rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7923

# **Abstract**

This paper presents the results of an investigation of selected census towns in northern India. Census towns are settlements that India’s census classifies as urban although they continue to be governed as rural settlements. The 2011 census featured a remarkable increase in the number of census towns, which nearly tripled between 2001 and 2011, from 1,362 to 3,894. This increase contributed to nearly a third (29.5 percent) of the total increase in the urban population during this period. Only part of this evolution can be attributed to the gradual urbanization of settlements in the vicinity or larger towns. Instead, the majority of census towns appear as small “market towns,” providing trade and other local services to a growing rural market. The case studies of representative census towns in Bihar, Jharkhand, Orissa, and West Bengal show the role of increased connectivity and growing rural incomes in driving the demand for the small-scale and non-tradable services, which are the main sources of nonfarm employment in these settlements. The case studies also reveal that the trade-offs between urban and rural administrative statuses are actively debated in many of these settlements. Although statistical comparisons do not show a significant impact of urban or rural administrative status on access to basic services, urban status is often favored by the social groups involved in the growing commercial and services sectors, and resisted by the residents still involved in the traditional farming sectors.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group. 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 atamaria@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 42. Importance of IPA services: Promotion of investment opportunities**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0163|0.0128|||0.0171|-0.00105|||
||(0.0557)|(0.0537)|||(0.113)|(0.115)|||
|Export % of sales|0.000245|8.00e-05|0.000113|6.74e-05|1.77e-05|-5.90e-05|-0.000194|-0.000111|
||(0.000739)|(0.000720)|(0.000736)|(0.000733)|(0.00149)|(0.00151)|(0.00152)|(0.00160)|
|Share of sourcing via imports|0.00120|0.000561|0.00107|0.000303|0.00238|0.000900|0.00199|0.000220|
||(0.000807)|(0.000811)|(0.000795)|(0.000792)|(0.00165)|(0.00172)|(0.00169)|(0.00175)|
|High-income source country|-0.228***|-0.118|-0.241***|-0.117|-0.488***|-0.256|-0.509***|-0.241|
||(0.0812)|(0.0797)|(0.0765)|(0.0765)|(0.173)|(0.178)|(0.171)|(0.179)|
|>250 employees|0.00367|-0.00907|-0.00389|-0.00438|-0.00665|-0.0159|-0.0200|0.00280|
||(0.0669)|(0.0668)|(0.0679)|(0.0673)|(0.135)|(0.141)|(0.140)|(0.146)|
|Cumulative inv. >US$10M|0.0432|0.103*|0.0548|0.115*|0.114|0.190|0.146|0.232*|
||(0.0637)|(0.0607)|(0.0643)|(0.0614)|(0.130)|(0.130)|(0.133)|(0.134)|
|Years in country|0.000437|-0.00143|0.000419|-0.00208|-0.000121|-0.00556|0.000395|-0.00638|
||(0.00188)|(0.00184)|(0.00189)|(0.00185)|(0.00373)|(0.00399)|(0.00379)|(0.00416)|
|Level of foreign ownership (%)|-0.00394***|-0.00119|-0.00405***|-0.00106|-0.00800***|-0.00205|-0.00843***|-0.00214|
||(0.00112)|(0.00121)|(0.00113)|(0.00118)|(0.00251)|(0.00267)|(0.00256)|(0.00276)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.027|0.092|0.049|0.116|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Promoting investment opportunities]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level. Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

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**TABLE 2**

BETWEEN ESTIMATES OF EMPLOYMENT SIZE DISTRIBUTION £ (Absolute values of t-statistics in parenthesis)

||**Trade ErDo**|**sure Measu**|**red withIm**|**gort andE **|**xport Shares**|
|---|---|---|---|---|---|
||||**Percent**|**ile**||
||10th|25th|50th|75th|90th|
|ln(M/Q)|-.184*|<br>-.317*|<br>-.432|-.5/3*|<br>-1.10*|
||(2.59)|<br>(2.78)|<br>(2.03)|<br>(2.24)|<br>(2.98)|
|ln(X/Q)|-.204*|<br>-.333*|<br>-.367*|<br>-.168|.004|
||(6.24)|<br>(6.36)|<br>(3.76)|<br>(1.43)|<br>(.022)|
|ln(Q)|-.268*|<br>-.496*|<br>-.414*|.251|.129|
||(3.81)|<br>(4.40)|<br>(1.97)|<br>(1.00)|<br>(.353)|
|TUR|-7.60*|-14.17*|-10.02|13.43|14.72|
||(3.31)|<br>(3.86)|<br>(1.46)|<br>(1.63)|<br>(1.23)|
|TR*ln(M/Q)|<br>.446*|<br>.663*|<br>1.11*|1.39*|2.72*|
||(3.04)|<br>(2.82)|<br>(2.31)|<br>(2.64)|<br>(3.56)|
|TUR*ln(X/Q)|<br>.772*|<br>1.29*|<br>1.51*|.695|.188|
|-|(5.43)|<br>(5.67)|<br>(3.54)|<br>(1.36)|<br>(.254)|
|TUR*ln(Q)|.691*|<br>1.21*|<br>.974*|<br>-.564|-.722|
||(4.65)|<br>(5.09)|<br>(2.19)|<br>(1.06)|<br>(.935)|
|ChileDummy|<br>-.019|.055|-.039|-.291|-.037|
||(.260)|<br>(.473)|<br>(.179)|<br>(1.12)|<br>(.097)|
|R<sup>2</sup>|.887|.903|.842|.866|.765|
||**Trade Expo**|**sure Measu**|**red with Ef**<br>|**fective Pr**<br>|**otection Rates**|
||||**Percenti**|**le**||
||10th|25th|50th|75th|90th|
|ERP|.244*|<br>.352*|<br>.361|.332|.368|
||(3.41)|<br>(2.52)|<br>(1.78)|<br>(1.j7)|<br>(1.36)|
|ln(Q)|.296*|<br>.422|.545|1.15*|1.24*|
||(2.39)|<br>(1.78)|<br>(1.58)|<br>(4.03)|<br>(2.70)|
|TUR|14.05*|<br>19.08|21.96|43.73*|45.28|
||(2.33)|<br>(1.64)|<br>(1.31)|<br>(3.13)|<br>(2.03)|
|TUR*ERP|-.707*|<br>-1.05*|<br>-1.01*|<br>-1.04*|<br>-1.11|
||(4.45)|<br>(3.43)|<br>(2.29)|<br>(2.84)|<br>(1.89)|
|TUR*ln(Q)|-.876*|.1.18|-1.41|-2.67*|<br>-2.73|
||(2.29)|<br>(1.62)|<br>(1.32)|<br>(3.03)|<br>(1.93)|
|Chile Dummy|<br>.003|-.038|-.113|-.517|-.474|
||(.014)|<br>(.097)|<br>(.198)|<br>(1.09)|<br>(.623)|
|R<sup>2</sup>|.664|.587|.596|.836|.647|

aIndustry dummies were included in the regressions but are not reported. *Significantly different from zero at the .05 level using a two-tail test.
# **Conclusion**

This analysis makes possible an evidence-based discussion of labor markets and jobs in Haiti. Using three harmonized household surveys from 2001 to 2012, the paper provides insights into the evolution of labor markets in a particularly turbulent decade for Haiti marked by the political crisis of 2004 and the earthquake of 2010. The focus is the determinants of labor income, providing a household-level perspective on the larger dynamics of labor markets.

The main message of the analysis is that in spite of the shocks of the period, labor markets in Haiti presented more continuity than change. Self-employment in low-productivity sectors remains the norm outside of the farm sector, a cause for worry. While the share of workers in non-primary informal activities has decreased, this has not translated into an expansion of the formal private sector, but into the swelling of the public sector and NGOs. This puts in question the sustainability of poverty reduction in urban areas, especially in the Metropolitan area, in an environment of decreasing aid. Furthermore, the significant rise in employment rates in recent years was driven mainly by young people taking unpaid work in family businesses.

The analysis of the determinants of labor income in urban areas further echoes those results, showing no signs of sustained structural transformations in the endowments of the labor force or the quality of jobs. Additional analysis details the role of the public and NGO sectors in the wage increase observed, particularly for the lower end of the wage distribution. The analysis also found a stable premium on education and an overall reduction of the wage gap, except in low-productivity sectors where this gap increased over the period to 60 percent.

Generating more and better jobs largely depends on macro-economic factors that are largely outside of households’ control. But the analysis points to persisting structural challenges weighing down the returns workers get from their labor. For that reason, the obvious role that the private sector will have to play in generating growth and employment will need important help from the Haitian state in helping households improve their assets and use them in the labor markets (World Bank, 2015). It is the authors’ hope that this paper will provide a data-informed basis for discussion among policy makers and stakeholders who all acknowledge the critical role of labor markets and jobs to reduce poverty and foster shared prosperity in Haiti.

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squares, and three-stage least squares estimation methods. The remaining paper is organized as follows: Section II describes our data on public procurement auctions from official development assistance (ODA) financed infrastructure projects. It aims to overview the relationship between these elements on an informal basis. Section III develops our formal empirical models to estimate the equilibrium bid function with possible endogeneity of entry and quality considerations. Section IV presents and interprets our main empirical results.

# **II. AN OVERVIEW OF ELECTRICITY PROJECT PROCUREMENT AUCTIONS: COMPETITION, ENTRY AND QUALITY**

Competition in public procurement auctions for infrastructure development is generally limited (Gupta, 2002; Foster, 2005). A fundamental reason is that a relatively small number of contractors are competent to participate in the competitive bidding for technically complex infrastructure projects. Classic are electricity projects. Normally, only two or three multinational enterprises may be able to make an entry to the selection process for large-scale electricity projects. Even in the road sector, which is deemed as relatively less complicated than other infrastructure sectors, the number of auction participants appears low at about 5 to 6 (Estache and Iimi, 2008a). In our sample composed of 44 electricity project auctions with 131 bids assisted by international aid donors, the average number of bidders is 4.7 with a median of 4 (Figure 1).

Figure 1. Degree of Competition in ODA-Financed Electricity-Sector Projects <!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18<br>Number of bidders<br>Frequency<br><!-- End of picture text --> Source: Author’s calculation.
A shift from agriculture to manufacturing has been one of the hallmarks of job creation, poverty reduction and rapid growth in low-income countries around the world. Industrialization is also one of the pillars of the African Union’s Agenda 2063 – the blueprint for transforming the continent into a global powerhouse (at https://au.int/en/agenda2063).

Some of the signs for industrialization in Africa are encouraging. The most comprehensive information about manufacturing employment in Africa only covers 18 countries, but based on those data, manufacturing employment in Africa’s low- and middle-income countries increased from 6 million to more than 20 million from 2000 to 2018, raising the share of employment in manufacturing from 7.2 percent to 8.4 percent (Kruse et al. 2021). In comparison, the 1990s saw zero growth in Africa’s manufacturing employment. Manufacturing exports from African nations have also grown at an annual average of 9.5 percent per year (Signé 2018). But while <mark>employment and value-added shares of manufacturing in Africa are rising, both remain very low in comparison to the rest of the world (Diao et al, 2017; Nguimkeu and Zeufack 2019).</mark> The performance of the manufacturing sector varies considerably across African countries; this heterogeneity is to be expected given the vast differences in labor and natural resource abundance across Africa (World Bank 2021). Nevertheless, most African countries have a low share of formal employment in the manufacturing sector. According to the INDSTAT database of the United Nations Industrial Development Organization (UNIDO 2020), only a handful of countries in Africa in 2018 had formal employment that exceeded a total of 100,000 in firms with 10 or more employees: Ethiopia, Ghana, Kenya, Nigeria, South Africa and Tanzania. Among these countries, Nigeria stands out, with around three million workers engaged in formal manufacturing between 2010-2012 (Nigerian Manufacturing Sector Report 2014). However, very little systematic information exists about the manufacturing sector in Nigeria. To the best of our knowledge, no longitudinal census of manufacturing firms in Nigeria exists. There is a World Bank Enterprise Survey for the year 2014, but it only covers 19 of Nigeria’s 36 states and the total employment generated using weights is implausibly small.

This essay considers the prospects for growth of manufacturing in Africa. We begin with a description of broad trends, drawing heavily on the results in Diao et al. (2021). Three striking patterns emerge. First, there has been a rapid increase in the number of African manufacturing enterprises with fewer than 10 employees. We call these firms “small” and “informal” although some of these firms are likely to be formally registered; for the most part, we do not have enough information to make this distinction. In contrast, in Asian comparator countries, the share of employment in small manufacturing 2
education erasing nearly half the baseline marriage penalty. At the same time, educated husbands exert a quantitatively similar effect. Because of positive assortative mating, spousal education levels are highly correlated, necessitating the inclusion of both interactions in a single regression. In this model, the wife’s education remains significant while the coefficient on the husband’s education falls to zero.

Interpreting these results through the lens of our hypotheses, we argue that a woman’s education affects _both_ household gender norms and her outside employment options. In contrast, her husband’s education affects household norms, but does not directly affect her employment prospects. This suggests that outside options at least in part play a role in determining the marriage penalty. Finally, we directly test the role of gender attitudes by interacting the marriage indicator with DHS measures of household gender attitudes. We find strong evidence that women in households with more liberal gender norms experience smaller marriage penalties. The effects of education and social norms appear to be independent, suggesting that both opportunity costs and social norms play a role in driving the marriage penalty.

We contribute to the large and growing literature on gender inequality. The specialization of women in the home and men in the marketplace relates to women’s real or perceived comparative advantage in the home (Becker, 1973). Under specialization, women invest in home specific human capital, while men invest in market specific human capital, raising wages. Empirically, married men have substantially higher wages than unmarried men, even after controlling for human capital and job characteristics (Hersch and Stratton, 2000), suggesting that specialization through marriage makes men more productive. Our evidence on men is consistent with this literature. Nevertheless, despite substantial attention to gender inequality in recent years, there is limited causal evidence about the impact of marriage on women’s labor market outcomes. This paper begins to fill that gap.

Despite rising women’s human capital and narrowing gender gaps in education across the world, female labor force participation remains low in many parts of the world, including South Asia. One potential explanation for low female labor force participation is gender norms, which can generate spousal disagreement over the provision of the household public good (Bertrand et al., 2016; Fernandez et al., 2004). Child penalties have been documented 5
# **8 References**

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A second variable we added was an estimate of the average baseline 2014 school readiness score for students in each school. This was calculated as the average baseline school readiness score for the students in the school that were linked to the 2014 TECHI. This variable was used to establish balance in baseline TEHCI school readiness between treatment and non-treatment students using our mapping approach. An analogous variable was defined using the TEHCI home activity index.

We defined three sub-samples of interest for estimating the effect of the school readiness intervention on TEGRA achievement. The first sub-sample consisted of 1<sup>st</sup> grade students sampled at the beginning and end of the 2016 school year, and the second sub-sample consisted of 1<sup>st</sup> grade students sampled at the beginning of the 2016 school year and end of the 2017 school year. Students starting first grade in 2016 would have been exposed to the school readiness intervention in the year prior to starting primary school. This cohort would have been mostly born in 2010. These two sub-samples are used to estimate the effect of one year of exposure to the school readiness intervention on 1 and 2 years of schooling, respectively. The third subsample consisted of students in 1<sup>st</sup> grade during the 2017 school year. They would have been exposed to the school readiness intervention in the two years prior to starting primary school and been born mostly in 2011. This sub-sample was used to estimate the effect of two years of the school readiness intervention and one year of schooling. It should be noted that 2015 was the implementation year of the school readiness intervention, and playgroups did not become established until approximately halfway through the year.

# _Empirical approach_

Let _i_ and _j_ index the _i_<sup>th</sup> student in the _j_<sup>th</sup> school, and let T��,�� denote whether the student came from a school readiness treatment community or not. We defined the following model for each school, _j_ , as where u�,�� is a mean-zero, student-level error term and v�,� is a school-level error term. If P��,� 33
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