rafmacalaba/data-use-mentions-extended huggingface dataset:
As before, we recover the country fixed effects from the first-stage regression and regress them on the same national-level variables (keeping only one institutional variable at a time). Table 5 presents the findings. Model (1) is the full specification where the institutional variable is the Quality of Land Administration. Models (2) and (3) respectively substitute Rural land tenure insecurity and Enabling the business of agriculture to the Quality of Land Administration. Model (3) is our preferred specification because it has the greatest fit, although it also has the lowest number of observations due to data limitations for Enabling the business of environment. The signs of the coefficients are consistent across all specifications, with cropland per capita consistently associated with greater movements into agriculture—possibly capturing countries that have greater specialization in agriculture—and with GDP per capita and the Gini index associated with lower movements into agriculture. This mirrors our results for land degradation, where national wealth and inequality were associated with faster cropland loss. As regards institutional variables, only Enabling the business of agriculture is loosely significant (at the 15 percent level) in Model (3): A one increase in the standard deviation of this indicator increases the pace of movement into agriculture by 2.1 percent.

**Table 5 – National-level drivers of cropland gains**

|VARIABLES|(1)<br>Model 1|(2)<br>Model 2|(3)<br>Model 3|
|---|---|---|---|
|Cropland per capita (2003)|0.00527†|0.00257|0.00500†|
||(0.00362)|(0.00267)|(0.00328)|
|Quality of land administration|0.000160<br>(0.00163)|||
|Rural land tenure insecurity||0.00629<br>(0.00696)||
|Enabling the business of
Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis.

The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees? ; 2) How different are refugees from “regular” populations?; 3) How poor are refugees? ; 4) What are the main predictors of refugees’ welfare and poverty? ; 5) How vulnerable are refugees from a monetary and non‐monetary perspective? ; 6) Do poverty and vulnerability statuses overlap? ; 7) How effective are refugee assistance programs? ; 8) What is the potential for alternative policies? ; 9) How does welfare compare across countries and data sets?; 10) How transferable are the findings between countries and data sets?

The study findings opened a window on a population little known to economists. Refugees were found to be a population with specific characteristics that derived from a multi‐stage selection process that started well before the conflict in Syria for economic and environmental reasons. Each stage of this selection process further impoverished those who eventually would seek refugee status. The resulting profile of refugees was rather different from that of Syrians before the crisis and that of host populations, a relevant finding to study the impact of refugees on host populations. Poverty was found to be very high. Over 70 percent of refugees in Jordan and Lebanon resulted to be poor if the income threshold used to assign cash grants
exposure to four types of disaster risk, one in each of the four categories: extreme rainfall, earthquakes, droughts, and floods. Subsequently, the survey module contains checklists designed to identify domains of household preparedness to deal with disasters that have been welldocumented in disaster research (see, e.g., Brown et al., 2021 and Levac et al., 2012). These checklists cover: supplies owned by the household to deal with a disaster or emergency, household discussion and plans of what to do if a disaster or emergency occurs, and whether the household lives in a community that has an early warning system to monitor natural hazards, detect risks, and inform community members about impending threats. The module also asks households, _inter alia_ ̧ about self-reported ability to cope with a disaster and the impacts of the COVID-19 pandemic on their ability to deal with a disaster. Appendix 1 displays the full disaster risk survey module.

We supplement the household survey data analysis with descriptive statistics based on EM-DAT, the International Disaster Database of the Centre for Research on the Epidemiology of Disasters. This database: _“defines disasters as situations or events which overwhelm local capacity, necessitating a request for external assistance at the national or international level. Disasters are unforeseen and often sudden events that cause significant damage, destruction, and human suffering. EM-DAT globally records at the country level human and economic losses for disasters with at least one of the following criteria:10 fatalities; 100 affected people; a declaration of state of emergency; a call for international assistance”_<sup>_7_</sup> The EM-DAT database covers a subset of the events captured in the survey: two types of meteorological events (tropical cyclones and severe storms), one type of geophysical event (earthquakes), two types of climatological events (cold waves and droughts) and three types of hydrological events (floods, landslides, and high sea-swells).

# **2.3 Methods**

We take a straightforward approach in
specifically, Cantwell suggests that the entry of US affiliates provided a highly beneficial competitive spur in the industries where local firms had some traditional technological strength, whereas local firms in other industries - especially in countries where markets were too small to allow both kinds of firms to operate at efficient scale - were forced out of business or pushed to market segments that were ignored by the foreign MNCs.

Recently, some authors have also explicitly discussed the apparent contradictions between the earlier statistical spillover studies. In line with Cantwell (1989), Kokko (1994) argues that spillovers should perhaps not be expected in all kinds of industries. In,particular, foreign MNCs may sometimes operate in "enclaves", where neither products nor technologies have much in comnmon with those of local firms. In such circumstances, there may be little scope for learning, and spillovers may not materialize. Conversely, when foreign affiliates and local firms are in more direct competition with each other, spillovers are more likely. Examining data for Mexican manufacturing, he finds no signs of spillovers in industries where the foreign affiliates have much higher productivity and larger market shares than local firms; in industries without these enclave characteristics, on the other hand, there appears to be a positive relation between foreign presence and local productivity. Kokko, Tansini and Zejan (1996a) present similar findings for the Uruguayan manufacturing sector.

Another possible explanation for the divergent findings from the existing statistical tests is proposed by Kokko (1996), who focuses on effects of competition in Mexican manufacturing. The earlier studies have tested the hypothesis that productivity spillovers are strictly proportional to foreign presence, but Kokko (1996) argues that this is not always the case. Spillovers from competition, in particular, are not determined by foreign presence alone, but rather by the simultaneous interactions between foreign and local firms. Hence, it is possible that the spillovers are larger in case where a few foreign MNC stir up a previously protected market than in a situation where foreign affiliates hold large market shares, but refrain
baseline in 2012-13) and VG classification status, along with information on the name of the household head among other variables.<sup>13</sup> What we refer to as the SBM data includes the SBM baseline information as well as a snapshot of the (continuously changing) live SBM dataset taken in September 2016, two years after the launch of SBM and the micro-credit interventions. SBM has since stopped making data available at the individual household level.

The incredible scope of the SBM data, covering every household in rural India, comes at a cost in terms of the information available. First, data on the date of toilet construction and subsidy receipt, needed to assess funding time gaps, is missing for most observations. Second, no information on the toilet, beyond its original existence is available. Once a household is recorded to have a toilet (the type of which is unknown) it continues to be recorded as such: usage, functionality and quality are among the dimensions not captured in the SBM data, but are critical to know in order to assess whether micro-credit complements SBM in affecting sanitation behaviour and open defecation.

# **Survey data**

We are able to overcome these limitations by augmenting the SBM data with primary survey data that we collected from a sample of MF clients in our 81 study GPs in rural Maharashtra at two points in time, in Sept-Oct 2014 and Aug-Sept 2017. The first data collection round (referred to as the ‘census data’) was completed on 14 October 2014, one day before the announcement of the SBM policy by the Indian Prime Minister, Narendra Modi. The census data cover basic information (e.g. names, caste, toilet ownership, etc.) on a large representative sample of our study population (all households in relatively small villages and all households living in a representative sample of segments of villages in larger villages). The census population formed the sampling frame for our study. The second dataset used in this study (referred
Companies and Productivity Spillovers: A Metaanalysis,”</mark> _<mark>Economic Journal</mark>_ <mark>, 111(475), F723–F739.</mark>

- <mark>Görg, H., A. Hanley, and E. Strobl. 2008. “Productivity effects of international outsourcing: evidence from plant-level data,”</mark> _<mark>Canadian Journal of Economics</mark>_ <mark>, 41, 2, 670-88.</mark>

- <mark>Görg, H., and D. Greenaway. 2004. “Much ado about nothing? Do domestic firms really benefit from foreign direct investment?”,</mark> _<mark>The World Bank Research Observer,</mark>_ <mark>19(2), 171-97.</mark>

- <mark>Gorodnichenko Y., Svejnar J., and K. Terrell. 2007. “When Does FDI Have Positive Spillovers? Evidence from 17 Emerging Market Economies,” IZA Discussion Papers No. 3079, Institute for the Study of Labor (IZA).</mark>

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period. In all three countries, food security has not returned to pre-pandemic levels.

# **3 Method**

We use two econometric approaches to investigate the causal effect of livelihood diversification on mitigating the impact of COVID-19 on food insecurity. The first approach is a dynamic panel data estimator in which food insecurity for a particular round is explained by the diversity index from the previous round. This approach is designed to capture the potential _ex post_ effects of changing livelihood diversification strategies in response to the pandemic. The second approach is an ANCOVA estimator, in which we regress food insecurity for a particular round on the preCOVID-19 diversity index. This approach is designed to capture the potential _ex ante_ effects of livelihood diversification in anticipation of a shock. For each specification, we run regressions for each country separately. Survey weights are included in all specifications.

> 7The standardization process creates a _z_ -score by subtracting the mean and dividing by the standard deviation to get _FIES ∼N_ (0 _,_ 1). In calculating the mean and standard deviation, we use weights so that the mean is the weighted mean and the standard deviation is the weighted standard deviation. This preserves the representativeness in the standardized variable.

10
reported (log) number of crime in each district from administrative data split into non-violent and violent crimes (data starting in 2011, means 3.54 for log violent crime and 3.31 for log non-violent crime), from ENAHO whether crime is a major national problem (12.7%), from LAPOP whether they have been a crime victim in the last two months (32.0%) and standardized variables from LAPOP on opinions about neighborhood safety and from Gallup on personal security. Lastly, we examine the impact on community outcomes. Specifically, we look at standardized indexes measuring quality of local services and trust in neighbors from LAPOP and indexes from Gallup on community attachment, the quality of the local community and whether the community likes diversity.

# **4.3 Control Variables**

Table 1 show the descriptive statistics for the control variables used in each analysis. The information we have available from each dataset varies, but we can always control for age, gender, education, marital status, whether employed and household size. ENPOVE collects additional relevant data about Venezuelans including how long they have been in Peru, 10
Y|
|Region FE|Y|Y|Y|

*** ** * Note : p _<_ 0.01, p _<_ 0.05, p _<_ 0.1. Data are from the list of establishments contacted by the enumerators. For each country, the level of employment was regressed on a dummy for respondent while controlling for stratification such as sectors, size groups (small, medium, and large), and regions. Estimates for Vietnam are based on the original list of 1500 firms, with 1346 respondents and 154 non-respondents. Robust standard errors in parenthesis.

Second, under the premise that any systematic relationship between firm characteristics and participation is continuous in their reluctance to participate in the survey, we can learn about sample differences between respondents and non-respondents by comparing firms across different percentiles of the distribution of the number of attempts it took for them 36The average response rate for the WMS is around 40 percent.The response rate for MOPS, implemented by the United States Census Bureau, was around 80 percent.

106
- **Khanna, Tarun and Krishna Palepu** , “Is Group Affiliation Profitable in Emerging Markets? An Analysis of Diversified Indian Business Groups,” _The Journal of Finance_ , 2000, _55_ (2), pp. 867–891.

- **Kyle, Albert S** , “Continuous auctions and insider trading,” _Econometrica: Journal of the Econometric Society_ , 1985, pp. 1315–1335.

- **Massa, Massimo and Zahid Rehman** , “Information flows within financial conglomerates: Evidence from the banksmutual funds relation,” _Journal of Financial Economics_ , 2008, _89_ –

- (2), 288 306.

- **Masulis, Ronald W, Peter Kien Pham, and Jason Zein** , “Family business groups around the world: financing advantages, control motivations, and organizational choices,” _Review of Financial Studies_ , 2011, p. hhr052.

- **OECD** , “Corporate Governance of Company Groups in Latin America,” _OECD Publishing, Paris_ , 2015.

- **Parrino, Robert, Richard W Sias, and Laura T Starks** , “Voting with their feet: institutional ownership changes around forced _{_ CEO _}_ turnover,” _Journal of Financial_ –

- _Economics_ , 2003, _68_ (1), 3 46.

- **Pedraza, Alvaro** , “Strategic interactions and portfolio choice in money management: Theory and evidence,” _Journal of Money, Credit and Banking_ , 2015, _47_ (8), 1531–1569.

- **, Fredy Pulga, and Jose Vasquez** , “The information advantage of domestic investors: Evidence from the Colombian stock market,” _Universidad de la Sabana, Mimeo_ , 2016.

- **Petersen, Mitchell** , “Estimating Standard Errors in Finance Panel Datasets: Comparing Aproaches,” _Review of Financial Studies_ , 2009, _22_ (1), 435–480.

- **Porta, Rafael, Florencio Lopez de
br>High Upper−middle Lower−middle Low<br>income income income income<br>countries countries countries countries<br>Cumulative severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization.

> 6 The variation in Figure 7 is not explained by developing countries facing high all-cause mortality to begin with. Crude mortality rates are in fact lower in developing countries than in HICs. As of 2016, 874 per 100,000 people died in HICs, compared to 730, 734 and 812 for UMICs, LMICs and LICs (WHO, 2018b). This contrasts with the monotonous decline one would see in the corresponding age-adjusted mortality rates  using a standard population (Ahmad and others, 2001).

14
actions taken by village governments to improve service provision, as measured in survey data. We examine the dynamic timing of these effects and we probe the robustness of these results. We then show that these gains are especially pronounced for socially distant citizens and in villages where turnover weakens nepotistic networks. Finally, we document their limited pass-through to citizen satisfaction and trust in the village government.

## **5.1 Service Provision in Administrative Data**

**Main Results.** Consistent with our findings on bureaucrat-level outcomes, turnover in village elections improves public service provision, as reflected in administrative _Podes_ data. For this analysis, we restrict the sample to villages that held their last election before 2021 (378 out of 512 villages), the year of the most recent _Podes_ survey. We construct a standardized index of service quality, encompassing all public goods managed by village governments: drinking water, sewage, garbage collection, street lighting, paved roads, public transit, kindergartens, primary schools, village maternity clinics ( _polindes_ ), and community health centers ( _puskesmas_ ). In Table 7, column 1, we find a substantial 0.50 standard deviation (s.d.) increase in this index of service provision at the RD cutoff (see also Figure 5, panel a). This effect is primarily driven by garbage collection, street lighting, and to a lesser extent, drinking water and public transit—Appendix Table A.14 reports RD estimates for each component.

16
check, we estimated these figures on a subset of the sample (814 institutions) for which we had complete data on every variable. Those results are very slightly lower than those reported below (the results from the balanced panel were so similar that we do not report them). With the balanced panel, we calculate that on average, subsidies amounted to $128 per borrower, with a median of $21 per borrower and again no subsidy at the 25<sup>th</sup> percentile. The average subsidy for commercial banks is $255 per borrower and median of $89. The median (non-profit) microfinance NGO used subsidy at a rate of $21 per borrower, and subsidy for the NGO at the 25<sup>th</sup> percentile was just $2 per borrower.

3
underlying income variable. We then take advantage of the fact that 50 of the included municipalities are autorrepresentative, meaning direct estimates from the household survey are representative for these 50 municipalities, given their sample size. This analysis is useful for the estimation of covariate vulnerability. Lastly, we conduct a sensitivity analysis by applying an alternative probability threshold, which defines if a person is vulnerable or not. The following subsection details the main findings and conclusions from these validity checks.

## 6.1. Different Model Estimations and Vulnerability Estimates

**We investigate the robustness of our findings to different model specifications.** These model specifications account for some of the shortfalls of our baseline specification. Table 4 illustrates the results. We conduct these robustness tests for the 2019 survey year. The first colum reports the baseline estimate. We first present the total vulnerability rate for the overall population, and those for the urban and rural populations separately. Column 2 refers to a model specification which excludes the educational variable in order to increase the underlying sample size. Row 1 reveals that the vulnerability rate is slightly higher than the one from the baseline specification (29.5 percent versus 23.7 percent). These differences remain when restricting the sample by urban/rural status.

**Our risk measures are self-reported and could be subject to reporting biases; we account for this potential shortcoming by measuring exposure to hazards via external data sources.** There are two possibilities to measure hazards. First, one can rely on self-reporting of exposure to shocks by households. Second, it is possible to rely on data collected externally, as for example weather or remote-sensed data. Both approaches are subject to potential limitiations and biases. While external data sources might suffer from so-called basis risks, self-reporting is endogenous and subjective (Nguyen & Nguyen, 2020). Column 3 presents results for a model specification which relies on external data sources to measure risk exposure and excludes the educational variable. Column 4 relies on external data sources to measure risk exposure
support can play a key role in helping such firms mitigate the adverse impacts of recessions and economic shocks and contribute to a more robust recovery. Export promotion policies helped firms in Belgium and Peru weather the GFC (Van Biesebroeck et al., 2016): supported firms were more likely to survive in the international market and to continue exporting to destination countries hit by the GFC. Support from government credit guarantees in Germany helped firms sustain their exports after the GFC, as the guarantees lifted liquidity constraints and cushioned increases in the cost of credit (Felbermayr et al., 2012). While an emerging set of studies is examining the set of policy measures that governments have relied on to help firms and households face the pandemic (e.g., Chetty et al., 2020; Kozeniauskas et al., 2020; Baldwin and Weder, 2020; Cirera et al., 2021a; Guerrero-Amezaga et al., 2022), we contribute to this literature by examining the differential access by globally engaged firms.

# **3. Data and Methodology**

## **3.1 Data**

To understand the impact of the COVID-19 pandemic on the performance of firms across countries, the World Bank, conducted Business Pulse Surveys (BPS) and the Enterprise Surveys (WBES) - henceforth designated as ’BPS data’ for simplicity - in three waves: May-September 2020, October 2020-March 2021, and April-October 2021. The survey questionnaire includes modules on firm characteristics and outcomes, including exports, since the onset of COVID-19. Other trade outcomes 7
model as such, given the potentially endogenous nature of conflict and poverty whereby some conflict events are concentrated around wealthier areas. One of the areas for future research might be to leverage the time series that area available for various types of geo‐ spatial data to try to account for some of these dynamics relating poverty rates to shocks and imbalances.

# 6. Conclusion

The HFS conducted several rounds of data collection at a time of upheaval in the short history of South Sudan. In particular, Wave 3 of the HFS consisted of a major data collection effort during what effectively became one of the deepest humanitarian crises in recent history. The HFS was conceived within the context of the crisis and was therefore designed to leverage new technologies for monitoring and implementation as well as methodological innovations in survey design. This allowed the HFS team to monitor closely the survey and facilitate the implementation while facing a multitude of challenges induced by the escalating crisis. Unfortunately, the growing intensity of the conflict eventually led to a shortened survey with deprived sample size. In the end, after almost 9 21