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Legislative Quota, Women Empowerment and Development: Evidence from Tanzania

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Master project by Gregory Raiffa, Ericka Sánchez, Jan Stübner, Feodora Teti, and Andreas Wohlhüter. Barcelona GSE Master's in International Trade, Finance, and Development

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Legislative Quota, Women Empowerment and Development: Evidence from Tanzania

  1. 1. Barcelona Graduate School of Economics Final Master Project Legislative Quota, Women Empowerment and Development: Evidence from Tanzania Authors: Gregory Raiffa Ericka Sánchez Jan Stübner Feodora Teti Andreas Wohlhüter Abstract 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. June 5th , 2015
  2. 2. 1 Introduction 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 1 The figures reported here refer to the ratio of female to male outcomes. 1
  3. 3. 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 readily available. 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 2
  4. 4. 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 (2007)). 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 3
  5. 5. 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 4
  6. 6. 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 5
  7. 7. made up between 17% and 19% of all women in parliament for each of the elections between 1985 and 2010. Table 1: Women in Parliament Year Special seats for Women Constituency Women Total Women Total Seats in Parliament % 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” POLIS 2015 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 6
  8. 8. 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 categories. 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 7
  9. 9. 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 8
  10. 10. 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 developing countries. 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 9
  11. 11. 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 (1) 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 10
  12. 12. 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. 6 Data 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, 2 The sample consists of 131 districts 3 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. 11
  13. 13. 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. 12
  14. 14. 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 13
  15. 15. 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. 7.1 Education 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 14
  16. 16. 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 4 age, age2 , wealth, type of residence, number of members in household and total number of MPs 15
  17. 17. 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 (column 9). 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: 5 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). 16
  18. 18. 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 6 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. 17
  19. 19. 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 18
  20. 20. 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 reverse causality. 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. 19
  21. 21. 7.3 Health 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 20
  22. 22. 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 outcomes positively. 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 21
  23. 23. 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 22
  24. 24. 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 (0.010)*** (0.015)*** l2*MPfemale -0.074 0.147 (0.015)*** (0.003)*** _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 treatment. 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. 23
  25. 25. 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 politically active. 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 24
  26. 26. 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 availability. 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 25
  27. 27. 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. 26
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  29. 29. Hausmann, R., Tyson, L., Bechouche, Y., and Zahidi, S. (2014). The global gender gap report 2014. World Economic Forum. Hodler, R. and Raschky, P. A. (2014). Regional Favoritism. The Quarterly Journal of Economics, 129(2):995–1033. Jones, M. (1998). Gender quotas, electoral laws, and the election of women: Lessons from the argentine provinces. Comparative Political Studies. Klasen, S. (2002). Low Schooling for Girls, Slower Growth for All? Cross-Country Evidence on the Effect of Gender Inequality in Education on Economic Development. World Bank Economic Review, 16(3):345–373. Kotsadam, A. and Mans. The effect of gender quotas in latin american national elections. February 2012. Meena, R. (2003). The implementation of quotas: African experiences. International Insti- tute for Democracy and Electoral Assistance (IDEA)/Electoral Institute of Southern Africa (EISA)/Southern African Development Community (SADC) Parliamentary Forum Conference. Pande, R. (2003). Can mandated political representation provide disadvantaged minorities policy influence? theory and evidence from india. American Economic Review. Paola, M. D., Ponzo, M., and Scoppa, V. (2015). Gender Differences in Attitudes Towards Competition: Evidence from the Italian Scientific Qualification. CSEF Working Papers 391, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy. Seguino, S. and Floro, M. S. (2003). Does Gender have any Effect on Aggregate Saving? An empirical analysis. International Review of Applied Economics, 17(2):147–166. Swamy, A., Knack, S., Lee, Y., and Azfar, O. (2000). Gender and Corruption. Center for Development Economics 158, Department of Economics, Williams College. Thomas, S. (1991). The impact of women on state legislative policies. Journal of Politics. UNDP (2013). The rise of the south: Human progress in a diverse world. Human Development Report. Yoon, M. Y. (2008). Special seats for women in the national legislature: The case of tanzania. Africa Today. Yoon, M. Y. (2011). More women in the tanzanian legislature: Do numbers matter? Journal of Contemporary African Studies. 28
  30. 30. Table A1: Variable Description Variable Description Type of Variable educ 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. Outcome headfemale Dummy variable that equals 1 if the respondent says that the head of the household is female and equals 0 otherwise Outcome health Dummy variable whether a household member has been very sick for at least three of the past 12 months in the households Outcome water Dummy variable distinguishing between “goo” and “bad” water sources, according to UNDP measures Outcome sanitation Dummy variable distinguishing between ”good” and ”bad” sanitation sources, according to UNDP measures Outcome age Age of the respondent Control female Dummy variable that equals 1 if the respondent is female and equals 0 if male Control MPfemale Number of female MP’s in a district Control MPtotal Number of total MPs in a district, including male and female MPs Control number of members in hh Total number of household members Control singlehh Dummy variable that equals 1 if the female re- spondent lives in a single household and equals 0 if not Control type of residence Type of place of residence where the household resides as either urban or rural Control wealth 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. Control 29
  31. 31. Table A2: Randomization Test MP_female MP_male 0.0134 (0.19) female -0.0001 (0.02) age 0.0004 (1.00) age2 -0.0000 (0.28) wealth 0.0310 (1.99)** type of residence 0.0701 (0.75) singlehh -0.0090 (0.43) _cons -0.0748 (0.28) R2 0.48 N 178,530 * p < 0.1; ** p < 0.05; *** p < 0.01 30
  32. 32. Table A3: Effects on Education - Detailed (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) MPfemale 0.0275 0.0063 -0.0036 -0.0046 -0.0032 0.0273 0.0096 0.0029 0.0016 (5.50)*** (1.45) (1.14) (1.11) (0.73) (4.50)*** (1.88)* (0.79) (0.34) 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)*** number of members in hh -0.0056 -0.0040 -0.0036 -0.0044 -0.0062 -0.0048 -0.0044 (5.69)*** (4.13)*** (4.08)*** (4.74)*** (5.85)*** (4.71)*** (4.76)*** type of residence 0.0155 -0.0075 -0.0079 -0.0056 0.0084 -0.0060 -0.0066 (1.95)* (1.01) (1.07) (0.74) (1.34) (0.80) (0.88) wealth 0.0744 0.0681 0.0668 0.0538 0.0614 0.0549 0.0537 (22.77)*** (25.48)*** (26.58)*** (19.41)*** (18.93)*** (18.96)*** (19.47)*** MPmale -0.0050 0.0073 0.0139 0.0215 -0.0018 0.0171 0.0205 (1.41) (0.65) (1.10) (1.61) (0.54) (1.51) (1.57) age 0.0052 0.0054 0.0054 0.0841 0.1123 0.1114 0.1110 (11.17)*** (11.86)*** (11.86)*** (16.86)*** (18.78)*** (18.45)*** (18.45)*** age2 -0.0002 -0.0002 -0.0002 -0.0026 -0.0030 -0.0030 -0.0030 (25.79)*** (27.35)*** (27.36)*** (16.90)*** (17.70)*** (17.46)*** (17.45)*** trend -0.0000 -0.0000 -0.0000 (0.29) (0.26) (0.13) trend*female -0.0000 -0.0000 -0.0000 (0.87) (1.47) (1.79)* age2 [14-19 years] 0.1204 -0.0738 -0.0745 -0.0740 (12.02)*** (8.61)*** (8.80)*** (8.92)*** age2*female -0.0904 -0.0906 -0.0904 -0.0906 (6.92)*** (7.18)*** (7.14)*** (7.17)*** age2*MPfemale -0.0135 -0.0123 -0.0107 -0.0108 (3.19)*** (2.93)*** (2.75)*** (2.75)*** age2*MPfemale*female 0.0117 0.0101 0.0106 0.0106 (2.34)** (2.00)** (2.05)** (2.03)** age3 [19-25 years] 0.0662 -0.1075 -0.1076 -0.1071 (8.01)*** (8.79)*** (8.91)*** (8.85)*** age3*female -0.1321 -0.1260 -0.1284 -0.1280 (11.68)*** (11.56)*** (11.80)*** (11.81)*** age3*MPfemale -0.0069 -0.0068 -0.0050 -0.0051 (2.03)** (1.86)* (1.60) (1.60) 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 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 31
  33. 33. Table A4: 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) type of residence -0.0555 -0.0663 -0.0680 -0.0704 -0.0588 -0.0689 -0.0701 (4.36)*** (5.59)*** (5.70)*** (5.27)*** (4.21)*** (5.22)*** (5.28)*** number of members in hh -0.0022 -0.0015 -0.0013 -0.0027 -0.0034 -0.0029 -0.0028 (1.82)* (1.06) (0.94) (1.81)* (2.89)*** (2.01)** (1.88)* wealth -0.0196 -0.0235 -0.0239 -0.0250 -0.0213 -0.0249 -0.0253 (6.13)*** (7.45)*** (7.59)*** (7.15)*** (6.11)*** (7.08)*** (7.23)*** MPmale 0.0047 0.0320 0.0219 0.0218 0.0034 0.0335 0.0217 (1.23) (3.36)*** (2.43)** (2.31)** (0.84) (3.30)*** (2.31)** age -0.0014 -0.0013 -0.0013 0.0104 0.0073 0.0070 0.0069 (8.55)*** (8.22)*** (8.31)*** (11.80)*** (7.70)*** (7.22)*** (7.22)*** age2 0.0000 0.0000 0.0000 -0.0003 -0.0003 -0.0003 -0.0003 (8.51)*** (7.95)*** (8.07)*** (9.62)*** (5.97)*** (5.59)*** (5.53)*** singlehh 0.5885 0.5829 0.5826 0.6568 0.6690 0.6619 0.6616 (46.13)*** (45.69)*** (45.91)*** (56.22)*** (58.84)*** (57.31)*** (57.37)*** trend -0.0001 -0.0001 -0.0001 (0.85) (0.91) (0.94) trend*female -0.0000 0.0000 0.0000 (0.26) (0.15) (0.20) age1 [7-13 years] 0.0352 0.0007 0.0001 0.0002 (6.25)*** (0.11) (0.02) (0.04) age1*female 0.0128 0.0106 0.0119 0.0119 (1.73)* (1.56) (1.73)* (1.70)* age1*MPfemale -0.0000 -0.0010 -0.0009 -0.0008 (0.01) (0.35) (0.30) (0.26) age1*MPfemale*female -0.0070 -0.0072 -0.0079 -0.0081 (1.41) (1.61) (1.74)* (1.86)* age2 [14-19 years] 0.0499 0.0542 0.0506 0.0510 (6.09)*** (4.60)*** (4.38)*** (4.46)*** age2*female 0.0097 0.0126 0.0138 0.0133 (0.93) (1.24) (1.37) (1.31) age2*MPfemale 0.0012 0.0013 0.0022 0.0018 (0.37) (0.32) (0.53) (0.43) age2*MPfemale*female -0.0001 -0.0020 -0.0009 -0.0006 (0.02) (0.38) (0.20) (0.12) age3 [19-25 years] 0.0121 0.0250 0.0237 0.0236 (1.61) (1.45) (1.41) (1.42) age3*female -0.0061 -0.0069 -0.0062 -0.0056 (0.53) (0.56) (0.51) (0.46) age3*MPfemale -0.0042 -0.0100 -0.0077 -0.0080 (1.08) (1.93)* (1.24) (1.34) 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 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 * p < 0.1; ** p < 0.05; *** p < 0.0132
  34. 34. Table A5: Effects on Health - Detailed (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) age -0.00027 -0.00027 -0.00027 0.00110 -0.00348 (0.00016) (0.00016) (0.00016) (0.00143) (0.02061) age2 0.00002 0.00002 0.00002 -0.00012 0.00011 (0.00000)*** (0.00000)*** (0.00000)*** (0.00027) (0.00057) wealth 0.00047 0.00022 0.00029 0.00020 0.00023 (0.00064) (0.00060) (0.00061) (0.00065) (0.00091) type of residence 0.00017 -0.00153 -0.00137 -0.00409 0.00220 (0.00241) (0.00338) (0.00348) (0.00488) (0.00464) number of members in hh -0.00020 0.00013 0.00013 -0.00019 0.00055 (0.00027) (0.00027) (0.00027) (0.00023) (0.00037) MPmale -0.00068 0.00015 0.00082 0.00165 -0.00041 (0.00054) (0.00030) (0.00022)*** (0.00066)** (0.00041) _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 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 33
  35. 35. Table A6: Effects on Water - Detailed (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) female 0.004 0.002 0.002 0.002 0.002 0.002 0.001 0.000 (0.003) (0.002) (0.002) (0.002) (0.002) (0.002) (0.003) (0.003) wealth 0.111 0.076 0.073 0.073 0.073 0.058 0.058 (0.008)*** (0.005)*** (0.005)*** (0.006)*** (0.006)*** (0.008)*** (0.008)*** type of residence -0.133 -0.126 -0.125 -0.125 -0.133 -0.290 -0.296 (0.043)*** (0.032)*** (0.031)*** (0.034)*** (0.034)*** (0.050)*** (0.051)*** number of members in hh -0.006 -0.001 -0.001 -0.001 -0.001 -0.005 -0.006 (0.002)*** (0.002) (0.002) (0.002) (0.002) (0.002)** (0.002)** MPmale 0.014 0.020 -0.014 (0.009) (0.032) (0.031) l1*MPfemale 0.048 0.045 (0.010)*** (0.015)*** l1*MPmale -0.023 -0.017 (0.013)* (0.016) l2*MPfemale -0.074 0.147 (0.015)*** (0.003)*** l2*MPmale 0.014 0.006 (0.004)*** (0.003)** _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 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 34
  36. 36. Table A7: Effects on Sanitation (1) (2) (3) (4) (5) (6) (7) (8) MPfemale -0.034 -0.029 -0.028 0.002 (0.008)*** (0.009)*** (0.018) (0.011) female -0.003 -0.002 -0.003 -0.003 -0.002 -0.002 -0.002 -0.003 (0.003) (0.003) (0.002) (0.002) (0.002) (0.002) (0.005) (0.005) wealth 0.012 0.007 0.005 0.001 -0.001 -0.020 -0.022 (0.007)* (0.005) (0.005) (0.005) (0.005) (0.009)** (0.009)** type of residence 0.084 0.107 0.105 0.116 0.119 0.159 0.150 (0.025)*** (0.019)*** (0.018)*** (0.021)*** (0.020)*** (0.038)*** (0.037)*** number of members in hh 0.005 0.008 0.008 0.008 0.009 0.009 0.010 (0.002)** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.004)** (0.004)*** MPmale -0.003 -0.016 0.007 (0.011) (0.027) (0.022) l1*MPfemale -0.026 0.007 (0.016) (0.009) l1*MPmale -0.006 0.013 (0.038) (0.018) l2*MPfemale -0.101 0.086 (0.014)*** (0.005)*** l2*MPmale -0.032 0.008 (0.003)*** (0.002)*** _cons 0.470 0.255 0.571 -0.024 0.170 0.030 0.303 -0.440 (0.015)*** (0.064)*** (0.068)*** (0.094)* (0.065) (0.087)*** (0.101)*** R2 0.01 0.01 0.37 0.40 0.29 0.33 0.18 0.19 N 170,271 170,265 170,265 170,265 130,943 130,943 46,298 46,298 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 35
  37. 37. Figure 5: Frequency of Women by Age at First Birth Source: DHS 2007-2008 36

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