Returns to Experience and the Misallocation of Labor2 ASIF ISLAM, REMI JEDWAB, PAUL ROMER AND DANIEL PEREIRA 1. Introduction Large productivity gaps still persist between poorer and

Returns to Experience and the Misallocation of Labor

Asif Islam, Remi Jedwab, Paul Romer, and Daniel Pereira ∗

October 3, 2018

Abstract

A recent literature argues that factors of production are misallocated across

sectors, types of firms, and locations. In this paper, we: (i) Study wage-experience

profiles and obtain measures of returns to experience across these dimensions using

data from 23 million individuals in more than 1,000 household surveys and census

samples for 145 countries; (ii) Document that returns vary across dimensions in

developing countries, which suggests that labor could be misallocated from the

perspective of the returns to work experience: services, cognitive occupations, the

formal sector, and urban areas exhibit relatively higher returns than non-services,

manual occupations, the informal sector, and rural areas, respectively; (iii) Show

that despite these “return gaps”, reallocating labor to the best sector/occupation/firm

type/location in the country does little to bridge the gap in aggregate returns between

developed countries and developing countries; and (iv) Use cross-country data on

income, economic and political institutions, and social norms, and find that country-

wide characteristics appear to be the main drivers of aggregate returns.

JEL: O11; O14; O18; R11; R12; J21; J24

Keywords: Returns to Experience; Income Gap; Misallocation; Structural Change

∗Corresponding author: Paul Romer: NYU Urbanization Project, [email protected]. Asif Islam:The World Bank, [email protected]. Remi Jedwab: Department of Economics, George WashingtonUniversity, [email protected]. Daniel Pereira: PhD Candidate, Department of Economics, GeorgeWashington University, [email protected]. This paper served as a basis for the fourth chapter ofthe World Development Report of 2019 on “The Changing Nature of Work”. We gratefully thank Ariel BenYishay, Barry Chiswick, Gabriel Demombynes, Simeon Djankov, Aaart Kray, Federica Saliola and AdmasuShiferaw and seminar audiences at George Washington University, William & Mary and the World Bankfor their helpful comments. We also thank Claudio Montenegro and David Newhouse for helping us withusing the I2D2 database. We gratefully acknowledge the generous support of the World Bank (Office of theSenior Vice-President and Chief Economist) and the Cities Program of the International Growth Center.

http://home.gwu.edu/~jedwab/

https://paulromer.net/

2 ASIF ISLAM, REMI JEDWAB, PAUL ROMER AND DANIEL PEREIRA

1. Introduction

Large productivity gaps still persist between poorer and richer countries. Significant

differences in productivity, wages, or assets have then been disproportionately observed

for developing countries across sectors (Bartelsman et al., 2013; Gollin et al., 2014;

Vollrath, 2014; McMillan et al., 2014), between larger/formal firms and smaller/informal

firms (Restuccia and Rogerson, 2008; Hsieh and Klenow, 2009) and between urban and

rural areas Gollin et al. (2014, 2017). While some studies question whether such gaps

actually reflect sorting of better able workers across sectors or locations (Young, 2013;

Hicks et al., 2017), overall the literature has argued that poorer economies are ineffective

in allocating resources to their most productive use (Restuccia and Rogerson, 2017).

If we believe that wage gaps are not due to sorting only, reallocating labor could have

strong effects on the economies examined. Likewise, many international organizations

and national governments advocate for policies that would accelerate structural change

out of agriculture into higher-productivity sectors such as industry and services. It

is also often argued that formalization and urbanization are desirable trends for

developing countries, given their potentially large aggregate effects on the economy.1

This study attempts to contribute to that literature by studying how returns

to experience, the dynamic wage gains from work experience, one of the main

components of human capital, vary across countries as well as across sectors,

occupations, types of firms, and locations, and test whether reallocating workers could

have measurable effects on aggregate returns to experience in developing countries.

First, we use the International Income Distribution Database (I2D2) of the World

Bank to estimate returns to experience for as many countries and dimensions as

possible. Our sample includes 23 million individuals in 1,041 household surveys and

census samples for 145 countries that account for 95% of the world’s population.

Second, we document that: (i) aggregate returns to experience are twice higher

in developed countries than in developing countries. In developed countries, wages

increase about 4% for each extra year of experience. In developing countries, wages

increase by only 2% of each extra year; (ii) returns vary across dimensions in both

developed countries and developing countries, which suggests that labor could be

1In this study we focus on the misallocation of labor. See Duranton et al. (2015) for a study on themisallocation of two other factors of production, land and financial capital, in India.

RETURNS TO EXPERIENCE AND THE MISALLOCATION OF LABOR 3

misallocated from the perspective of the returns to experience: services, cognitive

occupations, the formal sector, and urban areas show relatively higher returns than

non-services, manual occupations, the informal sector, and rural areas, respectively;

(iii) the gaps in the returns across dimensions are larger in developing countries

than in developed countries, which suggests that there is more labor misallocation

in developing countries; and (iv) dimension-specific returns are always lower in

developing countries than in developed countries. In other words, the “best” (i.e.

highest-return) sectors / occupations / firms / locations in developing countries exhibit

still lower returns than the “worse” (i.e. lowest-return) sectors / occupations / firms /

locations in developed countries.

(i) and (ii) imply that reallocating labor could have disproportionately large dynamic

effects on wages in developing countries, in line with the literature finding significant

gaps across sectors, firms or locations. However, (iii) also implies that these large

dynamic effects of reallocating labor might be too small to bridge the gap in the

aggregate returns observed in developed countries and developing countries.

Third, we more formally test this hypothesis by simulating the effects of various

policies. We find that despite these “return gaps”, reallocating labor to the best

sector/occupation/firm type/location in developing countries or giving developing

countries the labor shares of developed countries, i.e. their economic structure,

does little to bridge the gap in aggregate returns between developed countries and

developing countries. At best, only 20% of the gap in the aggregate returns between

developed countries and developing countries can be explained by misallocation.

Where our gaps explained by sorting, the contribution of misallocation would be even

smaller. Conversely, if we do not change the economic structure of developing countries

but give them the dimension-specific returns of developed countries, we find that the

gap in aggregate returns is reduced by at least 85%. Overall, we find little evidence that

labor misallocation has large aggregate effects on returns to experience.

Fourth, the contribution effects of labor misallocation suggests that country-wide

characteristics are the main drivers of aggregate returns, which we investigate for the

145 countries using country-level data on income, economic and political institutions,

and social norms. Conditional on income, countries with better economic institutions

that are essential to the functioning of markets and social norms associated with


reduced labor market frictions have higher returns. Interestingly, income and a few core

controls explain 32% of the variation in the data. Adding a few measures of economic

institutions and social norms is then enough to increase the predictive power of our

simple regression to 75%. This suggests that countries with similar income levels can

raise their returns to experience if implementing policies that make labor markets more

efficient.

This study contributes to the literature on labor misallocation. First, productivity

is often poorly measured in developing countries, which constrains the comparison

of productivities across dimensions between developed and developing countries.

Second, net wages are also poorly measured in developing countries, due to the lack

of location-specific data on prices, in particular housing prices and commuting costs,

and amenities or disamenities. Third, assets, which have been used to bypass the issue

raised by the lack of data on prices and amenities, measure wealth stocks instead of

wealth flows. Fourth, data availability issues often restrict these studies to focus on a

handful of countries. Since we do not have data on prices in the I2D2 database, we

cannot compare net wages. We also do not have data on wages, prices or amenities.

However, we can estimate returns to experience, and thus focus on the more dynamic

aspect of labor misallocation. We also analyze most countries in the world. Aware of the

limitations of our analysis, we believe our study complements rather than replaces or

improves existing studies.

In addition, the study contributes to a larger literature estimating the returns to

education and experience. For example, this study directly builds on Lagakos et al.

(2018) who estimate experience-wage profiles for about 20 economies using individual-

level data, with most of them being middle- or high-income countries. We conduct

similar estimations for 145 economies, including many low-income countries. This

study then builds on other studies exploring the relationship between returns to

experience along a number of factors including levels of development (Psacharopoulos,

1994; Bils and Klenow, 2000), education (Mincer, 1974; Heckman et al., 1998; Lemieux,

2006) and cities (Roca and Puga, 2017). Our analysis then relates to other studies that

have used I2D2 to measure the returns to human capital. However, most of these studies

focus on the returns to education, which is only one component of education along

with childhood investments and work experience, and concentrate their analysis on the


aggregate returns rather than how these returns vary across the dimensions studied in

our analysis (Patrinos, 2014; de Hoyos et al., 2015).

2. Data: 23 Million Individual Observations from 1,041

Surveys/Census in 145 Countries

Sources. The data source for the analysis is the International Income Distribution

Database (I2D2). The database consists of a larger number of individual-level surveys

and census samples. The surveys include nationally representative household surveys,

labor force surveys and budget surveys. The data was initially compiled by the World

Bank’s World Development Report unit between 2005 and 2011. The data is now

harmonized and compiled by the Development Economics Research Group of the

World Bank.2

Sample Size. The sample that we will focus on includes 22,904,660 individuals from

1,041 survey/census samples in 145 countries from 1990-2016. We include surveys from

the 1990s to make sure we increase the number of countries in our sample. The data

is highly representative of the world economy. We have at least one survey for 145

countries, hence 94% of the world’s population. The data is then representative for

both developed countries (76% of their population; based on the 2017 classification

of the World Bank) and developing countries (98%). Note that developed countries

are high-income countries circa 2016 according to the classification of the World Bank.

Developing countries are low-, lower-middle and upper-middle income countries.

3. Methodology

Specification. To be consistent with the most recent literature on the returns to

experience, we follow the econometric framework of Lagakos et al. (2018). For

individual i in country c and sample year t, we use OLS for each country c one by one to

estimate:

2The I2D2 database has been used by a number of studies to explore employment shares, wageearnings premia, and the returns to education (Patrinos, 2014; de Hoyos et al., 2015; Gindling andNewhouse, 2014; Gindling et al., 2016).


lnwageict = αc1eduyrsict + αc2eduyrssqict +7∑e=1

βceexpreict + θct + εict (1)

The dependent variable is the log of monthly wages (lnwage). The experience

variables are transformed in a number of ways. The experience variable is defined as age

minus years of education minus 6 for individuals with more than 12 years of education,

and age minus 18 for individuals with less than 12 years of education. Observations with

negative experience are dropped. With the resulting years of experience variable (expr),

seven bins are created. The experience bins are [5-9 years], [10-14],[15-19],[20-24],[25-

29],[30-34],[35+]. The omitted experience bin is [0-4]. Note that we also restrict our

analysis to individuals aged 18-67. We thus do not include child workers or seniors that

may still be working beyond 67. Surveys included in the analysis must have at least 10

observations in each experience bin. This ensures that we have enough observations to

estimate each coefficient. Finally, years of education (eduyrs) is obtained directly from

the survey3.

We control for the number of years of education, since the number of years of

education may be correlated with the number of years of experience. First, individuals

that study are mechanically less experienced for a given age. Second, individuals that

study more will have higher wages if returns to education are positive, and education

could also improve longevity, and thus work experience. We then include sample fixed

effects (θct) to account for country-year specific unobservables. This implies that we

only compare individuals from the same country and year.

Obtaining the Returns. From equation 1 we obtain a coefficient for each experience

bin, hence seven coefficients. These coefficients allow us to construct wage-experience

profiles for each country-dimension. Figure 2(a) and Figure 2(b) show the wage-

experience profiles for agriculture, industry, services, and the whole economy (“agg.”)

for developed countries and developing countries respectively. To obtain these profiles,

we obtain the average coefficients for each country (during the period 1990-2016). We

then obtain the mean coefficients for each group of countries using the population of

each country in 2010. Doing so, we give more weight to larger countries. As can be

3For the few samples for which the number of years of education is not available whereas educationalattainment is, we impute the number of years of education based on descriptions of educational stagesin the country of the sample.


seen, aggregate profiles are steeper in developed countries than in developed countries.

In developed countries, individuals with 5 and 30 years of experience earn almost 50%

and 100% more than individuals with no experience. In developing countries, they only

earn almost 25% and 50% more. A measure of the return should take the integral below

the profile, what Lagakos et al. (2018) call the “sum of heights”. We thus estimate an

annualized return for each experience bin. We then take the mean of these annualized

returns to obtain our measure, the mean annualized return throughout the experience

distribution. We find a mean annualized return of 4.1% for developed countries and

1.9% for developing countries. In other words, wages increase by 4.1% and 1.9% for each

extra year of experience in developed countries and developing countries, respectively.

Note that our specification may differ from the standard Mincer specification which

experience and its square. Our specification has several advantages. First, it is more

flexible, and thus contains more information that a quadratic function. As can be seen,

the profiles are not necessarily monotonic. Second, in future versions of the paper, we

will consider mean annualized returns that only depend on the coefficients of the 5-

20 years of experience bins. We will do so because profiles tend to flatten beyond 20

years, so most differences across countries and dimensions come from the first 20 years

of experience. The returns for the first 20 years may also better capture the current

dynamism of labor markets whereas the last 20-35 years of experience may capture

past trends. Finally, we will discuss later how selective mortality in poorer countries can

affect the coefficients of later experience bins. In other words, restricting our analysis to

the first 20 years of experience may help us estimate more causal returns.4

Methodological Choices. Our framework then departs from the framework used by

Lagakos et al. (2018) who study hourly wages and drop females, part-time workers, self-

employed workers and workers of the public sector, as they argue that including them

could bias the estimated returns to experience. Using hourly wages necessitates data on

the number of hours worked, which is available for fewer samples and countries. Since

we want our sample to include as many samples and countries as possible, we choose to

focus on monthly wages. Also, the number of hours worked could vary with experience,

4The other issue with the Mincer specification how to obtain the returns from the coefficients ofexperience and its square, say β1 and β2. It is standard to calculate the returns as β1 + β2 x themean or median of the number of years of experience. There are two major problems with that. Themean/median dramatically varies across countries and dimensions. In addition, this only captures thelocal slope/return at the mean/median, not the full distribution.


and thus be part of the returns. In addition, we keep females, part-time workers, self-

employed workers and workers of the public sector. First, we want our sample to be

as “full” as possible. Second, many workers are underemployed (work part time) in

developing countries. Due to the lack of unemployment benefits, unemployment is

actually measured by underemployment. In addition, most workers are self-employed

in low-income countries, so excluding them gets rids of a significant share of the labor

force. For 130 countries for we also have data on hourly wages, the coefficient of

correlation between the estimated aggregate returns based on hourly wages but keeping

the full sample and the estimated returns based on hourly wages and the selection

criteria of Lagakos et al. (2018) is 0.79. Therefore, we doubt that this will affect our main

analysis.

Endogeneity issues. Returns to experience compare for more experienced vs. less

experienced individuals. There are several endogeneity issues.

First, we measure “potential experience”, not true experience, since we do not know

the employment history of each individual (surveys and censuses do not collect this

type of information). We are thus likely to over-estimate total experience for more

experienced individuals (since they have more years of potential experience, they have

by construction more years that could have been spent not working). This should lead

us to over-estimate returns to experience. However, it is unclear to what extent these

measurement errors are classical, or actually correlated with the subdimension studied

and/or development status.

Second, potential experience is estimated based on age and the number of years

of education. By construction, more experienced individuals have less education, and

education has an effect on the wage if returns to education are positive. Since we are

controlling for the number of years of education and its square in equation (1), we are

controlling for this omitted variable bias.

Third, since we control for education, potential experience also captures the effect of

age. It is a standard problem in the literature, since one cannot simultaneously include

experience, education and age. Various imperfect approaches have been used in past

studies to deal with this issue (see for example Lagakos et al. (2018)). We will use these

approaches in future versions of the paper.

Fourth, international migration flows are small enough than selection issues


probably do not affect the comparison of returns across countries. But when comparing

the different subdimensions of a same country, selection issues could affect the results.

For example, if there is significant rural-to-urban migration, and migrants are overall

less experienced and positively selected, which means they are likely to obtain higher

wage anyway, the returns for urban (rural) areas will be under-(over-)estimated. When

comparing the returns for urban and rural areas, the return gap will thus be under-

estimated. Likewise, if there is significant structural change and workers are little

experienced when they decide which sector to specialize into and the ones choosing

the “best” sector(s) are positively selected, then the returns of the best (worse) sector(s)

will be under-(over-)estimated. When comparing the returns between the best and

worse sectors, the return gap will also be under-estimated. More generally, sorting

is only an issue if: (i) there is significant “mobility” across subdimensions; (ii) less

experienced workers are more mobile across subdimensions, which is likely;5 and (iii)

mobile workers are positively selected, which is also likely. If anything, this suggests

that the return gaps that we will estimate may be under-estimated, which should be

less of an issue. Now, the question is whether sorting differs between poorer and richer

countries.

As things stand, we cannot be sure the effects are causal. But we are not alone in that.

The whole literature on the returns to experience have not found a convincing way to

estimate causal effects. In addition, even if there is sorting and that sorting leads us to

over-estimate the gaps in the returns between the worse and best sectors / occupations

/ firms / locations, we will show that the estimated gaps are relatively small to start with.

Were the true gaps even smaller, this would if anything reinforce our main result that

labor misallocation does not contribute much in aggregate return gaps.

5Remember that potential experience is de facto a function of age, and we believe that older, and thusmore experienced, workers are less likely to change location or sector.


4. Estimated Returns to Experience

Returns for the World. We obtain for each of the 145 countries the average returns to

experience during the 1990-2016 period. We then calculate the mean return in the world

using the populations of each country in 2010 as weights (thus giving more weight to

larger countries, which is logical if we care more about individuals and the world as a

whole than countries). The overall returns to experience for the full sample is 2.2%.

In other words, for a same labor market, wages increase by 2.2% for each extra year of

experience.

The following is then observed in the data. Mean population-weighted returns

are higher for: (i) Services (2.6%) than for industry (2.0) and agriculture (1.3); (ii)

Cognitive/low automation occupations than for manual /high automation ones (2.6-

2.7 vs. 1.7-1.9);6 (iii) Formal jobs than for informal jobs (2.2 vs. 1.7);7 and (iv) Urban

areas than for rural areas (2.5 vs. 1.7). Note that the number of samples and countries

used varies across dimensions, as not all surveys/censuses contain variables that allow

us to identify the sector, the occupation, formality, or the location. The implied average

return for each dimension may thus be lower or higher than the aggregate return (2.2%)

for the world.

Developed vs. Developing Countries We define as “developed” countries classified as

high-income countries by the World Bank circa 2016. Note that developed countries

and developing countries have on average the per capita GDP as Belgium and Jordan.

The average labor shares and returns discussed below are thus not for the wealthiest

countries or the poorest countries but should be thought as representative an average

developed country and an average developing country.

By construction and for each dimension (e.g., three aggregate sectors), the aggregate

6Following Lagakos et al., 2018, we define cognitive occupations as managers, professionals,technicians, clerks, crafts and salespersons. Non-cognitive occupations are defined as machinists,elementary occupations, and skilled agricultural workers. Based on the automation score for 37 sub-occupations using the ISCO 8 2-digit classification (Arntz et al, 2017), we obtain the mean automationscore for our 10 occupations and define lowly automatable occupations as the ones below the meanautomation probability, i.e. managers, professionals, technicians and clerks. Highly automatableoccupations are the remaining occupations. Returns are similar if we use stricter definitions of bothcognitive and low automation occupations (reclassifying crafts/salespersons and clerks, respectively).

7Formal wage workers are those with a contract or health insurance or social security or unionmembership or those working in a firm of more than 10 employees. All other workers for which we haveinformation on any of the dimensions above are considered to be informal.


return should be equal to the weighted mean of the returns of all subdimensions

s (e.g., agriculture, industry and services) using as weights the labor shares of the

subdimensions:

expr =∑s

shares ∗ exprs (2)

Table 1 shows for each dimension, and each subdimension s of that dimension, the

mean population-weighted labor share (column (1)) and return (column (2)), for both

developed countries (high income countries based on the classification by income of

the World Bank in 2017) and developing countries (upper-middle, lower-middle and

low income countries), as well as the difference between the two. The table implies the

following stylized facts:

Aggregate sectors (Panel A of Table 1): While developed and developing countries have

almost the same shares of workers in industry (0.26 vs. 0.21), developed countries

have about 30 percentage points more workers in services than in developing countries,

where the share of workers in agriculture is symmetrically higher. Regarding the returns,

we find that: (i) For both developed and developing countries, returns are higher in

services than in industry and higher in industry than in agriculture. This can also be

seen in Figures 2(a)-fig:prof:g where the profile for services is systematically steeper

than the profiles for industry and agriculture. Interestingly, the gaps appear after only

5 years of experience and tend to become wider over time. Likewise, 3(a) shows that

the kernel distributions of agricultural returns and industrial returns are located more

to the left than the kernel distribution of services returns; (ii) The services-agriculture

and industry-agriculture gaps are larger in developing countries (+118% and +64%)

than in developed countries (+32% and +23%). This can also be seen in Figures 2(a)-

fig:prof:g where the gap is relatively larger for developing countries very early on (i.e.,

after just 5 years); (iii) Returns to experience are higher for developed economies than

developing economies for all three sectors; and (iv) The sector with the highest returns

in developing countries (services; 2.4) has lower returns than the sector with the lowest

returns in developed countries (agriculture; 3.1). Similarly, 3(b) shows that the countries

with low returns in agriculture also tend to have low returns in services (as well as in

industry; not shown but available upon request). Finally, results hold if we classify


employment into 10 sectors (ISIC Rev.2) instead of the 3 aggregate sectors (see Panel

A of Table 2). This shows that the results are not sensitive to the number of sectors we

partition the data into.

Occupational types (Panel B of Table 1): Developed economies have higher shares

of cognitive occupations (80%) than developing economies (47%). Regarding the

returns, we find that: (i) For both developed and developing countries, returns are

higher in cognitive occupations than in manual occupations; (ii) The cognitive-manual

gap is larger in developing countries (+64%) than in developed countries (+14%); (iii)

Returns to experience are higher for developed economies than developing economies

for all occupational types; and (iv) The occupational tyoe with the highest returns

in developing countries (cognitive; 2.3) has lower returns than the sector with the

lowest returns in developed countries (manual; 3.7). Finally, results hold if we

classify employment into 10 occupations (ISCO-08) instead of grouping them into two

subgroups (see Panel B of Table 2). This shows that the results are not sensitive to the

number of occupations we partition the data into. Likewise, results hold if instead of

classifying occupations based on their cognitiveness we classify them based their degree

of automatibility (see Panels C-D).

Formal status (Panel C of Table 1): Developed economies have higher formal shares

(70%) than developing economies (52%). Regarding the returns, we find that: (i) For

both developed and developing countries, returns are higher in the formal sector than in

the informal sector; (ii) The formal-informal gap is larger in developing countries (33%)

than in developed countries (23%); (iii) Returns to experience are higher for developed

economies than developing economies for all formal statuses; and (iv) The formal sector

in developing countries (2.0) has lower returns than the informal sector in developed

countries (3.1). Finally, results hold if we use an alternative definitions of formality

based on firm size only or self-employment whether we include or exclude employers

(see Panels F-H of Table 2).

Location (Panel D of Table 1): Developed economies have higher urban shares than

developing economies (72% vs. 44%). Regarding the returns, we find that: (i) For both

developed and developing countries, returns are higher in urban areas than in rural;

(ii) The urban-rural gap is larger in developing countries (+69%) than in developed

countries (+8%); (iii) Returns to experience are higher for developed economies than


developing economies for all locations; and (iv) The locations with the highest returns

in developing countries (urban; 2.2) has lower returns than the location with the lowest

returns in developed countries (rural; 3.8). These results also hold if we compare

locations within urban areas, i.e. the largest city and secondary cities (see Panel I of

2).

To summarize, we find that: (i) what we expected to be the “best” sectors /

occupations / firms / locations have relatively higher returns than other sectors /

occupations / firms / locations; (ii) these gaps are more apparent in developing

countries (+33-118%) than in developed countries (+8-32%), which suggests that there

is more labor misallocation in poorer countries; (iii) the “best” sectors / occupations

/ firms / locations of developing countries still have lower returns than the “worse”

sectors / occupations / firms / locations in developed countries. This suggests that even

if there is more labor misallocation in developing countries it may not contribute much

to explaining why aggregate returns are twice higher in developed countries. We now

more formally test that hypothesis.

5. Effects of Various Policies: Labor Reallocation vs.

Country-Wide Characteristics

With the previous results in place, and for each dimension (sector, occupation,

formal status, location), we can estimate the effects of policies that would reallocate

labor across subdimensions vs. other policies that would increase the returns of all

dimensions. Note that the effects discussed here do not include the general equilibrium

effects that such policies could have.

Methodology. Equation 2 shows that for each dimension (e.g., sectors), the aggregate

return should be equal to the weighted mean of the returns of all subdimensions

s (e.g., agriculture, industry and services) using as weights the labor shares of the

subdimensions. Developing countries have different shares and returns than developed

countries. In particular, developed countries have relatively more workers in non-

agriculture, cognitive and low-automation occupations, formal sectors, and urban

areas. In turn, non-agricultural sectors, cognitive and low-automation occupations,


formal sectors, and urban areas have relatively higher returns than agriculture, manual

and high-automation occupations, informal sectors, and rural areas, in both developed

and developing countries. In addition, the returns for the “best” subdimensions are

higher in developed countries than in developing countries. This suggests four possible

policies:

1. For each dimension, we reallocate all workers to the “best” subdimension in

developing countries, i.e. the subdimension with the highest return in developing

countries. For sectors, it is services. For occupational types, these are cognitive

occupations and low-automation occupations. For formality, these are formal

workers. For locations, these are urban areas. We call this policy All in “best”

Dimension in Developing Countries (see column (2.1) in Table 3). However, note

that this policy is not realistic. It is indeed impossible that all workers work in

services, cognitive occupations, the formal sector and urban areas.

2. For each dimension, we give developing countries the labor shares of developed

countries. Since developed countries have a higher share of their workers in the

best subdimension(s), and the best subdimension(s) in developed countries are

also the best subdimension(s) in developing countries, this policy is relatively

similar to the first policy, another policy that reallocates workers. We call this

policy Labor Shares of Developed Countries (see column (2.2) in Table 3).

3. For each dimension, we can give to all subdimensions in developing countries

the return of the “best” subdimension in developing countries, i.e. the highest

return across all subdimensions. This policy is equivalent to the first policy that

reallocates all workers to the best subdimension of developing countries, so we do

not treat it separately.

4. For each dimension and each subdimension, we can give to developing countries

the corresponding return in developed countries, since developed countries have

higher subdimension-specific returns than developing countries. This policy thus

does not reallocate workers, but make each subdimension in developing countries

as “good” as it is in developed countries. We call this policy Returns of Developed

Countries (see column (2.3) in Table 3).


Results. Table 3 shows for each dimension the effects of implementing the three

policies described above. First, column (1) shows for each dimension the estimated

aggregate returns when reconstructing them using the labor shares and the returns of

all subdimensions for that dimension. These estimated aggregate returns may differ

from each other, and also differ from the ones estimated directly for each group of

countries (4.1% for developed countries; 1.9% for developing countries). The reason

for that is that each subdimension-specific return is estimated using only the countries

and samples for which we have data on the subdimension. Therefore, the group

of countries used varies across subdimensions and dimensions.8 Nonetheless, the

reconstructed aggregate returns are very close to the estimated ones (3.6-4.0% and

1.7-1.8% respectively). No matter the dimension, the aggregate return gap between

developed and developing countries is about 2 percentage points. Thus, returns are

twice higher in developed than in developing countries.

Second, columns (2.1)-(2.3) show by how much this gap is reduced when

implementing each of the policies described above. Reallocating labor (columns (2.1)-

(2.2)) always has smaller effects than increasing returns to the levels seen in developed

countries ((2.3)). In particular, the effects are weaker for policy 2.2 than for policy 2.1.

However, policy 2.1 is not realistic since it reallocates all workers to one subdimension

only (e.g., 100% of workers in services instead of 42% as of now) whereas policy 2.2

gives developing countries the labor shares of developed countries (e.g., 70% of workers

in services). Comparing columns (2.2) and (2.3), one can see that there is not much

misallocation when studying returns to experience. Reallocating workers raises the

returns, sometimes by a significant share. But it is clearly not enough to bridge the gap

with developed countries. If anything, sorting could give an even smaller role to labor

reallocation.

In Table 3, we focus on three aggregate sectors, two times two occupational types,

two formal statuses, and two locations. In Table 4, we show these results hold if

we perform the same decomposition exercises for ten sectors, ten occupations, other

definitions of cognitiveness and automatibility, other definitions of formality, or focus

8In addition, the labor shares that we use are computed using all available observations from 2000-2016, even if the number of years of potential experience or education is missing. Indeed, we wanted thelabor shares to be representative of the world economy today rather than of the sample used to estimatethe returns (1990-2016).


on urban areas only and reallocate workers between primate and secondary cities.

The reason for the small effects of reallocation comes from the fact that returns are

much higher overall in developed countries than in developing countries, which has to

do with country-wide characteristics rather than subdimension-specific characteristics.

Indeed, for the 107 developing countries for which we were able to estimate the

aggregate return, the coefficient of correlation between the aggregate returns and the

subdimension-specific returns when available is always higher than 0.75, with the mean

coefficient of correlation equal to 0.86.9

6. Economic and Non-Economic Drivers of Aggregate

Returns

Given the results of the previous section, aggregate returns appear to be a sufficient

statistic to capture the return gap and we can ignore misallocation issues when

comparing countries. Therefore, we now use cross-sectional data for the 144 countries

for which we estimated the aggregate returns to investigate how they correlate with

economic and non-economic factors.

Aggregate Returns and Income. The correlation between aggregate returns and log

per capita GDP (PPP; constant 2011 USD; averaged over the period 2000-2016 to

avoid fluctuations due to changes in commodity prices for commodity exporters) for

144 countries is presented in figure 1. The returns to experience are conditional

on log life expectancy (averaged over the period 2000-2016), the HIV prevalence rate

(averaged over the period 2000-2016) and several controls for whether the country

had a communist regime, for how many years, and the year the country liberalized its

economy. The reason why we control for life expectancy and HIV is that adult mortality

is high in poorer countries. If there is positive selective mortality, in other words if

individuals with better abilities are more likely to survive until later ages (and thus

9For agriculture (N = 112), industry (134) and services (136), these correlations are 0.71, 0.83 and 0.92respectively. For cognitive (129) and non-cognitive (128) occupations, these are 0.92 and 0.85 respectively.For formal (116) and informal (112) workers, these are 0.87 and 0.78 respectively. For urban (136) and rural(132) areas, these are 0.95 and 0.91 respectively. The mean of these is 0.86.


reach more years of experience), and such individuals also get higher wages thanks

to their abilities, estimated returns will be over-estimated for poorer countries. We

then add the communism controls because former communist countries changed their

economic system when they transitioned to a market economy, thus making obsolete

skills accumulated during the pre-transition period by older and thus more experienced

individuals.

Figure ?? indicates a strong, positive relationship between income and the returns

to experience (the effect of log income per capita on the aggregate return is 0.72∗∗∗

vs. 0.41∗∗∗ when not controlling for life expectancy, HIV and communism). This is

consistent with Lagakos (2018) who find steeper wage-experience profiles for developed

than developing economies. There are two potential explanations the human capital

theory explanation and the search and match explanations. The human capital

theory suggests that richer economies have higher returns to experience as workers

accumulate more human capital over the life cycle than workers in poorer countries.

The search and match theory would suggest that there are larger labor market frictions

in poorer countries than rich countries. Lower market fluidity in developing economies

prevents workers from moving up the ladder to better jobs that fits their skills profile.

Improving the stock of human capital and reducing labor market frictions may be

important for developing economies to adapt workers to future jobs. With increasing

automation, the divergence between rich and poor economies may rise as the gap in

returns to experience widens.

Other Relationships. When studying the relationship between the returns and income,

the R-squared is 0.33 (0.17 if not controlling for life expectancy, HIV and communism),

thus much lower than 1. In other words, even when comparing two countries with the

same income, there are still significant differences in the returns. We thus investigate

if there is any relationship between the returns and various measures of economic and

political institutions and culture, conditional on income and the other core controls.

We are hoping that these measures proxy for human capital accumulation at work and

labor market frictions. Note that the observed relationships are not necessarily causal,

and we do not have an instrument for each of the measures. Figure 1 summarizes the

main results for selected variables, but we investigate the effects for many more.

Political and Economic Institutions. We first regress aggregate returns on measures


of governance from the World Governance Indicators of the World Bank. We find strong

conditional correlations between the returns and the following measures: control of

corruption (see Row 1 and Column 1 of 1), rule of law (not shown), political stability

and absence of violence/terrorism (ditto), voice and accountability (not shown),

government effectiveness (ditto) and regulatory quality (ditto). When using all six

measures altogether, only control of corruption remains significant (not shown). We

thus keep this measure in the remainder of the analysis. 10

Second, we investigate the relationship between the aggregate returns and measures

of economic freedom from the Doing Business Indicators of the World Bank. Row 2 of

Column 1 shows a strong negative effect of the ease of doing business rank, meaning

that countries with simpler regulations for businesses and stronger protections of

property rights have higher returns.11

Third, while corruption and ease of doing business capture more economic

institutions, we may wonder if democratic countries have higher returns. In Row 3

of Column 1, we find a positive but weak correlation between aggregate returns and

the Democracy Index of The Economist (only significant at 15%). Other measures give

either stronger or weaker, but always positive, effects.12

Labor market frictions. Fourth, we use internet penetration from the World

Development Indicators of the World Bank as a measure of technology use and labor

market frictions. We find a strong correlation as can be observed in Row 4 of Column 1.

No significant effect is found for cellphone penetration (not shown).

Fifth, we obtain measures of “values” from the World Values Surveys (WVS) available

for 70 countries in our sample. In particular, Row 5 of Column 1 shows a strong

correlation between the Emancipative Values Index (EVI) and the returns. According to

10Note that the result on corruption holds if we use instead the corruption perception index ofTransparency International (not shown). We also find that the share of natural resource exports (fuelexports and ores and metals exports) in total exports or GDP (source: World Development Indicators) isassociated with lower returns (not shown). Although it is not a measure of corruption per se, we know thatresource-rich countries are affected by rent seeking. We then find a negative effect of the urban primacyrate (source: World Development Indicators), the share of the largest city in the urban population, whichis also a measure of rent-seeking according to the literature.

11When using specific doing business indicators, we find that returns are higher in countries where it iseasy to trade across borders, start a business, resolve insolvency, register property, pay taxes and enforcecontracts (not shown).

12In particular, results are stronger if we use the Human Freedom Index of the CATO Institute or theWorld Press Freedom Index of Reporters Without Borders (not shown). Results are then weaker if we usethe Combined Polity Score of Polity IV (ditto).


Welzel (2014), “emancipative values appreciate a life free from external domination, for

which reason these values emphasize equal freedoms for everyone. Thus, emancipative

values involve a double emphasis on freedom of choice and equality of opportunities.”

Interestingly, we find no effect for the Secular Values Index of the WVS. Welzel writes

that “secular values dissociate people from external sources of quasi-sacred authority,

like religion, the nation, the state and group norms”. In other words, more economic

values matter, more social values do not.13

Sixth, we push this argument further and investigate the correlation between

measures of discrimination against economic and non-economic minorities. Row 6

of Column 1 shows a negative correlation between the returns and a measure of legal

disparities between men and women for getting a job (source: World Development

Indicators). Row 7 of Column 1 then shows a negative correlation between the returns

and a measure of racism (source: World Values Surveys), in particular the share of survey

respondents that say they would dislike having a neighbor from a different race.14

Seventh, there are countries where entrepreneurship may be frowned upon by a

large share of the population and where many people think the government should

play an important role in the economy, thus constraining markets, and labor markets

in particular. From the World Values Survey, we know the share of survey respondents

that favors state ownership of businesses. Row 8 of Figure 1 shows that it negatively

affects the returns.

In Columns 2-4 of Table 5, we include subsets of these eight main variables instead

of including them one by one. In Column 2, we simultaneously include the measures

of economic and political institutions and infrastructure. The effects of corruption

and internet penetration are weakened but survive. The effect of the Ease of Doing

Business index disappears but it is because it is very highly correlated with the control

13We obtain similar results if we focus on components of these indexes. We find a positivecorrelation for measures of “equality” (between men and women), “choice of life” and “autonomy” (notshown). We also find stronger returns for cultural areas that more self-expression values (European andneo-European countries in the sample, with the strongest effects observed for “Protestant-EuropeanCountries” according to the WVS classification).

14We find similarly negative effects for the shares of survey respondents that would dislike havingneighbors from a different religion or neighbors that are immigrants/foreign workers (not shown). Wefind negative effects for people who have more distrust in “people” (not shown). We also find negativeeffects of higher levels of inequality, when measured by the Gini index or the top decile share (notshown). Societies that are more unequal may be less mobile, and thus reinforce discriminations againstminorities.


Figure 1

of corruption index. These two measures capture economic institutions more, whereas

the democracy index captures political institutions, which do not seem to matter as

much. Interestingly, the R-squared is 0.56, vs. 0.34 if we focus on the same 127 countries

but only include income and the other core controls (life expectancy, HIV, communism).

Adding four controls for institutional quality and infrastructure is enough to increase

the R-squared by 0.22, which suggests that such country characteristics may really

matter for labor markets.

In Column 3, we simultaneously include all the values-related measures. The effects

of weakened but remain significant for all of them. The R-squared is 0.70, vs 0.47 if we

focus on the same 69 countries but only include income and the other core controls. In

Column 4, we only keep the significant variables of Columns 2 and 3. Some effects are

only significant at 15% now, and other effects become insignificant, one reason being


the high correlation across these measures. The R-squared is 0.75, vs. 0.47 if we focus

on the same 69 countries but only include income and the other core controls. Overall,

measures of institutional quality, infrastructure and values seem to have a strong effect

on the returns to experience, and thus human capital accumulation at work and the

efficiency of labor markets. With just a few variables, we are able to increase the R-

squared to 0.75.


7. Concluding Discussion

In this short paper, we have used data from 23 million individuals in more than

1,000 household surveys and census samples for 144 countries to show that there

are significant differences in the returns to experience across development levels and

dimensions (sectors, occupations, formal statuses and locations). While reallocating

labor subdimensions increase aggregate returns in developing countries, it does little to

bridge the gap in the estimated aggregate returns between developed and developed

countries. Based on returns alone, labor misallocation plays a minor role in cross-

country differences. Our analysis thus complements other studies that focus on

productivity or wage differentials, usually for one dimension at a time. We then find that

the returns are correlated with the level of development, with wealthier countries have

higher aggregate returns. Conditional on income, returns are also higher in countries

with better economic institutions as well as infrastructure and values that may reduce

labor market frictions. Finally, one important caveat with our analysis is that our

estimated returns and effects are not causal.


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Figure 2: Wage-Experience Profiles for Developed Countries vs. Developing Countries

(a) Profiles for Developed Countries (b) Profiles for Developing Countries

Notes: The figure shows for agriculture (“agri’), industry (“indu’), services (“serv’) and the whole economy(“agg.”) of both developed countries (Subfigures 2(a)) and developing countries (Subfigures 2(b)) themean population-weighted coefficients of the seven experience bins (“5” for 5-9 years, ..., “30” for 30-34years, “35” for 35-49 years). The “0” experience bin (0-4 years) is the omitted group.

Figure 3: Wage-Experience Profiles for Developed Countries vs. Developing Countries

(a) Profiles for Developed Countries (b) Profiles for Developing Countries

Notes: Subfigure 3(a) shows the kernel distributions of the returns to experience for agriculture (“agri’),industry (“indu’), services (“serv’) and the whole economy (“agg.”) for 111 countries for which we havean estimated return for all sectors. There are 145 - 111 = 34 countries for which we do not have enoughobservations in at least one sector. Subfigure 3(b) shows for the same 111 countries the relationshipbetween the returns for services and the returns for agriculture (correlation of 0.54).


Table 1: LABOR SHARES AND RETURNS TO EXPERIENCE ACROSS DIMENSIONS

Dimension Subdimension (1) Labor Shares (2) Returns to Experience

Developed Developing Diff. Developed Developing Diff.

A. Agg. Sectors a. Agriculture 0.05 0.38 -0.33 3.1 1.1 2.0b. Industry 0.26 0.21 0.05 3.8 1.8 2.0c. Services 0.70 0.41 0.29 4.1 2.4 1.7

B. Agg. Occup. a. Manual 0.20 0.53 -0.33 3.7 1.4 2.3b. Cognitive 0.80 0.47 0.33 4.2 2.3 1.9

C. Formal Status a. Informal 0.30 0.48 -0.18 3.1 1.5 1.6b. Formal 0.70 0.52 0.18 3.8 2.0 1.8

D. Location a. Rural 0.28 0.56 -0.28 3.8 1.3 2.5b. Urban 0.72 0.44 0.28 4.1 2.2 1.9

Notes: This table shows the labor shares and the estimated returns to experience for each group of countries and each dimension.Developed countries are countries classified as high-income countries by the World Bank circa 2016.


Table 2: LABOR SHARES AND RETURNS TO EXPERIENCE, ROBUSTNESS

Dimension Subdimension (1) Labor Shares (2) Returns to Exp.

Dev’d Dev’g Diff. Dev’d Dev’g Diff.

a. Agriculture 0.05 0.38 -0.33 3.1 1.1 2.0b. Mining 0.00 0.01 -0.01 4.0 1.8 2.2c. Manufacturing 0.17 0.12 0.05 3.9 2.0 1.9d. Utilities 0.01 0.02 -0.01 4.0 1.8 2.2

A. 10 Sectors e. Construction 0.07 0.06 0.01 3.6 1.6 2.0f. Commerce 0.19 0.15 0.04 4.2 2.1 2.1g. Transp. & Comm. 0.06 0.05 0.01 4.1 2.3 1.8h. Fin., Real Est. & Bus. 0.13 0.03 0.10 4.0 2.4 1.6i. Admin. 0.18 0.10 0.08 3.5 2.4 1.1j. Other Serv. 0.13 0.08 0.05 3.6 2.4 1.2

B. Agg. Occup. a. Manual 0.47 0.79 -0.32 3.9 1.6 2.3(Strict) b. Cognitive 0.53 0.21 0.32 4.1 2.7 1.4

C. Automation a. High Automation (Strict) 0.61 0.85 -0.24 3.9 1.7 2.2(Strict) b. Low Automation (Strict) 0.39 0.15 0.24 4.0 2.7 1.3

D. Automation a. High Automation (Broad) 0.46 0.70 -0.24 3.8 1.6 2.2(Broad) b. Low Automation (Broad) 0.54 0.30 0.24 4.3 2.5 1.8

a. Skilled Agriculture 0.03 0.28 -0.25 3.7 1.1 2.6b. Elementary Occ. 0.08 0.15 -0.07 3.5 1.1 2.4c. Machinists 0.08 0.07 0.01 3.7 1.9 1.8d. Sales 0.15 0.15 0.00 3.9 2.1 1.8

E. 10 Occupations e. Crafts 0.12 0.10 0.02 3.7 1.7 2.0f. Clerks 0.14 0.06 0.08 3.8 2.3 1.5g. Army & Others 0.01 0.04 -0.03 3.7 0.4 3.3h. Technicians 0.14 0.05 0.09 3.9 2.5 1.4i. Professionals 0.16 0.06 0.10 3.9 2.7 1.2j. Managers 0.10 0.04 0.06 4.0 2.5 1.5

F. Firm Size a. Firm Size Below 10 0.31 0.54 -0.23 3.1 1.4 1.7b. Firm Size Above 10 (Incl.) 0.69 0.46 0.23 3.8 1.9 1.9

G. Empl. Status a. Self-Employed (Broad) 0.13 0.39 -0.26 3.3 1.9 1.4(Broad) b. Employed (Broad) 0.87 0.60 0.27 4.1 2.0 2.1

H. Empl. Status a. Self-Employed (Strict) 0.11 0.38 -0.27 3.4 1.9 1.5(Strict) b. Employed (Strict) 0.89 0.62 0.27 4.1 2.0 2.1

I. Largest City a. Secondary Cities 0.89 0.83 0.06 4.1 2.2 1.9b. Largest City 0.11 0.17 -0.06 4.4 2.4 2.0

Notes: This table shows the labor shares and the estimated returns to experience for each group of countries and each dimension.Developed countries are countries classified as high-income countries by the World Bank circa 2016.


Table 3: EFFECTS OF THREE POLICIES

Dimension (1) Estimated Agg. Return (2) Reduction (%) in Agg. Return Gap

Dev’d Dev’g Diff. 1. All in Bestof Developing

2. Labor Sharesof Developed

3. Returns ofDeveloped

A. Agg. Sectors 4.00 1.80 2.20 27.3 18.2 86.4B. Agg. Occup. 4.10 1.80 2.30 21.7 13.0 91.3C. Formal Status 3.60 1.80 1.80 11.1 0.0 94.4D. Location 4.00 1.70 2.30 21.7 8.7 95.7

Notes: This table shows the effects of three policies (see text for a description of each policy).

Table 4: EFFECTS OF THREE POLICIES, ROBUSTNESS

Dimension (1) Estimated Agg. Return (2) Reduction (%) in Agg. Return Gap

Dev’d Dev’g Diff. 1. All in Bestof Developing

2. Labor Sharesof Developed

3. Returns ofDeveloped

A. 10 Sectors 3.8 1.7 2.1 33.3 19.0 90.5B. Agg. Occup. (Strict) 4.0 1.8 2.2 40.9 18.2 95.5C. Automation (Strict) 3.9 1.9 2.0 40.0 10.0 100.0D. Automation (Broad) 4.1 1.9 2.2 27.3 9.1 90.9E. 10 Occupations 3.9 1.6 2.3 47.8 21.7 91.3F. Firm Size 3.6 1.6 2.0 15.0 5.0 90.0G. Empl. Status (Broad) 4.0 1.9 2.1 4.8 4.8 85.7H. Empl. Status (Strict) 4.0 2.0 2.0 0.0 0.0 90.0I. Largest City 4.1 2.2 1.9 10.5 0.0 105.3

Notes: This table shows the effects of three policies (see text for a description of each policy).


Table 5: INSTITUTIONS, VALUES, AND RETURNS TO EXPERIENCE

Dependent Variable: Estimated Aggregate Return to Experience

(1) (2) (3) (4)

Control of Corruption Index, avg. 2000-2016 (N = 144) 0.83*** 0.61*** 0.37*

(0.11) (0.21) (0.22)

Ease of Doing Business Score, avg. 2010-2018 (N = 144) 0.03*** -0.02

(0.01) (0.01)

Democracy Index 2015 (N = 127) 0.14γ -0.08

(0.09) (0.09)

Internet Penetration (%), avg. 2000-2016 (N = 143) 0.05*** 0.03*** 0.02

(0.01) (0.01) (0.02)

Emancipative Values Index, avg. 2000-2014 (N = 72) 7.63*** 4.16** 0.20

(1.32) (1.87) (2.07)

Legal Gender disparities: Getting a Job, 2016 (N = 137) -0.10*** -0.06* -0.05γ

(0.02) (0.03) (0.03)

Dislike Neighbors of a Different Race, avg. 2000-2014 (N = 72) -4.37*** -2.34** -2.82***

(1.17) (1.15) (1.00)

Favors State Ownership of Business, avg. 2000-2014 (N = 70) -0.52*** -0.27* -0.11

(0.17) (0.14) (0.15)

Observations 70-144 127 69 69

R-squared 0.35-0.64 0.56 0.70 0.75

Income Control and Other Core Controls Yes Yes Yes Yes

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.10, γ <0.15.

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