rafmacalaba/data-use-mentions-extended huggingface dataset:
gender-specific constraints also include a dramatic soft-skills gender-gap in Sub-Saharan Africa, with men consistently scoring significantly higher on a range of soft-skills measures (Ajayi et al., 2022).<sup>12</sup> Closing the skills gender gap by strengthening inter and intrapersonal skills may help women negotiate within the household, expand their job search practices, and ultimately improve their labor market outcomes. The evidence base is still thin, with none of the papers focused on the effects of soft skills training for highly educated youth cited above conducting heterogeneity by gender, and with papers focused on less-educated samples finding mixed evidence.<sup>13</sup> We find similar take-up of soft skills training for women and men, and no significant differences in treatment effects.

The paper continues as follows. Section 2 presents the experimental design, data, and COVID19 context. Section 3 describes the methodology and presents the results. Finally, Section 4 concludes.

# **2 Experimental design, data, and COVID-19 context**

We evaluate a soft skills training for recent tertiary education graduates in Rwanda. The project was implemented by the University of Rwanda in collaboration with the Rwanda Development Board (RBD). RDB advertised the program on several local online job boards and invited youth who graduated from university or technical/vocational education programs after 2018 to apply. Ultimately, these advertisements yielded more eligible applicants for the training than could be accommodated by the implementing organization; we use this over-subscription together with in-person lotteries to randomize 900 eligible applicants—stratified by gender—to either the soft skills training or a control group.

Of the 2,390 eligible applicants who completed an initial short application form, 1,779 completed a longer baseline survey. Of these, 500 young men and 500 young women were randomly selected and invited to attend one of three regional, in-person lotteries to allocate the vacancies

> top 5 countries in the 2019 Africa Gender Equality Index—there are still prominent gaps in some areas. In 2021, labor force participation
End of picture text --> Figure 5 shows the performance in value added labor productivity per hour by integration status. Between 1993 and 1994 (the period of NAFTA without a peso devaluation), average productivity in all integration groups rose, with the fully integrated firms benefiting most. After the peso devaluation and until 1996, labor productivity of integrated and non-integrated firms decreased with the non-integrated firms experiencing

> 7Given that the peso crisis took place on the 20 December 1994, the effect of the devaluation is not captured by the data from year 1994.

8Yet another way of interpreting the increase in small integrated firms between 1993 and 1994 is by assuming that larger firms had a better chance of anticipating NAFTA, therefore integrating before the agreement was enacted.

11
interventions promoting cleaner and more efficient energy use. A robust approach to measuring energy poverty enables policymakers to pinpoint those most in need and track progress over time.

# **Furthermore, energy poverty has multiple drivers, including low income, poor energy efficiency, and**

**high energy prices** . As explored further in section 2, energy poverty manifests in various forms, including a lack of energy access due to power shortages or the inability to adequately heat one's home. Thomson et al. (2017) define energy poverty as inadequate access to home energy services, encompassing heating, cooling, lighting, and appliance use. The Energy Poverty Advisory Hub (EPAH)<sup>14</sup> (2022) categorizes the drivers of energy poverty into contextual and individual factors, identifying low income, low energy efficiency, and high energy prices as overarching causes. The consequences of energy poverty on human welfare can be severe in both the short and long term (ibid). Specific population groups may experience varying impacts based on socio-demographic characteristics, household composition, health, energy literacy, and cultural factors.

**This paper systematically reviews existing literature on energy poverty measurement, assesses the suitability of potential measures for Bulgaria, and constructs feasible measures based on the availability and quality of existing micro datasets.** This involves analyzing measures previously established in academic literature, white papers, and policy reports, focusing on the EU context. Then, utilizing available household-level microdata, we analyze the feasibility of implementing these indicators in Bulgaria. The examination involves several indicators using household expenditure data from the Household Budget Surveys and income and housing conditions from the European Living Condition Survey (EU-SILC). When selecting appropriate indicators, Palma and Gouveira (2022) recommend considering three key factors—context, scale, and availability.  Additionally, we experiment with the potential use of big data, including satellite data. Subsequently, we compare these measures, highlighting their respective strengths and limitations. The main objective is
|France|4|0.29%|
|10countries|3|2.19%|
|3countries|2|0.44%|
|4 countries|1|0.29%|
|Total tariff items subject to duties|1,368|Not applicable*|

Source: Based on data from the World Trade Atlas and the 2002 Antidumping Agreement.

The figures in Tables 11 and 12 serve to assess the actual impact of contingent protection mechanisms on current trade flows in Mexico. Most products affected by antidumping duties come from China; 96% of tariff items currently subject to antidumping duties are of Chinese origin (see Table 11 below). Nevertheless, when assessing the global impact of such duties on Mexican imports, it is worth noting that Mexican authorities have succeeded in not using these 42
by country _d_ against Brazil for product _p_ . (Threat)kd,t−1 is a binary indicator that takes a value of one if Brazil filed an AD investigation against country _d_ in the three-digit ISIC industry category _k_ in the previous year. We consider it plausible that firms in the same industry may be connected via unions, trade associations, and other business organizations. Thus, retaliation may be reflected at the industry level, although AD filings target a narrow product line.<sup>8,9</sup> In addition, this variable is zero for countries that have not implemented an AD law because they have no capacity to retaliate. We treat the European Union countries as a single country because AD is a European Union-wide policy.<sup>10</sup>

# **_<<B>>_** _Other Considerations_

We include firm-product-destination fixed effects in our specifications to control for firm-level unobservables, such as managerial ability, to account for the heterogeneity in the quantities’ units of measurement, which are used to create the unit export values and to control for time-invariant, country-specific factors, such as distance.<sup>11</sup> Moreover, we use the total export revenues of the firm, which measure not only the size but also the market power in the international market.<sup>12</sup> In addition, as stated in Verhoogen (2008) and Hallak and Sivadasan (2008), larger and more productive firms pay higher wages and employ better inputs, resulting in higher quality in exports, which are sold for higher prices. Although we do not have information on domestic sales, we use the “number of workers” obtained from the linked employment data to represent the size of the firm.<sup>13</sup> Recent literature (including, but not limited to, Hallak 2006, Kneller and Yu 2008, Harrigan and Deng 2008, Manova and Zhang 2009, Bastos and Silva 2010), on
Morogoro. In this section we summarize some key features of private schools in Morogoro and contrast them with those of public schools. Therefore, unless explicitly mentioned, data for this section come from the 2015 Morogoro data. In some places, these data are supplemented with 2013 IE data (details in Section 2).

Roughly half of the private schools in our sample are owned and managed by a faith-based organization, a third are owned and managed by a private business and the rest fall under the category of community/NGO/Trade Union schools.

Figure 8: Types of private schools in Urban Morogoro

|Type ofprivate schools|Percentage|
|---|---|
|Faith based/religious organization|54%|
|Private business|38%|
|Community|3%|
|Charity/NGO|3%|
|Trade Union|1%|

Source: Authors' calculations using 2015 Morogoro data.

Private schools are newer than public schools by an average of 6 years. Nearly 45 percent of public and approximately 61 percent of private schools in the sample were established between 2005-2014.

# 6.1 Size and Capacity

In the 2015 Morogoro data we find that private schools have significantly higher capacity for additional enrollment at the preprimary/primary level and slightly higher capacity for additional enrollment at the secondary level. Nearly 86 percent of private pre-primary/ primary schools have capacity for additional enrollment, compared to only 27 percent in public schools. At the secondary level, 83 percent of private schools have capacity for additional enrollment, compared to 65 percent in public schools. Around 86 percent of private schools admit every student who wishes to attend their school but only 42 percent of public schools admit every student who applies.

Figure 9: Filled capacity in public and private schools, by level

|Primary|Private|85%|
|---|---|---|
||Public|175%|
|Secondary|Private|65%|
||Public|107
many countries, but the size of the operation and level of organization are defining characteristics of the informal sector, while they are not in the case of the hidden economy. This study will focus on the informal sector, addressing the hidden economy only briefly in various parts of this report.

3.Mead and Morrisson (1996) quote Hemando de Soto: "The informal sector is like an elephant: we may not be able to defne it precisely, but we know it when we see it." Mead and Morrisson use data on small enterprises in seven countries to examine, empirically, how much consistency there is across countries when attempting to apply a single universal definition of the "informal sector." They find considerable "variability across countries in patterns of informality." (p. 1617).

ILO, 1993, also makes this point: "There is no general agreement on the exact meaning of 'informal sector.' The term is used in different contexts with different meanings and users of statistics may wish to define the sector for different analytical purposes.... This broadness is essential to an international statistical definition of the informal sector, which has to encompass the different ways in which the informal sector manifests itself in different countries and provide flexibility in the adoption of more specific definitions at the national level." p. 25.

4.The literature on the informal sector does provide a catalogue of features that are frequently associated with informality. Some defining characteristics used in empirical research by different scholars are: (i) the scale of operations, usually classifying enterprises with less than ten employees as informal; (ii) the use of self- or family labor; (iii) irregular and unstable income; (iv) the physical appearance of the enterprise structure, access to electricity; (v) the nature of the good or service being produced; (vi) the level of competition faced by enterprises; (vii) the level of technology, the labor-intensity of production, or the
to school by the children in the family and "distance to work" if the school<sup>distance data</sup> was not available. The hypothesized negative impact on non-farm participation<sup>was</sup> significant in the 1992 equation but not in the 1988 equation. Also, the distance<sup>variable</sup> is negative and significant in the agriculture equation for 1987-88, indicating<sup>that this</sup> variable is important for all of rural economy. These results confirm the<sup>now perceived</sup> wisdom that price incentives alone are not adequate to revise and<sup>rejuvenate the rural</sup> economy-markets and other infrastructure are necessary.

We expected non-labor income to be an important determinant of non-farm participation as hypothesized by others and since small amounts of capital are<sup>probably</sup> needed to start self-employment enterprises. Along those lines, some analysts<sup>argue that</sup> remittances are used to help households start new, non-farm activities. In this estimation, non-labor income is the sum of remittances, rental income, and "other" incomes<sup>listed</sup> (measured in 100,000 cedis). The results show that non-labor income is a positive<sup>and</sup> significant determinant of non-farm activity in both periods. The effect of non-labor income on agriculture is positive but not significant for the first two models for the 1987 survey, while it is negative and significant for the 1992 survey. Several explanations could be offered, one being that families with non-labor income moved out of<sup>agriculture</sup> in the latter period. It could also, by itself, indicate that agriculture families relied less on other sources of income than on the proceeds from agriculture. The first explanation<sup>is,</sup> however, more in keeping with the evidence on declining agricultural incomes, and the evidence on the
# **_An attempt to measure the trends in shadow employment in Poland_**

**_The transition probabilities out and into shadow employment using the LFS data augmented by the results of a dedicated survey performed by CASE in 2007._**<sup>**_1_**</sup>

_Mateusz Walewski_

> 1  This paper—a product of the Human Development Economics Unit, Europe and Central Asia Region—is part of an effort to understand the underlying factors that determine the size of informal employment in the shadow economy, providing background technical analysis for a forthcoming World Bank regional study ―In from the Shadow: Integrating Europe's Informal Labor‖. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted as follows: Mateusz.Walewski@case-research.eu
.S|T.S|C.S.|T.S.|C.S|T.S.|<br>C.S|
|Vehicles|1.0|0.9|_0.5_|1.0|1.0|0.8|1.0|0.9|0.1|<br>-0.4|
|Road length|_0.5_|0.5|0.4|1.0|0.1|0.7|0.5|0.8|-0.4|<br>-1.0|
|Vehicle-road ratio|0.4|0.4|0.3|0.0|0.9|0.0|0.5|0.1|0.5|<br>0.6|

**Urban spatial endowments.** The strong relation between income and population density at the urban level had produced unstable results in earlier regressions. Several of the urban equations reported on here use the deviation in area per capita (the inverse of density) from a predicted level for each city to summarize the effect of the durability of urban development patterns. This variable, termed an urban historical endowment index, worked well in the equations.

**Implications.** Many implications flow from the empirical results presented here. Most notably is the increase in urban congestion over time reflected in the remarkably rapid increase in the urban vehicle-road ratio with income over time. The magnitude of these trends is so large that it is alarming. Economic growth appears to be producing urban gridlock and promoting low density urban development.

During recent decades, urban areas have coped with congestion by spreading their activities over larger areas and adding road space by annexation. Decentralized urban development is most evident in high income countries, such as the U.S. and Australia, where land costs at the periphery of urban areas
oil demand and oil supply) are normalized such that they result in a 1 percent increase in real crude oil prices.

> 5 According to <u>https://www.worldstopexports.com/, the worldwide values of crude oil, natural gas, and coal exports in 2021 were</u> $982.6 billion, $374.6 billion, and $122.9 billion, respectively.

> 6 As data for production of natural gas and coal are not available in quarterly frequency for our sample period, the investigation is restricted to the effects of oil demand versus oil supply shocks on current account balances.

8
as poor. _b/_ FGT1 refers to the Foster,Greer, and Thorbecke (1984) measure of the poverty gap index, which adds up the extent to which individuals on average fall below the poverty line, and expresses it as a percentage of the poverty line. 32 _c/_ FGT2 refers to the Foster,Greer, and Thorbecke (1984) measure of poverty severity, calculated as the poverty gap index squared, which implicitly puts more weight on observations that fall well below the poverty line.
outcomes, which require further evidence for better understanding (Cerra _et al.,_ 2022; Ferreira _et al._ , 2022). In fact, we offer the first study to investigate all three development outcomes of poverty, inequality, and economic growth for Viet Nam.

Specifically, we investigate several research questions that are highly relevant to policy. Have average incomes across provinces converged (or diverged) over time? If yes, how were the poor segregated? Why were the poor spatially concentrated in some provinces and not others? Did factors such as inequality, urbanization, investment, and volume of government spending play a role in determining poverty reduction? Furthermore, what could we tell about the relationship between economic growth, inequality, and (different measures of) poverty?

Second, our analysis spans the period 2002-2020, which represents the longest-running period for the country that has been examined in an academic study. We complete this task by analyzing 10 rounds of the nationally representative Vietnam Household Living Standards Surveys (VHLSS). Our findings on the trends of poverty, inequality and economic growth, which are further disaggregated into between-province and within-province and regional variations, offer a nuanced picture of the evolution of these outcomes over time. To our knowledge, these findings are new and not available in previous studies.<sup>1</sup> Finally, we construct a database of panel data at the province level, which allows us to offer more granular analysis than the urban-rural dichotomy analyzed in previous studies. We further

> 1 Among the three development outcomes of poverty, inequality, and economic growth that we analyze, existing studies focus on either a single outcome or two outcomes. Their analyses are mostly restricted to survey data before 2010. For example, analyzing earlier VHLSS data covering the early 1990s to the early 2000s, Le and Booth (2014) and Nguyen _et al._ (2007) found widening urban-rural gaps in inequality. Benjamin _et al._ (2017) examine household income inequality during 2002-2014 through
countries. For example, in a precursor program in five Western Balkan countries, Cusolito et al. (2021) found that only 43.7% of firms attended at least one masterclass in-person, and then typically only the one held in their home country.

A final reason for the drop-off in training attendance is that it may have reflected some firms finding the training content less useful than others. Our own impressions from virtually attending the training sessions to observe were that they varied in the dynamism of the instructor, and in the extent to which they provided high-level strategic advice and broad overviews versus specific actionable steps. Some firms noted they either found some of the content too broad, not directly applicable to their business, or to be covering things they were already doing. However, even when content was somewhat familiar, several firms noted that it helped affirm that they were on the right track, or spurred them to get around to implementing practices they knew they should be doing but had not yet implemented. They also noted the one-on-one sessions had been helpful in addressing firm-specific issues. Firms showed most interest in improving customers and markets, and so were less likely to attend one-on-one sessions later in the program that were intended to focus on other business aspects such as financial performance. Only 9% of treated firms said the program had not been useful for their business, with 45% saying it was somewhat useful, and 46% very useful. Overall, 81% of treated firms said the program met or exceeded their expectations, and 90% would recommend it to other firms like theirs. The control group was less satisfied with the program, with only 27% saying it had been very useful to their firm, and 35% saying it did not meet their expectations.

# **3 Results**

## **3.1 Data collection and attrition**

Our baseline data come from the application form and diagnostic sheets submitted by all firms as they entered the program. Administrative data from the program
garments, processed food, etc.). In addition, we include year effects interacted with base-year product market characteristics that may influence regulatory changes. In particular we use six such characteristics: i) the product average import tariff rate; ii) a dummy equal to one if a product market is subject to at least one non-tariff measure;<sup>25</sup>

> 25To control for the impact of non-tariff measures, we aggregate HS 10 digit product-level information at the 5-digit industry-level using concordance tables provided by Indonesia Bureau of Statistics. We construct a dummy variable equal to 1 if a 5-digit industry has at least one non-tariff measure, and zero otherwise. The NTM and tariff data comes from Calì et al. (2021).

17
Work Research Center in Ajmer, and the Gramin Vigyan Vikas Samiti in Jodhpur are the most well-known examples. Most of these NGOs were initially set up to complement the state in the delivery of public services (Singh 2016).<sup>9</sup> In subsequent years, however, they broadened their focus and became important 6 Deininger and Liu (2009) use data from household surveys from 2004 and 2006 to document three main impacts: increases in social capital and economic empowerment, nutritional improvement (despite persistent drought at the time), and an increase in consumption for participants of new groups. The findings did not find increases in income or assets. There was however, evidence of spillovers within the communities in which SHGs were formed. 7 Under British rule, the region was indirectly governed through the Rajputana Agency, a collection of 18 princely states and 2 chiefdoms (Gupta and Bakshi 2008).

> 8 Singh (2016:104) describes many strategies used by the BJP government to promote the development of a panRajasthani identity throughout the 1990s. She notes that the speeches of leaders were replete with references to heroes of the past, highlighting the culture of valor and independence. The government also promoted tourism, supported cultural fairs and events, and demanded constitutional recognition of the Rajasthani language.

> 9 Under the fifth five year plan (1980-85), the government provided Rs. 500 million to support NGOs. In 1980, an important amendment to the Income Tax Act allowed corporations to deduct donations to NGOs from their taxable income (Kudva, 2005).

5