metadata: authors: 0: author: Choi,Yunji
1: author: Gronert Alvarez,Mario Andres
2: author: Honorati,Maddalena
3: author: Merfeld,Joshua David
4: author: Newhouse,David Locke
count: World
docna: 0: docna: Does Climbing the Jobs Ladder Promote
            Poverty Reduction ?
docty: Policy Research Working Paper
theme: Social protection delivery systems,Job Creation,Human Development and Gender,Social Protection,Social Development and Protection,Private Sector Development,Labor Market Policy and Programs,Active Labor Market Programs,Jobs
abstracts: cdata!: This paper explores trends in and the
            potential determinants of the types of jobs held by workers,
            and their relationship with poverty reduction, in an
            unbalanced panel of 89 countries over the past 30 years.
            Jobs are classified into five categories according to
            formality, occupation or level of skills required, and wage
            work versus self-employment. Net shifts into "upper
            tier" or skilled informal wage jobs, defined as
            professionals, managers, technicians, or clerks, from
            "lower tier" or lower skilled informal jobs were
            strongly associated with poverty reduction at the $1.90 and
            $3.20 lines. In contrast, net shifts into formal wage jobs
            from lower tier informal jobs were associated with modest
            poverty reductions at the $5.50 poverty line. The share of
            workers in informal upper tier jobs represents less than 2
            percent of the workforce and has increased little over the
            past 30 years in low- and middle-income countries. The
            findings show that increases in upper tier informal wage
            jobs are associated with shifts of the workforce from
            microenterprises to small firms in lower- and
            upper-middle-income countries, but they are not discernibly
            associated with higher educational attainment or
            urbanization. In contrast, increases in the share of formal
            wage jobs are strongly associated with increases in the
            share of workers with post-secondary education, driven by
            high-income countries. The results suggest that upper tier
            informal wage jobs and the skills they require play a
            potentially important role in poverty reduction but are not
            automatically generated by increased educational attainment,
            urbanization, or firm size.
display_title: Does Climbing the Jobs Ladder
            Promote Poverty Reduction ?
listing_relative_url: /research/2024/07/34366248/climbing-jobs-ladder-promote-poverty-reduction
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Jobs and Poverty m1327656 701,Informal Employment m1503994 2692
publishtoextweb_dt: 2024-07-18T00:00:00Z
dois: 10.1596/1813-9450-10856
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 55
author_keys: 0: author_key: 2104983
1: author_key: 1612687
2: author_key: 793445
3: author_key: 1762622
4: author_key: 450079
model_extractions: Policy Research Working Paper 10856 Does Climbing the Jobs Ladder Promote Poverty Reduction?

_Yunji Choi Mario Gronert Maddalena Honorati Joshua D. Merfeld David Newhouse_ Social Protection and Labor Global Practice July 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10856

# **Abstract**

This paper explores trends in and the potential determinants of the types of jobs held by workers, and their relationship with poverty reduction, in an unbalanced panel of 89 countries over the past 30 years. Jobs are classified into five categories according to formality, occupation or level of skills required, and wage work versus self-employment. Net shifts into “upper tier” or skilled informal wage jobs, defined as professionals, managers, technicians, or clerks, from “lower tier” or lower skilled informal jobs were strongly associated with poverty reduction at the $1.90 and $3.20 lines. In contrast, net shifts into formal wage jobs from lower tier informal jobs were associated with modest poverty reductions at the $5.50 poverty line. The share of workers in informal upper tier jobs represents less than 2 percent of the workforce and has increased little over the past 30 years in low- and middle-income countries. The findings show that increases in upper tier informal wage jobs are associated with shifts of the workforce from microenterprises to small firms in lower- and upper-middle-income countries, but they are not discernibly associated with higher educational attainment or urbanization. In contrast, increases in the share of formal wage jobs are strongly associated with increases in the share of workers with post-secondary education, driven by high-income countries. The results suggest that upper tier informal wage jobs and the skills they require play a potentially important role in poverty reduction but are not automatically generated by increased educational attainment, urbanization, or firm size.

This paper is a product of the Social Protection and Labor Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mhonorati@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Does Climbing the Jobs Ladder Promote Poverty Reduction?<sup>1</sup>

Yunji Choi<sup>*</sup> Mario Maddalena Joshua D. David Newhouse<sup>+</sup> Gronert<sup>†</sup> Honorati<sup>†</sup> Merfeld<sup>‡</sup>

## **Keywords:** jobs, growth, employment, poverty, education, skills, informality. **JEL codes:** I31, J21, J46, L16, O10, O50.

> 1 The paper is a background paper to _A Jobs Lens to Development, Navigating Old and New Employment Challenges_ analytical work program developed by the Jobs Group at the Social Protection and Jobs Global Practice. The program was funded by the Jobs Multi-Donor Trust Fund (MDTF) and managed by the Jobs Group. This work does not necessarily reflect the views of WBG and the MDTF donors. The authors would like to thank Wendy Cunningham and Nistha Sinha for helpful peer review comments; Dino Merotto, Federica Saliola, Markus Poschke and Andreas Eberhard for useful suggestions and comments, and Luis Diego Rojas Alvarado for the initial data analysis.

- Hong Kong University of Science and Technology

- World Bank

- World Bank (mhonora�@worldbank.org)

- KDI School and IZA

- + World Bank and IZA
# Introduction

Ever since Lewis’s (1954) seminal paper, economists have remained interested in the process of labor reallocation throughout the development process. Although Lewis was careful not to put it in such terms, it is clear that his paper is describing the movement of labor from the agriculture sector to the non-agriculture sector, a process now commonly referred to as structural transformation. Indeed, many papers have followed this line of reasoning, clearly documenting the differences in productivity and wages between the agriculture and nonagriculture sectors (Gollin et al., 2014; McCullough, 2017). However, the reallocation process itself is much more complex, as more productive firms exist in both sectors, as do less remunerative forms of employment. A case in point for the latter is the large number of workers in low-paid non-agricultural self-employment, especially among younger adults in African cities (Fox et al., 2016, Cunningham et al 2024) and in the service sector (Rodrik, 2016, Nayyar et al. 2021). A review of existing evidence on the sectoral wage gaps and labor market dynamics points to the presence of frictions that hinder worker reallocations to growing and more productive sectors (Donovan and Schoolman 2023).

Along with ideas about the evolution of labor across sectors, there was also a prediction that labor would move from the informal sector to the formal sector as countries developed, under the assumption that formal employment is more productive than its informal counterparts (John, 1970; Rauch, 1991). Yet, this prediction has largely failed to materialize in much of the developing world, even in countries with high growth rates, like India (Kanbur, 2017). While debates continue about the long-term prospects of the informal sector (see, for example, de Soto (2000) and Levy (2010) for competing views), a key fact has emerged: the informal sector remains 2
<!-- Start of picture text --> Existing studies document substantial<br>characteristics as employers and over 60 percent fall in the middle or high tercile of per capita<br>household consumption, suggesting potential to progress up the jobs ladder. Similar<br>extent to which the informal sector is characterized by exit versus exclusion is still debated, with<br><!-- End of picture text -->
<!-- Start of picture text --> Besides the prevalence of voluntary exit in the informal sector, other important questions<br>a stepping-stone for entrepreneurs with high-growth potential but who might be constrained by,<br>say, credit constraints? From the workers' perspective, especially young and female workers, do<br>transitions into formal employment very unlikely? For informal workers that choose to be<br>reduction?<br>The existing literature examining workers’ mobility within and across informal and formal<br>rotating panel of labor force surveys for about 50 countries, Donovan et al. (2023) find that<br>transitions in and out of employment and across job types are more common in developing<br><!-- End of picture text -->
<!-- Start of picture text --> consequences of informality would benefit from systematic analysis of linked data on informal<br>workers and production units, which is rarely available (Elgin et al, 2021).<br><!-- End of picture text -->
household surveys collect information on business registration for tax purposes among the self-employed. See Table 1 for definitions of each category.
Conditional on surveys (309) collecting information on earnings for both wage workers and self-employed. It is important to note that depending on survey design and implementation, some profits may have been reported by
that formal firms are hiring informally, which would be consistent with previous research (La Porta & Shleifer, 2014). Second, net shifts from lower tier self-employment to unemployment are associated with a smaller increase in poverty at the $1.90 and $3.20 rate, although only the latter is statistically significant. This suggests that increases in unemployment may slow poverty reduction. Finally, increased formal wage work is associated with a modest decline in poverty at the $5.50 line, while informal wage employment is not, suggesting that the benefits of increased formal wage work are not enjoyed by the poorest workers. Overall, these results suggest that net shifts from lower-tier self-employment to informal upper tier wage work are strongly associated with poverty reduction.

Third, we look at what country characteristics (in terms of factor endowments and employment structure by economic sector, geographical area and firm size) are associated with labor reallocations to pro-poor informal upper tier jobs and formal wage jobs. We find that improvements in higher education are strongly associated with shifts out of informal lower tier jobs to formal wage jobs, while improvements in basic education levels are not statistically significant when controlling for country fixed effects and GDP per capita. We find evidence that firm size is important for improvements up the job ladder, but not necessarily as important as education and as in the same ways as in previous research. Previous research has found that smaller firms are important drivers of job creation (Ayyagari et al., 2014). In contrast, we find that firm size is not discernibly correlated with an increased share of workers in formal wage employment, conditional on GDP growth. However, increases in the share of workers employed in small firms (with 6 to 20 employees) as opposed to micro-enterprises (0-5 employees) are significantly correlated with both increases in upper-tier informal wage employment and 8
informal lower-tier jobs. An increase in larger firms, defined as more than 20 employees, is also associated with a shift out of lower-tier self-employment into formal wage work, though the latter is only marginally statistically significant. Increases in large firms are less strongly associated with changes in job type. Increases in employment in any of these firms is associated with increased labor-force participation, though it is only significant for the firms with between 21 and 50 employees, while the coefficient for the smallest category is half the size of the larger categories. The results lend some support to the notion that firm size and wages may not be as strongly correlated in services-based economies as in manufacturing-based economies (Berlingieri et al., 2018), which is particularly relevant in light of the literature on “premature deindustrialization” (Rodrik, 2016). In addition, we do not have information on firm age, which is an important mediator (Lagakos et al., 2018).

Finally, the results do not show strong and significant correlations between urbanization and movement up the job ladder to formal and informal upper tier jobs, while labor reallocations from agriculture to manufacturing are associated with increases in formal wage employment. Similarly, we do not find significant correlations between improvements in the jobs ladder and openness to trade, and capital deepening measures. There is also no clear relationship between educational attainment, urbanization, or firm size and net shifts into the informal upper-tier wage jobs that are most conducive to poverty reduction. Overall, these results fail to reveal a clear pattern between the country characteristics considered and increases in the formal and informal upper tier jobs. This suggests that further research could be useful to better understand the country-level conditions that promote growth in the types of jobs most conducive to poverty reduction.

9
tatus such that it does not change for any given country in our entire sample, based on the income classification of
# 2.2 Descriptive statistics

Our main goal is to analyze changes in the types of jobs held by workers across different countries over the last three decades. We classify employees based on their job type and formality status, adapting the definitions used by Fields et al. (2023). Table 1 lists the five different “rungs” on the jobs ladder, which ranges from lower-tier informal wage employees (lowest) to upper-tier self-employed (highest) based on median hourly wage and earnings as criteria to rank the five job categories.

Table 1: Definitions of Rungs on Jobs Ladder

|1. Upper-tier self-employed|Self-employed professionals, managers, technicians, or<br>clerks OR employers with at least one employee.|
|---|---|
|2. Formal wage employees|Wage employees with social security, health insurance, or a<br>contract.|
|3. Upper-tier informal wage employees|Non-formal wage employees who are professionals,<br>managers, clerks, or technical employees.|
|4. Lower-tier informal wage employees|Non-formal wage employees who are neither professional,<br>managers, clerks, or technical employees and unpaid<br>workers.|
|5. Lower-tier informal self-employed|Self-employed who have no paid employees, and are not<br>professional, managers, clerks, or technical workers.|

To provide supporting evidence regarding the ordering of rungs, Table A3 in the appendix shows the median wage and earnings in our sample, by the rung of the ladder on which an employee is placed. Upper tier self-employed have the highest earnings, higher than formal wage workers, therefore the ordering of job status in terms of earnings has upper tier self-employed at the top. It has to be noted that when other monetary benefits (health insurance and other social security benefits) and non-monetary benefits (job security and having a written contract for example) are considered, formal wage work may be considered at the top of a jobs ladder. Self-employment rungs would be preferred and ranked at the top of the ladder when the 11
independence and flexibility of work arrangements are used as the main ranking criteria. What is clear is that lower-tier informal wage employees and self-employed receive the lowest median remuneration, validating the tier classification.

Over all countries, one in three people of working age is engaged in lower tier informal jobs, compared to 20 percent of people in formal wage jobs, 6 percent in upper tier informal jobs, 5 percent in unemployment and 38 percent who are inactive. Lower-tier informal wage jobs and self-employment yield the lowest earnings and account for most jobs in lower income countries (accounting for 48 percent of the working age population in LICs as opposed to 31 percent in LMICs, 21 percent in UMICs and 13 percent in HICs). Table A4, also in the appendix, presents a breakdown of the proportion of adults (15+) in each category, by income status and by region. There is a clear increase in the proportion of workers in higher forms of employment as country income increases; 37.3 percent of adults in high-income countries are classified as formal wage employees, while just 10.2 percent of adults in low-income countries are classified as such.

# 2.3 Methods

We collapse all labor data to the country-survey year level, using the provided survey weights. We reweight the data in two ways. Our preferred specification gives each country equal weight and reweights the data to correct for heteroskedasticity driven by the number of observations in the sample surveys. We calculate the average number of survey observations for each country, Ih we denote as . Putting these together, our final weight for each country-year cc NN<sup>�</sup> is ~~,~~ where is the number of survey waves for country . Giving each country equal weight NN<sup>�</sup> cc NNcc NNcc cc avoids skewing the results towards countries with more frequent surveys. Meanwhile, correcting 12
for heteroskedasticity gives greater weight to countries with larger surveys, since the labor outcomes are estimated more precisely in these countries. This forms the data for the descriptive changes of the job ladder indicators across years. However, in the appendix we also report key results without the heteroskedasticity correction, to verify that they are robust (Table A17-A19) without heteroscedasticity.

We also estimate linear regressions of different forms. The simplest form is used to estimate trends in the share of workers in different rungs: (1) R R cc,tt<sup>= αα</sup> cc<sup>+ β</sup> cctt<sup>,</sup> where R R is the proportion of adults (15+) on a given rung of the ladder in country<sup>(tt) + εε</sup> in (cc, tt) cc year , is country fixed effects, and , so that the coefficient represents the 10 tt cc tt αα ββ(tt) = ββ average change in the share of workers working in a particular rung over ten years.

To assess whether changes in the jobs ladder are correlated with changes in poverty. We estimate regressions of the form: (2) P P + ∑ R R 1 2 3P , rr=7 ccttcc cc tt rr=2 rr rrcctt cc,tt cc,tt cc,tt cctt where P P PPttyy is headcount poverty in country = αα + δδ γγ + ββ at time XX + ββ , measured at poverty line l. We GGββPP + ββ PP + εε ccttcc conside three separate poverty lines, set at $1.90, $3.20, and $5.50 in PPP 2011 terms. These PPttyy cc tt correspond to the extreme poverty line, and the poverty line for lower-middle-income countrie and upper- middle-income countries. R R rrcctt<sup>is the proportion of adults on rungPP, with the</sup> proportion of people in lower-tier self-employment being the omitted category. We also include the unemployed and those out of the labor force, leading to seven total categories. In all regressions, we cluster standard errors at the country level.

13
Finally, to better understand the determinants of net changes in the share of workers in each rung, we estimate the following equation: (3) R R 1<sup>XX</sup> 2<sup>GGββPP</sup> 3<sup>P</sup> cc,tt<sup>= αα</sup> cc<sup>+ δδ</sup> tt<sup>+ ββ</sup> cc,tt<sup>+ ββ</sup> cc,tt<sup>+ ββ</sup> cc,tt<sup>+ εε</sup> cctt<sup>,</sup> where R R is the proportion of adults (15+) on a given rung of the ladder in country<sup>PP</sup> in year , is country fixed effects, (cc, tt) is year fixed effects, is log per capita GDP in country cc cc tt cc,tt c and year t, tt αα P is log population in country c and year t and δδ GGββPP is an error term. cc,tt cctt includes variables of interest. In the simplest specifications, PP includes just the numeric year, εε XX(cc, tt) indicating a linear time trend, and we exclude the year fixed effects. Other specifications include XX(cc, tt) different types of variables of interest. For example, in one specification, includes the proportion of people with a secondary degree and the proportion of people with more than a XX(cc, tt) secondary degree in country in year , as well as GDP per capita and total population, both logged. cc tt

# 3. Results

In this section, we report the main results. We attempt to answer three separate questions, based on the three equations listed in the previous section: (1) What is the evolution of job ladder categories over time? (2) How are changes in the jobs ladder associated with growth and poverty reduction? And (3) What characteristics are associated with progression up the ladder and with poverty reducing jobs? We go through these three questions in turn in the following subsections.

14
# 3.1 What is the evolution of job ladder categories over time?

We begin with a simple graphical representation of changes in different job categories, across the entire sample, in Figure 1. We take 2000-2004 as the base period and estimate regressions with country fixed effects. The figure plots the resulting coefficients for different time periods, relative to 2000-2004. Several patterns jump out. First, there is a clear increase in formal wage employment; the proportion of workers in formal wage employment increased by around eight percentage points from 2000-2004 to 2015 or later. Since on average only 20 percent of working age people is classified as formal wage employees, eight percentage points is a relatively large increase. The second clear pattern is a decrease in both inactivity (i.e. more people in the labor force) and unemployment. Other categories show no clear patterns, with some years seeing increases and others decreases, though by 2015, both lower-tier self-employment and lower-tier informal wage employment see decreases of around 1.5-to-2 percentage points.

Figure 1: Changes in job categories over �me Notes. The figure shows coefficients from a regression with country fixed effects. 2000-2004 is the base time period, meaning changes are measured relative to 2000-2004. "Non-LFP" refers to the share of people aged 15 and above who are inactive, i.e. out of the labor force. Unemployed refers to the share of people aged 15 and above who are unemployed. The five job rungs refer to the shares of workers aged 15 and above in each job rung.

15
<!-- Start of picture text --> lo}+ HIC xtSo UMIC<br>a 2 § 2<br>fo} fo}<br>fo} So<br>xx<br>2A 2 1<br>oO oO<br>= =<br>oO oO<br>22<br>§- 1 o -1<br>5 (ss ) b& < 6 ey De 2) b x<br>esoe oe oe o® oe 3 oe oe o® x<br>oe oe 9° go? e ee oe qe oe?<br>t LMIC t LIC<br>lo} So<br>a 2 8 2<br>fo} So<br>fo} fo}<br>ax<br>2g<br>oO oO<br>3 0 _—— 3 0 —— |<br>oO oO<br>22<br>&-1 @ -1<br>5 (% Oo, IM < 6 cy De 2) b <<br>eeow oe oe o® ofoe ewe oe oe o® gwe<br>oe we oo oo as we oe oe oe?<br>—— Formal wage —— Upper-tier self —— Upper informal wage —— Lower-tier self<br>—— Lower informal wage —— Unemployed Non-LFP<br><!-- End of picture text -->

base year for all income categories. The country income group classification is based on the year the country enters the sample for the first time and is kept constant throughout the period. High-income countries (HIC) have very few observations prior to 2000, leading to no coefficient for that time period.
Figure 2 presents changes by country income group graphically. We use each country’s initial income status throughout the entire sample period, so that changes are driven only by changes within country and not by changing income status. The figure makes clear that the increase in formal wage employment was driven primarily by high-income countries; middle- and low-income countries saw relatively small changes. We break out these changes empirically in Table 2 . The coefficients, derived from equation (1), are directly interpretable as the average change, per decade, for each income status.

Upper tier self-employment which is at the top of the earning ladder does not have a clear pattern overall countries offsetting small decreases in all countries except in UMICs where it increased by 0.5 percent per decade. While we see clear increases in formal wage employment throughout our sample, this increase was driven by high-income countries; formal wage employment in these countries increased by 15.4 percentage points per decade, on average. The two middle-income categories saw small increases in formal wage employment of 2.7 and 3.4 percentage points per decade. The coefficient for low-income-countries is around twice as large as that for upper-middle-income countries, but the standard errors are much larger, meaning that the coefficient is not statistically significant. What is clear, however, is that decreases in all other ladder rungs are only clear in high-income countries.

17
Table 2: Changes in job categories by country income

||(1)<br>Upper-tier<br>self emp.|(2)<br>Formal<br>wage|(3)<br>Upper-tier<br>informal wage|(4)<br>Lower-tier<br>informal<br>wage|(5)<br>Lower-tier<br>self emp.|(6)<br>Unemployed.|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Year 10s|-0.008***|0.154**|-0.038**|-0.076**|-0.017***|-0.000|-<br>0.015***|
|(in HIC)|(0.001)|(0.029)|(0.009)|(0.017)|(0.002)|(0.000)|(0.000)|
|Year 10s|0.005**|0.027***|-0.001|-0.026***|-0.011**|-0.000|0.006|
|(in UMIC)|(0.002)|(0.005)|(0.001)|(0.005)|(0.005)|(0.007)|(0.005)|
|Year 10s|-0.001|0.034***|-0.001|-0.010|-0.004|-0.002|-0.016|
|(in LMIC)|(0.003)|(0.011)|(0.002)|(0.008)|(0.003)|(0.003)|(0.015)|
|Year 10s|-0.021*|0.052|0.002|-0.010|-0.015|-0.003|-0.005|
|(in LIC)|(0.011)|(0.057)|(0.004)|(0.020)|(0.038)|(0.006)|(0.026)|
|Observations|493|493|493|493|493|493|493|

Notes. Standard errors are in parentheses and are clustered at the country level. Country fixed effects are included in all regressions. Coefficients are marginal effects, not interaction terms, meaning the coefficients are directly interpretable as the average change for each income status. The country income classification is fixed based on the income classification of the country for the year the first survey is available. * p<0.10 ** p<0.05 *** p<0.01.

Which region showed the largest increases in formal wage employment? Figure 3 answers this question graphically. The largest increases for formal wage employment happened in East Asia and the Pacific (in our sample, Australia and the Republic of Korea dominate this increase), followed by Europe and Central Asia and Latin America and the Caribbean. Sub-Saharan Africa saw increases, but much more modest. The only clear decrease was in the Middle East and North Africa. Figure A2 in the appendix shows changes by gender and Figure A3 shows changes by age group graphically. The empirical results for gender are in Table A6 and A7 in the appendix. Overall results are generally similar, with the largest differences being labor force participation status. The empirical results for age groups are in table A8, A9 and A10. Interestingly, movements out of lower tiered employment and into formal wage employment is most pronounced for prime-aged adults. The youngest and the oldest see relatively less “positive” movements along the jobs ladder.

18
Figure 3: Changes in formal wage employment by region Notes. The figure shows coefficients from a regression with country fixed effects. 2000-2004 is the base time period, meaning changes are measured relative to 2000-2004. The figure includes only formal wage employment. Due to a lack of observations in earlier years, South Asia is omitted. We also break out these changes over time across education categories in table 3. We create three separate education categories: "low" includes people with primary education completed as highest attainment, "medium" refers to having completed lower secondary education and having some upper secondary education, and “high” refers to upper secondary education completed and any post-secondary education attainment.
Table 2: Changes across education categories

|Education:|(1)<br>Low|(2)<br>Medium|(3)<br>High|(4)<br>All|
|---|---|---|---|---|
|Upper tier self-employment|||||
|Year (10s)|-0.004|-0.006|0.004|-0.003|
||(0.003)|(0.005)|(0.007)|(0.003)|
|Formal wage employment|||||
|Year (10s)|0.016|0.007|-0.031|0.034**|
||(0.019)|(0.015)|(0.032)|(0.014)|
|Upper tier informal wage|||||
|Year (10s)|-0.003|0.001|-0.000|-0.010|
||(0.010)|(0.007)|(0.003)|(0.007)|
|Lower tier informal wage|||||
|Year (10s)|-0.003|0.001|-0.000|-0.010|
||(0.010)|(0.007)|(0.003)|(0.007)|
|Lower tier self-employment|||||
|Year (10s)|-0.049**|-0.002|0.006*|-0.009|
||(0.020)|(0.009)|(0.004)|(0.009)|
|Unemployed|||||
|Year (10s)|-0.007|-0.011|0.010**|-0.001|
||(0.005)|(0.012)|(0.004)|(0.003)|
|Inactive|||||
|Year (10s)|0.056**|0.016|0.004|-0.011|
||(0.024)|(0.011)|(0.011)|(0.011)|
|Share in education category|||||
|Year (10s)|-0.086***|0.037***|0.049***|N/A|
||(0.010)|(0.009)|(0.005)|N/A|

Notes. Each cell reports results from separate regressions. Standard errors are in parentheses and are clustered at the country level. Country and year fixed effects are included in all specifications. "Low" education is defined as primary or below, "medium" is lower secondary completion and some upper secondary education, and "high" is upper secondary completed and above. * p<0.10 ** p<0.05 *** p<0.01.

Focusing first on formal wage employment, we do not see any changes within each education category, but we do see the overall increase in the sample as a whole. This means that for each particular education category there was no significant increase in formal jobs nor in upper tier informal jobs. This is likely explained by the regressions in the bottom row, which show that educational attainment among workers is increasing on average in the sample, pointing to a positive correlation between the increase in formal jobs and the improvement in the overall education levels. Overall formal wage employment can increase even if it does not increase within each education category, since higher levels of education are associated with being 20
If we simply regress the ladder shares on the proportion of the population that has more than secondary education, we see large, positive correlations with formal wage employment and large, negative correlations with informal upper and lower tier wage employment. We discuss education more in the next section. Figure A1 in the appendix shows trends in education across country income categories. The increase in secondary completion is most pronounced for low-income countries, while secondary education increases most in high-income
<!-- Start of picture text --> 8<br>6 :<br>-<br>wo<br>& Ue<br>= oe a-n<br>< 4 _ ieee; oe<br>°s>ee<br>=<br>wo<br>0 ee ——<br>6 7 8 9 10 11<br>GDP per capita in 2017 international USD<br>— Formal wage-employees — Upper-tier self-employed — Upper-tier informal wage<br>— Lower-tier informal wage — Lower-tier self-employed —-— Unemployed<br>—— Non-LFP<br><!-- End of picture text --> <!-- Start of picture text --> Male Female<br>8 :<br>8<br>6<br>6 ee oe<br>Ss-°4 S—°+4 ”=“ a<br>5_ 5 2<br>5 LAO 5<br>2<br>SS an 2 :<br>) aS 0 soeeeeoo SS<br>6 7 8 9 10 "1 6 7 8 9 10 "1<br>GDP per capita in 2017 intemational USD GDP per capita in 2017 international USD<br>— Formal wage-employees —— Upper-tier self-employed — Upper-tier informal wage<br>— Lower-tier informal wage —— Lower-tier self-employed —-— Unemployed<br>— — Non-LFP<br><!-- End of picture text -->
Figure 5 presents the same graph separately for men (left graph) and women (right graph).

The stock of men in formal wage employment and upper tier informal self-employment increases as countries develop more than women, while women have a more pronounced movement out of the labor force than men<sup>8</sup> . However, men also exhibit slower movement out of lower-tier wage employment than women do, though both groups see similar decreases in lower-tier selfemployment – the category with the sharpest decreases across the GDP distribution<sup>9</sup> .

Table 4: Jobs ladder and extreme poverty

||(2)|(4)|(6)|
|---|---|---|---|
||$1.90<br>Headcount|$3.20 Headcount|$5.50 Headcount|
|Upper-tier self-employed|0.163|0.312|-0.048|
||(0.188)|(0.206)|(0.166)|
|Formal wage|0.053|0.023|-0.405***|
||(0.092)|(0.098)|(0.107)|
|Upper-tier informal wage|-1.938**|-2.307**|-0.253|
||(0.825)|(0.943)|(0.618)|
|Lower-tier Informal wage|-0.008|0.131|0.160|
||(0.170)|(0.128)|(0.117)|
|Unemployed|0.463*|0.751**|-0.059|
||(0.276)|(0.343)|(0.315)|
|Inactive|0.008|-0.069|-0.135|
||(0.189)|(0.200)|(0.119)|
|Total pop. (log)|-0.016|0.033|0.350**|
||(0.169)|(0.179)|(0.146)|
|GDP p.c. (log)|-0.042|-0.064|-0.118|
||(0.052)|(0.064)|(0.079)|
|Observations|338|338|338|

Notes. Standard errors are in parentheses and are clustered at the country level. Country-survey type and year fixed effects are included in all specifications. The country-survey type fixed effect takes into account significant changes in the survey used to measure poverty such that poverty rates are comparable across time. The omitted category is lower-tier self-employment. * p<0.10 ** p<0.05 *** p<0.01.

> 8 High female inac�vity rates in our sample are driven by Saudi Arabia and Türkiye.

9 As we measure net changes, not gross, we cannot distinguish transition in and out. The correlation captures levels, not individual transitions.

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<!-- Start of picture text --> Table A22 presents results without the heteroskedasticity correction. The key findings remain.<br><!-- End of picture text -->
The results show that changes in employment structure, conditional on changes in per capita GDP and population, can have discernible correlations with country poverty rates. What happens if we remove GDP from the regression? In general, the main results from above hold (Table A21). The only notable change is that informal upper wage employment is also correlated with lower levels of poverty at the highest poverty line. As such, these patterns are robust to the inclusion of GDP, which we interpret as meaning that the employment structure itself is a key determinant of poverty reduction, not just overall growth levels. Specifically informal upper-tier wage jobs are the types of jobs that matter to lift people out of extreme poverty - these are more probably the jobs poor people have access to - while formal wage jobs are key to reduce poverty measured at higher poverty lines.

3.3 What country characteristics are associated with progression up the ladder and poverty reducing jobs?

While we have seen that changes in education are likely correlated with movements up the jobs ladder, are there any other country-specific characteristics that predict changes in the job ladder rungs? We begin with a simple specification with educational attainment, based on equation (3), where we include the proportion of people with secondary education (meaning lower secondary education completed and having some upper secondary education) and with post-secondary (including upper secondary education completed and any post-secondary education attainment), treating the proportion of people with primary education completed or less as highest attainment as the omitted category. The interpretation of the coefficients is the conditional correlation of a shift from nobody in that education category to everyone in that category, relative to primary education completed and less.

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These results are presented in Table 5. Equivalently, dividing the coefficient by 10 gives the conditional correlation of a ten-percentage-point (pp) net shift in the category. We find that improvements in education levels are not significantly correlated with increases in upper tier selfemployment and formal wage employment. The most striking results are those for postsecondary education. A ten percentage points increase in post-secondary education is associated with a 3.7 pp increase in the share of workers in formal wage employment (although statistically significant at the 10-percent level.), a 3.8 pp decline in the share of workers in informal lowertier wage employment, and a 0.6 pp decline in the share of workers in upper tier wage employment. In contrast, the results on lower secondary education are less conclusive. Increased lower secondary education is associated with a modest shift out of lower tier self-employment into formal wage work and upper tier informal jobs, but the correlations are not statistically significant. Importantly, the above-mentioned results are most pronounced for men, again pointing to the fact that for the same level of education, women's human capital is not utilized in the labor market as productively as men's (Table A11 and A12).

To try to unpack the strong correlation between improvements in education and shifts to formal jobs, we interact the education variables with dummies for country income groups. We find that the increase in postsecondary attainment translated into more formal wage jobs in high income countries (HICs) but not in other countries, and into more upper tier self-employment (though not in a statistically significant way). The coefficients of the interaction terms of lower secondary education with country income group dummies are all not statistically significant. Possible explanations of why the improvement in education is strongly associated with shifts to formal jobs in HICs include the higher quality of education in HICs and higher investments in 26
complementary factors of production that enhance the demand and productivity of higher educated workers.

Table 5: Educational attainment and jobs ladder (All countries)

||(1)<br>Upper tier<br>self-emp.|(2)<br>Formal<br>wage|(3)<br>Upper tier<br>informal<br>wage|(4)<br>Lower tier<br>informal<br>wage|(5)<br>Lower tier<br>informal<br>self-emp.|(6)<br>Unemployed|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Lower secondary<br>completed|0.045|0.194|0.020|-0.070|-0.134|0.053|-0.108|
||(0.047)|(0.131)|(0.024)|(0.106)|(0.117)|(0.044)|(0.141)|
|Upper secondary|-0.042|0.369*|-0.062**|-0.379***|-0.059|-0.010|0.184|
|completed and||||||||
|above||||||||
||(0.049)|(0.208)|(0.029)|(0.118)|(0.135)|(0.048)|(0.177)|
|Total pop. (log)|-0.145**|-<br>0.198**|0.030|0.159|0.123|0.053|-0.022|
||(0.071)|(0.088)|(0.024)|(0.155)|(0.153)|(0.054)|(0.120)|
|GDP p.c. (log)|0.017|0.175|0.023*|0.012|-0.122|0.000|-0.105*|
||(0.025)|(0.116)|(0.012)|(0.045)|(0.094)|(0.019)|(0.061)|
|Observations|429|429|429|429|429|429|429|

Notes. Standard errors are in parentheses and are clustered at the country level. Country and year fixed effects are included in all specifications. The proportion of people with primary education completed as highest attainment or less is the omitted category. Shares refer to all workers with non-missing firm size. * p<0.10 ** p<0.05 *** p<0.01.

To put these coefficients in context, it is helpful to understand the within-country variation in the education variables. The within-country variation for lower secondary education completion is 0.035, while it is 0.03 for post-secondary education. Using the latter, a onestandard-deviation increase in post-secondary education leads to a decrease in informal uppertier wage employment of around 0.2 percentage points. A similar increase in post-secondary education leads to a decrease in informal lower-tier wage employment of around 1.4 percentage points. While these changes seem small, we note that they are relatively large when compared to the means of the five job ladder shares. The mean for informal upper wage employment is 0.015, meaning the one-standard-deviation increase in above secondary employment leads to a 27
decrease of approximately 13.3 percent of the mean, while the value for lower wage employment is around 8.6 percent of the mean. Table A17 in the appendix presents the same regression results, but without the heteroskedasticity correction. The signs of the coefficients are generally similar, but some change in magnitude. The most obvious change is the coefficient on above upper secondary for formal wage employment; while still positive, it is much smaller in magnitude.

Table 6: Urbanization and jobs ladder (All countries)

||(1)<br>Upper tier<br>Informal<br>self-emp.|(2)<br>Formal<br>wage|(3)<br>Upper tier<br>informal<br>wage|(4)<br>Lower tier<br>informal<br>wage|(5)<br>Lower tier<br>informal<br>self-emp|(6)<br>Unemployed|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Urban pop. (log)|-0.037|0.050|0.025|0.047|-0.127|0.060**|-0.017|
||(0.049)|(0.141)|(0.020)|(0.083)|(0.125)|(0.029)|(0.098)|
|Rural pop. (log)|-0.055**|-0.189**|0.002|0.028|0.183*|-0.027|0.059|
||(0.023)|(0.073)|(0.011)|(0.066)|(0.097)|(0.031)|(0.113)|
|GDP p.c. (log)|0.023|0.187*|0.010|-0.025|-0.108|-0.010|-0.077|
||(0.027)|(0.096)|(0.010)|(0.037)|(0.077)|(0.020)|(0.047)|
|Observations|466|466|466|466|466|466|466|

Notes. Standard errors are in parentheses and are clustered at the country level. Country and year fixed effects are included in all specifications. The proportion of people with primary education or less is the omitted category. Shares refer to all workers with non-missing firm size. * p<0.10 ** p<0.05 *** p<0.01.

We now turn to the relationship between changes in the job ladder and urbanization and population growth. Table 6 presents the results of the regressions based on equation (3) separating population into urban and rural population. There are three key coefficients worth noting. First, higher levels of urban population are modestly correlated with higher levels of unemployment. This is reminiscent of the recent work exploring the relationship between urban unemployment and the unskilled population (S. Feng et al., 2017), though we note that we do not find the same negative relationship between GDP and unemployment as others (Y. Feng et 28
al., 2024). Consistent with this story, urban employment increases unemployment for the youngest adults Table A13, with much smaller changes for older adults (Table A14 and A15).

Second, higher levels of population in rural areas appear to lead households to work in lower-tier informal jobs, possibly due to a lack of (high-paying) jobs in rural areas. Larger rural population, controlling for urban population, is associated with moderate shifts from formal wage work and upper tier self-employment to lower tier informal self-employment, indicating a concentration of employment in the lowest tier of the ladder. We also look at whether the relationship varies by country income groups: we find that urbanization is significantly associated with shifts from informal to formal wage jobs only in HICs and that the positive relationship with unemployment is driven by HICs. Interestingly, in UMICs larger rural populations are positively associated with shifts from lower-tier informal self-employment to upper tier self-employment and to movements up the job ladder in HICs. In the appendix we present results without heteroskedasticity weighting in Table A18. The main findings, especially around unemployment, remain.

Next, we investigate whether the share of the workforce working in larger firms impact the quality of jobs available. We turn to this question in Table 7 , which shows how the share of workers in each job ladder rung changes based on the proportion of the workers in firms of different sizes. The omitted category is the share of employment in firms with five or fewer employees, meaning that all coefficients can be interpreted relative to that group.

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Table 7: Firm size and movements on the jobs ladder (ALL COUNTRIES)

||(1)<br>Upper tier<br>self-emp.|(2)<br>Formal<br>wage|(3)<br>Upper tier<br>informal<br>wage|(4)<br>Lower tier<br>Informal<br>wage|(5)<br>Lower tier<br>self-emp|(6)<br>Unemployed|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Share in firms|0.031|0.011|0.048**|0.185*|-0.208**|0.020|-0.087|
|6-20|(0.021)|(0.159)|(0.024)|(0.106)|(0.092)|(0.018)|(0.080)|
|Share in firms|0.003|0.315|0.000|0.085|-0.230***|0.026|-0.199*|
|21-50|(0.046)|(0.189)|(0.016)|(0.078)|(0.084)|(0.027)|(0.117)|
|Share in firms|0.035|0.170|0.008|0.002|-0.126*|0.074|-0.163|
|51+|(0.063)|(0.120)|(0.017)|(0.079)|(0.072)|(0.044)|(0.170)|
|Total pop. (log)|-0.182|-0.197|0.025|0.362|-0.010|0.052|-0.049|
||(0.147)|(0.132)|(0.043)|(0.231)|(0.271)|(0.080)|(0.222)|
|GDP p.c. (log)|0.005|0.213***|0.009|-0.017|-0.168**|-0.016|-0.024|
||(0.040)|(0.071)|(0.012)|(0.044)|(0.068)|(0.017)|(0.064)|
|Observations|323|323|323|323|323|323|323|

Notes. Standard errors are in parentheses and are clustered at the country level. Country and year fixed effects are included in all specifications. Firms size 1-5 is the omitted category. Shares refer to all workers (including wage workers, self-employed, employers and unpaid workers) with non-missing firm size. We assume that selfemployed work in a 1-person business. * p<0.10 ** p<0.05 *** p<0.01.

Overall, the results indicate that higher shares of employment in firms with more than 5 employees compared to microenterprises are significantly associated with movements out of lower tier informal self-employment. In particular, the growth of small firms – with size between six and 20 employees –is positively correlated with shifts from informal lower tier selfemployment to upper-tier- but also lower-tier informal wage employment. Unpacking the correlation by country income group we find that increasing shares of the workforce in small firms (with 6 to 20 employees) lead to shifts to formal wage employment in UMICs and HICs, though the coefficient - albeit positive - is not significant in LMICs and LICs. In LMICs and UMICs the increasing incidence of medium firms (with 21-50 employees) is positively associated with shifts from formal wage work to informal upper tier wage employment.

We also see marginally significant increases in unemployment as larger firms become more common (and significant increases without heteroskedasticity corrections (Table A19). The changing industrial sector thus seems to be correlated with increases in unemployment, which 30
may be related to some of the trends documented in Y. Feng et al. (2024). We also note the decreases in non-labor force participation, but the coefficients are too imprecise for firm conclusions.

Finally, we look at changes in the jobs ladder based on changes in sector of employment (agriculture, manufacturing and services). Table 8 shows that movements out of agricultural employment and into manufacturing employment are associated with increases in formal wage employment but that growth in services does not show the same relationship. We also explore the extent to which changes in trade orientation and capital deepening are correlated with better jobs outcomes. Table A16 looks at correlations between exports and capital stocks and movements on the jobs ladder. However, many of these variables are available for only a subset of country-year surveys and, as such, we do not see any significant within-country correlations.

Table 8: Sectors of employment and changes in the jobs ladder

||(1)<br>Upper-tier<br>self emp.|(2)<br>Formal<br>wage|(3)<br>Upper-<br>tier<br>informal<br>wage|(4)<br>Lower-tier<br>informal<br>wage|(5)<br>Lower-tier<br>self emp.|(6)<br>Unemployed.|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Emp. share in|-0.337|0.811|-0.077|-0.550**|-0.281|0.121|0.313|
|manufacturing|(0.234)|(0.531)|(0.064)|(0.225)|(0.560)|(0.126)|(0.294)|
|Emp. share in|-0.285|0.546|-0.085|-0.380*|0.054|0.041|0.109|
|services|(0.195)|(0.348)|(0.052)|(0.201)|(0.324)|(0.115)|(0.281)|
|Total pop. (log)|-0.081**|-<br>0.340***|0.061***|0.227|0.138|0.033|-0.036|
||(0.040)|(0.105)|(0.022)|(0.151)|(0.142)|(0.066)|(0.151)|
|GDP p.c. (log)|0.045|0.094|0.024*|0.044|-0.090|-0.013|-0.105*|
||(0.040)|(0.071)|(0.014)|(0.054)|(0.101)|(0.022)|(0.056)|
|Observations|466|466|466|466|466|466|466|

Notes. All regressions include country fixed effects. Standard errors are clustered at the country level and are in parentheses. Only older adults are included. * p<0.10 ** p<0.05 *** p<0.01.

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# 4. Conclusion

In this paper, we document patterns of labor (re)allocation across about 90 countries over the last three decades. Overall, across the entire sample, formal wage employment has increased over time, inactivity and unemployment decreased, while the four types of informal jobs do not present a clear trend. Looking within country groups, however, formal wage employment growth is mostly driven by higher income countries. We similarly see large decreases in other forms of employment only in high-income countries. In our sample, these changes in high-income countries are driven by East Asia and the Pacific, specifically Korea and Australia. We also document large changes in education categories. In the entire sample, educational attainment increased markedly between 1990 and 2020. For each of the three educational attainment categories (low, medium and high) we do not see large changes in employment type, but we do see large increases in formal employment overall, indicating that changes in education patterns are correlated with changes in the employment structure; populations with increasing education also see increasing formal wage employment. We do see increases in unemployment for the lowest educated group, which could be related to migration patterns and urban populations; we find that larger urban population is associated with higher levels of unemployment, as expected. The main result is that the structure of employment is correlated with poverty rates. We find that informal upper-tier wage employment is a strong and statistically significant predictor of lower levels of poverty at the $1.90 and $3.30 poverty lines, while formal wage employment (both skilled and unskilled) is the most important at the higher poverty line of $5.50. This means that jobs that require higher skills matter more for poverty reductions in lower income countries than the formality status. Importantly, these patterns hold even when we control for per capita 32
GDP in the regressions, indicating that employment structure on its own, separate from aggregate growth, is highly correlated with poverty rates.

What country characteristics are associated with shifts to formal jobs and upper tier informal jobs? To answer this question, we look at how changes in the stock of human capital (measured through education attainment) and physical capital affect the employment structure. We find that increasing shares of people with completed lower secondary education is associated with shifts from lower to upper-tier informal jobs, but the correlations are not statistically significant. On the contrary, countries with increasing shares of people with post-secondary education are significantly correlated with increases in formal wage jobs, but not in upper tier informal jobs.

We also explore the relationship between the movement up the job ladder (as a proxy for labor productivity) and job structural transformations along the sectoral dimension (measured by the share of workers in non- agriculture sectors), the spatial dimension (measured by urban and rural populations) and the organizational dimensions (measured by the share of workers in larger firms and by the level of exports). Private sector development (organizational transformation) and higher shares in manufacturing are associated with climbing the job ladder. Specifically, shifts in the workforce from microenterprises to small firms are strongly associated with increases in pro-poor upper-tier informal wage employment, but at the cost of increases in informal lower-tier jobs. Labor reallocation from agriculture to manufacturing is associated with increases in formal wage employment. The results do not show strong and significant correlations between urbanization and movement up the job ladder to formal and informal upper tier jobs, while estimates on the contribution of exports and the stock of capital suffer 33
from imprecision due to the fact that many of these variables are available for only a subset of country-year combination.

While we document interesting patterns in labor allocation over time and correlations with countries’ endowments and structural transformation, future research could do similar analyses of government policies. For example, do government labor market, industrial, or trade policies or programs predict changes in the structure of employment? Answering these types of questions is important for better understanding how policies in developing countries can effectively change employment patterns to accelerate poverty reduction.

34