Surveys in Mathematics and its Applications

ISSN 1842-6298 (electronic), 1843-7265 (print)
 Volume 10 (2015), 113 -- 137

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

PUBLIC SERVICE ALLOCATION, SOCIAL UTILITY AND SPILLOVER EFFECTS: A REVISED BENEFIT INCIDENCE APPROACH

Stefano Mainardi

Abstract. In many developing countries, public service provision continues to fall short of demand. In the presence of severe infrastructure backlogs and different returns on public investment expenditure, marginal benefit incidence theory envisages that measures aimed at maximizing average access rates have contradictory impacts in the medium term. While relatively uniform expansion of access coverage across target areas can be achieved in some sectors, geographical disparities may persist or worsen in others. This study revises and extends a previous modeling approach by testing for endogenous eligibility, geographically-varying functional relationships, and number of uncompensated losers (numbers effect) as an additional social welfare objective. Relative to medium-term changes in access rates in primary schools and healthcare, spatial and geographically weighted regression models are applied to districts in Niger. Results point to an eligibility threshold which exceeds the average coverage rate for primary education, some evidence of numbers effect as a target for healthcare, and substantial spatial heterogeneity particularly for primary schools.

2010 Mathematics Subject Classification: 91B16; 91B18; 91B72.
Keywords: social welfare function; regional planning and public services; spatial and geographically weighted regressions.

Full text

References

  1. T. Abdoulaye and J. Sanders, New Technologies, Marketing Strategies and Public Policy for Traditional Food Crops: Millet in Niger, Agricultural Systems, 90 (2006), no. 1-3, 272-292.

  2. M. Ajwad and Q. Wodon, Do Local Governments Maximize Access Rates to Public Services across Areas? A Test Based on Marginal Benefit Incidence Analysis, Quarterly Review of Economics and Finance, 47 (2007), no. 1, 242-260.

  3. M. Ajwad and Q. Wodon, Who Benefits from Increased Access to Public Services at the Local Level? A Marginal Benefit Incidence Analysis for Education and Basic Infrastructure, World Bank Economists' Forum, 2 (2002), 155-175.

  4. L. Anselin, GeoDa 0.9 User's Guide, CSISS, University of Illinois, Urbana-Champaign, 2003.

  5. M. Beenstock and D. Felsenstein, Nonparametric Estimation of the Spatial Connectivity Matrix Using Spatial Panel Data, Geographical Analysis, 44 (2012), no. 4, 386-397.

  6. R. Brent, The Role of Public and Private Transfers in the Cost-Benefit Analysis of Mental Health Programs, Health Economics, 13 (2004), no. 11, 1125-36.

  7. R. Brent, An Axiomatic Basis for the Three-Objective Social Welfare Function within a Poverty Context, Economics Letters, 20 (1986), no. 1, 89-94. MR0824902.

  8. C. Briceño-Garmendia, K. Smits and V. Foster, Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues, World Bank, Washington DC, 2008.

  9. C. Brunsdon, S. Fotheringham and M. Charlton, Geographically Weighted Regression as a Statistical Model, Spatial Analysis Research Group, University of Newcastle-upon-Tyne, 2000. (available at: citeseerx.ist.psu.edu)

  10. C. Brunsdon, S. Fotheringham and M. Charlton, Some Notes on Parametric Significance Tests for Geographically Weighted Regression, Journal of Regional Science, 39 (1999), no. 3, 497-524.

  11. K. Burnham and D. Anderson, Multimodel Inference: Understanding AIC and BIC in Model Selection, Sociological Methods and Research, 33 (2004), no. 2, 261-304.

  12. F. Castro-Leal, J. Dayton, L. Demery and K. Mehra, Public Spending on Health Care in Africa: Do the Poor Benefit?, Bulletin of the World Health Organization, 78 (2000), no. 1, 66-74.

  13. W. Charemza and D. Deadman, New Directions in Econometric Practice, Edward Elgar, Aldershot, 1993.

  14. M. Charlton and S. Fotheringham, Geographically Weighted Regression, National University of Ireland Maynooth, Kildare, 2009. (available at: ncg.nuim.ie/ncg/GWR)

  15. M. Charlton, S. Fotheringham and C. Brunsdon, GWR 3. Software for Geographically Weighted Regression, Spatial Analysis Research Group, University of Newcastle-upon-Tyne, 2003.

  16. M. Devkota, G. Hatfield and R. Chintala, Effects of Sample Size on Performance of Ordinary Least Squares and Geographically Weighted Regression, British Journal of Mathematics and Computer Science, 4 (2014), no. 1, 1-21.

  17. M. Fafchamps and B. Minten, Public Service Provision, User Fees, and Political Turmoil, Journal of African Economies, 16 (2007), no. 3, 485-518.

  18. A. Fozzard, M. Holmes, J. Klogman and K. Withers, Public Spending for Poverty Reduction, World Bank, Washington DC, 2001. (available at: www.worldbank.org/poverty/strategies)

  19. B. Fredriksen, Rationale, Issues, and Conditions for Sustaining the Abolition of School Fees, in World Bank-UNICEF, Abolishing School Fees in Africa. Lessons from Ethiopia, Ghana, Kenya, Malawi, and Mozambique, World Bank, Washington DC (2009), 1-41.

  20. Golden Software, Surfer. Contouring and 3D Surface Mapping for Scientists and Engineers, Golden Software Inc., Golden-CO, 2009.

  21. D. Hendry and J. Doornik, Empirical Econometric Modelling Using PcGive 10, vol. I, Timberlake, London, 2001.

  22. C. Hurvich, J. Simonoff and C.-L. Tsai, Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion, Journal of the Royal Statistical Society, Series B, 60, (1998), no. 2, 271-293.

  23. Institut National de la Statistique (INS), Niger. Répertoire National des Communes (RENACOM), Niamey, 2006.

  24. G. Jenkins, C.-Y. Kuo and A. Harberger, The Shadow Price of Government Funds, Distributional Weights, and Basic Needs Externalities, Cost-Benefit Analysis for Investment Decisions (Development Discussion Papers), no. 14, Queen's University, Kingston, 2011. (available at: http://www.queensjdiexec.org/publications/qed_dp_207.pdf)

  25. Y. Kamarianakis, H. Feidas, G. Kokolatos, N. Chrysoulakis and V. Karatzias, Evaluating Remotely Sensed Rainfall Estimates Using Nonlinear Mixed Models and Geographically Weighted Regression, Environmental Modelling and Software, 23 (2008), no. 12, 1438-47.

  26. C. Lessmann, Fiscal Decentralization and Regional Disparity: Evidence from Cross-section and Panel Data, Environment and Planning A, 41 (2009), no. 10, 2455-73.

  27. S. Mainardi, Disparities in Public Service Provision in Niger: Cross-District Evidence on Access to Primary Schools and Healthcare, Regional Studies, 49 (2015), no. 12, 2017-36.

  28. L. Meuwissen, Problems of Cost Recovery Implementation in District Health Care: A Case Study for Niger, Health Policy and Planning, 17 (2002), no. 3, 304-313.

  29. Ministère de la Santé Publique et de la Lutte contre les Epidémies (MSP/LCE), Plan de Développement Sanitaire 2005-2009, MSP/LCE, Niamey, 2005.

  30. P. Mosley, J. Hudson and A. Verschoor, Aid, Poverty Reduction and the New `Conditionality', The Economic Journal, 114 (2004), no. 496, 217-243.

  31. L. Orr, Income Transfers as a Public Good: An Application to AFDC, American Economic Review, 66 (1976), no. 3, 359-371.

  32. A. Patunru, M. McCulloch and C. von Luebke, A Tale of Two Cities: The Political Economy of Local Investment Climate in Solo and Manado, Indonesia, IDS Working Papers, no. 338, University of Sussex, 2009.

  33. M. Thobani, Charging User Fees for Social Services: Education in Malawi, Comparative Education Review, 28 (1984), no. 3, 402-423.

  34. C. Tiebout, A Pure Theory of Local Expenditures, Journal of Political Economy, 64 (1956), no. 5, 416-442.

  35. D. van de Walle, Assessing the Welfare Impacts of Public Spending, World Development, 26 (1998), no. 3, 365-379.

  36. M. Verbeek, A Guide to Modern Econometrics, Wiley, Chichester-UK, 2012. MR1937619(2003g:62004). Zbl 1013.62109.

  37. I. Walker, P. Serrano and J. Halpern, Pricing, Subsidies and the Poor: Demand for Improved Water Services in Central America, Policy Research Working Paper Series, no. 2468, World Bank, Washington DC, 2000.

  38. D. Wheeler and M. Tiefelsdorf, Multicollinearity and Correlation among Local Regression Coefficients in Geographically Weighted Regression, Journal of Geographical Systems, 7 (2005), no. 2, 161-187.

  39. J. Wooldridge, Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge-Mass., 2002. MR2768559(2012k:62001). Zbl 05881239.

  40. World Bank, Water Supply and Sanitation in Niger, Water and Sanitation Program/Africa Region, Nairobi, 2011.

  41. S. Yitzhaki, Cost-Benefit Analysis and the Distributional Consequences of Government Projects, National Tax Journal, 56 (2003), no. 2, 319-336.



Stefano Mainardi
Natural Resources Dept., Falkland Islands Government
FIQQ 1ZZ Stanley, Falklands.
e-mail: stemaind@gmail.com, smainardi@fisheries.gov.fk

http://www.utgjiu.ro/math/sma