metadata: authors: 0: author: Herrera Aguilera,Santiago
1: author: Pang,Gaobo
count: World
docna: 0: docna: Efficiency of public spending in developing
            countries : an efficiency frontier approach
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Labor Markets,Public Sector Economics,Population & Development,Educational Sciences,Public Financial Management,Public Finance Decentralization and
            Poverty Reduction
teratopic: Macroeconomics and Economic Growth,Environment,Finance and Financial Sector Development,Health, Nutrition and Population
abstracts: cdata!: Government spending in developing
            countries typically account for between 15 and 30 percent of
            GDP. Hence, small changes in the efficiency of public
            spending could have a major impact on GDP and on the
            attainment of the government's objectives. The first
            challenge that stakeholders face is measuring efficiency.
            This paper attempts such quantification and has two major
            parts. The first part estimates efficiency as the distance
            between observed input-output combinations and an efficiency
            frontier (defined as the maximum attainable output for a
            given level of inputs). This frontier is estimated for
            several health and education output indicators by means of
            the Free Disposable Hull (FDH) and Data Envelopment Analysis
            (DEA) techniques. Both input-inefficiency (excess input
            consumption to achieve a level of output) and
            output-inefficiency (output shortfall for a given level of
            inputs) are scored in a sample of 140 countries using data
            from 1996 to 2002. The second part of the paper seeks to
            verify empirical regularities of the cross-country variation
            in efficiency. Results show that countries with higher
            expenditure levels register lower efficiency scores, as well
            as countries where the wage bill is a larger share of the
            government's budget. Similarly, countries with higher
            ratios of  public to private financing of the service
            provision score lower efficiency, as do countries plagued by
            the HIV/AIDS epidemic and those with higher income
            inequality. Countries with higher aid-dependency ratios also
            tend to score lower in efficiency, probably due to the
            volatility of this type of funding that impedes medium term
            planning and budgeting. Though no causality may be inferred
            from this exercise, it points at different factors to
            understand why some countries might need more resources than
            others to achieve similar educational and health outcomes.
repnme: repnme: Efficiency of public spending in developing
            countries : an efficiency frontier approach
display_title: Efficiency of public spending in
            developing countries : an efficiency frontier approach
listing_relative_url: /research/2005/06/5863405/efficiency-public-spending-developing-countries-efficiency-frontier-approach
topicv3: Public Finance Management m1326316 716,Education m1327048 470,Production Frontier m1503947 2688,Life Expectancy m1504363 2666,Health m1504661 3480
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Environment,Finance and Financial Sector Development,Health, Nutrition and Population
no_of_pages: 69
author_keys: 0: author_key: 167039
1: author_key: 679057
model_extractions: # WPS3645

## **Efficiency of Public Spending in Developing Countries: An Efficiency Frontier Approach**<sup>**1**</sup>

Santiago Herrera Gaobo Pang

## _Abstract_

_Governments of developing countries typically spend between 15 and 30 percent of GDP. Hence, small changes in the efficiency of public spending could have a major impact on GDP and on the attainment of the government’s objectives. The first challenge that stakeholders face is measuring efficiency. This paper attempts such quantification and has two major parts. The first one estimates efficiency as the distance between observed input-output combinations and an efficiency frontier (defined as the maximum attainable output for a given level of inputs). This frontier is estimated for several health and education output indicators by means of the Free Disposable Hull (FDH) and Data Envelopment Analysis (DEA) techniques. Both input-inefficiency (excess input consumption to achieve a level of output) and output-inefficiency (output shortfall for a given level of inputs) are scored in a sample of 140 countries using data from 1996 to 2002. The second part of the paper seeks to verify empirical regularities of the crosscountry variation in efficiency. Results show that countries with higher expenditure levels register lower efficiency scores, as well as countries where the wage bill is a larger share of the government’s budget. Similarly, countries with higher ratios of public to private financing of the service provision score lower efficiency, as do countries plagued by the HIV/AIDS epidemic and those with higher income inequality. Countries with higher aid-dependency ratios also tend to score lower in efficiency, probably due to the volatility of this type of funding that impedes medium term planning and budgeting. Though no causality may be inferred from this exercise, it points at different factors to understand why some countries might need more resources than others to achieve similar educational and health outcomes._

## World Bank Policy Research Working Paper 3645, June 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 The paper is a draft of work in progress that carries the names of the authors and should be cited accordingly. The authors thank Antonio Estache, Jorge Garcia, April Harding, Ulrich Lachler, Vikram Nehru, John Rust, and participants at seminars of the Economic Policy and Debt Department of the World Bank., the Banca d’Italia Workshop on Public Finance and the Economics Department at the University of Maryland for helpful comments on earlier drafts of the paper. The dataset, specific-country results, and programs are available in the World Bank PRMED (Economic Policy and Debt Department) website.
# Table of Contents

|I. Introduction..........................................................................................1|
|---|
|II. Measuring Efficiency: Methodologies and Overview of the Literature...................1|
|II.1. Methods for Measuring Efficiency........................................................2|
|II.2. Overview of Precursor Papers.............................................................5|
|III. Empirical Results.................................................................................8|
|III.1. Input and Output Indicators: Description, Assumptions and Limitations..........8|
|III.2. Single Input-output Results..............................................................12|
|III.3. Multiple Inputs and Multiple Outputs.................................................20|
|III.4. Efficiency Change Over Time..........................................................24|
|IV. Explaining Inefficiency Variation Across Countries......................................25|
|IV.1. Method, Variables and Data Description.............................................25|
|IV.2. Results.....................................................................................28|
|V. Concluding Remarks and Directions for Future Work.....................................31|
|References............................................................................................36|
# **I. Introduction**

Governments of developing countries typically spend resources equivalent to between 15 and 30 percent of GDP. Hence, small changes in the efficiency of public spending could have a significant impact on GDP and on the attainment of the government’s objectives whichever these are. The first challenge faced by stakeholders is measuring and scoring efficiency. This paper attempts such quantification. Additionally it verifies statistically some empirical regularities that describe the cross country-variation in the estimated efficiency scores.

The paper has four chapters following this Introduction. The first one presents the methodology that defines efficiency as the distance from the observed input-output combinations to an efficient frontier. This frontier, defined as the maximum attainable output for a given input level, is estimated using the Free Disposable Hull (FDH) and Data Envelopment Analysis (DEA) techniques. The exercise focuses on health and education expenditure because they absorb the largest share of most countries’ budgets, and because of lack of data availability for international comparisons in other types of expenditures.

The second chapter estimates the efficiency frontiers for nine education output indicators and four health output indicators, based on a sample of 140 countries and data for 19962002 Both input-efficiency (excess input consumption to achieve a level of output) and output-efficiency (output shortfall for a given level of inputs) are scored. The chapter presents both the single input-single-output and the multiple-inputs multiple-outputs frameworks. In addition, this chapter explores how expenditure efficiency has changed over time.

The third chapter seeks to identify empirical regularities that explain cross-country variation in the efficiency scores. Using a Tobit panel approach, this chapter shows that higher expenditure levels are generally associated with lower efficiency scores. Similarly, countries in which the wage bill is a larger share of the total budget tend to have lower efficiency scores. Three other variables that explain the cross country variation in efficiency scores are the degree of urbanization (positively correlated with efficiency, the prevalence of the HIV/AIDS epidemic (negatively associated with efficiency scores), and inequality in income distribution (higher inequality associated with lower efficiency).

The fourth and last chapter summarizes the conclusions.

# **II. Measuring Efficiency: Methodologies and Overview of the Literature**

The object of this chapter is to briefly describe the specific empirical methods applied in this paper to measure efficiency and to survey the literature more directly related to the analysis of public expenditure efficiency. Empirical and theoretical measures of efficiency are based on ratios of observed output levels to the maximum that could have 1
been obtained given the inputs utilized. This maximum constitutes the efficient frontier which will be the benchmark for measuring the relative efficiency of the observations. There are multiple techniques to estimate this frontier, surveyed recently by MurilloZamorano (2004), and the methods have been recently applied to examine the efficiency of public spending in several counties. These are the topics of the next two sections.

# **II.1.  Methods for Measuring Efficiency.**

The origin of the modern discussion of efficiency measurement dates back to Farell (1957), who identified two different ways in which productive agents could be inefficient: one, they could use more inputs than technically required to obtain a given level of output, or two, they could use a sub-optimal input combination given the input prices and their marginal productivities. The first type of inefficiency is termed technical inefficiency while the second one is known as allocative inefficiency.

These two types of inefficiency can be represented graphically by means of the unit isoquant curve in Figure1. The set of minimum inputs required for a unit of output lies on the isoquant curve YY’. An agent’s input-output combination defined by bundle P produces one unit of output using input quantities X1 and X2. Since the same output can be achieved by consuming less of both inputs along the radial back to bundle R, the segment RP represents the inefficiency in resource utilization. The technical efficiency (TE), input-oriented, is therefore defined as TE = OR/OP. Furthermore, the producer could achieve additional cost reduction by choosing a different input combination. The least cost combination of inputs that produces one unit of output is given by point T, where the marginal rate of technical substitution is equal to the input price ratio. To achieve this cost level implicit in the optimal combination of inputs, input use needs to be contracted to bundle S. The input allocative efficiency (AE) is defined as AE = OS/OR.

<!-- Start of picture text --> X2/Y<br>Y<br>T<br>P<br>R<br>S<br>Y’<br>O<br>X1/Y<br><!-- End of picture text -->

**Figure 1 Technical and Allocative Inefficiency**

2
The focus of this paper is measuring technical efficiency, given the lack of comparable input prices across the countries. This concept of efficiency is narrower than the one implicit in social welfare analysis. That is, countries may be producing the wrong output very efficiently (at low cost). We abstract from this consideration (discussed by Tanzi 2004), focusing on the narrow concept of efficiency.

Numerous techniques have been developed over the past decades to tackle the empirical problem of estimating the unknown and unobservable efficient frontier (in this case the isoquant YY”). These may be classified using several taxonomies. The two most widely used catalog methods into parametric or non-parametric, and into stochastic or deterministic. The parametric approach assumes a specific functional form for the relationship between the inputs and the outputs as well as for the inefficiency term incorporated in the deviation of the observed values from the frontier. The nonparametric approach calculates the frontier directly from the data without imposing specific functional restrictions. The first approach is based on econometric methods, while the second one uses mathematical programming techniques. The deterministic approach considers all deviations from the frontier explained by inefficiency, while the stochastic focus considers those deviations a combination of inefficiency and random shocks outside the control of the decision maker.

This paper uses non-parametric methods to avoid assuming specific functional forms for the relationship between inputs and outputs or for the inefficiency terms. A companion paper will explore the parametric approach, along the lines proposed by Greene (2003). The remainder of the section briefly describes the two methods: the Free Disposable Hull (FDH) and the Data Envelopment Analysis (DEA) The FDH method imposes the least amount of restrictions on the data, as it only assumes free-disposability of resources. Figure 2 illustrates the single-input single-output case of FDH production possibility frontier. Countries A and B use input XA and XB to produce outputs YA and YB, respectively. The _input efficiency_ score for country B is defined as the quotient XA/XB. The _output efficiency_ score is given by the quotient YB/YA. A score of one implies that the country is on the frontier. An input efficiency score of 0.75 indicates that this particular country uses inputs in excess of the most efficient producer to achieve the same output level. An output efficiency score of 0.75 indicates that the inefficient producer attains 75 percent of the output obtained by the most efficient producer with the same input intake. Multiple input and output efficiency tests can be defined in an analogous way.

3
<!-- Start of picture text --> Output D<br>E<br>C<br>A<br>YA<br>YB B<br>O<br>XA XB Input<br><!-- End of picture text -->

**Figure 2 Free Disposal Hull (FDH) production possibility frontier**

The second approach, Data Envelopment Analysis (DEA), assumes that linear combinations of the observed input-output bundles are feasible. Hence it assumes convexity of the production set to construct an envelope around the observed combinations. Figure 3 illustrates the single inputsingle output DEA production possibility frontier. In contrast to the vertical step-ups of FDH frontier, DEA frontier is a piecewise linear locus connecting all the efficient decision-making units (DMU). The feasibility assumption, displayed by the piecewise linearity, implies that the efficiency of C, for instance, is not only ranked against the real performers A and D, called the peers of C in the literature, but also evaluated with a virtual decision maker, V, which employs a weighted collection of A and D inputs to yield a virtual output. DMU C, which would have been considered to be efficient by FDH, is now lying below the variable returns to scale (VRS, further defined below) efficiency frontier, XADF, by DEA ranking. This example shows that FDH tends to assign efficiency to more DMUs than DEA does. The input-oriented technical efficiency of C is now defined by TE = YV/YC.

<!-- Start of picture text --> CRS F<br>D<br>Output<br>N<br>VRS<br>Y<br>V C<br>A<br>B<br>VRS<br>O X<br>Input<br><!-- End of picture text -->

**Figure 3 DEA production possibility frontier**

4
If constant returns to scale (CRS) characterize the production set, the frontier may be represented by a ray extending from the origin through the efficient DMU (ray OA). By this standard, only A would be rated efficient. The important feature of the XADF frontier is that this frontier reflects variable returns to scale. The segment XA reflects locally increasing returns to scale (IRS), that is, an increase in the inputs results in a greater than proportionate increase in output. Segments AD and DF reflect decreasing returns to scale. It is worth noticing that constant returns to scale technical efficiency (CRSTE) is equal to the product of variable returns to scale technical efficiency (VRSTE) and scale efficiency (SE). Accordingly, DMU D is technically efficient but scale inefficient, while DMU C is neither technically efficient nor scale efficient. The scale efficiency of C is calculated as YN/YV. For more detailed exploration of returns to scale, readers are referred to Charnes, Cooper, and Rhodes (1978) and Banker, Charnes, and Cooper (1984), among others.<sup>2</sup> The limitations of the non-parametric method derive mostly from the sensitivity of the results to sampling variability, to the quality of the data and to the presence of outliers. This has led recent literature to explore the relationship between statistical analysis and non-parametric methods (Simar and Wilson, 2000). Some solutions have been advanced. For instance, confidence intervals for the efficiency scores can be estimated using asymptotic theory in the single input case (for input-efficiency estimators) or singleoutput (in the output efficiency) case, given these are shown to be maximum likelihood estimators (Banker, 1993 and Goskpoff, 1996). For multiple input-output cases the distribution of the efficiency estimators is unknown or quite complicated and analysts recommend constructing the empirical distribution of the scores by means of bootstrapping methods (Simar and Wilson, 2000). Other solutions to the outlier or noisy data consist in constructing a frontier that does not envelop all the data point, building an expected minimum input function or expected maximum output functions (Cazals, Florens and Simar, 2002, and Wheelock and Wilson, 2003). Another limitation of the method, at least in the context in which we will apply it, is the inadequate treatment of dynamics, given the lag between input consumption (public expenditure) and output production (health and education outcomes).

# **II.2. Overview of Precursor Papers**

There is abundant literature measuring productive efficiency of diverse types of decision making units. For instance, there are papers measuring efficiency of museums (Bishop and Brand, 2003), container terminals (Cullinane and Song, 2003), electric generation plants (Cherchye and Post 2001), banks (Wheelock and Wilson, 2003), schools (Worthington, 2001) and hospitals (Bergess and Wilson, 1998), among others. Few papers, however, analyze aggregate public sector spending efficiency using cross-country data. These are the direct precursors of this paper and are the focus of this section’s survey.

> 2  The technical Appendix D provides more detailed exploration of the Data Envelopment Analysis, which shows how the peers are identified, how the virtual DMUs are constructed, and how weights to the different efficient DMUs and efficiency scores are calculated.

5
Gupta and Verhoeven (2001) employ the input-oriented FDH approach to assess the efficiency of government spending on education and health in 37 African countries in 1984-1995. Using several output indicators for health and education, they construct efficiency frontiers for each of the indicators and for each of the time periods they considered. That is, they used a single input-single output for each time period. They find that, on average, African countries are inefficient in providing education and health services relative to both Asian and the Western Hemisphere counties. They also report, however, an increase in the productivity of spending through time, as they document outward shifts in the efficiency frontier. Finally the authors report a negative relationship between the input efficiency scores and the level of public spending, which leads them to conclude that higher educational attainment and health output requires efficiency improvement more than increased budgetary allocations.

Evans and Tandon (2000) adopt a parametric approach to measure efficiency of national health systems for the World Health Organization, by estimating a fixed effects panel of 191 countries for the period 1993-1997. Health output was measured by the disability adjusted life expectancy (DALE) index, while health expenditures (public and private aggregated) and the average years of schooling of the adult population were considered as inputs. The output-efficiency score is defined as the ratio of actual performance above the potential maximum. The authors also introduce the square of the inputs (average years of schooling and expenditure), arguing it’s a second-order Taylor-series approximation to an unknown functional form. The fact that the quadratic terms are significant may be an indication of the importance of non-linearity, but may also reflect neglected dynamics or heterogeneity in the sample (Haque, Pesaran and Sharma, 1999), given that both developed and developing nations were included. An interesting contribution of the paper is a construction of a confidence interval for the efficiency estimates through a Monte-Carlo procedure. These authors document a positive relationship between their efficiency scores and the level of spending. The more efficient health systems are those of Oman, Chile and Costa Rica. The more inefficient countries are all African: Zimabawe, Zambia, Namibia, Botswana, Malawi and Lesotho.

Jarasuriya and Woodon (2002) also adopt a parametric approach to estimate efficiency of health and education provision in a sample of developing countries. The authors estimate the efficiency frontier by econometric methods. These authors consider separately an educational attainment indicator (net primary enrollment) and a health output indicator (life expectancy) and estimate a functional linear relationship between these output indicators and three inputs: per capita GDP, spending per-capita, and the adult literacy rate. Using a panel of 76 countries for the period 1990 to 1998, they found no relationship between expenditure and the educational or health output variables when they include the per-capita GDP. This led the authors to conclude that spending more is not a guarantee to obtain better education or health results. The authors do not point at the correlation between the two variables as a possible cause of this problem, which we discuss in the next section. The countries with the lowest efficiency in health indicators are all African (Malawi, Zambia, Mozambique, Ethiopia) as well as in education attainment (Ethiopia, Niger, Burkina Faso).

6
The authors go further by attempting to explain the cross-country variation in efficiency and find that the degree of urbanization and the quality of bureaucracy are the most relevant variables. To capture possible non-linearity, the authors introduce these variables squared. This stage of their work poses several problems. First, it is possible that the (non-linear) quadratic terms reflect heterogeneity across countries and dynamics across time. As shown by Haque, Pesaran, and Sharma (1999), this would produce inconsistent estimates. Second, the authors do not adjust for the fact that the dependent variable (efficiency scores) is censored, given that it can adopt only values between zero and one. And third, the authors do not consider the serial correlation of the efficiency scores (Simar and Wilson, 2004) Greene (2003a) combines the previous two papers in the sense he concentrated on health efficiency only using the WHO panel data and explained inefficiency scores variation across the sample of counties. Greene’s stochastic frontier estimation is much more general and flexible, as it allows for time variation of the coefficients and heterogeneity in the countries’ sensitivity to the explanatory variables. The author first estimates a health production function using expenditure (public and private together) and education as inputs, and then explains inefficiency with a set of explanatory variables of which the only significant ones are the income inequality measure, GDP per capita and a dummy variable for tropical location.

Afonso, Schuknecht and Tanzi (2003) examine the efficiency of public spending using a non-parametric approach. First, they construct composite indicators of public sector performance for 23 OECD countries, using variables that capture quality of administrative functions, educational and health attainment, and the quality of infrastructure. Taking the performance indicator as the output, and total public spending as the input, they perform single-input, single-output FDH to rank the expenditure efficiency of the sample. Their results show that countries with small public sectors exhibit the highest overall performance.

Afonso and St. Aubyn (2004) address the efficiency of expenditure in education and health for a sample of OECD countries applying both DEA and FDH. This paper presents detailed results by comparing input-oriented and output-oriented efficiency measurements. The small overlap of the samples limits the comparability of these results with those presented in the next section. An apparently strange result, reported in earlier drafts of the paper, was the inclusion of Mexico as one of the benchmark countries (on the efficiency frontier). The result is strange given that the sample is the OECD countries, and it counterintuitive. This is the result of Mexico having very low spending and low education attainment results, hence it cam be considered as the “origin” of the efficiency frontier. The next chapter discusses this topic and reports similar counterintuitive results but for other countries.

7
# **III.   Empirical Results**

# **III.1. Input and Output Indicators: Description, Assumptions and Limitations**

Cross-country comparisons assume some homogeneity across the world in the production technology of health and education.<sup>3</sup> There are two particular aspects in which the homogeneity assumption is important. First, the comparison assumes that there is a small number of factors of production that are the same across countries. Any omission of an important factor will yield as a result a high efficiency ranking of the country that uses more of the omitted input. Second, the comparison requires that the quality of the inputs is more or less the same, with the efficiency scores biased in favor of countries where the quality is of higher grade.

Factor heterogeneity will not be a problem, as long as it is evenly distributed across countries. It will be problematic if there are differences between countries in the average quality of a factor (Farrell, 1957). The exercise that we present suffers from this limitation, given that the main input in both production technologies is used more intensively in richer countries (with higher per-capita GDP). The main input is public spending per capita on education and health measured in constant 1995 US dollars in PPP terms. A clear positive association between this variable and per-capita GDP can be verified (Figures 4 and 5).

This positive association between expenditure and the level of economic development (as measured by per-capita-GDP) may be explained by several reasons.. One of them could be the Balassa-Samuelson effect, according to which price levels in wealthier countries tend to be higher than in poorer countries.<sup>4</sup> This applies to both final goods and factor prices. Thus price of the same service (health or education, for instance) will be higher in the country with higher GDP. Similarly, wages in the relatively richer counties are higher, given the higher marginal productivity of labor, which will tend to increase costs, especially in labor-intensive activities as health and education.

Figure 4 can be interpreted as evidence of the validity of Wagner’s hypothesis at the cross-country level. This hypothesis, postulates that there is a tendency for governments to increase their activities as economic activity increases. Since 1890 Wagner postulated that economic development implied rising complexities that required more governmental activity, or that the elasticity of demand for publicly provided services, in particular education was greater than one. This hypothesis has been tested econometrically (Chang, 2002) in time series and cross-country settings, showing that this is nothing particular of the series used for the present study.

> 3 See Table B.5 in Appendix B for the list of countries included in the study.

> 4 The Balassa-Samuelson effect refers to the fact that price levels are higher in richer countries than in poorer countries.  It can be shown that relative wages and relative prices are a function of the marginal productivity of labor in the traded goods. Given higher capital abundance in the richer countries, the productivity of labor tends to be higher in these countries, and hence will be wages and prices.

8
<!-- Start of picture text --> Education Spending vs GDP per capita Health Spending vs GDP per capita<br>SAUBRBKWT CZESVN<br>TZAMWIBDIETHNERTJKMLIYEMZMBMDGGNBMOZERITCDBENBFAKENRWACAFUGANP LMDASENUZBKGZTGOBGDLAOSDNMNGCIVGMBCOMKHMMRTCMRGEOPA KGHAAGOVNMGINDJISLBLSOARMNICAZEINDBOLPNGHNDZWEVUTIDNLKASYRMARCHNJAMGTMPHLJORUKRGUYLBNSWZKAZCPVBLRSLVPRYPERWSMFJIVCTBLZDZALCADMADOMIRNNAMTONPANROMTUNTURBGRMKDCOLGABTHAGRDRUSBWALVABRACRIME XTTOMY SLTUURYHRVCHLMUSPOLZAFES TATGGNQSVKKNAHUNARGOMNCZEKORBHRBHS MA CARE SLETZAMWIBDIETHNERTJKMLIZMBYEMMDGGNBMOZZARERITCDBENCOGBFAKENRWACAFUGANP LMDASENUZBKGZTGOLAOBGDSDNMNGCIVGMBHTICOMKHMMRTCMRGEOPA KGHAAGOVNMGINDJISLBLSOARMNICINDAZEBOLPNGHNDZWETKMVUTIDNLKAECUSYREGYMARCHNJAMALBGTMPHLJORUKRGUYLBNSWZKAZCPVBLRSLVPRYPERWSMFJIVCTDZABLZLCADMADOMVENIRNNAMTONPANROMTUNBGRMKDCOLGABTHAGRDRUSBWALVABRACRIME XTTOLTUMY SURYHRVCHLMUSPOLZAFES TATGGNQSVKKNAHUNARGSAUOMNKORBRBBHRBHSKWT ARESGP<br>SLE NGA<br>COG<br>6 7 8 9 10 6 7 8 9 10<br>lgdp lgdp<br>ledu Linear prediction lhea Linear prediction<br>8 8<br>6 6<br>4 4<br>ledu/Linear prediction 2 lhea/Linear prediction 2<br>0 0<br><!-- End of picture text -->

**Figure 4. Public Expenditure  and GDP (both per capita and in logs)**

Previous studies that measured the efficiency of public spending recognized the positive association and suggested alternative solutions. One possibility is to split the sample by groups of countries (Gupta and Verhoeven, 2001). We follow this approach by excluding the industrialized nations from the sample, and by presenting most of the results clustered regionally (Africa (AFR), East Asia and Pacific(EAP), Latin America and Caribbean (LAC), Middle-East and North Africa (MNA) and South Asia (SAS)). A second alternative incorporates directly the per-capita GDP as a factor of production, jointly with expenditure and other inputs (Jarasuriya and Woodon 2002). The problem with this approach is that it combines variables derived from a production function approach, and hence with clear interpretation, with others (GDP per capita) that are difficult to interpret from any viewpoint. When the two types of variables are combined, their effects cannot be disentangled.

A third option consists in using as an input the orthogonal component of public expenditure to GDP<sup>5</sup> . We scored the efficiency using as input both the original expenditure variable and the orthogonalized variable. The goodness-of-fit of each model was gauged based on the frequency distribution of the inefficiency measures, as suggested by. Farrell (1957) and Varian (1990). Comparing the efficiency distributions (Figures 5) it is clear that the orthogonalized expenditure version produces distributions that are not skewed towards extreme inefficient outcomes. On this basis, the paper considered the orthogonal component of expenditure on health and education.

> 5 The orthogonalized expenditure variable is the residual of the linear regression between pubic expenditure and GDP per capita. Since residuals may take positive and negative values, the variable was right-shifted to avoid negative values to facilitate graphical presentation of the frontiers.

9
<!-- Start of picture text --> FDH FDH<br>0 .2 .4 .6 .8 1 .4 .6 .8 1<br>score score<br>Unorthogonalized Public Expenditure Orthogonalized Public Expenditure<br>8 15<br>6<br>10<br>4<br>Density Density<br>5<br>2<br>0 0<br><!-- End of picture text -->

**Figure 5. Density of Efficiency Scores – Gross Primary School Enrollment**

This paper uses nine indicators of education output and four indicators of health output.<sup>6</sup> The education indicators are: primary school enrollment (gross and net), secondary school enrollment (gross and net), literacy of youth, average years of school, first level complete, second level complete, and learning scores. Though the ideal educational output indicator are comparable learning scores, international assessments are based on samples mostly composed of developed nations, limiting the applicability to the present paper. However, Crouch and Fasih (2004) recently combined several international assessments to obtain a larger sample of comparable results.<sup>7</sup> Unfortunately they only do it for one period. The correlation between the learning scores and other output variables is high (.81 with net secondary school enrollment and .76 with average years of school), as shown in Figure 6<sup>8</sup> . The health output indicators are: life expectancy at birth, immunization (DPT<sup>9</sup> and measles), and the disability-adjusted life expectancy (DALE).

The cross-country comparisons with this set of indicators assume some form of data homogeneity, which might be problematic given the diversity of counties in the sample considered. Even for a more homogeneous group of countries, such as the OECD, there is call for caution when comparing expenditure levels in member countries (Jounard,

> 6 The data sources are: the World Bank World Development Indicators (WDI), Barro-Lee database, and Crouch and Fasih (2004)  the World Health Organization (Mathers _et al,_ 2000).A complete list of variables and data sources can be found in Table B.6 of Appendix B.

> 7 Crouch and Fasih (2004) consider several international tests of learning achievement in math, science and literacy applied at different levels of the school system.  The tests are the following: TIMSS (Third International Mathematics and Science Survey),  PIRLS (Progress in International Literacy Study), PISA (program for International Student assessment), Reading Literacy Study,  LLECE (Laboratorio  Since the tests have different samples, they converted all test scores through iterative comparisons to a single numeraire.Latinoamericano de Evaluaciond ela Calidad de la Educacion, SACMEQ (Southern Africa Consortium for Monitoring of Education Quality), MLA (Monitoring Learning Achievement)

> 8 The correlation coefficients and Figure 6 exclude developed nations for the Crouch and Fasih (2004) sample.

> 9 DPT is Diphtheria-Pertussis and Tetanus

10
et.al. 2003). There is very little to do to overcome this limitation, except subdivide the sample into different groups. Probably a regional aggregation can be useful, but even at that level there may be extreme heterogeneity.

<!-- Start of picture text --> Correlation: Learning Scores and Net Secondary Enrollment Correlation: Learning Scores and Average Years of School<br>KOR SGP TWN KOR<br>HKG<br>SVK HUNCZESVN SV N HUNSVKCZE<br>MYS BGRLVA POL MYS BGRPOL<br>MEX MDATTO MKDROMCUB LTU THA MEX CUBTTO ROM<br>IDN TUR TUN BRAIRNALB ARG JOR BRAIDNTUNTURIRN J OR ARG<br>DOM PRY COLPER BOL CHL KWT HNDDOMCOLBOL PRYKWT CHLPER<br>VUT SYC<br>TZA MOZMDG KEN MARSWZ ZWE PHL BLZMUS MOZ TZA UGACMR KEN ZWE SWZMUS PHL<br>BWA BWA<br>BFA<br>ZAF ZAF<br>LSO NAM MLI SEN LSO ZMB<br>MWI<br>NE R NER<br>0 20 40 60 80 100 0 2 4 6 8 10<br>Net Secondary Enrollment Average Years of School<br>Data Source: World Bank WDI & Crouch and Fasih (2004) Data Source: World Bank WDI & Crouch and Fasih (2004)<br>600 600<br>500 500<br>400 400<br>Learning Scores Learning Scores<br>300 300<br>200 200<br><!-- End of picture text -->

**Figure 6. Correlation between Learnings Scores and Other Education Indicators**

Other four limitations of the analysis arising from the particular data sources are: First, the level of aggregation. The paper uses aggregate public spending on health and education, while using disaggregate measures of output, such as. primary enrollment or secondary enrollment. Ideally, the input should be use separately public spending in primary and secondary education. Similarly, health care spending could be disaggregated into primary care level care and secondary level. The data can be disaggregated even further, by analyzing efficiency at the school or hospital levels. Second, there are omitted factors of production. This is especially true in education, as the paper did not consider private spending due to data constraints for developing nations. If this factor were used more intensively in a particular group of countries, then the efficiency scores (reported in the next section) would be biased favoring efficiency in that group.

The third limitation arising from the data is the combination of monetary and nonmonetary factors of production. The paper uses together with public expenditure, other non-monetary factors of production such as the ratio of teachers to students, in the case of education, or literacy of adults in the case of health and education. Other factors of production that could have been used were the physical number of teaching hours (in education) or the number of doctors or in-patient beds, as Afonso and St. Aubyn did for the OECD countries. However, inexistent data for a large number of developing countries constrained the options.

A fourth limitation arising from the selected indicators, is that these don’t allow for a good differentiation between outputs and outcomes. For instance, most of the indicators of education, such as completion and enrollment rates do not measure how much learning is taking place in a particular country. In education, this paper advances by considering the learning scores as one of the indicators. In health, other outcomes such as the number 11
of sick-day leaves or the number of missed-school days because of health-related causes could be better reflections of outcomes. Two of the selected health output indicators, DPT and measles immunization are delivered in vertical programs, that is, in campaigns that are relatively independent of basic health systems and therefore may not be good indicators of the actual quality of the health system. Finally, the fact that life expectancy is influenced by diet, lifestyle, and a clean environment, that to the extent that are not included as factors of production may bias the efficiency scores.

# **III.2. Single Input-output Results**

III.2.1. FDH and DEA analysis: Education Figures 7a-c show both FDH and DEA estimation of the efficiency frontier for three of the nine output indicators: gross primary school enrollment, first level complete and learning scores. Individual country efficiency scores for the three indicators are reported in Table B.1-3 of Appendix B<sup>10</sup> . The graphical efficiency frontiers for other output indicators can be found in Appendix B (Figure B.1).

Figure 7.d illustrates the efficiency frontier for the learning scores if the developed countries are included in the sample, demonstrating the sensitivity of the results to the sample definition. This fact is particularly acute in the case of learning scores which capture the quality of education dimension that no other indicator captures. While in the sample of developing countries Chile, Hungary and the Czech Republic are on the frontier; once the developed nations are included they appear as inefficient. The complete set of efficiency scores (including and excluding the set of developed countries can be found in Table B.2 and B.3 in Appendix B<sup>11</sup>

> 10 The efficiency scores of all the indicators can be found at the PRMED website indicated in footnote 1. 11 The frontier depicted in Figure 7.d. excludes Japan, Korea, Ireland and Belgium to facilitate comparisons with the frontier without developed nations.

12
<!-- Start of picture text --> Free Disposable Hull (FDH) Data Envelopment Analysis (DEA)<br>Gross Primary Enrollment vs Education Expenditure Gross Primary Enrollment vs Education Expenditure<br>BRA BRA<br>GAB MWICP V GAB MWICP V<br>PER UGA T GO PER UGA TGO<br>BH SMAC URYBHRDOMGT MIDNSLVCH NBGRL BNL KAOMNCHLKORPN GRUSGINKHMT URCOGROMPAKGEOVNMT TARMAGOBGDCMRLAOCAFPHLMUSSYRGNBT CDORWATJKSLEZMBIN DKAZAZEMOZBE NMDGSLBGMBNPLMRTKGZTZANICCOMMKDGHAHNDSE NUK RCOLCIVHRVSVKGRDWSMGUYMEXPRYARGKENDZAKNAMNGMARYEMCZEBOLIRNLT UMDAUZ BCRIPANHUNT HABL RJORBLZSWZT ONJ AMFJI DMAPOLLVAVUTLSOMYSVCTT UNLCAZAFZWE ES T NA M BWA SAUKWTBR B BH SMAC URYBHRDOMGTMIDNSLVCH NBGRL BNOMNL KACHLKORPN GRUSGINKHMTURROMCOGPAKGEOVNMTT OARMAGOBGDCMRLAOCAFPHLMUS SYR GNBTCDRWASLETJK IN D MOZZMBAZEKAZBE NSLBMDGNPLMRTGMBKGZTZANICCOMMKDGHAHNDSE NUK RCOLHRVCIVSVKGRDGUYWSMMEXPRYARGKENKNADZAMNGMARCZEBOLYEMIRNLTUMDACRIUZBPANHUNTHABL RJORBLZSWZTONJ AMFJI DMAPOLLVAVUTLSOMYSVCTTUNLCAZAFZWE ES T NA M BWA SAUKWTBR B<br>MLIERIBD IET H SDN MLIERIBD IETH SDN<br>BFADJI BFADJI<br>NER NER<br>300 400 500 600 700 800 300 400 500 600 700 800<br>Orthogonalized Public Expditure on Education Orthogonalized Public Expditure on Education<br>Data Source: World Bank WDI Data Source: World Bank WDI<br>(a.1) (a.2)<br>First Level Complete vs Education Expenditure First Level Complete vs Education Expenditure<br>TUR TUR<br>POL POL<br>BGR ARG THA MYS BGR ARG THA MYS<br>URY DOMKOR GTMCHLSLVIDNCHNTTOGA BMUSROMPERLKASVKCZEPNGBRAHR VPAKPHLCOGSYRME XCOLCMRBGDCAFHUNINDUGARWANI CGNBZMBDZAMOZNP LBENSL ENERMLIGUYHNDGHATZAPRYCRISENIRNTGOMWIPANBOLKENSDNFJIJORSWZJAM TUNZAFLSO ZWE KWTBWABRB URY DOMKOR GTMSLVCHLIDNCHNTTOGA BMUSROMPERLKASVKPNGCZEBRAHR VPAKPHLCOGSYRME XCMRCOLBGDCAFHUNINDUGARWANI CGNBMOZNP LZMBDZABENSL ENERGUYHNDMLIGHAPRYCRITZASENIRNTGOMWIPANBOLKENSDNFJIJORSWZJAM TUNZAFLSO ZWE KWTBWABRB<br>0 100 200 300 400<br>0 100 200 300 400 Orthogonalized Public Expditure on Education<br>Orthogonalized Public Expditure on Education Data Source: World Bank WDI<br>Daat Source: World Bank WDI, Barro-Lee database<br>(b.1) (b.2)<br>Learning Scores vs Education Expenditure Learning Scores vs Education Expenditure<br>BGRRUS SV KCZE HUN POL LVAMYS BGRRUS SV KCZE HUN POL LVAMYS<br>CHLIDNTTOTURROMPERARGBRAMKDMEXCOLLTUHNDPRYIRNTHABOL MDAJOR TUN KWT CHLIDNTTOTURROMPERARGBRAMKDMEXCOLLTUHNDPRYIRNTHABOL MDAJOR TUN KWT<br>VUT VUT<br>MUS PHLCMRUGAMOZMDGTZAMARKENBLZSWZ ZWE MUS PHLCMRUGAMOZMDGTZAMARKENBLZSWZ ZWE<br>BFA CIV ZAF BWA BFA CIV ZAF BWA<br>ZMBMLISEN LSO NAM ZMBMLISENMWI LSO NAM<br>MWI<br>NE R<br>NE R<br>0 100 200 300 400<br>0 100 200 300 400 Orthogonalized Public Expditure on Education<br>Orthogonalized Public Expditure on Education<br>Data Source: World Bank WDI & Crouch and Fasih (2004)<br>Data Source: World Bank WDI & Crouch and Fasih (2004)<br>(c.1) (c.2)<br>140 140<br>120 120<br>100 100<br>80 80<br>60 60<br>Gross Primary School Enrollment Gross Primary School Enrollment<br>40 40<br>40 40<br>30 30<br>20 20<br>10<br>10<br>First Level Complete (age 15 +) First Level Complete (age 15 +)<br>0<br>0<br>600 600<br>500 500<br>400 400<br>Learning Scores Learning Scores<br>300 300<br>200<br>200<br><!-- End of picture text --> 13
<!-- Start of picture text --> Learning Scores vs Education Expenditure Learning Scores vs Education Expenditure<br>GRC GBRDE UESPDOMITANLDCHLAUSBGRCZETTOSVKRUSID NARGTURROMPERUSABRAHUNMEXMKDCOLLTUIR NPRYHNDTHABOLPOLCA NJORMDACH EL VAMYSCY PPRTTUNVUTFRAAUTKWT ISL FINNZL IS R SW E NOR DNK GRC GBRDE UESPITADOMNLDCHLAUSBGRCZETTOSVKRUSIDNARGTURROMPERUSABRAHUNMEXMKDCOLLTUIRNPRYHNDTHABOLPOLJORCA NMDACHEL VAMYSCY PPRTTUNVUTAUTFRAKWT ISL FINNZL IS R SW E NOR DNK<br>MUS PHLCMRBFAUGAMOZMD GMARTZACI VBLZKENSWZ ZAF ZWEBWA MUS PHLCMRBFAUGAMOZMDGMARTZACI VBLZKENSWZZAF ZWEBWA<br>ZMBMLISEN LSO NAM ZMBMLISENMWI LSO NAM<br>MWI<br>NER<br>NER<br>600 800 1000 1200 1400 1600<br>600 800 1000 1200 1400 1600 Orthogonalized Public Expditure on Education<br>Orthogonalized Public Expditure on Education<br>Data Source: World Bank WDI & Crouch and Fasih (2004)<br>Data Source: World Bank WDI & Crouch and Fasih (2004)<br>(d.1) (d.2)<br>600 600<br>500 500<br>400 400<br>Learning Scores Learning Scores<br>300 300<br>200<br>200<br><!-- End of picture text -->

**Figure 7 Education Efficiency Frontier: Single Input and Single Output**

Several results may be highlighted:

- a. In general, the rankings are robust to the output indicator selected. This can be can be verified by the Spearman rank-correlation coefficient (see Tables C.1 and C.2 in Appendix C), that are all positive, significant and high. The range oscillates from a minimum of .53 to a maximum of .94, with the mean of .70. This result implies that countries appearing as efficient (or inefficient) according to one indicator, are ranked similarly when other output indicator is used.

- b. Despite the orthogonalization by GDP, the relatively rich countries tend to be in the less efficient group, i.e. countries with higher per-capita GDP spend more than other countries in attaining similar education outcomes. Higher spending may reflect the higher cost of tertiary education. This is one factor that may help explain the stand-out of Estonia, Latvia, and Poland. Oil-rich countries, such as Kuwait and Saudi Arabia, tend to be in the group of relatively more inefficient producers.

- c. Another group of relatively inefficient producers are those with “average” expenditure levels but extremely low education attainment. Among those are mostly African counties (Angola, Niger, Burkina Faso, Sudan, and Ethiopia), some Middle Eastern countries (Djibouti, and Yemen) and South Asia (Bangladesh and Pakistan). Table B.1-3 in Appendix B list the output-efficiency scores for three of the indicators.

- d. Output-efficiency rankings also vary with the selected output indicators. The spearman correlation coefficient of the output-efficiency scores (see Tables C.3 and C.4 in Appendix) show that these are robust to the selected indicator, though the mean of the correlation coefficients is lower (.52) and the range is somewhat higher (.30 to .95) than those registered in the input-efficiency rankings.

- e. In an attempt to identify clusters of more efficient countries and more inefficient countries, the top (and bottom) 10 percent of the efficiency ranking were selected for each of the indicators. If a country appeared in the efficient (inefficient) tail in three or more of the indicators, it was included in Table 1.

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**Table 1. Education Attainment: Single Input, Single Output**

||Input-Efficient|OutputEfficient|
|---|---|---|
|More efficient|Uruguay, Korea, Dominican<br>Republic, Indonesia,<br>Guatemala, China, Bahamas,<br>Bahrain,ElSalvador|Uruguay, Korea, Bahrain,<br>Bahamas|
|Least efficient|Botswana, South Africa,<br>Kuwait, Tunisia, Lesotho,<br>Barbados, Saudi Arabia,<br>Zimbawe, Namibia, Malaysia,<br>St, Lucia, Jamaica, St,<br>Vincent,Latvia,|Niger, Mali, Tanzania,<br>Burkina Fasso, Gunea-Bissau,<br>Ethiopia, Guinea, Burundi,<br>Sudan, Sierra Leone, Chad|

- f. This clustering exercise reveals (Table 1) a group of African countries as the most inefficient.  Two oil-rich countries are included in this group as well. Among the more efficient group of countries we consistently find Uruguay, Korea, Bahamas, and Bahrain.  Explaining why these particular sets of countries appear in each cluster requires more in-depth analysis. The last section of this paper attempts to associate efficiency results with some explanatory variables.

- g. To grasp the order of magnitudes of the deviations from the efficiency frontier, we computed an average for all indicators for the inefficient countries.  The inputefficiency estimations indicate that the most inefficient decile could reach the same educational attainment levels by spending approximately 50 percent less. The output efficiency estimators indicate that, on average, with the expenditure level this group could reach educational attainment levels four times as high.

- h. It is critical to note that even if a country appears as efficient, there might still be a significant discrepancy between the observed output level and the desired or target output level. For instance, Bahamas, Bahrain, Dominican Republic and Guatemala appear as efficient countries on the efficiency frontier or very close to it (Figure 7 a.1).  However, these countries are still far away from where Gabon or Brazil are, and could consider desirable to achieve those target enrollment rates. Both Guatemala and Dominican Republic spend two percent of GDP on education but have (net) secondary enrollment rates below 40 percent. And net primary enrollment is about 80 percent.  It would be difficult to argue that that is a desirable outcome, though it is an efficient one. Similarly, though Chile appears as efficient with learning scores of about 400, the country could still achieve higher learning scores of over 500 points at the cost of additional public spending. The important thing is that the country moves along the efficiency frontier to the higher target output level.  Countries can even improve efficiency by exploiting scale economies if they are operating in the increasing returns to scale zone of the production possibility frontier (output levels smaller than that of point A, Figure 3)

- i. The regional aggregation of the efficiency scores by each individual output indicator shows that scores are lower when they are input oriented (Table 2) than

15
when they are output oriented (Table 3).12This is especially true for ECA. In general, we observe higher efficiency scores when primary enrollment is considered as the output indicator. Scores are lower for secondary enrollment, especially when output-oriented measures are considered. Africa and MNA have similar levels of input-inefficiency: in most cases, both regions use public spending in excess of 35 percent than the benchmark cases. EAP, ECA, LAC and SAS spend in excess between 20-30 percent of the benchmark level. The output efficiency scores are lower in Africa

**Table 2. Educational Attainment: Input-Efficiency scores by regions across the world**

# **- Single Input, Single Output**

||AFR|EAP|ECA|LAC|MNA|SAS|
|---|---|---|---|---|---|---|
|Gross primary enrollment|.69|.74|.67|.74|.65|.75|
|Net primary enrollment|.68|.78|.72|.77|.68|.71|
|Gross Secondary enrollment|.65|.69|.67|.69|.63|.70|
|Net secondary enrollment|.64|.71|.71|.69|.64|.72|
|Average years ofschool|.21|.36|.37|.32|.18|.25|
|First level complete|.21|.43|.48|.36|.20|.26|
|Secondlevelcomplete|.22|.37|.33|.32|.19|.27|
|Literacy ofyouth|.66|.73|.86|.72|.63|.72|

**Table 3. Educational Attainment: Output-Efficiency scores by regions across the world**

# **- Single Input-Single Output**

||AFR|EAP|ECA|LAC|MNA|SAS|
|---|---|---|---|---|---|---|
|Gross primary enrollment|.62|.79|.72|.82|.67|.72|
|Net primary enrollment|.64|.93|.90|.93|.79|.78|
|Gross Secondary enrollment|.23|.50|.70|.61|.54|.39|
|Net secondary enrollment|.26|.58|.84|.66|.60|.44|
|Average years ofschool|.32|.63|.79|.60|.53|.38|
|Firstlevelcomplete|.19|.49|.50|.36|.22|.20|
|Secondlevelcomplete|.09|.37|.38|.24|.26|.22|
|Literacy ofyouth|.72|.95|.99|.94|.88|.66|

> 12 The regional aggregation is for illustrative purposes only and was computed as the simple average of the individual country scores obtained for the whole sample.  The scores were not computed by constructing separate efficiency frontiers for each region.  Hence, they do not reflect the heterogeneity in the individual country scores and possibly do not reflect adequately variations across regions.

16
# III.2.2. FDH and DEA Analysis: Health

This section considers the case of one input (public expenditure on health per capita in PPP terms) and four alternative output indicators: life expectancy at birth, DPT immunization, measles immunization, and the disability adjusted life expectancy (DALE) index which takes into account both mortality and illness. The efficiency frontiers for each indicator are computed using both the FDH and DEA methodologies. Figures 8a-d show the efficiency frontier for one indicator. The specific country rankings for two of the health indicators are listed in Table B.4 of Appendix B.

<!-- Start of picture text --> Free Disposable Hull (FDH) Data Envelopment Analysis (DEA)<br>Life Expectancy vs Health Expenditure Life Expectancy vs Health Expenditure<br>KORMYSMUSARETT OOMN THADOMIDNKWTMEXBHSPHLCHLIRNMARBHRBLZKA ZAZECHNINDLKAEGYLBNGTMKNAFJIPERALBDZASY RL CAECUPAKGEOVUTVNMPRYATGBRATONVENKGZUKRTJKGRDHNDJ AMARMCPVUZBTKMSLVBOLNI CTURCOLMDABGRLVARUSSAUMNGVCTSLBGUYJORW SMDMABLRROMBRBPOLTUNLTUCRIPANURYESTMKD ARGSVKHUN HRV CZESVN KORMYSMUSARETT OOMN THADOMKWTIDNMEXBHSPHLCHLIRNMARBHRAZEKA ZBLZCHNINDLKAEGYLBNGTMKNAFJIPERALBDZASY RL CAECUPAKGEOVUTVNMPRYATGBRATONVENKGZUKRTJKGRDHNDARMJ AMCPVUZBTKMSLVBOLNI CTURCOLMDABGRLVARUSSAUMNGVCTSLBGUYJORW SMDMABLRROMBRBPOLTUNLTUCRIPANURYESTMKD ARGSVKHUN HRV CZESVN<br>BGDCOM BGDCOM<br>NPL NPL<br>SDNGHAYEMPNG SDNGHAYEMPNG<br>GA B SWZCMRKHMCIVAGOGI NLAOTGONGAMRTBENCAFHTICOGKE NZARBFAUGAMDGNERETHMLISENTCDBDIGMBRWATZAGNBZMBMWIERIMOZBWAZAFDJILSOZWE NAM GA B SWZCMRKHMCIVAGOGINLAOTGONGAMRTBENCAFHTICOGKE NZARBFAUGAMDGNERETHMLISENTCDBDIGMBRWATZAGNBZMBMWIERIMOZBWAZAFDJILSOZWE NAM<br>SLE SLE<br>300 400 500 600 700 800 300 400 500 600 700 800<br>Orthogonalized Public Expditure on Health Orthogonalized Public Expditure on Health<br>Daat Source: World Bank WDI Daat Source: World Bank WDI<br>(a.1) (a.2)<br>Immunization DPT vs Health Expenditure Immunization DPT vs Health Expenditure<br>KORMYSMUSARETT OOMN THADOMKWTIDNMEXBHSPHLCHL IRN MARBHRBLZAZEKA ZCHNLKAEGYGTMLBNSWZKNAFJIPERSY RDZAALBLCAECUGEOVUTVNMPRYATG BRA BGDCOMGHANPLTOKGZTJKBENUKRKE NGRDJ AMARMHCPVTKMUZBBDIRWASLVGMB N TZABOLZMBNI CTURMWIERIBWADCOLMDABGRZAFRUSLVASAUMNGVCTSLBGUYLSOZWEJORW SMDMABLRROMBRBPOLTUNLTUNAMCRIPANURYESTMKD ARGSVKHUN HRV CZESVN KORMYSMUSARETT OOMN THADOMKWTIDNMEXBHSPHLCHL IRN MARBHRBLZAZEKA ZCHNEGYLKAGTMLBNSWZKNAFJIPERSY RDZAALBL CAECUGEOVUTVNMPRYATGBGD BRA COMGHANPLTOKGZTJKBENUKRKE NGRDJ AMARMHCPVTKMUZBBDIRWASLVGMB N TZABOLZMBNI CMWITURBWAERIDCOLMDABGRZAFRUSLVASAUMNGVCTSLBGUYLSOZWEJORW SMDMABLRROMBRBPOLTUNLTUNAMCRIPANURYESTMKD ARGSVKHUN HRV CZESVN<br>YEM YEM<br>IND VEN IND VEN<br>PAKCIVLAOTGOUGAMDGSENPNGMOZ PAKCIVLAOTGOUGAMDGSENPNGMOZ<br>KHM KHM<br>CMRSDNGI NMLIETHGNB CMRSDNGI NMLIETHGNB<br>GA B MRTCAFHTISLE GA B MRTCAFHTISLE<br>BFA DJI BFA DJI<br>AGO AGO<br>COG COG<br>NGAZARNERTCD NGAZARNERTCD<br>300 400 500 600 700 800 300 400 500 600 700 800<br>Orthogonalized Public Expditure on Health Orthogonalized Public Expditure on Health<br>Daat Source: World Bank WDI Daat Source: World Bank WDI<br>(b.1) (b.2)<br>80 80<br>70 70<br>60 60<br>50 50<br>Life Expectancy at Birth Life Expectancy at Birth<br>40 40<br>100 100<br>80 80<br>60 60<br>Immunization DPT Immunization DPT<br>40 40<br>20 20<br><!-- End of picture text --> 17
<!-- Start of picture text --> Immunization Measles vs Health Expenditure Immunization Measles vs Health Expenditure<br>KORMYSMUSARETT OOMN THADOMIDNKWTMEXBHSPHLCHLMARIRNBHRBLZAZEKA ZCHNLKAEGYGTMLBNSWZKNAFJIPERDZAALBSY RECULCAGEOVUTVNMPRYATGBGDBRAGHANPLVENTONKGZTJKUKRKE NGRDJ AMHNDARMCPVTKMUZBBDIGMBRWASLVTZAZMBNICMWITURBWAERICOLMDABGRZAFLVARUSSAUMNGVCTSLBGUYLSOZWEJORW SMDMABLRROMBRBPOLTUNLTUCRIPANURYESTMKD ARGSVKHUN HRV CZESVN KORMYSMU SARETT OOMN THADOMKWTIDNMEXBHSPHLCHLIRNMARBHRAZEKA ZBLZCHNLKAEGYLBNGTMSWZKNAFJIPERSY RALBDZAEC UL CAGEOVUTVNMATGPRYBRABGDGH ANPLVENTONKGZTJKUKRKE NGRDJ AMARMHNDCPVTKMUZBBDIGMBRWASLVTZAZMBMWINI CTURBWAERICOLMDABGRZAF RUS LVASAUMNGVCTSLBGUYLSOZWEJORW SMDMABLRROMBRBPOLTUNLTUCRIPANURYESTMKD ARGSVKHUN HRV CZESVN<br>BENBOL BENBOL<br>CIVAGOLAOCOMMRTYEM NAM CIVAGOLAOCOMMRTYEM NAM<br>GA B INDCMRPAKSDNKHMGINTGOUGAHTIMDGSENSLEGNBPNGMOZ GA B INDCMRPAKSDNKHMGI NTGOUGAHTIMDGSENSLEGNBPNGMOZ<br>MLIETH MLIETH<br>NGABFA NGABFA<br>CAFNER DJI CAFNER DJI<br>TCD TCD<br>COGZAR COGZAR<br>300 400 500 600 700 800 300 400 500 600 700 800<br>Orthogonalized Public Expditure on Health Orthogonalized Public Expditure on Health<br>Daat Source: World Bank WDI Daat Source: World Bank WDI<br>(c.1) (c.2)<br>DALE vs Health Expenditure DALE vs Health Expenditure<br>DMA DMA<br>KORMYSMUSARETT OOMN THADOMKWTIDNMEXBHSPHLCHLIRNMARBHRKAZAZEBLZCHNINDLKAEGYLBNGTMKNAFJIPERDZAALBSY RL CAECUPAKGEOVUTVNMATGPRYBRAVE NTONKGZUKRTJKARMGRDJ AMHNDCPVUZBTKMSLVBOLTURNICCOLMDABGRLVARUSSAUMNGVCTSLBGUYJORW SMBLRROMBRBPOLTUNLTUCRIPANURYESTMKD ARGSVKHUN HRV CZE SVN KORMYSMU SARETT OOMN THADOMKWTIDNMEXBHSPHLCHLIRNMARBHRKAZAZEBLZCHNINDLKAEGYLBNGTMKNAFJIPERDZAALBSY RL CAEC UPAKGEOVUTVNMPRYATGBRAVE NTONKGZUKRTJKARMGRDHNDJ AMCPVUZBTKMSLVBOLNI CTURCOLMDABGRLVARUSSAUMNGVCTSLBGUYJORW SMBLRROMBRBPOLTUNLTUCRIPANURYESTMKD ARGSVKHUN HRV CZE SVN<br>BGDNPLYEM BGDNPLYEM<br>GA B SWZCMRSDNKHMCIVAGOGINLAOCOMTGOGHANGAMRTBENCOGHTIKE NZARBFAUGAMDGETHMLISENTCDBDIGMBTZARWAGNBPNGERIMOZBWAZAFDJILSOZWE NAM GA B SWZCMRSDNKHMCIVAGOGI NLAOCOMTGOGH ANGAMRTBENCOGHTIKE NZARBFAUGAMDGETHMLISENTC DBDIGMBTZARWAGNBPN GERIMOZBWAZAFDJILSOZWE NAM<br>NERZMBMWI NERZMBMWI<br>SLE SLE<br>300 400 500 600 700 800 300 400 500 600 700 800<br>Orthogonalized Public Expditure on Health Orthogonalized Public Expditure on Health<br>Daat Source: World Bank WDI, Mathers et al (2000) Daat Source: World Bank WDI, Mathers et al (2000)<br>(d.1) (d.1)<br>100 100<br>80 80<br>60 60<br>Immunization Measles 40 Immunization Measles 40<br>20 20<br>70 70<br>60 60<br>50 50<br>40 40<br>Disability Adjusted Life Expectancy Disability Adjusted Life Expectancy<br>30 30<br><!-- End of picture text -->

**Figure 8. Health Efficiency Frontier: Single Input and Single Output**

Several results may be highlighted:

- a. The input efficiency scores obtained for each of the output indicators are highly correlated. The Spearman rank-order correlation coefficient oscillates between .66 and .94, with a mean of 0.81 (see Tables C.1 and C.2 in Appendix C).  This indicates that the efficiency ranking is very similar regardless of the output indicator being used.

- b. Despite the orthogonalization by GDP the relatively rich countries tend to be in the less efficient group. The group of inefficient producers tend to concentrate in two groups of countries: one group of relatively rich countries like the Czech Republic, Croatia, Slovenia, and Hungary that have big expenditure levels and not extremely high output (input inefficiency) and other group of countries that spend relatively little but their output indicators could be substantially larger, like Sierra Leone, Namibia, Zimbawe, and Lesotho

18
- c. To capture this difference, it is convenient to examine the output- efficiency scoring (see Tables C.3 and C.4 in Appendix C).  The rankings between input and output orientations are highly correlated.

- d. With the four output indicators deciles, more efficient and least efficient countries are listed in Table 4.  The group of least efficient countries could, on average, increase output significantly for a given expenditure level. For instance, the decile of most inefficient countries could almost double the disability-adjusted life expectancy (DALE) index to achieve the same efficiency as the benchmark. Similarly the DPT immunization would have to triple to achieve the same efficiency level than the benchmark developing countries.

- e. The regional aggregation of the efficiency scores, by each individual output indicator shows that input efficiency scores (Table 5) are lower than output efficiency scores (Table  6).  This is especially true in ECA, LAC and MNA, and to a lesser extent in EAP and SAS. In Africa, both scores are strikingly similar, indicating that, on average, the region spend about 35 percent in excess of the benchmark cases to achieve the same output level.  Alternatively, the output level is 35 percent below comparable efficient countries that use the same input (expenditure) level.

**Table 4. Health Attainment: Single Input, Single Output**

||Input-Efficient|OutputEfficient|
|---|---|---|
|More efficient|Korea, Malaysia, Thailand,<br>Trinidad & Tobago, Oman,<br>United Arab Emirates,<br>Mauritius,Kuwait, Chile|Korea, Dominica, Oman,<br>United Arab Emirates, Anigua<br>and Barbuda|
|Least efficient|Argentina, Estonia, Czech<br>Republic, Slovenia,<br>Macedonia, Croatia, Namibia,<br>Tunisia, Latvia, Hungary,<br>Barbados|Sierra Leone, Ethipia,<br>Burkina Fasso, Central<br>African Republic, Mali|

**Table 5.  Health Attainment: Input-Efficiency scores by regions across the world**

**- Single Input, Single Output**

||AFR|EAP|ECA|LAC|MNA|SAS|
|---|---|---|---|---|---|---|
|LifeExpectancy at birth|.65|.72|.58|.69|.73|.69|
|Immunization DPT|.66|.73|.63|.68|.76|.71|
|Immunization Measles|.65|.73|.67|.69|.76|.71|
|DALE|.65|.72|.60|.70|.71|.69|

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**Table 6. Health Attainment: Output-Efficiency scores by regions across the world**

**- Single Input-Single Output**

||AFR|EAP|ECA|LAC|MNA|SAS|
|---|---|---|---|---|---|---|
|LifeExpectancy at birth|.63|.87|.91|.92|.90|.83|
|Immunization DPT|.62|.83|.95|.87|.90|.75|
|Immunization Measles|.63|.83|.95|.91|.90|.71|
|DALE|.56|.83|.90|.90|.86|.79|

# **III.3.  Multiple Inputs and Multiple Outputs**

Both education and health attainment are not solely determined by public spending. Other inputs, such as private spending also affect the output indicators. For health, the World Bank WDI database reports a comparable statistic across countries. Unfortunately, a comprehensive database of this variable does not exist for education: For the education production technology we have multiple indicators of educational attainment, and three inputs (public spending, teachers per pupil, and adult literacy rate). In health, besides public spending, two other inputs were included: private spending and the education level of adults. The analysis was limited to include up to three outputs Too many output indicators will complicate the analysis, biasing efficiency scores towards one, increasing the variance of the estimators, and reducing their speed of convergence to the true efficiency estimators (Simar and Wilson, 2000; Groskopff, 1996) In education, the selected input-output combinations produce ranking that are somewhat similar: the average rank correlation coefficient is .53. The frequency distribution of the efficiency estimators is similar in all the models, and as the model shifts from a basic two-input two-output model to a more complex three-input three–output model, the frequency distribution shifts to the right, that is, more concentrated around more efficient results.

The multi-input output model results (Table 7) in general confirm the results of Table 1. Some new countries that appear as efficient are Bangladesh, Congo and Argentina. In the case of Bangladesh and Congo, this is the result of considering literacy of adults as a factor of production, that in these countries is low, and hence, appearing as very efficient. Congo has also extremely low ratio of teachers per student, the other factor of production, reinforcing the bias towards the efficient score. Within the least efficient countries, the models point at Zimbabwe, Lesotho, Botswana, Malaysia, and Saudi Arabia as the singleinput models. In addition, Costa Rica and Swaziland appear as input inefficient.

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**Table 7. Educational Attainment: Multiple Inputs, Multiple Outputs**

||Input-Efficient|OutputEfficient|
|---|---|---|
|More efficient|Bangladesh, Bahrain,,<br>Dominican Republic,<br>Argentina,Estonia|Argentina, Bangladesh,,<br>Chile, Brazil, Bahrain,<br>Dominican Republic, Congo|
|Least efficient|Zimbabwe, Lesotho,<br>Botswana, Costa Rica,<br>Swaziland, Saudi Arabia,<br>Malaysia|Sudan, Ghana, Tanzania,<br>Ethiopia, Kenya, Niger|

The regional aggregation for input and output efficiency scores using the multiple inputoutput framework show (Tables 8 and 9) that as the model becomes more complex (adding inputs or outputs), scores tend to show more efficient regions. The input efficiency regional aggregation allows several interesting comparisons across the regions on the impact of an additional input on the efficiency scores. For instance, the first two rows of Table 8 allow examination of the impact of adding literacy of adults as an additional input. The biggest impact is in the MNA region, followed by ECA and LAC while the others the increase in efficiency scores is more marginal<sup>13</sup> . Output efficiency scores change substantially in MNA and Africa.

**Table 8. Education Attainment: Input-Efficiency scores by regions across the world**

**- Multiple Inputs, Multiple Outputs**

||AFR|EAP|ECA|LAC|MNA|SAS|
|---|---|---|---|---|---|---|
|2 inputs (public expenditure, teachers per pupil) – 2<br>outputs (gross primary and secondary enroll.)|.88|.83|.72|.82|.73|.91|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 2 outputs (gross primary and<br>secondary enroll.)|.92|.89|.86|.89|.92|.96|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 2 outputs (net primary and<br>secondary enroll.)|.87|.94|.93|.93|.92|1.0|
|2 inputs (public expenditure, literacy of adult)- 3<br>outputs (first complete, second level complete, avg<br>yrs ofschool)|.78|.92|.95|.84|.80|.91|
|3 inputs (public expenditure, literacy of adult,<br>teachers per pupil)- 3 outputs (first complete,<br>secondlevelcomplete, avg yrs ofschool)|.91|.97|.94|.89|.81|.95|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 3 outputs (literacy of youth, first<br>levelcomplete, secondlevelcomplete)|.91|.97|.94|.89|.80|.95|

> 13 The statistical significance of these changes has yet to be determined. The tests developed by Banker , and used in previous sections do not apply to the multiple-output cases we are analyzing here (Simar and Wilson, 2000)

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**Table 9. Education Attainment: Output-Efficiency scores by regions across the world**

# **- Multiple Inputs,  Multiple Outputs**

||AFR|EAP|ECA|LA|MNA|SAS|
|---|---|---|---|---|---|---|
|2 inputs (public expenditure, teachers per pupil) – 2<br>outputs (gross primary and secondary enroll.)|.68|.83|.80|.85|.71|.79|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 2 outputs (gross primary and<br>secondary enroll.)|.82|.88|.89|.89|.91|.90|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 2 outputs (net primary and<br>secondary enroll.)|.79|.97|.96|.96|.92|1.0|
|2 inputs (public expenditure, literacy of adult)- 3<br>outputs (first complete, second level complete, avg<br>yrs ofschool)|.64|.87|.94|.80|.79|.83|
|3 inputs (public expenditure, literacy of adult,<br>teachers per pupil)- 3 outputs (first complete,<br>secondlevelcomplete, avg yrs ofschool)|.86|.94|.93|.86|.80|.89|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 3 outputs (literacy of youth, first<br>levelcomplete, secondlevelcomplete)|.98|1.0|1.0|.98|.99|.99|

Rows 4 and 5 of Table 8 allow comparing the impact of adding the variable teachers per pupil as an additional input. In Africa the change is dramatic, while in ECA and MNA there is no significant change. Further analysis is required to explain this differential response to the inclusion of this input.

In health there are multiple combinations of inputs (public expenditure, private expenditure, and literacy of adults) and outputs (life expectancy at birth, immunization DPT, immunization measles, and Disability Adjusted life expectancy (DALE)). The combinations we selected produce rankings that are more homogeneous. The rank correlation is in the range of .65 to .98 (Tables 10-12). Bangladesh appears as efficient, as well as Niger, mainly due to the inclusion as of the ( low ) levels of literacy of adults as an input, and hence, making these countries to appear as efficient.

**Table 10. Health Attainment: Multiple Inputs, Multiple Outputs**

||Input-Efficient|OutputEfficient|
|---|---|---|
|More efficient|Bangladesh, Malaysia, Costa<br>Rica, Kuwait, Morocco, Oman,<br>Mauritius, Niger|Bangladesh, Costa Rica,<br>Kuwait, Malaysia, Morocco,<br>Mauritius, Oman, Niger|
|Least efficient|Russia, Belarus, Namibia,<br>Romania, Estonia, Croatia,<br>Lituania,, Hungary, Jordan|Namibia, Togo, Ethiopia,<br>Mozambique, Cote d”Ivoire,<br>Cameroon, Congo, Central<br>African Republlic, Nigeria,<br>Uganda|

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# **Table 11. Health Attainment: Input-Efficiency scores by regions across the world**

# **- Multiple Inputs,  Multiple Outputs**

||AFR|EAP|ECA|LA|MNA|SAS|
|---|---|---|---|---|---|---|
|2 inputs (public expenditure, literacy of adult) – 2<br>outputs (life expectancy,immunization DPT.)|.85|.82|.72|.82|.91|.93|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization DPT.)|.86|.82|.74|.83|.91|.94|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization measles.)|.86|.82|.77|.83|.91|.94|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 3 outputs (life expectancy,<br>immunization DPT.,DALE)|.86|.82|.80|.87|.93|.94|

**Table 12. Health Attainment: Output-Efficiency scores by regions across the world**

# **- Multiple Inputs,  Multiple Outputs**

||AFR|EAP|ECA|LA|MNA|SAS|
|---|---|---|---|---|---|---|
|2 inputs (public expenditure, literacy of adult) – 2<br>outputs (life expectancy,immunization DPT.)|.81|.91|.97|.93|.97|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization DPT.)|.81|.91|.97|.94|.97|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization measles.)|.80|.91|.96|.94|.98|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 3 outputs (life expectancy,<br>immunization DPT.,DALE)|.82|.91|.97|.95|.98|.97|

Tables 9 and 12 show that, on average, developing nations score between .85 and .95 in output efficiency in the multiple input-output framework. This figures imply that developing countries could raise their output levels by an average of 10 percent with the same input consumption, if they were as efficient as the comparable benchmark countries. This figure is simply indicative, as the precise estimate varies with the country and with the selected indicator, and has a large variance across countries: for instance, the bottom decile of (output) efficiency scores is about .66, implying that the scope for increasing health and education attainment levels is between 3 or 4 times higher than for the whole sample average.

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# **III.4. Efficiency Change Over Time**

To examine the evolution of input and output efficiency over time, we computed the efficiency scores in two different time periods: 1975-1980 and 1996-2002 for education study, and 1997-99 and 2000-02 for health study, the construction of which is driven by data availability. Appendix D reports the results on a regional basis<sup>14</sup> .

Comparison of different input-output bundles in different time periods has to be done carefully because the frontier can be shifting outward through time. In some cases the frontier displacement can be parallel (such as in the life expectancy case of Figure 9). In others, the frontier displacement can be very uneven (biased frontier shift in Figure 9) reflecting biased technological change (see Appendix D for detailed discussion).

<!-- Start of picture text --> Frontier Shift: Life Expectancy Frontier Shift: Gross Secondary Enrollment<br>LVA KOR BRB<br>CRI URY<br>KWT<br>CRI KWT<br>KWT<br>ARGIDN<br>GTM<br>KOR<br>OMN<br>KOR<br>600 800 1000 1200<br>350 400 450 500 550 600<br>Orthogonalized Public Expenditure on Education<br>Orthogonalized Public Expenditure on Education<br>Eff. Frontier 1975-80 Eff. Frontier 1996-02<br>Eff. Frontier 1997-99 Eff. Frontier 2000-02<br>Data Source: World Bank WDI<br>Data Source: World Bank WDI<br>Parallel Shift Biased Shift<br>78 100<br>80<br>76<br>60<br>40<br>Life Expectancy 74<br>20<br>Gross Secondary Enrollment<br>72 0<br><!-- End of picture text -->

**Figure 9. Efficiency Frontier Shift Over Time**

The detailed comparison between observed input-output combinations in different time periods distinguishes whether variations in the levels of input utilization or output production levels are due to changes in efficiency or changes in technology. This testing is possible with observed levels of inputs and outputs, and are based on the concept of a Malmquist Index (Fare, Grosskpof, Norris and Zhang, 1994). This method has been used to study productivity change in the OECD economies, as well as productivity in agriculture across the world (Coelli and Rao, 2003; Nin, Arndt, and Preckel, 2003). Appendix D describes details of the method that uses some of the efficiency scores calculated in previous sections, and the index that will facilitate the analysis of productivity change through time.

> 14 Scores for individual countries are available at the PRMED website indicated in footnote 1.

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Results show that over the two decades output-efficiency growth was faster in the most inefficient countries, showing that there is a “catching-up” phenomenon. However, when measuring input-efficiency, the previous results do not hold: most regions increased expenditure levels without increasing output. Appendix D summarizes, on a regional basis, the change in productivity of public spending decomposed into efficiency change and technological change.<sup>15</sup>

# **IV. Explaining Inefficiency Variation Across Countries**

This chapter seeks to identify factors correlated with inefficiency scores variation across countries. This two-stage approach attempts to identify statistically significant regularities common to efficient or inefficient countries using the more basic statistical techniques. This exercise does not try to identify supply or demand factors that affect health and education outcomes, such as those described by Filmer (2003). The scope is limited to verifying statistical association between the efficiency scores and environmental variables.

# **IV.1. Method, Variables and Data Description**

Given that the dependent variable, the efficiency scores, is continuous and distributed over a limited interval (between zero and one), it is appropriate to use a censored (Tobit) regression model to analyze the relationships with other variables. The panel consist of a large numebr of countries (varying from 70 to 140 depending on the output indicator) and only two time periods. The literature on panel estimation has shown that in panels with this configuration, that is, a large number of cross-section units (countries) and a relatively short time dimension (2 periods), the fixed-effects estimators of the coefficients will be inconsistent (Maddala, 1987) and their variance will be biased downward (Greene, 2003b). Hence the random effects panel estimation method was preferred.

The dependent variable in the Tobit panel is the input efficiency score calculated by DEA method in the first stage. The input-oriented estimator reflects the consideration that input choices are more under the policymaker’s control. The independent variables reflect environmental effects included in precursor papers, as well as suggested by others recently. We included the following independent variables<sup>16</sup> .

**a. The size of government expenditure** . Most of the papers surveyed in the previous section explore the relationship between the size of the government (or expenditure as a percentage of GDP) and efficiency levels.  The objective is to verify if additional pubic spending is associated with better education and health outcomes.  While some papers have found a negative association between efficiency and expenditure levels (GuptaVerhoeven 2001, Jarasuriya-Woodon 2003, and Afonso et.al. 2003), others have found a positive association (Evans et.al. 2003) and others have found no significant impact (Filmer and Pritchett, 1999)

> 15 The results on country-by-country basis can be found at the PRMED website indicated in footnote 1. 16 The precise definition and sources is can be found in Appendix B, Table B.6.

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**b** . **A government budget composition variable** .  Given that both education and health are labor-intensive activities, the government’s labor policies will determine the efficiency with which outputs are delivered.  We choose a budget composition indicator to reflect this, in particular, the ratio of the wage bill to the total budget.  A higher ratio is expected to be negatively correlated with efficiency.

**c** . **Per-capita GDP** . We included the per-capita GDP to control for the BalassaSamuleson effect in comparing across countries.  If richer countries tend to be more inefficient (given higher wages in these countries), a negative sign is expected. However, it must be recalled that to obtain the efficiency scores in the “fist stage” we constructed an auxiliary variable (the orthogonalized public expenditure). Hence the inclusion of this variable in the second stage is an attempt to control for any remaining Balassa- Samuleson effects.

**d** . **Urbanization** . The clustering of agents make it cheaper to provide services in urbanized areas rather than in rural.  Higher degree of urbanization should reflect in higher efficiency, making positive as the expected sign of the coefficient on this variable.

**e. Prevalence of AIDS.** Based on WHO mappings of the disease, we included a dummy variable in the most severely affected countries to control for the role of this epidemic in the poor health outcomes.  Evans _et. al._ (2000) report that AIDS lowers the Disability adjusted life expectancy (DALE) by 15 years or more.  Aids also affects education outcomes both directly and indirectly (Drake, _et. al._ 2003): directly because school-age children are affected: UNAIDS estimates that almost 4 million children have been infected since the epidemic began, and two thirds have died. However, the indirect channel is relatively more important: AIDS leaves orphaned children that are more likely to drop-out of school or repeat. All these factors reflect how AIDS affect the demand for education.  But the supply is also affected by the decreasing teacher labor force due to illness or death, or the need to care for family (Pigozzi, 2004). Prevalence of HIV/AIDS should be negatively associated with education and health outcomes.  Consequently, efficiency scores should be negatively associated with the dummy variable.

**f. Income distribution inequality** . Ravallion (2003) argues that, besides the mean income, its distribution affects social indicators because their attainment is mostly determined by the income of the poor.  Hence, we controlled for the distribution of income by including the Gini coefficient as an explanatory variable.  Higher inequality is expected to be associated with lower educational and health attainments, making negative the expected sign of this variable.

**g** . **Share of public sector in the provision of service.** Services can be provided by both the public and private sectors, and efficiency indicators will differ across countries depending on the relative productivities of both sectors. Previous studies have included this variable to explain differences in outcomes (Le Grand, 1987; Berger and Messer, 2002) or efficiency scores (Greene, 2003a). The specific variable we included was the ratio of publicly financed service over the total spending (sum of private and public spending).

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**h. External Aid.** To the extent that countries do not have to incur the burden of taxation, they may not have the incentive to use resources in the most cost-effective way.   Another channel through which aid-financing may affect efficiency is through the volatility and unpredictability of its flows.  Given that this financing source is more volatile than other types of fiscal revenue (Bulir and Hamann, 2000), it is difficult to undertake mediumterm planning with in activities funded with aid resources. If this is the case, we would expect a negative association between aid-dependence and efficiency in those activities funded with aid, mostly health services. To our knowledge there are no previous attempts to establish a relationship between efficiency and the degree to which activities are financed by external aid.  There is, however, recent evidence of a negative association between donor financing and some health outcomes (Bokhari, Gottret, and Gai, 2005)

**i. Institutional Variables.** Countries with better institutions, more transparency, and less corruption are expected to have higher efficiency scores. Similarly, countries that have suffered wars or state failures are expected to register lower efficiency scores.  To capture these effects we included different indicators: the ICRG International Country Risk Indicators, the Worldwide Governance Research Indicators, in particular the Control of Corruption component (Kaufmann, et.al, 2002).  We also included a dummy variable if there had been some type of state failure, such as internal wars, from the State Failure Task force database.

The data on educational and health indicators are not available on a continuous annual basis for many countries. Thus, averages of the variables were computed over sub-periods both in the first stage calculation of efficiency score and in the second stage of regression analysis. Specifically, educational indicators are averaged over two periods (1975-80 and 1996-2002) and health indicators over two periods (1996-99 and 2000-02). This discrepancy in the sub-period construction is due exclusively to the lack of data for earlier years. The averages are treated as separate observations. The advantages of this approach are threefold. First, the averages may serve as a better measure of the educational and health attainment, which can hardly be substantially improved on a yearly basis; Second, the averaging maximizes the coverage of countries for each period, since one observation of a certain year is sufficient to help the country survive in the cross sectional comparison; Third, the time series thus constructed for each country, although short, facilitates the implementation of econometric techniques on panel data to explore the efficiency variations across countries and through time.

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# **IV.2.  Results**

The Tobit estimation on panel data is defined as follows.

_VRSTEit_<sup>=</sup> _f_ ( _WAGEit_ , _GOVEXPit_ , _PUBTOTit_ , _GDPPCit_ , _URBAN it_ , _AIDSit_ , _GINI it_ , _EXTAIDit_ , _INSTit_ , _CONS_ ) where _VRSTEit_ = Variable returns to scale DEA efficiency score for single output and multiple output cases.

- _WAGEit_ = Wages and salaries (% of total public expenditure)

- _GOVEXPit_ = Total government expenditure (% of GDP)

- _PUBTOTit_ = Share of expenditures publicly financed (public/total)

- _GDPPCit_ = GDP per capita in constant 1995 US dollars

- _URBAN it_ = Urban population (% of total)

- _AIDSit_ = Dummy variable for HIV/AIDS

- _GINI it_ = Gini Coefficient

- _EXTAID  = it_ External aid (% of fiscal revenue)

- _INSTit_ = Institutional indicators including ICRG country risk, World

- Governance Research Indicators (Corruption Control), or a dummy for state failures from the State Failure Task Force database.

- _CONS_ = Constant

Tables 13 and 14 report the results for the single-input single-output case and the multiple-input multiple-output case, respectively. The more interesting findings are:

- a. We find that countries with larger expenditure levels also register the more inefficient scores.  This result is robust to changes in the output indicator selected, to considering health or education, and to adopting either the single –output or multiple output frameworks. The trade-off between size of expenditure and efficiency is quite robust.

- b. Countries in which the wage bill represents a higher fraction of total expenditure tend to be more inefficient.  This result does not hold for health in the multiple output framework. This difference could be due partly to the relatively decreasing number of health care professionals in the world, especially in the poorer countries (Liese, et.al., 2003).  Further investigation would be required to examine why this is not the case in education.

- c. Countries in which public financing is a larger share of total expenditure on the service also register lower efficiency scores.  This is probably due to differential productivity rates in the provision of services. Further research would be needed to explain why this is the case in health services.    Recent case studies of water

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companies in Argentina show that private companies were more efficient than public ones and provided better service quality leading to lower child mortality rates (Galiani, Gertler and Schargrodsky, 2005). In education, there is some evidence that efficiency scores are lower in public schools (Alexander and Jaforullah, 2004), though the evidence regarding the impact of privatizing education on outcomes is mixed (World Bank, 2003).

- d. Urbanization is positively associated with efficiency scores in both education and health.  However, when life expectancy is included as an output, the relationship is non-significant (single output) or negative (multiple-output).  Possibly the urbanization variable is capturing other effects such as crime.  There is ample literature studying the relationship between urbanization and crime (Glaeser E. and B. Sacerdote, 1999).  Alternatively, as urbanization intensifies, communicable diseases are more difficult and costly to control, hence the negative association found between both variables in health.

- e. The effect of the HIV/AIDS is clearly negative affecting health efficiency scores in the multiple-output models. However, its effect on education is less clear, as the expected negative sign is significant in few cases and has the opposite sign in equal number of cases. This confirms the difficulty of empirically verifying this relationship, reported in previous work (Wobst and Arndt, 2003).

- f. Income distribution has the expected negative effect on the educational and health efficiency scores.  The impact of inequality on health scores is less robust than in education, but confirms Greene’s findings (2003).  Other papers (Berger and Messer, 2002), have found a positive association between income inequality and health outcomes.

- g. Results showed a negative relationship between some of the efficiency scores and the external aid dependency ratio. Only in one of the multiple-output cases is the external aid associated with higher efficiency, but with border-line statistical significance.  Though no causality relationship can be inferred from the exercise, this is one of the results that merit more detailed research. This result might be explained by the volatility of aid as a funding source that limits medium term planning and effective budgeting. Probably this is why the negative sign is more robust in health than in education, given that donor funding is mostly directed towards the first. Recent research (Bokhari, Gottret, Gai, 2005) show a negative association between some health outcomes and the degree of donor funding, pointing in this same direction.  This result also coincides with research showing that the quality of policies is not only unrelated to donor financing, but that highly indebted countries with “bad” policies received more net transfers as a share of GDP (Birdsall, et.al. 2003).

- h. None of the institutional variables proved to be statistically significant.  We interpret this result as due to the data limitations, as some of the most crucial information, for instance the corruption index is only available since 1996 and the panel exercise was limited to a cross-section.  The state-failure dummy variable or

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the ICRG indicators did not prove to be significant either. Hence, these results are not reported in any of the tables.

To investigate the possibility of slope heterogeneity across countries, we followed the approach used in Haque, Pesaran, and Sharma (1999). Specifically, the slope coefficients in each country are assumed to be fixed over time, but varying across countries linearly with the individual sample mean of GDP per capita. The final results (Tables 15 and 16) only include the statistically significant interaction terms, in order to avoid co linearity arising from the correlation between original explanatory variables and the auxiliary variable capturing the interaction of these with the sample mean of GDP per capita. Hence the estimated model is: _VRSTEit_<sup>=</sup> _f_ ( _WAGEit_ , _GOVEXPit_ , _GDPPCit_ , _URBAN it_ , _AIDSit_ , _GINI it_ , _WAGEGit_ , _GOVGit_ , _GINIGitCONS_ ) where _VRSTEit_ = Variable returns to scale DEA efficiency score for single output and multiple output cases

- _WAGEit_ = Wages and salaries (% of total public expenditure)

_GOVEXPit_ = Total government expenditure (% of GDP) _PUBTOTit_ = Share of expenditures publicly financed (public/total) _GDPPCit_ = GDP per capita in constant 1995 US dollars _URBAN it_ = Urban population (% of total) _AIDSit_ = Dummy variable for HIV/AIDS _GINI it_ = Gini Coefficient _CONS_ = Constant

* _WAGEGit_ = _WAGEit GDPPCi_ * _GOVGit_ = _GOVEXPit GDPPCi_ * _GINIGit_ = _GINI it GDPPCi_ − 1 _T GDPPCi_ = _T_ ∑ _t_ = 1 _GDPPCit_

Results show that the interaction terms are significant, especially for the health regression, implying that there is a heterogeneous response of efficiency scores to the different explanatory variables. This confirms Greene’s (2003) results on the WHO data. One of the key results of this section is that the negative association between the size of government expenditure and efficiency is stronger in countries with higher per-capita GDP. Similarly, this happens with the wage variable. Results are somewhat similar to those of the homogeneous slopes, though statistical significance of many of the coefficients is lower. This is the result of co-linearity between the auxiliary variables and the original set of explanatory variables. This problem deserves further work in the future.

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Interpretation of these results requires caution due to several limitations. First, education and health outcomes are explained by multiple supply and demand factors (Filmer, 2003) that are not included here. This is not the object of the present paper. The omission of one of these factors in the health or education production functions in the previous stage could explain some of the cross-country co-variation of the efficiency results (Ravallion, 2003). The goodness-of-fit analysis of the first stage indicated that no important factor seemed to be omitted. Of course, there can always be additional factors that could be included but the curse of dimensionality<sup>17</sup> is particularly pressing in non-parametric statistical methods (even if the data were available) The second limitation derives from the intuitive question why the set of explanatory variables used in the second stage were not included in the first stage. The answer lies in that most of these variables are environmental and outside the control of the decisionmaking unit. The inclusion of these environmental variables would have had little justification from the production function perspective. Additionally, by maintaining the production function as simple as possible the dimensionality curse is avoided.

Finally, the third limitation arises from the fact that if the variables used in the first stage to obtain the efficiency estimator are correlated with the second stage explanatory variables, the coefficients will be inconsistent and biased (Simar and Wilson, 2004; Grosskopf, 1996; Ravallion, 2003). To examine the extent of this potential problem we calculated correlation coefficients between the “first-stage” inputs and the second stage explanatory variables. The largest correlation coefficients were between GDP per capita and the teachers per pupil ratio and the literacy of the adult. To examine the sensitivity of the results to the inclusion of GDP per capita, all the estimations were performed without this variable and none of the results changed.

# **V.  Concluding Remarks and Directions for Future Work**

The paper presented an application of non-parametric methods to analyze the efficiency of public spending. Based on a sample of more than 140 countries, the paper estimated efficiency scores for nine education output indicators and fourth health output indicators. Our results indicate that countries could achieve substantially higher education and health output levels: on average, developing countries score output efficiency of about .9 (in the multiple input-output framework) or around .7 (in the single input-single output model), implying that they could increase health and education attainment between by 10 percent or 30 percent while consuming the same input level, if they were as efficient as the comparable benchmark countries. This is just an indicative figure, as the figures vary across countries and with the selected output indicator. It is crucial to identify what are the institutional or economic factors that cause some countries to be more efficient than others in the service delivery.

> 17  As the number of outputs increase, the number of observations must increase exponentially to maintain a given mean-square error of the estimator.  See Simar and Wilson (2000).

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In terms of policy implications, it is vital to differentiate between the technically efficient level and the optimal or desired spending level. Even if a country is identified as an “efficient” benchmark country, it may very well still need to expand its public spending levels to achieve a target level of educational or health attainment indicators. Such is the case of countries with low spending levels and low attainment indicators, close to the origin of the efficient frontier.. The important thing is that countries expand their scale of operation along the efficient frontier.

The methods used in the paper can be interpreted as tools to identify extreme cases of efficient units and inefficient cases. Once the cases have been identified, more in-depth analysis is required to explain departures from the benchmark, as proposed and done by Sen (1981). Given that the methods are based on estimating the frontier directly from observed input-output combinations they are subject to sampling variability and are sensitive to the presence of outliers. Recent advances allow dealing with these problems such as in Wilson (2004). Additionally, it would be useful to contrast these results with those obtained with the use of parametric stochastic frontier estimation.

In a “second stage” the paper verified statistical association between the efficiency scores and environmental variables that are not under the control of the decision-making units. The panel Tobit regressions showed that the variables, which are negatively associated with efficiency scores, include the size of public expenditure, the share of the wage bill in the total public budget, the proportion of the service that is publicly financed, the prevalence of HIV/AIDS epidemic on health efficiency scores, income inequality on education efficiency scores, and external aid-financing on some of the efficiency scores. This last impact is probably due to the volatility of aid that impedes effective medium term planning and budgeting, and probably explains why the result is more robust in health than in education where most of the donor-funding is directed. This result point in the same direction of previous research showing that donor financing is unrelated to the quality of domestic policies and that, in the case of highly indebted counties, those with worse policies received more transfers. A positive association between urbanization and efficiency outcomes is also identified in education but some of the health efficiency scores are negatively associated. This last result probably is due to higher crime rates in the cities or the effect of communicable diseases that spread with agglomeration. These are topics for further research in case studies.

32
**Table 13. Explaining cross-country variation in efficiency, Single Input-Single Output**

|Independent<br>Variable|Gross<br>Primary<br>Enrollment|Net<br>Primary<br>Enrollment|Gross<br>Secondary<br>Enrollment|Net<br>Secondary<br>Enrollment|Literacy of<br>Youth|Average<br>Years of<br>School|First<br>Level<br>Complete|Secondary<br>Level<br>Complete|Life<br>Expectancy|Immuni-<br>zation<br>DPT|Immuni-<br>zation<br>Measles|
|---|---|---|---|---|---|---|---|---|---|---|---|
|WAGE|-.00117***|-.00357*|-.00172**|-.00680*|-.00189**|-.00570*|-.00470**|-.00546*|.00065|-.00052|-.00049|
|GOVEXP|-.00387*|-.00546*|-.00340*|-.00455**|-.00387*|-.00696*|-.00566*|-.00765*|-.00269**|-.00078|-.00227***|
|PUBTOT|--|--|--|--|--|--|--|--|-.00213*|-.00150*|-.00135***|
|GDPPC|-.00002*|-.00002*|-.00001*|.00002**|-.00002*|-1.5e-6|-.00001|-7.7e-6|7.6e-7|-.00001*|-.00001*|
|URBAN|.00167*|.00143***|.00168*|.00037|.00187*|.00532*|.00551*|.00555*|-.00018|.00099**|.00088|
|AIDS|-.04471**|-.08731**|-.02204|.01243|-.02974|.12717***|.1211***|.11041|-.05473|-.01108|-.02730|
|GINI|-.06688|.01507|-.19326**|-.42311|-.18484***|-.44658**|-.34402|-.45870**|.22118|.09510|.08692|
|EXTAID|-.00094|-.00196**|-.00021|-.00106|-.00054|.00089|-.00025|-.00006|-.00224***|-.00155|-.00324**|
|CONS|1.02996*|1.1282*|1.0472*|.84138*|1.0697*|.76791*|.70009*|.81705*|.79193*|.78734*|.84384*|
|# of Obs|79|44|79|34|72|71|71|71|118|118|118|
|(# ofCountrs)|(51)|(30)|(51)|(20)|(46)|(45)|(45)|(45)|(69)|(69)|(69)|
|Wald Chi2(6)|83.91|66.09|46.72|55.31|44.27|64.13|45.53|61.94|50.83|123.97|35.01|
|(Prob>Chi2)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|

Note: * 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise 33
**Table 14. Explaining cross-country variation in efficiency, Multiple Inputs-Multiple Outputs**

|Independent<br>Variable|EDU2-2|EDU2-2n|EDU3-2|EDU3-2n|EDU3-3|EDU3-3bl|HEA2-2|HEA3-2|HEA3-2m|HEA3-3|
|---|---|---|---|---|---|---|---|---|---|---|
|WAGE|-.00212**|-.00767*|-.00219**|-.00425|-.001000|-.00340***|.00126*|.00205*|.00203***|.00203***|
|GOVEXP|-.00321*|-.00365|-.00203***|.00099|-.00123***|-.00316***|-.0012***|-.00273*|-.0009|-.00090|
|PUBTOT|--|--|--|--|--|--|-.00151*|-.00142*|-.00159***|-.00151***|
|GDPPC|-.00001**|-6.6e-7|-.00001***|-.00003|-4.2e-6|1.98e-6|-2.7e-6|4.2e-6*|-7.1e-7|-9.3e-7|
|URBAN|.00138***|-.00045|.00191**|.001997|.00127*|.00091|-.00095*|-.00148*|-.00106|-.00105|
|AIDS|-.03295|-.05843|-.00956|-.14763|.01797|.06022|-.04815*|-.033147**|-.07162|-.06999|
|GINI|-.06485|.43602|-.14717|.27058|-.17237**|-.15697|-.03997|-.07958***|-.01015|-.01387|
|EXTAID|.00010|-.00622|.00152|-.00274|-.00066|.00123|.00087|.00128***|-.00095|-.00106|
|CONS|1.0655*|1.0223|1.0642*|1.0124*|1.06570*|1.1218*|1.0098|1.0117*|.98891*|.98787*|
|# of Obs|76|34|69|32|69|63|97|98|98|98|
|(# ofCountrs)|(49)|(20)|(44)|(19)|(44)|(40)|(55)|(56)|(56)|(56)|
|Wald Chi2(6)|24.48|11.69|20.84|7.44|18.72|9.18|185.21|229.98|19.25|18.62|
|(Prob>Chi2)|(.00)|(.11)|(.00)|(.38)|(.01)|(.24)|(.00)|(.00)|(.01)|(.02)|

# Note:

- 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise EDU2-2: Inputs: orthogonalized public spending on education per capita, teachers per pupil Outputs: gross primary and secondary enrollments

- EDU2-2n: same inputs as EDU2-2, outputs: net primary and secondary enrollment EDU3-2: literacy of adult is added to EDU2-2 as input

- EDU3-2n: literacy of adult is added to EDU2-2n as input EDU3-3: literacy of youth is added to EDU3-2 as output EDU3-3bl: same inputs as in EDU3-2,

outputs: average years of school, first level complete, and second level complete (Barro-Lee education indicators)

- HEA2-2: Inputs: orthogonalized public spending on health per capita, literacy of adult

- Outputs: life expectancy at birth, and immunization DPT

- HEA3-2: orthogonalized private spending on health per capita is added to HEA2-2 as input HEA3-2m: Immunization Measles is in place of DPT in HEA3-2 as output HEA3-3: Immunization Measles is added to HEA3-2 as output

34
**Table 15. Explaining cross-country variation in efficiency, Single Input and Single output - Heterogeneous Slopes**

|Independent<br>Variable|Gross<br>Primary<br>Enrollment|Net<br>Primary<br>Enrollment|Gross<br>Secondary<br>Enrollment|Net<br>Secondary<br>Enrollment|Literacy of<br>Youth|Average<br>Years of<br>School|First<br>Level<br>Complete|Secondary<br>Level<br>Complete|Life<br>Expectancy|Immuni-<br>zation<br>DPT|Immuni-<br>zation<br>Measles|
|---|---|---|---|---|---|---|---|---|---|---|---|
|WAGE|-.00006|.00076|-.00035|-.00228|-.00056|-.00200|-.00120|-.00419|-.00306***|-.00079|-.00241|
|GOVEXP|-.00363*|-.00255***|-.00377*|-.00727***|-.00552*|-.00595***|-.00453|-.00611***|.00337**|.00168***|.00221|
|PUBTOT|--|--|--|--|--|--|--|--|-.00162*|-.00162*|-.00097|
|GDPPC|-.00002*|-.00002*|-5.4e-6|.00003*|-.00002***|.00004*|.00003***|.00003***|.00002**|-.00002*|-.00001|
|URBAN|.00179*|.00132**|.00193*|.00139|.00212*|.00566*|.00601*|.00593*|-.00080|-.00117*|.00021|
|AIDS|-.03866***|-.06603**|-.03153|.01010|-.02177|.05491|.06656|.06464|-.02321|-.04147**|-.00826|
|GINI|-.14230|-.42098*|-.14976|-.29395|-.13107|-.09995|-.15463|-.24762|-.12865|-.38851*|-.42162**|
|WAGG|-4.4e-6***|-1.2e-6*|-4.6e-7***|-9.4e-7|-4.5e-7|-8.1e-7|-8.8e-7|-2.4e-7|8.9e-7**|6.95e-8|5.1e-7|
|GOVG|-8.6e-8|-5.2e-7***|4.3e-8|3.6e-7|4.0e-7|-4.3e-7|-4.4e-7|-5.3e-7|-1.4e-6*|-5.4e-7*|-9.4e-7*|
|GINIG|.00003|.00011*|-2.4e-6|-.00003|2.0e-6|-.00006|-.00005|-.00006|.00001|.00009*|.00006***|
|CONS|1.0156*|1.1036*|1.0098*|.74603*|1.0365*|.60371*|.53977*|.68648*|.82665*|1.0119*|.93820*|
|# of Obs|82|47|82|36|75|74|74|74|120|121|121|
|(# ofCountrs)|(52)|(31)|(52)|(21)|(47)|(46)|(46)|(46)|(70)|(71)|(71)|
|Wald Chi2(6)|87.32|93.98|62.74|105.34|58.40|94.00|69.32|82.38|74.33|450.54|52.71|
|(Prob>Chi2)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|

Note: * 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise 35
**Table 16. Explaining cross-country variation in efficiency, Multiple Inputs and Multiple Outputs - Heterogeneous Slopes**

|Independent<br>Variable|EDU2-2|EDU2-2n|EDU3-2|EDU3-2n|EDU3-3|EDU3-3bl|HEA2-2|HEA3-2|HEA3-2m|HEA3-3|
|---|---|---|---|---|---|---|---|---|---|---|
|WAGE|.00051|-.00140|.00005|.00494|-.00018|-.00045|-.00063|-.00065|-.00093|-.00092|
|GOVEXP|-.00323**|.00501|-.00385**|.00520|-.00256**|-.00459|.00122***|.00063|-.00070|-.00064|
|PUBTOT|--|--|--|--|--|--|-.00180*|-.00145**|-.00149***|-.00141***|
|GDPPC|-8.6e-6|.00002|1.7e-6|.00003|-1.8e-6|-2.1e-6|-.00001**|-.00001|-.00003**|-.00003**|
|URBAN|.00137**|.00079|.00166**|.00096|.00134*|.00064|-.00246*|-.00167***|-.00160|-.00159|
|AIDS|-.04139|-.06211|-.04744|-.20362*|.00646|.04633|-.06289*|-.04001|-.07217|-.07025|
|GINI|-.14418|-.18676|.07096|-.02601|-.07474|-.20029|-.32844*|-.45695**|-.29885|-.30857|
|WAGG|-8.3e-7**|-1.2e-6|-6.4e-7***|-1.9e-6|-2.0e-7|-7.9e-7|7.8e-7*|7.2e-7|6.0e-7|6.0e-7|
|GOVG|-6.3e-8|-2.6e-6***|3.5e-7|-1.2e-6|3.0e-7|3.5e-7|-5.98e-7*|-4.9e-7|2.7e-8|1.4e-8|
|GINIG|.00003|.00012|-.00003|.00005|-.00002|.00003|.00005*|.00005***|.00006|.00006***|
|CONS|1.0515*|.89986*|1.0021*|.84756*|1.0464|1.1257*|1.1494*|1.1457*|1.1512*|1.1495*|
|# of Obs|79|36|72|34|72|66|101|101|101|101|
|(# ofCountrs)|(50)|(21)|(45)|(20)|(45)|(41)|(58)|(58)|(58)|(58)|
|Wald Chi2(6)|41.93|18.57|31.15|18.71|23.89|13.22|600.70|37.22|25.33|24.74|
|(Prob>Chi2)|(.00)|(.03)|(.00)|(.22)|(.00)|(.15)|(.00)|(.00)|(.00)|(.01)|

# Note:

1. * 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise

2. EDU2-2: Inputs: orthogonalized public spending on education per capita, teachers per pupil Outputs: gross primary and secondary enrollments

- EDU2-2n: same inputs as EDU2-2, outputs: net primary and secondary enrollment EDU3-2: literacy of adult is added to EDU2-2 as input

- EDU3-2n: literacy of adult is added to EDU2-2n as input EDU3-3: literacy of youth is added to EDU3-2 as output EDU3-3bl: same inputs as in EDU3-2,

outputs: average years of school, first level complete, and second level complete (Barro-Lee education indicators) HEA2-2: Inputs: orthogonalized public spending on health per capita, literacy of adult

- Outputs: life expectancy at birth, and immunization DPT

HEA3-2: orthogonalized private spending on health per capita is added to HEA2-2 as input HEA3-2m: Immunization Measles is in place of DPT in HEA3-2 as output HEA3-3: Immunization Measles is added to HEA3-2 as output 36