rafmacalaba/data-use-mentions-extended huggingface dataset:
Immigration to the US_ , edited by G. Borjas. University of Chicago Press, pp.159-192 IOM 2015. “Migration Trends Across the Mediterranean: Connecting the Dots. Prepared by Altai Consulting for IOM, MENA Regional Office.” https://publications.iom.int/system/files/altai_migration_trends_accross_the_mediterranean.pdf.

IOM. 2016a. “Displacement Tracking Matrix, DTM Round 1 (database).” (accessed September 23, 2019), https://displacement.iom.int/system/tdf/reports/DTM%20Libya%20Round%201%20reportEN.pdf?file=1&type=node&id=473.

——— 2016b. “The Central Mediterranean Route: Deadlier Than Ever.” Data Briefing Series Issue No. 3. IOM Berlin. https://publications.iom.int/system/files/pdf/gmdac_data_briefing_series_issue3.pdf.

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contraction in economic activities as a result of COVID-19 for a number of reasons. The hypothesis that we focus on is that women have to shoulder the extra care responsibilities that are likely to result from government-mandated coronavirus containment policies and therefore are having to close their business. For example, the extra burden may come from staying home during a quarantine or caring for children out of school or family members who have fallen ill or who no longer have access to family or non-family caregiving.

In the rest of this paper, we use national school closure policies related to COVID-19 to examine the impact of a large negative childcare shock on the gender gap in time spent on care and domestic activities such as cleaning and cooking, and on business performance outcomes by gender.<sup>37</sup> We show that the increased burden of domestic work through school and childcare facility closures has a disproportionate effect on women entrepreneurs. In the following analysis we report results for all entrepreneurs in the sample and then examine the motherhood penalty by focusing the analysis on entrepreneurs who have any child dependents in the household. In addition to presenting the main business outcomes for entrepreneurs, we also explore intrahousehold dynamics by utilizing time-use data as reported by entrepreneurs about themselves and their spouse. We show that a significant proportion of entrepreneurs are having to devote time to domestic and care responsibilities after a school closure shock, with women doing the bulk of this work. The majority of the following analysis focuses on the 2020 survey waves. However, we supplement the 2020 results in Table 4a by showing the 2021 survey wave separately in Table 4b to examine whether any COVID-19 related school closure impacts persisted one year later.

> 37While data on the closure of childcare facilities within a country is unknown, in general, a national school closure policy due to COVID-19 is likely to be correlated with a closure in nurseries and day-care facilities too.

22
Beyond providing substantive insights regarding the impact of rotational practices, our paper is related and contributes to a growing body of evidence on machine learning and the use of remotely sensed data for analysis of agricultural production relations. We document the potential of machine learning by showing that a modest effort of training data collection allows generation of a crop map for the entire agricultural area of Ukraine. Although field-level crop cover data at slightly coarser resolution are freely available for the United States in the form of the crop data layer (CDL), there are few, if any studies outside the United States that have achieved national coverage with field-level data on crop cover.

We also contribute to the evidence on use of remotely sensed data by showing that use of common indices of crops’ vegetative development not only produces results consistent with those from combining crop cover data with statistical information on yields, but also affords insights (e.g., on soil water content) in their own right. The scope for disaggregation inherent in such data is a promising avenue for future research, including exploration of implications on profitability and implications for potential policy interventions to eliminate negative externalities resulting from rotational choices.

The rest of the paper is organized as follows. Section 2 briefly reviews available literature as well as the relevance of the topic within the context of Ukraine’s agricultural development. Section 3 describes the data sources, provides descriptive statistics on the incidence of crop rotations and yields, and outlines our econometric approach. Section 4 reviews evidence on the impact of rotational practice on yield, commonly used vegetation indices, and soil water content. Section 5 concludes by drawing out implications for future research as well as policy.

# **2. Literature and context**

Insights into the trade-offs involved in on-farm decision making regarding crop choice and sequence derived from detailed study of the productivity of different rotations can help identify economic effects of rotational choices, including whether such choices are likely to trigger external effects and, if yes, help identify suitable instruments to internalize such externalities
A. **The Firm**

# **Corporate governance6**

The fast growth of the corporate sector throughout the world makes corporate governance increasingly important. However, we have an especially poor understanding of corporate finance in developing countries, largely because of the lack of firm-level data. For instance, what are the ownership patterns in the developing countries? How are firms financed? Do firms face financial constraints? How well are creditors and shareholders protected in these countries? The answers to these illustrative questions can have important policy implications. The recent East Asian Crisis, for example, has been attributed to the poor protection of minority shareholders rights. The policy implications of these questions call for the collection of such data.

Bank surveys ask many good questions about corporate governance, although the coverage varies greatly. Consider the contrast between what theory suggests we should measure and what Bank surveys actually collect:

1. Theory suggests that competition in the managerial labor markets and reputation concerns force managers to perform well for future career prospects, making it important to measure aspects of managerial labor market, such as turnover, performnance criteria, rewards and punishment. Such questions are hard to find in Bank surveys. _Suggested questions:_ What determines managerial pay (current profits, stock options, relative performance, regulators' evaluations, etc)? If profits increase by one percent, what percentage increase does the managerial wage? Has a manager ever been removed because of poor performance? Can shareholders replace the CEO when performance is bad? What was the worst performance in the past x years? What consequences did the then-CEO suffer (income losses, demotion, or others)?

2. A firn's board of directors can be an important disciplinary force. Theory suggests that because of the free-riding problem associated with monitoring by shareholders, concentrated ownership may emerge to internalize the benefits and costs of monitoring. Thus it is important to uncover the ownership stake of the largest shareholders. Most Bank surveys ask about ownership type, but ask few questions beyond
surveys, from 2000 to 2009 for 73 low- and middle-income countries. That analysis reported that 58% of the total population and 78% of the extreme poor lived in the rural sector, as compared to 55% and 80%, respectively, in our sample. The slightly lower share of the total population living in rural areas in this analysis likely reflects increased urbanization in recent years, as this paper draws on newer household surveys and lines up the sample to 2013 instead of 2010. Although the two studies are not directly comparable, the higher share of the extreme poor in rural areas in the more recent data is suggestive that the rural sector has lagged in reducing poverty since 2010.<sup>22</sup> As mentioned above, the extent of poverty in rural areas is subject to two countervailing biases **.** On the one hand, the failure to spatially deflate the welfare aggregate in several countries overestimates the extent of rural poverty, by not adjusting for the lower cost of living in rural areas. On the other hand, the use of outdated urban/rural definitions in national surveys understates rural poverty by continuing to classify newly urbanized suburbs, which tend to be less poor than more remote areas, as rural. The former is mitigated by the use of separate urban and rural PPP exchange rates in China, India, and Indonesia. This, combined with evidence from South Asia presented below, suggests mild effects on the overall share of rural poor of less than five percentage points. It is not possible to assess the magnitude of the second source of bias with the data at hand.<sup>23</sup> In line with the high rate of rural poverty, however, poverty is also deeply ingrained in the agriculture sector **.** Nearly two-thirds of extremely poor workers aged 15 and above reported that their primary job is in the agricultural sector; and extreme poverty rates among these workers is more than four times higher than among non-agricultural workers (Figures
An increase in height between generations (in a stable population without significant in- or out-migration) is associated with better welfare conditions (McKee and Todd, 2011). This paper analyzes absolute intergenerational mobility (change of height between generations) and relative intergenerational mobility, defined as the degree to which parental height does _not_ predict the height of children.

# **V. Data**

We use the Evaluation of Rural Households Survey (ENCEL) 2017. ENCEL 2017 collected data on youth, heads of household,<sup>9</sup> and characteristics of both the individual’s household of origin and his/her new household.<sup>10</sup> Additionally, data from the Socioeconomic Characteristics of the Households Survey (ENCASEH) 1997 as well as the 1998 to 2000 ENCEL longitudinal surveys were used. ENCEL 2017 was established as a follow-up to a subsample of individuals from the 1997-2000 ENCASEH, to analyze elements of social mobility. Individuals who were in critical periods of growth and development when the program initiated were selected: those who could have received PROSPERA transfers during the first 1,000 days of life (under two years old), those who were about to start primary education (6 to 8 years old), or those who were transitioning to middle school (enrolled in the sixth grade). Measurements of intergenerational mobility are based on the comparisons of results between these youth and the people who, when the youth were 14 years old, were the primary household providers (hereafter referred to as ‘the providers’).

The sample was drawn from individuals in the three age categories identified from prior evaluation surveys and administrative registers.<sup>11</sup> The procedure ENCEL 2017 followed consisted of initially visiting the selected young adult’s households of origin, that is, the households they resided in between 1997 and 2000. When the original household was located, a survey was administered that collected data on: housing characteristics, household composition, each of the
A.6 Conflict (Figures S13 to S18)**

The Armed Conflict Location and Event Database Project (ACLED) uses reports from local, national, and international sources to generate geocoded event data on conflict around the world. Data from ACLED document numerous features of conflict events, including their timing, location, type, and the involved actors. ACLED prioritizes external validity in its data collection protocol and therefore captures a wider range of conflict types in comparison to other sources of conflict event data [107].

As of May 30, 2023, ACLED has documented over 306,000 conflict events on the African continent alone. This population of events forms the basis of the outcome measures we use to estimate the effect of AP management on conflict. We exclude from these data conflict events that cannot be geolocated to the town-level to minimize measurement error when determining where conflict occurred relative to protected areas’ boundaries and corresponding buffer areas. We also exclude from the ACLED data conflict events classified as strategic developments (“contextually important events which may contribute to a state’s political disorder and/or may trigger future events”). Such 10
**B. Implications of rising uncertainty on investment in EMDEs** . To assess the role of uncertainty for EMDE investment during 1998Q1‐2016Q2, aggregate vector autoregressive models for 18 EMDEs are applied. Given limited data availability, the sample varies for each indicator of uncertainty. Therefore, a series of separate vector autoregressive models are estimated. The results are statistically significant within the usual 16‐84 percent confidence bands.

The sample includes 18 EMDEs with available data for key quarterly macroeconomic indicators and stock market indexes: Brazil, Bulgaria, Chile, Costa Rica, Hungary, India, Indonesia, Malaysia, Mexico, Paraguay, Peru, Philippines, Poland, Romania, Russia, South Africa, Thailand, and Turkey. The literature on uncertainty often uses the option‐induced volatility measure VXO (e.g., Bloom 2009) or rich monthly macro‐data (279 macro and financial series in the case of Jurado, Ludvigson, and Ng 2015) to construct uncertainty measures. However, for many EMDEs, such measures cannot be constructed.

There are two sources of uncertainty: domestic and global. Global uncertainty is captured by financial market and policy uncertainty in the United States and the European Union (EU). Financial market uncertainty is proxied by the VIX for the United States and by the standard deviation of daily stock price changes for the Euro Area. Policy uncertainty is captured by the Economy Policy Uncertainty Index for the United States and the EU. Domestic financial market uncertainty is proxied by the standard deviation of daily stock market changes; domestic policy uncertainty is proxied by the ICRG index of political risk or, for Brazil, the Economic Policy Uncertainty Index.

_Global uncertainty_ . Vector autoregressions are used to estimate the impact of global uncertainty on EMDE investment. The data consist of investment‐weighted averages for 18 EMDEs for 1998Q1‐2016Q2. Endogenous variables follow this Cholesky ordering: global financial market or policy uncertainty, EMDE stock price index; EMDE bond price index, and aggregate real output and investment in EMDEs.
household head’s sector of employment. Panel a and c show the backcasted poverty rates at the US$1.90 and US$3.20 poverty lines, panel b and d show the backcasted series of number of people living below these two lines. Population estimates are from the WDI. Different values of the pass-through rate are calculated using household survey data from PovcalNet and National Accounts data from the WDI.

We also test the robustness of the results to relaxing the assumption that growth in the national accounts is passed through to growth in household consumption at the same rate across the entire consumption distribution.<sup>23</sup> To do this, we construct three separate “growth incidence curves” (GICs) using imputed consumption data from the survey-to-survey imputation exercise: the time periods considered are 2010/11 to 2018/19, 2010/11 to 2015/16, and 2015/16 to 2018/19 (see Table 12 in the Appendix for these GICs). Over the entire 2010/11 to 2018/19 period, Nigeria’s GIC was sloped _slightly_ downwards, implying that poorer Nigerians benefited slightly more from growth than richer Nigerians: this corresponds to a small drop in the Gini coefficient of just 0.6 points over this period.<sup>24</sup> However, this picture is somewhat distorted by the effects of the 2016 oil recession. The GICs based on imputed data indicate that richer households lost out significantly more than poorer households when the economic shock hit.<sup>25</sup> However, during the first part of the decade – when Nigeria was growing more strongly – richer Nigerians disproportionately enjoyed the gains. Put differently, the consumption of richer Nigerians was more sensitive to Nigeria’s overall growth performance than the consumption of poorer Nigerians. Thus, it is important to separate out the periods before and after the 2016 recession when constructing the GICs and testing the robustness of the backcasts. Once they have been constructed, the
MPI Methodological Notes 44, Oxford Poverty and Human Development Initiative, University of Oxford.

- Azevedo, J.P. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper, No. 9446. Washington, DC: World Bank

- Balseca, E., J. Cuesta, R. Damania, S. Feng, J. Moon, J. Rentschler, J. Russ & Margaret Triyana. 2020. The RISE Framework, Washington DC: World Bank and Korea Green Growth Partnership. Available at:

https://worldbankgroup.sharepoint.com/sites/ggs/Documents/RISE_MainReport_fnl.pdf

- Barron, P., L. Cord, J. Cuesta, S. Espinoza, G. Larson and M. Woolcock. 2022. Social Sustainability: A Position Paper. Washington DC: World Bank.

- Barron, L.,  and E. Gauntlett. 2002. Housing and Sustainable Communities Indicators Project: Stage 1 Report-Model of social sustainability. WACOSS(Western Australia Council of Social Service).

- Bostrom, M. 2012. A missing Pillar? Challenges in theorizing and practicing social sustainability: introduction. _Sustainability: Science, Practice & Policy_ , 8 (1), 3–14.

- Bramley G, Power S. 2009. Urban form and social sustainability: the role of density and housing type. Environment and Planning B 36: 30–48

- Becker, E., T. Jahn and I. Stiess, 1999. ‘Exploring uncommon ground: sustainability and the social sciences’, in Becker, Egon and Thomas Jahn (eds.) Sustainability and the Social Sciences: a cross-disciplinary approach to integrating environmental considerations into theoretical reorientation, Zed Books, London, pp.1-22

- CARE. 2020. “Gender Implications of COVID-19 Outbreaks in Development and Humanitarian Settings,” March 2020, https://reliefweb.int/report/world/gender-implications-covid-19outbreaks-development-and-humanitarian-settings.

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**Table 2: Life satisfaction across different scales**

||**Satisfaction**<br>**with life (1-**<br>**10 scale)**|**Satisfaction**<br>**with life**<br>**(disagree –**<br>**agree scale)**|**Satisfaction**<br>**with**<br>**finances**<br>**(disagree -**<br>**agree scale)**|
|---|---|---|---|
|Ln(household expenditures)|0.118***<br>(0.013)|0.112***<br>(0.014)|0.170***<br>(0.015)|
|HH asset index|0.161***<br>(0.006)|0.132***<br>(0.006)|0.134***<br>(0.006)|
|Primary education or less|-0.136***<br>(0.022)|-0.039*<br>(0.022)|-0.008<br>(0.023)|
|Secondary (baseline)||||
|Post-secondary education|0.139***<br>(0.015)|0.112***<br>(0.015)|0.155***<br>(0.016)|
|Did not work during past 12 months|-0.083***<br>(0.015)|-0.065***<br>(0.015
947)|
|Number of observations|632|262|553|223|643|264|632|261|
|r2_w|0.328|0.376|||||||
|r2_b|0.032|0.007|||||||
|r2_o|0.011|0.025|||||||
|rho|0.760|0.835|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Columns 3 through 8 report marginal effects from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

45
In this subsection we consider how syndicated loan flows aggregated at the bilateral borrower country, lender country level are related to bank regulatory stringency in both borrower and lender countries. We estimate equations as follows: I (bilateral)i,j,t = α + β0 Regulation (borrower)i,t + β1 X (borrower)i,t + γ0 Regulation (lender)j,t + γ1 X (lender)j,t + θ1 Log distancei,j + θ2 Common languagei,j + ηi + φj +  t + εi,j,t (2) in which I (bilateral)i,j,t is a variable characterizing the flow of loans to borrowers in country i from banks headquartered in country j at time t, and ηi, φj and  t are fixed effects for borrower and lender countries and time, respectively. Specification (2) includes regulatory variables and other institutional variables for both borrower and lender countries. We report standard errors that allow for clustering at the borrower country level.<sup>22</sup> The potential effects of regulatory variables in borrower and lender countries on absolute and relative credit variables are analogous to the discussion in Section 3.1.

Columns 1-2 of Table 4 report OLS regressions of Log(Volume + 1) and Arsinh(Volume), and a Tobit regression of Arsinh(Volume) with bilateral syndicated loan data, respectively. Capital regulation (Lender) enters the three regression with negative significant coefficients, suggesting that the total loan volume at the bilateral level declines with lender-country capital stringency consistent with regulatory arbitrage. Overall activity restrictions in the borrower country impact negatively and significantly on bilateral loan volumes in regressions 1-2, while overall activity restrictions in the lender country are positively and significantly related to loan volumes in regressions 1-3. This could reflect that

> 22 <mark>Two-way clustering at the borrower and lender country levels yields qualitatively similar results to the reported results.</mark>

20
and support the growth of more productive firms or result in an inefficient use of public money through helping unproductive firms to accumulate scarce physical and human capital and stay in the market. Reducing corruption in public procurement also reflects on the enforcement of competitive market structures in the economy and thus supports a policy environment that encourages competition based on innovation and efficiency instead of lobbying for policy privileges. Competitive public procurement practices can thus boost economic growth in two fundamental ways:

- Increasing the efficiency of resource allocations across firms and raising aggregate productivity growth

- Contributing to a level playing field among all firms in the economy, which encourages innovation and ensures survival and growth of the most competitive enterprises.

Against this background, the main goal of this paper is to provide a comprehensive analysis of corruption risks and state capture in public procurement in Bulgaria during 2011-2019. To measure these risks, this paper develops and validates a range of corruption risk indicators (CRIs) and builds a network-based state capture assessment framework. For measuring corruption risks, the paper calculates a composite score of individual ‘red flags’, called the corruption risk indicator (CRI), as an objective proxy measure of institutionalized corruption in public procurement (Fazekas and Kocsis 2020). For measuring state capture, the clustering of high corruption risk contracting relationships among buyers and suppliers is applied (Fazekas and Tóth 2016). These measurement innovations adapted to the specific Bulgarian context enable us to track the development, distribution, and structure of corruption risks and state capture across years, regions, and sectors, to name a few key analytical dimensions.

> 1 European Commission. Public administration and governance: Bulgaria. 2020. URL: https://commission.europa.eu/system/files/2021-10/bulgaria-ht0321342enn.en_.pdf. 2 The World Bank Worldwide Governance Indicators.

2
*|-0.007***|0.004|0.008**|
|(15-24/25-34/35-44/45-54)|(0.003)|(0.002)|(0.003)|(0.004)|
|Share of population with a primary|-0.001|0.001*|0.002***|0.001|
|education or less|(0.001)|(0.001)|(0.001)|(0.001)|
|Natural log of GRP per capita|0.071***|0.052***|0.050***|0.046***|
|(constant 2010 IDR)|(0.011)|(0.009)|(0.007)|(0.008)|
|Growth in GRP per capita|-0.000|0.001|0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Number of observations|272|272|272|272|
|R-squared|0.740|0.658|0.719|0.726|
|Year dummies|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

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