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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

Publié dans : Économie & finance
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Legislative Quota, Women Empowerment and Development: Evidence from Tanzania

  1. 1. Legisla(ve  Quotas,  Women   Empowerment  and  Development:   Evidence  from  Tanzania   Gregory  Raiffa   Ericka  Sánchez   Jan  Stübner   Feodora  Te;   Andreas  Wohlhüter  
  2. 2. 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)  
  3. 3. 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)   05.06.2015   ISP  Final  Presenta(on   3  
  4. 4. 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)   05.06.2015   ISP  Final  Presenta(on   4  
  5. 5. 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   05.06.2015   ISP  Final  Presenta(on   5   Did  the  legisla#ve  women’s  quota  reduce  the  exis#ng  gender  gap  in  TZ?  
  6. 6. 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  %     05.06.2015   ISP  Final  Presenta(on   6   Source:  Yoon  (2008);  Reith  (2011)  
  7. 7. Qualita#ve  Analysis  of  the  Quota  in  TZ   In  which  poli(cal  areas  do  women  in  TZ  engage?   05.06.2015   ISP  Final  Presenta(on   7   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)  
  8. 8. 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     05.06.2015   ISP  Final  Presenta(on   8  
  9. 9. Empirical  Strategy   What  is  the  source  of  varia(on  in  representa(on?   05.06.2015   ISP  Final  Presenta(on   9   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)  
  10. 10. -  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?   05.06.2015   ISP  Final  Presenta(on   10   —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
  11. 11. Data   How  is  our  dataset  constructed?   05.06.2015   ISP  Final  Presenta(on   11   GADM  DHS   POLIS   -­‐  Regions   -­‐  Districts   -­‐  GPS   -­‐  Microdata   -­‐  Villages   -­‐  GPS   -­‐  MP  database   -­‐  Cons(tuency/ elem.  school   -­‐  Districts   Final  Database   +   +  
  12. 12. 05.06.2015   ISP  Final  Presenta(on   12   DHS  data  +  GADM  Data    
  13. 13. 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   05.06.2015   ISP  Final  Presenta(on   13   Advantages   Disadvantages  
  14. 14. Results   Is  there  a  rela(onship  btw.  pol.  representa(on  &  educa(on?   05.06.2015   ISP  Final  Presenta(on   14   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)** 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
  15. 15. Results   Is  there  a  rela(onship  btw.  pol.  representa(on  &  empowerment?   05.06.2015   ISP  Final  Presenta(on   15   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)*** 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
  16. 16. Results   Is  there  a  rela(onship  btw.  pol.  representa(on  &  health?   05.06.2015   ISP  Final  Presenta(on   16   agnitude when including the region-year FE and the linear trend. Again this supports o ment 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) 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
  17. 17. Results   Is  there  a  rela(onship  btw.  pol.  representa(on  &  infrastructure?   05.06.2015   ISP  Final  Presenta(on   17   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)*** 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 is in this context is especially important because it seems likely that
  18. 18. 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   05.06.2015   ISP  Final  Presenta(on   18   Legisla#ve  women‘s  quota  successfully  decreased  the  gender  gap  in  TZ  
  19. 19. Appendix   05.06.2015   ISP  Final  Presenta(on   19  
  20. 20. Appendix   05.06.2015   ISP  Final  Presenta(on   20   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
  21. 21. 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   05.06.2015   ISP  Final  Presenta(on   21  
  22. 22. Appendix   05.06.2015   ISP  Final  Presenta(on   22   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
  23. 23. Appendix   05.06.2015   ISP  Final  Presenta(on   23   Table 2: Summary Statistics Gender Gap Having MP_female Obs 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 -206.94 MPfemale 178,610 0.814 1.244 0 9 -0.005 -0.90 -1.682úúú -387.35 MPtotal 178,610 3.002 2.307 0 17 -0.009 -0.80 -2.471úúú -267.81 educ 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 -10.64 educ [age 14 - 19] 23,002 0.912 0.283 0 1 0.0329úúú 8.82 -0.0511úúú -13.57 educ [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 -3.67 singlehh 182994 0.0589 0.236 0 1 -0.014úúú -12.54 0.006úúú 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 -0.001úú -2.20 -0.187úúú -67.64 sanitation 174177 0.432 0.495 0 1 0.002 1.09 0.0829úúú 34.57 wealth 182988 3.038 1.395 1 5 -0.003 -0.51 -0.648úúú -101.15 number of members in hh 182994 7.020 3.776 1 49 0.015 0.85 0.160úúú 9.01 age 182939 22.25 19.41 0 95 -0.585úúú -6.44 -0.475úúú -5.16 type of residence 182994 0.207 0.405 0 1 -0.008úúú -4.26 -0.117úúú -62.07 * p<0.10, ** p<0.05, *** p<0.01 Column (6) shows the di erence between males and females Column (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,
  24. 24. Appendix   05.06.2015   ISP  Final  Presenta(on   24   Figure 5: Frequency of Women by Age at First Birth Source: DHS 2007-2008
  25. 25. Appendix   05.06.2015   ISP  Final  Presenta(on   25   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) R2 0.01 0.01 0.37 0.40 0.29 0.33 N 170,271 170,265 170,265 170,265 130,943 130,943 Year & District FE no no yes yes yes yes Year-Region FE no no no yes no yes * p < 0.1; ** p < 0.05; *** p < 0.01

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