rafmacalaba/datause-encoder-data huggingface dataset:
restricted to the agricultural sector in rural areas and to the service sector in urban areas. Less than 4 percent of women are service or market sales workers and even fewer are technicians or senior officials— job categories that require certain level of higher education. Young people represent nearly 45 percent of the workers in skilled agriculture and fishing, with female youth representing 60 percent. Male youth are more likely to be in elementary occupations. An employer interviewed as a part of the qualitative research for this report suggests that agricultural workers are essential: a manager of a medium-size private agricultural operation in Nyala said, “In the last period, it is the opposite there is a lot of problems because of the war that was during the past thirteen years, so the agriculture you can say that the half of the farmers aren’t farmers anymore. Yes, it has deteriorated too much because of the conditions.”

**Figure 10. Employment shares by sector, gender, quintile, and locality, 2014**

<!-- Start of picture text --> 100%<br>19%<br>80% 38% 31% 35% 32% 1% 37%<br>47% 51%<br>63%<br>5% 6%<br>60% 2% 7% 79% 7% 76%<br>86%<br>8%<br>10%<br>40% 80%<br>60% 63% 58% 10% 62% 56%<br>20% 44% 39%<br>27% 13% 5% 15%<br>8% 9% 8%<br>0%<br>All Women Men B20% B40% T20% Rural Urban Rural Urban Rural Urban<br>women women men men<br>By gender By quintile By locality By locality and gender<br>Services Industries Agriculture<br><!-- End of picture text --> _Source:_ World Bank 2019b.

# **_Education and economic opportunities_**

Low educational attainment has far-reaching impacts across domains, including on people’s earnings and standards of living (Wodon et al. 2018). A recent study confirms that educational gaps between boys and girls in Sudan are still striking, and they perpetuate gender inequality, with child labor also contributing to keeping future generations uneducated (Berenger and Verdier-Chouchane 2015). Enrollment rates in Sudan have increased across the board in the last few decades, but especially noticeable is the 20 percentage point increase in girls’ enrollment in primary school and 16 percentage point increase in secondary school between 2001 and 2017 (compared to 15 and 8 percentage point increases for boys in primary and secondary school, respectively) (Figure 11). The primary enrollment increase was mainly driven by an increase in girls’ attendance in the poorest quintiles in urban areas. The difference in enrollment between the poorest and richest quintiles was 9 percentage points at the primary level and 11 percentage points at the secondary level. Although enrollment rates have increased, Sudan is still below the average of Sub-Saharan Africa countries at the primary and secondary levels. Sudanese boys have 7.9 expected years of schooling and girls 7.3 (WDI 2020, data for 2015). Access to school is affected 16
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Policy Research Working Paper 7333

# **Abstract**

Developing countries have urbanized rapidly since 1950. To explain urbanization, standard models emphasize ruralurban migration, focusing on _rural push factors_ (agricultural modernization and rural poverty) and _urban pull factors_ (industrialization and urban-biased policies). Using new historical data on urban birth and death rates for seven countries from Industrial Europe (1800–1910) and thirty-five developing countries (1960–2010), this paper argues that a non-negligible part of developing countries’ rapid urban growth and urbanization may also be linked to demographic factors, such as rapid internal urban population growth, or an _urban push._ High urban natural increase in today’s developing countries follows from lower urban mortality, relative to Industrial Europe, where higher urban deaths offset urban births. This compounds the effects of migration and displays strong associations with urban congestion, providing additional insight into the phenomenon of urbanization without growth.

This paper is a product of the Office of the Chief Economist, Africa Region of the World Bank, in collaboration with George Washington University. 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://econ.worldbank.org. The authors may be contacted at lchristiaensen@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
**Table O4: Net Effects of the Illness of a Worker on Household‐Level Labor Outcomes with EA x week controls**

|||Hou|seholds wi|th...||
|---|---|---|---|---|---|
|Sample of households|all|2 +<br>earners at<br>baseline|1+ male<br>earner at<br>baseline|2+ male<br>earners at<br>baseline|HH in which<br>woman is highest<br>earner at baseline|
|_Panel A: Dep_|_endent Va_|_riable = Total_|_household i_|_ncome_||
|Worker ill|‐89.583|‐70.927|‐110.175|‐134.369***|‐8.725|
||[59.373]|[56.415]|[81.249]|[48.739]|[22.589]|
|Worker ill X Female ill|29.856|19.969|61.025|113.892*|‐89.157*|
||[61.731]|[57.564]|[81.457]|[59.902]|[46.729]|
|Observations|2345|1268|1456|353|940|
|R‐squared|0.685|0.773|0.721|0.485|0.526|
|Mean dependent variable|202.60|259.80|248.80|307.80|183.90|
|Overall effect of female illness|‐70.320|‐44.240|‐35.160|‐20.480|‐97.880|
|P‐value|0.004|0.024|0.109|0.595|0.013|
|Expected male earnings loss from<br>Fraction of expected loss<br>compensated<br>Expected female earnings loss from<br>baseline|105.500<br>0.224|92.06<br>0.116|105.5<br>0.387|77.06<br>‐0.744|42.33<br>0.794|
|baseline|49.040|50.07|57.4|67.5|53.68|
|Fraction of expected loss<br>compensated|‐0.434|‐0.0393|0.217|0.697|‐0.824|

|_Panel B: De_|_pendent variab_|_le = Total Ho_|_usehold Lab_|_or Hours_||
|---|---|---|---|---|---|
|Worker ill|‐25.337***|‐34.593***|‐31.111***|‐40.165***|‐12.493|
||[6.823]|[10.055]|[7.535]|[9.999]|[8.897]|
|Worker ill X Female ill|11.908|20.073*|19.385**|30.129**|‐4.938|
||[7.765]|[10.896]|[9.295]|[11.931]|[8.244]|
|Observations|2345|1268|1456|353|940|
|R‐squared|0.859|0.862|0.876|0.709|0.809|
|Mean dependent variable|67.03|95.70|83.54|112.60|72.24|
|Overall effect of female illness|‐16.490|‐16.870|‐15.470|‐10.040|‐17.430|
|P‐value|0.000|0.000|0.004|0.178|0.001|

_Notes: Worker ill = 1 if a household member reported missing work for an entire week in which s/he was planning to work that week and the reason for missing work was injury/illness. Expected earnings loss from baseline is calculated by taking the taking the average usual reported income from the baseline survey of individuals who are unexpectedly sick over the course of the survey. All specifications include household fixed effects, week of interview dummies interacted with enumeration area, and controls for the number of household members surveyed in that round. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
reduce their ordinary saving by the same amount of their contributions leaving unchanged their total asset accumulation. At the same time, the ...rm uses the reduction on wages to ...nance the pension fund without modifying retained earnings. Thus, private saving remain unchanged.<sup>16</sup>

# 3.5 Funded programs-cross section

Hubbard (1986) used cross section data collected from a survey of the U.S. President’s Commission on Pension Policy in 1979 with a sample of more than 3,000 observations. The estimates imply that an increase in social security wealth of one dollar reduces ordinary wealth by thirty-three cents, while an increase in private pension wealth of one dollar reduces ordinary wealth by sixteen cents. Gale (1998) argues that most estimates of the e¤ect of private pensions on ordinary wealth are biased downwards.

# 3.6 Funded programs-cross country

Baillu and Reisen (1997) work with a sample of 10 countries and a panel of more than 100 observations to study the determinants of private saving rates in the same fashion as Edwards (1996). The purpose of their paper is to study the e¤ect of funded pension wealth as a determinant of private saving rates. They ...nd systematic evidence that funded pension wealth increases private saving rates in developing countries with mandatory funded pension programs.

> 16Even if the ...rm uses the reduction in wages to distribute dividends instead of ...nancing the pension fund, the shareholders can save those additional dividends leaving aggregate saving unchanged.

21
|Study and Location|Definition of<br>Empowerment|Measurement<br>Concept|Data Sources|Focus|Conclusions|Implication|Scope|
|---|---|---|---|---|---|---|---|
|||parliamentary<br>representation,<br>economic<br>participation and<br>decision making<br>(combining<br>administrative and<br>managerial positions<br>and professional and<br>technical positions)<br>and the gender<br>disparity in earned<br>incomes, reflecting<br>economic<br>independence|legislators,<br>senior officials,<br>and managers,<br>and among<br>professional and<br>technical<br>workers, as well<br>as male and<br>female<br>proportional<br>income share|actively in<br>economic and<br>political life|matters||domains.|
|Kvinnoforum<br>(2001)<br>_Namibia, Bostwana,_<br>_Zimbabwe, and_<br>_Mozambique_|No set definition<br>of empowerment<br>but focus on two<br>basic principles:<br>(1)<br>disempowerment<br>as starting point<br>for<br>empowerment<br>processes, and<br>(2)<br>empowerment<br>cannot be given<br>to someone by<br>somebody else;<br>it has to start<br>from within and<br>be owned by the|Project tested a<br>number of<br>qualitative and<br>quantitative<br>methods: Circle of<br>Important Areas,<br>House of Life,<br>ratings<br>questionnaire,<br>individual<br>interviews, focus<br>group discussion,<br>participatory<br>observations, case<br>studies|Participatory<br>methods that ask<br>women to reflect<br>on their own<br>situation|Women’s<br>empowerment|Importance of<br>empowering<br>methods and<br>acknowledging<br>that concepts<br>mean different<br>things to different<br>women.<br>Participants felt<br>that the House of<br>Life tool and the<br>individual<br>questionnaire best<br>captured their<br>own<br>empowerment<br>status|Participatory<br>methods;<br>indicators at<br>individual level,<br>group level, and<br>societal level are<br>different, even for<br>the same concept<br>(e.g., agency)|Participatory tools<br>rather than specific<br>instruments. Project<br>and individual levels.|

41
both variables shows that exporters become on average larger and the firm-size distribution becomes heavier at the top as exports grow.

To the extent that better resource allocation promotes export growth, we would expect average exporter size to increase and exporter concentration to intensify as exports grow. If differences in average exporter size and concentration are more closely related to the underlying firm productivity distribution than to allocative efficiency then there need not be any correlation between export growth and average exporter size and concentration, especially after controlling for country-sector-year effects and destination-sector-year fixed effects.

Table 8 reports results using the country-sector-destination-year level data with countrysector-destination (panel) fixed effects and country-sector-year and destination-sector-year fixed effects. Nearly 90 percent of the time-series variation in exports in comes from larger exporters. The estimated within-effects show that, as exports grow, the average size of exporters and the share of exports by the top 5 percent increase significantly. To the extent that export growth reflects improved resource allocation over time, these results suggest that export growth is associated with more resources being allocated to the largest (and presumably most productive) exporters.<sup>34</sup>

# **6. Conclusion**

While the literature on exporter dynamics is rapidly growing, there has not been a comparison across a large number of countries of different sizes and at differing stages of development. The

> 34 In a robustness check we estimated similar regressions using the country-sector-destination-year level data but focusing on a sample of countries for which 8 years of data are available, which allows to observe differences over a relatively longer period of time. The results are very similar to those in Table 8.

26
# SMEs.

The ranges of average annual sales used to classify SEs and MEs differ between Argentina and Chile, reflecting the different size of their economies (Table 1). In Argentina, a company is considered to be an SE when its average annual sales are approximately between US$300,000 and US$5,000,000. MEs are those with annual sales between US$5,000,000 and US$30,000,000. In Chile, these ranges have lower values: SEs have average annual sales of approximately US$90,000US$1,600,000 and MEs of US$1,600,000-US$2,3800,000.<sup>10</sup> All the companies with average annual sales below these ranges are considered to be micro enterprises and those above belong to the corporate sector. It should be noted that in Argentina, loans of AR$500,000 (US$ 166,667), or less can be treated as consumption loans according to Central Bank (BCRA) regulations, even when they are granted to a company (as long as the total debt the client has with the bank does not exceed this amount).

In the rest of the paper, we ignore the heterogeneity of ranges observed in the definition of SMEs and use whatever definition banks use to define them. To some degree, this makes the comparison across banks with very different definitions difficult. Nonetheless, it is useful to analyze how banks conduct business with what they consider to be SEs and MEs. Moreover, it would be very difficult to construct another working definition.

# **3. Extent and Type of SME Lending**

Bank involvement with SMEs in Argentina and Chile appears to be significant. All banks interviewed have SMEs among their clients, with the exception of one Argentine bank, which is planning to enter the middle-market segment (Figure

> 10 These ranges were calculated as the average minimum and the average maximum values of the criteria banks use to define SEs and MEs.

7
# **3. Data and Empirical Strategy**

# **3.1 Data on Political Representation for Women**

We exploit two types of variation in exposure to women leaders to identify the effect of PRs on labor force participation. The first captures cross-state differences in the timing of when the 73rd Amendment provisions were implemented. This information was collected from several publications documenting the implementation and progress of the reservations (Mathew 1995, 2000; GOI 2008); Appendix Table 1 depicts the considerable variation in timing of effective implementation of the Panchayati Raj across states/UTs.

The second level of identifying variation measures district-level chairperson reservations.<sup>5</sup> That is, one-third of district council chairperson (“Sarpanch”) positions in India were also reserved for women, and this reservation rotates across districts. While all districts within a state have an identical (one-third) fraction of their council membership reserved for women, they do differ at a given point in time whether the chairperson position is reserved for women, and differ over time as to how many election cycles the Sarpanch position has been reserved for a woman.

# **3.2 Data on Labor Market Outcomes and Public Employment**

The primary data source for labor force outcomes used in our analysis is India’s National Sample Survey Organization’s “Socio-Economic Survey—Schedule 10: Employment and Unemployment” (hereafter referred to as “NSS”). The NSS data contain a representative sample of households across all states/UTs approximately every five years (“thick” rounds).<sup>6</sup> We use data from the thick rounds conducted in fiscal years 1999-2000, 2004-05, 2007-08 and 2009-10 (hereafter referred to only by the initial year for simplicity). The village sampling frame comes from the most recent national population census, and the sample is stratified across households at the district-rural/urban level.

> enforcement in 1993. Bihar was prevented from implementation due to legal issues regarding certain provisions of the Amendments (Iyer et al, 2012). Some states (Meghalaya, Mizoram and Nagaland) were explicitly excluded from the purview of the Amendments (GOI 2008). Jammu and Kashmir introduced reservations at a level consistent with the Amendments via state-level legislation in 1997, although the election of panchayats under its own Act has not yet taken place (GOI 2008). Jharkhand has similarly never held reserved elections.

> 5 These data are available from the publisher of Iyer et al (2012).

> 6 The NSS’ “thin” or small rounds are conducted on a more frequent basis, although their smaller sample makes them less comparable to the thick rounds and less viable in empirical analysis also containing thick rounds.

8
**Table G6: Relationship between food security measures and height-for-age z-scores (Pakistan): Expanded Controls Version**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|||Height-for-|age z-scores|||
|Per capita kcal|0.023||||||
||(0.049)||||||
|1-SSR||0.056|||||
|||(0.056)|||||
|1 - SSEXR|||0.043||||
||||(0.059)||||
|DDS||||0.068|||
|||||(0.072)|||
|PC expenditure|||||0.043||
||||||(0.061)||
|Food share||||||-0.040|
|||||||(0.053)|
|Child is female|0.073|0.075|0.071|0.079|0.074|0.069|
||(0.109)|(0.109)|(0.109)|(0.109)|(0.109)|(0.110)|
|Age (in months)|-0.023|-0.023|-0.023|-0.023|-0.023|-0.023|
||(0.014)|(0.014)|(0.014)|(0.014)|(0.014)|(0.014)|
|Age squared|0.000*|0.000*|0.000*|0.000*|0.000*|0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|HH size|-0.003|-0.002|-0.004|-0.004|-0.002|-0.004|
||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|Mother is literate|0.083|0.082|0.082|0.078|0.080|0.089|
||(0.148)|(0.147)|(0.147)|(0.147)|(0.148)|(0.148)|
|Father is literate|0.122|0.121|0.123|0.117|0.119|0.118|
||(0.122)|(0.122)|(0.122)|(0.122)|(0.122)|(0.122)|
|Distance to health|||||||
|service|-0.006|-0.006|-0.006|-0.006|-0.006|-0.006|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Main source of<br>drinking water|||||||
|<br>(tap=1)|-0.097|-0.107|-0.100|-0.087|-0.100|-0.105|
||(0.136)|(0.136)|(0.136)|(0.136)|(0.137)|(0.136)|
|_Sanitation facility (re_|_ference categ_|_ory: No toilet)_|||||
|Latrine|0.082|0.087|0.086|0.050|0.076|0.078|
||(0.196)|(0.196)|(0.197)|(0.197)|(0.196)|(0.196)|
|Flush toilet|-0.078|-0.077|-0.076|-0.090|-0.083|-0.083|
||(0.158)|(0.158)|(0.158)|(0.158)|(0.158)|(0.158)|

78
Beyond a country’s fiscal space and public expenditure levels, we identify four different types of factors that are significantly correlated with intergenerational mobility. First, child health indicators are seen to be positively correlated with intergenerational mobility. We observe a negative coefficient for stunting, a positive coefficient for immunization rates, and negative coefficients for the prevalence of anemia and mortality rates. When children from disadvantaged backgrounds do not have access to adequate nutrition and health care, this may have long-term negative effects on their cognitive development. The resulting gaps in cognitive development means that disadvantaged and advantaged children could be learning at different rates, even when they would have access to schools of comparable quality.

Second, proxies for school quality are found to be positive factors: Intergenerational mobility tends to be higher in countries where there are more teachers per student (i.e. lower pupil-teacher ratios) and where the share of children dropping out of school early is low.

Third, proxies for segregation are negatively correlated with mobility: countries where the share of total inequality in years of schooling that is due to between-district variation in schooling (versus within-district variation) tend to report higher levels of intergenerational mobility in education. Ethno-linguistic fractionalization is similarly negatively correlated with mobility, albeit weakly.

Fourth, factors that may be indicative of the reach of public interventions are positively correlated with relative mobility: countries with higher mobility levels are more likely to be countries that are relatively small in terms of population and/or land area and tend to be more urbanized (possibly because it is easier to organize high-quality public interventions with a broad reach when countries are small with the population living in closer proximity to each other and to public services).

Labor market variables are found to exhibit little to no correlation with intergenerational mobility in education. These variables will conceivably report stronger correlations with measures of income mobility which, unlike education mobility, will be more sensitive to the functioning of the labor market.

# **6. Robustness**

In this section we re-evaluate the trends and patterns in intergenerational mobility using alternative measures for both absolute and relative mobility.

## **6.1. Intergenerational mobility across space and time**

### _Absolute mobility_

As a robustness check we also evaluate absolute mobility (denoted by _YOS_ ) using education data in its most disaggregated form, which is years of schooling completed. _YOS_ measures the share 24