Do Female Politicians Lead to Better Learning Outcomes?

Sadia Priyanka

doi:10.1515/bejeap-2021-0439

Article

Do Female Politicians Lead to Better Learning Outcomes?

Sadia Priyanka

Published/Copyright: September 21, 2022

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From the journal The B.E. Journal of Economic Analysis & Policy Volume 22 Issue 4

Abstract

Despite progress towards universal primary education, children in India lag in age and grade-appropriate learning competencies. This paper studies the effect of women’s political leadership in state legislatures on primary school-aged children’s learning outcomes. I use close elections won by women against men as an instrument for the election of a female politician and find significant gains in math and reading proficiency in children 8–11. Using a rich set of school and household-level data, I evaluate the impact on public and household investments. There is an increased likelihood of public schools receiving grants and inputs beneficial for learning. Further, female politicians lead households to exhibit more confidence in the ability of public schools to provide a better education. Consistent with this perception change, there are reduced time allocation and spending on supplementary household resources. The net positive effect suggests the policy effect outweighs the crowding out of private resources.

Keywords: female politicians; learning outcomes; primary school; India

Corresponding author: Sadia Priyanka, Department of Economics, Connecticut College, 270 Mohegan Ave Pkwy, 06320-4125, New London, CT, USA, E-mail: spriyanka@conncoll.edu

Appendix

Table A1:

Election year and states in the sample.

States	2006	2007	2008	2009	2010
Andhra Pradesh				•
Assam	•
Bihar					•
Gujarat		•
Haryana				•
Himachal Pradesh		•
Karnataka			•
Kerala	•
Madhya Pradesh			•
Maharashtra				•
Orissa				•
Punjab		•
Rajasthan			•
Tamil nadu	•
West Bengal	•

The black dot represents the election year data included in the sample for each state.

Table A2:

Distribution of proficiency levels.

Reading levels		Math levels		Writing levels
Cannot read	8%	Cannot recognize	12%	Cannot write	21%
Letters	16%	Numbers	36%	1–2 mistakes	41%
Words	24%	Subtraction	33%	No mistake	38%
Paragraph	23%	Division	19%
Story	29%

Weighted using sample weights.

Table A3:

First stage.

Dependent variable: share of seats won by women in a district (Female MLA)
	Linear margins (1)
Share of seats won by women in close elections against men in a district	1.045***
(Female MLA Close)	(0.104)
Share of seats with close elections between women and men in a district	−0.746***
(Total Close)	(0.199)
F statistic	103.3
R square	0.951
Individual and household controls	Yes
State fixed effects	Yes
Observations	1,970

Robust standard errors clustered at the district level in parentheses. Significance levels ***p < 0.01, **p < 0.05.

Table A4:

Test of identifying assumptions.

Panel A: personal covariates
	Age (1)	Female (2)	Hindu (3)	Muslim (4)	SCST (5)
Female MLAs	−0.179	−0.093	0.325	−0.024	−0.593*
(0.515)	(0.316)	(0.389)	(0.344)	(0.359)
Total Close	0.518	0.108	0.450	−0.482	−0.165
(0.629)	(0.346)	(0.602)	(0.397)	(0.432)
Observations	1,970	1,970	1,970	1,970	1,970
Panel B: Household covariates
	HH size	Income per	Adult years of
	(1)	capita (2)	education (3)
Female MLAs	−1.199	7,677	1.750
(1.512)	(28,028)	(4.870)
Total Close	−0.109	5,351	−1.248
(2.130)	(34,011)	(5.192)
Observations	1,970	1,970	1,970
Panel C: Probability of women winning elections
	Female MLAs
	(Close election-years) (1)
Share independent	0.621*
(0.335)
Share BJP	−0.176
(0.165)
Share Janata	−0.053
(0.175)
Share congress	−0.086
(0.171)
Share soft left	0.314
(0.366)
Share hard left	−0.307
(0.231)
Share regional	0.262
(0.169)
Total seats	0.0007
(0.001)
Total electors	−2.16e-07
(2.22e-07)
Total votes cast	1.02e-07
(3.16e-07)
Observations	1,970

Estimation applies sample survey weights so that the covariates are representative of the sample. Previous research shows that female MLAs increase primary education attainment (Clots-Figueras 2012), so we expect them to affect children’s years of education. Similarly, MLAs could affect short-term household expenditures. However, the main results are robust to whether we include them as controls or not. Significance levels *** p<0.01, ** p<0.05, * p<0.1.

Table A5:

Gender heterogeneity.

	Reading		Math
	Girls (1)	Boys (2)	Girls (3)	Boys (4)
Female MLAs	0.347	0.592**	0.650**	0.708**
	(0.221)	(0.259)	(0.260)	(0.311)
Observations	913	1,057	913	1,057
Electoral margins	Linear	Linear	Linear	Linear
Individual controls	Yes	Yes	Yes	Yes
Household controls	Yes	Yes	Yes	Yes
State fixed effect	Yes	Yes	Yes	Yes

Robust clustered standard errors at district level in parentheses. Regressions include share of seats in district with close man-woman elections. Individual child controls include age dummies, gender, religion, caste, and years of education completed. Household controls include household size, expenditure and income per capita, and highest adult years of education. Significance levels ***p < 0.01, **p < 0.05, *p < 0.1.

Table A6:

Accounting for ordinal test scores.

Test scores	≥1 (1)	≥2 (2)	≥3 (3)	≥4 (4)
Panel A: Reading
Female MLAs	0.330**	0.310	0.382	0.370*
(0.147)	(0.355)	(0.366)	(0.206)
Observations	1,970	1,970	1,970	1,970
Panel B: Math
Female MLAs	0.213*	0.064	0.697***
(0.111)	(0.324)	(0.249)
Observations	1,970	1,970	1,970
Electoral margins	Linear	Linear	Linear	Linear
Individual controls	Yes	Yes	Yes	Yes
Household controls	Yes	Yes	Yes	Yes
State fixed effect	Yes	Yes	Yes	Yes

Robust clustered standard errors at district level in parentheses. The dependent variable is a dummy variable equal to 1 if the child has mastered at least the skill level corresponding to each score. Regressions include share of seats in district with close man-woman elections. Individual child controls include age dummies, gender, religion, caste, and years of education completed. Household controls include household size, expenditure and income per capita, and highest adult years of education. Significance levels ***p < 0.01, **p < 0.05, *p < 0.1.

Table A7:

OLS and 2SLS estimates (full sample of 8–11 year olds).

	Reading		Math		Writing
	OLS (1)	2SLS (2)	OLS (3)	2SLS (4)	OLS (5)	2SLS (6)
Female MLAs	0.078	0.122	0.010	0.208	0.021	0.299
(0.108)	(0.220)	(0.078)	(0.248)	(0.105)	(0.388)
Observations	5,998	5,998	5,998	5,998	5,998	5,998
Electoral margins	Linear	Linear	Linear	Linear	Linear	Linear
Individual controls	Yes	Yes	Yes	Yes	Yes	Yes
Household controls	Yes	Yes	Yes	Yes	Yes	Yes
State fixed effect	Yes	Yes	Yes	Yes	Yes	Yes

Robust clustered standard errors at district level in parentheses. Full sample of 5998 observations include all children with non-missing test scores (including close and non-close election observations) Regressions include share of seats in district with close man-woman elections. Individual child controls include age dummies, gender, religion, caste, and years of education completed. Household controls include household size, expenditure and income per capita, and highest adult years of education. Significance levels ***p < 0.01, **p < 0.05, *p < 0.1.

Table A8:

Robustness.

	(1)	(2)	(3)	(4)	(5)	(6)
Female MLAs	1.195*	1.393	0.973**	0.714***	0.725**	1.000***
(Close elections)	(0.648)	(0.958)	(0.399)	(0.260)	(0.313)	(0.257)
Observations	1,970	1,970	1,970	1,537	1,970	1,970

Sample	Average	Continuous	Continuous	Exclusion	Sample	Sample
	z-score	test score	test score	8-year old	weights	weights
			reading	math	math	reading	math

Female MLAs	0.320	0.730***	0.722***	0.920***	0.387*	0.728***
(Close elections)	(0.363)	(0.266)	(0.269)	(0.217)	(0.205)	(0.250)
Observations	1,970	1,970	1,970	1,970	1,970	1,970

Sample	Quadratic	Quadratic	Political	Political	Reduced	Reduced
	margins	margins	controls	controls	Form	form
	reading	math	reading	math	reading	math
All controls	Yes	Yes	Yes	Yes	Yes	Yes
State fixed effect	Yes	Yes	Yes	Yes	Yes	Yes

Robust clustered standard errors at district level in parentheses. Regressions include share of seats in district with close man-woman elections. Individual child controls include age dummies, gender, religion, caste, and years of education completed. Household controls include household size, expenditure and income per capita, and highest adult years of education. Significance levels ***p < 0.01, **p < 0.05, *p < 0.1.

Table A9:

Effect of female MLAs on teachers.

	Received training (1)	Female teacher (2)	Years of education (3)
Female MLAs	0.265**	−0.487	−0.533
(Close elections)	(0.119)	(0.521)	(1.396)
Observations	2,484	2,473	2,484
Electoral margins	Linear	Linear	Linear
State fixed effects	Yes	Yes	Yes

Robust clustered standard errors at district level in parentheses. Regressions include share of seats with close man-woman elections. Significance levels ***p < 0.01, **p < 0.05, *p < 0.1.

Figure A1:

Illustration of first stage estimate.

The figure shows the share of seats won by female MLAs against the vote margins of all man-woman elections in the sample. The plot uses data-driven and evenly spaced bins to mimic the underlying variability of the data.

Figure A2:

Electoral margins in man-woman elections.

Notes: Panel A plots the distribution of electoral margins in man-woman elections. Panel B depicts the McCrary density test.

Figure A3:

Election margins and test scores.

The graph plots the effect of female MLAs on reading and math proficiency against various close election bandwidths ranging from 2.5 to 5%. The dashed line represents the 95% confidence intervals of the coefficient estimates.

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Received: 2021-11-24

Accepted: 2022-08-25

Published Online: 2022-09-21

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Articles in the same Issue

https://doi.org/10.1515/bejeap-2021-0439

Keywords for this article

female politicians; learning outcomes; primary school; India