Legislature Size, Local Government Expenditure and Taxation, and Public Service Access in Indonesia


This study examines the impact of legislature size on local public finance and service outcomes in Indonesia. The investigation employs both continuity- and randomization-based regression discontinuity methods to accommodate the endogeneity of council size and to identify its causal effects on local government spending, service delivery, and own-source revenue mobilization. Many studies have examined the influence of increasing legislature size on expenditures, but no consensus has emerged on the direction of impacts. Moreover, interpretation of the efficiency of derived spending effects has remained elusive and reliant on ad hoc theorizing. This is the first study to examine the causal impact of council size on service outcomes, thereby facilitating an empirically based understanding of efficiency effects. The study finds that increasing legislature size negatively affects local government total and capital spending. The investigation also shows that rising legislature size has a negative influence on citizen access to public services. Finally, the examination offers evidence to suggest that an increasing number of legislators have no impact on local own-source revenues. Taken together the results imply a decline in local efficiency: residents pay the same amount in taxes but receive fewer services. The findings in this investigation contradict recent theoretical predictions and empirical results from other research.

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  1. 1.

    The exception to the rule is Hohmann (2017). He finds negative spending impacts of increasing council size in Germany but reasonably suggests that further research would be required before a judgment can be made about the interpretation of effects.

  2. 2.

    Interestingly, Primo and Snyder Jr. (2008), using the same framework as Weingast et al. (1981), show that if home district project costs are partially subsidized by central government, the impact of the number of legislators on total local spending depends on the pureness of the public good and the initial number of legislators. In particular, if public goods are somewhat congested and/or the initial number of legislators is relatively large then an increase in the latter may result in lower local government spending.

  3. 3.

    Intergovernmental transfers, the main source of revenue in local budgets, are exogenously determined by central government. Mayors and councilors have no influence on the amount of transfers received.

  4. 4.

    In any case, local governments rarely borrow to finance capital investments (Lewis and Oosterman 2011).

  5. 5.

    Neither are local councilors time-constrained in Indonesia in the sense described by Pettersson-Lidbom (2012) for Sweden and Finland, making the associated agency framework plausibly relevant in those countries inappropriate in this case.

  6. 6.

    This assumes that other (non-local) taxes paid by residents (e.g., personal income taxes) that form the basis for funding some intergovernmental transfers are not affected by increasing council size either. Later, I will show that such payments-cum-transfers do not confound hypothesized effects.

  7. 7.

    In 2016, the specific-purpose grant was reconstructed as a proposal-based transfer to provinces and districts. The matching component was expunged.

  8. 8.

    Political parties are weak at the local level in Indonesia. They are essentially vehicles for individuals to seek and gain access to political office. See Aspinall (2013) for a discussion and analysis of the fragmentation and personalization of Indonesian political parties at the local level.

  9. 9.

    Comprehensive data on the 2014 elections are not yet available.

  10. 10.

    GOLKAR is the party of ex-president Suharto.

  11. 11.

    The figure shows districts with populations up to two million persons to best illustrate the relationship. There are 13 local jurisdictions with population greater than two million; the largest place (Surabaya) has a population of just over four million. Jakarta, population (approximately eight million) is not included in the study due to lack of data.

  12. 12.

    Service access variables are selected based on the availability of data. Data also exist on child immunization rates, but the variable has not been consistently measured across the years and so it cannot be used in the analysis here. There are no comprehensive data on service quality for Indonesia.

  13. 13.

    Alternatively, the service access index could be constructed by employing principal components analysis. This procedure was also carried out. The empirical results do not change appreciably; the qualitative conclusions reached here based on the analysis are robust with respect to the service access measure. I prefer to use average service access because it is more easily interpreted.

  14. 14.

    Districts in Eastern Indonesia comprise those in the provinces of Maluku, Maluku Utara, Nusa Tenggara Barat, Nusa Tenggara Timur, Papua, and Papua Barat. Eastern Indonesia is the least developed region of the country, and spending and service access in districts in there are significantly different from those in the rest of Indonesia.

  15. 15.

    For an up-to-date analysis of the determinants of district splitting, see Pierskalla (2016), and for recent investigations of the (largely deleterious) effects of pemekaran, see Burgess et al. (2012), Bazzi and Gudgeon (2016), and Lewis (2017a).

  16. 16.

    Garmann (2015) distinguishes between local governments with elected and appointed heads in his examination of council size effects in Germany. He finds a negative council size effect on spending for the former but no statistically significant results either way for the latter. It is not analytically feasible to draw that distinction in this study, owing to the small sample of districts with appointed heads (52).

  17. 17.

    See Brollo et al. (2013) for the use of similar pooling and normalization procedures in the context of multiple cutoff RD designs.

  18. 18.

    I use the Stata command “rdrobust” to estimate treatment effects in this article. See Calonico et al. (2017).

  19. 19.

    The MSE of the estimator is the sum of the bias squared plus the variance. As such, the bandwidth selection procedure optimizes the bias-variance trade-off. (Cattaneo et al. 2018a).

  20. 20.

    I present robust p values to be consistent with p values associated with the randomization-based RD procedures used later in the paper.

  21. 21.

    I find no impact of increasing council size other types of spending—personnel and goods and services. To save space, I do not present those results here.

  22. 22.

    Note that a decrease in total spending together with no change to own-source and transfer revenues implies that districts run a surplus. It might plausibly be argued that this surplus could be used augment fiscal reserves and increase capital spending and improve service delivery in the future. However, local governments in Indonesia tend to finance their capital spending out of gross operating budgets and do not use fiscal reserves (or borrowing) to any significant extent for such purposes (Lewis and Oosterman 2011).

  23. 23.

    Eggers et al. (2018) argue that treatment effects may also be confounded when election population thresholds are used for other policy decisions. They show that election thresholds are used to make a variety of other relevant decisions in France, Italy, and Germany. None of the policy decisions enumerated by those authors are made as a function legislative-seat-determining population thresholds in Indonesia. I am unaware of any other policy decisions that depend on population thresholds.

  24. 24.

    To illustrate the window selection algorithm, Table 12 in the Online Appendix reports the full output using log of total expenditure per capita as an example. The results for all other outcome variables are similar.

  25. 25.

    I also test the robustness of the derived results with respect to the use of an alternative kernel (uniform) and different bandwidths. Regarding the latter, I choose bandwidths that are 25% narrower and 25% wider than the bandwidths used in the baseline regressions. I also test the robustness of the main results by splitting the sample of districts into those that did and did not experience an increase in council seats between the two electoral periods. Finally, I examine the treatment effects for all individual services that make up the service index used in the main regressions. I find that the baseline results presented in the text are robust with respect to all these tests. Relevant output is presented in the Online Appendix in Tables 8,9,10, and 11.


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Table 7 Population size, number of council seats, and districts in sample
Table 8 Legislature size impact on district spending, service access, and own-source revenue—uniform kernel
Table 9 Legislature size impact on district spending, service access, and own-source revenue—alternative bandwidths
Table 10 Legislature size impact on district spending, service access, and own-source revenue—no change or increase in number seats across electoral periods
Table 11 Legislature size impact on individual service access variables
Table 12 Window selection algorithm results for randomization-based RD treatment effect estimation

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Lewis, B.D. Legislature Size, Local Government Expenditure and Taxation, and Public Service Access in Indonesia. St Comp Int Dev 54, 274–298 (2019). https://doi.org/10.1007/s12116-019-09278-1

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  • Legislature size
  • Local government
  • Pork barrel spending
  • Service delivery
  • Efficiency
  • Indonesia

JEL Classification

  • H72
  • H75
  • H76