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Appendix. Data Envelopment Analysis (DEA)23
Etibar Jafarov is from the European Department of the International Monetary Fund (IMF). Victoria Gunnarsson was in the Fiscal Affairs Department of the IMF at the time of the writing of this paper; currently, she is with the Independent Evaluation Group of the World Bank. The authors would like to thank Robert A. Feldman, Tomislav Galac, David Moore, Marijn Verhoeven, and participants in a seminar at the Croatian National Bank and a Zagreb conference (entitled “Welfare State Performance and Design”) sponsored by the Croatian Institute of Public Finance for helpful comments.
Expenditure-led fiscal adjustment will help to address Croatia’s large current account deficit, and maintain strong economic growth on a sustainable basis. At the same time, rationalizing spending is key for enhancing the flexibility of fiscal policy, a necessary ingredient for coping with shocks in the context of tightly managing the exchange rate.
The projection does not include spending related to the use of EU structural funds.
EU-10 countries are new EU members and comprise the Czech Republic, Estonia, Latvia, Hungary, Lithuania, Poland, Slovakia, Slovenia, Bulgaria, and Romania. EU-15 countries comprise Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom.
Results for the EU-10 are heavily influenced by the results for Bulgaria and Romania, which have significantly worse results than the other new EU members. But Croatia’s performance is still slightly better than the averages for the other EU-10 countries.
Twenty groups of people, including pensioners, unemployed, and students, are exempt from paying contributions. Only around 35 percent of the population pays contribution.
See Funding Health Care by Mossialos et al. (2002) for a description of cost sharing in Europe. Several countries, including Australia, Canada, and Switzerland, do not allow supplementary insurance to cover copayments associated with services paid for by the health insurance fund.
These lists were introduced in 2006. For drugs on the B list, the HZZO pays a reference price for drugs on the A list and consumers pay the difference between the sale and reference prices. As a result of strong bargaining, pharmaceutical spending was reduced by about 2 percent in 2007, despite a 6 percent increase in consumption of drugs.
Over a third of total health care spending in Croatia finances hospital (in-patient) care.
Croatia ranked 26th on the PISA science scale, ahead of some EU countries (e.g., Italy and Spain).
The sequencing of possible reforms and related political economy issues are beyond the scope of this paper.
The results are broadly comparable to those in Verhoeven, Gunnarsson, and Lugaresi (2007), which analyzes health care spending in the Slovak Republic.
This analysis does not provide estimates of causality. It is possible that causality goes the other way around or both ways. The small sample size precludes regression analysis in the second-stage.
Given the close relationship of spending and outcomes with income levels, correlations of efficiency scores and associated factors are conditional on GDP. GDP per capita is adversely related to efficiency since many of the factors that are associated with efficiency are also closely related to income level. In order to avoid attribution of factors whose effects on the variation in efficiency cannot be separated from the effect of GDP, only GDP per capita and factors that are correlated with efficiency independently of GDP per capita are considered in the second-stage analysis of this chapter. The association with efficiency of factors that are strongly correlated with GDP is assessed by regressing the efficiency score on both GDP and the associated factor.
The Croatian government adopted the National Health Care Development Strategy 2006–11 to enhance and secure better-quality health care for citizens. The strategy includes both system reforms and financing reforms.
The replacement rate is the ratio of benefits to (previously received) income.
About 6 percent of the labor force was on sick leave in 2005; anecdotal evidence suggests that sick leave is used to deal with excess employment at the business level.
Moreover, restricting the basic benefit package would stimulate private participation in the provision of additional insurance.
The share of obese people in Croatia is almost double the average of the EU-15. Mihaljek (2007) mentions an unhealthy lifestyle (high alcohol and tobacco consumption, and prevalence of physical inactivity) as the likely reason for the difference in mortality rates for non-communicable diseases between Croatia and EU-15 countries.
Efficiency in secondary education is estimated using both a combined set of secondary intermediary outputs and outcomes, and PISA scores only.
System efficiency was estimated only for the secondary education level, where PISA test scores were used as education outcome. The overall public sector efficiency (quartile) rankings in the primary and secondary levels presented in Table 7 are for the first stage of the production process (spending to intermediary outputs), since no education outcomes such as test scores are available at these levels.
There is well-established literature using DEA to assess the relative efficiency of public expenditure. Gupta and Verhoeven (2001) studied the relative efficiency of education spending in a broad sample of African countries during the 1984-95 period. Afonso and St. Aubyn (2004) applied DEA and a related frontier-based approach on health and education spending in a sample of OECD countries. Herrera and Pang (2005) studied the relative efficiency of spending in 140 countries using DEA. Afonso, Schuknecht and Tanzi (2006) applied DEA in a sample of EU and emerging market countries. An important contribution of their work was to apply truncated regression models based on procedures developed by Simar and Wilson (2007) to control for exogenous factors that impact efficiency but that are not directly controlled by policy makers. Coelli, Lefebvre, and Pestieau (2007) applied DEA to study social protection performance in the EU.
An output-based efficiency score of one corresponds to a relatively efficient country operating on the frontier. Scores exceeding one imply that spending could achieve better output performance. This differs from input-based efficiency scores that range between zero and one.
The input- and output-based efficiency scores are equal assuming constant returns to scale. However, the DEA models considered in this chapter permit variable returns to scale.
A key issue is how quickly the estimated efficiency scores converge to their unbiased true values if the sample of observations is expanded. This convergence speed is n-2/(p+q+1), where p is the number of inputs and q is the number of production items. In the 1 input/1 product examples of this Appendix, the convergence speed is n-2/3. This is faster than the convergence speed for a standard parametric regression of n-1/2, suggesting that reasonable estimates of efficiency scores and confidence intervals can be reached with a lower number of observations than would be needed for standard regression analysis. However, the convergence speed declines exponentially as the number of inputs and production items is increased, and already at two inputs and production items, the speed of convergence is markedly slower than for a parametric regression. This implies that an expansion in the numbers of inputs and production items comes at a significant cost in terms of the ability to draw conclusions on efficiency from a limited number of observations.