efficiency of public spending in education, health, …...policy research working paper 8586...

Policy Research Working Paper 8586

Efficiency of Public Spending in Education, Health, and Infrastructure

An International Benchmarking Exercise

Santiago HerreraAbdoulaye Ouedraogo

Macroeconomics, Trade and Investment Global Practice September 2018

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Produced by the Research Support Team

Abstract

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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Policy Research Working Paper 8586

Governments of developing countries typically spend between 20 and 30 percent of gross domestic product. Hence, small changes in the efficiency of public spending could have a major impact on aggregate productivity growth and gross domestic product levels. Therefore, measuring efficiency and comparing input-output combinations of different decision-making units becomes a central chal-lenge. This paper gauges efficiency as the distance between observed input-output combinations and an efficiency frontier estimated by means of the Free Disposal Hull and Data Envelopment Analysis techniques. 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 175 countries using

data from 2006–16 on education, health, and infra-structure. The paper verifies empirical regularities of the cross-country variation in efficiency, showing a negative association between efficiency and spending levels and the ratio of public-to-private financing of the service provision. Other variables, such as inequality, urbanization, and aid dependency, show mixed results. The efficiency of capi-tal spending is correlated with the quality of governance indicators, especially regulatory quality (positively) and per-ception of corruption (negatively). Although 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 education, health, and infrastructure outcomes.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at [email protected] and [email protected].

Efficiency of Public Spending in Education, Health, and Infrastructure: An International Benchmarking Exercise1

Santiago Herrera Abdoulaye Ouedraogo

JEL Classification: C14, H5, H51, H52, H54, I1, I2, E62

Keywords: efficiency frontiers, efficiency of public spending, education expenditure, health expenditure, infrastructure spending.

1 The paper carries the names of the authors and should be cited accordingly. It follows closely the presentation of Herrera-Pang (2005) and uses the data and STATA codes available in the WPS website. The findings, interpretations and conclusions are the authors’ responsibility and do not necessarily represent the view of the World Bank or its Executive Directors.

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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. The first challenge faced by stakeholders is measuring efficiency. This paper attempts such quantification and verifies empirical regularities in the cross country-variation in the efficiency scores. The paper has four chapters. The first one presents the methodology that defines efficiency as the distance from the observed input-output combinations to an efficient frontier. This unobservable frontier 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 it facilitates comparison of results with existing literature.2 We extend the analysis by measuring efficiency of capital spending in achieving several infrastructure output indicators. The second chapter estimates the efficiency frontiers for 14 education output indicators and seven health output indicators, based on a sample of data for 175 countries for the period 2006-2016, and six infrastructure output indicators based on a sample of 150 countries using 2007-2015 data. 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. The third chapter explores empirical regularities in the cross-country variation in the efficiency scores. Using a Tobit panel approach, this chapter explores the relationship between efficiency scores and expenditure levels, the wage bill as a share of the total budget, the degree of urbanization, and aid-dependency. The fourth and last chapter summarizes the conclusions. II. Measuring Efficiency: Methodologies and Overview of the Literature This chapter describes the empirical methods used in this paper to measure efficiency and surveys 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 been obtained given the inputs utilized. This maximum constitutes the efficient frontier which will be the benchmark for gauging the relative efficiency of the observations. There are multiple techniques to estimate this frontier and they have been applied to examine the efficiency of public spending in different contexts. These are the topics of the next two sections. 2 This paper follows closely the Herrera-Pang (2005) presentation to facilitate intertemporal comparisons.

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II.1. Methods for Measuring Efficiency

The origin of the discussion of efficiency measurement dates 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 Figure 1. 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.

Figure 1 Technical and Allocative Inefficiency

This paper focuses on technical efficiency, due to 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.

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Numerous techniques have been developed to estimate the 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 non-parametric 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. Other papers use the parametric approach (Greene (2003) Grigio (2013, 2014). The remainder of the section briefly describes the two methods: Free Disposable Hull (FDH) and 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 the FDH production possibility frontier. Countries A and B use inputs 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 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.

Figure 2 Free Disposal Hull (FDH) production possibility frontier

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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 input-single output DEA production possibility frontier. In contrast to the vertical step-ups of the FDH frontier, the 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 efficient by FDH, now lies below the variable returns to scale (VRS, further defined below) efficiency frontier, XADF, by DEA ranking. This example shows that FDH efficiency scores are higher than DEA ones. The input-oriented technical efficiency of C is now defined by TE = YV/YC.

Figure 3 DEA production possibility frontier

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.

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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 led the literature to explore the relationship between statistical analysis and non-parametric methods (Simar and Wilson, 2000) and propose solutions like constructing confidence intervals for the efficiency scores using asymptotic theory in the single input case (for input-efficiency estimators) or single-output (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 points, 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 of this paper, 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; Herrera-Pang 2006), 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 have been surveyed elsewhere.3 The more recent directly related papers are Herrera-Pang (2005), which use non-parametric methods (FDH and DEA) to gauge efficiency for numerous health and education output indicators. Using a large set of developing countries for the period 1996-2002, the paper estimates efficiency scores in a first stage and in a second stage they use panel methods to explore the correlates of efficiency variation across countries. One of the common results of that paper and previous literature is that countries with low attainment in results in health or education show up with high efficiency scores. The apparently counterintuitive result is due to some countries’ low spending levels and low education attainment results, which are considered as the “origin” of the efficiency frontier: though the country appears as efficient because the peer countries spend more (in the OECD context), its output levels are also higher and probably more desirable from a broader perspective. The next chapters discuss this topic and report similar results for other countries.

3 Gupta and Verhoeven (2001), Evans and Tandon (2000), Jarasuriya and Woodon (2002) Greene (2003a), Afonso, Schuknecht and Tanzi (2003), Afonso and St. Aubyn (2004) were surveyed in Herrera-Pang (2005).

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Afonso, Romero and Monsalve (2013) focus exclusively on Latin American and Caribbean countries, and define aggregate measures of public sector output, which include education, health infrastructure, macro stability, and economic performance. The aggregations are weighted and unweighted. Given the complexity of the aggregations, their results are not directly comparable to Herrera-Pang or Grigoli’s results described below. But their findings in general show output efficiency scores higher than input efficiency scores, like the Herrera-Pang results for LAC, especially in health. Grigoli and Kapsoli (2013) use stochastic frontier analysis (SFA) to examine efficiency in health spending in a sample of 80 countries. Using a single indicator for health outcomes, the health adjusted life expectancy (HALE) average for 2006-2010, and lagged values of spending and other correlates (average of 2001-2005), the authors compute efficiency scores. This is an innovative way to capture the dynamics in the relationship between spending and health outcomes, but still imposes restrictions on the data. The reported scores are high, and the authors acknowledge the fact when reporting average efficiency scores of .94 compared to significantly lower scores of .81 of other studies. That average also seems high compared to results presented in this paper. The efficiency rankings of Grigoli and Kapsoli (2013) are also strange, as the efficiency scores of Trinidad and Tobago, for instance, ranked within the most efficient group in Herrera-Pang (2005) and Afonso et.al (2013), show up in the third quartile of the efficiency distribution. Grigoli (2014) uses an envelopment methodology, variant of the DEA, designed by Wagstaff and Wang (2011), to examine efficiency of education spending. One of the advantages of this approach is that it allows dealing with heterogeneity across groups, as different frontiers are constructed for different groups of countries. It also uses a LOWESS method, which helps dealing with the measurement error, data outliers, and stochastic nature of the problem at hand. The added complexity comes at a cost, as the study uses only one education outcome variable, net secondary enrollment. The efficiency scores have a larger dispersion and little information is given on the distribution of the scores, but many countries about 15% of the total, are on the efficiency frontier. Perelman et al. (2017) use two concepts, performance and productivity, to compare Latin American countries’ efficiency in education and health spending. The performance concept compares outcomes without considering resource constraints. Their concept of productivity links the outcomes to the resources, like the efficiency concept used in this paper and other literature in this review. For education outcomes, the authors use the PISA scores exclusively, while the health outcome is a human development index which is an aggregation of several health outcomes.4 The education efficiency scores of the Perelman et al. study are, on average, .63 for input-efficiency and .75 for output-

4 The education outcome considers only six LAC countries. The health outcomes used to compute the human development index are Disability-Adjusted Life Years (DALY), the probability of dying between 30 and 70 years old from cardiovascular, cancer, diabetes, or chronic respiratory diseases (PROB); the percentage of out-of-pocket expenditure in total health expenditure as an indicator of financial protection (FIPR); and an indicator of equity in health EQTY.

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efficiency. In health, the average input-efficiency is .59 and output-efficiency is .90. These results match the pattern found in other studies, including the present one, of output-efficiency exceeding input-efficiency, and that of health efficiency tending to be higher than education, especially on the output measure.5 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, education, and infrastructure.6 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 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 if 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, health and infrastructure, and it shows a clear positive association with per-capita GDP (Figures 4A, 4B and 4C).7 This positive association may be explained by several factors. One of them is the Balassa-Samuelson effect, according to which price levels in wealthier countries tend to be higher than in poorer countries.8 This applies to both final goods and factor prices. Thus, the 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 such as health and education. 5 Averages of Perelman scores come from Tables 6 and 19. The order of magnitude of the averages is very similar to those reported in this study for the PISA scores of the same LAC country sample. The average reported in this study for education input efficiency is 0.67 and the output efficiency is .80. The LAC region health input efficiency score is .48 and output efficiency is .95, presented in tables in the next section. 6 See Table A.9 in Appendix A for the list of countries included in the study. 7 Education and health spending are measured in constant 2011 US dollars in PPP terms, and is an update of the figures presented in Herrera-Pang (2005), and the source is WDI from the World Bank. Capital spending comes from a different source, namely the IMF WEO. 8 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.

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The positive association (Figures 4A, 4B and 4C) can also 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; Afonso et. al. 2017) in time series and cross-country settings, showing that this is not particular to the series used for the present study.

Figure 4. Public Expenditure on Education, Health and Capital and GDP (all per capita and in logs) 2009-2015

Figure 4A. Public Expenditure on Education and

GDP 2009-2015 Figure 4B. Public Expenditure on Health and GDP

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Figure 4C. Public Capital Spending and GDP 2007-2015

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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 may partially control for this factor by excluding the industrialized nations from the sample, or by presenting the results clustered regionally (Africa (AFR), East Asia and Pacific (EAP), Eastern Europe and Central Asia (ECA), 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. We attempt to control for this factor by including GDP per capita as an explanatory variable in the “second stage” regressions presented in the last section. A third option consists in using as an input the orthogonal component of public expenditure to GDP.9 We estimated the efficiency scores using as input both the original and the orthogonalized variables. The goodness-of-fit of each model was gauged based 9 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. The orthogonalization of capital spending was done using a log-log regression, as this transformation of the variables resulted in a distribution of the efficiency scores that produced a better fitness observed by comparing Fig. 5B (log-log) and the figure reported in the Appendix without the log transformation.

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on the frequency distribution of the inefficiency measures, as suggested by Farrell (1957) and Varian (1990). Comparing the distributions of the efficiency scores computed alternatively with the public expenditure or the orthogonalized value (Figures 5A and 5B), we see that the orthogonalization reduces the bias towards inefficiency (in the case of education, Fig. 5A) or towards efficiency (capital spending, Fig. 5B).10 Given this mitigation of the skewedness, the remainder of the paper considers the orthogonal component of expenditure as the input in all the efficiency score calculations.

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Figure 5B. Density of Efficiency Scores – Capital Spending-Overall Quality of Infrastructure

10 The efficiency scores depicted in Figs. 5A and 5B are FDH efficiency scores, one for input efficiency (5A) and the other for output efficiency (5B).

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This paper uses 14 indicators of education output, seven indicators of health output, and six indicators of infrastructure output.11 The education indicators are: primary school enrollment (gross and net), secondary school enrollment (gross and net), gross tertiary school enrollment, literacy of youth, average years of school, second level complete, PISA learning scores for math, reading and science and the WEF Indices for Quality of Match and Science Education, Quality of Primary Education, and Quality of the education system. Though the PISA country sample is limited, it is a numerical score. The WEF indices are computed for a significantly larger sample but have a limited range from 1-7. The correlation between the PISA learning scores and other output variables is high (.80 with average years of school and .78 with net primary school enrollment), as shown in Figure 6.12 The health output indicators are: life expectancy at birth, immunization (DPT13 and measles), and the disability-adjusted life expectancy (DALE), maternal survival rate, infant survival rates and Tuberculosis Free Population.14 For infrastructure output, we considered six indexes produced by the World Economic Forum: quality of overall infrastructure, quality of roads, quality of railroad infrastructure, quality of port infrastructure, quality of transport infrastructure, and quality of electricity supply. The cross-country comparisons with this set of indicators assume some form of data homogeneity, which might be problematic given the diversity of countries 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, 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.

11 The data sources are: The World Bank World Development Indicators (WDI), Barro-Lee database, and Crouch and Fasih (2004), PISA, and the World Health Organization (Mathers et al, 2000). A complete list of variables and data sources can be found in Table A.10 of Appendix A. For capital spending the source is the WEO data base from the IMF and the infrastructure output indicators come from the World Economic Forum (WEF). 12 The correlation coefficients and Figure 6 exclude developed nations for the Crouch and Fasih (2004) sample. 13 DPT is Diphtheria-Pertussis and Tetanus 14 Maternal survival rates and infant survival rates are transformations of the mortality rates as follows: Maternal Survival Rate= (100000-Maternal Mortality Rate)/100000. The infant survival rate (ISR)=1000-Infant Mortality rate)/1000. Tuberculosis free population is a transformation of the incidence of tuberculosis variable (per 100,000 inhabitants).

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DEU

DNK

DOM

DZA

ERI

ESP

FIN

FRA

GBR

GEO

GRC

HRVHUN

IDN

ISLITA

JOR

KAZ

KGZ

KOR

LBN

LTULUXLVA

MDA

MEX

MKD

NLD

NOR

NZL

PANPER

POL

ROM

RUS

SVN

SWE

THATTO

TUN

TURURY

USA

VNM

350

400

450

500

550

PIS

A S

cien

ce S

core

s

40 60 80 100Net Primary Enrollment

Data Source: World Bank Indicators

Correlation: PISA Science Scores and Net Primary Enrollment

ALB

ARE

ARG

AUS

AUT

BGR

BRA

CAN

CHE

CHL

CHN

COL

CRI

CYP

CZE

DEU

DNK

DOM

DZA

ESP

FIN

FRA

GBR

GRC

HRVHUN

IDN

ISLITA

JOR

KAZ

KGZ

KOR

LTULUXLVA

MDA

MEX

NLD

NOR

NZL

PANPER

POL

ROM

RUSSVK

SVN

SWE

THATTO

TUN

TURURY

USA

VNM

350

400

450

500

550

PIS

A S

cien

ce S

core

s

0 5 10 15Average Years of School

Data Source: World Bank Indicators & Barro Lee

Correlation: PISA Science and Average Years of School

Figure 6. Correlation between Learning Scores and Other Education Indicators

There are four other limitations arising from the variables and data selected for the analysis. The first one refers to the level of aggregation. While the exercises use aggregate public spending on health, education and capital spending as inputs, they use disaggregate measures of output, such as primary enrollment or secondary enrollment. Ideally, the input should have differentiated between primary and secondary education expenditures. 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 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 non-monetary factors of production and outputs. 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 many developing countries constrained the options. In the case of infrastructure output, the physical measures such as those used in Herrera-Pang for container ports are not available, hence we are limited to using indexes. A fourth limitation arising from the selected indicators is that these do not 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 country. In education, this paper advances by considering the learning scores as one of the indicators. In health, other outcomes such as the number of sick-day leaves or the number of missed-school days because of health-related causes

14

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. Additionally, in most countries, these two activities (immunization) account for very small fractions of the health budgets. 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 Education Frontiers Figures 7a-d show both FDH and DEA efficiency frontiers for four of the 14 output indicators: gross primary school enrollment, secondary gross enrollment, quality of math and science education index (from WEF) and the PISA Science scores. Individual country efficiency scores for the four indicators are reported in Table A.1-2 of Appendix A.15 The graphical efficiency frontiers for other output indicators can be found in Appendix A (Figure A.1). Several results may be highlighted:

a. The input efficiency rankings (education) are robust to the output indicator selected, as implied by the Spearman rank-correlation coefficient (see Tables A.1 and A.3 in Appendix A), which is positive, significant and high. The range oscillates between .35 and .97. This result implies that countries ranked as efficient (or inefficient) per one indicator, are ranked similarly when other output indicators are used.

b. Despite the orthogonalization by GDP, the relatively rich countries tend to be in the more efficient group, especially when output efficiency is considered.

c. In general, output efficiency scores are higher than input efficiency scores.

15 The efficiency scores of all the indicators can be found at the MFM website indicated in footnote 1.

15

Free Disposable Hull (FDH)

Data Envelopment Analysis (DEA)

AGO

ALB

ARG

ARMATG

AUS

AUTAZE

BDI

BEN

BFA

BGD

BGR BLR

BLZ

BOL

BRA

BRB

BWA

CAF

CAN

CHECHL

CHN

CIV

CMRCOG

COL

COM

CPVCRI

CYPCZEDEU

DJI

DNK

DOM

ECU

EGYESP

EST

ETH

FIN

FJIFRAGBR

GEO

GHA

GINGMB

GNBGTM

GUY

HND

HRV

HUN

IDNINDIRN

ISLITA

KAZ

KEN

KGZ

KHM

KNA

KOR

LAO

LBN LKA

LSO

LTULVA

MAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN PER

PHL

POL

PRY

ROM

RUS

RWA

SAU

SDN

SEN

SLB

SLE

SLV

SVKSVN

SWE

SWZ

TCD

TGO

THA TJK

TKM

TTOTUN

TUR

TZA

UGA

UKR

URY

USA

VCTVEN

VNM

VUT

ZAFZARZWE

6080

100

120

140

Gro

ss P

rimar

y S

choo

l Enr

ollm

ent

500 1000 1500 2000 2500Orthogonalized Public Expditure on Education

Data Source: World Bank WDI

Gross Primary Enrollment vs Education Expenditure

AGO

ALB

ARG

ARMATG

AUS

AUTAZE

BDI

BEN

BFA

BGD

BGR BLR

BLZ

BOL

BRA

BRB

BWA

CAF

CAN

CHECHL

CHN

CIV

CMRCOG

COL

COM

CPVCRI

CYPCZEDEU

DJI

DNK

DOM

ECU

EGYESP

EST

ETH

FIN

FJIFRAGBR

GEO

GHA

GINGMB

GNBGTM

GUY

HND

HRV

HUN

IDNINDIRN

ISLITA

KAZ

KEN

KGZ

KHM

KNA

KOR

LAO

LBN LKA

LSO

LTULVA

MAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN PER

PHL

POL

PRY

ROM

RUS

RWA

SAU

SDN

SEN

SLB

SLE

SLV

SVKSVN

SWE

SWZ

TCD

TGO

THA TJK

TKM

TTOTUN

TUR

TZA

UGA

UKR

URY

USA

VCTVEN

VNM

VUT

ZAFZARZWE

6080

100

120

140

Gro

ss P

rimary

Sch

ool E

nrollm

ent



Gross Primary Enrollment vs Education Expenditure

(a.1) (a.2)

AGO

ALB ARGARM

ATGAUS

BDI

BEN

BFA

BGD

BGR

BLR

BLZ

BOL

BRA

BRB

CAF

CHE

CHL

CHN

CMR

COL

COM

CPV

CRI

CYPDNK

DOM

ECUEGY

ESPEST

ETH

FIN

FJI

FRA

GBR

GEO

GHA

GIN

GTM

GUY

HND

HRVHUN

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IND

IRN

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ITA

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KEN

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KOR

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OMN

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PAN

PER

PHL

POL

PRY

ROM

RWA

SAU

SLV

SVN SWE

SWZ

THATJKTUR

UKR

URY

USAVCT

VEN

VUT

ZWE

2040

6080

100

Gro

ss S

econd

ary

Sch

ool E

nro

llmen

t



Gross Secondary Enrollment vs Education Expenditure

AGO

ALB ARGARM

ATGAUS

BDI

BEN

BFA

BGD

BGR

BLR

BLZ

BOL

BRA

BRB

CAF

CHE

CHL

CHN

CMR

COL

COM

CPV

CRI

CYPDNK

DOM

ECUEGY

ESPEST

ETH

FIN

FJI

FRA

GBR

GEO

GHA

GIN

GTM

GUY

HND

HRVHUN

IDN

IND

IRN

ISL

ITA

JAM

KAZ

KEN

KGZKNA

KOR

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LBN

LCALKA

LSO

LTU

LVA

MAR

MDA

MDG

MEX

MLI

MOZ

MRT

MUS

MWI

MYS

NER

NIC

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NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROM

RWA

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SLV

SVN SWE

SWZ

THATJKTUR

UKR

URY

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VEN

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6080

100

Gro

ss S

econ

dary

Sch

ool E

nrol

lme

nt



Gross Secondary Enrollment vs Education Expenditure

(b.1) (b.2)

AGO

ALB

ARG

ARM

AUS

AUT

AZE BDI

BEN

BFA

BGD

BGR

BLZBOL

BRA

BRB

BWA

CAN

CHE

CHL

CHN

CIV

CMR

COL

CPV

CRI

CYP

CZE

DEU DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GTM

GUY

HND

HRV

HTI

HUNIDNINDIRN

ISL

ITA

JAM

KAZKEN

KGZKHM

KOR

LAO

LBN

LKA

LSO

LTU

LVA

MARMDA

MDG

MEX

MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROMRUS

RWASAU

SEN

SLE

SLV

SVK

SVN

SWE

SWZ

TCD

THA

TJK

TTO

TUN

TUR

TZA

UGA

UKR

URY

USA

VEN

VNM

ZAF

ZWE

23

45

6Q

ualit

y of

mat

h a

nd s

cien

ce


Data Source: World Bank WDI/World Economic Forum

Quality of math and science vs Education Expenditure

AGO

ALB

ARG

ARM

AUS

AUT

AZE BDI

BEN

BFA

BGD

BGR

BLZBOL

BRA

BRB

BWA

CAN

CHE

CHL

CHN

CIV

CMR

COL

CPV

CRI

CYP

CZE

DEU DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GTM

GUY

HND

HRV

HTI

HUNIDNINDIRN

ISL

ITA

JAM

KAZKEN

KGZKHM

KOR

LAO

LBN

LKA

LSO

LTU

LVA

MARMDA

MDG

MEX

MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROMRUS

RWASAU

SEN

SLE

SLV

SVK

SVN

SWE

SWZ

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THA

TJK

TTO

TUN

TUR

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UGA

UKR

URY

USA

VEN

VNM

ZAF

ZWE

23

45

6Q

ua

lity

of m

ath

an

d sc

ienc

e



Quality of math and science vs Education Expenditure

(c.1) (c.2)

16

ALB

ARG

AUS

AUT

AZE

BGR

BRA

CAN

CHE

CHL

CHN

COL

CRI

CYP

CZE

DEU

DNK

DOM

ESP

FIN

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GBR

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KOR

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550

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a S

cience

Lite

racy

sco

re

1000 1500 2000 2500Orthogonalized Public Expditure on Education

Data Source: World Bank WDI/NCES

Pisa Science Literacy score vs Education Expenditure

ALB

ARG

AUS

AUT

AZE

BGR

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CAN

CHE

CHL

CHN

COL

CRI

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CZE

DEU

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LTULVA

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a S

cien

ce L

itera

cy s

core



Pisa Science Literacy score vs Education Expenditure

(d.1) (d.2)

Figure 7 Education Efficiency Frontier: Single Input and Single Output

d. Output-efficiency rankings also vary with the selected output indicators. The Spearman correlation coefficient of the output-efficiency scores (see Tables A.2 and A.4 in Appendix) show that these are robust to the selected indicator, though the range is higher (.16 to .90).

e. To grasp the order of magnitudes of the deviations from the efficiency frontier, and the disparity in efficiency across countries, we computed an average for all indicators for the groups of more efficient and least efficient countries (Table 1).16 The input-efficiency estimations indicate that a country in the bottom of the distribution could reach the same educational attainment levels by spending approximately 50 percent less. The output efficiency estimators indicate that, on average, with the prevailing expenditure level a typical country of this group could reach educational attainment levels more than twice as high (Table 1A).

Table 1. Education Attainment: Single Input, Single Output Country Clustering

Input-Efficient Output Efficient More efficient Italy, Kazakhstan, Lebanon,

Czech Republic, Slovak Republic, China, Republic of Korea

Austria, Finland, Kazakhstan, Italy, Canada, Lebanon, Czech Republic, Great Britain, Sweden, Republic of Korea

Least efficient Denmark, Norway, Sweden, Saudi Arabia, Namibia, Botswana, Moldova, Costa Rica

Angola, Mozambique, Burundi, Mauritania, Tanzania, Peru

16 These are simple averages of the top 10 and bottom 10 countries in each ranking.

17

Table 1A. Education Attainment: Single Input, Single Output Efficiency Scores

Input-Efficient Output Efficient More efficient .89 .92 Least efficient .47 .39 f. This clustering exercise reveals (Table 1) that several developed economies, such

as Denmark, Norway and Sweden, appear within the least input-efficient category. This is due to the amount of spending these countries do. Resource-rich countries also appear in the inefficient group, such as Saudi Arabia, Botswana, Angola, and Mozambique. Mostly African countries populate this group. Among the more efficient group, Lebanon, Republic of Korea, China, Czech Republic, and Kazakhstan appear as both input and output efficient. Curiously, Sweden appears within the most output-efficient category. Explaining why these countries appear in each cluster requires more in-depth analysis, undertaken in the last section, which explores the association of efficiency scores with other variables.

g. 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, Azerbaijan, Lebanon and Romania appear as efficient countries on the FDH efficiency frontier or very close to it (Figure 7 a.1) because they spend relatively little on education; they are close to the origin of the frontier with low spending and low learning levels. But their PISA scores are far below those of countries that spend similar amounts but have significantly higher PISA scores, such as Italy or the Czech Republic. Within the LAC countries, Panama and Chile have the highest input efficiency scores, though Panama’s case is due to the low spending level effect discussed above. The important point 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).

h. The regional aggregation of the efficiency scores shows that input-oriented scores (Table 2A) are lower than output-oriented scores (Table 2B).17 This is especially true in ECA, EAP, and MNA. In general, scores are higher for primary enrollment and decrease with the level of education (secondary enrollment), especially when output-oriented measures are considered. Across regions, ECA, EAP, and MENA have similar levels of input-inefficiency of about 65-70 percent, while LAC and SAS average around 60 percent, and AFR about 55 percent.

17 The regional aggregate is 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. The regional classification exercise excludes the developed economies in all the regional aggregation exercises.

18

i. Output efficiency in ECA and EAP is in the range of 75 to 80 percent, in LAC and MNA around 70 percent, in SAS about 60 percent and in AFR 50 percent.18 In EAP, ECA, and LAC the output efficiency scores are about 15 percent higher than the input efficiency, while in SAS they are about the same and in AFR 7 percent lower. This indicates that the first three regions are closer to the frontier on the output side than on the input (spending) side.

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

- Single Input, Single Output

AFR EAP ECA LAC MNA SAS Gross primary enrollment .61 .65 .67 .65 .65 .70 Net primary enrollment .58 .72 .73 .70 .75 .69 Gross Secondary enrollment .57 .68 .66 .63 .70 .63 Net secondary enrollment .56 .61 .66 .63 .65 .62 Average years of school .56 .64 .69 .60 .59 .63 Second level complete .56 .63 .68 .60 .59 .63 PISA Science .82 .79 .68 .74 PISA Reading .78 .78 .68 .74 PISA Math .80 .79 .67 .74 Quality of Math and Science .57 .64 .69 .60 .69 .64

Table 2B. Educational Attainment: Output-Efficiency scores by regions across the world

- Single Input-Single Output

AFR EAP ECA LAC MNA SAS Gross primary enrollment .72 .79 .75 .77 .76 .79 Net primary enrollment .81 .93 .94 .94 .92 .90 Gross Secondary enrollment .34 .73 .89 .74 .77 .59 Net secondary enrollment .33 .59 .75 .68 .63 .49 Average years of school .42 .65 .86 .65 .59 .51 Second level complete .20 .39 .76 .41 .35 .32 PISA Science .90 .87 .77 .79 PISA Reading .88 .85 .79 .74 PISA Math .88 .86 .74 .78 Quality of Math and Science .57 .72 .72 .53 .73 .66 j. Two groups of indicators capture the quality of educational attainment: the PISA

scores (Math, Science and Reading) and the World Economic Forum (WEF) various indicators, of which we focus on one, the Quality of Math and Science

18 These are simple averages of the indicators by region. In output efficiency the primary and secondary level completed indicators were omitted due to problems with the data.

19

education. Both variables have limitations: the PISA country sample is smaller and with relatively greater weight of developed nations, while the WEF variable is an index constructed to reflect the quality in a large sample of countries.

III.2.2. FDH and DEA Health Frontiers This section presents the single input (public expenditure on health per capita in PPP terms)-single output efficiency frontiers using both the FDH and DEA methodologies; seven alternative output indicators are used: life expectancy at birth, the disability adjusted life expectancy (DALE), DPT immunization, measles immunization, tuberculosis free population, maternal survival, and infant survival rates.19 Figures 8a-d show the FDH and DEA efficiency frontiers for four indicators. The specific country rankings for four of the health indicators are listed in Table A.4-5 of Appendix A. The graphical efficiency frontiers for other output indicators can be found in Appendix A (Figure A.2).

Free Disposable Hull (FDH) Data Envelopment

AGO

ALBARGARM AUS AUTAZE

BDI

BENBFA

BGD

BGRBHR

BHS

BLR BLZ

BOL

BRABRB

BWA

CAF

CANCHECHL CHN

CIVCMR

COG

COL

COM

CPV CRICYP CZE DEU

DJI

DNK

DOMDZA

ECUEGY

ERI

ESPEST

ETH

FINFJI FRA

GAB

GBRGEO

GHA

GINGMB

GNB

GRCGRDGTM

GUY

HND

HRV

HTI

HUN

IDNIND

IRN ISLITA

JAMJOR

KAZ

KEN

KGZ

KHM

KOR

LAO

LBNLCALKA

LSO

LTU LUXLVA

MAR

MDA

MDG

MEXMKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLDNOR

NPL

NZLOMN

PAK

PANPER

PHL

PNG

POL

PRY

ROMRUS

RWA

SAU

SDNSEN

SLB

SLE

SLVSVK SVN SWE

SWZ

TCD

TGO

THA TJKTKM

TONTTO TUN

TUR

TZAUGA

UKRURY USAUZBVCTVEN

VNMVUTWSM

ZAF

ZAR

ZMB

ZWE

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00990

00995

00100

000

Mat

ern

al S

urv

ival R

ate

1000 2000 3000 4000 5000Orthogonalized Public Expditure on Health

Data Source: World Bank WDI, World Health Organization

Maternal Survival Rate vs Health Expenditure

AGO

ALBARGARM AUS AUTAZE

BDI

BENBFA

BGD

BGRBHR

BHS

BLR BLZ

BOL

BRABRB

BWA

CAF

CANCHECHL CHN

CIVCMR

COG

COL

COM

CPV CRICYP CZE DEU

DJI

DNK

DOMDZA

ECUEGY

ERI

ESPEST

ETH

FINFJI FRA

GAB

GBRGEO

GHA

GINGMB

GNB

GRCGRDGTM

GUY

HND

HRV

HTI

HUN

IDNIND

IRN ISLITA

JAMJOR

KAZ

KEN

KGZ

KHM

KOR

LAO

LBNLCALKA

LSO

LTU LUXLVA

MAR

MDA

MDG

MEXMKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLDNOR

NPL

NZLOMN

PAK

PANPER

PHL

PNG

POL

PRY

ROMRUS

RWA

SAU

SDNSEN

SLB

SLE

SLVSVK SVN SWE

SWZ

TCD

TGO

THA TJKTKM

TONTTO TUN

TUR

TZAUGA

UKRURY USAUZBVCTVEN

VNMVUTWSM

ZAF

ZAR

ZMB

ZWE

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09900

099

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1000

00M

ate

rnal S

urv

ival R

ate



Maternal Survival Rate vs Health Expenditure

(a.1) (a.2)

19 Tuberculosis free, maternal survival rates, and infant survival rates are transformations of the original variables, tuberculosis incidence, maternal mortality, and infant mortality, respectively. Transformations are described in footnote 15.

20

AGO

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ARM

ATGAUS AUT

AZE

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CHN

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COG

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CPV

CRI

CYP CZE DEU

DJI

DMA

D

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DZAECU

EGY

ERI

ESPEST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GRC

GRD

GTMGUY

HND

HRV

HTI

HUN

IDN

IND

IRN

ISLITA

JAMJORKAZ

KEN

KGZ

KHM

KNA

KOR

LAO

LBN

LCALKA

LSO

LTULUX

LVA

MAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZMRT

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MYS

NAM

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NLNOR

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NZL

OMN

PAK

PANPER

PHL

PNG

POL

PRY

ROMRUS

RWA

SAU

SDN

SEN

SLB

SLE

SLV

SVKSVN SWE

SWZ

TCD

TGO

THA

TJK

TKM

TONTTO

TUNTUR

TZAUGA

UKRURYUSA

UZB

VCTVEN

VNM

VUT

WSM

YEM

ZAF

ZAR

ZMBZWE

.9.9

2.9

4.9

6.9

81

Infa

nt S

urv

iva

l Ra

te

1000 2000 3000 4000Orthogonalized Public Expditure on Health


Infant Survival Rate vs Health Expenditure

AGO

ALBARG

ARM

ATGAUS AUT

AZE

BDI

BEN

BFA

BGD

BGRBHRBHS

BLR

BLZ

BOL

BRABRB

BWA

CAF

CANCHECHL

CHN

CIV

CMR

COG

COL

COM

CPV

CRI

CYP CZE DEU

DJI

DMA

DNK

DOM

DZAECU

EGY

ERI

ESPEST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GRC

GRD

GTMGUY

HND

HRV

HTI

HUN

IDN

IND

IRN

ISLITA

JAMJORKAZ

KEN

KGZ

KHM

KNA

KOR

LAO

LBN

LCALKA

LSO

LTULUX

LVA

MAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLDNOR

NPL

NZL

OMN

PAK

PANPER

PHL

PNG

POL

PRY

ROMRUS

RWA

SAU

SDN

SEN

SLB

SLE

SLV

SVKSVN SWE

SWZ

TCD

TGO

THA

TJK

TKM

TONTTO

TUNTUR

TZAUGA

UKRURYUSA

UZB

VCTVEN

VNM

VUT

WSM

YEM

ZAF

ZAR

ZMBZWE

.9.9

2.9

4.9

6.9

81

Infa

nt S

urv

iva

l Ra

te



Infant Survival Rate vs Health Expenditure

(b.1) (b.2)

AGO

ALBARGARM

AUS AUT

AZE

BDI

BENBFA

BGD

BGRBHR BLZ

BOL

BRABRB

BWA

CANCHECHL

CHN

CIVCMR

COL

CPV

CRICYP CZE DEU DNKDOM DZAECUEGY ESPEST

ETH

FIN FRA

GAB

GBR

GEO GHA

GINGMB

GRCGTM

GUYHND

HRV

HTI

HUN

IDN IND

IRN ISLITAJAM JOR

KAZ

KEN

KGZ

KHM

KOR

LAO

LBNLKA

LSO

LTULUX

LVAMAR

MDA

MDG

MEX MKD

MLIMNG

MOZ

MRT

MUS

MWI

MYS

NAM

NGA

NICNLDNOR

NPL

NZLOMN

PAK

PAN

PER

PHL

POL PRYROMRUS

RWA

SAU

SEN

SLE

SLVSVK SVN SWE

SWZ

TCD

THATJK

TTO TUNTUR

TZAUGA

UKR

URY USAVEN

VNM

YEM

ZAF

ZMB

ZWE

.985

.99

.995

1F

ree

Tu

be

rcu

losi

s



Free Tuberculosis vs Health Expenditure

AGO

ALBARGARM

AUS AUT

AZE

BDI

BENBFA

BGD

BGRBHR BLZ

BOL

BRABRB

BWA

CANCHECHL

CHN

CIV

CMR

COL

CPV

CRICYP CZE DEU DNKDOM DZAECUEGY ESPEST

ETH

FIN FRA

GAB

GBR

GEO GHA

GINGMB

GRCGTM

GUYHND

HRV

HTI

HUN

IDN IND

IRN ISLITAJAM JOR

KAZ

KEN

KGZ

KHM

KOR

LAO

LBNLKA

LSO

LTULUX

LVAMAR

MDA

MDG

MEX MKD

MLIMNG

MOZ

MRT

MUS

MWI

MYS

NAM

NGA

NICNLDNOR

NPL

NZLOMN

PAK

PAN

PER

PHL

POL PRYROMRUS

RWA

SAU

SEN

SLE

SLVSVK SVN SWE

SWZ

TCD

THATJK

TTO TUNTUR

TZAUGA

UKR

URY USAVEN

VNM

YEM

ZAF

ZMB

ZWE

.985

.99

.995

1F

ree

Tu

be

rcu

losi

s



Free Tuberculosis vs Health Expenditure

(c.1) (c.2)

AGO

ALBARG

ARM

ATG

AUSAUT

AZE

BDI

BEN

BFA

BGD

BGRBHR

BHS

BLRBLZBOL

BRA

BRB

BWA

CAF

CANCHE

CHL

CHN

CIVCMR

COG

COL

COM

CPV

CRICYP

CZE

DEU

DJI

DMA

DNK

DOM

DZAECU

EGY

ERI

ESP

EST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GRC

GRDGTM

GUY

HND

HRV

HTI

HUN

IDN

IND

IRN

ISLITA

JAM JOR

KAZ

KEN

KGZ

KHM

KOR

LAO

LBN

LCALKA

LSO

LTU

LUX

LVAMAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAMNER

NGA

NIC

NLDNOR

NPL

NZL

OMN

PAK

PANPER

PHL

PNG

POL

PRYROM

RUS

RWA

SAU

SDN

SEN

SLB

SLE

SLVSVK

SVN

SWE

SWZ

TCD

TGO

THA

TJK

TKM

TON

TTO

TUNTUR

TZA

UGA

UKR

URY

USA

UZB

VCT

VEN

VNM

VUT

WSM

YEM

ZAF

ZAR

ZMBZWE

5060

7080

Dis

ab

ility

Ad

just

ed

Life

Exp

ect

an

cy


Data Source: World Bank WDI, Institute for Health Metrics and Evaluation (IHME)

Disability Adjusted Life Expectancy vs Health Expenditure

AGO

ALBARG

ARM

ATG

AUSAUT

AZE

BDI

BEN

BFA

BGD

BGRBHR

BHS

BLRBLZBOL

BRA

BRB

BWA

CAF

CANCHE

CHL

CHN

CIVCMR

COG

COL

COM

CPV

CRICYP

CZE

DEU

DJI

DMA

DNK

DOM

DZAECU

EGY

ERI

ESP

EST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GRC

GRDGTM

GUY

HND

HRV

HTI

HUN

IDN

IND

IRN

ISLITA

JAM JOR

KAZ

KEN

KGZ

KHM

KOR

LAO

LBN

LCALKA

LSO

LTU

LUX

LVAMAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAMNER

NGA

NIC

NLDNOR

NPL

NZL

OMN

PAK

PANPER

PHL

PNG

POL

PRYROM

RUS

RWA

SAU

SDN

SEN

SLB

SLE

SLVSVK

SVN

SWE

SWZ

TCD

TGO

THA

TJK

TKM

TON

TTO

TUNTUR

TZA

UGA

UKR

URY

USA

UZB

VCT

VEN

VNM

VUT

WSM

YEM

ZAF

ZAR

ZMBZWE

5060

7080

Dis

ab

ility

Ad

just

ed

Life

Exp

ect

an

cy


Data Source: World Bank WDI, Institute for Health Metrics and Evaluation (IHME)

Disability Adjusted Life Expectancy vs Health Expenditure

(d.1) (d.2)

Figure 8: Health Efficiency Frontiers Single Input-Single Output

21

Several results may be highlighted:

a. The input-efficiency rankings of the health indicators are positively and significantly correlated, with the Spearman rank correlation coefficient oscillating between .19 and .93 (see Tables A.1 and A.3 in Appendix A). This indicates that the efficiency ranking is similar regardless of the output indicator being used.

b. Despite the orthogonalization by GDP, the richer countries tend to be more efficient (Table 3). The inefficient group has two types of countries: one small group of rich countries like the USA, Great Britain, and Switzerland which have high expenditure levels and not extremely high output (input inefficiency) and another much larger group of countries with relatively low spending but their output indicators could be substantially larger, like Lesotho, Malawi, and Sierra Leone. Other large African countries, Nigeria and Mozambique, appear in this group as well.

Table 3. Health Attainment: Single Input, Single Output

Input-Efficient Output Efficient More efficient Bahrain, Saudi Arabia, Oman,

Luxembourg, Trinidad and Tobago, Republic of Korea, Australia

Oman, Saudi Arabia, Bahrain, Luxembourg, Switzerland, Czech Republic, Finland, Italy

Least efficient Burundi, Mozambique, Democratic republic of Congo, Lesotho, Malawi, Switzerland, United States, Great Britain

Central African Republic, Sierra Leone, Nigeria, Lesotho, Malawi, Mozambique, Democratic Republic of Congo, Zimbabwe

Table 3A. Health Attainment: Single Input, Single Output Efficiency Scores

Input-Efficient Output Efficient More efficient .85 1.0 Least efficient .38 .77

c. The health efficiency rankings are closely correlated: the Spearman coefficient of output-efficiency ranking is .34 and the output one is .93 (see Tables A.2 and A.4 in Appendix A).

d. To examine the efficiency differences across groups of countries and the potential efficiency gains of moving closer to the frontier, countries were clustered into the least and most efficient (Tables 3 and 3A). The typical country of the least efficient group could maintain the same output levels with about 50% of the expenditure level. Or on the output side, the least efficient group could increase output levels by 35% for a given expenditure level (Table 3A) The divergence

22

between least and most efficient groups in output-efficiency is not as large as in education.

e. Output-efficiency scores are notoriously higher than the input-efficiency ones (Tables 4A and 4B). While the input-efficiency regional average oscillates between 40 and 50 percent, the output-efficiency average fluctuates between 90 and 95 percent, except in AFR where it is 85 percent. The large difference between the health input and output efficiency did not happen in the case of education, nor in previous studies based on data from the early 2000s. Comparing these results with efficiency scores of the early 2000s (Herrera-Pang, 2005), there seems to be a stagnation of input efficiency while output efficiency increased significantly, especially in AFR and SAS, which had the lowest achievement levels in the earlier studies.

Table 4A. Health Input-Efficiency scores by regions across the world


AFR EAP ECA LAC MNA SAS Immunization, measles .42 .46 .46 .46 .58 .44 Immunization .42 .55 .50 .47 .63 .47 Life Expectancy at birth .42 .48 .48 .48 .60 .44 DALE .49 .54 .54 .56 .65 .50 Free Tuberculosis Cases .42 .48 .48 .48 .61 .44 Infant Survival Rate .42 .48 .56 .47 .59 .44 Maternal Survival Rate .42 .46 .46 .46 .59 .44

Table 4B. Health Output-Efficiency scores by regions across the world

- Single Input-Single Output

AFR EAP ECA LAC MNA SAS Life Expectancy at birth .72 .87 .90 .90 .91 .85 Immunization DPT .82 .87 .95 .93 .94 .89 Immunization Measles .80 .87 .96 .93 .93 .85 DALE .73 .85 .90 .91 .92 .85 Tuberculosis free .99 1.0 1.0 1.0 1.0 1.0 Infant survival rate .95 .98 .99 .98 .98 .96 Maternal Survival rate 1.0 1.0 1.0 1.0 1.0 1.0

23

III.2.3. FDH and DEA Infrastructure Efficiency frontiers This section presents the single input (public capital expenditure per capita)- single output frontiers, using both the FDH and DEA methodologies, with six alternative output indicators: quality of overall infrastructure, quality of roads, quality of railroad infrastructure, quality of port infrastructure, quality of transport infrastructure, and quality of electricity supply. Figures 9a-c show the FDH efficiency frontier for three indicators. The specific country scores for all the infrastructure indicators and the DEA frontiers are can be found in Appendix A.7.

Free Disposable Hull (FDH) Data Envelopment

AGO

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN

BFABGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BTNBWA

CAN

CHE

CHL

CIV

CMR

COL

CPV

CRI

CZE

DEU

DOM DZAECU

EGY

EST

ETH

FINFRA

GAB

GBR

GEO

GHA

GIN

GMBGRC

GTM

GUYHND

HRV

HTI

IDN IND

IRL

IRN

ISL

ISR

ITA

JOR

JPN

KAZ

KENKHM

KOR

KWTLKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZL

OMN

PAK

PAN

PERPHL

POL

PRT

PRY

ROM

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SURSVK SWZ

SYC

TCD

THA

TJK

TTO

TUNTUR

TZA

UGA

UKRURY

USA

VEN

VNM

YEM

ZAF

ZMBZWE

AGO

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN

BFABGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BTNBWA

CAN

CHE

CHL

CIV

CMR

COL

CPV

CRI

CZE

DEU

DOM DZAECU

EGY

EST

ETH

FINFRA

GAB

GBR

GEO

GHA

GIN

GMBGRC

GTM

GUYHND

HRV

HTI

IDN IND

IRL

IRN

ISL

ISR

ITA

JOR

JPN

KAZ

KENKHM

KOR

KWTLKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZL

OMN

PAK

PAN

PERPHL

POL

PRT

PRY

ROM

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SURSVK SWZ

SYC

TCD

THA

TJK

TTO

TUNTUR

TZA

UGA

UKRURY

USA

VEN

VNM

YEM

ZAF

ZMBZWE

23

45

67

Qualit

y of O

vera

ll In

frast

ruct

ure

0 .5 1 1.5 2 2.5Log-Log Form: Orthogonalized Public Investment Per Capita

Data Source: World Economic Outlook-IMF/World Economic Forum

Quality of Overall Infrastructure vs Public Investment

AGO

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN

BFABGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BTNBWA

CAN

CHE

CHL

CIV

CMR

COL

CPV

CRI

CZE

DEU

DOM DZAECU

EGY

EST

ETH

FINFRA

GAB

GBR

GEO

GHA

GIN

GMBGRC

GTM

GUYHND

HRV

HTI

IDN IND

IRL

IRN

ISL

ISR

ITA

JOR

JPN

KAZ

KENKHM

KOR

KWTLKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZL

OMN

PAK

PAN

PERPHL

POL

PRT

PRY

ROM

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SURSVK SWZ

SYC

TCD

THA

TJK

TTO

TUNTUR

TZA

UGA

UKRURY

USA

VEN

VNM

YEM

ZAF

ZMBZWE

AGO

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN

BFABGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BTNBWA

CAN

CHE

CHL

CIV

CMR

COL

CPV

CRI

CZE

DEU

DOM DZAECU

EGY

EST

ETH

FINFRA

GAB

GBR

GEO

GHA

GIN

GMBGRC

GTM

GUYHND

HRV

HTI

IDN IND

IRL

IRN

ISL

ISR

ITA

JOR

JPN

KAZ

KENKHM

KOR

KWTLKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZL

OMN

PAK

PAN

PERPHL

POL

PRT

PRY

ROM

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SURSVK SWZ

SYC

TCD

THA

TJK

TTO

TUNTUR

TZA

UGA

UKRURY

USA

VEN

VNM

YEM

ZAF

ZMBZWE

23

45

67

Qualit

y of O

vera

ll In

frast

ruct

ure



Quality of Overall Infrastructure vs Public Investment

(a.1) (a.2)

AGO

ALB

ARE

ARGARM

AUS

AUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BTN

BWA

CANCHE

CHL

CIV

CMRCOL

CPV

CRI

CZE

DEU

DOM

DZA

ECUEGY

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHA

GIN

GMBGRC

GTM

GUYHND

HRV

HTI

IDN

INDIRL

IRN

ISL

ISR

ITA

JOR

JPN

KAZ

KEN

KHM

KOR

KWTLKA

LSO

LTU

LUX

LVAMAR

MDAMDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZLOMN

PAK

PAN

PERPHL POL

PRT

PRY

ROM

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SURSVK SWZ

SYC

TCD

THA

TJK

TTOTUN

TUR

TZA UGA

UKR URY

USA

VEN

VNM

YEM

ZAF

ZMBZWE

AGO

ALB

ARE

ARGARM

AUS

AUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BTN

BWA

CANCHE

CHL

CIV

CMRCOL

CPV

CRI

CZE

DEU

DOM

DZA

ECUEGY

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHA

GIN

GMBGRC

GTM

GUYHND

HRV

HTI

IDN

INDIRL

IRN

ISL

ISR

ITA

JOR

JPN

KAZ

KEN

KHM

KOR

KWTLKA

LSO

LTU

LUX

LVAMAR

MDAMDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZLOMN

PAK

PAN

PERPHL POL

PRT

PRY

ROM

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SURSVK SWZ

SYC

TCD

THA

TJK

TTOTUN

TUR

TZA UGA

UKR URY

USA

VEN

VNM

YEM

ZAF

ZMBZWE

23

45

67

Tra

nsp

ort

Infr

ast

ruct

ure



Transport Infrastructure vs Public Investment

AGO

ALB

ARE

ARGARM

AUS

AUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BTN

BWA

CANCHE

CHL

CIV

CMRCOL

CPV

CRI

CZE

DEU

DOM

DZA

ECUEGY

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHA

GIN

GMBGRC

GTM

GUYHND

HRV

HTI

IDN

INDIRL

IRN

ISL

ISR

ITA

JOR

JPN

KAZ

KEN

KHM

KOR

KWTLKA

LSO

LTU

LUX

LVAMAR

MDAMDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZLOMN

PAK

PAN

PERPHL POL

PRT

PRY

ROM

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SURSVK SWZ

SYC

TCD

THA

TJK

TTOTUN

TUR

TZA UGA

UKR URY

USA

VEN

VNM

YEM

ZAF

ZMBZWE

AGO

ALB

ARE

ARGARM

AUS

AUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BTN

BWA

CANCHE

CHL

CIV

CMRCOL

CPV

CRI

CZE

DEU

DOM

DZA

ECUEGY

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHA

GIN

GMBGRC

GTM

GUYHND

HRV

HTI

IDN

INDIRL

IRN

ISL

ISR

ITA

JOR

JPN

KAZ

KEN

KHM

KOR

KWTLKA

LSO

LTU

LUX

LVAMAR

MDAMDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZLOMN

PAK

PAN

PERPHL POL

PRT

PRY

ROM

RUS

RWA

SAU

SEN

SGP

SLE

SLV

SRB

SURSVK SWZ

SYC

TCD

THA

TJK

TTOTUN

TUR

TZA UGA

UKR URY

USA

VEN

VNM

YEM

ZAF

ZMBZWE

23

45

67

Tra

nspo

rt In

fra

stru

ctur

e



Transport Infrastructure vs Public Investment

(b.1) (b.2)

24

AGO

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN BFA

BGD

BGR

BHR

BIH

BLZBOL

BRA

BRBBTN

BWA

CANCHE

CHL

CIV

CMR

COL

CPV

CRI

CZEDEU

DOM

DZA

ECU

EGY

EST

ETH

FINFRA

GAB

GBR

GEO

GHA

GIN

GMB

GRCGTM

GUY

HND

HRV

HTI

IDN

IND

IRL

IRN

ISL

ISRITA JOR

JPN

KAZ

KEN

KHM

KOR

KWTLKA

LSO

LTU

LUX

LVAMAR

MDA

MDG

MEX

MLI

MMR

MNE

MOZ

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRT

PRY

ROMRUS

RWA

SAU

SEN

SGP

SLE

SLVSRB

SUR

SVK

SWZ

SYC

TCD

THA

TJK

TTOTUN

TUR

TZA UGA

UKR

URY

USA

VEN

VNM

YEM

ZAFZMB

ZWEAGO

ALB

ARE

ARG

ARM

AUS

AUT

AZE

BDI

BEL

BEN BFA

BGD

BGR

BHR

BIH

BLZBOL

BRA

BRBBTN

BWA

CANCHE

CHL

CIV

CMR

COL

CPV

CRI

CZEDEU

DOM

DZA

ECU

EGY

EST

ETH

FINFRA

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Quality of Electricity Supply vs Public Investment

(c.1) (c.2)

Figure 9: Infrastructure Frontiers Single Input-Single Output

As in previous cases, countries were clustered into the more efficient and least efficient groups (Table 5). The more efficient countries are the richer economies, with Chile included in that group. Within the inefficient group we find mostly African countries and a few LAC countries like Ecuador and Trinidad and Tobago. The discrepancy in efficiency between both groups is extraordinary (Table 5A) and much larger than in education or health.

Table 5. Infrastructure Single Input, Single Output)

Input-Efficient Output Efficient More efficient Germany, Belgium,

Switzerland, Singapore, United Kingdom, Austria, Chile, Finland, France.

Israel, United Araba Emirates, Germany, Slovenia, Switzerland, Netherlands, Great Britain, Iceland.

Least efficient Ecuador, Etiopia, Lesotho, Venezuela RB, Trinidad and Tobago, Angola, Botswana

Haiti, Tchad, Gabon, Uganda, Romania, Vietnam

Table 5A. Infrastructure Single Input, Single Output Efficiency Scores

Input-Efficient Output Efficient More efficient .68 .96 Least efficient .10 .32

Input-efficiency is extremely low, with the average regional input-efficiency score between 18% and 30% (Table 6A); AFR is the lower bound at 18% and EAP at 29%. Output-efficiency is significantly higher (Table 5B), indicating that capital spending

25

deviation from the frontier is larger than the output deviation from the frontier. This result could be explained due to the index of quality being used as output indicator (which is limited by nature of the construction from 1-7). Also, the computation of the frontiers including both developed and developing countries could be biasing the results, though when other inputs are included the problem is mitigated significantly. The efficiency scores are closely correlated with indicators of the regulatory quality, or the effectiveness of government (Table 7). This is especially true when output- efficiency indicators are used.20 These relationships are explored econometrically in the last section, when other factors are controlled for.

Table 6A Infrastructure: Input-Efficiency scores by regions across the world


AFR EAP ECA LAC MNA SAS

Quality of electricity supply 0.17 0.24 0.19 0.18 0.20 0.19

Transport infrastructure 0.17 0.33 0.18 0.19 0.25 0.20

Quality of air transport infrastructure 0.18 0.31 0.17 0.20 0.22 0.19

Quality of port infrastructure 0.19 0.32 0.19 0.22 0.24 0.20

Quality of railroad infrastructure 0.20 0.25 0.23 0.19 0.24 0.28

Quality of roads 0.18 0.30 0.18 0.20 0.24 0.19

Quality of overall infrastructure 0.17 0.26 0.18 0.19 0.22 0.19

Table 6B Infrastructure: Output-Efficiency scores by regions across the world





Quality of air transport infrastructure 0.57 0.71 0.62 0.66 0.71 0.60



Quality of roads 0.52 0.66 0.51 0.57 0.70 0.56


20 The overall infrastructure index is weakly correlated with ranking on the quality of the public investment management systems (Dabla-Norris, et. al.), probably due to the limited sample in the PIMS study.

26

Table 7 Correlation of Efficiency Rankings and Governance indicators.

III.3. Multiple Inputs and Multiple Outputs Education, health, and infrastructure attainment are not solely determined by public spending. Other inputs, such as private spending also affect the output indicators. For both health and capital spending, we could get data on private spending, but not for education.21 Hence the education production technology will have multiple indicators of educational attainment, and three possible inputs (public spending, teachers per pupil, and adult literacy rate). In health, besides public spending, three other inputs were included: private spending, improved sanitation condition, gross secondary school enrollment and the literacy of adults. The analysis was limited to include up to two inputs and two outputs to avoid the curse of dimensionality: increasing the number of output or input indicators biases efficiency scores towards one, increases the variance of the estimators, and reduces the speed of convergence to the true efficiency estimators (Simar and Wilson, 2000; Groskopff, 1996). III.3.1. DEA Education Frontiers The multi-input multi-output efficiency scores produce a ranking of countries like the single input-single output case (Table 8). Lebanon and Kazakhstan appear as efficient countries, close to the origin due to their low spending levels but low attainment levels. However, new countries appear within the more efficient group, such as Finland, the Islamic Republic of Iran and Spain. Within the least efficient countries, Norway appears as input-inefficient, as well as Brazil and Saudi Arabia. The least output efficient countries are mostly African counties, with Pakistan and Peru. The discrepancy in efficiency between the more efficient group and least efficient is lower than in the single input case, but still the typical country of the least efficient group could achieve the same output with 40% lower spending.

21 The source of health data is the WDI while for capital spending it is WEO.

Spearman Rank Correlation

Control of Corruption

Government Effectiveness

Political Stability and Absence of

Violence/Terrorism

Regulatory Quality

Rule of Law

Voice and Accountability

Input Efficiency

0.358*** (113)

0.374*** (113)

0.291*** (113)

0.439*** (113)

0.381*** (113)

0.378*** (113)

Output Efficiency

0.678*** (113)

0.752*** (113)

0.488*** (113)

0.720*** (113)

0.686*** (113)

0.420*** (113)

27

Table 8. Educational Attainment: Multiple Inputs, Multiple Outputs sample countries

Input-Efficient Output Efficient More efficient Lebanon, Panama, Bahrain,

Kazakhstan, Finland, China, Spain, Islamic Republic of Iran

Kazakhstan, Spain, Islamic Republic of Iran, Switzerland, Bahrain, Lebanon, Russian Federation

Least efficient Cyprus, Costa Rica, Norway, Iceland, Moldova, Brazil, Saudi Arabia

Mali, Senegal, Dominican Republic, Côte d’Ivoire, Angola, Mozambique, Sierra Leone, Pakistan, Peru.

Table 8A. Educational Attainment: Multiple Inputs, Multiple Outputs efficiency scores

Input-Efficient Output Efficient More efficient .99 .99 Least efficient .61 .56

When multiple inputs are considered, efficiency scores tend to rise. The regional aggregation for input and output efficiency scores shows (Tables 9A and 9B) that, as the model becomes more complex (adding inputs or outputs), scores rise due to the increasing uniqueness of the input-output bundle, and the relatively higher difficulty of finding peers. The first four rows of Table 8A allow gauging the impact on efficiency of adding literacy of adults as an additional input, by comparing scores with the single input case (Table 2A). In Africa and SAS, the change is dramatic, increasing from around 60 percent to the low 80s in AFR and low 90s in SAS. In EAP, ECA and MNA changes are of relatively lower importance, and in LAC the addition of the input is insignificant. This differential response to the inclusion of the additional input is due to the low levels of adult literacy in AFR and SAS, which yield gains in efficiency. In output efficiency there is no change in EAP and ECA, but in MENA and LAC the change is relevant with MNA being on the efficiency frontier (Table 8B, row 1 and Table 2B row 8). Individual country efficiency scores for the multiple inputs for education are reported in Table A.3 of Appendix A.

Table 9A. Education Attainment: Input-Efficiency scores by regions across the world

- Multiple Inputs, Multiple Outputs

AFR EAP ECA LAC MNA SAS 2 inputs (public expenditure, literacy of adult) – 1 outputs (Net primary enroll.)

0.84 0.83 0.78 0.76 0.82 0.91

2 inputs (public expenditure, literacy of adult) – 1 outputs (Net Secondary enroll.)

0.84 0.81 0.83 0.82 0.88 0.91

2 inputs (public expenditure, literacy of adult) – 1 outputs (Average Year of Schooling)

0.83 0.82 0.92 0.78 0.81 0.90

2 inputs (public expenditure, literacy of adult) – 1 outputs (Pisa Science)

0.96 0.91 0.91 1.00

2 inputs (public expenditure, teachers per pupil) – 2 outputs (Pisa Science & net primary enroll.)

0.92 0.86 0.89 0.88

28

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


AFR EAP ECA LAC MNA SAS 2 inputs (public expenditure, literacy of adult) – 1 outputs (Pisa Science)

0.91 0.87 0.80 1.00

2 inputs (public expenditure, literacy of adult) – 1 outputs (Net primary enroll.)

0.85 0.96 0.95 0.93 0.98 0.91

2 inputs (public expenditure, literacy of adult) – 1 outputs (Net Secondary enroll.)

0.57 0.71 0.81 0.74 0.81 0.76

2 inputs (public expenditure, literacy of adult) – 1 outputs (Average Year of Schooling)

0.69 0.73 0.92 0.74 0.79 0.82

2 inputs (public expenditure, teachers per pupil) – 2 outputs (Pisa Science & net primary enroll.)

0.98 0.95 0.96 0.98

III.3.2. DEA Health Multi Input-Output In health there are multiple possible 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)). Of the additional inputs, private sector health spending deserves special attention given its magnitude and pattern of use across countries. In many countries, private spending in health is larger than public spending, with the ratio of private to public spending being larger than one and decreasing with the level of GDP per capita (Fig 10). Hence, richer countries use this input less intensively than poorer countries, or, that poorer countries use more public financing relative to private to achieve health outcomes. There is a clear positive relationship between private spending in health and GDP per capita (Fig. 11), like the one displayed by public spending. Hence, we will use the orthogonalized component in the inputs.

29

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Ratio of Private to Public Health Spending as a Share of GDP vs GDP Per Capita

Figure 10 Ratio of Private to Public Health Spending and GDP per Capita

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Private Health Spending vs GDP Per Capita

Figure 11 Private Health Spending and GDP per Capita

30

Table 10. Health Attainment Country Clusters: Multiple Inputs-Multiple Outputs Sample of countries

Input-Efficient Output Efficient More efficient Oman, Bahrain, Cyprus,

Luxembourg, Saudi Arabia, Lebanon

Oman, Luxembourg, Cyprus, Iceland, Norway, Saudi Arabia, Bangladesh, Bahrain

Least efficient South Africa, Brazil, Georgia, Paraguay, Namibia, St. Lucia

South Africa, Lesotho, Zimbabwe, Angola, Sierra Leone

Table 10A. Health Attainment Country Clusters: Multiple Inputs-Multiple Outputs Efficiency scores

Input-Efficient Output Efficient More efficient 1.0 1.0 Least efficient .64 .84

As in previous cases, the countries were clustered into the most efficient and least efficient groups. In this more complex model, the more efficient group is directly on the frontier (score of 1) while the least efficient group on average scores .64 and .84 in input and output efficiency, respectively. The discrepancy between the groups narrows, but still the scope for expenditure reduction is about 30%. This is lower than in the education case.

31

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

- Multiple Inputs-Multiple Outputs

AFR EAP ECA LAC MNA SAS 2 inputs (public expenditure, Private expenditure) – 1 output (Immunization DPT)

0.41 0.52 0.46 0.40 0.59 0.44

2 inputs (public expenditure, Private expenditure) – 1 output (Maternal Survival Rate)

0.41 0.43 0.47 0.39 0.53 0.42

2 inputs (public expenditure, Private expenditure) – 1 output (Dale)

0.45 0.51 0.48 0.49 0.59 0.46

2 inputs (public expenditure, Improved Sanitation) – 1 output (Maternal Survival Rate)

0.85 0.83 0.83 0.82 0.90 0.84

2 inputs (public expenditure, Improved Sanitation) – 1 output (Dale)

0.86 0.79 0.72 0.82 0.88 0.80

2 inputs (public expenditure, Adult Literacy Rate) – 1 output (Immunization DPT)

0.80 0.76 0.65 0.64 0.83 0.86

2 inputs (public expenditure, Adult Literacy Rate) – 1 output (Maternal Survival Rate)

0.78 0.79 0.86 0.76 0.94 0.89

2 inputs (public expenditure, Adult Literacy Rate) – 1 output (Dale)

0.81 0.75 0.70 0.82 0.92 0.87

2 inputs (public expenditure, Improved Sanitation) – 2 outputs (Dale, Maternal Survival Rate)

0.88 0.86 0.89 0.85 0.86 0.84

2 inputs (public expenditure, Improved Sanitation) – 2 outputs (Dale, Infant Survival Rate)

0.87 0.82 0.84 0.84 0.84 0.83

2 inputs (public expenditure, Adult Literacy Rate) – 2 outputs (Dale, Maternal Survival Rate)

0.82 0.81 0.86 0.85 0.97 0.93

2 inputs (public expenditure, private expenditure) – 2 outputs (Dale, Infant Survival Rate)

0.94 0.98 0.99 0.98 0.98 0.96

Table 11B. Health Attainment: Output-Efficiency scores by regions across the world


AFR EAP ECA LAC MNA SAS 2 inputs (public expenditure, Private expenditure) – 1 outputs (Immunization, DPT)

0.81 0.87 0.95 .093 0.94 0.88

2 inputs (public expenditure, Improved Sanitation) – 1 outputs (Life Expectancy at Birth)

0.86 0.93 0.91 0.93 0.94 0.93

2 inputs (public expenditure, Improved Sanitation) – 1 outputs (Immunization, DPT)

0.86 0.88 0.95 0.92 0.97 0.90

2 inputs (public expenditure, Improved Sanitation) – 1 outputs (Dale)

0.88 0.91 0.91 0.94 0.95 0.93

2 inputs (public expenditure, Adult Literacy Rate) – 1 outputs (Immunization, DPT)

0.85 0.88 0.93 0.92 0.97 0.90

2 inputs (public expenditure, Adult Literacy Rate) – 1 outputs (Maternal Survival Rate)

1.0 1.0 1.0 1.0 1.0 1.0

2 inputs (public expenditure, Adult Literacy Rate) – 1 outputs (Dale)

0.84 0.90 0.91 0.95 0.98 0.94

2 inputs (public expenditure, Improved Sanitation) – 2 outputs (Dale, Maternal Survival Rate)

1.0 1.0 1.0 1.0 1.0 1.0

2 inputs (public expenditure, Adult Literacy Rate) – 2 outputs (Dale, Infant Survival Rate)

0.97 0.99 0.99 0.99 1.0 0.99

2 inputs (public expenditure, private expenditure) – 2 outputs (Dale, Infant Survival Rate)

0.94 0.98 0.99 0.98 0.98 0.96

32

The impact of private spending on efficiency scores is puzzling. The input efficiency scores remain unchanged (Table 11A, rows 1-3) compared to the single output case (Table 4A), though output efficiency increases (Tables 11B vs. 4B). But all the other combinations of inputs increase efficiency. For instance, when the adult literacy rate is included as an input, the AFR DALE efficiency increases from .49 to .81 and MNA efficiency increases from .65 to .92. Output efficiency increase as well, though not in the same proportion; from .73 to .84 in AFR and from .92 to .98 in MNA. Other additional inputs, such as improved sanitation conditions, have similar effects on efficiency measures. Similar results are obtained when the two input-two output models are considered, but efficiency scores increase when private health spending is considered. Table 11B shows that, on average, developing nations score between .84 and .89 in output efficiency in the multiple input-output framework. These figures imply that developing countries could raise their output levels by an average of 15 percent with the same input consumption, if they were as efficient as the comparable benchmark countries. This figure is simply indicative, as the estimate varies with the country and with the selected indicator and has a large variance across countries: for instance, the bottom decile of (input) efficiency scores is about .64, implying that the scope for increasing health and education attainment levels is between 3 or 4 times higher than for the whole sample average. Individual country efficiency scores for the multiple inputs for health are reported in Table A.6 of Appendix A. III.3.3. DEA Infrastructure Multi Input-Output In infrastructure we will focus on adding private capital spending as an input.to the existing production function of one input (quality of overall infrastructure). The private spending is generally larger than public capital spending, with the ratio being larger than one (Figure 12). However, there are cases where private spending is extremely low compared with public capital spending, such as Trinidad and Tobago, Venezuela, and Mozambique. The trend is for the ratio to increase with the level of GDP. Private capital spending is also positively correlated with GDP (Fig. 13). Hence, we will use the orthogonalized component. Countries were clustered into the most and least efficient (Table 12) and the efficiency scores of both groups show a marked increase in efficiency (Table 12A). The most efficient group lies on the frontier, while the least efficient group averages .28.

33

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Ratio of Private to Public Investment Spending vs GDP per Capita

Figure 12 Ratio of Private to Public Capital Spending

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Private Investment Spending vs GDP per Capita

Figure 13 Private Capital Spending per capita and GDP per capita

The regional aggregation shows that MNA and EAP have the highest efficiency scores, both input and output oriented (Table 13). Individual country efficiency scores for the multiple inputs for infrastructure are reported in Table A.8 of Appendix A.

34

Table 12. Capital Spending Multiple Inputs, Multiple Outputs

Input-Efficient Output Efficient More efficient Germany, Israel, Singapore,

Belgium, Switzerland, France, Finland, United Arab Emirates

Germany, Israel, United Arab Emirates, Barbados, Switzerland, Netherlands, Singapore

Least efficient Haiti, Bosnia-Herzegovina, Myanmar, Paraguay, Angola, Burundi, Sierra Leone, Venezuela RB

Haiti, Chad, Myanmar, Bosnia-Herzegovina, Paraguay, Bangladesh, Nigeria, Moldova, Lesotho

Table 12A. Capital Spending Multiple Inputs, Multiple Outputs

Input-Efficient Output Efficient More efficient .99 1.0 Least efficient .28 0.33

Table 13A. Infrastructure: Input-Efficiency scores by regions across the world





Quality of air transport infrastructure

0.49 0.57 0.49 0.56 0.60 0.46



Quality of roads 0.54 0.58 0.47 0.55 0.67 0.49


35

Table 13B. Infrastructure: Output-Efficiency scores by regions across the world






0.63 0.71 0.64 0.69 0.75 0.61



Quality of roads 0.59 0.66 0.52 0.60 0.74 0.57


IV. Covariates of Inefficiency Variation across Countries This chapter seeks to identify correlates of efficiency variation across countries. Efficiency scores were obtained in the first stage described up to this section. This two-stage approach seeks to identify statistically significant regularities common to efficient or inefficient countries. 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, as done in Herrera and Pang (2005). IV.1. Method, Variables and Data Description The cross-section consists of many countries (varying from 19 to 69 depending on the output indicator) and a single period, as we collapsed the 2009-2015 information into a period average for each variable. The dependent variable is the input efficiency score calculated by the DEA method in the first stage. Given that the dependent variable (the efficiency score) 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 input-oriented estimator reflects the consideration that input choices are more under the policy maker’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.22 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 (Gupta-Verhoeven 2001, Jarasuriya-Woodon 2003, and Afonso et.al. 2003 Herrera-Pang, 2005),

22 The precise definition and sources can be found in Appendix A, Table A.10.

36

others have found a positive association (Evans et.al. 2003) and others have found no significant impact (Filmer and Pritchett, 1999). 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 chose 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 Balassa-Samuleson 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 “first 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 makes 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 HIV/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 who are more likely to drop-out of school or repeat. All these factors reflect how AIDS affects 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,

37

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). 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 medium-term planning within 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 the World Bank Governance Indicators. 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 the 2009-2016 period for all variables. IV.2. Results The Tobit estimation on panel data is defined as follows.

),,,,,,

,,,(

CONSINSTEXTAIDGINIAIDSURBANGDPPC

PUBTOTGOVEXPWAGEfVRSTE

itititititit

itititit

where itVRSTE = Variable returns to scale DEA input or output efficiency scores.

itWAGE = Wages and salaries (% of total public expenditure)

itGOVEXP = Total government consumption expenditure (% of GDP)

itPUBTOT = Share of expenditures publicly financed (public/total)

itGDPPC = GDP per capita in constant 1995 US dollars

itURBAN = Urban population (% of total)

itAIDS = Dummy variable for HIV/AIDS

itGINI = Gini Coefficient

itEXTAID = External aid (% of fiscal revenue)

itINST = Institutional indicators WB Governance Indicators.

CONS = Constant

38

Tables 14, 15, and 16 show the results for the single-input single-output education, health and infrastructure scores, with input efficiency scores (a) as dependent variable and output efficiency scores (b), respectively. The findings show:

a. Government expenditure (GOVEXP) is negatively associated with efficiency scores in education (Tables 14 a and b). This result is robust to changes in the output indicator selected. In the output efficiency case, the impact is ambiguous specially when the PISA Math and Science scores are the output indicators (Table 14 b). In health (Tables 15 a and b), the negative association is present in both input and output efficiency. In infrastructure, the expenditure variables (GOVEXP and PUBGFC10PC) are negative in the six output indicators that are used (Table 16a).23 There is a robust trade-off between size of expenditure and efficiency.

b. The wage bill within total expenditure (WAGE) and efficiency is ambiguous in

education; it is positive and significant when the enrollment indicators (Table 14 a) are considered, but negative when the PISA scores are considered (Table 14b). WAGE is insignificant in health and positive in infrastructure.

c. The share of public financing within the total (sum of public and private) is

robustly associated with lower efficiency scores. In the case of education (EDUCPUBTOT) it is negatively associated when input efficiency is considered (Table 14a) and a similar result is obtained in health (HEAPUBTOT, Table 15a). In health, the share of public financing is positively associated with output efficiency, rendering this impact as ambiguous in health. Further research would be needed to explain why this is the case in health services. In infrastructure it is negative (Table 16A).

d. GDP per capita is positively associated with efficiency scores, indicating that efficiency scores are higher in richer countries despite the orthogonalization procedure (Tables 14a, 15a, 16a).

e. Urbanization is negatively associated with efficiency scores in education (Tables 13a and b), while it is positively associated in health. However, scores are unrelated to this variable.

f. The effect of HIV/AIDS is negative on both education and health (Tables 13b and 14b).

g. Income distribution (GINI) is negatively associated with efficiency in education

(table 14a) and health (Table 15b). However, there are cases of sign reversals that

23 The govexp variable is total consumption expenditure while PUBGFC10PC is capital spending per capita. Hence the infrastructure equations include both types of spending.

39

are puzzling, with the primary enrollment output efficiency scores being positively associated with GINI (Table 14b); in Health, GINI is positively associated with input efficiency in Measles Immunization and Maternal Survival (Table 15a) but negatively related with the output efficiency measures (Table 15b).

h. The external aid dependency ratio (EXTAID) is negatively associated with

efficiency in most education indicators, except with PISA output efficiency (Table 14b) where it is positive. In the case of health there is a negative association (Table 15b). 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.

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) which are not included here. 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 dimensionality24 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 decision-making 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 rate of adults. 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.

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

40

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 14 education output indicators, seven health output indicators, and seven infrastructure attainment indicators. Our results show that countries could achieve substantially higher education, health, and infrastructure quality levels: on average, developing countries reached output efficiency of about .6 in infrastructure, .8 in education, and .9 in health. That means that, for the same spending levels, peer countries achieve significantly higher outputs, with infrastructure being the area with more room for improvement. On the input side, efficiency is lower. Input efficiency scored .53, .75, and .86, for infrastructure, health, and education, respectively. These results indicate that developing countries deviate more from the frontier on the input (spending) side than on the output dimension. 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 service delivery. 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 education 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 countries and inefficient ones. 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. 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 Tobit regressions showed that the variables, which are negatively associated with efficiency scores, include the size of public expenditure, the proportion of the service that is publicly financed, the prevalence of the HIV/AIDS epidemic on health efficiency scores, income inequality on education efficiency scores, and external aid-financing on some of the efficiency scores.

41

Table 14A. Correlates of Cross-Country variation in Education Input efficiency

Note: ***0.01 significance level, ** 0.05 significance level, *0.10 significance level, and insignificant otherwise

VARIABLES Net Primary Enrollment

Gross Primary Enrollment

Gross Secondary Enrollment

Net Secondary Enrollment

Average Years of School

Literacy of Youth

First Level Complete

Secondary Level Complete

Math PISA Scores

Science PISA Scores

wage 0.30** 0.35** 0.21** 0.16** 0.12 0.344* 0.12 0.10 -0.037 0.14 govexp -1.502*** -1.534*** -0.619*** -0.733*** -1.172*** 1.043* -1.383*** -0.934*** -1.317 -1.591* educpubtot -0.434 -0.922** -0.968*** -0.630*** -0.24 -1.871*** -0.416 -0.238 -0.717 -0.692 gdppc 8.1e-06* 2.6e-05*** 1.6e-05*** 1.2e-05*** 9.5e-06** -7.8E-06 1.93e-05*** 1.27e-05*** 9.7E-06 8.7E-06 urban -0.002 -0.0035** -0.0001 -0.0012 -0.0025** 0.0056** -0.0041*** -0.0024*** 0.00013 0.0022 aids 0.00491 0.068 0.0238 0.0339 0.0404 0.220*** 0.0554 0.0306 gini -0.0028 7.5E-05 -0.003* -0.003** -0.007*** -0.01*** -0.001 -0.0046*** 0.002 -0.0002 aid 0.000256 0.00428 -0.0203** -0.00687** -0.00738*** -0.00505 -0.00648 -0.00642*** 0.407* 0.362* Constant 0.999*** 0.550** 0.676*** 0.788*** 1.049*** 0.347 0.877*** 0.871*** 0.636 0.482

Observations 56 45 46 57 42 54 53 53 19 19

42

Table 14B. Correlates of Cross-Country variation in Education Output efficiency


VARIABLES Net Primary Enrollment




Average Years of School

Literacy of Youth

First Level Complete

Second Level Complete

Math PISA Scores

Science PISA Scores

wage ‐0.121 ‐0.0124 ‐0.133 ‐0.0687 ‐0.219 ‐0.19 ‐0.00286 ‐0.304 ‐0.388*** ‐0.333***

govexp ‐0.251 ‐0.633** 0.475 0.610* 0.596* 0.509 ‐0.0435 1.091* 0.609** 0.439*

educpubtot ‐0.184 ‐0.556* 0.0138 ‐0.507 ‐0.216 ‐0.182 ‐0.21 ‐0.534 0.653* 0.331

gdppc 1.91E‐07 3.43E‐06 1.80e‐05*** 1.32e‐05*** 1.22e‐05** 1.12e‐05** ‐1.73E‐07 1.19E‐05 1.96e‐05*** 1.49e‐05***

urban ‐0.00017 ‐0.00188* ‐0.00035 0.000197 0.000425 ‐0.00091 ‐0.00245 0.000623 ‐0.00330** ‐0.00244*

aids ‐0.0890*** ‐0.0644* ‐0.145*** ‐0.173*** ‐0.0867* ‐0.142*** ‐0.0923 ‐0.041

gini 0.00351*** 0.00474*** ‐0.00153 ‐0.00664*** ‐0.00276 0.00415** 0.0066 ‐0.00779** ‐0.00218 ‐0.00058

aid 0.00700** 0.00438 ‐0.00362 ‐0.0340*** ‐0.0068 ‐0.0047 0.00173 ‐0.0103 0.376*** 0.283**

Constant 0.916*** 0.856*** 0.490*** 0.870*** 0.624*** 0.722*** 0.267 0.597*** 0.688*** 0.739***

Observations 58 61 59 48 54 55 54 54 19 19

43

Table 15A. Correlates of Cross-Country variation in HEALTH INPUT efficiency

VARIABLES Life Expectancy

Disability Adjusted Life Expectancy

Immunization DPT

Immunization Measles

Free Tuberculosis

Infant Survival Rate

Maternal Survival Rate

wage 0.0455 0.0684 0.0329 -0.0239 0.0406 -0.0794* -0.0226 govexp -0.436*** -0.421** -0.107 -0.231*** -0.0885 -0.190* -0.209*** heapubtot -0.105 0.106 -0.333 -0.296*** -0.160* -0.213** -0.312*** gdppc 6.50e-06*** 5.60e-06* 5.4E-06 6.44e-06*** 4.75e-06*** 1.14e-05*** 6.26e-06*** urban 0.000567 0.000925 -0.00054 -3.9E-05 0.00186*** 0.000492 9.09E-06 aids 0.0114 -0.00872 -0.0424 0.00163 -0.00517 0.000387 0.000924 gini -0.00053 0.000577 0.00129 0.000763* 0.000273 8.77E-05 0.000801** aid -0.00033 -4.2E-05 -0.0012 -0.00088 -0.00149 -0.00162 -0.00078 Constant 0.458*** 0.449*** 0.473*** 0.415*** 0.300*** 0.356*** 0.407***

Observations 67 67 67 67 62 64 67


44

Table 15B. Correlates of Cross-Country variation in HEALTH OUTPUTefficiency


VARIABLES Life Expectancy

Disability Adjusted Life Expectancy

Immunization DPT

Immunization Measles

Free Tuberculosis



wage 0.0189 0.0814** ‐0.0125 ‐0.08 0.00275 ‐0.0134 ‐0.00143

govexp ‐0.262*** ‐0.402*** 0.352 0.369 ‐0.00890** ‐0.0144 0.000172

heapubtot 0.294*** 0.266** 0.0612 0.29 0.00444 0.0918*** 0.000682

gdppc 9.48E‐07 1.68E‐06 5.11E‐06 5.56E‐06 ‐9.50E‐08 4.97E‐07 1.03E‐08

urban 0.000585 0.00131*** ‐0.00126 ‐0.00091 3.91e‐05** 6.34E‐05 8.60E‐07

aids ‐0.111*** ‐0.0948*** ‐0.0788** ‐0.0689* ‐0.00128* ‐0.0209*** ‐0.00261***

gini ‐0.00158** ‐0.00226*** 0.000412 ‐0.00029 ‐0.000121*** ‐0.00027 ‐4.73E‐06

aid ‐0.00422*** ‐0.00172 0.00246 0.00109 ‐0.0001 ‐0.00165*** ‐0.000342***

Constant 0.912*** 0.892*** 0.883*** 0.859*** 1.003*** 0.982*** 1.000***

Observations 69 69 69 69 63 69 69

45

Table 16A Correlates of cross-country variation in Capital Spending Input efficiency


VARIABLES Quality of overall infrastructure

Quality of roads Quality of railroad infrastructure

Quality of port infrastructure


Quality of electricity supply

wage 18.55* 19.21* 5.137 25.25** 11.31 16.74**

govexp ‐39.17* ‐39.59 ‐37.47* ‐45.27* ‐26.91 ‐35.98**

pubshare ‐0.183* ‐0.163 ‐0.308*** ‐0.168 ‐0.191* ‐0.235***

pubgfcp10pc ‐0.000353*** ‐0.000419*** ‐0.000202* ‐0.000461*** ‐0.000396*** ‐0.000291***

gdppc10 1.73e‐05** 2.86e‐05*** 9.76E‐06 2.42e‐05*** 1.89e‐05** 1.38e‐05**

urban 0.000526 ‐0.0002 ‐0.00011 0.000439 0.000532 0.000109

gini 0.00218 0.00247 0.00258* 0.00329** 0.00438*** 0.00155

aid 0.00155 0.00197 0.00161 0.00158 0.00285 0.000551

Voice and Accountability ‐0.0191 ‐0.0185 ‐0.0242 ‐0.0228 ‐0.0161 ‐0.00367

Rule of law 0.129** 0.131 0.115* 0.137* 0.149** 0.072

Regulatory Quality ‐0.0137 ‐0.00223 ‐0.0318 ‐0.00663 ‐0.0251 ‐0.0175

Political Stability and Absence of Violence

‐0.0272 ‐0.0296 ‐0.0115 ‐0.0322 ‐0.0311 ‐0.0227


0.00553 0.00565 ‐0.0125 0.0246 0.0378 0.00317

Control of Corruption ‐0.0627 ‐0.0605 ‐0.0463 ‐0.0696 ‐0.109** ‐0.0407

Constant 0.140* 0.142 0.248*** 0.0971 0.054 0.190***

Observations 58 58 53 58 58 58

46

Table 16B. Correlates of cross-country variation in Capital Spending Output efficiency


VARIABLES Quality of overall infrastructure

Quality of roads Quality of railroad infrastructure

Quality of port infrastructure


Quality of electricity supply

wage 1.797 20.85* ‐42.68** 21.80* 10.26 10.96

govexp 4.265 ‐39.83 49.14 ‐28.22 ‐54.46** 53.28*

pubshare ‐0.291*** ‐0.220* ‐0.412** ‐0.191 ‐0.0434 ‐0.155

pubgfcp10pc 5.01E‐05 4.85E‐05 0.000317 ‐5.75E‐05 ‐0.00017 ‐0.0002

gdppc10 ‐1.82E‐06 5.84E‐06 ‐1.81E‐05 2.70E‐06 1.87E‐06 1.23E‐05

urban 0.000218 ‐0.00131 0.000193 0.000241 0.00111 0.00224**

gini 4.27E‐05 0.00165 ‐0.00014 0.00165 0.000841 ‐0.00282

aid 0.00145 0.0037 ‐0.00112 0.000419 ‐0.00536 ‐0.00082

Voice and Accountability ‐0.100*** ‐0.0914*** ‐0.0708* ‐0.0331 ‐0.0619** ‐0.123***

Rule of law ‐0.0581 ‐0.113 0.0839 0.00519 0.0229 ‐0.126

Regulatory Quality 0.0491 0.019 ‐0.0954* 0.0164 0.113*** 0.0905*

Political Stability and Absence of Violence

0.0222 0.00201 0.0376 ‐0.0162 ‐0.0152 0.0107


0.146** 0.229*** 0.141 0.112 0.135* 0.218**

Control of Corruption 0.056 0.0948 ‐0.0865 ‐0.00461 ‐0.0629 0.085

Constant 0.654*** 0.624*** 0.577*** 0.544*** 0.652*** 0.546***

Observations 63 63 59 63 63 63

47

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51

Appendix A. International Benchmarking Exercise

Table A.1 Efficiency Score for Selected Education Indicators (Single Input - Output)

PISA Math Score PISA Science Score

Input Efficiency Output Efficiency Input Efficiency Output Efficiency

dmu FDH DEA FDH DEA FDH DEA FDH DEA

ALB 0.678 0.678 0.738 0.738 0.773 0.688 0.774 0.774

ARG 0.697 0.629 0.789 0.789 0.697 0.650 0.829 0.824

AUS 0.878 0.825 0.942 0.942 0.947 0.910 0.984 0.981

AUT 0.765 0.703 0.928 0.928 0.715 0.710 0.936 0.929

AZE 0.999 0.915 0.886 0.885 0.876 0.876 0.769 0.769

BGR 0.831 0.766 0.824 0.813 0.831 0.780 0.860 0.841

BRA 0.579 0.579 0.713 0.713 0.660 0.582 0.762 0.756

CAN 0.877 0.862 0.974 0.974 1.000 1.000 1.000 1.000

CHE 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

CHL 0.798 0.720 0.788 0.788 0.798 0.754 0.848 0.847

CHN 0.959 0.924 0.993 0.993 0.959 0.918 0.983 0.981

COL 0.633 0.633 0.720 0.720 0.721 0.642 0.774 0.771

CRI 0.530 0.530 0.748 0.748 0.604 0.547 0.794 0.783

CYP 0.548 0.507 0.817 0.817 0.548 0.506 0.819 0.802

CZE 1.000 0.973 1.000 0.981 1.000 1.000 1.000 1.000

DEU 0.973 0.927 0.966 0.961 1.000 0.989 1.000 0.996

DNK 0.420 0.398 0.948 0.948 0.420 0.400 0.923 0.923

DOM 0.745 0.745 0.621 0.615 0.745 0.745 0.644 0.636

ESP 0.870 0.868 0.918 0.914 0.910 0.889 0.959 0.949

FIN 0.567 0.565 0.983 0.983 1.000 1.000 1.000 1.000

FRA 0.768 0.704 0.925 0.925 0.768 0.719 0.940 0.933

GBR 0.706 0.686 0.920 0.920 0.776 0.756 0.968 0.961

GEO 0.699 0.699 0.755 0.755 0.797 0.712 0.780 0.779

HRV 0.813 0.783 0.864 0.864 0.813 0.808 0.912 0.911

HUN 0.798 0.794 0.903 0.903 0.834 0.814 0.930 0.928

IDN 0.679 0.679 0.708 0.708 0.679 0.679 0.744 0.743

ISL 0.512 0.472 0.930 0.930 0.458 0.458 0.894 0.894

ITA 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

KAZ 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

KGZ 0.571 0.571 0.619 0.619 0.571 0.571 0.625 0.618

KOR 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

LBN 0.913 0.913 0.978 0.860 0.913 0.913 0.966 0.844

LTU 0.772 0.762 0.893 0.893 0.772 0.770 0.915 0.914

LVA 0.665 0.661 0.901 0.901 0.696 0.684 0.931 0.923

MDA 0.614 0.552 0.784 0.784 0.614 0.564 0.810 0.799

MEX 0.707 0.629 0.773 0.773 0.707 0.636 0.787 0.783

52

Table A.1 (continued)

PISA Math Score PISA Science Score



MKD 0.653 0.653 0.694 0.694 0.653 0.653 0.726 0.724

NLD 0.789 0.772 0.970 0.970 0.851 0.805 0.975 0.970

NOR 0.482 0.448 0.934 0.934 0.482 0.457 0.921 0.921

NZL 0.607 0.575 0.948 0.948 0.655 0.642 0.989 0.968

PAN 0.855 0.855 0.740 0.727 0.855 0.855 0.776 0.765

PER 0.704 0.704 0.702 0.702 0.704 0.704 0.727 0.725

POL 0.855 0.794 0.934 0.934 0.855 0.830 0.955 0.954

ROM 0.955 0.882 0.884 0.866 0.955 0.881 0.869 0.869

RUS 0.915 0.907 0.976 0.927 0.915 0.912 0.972 0.953

SVK 0.946 0.945 0.987 0.960 0.946 0.933 0.958 0.953

SVN 0.764 0.720 0.945 0.945 0.785 0.768 0.970 0.963

SWE 0.524 0.479 0.923 0.923 0.490 0.486 0.911 0.911

THA 0.781 0.700 0.780 0.780 0.781 0.711 0.801 0.801

TTO 0.715 0.639 0.777 0.777 0.715 0.645 0.790 0.786

TUN 0.556 0.556 0.689 0.689 0.556 0.556 0.745 0.737

TUR 0.782 0.719 0.809 0.809 0.782 0.731 0.832 0.832

URY 0.716 0.647 0.790 0.790 0.716 0.660 0.816 0.812

USA 0.805 0.795 0.894 0.894 0.900 0.852 0.947 0.946

VNM 0.756 0.692 0.924 0.924 0.815 0.807 0.993 0.985

53

Table A.2 Efficiency Score for Selected Education Indicators (Single Input - Output)

School enrollment, primary (% gross) Secondary Education Gross

Enrollment Input Efficiency Output Efficiency Input Efficiency Output Efficiency

Country FDH DEA FDH DEA FDH DEA FDH DEA

AGO 0.985 0.715 0.991 0.840 0.613 0.613 0.170 0.170 ALB 0.645 0.645 0.926 0.776 0.651 0.651 0.639 0.588 ARG 0.910 0.617 0.778 0.778 0.571 0.571 0.644 0.644 ARM 0.642 0.642 0.883 0.738 0.647 0.647 0.740 0.677 ATG 0.944 0.944 0.913 0.890 AUS 0.614 0.614 0.887 0.757 1.000 1.000 1.000 1.000 AUT 0.526 0.526 0.690 0.690 0.648 0.549 0.708 0.708 AZE 0.865 0.865 0.909 0.851 0.867 0.867 0.992 0.892 BDI 0.966 0.776 0.886 0.886 0.550 0.550 0.172 0.172 BEN 0.964 0.696 0.831 0.831 0.559 0.559 0.295 0.295 BFA 0.554 0.554 0.566 0.566 0.559 0.559 0.156 0.156 BGD 0.877 0.613 0.920 0.780 0.596 0.596 0.347 0.347 BGR 0.695 0.695 0.920 0.771 0.701 0.701 0.740 0.721 BLR 0.584 0.584 0.836 0.704 BLZ 0.772 0.555 0.768 0.768 0.527 0.527 0.540 0.540 BOL 0.513 0.513 0.690 0.690 0.519 0.519 0.575 0.575 BRA 0.928 0.671 0.831 0.831 0.756 0.571 0.726 0.726 BRB 0.529 0.529 0.670 0.670 0.750 0.577 0.736 0.736 BWA 0.413 0.413 0.737 0.737 0.421 0.421 0.576 0.576 CAF 0.560 0.560 0.626 0.626 CAN 0.614 0.614 0.835 0.712 0.875 0.663 0.728 0.728 CHE 0.687 0.687 0.943 0.786 0.703 0.703 0.764 0.747 CHL 0.655 0.655 0.900 0.760 0.662 0.662 0.718 0.669 CHN 0.666 0.666 0.960 0.819 0.672 0.672 0.654 0.617 CIV 0.557 0.557 0.580 0.580 0.563 0.563 0.199 0.199

CMR 0.856 0.580 0.907 0.758 0.582 0.582 0.304 0.304 COG 0.791 0.564 0.765 0.765 COL 0.994 0.717 0.988 0.839 0.600 0.600 0.663 0.663 COM 0.551 0.551 0.717 0.717 CPV 0.822 0.588 0.767 0.767 0.560 0.560 0.609 0.609 CRI 0.782 0.529 0.777 0.777 0.592 0.493 0.700 0.700 CYP 0.422 0.422 0.689 0.689 0.431 0.431 0.688 0.688 CZE 0.793 0.793 0.926 0.831 0.801 0.801 0.940 0.816 DEU 0.688 0.688 0.948 0.791 0.979 0.740 0.814 0.794 DJI 0.563 0.563 0.449 0.449

DNK 0.278 0.278 0.689 0.689 0.400 0.377 0.859 0.859 DOM 0.720 0.720 0.958 0.818 0.724 0.724 0.759 0.610

54

Table A.2 (continued) School enrollment, primary (% gross) Secondary Education Gross Enrollment

Input Efficiency Output Efficiency Input Efficiency Output Efficiency Country FDH DEA FDH DEA FDH DEA FDH DEA

ECU 0.935 0.644 0.950 0.796 0.585 0.585 0.585 0.585 EGY 0.636 0.636 0.927 0.771 0.641 0.641 0.645 0.586 ESP 0.706 0.706 0.963 0.814 1.000 0.989 1.000 0.991 EST 0.585 0.585 0.837 0.706 0.830 0.640 0.737 0.737 ETH 0.551 0.551 0.657 0.657 0.557 0.557 0.234 0.234 FIN 0.384 0.384 0.685 0.685 0.551 0.456 0.778 0.778 FJI 0.600 0.600 0.885 0.745

FRA 0.536 0.536 0.731 0.731 0.764 0.658 0.801 0.801 GAB 0.805 0.805 0.533 0.465 GBR 0.525 0.525 0.731 0.731 0.646 0.546 0.707 0.707 GEO 1.000 0.736 1.000 0.858 0.678 0.678 0.741 0.704 GHA 0.532 0.532 0.736 0.736 0.538 0.538 0.421 0.421 GIN 0.558 0.558 0.597 0.597 0.564 0.564 0.280 0.280

GMB 0.560 0.560 0.586 0.586 0.565 0.565 0.380 0.380 GNB 0.844 0.620 0.922 0.780 GTM 0.943 0.651 0.971 0.808 0.640 0.640 0.483 0.438 GUY 0.616 0.616 0.721 0.616 0.748 0.639 0.712 0.712 HND 0.791 0.575 0.772 0.772 0.539 0.539 0.494 0.494 HRV 0.670 0.670 0.839 0.718 0.677 0.677 0.764 0.725 HTI 0.576 0.576 0.317 0.317

HUN 0.651 0.651 0.890 0.750 0.794 0.671 0.792 0.736 IDN 0.649 0.649 0.945 0.794 0.654 0.654 0.629 0.581 IND 0.592 0.592 0.904 0.767 0.598 0.598 0.444 0.444 IRN 0.752 0.752 0.993 0.868 0.757 0.757 0.851 0.708 ISL 0.344 0.344 0.675 0.675 0.494 0.412 0.782 0.782 ITA 0.821 0.821 0.930 0.849 1.000 0.860 1.000 0.894 JAM 0.533 0.533 0.636 0.636 KAZ 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 KEN 0.812 0.592 0.773 0.773 0.552 0.552 0.426 0.426 KGZ 0.536 0.536 0.700 0.700 0.542 0.542 0.616 0.616 KHM 1.000 0.740 1.000 0.853 0.602 0.602 0.308 0.308 KNA 0.971 0.971 0.806 0.796 KOR 0.668 0.668 0.884 0.756 0.678 0.678 0.779 0.740 LAO 1.000 0.739 1.000 0.854 0.621 0.621 0.340 0.340 LBN 0.913 0.913 0.909 0.872 0.913 0.913 0.814 0.761 LKA 0.757 0.757 0.913 0.800 0.760 0.760 0.925 0.772 LSO 0.521 0.521 0.744 0.744 0.527 0.527 0.330 0.330 LTU 0.626 0.626 0.886 0.731 0.888 0.662 0.806 0.727 LVA 0.532 0.532 0.704 0.704 0.541 0.541 0.694 0.694

55



MAR 0.914 0.623 0.927 0.781 0.571 0.571 0.440 0.440 MDA 0.500 0.500 0.638 0.638 0.507 0.507 0.628 0.628 MDG 1.000 1.000 1.000 1.000 0.569 0.569 0.231 0.231 MEX 0.576 0.576 0.859 0.718 0.583 0.583 0.631 0.631 MKD 0.616 0.616 0.744 0.635 0.623 0.623 0.596 0.596 MLI 0.562 0.562 0.545 0.545 0.567 0.567 0.278 0.278

MNG 0.916 0.664 0.969 0.812 0.623 0.623 0.669 0.669 MOZ 0.543 0.543 0.723 0.723 0.548 0.548 0.172 0.172 MRT 0.585 0.585 0.803 0.677 0.590 0.590 0.185 0.185 MUS 0.699 0.699 0.941 0.792 0.705 0.705 0.718 0.704 MWI 0.975 0.908 0.960 0.960 0.555 0.555 0.229 0.229 MYS 0.553 0.553 0.693 0.693 0.562 0.562 0.491 0.491 NAM 0.441 0.441 0.746 0.746 0.448 0.448 0.457 0.457 NER 0.549 0.549 0.461 0.461 NGA 0.643 0.643 0.783 0.655 0.647 0.647 0.307 0.281 NIC 1.000 0.742 1.000 0.851 0.580 0.580 0.492 0.492 NLD 0.543 0.543 0.731 0.731 0.776 0.755 0.881 0.881 NOR 0.315 0.315 0.681 0.681 0.454 0.388 0.795 0.795 NPL 0.997 0.916 0.953 0.953 0.567 0.567 0.363 0.363 NZL 0.417 0.417 0.680 0.680 0.596 0.556 0.852 0.852 OMN 0.751 0.751 0.985 0.860 0.764 0.764 0.941 0.788 PAK 0.612 0.612 0.780 0.664 0.617 0.617 0.247 0.247 PAN 0.836 0.836 0.968 0.891 0.839 0.839 0.758 0.665 PER 0.675 0.675 0.963 0.795 0.680 0.680 0.735 0.699 PHL 0.955 0.691 0.992 0.831 0.648 0.648 0.671 0.615 POL 0.620 0.620 0.878 0.720 0.757 0.634 0.787 0.704 PRY 0.586 0.586 0.863 0.728 0.592 0.592 0.482 0.482 ROM 0.813 0.813 0.872 0.793 0.818 0.818 0.933 0.824 RUS 0.762 0.762 0.916 0.806 0.769 0.769 0.885 0.746 RWA 0.979 0.894 0.949 0.949 0.557 0.557 0.220 0.220 SAU 0.458 0.458 0.706 0.706 0.657 0.509 0.740 0.740 SDN 0.613 0.613 0.585 0.498 SEN 0.537 0.537 0.557 0.557 0.543 0.543 0.253 0.253 SLB 0.815 0.559 0.782 0.782 SLE 1.000 0.782 1.000 0.873 0.569 0.569 0.239 0.239 SLV 1.000 0.712 1.000 0.840 0.624 0.624 0.473 0.473 SVK 0.788 0.788 0.930 0.832 0.795 0.795 0.922 0.797 SVN 0.541 0.541 0.674 0.674 0.550 0.550 0.690 0.690 SWE 0.522 0.355 0.750 0.750 0.505 0.376 0.719 0.719

56



SWZ 0.765 0.562 0.776 0.776 0.522 0.522 0.411 0.411 TCD 0.575 0.575 0.745 0.622 0.580 0.580 0.161 0.161 TGO 0.977 0.747 0.863 0.863 THA 0.648 0.648 0.869 0.731 0.655 0.655 0.647 0.598 TJK 0.560 0.560 0.675 0.675 0.565 0.565 0.611 0.611

TKM 0.711 0.711 0.818 0.694 TTO 0.566 0.566 0.863 0.728 0.577 0.577 0.627 0.627 TUN 0.763 0.519 0.750 0.750 0.521 0.521 0.649 0.649 TUR 0.641 0.641 0.900 0.752 0.649 0.649 0.659 0.604 TZA 0.562 0.562 0.622 0.622 0.568 0.568 0.214 0.214 UGA 0.918 0.642 0.937 0.793 0.574 0.574 0.190 0.190 UKR 0.510 0.510 0.715 0.715 0.517 0.517 0.682 0.682 URY 0.863 0.601 0.919 0.771 0.589 0.589 0.646 0.646 USA 0.620 0.620 0.881 0.723 0.634 0.634 0.757 0.681 VCT 0.552 0.552 0.713 0.713 VEN 0.591 0.591 0.840 0.712 0.599 0.599 0.600 0.600 VNM 0.558 0.558 0.728 0.728 0.564 0.564 0.515 0.515 VUT 0.923 0.673 0.819 0.819 ZAF 0.527 0.527 0.666 0.666 0.643 0.536 0.700 0.700 ZAR 0.557 0.557 0.683 0.683 ZWE 0.533 0.533 0.691 0.691 0.539 0.539 0.300 0.300

57

Table A.3 Efficiency Score for Selected Education Indicators (Two Inputs – Single Output)

Input (Public Expenditure‐ Adult Literacy Rate)

Primary Net Enrollment Secondary Net Enrollment



AGO 0.962 0.847 0.873 0.824 0.963 0.838 0.545 0.306

ALB 0.869 0.798 0.941 0.937 0.940 0.833 0.933 0.766

AZE 1.000 0.942 1.000 0.970

BDI 0.947 0.820 0.931 0.926 0.904 0.792 0.477 0.346

BEN 0.984 0.964 0.959 0.958 1.000 1.000 1.000 1.000

BFA 0.951 0.910 0.660 0.660 0.973 0.910 0.585 0.478

BGD 1.000 0.948 1.000 0.917 1.000 0.957 1.000 0.839

BGR 0.904 0.845 0.965 0.962 0.927 0.854 0.961 0.794

BLR 0.816 0.679 0.930 0.930 0.983 0.845 0.840 0.840

BOL 0.757 0.656 0.906 0.904 0.911 0.731 0.851 0.692

BRA 0.740 0.654 0.941 0.938 1.000 0.811 1.000 0.822

CAF 0.975 0.921 0.696 0.695 0.974 0.918 0.361 0.284

CHL 0.868 0.750 0.944 0.940 0.938 0.832 0.976 0.767

CHN 1.000 1.000 1.000 1.000 0.928 0.825 0.913 0.753

CIV 0.947 0.875 0.709 0.708 0.969 0.894 0.722 0.655

CMR 0.923 0.815 0.954 0.899 0.964 0.801 0.746 0.533

COG 0.804 0.719 0.917 0.909 0.874 0.710 0.685 0.513

COL 0.877 0.719 0.925 0.922 1.000 0.861 1.000 0.817

COM 0.957 0.861 0.826 0.823 1.000 0.908 1.000 0.833

CPV 0.856 0.766 0.973 0.971 1.000 0.833 1.000 0.790

CRI 0.727 0.623 0.980 0.976 1.000 0.850 1.000 0.868

CYP 0.551 0.504 0.982 0.982 0.923 0.717 0.756 0.756

DOM 1.000 0.848 1.000 0.878 0.999 0.862 0.898 0.731

ECU 0.873 0.742 0.952 0.949 0.995 0.859 1.000 0.820

EGY 1.000 0.904 1.000 0.980 1.000 0.900 1.000 0.848

ESP 1.000 0.867 1.000 0.997 1.000 1.000 1.000 1.000

EST 0.771 0.677 0.963 0.963 0.980 0.828 0.847 0.847

GEO 0.953 0.952 1.000 0.998 0.916 0.873 0.984 0.828

GHA 0.816 0.742 0.858 0.852 0.914 0.750 0.848 0.592

GIN 1.000 0.958 1.000 0.799 1.000 0.992 1.000 0.856

GMB 0.965 0.890 0.704 0.702 1.000 0.963 1.000 0.928

GNB 0.989 0.890 0.965 0.691

GTM 1.000 0.841 1.000 0.920 1.000 0.835 1.000 0.642

GUY 0.961 0.780 0.816 0.814 1.000 0.883 1.000 0.829

HND 0.857 0.732 0.961 0.960 0.857 0.725 0.796 0.626

HRV 0.851 0.728 0.880 0.880 0.997 0.850 0.976 0.799

58






IDN 0.898 0.797 0.924 0.921 0.898 0.803 0.812 0.694

IND 0.939 0.836 0.975 0.919 1.000 0.854 1.000 0.742

IRN 1.000 1.000 1.000 1.000 1.000 0.917 1.000 0.876

ITA 0.891 0.854 0.977 0.977 1.000 0.891 1.000 0.850

KAZ 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

KEN 0.855 0.741 0.851 0.843 0.910 0.746 0.759 0.572

KGZ 0.812 0.673 0.883 0.883 0.860 0.747 0.691 0.691

KHM 0.997 0.871 0.982 0.962

LAO 1.000 0.926 1.000 0.945 1.000 0.903 1.000 0.673

LBN 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.739

LKA 1.000 0.993 1.000 0.995 1.000 0.983 1.000 0.974

LSO 0.798 0.709 0.807 0.801 0.835 0.713 0.647 0.501

LTU 0.845 0.733 0.971 0.971 0.981 0.832 0.811 0.811

LVA 0.737 0.649 0.974 0.974 0.980 0.789 0.827 0.827

MAR 0.951 0.824 0.973 0.967 1.000 0.829 1.000 0.724

MDA 0.742 0.629 0.878 0.878 0.861 0.715 0.688 0.688

MDG 0.905 0.795 0.662 0.385

MEX 0.819 0.709 0.958 0.954 0.898 0.751 0.851 0.694

MLI 0.968 0.933 0.621 0.621 0.995 0.972 0.895 0.768

MNG 0.858 0.796 0.966 0.966 0.928 0.819 0.947 0.749

MOZ 0.931 0.851 0.878 0.875 0.931 0.827 0.461 0.375

MUS 0.921 0.863 0.967 0.964 0.996 0.880 0.937 0.827

MWI 0.966 0.858 0.989 0.984 0.886 0.794 0.581 0.415

MYS 0.802 0.681 0.983 0.980 0.791 0.658 0.713 0.577

NAM 0.632 0.568 0.879 0.877

NER 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.639

NPL 0.987 0.880 0.988 0.982 1.000 0.866 1.000 0.750

PAK 1.000 0.898 1.000 0.734 1.000 0.898 1.000 0.500

PAN 1.000 1.000 1.000 1.000 1.000 0.906 1.000 0.723

PER 0.921 0.848 0.948 0.945 0.971 0.887 0.962 0.838

PHL 0.908 0.824 0.940 0.937 0.946 0.816 0.873 0.736

PRY 0.882 0.723 0.895 0.891 0.894 0.729 0.715 0.572

ROM 0.954 0.854 0.918 0.906 0.982 0.916 0.987 0.879

RUS 0.915 0.852 0.962 0.959 0.915 0.861 0.973 0.812

RWA 0.967 0.835 0.979 0.973 0.876 0.781 0.573 0.388

SAU 0.547 0.488 0.933 0.930 1.000 0.817 1.000 0.840

59





FDH DEA FDH DEA FDH DEA FDH DEA

SEN 0.902 0.820 0.716 0.714 0.924 0.830 0.718 0.591

SLE 1.000 1.000 1.000 1.000 1.000 0.997 1.000 0.863

SLV 0.983 0.816 0.944 0.943 0.983 0.811 0.854 0.703

SWZ 0.752 0.663 0.811 0.803 0.834 0.679 0.759 0.546

TCD 1.000 1.000 1.000 0.854 1.000 0.992 1.000 0.580

TGO 0.965 0.835 0.947 0.942 0.981 0.828 0.875 0.646

THA 0.894 0.782 0.938 0.935 0.967 0.871 0.986 0.818

TUN 1.000 0.721 1.000 0.993 1.000 0.820 1.000 0.843

TUR 0.896 0.754 0.951 0.948 0.969 0.816 0.929 0.745

TZA 0.894 0.777 0.847 0.841 0.895 0.767 0.409 0.322

UGA 0.914 0.814 0.990 0.933 0.914 0.788 0.342 0.239

UKR 0.749 0.660 0.961 0.961 0.912 0.755 0.755 0.755

URY 0.857 0.725 0.983 0.983 0.927 0.785 0.751 0.751

VNM 0.902 0.778 0.989 0.986

ZAF 0.975 0.767 0.935 0.754

ZAR 0.895 0.766 0.516 0.405

ZWE 0.830 0.710 0.879 0.877 0.832 0.697 0.523 0.413

60

Table A.4 Efficiency Score for Selected Health Indicators (Single Inputs – Output)

Disability Adjusted Life Expectancy Immunization, DTP



AGO 0.504 0.504 0.76 0.746 0.439 0.439 0.704 0.704

ALB 0.527 0.527 0.954 0.94 0.775 0.638 0.997 0.997

ARG 0.551 0.551 0.951 0.94 0.495 0.495 0.944 0.941

ARM 0.521 0.521 0.927 0.913 0.454 0.454 0.951 0.951

ATG 0.844 0.594 0.96 0.952 0.826 0.68 1 0.997

AUS 1 1 1 1 0.415 0.415 0.929 0.929

AUT 0.768 0.653 0.98 0.98 0.31 0.31 0.918 0.918

AZE 0.619 0.619 0.875 0.874 0.565 0.565 0.899 0.897

BDI 0.472 0.472 0.7 0.7 0.404 0.404 0.962 0.962

BEN 0.481 0.481 0.777 0.759 0.413 0.413 0.789 0.789

BFA 0.476 0.476 0.711 0.711 0.409 0.409 0.915 0.915

BGD 0.491 0.491 0.882 0.863 0.424 0.424 0.968 0.968

BGR 0.529 0.529 0.924 0.911 0.463 0.463 0.941 0.941

BHR 0.897 0.897 0.984 0.975 0.881 0.881 1 0.999

BHS 0.586 0.586 0.921 0.916 0.523 0.523 0.983 0.98

BLR 0.529 0.529 0.893 0.88 0.472 0.472 0.987 0.987

BLZ 0.503 0.503 0.888 0.871 0.433 0.433 0.967 0.967

BOL 0.492 0.492 0.899 0.88 0.423 0.423 0.957 0.957

BRA 0.528 0.528 0.928 0.915 0.465 0.465 0.978 0.978

BRB 0.519 0.519 0.955 0.94 0.448 0.448 0.922 0.922

BWA 0.531 0.531 0.717 0.707 0.467 0.467 0.961 0.961

CAF 0.475 0.475 0.583 0.582 0.407 0.407 0.43 0.43

CAN 0.849 0.771 0.986 0.986 0.343 0.343 0.916 0.916

CHE 1 1 1 1 0.34 0.34 0.968 0.968

CHL 0.86 0.857 0.995 0.988 0.51 0.51 0.944 0.941

CHN 0.534 0.534 0.933 0.921 0.973 0.973 1 1

CIV 0.49 0.49 0.712 0.696 0.421 0.421 0.792 0.792

CMR 0.491 0.491 0.722 0.707 0.423 0.423 0.853 0.853

COG 0.501 0.501 0.752 0.737 0.431 0.431 0.828 0.828

COL 0.732 0.515 0.96 0.94 0.425 0.425 0.906 0.906

COM 0.478 0.478 0.826 0.807 0.411 0.411 0.853 0.853

CPV 0.495 0.495 0.906 0.888 0.429 0.429 0.957 0.957

CRI 0.973 0.777 0.98 0.979 0.394 0.394 0.906 0.906

CYP 1 1 1 1 1 0.823 1 0.999

CZE 0.706 0.578 0.95 0.95 0.42 0.414 0.993 0.993

DEU 0.776 0.612 0.974 0.974 0.311 0.311 0.961 0.961

DJI 0.473 0.473 0.785 0.785 0.406 0.406 0.85 0.85

61





DMA 0.512 0.512 0.915 0.899 0.442 0.442 0.986 0.986

DNK 0.532 0.496 0.967 0.967 0.278 0.278 0.931 0.931

DOM 0.546 0.546 0.933 0.922 0.477 0.477 0.863 0.863

DZA 0.773 0.527 0.957 0.942 0.444 0.444 0.96 0.96

ECU 0.784 0.558 0.961 0.947 0.454 0.454 0.877 0.877

EGY 0.537 0.537 0.89 0.879 0.472 0.472 0.962 0.962

ERI 0.48 0.48 0.778 0.76 0.412 0.412 0.945 0.945

ESP 1 0.943 1 0.998 0.386 0.386 0.977 0.977

EST 0.536 0.536 0.943 0.93 0.461 0.461 0.947 0.947

ETH 0.476 0.476 0.749 0.749 0.408 0.408 0.691 0.691

FIN 0.917 0.752 0.982 0.98 0.384 0.382 0.994 0.994

FJI 0.512 0.512 0.814 0.8 0.927 0.927 1 1

FRA 0.768 0.73 0.99 0.99 0.318 0.318 0.996 0.996

GAB 0.598 0.598 0.794 0.791 0.521 0.521 0.766 0.764

GBR 0.853 0.665 0.973 0.973 0.344 0.344 0.957 0.957

GEO 0.526 0.526 0.915 0.902 0.46 0.46 0.929 0.929

GHA 0.485 0.485 0.791 0.773 0.419 0.419 0.934 0.934

GIN 0.478 0.478 0.732 0.715 0.409 0.409 0.597 0.597

GMB 0.476 0.476 0.805 0.805 0.408 0.408 0.98 0.98

GNB 0.48 0.48 0.704 0.687 0.412 0.412 0.861 0.861

GRC 0.981 0.798 0.982 0.981 0.85 0.85 1 1

GRD 0.534 0.534 0.892 0.88 0.466 0.466 0.973 0.973

GTM 0.513 0.513 0.894 0.879 0.446 0.446 0.869 0.869

GUY 0.49 0.49 0.83 0.812 0.425 0.425 0.973 0.973

HND 0.483 0.483 0.897 0.877 0.415 0.415 0.98 0.98

HRV 0.46 0.46 0.932 0.931 0.399 0.399 0.967 0.967

HTI 0.481 0.481 0.765 0.747 0.413 0.413 0.638 0.638

HUN 0.515 0.515 0.933 0.917 0.953 0.953 1 1

IDN 0.548 0.548 0.878 0.868 0.484 0.484 0.816 0.814

IND 0.507 0.507 0.832 0.817 0.44 0.44 0.825 0.825

IRN 0.574 0.574 0.931 0.924 0.854 0.703 1 0.998

ISL 0.878 0.869 0.999 0.995 0.37 0.37 0.937 0.937

ITA 0.899 0.879 0.998 0.994 0.373 0.373 0.965 0.965

JAM 0.513 0.513 0.936 0.92 0.444 0.444 0.935 0.935

JOR 0.478 0.478 0.938 0.916 0.422 0.414 0.991 0.991

KAZ 0.632 0.632 0.86 0.856 0.576 0.576 0.993 0.991

KEN 0.485 0.485 0.784 0.766 0.416 0.416 0.918 0.918

KGZ 0.481 0.481 0.861 0.842 0.413 0.413 0.971 0.971

KHM 0.491 0.491 0.829 0.812 0.423 0.423 0.89 0.89

62





KNA 0.59 0.59 0.973 0.971

KOR 1 0.99 1 0.999 0.561 0.561 0.987 0.985

LAO 0.505 0.505 0.802 0.787 0.439 0.439 0.811 0.811

LBN 0.803 0.757 0.988 0.976 0.485 0.485 0.821 0.818

LCA 0.514 0.514 0.942 0.926 0.444 0.444 0.99 0.99

LKA 0.541 0.541 0.948 0.937 0.802 0.66 0.997 0.997

LSO 0.467 0.467 0.571 0.571 0.396 0.396 0.944 0.944

LTU 0.536 0.536 0.915 0.902 0.473 0.473 0.948 0.948

LUX 0.962 0.888 0.992 0.991 0.897 0.897 1 1

LVA 0.554 0.554 0.91 0.901 0.498 0.498 0.941 0.938

MAR 0.515 0.515 0.922 0.907 0.933 0.933 1 1

MDA 0.476 0.476 0.862 0.862 0.409 0.409 0.905 0.905

MDG 0.478 0.478 0.77 0.752 0.41 0.41 0.73 0.73

MEX 0.56 0.56 0.95 0.941 0.49 0.49 0.931 0.928

MKD 0.504 0.504 0.925 0.908 0.441 0.441 0.961 0.961

MLI 0.48 0.48 0.744 0.726 0.413 0.413 0.698 0.698

MNG 0.525 0.525 0.828 0.815 0.463 0.463 0.988 0.988

MOZ 0.475 0.475 0.688 0.688 0.407 0.407 0.775 0.775

MRT 0.495 0.495 0.853 0.836 0.427 0.427 0.75 0.75

MUS 0.592 0.592 0.92 0.915 0.528 0.528 0.993 0.991

MWI 0.47 0.47 0.663 0.663 0.402 0.402 0.934 0.934

MYS 0.655 0.655 0.94 0.938 0.596 0.596 0.983 0.981

NAM 0.494 0.494 0.733 0.718 0.423 0.423 0.867 0.867

NER 0.474 0.474 0.72 0.72 0.407 0.407 0.703 0.703

NGA 0.511 0.511 0.766 0.753 0.446 0.446 0.506 0.506

NIC 0.718 0.571 0.971 0.949 0.415 0.415 0.99 0.99

NLD 0.711 0.609 0.981 0.981 0.28 0.28 0.975 0.975

NOR 0.759 0.697 0.987 0.987 0.305 0.305 0.949 0.949

NPL 0.481 0.481 0.859 0.839 0.414 0.414 0.906 0.906

NZL 0.782 0.671 0.981 0.981 0.305 0.305 0.938 0.938

OMN 1 1 1 1 1 1 1 1

PAK 0.507 0.507 0.823 0.808 0.439 0.439 0.743 0.743

PAN 0.746 0.638 0.978 0.959 0.43 0.43 0.843 0.843

PER 0.785 0.709 0.983 0.969 0.462 0.462 0.921 0.921

PHL 0.515 0.515 0.865 0.85 0.448 0.448 0.804 0.804

PNG 0.478 0.478 0.741 0.724 0.411 0.411 0.737 0.737

POL 0.788 0.546 0.958 0.945 0.786 0.647 0.997 0.997

PRY 0.501 0.501 0.929 0.911 0.429 0.429 0.889 0.889

ROM 0.528 0.528 0.926 0.912 0.462 0.462 0.929 0.929

63





RUS 0.577 0.577 0.861 0.855 0.515 0.515 0.984 0.982

RWA 0.477 0.477 0.778 0.778 0.41 0.41 0.986 0.986

SAU 0.99 0.99 0.988 0.988 1 1 1 1

SDN 0.494 0.494 0.835 0.818 0.426 0.426 0.918 0.918

SEN 0.482 0.482 0.803 0.785 0.415 0.415 0.906 0.906

SLB 0.472 0.472 0.758 0.757 0.405 0.405 0.952 0.952

SLE 0.478 0.478 0.701 0.684 0.411 0.411 0.882 0.882

SLV 0.493 0.493 0.932 0.912 0.425 0.425 0.921 0.921

SVK 0.497 0.497 0.947 0.928 0.442 0.433 0.991 0.991

SVN 0.678 0.665 0.969 0.968 0.392 0.392 0.965 0.965

SWE 0.742 0.696 0.989 0.989 0.284 0.284 0.99 0.99

SWZ 0.473 0.473 0.613 0.613 0.403 0.403 0.937 0.937

TCD 0.484 0.484 0.712 0.696 0.416 0.416 0.372 0.372

TGO 0.478 0.478 0.748 0.73 0.41 0.41 0.85 0.85

THA 0.546 0.546 0.952 0.941 1 1 1 1

TJK 0.486 0.486 0.874 0.854 0.418 0.418 0.96 0.96

TKM 0.57 0.57 0.851 0.844 0.512 0.512 0.986 0.983

TON 0.486 0.486 0.876 0.856 0.422 0.422 0.815 0.815

TTO 0.697 0.697 0.946 0.912 0.631 0.631 0.93 0.928

TUN 0.759 0.542 0.961 0.944 0.441 0.441 0.99 0.99

TUR 0.799 0.686 0.978 0.966 0.475 0.475 0.978 0.978

TZA 0.482 0.482 0.752 0.735 0.415 0.415 0.929 0.929

UGA 0.479 0.479 0.725 0.708 0.412 0.412 0.798 0.798

UKR 0.496 0.496 0.875 0.857 0.43 0.43 0.535 0.535

URY 0.722 0.498 0.958 0.937 0.422 0.422 0.958 0.958

USA 0.562 0.494 0.952 0.952 0.316 0.316 0.958 0.958

UZB 0.495 0.495 0.859 0.841 0.89 0.74 0.999 0.999

VCT 0.506 0.506 0.893 0.877 0.436 0.436 0.986 0.986

VEN 0.605 0.605 0.94 0.937 0.542 0.542 0.822 0.82

VNM 0.494 0.494 0.917 0.898 0.425 0.425 0.912 0.912

VUT 0.481 0.481 0.789 0.771 0.413 0.413 0.652 0.652

WSM 0.481 0.481 0.895 0.874 0.411 0.411 0.629 0.629

YEM 0.497 0.497 0.827 0.81 0.43 0.43 0.726 0.726

ZAF 0.514 0.514 0.71 0.698 0.445 0.445 0.732 0.732

ZAR 0.475 0.475 0.717 0.717 0.407 0.407 0.745 0.745

ZMB 0.489 0.489 0.659 0.645 0.422 0.422 0.853 0.853

ZWE 0.481 0.481 0.656 0.641 0.412 0.412 0.899 0.899

64

Table A.5 Efficiency Score for Selected Health Indicators (Single Inputs – Output)





AGO 0.439 0.439 0.995 0.995 0.439 0.439 0.935 0.935

ALB 0.462 0.462 1.000 1.000 0.462 0.462 0.989 0.988

ARG 0.495 0.495 1.000 1.000 0.509 0.499 0.991 0.990

ARM 0.454 0.454 1.000 1.000 0.454 0.454 0.988 0.988

ATG 0.826 0.602 0.996 0.996

AUS 0.882 0.416 1.000 1.000 0.697 0.646 0.999 0.998

AUT 0.659 0.625 1.000 1.000 0.520 0.495 0.999 0.999

AZE 0.565 0.565 1.000 1.000 0.565 0.565 0.971 0.971

BDI 0.404 0.404 0.993 0.993 0.404 0.404 0.946 0.945

BEN 0.413 0.413 0.996 0.996 0.413 0.413 0.934 0.933

BFA 0.409 0.409 0.996 0.996 0.409 0.409 0.941 0.941

BGD 0.424 0.424 0.998 0.998 0.424 0.424 0.967 0.967

BGR 0.776 0.463 1.000 1.000 0.541 0.512 0.994 0.994

BHR 0.856 0.856 1.000 1.000 1.000 1.000 1.000 1.000

BHS 0.523 0.523 0.999 0.999 0.537 0.534 0.992 0.992

BLR 1.000 0.472 1.000 1.000 0.792 0.733 0.999 0.999

BLZ 0.433 0.433 1.000 1.000 0.433 0.433 0.988 0.987

BOL 0.423 0.423 0.998 0.998 0.423 0.423 0.968 0.968

BRA 0.465 0.465 1.000 1.000 0.465 0.465 0.986 0.986

BRB 0.448 0.448 1.000 1.000 0.461 0.449 0.990 0.990

BWA 0.467 0.467 0.999 0.999 0.467 0.467 0.964 0.964

CAF 0.407 0.407 0.991 0.991 0.407 0.407 0.905 0.904

CAN 0.577 0.344 1.000 1.000 0.576 0.491 0.997 0.997

CHE 0.723 0.341 1.000 1.000 0.571 0.524 0.998 0.998

CHL 0.510 0.510 1.000 1.000 0.595 0.585 0.995 0.995

CHN 0.467 0.467 1.000 1.000 0.480 0.471 0.991 0.991

CIV 0.421 0.421 0.994 0.994 0.421 0.421 0.928 0.928

CMR 0.423 0.423 0.994 0.994 0.423 0.423 0.939 0.939

COG 0.431 0.431 0.996 0.996 0.431 0.431 0.961 0.960

COL 0.425 0.425 0.999 0.999 0.425 0.425 0.987 0.987

COM 0.411 0.411 0.997 0.997 0.411 0.411 0.941 0.941

CPV 0.429 0.429 1.000 1.000 0.429 0.429 0.981 0.981

CRI 0.394 0.394 1.000 1.000 0.460 0.429 0.994 0.993

CYP 1.000 0.596 1.000 1.000 1.000 1.000 1.000 1.000

CZE 0.867 0.409 1.000 1.000 0.949 0.700 1.000 0.999

DEU 0.661 0.547 1.000 1.000 0.522 0.494 0.998 0.998

DJI 0.406 0.406 0.998 0.998 0.406 0.406 0.943 0.942

65


Maternal Survival Rate Infant Survival Rate



DMA 0.442 0.442 0.978 0.978

DNK 0.590 0.489 1.000 1.000 0.466 0.439 0.998 0.998

DOM 0.477 0.477 0.999 0.999 0.477 0.477 0.975 0.975

DZA 0.444 0.444 0.999 0.999 0.444 0.444 0.980 0.979

ECU 0.454 0.454 0.999 0.999 0.454 0.454 0.983 0.982

EGY 0.472 0.472 1.000 1.000 0.472 0.472 0.980 0.980

ERI 0.412 0.412 0.995 0.995 0.412 0.412 0.965 0.965

ESP 0.819 0.386 1.000 1.000 0.647 0.631 0.999 0.999

EST 0.774 0.462 1.000 1.000 0.774 0.745 0.999 0.999

ETH 0.408 0.408 0.996 0.997 0.408 0.408 0.953 0.953

FIN 0.799 0.375 1.000 1.000 0.869 0.801 1.000 1.000

FJI 0.445 0.445 1.000 1.000 0.445 0.445 0.983 0.982

FRA 0.520 0.466 1.000 1.000 0.519 0.491 0.998 0.998

GAB 0.521 0.521 0.997 0.997 0.521 0.521 0.963 0.963

GBR 0.578 0.345 1.000 1.000 0.577 0.518 0.998 0.998

GEO 0.460 0.460 1.000 1.000 0.460 0.460 0.990 0.989

GHA 0.418 0.418 0.997 0.997 0.419 0.419 0.955 0.955

GIN 0.409 0.409 0.993 0.993 0.409 0.409 0.936 0.936

GMB 0.408 0.408 0.993 0.993 0.408 0.408 0.956 0.956

GNB 0.412 0.412 0.995 0.995 0.412 0.412 0.935 0.935

GRC 0.871 0.408 1.000 1.000 0.684 0.657 0.999 0.999

GRD 0.466 0.466 1.000 1.000 0.466 0.466 0.989 0.989

GTM 0.446 0.446 0.999 0.999 0.446 0.446 0.975 0.975

GUY 0.425 0.425 0.998 0.998 0.425 0.425 0.973 0.973

HND 0.414 0.414 0.999 0.999 0.415 0.415 0.984 0.984

HRV 0.669 0.399 1.000 1.000 0.669 0.585 0.998 0.998

HTI 0.413 0.413 0.996 0.996 0.413 0.413 0.942 0.942

HUN 0.457 0.457 1.000 1.000 0.767 0.646 0.998 0.997

IDN 0.484 0.484 0.999 0.999 0.484 0.484 0.977 0.977

IND 0.440 0.440 0.998 0.998 0.440 0.440 0.961 0.960

IRN 0.509 0.509 1.000 1.000 0.509 0.509 0.987 0.987

ISL 0.791 0.371 1.000 1.000 1.000 1.000 1.000 1.000

ITA 0.791 0.373 1.000 1.000 0.625 0.600 0.999 0.999

JAM 0.444 0.444 0.999 0.999 0.444 0.444 0.988 0.987

JOR 0.411 0.411 0.999 0.999 0.411 0.411 0.985 0.985

KAZ 0.576 0.576 1.000 1.000 0.576 0.576 0.987 0.987

KEN 0.416 0.416 0.995 0.995 0.416 0.416 0.962 0.962

KGZ 0.413 0.413 0.999 0.999 0.413 0.413 0.979 0.979

KHM 0.423 0.423 0.998 0.998 0.423 0.423 0.969 0.969

66





KNA 0.689 0.628 0.994 0.994

KOR 0.941 0.561 1.000 1.000 0.941 0.896 0.999 0.999

LAO 0.439 0.439 0.998 0.998 0.439 0.439 0.947 0.947

LBN 0.485 0.485 1.000 1.000 0.566 0.539 0.994 0.994

LCA 0.444 0.444 1.000 1.000 0.444 0.444 0.990 0.989

LKA 0.478 0.478 1.000 1.000 0.558 0.507 0.993 0.993

LSO 0.396 0.396 0.995 0.995 0.396 0.396 0.927 0.927

LTU 0.794 0.473 1.000 1.000 0.793 0.697 0.998 0.998

LUX 0.725 0.433 1.000 1.000 1.000 0.955 1.000 1.000

LVA 0.498 0.498 1.000 1.000 0.835 0.659 0.997 0.997

MAR 0.448 0.448 0.999 0.999 0.448 0.448 0.976 0.975

MDA 0.408 0.408 1.000 1.000 0.409 0.409 0.988 0.988

MDG 0.410 0.410 0.996 0.997 0.410 0.410 0.962 0.962

MEX 0.490 0.490 1.000 1.000 0.490 0.490 0.989 0.988

MKD 0.740 0.441 1.000 1.000 0.515 0.457 0.993 0.993

MLI 0.413 0.413 0.994 0.994 0.413 0.413 0.927 0.927

MNG 0.463 0.463 1.000 1.000 0.463 0.463 0.983 0.983

MOZ 0.407 0.407 0.995 0.995 0.407 0.407 0.939 0.939

MRT 0.426 0.426 0.994 0.994 0.427 0.427 0.943 0.942

MUS 0.528 0.528 1.000 1.000 0.528 0.528 0.989 0.989

MWI 0.402 0.402 0.994 0.994 0.402 0.402 0.952 0.952

MYS 0.595 0.595 1.000 1.000 0.999 0.708 0.996 0.996

NAM 0.423 0.423 0.997 0.997 0.423 0.423 0.964 0.964

NER 0.407 0.407 0.994 0.995 0.407 0.407 0.944 0.944

NGA 0.446 0.446 0.992 0.992 0.446 0.446 0.926 0.926

NIC 0.415 0.415 0.999 0.999 0.415 0.415 0.983 0.983

NLD 0.470 0.457 1.000 1.000 0.469 0.438 0.998 0.998

NOR 0.648 0.576 1.000 1.000 0.709 0.591 0.999 0.999

NPL 0.413 0.413 0.997 0.997 0.414 0.414 0.968 0.968

NZL 0.513 0.344 1.000 1.000 0.512 0.430 0.997 0.997

OMN 0.972 0.972 1.000 1.000 1.000 1.000 1.000 1.000

PAK 0.438 0.438 0.998 0.998 0.439 0.439 0.932 0.932

PAN 0.430 0.430 0.999 0.999 0.430 0.430 0.986 0.986

PER 0.462 0.462 0.999 0.999 0.462 0.462 0.988 0.988

PHL 0.448 0.448 0.999 0.999 0.448 0.448 0.979 0.978

PNG 0.411 0.411 0.998 0.998 0.411 0.411 0.955 0.955

POL 1.000 0.469 1.000 1.000 0.786 0.671 0.998 0.998

PRY 0.429 0.429 0.999 0.999 0.429 0.429 0.983 0.983

ROM 0.462 0.462 1.000 1.000 0.540 0.494 0.994 0.993

67





RUS 0.515 0.515 1.000 1.000 0.601 0.580 0.995 0.995

RWA 0.410 0.410 0.997 0.997 0.410 0.410 0.964 0.964

SAU 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SDN 0.426 0.426 0.997 0.997 0.426 0.426 0.953 0.953

SEN 0.415 0.415 0.997 0.997 0.415 0.415 0.963 0.962

SLB 0.404 0.404 0.999 0.999 0.405 0.405 0.979 0.979

SLE 0.410 0.410 0.986 0.986 0.411 0.411 0.904 0.904

SLV 0.425 0.425 0.999 0.999 0.425 0.425 0.987 0.987

SVK 0.911 0.430 1.000 1.000 0.721 0.578 0.997 0.997

SVN 0.658 0.392 1.000 1.000 0.910 0.818 1.000 1.000

SWE 0.604 0.572 1.000 1.000 0.661 0.535 0.999 0.999

SWZ 0.403 0.403 0.996 0.996 0.403 0.403 0.943 0.942

TCD 0.415 0.415 0.991 0.991 0.416 0.416 0.920 0.920

TGO 0.410 0.410 0.996 0.996 0.410 0.410 0.946 0.946

THA 0.480 0.480 1.000 1.000 0.493 0.483 0.991 0.990

TJK 0.417 0.417 1.000 1.000 0.418 0.418 0.960 0.960

TKM 0.512 0.512 1.000 1.000 0.512 0.512 0.954 0.953

TON 0.421 0.421 0.999 0.999 0.422 0.422 0.987 0.987

TTO 0.630 0.630 0.999 0.999 0.631 0.631 0.988 0.985

TUN 0.441 0.441 0.999 0.999 0.441 0.441 0.989 0.988

TUR 0.475 0.475 1.000 1.000 0.475 0.475 0.988 0.988

TZA 0.415 0.415 0.996 0.996 0.415 0.415 0.957 0.957

UGA 0.411 0.411 0.997 0.997 0.412 0.412 0.955 0.955

UKR 0.429 0.429 1.000 1.000 0.501 0.452 0.993 0.993

URY 0.422 0.422 1.000 1.000 0.493 0.458 0.994 0.993

USA 0.317 0.317 1.000 1.000 0.529 0.405 0.996 0.996

UZB 0.427 0.427 1.000 1.000 0.427 0.427 0.974 0.974

VCT 0.436 0.436 1.000 1.000 0.436 0.436 0.985 0.985

VEN 0.542 0.542 0.999 0.999 0.542 0.542 0.988 0.988

VNM 0.425 0.425 0.999 0.999 0.425 0.425 0.984 0.984

VUT 0.413 0.413 0.999 0.999 0.413 0.413 0.978 0.978

WSM 0.411 0.411 1.000 1.000 0.411 0.411 0.986 0.986

YEM 0.430 0.430 0.959 0.958

ZAF 0.445 0.445 0.999 0.999 0.445 0.445 0.965 0.965

ZAR 0.406 0.406 0.993 0.993 0.407 0.407 0.922 0.922

ZMB 0.422 0.422 0.998 0.998 0.422 0.422 0.952 0.951

ZWE 0.412 0.412 0.996 0.996 0.412 0.412 0.951 0.951

68

Table A.6 Efficiency Score for Selected Health Indicators (Two Inputs – Single Output)

Input (Health Expenditure‐ Adult Literacy Rate)




AGO 0.924 0.780 0.824 0.814 0.931 0.765 0.704 0.704

ALB 0.927 0.832 0.993 0.963 0.923 0.754 0.997 0.997

ARG 0.914 0.783 0.956 0.953 0.791 0.642 0.941 0.941

ARM 0.899 0.744 0.970 0.938 0.909 0.693 0.954 0.951

AZE 0.899 0.833 0.916 0.905 0.909 0.843 0.900 0.899

BDI 0.922 0.788 0.840 0.802 0.893 0.830 0.982 0.965

BEN 1.000 0.926 1.000 0.974 1.000 0.921 1.000 0.892

BFA 0.991 0.915 0.988 0.922 1.000 1.000 1.000 1.000

BGD 1.000 0.957 1.000 0.983 1.000 1.000 1.000 1.000

BGR 0.851 0.693 0.929 0.926 0.749 0.613 0.941 0.941

BHR 0.948 0.862 0.984 0.969 0.959 0.871 1.000 0.998

BLR 0.798 0.639 0.898 0.894 0.791 0.647 0.987 0.987

BOL 0.873 0.703 0.909 0.907 0.796 0.665 0.957 0.957

BRA 0.922 0.747 0.939 0.938 0.815 0.679 0.978 0.978

CAF 0.970 0.882 0.768 0.726 0.987 0.865 0.488 0.464

CHL 1.000 1.000 1.000 1.000 0.800 0.649 0.941 0.941

CHN 0.915 0.758 0.976 0.943 0.969 0.969 1.000 1.000

CIV 1.000 0.885 1.000 0.842 1.000 0.874 1.000 0.840

CMR 0.936 0.768 0.782 0.765 0.947 0.743 0.853 0.853

COG 0.914 0.732 0.815 0.782 0.918 0.695 0.828 0.828

COL 0.859 0.823 0.971 0.963 0.685 0.574 0.906 0.906

COM 0.947 0.862 0.968 0.948 0.957 0.847 0.881 0.865

CPV 0.893 0.752 0.974 0.925 0.856 0.708 0.957 0.957

CRI 1.000 0.983 1.000 0.998 0.548 0.489 0.906 0.906

CYP 1.000 1.000 1.000 1.000 0.854 0.722 0.997 0.997

DOM 0.992 0.811 0.976 0.951 0.947 0.696 0.865 0.863

DZA 1.000 1.000 1.000 1.000

ECU 1.000 0.898 1.000 0.975 0.841 0.651 0.877 0.877

EGY 1.000 0.862 1.000 0.946 1.000 0.854 1.000 0.963

ERI 0.904 0.771 0.843 0.836

ESP 1.000 1.000 1.000 1.000 0.428 0.425 0.977 0.977

EST 0.768 0.697 0.947 0.938 0.601 0.528 0.947 0.947

ETH 0.987 0.868 0.987 0.923

GAB 1.000 0.829 1.000 0.830 1.000 0.787 1.000 0.764

GEO 0.900 0.732 0.958 0.928 0.910 0.694 0.932 0.930

GHA 0.882 0.733 0.857 0.835 0.898 0.763 0.934 0.934

69


Input (Health Expenditure‐ Adult Literacy Rate)




GIN 1.000 0.934 1.000 0.927 1.000 0.918 1.000 0.710

GMB 1.000 0.916 1.000 0.978 1.000 1.000 1.000 1.000

GNB 0.973 0.852 0.825 0.821 1.000 0.884 1.000 0.878

GRC 1.000 0.967 1.000 0.993 0.672 0.672 1.000 1.000

GTM 0.976 0.799 0.969 0.931 0.934 0.724 0.869 0.869

GUY 0.838 0.663 0.863 0.847 0.826 0.703 0.973 0.973

HND 0.866 0.730 0.964 0.915 0.824 0.706 0.980 0.980

HRV 0.745 0.739 0.951 0.943 0.483 0.479 0.967 0.967

HTI 0.964 0.840 0.896 0.879

IDN 0.958 0.769 0.919 0.898 0.965 0.755 0.817 0.815

IND 1.000 0.814 1.000 0.895 1.000 0.782 1.000 0.826

IRN 1.000 0.847 1.000 0.962 1.000 0.849 1.000 0.997

ITA 0.991 0.982 0.998 0.998 0.424 0.408 0.965 0.965

JOR 0.839 0.731 0.950 0.940 0.673 0.637 0.991 0.991

KAZ 0.899 0.755 0.895 0.875 0.909 0.784 0.994 0.992

KEN 0.904 0.731 0.850 0.821 0.890 0.722 0.918 0.918

KGZ 0.850 0.642 0.866 0.865 0.850 0.656 0.971 0.971

KHM 0.945 0.747 0.899 0.869 0.942 0.750 0.890 0.890

LAO 0.990 0.848 0.974 0.887 1.000 0.836 1.000 0.819

LBN 1.000 1.000 1.000 1.000 0.878 0.667 0.818 0.818

LKA 0.987 0.872 0.992 0.969 1.000 0.834 1.000 0.998

LSO 0.784 0.667 0.611 0.608 0.777 0.685 0.944 0.944

LTU 0.671 0.629 0.919 0.911 0.643 0.556 0.948 0.948

LVA 0.788 0.660 0.915 0.911 0.751 0.620 0.938 0.938

MAR 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

MDA 0.788 0.632 0.887 0.885 0.743 0.594 0.905 0.905

MDG 0.889 0.750 0.834 0.819 0.863 0.722 0.730 0.730

MEX 0.959 0.832 0.966 0.961 0.864 0.677 0.928 0.928

MLI 0.995 0.915 0.956 0.931 1.000 0.899 1.000 0.794

MNG 0.912 0.684 0.866 0.836 0.923 0.721 0.991 0.988

MOZ 0.933 0.813 0.826 0.803 0.931 0.788 0.800 0.785

MRT 1.000 0.991 1.000 0.996

MUS 0.979 0.804 0.962 0.941 0.990 0.803 0.994 0.991

MWI 0.843 0.745 0.736 0.734 0.867 0.787 0.953 0.935

MYS 0.963 0.849 0.978 0.961 0.974 0.838 0.984 0.982

NAM 0.779 0.647 0.763 0.753 0.736 0.595 0.867 0.867

70


Health Expenditure‐ Adult Literacy Rate




NER 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

NGA 1.000 0.893 1.000 0.881

NPL 0.945 0.848 0.974 0.940 0.927 0.815 0.936 0.910

OMN 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

PAK 1.000 0.867 1.000 0.925 1.000 0.852 1.000 0.754

PAN 0.961 0.888 0.990 0.978 0.616 0.503 0.843 0.843

PER 1.000 0.978 1.000 0.997 0.874 0.677 0.921 0.921

PHL 0.935 0.710 0.904 0.877 0.940 0.681 0.806 0.804

PRY 0.888 0.741 0.940 0.936 0.781 0.615 0.889 0.889

ROM 0.848 0.672 0.931 0.925 0.697 0.576 0.929 0.929

RUS 0.777 0.607 0.865 0.863 0.790 0.640 0.981 0.981

RWA 0.881 0.749 0.864 0.850 0.957 0.841 0.986 0.986

SAU 0.949 0.873 0.988 0.974 0.960 0.864 0.994 0.993

SDN 0.985 0.860 0.978 0.939

SEN 0.976 0.842 0.941 0.932 0.977 0.896 0.936 0.920

SLE 0.978 0.910 0.901 0.870 1.000 1.000 1.000 1.000

SLV 0.969 0.794 0.970 0.952 0.794 0.655 0.921 0.921

SWZ 0.707 0.605 0.653 0.642 0.692 0.615 0.937 0.937

TCD 1.000 0.976 1.000 0.927 1.000 0.959 1.000 0.474

TGO 0.885 0.783 0.848 0.819 0.925 0.772 0.878 0.853

THA 0.950 0.837 0.968 0.963 0.940 0.940 1.000 1.000

TON 0.824 0.630 0.880 0.878 0.783 0.602 0.815 0.815

TUN 1.000 0.977 1.000 0.995 0.807 0.759 0.990 0.990

TUR 0.974 0.910 0.990 0.983 0.715 0.628 0.978 0.978

TZA 0.885 0.728 0.816 0.789 0.893 0.750 0.929 0.929

UGA 0.887 0.739 0.786 0.765 0.870 0.709 0.798 0.798

UKR 0.800 0.620 0.879 0.877 0.747 0.585 0.535 0.535

URY 0.802 0.756 0.963 0.952 0.580 0.528 0.958 0.958

UZB 0.889 0.657 0.873 0.864 0.900 0.728 0.999 0.999

VEN 0.935 0.825 0.983 0.962 0.948 0.768 0.823 0.821

VNM 0.901 0.737 0.932 0.926 0.811 0.652 0.912 0.912

WSM 0.815 0.648 0.899 0.897 0.742 0.568 0.629 0.629

ZAF 0.788 0.629 0.718 0.715 0.760 0.589 0.732 0.732

ZAR 0.883 0.739 0.796 0.776 0.861 0.693 0.745 0.745

ZMB 0.902 0.745 0.714 0.695 0.887 0.678 0.853 0.853

ZWE 0.885 0.696 0.705 0.670 0.855 0.678 0.899 0.899

71

Table A.7 Efficiency Score for Selected Infrastructure Indicators (Single Inputs – Output)

Quality of the Overall Infrastructure Quality of Electricity Supply



AGO 0.100 0.100 0.321 0.321 0.100 0.100 0.233 0.233

ALB 0.143 0.143 0.522 0.522 0.143 0.143 0.555 0.555

ARE 0.259 0.254 0.936 0.936 0.259 0.243 0.956 0.956

ARG 0.230 0.230 0.507 0.484 0.230 0.230 0.502 0.491

ARM 0.258 0.258 0.614 0.594 0.258 0.258 0.700 0.687

AUS 0.334 0.193 0.772 0.772 0.364 0.216 0.905 0.905

AUT 1.000 0.614 1.000 0.948 1.000 0.835 1.000 0.987

AZE 0.087 0.087 0.662 0.662 0.087 0.087 0.650 0.650

BDI 0.153 0.153 0.369 0.369 0.153 0.153 0.329 0.329

BEL 0.877 0.699 0.922 0.911 0.877 0.870 0.999 0.992

BEN 0.168 0.168 0.427 0.427 0.168 0.168 0.371 0.371

BFA 0.150 0.150 0.392 0.392 0.150 0.150 0.357 0.357

BGD 0.177 0.177 0.398 0.398 0.177 0.177 0.290 0.290

BGR 0.165 0.165 0.471 0.471 0.165 0.165 0.573 0.573

BHR 0.383 0.258 0.824 0.824 0.139 0.139 0.848 0.848

BIH 0.151 0.151 0.361 0.361 0.151 0.151 0.783 0.783

BLZ 0.163 0.163 0.530 0.530 0.163 0.163 0.603 0.603

BOL 0.104 0.104 0.448 0.448 0.104 0.104 0.582 0.582

BRA 0.664 0.679 0.620 0.664 0.804 0.779

BRB 0.499 0.369 0.848 0.848 0.499 0.325 0.913 0.913

BTN 0.098 0.098 0.676 0.676 0.098 0.098 0.863 0.863

BWA 0.102 0.102 0.650 0.650 0.102 0.102 0.546 0.546

CAN 0.439 0.361 0.878 0.878 0.656 0.440 0.965 0.965

CHE 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

CHL 1.000 0.592 1.000 0.851 0.395 0.395 0.939 0.857

CIV 0.228 0.228 0.612 0.584 0.228 0.228 0.600 0.587

CMR 0.210 0.210 0.458 0.432 0.210 0.210 0.413 0.400

COL 0.133 0.133 0.497 0.497 0.133 0.133 0.747 0.747

CPV 0.109 0.109 0.542 0.542 0.109 0.109 0.324 0.324

CRI 0.242 0.242 0.533 0.512 0.242 0.242 0.847 0.831

CZE 0.411 0.195 0.737 0.737 0.448 0.361 0.936 0.936

DEU 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

DOM 0.217 0.217 0.554 0.525 0.217 0.217 0.295 0.287

DZA 0.084 0.084 0.516 0.516 0.084 0.084 0.647 0.647

ECU 0.098 0.098 0.531 0.531 0.098 0.098 0.556 0.556

EGY 0.377 0.377 0.716 0.599 0.377 0.377 0.741 0.672

EST 0.360 0.210 0.773 0.773 0.142 0.142 0.814 0.814

ETH 0.094 0.094 0.481 0.481 0.094 0.094 0.472 0.472

72





FIN 0.802 0.584 0.963 0.963 0.802 0.753 0.995 0.995

FRA 0.795 0.545 0.958 0.958 0.791 0.64 0.984 0.984

GAB 0.121 0.121 0.459 0.459 0.121 0.121 0.354 0.354

GBR 0.669 0.42 0.856 0.823 1 0.742 1 0.982

GEO 0.136 0.136 0.619 0.619 0.136 0.136 0.723 0.723

GHA 0.162 0.162 0.536 0.536 0.162 0.162 0.438 0.438

GIN 0.57 0.442 0.388 0.57 0.228 0.218

GMB 0.156 0.156 0.638 0.638 0.156 0.156 0.59 0.59

GRC 0.41 0.41 0.939 0.741 0.41 0.41 0.858 0.788

GTM 0.602 0.9 0.801 0.602 0.862 0.828

GUY 0.104 0.104 0.527 0.527 0.104 0.104 0.421 0.421

HND 0.291 0.291 0.571 0.559 0.291 0.291 0.592 0.585

HRV 0.452 0.182 0.713 0.713 0.179 0.179 0.81 0.81

HTI 0.109 0.109 0.306 0.306 0.109 0.109 0.252 0.252

IDN 0.332 0.332 0.563 0.559 0.332 0.332 0.6 0.598

IND 0.136 0.136 0.535 0.535 0.136 0.136 0.481 0.481

IRL 0.235 0.235 0.723 0.692 0.648 0.455 0.949 0.929

IRN 0.28 0.28 0.642 0.627 0.28 0.28 0.765 0.754

ISL 0.559 0.545 0.934 0.934 0.973 0.959 0.999 0.999

ISR 1 1 1 1 1 1

ITA 0.26 0.26 0.639 0.619 0.26 0.26 0.861 0.845

JOR 0.434 0.216 0.745 0.745 0.172 0.172 0.836 0.836

JPN 0.367 0.32 0.896 0.896 0.367 0.332 0.951 0.951

KAZ 0.162 0.162 0.596 0.596 0.162 0.162 0.648 0.648

KEN 0.187 0.187 0.556 0.556 0.187 0.187 0.535 0.535

KHM 0.135 0.135 0.536 0.536 0.135 0.135 0.445 0.445

KOR 0.404 0.307 0.855 0.855 0.404 0.199 0.889 0.889

KWT 0.15 0.15 0.687 0.687 0.15 0.15 0.735 0.735

LKA 0.18 0.18 0.669 0.669 0.18 0.18 0.702 0.702

LSO 0.1 0.1 0.466 0.466 0.1 0.1 0.532 0.532

LTU 0.448 0.204 0.731 0.731 0.177 0.177 0.816 0.816

LUX 0.418 0.357 0.889 0.889 0.418 0.361 0.945 0.945

LVA 0.197 0.197 0.676 0.676 0.197 0.197 0.778 0.778

MAR 0.198 0.198 0.629 0.629 0.198 0.198 0.767 0.767

MDA 0.223 0.223 0.541 0.515 0.223 0.223 0.648 0.632

MDG 0.294 0.294 0.461 0.452 0.294 0.294 0.342 0.338

MEX 0.169 0.169 0.596 0.596 0.169 0.169 0.636 0.636

MLI 0.161 0.161 0.493 0.493 0.161 0.161 0.501 0.501

MMR 0.14 0.14 0.342 0.342 0.14 0.14 0.418 0.418

73





MNE 0.145 0.145 0.489 0.489 0.145 0.145 0.572 0.572

MOZ 0.103 0.103 0.418 0.418 0.103 0.103 0.489 0.489

MUS 0.155 0.155 0.69 0.69 0.155 0.155 0.772 0.772

MWI 0.203 0.203 0.458 0.458 0.203 0.203 0.363 0.363

MYS 0.274 0.192 0.834 0.834 0.099 0.099 0.855 0.855

NAM 0.324 0.189 0.774 0.774 0.128 0.128 0.795 0.795

NGA 0.254 0.254 0.423 0.409 0.254 0.254 0.237 0.232

NIC 0.175 0.175 0.45 0.45 0.175 0.175 0.466 0.466

NLD 0.457 0.407 0.903 0.903 0.794 0.706 0.991 0.991

NPL 0.53 0.527 0.453 0.53 0.271 0.257

NZL 0.317 0.132 0.717 0.717 0.125 0.125 0.834 0.834

OMN 0.213 0.136 0.812 0.812 0.213 0.116 0.898 0.898

PAK 0.344 0.344 0.534 0.531 0.344 0.344 0.348 0.347

PAN 0.137 0.137 0.688 0.688 0.137 0.137 0.764 0.764

PER 0.165 0.165 0.477 0.477 0.165 0.165 0.708 0.708

PHL 0.403 0.403 0.692 0.543 0.403 0.403 0.664 0.608

POL 0.158 0.158 0.518 0.518 0.158 0.158 0.782 0.782

PRT 0.656 0.53 0.926 0.888 0.656 0.424 0.94 0.922

PRY 0.217 0.217 0.373 0.354 0.217 0.217 0.459 0.447

ROM 0.137 0.137 0.424 0.424 0.137 0.137 0.633 0.633

RUS 0.205 0.205 0.539 0.539 0.205 0.205 0.667 0.667

RWA 0.113 0.113 0.676 0.676 0.113 0.113 0.604 0.604

SAU 0.247 0.137 0.78 0.78 0.247 0.131 0.895 0.895

SEN 0.16 0.16 0.524 0.524 0.16 0.16 0.352 0.352

SGP 0.719 0.607 0.979 0.979 0.719 0.619 0.989 0.989

SLE 0.197 0.197 0.425 0.425 0.197 0.197 0.324 0.324

SLV 0.622 0.955 0.858 0.622 0.83 0.8

SRB 0.286 0.286 0.469 0.459 0.286 0.286 0.711 0.701

SUR 0.121 0.121 0.623 0.623 0.121 0.121 0.552 0.552

SVK 0.194 0.194 0.62 0.62 0.536 0.312 0.903 0.903

SWZ 0.163 0.163 0.626 0.626 0.163 0.163 0.58 0.58

SYC 0.14 0.14 0.699 0.699 0.14 0.14 0.73 0.73

TCD 0.127 0.127 0.337 0.337 0.127 0.127 0.22 0.22

THA 0.142 0.142 0.696 0.696 0.142 0.142 0.8 0.8

TJK 0.108 0.108 0.518 0.518 0.108 0.108 0.344 0.344

TTO 0.096 0.096 0.651 0.651 0.096 0.096 0.781 0.781

TUN 0.154 0.154 0.698 0.698 0.154 0.154 0.812 0.812

TUR 0.149 0.149 0.703 0.703 0.149 0.149 0.667 0.667

74





TZA 0.186 0.186 0.441 0.441 0.186 0.186 0.345 0.345

UGA 0.152 0.152 0.482 0.482 0.152 0.152 0.364 0.364

UKR 0.504 0.817 0.69 0.504 0.763 0.718

URY 0.173 0.173 0.597 0.597 0.173 0.173 0.841 0.841

USA 0.449 0.361 0.872 0.872 0.449 0.327 0.925 0.925

VEN 0.093 0.093 0.411 0.411 0.093 0.093 0.364 0.364

VNM 0.12 0.12 0.465 0.465 0.12 0.12 0.53 0.53

YEM 0.398 0.398 0.575 0.449 0.398 0.398 0.248 0.226

ZAF 0.284 0.284 0.718 0.702 0.284 0.284 0.552 0.544

ZMB 0.346 0.346 0.516 0.514 0.346 0.346 0.516 0.515

ZWE 0.751 0.665 0.626 0.751 0.321 0.314

75

Table A.8 Efficiency Score for Selected Infrastructure Indicators (Two Inputs – Single Output)

Input (Public ‐ Private Capital Expenditure)

Quality of overall infrastructure Quality of Electricity Supply



AGO 1 1 1 1 1 1 1 1

ALB 0.625 0.385 0.522 0.522 0.625 0.425 0.555 0.555

ARE 1 1 1 1 1 0.954 1 0.978

ARG 0.964 0.519 0.593 0.525 0.964 0.574 0.502 0.496

ARM 0.684 0.494 0.614 0.594 0.684 0.559 0.7 0.687

AUS 0.729 0.601 0.772 0.772 0.729 0.652 0.905 0.905

AUT 1 0.897 1 0.955 1 0.87 1 0.987

AZE 1 0.828 1 0.9 1 0.787 1 0.849

BDI 0.822 0.499 0.904 0.501 1 0.553 1 0.426

BEL 0.925 0.846 0.922 0.911 0.925 0.911 0.999 0.992

BEN 0.714 0.341 0.427 0.427 0.714 0.356 0.371 0.371

BFA 0.543 0.385 0.441 0.419 0.689 0.424 0.367 0.358

BGD 0.446 0.305 0.398 0.398 0.66 0.291 0.29 0.29

BGR 0.723 0.39 0.471 0.471 0.723 0.489 0.573 0.573

BHR 0.766 0.651 0.824 0.824 0.738 0.626 0.848 0.848

BIH 0.513 0.339 0.387 0.379 0.832 0.706 0.784 0.783

BLZ 0.714 0.5 0.568 0.553 0.895 0.58 0.603 0.603

BOL 0.894 0.484 0.715 0.552 0.894 0.643 0.999 0.675

BRA 0.995 0.799 0.679 0.62 0.995 0.808 0.804 0.779

BRB 1 1 1 1 1 1 1 1

BTN 0.528 0.379 0.676 0.676 0.528 0.453 0.863 0.863

BWA 0.608 0.458 0.65 0.65 0.503 0.404 0.546 0.546

CAN 0.833 0.765 0.912 0.884 0.801 0.781 0.967 0.966

CHE 1 1 1 1 1 1 1 1

CHL 1 0.798 1 0.851 0.903 0.776 0.939 0.857

CIV 1 0.829 1 0.831 1 0.864 1 0.822

CMR 0.864 0.417 0.495 0.446 0.864 0.452 0.413 0.401

COL 0.584 0.411 0.516 0.501 0.736 0.601 0.748 0.747

CPV 0.479 0.341 0.542 0.542 0.479 0.255 0.324 0.324

CRI 0.825 0.477 0.533 0.512 0.933 0.736 0.847 0.831

CZE 0.721 0.594 0.737 0.737 0.721 0.69 0.936 0.936

DEU 1 1 1 1 1 1 1 1

DOM 0.753 0.446 0.554 0.525 0.753 0.351 0.295 0.287

DZA 0.542 0.368 0.516 0.516 0.655 0.461 0.647 0.647

ECU 0.627 0.439 0.569 0.549 0.627 0.477 0.556 0.556

EGY 0.864 0.632 0.782 0.599 0.864 0.696 0.741 0.672

76






EST 0.73 0.609 0.773 0.773 0.73 0.605 0.814 0.814

ETH 0.605 0.382 0.514 0.492 0.605 0.41 0.473 0.472

FIN 1 0.94 1 0.978 0.858 0.851 0.996 0.995

FRA 0.992 0.917 0.994 0.97 0.851 0.826 0.985 0.984

GAB 0.531 0.338 0.459 0.459 0.531 0.317 0.354 0.354

GBR 1 0.92 1 0.92 1 1 1 1

GEO 0.752 0.525 0.657 0.629 0.752 0.599 0.724 0.724

GHA 0.709 0.537 0.632 0.576 0.709 0.495 0.451 0.442

GIN 1 1 1 1 1 1 1 0.59

GMB 0.839 0.569 0.683 0.651 0.839 0.538 0.591 0.59

GRC 1 0.839 1 0.815 1 0.896 1 0.868

GTM 0.968 0.88 0.942 0.86 1 0.916 1 0.892

GUY 0.976 0.603 0.912 0.685 0.976 0.544 0.722 0.521

HND 0.711 0.498 0.571 0.559 0.711 0.523 0.592 0.585

HRV 0.795 0.634 0.753 0.717 0.795 0.677 0.811 0.81

HTI 0.24 0.23 0.306 0.306 0.516 0.258 0.252 0.252

IDN 0.652 0.483 0.563 0.559 0.652 0.5 0.6 0.598

IND 0.598 0.37 0.535 0.535 0.598 0.36 0.481 0.481

IRL 0.969 0.675 0.781 0.707 0.969 0.845 0.949 0.93

IRN 0.747 0.557 0.642 0.627 0.747 0.651 0.765 0.754

ISL 1 1 1 1 1 1 1 1

ISR 1 1 1 1 1 1 1 1

ITA 0.92 0.622 0.887 0.636 1 0.819 1 0.846

JOR 0.749 0.627 0.745 0.745 0.749 0.657 0.836 0.836

JPN 0.861 0.79 0.951 0.911 0.829 0.778 0.952 0.952

KAZ 0.765 0.501 0.596 0.596 0.765 0.542 0.648 0.648

KEN 0.799 0.499 0.589 0.559 0.799 0.505 0.536 0.535

KHM 0.619 0.472 0.574 0.552 0.619 0.439 0.445 0.445

KOR 0.722 0.647 0.855 0.855 0.684 0.621 0.889 0.889

KWT 0.891 0.711 0.81 0.76 0.891 0.739 0.805 0.763

LKA 0.724 0.526 0.669 0.669 0.715 0.542 0.702 0.702

LSO 0.619 0.383 0.499 0.48 0.619 0.462 0.533 0.533

LTU 0.856 0.685 0.783 0.752 0.856 0.72 0.817 0.816

LUX 1 0.914 1 0.951 0.973 0.9 0.974 0.951

LVA 0.781 0.575 0.676 0.676 0.81 0.634 0.778 0.778

77






MAR 0.651 0.466 0.629 0.629 0.736 0.556 0.767 0.767

MDA 0.714 0.43 0.541 0.515 0.714 0.529 0.648 0.632

MDG 0.749 0.443 0.461 0.452 0.749 0.443 0.342 0.338

MEX 0.819 0.533 0.633 0.604 0.819 0.568 0.637 0.636

MLI 0.705 0.459 0.528 0.512 0.705 0.502 0.502 0.501

MMR 0.55 0.334 0.404 0.367 0.698 0.466 0.431 0.423

MNE 0.634 0.402 0.489 0.489 0.634 0.487 0.572 0.572

MOZ 0.789 0.414 0.493 0.477 0.789 0.521 0.535 0.523

MUS 0.796 0.592 0.716 0.693 0.796 0.632 0.773 0.772

MWI 0.888 0.523 0.794 0.529 0.888 0.497 0.622 0.385

MYS 0.781 0.759 0.893 0.869 0.781 0.715 0.856 0.855

NAM 0.707 0.599 0.774 0.774 0.707 0.586 0.795 0.795

NGA 0.639 0.417 0.423 0.411 0.863 0.409 0.237 0.232

NIC 0.719 0.354 0.45 0.45 0.719 0.408 0.466 0.466

NLD 0.93 0.872 0.967 0.942 0.93 0.915 0.992 0.992

NPL 0.762 0.649 0.527 0.453 0.762 0.649 0.271 0.257

NZL 0.778 0.642 0.768 0.748 0.778 0.705 0.835 0.835

OMN 0.802 0.746 0.868 0.852 0.802 0.784 0.899 0.898

PAK 0.993 0.618 0.754 0.584 0.993 0.535 0.45 0.37

PAN 0.62 0.474 0.688 0.688 0.62 0.502 0.764 0.764

PER 0.722 0.389 0.477 0.477 0.734 0.562 0.708 0.708

PHL 0.797 0.564 0.692 0.543 0.797 0.609 0.664 0.608

POL 0.694 0.471 0.555 0.533 0.861 0.682 0.783 0.782

PRT 1 0.907 1 0.923 0.98 0.874 0.94 0.923

PRY 0.554 0.387 0.436 0.39 0.953 0.541 0.459 0.454

ROM 0.601 0.321 0.424 0.424 0.713 0.485 0.633 0.633

RUS 0.843 0.509 0.571 0.55 0.843 0.625 0.688 0.668

RWA 0.743 0.571 0.724 0.696 0.743 0.519 0.605 0.605

SAU 0.82 0.715 0.834 0.823 0.82 0.799 0.896 0.896

SEN 0.701 0.411 0.524 0.524 0.701 0.336 0.352 0.352

SGP 0.943 0.881 0.979 0.979 0.768 0.752 0.989 0.989

SLE 0.863 0.454 0.527 0.463 0.863 0.433 0.334 0.33

SLV 1 0.966 1 0.96 1 0.93 1 0.91

SRB 0.845 0.47 0.469 0.459 0.845 0.681 0.711 0.701

SUR 0.522 0.365 0.623 0.623 0.522 0.336 0.552 0.552

SVK 0.801 0.554 0.62 0.62 0.801 0.744 0.903 0.903

SWZ 1 0.818 1 0.818 0.975 0.78 0.996 0.72

SYC 0.677 0.518 0.699 0.699 0.677 0.522 0.73 0.73

78






TCD 0.389 0.245 0.337 0.337 0.558 0.24 0.22 0.22

THA 0.738 0.552 0.696 0.696 0.738 0.602 0.8 0.8

TJK 0.917 0.578 0.828 0.65 0.917 0.467 0.59 0.407

TUN 0.788 0.593 0.724 0.699 0.788 0.651 0.812 0.812

TUR 0.726 0.558 0.703 0.703 0.726 0.522 0.667 0.667

TZA 0.642 0.332 0.441 0.441 0.642 0.313 0.345 0.345

UGA 0.655 0.353 0.482 0.482 0.655 0.321 0.364 0.364

UKR 0.917 0.749 0.855 0.712 0.917 0.786 0.885 0.733

URY 0.85 0.552 0.64 0.613 0.85 0.73 0.842 0.841

USA 0.945 0.838 0.933 0.914 0.909 0.818 0.926 0.925

VEN 1 0.637 1 0.736 1 0.662 1 0.676

VNM 0.524 0.341 0.465 0.465 0.524 0.412 0.53 0.53

YEM 1 0.805 1 0.809 1 0.681 1 0.449

ZAF 0.852 0.661 0.718 0.702 0.843 0.565 0.552 0.544

ZMB 0.681 0.483 0.516 0.514 0.681 0.483 0.516 0.515

ZWE 1 0.891 1 0.674 1 0.891 1 0.342

79

Table A.9. List of Countries

Code Region Country Code Region Country Code Region Country

AGO AFR Angola GRD LAC Grenada PER LAC Peru

ALB ECA Albania GTM LAC Guatemala PHL EAP Philippines

ARE MNA United Arab Emirates GUY LAC Guyana PNG EAP Papua New Guinea

ARG LAC Argentina HND LAC Honduras POL ECA Poland

ARM ECA Armenia HRV ECA Croatia PRI LAC Puerto Rico

ATG LAC Antigua and Barbuda HTI LAC Haiti PRY LAC Paraguay

AZE ECA Azerbaijan HUN ECA Hungary QAT MNA Qatar

BDI AFR Burundi IDN EAP Indonesia ROM ECA Romania

BEN AFR Benin IND SAS India RUS ECA Russian Federation

BFA AFR Burkina Faso IRN MNA Iran, Islamic Rep. RWA AFR Rwanda

BGD SAS Bangladesh JAM LAC Jamaica SAU MNA Saudi Arabia

BGR ECA Bulgaria JOR MNA Jordan SDN AFR Sudan

BHR MNA Bahrain KAZ ECA Kazakhstan SEN AFR Senegal

BHS LAC Bahamas, The KEN AFR Kenya SGP EAP Singapore

BIH ECA Bosnia & Herzegovina

KGZ ECA Kyrgyz Republic SLB EAP Solomon Islands

BLR ECA Belarus KHM EAP Cambodia SLE AFR Sierra Leone

BLZ LAC Belize KNA LAC St. Kitts and Nevis SLV LAC El Salvador

BOL LAC Bolivia KOR EAP Korea, Rep. SRB ECA Serbia

BRA LAC Brazil KWT MNA Kuwait SUR LAC Suriname

BRB LAC Barbados LAO EAP Lao PDR SVK ECA Slovak Republic

BRN EAP Brunei Darussalam LBN MNA Lebanon SVN ECA Slovenia

BTN SAS Bhutan LCA LAC St. Lucia SWZ AFR Eswatini

BWA AFR Botswana LKA SAS Sri Lanka SYC AFR Seychelles

CAF AFR Central African Rep. LSO AFR Lesotho SYR MNA Syrian Arab Republic

CHL LAC Chile LBR AFR Liberia TCD AFR Chad

CHN EAP China LBY MNA Lybia TGO AFR Togo

CIV AFR Côte d'Ivoire LTU ECA Lithuania THA EAP Thailand

CMR AFR Cameroon LVA ECA Latvia TJK ECA Tajikistan

COG AFR Congo, Rep. MAR MNA Morocco TKM ECA Turkmenistan

COL LAC Colombia MDA ECA Moldova TLS EAP Timor‐Leste

COM AFR Comoros MDG AFR Madagascar TON EAP Tonga

CPV AFR Cabo Verde MEX LAC Mexico TTO LAC Trinidad and Tobago

CRI LAC Costa Rica MKD ECA Macedonia, FYR TUN MNA Tunisia

CZE ECA Czech Republic MLI AFR Mali TUR ECA Turkey

80

Table A.9. Continued

Code Region Country Code Region Country Code Region Country

DJI MNA Djibouti MMR EAP Myanmar TZA AFR Tanzania

DMA LAC Dominica MNE EAP Montenegro, Rep. of UGA AFR Uganda

DOM LAC Dominican Republic

MNG EAP Mongolia UKR ECA Ukraine

DZA MNA Algeria MOZ AFR Mozambique URY LAC Uruguay

ECU LAC Ecuador MRT AFR Mauritania UZB ECA Uzbekistan

EGY MNA Egypt, Arab Rep. MUS AFR Mauritius VCT LAC St. Vincent & Grenadines

ERI AFR Eritrea MWI AFR Malawi VEN LAC Venezuela, RB

EST ECA Estonia MYS EAP Malaysia VNM EAP Vietnam

ETH AFR Ethiopia NAM AFR Namibia VUT EAP Vanuatu

FJI EAP Fiji NER AFR Niger WSM EAP Samoa

GAB AFR Gabon NGA AFR Nigeria YEM MNA Yemen, Rep.

GEO ECA Georgia NIC LAC Nicaragua ZAF AFR South Africa

GHA AFR Ghana NPL SAS Nepal ZAR AFR Congo, Dem. Rep.

GIN AFR Guinea OMN MNA Oman ZMB AFR Zambia

GMB AFR Gambia, The PAK SAS Pakistan ZWE AFR Zimbabwe

GNB AFR Guinea‐Bissau PAN LAC Panama

Table A.9. Continued

Developed countries included in the efficiency estimation for learning scores

Code Country Code Country Code Country

AUS Australia FIN Finland LUX Luxembourg

AUT Austria FRA France MLT Malta

BEL Belgium GBR United Kingdom NLD Netherlands

CAN Canada GRC Greece NOR Norway

CHE Switzerland IRL Ireland NZL New Zealand

CYP Cyprus ISL Iceland PRT Portugal

DEU Germany ISR Israel SWE Sweden

DNK Denmark ITA Italy USA United States

ESP Spain JPN Japan

81

Table A.10. Definition and Source of Variables Definition of Variable Source

Output variables for education

School enrollment, primary (% gross) World Bank WDI

School enrollment, primary (% net) World Bank WDI

School enrollment, secondary (% gross) World Bank WDI

School enrollment, secondary (% net) World Bank WDI

Tertiary level complete, ages 15+ Barro‐Lee Database

Literacy rate, youth total (% of people ages 15‐24) World Bank WDI

Average years of school, ages 15+ Barro‐Lee Database

First level complete, ages 15+ Barro‐Lee Database

second level complete, ages 15+ Barro‐Lee Database

PISA Science scores www.oecd.org/pisa/data

PISA Mathematics scores www.oecd.org/pisa/data

PISA Reading scores www.oecd.org/pisa/data

Quality of primary education, 1‐7 (best) World Economic Forum

Primary education Net Enrollment, net % World Economic Forum

Secondary Education Gross Enrollment, gross % World Economic Forum

Tertiary education enrollment, gross % World Economic Forum

Quality of the education system, 1‐7 (best) World Economic Forum

Quality of math and science education, 1‐7 (best) World Economic Forum

Input variables for education

Public education spending per capita in PPP terms, calculated World Bank WDI

Literacy rate, adult total (% of people ages 15 and above) World Bank WDI

Teachers per pupil, equal the reciprocal of pupils per teacher World Bank WDI

Output variables for health

Life expectancy at birth, total (years) World Bank WDI

Immunization, DPT (% of children ages 12‐23 months) World Bank WDI

Immunization, measles (% of children ages 12‐23 months) World Bank WDI

Disability Adjusted Life Expectancy Institute for Health Metrics and

Evaluation (IHME)

Tuberculosis cases/100,000 pop. World Economic Forum

Maternal Mortality Ratio (per 100 000 live births) World Health Organization

Infant mortality rate (probability of dying between birth and age 1 per 1000 live births)

World Health Organization

Input variables for health

Literacy rate, adult total (% of people ages 15 and above) World Bank WDI

public spending on health per capita in PPP terms, calculated World Bank WDI

public spending on health per capita in PPP terms, calculated World Bank WDI

Output variables for Infrastructure

Quality of overall infrastructure, 1‐7 (best) World Economic Forum

Quality of roads, 1‐7 (best) World Economic Forum

Quality of railroad infrastructure, 1‐7 (best) World Economic Forum

82

Table A.10 (Continued)

Definition of Variable Source

Quality of port infrastructure, 1‐7 (best) World Economic Forum

Quality of air transport infrastructure, 1‐7 (best) World Economic Forum

A. Transport infrastructure World Economic Forum

Quality of electricity supply, 1‐7 (best) World Economic Forum

Input variables for Infrastructure

Private investment spending per capita in real terms, calculated World Economic Outlook

Public investment spending per capita in real terms, calculated World Economic Outlook

Variables used in the calculation World Bank WDI

Pupil‐teacher ratio, primary World Bank WDI

Public spending on education, total (% of GDP) World Bank WDI

GDP per capita, PPP (constant 2011 international $) World Bank WDI

Health expenditure, private (% of GDP) World Bank WDI

Health expenditure, public (% of GDP) World Bank WDI

GDP (constant 2010 US$) World Economic Outlook

GDP (current US$) World Economic Outlook

National Currency per PPP dollar (Units National Currency per PPP dollar) World Economic Outlook

Population (Millions of Persons) World Economic Outlook

Variables used in the Panel Tobit regression

Wages and salaries (% of total public expenditure) World Bank WDI

Total government expenditure (% of GDP) World Bank WDI

Share of expenditures publicly financed (public/total) World Bank WDI

GDP per capita in constant 2011 US dollars World Bank WDI

Urban population (% of total) World Bank WDI

Dummy variable for HIV/AIDS WHO mappings of diseases

Gini Coefficient World Bank WDI

Aid (% of fiscal revenue) calculated as Official development assistance World Bank WDI

and official aid (current US$) *official exchange rate * PPP conversion factor

/ Revenue, excluding grants (current LCU)

Institutional Indicators including

a. Worldwide Governance Research Indicators World Bank WDI

Control of Corruption: Estimate World Bank WDI

Government Effectiveness: Estimate World Bank WDI

Political Stability and Absence of Violence/Terrorism: Estimate World Bank WDI

Regulatory Quality: Estimate World Bank WDI

Rule of Law: Estimate World Bank WDI

Voice and Accountability: Estimate World Bank WDI

83

Figure A. 1. Efficiency Frontiers for Education

Free Disposable Hull (FDH) Data Envelopment Analysis (DEA)

AGO

ALB

ARG

ARM

ATG

AUS

AZEBDI

BEN

BFA

BGD

BGR

BLR

BLZ

BOL

BRABRB

BWA

CAF

CAN

CHECHL

CHN

CIV

CMRCOG

COL

COM

CPV CRI CYPDEU

DJI

DNK

DOM

ECU

EGYESP

EST

ETH

FIN

FJIFRA

GBRGEO

GHA

GIN

GMBGNB

GTM

GUY

HND

HRV

HUNIDN IND

IRN ISLITA

KAZ

KEN

KGZ

KHM

KNA

KOR

LAO

LBN

LKA

LSO

LTU LVAMAR

MDA

MEX

MKD

MLI

MNG

MOZ

MRT

MUSMWIMYS

NAM

NER

NGA

NIC

NLD NOR

NPLNZL

OMN

PAK

PANPERPHL

POL

PRYROM

RUSRWA

SAU

SDN

SEN SLB

SLE

SLV

SVNSWE

SWZ

TCD

TGOTHA

TJK

TTO

TUN

TUR

TZA

UGA

UKRURY

USAVCT

VEN

VNM

VUTZWE

5060

7080

9010

0N

et P

rimar

y S

chool

Enr

ollm

ent



Net Primary Enrollment vs Education Expenditure

AGO

ALB

ARG

ARM

ATG

AUS

AZEBDI

BEN

BFA

BGD

BGR

BLR

BLZ

BOL

BRABRB

BWA

CAF

CAN

CHECHL

CHN

CIV

CMRCOG

COL

COM

CPV CRI CYPDEU

DJI

DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FJIFRA

GBRGEO

GHA

GIN

GMBGNB

GTM

GUY

HND

HRV

HUNIDN IND

IRN ISLITA

KAZ

KEN

KGZ

KHM

KNA

KOR

LAO

LBN

LKA

LSO

LTU LVAMAR

MDA

MEX

MKD

MLI

MNG

MOZ

MRT

MUSMWIMYS

NAM

NER

NGA

NIC

NLD NOR

NPLNZL

OMN

PAK

PANPERPHL

POL

PRYROM

RUSRWA

SAU

SDN

SEN SLB

SLE

SLV

SVNSWE

SWZ

TCD

TGOTHA

TJK

TTO

TUN

TUR

TZA

UGA

UKR

URY

USAVCT

VEN

VNM

VUTZWE

5060

7080

90100

Net P

rim

ary

Sch

ool E

nro

llmen

t



Net Primary Enrollment vs Education Expenditure

(a.1) (a.2)

AGO

ALB

ARG

ARM

ATG

AUS

AUT

BDI

BEN

BFA

BGD

BGR

BLR

BLZBOL

BRABRB

CAF

CAN

CHECHLCHN

CIV

CMRCOG

COL

COM

CPV

CRI

CYPCZE

DEU

DJI

DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FJI

FRAGBR

GEO

GHA

GIN

GMBGTM

GUY

HND

HRVHUN

IDN

IND

IRN

ISL

ITA

JAM

KAZ

KEN

KGZKNA

KOR

LAO

LBN

LCA

LKA

LSO

LTU LVA

MAR

MDA

MDG

MEXMKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NER

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROMRUS

RWA

SAU

SDN SEN

SLBSLE

SLV

SVK

SVN

SWE

SWZ

TCD

TGO

THA

TJKTKMTUNTUR

TZA

UGA

UKRURYUSA

VCT

VEN

VUT

ZAF

ZARZWE

050

100

150

Net S

eco

ndary

Sch

ool E

nro

llment



Net Secondary Enrollment vs Education Expenditure

AGO

ALB

ARG

ARM

ATG

AUS

AUT

BDI

BEN

BFA

BGD

BGR

BLR

BLZBOL

BRABRB

CAF

CAN

CHECHLCHN

CIV

CMRCOG

COL

COM

CPV

CRI

CYPCZE

DEU

DJI

DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FJI

FRAGBR

GEO

GHA

GIN

GMBGTM

GUY

HND

HRVHUN

IDN

IND

IRN

ISL

ITA

JAM

KAZ

KEN

KGZKNA

KOR

LAO

LBN

LCA

LKA

LSO

LTU LVA

MAR

MDA

MDG

MEXMKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NER

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROMRUS

RWA

SAU

SDN SEN

SLBSLE

SLV

SVK

SVN

SWE

SWZ

TCD

TGO

THA

TJKTKMTUNTUR

TZA

UGA

UKRURYUSA

VCT

VEN

VUT

ZAF

ZARZWE

050

100

150

Net

Sec

ond

ary

Sch

ool

Enr

ollm

ent



Net Secondary Enrollment vs Education Expenditure

(b.1) (b.2)

AGO

ALB

ARG

ARM

AUS

AUT

AZE

BDI

BEN

BFA

BGD

BGR

BLZ

BOL

BRA

BRB

BWA

CAN

CHE

CHL

CHN

CIVCMR

COL

CPV

CRI

CYP

CZE

DEU

DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHAGIN

GMB

GTM

GUY

HND

HRV

HUN

IDN

IND

IRN

ISL

ITA

JAM

KAZ

KEN

KGZ

KHM

KOR

LAO

LBN

LKA

LSO

LTU

LVA

MAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NAMNGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROM

RUS

RWA

SAU

SEN

SLE

SLV

SVK

SVN

SWE

SWZTCD

THA

TJK

TTO

TUN

TUR

TZAUGA

UKR

URY

USA

VEN

VNMZAF

ZWE

020

4060

80100

Tert

iary

Gro

ss E

nro

llment



Tertiary Gross Enrollment vs Education Expenditure

(c.1)

AGO

ALB

ARG

ARM

AUS

AUT

AZE

BDI

BEN

BFA

BGD

BGR

BLZ

BOL

BRA

BRB

BWA

CAN

CHE

CHL

CHN

CIVCMR

COL

CPV

CRI

CYP

CZE

DEU

DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHAGIN

GMB

GTM

GUY

HND

HRV

HUN

IDN

IND

IRN

ISL

ITA

JAM

KAZ

KEN

KGZ

KHM

KOR

LAO

LBN

LKA

LSO

LTU

LVA

MAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NAMNGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROM

RUS

RWA

SAU

SEN

SLE

SLV

SVK

SVN

SWE

SWZTCD

THA

TJK

TTO

TUN

TUR

TZAUGA

UKR

URY

USA

VEN

VNMZAF

ZWE

020

4060

80100

Tert

iary

Gro

ss E

nro

llment



Tertiary Gross Enrollment vs Education Expenditure

(c.2)

84

AGO

ALB

ARG

ARM

AUS

AUT

AZE

BDI

BEN

BFABGD

BGRBLZ

BOL

BRA

BRB

BWA

CAN

CHE

CHL

CHN

CIV

CMR

COL

CPV

CRI

CYP

CZE

DEU DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FRA

GAB

GBR

GEO GHA

GIN

GMB

GTM

GUY

HND

HRV

HTI

HUNIDN

IND

IRN

ISL

ITA

JAM

KAZ KEN

KGZKHM

KOR

LAO

LBN

LKA

LSO

LTULVA

MAR

MDA

MDGMEX

MKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAKPAN

PER

PHL

POL

PRY

ROM

RUSRWA

SAU

SEN

SLESLV

SVK

SVN SWE

SWZ

TCD

THA

TJK

TTO

TUN

TUR

TZA

UGA

UKR

URY

USA

VEN

VNM

ZAF

ZWE

23

45

67

Qu

alit

y of

pri

mar

y e

duca

tion



Quality of primary education vs Education Expenditure

(d.1)

AGO

ALB

ARG

ARM

AUS

AUT

AZE

BDI

BEN

BFABGD

BGRBLZ

BOL

BRA

BRB

BWA

CAN

CHE

CHL

CHN

CIV

CMR

COL

CPV

CRI

CYP

CZE

DEU DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FRA

GAB

GBR

GEO GHA

GIN

GMB

GTM

GUY

HND

HRV

HTI

HUNIDN

IND

IRN

ISL

ITA

JAM

KAZ KEN

KGZKHM

KOR

LAO

LBN

LKA

LSO

LTULVA

MAR

MDA

MDGMEX

MKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAKPAN

PER

PHL

POL

PRY

ROM

RUSRWA

SAU

SEN

SLESLV

SVK

SVN SWE

SWZ

TCD

THA

TJK

TTO

TUN

TUR

TZA

UGA

UKR

URY

USA

VEN

VNM

ZAF

ZWE

23

45

67

Qua

lity

of p

rima

ry e

duc

atio

n



Quality of primary education vs Education Expenditure

(d.2)

AGO

ALB

ARGARM

AUS

AUT

AZE

BDI

BEN

BFA

BGDBGR

BLZ

BOL

BRA

BRB

BWA

CAN

CHE

CHL

CHN

CIV

CMRCOL

CPV

CRI

CYP

CZE

DEU DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GTM

GUY

HND

HRV

HTI

HUN

IDN IND

IRN

ISL

ITA JAMKAZ

KEN

KGZ

KHM

KOR LAO

LBN

LKA

LSO

LTULVA

MARMDA

MDGMEX

MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROMRUS

RWA

SAU

SEN

SLESLV

SVK

SVN

SWE

SWZ

TCD

THATJK

TTO

TUN

TURTZA

UGA

UKR

URY

USA

VEN

VNM

ZAF

ZWE

23

45

6Q

ual

ity o

f th

e e

duc

atio

n sy

ste

m



Quality of the education system vs Education Expenditure

(e.1)

AGO

ALB

ARGARM

AUS

AUT

AZE

BDI

BEN

BFA

BGDBGR

BLZ

BOL

BRA

BRB

BWA

CAN

CHE

CHL

CHN

CIV

CMRCOL

CPV

CRI

CYP

CZE

DEU DNK

DOM

ECU

EGY

ESP

EST

ETH

FIN

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GTM

GUY

HND

HRV

HTI

HUN

IDN IND

IRN

ISL

ITA JAMKAZ

KEN

KGZ

KHM

KOR LAO

LBN

LKA

LSO

LTULVA

MARMDA

MDGMEX

MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRY

ROMRUS

RWA

SAU

SEN

SLESLV

SVK

SVN

SWE

SWZ

TCD

THATJK

TTO

TUN

TURTZA

UGA

UKR

URY

USA

VEN

VNM

ZAF

ZWE

23

45

6Q

ual

ity o

f the

edu

catio

n sy

stem



Quality of the education system vs Education Expenditure

(e.2)

ALB

ARG

AUSAUT

AZE BGR

BRA

CANCHE

CHL

CHN

COL

CRI

CYP

CZE

DEU DNK

DOM

ESP

FIN

FRAGBR

GEO

HRV

HUN

IDN

ISL

ITA

KAZ

KGZ

KOR

LBN

LTULVA

MDAMEX

MKD

NLD

NORNZL

PAN

PER

POL

ROM

RUSSVK

SVN

SWE

THA TTO

TUN

TURURY

USA

VNM

300

350

400

450

500

550

Pis

a M

athem

atic

s Lite

racy

sco

re



Pisa Mathematics Literacy score vs Education Expenditure

(f.1)

ALB

ARG

AUSAUT

AZE BGR

BRA

CANCHE

CHL

CHN

COL

CRI

CYP

CZE

DEU DNK

DOM

ESP

FIN

FRAGBR

GEO

HRV

HUN

IDN

ISL

ITA

KAZ

KGZ

KOR

LBN

LTULVA

MDAMEX

MKD

NLD

NORNZL

PAN

PER

POL

ROM

RUSSVK

SVN

SWE

THA TTO

TUN

TURURY

USA

VNM

300

350

400

450

500

550

Pis

a M

ath

em

atic

s Lite

racy

sco

re



Pisa Mathematics Literacy score vs Education Expenditure

(f.1)

85

ALB

ARG

AUS

AUT

AZE

BGR

BRA

CAN

CHE

CHL

CHN

COLCRI

CYP

CZE

DEUDNK

DOM

ESP

FIN

FRAGBR

GEO

HRVHUN

IDN

ISLITA

KAZ

KGZ

KOR

LBN

LTU

LVA

MDAMEX

MKD

NLD NORNZL

PAN

PER

POL

ROM

RUS

SVK

SVNSWE

THATTO

TUN

TUR

URY

USA

VNM

300

350

400

450

500

550

Pis

a R

eadin

g L

itera

cy



Pisa Reading Literacy vs Education Expenditure

(g.1)

ALB

ARG

AUS

AUT

AZE

BGR

BRA

CAN

CHE

CHL

CHN

COLCRI

CYP

CZE

DEUDNK

DOM

ESP

FIN

FRAGBR

GEO

HRVHUN

IDN

ISLITA

KAZ

KGZ

KOR

LBN

LTU

LVA

MDAMEX

MKD

NLD NORNZL

PAN

PER

POL

ROM

RUS

SVK

SVNSWE

THATTO

TUN

TUR

URY

USA

VNM

300

350

400

450

500

550

Pis

a R

ead

ing L

itera

cy



Pisa Reading Literacy vs Education Expenditure

(g.1)

ALBARG

ARM

AUS

AUT

BDI

BEN

BGD

BGR BLZ

BOL

BRA

BRBBWA

CAF

CANCHE

CHL

CHN

CIV

CMRCOG

COL

CRI

CYP

CZE DEU

DNK

DOM ECU

EGY

ESP

EST

FINFJIFRA

GAB

GBR

GHA

GMB

GTM

GUY

HND

HRV

HTI

HUN

IDN

IND

IRN

ISL

ITAJAM

KAZ

KEN

KGZ

KHM

KOR

LAO

LKA

LSO

LTULVA

MAR

MDA

MEX

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NIC

NLDNOR

NPL

NZL

PAK

PANPER

PHL

POL

PRY

ROM

RUS

RWA

SAU

SDNSEN

SLE

SLV

SVK

SVN SWE

SWZ

TGO

THA

TJKTTO

TUNTUR

TZAUGA

UKR

URY

USA

VEN

VNM

ZAF

ZAR

ZWE

05

1015

Ave

rage

Yea

r of

Sch

ool

500 1000 1500 2000 2500Orthogonalized Public Expenditure on Education

Data Source: World Bank WDI, Barro-Lee database

Average Year of School vs Education Expenditure

ALBARG

ARM

AUS

AUT

BDI

BEN

BGD

BGR BLZ

BOL

BRA

BRBBWA

CAF

CANCHE

CHL

CHN

CIV

CMRCOG

COL

CRI

CYP

CZE DEU

DNK

DOM ECU

EGY

ESP

EST

FINFJIFRA

GAB

GBR

GHA

GMB

GTM

GUY

HND

HRV

HTI

HUN

IDN

IND

IRN

ISL

ITAJAM

KAZ

KEN

KGZ

KHM

KOR

LAO

LKA

LSO

LTULVA

MAR

MDA

MEX

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NIC

NLDNOR

NPL

NZL

PAK

PANPER

PHL

POL

PRY

ROM

RUS

RWA

SAU

SDNSEN

SLE

SLV

SVK

SVN SWE

SWZ

TGO

THA

TJKTTO

TUNTUR

TZAUGA

UKR

URY

USA

VEN

VNM

ZAF

ZAR

ZWE0

510

15A

vera

ge Y

ear of S

chool



Average Year of School vs Education Expenditure

(h.1) (h.2)

ALB

ARG

ARM

AUS

AUT

BDI

BEN

BGD

BGR

BLZ

BOL

BRA

BRB

BWA

CAF

CAN

CHE

CHL

CHN

CIV

CMR

COG

COL

CRI

CYP

CZE

DEU

DNK

DOM

ECUEGY

ESP

EST

FIN

FJI FRA

GAB

GBR

GHA

GMB

GTM

GUY

HND

HRV

HTI

HUN

IDNIND

IRN

ISL

ITAJAM

KAZ

KEN

KGZ

KHM

KOR

LAO

LKA

LSO

LTU

LVA

MAR

MDA

MEX

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NIC

NLDNOR

NPL NZL

PAK

PAN

PER

PHL

POL

PRY

ROM

RUS

RWA

SAU

SDN SEN

SLE

SLV

SVK

SVN

SWE

SWZ

TGO

THA

TJK

TTO

TUN

TUR

TZAUGA

UKR

URY

USAVEN

VNM

ZAF

ZAR

ZWE

020

4060

80S

eco

nd

Lev

el C

omp

lete



Second Level Complete vs Education Expenditure

ALB

ARG

ARM

AUS

AUT

BDI

BEN

BGD

BGR

BLZ

BOL

BRA

BRB

BWA

CAF

CAN

CHE

CHL

CHN

CIV

CMR

COG

COL

CRI

CYP

CZE

DEU

DNK

DOM

ECUEGY

ESP

EST

FIN

FJI FRA

GAB

GBR

GHA

GMB

GTM

GUY

HND

HRV

HTI

HUN

IDNIND

IRN

ISL

ITAJAM

KAZ

KEN

KGZ

KHM

KOR

LAO

LKA

LSO

LTU

LVA

MAR

MDA

MEX

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NIC

NLDNOR

NPL NZL

PAK

PAN

PER

PHL

POL

PRY

ROM

RUS

RWA

SAU

SDN SEN

SLE

SLV

SVK

SVN

SWE

SWZ

TGO

THA

TJK

TTO

TUN

TUR

TZAUGA

UKR

URY

USAVEN

VNM

ZAF

ZAR

ZWE

020

4060

80S

eco

nd L

eve

l Com

ple

te



Second Level Complete vs Education Expenditure

(i.1) (i.2)

86

AGO

ALB ARGARMAZE

BDI

BENBFA

BGD

BGRBLR BOLBRA

CAF

CHLCHN

CIV

CMR COG

COL

COM

CPV CRI CYPDOM ECU

EGY

ESP EST

GAB

GEO

GHA

GIN

GMBGNB

GTMGUY HND

HRVIDN

IND

IRNITA

KEN

KGZ

KHM

LAO

LBN LKA

LSO

LTU LVA

MAR

MDA

MDG

MEX

MLI

MNG

MOZ

MUS

MWI

MYS

NAM

NER

NPL

OMN

PAK

PAN PERPHL PRYROM RUS

RWA

SAU

SEN

SLE

SLVSWZ

TCD

TGO

THA TUNTUR

TZA

UGA

UKRURYVENVNMZAF

ZAR

ZWE

2040

6080

100

You

th L

itera

cy R

ate

0 200 400 600 800 1000Orthogonalized Public Expenditure on Education


Youth Literacy Rate vs Education Expenditure

AGO

ALB ARGARMAZE

BDI

BENBFA

BGD

BGRBLR BOLBRA

CAF

CHLCHN

CIV

CMR COG

COL

COM

CPV CRI CYPDOM ECU

EGY

ESP EST

GAB

GEO

GHA

GIN

GMBGNB

GTMGUY HND

HRVIDN

IND

IRNITA

KEN

KGZ

KHM

LAO

LBN LKA

LSO

LTU LVA

MAR

MDA

MDG

MEX

MLI

MNG

MOZ

MUS

MWI

MYS

NAM

NER

NPL

OMN

PAK

PAN PERPHL PRYROM RUS

RWA

SAU

SEN

SLE

SLVSWZ

TCD

TGO

THA TUNTUR

TZA

UGA

UKRURYVENVNMZAF

ZAR

ZWE

2040

6080

100

Yout

h L

itera

cy R

ate

0 200 400 600 800 1000Orthogonalized Public Expenditure on Education


Youth Literacy Rate vs Education Expenditure

(j.1)

(j.2)

87

Figure A.2. Efficiency Frontiers for Health

Free Disposable Hull (FDH) Data Envelopment Analysis (DEA)

AGO

ALBARG

ARM

ATG

AUSAUT

AZE

BDI

BENBFA

BGD

BGR

BHRBHS

BLR

BLZ

BOL

BRABRB

BWA

CAF

CANCHE

CHL

CHN

CIV

CMR

COG

COL

COM

CPV

CRICYPCZE

DEU

DJI

DNK

DOM

DZAECU

EGY

ERI

ESP

EST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GRC

GRDGTM

GUY

HND

HRV

HTI

HUN

IDNIND

IRN

ISLITA

JAMJOR

KAZ

KEN

KGZ

KHM

KOR

LAO

LBN

LCALKA

LSO

LTU

LUX

LVAMAR

MDA

MDG

MEXMKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLDNOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

PNG

POL

PRY

ROM

RUS

RWA

SAU

SDN

SEN

SLB

SLE

SLV

SVK

SVN

SWE

SWZ

TCD

TGO

THA

TJK

TKM

TON

TTO

TUNTUR

TZA

UGA

UKR

URY

USA

UZB

VCTVEN

VNM

VUT

WSM

YEM

ZAF ZARZMB

ZWE

5060

7080

90Li

fe E

xpe

cta

ncy



Life Expectancy vs Health Expenditure

AGO

ALBARG

ARM

ATG

AUSAUT

AZE

BDI

BENBFA

BGD

BGR

BHRBHS

BLR

BLZ

BOL

BRABRB

BWA

CAF

CANCHE

CHL

CHN

CIV

CMR

COG

COL

COM

CPV

CRICYPCZE

DEU

DJI

DNK

DOM

DZAECU

EGY

ERI

ESP

EST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GRC

GRDGTM

GUY

HND

HRV

HTI

HUN

IDNIND

IRN

ISLITA

JAMJOR

KAZ

KEN

KGZ

KHM

KOR

LAO

LBN

LCALKA

LSO

LTU

LUX

LVAMAR

MDA

MDG

MEXMKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLDNOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

PNG

POL

PRY

ROM

RUS

RWA

SAU

SDN

SEN

SLB

SLE

SLV

SVK

SVN

SWE

SWZ

TCD

TGO

THA

TJK

TKM

TON

TTO

TUNTUR

TZA

UGA

UKR

URY

USA

UZB

VCTVEN

VNM

VUT

WSM

YEM

ZAF ZARZMB

ZWE

5060

7080

90Life

Exp

ecta

ncy



Life Expectancy vs Health Expenditure

(a.1) (a.2)

AGO

ALB

ARG ARM

ATG

AUSAUT

AZE

BDI

BEN

BFA

BGD

BGR

BHRBHS BLR

BLZBOLBRA

BRB

BWA

CAF

CAN

CHE

CHL

CHN

CIV

CMRCOG

COL

COM

CPV

CRI

CYP CZE

DEU

DJI

DMA

DNK

DOM

DZA

ECU

EGYERI

ESP

EST

ETH

FINFJI FRA

GAB

GBR

GEO GHA

GIN

GMB

GNB

GRC

GRD

GTM

GUYHNDHRV

HTI

HUN

IDNIND

IRN

ISL

ITA

JAM

JORKAZ

KEN

KGZ

KHM

KNAKOR

LAOLBN

LCALKA

LSOLTU

LUX

LVA

MAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NAM

NER

NGA

NICNLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

PNG

POL

PRY

ROM

RUS RWASAU

SDNSEN

SLB

SLE

SLV

SVKSVN

SWE

SWZ

TCD

TGO

THA

TJKTKM

TON

TTO

TUNTUR

TZA

UGA

UKR

URY USA

UZBVCT

VEN

VNM

VUTWSM

YEMZAFZAR

ZMB

ZWE

4060

8010

0Im

mun

izat

ion D

PT



Immunization DPT vs Health Expenditure

AGO

ALB

ARG ARM

ATG

AUSAUT

AZE

BDI

BEN

BFA

BGD

BGR

BHRBHS BLR

BLZBOLBRA

BRB

BWA

CAF

CAN

CHE

CHL

CHN

CIV

CMRCOG

COL

COM

CPV

CRI

CYP CZE

DEU

DJI

DMA

DNK

DOM

DZA

ECU

EGYERI

ESP

EST

ETH

FINFJI FRA

GAB

GBR

GEO GHA

GIN

GMB

GNB

GRC

GRD

GTM

GUYHNDHRV

HTI

HUN

IDNIND

IRN

ISL

ITA

JAM

JORKAZ

KEN

KGZ

KHM

KNAKOR

LAOLBN

LCALKA

LSOLTU

LUX

LVA

MAR

MDA

MDG

MEX

MKD

MLI

MNG

MOZMRT

MUS

MWI

MYS

NAM

NER

NGA

NICNLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

PNG

POL

PRY

ROM

RUS RWASAU

SDNSEN

SLB

SLE

SLV

SVKSVN

SWE

SWZ

TCD

TGO

THA

TJKTKM

TON

TTO

TUNTUR

TZA

UGA

UKR

URY USA

UZBVCT

VEN

VNM

VUTWSM

YEMZAFZAR

ZMB

ZWE

4060

80100

Imm

uniz

atio

n D

PT



Immunization DPT vs Health Expenditure

(b.1) (b.2)

AGO

ALB

ARG

ARMATG

AUS

AUT

AZE BDI

BEN

BFA

BGD

BGR

BHR

BHS

BLRBLZ

BOL

BRA

BRB

BWA

CAF

CANCHECHL

CHN

CIV

CMRCOG

COL

COM

CPV

CRICYP

CZEDEU

DJI

DMA

DNKDOM

DZAECU

EGY ERIESP

EST

ETH

FIN

FJI

FRA

GAB

GBRGEO

GHA

GIN

GMB

GNB

GRC

GRD

GTM

GUY

HND

HRV

HTI

HUN

IDN

IND

IRN

ISL

ITAJAM

JORKAZ

KEN

KGZ

KHM

KNAKOR

LAO

LBN

LCALKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MEX MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

PNG

POL

PRYROM

RUS

RWA

SAU

SDN

SEN

SLB

SLE

SLV

SVK

SVN

SWE

SWZ

TCD

TGO

THA

TJK

TKM

TON

TTO

TUNTURTZA

UGA

UKR

URY

USA

UZBVCT

VEN

VNM

VUT

WSMYEM

ZAF

ZAR

ZMB

ZWE

5060

7080

9010

0Im

mun

izatio

n M

easl

es



Immunization Measles vs Health Expenditure

AGO

ALB

ARG

ARMATG

AUS

AUT

AZE BDI

BEN

BFA

BGD

BGR

BHR

BHS

BLRBLZ

BOL

BRA

BRB

BWA

CAF

CANCHECHL

CHN

CIV

CMRCOG

COL

COM

CPV

CRICYP

CZEDEU

DJI

DMA

DNKDOM

DZAECU

EGY ERIESP

EST

ETH

FIN

FJI

FRA

GAB

GBRGEO

GHA

GIN

GMB

GNB

GRC

GRD

GTM

GUY

HND

HRV

HTI

HUN

IDN

IND

IRN

ISL

ITAJAM

JORKAZ

KEN

KGZ

KHM

KNAKOR

LAO

LBN

LCALKA

LSO

LTU

LUX

LVA

MAR

MDA

MDG

MEX MKD

MLI

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

PNG

POL

PRYROM

RUS

RWA

SAU

SDN

SEN

SLB

SLE

SLV

SVK

SVN

SWE

SWZ

TCD

TGO

THA

TJK

TKM

TON

TTO

TUNTURTZA

UGA

UKR

URY

USA

UZBVCT

VEN

VNM

VUT

WSMYEM

ZAF

ZAR

ZMB

ZWE

5060

7080

9010

0Im

muni

zatio

n M

easl

es



Immunization Measles vs Health Expenditure

(c.1) (c.2)

88

Table B.1. Spearman rank-correlation on input efficiency rankings (DEA)

Note 1. Figures are correlation coefficients from Spearman test, number of observations are in parentheses. 2. ***0.01 significance level, ** 0.05 significance level, *0.10 significance level, and insignificant otherwise

PISA

Science

Second Level

Complete

Average year of School



Gross Primary

Enrollment

Net Primary

Enrollment

DALE

Free Tuberculosis



PISA Science

1.000 (55)

0.72*** (51)

0.80*** (51)

0.74*** (44)

0.76*** (52)

0.63*** (55)

0.78*** (52)

0.19 (55)

0.37*** (55)

0.34** (55)

‐0.18 (55)


1.000 (113)

0.97*** (113)

0.96*** (87)

0.95*** (106)

0.80*** (110)

0.85*** (105)

0.23** (113)

0.28*** (106)

0.32*** (113)

0.23**


1.000 (113)

0.95*** (87)

0.95*** (106)

0.75*** (110)

0.87*** (105)

0.28*** (113)

0.30*** (106)

0.36*** (113)

0.17* (113)


1.000 (103)

0.96*** (103)

0.75*** (101)

0.88*** (100)

0.33*** (102)

0.27*** (93)

0.39*** (103)

0.35*** (101)


1.000 (128)

0.73*** (126)

0.87*** (119)

0.40*** (127)

0.34*** (111)

0.45*** (128)

0.32*** (126)


1.000 (133)

0.74*** (126)

0.08 (132)

0.09 (116)

0.14 (133)

0.13 (131)

Net Primary Enrollment

1.000 (126)

0.50*** (125)

0.41*** (111)

0.50*** (133)

037*** 124)

DALE

1.000 (151)

0.78*** (126)

0.88*** (151)

0.56*** (148)

Free Tuberculosis

1.000 (126)

0.79*** (126)

0.55*** (148)


1.000 (152)

0.62*** (148)


1.000 (148)

89

Table B.2. Spearman rank-correlation on output efficiency rankings (DEA)

Note

3. Figures are correlation coefficients from Spearman test, number of observations are in parentheses. 4. ***0.01 significance level, ** 0.05 significance level, *0.10 significance level, and insignificant otherwise

PISA

Science

Second Level

Complete




Gross Primary

Enrollment

Net Primary

Enrollment

DALE

Free Tuberculosi

s



PISA Science

1.000 (55)

0.21 (51)

0.61*** (51)

0.76*** (44)

0.68*** (52)

‐0.08 (55)

0.53*** (52)

0.39*** (55)

0.43*** (55)

0.64** (55)

0.66*** (55)


1.000 (113)

0.84*** (113)

0.71*** (87)

0.69*** (106)

‐0.008 (110)

0.41*** (105)

0.53*** (113)

0.50*** (106)

0.66*** (113)

0.72***


1.000 (113)

0.86*** (87)

0.86*** (106)

‐0.04 (110)

0.55*** (105)

0.69*** (113)

0.66*** (106)

0.85*** (113)

0.86*** (113)


1.000 (103)

0.91*** (103)

‐0.06 (101)

0.65*** (100)

0.73*** (102)

0.70*** (93)

0.87*** (103)

0.89*** (101)


1.000 (128)

0.0.16* (126)

0.66*** (119)

0.81*** (127)

0.75*** (111)

0.88*** (128)

0.88*** (126)


1.000 (133)

0.38*** (126)

0.10 (132)

‐0.007 (116)

0.08 (133)

0.05 (131)


1.000 (126)

0.65*** (125)

0.56*** (111)

0.67*** (126)

0.66*** 124)

DALE

1.000 (151)

0.88*** (126)

0.90*** (151)

0.84*** (148)

Free Tuberculosis

1.000 (126)

0.84*** (126)

0.82*** (148)


1.000 (152)

0.93*** (148)


1.000 (148)

90

Table B.3. Spearman rank-correlation on input efficiency rankings (FDH)

Note 5. Figures are correlation coefficients from Spearman test, number of observations are in parentheses. 6. ***0.01 significance level, ** 0.05 significance level, *0.10 significance level, and insignificant otherwise

PISA Science

Second Level

Complete




Gross Primary

Enrollment

Net Primary

Enrollment

DALE Free Tuberculosis



PISA Science

1.000 (55)

0.69*** (51)

0.80*** (51)

0.69*** (44)

0.71*** (52)

0.41*** (55)

0.76*** (52)

0.18 (55)

0.24* (55)

0.30** (55)

0.26* (55)


1.000 (113)

0.91*** (113)

0.87*** (87)

0.80*** (106)

0.50*** (110)

0.83*** (105)

0.19** (113)

0.16* (106)

0.26*** (113)

0.28*** (113)


1.000 (113)

0.88*** (87)

0.86*** (106)

0.44*** (110)

0.85*** (105)

0.30*** (113)

0.24** (106)

0.40*** (113)

0.39*** (113)


1.000 (103)

0.89*** (103)

0.43*** (101)

0.87*** (100)

0.37*** (102)

0.19* (93)

0.40*** (103)

0.41*** (101)


1.000 (128)

0.35*** (126)

0.82*** (119)

0.47*** (127)

0.34*** (111)

0.51*** (128)

0.47*** (126)


1.000 (133)

0.50*** (126)

‐0.05 (132)

‐0.13 (116)

‐0.11 (133)

‐0.13 (131)


1.000 (126)

0.49*** (125)

0.32*** (111)

0.49*** (133)

0.46*** 124)

DALE

1.000 (151)

0.75*** (126)

0.86*** (151)

0.81*** (148)

Free Tuberculosis

1.000 (126)

0.80*** (126)

0.73*** (148)


1.000 (152)

0.93*** (148)


1.000 (148)

91

Table B.4. Spearman rank-correlation on output efficiency rankings (FDH)

Note

Figures are correlation coefficients from Spearman test, number of observations are in parentheses. ***0.01 significance level, ** 0.05 significance level, *0.10 significance level, and insignificant otherwise

PISA Science

Second Level

Complete




Gross Primary

Enrollment

Net Primary

Enrollment

DALE

Free Tuberculos

is



PISA Science

1.000 (55)

0.20 (51)

0.63*** (51)

0.70*** (44)

0.64*** (52)

‐0.04 (55)

0.47*** (52)

0.34** (55)

0.43*** (55)

0.63** (55)

0.64*** (55)


1.000 (113)

0.84*** (113)

0.72*** (87)

0.70*** (106)

0.10 (110)

0.41*** (105)

0.52*** (113)

0.50*** (106)

0.67*** (113)

0.72*** (113)


1.000 (113)

0.87*** (87)

0.86*** (106)

0.06 (110)

0.57*** (105)

0.68*** (113)

0.65*** (106)

0.85*** (113)

0.86*** (113)


1.000 (103)

0.90*** (103)

0.03 (101)

0.66*** (100)

0.72*** (102)

0.70*** (93)

0.87*** (103)

0.89*** (101)


1.000 (128)

0.20*** (126)

0.68*** (119)

0.79*** (127)

0.74*** (111)

0.86*** (128)

0.88*** (126)


1.000 (133)

0.37*** (126)

0.19** (132)

0.00 (116)

0.11 (133)

0.11 (131)


1.000 (126)

0.65*** (125)

0.56*** (111)

0.67*** (133)

0.67*** 124)

DALE 1.000 (151)

0.87*** (126)

0.88*** (151)

0.83*** (148)

Free Tuberculosis

1.000 (126)

0.84*** (126)

0.82*** (148)


1.000 (152)

0.93*** (148)


1.000 (148)

efficiency of public spending in education, health, …...policy research working paper 8586...

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