Government Free Riding in Medical Research∗
Margaret K. Kyle†, David B. Ridley‡, Su Zhang‡
May 6, 2016
Abstract
Because knowledge spillovers cause the private sector to underinvest in research,
governments often fund research, especially medical research. However, knowledge
spillovers across borders might introduce free-riding by governments on each other.
We provide the first empirical evidence of how a government responds to medical
research funding by another government. Using data from 2007 to 2014 on infectious
and parasitic diseases, we examine how governments and foundations in 41 countries
changed their funding in response to outlays by the US government, which accounted
for about half of public outlays in our sample. Because funding decisions by the US
and by other countries might have common unobserved drivers, we instrument for US
spending using the political composition of the US Congress. Congress sets the budget
for the US National Institutes of Health, and thus affects US research funding, but does
not directly affect other countries’ research funding. We find that a 10% US funding
increase for a disease is associated with a 1% funding reduction for that disease by an
individual foundation or government agency and a 4% reduction for other governments
in aggregate.
JEL Codes: O3, L65, I18
Keywords: public goods, free riding, health, innovation, pharmaceuticals
∗
Preliminary and incomplete. We are grateful for helpful comments from participants at the International
Industrial Organization Conference and Journée de la Chaire Santée at Université Paris Dauphine, as well
as James Anton, Ashish Arora, Victor Bennett, Thomas Buchmueller, Thomas Nechyba, and Shannon
Seitz. This research was completed with the support of Health Chair - a joint initiative by PSL, Université
Paris-Dauphine, ENSAE, MGEN and ISTYA under the aegis of the Fondation du Risque (FDR).
†
MINES ParisTech (CERNA), PSL Research University, and CEPR, 75006 Paris, France
‡
Duke University, Durham, NC 27708, USA
1
1
Introduction
“Failure to provide global public goods is linked to collective action problems such
as ‘free-riding.’ The free-rider term describes a situation when no individual is
prepared to pay the cost of something that others may be expected to benefit
from; instead, all hope that someone else will pay for it and they will benefit
for free. This is particularly an issue for research and development (R&D) into
medicines to combat neglected diseases, which requires high-levels of investment.”
- World Health Organization1
Because the knowledge generated by research and development (R&D) is a public good,
for-profit firms are unable to appropriate the benefits of their investment. As a results, firms
tend to invest less than is socially optimal. To correct this market failure, governments grant
patents, use innovation prizes, provide R&D tax credits, and directly fund R&D. The latter is
particularly salient in the case of neglected infectious and parasitic diseases (Kremer, 2002).
But what about governments? Do they free ride as well? If funding by one government
creates spillovers in other countries, do governments free ride on the funding of others?
The potential for international free-riding is recognized in many other contexts, including
national defense (Olson and Zeckhauser, 1966; Lee, 1988) and environmental policy (Nordhaus, 2015). Because the deterrence created by military power is a public good shared by
allies, there is an incentive for each to under-invest. Similarly, investments in reducing carbon emissions create benefits globally, so a country may be tempted to allow others to incur
the costs of doing so. Military alliances such as the North Atlantic Treaty Organization
(NATO) and climate accords often commit countries to specific levels in order to restrict
their ability to free ride.
US drug makers and government officials sometimes complain that when other countries
cap drug prices, they are free riding on the US market (Scherer, 1993). High prices in the
US are said to provide the demand-side pull that encourages commercial drug development.
The potential for free-riding is one justification for the inclusion of intellectual property requirements in international trade agreements: the commitment to fixed periods of patent
protection reduces free-riding on demand-side “pull” policies to induce innovation. However, international free-riding on supply-side “push” policies, such as government grants,
has received less attention. In this paper, we examine whether foundations and government
agencies around the world free-ride on US government funding for basic medical research.
1
http://www.who.int/trade/glossary/story041/en/, accessed 15 March 2016.
2
We use detailed data on funding by government agencies and non-governmental organizations from 2007-2014 in 41 countries for 15 infectious and parasitic diseases. These diseases
are of particular interest for several reasons. First, these are typically considered “neglected”
by the private sector. Their burden is largely concentrated in poor countries, where the potential profits are low. The theoretical case for government intervention is strong, because
the social burden of these diseases is substantial, and government-funded research is unlikely
to crowd-out private investment. Second, many infectious and parasitic diseases may easily
cross borders, as the recent examples of the Ebola and Zika viruses illustrate. This suggests
that the benefits of curing them may not be confined to a single country, but rather all
countries to which they might spread. Third, detailed data on research funding for these
diseases across many countries and organizations is collected by the non-profit Policy Cures.
We are not aware of a similarly comprehensive source for other diseases.
Identifying free-riding is not straightforward. Many funders will prioritize diseases with
the greatest burden, or pursue areas where scientific breakthroughs are the most promising.
Ideally, we need an exogenous shock to one country’s funding allocation in order to observe
the response by other funders. We exploit the fact that the National Institutes of Health
(NIH) in the US plays an outsize role in financing research on these diseases relative to their
burden in the US population. Indeed, the NIH accounted for more than half of spending
on infectious and parasitic diseases during the time period covered by our data. The NIH
budget is set annually by Congress and fluctuates as the composition of Congress changes.
We argue that the party composition of Congress affects NIH funding for a given disease, but
does not directly affect funding for a disease in other countries. In other words, we use the
share of a political party in Congress as an instrumental variable to address the endogeneity
of government research funding.
In specifications that examine spending by individual funders (i.e. government agencies
or non-governmental organizations), we find that a 10% increase in US government outlays is
associated with about a 1% decrease in outlays by other funders in the following year. When
we combine spending by government agencies within a country, we find that a 10% increase in
US government outlays is associated with a 4% decrease by other governments. The results
are statistically significant and robust to alternative instrumental variable specifications.
This evidence is consistent with international free-riding.
3
1.1
Related literature
This is the first study to study whether governments free ride on other governments in funding basic medical research. However, there are several studies examining how government
R&D funding affects private investment. Government R&D funding can substitute for private funding because government funding bids up the wages of scientists and engineers which
makes private investment in innovation more expensive, at least in the short run (Goolsbee,
1998). Conversely, government funding can complement private investment, if government
invests in early-stage research, and private investors fund later-stage development (Toole,
2007; Blume-Kohout, 2012). The complementarity of government research and private development appears most substantial for early stage (Phase I) clinical trials rather than later,
costlier (Phase III) trials (Blume-Kohout, 2012). However, in this research it is difficult to
find a clean econometric experiment to identify these effects (David et al., 2000).
Government funding can not only crowd out private business, but also crowd out private
charity (Andreoni and Payne, 2011). During the Great Depression, government charitable
programs expanded, and private charities reduced funding for the poor and instead devoted
funding to other causes (Roberts, 1984; Gruber and Hungerman, 2007). Murray (2013) recommends that governments consider the role of charities: “In determining their own funding
strategies, they must no longer assume that their funding is the only source in shaping some
fields of research while recognizing that philanthropy may ignore other important fields.”
Although medical research is both supplied and used globally, most previous studies
have focused on only on NIH funding because the NIH is by far the largest funder and the
US government makes the data available. However, one shortcoming of analyzing only one
source of public investment, i.e. the NIH, is the possibility of biased estimates depending
on whether other public spending is positively or negatively correlated with NIH outlays.
If other governments devote resources to diseases in a pattern similar to the NIH, then
the effect of NIH spending is overestimated if the funding of other governments is omitted.
However, if an increase in spending by the NIH for a particular disease triggers a reduction
by other public funders, the effect of NIH spending is underestimated. For example, when
the George W. Bush administration prohibited federal funding for the development of and
research on new human embryonic cell lines, international researchers collaborated more with
US researchers (Furman et al., 2012). This response highlights the global nature of scientific
research and the importance of considering funding sources outside of the US.
4
2
Theory
We use a simple model of funding as a function of disease burden and the state of scientific
knowledge. According to NIH leaders, “NIH believes that a process that includes multiple
measurements of public health needs, but is also informed by scientific opportunity, allows
us to fund the best science” (Rockey and Wolinetz, 2015).
The model includes multiple funders which respond strategically to one another. For
example, a government funder in Europe might consider outlays by the NIH when choosing
how much to spend for a given disease. After all, knowledge generated by NIH-funded
research is rarely restricted to the US population.
The burden of disease d in the country of funder f is Bdf . The probability of developing
a new treatment is Sd xαd where Sd is an R&D productivity parameter specific to disease
d, xfd is the outlay, and α ∈ (0, 1). We define the expected benefits of xfd investment as
P
Bdf Sd f (xfd )α . Note that we sum across the outlays but not across the burdens, meaning
that the benefits to a funder include the other funder’s outlays but not the other funder’s
burden. That is, the funder does not consider the potential benefits of reducing disease
burden in the other country. The budget constraint for funder f is Y f . Therefore, the
optimization problem for a funder is:
max
xfd
X
Bdf Sd
d
s.t. :
X f
(xd )α
f
X
xfd
=Yf
(1)
d
We solve a simple version of the model with two funders, f = a, b, and three diseases,
d = i, j, g. To illustrate differences in disease burdens across countries, assume that funder a
has no burden from disease j (Bja = 0), and funder b has no burden from disease i (Bib = 0).
Both have burden from disease g which we refer to as the “global disease.” We refer to the
disease with burden only in the funder’s country as the “local disease” (for example disease
i for funder a).
Given the funding decisions for the other funder, we derive the optimal funding rule for
funder a as:
Y a − xag
1
Bia Si 1−α
=
[
]
g
a
b∗
xg + xg
Bg Sg
(2)
Then we solve for the equilibrium funding decision. An interior solution can be obtained
5
if:
1 + hig b
hig
Y >Ya >
Y b,
hjg
1 + hjg
(3)
where hmn = [(Bm Sm )/(Bn Sn )]1/(1−α) . Combining equation 2 and condition 3 gives us
optimal funding for funder a’s local disease i, as well as the disease g which burdens funders
in both countries.
hig Y a + hig Y b
1 + hig + hjg
(1 + hjg )Y a − hig Y b
=
,
1 + hig + hjg
xa∗
i =
xa∗
g
(4)
In this equilibrium, it is easy to show that an increase in the budget for funder a (Y a )
a∗
leads to a larger outlay for both diseases by funder a (xa∗
i and xg ). However, an increase in
the budget for the other funder (Y b ) leads funder a to reduce funding for the shared disease
a∗
(xa∗
g ) and increase funding for its local disease (xi ).
Hypothesis 1 If the budget for one funder increases, that funder will increase outlays on
diseases it is currently funding, while the other funder will free ride on the increased outlays,
shifting resources to other needs.
b∗
Giving the symmetry between funder a and b, and bringing xa∗
g into the equation of xg ,
we have:
xgb∗ =
Ya+Yb
− xa∗
g ,
1 + hig + hjg
(5)
Equation (5) shows the optimal funding level of funder b for global disease g as a function
of disease burden, R&D productivity, budget of both funders, and outlay for global disease
g from funder a. Specifically, we can see that, holding the budget of funder a as constant,
b∗
a dollar increase in xa∗
g is followed by a dollar decrease in xg . However, a dollar increase in
Y a induces xb∗
g to decrease by hjg /(1 + hig + hjg ) , which is less than 1.
Next consider the case in which one funder has a much larger budget (Y a >> Y b ), or the
burden or R&D productivity of domestic indication in one funder’s home country is much
larger than another (large hjg )2 . In this case, condition 3 does not hold. In this case funder
2
Condition 3 is also violated if hig is large. We ignore it here because in this case the corner solution is
symmetric to equations 6 and 7.
6
b will spend spend nothing on the global disease and everything on the local disease j.
b
xb∗
j = Y ,
(6)
xb∗
g = 0
Funder a allocates funding across the diseases according to the relative burden and science.
hig
Ya
1 + hig
1
=
Ya
1 + hig
xa∗
i =
xa∗
g
(7)
The corner solution presented above suggests that when funders are extremely unequal
in terms of budget size or disease burden, the smaller funder will completely free ride on the
larger funder, dedicating no funding to the global disease, and only funding the local disease.
Hypothesis 2 When there are multiple funders, and those funders differ in disease burden
or budget, a funder might free ride, so outlays from a given funder will not necessarily be
proportional to disease burden or science.
If government funding is not proportional to burden or science, it is not necessarily
because the government is incompetent or biased, but perhaps because the government is
free riding on others. A government will free ride even if the budgets are the same, but the
disease burdens are sufficiently different. The government with the larger disease burden will
free ride. In other words, some governments in poor countries might free ride on the US not
just because the US has a large GDP and NIH budget, but because the US has a smaller
burden of disease than many poor countries.
P
Next we discuss the aggregate funding support for each indication. Denote Xd = xfd ∗ ,
f
equation 4 suggests that
Xi
= hij
Xj
Xi
= hig
Xg
7
(8)
Similarly, combining equation 6 and 7, we have:
Xi
hig Y a
=
> hij
Xj
1 + hig Y b
Xi
= hig
Xg
(9)
Proofs are in Appendix B. Equations 8 and 9 show that while total outlays (aggregating
across all funders) are not necessarily proportional to disease burden (more on this below),
total outlays are at least positively correlated with disease burden. Furthermore, total outlays
are positively correlated with disease-specific R&D productivity which leads to our third
hypothesis:
Hypothesis 3 Diseases with greater burden and/or higher productivity of funding will receive more funding in aggregate.
While outlays and disease burden are positively correlated, they are not necessarily proportional. They are only proportional at the individual and aggregate levels if the funders’
budgets are equal. If funders’ budgets are different, then outlays across diseases will not
be proportional at the individual level, but could be at the aggregate level, provided that
the budgets are not dramatically different (Equation 8). If budgets differ dramatically, then
outlays across diseases will be proportional to disease burdens neither individually nor in
aggregate (Equation 9).
If funders’ budgets differ dramatically, then to optimize global social welfare, the funder
with a larger budget should allocate less to its local disease and more to the local disease of
funder with a smaller budget, until the aggregate funding for each disease is proportional to
its total disease burden and R&D productivity. In other words, if a richer funder considers
the global social optimum, it will fund some diseases for which it has no local burden.
Finally, our simple model takes budgets as given. However, another way in which one
funder might free ride on another is by choosing a smaller budget. For example, a rich
country might allocate fewer resources in total. We will look at the share of national income
devoted to medical research by rich countries.
3
Data
Our study requires measures of disease burden at the country and global levels, as well as
data on the state of science for each disease. The biggest challenge for a study like this
8
is obtaining data on outlays by government agencies and foundations for each disease and
year. While the NIH provides a long time-series of funding at the project level, collecting
similar information from funders in other countries is not straightforward. Another challenge
is linking funded projects to diseases based on keywords in project descriptions.
3.1
Funding data
We focus on infectious and parasitic diseases because of the availability of high-quality data
on funding from all relevant governments and foundations: the G-FINDER data, collected
by Policy Cures using an annual survey. The survey covers public and private funding for 35
neglected diseases, which include infectious and parasitic diseases that predominantly affect
people in developing countries. In these countries, treatments are needed, and there is not
a sufficient commercial market to attract R&D by private industry. Each record includes
the disease name, product category, funder name, funder type, home country of the funder,
funding amount, year, and recipient information. A key contribution of the survey is that
it allows a much more accurate classification of spending by disease than one based on
keywords, and much effort is made to avoid double-counting. For more details on the data,
see previous studies such as Moran (2010) and Røttingen et al. (2013).
We aggregate the G-FINDER data to the disease-funder-year level and adjust all figures
to 2013 US dollars. Our sample includes 398 funders based in 41 countries between 20072014. We set the outlay to zero for any disease-country-year and disease-funder-year that are
missing in the G-Finder data set, so the data are fully rectangular at the country-year-disease
level and funder-year-disease level. Thus, we have a sample of funders that ever funded one
of these diseases during 2007-2014.
3.2
Disease burden
We use a measure of disease burden from the World Health Organization’s (WHO) Global
Burden of Disease project. WHO reports the burden of disease in terms of disability-adjusted
life years (DALYs) lost, which measures years of life lost due to premature mortality and
years of life lost due to time lived in states of less than full health. Disease burdens are
reported by disease and country for the years 2000 and 2012. We linearly impute values
between 2000 and 2012. In a robustness check we assign year 2000 burden to years before
2012, and year 2012 burden to 2012 and later years. We match the country-level disease
burden to other country-level factors, including its World Bank income classification. We
9
match the disease burden data to the funding data at the disease level. Some diseases in
G-FINDER are not included in the burden data and vice versa, so our sample consists of 15
diseases.3 Our sample includes neither Ebola nor Zika because the diseases were not covered
in the WHO burden of disease data, and the G-FINDER data only included Ebola in the
2014 survey.
3.3
State of science
The state of existing knowledge about a disease is another potential determinant of research
investment for that disease. Funders might devote more money to diseases with a more
advanced state of science, perhaps because such funding is more likely to lead to successful
treatments. Furthermore, the existence of many scientific papers reflects the interest of
academic researchers who might submit more and better grant proposals. Hence, we include
a control variable for the state of science.
We use the stock of articles indexed in the PubMed database as a measure of the state
of knowledge. We include only publications that are coded in PubMed as journal articles,
excluding letters, editorials, reviews, etc. Each article in PubMed includes keywords (or
Medical Subject Headers) in its listing, which we use to assign to diseases. Specifically, we use
the “Entrez” tool from Biopython, an open source package written in Python, which allows
us to efficiently search the PubMed database for each disease and to extract information on
all publications for which the disease was listed among their Medical Subject Headers. We
rely on the accuracy of the algorithm used by the National Library of Medicine to assign
Medical Subject Headers.
As a robustness check, we also include measures of recent drug innovation as explanatory
variables. It is plausible that following the development of an effective treatment, funding
shifts from research subsidies to purchase and distribution of that treatment. It is also
possible that past success suggests a higher probability of finding effective treatments in the
future. Again, we are agnostic about the coefficient’s interpretation.
3.4
Summary of data
We complement the data described above with country-level information on GDP and population from the World Bank. We include these as control variables, because research
3
For example, rheumatic fever, included in the GFINDER survey, is not listed as a specific item in the
Global Burden of Disease data and thus not included in our sample.
10
funding might be a “normal good” (increasing in income) and the incentives to invest in
research might be higher if the benefits accrue to a larger local population. Furthermore,
non-government funders located in richer countries might have better fundraising opportunities.
4
Methods
An observation is a disease-funder-year. As in the theoretical model, we consider funding
outlays as a function of disease burden and the state of science. Recall that NIH leadership
stated that “NIH believes that a process that includes multiple measurements of public health
needs, but is also informed by scientific opportunity, allows us to fund the best science.”
Furthermore, “NIH funding levels relate to U.S. and global deaths and disability-adjusted
life years (DALYs)a measure that quantifies the number of healthy years of life lost due to
morbidity or premature mortality caused by disease” (Rockey and Wolinetz, 2015). Hence,
the model includes both US and global DALYs as measures of disease burden, as well as the
stock of scientific publications.
4.1
Identification
The challenge in identifying free-riding is that governments are likely to consider similar
factors in research funding. For example, diseases with high burdens will tend to attract
more funding from both the US and UK governments. If the UK free rides, it allocates less
to a disease when the US spends more, all else equal. We need an instrumental variable that
shifts US funding for a given disease in a given year but that does not directly change UK
funding, only affecting UK funding indirectly through US funding.
Our instrumental variables strategy uses the political composition of the US Congress,
which sets the overall NIH budget. NIH budgets have a political component. For example,
changes in Congressional committee chairmanships influence total federal spending (Cohen
et al., 2011). Likewise, Congressional representation influences medical research funding.
Researchers located in districts represented by members of the US House Appropriations
Committee receive more NIH grants than those without such representation (Hegde and
Mowery, 2008; Hegde, 2009). In our identification strategy, we rely on US political forces
that affect NIH funding but that do not directly affect funding in other countries.
The budget process begins more than 18 months before the US government fiscal year
begins (October 1) when the President submits a budget to Congress. Because the budget
11
reflects Congressional negotiations more than 18 months earlier, we use as instruments 2year lags of the number of Democrats in the House and Senate, as well as their interaction.
For example, fiscally-conservative Republicans, including new Tea Party members, gained
a majority in the House of Representatives in 2011 and the NIH budget fell (in inflationadjusted terms) in subsequent years.4
We argue that changes in the political composition of Congress are correlated with the
total NIH budget which is correlated with funding for a given disease. Because this process
is specific to the US, it should not affect funding of medical research in the UK or other
countries, except through their response to changes in NIH funding.
4.2
Estimation
We estimate non-US outlays for a given disease from funder f as with the following specification:
Log Non-US Funder Outlayf,t,d = β0 + β1 Log US Outlayt−1,d
β2 Log Local Disease Burdenc,d +
+β3 Log Disease Burden in Poor Countriesd +
β4 Infectious except HIVd + β5 HIV Indicator +
β6 Log Stock Science Articlest,d + β8 GDPc,t−1 + (10)
The explanatory variables include the disease burden, the state of science, and other
control variables. We measure the state of science as the stock of scientific publications. The
burden of disease in poor countries is net of the local burden for the disease, where poor
countries are defined as those listed as low income and lower-middle income by the World
Bank.5 We also control for whether the disease is infectious or parasitic. Finally, we include
an indicator variable for HIV, because HIV is unique in its global and rich-world disease
burdens, as well as the attention it receives.
Our main variable of interest is lagged US funding, which might be determined by unobservable factors that also drive non-US funding decisions. We address this endogeneity using
4
In contrast to the current Republican Congress, President George W. Bush supported expansion of NIH
funding.
5
http://data.worldbank.org/country
12
two-stage least squares, where the first stage is the following:
Log US Outlayc,t,d = α0 + α1 Democrats in Houset−2,d + α2 Democrats in Senatet−2,d +
α3 Democrats in House x Democrats in Senatet−2,d +
α4 Log Local Disease Burdenc,d +
α5 Log Disease Burden in Poor Countriesd +
α6 Infectious except HIVd + α7 HIV Indicator +
α8 Log Stock Science Articlest,d + α9 GDPc,t + (11)
The dependent variable is the (logged) annual US outlay for disease d in year t, and the
instrumental variables excluded from the second stage are the political composition in the
US Congress: share of Democrats in the Senate, share of Democrats in the House, and the
interaction term of former two variables.
In an alternative specification, we also examine funding at the government level (i.e.,
aggregated across all government agencies within a country) and funding from both government agencies and private funders. The approach is exactly the same as that described
above, except with different number of observations.
5
Results
Plots of the data show patterns that are consistent with free-riding. US government funding
for these neglected diseases is dramatically higher than by other governments both in absolute
terms (Table 1) and relative to national income (Figure 1). The US government accounts
for 59% of the total funding of these 15 diseases with the majority of that funding from the
NIH (Table 1). Furthermore, measured as a share of national income, US funding is at least
triple that of any other country except the United Kingdom (Figure 1). Large countries that
are in our sample but whose funding puts them below the top 10 include China, Japan, and
Russia. Also consistent with free-riding, we show that funding by other governments is often
the mirror image of funding by the US, rising when US funding falls in the previous year
(Figure 2).
The sample includes 15 diseases: 6 infectious diseases and 9 parasitic diseases for which
we have data on both funding (described in section 3.1) and the burden of the disease
13
(described in section 3.2). Consistent with hypothesis 3, diseases with greater burden and
stock of science tend to have greater outlays (Table 2). For example, HIV/AIDS has the
highest stock of scientific publications, global disease burden, and global outlays. Similarly,
the largest funder, the US government, has the highest outlays for HIV/AIDS.
For an individual country, outlays are not necessarily proportional to publications and
burden. For example, the government of Brazil provides less medical research funding for
HIV/AIDS than for dengue, leishmaniasis, and malaria, all of which have smaller burdens of
disease in Brazil than HIV/AIDS (Table 2). One interpretation of this pattern is that Brazil
can spend less on HIV/AIDS, because it benefits from the funding for HIV/AIDS provided
by the US, behavior that is consistent with hypothesis 2.
Summary statistics for the variables used in the regression are shown in Table 3. The
average funding for a given disease-country-year is $1.54 million with a standard deviation
of $10.57 million. There are 47,760 observations in total, but we lose one year due to lagged
values when estimating the regressions, so the number of observations used in the regressions
falls to 41,790. The disease burden in poor countries is massive – about 13 million DALYs
lost for a given disease – compared to the burden in the funder’s country, which is about 2
orders of magnitude smaller.
The dependent variable is the log of disease-funder-year funding. Several of the independent variables are also in logs, so their coefficients may be interpreted as elasticities. In the
tables with the regression results (beginning with Table 4), we include both the instrumental variables results and the ordinary least squares results. We present regression results for
government agencies only (Table4), all funders – including both government agencies and
private foundations (Table 5), and government agencies aggregated by country (Table 6).
The first stage instrumental variables regression, in which US spending is the dependent
variable, shows that our instruments are reasonably strong. The coefficients on all the
measures of the Democrats’ share in Congress are statistically significant. The first stage
F-statistic is well above the threshold used to test for weak instruments. The results suggest
that a higher share in Congress for Democrats yields higher research funding.
The sign on US government outlays differs between the OLS and instrumental variables
specifications (Table 4). Whereas the OLS results have a positive coefficient on US outlays,
the instrumental variables results have a negative coefficient on US outlays. The difference
in sign suggests that unobserved factors drive R&D in both the US and other countries and
create a spurious positive correlation. Furthermore, the Wu-Hausman test is consistent with
the endogeneity of US funding.
14
Consistent with free-riding, the instrumental variables results indicate that a 10% increase
in US government outlays is associated with a 1% decrease in outlays by government funders
in other countries in the following year. The positive coefficient on budget in the first stage,
and negative coefficient on US outlays in the second stage are consistent with hypothesis 1.
The coefficients on other variables are consistent with our priors, and have the same
sign and statistical significance regardless of whether we use OLS or instrumental variables.
The positive coefficients on scientific publications and burden of disease are consistent with
hypothesis 3. A 10% greater disease burden in own countries is associated with about 1%
greater funding by the US government. Similarly, a 10% greater local disease burden is
associated with 1% greater funding from government funders in other countries (holding US
government funding constant). A 10% greater disease burden in poor countries is associated
with nearly 2% greater funding by the US government. For other countries, the effect of disease burden is similar. A 10% greater disease burden in poor countries is associated with 2%
greater funding from government funders in other countries (holding US government funding constant). Note that because most of the burden of these diseases is in poor countries,
burden in poor countries is largely equivalent to global burden.
Funding is also related to the state of science. A 10% greater stock of scientific articles
for a disease in a year is associated with a 10% increase in funding for the US government
funders. For government funders from other countries, the effect on funding is a 5% increase
(holding US funding constant) (Table 4). Funding is increasing in a country’s income per
capita.
For the US government, HIV/AIDS receives more funding than all other infectious and
parasitic diseases combined (Table 2). Likewise, the coefficient on the HIV/AIDS indicator
variable is positive and significant (Table 4).
We have similar results when private funders are included in the sample(Table 5). In
particular, a 10% increase in US government outlays is associated with a 1% decrease in outlays by US private funders and funders in other countries in the following year. Coefficients
of all the other variables have the same sign and statistical significance as in Table 5.
When we combine all funding for a given disease in a given year at the country level
(combining all government agencies), the only variation is by country (38 countries), year (7
years) and disease (15 diseases), for a total of 3990 observations. The results are qualitatively
consistent with those obtained using funder-level information. In particular, we again find
evidence of free-riding. A 10% increase in US funding for a disease is associated with a
reduction of 4% in government funders from other countries.
15
To verify the robustness of these results, we include five alternative specifications in Appendix A. First, we include only the non-US government agencies from top 20% governments,
which represent about half of total funding. The magnitude of the coefficient increases from
-0.1 to -0.2 (Table 7), suggesting that the top funders are even more responsive to US outlays.
Second, we replace the one-year lag for US outlays with a two-year lag. We chose a
shorter lag for the main results in order to avoid discarding two years of data. However,
when using a two-year lag, the magnitude on the US outlay coefficient increases (Table 8).
Third, we include a count of the number of treatments introduced since 1987 (Table 9).
In general, the availability of treatments is associated with higher levels of funding. Perhaps
these diseases receive more funding because there is a proven record of success in developing
treatments for these diseases. Regardless of the reason for the positive relationship, the
results for free-riding remain, although they are somewhat smaller.
Fourth, we incorporate alternative measures of disease burden (Table 10). Recall that
WHO provides data on DALYs only for the years 2000 and 2012. In the main specification, we
use a smoothed burden measurement. In the robustness check, observations from the years
2007-2011 are assigned the values of disease burden from the year 2000, and observations
from 2012-2014 are assigned the 2012 value.6 Funders may respond to disease outbreaks
by increasing research support in those therapeutic areas, and it is possible that failing to
incorporate changes in burden could bias our results. However, we continue to see a negative
and significant coefficient on US funding, so the free-riding result persists.
Fifth, we consider the flow, rather than stock of scientific publications (Table 11). The
flow variable is more appropriate if funding decisions are closely tied to recent publications.
The results show that even in the extreme where only contemporaneous publication matters,
the coefficient on lagged US outlay is still negative and significant.
Sixth, we replace the share of Democrats in Congress with the NIH budget as the instrumental variable (Table 12). Our argument for the validity of this instrument is the same
as that for the composition of Congress: disease funding in the US is correlated with the
total NIH budget, but the total NIH budget should not affect the decisions of other funders directly. In this specification, we observe a stronger free-riding effect, with a decline of
1% in response to a 10% increase in the US. Thus, the free-riding result appears robust to
alternative regression specifications.
6
Implicitly, we are assuming that this specification is an accurate representation of the information available to policymakers. Usually, there is a significant lag between the collection of the data and its release to
the public, but it is possible that the WHO disseminates information to policymakers in advance. We also
tried using the change in burden from 2000-2012, and obtained similar results.
16
We estimate that if the US increases outlays by $1 for a disease, then the net outlay will
be $0.60 to $0.90, because other funders will reduce their outlays for that disease, shifting
resources to other needs. These results are based on two factors. First, the US accounts for
about half of outlays. Second, for non-US funders, the elasticities on US outlays are between
-0.1 and -0.4.
6
Conclusions
Governments and foundations play an important role in advancing science by funding research and then sharing the results with the public. Indeed, the NIH requires that authors
of scientific publications who received NIH funding for their work make their papers publicly
available through PubMed.7
However, government funding of medical research may confront free-riding, particularly
among the relatively rich countries that are capable of financing research. We provide evidence of free-riding for infectious and parasitic diseases. We use instrumental variables to
address the endogeneity of funding, and estimate several robustness checks with alternative measures of key variables. Our results suggest that free-riding is both statistically and
economically important.
Funding for diseases endemic throughout the world might be especially vulnerable to
free-riding by government funders. However, these same diseases might not be as vulnerable
to free-riding by drug makers, because they can exploit intellectual property rights. These
intellectual property rights have been expanding (Kyle and McGahan, 2012). While intellectual property rights can reduce free-riding, they can also harm consumers, at least in the
short run (Chaudhuri et al., 2006). Furthermore, even diseases present around the world will
not receive much attention if nearly all of the people suffering from the diseases live in poor
countries. Hence, intellectual property rights might be most effective for diseases endemic
in both rich and poor countries, such as cancer.
We found a negative relationship between medical research funding by the US and other
countries. While it is consistent with free-riding, which sounds pejorative, we cannot make
claims about social welfare. It might be optimal for other governments to devote resources
to other causes. Nevertheless, it is useful to be aware of how resources shift when the US
changes its spending.
While the relationship is negative for outlays across funders, the magnitude is less than
7
https://publicaccess.nih.gov/policy.htm
17
one, meaning that other governments reduce outlays by less than a dollar when the US
increases outlays by a dollar. The model predicts that the relationship will be less than oneto-one when there are differences in budgets across funders. Furthermore, there could be
other objectives for funders, including a desire to fund favored scientists or to show progress
fighting a disease. Finally, there might be frictions that delay awareness or action on funding
changes. Indeed, we find evidence that the free-riding effect is stronger with a two-year lag
than a one-year lag.
We also showed that the US provides a much higher share of funding for these diseases
than other governments (Figure 1). In 2003, while serving as Commissioner of the US Food
& Drug Administration, Mark McClellan said:
“Our governments need to start by sharing the burden of the increasingly complex
basic science that goes into the development of new drugs and biologics. In the
United States, we’ve responded to the new opportunities that exist in the lab, by
doubling our NIH budget to over $27 billion. As a share of GDP, this is about
four times as much as European Union countries spend. But on an interconnected
planet, all of this spending turns into biomedical knowledge that is transmitted
worldwide for the good of public health worldwide. If other developed countries
contributed to this worldwide effort in proportion to their GDP, we would build
the foundations for better treatments much faster” (McClellan (2003)).
Commissioner McClellan was speaking to other rich countries. In the case of poor countries, it might be sensible for the NIH to fund research for global diseases. In cases where the
burden of a particular disease is high in countries poor countries, the total level of funding is
likely to be lower than the social optimum even if each individual government is maximizing
local welfare. Furthermore, economies of scale in research might limit the output from having many small, local programs in budget-constrained countries relative to those in large,
wealthy countries.
18
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21
Table 1: Summary Statistics: Top Funders
Funder
Share
Aggregated Government
United States
United Kingdom
France
India
Australia
Germany
Canada
Netherlands
Brazil
Sweden
Government Agencies
National Institutes of Health, USA
Agency for International Development (USAID), USA
Department of Defense (DOD), USA
Department of International Development, UK
Medical Reseach Council, UK
Institut Pasteur, France
National Health and Medical Research Council (NHMRC), Australia
Inserm - Institute of Infectious Diseases, France
Council of Medical Research, India
Centers for Disease Control, USA
Non-Governmental Organizations
Bill & Melinda Gates Foundation, USA
The Wellcome Trust, UK
UBS Optimus Foundation, Switzerland
Fundacio La Caixa, Spain
Starr Foundation, USA
Global Alliance for Vaccines and Immunizations (GAVI), Switzerland
ExxonMobil Foundation, USA
amfAR, The Foundation for AIDS Research, USA
Global Fund to Fight AIDS, TB and Malaria (GFATM), Switzerland
OPEC Foundation for International Development (OFID), Austria
22
Total Outlay($MM)
Infectious
Parasitic
58.63
4.72
2.86
1.16
1.14
1.06
0.76
0.67
0.57
0.48
8752
891
557
110
80
130
137
99
63
65
1989
502
286
102
130
64
1
25
41
23
50.32
3.89
3.46
2.27
2.25
1.07
0.93
0.83
0.75
0.65
7572
642
406
296
216
102
61
107
68
91
1645
70
227
120
196
94
110
45
69
29
20.41
3.52
0.06
0.06
0.06
0.05
0.04
0.03
0.03
0.03
2257
273
7
7
11
8
1
6
4
5
1482
372
4
4
0
0
7
0
2
0
Table 2: Summary Statistics: Diseases
Indication
Stock of
Articles
(MM)
Infectious Disease
Dengue
HIV/AIDS
Leprosy
Meningitis
Trachoma
Tuberculosis
Parasitic Disease
Chagas disease
Hookworm disease
Leishmaniasis
Lymphatic filariasis
Malaria
Onchocerciasis
Schistosomiasis
Trichuriasis
Trypanosomiasis
Global
Burden
Outlay
(000)
($MM)
USA Goverment
Burden
Outlay
(000)
($MM)
Brazil Goverment
Burden
Outlay
(000)
($MM)
6
74
11
7
11
72
1007
101632
219
41693
433
60296
83
1124
10
41
5
410
0
494
0
2
0
32
53
840
5
8
4
180
4
838
1
118
62
326
7
0.8
1
0.5
0
1
8
1
13
9
36
2
12
1
3
571
3469
4986
2534
78236
590
3137
696
3741
18
9
48
13
458
7
23
1
41
0
0
0
0
46
0
0
0
1
12
2
22
5
174
2
15
0.5
16
3
15
37
17
130
2
181
0.4
11
1
0
2
0
2
0
1
0
0
The disease burden is measured in disability-adjusted life years (DALY)
lost.
Table 3: Summary Statistics: Units of Observation
Unit of Observation (observations)
Disease, Funder, Year (47760)
Disease, Country, Year (4920)
Country, Year (328)
Disease, Year (120)
Year(7)
Variable
Total Outlay ($MM)
Total Outlay ($MM)
Local Disease Burden (000)
GDP per Capita ($)
US Government Outlay ($MM)
Stock of Science Articles (MM)
Disease Burden in Poor Countries (000)
Democrats in Senate
Democrats in House
23
Mean
0.2
1.5
166
29764
88
18
12862
52
222
Std Dev
3
11
1093
22768
211
23
21043
5
26
Table 4: The dependent variable is the log outlay by a government agency for a disease in
a year.
OLS
0.091∗∗∗
(0.010)
Lag Log Annual Outlay of US
Instrumental Variables
First Stage Second Stage
-0.114∗∗
(0.041)
Log Local Burden
0.049∗∗∗
(0.014)
0.091∗∗∗
(0.009)
0.068∗∗∗
(0.015)
Log Burden in Poor Countries
0.116∗∗∗
(0.012)
0.164∗∗∗
(0.007)
0.147∗∗∗
(0.013)
Infectious Except HIV Indicator
0.004
(0.044)
-0.542∗∗∗
(0.026)
-0.114∗
(0.049)
HIV Indicator
-0.078
(0.089)
1.620∗∗∗
(0.053)
0.249∗
(0.110)
Log Stock of Scientific Publications
0.275∗∗∗
(0.023)
1.090∗∗∗
(0.012)
0.505∗∗∗
(0.051)
Lag Log GDP per Capita
0.073∗∗
(0.022)
0.093∗∗∗
(0.013)
0.096∗∗∗
(0.023)
Share of Democrats in House, 2-period Lag
0.804∗∗∗
(0.028)
Share Democrats in Senate, 2-period Lag
0.850∗∗∗
(0.027)
Share of Democrats in House x Share of Democrats in Senate, 2-period Lag
-0.015∗∗∗
(0.001)
-4.887∗∗∗
(0.246)
27195
0.059
241.50
9.08
Constant
Observations
R2
F-Statistics
F-Stat critical value(10%)
p-value of Durbin Test
p-value of Wu-Hausman Test
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
24
-41.526∗∗∗
(1.484)
27195
0.522
3297.38
9.08
0.00
0.00
-4.119∗∗∗
(0.290)
27195
0.043
1585.33
9.08
Table 5: The dependent variable is the log outlay by a funder (foundation or government
agency) for a disease in a year.
OLS
0.070∗∗∗
(0.007)
Lag Log Annual Outlay of US
Instrumental Variables
First Stage Second Stage
-0.079∗
(0.031)
Log Local Burden
0.056∗∗∗
(0.012)
0.119∗∗∗
(0.008)
0.073∗∗∗
(0.012)
Log Burden in Poor Countries
0.089∗∗∗
(0.009)
0.158∗∗∗
(0.006)
0.111∗∗∗
(0.010)
Infectious Except HIV Indicator
0.011
(0.033)
-0.537∗∗∗
(0.021)
-0.074∗
(0.038)
HIV Indicator
-0.045
(0.069)
1.546∗∗∗
(0.043)
0.183∗
(0.083)
Log Stock of Scientific Publications
0.220∗∗∗
(0.018)
1.077∗∗∗
(0.010)
0.386∗∗∗
(0.038)
Lag Log GDP per Capita
0.034+
(0.019)
0.114∗∗∗
(0.012)
0.053∗∗
(0.019)
Share of Democrats in House, 2-period Lag
0.804∗∗∗
(0.023)
Share Democrats in Senate, 2-period Lag
0.851∗∗∗
(0.022)
Share of Democrats in House x Share of Democrats in Senate, 2-period Lag
-0.015∗∗∗
(0.000)
-3.555∗∗∗
(0.202)
41790
0.044
276.25
9.08
Constant
Observations
R2
F-Statistics
F-Stat critical value(10%)
p-value of Durbin Test
p-value of Wu-Hausman Test
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
25
-41.672∗∗∗
(1.197)
41790
0.523
5085.68
9.08
0.00
0.00
-3.005∗∗∗
(0.232)
41790
0.035
1834.24
9.08
Table 6: The dependent variable is the log outlay by a government (government agencies
aggregated) for a disease in a year.
OLS
0.296∗∗∗
(0.043)
Lag Log Annual Outlay of US
Instrumental Variables
First Stage Second Stage
-0.353+
(0.183)
Smoothed Local Burden
0.360∗∗∗
(0.056)
0.058∗∗
(0.020)
0.401∗∗∗
(0.059)
Smoothed Burden in Poor Countries
0.231∗∗∗
(0.051)
0.170∗∗∗
(0.018)
0.333∗∗∗
(0.060)
Infectious Except HIV Indicator
0.096
(0.190)
-0.534∗∗∗
(0.068)
-0.270
(0.219)
HIV Indicator
0.449
(0.387)
1.656∗∗∗
(0.137)
1.506∗∗
(0.492)
Log Stock of Scientific Publications
0.791∗∗∗
(0.101)
1.105∗∗∗
(0.032)
1.529∗∗∗
(0.228)
Lag Log GDP per Capita
0.960∗∗∗
(0.076)
0.047+
(0.028)
1.001∗∗∗
(0.079)
Share of Democrats in House, 2-period Lag
0.802∗∗∗
(0.074)
Share Democrats in Senate, 2-period Lag
0.849∗∗∗
(0.072)
Share of Democrats in House x Share of Democrats in Senate, 2-period Lag
-0.015∗∗∗
(0.001)
-20.722∗∗∗
(0.931)
3990
0.209
150.01
9.08
Constant
Observations
R2
F-Statistics
F-Stat critical value(10%)
p-value of Durbin Test
p-value of Wu-Hausman Test
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
26
-41.142∗∗∗
(3.876)
3990
0.521
480.82
9.08
0.00
0.00
-18.061∗∗∗
(1.204)
3990
0.163
952.53
9.08
Japan
Canada
Germany
France
Australia
Switzerland
Britain
USA
0
0.00002
0.00004
0.00006
0.00008
2014 Funding / GDP
0.0001
Figure 1: Spending on 15 infectious and parasitic diseases
27
0.00012
65
860
60
55
50
820
45
800
40
1.6
15.0
1.4
14.0
1.2
13.0
1.0
12.0
0.8
11.0
0.6
780
35
10.0
0.4
760
30
9.0
0.2
740
25
8.0
2007
2008
2009
2010
2011
2012
2013
2014
0.0
2007
Lag US Government Outlay
Government Outlay From UK
2008
14.0
12.0
Millions
220.0
200.0
10.0
190.0
2010
2011
2012
2013
2014
Schistosomiasis
Millions
Millions
TB
210.0
2009
Lag US Government Outlay
Government Outlay From France
20.0
1.6
19.0
1.4
18.0
1.2
17.0
180.0
8.0
16.0
1.0
170.0
6.0
15.0
0.8
160.0
4.0
14.0
0.6
150.0
2.0
13.0
0.4
140.0
0.0
12.0
2007 2008 2009 2010 2011 2012 2013 2014
0.2
2007
Lag US Government Outlay
Government Outlay From Netherlands
Millions
840
16.0
Millions
880
Millions
Chagas disease
70
Millions
Millions
HIV
900
2008
2009
2010
2011
2012
2013
2014
Lag US Government Outlay
Government Outlay From Australia
Figure 2: Lagged US funding and current-year other country funding for HIV, TB, Chagas
and Schistosomiasis. For these diseases, there appears to be an inverse relationship between
funding sources.
28
$34
70%
$32
60%
$30
$26
40%
$24
30%
$22
Share in Congress
Billions of Dollars
50%
$28
20%
$20
10%
$18
$16
0%
2006
2007
2008
2009
2010
2011
2012
2013
2014
NIH Budget
Democrats in House, 2-period lagged
Democrats in Senate, 2-period lagged
Figure 3: The magnitude of the NIH budget appears to depend in part on the party in
control of the House and Senate.
29
7
Appendix A
Table 7: The dependent variable is the log outlay by a government agency for a disease in
a year. This specification includes only government agencies from top 20% governments.
OLS
0.132∗∗∗
(0.027)
Lag Log Annual Outlay of US
Instrumental Variables
First Stage Second Stage
-0.184+
(0.111)
Log Local Burden
0.066
(0.080)
0.373∗∗∗
(0.037)
0.168+
(0.088)
Log Burden in Poor Countries
0.220∗∗∗
(0.036)
0.106∗∗∗
(0.017)
0.253∗∗∗
(0.038)
Infectious Except HIV Indicator
-0.003
(0.121)
-0.528∗∗∗
(0.056)
-0.180
(0.136)
HIV Indicator
-0.530∗
(0.256)
1.331∗∗∗
(0.118)
-0.100
(0.298)
Log Stock of Scientific Publications
0.450∗∗∗
(0.069)
0.982∗∗∗
(0.030)
0.777∗∗∗
(0.132)
Lag Log GDP per Capita
0.058
(0.161)
0.214∗∗
(0.076)
0.091
(0.164)
Share of Democrats in House, 2-period Lag
0.813∗∗∗
(0.061)
Share Democrats in Senate, 2-period Lag
0.868∗∗∗
(0.059)
Share of Democrats in House x Share of Democrats in Senate, 2-period Lag
-0.015∗∗∗
(0.001)
-7.247∗∗∗
(1.691)
5775
0.084
Constant
Observations
R2
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
30
-42.363∗∗∗
(3.315)
5775
0.528
-5.729∗∗
(1.786)
5775
0.063
Table 8: The dependent variable is the log outlay by a government agency for a disease
in a year. This specification uses 2-year lagged, rather than 1-year lagged outlay, from US
goverment
OLS
0.091∗∗∗
(0.010)
Log 2-year Lagged Annual Outlay of US
Instrumental Variables
First Stage Second Stage
-0.204∗∗∗
(0.040)
Log Local Burden
0.034∗
(0.015)
0.086∗∗∗
(0.010)
0.061∗∗∗
(0.016)
Log Burden in Poor Countries
0.110∗∗∗
(0.012)
0.140∗∗∗
(0.008)
0.148∗∗∗
(0.013)
Infectious Except HIV Indicator
-0.009
(0.047)
-0.619∗∗∗
(0.030)
-0.200∗∗∗
(0.054)
HIV Indicator
-0.070
(0.095)
1.704∗∗∗
(0.061)
0.425∗∗∗
(0.117)
Log Stock of Scientific Publications
0.290∗∗∗
(0.025)
1.132∗∗∗
(0.015)
0.633∗∗∗
(0.052)
Lag Log GDP per Capita
0.054∗
(0.024)
0.084∗∗∗
(0.016)
0.086∗∗∗
(0.025)
Share of Democrats in House, 2-period Lag
7.657∗∗∗
(1.566)
Share Democrats in Senate, 2-period Lag
8.388∗∗∗
(1.680)
Share of Democrats in House x Share of Democrats in Senate, 2-period Lag
-0.144∗∗∗
(0.029)
-4.783∗∗∗
(0.265)
23310
0.058
Constant
Observations
R2
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
31
-442.356∗∗∗
(89.912)
23310
0.483
-3.728∗∗∗
(0.304)
23310
0.022
Table 9: The dependent variable is the log outlay by a government agency for a disease in
a year. This specification is with number of existing treatment
OLS
0.086∗∗∗
(0.010)
Lag Log Annual Outlay of US
Instrumental Variables
First Stage Second Stage
-0.108∗∗
(0.041)
Log Local Burden
0.030∗
(0.014)
0.067∗∗∗
(0.009)
0.044∗∗
(0.015)
Log Burden in Poor Countries
0.110∗∗∗
(0.012)
0.155∗∗∗
(0.007)
0.137∗∗∗
(0.013)
Infectious Except HIV Indicator
0.121∗∗
(0.047)
-0.396∗∗∗
(0.028)
0.038
(0.050)
HIV Indicator
-1.228∗∗∗
(0.187)
0.208+
(0.112)
-1.189∗∗∗
(0.188)
Log Stock of Scientific Publications
0.205∗∗∗
(0.025)
0.997∗∗∗
(0.014)
0.405∗∗∗
(0.048)
Number of Treatment Launched Since 1987
0.081∗∗∗
(0.012)
0.099∗∗∗
(0.007)
0.100∗∗∗
(0.012)
Lag Log GDP per Capita
0.053∗
(0.022)
0.068∗∗∗
(0.014)
0.070∗∗
(0.023)
Share of Democrats in House, 2-period Lag
0.810∗∗∗
(0.028)
Share Democrats in Senate, 2-period Lag
0.854∗∗∗
(0.027)
Share of Democrats in House x Share of Democrats in Senate, 2-period Lag
-0.015∗∗∗
(0.001)
-4.013∗∗∗
(0.276)
27195
0.060
Constant
Observations
R2
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
32
-40.756∗∗∗
(1.479)
27195
0.525
-3.088∗∗∗
(0.337)
27195
0.047
Table 10: The dependent variable is the log outlay by a government agency for a disease in
a year. This specification is with time varying disease burden
OLS
0.076∗∗∗
(0.010)
Lag Log Annual Outlay of US
Instrumental Variables
First Stage Second Stage
-0.123∗∗
(0.041)
Log Local Disease Burden, Varying by time
0.059∗∗∗
(0.013)
0.055∗∗∗
(0.007)
0.071∗∗∗
(0.013)
Log Burden in Poor Countries, Varying by time
0.115∗∗∗
(0.011)
0.263∗∗∗
(0.006)
0.165∗∗∗
(0.015)
Infectious Except HIV Indicator
-0.071
(0.044)
-0.563∗∗∗
(0.025)
-0.189∗∗∗
(0.050)
HIV Indicator
-0.048
(0.087)
1.505∗∗∗
(0.051)
0.245∗
(0.105)
Log Stock of Scientific Publications
0.297∗∗∗
(0.022)
1.024∗∗∗
(0.012)
0.508∗∗∗
(0.047)
Lag Log GDP per Capita
0.080∗∗∗
(0.021)
0.052∗∗∗
(0.012)
0.094∗∗∗
(0.021)
Share of Democrats in House, 2-period Lag
0.810∗∗∗
(0.028)
Share Democrats in Senate, 2-period Lag
0.864∗∗∗
(0.027)
Share of Democrats in House x Share of Democrats in Senate, 2-period Lag
-0.015∗∗∗
(0.001)
-4.901∗∗∗
(0.246)
27195
0.060
Constant
Observations
R2
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
33
-41.781∗∗∗
(1.449)
27195
0.544
-4.110∗∗∗
(0.293)
27195
0.046
Table 11: The dependent variable is the log outlay by a government agency for a disease in
a year. This specification is with flow scientific publication
OLS
0.048∗∗∗
(0.010)
Lag Log Annual Outlay of US
Instrumental Variables
First Stage Second Stage
-0.107∗∗
(0.037)
Log Local Burden
0.035∗
(0.014)
0.037∗∗∗
(0.008)
0.042∗∗
(0.014)
Log Burden in Poor Countries
0.115∗∗∗
(0.011)
0.152∗∗∗
(0.006)
0.137∗∗∗
(0.013)
Infectious Except HIV Indicator
-0.078+
(0.043)
-0.687∗∗∗
(0.024)
-0.187∗∗∗
(0.050)
HIV Indicator
-0.043
(0.089)
1.537∗∗∗
(0.049)
0.193+
(0.104)
Log Flow of Scientific Publications
0.367∗∗∗
(0.022)
1.186∗∗∗
(0.010)
0.552∗∗∗
(0.048)
Lag Log GDP per Capita
0.060∗∗
(0.022)
0.037∗∗
(0.012)
0.071∗∗
(0.022)
Share of Democrats in House, 2-period Lag
0.873∗∗∗
(0.026)
Share Democrats in Senate, 2-period Lag
0.958∗∗∗
(0.025)
Share of Democrats in House x Share of Democrats in Senate, 2-period Lag
-0.017∗∗∗
(0.000)
-3.718∗∗∗
(0.235)
27195
0.063
Constant
Observations
R2
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
34
-42.614∗∗∗
(1.374)
27195
0.590
-2.573∗∗∗
(0.353)
27195
0.056
Table 12: The dependent variable is the log outlay by a government agency for a disease in
a year. This specification includes NIH budget (net of funding for the disease in question)
as the instrument variable, rather than Congressional composition
Instrumental Variables
OLS
First Stage Second Stage
∗∗∗
Lag Log Annual Outlay of US
0.091
-0.202∗∗∗
(0.010)
(0.056)
Log Local Burden
0.049∗∗∗
(0.014)
0.094∗∗∗
(0.009)
0.077∗∗∗
(0.015)
Log Burden in Poor Countries
0.116∗∗∗
(0.012)
0.158∗∗∗
(0.007)
0.161∗∗∗
(0.015)
Infectious Except HIV Indicator
0.004
(0.044)
-0.560∗∗∗
(0.026)
-0.165∗∗
(0.054)
HIV Indicator
-0.078
(0.089)
1.604∗∗∗
(0.053)
0.390∗∗
(0.126)
Log Stock of Scientific Publications
0.275∗∗∗
(0.023)
1.107∗∗∗
(0.013)
0.604∗∗∗
(0.067)
Lag Log GDP per Capita
0.073∗∗
(0.022)
0.106∗∗∗
(0.014)
0.106∗∗∗
(0.024)
12.978∗∗∗
(0.440)
Lag Log NIH Budget
-4.887∗∗∗
(0.246)
27195
0.059
Constant
Observations
R2
Standard errors in parentheses
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01,
∗∗∗
p < 0.001
35
-40.766∗∗∗
(1.515)
27195
0.509
-3.788∗∗∗
(0.325)
27195
0.027
8
Appendix B
This section provides the proofs for equations (8) and (9).
We start by proving equation (8). From Equation (4) we have:
hig Y i + hig Y j
=
1 + hig + hjg
(1 + hjg )Y i − hig Y j
Xgi∗ =
1 + hig + hjg
Xii∗
and
hjg Y j + hjg Y j
1 + hig + hjg
(1 + hig )Y j − hjg Y i
,
=
1 + hig + hjg
Xjj∗ =
Xgj∗
which implies that
xi
Xii∗ + Xij∗
=
= i∗
xj
Xj + Xjj∗
hig Y i +hig Y j
1+hig +hjg
hjg Y j +hjg Y j
1+hig +hjg
Xii∗ + Xij∗
xi
=
=
xg
Xgi∗ + Xgj∗
hig Y i +hig Y j
1+hig +hjg
(1+hig )Y j −hjg Y i
(1+hjg )Y i −hig Y j
+ 1+h
1+hig +hjg
ig +hjg
hig
=
=
hjg
Bi Si
Bg Sg
Bj Sj
Bg Sg
=
Bi Si
= hij
Bj Sj
=
hig Y i +hig Y j
1+hig +hjg
Y i +Y j
1+hig +hjg]
Now we prove equation (9). Equations (6) and (7) suggest that:
Xjj∗ = Y j ,
Xgj∗ = 0
and
hig
Yi
1 + hig
1
Xgi∗ =
Y i.
1 + hig
Xii∗ =
Therefore,
36
= hig .
hig Y i
xi
=
xj
1 + hig Yj
xi
Xii∗ + Xij∗
=
=
xg
Xgi∗ + Xgj∗
hig
Yi
1+hig
1
Yi
1+hig
= hig .
In addition, because the prerequisite of the existence of the corner solution is that Y i >>
1+h
Y j and equation 3 is violated, thus, Y i > hjgig Y j . Therefore, we have
hig Y i
hig
xi
=
>
xj
1 + hig Yj
1 + hig
1+hig j
Y
hjg
Yj
37
hig
=
=
hjg
Bi Si
Bg Sg
Bj Sj
Bg Sg
=
Bi Si
= hij .
Bj Sj