The Yale Undergraduate
Journal of Economics and
Politics
ARTICLE
10
Expansions in the Affordable Care Act on Job Search and Part-Time Employment
James Bien - Northwestern University
53
Rational Radicals: Japanese Foreign Policy in the 21st Century
Syrus Jin - Washington University in Saint Louis
65
Nudge: The Role of Choice Architecture in Addressing Underinvestment in Education
Bapuchandra Kotapati - University of California, Los Angeles
78
Moving Toward a Brighter Future: India’s Solar Energy Aspirations
Rhea Kumar - Yale University
99
Motivations Behind China’s Bilateral Currency Swap Agreements
Rachel Yang - University of California, San Diego
114
The CNN Effect and Humanitarian Interventions: The Effect of Media on Foreign Policy
Audrey Zhang - University of Chicago
NOTE
153
Rethinking the Fight for Fifteen
Alec Bania - Brown University
THE YALE UNDERGRADUATE
JOURNAL OF ECONOMICS AND POLITICS
VOLUME 1 | ISSUE 1 | FALL 2016
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Eli Lininger ‘19
Editor-in-Chief
Yale Undergraduate Journal of Economics and Politics
Expansions in the Affordable Care Act on Job Search and Part-Time Employment
James Bien
Abstract
Between 2014 and 2016 thirty-two states opted into the Affordable Care Act’s Medicaid
expansion component, which increased the Medicaid eligibility income limit to 138% of the
federal poverty level. This paper studies the effects of these expansions on job search behavior
and part-time employment, using data from Google Trends and the Current Population Survey
(CPS). By comparing several labor market outcomes and metrics of job search activity in states
that expanded their Medicaid programs and states that did not, I estimate that the policy changes
led to modest increases in online job search and various levels of part-time employment.
Furthermore, I find that increases in job search activity are concentrated among those looking
for part-time opportunities. These results are consistent with previous literature also studying
the effects of the ACA Medicaid expansions on labor market outcomes.
10
Table of Contents
Introduction
I.
Public health insurance and employment
II.
Google search data as a measure of job search activity
III.
Data
IV.
Theoretical foundations
V.
Effects of Medicaid expansions on job search activity
VI.
Effects of Medicaid expansions on voluntary part-time employment: evidence
from CPS data
Conclusion
11
I. Introduction
The Affordable Care Act (ACA), signed into law in March 2010, represents the largest
regulatory overhaul in the United States healthcare system since the Social Security
Amendments of 1965, which introduced the Medicare and Medicaid programs to the original
Social Security Act of 1935. The ACA is composed of various laws and reforms that collectively
aim to increase accessibility of healthcare and to control healthcare costs in the U.S. Its
subcomponents include:
•
an individual mandate that requires all U.S. citizens and legal residents to obtain
qualifying health insurance coverage;
•
a subsidy system for low income individuals and families to purchase benchmark health
insurance plans;
•
a mandate that forbids insurance companies from denying citizens with preexisting
conditions coverage;
•
the creation of Health Insurance Exchanges (websites administered by governmental
agencies and non-profit organizations that facilitate purchase of health insurance);
•
requirements on employers with 200 or more full-time employees to provide workers
with health insurance plans; and
•
an opt-in expansion of state-level Medicaid programs to cover adults with incomes within
138% of the federal poverty level.
Despite its goal of increasing access to healthcare among low-income households in the
U.S., the ACA has raised concerns among policymakers and civilians alike. Criticisms of the
ACA include concerns about an increased financial burden on American firms (which many fear
will lead to job cuts), an excessively regulated healthcare system, and an unsuccessful launch of
Health Insurance Exchanges; as a result of these criticisms, the House of Representatives has
attempted to repeal the law more than 50 times. Still, the ACA has been successful in providing
coverage to the previously uninsured; between 2013 and 2014, the share of those without health
12
insurance coverage dropped from 13.3 percent to 10.4i, accounting for the largest single-year
drop since 1987.
However, questions surrounding the auxiliary impacts the ACA may have, specifically
the effects on labor supply, are largely unanswered, though some economists have calculated
crude estimates of these effects in the short time since the legislatures were enacted. This paper
aims to further current understanding of the effects of the ACA – specifically its Medicaid
expansion component – on labor market outcomes such as job search behavior and part-time
employment.
The interaction between health insurance and employment has been a focus of many labor and
health economists; certainly, the two are tightly connected in the U.S. “Employment lock”, a
term coined by Garthwaite et al. (2014) posits that more generous public health insurance
policies (like the ACA Medicaid expansions) will induce many workers who are only employed
to receive health coverage to leave the labor force; however, recent evidence suggests that the
expansions may have instead increased labor force participation. Prior to the ACA, Medicaid
eligibility differed greatly from state to state, and many states did not offer Medicaid to childless
adults; however, as a result of the recent policy exchanges, a consistent standard for eligibility
was created for all adults earning incomes less than 138% of the federal poverty level, although
several states expanded their eligibility beyond this limit. This new threshold corresponds to
$16,243 in individual annual income or $33,465 in household income for a family of fourii. As a
result of such a large change in Medicaid coverage, drastic auxiliary outcomes in the labor
market may be observed. A thorough understanding of the program’s impact on job search
behavior, willingness to work, and labor supply in general is necessary to properly evaluate the
economic repercussions of the ACA and to discuss potential policies to mitigate some of its
negative outcomes. Due to the uncertain nature of these effects, this is a subject that warrants
significant investigation.
This paper uses two broad categories of variables to evaluate the effects of Medicaid
expansion on labor supply. First, to study the effects of the state-level policy changes on job
search behavior, I use two metrics derived from Google Trends search data, the Google Job
Search Index (GJSI) and the Part-Time Search Index (PTSI). Although most existing research
used more traditional metrics of labor supply (like employment and hours worked), Google
Trends represents a novel and direct approach to measuring job search behavior. Furthermore, a
13
review of Baker and Fradkin (2015) verifies that Google search results are an accurate measure
for aggregate job search activity in a study of the economic impacts of unemployment insurance.
Specifically, this paper observes changes in search volumes for the terms, “jobs” and “part-time”
as a result of the expansions. Second, to internally validate the results obtained from Google
search data, as well as to evaluate hypotheses made by other economists regarding the impact of
Medicaid expansions on working parents, I use 2010 – 2016 Current Population Survey (CPS)
data and extrapolate changes in the likelihood of part-time employment as a result of the ACA
Medicaid expansions. Three classifications of part-time employment are used: aggregate parttime employment, voluntary part-time employment, and part-time employment due to childcare
and family reasons. I employ the differences-in-differences method and a series of ordinary least
squares (OLS) regressions to estimate the effect of the expansions on these metrics.
To isolate the effects of Medicaid expansions from other macroeconomic trends, I take
advantage of the opt-in nature of the ACA’s Medicaid expansion component; each state can
decide whether to expand its Medicaid income limit to at least 138% of the federal poverty level.
Prior to the ACA, income limits for Medicaid eligibility varied greatly by state, and to a lesser
extent the same diversity is still seen in the non-expansion states today. To illustrate, the current
income limits for Medicaid eligibility for parents in a family of three in Alabama and Wisconsin
are 18% and 100% of the federal poverty level, respectivelyiii. To date, thirty-two states
(including the District of Columbia) have adopted the ACA Medicaid expansions. Although
most of these expanded their Medicaid programs in January 2014, a select few expanded later;
for example, Montana expanded its income limits in January 2016.
The division of states into the expansion and non-expansion categories is analogous to
having “treatment” and “control” groups; as a result, the opt-in policy creates a quasiexperimental environment where the effects of Medicaid expansion on job search behavior can
be isolated. Furthermore, using the specification outlined in a paper by Kaestner et al. (2016),
this paper stratifies the expansion states into states that had prior and comprehensive expansion
similar to the ACA, states that had no prior Medicaid expansion, and states that had a prior, but
not comprehensive, Medicaid expansion. Using these two specifications, I study the effects of
ACA implementation on online job search behavior and part-time employment, and how these
effects vary with prior state-level expansion efforts. A shortcoming of relying on expansion
status as an experimental “treatment” for this study is that expansion states are non-randomly
14
distributed across the country. A method of incorporating the geographical differences in these
states are included at the end of the paper.
Based on the theoretical foundations outlined in this paper, I hypothesize that all
measures of job search activity and part-time employment increased as a result of the ACA
Medicaid expansions. If Jorgensen and Baker’s (2014) findings that employment increases are
focused on part-time work are accurate, I also expect the PTSI to be more heavily affected than
the GJSI. Finally, because Google Trends data reflect willingness to work, whereas CPS data
show job-finding rates, I expect the expansions to affect the latter metrics more modestly.
However, since many existing estimates for expansion effects are statistically insignificant, it is
likely that my results will also show large standard errors.
Results from this paper broadly follow expectations. Regarding online job search activity,
this paper finds that the ACA Medicaid expansions increased searches for both “jobs” and “parttime” by 1.3 and 2.6 percentage points, respectively. Furthermore, results show that both
measures increased in all states, regardless of expansion history, although increases were more
significant in states that had some prior Medicaid expansions. The magnitudes of these estimates
also tend to be greater for searches with the term “part-time”, although not all estimates are
statistically significant. Similarly, using CPS data this paper also finds that the ACA Medicaid
expansions increased part-time employment, voluntary part-time employment, and part-time
employment due to childcare and family reasons by 0.04, 0.2 and 0.07 percentage points,
respectively. The concentrated increase in voluntary part-time employment concurs with the
earlier results discussed in this paper. However, in contrast to the results from the GJSI and
PTSI, the relative increases in each classification of part-time employment seems to depend on
prior expansion policies.
This paper begins with an introduction of current evidence for the direction and
magnitude of changes in the labor market as a result of the Medicaid expansions, then moves on
to a brief discussion on the validity of Google Trends data as a proxy for job search activity. A
description of the data used and the economic theories behind this paper follows. Finally, the
paper closes with an examination of the econometrics used and an analysis of the results
obtained.
15
II. Public health insurance and employment
Numerous studies have focused on the relationship between government-provided health
insurance coverage and labor market conditions, but the findings of the interaction between the
two are conflicted; while some researchers concluded that more generous public insurance
programs create disincentives for work, others found that they lead to greater job search
behavior.
To begin, Garthwaite et al. (2014) explored the relationship between health insurance
coverage and labor supply through a difference-in-difference analysis of the 2005-6 TennCare
disenrollment. TennCare was Tennessee’s Medicaid expansion program, enacted in 1994 and
which increased eligibility to many childless adults in Tennessee. Starting in 2002 various
restrictions on eligibility to the original TennCare program caused a number of individuals in this
population segment to lose coverage, with the sharpest decline in enrollment from 2005-6. The
paper found that TennCare disenrollment caused a 4.6 percentage point increase in employment
as compared to other Southern states. Furthermore, the paper observed that this increase is
concentrated among those who worked more than 20 hours a week and who received employerprovided private health insurance. This paper’s findings suggest that a significant portion of
Tennessee’s workers would not have entered the labor force had they not lost Medicaid coverage
through the TennCare disenrollment. The authors refer to the phenomenon of individuals
working to gain employer-provided private health insurance coverage as “employment lock”.
Similarly, a study by Dague et al. (2014) used a regression discontinuity model to
measure the effects of the BadgerCare Core Plan in Wisconsin – which was expanded in 2009
and subsequently frozen after a few months – on labor supply. The authors found that public
insurance enrollment during this time reduced the likelihood of employment by 6.1 to 10.6
percentage points, supporting the “employment lock” theory put forth by Garthwaite et al.
(2014). Based on the findings from these papers, it may be expected that a similar trend would
occur in states that expanded Medicaid eligibility as a result of the Affordable Care Act. As more
individuals are eligible for Medicaid coverage labor supply may shrink, since workers are no
longer “locked” to their jobs for health insurance coverage.
Contrary to these expectations, two papers have found that the ACA has increased labor
force participation in states that expanded Medicaid eligibility. First, Jorgensen and Baker (2014)
16
explored the high-level effects of Medicaid expansion on voluntary part-time employment across
the nation. They found that during the first seven months of Medicaid expansion voluntary parttime employment rose by 2.08 percentage points. Furthermore, this uptick is concentrated among
women, who experienced a 3.18 percentage point increase in voluntary part-time employment
(while men experienced a 0.19 percentage point decrease) and among families with three or
more children, which experienced a 15.41 percentage point increase from 2013 to 2014.
Jorgensen and Baker (2014) hypothesized that increased Medicaid income limits provided
workers with the option to pursue a part-time job that allows them to be with young children or
family members in need of care without having to sacrifice health insurance coverage.
However, what is perhaps more interesting are the findings from a recent paper by
Kaestner et al. (2015), which focuses on the state-level impact of Medicaid expansion on labor
supply, using data from the 2010 – 2014 American Community Survey (ACS). Taking advantage
of the Supreme Court ruling that allowed states to opt out of Medicaid expansion, the study uses
a differences-in-differences research design to examine changes in insurance coverage and labor
supply in states that expanded Medicaid versus states that did not. Regarding the changes in
insurance coverage, the authors found that the ACA Medicaid expansions led to an increase in
Medicaid coverage of 4 percentage points, a decrease in the proportion uninsured by 3
percentage points and a decrease in the proportion privately insured by 1 percentage point,
indicating private insurance crowd out. More relevantly, the paper estimated that Medicaid
expansion also increased the proportion employed by 0.5 percentage points, the usual hours
worked per week by 21.7 percentage points and the proportion who worked 30 or more hours per
week by 0.7 percentage points, though not all estimates were statistically significant. While the
magnitudes of these estimates are not particularly notable, the positive direction in the short time
since Medicaid expansion indicates a positive effect on labor supply. Finally, the paper also
stratifies states by its previous Medicaid expansion policies and finds that the ACA Medicaid
expansion modestly increased labor force participation in states regardless of prior expansion
policies.
In considering the conflicting findings from these studies, it is important to note that
while the first two studies generated significant results and furthered our general understanding
of the effects of public health insurance policies on labor supply, the results from Jorgensen and
Baker (2014) and Kaestner et al. (2015) may provide a more accurate reference point for the
17
effects of recent Medicaid expansions on job search behavior as they capitalize on data obtained
since the ACA expansions went into effect. Furthermore, asymmetric responses between an
individual losing and gaining health insurance coverage may challenge the applicability of the
“employment lock” phenomenon on recent Medicaid expansions; the phenomenon may be
monotonic. Finally, although the first two papers provide valuable insight on the broad labor
market outcomes of health insurance policies, the findings are not specific to types of
occupations being pursued. The theory put forth by Jorgensen and Baker (2014), that increased
Medicaid eligibility may encourage more individuals in the labor force to voluntarily pursue
part-time work, may shed light on the types of jobs that people search for following the
expansions.
III. Google search data as a measure of job search activity
While the papers discussed thus far took conventional approaches to measuring labor
supply and the impact of public health insurance policy changes, Baker and Fradkin (2015)
explored a more experimental technique to assess the impact of unemployment insurance on job
search. A new metric, the Google Job Search Index (GJSI), measures job search activity based
on publicly available Google search data. As the first researchers to utilize proprietary Google
search trends to proxy actual job search, Baker and Fradkin (2015) devoted a significant portion
of their paper to defend the validity of using the GJSI as a proxy for overall job search. In
particular, the authors cited the GJSI’s correlation with comScore web panel and American Time
Use Survey (ATUS) results, as well as the GJSI’s fluctuations in response to macroeconomic
drivers like unemployment and labor market tightness. Baker and Fradkin (2015) concluded that
a true link can be drawn between job search undertaken in the real world and job search observed
through Google search behavior, making the GJSI a reliable tracker for job search efforts.
Baker and Fradkin’s comparative study of job search activity as recorded on Google
Trends and in other systems creates confidence in the legitimacy of the GJSI as a measure of
online job search. In this paper, I use aggregate information on state-level health insurance
programs and macroeconomic variables to study the effects of the ACA Medicaid expansions on
Google job search activity; the results from this analysis are supported by separate regressions
using data derived from the Current Population Survey (CPS).
18
IV. Data
A. Google Trends Search Data
Although previous studies linking public health insurance policies and labor market
outcomes have focused on employment and job finding rates, the primary outcome variable
chosen for this paper is a measure of job search. The reason for this is twofold. First, the
economic phenomenon described by Garthwaite et al. (2014) as “employment lock” is concerned
with job search behavior, not job finding rates; it is defined as the behavior of individuals finding
a job to gain employer-provided private health insurance. While employment rates and hours
worked provide a good proxy for job search effort, search result data is a more direct and
observable measure for this study. Second, before the accessibility created by Google Trends, job
search effort has been a variable that is largely unobservable and thus not a variable of interest
for economic studies. Baker and Fradkin (2015) present a proprietary method of using Google
search results to relate unemployment insurance coverage and job search behavior that can be
applied to a study involving health insurance instead.
One measure that is used in this paper is defined by Baker and Fradkin (2015) as the
Google Job Search Index (GJSI), and is constructed using the Google Trends tool. The GJSI is
calculated as the amount of Google searches that contain the term ‘jobs’ in a specified subset of
time over the maximum amount of Google searches with the same term during the entire time
frame. For example, if the entire time frame being studied is a duration of two weeks, and there
were 120 searches with the term ‘jobs’ in week 1 and 160 in week 2, then the GJSI for weeks 1
and 2 would be 75 and 100, respectively. Actual search volumes are not recorded as they violate
Google’s privacy restrictions, but for the purposes of the study, exact values are unnecessary;
state-level search volumes are weighted by population to provide for actual search volume. In
addition to the GJSI, this paper will also study a new measure denoted as the Part Time Search
Index (PTSI), which uses ‘part time’ as a search term instead to capture the effects of Medicaid
expansions on part-time job search and voluntary part-time employment.
The values considered in this paper are state-week Google Trends data from 1/2/2011 to
2/6/2016 in all 50 states and the District of Columbia, although some states are omitted due to
19
data restrictions. This range was chosen primarily to provide sufficiently long periods before and
after Medicaid expansion for observable effects in a differences-in-differences analysis. In 2010,
Google Trends changed their analysis algorithms; to omit the effects of this change, the analysis
period begins from the first week of 2011. An example of Google Trends output is shown in
Appendix 1.
There are several benefits to using online search data, chief among which is its ready
availability. Google Trends is an open data source that is easily downloadable for economic
analysis. Furthermore, search results also present less sampling bias than data from the Current
Population Survey (CPS) and American Time Use Survey (ATUS) as the metric is obtained
through measurement of search volume rather than surveying households.
As online job search platforms become more popular, more tools have become available
for researchers to use to study job search effort. Activity on websites like CareerBuilder and
SnagAJob have been used as proxies for actual job search, but the GJSI presents multiple
advantages over these tools. First, access to other online platforms is often limited to privacy
restrictions, whereas Google Trends, as an open data source, generates flexibility for different
search terms and time periods. Furthermore, while the popularities of different job search
websites fluctuate with time, Google has been the predominant search engine in the US for over
10 years, and is often the first step of any job search process.
In spite of the advantages the GJSI holds for the purposes of this paper, the index is also
vulnerable to a number of shortcomings. The lack of personal detail (as compared to survey
results from the CPS or ATUS) makes it impossible to ascertain characteristics that may be
important to our understanding of the individuals being affected, such as employment status.
Furthermore, actual search volume is also unavailable from Google Trends; all metrics used in
this paper are relative search volumes within a specified period, which presents a slight
inconvenience. As a result, this paper uses state-level population data from 2011 to weight each
state’s relative search volumes. Finally, data availability on Google Trends is inconsistent; many
states had to be dropped when searching for ‘part-time’ queries due to lack of valid weekly
search data. The 30 states that will be included in analyses using ‘part-time’ as a search term are
as follows:
20
•
AL, AZ, CA, CO, CT, FL, GA, IA, IN, KS, KY, MA, MD, MI, MN, MO, NC, NE, NJ,
NY, OH, OK, OR, PA, SC, TN, TX, UT, VA, WA, WI (30)
A number of external data points was also used to improve the validity of the
econometric analysis conducted. First, state-level data on Medicaid expansions, including the
date of expansion, were used to stratify the data set into control and treatment groups. 2011
populations in each state, as reported by the US census, was used to weight each state in the
regression. Finally, monthly average home values, taken from Zillow.com, were used to capture
state-level economic trends in an effort to make estimates more precise. Because Zillow.com’s
collection was incomprehensive, Louisiana, Maine, and the District of Columbia were dropped
from the aggregate data set.
B. Current Population Survey (CPS) Data
Current Population Survey (CPS) responses were also used to address some of the
shortcomings of the GJSI and PTSI. The CPS is a monthly survey sponsored by the U.S. Census
Bureau and U.S. Bureau of Labor Statistics (BLS), and is the primary data set used to study the
U.S. population’s labor force characteristics. The CPS surveys approximately 50,000 households
each month.
Three binary outcome variables derived from CPS responses are defined for this paper:
part-time employment, voluntary part-time employment (defined as “part-time employment for
non-economic reasons”, a subset of part-time employment), as well as part-time employment due
to childcare and family reasons (defined as those whose reason for working part-time is “Child
care problems” or “Other family/personal obligations”, a subset of voluntary part-time
employment). These variables were chosen to specifically examine the claims made by
Jorgensen and Baker (2014) that the increase in employment is concentrated among those who
capitalize on the increased flexibility provided by the increased Medicaid income limits.
Monthly survey results from January 2010 to March 2015 are used in regressions
involving CPS responses. These data points are used to address some of the shortcomings of the
GJSI and PTSI, as the individual-level characteristics provide a more controlled research
environment. Like the regressions involving Google Trends data, those using CPS responses
21
utilize variables corresponding to each state’s expansion policies and implementation dates.
Because the CPS regressions do not rely on other external data points (like Zillow.com average
home values for control variables) they are not limited to the restricted data available in these
external sources; as a result, all 50 states are included in the analysis, as well as the District of
Columbia.
In addition to evaluating the claims made by Jorgensen and Baker (2014), this paper uses
results from CPS data as a method of internal validation by comparing the magnitude and
direction of the Medicaid Expansions’ impact on the GJSI, PTSI, and various definitions of parttime employment.
V. Theoretical foundations
This section summarizes the economic theory presented in the previous sections to
generate and justify expectations for the outcomes of this study.
Although the literature on the effects of Medicaid expansions on job search behavior is
conflicted, findings from Kaestner et al. (2015), which studies employment rates before and after
Medicaid expansion, should lead to the most relevant expectations for this study. The paper
found that Medicaid expansions led to a modest increase in employment, though most estimates
for this effect were statistically insignificant. Similarly, the hypothesis of this paper is that
Medicaid expansions will increase job search activity (as measured by the GJSI and PTSI). In
addition, this paper expects the magnitudes of estimates for the effect on voluntary part-time
employment and part-time employment due to childcare and family reasons to be larger,
although these results may also have small statistical significance.
Under the ACA, Medicaid expansions will increase the threshold for Medicaid eligibility
to 138% of the federal poverty line, which corresponds to $16,243 in individual annual income
or $33,465 in household income for a family of four. This greater threshold creates flexibility for
low-income individuals and affects job search behavior in two ways: first, previously
unemployed individuals may now obtain a low-income job (that likely will not come with
employer-provided private health insurance) without sacrificing Medicaid eligibility, and;
second, individuals that are employed full-time may be induced to work at a part-time job instead
22
to spend more time with family while maintaining health coverage, leading to private insurance
crowd out.
The second effect is corroborated by evidence from Jorgensen and Baker (2014), which
found that within seven months of Medicaid expansion there was an observable increase in
voluntary part-time employment. Moreover, these results are focused among female workers and
individuals in families with three or more children, giving further validity to the hypothesis that
increases in job search behavior will be concentrated in part-time job search.
To study the effects of Medicaid expansions on job search behavior, I look at two
variables in particular, both of which are derived from Google Trends results. First, the GJSI is
used to observe the high-level impact of Medicaid expansions on job search. Since the GJSI
captures aggregate job search activity on Google, I expect to see a slight increase in the values
following Medicaid expansion. Second, the PTSI is used to observe changes in search volume
for part-time jobs. Similarly, I expect to see an increase in PTSI following Medicaid expansion,
though the magnitude of the difference may be larger due to more focused increases in part-time
job search.
To evaluate the claims made by Jorgensen and Baker (2014), I look at three definitions of
part-time employment and compare the magnitudes of their estimates. Using these definitions,
each applicable respondent in the CPS data set is assigned a dummy variable whose value is 1 if
the individual fits that definition of a part-time worker. For example, if a respondent indicates
that she works part-time for “child care problems”, she is classified as a “part-time worker”, a
“voluntary part-time worker”, and a “part-time worker due to child care and family reasons”. In
contrast, if a respondent indicates that she works part-time because she “could not find full-time
work”, she is only classified as a “part-time worker”.
Because these variables measure job-finding rates (as a proxy for job search activity),
they are expected to increase with an increase in income limits; the estimated effects of Medicaid
expansions should be positive. Furthermore, since each definition of part-time employment is a
subset of a larger outcome variable, I expect to see an increase in the magnitudes of these
estimates as the definition of part-time employment become more specific, according to theories
posited by Jorgensen and Baker (2014). Finally, because these variables do not reflect job search
behavior directly, I expect the magnitude of increases to be less than those of the GJSI and PTSI.
23
The econometric methodology of this paper resembles that of Kaestner et al. (2015), with
some minor difference. Percentage changes in job search measures are estimated by regressing
the dependent variables on dummy variables that indicate whether a state is an expansion state
and whether the outcome was measured after expansion using a differences-in-differences
approach. As a result, the states in the data set are first stratified into two groups: states that did
not expand Medicaid after 2014 (the “control” group) and states that expanded Medicaid after
2014 (the “treatment” group). Kaestner et al. (2015) also used a more specific stratification to
classify each state with a greater level of detail based on Medicaid expansions prior to the ACA.
In particular, the paper divides states that expanded Medicaid after 2014 into “states that had
prior and comprehensive Medicaid expansion similar to ACA” (Group 1), “states that had no
prior Medicaid expansion” (Group 2), and “states that had a prior, but not comprehensive,
Medicaid expansion” (Group 3). Although the authors of the paper assume that Group 1 states
will behave like states in the control group, I classify them as “treatment” states to create
estimates for effects on this group and to verify these assumptions.
Appendix 2 summarizes information on the inclusion and treatment of each state in this
paper.
VI. of Medicaid expansions on job search activity: Evidence from Google search data
To study the high-level impact of Medicaid expansions on the labor market, I estimate
the regression in the following form:
(1)
𝑦!" = 𝛼! + 𝛽! + 𝛾! + 𝜆(𝑒𝑥𝑝𝑎𝑛𝑑! ∗ 𝑎𝑓𝑡𝑒𝑟!" ) + 𝛿(ℎ𝑜𝑚𝑒 𝑣𝑎𝑙𝑢𝑒!" ) + 𝑒!"
The variable yst represents a labor market outcome in state s and week t, which is either the log of
the GJSI or the log of the PTSI. A number of variables on the right-hand side of equation (1)
capture the fixed effects for each data point. State fixed effects are captured by 𝛽s, weekly fixed
effects are captured by 𝛾t, and overall economic effects in state s and week t are captured by the
estimate of monthly home values’ (obtained from Zillow.com) impact on job search, 𝛿. All other
effects that are not correlated with Medicaid expansion are captured in the error term, est.
24
The two independent variables, expands and afterst, are binary factors that indicate
whether a state expanded its Medicaid program as a result of the ACA, and whether the outcome
was measured after Medicaid expansion, respectively. The classification for each state that was
included in the analysis of the GJSI are as follows:
FIGURE 1: Classification of states as expansion or non-expansioniv
States that did not expand Medicaid
States that expanded
after 2014
Medicaid after 2014
(the “control” group)
(the “treatment” group)
•
AL, FL, GA, ID, KS, MO,
•
AK, AR, AZ, CA, CO, CT,
MS, NC, NE, OK, SC, SD,
DE, HI, IA, IL, IN, KY, MA,
TN, TX, UT, VA, WI, WY
MD, MI, MN, MT, ND, NH,
(18)
NJ, NM, NV, NY, OH, OR,
PA, RI, VT, WA, WV (30)
The regressor in equation (1) is the interaction of expands and afterst because both conditions
must be met for treatment to occur. As a result, the key coefficient of interest in equation (1) is 𝜆,
which is the estimate of the effect of ACA-induced Medicaid expansions.
Estimates of the effect on the GJSI and PTSI are shown in the third column of tables 1
and 2, respectively. These estimates show that as expected, ACA-induced Medicaid expansions
increase both aggregate online job search and part-time specific job search by 1.3 percentage
points and 2.6 percentage points, respectively. As a point of reference, Baker and Fradkin (2015)
found that a 10-week increase in unemployment insurance benefits led to a decrease in GJSI of
1.5 to 2.4 percentage points. If we assume symmetric responses, the effect of the ACA Medicaid
expansions on GJSI is similar to that of a 10-week reduction in unemployment insurance
benefits. However, again as expected, the estimates shown here have large standard errors and do
not demonstrate statistical significance at most levels. Still, it is important to note that the
estimated magnitude of the ACA Medicaid expansions’ effects on part-time job search is double
that of aggregate job search behavior. Furthermore, the estimated effect of the PTSI is also more
precise than that of the GJSI, suggesting that on average, Medicaid expansions have a more
observable impact on the search for part-time jobs than for jobs in general. These initial results
25
seem to corroborate findings from Jorgensen and Baker (2014) that the impact of Medicaid
expansion is focused on voluntary part-time employment.
While estimates of the high-level impact of the ACA indicate expected trends of
increases in both aggregate job search and voluntary part-time employment, it is important to
further stratify the treatment states according to each expansion states’ history of expanding
Medicaid, prior to 2014. To stratify the 30 “treatment” states, this paper follows the classification
put forth by Kaestner et al. (2015) as follows:
FIGURE 2: Classification of states as Group 1, 2, or 3v
Group 1
Group 2
Group 3
(States that had prior
(States that had no
(States that had a
and comprehensive
prior Medicaid
prior, but not
expansion similar to
expansion)
comprehensive,
ACA)
•
DE, MA, NY,
VT (4)
Medicaid expansion)
•
AK, AR, IN, KY,
MI,
MT,
ND,
•
AZ,
CA,
CO,
CT, HI, IA, IL,
NH, NM, NV,
MD,
OH,
OR, RI, WA (13)
PA,
WV
MN,
NJ,
(13)
Using this classification, I can also verify or disprove the assumption made by Kaestner et al.
(2016) that Group 1 states can be treated as “control” states.
To assess the impact of prior, as well as ACA-induced Medicaid expansion, the following
regression was used:
(2)
𝑦!" = 𝛼! + 𝛽! + 𝛾! + 𝜆! 𝑔𝑟𝑜𝑢𝑝 = 1! ∗ 𝑎𝑓𝑡𝑒𝑟!" + 𝜆! 𝑔𝑟𝑜𝑢𝑝 = 2! ∗ 𝑎𝑓𝑡𝑒𝑟!" +
𝜆! 𝑔𝑟𝑜𝑢𝑝 = 3! ∗ 𝑎𝑓𝑡𝑒𝑟!" + 𝛿!" ℎ𝑜𝑚𝑒 𝑣𝑎𝑙𝑢𝑒!" + 𝑒!"
All of the variables in equation (1) remain in equation (2) with the exception of expandst, which
is replaced with three binary factors that reflect the classification of each state into its respective
group. Similarly, the regressors in equation (2) are the interactions of each groups variable with a
26
binary indicator of whether the outcome was measured after the state’s expansion date. Here, the
key coefficients 𝜆1, 𝜆2 and 𝜆3 represent the effects of Medicaid expansion on job search efforts in
states with varying levels of prior Medicaid expansion.
Estimates for these effects on the GJSI and PTSI are displayed in column 6 of tables 1
and 2, respectively. These estimates show that Medicaid expansions increased aggregate job
search on Google by 0.9 percentage points in states that had prior and comprehensive Medicaid
expansion, 1.2 percentage points in states that had no prior Medicaid expansion, and 1.5
percentage points in states that had prior, but incomprehensive, Medicaid expansion. Although
these results, again, demonstrate low statistical significance, it is interesting to note that the
effect of the ACA on aggregate online job search is largest in states that expanded Medicaid
incomprehensively in the past, which goes against expectations that states with no prior
Medicaid expansions should be most greatly affected by the ACA expansions.
In contrast, the estimates in Table 2 show that Medicaid expansions increased part-time
job search by 7.7 percentage points in states that had prior and comprehensive Medicaid
expansion, 1.5 percentage points in states that had no prior Medicaid expansion, and 1.4
percentage points in states that had prior, but incomprehensive, Medicaid expansion. The relative
magnitudes of these estimates are much greater than the GJSI estimates, which concurs with our
earlier findings that the expansions had a greater average effect on PTSI than GJSI. Furthermore,
the estimate for states that had prior and comprehensive Medicaid expansion showed
considerably high precision, indicating that the effect is statistically significant. These results
surprisingly suggest that voluntary part-time employment is most greatly affected by the ACAinduced Medicaid expansions in Massachusetts and New York, states that already had generous
Medicaid eligibility requirements prior to ACA Medicaid expansions. Due to the small number
of states that are classified with this status, there is a possibility that the result is skewed by
limited data availability; however, its statistical significance makes this result notable and
therefore refutes the assumption that Group 1 states can be treated as “control” states.
27
VII. Effects of Medicaid expansions on voluntary part-time employment: evidence from
CPS data
While the regressions discussed thus far focused on the effects of Medicaid expansions
on job search activity, a separate set of regressions using CPS responses were used to evaluate
Jorgensen and Baker’s (2014) hypothesis that the increase in income eligibility allowed part-time
workers to spend more time with family, as well as to validate previous findings in this paper.
The regression used took the following form:
(3)
𝑦 = 𝛼! + 𝛽! + 𝛾! + 𝜆 𝑒𝑥𝑝𝑎𝑛𝑑! ∗ 𝑎𝑓𝑡𝑒𝑟!" + 𝛿! 𝑐ℎ𝑖𝑙𝑑 𝑖𝑛 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 + 𝛿! 𝑏𝑙𝑎𝑐𝑘 +
𝛿! 𝑓𝑒𝑚𝑎𝑙𝑒 + 𝛿! 𝑎𝑔𝑒!"!!" + 𝛿! 𝑎𝑔𝑒!"!!" + 𝑒
The outcome variable, y, represents one of three binary variables being studied in this paper:
part-time employment, voluntary part-time employment, and part-time employment due to
childcare and family reasons. The variables to the right of the equation are similar to those of
equation (1); the independent variables of interest, expands and afterst, illustrate the effect of the
Medicaid expansions on part-time employment. Expansion and non-expansion states are
classified according to Figure 1, with the exceptions of Louisiana and Maine, which are
classified as non-expansion states in these regressions, and the District of Columbia, which is
classified as an expansion statevi. Due to the greater number of characteristics captured by the
CPS, more control variables were incorporated in addition to state and temporal (monthly)
effects. Demographic data, such as a person’s race, gender, and age were used as controls, as
well as a binary variable indicating whether children are present in a household. Log home
values were dropped because the dependent variable in these regressions are no longer on the
state level. The error term, e, captures all other effects not correlated with the Medicaid
expansions.
The estimates represented in the third column of tables 3, 4, and 5, show that Medicaid
expansions increased part-time employment, voluntary part-time employment, and part-time
employment due to childcare and family reasons, by 0.04, 0.2, and 0.07 percentage points,
28
respectively. Although these estimates vary in their level of statistical significance, a comparison
of their relative magnitudes yields important insights. First, we note that on average, Medicaid
expansions have a much larger effect on voluntary part-time employment (0.2 percentage points)
than on aggregate part-time employment (0.04 percentage points). This finding seems to
corroborate results from Jorgensen and Baker (2014), which stated that the increase in part-time
employment is concentrated among those who are voluntarily pursuing part-time opportunities.
Previous findings from this paper also support this result; since the relative magnitude for
estimated increases in Google searches for part-time jobs were double those for jobs in general,
we may expect to see that an individual is more likely to opt for part-time employment
opportunities following Medicaid expansion. However, the magnitude of this increase is
considerably more modest than what one might expect based on previous estimates; Jorgensen
and Baker (2014) approximate an increase in voluntary part-time employment by 2.08
percentage points, this paper finds only a 0.2 percentage point increase in this measure.
Furthermore, these estimates are also smaller in magnitude to the 2.6 percentage point increase in
PTSI; this is unsurprising, since the PTSI measures part-time job search activity, while CPS
metrics measure job-finding rates. In contrast, the magnitudes of the estimates here are more
comparable to Kaestner et al.’s (2016) results, which predicted a 0.5 percentage point increase in
the aggregate proportion employed.
Another interesting finding is that voluntary part-time employment is more greatly
impacted by Medicaid expansions than part-time employment due to childcare and family
reasons (0.07 percentage points). The relative magnitudes of these estimates indicate that
although the expansions increased all levels of part-time employment, the greatest increases in
voluntary part-time employment were not concentrated among those who are obtaining part-time
work to spend more time with their families, which goes against our initial expectations.
However, it is still likely that the increased Medicaid income limit induced some workers to
pursue part-time work and spend more time with their families, as seen in the positive direction
of the estimate.
An alternative way of understanding the impact of children on employment changes is
through the estimated effects the binary variable, children, has on part-time employment and
voluntary part-time employment. Expectedly, the coefficients for both definitions of part-time
employment are negative because households with children likely have a greater financial burden
29
that restricts workers from pursuing part-time work. That said, a comparison of magnitudes
between the two estimates again yields interesting results: aggregate part-time employment is
slightly more affected (1.7 percentage points) by the existence of children than voluntary parttime employment (1.1 percentage points). This comparison suggests that although individuals
with children are as a whole less likely to be employed part-time, they are more likely to work
part-time voluntarily than involuntarily, which is in line with the description posited by
Jorgensen and Baker (2014) that certain workers pursue part-time opportunities to increase time
spent with children.
Using the classification of states outlined in Figure 2, we can again take into account the
impact of prior state-level Medicaid expansions on employment changes. One amendment to the
figure is the inclusion of District of Columbia as a Group 1 state. To study the effects of prior
expansion policies, this paper uses a regression in the following form:
(4)
𝑦 = 𝛼! + 𝛽! + 𝛾! + 𝜆! 𝑔𝑟𝑜𝑢𝑝 = 1! ∗ 𝑎𝑓𝑡𝑒𝑟!" + 𝜆! 𝑔𝑟𝑜𝑢𝑝 = 2! ∗ 𝑎𝑓𝑡𝑒𝑟!" +
𝜆! 𝑔𝑟𝑜𝑢𝑝 = 3! ∗ 𝑎𝑓𝑡𝑒𝑟!" + 𝛿! 𝑐ℎ𝑖𝑙𝑑 𝑖𝑛 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 + 𝛿! 𝑏𝑙𝑎𝑐𝑘 + 𝛿! 𝑓𝑒𝑚𝑎𝑙𝑒 +
𝛿! 𝑎𝑔𝑒!"!!" + 𝛿! 𝑎𝑔𝑒!"!!" + 𝑒
Again, the coefficients 𝜆1, 𝜆2 and 𝜆3 represent the effects of Medicaid expansion on employment
outcome y in states with varying levels of prior expansions. Column 6 of tables 3, 4, and 5 show
the estimated coefficients.
Among Group 1 states (states that had comprehensive Medicaid expansions prior to the
ACA), the likelihood of part-time employment, voluntary part-time employment, and part-time
employment due to childcare and family reasons rose by 0.4, 0.2 and 0.06 percentage points,
respectively, following expansion. Among Group 2 states (states that had no prior Medicaid
expansions), the likelihood of part-time employment and voluntary part-time employment fell by
0.3 and 0.2 percentage points, while part-time employment due to childcare and family reasons
rose by 0.04 percentage points. Finally, among group 3 states (states that had prior, but
incomprehensive Medicaid expansions), the likelihood of part-time employment, voluntary parttime employment and part-time employment due to childcare and family reasons rose by 0.07,
0.4, and 0.08 percentage points, respectively. Again, the magnitudes of these estimates suggest
30
that the effects of Medicaid expansions on voluntary part-time employment discussed in
Jorgensen and Baker (2014)’s study might be overstated.
Comparisons within each state category yield remarkable trends. Surprisingly, in states
that had no prior Medicaid expansions, the only measure that increased after the expansions was
part-time employment due to childcare and family reasons. This suggests that these households
are the first to react to the change in Medicaid eligibility by pursuing more part-time work, a
result that corroborates the hypothesis that expansions give workers in households with children
more flexibility in choosing how to spend their time. Results also show that individuals in these
states were less likely to work part-time or pursue part-time employment following Medicaid
expansions, which is unexpected based on the theoretical foundations discussed in this paper.
The decrease in part-time employment may be partially attributed to the time-frame chosen for
this study. The CPS data extracted ranged from January 2010 to March 2015, and most states
that expanded their Medicaid programs did so in January 2014. However, due to asymmetric
responses to changes in public health insurance policies, workers who learn that they are eligible
for Medicaid likely will not change their employment decisions immediately after the policy
change, which may account for some of the lag in response captured by the individuals in these
states. Still, the negative direction of changes in part-time employment and voluntary part-time
employment indicates that the time-frame chosen for this study may not capture the long-term
employment effects of Medicaid expansions.
In states that had some, but incomprehensive, prior Medicaid expansions, the likelihood
of voluntary part-time employment rose relatively dramatically. Individuals in these states
behave most similarly to the national average, according to estimates for 𝜆 in equation (3). In
comparison, in states that had comprehensive prior Medicaid expansions, the likelihood of parttime employment experienced the largest increase. Combining the effects of the Medicaid
expansions from all three categories, the results indicate that when a state first undergoes
comprehensive Medicaid expansion, the first individuals to respond by changing their
employment outcome are workers with children to care for (as evidenced by estimates for Group
2 states). Then, voluntary part-time employment as a whole, including workers without children,
starts to increase (as evidenced by estimates for Group 3 states). Finally, when a state has already
undergone comprehensive Medicaid expansion, and receives an additional increase in income
31
limit, involuntary part-time employment overtakes voluntary part-time employment as the driver
for growth in part-time employment.
The staggered entry into the part-time labor force indicated in the CPS data produces a
source of diversion from results obtained when studying the GJSI and PTSI. While the direction
and magnitudes of the effects of Medicaid expansions on online job search behavior were
relatively comparable across all state categories, analysis of CPS data shows that the effect of
expansions on different classifications of part-time employment vary greatly with prior
expansion policies; that said, it is unsurprising that a state’s historical Medicaid records should
play a large role in how labor force characteristics change with newer Medicaid shocks.
The geographical distribution of expansion states may play a role in our understanding of
the Medicaid expansions’ effect on working families, too. Figure 3 represents a geographical
view of expansion states in the U.S. and shows that states that expanded are geographically
different from states that did not. As one might expect, the distribution of expansion versus nonexpansion state is not random across the country; there are clear geographical differences
between regions that have a high density of expansion states (such as the West and the
Northeast) and regions that have a high density of non-expansion states (such as the South).
Similarly, Figure 4 shows a geographical view of the different state categories across the U.S.
Here, geographical differences between state types are not as obvious. Group 2 and 3 states are
relatively evenly distributed across the country, although Group 1 states are concentrated in the
Northeast region. Because we treat the expansion status of each state as an experimental
“treatment” in this paper, the obvious non-random geographical differences between expansion
and non-expansion states prompted a further investigation into how the estimated effects may be
affected by region-time trends.
32
FIGURE 3: Geographical view of expansion states in the U.S.
FIGURE 4: Geographical view of state categories in the U.S.
Table 6 shows a summary of the estimated effects of the ACA Medicaid expansions on
part-time employment, when region-month effects are taken into account. Comparing these
values to the estimates in Tables 3, 4, and 5, one generally sees that the magnitudes of estimates
do not change dramatically, though there are some notable differences. For example, the
33
estimated effect of Medicaid expansion on part-time employment increases from 0.04 percentage
points to 0.19 percentage points; however, both estimates exhibit small statistical significance,
indicating that including region-month effects does not minimize the standard errors associated
with these estimates. Furthermore, the trends in relative magnitudes and directions of these
estimates remain intact after the inclusion of region-month effects, indicating that the theories
put forth regarding the staggered entry of part-time laborers are not impacted by these forces. It
is important to note that regardless of the inclusion of region-month effects, the standard errors
of most estimates are high, indicating that there is a considerable amount of noise in my data.
The high-level trends on labor supply that are observed in both sets of data yield
important insights on the effects of the ACA. First, this paper can conclude that on average job
search activity did increase as a result of the ACA Medicaid expansions, as seen in the increases
in GJSI, PTSI, and proportion of those employed part-time voluntarily. These results corroborate
previous studies of the expansions’ positive effect on labor supply, and should serve to disprove
concerns that a more generous Medicaid program will induce the labor force to be less active;
not only are these negative effects not observed in my estimates, where there are statistically
significant estimates they are largely positive. Second, results also suggest that Medicaid
expansions increased the likelihood of a worker pursuing part-time opportunities, as seen in the
relative magnitudes of the estimated effects on the GJSI and PTSI, as well as those on aggregate
part-time employment and voluntary part-time employment. Although the specific reasons for
each household’s increased proportion for voluntary part-time employment cannot be identified,
at least some of the affected workers likely pursued part-time work to increase time spent with
family and children, as there is an estimated increase in the proportion voluntarily working parttime due to childcare and family reasons. Since returns to education and academic performance
can be linked to parental involvement, these results suggest that the ACA may have broader
societal impacts.
VIII. Conclusion
This paper employs a differences-in-differences approach to study the state-level opt-in
Medicaid expansions implemented between 2014 and 2016 as a result of the Affordable Care Act
and finds that these expansions had a positive effect on job search behavior and voluntary part-
34
time employment. Using the definition of “employment lock” identified by Garthwaite et al.
(2014), one might expect that increased Medicaid enrollment would decrease labor force
participation, but this paper finds that both aggregate searches for jobs and searches specifically
for part-time roles on Google increased as a result of recent policy changes, although not all
estimates were statistically significant at the 5% level. Furthermore, increases in job search
behavior seem to be concentrated among part-time jobs, as indicated by the comparatively high
estimates for effects on the PTSI and on the proportion of voluntarily part-time employed. On
average, measures such as the GJSI, PTSI, part-time employment, voluntary part-time
employment, and part-time employment due to childcare and family reasons increased across
states. However, some surprising results were seen in states that had no prior Medicaid
expansion; specifically, both part-time employment and voluntary part-time employment
decreased following Medicaid expansion.
Different experimental designs may lead to more precise estimates for the effects of
Medicaid expansions. First, the relevance of using state stratification shown in Figure 2, which
was taken from Kaestner et al. (2016), should be more carefully evaluated. As part of an analysis
conducted on the effects of these states on job search behavior, this paper concludes that
individuals in Group 1 states (states that had prior and comprehensive expansion) do not behave
similarly to individuals in non-expansion states, calling into question assumptions made by the
original authors. Another way to stratify states is by Medicaid income limits prior to expansion; a
study using that specification may yield more telling results and may also provide a basis for
calculating changes in job search activity that can be associated with an incremental increase in
Medicaid income limit. Furthermore, because the theoretical foundations this paper builds on
depend on the validity of the hypothesis that workers with children and dependent family
members are more likely to pursue part-time work, more variables illustrating the reason for such
employment increases should be used to validate the hypotheses made. Due to privacy
restrictions, these details are unobtainable using Google Trends data, but to some extent they are
reflected in the use of control variables with CPS responses. Still, a more detailed profile of the
households impacted by the ACA expansions will play a large role in subsequent policy
decisions.
While this paper has focused on metrics of labor supply, other household-level economic
characteristics may also be of interest when analyzing the non-health impacts of the ACA
35
Medicaid expansions. Based on the findings from this paper, one might expect measures such as
household income to increase in the short run, but other longer-term metrics may also be
impacted as a result of these policy changes. A comprehensive study of multiple economic
variables can provide a more complete understanding of the auxiliary effects from the recent
Medicaid expansions.
36
REFERENCES
Baker, Scott R., and Andrey Fradkin. "The Impact of Unemployment Insurance on Job Search:
Evidence from Google Search Data." SSRN Electronic Journal SSRN Journal (n.d.): n. pag.
Web.
Dague, Laura, Thomas C. DeLeire, and Lindsey Leininger. "The Effect of Public Insurance Coverage
for Childless Adults on Labor Supply." NBER. Upjohn Institute, 2014. Web.
Garthwaite, Craig, Tal Gross, and Matthew J. Notowidigdo. "Public Health Insurance, Labor Supply,
and Employment Lock." NBER. The Quarterly Journal of Economics, 7 Mar. 2014. Web.
Jorgensen, Helene, and Dean Baker. "The Affordable Care Act: A Family-Friendly Policy." The Center
for Economic and Policy Research. N.p., Sept. 2014. Web.
Kaestner, Robert, Bowen Garrett, Anuj Gangopadhyaya, and Caitlin Fleming. "Effects of ACA
Medicaid Expansions on Health Insurance Coverage and Labor Supply." NBER, Dec. 2015.
Web.
37
TABLE 1: Effects of ACA-induced and Prior Medicaid Expansions on GJSI
log(GJSIst)
expands * afterst
(1)
(2)
(3)
-0.0094
0.0132
0.0132
(0.0030)
(0.0025)
(0.0227)
[0.002]
[0.000]
[0.566]
group1s * afterst
group2s * afterst
group3s * afterst
log(home valuest)
(4)
(5)
(6)
-0.0500
0.0093
0.0093
(0.0067)
(0.0045)
(0.0317)
[0.000]
[0.038]
[0.770]
-0.0336
0.0119
0.0119
(0.0053)
(0.0035)
(0.0200)
[0.000]
[0.001]
[0.554]
0.0125
0.0152
0.0152
(0.0037)
(0.0030)
(0.0294)
[0.000]
[0.000]
[0.607]
0.1507
0.1507
0.1453
0.1453
(0.0119)
(0.1030)
(0.0127)
(0.0944)
[0.000]
[0.150]
[0.000]
[0.131]
4.3636
2.7547
2.7547
4.3636
2.8201
2.8201
(0.0015)
(0.1458)
(1.2528)
(0.0014)
(0.1544)
(1.1479)
[0.000]
[0.000]
[0.033]
[0.000]
[0.000]
[0.018]
State fixed effects
No
Yes
Yes
No
Yes
Yes
Week fixed effects
No
Yes
Yes
No
Yes
Yes
Home value effects
No
Yes
Yes
No
Yes
Yes
Clustered SE
No
No
Yes
No
No
Yes
constant
38
R2
0.0008
0.7804
0.7804
0.0087
0.7804
0.7804
Note: The GJSI is calculated as the amount of Google searches that contain the term ‘jobs’ in a specified subset of time over the maximum amount of Google
searches with the same term during the entire time frame. For example, if the entire time frame being studied is a duration of two weeks, and there were 120
searches with the term ‘jobs’ in week 1 and 160 in week 2, then the GJSI for weeks 1 and 2 would be 75 and 100, respectively.
39
TABLE 2: Effects of ACA-induced and Prior Medicaid Expansions on PTSI
log(PTSIst)
expands * afterst
(1)
(2)
(3)
0.1116
0.0263
0.0263
(0.0044)
(0.0042)
(0.0225)
[0.000]
[0.000]
[0.251]
group1s * afterst
group2s * afterst
group3s * afterst
log(home valuest)
(4)
(5)
(6)
0.1521
0.0774
0.0774
(0.0094)
(0.0075)
(0.0206)
[0.000]
[0.000]
[0.001]
0.0678
0.0145
0.0145
(0.0083)
(0.0063)
(0.0233)
[0.000]
[0.022]
[0.538]
0.1168
0.0137
0.0137
(0.0054)
(0.0052)
(0.0239)
[0.000]
[0.009]
[0.570]
0.2412
0.2412
0.2866
0.2866
(0.0215)
(0.0904)
(0.0235)
(0.0775)
[0.000]
[0.012]
[0.000]
[0.001]
4.2335
1.5729
1.5729
4.2335
1.034
1.034
(0.0021)
(0.2562)
(1.0728)
(0.0021)
(0.2803)
(0.9200)
[0.000]
[0.000]
[0.153]
[0.000]
[0.000]
[0.270]
State fixed effects
No
Yes
Yes
No
Yes
Yes
Week fixed effects
No
Yes
Yes
No
Yes
Yes
Home value effects
No
Yes
Yes
No
Yes
Yes
Clustered SE
No
No
Yes
No
No
Yes
constant
40
R2
0.0723
0.7123
0.7123
0.0780
0.7147
0.7804
Note: The PTSI is calculated as the amount of Google searches that contain the term ‘part-time’ in a specified subset of time over the maximum amount of
Google searches with the same term during the entire time frame.
41
TABLE 3: Effects of ACA-induced and Prior Medicaid Expansions on
Part-Time Employment
Part-Time Employment
(4)
(5)
(6)
group1s *
-0.0003
0.0038
0.0038
afterst
(0.0013)
(0.0015)
(0.0021)
[0.833]
[0.014]
[0.083]
0.0002
-0.0033
-0.0033
(0.0011)
(0.0013)
(0.0022)
[0.815]
[0.010]
[0.129]
0.0074
0.0007
0.0007
(0.0007)
(0.0009)
(0.0019)
[0.000]
[0.426]
[0.703]
expands *
afterst
(1)
(2)
(3)
0.0046
0.0004
0.0004
(0.0005)
(0.0008)
(0.0018)
[0.000]
[0.604]
[0.815]
group2s *
afterst
group3s *
afterst
children in
-0.0174
-0.0173
-0.0173
-0.0175
-0.0173
-0.0173
household
(0.0003)
(0.0003)
(0.0018)
(0.0003)
(0.0003)
(0.0018)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
-0.0236
-0.0166
-0.0166
-0.0236
-0.0166
-0.0166
(0.0005)
(0.0005)
(0.0023)
(0.0005)
(0.0005)
(0.0023)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
0.0709
0.0710
0.0710
0.0709
0.0710
0.0710
(0.0003)
(0.0003)
(0.0037)
(0.0003)
(0.0003)
(0.0037)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
0.0640
0.0642
0.0642
0.0640
0.0642
0.0642
(0.0005)
(0.0005)
(0.0026)
(0.0005)
(0.0005)
(0.0026)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
0.0016
0.0015
0.0015
0.0015
0.0015
0.0015
(0.0004)
(0.0004)
(0.0009)
(0.0004)
(0.0004)
(0.0009)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.090]
No
Yes
Yes
No
Yes
Yes
black
female
age 18-34
age 35-54
State fixed
effects
42
Month fixed
No
Yes
Yes
No
Yes
Yes
Clustered SE
No
No
Yes
No
No
Yes
Observations
4,907,307
4,907,307
4,907,307
4,907,307
4,907,307
4,907,307
0.023
0.025
0.025
0.023
0.025
0.025
effects
R2
Note: Part-time employment is a binary variable that represents whether a respondent participated in part-time in the week preceding
surveying. Children in household is a binary variable that represents whether the respondent has at least one dependent child. Black is
a binary variable that represents whether the respondent has African-American ancestry. Female is a binary variable indicating the
respondent’s gender. Age 18-34 and Age 35-54 reflect the age group the respondent belongs to.
43
TABLE 4: Effects of ACA-induced and Prior Medicaid Expansions on
Voluntary Part-Time Employment
Voluntary Part-Time Employment
expand *
after
(1)
(2)
(3)
0.0057
0.0024
0.0024
(0.0005)
(0.0007)
(0.0016)
[0.000]
[0.001]
[0.151]
group1 *
after
group2 *
after
group3 *
after
(4)
(5)
(6)
0.0037
0.0017
0.0017
(0.0011)
(0.0013)
(0.0016)
[0.001]
[0.206]
[0.297]
0.0050
-0.0018
-0.0018
(0.0009)
(0.0011)
(0.0020)
[0.000]
[0.117]
[0.364]
0.0064
0.0039
0.0039
(0.0006)
(0.0008)
(0.0016)
[0.000]
[0.000]
[0.018]
children in
-0.0111
-0.0110
-0.0110
-0.0111
-0.0110
-0.110
household
(0.0003)
(0.0003)
(0.0018)
(0.0003)
(0.0003)
(0.0018)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
-0.0300
-0.0254
-0.0254
-0.0300
-0.0254
-0.0254
(0.0004)
(0.0004)
(0.0019)
(0.0004)
(0.0004)
(0.0018)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
0.0668
0.0669
0.0669
0.0668
0.0669
0.0669
(0.0003)
(0.0003)
(0.0033)
(0.0003)
(0.0003)
(0.0033)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
0.0404
0.0407
0.0407
0.0404
0.0407
0.0407
(0.0004)
(0.0004)
(0.0018)
(0.0004)
(0.0004)
(0.0018)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
-0.0072
-0.0070
-0.0070
0.0072
-0.0070
-0.0070
(0.0003)
(0.0003)
(0.0007)
(0.0003)
(0.0003)
(0.0007)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
No
Yes
Yes
No
Yes
Yes
black
female
age 18-34
age 35-54
State fixed
effects
44
Month fixed
No
Yes
Yes
No
Yes
Yes
Clustered SE
No
No
Yes
No
No
Yes
Observations
4,907,307
4,907,307
4,907,307
4,907,307
4,907,307
4,907,307
0.023
0.025
0.025
0.023
0.025
0.025
effects
R2
Note: Voluntary part-time employment is a binary variable that represents whether a respondent was working part-time for “noneconomic reasons”.
45
TABLE 5: Effects of ACA-induced and Prior Medicaid Expansions on
Part-Time Employment due to Childcare and Family Reasons
Part-Time Employment due to Childcare and Family Reasons
expand *
after
(1)
(2)
(3)
0.0020
0.0007
0.0007
(0.0003)
(0.0004)
(0.0007)
[0.000]
[0.110]
[0.327]
group1 *
after
group2 *
after
group3 *
after
(4)
(5)
(6)
0.0036
0.0006
0.0006
(0.0007)
(0.0008)
(0.0008)
[0.000]
[0.472]
[0.502]
0.0022
0.0004
0.0004
(0.0006)
(0.0007)
(0.0009)
[0.000]
[0.515]
[0.630]
0.0016
0.0008
0.0008
(0.0003)
(0.0005)
(0.0008)
[0.000]
[0.106]
[0.325]
children in
0.0280
0.0281
0.0281
0.0280
0.0281
0.0281
household
(0.0002)
(0.0002)
(0.0015)
(0.0002)
(0.0002)
(0.0015)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
-0.0160
-0.0140
-0.0140
-0.0160
-0.0140
-0.0140
(0.0002)
(0.0002)
(0.0009)
(0.0002)
(0.0002)
(0.0009)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
0.0388
0.0388
0.0388
0.0388
0.0388
0.0388
(0.0002)
(0.0002)
(0.0019)
(0.0002)
(0.0002)
(0.0019)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
0.0027
0.0029
0.0029
0.0027
0.0029
0.0029
(0.0002)
(0.0002)
(0.0003)
(0.0002)
(0.0002)
(0.0003)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
0.0050
0.0051
0.0051
0.0050
0.0051
0.0051
(0.0002)
(0.0002)
(0.0006)
(0.0002)
(0.0002)
(0.0006)
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
No
Yes
Yes
No
Yes
Yes
black
female
age 18-34
age 35-54
State fixed
effects
46
Month fixed
No
Yes
Yes
No
Yes
Yes
Clustered SE
No
No
Yes
No
No
Yes
Observations
4,907,307
4,907,307
4,907,307
4,907,307
4,907,307
4,907,307
0.025
0.026
0.026
0.025
0.026
0.026
effects
R2
Note: Part-time employment due to childcare and family reasons is a binary variable that represents whether the reason for a
respondent’s part-time employment is “Child care problems” or “Other family/personal obligations”.
47
TABLE 6: Effects of ACA-induced and Prior Medicaid Expansions on
Part-Time Employment, including Region-Month Effects
expand *
after
(1)
(2)
(3)
(4)
(5)
(6)
PT
PT
VPT
VPT
PT-F
PT-F
0.0019
0.0025
0.0009
(0.0022)
(0.0020)
(0.0009)
[0.390]
[0.223]
[0.336]
group1 *
0.0023
0.0026
0.0024
(0.0030)
(0.0028)
(0.0013)
[0.445]
[0.349]
[0.075]
group2 *
-0.0005
-0.0007
0.0003
after
(0.0028)
(0.0024)
(0.0012)
[0.858]
[0.782]
[0.832]
0.0035
0.0047
0.0007
(0.0026)
(0.0025)
(0.0010)
[0.181]
[0.063]
[0.492]
after
group3 *
after
State fixed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Clustered SE
Yes
Yes
Yes
Yes
Yes
Yes
Observations
4,907,307
4,907,307
4,907,307
4,907,307
4,907,307
4,907,307
0.025
0.025
0.024
0.024
0.026
0.025
effects
Month fixed
effects
Region-month
effects
R
2
Note: PT refers to part-time employment, VPT refers to voluntary part-time employment, and PT-F refers to part-time employment
due to childcare and family reasons. All three are binary variables that represents an individual’s employment status.
48
APPENDIX 1: Google Trends Example Search
Note: This figure shows the output when the search query is ‘jobs –steve –apple’, during the time frame of January 2011 – January 2016 in the United States. The latter half of the
query is included to remove all searches pertaining to the former Apple CEO.
49
APPENDIX 2: Summary of Each State’s Inclusion and Classification in Regressions
Inclusion
State
Treatment
Used in GJSI
Used in PTSI
Used in CPS
Regression
Regression
Regression
AK
Yes
AL
Yes
AR
Yes
AZ
Yes
CA
Yes
Expansion
Group1
Group2
Group3
1
1
Yes
1
1
Yes
Yes
1
1
Yes
Yes
Yes
1
1
CO
Yes
Yes
Yes
1
1
CT
Yes
Yes
Yes
1
1
Yes
1
1
1
1
Yes
DC
Yes
DE
Yes
Yes
FL
Yes
Yes
GA
Yes
Yes
Yes
HI
Yes
Yes
Yes
1
1
IA
Yes
Yes
Yes
1
1
ID
Yes
Yes
IL
Yes
Yes
1
1
IN
Yes
Yes
Yes
1
1
KS
Yes
Yes
Yes
KY
Yes
Yes
Yes
1
1
LA
MA
Yes
Yes
Yes
Yes
1
1
50
APPENDIX 2: Summary of Each State’s Inclusion and Classification in Regressions
(Continued)
MD
Yes
Yes
ME
Yes
1
1
Yes
MI
Yes
Yes
Yes
1
MN
Yes
Yes
Yes
1
MO
Yes
Yes
Yes
MS
Yes
Yes
MT
Yes
Yes
NC
Yes
ND
Yes
NE
Yes
NH
Yes
NJ
Yes
NM
1
1
1
1
1
1
Yes
1
1
Yes
1
Yes
Yes
1
1
NV
Yes
Yes
1
1
NY
Yes
Yes
Yes
1
OH
Yes
Yes
Yes
1
OK
Yes
OR
Yes
Yes
Yes
1
PA
Yes
Yes
Yes
1
RI
Yes
Yes
1
Yes
Yes
Yes
Yes
Yes
Yes
1
1
1
Yes
1
1
1
51
SC
Yes
Yes
Yes
SD
Yes
TN
Yes
Yes
Yes
TX
Yes
Yes
Yes
UT
Yes
Yes
Yes
VA
Yes
Yes
Yes
VT
Yes
WA
Yes
WI
Yes
WV
Yes
Yes
WY
Yes
Yes
Yes
Yes
1
Yes
Yes
1
Yes
Yes
1
1
1
1
i
Uninsured rates taken from the Center on Budget and Policy Priorities (cbpp.org), which calculates these metrics using census data from the Current Population Survey (CPS) and American Community Survey (ACS)
Estimates for annual income taken from obamacarefacts.com.
iii
Medicaid income eligibility limits as a percent of federal poverty level taken from kff.org.
iv
AK, AR, DE, HI, ID, MS, MT, ND, ND, NM, NV, RI, SD, VT, WV, WY were excluded from analyses using the PTSI due to data insufficiency
v
AK, AR, DE, HI, ID, MS, MT, ND, ND, NM, NV, RI, SD, VT, WV, WY were excluded from analyses using the PTSI due to data insufficiency
vi
Louisiana, Maine and the District of Columbia were omitted from the first set of regressions because of data insufficiency.
ii
52
Rational Radicals: Japanese Foreign Policy in the 21​st​ Century
Syrus Jin
Abstract
This research paper analyzes the foreign policy platform of the Japanese Liberal
Democratic Party (LDP) and presents the view that the LDP has continued to seek a
middle-course of balancing U.S. and national interests despite the rise of nominally nationalist
LDP politicians to positions of leadership. The doctrine of pragmatic policy-making that
subordinated national autonomy to economic growth developed among Japanese statesmen
immediately after the end of U.S. occupation of Japan in the 1950s, amidst a debate between
economic pragmatists and nationalist-orientated revisionists in the LDP, where the revisionists
were ideologically characterized as having a more glorified view of Japan’s imperial past and
muscular notion of providing for Japanese security. Although the turn of the century has seen the
rise in popularity and influence of LDP revisionists, the expansion of the Japanese military and
the rise in Japanese military operations abroad has been more of a reflection of changes in
geopolitical threats from China and changing perceptions in the public of national interests
rather than pure ideological motivations. A number of barriers prevent the implementation of
aggressive revisionist policies, including the government bureaucracy and relations with the
United States. The full effects of pursuing ideologically-based policies were shown when the DPJ
opposition party held power from 2009-2012 and soon showed its inability to deal with
institutional moderating factors. Despite the fact that LDP revisionists almost entirely controlled
​
the office of​ the
Prime Minister in the 21st century, Japan continues to go down a familiar path of
taking pragmatic action devoid of nationalist rhetoric which demonstrates an inherent flexibility
for future Japanese policies.
53
Introduction
At a glance, Japan is a nation in a peculiar situation. Having once fought a fierce and
bloody war with the United States, Japan now stands as one of the United States’ closest allies.
During the 1970s, Japan seemed to break out on the world stage as the second largest economy on
the globe. Although its position was later surpassed, Japan’s economic might in the 1990s gave it
the paradoxical situation of being an economic giant with little international influence.
Comparatively, Japan still has not recovered the regional dominance and influence that it had held
prior to the Second World War. During the Cold War, Japan largely pursued pragmatic policies
while staying in line with the United States, keeping a passive stance to international affairs. Since
the Cold War, the direction of Japan’s foreign policy has been contested between the “pragmatist”,
non-ideologically based political groups, and the “revisionist” factions that have a glorified view
of Japan’s imperial past, with both sides having differing visions on how to recover Japan’s
national prestige and autonomy (Bowen). This paper argues that due to the geopolitical changes of
the 21​st​ century and a combination of various external and internal factors, Japan has still
maintained pragmatism as a center pillar of its policy-making despite the presence of revisionist
politicians in office. While autonomy and national prestige have long been ideological goals in the
minds of many Japanese politicians, Japan still is more comfortable with pragmatically reacting to
immediate concerns without being married to ideology. As such, Japan’s current strategy towards
the Asia-Pacific region is dependent on the developments in its relationship with its neighbors and
the United States, and is directed by overriding concerns about national security and safety.
I. Historical Context
The conflict of ideology began with Japan’s surrender in the Second World War. Japan’s
total defeat and the subsequent American occupation of the home islands was extremely
significant in shaping the psyche and mentality of many Japanese postwar politicians. Not only
was Japan no longer an autonomous nation, it was now entirely dependent on the protection and
aid of its former enemy. The American occupation permitted no domestic opposition, and the
54
US-imposed constitution on Japan held a key tenet: Article 9, which committed Japan to
“renounce war” as a legitimate action of the state (Bowen). Japan had been rendered powerless,
politically and militarily.
With the end of American occupation in 1951, Japanese politicians had several major
concerns: shoring up Japan’s national security, economic rebuilding, and recovering lost national
prestige and autonomy. The drive for achieving national security was complicated by Article 9 and
a Japanese public that was still weary from war (Tōgō 33). However, the first prime minister since
the end of the war, Shigeru Yoshida, accurately gauged the powers at play with the US-USSR
conflict. With the spread of communism throughout Asia, the United States had begun to view
Japan less as a defeated enemy and more as a bulwark against the Soviets. Yoshida sought to take
advantage of America’s Cold War strategy, and became the leader of a faction aiming to achieve
security through economic growth without military spending, thus enjoying American defense
guarantees (Samuels 31).
This angle at policy-making, known as the Yoshida Doctrine, became the beginning of a
trend of ​pragmatism in Japanese politics. While some militarist factions in Japan—known as
historical “revisionists”—argued for the restoration of national prestige through military buildup,
pragmatist politicians sought a middle-course to accelerate economic recovery without
unnecessary risk (Tōgō 404). A series of election defeats eventually created an alliance of
necessity between Yoshida pragmatists and the revisionists, founding the Liberal Democratic
Party (LDP) in 1955. The LDP would come to dominate Japanese politics for the next three
decades, and while the factional split between pragmatists and revisionists persisted, the Yoshida
Doctrine of economic growth with minimal defense buildup remained as the LDP’s central
strategy (Keddell 8).
Autonomy and national prestige were ideological goals for both the pragmatist and
revisionist factions in the LDP. James Samuels, a renowned MIT professor and Japanese policy
expert, notes that pragmatists believed autonomy and prestige could be achieved through a strong
economy and keeping a careful balance between pacifism and assisting American interests
(Samuels 35). Peaceful economic influence, not military power, would be the backbone of Japan’s
55
new role in the world. Threats needed to be dealt with cautiously and the nation should be
protected from unnecessary risks. Revisionists, on the other hand, argued that economic wealth did
not translate directly into influence without the necessary step of building up a military (Samuels
37). Early on, revisionists maintained a muscular notion of national identity linked with the belief
in a nation’s duty to ensure its own security and prestige. In practice, this would mean reducing
military reliance on the US and Japan pursuing an Asian strategy unhindered by any need to
placate the United States or other Asian allies of the United States, such as South Korea.This
militarist view was compounded by their more positive views on Japan’s imperial past and a proud
view of Japan’s time as Asia’s hegemon.
After the end of the Cold War, Japan’s major economic recession in the 1990s and the
perception of changing geopolitical priorities—namely the growth of more external threats in
China and North Korea—lowered the popularity of the pragmatist faction of the LDP in the eyes
of Japan’s public. This culminated in the election of Junichiro Koizumi, an openly revisionist
Liberal Democrat politician, to the office of Prime Minister in 2000. Although the LDP lost power
in 2009 to the Democratic Party of Japan (DPJ), it quickly retook its majority in 2012 with the
election of Prime Minister Shinzo Abe, another LDP right-winger with a revisionist and nationalist
reputation (Scanlon).
II. Analysis
Besides the break in 2009-2012, it has seemed like the LDP revisionists have secured their
grip on power. Ostensibly, the turn of the millennia has offered an opportunity for the revisionist
strategy to come into practice. Koizumi planned for a $216 billion defense buildup in 2005
(Stalenheim 310), and in 2015 Japan’s parliament voted in constitutional changes to allow Japan’s
Self Defense Force (JSDF) to fight on foreign soil to enforce “collective self-defense” (“Japan
Passes Changes”). The statements of revisionist politicians called for a more “equal relationship”
with the United States, pointing forward to a more internationally assertive Japan (Samuels 203).
If ideology translates to policy, the revisionists are now in a position to finally implement their
56
long held vision of restoring Japan as a leader of the Asia-Pacific, breaking out the box of its US
alliance and the trend of pragmatic, short-term policies.
However, despite the appearance of a radical change in Japanese policy, the developments
in Japan’s foreign and defense policies have not been the result of an ideological shift under
revisionist governments. Although it seems as if revisionists are finally instituting their militarist
grand strategy into Japanese policy, the motive and reasoning behind revisionist policies are not at
all that different from pragmatist policies. Which is to say, pragmatic, rationalist decision-making
has not been replaced by nationalist and ideological motivations.
Traditionally expounded revisionist doctrine emphasizes Japan’s independence and
military self reliance when pursuing its foreign policy goals. This would, in practice, require Japan
to distance itself from the United States or break from its historically junior status in its American
alliance in order to take a more assertive stance when conflicts of interest arise and obtain a fuller
freedom of action unobstructed by American interests. But Prime Minister Koizumi did not loosen
the ties with the United States to allow Japan to pursue a more independent defense policy; he
tightened them. After 9/11, Koizumi took the step of “providing diplomatic and logistical support
for US military operations against both Afghanistan and Iraq” (Mochizuki “Japan’s Changing
International Role” 11). Koizumi’s policies signaled the beginning of a more robust and active
Japanese participation with its US alliance. Several programs since then have increased US-Japan
cooperation and intelligence sharing, including the creation of a joint military command center
(Samuels 178). This strengthening of defense ties between the two countries has been continued
by PM Abe, who, in an address to the US congress in April 2015, ended his speech by stating:
“…let us call the US-Japan alliance, an Alliance of Hope” (Abe). It is clear that LDP revisionists
have maintained the United States as a key cornerstone of Japanese foreign policy.
On the other hand, Koizumi and Abe’s stances to the US are not a static continuation of
Japanese attitudes during the Cold War. During a visit to the US in 1982, the PM Suzuki referred
to the US-Japan security treaty as an “alliance”. This caused an uproar in Japan as other politicians
feared that the usage of that term would be the pretext to more American pressure on Japan to take
an active role in the relationship, breaking with the tenets of the Yoshida Doctrine (Kawashima
57
31). From this episode, it is undeniable that the “Alliance of Hope” has undergone a significant
transition since the end of the Cold War. Yet, the transition does not entirely fit the prescribed
revisionist ideology of recovering lost autonomy.
Instead, the policies enacted under revisionist governments reflect the changes in the
geopolitical landscape rather than possible ideological motivations. The fall of the Soviet Union
brought an end to the bipolar stability of communism vs. non-communism that had lasted through
the Cold War. The possibility of diminished American support became a distinct concern for
Japan, with the Japanese ambassador to the U.S. in 1992 stating, “What we are concerned with is
an America turning inward, politically and economically” (qtd. in Sanger). These concerns were
followed by a series of new threats on the horizon with Japan’s neighbors, China and North Korea,
increasing fears about Japan’s national security. In 1996, large-scale Chinese military exercises
took place in the Taiwan Strait, and in 1998 North Korea launched a missile over the Sea of Japan
(Mochizuki “Japan’s Changing International Role” 5). Then, a financial crisis among Asian
countries in 1997 prompted Japan to begin regional negotiations to forge economic cooperation
among Southeast Asian nations (Mochizuki “Japan’s Changing International Role” 19). After this,
Japan began to increase its diplomatic interactions and forge stronger relationships outside of its
US relationship when it seemed clear that China was becoming more outspoken in its regional
ambition (Hughes 114). While these changes can be explained as a prologue to a Japanese attempt
at retaking its position as the leader of the Asia-Pacific, it is more likely that these developments
are rational responses to changing economic and political tides. Japan reacted to economic crises
and the possibility of weakened US influence in the region by engaging with its Asian neighbors,
and then responded to the rise of North Korea and Chinese militarism by strengthening its defense
alliance with the United States.
III. Regional Tensions with China
China’s rise is particularly concerning for Japan. Partially due to an utter absence of
Sino-Japanese engagement during the Mao era, relations have continued to be colored by lingering
Chinese resentment over Japan’s occupation of China and wartime atrocities during the Second
58
World War. Although the two countries experienced a brief thaw during the 1970s with a warming
of US-Chinese relations, the 1989 Tiananmen massacre sealed Japanese public opinion against
their Communist, authoritarian mainland neighbor (Mochizuki “Dealing with a Rising China”
239). Japan and China’s territorial disputes over the Diaoyu/Senkaku Islands in the East China
Sea, while rooted in conflicts of economic and strategic interests, have become massive issues of
nationalism and sovereignty. For Japanese policy-makers, rising Chinese military belligerence in
the Pacific and China’s economic strength have raised practical worries about the relative security
of Japan.
In response to the rise of a regional threat from China, LDP revisionists have often framed
Japanese militarization as a practical, strategic move. Chinese threats to sea lanes in the East and
South China Sea help explain the increase of Japanese security ties with Southeast Asian countries
and with the United States (Mochizuki “Dealing with a Rising China” 251). Yet, even with the rise
of China as a belligerent military power, Japan under LDP revisionists have not completely
adopted an anti-Chinese, nationalistic stance, showing that pragmatic decision-making has
continued to be the driving force behind Japanese foreign relations. With younger generations of
Japanese citizens less influenced by the period of postwar pacifism and more inclined to view
China as an aggressive neighbor, Japan’s government has notably sought to keep its diplomatic
options open. Regular meetings between Chinese President Xi Jinping and Prime Minister Abe
demonstrate an active Japanese effort to prevent tensions from damaging relations beyond repair,
despite the ideological and domestic pressures to respond in kind to Chinese provocations (Perlez).
While clearly Prime Minister Abe and LDP revisionists see the need to balance the rise of Chinese
power, they have maintained their options to keep a diplomatic avenue open despite Chinese
militarism. In this framework, Japanese military and diplomatic developments have acted as
defensive reactions to the changes in China. PM Abe has not been conciliatory or weak in
interacting with China, but neither has he demonstrated the nationalistic aggressiveness as
expected of a right-wing revisionist. Sino-Japanese interactions show a complex and deeply-rooted
rationalist mindset among revisionist policy-makers, where national prestige takes a backseat to
the concerns of survival and security.
59
These geopolitical developments have also allowed for another effect: the increased
popularity of revisionists in Japan. The supposedly more militaristic stance of the revisionists has
become more popular among the public because of these external events. Although the changes to
Japan’s constitution in 2015 which allowed the overseas deployment of the JSDF seemed to be
sudden and frightening for outside observers, this has only been a continuation of a gradual
realignment of Japan’s international involvement. In 2006, legislation which increased JSDF
participation in UN peacekeeping missions and further integrated Japan and US military
capabilities was passed in the parliament not only with the support of the ruling LDP but also with
the vote of several opposition parties (Shinoda 112). This legislative push for Japan to engage
more broadly with the world was not solely the vision of LDP revisionists, but supported by other
parties usually opposed to the LDP. Other parts of the political landscape and, by extension, more
of Japan’s public have embraced a more active Japanese international role. Terrorist risks, with
9/11 and more recently the killing of two Japanese citizens by ISIL, have also galvanized Japan’s
government into taking a greater role in international efforts, both alongside the United States and
with the United Nations (Ito 84). A motley of external pressures in the 21​st​ century has opened the
door for revisionist politicians to gain popularity and control of the office of the prime minister.
IV. Japan’s Bureaucracy and the American Alliance
However, numerous other factors, both internal and external, act as barriers to the
promulgation of aggressive revisionist reforms. Certainly, the personalities of revisionist prime
ministers such as Junichiro Koizumi and Shinzo Abe, both nominally “revisionist” but evidently
cautious and pragmatic, play a significant role in this restraint. But even without their
personalities, there remains a significant number of internal pressures that have retained the
tradition of pragmatism and thus prevented the possibility of a complete takeover of revisionist
ideologues. The bureaucracy, or the sum total of the ministries and administrative sectors of the
government, plays an unusually active and influential role in Japanese policy-making. During the
period of American occupation, the bureaucracy grew to have an immense amount of influence
over legislation; both in interpreting passed laws in Parliament and in presenting new legislation to
60
the Parliament (Keddell 13). This has carried over to the present day, where elite bureaucrats have
grown to have the knowledge needed to legislatively block other political actors and advance the
interests of their particular ministry. As described by Tomohito Shinoda, a professor at the
International University of Japan, organizational factors created a “bottom-up policy-making
process, [where] the bureaucracy played a pivotal role, serving as an instrumental veto player in
policy-making” (18). Bureaucrats often serve their entire careers within one particular ministry,
allowing them to act as a braking effect on prime ministers who may have wished to radically
change policy initiatives.
The bureaucracy’s pivotal role in administration also forces prime ministers to maintain
some bureaucratic support in order to pass legislation or respond effectively to crises. The
consequence of distrust from a prime minister to the bureaucracy was evidenced in the 2011
earthquake and Fukushima nuclear meltdown, where the PM Kan failed to give political
leadership on ​interagency coordination with recovery efforts due to his mistrust of the
bureaucracy, limiting the effectiveness of recovery operations in a time of crisis (Shinoda 220). In
the Japanese government—regardless of the party in power—the relative lack of centralization and
necessary collective consultation for policy-making prevents radical paradigm shifts from the
prime minister. Bureaucratic pressure helps to explain how LDP revisionist prime ministers,
despite their ties with revisionist and militarist groups, have largely refrained from breaking away
from the pragmatist status quo of policy-making.
Other external factors encourage pragmatist, cautious decision-making and act as
constraints on ideologically-based policies. Sixty years of close relations with the United States
make for a number of military, economic, and scientific ties that would be difficult to break if
Japan sought to achieve complete autonomy. The recently-passed Trans-Pacific Partnership,
fiercely pushed forward by PM Abe and President Obama, demonstrates the closeness of the two
countries’ economic interests in Asia (“Japan, US Make Economic Changes”). Additionally, most
Japanese and American security interests in Asia coincide, as both generally wish to preserve
stability in the region, maintain international sea-lanes of communication and commerce, and
cooperate against the spread of terrorism (Samuels 152). Although there are divergences in values,
both nations see it in their self-interest to maintain their relationship. Any drive to break away
61
from the US alliance would lead to significant strains on Japanese diplomatic power and security,
lending to a powerful reason not to blindly follow revisionist ideologies of autonomy. In terms of
military factors, Japan still enjoys the defensive presence of the U.S. 7​th​ Fleet and an entire U.S.
marine expeditionary force. While the Japanese-stationed American forces do not give the US a
massive amount of leverage over Japan, the clear and present benefits of the security alliance are
too great for Japan to push it to the side for the sake of achieving full autonomy, subsequently
giving the United States a pathway to promote its interests and prevent Japan from pursuing an
entirely independent strategy.
V. DPJ Failed Ideological Campaign
The deficiencies of pushing for an ideologically, rather than rationally, based grand
strategy were evidenced in 2009-2012 when the Democratic Party of Japan (DPJ) controlled a
majority in parliament. The DPJ, now called the Democratic Party, was the main opposition party
to the long-ruling LDP and managed to win the 2009 election on a campaign platform of
legislative reform, policy change, and a shift away from an American-centric regional strategy to a
more flexible one (Kushida 22). In general, the DPJ strongly opposed the talk among LDP
revisionists of changing Japan’s pacifist constitution, and believed that distancing Japan from the
interests of the United States would allow the nation to become fully autonomous and more widely
accepted in the global community as a trusted, peaceful country.
However, the DPJ’s lofty goals immediately ran into trouble. Christopher Hughes, a
published scholar in Japanese defense policy, puts forward the argument that the DPJ’s grand
strategy of how Japan should engage with the world fell victim to poor management and
institutional constraints: “The DPJ leadership…has[sic] curtailed many of its grander ambitions in
order to avoid costly domestic and international controversies” (Hughes 137). The DPJ’s time in
government coincided with a period of unheard of legislative lethargy, as the DPJ came into direct
conflict with the bureaucracy due to their ambitious policies, leading to a dysfunctional and
unproductive Parliament as fewer and fewer government-sponsored bills could make any headway
(Shinoda 18). The DPJ, attempting to shift away from its US alliance and realign itself with Asia,
immediately ran into complex, drawn-out talks with the US in attempting to close down American
62
military bases, ultimately leading to a series of embarrassing blunders in negotiations (Kushida
39). DPJ prime ministers failed to achieve their ambitious and explicitly stated goals since their
strained relationship with the US and the bureaucracy hindered the implementation of their plans.
With the public, the failure to live up to campaign promises led to the DPJ’s precipitous fall from
power as it was ousted in the 2012 elections with the LDP retaking its parliamentary majority once
more. The brief tenure of the DPJ in power loudly proclaimed the problems with ideology driving
policy. The DPJ’s attempt to charge forward despite the internal and external constraints present
definitively exposed the reasons behind the continuation of the pragmatist stances under
revisionist prime ministers.
Conclusion
Pragmatism, of course, is more logically convincing than ideologically-based decision
making. Between a potentially damaging, audacious move and a safe, beneficial pragmatic policy,
a politician whose career depends on visible good performance to the public while in office is
going to lean towards the latter. Geopolitical changes have led Japan to increase its ties with
Southeast Asian countries and attempt to balance new threats, while simultaneously causing the
Japanese public to be more open to the idea of a non-pacifist Japan that is active in the world’s
affairs. But although the abstract goals of autonomy and national prestige have been central in the
minds of Japanese political parties, external and internal constraints combined with the clear
benefits of continuing pragmatist policies have dampened the aggressive spirit of the revisionist
school of thought. Revisionist prime ministers have continued to maintain rational,
non-ideological policies. The obstacles with pursuing ideologically-based goals of autonomy and
prestige were shown with the DPJ’s brief tenure in 2009-2012. For LDP revisionists, no matter
what the ideological goals may be, national security and safety will always be the primary
concern. Any Japanese strategy that may arise is likely to be dependent on the developments in
Japan’s neighbors, rooted in the attitude of achieving survival through pragmatic means and being
ever-alert in the scanning the horizon for new threats and opportunities.
63
Works Cited
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Bowen, Roger. "Japan's Foreign Policy." ​The American Political Science Association 25.1 (1992): 57-73. J​ STOR. Web. 20 Apr. 2016.
Hughes, Christopher W. "The Democratic Party of Japan's New (but Failing) Grand Security Strategy: From "Reluctant Realism" to "Resentful
Realism"?" ​The Journal of Japanese Studies 38.1 (2012): 109-40. ​Project MUSE [Johns Hopkins UP]. Web. 20 Apr. 2016.
Ito, Go. "Participation in UN Peacekeeping Operations." Ed. Thomas U. Berger, Mike Mochizuki, and Jitsuo Tsuchiyama. ​Japan in International
Politics: The Foreign Policies of an Adaptive State. Boulder: Lynne Rienner, 2007. 75-96. Print.
"Japan Passes Changes to Pacifist Constitution to Allow Troops to Fight Abroad." ​ABC News. Australian Broadcasting Corporation, 18 Sept. 2015.
Web. 20 Apr. 2016.
"Japan, US Make Economic Rules under TPP: Abe." ​ABC News. Australian Broadcasting Corporation, 06 Nov. 2015. Web. 20 Apr. 2016.
Kawashima, Yutaka. ​Japanese Foreign Policy at the Crossroads: Challenges and Options for the Twenty-first Century. Washington, D.C.:
Brookings Institutions, 2003. Print.
Keddell, Joseph P. ​The Politics of Defense in Japan: Managing Internal and External Pressures. Armonk, NY: M.E. Sharpe, 1993. Print.
Kushida, Kenji, and Philip Lipscy. "The Rise and Fall of the Democratic Party of Japan." Ed. Kenji Kushida and Philip Lipscy. ​Japan under the
DPJ: The Politics of Transition and Governance. Stanford: Shorenstein APARC, 2013. 1-41. ​Stanford FSI. Web. 20 Apr. 2016.
Mochizuki, Mike. "Dealing with a Rising China." Ed. Thomas U. Berger, Mike Mochizuki, and Jitsuo Tsuchiyama. ​Japan in International Politics:
The Foreign Policies of an Adaptive State. Boulder: Lynne Rienner, 2007. 229-53. Print.
Mochizuki, Mike. "Japan's Changing International Role." Ed. Thomas U. Berger, Mike Mochizuki, and Jitsuo Tsuchiyama. ​Japan in International
Politics: The Foreign Policies of an Adaptive State. Boulder: Lynne Rienner, 2007. 1-23. Print.
Perlez, Jane. "Xi Jinping of China and Shinzo Abe of Japan Meet Amid Slight Thaw in Ties." ​The New York Times. The New York Times, 22 Apr.
2015. Web. 20 Apr. 2016.
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Shinoda, Tomohito. ​Contemporary Japanese Politics: Institutional Changes and Power Shifts. New York: Columbia UP, 2013. Print.
Stalenheim, Petter, Catalina Perdomo, and Elisabeth Skons. "Military Expenditure." ​Stockholm International Peace Research Institute Yearbook
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64
Nudge: The Role of Choice Architecture in Addressing Underinvestment in Education
Bapuchandra Kotapati
Abstract
The extent to which human capital, especially schooling, contributes to social well-being
and economic growth beyond labor-market productivity has been well established. Yet,
policymakers have raised concerns that individuals systematically underinvest in education,
which is evidenced by suboptimal rates of enrollment in primary, secondary and postsecondary
education in both developing and developed economies. Advancements in the field of behavioral
economics have brought forth a significant underlying source for the underconsumption of
education in market economies: individuals’ cognitive biases and the limitations imposed on
human rationality by cognitive thresholds. For this reason, intelligent, nuanced choice
architecture can lead to the development of policy tools that better motivate the consumption of a
socially desirable level of education. The paper will show this by examining the effectiveness of
changing the default option, simplification, and labeling in addressing underinvestment in
education in three different policy contexts: secondary education in India, postsecondary
education in the United States, and primary education in Morocco, respectively. There are at
least three substantive insights that come from reviewing the choice architecture tools presented
in this paper. First, the cognitive biases and behavioral tendencies of individuals can contribute
greatly to the suboptimal consumption of education with respect to the Mincer human capital
earnings function (HCEF), contrary to the traditional economics assumption of rational,
utility-maximizing behavior. Second, the effectiveness of traditional policy tools in the domain of
education may be significantly impacted by time discounting and biases towards present
rewards. And finally, the incorporation of behavioral insights can substantially expand the scope
of policy tools available to remedy underinvestment in education.
65
Introduction
Mincer’s human capital earnings function (HCEF), which is one of the most widely used
models in empirical economics, describes individuals’ lifetime earnings as a function of
education and experience.[1] Under the assumptions of the HCEF model, rational individuals
would carefully weigh the future benefits against costs to attain efficient levels of education. The
extent to which human capital, especially schooling, contributes to social well-being and
economic growth beyond labor-market productivity has been well established. Yet, policymakers
have raised concerns that individuals systematically underinvest in education, which is evidenced
by suboptimal rates of enrollment in primary, secondary and postsecondary education in both
developing and developed economies. Advancements in the field of behavioral economics have
brought forth a significant underlying source for the underconsumption of education in free
markets: individuals’ cognitive biases and the limitations imposed on human rationality by
cognitive thresholds. In this context, choice architecture—which is essentially organizing the
setting in which individuals make decisions—can refine and improve the effectiveness of policy
tools traditionally employed in encouraging increased participation in education.
Specifically, this paper argues that intelligent, nuanced choice architecture can lead to the
development of policy tools that better motivate the consumption of a socially desirable level of
education. The paper will show this by examining the effectiveness of changing the default
option, simplification, and labeling in addressing underinvestment in education in three different
policy contexts: secondary education in India, postsecondary education in the United States, and
primary education in Morocco, respectively. Subsequent sections will address the significance of
each policy issue, the underlying behavioral tendencies that contribute to these issues, and
pertinent choice architecture to mitigate underinvestment in education in each of the three cases.
The paper concludes by presenting a general framework to evaluate the effectiveness of choice
architecture in constructing behaviorally informed policy tools in education.
66
I. Choice architecture in education
Typically, choice architecture can mitigate the effects of flaws and inconsistencies in
decision-making by organizing the context in which individuals make decisions. Individuals
may want to complete secondary school, attend college or send their children to school, all of
which have positive externalities for society. However, education may be underconsumed in the
private market due to a variety of reasons, including individuals’ present bias, the complexity of
the decision-making setting, inattention, and cognitive strain. Small and apparently insignificant
details have an effect on individual behavior because they focus the attention of individuals in a
particular direction (Thaler and Sunstein, 2008). For this reason, behavioral insights can be
incorporated in policy tools to motivate the attainment of socially desirable levels of education.
Choice architecture can improve the context in which individuals make decisions and minimize
the effects of biases. Such an approach is libertarian and paternal at the same time in the sense
that choice architecture in policymaking is limited to offering individuals a better chance at
making efficient decisions in the private and social context; it does not unduly burden those who
want to exercise their freedom. In this manner, choice architecture can be implemented to
address low secondary school enrollment rates among girls in India, improve higher education
attainment in the United States, and lower primary school dropout rates in Morocco.
II​. ​Addressing low secondary school enrollment rates among girls in India
A. Policy issue
India, classified as a lower middle-income country as of 2016, has the second largest
education system in the world (British Council, 2014). It has seen a steady increase in primary
school enrollment over the last decade with over 96% of Indian children of primary school age
enrolled in the schooling system. However, the policy issue arises in retaining students— female
students in particular— in the period between the end of primary school and the beginning of
secondary school. It is estimated that for every 100 girls that enroll in school in rural India, 40
will reach grade 4, 18 will reach grade 8, nine will reach grade 9, and only one will make it to
grade 12 (Cheney, 2015). As a result, there is a wide gender gap in the secondary schools. In
many instances, girls are encouraged by their families to take up employment in the shadow
67
workforce after completing primary school.[2] India’s high fertility rates promote a social bias
against educating young girls. Parents lack the resources to provide a quality education for all of
their children, and therefore invest scarce resources in boys, for whom the market returns to the
investment in education are perceived to be higher.
There are many reasons for which the Indian education system’s failure to retain female
students has a negative impact on society. Mincerian estimates, which incorporate both years of
education and experience to project future earnings, show that a premature end to schooling can
depress the future earnings of women in the workforce. Moreover, there are a host of public
health benefits to educating women, including lower infant mortality rates, lower fertility rates,
and lower rates of incidence of sexually transmitted diseases (STDs) (Akmam, 2002). It has been
widely recognized that the education of women is a starting point in establishing equality in
traditionally patriarchal low-income countries (UNESCO, 2001). Education also provides skills
for girls to become more financially independent and emancipate themselves from regressive
social norms such as child marriage. However, it must be noted that there is a wide discrepancy
in the social norms of different states in India, which are closely correlated with the level of
literacy. At one end, the southwestern state of Kerala has achieved 94% literacy, while at the
other end, the northern state of Bihar has achieved only 39% literacy (Ray and Saini, 2016).
B. Underlying behavioral tendencies
There are two key behavioral mechanisms at play in limiting girls’ access to education in
India: the present bias and the herding behavior that arises from social normative influences. In
rural India, which has particularly low rates of school attendance for girls, families typically
pressurize girls to drop out from school after a few years of primary education. Due to the
present bias in their decision-making, predominantly uneducated families overvalue the present
benefits of monetary gain from girls working in the shadow workforce relative to the future
benefits of greater earning potential and job eligibility. Individuals display the present bias
because they discount future rewards in a non-linear, hyperbolic fashion (Shane et al., 2002).
That is, families discount the value of education by a large factor because the economic benefits
68
of education are projected in the future while the benefits of leaving school and joining the
shadow workforce are realized in the present. Studies have shown that valuations fall very
rapidly for small delay periods, but then fall slowly for longer delay period in a hyperbolic
fashion (Thaler, 1981). Therefore, the amount a future reward is discounted depends on the
length of the delay, given the discount factor such that:
where t is the delay in the reward offered by education, and k is a parameter governing the
degree of discounting. Because families systematically undervalue the benefits of greater future
earning potential offered by education, they pressurize girls to leave school upon completion of
primary school and urge them to take up jobs in the shadow workforce.
In addition, social normative influences widen the gender gap in secondary education
attainment in India. Specifically, herding behavior causes families to discourage girls from
attaining secondary education because the social norms in traditionally patriarchal rural India
dictate that girls must be trained for a role as a wife, mother and daughter-in-law (Lee, 2015).
Moreover, child marriage is most common in large swathes of rural India (UNICEF, 2014). In
this choice environment, families look to social norms for behavioral cues, and default to the
group behavior. It is also important to consider that most heads of families are not only
uneducated; they have also never had any contact with the schooling system. Because of their
status quo bias, families no longer weigh the benefits of an education versus that of marriage,
and they merely default to the social norm. This is particularly damaging when considering the
results of a study that found strong correlation between marital status and school attendance
rates: married children were over twice as likely to not attend school than single children
(Bhabha and Orla, 2013). For these reasons, students and their families undervalue education
because they make decisions skewed by strong present biases and regressive social norms.
69
C. Changing the default
Modifying the context in which students make their choices would at least partially
mitigate the effects of these cognitive biases. Implementing choice architecture, by enrolling
primary school-leaving girls in secondary school by default would increase participation. The
default would dictate that girls completing the fifth and final year of primary school are
automatically enrolled in a secondary school within reasonable geographical proximity. Such a
default would ensure that families would not actively debate withdrawing girls from education in
the reapplication period between the end of primary school and the beginning of secondary
school. This would wholly, or at least, in part mitigate the effects of the present bias. By
removing the active choice, the effect of social norms can be minimized because the stable state
associated with inaction would be attending secondary school. In traditional economic models,
defaults should have little impact on economic outcomes when the transaction costs are
negligible (Madrian, 2014). Because transaction costs of opting out of the default are minimal,
individuals would opt out of any default that is not consistent with their true preferences. In
reality, however, defaults significantly impact decision-making, even when the direct transaction
costs of opting out of the default are negligibly small, and even in domains where the decision is
highly consequential, such as education.
Another possible solution would be to reward girls’ successful completion of each year of
education with conditional cash deposits of $10 that are only redeemable upon completing
secondary school.[3] If a girl completes five years of primary school and is then pulled out from
secondary school, the cash deposit of $50 would no longer be available. If a girl misses school
on a regular basis, the family would be notified by text about the deposit they could lose, using
regret aversion as a motivational tool. A $100 deposit accrued over 10 years of education, would
represent a significant incentive to most families in the target areas of rural India where the
average per capita income is about $700. Because families are likely to fear that their decision
would turn out to be wrong in hindsight, they would exhibit regret aversion (Hayashi, 2008).
Regret-averse families may consider more carefully the costs of withdrawing girls from
schooling. In this manner, decision inertia wherein families prefer to retain the status quo of
70
limiting girls’ education can be successfully counteracted by setting defaults and devising
mechanisms that induce regret aversion.
III. Improving postsecondary education attainment in the United States
A. Policy issue
Each year, more than one million high school-leaving students in the United States who
are eligible for grant aid fail to complete the necessary forms to receive it (Lavecchia et al.,
2015). Given the high cost of postsecondary education in the United States, the number of
students attending college falls dramatically because fewer students can receive the financial
assistance they need.[4] Underconsumption of postsecondary education represents an inefficient
outcome due to the private and social benefits that a college education entails. While benefits
vary significantly across all college programs and occupations, college graduates enjoy an
earnings premium in all major occupation sectors. Lavecchia et al. (2015) posit that skill-biased
technological change has caused a large growth in demand for college educated workers,
especially those with skills that cannot easily be automated. Other empirical research argues that
there are likely large non-monetary returns to higher education, including higher job satisfaction,
increased quality of living, and better health outcomes (Oreopoulos and Salvanes, 2011).
Increased levels of higher educational attainment also show a correlation with lowered welfare
and criminal justice spending by the state. Yet, the complexity of the financial aid application
and erroneous evaluations of the likelihood of receiving financial aid dissuade a large number of
students from applying for aid that could finance their college education.
B. Cognitive thresholds and the representativeness heuristic
The complexity of the college financial aid application form in the United States has been
shown to lead to underconsumption of postsecondary education. The Free Application for
Financial Aid (FAFSA) form, which is the primary path to apply for college financial aid in the
United States, is eight pages long and includes well over 100 questions. The length and
complexity of the form dissuades a sizable proportion of high school-leaving students from
applying for financial aid (Dynarski and Scott-Clayton, 2006). The FAFSA includes detailed
71
questions on topics ranging from earnings, savings, the receipt of government benefits, parental
education attainment, driver's license number, previous drug convictions, and intended college
plans.[5] Then, under the threat of fines and prison, applicants and the parents of dependents
must formally attest that all responses are accurate. The length and complexity of the financial
aid application therefore induces cognitive strain in students who are in the process of deciding
whether to attend college. Such a complex decision environment is detrimental because the
human brain has a limited capacity in absorbing even modest amounts of complex information
given individuals’ cognitive thresholds. In addition, a report by the Advisory Committee on
Student Financial Assistance (2001) suggests that students and families, as well as adult learners,
are often heavily influenced by news stories about college being unaffordable. Because of the
representativeness heuristic, individuals estimate what is more likely by what is more available
in memory, which is biased towards unusual, or extraordinary examples. Subsequently, students
are more likely to be dissuaded from applying for aid once they consider the cognitive strain the
application process entails. In this case, underconsumption of postsecondary education arises at
least in part due to the complexity of the financial aid form.
C. Simplification
In such cases where complexity is identified as a significant contributor to suboptimal
decision-making, simplifying the choice set can be an effective policy tool. Bettinger et al.
(2012) conducted a field experiment designed to simplify the financial aid application process.
They introduced paid tax preparers to help individuals complete the FAFSA form at the time
when applicants file their federal taxes. They found that participants who were provided a
simplified method to complete the FAFSA were not only more likely to apply for financial aid,
they were significantly more likely to attend college and receive aid. College enrollment rates for
high school seniors and recent high school graduates rose 8 percentage points, from 34 to 42
percent in the year. Simplifying the FAFSA form also increased enrollment by 16 percent for
adults out of high school with no prior college experience. The effects of this relatively
inexpensive change in the decision-making context are large, particularly when compared to the
estimated effects of changing the price of college.[6] Accordingly, simplification is a powerful
72
policy tool in situations where complexity and cognitive strain are identified as significant
contributors to underconsumption of education.
IV. Lowering primary school dropout rates in Morocco
A.
Policy issue
Morocco is a lower middle-income country, with income per capita estimated at $6,850
in purchasing power parity terms (UNDP, 2015). Education levels in the general population are
relatively low, with only about 56% of the adult population literate (Benhassine et al., 2013). The
prevalence of low levels of literacy and high primary school dropout rates is particularly acute
among low-income individuals in rural areas. The Ministry of Education estimates that over 90%
of rural children start primary school, but 40% of these students drop out before completing the
full six years of primary education. Despite this, Mincerian estimates of the benefits to schooling
have been found to be large even among rural households. Primary school completion for either
the male or the female head of the household is correlated with 20% higher consumption at the
household level, and these effects are additive (Benhassine et al., 2013). However, it must be
noted that part or all of these correlations could be driven by selection effects.
B. Status quo bias, representativeness heuristic and priming effect
Apart from the economic disincentive of enrolling children in primary school, there are at
least three important biases that affect enrollment and retention, including the status quo bias,
representativeness heuristic and the priming effect. Status quo bias is primarily an emotional
bias; it describes a preference for the current state of affairs (Kahneman et al., 1991). Given that
44% of Morocco is illiterate, the current baseline is taken as a reference point, and only a change
from that baseline is perceived as a loss by families. That is, families do not necessarily perceive
their
children’s
potential illiteracy
and diminished employability
as
a loss.
The
representativeness heuristic takes effect because we make conclusions from limited sample
spaces. In a similar sense, Moroccan families might draw inferences from a limited sample space
of family members, relatives and friends and erroneously conclude that education might not be
important for job market eligibility. Finally, low-income families are primed by the ubiquity of
73
illiteracy in their communities and as a result may grossly undervalue education. In particular,
this effect is marked among rural low-income households in Morocco that are typically
characterized by a low marginal propensity to consume education.
C. Labeling as a policy tool
A policy tool that could be implemented to address under-enrollment in public schools in
Morocco is a cash transfer program targeted at low-income households. Benhassine et al. (2013)
conducted a field study with policymakers who could implement two different iterations of such
a cash transfer program. The first was a conditional cash transfer program in which payments
were only made if the child does attend school. The other was a labeled cash transfer (LCT) that
designated funds for children’s education; the funds could still be used for other purposes.[7]
The program was thus a “labeled” cash transfer, explicitly tied to an education goal but without
formal requirements on attendance or enrollment. Benhassine et al. (2013) found that the labeled
cash transfer program was at least as successful as the conditional transfer program. In fact,
households’ marginal propensity to consume education was slightly higher out of the labeled
cash transfer than out of the restrictive conditional transfer program. Additionally, Benhassine et
al. (2013) suggest that labeling the cash transfer as a ‘‘child benefit’’ apparently creates in
parents a moral obligation to actually spend that money on their children. Students who received
the labeled cash transfer were also 7.9 percentage points more likely to complete primary school.
Moreover, the labeled cash transfer program is significantly less expensive to administer than the
conditional cash transfer program. In this manner, intelligent labeling can significantly impact
the choice architecture, which can then be used to improve and reinvent existing policy tools.
Nonetheless, one limitation to the scope of Benhassine et al.’s (2013) study is the fact that the
program was run from schools, which may have discouraged or even excluded families who had
never had any prior contact with the school system.
Conclusion
In summary, choice architecture has broad implications for the design of policy solutions
to remedy underinvestment in education. The example of the simplification of the FAFSA
74
financial aid application illustrates the potential of simplification in shaping more cost-effective
policy solutions. Setting defaults can successfully counteract decision inertia wherein individuals
prefer to retain the status quo, as in the case of girls’ secondary school education in India. In
other contexts, labeling can be incorporated into the choice architecture to refine and improve the
effectiveness of existing policy tools, consistent with the results of the labeled cash transfer
program in Morocco. As the examples discussed in the paper suggest, nuanced choice
architecture has had, or has the potential to have, an impact on consequential policy outcomes in
education.
Accordingly, there are at least three substantive insights that come from reviewing the
choice architecture tools presented in this paper. First, the cognitive biases and behavioral
tendencies of individuals can contribute greatly to the suboptimal consumption of education with
respect to the human capital earnings function (HCEF), contrary to the traditional economics
assumption of rational, utility-maximizing behavior. Second, the effectiveness of traditional
policy tools in the domain of education may be significantly impacted by time discounting and
biases towards present rewards. These biases impact the effectiveness of policy tools in both
developing countries such as India and Morocco, and developed countries like the United States,
but the nature of and extent to which the biases affect policy goals may vary. And third, an
incorporation of behavioral insights can substantially expand the scope of policy tools available
to remedy underinvestment in education. Moreover, policy solutions that are crafted by nuanced,
intelligent choice architecture generally come at a lower private and social cost than traditional
policy tools. Ultimately, behaviorally informed policy interventions that motivate individuals to
consume socially desirable levels of education can result in Pareto improvement—they can
improve net social welfare without making anyone worse off.
75
Endnotes
[1]​ ​The human capital earnings function, named after Jacob Mincer, models the logarithm of earnings as a sum of years of education and a
2
quadratic function of years of potential experience such that: ln y = ln y​0 +
​ ​rS + ​β​1​X + β​1​X​
where is earnings, is the earnings of an individual with zero years of education and experience, describes the years of education and is years of
potential experience (Mincer, 1974).
[2] The Indian school system follows the British structure. Primary school consists of grades 1-5 (ages 6-11), secondary school consists of grades
6-10 (ages 11-16) and senior secondary school consists of grades 11 and 12 (ages 16-18). Attendance up to grade 10 is mandated by law (Right to
Education Act 2009) (Dhar, 2010). Any individuals looking to take up jobs before age 14 generally do so illegally in the informal ‘‘shadow’’
workforce.
[3] The Government of India’s estimated annual spending per student was $237 for the 2015-16 budgetary period (Nagarajan, 2015). An annual
$10 deposit would therefore represent a 4% increase.
[4] In the last 30 years, the cost of obtaining a college degree has increased by more than 1000 percent—four times faster than the increase in the
consumer price index (MP1, 2015).
[5] Students who are still financially dependent on their parents must also include information about their parents’ incomes, the year their parents
were married or divorced, and their parents’ social security numbers.
[6] The US Department of Education has subsequently implemented its own efforts to simplify the financial aid application process (Madrian
2014).
[7] The ​Tayssir labeled cash transfer (LCT) was tested in the five poorest regions of Morocco: Marrakech-Tensift-Al Haouz, Meknès-Tafilalet,
l'Oriental, Souss-Massa-Draa and Tadla-Azilal (Benhassine et al., 2013).
76
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77
Moving Towards a Brighter Future: India’s Solar Energy Aspirations
Rhea Kumar
Abstract
This paper examines India’s role in solar energy expansion, particularly in the electricity
sector. In 2015, the Indian government announced its intent to increase India’s renewable
energy capacity from 77 gigawatts (approximately 13% of current installed capacity) to 175
gigawatts by 2022 (40% of expected
​ ​ capacity)[1]. Much of the increase in renewables will be
driven by solar energy, which will account for 100 gigawatts (or GW) of the 175 GW, followed
by 60 GW from wind power, 10 GW from biomass and 5 GW from small scale
​ ​ hydropower[2].
Solar power is abundant in India and has untapped potential for expansion. At the same time,
such an ambitious solar plan, while commendable for India’s level of development, is
challenging to implement.
78
Introduction
Reaching the goal of an additional 20 GW per year requires an exorbitant amount of
physical and financial resources. Solar power also runs the risk of severely destabilizing the grid
due to the intermittent and fluctuating supply of solar energy. Nevertheless, even if the Indian
government does not reach its 100 GW target by 2022, solar will be crucial for India to address
the juxtaposing challenges of demand-supply gap and need for clean renewable energy.
Regardless of whether the Indian government meets its 100 GW target, it is important to
confront the issues associated with solar in order to spearhead its adoption in India’s electricity
market. In the current no-carbon pricing scenario​[3]​, the challenges to solar energy are addressed
by using either one of two broad strategies. The first is an expansion of and improvement in the
functioning of large scale grid based solar power such as through solar parks. The second is a
greater focus on small scale solar plants comprising either grid connected distributed systems or
completely standalone ( otherwise known as decentralized systems) that are disconnected from
the grid. India’s policies currently focus both on large scale grid based solar plants through its
solar parks policy, and on small scale distributed or decentralized plants through its rooftop solar
policy as well as the Pradhan Mantri Entrepreneurship Scheme. This deviates from the developed
world where solar energy expanded via centralized on-grid systems. Throughout this paper, my
objective is to analyze the advantages and disadvantages of each kind of strategy using case
studies of centralized and distributed projects in India, and also include Germany’s experience in
solar power expansion. I hope to use this to describe the enabling conditions and extent of the
shift from centralized to decentralized solar power in India.
I start by reviewing relevant literature that defines the opportunity for solar power in
India, current government policies towards solar power, and the four major challenges to
realizing India’s 100 GW target: scale, technical constraints, high tariffs and financial
constraints. I then raise the main question of my paper: will improving grid based systems or
expanding small scale solar energy be a better solution to India’s solar energy problems? Then, I
determine how these two strategies have worked in practice by looking at three case studies: a
pilot project to introduce purely off grid solar energy in rural areas launched by the Indian
79
Institute of Technology, policy implemented by provinces of Madras, Rajasthan’s/Gujarat’s to
facilitate construction of solar parks, and Germany’s grid based solar expansion plans of 38 GW.
In each case study, I attempt to not just understand the distinction between grid based and
decentralized power projects, but also what specific conditions allowed each kind of project to
work. I arrived at three conclusions. First, there is an active movement towards not just small
scale distributed solar power that is eventually connected to the large grid, but also towards
off-grid decentralized standalone solar systems, especially in rural India. Second, small scale
systems’ ability to accommodate both demand and supply-side challenges of solar energy have
made them a popular alternative to large scale solar plants across India. Third, while centralized
solar parks continue to play an important role in states like Gujarat and have contributed to
India’s solar energy expansion plans, their success depends on factors such as institutions and
location. On the other hand, small scale solar systems depend much less on such factors, and
hence entertain a broader appeal across India. Simultaneously, I find it necessary to look at the
specific contexts in which each type of system thrives and apply them to the right type of solar
energy strategy in various locations across India.
I. Literature Review
The existing literature on solar power is vast and describes the opportunities for solar
energy expansion both globally and in India, as well as the challenges with India’s solar energy
expansion plans of 100 GW by 2022.
Solar energy expansion in India is critical in order to simultaneously achieve growth and
sustainability. A McKinsey report titled “Disruptive Technologies: Advances that will transform
life, business and the global economy” had listed renewable energy as one of the 12 “disruptive
technologies” over the next decade.​[4]​ Specifically, it argues that global demand for solar and
wind energy has been driven by two factors: to supply the ever rising demand for energy and
80
electricity while simultaneously mitigating environmental degradation and climate change.​[5]
Both of these factors manifest themselves in India. Currently, about 32% of India’s population
lives without electricity.​[6]​ And combined with economic growth, industrialization and
population growth, demand for electricity is projected to rise. As opposed to burning fossil fuels
to meet this demand, the Indian government aims to produce 500 GW of electricity by 2022​[7]
Thus the expansion of renewable energy programs in India is inevitable. Solar will be a critical
driver of the renewable energy expansion. According to the McKinsey report, while wind power
is a relatively mature technology, solar power is relatively untapped but has potential for
technological innovation and growth. The report estimates that the costs of solar energy could
drop by 65% over the next decade due to technological improvements.​[8]​ These falling costs will
drive expanded production in solar energy, further reducing costs per unit of producing solar
energy due to economies of scale. Additionally, solar carries several distinct advantages over
other forms of renewable energy: it has greater power density (power produced per unit area or
volume), is more predictable in its supply than wind and is more energy efficient than biofuels or
hydro. Finally, solar power is suitable for India’s tropical climate. Ummadisingu and Soni argue
that India receives about 5000 kWh per year of solar energy, with most regions receiving 4-7
kwH per sq m.​[9]​ In a recent article, the economist Swaminathan Aiyar mentions that solar plants
can capture 15-22% of the sun’s energy based on the specific design, compared to natural plant
sources of biofuels, which capture only about 7% of the sun’s energy during photosynthesis.​[10]
Thus, renewable energy expansion is paramount for India to develop sustainably, and among
renewable energy sources, there is general consensus in the literature that solar will play a
critical, if not the most critical role in India’s renewable energy expansion.
For all these reasons, the government of India has chosen to prioritize solar in its
renewable energy expansion plans. In 2011, the government launched the Jawaharlal Nehru
National Solar Mission to provide technical and financial support for the expansion of solar
power under the ambit of the National Action Plan for Climate Change (NAPCC). The new
Bhartiya Janata Party government, elected to power in 2014, expanded on the previous mission
by mandating 100 GW (roughly 20% of the grid), a huge step up from the initial target of 22
GW. Government websites say that 40 GW of the 100 GW will be met through large scale solar
81
parks and utility scale solar, another 40 GW through grid connected rooftop solar projects and 10
GW through independent decentralized solar entrepreneurship schemes.​[11]​ The new policy hopes
to reach at least 11 GW of solar power capacity by 2017, a figure that the government believes
will lead to grid parity or equal cost per unit of solar energy and fossil fuels.​[12]​ As of now, 10
GW of utility scale solar is already under development, with an estimated additional capacity of
13.8 GW over the next two years. If the capacity addition indeed takes place, India will be able
to achieve grid parity.
However, it is unclear whether the Indian government will actually be able to achieve its
stated targets. The scale of solar capacity addition is unprecedented, both for the world and for
India, whose renewable energy capacity addition so far has been well below average. In order to
reach 100 GW, India will need to increase its pace of renewable energy capacity from 3 GW per
year to more than 20 GW per year. So far, India has added just over 10 GW​[13]​, which means
there’s a long way to go before the target is reached. No other country in the world has attained
this level of solar energy capacity, not even Germany, which recently invested in adding 38 GW
of solar energy expansion.
For India, producing 100 GW of solar energy would require huge amounts of both
physical and financial resources. In terms of physical resources, solar plants require vast amounts
of land, in addition to investments in transmission and distribution infrastructure. The power
density, or power per unit area, is much lower for solar energy compared to that of conventional
resources such as coal. Power density for solar energy ranges anything between 10 MW-62.5
MW per square kilometer based on estimates from different studies and whether the solar plant is
concentrated, photovoltaic or rooftop.​[14]​ This is less than the power density of coal-fired thermal
plants, which can range anything between 100 MW-1000 MW per square kilometer of land.
India will require more than 1000 sq km of land for solar power expansion. And in a densely
populated country like India, land is consequently scarce. Not only is diverting so much land
towards solar energy undesirable, but procuring such land is also difficult. The Land Acquisition
bill, a piece of legislation that allows the government to allocate eligible land for industrial bills,
is still pending in the Indian parliament. Even if passed, different Indian states have different
policies with respect to land. On the infrastructure side, India’s current grid based electricity
82
infrastructure is not very strong, and results in transmission and distribution (T&D) losses as
electricity is transported over large distances. Solar energy expansion at the 100 GW scale will
require huge investments upgrading and expanding the (T&D) network, a massive physical as
well as financial challenge.
Solar power’s current intermittency problems also deem it undesirable from both a
reliability and cost perspective. In his paper on renewable energy expansion, Bird argues that
while it is easy to predict the amount of sunlight available over the course of the day compared
to, for example, the available wind energy, the amount of sunlight fluctuates based on the cloud
cover at a point of time.​[15]​ These relatively small fluctuations, when combined with unexpected
changes in demands for electricity, can cause instability in the grid. Ghosh supports this claim:
"To maintain grid frequency, grid operators must be able to predict precisely what the solar
energy input at any given hour will be. But such an exact prediction every time is impossible. A
small error in judgment will trigger frequency fluctuations and, thereby, instability in the
grid.”​[16]​ Even the smallest errors in predicting solar input will lead to grid frequency
fluctuations. According to the CEO of General Electric South Asia, more than 20% renewable
energy on the grid could destabilize it.​[17]​ Ghosh argues that India will have to invest in backup
infrastructure to overcome the intermittency problem.​[18]​ The investment will translate into higher
setup costs for solar plants. The literature suggests two things. First, such a huge expansion of
solar power may not be desirable for India given the current stage of solar technology. Second, if
India wants to continue with its ambitious solar energy plans and simultaneously avoid the
intermittency problem, it will have to face higher spinning reserve costs as it installs the backup
infrastructure necessary for a reliable supply of electricity.
Solar energy faces high tariffs and costs of production, making it difficult to compete
with conventional energy resources. Globally and in India, solar is not cost competitive with
other energy technologies. Several levelized cost analyses of solar versus conventional energy
resources conclude that solar energy still carries significant cost disadvantages compared to
conventional energy resources. In India, solar power costs Rs. 6 or 7 per unit compared to
conventional power at Rs. 4 per unit.​[19]​ The government aims to generate 11 GW of solar energy
by 2017 in order to achieve grid parity or equal prices of solar and conventional fuels. India has
83
about 10 GW under construction right now and another 13.8 GW in the works​[20]​ over the next
few years, which makes this target more realistic. However, economists such as Ghosh point out
that grid parity is not the only goal for India -- stability is also a priority. Ghosh argues that most
of the lower costs of solar energy are due to the cheap imports of solar photovoltaic cells from
China, which exposes India to exchange rate fluctuations. Thus solar energy, which was partially
brought in to improve India’s energy security situation, could potentially threaten its energy
security situation. It is important for India to find ways to expand solar in an economical manner,
while simultaneously ensuring energy security and balance of trade.
The structure of the solar industry as well as government policies so far have not been
conducive to financing solar energy. In terms of industry structure, the costs of setting up solar
plants are extremely high and require huge investments upfront with uncertain returns. Hence
firms are reluctant to invest in solar energy. As an example of firm reluctance, Mahajan mentions
that 200 private companies have signed agreements with the new government to support 266 GW
of renewable energy in India, but most announcements are in the forms of Memorandum of
Understanding rather than firm commitments.​[21]​ So far, the private companies that have invested
in solar energy generation have not been performing as well as expected. In one example,
SunEdison Asia Pacific, which invested in a 500 MW solar park in Andhra Pradesh, quoted an
all time low tariff of Rs. 4.63 per kwH, compared to average tariffs of Rs. 6 to 7 in other
plants.​[22]​ However, recent reports show that SunEdison is now suffering from severe financial
issues and has delayed filing its annual income statements. In a recent article, Mahajan
summarizes: “No two [solar energy] projects can get you a similar price.”​[23]​ The perceived low
return-risk ratio of solar energy, combined with the track record of utilities and private
companies, has made it difficult to attract funding. In terms of the market, India’s policies
enforce many restrictions on financing solar energy projects. Mahajan provides a detailed review
of the challenges in India’s electricity market. He argues that the banking sector has already hit
its limit of extending 16% of its total loans to the power sector. This means that the government
will have to turn to equity from promoters in order to finance power sector expansion. However,
the total corporate debt in India (all sectors) is Rs. 41 lakh crore ($600bn), which has led to a
debt overhang problem and discouraged promoter equity towards new projects. In addition, the
84
subsidies on solar power manufacturing within India have deterred foreign funding for solar
energy.​[24]
While the literature acknowledges solar energy to be a crucial part of India’s renewable
energy expansion, it is simultaneously skeptical of the Indian government’s expansion targets of
100 GW by 2022. However, regardless of whether solar achieves the 100 GW target by 2022, it
will be useful to understand some of the possible solutions to these challenges in order to
facilitate the expansion of solar energy and ultimately reach grid parity. The next section
discusses two types of solar power plants -- centralized grid based systems and distributed or
decentralized solar plants. Analysis of each solar power plant will try to address potential
challenges.
II. Key Unresolved Questions
India’s current solar energy policy allocates 40 GW production to utility scale solar
power, 40 GW production to rooftop grid connected solar power and 20 GW to decentralized
solar projects through the Pradhan Mantri (Solar) Entrepreneurship Scheme.​[25]​ In this section, I
compare the key features of large and small scale solar power, review the history of large and
small scale solar power in India and hypothesize that smaller scaled distributed and decentralized
systems are a better option. Since I am primarily attempting to determine how the size of solar
power plants affects their ability to respond to technical and economic challenges, I choose to
group distributed and decentralized power under a single category--small scale solar plants.
Large scale solar plants have over 200 KW of generation capacity and are connected to
the electric grid. The plants consist of two broad types: concentrated solar power, where solar
energy is used to heat fluid that powers a generator through steam power, or photovoltaic, where
silicon cells directly convert solar energy to electricity. Currently, the costs for concentrated
solar power are lower than those of photovoltaics. However, due to technological innovation, the
costs of photovoltaics are rapidly declining . India’s pre-2014 policies towards grid based solar
focused on a 50-50 division between these two types of large scale solar plants​[26]​. At 100-300
MW, the expansion of India’s solar energy parks is a very ambitious ordeal. Smaller
manufacturing facilities connected to the grid, such as in the states of Rajasthan, Uttar Pradesh
85
and Andhra Pradesh, are also classified as large scale solar plants. Several developed markets,
such as Spain, US and Germany, have fueled the expansion of their solar energy through large
scale centralized solar plants. However, it is unclear whether large scale solar plants is the most
desirable route for India. Many of the challenges described above are amplified when applied to
large scale solar power plants. At the same time, there are ways to meet these challenges. The
size of these plants will require large amounts of land substitution. Assuming a solar power
density of 40 MW per square kilometer, a 100 MW solar park will require 2.5 square kilometers
of land, or the equivalent of 250 family sized farms. The large size of solar plants also translates
into heavy upfront costs, and raising the funds has been extremely difficult. Although in general
solar power suffers from intermittency problems and could destabilize the grids, some theories
suggest that large scale solar power may be more suited to deal with the issue than small scale
solar power. For instance, Lori Bird, an analyst at the National Renewable Energy Laboratory,
argues that since large photovoltaic solar plants are never completely covered by clouds, they
will be less affected by volatilities in output than small scale solar plants.​[27]​ Given the scale of
production in India’s power plants, they will be well equipped to deal with this challenge.
Additionally, unlike distributed solar power plants, photovoltaic solar plants can provide
real time data on demand and power generation to plant operators. According to Bird, distributed
solar power “makes it difficult for a system operator to know whether an increase in net load is
because of increasing demand or decreasing solar generation”.​[28]​ Finally, Bird argues that as the
number of solar plants in a certain geographical area increases, they will be better equipped to
deal with intermittency problems.​[29]​ This suggests that as grid connected solar power increases
in India, it will be less prone to fluctuations, ensuring the stability of the grid. However, given
India’s growing population, numerous solar plants must be built for this strategy to work.
Even as large scale solar plants attempt to resolve their challenges, small scale solar
plants with capacities are instead being recommended as a possible alternative.. India’s policies
currently envisage 40 GW of power generation through distributed rooftop solar plants and
another 10 GW of power generation through decentralized off grid solar. Since the 1980s, many
privately funded solar home systems have sprung up, providing basic electricity needs such as a
fan, light and television or radio​[30]​ while overcoming at least some of the key challenges faced
86
by large-scale centralized solar power. First, many of these systems are based on rooftops,
requiring less land substitution and diversion. Second, by avoiding investments in transmission
and distribution infrastructure, these systems could be more cost effective than large scale solar
systems. Since small scale solar systems are typically closer to the point of generation, they will
avoid the high costs of expanding the grid over a large landmass like India. Timilsina et al
support this point by arguing that in particular off grid solutions have become extremely popular
in China and India​[31]​. Additionally, off grid solar systems are have become competitive with
other indigenous sources at the rural level. In fact, according to a 2000 World Bank study, these
off grid systems offer a cost advantage over alternatives such as kerosene .​[32]​Meanwhile,
distributed and decentralized solar systems suffer from a major financing challenge. The World
Bank study mentions that they require about 15-20 years of upfront capital costs, which is
difficult for poor rural households to meet on their own. Apart from projects funded by the
private and social sector, there is a real need to encourage financing systems at the local level to
fund such projects independent of the grid. Alternatively, the projects can be integrated back into
the grid, which has historically been the preferred option. A 2000 World Bank study says: ““This
does not mean that rural households would not rather have a connection to the electricity
grid…in the absence of a grid, SHS is competitive with the technologies to which the rural
households would otherwise turn.”​[33]
It is clear that there is a paradigm shift to small scale distributed and decentralized solar
from large scale plants, as these systems grow in popularity. What are the enabling conditions
that have promoted this shift in India and other developing countries, and how far are we in this
shift? Should we try to integrate decentralized systems into the grid as the World Bank study
suggests, or allow them to exist on their own? Finally, does the shift to decentralized solar
energy mean a complete elimination of centralized large scale solar plants, or are there unique
conditions in India under which these plants could flourish? In the next section, I attempt to
answer these questions through case studies from outside and within India.
III. Methodology
87
For methodology, I examined three case studies that illustrate the advantages and disadvantages
associated with centralized and distributed solar systems. The first case describes Germany’s
development plan that launched a 38 GW expansion that was to be implemented by large scale
centralized solar plants. The second case looks at the potential benefits of a decentralized solar
home system pilot project pioneered in rural India by a team of scientists from the Indian
Institute of Technology in Madras. The final case analyzes a policy to centralize solar parks in
Gujarat that overcomes some of the potential drawbacks of Germany.
IV. Germany
Like India, Germany has heavily relied on coal for its electricity needs. While it does not
suffer from the same capacity expansion needs as India, the growing air pollution stemming from
thermal plants’ reliance upon lignite, an inferior grade coal, has prompted the government to
look at renewable energy options. Germany has invested in 38 GW of solar power expansion,
meeting approximately 6% of total electricity demand in 2014.​[34]​ As in other parts of the
developed world, the expansion of solar power has been dominated by centralized large scale
solar power plants, as well as some expansion of distributed rooftop solar plants. Many sources
reference Germany as a leader of large scale photovoltaic solar plants, which countries such as
India are now emulating. The German government played a major role in financing the
expansion of solar energy through two policy instruments: feed-in tariffs and subsidies. Feed in
tariffs (FITs) provide a fixed payment to solar energy producers that are not yet competitive with
conventional energy producers in order to encourage innovation and ultimately drive down the
costs of production of solar energy.​[35]​ Germany’s FITs vary with the technology and size of
plants, which has allowed it to charge the most efficient tariffs based on the perceived benefits
from the project. Timilsina argues that Germany’s approach to FITs has allowed it to rapidly
expand solar energy.​[36]
While FITs and subsidies have allowed for a rapid expansion of solar power, this
expansion has exerted significant pressure on the government budget. Since FITs are based on
costs and expected returns from solar power plants and the costs of generating solar energy still
remain high, the expansion required a huge upfront expenditure from the German government.
88
The McKinsey report on “Disruptive Technologies” mentions that FITs are sustainable to
an extent, as the shrinking fiscal space has forced the German government to cut back on FITs.​[37]
Similarly, the heavy subsidies to solar plants-- particularly rooftop solar plants, has exerted
pressure on the government’s budget with little benefit. Ghosh argues that solar energy is the
least efficient among all renewable energy technologies, converting only a small percentage of
the sun’s rays into actual electricity. Yet it receives more than 50% of the government’s green
power subsidies.​[38]​ Thus not only has financing solar energy exerted pressure on government
resources, it also has not led to tangible economic benefits.
Some sources suggest that solar energy has, in fact, been a detriment to German
economic development. As a capital intensive industry, solar energy expansion has put more
than 800,000 jobs in Germany at risk​[39]​ and affected employment prospects. Ghosh also points
out that solar energy expansion has greatly destabilized the grid by contributing to intermittency
issues and also been detrimental to industrial development in Germany.​[40]​ These findings show
that even large grid based plants are susceptible to intermittency fluctuations, and can severely
destabilize the grid. Furthermore, the expansion of solar energy has significant implications for
industrial development that need to be addressed.
Not only does Germany’s case emphasize and exemplify the challenges with solar power
expansion described in the literature review, it also shows the grave consequences of this
expansion for both government and industry. While one can argue that India has more potential
for solar power expansion than Germany due to natural factors, the problems of economic
development and limited government resources are much more serious in India and Germany.
Thus the question is not simply whether achieving 100 GW is possible for India, but whether it is
desirable for India, given its potential implications for India’s economic development.
Furthermore, are the technical and financial challenges faced by solar power plants in
expansion common to all forms of solar energy expansion, or particularly to centralized large
scale solar plants? The next two sections try to answer this question by looking at an example
each of small and large scale solar plants in India.
V. Decentralized Solar Systems: IIT Madras’ pilot projects
89
The second case looks at a project introducing Direct Current-powered off-grid solar
devices, to be launched in 1[1] [2] [3] 00,000 rural houses on a trial basis by the Indian Institute
of Technology, Madras. Previously implemented in residential and small commercial complexes
in Hyderabad and Chennai, the off grid solar designs are now being implemented in rural areas.
If successful, they could potentially spearhead a solar energy revolution in rural India.
Led by Ashok Jhunjhunwala, a professor and scientist at IIT Madras, the system of DC
powered solar devices has the potential to meet electricity demands in a cost effective manner
compared to both grid based solar power as well as conventional energy resources at the local
level. About 32% of Indian homes do not have any access to electricity at all.​[41]​ Many of these
homes are located in rural and remote areas where it will require huge investments to extend the
grid through transmission and distribution lines. Since photovoltaic solar energy is produced in
direct current form and the grid supplies power in alternative current form, much of the energy
initially produced is lost through the conversion process. As a result, the electricity generation
from solar power is too low to justify the high investment costs of setting up a grid.
Jhunjhunwala’s 48 Volt DC powered decentralized solar devices eliminate the need for
distribution networks, thus eliminating the conversion losses. A recent article covering the
project estimates that the DC-run solar devices, by overcoming conversion loss, consume 50%
less power than regular devices that run on AC-power from the grid.​[42]​ Jhunjhunwala’s
individual household solar systems will be able to produce about 500 W of electricity, enough to
power three LED lights, two fans and a cellphone charger or three lights, one fan and a 24 inch
TV. At the community level, he has designed a Green Offices and Apartments (GOA) model
where every home receives 100-400 W from a common grid.
In addition to their role in rural community electrification, Jhunjhunwala’s devices
promise to overcome the power shortages caused by the intermittent nature of solar energy. First,
by virtue of being close to the load centers, it is more capable of responding rapidly to changes in
demand. In the community based systems, any home can draw on the excess capacity of the mini
grid during a power shortage. In addition, even during times of peak demand, the system
safeguards against a total blackout. Jhunjhunwala’s solar distribution systems consist of two
lines: the main DC line, and the backup line, which supplies 10% of the normal power supply
90
from a DC line.​[43]​ Thus, at any given point of time, households and consumers will have access
to some form of electricity.
The IIT Madras model of off grid solar electricity is the first of its kind, and has the
potential to revolutionize rural electrification. The innovative use of technology and the focus on
rural areas that have large unmet electricity demand are key to the model’s success. However,
currently, there are several limitations to the model. First, it is still in a testing phase and has only
been implemented in city complexes before. Implementing it in rural areas, especially ones that
have had no access to electricity before could prove challenging. Second, while these systems
work well for small establishments, the scale of the systems is too small to meet commercial or
industrial demands for electricity. If solar energy is to become a major part of India’s renewable
energy mix, it will need to meet the electricity demands from the burgeoning manufacturing
sector in addition to domestic demand. Given the current state of the off grid model, we will
need utility scale grid connected solar power to meet this industrial demand. Third, the financing
mechanisms for projects similar to IIT Madras needs to be determined. While these solar systems
have been funded by a research institution, it is unclear how much funding can come from this
source. A report by the World Bank on lending to off grid photovoltaic systems in India argues
that the high level of credit risk in rural areas has deterred entrepreneurs from investing in rural
off grid solar systems.​[44]​ While the report is dated, it does reveal the need for government level
renewable energy institutions to provide not just technical but also financial advice on
entrepreneurs entering the solar energy market in rural areas.​[45]​ More recent reports suggest that
the Indian Renewable Energy Development Agency, the primary agency responsible for
supporting and advising solar energy expansion, is extremely uncoordinated.​[46]​ Improving the
functioning of this agency is critical to getting the credit to sustainably expand decentralized
solar energy. Even so, decentralized solar systems can at least partially meet the challenges
associated with large scale solar plants.
Germany showed the reasons why solar power may be disadvantageous, and the IIT
Madras experience revealed how decentralized solar was able to meet some of the challenges
associated with solar energy in Germany. I now look at the development of Charanka, a large
scale solar park in Gujarat, to determine whether centralized systems can change to address the
91
challenges described in Germany’s case. I also attempt to understand whether there may be cases
where centralized systems are preferred to decentralized ones.
VI. Charanka, Gujarat
Before 2010, Charanka was a small unknown village in Gujarat. Today, it houses one of
the largest solar power plants in Asia. At a current capacity of 345 MW(MegaWatts)​[47]​,
Charanka’s solar power plant is an example of a successful large scale solar power plant and is
inspiring the creation of other large solar photovoltaic plants across India. This section focuses
on the specific factors that have led to Gujarat, and particularly Charanka’s emergence as a major
hub of grid based large scale solar power in India, and how easily this model can be replicated.
At the outset, natural and economic factors had a huge role to play in Charanka’s
development. First, its location allows the plant to overcome some of the scale and financing
challenges faced by solar power plants. In his paper on solar power in India, Sharma points out
that Charanka is located in Northern Gujarat, which receives one of the highest amounts of solar
irradiation in the world of 5.6-6kwH/sq meters/day. The higher power density has allowed
Charanka to tap more solar energy and generate greater power per unit area compared to other
regions in India, as well as reduce the long payback periods of loans that finance solar energy
expansion.​[48]​ Second, the large tracts of wasteland allow Gujarat to set up large scale solar plants
with very low land substitution costs. Yenneti argues that Gujarat contains as many as 14.4
million acres of waste land in the areas of high solar radiation and has plans to construct large
scale solar projects [similar to Charanka] in these areas.​[49]​Additionally, Gujarat has become one
of the most economically prosperous states in India, with 20% higher per capita income than the
national average.​[50]​ However, Gujarat’s natural advantages were supplemented by beneficial
policies that promoted rapid solar expansion and funding from sources outside the government,
unlike other places that have experimented with large scale grid based expansion.
Due to the resources at its disposal, Gujarat had already taken the initiative to promote
solar power before the Jawaharlal Nehru Missions of 2011 and 2014. Gujarat had a state level
solar policy, the Gujarat Solar Power Policy, in 2009, before the national level policies were
announced. Thus Gujarat already had a strong base of solar power, allowing it to capitalize on
92
the 2011 central government policies that encouraged solar power expansion. For instance, under
the original JNNSM, Gujarat added 900 MW of capacity between 2011 and 2014.​[51]​ Gujarat’s
track record, combined with national level policies, created synergies that allowed it to attract
funding from private and foreign players. Yenneti explains that Charanka was able to attract a
large amount of funding from a wide variety of sources: $100m in loans from the Asian
Development Bank, $37m from the Gujarat government as well as significant funding from
project developers’ equity.
At the same time, Gujarat’s policies carry significant differences from national level
policies. Gujarat’s use of a feed in tariff policy rather than the competitive bidding process
instituted at the national level attracted firms by initially keeping tariffs high. Gujarat’s tariff
rates were USD 0.26 for the first 12 years, and then went down to USD 0.06 from the 13​th​ year,
as tariffs fell due to economies of scale and grid expansion.​[52]​ This policy encouraged private
investments in solar energy expansion, which ultimately led to a decline in costs and
subsequently tariffs. In comparison, the national level policies were based on a competitive
bidding process, that allowed investors to bid competitive tariffs for power generation, led to a
steep decline in tariffs due to the huge number of bidders. Yenneti argues that while the total
amount of capacity on offer in July 2010 was as high as 5126 MW, the bid capacity on offer
reduced to 650 MW in September 2010 due to a steep fall in the JNNSM tariff.​[53]​ The FIT policy
made large scale solar power projects such as Charanka much more attractive to investors in
Gujarat compared to other states.
Within Gujarat, the single window clearing systems and flexible import policies for large
scale solar plants reduced the costs of setting up large solar plants, making them relatively
attractive to small scale ones. Under the single window clearing system, the government takes
care of all necessary permissions such as land acquisition to set up solar projects. In the Yenneti
study, a business developer mentioned that the single window clearing process encouraged him
to develop interest in the Charanka solar plant rather than a smaller 5 MW plant in a different
location, that would have cost less in terms of initial capital costs.​[54]​ In addition, the Gujarat
government has also adopted a flexible stance towards the import of technology, allowing
developers to choose the most advanced and cost effective technology.​[55]​ Large scale solar plants
93
are able to provide more power and returns to developers due to economies of scale, however
their huge costs deter investors from participating in them. By removing these major costs, the
Gujarat government has enabled investors to take advantage of the economies of scale, and
hence invest in large scale solar projects.
The Charanka case shows that favorable policies--fixed FITs, single window clearances
and a flexible import policy--can indeed create an environment where large scale grid based
plants can be a more attractive opportunity than small scale solar plants. At the same time, it is
important to acknowledge that the institutional conditions in Gujarat had a huge factor to play in
its solar energy expansion. Gujarat’s government was very committed to solar energy expansion,
and has taken a lot of initiatives to attract the private sector. The scholar Yenneti mentions that
Gujarat holds the “Vibrant Gujarat” summit every two years in order to encourage private
investment in upcoming industries such as renewable energy.​[56]​ The case also shows how
Gujarat’s state policies sucessfully tapped into the advantages of the national policy, while
simultaneously introducing innovations of its own. Other Indian states, such as Telangana, have
many of the necessary natural conditions for solar energy, but have been unable to achieve large
scale of solar energy expansion due to protests against land acquisition or inadequate
investment.​[57]​ With stronger institutions and policies such as FITs and faster clearances, large
scale solar power plants could be more beneficial than small scale ones
VII. Key Findings
The three cases have revealed some key insights about the shift to small scale solar power
plants in India, which are summarized below.
First, not only is there a shift from large scale centralized to small scale distributed, but
also from distributed to decentralized systems. So far, the focus has been on the shift from
centralized systems to distributed small scale systems that are ultimately integrated into the grid,
while pure decentralized systems were not seen as sustainable in the long term. However, the IIT
Madras case shows that the third kind of solar power system- decentralized systems may have
the ability to exist self sufficiently without the need to integrate them with the grid. The mini
grids in which they exist have sufficient reserves and in built technological advances to meet
94
demand without having to feed into regional grids. While the success of the IIT Madras project
remains to be seen, it carries huge benefits for rural development, and could mean lower
investments in expanding grid infrastructure, either now or in the future.
Second, several key supply and demand side factors have prompted the shift away from
centralized systems. On the supply side, as we have seen, large scale systems are more prone to
technical and financial challenges than small scale ones. Germany offers the best example of the
challenges faced by large scale solar energy systems, namely destabilization of the grid and
financial pressure on the government. On the other hand, the IIT Madras case successfully
demonstrated how recent innovations in off grid decentralized systems allow it to meet
traditional supply side challenges with large scale plants. Although they have set up costs that
require financing at the local level, small scale systems partially overcome the financing
challenge by saving on infrastructure costs of connecting remote areas to the grid. The IIT
Madras case shows that decentralized home systems have been able to reach rural areas that are
not connected to the grid and thus help India meet its goal of rural electrification. By being
present close to the source of demand and using the direct current innovation, decentralized
systems have huge potential to save on efficiency and transmission and distribution losses. Not
only do small scale systems meet electricity demand, but they also address it in a cost efficient
and reliable.
Finally, while large scale solar systems continue to be important to India’s solar energy
plans, small scale systems have a broader appeal across India because they are not affected as
much by institutions and location. The success story of Charanka demonstrates how the plant
saved emissions and benefited Gujarat’s industrial development, allowing it to become an
exporter of electricity to other parts of India. The Gujarat case shows a few successful strategies
that national and state governments could incorporate to encourage expansion of large scale solar
energy systems, namely, careful selection of wasteland with high horizontal irradiance to
minimize land substitution, faster clearance policies and FITs rather than competitive bidding. At
300+ MW, solar parks truly have the capability to meet demand on a commercial scale, as well
as in densely populated urban areas. However, passing favorable policies and encouraging large
scale solar expansion is highly dependent on the quality of institutions and the transparency of
95
institutions, which widely vary across states. While states such as Gujarat have strong
institutions and have benefited from creating solar parks, distributed and decentralized systems
may be better suited to areas with relatively unfavorable location or state policy.
Conclusion
India’s growing electricity demand, coupled with environmental concerns, has prompted
the expansion of renewable energy, particularly solar energy. The most recent version of the
Jawaharlal Nehru National Solar Mission Plan aims to achieve 100 GW of solar energy
expansion by 2022. However, there are several challenges to achieving this ambitious target of
solar energy: the availability of land, the intermittent supply of electricity, the high tariffs and
restrictions to financing. By looking at case studies of large scale centralized and small scale
distributed and decentralized systems, I concluded that many of the typical problems with solar
energy could be attributed to large scale solar plants. Small scale solar plants, which have been
utilized primarily in developing countries, have shown some success in overcoming the
challenges associated with large scale solar plants. This, combined with the growing needs for
electricity in rural areas, has prompted a large shift to small scale solar plants in India.
Simultaneously, large scale solar parks have proven to be more successful means of generating
solar energy than small scale plants in areas of high industrial demand where the government
policies are favorable, such as Gujarat. While acknowledging the large shift to small scale solar
systems, I also believe that the means of expanding solar power is extremely context specific and
hence the shift will never be a complete one. In order to achieve its target of 100GW in an
economically and environmentally sustainable manner, India needs to simultaneously promote
the development of both large scale and small scale systems in contexts suited to each, taking
care to meet the problems faced by each kind of system.
96
Endnotes
“Make in India: Renewable Energy”, Make in India, 10 March 2016,​ ​http://www.makeinindia.com/sector/renewable-energy
“Make in India: Renewable Energy”, Make in India, 10 March 2016,​ ​http://www.makeinindia.com/sector/renewable-energy
[3]​
​Under a carbon pricing scenario, the prices of conventional fuel sources would go up, helping India achieve grid parity, However, since the
current climate negotiations do not point to a universal carbon price in the near future, I have chosen to consider the current policies scenario for
the scope of my paper.
[4]​
James Manyika et al., ​Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy, vol. 12 (McKinsey
Global Institute New York, 2013).
[5]​
Ibid, 140.
[6]​
Geetha Rao, "Reengineering Rural India," Institute of Electrical and Electronics Engineers
http://spectrum.ieee.org/geek-life/profiles/ashok-jhunjhunwala-reengineering-rural-india.
[7]​
“Make in India: Renewable Energy”, Make in India, 10 March 2016, http://www.makeinindia.com/sector/renewable-energy
[8]​
Manyika et al., ​Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy, 12.
[9]​
M.S. Soni Amita Ummadisingu, "Concentrating Solar Power- Technology, Potential and Policy in India " ​Renewable and Sustainable Energy
Reviews 15 (2011).
[10]​
Swaminathan Aiyar, "Time to Shift Gears; Scrap Biofuels, Go for Solar Power Instead," T
​ imes of India 2016.
[11]​
“Make in India: Renewable Energy”, Make in India, 10 March 2016,​ ​http://www.makeinindia.com/sector/renewable-energy
[12]​
Anilesh S. Mahajan, "The 100gw Headache," ​Business Today May 10 2015
[13]​
Anilesh Mahajan, "Solar under Cloud " ibid., April 24, 2016 2016
[14]​
Ghosh “Solar Power” ; Mahajan “The 100GW Headache”; Lecture 7 “Renewable Energy”
[15]​
M. Milligan and D. Lew L. Bird, "Integrating Variable Renewable Energy: Challenges and Solutions ", ed. US Department of Energy National
Renewable Energy Laboratory (Denver, Colorado 2013 ).
[16]​
Sajal Ghosh, "Solar Power: Truth Vs Hype " ​Livemint 2016
[17]​
Mahajan, "The 100gw Headache."
[18]​
Ibid
[19]​
Mahajan, "The 100gw Headache."
[20]​
Anilesh Mahajan, "Solar under Cloud " ibid., April 24, 2016 2016
[21]​
Anilesh S. Mahajan, "The 100gw Headache," ibid., May 10 2015
[22]​
Anilesh Mahajan, "Solar under Cloud " ibid., April 24, 2016 2016
[23]​
Ibid.
[24]​
Anilesh S. Mahajan, "The 100gw Headache," ibid., May 10 2015
[25]​
L. Bird, "Integrating Variable Renewable Energy: Challenges and Solutions ".
[26]​
Vikas Khare, Savita Nema, and Prashant Baredar, "Status of Solar Wind Renewable Energy in India," ​Renewable and Sustainable Energy
Reviews 27 (2013).
[27]​
L. Bird, "Integrating Variable Renewable Energy: Challenges and Solutions ".
[28]​
Ibid, 3.
[29]​
Ibid, 3.
[30]​
Chris Hope Damian Miller, "Learning to Lend for Off-Grid Solar Power: Policy Lessons from World Bank Loans to India, Indonesia, and Sri
Lanka " ​Energy Policy 28 (2000).
[31]​
Govinda R Timilsina, Lado Kurdgelashvili, and Patrick A Narbel, "Solar Energy: Markets, Economics and Policies," ​Renewable and
Sustainable Energy Reviews 16, no. 1 (2012).
[32]​
Damian Miller, "Learning to Lend for Off-Grid Solar Power: Policy Lessons from World Bank Loans to India, Indonesia, and Sri Lanka ".
[33]​
world bank paper
[34]​
Ghosh, "Solar Power: Truth Vs Hype ".
[35]​
Timilsina, Kurdgelashvili, and Narbel, "Solar Energy: Markets, Economics and Policies."
[36]​
Ibid.
[37]​
Manyika et al., ​Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy, 12.
[38]​
Ghosh, "Solar Power: Truth Vs Hype ".
[39]​
Ibid.
[40]​
Ibid.
[41]​
Rao, "Reengineering Rural India".
[42]​
"Iit Madras to Power 1 Lakh Houses," ​The Deccan Chronicle
[43]​
"Reengineering Rural India".
[44]​
Damian Miller, "Learning to Lend for Off-Grid Solar Power: Policy Lessons from World Bank Loans to India, Indonesia, and Sri Lanka ".
[1]​
[2]​
97
[45]​
Ibid.
Khare, Nema, and Baredar, "Status of Solar Wind Renewable Energy in India."
[47]​
Mahajan, "The 100gw Headache."
[48]​
Atul Sharma, "A Comprehensive Study of Solar Power in India and World," ​Renewable and Sustainable Energy Reviews 15, no. 4 (2011).
[49]​
Komali Yenneti, "Industry Perceptions on Feed in Tariff (Fit) Based Solar Power Policies–a Case of Gujarat, India," ibid.57 (2016).
[50]​
Ibid.
[51]​
Ibid.
[52]​
Ibid.
[53]​
Ibid.
[54]​
Ibid.
[55]​
Ibid.
[56]​
Ibid.
[57]​
Mahajan, "Solar under Cloud ".
[46]​
Works Cited
Aiyar, Swaminathan. "Time to Shift Gears; Scrap Biofuels, Go for Solar Power Instead." Times of India 2016.
Amita Ummadisingu, M.S. Soni "Concentrating Solar Power- Technology, Potential and Policy in India ". Renewable and Sustainable Energy
Reviews 15 (2011): 5169-75.
Damian Miller, Chris Hope "Learning to Lend for Off-Grid Solar Power: Policy Lessons from World Bank Loans to India, Indonesia, and Sri
Lanka ". Energy Policy 28 (2000): 87-105.
Ghosh, Sajal. "Solar Power: Truth Vs Hype " Livemint 2016
"Iit Madras to Power 1 Lakh Houses." The Deccan Chronicle
Khare, Vikas, Savita Nema, and Prashant Baredar. "Status of Solar Wind Renewable Energy in India." Renewable and Sustainable Energy
Reviews 27 (2013): 1-10.
L. Bird, M. Milligan and D. Lew "Integrating Variable Renewable Energy: Challenges and Solutions ", edited by US Department of Energy
National Renewable Energy Laboratory. Denver, Colorado 2013
Lecture 7, "Renewable Energy", ECON 452, 2016.
Mahajan, Anilesh. "Solar under Cloud " Business Today April 24, 2016 2016
Mahajan, Anilesh S. "The 100gw Headache." Business Today May 10 2015
Manyika, James, Michael Chui, Jacques Bughin, Richard Dobbs, Peter Bisson, and Alex Marrs. Disruptive Technologies: Advances That Will
Transform Life, Business, and the Global Economy. Vol. 12: McKinsey Global Institute New York, 2013.
“Make in India: Renewable Energy”, Make in India, 10 March 2016,​ ​http://www.makeinindia.com/sector/renewable-energy
Rao, Geetha. "Reengineering Rural India." Institute of Electrical and Electronics Engineers
http://spectrum.ieee.org/geek-life/profiles/ashok-jhunjhunwala-reengineering-rural-india​.
Sharma, Atul. "A Comprehensive Study of Solar Power in India and World." Renewable and Sustainable Energy Reviews 15, no. 4 (2011):
1767-76.
Timilsina, Govinda R, Lado Kurdgelashvili, and Patrick A Narbel. "Solar Energy: Markets, Economics and Policies." Renewable and Sustainable
Energy Reviews 16, no. 1 (2012): 449-65.
Yenneti, Komali. "Industry Perceptions on Feed in Tariff (Fit) Based Solar Power Policies–a Case of Gujarat, India." Renewable and Sustainable
Energy Reviews 57 (2016): 988-98.
Works Consulted
Energy Alternatives India. "India Solar Energy ". 2016 from​ ​http://www.eai.in/ref/ae/sol/sol.html.
Ministry of New and Renewable Energy (2016). " Solar ". 2016, from​ ​http://www.mnre.gov.in/schemes/grid-connected/solar/.
98
Motivations Behind China’s Bilateral Currency Swap Agreements
Rachel Yang
Abstract
During the global financial crisis of 2007, central banks around the world arranged
bilateral currency swap agreements with each other as a strategy to stabilize their nation's’
financial conditions. China was lightly impacted by the global financial crisis, yet it decided to
also participate in the establishment of these agreements. As a matter of fact, China has signed
an incredible number of thirty-two bilateral currency swap agreements with a wide range of
nations. This research paper evaluates the motivations behind China’s decision to sign these
agreements with such a diverse set of nations. The analysis of the paper indicates that
internationalizing the RMB currency and securing access to natural resources are highly likely
motivations behind China’s swap agreement decisions.
99
Introduction
During the global financial crisis of 2007, central banks around the world have arranged
bilateral currency swap agreements with each other as a strategy to stabilize their nations’
financial conditions. While China did not suffer heavily from the global financial crisis, it
decided to also take part in the establishment of these agreements. In fact, over the past few
years, China has signed an extraordinary number of bilateral currency swap agreements with a
wide variety of developed and developing countries. The aim of this research project is to
explain why China has moved to establish bilateral central bank currency swap arrangements
with such a diverse set of countries.
For this research project, there are two theories explaining China’s bilateral currency
swap agreement strategy. The first theory involves China using swap agreements as a means to
internationalize their RMB currency. The second theory suggests that China is using the swap
lines to secure access to raw material goods. To test these two theories, I collected quantitative
trade data on China and all its trade partners. From there, I used RMB clearing bank and
participation in RMB Qualified Foreign Institutional Investor pilot program as variables to
represent the effect of the first theory, and then I employed the fuel and mining export trade
value as a variable to proxy the effect of the second theory.
The result of this project largely supports the theories presented; internationalizing the
RMB currency and securing access to natural resources are highly likely motivations behind
China’s swap agreement decisions. In particular, my analysis suggests that internationalizing the
RMB currency is a stronger motivation for China’s swap line strategy. This result opens up a
new world of questions on China’s economic goals and policies that social scientists could study
in the near future.
100
I.
Unclear motivation behind China’s currency swap agreements
A bilateral currency swap agreement, or otherwise known as a “swap line,” between two
countries allows one country’s central bank to sell its domestic currency to the other country’s
central bank for foreign currency, and at the same time, agrees to reverse the transaction at a
predetermined exchange rate and date in the future. After attaining the foreign currency, the
central bank can then lend out the currency to its domestic banks that are in need of liquidity in
that currency, thereby helping to stabilize the financial markets.
The bilateral currency swap agreement is not an entirely new tool for central banks to
employ to stabilize the financial markets, but it did not become a common practice until after the
recent 2007 financial crisis. During the 2007-08 financial crisis, U.S. and non-U.S. banks alike
started to worry about funding their operations as they faced potential losses from their creditand mortgage-related products. Consequently, they became reluctant to lend dollars to each
other. Soon enough, the interbank money markets became impaired. At the same time, non-U.S.
banks without a natural source of dollar deposits had a growing need to support their
dollar-denominated assets. As a result, the dollar supply shrank and the dollar demand grew,
causing a dollar shortage on the global market. To address this dollar shortage that the
international monetary system was facing, the Federal Reserve, more commonly known as the
“Fed”, arranged swap lines with major central banks around the world. Around the same period,
China began to also arrange swap lines with many nations. However, the purpose of China’s
arrangements is less clear compared to that of the Fed’s.
As of 2015, China has entered into thirty-two swap agreements with countries that appear
to have little in common. China has agreements with countries from all regions of the world –
not just in Asia, but also in Africa, Latin America, and Europe, as one might expect if the motive
was regional financial stabilization. Furthermore, these swap agreements have been made with
both developed (e.g., Qatar, United Arab Emirates, Australia) and developing countries, (e.g.,
Indonesia, Armenia, Nepal). In short, since 2008, China has been aggressively establishing
101
central bank swap accords with a diverse set of nations from all regions of the world. ​Table 1 ​in
the Appendix Section lists all of China’s swap agreements established since 2008, which is
compiled from announcements and press release statements made by People’s Bank of China.
II. Theory
The diversity of China’s swap agreements suggests that China is pursuing more than one
objective when it decides the nations with which it engages in central bank cooperative
agreements. I argue that China is pursuing at least two different objectives with its swap
agreement strategy.
China is a growing power. Its’ economy has been growing at rates that powerhouses such
as United States and England cannot achieve in the current global economic climate. While it is
certainly growing at an incredible rate, China is still catching up to the powerhouse nations in
terms of status quo and international investment opportunities. By internationalizing its currency,
not only will China gain global prestige, but it will help to cut Chinese companies’ borrowing
cost when they venture abroad, thereby expanding their investments abroad and potentially
boosting the Chinese economy. Thus, I argue that one of China’s objectives is to internationalize
its currency. This argument suggests that China is more likely to arrange swap agreements with
large, financially developed trading partners in Europe and Asia.
Many of the financially stable countries are located in European and Asian regions. These
financially developed nations are key players in the global trade and financial markets. By
concluding swap agreements with these financially developed countries in Europe and Asia, it
will help enable China to conclude trade settlement in RMB, which will increase the usage of
RMB currency in the global trade market. Therefore, my first hypothesis is that China is more
likely to sign a currency swap agreement with a developed nation with important financial
centers than one without financial centers.
As previously discussed, the nations with which China has arranged swap lines seem to
have little in common. However, upon closer inspection, it appears that many of these countries,
102
such as Russia, Canada, Australia etc., are rich in natural resources. More specifically, if we look
at the import and export data of China provided by the UN Comtrade Database, we can see that
many of these nations export fuels and mining goods to China. Consequently, I argue that
another one of China’s objectives is to secure access to raw materials. This argument suggests
that China is more likely to conclude swap arrangements with nations in Africa and Latin
America that export raw materials to China than with nations that do not export large amounts of
raw materials to China.
Many African and Latin American countries are rich in raw material goods. By extending
a bilateral currency swap agreement to a nation rich in raw materials, China establishes itself as a
financial support or ally to that nation, which allows the two countries form a friendlier relation.
In doing so, China has more likelihood of securing goods produced by that nation since the
nation will probably want to return the favor of China providing the swap line. Therefore, my
second hypothesis is that countries that are rich in natural resources have a higher chance of
arranging a swap line with China than those who are not.
III. Method
In order to evaluate these arguments, I gathered quantitative data on China and its trading
and financial partners over the five-year period 2010 to 2014. My research design is
probabilistic, which is to say that I seek to estimate the likelihood that China will conclude a
currency swap agreement with a foreign central bank.
For my first hypothesis, I planned to use offshore RMB clearing bank and participation in
RMB Qualified Foreign Institutional Investor (RQFII) pilot program as proxies for a nation’s
financial stability. The offshore RMB clearing banks and the RQFII pilot program were both
established in an effort to internationalize the RMB currency. Since RMB is still not fully
convertible, the Chinese government has been promoting an “offshore” market with the RMB
clearing banks where RMB can be used outside Mainland China. For instance, establishing an
offshore RMB clearing bank in London would help to stimulate the usage of RMB in the London
103
area. The RQFII program allows qualified foreign institutions to invest offshore RMB back into
Mainland China’s capital market, so the program expands the number of investment
opportunities with RMB and therefore encourages the usage of the RMB currency. As previously
discussed, financially developed nations are the key players in global trade, so the efforts to
internalize the RMB currency are more likely to take place in the financially stable countries.
Consequently, if China has established an offshore RMB clearing bank in a country or if a
country is participating in the RMB Qualified Foreign Institutional Investor pilot program, then
that country is considered as a financially developed country in this research design. t. As for my
second hypothesis, I utilized data on fuel and mining exports to China as a measure for a nation’s
production of natural resources. More specifically, if a country has a very high trade value of fuel
and mining exports to China, then that country would be regarded as rich in natural resources.
To test the true motivation behind China’s swap agreement decisions, I considered a
panel of bilateral trade data between China and 179 countries from 2010 to 2014, taken from the
UN Comtrade Database. The model I used to test my hypotheses is:
SWAP AGREEMENT = β​0​ +​ β​1​ FUEL AND MINING EXPORTS +​ β​2​ALL COMMODITIES
IMPORTS AND EXPORTS +​ β​3​RMB CLEARING BANK +​ β​4​ ​RQFII +​ β​4​INFLATION +
β​5​EUROPE + β​6​WESTERN ASIA + β​7​AFRICA + β​8​OCEANIA + β​9​AMERICAS+ Εrror
where the qualitative response variable ​SWAP AGREEMENT is 1 when China has signed a
bilateral currency swap agreement with the country, and 0 otherwise.
The explanatory variables are ​FUEL AND MINING EXPORTS, ​ALL COMMODITIES
IMPORTS AND EXPORTS, ​RMB CLEARING BANK, and ​RQFII. The variable​ FUEL AND
MINING EXPORTS is a continuous variable, where it measures the average trade value from
2010 to 2014 of fuel and mining product exports to China, and this serves to represent the
importance of maintaining trade relations with nations rich in natural resources. ​RMB
CLEARING BANK ​is a binary variable with the value being 1 if a country has a RMB clearing
arrangement, and 0 otherwise. ​RQFII is also a binary variable attaining the value 1 when the
104
country participates in RMB Qualified Foreign Institutional Investor pilot program, and has a
value of 0 otherwise. Both ​RMB CLEARING BANK ​and ​RQFII are used to characterize the
motivation of internationalizing the Chinese Yuan since both RMB clearing arrangements and
RMB Qualified Foreign Institutional Investor pilot program were solely created to globalize the
Chinese Yuan. ​ALL COMMODITIES IMPORTS AND EXPORTS ​is a continuous variable
measuring the average trade value of imports and exports of all commodities to China, and it can
be a weak proxy for the intention of internationalizing the Chinese RMB since China will likely
want to first expand the usage of its domestic currency with its most important trade partners.
For ​FUEL AND MINING EXPORTS, I gathered the trade values of fuel and mining
exports to China from year 2010 to 2014. Then, I averaged the trade values of those five years.
Afterwards, I changed the average trade values in terms of a hundred million dollars. So, the unit
for the variable ​FUEL AND MINING EXPORTS is a hundred million dollars. Occasionally, a
country would not have a value in one of the years. In that case, I simply just average the values
that were available. For the variable ​ALL COMMODITIES IMPORTS AND EXPORTS, it was the
exact same process except that the trade values were of imports and exports of all commodities.
With binary variables ​RMB CLEARING BANK, ​RQFII, and ​SWAP AGREEMENT, I was
able to determine the values through information offered by People’s Bank of China. The
countries participating in the Chiang Mai Initiative Multilateralization, or CMIM, were not
assigned a value of 1 in the ​SWAP AGREEMENT variable unless they separately had a bilateral
currency swap agreement. This is because China did not purposefully choose to sign an
agreement with those countries. The participants in CMIM simply pledge a certain amount of
their currency to the program, and all participants have the opportunity to utilize the currency
others have pledged. Thus, including those participating in the CMIM would not help to
determine the motivations behind China’s own swap line decisions.
All other terms in the model are control variables, where ​INFLATION measures the
average inflation rate from 2010 to 2014 of each country, and ​EUROPE, ​WESTERN ASIA,
AFRICA, ​OCEANIA, ​AMERICAS are binary variables that stand for Europe, Western Asia or
105
Middle East, Africa, Oceania, and the Americas, respectively, accounting for the specific region
effects. For example, if a country is considered to be a part of the European region, then the
binary variable, ​EUROPE, would be assigned the value of 1 and the other binary region variables
would all have values of 0. There is also a binary variable—​ASIA—representing countries in
Central and Eastern Asia, but it was dropped to avoid perfect multicollinearity.
Inflation rate was included as a control variable, because inflation rate is a good indicator
of the economic stability of a nation. So in this model, the inflation rate is used to control for the
economic conditions of the partner countries. The model also incorporated regional control
variables. The reason is that certain regions often experience similar economic events or have
similar agricultural conditions, and those specific regional conditions may increase or decrease
the chance of attaining a swap agreement for nations in that region. As a result, I put in the
regional variables to account for those regional effects.
The data for ​INFLATION was gathered from the World Bank. I collected the Inflation or
GDP Deflator rate for all countries in my sample from year 2010 to 2014. Then again, I averaged
the values from all five years, and the averaged value is used as the data for the variable
INFLATION. For the binary variables representing different regions, I was able to assign the
countries to each region by following the geographical region list provided by United Nations
Statistics Division. However, instead of using a single Asia category, I separated the Asian
countries into Central and Eastern Asia, and Western Asia, because the nations under the
Western Asia category are what we usually consider as Middle Eastern. Therefore, the variable
WESTERN ASIA is actually representing the region Middle East.
IV. Empirical Analysis
To begin the analysis, I did a quick two-sample t-test to check if there was indeed a
difference in means between countries with high fuel and mineral exports to China and those that
do not. I bisected the sample into a high category, which included countries that were in the top
50% of ​FUEL AND MINING EXPORTS values, and a low category, which included those that
106
were in the bottom 50% range. Then, I generated a new binary variable—​HIGH—with a value 1
if the country belonged to the high category, and a value 0 otherwise. The exact same process
was also done to check if there was a difference in means between countries who traded heavily
with China and those who did not. The binary variable—​HIGH1—was assigned a value 1 if the
country was in the top 50% of the ​ALL COMMODITIES IMPORTS AND EXPORTS values, and
0 otherwise. ​Table 2​ and ​Table 3​ shows the results of the two t-tests. Both of the t-tests
demonstrated that the means are indeed different. Therefore, we have a cause to continue our
analysis.
After establishing that ​FUEL AND MINING EXPORTS, ​ALL COMMODITIES IMPORTS
AND EXPORTS, ​RMB CLEARING BANK, and ​RQFII were not highly correlated (shown in
Table 4​), I was able to include all four variables in the same regression. The final regression
yielded results shown in ​Table 5​. The coefficients of the explanatory variables were all positive,
but only ​FUEL AND MINING EXPORTS and ​RQFII were statistically significant at 95%
confidence. Since the regression is nonlinear, the coefficients cannot tell us anything meaningful
besides the direction of influence. Therefore, I needed the marginal effects to provide more
information on the effect of these variables. ​Table 6​ depicts the marginal effects at means for
each variable. Marginal effect at means tells us the effect of one unit increase of a variable when
all the other variables are held constant at its mean value. In the case of ​FUEL AND MINING
EXPORTS, if a Central or Eastern Asian nation had an additional hundred million dollars in
average trade value of fuel and mining exports, then that country would have an approximately
0.285% higher chance of receiving a swap line from China, when all the other variables are held
at their mean values. While the effect is not big, ​FUEL AND MINING EXPORTS’s marginal
effect is still statistically significant at 95%. The other explanatory variable with a statistically
significant marginal effect is ​RQFII, and its marginal effect at means was about 0.48. That means
if a Central or Eastern Asian country was in the RMB Qualified Foreign Institutional Investor
pilot program, then that nation is estimated 48% more likely to get a swap agreement from
China, when the other variables are held at their mean values.
107
V. Conclusion
While both the coefficients and marginal effects of the explanatory variables ​ALL
COMMODITIES IMPORTS AND EXPORTS and ​RMB CLEARING BANK were not statistically
significant at the 95% confidence level, those of the other two variables ​FUEL AND MINING
EXPORTS and ​RQFII did turn out to be statistically significant. The significance of the
coefficients and marginal effects tells us that these two variables do play a part in China’s swap
agreement strategy. In particular, it tells us that securing access to raw material goods and
internationalizing the RMB currency are most likely the determinants of China’s decision to
arrange a swap line with a foreign central bank. Moreover, the coefficient and marginal effect of
RQFII are large, and its z values for coefficient and marginal effect are larger than those of
FUEL AND MINING EXPORTS. This result suggests that internationalizing the RMB currency
has more weight than securing raw materials when it comes to China’s swap agreement
decisions.
The result from the analysis makes sense. As mentioned earlier, internationalizing the
RMB currency would be greatly beneficial to the Chinese economy. Specifically, it would
reduce Chinese companies’ borrowing cost when they venture abroad, thus increasing their
investments abroad and stimulating the Chinese economy. Additionally, the desire to secure
more raw materials is a logical motivation because China is not extremely rich in natural
resources. Therefore, it is reasonable for China to want more access to raw materials if it wants
to maintain the economic growth it has been experiencing. However, prioritizing the
internationalization of the RMB currency over the security of raw materials is reasonable. A
probable explanation could be that the internationalization of RMB is likely to have a greater
impact on the Chinese economy than the access to natural resources. If the RMB currency is
internationalized, the investment opportunities for Chinese companies will likely to expand
significantly in many different business sectors and geographic regions. Consequently, there
would be a very significant effect on the Chinese economy, especially since the benefits from
good investments can be compounded. Access to raw resources would not have the same impact
108
because there is always a limit on the availability of raw materials, therefore the effects of having
natural resources on the Chinese economy cannot be compounded the way that investments can.
Overall, the result of this analysis supports my hypotheses. Consequently, we can
probably expect China to pass more policies focusing on the internationalization of its RMB
currency. This result also raises a new round of questions surrounding China’s goals and policies
that social scientists could evaluate in the near future. Specifically, social scientists could
evaluate how successful China is in achieving internationalization of its’ currency and securing
raw material goods by implementing this swap line strategy. Additionally, since China’s usage of
swap agreements deviates from its original purpose, which is to stabilize financial conditions, it
will be interesting to see if any other country might follow the same footsteps as China when it
wants to achieve similar goals.
Appendix
Table 1. China’s Bilateral Currency Swap Agreements, as of mid-2015
Partner Central Banks
Swap Agreement Size
Partner Central Banks
Swap Agreement Size
1. Bank of Korea
RMB 360 Billion/KRW 64 Trillion
2. Hong Kong Monetary Authority
RMB 400 Billion/HKD 505 Billion
17. Central Bank of the Republic of
Turkey
18. Reserve Bank of Australia
3. Bank Negara Malaysia
RMB 180 Billion/MYR 90 Billion
19. National Ukraine
4. National Bank of the Republic
of Belarus
5. Bank Indonesia
RMB 7 Billion
20. Central Bank of Brazil
RMB 100 Billion/IDR 175 Trillion
21. Bank of England
6. Central Bank of Argentina
RMB 70 Billion/ARS 90 Billion
22. Hungary National Bank
7. Central Bank of Iceland
RMB 3.5 Billion/ISK 66 Billion
23. Bank of Albania
8. Monetary Authority of
Singapore
9. Reserve Bank of New Zealand
RMB 300 Billion/SGD 60 Billion
24. European Central Bank
RMB 25 Billion/NZD 5 Billion
25. Swiss National Bank
10. Central Bank of the Republic
of Uzbekistan
11. Bank of Mongolia
RMB 0.7 Billion
26. Central Bank of Sri Lanka
RMB 15 Billion/MNT 4.5 Trillion
27. Qatar Central Bank
12. National Bank of Kazakhstan
RMB 7 Billion/KZT 200 Billion
28. Bank of Canada
RMB 10 Billion/TRY 3
Billion
RMB 200 Billion/AUD 40
Billion
RMB 15 Billion/UAH 19
Billion
RMB 190 Billion/BRL 60
Billion
RMB 200 Billion/GBP 20
Billion
RMB 10 Billion/HUF 375
Billion
RMB 2 Billion/ALL 35.8
Billion
RMB 350 Billion/EUR 45
Billion
RMB 150 Billion/CHF 21
Billion
RMB 10 Billion/LKR 225
Billion
RMB 35 Billion/QAR 20.8
Billion
RMB 200 Billion/CAD 30
Billion
109
13. Central Bank of Russia
RMB 150 Billion/RUB 815 Billion
29. Central Bank of Suriname
14. State Bank of Pakistan
RMB 10 Billion/PKR 165 Billion
15. Bank of Thailand
RMB 70 Billion/THB 370 Billion
30. Central Bank of the Republic of
Armenia
31. South African Reserve Bank
16. Central Bank of the United
Arab Emirates
RMB 35 Billion/AED 20 Billion
32. Central Bank of Chile
RMB 1 Billion/SRD 520
Million
RMB 1 Billion/AMD 77
Billion
RMB 30 Billion/ZAR 57
Billion
RMB 22 Billion/CLP 2.2
Trillion
Table 2. Two-sample t-test with Equal Variances (for ​HIGH)
Group
Observatio
n
Mean
Standard Error
Standard Deviation
0
58
0.1551724
0.0479572
0.3652312
0.0591398
0.2512051
1
58
0.3448276
0.0629566
0.4794633
0.2187591
0.470896
Combine
d
116
0.25
0.0403786
0.4348913
0.1700177
0.3299823
-0.1896552
0.0791418
-0.3464345
-0.0328758
Differenc
e
95% Confidence Interval
Difference = mean(0) - mean(1)
t = -2.3964
H0: Difference = 0
degrees of freedom = 114
Ha: Difference <0
Ha: Difference! = 0
Ha: Difference >0
Pr(T<t) = 0.0091
Pr(|T|>|t|) = 0.0182
Pr(T>t) = 0.9909
Table 3. Two-sample t-test with Equal Variances (for ​HIGH1)
Group
Observatio
n
Mean
Standard Error
Standard Deviation
0
89
0.0449438
0.0220855
0.2083546
0.0010534
0.0888342
1
90
0.2888889
0.048044
0.4557854
0.1934265
0.3843513
Combine
d
179
0.1675978
0.0279957
0.3745564
0.1123517
0.2228439
-0.2439451
0.0530719
-0.3486802
-0.1392099
Differenc
e
95% Confidence Interval
Difference = mean(0) - mean(1)
t = -4.5965
H0: Difference = 0
degrees of freedom = 177
Ha: Difference <0
Ha: Difference! = 0
Ha: Difference >0
Pr(T<t) = 0.0000
Pr(|T|>|t|) = 0.0000
Pr(T>t) = 1.0000
110
Table 4. Correlation Between the Explanatory Variables
Variables
FUEL AND MINING
EXPORTS
ALL COMMODITIES
IMPORTS AND EXPORTS
RMB CLEARING BANK
FUEL AND MINING
EXPORTS
1.0000
ALL COMMODITIES
IMPORTS AND EXPORTS
0.2093
1.0000
RMB CLEARING BANK
0.1704
0.2057
1.0000
RQFII
0.1840
0.1013
0.4013
RQFII
1.000
Table 5. Probit Regression Result
Variables
Coefficients
Robust Standard Error
z
P>|z|
95% Confidence Interval
FUEL AND MINING EXPORTS
0.0104238
0.0048753
2.14
0.033
0.0008685
0.0199791
ALL COMMODITIES IMPORTS
AND EXPORTS
0.0001363
0.0001554
0.88
0.381
-0.0001683
0.0004409
RMB CLEARING BANK
0.7735685
0.5403359
1.43
0.152
-0.2854704
1.832607
RQFII
1.756936
0.6931644
2.53
0.011
0.3983587
3.115513
INFLATION
0.059838
0.0235389
2.54
0.011
0.0137027
0.1059733
WESTERN ASIA
-0.4194658
0.6070905
-0.69
0.490
-1.609341
0.7704096
EUROPE
-0.5934233
0.4046801
-1.47
0.143
-1.386582
0.1997351
OCEANIA
-0.0055955
0.735138
-0.01
0.994
-1.44644
1.435248
AMERICAS
-0.4237097
0.4499069
-0.94
0.346
-1.305511
0.4580916
AFRICA
-1.592917
0.6706781
-2.38
0.018
-2.907422
-0.2784122
CONSTANT TERM (β​0 )​
-0.8710235
0.3661116
-2.38
0.017
-1.588589
-0.153458
Table 6. Marginal Effects at Means
Variables
Marginal Effect
Delta-Method Standard Error
z
P>|z|
FUEL AND MINING
EXPORTS
0.0028544
0.0013747
2.08
0.038
0.00016
0.0055488
ALL COMMODITIES
IMPORTS AND EXPORTS
0.0000373
0.0000426
0.88
0.381
-0.0000463
0.0001209
RMB CLEARING BANK
0.2118327
0.1519528
1.39
0.163
-0.0859893
0.5096546
111
95% Confidence Interval
RQFII
0.4811164
0.2079915
2.31
0.021
0.0734605
0.8887722
INFLATION
0.0163859
0.0061906
2.65
0.008
0.0042526
0.0285192
WESTERN ASIA
-0.1148658
0.1658388
-0.69
0.489
-0.4399038
0.2101722
EUROPE
-0.162502
0.1123133
-1.45
0.148
-0.3826321
0.057628
OCEANIA
-0.0015323
0.2013081
-0.01
0.994
-0.3960889
0.3930244
AMERICAS
-0.1160279
0.1248635
-0.93
0.353
-0.3607559
0.1287
AFRICA
-0.4362017
0.1567932
-2.78
0.005
-0.7435107
-0.1288927
Table 7. Summary Statistics of All the Variables
Variable
Mean
Standard Deviation
Minimum
Maximum
SWAP AGREEMENT
0.1675978
0.3745564
0
1
FUEL AND MINING EXPORTS
10.89873
28.59188
3.00e-08
220
ALL COMMODITIES IMPORTS AND EXPORTS
198.3632
660.3462
0.0000721
5500
RMB CLEARING BANK
0.0558659
0.2303068
0
1
RMB QUALIFIED FOREIGN INSTITUTIONAL INVESTOR
0.0446927
0.207208
0
1
INFLATION
5.534671
5.737034
-1.370094
39.36664
ASIA
0.150838
0.3588948
0
1
WESTERN ASIA
0.0949721
0.2939987
0
1
EUROPE
0.2346369
0.4249604
0
1
OCEANIA
0.0782123
0.2692585
0
1
AMERICAS
0.2067039
0.4060771
0
1
AFRICA
0.2346369
0.4249604
0
1
112
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113
The CNN Effect and Humanitarian Interventions:
A Systematic Evaluation of the Effect of Media on Foreign Policy Decisions
You You Zhang
Abstract
The rationale behind military action in humanitarian crises abroad is one that has
puzzled many scholars. What factors lead to the decision to intervene? Traditional debate has
focused around realist and liberalist theories, which use national interest and media coverage
(the CNN effect) respectively as explanatory variables for engaging in costly military action
abroad. While many have acknowledged the lack of clear national interest in most cases of
military intervention in humanitarian crises, research on the CNN effect has produced sporadic
and inconclusive results. This paper analyzes the trends in media coverage in ten cases of human
rights crises in which the U.S. did not have a clear national interest in intervention. The cases,
which span more or less from 1990 to 2000 (post-Cold War and pre-War on Terror), are: the
Kurdish uprisings in northern Iraq; the civil war in Sierra Leone; the Afghan Civil War; the
Somali Civil War; Haiti under Cédras’s military regime; the Bosnian War; the Burundian
Genocide; the Rwandan Genocide; the First Chechen War; and the Kosovo War. In applying a
quantitative and qualitative analysis of the trends in coverage in cases where the U.S. intervened
as well as cases where it didn’t, this paper provides a systematic evaluation of the effect of media
on foreign policymaking. The evidence shows that between cases of intervention and
nonintervention, the variation in media coverage that underlies the CNN effect theory does not
exist. The theory therefore cannot accurately predict intervention in humanitarian crises abroad.
This conclusion also identifies the need for the international community to embrace defined
criteria for future military interventions in the face of humanitarian crises.
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Contents
Introduction
I.
Literature Analysis: Contenting Theories of Humanitarian Interventions
II.
Methodology
a. Shortcomings of Existing Research
b. Cases
c. Measuring Media Impact
d. Potential Criticisms and Concerns
III.
Results
a. Summary Statistics
b. Cases of Intervention
i. Northern Iraq
ii. Somalia
iii. Kosovo
iv. Haiti
c. Cases of nonintervention
i. Rwanda
ii. Chechnya
iii. Sierra Leone
d. General Trends
e. Case Study – Bosnia
Conclusion
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Introduction
Since the end of the Cold-War era, the world’s major powers have redefined the role of
the international community in securing and advancing human rights. They have conducted
humanitarian military interventions in the internal conflicts of various countries, both with and
without the formal endorsement of the United Nations. Operation Provide Comfort in Iraq
(1991), the Unified Task Force in Somalia (1992), and the United Nations Assistance Mission
for Rwanda (1993) are some examples of international military action in response to mass
violations of human rights. Many scholars have sought to explain the rationale behind these
interventions, defined as the “use of force across state borders by a state (or group of states)
aimed at preventing or ending widespread and grave violations of the fundamental human rights
of individuals other than its own citizens, without the permission of the state within whose
territory force is applied.” 1
The question of what factors drive states to intervene in humanitarian crises abroad
remains relevant today. The ratification of the “Responsibility to Protect” (R2P) doctrine by the
United Nations in 2005 formally obliges the international community to protect populations from
genocide, war crimes, ethnic cleansing, and crimes against humanity. The doctrine does not
outline standards for when and how to intervene, however. Left up to the interpretation of
member states, R2P was invoked in the intervention in Libya in 2011. In contrast, no multilateral
military action was taken in the Syrian Civil War for humanitarian reasons; military intervention
only began when the global threat of the Islamic State of Iraq and the Levant has become too
alarming to ignore. 2 3 Understanding the factors behind the decision to intervene could allow the
international community to harness these motivations to initiate intervention future cases of
humanitarian crises.
Until recently, academic debate has been focused on the significant costs—in terms of
funds, casualties, and domestic political resistance—of taking military action abroad, and
possible motivators for a state to be willing to overcome such costs to save the lives of foreign
nationals. The two most prominent arguments come from realist and liberalist schools of
thought. The realist camp contends that states have little to no motivation to take action and pay
the costs for another country’s citizens, unless there is significant national interest involved. The
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protection of human rights is merely a pretext for advancing national interest, exemplifying a
new type of imperialism in the post-Cold War era that has nothing to do with humanitarian
goals.4 While this theory applies to U.S. intervention when Saddam Hussein invaded Kuwait, it
cannot explain why the U.S. has intervened in places like Somalia and Haiti, where little or no
strategic interests were at stake.
In the other camp, liberalists advance the theory that states can be pressured by the public
to take costly action. Referred to as the CNN effect, the argument postulates that although states
are not interested in humanitarian interventions because the costs outweigh the benefits, western
liberal democracies are compelled to take action due to pressure from the domestic sphere.
Driven by the enhancement of international solidarity through mass media, citizens of liberal
democracies express moral outrage at acts of atrocities abroad.5 This then pressures on
policymakers to take military action in humanitarian crises on foreign soil.6 This theory suggests
that the media should have a strong influence on foreign policymaking and that the public should
hold powerful sway over their government. While a plausible explanation, research on the
validity and generalizability of the CNN effect has produced inconclusive results.
This paper approaches the puzzle with a different methodology that moves beyond
focusing on a small number of cases, providing a systematic evaluation of the CNN effect in
cases of both intervention and nonintervention by the United States that occurred between 1990
and 2000. Conducting both a quantitative and qualitative analysis of coverage trends of
humanitarian crises that took place during this period, I argue that, while trends in some cases of
intervention may show a media effect, there are similar trends in coverage that appeared in cases
of nonintervention which did not lead to military action. Since there is no consistent difference
in the intensity and trends of media coverage between cases of intervention and cases of
nonintervention, I conclude that media is not a reliable predictor of whether a state intervenes
militarily in humanitarian crises abroad, and the CNN effect is not a sufficient theory of why
states intervene.
I. Literature Analysis: Contending Theories of Humanitarian Interventions
Scholars have sought to understand under what conditions a state would bear costly
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military action for the sake of foreign citizens caught in an internal conflict. Advocates of
realism apply a cost-benefit analysis. Morgenthau argues that nations are inherently selfinterested, with interest defined in terms of power.7 Realist scholars argue that states only
intervene in circumstances where non-intervention would result in chaos in an area of strategic
importance and therefore directly threaten a state’s national interests.8 They assume that
humanitarian interventions, often exercised by strong states against the weak, have merely been
“thinly disguised pretext[s] for intervention for its own sake… there have been few, if any,
interventions that were designed exclusively to save the lives of foreign nationals”.9
Under the realist assumption, there are extremely few conflicts which would meet the
threshold threat to a state’s national interest to justify military intervention in another country’s
internal affairs. A quick survey of the cases of intervention since the end of WWII challenges
this theory, and Somalia is an oft-cited example. Oberdorfer’s account of the decision-making
process and public statements made by the Bush Administration suggests that policymakers did
not perceive any economic and strategic interests in intervening in Somalia.10 11 In the words of
Sam Jameson, a former staffer of the State Department during the Somalia crisis, “it was hard to
get the [Department of African Affairs] as a whole to focus on the tragedy. After all, Somalia
was just not as important to the United States national interests as it once was.” 12
Of course, to say that realism cannot accurately explain decisions to intervene in
domestic conflicts in other nations is not to say there are never self-interested concerns
throughout the course of events. Since humanitarian interventions are military operations with
real costs, states may be inclined to do what is necessary to “uphold their reputation without
actually focusing on what has to be done.” Without the motivation of preserving national
interest, states would go through considerable effort to keep costs low throughout the course of
intervention, resulting in a “half-hearted” commitment.13 Nevertheless, the lack of sufficient
national interest as a motivating factor in most cases of military interventions have pushed
scholars to turn to other plausible theories.
Alternatively, in the liberal camp, Gary Bass attributes the main reason behind
interventions to the “CNN effect”. He argues that the three distinctive institutions of liberal
states—free press, free civil society, and governments that respond to public opinion—make it
possible for the public to create pressure on the government to defend human rights abroad.14
This allows mass media to influence public opinion and generate enough public pressure on the
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administration to take action.15 Although Samantha Power does not explicitly argue for the CNN
effect in her book, A Problem from Hell, she pays considerate attention to media coverage of
humanitarian crises in her analysis of U.S. foreign policy, arguing that “the U.S. media can
sometimes play a role in helping draw public attention to an injustice abroad or to the stakes of a
legislative sequence,” and that inconsistent reporting could “ensure that lawmakers and
administration officials could oppose sanction bills [among other things] without attracting
negative publicity.”16
Other scholars have cited this theory to justify why states intervene. Jakobsen applies
both the national interest and the CNN effect frameworks to undertake a structured comparison
of five post-Cold War UN peace enforcement operations (Kuwait, Northern Iraq, Somalia,
Rwanda, and Haiti).17 He concludes that of the five cases studied, only one was driven by
national interest: Kuwait.18 In the remaining four, perceptions of national interest were absent.19
Thus, realist explanations are of little help when analyzing peace enforcements after the Cold
War; rather, when no national interest is at stake,
The CNN effect is necessary to mobilize pressure on (the Western) governments
to act. Once the CNN effect has placed a conflict on the agenda, the perceived
chances of success become the principal factor determining whether an
enforcement operation will take place or not. Since governments are more risk
averse in conflicts not involving national interests, an enforcement operation is
unlikely to be undertaken unless the risk of casualties can be kept to a minimum
and the operation can be limited in time.20
In Jakobsen’s analysis, although the media does not determine once and for all whether
military action will be taken, it has the unique power of placing the conflict on the agenda. In an
era where the U.S. faces no severe national interest threats (the 1990s were a time of relative
calm in American foreign policy), media coverage predetermines which crises merit attention
from policymakers in the first place.
This theory has gained further traction over the past decade. In 1993, the then U.S.
ambassador to the UN Madeleine Albright stated, “Every day we witness the challenge of
collective security on television—some call it the CNN effect… Aggression and atrocities are
beamed into our living rooms and cars with astonishing immediacy. No civilized human being
can learn of these horrid acts occurring on a daily basis and stand aloof from them.” 21 Former
Secretary of State Lawrence Eagleburger commented that “The public hears of an event now in
real time, before the State Department has had time to think about it. Consequently, we find
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ourselves reacting before we’ve had time to think. This is now the way we determine foreign
policy—it is driven more by the daily events reported on TV than it used to be.” 22 Several highranking officials have also stated similar opinions. Assistant Secretary of State John Shattuck
wrote, “The media got us into Somalia and then got us out.” 23 The UN Secretary General
Boutros Boutros-Ghali stated, “CNN is the sixteenth member of the Security Council.” 24 George
Kennan, renowned American diplomat, wrote in a New York Times Op-ed that “the reason for
this acceptance [of intervention in Somalia] lies primarily with the exposure of the Somalia
situation by the American media, above all, television.”25 Colin Powell, who served as the
National Security advisor and chairman of the Joint Chiefs of Staff, provided a more complex
analysis – “Live television coverage does not change the policy, but it does create the
environment in which the policy is made.” 26
The realist model and the CNN effect model of analyzing international humanitarian
interventions both focus on overcoming the high cost of intervention—the first driven by
national interest, and the second driven by public pressure. As previously discussed, the realist
model is insufficient in explaining the majority of interventions that occurred after the Cold War.
The CNN effect, although plausible, has been the subject of a wide range of academic studies, all
of which yielded contradictory results and are thus insufficient to form a conclusive, generalized
theory regarding the role of media coverage in humanitarian interventions.27
II. Methodology
A. Shortcomings of Existing Research
No previous studies of the effect of mass media on foreign policy decisions have included
a large enough number of cases for the findings to be generalizable. Moreover, the research
methodologies employed vary widely. The inconsistency in methodology reduces the validity of
cross-case comparisons between the findings. Without a broader, systematic study of various
cases of over time, the deductive argument that media coverage affects whether a state intervenes
in a foreign conflict is ungrounded.
For instance, Martin Shaw conducts a survey of the coverage of the Kurdish crisis in the
British print and electronic media, as well as reviews local and national public polls, and finds a
correlation between media attitudes and public opinion.28 Livingston and Eachus analyzes news
content and interviews of officials in Washington and Africa, operationalizing the CNN effect
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theory into two variables: frequency of news coverage, and impact on officials. They argue that
the U.S. decision to intervene in Somalia was the result of diplomatic and bureaucratic
operations, with news coverage spiking to cover those decisions.29 Both studies focus solely on
an individual case, and the results are not generalizable to other cases of intervention.
Furthermore, Mandelbaum looks for support for the realist theory of international
interventions, and critically reviews the policy interests of the U.S. in Somalia under a realist
framework. Finding no national interest that would have explained the decision to intervene, he
concludes that the decision was propelled by the CNN effect, reaching this conclusion through a
failure to find support for a realist framework.30 Gibbs also studies the case of Somalia under a
realist framework, concluding that the U.S. intervened in Somalia due to strategic and economic
interests, and policymakers were able to exploit television pictures to provide a convenient and
moralistic cover for national interest.31 Although both authors analyze the same event using a
realist frameworks, they reach conflicting conclusions, exemplifying the inconclusiveness of
current academic research on the CNN effect theory.32
The inconsistency in research methodologies, as well as the tendency to focus on only
one or two cases, make previous scholarly research results inconclusive and hard to generalize
into a broader theory. I hypothesize that when a number of cases of intervention are compared
with a number of cases of nonintervention, there is not a consistent difference in the intensity and
trend of media coverage. Media is therefore not a reliable predictor of whether a state decides to
intervene in humanitarian conflicts abroad. To test this hypothesis, I employ a methodology
different from those used in previous scholarly research, and combine both a quantitative and
qualitative analysis of media trends in cases of intervention as well as cases of nonintervention.
B. Cases
Gilboa argues that a valid scientific approach to the study of the CNN effect hypothesis
requires both an assessment of the effect of mass media on specific foreign-policy decision in
comparison with the relative impact of other factors, and the application of this procedure to
several relevant cases.33 It is unrealistic to attempt an in-depth analysis of the impact of mass
media versus other factors in foreign policymaking for a wide selection of cases. It is, however,
possible to make a general assessment of the media coverage trends across a broad range of cases
so as to determine whether any correlation could be established between media and intervention
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in the first place, prior to more in-depth research projects. In this study, I examine the 10
instances of serious humanitarian crises between 1990 and 2000 in which the U.S. did not have
obvious national interest for intervention, and analyze the distribution of news coverage of each
event throughout the timeline of each crisis. It should be noted that while it may make sense to
do a comparison of U.S. interventions before and after the birth of cable news networks in 1980,
the fact that the Cold War lasted until 1991 makes it hard to tease out the effects of the media in
comparison with considerations of foreign interest due to the Cold War in cases prior to its end.
The structural change of the global climate between the 1980s and the 1990s is a huge factor that
may obfuscate the results, especially since most of the interventions occurred after the end of the
Cold War. The limitation of this time period controls for effects of the Cold War and the War on
Terror, as these two events significantly redefined the U.S.’s international policy interests (as
well as public attention to affairs in certain regions abroad). The cases to be analyzed are: the
uprisings in northern Iraq (1991); the civil war in Sierra Leone (1991-2002); the Afghan Civil
War (1996 -2001); the Somali Civil War (1991-1995); Haiti under the military regime led by
Raoul Cédras (1991-1995); the Bosnian War (1992-1995); the Burundian Genocide (1993); the
Rwandan Genocide (1994); the First Chechen War (1994-1996); and the Kosovo War (19981999).
The intervention of Kuwait is not included in the analysis due to the relatively wellestablished understanding of U.S. national interest in the region. 34 There are also several cases of
humanitarian crises occurring during this period that are not included in the analysis. The
Angolan Civil War (1975-2002) is not included because it began during the Cold War, and
served as a surrogate battleground, which may result in differences in national interest
considerations for the U.S. that other cases in the analysis do not encounter. The first and second
civil wars in the Democratic Republic of Congo (1993-2003), as well as the Algerian Civil War
(1991-2002), are not included because they extend beyond 2001; the U.S.’s national interest
shifted dramatically after the September 11th attacks, introducing an important factor in foreign
policymaking decisions that is not accounted for in this analysis. While the Algerian Civil War
ended in February 2002, a few months after the declaration of the global War on Terror in
October 2001, its Islamist movements and the ties of insurgency groups to al-Qaeda dramatically
impacted the U.S. response to the conflict after the September 11th attacks. Sierra Leone, on the
other hand, is included because the fighting (and genocidal campaigns) had mostly stopped by
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2000, and only the negotiation process was drawn out until January 2002.
C. Measuring Media Impact
The effects of media coverage of a humanitarian crisis abroad could simultaneously
affect many aspects of the domestic political sphere. It could lead to a dramatic shift of public
opinion in favor of military intervention, or a surge of political elites and journalists criticizing
the government for inaction. If media could “convince a handful of well-placed persons of the
need to intervene”, that could be a manifestation of the CNN effect as well.35 While the specifics
of the causal mechanism remain somewhat vague, underlying the argument is the idea that there
must be high media coverage in cases of intervention that influenced one or more groups within
the domestic political sphere, thereby pushing the government to action. In cases of
nonintervention, then, there must be a comparatively lower amount of news coverage.
Furthermore, if media is the main causal factor of intervention, then there should be an
observable trend within the cases of intervention that isn’t present in cases of nonintervention.
There cannot be a causal relationship between mass media and military intervention if there was
no difference in media coverage between the two sets of cases. Media coverage trends are
therefore the key independent variable to systematically examine the CNN effect.
To analyze the trends in media coverage, I look at news articles from major news sources
which have a broad readership across the United States: Wall Street Journal, New York Times,
and the Washington Post. These papers are selected for their wide circulation and national
readership, as well as accessibility to a database of historical articles. I examine the distribution
of articles mentioning each event over time, and compare the trends for cases of intervention and
nonintervention, so as to find an observable, distinct difference in cases of intervention and
nonintervention that may support the CNN effect theory. It is assumed that the frequency of
coverage in TV and radio news channels reflect the prevalence of each event in print news
coverage; thus, news articles are taken to be representative of other types of media. Media
coverage is measured by the average number of articles per month that mentioned the event. For
cases of intervention, only articles prior to the date of intervention was included; the causal
mechanism of the CNN effect suggests that prevalence in media must precede military action.
The data are drawn from ProQuest Historical News Archive. To ensure the soundness of the
research, very general search terms for each conflict were employed, so as to find as many
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relevant articles regarding each case as possible. I included all articles in which the name of the
country or countries in crisis appeared at least once throughout the duration of the conflict.
Furthermore, in the Sierra Leone Civil War, the U.K. intervened whereas the U.S. did
not. To supplement my research, I also collected data on one of the most widely distributed
newspapers in the U.K. (the Guardian), and analyzed the distribution trends. Since the theory
makes a claim regarding the nature of media and public influence on foreign policy issues in
western liberal democracies, comparing media coverage between a country that intervened and a
country that did not can increase the external validity of the research.
For cases of differing duration, the unit of time used to graph average news article
distribution also differs, so as to best reflect the variations in article distributions over the course
of the event. For cross-comparisons across cases, however, the values are calculated to a
standard unit of average number of articles per month (calculated from the total number of
articles from all three newspapers, averaged across the number of months during the conflict).
For cases in which daily data are collected, the monthly average is calculated as the daily
average multiplied by 365 days, then divided by 12. Likewise, if yearly data are collected
initially, the monthly average is calculated by the yearly average divided by 12. This method
ensures the most accurate calculation for the monthly average of news coverage.
D. Potential Criticisms and Concerns
There are two potential concerns to be addressed, the first of which is whether the
quantity and coverage trend of news articles regarding each event sufficiently isolates the media
effect. By selecting a defined time period for cases used for analysis, broader foreign policy
concerns stayed relatively the same, and therefore are not likely to have an impact on the
dynamic between media and intervention. By focusing on the U.S. only, the study ensures that
domestic policy conditions, as well as the relationship between the public, political elites, and
policymakers are the same between cases. Looking at the same news sources also eliminates the
possibility that an effect is perceived due to the different weights that the multitude of media
outlets place on foreign policy issues. Of course, the quantity of coverage alone is not a perfect
measure of the effects of media; a lengthier, detailed article describing the conflict may be more
impactful than several small articles that give a cursory overview of the events. Due to the
difficulty in comparing individual articles in every case analyzed, I do not attempt to quantify the
effects of individual articles in this study; thus, the biggest assumption underlying this analysis is
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that the more frequent a conflict appears in the media (the more times the public reads about the
conflict), the bigger effect the media should theoretically have on changing public opinion
regarding intervention. This assumption should be tested in future research.
It should be noted that there are also smaller case-by-case variations of factors that may
play a role in the decision to intervene. If a correlation between media and intervention can be
established, further research is required to tease out whether the relationship is causal, or is a
spurious one caused by confounding factors. If a correlation could not be established, however,
then there is no relationship, and no CNN effect, to speak of in the first place.
The second concern is that the CNN effect focuses on broadcast media in general, which
uses televised images to tugs on the heartstrings of the public and therefore leads to a pressure to
intervene. Due to the difficulty in assembling resources of television footage of historical events,
this paper uses print media as a substitute for two reasons. Firstly, while a majority of people
reported television to be their main source of news (71 percent in a 1996 poll, and 60 percent in a
1998 one), people still read newspapers on a regular basis.36 37 In a 1996 poll, 71 percent of
respondents reported that they read newspapers regularly, and 48 percent of respondents reported
that they read the newspaper every day in a 1998 poll.38 39 Newspapers remained an important
source of public information throughout 1990 and 2000. Secondly, it is unlikely that an issue
highly prevalent on cable news networks would not appear with frequency in print media, or that
a conflict not receiving much airtime on television would be heavily covered in print media.
Both print and broadcast media are tailored to the consumer’s interests, and it is doubtful that
consumers of broadcast media would have significantly different interests than consumers of
print media. I thus assume that coverage in print media is a suitable representation of the
frequency that the public received information on a foreign conflict in all forms of media.
Although this research ensures the external validity of the result through including a large
number of cases, it does not take into consideration unique aspects of each humanitarian crisis
which may influence the U.S. decision to intervene, or domestic conditions such as whether the
year of the crisis was an election year. Thus, it cannot provide a detailed account of the reasons
for intervention or nonintervention in each case; nor can it examine whether there are other
differing elements between cases of intervention and cases of nonintervention that may explain
the decision to intervene. Furthermore, the timeframe of analysis (between 1990 and 2000)
controls for a sample of cases that occurred post-Cold War and pre-War on Terror, and
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consequently does not factor in the influence of other foreign policy considerations that merits
analysis in cases outside of the selected timeframe. The research aims to only provide a general
survey of the distribution of media coverage during a humanitarian crisis between 1990 and
2000, and whether it supports the hypothetical causal relationship between media and foreign
policy decisions posited in the CNN effect theory. Nonetheless, this analysis examines the
fundamental premise of the CNN effect. By analyzing a representative sample of cases using an
internally and externally valid framework, this paper provides an answer to whether media truly
has an effect on international politics.
III. Results
A. Summary Statistics
Of the 10 cases analyzed, five are cases of intervention and five are cases of
nonintervention. The average number of news articles per month for cases where the U.S.
decided to intervene is 52.77, and the average number of news articles per month for cases where
the U.S. did not intervene is 25.83. Chart 1 provides the five number statistical summary of both
samples.
Chart 1: Average Number of Articles per Month
Summary
Statistics
Cases of
Nonintervention
Cases of
Intervention
(with Bosnia)
Cases of
Intervention
(without Bosnia)
Min
2.85
13.49
13.49
Max
74.21
108.90
52.03
Q1
3.53
40.27
33.58
Q3
39.59
52.04
49.87
mean
25.83
52.77
38.74
n
5
5
4
Interestingly, Bosnia is a statistical outlier (lying outside the 1.5 * IQR range), which
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pulls the average for cases of intervention disproportionately higher. Since the mean is a
statistical measure of center that is very sensitive to outliers on either extreme, I also calculated
the summary statistics of the cases of intervention without Bosnia as comparison, as shown in the
third column of Chart 1. The new average for cases of intervention is brought down to 38.74
articles per month, lowering the original average by 14.03 articles per month. The data suggest
that, not including Bosnia, the average monthly news coverage of cases of intervention is only
slightly higher than the average monthly coverage of cases of nonintervention. It is unconvincing
that a difference of 13 articles per month alone contributed to a dramatic shift in attitude from
journalists, political elites, or the public in favor of intervention, or that it convinced the United
States to take military action abroad, and risk its people and resources for the sake of foreign
populations. Moreover, there were cases of intervention which received less coverage than the
average number of articles covering cases of nonintervention, suggesting that even if there is a
relationship between media and the likelihood of intervention, media is definitely not the
deciding factor in the decision to intervene. In summary, including Bosnia, there would appear to
be a correlation between the number of articles and the likelihood of intervention, but the
strength of the correlation is dramatically reduced when Bosnia is not included in the
calculations.
Bosnia is a crucial case where the high amount of news coverage is correlated with
military intervention. It appears to provide the strongest support for the CNN effect framework
where intervention was caused by high media coverage. It is therefore examined in-depth as a
case study to determine whether the apparent correlation is causal in nature.
It should be noted that while a difference of means statistical analysis could present a
more valid analysis regarding whether the difference is significant, the small number of cases
make the test results statistically unreliable. The results are therefore not reported. By looking at
the averages alone, the results undermine the argument of that mass media coverage of an event
influences the decision to intervene; since there is not a large difference in the amount of media
coverage between cases of intervention and nonintervention, media could not have been a
decisive factor in the decision to intervene. Graph 2 shows the distribution for the average
number of articles per month that covered each case in the sample of study.
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The graph visualizes inconsistencies in the amount of media coverage between cases of
intervention and intervention. Rwanda and Chechnya are cases of nonintervention that received
more media attention than the average amount of coverage in the cases of intervention (not
including Bosnia). Somalia, on the other hand, is a case of intervention that received less media
attention than the average amount of coverage in cases of nonintervention. Initial analysis of the
data finds a lack of evidentiary support for the CNN effect. Nonetheless, comparing the summary
statistics between cases of intervention and cases of nonintervention alone does not tell the whole
story. To further explore the correlation between media and intervention, I also graph the newscoverage trend for individual cases to analyze whether the coverage trends follow the causal
framework of the theory. The following section discusses the data on a case-by-case basis.
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B. Cases of Intervention
i. Northern Iraq
40.27 articles/month* 40
In April 1991, after Saddam Hussein lost control of Kuwait in the Persian Gulf War, he
cracked down ruthlessly on the spontaneous uprisings in the Kurdish north. His forces
committed massacres and other gross human rights violations against the Kurds, causing a
refugee crisis as Kurds fled to the Turkish border. News reports of the event fluctuated during
the short period of violence, peaking once in the first few days of the crisis, and then again just
before the United States initiated the first air drops during Operation Provide Comfort.
The overage trend shows that following the first peak at the onset of crisis, media
attention declined until mid-March, and then increased until the start of intervention. This trend
of increasing coverage prior to intervention is consistent with the framework of the CNN-effect
theory. However, the timeframe was extremely short; it is unlikely that two weeks of coverage
alone could sway public opinion in favor of military action. It is also difficult to identify
whether increasing news coverage created pressure for intervention, or whether news about the
government’s plans for intervention resulted in an increase in coverage. Since it often takes a few
weeks for military forces to mobilize for intervention, the spike in coverage may just as likely to
be due to reporters catching wind of the impending intervention, rather than a reflection of public
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pressure for action. A more in-depth analysis of the content of news articles is conducted for the
Bosnia case study.
The spike in coverage in early March reaches same height as the spike in early April
preceding the intervention. If media drove the decision to intervene, just looking at the trend and
intensity of coverage alone does not reveal why intervention did not happen after the spike in
early March but happened after a spike of similar degree in early April. Finally, the fact that
media coverage is not consistently high after the peak in early March reveals a decline in media
attention to the issue until the weeks prior to intervention. If the media or the public was
pressuring for action, the coverage should have stayed somewhat level to indicate persistent
interest in the issue. The coverage trends of the Northern Iraq crisis does not indicate that a CNN
effect existed in this case.
ii. Somalia
13.49 articles/month
After Siad Barre was ousted in January 1991, the situation in Somalia quickly
deteriorated as factions warred and the environment degraded. Soon, the country fell into a
famine, declared the world’s worst humanitarian emergency at the time.41
Somalia is often used as an example in favor of the CNN effect. Assistant Secretary John
Shattuck and renowned diplomat George Kennan both stated that the media drove the decision to
intervene in Somalia.42 43 Many scholars use Somalia as the quintessential example of media
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influence on the policy decision to intervene. 44 45 46 However, the distribution of news coverage
through time seems relatively flat throughout the beginning of the crisis. It increases somewhat
in July 1992, but the peak is relatively small compared to the two spikes in January 1993 and
November 1993, after the intervention has begun. While some media attention was focused on
the issue from July to November 1992, much higher attention was paid to the crisis after the start
of military action. In order for media to have had an effect on the decision to intervene, causal
logic would require that there be high media coverage prior to military action as well.
The intensity of coverage for this case is also quite low compared to cases of
nonintervention. At merely 13.49 articles per month covering the situation prior to the
intervention, the average coverage is actually less than the average coverage of several cases of
nonintervention (Rwanda and Chechnya). If the intensity of media coverage alone drove the
decision to intervene in Somalia, then the other cases with more average news coverage should,
in theory, have also received intervention.
iii. Kosovo
49.15 articles/month
The Kosovo War was a bloody conflict that lasted from February 1998 to June 1999.
During the conflict, both sides committed multiple large-scale massacres.47 The trajectory of
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media coverage does not reflect a significant amount of media attention prior to the beginning of
military action in March 1999. The two small peaks in that timespan occurred in October 1998
and February 1999, with relatively similar rates of coverage. Again, if the amount of media
coverage in February 1999 was the driving force behind the decision to intervene, why was
nothing done after the peak of similar height in October 1998? The rise in media interest in the
conflict during the month prior to intervention could also be a factor of news outlets getting wind
of the impending military action.
The dip from October to November 1998 reveals a drop in media coverage to a level
lower than that of the months preceding the October 1998 peak (average coverage in July 1998
was 41.67 articles per month, while it was 35.33 and 36.67 for November and December,
respectively). If the high coverage in October convinced policymakers to intervene, we should
see a consistent level of coverage indicating the momentum generated by media attention. The
decline indicates an inconsistent level of attention, and again brings into question the ability of a
single month of high coverage to push policymakers to take military action abroad.
iv. Haiti
52.03 articles/month
Under Joseph Raoul Cédras’s de facto government after the coup d’état in September
1991, more than 3,000 innocent men, women, and children were murdered. A Truth Commission
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from the United States Institute of Peace later identified more than 18,000 human rights
violations under Cédras’s rule.48 In September, 1994, the UN-authorized Operation Uphold
Democracy began action to remove Cédras’s regime.
With the highest average number of reports prior to intervention of all the cases of
intervention (excluding Bosnia), the news distribution of the crisis in Haiti began relatively flat
throughout the beginning of the crisis. It peaked once in late 1993, reaching almost 150 reports
per month. However, no military action was taken, and the coverage dropped again to previous
levels. In July 1994, coverage rose once more almost as high as the first peak, then dipped down
in August, the month of the beginning of intervention. Once military action began to unfold,
coverage surged in September, reaching almost 250 articles in that month alone, almost five
times as high as the average amount of coverage prior to intervention.
The dip in coverage from December 1993 to March 1994 suggest a loss of media focus
on the issue. Again, if the CNN effect theory is true, the coverage should have remained at a
somewhat consistent level after the peak, indicating ongoing pressure for the government to act.
In May 1994, the UN and the U.S. tightened economic sanctions, leading to the adoption of
Resolution 940 authorizing member states to use all necessary means to overthrow the military
leadership and restore constitutional rule.49 This could account for the increase in media
coverage from March to May 1994 as reporters picked up on the diplomatic responses to the
crisis.
In this case, the changes in media coverage occur concurrently with the actions of
diplomats and politicians, rather than high media attention preceding foreign policy decisions. If
media had an effect, the question of why intervention didn’t occur after the first peak in October
1994 but occurred after a peak of similar height in July 1994 is once again left unexplained by
the mechanism of the CNN-effect theory.
C. Cases of Nonintervention
This section explores the media distribution for cases of nonintervention. Afghanistan
and Burundi received the least coverage in the ten cases analyzed, with an average of 8.95, 3.53,
and 2.86 articles per month respectively. In terms of analysis, not much can be said other than
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that a lack of media coverage may or may not explain the lack of military intervention for each
crisis. The more interesting cases are those that have higher news coverage, but did not receive
intervention. The news trajectory of the cases of Rwanda and Chechnya would provide insightful
information on whether media drove humanitarian action in cases of intervention. While Sierra
Leone also received a low amount of coverage, media trends during this crisis is analyzed in
comparison with media trends in the U.K. to understand whether differences in coverage resulted
in the U.K. decision to intervene, versus the U.S. decision not to intervene.
i. Rwanda
74.21 articles/month
The Rwandan genocide was the fastest, most efficient killing spree of the 20th Century. In
100 days, some 800,000 Tutsi and politically moderate Hutu were murdered.50 The lack of action
from the U.S. and other western democracies has been disparaged as a failure on the part of the
international community. Many scholars criticize the fact that the Clinton administration knew of
the atrocities being committed in Rwanda, but the lack of media and the subsequent lack of
public pressure resulted in the decision not to intervene.51 52 53 This makes Rwanda another oftcited cases in support of the CNN effect—the lack of media exposure resulted in a lack of
response. 54
The trajectory of news coverage of the atrocities shows, however, that there was
substantial media attention paid to the crisis from the onset of violence. Starting at close to 60
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articles per month, the number of reports kept increasing in the short three-month span of the
crisis. The high news coverage discredits the claim that the public had little idea of what was
going on in Rwanda. The coverage was also consistently high, with no dips in the trend
indicating a loss of attention on the issue. Most importantly, the average number of articles
throughout is higher than the coverage of all four cases of intervention (excluding Bosnia)
included in this study. This media trend suggests that it was not a lack of media exposure that
resulted in the lack of action; the trend of news coverage for this event would have been the
poster-child case for a media-driven decision to intervene.
This consistently increasing amount of coverage throughout the event mirrors the
trajectory in the three months prior to intervention in Kosovo, and (to a lesser extent) Haiti.
Looking at the frequency and trend of news coverage of these crisis, if media drove the
intervention in Haiti and Kosovo, it should have achieved intervention in Rwanda as well. The
fact that intervention didn’t occur in Rwanda, despite it having more news coverage than the
other two cases, further undermines the logic of the CNN effect.
ii. Chechnya
39.59 articles/month
The First Chechen War spanned from December 1994 to August 1996, during which
Russian forces “indiscriminately and disproportionately bombed and shelled civilian objects”.55
In addition to the carpet-bombing campaign, multiple massacres were committed by Russian
authorities.56 The crisis received high media alert from the start, with coverage peaking a mere
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one month after the beginning of the war. Media attention dropped subsequently, but it remained
somewhat consistent above 15 articles per month. The average coverage of 39.59 articles per
month is also just slightly above the average amount of coverage for the cases of intervention,
excluding the outlier Bosnia. Despite this high media coverage, however, it is difficult to
establish the same argument as in Rwanda that media had no effect on the decision to intervene.
Strategic considerations of U.S.-Russia relations may be a spurious factor that have affected
foreign policy in a way not accounted for in other cases in this study.
iii. Sierra Leone
3.53 articles/month
Civil War erupted in Sierra Leone in March 1991, when the rebel army called the
Revolutionary United Front (RUF) attempted to overthrow the Joseph Momoh government. By
the year’s end, the RUF controlled two-thirds of Sierra Leone. Government forces were brutal
and indiscriminate in their search for rebels among the civilian population. They transported
townspeople to concentration camp-like “strategic hamlets”, after which they would loot the
towns. The RUF were also extremely brutal in their treatment of civilians, waging a campaign of
amputation and rape. A grassroots militia force, the Kamajors, originally protected civilians and
fought against both the government and RUF troops, but by the end of the war, the Kamajors
also became corrupt, and partook in systematic extortion, murder, and kidnappings.57
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A peace accord was signed in 1996, but it quickly unraveled following a coup initiated by
a group of disgruntled Sierra Leone Army officers. In May 1997, the Armed Forces
Revolutionary Council (AFRC) proclaimed itself the new government of Sierra Leone, and
joined forces with the RUF. In the same year, the Commonwealth suspended Sierra Leone, and
the UN Security Council imposed sanctions on the country, barring trade in arms and petroleum
products.58 In January 1999, the AFRC/RUF set upon Freetown, the capital of Sierra Leone, in a
bloody assault called “Operation No Living Thing”. During the attack, rebels entered the
neighborhoods to loot, rape, mutilate, and kill indiscriminately, and over 7,000 people were
reported killed in this offensive.59 This was one of the bloodiest and disturbing incidents of mass
human rights violations during the war.
In May 1999, a ceasefire was negotiated with the help of the UN, and a peace agreement
was negotiated in July. Peacekeeping troops arrived in November to supervise the
demobilization and disarmament of RUF forces. Many rebels refused to commit themselves to
the process, and UN forces came under attack in April 2000. That May, several hundred UN
troops were abducted. On May 7th, the U.K. launched a military intervention under the codename
Operation Palliser. British forces evacuated foreign citizens and besieged international
peacekeepers.60 It also assisted the UN Mission in Sierra Leone (UNAMSIL), and the Sierra
Leone Army. After coming into direct confrontation with the RUF on May 17th, British Forces
began training the SLA. By September 2000, the RUF began to disarm under the immense
political pressure. The Sierra Leone government eventually signed a ceasefire. The civil war was
declared over in January 2002.
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News coverage of the event in both the United States and the United Kingdom follows
similar trajectories; coverage was low until 1996, and peaked in 2000 when the U.K. intervened.
There is a slight difference between average articles per month. In the U.S., there were an
average of 3.53 news articles regarding the civil war per month (2.48 if only articles prior to the
U.K. intervention are considered). In the U.K., there were an average of 8.03 news articles
covering the event each month. In the period prior to U.K. intervention in May 2000, there was a
difference of 5.55 articles between the monthly average coverage by the Guardian (the third most
circulated British newspaper)61, and by the top three most circulated newspapers in the U.S. It is
hard to determine whether this number would have been large enough to generate the so called
“CNN effect” and account for the different responses of the U.K. and the U.S. to the same
humanitarian crisis. Furthermore, media coverage in the two countries followed similar trends,
and if media drove the U.K. to intervention, why it didn’t achieve the same effect in the U.S.
It is important to consider that Sierra Leone is an ex-British colony and a member of the
Commonwealth, which may have heavily influenced the decision to intervene. Michael Karbo
argues that “[it] is difficult to sustain an argument that there was any link between media interest
and an activist British policy towards Sierra Leone”; coverage of the war had been patchy before
1997, and coverage only spiked in May 1997 when the evacuation of British and Western
nationals by British troops was extensively covered.62 Further research would be required to
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understand why the U.K. intervened in Sierra Leone, but in regard to this paper, it suffices to
know that there was a very small difference in the coverage trend and quantity of the event in the
two western liberal democracies, one of which intervened when the other didn’t. If the U.K.
intervention was driven in large part by media pressure, then based on the logic of the CNNeffect theory, the similar trends in coverage in the two countries should have led to intervention
from the U.S. as well.
D. General Trends
Three general conclusions could be derived from the above analysis. First, while there are
some instances of spikes in media coverage in the month or two preceding the military
intervention, appearing to follow the model of the CNN-effect theory, there are also peaks of
similar heights earlier during those same crises which did not lead to intervention. This can be
seen in the cases of Iraq, Haiti, and Kosovo. Furthermore, the same trend of rising media
coverage could be seen in the cases of Rwanda and Sierra Leone, both of which did not receive
intervention. The media trend in Rwanda is particularly notable since its average amount of
coverage is much higher than those of all four cases of intervention, excluding Bosnia. Second,
in some cases there is a decline in coverage in the month prior to intervention, indicating
inconsistent media attention on the issue, rather than continually building pressure in the media
or in public opinion for the government to take action—this is apparent in the trends for Haiti
and Somalia. Third, even when there is a consistent rise in media coverage in the Sierra Leone
case leading to intervention by the U.K., the same trend and similar amounts in coverage did not
lead to intervention by the U.S. Even if, for the sake of this argument, media coverage is
assumed to be the deciding factor in the U.K. decision to intervene, the lack of action by the U.S.
highlights the inconsistent effects of the proposed mechanism.
These trends suggest that while there may be partial correlations between high media
coverage and intervention, the evidence is weak at best, and is overwhelmed by instances of
media “peaks” which did not receive intervention—either during the earlier stages of the cases of
intervention, or during the cases of nonintervention. Media coverage, quantified in this study by
the average number of articles that were relevant to each of the cases analyzed, cannot accurately
predict whether a liberal democracy takes military action in humanitarian crises abroad.
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E. Case Study – Bosnia
As the outlier case with the highest average news coverage prior to intervention, Bosnia
seems to present the strongest evidence in support of the CNN-effect theory. An in-depth study
of this case through examination of the news coverage trends and the historical progression of
the crisis allows us to further understand the causal mechanism in the CNN effect theory. To do
so, I review the news articles at each peak in the trend – August-September 1992, April-June
1993, February-April 1994, and June-August 1995 – to determine whether the media at those
peaks reflected increasing public pressure to take action, or were covering the debate by
policymakers in the White House, or reported the on-the-ground action in Bosnia itself. Public
opinion polls are also analyzed to reveal public sentiment on the issue and to establish whether
there was strong public support in favor of intervention prior to military action.
The conflict in Bosnia broke out in April 1992 after the breakup of Yugoslavia.
Following Bosnia and Herzegovina’s declaration of independence, Bosnian Serbs mobilized
their forces to secure Serb territory in the region. Ethnic cleansing of the Bosniak Muslim and
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Croat populations ensued, and brutal violence spreading throughout the region. Three bitter years
of fighting followed, characterized by indiscriminate shelling of civilian areas, systematic massrape, and large-scale massacres.
Western media immediately picked up on the conflict, with reports of air strikes against
Croat and Muslim towns following the Western community’s decision to recognize the
independence of the Republic of Bosnia and Herzegovina.63 64 65 66 Just one week after the
bloody violence began, a New York Times article was already reporting the Muslim Slavs’ hopes
for U.S. intervention, calling for help to “spare [the] country from more ‘mass massacres’…
being committed by irregular forces sent from Serbia.”67 In the report, Secretary of State James
Baker stated that the U.S. had sent a “strongly worded protest note to the Serbian leadership.” 68
Journalists called for Washington and the European Community to “Stop the Butcher of the
Balkans,” portraying Milosevic (President of Yugoslavia) as “Europe’s last Communist tyrant”,
and the conflict as “a war that can and must be stopped.” 69 70 In response, the U.S. warned the
Serbian-led government in Yugoslavia that unless it stops its aggression in Bosnia and withdraws
its forces in the next two weeks, Washington would press for Belgrade’s suspension from the
Conference on Security and Cooperation in Europe.71
Despite the calls for Western action to stop the aggression, “neither European
governments nor the United States had ‘contingency plans or specific ideas’ for clamping down
on the Serbian leadership.” 72 Rather, statements of condemnation were issued, most of which
Serbia blatantly ignored. The UN also formed a peacekeeping force (UNPROFOR) in February
1992, providing humanitarian relief convoys and ground transportation for refugees. In May, the
European Community imposed sanctions against Serbia, and the UN imposed an arms embargo
on the region, seeking to drain the flow or resources and arms to the combatants. NATO foreign
ministers agreed to assist the UN in monitoring compliance with the sanctions under Operation
Maritime Monitor. None of these actions directly impacted the amount of fighting, and the
sanctions were criticized as being “much too late” to stop the violence and end the war.73
As months of diplomatic hand-wringing dragged on, Islamic leaders began to criticize the
lethargic action as a result of Western reluctance “at the idea of an Islamic state on the European
continent,” reinforcing the notion that “the U.S. and its allies secretly want to see Muslims
around the world weakened, suppressed, or even killed.” 74 Meanwhile, Bosnia’s Chief for
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Military Action urged President Bush for “peace-making forces” and “military action,” but the
White House reiterated the sharp distinction between provision of food and supplies and trying to
stop the violence. Secretary of State Baker indicated that “the focus of United States efforts is on
the provision of humanitarian relief and not an ultimate solution to the political conflict.” 75
In the first few months of the conflict, demands for U.S. military action by political elites
and journalists were prevalent in newspapers, along with criticisms that the Bush administration
has responded “too meekly” to the humanitarian crisis, which led to the first peak in the news
coverage trend between August and September 1992.76 77 The Bush Administration reacted with
calls to tightening sanctions and soliciting diplomatic pressure from Russia against Yugoslavia’s
Serbs, but the administration “gave no indication that the U.S. or any of its allies are considering
military action”, despite urges by various members of Congress and the Democratic presidential
nominee Bill Clinton for President Bush to take more aggressive steps.78 79 Not all reports during
this period called for military action, though a vast majority did pressure Bush to take the
leadership role and “organize the world to douse the Serbian brushfire before it spreads.” 80
In spite of considerate media coverage of the brutality of the crisis and the increasing
death toll, the American public was not paying attention. In a survey by Times Mirror Center,
only 27 percent of U.S. adults surveyed followed the Bosnia story fairly closely, and 10 percent
followed it very closely.81 A Harris Poll conducted in August 1992 shows that 21 percent of
American adults had not heard about the fighting in the area.82 In the same poll, 80 percent of
adults supported sending in UN peacekeeping forces to maintain peace, but only 50 percent
supported sending UN forces to actively stop the aggression. 30 percent of those surveyed
supported sending in U.S. troops to repel the aggressor. Clearly, at this early stage of the crisis,
there was high elite and journalist pressure for intervention, but little public support. Since the
CNN effect is based on media coverage driving public opinion in favor of intervention and
ultimately resulting policymakers deciding to intervene, the argument is undermined by this
example of how media bias towards intervention does not necessarily translate to public support
for military action.
The second and the highest peak in news coverage occurred between April and June
1993, a few months after Clinton took office as President. By this point, over 150,000 people had
been reported dead and missing as a result of the war, and Serbian leaders watched the West
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stand meekly by as they conquered 30 percent of Croatia and 70 percent of Bosnia. Western
policymakers remained divided—Britain and France supported air strikes, but opposed lifting the
arms embargo; Clinton’s administration ruled out ground troops but advocated a “lift and strike”
policy.
8384
Others believed that “[a]nything short of pushing the Serbs off their conquests…
could set a precedent—aggression pays—for other nationalist adventurism in eastern Europe”,
signaling the impotence of NATO in preventing such conflagrations within its European
backyard.85
Finally, in April, the UN Security Council approved the enforcement of a no-fly zone
over Bosnia under Operation Deny Flight: NATO’s first military operation, and a mission of
“more symbolic than real protective value” since most of the killing had been done on the
ground.86 87 Nonetheless, member states were unwilling to provide the 70,000 peacekeepers
needed for the mission.88 Despite the lack of moral ambiguity in Bosnia, and the fact that both
the UN and NATO have admitted that an end to the fighting depends on their involvement, this
mission wasn’t much more than a “paper tiger.” 89 As reports of the suffering of Bosnian
Muslims continue to pour in, the Clinton Administration remained undecided on whether to send
in ground troops.90 91 92
Clinton’s inaction contrasted with the strong position he favored as a candidate the
previous summer. Journalists attributed it to various reasons, including the lack of domestic
support.93 Articles calling for government action now also called for support from the American
public.94 95 96 A team of experts sent to Bosnia by President Clinton returned in April and also
urged Washington to seriously consider military intervention.97
Pressure continued to build until June, when the UN authorized NATO to expand its role
and provide close air support to UNPROFOR. U.S. representative Madeleine Albright made it
clear that the administration only supported the resolution reluctantly and as a stop-gap
measure.98 News outlets were now invoking language of the Holocaust, seeking to alarm both the
Clinton Administration and the general public.99 100
Public opinion during this period remained erratic. Generally, Americans were in support
of taking stronger steps towards stopping the war, especially when the issue is framed in
comparison with Hitler’s genocide against Jews. A poll in May 1993 shows that 48 percent of
respondents found it very convincing, and 20 percent found it somewhat convincing, that the
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Serbian attack in Bosnia is essentially a small version of Hitler’s genocide, and that the U.S.
should take strong steps to stop the aggression.101 When the question was framed in terms of U.S.
ground troop involvement, however, respondents were less supportive of military action in
Bosnia. In a Newsweek poll, 46 percent of respondents opposed sending U.S. ground forces to
restore peace in Bosnia.102 Another poll reports 56 percent of respondents in opposition of the
U.S. taking an active military role to stop the fighting in Bosnia, and only 34 percent in
support.103
In early February 1994, a mortar attack on Sarajevo’s central marketplace killed 68
people and injured 120, leading to four months of high coverage. News articles reiterated the
moral imperative to act, and the lack of a meaningful U.S. policy on Bosnia.104 105 Clinton
responded with threats of air strikes, but took no firm steps towards ending the fighting
otherwise.106 While the U.S. endorsed the UN’s plan for NATO aircrafts to backup peacekeepers,
President Clinton made it clear that the U.S. was not willing to send in ground forces into Bosnia
as peacekeepers. Finally, on February 9th, western states agreed for NATO to set the deadline for
the withdrawal of all heavy weapon from around Sarajevo within a week, backing up the demand
with threat of airstrikes.107 Clinton released statements declaring that the U.S. had clear interests
at stake in the conflict: preventing a wider war in Europe and keeping NATO’s credibility.108 The
extent of military action would be limited to tactical air strikes, and ground troops would not be
involved.109
Faced with the deadline, Bosnian Serbs withdrew most of their heavy artillery around
Sarajevo, and relief spread throughout the West that air strikes were not needed in the end.110 Yet
just days later on February 28th, four Serb jets violated the “no-fly zone” and were shot down by
U.S. fighter jets, signaling NATO’s first military engagement in Bosnia since establishing the
no-fly zone in April 1993, and the military alliance’s first offensive action anywhere.111 By
March 2nd, Serbian attacks have intensified in scattered areas of Bosnia, violating the cease-fire
accord reached three weeks earlier in Sarajevo.112 NATO aircrafts doubled their daily sorties over
Bosnia as increasing tension settles over the area, and the UN appealed for more than 10,000
new peacekeeping troops to prevent more violations of the short-lived truce.113 114
An NBC News/Wall Street Journal poll shows that by March 1994, 53 percent of
respondents favored the U.S. military taking part in UN peacekeeping forces on the ground to
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enforce a peace agreement in Bosnia.115 A similar poll conducted by the Program on International
Policy Attitudes shows 73 percent respondents in favor of ground troops participating in a UN
peacekeeping operation.116 41 percent of respondents felt the U.S. had a moral obligation to
protect the citizens of Bosnia against Serbian attacks, and 70 percent of respondents recognized
that there is a parallel between the holocaust and the ethnic cleansing that had been occurring in
Bosnia.117 118 Yet when asked whether they would “rather see the Muslims win or the Serbians
win,” 81 percent of respondents in the Gallup/CNN/USA Today Poll responded “not sure.” 119
Clearly, the majority of citizens weren’t paying close attention to the war, despite the general
sense of moral obligation that the U.S. should do something to protect Bosnians. In the same
poll, 61 percent of respondents felt the level of U.S. involvement in Bosnia was “about right” or
“too involved”—a majority of citizens had no strong desire for more involvement in the
situation.120
Despite the slight majority in support of U.S participation in peacekeeping troops
reported in some polls, Washington reversed its position last minute on a Security Council
resolution to send 10,000 new peacekeeping troops, due to a lack of available funding.121 Eight
days later, Bosnian Serbs had mounted an aggressive assault on Gorazde, and pounded its 65,000
citizens with heavy artillery. The next day, two U.S. fighters bombed Bosnian Serb targets in
Gorazde, NATO’s first-ever attack on ground positions. NATO launched several other limited
air strikes throughout the rest of the year, acting in coordination with the UN. Nonetheless,
Washington’s reluctance to commit more troops was apparent throughout this period.
The final peak in news coverage occurred in June 1995, by which point Bosnian Serbs
had resumed the siege of Sarajevo and captured some 400 UN peacekeepers as an insurance
against further NATO airstrikes. Former U.S. President Carter had brokered a four-month ceasefire among the warring factions in January 1995, but fighting soon broke out again, this time
with Muslim and Croatian forces now on the offensive. In June, Bosnian Serbs announced the
resumption of cooperation with the UN and freed the peacekeepers they had captured by
hostages. This, too, ended within a month; on July 6th, the UN safe enclaves of Srebrenica and
Zepa came under attack by Bosnian Serbs, and some 7,500 Muslin men and boys were killed.
Finally, Western countries realized the imperative to act before any more damage is done.
After the London Conference in July, UN Secretary General Boutros Boutros-Ghali gave the UN
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military commander the authority to request NATO airstrikes without consulting civilian UN
officials. NATO and the UN also agreed to use NATO air strikes in response to attacks on any
other safe areas in Bosnia, as well as to future acts of Serb aggression. On August 25th, a mortar
shell once again tore through a crowded market in Sarajevo, killing 43 people and wounding 75
others just hours after Bosnian Serb authorities expressed a tentative willingness to accept a
peace plan proposed by Richard Holbrooke.122 This time, with the full endorsement of the United
Nations, NATO officially launched Operation Deliberate Force on August 30th 1995 with largescale bombing of Serb targets, marking the official beginning of intervention.
By this time, calls for U.S. action had actually long disappeared from the news articles,
and were replaced instead by reactionary pieces describing the course of events. The few opinion
pieces that were published in June actually criticized Clinton’s decision instead, warning of a
“slippery slope” that unfairly thrusts Americans “into dangers that are justified by no vital
interest of the United States”.123 In fact, reactions to Clinton’s offer to send U.S. troops to help
reposition UN peacekeeping forces in Bosnia were so negative that Clinton had to reassure
members of Congress, senior officials, and the general public that “no U.S. ground troops will be
sent into Bosnia for any reason without extensive congressional consultation.”124 125
As tensions in the region increased during July and August, media coverage dropped,
indicating media fatigue of the situation and a loss of general interest in the issue. 52 percent of
respondents in a CBS News/New York Times poll reported that they followed the Bosnian crisis
“not too closely” or “not at all”.126 Despite the three years of rather consistent coverage, 57
percent of respondents still did not know “the name of the ethnic group that had conquered much
of Bosnia, and has surrounded Sarajevo.” 127 61 percent of respondents believed it was not in the
national interest of the United States to be involved in the conflict in Bosnia.128
The air campaign ended in September, as Bosnian Serbs had complied with the
conditions set out by the UN for negotiations for a ceasefire. Contrary to an influx of articles
celebrating the end of the atrocities, news coverage actually dropped to a local minimum during
September. The belligerents met in Dayton, Ohio in November 1995, and signed the Dayton
Accords, marking the end of three years of bloody war that took the lives of some 200,000
Bosnians. News coverage rose slightly through October and November, but compared to the
extent of coverage in May 1993, the coverage trend shows the media had moved on from the
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Bosnian story.
The Bosnian War has been used by many as a case that demonstrates the effect of media
on foreign policy. An in-depth analysis shows that, on the contrary, the media did not play a big
role in the U.S. policymaking process at all. At the beginning of the conflict, various journalists
and elites called for U.S. intervention before the crisis got out of hand, demands which remained
unheeded by the government. Despite the heavy media attention to the war throughout, public
opinion shifted very little, and the public remained generally supportive of a multilateral
humanitarian aid, while opposing unilateral active intervention. As the event dragged on,
coverage of the atrocities did not increase consistently, and fewer articles calling directly for
U.S. intervention appeared. Public opinion remained inconsistent throughout the crisis.
If the media had been able to shape U.S. foreign policy or set the agenda for decision
makers, it would seem likely that some kind of action be taken in May 1993 in response to the
pressure caused by high media coverage. Instead, the Clinton Administration offered a “paper
tiger” move that was a largely empty threat. In September 1994, the U.S. decided to intervene in
Haiti instead of doing something about Bosnia, despite the clear focus of the media on the
Bosnian crisis compared to the Haitian one. When the West finally took action three years later,
the media actually seemed to have lost somewhat of an interest, with no surge in reporting that
logically would have occurred if journalists or the public still cared deeply about the Bosnian
crisis. If media drove the decision to intervene in Iraq, Somalia, Kosovo, or Haiti, then why did
the U.S. administration wait three years before intervention, despite the much high amount of
coverage compared to the other four cases? Clearly, media was not a decisive factor in foreign
policy considerations.
Conclusion
This paper tests the ability of media coverage to explain the cases of intervention between
1990 and 2000, and aims to help scholars and policymakers understand what factors drive
decisions to intervene. The analysis of the trends in news coverage in the ten cases studied show
that there was—on average—very little difference between cases of intervention and cases of
nonintervention. Furthermore, cases which scholars have previously used in support of the CNN
effect, Somalia and Rwanda, do not support the proposed causal mechanism of the effect. While
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in some other cases of intervention, the distribution of media trends in the months prior to
military action may appear to reflect the causal mechanism of media leading to military action,
there are often other spikes of similar height earlier on in each of those crises that didn’t receive
military action. Moreover, similar media trends and trajectories exist in cases of nonintervention,
which did not lead to intervention. Media attention often fluctuated in cases of intervention,
rather than maintaining a constant or increasing level of coverage indicating steady media
pressure for the government to intervene. The data indicate no consistent pattern in media trends
between cases of intervention and nonintervention. Media coverage is an unreliable predictor of
whether a western liberal democracy decides to intervene militarily in humanitarian conflicts
abroad.
The in-depth case study of media trends and responses from the U.S. government during
the Bosnian War shows that despite the high media coverage throughout the crisis, no action was
taken when there were many articles calling for intervention, and neither was there a clear
majority of the public swayed in favor intervention throughout the crisis. Intervention actually
occurred at a point which the media was losing interest in Bosnia, contrary to the logical basis of
the CNN effect theory. Public support for intervention fluctuated throughout, and most of the
public have also lost interest in the crisis by the time the Clinton administration finally
intervened. Overall, there is a very episodic correlation between media coverage and
intervention, and support for the CNN effect theory is inconsistent. An argument based on media
coverage alone does not suffice in predicting cases of intervention.
But if media plays very little role in determining whether a country intervenes, then what
does? There are already various ideas within academic literature that looks to other factors. For
instance, Robert Pape argues that a mass-homicidal campaign, a relatively low-cost intervention
strategy, the reasonable prospect for the provision of enduring security for victims are factors
that lead to successful humanitarian interventions. 150 These alternative theories merit future
research and discussion, and should be systematically examined with a method similar to that
employed in this paper. Only then could we have a better understanding of international
humanitarian interventions.
148
Endnotes
1
J. L. Holzgrefe and Robert O. Keohane, Humanitarian Intervention: Ethical, Legal and Political Dilemmas (Cambridge University Press, 2003),
1
2
Edward, “More than 55,000 Killed in Syria in 2015.”
“UN News - Libya.”
4
Larry May, “Conflicting Responsibilities to Protect Human Rights.” in Human Rights: The Hard Questions, ed. Cindy Holder and David Reidy
(New York: Cambridge University Press, 2013), 347-61.
5
Ibid.
6
Ibid.
7
Hans Morgenthau, Kenneth Thompson, and David Clinton, Politics Among Nations, 7 edition (Boston: McGraw-Hill Humanities/Social
Sciences/Languages, 2005).
8
Andrew Mason and Nick Wheeler, “Realist Objections to Humanitarian Intervention,” The Ethical Dimensions of Global Change, ed. Barry
Holden (Macmillan Press, Basingstoke, 1996). 106.
9
Thomas M. Franck and Nigel S. Rodley, “After Bangladesh: The Law of Humanitarian Intervention by Military Force,” American Journal of
International Law 67 (1973): 340-41.
10
Peter Viggo Jakobsen, “National Interest, Humanitarianism or CNN: What Triggers UN Peace Enforcement after the Cold War?,” Journal of
Peace Research 33, no. 2 (1996): 205–15.
11
See Oberdorfer, Don, 1992. 'The Path to Intervention: A Massive Tragedy We Could Do Something About', The Wishington Post, 6 December.
12
Maryann K. Cusimano, Operation Restore Hope: The Bush Administration’s Decision to Intervene in Somalia, Pew Case Studies in
International Affairs, Institute for the Study of Diplomacy Publications, School of Foreign Service, Georgetown University, Washington DC,
1995, 4.
13
Andreas Krieg, Motivations for Humanitarian Intervention: Theoretical and Empirical Considerations (Springer Science & Business Media,
2012). 44
14
ibid. 28
15
Gary J. Bass, Freedom’s Battle: The Origins of Humanitarian Intervention (New York: Vintage, 2009).
16
Samantha Power, A Problem From Hell: America and the Age of Genocide, Second Edition (New York, NY: Basic Books, 2013). 229
17
Peter Viggo Jakobsen, “National Interest, Humanitarianism or CNN: What Triggers UN Peace Enforcement after the Cold War?,” Journal of
Peace Research 33, no. 2 (1996): 205–15.
18
Ibid. 212
19
Ibid. 212
20
Ibid. 213
21
U.S. Department of State Dispatch (Office of Public Communication, Bureau of Public Affairs, U.S. Department of State, 1993). 331
22
Kevin Dougherty, Military Decision-Making Processes: Case Studies Involving the Preparation, Commitment, Application and Withdrawal of
Force (McFarland, 2013). 7
23
Philip Seib, Media and Conflict in the Twenty-First Century (Palgrave Macmillan, 2005). 4-7
24
Ms Ekaterina Balabanova, Media, Wars and Politics: Comparing the Incomparable in Western and Eastern Europe (Ashgate Publishing, Ltd.,
2013)
25
George F. Kennan, “Somalia, Through a Glass Darkly,” The New York Times, September 30, 1993, sec. Opinion
26
Seib, Media and Conflict 5
27
For an in-depth comparison of various academic articles regarding the CNN effect, see Seib, Media and Conflict
28
Robinson, The CNN Effect. p.20
29
Livingston and Eachus, “Humanitarian Crises and U.S. Foreign Policy.”
30
Mandelbaum, “The Reluctance to Intervene.”
31
Gibbs, “Realpolitik and Humanitarian Interventions: The Case of Somalia” in Global Society in Transition: An International Politics Reader
32
Gilboa’s article, “Global Television News and Foreign Policy”, provides a more detailed review of the fragmented and confusing existing
literature on the CNN effect and humanitarian interventions.
33
Gilboa, “Global Television News and Foreign Policy.” 334-7
34
Ibid. 212
35
Ibid. 211
36
Washington Post. Washington Post Poll, Apr, 1996 [survey question]. USWASHP.916L.Q2A. Washington Post [producer]. Storrs, CT:Roper
Center for Public Opinion Research, iPOLL [distributor], accessed Feb-29-2016.
37
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[survey question]. USPSRA.98JUN8.R23. Princeton Survey Research Associates [producer]. Storrs, CT:Roper Center for Public Opinion
Research, iPOLL [distributor], accessed Feb-29-2016.
38
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[survey question]. USPSRA.051396.R02. Princeton Survey Research Associates [producer]. Storrs, CT:Roper Center for Public Opinion
Research, iPOLL [distributor], accessed Feb-29-2016.
39
Gallup Organization. Gallup Poll, Mar, 1998 [survey question]. USGALLUP.803011.R03A1. Gallup Organization [producer]. Storrs,
CT:Roper Center for Public Opinion Research, iPOLL [distributor], accessed Feb-29-2016.
40
* Due to the short span of the crisis, coverage data for Iraq is graphed on a daily, not monthly, basis.
As discussed previously, the average news coverage for cases of intervention only includes coverage prior to the beginning of intervention, so as
to examine the causal effect between coverage and intervention
41
Jon Western, “Sources of Humanitarian Intervention: Beliefs, Information, and Advocacy in the U.S. Decisions on Somalia and Bosnia,”
International Security 26, no. 4 (2002): 114-5
3
149
42
Peter Viggo Jakobsen, “National Interest, Humanitarianism or CNN” 214
43
George F. Kennan, “Somalia, Through a Glass Darkly,” The New York Times, September 30, 1993, sec. Opinion
Baum MA. 2004b. How public opinion constrains the use of force: the case of Operation Restore Hope. Pres. Stud. Q. 34:187–226
45
Matthew A. Baum and Philip B. K. Potter, “The Relationships Between Mass Media, Public Opinion, and Foreign Policy: Toward a
Theoretical Synthesis,” Annual Review of Political Science 11, no. 1 (2008): 39–65, doi:10.1146/annurev.polisci.11.060406.214132.
44
46
Louis Klarevas, “The ‘Essential Domino’ of Military Operations: American Public Opinion and the Use of Force,” International Studies
Perspectives 3, no. 4 (November 1, 2002): 417–37
47
"Human Rights Watch, Kosovo: Focus on Human Rights." Human Rights Watch, Kosovo: Focus on Human Rights. Web. 02 Feb. 2016.
48
“Truth Commission: Haiti,” United States Institute of Peace, accessed February 3, 2016, http://www.usip.org/publications/truth-commissionhaiti.
49
"Security Council Resolutions 1994." UN News Center. UN. Web. 02 Feb. 2016.
50
Power, Problem from Hell, 333-5
Colin McInnes and Nicholas J. Wheeler, Dimensions of Western Military Intervention (Routledge, 2012).
51
52
Monroe Edwin Price and Mark Thompson, Forging Peace: Intervention, Human Rights, and the Management of Media Space (Indiana
University Press, 2002).
53
Allan Thompson, The Media and the Rwanda Genocide (IDRC, 2007).
54
Richard Holbrooke, “No Media - No War,” Index on Censorship 28, no. 3 (May 1, 1999): 20–21, doi:10.1080/03064229908536578.
55
Human Rights Watch | 350 Fifth Avenue, 34th Floor | New York, and NY 10118-3299 USA | t 1.212.290.4700, “War Crimes In Chechnya and
the Response of the West,” Human Rights Watch, February 29, 2000, https://www.hrw.org/news/2000/02/29/war-crimes-chechnya-and-responsewest.
56
57
Ibid.
“Sierra Leone Profile - Timeline,” BBC News, accessed February 28, 2016, http://www.bbc.com/news/world-africa-14094419.
58
Resolution 1132 (1997). New York: UN, 1997.
"Getting Away with Murder, Mutilation, Rape: New Testimony from Sierra Leone". Human Rights Watch. July 1999. Retrieved 26
December 2010.
60
Eadie, Pauline, and Wyn Rees. The evolution of military power in the West and Asia: security policy in the post-Cold War era. (Routledge,
2016). 42
61
Due to the constraints of resources, only data for The Guardian were available for the full duration of the crisis.
62
Michael S. Kargbo, British Foreign Policy and the Conflict in Sierra Leone, 1991-2001 (Peter Lang, 2006). 132
59
63
Chuck Sudetics, “Shelling by Serbs in Bosnia Intensifies,” New York Times, April 7, 1992, sec. International.
64
Chuck Sudetics, “Croat Towns Bombed in Bosnia and Herzegovina,” New York Times, April 8, 1992, sec. International.
65
“World-Wide: Fighting Flared in Sarajevo,” Wall Street Journal, Eastern Edition, April 6, 1992.
66
“Fighting Spread across Bosnia-Hercegovina,” Wall Street Journal, Eastern Edition, April 10, 1992.
67
Chuck Sudetics, “U.S. Help Is Sought,” New York Times, April 15, 1992, sec. INTERNATIONAL.
68
Ibid.
“Violence Flares as EC Recognizes Bosnia: Gunmen Fire on Peace March in Sarajevo; 15 Casualties Reported,” The Washington Post (1974Current File), April 7, 1992.
69
70
“Bosnia’s War,” The Washington Post (1974-Current File), April 12, 1992, sec. Outlook Commentary and Opinion
71
David Binders, “U.S. Warns Serbia It Faces Reprisals: Threatens to Expel Belgrade From Conference Unless Its Forces Pull Back,” New York
Times, April 16, 1992, sec. International.
72
Ibid.
Roger Thurow, “War-Weary of Bosnia, Croatia Criticize Sanctions as Too Late,” Wall Street Journal, Eastern Edition, June 3, 1992, sec.
International.
73
74
Gerald Seib, “Split Between Islam and West Widens Because of Crisis in Bosnia-Herzegovina: Washington Insight,” Wall Street Journal (1923
- Current File), August 17, 1992.
75
Michel McQueen, “Bush Urges A Joint Effort On Ethnic Strife --- At Finnish Parley, President Asked by Bosnia’s Chief For `Military
Action’,” Wall Street Journal, Eastern Edition, July 10, 1992, sec. Politics & Policy.
150
76
Gerald F. Seib, “U.S. Calls for UN Diplomatic Actions to Shut Any Serbian-Run Death Camps,” Wall Street Journal, Eastern Edition, August
6, 1992, sec. Politics & Policy.
77
Fazlur Rahman, “Counterpoint: The New World Order Dies in Bosnia,” Wall Street Journal, Eastern Edition, September 17, 1992.
78
Seib, “U.S. Calls for UN Diplomatic Actions”
Gerald Seib, “Bush Is Being Pressured to Do More To Stop Violence in Former Yugoslavia,” Wall Street Journal (1923 - Current File),
August 5, 1992.
79
80
Ibid.
Times Mirror Center for the People & the Press. Times Mirror Poll # 1992-NII0992: September News Interest Index, Sep, 1992 [dataset].
USTM1992-NII0992, Version 2. Princeton Survey Research Associates [producer]. Storrs, CT:Roper Center for Public Opinion Research,
RoperExpress [distributor], accessed February 17, 2016.
82
Taylor, Humphrey. "Modest Plurality of Confused Public Favor Sending United Nations Fighting Force to Repel Aggression in Bosnia." The
Harris Poll. August 11, 1992. Accessed February 17, 2016.
83
The “lift and strike” policy involved simultaneously arming the Muslims by lifting the arms embargo, and threatening air strikes against the
Serbs should they launce an offensive before the weapons arrive. The strikes would also provide some military cover to the Muslims as they train
and rearm.
84
Thurow and Robbins, “A Costly Restraint”
81
85
Ibid.
Robert S. Greenberger, “UN Approves Enforcement Of No-Fly Zone --- Vote Sets Up Confrontation Between Bosnian Serbs And Western
Forces,” Wall Street Journal, Eastern Edition, April 1, 1993.
86
87
Alan Riding, “NATO Agrees to Enforce Flight Ban Over Bosnia Ordered by UN: By Mid-April, Warplanes Should Be Patrolling the
Embattled Nation.,” New York Times, April 3, 1993, sec. International.
88
James Adams, “NATO as Play-Doh: Why the Allied Forces in Europe Aren’t Ready to Move Into Bosnia,” The Washington Post (1974Current File), April 4, 1993, sec. Outlook Commentary and Opinion.
89
90
Ibid.
Chuck Sudetics, “From Bosnian Children, Tales of Hunger and Horror,” New York Times, April 2, 1993.
91
Chuck Sudetics, “6 Die as Muslims Flee Bosnian Town in UN Trucks: 2,000 Refugees Cram Aboard a Second Convoy to Escape the War.,”
New York Times, April 1, 1993, sec. International.
92
Stephen Engleberg, “Clinton Is Caught by Bosnia Dilemma: Serbs Dismiss Plan, Reviving Issue of Using U.S. Troops,” New York Times, April
4, 1993.
93
Anthony Lewis, “The Limit of Shame,” New York Times, April 5, 1993, sec. OP ED.
94
Ibid.
Chuck Sudetics, “In Sarajevo, Silence Turns to Despair,” New York Times, June 25, 1993, sec. International.
95
96
John Burns, “Serbs Advance in Bosnian Town, And Its Capture Seems Imminent: As Serbs Advance in Town, Its Capture Seems Imminent,”
New York Times, April 17, 1993.
97
Michael Gordon, “President Is Urged to Consider Force to Help Bosnians, but Congress Isnt Told: Expert Team Says U.S. Should Shield
Civilians -- Officials Omit Plan in Briefings Bosnia Panel Urges Clinton to Consider Using Force to Save Lives,” New York Times, April 11,
1993.
98
Ibid.
John Darntonbel, “Does the World Still Recognize a Holocaust?: Can the World Still Recognize a Holocaust in Progress? Bosnia Evokes
Europe’s Worst Memories; Appeasement Is One of Them.,” New York Times, April 25, 1993, sec. The Week in Review.
99
100
Mary Germain, “Letters to the Editor: What Did We Learn From the Holocaust?,” The Washington Post (1974-Current File), April 15, 1993.
101
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[producer]. Storrs, CT:Roper Center for Public Opinion Research, iPOLL [distributor], accessed Feb-23-2016.
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104
“World-Wide: Serbs Are Intensifying Persecution,” Wall Street Journal, Eastern Edition, February 2, 1994.
105
Sadruddin Aga Khan, “War Crimes Without Punishment: Civilians Die. The UN Guts a Tribunal.,” New York Times, February 8, 1994, sec.
OP-ED.
106
Elaine Sciolino, “Washington’s Mixed Signals: U.S. Again Warning of Military Action; Clinton Hits ‘Cowardly Act,’ but Takes No Step to
151
End the Fighting in Bosnia,” New York Times, February 6, 1994.
107
Carla Anne Robbins, “U.S. Will Seek End of Weapons Around Sarajevo --- NATO to Be Asked to Set Deadline for Removal, Use Air-Strikes
Threat,” Wall Street Journal, Eastern Edition, February 9, 1994.
108
Douglas Jehl, “Clinton Outlines U.S. Interest in Bosnia Air Strikes,” New York Times, February 10, 1994, sec. International.
109
Ibid.
110
John Kifner, “An Envoy Finds ‘No Need’ for Air Strikes: Conflict in the Balkans: Relief, Revelry and Russians UN Reports Serbs Are
Complying With Deadline for Withdrawal,” New York Times, February 21, 1994.
111
Carla Anne Robbins and Thomas E. Ricks, “U.S. Jets down Serb Planes over Bosnia, as They Violated NATO ‘No-Fly Zone,’” Wall Street
Journal, Eastern Edition, March 1, 1994.
112
“Fighting Intensifies in Bosnia,” New York Times, March 3, 1994, sec. International.
113
Rick Atkinson, “NATO Aircraft Have Doubled Daily Sorties Over Bosnia,” The Washington Post (1974-Current File), March 2, 1994.
114
Stephen Kinzer, “UN Seeks More Troops to Keep the Peace in Bosnia,” New York Times, March 4, 1994, sec. International.
115
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124
Thomas W. Lippman and Ann Devroy, “Clinton Defends Policy on Bosnia: Advisers Try to Calm Fears as Criticism Builds,” The Washington
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125
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152
Rethinking the Fight for Fifteen
Alec Bania
In looking back on the chaos of the last election cycle, we see that the call for a
fifteen-dollar minimum wage became something of a litmus test for progressives as well as a
central pillar of Bernie Sanders’ surprising bid for the Democratic nomination. While Sanders’
attempt ultimately fell short, his candidacy ultimately pushed the DNC to support a $15-dollar
minimum wage in their 2016 platform,​1​ and even caused the ultimate nominee, Hillary Clinton,
to express qualified support for the policy.​2
There is a strong impetus behind the $15 dollar an hour push, as the purchasing power of
the minimum wage has eroded significantly since the last raise in July 2009. Over the subsequent
period, the CPI increased 11%.​3​ Additionally, costs in key sectors like healthcare and housing
continue to grow at a rapid pace, a phenomenon that erodes the wages of the working poor.
Despite these challenges, the fifteen-dollar minimum wage, while a well-intentioned solution to a
very real problem, is a poor solution from a policy perspective. A revenue neutral wage subsidy
program, funded by payroll taxes, could achieve the same policy goals as a drastic minimum
wage hike without many of the negative consequences.
The potential drawbacks of a national $15 minimum wage have been well cataloged,
from the potential effects on employment and pricing, to the regional variations in cost of living
that could lead to distortionary effects. The difference between Manhattan, NY and Omaha, NE
effectively illustrates the vast regional variations in the policy implications of a fifeteen dollar
wage policy. A person working full time for $15 per hour will gross $600 a week and roughly
153
$2600 a month. In New York, these wages pale in comparison to the $2647​4​ that the median
studio apartment costs monthly, as well as the city’s median weekly wage of $2847.​5​ By
contrast, in Omaha, such a pay rate is not far off the region’s median weekly wage of $863,​6​ and
would be sufficient to easily afford a studio apartment at the median rate of $440 monthly.​7​ It is
clear that a $15 hourly minimum wage might both be a lifeline for the working poor in
Manhattan while also profoundly distorting the bottom end of the labor market of an area with a
more modest price level like Omaha. In labor markets with low prevailing price levels, sharp
minimum wage increases would likely cause significant inflationary stresses for consumers and
firms alike. If large segments of the population were to suddenly see wages adjusted upward, the
result would be pressure on prices from both the demand side, as money floods the market for
previously cheap goods (like housing) and from the supply side, as firm’s labor costs rise
sharply.
There are also broader worries about the effects of minimum wage increases on
employment. Previous empirical studies have suggested that employment effects are likely
small;​8​ however, such inquiries have generally been limited to the study of small minimum wage
increases. There is no empirical evidence to suggest what the potential impacts of a doubling in
the federal minimum wage could be, but it is entirely reasonable to assume the neoclassical
prediction of significant decreases in employment might well come to pass, especially given that
a $15 minimum wage is 65% of the current national median hourly wage​9​ and that, by one
estimate, 42% of US workers currently make $15 an hour or less.​10
If a $15 policy would create nearly as many issues as it solves, what policy steps can we
take instead to alleviate the plight of the low-skill, low-wage worker? For inspiration, we can
154
look to the EITC, a cash transfer program for the working poor designed to supplement incomes
without the incentive distortions that often accompany means tested programs. The EITC is,
essentially, a reverse income tax. However, the EITC does have significant flaws. Most notably,
the EITC comes in an annual lump sum payment administered through the federal tax system,
limiting the usefulness of the program in paying for recurrent or unexpected expenses that the
poor often face. Further, the working poor are prone to uncertainty in the earnings level and are
susceptible to the negative effects of income instability. The EITC is thus less helpful than it
could be to poor families who face unexpected mid-year financial crises.
The EITC is also somewhat expensive: it costs roughly $56 billion dollars a year. In a
political atmosphere with little appetite for more discretionary spending, near future initiatives to
help the poor will likely have to be revenue-neutral (i.e. paid for).
A federal wage subsidy program would effectively address the problems posed by both a
sharp minimum wage hike and the EITC. Since a subsidy would be built into to weekly
paychecks, it could more effectively help the working poor deal with unexpected expenses and
could be designed to soften the blow of negative income shocks that often prove so disruptive.
Wage subsidies could also easily be indexed to local prevailing wages and cost of living to more
effectively deal with the regional contours of US price levels. Finally, the whole program could
be made revenue neutral through a Social Security style payroll tax. Though such a levy would
have some deleterious effects on employment, they would be spread evenly across the whole
labor market, rather than concentrated on the low-skill workers at margins of the labor force, as
in the case of the minimum wage.
155
Progressive activists have identified important issues surrounding the living standards
associated with low skill and service sector employment, but it is clear that their goals would be
much better served by advocating for federal wage supports (which need not add to the deficit)
rather than an ill-considered, one-size fits all, drastic increase in the minimum wage.
156
Endnotes
1.
Gass, Nick. "DNC draft platform includes $15 minimum wage." ​POLITICO. Capitol News Company, 1 July 2016.
Web.
2.
Carroll, Lauren. "Does Hillary Clinton want a $15 or $12 minimum wage?" ​Politifact. Tampa Bay Times, 15 Apr.
2016. Web.
3.
"Consumer Price Index Data from 1913 to 2017." ​US Inflation Calculator. COINNEWS MEDIA GROUP LLC, 18
Jan. 2017. Web.
4.
"MNS Real Impact Real Estate." ​Manhattan Rental Market Report . MNS Brands, Dec. 2016. Web.
5.
"County Employment and Wages in New York City – First Quarter 2016 : New York–New Jersey Information Office."
U.S. Bureau of Labor Statistics. U.S. Bureau of Labor Statistics, 09 Nov. 2016. Web.
6.
"Occupational Employment and Wages in Omaha-Council Bluffs - May 2015 : Midwest Information Office." ​U.S.
Bureau of Labor Statistics. U.S. Bureau of Labor Statistics, 27 June 2016. Web.
7.
"Nebraska Apartments and Homes." ​Rent.com®. RentPath, LLC, n.d. Web.
8.
Schmitt, John. "Why Does the Minimum Wage Have No Discernible Effect on Employment?" ​CEPR.org. Center for
Economic and Policy Research, Feb. 2013. Web.
9.
Ibid.
10. ​Weissmann, Jordan. "A $15 Minimum Wage Would Give Almost Half of American Workers a Raise. Is That Crazy?" ​Slate
Magazine. The Slate Group, 28 Mar. 2016. Web.
157