Legislative Quota, Women Empowerment and Development: Evidence from Tanzania
Barcelona Graduate School of Economics
Final Master Project
Legislative Quota, Women Empowerment
and Development: Evidence from Tanzania
This paper analyzes whether the legislative women’s quota implemented in Tanzania has
helped to reduce the existing gender gap in that country. We focus on a set of development
indicators indicated by the literature and an analysis of female political activity. We exploit
the variation in the number of female representatives across the 131 districts of Tanzania,
employing a Diﬀerence and Diﬀerences approach including ﬁxed eﬀects and controlling for a
number of socioeconomic variables. Our analysis indicates that the legislative women’s quota in
Tanzania has led to signiﬁcant reductions in the gender gap and improvements for women. The
quota has eﬀectively increased political participation in accordance with its goals, and the level
of female representation continues to rise. We ﬁnd evidence that the quota has reduced the
gender gap in education for certain age groups, and we ﬁnd indications of small improvements
to female empowerment. In accordance with previous ﬁndings in other countries, we ﬁnd that
the increased female representation has led to substantial investments in water infrastructure
that has greatly increased the number of people with access to clean water. While we do not
ﬁnd signiﬁcant health impacts, this may be due to limitations in our dataset.
The improvement of global gender equality and the empowerment of women worldwide is one
of the eight UN millennium development goals. In the past two decades signiﬁcant progress has
been made in achieving this goal. According to the world gender gap report in 2014, the gender
gaps in women’s educational attainment (94%) and in health and survival (96%) have almost
been closed 1. In contrast, the gender inequality in economic participation and opportunity (60%),
and in particular the gender gap in political empowerment (21%) remain far from being balanced
Hausmann et al. (2014).
Although there has been great progress in some areas, gender inequality is still prominent
in many societal aspects, particularly in the developing world. Women are often not granted the
same rights and opportunities as men and are left with social and economic disadvantages, which
have negative eﬀects for an economy as a whole. Since human capital is one of the main drivers of
an economy, the underuse of half of a country’s population can have far-reaching consequences for
long-term economic growth and development.
Figure 1: Global Levels of Discrimination against Women
Source: Social Institutions and Gender Index 2015
Figure 1 illustrates that discrimination against women is most prominent on the African
continent. Africa lags behind most parts of the world in closing its gender gap on education and
health, but is well ahead of many emerging regions on closing the gap in political empowerment. In
order to redress gender inequality and its hampering consequences, a handful of African countries
(e.g. Eritrea, Rwanda, Sudan, Tanzania and Uganda) have employed women quotas in legislature.
Since many African countries have severe gender imbalances in legislature, but only a few are
The ﬁgures reported here refer to the ratio of female to male outcomes.
employing a quota system to address this disparity, it is crucial to evaluate the impact of such a
policy in order to evaluate its usefulness for other developing countries.
In this paper we focus on the special seat system for women in Tanzania. Our main objective
is to analyze the eﬀects of this quota system on a set of development indicators and by these means
to provide a sophisticated answer to the following policy question: Did the legislative women’s
quota reduce the existing gender gap in Tanzania? In particular, we are interested in outcomes
related to education, health, the quality of infrastructure and female empowerment.
Tanzania’s quota system was ﬁrst introduced with relatively mild requirements in 1985,
though the requirements were increased substantially for the 1995 elections to require female
representation to account for at least 15% of traditional seats in parliament. This requirement
was increased to 20% in 2000 and to 30% in 2005. Tanzania is a particularly interesting case
to study for a number of reasons. Firstly, the prevailing patriarchal society, favoring segregate
gender roles, makes it a good starting point to analyze the eﬀect of the legislative women’s quota.
Furthermore, after the special seat system for women was ﬁrst introduced, women managed to
push for laws that address women’s concerns in several areas, such as maternity leave for mothers,
a sexual oﬀence bill, a law that promotes enrollment of women in tertiary education and a land
law reform that addresses discriminatory practices against women (Meena (2003)). Furthermore,
while data availability is typically a major issue for developing countries, data for Tanzania is
Besides the direct channel of more female-oriented policies, the quota might also induce an
indirect change in women’s roles in society, i.e. a higher representation of women in politically
inﬂuential positions might incentivize young women to pursue similar paths and at the same
time lead to changes in cultural norms. However, the eﬀectiveness of a legislative gender quota is
debatable. Duﬂo (2012) concludes that a one-time impulsion of women’s rights is not suﬃcient
in order to change entrenched political norms and values that discriminate against women, but
instead further complementing measures are required.
In order to measure these eﬀects we exploit the variation in the number of female MPs across
the 131 districts of Tanzania employing a Diﬀerence -in- Diﬀerences (DiD) approach including
ﬁxed eﬀects and controlling for a number of socioeconomic variables. For this purpose we are
using various data sources. We are working with four extensive micro level datasets (Demographic
and Health Surveys (DHS) with more than 178,000 observations), ranging from 2003 to 2012
and a self-generated database, which contains information about Tanzanian MPs for the past
three legislative terms (2000, 2005, 2010). Using GPS data we match villages from the micro-level
dataset with the districts of the country and the information on female representation by district.
We ﬁnd signiﬁcant evidence that the quota has reduced the gender gap in education for certain age
groups, moreover we ﬁnd indications of small improvements for female empowerment for some age
groups. In accordance with previous ﬁndings in other countries, we ﬁnd that an increase in female
representation leads to substantial investments in water infrastructure that greatly increased the
number of people with access to clean water. While we do not ﬁnd signiﬁcant health impacts, this
may be due to limitations in our dataset.
The rest of this paper is structured as follows. Section 2 provides a literature review. Section
3 gives an overview of the quota in Tanzania, its implementation and its eﬀect on female political
participation. Section 4 looks at testable implications. Section 5 describes our empirical strategy
and Section 6 describes our dataset in more detail. Section 7 provides our main analysis and
section 8 provides our policy evaluation.
2 Literature Review
There is substantial research that analyzes the relationship between gender inequality and economic
growth and development. The theoretical literature regarding gender inequality in education focuses
on the insuﬃcient exploitation of human capital. Klasen (2002) argues that a higher marginal
return to education exists for girls, that if exploited could lead to substantial growth. Furthermore,
higher education of women is expected to lead to both lower fertility and child mortality rates
as well as a better educated following generation (Esteve-Volart (2004) ; Cavalcanti and Tavares
The same argument is often applied when considering the eﬀect of gender gaps in labor
market participation on economic growth, i.e. that existing human capital is not being eﬃciently
exploited (Klasen (2002)). Moreover, higher female employment has been shown to increase
women’s bargaining power at home, which consequently might lead to higher investments in
children’s health and education, fostering human capital formation of the following generation
(Seguino and Floro (2003)). Finally, recent literature has argued that women tend to be less
prone to corruption than men (Dollar et al. (2001), Swamy et al. (2000)). Hence, a higher female
participation in the labor force and higher education for women may lead to less corrupt governance
in business and policymaking.
Another line of research has demonstrated that increasing female political participation can
reduce the gender gap in a variety of areas. Thomas (1991) as well as Besley and Case (2003) ﬁnd
evidence that increased political representation of women is correlated with diﬀerent spending
priorities, and Clots-Figueroa (2011) leverages close elections between men and women in India
to show that women tend to invest more in education and make more pro-female policies. Aside
from the direct eﬀect of passing more female-oriented policies, there is increasing evidence that
increased female representation can reduce the gender gap through its eﬀect on social norms.
Beaman et al. (2012) demonstrate that female leadership has an impact on adolescent girls’ career
aspirations and educational attainments, which they attribute to a role-model eﬀect. According
to them, this role-model eﬀect may inﬂuence girls’ notion of women’s status in society and thus
may inﬂuence them to break with prevalent gender stereotypes. Therefore, being exposed to a
female leader might increase girls’ ambitions and their propensity to enter male dominated areas.
For rural India, the gender gap in aspirations closed by 25% for parents and by 32% for youths
in villages that had exposure to a female leader for two election periods. Furthermore, in these
villages the gender gap in educational attainment was eradicated and girls tended to spend less
time on household work Beaman et al. (2012).
Recent literature has demonstrated that women quotas lead to increases in women participation
in government. Yoon (2011) gives evidence that women quotas in Africa increase female legislative
representation, and Jones (1998) ﬁnds similar evidence for Argentina. Dahlerup (2003) also
documents the cases of Rwanda, South Africa and Costa Rica, where gender quotas have led to
large increases of women representation in government.
Evidence on such quotas from a variety of settings indicates that required political represen-
tation has an eﬀect on policy choices and outcomes. Chattopadhyay and Duﬂo (2004) study a
reservation policy for women in rural India. They ﬁnd that gender-speciﬁc preferences of political
leaders have signiﬁcant eﬀects on policy choices, implying that female political leaders better
represent women’s preferences. In regions where women complained relatively more about speciﬁc
types of infrastructure, women-led councils showed higher public spending for these types of infras-
tructure. Beaman et al. (2010) use data from the Millennial Survey spanning eleven Indian states
and show that on average, gender quotas result in increased investment in water infrastructure
and education. Pande (2003), when looking instead at required political participation for various
caste groups in India, ﬁnds increased transfers to those groups. On the other hand, Kotsadam and
Mans investigate the eﬀects of gender quotas in national elections in Latin America and ﬁnd that
while quotas substantially increased the number of women in parliament, they had no eﬀect on
political participation, public policy, or corruption.
Multiple studies demonstrate a change in cultural norms following the introduction of women
quotas. Beaman et al. (2009) present evidence for changes in voter attitudes after being exposed
to the quotas. According to their results, women were more likely to campaign and get elected
conventionally in councils that were required to have a female leader in the previous two elections.
Furthermore, reservation led to a decrease in gender discrimination by men. Beaman et al. (2010)
further show that the likelihood that a woman speaks at a village meeting in India increases by
25% when local political leader positions are reserved for women. Furthermore, there is evidence
that the eﬀects of women’s quotas persist over time. Paola et al. (2015) show that gender quotas
in Italy increase women’s representation in politics even after the quota was terminated. To our
best knowledge, we are the ﬁrst to quantitatively analyze the eﬀects of the legislative women’s
quota in Tanzania.
3 Quota in Tanzania
In order to better understand the eﬀects of the quota system in Tanzania and to guide our
micro-level analysis of outcomes, we ﬁrst investigate the direct eﬀects of the quota on female
political participation in Tanzania. We use a three phase analysis consisting of 1) identifying the
quota framework 2) evaluating the implementation of the quota and 3) analysing the political
activity of female MPs.
3.1 Quota Framework
The quota in Tanzania was implemented to address large gender gaps in parliamentary represen-
tation. High female participation in the struggle for independence and the nationalist movement
attracted women to politics and helped motivate the need to address the gender gap in repre-
sentation (Yoon (2008)). The quota is implemented through reserved seats called Special Seats.
The system was implemented in 1985, originally with 15 seats reserved for women. In 1995 the
quota increased to require that 15% (37 seats in 1995) of the total number of traditional seats in
parliament be added as special seats for women. In 2000 it was increased to 20%, and in 2005 it
was increased again to 30% (Meena (2003); Yoon (2008); Yoon (2011)). The total size of parliament
has been increasing over this timeline as well.
3.2 Implementation of the Quota
Table 1 shows the progression of the quota and female representation in parliament for the years
1985 to 2010. In each year the number of special seats women in parliament surpassed the level
mandated by the quota. Furthermore, female representation continued to increase in the 2010
elections despite no increase in the quota. We are also interested in how the number of women
elected to a constituency has changed over time. If the increased female representation resulting
from the quota has caused more women to feel capable of leadership, or if the increased female
representation has caused the public of Tanzania to have more faith in women as leaders, we might
expect to see more women winning constituency seats. Indeed we ﬁnd that the number of women
elected to a constituency has also been increasing to keep track with the quota. These women
made up between 17% and 19% of all women in parliament for each of the elections between 1985
Table 1: Women in Parliament
Year Special seats
Total Seats in
% Women Quota
1985 15 4 24 244 9.84% 15 seats
1990 15 5 28 255 10.98% 15 seats
1995 37 8 47 275 17.09% 15%
2000 48 12 63 295 21.36% 20%
2005 75 17 97 323 30.03% 30%
2010 102 21 126 357 35.29% 30%
Source: Yoon (2008), Keith (2011)
3.3 Political Activity of Female MPs
Thus far we have established that the quota has successfully led to a corresponding increase in
female representation. In order to fully assess the impact of the quota, we next evaluate what these
additional women have done once they have reached parliament. Ideally we would analyze the
number and scale of policies put forth by female MPs compared to their male counterparts, as well
as the pass rate of such policies. Furthermore we would identify any systematic diﬀerences in the
type of policies put forth by men vs. women. Unfortunately we were unable to obtain data at this
level of detail. Instead, we are limited to data on the gender makeup of parliamentary committees
over the past four terms. Committees in Tanzania are made up of “[. . . ] several members of
parliament with a speciﬁc goal and time-frame regarding a particular/distinct subject of concern”
Under the assumption that MPs are active in the subject area of a committee they are on, a
higher percentage female makeup of a committee would indicate that female MPs have a larger
platform in that area. Tanzania Parliament’s website (2015) lists 31 committees with more than
ﬁve members over the past four terms (1995, 2000, 2005, 2010). Committees with ﬁve or fewer
members over this time period were dropped from this analysis so that only major committees are
considered. These committees were grouped into ten overarching policy categories. Figure 2 shows
the percentage female representation across the ten policy areas for the pooled data from 1995
to 2010. We do indeed see variation in committee makeup, with higher concentration in areas
indicated by the literature like health and social welfare (Duﬂo (2012)), and lower concentration
in more stereotypically male-dominated areas like foreign aﬀairs, defense and security.
Figure 3 shows the evolution of the percentage of female representation, as well as the total
number of female representatives in these committees over time. A few things are worth noting.
First, while certain categories of committees tend to be made up by a higher percentage of women,
there is no clear trend in this over time. In particular, no category appears to be becoming more
female-centered over time. Second, the categories that have the highest female representation
are mid-size categories. The Health category only consists of the HIV/AIDS Aﬀairs committee,
although some committees in the Social Welfare/Development category likely have health-related
responsibilities. Third, there is a general increase in the number of women in the mid-to-large size
4 Testable Implications
Based on our literature review and analysis of female political activity in Tanzania, we identify four
channels through which the increase in female political participation is likely to aﬀect outcomes
in Tanzania, including 1) the direct eﬀect of policy changes 2) the eﬀect on social norms 3)
the role-model eﬀect and 4) the eﬀect of incentives for re-election. The ﬁrst three are stressed
throughout the literature on both female representation and quotas, and the fourth is relevant as
more female-oriented policies could encourage the support of more female voters in the future.
This last point is relevant as many special seats women attempt to win constituency seats later in
their careers (Yoon (2008)).
We further draw on the relevant literature and political analysis to identify relevant outcome
areas where the increased female representation in Tanzania is likely to have an impact. These
include 1) education 2) health 3) female empowerment and 4) water infrastructure. Previous
studies have found positive impacts for each of these outcomes as the result of increased female
representation. Furthermore, all four outcome areas are potentially aﬀected by the two committee
categories in Tanzania with the highest percentage of female representation, Health and Social
Welfare/Development. We would ideally include additional outcomes indicated by the literature
such as labor force participation, but we are limited by the data.
5 Empirical Strategy
This section describes the empirical strategy that is used to measure how an increase in political
representation aﬀects the gender gap in development outcomes. As the parliamentary quota
is imposed simultaneously throughout Tanzania, there is no variation in the quota start date.
Furthermore, while the quota extends to local councils, local government data for Tanzania
is unavailable. Instead we use variation in female representation across districts to estimate
Figure 2: Female Representation in Committees
Source: POLIS 2015
Figure 3: Evolution of Female Representation in Committees
Source: POLIS 2015
the eﬀects of the quota. In order to validate this approach it is important to understand how
women become representatives and how these representatives are distributed across districts. If
female representatives come overwhelmingly from one geographical area, we may not have the
necessary variation for our analysis. Furthermore, if female representatives come predominantly
from well-educated areas we may encounter problems of reverse causality.
Prior to 1992 Tanzania operated under a single party system, and special seat MPs were
nominated by the National Executive Committee (NEC) of Chama Cha Mapinduzi (the ruling
party henceforth CCM) and elected by constituency members in the National Assembly. In 1992
Tanzania switched to a multi-party system, and for the 1995 and 2000 elections special seats were
distributed ”on the basis of the proportional representation among the parties which won elections
in constituencies and secured seats in the National Assembly” (Government of Tanzania (1995)).
The mechanism changed again in 2005, and since then special seats are allocated to each party in
proportion to the number of votes won in the parliamentary election (only parties that won at
least 5% of the votes are included), as opposed to the number of seats won (Yoon (2008)).
Unlike constituency MPs that serve a particular constituency that exists within a particular
district, special seat MPs serve a region, consisting of four to nine districts, or a group (e.g.
university, disabled, youth, and NGOs). Women apply regionally to parties, typically in the region
that contains their home town, in order to be considered for appointment to one of the party’s
special seats. Parties then provide nominations to the NEC who has ultimate authority. There
is no national rule for how women should be nominated for special seat positions. In practice,
successful nomination within a party is primarily due to standing within the party and party
loyalty (Interview with Richard Faustine - Yoon (2008)).
CCM is by far the largest party in Tanzania, controlling 80-90% of parliament in the last
three elections (Yoon (2011)), and as such determines in large part how special seat representatives
are distributed. In 2005 CCM appointed two special seat MPs to each of the country’s 26 regions
and assigned their remaining special seat MPs to one of the groups mentioned above. Smaller
parties (only two other than CCM met the 5% threshold in 2005) spread less than 26 special
seat representatives across the 28 regions. Accordingly there is very little variation in female
representation across regions. However, as each region contains four to nine districts, and as
regional representatives typically come from one of the districts in their region, by linking these
representatives to their home district, we obtain variation in representation across districts. The key
assumption is that the four channels mentioned above may be stronger between a representative
and her home district. Hodler and Raschky (2014) ﬁnd evidence of such regional favoritism in
Similarly to Beaman et al. (2009) we use a DiD approach in order to investigate whether an
additional female MP in a district leads to a lower gender gap measured in terms of education,
perception of female empowerment and health. Motivated by Chattopadhyay and Duﬂo (2004) we
further look at how an additional MP might change the access to clean water. Equation 1 shows
the main regression equation, where yitd equals one of the four outcomes, femaleitd is a dummy
variable that equals 1 if the respondent is female and 0 otherwise, and MPfemaletd indicates
the number of female MPs by district. The coeﬃcient of the interaction of those two variables
β3 is the actual coeﬃcient of interest as it measures how much an additional female MP in a
district correlates with a change in the outcome variables for female over male individuals and
thus measures any potential changes in the gender gap induced by female representation.
yitd = β0 + β1femaleitd + β2MPfemaletd + β3(femaleitd ∗ MPfemaletd)
+ βkXkitd + δd + θt + θt ∗ γr + trenditd + uitd
i = individual; d = district; r = region; t = time
In order to ensure exogenous variation in our treatment, it is necessary that the probability
that a district is represented by a female MP in any year is independent of district characteristics.
The gender of the respondent can be safely assumed to be random. If the probability that a
district is represented by a female MP is independent of district characteristics, then any observed
diﬀerences in outcomes could be attributed to the presence of the MP. Thanks to the speciﬁc
political party assignment mechanism mentioned above it might be reasonable to think that female
MP assignment is as good as random conditional on the regions, as the only apparent criteria
according to which the female MPs are assigned is the region. If this assumption holds then the
simple diﬀerence in means estimator described in Equation 1 would yield an unbiased estimate of
the desired eﬀect.
We test for randomization in MP assignment to districts by regressing the number of female
MPs in a district on a set of control variables, including the total number of male MPs from that
district, female, age, age2, wealth quintile, whether the individual lives in a single household, type
of residence, the size of the household, region and year FE. The results are shown in Table A2 of
the appendix. All coeﬃcients except for the wealth indicator enter the regression insigniﬁcantly.
The coeﬃcient on wealth quintile is signiﬁcant at the 5% level, however the point estimate is rather
small.These results provide some support for successful randomization. However, controlling for
these household characteristics allows us to control for socioeconomic status of the respondents.
We include these controls in our analysis to eliminate any potential endogeneity threat as well as
to increase the precision of the estimates.
As Equation 1 shows we include FE in order to eliminate additional potentially omitted
variables. District FE δd capture any district time-invariant speciﬁc trends (e.g. persistent cultural
diﬀerences across districts), and year FE θt control for time trends that aﬀect the whole country
equally (e.g. country-wide trends in social norms). We also include region-year FE θt ∗ γr in order
to capture any trends on a regional level (e.g. regional development trends or trends in regional
politics). The coeﬃcient of interest would still be biased if there were any local policies (on a
district level) that aﬀect outcomes for women and men diﬀerently, but allowing women and men
in the same district to follow diﬀerent trends over time should discourage most of these concerns.
To allow the error term uitd to correlate within a district we cluster on a district level 2.
This section describes the dataset we constructed in order to analyze how an increase in female rep-
resentation induced by the adoption of a female legislative quota can aﬀect development indicators.
Our dataset was constructed from three sources: Demographic and Health Surveys (DHS), Global
Administrative Areas (GADM 2012) and Tanzania’s Parliamentary Online Information System
(POLIS 2015). DHS provides large sample size surveys with repeated cross-sectional information
about population, health and nutrition. We use the surveys corresponding to Tanzania in the
years 2003-2004, 2007-2008, 2010 and 2011-2012. These surveys provide GPS coordinates 3 for
the households surveyed. The geospatial tool QGIS is used to link the household GPS data to
geospatial data for districts in Tanzania obtained from GADM(2012).
POLIS (2015) provides information on MPs. We collected information on gender, the type of
member, which term(s) they have been in parliament, the constituency they represent and the
political party they belong to. We use the constituency of each member to identify the district
represented. However women occupying special seats are not linked to a particular constituency.
For these women we use the elementary school they attended as a proxy for constituency where
such information is available. We are able to obtain constituency or elementary school information
for approximately 81% of female representatives for the terms 2000, 2005 and 2010.
Figure 4 shows the variation in number of female MPs per district for the past three terms.
The percentage of districts not represented by a female MP decreased from 68% in 2000 to 53% in
2010. Of those districts represented by a female MP, the number represented by only one decreased
from 80% in 2000 to 64% in 2010.
6.1 Advantages of the Data
The scope of our analysis is due largely to the information gathered for our unique dataset. To our
knowledge, it is the ﬁrst dataset that combines DHS household information with information from
the parliament of Tanzania and is able to match the information at a district level. Furthermore,
The sample consists of 131 districts
To ensure respondent conﬁdentiality, DHS randomly displace the GPS latitude/longitude positions. The
displacement is restricted so that the points stay within the country and within the DHS survey region.
Figure 4: Number of District with MPs
Source: POLIS, 2015
the quality of the data used is very good, considering that each set (DHS and POLIS) comes from
a single credible source. DHS provides a representative sample of the country and the information
gathered from POLIS is from an oﬃcial source of the country. Finally it is worthwhile mentioning
that the large size of the dataset (178,000 observations) allows us to include district- , year- and
year-region FE. These controls capture potential omitted variables and enable us to better identify
the eﬀect of interest.
6.2 Limitations of the Data
There are two caveats that should be considered regarding the information available in both the
DHS and the POLIS datasets. Even though DHS contain national-wide surveys, representative
districts are randomly selected, and not every district is represented in each survey. This means
that there are districts, for which we have MP information but no DHS data, that could not
be used in the analysis. 22% of districts are not represented by DHS data. While these districts
contain fewer administrative divisions on average than those represented, as DHS selects districts
randomly, we do not expect a bias in the results because of this.
With respect to the POLIS dataset, as mentioned above there are some MPs for which there
is no information available regarding constituency or elementary school attended. Therefore, we
were not able to assign them to any district. This was the case for between 18% and 20% of the
total female MPs for the three elections considered. These ”missing” MPs were on average more
likely to have only served one term out of the three terms considered than the MPs for which
data is available, and they were more likely on average to belong to minority political parties.
6.3 Variables of Interest
In this section we deﬁne the central variables used throughout our analysis. Our main treatment
variable is MPfemaletd ∗ femaleitd, an interaction between the absolute numbers of female MPs
in a district and the gender of the respondent of the survey. The main outcomes we will discuss
relate to education, health, women empowerment and access to good water quality. For education
we created a dummy variable that is equal to 1 if the respondent has completed any year of
education and 0 if the respondent has not completed any years of schooling. Since in Tanzania the
ﬁrst year of compulsory schooling ends when children are eight, all children younger than eight
are treated as missing. This leaves us with 130,716 observations for this outcome variable.
For the health outcome we use a dummy variable that is equal to 1 if the respondent has
been sick for at least three out of the last twelve months and 0 otherwise. For the measurement
of women empowerment we use another dummy variable that is equal to 1 if the head of the
household is reported to be female and 0 if it is male. For our women empowerment analysis we
created an additional control: single household. This dummy is equal to 1 if there is just one adult
in the surveyed household, allowing us to control for households in which the head is a female
because there is no male present.
Regarding access to water, we create a dummy that is equal to 1 if the household has access
to a source of good quality water and 0 otherwise. The classiﬁcation of the quality of the water is
from UNDP (2013) . A detailed description of all the variables is included in the appendix, table
A1. Table 2, columns (1) - (5), shows some summary statistics for the variables described above,
as well as for the controls included in the regressions. Columns (6) and (8) show the diﬀerence
between males and females, and between districts with at least one female MP and those with
no female MPs respectively. As a ﬁrst overview we observe that for education and our measure
of women empowerment there is a statistically signiﬁcant gender diﬀerence: the probability of
having any education as well as the probability of being reported the head of the household is
higher for men. For the probability of being healthy and having access to clean water we cannot
reject the hypothesis that the diﬀerence is zero. For all outcome variables except for the health
measure the districts with female representations perform better than the ones without female MPs.
These descriptive results conﬁrm our hypothesis that there is a gender gap in many development
outcomes for Tanzania and that female representation is correlated with improvements.
7 Empirical Analysis
The following section presents and discusses our empirical results. Leveraging the relevant literature
and our analysis of the political activity of female MPs, we consider four diﬀerent outcome areas
Table 2: Summary Statistics
Gender Gap Having MP_female
Obs Mean St. Dev. Min Max Diﬀ t-stat Diﬀ t-stat
(1) (2) (3) (4) (5) (6) (7) (8) (9)
MPfemale*female 178,591 0.420 0.985 0 9 -0.817∗∗∗ -192.45 -0.867∗∗∗ -206.94
MPfemale 178,610 0.814 1.244 0 9 -0.005 -0.90 -1.682∗∗∗ -387.35
MPtotal 178,610 3.002 2.307 0 17 -0.009 -0.80 -2.471∗∗∗ -267.81
educ 133,941 0.780 0.414 0 1 0.0922∗∗∗ 40.95 -0.070∗∗∗ -30.66
educ [age 8 - 13] 31,224 0.828 0.378 0 1 -0.0427∗∗∗ -9.99 -0.0461∗∗∗ -10.64
educ [age 14 - 19] 23,002 0.912 0.283 0 1 0.0329∗∗∗ 8.82 -0.0511∗∗∗ -13.57
educ [age 20 - 25] 16,906 0.844 0.363 0 1 0.0731∗∗∗ 13.08 -0.0735∗∗∗ -13.06
head_fem 182994 0.198 0.398 0 1 -0.085∗∗∗ -45.62 -0.007∗∗∗ -3.67
singlehh 182994 0.0589 0.236 0 1 -0.014∗∗∗ -12.54 0.006∗∗∗ 5.13
health 45132 0.0119 0.109 0 1 -0.001 -0.70 -0.0004 -0.44
water 127006 0.427 0.495 0 1 -0.001∗∗ -2.20 -0.187∗∗∗ -67.64
sanitation 174177 0.432 0.495 0 1 0.002 1.09 0.0829∗∗∗ 34.57
wealth 182988 3.038 1.395 1 5 -0.003 -0.51 -0.648∗∗∗ -101.15
number of members in hh 182994 7.020 3.776 1 49 0.015 0.85 0.160∗∗∗ 9.01
age 182939 22.25 19.41 0 95 -0.585∗∗∗ -6.44 -0.475∗∗∗ -5.16
type of residence 182994 0.207 0.405 0 1 -0.008∗∗∗ -4.26 -0.117∗∗∗ -62.07
* p<0.10, ** p<0.05, *** p<0.01
Column (6) shows the diﬀerence between males and females
Column (8) is the diﬀerence between not having an MP female and having at least one
that an increase in female representation is likely to have impacted: education, female empowerment,
health and access to clean water.
One can think of at least three diﬀerent channels through which an increase in female political
presentation might aﬀect on educational attainment. First of all the direct policy channel might
be at work as the Tanzanian parliament started reforms in the tertiary education sector with the
particular goal of reducing the gender gap. Secondly, young girls might have higher incentives to
invest in education through role model eﬀect because they see that not only men have good career
prospects and in order to be qualiﬁed for these sorts of jobs one might need a better education
than before. Thirdly, the society and its beliefs might change due to the diﬀerent perception of
women, which could be a reason why parents now focus more of their time and money on their
daughters. If this hypothesis were true once regressing the education outcome on the controls
speciﬁed in equation (1) the coeﬃcient of the interaction term between the female dummy and
the number of female MPs in a district β3 should be positive.
Table 3 shows the results of this analysis, where the outcome variable is a dummy that equals
1 if the respondent has 1 or more years of education attained at the time of the interview and
0 otherwise. Column (1) - (4) shows the results of the regression using the full sample, where
Table 3: Eﬀects on Education
(1) (2) (3) (4) (5) (6) (7) (8) (9)
MPfemale*female 0.0091 0.0086 0.0086 0.0084 0.0022 -0.0049 -0.0045 -0.0053 -0.0052
(4.11)*** (3.57)*** (3.64)*** (3.56)*** (1.22) (1.46) (1.16) (1.31) (1.30)
female -0.1010 -0.1008 -0.1022 -0.0982 -0.0111 0.0452 0.0402 0.0409 0.0487
(23.44)*** (22.65)*** (22.64)*** (14.67)*** (1.74)* (5.88)*** (5.47)*** (5.43)*** (5.62)***
age2*female -0.0904 -0.0906 -0.0904 -0.0906
(6.92)*** (7.18)*** (7.14)*** (7.17)***
age2*MPfemale*female 0.0117 0.0101 0.0106 0.0106
(2.34)** (2.00)** (2.05)** (2.03)**
age3*female -0.1321 -0.1260 -0.1284 -0.1280
(11.68)*** (11.56)*** (11.80)*** (11.81)***
age3*MPfemale*female 0.0164 0.0163 0.0181 0.0178
(3.54)*** (2.88)*** (2.99)*** (2.92)***
_cons 0.8101 0.6528 0.6576 0.6521 0.0607 0.7907 -0.1744 -0.1666 -0.1824
(87.44)*** (29.09)*** (24.70)*** (10.94)*** (0.74) (61.08)*** (3.47)*** (3.22)*** (2.05)**
R2 0.03 0.20 0.22 0.23 0.14 0.02 0.11 0.14 0.15
N 130,716 130,659 130,659 130,659 69,465 69,467 69,465 69,465 69,465
Controls no yes yes yes yes no yes yes yes
Year & District FE no no yes yes yes no no yes yes
Year-Region FE & Trend no no no yes yes no no no yes
Full Sample yes yes yes yes no no no no no
* p < 0.1; ** p < 0.05; *** p < 0.01
In columns (5) - (9) the sample is restricted to individuals under 26 years
column (1) is the most parsimonious speciﬁcation, (2) includes a set of control variables4 , (3)
additionally controls for district- and year-FE and (4) includes region-year-FE and a linear trend
that controls for diﬀerent trends over time for women and men in the same district.
The treatment eﬀect indicates that on average, an additional female MP in a district is
correlated with almost a 1%-point increase in the likelihood of having received any years of education
for women. This eﬀect is highly signiﬁcant and quite stable throughout all four speciﬁcations. In
order to understand the size of the coeﬃcient better we compare it with the existing gender gap,
which is equal to the coeﬃcient of the regressor femaleitd and amounts to 10%-points, meaning
that women on average have a 10%-points lower probability of receiving any education compared
to men. Therefore adding an additional female MP in a district is associated with a reduction
of the gender gap in education by 10%. As expected once controlling for socio-economic status
the coeﬃcient of interest decreases, however only slightly from 0.91%-points to 0.86%-points
(signiﬁcant at the 1%-level). Except for MPfemale and MPmale all added controls enter the
regression signiﬁcantly and point in the direction that one would expect: being female and living
in a bigger household decrease the chances of receiving an education, the richer and the older
the respondent the more probable he/she is to have spent at least 1 year in school (see appendix
, wealth, type of residence, number of members in household and total number of MPs
Table A3). Adding the year-and district- FE, thus eliminating concerns about all time invariant
confounding factors that vary over districts like ethnic composition, as well as all trends that
aﬀect the whole country in the same way (e.g. macro-trends), changes the results only slightly.
Controlling for region-year FE and allowing women and men on a district level to be on diﬀerent
trends decreases the magnitude of the coeﬃcients somewhat (column (4)), but the coeﬃcient is
still signiﬁcant on a 1%-level.
Since the introduction of the quota is a rather recent event many individuals in our sample
should not have been aﬀected by it as they were no longer investing in education but instead were
already part of the labor force or even retired. This should attenuate the results in the ﬁrst part
of table 1. To account for this we have restricted the sample to individuals that were younger
than 26 at the date of the interview. The results (column (5)) show an insigniﬁcant coeﬃcient of
interest close to 0. All controls except for age are quite similar to the speciﬁcation that uses the
full sample (see appendix Table A3 for details). These results are somewhat surprising, as one
would have expected the coeﬃcient of MPfemale*female to be bigger in comparison to the full
sample. To scrutinize this puzzle in more detail we control for diﬀerent age groups, namely 7-13
years (age1), 14-19 years (age2) and 20-25 years (age3) and allow the treatment eﬀect to vary
depending on the age group, where the baseline group is the youngest age group5. The results of
this modiﬁcation are displayed in columns (6) - (9).
As the baseline results show (column (6)), girls aged 7-13 years are not disadvantaged
compared to their male counterparts but instead have a 5%-point higher probability to have had
any education. Since there is no gender gap to begin with it is not too surprising that the data
does not show a signiﬁcant treatment eﬀect. In the second age group this however changes: women
are 10%-points less likely to have received any education in this age group. An increase in the
number of female MPs correlates with an increase in the probability of having any education by
1%-point (signiﬁcant at the 10%-level). For the oldest age group, namely the ones aged 20-25, the
gender gap amounts to 13%-points and the coeﬃcient of interest equals 2%-points (signiﬁcant on
the 5%-level), thus higher female political representation is associated with a 15% decrease in the
gender gap. Again including the diﬀerent controls does not change the results much. The results
even stand once we allow for diﬀerent trends for females and males and include region-year-FE
These results suggest that a higher number of female MPs, generated through the legislative
women’s quota, is associated with better educational outcomes for girls who are between 14
and 25 years old. The biggest threat to validity in this analysis is potential reverse causality:
All individuals aged 0-6 are treated as missing variables since they cannot have completed a year of education
already as they are too young (see data section).
if a district has better educated inhabitants, the probability of having a suitable candidate for
parliament may be greater and thus also the probability of having a female MP. Since we do not
have any exogenous source of variation, there is no direct way of resolving this problem by a
more sophisticated empirical analysis e.g. using instrumental variables. However, once including
region-year FE, we control for all omitted variables that vary on a regional level over time, for
example regional educational reforms. The coeﬃcient of interest would still be biased if there were
any local policies (on a district level) that aﬀect women and men in their educational outcomes
diﬀerently, but allowing women and men in the same district to follow diﬀerent trends over
time should discourage most of these concerns. Neither the baseline results nor the ones in the
robustness checks are sensitive to the two modiﬁcations just explained, which is reassuring in the
sense that the estimation results are capturing the real eﬀect instead of being driven by reverse
causality. Nevertheless, one should interpret the results with caution.
7.2 Female Empowerment
We next investigate whether a higher number of women in parliament lead to a change in societal
norms measured as the probability of having a (reported) female head of the household. We use
a dummy variable that equals 1 if the respondent says that the head of the household is female
and equals 0 otherwise. We regress this indicator for female empowerment in households on the
number of female MPs, a gender dummy and the interaction of both. The results are displayed in
Table 4. The ﬁrst section of the table shows the baseline results for the whole sample and the
second section again displays the results of robustness checks.
In the most parsimonious speciﬁcation we ﬁnd that an additional MP does not have an eﬀect
on men’s perception of women as the heads of households (MPfemale). There is an eﬀect on
women, as their probability of reporting that the head of the household is female increases by
0.6%-point with female representation. Once we control for single households in column (2) the
results for men remain insigniﬁcant and close to 0; for women the coeﬃcient of interest changes
a little and increases from 0.6%-points to 0.9%-points (signiﬁcant on the 1%-level)6. All other
controls enter the regression again signiﬁcantly and point in the direction as one would expect
them to: the bigger and richer a household the smaller the probability, and the coeﬃcient for age
is negative, which implies that for the older cohorts a diﬀerent picture of society applies than for
the younger ones (see appendix Table A4 for more details).
Adding the remaining controls for men does not alter results: the coeﬃcient for MPfemale
Intuitively, when a single household consists of only one woman, this will correlate perfectly with the probability
of having a female head of the household and also be correlated positively with the interaction term by construction.
The combination of those two things should lead to an overestimation of the eﬀect of interest when there is no
control for single household, and the coeﬃcient should decrease once we include the control. In our data this however
does not happen, but the diﬀerence between the two coeﬃcients is negligibly small.
Table 4: Eﬀects on Female Empowerment
(1) (2) (3) (4) (5) (6) (7) (8) (9)
MPfemale*female 0.0061 0.0097 0.0097 0.0095 0.0057 0.0022 0.0049 0.0051 0.0048
(3.16)*** (4.47)*** (4.35)*** (4.05)*** (1.68)* (0.97) (2.14)** (2.30)** (2.22)**
MPfemale 0.0006 -0.0020 -0.0022 -0.0020 0.0005 0.0024 0.0020 0.0021 0.0018
(0.23) (0.70) (0.43) (0.34) (0.08) (0.67) (0.59) (0.31) (0.24)
female 0.0830 0.0722 0.0708 0.0718 0.0046 0.0042 0.0017 0.0011 0.0006
(23.54)*** (20.89)*** (20.43)*** (13.33)*** (0.95) (0.90) (0.40) (0.26) (0.10)
age1*female 0.0128 0.0106 0.0119 0.0119
(1.73)* (1.56) (1.73)* (1.70)*
age1*MPfemale*female -0.0070 -0.0072 -0.0079 -0.0081
(1.41) (1.61) (1.74)* (1.86)*
age2*female 0.0097 0.0126 0.0138 0.0133
(0.93) (1.24) (1.37) (1.31)
age2*MPfemale*female -0.0001 -0.0020 -0.0009 -0.0006
(0.02) (0.38) (0.20) (0.12)
age3*female -0.0061 -0.0069 -0.0062 -0.0056
(0.53) (0.56) (0.51) (0.46)
age3*MPfemale*female 0.0113 0.0137 0.0134 0.0136
(1.38) (1.41) (1.38) (1.42)
_cons 0.1556 0.2992 0.2305 0.1706 0.1685 0.1799 0.3058 0.2324 0.1825
(30.11)*** (10.12)*** (6.90)*** (2.79)*** (2.54)** (27.81)*** (9.62)*** (6.48)*** (2.75)***
R2 0.01 0.15 0.17 0.18 0.19 0.00 0.17 0.18 0.19
N 178,591 178,530 178,530 178,530 117,088 117,091 117,088 117,088 117,088
Controls no yes yes yes yes no yes yes yes
Year & District FE no no yes yes yes no no yes yes
Year-Region FE & Trend no no no yes yes no no no yes
Full Sample yes yes yes yes no no no no no
* p < 0.1; ** p < 0.05; *** p < 0.01
In columns (5) - (9) the sample is restricted to individuals under 26 years
still remains insigniﬁcant and close to zero. So as expected increasing the female representation in
a district does not alter the views of men. For women instead we ﬁnd a positive increase in the
probability of reporting that the head of the household is female by 1%-point. The interaction
term MPfemale ∗ female is again robust to the inclusion of the various FE as in the previous
section education was, which gives suggestive evidence that the ﬁndings do not arise because of
Again, we expect the heterogeneous treatment eﬀect depending on the cohort: if the introduc-
tion of the quota led to a change in societal norms (e.g. newspapers start bringing stories about
female leaders, female topics like maternity leave are put on the political agenda and women gain
importance in society in general) we would expect the younger cohorts to be aﬀected the most as
they were exposed the longest. To account for this we restrict the sample to individuals no older
than 25. Column (5) displays these results when using the full set of controls. The coeﬃcient of
interest for men is still insigniﬁcant, for women it goes down from 0.9%-points to 0.6%-points. The
biggest changes in the control variable happen in female and age: the coeﬃcient for age becomes
positive; female does not enter the regression signiﬁcantly anymore and is greatly attenuated
(see appendix Table A4). One possible explanation for this might be that many of the women
still live with their parents and do not have their own household yet.
To examine the responses of the younger cohorts in the sample in more detail the rest of
table 2 again includes the diﬀerent age groups and their interaction with the number of female
MPs. Respondents aged 0-6 form the baseline group, the age1 dummy groups all individuals
between 7-13, age2 between 14-19, and age3 between 20-25. Other than for the baseline and the
group of individuals between 14 and 19, having an additional MP does not change the outcome
variable signiﬁcantly. Since it is likely that the mean age of the parents is lower for the young
children (baseline) than for the older ones (age1) the positive coeﬃcient for the baseline could be
interpreted as conﬁrming our hypothesis that an increase of women in parliament goes in line
with a change in societal norms for the youngest cohort, namely the one that was exposed to the
changes in society the most. The eﬀect for men in this cohort is again small and insigniﬁcant as in
the full sample, once controlling for region-year FE and allowing for diﬀerent trends by gender,
which supports the argument as well.
Summing up one can say that the results for female empowerment are less pronounced than
for the education outcome. Nevertheless, there is some suggestive evidence for changes in societal
norms for the youngest cohort that has already started a family. Furthermore the robustness of
the coeﬃcients to the inclusion of the region-year FE and linear trend controls again indicate that
reverse causality might not confound results much.
The literature on female policymakers suggests that they are on average more concerned with
(child) health issues than are their male counterparts (e.g. Duﬂo (2012)). As was pointed out in
section 3, committees relating to health and welfare have had the highest percentage of women
representation of all committee categories. In view of the strong representation of women in
health-related committees, the direct policy channel may be a key mechanism through which
women achieve changes in the health sector. In fact, Tanzanian women pushed for policies such
as a paid maternity leave, a breastfeeding leave during working hours and a paid leave in case
of sickness or death of a child (USAID, 2009). We expect children and childbearing women in
particular to be positively aﬀected by these female-driven policy changes. As the histogram in
Figure 5 in the appendix shows, the typical childbearing age in Tanzania is between 16 and 21.
80% of women have their ﬁrst child while in this age group.
While there are limited health outcomes in our dataset, we are able to analyze the dummy
variable, whether the respondent has been very sick for at least three of the past 12 months. Since
this variable has only been included in the 2007/2008 DHS survey, our sample is reduced to about
a fourth of the original size. Note that this variable is equal to 1 for only 1.2% (n=538) of the
individuals in this sample. Columns (1) to (4) in table 5 show our analysis for the non-restricted
sample, where the outcome variable is the dummy equal to 1 if the respondent has been very sick
for at least three of the last 12 months and 0 otherwise. Again column (1) is the most parsimonious
speciﬁcation, column (2) includes a set of socio-economic controls, column (3) controls for district-
and year-FE and column (4) includes region-year-FE plus the linear trend that allows for a
diﬀerent trend for women.
There does not appear to be any noteworthy health gender gap. Throughout our diﬀerent
speciﬁcations, the relevant coeﬃcient female is not signiﬁcant. In view of this, it is also not
surprising that the interaction term MPfemale ∗ female is not signiﬁcant throughout all our
speciﬁcations. We ﬁnd little support for our hypothesis that in particular young children and
mothers beneﬁts from having more female MPs. If we restrict our sample to children under the
age of seven, out of this limited sample only 55 children (0.5% of sample) were sick three of the
last twelve months. We ﬁnd similar results for mothers. Restricting our sample to the age group
in which 80% of women are having their ﬁrst child, namely between 16 and 21, only 21 women
(0.4% of sample) were sick three of the last twelve months. Column (5) and (6) in Table 5 show
the results for children under seven and respondents between 16 and 21 respectively including
region-year-FE plus the linear trend.
Even for the restricted samples, for which we would have expected to observe improvements
in health outcomes, we do not ﬁnd convincing results. Again, we do not observe any indication
of a gender gap. Also we do not ﬁnd any signiﬁcant changes in both age groups if the number
of female MPs increases. Direct policy may not be the primary channel in this case. As these
policies are passed at a national level, we would not expect them to aﬀect districts diﬀerentially.
This is true unless female MPs show some form of favoritism in health-related issues, for example
by putting forward the construction of health facilities in their district of origin. Still, having an
additional female MP in a district might change societal norms in a way that inﬂuences health
Nevertheless, our analysis might be ﬂawed for two reasons: ﬁrstly, as stated above, our data
allows us to observe this outcome variable for only one DHS wave (2007/2008), which restricts our
sample considerably. The second issue has to do with the small fraction of respondents reporting to
very sick, varying between 0.4% and 1.2% in our diﬀerent samples. The lack of suﬃcient variation
in this variable might therefore be another reason that hinders us from ﬁnding signiﬁcant results.
In order to overcome these data limitations we would ideally have a variable at hand that is
available for various DHS years and exhibits a larger degree of variation.
Regardless, the threat of reverse causality is again present, as a healthier society might also
raise more female MPs. Although the coeﬃcient of interest is insigniﬁcant, it stays robust in terms
of magnitude when including the region-year FE and the linear trend. Again this supports our
argument that reverse causality may not be driving our results.
Table 5: Eﬀects on Health
(1) (2) (3) (4) (5) (6)
MPfemale*female -0.00118 -0.00101 -0.00106 -0.00106 -0.00171 -0.00031
(0.00083) (0.00087) (0.00085) (0.00083) (0.00128) (0.00263)
MPfemale 0.00061 0.00046 0.00045 -0.00024 0.00440 0.00125
(0.00097) (0.00094) (0.00052) (0.00083) (0.00267) (0.00156)
female 0.00313 0.00279 0.00276 0.00395 -0.00053 0.00738
(0.00176)* (0.00177) (0.00175) (0.00265) (0.00214) (0.00667)
_cons 0.01170 0.00526 -0.00151 0.00360 -0.04116 0.01776
(0.00134)*** (0.00665) (0.00792) (0.00929) (0.03229) (0.18070)
R2 0.00 0.02 0.03 0.03 0.03 0.04
N 44,466 44,437 44,437 44,437 10,439 5,112
Controls no yes yes yes yes yes
Year & District FE no no yes yes yes yes
Year-Region FE & Trend no no no yes yes yes
Full sample yes yes yes yes no no
* p < 0.1; ** p < 0.05; *** p < 0.01
Column (5) is restricted to individuals under 7 years
Column (6) is restricted to individuals between 16 and 21 years
7.4 Infrastructure - Access to Clean Water
In an inﬂuential paper based on evidence from India, Chattopadhyay and Duﬂo (2004) convincingly
demonstrate that women as policymakers are generally more concerned with certain types of
infrastructure projects than their male counterparts. In particular, they ﬁnd that women tend
to invest more in drinking water infrastructure, recycled fuel equipment and road construction.
Beaman et al. (2010) also ﬁnd that that on average, gender quotas result in increased investment
in water infrastructure. In view of these results, we are therefore interested whether an increase in
female MPs in parliament has an impact on the quality of infrastructure.
Two mechanisms might be important here. The ﬁrst one is certainly the direct policy channel.
A larger number of women in parliament may result in more policies that align with female
preferences, one of them potentially being infrastructure improvement as documented above.
There is a second channel because, as mentioned in section 4 many special seats women attempt
to win constituency seats later in their careers. By visibly improving living conditions in their
districts, chances for re-election increase.
For this outcome we do not expect any diﬀerential eﬀect on household members, which is why
we are excluding the treatment variable MPfemale ∗ female from our regressions as well as not
controlling for diﬀerent trends by gender. Instead our key variable of interest now is MPfemale,
i.e. we are analyzing whether an increase in the number of MPs in a district has an eﬀect on
the quality of infrastructure a household has access to. Our database allows us to analyze the
quality of drinking water that is available to households. For this purpose, we create a dummy
variable distinguishing between “good” and “bad” water sources . We did the same for the quality
of sanitation of households, which we will use as a robustness check. 43% of households reported
having access to good water quality, while 43% also have decent sanitation facilities.
Table 6 shows the results of our analysis. In our simple speciﬁcation without controls in
column (1) the coeﬃcient of MPfemale is signiﬁcant at the 1%-level. Once we include a set of
controls for socio-economic status (column (2)) and control for district- and year-FE (column (3))
and region-year-FE (column (4)) the signiﬁcance of this coeﬃcient vanishes. This is not surprising.
Implementation and completion of infrastructure projects typically takes a while (e.g. improving
water quality requires pipes to be laid and boreholes to be constructed), so we would not expect
to ﬁnd any immediate direct eﬀects. Rather we would assume some delay after treatment before
we see results. The correlation in column (1) is possibly driven by third factors, for example a
more prosperous district might have both more female MPs and better quality of water.
In columns (5) to (8) we use instead of the contemporaneous the lagged values of our variables
of interest. In columns (5) and (7) we control for district- and year- FE, and in columns (6) and
Table 6: Eﬀects on Quality of Water
(1) (2) (3) (4) (5) (6) (7) (8)
MPfemale 0.038 -0.003 0.029 -0.002
(0.012)*** (0.009) (0.019) (0.012)
l1*MPfemale 0.048 0.045
l2*MPfemale -0.074 0.147
_cons 0.308 0.258 0.829 1.180 0.396 0.460 0.724 0.754
(0.022)*** (0.091)*** (0.098)*** (0.173)*** (0.084)*** (0.152)*** (0.101)*** (0.213)***
R2 0.01 0.17 0.36 0.40 0.30 0.32 0.46 0.47
N 123,716 123,715 123,715 123,715 105,929 105,929 33,492 33,492
Controls no yes yes yes yes yes yes yes
Year & District FE no no yes yes yes yes yes yes
Year-Region FE no no no yes no yes no yes
* p < 0.1; ** p < 0.05; *** p < 0.01
(8) we are using our most sophisticated speciﬁcation controlling additionally for region-year-FE.
Controlling for region-year FE in this context is especially important because it seems likely that
certain regions, for example the most densely populated or the capital region, receive preferential
The lagged values are statistically signiﬁcant at the 1%-level in all 4 speciﬁcations, both
for the ﬁrst and the second lagged values. The magnitude of the eﬀect is even larger for the
second lag, which goes in line with the reasoning provided above. Because of the importance of
the region-year FE, this is our preferred speciﬁcation: according to our estimates in column (6)
and (8), having an additional female MP in the ultimate (penultimate) term is associated with a
4.5%-point (14.7%-point) higher probability of having access to good water quality. Keeping in
mind that the average probability of having access to clean water for the sample equals 43% and
the standard deviation equals 49%, these eﬀects can be considered as large.
We might interpret this ﬁnding as a sign for regional favoritism. Hodler and Raschky (2014)
ﬁnd evidence that particularly in developing countries political leaders favor their area of origin
by channeling a disproportional amount of public goods there. This argumentation is directly
linked to the re-election channel mentioned above. In order to secure their re-election MPs have a
strong incentive to favor their district of origin. By achieving better infrastructure and making
voters happy, MPs increase their chances to be re-elected. A further point worth mentioning in
this context is corruption. As the literature has shown that female political leaders tend to be less
corrupt and since typically infrastructure is an area that is highly prone to corruption, we can
make the case for a third possible channel Beaman et al. (2009). In other words, women might
achieve better outcomes in infrastructure projects as they tend to be less corrupt.
In our crosscheck using sanitation as outcome variable, we observe a similar pattern. We do
not obtain any signiﬁcant results for the contemporaneous eﬀects of an additional female MP on
good sanitation quality. However, the lagged eﬀects, although only the second lag, turn out to be
statistically signiﬁcant and enter with the expected positive sign again. The same goes for the
second lag of the total number of MPs. The results can be found in table A7 in the appendix.
As with our previous results, however, these results should be interpreted with caution since
we cannot rule out the possibility of reverse causality. In this particular case, more progressive
districts in the past may have better infrastructure, and may also lead to the development of
more female MPs than other districts. Furthermore, our dataset does not provide information
on whether the individual moved or not. Migration could bias our results because it is possible
that people observe improvements in the access to clean water in certain districts and move their
because of that. As long as the migration is not correlated with any other characteristic in a
systematic way this problem should not bias the results. If this is not the case then as long as it
occurs on a region level, the region-year FE eﬀects should capture this omitted variable. If moving
also happens across districts, we do not have a variable accounting for this problem. However,
exactly this type of migration seems more likely as the costs of moving between diﬀerent districts
most likely are lower then when moving between regions.
8 Policy Evaluation
In this section we discuss our ﬁndings and provide a ﬁnal evaluation on whether the implementation
of the legislative women’s quota in Tanzania successfully reduced the gender gap. The increase in
female political representation and participation is one of the clearest results of our analysis. In
1985 when the quota was ﬁrst introduced the women in parliament amounted to 24 or less than
10% of parliament. The quota increased to 30% by 2005, and in the last elections held in 2010 the
share of women elected to parliament was nearly 35%. Data on parliamentary committee makeup
and the little data available on recent legislation further indicate that female representatives are
While the number of conventionally elected women also increased over this time period
in proportion to the rise in the quota, women still win a very small percentage of traditional
elections. One possible explanation for this might be that parties do not have enough suitable
female candidates. In this case increasing the legislative quota further might incentivize parties to
push for policy changes that increase the quality of female candidates like educational or labor
market reforms. Another reason might be that society is still rather conservative and needs more
time to adjust its norms. If this is the case we think the quota should be in place until society has
transformed in order to ensure that female political representation is persistent.
Our microdata analysis allows us to analyze key outcome areas - education, female empower-
ment, health and infrastructure - to determine ﬁrstly whether a gender gap exists, and secondly
whether an increase in female representation is correlated with a reduction in that gap. For
education we ﬁnd that at a young age (7-13 years old) women are actually 5%-points more likely
to have received any schooling than their male counterparts. However, within the 14-25 age group,
women are on average 10%-points less likely to have received any education, as expected. Adding
an additional female representative is associated with a reduction of this gap by 10%.
With regard to female empowerment and health the eﬀects of the quota are less clear: we
ﬁnd that female empowerment, which is measured as the probability of reporting a female head of
the household, increases by around 1%-points with every additional female MP for the youngest
generation of parents who have been exposed to the gradual increase of female representation
throughout most of their lives. This gives suggestive evidence that the legislative quota has
inﬂuence on societal norms, however the ﬁndings are not clear for the other age groups. For
health we do not ﬁnd any statistically signiﬁcant results, which we attribute mostly to poor data
The literature in the area of female political representation and empowerment suggests that
female representatives may lobby more for improvements in health infrastructure. Our results show
indeed that higher female representation is associated with better water quality, although this
eﬀect appears to operate on a delay. One additional female MP in the previous term is associated
with a 6%-points higher probability of having access to clean water, and one additional female
MP in the second-to-last term increases the probability by 14%-points.
The biggest threat to internal validity of these results is potential reverse causality: an improve-
ment in a district’s outcome variables might also increase the probability of that district obtaining
a female MP. Due to the lack of exogenous variation it is diﬃcult to use a more sophisticated
empirical strategy that eliminates this problem, like instrumental variables. Furthermore, if there is
migration of predominantly high socio-economic individuals to districts with high socio-ecnonomic
outcomes this could bias our results as well. Access to panel data would help to alleviate some of
these concerns. However, thanks to the size of our dataset we were able to control for region-year
FE, which capture all changes that aﬀect regions over time. Further by including a linear trend
we allow women and men to be on diﬀerent trends by district. Our results remain stable even
when adding these controls, which suggests that our coeﬃcients of interest are not just a result of
reverse causality and endogeneity.
Furthermore, it may be the case that the eﬀects of increased female representation are
nonlinear. The reasoning behind this is that when there are only very few women in parliament it
may be hard for the female MPs to push policies for women through. However, their eﬀectiveness
may increase substantially once a critical mass is met. It might also be the case that at some point
an increase in female representation does not lead to further improvements.
Summing up, our analysis indicates that the legislative women’s quota in Tanzania has led to
signiﬁcant reductions in the gender gap. The quota has eﬀectively increased political participation
in accordance with its goals, and the level of female representation continues to rise. We ﬁnd
evidence that the quota has reduced the gender gap in education for certain age groups, and we ﬁnd
indications of small improvements to female empowerment for certain age groups. In accordance
with previous ﬁndings in other countries, we ﬁnd that the increased female representation led to
substantial improvements in water infrastructure that greatly increased the number of people
with access to clean water. While we do not ﬁnd signiﬁcant health impacts, this may be due to
limitations in our dataset. It is thus apparent that the quota likely had positive eﬀects on a variety
of relevant outcomes. We hope to assess the impacts on additional impacts in the future to further
understand the breadth and persistence of the quota’s impact.
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Table A1: Variable Description
Variable Description Type of Variable
Dummy variable that equals 1 if the respondent
has 1 or more years of education attained at the
time of the interview and 0 otherwise.
Dummy variable that equals 1 if the respondent
says that the head of the household is female and
equals 0 otherwise
Dummy variable whether a household member
has been very sick for at least three of the past
12 months in the households
Dummy variable distinguishing between “goo”
and “bad” water sources, according to UNDP
Dummy variable distinguishing between ”good”
and ”bad” sanitation sources, according to
age Age of the respondent Control
Dummy variable that equals 1 if the respondent
is female and equals 0 if male
MPfemale Number of female MP’s in a district Control
Number of total MPs in a district, including male
and female MPs
number of members in hh Total number of household members Control
Dummy variable that equals 1 if the female re-
spondent lives in a single household and equals
0 if not
type of residence
Type of place of residence where the household
resides as either urban or rural
The wealth index is a composite measure of
a household’s cumulative living standard. The
wealth index is calculated using easy-to-collect
data on a household’s ownership of selected as-
sets, such as televisions and bicycles; materials
used for housing construction; and types of water
access and sanitation facilities.
Table A2: Randomization Test
type of residence 0.0701
* p < 0.1; ** p < 0.05; *** p < 0.01