WPS4078
Informality Trends and Cycles*
Norman V. Loayza
The World Bank
Jamele Rigolini
The World Bank
Abstract
This paper studies the trends and cycles of informal employment. It first presents a theoretical model where
the size of informal employment is determined by the relative costs and benefits of informality and the
distribution of workers' skills. In the long run, informal employment varies with the trends in these
variables, and in the short run it reacts to accommodate transient shocks and to close the gap that separates
it from its trend level. The paper then uses an error-correction framework to examine empirically
informality's long- and short-run relationships. For this purpose, it uses country-level data at annual
frequency for a sample of developed and developing countries, with the share of self-employment in the
labor force as the proxy for informal employment. The paper finds that, in the long run, informality is
larger in countries that have lower GDP per capita and impose more costs to formal firms, in the form of
more rigid business regulations, less valuable police and judicial services, and weaker monitoring of
informality. In the short run, informal employment is found to be counter-cyclical for the majority of
countries, with the degree of counter-cyclicality being lower in countries with larger informal employment
and better police and judicial services. Moreover, informal employment follows a stable, trend-reverting
process. These results are robust to changes in the sample and to the influence of outliers, even when only
developing countries are considered in the analysis.
World Bank Policy Research Working Paper 4078, December 2006
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
*We would like to thank Koichi Kume for excellent research assistance. We would also like to
thank Omar Arias, Bill Maloney, and Jaime Saavedra for very useful comments and discussions.
1 Introduction
Informality is a fundamental characteristic of underdevelopment. It is shaped both
by the modes of production inherent to economies in the transition to modernity and
by the relationship that the state establishes with private agents through regulation
and monitoring. The received literature finds evidence that the relative size of the
informal sector declines with overall development, rises with the burden of regulation,
and decreases with the strength of monitoring (see Johnson et al., 1997; Schneider and
Enste, 2000; Friedman et al., 2000; and Loayza, Oviedo, and Serv´en, 2005).
Notwithstanding the importance of these long-run relationships, the behavior of the
informal sector is quite dynamic over time. This indicates that informality not only
responds to fundamental, long-run forces but also to inter-temporal economic conditions
related to the business cycle and transient policies. Thus, for example, the informal
sector could act as a buffer that expands in economic recessions or as an adjustment
mechanism during temporarily high tax regimes.
This paper studies the behavior of the informal sector specifically informal em-
ployment over the long and short runs, linking the two aspects both theoretically and
empirically. In the long run, informal employment is determined by the trends in the
relative costs and benefits of informality. In the short run, informal employment reacts
to the temporary conditions created by the business cycle and moves to close the gap
that separates it from its equilibrium, long-run trend. In so doing, the paper examines
how the variables that drive informality in the long run also determine the way the
informal sector reacts to the business cycle.
The literature on informality over the business cycle is relatively scarce and has
concentrated on the analysis of the time series of selected countries (see, for instance,
Bosch and Maloney, 2005). A further reduced number of papers address the issue
of informality cycles in the context of long-run relationships. One of them is Fiess,
2
Fugazza, and Maloney (2006), which examines the reaction of formal and informal labor
markets to permanent and temporary macroeconomic shocks. The paper develops a
theoretical model whereby the comovement of relative earnings, sector sizes, and the
real exchange rate can indicate the type of shock affecting the economy and whether
labor rigidities become binding. It then studies these comovement patterns using time
series for Argentina, Brazil, Colombia, and Mexico. Although Fiess, Fugazza, and
Maloney study informality over the business cycle as deviation from a steady state,
they do not characterize the evolution of the steady state itself. Our paper studies this
evolution theoretically and uses cross-country variation to identify it. Once this is done,
this paper uses the time-series variation to identify and assess the cyclical properties of
informal employment.
The paper first presents a two-sector theoretical model that endogenizes the relative
size of informal employment, making it a function of the productivity differential be-
tween formal and informal workers. This differential is, in turn, determined by the cost
to become and remain formal and the distribution of skills in the labor force. Specif-
ically, the productivity differential has a worker-driven component, given by workers'
individual skills, and a sector-related component, given by the relative formal-informal
regulatory burden, the strength of enforcement, and the access to productive public
services. The size of informal employment is then given by the proportion of workers
whose skills fall below a threshold level where the worker is indifferent between the two
sectors.
In the model, cycles appear as productivity shocks occur, and informal employment's
behavior over the cycle depends on how these shocks affect the productivity differential
between the two sectors. The model derives its main results conducting comparative
statics exercises on the cumulative function of the skill distribution (i.e,, the relative
size of informal employment) and the elasticity of this cumulative function with respect
to productivity shocks (i.e., the short-run response of informal employment). In the
3
model, informal employment is predicted to be countercyclical. This happens as shocks
affect both sectors homogeneously so that the costs of formality become more binding
when negative shocks occur, and conversely in the face of positive shocks. Moreover,
the model shows that higher levels of informal employment reduce the counter-cyclical
response of informality.
Our theoretical model draws from Rausch (1991), who looks at the relationship
between the size of the informal sector and the minimum wage. In his model, peo-
ple choose to become workers, formal, or informal entrepreneurs depending on their
managerial ability; similarly, in our model workers choose to be active in the formal
or informal sector depending on their skills. Less directly, our model also draws from
previous theoretical papers that have studied the relationship between informality and
growth (Loayza, 1996), informality and rent-seeking (Sarte, 2000), inequality and the
size of the informal sector (Dessy and Pallage, 2003), and the relationship between
informality and the structure of taxation (de Paula and Sheinkman, 2006).
The paper then tests the relevance of the model using cross-country and time-series
data. It does so in the context of an error-correction framework: in the long run,
informal employment moves in conjunction with the development characteristics that
influence and are influenced by informality; in the short run, informal employment
moves both in response to production shocks, and in order to close the gap that sepa-
rates it from its trend, equilibrium level. We also examine how the response of informal
employment to production shocks varies with the level of informality itself and its de-
terminants. The empirical findings are broadly consistent with the model's results.
The data consist of annual observations for a collection of developing and developed
countries over diverse time periods. The observations are given at the national level,
so that the variation in the data (and, thus, its informational value) resides on their
cross-country and time-series dimensions. The key piece of information for the paper's
empirical analysis (and its main bottleneck) is data on informal employment. For this
4
purpose, the proxy used in the paper is the share of self-employed in the labor force,
as reported in the surveys collected by the International Labor Organization. The final
sample consists of 471 observations in 42 countries when we consider all countries, and
of 182 observations in 18 countries when we consider developing countries only.
The rest of the paper proceeds as follows. Section 2 presents a stochastic model of
employment and production in a dual (formal/informal) economy. Section 3 presents
the empirical analysis, introducing first the data and sample and then discussing the
estimation results for, respectively, the long- and short-run relationships. The empirical
section includes several robustness checks on both the criteria to obtain the sample and
the influence of outliers.
2 A Basic Model of Informal Activity
The economy consists of a measure one of workers, and of two sectors, a formal and
an informal one. Each worker is the owner of her own firm, hence, in what follows we
shall use equivalently the concept of workers and firms. In each sector workers have
different productivities depending on their skill level s, which has a distribution (s) over
s [0, 1] . More precisely, productivity in the formal sector is equal to R · F(s) - R,
where represents the overall productivity level of the economy, s the workers' skills,
R the level of regulation, 0 < < 1, and R represents the efficiency with which the
government uses regulation (i.e. countries with a higher parameter make a better use
of regulation). We also assume that workers' productivity increases with their skills in
a concave manner, so that F (s) > 0, F (s) < 0.
The way we introduce regulation R wants to capture its dual role. Some degree of
regulation can increase firms' productivity by allowing the government to provide better
services to firms, such as the ability to solve disputes through an efficient judicial system,
standardized procedures, and the ability to solve moral hazard and adverse selection
5
problems. This "efficient provision of services through regulation" is captured in the
productivity function by the term R. However, obeying regulations is also costly to
firms, in particular when they are used inefficiently and lead to red tape and corrupt
practices. De Soto (1989), for instance, estimates that to register a small industry in
Peru it takes 289 days and $1231 to fulfill all the bureaucratic procedures, and Djankov
et al. (2002) find similar results in a large number of developing countries. Moreover,
staying formal can also be very costly. De Soto (1989) finds that, in a sample of 50 small
manufacturing firms, the costs of staying formal represent an average of 348 percent
of after-tax profits. Interestingly, only 22 percent of such costs are due to taxes, and
5 percent are due to higher public utility rates, while 73 percent of the costs are due
to regulation and bureaucratic requirements. To consider the benefits and costs of
regulation, we assume therefore that regulation has a constant unit marginal cost, but
that the efficiency with which it is used can vary: in the model, the varying efficiency is
captured by the parameter , where countries with a higher parameter perform better
in transforming regulation in productivity-enhancing services. Notice that the function
R · F(s) - R is strictly concave, so that there exists an "optimal" level of regulation
R(s) that maximizes firms' productivity, where R(s) increases with workers' skills s.
Next, we turn the attention to firms in the informal sector, which in our model are
firms that choose not to obey regulations. Factual evidence suggests that, aside from
the fact that they do not have to bear the cost of regulation, informal firms tend to be
less productive than formal ones. Penalties for engaging in informal activity can be stiff:
detected firms may have to surrender a considerable part of their output or physical
capital stock, and to avoid being caught, firms scale down the size of their informal
operations, becoming therefore less productive. At least part of the penalties informal
firms pay are in the form of bribes. De Soto (1989), for instance, finds that informal
entrepreneurs pay between 10 to 15 percent of their gross income in bribes to corrupt
government officials, whereas formal entrepreneurs pay an average of only 1 percent
6
of gross income in bribes (without counting bribes used to become formal). Another
cost of informality is the inability to take full advantage of government provided goods,
such as the legal and judicial system, and the police. In order to represent the lower
productivity of the informal sector, we assume that informal workers have productivity
(E)R · F(s), where E represents enforcement of regulation compliance, (E) the
level with which informal firms can benefit from the government's provision of services
without paying for them, and (E) < 1, (E) < 0. The productivity differential
depends therefore on the intensity with which the government fights informal activity
by constraining firms to obey regulation and to pay for the services it provides.
In the model, regulation implies a fixed cost that workers have to pay irrespective of
their productivity. Therefore, in equilibrium there exists a "skill threshold" sI [0, 1]
such that workers with skills s < sI choose to be informal, while workers with skills
s > sI choose to be formal. This perspective is consistent with the view of informality
as a voluntary, equilibrium choice, as proposed in Maloney (2004) and Loayza (1996).
Although depending on the parameters , , R, E, a fully formal or informal economy
can exist, in what follows we focus attention on the case where formal and informal
activities coexist. Under this situation, the skill threshold sI is defined by:
R1-
F(sI) = (1)
(1 - (E))
and the size of the informal sector is given by [sI(, , R, E)], where [s] represents
the cumulative function of the skill distribution (s). The next proposition describes
how the size of the informal sector varies with the parameters of the economy:
Proposition 1 The size of the informal sector increases with the regulatory burden
R, while it decreases with overall productivity , with the efficiency of the provision of
services , and with enforcement E.
7
The intuition runs as follows. Firms weight the benefits of being formal against the
costs; thus, when regulation decreases or enforcement increases, the formal sector be-
comes relatively more attractive for them, and more firms join it. Moreover, regulation
is a fixed cost that all formal firms have to bear. Therefore, when overall productivity
increases, the cost of regulation becomes proportionally smaller, so that more firms join
the formal sector.
Informality Cycles
The previous section describes how informal activity (measured by the size of the in-
formal sector) reacts in the long run to changes in overall productivity, the quality of
public services, regulation, and enforcement. Next, we shall see how the same setup also
explains how informal activity reacts to the business cycle. To this end, we presume
that the cycle is caused by changes in overall productivity , so that the relationship be-
tween informal activity and the business cycle is described by the elasticity of informal
activity with respect to , which is equal to:
d[sI] d[sI] dsI F(sI) (sI)
= = = - (2)
d [sI] dsI d [sI] F (sI) [sI]
In our model, informal activity reacts therefore counter-cyclically to the business cycle,
as regulation being a fixed cost, more workers are willing to join the formal sector during
a high cycle. 1 Several other factors also affect the elasticity of informal activity (and,
therefore, the size of the cycle). Equation (2) shows that, everything else being equal,
counter-cyclicality is smaller in countries with large informal economies. The reason is
1 If our model had allowed for different productivity shocks to informal and formal firms, the possi-
bility of pro-cyclical informality would have arisen. Fiess, Fugazza, and Maloney (2006) considers this
possibility, obtaining pro-cyclical informal behavior when shocks to the non-tradable sector dominate.
8
simple: a productivity shock affects the absolute number of workers switching sectors,
so that in relative terms, countries with larger informal economies have cycles of smaller
magnitude. Similarly, the skill density at the threshold level (sI) also influences the
elasticity of informal activity, as, for equal productivity shocks, if more workers have
skills close to the threshold level sI, the magnitude of the cycle becomes larger. Finally,
the term F(sI)/F (sI) takes into account how much workers' productivity varies around
the skill threshold sI. More precisely, if productivity varies much around the skill
threshold sI (i.e., F(sI)/F (sI) is small), then, everything else being equal, under a
shock few workers are going to switch sectors as the required change in sI necessary
to adjust to the shock is small; in contrast, if productivity varies little around the skill
threshold sI (i.e., F(sI)/F (sI) is large), then, everything else being equal, under a
shock more workers are going to switch sectors. Notice, also, that the skill threshold
sI represents a sufficient statistics in describing the elasticity , so that to study how
informal activity reacts to the cycle it is sufficient to look at how the elasticity varies
with sI. In doing the comparative statics, however, we shall make two assumptions
that will be justified by the empirical analysis. First, notice that the skill distribution
(s) varies from country to country, and that it is empirically not possible to measure
it. Hence, in this section we consider it as constant, and in the empirical section we
shall consider it as an exogenous random error term. Similarly, as the size of the
informal sector is empirically measurable, and across countries there is not necessarily
a relationship between sI and , in doing the comparative statics we shall consider the
size of the informal sector as an exogenous variable, and in the empirical section we
shall then control for it. The next proposition summarizes the behavior of the elasticity:
Proposition 2
1. The informal sector behaves counter-cyclically with respect to the business cycle.
9
2. Everything else being equal, informality is less counter-cyclical in countries with
larger informal economies.
3. Everything else being equal, informality is less counter-cyclical in countries with
higher overall productivity, stronger enforcement, and in countries which provide
better public services.
4. Everything else being equal, informality is more counter-cyclical in countries with
higher regulation.
Proof of Proposition 2 The derivative of the elasticity with respect to is equal to:
F(sI)
(sI, ) = > 0 (3)
F (sI) 2
Similarly, the derivative of the elasticity with respect to sI is equal to:
F(sI)F (sI)
(sI, ) = - 1 - < 0 (4)
sI F (sI)2
where the negative sign stems from the concavity assumption on F(s). Equation (1) concludes the
proof. End of Proof.
Propositions 1 and 2 have clear implication on how overall productivity, regulation,
enforcement, and the efficient provision of public services affect informality trends and
cycles. Next, we shall look at how these implications compare with empirical evidence.
10
3 Empirics
The empirical section of the paper has a dual objective. First, it will analyze the long-
run relationship between informal employment and its main correlates suggested by the
theory. This will serve to validate the long-run component of the model and to derive
a measure of disequilibrium in informal employment that will be used in the next part
of the analysis.
Second, the empirical section will study the short-run movements in informal em-
ployment. In particular it will examine how informality reacts to changes in aggregate
production, that is, whether informal employment behaves counter- or pro-cyclically
with respect to the business cycle. The analysis will allow the informality response
to be heterogeneous across countries and will consider whether it varies systematically
with the level of informality itself, as suggested by the model.
The connection between the two sections of the empirical analysis is given by an
error-correction framework. It is this framework which best fits our theoretical model:
In the short run, informal employment moves both in response to production shocks and
in order to close the gap that separates it from its trend, equilibrium level; in the long
run, informal employment moves in conjunction with the development characteristics
that influence and are influenced by informality.
The data consist of annual observations for a collection of developing and developed
countries over diverse time periods. 2 The observations are given at the national level, so
that the variation in the data (and, thus, its informational value) resides on their cross-
country and time-series dimensions. In principle, if we had sufficiently large time series
per country, we could estimate both long- and short-run relationships using individual
country data. Since this is not the case, we need to make some identifying assumptions,
which are either directly supported by the data or validated by our results. The first is
2The next subsection provides detailed information on sample composition, variable definitions,
and data sources.
11
that the long-run relationship can be estimated from the cross-country variation in the
data. Although for efficiency purposes time-series data are also used, the within-country
variation is quite small relative to the cross-country variation. Therefore, identification
of long-run parameters comes from the comparison across countries. Figure 1 illustrates
this point for the relationship between informal employment and GDP per capita.
The second assumption is that the speed of adjustment to the (equilibrium) trend
relationship is homogeneous across countries. This implies a stable dynamic process for
informal employment that is common to all countries, including those whose limited
time-series data would not clearly reveal such a process. Finally, the third assumption is
that the short-run response of informal employment to cyclical movements in aggregate
output is heterogeneous across countries but in a systematic way that links this response
to specific country characteristics. Next we describe how these assumptions affect the
long- and short-run specifications of the empirical model.
Long Run
First we examine whether in the long run informal employment is determined by the
flexibility of the formal business environment, the quality of public services available
to formal enterprises (e.g., the police and judicial system), and the enforcement of
taxes and business regulations. The first two factors should determine the opportunity
cost of informality, while the last one would represent its direct cost. Since informal
employment is also related to other features of underdevelopment such as the lack of
education, rudimentary infrastructure, and laggard technology we also relate the level
of informal employment to a country's per capita GDP. In light of these considerations,
a straightforward representation of the long-run regression equation is given by:
Ict = 0 + 1Yct + 2Flexc + 3Lawc + 4Govexc + ct (5)
12
where the subscripts c and t represent country and time period, respectively. I repre-
sents informal employment, proxied by the ratio of self to total employment, as reported
by the International Labor Organization. Y denotes the average level of income, as mea-
sured by the log of per capita GDP. Flex represents business flexibility, proxied by the
Fraser Institute index of credit, labor, and regulatory flexibility. Law measures the
enforcement of contracts and the prevalence of the rule of law and the efficiency of the
police and judicial systems, proxied by the International Country Risk Guide index of
law and order. Finally, Govex is the ratio of government expenditures to GDP and
attempts to measure the government's ability to monitor and enforce formal taxes and
regulations; we assume, therefore, that this ability is linked to the availability of govern-
ment's financial resources. The variables Flex, Law, and Govex are measured as country
averages due to their stability over time and, for Flex and Law, to the incompleteness
of their data in the time dimension.
Short Run
As mentioned above, the short run is modeled as an error-correction process, where
informal employment changes in response to output shocks and in order to close the
gap that separates it from its long-run equilibrium level. A simple formulation of the
short-run process is given by:
Ict = 0 + 1 Yct + 2LRdevct
c -1 + ct (6)
where LRdev represents the deviation of informal employment from its trend value,
as derived from the long-run equation; and is the difference operator denoting the
(proportional) change with respect to the previous year's value.3 In this formulation the
3In the case of informal employment, the proportional difference is computed as the absolute dif-
ference divided by the previous year's value: (It - It )/It . In the case of GDP per capita, the
-1 -1
proportional difference is simply computed as the difference of the logs.
13
response of informal employment to output changes can vary from country to country
(which is indicated by the subscript c attached to 1). We consider whether this
heterogeneity is systematic by allowing 1 to be a (linear) function of the (average)
levels of informal employment and, potentially, per capita GDP, business flexibility,
rule of law, and government expenditures. Thus, the short-run regression equation can
be written as:
Ict = 0 + (10 + 11 Ic + 12 Yc + 13 Flexc + 14 Lawc (7)
+ 15 Govexc) Yct + 2 LRdevct -1+ ct
Estimation is conducted in two sequential steps: first, we estimate the long-run rela-
tionship, analyze it, and use it to derive the deviations from equilibrium. Second, we
estimate and analyze the short-run equation. Before discussing the results, the next
subsection presents our data set, providing information on sample composition, variable
definitions, and data sources.
Data and Sample
Our measure of informal employment corresponds to the percentage of the active labor
force that is self employed. In most developing countries, there is a strong association
between self-employment and informal activity, as most self employed workers tend to
be low-skilled, unregistered workers. In fact, self employment correlates well with other
estimates of informal activity such as the Schneider (2005) measure of informal produc-
tion: the correlation among non-Eastern European countries equals 0.75. In addition, it
presents some advantages with respect to traditional estimates of informal production
14
based on excess currency demand or latent variable methodologies (e.g., the MIMIC
model). The first advantage is conceptual: what self-employment measures is clear
and well defined (although we can argue that self-employment does not comprise all
informal activity). In contrast, methodologies based on estimated residuals or derived
latent variables render data whose meaning is subject to multiple interpretations. The
second advantage is practical: self-employment data are available not only for a cross
section of countries but also for several consecutive years per country. Conversely, most
other measures are either only available across countries or have very limited time-
series. Needless to say, for the type of research we undertake here both cross-country
and time-series dimensions are crucial.
Data on self-employment are obtained from the International Labor Organization,
which on its website (http://laborsta.ilo.org) publishes yearly employment statistics
for most countries. Self-employment is measured as the percentage of self employed
workers with respect to the total active population, and the full dataset contains 783
observations in 93 countries. We drop, however, some observations for the following
reasons.
First, we drop countries from Eastern Europe, as self employment in these coun-
tries appears to be still in transition to market-economy levels. In particular, self-
employment levels in Eastern Europe remain substantially lower than in non-Eastern
European countries, and the gap persists after correcting for their level of per capita
GDP and their institutional characteristics. This gap is likely the result of their social-
ist past, when employment took place exclusively in state enterprises. Since the gap is
large and changing over time, including Eastern European countries would bias both
the long and short run estimates.
Second, we drop countries that do not have at least four consecutive pairs of obser-
vations, which corresponds to the minimum threshold of observations for the short-run
regression. This choice is a compromise between having sufficient time-series observa-
15
tions per country and not eliminating too many countries from the sample. At any
rate, as a robustness check we shall vary the threshold from a minimum of two to a
maximum of eight consecutive pairs of observations.
The variable Flex measures the regulatory environment, and consists of the Fraser
Institute index of credit, labor, and regulatory flexibility (http://www.fraserinstitute.ca).
Flex measures how much the regulation of credit and labor markets, and of the business
environment, "restricts entry into markets and interferes with the freedom to engage
in voluntary exchange" (Economic Freedom of the World, 2005). The index considers
factors such as the presence of foreign Banks, interest rates controls, minimum wages,
and firms' entry costs. It varies from zero to ten, where ten represents the highest
degree of flexibility.
The variable Law measures the degree to which contracts are enforced and the
efficiency of the police and judicial systems. It consists of the International Country
Risk Guide index of law and order (http://www.icrgonline.com), which measures the
strength and impartiality of the legal system, and the popular observance of the law.
The index varies from one to six, where six represents the highest degree of law and
order.4 Finally, the variables Y and Govex represent, respectively, the log of GDP per
capita in constant 2000 US Dollars, and general government expenditure as a percentage
of GDP. Both variables are from the World Development Indicators (2005). Appendix 2
shows univariate summary statistics and bivariate correlations for all variables included
in the empirical analysis.
We also consider two different samples of countries: all countries, and only low and
middle income countries with a per capita GDP below 9000 US dollars. We work with
4A main caveat of the Flex and Law indexes is that they are based to a large extent on subjective
assessments, such as firms' surveys (for instance, 10 out of 15 components used to create the Flex
index are based on survey data). Thus, the indexes can change from one year to the next if people's
perceptions changes (because, for instance, of a corruption scandal), even if structurally little has
changed in the country. It is also to avoid these noisy fluctuations that we take countries' averages of
Flex and Law.
16
the two samples partly to check the robustness of the results and partly to consider
the possibility that self-employment may have a different worker composition in rich as
in developing countries. The final sample consists therefore of 471 observations in 42
countries when we consider all countries, and of 182 observations in 18 countries when
we only consider low and middle-income countries. Appendix 1 lists the countries and
corresponding years of coverage for each sample under study.
Results
Table 1 presents the results on the estimation of the basic long-run relationship. The
connection between informal employment and GDP per capita is quite strong: higher
informal employment is associated with lower GDP per capita, and this association is
not only statistically significant but also economically meaningful (see Col. 1). In fact,
80% of the variation in informal employment can be explained by the variation in GDP
per capita. The close connection between self-employment and GDP per capita has
already been documented by, among others, Blau (1987), Maloney (2001), and Gollin
(2002). Using Schneider's measures of informal production, Loayza, Oviedo, and Serv´en
(2005) also find a strong relationship between informality and national income.
If we replace GDP per capita by the determinants of the opportunity and direct costs
of informality, all of them carry coefficients with the expected negative sign and high
statistical significance. Thus, informal employment is more prevalent when business
flexibility, the rule of law, and government resources are weaker. The variation in these
three variables explains 72% of the variation in informal employment, slightly less than
the explanatory power of GDP per capita alone.
When we consider the most comprehensive model (which adds GDP per capita
to business flexibility, the rule of law, and government resources as determinants of
informal employment), we find that all variables retain their negative sign and three
17
of the four remain statistically significant the exception being government resources.
In all cases, the size of the coefficients is somewhat reduced, indicating that GDP per
capita captures some of the effects of the variables measuring the costs of informality,
and vice versa. Government resources is in fact so much associated with GDP per
capita that its relationship with informal employment appears to be embedded in the
informality-income relationship. Figure 2 compares the average informal employment
per country with the corresponding predicted level according to this model. With an
R-squared of 85%, the accuracy of the model's fit is shared by most countries in the
sample, with no discernable bias for countries in different income levels (or those in
Latin America in particular).
As the following exercises show, the basic results are quite robust to different samples
and to the influence of outliers. Table 2 presents the results for the samples obtained
using different criteria for the minimum number of time-series observations per country.
The estimation of the error-correction model relies on having sufficient consecutive ob-
servations for each country. Using a large time-series dimension improves the model's
ability of capturing informality's dynamic process but comes at the cost of eliminating
countries with few annual observations. For our basic specification, we chose 4 con-
secutive pairs of annual observations as the minimum threshold. In this robustness
exercise, we first relax this criterion (applying a minimum of 2 consecutive pairs and,
thus, allowing more countries in the sample) and then restrict it further (applying a
threshold of 8 pairs and, therefore, including fewer countries). Moreover, for each of
the three cases, we consider not only OLS estimation but also a weighted least squares
(WLS) procedure that reduces the influence of outliers. The results are remarkably
robust, despite the considerable variations in the data and the potential presence of
outlying observations.
Also, our basic results are obtained using a sample of countries comprising both
developed and developing countries. Table 3 examines the robustness of these results
18
to the use of a sample of developing countries only. In this case the variation in all
variables is significantly more limited and the sample size is reduced to fewer than half
observations and countries. Not surprisingly, the R-squared falls to 66%, indicating
that the informational value that developed countries bring about is rather important.
Despite this large change, most results are robust. GDP per capita and the rule of law
remain negatively and significantly related to informal employment. The coefficient on
government resources retains its negative sign and now becomes statistically significant;
on the other hand, business flexibility loses relevance. The simpler models (not shown in
the tables) are more robust. When only GDP per capita is included in the regression, it
carries a negative and highly significant coefficient. Likewise, when we exclude GDP per
capita from the regression replacing it by the variables that measure the opportunity
and direct costs of informality business flexibility, the rule of law, and government
expenditures all carry negative and significant coefficients, just as it happened for the
full country sample. The table also shows the results obtained with the WLS procedure
that limits the influence of extreme observations. They are basically the same as those
obtained under OLS, confirming the results' robustness to potential outliers.
Table 3 also presents the long-run estimation using only Latin American and Caribbean
countries. Despite the fact that the sample of observations and countries is smaller than
one-third of the full sample, the results are quite similar; in fact, the only exception is
that business flexibility does not carry a significant coefficient when GDP per capita
is also considered as an explanatory variable. In the simpler model where only the
variables representing the costs of informality are included business flexibility, the rule
of law and government expenditures are negative and significantly related to informal
employment, as it happened with the full sample of countries.
In summary, informal employment is more pervasive in countries having lower GDP
per capita and imposing more costs (or generating less advantage) to formal enterprises,
in the form of more rigid business regulations, less valuable public services, and weaker
19
monitoring of informality. The results are robust to changes in the sample and the
influence of outlier observations.
Short Run
We now examine how informal employment reacts to deviations from its equilibrium
level and to fluctuations in GDP per capita growth. The basic results are presented in
Table 4. Column 1 shows the simplest error-correction model, where full cross-country
homogeneity is assumed. The coefficient on the deviation from the long run (also known
as "adjustment" coefficient) is negative and statistically significant. This supports the
assumption of dynamic stability in the sense that informal employment moves, at least
in part, to return to its long-run equilibrium. The coefficient on the GDP per capita
growth is also negative and statistically significant, indicating that in average informal
employment behaves counter-cyclically.
In column 2, we allow the coefficient on GDP per capita growth to vary with the aver-
age level of informal employment. We find the interaction coefficient to be significantly
positive, meaning that in countries with larger informal sector, informal employment
tends to be less counter-cyclical. Figure 3 simulates the change in informal employment
growth (and corresponding 90% confidence bands) due to a 5 percentage-point increase
in GDP per capita growth as a function of the average level of informal employment.
Figure 3 also identifies the predicted values for the Latin American countries in the
sample. For the majority of countries, we estimate that informal employment moves
counter to the business cycle. The exceptions are the countries with the largest infor-
mal sectors (such as Peru and Bolivia), for which informal employment appears to be
a-cyclical.
In column 3 of Table 4, we consider the possibility that the determinants of infor-
mality have an independent effect on the counter-cyclicality of informal employment.
20
For this purpose, we enlarge the set of interactions with GDP per capita growth. We
find that neither the level of income nor business flexibility affect the cyclical response
of informality. Larger government expenditure appears to induce a more pro-cyclical
response, but, as we will see below, this result is not robust. On the other hand, the
interaction with law and order does appear to be robust, indicating that stronger rule
of law reduces the counter-cyclicality of informal employment.
Since the level of informality and the measure of the rule of law tend to go in opposite
directions, their effects on the cyclicality of informal employment could cancel each
other. To consider this possibility, we estimate the elasticity of informal employment
growth to changes in GDP per capita growth for all countries in the sample, taking into
account only the significant interactions that is, a model where GDP per capita growth
is interacted with informal employment and rule of law only (not shown in the table).
The results are presented in Figure 4. For ease of presentation, the elasticities (and
corresponding 90% confidence bands) are ordered by country income and smoothed out
by fitting a cubic spline. For 83% of the sample, the response of informality to economic
growth is significantly negative, that is, counter-cyclical. For 15%, this response is not
statistically different from zero, and only in one case (Vietnam) informality is estimated
to be pro-cyclical.
To check how sensible the elasticities derived from the empirical model are, we match
them with the elasticities obtained on a country-by-country basis (that is, through a
set of country dummy variables interacted with GDP per capita growth in the error-
correction model). The latter are subject to large imprecision due to the small size of
the time-series dimension of most countries. However, in spite of this, the two sets of
elasticities are reasonably in line with each other, having a correlation coefficient of the
order of 0.65.
Next, we conduct robustness checks similar to those in the long-run analysis. In
Table 5 we consider the robustness of the results to the presence of potential outliers
21
and changes in the minimum threshold of time-series coverage. In Table 6 we examine
the robustness of the results to the samples of all developing and only Latin American
countries, as well as to potential outliers in these samples. In both cases, results remain
basically the same.
To summarize the robust results, the deviation from the long-run (or "error-correction"
term) always carries a negative and highly significant coefficient, indicating informal
employment's tendency to trend reversion. The growth rate of GDP per capita also
carries a negative and significant coefficient, which has to be considered jointly with the
significant interaction coefficients to establish the cyclicality of informal employment.
Only two interactions are significant in all robustness exercises: they are the interac-
tions of GDP per capita growth with the level of informal employment and with the
rule of law index. Informality's counter-cyclicality decreases with the level of informal
employment and, independently, decreases with the rule of law. Since improvements
in the rule of law have the effect of reducing informal employment in the long run, the
two interactions with GDP per capita growth would tend to go in opposite directions.
However, using the actual values in the sample under study, we find that for the large
majority of countries (and not only for the average), informal employment is signifi-
cantly and robustly countercyclical. In brief, the short-run results are robust to changes
in the sample and the influence of outliers, including the cases where the informational
value of the variables is reduced as when only developing and Latin American countries
are considered.
4 Conclusions
This paper studies the trends and cycles of informal employment. It first presents a
theoretical model where the size of informal employment is determined by the relative
22
costs and benefits of informality (in terms of regulatory burden, enforcement, and access
to public services) and the distribution of workers' skills. In the long run informal
employment varies with the trends in these variables, and in the short run it reacts to
accommodate transient shocks and also to close the gap that separates it from its trend
level.
The paper then examines empirically informality's long- and short-run relationships.
It does so in the context of an error-correction framework, using country-level data at
annual frequency for a sample of developed and developing countries. Using the share
of self-employment in the labor force as proxy for informal employment, the paper finds
that in the long run informality is larger in countries that have lower GDP per capita and
impose more costs to formal firms, in the form of more rigid business regulations, less
valuable public services, and weaker monitoring of informality. The results are robust
to the criteria used to obtain the sample and to the influence of outlier observations.
The short-run results indicate that informal employment follows a stable, trend-
reverting process. Moreover, informal employment is found to be counter-cyclical for
the majority of countries. Informality's counter-cyclicality decreases with the level of
informal employment and, independently, decreases with the quality of policy and judi-
cial services (and less significantly with GDP per capita, business regulatory flexibility,
and strength of enforcement). These results are robust to changes in the sample and
to the influence of outliers, even when only developing countries are considered in the
analysis.
23
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25
Table 1. Long-Run Informality Relationships
Dependent variable: Self employment rate (as ratio to total workers)
OLS OLS OLS
(1) (2) (3)
GDP per capita -0.0759 *** -0.0516 ***
(in logs, annual) 0.0043 0.0060
Business flexibility -0.0293 ** -0.0167 *
(index from Fraser Institute, range:0-10, country average) 0.0111 0.0092
Law and order -0.0457 *** -0.0191 ***
(index from ICRG, range:0-6, country average) 0.0072 0.0050
Government expenditure -0.0050 ** -0.0015
(as % of GDP, country average) 0.0022 0.0015
Constant 0.9065 *** 0.6954 *** 0.9030 ***
0.0388 0.0666 0.0424
Observations/Countries 525/42 525/42 525/42
R-squared 0.80 0.72 0.85
Numbers in parentheses are robust, (country) clustered standard errors.
* significant at 10%; ** significant at 5%; *** significant at 1%
26
Table 2. Robustness to Outliers and Minimum Time-Series Coverage, Long-Run
Dependent variable: Self employment rate (as ratio to total workers)
Min.Obs. = 4 Min.Obs. = 2 Min.Obs. = 8
OLS WLS OLS WLS OLS WLS
(1) (2) (3) (4) (5) (6)
GDP per capita -0.0516 *** -0.0529 *** -0.0515 *** -0.0530 *** -0.0573 *** -0.0580 ***
(in logs, annual) 0.0060 0.0052 0.0058 0.0051 0.0072 0.0066
Business flexibility -0.0167 * -0.0158 * -0.0169 * -0.0158 * -0.0189 ** -0.0178 **
(index from Fraser Institute, range:0-10, country average) 0.0092 0.0082 0.0092 0.0081 0.0094 0.0083
Law and order -0.0191 *** -0.0184 *** -0.0185 *** -0.0177 *** -0.0127 ** -0.0125 **
(index from ICRG, range:0-6, country average) 0.0050 0.0045 0.0046 0.0041 0.0052 0.0048
Government expenditure -0.0015 -0.0013 -0.0017 -0.0016 -0.0013 -0.0011
(as % of GDP, country average) 0.0015 0.0014 0.0014 0.0013 0.0018 0.0016
Constant 0.9030 *** 0.9047 *** 0.9036 *** 0.9049 *** 0.9390 *** 0.9329 ***
0.0424 0.0380 0.0421 0.0375 0.0520 0.0464
Observations/Countries 525/42 525/42 546/47 546/47 457/33 457/33
R-squared 0.85 0.87 0.85 0.87 0.83 0.85
Numbers in parentheses are (robust) standard errors.
* significant at 10%; ** significant at 5%; *** significant at 1%
Notes:1. Min.Obs. Refers to the minimum number of consecutive pairs of annual self-employment observations that a country must
have in order to be included in the sample. Consecutive pairs are needed to obtain observations in growth rates as required
by the short-run analysis.
2.WLS is a weighted least squares procedure that reduces the influence of outliers in the estimation.
27
Table 3. Robustness to Sample of Countries, Long-Run
Dependent variable: Self employment rate (as ratio to total workers)
Full Developing LAC
OLS WLS OLS WLS OLS WLS
(1) (2) (3) (4) (5) (6)
GDP per capita -0.0516 *** -0.0529 *** -0.0579 *** -0.0575 *** -0.0607 *** -0.0597 ***
(in logs, annual) 0.0060 0.0052 0.0086 0.0080 0.0126 0.0113
Business flexibility -0.0167 * -0.0158 * 0.0045 0.0043 0.0036 0.0022
(index from Fraser Institute, range:0-10, country average) 0.0092 0.0082 0.0111 0.0096 0.0121 0.0108
Law and order -0.0191 *** -0.0184 *** -0.0301 *** -0.0290 *** -0.0243 ** -0.0229 **
(index from ICRG, range:0-6, country average) 0.0050 0.0045 0.0079 0.0070 0.0096 0.0087
Government expenditure -0.0015 -0.0013 -0.0062 * -0.0063 * -0.0054 -0.0051
(as % of GDP, country average) 0.0015 0.0014 0.0035 0.0030 0.0052 0.0046
Constant 0.9030 *** 0.9047 *** 0.9155 *** 0.9110 *** 0.9215 *** 0.9115 ***
0.0424 0.0380 0.0849 0.0765 0.1045 0.0946
Observations/Countries 525/42 525/42 205/18 205/18 149/12 149/12
R-squared 0.85 0.87 0.66 0.69 0.65 0.67
Numbers in parentheses are (robust) standard errors.
* significant at 10%; ** significant at 5%; *** significant at 1%
Note: 1. "Full" sample includes all high-income and developing countries with available data.
"Developing" sample includes only developing countries --those with avg. per capita income lower than $9,000 (at 2000 prices).
"LAC" sample includes only countries in Latin America and the Caribbean.
2. WLS is a weighted least squares procedure that reduces the influence of outliers in the estimation.
28
Table 4. Short-Run Informality Relationships
Dependent variable: Annual change in the self employment rate
OLS OLS OLS
(1) (2) (3)
Deviation from Long-Run -0.1752 *** -0.2014 *** -0.2880 ***
0.0460 0.0552 0.0862
ln(GDPpc) -0.3132 *** -0.8603 *** -5.0215 **
0.0791 0.2008 2.2080
Interactions:
ln(GDPpc) * Self 2.1899 *** 6.5559 ***
0.6247 2.3540
ln(GDPpc) * GDPpc 0.1613
0.1265
ln(GDPpc) * Business 0.0864
0.0765
ln(GDPpc) * Law and Order 0.1897 ***
0.0552
ln(GDPpc) * Gov. Expenditure 0.0232 *
0.0134
Constant 0.0073 ** 0.0087 ** 0.0073 **
0.0026 0.0027 0.0027
Observations/Countries 475/42 475/42 475/42
R-squared 0.08 0.11 0.13
Numbers in parentheses are (robust) standard errors.
* significant at 10%; ** significant at 5%; *** significant at 1%
Notes:1. The deviation from the long-run relationship is the difference between
the actual and the projected self-employment rate, where the latter is
given by the estimated long-run relationship in the benchmark case
(see Table1, Col.3)
2. Variables interacted with the change in GDP per capita correspond to
their country (time invariant) averages.
29
Table 5. Robustness to Outliers and Minimum Time-Series Coverage, Short-Run
Dependent variable: Annual change in the self employment rate
Min.Obs. = 4 Min.Obs. = 2 Min.Obs. = 8
OLS WLS OLS WLS OLS WLS
(1) (2) (3) (4) (5) (6)
Deviation from Long-Run -0.2880 *** -0.2021 *** -0.2930 *** -0.2092 *** -0.2724 *** -0.1926 ***
0.0862 0.0406 0.0855 0.0422 0.0979 0.0476
ln(GDPpc) -5.0215 ** -3.2063 *** -5.3415 ** -3.5869 *** -5.5050 ** -4.0404 ***
2.2080 1.1178 2.2471 1.2361 2.5333 1.2647
Interactions:
ln(GDPpc) * Self 6.5559 *** 4.2601 *** 6.8706 *** 4.7160 *** 6.7256 *** 4.9347 ***
2.3540 1.1441 2.3916 1.2765 2.6529 1.3130
ln(GDPpc) * GDPpc 0.1613 0.1090 0.1632 0.1224 0.2160 0.1605 **
0.1265 0.0688 0.1329 0.0773 0.1491 0.0737
ln(GDPpc) * Business 0.0864 0.0515 0.0947 0.0322 0.1099 0.1255 **
0.0765 0.0394 0.0769 0.0425 0.0871 0.0545
ln(GDPpc) * Law and Order 0.1897 *** 0.1566 *** 0.2223 *** 0.2217 *** 0.1419 *** 0.0545 **
0.0552 0.0336 0.0572 0.0433 0.0553 0.0442
ln(GDPpc) * Gov. Expenditure 0.0232 * -0.0025 0.0246 * -0.0055 0.0236 0.0011
0.0134 0.0087 0.0136 0.0087 0.0146 0.0120
Constant 0.0073 ** 0.0038 ** 0.0072 ** 0.0041 ** 0.0076 *** 0.0030
0.0027 0.0019 0.0026 0.0018 0.0029 0.0023
Observations/Countries 475/42 471/42 489/47 485/47 419/33 415/33
R-squared 0.13 0.13 0.15 0.17 0.13 0.12
Numbers in parentheses are (robust) standard errors.
* significant at 10%; ** significant at 5%; *** significant at 1%
Notes:1. The deviation from the long-run relationship is the difference between the actual and projected
self-employment rate, where the latter is given by the estimated long-run relationship in the benchmark case
(see Table1, Col.3)
2. Variables interacted with the change in GDP per capita correspond to their country (time invariant) averages.
3. Min.Obs. Refers to the minimum number of consecutive pairs of annual self-employment observations that
a country must have in order to be included in the sample. Consecutive pairs are needed to obtain observations
in growth rates as required by the short-tun analysis.
4. WLS is a weighted least squares procedure that reduces the influence of outliers in the estimation.
30
Table 6. Robustness to Sample of Countries, Short-Run
Dependent variable: Annual change in the self employment rate
Full Developing LAC
OLS WLS OLS WLS OLS WLS
(1) (2) (3) (4) (5) (6)
Deviation from Long-Run -0.2880 *** -0.2021 *** -0.2391 ** -0.2198 *** -0.3090 *** -0.2941 ***
0.0862 0.0406 0.0967 0.0682 0.0774 0.0590
ln(GDPpc) -5.0215 ** -3.2063 *** -2.4896 -2.6317 * -5.1583 *** -5.5224 ***
2.2080 1.1178 1.5949 1.4368 0.9309 0.9558
Interactions:
ln(GDPpc) * Self 6.5559 *** 4.2601 *** 3.2541 * 2.8307 * 6.5929 *** 7.3597 ***
2.3540 1.1441 1.5838 1.5579 0.9214 0.8482
ln(GDPpc) * GDPpc 0.1613 0.1090 0.0036 0.0692 0.2149 ** 0.2649 ***
0.1265 0.0688 0.1199 0.1040 0.0733 0.0705
ln(GDPpc) * Business 0.0864 0.0515 -0.0520 -0.0954 0.0090 0.0242
0.0765 0.0394 0.0817 0.0642 0.0785 0.0613
ln(GDPpc) * Law and Order 0.1897 *** 0.1566 *** 0.2449 *** 0.2746 *** 0.2517 *** 0.2892 ***
0.0552 0.0336 0.0841 0.0754 0.0332 0.0362
ln(GDPpc) * Gov. Expenditure 0.0232 * -0.0025 0.0521 0.0413 0.0171 -0.0244
0.0134 0.0087 0.0420 0.0393 0.0254 0.0283
Constant 0.0073 ** 0.0038 ** 0.0079 ** 0.0090 *** 0.0080 ** 0.0090 ***
0.0027 0.0019 0.0031 0.0024 0.0031 0.0025
Observations/Countries 475/42 471/42 182/18 182/18 133/12 133/12
R-squared 0.13 0.13 0.11 0.16 0.15 0.26
Numbers in parentheses are (robust) standard errors.
* significant at 10%; ** significant at 5%; *** significant at 1%
Notes:1. The deviation from the long-run relationship is the difference between the actual and projected
self-employment rate, where the latter is given by the estimated long-run relationship in the benchmark case
(see Table1, Col.3)
2. Variables interacted with the change in GDP per capita correspond their country (time invariant) averages.
3. "Full" sample includes all high-income and developing countries with available data.
"Developing" sample includes only developing countries --those with avg. per capita income lower than
$9,000 (at 2000 prices).
"LAC" sample includes only countries in Latin America and the Caribbean.
4. WLS is a weighted least squares procedure that reduces the influence of outliers in the estimation.
31
Figure 1. Informal Employment and GDP per Capita
.5
Vnm
VnmPakPak Hnd
Bol Per
Per
Vnm Vnm
Pak
Pak Bol Per
Per
BolBol
Hnd
Hnd
.4 VnmVnm
Vnm Per
Hnd
Hnd
Pak Per
Per
BolPhl
BolPhl
Hnd
Col
PerPerJam
Jam
Vnm BolPhl
Bol
Bol Col Jam
Jam
Jam
rate BolPhlEcu
Ecu
Ecu
Ecu
Ecu
Ecu
Ecu
ThaEcuThaTha
Ecu
Ecu
Ecu
Ecu ThaTha
Ecu
Slv Grc
Tha
Slv
Col
Slv
Ecu Tha Tha
Tha
Tha
Col
Tha
Slv
Col
Slv Pan
Tha GrcGrc
GrcGrc
Grc
GrcGrc
.3 ThaThaThaSlv
Tha Pan GrcGrc
ThaEgyCol
EgyCol
ThaCol
Slv
Slv PanBraPanMex
PanPan Kor
BraPanKorKor
PanPanUry
Mex
Egy
Egy ChlMexKor
Mex
Egy
Egy KorKor
Kor
Tun Arg Kor
Ury ArgPrt
Prt Prt
PrtPrt
Tun
Tun PanCri
Pan CriMexKorKor KorKorKor
PanPanMexMex
PanCriChlMex KorKorKor
Pan
PanCriChlChlKor
Pan
PanCriChlMex
CriCriChlMexArgArgPrt
ChlUry ArgArg
Ury Arg
MexArgKorKor
CriCri
CriCri
CriCriCri
employment CriCri
Cri Prt
Prt
Tun Tun
Tun Prt
PrtEspIrlTwn ItaIta
Prt ItaItaIta
Ita
EspCypIrl
TwnTwn ItaIta
IrlIrlCypIrlIrl
EspEsp
NzlCyp
NzlNzlNzl
EspNzl IrlIrlIrlIrlIsl
EspTwn
EspTwn
IrlTwn
IrlIrl
Self .2 Mys
Mys
Mys TtoTtoTto EspCyp Irl
EspCyp
NzlNzl
Esp
Esp
NzlNzl
Esp
Nzl
Cyp
Mys
Mys
MysTtoTtoTto
Tto
Tto
TtoTto
TtoTto EspTwnTwnTwn Ita
Mys Tto
Mys
Mys NzlNzl
Esp
Nzl
Esp
Esp
Mlt EspAusCanIslIslIrlIslChe
EspEspCanCan IslChe
CanIslIslIsl
CanIsl Irl
CanIrl
IrlIrlIslIsl
MltSgpSgpSgpSgp
Sgp AusAus CanJpnChe
AusCan Che
AusAus
AusCanIsl Jpn
SgpSgpSgpAusHkgChe
AusCan
Aus Can Jpn
Che
SgpIsrGbrGbr CheJpn
.1 IsrGbrNldHkgJpn
AusSgp
CanAus
Can
Aus
CanCan Che Jpn
Aus
SgpGbr
Aus
GbrHkg
Aus
Aus
GbrGbrChe
SgpGbr Jpn
GbrGbr
Sgp Jpn
Jpn
IsrIsrNldDeu
IsrSgpAut Jpn
IsrNldHkg
SgpAut
Deu DnkNor
AutHkgUsaUsa
NldGbr Jpn
AutAut
Deu
Hkg
AutHkg
Gbr Jpn
Jpn
Jpn
Deu
Deu
Deu
NldHkg
DeuUsa
HkgUsa
Sgp
Deu
Deu
Aut
Nld
Nld
Aut
Deu Usa
Deu DnkUsaNor
DnkUsa
DnkUsaNor
Usa
Usa
DnkNorNor
Usa
DnkUsaUsa
Usa
DnkNor
Nor
6 7 8 9 10 11
GDP per capita in logs
Note: Countries in Latin America and the Caribbean are highlighted in red.
32
Figure 2. Actual vs. Predicted Informal Employment
.5
Pak
.4 Per BolVnm
Hnd
rate Jam Phl
Ecu
Grc Tha SlvCol
.3 Pan Bra
Chl Kor Ury
Mex Egy
Arg
employment Prt Cri
Tun
Ita
Twn
self .2 NzlIrl Cyp Tto
Esp Mys
Isl
Can
Che
Aus Mlt
GbrJpn Sgp
Average .1 NldHkgDeu
Aut Isr
Dnk
Nor
Usa
0
0 .1 .2 .3 .4 .5
Average predicted self employment rate
Note: 1. The predicted self-employment rate is derived from the long-run regression presented in
Table 2, Col. 3 (min.obs.=2).
2. Countries in Latin America and the Caribbean are highlighted in red.
33
Figure 3. Informal Employment Reaction to the Business Cycle
.01
rate 0 Hnd
Per
Bol
Jam
growth Ecu
Slv
Col
-.01
UryMex
PanBra
Chl
CriArg
employment -.02 Tto
self
in
-.03
Change
-.04
.1 .2 .3 .4
Average self employment rate
Note: 1. The graph simulates the change in informal employment growth due to a 5 percentage-
point change in the GDP per capita growth rate. The simulation is based on a short-run
regression like the one shown in Table 4, Col. 2, but with a sample resulting from
applying the constraint of min. obs. =2.
2. The dotted lines are the 90% confidence bands.
3. Only countries in Latin America and the Caribbean are highlighted. From left to right
they are Trinidad and Tobago, Costa Rica, Argentina, Uruguay, Chile, Mexico, Panama,
Brazil, El Salvador, Colombia, Ecuador, Jamaica, Bolivia, Honduras, and Peru.
34
Figure 4. The Response of Informality to Changes in GDP per capita Growth
.5
growth
0
economic
to
informality -.5
of
Elasticity
-1
6 7 8 9 10
Average GDPpc (in logs)
Note: 1. The elasticity of informality to economic growth is obtained from the regression:
Ict = 0 +(10 + 11Ic + 12Lawc)yct + 2LRdevct +ct. For each country, the
-1
elasticity is given by ^10 + ^11Ic + ^12Lawc and the corresponding variance is equal to
Var[Ict ]=Var(10 )+ Var(11)Ic +Var(12 )Lawc + 2Cov(11, 12 )IcLawc
2 2
+ 2Cov(10,11)Ic + 2Cov(10,12)Lawc . The sample consists of all countries (47)
with min.obs.=2.
2. The point estimates correspond to the 90% confidence bands are smoothed out using the
cross medians as knots to fit a cubic spline.
3. Average GDP per capita (in the horizontal axis) is used only as an order criterion for the
elasticities.
35
Appendix 1. Sample of Countries
Min.Obs.=2 Min.Obs.=4 Min.Obs.=8 Developing LAC
Country Name Period (47countries) (42countries) (33countries) (18countries) (12countries)
Argentina 1996 - 2003
Australia 1987 - 2004
Austria 1994 - 2004
Bolivia 1989 - 2000
Brazil 2001 - 2003
Canada 1987 - 2004
Chile 1996 - 2004
Colombia 1992 - 2000
Costa Rica 1987 - 2004
Cyprus 1999 - 2004
Denmark 1995 - 2004
Ecuador 1988 - 2004
Egypt, Arab Rep. 1997 - 2003
El Salvador 1995 - 2004
Germany 1991 - 2004
Greece 1993 - 2003
Hong Kong, China 1993 - 2004
Honduras 1996 - 2004
Ireland 1986 - 2004
Iceland 1991 - 2002
Israel 1995 - 2003
Italy 1993 - 2003
Jamaica 1997 - 2004
Japan 1987 - 2004
Korea, Rep. 1986 - 2004
Mexico 1991 - 2004
Malta 2001 - 2004
Malaysia 1995 - 2003
Netherlands 1995 - 2003
Norway 1996 - 2004
New Zealand 1991 - 2004
Pakistan 1995 - 2002
Panama 1982 - 2004
Peru 1996 - 2004
Philippines 2001 - 2004
Portugal 1992 - 2003
Singapore 1986 - 2003
Spain 1986 - 2004
Switzerland 1991 - 2004
Taiwan, China 1994 - 2002
Thailand 1987 - 2004
Trinidad and Tobago 1987 - 2002
Tunisia 1999 - 2003
United Kingdom 1992 - 2004
Uruguay 2000 - 2003
United States 1987 - 2004
Vietnam 1996 - 2003
36
Appendix 2. Descriptive Statistics for the 42-Country Sample
5 2 7 4
sd 06 73 65 09 04
0. 0. 0. 1. .23 nt eru
me 49 05 11 04 00
4 ern 0.- 0. 0. 0. 1.
ax
m 433 016 100 000 73
0. 9. 7. 5. 2.2 Gov expendit
ngi
lopeveD ni 85 92 00 00 3
m .10 .75 .93 .01 23
6. redrO 4
.7 73 41 00
043 387 0. 0. 1.
001 676 40 and -0 -0.31
.7
median 0. 7. 6. 3. 11 Law
2
31
mean 0. 977.7 1 5 17
00 47 .3 ple
6. 3. 12 ss yt
samy
8 8 6 2 74 neisuB ili
ibx 05 44 00 13 21
-0. 0. 1. 0. 0.
fle
sd 09 15 91 35
0. 1. 0. 1. .74 countr
ax 76 9
m 433 .5 800 000 22
0. 10 8. 6. 1.3 lopinge
dev 00 49 12 22
GDP capita
ll -0.89 1. 0. 0. 0.
n 67 92
Fu mi .00 .75 009.3 000.1 3 for
23 per
6.
10
median .20 263.9 00 00 48
.36 .05 .6 iangletrre nte
14 low m
00
0
22
mean 0. 630.9 0 8 97 Self
and ploy rate 1. -0.73 -0.37 -0.31 -0.14
32 65 .2
6. 4. le em
15
samp
full
)s for
orkerw
alt
to )
ot ex P)DGfo iangletrre
Upp
io ind
atr( )x (%
0 on:i er
te ogs)l
rat (in 0-1(y inde uretdi te itu
y nd
et a (0-6r
men pit enpxe laterr rat
Co a r pe
ia e men pit ext
ar oyl ilitibxelf derO nte loy ilitibxlef derO
ivnU elbair car car
emp pe ess and pe sse and nmen
er
a)( va elfS PDG usinB awL mnrevoG riata
Biv)b( elbair
va emplfeS PDG sinuB awL ovG
37