Determinants of Social Spending in Latin America. A Dynamic Panel Data Errorcorrection Model Analysis
Fernando Martin Mayoral
Markus Nabernegg
FLACSO–Ecuador
ABSTRACT The aim of our research is the study of economic, demographic, and political
determinants of social spending in Latin America between 1990 and 2010 We apply system
General Method of Moments (GMM) estimation for panel data error-correction models.
The empirical results suggest a long-term relationship between social expenditure and its
determinants, in all cases indicating diminishing returns; this confirms the convergence
hypothesis toward an upper-bounded steady state. Globalisation and growth of interest
payments on debt crowd out total social expenditure in the first case and health
expenditure in the second, while higher unemployment rates increase total social spending.
For demographic factors, Latin American governments seem to prioritise social security
for the working-age and elderly populations at the expense of the young population. On the
other hand, the movement to less-democratic governments increases total social
expenditure, as well as spending on education and health; the latter is also negatively
affected by left-wing governments. Finally, countries' specific structural characteristics not
included in the model absorb an important proportion of the explanatory power of our
model. There are positive effects in the case of health and education, and negative effects
for social security expenditure.
JEL: E62, H53, N16, C23
KEYWORDS: social spending; health; education; social security; Latin America;
dynamic data panels; error-correction model
Determinants of Social Spending in Latin America. A Dynamic Panel Data Errorcorrection Model Analysis
I. Introduction
During the decades of 1990 and 2000, Latin American countries carried out deep structural
and institutional reforms in order to offset the fall in social expenditure, which had occurred
in the 1980s due to governments’ prioritisation of debt-service payments over social
programs. Social expenditure increased both as a percentage of GDP and of total public
spending in almost all Latin American countries, growing on average from 9.3 per cent of
GDP and 45.7 per cent of total expenditure in 1990-1991 to 15.4 per cent of GDP and 62.6
per cent of total expenditure in 2009-2010 (CEPAL, 2012). Spending on social security
followed by education were the social expenditures that experienced the largest increases.
This growth contributed to indigence reduction and less poverty in the last three decades,
although the region still has the highest levels of inequality in the world.
Despite these expenditure increases, most researchers agree that there has been
limited effectiveness in Latin American social policies in terms of access to education
among low-income groups, the creation of employment opportunities, and the social
protection of certain excluded population groups (CEPAL, 2011). Among the causes that
explain the shortcoming of social expenditures in the region, one strand focuses on the
revenue side arguing that the fragile ability of fiscal institutions to collect taxes could be
responsible. But there are also limitations on the expenditure side related to economic,
demographic, political, and even institutional factors.
Succinctly, the aim of this paper is to identify, through dynamic panel data analysis,
the determinants of aggregate social expenditure and its three major categories (education,
health, and social security) in Latin America for the period 1990 to 2010. The paper is
organised as follows. Section II reviews the main theoretical contributions on the
determinants of social spending, paying special attention to developing and transition
countries. Section III presents a descriptive-comparative analysis of social spending in
Latin America between 1990 and 2010. In section IV we review methodological
considerations that have been highlighted in the literature, introducing an error-correction
model specification over an autoregressive distributed lag model ARDL(1,1). Then, we
present empirical evidence on determinants of social spending in Latin America and its
three categories (education, health, and social security) for the period 1990-2010 which is
produced with GMM system estimators. Section VII contains our conclusions.
II. Determinants of Social Spending
Studies about determinants of social spending have increased in number considerably since
the late 1970s, coinciding with the implementation of social-policy reforms carried out in
developed countries in order to strengthen their welfare states (Kittel and Obinger, 2003).
The literature has analysed variables related to the ideological orientation of governments,
and globalisation or the degree of fiscal decentralisation that may have affected social
spending and its major categories1 For the period since 2000, we find an increasing interest
in developing and transition countries, assuming that there are clear differences from
industrialised countries in their political, institutional, and demographic characteristics, as
well as in economic factors. In what follows, we review the main contributions made in
these respects.
From an economic perspective, an extensive literature has concentrated on
globalisation as the main determinant of social spending (see Kaufman and SeguraUbiergo, 2001; Avelino et al., 2005; Dreher et al., 2008 and Leibrecht et al., 2011 to give
but a few examples). In this respect, there exist two main opposing views. The first one—
the efficiency hypothesis—considers that globalisation has negative effects on social
expenditures. The cornerstone of this is the belief that public expenditure and, particularly,
social spending, may disturb labour markets and bias private investment decisions, thereby
reducing producers' international competitiveness. This could be the consequence of fiscal
redistributive policies based on progressive taxation or of the application of high payroll
taxes. Another source of inefficiency comes from the financing of social policies, which
may increase interest rates, crowd out private investment, and (or) cause real exchange rate
appreciation. Therefore, globalisation increasingly imposes pressure on governments that
favour market interests over social issues. Greater integration of financial markets also
deepens this pressure, since it increases opportunities for capital flows.
Another argument comes from Wibbels (2006), who believes that the negative
relationship between openness and social spending in developing countries is caused by
their dependent position in global markets. Prices of primary products tend to be more
volatile, provoking intense business cycles in developing countries. Limitations on these
governments in obtaining finance in international markets during times of crisis, and any
ensuing production shocks, may reduce their spending capacity.
On the other hand, there are arguments in favour of a positive relationship between
globalisation and social expenditure. This is the so-called compensation hypothesis, which
is based on the idea that integration into international markets would encourage
governments to increase social spending to boost workers’ productivity. Taking into
account that public investment in human capital represents a public good, the business
sector may also demand greater investment in education, which should help them to
improve productivity and thereby improve their international competitiveness. For
Kaufman and Segura-Ubiergo (2001: 557) the increase in international competition may
cause 'social dislocation, uncertainty and unequal distributive effects'. In this scenario,
governments would be interested in increasing social expenditure in order to avoid political
instability, thus redistributing the risks of the increased openness of the economy.
Other authors argue that globalisation forces governments to restructure their
welfare state—according to their ideology—converging in the long run to similar welfare
states of an upper steady state (convergence or catch-up hypothesis) (Flora and
Heidenheimer, 1981; Flora, 1986; Pierson, 1994, 2001; Scharpf, 2000).
At a disaggregated social expenditure level, Dion (2000, 2006) for developing
countries and Kaufman and Segura-Ubiergo (2001) and Avelino et al. (2005) for Latin
America find that trade liberalisation has a positive impact on spending on education and
health, because it encourages governments to improve productivity and human capital.
With respect to social security, they find a negative correlation with openness. They argue
that social security protection may harm the comparative advantage of developing countries
by raising the cost of their relatively abundant labour (following the Heckscher-Ohlin
model guidelines). Financial openness should follow a similar logic, as foreign direct
investment seeks intensive unskilled labour investments. Instead, Avelino et al. (2005)
observe that financial openness puts no constraint on government spending.
The economic cycle has also been analysed as a possible determinant of social
spending. Pierson (1996, 2001) and Bonoli et al. (2000) agree that the share of social
spending in GDP behaves pro-cyclically. In Latin America, Hicks and Wodon (2001),
Braun and Di Gresia (2002), and Aldunate and Martner (2006) observe that social spending
grows fast in periods of economic expansion but declines even faster in periods of
recession. With respect to different social spending categories, Wibbels (2006) argues that
spending on health and education are more vulnerable to economic cycles than is social
security spending, as the beneficiaries of the first two programs have a 'relatively
unorganized constituency' (Wibbels, 2006: 440) in comparison to the small and highly
organised constituency of social security spending.
The level of economic development of a country may also influence social
expenditures. In this respect, Warner’s law of increasing state activity, subsequently
validated by the displacement hypothesis introduced by Peacock and Wiseman (1961),
affirms that government activities grow with the economic development of the country over
time through increasing public expenditure to satisfy the needs of the population. This
process is associated with the rise in revenue collection that accompanies economic growth.
This positive relationship has been observed by Gupta (1967), Diamond (1977), and
Nomura (1991, 1995
Another economic factor that influences social expenditures is public indebtedness.
Lora and Olivera (2007) and Lora (2009) find that excessive debt ratios and high interest
payments on debt crowd out social expenditures, with a greater effect in Latin America
than in other regions. This constitutes a limitation not only for social security but also for
education and health spending (Hunter and Brown, 2000; Dion, 2006), although empirical
evidence is not unambiguous (Lora and Olivera, 2007; Lora, 2009).
Higher unemployment rates, which usually come with a declining economy, are
considered by various authors to increase social spending (Snyder and Yackovlev, 2000;
Kittel and Obinger, 2003; Avelino et al., 2005) due to higher costs for the state in
maintaining its social programs. Avelino et al. (2005) argue that even if there exist few
unemployment programs in Latin America, there should be a positive relationship between
these variables, due to governments' efforts to counteract the negative effects of crisis and
to promote employment generation2.
A second group of determinants that may affect social spending are related to
demographic structure. The increasing aging of the population—caused by the lengthening
of life expectancy and the fall in birth rates—is starting to have an impact on social
expenditure—mostly in health and retirement pensions—forcing many governments
(especially in developed countries) to restructure their social policy because of financial
constraints.
Lindert (1994), analysing OECD countries during the period 1880 to 1930, finds
that an increase in the size of the population older than 65 years has strong positive effects
on social spending, especially on pensions, and negative effects on education. Lindert
(1996) analyses the period 1962-1985 and finds the same pattern, although at a diminishing
rate, with a deceleration from the 1980s. Gonzales-Eiras and Niepelt (2007: 24) apply an
overlapping-generations model to disaggregate social spending in the United States. They
observe that a demographic transition towards an older society leads to a 'reallocation of
government spending from productive public education to unproductive intergenerational
transfers'. This reallocation negatively affects productivity growth and can even decrease
per capita income if there are no positive human capital externalities to offset the loss of
efficiency.
On the other hand, countries with a high percentage of young people (under 15) are
more likely to have higher education and health spending and lower social security
spending. Huber et al. (2008) find that health expenditure in Latin American and Caribbean
countries rises with a large young population, while in developed countries it rises with a
large elderly population.
A third set of factors that influence spending on social programs is linked to
political organisation and political institutions. There exists a vast literature that considers
that the political ideology of governments affects social spending3. Ruggie (1983),
Katzenstein (1985) and Rodrik (1997) (based on Polanyi (1944)) highlight that economic
liberalism has been accompanied by an increase in social protection, not only in
industrialised countries but also on a global scale, weakening the efficiency hypothesis.
This fact has been questioned by several authors, such as Sinn (1997), for whom the public
budget is subordinated to market forces, regardless of the ideological tendency of
governments.
For Ross (1997, 2000) and Armingeon et al. (2001) left-wing parties are more
susceptible to applying policies that sustain the welfare state, due to their greater
preoccupation with workers' protection than are right-wing parties. Nevertheless, Kitschelt
(2001) holds that centre and right-wing parties are more reluctant to cut benefits or to
impose fiscal austerity in times of economic recession.
It is also argued by some that a government would adopt strategies of efficiency or
compensation depending on the power of the population to defend their interests and the
effects on the political process, regardless of the government’s political orientation. For
Kaufman and Segura-Ubiergo (2001) the power correlations between interest groups and
the political organisations is crucial. For example, the presence of strong worker unions in
the social democratic governments of the OECD has contributed to increased social
expenditure. Huber et al. (2008) point out that the expansion of social security has occurred
especially in countries with close ties between the government and syndicates, independent
of their regimes (democratic or authoritarian). But, unlike the position in industrialised
countries where unions and social democratic parties have pushed the welfare state, Latin
America and many other developing countries do not have strong workers' organisations to
defend social protection policies. Therefore, developing countries depend more on the
political orientation of the parties in charge.
Another question that arises in this context is whether authoritarian or democratic
regimes affect social spending differently. Some authors believe that democratic regimes
have higher social spending due to high electoral risks. Avelino et al. (2005) observes that
countries in transition towards democracy may be able to increase their social spending for
the poor because of the strength of the voting power of the poor.
Relating social expenditure categories, Avelino et al. (2005) find a strong positive
association between democracy and education spending in Latin America, as governments
attempt to attract more voters through proper educational programs (consistent with
Kaufman and Segura-Ubiergo, 2001), or because there is a high percentage of young
people in the population, which makes it more attractive for the government to spend on
education. With respect to spending on health and social security, Avelino et al. (2005) do
not find any significant correlation with democracy.
For Huber et al. (2008), regardless of their ideological orientation, democratic
regimes do have a long-term positive impact on both social security welfare spending and
health and education spending. Highly repressive authoritarian governments have negative
effects on health and education expenditures, but they do not affect social security and
welfare spending. This difference can be explained with the median voter theorem (Boix,
1998; Dion, 2006). It is argued that democratic regimes have higher social expenditures
because they consider the whole population in their welfare decisions and therefore the
income of the median voter is lower than it would be in authoritarian regimes, where social
spending would be targeted to the smaller group of supporters who benefit from the regime.
On the other hand, authoritarian regimes may take more drastic decisions for or against
social spending than in the case of democratic regimes, due to the absence of 'veto players'
(Tsebelis, 2002: 19) whose anonymous decision is required to change the status quo (Dion,
2006). This theory could explain the findings of Kaufman and Segura-Ubiergo (2001) who
observe that social security spending seems to be more vulnerable than education and
health spending in less developed countries, because this category is not integrated by a
broad group of stakeholders and therefore it is not as important as education and health
spending are in the electoral calculations.
III. Evolution of Social Spending in Latin America
During the 1990s and 2000s, Latin American countries carried out deep structural and
institutional reforms. This was intended to reverse the fall in social expenditures which had
occurred in the 1980s, as a consequence of the sharp fiscal adjustments that had caused the
debt crisis originating at the beginning of the decade (Cominetti and Ruiz, 1998). Social
expenditure increased as percentage of GDP and as a percentage of total public spending in
almost all Latin American countries. The increases were on average from 9.3 per cent of
GDP (11% as a weighted average) and 45.7 per cent of total expenditure in 1990-1991 to
15.4 per cent of GDP (18.6% as a weighted average) and 62.6 per cent of total expenditures
in 2009-2010 (CEPAL, 2012).
Despite these results, Latin America’s effort in social policy is still well below the
developed countries. OECD countries, according to the OECD Social Expenditure
Database (SOCX), averaged (without applying weights) 22 per cent of GDP in 2010.
France was the country that spent the most on social policy (32.2% of GDP), followed by
Denmark (30.1%) and Belgium (29.5%).
Among Latin American countries we find a great heterogeneity of social-spending
behaviour. At the beginning of the 1980s, Chile ranked first in the list of Latin American
countries with a social expenditure of 17.6 per cent of its GDP, followed by Argentina and
Costa Rica (both with 16.1%), and Panama (15%). In the subsequent years, Chile, Panama,
and other countries from the Andean Community (ANCOM) and Central America
experienced significant reductions in their social programs due to the sharp economic
contraction and the subsequent neoliberal adjustments that led governments to lower their
priorities on social expenditures, paying more attention to fiscal balance (Mostajo, 2000).
Panama was the country that made the deepest cuts in per capita social spending during
1980 and 1990—over 50 per cent in real terms—followed by El Salvador (38%), and
Guatemala, Mexico, and Ecuador (35%). On average, social expenditure cuts during the
1980s reached 18 per cent for the 17 countries for which information is available.
This trend changed during the 1990s, with generalised increases in social spending,
except for Ecuador that continued reducing the weight of social spending in its total
expenditure. The consolidation of welfare policies comes in the 2000s, with important
advances in this area due to economic growth in the region and the increasing importance
given to social policies in Latin America (CEPAL, 2007). Chile and Panama faced sharp
increases in social expenditures during the 1990s and 2000s, but they never regained their
position of the beginning of the 1980s. The case of Bolivia is outstanding. After the drastic
reduction of its social spending during the 1980s, they implemented deep structural reforms
after 1992 (the Social Strategy) to improve quality and coverage in education and health.
Social expenditure grew significantly during the 2000s, in 2010 reaching fifth position in
the regional ranking.
Moreover, social expenditures have shown a positive correlation with GDP per
30
20
capita in Latin America4 (Figure 1), although with important cross-country differences.
ARG
BRA
ARG
BRA
25
URY
15
CRI
URY
CRI
10
20
CHL
BOL
BOL
CHL
PER
GTM
MEX
15
PAN
COL
HND
5
NIC
VEN
COL
NIC
ECU
10
PRY
HND
PRY
SLV
0
ECU
PER
VENMEX
PAN
GTM
2000
4000
6000
8000
GDP per capita, PPP (constant 2005 international $)
GSTotal
Fitted values
10000
0
5000
10000
GDP per capita, PPP (constant 2005 international $)
GSTotal
15000
Fitted values
Source: CEPALSTAT, 2013
Figure 1. GDP per capita and social expenditures in Latin American countries
1990 to 2010
The group of countries located above the regression line are spending more than the
estimated average according to their GDP per capita level; this is the opposite for those
countries located below the regression line. Three countries from MERCOSUR (Argentina,
Brazil, Uruguay) followed by Costa Rica and Bolivia systematically prioritised their
investments in social programs, with Bolivia showing an important boost to social spending
between 1990 and 2010. Chile, on the other hand, shifted from being above the average in
1990 to being below it in 2010, which shows that the effort made by its government
between 1990 and 2010 is still insufficient, given its level of per capita income.
Below the regression line we observe more heterogeneity: Mexico, Venezuela,
Ecuador, Peru, and Guatemala seem to be the countries that have paid less attention to
social expenditure in relation to their GDP per capita. The situation in Panama worsens
between 1990 and 2010, whereas Colombia and Paraguay move closer to the estimated
average. Finally, Honduras, El Salvador, and Nicaragua appear to have maintained social
policies consistent with the spending capacities enabled by their respective per capita
incomes.
With respect to the different categories of social expenditure, education and social
security5 experienced the highest growth in Latin America, reaching on average 5.5 per
cent and 5.2 per cent of GDP, respectively, in 2010. This was a doubling of their weights
since 1990 according to the Economic Commission for Latin America and the Caribbean
(ECLAC) statistical database for 2013. The increase in Latin American education spending
can be explained by the investments undertaken to improve the educational infrastructure
and teachers' pay in order to expand primary education access for the poorest countries, and
secondary education for the rest. Social security growth can be explained by three factors.
The first is the increased growth rate in population aging, which has put pressure on
governments to expand their pension systems. The second is the spread of direct transfers
to households, which has been oriented to poverty-reduction since the 2000s. The last
factor is the international financial crisis, regarded as being responsible for the strong
increase since 2007 in social security expenditure for the most vulnerable households
(CEPAL, 2012).
Finally, health expenditure reached 3.5 per cent of GDP in 2010, representing an
increase of 1.3 percentage points from 1990; other social expenditures (housing, water and
sanitation, and environment) absorbed 1.5 per cent of GDP in 2010, an increase of 0.39
percentage points since 1990.
IV. Methodological Considerations and Model Specification
As Dion (2006) points out, there exists a strong debate about the appropriate models and
estimation methods to analyse the determinants of social expenditure. The studies for Latin
American countries based on panel analysis highlight five main issues to take into account6:
(i) modelling in levels or in differences, (ii) correcting for serial correlation in the error
terms, (iii) obtaining more efficient estimators in the presence of contemporaneous
correlation across units, and heteroskedasticity in panel data models, (iv) controlling for
heterogeneities across observations and (or) common time shocks, and (v) addressing
potential endogeneity bias.
With respect to the first concern, the justification for using levels or differences is in
the first place theoretical and depends on expectations of whether the independent variables
influence social spending. Modelling the dependent variable and its regressors in levels
puts emphasis on the long-term relationships between them (Avelino et al., 2005; Dion,
2006; Huber et al., 2008; Lora, 2009), whereas using the variables in first differences gives
us information about the short-run effects—that is, how changes in economic,
demographic, or political variables are related to changes in social spending (Brown, 1995;
Snyder and Yackovlev, 2000; Kaufman and Segura-Ubiergo, 2001; Wibbels, 2006).
However, this is not only a theoretical choice; behind the selection of the model there are
econometric considerations to take into account, as we discuss below.
Indeed, the empirical strategy has to deal with several problems. One of them is the
existence of serial correlation in the error terms, which typically causes an underestimation
of the standard errors, incrementing the statistical significance of hypothesis tests
(Studenmund, 2011). For this reason, social expenditure models have been frequently
estimated using Prais-Winsten generalised linear regressions (Snyder and Yackovlev, 2000;
Dion, 2006, Huber et al., 2008). These transform the variables into quasi-differences to
correct for first order serial correlation in the residuals caused by time series and trends
based on explanatory variables with small samples (Bence, 1995). An alternative method is
modelling a first order autoregressive panel data model, where one or more lagged values
of the dependent variable are added as regressors. This inclusion also allows us to account
for the persistent effect of the dependent variable in the past. (Wawro, 2002). Beck and
Katz (1995) conducted simulations with both methods, recommending the use of the lag
correction. However, it is still important to verify that the lagged dependent variable
effectively removes the serial correlation through, for example, a Lagrange multiplier test
(Kristensen and Wawro, 2003).
In this respect, Achen (2000), Beck and Katz (2004) and Huber et al. (2008) have
argued that in the presence of serial correlation and highly persistent data, the inclusion of a
lagged dependent variable as a control would dominate the regression and obscure the true
effect of other independent variables. In other words, this method carries some risks that
causal hypotheses will be rejected prematurely (Kaufman and Segura-Ubiergo, 2001) if
they are not performing as well as expected in empirical analysis (Reed and Webb, 2010).
With respect to social expenditures in Latin America, we find that they are highly
persistent, with correlations between the dependent variable and its lag of 0.99 on average.
On the other hand, since most economic variables are non-stationary, frequently
showing time trends, the models estimated in levels would show a spurious relationship,
obtaining apparently significant coefficients from unrelated variables. To avoid this
problem, it is possible to stationarise the series, transforming them into new variables with
zero mean and constant variance by applying, for example, first differences. Nevertheless,
the estimation of a stationarised model allows us to capture only the short-run adjustments.
Social expenditure series in Latin America are non-stationary—with time trends for the
majority of countries—while social expenditure growth series are stationary.
Alternatively, it may be that non-stationary level variables move together in the long
run, which means that there exists a stable equilibrium relationship between the original
series. In this case, the series are said to be co-integrated, which refers to a linear
combination of non-stationary variables that become stationary. Otherwise, it will only be
possible to explain short-term effects between the dependent and independent variables. A
common method for testing for the existence of co-integration is to verify whether the
residuals of the regression are stationary (Engle and Granger, 1987). The idea behind the
long-term equilibrium is that the variables need to adjust in the short run to come back to
the steady state. The error-correction models can help us to describe this process (Asirim,
1996).
Snyder and Yackovlev (2000), Kaufman and Segura-Ubiergo (2001) and Wibbels
(2006) have used error-correction models, regressing social expenditure growth against its
own lag (to control for serial correlation), the lagged level of social expenditure, and other
explanatory variables in lagged levels and differences to capture short and long-term
effects.
Having controlled for autocorrelation, the next step is to account for
contemporaneously correlated errors across units (due to common shocks in a given time
period), and panel heteroskedasticity (the error variance differs across units due to timeinvariant individual characteristics of each unit), which render Ordinary Least Squares
(OLS) estimators inefficient. To correct both problems, Beck and Katz (1995) propose the
use of OLS with panel-corrected standard errors (PCSE); this simple method has been
widely used in research in political science (Kristensen and Wawro, 2003).
The fourth issue to take into account is the unobservable heterogeneity between
units (individual effects, or 𝜂𝑖 ) in panel data analysis, which may cause omitted variable
bias in the estimates. In dynamic panel data models (AR1 models), if the unit-specific
effects are stochastic, and they are correlated with the lagged dependent variable (𝑦𝑖𝑡−1 ),
then the OLS estimator is inconsistent due to the correlation of 𝑦𝑖𝑡−1 with the error term
(𝜂𝑖 + 𝑢𝑖𝑡 ) (Bond, 2002). Therefore, we need to control for those individual effects before
using a PCSE model. The solution is to apply the within-group estimator (also called the
fixed-effect estimator (FE)), or first differences, which are methods that eliminate the
individual effects and which produce robust standard errors. Another possibility is to
include dummies for each unit (Least Square Dummy Variable estimator (LSDV)) to
account for those effects.
One limitation of controlling fixed effects is that they will drop other observable
variables that are time-invariant or will affect the coefficients of those variables that change
slowly due to collinearity with individual effects (Kristensen and Wawro, 2003).
Additionally, when the time periods of the sample are short, FE transformation induces a
negative correlation between the transformed lagged dependent variable and the
transformed error terms. This correlation does not decrease with the number of units, so the
within-group estimator is also inconsistent (Bond, 2002).
This introduces us to the final issue to take into account—the possible endogeneity
of some or all of the explanatory variables. That is, whether they are correlated with the
error term, thus rendering the OLS estimator to be biased and inconsistent. This is the case
of the autoregressive variable, as we stated before. A common approach to control for
endogeneity is through instrumental variables. That is, the GMM developed by Hansen
(1982), which is one of the most widely used methods to obtain efficient estimators in the
presence of heteroskedasticity (Baum et al., 2003). This method transforms the original
model by applying first differences, thereby eliminating the unobservable fixed effects.
Then, it instruments the endogenous variables by a matrix of lagged variables in levels for
two or more periods (Bond, et al., 2001). This is the so-called difference GMM estimator
used by Lora (2009) to estimate the vulnerability of social expenditure to several fiscal
variables.
However, in the presence of time-persistent series with a small number of
observations, lagged levels of the variables are weak instruments, it being preferred to use
the system GMM estimator as introduced in Arellano and Bover (1995) and Blundell and
Bond (2000). This estimator transforms the model into a system of equations in both first
differences and levels, thus adding a new subset of instruments in first differences for the
levels equations (Bond et al., 2001). System GMM estimation has also the advantage of
allowing us to reinstate the unobservable fixed effects in the model.
The analysis above will guide us to choose the model with which to estimate social
expenditures in Latin America. We start with an autoregressive distributed lag model
ARDL (1,1)7.
𝑦𝑖𝑡 = 𝛼0 + 𝛼1 𝑦𝑖𝑡−1 + 𝑥𝑖𝑡 𝛽1𝑘 + 𝑥𝑖𝑡−1 𝛽2𝑘 + 𝜂𝑖 + 𝑣𝑖𝑡 ,
(1)
where 𝑦𝑖𝑡 is the social expenditure of country i in period t, 𝑦𝑖𝑡−1 is the lagged dependent
variable that corrects for autocorrelation in the error terms, 𝑥𝑖𝑡 is a vector of economic,
demographic, and political variables, 𝜂𝑖 are the unobservable time-invariant individual
effects correlated with the explanatory variables, but not with their differences. Finally, 𝑣𝑖𝑡
is the error term assumed to be iid(0, 𝜎𝑣2 ). The parameter 𝛼1 has to be <1 for the model to
be stable dynamically.
The variables 𝑦𝑖𝑡 and 𝑥𝑖𝑡 are non-stationary I(1), but the linear combination is cointegrated presenting stationary residuals. Under these circumstances, there exists a longrun equilibrium relationship between social spending and the independent variables in
equation (1). According to Engle-Granger's representation theorem (Engle and Granger,
1987), this process is equivalent to an error-correction model (ECM), where its coefficient
gives us information about the short-run adjustments to equilibrium after an exogenous
shock. To derive the ECM we apply some linear transformations to equation (1) obtaining:
𝛥𝑦𝑖𝑡 = 𝛼0 + 𝜃𝑦𝑖𝑡−1 + 𝛥𝑥𝑖𝑡 𝛽1𝑘 + 𝑥𝑖𝑡−1 𝛾𝑘 + 𝜂𝑖 + 𝑣𝑖𝑡 ,
(2)
with 𝜃 = (𝛼1 − 1) and 𝛾𝑘 = (𝛽1𝑘 + 𝛽2𝑘 ). This model can be written in an ECM form
(Keele and De Boef, 2004):
𝛥𝑦𝑖𝑡 = 𝛼0 + 𝜃(𝑦𝑖𝑡−1 − 𝑥𝑖𝑡−1 𝛾𝑘 ) + 𝛥𝑥𝑖𝑡 𝛽1𝑘 + 𝜂𝑖 + 𝑣𝑖𝑡 ,
(3)
where 𝜃 is the error-correction coefficient that measures the speed of adjustment to longrun equilibrium. If 𝜃 is significant and negative, there exists a long-term relationship
between social expenditure and the independent variables, and any deviation from the
equilibrium in the previous period will be adjusted at that rate.
Equations 1 to 3 are equivalent, so we can estimate any of them to determine long
and short-run relationships between dependent and independent variables. We base our
estimation on equation (2) to perform the empirical analysis of the economic, demographic,
and political determinants of social spending in Latin America for the period 1990-2010 at
an aggregated level, and for the three main categories of social expenditure (education,
health, and social security).
V. Variables and Data Sources
The economic variables included in the model are the deviation of GDP per capita from the
Latin American average each year (GDPpc)8, its annual growth (GDPpc growth), the
unemployment rate and its growth (unemployment), the sum of exports and imports as a
percentage of GDP (trade), the inflow of foreign direct investment (FDI) (FDI inflows), the
stock of external debt to GNP (external debt), and the average interest on new external debt
commitments (interest payments on debt).
For demographic factors, using the population over 65 years presented
multicollinearity problems in all estimations and absorbed the significance of all dummy
variables. For that reason, we created a dependent-population index, composed of those
older than 65 years and younger than 14 years in a new variable called dependency,
calculated as percentage of the working-age population. For education expenditure, we
used the percentage of the population younger than 14 instead, because the educational
programs are mainly focused on this group (0-14 years). With regard to social security,
there is a great deal of difference between developed and Latin American countries. In the
first group, old age pensions absorb the highest percentage of this expenditure, while in
Latin America only four out of 10 people older than 65 years are covered, with differences
in 2009 that ranged from 90 per cent in Argentina to 7 per cent in Honduras (CEPAL,
2011). For that reason, we prefer to consider the working-age and retired populations
together in a new variable called dependence social security.
Under political determinants, we first included a discrete variable that represents
civil liberties and political rights (democracy), which ranges between 1 (highest level of
freedom) and 7 (lowest level of freedom). Second, we added government politicalorientation dummies which control left and centre governments against right ones (left
governments, centre governments). Finally, we considered country dummies to capture
explicitly the unobservable individual effects which are specific to each country i and are
constant over time9.
For several variables, we only care about levels and not their differences, because
they change slowly from year to year; this is the case for the demographic and left and
centre government variables.
Data sources for economic and demographic variables are the World Development
Indicators of the World Bank and the social expenditure database of the ECLAC. Data
about democracy (political rights and civil liberties) come from the Development Statistics
for Latin America and the Caribbean compiled by the USAID, based on the data from
Freedom House10. The series for government political orientation were obtained from the
World Bank database of Political Institutions, 2012.
VI. Empirical Evidence of Determinants of Social Spending in Latin America
We estimate equation (2) through the system GMM to control for the endogeneity of the
autoregressive variable (the lagged level of social expenditure) using, as instruments, the
levels of this variable and its differences, lagged four periods. The estimation uses clusterrobust
standard
errors
to
control
for
heteroskedasticity and
contemporaneous
autocorrelation within individuals.
Table 1. Determinants of social spending in 17 Latin American countries (1990-2010)
GMM system estimates
VARIABLES
Social Expenditures (t-1)
(1)
Total Social
Expenditure
-2.897**
(1.241)
Education (t-1)
(2)
Education
Expenditure
(3)
Health
Expenditure
-10.37**
(4.300)
Health (t-1)
-9.266***
(3.463)
Social security (t-1)
GDP pc (t-1)
GDP growth
Trade (% GDP) (t-1)
(4)
Social Security
Expenditure
-8.283
(10.41)
-0.267
(0.274)
0.0534
-0.340
(10.64)
-0.318
(0.287)
0.105
-11.50
(14.86)
-0.279
(0.428)
0.0275
-14.25***
(5.520)
-70.52***
(26.97)
-0.601
(0.494)
-0.0292
Trade growth
FDI inflows (%GNI) (t-1)
FDI inflows growth
External debt (%GNI) (t-1)
External debt growth
Interest payments on debt (t-1)
Interest payments on debt growth
Unemployment (t-1)
Unemployment growth
Dependent
0-14 and >65 (t-1)
(0.0430)
-0.0932
(0.107)
-0.272
(0.375)
-0.0245
(0.0177)
0.00628
(0.00621)
-1.831
(4.515)
0.191
(0.462)
-0.528
(1.164)
-0.230
(0.430)
0.0788*
(0.0445)
-0.793***
(0.0821)
-0.199**
(0.0858)
0.272
(0.324)
-0.0146
(0.0127)
0.00886
(0.00813)
-0.784
(4.565)
0.389
(0.600)
1.617
(1.634)
-0.146
(0.426)
0.0257
(0.0524)
(0.287)
Pop 0-14 (t-1)
(0.0505)
-0.0699
(0.100)
-0.259
(0.405)
-0.0404***
(0.0137)
-0.00132
(0.00792)
0.661
(8.491)
-0.0668
(0.418)
-3.290***
(1.219)
-0.573
(0.708)
-0.00280
(0.0611)
-0.399
(0.243)
-2.598**
(1.181)
Dependent social security
15-64 and >65 (t-1)
Democracy (t-1)
Democracy growth
Left governments (t-1)
Centre governments (t-1)
Observations
Arellano-Bond Test AR (1)
(p value)
Arellano-Bond Test AR (2)
(p value)
Sargan/Hansen Test Prob > chi2
Difference-in-Sargan Test Prob >
chi2
Lags
(0.138)
0.219
(0.206)
-0.409
(2.161)
-0.0565
(0.0727)
0.0786***
(0.0140)
-22.57
(24.71)
0.649
(1.658)
1.331
(5.558)
-0.461
(0.523)
0.106
(0.124)
3.711***
0.795
(1.406)
0.0634**
(0.0323)
-0.523
(1.696)
2.057
(4.111)
3.822**
(1.701)
0.0864***
(0.0276)
-0.902
(2.245)
3.677
(4.105)
-0.962
(2.087)
0.0704*
(0.0364)
-2.767*
(1.615)
-0.532
(2.542)
(1.187)
-0.895
(3.842)
0.102
(0.0890)
0.665
(5.335)
-3.015
(5.040)
305
0.00711
305
0.00503
305
0.00231
305
0.0581
0.329
0.244
0.173
0.299
0.696
1.000
0.523
1.000
0.961
1.000
0.306
1.000
𝑦𝑖𝑡−1 and all
its lags
𝑦𝑖𝑡−1 and all
its lags
𝑦𝑖𝑡−1 and all
its lags
𝑦𝑖𝑡−1 and
𝑦𝑖𝑡−1
Asymptotically robust standard errors are reported in parentheses
*Significant at 1% level. ** Significant at 5% level. ***Significant at 10% level
The validity of GMM estimators is confirmed by various tests. The Arellano and
Bond (1991) test for autocorrelation should show first order serial correlation, but not
second order correlation in the error terms. For all estimations, the Arellano-Bond test
rejects the null hypothesis of no autocorrelation in first-differenced errors, and accepts the
null hypothesis of no autocorrelation in the second-differenced errors; this means that the
estimated coefficients are efficient and unbiased. The Sargan (1958) and Hansen (1982)
tests of over-identifying restrictions allow us to verify whether the instruments chosen to
substitute for the endogenous variables are correlated with the error term when evaluated
with the different GMM estimators. This test is analogous to a Lagrange multiplier (LM)
test and verifies whether the lagged dependent variable eliminates serial correlation of the
error terms (Baum et al., 2003). The Sargan/Hansen J-statistic test verifies the validity of
the set of instruments in levels. Finally, the difference-in-Hansen test detects problems of
exogeneity in the new set of instruments in differences, which is added to the system GMM
estimator (Arellano and Bond, 1991). The subset of instruments (levels, first differences,
and exogenous variables instrumented by themselves) used by the GMM system estimator
is not rejected by the difference-in-Sargan/Hansen test.
The coefficients of lagged levels of social spending are significant and negative in
all estimations. As already stated, these are the coefficients associated with the errorcorrection terms (𝜃), showing that there exists a long-run relationship between social
expenditure levels and the explicative level variables. Any unanticipated shock that changes
the equilibrium path will be restored in future periods at speeds that range between 2.9 per
cent of total expenditure and 14.2 per cent of social security spending per year. From an
economic perspective, this result can be interpreted as the presence of diminishing marginal
returns in social expenditures, which means that the higher the level of social spending, the
lower the growth rates in this variable. This result sustains the convergence or catch-up
hypothesis towards a superior welfare steady state in the region.
With respect to the rest of the economic variables, lagged levels of GDP per capita
have a significant impact only on social security growth, with a negative sign, which
contradicts Warner’s law of increasing state activity. Economic growth estimates are also
negative, but they were not significant in any case.
For the variables related to globalisation, trade openness and FDI inflow levels have
no significant effect on social expenditure growth, although their growth rates are
significant for education in the case of trade, and for health in the case of FDI inflows—in
both cases with a negative sign. This shows that Latin American governments seem to
follow an efficiency hypothesis with respect to health and education, in opposition to the
views of Kaufman and Segura-Ubiergo (2001) and Avelino et al. (2005).
Unemployment rates increase total social spending but they do not affect any of the
three categories of social expenditure. Finally, external debt and its growth were not
significant in any case except for social security with a discrete positive effect, while an
increase in interest payments on debt reduced health expenditure significantly.
Regarding demographic variables, we find a significant correlation with all
expenditure items except for health. This correlation is negative in the case of social
expenditure at an aggregated level (using the population groups below 14 and over 65
years), and education (with the population below 14 years), and it is positive in the case of
social security (with the population over 15 years). It seems that there is a generalised
concern among Latin American governments to increase social security for the workingage and elderly populations at the expense of the population younger than 14 years.
Within the set of political variables, the democracy level shows a positive and
significant effect only on education expenditures, while democracy growth has a positive
impact on all expenditure items except for social security, although with a more discrete
effect. This variable represents civil liberties and political rights, ranging between 1
(highest freedom) and 7 (lowest freedom). Due to the construction of this variable, a
positive correlation means that the authoritarian governments in Latin America tend to
spend more on social benefits than their democratic counterparts do. Relating to the
ideology of the governments, we only find a significant and negative relationship between
left-wing government and health expenditure.
The estimated model also includes country dummies to capture the unobservable
individual effects that may have influenced social expenditure in Latin American countries.
Table 2 contains the results. For total social expenditure, all countries have specific
structural characteristics (not included among the control variables) that have allowed them
to have greater social expenditure during the period 1990-2010. Eight countries are above
the average and 10 are below. The greatest effects are found in three countries from
MERCOSUR (Argentina, Uruguay, and Brazil), followed by Bolivia and Costa Rica, while
the last positions belong to Ecuador, Panama, Peru, and Paraguay.
When we observe the three subcategories of social expenditure, we find some
peculiarities noted below. Unobservable fixed effects had the greatest positive impact on
education expenditure growth, with Bolivia, Honduras, and Nicaragua ranking first and
Uruguay and Chile in the last positions, followed by Panama, Peru, and Colombia, all of
them below the average. Health expenditure had a more discrete, positive, unobserved
individual effect. In Argentina, Costa Rica, El Salvador, and Uruguay these characteristics
had the greatest positive effects while Ecuador, Paraguay, Peru, and Venezuela are in the
tail. Finally, social security’s unobserved individual effects are in all cases negatively
correlated with its growth. Here, we also observe the greatest dispersion between countries,
with Uruguay, Brazil, and Argentina at the top (although having no significant
coefficients), and Honduras, Ecuador, Nicaragua, and Guatemala at the bottom.
Table 2. Unobservable country effects
Argentina
Uruguay
Brazil
Bolivia
Costa Rica
El Salvador
Guatemala
Average
Nicaragua
Chile
Honduras
Colombia
Venezuela
Mexico
Paraguay
Peru
Panama
Ecuador
(1)
Total Social
Expenditure
120.3***
(37.71)
116.9***
(37.12)
113.1***
(37.12)
110.7***
(36.24)
103.0***
(32.84)
98.44***
(28.65)
95.30***
(28.88)
94.75
(2)
Education
Expenditure
113.1**
(43.94)
91.02***
(34.28)
116.2**
(46.73)
155.3**
(63.59)
121.8**
(47.36)
116.7**
(46.85)
123.9**
(51.71)
115.33
(3)
Health
Expenditure
95.78***
(28.16)
80.63***
(25.47)
80.03***
(22.81)
74.61***
(24.70)
89.22***
(28.00)
84.52***
(24.68)
63.19***
(21.74)
72.70
(4)
Social Security
Expenditure
-37.13
(66.04)
-16.64
(68.47)
-30.65
(65.87)
-126.5**
(63.06)
-106.8*
(62.69)
-122.9*
(66.82)
-159.5**
(67.51)
-116.00
92.34***
(30.55)
91.56***
(27.69)
91.31***
(29.21)
88.66***
(26.57)
87.43***
(25.96)
84.77***
(23.66)
84.09***
(25.85)
81.96***
(24.64)
79.26***
(22.74)
71.56***
127.9**
(54.16)
94.31**
(37.55)
142.4**
(58.74)
103.4**
(43.33)
117.9**
(47.95)
106.8**
(44.89)
119.5**
(49.66)
101.9**
(42.32)
101.4**
(42.56)
107.0**
78.20***
(25.72)
72.90***
(25.79)
71.87***
(25.55)
69.95***
(21.88)
61.31**
(25.22)
70.68***
(23.29)
60.30***
(20.39)
61.13***
(19.79)
67.69**
(28.82)
53.85***
-164.3**
(73.60)
-71.89
(65.39)
-176.4**
(74.24)
-108.4
(68.95)
-106.7
(67.33)
-133.6**
(68.13)
-149.0**
(68.19)
-139.6*
(71.54)
-146.3**
(74.52)
-175.7**
(20.66)
(43.77)
(20.08)
Coefficient of Variation
0.15
0.14
0.15
Asymptotically robust standard errors are reported in parentheses
*Significant at 1% level. ** Significant at 5% level. ***Significant at 10% level
(75.78)
0.43
VII. Conclusions
We have analysed the determinants of social expenditures in Latin America during the
period 1990-2010 through system GMM estimations applied over a dynamic panel data
ECM that control for several statistical problems that have been highlighted in the
literature. This model allows us to capture short and long-term relationships between social
expenditure and its regressors, a set of economic, demographic, and political variables that
have strong support in the literature, including country dummy variables that capture
unobserved structural effects.
The empirical results suggest that there exists a long-term relationship between levels
of total social expenditure and any of its categories with their determinants (also in levels).
The error-correction coefficient shows diminishing returns, confirming the convergence (or
catch-up hypothesis) toward an upper steady state.
With respect to the economic variables included in the model, we find that
globalisation increasingly imposes pressure on governments who favour market interests
over social issues (efficiency hypothesis). There is no proof of pro-cyclical effects in total
social-spending growth or in any of the social-spending categories. On the other hand, debt
ratios seem to have no effect on social expenditure, except for social security at a very low
positive rate. With respect to interest payments on debt, they have no significant effects on
total social spending, but its growth seems to crowd out health expenditure, consistent with
Hunter and Brown (2000) and Dion (2006). Finally, higher unemployment rates increase
total social spending, as found by Snyder and Yackovlev (2000), Kittel and Obinger
(2003) and Avelino et al. (2005), but we do not find a significant effect in any of the three
categories of social expenditure.
For demographic variables, the results indicate that Latin American governments are
interested in increasing social security for working-age and elderly populations at the
expense of their young population.
The political variables show an important positive association between less-democratic
governments and education expenditure, which is opposite to the views of Avelino et al.
(2005) and Huber et al. (2008). Moreover, the movement to less-democratic governments
increases total social expenditure, as well as education and health expenditures also being
positive; this is not significant for social security. This result supports the absence of veto
payers, as stated by Tsebelis (2002), helping authoritarian regimes to take more drastic
decisions in favour of social spending than in the case of democratic regimes. On the other
hand, the partisanship of governments has no significant effects on social expenditure
growth, except for a negative relationship between health expenditure growth and left-wing
governments.
To conclude, we detected that all countries have specific structural characteristics—not
included in the set of determinants—that absorb an important part of the explanatory power
of our model. They are responsible for higher growth in social expenditure at an aggregated
level, as well as in health and education. For social security, instead, we find negative
impacts on its growth for all Latin American countries, together with greater dispersion
between countries. This result opens the door to future research to obtain new determinants
that might allow us to have a better understanding of social-spending dynamics in Latin
America.
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Authors
Snyder and
Yackovlev
(2000)
Methodology
Increment
budgeting
model in
changes
Model
𝛥𝑆𝑠𝑖𝑡 = 𝛽0 + 𝛽1 ∆𝑃𝐼𝐵 + 𝛽2 𝛥𝑃𝐼𝐵
𝑖𝑡
𝑘
𝑖𝑡−1
+ ∑ 𝑋𝑘𝑖𝑡 𝛿𝑘 + 𝑢𝑖𝑡
𝑘=1
Kaufman and
SeguraUbiergo
(2001)
ECM in
changes with
PCSE and
LSDV
𝛥𝑆𝑠𝑖𝑡 = 𝛼𝑑𝑢𝑚𝑖 + 𝛽1 𝑆𝑠
Avelino et al.
(2005)
LDV in levels
with PCSE
and LSDV
𝑆𝑠𝑖𝑡 = 𝛼𝑑𝑢𝑚𝑖 + 𝛽1 𝑆𝑠
𝑖𝑡−1
𝛽2 𝛥𝑆𝑠
∑
+
+ ∑𝑘𝑘=1 𝑋𝑘𝑖𝑡−1 𝛿𝑘 +
𝑖𝑡−1
𝑘
𝑘
𝑘=1 𝛥𝑋𝑖𝑡−1 𝜆𝑘
+ 𝑇𝑑𝑢𝑚𝑡 + 𝑢𝑖𝑡
∑𝑘𝑘=1 𝑋𝑘𝑖𝑡 𝛿𝑘 + ∑
+
𝑖𝑡−1
𝑘
𝑘
𝑘=1 𝐼𝑇𝑎𝑖𝑡 𝜆𝑘
+ 𝑇𝑑𝑢𝑚𝑡 +
𝑢𝑖𝑡
Prais-Winsten
in levels with
PCSE and
LSDV
DHSY ECM
in changes
with PCSE
and LSDV
Sample
USA (19481998) and 19
Latin American
countries (19701996)
Significant resultsa
SSTb: GDPpc (d) (+), deficit (l) (-), new democratic regime (+), old
democratic regime (+), GDPpc (d)*dem (+), GDPpc (d)*authorit (+),
GDPpc (d)*positive shock (+), GDPpc (d)*negative shock (+)
SSE+SSH: GDPpc (d) (+), deficit (l) (-), new democratic regime (+),
GDPpc (d)*dem (+), GDPpc (d)*authorit (+), GDPpc (d)*positive shock
(+)
SSS: GDPpc (d) (+), deficit (l) (-), GDPpc (d)*dem (+), GDPpc
(d)*positive shock (+)
14 Latin
America
countries (19731997)
SST: GDPpc (l) (+), age65 (+), gov (l+d) (+), trade (d) (-), popular (l) (-),
trade*financial (l+d) (-)
SSE+SSH: GDPpc (l) (+), government size (l+d) (+/-), financial (l+d) (+),
dem (l) (+), popular (d) (-)
SSS: GDPpc (l) (+), age65 (+), government size (l+d) (+), trade (d+l) (-),
financial (l) (-), dem (l) (-), popular (d+l) (+)
19 Latin
American
countries (19801999)
LDV model (dependent variable in levels):
SST: urban (l) (+), unempl (l) (+), inflation (l) (-), trade (l) (+), dem (+),
trade*financial (l) (-)
SSE: urban (l) (+), age65 (l) (-), inflation (l) (-), trade (l) (+), dem (+)
SSH: GDPpc (l) (+), GDPpc (d) (-)
SSS: urban (l) (+), age65 (l) (+), GDPpc (l) (-), GDPpc (d) (-), unempl (l)
(+), trade (l) (+)
𝑆𝑠𝑖𝑡 = 𝛼𝑑𝑢𝑚𝑖 + ∑𝑘𝑘=1 𝑋𝑘𝑖𝑡 𝛿𝑘 +
𝑇𝑑𝑢𝑚𝑡 + 𝑢𝑖𝑡
see Kaufman and Segura-Ubiergo (2001)
DHSY model (dependent variable in changes; with new measure for
trade openness):
SST: urban (l) (+), GDPpc (l) (-), inflation (l) (-), trade (l) (+), dem (l) (+)
Dion (2006)
LDV in levels
with PCSE
and LSDV
(with and
without
regional
dummies)
𝑆𝑠𝑖𝑡 = (𝛼𝑑𝑢𝑚𝑖 ) + 𝛽1 𝑆𝑠
∑𝑘𝑘=1 𝑋𝑘𝑖𝑡−1 𝛿𝑘 + ∑
+
𝑖𝑡−1
𝑘
𝑘
𝑘=1 𝐼𝑇𝑎𝑖𝑡−1 𝜆𝑘
+ 𝑢𝑖𝑡
36 middleincome
developing
countries,
including Latin
American
countries (19801999)
SSE: dem (l) (+), financial (l) (+), trade (l) (+), dem*trade (l) (+)
SSH: dem (l) (+), trade (l) (+), dem*trade (l) (-), GDPpc (l) (-)
SSS: dem (l) (+), financial (l) (+), pop65 (l) (+)
Wibbels
(2006)
ECM in
changes with
PCSE and
LSDV
see Kaufman and Segura-Ubiergo (2001)
12 Latin
American
countries (mid1970s - mid1990s)
SST: trade (d+l) (-), financial (l) (+), party fragmentation (l) (+), total
public spending (d) (-)SSE+SSH: negative shock (d) (-), dem (l) (+), total
spending (d+l) (-)in negative shock observations: trade (d+l) (-), financial
(d+l) (+), age65 (l) (-), total spending (d+l) (-)in positive shock
observations: trade (d) (+), financial (l) (-), left (d) (-), dem (d+l) (+), total
spending (d+l) (-)SSS: trade (d+l) (-), financial (l) (+), total spending (d) (), union (l) (-)in negative shock observations: trade (d+l) (-), pop65 (l) (+),
GDPpc (l) (-), total spending (d+l) (-)in positive shock observations: trade
(d+l) (-), financial (l) (+), left (l) (-), party fragmentation (l) (+), total
spending (d) (-)
Huber et al.
(2008)
Prais-Winsten
in levels with
PCSE
𝑆𝑠𝑖𝑡 = 𝛼0 + ∑𝑘𝑘=1 𝑋𝑘𝑖𝑡 𝛿𝑘 + 𝑇𝑑𝑢𝑚𝑡 +
𝑢𝑖𝑡
18 Latin
American
countries (19702000)
SSE+SSH: GDPpc (l), pop14 (l) (+), budget deficit (l) (-), IMF agreement
(l) (+), dem (l) (+), authorit (l) (-), partisan balance (l) (-)
SSS: pop65 (l) (+), dem (l) (+)
Lora (2009)
GMM-Diff in
levels
𝑆𝑠𝑖𝑡 = 𝛼0 + 𝛽1 𝑆𝑠
+ ∑𝑘𝑘=1 𝑋𝑘𝑖𝑡 𝛿𝑘 +
𝑖𝑡−1
57 developing
countries
including Latin
American
countries (19852003)
SSTb: primary spending (-), debt interest (-), public debt (+), debt default (), debt default*public debt (+)
SSE: primary spending (-), debt interest (-), public debt (+)
SSH: primary spending (-), debt interest (-)
𝑢𝑖𝑡
a: significant results are taken from the principal model following the author(s)
b: results for regression with Latin American countries
c: shocks are deviations from trend GDP, where the variable positive (negative) shock is 1 in positive (negative) deviations and 0 in negative (positive) deviations
Note: SST: Social Spending Total, SSE: Social Spending Education, SSH: Social Spending Health, SSS: Social Spending Social Security.
l: level, d: differences; dem: democracy index, authorit: authoritarian regime, age65: percentage of population 65 years or older, popular: popularly based presidents , left: electoral
strength of left wing, union: union strength , trade: trade openness, financial: financial openness, urban: percentage urban population, unempl: unemployment rate; partisan
balance: index of ideological centre of gravity (right...0...left)
1
See Snyder and Yackovlev (2000) for a detailed review of studies for this period.
Other authors have accounted for additional economic variables that may have affected social spending.
Perotti (1996) and Lindert (1996) study the effects of inequality and skewness in income distribution within
OECD countries; they find a negative relationship with social spending for inequality, and a positive one for
skewness. Avelino et al. (2005), include the inflation rate, which shows a negative effect because it causes a
reduction in public revenues (Olivera-Tanzi effect), and urbanisation, which is associated with
industrialisation and labour unions that lobby for higher wages and social benefits, obtaining a positive effect.
3
See Kittel and Obinger(2003) for an extensive review of partisan politics and political institutions as
determinants of social expenditures in industrialised countries.
4
We have excluded from our analysis the four Caribbean countries for which the ECLAC provides
information (Cuba, Jamaica, the Dominican Republic, and Trinidad Tobago).
5
In social security, sickness, maternity, industrial accidents and occupational illnesses, disability, old age
pensions and widowhood, and death, and family benefits were included.
6
See Appendix 1 for a summary of these contributions. WHERE IS APPENDIX 1? Is it the landscape
table?
7
Akaike, Hannan-Quinn and Schwarz information criterion helps us to choose the lag order of the model
following Pesaran and Shin (1999).
8
We use a proportion of GDP per capita instead of simply the logarithm of GDP per capita, due to
multicollinearity problems with other variables.
9
We also included a discrete variable to control for time fixed effects, common to all countries, but varying
across time; it was not significant in any of the regressions with different panel data methodologies.
10
Specifically, we used discrete indicators (overall freedom index, civil liberties index, political rights index).
2