Afonso, António; Baquero Fraga, Gabriela
Working Paper
Government Spending Efficiency in Latin America
CESifo Working Paper, No. 10096
Provided in Cooperation with:
Ifo Institute – Leibniz Institute for Economic Research at the University of Munich
Suggested Citation: Afonso, António; Baquero Fraga, Gabriela (2022) : Government Spending
Efficiency in Latin America, CESifo Working Paper, No. 10096, Center for Economic Studies
and ifo Institute (CESifo), Munich
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10096
2022
November 2022
Government Spending
Efficiency in Latin America
António Afonso, Gabriela Baquero Fraga
Impressum:
CESifo Working Papers
ISSN 2364-1428 (electronic version)
Publisher and distributor: Munich Society for the Promotion of Economic Research - CESifo
GmbH
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CESifo Working Paper No. 10096
Government Spending Efficiency in Latin America
Abstract
We assess the public spending efficiency of 20 Latin American countries over the period of 20002019, computing Data Envelopment Analysis efficiency scores. For the Public Sector
Performance composite indicator, we use the annual data of socio-economic indicators, and for
the input measure we consider Total Public Spending as a percentage of GDP, by spending
category. The results show that public spending during the period under study increased, but that
overall governments were not efficient, as on average they could have used 27% less spending to
achieve the same levels of performance. On the other hand, governments could have increased
their performance by 18% whilst maintaining the same level of spending. The most-efficient
countries were Chile, Guatemala, Panama, and Paraguay, with the least efficient being Bolivia,
Venezuela, Nicaragua, Suriname, and Brazil.
JEL-Codes: H110, C130, C140, H500.
Keywords: government efficiency, Data Envelopment Analysis, government spending, Latin
America.
António Afonso
ISEG – School of Economics and
Management, Universidade de Lisboa
Lisbon / Portugal
aafonso@iseg.lisboa.pt
Gabriela Baquero Fraga
ISEG – School of Economics and
Management, Universidade de Lisboa
Lisbon / Portugal
gabi_baquero@hotmail.com
November 2022
This research was supported by the FCT (Fundação para a Ciência e a Tecnologia) [grant number
UIDB/05069/2020]. The opinions expressed herein are those of the authors and do not necessarily
reflect those of the authors’ employers. Any remaining errors are the authors’ sole responsibility.
1. INTRODUCTION
The role of fiscal policy broadly covers the accomplishment of the three Musgravian
functions: allocation of resources, distribution of income, and the stabilisation of the economy.
As governments endeavour to accomplish these roles, it is important to analyse the quality of
government spending and how effective has been the use of public resources, especially in a
region that mostly depends on revenues form commodities, which is non-permanent income.
Accordingly, this paper analyses government expenditure and its efficiency for 20 Latin
American countries between 2000 and 2019. This cross-country analysis enables drawing
relative comparisons for the region as a whole and highlights which countries used their
economic resources better and performed well within the same region.
The reality in Latin America is that government spending increased over the last two
decades, and at a relatively greater speed since 2010. With the onset of the global financial and
economic crisis, most of the region's countries implemented expansionary fiscal policies that
were intended to increase aggregate demand. Spending on subsidies, transfers, and certain
social programmes was accordingly increased, which, although it helped mitigate the impact of
the crisis on the most vulnerable sectors, in some cases it led to a permanent rise in government
spending. Consequently, public spending as percentage of GDP in the 20 countries comprising
our analysis increased by 7% of GDP from 2000 (19.3%) to 2020 (26.3%). In addition, the
average spending in the Latin American countries under study in the areas of health, education,
and social protection increased from representing 1.5%, 3.2% and 3.4% of GDP in 2000, to
representing 2.8%, 4.3% and 5.6% of GDP in 2020, respectively.
Our contribution to the literature lies in the study’s focus on the analysis of public sector
efficiency in Latin American countries, especially with regards the provision of both public
sector performance indicators and (output and input) efficiency scores ensuing from the
implementation of the Data Envelopment Analysis. As mentioned in Afonso et al. (2020), less
evidence is available for Asia, Africa, or Latin America regarding public sector efficiency.
Hence, our paper provides a public sector performance and efficiency analysis for a time span
of 20 years covering 20 countries, with our dataset including many socio-economic areas, which
thus make it possible to analyse individual categories of spending areas.
The remainder of the paper is organised as follows. Section 2 provides the literature review,
while Section 3 describes the methodology applied to compute the performance indicators and
the Data Envelopment Analysis methodology. Section 4 report the empirical analysis and
Section 5 concludes.
2. LITERATURE
Using efficiency analysis, along the lines of the seminal work of Farrell (1957), the related
literature expands the use of methods such as Free Disposal Hull (FDH), Data Envelopment
Analysis (DEA), and composite performance indicators to study the efficiency of government
spending – notably across countries.
For example, Afonso and St. Aubyn (2004) computed the efficiency of public spending
specifically for the education and health sectors for a sample of OECD countries. These same
authors compared both non-parametric methods: Free Disposal Hull (FDH) analysis and Data
Envelopment Analysis (DEA) and for both methodologies, they found that in the education
sector (input and output), Finland, Japan, Korea and Sweden were the most efficient countries,
whereby their students achieved the best results with fewer resources, whereas Belgium (input)
and Portugal (output) were the least efficient. The average input efficiency score in their study
education was 0.89, which means that on average, countries could have used 11% less resources
to achieve the same output.
Afonso et al. (2005) carried out one of the first efficiency analyses, using public sector
performance (PSP) composite indicators and public sector efficiency (PSE) indicators for 23
OECD industrialised countries for the period of 1990 and 2000. For the countries analysed, the
division into small, medium, and large governments corresponded to spending below 40% of
GDP, between 40% and 50% of GDP, and above 50% of GDP respectively. The analysis was
divided into four expenditure categories, namely education, health, public infrastructure, and
administration. These were called the “Opportunity Indicators”, and were the “Musgravian”
indicators that reflected allocation, distribution, and stabilisation. The results showed that, on
balance, small governments report better economic performance (PSP) than large governments
or medium sized governments. The FDH analysis results showed that Japan, United States, and
Luxembourg were placed on the “production possibility frontier”, in that large governments,
on average, are able to use 35% less spending to achieve the same PSP. Furthermore, the EU15
countries were identified as being relatively less efficient when compared with both the United
States and the average of the other OECD countries in the sample.
Afonso et al. (2010) also studied public sector performance and efficiency for the period of
2001-2003 for 22 countries, including the 12 new EU members at that time, as well as emerging
markets, such as Brazil, Chile, Mexico and others. The authors found important differences
across the countries, with Brazil being one of the worst countries in terms of PSP. Even though
most of the emerging economies performed less-well than the old, industrialised countries, the
economies of the recently-industrialised Asian countries performed well. Regarding the
3
efficiency scores, the Asian countries achieved higher scores with lower public spending. By
analysing the DEA results, Thailand, Cyprus, Ireland, and Korea were found to be on the
production frontier, with Chile next on the list. Finally, the Tobit analysis showed that per capita
GDP, public sector competence, educational levels, and the security of property rights all
appeared to contribute to the prevention of inefficiencies in the public sector.
There are few studies that address public efficiency in Latin America. Clements et al. (2007)
calculated the efficiency of spending on infrastructure (rails, roads, electricity, water, and
telecommunications) in seven Latin American countries (Argentina, Bolivia, Brazil, Chile,
Colombia, Mexico, and Peru) during the 1990s and the early 2000s, using the Free Disposal
Hull Analysis technique. The results showed that Chile and Mexico demonstrated higher levels
of efficiency.
Afonso et al. (2013) analysed 23 countries, using the Public Sector performance (PSP),
Public Sector Efficiency (PSE) indicators and Data Envelopment Analysis (DEA) efficiency
scores for the period of 2001-2010. They divided the countries according to their public
spending as a percentage of GDP, namely: below 25% of GD; between 26% and 30% of GDP;
and above 30% of GDP. Their results showed again that the larger the size of the government,
the less efficient it is. The results of PSP placed Chile, Trinidad and Tobago, Panama, and Costa
Rica as the best performers. For education, Costa Rica, Trinidad and Tobago, and Guyana were
ranked in first place in that order. In terms of health, Costa Rica and Chile topped the list, while
Chile was ranked first for the provision of public infrastructure. Next, the overall PSE score
placed Guatemala, Chile, and Peru at the top of the group, followed by the Dominican Republic,
Ecuador, and El Salvador. It is also important to remark that Trinidad & Tobago and Panama
no not feature at the top of the list of PSE scores. In the DEA Chile, Guatemala and Perú were
placed on the efficiency frontier, with, on average, countries being able to achieve the same
level of outcome by using 40% less spending or they could increase their performance by 19%
with the same level of inputs.
Ribeiro (2008) also analyses 17 countries of Latin America from 1998 to 2002. Following
the same process, the author computed the PSP indicator for five areas: health, education, public
administration, equality, and economic performance. Finally, the author computed DEA
analysis to gain efficiency scores and analysis the non-discretionary variables, but using the
bootstrap methodology. The countries with the best PSP scores were Chile for health,
administration, and economic performance, Costa Rica for education and health, and Uruguay
for equality, and the lowest scores in the region were for Guatemala, Paraguay and Bolivia.
According to the DEA analysis, the countries located at the production frontier are Costa Rica,
4
the Dominican Republic, and Guatemala. On the contrary, Bolivia, Brazil and Honduras were
the more inefficient countries.
Finally, one of the latest studies of efficiency in Latin America is that of Izquierdo et al.
(2018), in which the analysis compares countries of Latin America versus OECD countries.
The methodology used was DEA for the sectors of health, security, and public administration,
employing indicators such as public salaries, transfers and subsidies, and public purchases.
These authors estimated on average about 4.4% of inefficiency for GDP, which represents about
16% of public spending. Regarding security, their results showed an average 70% of efficiency,
which equates to 30% of crime not being prevented. The results of Izquierdo et al.’s research
are diverse across countries, and the authors detected a correlation between better institutions
and greater efficiency. In addition, in the health sector, Chile was the only Latin-American
country to be placed at the production frontier, while Barbados, Costa Rica, Cuba, and Uruguay
also received good efficiency scores. On the other hand, Bolivia, Ecuador, Guatemala, Guyana,
Panama and Suriname all registered low efficiency scores for health.
The recent literature has also investigated the relationship between the tax system and
spending efficiency, the idea being that it is not only changes in revenues that affect the level
of public spending. For instance, Afonso et al. (2020) assessed whether structural tax reforms
positively or negatively affect public spending efficiency for OECD economies during the
period of 2007-2016. They calculated the composite indicators of government performance and
then the input efficiency scores for 2016-2017 using DEA technique for 3 different models. The
results, showing an average efficiency score of around 0.6-0.7, and therefore spending was
30%-40% lower, on average. Furthermore, Chile, Korea, and Switzerland were located at the
efficiency frontier. Later on, the same authors used a panel analysis to assess the impact of tax
reforms on the computed DEA input efficiency scores, reporting that those countries that
increased their tax rates experienced lower spending efficiency. When the authors controlled
for endogeneity, they achieved two specific results: i) increasing tax rate reforms worsens
public sector efficiency, and ii) increasing tax base reforms improves efficiency. Regarding the
control variables, the authors found that population, primary balance, and number of internet
users all positively affect public sector efficiency.
Following up on this topic, Afonso et al. (2021) evaluated the relevance of taxation for
public spending efficiency from 2003 to 2017 for the OECD countries. Having calculating DEA
efficiency scores and measuring the impact of tax structures, the main conclusions were that
inputs could be theoretically lower by approximately 32%–34%, and that expenditure
efficiency is negatively associated with taxation.
5
3. METHODOLOGY
3.1. Public Sector Performance
The total number of countries in Latin America and the Caribbean is 42, but data is not
available for all of them, especially the Caribbean countries. The sources used to collect the
information for the social and economic indicators are mainly the World Bank, the International
Monetary Fund (IMF), and the Economic Commission for Latin America and the Caribbean
(ECLAC). From the 42 countries, we analyse 20 countries from both Central (10) and South
(10) America for the period of 1990 to 2019.
Following the methodology of Afonso et al. (2005), we first compute a Public Sector
Performance (PSP) composite indicator, which includes seven socio-economic areas of
government activity, which are referred to as the PSP sub-indicators (including the Musgravian
functions of the Sate, distribution, economic performance/allocation, and stabilisation).
Administration: is proxied by the Governance indicators of the World Bank, which reflect
the perceptions of the quality of public services, capacity to regulate and implement policies
and rules of society, freedom of expression, as well as the active participation of society in
government. The four indicators used are available for all the countries during the whole period
of 1996-2019. The original data ranges from -2.5 (bad) to 2.5 (good) and they were then
rescaled from 0 to 5 for the calculation.
Education: is measured by the average years of secondary school enrolment and the quality
of the education system over the period of 1990-2019 for the first indicator, and for only 20082018 for the second indicator, without information for Belize, Guatemala, and Suriname. The
countries with less data available for the whole the period are Guyana, Nicaragua, Paraguay,
Honduras, Suriname, Bolivia, and Brazil.
Health: comprises three indicators, two of which have complete data for the whole series
since 1990, albeit Maternal Mortality only has information for all countries since 2000, with
data missing before that date for Honduras, Nicaragua, Panama, Suriname, Bolivia, Paraguay,
and Peru. These three indicators were: i) Mortality rate, under-5 years old (per 1,000 live births):
changed to (1,000 - Value)/1.000; ii) Adolescent fertility rate (births per 1,000 women aged 1519): Changed to (1.000-Value)/1.000; and iii) Maternal mortality ratio (modelled estimate, per
100,000 live births): Changed to (100,000-Value)/100,000.
Infrastructure: is measured by the indicator “Quality of Infrastructure” from the World
Economic Forum, with information only being available for the period of 2008-2018, except
for Belize, Guyana, and Suriname.
6
Distribution: includes the Gini Index, although data is missing for several countries during
the period, with more fully-available data only being available since 2000. Countries such as
Belize, Guatemala, Guyana, Nicaragua, and Suriname lack information. For calculations, the
data were changed to the 100-Gini Value.
Economic performance: consists of three indicators: i) unemployment rate; ii) GDP per
capita; and iii) GDP growth. The values are a five-year average, as they are macro indicators
that change in the long term. Data is available for all countries.
Stability: is composed of a five-year average of the coefficient of the variation of growth
and the inflation rate. All countries possess information for the whole period. The coefficient
of variation of Growth is Standard Deviation (five-year average)/Five-year average, which is
changed to 1/x and Inflation is the five-year average (used as 1/x).
For further details on the indicators, see Appendix Table A.1. After all the transformations
had been carried out, each indicator was normalised by its sample mean and the resultant values
were used to construct the performance composite indicators.
Each PSP sub-indicator is the average of its indicators for each country for every year, with
the total PSP being the average of the seven PSP sub-indicators (with equal weights assigned).
The first four categories of administration, education, health, and infrastructure are considered
to be “Opportunity Indicators”, which refer to the government as being the provider of both
public services and equal opportunities to the society. The following three categories are
distribution, economic performance, and stability, which are called the “Musgravian
Indicators”, and they represent the ability of the government to promote the functions of
distribution, allocation, and stabilisation.
The PSP was computed for the period of 1990-2019, subject to the limitation of the data
described above. It is important to mention that the PSP values over time are measured relative
to those of other countries, which means that over time the PSP values could increase or
decrease, not just because of the evolution of the indicators, but also as a result of the behaviour
of the other countries.
3.2. Data Envelopment Analysis (DEA)
As mentioned by Afonso et al. (2007), this strand of analysis has its roots in the seminal
work of Farrell (1957), in which the author provided a measure of productive efficiency which
considers inputs and outputs, and went on to obtain a production function with constant returns
to scale. Recent papers have used non-parametric approaches for measuring relative
expenditure efficiency across countries and this thesis follows the description of DEA
7
constructed by Afonso et al. (2007), and thus the measurement of public sector efficiency
follows a function for each country i from a total of 20, calculated by:
𝑌𝑖 (𝑡) = 𝑓(𝑋𝑖 (𝑡))
(1)
where 𝑌𝑖 = Composite indicator representing the output, and 𝑋𝑖 = Government Spending
representing input. Accordingly, country i will be efficient if 𝑌𝑖 > f (𝑋𝑖 ), which implies that the
output is the best that can be obtained for the respective input level.
By using DEA, it is possible to compute a theoretical efficiency frontier, where the
inefficiency of country i is measured by computing the distance to the theoretical frontier. The
linear programming problem involved supposes k inputs and m outputs for the 20 countries
under analysis. For the i-th country, yi is the column vector of the outputs and xi is the column
vector of the inputs. X can be defined as the (k×n) input matrix, and Y as the (m×n) output
matrix.
The DEA model is then specified for a given i-th country, and, as an illustration, adopting
an input-oriented approach, the efficiency scores are computed by means of the following linear
programming problem:
min 𝜃
𝜃,𝜆
𝑠. 𝑡. − 𝑦𝑖 + 𝑌𝜆 ≥ 0
𝜃𝑥𝑖 − 𝑋𝜆 ≥ 0
𝐼1’𝜆 = 1
(2)
𝜆≥ 0
where 𝑦𝑖 is a vector of outputs, 𝑥𝑖 is a vector of inputs, 𝜆 is a vector of constants, 𝐼1’ is a vector
of ones, 𝑋 is the input matrix, and 𝑌 is the output matrix. The efficiency scores of 𝜃, range from
0 to 1, such that countries performing on the frontier are awarded a score of 1. More specifically,
if θ<1, the country is within the production frontier (i.e., it is inefficient), and if θ=1, then the
country is situated on the frontier (i.e., it is efficient).
DEA can provide two sets of results, both of which are input- and output-oriented. Input
efficiency scores represent the proportional reduction in inputs, while the output constant holds
firm and the output-oriented scores measure the proportional increase in outputs while the
inputs remain constant.
8
4. EMPIRICAL ANALYSIS
4.1. Government Spending Data
Due to the limited availability of data for government spending, our analysis only focusses
on the period of 2000 onwards. Over the last two decades government spending in Latin
America has shown an upward trend, as presented in Figure 1. On average, it represented 19.3%
of GDP in 2000 and increased to 25.6% of GDP in 2020, with a growth rate of 32.9%. The
results also show that the government spending in South America is greater than the level in
Central America. When comparing the two regions, average spending in 2000 was 20.8% in
South America, and 17.0% in Central America, which increased to 26.3% and 25.1% of GDP
in 2020 respectively1. However, the growth rate of the percentage of government spending
between 2000 and 2020 was larger for Central America (46.9%) than South America (26.4%).
Figure 1 displays the evolution of this indicator, where government spending reached a peak
during the years of the global economic crisis in 2008-2009, which was mostly due to the
expansive fiscal policies adopted by governments to increase aggregate demand and mitigate
the impact of the crisis on the private sector and on households.
During the years following the crisis, the increase of public spending continued at a good
pace, reaching its next peak in 2014. The average growth rate between 2010 and 2014 was
2.6%, while between 2000 and 2009 it was 1.1%, with values showing a slight reduction from
2015 up until 2018, after which it increased again, attaining the highest value of total
government spending in 2020, which can be explained by the measures and policies taken by
governments to face the economic, social and health crisis caused by Covid-19. It was during
2020 that governments increased spending to completely unexpected rates, with many countries
registering values over 17% (El Salvador, Guatemala, Argentina, Brazil, Paraguay, and Peru).
1
Currently no data is available for Panama (2018, 2019, and 2020), Bolivia (2019 and 2020) and Venezuela (2015
to 2020)
9
Figure 1: Average Government Spending (% GDP)
27,0
26,0
25,0
24,0
23,0
22,0
21,0
20,0
19,0
18,0
17,0
16,0
Total (a)
South America (b)
Central America (c)
Source: Economic Commission for Latin America and the Caribbean (ECLAC)
(a) Belize (since 2012), Costa Rica, El Salvador, Guatemala, Guyana, Honduras, Mexico, Nicaragua, Panama, Suriname
(since 2013), Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, and Venezuela.
(b) Belize (since 2012), Costa Rica, El Salvador, Guatemala, Guyana, Honduras, Mexico, Nicaragua, Panama, Suriname
(since 2013).
(c) Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, and Venezuela.
*Corresponds to Central Government Spending for all countries, except Peru, where General Government Spending is
used.
This increase of public spending during the period under study was largely due to the
commodities prices boom, which started at the beginning of the 2000s and lasted for about a
decade, which gave rise to an increase in public revenues. Furthermore, even though these price
increases stopped during the months immediately following the economic crisis of 2008-2009,
the strong level of Chinese demand soon resumed and prices increased again (Ocampo, 2017).
It is important to mention that Latin American countries mostly rely on commodities
exports, and therefore government revenues depend on taxes levied on commodity sectors and
profits from state-owned enterprises in sectors such as oil and minerals. Latin America is highly
natural resource dependent, with countries such as Bolivia, Colombia, Ecuador, and Venezuela
depending mainly on Fuels, whereas Brazil, Chile, Peru are dependent on Minerals and
Argentina, Paraguay, Uruguay, Guatemala, Honduras, and Nicaragua depend on Agriculture.
According to the analysis of Ocampo (2017), the commodity boom between 2003 and 2013
was stronger for oil and metals than it was for agricultural goods.
4.2. Public Sector Performance Results
Table I shows the results of the standardised PSP for 1990, 2000, 2010, and also 2019 for
the 20 countries under analysis. These results represent outcome indicators, without considering
10
the amount of spending incurred. Panama registered the highest total PSP in 1990 and 2000
(2.23 and 1.43 respectively) compared with Guyana (0.42) in 1990, and both Venezuela and
Ecuador in 2000 (which registered almost the same value of 0.74), which recorded the lowest
PSP during the same years. Then in 2010 the first place was passed on to Chile, which registered
the highest score (1.24) between the countries and maintained this position for many years, up
until 2013, whereas the worst place was occupied by Nicaragua which recorded 0.78. Next, the
best and worst ranked performers changed back to Panama (1.36) and Venezuela (-0.5) in 2019.
Table I: Public Sector Performance Indicator by type
Musg.
1990
Opp.
Total
Musg.
2019
Opp.
Total
Belize
2.02
0.99
1.67
1.68
1.04
1.30
0.94
0.97
Costa Rica
1.25
0.89
1.11
1.14
1.06
1.10
0.92
1.17
0.96
1.72
0.95
1.25
1.06
1.27
1.30
1.28
El Salvador
0.67
0.99
0.78
1.02
0.91
0.96
0.89
1.08
1.00
1.81
0.90
1.36
Guatemala
1.15
0.97
1.09
1.45
0.75
1.10
Guyana
0.13
1.00
0.42
0.76
1.10
0.96
1.03
0.95
0.98
1.61
0.79
1.12
0.68
1.01
0.88
1.28
0.98
1.13
Honduras
1.00
0.98
0.99
0.90
0.90
0.90
0.87
0.93
0.90
1.18
0.83
1.01
Mexico
0.91
0.99
0.95
0.96
1.03
1.00
0.96
1.03
1.00
1.05
1.05
1.05
Nicaragua
0.06
0.80
0.43
Panama
3.45
1.02
2.23
1.22
0.86
1.00
0.61
0.86
0.78
0.64
0.84
0.74
1.83
1.03
1.43
1.17
1.05
1.10
1.72
1.01
1.36
Suriname
0.41
1.00
0.71
0.82
1.00
0.91
1.35
0.96
1.11
0.38
0.96
0.72
Argentina
0.47
Bolivia
0.41
1.13
0.80
0.75
1.16
0.95
0.96
0.99
0.98
0.59
1.07
0.83
0.96
0.59
1.06
1.00
1.03
1.43
0.87
1.05
1.33
0.91
1.12
Brazil
0.91
1.00
0.93
0.75
1.04
0.89
0.94
1.05
1.01
0.50
1.06
0.78
Chile
1.15
1.16
1.16
1.16
1.25
1.21
1.09
1.32
1.24
1.01
1.24
1.14
Colombia
1.69
1.01
1.46
0.66
0.97
0.81
0.95
1.05
1.00
0.96
1.07
1.01
Ecuador
0.66
1.01
0.83
0.61
0.88
0.74
1.00
0.89
0.94
0.90
0.99
0.95
Paraguay
1.21
0.77
1.03
0.66
0.85
0.77
0.89
0.79
0.83
1.01
0.92
0.97
Peru
0.26
1.08
0.67
0.92
1.04
0.98
1.23
0.96
1.08
1.17
1.10
1.14
Uruguay
0.84
1.22
1.03
0.78
1.24
1.01
1.22
1.20
1.21
0.85
1.25
1.05
Venezuela
0.68
0.98
0.83
0.53
0.89
0.74
0.89
0.82
0.85
-1.61
0.61
-0.50
Avg
1.00
Max.
1.00
2.23
1.00
1.83
1.00
1.25
1.00
1.43
1.00
1.43
1.00
1.32
1.00
1.24
1.00
1.81
1.00
1.30
1.00
1.36
Min.
0.42
0.53
0.75
0.74
0.61
0.79
0.78
-1.61
0.61
-0.50
Country
1.00
2000
2010
Musg. Opp. Total Musg. Opp. Total
Source: Authors calculations. It is important to notice that PSP in 1990 includes only Education, Health, Economic,
and Stability, because of data availability. Since 2000, PSP includes more less all sub indicators. Infrastructure is
only since 2008 until 2018 and Administration only since 1996.
Analysing by type of indicator, i.e., whether it is Musgravian or Opportunity PSP, the results
show that best or worst scores do not represent the same countries as in the cases of Total PSP.
For example, if only Musgravian PSP (economic indicators) are checked, then the best country
was Bolivia (1.43) in 2010 and the worst country was Nicaragua in both 1990 and 2010. In
11
addition, the same occurred with Opportunity PSP (social indicators), where Uruguay was the
best-ranked in 1990 (1.22), in 2000 and 2010 it was Chile, and in 2019 it was Costa Rica. On
the contrary, Paraguay and Guatemala registered the lowest results in 1990 and 2000.
By ranking, if we consider the first and last three positions during the period of 1990-2019,
the best country for many years are Panama, Chile, and Belize, while the worst scores are
Venezuela, Nicaragua, and Paraguay.
Figure 1 shows the evolution of the PSP indicator of the best performers, with scores
ranging from 0.94 to 2.23. During the first decade, Panama was the best performer, however
the three countries registered a decrease in their PSP. On the contrary, Belize started to improve
in 1998, which is probably because this year fell during a period of economic crisis in most of
the countries and inflation values were very high. Accordingly, when comparing between the
countries under analysis, Belize was among the countries with a lower inflation rate, which
helped to improve its PSP. Chile demonstrates the most constant pattern during the whole
period.
Figure 1: Evolution of the Total PSP - top three countries
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2,50
2,30
2,10
1,90
1,70
1,50
1,30
1,10
0,90
0,70
0,50
Belize - CA
Panama - CA
Chile - SA
Source: Authors’ calculations, CA - Central America, SA - South America,
On the other hand, the three weakest PSP performers in the sample are Venezuela,
Nicaragua, and Paraguay (Figure 2), as shown by scores ranging from 0.43 to 1.08, except
during 2019 and 2020, which is when Venezuela registered a negative PSP value in 2019 (as a
result of the consequence of negative GDP growth rates). None of the countries appears to have
improved, at least not along the period under study, all remaining within the same range.
12
Figure 2: Evolution of Total PSP – bottom three countries
1,20
1,00
0,80
0,60
0,40
0,20
-0,20
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
0,00
-0,40
-0,60
Nicaragua - CA
Paraguay - SA
Venezuela - SA
Source: Authors’ calculations, CA - Central America, SA - South America,
Analysing PSP by the areas and for each 10 years, as shown in Figure 3, Uruguay was
leading the group of countries in Education, however Costa Rica took the first place in 2000,
and later in 2010 and 2019. The worst country in Latin America in 2000 was Guatemala, while
Paraguay was in 2010, and Guatemala was again the worst country in 2019. Moving to the
results for Health, the first-placed country for the same three years was Chile, with the worstranked country being respectively Guatemala (2000), Guyana (2010), and Venezuela (2019).
For Administration, Chile once again led the ranking for the three years in question, with the
worst performers being Paraguay and Venezuela.
13
Figure 3: PSP by Area: Education, Health, and Administration
Education PSP
Administration PSP
Health PSP
Venezuela
Venezuela
Venezuela
Nicaragua
Nicaragua
Uruguay
Guyana
Guyana
Peru
Costa Rica
Costa Rica
Paraguay
Uruguay
Uruguay
Ecuador
Peru
Peru
Colombia
Argentina
Argentina
Chile
Mexico
Mexico
Brazil
Brazil
Brazil
Bolivia
Chile
Chile
Argentina
Colombia
Colombia
Suriname
Ecuador
Ecuador
Panama
Bolivia
Bolivia
Nicaragua
Belize
Belize
Mexico
Suriname
Suriname
Honduras
Panama
Panama
Guyana
Paraguay
Paraguay
Guatemala
El Salvador
El Salvador
El Salvador
Honduras
Honduras
Costa Rica
Guatemala
Guatemala
Belize
-0,49
2000
0,51
2010
1,51
2019
0,96 0,98 1,00 1,02
2000
2010
2019
0,00
2000
1,00
2010
2,00
2019
Source: Authors’ calculations.
Contrary to Economic PSP, Belize was the best performer in 1990, Mexico in 2000, and
lately in Panama occupied this place in 2010 and 2019. The worst-performing countries were
Guyana in 1990, Ecuador in 2000, Belize in 2010, and Venezuela in 2019. In terms of the
stability indicators, Panama is also highlighted in 1990 and 2000, with Bolivia being ranked in
first place in 2010, and in 2019 this place changed to El Salvador. On the other hand, Peru was
placed at the bottom of the ranking in 1990 for Stability, followed by Venezuela in 2000 and
2010, while Suriname was ranked with the worst result for this factor in 2019.
14
4.3. DEA Efficiency Scores
Three models each using different inputs were used for the application of the DEA
methodology, applying both input-oriented and output-oriented approaches.
The first model uses Total Public Spending as percentage of GDP as the input and Total
PSP Scores as the output, while Table 2 reports the results.
Table2: DEA Efficiency Scores Model 1 (total PSP)
2009
Input oriented
Country
Belize
VRSTE
2013
Output oriented
Input oriented
Rank. VRSTE Rank. VRSTE Rank.
.
.
.
0.54
2019
Output oriented
VRSTE
16
0.89
Input oriented
Output oriented
Rank. VRSTE Rank. VRSTE Rank.
10
0.48
15
0.93
4
Costa Rica
0.94
5
0.96
6
0.88
5
0.94
5
0.85
3
0.95
3
El Salvador
0.68
11
0.82
12
0.73
11
0.86
12
1.00
1
1.00
2
Guatemala
1.00
1
1.00
1
1.00
1
1.00
1
1.00
1
1.00
1
Guyana
0.65
13
0.73
17
0.80
8
0.90
7
0.51
12
0.83
7
Honduras
0.64
14
0.77
14
0.59
14
0.71
19
0.64
9
0.74
12
Mexico
0.80
7
0.87
7
0.76
10
0.88
11
0.73
5
0.80
8
Nicaragua
0.70
10
0.71
18
0.82
7
0.75
18
0.71
6
0.56
16
Panama
1.00
1
1.00
1
0.95
4
0.98
3
.
.
.
.
.
.
0.42
20
0.85
13
0.34
17
0.53
17
Suriname
.
Argentina
0.72
9
0.83
10
0.62
13
0.77
17
0.61
10
0.61
14
Bolivia
0.44
18
0.82
11
0.50
18
0.90
6
.
.
.
.
Brazil
0.54
16
0.79
13
0.52
17
0.79
16
0.46
16
0.58
15
Chile
1.00
4
1.00
1
1.00
1
1.00
1
0.58
11
0.84
5
Colombia
0.68
12
0.83
9
0.70
12
0.84
14
0.70
7
0.76
11
Ecuador
0.59
15
0.76
15
0.56
15
0.83
15
0.49
14
0.70
13
Paraguay
1.00
1
1.00
1
0.99
3
0.89
8
0.81
4
0.78
9
Peru
0.74
8
0.86
8
0.79
9
0.89
9
0.68
8
0.84
6
Uruguay
0.91
6
0.97
5
0.87
6
0.96
4
0.50
13
0.77
10
Venezuela
0.50
17
0.73
16
0.44
19
0.65
20
.
.
.
.
Average
0.75
0.86
0.72
0.86
0.65
0.78
Maximum
1.00
1.00
1.00
1.00
1.00
1.00
Minimum
0.44
0.71
0.42
0.65
0.34
0.53
Std Deviation
0.19
0.10
0.19
0.09
0.19
0.15
Source: Authors’ calculations.
Assuming variable returns to scale and considering an input-oriented approach (how much
input quantities can be proportionally reduced without changing the output quantities
produced), DEA displayed the results described below.
15
Using available data from 17 of the 20 countries, the average input efficiency score in 2000
was 80%, which implies that countries could achieve the same level of PSP by using 20% less
government spending. The countries situated on the production possibility frontier are
Guatemala and Panama, with Mexico and Argentina ranked after them, while the countries
ranked in the last positions are Brazil, Venezuela, and Bolivia, which means that they are
located the furthest from the efficiency frontier. A summary of efficiency scores is reported in
Appendix A (the detailed results per year are described in the Online Appendix).
During the period under analysis, the most efficient countries are Guatemala, Panama,
Chile, and Paraguay. This contrasts with the previously-obtained PSP results, where Panama
and Chile are also ranked as the best performers, although, interestingly, Paraguay is not in this
case, as it was ranked the worst in the PSP results. On analysing the data for Paraguay, it can
be seen that its efficiency score is high as a result of the low values of government spending as
a percentage of GDP when compared to the other countries.
With regards the results of the output-oriented approach (i.e., how much output quantities
can be proportionally increased without changing the input quantities used), the same 17
countries had an output efficiency score on average of 73% in 2000, implying that countries
could have increased their performance by 27% with the same level of inputs. The countries
located on the production possibility frontier were once again Guatemala and Panamá, followed
by Mexico and Chile in third and fourth place respectively. The worst-performing countries
were Venezuela, Brazil, and Peru.
The efficient countries located on the production possibility frontier in 2009 are Guatemala,
Panama, Chile, and Paraguay. Guatemala and Chile remain in the same ranking in 2013 and,
interestingly another country emerged at the top of the ranking in 2019, which is El Salvador,
together again with Guatemala. In addition, in the same year Chile dropped to 11th position, and
2019 was therefore obviously not a good year for Chile.
Analysing the differences in the results according to the method used (input – output), most
countries remain in the same position, or close to it. In particular there are some countries, such
as Bolivia or Nicaragua that demonstrate a large difference in the results. Nicaragua is more
efficient in terms of inputs and is ranked in the Top 10, however, when in terms of output it is
ranked among the last positions, and is this one of the worst countries. Bolivia is the opposite,
as the inputs results show zero efficiency, albeit this result improves for outputs. It is important
to mention that Bolivia has one of the highest percentages of public spending (over 30%), and
that Nicaragua belongs to the group of countries that spend less than 20%.
16
Figure 4 illustrates the production possibility frontier for Model 1 (with one input and one
output) over a period of four years. The efficient countries in 2000 are Guatemala and Panama,
with Mexico lying very close. The efficient countries in 2009 are Guatemala, Panama, Chile,
and Paraguay while the efficient countries in 2013 are Guatemala and Chile. Finally, in 2019,
the efficient countries are El Salvador and Guatemala.
Figure 4: Production Possibility Frontiers, Model 1.
Source: Authors’ calculations.
We also assessed the level of efficiency by using two alternatives specifications. First, using
Public Spending on Health (% of GDP) as the input and Health PSP as the output (Model 2),
(mostly because of the health crisis due to Covid-19 and also in order to gain a view of this
sector before this pandemic). Second, Model 3 uses Total Public Spending (% of GDP) as the
input and the Economic component of the PSP as the output. The efficiency score results are
reported in Table 3 and Table 4, respectively
17
From Table 3 we can observe that the input efficiency score increases from 33% in 2009 to
48% in 2013, which is the highest score, and then it starts to decrease until 2019, with 41%.
The best year for the health sector appears to have been 2013, during which the increase of
public spending was able to achieve better efficiency results.
Table 3: DEA Efficiency Scores - Model 2 (health performance)
2009
Input oriented Output oriented
Country
2013
Input oriented Output oriented
2019
Input oriented Output oriented
VRSTE
Rank.
.
.
.
.
0.24
19
0.99
12
0.20
16
0.98
12
Costa Rica
1.00
1
1.00
1
1.00
1
1.00
1
1.00
1
1.00
1
El Salvador
0.21
15
0.99
9
0.34
14
0.99
11
0.33
9
0.99
10
Guatemala
0.35
5
0.98
15
0.70
4
0.99
17
0.59
4
0.98
15
Guyana
0.26
11
0.98
17
0.43
10
0.98
19
0.25
12
0.98
17
Honduras
0.16
17
0.98
14
0.29
17
0.99
14
0.34
8
0.98
13
Mexico
0.42
3
0.99
5
0.66
6
0.99
7
0.77
3
0.99
5
Nicaragua
0.16
16
0.98
18
0.27
18
0.98
20
0.22
15
0.98
16
Panama
0.25
12
0.99
13
0.50
7
0.99
15
.
.
.
.
Suriname
.
.
.
.
0.70
3
0.99
8
0.36
6
0.99
8
Argentina
0.65
2
1.00
4
0.96
2
1.00
4
0.98
2
0.99
3
Bolivia
0.33
6
0.98
16
0.69
5
0.99
16
.
.
.
.
Brazil
0.27
10
0.99
6
0.44
9
0.99
6
0.36
7
0.99
7
Chile
0.13
18
1.00
1
0.22
20
1.00
1
0.15
17
1.00
1
Colombia
0.28
9
0.99
8
0.36
13
0.99
10
0.24
13
0.99
9
Ecuador
0.31
7
0.99
10
0.38
12
0.99
9
0.29
11
0.98
11
Paraguay
0.38
4
0.99
11
0.50
8
0.99
13
0.41
5
0.98
14
Peru
0.28
8
0.99
7
0.40
11
0.99
5
0.32
10
0.99
4
Uruguay
0.24
13
1.00
3
0.31
15
1.00
3
0.22
14
0.99
6
Venezuela
0.23
14
0.99
12
0.30
16
0.98
18
.
.
.
.
Average
0.33
0.99
0.48
0.99
0.41
0.99
Maximum
1.00
1.00
1.00
1.00
1.00
1.00
Minimum
Standard
Deviation
0.13
0.98
0.22
0.98
0.15
0.98
0.21
0.01
0.23
0.01
0.26
0.01
Belize
VRSTE Rank. VRSTE Rank. VRSTE Rank. VRSTE Rank. VRSTE Rank.
Source: Authors’ calculations
In contrast, the output-oriented score is 99% for the three years, where there are proportional
changes in terms of public spending, although these did not affect the level of efficiency and
neither is there enough space for efficiency improvement.
In the case of Chile, it is interesting to see that while in the output-oriented approach it is
the most efficient country, it is not in the input-oriented category. This is because Chile is the
country with the highest percentage of Public Spending in Health on average, with government
spending being 3.53% of GDP during the period under analysis and it registered a significant
18
increase over the years, e.g., the growth rate is 92% from 2000 to 2019. It is only when analysing
output that Chile obtains the best score, however when both input and output are contrasted and
compared between other countries, DEA estimates highlight the efficiency of Costa Rica. The
level of public spending in Costa Rica is on average 0.61% of GDP, which represents a vast
difference from Chile. In terms of output, Costa Rica is ranked in second and third place, and,
as a result, DEA methodology punishes Chile and it is calculated that in 2009 it could have
obtained the same PSP results by using 87% less spending, and that in 2013 it could have used
78% less, while in 2019 it could have used 85% less on government spending. The overall
conclusion is that Chile is not an efficient country in the health sector, and that it has
considerable scope for improvement.
In Table 4, which shows the assessment for economic performance, it can be seen that
countries could have achieved the same average level of PSP by using 32% less on government
spending, and that countries could have increased their PSP by 24%, while using the same levels
of spending. Furthermore, these scores worsen for 2019, during which countries could achieve
the same level of PSP by using 39% less government spending, or countries could have
increased their PSP by 38% by maintaining the same levels of spending. Economicallyspeaking, 2019 is the less-efficient year across all the countries.
See the Appendices for a summary of the main results of the three models for both inputand output oriented efficiency scores and also the complete data set for 2000-2019.
19
Table 4: DEA Efficiency Scores - Model 3 (economic performance)
2009
Input oriented Output oriented
Country
2013
Input oriented Output oriented
2019
Input oriented Output oriented
VRSTE
Rank.
.
.
.
.
0.44
18
0.31
20
0.38
16
0.48
14
Costa Rica
0.84
4
0.88
5
0.73
5
0.64
12
0.65
8
0.79
3
El Salvador
0.57
14
0.48
17
0.73
6
0.43
19
0.68
6
0.56
11
Belize
Guatemala
VRSTE Rank. VRSTE Rank. VRSTE Rank. VRSTE Rank. VRSTE Rank.
1
1
1
1
1
1
1
2
1
1
1
1
Guyana
0.63
9
0.48
18
0.70
9
0.48
18
0.50
12
0.65
9
Honduras
0.63
10
0.75
11
0.59
14
0.55
16
0.64
9
0.73
5
Mexico
0.76
5
0.86
7
0.73
7
0.66
11
0.73
3
0.81
2
Nicaragua
0.70
7
0.53
16
0.82
4
0.56
15
0.71
4
0.53
13
Panama
1
1
1
2
1
1
1
1
.
.
.
.
Suriname
.
.
.
.
0.38
20
0.67
8
0.34
17
0.37
15
Argentina
0.76
6
0.90
4
0.62
13
0.67
9
0.61
10
0.34
16
Bolivia
0.38
18
0.76
10
0.51
17
0.88
4
.
.
.
.
Brazil
0.46
17
0.64
14
0.52
16
0.57
13
0.46
15
0.25
17
Chile
0.59
12
0.81
8
0.67
11
0.70
6
0.55
11
0.71
7
Colombia
0.56
15
0.62
15
0.65
12
0.53
17
0.70
5
0.66
8
Ecuador
0.54
16
0.69
13
0.53
15
0.68
7
0.49
14
0.53
12
Paraguay
1
1
0.87
6
0.99
3
0.96
3
0.81
2
0.77
4
Peru
0.62
11
0.70
12
0.68
10
0.66
10
0.66
7
0.71
6
Uruguay
0.59
13
0.79
9
0.70
8
0.77
5
0.50
13
0.58
10
Venezuela
0.67
8
0.99
3
0.44
19
0.56
14
.
.
.
Average
0.68
0.76
0.67
0.66
0.61
0.62
Maximum
1.00
1.00
1.00
1.00
1.00
1.00
Minimum
Standard
Deviation
0.38
0.48
0.38
0.31
0.34
0.25
0.18
0.17
0.18
0.18
0.16
0.19
5. CONCLUSION
On average, governments in Latin America spent about 25.6% of GDP on the provision of
public goods, services, and transfers in 2020. Furthermore, the available statistics reflect a vast
increment in public spending for the last years (2013 and 2019), with an average growth rate of
32.9%. During periods of recession, such as the economic crisis of 2008-2009 or the health and
economic crisis of 2019, governments tend to spent more on public expenditures. This paper
aims to calculate how efficient public spending has been over the last 20 years.
By collecting indicators for different areas of government activities for all countries in Latin
America, the biggest challenge was to find comparable measures for all the countries and a
complete data set for every year, and consequently the final sample analysed is for 10 countries
in South America, and 10 from Central America for the period of 2000-2019. Once the
indicators were determined, transformations were applied to each with the aim to obtain the
20
same scales and then normalise them. The next phase was to calculate the “Public Sector
Performance (PSP)” composite indicator, in order to obtain a comparable and unique measure
that represents the outcome for all the countries in the overall sample. Finally, Data
Envelopment Analysis technique was applied to compute efficiency scores and rankings for
each year.
With regards the original indicator of “Public Spending as a percentage of GDP”, figures
show that health, education, and social protection are key areas of spending, albeit within the
group of countries the level of spending differs, while in the South American region, the highest
spending is on social protection, whereas in Central America it is on education.
From the analysis of the PSP indicator, those countries that performed better during the
period of 1990-2019 are Panama, Chile, and Belize. Interestingly, these three countries are
diverse in their level of public spending, while Belize is situated in the group that spend over
30% of GDP, while Chile belongs to the group that spend between 20%-30% of GDP and
Panama is within the group that spend less than 20% of GDP. On the other hand, the worseperforming performance countries are Venezuela, Nicaragua, and Paraguay, where the public
spending of Nicaragua and Paraguay is less than 20% of GDP and Venezuela spends between
20%-30% of GDP.
Furthermore, DEA results showed a degree of diversity between the countries, but
commonly there is potential for increase efficiency in public spending. Three models were
applied for each year: i) a general model, using Total Public Spending (% GDP) as the input
and Total PSP as the output; ii) Model 2, which uses Health PSP as the input and Public
Spending on Health as the output; iii) Model 3, with Economic PSP as the input and Total
Public Spending as the output.
With respect to Model 1, assuming variable returns to scale and adopting both the input and
output approach, the set of countries that define the theoretical production possibility frontier
in 2019 are El Salvador and Guatemala. The average input score during the period of 2000 to
2019 decreased from 80% in 2000 to 65% in 2019 (when countries could have used 35% less
spending to achieve the same levels of PSP). On the contrary, using the output-oriented
approach, the efficiency scores slightly increased from 73% in 2000 to 78% in 2019 (when
countries could have increased their performance by 22% with the same level of inputs).
Analysing the years of the global economic crisis of 2008-2009, scores in both approaches
are worse, suggesting that when Latin American countries passed through crises and increased
the public spending, their efficiency decreased, with 2019 recording an emphasised decrease.
21
The least efficient countries differ in their approaches, with Suriname, Brazil, and Belize
being at the bottom of the ranking in 2019 for the input approach. It should be noticed that
Belize was among the best performers in PSP, but when this is contrasted with the spending
incurred, it is not efficient, representing a case that shows the importance of applying DEA
methodology to describe efficiency. In addition, in terms of output approach, the worst
countries are Nicaragua, Suriname, and Brazil.
Model 2 gave rise to interesting findings, in that the average input efficiency score during
the period is 40%, which is a very low score, indicating that countries could have used 60%
less spending on health to attain the same outcomes if they had been fully efficient. On the other
hand, the average output score is surprisingly high, with an average of 99%, suggesting that
countries are almost attaining the maximum possible return from most of the outputs with the
level of spending in the health area. From the point-of-view of the input-oriented approach, the
best country for health is Costa Rica, which remain fully efficient along the period under
analysis, with Chile positioned at the bottom of the ranking.
Finally, the results of Model 3 led to an average input efficiency score of 68% from 2000
to 2019, with those countries that could have achieved the same level of PSP using 32% less
government spending. In contrast, the average output efficiency score is 66%, implying that
countries could have increased their performance by 34% with the same level of inputs.
Accordingly, governments have a large space for improvement in economic areas.
Surprisingly, the findings from Model 3 suggest that the lower the spending ratios, the more
efficient are the countries. Guatemala, Panama, and Paraguay rank as the best performers in
both the input- and output-oriented approach, all of which are countries with a public
expenditure as a % of GDP less than 20%.
To conclude, the analysis of the three models provides an important understanding of the
differences between countries when analysing public spending in general vs in particular areas,
such as health or Total PSP (which aggregates many fields) vs Economic or Health PSP. For
example, Chile, which topped the ranking for many years of the more efficient countries in
Model 1, does not do so when only health or economic areas are analysed. The results divided
by individual spending areas seem to present a more promising approach for measuring
efficiency and effectiveness on a cross-country basis.
Future research can continue this analysis with the application of appropriate methodologies
to better understand the determinants of the efficiency scores already calculated, and, in
addition, they could identify what governments can do to achieve greater efficiency. Advances
also need to be made regarding, for instance, the effects of taxation on the efficiency scores as
22
manifested in the literature review. In addition, in order to make the most use of the large crosscountry panel dataset presented in this study, it would be interesting to apply alternative DEA
models, such as the DEA-Windows method, which would enable a year-to-year comparison of
the results and help contrast the existing scores obtained for each year.
REFERENCES
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Afonso, A., Jalles, J. T. & Venâncio, A., (2021). Taxation and Public Spending Efficiency: An
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Clements, B., Faircloth, C. & Verhoeven, M., (2007). Public Spending in Latin America:
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Gavin, M. & Perotti, R., (1997). Fiscal policy in Latin America. NBER Macroeconomics
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24
Table A.1: Indicators - Description and Sources
Opportunity Indicators
Indicator
Voice and Accountability: Estimate
Rule of Law: Estimate
Administration
Regulatory Quality: Estimate
Government Effectiveness: Estimate
Description
Captures perceptions of the extent to which a country's citizens are able to
participate in selecting their government, as well as freedom of
expression, freedom of association, and a free media.
Captures perceptions of the extent to which agents have confidence in and
abide by the rules of society, and in particular the quality of contract
enforcement, property rights, the police, and the courts, as well as the
likelihood of crime and violence.
Captures perceptions of the ability of the government to formulate and
implement sound policies and regulations that permit and promote private
sector development.
Captures perceptions of the quality of public services, civil service and the
degree of its independence from political pressures, quality of policy
formulation and implementation, and the credibility of the government's
commitment to such policies.
School enrollment, secondary (% gross) Ratio of total enrollment, regardless of age, on secondary education
Education
Quality of the education system
Health
Mortality rate, under-5 (per 1,000 live
births)
Adolescent fertility rate (births per
1,000 women ages 15-19)
Maternal mortality ratio (modeled
estimate, per 100,000 live births)
Infrastructure
Musgravian Indicators
Distribution
Economic
Quality of overall infrastructure, 1-7
(best)
Indicator
Source
Governance Indicators World Bank
Modifications
Serie Availability
Original estimates ranging from 1996,1998,2000,2002,
2.5(bad) to 2.5(good). Changed to 0
2003-2019
to 5.
Governance Indicators World Bank
Original estimates ranging from 1996,1998,2000,2002,
2.5(bad) to 2.5(good). Changed to 0
2003-2019
to 5.
Governance Indicators World Bank
Original estimates ranging from 1996,1998,2000,2002,
2.5(bad) to 2.5(good). Changed to 0
2003-2019
to 5.
Governance Indicators World Bank
Original estimates ranging from 1996,1998,2000,2002,
2.5(bad) to 2.5(good). Changed to 0
2003-2019
to 5.
World Bank: UNESCO Institute for Statistics
Quality of educational system on a scale from 7 (very well) to 1 (not well at The Global Competitiveness Index Historical
all).
Dataset © 2007-2017 World Economic Forum.
1990-2019
2008-2018
Probability per 1,000 that a newborn baby will die before reaching age five.
World Bank
1990-2019
Changed to (1000-IMR)/1000
Number of births per 1,000 women ages 15-19.
World Bank
1990-2019
Changed to (1000-AFR)/1000
2000-2017
Changed to (100000-MM)/100000
WHO, UNICEF, UNFPA, World Bank Group, and
Number of women who die from pregnancy-related causes while pregnant
the United Nations Population Division. Trends
or within 42 days of pregnancy termination per 100,000 live births.
in Maternal Mortality: 2000 to 2017.
Infrastructure quality on a scale from 7 (extensive and efficient) to 1
The Global Competitiveness Index Historical
(extremely underdeveloped)
Dataset © 2007-2017 World Economic Forum.
Description
Source
2008-2018
Serie Availability
World Bank
1990-2019
Number of unemployed persons as a percentage of the labor force
International Monetary Fund
1990-2019
Gross domestic product per capita,
constant prices
GDP is expressed in constant international dollars per person. Data are
derived by dividing constant price purchasing-power parity (PPP) GDP by
total population.
International Monetary Fund
1990-2019
Gross domestic product, constant
prices (Percent change)
Annual percentages of GDP constant price
International Monetary Fund
1990-2019
Gini index (estimate)
Unemployment rate (% of total labor
force)
Gini index on a scale from 100(perfect inequality) to 0 (perfect equality).
Coefficient of variation of Growth
Coefficient of variation=standard deviation/mean of GDP growth based on
5 year data. GDP constant prices (percent change).
International Monetary Fund
1990-2019
Inflation, average consumer prices
Annual percentages of average consumer prices.
International Monetary Fund
1990-2019
Stability
Modifications
Changed to 100-GINI
5 year average and Reciprocal
value 1/x
5 year average
5 year average
Coefficient of variation: Standard
Deviation/Average and Reciprocal
value 1/x
5 year average and Reciprocal
value 1/x
Table A.2: Total Public Sector Performance (PSP) 1990-2019
Country
1990 1991 1992 1993 1994 1995 1996 1997 1998
1999
2000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
2019
Belize
1.67 1.72 1.94 1.61 1.45 1.41 1.17 1.08 1.02 1.18
1.30 1.43 1.74 1.62 1.32 1.13 1.06 0.98 0.94 0.96 0.96 0.97 1.03 1.07 1.23 1.20 1.17 1.14 1.21
1.25
Costa Rica
1.11 1.16 1.03 1.07 1.05 1.03 1.03 1.00 1.04 1.07
1.10 1.07 1.19 1.22 1.45 1.36 1.11 1.08 1.05 1.04 1.06 1.07 1.07 1.06 1.07 1.11 1.15 1.17 1.18
1.28
El Salvador
0.78 0.80 0.80 0.83 0.86 0.98 0.90 0.89 0.87 0.98
0.96 1.00 1.03 1.09 1.06 0.97 0.96 0.96 1.02 0.99 1.00 0.99 0.97 0.98 1.02 1.11 1.10 1.22 1.26
1.36
Guatemala
1.09 0.98 1.27 1.22 1.17 1.13 1.02 1.10 0.98 1.28
1.10 1.17 1.13 1.11 1.07 1.01 0.96 0.94 0.97 0.98 0.98 0.98 0.98 0.98 0.99 1.05 1.05 1.04 1.06
1.12
Guyana
0.42 0.47 0.49 0.56 0.67 0.78 1.13 1.20 0.94 0.99
0.96 0.98 0.89 0.82 0.85 0.79 0.83 0.83 0.82 0.86 0.88 0.99 1.00 1.04 1.06 0.75 1.03 1.08 1.10
1.13
Honduras
0.99 0.86 0.88 0.89 0.86 0.86 0.86 0.91 0.83 0.85
0.90 0.94 0.94 0.93 0.91 0.96 0.95 1.00 1.00 0.93 0.90 0.89 0.87 0.85 0.82 0.98 1.01 0.98 0.99
1.01
Mexico
0.95 0.97 1.07 1.15 1.24 0.95 0.95 0.93 0.95 0.96
1.00 1.14 1.10 1.10 1.07 1.06 1.07 1.10 1.08 1.02 1.00 0.99 1.00 1.00 0.98 1.05 1.07 1.07 1.08
1.05
Nicaragua
0.43 0.48 0.48 0.58 0.53 0.59 0.68 0.72 0.79 1.05
1.00 1.00 0.88 0.91 0.87 0.89 0.87 0.85 0.86 0.81 0.78 0.77 0.78 0.81 0.86 0.82 1.03 1.08 0.83
0.74
Panama
2.23 1.92 1.98 2.07 2.03 1.78 1.77 1.78 1.46 1.56
1.43 1.43 1.26 1.35 1.35 1.27 1.24 1.17 1.17 1.16 1.10 1.09 1.11 1.14 1.16 1.15 1.18 1.22 1.28
1.36
Suriname
0.71 0.68 0.73 0.70 0.50 0.53 0.66 0.71 0.75 0.80
0.91 0.90 0.89 0.89 0.88 0.92 1.08 1.04 1.02 1.05 1.11 1.08 1.04 1.02 0.94 0.83 0.73 0.69 0.74
0.72
Argentina
0.80 0.96 0.85 0.90 0.99 1.01 1.07 1.02 1.46 0.98
0.95 0.90 0.85 0.85 0.86 0.91 0.93 0.96 1.06 0.98 0.98 0.98 0.95 0.92 0.88 0.86 0.88 0.89 0.87
0.83
Bolivia
0.59 0.71 0.95 0.97 1.02 1.07 1.05 1.03 1.53 1.10
1.03 1.04 1.03 1.08 1.04 1.03 1.03 1.04 0.93 1.02 1.05 1.08 1.11 1.09 1.12 1.08 1.10 1.07 1.07
1.12
Brazil
0.93 0.80 0.71 0.68 0.65 0.69 0.81 0.77 0.82 0.79
0.89 0.88 1.02 0.98 0.97 0.98 0.97 0.97 0.94 0.98 1.01 1.00 0.97 0.95 0.92 0.85 0.80 0.79 0.79
0.78
Chile
1.16 1.40 1.25 1.25 1.08 1.14 1.24 1.22 1.18 1.14
1.21 1.16 1.22 1.21 1.24 1.34 1.29 1.33 1.33 1.24 1.24 1.19 1.23 1.20 1.21 1.17 1.17 1.13 1.18
1.14
Colombia
1.46 1.17 1.11 1.12 1.08 1.12 0.97 1.03 0.89 0.84
0.81 0.78 0.80 0.82 0.84 0.96 0.99 1.03 1.00 1.01 1.00 1.00 1.01 1.01 1.02 1.01 0.97 0.95 0.96
1.01
Ecuador
0.83 0.82 0.81 0.88 0.86 0.94 0.88 0.87 0.84 0.76
0.74 0.75 0.73 0.77 0.77 0.84 0.90 0.89 0.92 0.94 0.94 0.95 0.97 1.00 1.03 0.99 1.00 1.00 1.01
0.95
Paraguay
1.03 0.93 0.95 0.96 0.94 0.93 0.88 0.91 0.81 0.89
0.77 0.78 0.72 0.72 0.75 0.78 0.81 0.84 0.85 0.84 0.83 0.84 0.85 0.88 0.87 0.75 0.87 0.89 0.97
0.97
Peru
0.67 0.69 0.64 0.63 0.67 0.78 0.87 0.90 0.91 0.97
0.98 0.95 0.97 0.98 1.04 1.07 1.12 1.19 1.05 1.03 1.08 1.08 1.07 1.05 1.03 1.00 1.01 0.99 1.02
1.14
Uruguay
1.03 1.06 1.04 0.98 0.98 0.99 1.08 1.03 1.04 0.93
1.01 0.94 0.95 0.93 0.91 0.94 0.96 0.97 1.08 1.20 1.21 1.19 1.19 1.16 1.14 1.04 1.05 1.04 1.02
1.05
Venezuela
0.83 0.90 0.93 0.86 0.70 0.92 0.80 0.75 0.80 0.78
0.74 0.81 0.74 0.69 0.74 0.80 0.83 0.84 0.86 0.91 0.85 0.82 0.81 0.78 0.72 0.62 0.56 0.48 0.32 -0.50
Average
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
*The values highlighted are the best scores for each year.
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Table A.3: Summary of DEA Input Efficiency Scores
Model 1
Total PSP
Average
Maximum
Minimum
Standard Deviation
Total Efficient Countries
Countries on the frontier
Model 2
Health PSP
2000
0.80
1.00
0.50
0.15
2001
0.79
1.00
0.46
0.17
2002
0.78
1.00
0.42
0.18
2003
0.77
1.00
0.47
0.18
2004
0.75
1.00
0.42
0.17
2005
0.75
1.00
0.42
0.18
2006
0.76
1.00
0.40
0.19
2007
0.74
1.00
0.40
0.19
2008
0.72
1.00
0.36
0.21
2
3
3
2
3
2
3
2
2
GUA, GUA,
COS,
GUA,
PAN,
COS,
MEX, PAN,
GUA,
PAN
PAR
PAR
PAN ARG
PAR
COS,
CHI,
PAR
CHI,
PAR
CHI,
PAR
2009
0.75
1.00
0.44
0.19
2010
0.78
1.00
0.55
0.16
2011
0.79
1.00
0.48
0.18
2012
0.76
1.00
0.49
0.17
2013
0.72
1.00
0.42
0.19
2014
0.70
1.00
0.36
0.21
2015
0.68
1.00
0.35
0.21
2016
0.63
1.00
0.40
0.18
2017
0.65
1.00
0.39
0.19
4
3
3
2
2
4
4
2
3
2018 2019
0.64 0.65
1.00 1.00
0.40 0.34
0.19 0.19
2
2
GUA,
BEL, BEL,
GUA, GUA,
SAL,
GUA, GUA, GUA, GUA, GUA,
SAL, SAL,
PAN,
CHI, CHI,
GUA,
CHI CHI PAN, PAN, PAN
BOL GUA
CHI,
PAR PAR
PAN
PAR
CHI CHI
Average
Maximum
Minimum
Standard Deviation
0.39
1.00
0.17
0.23
0.38
1.00
0.17
0.22
0.35
1.00
0.16
0.22
0.44
1.00
0.16
0.25
0.36
1.00
0.15
0.24
0.35
1.00
0.15
0.23
0.31
1.00
0.14
0.23
0.31
1.00
0.14
0.22
0.35
1.00
0.15
0.22
0.33
1.00
0.13
0.21
0.39
1.00
0.16
0.21
0.37
1.00
0.16
0.21
0.45
1.00
0.20
0.22
0.48
1.00
0.22
0.23
0.45
1.00
0.20
0.23
0.45
1.00
0.19
0.23
0.45
1.00
0.18
0.23
0.45
1.00
0.17
0.24
0.43
1.00
0.17
0.26
Total Efficient Countries
Countries on the frontier
1
COS
1
COS
1
COS
1
COS
1
COS
1
COS
1
COS
1
COS
1
COS
1
COS
1
COS
1
COS
1
ARG
1
COS
1
COS
1
COS
1
COS
1
COS
1
1
ARG COS
0.78
1.00
0.50
0.14
0.77
1.00
0.46
0.16
0.76
1.00
0.42
0.17
0.75
1.00
0.45
0.18
0.72
1.00
0.40
0.18
0.72
1.00
0.40
0.19
0.73
1.00
0.43
0.20
0.71
1.00
0.40
0.20
0.66
1.00
0.35
0.22
0.68
1.00
0.38
0.18
0.68
1.00
0.42
0.18
0.69
1.00
0.42
0.17
0.71
1.00
0.47
0.16
0.67
1.00
0.38
0.18
0.63
1.00
0.36
0.19
0.61
1.00
0.37
0.18
0.60
1.00
0.37
0.18
0.61
1.00
0.41
0.17
0.59
1.00
0.37
0.17
0.61
1.00
0.34
0.16
2
1
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
1
1
Average
Model 3
Economic PSP Maximum
Minimum
Standard Deviation
Total Efficient Countries
Countries on the frontier
0.41
1.00
0.15
0.26
GUA, GUA, GUA, GUA, GUA, GUA, GUA, GUA, GUA,
GUA,
PAN, GUA, GUA, GUA, GUA, GUA, GUA,
MEX MEX, MEX, MEX, MEX, MEX, MEX, ARG, PAN, PAN,
BOL GUA
MEX
PAR PAN PAN PAN PAN PAN PAN
ARG PAR PAR PAR PAR PAR PAR PAR PAR
*GUA – Guatemala; PAN – Panama; MEX – Mexico; ARG – Argentina; COS – Costa Rica; PAR – Paraguay; CHI – Chile; BEL – Belize: SAL – Salvador; BOL–Bolivia
Table A.4: Summary of DEA Output Efficiency Scores
Model 1
Total PSP
Average
Maximum
Minimum
Standard Deviation
Total Efficient Countries
Countries on the frontier
Model 2
Health PSP
Average
Maximum
Minimum
Standard Deviation
2000
0.73
1.00
0.52
0.14
2001
0.74
1.00
0.54
0.15
2002
0.80
1.00
0.59
0.14
2003
0.75
1.00
0.51
0.13
2004
0.72
1.00
0.51
0.15
2005
0.76
1.00
0.58
0.12
2006
0.82
1.00
0.64
0.12
2007
0.80
1.00
0.62
0.12
2008
0.82
1.00
0.65
0.12
2
3
2
1
2
1
3
2
2
COS
COS,
CHI,
PAR
CHI,
PAR
CHI,
PAR
GUA,
GUA,
GUA,
COS,
PAN
MEX,
PAN
PAN
GUA
PAN
2009
0.86
1.00
0.71
0.10
2010
0.87
1.00
0.69
0.10
2011
0.89
1.00
0.69
0.10
2013
0.86
1.00
0.65
0.09
2014
0.85
1.00
0.59
0.11
2015
0.85
1.00
0.65
0.12
2016
0.87
1.00
0.62
0.11
2017
0.85
1.00
0.57
0.12
2018
0.83
1.00
0.59
0.13
2019
0.78
1.00
0.53
0.15
4
3
4
2
2
4
4
2
3
2
2
GUA,
GUA,
BEL, BEL,
GUA,
SAL,
SAL, SAL,
PAN,
CHI, GUA, GUA, GUA, GUA, GUA,
CHI,
GUA,
CHI,
PAR, CHI CHI PAN, PAN, PAN
BOL GUA
PAR
PAN
PAR
URU
CHI CHI
0.99
1.00
0.97
0.01
0.99
1.00
0.97
0.01
0.99
1.00
0.97
0.01
0.99
1.00
0.97
0.01
0.99
1.00
0.97
0.01
0.99
1.00
0.97
0.01
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
Total Efficient Countries
2
2
2
2
2
2
2
2
2
2
2
2
Countries on the frontier
COS,
CHI
COS,
CHI
COS, COS, COS,
CHI CHI CHI
COS, COS,
CHI CHI
COS,
CHI
COS, COS,
CHI CHI
0.67
1.00
0.45
0.17
0.56
1.00
0.36
0.18
0.56
1.00
0.27
0.22
0.56
1.00
0.14
0.24
0.59
1.00
0.25
0.20
0.65
1.00
0.31
0.18
0.70
1.00
0.40
0.17
0.75
1.00
0.44
0.18
0.79
1.00
0.45
0.18
Total Efficient Countries
1
1
2
1
2
2
2
Countries on the frontier
MEX
Average
Model 3
Economic PSP Maximum
Minimum
Standard Deviation
2012
0.86
1.00
0.66
0.10
0.76
1.00
0.48
0.17
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
3
2
COS,
COS, COS,
COS,
ARG,
CHI CHI
CHI
CHI
0.76
1.00
0.47
0.16
0.72
1.00
0.45
0.16
0.66
1.00
0.31
0.17
0.66
1.00
0.31
0.18
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
0.99
1.00
0.98
0.01
2
2
2
2
COS,
CHI
COS, COS,
CHI CHI
0.64
1.00
0.32
0.19
0.63
1.00
0.32
0.18
0.59
1.00
0.29
0.19
0.99
1.00
0.97
0.01
0.99
1.00
0.98
0.01
3
2
COS,
COS,
COS,
ARG,
CHI
CHI
CHI
0.59
1.00
0.30
0.20
0.65
1.00
0.33
0.18
0.62
1.00
0.25
0.19
3
3
2
3
2
2
2
2
2
2
2
1
1
GUA, GUA,
GUA,
GUA,
GUA, GUA, GUA,
GUA,
PAN, GUA, GUA, GUA, GUA, GUA, GUA,
MEX, ARG,
PAN,
MEX
MEX
BOL GUA
MEX
MEX MEX MEX
PAN
PAR PAN PAN PAN PAN PAN PAN
PAR PAR
PAR