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PRELIMINARY FIRST DRAFT—PLEASE DO NOT QUOTE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 .