self-employment penalties, growth and labor market regulations
TRANSCRIPT
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PRELIMINARY FIRST DRAFT—PLEASE DO NOT QUOTE
Self-employment Penalties, Growth and Labor Market Regulations
T. H. Gindling (UMBC), Nadwa Mossaad (UMBC)
and David Newhouse (The World Bank)
April, 2013
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Abstract:
While several studies have examined differences in the wage structure between informal
and formal sectors in specific developing countries, little is known about how wage gaps
between the self-employed and employees differ across countries. This paper uses a
unique database of household surveys collected by the World Bank to document wage
differentials between the self-employed and wage and salary employees over time and
across more than 60 low, middle and high income countries. We find that in
approximately three quarters of the surveys there is a wage penalty for self-employment.
On average, the self-employment wage penalty is large for low-income countries, falls as
per capita income increases, and then rises again for high-income countries. Patterns
with respect to labor market regulations are partially consistent with the canonical two-
sector model. There is weak evidence that countries where it is harder to start a business
have moderately smaller self-employment penalties, presumably due to the exit of less
productive entrepreneurs.
While there have been several recent studies of the impact of labor market regulations on
the size of the informal sector, ours is the first study that we know of that takes a broad
view of how labor market segmentation depends on a country’s level of development and
the strength of their labor market regulations.
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I. Introduction
Over 35% of workers in developing economies, including over 50% of workers in low
income countries, are self-employed (Gindling and Newhouse 2013). While there have
been many studies of differences in the wage structure between informal and formal
sectors in individual developing countries, there is very little comparative information on
how and why the wage gaps between the self-employed and employees differ between
countries. This paper uses a comprehensive set of household surveys, the World Bank
International Income Distribution data base, to document wage differentials between the
self-employed and wage and salary employees over time and across more than 60 low,
middle and high income countries. We then combine these self-employment wage
differentials with country-level data from the World Bank Doing Business project to
examine the relationship between self-employment wage differentials, per capita income,
and labor market regulations.
Specifically, we address the following questions: Do workers earn a wage premium or
pay a wage penalty for self-employment? How does this premium/penalty vary across
countries and regions? How does the self-employment wage penalty/premium change as
countries develop? And finally, how do labor market regulations affect the self-
employment wage premium/penalty? The results of this analysis can shed new light on
the impacts of labor market regulations and reforms in developing countries. While there
have been several recent studies of the impact of labor market regulations on the size of
the informal sector, ours is the first study that we know of that takes uses cross-country
panel data to examine how labor market segmentation depends on a country’s level of
development and the strength of their labor market regulations.
Broadly, views of the role of self-employment in the labor markets of developing
countries can be divided into two camps: those who see workers as involuntarily forced
into self-employment because they cannot obtain better paid formal wage and salary
employment, and those who see workers voluntarily choosing self-employment. Within
the latter category, workers may choose to become self-employed because they are
innovative and potentially successful entrepreneurs and/or because they prefer the
autonomy and flexibility of self-employment to working for others.
The first, more traditional view of self-employment in developing economies, associates
self-employment and informality with a segmented or dualistic labor market where
formal sector jobs are restricted by minimum wages, tax laws and labor market
regulations that limit the growth of employment in the formal sector. The dualist view
subscribes to the notion that informality stems from an imbalance between high
population growth and the slow growth of “good” formal employment (Fields, 2004,
2009; Tokman, 1978; De Mel, et al. 2010). This view argues that workers unable to find
adequate employment opportunities in the formal sector are forced to take employment in
the low paid, marginal informal sector. This view was supported by evidence of increased
self-employment during periods of economic crisis in Latin America in the 1980’s and
South Asia in the 1990’s crises (Tokman, 1984; Lee 1998; Smith, et al, 2002).
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One distinguishing feature of labor market segmentation and dual labor markets is wage
differentials; an earnings gap between informal sector workers and equally-qualified
formal wage and salaried employees which has often been interpreted as a measure of the
degree of labor market segmentation (Schultz 1961; Becker 1962; Mincer 1962). For
example, Fields (2009) notes, “The distinguishing feature used by Nobel laureates Arthur
Lewis (1954) and Simon Kuznets (1955) as well as other dual economy modelers is the
fact that workers earn different wages depending on the sector of the economy in which
they are able to find work.” In this view, self-employment is prevalent in low-income
economies because the formal economy is incapable of providing enough “good”, high-
wage jobs. As countries develop, the proportion of workers who are self-employed falls
and the wage differential between the self-employed and employees should fall and
eventually disappear.
An alternative view is that self-employed maximize their expected income by choosing to
be self-employed due to their comparative advantage as potentially innovative and
successful entrepreneurs (Maloney 2004). In this case, we would expect self-employed
workers to earn a wage premium over equally qualified wage and salary employees, both
because of the returns to the risk of entrepreneurship and as compensating differentials
for the loss of the non-wage benefits (i.e. pensions, severance pay, entitlements) that
often accompany wage and salary employment (Meghir, Narita, and Robin 2012). On the
other hand, workers may also value the autonomy and flexibility associated with self-
employment. In this case, workers may be willing to accept lower profits than the wages
they could earn as employees, creating a wage penalty for self-employment.
Labor market regulations, like segmentation, are a source of considerable controversy in
the literature. Proponents argue that regulations protect workers from being taken
advantage of by firms that have greater market power, and reduce shocks. Critics,
meanwhile, claim that regulations often benefit insiders at the expense of less
experienced and skilled outsiders. In addition, they point to evidence that employment
protection regulations increase informal employment and reduce the gross labor mobility
that is crucial for creative destruction and productivity growth (Freeman, 2009; Heckman
and Pagés, 2004). In addition to these two camps, a third view is emerging that in most
contexts, the effects of regulatory reform are generally mild, particularly when compared
to the intensity of the debate over regulations (World Bank, 2013).
Calls to relax labor market regulation are often based on the classic two-sector model, in
which stringent hiring and firing regulations ration workers out of the formal sector and
increase the penalty to self-employment. In contrast, stronger barriers to starting a
business would discourage workers from entering self-employment, drive down returns
to wage employment. In addition, the remaining enterpreneurs would be those who
expected to earn a sufficiently high return to starting a business to make it worthwhile
(Maloney 2004; de Soto 1989). This would further diminish the observed penalty to self-
employment in countries with more onerous procedures for starting a business.
Most recent studies on regulations rely on natural experiments that examine how labor
market regulations affect outcomes relative to a control group. Our study takes a different
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but complementary approach by examining a broader cross-section of countries. This
comes at a cost; the source of variation in regulations is unknown, and may be related to
unobserved labor market outcomes. On the other hand, we observe and focus on self-
employment penalties, one of the main channels through which regulations are
hypothesized to affect labor markets. Furthermore, we include country fixed effects in
some specifications in order to examine how variation in regulations over time is
associated with changes in the self-employment penalty.
Overall, we find that there is a substantial self-employment penalty in most countries,
averaging approximately 20 percent. Self-employment penalties are relatively low in
Latin America, and highest in Europe and Central Asia, Sub-Saharan Africa, and South
Asia, sounding a note of caution about generalizing from Latin America to other regions.
Consistent with this, penalties are highest in low-income and high-income countries, and
lowest for upper-middle income countries. We find weak evidence that countries in
which it is more difficult to start a business have smaller self-employment premiums.
This suggests that relaxing barriers to self-employment can modestly improve labor
market by attracting less productive workers into self-employment.
In the empirical section of this paper, we start by using the DEC I2D2 data base to
estimate self-employment wage penalties or premiums for countries throughout the world
and over time within countries. Combining these estimates of self-employment wage
gaps with cross-country data on labor market and other regulations and macroeconomic
variables, we examine the correlations between economic growth, labor regulations and
the self-employment wage gap, controlling for observed individual characteristics,
country-level fixed effects and time fixed effects.
II. Literature Review:
Our paper contributes to two broad strands in the literature. The first is the literature on
the impact of labor market regulations and other government policies on informality and
other labor market outcomes. The second is the literature on the magnitude and causes of
the wage differentials between wage and salaried employees and self-employed workers.
There are numerous country-level and regional studies that examine rigid labor market
laws and regulations on host of economic outcomes1. The two studies that inspired such
literature are Heckman and Pagés (2004) in Latin America and Besley and Burgess
(2004) in India. Heckman and Pagés (2004), a collection of essays, examine the impact of
mandated worker benefits, payroll taxes, minimum wage, and employment protection
laws on employment. They find negative consequences of regulations on employment in
general, and also find that the negative effects are worse for young and unskilled workers.
They conclude that in the case of Latin America, rigid labor regulations protect workers
already in the system at the expense of those considered outside, promoting inequality
among the later group. Besley and Burgess (2004) explore the Industrial Disputes Act
(IDA) of 1947, a set of labor and employment laws aimed at protecting workers in the
1 See Djankov and Ramalho (2009) for a detailed review.
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organized sector and how they affect long-run manufacturing development. They find
that Indian states that amended the laws in a pro-worker direction grew more slowly than
states that amended the laws in a pro-employer direction. Consequently, labor
amendments, originally aimed at protecting workers, resulted in higher poverty and
informality and low levels of productivity, investment, and employment in formal sectors
in pro-worker states.
Our research is most closely related to the extensive recent literature, inspired by Botero
et al. (2004), on the impact of rigid labor market regulations and other government
policies on labor market outcomes such as high unemployment, size informal economy,
growth and so on. Botero et al. (2004), the first cross-country study on labor market
regulations to include developing countries, construct indices of labor market regulation
for 85 countries and examine the correlations between the rigidity of employment laws,
collective bargaining and social security laws on the size of the unofficial economy, labor
force participation rates and unemployment. They find that heavier labor market
regulation is associated with a larger informal sector, lower labor force participation and
higher unemployment, especially among youth.
Since then, a number of studies have used Botero’s et al. (2004) data and methodology to
examine the effects of labor market regulation on a range of economic outcomes. On
employment for example, Micco and Pagés (2006) adopted Botero et al. (2004)
methodology and used data on industry sectors in 69 countries to show that stringent
employment protection regulations reduce productivity, net firm entry, turnover,
employment and value added. The effects of the regulations on Job flow are mostly
concentrated in highly volatile sectors, which require higher level of hiring flexibility.
Pierre and Scarpetta (2004) suggest that countries with onerous labor regulations - where
hiring and firing can be very costly - tend to hire less, rely more on on-the-job training
and make greater use of temporary employment. Medium sized and innovating firms tend
to be negatively affected the most. Feldmann (2009) uses an alternative dataset on labor
regulations for 73 countries during the period 2000-03, from the World Economic Forum
(WEF), to also find similar results. They conclude that stricter regulations generally
reduce employment and centralized collective bargaining increase female unemployment.
Again, the size of the effects seems to be larger for younger workers. They also look at
the impact of minimum wages or unemployment benefits but find no statistically
significant effects.
Similarly, Djankov and Ramalho (2009) conducted a cross-country correlation analysis
using data from the WEF and the Doing Business indicators as well as the Global
Competitiveness Report. Expanding on the number of countries from Botero et al.
(2004), they use data from over 150 countries and show that developing countries with
more rigid employment laws tend to have larger informal sectors and higher
unemployment, especially among younger workers. They also show a large, significant
and negative impact of cumbersome administrative procedures to start a business and the
tax costs associated with operating a formal business on the size of the informal sector.
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On investment and trade, Hallward-Driemeier and Stewart (2004) use the World Bank's
surveys of firms and Doing Business indicators, and provide insights into the cost of
regulatory burdens on the investment climate. They conclude that improvements in
regulations increase productivity, investment and job growth disproportionately
benefiting small firms. Helpman and Itskhoki (2010) provide an analytical framework of
labor market rigidities and trade impediments in shaping productivity and unemployment.
They argue that countries with more flexible labor market institutions (ie lower labor
market frictions) enjoy higher productivity and gain more from trade but also caution that
higher trade unemployment may not due to high labor market rigidity.
This analytical framework is supported by cross-country empirical evidence presented in
Freund and Bolaky (2008), Busse and Groizard (2008) and Cuñat and Melitz (2011).
Freund and Bolaky (2008) find that excessively regulated countries do not benefit from
trade openness and that trade promotes higher income and standards of living in well-
regulated but not rigid economies. While Busse and Groizard (2008) show that trade
liberalization and Foreign Direct Investment does not stimulate growth in economies with
excessive business and labor regulations and inadequate government institutions.
Similarly, Cuñat and Melitz (2011) examine the link between volatility, labor market
rigidity, and international trade using the DB dataset. They find that, all other things
being equal, countries with less rigid labor markets specialize in sectors with higher
volatility, where the ability to adjust to labor demand is more important. They find
evidence that more flexible countries export relatively more in high volatility industries
and suggest trade liberalization as an alternative to labor market to improve welfare.
Another recent strand of literature examines the effects of regulation and taxation on size
of the informal economy where most own-account workers operate. Schneider et al.
(2010), the most comprehensive study to date on informal economic activities, find that
an increased burden of taxation, combined with inflexible labor market regulations and
the quality of public institutions and services are the leading causes of the existence and
growth of the shadow economy.Using the same shadow economy variable as Schneider
et al. (2010), Lehmann and Muravyev (2012) find similar results. Using country-level
panel data from transition economies and Latin America, they find that higher
employment protection legislation and larger tax wedge increase the size of the informal
economy. Sabirianova Peter (2009) uses instrumental variables and a longer-time span
panel data to measure the effect of a global transition to flatter taxes on the size and
growth of the shadow economy. She finds that flatter and simpler taxes reduce the size of
the informal economy in the short run and that the effects are significantly larger with
improved government institutions, low corruption and strong legal system. She does
however caution that adopting flatter taxes might not be socially as it might increase
income inequality.
On entrepreneurship; Van Stel et al. (2007) use individual level data set (Global
Entrepreneurship Monitor (GEM) data combine with Doing Business (DB) dataset to
examine the relationship between regulations and entrepreneurship. First, they find that
administrative barriers to start a business such as the time, the cost, or the number of
procedures needed to start a business, have no impact on the rate of entrepreneurship.
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Interestingly, they find that it is labor market regulations that strongly influence the rate
on entrepreneurship amongst young and potential entrepreneurs. And finally, “necessity”
entrepreneurs ignore regulations all together as tend or prefer to operate informally. This
last finding has strong policy implications, especially for countries where the majority of
entrepreneurs are such by necessity.
Similarly, Ardagna and Lusardi (2008, 2009a) use the GEM data and DB to investigate
the determinants of entrepreneurial activity in several developed and developing
countries. They argue that rigid labor regulations, through working status, social network
and business skills, play a detrimental role in entrepreneurship, especially for those
pursuing business opportunities. In particular they find that tougher entry regulations,
contract enforcement and labor regulations reduce the likelihood to engage in new
entrepreneurship activity for existing entrepreneurs. Unfortunately, both studies are
limited in that they 1) do not offer information on income or measurable success 2) are
limited to mainly advanced and industrialized countries and 3) are based on self-reported
information. Our proposed study will use similar data to Botero, et al. (2004) and
Djankov and Ramalho (2009) from the Doing Business database and the World
Development indicators, and will extend this literature by examining the impact of labor
market and other government regulations on the earnings gap between self employed
workers and wage and salaried employees
The second broad strand in the literature to which our paper contributes is the estimation
of the magnitudes and causes of wage differentials between self-employed and informal
sector employees relative to formal sector wage and salary employees. Many of these
studies examine wages in middle-income countries and conclude that workers in the
informal sector earn less than equally qualified employees in the formal sector (i.e.
Heckman and Hotz, 1986; Gindling, 1991; Basch and Paredes-Molina, 1996; Günther
and Launov, 2006). However, not all informal sector workers are self-employed, and the
self-employed may be very different from informal sector employees. For example, in a
review of the evidence from Latin America, Perry et al. (2007, p.6) write that “most of
the self-employed do not appear to be “excluded” from the formal sector; rather, after
implicitly making a cost-benefit analysis, they opt out of formality. A different picture
emerges, however, for the majority of informal salaried workers in the countries studied.
Indeed, most of the informal salaried appear to be queuing for more desirable jobs in
either the formal salaried sector or as self-employed workers.”
When researchers estimate formal-informal wage differentials separately for informal
sector employees and self-employment workers, they often find different results for the
two groups. Compared to formal sector wage and salary employees, Arias and Khamis
(2009) find a wage penalty for informal wage and salary employees but a wage premium
for self-employed workers in Argentina. Nguyen et al. (2013) find the same thing in
Vietnam. Bargain and Kwenda (2011) find similar results in Brazil and Mexico.
However, for South Africa they find that both informal sector employees and self-
employed workers pay a wage penalty, relative to formal sector employees.2 Maloney
2 Bargain and Kwenda (2010) and Nguyen, Nordman and Roubaud (2013) also use quantile regressions to
estimate wage differentials at different points in the conditional wage distribution. Both papers find that for
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(1999) finds that workers who transition from wage and salary employment into self-
employment in Mexico experience higher earnings, while workers who transition into
informal sector wage and salary employment experience a decline in earnings. Saavedra
and Chong (1999) find a wage penalty for informal sector employees, but no difference
between the wages of informal self-employed workers and formal sector employees.
In summary, while the literature on wage differentials points to consistent wage penalties
for informal sector wage and salary employment, this is not the case for self-employment
relative to formal wage and salaried employment. Most published studies conclude that
self-employed workers do not earn less than equally qualified formal sector wage and
salaried employees. However, most of these studies are from middle income and/or Latin
American countries; there are few studies of self-employment wage penalties or
premiums in low-income countries outside of Latin America. In at least one African
country (South Africa), a published study has shown that self-employed workers pay a
wage penalty. Our paper contributes to the literature on wage differentials between self-
employment and wage and salary employment by estimating and comparing these
differentials for a wider range of developing and high income countries than currently
exists in the literature. The countries that we examine represent all income levels and
across all regions of the world.
Our paper also contributes to the literature by using a country-level panel data set to
examine the impact of labor market regulations on the self-employment
penalty/premium. While we have reviewed several recent studies of the impact of labor
market regulations on employment, unemployment and the size of the informal sector,
ours is the first study that we know of that uses cross-country panel data to examine the
impact of economic growth and the impact of labor market regulations on the wages of
the self-employed and wage and salary employees.
III. DATA & METHODS:
1) Data
One objective of this research is to estimate the average self-employment wage penalties
or premiums, as well as the distribution of those premiums/penalties for countries
throughout the world and within countries over time. A second objective of this research
is to estimate the relationship between labor market regulations and the magnitude and
distribution of the self-employment earnings premium. This section describes the data
workers at the lower end of the conditional wage distribution there is a wage penalty for self-employment,
while for workers in the high end of the wage distribution there is a wage premium. This suggests that the
reasons for self-employment may be different for high productivity and low productivity workers, and that
while low productivity workers may be forced into self-employment because they are rationed out of
formal sector jobs, high productivity workers choose to be self-employed. This suggests that it is important
to estimate self-employment wage differentials separately for workers in different parts of the wage
distribution. In the next stage of our research, we will examine the distribution of the self-employed wage
premium among workers by estimating local linear regressions that allow us to estimate a self-employment
wage premium for each worker.
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we employ in the empirical analysis. We begin by discussing the standardized household
survey data, and then we discuss the institutional and regulatory data.
I2D2 data used to estimate the self-employment earnings penalty/premium
Our analysis is based on multiple data sources. The first and main data source that we
use are micro-level household surveys collected by the Development Economics Group
(DEC) of the World Bank, the I2D2 data warehouse. This database consists of already
existing data sets that have been collected and standardized. Most original country
datasets come from labor force surveys, budget surveys or living standards measurement
surveys. The database is an updated version of that described in Montenegro and Hirn
(2009). The main advantage of these household surveys is that they provide information
on the incomes of the self-employed as well as of wage and salary employees, in addition
to other relevant information on individual socioeconomic characteristics. The data
include four sets of consistently defined and coded variables: (i) demographic variables,
(ii) education variables, (iii) labor force variables, and (iv) household per capita
consumption.
Not all variables are available in all countries and years. In our analysis, we only use
surveys where we can identify whether the worker is self-employed3 or a wage and salary
employee, and where we also have data on the earnings of both the self-employed and
wage and salaried workers. Most countries datasets are available for multiple years; we
include countries from the period 2004 to 2010 to match our regulatory data sets. Within
each country, we limit our samples to the working age population, 15-65 years old. The
countries and years available for our analysis are listed in the appendix in table A1. Our
data set includes 260 surveys (country/year combinations) for 86 countries. For most
countries there are multiple years, which will allow us to control for country-level fixed
effects.
In the first stage of our analysis, we use these data sets to estimate the wage
premium/penalty for workers in each county/year combination in the data.
Data sources for the regulatory and institutional variables
For the second part of the research, we merge the data set of the estimated self-
employment earnings premiums for each year and country with data on individual
countries’ regulatory and institutional characteristics. We examine the correlations
between the self-employment earnings premium and country-level characteristics such as
labor market regulations, tax policies, national income per capita, etc.
There is a large body of literature on the causes of increased informality and shadow
economies (Djankov and Ramalho, 2009; Peter, 2009; Lehmann and Muravyev, 2012).
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Self-employed workers include those who self-identify as either an own account worker or an
owner/employer. We use the ILO definition of own account workers as “workers who, working on their
own account or with one or more partners, hold the type of job defined as a self- employed job, and have
not engaged on a continuous basis any employees to work for them during the reference period”
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The most cited culprits of such increases tend to be the burden of taxation, government
regulatory constraints (i.e. red tape) such as labor regulations, procedures to start and
operate a business, licenses etc., and the quality of the legal system (i.e corruption, rule of
law, property right etc.). High taxation burden, regulatory constraints and low
government quality might provide strong incentives to operate in a shadow economy but
it is unclear how they might effect the wages of those employed in it. On the one hand,
self-employed workers might choose to operate outside the formal economy to avoid the
associated costs (such as taxation) and as such earn wages higher to similar workers
operating in a formal economy who pay taxes and other costs.
Data on labor and business regulations come from the World Bank Doing Business
(WBDB) project. This dataset is one of the first to measure business regulations in a
comparable way across multiple countries including a large number of developing and
transitioning economies. The data is available for 185 economies and according to the
DB website "… provides objective measures of business regulations” and an opportunity
to study the effect of such regulations on a host of economic factors (World Bank, 2013).
The Doing Business project collects information on labor laws through questionnaires
administered to local business experts (this includes business consultants, accountants as
well as labor lawyers and government officials).
One of the key variables of the DB database is the Rigidity of Employment Index (REI),
which measures the cost and inflexibility of employment regulations. The doing business
index is modeled after the Employment Laws Index of Botero et al. (2004) which ranks
economies based how their labor laws hamper doing business. The REI is a key policy
variable in the growing literature on the relationship between labor market regulation,
economic growth, informality, etc. (Ardagna and Lusardi, 2008; Freund and Bolaky,
2008; Djankov and Ramalho 2009; Cuñat and Melitz 2011; and Helpman and Itskhoki
2010)
The REI is the average of three sub-indices; 1) difficulty of hiring, 2) rigidity if working
hours and 3) difficulty of redundancy. REI takes a score between 0 and 100, with higher
scores indicating larger barriers to employment. Using the availability of fixed-term
contacts and minimum wage regulations (ratio of minimum wage to the average wage),
the first sub-index measures the flexibility of small to mid-size firms to add on new
workers. The second sub-index measures the flexibility of working nights and weekends,
the length of a workweek and the number of paid vacation days. The third sub-index,
difficulty of redundancy, is a measure of the firm’s cost to dismiss workers, in weeks of
salary, due to redundancy. It includes length of notice requirements, penalties and
severance pay for terminating a redundant worker4. Lower scores for all three sub-indices
indicate reduced restrictions on employment regulations.
4 The data collected refer to businesses in the economy’s largest business city (which in some
economies differs from the capital) and may not be representative of regulation in other parts of
the economy It should be noted that the measure favors flexible employment regulations. The
index has also been subject to strong criticism; it assumes that rigid labor regulation is the result
of rent seeking behaviors from those already in the system at the expense of those who are out.
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Other WBDB variables include Procedures to Start a Business (PSB) and Total Tax Rate
(TTR). PSB is a measure of the number of procedures, time and cost officially required to
start and operate a new business. A growing body of literature has shown that higher
entry barriers lead to low levels of entrepreneurship, legally registered businesses, higher
levels of corruption and higher levels of informality (Djankov et al., 2002; Ardagna and
Lusardi, 2010b). TTR documents the tax burden on new businesses. These are taxes born
by a business in the second year of operation as a percent of commercial profit before
taxes are applied. Djankovet al. (2008) found that a high corporate tax burden had large
and negative impact on investment, entrepreneurial activities, and growth. They also
found a large impact on the size of the informal sector as firms facing higher tax burden
choose to opt out of the formal sector. Finally, we add a key macro-economic variable,
gross national income per capita (GNI pc), as a measure of development of living
standards between countries and overtime.
2) Methodology
Estimating the Self-employment Wage Penalty/Premium
We estimate the wage premium/penalty in each country/year using individual worker
level data to estimate an earnings regression,
LnYi = EP*SEi + B*Xi + ui [1]
Where
LnYi is our dependent variable, self-reported monthly earnings of worker i.
SEi is a dummy variable indicating whether the worker is self-employed (1) or a
wage and salary worker (0); EP is the estimate of the average self-employment
wage premium, estimated separately for each country/year.
Xi is a vector of worker specific variables; B is a vector of coefficients on these
variables. Worker-specific explanatory variables include: years of education,
years of education squared, age, age squared, a gender dummy variable and an
urban/rural dummy variable.
ui is the error term.
Equation 1 is estimated separately for every county (j) and year (t) for which we have the
appropriate variables in the I2D2 data set. These estimates result in an estimate of the
wage premium for each country (j) and year (t) combination in the I2D2 data set, EPjt.
EPjt is that percent by which the earnings of the self-employed differ from the earnings
of wage and salary workers. If EPjt is positive, that indicates that there is an earnings
premium for self-employment; if EPjt is negative, that indicates an earnings penalty for
self-employment.
We compare the earnings of wage and salaried employees using the log of monthly
earnings. We estimate the earnings premium/penalty in each country/year using
individual worker level data to estimate an earnings regression, where the dependent
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variable is earnings and the key independent variable is a dummy variable that is one if
the worker is self-employed, zero if a wage and salaried employee. Other control
variables in the earnings regressions include: years of education, years of education
squared, age, age squared, a gender dummy variable and an urban/rural dummy variable.
Using reported earnings for the self-employed has two limitations: (i) it is available for a
limited number of countries/years and (ii) the earnings of the self-employed may be mis-
measured. One source of mis-measurement is that self-reported earnings of the self-
employed could include returns to capital as well as labor, while the earnings of wage and
salaried employees include only returns to labor. Another source of mis-measurement,
especially in family enterprises, is that the total earnings of a family enterprise may be
attributed to the household head.
In this draft of our paper, we use simple OLS to estimate the average earnings
premium/penalty. In the next phase of our analysis, we also plan to estimate a self-
employment earnings premium/penalty for each worker (rather than an average) using
local linear regression techniques--we hope to have this available by the time of the
conference. The latter technique will allow us to examine earnings premiums across the
full distribution of workers and examine how the earnings premium differs for workers
with different characteristics (for example, men vs. women, rural vs. urban, etc.).
Estimating the Impact of Labor Market Regulations on the Wage Premium
To estimate the impact of labor market regulations and level of development on the
earnings premium we use country-level panel data, where the dependent variable is the
estimated self-employment earnings premium/penalty described in the last sub-section.
Because our data consists of multiple years of observations for many countries, this will
allow us to control for time-invariant country-level fixed effects and also variables that
change over time but not across countries. We estimate the following equation:
EPjt = β Xjt + γj Zj + Tt + ujt [2]
Where
EPjt is our dependent variable (self-employment wage premium/penalty), where j
= country and t = year.
Xjt is is a vector of country-specific time-varying variables (such as regulations
and macroeconomic variables); β is a vector of coefficients on these variables.
Zj is a vector of covariates that vary across countries but not over time. In the
fixed effects estimates, these are country-level fixed effects. In the random
effects model these are dummies indicating the region of the country; γj are the
coefficients on these variables.
Tt (t=1…t) is a vector of time dummy variables. These capture the year fixed
effects, which capture shocks common across countries in a given year (such as
international economic crisis).
ujt is the error term for country j at time t.
14
We estimate OLS, fixed and random effects regressions of equation [2]. As additional
diagnostic checks, we run the Breusch-Pagan Lagrange multiplier (LM) test to help
decide between the random effects and OLS regressions; the null hypothesis in the LM
test is that variances across countries is zero, that is, no significant difference across
countries. To decide between fixed or random effects we run a Hausman test where the
null hypothesis is that the preferred model is random effects vs. the alternative fixed
effects. It basically tests whether the unique errors (ujt) are correlated with the regressors,
the null hypothesis is that they are not.
The dependent variable EPjt, based on self-reported earnings, is discussed above. Xjt
includes the Rigidity of Employment Index (REI), Total Tax Rate (TTR), Procedures to
Start a Business (PSB) and the log of per capita gross national income. In the OLS and
random effects estimates, Zj includes dummy variables that indicate the region of the
world for country j. In the fixed effects estimates, Zj are the country fixed effects.
The sample includes variables from multiple datasets which report different years of data.
First, data from the World Bank Doing Business data base is available only for the years
2004-2013, with few numbers of countries participating in earlier years. Moreover, some
of the variables in the data set were not collected before 2006. Second, not all countries in
our I2D2 sample have data for all years. Several countries appear only once or twice,
leading to an unbalanced data. We estimate equations (2) using data for countries over
the period 2004 to 2011. Unfortunately the data are not complete. After removing
missing observations we are left with an unbalanced panel of 155 observations distributed
over 62 countries from 2004 through 2010.
IV. Results:
The Self-employment Wage Penalty/Premium
Table 1 displays basic summary statistics for the key variables used in the earnings’
equations. Panel A includes the wage premiums/penalties while Panel B lists the
descriptive statistics for the explanatory variables5. The summary statistics are presented
for the entire sample of countries as well as by countries’ income group and regions.
Most of the sample is concentrated within high and middle-income countries.
Regionally, the sample is concentrated in Europe & Central Asia (ECA) and Latin
America & Caribbean (LAC).
Overall, the mean wage penalty/premium for self-employment is a -22%, indicating 22%
a penalty for self-employed workers. For 75% of country/year observations there is a
wage penalty for self-employment vs. wage and salary employment, while for 25% of
observations there is a wage premium for self-employment. The average wage penalty is
largest in ECA, South Asia and Sub-Saharan Africa (SSF), and lowest in LAC and the
Middle East and North Africa (MENA). However, the wage penalty/premium varies
5 We also report the number of years for each country in the sample and the mean of the wage
premiums/penalties, rigidity of employment index, total tax rate, number of procedures to start a business
and log (GNI) in the appendix in table A2.
15
tremendously between and within countries, ranging from a penalty of 244% to a
premium of 96%. Interestingly, this variation is large in high- and low-income countries
but narrows dramatically in upper- and lower middle-income countries. Within regions,
variation in the wage penalty/premium is largest in East Asia and the Pacific (EAS) and
(ECA), and lowest in LAC and MENA. Even in Latin America, where the mean wage
penalty and variation is low, it is still true that in 65% of observations there is a wage
penalty for self-employment.
The summary statistics suggest that there is a non-linear relationship between the self-
employment wage penalty and per capita GNI, where earnings of self-employed vs. wage
and salary employees rise with income for low, low middle and upper middle income
countries, and then fall for high income countries. The self-employment wage penalty is
largest for low and high income countries, and essentially disappears for middle income
countries. Figure 1 illustrates this non-linear relationship, showing that, starting at low
incomes, the earnings of the self-employed vs. employees rises with increases in income
until the log GNI per capita is about 8 (approximately $3,000 per capita). After that,
further increases in national income per capita are correlated with decreases in the
earnings of the self-employed relative to employees.
Figure 2 illustrates the unconditional correlations between the labor market
regulation/policy variables and the wage penalties/premiums. These unconditional
correlations suggest a negative correlation between the self-employment wage premium
and the Rigidity of Employment Index, a negative correlation between the self-
employment wage premium and the tax burden, and a positive correlation between the
self-employment wage premium and the number of procedures for starting a business.
The impact of labor market regulations and national income per capita on the self-
employment wage premium
Table 2 presents the regression results using three different specifications of equation 2;
OLS, fixed effects (FE) and random effects (RE). We also report the results of the two
standard diagnostics tests to help us decide between RE and OLS regressions as well as
RE and FE. First, the Breusch-Pagan test helps decide if OLS is appropriate. The null
hypothesis in the Breusch-Pagan test is that the error terms are homoskedastic--that
variances in the error terms across countries are equal to zero (ie no significant difference
across units). Using the Breush-Pagan test, we cannot reject the null, which leads us to
conclude that the variances of the error terms are correlated across time within countries,
and that therefore OLS is not appropriate. If the RE assumptions are valid, FE and RE
models coefficients should be the same. To test this, (decide between fixed or random
effects) we run a Hausman test where the null hypothesis is that the preferred model is
random effects vs. the alternative, the fixed effects. It basically tests whether the unique
errors (ui) are correlated with the independent variables; the null hypothesis is that they
are not. The Hausman statistic in this case is very big. All of the tests performed using the
wage premium/penalty have insignificant p-values (Prob>chi2 larger than .05), meaning
that the coefficients are not statistically different and that the random-effects model is the
appropriate one to use. The results of our diagnostic test suggest that the random effects
16
(RE) estimates are preferred to both the OLS and fixed effects estimates in the wage
premiums/penalty regressions. For comparison purposes we present the estimates from
all three specifications. 6
All specifications suggest that the relationship between the wage penalty for self-
employed workers and GNI per capita is non-linear. The coefficients on the log of GNI
per capita and the log of GNI per capita square are both statistically significant. Figure 3
illustrates this non-linear relationship for the range of GNI per capita in the countries in
our data set, using our preferred RE specification.7 As is illustrated in figure 3, starting
from low GNI per capita, increases in national income lead to an increase in the wages of
the self-employed relative to employees (a reduction in the self-employment wage
penalty). Then, after a country reaches approximately the upper middle income level,
further increases in GNI per capita lead to a decrease in the wages of the self-employed
relative to employees. The turning point is around $8,000 per capita national income.
After countries reach this level of GNI per capita, further increases in national income
lead to decreases in the wages of the self-employed relative to employees (an increase in
the self-employment wage penalty). That is, our results suggests that for low income
countries the self-employment wage penalty falls with increased in GNI per capita, and
then after reaching the upper-middle income level, the wage penalty increases with GNI
per capita.
Table 2 also presents our preliminary estimates of the impact of labor market and other
regulations on the wage penalty for self-employed workers. In all specifications of the
wage penalty/premium regressions, the results suggest that regulations that increase the
difficulty of starting a business lead to higher wages for self-employed workers relative
to the wages of employees (a fall in the self-employment wage penalty), although this
coefficient is only statistically significant in the OLS specification, so the evidence in
favor of this point is weak. For the other regulatory variables the coefficients are
insignificant in all specifications (for the rigidity of employment index) or are mostly
insignificant and of different signs depending on the specification (for the tax burden of
starting a new business).
V. CONCLUSIONS
In this paper we estimate the self-employment earnings penalties or premiums for over
60 countries throughout the world, and within countries over time. We are able to
estimate self-employment penalties/premiums for countries from across all regions and
income groups using the unique DEC I2D2 data base of the World Bank. Combining
these estimates with regulatory variables from the World Bank Doing Business database,
we examine the effects of economic growth, labor regulations, and other business
regulations on self-employment earnings premiums/penalties.
6 We further test to see if the year dummies are equal to 0 and if the time fixed effects are needed. We
failed to reject the null that all years coefficients are jointly equal to zero therefore no time fixed- effects
are needed. 7 For illustrative purposes, the simulation in figure 4 assumes the mean level of the labor regulation
variables for 2010 in the Middle East and North Africa.
17
Our results indicate that there is, generally, a sizeable penalty to self-employment. This
penalty, however, varies greatly according to a country’s level of development (measured
by GNI per capita). In low-income countries, self-employed workers face a substantial
wage penalty. This wage penalty decreases as countries develop, until countries reach
upper middle-income levels. After countries reach upper-middle income levels, further
increases in GNI per capita are correlated with increase in the self-employment wage
penalty. Our findings suggest some support for the broad view that workers in low-
income developing countries seek self-employment because they cannot obtain better-
paid formal wage and salary employment, but that as countries begin to develop and the
formal sector expands, self-employment becomes a voluntary choice.
Patterns with respect to labor market regulations are partially consistent with the
canonical two-sector model. There is some weak evidence that countries where it is
harder to start a business have moderately smaller self-employment penalties, presumably
due to the exit of less productive entrepreneurs.
Most previous estimates of the self-employment wage premium/penalty in developing
countries come from middle-income countries in Latin America. These studies tend to
find that self-employed workers do not earn less than wage and salary employees (and
often earn a premium). In this paper, we also find that on average there is, at most, a
small self-employment wage penalty in Latin America and the Caribbean. On the other
hand, we find that there are large self-employment wage penalties, on average, in South
Asia and Sub-Saharan Africa. Our results suggest a note of caution about generalizing
from Latin America to other regions.
The results presented here are the beginning of a more comprehensive examination of
determinants of earnings premiums/penalties. We plan to further examine the relationship
between earnings premiums/penalties and regulations, policies and country
characteristics by including alternative measures of regulations, more years and countries
for which we have earnings’ data. For added robustness of our results, we also plan to
explore different methodologies and estimate a self-employment earnings
premium/penalty for each worker (rather than an average) using local linear regression
techniques. This will allow us to examine earnings premiums across the full distribution
of workers and examine how the earnings premium differs for workers with different
characteristics (for example, men vs. women, rural vs. urban, etc.).
18
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21
TABLES:
Table 1: Descriptive Statistics
Panel A: Descriptive Statistics - Dependent variables
Wage premiums/penalties Obs Mean Std.
Dev. Min Max
Negative
(%)
Positive
(%)
Total 193 -0.22 0.46 -2.44 0.96 75 25 Income group
Low Income 8 -0.38 0.94 -2.44 0.93 88 12 Low Middle Income 49 -0.16 0.26 -1.26 0.79 88 12 Upper Middle Income 61 0.01 0.25 -0.46 0.96 52 48 High Income 75 -0.44 0.52 -2.04 0.47 84 16
Region
East Asia & Pacific 13 -0.16 0.79 -2.44 0.93 77 23 Europe & Central Asia 86 -0.38 0.54 -2.04 0.96 81 19 Latin America & Caribbean 77 -0.06 0.20 -0.47 0.62 65 35 Middle East & North Africa 2 0.03 0.18 -0.10 0.16 50 50 North America 1 -0.23 . -0.23 -0.23 100 0 South Asia 7 -0.27 0.27 -0.72 0.09 56 14 Sub-Saharan Africa 7 -0.26 0.15 -0.46 -0.04 100 0
22
Panel B: Descriptive Statistics - Independent variables
Obs. Mean Std. Dev. Min Max
Obs. Mean Std. Dev. Min Max
Rigidity of Employment Index
Procedures to start a business
Income Group: Total 170 31.32 15.04 3.33 66.33
257 9.01 3.64 3 19
Low Income 8 29.43 9.52 13.33 44.44
13 9.38 2.40 4 13 Low Middle Income 43 36.04 13.94 10.33 66.33
62 11.87 3.28 3 17
Upper Middle Income 48 29.78 14.59 3.67 62.56
69 9.87 3.05 5 19 High Income 71 29.71 16.10 3.33 55.56
113 6.88 2.92 3 15
Region East Asia & Pacific 16 28.48 13.06 10.33 50.78
17 10.53 3.57 6 17
Europe & Central Asia 80 29.47 15.63 3.33 55.56
125 7.07 3.03 3 15 Latin America & Caribbean 53 35.54 15.03 3.67 62.56
81 11.53 3.20 6 19
Middle East & North Africa 5 27.40 6.32 16.67 31.44
5 10.00 2.92 5 12 North America -- -- -- -- --
1 6.00 . 6 6
South Asia 6 28.07 12.39 13.33 49.22
10 9.10 2.96 4 13 Sub-Saharan Africa 10 32.16 15.63 13.33 66.33
18 9.56 2.01 6 13
Business Taxes (as % profit)
Log (GNI - Per Capita)
Income Group: Total 257 47.20 18.36 9.3 217.9
258 8.94 1.42 5.14 11.36
Low Income 8 35.36 10.01 22.60 52.80
13 6.09 0.44 5.14 6.63 Low Middle Income 45 43.36 15.12 15.30 80.00
60 7.48 0.46 6.43 8.26
Upper Middle Income 48 48.97 16.96 17.10 82.60
69 8.54 0.39 7.78 9.39 High Income 68 47.51 12.73 21.10 77.50
116 10.26 0.62 8.83 11.36
Region East Asia & Pacific 17 37.94 8.28 22.6 51.3
17 7.33 0.55 6.38 8.30
Europe & Central Asia 125 46.45 13.52 15.3 77.5
128 10.07 0.83 7.34 11.36 Latin America & Caribbean 81 50.99 16.37 25.6 82.6
81 8.15 0.60 6.94 9.58
Middle East & North Africa 5 32.34 16.44 16.8 54.3
3 7.96 1.02 7.21 9.12 North America 1 46.80 . 46.8 46.8
1 10.71 . 10.71 10.71
South Asia 10 44.99 19.26 9.3 72.8
10 6.74 0.72 5.56 8.20 Sub-Saharan Africa 18 49.46 43.85 17.1 217.9
18 7.32 1.31 5.14 8.83
23
Table 2: Self-employment wage premium regressions
Dependent Variable =
Wage premiums
(1) (2) (3)
OLS FE RE
Rigidity of Employment Index
-0.004 -0.029 -0.005 (0.003) (0.031) (0.004)
Paying Taxes - Total tax rate (% profit)
-0.006** 0.001 -0.005 (0.003) (0.013) (0.004)
Starting a Business - Procedures (number)
0.029** 0.036 0.021 (0.013) (0.043) (0.014)
Log(GNI) 1.246* 7.019 1.330*
(0.697) (7.679) (0.693)
Log(GNI)^2 -0.068* -0.384 -0.074*
(0.038) (0.455) (0.040)
Region Dummies YES NO YES
Year Dummies YES YES YES
N (obs.) 123 123 123
N (countries) 52 52 52
R-sq 0.215 0.1 0.211
Hausman chi2 4.63
(0.865)
Breusch and Pagan
11.58
(0.00) ***Indicate statistical significance at the 1% level. **For the 5% level. *For the 10% level. Robust standard errors are shown in parentheses.
24
FIGURES:
Figure 1: Self-employment premiums and Gross National Income per Capita
-.8
-.6
-.4
-.2
0
self-e
mplo
ym
ent w
ag
e p
rem
ium
4 6 8 10 12log GNI per capita
25
Figure 2: Regulations and self-employment premiums/penalties
26
Figure 3: Predicted self-employment premium and per capita Gross National
Income
-1.2
-1-.
8-.
6-.
4-.
2
pre
dic
ted_
wa
e_
pre
miu
m
4 6 8 10 12Log GNI per capita
27
APPENDIX A
Table A1: Countries and years
Country Years Country Years
Afghanistan 2007 Lao PDR 2008
Albania 2005 Latvia 2005-2008
Austria 2004-2008 Lebanon 2011
Bangladesh 2005, 2010 Liberia 2007
Belgium 2004-2008 Lithuania 2005-2008
Bhutan 2007 Luxembourg 2004-2008
Bolivia 2005-2008 Macedonia, FYR 2004, 2005
Botswana 2009 Maldives 2004
Brazil 2004-2009 Mauritius 2004, 2007, 2008
Bulgaria 2008, 2009 Mexico 2004-2006, 2010
Cambodia 2007, 2008 Mongolia 2008, 2009
Cape Verde 2007 Mozambique 2008
Chile 2006, 2009 Netherlands 2004, 2007, 2008
Colombia 2004-2010 Nicaragua 2005
Comoros 2004 Norway 2004, 2006-2008
Congo, Rep. 2006 Pakistan 2004, 2007
Costa Rica 2004-2009 Panama 2004, 2006, 2009, 2010
Croatia 2004 Papua New Guinea 2010
Cyprus 2005-2008 Paraguay 2004-2010
Czech Republic 2005-2008 Peru 2004-2010
Denmark 2004-2008 Philippines 2006-2008
Dominican Rep. 2004-2010 Poland 2005, 2007
Ecuador 2004-2010 Portugal 2004-2008
Egypt 2006 Puerto Rico 2005
El Salvador 2004- 2009 Romania 2007, 2008
Estonia 2004-2008 Rwanda 2005
Fiji 2008 Senegal 2005
Finland 2004-2008 Serbia 2010
France 2004-2008 Slovak Republic 2005-2008
Gabon 2005 Slovenia 2004-2007
Georgia 2010 South Africa 2004, 2006, 2007
Germany 2005-2008 Spain 2004-2008
Ghana 2005 Sweden 2004-2009
Greece 2004-2008 Syria 2007
Guatemala 2004, 2006 Thailand 2009
Honduras 2004-2009 Timor-Leste 2007
Hungary 2004-2008 Togo 2006
Iceland 2004-2008 Ukraine 2005
India 2004, 2007, 2009 United Kingdom 2005-2008
Indonesia 2004, 2008-2010 United States 2005
Ireland 2004-2008 Uruguay 2005, 2007, 2008, 2010
Italy 2004-2008 Vietnam 2008
Kenya 2005 West Bank & Gaza 2008, 2009
28
Table A2: Main indicators by country
Country N.
Obs.
Wage
premiums/
penalties
Rigidity of
Employme
nt Index
Paying
Taxes -
Total
tax rate
(%
profit)
Starting a
Business -
Procedures
(number)
Log
(GNI)
Afghanistan 1 -0.16 13.33 36.40 4 5.56
Albania 1 -0.14 -- 57.75 11 7.86
Austria 5 -0.59 19.11 55.80 8 10.58
Bangladesh 2 -0.18 34.78 35.15 8 6.36
Belgium 5 -0.55 10.33 59.70 4 10.56
Bhutan 1 -- 24.44 40.80 10 7.39
Bolivia 4 -0.17 32.56 80.00 15 7.10
Botswana 1 -- 13.33 17.10 10 8.74
Brazil 6 0.05 34.06 69.12 16 8.58
Bulgaria 2 0.37 12.22 39.60 9 8.53
Cambodia 2 -0.76 30.00 22.60 10 6.44
Cape Verde 1 -- 31.11 53.10 12 7.85
Chile 2 0.60 17.78 25.65 9 9.05
Colombia 7 -0.28 7.27 81.20 12 8.27
Comoros 1 -0.29 -- 217.90 11 6.25
Congo, Rep. 1 -- 66.33 65.40 10 7.11
Costa Rica 6 0.05 41.11 54.80 12 8.57
Croatia 1 -0.09 -- 32.50 11 9.01
Cyprus 4 0.34 24.44 22.60 6 10.08
Czech Republic 4 -- 13.33 49.16 10 9.57
Denmark 5 -- 3.33 33.82 4 10.84
Dominican Rep. 7 0.27 30.38 37.16 9 8.24
Ecuador 7 -0.14 37.62 35.19 14 8.03
Egypt, Arab Rep. 1 -- 26.67 54.30 10 7.21
El Salvador 6 0.00 31.11 34.82 11 8.02
Estonia 5 -1.62 54.44 50.36 6 9.32
Fiji 1 -- 14.11 41.50 8 8.30
Finland 5 -- 38.11 48.62 3 10.62
France 5 -0.05 55.56 65.92 6 10.50
Gabon 1 -0.46 -- 44.90 9 8.54
Georgia 1 -1.26 13.33 15.30 3 7.89
Germany 4 -0.33 27.78 49.31 9 10.56
Ghana 1 -- -- 38.10 11 6.13
Greece 5 0.08 44.78 52.25 15 10.04
Guatemala 2 -0.26 28.11 38.70 15 7.63
Honduras 6 -0.17 54.83 44.48 13 7.34
Hungary 5 -0.35 15.78 56.25 6 9.28
Iceland 5 -- 15.67 27.44 5 10.80
India 3 -0.52 23.33 69.10 12 6.78
Indonesia 4 -0.08 48.26 37.38 11 7.51
Ireland 5 -0.12 10.33 26.40 4 10.70
Italy 5 -0.09 40.00 76.85 9 10.37
Kenya 1 -- -- 50.20 12 6.25
Lao PDR 1 -0.24 20.33 35.50 7 6.63
Latvia 4 -0.18 38.15 36.89 5 9.11
Lebanon 1 0.16 31.44 30.20 5 9.12
Liberia 1 -- 27.78 43.70 10 5.14
Lithuania 4 -0.02 31.11 49.70 8 9.14
29
Luxembourg 5 -0.81 48.89 21.41 6 11.17
Macedonia, FYR 2 0.04 -- 21.60 13 7.90
Maldives 1 0.09 -- 9.30 5 8.20
Mauritius 3 -0.18 16.67 22.83 6 8.68
Mexico 4 -0.19 44.44 54.20 9 9.00
Mongolia 2 0.03 17.44 36.80 7 7.48
Mozambique 1 -0.45 44.44 34.30 10 5.94
Netherlands 3 -- 42.22 44.98 6 10.71
Nicaragua 1 -0.13 -- 63.50 8 7.06
Norway 4 -- 41.81 42.00 5 11.16
Pakistan 2 -0.47 49.22 42.90 11 6.59
Panama 4 -0.16 61.44 49.00 6 8.63
Papua New Guinea 1 -0.21 10.33 42.30 6 7.17
Paraguay 7 -0.16 51.89 43.99 13 7.44
Peru 7 -0.10 38.78 41.32 10 8.13
Philippines 3 -0.31 35.22 50.43 17 7.33
Poland 2 -0.26 13.33 43.80 10 9.04
Portugal 5 -0.55 37.78 45.12 10 9.84
Puerto Rico 1 0.08 -- 57.60 7 9.58
Romania 2 -0.17 40.72 48.20 5 8.90
Rwanda 1 -- -- 42.15 9 5.60
Senegal 1 -- -- 48.35 10 6.68
Serbia 1 0.96 35.89 34.00 7 8.64
Slovak Republic 4 -- 18.89 48.89 9 9.50
Slovenia 4 -0.65 40.33 40.00 9 9.83
South Africa 3 -0.15 35.22 37.93 9 8.49
Spain 5 -0.38 49.22 61.84 10 10.20
Sweden 6 -1.18 30.28 54.77 3 10.73
Syrian Arab Rep. 1 -- 16.67 43.30 12 7.54
Thailand 1 -0.13 17.78 37.80 8 8.22
Timor-Leste 1 0.79 34.44 28.30 10 7.52
Togo 1 -- 34.78 52.80 13 5.99
Ukraine 1 -0.30 -- 57.15 15 7.34
United Kingdom 4 -0.28 3.67 35.78 6 10.66
United States 1 -0.23 -- 46.80 6 10.71
Uruguay 4 -0.02 13.33 64.05 11 8.85
Vietnam 1 -- 13.33 40.00 11 6.82
West Bank & Gaza 2 -0.10 31.11 16.95 12 .