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 The  Effects  of  India’s  Gender  Quota  in  

Local  Government  on  Rates  of  Reporting  Rapes  of  Women  from  Scheduled  Castes  

and  Tribes        

Nadia  Kale    Advisor:  Professor  Anna  Harvey  

   

New  York  University    Politics  Honors  Thesis    

     

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Abstract    

This  research  project  asks  whether  increased  female  representation  in  India  increases  rates  of  reporting  rapes  of  women  from  scheduled  castes  and  tribes.  The  impact  of  female  representation  on  the  incidence  of  violence  against  women  has  yet  to  be  extensively  explored,  due  to  the  nonrandom  assignment  of  female  representation  across  electoral  districts.  In  India,  however,  the  Panchayati  Raj  Act  of  1993  introduced  a  quota  system  in  local  levels  of  government,  mandating  that  one  third  of  seats  be  reserved  for  women.  For  several  idiosyncratic  reasons,  the  Act  was  implemented  in  different  states  at  different  times,  creating  increases  in  women’s  representation  that  were  as-­‐if  random.  One  recent  study  looked  at  the  gender  quota’s  impact  on  crimes  committed  against  all  women  and  found  an  increase  in  rates  of  reporting  (Iyer  2012).  However,  this  study  did  not  account  for  caste  and  socioeconomic  distinctions  that  may  influence  which  women  are  empowered  to  report.  In  exploring  the  impact  of  mandated  increases  in  female  representation  on  rates  of  reporting  rapes  of  India’s  most  marginalized  women,  this  project  finds  that  while  gender  quotas  have  a  positive  impact  on  rates  of  reporting  amongst  all  women,  the  same  does  not  hold  true  for  women  from  scheduled  castes  and  tribes.          Acknowledgement    I  am  extremely  grateful  to  Professor  Harvey  and  Hannah  Simpson  for  the  time  they  dedicated  to  teaching  me  about  quantitative  methods  and  for  their  guidance  in  helping  me  write  my  thesis.  What  I  have  learned  this  year  surpassed  my  academic  goals  and  expectations  and  I  hope  to  continue  incorporating  quantitative  analysis  into  my  future  studies!                                    

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Statement  of  Research  Question      

My  project  asks  whether  increased  female  representation  in  India  reduces  

violence  against  women  from  scheduled  castes  and  tribes.  The  impact  of  female  

representation  on  the  incidence  of  violence  against  women  has  yet  to  be  extensively  

explored,  due  to  the  nonrandom  assignment  of  female  representation  across  

electoral  districts.  In  India,  however,  the  Panchayati  Raj  Act  of  1993  introduced  a  

quota  system  in  local  levels  of  government,  mandating  that  one  third  of  seats  be  

reserved  for  women.  For  several  idiosyncratic  reasons,  the  Act  was  implemented  in  

different  states  at  different  times,  creating  increases  in  women’s  representation  that  

were  as-­‐if  random.  One  recent  study  looked  at  the  gender  quota’s  impact  on  crimes  

against  women  and  found  an  increase  in  rates  of  reporting  all  types  of  crimes  

committed  against  women  (Iyer  2012).  However,  this  study  did  not  account  for  class  

distinctions  that  may  influence  which  women  are  empowered  to  report  crimes  

committed  against  them.  My  project  will  explore  the  impact  of  the  increased  female  

representation  mandated  by  the  Panchayati  Raj  Act  on  rates  of  reported  rapes  of  

women  from  scheduled  castes  and  tribes.  This  project  will  allow  me  to  assess  the  

impact  of  increased  female  representation  on  India's  most  marginalized  women.    

 

 

 

 

 

 

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Literature  Review  

 

  Rape  is  one  of  many  violent  crimes  perpetrated  against  women  by  both  

known  and  unknown  attackers,  though  the  former  is  far  more  likely.  In  a  telephone  

survey  conducted  from  1995  to  1996  on  a  total  of  8,000  women  in  the  District  of  

Columbia,  Kruttschnitt  and  Macmillan  found  that  over  three-­‐quarters  (78%)  of  

attackers  in  violent  crimes  against  women  are  known  to  their  female  victims  

(“Patterns  of  Violence  Against  Women:  Risk  Factors  and  Consequences”  (2005)).  

Most  studies  of  rape  and  other  violent  crimes  against  women  thus  focus  on  factors  

motivating  victims’  intimate  partners  to  commit  such  crimes.  

  Reported  rates  of  violent  crimes  against  women  are  the  product  of  two  kinds  

of  factors:  those  responsible  for  the  actual  crimes,  and  those  responsible  for  the  

rates  of  reporting  these  crimes.  Most  studies  focus  on  the  former  set  of  factors.  

However,  a  few  studies  have  looked  at  factors  motivating  reporting  of  violent  crimes  

against  women.  

A  theory  often  cited  in  explaining  causes  of  violence  against  women  is  a  

community  or  region’s  poverty  level  and/or  relative  level  of  development.  Among  

the  first  quantitative  analyses  of  the  relationship  between  poverty  and  violence  

against  women  was  Miles-­‐Doan’s  article,  “Violence  Between  Spouses  and  Intimates:  

Does  Neighborhood  Context  Matter?”  (1998).  In  this  study,  the  dependent  variable  

analyzed  is  reported  incidence  of  domestic  violence,  which  includes  a  number  of  

acts  of  aggression,  such  as  rape.  Using  law  enforcement  data  from  1992  and  the  

1990  census  data  from  one  county  in  Florida  with  exceptionally  high  death  rates  

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due  to  violence,  Miles-­‐Doan  found  that  within  Duval  county,  “neighborhoods  with  a  

high  concentration  of  residents  living  in  poverty  [and]  unemployed  males…have  

drastically  higher  rates”  of  domestic  violence  than  neighborhoods  with  

comparatively  lower  concentrations  (Miles-­‐Doan,  1998;  p.  637).  The  causal  

explanation  that  Miles-­‐Doan  presents  to  explain  this  observed  neighborhood  effect  

is  that  one’s  geographical  location  influences  the  networks  that  one  operates  within  

and  will  consequently  affect  “prospects  for  employment,  for  public  services,  for  

educational  advancement…  and  much  more”  (Miles-­‐Doan,  1998;  p.  626).    In  cases  

where  prospects  are  low,  the  assumption  is  that  there  will  be  higher  rates  of  

domestic  violence,  because  unemployment,  a  lack  of  public  services  and  resources,  

and  low  levels  of  education  are  all  risk  factors  associated  with  both  domestic  

violence,  and  violence  against  women  more  generally  (Campbell,  2005;  Kruttschnitt,  

2006).      

  Despite  Miles-­‐Doan’s  findings  in  favor  of  neighborhood  effect  theories,  there  

is  no  way  to  thoroughly  distinguish  whether  the  observed  results  are  truly  due  to  a  

neighborhood  effect,  which  asserts  that  it  is  the  poverty  and  underdeveloped  nature  

of  a  specific  community  that  motivates  a  higher  number  of  domestic  violence  cases.  

The  reason  that  no  concrete  conclusions  can  be  determined  is  because  the  

conditions  of  each  neighborhood  are  non-­‐random,  which  presents  a  selection  

problem.  Without  randomization  of  economic  conditions,  it  is  not  possible  to  

accurately  deduce  what  effect  a  neighborhood’s  level  of  development  or  affluence  

has  on  rates  of  domestic  violence,  because  extenuating  factors  that  may  influence  

neighborhood  conditions  may  also  be  exogenous  variables  that  affect  levels  of  

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domestic  violence.  One  possibility  is  that  a  historically  high  concentration  of  families  

with  issues  of  domestic  violence  could  affect  a  neighborhood’s  economic  conditions,  

which  would  imply  reverse  causality.      

Another  investigation  of  the  relationship  between  economic  conditions  and  

domestic  violence  is  Aizer’s  article,  “The  Gender  Wage  Gap  and  Domestic  Violence”  

(2010).  Aizer’s  study  uses  an  instrumental  variable  design,  exploiting  “exogenous  

changes  in  the  demand  for  labor  in  female-­‐dominated  industries”  to  estimate  the  

effects  of  a  decreasing  male-­‐female  wage  gap  on  domestic  violence  (Aizer,  2010;  

p.1).  Aizer’s  measure  of  the  female-­‐male  wage  gap  is  constructed  to  reflect  a  

particular  county’s  proportions  of  male  and  female  workers  in  a  given  industry  in  

that  county.  This  is  then  indexed  by  the  statewide  wage  for  that  industry,  which  

Aizer  argues  makes  the  measure  of  the  wage  gap  exogenous,  because  of  the  fact  that  

she  is  using  state-­‐wide  wages  averaged  across  all  industries,  as  opposed  to  using  

county-­‐specific  wages,  which  would  not  be  random  when  observing  those  counties.        

The  instrumental  variable  used  in  Aizer’s  investigation  is  derived  from  the  same  

strategy  of  indexing  county-­‐specific  proportions  of  workers  in  a  given  industry  by  

statewide  growth  in  that  industry.  Again,  the  argument  is  that  this  variable  is  

exogenous  to  county-­‐specific  conditions,  because  Aizer  uses  statewide  growth  in  an  

industry,  as  opposed  to  county-­‐specific  growth.    

  Aizer  ultimately  finds  that  over  a  span  of  fifteen  years,  from  1995-­‐2010,  

violence  against  women  declined  as  employment  and  earnings  amongst  women  

increased.  More  specifically,  Aizer  concludes  that  a  decline  in  the  wage  gap  

witnessed  over  the  same  time  period  can  explain  9  percent  of  the  reduction  in  

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violence  against  women  (Aizer,  2010;  pp.  18).  As  with  Miles-­‐Doan’s  article,  Aizer’s  

dependent  variable  is  rates  of  domestic  violence,  which  includes  intimate  partner  

rape.  The  problem  with  Aizer’s  analysis,  however,  is  that  the  proportion  of  workers  

in  a  given  industry  could  be  dependent  on  a  number  of  variables  that  are  not  

accounted  for  in  this  research  design.  One  possibility  is  that  in  areas  where  there  is  

more  domestic  violence,  one  might  observe  a  higher  proportion  of  women  working  

in  service  jobs,  which  are  historically  considered  part  of  a  lower  wage  female-­‐

dominated  industry.    

Further  exploring  the  association  between  economic  development  and  

violence  against  women,  Hackett’s  article,  “Domestic  Violence  Against  Women:  

Statistical  Analysis  of  Crimes  Across  India”  (2011),  uses  the  National  Crime  Records  

Bureau  of  India’s  “Crimes  against  Women”  data  to  analyze  how  a  state’s  level  of  

development  impacts  certain  types  of  crime  rates  against  women.  Using  

multivariate  linear  regressions  involving  a  number  of  development  indicator  

variables,  Hackett  looks  specifically  at  dowry  deaths  and  cruelty  (wife  abuse)  to  

analyze  potential  causal  effects.  Both  dowry  deaths  and  cruelty  are  forms  of  

intimate  partner  violence  perpetrated  against  female  victims  by  members  of  their  

immediate  family.  Although  not  limited  to  sexual  violence,  cruelty  as  a  form  of  

intimate  partner  violence  accounts  for  rapes  committed  against  married  women  by  

their  partners  and  other  family  members.    

The  independent  variables  that  Hackett  employs,  each  of  which  groups  

together  a  number  of  variables  within  them,  are  human  development,  gender-­‐

equality  development,  and  urban  development.  Here,  Hackett  uses  a  factor  analysis  

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approach,  taking  a  number  of  state  indicators,  such  as  female  literacy  rate,  child  sex  

ratio,  percentage  of  population  with  electricity,  and  female  employment,  and  

weighting  them  into  three  groups,  with  each  group  representing  a  specific  type  of  

development.  Results  indicate  that  states  with  higher  rates  of  urbanization,  health,  

and  education  have  lower  rates  of  dowry  death  and  cruelty.  Further,  in  regards  to  

the  gender-­‐equality  development  factor,  Hackett  found  that  the  less  developed  a  

state  is  in  terms  of  gender-­‐equality,  the  higher  the  incidence  of  dowry  deaths  and  

cruelty.    

  One  problem  with  Hackett’s  study  is  that  the  independent  variables  

identified  using  the  factor  analysis  approach  are  nonrandom  across  the  Indian  

states  being  analyzed,  which  creates  a  causal  inference  problem.  Any  number  of  

extenuating  factors  could  impact  one  of  Hackett’s  three  independent  variables,  as  

well  as  cruelty  and  dowry  death  rates.    

Contrary  to  Hackett’s  findings,  Johnson  proposes  that  improved  

socioeconomic  conditions  might  instead  lead  to  increases  in  violence  against  

women.  In  his  article,  “Rape  and  Gender  Conflict  in  a  Patriarchal  State”  (2014),  

Johnson  examines  the  empirical  relationship  between  female  socioeconomic  and  

political  power  and  rape  rates  in  Kansas.  He  hypothesizes  that  as  women  begin  to  

progress  towards  equality,  both  economically  and  politically,  men  react  to  increased  

competition  within  their  community  cohort  by  trying  to  thwart  such  advancement  

and  asserting  their  dominance  and  superiority.  Johnson’s  results  suggest  that  there  

is  a  positive  and  strong  correlation  between  county  rape  rates  and  female  

sociopolitical  power.  According  to  Johnson’s  findings,  controlling  for  overall  violent  

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crime  rates  and  specific  county  characteristics,  an  increase  in  the  number  of  female-­‐

headed  households,  female-­‐owned  businesses,  and  female  politicians  and  police  

officers  motivated  an  increase  in  rapes  across  all  Kansas  counties.    

  However,  Johnson’s  causal  story  is  inconclusive.  Because  the  increase  in  

female  sociopolitical  power,  which  Johnson  observes  through  a  number  of  variables,  

is  non-­‐random  across  the  counties  within  Kansas,  no  accurate  conclusions  can  be  

drawn  as  to  how  increased  socioeconomic  status  for  women  affects  rape  rates  

across  the  state.  Moreover,  Johnson  does  not  further  investigate  the  possibility  that  

the  increase  in  rates  is  actually  an  increase  in  reporting  of  crimes  committed  against  

women,  which  in  itself  could  be  a  result  of  increased  socioeconomic  and  political  

status  of  women  in  the  historically  patriarchal  state  of  Kansas.  Unlike  Aizer  and  

Hackett’s  articles,  where  the  argument  is  that  an  increase  in  status  for  women,  

whether  in  levels  of  bargaining  power  or  equality,  leads  to  a  consequent  decrease  in  

rates  of  violence  against  women,  Johnson’s  article  does  not  consider  the  

psychological  effects  of  elevated  status  on  women.  Thus,  his  assumption  of  backlash  

needs  to  be  further  explored.      

  Similar  to  Johnson’s  findings,  Iyer  et  al’s  article,  “The  Power  of  Political  Voice:  

Women’s  Political  Representation  and  Crime  in  India”  (2012)  finds  that  increased  

political  representation  for  women  in  local  government  increases  rates  of  violence  

against  women  across  states  in  India.  Despite  these  results,  however,  Iyer  et  al  

conclude,  after  further  analysis,  that  the  observed  increases  in  rates  of  violence  

against  women  are  actually  increases  in  rates  of  reporting,  which  suggests  a  much  

more  positive  effect  of  rising  status  for  women  in  patriarchal  societies.    

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In  order  to  investigate  the  effects  of  increased  political  representation  for  

women  on  rates  of  violence  against  women,  Iyer  et  al  take  advantage  of  India’s  as-­‐if  

random  gender  quota,  which  eliminates  the  problems  of  non-­‐randomization  that  

Johnson  faced  in  his  study.  India’s  gender  quota  was  mandated  for  all  17  of  India’s  

major  states  in  the  1993  amendment  to  the  Panchayati  Raj  Act.  This  presents  an  

opportunity  for  an  as-­‐if  random  analysis,  because  the  reservation  of  seats  was  

mandated  at  the  federal  level,  so  the  sudden  increase  in  female  political  

representatives  is  consistent  across  states  and  cannot  be  attributed  to  variation  in  

state  conditions,  which  otherwise  might  affect  each  state’s  rates  of  violence  against  

women.  Further,  the  variation  in  dates  of  implementation  of  the  reservations  for  

women  across  states  addresses  potential  endogeneity  of  the  passage  of  the  

Panchayati  Raj  Act  itself.  Unlike  other  policy  implementations  used  as  treatments  

that  may  have  been  implemented  due  to  a  specific  incident  occurring  at  a  particular  

time  or  in  a  particular  state,  the  Panchayati  Raj’s  73rd  amendment  was  passed  and  

implemented  over  a  span  of  years  with  no  potential  confounding  variable  that  both  

led  to  its  initiation  and  will  also  impact  crime  rates  against  women.    

Comparing  state-­‐level  crime  rates  pre  and  post  reservations  for  women,  Iyer  

et  al  gauge  the  impact  of  increased  female  political  representation  while  controlling  

for  a  number  of  factors,  such  as  literacy  rates,  per  capita  incomes,  male-­‐female  ratio,  

level  of  urbanization,  and  size  of  police  force.  Iyer  et  al’s  independent  variable  was  

reservations  for  women,  and  their  dependent  variable  was  overall  rates  of  violence  

against  all  women  across  Indian  states.    

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Results  from  these  regressions  indicate  that  “political  representation  for  

women  is  associated  with  a  large  and  significant  increase  in  the  documented  crimes  

against  women”  (Iyer,  2012;  p.176).  There  are  two  possible  causal  mechanisms  to  

explain  these  relationships.  The  first  is  backlash  theory,  which  assumes  that  the  

increase  in  crimes  against  women  is  a  result  of  a  rise  in  hostility  towards  women  

due  to  their  rising  status,  or  that  in  the  presence  of  increased  representation  for  

women,  there  is  a  decrease  in  overall  law  and  order.  The  second  potential  causal  

mechanism  is  that  the  observed  increase  in  documented  crimes  is  actually  an  

increase  in  rates  of  reporting.  This  hypothesis  assumes  that  with  an  increase  in  

female  representation,  more  women  feel  empowered  to  come  forward  and  report  

violent  crimes  committed  against  them.  In  this  case,  increased  levels  of  confidence  

that  a  victim’s  claims  will  be  handled  responsibly  and  that  potential  backlash  from  

reporting  is  no  longer  a  threat  are  possible  explanations.          

In  order  to  support  the  latter  causal  mechanism  and  disprove  backlash  

theory,  Iyer  et  al  needed  to  prove  that  the  rise  in  reporting  of  violence  against  

women  was  not  an  increase  in  actual  crimes  against  women  and  crime  rates  overall.  

First  they  ran  the  same  regressions  run  for  gender  quotas  and  gender  violence,  but  

instead  analyzed  the  impact  of  the  gender  quotas  on  overall  crime  rates.  The  results  

of  these  regressions  indicate  that  only  crimes  specifically  relating  to  women  

increased,  which  suggests  that  increased  female  representation  has  no  negative  

effect  on  overall  levels  of  law  and  order.  Instead,  these  results  show  that  effects  of  

increased  female  representation  are  gender  specific.      

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To  further  distinguish  between  incidence  of  reporting  and  incidence  of  actual  

crimes,  the  authors  first  identified  categories  of  crime  where  incidence  of  

underreporting  would  be  lower,  such  as  murder.  The  authors  then  looked  at  the  

effects  of  increased  female  representation  on  murder  rates,  both  overall  and  

specifically  of  women,  finding  that  there  was  no  increase  in  murder  rates  post  

reservation  implementation.  By  doing  the  same  with  other  crime  categories  bound  

to  go  less  underreported,  such  as  suicide,  the  authors  concluded  that  the  increased  

rates  were  isolated  to  crimes  typically  underreported,  such  as  rape  and  harassment,  

which  supports  the  argument  that  reporting  rates  increased,  not  actual  crime  rates.    

In  a  second  analysis  of  the  impact  of  increased  political  representation  for  

women,  Iyer  et  al  looked  specifically  at  the  reservation  of  Pradhan  (chief  person)  

seats  for  women  at  the  district  level.  The  purpose  of  this  second  analysis  was  to  

investigate  at  what  level  increased  representation  for  women  is  most  effective  in  

increasing  rates  of  reporting  violence  against  women.  Using  district  level  data  from  

ten  states  across  India,  Iyer  et  al  again  utilized  the  Panchayati  Raj  Act  amendment,  

but  this  time  took  advantage  of  a  different  aspect  of  the  stipulated  reservations  for  

women.  Perhaps  more  soundly  randomized  than  their  first  experiment,  Pradhan  

seats  are  reserved  for  women  so  that  each  election  cycle,  one-­‐third  of  a  state’s  

districts  implement  reservations  for  a  woman  Pradhan.  Every  five  years,  a  different  

group  representing  a  predetermined  and  randomly  selected  one-­‐third  of  a  state’s  

districts  will  be  required  to  reserve  their  seat  based  on  a  rotating  system.  To  carry  

out  this  analysis,  Iyer  et  al  obtained  data  from  the  election  commission  of  ten  states  

in  India  and  ran  regressions  that  looked  at  the  effects  of  a  district  having  a  female  

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Pradhan  on  overall  crime  rates  against  women.  Only  ten  states  of  the  original  

seventeen  examined  were  used  in  this  second  analysis,  because  the  authors  were  

not  able  to  obtain  the  necessary  data  from  seven  states’  election  commissions.  

Nonetheless,  the  authors  had  a  significantly  larger  number  of  observations  to  work  

with  due  to  the  district-­‐level  analysis.  Despite  this,  Iyer  et  al  did  not  find  the  same  

similarly  robust  results  as  those  found  in  their  initial  analysis.  What  this  indicates  is  

that  at  the  higher  level,  the  effects  of  female  representatives  are  diminished.    

According  to  Iyer  et  al,  the  causal  story  behind  the  diminishing  effectiveness  

of  female  representatives  at  higher  levels  is  that  proximity  matters  in  terms  of  

female  representatives  increasing  confidence  amongst  female  constituents  to  report  

violence  against  women.  Iyer  et  al’s  finding  supports  the  supposition  that  higher-­‐

level  political  figures  will  not  influence  a  woman’s  likelihood  to  report  crimes,  

because  a  lack  of  proximity  or  immediate  jurisdiction  regarding  a  case  may  hinder  a  

higher-­‐level  female  representative’s  ability  to  positively  exert  influence.        

    Iyer  et  al’s  paper  also  looks  at  scheduled  castes  and  tribes  (SC/STs)  and  the  

impacts  of  reservations  for  these  two  minority  groups  on  the  reporting  of  crimes  

specifically  targeted  at  their  communities.  What  the  authors  find  is  consistent  with  

their  results  regarding  gender  quotas;  reporting  of  SC/ST  specific  crimes  increase  

with  an  increase  in  representation  by  SC/STs.  However,  the  authors  do  not  examine  

the  effects  of  gender  quotas  on  violent  crimes  reported  by  women  from  SC/STs,  

which  results  in  a  failure  to  account  for  class  distinctions  that  may  impact  rates  of  

reporting.  Without  fully  analyzing  the  effects  of  reservations  for  women  on  arguably  

the  most  marginalized  group  in  India-­‐  women  from  scheduled  castes  and  tribes-­‐  

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conclusions  cannot  be  drawn  about  the  effectiveness  of  quota  systems  in  creating  

access  to  political  voice  for  the  most  underrepresented  people.  Further,  when  

investigating  issues  of  violence  against  women,  especially  in  a  caste-­‐conscious  

county  like  India,  it  is  imperative  to  account  for  these  social  disparities.    

 

Causal  Model  

 

Based  on  their  results,  Iyer  et  al  (2012)  conclude  that  the  increase  in  

reported  crimes  in  the  presence  of  increased  female  political  representation  is  a  

result  of  a  rising  willingness  amongst  women  to  report.  This  increased  willingness  

to  report  is  a  result  of  political  empowerment,  which  Iyer  et  al  argue  is  spurred  by  

the  identity  of  politicians,  in  this  case  the  fact  that  they  are  female.  According  to  this  

causal  story,  violence  perpetrated  against  women  often  goes  underreported,  

because  victims  do  not  feel  confident  that  their  claims  will  be  handled  responsibly  

or  that  further  humiliation  or  aggression  will  not  ensue.  However,  with  the  

reservation  of  one  third  of  local  government  seats  for  women,  Iyer  et  al  suggest  that  

this  increased  female  representation  and  the  establishment  of  a  political  voice  lead  

to  feelings  of  empowerment  and  rising  levels  of  confidence.  

 An  article  by  Beaman  et  al,  titled  “Female  Leadership  Raises  Aspirations  and  

Educational  Attainment  for  Girls:  A  Policy  Experiment  in  India,”  supports  this  claim  

(Beaman,  2012).  Also  using  India’s  gender  quota,  Beaman  et  al  investigate  the  

effects  of  increased  female  leadership  in  the  form  of  reserved  council  chief  

(Pradhan)  seats  for  women  on  the  aspirations  of  girls.  Similar  to  Iyer  et  al’s  second  

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analysis  at  the  district  level,  Beaman  et  al  take  advantage  of  the  Panchayati  Raj’s  

stipulation  that  one  third  of  a  state’s  districts  have  their  Pradhan  seat  be  reserved  

for  women  based  on  a  rotating  system.  Using  this  as-­‐if  random  selection,  the  authors  

are  able  to  identify  a  causal  relationship  between  the  election  of  female  leaders  and  

the  aspirations  of  adolescent  girls  and  their  levels  of  educational  attainment.    

Using  survey  data  at  the  village  level,  the  authors  compare  villages  that  have  

never  had  seats  reserved  for  women  with  villages  assigned  female  leaders  for  two  

election  cycles.  Beaman  et  al  find  that  the  aspirations  of  adolescent  girls  for  

themselves,  as  well  as  the  aspirations  held  by  their  parents  for  them,  rise  as  

exposure  to  female  council  chiefs  increases.  Further,  as  the  number  of  times  a  

district  has  had  a  female  Pradhan  as  a  result  of  reservations  increases,  the  education  

gap  between  boys  and  girls  and  time  spent  by  girls  doing  household  chores  

decreases.  The  authors  conclude  that  in  leadership  roles,  women  have  a  positive  

effect  on  aspirations  and  educational  attainment  for  girls  in  two  ways.  Firstly,  as  a  

Pradhan,  women  are  able  to  influence  the  implementation  of  policies  that  improve  

access  to  education  and  other  means  necessary  to  succeed.  Secondly,  as  female  

leaders  in  the  public  eye,  women  Pradhans  become  role  models,  representing  

possibilities  of  success  and  influence.    

This  second  line  of  reasoning  is  particularly  relevant  to  the  question  posed  in  

this  thesis.  While  implementation  of  the  gender  quotas  should  have  no  significant  

effect  on  actual  policies  related  to  crime,  because  Panchayat  level  politicians  have  no  

control  over  such  policies  or  the  police  force,  the  reporting  of  crimes  committed  

against  women  is  arguably  dependent  on  female  confidence  in  the  justice  system.    

  16  

Women  in  positions  of  power  can  inspire  this  confidence.  So,  whether  in  terms  of  

women  constituents’  confidence  or  their  aspirations,  the  argument  presented  by  

both  Iyer  et  al  and  Beaman  et  al  is  that  increased  female  representation  is  positively  

correlated  with  feelings  of  empowerment.    

  Despite  Iyer  et  al’s  convincing  causal  story,  however,  the  effects  of  

reservations  on  women  from  scheduled  castes  and  tribes  may  not  yield  the  same  

significant  and  positive  results.  Because  caste  in  India  is  an  important  determinant  

of  identity  that  divides  interests,  it  is  imperative  that  it  be  accounted  for  when  

analyzing  the  effects  of  reservations  for  women.  Given  historical  and  still  currently  

relevant  social  disparities  within  India,  I  do  not  expect  to  find  a  positive  causal  

relationship  between  reservations  for  women  and  rates  of  rapes  committed  against  

women  from  scheduled  castes  and  tribes.  The  causal  model  behind  this  is  informed  

by  Clots-­‐Figueras’  and  Bardhan  et  al’s  articles,  both  of  which  conclude  that  the  

effects  of  representation  for  women  are  not  equally  distributed  across  classes  and  

communities  of  varying  status.  

  In  Bardhan  et  al’s  article,  “Impact  of  Political  Reservations  in  West  Bengal  

Local  Governments  on  Anti-­‐Poverty  Targeting”  (2010),  the  authors  use  India’s  

gender  quota  to  analyze  the  impacts  of  women  as  policy  makers  in  West  Bengal  

using  survey  data  from  16  agricultural  districts  within  the  state.  Bardhan  et  al  look  

specifically  at  outcomes  for  anti-­‐poverty  targeting  and  analyze  how  reservations  for  

women  impact  programs  aimed  at  women,  as  well  as  how  they  impact  initiatives  for  

scheduled  castes  and  tribes.  The  authors  also  do  the  same  for  SC/ST  reservations  in  

  17  

an  effort  to  see  if  elected  politicians’  policy  decisions  cater  solely  to  their  own  

disadvantaged  group  or  are  all  encompassing.    

  To  avoid  potential  heterogeneity  of  the  impact  of  reservations,  the  authors  

controlled  for  a  number  of  village  characteristics,  such  as  levels  of  land  inequality  

and  demographic  share  of  SC/ST  groups.  The  main  finding  with  regard  to  

reservations  for  women  is  that  there  is  no  associated  improvement  in  any  aspect  of  

anti-­‐poverty  targeting.  Furthermore,  the  authors  find  that  reservations  for  women  

have  a  negative  impact  on  intra-­‐village  anti-­‐poverty  targeting  to  SC/ST  groups.  On  

the  other  hand,  results  pertaining  to  reservations  for  SC/STs  differ  drastically;  the  

authors  found  that  there  are  significant  positive  effects  of  SC/ST  Pradhan  (council  

chief)  reservations  in  terms  of  village  level  benefits,  as  well  as  for  targeting  to  

female-­‐headed  households.  These  results  suggest  that  there  are  major  distinctions  

amongst  the  different  benefactors  of  quotas,  specifically  in  terms  of  reservations  for  

women.  These  findings  speak  to  the  importance  of  accounting  for  class  distinctions,  

such  as  caste  and  tribe  affiliations,  when  investigating  how  increased  representation  

for  women  affects  incidence  of  reporting  violence  against  women.  Bardhan  et  al’s  

results  indicate  that,  just  as  it  has  been  shown  that  anti-­‐poverty  targeting  to  

scheduled  castes  and  tribes  will  not  be  improved  in  the  presence  of  increased  

female  representation,  it  cannot  be  assumed  that  increased  representation  for  

women  will  improve  rates  of  reporting  violence  amongst  women  from  scheduled  

castes  and  tribes.      

  Another  study  that  supports  this  reasoning  is  Clots-­‐Figueras’  article,  “Women  

In  Politics:  Evidence  from  the  Indian  States”  (2011).  This  paper  looks  at  the  effects  

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of  female  representation  on  policy,  expenditure,  and  the  allocation  of  public  goods  

by  using  panel  data  for  the  16  main  states  in  India  from  1968-­‐2000  and  a  

regression-­‐discontinuity  design  to  identify  the  causal  effect  of  female  

representation,  in  both  SC/ST  and  general  seats.  Beyond  finding  that  politicians’  

gender  does  affect  policy,  Clots-­‐Figueras  also  finds  that  social  position,  namely  a  

politician’s  caste,  should  also  be  accounted  for  when  analyzing  effects  on  policy.    

  One  example  of  the  differences  in  policy  choices  between  women  politicians  

filling  seats  reserved  for  SC/STs  and  those  filling  general  seats  is  in  how  the  two  

groups  invest  in  education.  While  both  general  female  politicians  and  those  filling  

seats  reserved  for  SC/STs  favor  higher  education  levels  in  their  policy  making,  

SC/ST  female  politicians  invest  more  in  primary  education,  whereas  general  women  

politicians  invest  more  in  middle  and  secondary  education.  This  is  indicative  of  

class-­‐conscious  policy  choices,  because  for  general  female  politicians,  who  most  

often  represent  the  “upper  castes  [and]  educated  middle  classes,  higher  education  is  

something  more  accessible  and  more  important”  (Raman,  1999).  

  For  SC/ST  female  legislators,  however,  Clots-­‐Figueras  finds  that  increasing  

the  quality  of  and  access  to  basic  primary  education  is  far  more  important.  The  

results  also  indicate  that  SC/ST  female  legislators  favor  more  investments  in  beds  at  

hospitals,  pro-­‐poor  redistributive  policies,  and  “women-­‐friendly”  laws.  Female  

legislators  from  higher  castes,  however,  have  no  impact  on  “women-­‐friendly”  laws,  

reduce  social  expenditure,  and  oppose  land  reform,  which  along  with  investment  in  

higher  education,  indicates  that  there  are  distinct  differences  based  on  class  that  

impact  the  policy  decisions  of  female  legislators.  Given  Bardhan  et  al  and  Clots-­‐

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Figueras’  findings,  then,  the  expectation  is  that  women  from  SC/STs  will  not  be  

affected  in  the  same  ways  as  women  of  higher  social  status  when  it  comes  to  

increased  female  political  representation.  Thus,  women  from  SC/STs  will  not  feel  

empowered  or  more  confident  as  a  result  of  an  increase  in  female  political  

representation,  and  will  not  come  forward  to  report  violence  committed  against  

them  at  the  same  rate  as  women  of  higher  status.    

 

Problems  with  Causal  Inference  

 

  The  problem  with  making  inferences  about  the  effects  of  increased  female  

representation,  or  any  representation  by  any  group,  is  that  generally  this  

representation  is  not  random.  Most  often,  political  representation  at  any  level  is  the  

product  of  a  number  of  factors  that  have  contributed  to  electing  a  particular  

representative.  In  such  cases,  conclusions  about  the  effects  of  these  representatives  

cannot  be  deduced,  because  any  number  of  endogenous  factors  could  be  affecting  

both  the  presence  of  the  representative  and  whatever  outcome  is  being  observed.  

When  investigating  the  effects  of  a  policy  change  or  law,  in  this  case  an  amendment  

that  required  reservations  of  council  seats  for  women,  it  is  necessary  to  prove  that  

the  implemented  policy  was  randomly  assigned  across  the  sample  being  observed.  

Using  reservations  for  women  as  an  example,  had  the  reservation  policy  not  

been  imposed  across  all  17  of  the  major  states  in  India,  observing  the  effects  of  

increases  in  female  representation  in  only  states  that  chose  to  implement  

reservations  would  not  permit  causal  inference.  This  is  because  states  that  

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implemented  reservations  for  women  might  have  a  number  of  other  conditions  that  

might  positively  affect  rates  of  reporting  violence  against  women.  Conversely,  states  

that  were  unwilling  to  increase  representation  for  women  might  have  a  number  of  

other  women-­‐unfriendly  laws,  which  might  deter  women  from  reporting  violence  

committed  against  them.    

A  number  of  authors  have  used  India’s  gender  quota  as  an  as-­‐if  random  

independent  variable  in  order  to  investigate  the  effects  of  increased  political  

representation  on  various  outcomes.  One  of  the  first  articles  to  exploit  the  as-­‐if  

random  assignment  of  India’s  gender  quota  was  Chattopadhyay  and  Duflo’s  

“Women  as  Policy  Makers:  Evidence  From  a  Randomized  Policy  Experiment  in  

India”  (2004).  In  their  investigation,  the  authors  assess  the  effects  of  both  the  one-­‐

third  reservation  of  all  council  seats  as  well  as  the  rotating  reservation  of  one-­‐third  

of  Pradhan  positions  for  women.  The  purpose  of  their  investigation  is  to  analyze  the  

impacts  of  women’s  leadership  on  policy  making  using  detailed  survey  data  on  

investments  in  local  public  goods  in  a  number  of  villages  within  two  districts  in  

West  Bengal.  What  the  authors  find,  when  looking  at  both  the  effects  of  women  

council  seat  holders  and  women  Pradhans,  is  that  reservations  do  in  fact  affect  

policy  choices.  The  authors  conclude  that  the  policy  decisions  reflect  gender  

preferences,  with  women  choosing  to  “invest  more  in  infrastructure  directly  

relevant  to  the  needs  of  their  own  gender”  (Chattopadhyay,  2004;  p.  1409).    

In  Iyer  et  al’s  (2012)  article,  which  this  thesis  uses  as  a  model,  the  authors  

argue  that  they  are  “able  to  address  endogeneity  issues  by  taking  advantage  of  a  

unique,  countrywide  policy  experiment  in  India”  (Iyer,  2012;  p.  166)  In  reference  to  

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India’s  implementation  of  reservations  for  women,  Iyer  et  al  argue  that  because  the  

policy  was  “implemented  at  varying  dates  across  Indian  states,”  it  is  possible  to  

construct  a  differences-­‐in-­‐differences  design  using  a  randomized  independent  

variable.    

While  the  varying  dates  of  implementation  across  states  addresses  the  

potential  endogeneity  of  the  passage  of  the  Panchayati  Raj  amendment  itself,  it  is  

possible  that  this  variation  in  timing  was  driven  by  state-­‐level  factors  that  could  be  

associated  with  rates  of  reporting  of  violent  crimes  against  women.  Iyer  et  al  

present  three  explanations  for  this  variation,  which  is  reported  in  Table  1.    

First,  several  states  already  had  some  level  of  reservation  for  women  in  their  

electoral  systems  prior  to  the  1993  amendment.  The  states  the  authors  reference  

are  Kerala,  Karnataka,  and  Maharashtra.    

In  the  cases  of  both  Kerala  and  Maharashtra,  Iyer  et  al’s  argument  in  favor  of  

the  as-­‐if  random  nature  of  their  reservations  is  that  both  states  proactively  

implemented  reservations  in  1991  and  1992  respectively,  because  the  73rd  

amendment,  which  was  introduced  in  national  parliament  that  year,  was  considered  

imminent.  Arguably,  the  reasoning  behind  their  initiation  of  reservations  for  women  

was  not  a  bias  towards  woman-­‐friendly  laws  that  might  impact  rates  of  reporting  

violence  against  women,  but  was  instead  motivated  by  convenience  and  a  statewide  

understanding  that  the  amendment  was  going  to  be  passed.    

Similarly,  Karnataka  implemented  reservations  for  women  before  the  

passing  of  the  73rd  amendment.  However,  in  Karnataka’s  case,  this  introduction  of  

gender  quotas  came  in  1987,  a  significant  amount  of  time  before  the  amendment  

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was  enacted  in  1993.  Here,  the  problem  of  bias  is  more  significant,  because  the  

reservation  was  substantial,  with  25  percent  of  local  Panchayat  seats  allotted  to  

women.  The  fact  that  Karnataka  also  reserved  30  percent  of  government  jobs  for  

women  serves  to  highlight  how  the  varying  implementation  dates  may  not  

represent  randomization  (Raman,  1999).  Karnataka’s  significant  steps  to  increase  

the  political  voice  of  women  and  implement  woman-­‐friendly  policies  could  also  

mean  that  other  aspects  of  the  state’s  policies  encourage  higher  rates  of  reporting  

crime  against  women.    

Iyer  et  al  account  for  these  individual  state  discrepancies  using  state  fixed  

effects,  which  are  “included  in  all  [their]  regressions  [to]  capture  time  invariant  

characteristics  across  states,  such  as  the  presence  of  a  prescheduled  local  

government  election”  (Iyer,  2012;  p.  170).    

The  second  explanation  behind  variation  in  differences  in  the  timing  of  the  

implementation  of  the  1993  amendment  is  that  some  states  chose  to  challenge  

portions  of  the  Panchayati  Raj  act  with  lawsuits.  The  authors  argue  that  these  

lawsuits  can  be  “regarded  as  reasonably  exogenous  factors  in  causing  the  delay,”  

because  none  of  the  lawsuits  related  specifically  to  objections  to  reservations  for  

women  (Iyer,  2012;  p.  171).  Bihar  is  one  state  that  chose  to  file  a  lawsuit,  which  

specifically  challenged  reservations  for  Other  Backwards  Classes.  The  distinction  

between  Scheduled  Castes  and  Other  Backwards  Classes  (OBCs)  is  that  the  latter  

represent  lower  classes  that  are  educationally  and  socially  disadvantaged,  while  

Scheduled  Castes  represent  dalits,  who  are  considered  part  of  India’s  untouchable  

caste,  making  them  the  most  marginalized  and  vulnerable  group  in  the  country.    

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Because  this  lawsuit  was  against  reservations  for  OBCs,  it  does  not  present  a  

challenge  to  the  as-­‐if  random  nature  of  Bihar’s  implementation  of  gender  quotas.  

Had  the  lawsuit  been  against  the  reservations  for  women,  however,  this  would  

suggest  a  state’s  bias  against  woman-­‐friendly  policies,  which  could  potentially  affect  

rates  of  reporting  violence  against  women.    

The  final  reason  presented  to  explain  variation  in  dates  is  that  some  states  

had  to  delay  elections  due  to  budgetary  constraints.  Assam,  for  example,  which  had  

elections  in  1992  and  therefore  should  have  implemented  the  reservations  in  

elections  by  1997,  did  not  do  so  until  2001.  In  order  to  substantiate  the  inclusion  of  

states  with  such  budgetary  constraints,  Iyer  et  al  argue  that  their  “main  results  are  

robust  to  the  exclusion  of  any  specific  state,”  which  implies  that  regardless  of  these  

aforementioned  challenges  to  randomization,  results  are  consistent  when  all  states  

are  included  in  analysis  and  when  some  states  are  dropped  (Iyer,  2012;  171).    

Ultimately,  despite  the  fact  that  the  explanations  for  premature  or  late  

implementation  of  the  gender  quota  could  be  considered  nonrandom,  Iyer  et  al  

account  for  these  variations  by  using  fixed  effects.  Further,  through  investigating  the  

specific  causes  associated  with  each  state,  the  argument  as  to  why  the  gender  quota  

remains  as-­‐if  random  becomes  stronger,  because  in  all  but  one  case-­‐  Karnataka-­‐  the  

explanation  for  the  variation  does  not  present  a  bias  in  favor  of  or  against  increased  

female  representation.    

In  their  second  analysis,  which  looks  only  at  reservation  of  Pradhan  seats  for  

women,  Iyer  et  al’s  argument  for  randomization  is  made  more  compelling,  both  by  

the  way  that  this  specific  reservation  is  administered,  and  because  a  number  of  

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other  political  scientists  have  also  used  the  Pradhan  reservations  as  the  randomized  

independent  variable  in  their  respective  papers,  thus  supporting  Iyer  et  al’s  case.    

To  implement  reservations  of  the  Pradhan  (council  chief)  position  for  women,  

districts  are  randomly  distributed  into  three  groups,  with  each  group  of  districts  

reserving  one  third  of  Pradhan  seats  for  women  each  election  cycle.  The  group  of  

districts  is  selected  by  a  rotating  cycle,  so  that  every  five  years,  one  of  the  groups  

will  have  districts  with  female  Pradhans,  while  the  other  two  groups  will  not  

implement  reservations.  Here,  fewer  problems  of  endogeneity  are  presented,  

because  of  the  completely  random  way  that  a  state’s  districts  are  divided  into  

groups,  and  the  rotating  cycle  used  to  decide  which  group  will  have  to  implement  

reservations  for  women  at  any  given  time.  

 

Research  Design  

 

  As  previously  discussed,  the  problem  with  trying  to  analyze  the  effects  of  

increased  representation  on  a  given  outcome  is  that  most  often,  an  increase  in  

representation  by  a  particular  group  is  the  result  of  extenuating  factors.  As  

demonstrated  in  a  number  of  previously  cited  papers  (Chattopadhyay  et  al,  2004;  

Bardhan  et  al,  2008;  Iyer  et  al,  2011;  Beaman  et  al,  2012),  India’s  gender  quotas  

present  a  solution  by  providing  an  opportunity  to  study  the  as-­‐if  random  

assignment  of  increases  in  female  representation  across  states  in  India  to  observe  a  

number  of  outcomes.      

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  Much  like  Chattopadhyay  and  Duflo  and  Iyer  et  al’s  research  designs,  this  

thesis  will  incorporate  two  aspects  of  Panchayati  Raj  reservations  for  women,  

looking  at  both  general  council  seats  and  Pradhan  positions  in  two  separate  

analyses.  The  first  set  of  regressions  will  look  specifically  at  the  reservation  of  

general  council  seats  to  assess  the  impact  of  increased  female  political  

representation  on  rates  of  violence  against  women  from  scheduled  castes  and  

tribes.  Here,  regressions  like  those  performed  by  Iyer  et  al  will  be  used  to  compare  

rates  of  violence  against  all  women  and  women  from  scheduled  castes  and  tribes.  

The  second  set  of  regressions  will  look  specifically  at  the  reservation  of  Pradhan  

seats  for  women  to  gauge  whether  there  is  an  effect  of  a  female  council  chief  on  

reported  rates  of  violence  committed  against  women  from  SC/STs.    

The  treatment  in  this  study  is  the  reservation  of  local  government  seats  for  

women,  and  the  analysis  will  be  of  rape  rates,  comparing  rates  before  and  after  

implementation  of  the  treatment.    

 

Testable  Hypotheses  

 

State-­‐Level  Analysis  

Hypothesis  1:  An  increase  in  the  number  of  women  holding  Panchayat  council  seats  

will  not  lead  to  an  increase  in  the  rate  of  reported  rapes  committed  against  women  

from  scheduled  castes  and  tribes.    

 

 

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District-­‐Level  Analysis  

Hypothesis  2:  An  increase  in  the  number  of  women  holding  Pradhan  (district  

chairwoman)  seats  will  not  lead  to  an  increase  in  the  rate  of  reported  rapes  

committed  against  women  from  scheduled  castes  and  tribes.    

In  this  paper,  the  independent  variable  will  be  increased  representation  for  

women,  which  will  be  represented  by  the  implementation  of  India’s  gender  quota.  

However,  the  actual  measurement  of  increased  representation  for  women  will  differ  

between  the  two  analyses.  In  the  first  analysis,  the  independent  variable  will  be  date  

of  implementation  of  the  Panchayati  Raj  Act,  which  resulted  in  the  introduction  of  

one  third  of  all  council  seats  being  reserved  for  women.  In  the  second  analysis,  the  

independent  variable  will  be  reservation  of  Pradhan  seats  for  women,  which  will  not  

be  represented  by  a  single  implementation  date,  but  will  instead  be  a  dummy  

variable  that  represents  whether  a  district  had  a  reservation  for  women  within  a  

given  year.    

  For  both  analyses,  the  dependent  variables  will  be  rates  of  rapes  committed  

against  women  from  scheduled  castes  and  tribes  respectively.  Overall  rape  rates  

against  all  women  will  also  be  used  as  a  dependent  variable  in  order  to  compare  the  

effects  of  increased  representation  for  women  on  all  women  as  opposed  to  women  

from  marginalized  sections  of  Indian  society.  The  expectation  is  that  while  there  will  

be  an  observed  increase  in  rape  rates  committed  against  all  women,  the  same  

increase  will  not  be  found  when  comparing  rape  rates  of  women  from  scheduled  

castes  and  tribes.    

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  In  order  to  account  for  other  factors  that  may  affect  the  dependent  variables,  

the  following  controls  will  be  utilized:  overall  literacy  rates,  female  literacy  rates,  

male-­‐female  ratio,  whether  a  region  is  rural  or  urban,  the  proportion  of  a  state’s  

population  working  in  farming,  state  GDP  per  capita,  strength  of  police  as  measured  

by  number  of  police  per  1000  people,  and  presence  of  a  female  chief  executive.  The  

purpose  of  including  this  range  of  variables  is  to  control  for  economic,  political,  and  

social  development  factors  that  could  impact  crime  statistics,  specifically  rape  rates,  

within  a  state.  Finally,  to  account  for  unmeasured  state  specific  factors  and  state  

specific  time  trends,  fixed  effects  will  be  used,  along  with  an  interaction  term  for  

each  state  and  year  observation.    

 

Description  of  Data    

 India’s  National  Crime  Records  Bureau  does  not  specifically  document  crimes  

committed  against  women  from  scheduled  castes  and  tribes.  However,  it  does  

report  rapes  of  members  of  scheduled  castes  and  tribes.  Assuming  that  the  vast  

majority  of  these  reported  rapes  are  of  female  victims,  rape  rates  plausibly  

represent  crimes  committed  against  women  from  scheduled  castes  and  tribes.  It  is  

important  to  note  that  despite  NCRB  beginning  to  make  available  data  on  rapes  

committed  against  women  from  scheduled  castes  and  tribes  in  1992,  many  states  

are  not  consistent  in  their  reporting  of  violence  against  these  marginalized  groups.  

Thus,  there  are  a  number  of  states  missing  data  for  rapes  committed  against  women  

from  scheduled  castes  or  tribes  in  various  years  between  1992  and  2007.    

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The  limitations  in  data  available  for  violent  crimes  committed  against  women  

from  scheduled  castes  and  tribes  also  eliminate  the  possibility  of  distinguishing  

between  actual  rape  rates  and  rates  of  reporting  rapes.  In  Iyer  et  al  (2012),  

distinguishing  between  increases  in  actual  crime  rates  as  opposed  to  an  increase  in  

rates  of  reporting  required  a  large  number  of  crime  variables  specifically  pertaining  

to  women.  With  this  data,  they  ran  regressions  on  different  types  of  crimes  to  

compare  which  types  of  crimes-­‐  those  more  likely  versus  those  less  likely  to  go  

underreported-­‐  saw  an  increase  in  rates  after  the  implementation  of  reservations  

for  women.  However,  Iyer  et  al  found  that  rape  is  a  type  of  crime  more  likely  to  go  

underreported,  and  is  one  for  which  they  observed  significant  increases  after  the  

implementation  of  reservations  for  women.  The  assumption  for  this  thesis  is  thus  

that  fluctuations  in  rape  rates  represent  fluctuations  in  rates  of  reporting,  as  

opposed  to  actual  increases  or  decreases  in  the  number  of  rapes  committed.    

In  the  state-­‐level  analysis,  my  independent  variable  is  coded  from  state-­‐level  

data  on  the  date  of  Panchayati  Raj  implementation,  specifically  the  date  of  the  first  

cycle  of  elections  within  each  state  that  adhered  to  the  provisions  of  the  Panchayati  

Raj’s  73rd  amendment.  I  use  data  collected  by  Iyer  et  al  for  their  original  dataset,  in  

which  the  variable  is  coded  as  a  dummy  variable,  where  1  indicates  the  presence  of  

reservations  for  women  and  0  otherwise.  The  three  dependent  variables  in  my  

state-­‐level  analysis  are  drawn  from  state-­‐level  data  on  crime  rates  across  India  and  

were  collected  by  Iyer  et  al  from  India’s  National  Crime  Records  Bureau.  Rape  rates  

for  all  women  are  divided  by  the  entire  population  (women  and  men)  for  each  state,  

while  rape  rates  for  women  from  scheduled  castes  and  tribes  are  divided  by  the  

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respective  populations  in  each  state.  Population  data  for  all  women  came  from  Iyer  

et  al’s  dataset;  I  obtained  the  scheduled  caste  and  tribe  population  data  from  India’s  

1981,  1991,  2001,  and  2011  censuses.    

Data  for  control  variables  came  from  Iyer  et  al  (2011),  and  were  originally  

collected  from  a  number  of  sources,  including  India’s  1981,  1991,  and  2001  

censuses  and  the  Government  of  India’s  Ministry  of  Statistics  and  Program  

Implementation.  

In  the  state-­‐level  analysis,  the  years  observed  range  from  1992-­‐2007.  In  Iyer  

et  al’s  analysis  of  crimes  committed  against  all  women,  the  years  ranged  from  1985-­‐

2007.  However,  because  rapes  committed  against  women  from  scheduled  castes  

and  tribes  were  not  recorded  until  1992,  my  analysis’  years  of  observations  are  

limited.  A  summary  of  all  variables  used  in  my  state-­‐level  analysis  can  be  found  in  

Table  2.      

For  the  district-­‐level  analysis,  district-­‐level  electoral  data  on  which  districts  

have  had  to  reserve  Pradhan  positions  for  females  was  used  to  code  the  

independent  variable.  Iyer  et  al  collected  this  data  from  the  ten  state  government  

websites  that  made  this  data  readily  available.  Again,  the  independent  variable  is  a  

dummy,  coded  as  1  when  a  district  has  a  female  Pradhan,  and  0  otherwise.  District-­‐

level  crime  data  was  used  to  construct  the  dependent  variable.  Because  Iyer  et  al  

only  report  district-­‐level  data  on  overall  crimes  against  women,  I  retrieved  district-­‐

level  data  on  reported  rapes  of  all  women  and  reported  rapes  of  women  from  

scheduled  castes  and  tribes  from  India’s  National  Crime  Records  Bureau.  For  this  

analysis,  the  observed  years  run  from  2001  to  2007,  as  opposed  to  Iyer  et  al’s  

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observations,  which  span  from  1992  to  2007.  This  is  a  result  of  limited  access  to  

crime  data  from  the  National  Crime  Records  Bureau,  which  currently  only  has  crime  

data  available  online  beginning  in  2001.  Data  for  control  variables  again  came  from  

Iyer  et  al  (2011),  and  were  originally  collected  from  a  number  of  sources,  including  

India’s  1981,  1991,  and  2001  censuses  and  the  Government  of  India’s  Ministry  of  

Statistics  and  Program  Implementation.  Summary  statistics  for  my  district-­‐level  

analysis  can  be  found  in  Table  3.        

 

Empirical  Method  &  Expected  Results    

 State-­‐Level  Analysis:  

  In  order  to  observe  the  effects  of  reservations  of  council  seats  for  women  on  

rates  of  reporting  rapes  against  women  from  scheduled  castes  and  tribes,  I  will  use  

the  following  equation:    

   

In  this  equation,  𝑅𝑎𝑝𝑒𝑠!"  represents  the  number  of  rapes  in  a  given  state  and  

year,  and  𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛!"  represents  the  total  population-­‐  both  men  and  women-­‐  of  

the  specific  group  being  analyzed  in  a  given  state  and  year.  For  example,  when  

looking  at  rape  rates  of  all  women,  the  denominator  will  be  the  entire  population  

within  a  state  in  a  given  year,  whereas  for  rape  rates  of  women  from  scheduled  

castes,  the  denominator  will  be  the  total  population  of  all  scheduled  castes  within  a  

state  in  a  given  year.  This  equation  will  be  run  three  times  to  determine  the  effects  

ln(𝑅𝑎𝑝𝑒𝑠!"/𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛!")=  𝛼!  +  𝛽!  +  𝑓𝐷!"  +  𝑑’𝑿𝒔𝒕  +  𝜀!"  

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of  the  gender  quota  on  all  women,  women  from  scheduled  castes,  and  women  from  

scheduled  tribes,  in  relation  to  their  relative  populations.    

    α!  represents  state  fixed  effects;  𝛽!represent  year  fixed  effects;  𝐷!"  is  the  

dummy  that  represents  the  reservation  implementation,  equaling  1  if  the  

reservation  for  women  was  implemented  in  that  year  or  any  year  after,  and  0  

otherwise;  and  𝑋!"  represents  a  number  of  state  and  time  varying  controls:  strength  

of  police  force,  a  state’s  GDP  per  capita,  female-­‐male  ratio,  overall  literacy  rates,  

female  literacy  rates,  urbanization,  proportion  of  state  population  in  farming,  

whether  a  state  has  a  female  chief  minister,  and  state  time  trends.  

The  purpose  of  the  fixed  effects  is  to  account  for  state  or  time-­‐specific  

variation  that  could  impact  rape  rates  across  states  and  over  time.  To  account  for  

potential  spikes  in  rape  rates  over  time  that  could  be  correlated  with  specific  state  

conditions,  all  standard  errors  are  clustered  at  the  state  level.    

In  this  state-­‐level  analysis,  the  coefficient  on  my  reservation  dummy  variable  

𝐷!" ,  which  indicates  the  year  in  which  the  electoral  gender  quotas  were  

implemented  in  a  given  state,  will  signify  whether  the  quota  had  an  effect  on  the  

reported  incidence  of  rapes  committed  against  all  women,  women  from  scheduled  

castes,  and  women  from  scheduled  tribes.  I  expect  that  the  coefficient  for  all  women  

will  be  positive  and  statistically  significant,  whereas  the  coefficients  for  women  from  

scheduled  castes  and  tribes  will  not  be  significantly  different  from  zero.  This  would  

suggest  that  increased  representation  for  women  does  not  have  a  positive  impact  on  

the  reporting  of  rapes  against  women  from  scheduled  castes  and  tribes.    

 

  32  

District-­‐Level  Analysis:    

  In  order  to  observe  the  effects  of  reservations  of  district  Pradhan  seats  for  

women  on  rates  of  reporting  rapes  against  women  from  scheduled  castes  and  tribes,  

the  following  equation  will  be  used:  

This  equation  is  similar  to  that  used  in  the  first  analysis  and  is  again  modeled  

on  the  equation  used  in  Iyer  et  al  (2012).  Here,  the  dependent  variable  is  the  rate  of  

rapes  within  a  given  district  during  a  given  year.  𝑎!  represents  district  fixed  effects,  

while  𝑏!  represents  year  fixed  effects.  The  main  independent  variable  is  

ChairPersondt,  which  is  a  dummy  variable  equaling  one  if  the  observed  district’s  

chairperson  in  a  given  year  is  reserved  for  a  woman  Pradhan,  and  zero  if  not.  

Controls  are  included  for  female-­‐male  population  ratio,  literacy  rates,  and  a  district’s  

level  of  urbanization.  𝐷!"  is  the  final  control,  which  accounts  for  the  timing  of  

Panchayati  Raj  implementation,  and  is  therefore  measured  at  the  state  level.  As  with  

my  first  analysis,  this  equation  will  be  run  three  times,  for  the  three  dependent  

variables:  rape  rates  of  all  women,  rape  rates  of  women  from  scheduled  castes,  and  

rape  rates  of  women  from  scheduled  tribes.  All  errors  are  clustered  at  the  district  

level  in  an  effort  to  account  for  district-­‐level  conditions  that  significantly  affect  rape  

rates.    

In  terms  of  expectations,  Iyer  et  al  found  that  having  a  Pradhan  seat  reserved  

for  a  woman  in  a  given  year  has  no  statistically  significant  effect  on  rates  of  

ln  (𝑅𝑎𝑝𝑒𝑠!"/𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛!")  =  𝑎!+  𝑏!+  𝑔𝐶ℎ𝑎𝑖𝑟𝑃𝑒𝑟𝑠𝑜𝑛!"+  𝑑’𝑋!"  +  f𝐷!"  +  𝑒!"  

  33  

reporting  crimes  committed  against  women.  Based  on  their  findings,  I  expect  that  

the  coefficient  on  the  ChairPerson  dummy  will  not  be  significantly  different  from  

zero  when  analyzing  the  effects  on  all  women,  women  from  scheduled  castes,  and  

women  from  scheduled  tribes.    

 

State-­‐Level  Analysis  Results         In  the  state-­‐level  analysis,  no  effect  is  seen  when  rape  rates  for  all  women  are  

regressed  on  the  implementation  of  reservations,  without  any  controls.  Similarly,  no  

increases  in  rates  of  reporting  among  all  women  are  seen  when  demographic,  

political  and  economic  controls  are  added,  or  when  controls  for  strength  of  police  

force  and  female  literacy  are  included.  However,  when  state-­‐specific  time  trends  are  

controlled  for,  the  implementation  of  the  gender  quota  is  associated  with  an  

approximate  9%  increase  in  reported  rapes  amongst  all  women  at  the  95%  

significance  level.  When  all  controls  are  included,  an  8.9%  increase  in  reported  

rapes  is  observed  at  the  95%  significance  level.  These  results  are  reported  in  Tables  

4-­‐10.  

These  are  smaller  effects  than  those  found  by  Iyer  et  al  (2012).  In  their  

analysis,  with  all  controls,  the  implementation  of  the  gender  quota  was  associated  

with  a  20%  increase  in  reported  rapes  against  all  women,  significant  at  the  1%  level.  

However,  the  two  analyses  use  different  samples;  in  order  to  compare  the  results  for  

all  rapes  to  those  for  rapes  of  women  from  scheduled  castes  and  tribes,  I  use  fewer  

years  of  observation.    

  34  

  Unlike  the  observed  significant  increase  in  rates  of  reporting  rapes  amongst  

all  women  after  the  implementation  of  the  gender  quota,  the  coefficients  for  the  

effect  of  the  gender  quota  on  reported  rape  rates  for  women  from  scheduled  castes  

and  tribes  remain  statistically  insignificant  regardless  of  controls.  These  findings  

suggest  that  the  positive  effects  of  increased  female  political  representation  are  not  

equally  distributed  across  all  populations  of  women.  Instead,  gender  quotas  appear  

to  be  ineffective  in  terms  of  empowering  women  from  scheduled  castes  and  tribes  to  

report  rapes  committed  against  them.    

Figures  1-­‐3  depict  rape  rates  for  all  three  populations  using  two  different  

colored  points  for  pre  and  post-­‐quota  implementation,  and  plotting  predicted  rape  

rates  from  regressions  of  rape  rates  on  year.  In  Figure  1,  which  displays  rape  rates  

for  all  women,  the  purple  dots  represent  rates  of  reporting  prior  to  implementation  

of  the  gender  quota  in  individual  states.  There  does  not  appear  to  be  an  upward  

sloping  pattern  over  time  in  these  purple  dots.  Green  dots  represent  rates  of  

reporting  after  the  implementation  of  the  gender  quota  in  individual  states.  There  

does  appear  to  be  an  upward  sloping  pattern  in  the  green  dots.  This  upward  sloping  

pattern  of  green  dots,  and  the  corresponding  upward  sloping  regression  line  for  all  

observations,  appear  to  represent  the  increasing  rates  of  reporting  rapes  amongst  

all  women  in  the  presence  of  the  gender  quota.    

However,  in  Figures  2  and  3,  which  display  scheduled  caste  and  tribe  rape  

rates  respectively,  there  are  no  upward  sloping  patterns  amongst  either  the  purple  

or  the  green  dots.  The  lack  of  a  clear  pattern  in  the  green  dots  and  the  

corresponding  regression  lines  for  all  observations  suggest  that  the  gender  quota  

  35  

had  no  effect  on  rates  of  reporting  rapes  amongst  women  from  either  scheduled  

castes  or  tribes.      

These  results  suggest  that  the  observed  increase  in  rates  of  reporting  rapes  

amongst  all  women  in  the  presence  of  increased  female  representation  does  not  

actually  apply  to  women  of  lower  caste  and  socioeconomic  status.  This  finding  is  

consistent  with  the  interpretation  that  increased  female  representation  does  not  

empower  women  of  lower  caste  and  socioeconomic  status  to  report  violence  

committed  against  them  in  a  pattern  similar  to  their  higher  caste  counterparts.      

 

State-­‐Level  Analysis  Robustness    

 

  Because  India’s  gender  quota  was  implemented  in  1993  at  the  federal  level,  

the  consequent  reservations  for  women  are  thought  to  not  be  subject  to  any  state-­‐

level  biases  or  conditions  that  could  have  affected  rates  of  reporting  violence  against  

women.  However,  as  seen  in  Table  1,  Karnataka,  Kerala,  Maharashtra,  and  Orissa  all  

implemented  gender  quotas  prior  to  1993.  In  order  to  ensure  that  the  results  from  

the  state-­‐level  analysis  aren’t  being  affected  by  any  pre-­‐existing  conditions  within  

these  four  states,  I  ran  the  same  regressions  as  reported  above  but  excluded  

observations  from  these  four  states.    

  The  results  of  this  first  robustness  test,  displayed  in  Table  11  and  Figure  4,  

are  consistent  with  my  initial  results.  When  excluding  the  states  that  implemented  

reservations  for  women  prior  to  1993  and  using  no  controls,  a  13.2%  increase  in  

rates  of  reporting  amongst  all  women  is  seen  at  the  95%  significance  level.  When  

  36  

controls  for  economic,  political,  and  demographic  or  police  strength  factors  are  

added,  no  significance  is  seen.  However,  controlling  for  state-­‐specific  time  trends  

yields  an  11.9%  increase  in  rates  of  reporting  for  all  women  at  the  1%  significance  

level.  Finally,  when  all  controls  are  added,  the  result  is  an  8.2%  increase  at  the  5%  

significance  level.  For  scheduled  castes  and  tribes,  there  are  no  significant  

coefficients  on  the  gender  quota  variable,  regardless  of  the  controls  included.  These  

results  further  suggest  that,  while  there  was  a  significant  effect  of  reservations  for  

women  on  rates  of  reporting  rapes  among  all  women,  gender  quotas  had  no  positive  

impact  on  rates  of  reporting  rapes  among  women  from  scheduled  castes  and  tribes.    

It  is,  however,  important  to  note  that  each  dependent  variable  in  Table  11  

(all  rapes,  scheduled  caste  rapes,  and  scheduled  tribe  rapes)  has  a  different  number  

of  observations.  There  are  more  observations  for  rapes  reported  by  all  women  -­‐  221  

observations  for  all  women,  183  for  SC  women,  and  121  for  ST  women-­‐,  because  of  

inconsistently  documented  rape  rates  for  scheduled  castes  and  tribes,  which  can  be  

seen  in  Table  12.  As  a  result,  the  larger  number  of  observations  for  all  rapes  may  

have  contributed  to  the  statistical  significance  of  the  gender  quota  variable  for  this  

sample.    

To  address  this  concern,  I  conducted  an  additional  robustness  test  wherein  I  

ran  the  same  regressions  on  all  rapes  as  those  reported  in  Table  11,  but  further  

restricted  the  samples  to  years  for  which  there  were  corresponding  observations  for  

scheduled  caste  rapes  and  then  scheduled  tribe  rapes.  The  results,  shown  in  Table  

13,  reveal  that  when  the  analysis  of  all  rapes  is  limited  to  states  and  years  wherein  

there  is  data  for  rapes  of  women  from  scheduled  castes,  there  is  still  a  significant  

  37  

increase  in  rates  of  reporting  all  rapes  as  a  function  of  the  implementation  of  gender  

quotas.  This  significance  disappears  for  an  analysis  of  all  rapes  in  states  and  years  

wherein  there  is  data  for  scheduled  tribe  rapes.  

These  results  support  the  inferences  made  above  about  the  effect  of  the  

gender  quotas  on  rates  of  reporting  rapes  among  all  women,  relative  to  rates  of  

reporting  rapes  among  women  from  scheduled  castes.  When  the  analysis  is  limited  

to  states  and  years  wherein  there  is  data  for  rapes  of  women  from  scheduled  castes,  

there  is  a  significant  increase  in  rates  of  reporting  all  rapes  as  a  function  of  the  

implementation  of  gender  quotas,  but  no  effect  on  rates  of  reporting  rapes  among  

women  from  scheduled  castes.  Again,  this  finding  is  consistent  with  the  

interpretation  that  reservations  for  women  are  not  equally  beneficial  for  the  most  

marginalized  groups  of  women  in  India.    

However,  when  the  analysis  is  limited  to  states  and  years  wherein  there  is  

data  for  rapes  of  women  from  scheduled  tribes,  there  is  no  effect  on  rates  of  

reporting  rapes  as  a  function  of  the  implementation  of  gender  quotas  either  for  all  

women,  or  for  women  from  scheduled  tribes.  This  is  most  likely  due  to  the  high  level  

of  missing  data  on  rapes  of  women  from  scheduled  tribes.  This  lack  of  data  means  

that  we  cannot  draw  robust  inferences  about  the  relative  effects  of  the  gender  quota  

on  rates  of  reporting  rapes  across  all  women  and  women  from  scheduled  tribes.  

This  robustness  test  draws  attention  to  the  problem  of  missing  data  on  

crimes  committed  against  women  from  scheduled  tribes  in  India.  As  seen  in  Table  

12,  Haryana  and  Punjab  do  not  report  any  crimes  committed  against  women  from  

scheduled  tribes  because  these  two  states  do  not  have  tribal  populations.  Similarly,  

  38  

the  failure  of  Tamil  Nadu  and  Himachal  Pradesh  to  consistently  report  crimes  

committed  against  women  from  scheduled  tribes  could  be  because  both  states  have  

such  small  tribal  populations.  However,  in  Assam,  Kashmir,  and  West  Bengal,  the  

inconsistently  reported  scheduled  tribe  rape  rates  cannot  be  attributed  to  small  

tribal  populations,  because  Assam  and  Kashmir  have  significant  tribal  populations,  

and  all  three  are  also  significantly  underreporting  for  scheduled  castes.      

Some  states  might  not  report  rape  rates  because  they  are  less  female-­‐friendly  

or  because  they  lack  the  resources  necessary  to  ensure  properly  recorded  data.  

Table  12  also  reports  state  demographic  data  that  might  measure  these  factors.  

However,  Assam  and  Kashmir  do  not  stand  out  significantly  in  terms  of  their  female-­‐

male  ratios,  fractions  of  literate  women,  or  state  GDP  per  capita.  In  the  case  of  West  

Bengal,  it  is  the  second  poorest  state  with  an  average  female-­‐male  ratio  and  the  

lowest  overall  literacy  rates  and  female  literacy  rates.    These  particular  

demographics  potentially  indicate  that  a  lack  of  funds  or  resources  could  contribute  

to  the  state’s  inconsistent  reporting.  Ultimately,  though,  there  are  no  clear  patterns  

or  parallels  indicating  why  these  states  only  inconsistently  report  rape  data  for  

women  from  scheduled  castes  and  tribes.    

 

District-­‐Level  Analysis  Results  

 

  The  district-­‐level  analysis  looks  at  how  the  election  of  a  female  chairperson  

in  one-­‐third  of  a  state’s  districts  affects  rates  of  reporting  rapes  amongst  different  

groups  of  women.  As  found  in  Iyer  et  al’s  investigation  of  reservations  for  female  

  39  

Pradhans,  the  effects  of  female  chairpersons  on  rates  of  reporting  rape  against  all  

women,  women  from  scheduled  castes,  or  women  from  scheduled  tribes  are  not  

significantly  different  from  zero.  These  results,  shown  in  Tables  14-­‐16,  imply  that  at  

higher  levels  of  government,  the  effect  of  elected  female  representatives  on  all  

reported  rapes  diminishes,  and  there  is  no  longer  a  difference  in  the  effect  of  the  

gender  quota  on  reported  rape  rates  across  these  three  populations.  

One  explanation  for  this  finding  could  be  that  female  representatives  with  

greater  proximity  to  victims  have  a  greater  impact  on  victims’  feelings  of  

empowerment,  because  they  are  more  visible.  The  visibility  of  female  

representatives  encourages  more  women  to  come  forward  with  the  belief  that  their  

cases  will  be  taken  seriously  and  handled  responsibly.  So,  because  female  Pradhans  

hold  a  higher  seat  in  local  government  than  the  women  elected  to  general  Panchayat  

seats,  the  assumption  made  by  female  constituents  might  be  that  female  Pradhans  

are  less  capable  of  exerting  influence  at  such  a  local  level,  whereas  the  women  

elected  to  general  seats  can  more  effectively  advocate  on  behalf  of  local  women.  The  

fact  that  no  difference  is  seen  amongst  women  from  scheduled  castes  and  tribes  is  

also  consistent  with  previous  findings,  because  it  reiterates  the  assertion  that  

women  from  scheduled  castes  and  tribes  are  not  feeling  empowered  by  increased  

female  political  representation  and  are  not  reaping  any  of  the  benefits.      

                         

 

 

  40  

Conclusion  

 

  My  thesis  addresses  whether  increased  female  representation  is  as  effective  

at  empowering  women  from  marginalized  groups  in  India,  as  it  is  at  empowering  

women  from  higher  status  groups.    

  In  my  state-­‐level  analysis,  my  initial  findings  are  consistent  with  Iyer  et  al’s  

results,  which  indicate  that  increased  political  representation  for  women  has  a  

positive  and  statistically  significant  impact  on  rates  of  reporting  crimes  committed  

against  all  women.    For  women  from  scheduled  castes  and  tribes,  however,  the  same  

increase  in  rates  of  reporting  is  not  observed.  For  women  from  scheduled  castes,  the  

lack  of  a  statistically  significant  increase  in  rates  of  reporting  after  the  introduction  

of  gender  quotas  does  not  appear  to  be  due  to  missing  data.    For  women  from  

scheduled  tribes,  however,  the  lack  of  a  statistically  significant  increase  in  rates  of  

reporting  after  the  implementation  of  gender  quotas  may  be  simply  due  to  missing  

data.    

In  my  district-­‐level  analysis,  as  in  Iyer  et  al,  the  effect  of  female  chief  

ministers  on  the  reporting  of  all  rapes  is  negligible.  There  is  also  no  effect  of  female  

chief  ministers  on  the  reporting  of  rapes  of  women  from  scheduled  castes  and  

tribes.  This  finding  suggests  that  gender  quotas  are  not  effective  at  empowering  

women  to  report  crimes  committed  against  them  when  female  political  

representatives  are  less  visible  and  accessible  to  constituents.    

The  findings  of  this  thesis  may  be  relevant  to  current  policy  debates.  The  

Indian  Parliament  is  currently  debating  the  Women’s  Reservation  Bill,  which  would  

  41  

further  extend  India’s  gender  quota  system  to  parliament.  The  bill  was  passed  in  the  

Rajya  Sabha  (upper  house  of  parliament)  in  2010  and  is  currently  awaiting  decision  

in  the  Lok  Sabha  (lower  house).  While  the  results  reported  here  speak  to  the  

positive  effects  that  increased  political  representation  for  women  can  have  on  rates  

of  reporting  violence  against  all  women,  they  also  raise  questions  about  possible  

shortcomings  of  gender  quotas.  My  results  suggest  that  quotas  alone  may  not  be  

enough  to  adequately  empower  and  improve  conditions  for  women  from  scheduled  

castes  and  tribes.  Thus,  legislators  may  need  to  move  beyond  quotas  for  women  or  

for  scheduled  castes  and  tribes  in  order  to  better  address  issues  plaguing  women  

from  these  vulnerable  populations.    

Finally,  perhaps  the  most  important  contribution  made  by  my  findings  is  that  

there  are  serious  deficiencies  regarding  the  collection  of  data  concerning  violence  

against  women  from  scheduled  castes  and  tribes  in  India.  A  more  complete  

understanding  of  the  circumstances  contributing  to  the  underreporting  of  this  data  

is  necessary  before  we  can  begin  to  understand  rates  of  reporting  violent  crimes  

committed  against  women  belonging  to  India’s  most  marginalized  groups.  Better  

policies  need  to  be  implemented  in  order  to  ensure  that  data  are  documented  within  

a  given  state  and  year  and  are  compiled  and  reported  nationally.    

       

  42  

Table  1    

Year  of  Women’s  Reservation  Implementation      

Year  of  Women's  Reservation  Implementation  

Number  of  States  

State  Name  

1987   1   Karnataka  1991   1   Kerala  1992   2   Maharashtra,  Orissa  1993   1   West  Bengal  1994   2   Punjab,  Madhya  Pradesh  1995   5   Gujarat,  Haryana,  Rajasthan,  Himachal  Pradesh,  Andhra  Pradesh  1996   1   Tamil  Nadu  2001   2   Kashmir,  Bihar  2002   1   Assam  2006   1   Uttar  Pradesh  

     

  43  

Table  2    

Summary  Statistics  for  State-­‐level  Analysis        

Variables   Observations   Mean   SD   Min   Max  All  Rapes  per  1000  total  pop   272   -­‐4.27   0.58   -­‐5.74   -­‐3.01  SC  Rapes  per  1000  SC  pop   247   -­‐5.61   1.33   -­‐9.9   -­‐3.21  ST  Rapes  per  1000  ST  pop   184   -­‐5.63   1.27   -­‐8.47   -­‐2.64  Year  women's  reservation  implemented  

391   1996   4.49   1987   2006  

Per  capita  state  GDP   391   1.67   0.8   0   4.24  Fraction  of  population  literate   391   0.51   0.12   0.27   0.81  Fraction  of  literate  women   391   0.42   0.14   0.14   0.8  Female-­‐male  ratio   391   0.94   0.05   0.86   1.07  Population  rural   391   0.75   0.09   0.51   0.92  Fraction  of  state  pop.  in  farming   391   0.18   0.05   0.02   0.29  Police  strength  (#  of  police/1000  pop.)   391   1.54   0.89   0.08   5.92  Female  Chief  Minister     391   0.08   0.27   0   1  

 Note:  All  rape  rates  in  logs.  Observations  range  from  1992-­‐2007.  Each  rape  rate  is  per  1000  total  population  of  the  respective  group.  Female  chief  minister  is  a  dummy  that  equals  1  if  a  state  has  a  female  chief  minister  and  0  if  male  chief  minister.          

  44  

Table  3    

Summary  Statistics  for  District-­‐level  Analysis      

Variable   Observations   Mean   Std.  Dev.   Min   Max  

All  Women  Rapes   1752   2.95   0.97   0   5.46  

SC  Rapes   924   0.81   0.72   0   3.09  

ST  Rapes   501   0.57   0.63   0   2.38  

Female  Chairperson   1128   0.29   0.42   0   1  

Female-­‐male  Ratio     1128   0.94   0.06   0.85   1.34  

Fraction  urban   1128   0.23   0.14   0.03   0.82  

Fraction  of  females  literate  

1128   0.48   0.15   0.17   0.85  

 Note:  All  rape  values  represent  total  rapes  in  a  given  year  in  a  given  district.  Rapes  are  in  logs  and  are  not  divided  by  population.  All  variables  are  at  the  district  level  and  represent  the  years  2001-­‐2006.            

  45  

Table  4      

State-­‐level  Analysis      

No  Controls      

            (1)   (2)   (3)  

VARIABLES  

All  Rapes  (per  1000  total  

pop)  

Scheduled  Caste  Rapes  

(per  1000  SC  pop)  

Scheduled  Tribe    Rapes    

(per  1000  ST  pop)    

             

Women’s  Reservation  Implemented     0.049   0.038   -­‐0.101  

 (0.050)   (0.118)   (0.174)  

Constant   -­‐4.605***   -­‐5.743***   -­‐5.833***  

 (0.054)   (0.157)   (0.222)  

       Observations   289   247   184  R-­‐squared   0.875   0.899   0.854  Standard  errors  in  parentheses  

   ***  p<0.01,  **  p<0.05,  *  p<0.1        

Note:  Regression  for  17  major  states  and  years  1992-­‐2007.  All  rape  variables  are  in  logs.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  corrected  for  clustering  at  the  state  level.          

  46  

Table  5    

State-­‐level  Analysis      

GDP  &  Demographic  Controls    

            (1)   (2)   (3)  

VARIABLES  

All  Rapes    (per  1000  total  

pop)  

Scheduled  Caste  Rapes  

(per  1000  SC  pop)  

Scheduled  Tribe    Rapes    

(per  1000  ST  pop)    

               Women’s  Reservation  Implemented   0.040   0.069   0.006  

 (0.048)   (0.114)   (0.169)  

Female-­‐male  ratio   -­‐1.861   -­‐7.169   -­‐6.846  

 (3.206)   (7.425)   (10.278)  

Fraction  rural   -­‐3.600***   -­‐9.793***   10.467*  

 (1.198)   (2.735)   (5.865)  

Fraction  of  state  population  literate   -­‐3.441***   0.765   1.843  

 (0.795)   (1.906)   (2.255)  

Fraction  of  state  population  in  farming   -­‐3.107   -­‐14.244***   -­‐30.712***  

 (1.888)   (4.604)   (6.080)  

Presence  of  female  chief  minister   -­‐0.100*   -­‐0.060   -­‐0.217  

 (0.057)   (0.120)   (0.159)  

State  per  capita  GDP   -­‐0.170***   0.190   0.005  

 (0.061)   (0.136)   (0.175)  

Constant   2.300   9.203   -­‐3.164  

 (3.262)   (7.663)   (9.690)  

       Observations   289   247   184  R-­‐squared   0.893   0.914   0.883  Standard  errors  in  parentheses  

   ***  p<0.01,  **  p<0.05,  *  p<0.1        

Note:  Regression  for  17  major  states  and  years  1992-­‐2007.  All  rape  variables  are  in  logs.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  corrected  for  clustering  at  the  state  level.          

  47  

Table  6    

State-­‐level  Analysis      

Police  Controls           (1)   (2)   (3)  

VARIABLES  

All  Rapes    (per  1000  total  

pop)  

Scheduled  Caste  Rapes  

(per  1000  SC  pop)  

Scheduled  Tribe    Rapes    

(per  1000  ST  pop)                    Women’s  Reservation  Implemented   0.041   0.072   0.017  

 (0.049)   (0.115)   (0.172)  

Female-­‐male  ratio   -­‐1.865   -­‐6.994   -­‐7.183  

 (3.212)   (7.460)   (10.345)  

Fraction  rural   -­‐3.601***   -­‐9.817***   10.261*  

 (1.201)   (2.742)   (5.906)  

Fraction  of  state  population  literate   -­‐3.463***   0.680   1.843  

 (0.824)   (1.927)   (2.261)  

Fraction  of  state  population  in  farming   -­‐3.066   -­‐14.118***   -­‐30.625***  

 (1.931)   (4.630)   (6.102)  

Presence  of  female  chief  minister   -­‐0.101*   -­‐0.065   -­‐0.211  

 (0.058)   (0.121)   (0.161)  

State  per  capita  GDP   -­‐0.171***   0.190   0.000  

 (0.062)   (0.137)   (0.176)  

Number  of  police  per  1000  state  population   -­‐0.005   -­‐0.047   0.097  

 (0.052)   (0.141)   (0.252)  

Constant   2.315   9.155   -­‐2.815  

 (3.272)   (7.681)   (9.760)  

       Observations   289   247   184  R-­‐squared   0.893   0.914   0.883  Standard  errors  in  parentheses  

   ***  p<0.01,  **  p<0.05,  *  p<0.1        

Note:  Regression  for  17  major  states  and  years  1992-­‐2007.  All  rape  variables  are  in  logs.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  corrected  for  clustering  at  the  state  level.        

  48  

Table  7    

State-­‐level  Analysis      

Female  Literacy  Controls      

    (1)   (2)   (3)  

VARIABLES  

All  Rapes    (per  1000  total  

pop)  

Scheduled  Caste  Rapes  

(per  1000  SC  pop)  

Scheduled  Tribe    Rapes    

(per  1000  ST  pop)                    Women’s  Reservation  Implemented   0.044   0.056   0.014  

 (0.049)   (0.114)   (0.173)  

Female-­‐male  ratio   -­‐1.682   -­‐8.868   -­‐7.782  

 (3.223)   (7.471)   (10.678)  

Fraction  rural   -­‐2.996**   -­‐13.282***   9.467  

 (1.430)   (3.248)   (6.796)  

Fraction  of  state  population  literate   -­‐8.936   33.671**   8.390  

 (7.065)   (16.955)   (27.519)  

Fraction  of  state  population  in  farming   -­‐3.357*   -­‐12.384***   -­‐30.526***  

 (1.968)   (4.683)   (6.135)  

Presence  of  female  chief  minister   -­‐0.098*   -­‐0.085   -­‐0.217  

 (0.058)   (0.120)   (0.163)  

State  per  capita  GDP   -­‐0.183***   0.237*   -­‐0.005  

 (0.064)   (0.138)   (0.177)  

Number  of  police  per  1000  state  population   0.015   -­‐0.124   0.098  

 (0.058)   (0.145)   (0.253)  

Fraction  of  female  population  literate   5.203   -­‐31.631*   -­‐6.378  

 (6.671)   (16.152)   (26.716)  

Constant   2.386   9.727   -­‐2.318  

 (3.275)   (7.635)   (10.010)  

       Observations   289   247   184  R-­‐squared   0.893   0.915   0.883  Standard  errors  in  parentheses  

   ***  p<0.01,  **  p<0.05,  *  p<0.1        

Note:  Regression  for  17  major  states  and  years  1992-­‐2007.  All  rape  variables  are  in  logs.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  corrected  for  clustering  at  the  state  level.    

  49  

 Table  8  

 State-­‐level  Analysis  

 State-­‐Specific  Time  Trend  Controls    

      (1)   (2)   (3)  

VARIABLES  

All  Rapes    (per  1000  total  

pop)  

Scheduled  Caste  Rapes  

(per  1000  SC  pop)  

Scheduled  Tribe    Rapes    

(per  1000  ST  pop)    

               Women’s  Reservation  Implemented   0.090**   0.147   -­‐0.016  

 (0.035)   (0.126)   (0.195)  

State  x  Year  Interaction  term   0.008   0.023   0.067**  

 (0.007)   (0.025)   (0.031)  

Constant   -­‐60.719***   -­‐45.041*   -­‐66.056*  

 (6.864)   (22.980)   (34.103)  

       Observations   289   247   184  R-­‐squared   0.957   0.926   0.894  Standard  errors  in  parentheses  

   ***  p<0.01,  **  p<0.05,  *  p<0.1      

         Note:  Regression  for  17  major  states  and  years  1992-­‐2007.  All  rape  variables  are  in  logs.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  corrected  for  clustering  at  the  state  level.          

  50  

Table  9    

State-­‐level  Analysis    

All  Controls    

            (1)   (2)   (3)  

VARIABLES  

All  Rapes    (per  1000  total  

pop)  

Scheduled  Caste  Rapes  

(per  1000  SC  pop)  

Scheduled  Tribe    Rapes    

(per  1000  ST  pop)    

               Women’s  Reservation  Implemented   0.089**   0.089   0.054  

 (0.039)   (0.141)   (0.245)  

Female-­‐male  ratio   -­‐2.151   151.817   250.298  

 (28.324)   (102.785)   (207.277)  

Fraction  rural   3.212   -­‐77.019   -­‐51.805  

 (14.522)   (52.553)   (148.663)  

Fraction  of  state  population  literate   -­‐0.478   2.568   -­‐22.614  

 (5.309)   (19.653)   (30.089)  

Fraction  of  state  population  in  farming   -­‐1.125   144.653**   -­‐17.817  

 (17.910)   (71.331)   (98.246)  

Presence  of  female  chief  minister   -­‐0.035   0.027   -­‐0.520**  

 (0.041)   (0.125)   (0.199)  

State  per  capita  GDP   -­‐0.130**   0.041   0.026  

 (0.052)   (0.168)   (0.204)  

Number  of  police  per  1000  state  population   -­‐0.025   -­‐0.390**   0.056  

 (0.049)   (0.173)   (0.254)  

State  x  Year  Interaction  term   0.021   0.767*   0.112  

 (0.110)   (0.422)   (0.555)  

Constant   -­‐94.973   -­‐1,055.040   -­‐92.730  

 (185.633)   (721.823)   (1,001.596)  

       Observations   289   247   184  R-­‐squared   0.959   0.930   0.902  Standard  errors  in  parentheses  

   ***  p<0.01,  **  p<0.05,  *  p<0.1        

Note:  Regression  for  17  major  states  and  years  1992-­‐2007.  All  rape  variables  are  in  logs.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  corrected  for  clustering  at  the  state  level.          

  51  

Table  10    

State-­‐level  Analysis    

                                 Consolidated  Table    

   

Observations:  All  women-­‐  289  |  Scheduled  Castes-­‐  247  |  Scheduled  Tribes-­‐  184    

Note:  Regression  for  17  major  states  and  years  1992-­‐2007.  All  rape  variables  are  in  logs.  State  and  year  fixed  effects  included  in  all  six  regressions.  Standard  errors  are  in  brackets,  corrected  for  clustering  at  the  state  level.        

  52  

 Table  11  

 State-­‐level  Analysis    

 Robustness  Test  

 Regressions  excluding  pre-­‐1993  states  

Karnataka  (’87),  Kerala  (’91),  Maharashtra  (’92),  Orissa  (’92)    

   

Note:  Regression  for  13  major  states  and  years  1992-­‐2007.  All  rape  variables  are  in  logs.  State  and  year  fixed  effects  included  in  all  six  regressions.  Standard  errors  are  in  brackets,  corrected  for  clustering  at  the  state  level.        

  53  

Table  12    

State-­‐level  Analysis    

Consolidated  State-­‐level  Demographic  Table    

   

 Note:  This  table  displays  the  number  of  years  for  which  each  state  has  available  scheduled  caste  and  tribe  rape  rate  data.  It  also  includes  demographic  data  from  1995,  because  that  is  the  year  that  the  majority  of  states  implemented  reservations  for  women  in  local  levels  of  government.  Female  chief  minister  is  a  dummy  that  equals  1  if  a  state  has  a  female  chief  minister  and  0  if  their  chief  minister  is  male.  Scheduled  caste  and  tribe  population  data  is  from  the  1991  census.    

 *  SC/ST  data  was  not  recorded  for  Kashmir  in  the  1991  census.  The  percentage  shown  for  ST  %  of  population  in  Kashmir  is  from  the  2011  census  and  was  obtained  from  the  Government  of  India’s  Ministry  of  Tribal  Affairs  website.          

  54  

Table  13    

State-­‐level  Analysis    

Robustness  Test    Excluding  all  pre-­‐1993  states  &  limiting  sample  size  

      (1)   (2)   (3)  

VARIABLES  

All  Rape  Observations  1992  -­‐2007  

 

All  Rapes    (limiting  analysis  to  years/states  

with  corresponding    

SC  observations)  

All  Rapes    (limiting  analysis  to  years/states  with  corresponding    

ST  observations)                  Women’s  Reservation  Implemented   0.082**   0.083*   0.036  

 (0.038)   (0.045)   (0.063)  

Female-­‐male  ratio   -­‐42.999   -­‐28.107   26.901  

 (31.537)   (40.212)   (61.446)  

Fraction  rural   -­‐37.369***   -­‐59.102***   -­‐111.559***  

 (14.110)   (17.560)   (40.770)  

Fraction  of  state  population  literate   21.656***   19.198**   27.848***  

 (5.778)   (7.553)   (10.002)  

Fraction  of  state  population  in  farming   -­‐30.555   -­‐31.362   -­‐32.026  

 (21.096)   (29.358)   (32.379)  

Presence  of  female  chief  minister   -­‐0.016   -­‐0.020   -­‐0.014  

 (0.037)   (0.038)   (0.050)  

State  per  capita  GDP   -­‐0.047   -­‐0.021   -­‐0.042  

 (0.049)   (0.053)   (0.052)  

Number  of  police  per  1000  state  population   -­‐0.019   -­‐0.016   -­‐0.055  

 (0.045)   (0.053)   (0.062)  

State  x  Year  Interaction  term   -­‐0.484***   -­‐0.470**   -­‐0.644***  

 (0.147)   (0.210)   (0.244)  

Constant   871.495***   960.682***   1,358.913***  

 (252.220)   (367.468)   (414.982)  

       Observations   221   183   121  R-­‐squared   0.970   0.973   0.981  Standard  errors  in  parentheses  

   ***  p<0.01,  **  p<0.05,  *  p<0.1        

   

  55  

Table  14    

District-­‐level  Analysis  Pradhan  =  District  Chairperson  

 No  Controls  

 

            (1)   (2)   (3)  

VARIABLES   All  Rapes  Scheduled  Caste  

Rapes  Scheduled  Tribe  

Rapes                  Female  Pradhan   -­‐0.047   -­‐0.034   -­‐0.073  

 (0.051)   (0.075)   (0.126)  

Constant   3.110***   0.912***   0.755***  

 (0.032)   (0.062)   (0.093)  

       Observations   1,021   635   331  R-­‐squared   0.811   0.545   0.562  Robust  standard  errors  in  parentheses  

 ***  p<0.01,  **  p<0.05,  *  p<0.1        

Notes:  Regressions  are  for  188  districts  in  10  major  states  in  India  and  years  2001-­‐2006.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  and  are  clustered  at  the  district-­‐level.    All  rape  variables  are  in  logs.      

  56  

Table  15    

District-­‐level  Analysis    

Demographic  controls:  female  literacy,  urbanization,  and  female-­‐male  ratio  

            (1)   (2)   (3)  

VARIABLES   All  Rapes  Scheduled  Caste  

Rapes  Scheduled  Tribe  

Rapes                  Female  Pradhan   -­‐0.046   -­‐0.041   -­‐0.083  

 (0.051)   (0.077)   (0.131)  

Female-­‐male  ratio   -­‐1.553   -­‐17.806   -­‐28.530  

 (9.317)   (13.151)   (22.931)  

Population  urban   3.179   4.378   -­‐12.312  

 (5.185)   (8.834)   (22.164)  

Fraction  of  female  population  literate     0.486   3.092   -­‐1.608  

 (2.140)   (3.836)   (5.733)  

Observations   1,021   635   331  R-­‐squared   0.812   0.550   0.566  Robust  standard  errors  in  parentheses  

 ***  p<0.01,  **  p<0.05,  *  p<0.1        

 Notes:  Regressions  are  for  188  districts  in  10  major  states  in  India  and  years  2001-­‐2006.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  and  are  clustered  at  the  district-­‐level.    All  rape  variables  are  in  logs.      

  57  

Table  16    

District-­‐level  analysis      

State  Specific  Time  Trends  (interaction  term)  &  Demographic  controls:  

            (1)   (2)   (3)  

VARIABLES   All  Rapes    Scheduled  Caste  Rapes  

Scheduled  Tribe  Rapes  

               Female  Pradhan   -­‐0.072   -­‐0.048   -­‐0.124  

 (0.044)   (0.078)   (0.127)  

Female-­‐male  ratio   -­‐3.837   -­‐9.644   -­‐44.920*  

 (9.939)   (16.998)   (26.284)  

Population  urban   -­‐2.512   -­‐1.781   -­‐10.424  

 (7.151)   (11.678)   (23.854)  

Fraction  of  female  population  literate   4.306   9.541   10.582  

 (4.165)   (5.801)   (10.578)  

State  x  Year  Interaction  Term   -­‐0.045   -­‐0.143*   -­‐0.070  

 (0.066)   (0.085)   (0.167)  

Constant   -­‐12.749   196.300   191.717  

 (82.392)   (119.296)   (230.578)  

Observations   1,021   635   331  R-­‐squared   0.829   0.560   0.588  Robust  standard  errors  in  parentheses  

 ***  p<0.01,  **  p<0.05,  *  p<0.1        

 Notes:  Regressions  are  for  188  districts  in  10  major  states  in  India  and  years  2001-­‐2006.  State  and  year  fixed  effects  used.  Standard  errors  are  in  brackets,  and  are  clustered  at  the  district-­‐level.    All  rape  variables  are  in  logs.      

  58  

Figure 1

State-level Analysis

   Note:  this  graph  reports  rape  rates  for  all  women  from  1992-­‐2007.  Rape  rates  are  in  logs.  Purple  dots  indicate  state-­‐level  logged  rape  rates  prior  to  implementation  of  the  gender  quota,  while  green  dots  represent  post-­‐reservation  rape  rates.  The  fitted  values  line  represents  a  regression  of  logged  rape  rates  on  year  for  all  observations,  and  suggests  that  there  is  a  positive  and  statistically  significant  increase  in  rates  of  reporting  rapes  amongst  all  women  over  time.              

  59  

Figure  2    

State-­‐level  Analysis    

   Note:  this  graph  reports  rape  rates  for  women  from  scheduled  castes  from  1992-­‐2007.  Rape  rates  are  in  logs.  Purple  dots  indicate  state-­‐level  logged  rape  rates  prior  to  implementation  of  the  gender  quota,  while  green  dots  represent  post-­‐reservation  rape  rates.  The  fitted  values  line  represents  a  regression  of  logged  rape  rates  on  year  for  all  observations,  and  suggests  that  there  is  no  statistically  significant  change  in  rates  of  reporting  rapes  amongst  women  from  scheduled  castes  over  time.        

  60  

Figure  3    

State-­‐level  Analysis    

        Note:  this  graph  reports  rape  rates  for  women  from  scheduled  tribes  from  1992-­‐

2007.  Rape  rates  are  in  logs.  Purple  dots  indicate  state-­‐level  logged  rape  rates  prior  to  implementation  of  the  gender  quota,  while  green  dots  represent  post-­‐reservation  rape  rates.  The  fitted  values  line  represents  a  regression  of  logged  rape  rates  on  year  for  all  observations,  and  suggests  that  there  is  no  statistically  significant  change  in  rates  of  reporting  rapes  amongst  women  from  scheduled  tribes  over  time.      

  61  

 Figure  4  

 State-­‐level  Analysis  

 Robustness  Test  

Excluding  Karnataka,  Kerala,  Maharashtra,  &  Orissa    

   Note:  this  graph  reports  rape  rates  for  all  women  from  1993-­‐2007  for  those  states  that  implemented  reservations  for  women  after  1992.  Rape  rates  are  in  logs.  Purple  dots  indicate  state-­‐level  logged  rape  rates  prior  to  implementation  of  the  gender  quota,  while  green  dots  represent  post-­‐reservation  rape  rates.  The  fitted  values  line  represents  a  regression  of  logged  rape  rates  on  year  for  all  observations,  and  suggests  that  there  is  a  positive  and  statistically  significant  increase  in  rates  of  reporting  rapes  amongst  all  women  over  time  in  states  that  implemented  reservations  after  1992.      

  62  

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