legislative quota, women empowerment and development: evidence from tanzania

Legisla(ve Quotas, Women Empowerment and Development:

Evidence from Tanzania

Gregory Raiffa Ericka Sánchez Jan Stübner Feodora Te;

Andreas Wohlhüter

Mo#va#on and Introduc#on Why is it important to analyze gender gaps in dev. countries?

05.06.2015 ISP Final Presenta(on 2

Source: OECD Social Ins(tu(ons & Gender Index (SIGI)

Mo#va#on and Introduc#on Does a reduc(on in the gender gap encourage growth and dev.?

-  Nega(ve associa(on between gender gap and economic growth (Dollar and GaT, 1999; Klasen, 2002; Knowles, Lorgelly and Owen, 2002)

-  Reducing gender gap in educa(on •  Exploi(ng higher return to educa(on for women (Klasen, 2002)

•  Lower fer(lity and child mortality rates, beYer educated subsequent genera(on (Esteve-‐Volart, 2004; Cavalcan( and Tavares, 2007)

-  Reducing gender gap in labor market par(cipa(on

•  More efficient use of human capital (Klasen, 2002)

•  Higher women’s bargaining power at home, higher investments in children’s health and educa(on (Seguino and Floro 2003)

•  Less corrupt governance in business and policymaking (Dollar et al., 2001, Swamy et al., 2001)


Mo#va#on and Introduc#on Do quotas and increased representa(on make a difference?

-  Increasing female poli(cal par(cipa(on

•  Greater investment in educa(on and female-‐oriented policies (Clots-‐Figueras 2011)

•  Elevated adolescent girls’ career aspira(ons and educa(onal aYainments (Beaman et al. 2012)

•  Reduc(on in educa(on gender gap (Beaman et al. 2012)

-  Women Quotas

•  Increased female representa(on (Yoon, 2010; Dahlerup, 2003; Jones, 1998)

•  Increased female representa(on even ajer quota removed (De Paola et al. 2010)

•  More female-‐supported policies (ChaYopadhyay and Duflo, 2004)

•  Addi(onal investment in water infrastructure and educa(on (Beaman, 2010)


Mo#va#on and Introduc#on Why is Tanzania an interes(ng case?

-  Patriarchal society (Meena, 2003) -  Exis(ng gender gap in many areas (SIGI)

•  Poli(cal representa(on •  Educa(on •  Labor par(cipa(on

-  Quota has been introduced successfully (Yoon, 2008) •  Current quota is large •  Women ac(vely par(cipated in poli(cs

-  Data availability


Did the legisla#ve women’s quota reduce the exis#ng gender gap in TZ?

Qualita#ve Analysis of the Quota in TZ Did the quota increase female representa(on in poli(cs?

Year Special Seats

Const. Women

Total Women

Total # of seats

Share of Women Quota

1985 15 4 24 244 9 % 15 seats

1990 15 5 28 255 11 % 15 seats

1995 37 8 47 275 17 % 15 %

2000 48 12 63 295 21 % 20 %

2005 75 17 97 323 30 % 30 %

2010 102 21 126 357 35 % 30 %


Source: Yoon (2008); Reith (2011)

Qualita#ve Analysis of the Quota in TZ In which poli(cal areas do women in TZ engage?


Defence and Security

Foreign Affairs/Defense and Security

Foreign Affairs

Economics

Infrastructure

Administra(on/Governance

Land, Natural Resources and Environment

Industries and Trade

Social Welfare/Development

Health

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% % Female Representa(on

Source: POLIS (2015)

Mechanisms What are poten(al mechanisms we are expec(ng?

-  Legisla(ve women quota à  Female representa(on in poli(cs ñ à  Probability of having a female MP in district ñ à  Changes in outcomes

-  Poten(al channels? •  Direct effect through policy changes •  Change of societal norms •  Role model effect •  Incen(ve for re-‐elec(on

-  Mo(va(on for specific outcomes •  Literature, commiYee makeup, channels


Empirical Strategy What is the source of varia(on in representa(on?


0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 More

# of Districts

# of Female MP's

2000

2005

2010

Source: POLIS (2015)

-  DiD exploi(ng distribu(on of female MPs and gender

•  i: individuals, t: (me, d: districts, r: region -  Controls:

•  Year-‐FE, district-‐FE, region-‐year FE, linear trend •  # of HH members, wealth, age, age2 single HH, type of resid., # MPs

-  Dependent variables: dummy •  Educ: any level of educa(on? •  Head: is the head of the household female? •  Health: did you feel sick 3+ months in the last year? •  Water: access to clean water?

Empirical Strategy What empirical strategy are we using?


—3 is the actual coe�cient 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 = —0 + —1femaleitd + —2MPfemaletd + —3(femaleitd ú MPfemaletd)

+ÿ

—kXkitd + ”d + ◊t + ◊t ú “r + 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

di�erences 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 di�erence in means estimator described in Equation 1 would yield an unbiased estimate of

the desired e�ect.

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, age

2, 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 coe�cients except for the wealth indicator enter the regression insignificantly.

The coe�cient 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 ”d capture any district time-invariant specific trends (e.g. persistent cultural

di�erences across districts), and year FE ◊t control for time trends that a�ect the whole country

equally (e.g. country-wide trends in social norms). We also include region-year FE ◊t ú “r in order

10

Data How is our dataset constructed?


GADM DHS POLIS

-‐  Regions -‐  Districts -‐  GPS

-‐  Microdata -‐  Villages -‐  GPS

-‐  MP database -‐  Cons(tuency/

elem. school -‐  Districts

Final Database

+ +


DHS data + GADM Data

Data What are the advantages and disadvantages of our dataset?

•  Novel dataset •  Matches micro-‐level data

with informa(on on MPs •  High quality data

–  Same source –  DHS are representa(ve

•  Big dataset –  Various FE controls

•  DHS: not all districts are represented in the survey –  Around 20% are missing –  Mostly smaller districts

•  Polis: some MPs could not be matched to a district –  Around 20% are missing –  1 term MPs overrepresented –  Minority par(es

overrepresented


Advantages Disadvantages

Results Is there a rela(onship btw. pol. representa(on & educa(on?


Table 3: E�ects 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)**R

2 0.03 0.20 0.22 0.23 0.14 0.02 0.11 0.14 0.15N 130,716 130,659 130,659 130,659 69,465 69,467 69,465 69,465 69,465Controls no yes yes yes yes no yes yes yesYear & District FE no no yes yes yes no no yes yesYear-Region FE & Trend no no no yes yes no no no yesFull Sample yes yes yes yes no no no no no

* p < 0.1; ** p < 0.05; *** p < 0.01

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 di�erent trends over time for women and men in the same district.

The treatment e�ect 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 e�ect is highly significant and quite stable throughout all four specifications. In

order to understand the size of the coe�cient better we compare it with the existing gender gap,

which is equal to the coe�cient 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 coe�cient 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

Table A3). Adding the year-and district- FE, thus eliminating concerns about all time invariant4age, age2, wealth, type of residence, number of members in household and total number of MPs

15

Results Is there a rela(onship btw. pol. representa(on & empowerment?


Table 4: E�ects 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)***R

2 0.01 0.15 0.17 0.18 0.19 0.00 0.17 0.18 0.19N 178,591 178,530 178,530 178,530 117,088 117,091 117,088 117,088 117,088Controls no yes yes yes yes no yes yes yesYear & District FE no no yes yes yes no no yes yesYear-Region FE & Trend no no no yes yes no no no yesFull Sample yes yes yes yes no no no no no

* p < 0.1; ** p < 0.05; *** p < 0.01In columns (5) - (9) the sample is restricted to individuals under 26 years

18

Results Is there a rela(onship btw. pol. representa(on & health?


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 a�ect districts di�erentially.

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 di�erent samples. The lack of su�cient 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 coe�cient 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: E�ects 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)R

2 0.00 0.02 0.03 0.03 0.03 0.04N 44,466 44,437 44,437 44,437 10,439 5,112Controls no yes yes yes yes yesYear & District FE no no yes yes yes yesYear-Region FE & Trend no no no yes yes yesFull 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

Results Is there a rela(onship btw. pol. representa(on & infrastructure?


Table 6: E�ects 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)***R

2 0.01 0.17 0.36 0.40 0.30 0.32 0.46 0.47N 123,716 123,715 123,715 123,715 105,929 105,929 33,492 33,492Controls no yes yes yes yes yes yes yesYear & District FE no no yes yes yes yes yes yesYear-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 is 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 e�ect 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

6.2%-point (14.1%-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 e�ects 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

Evalua#on of the Policy What are we taking away from this analysis?

•  Improvements in –  Female poli(cal par(cipa(on –  Educa(onal outcomes –  Access to clean water –  (Some evidence) female

empowerment & health

•  Caveats –  Reverse causality –  Movers –  Data issues

•  No test for nonlineari(es


Legisla#ve women‘s quota successfully decreased the gender gap in TZ

Appendix


Appendix


Figure 2: Female Representation in Committees

Source: POLIS 2015

Figure 3: Evolution of Female Representation in Committees

Source: POLIS 2015

the e�ects 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

Appendix

-  Par(es (> 5% of votes) appoint SS based on their propor(on of total representa(on •  Women apply regionally to par(es •  Par(es provide nomina(ons to the Na(onal Electoral Commission

(NEC) who has ul(mate authority •  Successful nomina(on within party due primarily to standing within

party / party loyalty

-  CCM party (80-‐90% of parliament in last 3 elec(ons) in 2005 appointed two SS to each region and assigned remaining representa(ves to special issues, e.g. youth, NGO

-  Smaller par(es (only 2 met 5% threshold in 2005) spread less than 26 SS representa(ves across the 26 regions


Appendix


Table A2: Randomization Test

MP_female

MP_male 0.0134(0.19)

female -0.0001(0.02)

age 0.0004(1.00)

age

2 -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)

R

2 0.48N 178,530* p < 0.1; ** p < 0.05; *** p < 0.01

30

Appendix


Table 2: Summary Statistics

Gender Gap Having MP_femaleObs Mean St. Dev. Min Max Di� t-stat Di� t-stat(1) (2) (3) (4) (5) (6) (7) (8) (9)

MPfemale*female 178,591 0.420 0.985 0 9 -0.817úúú -192.45 -0.867úúú -206.94MPfemale 178,610 0.814 1.244 0 9 -0.005 -0.90 -1.682úúú -387.35MPtotal 178,610 3.002 2.307 0 17 -0.009 -0.80 -2.471úúú -267.81educ 133,941 0.780 0.414 0 1 0.0922úúú 40.95 -0.070úúú -30.66

educ [age 8 - 13] 31,224 0.828 0.378 0 1 -0.0427úúú -9.99 -0.0461úúú -10.64educ [age 14 - 19] 23,002 0.912 0.283 0 1 0.0329úúú 8.82 -0.0511úúú -13.57educ [age 20 - 25] 16,906 0.844 0.363 0 1 0.0731úúú 13.08 -0.0735úúú -13.06

head_fem 182994 0.198 0.398 0 1 -0.085úúú -45.62 -0.007úúú -3.67singlehh 182994 0.0589 0.236 0 1 -0.014úúú -12.54 0.006úúú 5.13health 45132 0.0119 0.109 0 1 -0.001 -0.70 -0.0004 -0.44water 127006 0.427 0.495 0 1 -0.001úú -2.20 -0.187úúú -67.64sanitation 174177 0.432 0.495 0 1 0.002 1.09 0.0829úúú 34.57wealth 182988 3.038 1.395 1 5 -0.003 -0.51 -0.648úúú -101.15number of members in hh 182994 7.020 3.776 1 49 0.015 0.85 0.160úúú 9.01age 182939 22.25 19.41 0 95 -0.585úúú -6.44 -0.475úúú -5.16type of residence 182994 0.207 0.405 0 1 -0.008úúú -4.26 -0.117úúú -62.07* p<0.10, ** p<0.05, *** p<0.01Column (6) shows the di�erence between males and femalesColumn (8) is the di�erence 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 di�erent channels through which an increase in female political

presentation might a�ect 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 e�ect 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 di�erent 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 coe�cient of the interaction term between the female dummy and

the number of female MPs in a district —3 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

Appendix


Figure 5: Frequency of Women by Age at First Birth

Source: DHS 2007-2008

Table A7: E�ects on Sanitation

(1) (2) (3) (4) (5) (6)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

(0.003) (0.003) (0.002) (0.002) (0.002) (0.002)wealth 0.012 0.007 0.005 0.001 -0.001

(0.007)* (0.005) (0.005) (0.005) (0.005)type of residence 0.084 0.107 0.105 0.116 0.119

(0.025)*** (0.019)*** (0.018)*** (0.021)*** (0.020)***numbers of members in hh 0.005 0.008 0.008 0.008 0.009

(0.002)** (0.002)*** (0.002)*** (0.002)*** (0.002)***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)_cons 0.470 0.255 0.571 -0.024 0.170 0.030

(0.015)*** (0.064)*** (0.068)*** (0.094)* (0.065)R

2 0.01 0.01 0.37 0.40 0.29 0.33N 170,271 170,265 170,265 170,265 130,943 130,943Year & District FE no no yes yes yes yesYear-Region FE no no no yes no yes

* p < 0.1; ** p < 0.05; *** p < 0.01

35

legislative quota, women empowerment and development: evidence from tanzania

Economy & Finance