rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

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- Bj ̈orkman-Nyqvist, M. (2013). Income shocks and gender gaps in education: Evidence from uganda. _Journal of Development Economics_ , _105_ , 237-253. Retrieved from https://www.sciencedirect.com/science/article/pii/ S0304387813001120 doi: https://doi.org/10.1016/j.jdeveco.2013.07.013

- Brunello, G., & Kiss, D. (2022). Math scores in high stakes grades. _Economics of Education Review_ , _87_ , 102219. Retrieved from https://www.sciencedirect.com/ science/article/pii/S027277572100131X doi: https://doi.org/10.1016/ j.econedurev.2021.102219

- Carleton, T., Jina, A., Delgado, M., Greenstone, M., Houser, T., Hsiang, S., ... Zhang, A. T. (2022, 04). Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits*. _The Quarterly Journal of Economics_ , _137_ (4), 2037-2105. Retrieved from https://doi.org/10.1093/qje/qjac020 doi: 10.1093/qje/qjac020

- Center for International Earth Science Information Network - CIESIN. (2018). _Population estimation service, version 3 (pes-v3)._ Retrieved 2022-11-01, from https://doi.org/ 10.7927/H4DR2SK5

- Cho, H. (2017). The effects of summer heat on academic achievement: A cohort analysis. _Journal of Environmental Economics and Management_ , _83_ (C), 185196. Retrieved from https://EconPapers.repec.org/RePEc:eee:jeeman: v:83:y:2017:i:c:p:185-196

- CSA. (2012). _Rural socioeconomic survey 2011-2012._ Retrieved 2023-03-01, from https:// microdata.worldbank.org/index.php/catalog/2053

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- CSA. (2016). _Ethiopia socioeconomic survey, wave 3 (ess3) 2015-2016._ Retrieved 2023-03-01, from https://microdata.worldbank.org/index.php/catalog/2783

- Deschenes, O., Greenstone, M., & Guryan, J. (2009, May). Climate change and birth weight. _American Economic Review_ , _99_ (2), 211-17. Retrieved from https://www.aeaweb .org/articles?id=10.1257/aer.99.2.211 doi: 10.1257/aer.99.2.211

- Duflo, E., Dupas, P., & Kremer, M. (2015). School governance, teacher incentives, and pupil–teacher ratios: Experimental evidence from kenyan primary schools. _Journal of Public Economics_ , _123_ , 92-110. Retrieved from https://www.sciencedirect .com/science/article/pii/S0047272714002412 doi: https://doi.org/10 .1016/j.jpubeco.2014.11.008

- Garg, T., Jagnani, M., & Taraz, V. (2020). Temperature and human capital in india. _Journal of the Association of Environmental and Resource Economists_ , _7_ (6), 1113-1150. Retrieved from https://doi.org/10.1086/710066 doi: 10.1086/710066

- Graff Zivin, J., Song, Y., Tang, Q., & Zhang, P. (2020). Temperature and high-

17
3 Gine and Townsend, 2004; and Townsend and Ueda, 2006). For example, financial development by enhancing economic activity could boost the demand for labor. If this increased labor demand falls primarily on low-skilled workers, this indirect effect of financial development could increase the income of the poor and reduce income inequality. Thus, even if financial development does not increase the direct use of financial services by the poor, it might alleviate poverty and tighten the income distribution by creating job opportunities for the poor.

Similarly, financial development could intensify the competition in the non-financial sector by reducing the barriers to the entry of new firms. According to Becker (1957), this increased competition could reduce discrimination in hiring workers and expand the economic opportunities of disadvantaged groups, i.e. the poor.

Empirically, many studies looking at micro data find evidence of a positive correlation between access to finance and poverty. Jacoby (1994), for instance, finds that lack of access to credit perpetuates poverty in Peru because poor households cannot afford to provide their children with appropriate education. Jacoby and Skoufias (1997) show that households from Indian villages without access to credit markets tend to reduce their children’s schooling when transitory shocks reduce their income. Similarly, Dehejia and Gatti (2003) and Beegle et al. (2003) show that child labor rates are higher in countries with poorly functioning financial systems.

Rosenzweig and Wolpin (1993) and Rosenzweig and Binswanger (1993) find evidence to support the risk diversification view. Their results suggest that low-wealth households without access to services that would help to manage their risks choose lower-return, lowerrisk activities compared to households without these constraints.

Turning to the job creation channel, Burgess and Pande (2005) look at India’s policy on bank branches that led to the opening of thousands of new rural bank branches in states with initially low levels of financial development. Their results suggest that opening a bank branch led to faster growth in wages of agricultural workers, while the wages of urban factory workers did not show the same time pattern.

Beck et al. (2009) examine the effects of the deregulation of geographic restrictions on banking across individual states of the United States. They find that deregulation exerts a disproportionately positive increase in the demand for lower-skilled workers, which increases their annual earnings relative to higher-income individuals and, hence, narrows income disparities.

Levine et al. (2009) study the same event to assess whether racial discrimination fell after states deregulated in the United States. They find that the difference between the wage rates of white males and black males narrowed after bank deregulation. Financial development seems to have broadened the economic opportunities of a group that had been discriminated against.
Grant Thornton Associates, Contour Project Managers, and Bicon Zambia, 2005. _Audit of Zambia Wildlife Authority’s (ZAWA’s) Current Capabilities & Development of Project proposals for Institutional Capacity Development over a Five Year Period (20042008)_ , Zambia.

- Government of the Republic of Zambia 1998. _The Zambia Wildlife Act N0 10 of 1998_ , Government printers, Lusaka.

- Ravallion, M. 2001. “The Mystery of the Vanishing Benefits: An Introduction to Impact Evaluation.” _The World Bank Economic Review_ , 15 (1): 115-140.

Ravallion, M. 2003 “Assessing the Poverty Impact of an Assigned Program.” In François Bourguignon, Luiz A. Pereira da Silva (ed) _The Impact of Economic Policies on Poverty and Income Distribution: Evaluation Techniques and Tools_ , Chapter 5, Oxford University Press.

- Simwanza, H. I. (2007): Financial Viability of Current National Parks and Game Management Areas in Zambia: A Case for 2006 (2007). GRZ / GEF / UNDP funded project for the Reclassification and Effective Management of the National Protected Areas System.

- Shyamsundar, P. E. Araral, and S. Weeraratne 2005. Devolution of resource rights, poverty and natural resource management: A review. Environment Department Paper No. 104. Washington, D.C.: World Bank.

UNDP-GEF (United Nations Development Program and the Global Environmental Facility) 2004. Developing capacity, conserving biodiversity, sustaining livelihoods. New York: UNDP-GEF.

USAID 2003. Biodiversity conservation: A guide to staff and partners. Prepared by ARD Inc. for USAID.Washington, D.C.: USAID.

- Maddala, G.S. 1983. _Limited-dependent and qualitative variables in econometrics._ Cambridge University Press, UK.

ZAWA, 2007. “Community Based National Resources Management” in <u>http://www.zawa.org.zm/cbnrm.htm, accessed on 7 July 2008.</u> 15
If a recent migrant was residing in a country other than their birth country, we consider them to be transit migrants. No other data source, to our knowledge, has this level of detailed and extensive information about immigrants and their migration paths. Despite this unique information, we do not actually know when transit migrants moved to their country of last residence. This information is not needed for our modeling purposes but would be quite useful for future analysis.

We construct our database in the following manner. We only include people between the ages of 18 and 65 at the time of the survey. Dropping the elderly (65+) reduces the sample by around 4% while removing the children and young adults shrinks it by around 24%. The main reason for removing young migrants is that they are likely to be moving with their parents and are unlikely to be making the individual mobility decisions that form the basis of our model. We include everybody in our sample who states that they arrived during the year of the survey or during the previous year and were abroad in the year before they arrived.<sup>9</sup> This leads to slightly over 9 million people in the sample.

The ACS surveys have, on average, 175 different countries and territories as possible answers to the place of birth question. On the other hand, ‘country of residence 1 year ago’ question has only 67 categories listed. Many smaller countries in a given region are aggregated into regional categories, such as ‘Other South America’ or ‘Other West Asia’. To complicate the

> 9There are two questions we use for this purpose. The first question asks when the migrant moved to the United States and the second asks the following: ‘Where did this person live 1 year ago?’ We should note these two questions seem to create a certain level of confusion which possibly arises from when the survey is conducted within that year. Suppose the survey is conducted in September 2006 and the migrant arrived in the United States in March 2005. The answer to the first question will be that the migrant moved to the United States during 2005. In other words, this question is likely to capture more than 12 months of arrivals. That is why, the total number of recent arrivals (current or previous year) obtained by summing up each individual survey year is actually more than the number of people who arrived during 2001-11 based on the 2012 ACS data. For the second question, some people will interpret ‘1 year ago’ as November 2005 and state where they were living in the United States. Other respondents will interpret one year as the previous calendar year and report the foreign country they were in before coming to the United States. As a result, around 40% of the people who stated they migrated during that year or the previous year also reported a location in the United States to this second question. We drop these observations from our sample. This adjustment brings our total number of observations to 9.03 million, which is now 10% less than the number of people who arrived during 2001-11 according to the 2012 ACS data.

15
Policy Research Working Paper 10058

# **Abstract**

In Africa, evidence on the interactions among poverty, growth, and income distribution presents a puzzle: While growth has been robust in recent decades, the growth elasticity of poverty has remained low. This suggests that inequality has dampened the pro-poor effects of growth. However, when using standard inequality measures, there is only scattered evidence of high and growing inequality in Africa outside the extremely unequal southern cone. This paper argues that inequality mismeasurement could be the main culprit responsible for this paradox: consumption-based measures miss important information at the top end of the consumption distribution, leading to underestimation of inequality. This paper proposes distinct solutions, arguing that by reevaluating the importance of distributional issues in Africa, the need becomes apparent for refreshing the research agenda on African development in such a way that the interaction between poverty and inequality becomes a core concern.

This paper is a product of the Development Data Group, Development Economics. 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 vmolini@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
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39
in my sample, and I use it to test whether the e¤ect on registration and income is greater in municipalities with greater reductions in registration procedures. This test corresponds to the following regression where �Procedures is the reduction in the number of procedures in municipality c. Conditional on having the reform, the average reduction in the number of procedures was 5, with a standard deviation of 6. The minimum was 0 and the maximum 22<sup>15</sup> . Column 1 of Table 6 shows the regression results for the registration dummy, where the regression includes individual background variables and Economic Census variables interacted with a linear time trend. As expected, the coe¢ cient on the interaction term of the reform and reduction in procedures is positive and signi...cant, implying that the reform had a bigger e¤ect on registration in municipalities that saw a higher reduction in procedures. For a municipality with the average reduction in procedures (5), the increase in registration was 1.4 percent.

The model from Section 3 predicts that the reform should a¤ect individuals in di¤erent pre-reform occupations di¤erently. To test these predictions, I make use of the panel structure of my data. For each individual, I create four dummy variables that specify which of four occupations they held when I ...rst observed them in the pre-reform period. The four possible occupations are registered business owner, non-registered business owner, wage earner, and not employed (unemployed or out of the labor force)<sup>16</sup> . For example, for somebody who was a wage earner in the ...rst period when I observed her, the “past wage earner”dummy is equal to one. I then drop the ...rst period of observation for each person and run the following regression for the remaining periods which includes the reform dummy interacted with all ...ve past occupation dummies. The regression also includes quarter, municipality and past occupation dummies, PastOccupo, as well as the interactions of quarter with occupation dummies and time with occupation dum-

> 15 The minimum of 0 only applies to three counties. In these counties, the number of procedures was already low pre-reform. However, the reform was still e¤ective in these counties since it reduced the number of days it takes to register a business in all three counties. In two of the counties the reform also reduced the number of o¢ ce visits.

> 16 In the category "not employed", I also include the small fration of people who say that they are not currently working, but have a job lined up that they are planning to start within one month.

20
to question citizens’ ability to make efficient electoral choices at the local level, especially in the rural areas. Voters in rural Burkina Faso overwhelmingly live in poverty. The vast majority have been excluded from educational opportunities. Literacy rates are low, with only 16.3 percent of the study population having ever attended school. Without widespread literacy and formal education, and often even without electricity and cell phone coverage, word-of-mouth and radio are by far the most important sources of political information at the local level. However, the presence of municipal-level politics in public discussion falls far behind national-level politics.

In such a low-information and educationally disadvantaged environment, are democratic elections an efficient way of selecting public decision makers? Are voters able to identify benevolent candidates? How do elections influence citizens’ expectations towards and evaluation of public decision makers? How willing are citizens to sanction embezzlement by local decision makers, and does this depend on whether they are elected or not? To improve the evidence base on these questions, this study was carried out with voting-age citizens in rural Burkina Faso between June and July 2015, prior to the transitional municipal elections of 2016.

The study participants are a stratified random sample of the population of all 118 rural municipalities that are located within six of Burkina Faso’s 13 regions: Cascades, Centre-Est, Centre-Nord, Centre-Sud, Plateau Central, and Sahel (Figure 1). Within each municipality, ten villages were sampled at random. In municipalities with fewer than ten villages, all villages were included. Per village, two voting-age adults were invited to take part in the study. The invited study participants were sampled at random (with equal inclusion probabilities) from a census of the voting-age population in each village (census lists of residents between 18 and 70 years of age in 2014).

The 2,360 study participants provide a typical picture of the voting-age population in the rural areas of Burkina Faso. Only 16.3 percent of study participants report ever attending school (weighted population estimate for the six study regions: 17.8 percent).<sup>1</sup> Among those who attended school, the mean level of schooling is 5.4 years. Median age among study participants is 36 years (population estimate: 36 years) and 53.5 percent of study participants are female (population estimate: 52.1

> 1Another 11.2 percent report to have attended coranic school (population: 11.6 percent).

10
Table A.1: Summary Statistics

||National||Urban||Rural||
|---|---|---|---|---|---|---|
||Mean<br>SD|M|ean<br>SD|M|ean|SD|
||||2010||||
|Real Per Capita Expemditure (taka)|1254.8|973.6|1454.6|1205.3|1142.7|793.5|
|Average years of education|3.547|1.455|4.534|1.593|2.994|1.014|
|Prop. In skilled job|0.108|0.117|0.173|0.146|0.071|0.074|
|Prop. In semi-skilled job|0.344|0.195|0.452|0.189|0.283|0.170|
|Prop. In manufacturing|0.164|0.154|0.228|0.177|0.129|0.125|
|Prop. In services|0.445|0.235|0.621|0.212|0.346|0.184|
|Prop. of household with phone|0.652|0.209|0.790|0.159|0.574|0.193|
|Prop. of household with electricity|0.576|0.340|0.844|0.203|0.426|0.307|
|sanitarty latrine|0.198|0.249|0.290|0.288|0.146|0.207|
|pacca(water seal) latrine|0.172|0.195|0.243|0.222|0.133|0.164|
|pacca(pit) latrine|0.157|0.196|0.160|0.184|0.156|0.202|
|katcha(perm) latrine|0.240|0.233|0.186|0.216|0.270|0.237|
|supply|0.080|0.222|0.197|0.326|0.014|0.070|
|Tubewell|0.884|0.259|0.773|0.343|0.947|0.167|
|Distance to large cities|331.773|158.545|302.742|168.268|348.066|150.394|
|Monsoon Rain COV|0.181|0.026|0.180|0.025|0.182|0.027|
|Rice Price|44.465|2.834|45.745|2.977|43.746|2.476|
|Non-traded Price|71.244|22.006|73.499|22.743|69.978|21.480|
|HousingValue Index|1589794|295172|1619459|300214|1573145|291002|
||||2000||||
|Real Per Capita Expemditure (taka)|868.043|771.171|1098.000|1150.917|758.540|458.417|
|Average years of education|3.431|2.257|5.133|2.752|2.620|1.369|
|Prop. In skilled job|0.077|0.103|0.127|0.149|0.053|0.058|
|Prop. In semi-skilled job|0.283|0.206|0.461|0.201|0.197|0.145|
|Prop. In manufacturing|0.112|0.134|0.182|0.161|0.078|0.104|
|Prop. In services|0.369|0.231|0.580|0.221|0.268|0.155|
|Prop. of household with phone|0.021|0.074|0.059|0.120|0.003|0.017|
|Prop. of household with electricity|0.373|0.364|0.765|0.272|0.187|0.229|
|sanitarty latrine|0.173|0.240|0.323|0.314|0.102|0.150|
|pacca(water seal) latrine|0.057|0.114|0.130|0.168|0.022|0.044|
|pacca(pit) latrine|0.126|0.187|0.206|0.248|0.088|0.134|
|katcha(perm) latrine|0.208|0.212|0.146|0.198|0.237|0.212|
|supply|0.088|0.253|0.264|0.384|0.004|0.051|
|Tubewell|0.881|0.263|0.725|0.381|0.956|0.128|
|Distance to large cities|308.0|171.6|229.6|184.0|345.3|151.8|
|Monsoon Rain COV|0.183|0.026|0.180|0.026|0.184|0.026|
|Rice Price|19.295|1.506|20.225|1.437|18.852|1.326|
|Non-traded Price|50.338|16.237|52.438|14.702|49.329|16.833|
|HousingValue Index|435477|87674|470015|132296|419031|46654|
# <u>Data</u>

This paper uses data from the second wave of the Labor Skills Survey (LSS) conducted in Pakistan in the last quarter of 2013. The survey was designed to be nationally representative and covers all regions of Pakistan except Balochistan and the Federal Administered Tribal Areas, which jointly represent less than 7% of the total population. The sample was drawn using a stratified three-stage design. Twenty districts (7 in urban and 13 in rural areas) were first selected through a random sampling in each of the urban and rural strata. In the second stage, 100 primary sample units at the union council level were selected within each stratum systematically with probability proportional to size. Finally, in the third stage, a random systematic sampling was used to select 25 target households. A total of 2,500 households in 20 districts and 100 union councils were finally selected. Interviews were completed for 2,354 households in 94 union councils, due to security issues in six union councils of Khyber Pakhtunkhwa (KPK).

The LSS household survey consists of a questionnaire for the household head as well as a separate questionnaire for a subset of one male and one female randomly selected within the household, among all mentally able household members aged between 15 and 64. The household head questionnaire collected general information on all household members including age, gender, height and general education. The male and female questionnaires collected detailed information on individual employment, income, and individual skills reported by the individual himself. Additionally, cognitive abilities were assessed for all males and females aged 15 to 64 in the household. The height variable used in the paper was obtained from actual measurement of all household members aged 2 and above.

Our measure of cognitive ability is derived from the Raven’s test of progressive matrices, administered to all adults aged 15 to 64 in the LSS households. The Raven’s test of progressive matrices aims at measuring logical reasoning ability. The adult instrument consists of 60 questions 5