rafmacalaba/data-use-mentions-extended huggingface dataset:
industry<br>that are located in the same district|_M_|41,410|<br>186|281|1|<br>1,891|
|Inventory analysis:|||||||
|Amount of inventories at the end of the|_Q_|17,475|<br>113,447|1,290,961|0.00|<br>104,000,000|
|period (1,000 tenge)|||||||
|Transport costs to the nearest city ($/ton)|_CITY_|17,475|<br>1.37|2.02|0.05|<br>14.31|
|Transport costs to the nearest border|_BP_|17,475|<br>35.61|17.85|5.36|<br>80.98|
|point ($/ton)|||||||
|<br>Market Access Index based on transport|_MAI_|17,475|<br>0.22|0.32|0.01|<br>1.00|
|costs|||||||
|Interest rate (financing costs divided by<br>total|_I_|17,475|<br>6.43|507.44|0.00|<br>62,446|
|Total revenue (1,000 tenge)|_Y_|17,475|<br>740,949|8,362,113|1.00|<br>410,000,000|
|Number of employees|_L_|17,475|<br>33.49|29.72|1|<br>999|
|Share of the major business revenue in<br>total revenue|_diverse_|17,475|<br>0.07|0.44|
phase-3 SLQAT takers, applying a partially-fuzzy RD design, we find no evidence of program impacts at the cutoff on our outcomes of interest. We posit that this is likely due to weak identification arising from a small jump in the probability of treatment at the cutoff. In contrast, for phase-4 SLQAT takers, applying a sharp RD design, we find robust evidence that the program significantly increased the number of students, teachers, classrooms, and blackboards in marginal passers. The impact estimates at the cutoff were sizeable: our conservative estimates indicate that the program expanded marginal passers by roughly 85 students, and 3–4 teachers, classrooms, and blackboards. These impact estimates are also large relative to the mean baseline values for these outcomes for phase-4 SLQAT takers near the cutoff. What is more, they are particularly impressive given that phase-4 program schools were exposed to only about ten months of treatment before the follow-up telephone interview data were gathered. Finally, our cost-effectiveness estimates suggest that the program is among the cheapest interventions in developing countries for inducing enrollment gains.

Two potential threats to the internal validity of our impact estimates are however present. These threats arise from specific design and implementation features of the program. The first threat is program spillovers to non-program schools (of which marginal failers are but a specific subset) that operate in the same local schooling markets as program schools. Design features of the program such as the free-schooling condition for program benefit maintenance can alter the terms of local market competition, providing program schools with a competitive edge vis-à-vis non-program schools. The altered terms could result in impact estimates being upwardly biased if, for example, they induce the flow of students (and accompanying them, teachers) from nonprogram to program schools leading to the shrinking (or, at an extreme, the shutdown) of nonprogram schools and/or they discourage investments in the quantity and quality of inputs. On the 36
30 <mark>market orientation, the adoption of the improved production packages on the amount of rice sold per capita was also analyzed. It increases by 80, 94 and 374 kg of rice sold per capita for the STP, SMP, and MP respectively.</mark> <mark>To address the question whether greater rice orientation following intensification also results in greater welfare, household income and or consumption data would be needed. In the absence of such information, Table 9 looks at household food insecurity instead. Consistent with the notion of reduced poverty (and increased welfare),</mark> food insecurity decreases significantly by 2.22, and 3.17 points on the FAO household food insecurity experience scale in the semi-modern and modern production packages respectively.
<br>|13.9<br>|35.9<br>|
|% of Households<br>with Access to Electricity|30.5|71.1|14.5|57.2|
|Gross Primary Enrollment Ratio|95|97|91|95|
|Gross Secondary Enrollment Ratio|47|58|44|56|
|% of Heads of Household who are Farmers|71|67|92|**771/**|
|Household Expenditure per Capita<br>('000 Rupiah/year)<br>2'|575.3|612.4|453.8|547.12'|
|Gini-coefficient<br>|0.29|0.36|0.37|1<br>0.351_|
|1 Based on the three study areas only.<br>|||<br>|<br>|
|2. At the time of data collection (Fall 1996) the ex<br>|change rate was in th<br>|e range of $1 =<br> <br>|2,300-2,400<br>R<br>|<br>upiah.<br>|
|Sources: Statistical Yearbook of Indonesia1995; <br>Tengah, NTT-Hasil<br>Survei Penduduk A|Statistik Pendidik<br>ntar Sensus 1995; au|an<br>1994/95;Pen<br>thor's calcula|dudukIndone<br>tions.|sia,<br>Jambi, Jawa|

- 7 The data were collected in the fall of 1996, i.e. prior to the recent social and economic crisis in Indonesia. The macroeconomic evolution in the country is reviewed in Thorbecke (1991) and World Bank (1996). Tjiptoherijanto (1996) reviews the evolution of poverty and inequality. Thorbecke (1998) provides an initial assessment of the social costs
# **1. Introduction**

Governments have grappled with policies to improve women’s job market prospects. Of late, the spotlight has turned to paternity leave. Two articles in the media ( _Fortune_ , February 2015;<sup>1</sup> _The New York Times_ , November 2014<sup>2</sup> ) highlight the potential benefits of paternity leave, including better job prospects for women. However, the discussion on paternity leave in the informal and formal literature is restricted to developed countries. Do paternity leave laws exist in developing economies? If so, can we uncover any relationship with employment outcomes?

The formal empirical literature suggests two mechanisms for the impact of paternity leave on employment of men and women. First, employer attitudes may change. That is, if a particular position requires investment in job training, employers may discriminate against women, anticipating frequent absence due to child birth and child rearing activities. However, if paternity leave allows men to spend more time outside employment, assuming considerable uptake of paternity leave, this may limit discrimination against women. Second, Becker (1985) indicated that significant changes in the household’s time allocation can be achieved by minor changes in initial conditions. Therefore, changing the allocation of labor via paternity leave could change the trajectory of women’s time spent on market activities. Empirical evidence on the impact of paternity leave on women’s employment is mixed. For example, while Amarson and Mitra find a positive impact in Iceland, other studies find no such impact (see for example, Cools et al. 2015, Rege and Solli 2013, Ekberg et al. 2013 and Mansdotter et al. 2007).

The present study extends the above literature to the case of developing countries. Using a cross-section of firm-level survey data for private firms in 53 developing countries, the study finds

> 1 <u>http://fortune.com/2015/02/01/paternity-leave/</u>

> 2
|
|Ratio of formal<br>to<br>modern<br>informal<br>productivity<br>(a)|Total factor productivity in the<br>informal sector is a fraction,_a_, of<br>productivity in the formal sector<br>(A).|We use an estimate from Perry (2007) that,<br>controlling for firm characteristics, informal firms<br>are 71% as productive as formal firms. For the<br>persistent informality parameter options, we set<br>the growth rate of_a_to be 0.25% percent annually.|
|Ratio of formal<br>to rudimentary<br>informal<br>productivity<br>(b)|Labor<br>productivity<br>in<br>the<br>rudimentary<br>economy<br>is<br>a<br>fraction,<br>_b_,<br>of<br>total<br>factor<br>productivity in the formal sector.|Country‐specific estimates are obtained through<br>calibration of the model. For the persistent<br>informality parameter options, we set the growth<br>rate of_b_to be 0.25% percent annually.|
|Formal Labor<br>(LF)|The total number of workers<br>employed in formal firms, which<br>are subject to labor legislation.|We use the latest available estimates of the rate of<br>contribution to mandatory pensions systems<br>around the world (World Bank; ILO), combined<br>with average employment and labor force<br>estimates from 2008‐2012 from WDI. We assume<br>that unemployed and informal workers contribute<br>to pensions at a fraction,_d,_set to 2%, of the rate<br>among formal employees, and adjust our estimates<br>accordingly.|
|
for years. As no official datasets of school locations could be obtained for this analysis, just as with hotels, volunteer-contributed geolocations of schools were sourced from the OSM project (OpenStreetMap, 2019). Consequently, all the caveats mentioned in section 3.5 for the hotel dataset also apply here. The OSM school dataset contains 1,546 schools, 864 of which are located in coastal provinces. According to 2017 data from GSO, these 864 schools represent about 12 percent of all schools in coastal provinces (General Statistics Office of Vietnam, n.d.).

Importantly, the schools in the dataset do not seem to represent a random subsample of all schools, as some provinces contain only very few school, and three provinces have no schools at all in the dataset. Table 3.2 gives an overview over the provincial distribution of the geolocated dataset from OSM and compares it to the number of schools in each district according to the official statistics.

**Table 3.2: School numbers comparison across datasets**

|**Coastalprovince**|**Number of schools(GSO)**|**Number of school locations(OSM)**|**Coverage rate(%)**|
|---|---|---|---|
|Ba Ria-VungTau|167|27|16|
|Bac Lieu|88|0|0|
|Ben Tre|178|13|7|
|Binh Dinh|338|31|9|
|Binh Thuan|193|33|17|
|Ca Mau|132|4|3|
|Da NangCity|199|38|19|
|Ha Tinh|273|7|3|
|Hai PhongCity|311|58|19|
|Ho Chi Minh City|1208|505|42|
|Khanh Hoa|199|32|16|
|Kien Giang|155|10|6|
|Nam Dinh|266|10|
ES, making it possible to link performance during the COVID-19 pandemic back to firm characteristics collected through the ES. The ES cover formal (registered) firms with five or more employees and are designed to be nationally representative for the manufacturing and service sectors. For the countries in our sample, the most recent ES was conducted in 2019 or early 2020.<sup>6</sup> The ES COVID-19 Follow-up Surveys include questions on government support received during the pandemic. They also ask firms about changes in performance during the crisis, as well as expectations for the next months. Table A.2 provides the definitions of the variables included in our analysis. Tables 1 and 2 present the summary statistics of the share of firms that received government support and when they did so. Table 3 presents the summary statistics on firm characteristics and performance.

> 2 More information on the ES COVID-19 Follow-up Surveys is available at https://www.enterprisesurveys.org/en/covid-19.

> 3 Four rounds of data were collected in Jordan.

> 4 At least three rounds of survey data are also available for 3 countries in the Middle East and North Africa, 2 countries in Sub-Saharan Africa, and 4 countries in high-income Europe and Central Asia.

> 5 Armenia and North Macedonia have later Rounds 2 and Rounds 3 compared to the other 13 countries in our sample. Our results in this paper are robust for a sub-sample of 13 countries which drops Armenia and North Macedonia from the original sample.

> 6 For most countries, the surveys were completed before March 2020. For Romania, 92 percent of the interviews were completed before March 2020. Our results are similar when we exclude the 8 percent of observations from Romania that were collected in or after March 2020.

3
The treatment and control groups are well balanced with respect to observable characteristics (Table 1). While there are small differences in demographics (the age of the household head, who is almost always the beneficiary’s husband;<sup>11</sup> the beneficiary is female; and the number of children aged 6-15 years), work (the proportion of other adults, excluding the female beneficiary, who are inactive or are day laborers), and food expenditures, they are not jointly significant.<sup>12</sup> In our analysis we control for these unbalanced baseline characteristics. We also compare our main results to those without baseline controls and using a double lasso procedure to select a more parsimonious set of baseline controls (Annex B Table 2); neither alter the main findings.

Consistent with Djibouti’s national statistics, a staggering 82 percent of women in our study have no formal education, matched by a share of 66 percent illiterate household heads who are mostly (90 percent) husbands. In contrast, 77 percent of children aged 6-15 years are formally enrolled in school. At baseline the proportion of women employed or looking for work is very low; only one in ten women worked for income in the 7 days prior to the interview. Half of these women are self-employed, meaning that they almost exclusively selling food, either as street vendors or at a fixed location (e.g., baked goods, khat, ice, produce). Almost no women are self-employed as hairdressers, tailors, construction workers, or transport drivers). Women who report working usually belong to poorer households, are older, and rely significantly more on money from family members to cover the cost of household needs compared to women who do not work. Despite having very little schooling and low socioeconomic status as well as residing under strict social norms, the vast majority of women reported at baseline that they have high aspirations for their daughters’ education (completed secondary), marriage (after
Policy Research Working Paper 8652

# **Abstract**

Earnings inequality and job polarization have increased significantly in several countries since the early 1990s. Using data from European countries covering a 20-year period, this paper provides new evidence that the decline of middle-skilled occupations and the simultaneous increase of high- and low-skilled occupations are important factors accounting for the rise of inequality, especially at the bottom of the distribution. Job polarization accounts for a large share of the increasing inequality between the 10th and the 50th percentiles, but it explains little or none of the increasing inequality between the 50th and 90th percentiles. Other important developments during this period, such as changing wage returns, higher educational attainment, and increased female labor force participation, account for a small portion of the changes in inequality.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia 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/research. The authors may be contacted at mbussolo@worldbank.org, itorre@worldbank.org, and hwinkler@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
on the unilateral depth variable interacted with the same-country dummy turns positive in column (3) of Table 3, where we control for the influence of GDP and GDP per capita of the importer (as proxies for country size and economic development) on the border effect. This seemingly surprising result is confirmed in column (5), where we include interactions with a measure of the quality of institutions (the average across the six categories of the World Bank’s World Governance Indicators (WGI) database, which is found to decrease the border effect, consistent with the evidence from Beverelli et al. (2018)), and with other TA – variables that can have non-discriminatory effect the EU and GPA dummies equal one if the importer is a member of these TAs. The positive and significant interaction effect found also for trade in private markets, where unilateral depth in procurement provisions should not matter, casts doubts on the reliability of these findings. All in all, the results indicate that domestic firms might take advantage of more transparent procedures and open data on government procurement in goods, a the expenses of foreign firms.

Different than for goods, results in services, where the border effect is found to be significantly larger in public than private markets (see columns (1) and (2) of Table 2), suggest that countries with deep provisions on government procurement are more open to international trade. The interaction effect in Table 4 is consistently negative and significant across specifications for public markets, and the effect becomes weaker as we control for size and economic development. As an indication that the unilateral depth measures captures policies relevant to government procurement in services, the interaction effect is not significant for private markets. We thus find that deep non-discriminatory provisions on government procurement in TAs have been instrumental in promoting cross-border procurement flows in services, where governments have been buying significantly more locally than firms.

An important result from Tables 3 and 4 is that the bilateral trade policy effects become much lower and lose significance as we control
WaterAid (2009) provided several recommendations that could potentially improve the country’s rural water access: instituting, monitoring and regulation of COWSOs, improvement of the WPM data<sup>8</sup> ; promoting more community participation; providing autonomy in managing their water infrastructure; and the provision of technical support for COWSOs to conduct complex maintenance works.

With around 21 million Tanzanians still lacking access to improved water, the country fell short of reaching its Millennium Development Goal (MDG) targets to halve the proportion of people without improved drinking water and sanitation in 1990, by 2015. The new Sustainable Development Goal (SDG) of reaching universal access to safe water and sanitation by 2030, will prove even more challenging. This goal includes not only providing water access to the population, but also ensuring a continuous supply of sufficient, affordable and clean water for all. While the World Bank’s support has made headway in improving access to rural water supply in the country through Rural Water Supply and Sanitation

> 7 It is reported that some were paying close to 30 percent of the household income. See DFID (2016): “Measuring and Maximizing Value for Money of Rural Water Supply (RWS) Investments in TZ”.

> 8 Better WPM data is essential to the success of Tanzania’s water targets. According to Verplanke and Georgiadou (2017) and Water Aid (2009)  the WPM database for Tanzania is riddled with errors and inaccuracies.

5
violence. Systematic variation in response patterns could also have contributed to the divergence. People experiencing severe outcomes that are seen as socially undesirable—aggressive tendencies or indicators of risk for mental health problems, for example—may have been more likely to have reported their experiences honestly in a self-administered online questionnaire than in an in-person interview. A different set of respondents, who were not themselves experiencing severe outcomes but perhaps had friends or relatives in difficult circumstances, may purposely have reported more severe outcomes than they experienced personally with the goal of influencing resource allocation toward the needs they observed. While the PPCS and Facebook survey were not designed to isolate the effects of these different mechanisms, we explore each of them further in the sections that follow.

Were people who experienced more severe outcomes more likely to complete the Facebook survey?

The PPCS team secured high response and completion rates for the detailed adult individual interview, but the Facebook survey reflected the nonresponse and dropout challenges that are common to online questionnaires. Almost 96 percent of the adults selected for the PPCS individual interview agreed to participate, and virtually all of those adults completed the questionnaire. In contrast, about 12 percent of the people who clicked on the Facebook-advertised link (themselves a fraction of the Facebook users for whom the advertisement appeared) agreed to start the survey, and of those respondents only 54 percent remained to comprise the final sample.

The Facebook and PPCS samples differ substantially in some observed characteristics (further discussed below in the section on weighting strategies), and it is likely that they also differ in important unobserved characteristics given the high nonresponse rates of the Facebook survey. One plausible unobserved selection mechanism is interest in the survey topics. According to the introduction to the Facebook survey that preceded agreement to participate, the questionnaire was “about the psychological conditions of Palestinians living in the West Bank and Gaza” and the research team was “trying to understand how people have been affected by the conflict that took place last May.” Other topics mentioned include background information, employment status
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