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Problemas del desarrollo

versión impresa ISSN 0301-7036

Prob. Des vol.54 no.215 Ciudad de México oct./dic. 2023  Epub 18-Mar-2024

https://doi.org/10.22201/iiec.20078951e.2023.215.69977 

Articles

Technical Efficiency of healthcare systems: a response to pandemic mortality

Luis Suin-Guaraca* 

*Servicio Nacional de Aduana del Ecuador (SENAE), Ecuador. Correo electrónico: luis_suin_g@hotmail.com


Abstract:

The Covid-19 pandemic caused an unusual population mortality rate. This paper aims to determine a causal relationship and its incidence between the Technical Efficiency (TE) of health systems and the Covid -19 mortality rate. Using the Data Envelopment Analysis (DEA) methodology and the OLS, GLS and 2SLS adjustment methods, in 108 countries grouped according to per capita health expenditure, it was found that a 1% increase in the TE of the health systems of the analyzed countries reduces the number of deaths from Covid-19 by between 61 and 127 per hundred thousand inhabitants, concluding that the efficiency of expenditure was transcendental in the prevention of mortality caused by the pandemic.

Key Words: Covid-19; efficiency; public spending; mortality; public health

Resumen:

La pandemia de Covid-19 ocasionó una inusual tasa de mortalidad poblacional. El presente trabajo pretende determinar la existencia de relación causal y su incidencia entre la Eficiencia Técnica (ET) de los sistemas de salud y la tasa de mortalidad por Covid-19. Usando la metodología Análisis Envolvente de Datos (DEA, por sus siglas en inglés) y los métodos de ajuste MCO, MCG y MC2E, en 108 países agrupados de acuerdo al gasto en salud per cápita, se encontró que un incremento en un 1% en la ET de los sistemas de salud de los países analizados, disminuye entre 61 y 127 fallecidos por Covid-19 por cada cien mil habitantes, concluyendo que la eficiencia en el gasto resultó trascendental en la prevención de la mortalidad ocasionada por la pandemia.

Palabras clave: Covid-19; eficiencia; gasto público; mortalidad; sanidad pública

Clasificación JEL: D61; H51; I12; I18

1. Introduction

In early 2020, humanity faced the COVID-19 pandemic, one of the most significant challenges of recent decades, making it necessary to build resilient, flexible and adaptable structures with institutions that would provide an effective and efficient response while being able to overcome traumatic situations, such as the one generated by the virus, with the most negligible social impact. On March 11, 2020, the World Health Organization (WHO) declared a global pandemic when a highly dangerous transmission of the SARS-CoV-2 virus became evident (Pan American Health Organization [PAHO, 2021]). Control measures were the primary basis for prevention: reduction of its spread through hand hygiene and when coughing, standard contact and airborne transmission precautions, and establishing of isolation measures.

At the end of April 2020, more than 3.1 million cases and 217,132 deaths were reported worldwide. By August, these figures had reached 21.1 million cases and 750,660 deaths. In April 2021, the number of infections was 147.2 million and 3.1 million deaths. In May of the same year, the figures increased alarmingly, with nearly 176 million cases worldwide and almost 4 million deaths. These figures show a highly transmissible infectious process, easily transmitted by nasal or oral particles, which brought the planet to a standstill (see Table 1).

Table 1 Total cases and deaths due to Covid-19 by region (May 2021) 

Region Cases Cases per 100,000 inhab. Deaths Deaths per 100,000 inhab
África 5 087 990 391 135 047 10
North America 34 720 246 9 645 622 967 173
Latin America and the Caribbean 35 248 418 5 595 1 213 426 192
Asia 38 427 004 937 536 011 13
Europe 53 609 712 7 344 1 125 119 154
Middle East 9 126 481 2 226 160 297 39
Oceania 78 099 186 1 387 3
World total 176 297 950 3 794 254

Source: Compiled by the autor based on BBC (2021).

Against this background, there was widespread ignorance about the causes, consequences and, above all, how health institutions responded and offered the necessary reassurance to the population. The scientific community warned that the presence of pandemics will be more frequent and their consequences more devastating, with elevated levels of contagion and higher mortality (IPBES, 2020; Han et al., 2015 and 2016; Menachery et al., 2015; Allen et al., 2017). It is estimated that about 1.7 million undiscovered viruses exist, of which more than 850,000 are capable of human transmission (IPBES, 2020). A bleak future seems imminent, making it necessary to direct efforts towards prevention in the field of health.

The healthcare service understood as a right, has constituent elements that the State should guarantee in order to satisfy one of the basic needs, as well as social justice and equality (Vanhulst, 2015). In this respect, the WHO and the PAHO have developed a series of indicators that determine minimum thresholds for healthcare services to correspond with effectiveness in care. The most usual are current public expenditure on health per capita and out-of-pocket expenses.1 It has also been found that for every thousand inhabitants, 2.28 health professionals and 2.4 beds are required in the health system in order to provide a minimum coverage of 80% of care (WHO, 2006).

Table 2 shows that Argentina and Brazil have the highest per capita health spending in South America. In contrast, Ecuador, Paraguay and Venezuela show a higher percentage of out-of-pocket spending, although this has decreased in Venezuela.

Table 2 Use of health resources by country and by year in South America 

Countries Out-of.pocket halth care spending as % of total spending Current health expenditure per capita in ppa
2013 n 2014 % 2015 % 2013 $ 2014 $ 2015 $
Argentina 19 19 18 1 287.70 1 268.30 1 389.80
Bolivia 30 26 23 348.20 384.90 445.80
Brasil 22 21 20 1 275.60 1 365.30 1 391.50
Colombia 14 15 18 765.40 856.00 852.80
Chile 32 32 31 1 677.70 1 774.10 1 903.10
Ecuador 40 40 42 942.00 994.40 980.20
Paraguay 38 36 35 593.40 682.90 724.30
Perú 31 28 31 572.60 621.80 671.00
Uruguay 17 16 16 1 690.80 1 754.10 1 747.80
Venezuela 35 31 28 641.60 640.10 579.40

Source: Copiled by the autor base on PAHO (2020).

Nevertheless, efficiency parameters are not established; instead, inefficiencies of between 20 and 40% of the resources allocated to the health field have been identified (WHO, 2010).

Efficiency should be conceived as the capacity to produce with limited resources measured in the amount of goods and services that can be obtained for each unit of resource used (Mankiw, 2012). Meanwhile, Hurley (2000) indicates that it is essential to discuss the efficiency of a service, good or activity if an explicit objective has been articulated against which this efficiency can be evaluated.

Farrell (1957) states the need to measure production efficiency in a given industry to understand how much that production unit can increase its product simply by increasing its efficiency without absorbing more resources than it has available.

Hurley (2000) and Cid et al. (2016) define TE as that which is achieved by producing a given output with the minimum use of inputs, understood as the adequate and optimal use of resources in production, with various combinations of inputs to achieve a given output. Soto and Casado (2019) contribute by indicating that TE is achieved by obtaining the maximum result from given resources, or that these results are at least as high as the opportunity cost or, if producing the same results, a smaller amount of resources is consumed.

From a sample of 32 public hospitals in Chile from 2011-2013, Santelices (2017) found an average efficiency of 77%. Another sample of 40 units in 2012 reported an efficiency of 86%. In Colombia, Fontalvo (2017) indicates that 12 of the 17 units analyzed present optimal efficiency. Meanwhile, Meza (2018) observed that only 14.5% of the 29 Colombian entities studied were 100% efficient.

Rodriguez et al. (2015) measured the TE of four clinics specializing in neurological diseases in Cuba, finding a mean scale efficiency of 66.8% in 2012 and 78.7% in 2013. In Ecuador, Suin et al. (2021) found higher TE in the public rather than in the private health system. However, they warn that this could be due to the very nature of the private service reflected in the variables used.

Multinational studies, such as that of Maza and Vergara (2017), which analyze the efficiency of high-complexity hospitals and clinics in Latin America during the period 2010-2011, found that 65% of the units were totally efficient and 48% experienced growth in their productivity due to increases in their efficiency and technological improvements. Sanmartín et al. (2019) quantified the relative efficiency of total health spending in 62 countries in Latin America and the Caribbean (LAC) and the Organization for Economic Cooperation and Development (OECD), finding that in 2014, the most efficient countries in LAC were Chile, Cuba, the Dominican Republic, Venezuela and Jamaica, and in the OECD, Japan, Luxembourg and Turkey.

The Inter-American Development Bank (Banco Interamericano de Desarrollo [IDB], 2018), which measures efficiency levels of healthcare systems in LAC and middle-income OECD countries, found that Latin America shows significant variations in terms of efficiency, with Chile being the best-ranked country (eighth place), together with most OECD countries in the top 25%. Meanwhile, another 22 of the 27 countries are located in the bottom half of average efficiency. Bolivia, Ecuador, Guatemala, Guyana, Panama and Suriname were the lowest-performing countries.

Regarding the use of variables in Table 3, different studies have employed Data Envelopment Analysis (DEA) in the analysis of TE. This methodology is used with diverse types of data because of its excellent versatility.

Table 3 Variables usedwhen applying DEA in a review of national and multinational literature 

Input Output
Operational variables
Health personnel 1, 3, 4, 5, 7, 9, 10, 11, 12, 14, 18, 19, 21, 23
Number of beds 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 18, 20, 21, 23
Administrative personnel 4, 5, 7, 9, 10, 11, 14, 21, 23
Miscellaneous units 4, 6, 7, 10, 18, 20, 21
Age-construction technology 12, 4
Expenditure 1, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 18, 19, 20, 23
Activities and services 4, 5, 6, 7, 10, 11, 14, 18, 19, 20, 21
Health indicators 3, 4, 8, 13, 22
Hospital stay 7, 9, 13, 15, 20
User satisfaction 18, 21
Hospital occupancy 12, 13
Readmission 20
Administrative variables
Assets 2, 16, 17
Cost of expenses 2
Operating expenses 2, 4, 20, 21, 23, 5, 14, 7, 9, 15, 13, 18, 19
Public expenditure 3, 8, 22
Revenues 16, 17, 21
Gross profit 16, 2

Source: Compiled by the autor based on 1. Barahona (2011); 2. Fontalvo et al. (2015); 3. Gómez et al. (2019); 4. Peñaloza (2003); 5. Pérez et al. (2017); 6. Pinzón (2003); 7. Portillo et al. (2018); 8. Sanmartín et al. (2019); 9. Martín y Ortega (2016); 10. Paredes y Cutipa (2017); 11. Lau (2017); 12. Maza y Vergara (2017); 13. Perera (2018); 14. Pérez et al. (2019); 15. Meza (2018); 16. Fontalvo (2017); 17. Franco y Fullana (2020); 18. Franco y Fullana (2018); 19. Ferrándiz (2017); 20. Vivas (2019); 21. Santelices (2017); 22. BID (2018) y 23. Rodríguez et al. (2015).

Against this background, this study aims to determine a causal relationship and the incidence of TE in healthcare systems in their response to and management of mortality caused by the worldwide presence of the COVID-19 pandemic.

In terms of formality, this document is divided into five sections. The first is the introductory section with a review of the literature. The second section explains the methodologies used and a complete reference of the data that served as the basis for the analysis. The third section presents the results obtained and their interpretation and contribution based on the research. The fourth section discusses the results and refers to the limitations and new research alternatives from other perspectives and methodological resources. Finally, the fifth section presents the conclusions of the research.

2. Materials and methods

Technical efficiency (TE)

The TE of healthcare systems was measured using the DEA, which is a deterministic and non-parametric frontier method widely used due to its versatility in the use of variables, especially when information is scarce and incomplete (Peñaloza, 2003; García, 1997; Martín, 2008; Yates, 1983).

The methodology presented by Farrell (1957) proposes the existence of Decision-Making Units (DMU) and the use of inputs and outputs, creating an empirical production frontier and measuring the distance to the DMU to obtain a relative efficiency measure. Charnes et al. (1978 and 1997) construct ratios resulting from the ratio of the weighted sum of the outputs to the weighted sum of the inputs and, pursuant to Paretian criteria, obtain an efficiency value between 0 (zero) or not at all efficient and 1 (one) or totally efficient, giving rise to the DEA, which assumes Consistent Returns at Scale (CRS).

In addition, Charners et al. (1978) obtained two more versions of the DEA: the first minimizes the quantity of inputs to obtain the same output (input orientation), and the second, while maintaining the same quantity of inputs, maximizes the output (output orientation). Meanwhile, Banker et al. (1984) propose dual models and add a convexity constraint to obtain the DEA with Variable Returns at Scale (VRS). For the analysis in this study, CRS and VRS models were used, with input orientation, whose mathematical expressions are:

Modelo CRS con orientación inputModelo VRS con orientación inputMinλ,h.si-,sr+(1)jS.A,:λjXij+Si-=Xij0ijλjXrj-Sr+=Yrj0Si+,Sr-0λj0ri,jjMinλ,h,si-,sr+(2)jS.A.:λjXij+Si+=Xij0ijλjXrj-Sr-=Yrj0iλj=1Si+,Sr-0λj0ri,j

Where:

Si+,Sr-: slack variables

Ф: Objective function. Efficiency measure

Yrj: i-th output of the j-th DMU

Xij : i-th input of the j-th DMU

Variables used

The information used comes from the open database of the World Bank (2021). Table 4 presents a total sample of 108 countries as DMU, making a distinction by their per capita health expenditure, divided into 40 and 68 countries, respectively, in order to locate each country within its production area. As far as the inputs and outputs are concerned, the variables used are based on those proposed by the IDB (2018) and Sanmartín (2019), highlighting the fact that the number used in each of the calculations considers the formula proposed by Banker et al. (1984) to guarantee correct discrimination between each DMU.

DMUmaxinp*out;3*inp+out (3)

Table 4 Variables used when applying DEA in a review of national and multinational literature 

DMU Input variables Label Output variables Label Number of variables
Countries whose per capita health expenditure exceeds US$500 Health expenditure per capita i_gastsalpib Life expectancy at birth o_esvinacdi 68 ≥ [3 ; 12]
Health expenditure as % GDP i_gastsalpcap Older tan 65 years (%) o_masesycina
Child survival rate o_tassuperv
Countries whose per capita health expenditure is less tan US$500 Health expenditure per capita i_gastsalpib Life expectancy at birth o_esvinacdi 40 ≥ [3 ; 12]
Health expenditure as % GDP i_gastsalpcap Older tan 65 years (%) o_masesycina
Child survival rate o_tassuperv

Source: Compiled y the autor base don the World Bank (2021) and the daily update on the progress of the pandemic from the British Broadcasting Corporation (BBC, 2021).

The mathematical models applying DEA are presented as follows:

dea i_gastsalpib = o_esvinacdi o_masesycina o_tassuperv, rts(CRS) ort(in) stage(2)

dea i_gastsalpib = o_esvinacdi o_masesycina o_tassuperv, rts(CRS) ort(in) stage(2)

dea i_gastsalpcap = o_esvinacdi o_masesycina o_tassuperv, rts(VRS) ort(in) stage(2)

Regression analysis

Ordinary Least Squares (OLS), General Least Squares (GLS) and 2-Stage Least Squares (2SLS) were used to determine the relationship between the TE of the countries' healthcare systems and the mortality caused by the COVID-19 pandemic.

OLS -attributable to Carl Friedrich Gauss- is one of the most efficient and popular regression analyses due to its statistical properties and assumptions: homoscedastic variance, explanatory variables not sharing information, and errors not correlated with each other. However, if there is evidence of heteroscedasticity, it should be changed to GLS, which will help to correct the lack of efficiency of OLS estimators (Gujarati and Porter, 2009; Girón, 2017).

Meanwhile, suppose inconsistencies occur due to a probable correlation between the stochastic explanatory variable and the stochastic disturbance term. In that case, instrumental variables can be used and 2SLS, developed by Arnold Zellner and Henri Theil (1962) and Robert Basmann (1957), can be applied. Finally, it is important to mention that GLS will present results similar to those of OLS. 2SLS will do the same if the equation explains all the variability in the data around the mean (Gujarati and Porter, 2009; Girón, 2017).

Variables used

The dependent variable will be the mortality rate caused by Covid-19 and the independent variable will be the TE index of the healthcare systems. In addition, control variables were used (see Table 5).

Table 5 Description of variables used i the regression analysis 

Variables Label Specification Concept and justificationn
Dependent muecovid Deaths per Covid Number of deaths caused by Covid-19 per 100,000 inhabitants
Independent eftecppcvrs Technical Efficiency Input health expenditure per capita and variable returns
eftecpibvrs Technical Efficiency Input health expenditure as % GDP and variable returns
eftecpibcrs Technical Efficiency Input health expenditure as % GDP and constant returns
Control denpobl Population Density Population divided by km2. Variable justified by the transmission and infection capacity of the virus.
gasbolsil Out-of-Pocket Expenditure Health expenditre through out-of-pocket payments per capita in dollars Variable that is justified by the population’s ability to access care in private healthcare systems.
crecpib GDP growth GDP growth rate in constant 2010 dollars. Variable that is justified as an indicator of a country’s economic capacity to face crises.
desnut Malnutrition Percentaje of the population whose food intake is insuffients to continuosly meet dietary energy needs. Variable that is justified by people’s health conditions when facing the virus.
gini Gini Index The extent to which income distribution ammog individual sor households within an economy deviates from a perfectly equal distribution. Avariable that is justified by reflecting the level or index of human development an involves key social factors.

Source: Compiled by the author based on the World Bank (2021).

The mathematical models of the regression are presented as follows:

muecovid=β0+β1eftecppcvrs+ε

muecovid=β0+β1eftecpibvrs+ε

muecovid=β0+β1eftecpibcrs+ε

muecovid=β0+β1eftecppcvrs+β2denpobl+β3gasbolsil+β4crecpib+ε

muecovid=β0+β1eftecpibvrs+β2denpobl+β3gasbolsil+β4crecpib+ε

muecovid=β0+β1eftecpibcrs+β2denpobl+β3gasbolsil+β4crecpib+ε

muecovid=β0+β1eftecppcvrs+β2denpobl+β3gasbolsil+β4crecpib+β5desnut+β6gini+ε

muecovid=β0+β1eftecpibvrs+β2denpobl+β3gasbolsil+β4crecpib+β5desnut+β6gini+ε

muecovid=β0+β1eftecpibcrs+β2denpobl+β3gasbolsil+β4crecpib+β5desnut+β6gini+ε

The models would be interpreted as the relationship between the number of deaths caused by COVID-19 and the technical efficiency of the healthcare systems of the sample countries. Control variables are used to ratify the results obtained.

The control variables used were selected based on what the WHO (2009 and 2017) defines as the Social Determinants of Health by referring to the set of social, political, economic, environmental and cultural factors that exert significant influence on the state of health, omitting those that allude to the health condition per se.

3. Results

Technical efficiency

In the Appendix, Tables A1 and A2 show the TE results of the healthcare systems of 40 and 68 countries, respectively, differentiated by per capita health expenditure, while Table A3 shows the countries used as a sample. The first group includes Bangladesh, Djibouti, Samoa, Morocco, Honduras, the Solomon Islands and Vietnam, which maintain a TE of 100%, while Gabon and the Central African Republic are the least efficient.

In group 2, Singapore, Japan and Qatar are 100% efficient; the first two in the three scenarios considered. Meanwhile, Kuwait with 14%, South Africa with 25% and Namibia with 21% are the countries with the lowest resource use efficiency. The values depend on the inputs and methods used (CRS or VRS).

Regression analysis

The results are presented in Table 6 and show an inverse relationship between TE and COVID-19 mortality, except for panel B, whose t-value indicates that the results are unreliable. In panel A, in all the proposed scenarios, the results have a significant t-value of less than 1%, and although the R2 barely reaches 22%, the relationship between the two variables is reliable. These results are supported and exhibit similar behavior in panel C, which uses the 108 observations; the inverse relationship between the variables is maintained. However, the value of the parameter of the independent variable changes: considering the absolute value, it goes from a minimum of 61.17838 to a maximum of 127.88 depending on the input used for the calculation of the TE and the model used: VRS or CRS.

Table 6 Modelo de regresión con muertes por Covid-19 como variable dependiente 

Health expenditure per capita Health expenditure as % of GDP
Modelo VRS Modelo VRS Modelo CRS
Panel A: Countries with health expenditure per capita above US$500
Technical Efficincy -127.88 *** -83.72164 *** -95.27453 ***
[27.25197] [30.55511] [30.18117]
Constant 145.1589 *** 117.9721 *** 120.9956 ***
R2 0.2212 0.0857 0.099
Numer of observations 68 68 68
Panel B: Countries with health expenditure per capita below US$500
Technical Efficincy -4.820077 5.167789 -8.703761
[12.19875] [9.414235] [9.859381]
Constant 17.47132 ** 11.19243 19.62029**
R2 0.0026 0.0032 0.0070
Numer of observations 40 40 40
Panel C: All countries
Technical Efficincy -71.35872 *** -61.17838 *** -65.23424 ***
[18.52188] [20.12675] [30.18117]
Constant 91.40339 *** 86.81011 *** 120.9956***
R2 0.0889 0.0659 0.0607
Numer of observations 108 108 108

Note: p-value: *** p < 0.01; ** p < 0.05; * p < 0.1; estandard errors in square brackets.

Source: Compiled by the autor base don result using STATA.

These deductions were tested using control variables in two scenarios. The first used only three variables: Population Density, Out-of-Pocket Expenditures and GDP Growth. Meanwhile, the Malnutrition and Gini Indexes were added to the second (see Tables 7, 8 and 9). Countries with a per capita health expenditure of less than US$500 have been omitted as they exhibit unreliable results in the relationship between variables.

Table 7 Sample of countries with health expenditure greater tan US$500 using health expenditure as % GDP as input 

MCO MCG MC2E
Panel A: 3 control variables
Technical Efficiency -97.27008 *** -97.27008 *** -97.27008 ***
[35.57986] [29.47611] [35.57986]
Population Density -0.0065469 -0.0065469 *** -0.0065469
[0.0073445] [0.0023551] [0.0073445]
Out-of-Pocket Spending 0.0355446 ** 0.0355446 ** 0.0355446 **
[0.0177603] [0.0144314] [ 0.0177603]
GDP Growth -2.029535 -2.029535 -2.029535
[2.997956] [2.604703] [2.997956]
Constant 117.6005*** 117.6005*** 117.6005***
R2 0.1554 0.1554
Number of observations 68 68 68
Panel B: 5 control variables
Technical Efficiency -129.4118 ** -129.4118 ** -129.4118 **
[57.24287] [50.64491] [57.24287]
Population Density 0.0088023 0.0088023 0.0088023
[0.0757527] [0.0949092] [0.0757527]
Out-of-Pocket Spending 0.0123035 0.0123035 0.0123035
[0.022711] [0.0144922] [0.022711]
GDP Growth -3.01469 -3.01469 -3.01469
[4.399288] [4.569196] [4.399288]
Malnutrition Index -6.281668 ** -6.281668 ** -6.281668 **
[2.386539] [1.813643] [2.386539]
Gini Index 1.010878 1.010878 1.010878
[1.127851] [1.377753] [1.127851]
Constant 163.9679 ** 163.9679 ** 163.9679 **
R2 0.2984 0.2984 0.2984
Number of observations 40 40 40

Note: p-valor: *** p < 0.01; ** p < 0.05; * p < 0.1; estándar errors in square brackets.

Source: Compiled by the autor base don results using STATA.

Table 8 Sample with all countries using health expenditure as an input as % GDP 

Variables MCO MCG MC2E
Panel A: 3 control variables
Technical Efficiency -61.41377 *** -61.41377 *** -61.41377 ***
[21.90137] [18.52394] [21.90137]
Population Density -0.0074658 -0.0074658 *** -0.0074658
[0.00643673] [0.0022019] [0.00643673]
Out-of-Pocket Spending 0.0516782 *** 0.0516782 *** 0.0516782 ***
[0.01406] [0.0166841] [0.01406]
GDP Growth -2.156442 -2.156442 -2.156442
[1.773367] [1.62062] [1.773367]
Constant 81.04987 *** 81.04987 *** 81.04987 ***
R2 0.2019 0.2019 0.2019
Number of observations 107 107 107
Panel B: 5 control variables
Technical Efficiency -94.82057 *** -94.82057 *** -94.82057 ***
[37.75897] [36.08694] [37.75897]
Population Density 0.0188223 0.0188223 0.0188223
[0.040936] [0.0366725] [0.040936]
Out-of-Pocket Spending 0.0147634 0.0147634 0.0147634
[0.0195646] [0.0140777] [0.0195646]
GDP Growth -6.299436 -6.299436 -6.299436
[3.834391] [3.991093] [3.834391]
Malnutrition Index -4.118347 *** -4.118347 *** -4.118347 ***
[1.449592] [1.434888] [1.449592]
Gini Index 1.198691 1.198691 1.198691
[0.08835683] [1.103968] [0.08835683]
Constant 132.0499 *** 132.0499 ** 132.0499 ***
R2 0.4050 0.4050 0.4050
Number of observations 56 56 56

Note: p-valor: *** p < 0.01; ** p < 0.05; * p < 0.1; estandard errors in square brackets.

Source: compiled by the autor based on results using STATA.

Table 9 Regression with all countries using per capita health expenditure as input 

Variables MCO MCG MC2E
Panel A: 3 control variables
Technical Efficiency -59.39136 *** -59.39136 *** -59.39136 ***
[22.00209] [17.5452] [22.00209]
Population Density -0.0065612 -0.0065612 *** -0.0065612
[0.0065141] [0.0021361] [0.0065141]
Out-of-Pocket Spending 0.045497 *** 0.045497 *** 0.045497 ***
[0.013999] [0.0153225] [0.013999]
GDP Growth -2.114709 -2.114709 -2.114709
[1.782879] [1.701794] [1.782879]
Constant 79.9014 *** 79.9014 *** 79.9014 ***
R2 0.1977 0.1977 0.1977
Number of observations 107 107 107
Panel B: 5 control variables
Technical Efficiency -96.61758 *** -96.61758 *** -96.61758 ***
[34.58586] [32.11552] [34.58586]
Population Density 0.0183775 0.0183775 0.0183775
[0.0402342] [0.033232] [0.0402342]
Out-of-Pocket Spending 0.0103406 0.0103406 0.0103406
[0.0194206] [0.0134213] [0.0194206]
GDP Growth -7.37595 * -7.37595 * -7.37595 *
[3.714136] [4.31656] [3.714136]
Malnutrition Index -3.186764 ** -3.186764 ** -3.186764 **
[1.380618] [1.462169] [1.380618]
Gini Index 1.249489 1.249489 1.249489
[0.8674487] [1.120615] [0.8674487]
Constant 125.7329 *** 125.7329 ** 125.7329 ***
R2 0.4207 0.4207 0.4207
Number of observations 56 56 56

Note: p-valor: *** p < 0.01; ** p < 0.05; * p < 0.1; estandard errors in square brackets.

Source: compiled by the autor based on results using STATA.

Table 7 shows the results of the model for the sample of countries with a per capita health expenditure of more than US$500 with the inclusion of the control variables. The dependent and independent variables maintain their inverse relationship, as well as for calculation using OLS, GLS and 2SLS. Panel A shows statistical confidence, although its R2 has been reduced to 15.54. Panel B maintains the inverse relationship between the dependent and independent variables, preserving its statistical significance, and its R2 increases to 29.84. It is important to note that the values of parameter β vary depending on the number of control variables included, with no differentiation between the regression models used.

This behavior is maintained when all observations are used and the TE is calculated with health spending as a percentage of GDP and health spending per capita, both with VRS. These values are observed in Tables 8 and 9. The results do not vary. The relationship between the slope and independent variables continues to be inverse and the values maintain their statistical significance in all the proposed scenarios. Finally, it is essential to note that the model's fit improves as the number of observations increases, ending with an R2 of 42.70.

4. Discussion

The TE shows values with expected behavior. There is a more significant difference when the calculations are carried out using CRS or VRS models, although this difference is not greater. Likewise, when the input is changed, the results are not subject to significant alterations. In the sample of countries with a health expenditure of less than US$500, Bangladesh is the only one that maintains a TE of 100% in all the proposed scenarios. The same occurs with Singapore and Japan in the sample of countries with a health expenditure of more than US$500.

Meanwhile, in the regression analysis, the tests were performed for the three types of samples using OLS, GLS, and 2SLS, and the results are homogeneous and statistically significant. The independent variable Deaths due to Covid is inverse to the dependent variable TE. However, it is worth mentioning that, for countries with health expenditure below US$500, the deductions are not reliable.

The results finally translate into a 1% increase in the TE of the countries' healthcare systems taken as a sample, which would reduce deaths due to COVID-19 by between 61 and 127 per 100,000 inhabitants. These results are supported when all countries are sampled: the regressor of the independent variable maintains its inverse relationship and statistical significance, which indicates that the values and, above all, the deductions that can be obtained based on these results are statistically reliable.

The results also show the importance of maintaining high percentages of TE to meet the population's needs. Gómez et al. (2019) indicate that positive changes in the levels of TE will lead to productivity increases in the operational and financial factors of the national healthcare systems of 28 countries of the European Union.

Similarly, the IDB (2018) suggests that several Latin American countries could significantly improve health output indicators while maintaining their current budget stable. The analysis indicates that, if efficient, the region would lengthen its life expectancy by four years; under-five mortality could be reduced by 10 deaths per 1,000 live births; Disability Adjusted Life Years (DALY) lost due to all causes could be reduced on average by 6.1432 per 100,000 inhabitants; specialized care during childbirth could be improved by 4.4% and DTP2 immunization rates could reach 96.9%.

Furthermore, the R2 is relatively low and the model cannot adjust to the dependent variable. However, although the model does not reliably explain the variability of the data, the causes of mortality are based on the specific health situations of each person, resulting in the logical value of the R2.

As for the control variables, when only three are used, Out-of-Pocket Expenditures show a direct relationship with deaths due to COVID-19 and their value is reliable. In this case, their behavior could be explained by the fact that a deficient health system causes higher Out-of-Pocket Expenditures. Finally, when five control variables are used, the Malnutrition Index maintains statistical significance, although with an inverse relationship to the dependent variable, which could be explained by health factors specific to each person and the relationship with COVID-19.

Given the lack of complete, updated and relevant data, the study has a major limitation given that there is no quality information available, especially in Latin American and African countries and, in some cases, even in first-world countries. This makes it challenging to work with a larger number of variables to compare results.

By its very essence, the DEA also presents the difficulty of contrasting hypotheses since it does not have statistical characteristics such as the presence of error, translating any deviation from the data into ineffective behavior of the DMU. However, it is a valid method used in scientific research.

As for the regression analysis, working with few observations results in an insignificant R2. The scarce knowledge and heterogeneous nature of the dependent variable means that the model cannot provide a reliable explanation. However, it must be understood that the explained variable will depend on medical factors, which have also failed to provide a conclusive explanation.

In terms of scope, the study does not perform a slacks analysis, so it does not know exactly which variables are a source of inefficiencies. Finally, mortality rates by age group have not been standardized in order to determine the level of response to this condition in each country and to be able to compare them.

5. Conclusions and recommendations

The study established a relationship between deaths from COVID-19 and the TE of healthcare systems. The better the use of available resources, the more prepared countries will be to face situations such as those that occurred in the last two years. The study shows that a 1% increase in the TE of the healthcare systems of the countries analyzed would reduce deaths from COVID-19 by between 61 and 127 per 100,000 inhabitants.

It should also be noted that the diversity of the countries, the structure of the healthcare systems, the physical conditions of the people, the behavior and vertiginous mutation of the virus, as well as the structure and economic development of the States, played a dominant role in the effectiveness of the fight against the pandemic. The main challenge initially was to attenuate and contain the accelerated advance of the epidemic.

Extensive literature indicates that pandemics will continue. There is a high probability that humanity will again face other health emergencies, which are expected to be mostly catastrophic and devastating. Given this scenario, a new approach and orientation of public policies in the field of health economics is necessary, acting from a more proactive viewpoint, preparing and improving the response capacity of healthcare systems in order to face, with minimum impact, the consequences of these new epidemics.

It is important to provide policymakers with the technical tools to help them make decisions that can prevent and correct the consequences of situations such as the presence of COVID-19, especially in terms of the use and destination of capital. It is not only a matter of increasing or correctly allocating more resources to the health field -at least in the first instance-, but also of improving their destination and use.

Efficient spending is therefore essential, not only to guarantee people's right to free access and high levels of healthcare coverage but also to ensure that healthcare services and systems respond in an efficient and timely manner to the needs and requirements of the population, being resilient and managing to overcome adverse and highly vulnerable situations, such as the recent pandemic, with the least possible impact.

It is imperative to start thinking about a new way of taking action. The purpose is not to obtain more available resources but to obtain more of the resources available -especially because of their scarcity as opposed to unlimited needs- by being cautious, pragmatic and flexible in prioritizing spending and allocating resources.

REFERENCES

Allen, T., Murray, K., Zambrana, C., Morse, S., Rondini, C., Di Marco, M., Breit, N., Olival, H. y Daszak, P. (2017). Global hotspots and correlates of emerging zoonotic diseases. Nat Commun 8. http://dx.doi.org/10.1038/s41467-017-00923-8 [ Links ]

Alvis, L., Alvis, N. y De La Hoz, F. (2007). Household spending on private health care in Cartagena de Indias, 2004. Revista de Salud Pública, 9. https://bit.ly/3pPB6IYLinks ]

Banco Interamericano de Desarrollo (BID) (2018). Mejor gasto para mejores vidas: cómo América Latina y el Caribe puede hacer más con menos. https://bit.ly/2Of5acvLinks ]

Banco Mundial (2021). Indicadores. [2021, 31 de marzo]. https://datos.bancomundial.org/indicatorLinks ]

Banker, R., Charnes, A. y Cooper, W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9). https://doi.org/10.1287/mnsc.30.9.1078 [ Links ]

Barahona, P. (2011). Análisis de eficiencia hospitalaria en Chile. Anales Facultad de Medicina, 72(1).https://bit.ly/2A3XDIxLinks ]

Basmann, R. L. (1957). A Generalized classical method of linear estimation of coefficients in a structural equation. Econometrica, 25(1). https://doi.org/10.2307/1907743 [ Links ]

British Broadcasting Corporation (BBC) (2021, 19 de abril). Tres millones de muertos por coronavirus: el mapa que muestra dónde han fallecido las víctimas de Covid-19. https://bbc.in/3gDnJVBLinks ]

Charnes, A. (1997). Data enveloment analysis: Theory, methodology and applications. Editorial Kluwer Academic Publishers. [ Links ]

Charnes, A., Cooper, W. y Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operations Research, 2(6). https://doi.org/10.1016/0377-2217(78)90138-8 [ Links ]

Cid, C., Báscolo, E. y Morales, C. (2016). La eficiencia en la agenda de la estrategia de acceso y cobertura universales en salud en las Américas. Salud Pública de México, 58(5). http://dx.doi.org/10.21149/spm.v58i5.8182 [ Links ]

Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3). https://doi.org/10.2307/2343100 [ Links ]

Ferrándiz, R. (2017). Análisis de la eficiencia de los hospitales públicos de la Comunidad Autónoma de la Región de Murcia [Tesis Doctoral, Universidad Católica de Murcia]. https://bit.ly/2Yy6dJdLinks ]

Fontalvo, T. (2017). Eficiencia de las Entidades Prestadoras de Salud (EPS) en Colombia por medio de análisis envolvente de datos. Ingeniare, Revista Chilena de Ingeniería, 25(4). http://dx.doi.org/10.4067/S0718-33 052017000400681 [ Links ]

Fontalvo, T., Adel, M. y Delimiro, V. (2015). Medición de la eficiencia financiera de las entidades promotoras de salud (EPS) del régimen contributivo mediante el análisis envolvente de datos (AED). Universidad and Empresa, 17(29 ). http://dx.doi.org/10.12804/rev.univ.empresa.29.2015.04 [ Links ]

Franco, J. y Fullana, C. (2018). Influencia de los modelos de gestión en la eficiencia de los hospitales del sistema sanitario público. Repositorio Comillas. https://bit.ly/3dAjFAeLinks ]

______ y Fullana, C. (2020). Influencia de los modelos de gestión basados en la colaboración público-privada en la eficiencia técnica e investigadora de los hospitales del sistema sanitario público. Revista de Contabilidad Spanish Accounting Review, 23(1). https://doi.org/10.6018/rcsar.389261 [ Links ]

García, B. (1997). Análisis de eficiencia del sector hospitalario: una revisión de métodos. Cuaderno de estudios empresariales , (7).https://bit.ly/3MKEtKgLinks ]

Girón, L. (2017). Econometría aplicada. Editorial Pontificia Universidad Javeriana. [ Links ]

Gómez, J., García, J. y Gómez, M. (2019). Eficiencia y productividad de los sistemas de salud en los países de la Unión Europea. Estudios de Economía Aplicada, 37(2). https://bit.ly/31i2m4sLinks ]

Gujarati, D. y Porter, D. (2009). Econometría. Editorial McGraw-Hill. [ Links ]

Han, B., Schmidt, J., Bowden, E. y Drake, J. (2015). Reservoirs of future zoonotic diseases. Proc. Natl. Acad. Sci. U. S. A. 112(7). http://dx.doi.org/10.1073/pnas.1501598112 [ Links ]

______, Scmidt, J., Alexander, L., Bowden, E., Hayman, D. y Drake, J. (2016). Undiscovered bat hosts of filoviruses.PLoS Negl. Trop. Dis. 10(7). http://dx.doi.org/10.1371/journal.pntd.0004815 [ Links ]

Hurley, J. (2000). An overview of the normative economics of the health sector. En A. Culyer y J. Newhouse (ed.). Handbook on health economics. Elsevier Science B.V. [ Links ]

IPBES (2020). Workshop Report on Biodiversity and Pandemics of the Inter‑governmental Platform on Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, Germany. https://doi.org/10.5281/zenodo.4147317 [ Links ]

Lau, E. (2017). Eficiencia y productividad del sistema hospitalario en Panamá. [Tesis Doctoral, Universidad de Extremadura]. https://bit.ly/387Q5BfLinks ]

Mankiw, N. (2012). Principios de Economía. Editorial Cengage Learning. [ Links ]

Martín, R. (2008). La medición de la eficiencia universitaria: una aplicación del Análisis Envolvente de Datos. Formación Universitaria, 1(2). http://dx.doi.org/10.4067/S0718-50062008000200004 [ Links ]

Martín, J. y Ortega, M. (2016). Rendimiento hospitalario y benchmarking. Revista de Economía Aplicada, 24(70). https://bit.ly/2BLN6SYLinks ]

Maza, F. y Vergara, J. (2017). Eficiencia y productividad de los hospitales y clínicas latinoamericanos de alta complejidad. Saber, Ciencia y Libertad, 12(1). https://doi.org/10.18041/2382-3240/saber.2017v12n1.1471 [ Links ]

Menachery, V., Yount, V., Debbink, K., Agnihothram, S., Gralinski, L., Plante, J., Graham, R., Scobey, T., Ge, X., Donaldson, E., Randell, S., Lanzavecchia, A., Marasco, W., Shi, Z. y Baric, R. (2015). A SARS-like cluster of circulating bat Coronaviruses shows potential for human emergence. Nature Medicine, 21. http://dx.doi.org/doi:10.1038/nm.3985 [ Links ]

Meza, M. (2018). Análisis de la eficiencia financiera de las instituciones acreditadas en salud en Colombia a través de Análisis Envolvente de Datos (DEA). [Tesis de Grado, Universidad de Cartagena]. https://bit.ly/386yCsXLinks ]

Organización Mundial de la Salud (OMS) (2006). Colaboremos por la salud. (Informe sobre la salud en el mundo 2006). https://bit.ly/3dW9DNqLinks ]

______ (2009). Comisión sobre determinantes sociales de la salud. Subsanar las desigualdades en una generación. (Informe Final). https://apps.who.int/gb/ebwha/pdf_files/A62/A62_9-sp.pdfLinks ]

______ (2010). La financiación de los sistemas de salud: el camino hacia la cobertura universal. (Informe sobre la salud en el mundo 2010). https://bit.ly/3dU1x86Links ]

______ (2017). Salud en las Américas. (Informe Determinantes e Inequidades en Salud). https://bit.ly/2PB66WeLinks ]

Organización Panamericana de la Salud (OPS) (2020). Plataforma de Información en Salud para las Américas. [2023, 15 de abril]. https://www.paho.org/data/index.php/es/?option=com_content&view=article&id=515:indicadoresviz&Itemid=347Links ]

______ (2021). La OMS caracteriza a Covid-19 como una pandemia. PAHO. https://www.paho.org/es/noticias/11-3-2020-oms-caracteriza-covid-19-como-pandemiaLinks ]

Paredes, R. y Cutipa, E. (2017). Medición de la eficiencia técnica de los hospitales en la región de Puno: una aplicación del Análisis Envolvente de Datos (DEA). Semestre Económico, 6(2). http://dx.doi.org/10.26867/se.2017.2.66 [ Links ]

Peñaloza, M. (2003). Evaluación de la eficiencia en instituciones hospitalarias públicas y privadas con Data Envelopment Analysis (DEA). Archivos de Economía. https://bit.ly/388ZE2DLinks ]

Perera, H. (2018). Eficiencia y productividad hospitalaria en Costa Rica: modelo DEA e índice de Hicks-Moorsteen. [Tesis de Maestría, Universidad de Chile]. https://bit.ly/31wpqMWLinks ]

Pérez, C., Ortega, M., Ocaña, R. y Martín, J. (2017). Análisis de la eficiencia técnica en los hospitales del Sistema Nacional de Salud español. Gaceta Sanitaria, 31(2). http://dx.doi.org/10.1016/j.gaceta.2016.10.007 [ Links ]

______, Ortega, M., Ocaña, R. y Martín J. (2019). Análisis multinivel de la eficiencia técnica de los hospitales del Sistema Nacional de Salud español por tipo de propiedad y gestión. Gaceta Sanitaria, 33(4). https://doi.org/10.1016/j.gaceta.2018.02.005 [ Links ]

Pinzón, M. (2003). Medición de eficiencia técnica relativa en hospitales públicos de baja complejidad mediante la metodología Data Envelopment Analysis (DEA). [Tesis de maestría, Pontificia Universidad Javeriana]. https://bit.ly/2VsVZHXLinks ]

Portillo, D., Enríquez, F. y Riascos, J. (2018). Factores económicos que inciden sobre el índice de eficiencia técnica de los hospitales públicos del departamento de Nariño - Colombia 2008-2014. Apuntes del Cenes, 38(67). https://doi.org/10.19053/01203053.v38.n67.2019.7364 [ Links ]

Rodríguez, I., García, A. y García, G. (2015). Eficiencia técnica de clínicas del Centro Internacional de Restauración Neurológica. Revista Cubana de Salud Pública, 42(1).https://bit.ly/2YG50j5Links ]

Sanmartín, D., Henao, M., Valencia, Y. y Restrepo, J. (2019). Eficiencia del gasto en salud en la OCDE y ALC: un análisis envolvente de datos. Lecturas de Economía, 91. https://doi.org/10.17533/udea.le.n91a02 [ Links ]

Santelices, E. (2017). Evaluación de la Eficiencia Técnica de hospitales públicos de Chile. [Tesis Doctoral, Universidad de Chile]. https://bit.ly/3eJ9TNHLinks ]

Soto, J. y Casado, M. (2019). La trayectoria metodológica de la evaluación de la eficiencia y su futuro. Papeles de Economía, 160. http://bitly.ws/PthxLinks ]

Suin, L., Feijoo, E. y Suin, F. (2021). La salud en territorio: una aproximación a la Eficiencia Técnica del Sistema de Salud en el Ecuador mediante el Análisis Envolvente de Datos DEA. uda akadem , (7). https://doi.org/10.33324/udaakadem.vi7.372 [ Links ]

Vanhulst, J. (2015). El laberinto de los discursos del Buen Vivir: entre el Sumak Kawsay y el socialismo del siglo XXI. Polis, 14(40). http://dx.doi.org/10.4067/S0718-65682015000100012 [ Links ]

Vivas, C. (2019). Eficiencia técnica y satisfacción de usuarios pre y post reformas de salud: el caso de los hospitales públicos colombianos período 2010 a 2017. [Tesis Maestría, Pontificia Universidad Javeriana]. https://bit.ly/2BJtBujLinks ]

Yates, J. (1983). When will players get involved? Health Social Services Journal, (15 ). Sin DOI. [ Links ]

Zellner, A. y Theil, H. (1962). Three-stage least squares: simultaneous estimation of simultaneous equations. Econometrica, 30(1). https://doi.org/10.2307/1911287 [ Links ]

1Out-of-pocket expenditure understood as any expenditure of family resources for the acquisition of goods and services useful for restoring or improving health, which are not covered by the health system (Alvis et al., 2007).

2Vaccination against diphtheria, tetanus and pertussis or whooping cough.

Appendix

Table A1 Technical Efficiency calculated by DEA for a simple of countries with per capita health expenditure below US$500 

Health expenditure per capita Health expenditure as a % of GDP
VRS Model VRS Model CRS Model
DMU Score DMU Score DMU Score DMU Score DMU Score DMU Score
1 BGD 1.00 21 IDN 0.501237 1 BEN 1.00 21 PAK 0.666667 1 BGD 1.00 21 COD 0.556477
2 COD 1.00 22 GMB 0.486177 2 BGD 1.00 22 PHL 0.663285 2 DJI 1.00 22 VNM 0.553968
3 ETH 1.00 23 RWA 0.484095 3 COG 1.00 23 UZB 0.652294 3 COG 0.958463 23 HND 0.484472
4 HND 1.00 24 GTM 0.475864 4 DJI 1.00 24 GTM 0.628013 4 SLB 0.953804 24 MMR 0.480000
5 MAR 1.00 25 BOL 0.472602 5 HND 1.00 25 BOL 0.612948 5 VUT 0.943525 25 SEN 0.467818
6 SLB 1.00 26 BEN 0.455022 6 IDN 1.00 26 MMR 0.566667 6 IDN 0.934921 26 BOL 0.466667
7 VNM 1.00 27 COG 0.441228 7 MAR 1.00 27 SEN 0.500000 7 PNG 0.888586 27 GTM 0.453174
8 WSM 1.00 28 MWI 0.434306 8 PNG 1.00 28 TCD 0.500000 8 BEN 0.849983 28 KGZ 0.380923
9 MDG 0.883558 29 PAK 0.406931 9 SLB 1.00 29 NIC 0.474914 9 BTN 0.800000 29 MDG 0.378319
10 VUT 0.847524 30 CPV 0.403699 10 VNM 1.00 30 COM 0.400000 10 WSM 0.763043 30 TCD 0.373186
11 DJI 0.814815 31 COM 0.402888 11 WSM 1.00 31 MDG 0.400000 11 VEN 0.700000 31 MRT 0.357874
12 HTI 0.763889 32 TCD 0.392405 12 VUT 0.968957 32 MRT 0.400000 12 CPV 0.678261 32 COM 0.354631
13 IND 0.624242 33 KGZ 0.381055 13 BTN 0.944904 33 ZMB 0.400000 13 PHL 0.640792 33 ZMB 0.351267
14 VEN 0.614654 34 PHL 0.326363 14 VEN 0.930648 34 ZWE 0.400000 14 GAB 0.620477 34 NIC 0.344086
15 MMR 0.587900 35 CAF 0.319588 15 CPV 0.800959 35 KGZ 0.393143 15 UZB 0.618773 35 ZWE 0.338463
16 PNG 0.543999 36 MRT 0.303054 16 IND 0.708333 36 NER 0.285714 16 PAK 0.618681 36 RWA 0.276615
17 BTN 0.533545 37 ZMB 0.241849 17 COD 0.666667 37 RWA 0.279762 17 ETH 0.610625 37 HTI 0.250000
18 NIC 0.529080 38 UZB 0.218186 18 ETH 0.666667 38 HTI 0.250000 18 IND 0.600000 38 NER 0.245039
19 NER 0.528055 39 ZWE 0.182251 19 GAB 0.666667 39 MWI 0.222222 19 GMB 0.569080 39 MWI 0.206861
20 SEN 0.519572 40 GAB 0.148198 20 GMB 0.666667 40 CAF 0.181818 20 MAR 0.560000 40 CAF 0.132756

Fuente: elaboración propia. Resultados DEA mediante uso de STATA.

Table A2 Technical Efficiency calculated by DEA for a simple of countries with per capita health expenditure greater than US$500 

Health expenditure per capita Health expenditure as a % of GDP
VRS Model VRS Model CRS Model
DMU Score DMU Score DMU Score DMU Score DMU Score DMU Score
1 BLR 1.00 35 DZA 0.536794 1 JPN 1.00 35 SYC 0.560000 1 QAT 1.00 35 BHR 0.520963
2 CYP 1.00 36 ARM 0.525233 2 QAT 1.00 36 CAN 0.555214 2 SGP 1.00 36 TTO 0.511823
3 GUY 1.00 37 RUS 0.523297 3 SGP 1.00 37 BHR 0.534009 3 THA 0.970276 37 MEX 0.505604
4 JPN 1.00 38 DOM 0.506229 4 LUX 0.997516 38 URY 0.533460 4 RUS 0.929066 38 GNQ 0.497495
5 LKA 1.00 39 LUX 0.495928 5 THA 0.987377 39 TTO 0.514286 5 LUX 0.916618 39 OMN 0.491539
6 SGP 1.00 40 CHE 0.487837 6 RUS 0.933333 40 MEX 0.506667 6 KAZ 0.837292 40 CHE 0.484286
7 MNG 0.991341 41 CAN 0.487635 7 FIN 0.895691 41 IRQ 0.500000 7 LKA 0.836404 41 CAN 0.478369
8 KOR 0.954101 42 MEX 0.484020 8 LKA 0.850952 42 MNG 0.500000 8 LCA 0.834753 42 MNG 0.456093
9 VCT 0.940000 43 QAT 0.482332 9 KAZ 0.844444 43 OMN 0.500000 9 VCT 0.826766 43 IRQ 0.454077
10 BRB 0.906548 44 COL 0.475884 10 LCA 0.841477 44 CUB 0.440840 10 GRD 0.826699 44 CUB 0.429435
11 SLV 0.870330 45 BWA 0.471161 11 GRD 0.833333 45 BHS 0.422222 11 BLR 0.804979 45 DOM 0.419731
12 SRB 0.849233 46 NOR 0.467441 12 VCT 0.833333 46 DOM 0.422222 12 JPN 0.794969 46 BHS 0.419523
13 LCA 0.846154 47 IRL 0.456198 13 BLR 0.809001 47 PAN 0.420075 13 FIN 0.748609 47 PAN 0.403499
14 EGY 0.837402 48 ZAF 0.455135 14 BRB 0.743000 48 TUN 0.406962 14 BRB 0.709832 48 TUN 0.401213
15 THA 0.826425 49 GBR 0.444577 15 IRL 0.711485 49 EGY 0.400000 15 CYP 0.655725 49 SLV 0.396945
16 AZE 0.811917 50 ATG 0.442835 16 ISL 0.688423 50 KWT 0.400000 16 IRL 0.642195 50 EGY 0.393164
17 ISL 0.757264 51 BEL 0.442619 17 NLD 0.684095 51 SLV 0.400000 17 MYS 0.634821 51 KWT 0.387610
18 GRD 0.744957 52 SUR 0.435610 18 AUT 0.682035 52 DZA 0.386435 18 ISL 0.627925 52 DZA 0.379287
19 SWZ 0.737103 53 MYS 0.432998 19 BEL 0.680311 53 GUY 0.377778 19 ATG 0.616007 53 GUY 0.369178
20 GNQ 0.736012 54 BHR 0.427820 20 DEU 0.677795 54 ARG 0.365902 20 SRB 0.608364 54 ARG 0.361029
21 IRQ 0.717020 55 NLD 0.418186 21 FRA 0.676279 55 ARM 0.360000 21 NLD 0.579440 55 ARM 0.359647
22 JOR 0.698689 56 AUT 0.412447 22 CYP 0.675836 56 COL 0.359899 22 AUT 0.579337 56 COL 0.351725
23 KAZ 0.659169 57 URY 0.405119 23 SWE 0.672665 57 SAU 0.343544 23 BEL 0.579250 57 SAU 0.333170
24 FIN 0.653804 58 PAN 0.367756 24 GNQ 0.666667 58 BWA 0.333333 24 DEU 0.574617 58 PRY 0.321894
25 AUS 0.611588 59 OMN 0.365189 25 AUS 0.650260 59 ECU 0.324225 25 KOR 0.571072 59 ECU 0.317984
26 NZL 0.594309 60 DEU 0.352722 26 MYS 0.638355 60 PRY 0.323810 26 SWE 0.561753 60 SUR 0.312252
27 CUB 0.594226 61 BRA 0.337265 27 GBR 0.635608 61 SUR 0.316667 27 AZE 0.560534 61 BRA 0.306841
28 MDV 0.592901 62 SYC 0.332473 28 NZL 0.631800 62 BRA 0.307665 28 AUS 0.556405 62 BWA 0.302364
29 NAM 0.580974 63 IRN 0.305625 29 KOR 0.630692 63 SWZ 0.285714 29 SYC 0.555275 63 IRN 0.252650
30 FRA 0.579516 64 ARG 0.277645 30 ATG 0.627542 64 IRN 0.256429 30 NZL 0.554795 64 JOR 0.242573
31 TUN 0.566695 65 TTO 0.259365 31 NOR 0.625334 65 MDV 0.250657 31 GBR 0.552231 65 ZAF 0.236996
32 PRY 0.551312 66 BHS 0.256988 32 CHE 0.624124 66 JOR 0.250000 32 FRA 0.551783 66 MDV 0.227175
33 SWE 0.545378 67 SAU 0.159971 33 SRB 0.612894 67 NAM 0.250000 33 NOR 0.536154 67 SWZ 0.227095
34 ECU 0.541355 68 KWT 0.140878 34 AZE 0.566667 68 ZAF 0.250000 34 URY 0.521123 68 NAM 0.208603

Fuente: elaboración propia. Resultados DEA mediante uso de STATA.

Table A3 Name and code of simple countries 

Country Code Country name Country Code Country name Country Code Country name Country Code Country name
ARG Argentina DEU Germany KGZ Kyrgyztan QAT Qatar
ARM Armenia DJI Djibouti KOR South Korea RUS Russian Federation
ATG Antigua and Barbuda DOM Dominica KWT Kuwait RWA Rwanda
AUS Australia DZA Algeria LCA Saint Lucia SAU Saudi Arabia
AUT Austria ECU Ecuador LKA Sri Lanka SEN Senegal
AZE Azerbaijann EGY Egypt LUX Luxembourg SGP Singapore
BEL Belgium ETH Ethiopia MAR Morocco SLB Solomon Islands
BEN Benin FIN Finland MDG Madagascar SLV El Salvador
BGD Bangladesh FRA France MDV Maldives SRB Serbia
BHR Bahrain GAB Gabon MEX Mexico SUR Suriname
BHS Bahamas GBR Great Bretain MMR Myanmar SWE Sweden
BLR Belarus GMB Gambia MNG Mongolia SWZ Eswatini
BOL Bolivia GNQ Equatorial Guinea MRT Mauritania SYC Seychelles
BRA Brazil GRD Grenada MWI Malawi TCD Chad
BRB Barbados GTM Guatemala MYS Malaysia THA Thailand
BTN Bhutan GUY Guyana NAM Namibia TTO Trinidad and Tobago
BWA Botswana HND Honduras NER Niger TUN Tunisia
CAF Central African Rep. HTI Haití NIC Nicaragua URY Uruguay
CAN Canada IDN Indonesia NLD Netherlands UZB Uzbekistan
CHE Switzerland IND India NOR Norway VCT St. Vincent and the Grenadines
COD Congo, Democratic IRL Ireland NZL New Zealand VEN Venezuela
COG Congo IRN Iran OMN Oman VNM Viet Nam
COL Colombia IRQ Iraq PAK Pakistan VUT Vanuatu
COM Comoros ISL Isle of Man PAN Panama WSM Samoa
CPV Cabo Verde JOR Jordan PHL Philippines ZAF South Africa
CUB Cuba JPN Japan PNG Papua New Guinea ZMB Zambia
CYP Cyprus KAZ Kazakhtan PRY Paraguay ZWE Zimbabwe

Source: Compiled by the autor.

Received: November 05, 2022; Accepted: May 02, 2023

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