The political economy of lean season transfers: is hunger the only targeting criterion?

1. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References The political economy of lean season transfers in Malawi Is hunger the only targeting criterion? Jan Duchoslav Edwin Kenamu Jack Thunde j.duchoslav@cgiar.org 20 January 2021 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 1 / 12

2. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Outline of this paper Do electoral considerations play a role in the targeting of humanitarian transfers? Household survey data on direct transfers following an exceptionally poor harvest in 2016 Remotely sensed measure of drought (SPEI) 2014 and 2019 parliamentary election results Transfers are disproportionately targeted at marginal constituencies Voters do not reciprocate by increasing their support for the ruling party Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 2 / 12

5. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Literature on direct transfers and elections Direct transfers improve individual and household welfare from food security to psychosocial wellbeing (Bhalla et al., 2018; Natali et al., 2018) increase satisfaction with authorities (Evans et al., 2019) may increase voter turnout and incumbent’s vote share (De La O, 2013; Galiani et al., 2019) ... or not (Imai and Rivera, 2019; Zucco, 2013) Incentive to use transfers to improve electoral prospects Theoretical models: base mobilization (Cox and McCubbins, 1986) or swing voter persua- sion (Lindbeck and Weibull, 1987) Evidence supporting either or both models (Ansolabehere and Snyder, 2006; Herron and Theodos, 2004; Kang, 2015) ... but only from high- and upper-middle-income countries and non-emergency contexts Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 3 / 12

9. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Timeline Targeting Context 2014: elections 2015: poor harvest 2016: 2.8 million people receiving transfers 2016: very poor harvest 2017: 6.7 million people receiving transfers 2019: elections Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 4 / 12

10. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Timeline Targeting Context: Transfer targeting Transfer targeting Extent of disaster assessed by MVAC Timing of transfers and number of beneficiaries set by DoDMA at the TA level TA quotas divided by district officials to smaller administrative units down to village level Beneficiary households identified through community-based targeting using JEFAP criteria JEFAP criteria Administrative divisions 28 districts 193 parliamentary constituencies (first-past-the-post) 328 Traditional Authorities (TAs) Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 5 / 12

11. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Timeline Targeting Context: Transfer targeting Transfer targeting Extent of disaster assessed by MVAC Timing of transfers and number of beneficiaries set by DoDMA at the TA level TA quotas divided by district officials to smaller administrative units down to village level Beneficiary households identified through community-based targeting using JEFAP criteria JEFAP criteria Administrative divisions 28 districts 193 parliamentary constituencies (first-past-the-post) 328 Traditional Authorities (TAs) Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 5 / 12

12. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References SPEI/Survey Elections Data: Drought and transfers Remote sensing Std. Precipitation-Evapotranspiration Index (SPEI04 wet season mean) Avoids response bias Household survey (IHS4) If/when received transfer Targeting criteria Self-reported drought exposure and yield Summary statistics by household Summary statistics by constituency Drought measure comparison Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 6 / 12

13. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References SPEI/Survey Elections Data: Drought and transfers Remote sensing Std. Precipitation-Evapotranspiration Index (SPEI04 wet season mean) Avoids response bias Household survey (IHS4) If/when received transfer Targeting criteria Self-reported drought exposure and yield Summary statistics by household Summary statistics by constituency Drought measure comparison Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 6 / 12

14. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References SPEI/Survey Elections Data: Elections Parliamentary elections 2014 and 2019 193 single-seat constituencies First-past-the-post system 4 major parties Ruling party won plurality of votes and seats Election results Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 7 / 12

15. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Hypotheses 1 Transfers are disproportionately targeted at ruling party strongholds 2 Transfers are disproportionately targeted at marginal constituencies 3 Voters reward transfers by voting for ruling party candidate ...controlling for drought severity and targeting criteria Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 8 / 12

18. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Targeting Voter response Transfers are skewed towards marginal constituencies Received transfer 2017 (1) (2) (3) (4) Vote share 2014 0.036 0.001 (0.073) (0.069) Abs. margin 2014 -0.188** -0.187** (0.077) (0.078) SPEI 2016 -0.155*** -0.147*** -0.123*** -0.123*** (0.032) (0.035) (0.033) (0.032) Targeting criteria yes yes yes yes Distribution yes yes yes yes Month FE yes yes yes yes N 9,246 9,246 9,246 9246 Pseudo R2 0.224 0.224 0.230 0.230 AUC 0.821 0.820 0.823 0.823 Notes: Probit average marginal effects. Standard errors clustered by TA in all four models and by constituency in models (2), (3) and (4) in parentheses. AUC = area under ROC curve. * p < 0.10, ** p < 0.05, *** p < 0.01. Drought measure comparison Ethnicity controls Self-reported drought exp. Self-reported yield HHHI Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 9 / 12

19. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Targeting Voter response Voters do not reward transfers with votes for ruling party candidates Vote share 2019 (1) (2) Received transfer 2017 0.011 -0.005 (0.087) (0.082) Vote share 2014 0.443*** 0.363*** (0.082) (0.096) SPEI 2016 -0.071* -0.121*** (0.038) (0.040) Targeting criteria yes yes Ethnicity no yes N 166 166 Adj. R2 0.237 0.322 Notes: Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. Robustness checks Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 10 / 12

20. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Conclusions Targeting of direct transfers distributed in Malawi in response to an exceptionally poor harvest following a late and erratic rainy season of 2015-16 Combine household survey data on transfers with a remotely sensed measure of drought and with the results of the 2014 and 2019 parliamentary elections Transfers were targeted at the most drought-stressed areas ...but also disproportionately at marginal constituencies Voters do not reward transfers distributed in the middle of the electoral term by voting for ruling party candidates ...but may respond to transfers closer to an election ...more data needed Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 11 / 12

24. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Remaining questions Do voters respond to transfers closer to an election? Are transfers closer to an election even more skewed towards marginal constituencies? Geocoded data on size of lean season transfers needed for 2014–2019 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 12 / 12

25. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Remaining questions Do voters respond to transfers closer to an election? Are transfers closer to an election even more skewed towards marginal constituencies? Geocoded data on size of lean season transfers needed for 2014–2019 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 12 / 12

26. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions ———————————————— Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 13 / 12

27. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions JEFAP criteria Households that fall in two or more of the following categories: 1 are headed by children 2 are headed by elderly people (more than 60 years old) 3 are headed by females 4 are caring for orphaned children less than 18 years old 5 have chronically ill members 6 have children receiving supplementary or therapeutic feeding 7 have experienced crop failure in the previous agricultural season Context Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 13 / 12

28. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Summary statistics by household Variable Mean Std. Dev. Min. Max. N Received transfer 2017 0.232 0.422 0 1 10 175 Distribution 0.418 0.493 0 1 10 175 Child head 0.001 0.038 0 1 10 175 Elderly head 0.181 0.385 0 1 10 175 Female head 0.308 0.462 0 1 10 175 Caring for orphans 0.021 0.144 0 1 10 175 Chronically ill member 0.227 0.419 0 1 10 175 Children on supplementary/therapeutic feeding 0.022 0.147 0 1 10 175 Harvest failure 0.231 0.421 0 1 10 175 Satisfied targeting criteria 0.991 0.970 0 4 10 175 SPEI 2016 −0.894 0.413 −1.664 0.969 9328 Experienced drought 2016 0.810 0.393 0 1 10 175 Maize yield (kg/ha)2016 863.667 810.724 2.073 3013.48 5465 Notes: Distribution = dummy equal to 1 if household was interviewed after transfer distribution began in their TA. Data: Drought and transfers Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 14 / 12

29. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Summary statistics by constituency Variable Mean Std. Dev. Min. Max. N Received transfer 2017 0.222 0.196 0 0.834 176 Child head 0.001 0.004 0 0.035 176 Elderly head 0.179 0.067 0 0.373 176 Female head 0.303 0.102 0.022 0.563 176 Caring for orphans 0.021 0.025 0 0.172 176 Chronically ill member 0.228 0.094 0 0.511 176 Children on supplementary/therapeutic feeding 0.022 0.037 0 0.161 176 Harvest failure 0.225 0.158 0 0.764 176 SPEI 2016 −0.794 0.425 −1.543 0.907 168 Experienced drought 2016 0.791 0.158 0.257 1 176 Maize yield (kg/ha) 2016 885.968 391.289 49.239 2534.458 168 Chewa 0.582 0.38 0 1 176 Lomwe 0.018 0.038 0 0.25 176 Yao 0.096 0.234 0 1 176 Ngoni 0.024 0.092 0 0.625 176 Tumbuka 0.126 0.301 0 1 176 Other language 0.154 0.267 0 1 176 Data: Drought and transfers Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 15 / 12

30. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Ruling party parliamentary candidates results Variable Mean Std. Dev. Min. Max. N Vote share 2014 0.209 0.17 0 0.9 175 Won 2014 0.234 0.425 0 1 175 Winning margin 2014 0.209 0.178 0.001 0.856 41 Losing margin 2014 −0.303 0.169 −0.886 −0.004 134 Margin 2014 −0.183 0.276 −0.886 0.856 175 Abs. margin 2014 0.281 0.175 0.001 0.886 175 HHI 2014 0.315 0.107 0.13 0.822 175 Vote share 2019 0.272 0.191 0 0.872 175 Notes: HHI = Herfindahl–Hirschman Index. Data: Elections Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 16 / 12

31. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions SPEI is a good predictor of direct transfers Received transfer 2017 (1) (2) (3) SPEI 2016 -0.155*** (0.032) Experienced drought 2016 0.084*** (0.018) Maize yield (t/ha) 2016 -0.029* (0.015) Targeting criteria yes yes yes Distribution yes yes yes Month FE yes yes yes N 9,294 10,175 5,459 Pseudo R2 0.225 0.210 0.124 AUC1 0.821 0.801 0.744 Notes: Probit average marginal effects. Standard errors clustered by TA in parentheses. AUC = area under ROC curve. * p < 0.10, ** p < 0.05, *** p < 0.01. Data: Drought and transfers Result 1 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 17 / 12

32. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Result 1 robustness: Ethnicity controls Received transfer 2017 (1) (2) (3) (4) Vote share 2014 0.010 -0.019 (0.074) (0.078) Abs. margin 2014 -0.188** -0.190** (0.078) (0.079) SPEI 2016 -0.134*** -0.133*** -0.109*** -0.110*** (0.029) (0.030) (0.030) (0.030) Targeting criteria yes yes yes yes Distribution began yes yes yes yes Month FE yes yes yes yes Ethnicity yes yes yes yes N 9246 9246 9246 9246 Pseudo R2 0.235 0.235 0.241 0.241 AUC 0.821 0.820 0.822 0.822 Notes: Probit average marginal effects. Standard errors clustered by TA in all four models and by constituency in models (2), (3) and (4) in parentheses. AUC = area under ROC curve. * p < 0.10, ** p < 0.05, *** p < 0.01. Result 1 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 18 / 12

33. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Result 1 robustness: Self-reported drought exposure Received transfer 2017 (1) (2) (3) (4) Vote share 2014 0.151** 0.084 (0.070) (0.069) Abs. margin 2014 -0.252*** -0.227*** (0.075) (0.076) Experienced drought 2016 0.085*** 0.085*** 0.083*** 0.083*** (0.018) (0.018) (0.017) (0.017) Targeting criteria yes yes yes yes Distribution yes yes yes yes Month FE yes yes yes yes N 10,127 10,127 10,127 10,127 Pseudo R2 0.209 0.212 0.220 0.221 AUC 0.801 0.805 0.810 0.811 Notes: Probit average marginal effects. Standard errors clustered by TA in all four models and by constituency in models (2), (3) and (4) in parentheses. AUC = area under ROC curve. * p < 0.10, ** p < 0.05, *** p < 0.01. Result 1 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 19 / 12

34. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Result 1 robustness: Self-reported yield Received transfer 2017 (1) (2) (3) (4) Vote share 2014 0.259** 0.156 (0.111) (0.105) Abs. margin 2014 -0.419*** -0.376*** (0.106) (0.108) Maize yield (t/ha) 2016 -0.029* -0.028* -0.030* -0.030* (0.015) (0.016) (0.016) (0.016) Targeting criteria yes yes yes yes Distribution yes yes yes yes Month FE yes yes yes yes N 5,422 5,422 5,422 5,422 Pseudo R2 0.122 0.129 0.142 0.145 AUC 0.744 0.751 0.758 0.762 Notes: Probit average marginal effects. Standard errors clustered by TA in all four models and by constituency in models (2), (3) and (4) in parentheses. AUC = area under ROC curve. * p < 0.10, ** p < 0.05, *** p < 0.01. Result 1 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 20 / 12

35. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Result 1 robustness: Herfindahl–Hirschman Index Received transfer 2017 (1) (2) Vote share 2014 0.092 (0.072) HHI 2014 -0.322** -0.357*** (0.132) (0.137) SPEI -0.140*** -0.121*** (0.034) (0.033) Distribution began 0.146*** 0.145*** (0.029) (0.028) Month FE yes yes Targeting criteria yes yes N 9,246 9,246 Pseudo R2 0.230 0.231 AUC 0.822 0.823 Notes: Probit average marginal effects. Standard errors clustered by TA and constituency in parentheses. HHI = Herfindahl–Hirschman Index. AUC = area under ROC curve. * p < 0.10, ** p < 0.05, *** p < 0.01. Result 1 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 21 / 12

36. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References Summary Questions Result 2 robustness Vote share 2019 (1) (2) (3) (4) (5) (6) Received transfer 2017 0.011 -0.005 0.059 0.071 0.063 0.078 (0.087) (0.082) (0.081) (0.079) (0.082) (0.080) Vote share 2014 0.443*** 0.363*** 0.480*** 0.422*** 0.455*** 0.419*** (0.082) (0.096) (0.078) (0.095) (0.079) (0.096) SPEI 2016 -0.071* -0.121*** (0.038) (0.040) Experienced drought 2016 0.063 0.035 (0.091) (0.098) Maize yield (t/ha) 2016 -0.055 -0.054 (0.048) (0.048) Targeting criteria yes yes yes yes yes yes Language no yes no yes no yes N 166 166 174 174 166 166 Adj. R2 0.237 0.322 0.226 0.281 0.227 0.280 Notes: Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. Result 2 Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 22 / 12

37. Introduction Background Context: Timeline Data Hypotheses Results Conclusion References References Ansolabehere, S. and Snyder, J. M. (2006). Party control of state government and the distribution of public expenditures. Scandinavian Journal of Economics, 108(4):547–569. Bhalla, G., Handa, S., Angeles, G., and Seidenfeld, D. (2018). The effect of cash transfers and household vulnerability on food security in Zimbabwe. Food Policy, 74:82–99. Cox, G. W. and McCubbins, M. D. (1986). Electoral Politics as a Redistributive Game. The Journal of Politics, 48(2):370–389. De La O, A. L. (2013). Do Conditional Cash Transfers Affect Electoral Behavior? Evidence from a Randomized Experiment in Mexico. American Journal of Political Science, 57(1):1–14. Evans, D. K., Holtemeyer, B., and Kosec, K. (2019). Cash transfers increase trust in local government. World Development, 114:138–155. Galiani, S., Hajj, N., McEwan, P. J., Ibarrarán, P., and Krishnaswamy, N. (2019). Voter response to peak and end transfers: Evidence from a conditional cash transfer experiment. American Economic Journal: Economic Policy, 11(3):232–260. Herron, M. C. and Theodos, B. A. (2004). Government redistribution in the shadow of legislative elections: A study of the Illinois member initiative grants program. Legislative Studies Quarterly, 29(2):287–311. Imai, K. and Rivera, C. V. (2019). Do nonpartisan programmatic policies have partisan electoral effects? Evidence from two large-scale experiments. Journal of Politics, 82(2):714–730. Kang, W. C. (2015). Electoral cycles in pork barrel politics: Evidence from South Korea 1989-2008. Electoral Studies, 38:46–58. Lindbeck, A. and Weibull, J. W. (1987). Balanced-budget redistribution as the outcome of political competition. Public Choice, 52(3):273–297. Natali, L., Handa, S., Peterman, A., Seidenfeld, D., and Tembo, G. (2018). Does money buy happiness? Evidence from an unconditional cash transfer in Zambia. SSM - Population Health, 4:225–235. Zucco, C. (2013). When Payouts Pay Off: Conditional Cash Transfers and Voting Behavior in Brazil 2002-10. American Journal of Political Science, 57(4):810–822. Duchoslav et al. (IFPRI Malawi) Political economy of humanitarian transfers 20 January 2021 23 / 12

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