metadata: authors: 0: author: Kamei,Akito
1: author: Nakamura,Shohei
count: Ethiopia
docna: 0: docna: Urban Agglomerations and Employment
            Transitions in Ethiopia
docty: Policy Research Working Paper
subtopic: Employment and Unemployment,Educational Sciences,Labor Markets,Inequality,Wages, Compensation & Benefits
teratopic: Education,Poverty Reduction,Social Protections and Labor
abstracts: cdata!: Agglomeration boosts economic growth. A
            vast literature has empirically assessed the effects of
            agglomeration by estimating the city population elasticity
            on wages. This conventional approach is not necessarily
            suitable for analyzing urbanization at the early stage in
            developing countries, where a majority of urban workers
            engage in self-employment and/or informal jobs.  Focusing on
            one of the poorest and largest among those countries, this
            paper sheds light on an aspect of urbanization and
            agglomeration: the transition in the mode of labor from
            self-employment/informal jobs to wage employment/formal
            jobs. Applying the instrumental variable approach to
            national labor force survey data sets, the analysis
            underscores several labor market transitions across space in
            urban Ethiopia. First, the town population size and the
            share of workers with wage employment are strongly
            correlated. The probability of engaging in wage work
            increases by 4.5 percentage points with a log increase in
            population size. Second, this relationship is particularly
            strong among disadvantaged workers, such as the female,
            young, and/or less educated population. Finally, the study
            documents higher labor force participation and lower
            underemployment in larger towns.
geo_reg_key: 517191
repnme: repnme: Urban Agglomerations and Employment
            Transitions in Ethiopia
display_title: Urban Agglomerations and Employment
            Transitions in Ethiopia
listing_relative_url: /research/2020/03/31865419/urban-agglomerations-employment-transitions-ethiopia
topicv3: Poverty m1326687 695,Education m1327048 470,Jobs m1503931 2670,Self-Employment m1504000 2698,Wage Employment m1504005 2703
publishtoextweb_dt: 2020-03-13T00:00:00Z
historic_topic: Education,Poverty Reduction,Social Protections and Labor
origu: EFI-AFR1-POV-Poverty and Equity (EAEPV)
no_of_pages: 38
author_keys: 0: author_key: 1677977
1: author_key: 1389722
model_extractions: Policy Research Working Paper 9184 Urban Agglomerations and Employment Transitions in Ethiopia _Akito Kamei Shohei Nakamura_ Poverty and Equity Global Practice March 2020
Policy Research Working Paper 9184

# **Abstract**

Agglomeration boosts economic growth. A vast literature has empirically assessed the effects of agglomeration by estimating the city population elasticity on wages. This conventional approach is not necessarily suitable for analyzing urbanization at the early stage in developing countries, where a majority of urban workers engage in self-employment and/or informal jobs. Focusing on one of the poorest and largest among those countries, this paper sheds light on an aspect of urbanization and agglomeration: the transition in the mode of labor from self-employment/informal jobs to wage employment/formal jobs. Applying the instrumental variable approach to national labor force survey data sets, the analysis underscores several labor market transitions across space in urban Ethiopia. First, the town population size and the share of workers with wage employment are strongly correlated. The probability of engaging in wage work increases by 4.5 percentage points with a log increase in population size. Second, this relationship is particularly strong among disadvantaged workers, such as the female, young, and/or less educated population. Finally, the study documents higher labor force participation and lower underemployment in larger towns.

This paper is a product of the Poverty and Equity 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 snakamura2@worldbank.org.

_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
# Urban Agglomerations and Employment Transitions in Ethiopia<sup>_⇤_</sup>

Akito Kamei _University of Illinois at Urbana-Champaign_<sup>_†_</sup> Shohei Nakamura _World Bank_<sup>_‡_</sup> JEL classification: J01, O17, R12 Keywords: Urbanization; Labor Market; Urban Agglomerations; Informality; Ethiopia

> _⇤_ This paper was produced as a background paper for the forthcoming World Bank report “Ethiopia Poverty Assessment: Harnessing Continued Growth for Accelerated Poverty Reduction.” The authors would like to thank David Newhouse and Tom Bundervoet for useful comments. 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

> _†_ Corresponding author e-mail: kamei2@illinois.edu

> _‡_ Corresponding author, e-mail: snakamura2@worldbank.org
# **1 Introduction**

Urbanization potentially stimulates economic growth and poverty reduction through facilitating sectoral shifts and enhancing productivity gains. As a country urbanizes, its economy shifts from less productive (for example, agriculture) to more productive sectors (manufacturing and services). Rural to urban migration usually accompanies such transition. This structural and spatial transformation is expected to spur economic growth and reduce poverty in the developing world. In addition to the sectoral shift, urbanization also enhances productivity within sectors. When managed well, agglomeration economies boost the productivity of firms and workers through sharing, matching, and learning e↵ects (Duranton 2015).

Recent studies in developing countries (particularly in Africa), however, highlight the lack of the link between spatial and structural transformation (Gollin, Jedwab, and Vollrath 2016; Henderson, Storeygard, and Deichmann 2017; Jedwab, Christiaensen, and Gindelsky 2015). Urban population growth has been mainly driven by natural population growth, while rural to urban migration has been driven by push factors such as natural disasters and conflicts (instead of pull factors). Export of natural resource and institutional weakness, among many other factors, prevent the development of the manufacturing sector and the production of internationally tradable goods in developing countries. The lack of adequate infrastructure hampers economic agglomeration in African cities as well.

Applying the existing conceptual and empirical strategy developed in the agglomeration economies literature to developing countries has some practical challenges. A large number of empirical studies demonstrate the sizable agglomeration e↵ects in the developed world as the positive elasticity of nominal wages, which is a commonly used proxy of labor productivity, with respect to the city population size (Melo, Graham, and Noland 2009; Puga 2010; Rosenthal and Strange 2004). Although still limited, a growing literature on the agglomeration economies in the middle income countries also reports a similar scale of agglomeration e↵ects (for example, Chauvin, Glaeser, Ma, and Tobio 2017; Duranton 2016; Quintero and Roberts 2018). However, with a few exceptions (for example, Mukim 2014; Tanaka and Hashiguchi 2017; Tran and La 2018), the previous studies in agglomeration pay little attention to the prevalence of informal jobs in developing countries. It is not a priori evident that informal workers also benefit from agglomeration economies (Moreno-Monroy 2012).

2
In addition, there is a methodological concern in estimating agglomeration e↵ects based on wages in developing countries, where a majority work in self-employment instead of wage employment (Gindling and Newhouse 2014).<sup>1</sup> Equally important but understudied is the link between urbanization and the way in which workers are allocated to tasks. As well described by Fafchamps (2012), agglomeration potentially has a profound e↵ect on the mechanism by which workers are allocated to tasks by extending gains from specialization and circulating information conducive to innovation in technology, organization, and business practices. Of particular relevance is the transition from specialization driven by the market-based task allocations to hierarchical task allocations within firms. The former is dominated by self-employment in small towns, while the latter prevails in large cities in the form of wage employment.<sup>2</sup> The shift from informal/self-employment jobs to formal/wage employment potentially helps low-skilled workers escape from poverty. However, this transition to wage employment does not automatically take place as self-employment remains prevalent in many large cities in the developing world.

Our study examines the nexus between town population size and job allocation mechanisms in one of the least-urbanized Sub-Saharan African countries: Ethiopia. We focus on urban Ethiopia, where approximately a quarter of the country’s population currently resides. This study reveals whether the transition of self-employment to wage employment, as well as other labor outcomes, is associated with the town population at the early stage of urbanization. This paper focuses on the following key questions. First, we examine whether and to what extent larger towns create more wage and/or formal jobs than smaller towns. Second, we assess who are likely to benefit from labor transitions due to the larger population size (or agglomeration). Finally, we investigate how other labor outcomes, such as labor force participation and unemployment/underemployment, change in tandem.

Our empirical approach to cross-sectional labor force surveys is an instrumental variable (IV) regression of the town population. To reduce an endogeneity bias, we instrument the current town

> 1Teal (2011) puts this well: “[i]n almost all the literature the empirical analysis has been confined to wage employment. In extending it to developing countries that is a rather serious limitation as most employment in such countries is... not wage but self-employment.”

> 2A study of Nepal by Fafchamps and Shilpi (2005) examines the relationship between the proximity to urban centers and various labor outcomes. They find that the transition to wage employment took place mainly in the public sector.

3
population with the historical population, following previous studies (Chauvin et al. 2017; Ciccone and Hall 1996; Combes, Duranton, Gobillon, and Roux 2010; Duranton 2016).

Addressing a selection bias is rather challenging given our cross-sectional data, which do not allow us to add worker fixed e↵ects. However, adding worker fixed e↵ects makes it possible to analyze agglomeration e↵ects only for movers. There is an argument that a pooled OLS works better when considering the net e↵ects of agglomeration (Henderson and Kriticos 2018; Roca and Puga 2017). In addition to including a host of observed worker and household characteristics to reduce the bias, we estimate the regression models for the subsample of non-migrants. We expect this analysis to be less susceptible. Despite all these e↵orts, sorting is admittedly an unsolved issue in our study.

Our analysis confirms the labor market transition from self-employment to wage employment across space in urban Ethiopia. First, the town population and the share of workers with wage employment are positively correlated. Based on our two-stage least squares (2SLS) estimation, the share increases 4.5 percentage points with a log increase in town population. Second, this association is particularly strong among disadvantaged workers: the estimate is 6.5 percentage points among female workers (as opposed to 2.6 percentage points among male workers) and 7.8 percentage points among the youth (as opposed to 2.8 percentage points among the non-youth). In addition, it is mostly the less-educated workers who are more likely to find wage employment in larger towns (7.5 percentage points). Finally, underemployment declines as the town population size increases. Women are more likely to be in active labor forces in larger towns, which pushes up their unemployment. Overall, these results demonstrate the crucial link between urbanization and labor market transitions.

Our study is unique as it bridges two lines of literature that have not conversed: urban economics on agglomeration economies and the literature on informality in the labor markets of developing countries. The urban economics literature expects agglomeration e↵ects on labor productivity in developing countries as well (Duranton 2015). However, it is not clear if its conceived channels, such as sharing, matching, and learning, are present in low-income and least-urbanized countries, where a majority of urban workers still engage in informal non-wage jobs. In particular, various factors, such as congestion due to inadequate infrastructure, are considered to constrain the realization of agglomeration e↵ects in African cities (Lall, Henderson, and Venables 2017). On the other hand, 4
the informality literature has long discussed advantages and disadvantages of informal non-wage employment over formal wage employment. For example, some empirical studies demonstrate that informal jobs are not necessarily ‘last resort’ options. The earning premium of formal jobs disappears once unobserved worker characteristics are taken into account (Falco and Haywood 2016; Falco, Kerr, Rankin, Sandefur, and Teal 2011). The literature, however, does not discuss in detail how urban agglomerations influence the transition from the former to the latter.<sup>3</sup> Our paper attempts to bridge these two lines of literature that are closely related but not conceptually and empirically linked to date.

The rest of this paper is structured as follows. Section 2 introduces the background of urban Ethiopia. Section 3 describes the data used for our analysis. Section 4 explains the empirical methodology. Section 5 reports the results. Section 6 provides a conclusion.

# **2 Background: Urban Economy in Ethiopia**

Despite remaining one of the poorest countries in the world, Ethiopia has achieved tremendous economic growth over the last two decades. Real GDP growth averaged 11 percent annually between 2004 and 2014, driven by public infrastructure development supported by a conducive external environment (World Bank 2016b). In tandem, the poverty rate has declined from 45.5 percent in 1995/96 to 23.5 percent in 2015/16 (National Planning Commission 2017).<sup>4</sup> About 15 percent of urban population—or around 2.4 billion individuals—is in consumption poverty. According to the World Bank’s global poverty estimate, Ethiopia accommodates the fourth largest number of the extreme poor (per capita daily expenditure lower than $1.9 in 2011 PPP) in the world (World Bank 2018).

Despite the rapid pace of economic growth, Ethiopia’s spatial transformation has been moderate in that its urbanization level has reached only 20 percent in 2017 (World Bank 2019).<sup>5</sup> The majority of urban population lives in small towns. As of 2015, 3.3 million people live in Addis Ababa, the

> 3There is a line of literature that theorizes the transition of rural to urban economies, initiated by Harris and Todaro (1970).

> 4The official poverty in Ethiopia is measured based on per adult-equivalent consumption.

> 5While Ethiopia’s urbanization level changed by only 5 percentage points between 2000 and 2017, the pace of urban population growth has been tremendous. For example, urban population has been increasing by 6.2 percent annually since 2011, adding nearly 1 million people to urban population.

5
largest city, accounting for nearly 20 percent of the urban population.<sup>6</sup> Another 8.9 million people (57 percent) live in small and medium towns with a population of 50,000 or less. The remaining 5.3 million people live in major towns with a population of 50,000 or greater. Within urban areas, poverty incidence is relatively high in small towns with a population of less than 20,000 (20 percent) (World Bank 2020). Living standards in terms of access to basic services are also lagging behind in those small towns, though they are still better than rural villages.

Structural transformation has been lackluster in Ethiopia, as well. The low productivity in Ethiopian economy is due in large part to its low-skilled labor force. Nearly half of the urban workers have not completed primary education as of 2010. Nevertheless, the education levels of workers have been gradually improving in urban Ethiopia. The share of workers who have not completed primary education declined from 46 percent in 2010 to 38 percent in 2016.

Despite the improvement in labor force skills, the worker mobility to more productive sectors has been limited. Figure 1 shows that the share of workers in each industry has been remarkably stable. As of 2016, 6 percent of urban workers were engaged in agricultural jobs, while 12 percent and 72 percent were engaged in the manufacturing and service sectors, respectively. Despite such moderate scale, the shift in labor from agriculture to services and construction explains up to a quarter of per capita growth between 2005 and 2013 due to a large productivity gap between the sectors (World Bank 2016b).

Although consumption gains among self-employment households have made a significant contribution to urban poverty reduction, households with non-wage workers in the private sector are still more likely to be poor. Ethiopia’s urban poverty rate dropped from 35.2 percent in 2005 to 14.6 percent in 2016 (World Bank 2020).

Although the share of household heads with self-employment jobs remained at 45 percent during the decade, the rise in their consumption supported their escape from poverty. Nearly half of the urban poverty reduction between 2005 and 2016 took place among households headed by selfemployed workers. Nevertheless, house- holds headed by wage workers in the private sector are less likely to be poor than those headed by self-employed workers. Even among the latter group of households, those that have a member working with a private wage job tend to be less in poverty.

> 6While the population of Addis Ababa is by far the largest, its primacy rate (20 percent) is not so high as other African countries (Henderson and Kriticos 2018).

6
Figure 1: Industry Compositions in Urban Ethiopia (percent), 2006–16 <!-- Start of picture text --> 100<br>24 24 25<br>80<br>8 8 6<br>4 7<br>60 5 3 5 6<br>40 32 28 28<br>1 6 1 8<br>20 2 7<br>14 12 12<br>0<br>7 0 9 6 0<br>0<br>2006 2010 2016<br>Public<br>Agriculture Construction Administration<br>Other<br>Mining Commerce Services<br>Transport<br>Manufacturing Communications<br>Financial<br>Public utilities<br>Business Services<br>Sources: Urban Employment and Unemployment Survey (UEUS) 2010/2016<br><!-- End of picture text --> In addition, the analysis of poverty dynamics based on the three waves of Ethiopian panel data demonstrates that self-employment is associated with frequent movement into poverty compared to wage employment (World Bank 2020). Among households headed by self-employed workers, informal self-employment is particularly volatile in that they are relatively more likely to fall into poverty.

# **3 Data**

## **3.1 Towns and Populations**

The main data sets used in this study are the 2012 Urban Employment and Unemployment Survey (UEUS) and the 2013 Labor Force Survey (LFS) collected by the Central Statistical Agency (CSA) of the Ethiopian government. Although core labor-related questions are common in both surveys, the LFS includes additional information, such as migration and secondary jobs. In addition, though both surveys are nationwide, the coverages of towns in the 2013 LFS data sets is larger than that 7
<!-- Start of picture text --> :<br>:<br>°o * |<br>es. co :<br>i?<br>o ee<br>are * 58°<br>Nee<br>ey<br>ee<br>e<br>|<br><!-- End of picture text -->
populations in 2012 (for the UEUS) and 2013 (for the LFS) as the weighted average of the 2007 Census population and the 2014 projected populations by the intervals from the survey years. Below is how the town population is calculated in each year _i_ (=2012, 2013): Figure 3 illustrates the population distributions of the UEUS/LFS towns. As is clearly shown, Addis Ababa has by far the largest population. Since the capital city is influential on any regression estimation, we estimate specifications with and without Addis Ababa fixed e↵ects in every analysis.

Figure 3: Population Distributions in the UEUS/LFS Data Sets <!-- Start of picture text --> Addis Ababa<br>6 8 10 12 14 16<br>Ln Pop<br>UEUS LFS<br>23<br>17.25<br>11.5<br>Frequency in 100,000s<br>5.75<br>0<br><!-- End of picture text --> We conduct analysis with several other population variables to check the robustness of the results. As an alternative town population measure, we draw on 100 m by 100 m grid-level population data from the World Pop 2015. We identify urban agglomerations following the European Union’s definition of high density urban clusters (contiguous grid cells of 1 km<sup>2</sup> with a density of at least 1,500 inhabitants per km<sup>2</sup> and a minimum population of 50,000).<sup>9</sup> The result using the World Population 2015 is presented in Section 5.5.

> 9See measurement details at Eurostat Statistical Explained, Degree of urbanisation classification - 2011 revision.

9
We calculate a Market Accessibility Index (MAI) as another agglomeration measure. Treating the population size within the town administrative boundaries could mismeasure agglomerations. For instance, the presence of economically vibrant, small satellite towns surrounding major towns could result in underestimating our estimates. To take account of such connectivity between towns, we calculate the MAI, following Donaldson and Hornbeck (2016) and Berg, Blankespoor, and Selod (2018). It is calculated as the sum of its own population and surrounding towns’ populations that are discounted based on its travel time from the town (see Appendix A for the details).

# **3.2 Labor Outcomes**

Table 1: Employment Characteristics of Whole Sample and Among Employed

||Total|Size 1|Size 2|Size 3|Size 4|Addis<br>Ababa|Min|Max|
|---|---|---|---|---|---|---|---|---|
|**Employment Status**|||||||||
|Employed|0.62|0.72|0.66|0.61|0.60|0.55|0|1|
|Unemployed|0.13|0.08|0.11|0.13|0.14|0.17|0|1|
|Non-Labor Force|0.25|0.20|0.23|0.26|0.25|0.28|0|1|
|Observations|124,354|9,240|16,727|43,732|26,774|27,486|||
|**Among Employed**|||||||||
|Sector of employment|||||||||
|Government|0.23|0.24|0.22|0.24|0.23|0.23|0|1|
|Private|0.21|0.07|0.14|0.19|0.28|0.37|0|1|
|Self-employed|0.48|0.65|0.59|0.51|0.39|0.27|0|1|
|Other|0.08|0.04|0.05|0.06|0.10|0.13|0|1|
|Formality|||||||||
|Book account of enterprise|||||||||
|Have book account|0.41|0.30|0.30|0.38|0.45|0.62|0|1|
|Do not have book account|0.33|0.64|0.38|0.29|0.29|0.17|0|1|
|Do not know|0.24|0.07|0.31|0.31|0.22|0.17|0|1|
|License of enterprise|||||||||
|Have License|0.66|0.55|0.58|0.62|0.72|0.84|0|1|
|Do not have License|0.33|0.44|0.41|0.36|0.26|0.13|0|1|
|Do not know|0.02|0.01|0.01|0.02|0.02|0.03|0|1|
|Labor outcome|||||||||
|Working hours|43|35|39|43|47|50|0|97|
|Underemployment|0.13|0.21|0.19|0.14|0.09|0.05|0|1|
|Monthly nominal earnings|1047|762|798|920|952|1293|50|81000|
|Observations|74,578|6,566|10,946|25,900|16,057|14,828|||

Notes: The city size is grouped in quantile excluding Addis Ababa; Size 1 is up to 15,000, Size 2 is up to 40,000, Size 3 is up to 150,000, and size 4 is up to 500,000. Underemployment is defined as workers who work less than 30 hours per week and are willing to work more. Unit of nominal earnings is Ethiopian Birr (ETB).

The labor status of urban individuals is associated with the town population size. Table 1 10
provides descriptive statistics of labor characteristics by the category of city size: 1) population up to 15,000; 2) between 15,001 and 40,000; 3) between 40,001 and 150,000; 4) between 150,000 and 500,000; and 5) Addis Ababa. Compared to smaller towns, labor force participation rates are lower in larger towns, while their unemployment rates are higher. Based on the UEUS/LFS data, around 75 percent of working-age urban populations are active labor forces, 83 percent of whom are employed and 17 percent are unemployed. The labor force participation rates are 72 percent in Addis Ababa (64.1 percent for female and 81 percent for male), while the rates in small towns (town size less than 15,000) are 80 percent. By contrast, unemployment rates tend to be higher in larger towns, and it is 20.6 percent for women and 12.6 percent for men in Addis Ababa.<sup>10</sup> In terms of employment types, wage employment (private and public) is common in larger towns, whereas the majority of workers are self-employed in smaller towns. The data show that 48 percent of urban workers are engaged in self-employment, 21 percent are private employees, and 23 percent are public employees. Unskilled workers tend to engage in self-employment jobs (commonly in the sectors of agriculture, manufacturing, and construction).

A third of urban workers are engaged in informal jobs. The UEUS/LFS allows to define informal jobs in two ways: whether the job has a book account or whether the job has a license. According to the former definition, 23 percent of private employees and 90 percent of self-employed workers are in the informal sector. Based on the license definition, only 9 percent of wage workers in the private sectors are informal, while nearly 60 percent of self-employed workers are informal.

The average hours worked per week is only 35 hours in small towns, in contrast to 50 hours in Addis Ababa. A sizable portion of urban workers are potentially under- employed. Underemployment is defined as workers who work fewer than 30 hours per week and are willing to work more in the analysis. About 13 percent of workers are underemployed. Underemployment is prevalent among less-educated workers and/or those engaged in self-employment jobs. In small towns, nearly 30 percent of workers can be classified as underemployed.

Nominal wages are higher among public employees (relative to private employees) and/or in larger towns. The UEUS/LFS reports the amount of income and frequency of payments for those who are private and public employees. Importantly, there is no income information available in

> 10In the UEUS/LFS, an individual is classified as unemployed if she or he is not engaged in a productive activity and is available for work in the next month if the opportunity arises.

11
the UEUS/LFS for those who are engaged in self-employment jobs. The monthly wage is overall higher among public employees than private employees. In addition, monthly wages are higher in larger towns, as the average wages are ETB 762 in small towns and ETB 1,292 in Addis Ababa. We also calculate hourly wages by dividing earnings by the number of hours worked.

# **4 Empirical Strategy**

Our primary econometric approach is pooled ordinary least squares (OLS) and 2SLS at the individual level. The baseline pooled OLS model for individual _i_ in town _j_ at year _t_ (either 2012 or 2013) is where _yijt_ , is a binary indicator of a labor outcome for individual _i_ (for example, one if self-employed and zero otherwise), _Popjt_ indicates the log population of town _j_ at year _t_ , and _β_ 1 is the parameter of interest indicating the probability change in the outcome with respect to the change in a log town population. _Xijt_ is a vector of individual or household characteristics for control.

We control for a set of individual and household characteristics. Those include individual’s age, sex, marital status, and household size. Their educational attainment is controlled by the following categories: no education, primary incomplete, primary complete, secondary incomplete, secondary complete, higher education, and adult education. The summary statistics are reported in Appendix B.

_γt_ is a year fixed e↵ects to deal with year-specific e↵ects since the UEUS and the LFS data were collected in di↵erent years. The standard error is clustered by the town level.

We rely on IVs to reduce an endogeneity bias. There are three sources of possible endogeneity problems in our models. The first stems from the correlation of unobserved town characteristics with town population and labor outcomes. Another problem is reverse causality, that is, workers move to towns where labor outcomes would be better. These two problems are econometrically equivalent. The third problem is about how to distinguish location e↵ects from the sorting of workers by observed and unobserved skills. We deal with the first and second problems by employing an IV approach that is commonly used in the literature (Chauvin et al. 2017; Ciccone and Hall 1996; Combes et al. 2010; Duranton 2016). We instrument the current town population by the populations 12
in 1960 (and 1984, when there are two endogenous variables). The identification assumption is that the past populations a↵ect the current labor outcomes only through the current population size. As is mentioned in the previous literature, we consider this instrumental variable as a strategy to reduce the bias rather than a perfect solution for the endogeneity issue.

where _Zj_ is historical population of town _j_ . The second stage estimation is To examine heterogeneity in the agglomeration e↵ects, we estimate the 2SLS models in equations (3) and (4) for subsamples based on workers’ demographic and educational characteristics as well.

# **5 Results**

## **5.1 Employment Types: Self-Employment versus Wage Employment**

We first report OLS estimation results for employment types, measured as the binary indicator. Table 2 shows the association between the probability of individuals engaging in self-employment and the town population. The baseline result with no controls in column 1 shows the coefficient estimate for the log of population as -0.067, meaning that a log increase in population size is associated with a 6.7 percentage point decrease in the chance of individuals engaging in self-employment (or the share of self-employment at the town level) as opposed to wage employment. Additional controls for Addis Ababa fixed e↵ect (column 2), regional fixed e↵ects (column 3), demographic (column 4), and education (column 5) do not substantially change the estimates.

We then estimate the 2SLS models in equations (3) and (4) to reduce an endogeneity bias. Table 3 summarizes the results of 2SLS estimations with subsamples, as well as OLS results for the sake of comparison. The results confirm that the town population size is negatively correlated with the probability of workers taking on self-employment jobs (as opposed to wage employment). The estimated probability change is 4.5 percentage points (column 2), which is smaller than the OLS estimate (5.8 percentage points in column 1). Nevertheless, the magnitude is not negligible as a log increase in population is associated with 4.5 percentage points higher chance in wage employment.

13
Table 2: Probability of Engaging in Self-employment and Population Size (OLS)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Ln Pop|-0.067<sup>_⇤⇤⇤_</sup>|-0.077<sup>_⇤⇤⇤_</sup>|-0.082<sup>_⇤⇤⇤_</sup>|-0.077<sup>_⇤⇤⇤_</sup>|-0.062<sup>_⇤⇤⇤_</sup>|
||(0.003)|(0.008)|(0.008)|(0.007)|(0.006)|
|Addis Ababa||0.055<br>(0.036)|0.098<sup>_⇤⇤⇤_</sup><br>(0.034)|0.089<sup>_⇤⇤⇤_</sup><br>(0.032)|0.065<sup>_⇤⇤⇤_</sup><br>(0.025)|
|Demographic|No|No|No|Yes|Yes|
|Education|No|No|No|No|Yes|
|Region|No|No|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|
|Mean|0.48|0.48|0.48|0.48|0.48|
|Adjusted _R_<sup>2</sup>|0.07|0.07|0.08|0.11|0.24|
|N|74,297|74,297|74,297|74,285|73,955|

Notes: Standard errors in parentheses, _⇤p < ._ 10 _,⇤⇤ p < ._ 05 _,⇤⇤⇤ p < ._ 01. Dependent variable is a binary indicator about individual’s employment type (one=self-employment; zero otherwise). Control variables are demographic: gender, age (ten years increment), marital status, and household size, and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education.

For comparison, completing secondary education (compared to incomplete of secondary school), on average, is expected to raise the probability of obtaining a wage job by 4.8 percentage points (see Appendix C for the coefficients for education covariates). A magnitude of a log population increase is equivalent to the e↵ect size of this educational attainment.

In columns 3-6 in Table 3, we explore the heterogeneity in the link between the probability of engaging in self-employment jobs and town population size. We find that female and/or young workers are particularly more likely to find self-employment opportunities in larger towns. The estimate is smaller among male workers (-0.027 in column 3) than female workers (-0.065 in column 4). Column 5 reports the coefficient when focusing on only young workers (-0.078), which is by far larger than the non-youth workers (-0.028 in column 6). These results suggest that agglomeration economies bring benefit to female and young workers who would not otherwise find better employment opportunities.

Another important result is that less-educated workers are more likely to find better employment opportunities in larger towns. Table 4 examines the heterogeneity by workers’ education levels.

14
Table 3: 2SLS for the Probability of Engaging in Self-employment by Demographic

||OLS|||2SLS|||
|---|---|---|---|---|---|---|
|Sample|(1)<br>All|(2)<br>All|(3)<br>Male|(4)<br>Female|(5)<br>Youth|(6)<br>Non-Youth|
|Ln Pop|-0.058<sup>_⇤⇤⇤_</sup><br>(0.006)|-0.045<sup>_⇤⇤⇤_</sup><br>(0.010)|-0.026<sup>_⇤⇤_</sup><br>(0.011)|-0.065<sup>_⇤⇤⇤_</sup><br>(0.012)|-0.078<sup>_⇤⇤⇤_</sup><br>(0.015)|-0.028<sup>_⇤⇤⇤_</sup><br>(0.009)|
|Addis Ababa|Yes|Yes|Yes|Yes|Yes|Yes|
|Control|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean|0.48|0.48|0.44|0.53|0.46|0.48|
|Underidentifcation||18.62|19.32|17.73|17.00|19.16|
|Weak identifcation||73.71|69.62|78.16|83.99|69.67|
|N|73,955|73,955|41,241|32,714|18,617|55,338|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Youth is defined as worker age less than 25 years. Dependent variable is a binary indicator about individual’s employment type (one=selfemployment; zero otherwise). Control variables are demographic: gender, age (ten years increment), marital status, and household size, and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education.

Columns 1 and 2 report benchmark OLS and 2SLS results, respectively. The absolute value of the coefficient estimate for the town population is higher when the 2SLS is estimated for workers with lower education levels (-0.075 in column (3)) than those with higher education levels (not significantly di↵erent from zero in column (4)). It demonstrates that workers with low education levels tend to benefit more from agglomeration economies in terms of employment types. Their chance of engaging in self-employment jobs significantly decreases in larger towns. By contrast, the chance of workers with higher education levels, such as those who have completed secondary or higher education, engaging in self-employment jobs is not so di↵erent depending on town population size. This is probably because they could work in wage jobs either in small towns or large towns.

# **5.2 Formal and Informal Jobs**

Next, we present the results of our analyses about the associations between the town population and the share of formal and informal jobs. We use two di↵erent definitions of formal jobs: jobs with book account (columns 1 and 2) and jobs with license (columns 3 and 4). For each of these, we separately estimate the 2SLS for all samples and the subsample with public employees excluded.

15
Table 4: 2SLS for the Probability of Engaging in Self-employment by Education

||OLS||2SLS||
|---|---|---|---|---|
|Sample|(1)<br>All|(2)<br>All|(3)<br>Low education<br>(Primary Completed)|(4)<br>High education<br>(Higher than Primary)|
|Ln Pop|-0.058<sup>_⇤⇤⇤_</sup>|-0.045<sup>_⇤⇤⇤_</sup>|-0.075<sup>_⇤⇤⇤_</sup>|0.001|
||(0.006)|(0.010)|(0.015)|(0.010)|
|Addis Ababa|Yes|Yes|Yes|Yes|
|Control|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|Mean|0.48|0.48|0.64|0.29|
|Underidentifcation||18.62|17.11|19.94|
|Weak identifcation||73.71|62.05|83.99|
|N|73,955|73,955|38,017|35,938|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Control variables are demographic: gender, age (ten years increment), marital status, and household size, and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education.

The main results in columns 2 and 4 show that the estimated probability change of formal jobs with respect to the town population is 2.5 percentage points (book account) and 5.1 percentage points (license), respectively.

# **5.3 Other Labor Outcomes**

## **Labor Force Participation and Unemployment**

Table 6 presents the labor force status of individual workers; whether the individual is active in the labor force or not. Given the di↵erence in the motivation of labor force participation between single/married and male/female, we estimate OLS and 2SLS models for each of their combinations separately. The 2SLS results in columns 5 to 8 do not show clear link between the town population and labor force participation, except for female. A log change in town population is associated with 3.6 percentage point increase in labor participation for single women. The coefficient estimate is also positive for married women but not statistically significant. That said, the chance of unemployment among active labor forces is likely to be higher in larger towns.

Panel B, Table 6 summarizes the estimation results of OLS and 2SLS for individual workers’ 16
Table 5: 2SLS for the Probability of Engaging in Formality Employment

||Form<br>Have boo|ality<br>k account|Form<br>Have|ality<br>licence|
|---|---|---|---|---|
|Sample|(1)<br>Public<br>Private<br>Self-employment|(2)<br>Private<br>Self-employment|(3)<br>Public<br>Private<br>Self-employment|(4)<br>Private<br>Self-employment|
|Ln Pop|-0.000|0.024<sup>_⇤⇤_</sup>|0.029<sup>_⇤⇤⇤_</sup>|0.051<sup>_⇤⇤⇤_</sup>|
||(0.010)|(0.011)|(0.010)|(0.013)|
|Addis Ababa|Yes|Yes|Yes|Yes|
|Control|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|Mean|0.41|0.20|0.66|0.54|
|Underidentifcation|18.58|17.61|18.58|17.61|
|Weak identifcation|72.90|72.36|72.90|72.36|
|N|66,578|48,447|66,578|48,447|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Control variables are demographic: gender, age (ten years increment), marital status, and household size (and its squared term), and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education unemployment status. In general, well-educated workers attempt a transition to better-paid and/or formal jobs that are o↵ered more in larger towns. As described in World Bank (2016a), undertaking underpaid informal jobs during such a job seeking period could lower the chance of the worker’s finding a formal job later on. Workers who are aware of this tend choose to be unemployed until his or her job searching succeeds. However, the 2SLS estimation results imply that unemployment and town population size are associated only among married women.

# **Working Hours and Underemployment**

As discussed in Section 3, underemployment—rather than unemployment—is prevalent among lesseducated poor workers. Thus, it is useful to examine how working hours and underemployment among employed workers varies across towns with a di↵erent population size. Table 7 summarizes the estimation results of OLS and 2SLS on the log of minutes worked during the last month. The estimated elasticity of working hours to the town population size in column 2 is 6.2 percent. Distinguishing the e↵ects for working hours on private wage jobs (columns 4–6) and self-employment jobs (columns 7–9) shows that the elasticity is 6.1 percent among self-employed workers. This 17
Table 6: 2SLS for Labor Force Participation and Unemployment

||OL<br>|S<br>|||2S<br>|LS<br>||
|---|---|---|---|---|---|---|---|
|(1)<br>Single<br>Male|(2)<br>Married<br>Male|(3)<br>Single<br>Female|(4)<br>Married<br>Female|(5)<br>Single<br>Male|(6)<br>Married<br>Male|(7)<br>Single<br>Female|(8)<br>Married<br>Female|
|**Panel A: Not in labo**|**r force**|||||||
|Ln Pop<br>0.013<sup>_⇤_</sup>|0.004<sup>_⇤⇤⇤_</sup>|-0.001|0.026<sup>_⇤⇤⇤_</sup>|-0.017|-0.002|-0.035<sup>_⇤⇤_</sup>|-0.012|
|(0.007)|(0.002)|(0.008)|(0.007)|(0.016)|(0.003)|(0.014)|(0.015)|
|Mean<br>0.32|0.034|0.42|0.27|0.32|0.034|0.42|0.27|
|Adjusted _R_<sup>2</sup><br>0.2|0.053|0.17|0.082|0.19|0.052|0.16|0.075|
|Observation<br>28,917|26,061|24,974|29,657|28,917|26,061|24,974|29,657|
|**Panel B: Unemploym**|**ent**|||||||
|Ln Pop<br>0.021<sup>_⇤⇤⇤_</sup>|0.007<sup>_⇤⇤⇤_</sup>|0.021<sup>_⇤⇤⇤_</sup>|0.060<sup>_⇤⇤⇤_</sup>|0.005|0.002|0.010|0.056<sup>_⇤⇤⇤_</sup>|
|(0.006)|(0.002)|(0.007)|(0.009)|(0.010)|(0.005)|(0.011)|(0.018)|
|Mean<br>0.19|0.049|0.24|0.28|0.19|0.049|0.24|0.28|
|Adjusted _R_<sup>2</sup><br>0.057|0.015|0.043|0.095|0.056|0.014|0.043|0.094|
|N<br>19,493|25,025|14,330|21,209|19,493|25,025|14,330|21,209|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Dependent variables are (a) a binary indicator about labor force status (one = not active; zero = active) and (b) a binary indicator about unemployment (one = unemployed; zero = employed). The estimation includes control variables for demographic: gender, age (10 years increment), marital status, and household size (and square term) and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education. The estimation controls for capital city, Addis Ababa, and the year of the data collection.

finding is corroborated by the results in Panel B, Table 7, which shows the OLS and 2SLS estimations for individual workers’ underemployment status. The estimation result of the 2SLS model without Addis Ababa fixed e↵ects in column 2 shows that underemployment rates are lower in larger towns. Estimating the same models separately for private wage workers (column 5) and self-employed workers (column 8) suggests that the impacts are larger among the latter group.

# **5.4 Nominal Wages**

We then examine the agglomeration e↵ects on labor productivity by regressing the log of nominal wages on the log of city population (Table 8). Caution is required to interpret the results since labor income is reported only for wage workers in the UEUS/LFS, and thus, this analysis does not include self-employed workers. To account for this, we control for workers’ education levels and other characteristics in the models. The baseline OLS models show that the wage elasticity with respect to the town population is positive and statistically significant (7.0 percent without Addis Ababa fixed e↵ect in column 1 and 6.0 percent with Addis Ababa fixed e↵ect in column 2). The 18
Table 7: 2SLS for Working Hours and Underemployment

|Sample|Private|+Self-emplo|yment||Private||Sel|f-employme|nt|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||OLS|2SLS|2SLS|OLS|2SLS|2SLS|OLS|2SLS|2SLS|
|**Panel A: Lo**|**g of workin**|**g hours**||||||||
|Ln Pop|0.121<sup>_⇤⇤⇤_</sup>|0.063<sup>_⇤⇤⇤_</sup>|0.070<sup>_⇤⇤⇤_</sup>|0.031<sup>_⇤⇤⇤_</sup>|0.014<sup>_⇤⇤⇤_</sup>|0.044<sup>_⇤⇤⇤_</sup>|0.110<sup>_⇤⇤⇤_</sup>|0.061<sup>_⇤⇤⇤_</sup>|0.042|
||(0.013)|(0.006)|(0.026)|(0.007)|(0.003)|(0.011)|(0.014)|(0.011)|(0.031)|
|Addis Ababa|Yes|No|Yes|Yes|No|Yes|Yes|No|Yes|
|Underidentifc|ation|2.839|17.97||3.706|16.45||2.743|18.06|
|Weakidentifca|tion|58.54|72.39||188.0|96.59||32.18|62.50|
|N|47,153|47,153|47,153|16,199|16,199|16,199|30,954|30,954|30,954|
|**Panel B: Un**|**deremploy**<br>|**ment**<br>||||||||
|Ln Pop|-0.038<sup>_⇤⇤⇤_</sup>|-0.023<sup>_⇤⇤⇤_</sup>|-0.022<sup>_⇤⇤_</sup>|-0.018<sup>_⇤⇤⇤_</sup>|-0.012<sup>_⇤⇤⇤_</sup>|-0.018<sup>_⇤⇤⇤_</sup>|-0.034<sup>_⇤⇤⇤_</sup>|-0.021<sup>_⇤⇤⇤_</sup>|-0.015|
||(0.005)|(0.003)|(0.010)|(0.005)|(0.002)|(0.007)|(0.006)|(0.004)|(0.012)|
|Addis Ababa|Yes|No|Yes|Yes|No|Yes|Yes|No|Yes|
|Underidentifc|ation|2.832|17.74||3.694|16.61||2.736|17.72|
|Weakidentifca|tion|57.67|71.83||185.5|97.90||31.91|61.77|
|N|49,884|49,884|49,884|16,979|16,979|16,979|32,905|32,905|32,905|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Dependent variables are (a) the log of hours worked during the last week and (b) a binary indicator about underemployment (one = underemployment; zero = not underemployment). The estimation includes control variables for Demographic: Gender, Age (10 years), Marital Status, and Household Size (and square term) and Education: No education, Incomplete primary, Complete primary, Incomplete secondary, Complete secondary, Post-secondary, and Adult education, as well as year fixed e↵ects.

elasticity estimate in the 2SLS without Addis Ababa fixed e↵ects is 5.7 percent (column 3). Adding Addis Ababa fixed e↵ect reduces the elasticity to 3.2 percent, which is not statistically significant. These results imply that the wage premium of large towns is driven by Addis Ababa.

# **5.5 Robustness Check**

## **Excluding Recent Migrants**

Although our analysis has instrumented the town population size, an endogeneity bias due to the sorting of workers is a lingering concern. To assess the bias, we tap into the migration information available in our data.<sup>11</sup> We reestimate our OLS and 2SLS models by excluding recent migrants to towns to highlight agglomeration e↵ects on the stayers. We expect this to reduce the size of the estimated agglomeration e↵ects if sorting has contributed to the e↵ects. Table 9 summarizes the results. As expected, excluding rural-to-urban migrants (columns 2 and 5) and both rural-tourban and urban-to-urban migrants (columns 3 and 6) lower the absolute values of the coefficient estimates for the log of town population. Nevertheless, we still observe significant associations between town population size and the probability of engaging in self-employment jobs.

> 11Unfortunately, only the LFS has such migration information, reducing the sample size for this analysis.

19
Table 8: OLS and 2SLS for Nominal Wages

||(1)<br>OLS|(2)<br>OLS|(3)<br>2SLS|(4)<br>2SLS|
|---|---|---|---|---|
|Ln Pop|0.070<sup>_⇤⇤⇤_</sup>|0.063<sup>_⇤⇤⇤_</sup>|0.057<sup>_⇤⇤⇤_</sup>|-0.032|
||(0.005)|(0.019)|(0.008)|(0.040)|
|Addis Ababa|No|Yes|No|Yes|
|Control|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|Mean|6.52|6.52|6.52|6.52|
|Underidentifcation|||3.71|17.08|
|Weak identifcation|||201.55|95.15|
|N|20,351|20,351|20,351|20,351|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Dependent variable is the log of nominal monthly wages. The estimation includes control variables for demographic: gender, age (ten years increment), marital status, and household size (and square term) and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education.

# **Market Accessibility as Agglomeration Measure**

The analyses so far define the populations of towns based on their administrative boundaries. However, this may not appropriately measure the agglomeration of towns that are well connected with each other. Thus, we estimate the OLS and 2SLS models in Equations 2–4 by replacing the log of town populations with the MAI. The estimation results of OLS and 2SLS in Table 10 support the main findings as workers are more likely to find wage jobs and earn more in towns with the higher MAI. Similarly, underemployment is lower in larger towns. The absolute values of the coefficient estimates for the MAI are larger than those for the log of town population that are reported in the earlier sections.

# **Trimming Small Towns**

Alternatively, one may argue that towns with very small populations should not be considered when measuring agglomeration e↵ects. We estimate our models by excluding towns with a population of less than 10,000, which nevertheless does not substantially change the key findings (see Appendix D). Raising the population threshold from 10,000 to 25,000 does not change the conclusions.

20
Table 9: Population Size and Probability of Engaging in Self-employment by Migrant Status (LFS)

|||OLS|||2SLS||
|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>Exclude<br>rural migrant|(3)<br>Exclude<br>any migrant|(4)<br>All|(5)<br>Exclude<br>rural migrant|(6)<br>Exclude<br>any migrant|
|Ln Pop|-0.058<sup>_⇤⇤⇤_</sup>|-0.045<sup>_⇤⇤⇤_</sup>|-0.052<sup>_⇤⇤⇤_</sup>|-0.039<sup>_⇤⇤⇤_</sup>|-0.020<sup>_⇤⇤_</sup>|-0.030<sup>_⇤⇤_</sup>|
||(0.006)|(0.006)|(0.009)|(0.009)|(0.009)|(0.013)|
|Addis Ababa|Yes|Yes|Yes|Yes|Yes|Yes|
|Control|Yes|Yes|Yes|Yes|Yes|Yes|
|N|46,914|31,638|18,370|46,914|31,638|18,370|

Note: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. The estimation includes control variables for demographic: gender, age (ten years increment), marital status, and household size (and square term) and education: no education, incomplete primary, complete primary, incomplete secondary, complete sec- ondary, postsecondary, and adult education. No migrant from rural excluding rural migrant, but include migrations between urban areas.

Table 10: Summary of 2SLS Estimation: Market Accessibility Index (MAI)

||Self-Emp|loyment|Formal:|License|Underem|ployment|Nominal|Earnings|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||OLS|2SLS|OLS|2SLS|OLS|2SLS|OLS|2SLS|
|MAI|-0.050<sup>_⇤⇤⇤_</sup>|-0.109<sup>_⇤⇤⇤_</sup>|0.021<sup>_⇤_</sup>|0.063<sup>_⇤⇤⇤_</sup>|-0.018<sup>_⇤⇤_</sup>|-0.047<sup>_⇤⇤⇤_</sup>|0.064<sup>_⇤⇤_</sup>|0.164<sup>_⇤⇤⇤_</sup>|
||(0.019)|(0.015)|(0.013)|(0.011)|(0.009)|(0.007)|(0.026)|(0.023)|
|Control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Underidentifcation||3.53||3.63||3.53||3.72|
|Weak identifcation||205.47||201.38||205.47||76.45|
|N|69,571|69,527|65,055|65,012|69,571|69,527|19,577|19,576|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. The estimation includes control variables for demographic: gender, age (ten years increment), marital status, and household size (squared term) and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education.

21
## **High-Density Urban Clusters Based on Grid-Level Population Data**

To check the robustness of the main result using other population data, we conducted the same estimation using the alternative population measure based on the grid-level WorldPop data. While the significance level di↵ers, the rest of the subpopulation analysis shows a similar relationship and magnitude as the analysis in the main text (Appendix E).

## **Using Data from Di↵erent Years**

Finally, we run the regressions for the 2006 and 2015 UEUS data separately to examine whether the associations between the town population size and labor outcomes hold in di↵erent years. The overall results remain unchanged (see Appendix F for the results).

## **Sectoral Shifts: Excluding Agricultural Employment**

Although agricultural employment is a small share of urban employment, it could contribute to a negative relationship between city size and self-employment (because agriculture is less prevalent in larger cities). To eliminate the possibility that the estimation result is driven by changes in sector (which tends to be self-employed), this subsection provides an estimate excluding the workers in the agriculture sector. The overall results remain unchanged (see Appendix G for the results).

# **6 Discussion and Conclusion**

Among the channels of urbanization for economic growth and poverty reduction, the transition in the mode of labor is a critically important factor. Focusing on urban Ethiopia, this study empirically examines the nexus between urban agglomerations and employment changes from selfemployment/informal jobs to wage/formal jobs. Overall, our IV regressions based on individual workers in the 2012/13 LFSs confirm such link, providing several important findings. First, town population size is positively correlated with the probability of individuals engaging in wage/formal jobs (as opposed to self-employment/informal jobs). According to our preferred speci- fication, the probability change in an individual’s chance (or the town-level share) of wage employment with respect to the town population size is 4.4 percentage points. Similarly, positive probability change is estimated for formal employment (5.1 percentage points). As emphasized in the literature on 22
informality (Falco and Haywood 2016; Falco et al. 2011), self-employment or informal jobs are not necessarily ‘last resort’ options in low-income countries. Indeed, profound consumption gains among self-employment households have contributed to urban poverty reduction during the last decade in Ethiopia (World Bank 2019). Nevertheless, there is no doubt that increasing wage/formal jobs is a key to promoting economic development in the long run.

Second, the link between town population size and wage/formal employment is found to be particularly strong among disadvantaged workers. The estimated probability change in wage employment to town population size is 6.4 percentage points among women (as opposed to 2.5 percentage points among men) and 7.7 percentage points among the youth (as opposed to 2.7 percentage points among the non-youth workers). Moreover, a similarly high e↵ect is estimated for unskilled workers (7.4 percentage points). The previous studies of the U.S. and European contexts demonstrate strong agglomeration e↵ects among high-skilled workers. Our analysis of urban Ethiopia instead suggests that unskilled workers may find more opportunities to find better employment arrangements in larger towns, conditional on their observed characteristics.

Third, we also find that other important labor outcomes are associated with town population size in Ethiopia. For example, women are more likely to be active labor forces in larger towns. In addition, larger towns tend to reduce underemployment, the status in which workers are willing to work additional hours. In this respect, wage employment has an advantage, also reflected in the low transition of wage (or formal) workers into poverty over time between 2012 and 2016 (World Bank 2019). These may not necessarily indicate a positive situation if they are simply ‘working poor’, as they are required to work more just to make ends meet in larger towns, where costs of living are also higher. Nevertheless, poverty incidence is lower in larger towns in Ethiopia.

All these findings suggest that it is important to remove barriers to migrations to cities, such as low human capital, low connectivity to cities, lack of property rights for farmland, and lack of information about economic opportunities in cities, to name a few.

Finally, our analysis shows that nominal wages are higher in larger towns after observed worker characteristics are controlled for. In our preferred specification, the elasticity of nominal wages with respect to the town population is estimated to be 5.7 percent. This elasticity, however, becomes smaller if Addis Ababa is excluded (4.0 percent and not statistically significant at the 10 percent level). Our elasticity estimate is similar to the previously reported estimates in other developing 23
countries, such as Brazil and India (3 percent and 8 percent, respectively) (Chauvin et al. 2017) and Colombia (5 percent in Duranton (2016)). That said, it is important to keep in mind that wage employment accounts only for a small portion of existing jobs in Ethiopia. Also, nominal wages may not reflect labor productivity for several reasons, such as the presence of heavily regulated public wages and the lack of worker mobility, among others.

24