SOCIAL GROUP IDENTITY, ELECTORAL RESERVATIONS, AND ENTREPRENEURSHIP Venkata Krishna Nadella Indiana University, Bloomington October 11, 2017 UNU-WIDER Helsinki, Finland
OUTLINE Caste as social groups Quota system and electoral reservations Literature and Research question Data and variables Baseline comparisons Analysis: Matched-pair (policy feature) and RDD (politics) Working hypotheses
CASTE AS SOCIAL GROUPS Caste groups are endogamous social groups whose membership is confined by birth (Ketkar, 1909) Hundreds of localized caste groups across the country; with identity formation routed in history of human displacement (Ambedkar, 1944) Genome research traces intermarriage restrictions to 70 generations ago with asymmetric mixing between immigrant and indigenous populations irrespective of ancestry (Basu et al., 2016) Socio-cultural norms restricting intermarriage among large populations has created a system of social stratification, with strong in-group identities, most dominant in rural India (Karve, 1961; Srinivas, 1976)
THE QUOTA SYSTEM: RESERVATIONS Indian constitution recognizes dalit (SCs) and adivasi (ST) populations for targeted affirmative-action policies through the quota system, which includes Public, and ‘publicly-funded’, education Public sector employment including administrative civil service Parliamentary and state elections
ELECTORAL RESERVATIONS Electoral reservations for SCs and STs exist at the national (parliamentary) and state (assembly) levels of government and are the most comprehensive mandate to ensure proportional political representation The delimitation commission assigns reservation status and constituency boundaries after every decennial population census In reserved constituencies, only members from SC or ST, are nominated to contest elections while the entire electorate votes Changes to the number and status of reserved constituencies have been made four times since Independence - 1952, 1962, 1972 & 2002. Reserved constituencies drafted in 1972 remained in place for elections between 1976 to 2008 (the time period of this study).
ONE VOTE ONE VALUE “ On the 26 th of January 1950, we are going to enter into a life of contradictions. In politics we will have equality and in social and economic life we will have inequality. In politics we will be recognizing the principle of one man one vote and one vote one value. In our social and economic life, we shall, by reason of our social and economic structure, continue to deny the principle of one man one value. ” (emphasis added) - B.R. Ambedkar addressing the Constituent Assembly of India on 25th November, 1949 Source: Constituent Assembly of India, Volume XI http://parliamentofindia.nic.in/ls/debates/vol11p11.htm
LITERATURE: TWO STYLIZED FACTS 1. Electoral quotas are effective in aggregating policy preferences of disadvantaged groups with regards to ‘ low spillover goods ’ Public spending (Pande, 2003) Public good provision (Besley et al., 2004; Chattopadhyay and Duflo, 2004) Poverty alleviation (Chin and Prakash, 2011) 2. Despite welfare gains over time, there are no relative gains of political representation on ‘ long-run development outcomes ’ of targeted groups Development indicators (Jensenius, 2015) Literacy rates (Bhavnani and Jensenius, 2017)
RESEARCH QUESTION/S Do electoral quotas improve allocation to productive entrepreneurship among disadvantaged social groups? Policy (and programmatic) evaluation approach focusing on 1. Target population: SC and ST populations in reserved constituencies 2. Treatment Condition: a) quota assignment, and b) effect of politics 3. Programmatic Aspects of Quota Policy and Implementation: Freezing of reserved status for large periods of time, and method of picking reserved constituencies
DATA CHALLENGE Secondary data sources, at the individual, household, enterprise, and industry levels, are sampled at the district level and cannot be disaggregated at geographies below district boundaries. As a consequence, reliable estimates for policy evaluations using secondary data sources can only be obtained at the district levels This challenge can be circumvented by using population and economic censuses of India, which focus on complete enumeration of household populations, with metadata on names of villages, towns, blocks, sub-districts, and other administrative boundaries.
CONSTITUENCY LEVEL DATASET Construction of the constituency level dataset is similar to Asher & Novosad (2017) by matching village and town names across waves of the economic census and population census datasets, AND identifying the location of these villages and towns within state assembly constituency boundaries using GIS metadata provided by ML Infomap (a private mapping firm based in New Delhi)
CENSUS DATA & VARIABLES PC-2001 PC-1971 PC-1991 Socio-economic variables Policy Change 1976 TOWNS MATCHED (1990 ‐ 2005) VILLAGES MATCHED (1990 ‐ 2005) EC-1990 EC-1998 EC-2005 Individual Enterprise variables
BASELINE COMPARISONS: 1991 POPULATION CENSUS Constituency Type Variables Overall GEN SC ST Rural Public Goods (Share villages), 1991 paved road access 0.61 0.63 0.56 0.53 power supply 0.80 0.83 0.77 0.64 primary schools 0.86 0.87 0.84 0.86 Urban public goods (real values), 1991 paved roads (kms.) 9.36 10.12 7.38 6.08 # electricity connections 2381 2547 2238 1072 # primary schools 6.85 7.30 6.26 3.73
GROWTH AND URBANIZATION (1990-2005) Constituency Type Variables Overall GEN SC ST Period 0 (1990-1998) Annualized Employment Growth (change in log emp) 0.020 0.020 0.021 0.024 Urbanization 1998 (prop. urban jobs) 0.21 0.22 0.20 0.16 Period 1 (1998-2005) Annualized Employment Growth (change in log emp) 0.029 0.030 0.030 0.021 Urbanization 2005 (prop. urban jobs) 0.31 0.33 0.28 0.24
EMPIRICAL SETTING (1): MATCHED PAIR The Delimitation commission of India assigns reservation status to constituencies, proportional to the population share within a state, on the basis of two criteria (Jensenius, 2015): 1. Highest proportion of SCs in the district in the state (rank) 2. Preference to geographical spread within the state (spread) This treatment assignment feature of the delimitation commission meant that constituencies within a district (in a state) that were very similar to the treated constituency in all aspects (with the exception atleast one of the above criteria) failed to receive treatment.
EMPIRICAL SETTING (2): MATCHED PAIR To evaluate the intended effect of treatment (reservation assignment) on the treated (reserved) units, I compare the outcomes in treated units by matching each treated constituency with a untreated (general) constituency. This approach is similar to Jensenius (2015) Data: Matched 279 reserved constituencies to general constituencies Treatment: SC Reservation Status for constituencies Outcomes of Interest: 1. Self-employment rates of Scheduled Caste populations 2. Employment by SC firms 3. Relative Gaps in SC and Non-SC economic outcomes
PRETREATMENT SAMPLE BALANCE Variable (1971 Population Census) General Reserved Difference p-value (1) (2) (2)-(1) Literacy Rate (Overall) 24.80 24.49 -0.30 0.24 SC Literacy Rate 11.91 11.97 0.06 0.789 Non-SC Literacy Rate 28.09 27.85 -0.25 0.365 Employment Rate (Overall) 32.83 32.84 0.01 0.896 SC Employment Rate 37.12 37.09 -0.03 0.856 Non-SC Employment Rate 31.82 31.82 0.00 0.986 Agri Labor (Overall) 21.25 20.96 -0.28 0.352 SC Agri Labor 53.24 52.74 -0.51 0.485 Non-SC Agri Labor 20.40 20.09 -0.31 0.357 SC Percent in Constituency 20.30 21.79 1.49 <0.001 SC Population 31,576 33,669 2,094 <0.001 Total Population 155,392 154,062 -1,330 <0.001
SC ECONOMIC OUTCOMES 1990 1998 2005 Difference p-value Difference p-value Difference p-value SC Self-employment Rates 0.011 0.011 0.003 0.188 0.002 0.498 SC Firms (1-9 employees) 0.001 0.250 0.001 0.149 0.002 0.018 SC Firms > 10 emps. -0.001 0.317 -0.001 0.540 0.000 0.483 SC Firms > 20 emps. 0.000 0.648 -0.001 0.330 0.000 0.503 All SC Firms employment 0.012 0.009 0.004 0.119 0.004 0.195 Notes: Standard errors clustered at state level
GAP IN ECONOMIC OUTCOMES Non SC minus SC comparisons. A negative sign means that gap is lower in reserved cons. 1990 1998 2005 Difference p-value Difference p-value Difference p-value Self-employment Rates -0.008 0.230 -0.005 0.474 0.004 0.439 Firms (1-9 employees) -0.001 0.800 -0.008 0.084 -0.005 0.276 Firms > 10 emps -0.014 0.097 0.002 0.699 -0.012 0.038 Firms > 20 emps -0.016 0.036 0.002 0.716 -0.010 0.049 All Firms employment -0.024 0.009 -0.011 0.229 -0.013 0.065 Notes: Standard errors clustered at state level
EMPIRICAL SETTING: RDD Political favoritism is a key mechanism through which ruling party constituencies gain favors from winning coalitions (Asher & Novosad, 2017) Specific mechanisms include local regulations (occupational licensing, health and safety standards), public contracting (big-ticket projects, grants), and political control over bureaucratic discretion (management of public infrastructure and rule of law) Instrument: Using the discontinuity in close elections for two time periods between the three waves of the economic census i.e.;1990- 1998; and 1998-2005
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