The Performance of Health Spending: Evidence from American Area

Economic Management Journal February 2015, Volume 4, Issue 1, PP.1-7

The Performance of Health Spending: Evidence from American Area Shan-Ju Ho Department of Finance/National Sun Yat-sen University/Doctoral program student, 80424, Taiwan Email: shanju71@gmail.com

Abstract This article measures the performance of health expenditure and demonstrates how productivity has changed over time for 31 selected countries in the American area by applying Super-SBM model and Malmquist productivity Index, respectively.

Efficiency scores are measured using traditional BCC model and Super-SBM model. Based on the Super-SBM model, we obtain proper scores and find the most efficient country as well. The findings of decreased productivity growth are related to technical change and pure efficiency change. In addition, this paper demonstrates a SSBM-MPI matrix that could distinguish all countries into four quadrants. Different quadrant has different policy implication for health spending.

Keywords: Data Envelopment Analysis; Health Spending; Efficiency; Productivity; Slack-Based Measure

1 INTRODUCTION The so-called super-efficiency data envelopment analysis (DEA) model has the desirable feature of differentiating some of the efficient decision-making units (DMUs) that have identical efficiency scores that are equal to one in the basic DEA models. 1 The purpose of this paper is to investigate the performance of health expenditure and demonstrate how productivity has changed over time for 31 countries in the American area by addressing the super slacks-based measurement model (hereafter Super-SBM) and Malmquist productivity index (hereafter MPI), respectively.

Efficiency measurement is an important issue of continuing interest in the health economics literature. With budget constraints, it is important to examine the efficiency of health spending as small changes can have a major impact in terms of achieving the millennium development goals. This effect is particularly pronounced in countries where public resources are normally insufficient. According to the World Health Report (2000), health spending as a proportion of world GDP rose from 3% in 1948 to 7.9% in 1997. Globally in 2006, it was about 8.7%, with the highest level in the Americas at 12.8%. During this period of rapidly rising medical costs, policy-makers started to focus on the productivity and efficiency of health care and have increasingly made the regular measurement of the efficiency of health care systems central to their work. Several approaches to measuring the efficiency of government expenditure have been presented in the literature. In general, the government has engaged in the production of various outputs by combining labor with other inputs. Therefore, the government can be viewed as a producer. When a government produces more outputs but spends less on inputs, it can be viewed as being more efficient than a government that produces fewer outputs and uses more inputs (Gupta and Verhoeven, 2001).

2 Although some studies focus on the relationship between health expenditure and economic growth, they do not pay much attention to the productivity and efficiency of health spending apart from Lavado and Cabanda (2009) and Keng and Li (2010). However, while very insightful, country analyses are rarely used in policy analysis.

Studies on the efficiency of government spending have developed broadly along certain lines. First, some studies 1

See Xue and Harker (2002).

Klomp and de Haan (2008) explored the relation between governance and the health of individuals and the health care sector for 101 countries over the period 2000 to 2005.

2

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have concentrated on public spending and economic growth (Ventelou and Bry, 2006; Sung, 2007). Another strand of the literature has focused on certain specific types of government spending. Gupta et al. (2002) use cross-sectional data for 50 countries to show that an increase in public expenditure on education and health care is associated with improvements in both access to and attainment in schools, and reduces mortality rates for infants and children. Gupta et al. (2003) also confirm that the poor have significantly worse health status than the nonpoor, and the regression results provide new evidence that public spending on health care matters more to them. Lavado and Cabanda’s paper (2009) attempts to measure the efficiency of provinces in the Philippines in utilizing public resources for health and education. In the case of China, Han and Miao (2010) analyze local health expenditure efficiencies by using the twostage framework of the DEA-Tobit model based on panel data for 31 provinces in China from 1997 to 2007. Keng and Li (2010) measure and compare the efficiency of health production across countries and through time by decomposing productivity change into three components, namely, technical change, the scale component and efficiency change. In the article of Carrington et al. (2011), they examine level of efficiency and productivity growth in Australian health funds. Efficiency frontiers are constructed using DEA methods, and MPI are constructed for the health funds.

Besides concentrating on the efficiency of public spending or on certain specific types of government spending, the analysis of efficiency should consider discrimination between efficient decision-making units. In recent years, superefficiency DEA models have become an interesting research subject. Cooper et al. (2000) introduce a nonradial measure of efficiency referred to as the Slacks-Based Measure (SBM) of efficiency. Herrero et al. (2006) examine the mix efficiency of a fleet operating in a multispecies fishery. Liu and Wang (2008) employ DEA to measure the Malmquist productivity of semiconductor packaging and testing firms in Taiwan from 2000 to 2003, and also use slacks-based measurement (SBM) and Super-SBM models to obtain more accurate measurements. Chiu and Chen (2009) adopt a three-stage approach to estimate bank efficiency. In the first stage, they employ a Super-SBM model to estimate the scores related to bank efficiency that include internal risk. Drake et al. (2009) also examine the efficiency of the Japanese banking system using the slacks-based measure.

In addition, Kritikos et al. (2010) apply three nonparametric DEA models that assume constant returns-to-scale (CRS), variable returns-to-scale (VRS) and slacks-based measures (SBM), respectively, for evaluating the relative efficiency of corporate real estate usage across decision-making units. Chiu et al. (2011) adopt two DEA methods, including the Banker-Charnes-Cooper (BCC) and slacks-based measure (SBM) of super efficiency, to investigate whether a bank’s technical efficiency index’s results, that incorporate account risk and do not incorporate account risk, differ significantly. To the author’s knowledge, little use has been made in government health expenditure to date of the Tone (2001) SBM DEA program.

Although the evidence presented in the above-mentioned studies has in general contributed to the literature, few papers have analyzed the use of Super-SBM model and the Malmquist productivity index as tools for possible application in evaluating the performance of government health spending. This article extends a framework to analyze the performance and productivity of health spending for 31 countries in the American area.

The main purposes and contribution of this paper are as follows. First of all, we evaluate the efficiency scores of the Super-SBM model for government spending on health in the American area. A comparison is made between the results of the BCC and Super-SBM models as well. Secondly, efficiency can change over time, and indeed we would suggest that the examination of a single cross section of data, when estimating production efficiency with regard to health spending, gives us only a snapshot of what is really going on. Only the analysis of a panel of data can reveal the true effects. A DEA-based method that is suitable for analyzing such data – the Malmquist productivity index – is used to measure the concept of productivity. We use a decomposition of the Malmquist productivity index to locate the sources of productivity growth.

2 DATA The sample used in this study comprises 31 countries in the American area over the period 2005-2007 obtained from

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World Development Indicators (WDI) and Global Development Finance (GDF).3 The use of panel data has clear advantages over the use of cross-sectional data. The data are limited to a 3-year time period in this analysis due to missing data. The DEA model includes one input and three outputs. The input variable used in our empirical analysis is measures by health expenditure per capital (hereafter HEX, see Gupta and Verhoeven, 2001; Han and Miao, 2010).

The choice of output variables for the model used in the analysis was largely based on the studies of Gupta and Verhoeven (2001) and Spinks and Hollingsworth (2009). They are measured by the year of life expectancy (hereafter LEX), by the rate of infant mortality per 1,000 live births (hereafter INF, see Gupta and Verhoeven, 2001; Adang and Borm, 2007) and by immunizations against measles (hereafter MEA, see Hsu, 2012).4 Since infant mortality is bad, a transformation is necessary to conform to the isotonicity property. This paper uses the reciprocals of infant mortality as output. This has the advantage of leaving relative positions of governments unchanged (see Rayp and van de Sijpe, 2007).5 TABLE 1. EFFICIENT SCORES FOR AMERICAN AREA

Country

Country Code

Argentina Bahamas, The Barbados Belize Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB Canada United States Mean

ARG BHS BRB BLZ BOL BRA CHL COL CRI CUB DOM ECU SLV GRD GTM GUY HTI HND JAM MEX NIC PAN PRY PER VCT SUR TTO URY VEN CAN USA

2005 TE SE 0.458 0.147 0.121 0.332 0.261 0.203 1.000 0.309 1.000 0.281 1.000 0.112 0.939 0.109 0.466 0.338 1.000 0.103 1.000 0.106 0.565 0.320 1.000 0.159 1.000 0.186 1.000 0.160 1.000 0.294 1.000 0.407 1.000 1.000 1.000 0.343 0.633 0.352 0.378 0.267 1.000 0.340 1.000 0.109 0.750 0.350 0.697 0.309 0.664 0.266 0.385 0.378 0.212 0.355 0.316 0.276 0.457 0.281 1.000 0.021 0.089 0.121 0.722 0.269

2006 TE SE 0.195 0.422 0.114 0.434 0.272 0.253 1.000 0.414 1.000 0.405 0.326 0.439 1.000 0.137 0.456 0.449 1.000 0.123 1.000 0.150 0.530 0.400 1.000 0.202 0.569 0.448 0.439 0.422 1.000 0.338 1.000 0.546 1.000 1.000 0.972 0.447 0.654 0.464 0.301 0.427 1.000 0.439 0.344 0.402 0.778 0.463 0.791 0.436 0.497 0.439 0.333 0.515 0.188 0.502 0.273 0.394 0.396 0.339 1.000 0.026 0.090 0.154 0.630 0.388

2007 TE SE 1.000 0.073 0.125 0.372 0.297 0.174 1.000 0.338 1.000 0.373 0.323 0.377 1.000 0.118 0.480 0.377 1.000 0.101 1.000 0.094 0.550 0.344 1.000 0.164 0.642 0.377 1.000 0.163 1.000 0.272 1.000 0.478 1.000 1.000 0.938 0.387 0.600 0.349 0.327 0.351 1.000 0.377 0.351 0.323 0.906 0.391 0.766 0.373 0.544 0.377 0.360 0.411 0.175 0.433 0.279 0.340 0.378 0.288 1.000 0.024 0.096 0.129 0.682 0.314

Average TE SE 0.551 0.214 0.120 0.379 0.277 0.210 1.000 0.354 1.000 0.353 0.550 0.309 0.980 0.121 0.467 0.388 1.000 0.109 1.000 0.117 0.548 0.355 1.000 0.175 0.737 0.337 0.813 0.248 1.000 0.301 1.000 0.477 1.000 1.000 0.970 0.392 0.629 0.388 0.335 0.348 1.000 0.385 0.565 0.278 0.811 0.401 0.751 0.373 0.568 0.361 0.359 0.435 0.192 0.430 0.289 0.337 0.410 0.303 1.000 0.024 0.092 0.135 0.678 0.324

Note: TE denotes technical efficiency and SE denotes scale efficiency.

3

World Development Indicators (WDI) is the primary World Bank database for development data from officially-recognized international sources. Global Development Finance (GDF) provides external debt and financial flows statistics for countries that report public and publicly-guaranteed debt under the World Bank’s Debtor Reporting System (DRS).

4 O’Brien et al. (2010) find that generates income from salary and wages and household enterprises, as well as government transfers, has produced differentiation in the subjective psychological as well as material quality of rural residents’ lives.

5 Bowlin (1998) suggests adding the same positive amount to the values of the variable concerned for all DMUs in order to solve the non-positivity problem. Adam et al. (2011) use the reciprocals of bads as outputs. In addition, Chung et al. (1997) also introduce a directional distance function and use it as a component in a new productivity index that readily models joint production of goods and bads, credits firms for reductions in bads and increases in goods, and does not require shadow prices of bad outputs.

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3 EMPIRICAL RESULTS 3.1 Results of the DEA analysis Table 1 presents the results of the efficiency scores for the BCC and Super-SBM models. In BCC model, Table 2 shows that the average level of the efficiency scores in the American area for the period as a whole is 0.674, suggesting that the countries could achieve an average of the same level of output with roughly 33% fewer resources, i.e., the performance of countries could be improved without necessarily increasing health spending. Specifically, the inefficient countries could adopt the best practice from amongst the high-performing peers.

On average, we have seven countries that are operating efficiently in BCC model. They are Belize (BLZ), Costa Rica (CRI), Cuba (CUB), Guyana (GUY), Haiti (HTI), Nicaragua (NIC) and Canada (CAN). Six of them are in the central and southern American area and one is in the northern American area. In particular, the United States exhibits the lowest technical efficiency of 0.096. With reference to the result for the United States, there may be a slight possibility that a great size of health spending will be its burden. The explanation should be sought in the reduction of the whole health or medical care spending and the introduction of new technology. In other words, our results suggest that the inefficient estimates observed for sample countries are attributed to the inefficient usage of input resources.

TABLE 2. MALMQUIST INDEX SUMMARY OF COUNTRIES MEANS

Country Argentina Bahamas, The Barbados Belize Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB Canada United States Mean

Country Code ARG BHS BRB BLZ BOL BRA CHL COL CRI CUB DOM ECU SLV GRD GTM GUY HTI HND JAM MEX NIC PAN PRY PER VCT SUR TTO URY VEN CAN USA

EFFCH 1.041 1.078 0.989 1.047 1.152 1.042 1.071 1.071 0.990 0.943 1.022 1.016 1.142 1.011 0.963 1.084 1.000 1.029 0.969 1.067 1.054 1.018 1.160 1.152 1.078 1.044 1.004 1.045 0.922 1.072 1.074 1.042

TECHCH 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.883 0.879 0.879 0.879 0.879 0.880 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.879 0.882 0.879 0.879 0.879

PECH 1.477 1.018 1.067 1.000 1.000 0.568 1.032 1.015 1.000 1.000 0.986 1.000 0.801 1.000 1.000 1.000 1.000 0.969 0.974 0.930 1.000 0.592 1.099 1.049 0.905 0.967 0.909 0.940 0.910 1.000 1.039 0.964

SECH 0.705 1.059 0.926 1.047 1.152 1.833 1.038 1.056 0.990 0.943 1.036 1.016 1.425 1.011 0.963 1.084 1.000 1.062 0.995 1.147 1.054 1.718 1.056 1.099 1.190 1.079 1.105 1.111 1.013 1.072 1.033 1.081

MPI 0.915 0.947 0.869 0.920 1.012 0.915 0.941 0.941 0.870 0.828 0.898 0.897 1.003 0.888 0.846 0.953 0.880 0.904 0.852 0.938 0.926 0.894 1.019 1.012 0.947 0.917 0.883 0.918 0.813 0.942 0.943 0.916

Rank 17 6 27 14 2 17 10 10 26 30 20 21 4 23 29 5 25 19 28 12 13 22 1 2 6 16 24 15 31 9 8

Note: EFFCH is technical efficiency change, TECHCH is technical change, PECH is pure efficiency change, SECH is scale efficiency change, TFPCH is the total factor productivity change (Malmquist productivity index).

As can be seen in Table 2, Belize, Costa Rica, Cuba, Guyana, Haiti, Nicaragua and Canada have the best performance. However, we cannot identify which country is the best one. The Super-SBM model, however, -4www.emj-journal.org

discriminates between these efficient countries. The basic idea is that we delete the efficient country concerned from the production possibility set (PPS) and measure the distance from the country to the remaining PPS. If the distance is small, the super-efficiency of the country is judged to be lower as the country only marginally outperforms other countries. On the other hand, if the distance is large, the super-efficiency of the country is high compared to the remaining countries. Hence, it will make sense to rank the efficient countries in the order of the distance thus obtained. Among the Super-SBM efficiency scores of Table 1 or Table 2, Haiti has the best performance in the sample.

3.2 Analysis of the Results on the Malmquist Productivity Index A DEA study in general considers performance analysis at a given point of time. However, extensions of the DEA procedures, such as the Malmquist productivity index approach, have been reported to provide performance analysis over a period of time. A summary of results listing the efficiency change (EFFCH), technological change (TECHCH) and Malmquist productivity index (MPI) for each country is presented in Table 4. The Malmquist productivity index mean and rank are also listed. If the value of the Malmquist productivity index or any of its components is less (greater) than one, it denotes a deterioration (an improvement) in performance.

EFFCH provides a measure of how far each country has moved from the efficient frontier over the time period of interest. The mean value of 1.042 for samples suggests that, overall, member countries have moved closer to the frontier, representing an increase in efficiency change. On the contrary, the mean technological change (TECHCH) value of 0.879 would suggest that the technology with respect to which individual countries are producing outputs has declined slightly, that is, the efficiency of the whole sample has remained steady (or declined slightly), and that over this time period absolute output values have decreased. The MPI value of 0.916 may be interpreted as reflecting the sum of movements.

In addition, these results indicate that the Malmquist productivity index has decreased by 8.4% per year over the 2005-2007 period. On average, this deterioration is ascribed to a technical regress (TECHCH) of 12.1% and to an efficiency improvement (EFFCH) of 4.2%. The latter, in turn, is attributed to a scale efficiency improvement (SECH) of 8.1% but to a smaller pure efficiency deterioration (PECH) of 3.6%.

For various countries, negative values of technical change as well as MPI have been indicative of their difficulties in exploiting new technologies. Negative rates of technical change should be attributed to the cooperation of medical technologies in the health sector, such as an acute inpatient care system (Gaal et al., 2006), and the poor qualification of human resources (Varela et al., 2010). It is worth noting that over the period under examination, higher efficiency from one period to another does not necessarily suggest that the operating unit achieves higher productivity since the technology may have changed. As can be seen in Table 4, only 12.9% of American area countries show MPI progress, but most of them are regressing. The top four countries are Paraguay, Peru, Bolivia and El Salvador in the rank order of MPI. In the case of Paraguay, the gains in productivity are due to gains in efficiency progress, and thus productivity is found to have grown above the sample average in the period. Venezuela, Cuba and Guatemala, on the contrary, experienced losses in all the periods considered. The important finding for these three countries is the lower efficiency change.

4 CONCLUSIONS AND POLICY IMPLICATIONS The empirical evidence of this paper provides some implications and suggestions for countries in the American area seeking to improve their efficiency. First, since the results show that the mean efficiency score of Super-SBM model is 73.7%, it would appear that countries could have increased their output by 26.3% with the existing level of inputs.

The United States has the lowest efficiency score, but Haiti has the highest score. Second, the results indicate that productivity growth decreased on average by 8.4% per year over the sample period. The findings of decreased productivity growth are related to technical change and pure efficiency change. Thus, the pressure on countries to improve their productivity will only mount or medical technology will be transferred. Third, based on the SuperSBM efficiency scores and MPI index, we find that most countries have lower efficiency and contribute less effort to improving technical change. Finally, most countries with lower efficiency should carry out a careful review of their -5www.emj-journal.org

spending on health since the inefficiency problem plays a very important role in a country’s economic growth and welfare, and a healthy country should be regarded as a prerequisite for a level playing field with well-functioning markets.

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AUTHORS Shan-Ju Ho (1975- ), Ph. D. student, Department of Finance, National Sun Yat-sen University, Kaohsiung, Taiwan.

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