Gender and Conflict in Nepal:
Testing for “Added Worker” Effects
Nidhiya Menon, Brandeis University
Yana Rodgers, Rutgers University
June 10, 2010
Motivation


Nepal’s “People’s War” ranked among the most intense civil
conflicts in the world in recent times
– Duration: 1996-2006
– Repercussions: deaths, injuries, and migration
Goal of study: examine how Nepal’s civil war affected
women’s decisions about employment
– Test for an “added worker” effect: women may join labor
force to compensate for declines in HH income due to
war-related disruptions in husband’s earnings
Motivation

Previous studies on added worker effect
– Historically has been strong in industrialized countries
(World Wars, Great Depression), but less important as
women’s labor market status improved
o e.g. Finegan and Margo (1994); Prieto-Rodriguez and
Rodriguez-Gutierrez (2003)
– Substantial in developing countries, especially in times of
crisis and economic downturns
o e.g. Parker and Skoufias (2004); Bhalotra and
Umana-Aponte (2010)
Motivation



Approach: differences-in-differences procedure to identify
impact of war on women’s employment decisions
Data: Nepal Demographic and Health Survey (DHS) for
1996, 2001, and 2006
Preview of findings
–
–
–
–
Find strong evidence of added worker effect for women:
compared to the outbreak of war in 1996, women’s
employment probabilities are substantially higher in 2001 and
2006
Similar patterns evident for women’s self-employment
Main result robust to regressions that condition on husband’s
migration status and women’s status as household heads
Main result robust to alternative estimation procedures
Conflict Background


War erupted in 1996 when Communist Nepal-Maoist party
attacked a police outpost
Motivation for attack and subsequent 10-year insurgency:
–
–
–

Anger by lower castes over landlessness and deprivation
Overall poverty and lack of economic development
Dissatisfaction against government for targeting Maoist
activities
Maoist objectives: weaken and eliminate the monarchy,
establish a new Constituent Assembly and constitution
–
Goals achieved by 2006; in 2008 a former Maoist leader was
elected Prime Minister of the new republic
Conflict Background

Social costs enormous:
–
–
–

Death toll over 13,300 (about 2/3 caused by state forces, 1/3
caused by Maoist insurgents); see figure
Infrastructure destroyed, new projects postponed
Growing amount of migration over time, especially by men,
within and outside of Nepal; see figures
Did conflict-induced changes in household composition and
income cause more women to join labor force?
–
A priori, answer not clear; previous findings for Nepal mixed
o
o
inverse relationship between men’s migration and women’s market
work in 2004 (Lokshin and Glinskaya 2009)
absence of husbands led to large increase in women’s overall
work burdens (World Bank 2004)
Conflict-related deaths, 1996-2006
5000
Number of deaths
4000
3000
2000
1000
0
1996
1997
1998
1999
Killed by state
2000
2001
2002
Killed by Maoists
2003
2004
2005
2006
Status of Husband’s Presence
Percent of all women
100
80
60
1996
Husband Present
2001
Husband Migrated
2006
Widowed, Separated, Divorced
Household Headship
Percent of all women
100
80
60
1996
Woman not HHH
2001
Woman HHH: Migration
Woman HHH: Div/Wid/Sep
2006
Woman HHH: Husband Incapacitated
Conflict and Non-Conflict Regions

Research design centers on idea that geographical terrain
(measured by proportion of forest cover) determined
intensity of conflict
–

Conflict-related deaths substantially higher in districts with
higher elevation and forest coverage (Do and Iyer 2009)
Our first-stage procedure: classify regions based on
geography from 1994, a pre-conflict year
–
–
–
–
Objective: use geographical measures from a pre-conflict year
as instruments to approximate conflict intensity from 1996-2006
Conflict measures likely to be co-determined with other
variables affecting women’s employment (e.g. poverty)
Geographical measures from pre-conflict year provide
exogenous variation required to identify the impact of conflict
on women’s work
Strategy similar to Angrist and Kugler (2008)
Conflict and Non-Conflict Regions
●
Specific steps:
–
Aggregated 75 districts into 15 sub-regions:
o
−
−
●
5 regions (East, Central, West, Mid-West, Far-West), combined
with 3 terrains (Mountain, Hill, Terai Grasslands)
Measured conflict intensity as total number of casualties due to
state and Maoist action
Initially used 6 exogenous measures from 1994: forest
coverage, altitude, number of rivers, length of road network,
annual rainfall, and average temperature
First stage regression results:
●
●
Strong correlation between conflict-induced casualties and forest
coverage
Forest coverage interacted with year dummies used as instruments
Conflict and Women’s Employment

Data for women’s employment
–
–
–
–

Used Nepal DHS for 1996, 2001, and 2006: survey of women
aged 15-49 and members of their households
Working sample: kept all ever-married women aged 15-49 with
measured values for all indicators in empirical analysis
Sample size: about 25,700 observations in pooled sample
Note: employment includes work for cash, work for in-kind, and
non-remunerated work; cannot be separated
Sample stats (see table)
–
–
High proportion of women employed, growing share over time
living without husbands, majority of women have no education,
improvements in education & socioeconomic status over time
Measured characteristics in 1996 (start of conflict) are
comparable for treatment and control samples; this satisfies a
required check for difference-in-difference method
Sample Statistics (% of sample, weighted)
Overall Sample
Woman Employed
Yes
No
Husband Gone
Yes
No
Woman’s Education
No schooling
Some or all primary school
Some secondary school
Completed secondary school +
Husband's education
No schooling
Some or all primary school
Some secondary school
Completed secondary school +
House has electricity
Yes
No
1996
100.0
2001
100.0
2006
100.0
77.3
22.7
82.9
17.1
73.6
26.4
20.8
79.2
25.0
75.0
29.7
70.3
80.0
11.0
6.3
2.7
72.0
14.8
9.3
3.9
62.6
16.8
14.1
6.4
40.7
22.0
19.4
17.9
37.3
24.8
22.8
15.1
26.2
27.6
28.2
17.9
17.3
82.7
22.5
77.5
47.4
52.6
Conflict and Women’s Employment
●
Women’s employment decisions: naïve probit model
−
Estimate standard labor supply equation for women:
−
Vector Sijt is a catch-all variable that indicates effect of conflictrelated measures on women’s employment over and above
those in Xijt
o Include conflict casualties, indicator for husband has
migrated, and indicator for widowed/divorced/separated/HH
head
Use probit model to estimate labor supply equation; find
probability of women’s employment rose in 2001 and 2006
o These estimates serve as qualitative benchmark; need to
correct for fact that variables in Sijt could be endogenous
−
Conflict and Women’s Employment
●
Women’s employment decisions: difference-in-difference
model
−
Amend the labor supply equation for women as follows:
−
Σs α0s Fjst is the key term; measured as interactions of dummy
variables for the conflict years (2001, 2006) and a categorical
variable for forest coverage
Coefficients on the interaction terms (converted into marginal
probabilities) represent marginal effects of Nepal’s conflict on
likelihood of women’s employment, as well as likelihood of
women’s self-employment
Equation estimated with probit models
−
−
o
Run for full sample, and 2 sub-samples: (1) women whose husbands have
migrated, and (2) women who are widowed/divorced/separated/HH head
Conflict and Women’s Employment
●
Main results:
−
−
−
−
Women living in more conflict-prone areas had a higher
likelihood of employment in 2001 and 2006 compared to the
outbreak of war in 1996 (see Table)
Estimates indicate that relative to 1996, the probability of
employment was 0.098 higher in 2001 and 0.095 higher in
2006 for comparable women in more conflict-prone areas
Similar results for the likelihood of women’s self-employment in
the overall sample; however, magnitude of estimates is smaller
Employment result also holds for sub-sample of women whose
husbands migrated, and for women managing their households
due to other reasons
Conflict and Women’s Employment
Women with Husbands Widow/Sep/Div/HH
Migrated
Head
SelfSelfSelfEmployed
Employed
Employed
Employed
Employed
Employed
All Women
Interaction Terms (reference=conflict_1996)
Conflict_2001
Conflict_2006
0.098**
0.063***
0.134**
0.001
0.099**
0.149***
(0.032)
(0.010)
(0.032)
(0.018)
(0.023)
(0.012)
0.095***
0.046***
0.136***
-0.013
0.079*
0.090*
(0.016)
(0.012)
(0.017)
(0.020)
(0.029)
(0.031)
Conflict and Women’s Employment
●
Robustness Checks: conducted 3 sets of tests
1)
2)
3)
Estimated marginal probabilities for likelihood of employment using
first-stage predicted values of the conflict-year interactions
−
Main result holds for employment and self-employment
Estimated instrumental variable probits for likelihood of employment
using alternative measures of conflict in the ivprobit regressions
−
Conflict measured by mortality, migration, and women
wid/div/sep/HH head
−
Main result holds for mortality and migration measures
Estimated Two State Least Squares for employment using data at
sub-region level and alternative measures of conflict
−
Main result holds, except for conflict proxied by husbands
migrated; found some evidence supporting idea that remittances
may discourage women from engaging in employment
Conclusion and Policy Implications
●
Main finding: a strong “added worker” effect during Nepal’s
civil war
−
−
−
●
Women living in areas with high conflict intensity more likely to engage
in employment relative to comparable women in other areas
Similar trends evident for self-employment
Main results robust to alternative measures of conflict intensity,
sample composition, and estimation strategies
Policy implications
−
−
−
Women responsive to employment opportunities; promoting
employment in Nepal’s industry sector will reduce poverty and
inequality (Acharya 2008)
Educating girls and women remains a policy priority given low literacy
rates and traditional norms
Added worker effect could lead to girl children being withdrawn from
school; need policies to support families with childcare and work
burdens
Next Steps

Further checks on sample selection bias from husband’s
migration
–
–


Measure separate effects for women whose husband’s did not
migrate and ensure that these effects are similar to the results for
the complete sample
Include husband’s migration status directly in the control variables
Include linear trends for each sub-region type to control
for possible omitted variable and serial correlation bias
Consider separate effects for non-self employment work