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Effect of fiscal policies on gender equality ∗
Lisa KolovichIMF†
Angelica Martinez LeyvaIMF†
January 2019
AbstractThis paper analyzes the effect of fiscal policies in Uganda and Rwanda on gender equalityin education, health and infrastructure outcomes. Government budgets and fiscal measurescan play an important role in promoting women’s development and gender equality, an ap-proach commonly referred to as gender budgeting. First using a fixed effect approach, wefind that fiscal policies have a significant, positive impact on education. Using years oneducation as one of our education indicators, we find that on average, the girls increase theireducation by 0.7 years. In the same way, when we analyze infrastructure outcomes we finda significant reduction on the time it takes for women to collect water. Similarly, for healthoutcomes, the probability of women of having children in their houses decreases when genderbudgeting policies are present. For our second approach we use the synthetic control methodand we find that if the gender budgeting policies wouldn’t have been implement, educationand health outcomes for both countries wouldn’t have performed as good as with the policyimplementation. On average the gap between the years of education and the contrafactualis 0.5 years of education for Rwanda and 0.2 years for Uganda. We found positive resultsfor our health outcomes as well.
∗The views express herein are those of the authors and do not necessarily reflect the views of the IMF†E-mail: [email protected]†E-mail: [email protected]
1
1 Introduction
Historically, women around the world have had less opportunity than men in education,
employment, and health care, and less political representation. And though many global
gender gaps have narrowed in recent decades, particularly in education enrollment, the World
Economic Forum estimates that at the current rate of progress it will take more than 200
years to close the overall global gender gap in economic participation and opportunity.1
The moral argument for gender equality is clear, nonetheless, and the economic evidence is
mounting. Eliminating gender inequalities can increase female economic participation, boost
economic growth, and improve health outcomes for women and children (see Cuberes and
Teignier (2014); Klasen and Lamanna (2009); Bank (2011); and Knowles et al. (2002)). Hill
and King (1993) find that large gender disparities in education reduce gross national prod-
uct. In countries where the female-to-male school-enrollment ratio is lower than 0.75, gross
national product is approximately 25 percent lower than in countries with greater gender
parity in education. Klasen and Lamanna (2009) show that gender gaps in education had
a negative impact on economic growth in the 1990s. Agenor and Canuto (2015) examine
how increasing investment in infrastructure reduces the time women spend on unpaid work,
which ultimately leads to economic growth. Duflo (2012) finds that there is a bidirectional
relationship between economic development and women’s empowerment defined as improv-
ing the ability of women to access the constituents of development — in particular health,
education, earning opportunities, rights, and political participation. In one direction, devel-
opment alone can play a major role in driving down inequality between men and women;
in the other direction, continuing discrimination against women can, as Sen has forcefully
argued, hinder development. Empowerment can, in other words, accelerate development.
Given the potential economic benefits of gender equality, there is interest in the types of tools
that countries may have at their disposal to promote gender equality. One such tool is gender
budgeting. Governments at the national, state, and local levels can use budgets and fiscal
measures and institutions to promote women’s development and gender equality. Originating
in Australia in the mid-1980s, gender budgeting spread to Canada, South Africa, and the
United Kingdom by the mid-1990s, and has now been applied at varying levels of complexity
and diversity in more than 80 countries. Gender budgeting has targeted a wide variety of
1The 2016 World Economic Forum Global Gender Gap Index consists of four sub-indices: EconomicOpportunity and Participation, Educational Attainment, Health and Survival, and Political Empowerment,and notes that while gaps in education and political empowerment have improved, challenges remain ineconomic opportunity and health outcomes. In fact, the 2016 economic gender gap returned to its 2008level, and the time it will take to close the gender gap in health outcomes is still undefined.
2
goals, including increasing access to education, childcare, and health services; raising female
labor force participation; and eradicating violence against women. (see Budlender and Hewitt
(2003); Budlender et al. (1998); Elson (2003); Stotsky (2006); Stotsky (2016); and IMF 2017)
Kadama et al. (2018) highlights some of the lessons learned from a global survey of gen-
der budgeting practices. Results from the survey show that legal requirements for gender
budgeting matter. Providing gender budgeting with legal status helps ensure its continuity
when political actors change. In addition, the leadership of the ministry of finance is crucial.
The analysis shows that gender budgeting initiatives appear more likely to be sustained if
the ministry of finance leads the gender budgeting effort. Civil society, gender and other
ministries, parliaments, and academia are also key players. Gender budgeting can be imple-
mented successfully at the national, state, and/or local levels. Uganda’s local efforts have
focused on gender-aware budget statements and sex-disaggregated analysis of the gender im-
balance in unpaid care burdens. ”Local budget clubs” also allow citizens to discuss spending
priorities and hold governments accountable.
The survey also highlights how gender budgeting goals should be aligned with national gender
equality plans or the Sustainable Development Goals. In addition, though gender budgeting
has typically focused on expenditures and overlooked taxes, tax policies are not always
gender neutral. Discriminatory tax and financial laws remain in many of the countries’ legal
systems, as do legal barriers constraining women’s right to work in nearly every country in
the Middle East and North Africa (World Bank 2015). Countries should aim to improve
reporting and transparency and gender budgeting statements can help ensure transparency.
Furthermore, countries should also work to increase monitoring and evaluation of gender
budgeting efforts.
However, the global survey did not examine the causal links between fiscal policies and
gender inequality. In fact, to date, there have been few papers in the literature analyzing the
causal links between gender budgeting and gender equality outcomes. Stotsky and Zaman
(2016) show that Indian states with gender budgeting efforts have made more progress on
gender equality in primary school enrollment than those without. This paper aims to start
filling in the gap on the research between the casual relationship between fiscal policies and
gender inequality.
Our analysis sheds light on how the innovative gender budgeting practices in Uganda and
Rwanda have led to higher levels of gender equality when compared to regional counterparts
without such extensive gender budgeting efforts. This study adds an important dimension
to research on gender budgeting. We use micro-level data to analyze the effect that gender
3
budgeting polices, and we employ a relatively novel approach — the synthetic control method
- to create a counter-factual of what would have happened if fiscal policies aimed at gender
equality were not implemented. Our results show that the implementation of policies that
focus on reducing gender inequality gaps has a positive effect across a range of outcomes,
which can further be translated to gains in economic growth within the countries.
The paper organized as follows: Section 2 provides the context of Rwanda and Uganda and
some stylized facts comparing them with regional peer countries. Section 3 explains the data
used in detail. Section 4 describes the model, and Section 5 discusses the empirical findings.
Section 6 describes the synthetic control method and the results of using this approach, and
in Section 7 we offer our conclusions.
2 The context: Rwanda and Uganda
Gender inequality in access to education and adequate health care for women pose challenges
in sub-Saharan Africa, though the region has seen improvement in recent years. Figure 1
shows the evolution of GDP per capita and the gender inequality index since 2000.2 Despite
having a GDP per capita below the regional average (panel 1), Rwanda and Uganda have
seen a decrease in gender inequality (panel 2).
Figure 1: Economic Growth and Inequality
200
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700
800
900
1000
2000
2001
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2003
2004
2005
2006
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2008
2009
2010
2011
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2015
2016
2017
$ U
S (c
on
stan
t 2
01
0)
GDP per capita
SSA (LIDCs) Rwanda Uganda
Source: World Development Indicators
0.10
0.20
0.30
0.40
0.50
0.60
0.70
2000 2005 2010 2011 2012 2013 2014 2015 2016 2017
Gender Inequality Index
Rwanda Uganda SSA (LIDCs)
Source: UNDP, Human development indicators
2The United Nations Development Programme measures gender inequalities in three important aspectsof human development creating the gender inequality index: reproductive health, measured by maternalmortality ratio and adolescent birth rates; empowerment, measured by proportion of parliamentary seatsoccupied by females and proportion of adult females and males aged 25 years and older with at least somesecondary education; and economic status, expressed as labour market participation and measured by labourforce participation rate of female and male populations aged 15 years and older.
4
Turning to average years of schooling, in Figure 2 we see that Uganda has higher average
years of schooling for its total population (age 15 and up) and for girls (age 15 and up)
than its peers. Rwandan’s girls have higher average years of schooling than the regional
counterparts. Looking at the education level for Rwanda and Uganda in urban and rural
areas, we can see that in both countries, the percentage of girls with no education has been
cut approximately in half in the rural regions in both countries.
Figure 2: Education
3.23
3.83
4.364.33
4.97
5.7
3.91
4.31
4.70
0
1
2
3
4
5
6
2000 2005 2010
Years of education (total)
Rwanda Uganda SSA (LIDCs)
Barro-Lee: Average years of total schooling, age 15+, Total
2.97
3.61
4.25
3.64
4.37
5.23
3.19
3.63
4.12
0
1
2
3
4
5
6
2000 2005 2010
Years of education (female)
Rwanda Uganda SSA (LIDCs)
Barro-Lee: Average years of total schooling, age 15+, female
0
10
20
30
40
50
60
70
2000 2014 2000 2014
Urban Rural
%
Rwanda: Education level
No education Primary
Source: DHS, Female +15
0
10
20
30
40
50
60
70
2000 2016 2000 2016
Urban Rural
%
Uganda: Education level
No education Primary
Source: DHS, Female +15
In 2000, both Rwanda and Uganda had a lower share of births attended by skilled health
staff than their counterparts in sub-Saharan Africa (Figure 3, panel 1). However, by 2014-
2015, more than 90 percent of births in Rwanda and close to 80 percent of births in Uganda
were attended to by skilled health staff. The rate in the rest of low-income and developing
sub-Saharan Africa was below 60 percent. In addition, in rural Rwanda, the percent of births
at home fell by more than 50 percentage points in fifteen years. We can see also that both
countries have a reduction on the percentage of households that have to go further than 30
minutes to get water (Figure 3, panel 2).
As the previous figures show, Rwanda and Uganda are two countries that have had suc-
5
Figure 3
0
10
20
30
40
50
60
70
80
90
100
2000 2005-06 2014 -15
pe
rce
nta
ge
po
ints
Births attended by skilled health staff (% of total)
Rwanda Uganda SSA (excluding high income)
Source: WB Gender statistics
10
20
30
40
50
60
70
2000 2014
pe
rce
nta
ge
po
ints
Households with water 30 minutes or longer away round trip (%)
Rwanda Uganda SSA (LIDCs)
Source: WB data Gender statistics
cess in advancing gender equality. Strong political will to mainstream gender into govern-
ment programs, underpinned by institutional and policy reforms, has been key to Rwanda’s
progress in addressing gender inequality. More broadly, the country’s socioeconomic out-
comes have improved significantly in the last two decades, and it is among sub-Saharan
Africa’s fastest growing economies. In this, it has emerged as a global leader in women’s eco-
nomic opportunities and endowments, and further advance could yield a significant growth
dividend. Gender budgeting has emerged as one of Rwanda’s key policy tools, and the prac-
tice helps evaluate how fiscal policies may affect men and women differently. The government
launched its initial gender budgeting initiative in 2002, led by the Ministry of Gender and
Family Promotion. Beginning with four focus sectors—agriculture, education, health, and
infrastructure—gender budgeting included reporting instruments, such as the Gender Bud-
get Statement, to ensure accountability, require analysis of gender-related goals, and report
gender-disaggregated employment data for the civil service. The positive impacts of the
Gender Budget Statement include improved access to water, new classrooms at the primary
and secondary levels, and greater access to health services due to the construction of new
facilities. The Gender Monitoring Office notes that, among other recommendations, gen-
der disaggregated data collection and analysis needs to improve, which would enhance the
government’s ability to plan, budget, and report.
Starting in the late 1990s, the Forum for Women in Democracy drove Uganda’s initial gender
budgeting effort, with a primary focus on education, health (in particular, maternal mor-
tality), and agriculture. By the mid-2000s, the country’s Poverty Eradication Action Plan
incorporated a gender focus. The fiscal 2004/2005 budget call circular, issued by the Min-
istry of Finance, Planning, and Economic Development, incorporated guidelines for gender
budgeting for ministries and local governments. Promoting gender equality was one of the
6
objectives of the 2010–15 National Development Plan, with gender budgeting included as a
tool for achieving gender equality; local governments have also incorporated gender-related
objectives into their five-year development plans. An innovative aspect of Uganda’s gender
budgeting program is its Gender and Equity Compliance Certificate. The Equal Opportuni-
ties Commission is tasked with reviewing Ministerial Policy Statements and assessing their
responsiveness to and compliance with legal gender and equity reporting and outcomes.
3 Data
We use micro-level data from the Demographic and Health survey (DHS) throughout the
first part of our analysis. The DHS collect data that are comparable across countries and
focus on marriage, fertility, mortality, family planning, reproductive health, child health,
nutrition, and HIV/AIDS and due to the subject matter of the survey, the focus of the
survey are women of reproductive age, between 15 and 49 years. We use data from the
DHS for Rwanda and Uganda, countries with sustained, well-developed gender budgeting
initiatives and Kenya and Ghana for our control group. Neither Kenya nor Ghana had
prominent gender budgeting initiatives during the timeframe of our analysis; furthermore,
the DHS surveys cover these two countries over the same time period. We use four different
years from 1998 to 2016 to evaluate the impact that gender budgeting has on the education,
health and infrastructure outcomes.3 In addition to the micro data from the DHS, we also
use macro data from the World Bank’s World Development Indicators dataset to control
for characteristics of the country, such as per capita GDP, rural and urban, labor force
participation, and foreign aid. Our panel dataset and use of fixed effects allows us to control
for variables that we cannot observe or measure such as cultural factors. We present our
fixed effects results first as we are interested in analyzing the impact that gender budgeting
has had over time. This will allow us to explore the relationship between gender budgeting
and outcome variables within a country.
For our second approach we use the synthetic control method. Unlike the fixed effect ap-
proach, we will rely only on country-level data for all the low-income and developing countries
in sub-Saharan Africa. We create a panel data set from 1990-2015 using macro level data
from the World Bank’s World Development Indicators dataset.
3For Rwanda we use 1998, 2004, 2008 and 2009; Uganda 2000, 2006, 2011 and 2016, Kenya 1998, 2003,2008 and 2014; Ghana 1998, 2003, 2008 and 2014.
7
4 Empirical approach
We now study the effects of gender budgeting on education, health and infrastructure out-
comes. For our econometric model, we tested ordinary least squares (OLS) and fixed effects
(FE).4 We present only the fixed effect models. Our regression takes the form
Yij = α + β1GBit + βkXk,ij + ϕj + µt + εit
where Yij is the dependent variable in country i in year t, representing education, health, and
infrastructure indicators; GBij is the gender budgeting dummy that accounts for whether
there is an ongoing gender budgeting effort in country i in year t with β1 being the coefficient
of interest; and Xk,ij is a vector of k explanatory variables with βk the coefficient of the
explanatory variables. The model also include country fixed effects, ϕj, which control for
the time-invariant characteristics of the country i, and time fixed effects, µt, to control for
common time-variant factors affecting the dependent variable across countries, and εit the
error term.
We measure the effect of gender budgeting using a dummy variable, where the variable takes
a value of 1 if the country has a gender budgeting effort in place and 0 if not. To measure the
impact of gender budgeting on education, we use years of education and literacy.5 For health
we use the place of delivery of the child (home versus clinic/hospital) and the type of person
who assisted with the delivery of the child, (family/friend vs someone qualified).6 Finally,
for the infrastructure variables we use access to electricity and time to get water.7 Time
spent collecting water is particularly interesting as this chore overwhelmingly is completed
by women, and it can often be quite time consuming, with some women spending hours each
day on the task. Reducing the amount of time women dedicate to getting water may be
translated into increased time for them to invest education or formal labor force participation
for example.
Given the availability of the data, we try to use the same wave years but when this is not
possible, we use the closest year available, with no more than one-year gap.
4For our binomial dependent variables we will use a probit analysis5Years of education is a continuous variable from zero to 12. Literacy takes the value of 1 if the person
can read and write, and zero if not.6Place of delivery will take the value of 1 if the place of delivery was a hospital, clinic, or any medical
facility, and zero if the place of delivery was in the respondents’ home or other home. For the delivery ofthe child variable, it will take a value of 1 if it was delivery by a relative or a friend and zero if it was notdelivery by a friend or relative.
7Electricity will take the value of 1 if the household has electricity and zero if not.
8
Table 1: Education
(1) (2) (3) (4)
Years Years Literacy Literacyof education of education
Gender Budgeting 0.183*** 0.722*** 0.00856 0.207***(0.0338) (0.0521) (0.0059) (0.0087)
Country FE yes yesCountry*Time FE yes yesObservations 147,948 147,948 151,009 151,009R-squared 0.322 0.328
Standard errors in parenthesis; ***p < 0.01, **p < 0.05, *p < 0.1.Note: Literacy results were using a probit regression. We are presenting the marginal effects.
5 Quantitative results
Table 1 presents our results when we analyze the impact that gender budgeting has on
education outcomes.8 Odd numbered columns present the results using country fixed effects
while even numbered columns use country and time fixed effects. We can see that our
variable of interest, gender budgeting, is positive and significant in most of the cases analyzed.
Column 1 and 2 shows that gender budgeting has a positive and significant effect on the
years of education and when we introduce country and time fixed effects the impact increases
from 0.183 to 0.722. On average the girls increase their education by 0.72 years in countries
where gender budgeting is in place. When we analyze the impact that gender budgeting
has on women’s literacy we can see in column 3 the effect is positive but not significant.
However when we introduced country and time fixed effects, the effect become significant.
In particular, column 4 shows that the probability of women be literate is 20 percent higher
for countries with gender budgeting initiatives.
Table 2 presents the results for the infrastructure outcomes that we use. Column 1 and
2 present the effect that gender budgeting has on electricity when we control for country
fixed effect and the interaction between country and time fixed effect respectively. When
we control only for country fixed effects, our variable of interest has the opposite effect of
what we expect, however when we also include time fixed effects the effect becomes positive
and significative. On average, the probability of the house where the woman lives to have
electricity is 12 percent higher on countries with gender budgeting. The relationship between
8You can find the complete regression tables is the annex.
9
Table 2: Infrastructure
(1) (2) (3) (4)
Electricity Electricity Time to Time toget water get water
Gender Budgeting -0.0262*** 0.123*** -6.082*** -20.65***(0.0027) (0.00523 (0.435) (0.662)
Country FE yes yesCountry*Time FE yes yesObservations 152,120 152,120 155,949 155,949R-squared 0.106 0.126
Standard errors in parenthesis; ***p < 0.01, **p < 0.05, *p < 0.1.Note: Electricity results were using a probit regression. We present the marginal effects.
time to get water and gender budgeting is expected to have a negative sign since we expect
that with the introduction of gender budgeting polices the amount of time that women spent
collecting water to decrease. Column 3 and 4 shows that the coefficient for gender budgeting
is negative and significative. On average, the time that women spent collecting water reduces
in 6 minutes when using country fixed effect. We can see that the effect increases when we
control for time and country effects to a 20 minutes decrease.
Finally, Table 3 show the results for our health outcomes. When we analyzed what is the
effect that gender budgeting policies has had on the place of were the child is deliver the
coefficient is significative and positive. In particular, we found that the probability of having
a baby in a hospital or clinic is between 20-22 percent higher in countries that have gender
budgeting policies, the effect intensifies when we control for country and time effects (column
2). For the variable delivery of a child, we expected the effect to be negative since the dummy
variable takes the value of zero when the child was not deliver by a friend or relative. Hence
the gender budgeting policies should impact the variable in a negative way. In this sense,
the coefficient is significant and negative. We can see that the probability of having a baby
delivered by a friend or relative declines in 6 to 18 percent.
10
Table 3: Health
(1) (2) (3) (4)
Place of Place of Delivery by Delivery bydelivery delivery relative relative
Gender Budgeting 0.206*** 0.229*** -0.180*** -0.0592***(0.0076) (0.0105) (0.0074) (0.0048)
Country FE yes yesCountry*Time FE yes yesObservations 77,218 77,218 74,829 74,829
Standard errors in parenthesis; ***p < 0.01, **p < 0.05, *p < 0.1.Note: Place delivery and delivery by relative results were using a probit regression. We present the marginaleffects.
6 Synthetic Control Method
For our second approach, we use the synthetic control method to analyze the effect that
gender budgeting has on health and education outcomes. The main goal of this approach is
to measure whether the policy actually had an impact, and to do this we need to develop an
unobservable contrafactual, that is, what would had happened without the policy interven-
tion. We want to compare the outcome of the unit affected by the policy to the evolution of
the outcome for some control group. Abadie et al. (2010) explain how the synthetic control
method gives a data driven procedure to construct more comparable control groups. In
particular, the authors mention that the synthetic method provides an algorithm that finds
weighted combination of comparison units with the weights are chosen such that the best
resembles the values used of predictors of the outcome variable for the effective unit before
the intervention.
To construct the control group, we use low-income and developing countries in sub-Saharan
Africa that have not implement gender budgeting policies. Our data are at an aggregate
level from 1990 to 2015. Unlike our fixed effects model, here for the health outcome we focus
on maternal mortality and, for education we use years of education. The control variables
include log GDP per capita, education expenditure, health expenditure, percentage of rural
population, female labor force participation, log of foreign aid and natural resources as a
percentage of GDP.9
9See annex for a list of the countries used for each of the experiments analyzed using the synthetic controlmethod.
11
Figure 5 shows the results for Rwanda. We can see in the left-hand panel that maternal
mortality would not have decreased as much without gender budgeting. The gap between
the treated and the synthetic control groups show the difference between the counter-factual
and the actual outcomes. On average, the gap from 2002 to 2015 is approximately 250,
which means that the maternal mortality rate would have been 250 points higher without
the gender policy implementation. In the same way, when we analyzed years of education,
the right-hand panel shows a significant gap between the counter-factual and the treatment
group. In this case, the increase in education without the intervention would have been on
average 0.5 fewer years.
Figure 4: Rwanda, Treatment vs synthetic control
200
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1400
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Rwanda_treated Rwanda_synthetic
Doing the same experiment for Uganda, Figure 6 shows the results for the treatment and
synthetic control. Similar to Rwanda, the gap in maternal mortality between the treatment
and the counter-factual groups shows that gender budgeting helped reduced maternal mor-
tality more than if the than in the counter-factual control group without gender budgeting.
On average, the difference between treatment and synthetic control from 2006 to 2015 is ap-
proximately 30. The difference between both gaps is that maternal mortality in Rwanda, at
the beginning of the 90s was almost as double than the maternal mortality in Uganda. In the
right-hand panel we can see the gap for years of education. The difference is around 0.2 years
of education. Overall, we can see that both countries have better outcomes in health and
education comparing with compared to the counter-factual of a country not implementing
gender budgeting.
12
Figure 5: Uganda, Treatment vs synthetic control
200
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7 Policy Conclusions
The key message from our analysis is that gender budgeting can and does reduce the gender
inequality within countries. Our results show that the implementation of policies that focus
on reducing gender inequality gaps has a positive effect across a range of outcomes, which
can further be translated to gains in economic growth within the countries.
Rwanda and Uganda had made great steps in reducing gender inequality over the last two
decades, and much of this improvement coincides with the country’s gender budgeting ef-
fort. Identifying and integrating gender-oriented goals into the normal budgetary process
reinforced the countries’ effort. Several opportunities exist to consolidate these gains in
making gender equality an integral part of inclusive growth. Female labor force partici-
pation beyond the agricultural sector could be enhanced through further advancement of
women’s access to quality health and education services. In this respect, there is still a wide
range of alternatives that the government can take to empower women that can accelerate
development.
13
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15
Appendix A
In Table 4, we report the complete results for the Education regression.
Table 4: Education
(1) (2) (3) (4)
Years Years Literacy Literacyof education of education
Gender Budgeting 0.183*** 0.722*** 0.00856 0.207***(0.0338) (0.0521) (0.0059) (0.0087)
Age 0.203*** 0.206*** 0.0107*** 0.0111***(0.0067) (0.0067) (0.0012) (0.0012)
Age2 -0.0040*** -0.00405*** -0.000268*** -0.000277***(0.000102) (0.000102) (1.80e-05) (1.81e-05)
Marital status -0.989*** -0.983*** -0.153*** -0.155***(0.0247) (0.0246) (0.00404) (0.00408)
Area 0.503*** 0.434*** 0.0579*** 0.0458***(0.0216) (0.0217) (0.00376) (0.00384)
GDP pc 2.130*** -3.885*** 0.872*** -1.388***(0.164) (0.815) (0.0297) (0.140)
LFP ratio 0.0153 25.35*** -5.572*** -0.794*(0.588) (2.668) (0.106) (0.459)
Currently working -0.0204 -0.0267 -0.00131 -0.00495(0.0182) (0.0182) (0.00322) (0.00326)
Wealth index 0.851*** 0.866*** 0.100*** 0.104***(0.00630) (0.00629) (0.00111) (0.00112)
Aid 0.366*** 0.803*** 0.102*** 0.462***(0.0296) (0.0935) (0.00529) (0.0176)
Constant -12.62*** -27.87***(0.852) (2.296)
Country FE yes yesCountry*Time FE yes yesObservations 147,948 147,948 151,009 151,009R-squared 0.318 0.303
Standard errors in parenthesis; ***p < 0.01, **p < 0.05, *p < 0.1.Note: Literacy results were using a probit regression. We are presenting the marginal effects.
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In Table 5, we report the complete results for the Infrastructure regression.
Table 5: Infrastructure
(1) (2) (3) (4)
Electricity Electricity Time to Time toget water get water
Gender Budgeting -0.0262*** 0.123*** -6.082*** -20.65***(0.00267) (0.00523) (0.435) (0.662)
Age 0.00435*** 0.00379*** -0.246*** -0.211**(0.000561) (0.000499) (0.0857) (0.0853)
Age2 -0.001*** -0.000*** 0.00434*** 0.00395***(0.000) (0.000) (0.00131) (0.00130)
Marital status -0.0250*** -0.0220*** 0.680** 0.563*(0.00221) (0.00198) (0.315) (0.314)
Area 0.116*** 0.0913*** -12.40*** -11.62***(0.00278) (0.00251) (0.280) (0.280)
GDP pc 0.632*** -1.857*** 6.913*** 222.4***(0.0141) (0.0929) (2.136) (10.26)
LFP ratio 0.0481 8.080*** -27.58*** -899.5***(0.0484) (0.278) (7.870) (33.37)
Currently working -0.00108 -0.000180 -1.047*** -1.428***(0.00151) (0.00133) (0.237) (0.236)
Wealth index 0.117*** 0.111*** -4.312*** -4.381***(0.00109) (0.00121) (0.0813) (0.0809)
Aid 0.0237*** -3.199*** 24.81***(0.00228) (0.390) (1.186)
Constant 109.0*** -282.9***(11.10) (28.98)
Country FE yes yesCountry*Time FE yes yesObservations 152,120 152,120 155,949 155,949R-squared 0.106 0.126
Standard errors in parenthesis; ***p < 0.01, **p < 0.05, *p < 0.1.Note: Electricity results were using a probit regression. We are presenting the marginal effects.
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In Table 6, we report the complete results for the Health regression.
Table 6: Health
(1) (2) (3) (4)
Place of Place of Delivery by Delivery bydelivery delivery relative relative
Gender Budgeting 0.206*** 0.229*** 0.180*** -0.0592***(0.00761) (0.0105) (0.00743) (0.00478)
Age -0.00806*** -0.00799*** 0.00128 0.00247*(0.00211) (0.00211) (0.00144) (0.00145)
Age2 0.000 0.000 -0.000 -0.000(0.000) (0.000) (0.000) (0.000)
Marital status -0.0409*** -0.0379*** 0.00408 -0.00411(0.00830) (0.00835) (0.00585) (0.00604)
Area 0.180*** 0.178*** -0.0562*** -0.0655***(0.00510) (0.00514) (0.00364) (0.00355)
GDP pc 0.856*** -0.522*** -1.281*** -0.248***(0.0425) (0.184) (0.0381) (0.00922)
LFP ratio -0.0624 3.060*** -0.902*** -1.201***(0.148) (0.609) (0.103) (0.0392)
Currently working 0.00344 -0.000555 0.0197*** -0.000741(0.00469) (0.00471) (0.00313) (0.00327)
Wealth index 0.0999*** 0.101*** -0.0361*** -0.0347***(0.00155) (0.00156) (0.00106) (0.00107)
Aid 0.101*** 0.234*** 0.00322(0.00746) (0.0211) (0.00586)
Country FE yes yesCountry*Time FE yes yesObservations 77,218 77,218 74,829 74,829
Standard errors in parenthesis; ***p < 0.01, **p < 0.05, *p < 0.1.Note: Place delivery and delivery by relative results were using a probit regression. We are presenting themarginal effects.
Appendix B
In Table 7 and Table 8, we report the donors pool for Rwanda for maternal mortality years
of education respectively.
In Table 9 and Table 10 , we report the donors pool for Uganda for maternal mortality years
of education respectively.
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Table 7: Rwanda - Unit weights for maternal mortality
Country Weights
Benin 0Burkina Faso 0Cameroon 0Central African Rep. 0Chad 0Cote d’Ivoire 0Eritrea 0.063Ethiopia 0Ghana 0Guinea 0Guinea Bissau 0Kenya 0Madagascar 0Malawi 0Mali 0Mozambique 0.395Niger 0Senegal 0Sierra Leone 0.175Togo 0.366Zambia 0
Table 8: Rwanda - Unit weights for years of education
Country Weights
Benin 0Cameroon 0Central African Rep. 0Cote d’Ivoire 0Ghana 0Kenya 0Malawi 0.083Mali 0Mozambique 0.102Niger 0.231Senegal 0Sierra Leone 0Togo 0.584Zambia 0
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Table 9: Uganda - Unit weights for maternal mortality
Country Weights
Benin 0Burkina Faso 0Cameroon 0Central African Rep. 0Chad 0Cote d’Ivoire 0Eritrea 0Ethiopia 0Ghana 0Guinea 0Guinea Bissau 0Kenya 0Madagascar 0Malawi 0Mali 0Mozambique 0Niger 0.149Sao Tome Principe 0.122Senegal 0Sierra Leone 0.05Togo 0.493Zambia 0.186
Table 10: Uganda - Unit weights for years of education
Country Weights
Benin 0Burundi 0.23Cameroon 0Central African Rep. 0Cote d’Ivoire 0Ghana 0.445Malawi 0Mali 0Mozambique 0.325Niger 0Senegal 0Sierra Leone 0Togo 0Zambia 0
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