metadata: authors: 0: author: Batana,Yele Maweki
1: author: Jarotschkin,Alexandra
2: author: Konou,Akakpo Domefa
3: author: Masaki,Takaaki
4: author: Nakamura,Shohei
5: author: Viboudoulou Vilpoux,Mervy Ever
count: Congo, Democratic Republic of
docna: 0: docna: Demographic and Spatial Disparities in Labor
            Market Outcomes within the Kinshasa Urban Landscape
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Employment and Unemployment,Transport Services
teratopic: Education,Social Protections and Labor,Transport
abstracts: cdata!: This paper examines the labor market and
            jobs in urban Kinshasa, by drawing on a recently collected
            household survey and other data sets. It particularly
            focuses on labor supply and employment patterns, job
            characteristics, and their spatial nexus. The analysis first
            shows that female and young workers are more likely to
            experience unemployment and underemployment than other
            workers in Kinshasa. Second, the availability of good
            quality jobs is still limited in Kinshasa. Third, in
            addition to the scarcity of good jobs, which are
            concentrated in the city core, poor accessibility due to the
            limited connective infrastructure and transport system
            further reduces job opportunities for people living in the outskirts.
geo_reg_key: 517196,517191
repnme: repnme: Demographic and Spatial Disparities in
            Labor Market Outcomes within the Kinshasa Urban Landscape
display_title: Demographic and Spatial Disparities
            in Labor Market Outcomes within the Kinshasa Urban Landscape
listing_relative_url: /research/2021/11/33631241/demographic-spatial-disparities-labor-market-outcomes-within-kinshasa-urban-landscape
topicv3: Transport m1326661 135,Education m1327048 470,Jobs m1503931 2670,Self-Employment m1504000 2698,Unemployment m1504051 2747
publishtoextweb_dt: 2021-11-19T00:00:00Z
dois: 10.1596/1813-9450-9856
historic_topic: Education,Social Protections and Labor,Transport
origu: EFI-AFR1-POV-Poverty and Equity (EAEPV)
no_of_pages: 31
author_keys: 0: author_key: 1156965
1: author_key: 1122717
2: author_key: 1614827
3: author_key: 1620251
4: author_key: 1389722
5: author_key: 1542968
model_extractions: Policy Research Working Paper 9856

# Demographic and Spatial Disparities in Labor Market Outcomes within the Kinshasa Urban Landscape

_Yele Maweki Batana Alexandra Jarotschkin Akakpo Konou Takaaki Masaki Shohei Nakamura Mervy Ever Viboudoulou Vilpoux_ Poverty and Equity Global Practice November 2021
Policy Research Working Paper 9856

# **Abstract**

This paper examines the labor market and jobs in urban Kinshasa, by drawing on a recently collected household survey and other data sets. It particularly focuses on labor supply and employment patterns, job characteristics, and their spatial nexus. The analysis first shows that female and young workers are more likely to experience unemployment and underemployment than other workers in Kinshasa.

Second, the availability of good quality jobs is still limited in Kinshasa. Third, in addition to the scarcity of good jobs, which are concentrated in the city core, poor accessibility due to the limited connective infrastructure and transport system further reduces job opportunities for people living in the outskirts.

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 ybatana@worldbank.org or 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
# **Demographic and Spatial Disparities in Labor Market Outcomes within the Kinshasa Urban Landscape**

Yele Maweki Batana, World Bank Alexandra Jarotschkin, World Bank Akakpo Konou, World Bank Takaaki Masaki, World Bank Shohei Nakamura, World Bank<sup>1</sup> Mervy Ever Viboudoulou Vilpoux, World Bank Keywords: Urban poverty; jobs, unemployment, accessibility, gender JEL Codes: C18, D31, I14, I24, I31, I32 1 Corresponding Author: snakamura2@worldbank.org. We would like to thank Andrew Dabalen, Ruth Hill, Kirsten Hommann and Clarence Tsimpo for useful comments. We also thank the Japan International Cooperation Agency (JICA) and Oriental Consultants Global for sharing the travel survey data. 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.
# **1. Introduction**

The Democratic Republic of Congo (DRC) has been in the seemingly paradoxical situation of being a fast-growing economy with a slow pace of poverty reduction and an increasing number of poor. As peace and security have been gradually restored after years of civil war and violence, the DRC has undergone impressive economic progress. The rate of economic growth ranged from 5.6 percent to 6.2 percent between 2002 and 2008, with a period of decline during the financial crisis in 2009. It picked up once again, registering an average growth rate of 7.5 percent in 2010–2015.

This impressive growth, however, has not translated into much benefit for the poor. Poverty incidence (that is, the population's share of poor people) is declining, but by less than 1 percentage point a year (the poverty rate fell from 69 to 64 percent between 2005 and 2012). Fast population growth driven by a high fertility rate (6.6 per woman in 2014) has resulted in an increase in the number of poor and has added pressure on the DRC’s already low human development. If current demographic and economic trends continue, the number of poor may increase by 8 million by 2030.

A lack of job opportunities—particularly for the youth—has been one of the factors contributing to the slow pace of poverty reduction in the DRC. A high rate of unemployment and underemployment is cited as an indication that the country’s economic growth has not been inclusive. A lack of job opportunities for the youth may create a fertile ground for social unrest, particularly in the context of a post-conflict and fragile state. The World Bank estimates suggest that the DRC needs to create 2 million to 4 million jobs every year to absorb new entrants into the labor market and reduce poverty (World Bank 2018).

This study provides an analysis of the labor market and jobs in urban Kinshasa—the capital of DRC. This focus on Kinshasa is predicated by the fact that it has vast potential to serve as a growth engine for job creation and poverty reduction in the country. Kinshasa is on its way to becoming the largest megacity in Africa over the next 25 to 30 years, registering an influx of 400,000 new inhabitants every year. Thus, the future of economic growth and poverty eradication rests upon the city’s ability to absorb these new entrants and provide them with access to jobs, more importantly, quality jobs. In particular, this study seeks to answer the following questions:

- (a) What are the labor supply and employment patterns in Kinshasa? And who is excluded from the labor market, either being inactive, unemployed, or underemployed?

- (b) What types of jobs, in terms of the economic sectors, informality, and self-employment versus wage, exist in Kinshasa? And where do they spatially concentrate in the city?

- (c) How is the labor market status related to neighborhoods and location characteristics?

To answer these questions, this paper relies primarily on a recently conducted household survey in Kinshasa, and is supplemented with other existing surveys and spatial data sets. The findings are summarized as follows. First, female and young workers are more likely to experience unemployment and underemployment than other workers in Kinshasa. The policy needs to support female and young workers by removing barriers—such as discrimination against them—to their accessing better jobs. Second, the availability of good-quality jobs is still limited in Kinshasa. The majority of existing jobs are low-productive, informal self-employment jobs. Third, in addition to 2
the scarcity of good jobs, which are concentrated in the city core, poor accessibility due to limited connective infrastructure and transport system further reduces job opportunities for people living in the outskirts. In fact, those people with inadequate job accessibility in Kinshasa are poorer, female, and/or young workers. This suggests that a mismatch in the labor market could be effectively reduced by complementing labor policies that support disadvantaged workers (such as female and young workers) with spatial interventions to enhance their mobility and access to economic opportunities.

This paper is structured as follows. Section 2 explains the data used in this paper and describes the labor force and employment conditions in Kinshasa. Section 3 assesses the employment conditions by examining who engages in better/worse jobs and how jobs are spatially distributed. Section 4 concludes.

# **2. Data and background**

## **2.1 Data**

The analysis of this paper relies primarily on a recently collected household survey in Kinshasa. Conducted by the INS of the DRC under the World Bank’s statistical operation in the country, it is an integrated survey, consisting of two surveys, targeting individuals and households. The first part of the survey includes sociodemographic characteristics, education, health, living conditions, employment, urban agriculture and non-agricultural household entrepreneurship. The second component is the household consumption survey.

The Kinshasa Household Survey was collected in two 18-day phases between mid-October and the end of November 2018 on a sample of 2,592 households. Given the outdated last population census—which was collected in 1984—the sampling method for the Kinshasa Household Survey was based on high-resolution satellite imagery. A two-stage stratified sampling design with equal allocation of first-degree enumeration area (EAs) was used for the survey. Kinshasa had been divided into two clusters: urban and rural. The 23 communes/districts of the city were considered as urban and some areas of Maluku, as part of the survey, were considered rural.

Two criteria were used to stratify the city (a) the building density of EAs and (b) typology of Kinshasa areas. The stratification of EAs according to the density criterion was carried out as follows: 1) Low density; fewer than 10 buildings per hectare, 2) Medium density; from 10 to 20 buildings per hectare, and 3) High density; more than 20 buildings per hectare. Precarious neighborhoods are those not suitable for habitation. In the survey, precarious areas are defined based on their characteristics of dwellings and geographic and environmental features.

- Precarious areas. This is a neighborhood whose dwellings are built by the occupants on land acquired in undeveloped areas, on farmland and market gardens, or on uneven ground (exposure to erosion and flooding). Buildings are anarchic and the use of recycled materials (cardboard, plastics, and sheet metal) is common. Most often, they are located in parts of the city abandoned by the more affluent categories: on steep slopes or near industrial areas, which makes them all the more dangerous and where misery is concentrated. Collective facilities (water, electricity, sanitation, and transport) are reduced and availability is low.

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- Non-precarious areas. These are neighborhoods where homes are self-constructed, constructed by stages, or constructed with the intervention of a contractor. They are also neighborhoods built entirely by the state for social housing in accordance with urban standards (drainage system, access to water and electricity, tracing roads, and so on) and habitat.

Each EA is then classified as either precarious or non-precarious by the knowledge of local experts based on the definition above (Figure 1).

The sample was drawn from the following seven strata: (a) low-density non-precarious zone, (b) medium-density non-precarious zone, (c) high-density non-precarious zone, (d) low-density precarious zone, (e) medium-density precarious zone, (f) high-density precarious zone, and (g) rural area.

While the Kinshasa Survey is prepared with an innovative approach, the use of the survey requires caution against its data quality and comparability. It is extremely challenging to design a sampling survey to collect information about households and individuals that represents Kinshasa, due to the lack of a recent population census. The sampling of the Kinshasa Survey, which is the primary data for this study, was prepared by drawing on satellite imagery. In addition, unlike the 1-2-3 household survey of 2012, the stratification of the 2018 survey explicitly captures precarious areas. Despite such an innovative sampling approach, the Kinshasa Survey has concerns about the data quality because of a problem in the data collection process (see Batana et al. 2021a for detailed descriptions). Considering all these factors, this study does not report any absolute number of population based on the survey and does not make a direct comparison between the Kinshasa Survey and the 1-2-3 household survey.

The analysis of job accessibility draws on the 2017 JICA Commuter Survey, which was conducted in 2017 with the aims of informing the Urban Transport Master Plan toward 2030 and identifying priority projects. The study area of the survey mainly covers urban parts of Kinshasa Province and spans over 1,450 km<sup>2</sup> (out of the total area of Kinshasa covering 9,985 km<sup>2</sup> ). For the survey, 8,000 households were sampled and randomly selected based on a satellite image of the study area. Surveyors were dispatched to visit the selected households and conduct face-to-face interviews with their representatives to answer the survey questionnaires. The questionnaire was designed to collect socioeconomic and commuting information of each household member.

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which is about 10 percentage points lower than that of men (Table 2).<sup>3</sup> While the male workingage population is more likely to enter the labor market, male labor force participants are more likely to be unemployed than women. The unemployment rate among men (8.7 percent) is higher than among women (5.6 percent), implying the existence of discouraged women who do not seek employment. The unemployment rate is particularly high among male and young workers, reaching as high as 18.4 percent.

Poor male workers are more likely to be unemployed than non-poor male workers, while there is no substantial difference in labor status between poor and non-poor women. The unemployment rate among the poor male labor force is relatively high (12 percent). By contrast, poor women’s unemployment rate is only 5 percent. Poverty does not appear to have influenced labor force participation status, as the LFPR of poor women is not particularly low (54 percent) compared to other women.

**Table 2: Labor force status in Kinshasa by age, sex, and poverty (percentage)**

||**All**|**Male**|**Female**|
|---|---|---|---|
|**(A) Working-age population**||||
|Employed|56.1|59.8|52.7|
|Unemployed|4.4|5.7|3.1|
|Not active|39.5|34.5|44.1|
|Labor force participation rate|60.5|65.5|55.9|
|Unemployment rate|7.2|8.7|5.6|
|**(B) Only youth**||||
|Employed|23.8|23.0|24.4|
|Unemployed|3.7|5.2|2.4|
|Not active|72.5|71.8|73.2|
|Labor force participation rate|27.5|28.2|26.8|
|Unemployment rate|13.5|18.4|8.9|
|**(C) Poor**||||
|Employed|53.2|55.3|51.2|
|Unemployed|5.0|7.3|2.7|
|Not active|41.8|37.4|46.1|
|Labor force participation rate|58.2|62.6|53.9|
|Unemployment rate|8.5|11.8|5.0|

_Source_ : Kinshasa Survey 2018.

The female (particularly young) working-age population with higher education faces a particularly high rate of unemployment. A simple look at unemployment rates and workers’ educational attainment does not show any clear correlation (Panel A in Figure 3). The unemployment rate is 5–8 percent among those with primary education, secondary education, and higher education. Breaking down the relationship by gender, however, shows a contrasting pattern. The

> 3 Labor force is defined in this report as active if an individual either (a) worked during the last 7 days, (b) did not work during the last 7 days but had a job (that is, absent), or (c) did not work but looked for a job. The labor force participation rate is calculated as the share of active working-age population. Unemployment rate is calculated as the ratio of working-age population with no job to the total number of active working-age population.

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A poor business environment in urban Kinshasa may be at the heart of underemployment. The DRC remains among the most challenging countries in the world to do business. The 2017 Doing Business Report ranks the country 184 of 190 and notes that it performs poorly across all indicators. Similarly, the 2016–2017 Global Competitiveness Index ranked the country 129 of 138. A lack of basic services and core infrastructure—coupled with a weak regulatory environment— coalesces to impede firm entry and the creation of good-quality jobs. This results in a proliferation of informal self-employment jobs with low productivity, and of erratic piecemeal activities with irregular working hours.

# **2.3. Job characteristics**

A large proportion of jobs in Kinshasa are private, informal, and/or self-employment jobs. In 2018, public sector jobs accounted for only 14 percent of jobs in Kinshasa.<sup>4</sup> Around 60 percent of jobs are informal, in that they do not offer a national identification number (IDN) and/or do not keep formal accounting books (Table 3). About half the jobs in Kinshasa are wage jobs, while the other half are self-employment jobs. Not surprisingly, informal jobs are predominantly selfemployment jobs. However, formal jobs are composed of both wage and self-employment jobs. In other words, all self-employment jobs are not necessarily informal, as 32 percent of formal jobs are actually self-employment.

**Table 3: Formality and occupation types in Kinshasa**

||**%**|
|---|---|
|Formal and wage|23.9|
|Formal and self-employment|14.2|
|Informal and wage|9.1|
|Informal and self-employment|43.4|
|Formal total|40.3|
|Informal total|59.7|
|Wage total|45.8|
|Self-employment total|44.9|

_Source_ : Kinshasa Survey 2018.

_Note:_ Occupation types other than wage and self-employment (such as employer and family worker) are not shown.

The majority of jobs in Kinshasa are service sector jobs. With a small fraction of jobs in agriculture (5.9 percent) and manufacturing (7.8 percent), about 80 percent of jobs in Kinshasa are in the service sector (Table 4).<sup>5</sup> Kinshasa remains largely a local city—as opposed to a global city that is open to regional and global trade, markets, and investments—and it produces fewer internationally tradable goods and services (60 percent) than other world cities (Lall, Henderson, and Venables 2017).

> 4 Unless otherwise noted, this section only looks at jobs of the working-age population.

> 5 For this analysis, the service sector includes commerce; transportation, storage, and communication; financial, insurance, and real estate; public administration; and other services.

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was significantly higher than the share of full-time jobs (41 percent). Full-time jobs are significantly more likely to be in wage employment than part-time jobs. Moreover, part-time jobs are more likely to be in agriculture and fishing. Finally, full-time jobs are statistically significantly more likely to be in jobs related to electricity, gas and water supply.

**Table 5: Characteristics of full-time and part-time jobs (percentage)**

||**Full-time**|**Part-time**|**Difference**|
|---|---|---|---|
|Formal|46.4|29.5|16.9***|
|Informal|53.6|70.5|−16.9***|
|Wage|51.1|34.7|16.4***|
|Self-employment|41.4|51.0|−9.5**|
|Agriculture and fishing|3.7|10.3|−6.6***|
|Mining and quarrying|0.0|0.0|0.0|
|Manufacturing|8.0|5.8|2.3|
|Electricity, gas, and water supply|0.6|0.1|0.5**|
|Construction|5.1|8.1|−3.0|
|Commerce|44.4|36.7|7.8*|
|Transportation, storage, and communication|7.0|6.0|0.9|
|Financial, insurance, and real estate|1.4|0.7|0.7|
|Public administration|7.5|6.1|1.4|
|Other services|22.1|26.1|4.0|

_Source_ : Kinshasa Survey 2018. _Note_ : Difference-in-means tests are conducted. ***p < 0.01; **p < 0.05; *p < 0.1.

# **3. Assessment of employment conditions**

## **3.1 Who engages in better/worse jobs?**

Younger workers endure less favorable employment situations, as they are more likely to engage in private sector jobs, informal jobs, and/or part-time jobs. The previous section shows that the youth are facing higher unemployment rates. Even among the employed, young workers appear to work in unfavorable conditions (Table 6). The chance of working in public sector jobs is limited among workers in the younger cohort. Only 6 percent of workers in the cohort of ages 15–24 (and 9 percent of workers of ages 25–34) engaged in public sector jobs in 2018.<sup>6</sup> In addition, the youth (ages 15–24) tend to work in informal jobs (70 percent) and/or work for less than 25 hours per week (45 percent).

Gender gap in employment conditions persists, as demonstrated by higher unemployment rates among highly educated women and the higher share of female workers in informal employment. As observed in the previous section, the unemployment rate of female working-age population with tertiary education is remarkably high. Female workers are also more likely to engage in informal jobs. In 2018, 70 percent female workers engaged in informal jobs, as opposed to 48 percent male workers (Table 6). As such, only a small fraction of female workers are managers or skilled employees.

> 6 Among the public sector employees, the workers of ages 15–24 and 25–34 constitute 5 percent and 19 percent, respectively.

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Compared to non-poor workers, poor workers are more likely to engage with jobs that are in the private sector, informal, part-time, and self-employment. Working in the public sector is a limited option to poor workers (10 percent, as opposed to 19 percent among non-poor workers) (Table 6). More than 70 percent of poor workers have informal jobs (as opposed to 49 percent among nonpoor workers). Poor workers are also more likely to work with part-time jobs (37 percent) and selfemployment jobs (57 percent).

**Table 6: Employment conditions in Kinshasa by age (percentage)**

|||**Age **||**S**|**ex**|**Po**|**verty**|
|---|---|---|---|---|---|---|---|
||**15–24**|**25–34**|**35–64**|**Male**|**Female**|**Poor**|**Non-poor**|
|Private|93.8|90.2|82.5|82.0|90.6|90.0|80.9|
|Public|6.2|9.3|17.3|18.0|9.4|10.0|19.1|
|Formal|30.0|39.1|42.8|52.1|28.8|28.9|51.2|
|Informal|70.0|60.9|57.2|47.9|71.2|71.1|48.8|
|Part-time (<25 hours/week)|44.6|30.4|26.6|27.4|32.4|36.9|23.2|
|Full-time (>25 hours/week)|55.4|69.6|73.4|72.6|67.6|63.1|76.8|
|_Types of occupation_||||||||
|Managers|2.3|9.6|12.4|14.5|6.0|6.0|15.9|
|Skilled employee|9.9|18.0|17.1|23.2|9.0|13.1|22.5|
|Unskilled employee|16.9|10.1|9.1|14.2|5.5|9.9|9.4|
|Family worker|26.8|6.9|9.1|12.1|8.3|14.1|4.9|
|Own-account worker|44.1|55.3|52.2|35.9|71.1|56.9|47.2|

_Source_ : Kinshasa Survey 2018.

Workers’ education levels are clearly associated with their employment conditions. More specifically, workers with higher education are more likely to have wage/salaried jobs (76 percent), and work in the formal (68 percent) and/or public sectors (36 percent) compared to those with lower levels of education (Figure 6). In contrast, workers who have only preschool/primary education usually end up in the informal sector (83 percent) and are mostly self-employed (64 percent) in the private sector.

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<!-- Start of picture text --> (C) Economic sectors<br>Agriculture ——<br>. —iod+4<br>Manufacturing | oo<br>Utilities | pp<br>. Hii<br>Construction |<br>Commerce —_Ey}=<br>Transport —_Ey|<br>+ . —#!\!"_ ld<br>Financial | |_g ———___,<br>Public administration a 1<br>Other . LL +4<br> services | , a<br>0 200 400 600 800 1,000<br>Wage/revenue from primary work last month<br>excludes outside values<br><!-- End of picture text -->
There is a clear gender gap with regard to labor income. As previously discussed, the education levels of women and their employment characteristics are different from men in that the former tend to be less educated and are more likely to be employed in the informal sector. Even after accounting for all these demographic and employment characteristics, male workers seem to have much higher labor income than female workers. Our regression results suggest that the earnings of male workers are 57–82 percent higher than those of female workers.<sup>8</sup> As such, gender inequalities are observed not only along demographic and employment characteristics but also in the baseline productivity of jobs that they have and/or gender-based discrimination.

The formality of jobs positively correlates with earning levels—that is, workers in the informal sector have about 64–68 percent lower income than workers in the formal sector. As discussed earlier, a significant share of jobs available in Kinshasa is informal in nature (without proper registration or formal accounting books). One of the possible reasons for this is a perennial shortage of wage jobs, which pushes workers into low-quality, informal jobs. There is a growing need for the creation of good-quality jobs by improving the business environment, which would in turn attract productive firms and private investments. Not only does it entail greater investments in key urban services and infrastructure, expanding access to jobs also requires capacity building for the government to efficiently administer rules and regulations that could facilitate the creation of quality jobs in Kinshasa (for example, reducing administrative time and costs associated with the formalization process).<sup>9</sup> Earnings differentials across sectors appear to be driven partly by different hours of work coupled with varying levels of productivity. There is a significant gap in earnings between service and industry jobs, on one hand, and agriculture, on the other. However, once hours of work and other demographic characteristics are accounted for, this gap is no longer statistically significant. In agriculture, the average hours of work per week was 28 hours, which was significantly lower than other sectors like industry (37 hours) and services (46 hours). Many workers who engage in agriculture, agro-business and fishing in Kinshasa are indeed underemployed compared to other sectors. The underemployment in this sector is likely due to the lack of connectivity and other incentives for the development of agricultural production. However, these workers also have limited access to more productive jobs in urban centers due to higher costs of living and a lack of connective infrastructure—a topic that is discussed more extensively in the following section.

# **3.2. Spatial distribution of jobs**

The geographical distribution of jobs within urban Kinshasa reveals high concentration of jobs in a few pockets within the city. In Kinshasa, more than 70 percent of workers work at office or a particular, non-home workplace. For the spatial analysis of jobs within urban Kinshasa, we rely on the 2017 JICA Commuter Survey, which provides detailed information on the locations of jobs and means of transportation to reach those jobs.<sup>10</sup> Panel (A) in Figure 8 shows the estimated number of jobs per square kilometers at the quartier level. Panel (B) presents spatial variation in the index

> 8 A previous World Bank study in the DRC also highlights the importance of income disparities between men and women especially in urban areas where women appear to be disadvantaged (World Bank 2014). 9 World Bank (2018b, 39).

> 10 See Annex A for details on the 2017/18 JICA Commuter Survey.

16
<!-- Start of picture text -->
[Eegena —<br>a<br>° CBD ;<br>\Skm J<br>| 40km aay<br>15km He .,<br>| Study area co | |Fe<br>I% of jobs. accessible9 (all jobs| i| Sibico)et |of<br>[SS 1.5% - 5.1% a ee eet<br>MMM 5.2% - 13.8% a 7 Heed<br>MM 13.9% - 26.5% f _ \ foo<br>MM 26.6% - 40.3% f TM~ \ ATH<br>ealize a “pieces tania<br>| Pr Pate ee(Pet eee<br>Eat eh eet eee EH EF<br>oTM4 :} Z oe~<br>\ ia S,<br>Nop f<br>* (4 4 :<br>0 275 55 "1 16.5 2 , a<br>Seics .<br><!-- End of picture text -->

<!-- Start of picture text --> Legend =]<br>* CBD a<br>15k va<br>__110km th<br>15k y<br>| Study area fh -<br>_...| Quartier boundarie — >»<br>mm113op Geney- - 746falllobs) i5 5q<br>lm 747 - 2823 |<br>mmm 2824 - 6075 a pa<br>ll 6076 - 17039 : _. : yen<br>mm >=17040 yo \ ; 4 fo<br>is ee 8<br>y # eis<br>\e ‘ Seat<br>‘5‘ :i an weae<br>N f ‘ ~~<br>So f<br>i ‘oan<br>0 275 55 1" 16.5 2s. « ae<br>OO ———<br><!-- End of picture text -->
<!-- Start of picture text --> Net monthly income<br>31<br>° * -<br>8<br>2 50<br>z<br>=o<br>—T<br>3<br>z 28 4<br>oN 16 10 3<br>7 6 5 5<br>><br>o 1 1<br><=5km 5-10km 10-15km >15km<br>(MG Less than $25 (EL $25-s199<br>[MG $200-$399 (755 More than $400<br><!-- End of picture text --> <!-- Start of picture text --> Distance from City Core<br>8 73<br>63 66<br>Qofo} 58<br>g<br>Zo<br>5<br>s<br>o<br>g ot<br>0122209 1 112335579 | 253328998 | 6.24328<br>lo} <=5km 5-10km 10-15km >15km<br>(ER) Agricutture and Fishing [EEE Mining and quarrying<br>ha Manufacturing Lay oeaiciy, gas and water<br>[2 construction GE) commerce<br>[EG] Transportation, storage and Financial, insurance, and<br>[EEE Pubic Administration (I other services<br><!-- End of picture text -->

<!-- Start of picture text --> Employment Status<br>So<br>© 74<br>69 64<br>8<br>£g 47 44<br>Ss<br>= J<br>xe}<br>x 9s 29<br>»<br>i=}<br>N<br>6 8 6 4<br>o <=5km 5-10km 10-15km >15km<br>(GG Wagelsalaried © Employer<br>[GS Self-employed<br><!-- End of picture text --> <!-- Start of picture text --> Sector<br>oS<br>=<br>83 83 so<br>Ed<br>S 2<br>=¥o 55<br>So<br>= 45<br>k>S° So<br>8 17 17<br>10<br>o<br><=5km 5-10km 10-15km >15km<br>HES Public §=E Private<br><!-- End of picture text -->
<!-- Start of picture text --> Sex Youth<br>2 = | 2 =<br>ge 83 m8<br>=2 a 25 an 25 =Zo 2 23 16 —<br>So 20 2120 5TM<br>=e md 15. od 2<br>2<br>=<br>o Non-youth ‘Youth<br>Female Male (25-64) (15-24)<br>Education Net monthly income<br>Fs = Es 7a<br>w s o at<br>== = 36 = Sits) - =<br>s x a ( 5 en<br>28 i ra] Pa] 18 46 FA] 9 10 14 32 ;<br>: o<br>Less S25-5199 §200-5209 More<br>a than than<br>Preschool/Primary Secondary University B25 3400<br>[) <=5km [Ss 5km-10km<br>[9 10km-15km_—— [== 15km<br><!-- End of picture text -->
<!-- Start of picture text --> Legend a<br>* — Job locations yd e<br>——— Origins to job destinations K io<br>| | 10km yf» 4 \<br>|_| Study area a Vif WY Lif » veo<br>— WEG Wid ; “f]<br>: Hl PS ae Ny. ‘<br>Nhe ra<br>cl eC , p j<br>| ,<br>O 275 55 W 16.5 22 Oe - a ed<br><!-- End of picture text -->

<!-- Start of picture text --> 31<br>3<br>23<br>5 g<br>5 2<br>¢ © ¢ © RY ¢ ¢<br>5 $<br>3<br><!-- End of picture text -->
<!-- Start of picture text --> Sex Youth<br>62 65<br>¢ o° ~ 2 S 50<br>a<br>=$3 So | os<br>Be= 36 ra£ &g =z 13 37<br>eM 42 43. «14 9 10<br>3 oO !<br>° Non-youth Youth<br>Female Male (25-64) (15-24)<br>Education Net monthly income<br>PaoO2 7 60 ,Bo 2 at 61<br>é o 53 49 3 - 54 nu 4245<br>29 5 23<br>‘So oT 25 #28 15 14 1948 43 3<br>Ro 14 20 25 1<br>as} 3 13 o<br>0 1 Less 25-6199 3200-3399 More<br>a than than<br>Preschool/Primary Secondary University $25 $400<br>(Welk = Busstaxi<br>(SS car =J Motorcycle<br><!-- End of picture text -->
<!-- Start of picture text --> °<br>3S<br>eo)<br>679<br>651<br>aboSs<br>1S)<br>£&<br>osZzfo) 424<br>vt<br>S<br>a<br>no<br>c<br>£<br>Fo<br>S<br>A<br>131<br>°o<br>Less $25-$199 $200-$399 More<br>than than<br>$25 $400<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>o<br>702<br>& i]<br>Ss<br>3<br>3°8<br>5<br>oy 457<br>£ 387<br>Q—<<br>£<br>fo)<br>Bo<br>eR 163<br>= N<br>Co<br><=5km 5-10km 10-15km >15km<br><!-- End of picture text --> <!-- Start of picture text --> °<br>v<br>36<br>$8<br>3<br>z<br>‘= 24<br>5 23<br>Eo<br>7c<br>= 14<br>=<br>8<br>2<br>So<br><=5km 5-10km 10-15km >15km<br><!-- End of picture text -->
<!-- Start of picture text --> Legend a<br>© Pax Van Sto AY<br>° Bus Stop aafe<br>* Minibus Stop v .<br>5k hb \<br>10km is i<br>15km<br>Study_Area foo a : \;} “i ceetetcc\<br>/ if? A<br>ey... ve SN<br>xwoe4 . pg2, w ;<br>tet aN a<br>oNNoLTE NEZ<br>a— ra faeen A ?<br>ae iA /fad<br>c ;|<br>0. 275 55 11 16.5 Zi iaes on<br><!-- End of picture text -->

<!-- Start of picture text --> 52<br>o 50<br>o<br>e<br>5<br>=<br>Bo<br>6°<br>= 33<br>&o<br>s N ¥v &Rw<br>s &<br>Ye2 w§<br><!-- End of picture text -->
for the development of an affordable transport system (e.g., minibus) and transport subsidies to disadvantaged workers for job searching.

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# **Annex A: Supplementary tables and figures**

**Table A.1: Labor income regression (Heckman’s selection model)**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|_Demographic variables_|||||
|Age|0.109<sup>***</sup>|0.109<sup>***</sup>|0.110<sup>***</sup>|0.111<sup>***</sup>|
||(0.026)|(0.026)|(0.030)|(0.030)|
|Age squared|-0.001<sup>***</sup>|-0.001<sup>***</sup>|-0.001<sup>***</sup>|-0.001<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Male|0.579<sup>***</sup>|0.600<sup>***</sup>|0.454<sup>***</sup>|0.466<sup>***</sup>|
||(0.080)|(0.082)|(0.092)|(0.092)|
|Secondary|0.308<sup>***</sup>|0.275<sup>***</sup>|0.241<sup>**</sup>|0.198|
||(0.104)|(0.106)|(0.123)|(0.128)|
|University|0.957<sup>***</sup>|0.888<sup>***</sup>|0.850<sup>***</sup>|0.772<sup>***</sup>|
||(0.119)|(0.123)|(0.152)|(0.158)|
|Other|-0.182|-0.224|-0.197|-0.339|
||(0.279)|(0.276)|(0.262)|(0.255)|
|_Job characteristics_|||||
|Industry||0.670<sup>***</sup>|0.487<sup>**</sup>|0.332|
|||(0.210)|(0.220)|(0.254)|
|Services||0.739<sup>***</sup>|0.375<sup>*</sup>|0.221|
|||(0.195)|(0.198)|(0.236)|
|Other||0.680<sup>***</sup>|0.306|0.172|
|||(0.202)|(0.220)|(0.256)|
|Private||-0.057|||
|||(0.103)|||
|International organization||1.301<sup>***</sup>|||
|||(0.328)|||
|Informal|||-0.447<sup>***</sup>|-0.393<sup>***</sup>|
||||(0.094)|(0.093)|
|Hours of work last week|||0.006<sup>***</sup>|0.006<sup>***</sup>|
||||(0.002)|(0.002)|
|_Geographic characteristics_|||||
|Distance to CBD||||-0.041<sup>**</sup><br>(0.018)|
|Distance to CBD squared||||0.001<br>(0.001)|
|Obs.|6,759|6,755|6,041|5,908|

_Note:_ Standard errors in parentheses.* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01. Other control variables include marital status. The results of the first-stage selection models are not reported.

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**Table A.2: Unemployment and informality**

||**(1)**<br>**Unemployment**|**(2)**<br>**Informal**|
|---|---|---|
|_Demographic variables_|||
|Age|−0.008**|−0.007|
||(0.003)|(0.010)|
|Age squared|0.000*|0.000|
||(0.000)|(0.000)|
|Male|0.051|−0.033|
||(0.040)|(0.087)|
|Secondary|0.001|−0.130**|
||(0.016)|(0.054)|
|University|0.094***|−0.338***|
||(0.034)|(0.098)|
|Other|−0.055**|0.099|
||(0.022)|(0.087)|
|Secondary × Male|0.013|−0.162|
||(0.045)|(0.099)|
|University × Male|−0.128**|−0.154|
||(0.053)|(0.131)|
|Other × Male|−0.028|0.050|
||(0.044)|(0.163)|
|_Geographic characteristics_|||
|Distance to CBD|−0.008**|0.029***|
||(0.003)|(0.008)|
|Distance to CBD squared|0.000|−0.000**|
||(0.000)|(0.000)|
|Adj-R<sup>2</sup>|0.038|0.160|
|Obs.|2,862|1,879|

_Note:_ Standard errors in parentheses.* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.

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**Table A.3: Use of public transportation**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|_Distance to public transportation stops_||||
|1–2 km|−0.017|0.021|0.032*|
||(0.018)|(0.018)|(0.018)|
|2–3 km|−0.117***|−0.083***|−0.084***|
||(0.027)|(0.027)|(0.027)|
|More than 4 km|−0.266***|−0.198***|−0.169***|
||(0.025)|(0.026)|(0.027)|
|_Demographic characteristics_||||
|Male||0.100***|0.071***|
|||(0.015)|(0.015)|
|Secondary||0.189***|0.165***|
|||(0.021)|(0.021)|
|University||0.302***|0.206***|
|||(0.023)|(0.024)|
|Other||0.113***|0.080***|
|||(0.027)|(0.027)|
|Youth (15–24)||−0.072**|−0.077**|
|||(0.033)|(0.032)|
|_Net monthly income_||||
|<br>US$25–US$199||0.277***|0.251***|
|||(0.025)|(0.025)|
|US$200–US$399||0.259***|0.213***|
|||(0.031)|(0.032)|
|More than US$400||0.038|−0.017|
|||(0.034)|(0.035)|
|_Job characteristics_||||
|Employer|||−0.141***|
||||(0.031)|
|Self-employed|||−0.158***<br>|
||||(0.019)|
|Private|||−0.030*|
||||(0.018)|
|Industry|||0.218***|
||||(0.046)|
|Services|||<br>0.250***|
||||(0.042)|
|Other|||<br>0.233***|
||||(0.040)|
|Adj-R<sup>2</sup>|0.012|0.097|0.124|
|Obs.|5,747|5,645|5,490|

_Note:_ Standard errors in parentheses.* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.

27
# **Annex B: Methodology**

## **Labor income regression**

For the analysis of correlates with individual labor income, a Heckman’s selection model is estimated. The baseline OLS model is set as follows: ln( , where is the log monthly wage or labor income of individual yyii) = αα+ ββXXii + εεii _i_ , is a vector of covariates including various demographic characteristics (for example, age, age squared, gender, education ii ii attainment), characteristics of jobs (for example, economic sectors, public/private, and yy XX formal/informal), and geographic characteristics of an enumeration area (for example, distance to the CBD). is an iid error term. In order to take account of the selection of individuals reporting labor income, Heckman’s selection model is estimated by adding a first-stage selection model ii based on individual’s demographic characteristics. The results are presented in Table A.1. εε We also employed a regression analysis to identify factors that determine unemployment and informality of jobs. The outcome variable is a binary variable coded 1 if unemployed or working in the informal sector. An OLS (linear probability) model was used. The same set of demographic characteristics and geographic characteristics, as described above, was adopted for these regressions as well, although we also introduced interaction terms between education levels and sex to explore if there are any differential effects of education on employment conditions by sex. The results are presented in Table A.2.

## **Job accessibility index**

The index of job accessibility that we used in this report measures the share of job opportunities that can be reached within a certain travel time (Nakamura and Avner 2018). This index can be formalized as follows: where _i_ indexes each location (or origin), _Oj_ represents the number of job opportunities in location _j_ , tij is the travel time between location _i_ and location _j_ , and is a time threshold. 1[.] is an indicator function that takes the value of 1 if the statement in the brackets is true, 0 otherwise. In other words, this index captures the share of jobs that are accessible from location aa̅ _i_ within travel time = . The index is computed for each 30-arc seconds grid (roughly squared kilometers) where travel aa̅ time by walk between the centroid of each grid and the locations of jobs is derived based on OpenStreetMap and Open Source Routing Machine (OSRM).<sup>15</sup>

> 15 ‘osrmtime’ in STATA is used to compute travel time between the centroid of each grid and the locations of jobs as recorded in the 2017 JICA CS.

28