rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>-20 Men<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br><!-- End of picture text -->

**Similarly, a statistical model developed using the collected survey data suggests that the odds of becoming a** **_new_ user of private motorized vehicles just since the onset of pandemic were significantly higher for women – by about 31 percent compared to men** . The estimated odds of being a new user of a car/van or motorcycle are also higher for people with at least some university education and people who had at least some type of change in their occupation status (e.g., work becoming remote or hybrid). The odds decrease with the person’s age, meaning that the older the respondent, the more likely he or she was already using the private motorized vehicle already before the pandemic as opposed to becoming a new user. The odds of recently switching to private motorized modes are also higher if the start of the trip is in CABA rather than in PBA, likely explained by the higher financial ability of households residing in CABA. As people already before the pandemic relied disproportionately more on private motorized modes to go on trips for ad-hoc reasons such as recreation or to see a doctor, the odds of _switching_ to those modes since the start of the pandemic were lower compared to other (more regular) types of trips such as to work or school. The results are also intuitive regarding the statistical association with the person’s stated most important travel attributes: the odds of becoming a new user of private motorized modes are
workers from sectors exposed to trade with the US or by input-output linkages, this specification aims to capture direct impacts. Panels A and B use wage employment to population (12 years or older) ratios from the Population Census counts. One limitation of this measure is that the publicly available counts do not allow to restrict the sample further to people older than 15 years of age, or to the private sector. Panel C provides results for private wage employment in the private sector for people aged 15 years or older using the IPUMS samples. We also provide results for separate age groups below. In addition to the control variables included in the previous section, we also include the initial share of exports and imports in the region’s value added as right-hand side variables. This would control for any differential employment trends by regions with different degrees of trade openness. In addition, we control for the initial share of manufacturing employment in all specifications, as this variable is highly correlated with employment growth in the tradable sector, possibly capturing secular trends in manufacturing jobs. Finally, we control simultaneously for employment- and export-weighted exposure to local automation. This is because export-weighted exposure to local automation is correlated with exposure to US automation. Thereby, its omission would likely bias the estimated impact of US automation on employment, as it would also capture any impacts that Mexico’s automation in sectors with a comparative advantage may have on the labor market. At the same time, we need to include employment-weighted exposure to local automation to control for the direct impact of local automation on employment that operates outside the trade channel. The reduced-form estimates show that US automation did not affect employment rates in Mexico’s tradable sector, since the coefficients are not statistically different from zero in all specifications.

Table 12 shows the estimates of trade with the US on Mexico’s employment in the tradable sector, driven by exposure to US automation. In other words, changes in trade outcomes at the area level are instrumented with exposure to US automation.
Policy Research Working Paper 7630

# **Abstract**

This paper seeks to find an empirical explanation of Vietnam’s outstanding performance on the Programme for International Student Assessment (PISA) in 2012. Only a few developing countries participate in the assessment. Those who do, with the unique exception of Vietnam, are typically clustered at the lower end of the range of the Programme for International student Assessment scores. The paper compares Vietnam’s performance with that of a set of seven developing countries from the 2012 assessment’s data set, using a cut-off per capita GDP (in 2010 purchasing power parity dollars) of $10,000. The seven developing countries’ average performance lags Vietnam’s by more than 100 points. The “Vietnam effect” is difficult to unscramble, but the paper is able to explain about half of the gap between Vietnam and the seven countries. The analysis reveals that Vietnamese students may be approaching their studies with higher diligence and discipline, their parents may have higher expectations, and the parents may be following up with teachers regarding those expectations. The teachers themselves may be working in a more disciplined environment, with tabs being kept on their own performance as teachers. Vietnam may also be benefiting from investments in pre-school education and in school infrastructure that are disproportionately higher when compared with Vietnam’s per capita income level.

This paper is a product of the Education Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at esedmik@ 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
workers. This combination suggests a strong increase in the relative demand for skilled labor. In contrast, the 2000s have

> 25 The pattern of a roughly constant wage skill premium and sizeable increase in the relative supply of skilled labor in the 1990s, and the conclusion on the relevance of changes in the relative demand for this factor over the decade, coincide with the findings of Gallego (2011), who uses a longer data series that covers only the Greater Santiago metropolitan area.

41
# Fi ure 3a: Shares of female ownershi b industr <u>g p y y</u>

<!-- Start of picture text --> A. Female ownership of manufacturing in 2000 B. Female ownership of services in 2000<br>90% 90%<br>80% 80%<br>70%<br>70%<br>60%<br>60%<br>50%<br>50%<br>40%<br>40%<br>30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0%<br>0 500 1000 1500 2000 2500 3000<br>-10% 0%<br>0 500 1000 1500 2000 2500 3000<br>-20%<br>Average industry wage in 2010 Average industry wage in 2010<br>C. Growth in female ownership share of mfg., 2001- D. Growth of female ownership share of services,<br>2010 2001-2010<br>25% 20%<br>10%<br>20%<br>0%<br>15%<br>0 500 1000 1500 2000 2500 3000<br>-10%<br>10%<br>-20%<br>5%<br>-30%<br>0%<br>-40%<br>0 500 1000 1500 2000 2500 3000<br>-5% -50%<br>-10% -60%<br>Average industry wage in 2010 Average industry wage in 2010<br>Female-owned share of firms<br>Female-owned share of firms<br>ownership ownership<br>Growth in share of female Growth in share of female<br><!-- End of picture text -->
br>30000 Ireland<br>Denmark<br>25000 Finland<br>Sweden<br>20000<br>Slovenia<br>15000<br>Czech Republic<br>Hungary<br>Slovak Republic Estonia<br>10000<br>Poland<br>Russian Federation Lithuania<br>Bulgaria LATVIA<br>5000 Romania<br>0<br>5 6 7 8 9<br>Knowledge Economy Index<br><!-- End of picture text --> _Source_ : WBIKAM 29
# **Introduction**

Cultivated areas have constantly been increasing since the beginning of agriculture about 10,000 years ago, reflecting the history of human settlements and their economic development (Ramankutty and Foley, 1999, Ellis, 2011). This trend has accelerated over the past three centuries, with a fivefold increase in the global amount of cropland between 1700 and 1990 (Goldewijk and Ramankutty, 2004). Over the recent decades, megatrends have emerged that will have a significant impact on the amount of land that is cropped globally and its distribution across and within countries. These megatrends include climate change which started half a century ago and is predicted to dramatically accelerate (IPCC, 2019), and the ongoing massive urban population growth in sub-Saharan Africa and Asia, which will likely continue over the next two decades (World Urbanization Prospects, 2018), increased demand for food from emerging economies such as China (van Dijk et al. 2021), and the rising global interest in land FDI since the 2008 commodity price boom (Arezki et al., 2015).

With the availability of satellite imagery products and increasing computational capacities, it has been possible to measure global cropland dynamics from space (Ramankutty and Foley, 1999, Kolb et al., 2013, Klimanova et al., 2018, Li et al., 2018, Liu et al., 2018, Song et al., 2018, Mousivand and Arsanjani, 2019, Vaclavik et al., 2013, Potapov et al., 2021, Winkler et al., 2021). Such studies offered the possibility of a consistent measure of global cropland, circumventing issues of national definitions and incompleteness of reporting when relying only on national statistics. They also make it possible to study spatial patterns of cropland dynamics (Haney and Cohen, 2015).

Satellite imagery assessments of cropland, however, are not exempt from issues ranging from measurement errors associated with spatial and temporal resolution and the use of different classification algorithms to identify cropland. In fact, depending
where φ(<sup>b</sup> i) =<sup>∂F2(</sup> ∂t<sup>h(i),1)</sup> . Again, using the same steps has in the previous section, it can be shown that the weighted growth rates of wages within brackets in equation (13a) is equal the growth in average wages minus the growth in different types of labor weighted by their wages shares. In this case, equation (13a) can be rewritten as in equation (10):

# 3 Data Set and Sample Characteristics

The data set used, “Quadros do Pessoal”, is based on an annual compulsory survey conducted by the Portuguese Ministry of Labor. It is a longitudinal data file covering all the firms and plants operating in Portugal<sup>18</sup> . Records are available at worker, plant and firm level, and provide a detailed description of the labor force characteristics. At the firm and plant level, there is information for the sector of activity, regional location and the number of employees. The analysis covers the period 1985-1994<sup>19</sup> . The original data has 123, 294 and 214, 047 plants and 1,8 million and 2,2 million workers in 1985 and 1994, respectively.

An important issue is the choice of the geographical unit of analysis. The location specific knowledge of a firm can be specific to the city, to the district or to another geographical unit, depending on the type of knowledge considered. The components of knowledge range from very location specific to more general ones, that cross international borders. There is evidence that knowledge spillovers are geographically localized (Jaffe et al., 1993) and most of the empirical literature has focused on metropolitan areas, based on the fact that communication is more intensive there (see Lucas, 1988). To widen the scope of the study to non-urban areas I use “concelhos” as the
_Figure 7: Governorate level poverty estimates based on nominal and spatially adjusted welfare_ <!-- Start of picture text --> $2.15 - 2017 PPP $3.65 - 2017 PPP<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>nominal spatially adj nominal spatially adj<br>Source : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey<br>Overall, while acknowledging the importance of controlling for spatial price differences, we do not think<br>that using an imperfect spatial deflator would be the best way forward, particularly when considering how<br>much a fixed basket approach would deviate from the theoretical underpinnings of welfare measurement.<br>Moreover, using a nominal aggregate could also be aligned with the impossibility to account for spatial<br>price differences in pre-conflict poverty estimates based on 2009 grouped data.<br>Figure 8 below maps poverty at the governorate level, using the nominal aggregate.<br>Figure 8: Poverty estimates based on HNAP 2022 data, nominal aggregate<br>$ 2.15 (2017 PPP) $3.65 (2017 PPP)<br>Al-Hasakeh Al-Hasakeh<br>Aleppo Aleppo<br>Ar-Raqqa Ar-Raqqa<br>Idleb Idleb<br>Lattakia Lattakia<br>Hama Hama<br>Deir-ez-Zor Deir-ez-Zor<br>Tartous Tartous<br>Homs Homs<br>Damascus Rural Damascus Poverty Rate(%)88 - 100 Damascus Rural Damascus Poverty Rate(%)88 - 100<br>Quneitra 75 - 8862 - 75 Qu neitra 75 - 8862 - 75<br>Dar'a As-Sweida 50 - 6238 - 5025 - 38 Dar'a As-SweidaAs-Sweida 50 - 6238 - 5025 - 38<br>12 - 25 12 - 25<br>0 - 12 0 - 12<br>Percent Percent<br><!--
Still, even in places where NERICAs have been introduced, overwhelming evidence from the field of substantial yield benefits are slim. This point is emphasized by Orr et al. (2008), who discuss the gap between the potential of NERICA varieties and evidence of its success. For example, the studies by Diagne cited earlier show mixed outcomes. On one hand, an impact evaluation suggests that NERICA varieties generated significantly higher yields in Benin, but similarly structured evaluations provide no strong evidence of broad yield improvements in Côte d’Ivoire, or Guinea. In Uganda, where rice is a relatively new crop, Kijima et al. (2008, 2010) report favorable yield outcomes for some NERICA varieties. Moreover, consistent with evidence on yields, studies suggest consumption benefits in Benin (Adekambi et al., 2007) and Uganda (Bergman-Lodin 2005; Kijima et al. 2006). At the same time, a recent study by Kijima et al. (2010), suggests that heterogeneous outcomes should be expected. Based on a panel of rice farmers in Uganda, their study suggests that differences related to climate and alternative income sources, as well as differences among farmers in their capacity to replicate seeds of NERICA varieties, can lead to different adoption outcomes.

While there is an urgent need to increase the number of evaluations of the impact of NERICA varieties on yield, there are suggestions that evaluations should be broadened beyond measures of land productivity. Wopereis et al. (2008) emphasize this point in their discussion of the role farmer preferences should play in the evaluation of new varieties. For example, Diagne suggests the reason farmers might prefer NERICA varieties to traditional varieties with similar yields has to do with the shorter growing season for NERICAs. This characteristic reduces the risks associated with terminal droughts, saves on labor and sometimes allows for a second rice crop. Because evaluations tend to focus on yield (land productivity) rather than labor or total factor productivity, the benefits of NERICAs go unmeasured under normal weather conditions. There is
0.49 ***|0.10|0.49|212 (10)|0.45 ***|0.10|0.45|126 (3)|0.55|0.36|0.55|
|North America|<br>1114 (2)|0.63 *|0.19|0.63|<br>496 (1)|0.31 ***|0.19|0.31|<br>618 (2)|0.72 **|0.11|0.72|
|South Asia|175 (7)|0.83|0.15|0.83|175 (7)|0.83|0.15|0.83|||||
|Sub-Saharan Africa|410 (44)|0.75 *|0.13|0.75|409 (44)|0.77 *|0.13|0.77|||||

**Note:** The panels shows results for no-constant regressions of growth rates from surveys on growth rates from national accounts, as done by Ravallion (2003). Robust standard errors clustered at the country level (based on robust sandwich estimator). Number of countries (clusters) are shown in parentheses next to the number of observations. Statistical significance of difference from _one_ , denoted by *p < = .1, **p < = .05, ***p < = .01.
the middle class also implies more market-oriented economic policy on trade and finance. The impact of a larger middle class appears to be more robust than the impact on the same outcomes of lower poverty, lower inequality, and also of higher GDP per capita. Overall, our findings suggest that the development of the literature studying the association between income and socioeconomic factors may have been hindered by its limitation in measuring household income directly.

The paper is organized as follows. Section 2 discusses the dataset; Section 3 presents the empirical approach; Section 4 discusses the results, and Section 5 concludes.

# **2. Data Description**

The analysis is based on a cross-country panel dataset that contains information about headcount indexes for various thresholds that we have purposely built for the analysis. The dataset spans 672 yearly observations across 128 countries, from 1967 to 2009 (around 90 percent of the observations are however from the 1990s and 2000s). To compute the headcount indexes we draw from various World Bank collections of harmonized nationally representative household surveys that contain information on income or expenditures, and from simulated distributions of income and expenditures from the World Bank’s _PovCal_ database, whose parameters fit the distribution of nationally representative household surveys (see Table 1). Because of the nature of the primary data, 17 percent of the countries and 38 percent of the annual observations are from Latin America. The dataset is fairly balanced across levels of economic development: 21 percent of the observations are from high income countries, 37 percent from upper middle income, 30 percent from lower middle income, and 11 percent from low income countries. Because surveys tend to report information either for income or expenditures, we report for each country only one of the two measures: 57 percent of the sample reports information on income, and 43 percent on expenditures.

All income and expenditures data are in 2005 PPP US dollars. For each survey, we first correct current units for inflation using the national CPIs, and then convert them into 2005 US dollars PPP using
education, and sector of employment have been shown to be highly predictive of household welfare. Estimates based on recent census data are expected to be more accurate and precise than estimates based on geospatial data, which is often only available at an aggregated level (see for example Corral et al., 2021).

In this paper, however, we avoid using household-level predictors in the model because information for the same predictors from a recent census is not available. Using old census data can be problematic because it is not guaranteed to capture developments since the last census, especially in countries impacted by rapid changes. Interpreting the estimates as if these arise from the census year requires that the distribution of the census predictors, as well as their relationship to poverty, has not changed over time. This is a particular concern in countries such as these under study in this paper, which have among the highest fertility rates in the world and, in addition, have suffered from recent conflict and climate shocks which likely affected the geographic distribution of poverty and the geographic distribution of the population. Alternative sources of administrative data, such as health, land, or other administrative records, can also be useful sources of auxiliary data for small area estimation. However, these were not possible to obtain, and would not necessarily be commonly available for all four countries. We therefore decided to use publicly available, up-to-date geospatial data as covariates in small area models. The full list of candidate geospatial covariates, as well as a brief description of each of them are included in Table A1 in Appendix A.

To estimate the model, we use survey data from the 2018 EHCVM surveys in the focus countries. The process of integrating the geospatial covariates with the survey data in each
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- Brooks
based quotas, primarily in India (Basant & Sen, 2020; Bertrand et al., 2010; Cassan, 2019) and Israel (Alon & Malamud, 2014); and geographic quotas, in Sri Lanka (de Silva et al., 2021). Most of the literature indicates that affirmative action is instrumental in increasing access to education in historically underrepresented populations.

Public universities in Afghanistan operate under a stringent centralized system. Admission to a public university is determined solely by the applicants’ scores on the annual nationwide entrance exam for public universities, known as the _Kankor_ examination. Female applicants tended to score lower than their male counterparts in the Kankor. This disparity in performance is attributable to the systematic underinvestment and cultural barriers Afghan women face in accessing post-secondary education (Bamik, 2018; Hayward & Babury, 2015; Najam & Johnston, 2023), which is exacerbated by conflict (Darwish & Wotipka, 2022). In 2016, the Afghan government introduced a gender quota for admissions to address gender disparities and promote women’s long-term labor participation. The policy expanded until the Taliban’s return. This policy allocated seats at the enrollment unit level ( _concentrations_ ) by gender in particular universities and courses of study. These concentrations served as the _treated units_ . Consequently, the admissions competition was gender-specific—female applicants competed with other females, and male applicants competed with other males—for gender-designated seats in treated concentrations. Conversely, the other concentrations maintained open competition<sup>1</sup> (hereafter, _untreated units_ ).

In this paper, I examine the short-term efficacy of gender quotas on women’s matriculation<sup>2</sup> , especially those from low socioeconomic status—proxied by districts’ average nightlight data—in Afghan public universities. Specifically, I explore variations across study units and over time to assess the causal impact of gender quotas on the share of matriculated female students and score-related outcomes using the difference-in-differences (DD) methods. The causality of the DD estimates relies