Does political competition lessen ethnic discrimination? Evidence from Sri Lanka

Journal of Development Economics 94 (2011) 277–289

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Journal of Development Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d eve c

Does political competition lessen ethnic discrimination? Evidence from Sri Lanka Iffath A. Sharif ⁎ South Asia Social Protection, The World Bank, 1818 H St. N.W., Washington, DC 20433, United States

a r t i c l e

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Article history: Received 28 May 2007 Received in revised form 7 January 2010 Accepted 8 January 2010 JEL classification: H11 I38 D72 Keywords: Political competition Ethnic discrimination Food stamps Sri Lanka

a b s t r a c t The impact of political competition on ethnic discrimination remains largely unexplored. To address this gap, this paper explores the relationship between the level of political competition and the probability of receiving government transfers among ethnic minorities in Sri Lanka in the run up to the national elections of 2000. The paper shows that making politicians dependent on the votes of members of ethnic groups other their own can encourage moderation in discriminatory practices towards ethnic minorities. Specifically we find that political competition positively influenced the distribution of government food stamps among Sri Lankan Tamils, who otherwise are less likely to receive food stamps relative to the Sinhalese majority. The negative impact of political competition on discrimination is higher when minorities form part of swing constituencies than when they form part of the base support for political parties. Lessons learnt here suggest that having built-in incentives in the design of the electoral process for intergroup bargaining and cooperation in countries with ethnically heterogeneous societies can be an effective restraint on ethnic discrimination. This is consistent with other research that considers political institutions to be a key lever for making ethnically divided societies more inclusive. © 2010 Elsevier B.V. All rights reserved.

1. Introduction There are strong moral and economic arguments that suggest ethnically diverse countries should ensure an inclusive society by giving minority groups a level-playing field in all aspects of life. Yet the reality remains that ethnic conflict often resulting from underlying ethnic discrimination persists in many countries. Despite the association made by neo-classical economists between sectarian conflicts and precapitalism, and the expectation that ethnic divisions fade away with economic development, ethnic discrimination and conflict persists even in growing economies.1 The recent example of Kenya where underlying ethnic inequalities ignited one of its worst periods of ethnic conflict is a case in point. Much of the discourse among economists on ethnic discrimination has largely focused on explanations for the phenomenon of ethnic discrimination2 and its consequences3, and much less so on how to address

⁎ Tel.: +1 202 473 9378; fax: +1 202 522 2955. E-mail address: [email protected]. 1 Some argue that market imperfections such as monopoly or oligopoly are plausible reasons behind persisting ethnic discrimination (Thurow, 1969; Bergmann, 1971). Another view sees discrimination arising from rational efforts by employers to minimize personnel — including information and transaction costs (Arrow, 1972). 2 See Becker (1957), Thurow (1969), Arrow (1972, 1998), Darity (1989), and Caselli and Coleman (2006). 3 See Alesina, Baqir, and Easterly (1999), Goldin and Katz (1997) La Ferrara (2002), Miguel and Gugerty (2005), Alesina and La Ferrara (2005), Habyarimana et al. (2007), and Karlan (2007).

0304-3878/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jdeveco.2010.01.006

it.4A plausible solution to this complex problem has been offered by leading political theorists who propose proportional representation for an electoral system in divided societies to allow for a peaceful coexistence among different socio-cultural groups (Horowitz, 1994; Lijphart, 1995; Bogaards, 2001; Wilkinson, 2004). They explain that under proportional representation strong electoral incentives force politicians in heterogeneous societies to seek the votes outside their own ethnic group to win elections. The necessity to engage in such “vote pooling” to win elections and maintain coalitions is what encourages politicians to accommodate the interests of ethnic minorities, and thus mitigate discriminatory practices.5 The underlying implications of these studies are that the conditions within which politicians may seek to not discriminate against minority groups include: (i) when minority groups are an important part of their party's current core support base; or (ii) when the overall electoral system is so competitive that there is a high probability that the governing party will need minority support (Horowitz, 1991; Wilkinson, 2004). Most of these theoretical observations on political competition and inter—ethnic cooperation are normative and have not been put to an empirical test, primarily because

4

See Bardhan (1997). There is however a contentious debate led over which form of proportional representation may generate moderation. Drawing any conclusions however, is premature and impossible partly because the ultimate effect on moderation depends on specific contexts and the quality of non-electoral institutions. See Bogaards (2001) for an excellent summary of the different schools of thought led by Lijphart and Horowitz on the type of electoral system that leads to moderation. 5

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of the lack of systematic data on ethnic discrimination.6 Using the context of many decades of ethnic heterogeneity and turmoil in Sri Lanka, this paper uses data on the allocation of government food stamps to see whether political competition affects the probability of receiving government transfers by ethnic minorities. Since one of the manifestations of ethnic discrimination is the lack of access to public resources such an analysis presents an empirical test of the hypothesis that democratic competition characterized by incentives provided by electoral arithmetic and the composition of constituencies can lessen ethnic discrimination. Our motivation to use the allocation of Samurdhi food stamps to test this hypothesis is rooted in the allegation that in the run up to general elections of October 2000 the allocation of Samurdhi benefits was politically motivated (Sunday Times, 2000). Parker and Silva (2000) and Salih (2000) also document the fact that program officials may have been under pressure to allocate food stamps to households in return for promises of support for the incumbent government party in the 2000 elections. The idea that the vote-maximizing behaviour of politicians influences the allocation of discretionary social assistance is well established and empirically shown in the literature on “pork barrel” politics7 (Cox and McCubbins, 1986; Lindbeck and Weibull, 1987; Dixit and Londregan, 1996; Schady, 2000; Case, 2001; Dahlberg and Johansson, 2002). These studies have shown that the resulting optimization problem for an incumbent seeking re-election is to tactically distribute discretionary funds to core voters as well as “marginal” or “swing” voters. These results are derived from the theory that voters base their decisions not only on the ideology of the candidate, but also on the changes in their welfare that they expect to see after an election.8 The basic electoral incentives model tested in this paper applies both the theory of vote pooling and of pork barrel politics to test whether the ethnicity of the voter matters in the allocation of Samurdhi food stamps in politically important areas. The paper uses data on divisional level voting patterns in the Sri Lankan general elections of 1994 and the 1999/2000 Sri Lankan Integrated Household Survey (SLIS) to test: (i) whether there were ethnic discrimination in the allocation of Samurdhi food stamps; and (ii) to what extent political competition undermined the ethnic discrimination effects in the allocation of these food stamps. The latter effect is teased out using two measures of political competition: the level of core and marginal support for the incumbent at the divisional level. The results lend support to the hypothesis that political competition helps to lessen discrimination towards ethnic minorities. We find evidence that Sri Lankan Tamil households who are generally less likely to receive government transfers relative to the majority Sinhalese population face a higher chance of receiving such transfers when they live in areas where the political competition is tight or the support for the incumbent political party is high. The negative impact of political competition on discrimination is higher when Tamil minorities form part of swing constituencies than when they form part of the base

6 Wilkinson (2004) is an exception in that he uses state and town level data to test the effect of electoral incentives as determined by the number of parties and the effective fractionalization of the electorate on ethnic riots between Hindus and Muslims in India and finds higher levels of party competition are associated with lower levels of Hindu-Muslim violence. While his analysis does not reflect discrimination per se, the implications of political competition on reduced ethnic tension can be extended to the analysis presented here. 7 A “pork barrel” project is a publicly funded project promoted by a legislator to bring money and jobs to his or her own district. The “pork” is allocated not on the basis of need, merit or entitlement; it is solely the result of political patronage, the desire of legislators to promote the interests of their own district, and thereby build up their local support. 8 Furthermore, Cox and McCubbins (1986) argue if candidates want to maximize the total number of votes won then it is the incumbent's attitude towards risk that will determine which type of voter is allocated the discretionary funds. Incumbents who are risk-averse will invest in their core supporters, whereas more risk-accepting incumbents will invest more aggressively in swing voters.

support. We try to minimize though not exclude the possibility of omitted variable bias in our results by using a host of household and community level variables including community level ethnic heterogeneity to control for differences between politically important areas and the rest of the country. Though not conclusive, the results presented here are consistent with other research that considers the design of political institutions to be a key lever for making ethnically divided societies more inclusive. The rest of the paper is organized as follows. Section 2 briefly describes the ethnic divisions prevalent in Sri Lankan society and the country's political history to set the research question into context. In Section 3, we explain the motivation behind using the Samurdhi Food Stamp Program to explore the question of tactical distribution of transfers and its impact on ethnic discrimination. The simple empirical strategy used to identify the effect of political competition on ethnic discrimination is explained in Section 4. Section 5 presents the empirical results while Section 6 concludes the paper. 2. Ethnic and political divisions in Sri Lanka Sri Lanka is an ethnically heterogeneous country with a population of around 20 million. The largest and most consequential division in Sri Lankan society is that between the majority Sinhalese who make up about 74% of the population and the Sri Lankan Tamils who account for around 13% (as per 1989 CIA estimate).9 Indian Tamils and the Muslim Moor population make up the remaining main ethnic groups. The Sinhalese dominate in the central, southern, and western parts of the country, while the Sri Lankan Tamils are concentrated in the north and east, especially in the Jaffna Peninsula to the far north. Parts of the north and east are also shared by the Sinhalese and Muslim population. The Indian Tamils, also known as “Estate Tamils,” are immigrants from South India and make up a small population mostly located in the central parts of the country. The Sinhalese speak their own language, known as Sinhala, and are predominantly Buddhists; the Tamils also speak their own language and are mainly Hindus. Sri Lanka has been in the midst of an ethnic conflict for the last 26 years which have resulted in clear ethnic divisions within the Sri Lankan society. The origin of the conflict however, dates back to the 60s when a bill was passed by a Sinhalese government making Sinhala the only official language of the country in response to a massive upsurge of Sinhalese nationalism. Since then there has been a widespread perception among Tamils that public services and jobs have been systematically denied to the Tamil population. Such Tamil grievances against both social and economic exclusion still persist to this day. Most Muslims although speak Tamil do not identify with the Sri Lankan Tamils, and have generally sided with the Sinhalese majority in this conflict. Sri Lankan political history suggests that the upsurge in Sinhalese nationalism was instigated by political divisions within the Sinhalese majority. Since the early 1960s, political competition between two mainly Sinhalese parties, the United National Party (UNP) and the Sri Lanka Freedom Party (SLFP) has dominated electoral politics in Sri Lanka. The nature of this political competition made it incumbent on each of the major Sinhalese parties to champion the cause of Sinhalese ethnic assertion against the interests of the Sri Lankan Tamils. Underlying this process of bidding and outbidding for the Sinhalese vote was an electoral system that translated small swings in popular votes into large swings in parliamentary seats. The system was firstpast-the-post (or election by plurality) in mainly single-member constituencies which made the vote of minority Tamils living in the Sinhalese-dominated Southern Sri Lanka insignificant. In such a situation an ethnic group does not have much to offer electorally to the moderates in a party of a dominant ethnic group. Rather, each party caught up in intra-ethnic competition has an incentive to pander to its 9 Census data since 2001 is only available for all provinces except the North and East where the majority of Sri Lankan Tamils live.

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extremist views. In the areas where the Tamils had a plurality, they had an overwhelming majority but only in a small number of constituencies in the North. The plurality system however, was changed in 1978 to a party list system with proportional representation in multi-member constituencies. Electoral incentives as a result were such that the Sinhalese political parties had to be more moderate towards ethnic minorities now that every vote in each constituency counted.10 Thus, the new electoral system could be expected to produce a change in the character of the party system whereby heterogeneity in the composition of the electorate would facilitate inter-ethnic cooperation on the basis of exchange of votes especially in areas where either party does not have a clear Sinhalese majority (Horowitz, 1989). The implication of these observations is that under normal circumstances discrimination towards ethnic minorities by both the UNP and SLFP would somehow lessen since both parties would have to try to: one, woo Tamils for their votes in marginal areas; and two, appeal to Tamil interests in the hope of forging coalitions with smaller Tamil parties11 in the event that a majority of parliamentary seats are not won. None of these observations however came to fruition due to a series of unfortunate chain of events. Soon after these changes in the electoral system, the Tamil separatist violence broke out in the North as did insurrectionary violence in the Sinhalese South that made electoral conditions anything but normal. The Tamil United Liberation Front, the leading Tamil political party, was excluded from parliament. Sinhalese and Tamil opinion became so polarized that the electoral system was unable to foster any moderation. Instead, extremist positions benefited the respective parties, and a full blown ethnic conflict led by the Liberation Tigers of Tamil Eelam (LTTE) engulfed the country in 1983. The 26 year old ethnic conflict finally ended in May 2009. It remains to be seen however what this would mean for Tamil grievances against both social and economic exclusion in a majority of Sinhalese society. 3. The political economy of the Samurdhi Food Stamp Program Sri Lanka has a long history of politically motivated government transfer programs. Transfer programs have often been set up by one government only to be replaced by a subsequent government of a different party. The main state-sponsored transfer program currently in place, the Samurdhi Food Stamp Program (SFSP), was initiated by the SFLP to replace the Janasaviya program that was developed by the previous UNP government. The program is implemented by an extensive administrative structure that runs parallel to the existing government structure with some overlap. There are district, divisional and zonal and village level Samurdhi officers. Samurdhi managers, the key persons in charge of program implementation at the zonal level, are accountable to the government officials at the District and Divisional levels. Samurdhi Development Officers, often hired on the recommendations of local politicians (Gunatilaka et al., 1997), are responsible for the screening of Samurdhi beneficiaries and for distributing food stamps at the village level. These officers identify potential beneficiaries using household questionnaires about income sources, living conditions, and possession of durable goods (World Bank, 2001).12 Households with a combined monthly income of less 10 A separately-elected presidency was also instituted whereby the president was elected by a system of preferential voting that accords weight to voters' second choices. Presidential candidates could hardly ignore Tamil interests under such a system: almost equitable Sinhalese divisions along party lines made it likely that the presidential elections would be decided on second preferences, particularly Tamil second preferences. 11 Since the onset of the Tamil separatist movement in the north of the country since 1983, many of the major Tamil political parties have not participated in electoral politics in the South but fielded candidates mainly in the North. 12 This means-tested targeting methodology has been replaced by a community based one in 2006.

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Table 1 Distribution of Samurdhi households by per capita expenditure quintiles. Pre-transfer per capita expenditure quintiles

Total sample

No. of Samurdhi households

% of Samurdhi households

Full sample Bottom 20thpercentile 20th–40th percentile 40th–60th percentile 60th–80th percentile Top 20th percentile

5524a 1043 1058 1020 1077 1326

2213b 659 581 457 339 177

100 30 26.2 20.6 15.3 7.8

a b

Six observations were dropped since these households lacked expenditure data. Two observations in the Samurdhi sample lacked expenditure data.

than Rs. 1500 are eligible for Samurdhi food stamps. Table 1 presents the distribution of the Samurdhi households by per capita pre-transfer expenditure quintiles which shows that a good proportion of households do not receive food stamps despite being eligible to do so. The district level allocation of Samurdhi funds is fixed by the central government. The fiscal transfer formula used favours in poorer districts. This pro-poor allocation formula is illustrated by a relatively high correlation coefficient of 0.814 between funds allocated to each district and district-specific poverty measures. However, the Sri Lanka Integrated Survey 1999/2000 (SLIS) data shows that poorer districts did not necessarily allocate significantly higher shares of their Samurdhi funds to the poor residing in these districts. The correlation between the share of district-specific Samurdhi funds transferred to the poorest strata13 and district-specific poverty rate is 0.017 (see Table A1 in Appendix A). These correlation coefficients suggest that within district allocation of Samurdhi funds was far from being pro-poor. Having an income-based eligibility criteria could be one reason for leakages in the allocation of the stamps.14 The other possible reason that is suggested is the somewhat discretionary nature of the allocations at the division level that allow political considerations to play a role in the distribution of these stamps (Gunatilaka et al., 1997; Parker and Silva, 2000; Salih, 2000). For example, although Samurdhi Managers supervised the distribution of the food stamps at the village level, there was no formal authority of the Divisional Secretary to oversee this process. This created a management vacuum at the divisional level, the domain where the actual program implementation decisions take place. This in turn allowed local area politicians to influence the distribution of the program funds once they were earmarked for each district. Allegations of such politicization of the program were widespread. For example, prior to the general elections of 2000, a daily newspaper reported that the government hired additional Samurdhi field workers and promoted many existing staff, all in violation of the program's employment policy (Daily News, July 18 and 20, 2000). The opposition parties alleged that such recruitment and promotions were meant to be incentives to help with the government party's re-election campaign in the guise of transfer payments. That Samurdhi workers are de facto political appointees makes it quite plausible that Samurdhi development officers succumb to pressure from politicians when carrying out their duties (Gunatilaka et al., 1997; Salih, 2000). The notion that the allocation of Samurdhi resources may have been politically motivated allows us to make empirical headway in isolating the impact political competition on the allocation of Samurdhi food stamps among ethnic minorities, particularly the Sri Lankan Tamil population in the run up to the elections of 2000.

13 The poorest strata are determined by the welfare ranking of households on the basis of their position in the per capita expenditure distribution before receiving Samurdhi food stamps. Expenditure measures were adjusted by province specific price indices that represented differences in the cost of living of the low income population. 14 Reported household incomes are quite volatile in less developed countries as has been documented by many studies.

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4. Identifying the effects of political competition on ethnic discrimination According to the theory on pork barrel politics, in a two-party framework, the main dilemma facing political parties when distributing discretionary funds is whether to favour core supporters or swing voters, and that the ultimate decision depends on the political objective functions of the parties. Lindbeck and Weibull (1987) and the Dixit and Londregan (1996) models suggest that if the objective of the political party is to maximize the number of total votes — as in some parliamentary elections — more funds will be allocated to swing areas where the race is tight. The Dixit and Londregan model explains that swing voters who are less attached to the candidate's political ideology will be more willing to switch votes in response to promises of economic benefits, and thus will be pursued by candidates of both parties.15 The decision to favour swing voters over core supporters is also suggested by Case (2001) but for a slightly different political objective. The Case model shows that to maximize the effects of allocating funds on the number of seats won in the legislature, more money is allocated to swing areas. This happens because for any given level of assistance, the marginal effect of assistance on the probability of winning a seat is higher when the partisan bias in favour of either party is lower. The Lindbeck and Weibull model discusses a third case where the political objective of the incumbent is to maximize the probability of winning a majority of seats in the legislature — as is needed to form an executive in some countries. In this case if the parties are not equally popular and the incumbent party has an overall advantage, funds are likely to be diverted to areas where there is a high level of core voter support (i.e. the core voter budget effect). Parties will place greater weight on these “safe” areas without which it would be hardest for the party to win a majority. The same result is discussed in a closely related paper on campaign spending (Snyder, 1989; Levitt and Snyder, 1995). These predictions are consistent with those provided by political science models that study the relationship between party competition and a state's response to anti-minority polarization and bias (Horowitz, 1991; Bardhan, 1997; Bogaards, 2001; Wilkinson, 2004). According to these models, given an electoral system based on proportional representation ethnic minority interests will be safeguarded when it is in their government's electoral interest to do so. This happens because this system encourages parties to appeal to a wide spectrum of society to maximize their overall national vote. While there is a fair amount of debate on the extent to which a proportional system generates vote pooling across ethnic lines the basic idea is that in all of the variants of the proportional representation system there are electoral incentives embedded that make the vote of minority groups count (Bogaards, 2001). For example, if the electoral system functions well in a multiparty system in an ethnically heterogeneous context, politicians in government will pander to minority interests when either of these two conditions applies: (i) when minority groups are an important part of their party's current support base, or the support base of one of their coalition partners in a coalition government (the core voter effect); or (ii) when the overall electoral system is so competitive that there is a high probability that the governing party will have to negotiate or form coalitions with minority supported parties in the future (the swing voter effect). Both sets of these models on electoral incentives suggest the following testable implication: political competition can lessen ethnic discrimination in the allocation of discretionary grants by incumbent

15 However, in a closely related paper Cox and McCubbins (1986) argue if candidates want to maximize the total number of votes won than it is the incumbent's attitude towards risk that will determine which type of voter is allocated the discretionary funds. Incumbents who are risk-averse will invest in their core supporters, whereas more risk-accepting incumbents will invest more aggressively in swing voters.

governments when ethnic minorities form part of the core and/or the swing voter base. The Sri Lankan electoral system, political landscape and the institutional set up of the Samurdhi Food Stamp Program offer an appropriate context within which to test the above hypothesis. The Sri Lankan Parliament consists of 225 members out of whom 196 members are elected from the 23 electoral districts that are drawn from the 25 administrative districts. The remaining 29 are allocated among the political parties that contest the election in proportion to the total number of votes polled by each party nation-wide. Voters indicate their choice of party on the ballot and then indicate their three preferences from the list of party candidates from that party. Once polling is closed, the party votes are counted first. This determines the seat allocation for each of the parties competing in the electorate. The votes for each candidate are then counted and the seats are allocated to those candidates who secure enough votes. For example, if the SLFP party wins three seats, the three candidates for the SLFP with the most votes in the electorate will be allocated seats. Thus on the one hand, the equitable nature of proportional representation is diluted by a constitutional provision that grants the party with the largest percentage of votes in each district a “bonus” seat in addition to those gained through proportional representation. On the other hand, parties have an incentive to field candidates who are moderate towards ethnic minorities in order for their candidates to get a maximum number of votes and win. Political parties are thus faced with a two-prong objective: to maximize the number of votes received and to maximize the probability of winning a majority of the seats. Consistent with a twoparty model in which the SLFP government was attempting to maximize the probability of its own re-election in 2000 the models on pork barrel politics and on vote pooling and ethnic conflict would suggest the following testable prediction: that a proportionately higher allocation of Samurdhi resources are allocated to areas of political importance such as swing areas and areas with high core SLFP support, and that ethnic minority groups, particularly Sri Lankan Tamils who make up the largest minority group, are likely to have a relatively higher probability of receiving Samurdhi resources in these areas. In what follows, we describe the empirical strategy used to tease out these political and ethnic effects on the distribution of Samurdhi food stamps among the ethnic minority population in the run up to the elections of 2000. The two main political parties, the United National Party (UNP) and the Sri Lanka Freedom Party (SLFP) were the leading contenders in both the 1994 and 2000 elections. The 2000 election was heavily influenced by the previous elections of 1994 which proved to be a game changer for the SLFP: the party leadership put together the People's Alliance (PA) coalition as an alternative approach to contesting the elections which was able to overturn the political dominance enjoyed by the UNP for the previous 17 consecutive years. Combined with the unexpected assassination of the UNP leader, Premadasa on May Day 1993, and the assuming of the SLFP leadership by the charismatic daughter of two previous presidents16, Chandrika Kumaratunga and her successful management of an unlikely coalition, the 1994 elections was an exogenous shock to the usual election culture that previously prevailed.17 Defying all expectations, the PA won the election of 1994 although by a small margin — it won 105 seats and formed a majority government by cobbling together a single vote majority of 113 with the help of its electoral ally, the Sri Lanka Muslim Congress and an independent. The UNP won 94 seats. The 2000 election results show a similar close race between the two parties: the PA won 107 seats while the UNP won 89 seats. Despite losing its parliamentary majority in the 2000 elections the PA still managed to

16

Mrs. Srimavo Bandarnaike and S.W.R.D Bandarnaike. See Schaffer (1995) for a detailed account of the 1994 elections and factors leading to the transformation of the political landscape in Sri Lanka. 17

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form a minority government due to support from the Sri Lanka Muslim Congress.18 During the 1994 elections, both the UNP and the PA represented the majority Sinhalese population. The PA also had the support of the Muslim population due to a pact with the Sri Lanka Muslim Congress. Smaller parties representing Sinhalese electorates also ran. The two main Tamil political parties, Tamil United Liberation Front (TULF) and the Tamil Eelam Liberation Organization (TELO), were the main political parties in the Northeastern Province. The support of the Sinhalese majority across Sri Lanka (except for the Northeast) is more or less evenly divided between the two main political parties — the SLFP and the UNP. The scenario did not change during the 2000 elections. Since our analysis excludes the data from the Northeast (due to sampling concerns), in areas in the rest of the country where the largest minority group, the Sri Lankan Tamils, were not represented by their respective ethnic political parties, it is plausible to expect the mainstream Sinhalese political parties to woo this group in politically important areas as part of a strategy to maximize votes as well as the probability of winning a majority of votes. Thus given the tight political competition faced by the PA in the 2000 elections, it is reasonable to expect the incumbent government party to reach out to Sri Lankan Tamils who form part of the core and swing voter blocks as part of its fundamental electoral strategy. Such a tactic should be reflected in the distribution of Samurdhi food stamps if they were indeed politically motivated. Whilst it is unrealistic to consider that any political party can observe voter preferences directly, it is reasonable to assume that the distribution of political preferences differs systematically across areas (Dixit and Londregan, 1996). Politicians can assess these differences by looking at geographic patterns in previous electoral outcomes. Deacon and Shapiro (1975) show that the probability that a randomly selected voter in an electoral district A will have voted for party X is equivalent to the share of the vote for party X in that district A. Thus voters in districts in which 50% of the population voted for the incumbent in the last election are most likely to be marginal voters — i.e. the probability they supported the incumbent is exactly the same as the probability that they supported any other candidate. Similarly, districts that overwhelmingly voted for the incumbent in the last election can be considered to be “safe” or “core support” areas. Thus, the extent to which a division is swing is proxied by the absolute deviation of the percentage of PA vote from 50 (e.g. |x − 50|) and the extent to which a division represents a core PA area by the percentage of PA vote (e.g. x) in the 1994 general elections.19 We use these political variables to assess the impact political competition may have on the allocation of Samurdhi food stamps to ethnic minority households. Accordingly, we estimate three simple participation equations, each of which captures three different effects — (i) the ethnic discrimination effect; (ii) the political competition effect; and (iii) the joint effect of political competition and ethnic bias — on the household probability of receiving Samurdhi food stamps. The first equation is a straightforward assessment of the determinants of participation in 18 However, the PA-led minority government turned out to be unstable. General elections were held again in 2001 in which the UNP managed to recapture governmental powers by winning a majority of parliamentary seats only to see the parliament dissolved in early 2004. SLFP formed another coalition for the elections of 2004 under the name of Freedom Alliance (FA) under the leadership of Mahinda Rajapakse who currently heads a minority government in Sri Lanka. Thus the 1994 elections served as the beginning of an era of SLFP dominance in Sri Lankan politics that is yet to end. 19 Both Case (2001) and Schady (2000) use this formulation for creating the political variables. However, it could be argued that in the case of three party or multi-party constituencies, the level of “swingness” of a division would have to be calculated differently. Since some of the constituencies in Sri Lanka have multiple parties competing, we explore this point further. We find that aside from the leading PA and UNP, the smaller parties and independent candidates together constituted on average of 3% of the total votes per district in the general elections of 1994 (excluding the Northeastern districts). Excluding such a small percentage of the voters in the calculation of the level of marginality of polling divisions is unlikely to have a significant impact on our measure of “swingness.”

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Samurdhi focusing essentially on household ethnicity. Early work on social identity theory established that patterns of intra-group behaviour can be understood by considering that individuals may attribute positive utility to the well being of members of their own clan or ethnicity, and a negative utility to that of members of other clans (Tajfel et al., 1971). This is consistent with Becker's (1957) formulation of a “discrimination coefficient” which measures the value placed on the non-pecuniary cost of a transaction with a member of a group with who one prefers to not associate. This would imply Samurdhi officers, who are mainly Sinhalese, may be more likely to select Sinhalese beneficiaries over other ethnic minorities when distributing Samurdhi food stamps. This discrimination effect would be particularly acute towards Sri Lankan Tamils given the prevailing ethnic conflict. We try to explore the extent of the ethnic bias in the allocation of Samurdhi food stamps in the following equation for household i at time t: Pi = ETHNICi ψ + Vi γ + Dϕ + ηid

ð1Þ

where Pi takes the value 1 if the household receives food stamps and 0 otherwise. ETHNICi takes the value 1 if the household belongs to an ethnic minority group; Vit is a vector of household and village characteristics that we might expect to affect household welfare as well as other characteristics that make households more likely to be selected into the program; D is a dummy variable representing the provinces in Sri Lanka (except for the Northeastern Province); and ηi is the error term which represents unmeasured and unobserved factors determining whether household i receives Samurdhi food stamps. We do not include the eligibility criteria since it is incomebased and thus unlikely to be strictly followed to select beneficiaries. Anecdotal evidence suggests Samurdhi officers essentially use household characteristics to determine eligibility (World Bank, 2001). In a second set of equations, we try to tease out the impact of a tactical increase in Samurdhi fund allocation to core and swing areas at the divisional level on the household probability of receiving Samurdhi food stamps. Despite poverty-based fixed Samurdhi allocations at the district level, we expect political objectives to motivate increased allocations of Samurdhi food stamps in “politically productive” divisions. Such a practice is likely to increase the probability of receiving Samurdhi food stamps by an average household living in core PA areas and/or in swing areas irrespective of the household poverty status. We try to capture this “political effect” in the following equation for household i at time t: Pit = Cdt−5 β + Sdt−5 χ + ETHNICit ψ + V it γ + Dϕ + ηidt :

ð2Þ

Cdt − 5 measures “the level of core support” or the percentage of votes received by PA in the general elections of 1994 in division d, and hence the time period is t − 5. Sdt − 5 measures the “swingness” of the division or the absolute value of the difference between the percentage votes received by the PA party in the 1994 general elections and 50. In the third set of equations, using simple interaction terms we estimate the impact of a tactical distribution of Samurdhi food stamps at the divisional level on the probability of receiving Samurdhi food stamps by ethnic minority households. We try to capture this “ethnopolitical effect” by the interaction between the political variables and the ethnic dummy variables as in the following equation for household i at time t: Pit = ETHNIC*Cdt−5 β + ETHNIC*Sdt−5 χ + ETHNICit ψ + Vit γ + Dϕ + ηidt :

ð3Þ A potential problem associated with Eqs. (2) and (3) is reverse causality should the 1994 electoral outcomes be “contaminated” by household receipt of Samurdhi food stamps in previous time periods. The

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Samurdhi program was set up in 1995 as a distinct PA government initiative. Thus the election results of the 1994 have the advantage of not being affected by specifically Samurdhi expenditures. One could argue however that the PA's election promise to reform the welfare program could have influenced the outcome of the 1994 election on the premise that these promises were regarded credible. However the win of the PA party has been largely attributed to “UNP fatigue” (Sives, 2001) and an effective coalition (Schaffer, 1995) and not to the promise of welfare reform. This observation is consistent with the fact that historically welfare reform has been part of the election manifesto of the SLFP in all previous elections (Sahn and Edirisinghe, 1993). Thus, there is no reason to believe that the promise of welfare reform will have a large impact on voters in the 1994 elections if it did not do so in the elections over the previous 17 years where the SLFP faced repeated electoral defeat by the UNP. Chart 1 presents the difference in the votes received by the UNP and the SFLP (difference=percentage of votes received by UNP− percentage of votes received by SLFP/PA) over the course of the election period from 1982 to 2000, i.e., since the introduction of the proportional representation system in 1978. We find that indeed 1994 presented a clear break for the SFLP. Rather, the key econometric issue needing consideration for our main analysis in Eq. (3) is whether tightly contested areas or swing areas and core PA areas are different in some way that also affects ethnic relations in those areas. For example are these areas more ethnically diverse than the rest of the sample which might suggest less social harmony? Is there a higher incidence of participation in village level informal groups which may indicate greater inter-ethnic

Chart 1 (continued).

cooperation? Are there other differences in terms of educational or income levels that might suggest less ethnic discrimination? We address these concerns by examining the data to control for any significant differences between core and swing areas with the rest of the country when estimating these equations. 5. Data and empirical results

Chart 1. District-wise difference in votes received by UNP and SFLP in parliamentary elections: 1982–2000.

The data used in this paper are from a nationally representative survey of 7500 Sri Lankan households conducted between October 1999 and July 2000. The Sri Lankan Ministry of Finance undertook this household survey, known as the Sri Lankan Integrated Household Survey 1999/2000 (SLIS), with technical and financial assistance from the World Bank. The SLIS is a multi-topic household survey in the style of a Living Standard Measurement Survey with modules on consumption, income, employment, health, nutrition, fertility, education and living conditions. It also includes household level questions on benefits received from the Samurdhi Food Stamp Program, along with a detailed community module. The data collected covered the war-torn Northeastern province. However, the analysis in this paper excludes this region because data collection was disrupted by the prevailing conflict conditions, and we expect sampling problems associated with the data from the Northeast. The exclusion of the Northeast leaves us with around 5530 households for the rest of Sri Lanka, out of which 2215

I.A. Sharif / Journal of Development Economics 94 (2011) 277–289 Table 2 The distribution of households by ethnicity. Ethnicity

Total sample (including the NE)

Total sample (excluding the NE)

Samurdhi households (excluding the NE)

Non-Samurdhi households (excluding the NE)

N Sinhalese Sri Lankan Tamil Indian Tamil Muslim Other Total

7401 78.36 13.34

5527 86.13 7.57

3313 92.48 3.63

2214 82.0 10.13

2.11

2.40

0.91

3.37

5.75 0.43 100

3.51 0.40 100

2.84 0.14 100

3.95 0.56 100

households received Samurdhi food stamps. Taking into account the sample weights, the Northeast sample amounted to about 12% of the total sample, which is consistent with this region's estimated share in the country's population. Using the sampling weights the residual sample by design is representative for the entire country excluding the Northeast. The distribution of the population by the main ethnic groups provided in Table 2, both including and excluding the North Eastern Province, shows that the ethnic distribution of the sample is fairly representative at the national level. The data also suggests that Sinhalese households are proportionately over-represented in Samurdhi compared to other minority groups. The SLIS data set does not however, have information on household level voting behaviour. Sri Lanka consists of 9 provinces divided into 25 districts, which in turn are divided into 324 divisions. The divisional level results of the October 1994 parliamentary elections were obtained from the Election Secretariat for the purpose of this paper and then merged with the SLIS. This allows us to create the two political variables that measure the extent of PA support in a division, and the extent to which the division is marginal. The interactions between the ethnic dummy variables and the political variables help identify the impact of political competition on the probability of ethnic minorities particularly Sri Lankan Tamils receiving Samurdhi food stamps just prior to the elections of 2000. Accordingly, our dependent variable takes the value 1 if the household reports to have received food stamps from Samurdhi in the last 12 months, and 0 otherwise. In addition to the ethnic dummy variables and political variables we include as control variables a host of observable characteristics that proxy for household poverty status. These include variables that represent household demographic information, the occupation of the household head, information on household ownership of land and non-land productive assets (e.g. livestock and farming equipment), the type of housing and sanitation available to the household, and whether the household has access to electricity. To control for any village level unobservables affecting household poverty, dummy variables are included that respectively take the value 1 if the household resides in what is considered as a traditional village; in an irrigation colony20; in a village expansion colony21; in a settlement scheme22; in a plantation estate23; in an urban slum; and in an urban

20 Irrigation colonies are areas in the dry zone in the North and Central parts of the country that have been irrigated for mostly paddy cultivation under an ambitious Mahaweli Development Project. 21 Village expansion colonies are settlements of peasants on land near traditional villages. These areas are generally backward with no proper water and sanitation systems; electricity; and access to roads, markets and health services. 22 Subsequent to land reforms in 1972, the Government of Sri Lanka distributed small tea, rubber, coconut estates as well as undeveloped land under land settlement schemes. Settlers are only allowed to cultivate the land while full ownership and rights of disposal remain with the Government. 23 Plantation estates are large commercial ventures that employ mostly Indian Tamil workers, and are located in geographically isolated areas.

283

middle class neighbourhood, and zero otherwise. Dummy variables are also included to control for household migration over the last 50 years. These variables are included since we expect households who have moved within the last five years to be less likely to receive Samurdhi food stamps. Change of location cancels any Samurdhi entitlements, and new applications are generally not encouraged. To control for another program policy we include another dummy variable that takes the value 1 if the household has a member in the Sri Lankan military, and zero otherwise. Households with members in the army are not required to be means tested and automatically become eligible to receive Samurdhi food stamps24 (World Bank, 2001). We also include a community level ethnic heterogeneity index25 in the participation equation to control for village level ethnic relations. Ethnic diversity has been widely used in the literature to proxy for interactions within and across ethnic groups to explain the underprovision of public goods. For instance Miguel and Gugerty (2005) provide evidence that higher levels of local ethnic diversity is associated with sharply lower household contributions to local schools and worse school facilities in western Kenya. These results are consistent with other studies in the literature on ethnic heterogeneity. Using data from urban areas in Kampala, Habyarimana et al. (2007) find that ethnic diversity is negatively related to collective action by communities. They observe that more ethnically fragmented neighbourhoods have more difficulty providing for their own security than do homogenous neighbourhoods. They run a series of experimental games to find that ethnic diversity has an independent impact on the likelihood that communities can organize collectively to improve their welfare. They explain that homogenous communities are better able to impose social sanctions on one another by virtue of shared norms and institutions. This finding is supported by other related studies on ethnic diversity. La Ferrara (2003) for instance using data on credit groups in Kenya finds that ethnicity matters for gaining access to group members who share the same ethnicity as the group chairperson are 20 to 25 percentage points more likely to borrow from the group or from other members. Karlan (2007) explains that having similar backgrounds often allow better monitoring and enforcement of group lending contracts, and he finds evidence to that effect among credit group members in Peru. Alesina and La Ferrara (2000) find greater social harmony among households located in homogeneous communities, measured by their ethnic composition. These studies along with a larger body of work (Easterly and Levine, 1997; Collier and Gunning, 1999; Alesina et al., 1999; Alesina, Baqir, and Easterly, 1999) suggest that ethnic fragmentation at the community level is associated with poor ethnic cooperation and thus constitutes an important control variable for our analysis. We expect a higher level of community ethnic heterogeneity to be associated with a lower probability of household participation in Samurdhi by ethnic minorities.26 Finally, dummy variables are included to control for any provincial level fixed effects. Table A2 in Appendix A provides descriptive statistics of the independent variables. Table 3 looks at the differences in a large number of characteristics between “swing areas” and “core areas,” and the rest of the sample respectively. Since both the variables representing the level of “swingness” and core support for the PA are continuous, we create 24 As of end of 2002, the Government cancelled this program and households with members in the military are no longer automatically eligible for Samurdhi food stamps. This policy was implemented as a cost-cutting measure. 25 The ethnic heterogeneity index is calculated as follows: 1 − ∑kski2 where s represents the proportion of households who belong to the ethnic group k in village i. This index captures the probability that two households randomly drawn from the population belong to two different ethnic groups. A higher index represents higher levels of ethnic heterogeneity. The index reaches 1 when every individual belongs to a different group and it becomes 0 when the population is homogenous. The ethnic groups used to calculate this index includes the Sinhalese, Sri Lankan Tamil, Indian Tamil, Sri Lankan Moors and other smaller ethnic groups such as Burghers and Malays. 26 It can also be argued that the mere presence of multiple ethnic identities does not inevitably produce ethnic tension, but on the contrary increase cohesiveness among various ethnic groups over a period of shared experiences and social interactions.

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I.A. Sharif / Journal of Development Economics 94 (2011) 277–289

Table 3 Comparisons between “Swing” and “Core PA” areas.

N Dependent variable Proportion of hh who receives Samurdhi food stamps

Explanatory variables % vote for PA in 1994 Abs. value (% vote for PA — 50) hh pre-transfer per capita monthly expenditure (Rs.) Years of education of household head Age of household head (years) No. of children aged 0 to 17 years No. of elderly (60+ years) Proportion of female-headed households Household size Proportion of households with disabled members Proportion of Sinhalese households Proportion of Sri Lankan Tamil households Proportion of Indian Tamil households Proportion of Muslim Moor households Proportion of households who own livestock Proportion of households who own farming assets Proportion of landless household Proportion of households owning less than 1 acre of land Proportion of households owning more than 1 acre of land Proportion of households with member in military Proportion of households who never moved Proportion of households who moved before 1950 Proportion of households who moved between 1950 and 1970 Proportion of households who moved between 1970 and 1990 Proportion of households who moved between 1990 and 1995 Proportion of households who moved between 1995 and 1999 Proportion of self-owned houses Proportion of attached houses Proportion of flats Proportion of shanties Proportion of other type of housing Proportion of hhs who own latrine Proportion of hhs who has no access to latrines Proportion of hhs who has ccess to community latrine

Total (COL 1)

Swing (COL 2)

Core (COL 3)

Swing vs ROC (p-value)

Core vs ROC (p-value)

COL2 vs COL3 (p-value)

5530

645

1837

0.401 (0.490)

0.405 (0.491)

0.43 (0.495)

0.828

0.00

0.282

50.790 (7.248) 5.508 (4.777) 8261 (7433) 7.434 (3.384) 50.406 (13.619) 1.267 (1.247) 0.395 (0.646) 0.100 (0.300) 4.515 (1.749) 0.310 (0.463) 0.861 (0.346) 0.073 (0.261) 0.024 (0.152) 0.051 (0.221) 0.078 (0.268) 0.293 (0.455) 0.297 (0.457) 0.180 (0.384) 0.115 (0.319) 0.046 (0.209) 0.703 (0.457) 0.025 (0.157) 0.088 (0.283) 0.143 (0.350) 0.020 (0.139) 0.012 (0.110) 0.883 (0.321) 0.049 (0.216) 0.010 (0.100) 0.016 (0.127) 0.042 (0.202) 0.863 (0.344) 0.092 (0.289) 0.037 (0.189)

49.9 (0.539) 0.459 (0.319) 8244 (6788) 7.762 (3.305) 48.96 (13.53) 1.20 (1.16) 0.3758 (0.632) 0.102 (0.303) 4.321 (1.57) 0.306 (0.461) 0.877 (0.328) 0.047 (0.210) 0.0201 (0.141) 0.053 (0.224) 0.079 (0.270) 0.297 (0.457) 0.311 (0.463) 0.203 (0.402) 0.107 (0.309) 0.062 (0.241) 0.722 (0.448) 0.020 (0.140) 0.104 (0.305) 0.109 (0.311) 0.032 (0.177) 0.011 (0.103) 0.896 (0.305) 0.036 (0.186) 0.017 (0.129) 0.012 (0.110) 0.0357 (0.185) 0.889 (0.313) 0.066 (0.249) 0.043 (0.196)

57.32 (2.52) 7.32 (2.53) 8007 (7583) 7.53 (3.350) 51.03 (14.03) 1.22 (1.23) 0.417 (0.656) 0.105 (0.307) 4.46 (1.73) 0.275 (0.446) 0.952 (0.218) 0.023 (0.152) 0.005 (0.073) 0.016 (0.128) 0.082 (0.274) 0.288 (0.453) 0.297 (0.457) 0.178 (0.383) 0.118 (0.322) 0.048 (0.213) 0.703 (0.457) 0.019 (0.136) 0.091 (0.289) 0.149 (0.356) 0.020 (0.141) 0.013 (0.113) 0.949 (0.220) 0.02 (0.135) 0.005 (0.073) 0.009 (0.095) 0.016 (0.127) 0.889 (0.314) 0.095 (0.29) 0.014 (0.118)

0.00

0.00

0.00

0.00

0.00

0.00

0.95

0.07

0.48

0.01

0.128

0.138

0.00

0.02

0.00

0.157

0.03

0.79

0.42

0.07

0.16

0.834

0.32

0.81

0.00

0.08

0.08

0.82

0.00

0.13

0.014

0.00

0.00

0.00

0.00

0.00

0.55

0.00

0.00

0.87

0.00

0.00

0.87

0.36

0.81

0.84

0.55

0.68

0.387

0.97

0.47

0.11

0.83

0.16

0.459

0.69

0.44

0.037

0.622

0.16

0.41

0.59

0.37

0.39

0.04

0.86

0.13

0.46

0.37

0.00

0.31

0.01

0.012

0.871

0.07

0.723

0.71

0.66

0.20

0.00

0.00

0.09

0.00

0.00

0.06

0.01

0.00

0.41

0.00

0.00

0.36

0.00

0.00

0.095

0.00

0.95

0.017

0.651

0.03

0.66

0.00

0.00

I.A. Sharif / Journal of Development Economics 94 (2011) 277–289

285

Table 3 (continued)

Explanatory variables Proportion of hhs who use electricity Proportion of hhs who has no light Proportion of hhs who use kerosene for light Proportion of traditional villages Proportion of irrigation colony Proportion of expansion colony Proportion of settlement scheme Proportion of urban low income neighbourhood Proportion of urban middle income neighbourhood Proportion of plantation estates Ethnic diversity Proportion of spontaneous Samurdhi savings & credit grps Proportion of officer-formed Samurdhi savings & credit grps Proportion of households in Western Province Proportion of households in Central Province Proportion of households in Southern Province Proportion of households in North Western Province Proportion of households in North Central Province Proportion of households in Uva Province Proportion of households in Sabaragamuwa Province

Total (COL 1)

Swing (COL 2)

Core (COL 3)

Swing vs ROC (p-value)

Core vs ROC (p-value)

COL2 vs COL3 (p-value)

0.611 (0.488) 0.037 (0.190) 0.333 (0.471) 0.733 (0.442) 0.050 (0.218) 0.055 (0.227) 0.059 (0.236) 0.011 (0.103) 0.075 (0.264) 0.043 (0.204) 0.129 (0.193) 0.195 (0.396) 0.031 (0.174) 0.24 (0.427) 0.166 (0.372) 0.165 (0.371) 0.126 (0.332) 0.091 (0.287) 0.097 (0.295) 0.117 (0.321)

0.631 (0.482) 0.038 (0.193) 0.324 (0.468) 0.706 (0.455) 0.066 (0.249) 0.063 (0.244) 0.046 (0.210) 0.023 (0.150) 0.046 (0.210) 0.046 (0.210) 0.138 (0.180) 0.181 (0.385) 0.076 (0.265) 0.251 (0.434) 0.068 (0.252) 0 0 0.204 (0.403) 0.203 (0.402) 0 0 0.273 (0.446)

0.651 (0.476) 0.044 (0.205) 0.300 (0.450) 0.743 (0.437) 0.046 (0.210) 0.015 (0124) 0.057 (0.232) 0 0 0.130 (0.336) 0.008 (0.09) 0.053 (0.053) 0.185 (0.384) 0.045 (0.206) 0.30 (0.462) 0 0 0.32 (0.466) 0.128 (0.334) 0.130 (0.335) 0.194 (0.073) 0.073 (0.260)

0.77

0.00

0.34

0.85

0.065

0.56

0.603

0.00

0.26

0.977

0.00

0.07

0.04

0.35

0.04

0.29

0.00

0.00

0.143

0.63

0.30

0.00

0.00

0.00

0.003

0.00

0.00

0.68

0.00

0.00

0.224

0.00

0.00

0.36

0.19

0.83

0.00

0.00

0.00

0.46

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.66

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

dummy variables to define extremely competitive areas and areas of high PA support. Divisions with a difference of 1% or less are defined as being swing areas whereas core areas are defined as divisions where the percentage of PA votes was equal or greater than 55%. This gives us a sub-sample of 645 and 1837 households living in “swing areas” and “core” areas respectively. We find households in swing areas are generally not significantly different from the rest of the sample other than in their political affiliation, household size, age and education levels of household head, location, and ethnicity. We find that households living in swing areas are smaller and household heads are younger and more educated compared to households living in the rest of the country (excluding the Northeast). Swing areas have significantly more Sinhalese residents and less Sri Lankan Tamil population than the rest of the country but are no less ethnically heterogeneous than the total sample. The percentage of households who join spontaneously formed credit and savings in swing areas is also not significantly different from the percentage of households who do the same in the rest of the sample. Swings areas are more likely to be found in irrigation colonies and low income neighbourhoods located mostly in the Northwestern and central provinces as well as in the Sabaragamuwa province located in the Southern part of the country. Households located in core areas have a few significant differences from the rest of the country. In addition to the political variables, these differences can be found in terms of the community level ethnic diversity, household demographics and ethnic composition, and location. We find that core areas are significantly more homogeneous with proportionately more Sinhalese households compared to the rest

of the country (excluding the Northeast). Minority households (Sri Lankan Tamils, Indian Tamils and Muslims) are under-represented in these areas. Core areas are mostly located in traditional villages and in urban middle class neighbourhoods. This is consistent with the data on their location within Provinces where we find that a significantly higher number of households located in core areas are from the highly urbanized Western province and from the Central and Southern Provinces. The percentage of households who join spontaneous credit and savings in core areas is not significantly different from the percentage of households who do the same in the rest of the sample. Thus overall the data suggests that households located in politically important “core” and “swing” areas have some meaningful household and community level differences compared to those areas located in the rest of the country. Whilst not ideal, controlling for these household level demographic variables and the community level ethnic diversity measure, along with a rich set of additional household and community level control variables that may affect household participation in the food stamp program, allows us to minimize to the extent possible any omitted variable bias when assessing the impact of political competition on the distribution of Samurdhi food stamps among ethnic minorities. Tables 4 and 5 present the probit estimates of the binary models described in the previous section. The standard errors reported are robust to the presence of heteroscedasticity, and allow for the clustered nature of the household survey data. For ease of interpretation, the probit estimates reported are transformed into marginal and impact effects for the continuous and dummy variables respectively. These values are calculated at the means of the independent variables.

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Table 4 Samurdhi food stamp allocation: ethnic discrimination and political effects. (1) Ethnic dummy variables (If 1, if a Sri Lankan Tamil household 1, if an Indian Tamil household 1, if a Muslim household

a Sinhalese hh, − 0.182*** (0.034) − 0.218*** (0.058) − 0.085*** (0.038)

Political variables % vote for PA in 1994 Abs. value (% vote for PA — 50) F-test: political variables (p-value) Ethnic diversity index Other household variables Other community variables Province dummies Psuedo R2 No. of observations

− 0.256*** (0.068) Yes Yes Yes 0.20 5232

(2)

(3)

(4)

omitted) − 0.175*** (0.033) − 0.218*** (0.061) − 0.082** (0.037)

− 0.179*** (0.033) − 0.218*** (0.059) − 0.087** (0.038)

− 0.175*** (0.033) − 0.218*** (0.061) − 0.812** (0.038)

0.0045** (0.002) − 0.0005 (0.003) 4.32 (0.11) − 0.233*** (0.069) Yes Yes Yes 0.20 5232

0.0046** (0.002) − 0.0018 (0.002)

− 0.259*** (0.068) Yes Yes Yes 0.20 5232

− 0.231*** (0.069) Yes Yes Yes 0.20 5232

Robust standard errors, adjusted for clustering, are reported in parentheses. ***denotes statistical significance at the 1% level, ** at the 5% level, and * at the 10% level using twotailed tests. The probit estimates are transformed into marginal effects for the continuous variables and impact effects for the binary variables. The other household variables include years of education, age, sex, ethnicity of household head; the number of household members aged 0–17; and the number of household members aged 65; proportion of disabled household members; household ownership of land, livestock and farming assets; household member in the army; household mobility; and access to housing, electricity and latrine facilities. The community dummy variables represent ethnic diversity and the type of community the household resides in — traditional village; irrigation colony, expansion colony, settlement scheme; urban low or middle income neighbourhood. The province dummy variables control for all provinces in Sri Lanka except for the North–East.

5.1. Estimates of the ethnic discrimination effect We find that the ethnicity of the household head has both significant and large effects on the probability of participating in SFSP (see column 1 in Table 4). Compared to Sinhalese households, Sri Lankan Tamil households are less likely to receive Samurdhi food stamps: being a Sri Lankan Tamil household reduces the probability of participating in SFSP by 18%. Indian Tamils also face a similar discrimination effect. Compared to a Sinhalese household, being an Indian Tamil and Muslim household reduces the probability of participating in SFSP by 22 and 8.5% respectively. The results suggest systematic discrimination against ethnic minorities in the allocation of Samurdhi food stamps. While being a Muslim household reduces the probability of receiving Samurdhi benefits relative to Sinhalese households, the level of discrimination is far less compared to any other ethnic minority group. Indian Tamil households who mainly live in plantation estates are the least likely to receive Samurdhi. These results are consistent with the political realities in Sri Lanka where Muslims are often seen to associate with the Sinhalese majority while Indian Tamils, given their relatively small population size and poor standing in society, are politically not an important constituency. We also find that higher ethnic heterogeneity at the community level reduces the probability of participating in Samurdhi: the estimates suggest that ceteris paribus going from perfect homogeneity to maximum heterogeneity (i.e. increasing the ethnic diversity index from 0 to 1) decreases the probability that a household participates in Samurdhi by 25%.27 The result is consistent with the studies that argue increased diversity leads to lower level of welfare spending due to ethnic divisions and tensions associated with higher diversity. Alternate explanations of

27 To explore whether the discrimination effect increases or lessens in ethnically diverse areas, we also interact the ethnic dummies with the ethnic heterogeneity index. However, we find these interaction terms to be insignificant determinants of participation.

this relationship are also possible. For example, it is also reasonable to expect areas that are economically better off are more likely to attract migrant households belonging to ethnic minorities. Households will generally be willing to incur the “social costs” associated with being a minority if they can secure economic prosperity. This would imply ethnically diverse villages are economically better off than homogenous ones, thereby explaining the result that higher diversity is associated with on average lower probability of receiving Samurdhi food stamps. However, as we find in the data, the majority of households (over 70% of the sample) do not move unless it is part of a government-sponsored resettlement of communities (which we control for), and land sales are fairly restricted. Ethnic composition of communities thus is relatively stable in Sri Lanka. The disincentive to move is also picked by the result that households who moved between 1990 and 1995 were significantly less likely to have received Samurdhi food stamps compared to households who never moved. This result is also consistent with the finding that households who moved to irrigation and expansion colonies are less likely to receive Samurdhi benefits relative to households who live in traditional villages. The results also show that a number of socio-economic characteristics associated with low welfare make households significantly more likely to be Samurdhi food stamp recipients. For example, higher levels of the household head's education have a significant negative effect on the chances of the household receiving food stamps. Having a higher number of children under the age of 17 (which would indicate a higher number of dependents) significantly increases the probability of joining SFSP. In terms of some of the other household demographic variables, the results show that a female-headed household is significantly more likely to receive Samurdhi food stamps. Variables that measure household access to facilities are also significant determinants of household participation in SFSP. For example, households who live in shanties (relative to those who live in their own home), those who do not have access to a latrine (relative to those who own a latrine), and those who use kerosene for lighting (relative to those who have electricity), all have a higher chance of joining the SFSP. Variables representing land ownership also show that households who own land compared to those who do not, are also more likely to receive Samurdhi food stamps. These land effects are quite large. The overall results indicate that the Samurdhi food stamp distribution was progressive for the average household but discriminatory in nature for households belonging to the ethnic minority communities. 5.2. Estimates of the political effect The results presented in Table 4 also provide some evidence of the role played by political competition in determining household participation in the Samurdhi program. After controlling for the various household and community level control variables, we find that a higher share of PA votes in a division is associated with a higher probability of a household participating in Samurdhi. We also find that the level of “swingness” of a division or the absolute deviation of the percentage of PA votes from 50 is negatively associated with the probability of participating in Samurdhi but not significant (see column 2). Evaluated at the mean, a one percentage point increase in the percentage of votes for PA in a division increases the probability of receiving Samurdhi benefits by a household living in that division by 0.5%, holding constant the extent of “swingness” of a division, other household and community level control variables and any provincial level fixed effects. This result, although relatively small in magnitude compared to the marginal effect of some of the other independent variables, suggests relatively higher amounts of Samurdhi fund allocation to more politically important divisions for the PA. In columns 3 and 4 we report the estimates of the impact of the percent voting in favour of the PA party and the distance of that from 50% separately. We include these additional specifications to ensure that the coefficients on the two political variables in column 2 are not being driven by their joint significance (the F-test of joint significance suggests some degree of collinearity between the political variables). The same pattern

I.A. Sharif / Journal of Development Economics 94 (2011) 277–289

287

Table 5 Samurdhi food stamp allocation: ethno-political effect.

Ethnic dummy variables (If a Sinhalese household, omitted) 1, if a Sri Lankan Tamil household 1, if an Indian Tamil household 1, if a Muslim household

(1)

(2)

(3)

(4)

(5)

− 0.081* (0.046) − 0.220* (0.101) − 0.077* (0.042)

− 0.427*** (0.032) − 0.298 (0.222) − 0.262 (0.184)

− 0.175*** (0.033) − 0.209 (0.343) − 0.081** (0.037)

− 0.176*** (0.033) − 0.219*** (0.061) − 0.201 (0.231)

− 0.389** (0.087) − 0.397 (0.041) − 0.370 (0.098)

0.003 (0.002)

0.005** (0.002)

0.005** (0.002)

0.003 (0.002) 0.001 (0.002)

Political variables % vote for PA in 1994 Abs. value (% vote for PA — 50)

0.001 (0.002)

Ethno-political variables [Abs. value (% vote for PA — 50)] ⁎ SL Tamil

− 0.017*** (0.005) − 0.002 (0.014) − 0.002 (0.006)

[Abs. value (% vote for PA — 50)] ⁎ Indian Tamil [Abs. value (% vote for PA — 50)] ⁎ Muslim [% vote for PA in 1994] ⁎ SL Tamil

0.013*** (0.004) 0.003 (0.011) 0.004 (0.007)

[% vote for PA in 1994] ⁎ Indian Tamil [% vote for PA in 1994] ⁎ Muslim

F-test: ethno-political variables (p-value) [Abs. value (% vote for PA — 50)] ⁎ Indian Tamil & [Abs. value (% vote for PA — 50)] ⁎ Muslim

− 0.0003 (0.011) 0.0029 (0.006)

0.10 (0.951)

[% vote for PA in 1994] ⁎ Indian Tamil & [% vote for PA in 1994] ⁎ Muslim

0.64 (0.727)

(% vote for PA — 50) ⁎ Indian Tamil & Indian Tamil

8.00 (0.018)

(% vote for PA — 50) ⁎ Muslim & Muslim hh

4.29 (0.117)

[% vote for PA in 1994] ⁎ SL Tamil & [Abs. value (% vote for PA — 50)] ⁎ SL Tamil hh Ethnic diversity index Other household variables Other community variables Province dummies Psuedo R2 No. of observations

− 0.006 (0.008) 0.0139 (0.026) 0.008 (0.009) 0.010* (0.006) 0.0137 (0.019) 0.009 (0.007)

− 0.276*** (0.067) Yes Yes Yes 0.20 5232

− 0.249*** (0.068) Yes Yes Yes 0.20 5232

− 0.231*** (0.068) Yes Yes Yes 0.20 5232

− 0.231*** (0.069) Yes Yes Yes 0.20 5232

10.55 (0.00) − 0.256*** (0.068) Yes Yes Yes 0.20 5232

Robust standard errors, adjusted for clustering, are reported in parentheses. ***denotes statistical significance at the 1% level, ** at the 5% level, and * at the 10% level using two-tailed tests. The probit estimates are transformed into marginal effects for the continuous variables and impact effects for the binary variables. The other household variables include years of education, age, sex, ethnicity of household head; the number of household members aged 0–17; and the number of household members aged 65; proportion of disabled household members; household ownership of land, livestock and farming assets; household member in the army; household mobility; and access to housing, electricity and latrine facilities. The community dummy variables represent the type of community the household resides in — traditional village; irrigation colony, expansion colony, settlement scheme; urban low or middle income neighbourhood. The province dummy variables control for all provinces in Sri Lanka except for the North–East.

however, emerges for the political variable coefficients in columns 3 and 4. The ethnic discrimination effects remain the same for all minority groups except for Sri Lankan Tamils: the magnitude reduces from 18 to 17%. We find similar coefficient estimates of other control variables in all the specifications. Our finding that the political competition variable representing the extent of core PA voter support significantly affects the distribution of Samurdhi food stamps is consistent with the results of existing empirical studies on pork barrel politics.28 That political objectives appear to have played a significant role in the distribution of Samurdhi 28 For instance, Case (2001) finds that not only was more social assistance allocated to core support areas for President Berisha, but also to areas that were marginal during the Berisha administration in the 1990s. Schady (2000) also finds that the distribution of the Peruvian social fund, FONCODES, was politically motivated. Expenditures were boosted just before the 1995 national elections, and were channeled to those provinces that had high political returns for the incumbent, President Fujimori. In a similar paper Dahlberg and Johansson (2002) find evidence that political parties in Sweden distributed transfers to regions where there were many swing voters, but not to their core voters.

food stamps provides further motivation for the main analysis of this paper on how political competition affected the discriminatory effect of the program on ethnic minorities, particularly on Sri Lankan Tamil households. 5.3. Estimates of the ethno-political effect The results in Tables 5 suggest that the distribution of Samurdhi food stamps was indeed tactical and largely motivated by electoral incentives and ethnic sensitivities. Column 1 shows the joint effect of the extent to which a division is swing and the ethnicity of the household on the probability of household participation in SFSP. We find that when ethnicity is interacted with the absolute difference between the percent of PA votes from 50, out of the all the ethnic minorities, only the interaction with being a Sri Lankan Tamil is significant. Evaluated at the mean, a one percentage point decrease in the absolute deviation of the percentage of votes for PA in a division from 50 increases the probability

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of receiving Samurdhi benefits by a Sri Lanka Tamil household living in that division by 1.76% relative to a Sinhalese household, holding constant all other household and community level control variables and any provincial level fixed effects. Also, after controlling for the interaction between being Sri Lankan Tamil and the extent of marginality of a division, we find that the ethnic discrimination effect towards Sri Lankan Tamils is diminished. The coefficients of all other control variables remain unchanged. The results suggest the important role played by specifically Sri Lankan Tamil minorities as “swing constituencies” compared to all other minority groups. A joint significant test of the interaction terms of the other ethnic minorities confirms this result. This is consistent with the political realities on the ground where Sri Lankan Tamils living outside of the North and East did not necessarily have any political party representing them in the elections of 1994. The Muslims on the other hand who constitute the second largest minority group were represented by the Sri Lanka Muslim Congress which was essentially a part of the PA coalition. 29 Column 2 in Table 5 explores the effect of core PA support on all ethnic groups. Once again, we find that the interaction between ethnicity and the level of core PA votes is a significant determinant of receiving Samurdhi food stamps among Sri Lankan Tamils. Evaluated at the mean, a one percentage point increase in the percentage of votes for PA in a division increases the probability of receiving Samurdhi benefits by a Sri Lankan Tamil relative to a Sinhalese household living in that division by 1.3%, holding constant all other household and community level control variables and any provincial level fixed effects. As in the case of swing areas, the core voter effect on other ethnic minority groups is insignificant. The results in columns 1 and 2 suggest that the tactical distribution found in the allocation of Samurdhi food stamps may have been essentially geared towards wooing Sri Lankan Tamils who form part of the base PA support or were living in swing areas. To ensure the results are not being driven by collinearity between the variables, we estimate separately the effects of the interaction of being Indian Tamil and Muslims with the percentage of core PA vote separately (columns 3 and 4). The results suggest once more that political competition when it comes to minority groups other than Sri Lankan Tamils have no impact on their probability of receiving Samurdhi food stamps, but maintains its independent effect on the dependent variable. Joint significance tests suggest that a high degree collinearity between the ethno-political variables and the respective ethnic dummy variables increase the standard errors on the latter, rendering them insignificant. The magnitude of the impact of political competition on the probability that a Sri Lankan Tamil household receives Samurdhi stamps is larger than the political effect on the average household found in Table 4. Where we find some difference is in the impact on the discrimination effect when we control for either of the two different types of ethno-political effect. When we control for the “swing” voter effect, the discrimination effect towards Sri Lankan Tamils diminishes whereas it is not the case when we control for the “core voter” effect. These results suggest that for Sri Lankan Tamils there are higher “political” returns to being part of a swing constituency than being part of the core support base of the PA. In column 5 we provide the results of a “kitchen sink” model which includes all the interaction terms despite the high degree of collinearity between the percent voting in favour of the PA and the distance of that from 50%. We find that when we put the maximum number of

29 We also estimate alternate specifications by replacing the continuous “swing” variables with a dummy variable to define extremely competitive areas (difference between 50 and percent of PA vote of 1% or less). We find that the probability of receiving Samurdhi food stamps by Sri Lankan Tamils living in these tightly contested areas is positive whereas it is negative for all other ethnic minorities living in the same areas. The result however is not statistically significant. The coefficients for all other explanatory variables including the ethnic dummies remain the same. However, when we relax the definition to include a higher difference, which gives us a larger sample of households, the coefficient on the interaction term between Tamil and swing is significant at the 10% level.

restrictions on the model, the interaction between the dummy variable representing a Sri Lankan Tamil household and the percent voting for the PA remains weakly significant while the interaction with the level of marginality of a division ceases to be so. However, the interactions between the Sri Lankan Tamil dummy with the extent to which a division is swing and the extent to which it is pivotal are jointly significant. The F-tests of the joint significance of these variables are provided in column 5. The discrimination effect towards the Sri Lankan Tamils (but not for the other minority groups) remains significant. All other results do not change. The results in Table 4 are not necessarily inconsistent with the incentive model or with the realities of Sri Lanka. Given that Sri Lankan Tamils other than those in the Northeastern Province are not represented by their own political party, unlike any of the other ethnic minority group, they represent important political blocks for the mainstream Sinhalese parties despite prevailing hostilities resulting from the ethnic conflict. It is plausible that this otherwise discriminated group are viewed favourably in the allocation of government transfer as part of an effort to woo Tamil votes in areas where the Sinhalese vote may be divided, such as in swing areas, and/or in areas where Tamils make up part of the core base support. While the evidence presented here is far from being conclusive, we are able to discern sufficient indication that electoral competition could reduce ethnic discrimination to warrant further research on the topic with appropriate data. Our results are consistent with a growing body of literature on the political economy of local public resource allocation where the emerging evidence suggests that while electoral incentives do not eliminate politician opportunism, socially and economically disadvantaged voters appear able to use their electoral clout to gain greater access to public resources (Besley et al., 2007). This link between the nature of political systems and the incentives they espouse among political establishments, and their impact on reduced discrimination towards otherwise disadvantaged groups is also documented in recent works on political reservation practiced in India (Besly et al., 2004; Pande, 2003; Duflo, 2004).

6. Conclusion This paper argues that in ethnically heterogeneous areas, the interdependence of ethnically-based political parties on inter-ethnic coalitions and cooperation to win elections under an electoral system based on proportional representation create the conditions for inclusiveness in an otherwise ethnically divided country. We use a basic electoral incentive model to show that incentives provided by democratic competition and the composition of the constituencies can play a role in mitigating discriminatory practices towards ethnic minorities. Using data on the allocation of food stamps in Sri Lanka in the run up to the elections of 2000, we show that political competition positively influenced the distribution of government food stamps among Sri Lankan Tamils, who otherwise are less likely to receive food stamps relative to the Sinhalese majority. We find that the tighter the political competition, the higher the probability that Tamil minorities living in these swing areas receive food stamps. We find a similar result for Tamils who form part of the core support of the incumbent political party. However, the swing voter effect is particularly stronger in negating ethnic bias in resource allocation than the core voter effect. A noteworthy caveat to these results is the possibility of omitted variable bias in our results. We try to alleviate this problem by using a large set of household and community level variables to control for any differences between the politically important areas and the rest of the country. The results lend support to models on pork barrel politics that suggest incumbent governments “purchase” votes by allocating discretionary transfers to sections of the population considered to represent the “swing” or “core” electorate. We also find support for the prediction derived from vote pooling models that suggest under certain electoral conditions politicians in heterogeneous societies are

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forced to accommodate the interests of those outside their own ethnic group to win elections. The results of this paper emphasize the importance of having mediating political institutions to help advance the socio-economic agenda of reducing ethnic discrimination.30 They suggest political competition in ethnically heterogeneous contexts may serve as a check on the extent of ethnic bias found in the allocation of public resources. The results of this paper are consistent with the predictions of research that argues that certain types of institutions are conducive to ethnic harmony, and democracy is one of them (Collier, 2000, 2001; Sambanis, 1999; Miguel, 2004). That said, it is also true that there is always scope for the manipulation of the electoral system and studies have yet to identify any particular variant of the proportional representation system that has been successful in minimizing ethnic divisions and discrimination on a permanent basis. Each situation is complex, tailored to the constraints of the historical, political and power dynamics in which it is formed and the negotiating skills of the parties involved. Further research is needed to test the effectiveness of different political approaches countries could adopt to address the issue of ethnic discrimination. Acknowledgments The author is grateful to Tim Besley, Markus Goldstein, Stefan Dercon, Oriana Bandiera, Hassan Zaman and an anonymous referee for helpful comments on earlier drafts of this paper. The views expressed in this paper are those of the author and do not necessarily reflect those of the World Bank, its Board of Executive Directors, or the countries they represent. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jdeveco.2010.01.006. References Alesina, A., La Ferrara, E., 2000. Participation in heterogeneous communities. Quarterly Journal of Economics 115 (3), 847–904. Alesina, A., La Ferrara, E., 2005. Ethnic diversity and economic performance. Journal of Economic Literature 43 (3), 762–800. Alesina, A., Baqir, R., Easterly, W., 1999. Public goods and ethnic divisions. Quarterly Journal of Economics 111 (4), 1243–1284. Arrow, K., 1972. Models of job discrimination. In: Pascal, A.H. (Ed.), Racial Discrimination in Economic Life. D.C. Health, Lexington, Mass. Arrow, K., 1998. What has economics to say about racial discrimination? Journal of Economic Perspectives 12 (2), 91–100. Bardhan, P., 1997. Method in madness? A political economy analysis of the ethnic conflicts in less developed countries. World Development 25 (9), 1381–1398. Becker, G., 1957. The Economics of Discrimination. University of Chicago, Chicago. Besley, T., Pande, R. and Rao, V., 2007. “Just rewards? Local politics and public resource allocation in India”, unpublished paper. Bogaards, M., 2001. “Electoral choices for divided societies: moderation through vote pooling and constituency pooling”. Department of Politics, University of Southampton. Mimeo. Case, A., 2001. Election goals and income distribution: recent evidence from Albania,. European Economic Review 45, 405–423. Caselli, F., Coleman II, W.J., 2006. On the theory of ethnic conflict. CEPR Discussion Papers 5622, C.E.P.R. Discussion Papers. Collier, P., 2000. Ethnicity, politics and economic performance. Economics and Politics 12, 225–245. Collier, P., 2001. Implications of ethnic diversity. Economic Policy 32, 129–166.

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