rafmacalaba/data-use-mentions-extended huggingface dataset:
)|-0.87|(3.89)|[0.45]|42.05|(29.65)|1,151|
|Dwelling can be locked (=1)|0.14**|(0.07)|[0.16]|0.79|(0.41)|1,610|

**_Notes_** :

- The table shows the regression results on pre-specified housing quality and housing-related finances using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The outcomes that require definitions are: Overall housing quality is defined as a normalized housing quality index that includes indicators for quality floors and roofs, indicators for access to grid electricity and piped water, and the number of people per room. Housingmaterial quality is defined as the summation of three indicators for high-quality floors and roofs. Clean water is defined as an indicator for households having treated drinking water (such as by a filter). _Total_ Monthly housing expenditures is defined as the sum of rent paid, mortgage, and upgrade cost. It includes the subsidy payments by the implementing organization. The _Per capita_ housing expenditures is divided by household size. Total ID Cards is defined as the sum of the ID cards possessed by the respondent (MOI card, Passport, Residency permit, Work permit, Family book, Syrian ID, and UNHCR file). Hours spent on childcare and chores are measured over the last week.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions
from Hill and Porter (2017), we estimate the vulnerability to absolute poverty for Afghan households due to drought. We combine the historical weather data with the estimated impacts of droughts on consumption to predict future consumption based on a posited threshold for the probability of falling into poverty and the estimated likelihood of future shocks based on the past weather shocks.

For simplicity, we rely on the linear estimates of NDVI anomalies on log consumption. As is clear from Figure 8a, imposing a linear relationship through the range of z-scores may underestimate the effect of severe droughts (NDVI z-score <-1), so these estimates should be considered a lower bound of household vulnerability to weather shocks.

> 33Over four-fifths of cultivating households in IE-LFS 2019-20 possess livestock, and livestock rearing is the second-largest income-generating activity for over two-thirds of the farming households.

17
viable organic alternatives were virtually nonexistent, the import ban created an economy-wide shock— effectively triggering a sudden, nationwide disruption in the supply of a critical production input.<sup>5</sup> This trade policy shock provides a rare opportunity to rigorously quantify the effects of large-scale fertilizer shortages and shed light on the importance of GE adjustments.

Our analysis proceeds in four steps. In the first step, we leverage a rich set of novel data sources 1Chemical fertilizers are shown to significantly boost crop yields, often by up to 50% in temperate settings (Stewart and Roberts, 2012). FAO (2025) reports that two out of every five employed individuals worldwide worked in the agrifood sector in 2022.

2More than 80% of countries rely on imports for 75% or more of their fertilizer needs —a consequence of highly concentrated global production: just five countries account for more than 60% of fertilizer nutrient output worldwide, while ten countries control 95% of the global supply of potassium, a key fertilizer nutrient (U.S. Department of Agriculture, Foreign Agricultural Service, 2022). The Global Trade Alert database described in Evenett and Fritz (2020) documents an increase in fertilizer import bans across countries from 0 in 2019 and 4 in 2020 to 61 in 2022.

> 3Besides scale, another difference between field experiments and large-scale access disruptions is that the former target agents who are initially _not_ using fertilizer, whereas the latter affect agents who _are_ initially using it, and who may thus value it more (by a revealed preference argument). These effects need not be symmetric.

4Sri Lankan subsidies covered 78–83% of the world price (Kishore et al., 2021) and accounted for 53.6% of agricultural spending in 2020 (Weerahewa et al., 2021).

> 5https://www.fao.org/4/ag120e/AG120E12.htm. The stated aim of the ban was a shift to organic farming; the unstated aim was to save
_Source:_ Own estimates from official poverty lines reported by the Bulgarian National Statistics Office and OECD Tax Ben data estimates. Poverty lines reflect income year, not survey year. For example, the 2021 poverty line is estimated with the 2022 EUSILC.

**The government reformed the MSA scheme by introducing a new “base/benchmark for assistance,” equal to 30% of the poverty line, aiming to increase the coverage and adequacy of the benefits linked to it** . While this reform could affect other programs linked to the GMI, this paper focuses on two critical programs designed to assist low-income families and individuals: the Monthly Social Allowance (MSA) scheme and the targeted heating allowance **.** The MSA is the main poverty-targeted program in the country, and the heating allowance is also critical to protect the energy poor, which is critical in the current context of rising energy prices. The Monthly Social Allowance (MSA) program is specifically crafted to address the modern social risks of poverty and social exclusion, offering a critical last resort benefit to ensure subsistence while still incentivizing employment. The primary objective of the MSA scheme is to provide households with an income sufficient to shield them from poverty.

# Monthly Social Allowance Scheme

## **The Monthly Social Allowance Scheme (MSA) is a means- and asset-tested national non-contributory**

**scheme that grants non-contributory allowance to low-income households.** The means test seeks to establish and measure the need of an individual or a Bulgarian family for benefits. Principal eligibility for income support is established by assessing a family's income level and potential sources (such as support for family members or potential revenues from the sale of assets). The allowance is given to households whose average gross income per member is below the Differentiated Minimum Income (DMI) threshold. The DMI threshold varies based on age, health, family, and educational status and is calculated as a proportion of the GMI threshold.<sup>11</sup> Certain
<!-- Start of picture text --> a) Skill-specific unemployment rate<br>8000 10000 14000 18000 25000 35000 40000<br>Researvation wage (MKD)<br>b) Field-specific unemployment rate<br>8000 10000 14000 18000 25000 35000 40000<br>Researvation wage (MKD)<br>30<br>20<br>10<br>Average skill-specific unemployment rate<br>0<br>30<br>20<br>10<br>Average field-specific unemployment rate<br>0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._ In conclusion, reservation wages in North Macedonia follow similar, though more compressed, distribution as market wages. However, their formation may be interfering with factors which are potentially more relevant for unemployed persons, like the searching costs and local employment conditions.

# **5. Methodology**

This paper has dual objectives: i) to analyze reservation wage determinants; and ii) to calculate the reservation wage premium or discount. The economic model to analyze reservation wage determinants 10
findings and the extent to which our findings for formal manufacturing firms are likely to be relevant for other industries. Political connectedness among manufacturing firms is on average slightly lower, with an average score of 2.88, than political connectedness in the financial and services sectors where firms on average have scores of 3.37 and 3.48 respectively (see Table B2).

To build confidence in our data and assess the importance of survivor bias, we first replicate the key regressions from Fisman’s (2001) seminal paper using our smaller sample. The results are shown in Table B3. While the pattern of the coefficient estimates is qualitatively similar to the results in Fisman (2001), quantitatively the estimates are somewhat different.<sup>29</sup> Perhaps the biggest difference is that the interaction between the return on the Jakarta Stock Exchange Composite Index net of broader Southeast Asian Effects (referred to using _NR JCI_ ) and the measure of political connectedness, though positive, is not statistically significant. This could be due to sample selection and/or differences in the way the _NR JCI_ proxy is constructed.<sup>30</sup> The data we use in our paper is restricted to manufacturing firms, so to assess whether political connections might be especially valuable (or not) for manufacturing firms, we run a regression in which we add two crude sector dummies and interact them with the _POL_ measure. The results are presented in column 9: we cannot reject the null hypothesis that political connections are less valuable in manufacturing 29 For example, using the sample of surviving firms, we find no evidence of connections playing a significant role in the response of firm’s share prices to rumors about Suharto’s health On April 1-3, 1997, even though the estimated coefficient estimate is negative (-0.53).

30 The net return measure is calculated as follows; first, a “market model” for daily returns is estimated: ii mm ii ii RR (JJCCHH)
Table A.4: World Income Inequality data (2019)

|Country<br>Data quali|ty<br>Imputation|
|---|---|
|_Pivotal benchmark countries_||
|China<br>3|Surveys and tax data|
|France<br>5|Surveys and tax microdata|
|India<br>3|Surveys and tax data|
|South Africa<br>3|Rescaled fscal income|
|United States<br>5|Surveys and tax microdata|
|_High income share_||
|Angola<br>1|Adjusted surveys|
|Bahrain<br>1|Adjusted surveys|
|Central African Rep<br>1|Adjusted surveys|
|Mozambique<br>1|Adjusted surveys|
|Yemen<br>1|Adjusted surveys|
|_LAC (good data quality)_||
|Argentina<br>4|Surveys and tax data|
|Brazil<br>4|Surveys and tax data|
|Chile<br>4|Surveys and tax data|
|Colombia<br>4|Surveys and tax data|
|Costa Rica<br>4|Surveys and tax data|
|Dominican Rep<br>4|Surveys and tax data|
|Ecuador<br>4|Surveys and tax data|
|El Salvador<br>4|Surveys and tax data|
|_Honduras (WID data)_<br>0|Regional imputation|
|Mexico<br>4|Surveys and tax data|
|Peru<br>4|Surveys and tax data|
|Uruguay<br>4|Surveys and tax data|

_Note:_ This table presents WID ”data quality index” and the method of calculating inequality measures for selected countries. Data is from the World Inequality Database (WID).

# **B Corporate Income Tax & Shareholder Registry**

In this section we explain the process of assigning undistributed profits to individuals.

47
Figure A1: Survey period in context–contd.

<!-- Start of picture text --> LBR MDA<br>0<br>80 80<br>0<br>-20<br>-20 60 60<br>-40<br>-40<br>40 40<br>-60<br>-60<br>20 20<br>-80 -80<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>MDG MLI<br>100 0 80<br>0 80 -20<br>60<br>-20<br>60 -40<br>-40 40<br>40 -60<br>-60<br>20<br>-80 20 -80<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>MNG NER<br>90 0 60<br>80 -20<br>0 40<br>-40<br>-20 70<br>-40 -60<br>20<br>-60 60<br>-80<br>-80<br>-100 50 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>NIC NPL<br>0 16 0 100<br>-20 -20 80<br>14<br>-40 -40<br>60<br>12<br>-60 -60<br>10 40<br>-80 -80<br>-100 8 -100 20<br
Policy Research Working Paper 9325

# **Abstract**

Many countries in Sub-Saharan Africa face a rapidly growing population and labor force in demand of good jobs. Ethiopia has reacted to this challenge by prioritizing largescale industrial development through the construction of industrial parks to drive exports, job creation, and growth. However, the African experience with industrial parks so far has been mixed. To provide further evidence on the welfare effects of factory jobs in Ethiopia, this study conducted an experiment that facilitated the job application and onboarding process for young female job seekers at three factories. Using panel data from 827 applicants, the study finds that the extra support increased the likelihood of being employed in the treatment group in the short run, largely driven by wage and factory work. Further, the intervention raised reported monthly income by nearly 30 percent in the treatment group. However, the study also finds an adverse impact on health outcomes as well as downward adjustments of applicants’ expectations and perceptions of the earnings potential and desirability of factory work in response to the treatment.

This paper is a product of the Africa Gender Innovation Lab, Africa Region. 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 gtefera@worldbank.org; nbuehren@worldbank.org and mgoldstein@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
man rights organizations.<sup>16</sup> Their campaigns led to the central government passing the Prenatal Sex Diagnostic Techniques (Regulation and Prevention of Misuse) (PNDT) Act in 1994. This act was effective from January 1, 1996. The PNDT Act made it illegal to use prenatal sex-diagnostic techniques (like ultrasound) to reveal the sex of a fetus. Following the revelation in the 2001 Census of a continuing deterioration in the sex ratio, the PNDT Act was strengthened by a 2002 Amendment (effective 2003) incorporating a ban on advertising prenatal sex determination and increased penalties for violations.<sup>17</sup> It is widely believed that these regulations have made little difference (Visaria (2005)), although Nandi and Deolalikar (2013) find that they did have some impact. These bans are difficult to enforce because ultrasound (or alternatives like amniocentesis) is also used for medical purposes and in routine prenatal care, making it easy to cover up sex determination as a motive.

Before we move on to the empirical strategy, it is useful to also consider whether we expect an _underlying_ trend in the sex ratio at birth, i.e., a trend in the absence of parental interventions, and in what direction. In general, the fetal environment has improved in India. The growth in income and the decline in poverty since the early 1980s has been widely documented; fertility decline set in from 1981 (Bhalotra and van Soest (2008)); and neonatal mortality rates have been decreasing. Maternal mortality is estimated to have declined (Bhat (2002)) and maternal age at birth has risen. These trends will all have led to improvements in fetal health. Indeed, the availability of ultrasound scans that monitor fetal development will also have contributed to better fetal health. Bio-medical studies show that fetal health improvements tend to favor boys (Trivers and Willard (1973), Almond and Edlund (2007)) and thus the underlying tendency, if any, would be
employees), and large firms (100 or more employees)), and _Geographical location_ (selected based on centers of economic activity in the country).

While the Enterprise Surveys have been produced since 2002, we restrict our sample to surveys administered during 2006-2010 since these provide sampling weights that take care of the varying probabilities of selection across different strata and are thus indispensable to making assertions about the whole population.<sup>8</sup> For countries that were surveyed twice during this period, we retain data for the year with the largest number of firms surveyed. Our results are also robust to retaining the most recent survey data for each country. Our final sample consists of surveys across 104 countries.

We examine and compare the role of small firms in each country along two dimensions. First we construct the small firm share of **Total Employment** where total employment is the population estimate of the number of permanent, full-time employees in the country derived by aggregating the employment reported each firm in the country multiplied by its sampling weight. Second, we construct the small firm share of **Job Creation** where job creation is the population estimate of the change in the number of permanent, full-time employees over two years, also derived by aggregating the change in employment reported by each firm in the survey multiplied by its sampling weight.<sup>9</sup>

> 8 Most surveys contain three sets of weights – strict, median, and weak weights depending on the eligibility criteria used to construct the sample universe. Under the strict assumption, eligible establishments are those for which it was possible to directly determine eligibility, under the median assumption, eligible establishments are those for which it was possible to directly determine eligibility and those that rejected the screener questionnaire and under the weak assumption only observed non-eligible units were excluded from universe projections. So under the weak assumption, all establishments for which it was not possible to finalize a contact were assumed eligible. The survey implementation manual recommends the use
while the inefficiency of the aging heavy industries results in the relative decline of the Northern provinces.

# **3. THE SPATIAL CONCENTRATION OF TECHNOLOGY AND LABOR INTENSIVE INDUSTRIES: A TEST WITH PANEL DATA**

# **3.1. Statistics**

The strong overall industrial growth in China masks overwhelming differences at the sectorial level. The Chinese industrial economic statistical yearbooks classify the industries in 30 groups<sup>13</sup> . We divide these 30 industries into 2 large categories: 4 high-tech industries (electronic and telecommunications equipment manufacturing; instruments, meters and other measuring equipment manufacturing; medical and pharmaceutical products; and electric equipment and machinery manufacturing) as category A, and the other 26 industries as category B. Among the 26 industries of category B, we further divide them into two subcategories: 9 industries as category C “primary materials, energy and foodstuffs”, and the other 17 industries as category D “labor intensive industries”.<sup>14</sup> We measure the importance of a category of industry in time _t_ , noted as _ICATcate_ , _t_<sup>, by the ratio of the value-added of all the industries of the category in</sup> question (A, B, C, or D) to the value-added of the entire 30 industries:

> where _j_ stands for the provinces, _i_ the industries, _t_ the year, _cate_ the category, and _va_ the value–added of the industry.

Our results show that the high-tech industries grew more rapidly than the labor intensive industries. In 1988, the value added of the industries of category A represented only 12% of the total value-added, while it increased to 16% in 1997. To a large extent, the growth of the industries of category B is pulled up by that of the industries of category C – the value-added of the industries of category C was 24% of the entire category B in 1988, and 38% in 1997. In contrast, the valueadded of
# **<mark>4. Estimation method</mark>**

To measure the impact of childcare attendance on maternal employment, we use a regression discontinuity method. Development and discussion of regression discontinuity design methods can be found in a large number of studies (e.g., Van der Klaauw, 2002; Imbens and Lemieux, 2008; and Lee and Lemieux, 2010). In regression discontinuity designs (RDD), a treatment group is selected for treatment if the value of at least one observed variable crosses a cut-off threshold. In particular, there exists a conditioning variable _Z,_ such that the treatment variable, denoted by _D,_ is equal to 1 if and only if _Z_ is larger than a specific value _Z=c_ . We can identify the treatment effect by comparing outcomes for individuals (denoted by _Y_ ) just above and below the threshold _c_ :  _RD_  _Y_   _Y_  .

The proportion of children attending childcare increases by age, and as discussed earlier, children’s current age is used to determine their enrollment eligibility. We thus use birth months as the conditioning variable that determines childcare attendance. Specifically, we compare the employment of women whose children were born in December and January in two contiguous years. Since the school year starts in September, a child born in January of a given year is more likely to start attending childcare 1 year later than a child born in December of the previous year, even though the two children differ in age by only 1 month.<sup>11</sup> Figure 3 shows the proportion of children age 1-5 attending childcare by birth month in two consecutive years. Older children were born from July to December, while younger children were born from January to June of the following year. Children born in January are one month younger than those born in December. Panel A of Figure 3 presents a graph using data from the pooled sample of children. Other panels of the figure present graphs for different
indicators for the different teams of enumerators who administered the survey across the communities in our sample. The error terms are clustered at the school level, which represents the unit of randomization of the treatments. We complement the usual asymptotic inference with two alternative procedures. First, we display _p_ -values based on randomization inference, which are accurate

> 5The distribution of missing schools in the analysis of transition to secondary school is 18 schools in the control group, 14 in the _Original_ group and 16 in the _Plus_ group. Due to the different individual identifiers, we are not able to match this dataset to the survey data. The estimates reported in Table B-6 document no program effects on grade repetition and attrition, which suggest that conditioning on grade attainment is not problematic in our context.

> 6Appendix A.3 reports more details about these interviews. Tables B-4 and B-5 show that the characteristics of these survey respondents are broadly comparable to those of the mentors and the local instructors in our main sample.

10
<!-- Start of picture text --> Sok =<br>Pcas<br>eae<br>Oe<br>7 fe<br>N<br>(3 -3.80 - -0.65 CINo data<br><!-- End of picture text --> <!-- Start of picture text --> BEG =<br>oes<br>Seo oe<br>SOUS<br>See<br>XSNi<br>(4 -1.20 - -0.80 ( -3.60 - -1.20 CINo data<br><!-- End of picture text --> <!-- Start of picture text --> as<br>Sey<br>Oe A<br>aSWie<br>(OsNSIN eS<br>Statseri an<br>2Chi? —N IR —<br><!-- End of picture text --> <!-- Start of picture text --> »<br>RE ~<br>CA aa<br>Pantie YS j<br>oNIRL aS<br>CaN yi a ae<br>cy SL |ee oe-<br>\P<br>RNs<br><!-- End of picture text -->

<!-- Start of picture text --> 1S<br>ROSS<br>S vA<br>ee | Ce<br>vat, MD<br>Cy<br>N<br>a 370 : 0.60 = a rd 3-0.95 - -0.80<br><!-- End of picture text -->

<!-- Start of picture text --> k<br>aS<br>ANS<br>PS<br>Oe 7)<br>Sap 2<br>AN<