rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10234

# **Abstract**

Formal sector entry-level jobs in Mexico offer low starting salaries but substantial wage growth. This paper experimentally tests whether a six-months wage incentive can increase formal employment among secondary school graduates. Combining survey and high-frequency social security data, the paper shows that the incentive increases formal employment among vocational school graduates by 4.2 percentage points (14.5 percent) over the first two years driven by a 5 percentage point (25 percent) increase in permanent formal jobs. These employment gains are due to both extensive and intensive margin effects. Treatment effects are concentrated among youths with binding reservation wages who also tend to underestimate formal wage growth.

This paper is a product of the Social Protection and Jobs 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 m.abel@bowdoin.edu; ecarranza@worldbank.org; kgeronimo@lampforum.org; and mortegahesles@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
<!-- Start of picture text --> House PricesBase Index,100in quarterly1991 data<br>4% 230<br>3% 220<br>2%<br>210<br>1%<br>200<br>0% 7 i T<br>oa am om am ama<br>1% CPF¢@/éoroNNNocSSS&FFS 8GANNtSEEEEBEBZBaSFFFSo & oFsesegegsgANONco co 3&2NN2&2 8&2A8g& 190<br>-2% 180<br>-3% 170<br>% change over previous quarter (left)<br>—— Purchase index (199101=100)<br>Source:<br>Federal Housing Finance Agency<br><!-- End of picture text --> <!-- Start of picture text --> Charge-off° and Delinquency Rates '<br>3 All Commercial Banks<br>7 2%<br>6 :<br>:<br>5 :<br>4 :<br>3 Pa<br>2 4+, ow ..a<br>Le<br>1<br>0<br>200401 200503 200701 200803 201001<br>———Charge-off +++++++ Deliquency<br>Source: Federal Reserve Board<br><!-- End of picture text -->
national regions, both the census and surveys generally have information from the same geographical locations. When this is not the case, it is necessary to aggregate locations to match the minimum common factor. For example, given that the Colombian census of 1985 aggregates the information of three departments (Caldas, Quindio, and Risaralda) but the same departments appear separately in the household survey, it was necessary to group them in the survey to match the census disaggregation. In Mexico, the subnational breakdown of the household surveys comprises only eight regions, whereas the census has information for 32 states. In these cases, the census data had to be aggregated to match the household survey.<sup>4</sup> This paper aims to shed light on the relationship between access to opportunities during childhood and household income in adulthood at the subnational level for different demographic groups, relying on both census records and data from household surveys, although household surveys are typically not statistically representative at these levels of disaggregation. Thus, in spite that our results are consistent across countries and opportunities, they should be interpreted considering these limitations.

# **3. Stylized facts**

4 The Mexican household survey ENIGH-MCS includes a module that is representative at the same level of disaggregation as the census. However, the survey used in the SEDLAC project is the standard ENIGH, which does not have such statistical power. The reason for including the ENIGH rather than the ENIGH-MCS was that, by being the former part of the SEDLAC project, we are guaranteed a better level of comparability between countries.

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THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 9. Urban versus rural access to water supply and sanitation, 2006**

<!-- Start of picture text --> a. Water supply b. Sanitation<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Piped Stand Posts/ Wells and Surface Water Flush toilets Latrines Open Defecation<br>Shared Boreholes<br>Urban Rural Urban Rural<br>% population % population<br><!-- End of picture text --> _Source:_ AICD water supply and sanitation utilities database (http://www.infrastructureafrica.org/aicd/tools/data). Access figures calculated by the AICD using data from the 2006 Multiple Indicators Cluster Surveys published by the JMP in March 2010. ß Availability of utility water in urban areas is extremely low relative to subsistence benchmarks, partly due to deficiencies in water treatment infrastructure. Domestic water consumption is on average 6.5 liters per capita per day, an insufficient amount to satisfy absolute subsistence at 25 liters per capita per day and only one-fifth of the average consumption of other fragile, rich countries (table 7). Whereas in Bangui the consumption is around 10 liters per day, in the other seven urban centers served by SODECA the daily consumption is limited to 5 liters. It is estimated that only 21 percent and 4 percent of the population living in Bangui and the other seven urban centers, respectively, consume the levels required to satisfy basic needs (World Bank 2010g). In view of the insufficient supply of water, there is an urgent need to secure access to chemicals and power for treatment (factors that have contributed to water shortages in the past), as well as to expand
so that the indirect effects of a policy reform are allowed to differ because of the difference in policy complementarity.

Since the business environment measures, , are observed only once at the end of the sample period, to avoid exaggerating estimation precision, we only use observations in year 2002 for our cross-sectional estimation. Thus, although we have panel data for three years for financial variables, the regression samples consist of the cross-sectional sample of the final year (2002). However, in estimating productivity, we made use of all available data from 2000 to 2002 to improve the reliability of the productivity measures. Since some of the explanatory variables vary only at the city level, we cluster the standard errors at the city level to account for within-city correlation of the error terms and to avoid overstating estimation precision (Bertrand, Duflo and Mullainathan 2004).

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the same GPN.

An alternative approach used to map activities within GPNs is to combine international trade data with input-output (IO) table data. The advantage of IO data is that – for domestic activities – it unambiguously defines intermediate inputs by their use, i.e., in which industry they are put to use and what share of the industry’s output is exported. This information on main use, however, is not available for imported goods in many input-output tables . Lacking this information, researchers have adopted the purpose, they have relied on lists of components and their factory prices from industry analysts’ “teardown” reports, which capture the composition of the product at a specific point in time.

6
could be a source of endogeneity given potential reverse causality (for instance, if places that have stable cropland attract roads). Although this omission could be a source of omitted variable bias, the risk of such bias is greatly reduced by controlling for distance to the nearest city, which also proxies for the presence of infrastructure.

Following this first step, we recover the country-fixed effects and then run a country-level regression of those fixed effects on variables that capture the national context. The specification is: where FEc is the set of country fixed effects estimated in the first stage, Zcl are the national-level variables for country c , and μl is the error term. We use four national variables, including GDP per capita in constant dollar terms, the Gini Index for 2018, cropland per capita for 2018, and the Doing Business Quality of Land Administration for 2020.<sup>7</sup> We also experimented with two other institutional variables in lieu of the Doing Business variable, including a measure of Rural land tenure insecurity that we constructed using the 2012 Institutional Profile Database and the overall indicator in the 2017 Enabling Business of Agriculture dataset (see the data section above).

For movements into cropland, the approach is similar to assessing the determinants of movements out of cropland. The sample is different though as we need to consider the hundreds of millions of pixels that were not cropland in 2003, raising computational issues. To address the large dimensionality of the problem, we restrict ourselves to the sample of pixels within 3 kilometers of other pixels that were initially classified as cropland in 2003. There are 30.3 million such pixels, of which 4.29 million (covering 107.2 million hectares) transitioned to cropland between 2003 and 2018. This 3-kilometer buffer thus captures 90.1 percent of cropland gains over the period (119 million hectares). We then proceed to a similar 2- stage approach for our assessment of the drivers of cropland loss. In the first stage, we run a regression similar
**Table 5** . Distribution of gender norm index and personality <u>groups, by men and women (</u> _weighted_ <u>)</u>

||**Pooled S**<br>**_Mean_**|**ample**<br>**_SD_**|**Women**<br>**_Mean_**|<br>**_SD_**|**Men**<br>**_Mean_**|**_SD_**|**_T-tests_**|
|---|---|---|---|---|---|---|---|
|**_Main explanatory variables_**||||||||
|<br>Gender role attitudes index|41.38|11.12|40.31|10.06|42.75|12.22|-2.504***|
|Personality Groups||||||||
|<br>Impulsiveness|2.47|0.56|2.46|0.55|2.47|0.57|0.0095|
|<br>Work centrality|4.14|0.75|4.1|0.78|4.2|0.7|-0.0951***|
|<br>Tenacity|3.91|0.72|3.87|0.74|3.96|0.7|-0.0961***|
|<br>Polychronicity|2.13|0.67|2.15|0.69|2.09|0.64|0.0541**|
|<br>Locus of control|2.88|0.7|2.93
**Figure 1.6. Short-Term Correlates of Mobility, Ordered Logit Model with Random Effects, Marginal effects, RLMS 1994-2015**

<!-- Start of picture text --> Panel A: 1994-1998 Panel B: 1998-2004<br>15<br>10<br>5<br>0<br>-5<br>Panel C: 2004-2009 Panel D: 2009-2015<br>15<br>10<br>5<br>0<br>-5<br>To formal sector To full-time employmentUpward skills mobility To public sectorTo formal sector To full-time employmentUpward skills mobility To public sector<br>No transition No transition No transition No transition No transition No transition No transition No transition<br>Mobility(%)<br>Mobility(%)<br><!-- End of picture text -->

**Note:** Orange/green lines are related to 95% confidence intervals. The dependent variable is individual labor earning mobility between year _t-1_ and year _t_ . The terciles are defined using the cross-sectional sample for each year. Data on formal sector are available since 1998 and data on public sector are available since 2004.

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**4.1 Literacy rates by region and gender in Ghana**

Literacy raises the productivity and earning potential of the population, and improves their quality of life. However, low literacy rates continue to be a problem in most of sub-Saharan African countries. Most of the educational systems with high enrollment rates are often plagued by high dropout rates. Even though there is no clear cut definition of literacy, we define a person to be "literate" if he can read and write. Of course this definition is very subjective. A person is considered to be "read literate" if he can read the newspaper, "write literate" if he can write a letter and "math literate" if he can do simple calculations. This section will examine the status of literacy in Ghana, based on the GLSS survey. This will help us better understand the extent of differences across such social lines as gender, region and age groups. Table 26 shows the literacy rates for the whole population. Literacy rates(read&write) have increased from 37% in 1987 to 49% in 199 15. Literacy rates are higher in the urban areas(65%) than in the rural(40%) in 1991. Also, the male literacy rates are higher than the female literacy rates. Looking at literacy rates within the age groups cohorts, we find that younger the age cohort, the more literates. Higher literacy rates in the younger age groups is an indication of advancement in the primary and secondary schooling, while lower literacy rates among age cohort 25 and above is an indication of lower access to primary education in the past. The main results are: The adult Literacy rates(read and write) is about 49%; Government estimates around 44% in the 1984 census. Male literacy is higher than female literacy. Urban literacy is higher than rural literacy and by age-cohort, the literacy rates are higher for younger people.

# **4.2 Dropouts and repetition:**

The
historically been the main drivers of increasing CO2e emissions.<sup>7</sup> This raises the question whether, and under which conditions, containing climate change and eradicating poverty are compatible goals.

Existing research has approached this question by calculating the carbon footprint associated with the consumption of individuals at different income levels using consumption and expenditure surveys.<sup>8–10</sup> These studies simulate shifting up the consumption value of the world’s extreme poor to the poverty line and estimate the emissions associated with this hypothetical consumption shift. Studies using this approach have generally found that eradicating poverty leads to modest increases in global emissions, with estimates ranging from less than 1% to about 3%.

Here we approach this question with a different, more realistic, framing. Poverty reduction occurs by a combination of economic growth and distribution of this growth across households, with 90% of historical poverty alleviation driven by economic growth.<sup>11–14</sup> We estimate the carbon emissions implications of various growth scenarios (for aggregate growth and the distribution of this growth among the population) under which poverty would be eradicated. With this framing, eradicating poverty requires not only to increase the consumption of poor people but also, under realistic assumptions for the distribution of growth based on historical patterns, to increase the consumption of non-poor people. Therefore, eradicating poverty is expected to lead to higher emissions with this framing than in previous studies if the energy and carbon contents of economic growth follow historical patterns.

To achieve the world’s poverty and climate objectives, the future will need to differ markedly from the past. In particular, we analyze how changes in income inequality and in the energy and carbon intensity of economic growth can contribute to reconciling poverty and climate goals. Previous studies have documented stark inequalities in emissions between poorer and richer individuals and households, drawing attention to high emitters in all countries, which contrast with the minimal emissions contributions of the world’s poorest.<sup
figures differ slightly from those calculated for Table 1. This is partially because the published figures were calculated on a per capita (rather than per adult equivalent) basis.<sup>2</sup> 2 Also, for this analysis, the 1991/92 survey figures were converted to 2001/02 Shilling figures by multiplying by 2.611811, the ratio of the (Basic Needs) poverty lines from the two years. In the official figures, a price adjustment of 2.49 was used. The official figures and the analysis here also employ regional price adjustments. This combination of price adjustments may have been implemented differently for the official mean consumption figures than is done here. The report indicates explicitly that the mean consumption figures were calculated on a per capita basis, rather than the
effectively in a pro-poor transfer, leading to a situation where post-fisc incomes are more equal than in the absence of that regressive tax.

Figure 4. Poland. Distributional Impact <u>of the Tax and Benefit System in 2014</u> <!-- Start of picture text --> 200%<br>150%<br>100%<br>50%<br>0%<br>-50%<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Market income deciles<br>Direct taxes Direct transfers VAT (per capita) Excise (per capita)<br>Education Health Net cash position Total<br>Source: Own estimates based on the 2014 HBS.<br>Share of disposable income<br><!-- End of picture text --> One way to calculate the effect of a particular fiscal instrument on inequality is to calculate its marginal contribution. The marginal contribution of a tax (or transfer) is calculated by taking the difference between the inequality indicator _with_ and _without_ the tax (or transfer). Table 6 shows both the Kakwani progressivity index for each tax and transfer along with its marginal contribution to reducing inequality and poverty for each tax and transfer intervention in 2014 under the main scenario. Results for the alternative scenario where old age pensions are considered as deferred savings are presented in Appendix 2. We describe each of these in turn.

# **Taxes**

Direct taxes and social contributions are only slightly progressive overall, and while they are redistributive, they place an important burden on the poor. This can be seen by the fact that the Kakwani coefficient is positive, indicating that taken together direct taxes and contributions are progressive, and by positive marginal contributions, indicating that they are redistributive (Table 6). However, direct taxes and contributions are also poverty increasing, as shown by a negative 20
# **2 Data**

Our analysis is primarily based on data on refugee stocks compiled by the UNHCR. UNHCR annually publishes the data on refugee stocks by source and destination country pair. The term “refugee” includes both refugees and asylum seekers. Under the 1951 Convention Relating to the Status of Refugees and the 1967 Protocol, a refugee is defined as “a person who has been forced to flee his or her country because of persecution for reasons of race, religion, nationality, political opinion or membership in a particular social group” (Art 1.A.2.).

The UNHCR Population Statistics Reference database contains data for the period 1951 – 2017 (released on June 19, 2019). The data set compiles annual stocks of refugees and asylum seekers at the source-destination level for 197 destination and 223 source countries. The ultimate source of the data is the authorities of each receiving country. While in principle there are observations going back to 1951, coverage prior to the late 1980s is too sparse to be usable. Thus, our analysis covers the period 1987-2017. Overall, we have 112,522 non-zero observations for bilateral stocks over the period 1987-2017.

Since the data are not recorded at the individual level, we cannot reliably calculate refugee flows. Thus, the main variable used in the analysis is the refugee stocks. By definition, the stock of refugees in any particular year mixes individuals that arrived at different times. Since our main object of interest is changes in refugee behavior over time, analyzing stocks will if anything attenuate temporal differences.

To better approximate flows, we restrict the sample to large refugee events. A refugee event begins in the year in which the global stock of refugees from a particular source country first exceeds 25,000. An event ends when the stock falls below 25,000, or 10 years after initiation if the destination is an OECD country, whichever comes first.<sup>2</sup> Capping the termination date of the event also puts earlier
-person|16.6|20.3|17.6|7.3|21.3|12.6|
|Unaccompanied children|0.7|1.1|0.8|0.1|0.2|0.1|
|Non-nuclear and other hhs|6.5|21.2|10.5|7.7|26.5|14.9|

_Source_ : Own calculations based on ProGres and JD-HV database. _Note_ : PA = principal applicant. PA is determined at the time of registration. This table contains data on 42,505 observations in 2013 and 40,483 observations in 2018. To make both waves comparable, we excluded observations in wave one that did not receive the home visit questionnaire or that reported having no expenditure. For this reason, this table contains figures that slightly differ from Table 1 in Hanmer et al. (2019), which had 54,408 observations.

Our data enable insights into the change in refugee households’ composition five years after their original displacement. The share of PAs (that we classify as household heads) that are women has risen from 27 to 38 percent between 2013 and 2018. Interestingly however, the characteristics of Syrian households observed in the data seem to be similar in both waves. For example, PAs in 201718 are about four years older on average compared to PAs in 2013-14. This is partly because most 11
(see below), our final sample includes 444 village heads, 1,067 village bureaucrats, and 8,880 citizens. Appendix B provides additional details on our survey design.

**Electoral Data.** As part of our survey, we collected official voting tallies for all candidates running in the last village head election. We obtained complete electoral data for 799 among the 852 villages in our sample (94%). Under the Village Law, village heads are elected every six years via first-past-the-post voting, and local elections are staggered across districts, with all village elections occurring in the same year within a district. Thus, elections were held in different years across villages in our sample: less than 1% were held before 2016, 11% in 2016, 13% in 2017, 16% in 2018, 28% in 2019, 2% in 2020, 27% in 2021, and 1% in 2022. On average, 3.6 candidates competed in these elections with a turnout of 82% (calculated as votes cast divided by the number of registered voters in each village).<sup>2</sup> We report various checks on the electoral data in Section 3.3 (see also Appendix Table A.1 and Appendix Figures A.1–A.2).

The voting data indicates which candidate was the incumbent at the time of the last election. Overall, 512 village elections featured an incumbent candidate. These villages constitute the main sample for our empirical analysis.<sup>3</sup> Women comprised only 5% of incumbent candidates, and 6% of all candidates. Figure 1 (panel a) plots the density of the difference between the vote share received by the highestranking challenger candidate and the incumbent’s vote share. We use this difference as the running variable in our regression discontinuity (RD) design, described in Section 3.2.

**Administrative Data.** To measure bureaucratic performance, in addition