Assessing Health Efficiency across Countries with a Two-step and Bootstrap Analysis Miguel St. Aubyn (ISEG-UTL, Technical University of Lisbon) António Afonso (ECB and ISEG-UTL) Fiscal Policy Challenges in Europe German Federal Ministry of Finance, Berlin, 23 March 2007

Assessing health efficiency across countries Fiscal Policy Challenges in Europe2 Motivation  Importance of health spending Germany, 2003 – 11.1% of GDP, of which 78.2 percent is public spending.Germany, 2003 – 11.1% of GDP, of which 78.2 percent is public spending. OECD countries, 2003 – 8.7 % of GDP, of which 72.5 percent is public spending.OECD countries, 2003 – 8.7 % of GDP, of which 72.5 percent is public spending.  European Union, 25 countries Total government spending, 2003 – 47.7 % of GDPTotal government spending, 2003 – 47.7 % of GDP Health government spending, 2003 – 6.4 % of GDPHealth government spending, 2003 – 6.4 % of GDP Health spending is 13.4 % of government spendingHealth spending is 13.4 % of government spending  There is an increased concern about health spending and with cross-country comparison, namely: OECDOECD ECEC

Assessing health efficiency across countries Fiscal Policy Challenges in Europe3 Motivation  Main questions Are “health results” satisfactory considering the amount of resources allocated to this activity?Are “health results” satisfactory considering the amount of resources allocated to this activity? Could we have better results using the same resources?Could we have better results using the same resources? Could we have the same results with lower expenses?Could we have the same results with lower expenses? Can we measure inefficiency across countries?Can we measure inefficiency across countries? Can we explain measured inefficiency?Can we explain measured inefficiency? –a systemic component, –and an environmental or non-discretionary component.

Assessing health efficiency across countries Fiscal Policy Challenges in Europe4 Public sector efficiency – some related references Evans, D.; Tandon, A.; Murray, C. and Lauer, J. (2000). “The Comparative Efficiency of National Health Systems in Producing Health: an Analysis of 191 Countries”, GPE Discussion Paper Series 29, Geneva, World Health Organisation. Afonso, A. and M. St. Aubyn (2005). "Non-parametric Approaches to Education and Health Efficiency in OECD Countries", Journal of Applied Economics, 8 (2), p Afonso, A., L. Schuknecht and V. Tanzi (2005). "Public sector efficiency: An international comparison," Public Choice, Springer, 123 (3), pages , June. Afonso, A. and M. St. Aubyn (2006). "Cross-country Efficiency of Secondary Education Provision: a Semi-parametric Analysis with Non-discretionary Inputs", Economic Modelling, 23 (3), p Simar, L. and Wilson, P. (2007). “Estimation and Inference in Two-Stage, Semi-Parametric Models of Production Processes”, Journal of Econometrics, 136 (1),

Assessing health efficiency across countries Fiscal Policy Challenges in Europe5 Data Envelopment Analysis  Efficiency measurement: Comparison of resources used to provide certain services, the inputs;Comparison of resources used to provide certain services, the inputs; with outputs, or results.with outputs, or results. Efficiency frontiers are estimated …Efficiency frontiers are estimated … … and inefficient situations detected (efficiency scores are computed).… and inefficient situations detected (efficiency scores are computed).  There are different techniques to deal with efficiency frontier estimation. We have used Data Envelopment Analysis (DEA).

Assessing health efficiency across countries Fiscal Policy Challenges in Europe6 Data Envelopment Analysis

Assessing health efficiency across countries Fiscal Policy Challenges in Europe7 Data Envelopment Analysis The more common “production function” relates several inputs to the output:The more common “production function” relates several inputs to the output: y = F(x 1, x 2 )y = F(x 1, x 2 ) However, it is conceivable that:However, it is conceivable that: y ≤ F(x 1, x 2 )y ≤ F(x 1, x 2 ) New interpretation:New interpretation: F(x 1, x 2 ) is a production possibilities frontierF(x 1, x 2 ) is a production possibilities frontier Note that:Note that: Usually there are several outputs.Usually there are several outputs. Their joint production depends on several inputs…Their joint production depends on several inputs… and on other variables (“environment variables”).and on other variables (“environment variables”).

Assessing health efficiency across countries Fiscal Policy Challenges in Europe8 Data Envelopment Analysis Country D vertical inefficiency score: (d 1 +d 2 )/d 1 Part of Country D inefficiency may be due to a harsh environment. Corrected inefficiency score: (d 1c +d 2c )/d 1c < (d 1 +d 2 )/d 1 Corrected inefficiency score: (d 1c +d 2c )/d 1c < (d 1 +d 2 )/d 1

Assessing health efficiency across countries Fiscal Policy Challenges in Europe9 Health – the outputs The considered outputs in each country were:The considered outputs in each country were: Life expectancyLife expectancy Infant survival rate (ISR)Infant survival rate (ISR) [children that survived]/[children that died before 1 year][children that survived]/[children that died before 1 year] ISR = [1000-infant mortality rate]/[infant mortality rate]ISR = [1000-infant mortality rate]/[infant mortality rate] Potential Years of Life Not Lost, PYLNLPotential Years of Life Not Lost, PYLNL [number of potential years of life till 70] – [number of life years lost due to all causes before the age of 70 and that could be prevented][number of potential years of life till 70] – [number of life years lost due to all causes before the age of 70 and that could be prevented] Source: OECD Health Data 2005Source: OECD Health Data 2005

Assessing health efficiency across countries Fiscal Policy Challenges in Europe10 Health – the inputs Inputs were:Inputs were: number of practising physiciansnumber of practising physicians practising nursespractising nurses acute care beds per thousand habitantsacute care beds per thousand habitants high-tech diagnostic medical equipment [magnetic resonance imagers (MRI)].high-tech diagnostic medical equipment [magnetic resonance imagers (MRI)]. Source: OECD Health Data 2005Source: OECD Health Data 2005

Assessing health efficiency across countries Fiscal Policy Challenges in Europe11 A look at the data

Assessing health efficiency across countries Fiscal Policy Challenges in Europe12 Principal components The use of PCA reduces the dimensionality of multivariate dataThe use of PCA reduces the dimensionality of multivariate data We applied PCA to the four input variablesWe applied PCA to the four input variables We used the first three principal components as the three input measures (they explain around 88 per cent of the variation)We used the first three principal components as the three input measures (they explain around 88 per cent of the variation) We also applied PCA to the three output variablesWe also applied PCA to the three output variables We selected the first principal component (it accounts for around 84 per cent of the variation)We selected the first principal component (it accounts for around 84 per cent of the variation) This reduces the problem to one output – three inputsThis reduces the problem to one output – three inputs

Assessing health efficiency across countries Fiscal Policy Challenges in Europe13 Empirical results Two step procedureTwo step procedure First step:First step: Data envelopment analysis (inputs, outputs)Data envelopment analysis (inputs, outputs) Inefficient scores are computed for each countryInefficient scores are computed for each country Second step:Second step: Regression analysisRegression analysis Inefficient scores are explained by environment variablesInefficient scores are explained by environment variables Two regression methods – Tobit and bootstrapTwo regression methods – Tobit and bootstrap

Assessing health efficiency across countries Fiscal Policy Challenges in Europe14 Empirical results – first step (DEA)

Assessing health efficiency across countries Fiscal Policy Challenges in Europe15 Empirical results – second step Regression of efficiency scores on GDP per capita, Y, educational level, E, obesity, O, and tobacco consumpion, T.Regression of efficiency scores on GDP per capita, Y, educational level, E, obesity, O, and tobacco consumpion, T. Tobit regressionTobit regression Bootstrap, algorithm 1Bootstrap, algorithm 1 Bootstrap, algorithm 2Bootstrap, algorithm 2 Results are similarResults are similar

Assessing health efficiency across countries Fiscal Policy Challenges in Europe16 Empirical results – second step

Assessing health efficiency across countries Fiscal Policy Challenges in Europe17 Empirical results – second step

Assessing health efficiency across countries Fiscal Policy Challenges in Europe18 Empirical results – second step

Assessing health efficiency across countries Fiscal Policy Challenges in Europe19 Empirical results – second step decomposition of the output efficiency score into two distinct parts:decomposition of the output efficiency score into two distinct parts: the result of a country’s environment,the result of a country’s environment, all other factors having an influence on efficiency, including therefore inefficiencies associated with the health system itself.all other factors having an influence on efficiency, including therefore inefficiencies associated with the health system itself.

Assessing health efficiency across countries Fiscal Policy Challenges in Europe20 Empirical results – second step

Assessing health efficiency across countries Fiscal Policy Challenges in Europe21 Empirical results – second step

Assessing health efficiency across countries Fiscal Policy Challenges in Europe22 Conclusions Inefficiencies may be quite high.Inefficiencies may be quite high. On average, and as a conservative estimate, countries could have increased their results by 40 per cent using the same resources. (Hungary, the Slovak Republic and Poland)On average, and as a conservative estimate, countries could have increased their results by 40 per cent using the same resources. (Hungary, the Slovak Republic and Poland) GDP per head, educational attainment, tobacco consumption, and obesity are highly and significantly correlated to output scores.GDP per head, educational attainment, tobacco consumption, and obesity are highly and significantly correlated to output scores. Country rankings and output scores derived from this correction can be substantially different from standard DEA results.Country rankings and output scores derived from this correction can be substantially different from standard DEA results. Non-discretionary outputs cannot be changed in the short run (education, smoking habits, obesity).Non-discretionary outputs cannot be changed in the short run (education, smoking habits, obesity). Results were strikingly similar with three different estimation processes, which bring increased confidence to obtained conclusions.Results were strikingly similar with three different estimation processes, which bring increased confidence to obtained conclusions.