Aiyer, Sri-ram, Dean T. Jamison, and Juan Luis Londoño, 1995, “Health Policy in Latin America: Progress, Problems, and Policy Options,” Cuadernos de Economia, Vol. 32, No. 95 (April), pp. 11-28.
Alesina, Alberto, (forthcoming), “Too Large and Too Small Governments,” in Economic Policy and Equity, ed. by Vito Tanzi, Ke-young Chu, and Sanjeev Gupta (Washington: International Monetary Fund).
Anand, Sudhir, and Martin Ravallion, 1993, “Human Development in Poor Countries: On the Role of Private Incomes and Public Services,”Journal of Economic Perspectives, Vol. 7, No. 1 (Winter), pp. 133–50.
Appleton, Simon, John Hoddinott, and John Mackinnon, 1996, “Education and Health in Sub-Saharan Africa,”Journal of International Development, Vol. 8, No. 3 (May-June), pp. 307–39.
Appleton, Simon, and John Mackinnon, 1996, “Enhancing Human Capacities in Africa,” in Agenda for Africa’s Economic Renewal, ed. by Benno Ndulu , and others (Washington: Overseas Development Council).
Barro, R.J, 1991, “Economic Growth in a Cross-Section of Countries,” Quarterly Journal of Economics, Vol. 106, No. 2 (May), pp. 407–14.
Bennell, Paul, 1996, “Rates of Return to Education: Does the Conventional Pattern Prevail in Sub-Saharan Africa,” World Development, Vol. 24 (January), pp. 183–200.
Bennell, Paul, 1995, “Rates of Return to Education in Asia: A Review of the Evidence,” Working Paper No. 24 (Oxford: Institute of Development Studies).
Bidani, Benu, and Martin Ravallion, 1997, “Decomposing Social Indicators Using Distributional Data,” Journal of Econometrics, Vol. 77, No. 1 (March): pp. 125-39.
Bredie, Joseph W.B, and Girindre K. Beeharry, 1998, “School Enrollment Decline in Sub-Saharan Africa: Beyond the Supply Constraint,” World Bank Discussion Paper No. 3951 (Washington).
Camdessus, Michel, 1997, “Toward a Second Generation of Structural Reform in Latin America,” keynote address delivered at the 1997 National Banks Convention of Asociación de Bancos Argentinos (ADEBA), Buenos Aires, May.
Canin, G., and C. Politi, 1995, “Exploring the Health Impact of Economic Growth, Poverty Reduction, and Public Health Expenditure,” Tijdschrift voor Economie en Management, Vol. 40, no. 3-4 (October), pp. 227-246.
Cassen, Robert, 1996, Human Development: Research and Policy Choices, ODC Occasional Paper No. 3 (Washington: Overseas Development Council).
Chu, Ke-young , and others, 1995, Unproductive Public Expenditures: A Pragmatic Approach to Policy Analysis, IMF Pamphlet Series, No. 48 (Washington: International Monetary Fund).
Demery, Lionel, and Michael Walton, 1998, Are Poverty Reduction and Other 21st Century Social Goals Attainable? (Washington: World Bank).
Filmer, Deon, Jeffrey Hammer, and Lant Pritchett, 1998, “Health Policy in Poor Countries: Weak Links in the Chain,” Policy Research Working Paper No. 1874 (Washington: World Bank).
Filmer, Deon, and Lant Pritchett, 1997, “Child Mortality and Public Spending on Health: How Much Does Money Matter?” Policy Research Working Paper No. 1864 (Washington: World Bank).
Flug, Karnit, Antonio Spilimbergo, and Erik Wachtenheim, 1998, “Investment in Education: Do Economic Volatility and Credit Constraints Matter?”, Journal of Development Economics, Vol. 55 (April), pp. 465–81.
Gallagher, Mark, 1993, “A Public Choice Theory of Budgets: Implications for Education in Less Developed Countries,” Comparative Education Review, Vol. 37, No. 2 (May), pp. 90–106.
Glewwe, Paul, and Hanan G. Jacoby, 1995, “An Economic Analysis of Delayed Primary School Enrollment in a Low–Income Country: The Role of Early Childhood Nutrition,” Review of Economics and Statistics, Vol. 77 (May), pp. 156-69.
Gupta, Sanjeev, Benedict Clements, and Erwin Tiongson, 1998, “Public Spending on Human Development,” Finance and Development, Vol. 35, No. 3 (September), pp. 10–13.
Gupta, Sanjeev, Keiko Honjo, and Marijn Verhoeven, 1997, “The Efficiency of Government Expenditure: Experiences from Africa,” IMF Working Paper WP/97/153 (Washington: International Monetary Fund).
Hojman, David E., 1996, “Economic and Other Determinants of Infant and Child Mortality in Small Developing Countries: The Case of Central America and the Caribbean,” Applied Economics, Vol. 28 (March), pp. 281–90.
Kim, Kwangkee, and Philip M. Moody, 1992, “More Resources Better Health? A Cross-National Perspective,” Social Science and Medicine, Vol. 34, No. 8 (April), pp. 837-42.
Landau, Daniel, 1986, “Government and Economic Growth in the Less Developed Countries: An Empirical Study for 1960–80,” Economic Development and Cultural Change, Vol. 35, No. 1 (October), pp. 35–75.
McGuire, Alistair, David Parkin, David Hughes, and Karen Gerard, 1993, “Econometric Analyses of National Health Expenditures: Can Positive Economics Help Answer Normative Questions?”, Health Economics, Vol. 2, No. 2 (July), pp. 113-26.
Mehrotra, Santosh, 1998, “Education for All: Policy Lessons from High-Achieving Countries,” UNICEF Staff Working Papers, Evaluation, Policy and Planning Series No. EPP-EVL-98-005 (New York: UNICEF).
Mehrotra, Santosh, and Peter Buckland, 1998, “Managing Teacher Costs for Access and Quality,” UNICEF Staff Working Papers, Evaluation, Policy and Planning Series No. EPP-EVL-98-004 (New York: UNICEF).
Mingat, Alain, and Jee-Peng Tan, 1998, “The Mechanics of Progress in Education: Evidence from Cross-Country Data,” Policy Research Working Paper No. 2015 (Washington: World Bank).
Musgrove, Philip, 1996, “Public and Private Roles in Health: Theory and Financing Patterns,” World Bank Discussion Paper No. 339 (Washington).
OECD (Organisation for Economic Co-operation and Development)/DAC (Development Assistance Committee), 1996, Shaping the 21st Century: The Contribution of Development Cooperation (Paris).
Ogbu, Osita M., and Mark Gallagher, 1992, “Public Expenditures and Health Care in Africa,” Social Science and Medicine, Vol. 34, No. 6 (February), 615–24.
Ogbu, Osita M., and Mark Gallagher, 1991, “On Public Expenditures and Delivery of Education in Sub-Saharan Africa,” Comparative Education Review, Vol. 35, No. 2 (May), 295–318.
Pesaran, M.H., and R.J. Smith, 1994, “A Generalized R2 Criterion for Regression Models Estimated by the Instrumental Variables Method,” Econometrica, Vol. 62 (May), pp. 705–10.
Plank, David N., 1987, “The Expansion of Education: A Brazilian Case Study,” Comparative Education Review, Vol. 31, No. 3 (August), pp. 361–76.
Pradhan, Sanjay, 1996, “Evaluating Public Spending: A Framework for Public Expenditure Reviews,” World Bank Discussion Paper No. 323 (Washington).
Psacharopoulos, George, 1994, “Returns to Investment in Education: A Global Update,” World Development, Vol. 22, No. 9 (September), pp. 1325–43.
Psacharopoulos, George, and Nguyen Xuan Nguyen, 1997, “The Role of Government and the Private Sector in Fighting Poverty,” World Bank Technical Paper No. 346 (Washington).
Sahn, David, and Rene Bernier, 1993, “Evidence from Africa on the Intrasectoral Allocation of Social Sector Expenditures,” Cornell Food and Nutrition Policy Program, Cornell University, Working Paper No. 45 (Ithaca).
Sargan, J.D. (1964), “Wages and Prices in the United Kingdom: A Study in Econometric Methodology,” in Econometric Analysis for National Economic Planning, ed. by P.E. Hart, G. Mills, and J.K. Whitaker (London: Butterworth), pp. 25-63.
Schultz, T. Paul, 1993, “Mortality Decline in the Low Income World: Causes and Consequence,” Economic Growth Center Discussion Paper No. 681 (New Haven: Yale University).
Sen, Amartya, (forthcoming), “Economic Policy and Equity: An Overview,” in Economic Policy and Equity, ed. by Vito Tanzi, Ke-young Chu, and Sanjeev Gupta (Washington: International Monetary Fund).
Shaw, R. Paul, and Charles C. Griffin, 1995, Financing Health Care in Sub-Saharan Africa Through User Fees and Insurance (Washington: World Bank).
Tresserras, R.J. Canela, J. Alvarez, J. Sentis, and L. Salleras, 1992, “Infant Mortality, Per Capita Income, and Adult Illiteracy: An Ecological Approach,” American Journal of Public Health, Vol. 82, No. 3 (March), pp. 435–37.
White, Halbert, 1980, “A Heteroskedasticity-Consistent Covariance Matrix and a Direct Test for Heteroskedasticity,” Econometrica, Vol. 48 (May), pp. 817-38.
The authors wish to thank Benedict Clements, Hamid Davoodi, Luiz de Mello, Robert Gillingman, Henry Ma, Edgardo Ruggiero, Christian Schiller, and Gustavo Yamada for their helpful comments on the earlier drafts. The usual disclaimer applies.
Recently, the methodological basis of studies estimating social rates of return of education has been questioned. For example, Bennell (1995; 1996) does not find support for the proposition that basic education has a higher social return than other levels of education (also see Appleton, Hoddinott, and Mackinnon, 1996; Cassen, 1996).
Also, the absence of a measurable impact of public spending on indicators could be due to a differential effect on poor and nonpoor groups, which are not captured by aggregated social indicators (Bidani and Ravallion, 1997). This possibility is not addressed here because of lack of data on disaggregated social indicators for poor and nonpoor groups.
A drawback of measuring education spending as a share of GDP is that the associated spending per student can vary greatly among countries depending on the level of GDP. However, the results presented in this paper continue to hold even when education and health care spending is expressed in per capita terms. This drawback was overcome by the inclusion of GDP per capita as a control variable (see below). Indeed, the effect of per capita spending can be gauged from the coefficients for spending as a percent of GDP and GDP per capita (the product of these variables equals spending per capita).
It should be noted that an increase in public allocations for, say, primary education, while holding all other spending constant, has an effect on education indicators both directly through X2i, and indirectly, through the overall level of education spending X1i.
The list of core indicators for education and health include: net enrollment in primary education, persistence through grade four, literacy rate of 15 to 24 year olds, adult literacy rate, infant mortality rate, child mortality rate, maternity mortality ratio, births attended by skilled health personnel, contraceptive prevalence rate, HIV infection rate in 15 to 24 year-old pregnant women, and life expectancy at birth.
Other proxies of child nutrition, such as indicators of malnourishment and birth weight, were not available.
In fact, female illiteracy was found to have a weaker effect than overall illiteracy.
Using DPT immunizations yields the same results. The two types of immunizations are highly correlated, and therefore, only one was included in the regressions.
An attempt was made to circumvent the problem of missing control variables by adding dummies for regions, under the assumption that the variation of omitted controls within regions is dominated by the variance among regions. However, regional dummies were not significant in the education regressions and can not be used in conjunction with other explanatory variables in the health regressions (see below).
The correlation coefficient between illiteracy rates and average teacher salaries as a multiple of GDP per capita was -0.80 for 24 countries for which data were available (data on teacher salaries are from Mehrotra and Buckland, 1998). The correlation coefficient with child mortality rates was -0.72. The correlation coefficient between income per capita in PPP terms and the relative teacher salaries was also relatively high at -0.48.
If the deviation between the sum of intrasectoral spending and total sectoral spending exceeded 10 percent, the observation was dropped.
This measure of primary health care, which includes services provided by clinics and medical, dental, and paramedical practitioners, appropriately captures primary-level health care, as it is the “first point of contact” between clients and a facility in a health system (e.g., Shaw and Griffin, 1995). The GFS disaggregation of health spending—into hospitals, clinics and practitioners, and other spending—is also used by others to examine priorities in the health sector (e.g., Appleton and Mackinnon, 1996).
For example, intrasectoral education data for 1994 were matched with enrollment data for 1994, if available. If enrollment data for 1994 were not available, observations in the range of 3 years before and after the year of spending were used (1991–1997). Potential problems of measurement error were addressed by running two-stage least squares regressions.
There is no a priori preferred functional form for the “production function” for education and health services. Therefore, regressions were also run in loglinear form, for which summary results are presented.
The data set includes some outlying observations (e.g., Papua New Guinea). However, these outliers did not critically affect the regression results after corrections for heteroskedasticity were made.
The correlation coefficient between total health spending and measles immunization was 0.40, and -0.39 for overall health expenditure and adult illiteracy. The two coefficients were significant at the 1 percent level.
To address heteroskedasticity, White’s (1980) corrected covariance and standard errors are used, except for the equation with gross primary plus secondary enrollment as the dependent variable. The latter regression was estimated using the weighted-least squares (WLS) technique, with adult illiteracy used as a weight. This weight can be interpreted as a scaling factor, indicative of the challenge of achieving targeted levels of education attainment, and yields better results than White’s corrected regression. The use of a consistent set of instruments in the 2SLS regressions was checked for validity using Sargan’s (1964) general misspecification test.
The inclusion of dummy variables for regions did not improve the explanatory power of the regression models, nor did it affect the coefficient estimates and their significance levels.
The coefficient estimate of the share of spending on primary plus secondary education from the WLS regression with gross primary enrollment as the dependent variable was 0.21. The coefficient estimate from an OLS regression with net secondary enrollment as the dependent variable was 0.19. They were both significant at the 5 percent level. OLS regressions with the spending and education attainment variables in logs were also run. These regressions yield similar results for gross primary and secondary enrollment and gross secondary enrollment as dependent variables, but the statistical significance of the intrasectoral spending variable for persistence through grade four regression was reduced.
The regressions do not permit drawing up of conclusions about the effect of changes in the level of spending on primary and secondary education—as opposed to the share of such spending in total education expenditure. This issue was addressed by re-estimating the education regressions including spending on primary and secondary education as a percent of GDP and omitting the variables for intrasectoral spending and the overall spending. In the four regressions for enrollment, this newly defined spending variable was significant at the 1 percent level; the coefficient estimated ranges between 3.0 and 4.0. In the two regressions for persistence through grade four, spending on primary and secondary education as a percent of GDP was only significant at the 10 percent level, with a coefficient of 2.7 for the OLS regression and 5.5 for the 2SLS regression. These results suggest that, irrespective of the specification, spending for the two sectors matters.
Partial variance analysis only yields accurate results if the underlying assumption on the ordering of casual effects is correct (i.e., partial variance analysis assumes here that public spending impacts on social indicators only after all other variables have taken effect). Alternatively, the results of partial variable analysis would be correct if spending has an effect independent from the other explanatory variables. Obviously, these are demanding assumptions, and the results presented here should be interpreted with caution.
In loglinear form, the regressions yield a different result: intrasectoral spending is insignificant at the 10 percent level but total health spending is significant at the 1 percent level in both equations.
To test for a discernable impact of an inconsistent definition of primary health care, the regressions with a dummy variable to reflect the use of the two different definitions were run. The dummy variable was statistically insignificant, suggesting that the inconsistent measurement of primary health care spending does not bias the results.
These observations were included in order not to reduce the already small number of observations. The regressions were also run for the sample excluding the countries with zero primary health care spending. Although coefficient estimates did not change by much, the statistical significance of intrasectoral spending and total health expenditures increased.
Re-estimating the regressions with primary health care in relation to GDP as an independent variable in place of other health spending variables yields statistically insignificant results, in part due to multicollinearity.
Regional dummies were omitted from the health regressions, just as in the case of the education regressions, but for a different reason. In the health regressions, the dummy variable for Africa is strongly significant, and draws away the statistical significance of the other explanatory variables, including the public spending variables. However, since the focus of this study is to explain cross-country variation in child and infant mortality rates without regard to geography, regional dummies were omitted from the regressions.
The additional variance explained by intrasectoral health spending increases to over 10 percent if insignificant control variables are dropped from the regressions.
Targets in social areas have been established at different foras. For example, the Development Assistance Committee (DAC) of the OECD, building on the results of the 1995 Social Summit in Copenhagen, has established goals that include reaching universal enrollment in primary education and reducing infant and child mortality by two-thirds in all developing countries by 2015 (OECD/DAC, 1996).