rafmacalaba/datause-encoder-data huggingface dataset:
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of periods of being out of the labor force. We now investigate mobility by using simple transition matrices.

A variable for primary activity/occupation was constructed in this analysis using the combination of the 8 categories for jobs described earlier with categories that cover the following non‐employment activities‐ _12.Job Search_ , _13.Education_ , and _19.Not in the labor force and not in education_ (includes housekeeping, retirement, being sick or disabled, being on vacation and “other”). Transition matrices for occupational status are listed and described below, for various age cohorts and time periods in IFLS. The tables are presented at the end of the paper.

**List and description of transition matrices for occupational change over time**

|**Table**|**Time period**|**Gender**|**Age**|
|---|---|---|---|
|Table 1|1993/2007|male|15‐24 in 1993|
|Table 2|1993/2000|male|15‐24 in 1993|
|Table 3|2000/2007|male|15‐24 in 2000|
|Table 4|1993/2007|male|aged 25 and over in 1993|
|Table 5|1993/2000|male|aged 25 and over in 1993|
|Table 6|2000/2007|male|aged 25 and over in 2000|
|Table 7|1993/2007|female|15‐24 in 1993|
|Table 8|1993/2000|female|15‐24 in 1993|
|Table 9|2000/2007|female|15‐24 in 2000|
|Table 10|1993/2007|female|aged 25 and over in 1993|
|Table 11|1993/2000|female|aged 25 and over in 1993|
|Table 12|2000/2007|female|aged 25 and over in 2000|

There are several interesting results that can be seen in the transition matrices. The first striking result is for transitions from self‐employment. Overall, a very small percentage of individuals (both adults and youth) transition from self‐employment by themselves and self‐ employment with a family member temp/work to being self‐employed with a permanent work (Generally about 1%‐2%, regardless of age, gender or the time period considered). This is reflective of the fact that in 1993, 2000, and 2007 there were only small numbers of individuals in the “self‐employment with permanent worker category” (113, 140 and 226 24
# **Introduction**

South Asia has been undergoing rapid economic transformation, but despite high economic growth, most countries in the region are under pressure to quickly create more and better jobs to cope with fastgrowing populations and to sustain growth. Furthermore, women are being excluded from labor markets—as evidenced by low female labor force participation rates, persistent gender disparity in male and female employment rates, and greater likelihood of women being engaged in unpaid employment. Therefore, expanding productive employment for women and lowering barriers for their entry into the labor market are necessary for inclusive growth in South Asia.

Female engagement in market work is desirable for a variety of reasons. In addition to economic returns, female employment has positive development spillover effects: households invest more in the health and education of their children as female earnings increase, and women being paid for work gives them greater “voice” at home and stronger engagement in society. For example, female employment in Bangladesh’s garment industry is associated with reduced fertility, increased age at marriage, and higher educational attainment (Heath and Mobarak, 2015). In India, employment in the business processing sector is associated with increased household investment in girls’ education and health (Jensen, 2012). Similarly, outside of South Asia, increased demand for labor in the female-intensive manufacturing sector in Mexico is associated with increased household decision-making power for women and improved health of their children<sup>2</sup> .

Rigorously assessing trends in female employment in South Asia is difficult due to data limitations. Agreeing on what represents a “job” is challenging as many forms of work fall in the gray areas between employment, unemployment, and inactivity. But available comparisons show that employment rates for women generally lag what we might credibly expect given South Asia’s development level. This is in contrast to male employment rates, which are as high as those in other developing countries, as shown in Figure 1.

Figure 1 raises an important question: _why_ are female employment rates in South Asia—particularly in Sri Lanka, Pakistan, India, and Bangladesh—far below average given their GDP per capita? This paper works towards answering this question by:

- (i) Charting trends and patterns in female employment rates across countries over time to investigate whether they have always been low.

- (ii) Using a variety of censuses, labor force surveys, and household surveys, which is complicated because definitions of “employed work” differ (explained below), to look at female employment in South Asia from a broad perspective.

> 2Majlesi (2016) uses the Mexican Family Life Survey, which asks the household decision maker respondents questions related to twelve different categories. Hence, they construct a direct measure of decision-making power for each spouse within households.

2
# _2.2. Data and stylized facts_

We use firm-level information from Bureau van Dijk’s Orbis global database, the largest cross-country database containing information on firms’ financial statements, production activity, and firm ownership (Kalemli-Ozcan et al., 2019). We consider all industrial sectors, including financial companies and services (see table A.1 in Appendix A). We observe European firms over a full economic cycle between 2010 and 2016. To clean up the data for our intended purposes, we follow Cusolito and Didier (2020) and select firms for which we have information on the key variables in our study, namely total sales, compensation of employees, interest payments, taxes, paid-in shareholder equity, total debt and date of establishment. Our final sample comprises information on approximately 7.8 million observations across 24 European countries.

Table 1 reports the number of countries and firms in our sample. Of the 24 countries, according to the 2021 World Bank classification of countries into income groups, 18 are high-income countries and 6 are upper-middle-income countries. Italy is the country with the largest number of firms (25% of the sample) and Luxembourg the lowest (0.04% of the sample). On average, more than 60% of total assets are financed through debt. Liabilities to total assets range from 41% (Ukraine) to 74% (Italy). The ratio of value-added (VA) to total assets is on average around 0.9, varying between a minimum of 43% (Bosnia and Herzegovina) to a maximum of 143% (Bulgaria).

Table 2 summarizes the cost of financing through debt of firms classified according to their size. In all years, smaller firms face a higher cost of financing through debt. Moreover, not only does the average cost of finance appear to be higher for smaller firms, but also there is a higher dispersion, suggesting potentially greater misallocation. Table 3 shows similar patterns but with some distinctions. The cost of equity financing is still greater for smaller firms but variability within size clusters appears to be elevated also for larger firms.

Tables 4 and 5 summarize the cost of debt and equity by age group. We do not observe a clear pattern in the cost of funding among age groups. In general, firms 14
|Husband<br>Wants More<br>Children<br>(7)<br>-0.029<br>(0.052)<br>[0.570]|0.370<br>5.89<br>12,761<br>ge estimate of the<br>ndicator variables<br>actly describe the<br>n (FPE) intensity<br>gions without and<br>d women in birth<br>. Each estimate is<br>rackets.|
|---|---|
|tion<br>Agriculture<br>/ Unskilled<br>Manual<br>(6)<br>-0.130<br>(0.045)<br>[0.004]|0.792<br>6.40<br>19,814<br>, and 2016).<br>ns. The frst-sta<br>olumns 4–7 are i<br>her categories ex<br>primary educatio<br>sures for MTI re<br>es include marrie<br>d a cubic for age<br>hown in square b|
|band’s Occupa<br>Service<br>/ Sales<br>(5)<br>0.099<br>(0.031)<br>[0.001]|0.081<br>6.40<br>19,814<br>ears 2005, 2011<br>f the six colum<br>t variables in c<br> groups; the ot<br> with the free <br>denote the mea<br>red. All sampl<br>near trends, an<br>; _p_-values are s|
|Hus<br>Husband’s<br>Years of<br>Skilled /<br>Schooling<br>Professional<br>(3)<br>(4)<br>1.116<br>0.026<br>(0.372)<br>(0.028)<br>[0.003]<br>[0.350]|2.28<br>0.083<br>7.10<br>6.40<br>19,784<br>19,814<br>mographic and Health Survey (in y<br>le is described at the top of each o<br> shown in column 1. The dependen<br>al, clerical, and skilled manual job <br>ed level of schooling, instrumented<br>easures _I_MTI-T<br>_zy_<br>and _I_MTI<br>_zy_<br>, which <br> in which two interventions occur<br>d zone fxed efects, zone-specifc li<br>ne level and shown in parentheses|
|Husband’s<br>Age<br>(2)<br>1.009<br>(0.884)<br>[0.254]|49.48<br>6.35<br>18,174<br>the Ethiopian De<br> dependent variab<br>married women is <br> include profession<br>g_izy_ is the predict<br> (MTI) intensity m<br>actions for regions<br>ude birth year an<br>clustered at the zo|
|Wife’s Years<br>of Schooling<br>[ First Stage<br>- Married Only ]<br>(1)<br>0.009<br>(0.063)<br>[0.891]<br>0.066<br>(0.058)<br>[0.261]<br>-0.212<br>(0.080)<br>[0.010]<br>0.001<br>(0.010)<br>[0.947]<br>0.009<br>(0.007)<br>[0.207]|1.15<br>4.69<br>20,959<br>ased on data from <br>ue instruction. The <br>rs of schooling for <br>d/Professional jobs <br>.<br>�<br>Years of Schoolin<br> tongue instruction <br>ively, and the inter<br> All regressions incl<br>tandard errors are|
|Add’l Years of Free<br>Schooling<br>�<br>_IF P E_<br>_zy_<br>�<br>Add’l Year of MTI<br>No Script Change<br>�<br>_IMT I−T_<br>_zy_<br>�<br>Add’l Year of MTI<br>with Script Change<br>�<br>_IMT I_<br>_zy_<br>�<br>_IF P E_<br>_zy_<br>_× IMT I−T_<br>_zy_<br>_IF P E_<br>_zy_<br>_× IMT I_<br>_zy_<br>�<br>Years of Schoolingizy|Mean of Dependant<br>(Pre-Reform Cohorts)<br>First Stage F-Statistic<br>N<br>_Source_: Author’s analysis b<br>_Note_: MTI = mother tong<br>efect of the reforms on yea<br>that equal 1 if true. Skille<br>occupation groups included<br>measure _I_FPE<br>_zy_<br>, two mother <br>with script change, respect<br>cohorts from 1970 to 1988. <br>from a unique regression. S|

A.29
The Unintended Impacts of an Intimate Partner Violence Prevention Program:

# Experimental Evidence from Rwanda

Claire Cullen, Arthur Alik-Lagrange, M ̃uthoni Ngatia, Julia Vaillant<sup>_∗_</sup>

**_JEL_ : C93, J12, J16, O12**

**Keywords: Intimate Partner Violence, Gender, Identity, Spillovers, Norms, Field Experiments**

> _∗_ Cullen (claire.cullen010@gmail.com), Alik-Lagrange (aaliklagrange@worldbank.org), Ngatia (ngatia@gmail.com), Vaillant (jvaillant@worldbank.org). Acknowledgments: We thank Julien Labonne, Markus Goldstein, and Rachael Pierotti for support throughout the project. We thank the respondents for participating in the surveys, and the staff at Laterite Ltd for their outstanding data collection work. We also thank Jean Paul Ngabonziza, Victoria Isika, Harald Hinkel, Verena Phipps, Patricia Fernandes, Jessica Ott, Stephanie Stahlberg, Ludovic Bequet, and Alex Twahirwa and other staff at MIGEPROF, and CARE, RWAMREC, and Rwandan Women’s Network for all their support, programming, research assistance, or insights into program administration. We are grateful to Lori Heise, Kristin Dunkle, Erin Stern, and other members of the Indashyikirwa team for their roles in the original program and for fruitful discussions of the results. We are grateful for comments and suggestions from Noam Angrist, Diana Arango, Leonardo Burzstyn, Cheryl Doss, Eliana La Ferrara, Anandi Mani, Ted Miguel, Berk Özler, Simon Quinn, Maria Micaela Sviatschi, and seminar participants at NEUDC, Oxford University, Princeton ECBE, CSAE, Melbourne University, Australian Gender Economics Workshop, the World Bank, and SVRI. All errors are our own. The study protocols were approved by the Rwandan National Ethics Committee (722/RNEC/2018) and the Oxford University Central University Research Ethics Committee (R50347), and a research visa was granted by the National Institute of Statistics Rwanda (0676/2018/10/NISR). This RCT was registered as AEARCTR-0002282. This paper is a product of the World Bank Africa Gender Innovation Lab, within the Office of the Africa Region Chief Economist. We are grateful to the World Bank’s Umbrella Facility for Gender Equality (UFGE) for financial support. The UFGE is a multi-donor trust fund administered by the World Bank to advance gender equality and women’s empowerment, and is supported by Australia, Canada, Denmark, Finland, Germany, Iceland, Ireland, Latvia, the Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States, the Bill and Melinda Gates Foundation, and the Wellspring Philanthropic Fund. We are also grateful for support from the World Bank’s Nordic Trust Fund and the Swiss Development Cooperation. The findings and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the Government of Rwanda, of the World Bank and its affiliated organizations, or those of its Executive Directors or the governments they represent.
|Arellano-Bond test for AR(1)|-7.365|-7.437|-6.411|
|---|---|---|---|
|Arellano-Bond test for AR(2)|2.939|2.837|4.253|
|Sargan test|311.1|310.8|135|

Note: i) Authors’ estimation from VHLSSs using province FE model (columns 1 to 3) and GMM model (columns 4 to 6), ii) Robust standard errors in parentheses clustered at the province level, iii)*** p<0.01, ** p<0.05, * p<0.1.

31
<!-- Start of picture text --> Figure 7. Growth rates of the poorest and<br>richest quintiles of countries<br>(population-weighted)<br><!-- End of picture text --> <!-- Start of picture text --> 4.0%<br>3.3%<br>1.8% 1.7%<br>1960-80 1980-97 1960-80 1980-97<br>Poorest countries Richest countries<br><!-- End of picture text --> Now, the adjective “population-weighted” is very important. If you ignore differences in population and just take an average of poor-country growth rates, you will find average growth of about zero for poor countries in the 1980-2000 period. Among the poorest quintile of countries in 1980 you have both China and India, and you also have quite a few small countries, particularly in Africa. Ignoring population, the average growth of Chad and China is about zero, and the average growth of Togo and India is about zero. Taking account of differences in population, on the other hand, one would say that the average growth of poor countries has been very good in the past 20 years. China obviously carries a large weight in any such calculation about the growth of countries that were poor in 1980. But it is not the only poor country that did well. India, Bangladesh, and Vietnam have also had accelerated growth and grown faster than rich countries in the recent period. A number of African economies, notably Uganda, have also had accelerated growth.

15
Statistical Business Process Model (GSBPM) has been used by many countries to guide development of their National Statistical System alongside the Generic Activity Model for Statistical Organizations (GAMSO). In the European Union, the model has been developed into a detailed handbook.

In order to understand how to make these generic models operational, PARIS21, Open Data Watch, the Global Partnership for Sustainable Development Data, the OECD and others have defined a virtuous data cycle where the end goals of data use, data value and data impact are achieved through building up each element of the data ecosystem. The approach used in the SPI draws on these underpinning concepts.

We identify five key pillars of a country’s statistical performance, as shown in Figure 1. These are data use, data services, data products, data sources, and data infrastructure, which can be further disaggregated into 22 dimensions. This figure shows these pillars and dimensions in the form of a dashboard, which can help countries identify areas for development in their statistical system. We briefly describe these pillars below. More details on the dimensions of the SPI are provided in Appendix A, Table A.1.

Since statistics have no value unless they are used, the first pillar of the SPI is data use. In order to meet user needs, the statistical system must develop a range of services that connect data users and producers and facilitate dialogue between them. The second pillar of the SPI is therefore data services that are trusted by users. The dialogue between users and suppliers in turn drives the design of statistical products that are to be created including the quality of product needed for the country requirement. This will incorporate accuracy, timeliness, frequency, comparability, and levels of disaggregation. The third pillar of the SPI is therefore data products. In order to create the products required, the statistical system needs to make use of a variety of sources from both 7
# **The Effect of the Swedish Payroll Tax Cut for Youths on Firm Profitability**

Arvid Malm<sup>ab</sup> , Johan Eklund<sup>cb</sup> , David Francis<sup>d</sup> , Nan Jiang<sup>d</sup> a Royal Institute of Technology (KTH), Department of Industrial Economics and Management b Swedish Entrepreneurship Forum (SEF) c Jönköping International Business School (JIBS) d The World Bank (IBRD) <u>arvid.malm@indek.kth.se</u> <u>johan.eklund@jibs.hj.se</u> <u>dfrancis@worldbank.org njiang1@worldbank.org</u> Keywords: payroll taxes, firm profitability, incidence JEL: H22, H25
unemployment duration and its causes in Turkey relative to other developed and developing countries, but they do not try to estimate any possible scarring that results from greater unemployment duration.

However, there is one notable working paper by Cruces, Ham and Viollaz (2012) that studies the scarring effects of both youth unemployment and youth work in the informal sector in Argentina and Brazil. They use repeated cross sectional data from the Socio‐Economic Database for Latin America and the Caribbean (SEDLAC) to track age cohorts of individuals over time, with individuals in these cohorts being born between 1965 and 1967. Their main findings are that youth unemployment and youth informal work have a scarring effect in terms of future wages and future probability of unemployment and informal work. This fits with the broadly defined phenomenon of labor market scarring.

# **_2.5 Job quality and occupational mobility_**

While the informal/formal dichotomy may be a simple and useful marker of employment quality, there are many ways in which job quality can be defined. In developed countries, earnings are a common measure of labor market outcomes. However, even though reliable earnings data often available in developed countries, they are not easy to measure and compare across individuals in developing countries. Also, they are often not the most important job characteristic to workers (D’Addio, Eriksson and Frijters, 2007). Additionally, higher wages in some occupations may reflect compensating wage differentials for other job characteristics (e.g., a premium paid for risky work).

A multi‐dimensional approach to defining job quality is widely recognized as being a more accurate measure than earnings. The Employment in Europe (2001) report for example, states that “in the absence of a single composite indicator, any analysis of job quality must be based on data on both objective and subjective evaluations of the worker‐job match”. Following many others, D’Addio, Eriksson and Frijters (2007), suggest that self‐reported job satisfaction measures are a common and appropriate approximation of job quality citing the Employment in Europe (2002) report, “in all Member States self‐reported job satisfaction is strongly positively correlated with wages, job status and job related skills acquired through training”. Even very simple categories such as “non‐agricultural own account workers” (Gindling and Newhouse, 2012) or “work with a formal contract” may be useful proxies for job quality.

Interestingly, while many of the characteristics associated with job satisfaction are indeed measures of private benefits (e.g. wages), there are some characteristics (e.g. job training and job security) that may have a range of public benefits such as promoting social cohesion and productivity. Ideally, in evaluating job quality, both the private and societal benefits should be considered. Failing to consider all characteristics may mean that certain shortcomings of the labor market are not identified. For example, if human capital accumulated during employment in a particular job is transferrable across firms; such 9
CLEMENS & TIONGSON entrepreneurial activity—except perhaps drawing some families’ breadwinners out of farming. It does not raise savings, but causes borrowing to markedly decrease.

Our research design cannot answer several questions about the effects of migration. It cannot measure how the effect depends purely on the gender of the migrant for theoretical reasons (women who self-select to apply for an overseas job could be quite different from men who do so) and empirical reasons (the applicant is female in only 179 [20%] of our sampled households). Our design also cannot measure any external effects, positive or negative, on other households—households from which no member applied to an EPS-Korea job. It cannot measure the effect of strategic decisions made prior to migration caused by foresight of the future option to migrate (Batista et al. 2012; Jensen and Miller 2012). And it cannot reliably measure the effects of migration experience on return migrants (Reinhold and Thom 2011), because return migrants are self-selected from current migrants.

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26