rafmacalaba/data-use-mentions-extended huggingface dataset:
included in this list.<sup>19</sup> Summary statistics for these variables are shown in Table A1.

# **4.3 Other data**

**Economic activity in Libya** To compute a proxy for economic activity around the FMP, we use the intensity of night lights in the 5 km radius from the FMP’s location in the corresponding month. Night lights data are often used to measure economic activity when data to construct economic indicators are missing or badly measured, as in our case.<sup>20</sup> In our analysis, we use 18The tone of a given news article reflects the attitude towards migration of the media outlet on which it is published, e.g., its political orientation. As long as the number of outlets does not vary systematically across periods, our measure captures - given the average political orientation of all the outlets in a given country - the variation in the tone of migration-related news across time. 19 For more details on the construction of these variables, see Appendix C. The correlation between the variable _Tone News Destinations_ and a variable given by the sum of the tone of news articles by topic (i.e., _Tone News Destinations Discrimination_ + _Tone News Destinations Crime_ + _Tone News Destinations Economic conditions_ + _Tone News Destinations Sea incidents_ + _Tone News Destinations Government announcements_ + _Tone News Destinations Other_ ) is 64%. This depends on two factors: i) some news could be double counted (e.g., they refer to both discrimination and crime) and used in the construction of more than one variable; ii) the average tone of the news in a destination country is averaged with the average tone of the news in other destination countries indicated in that FMP.

> 20Data on the Libyan economy are extremely limited. Official data on economic activities were collected only until 2011. After that, statistics on the Libyan economy have been largely unreliable due to the limited capacity

8
Harrison (2003) found no evidence that abatement costs affected incoming foreign investment in four developing countries: Cote d’Ivoire, Morocco, Mexico, and Venezuela. Cole and Elliott (2005) studied outbound investment from the US to Brazil and Mexico. They cite evidence that pollution intensive industry is on average capital intensive and so chose Brazil and Mexico as host countries because they have weaker environmental regulation than the US but are also relatively abundant in capital. They found some evidence that industries with high abatement costs in the US were more likely to have high outbound investment to Brazil and Mexico. Both the Eskeland and Harrison, and Cole and Elliot studies lacked data on abatement costs in the host countries and so used US abatement cost data as their measure of policy stringency. Elliot and Shimamoto (2008) studied outbound Japanese foreign direct investment to Malaysia, Indonesia and the Philippines in 10 manufacturing industries. They used Japanese abatement costs as their measure of the stringency of pollution regulations. They found no evidence that higher Japanese abatement costs increased outbound investment to any of the three host countries, and instead found that all else equal, high abatement costs in Japan had a negative effect in investment in the Philippines (contrary to the pollution haven hypothesis). As with the Eskeland and Harrison (2003) study, Elliot and Shimamoto did not have data on abatement costs in the host countries.

There is still much to learn more about the effects of environmental policy on international competitiveness in newly industrializing countries. Since much of the empirical work has been based on the experience of OECD countries, it is important to investigate potential channels through which effects may be different in a developing economy. For example, one important question is whether the competitiveness effects of tightening up environmental policy are larger or smaller in developing countries than in developed countries. On the one hand, _marginal_ abatement costs could be relatively lower in developing countries because of weaker pre-existing regulation – this is the "low-hanging fruit" story. And rapidly growing economies have the advantage that it
sizes by country, region, and gender are shown in Table A1.<sup>13</sup> The paper’s dataset comprises 157,483 observations, encompassing all respondents who provided complete information on individual characteristics. However, the sample size varies when analyzing different outcomes, as detailed in A.7. Questions posed later in the survey experienced higher rates of nonresponse.

The sample for the Gender Equality at Home survey is comprised of individuals who have access to the internet and personal Facebook accounts. As such, the results should not be viewed as representative of a general population in each country. Nevertheless, the survey’s extensive geographic reach and substantial sample size provide a unique opportunity to explore the research questions on a global scale. Given the significant increase in internet bandwidth and Facebook’s expanding user base, which stands at approximately 2.9 billion monthly active users at the time of this writing, this survey offers valuable insights despite its limitations.<sup>14</sup> Table 1 outlines the demographic characteristics of our sample, split by gender of the respondent. The total sample comprises of 152,555 individuals, with 52% of the sample being women. The demographic characteristics are broadly similar across female and male respondents. On average, 58% of the individuals in our sample are in a long-term partnership or married. The majority, 73%, are over 25 years old. Education levels are high, with 60% possessing an education beyond secondary level. Additionally, 60% of respondents reside in urban areas. Notable gender-based differences in the sample include a 19% lower likelihood of women identifying as the head of the household compared to men, and a 7% lower likelihood of women owning land.

# **2.3 Description of Key Variables**

## **2.3.1 Economic Behaviors**

The dependent variables in our study are categorized as follows: labor supply (employment status in the previous year and weekly hours worked
Table B2: ANCOVA Regressions [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 2<br>Ethiopia Malawi<br>-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5<br>Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria<br>-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5<br>Effect on Food Insecurity<br>Mild Moderate Severe Index<br>45<br><!-- End of picture text --
in coresidency rate.

The analysis and findings presented below have important and wide ranging implications for research on intergenerational economic mobility. First, our analysis implies that much progress in understanding intergenerational mobility can be made with the household surveys available in developing countries by focusing on IGC as the measure of mobility. These data sets are currently shunned by most researchers because of the worry that the estimates from the coresident sample suffer from bias of unknown direction, and possibly of very high magnitude.<sup>10</sup> Second, the results in this paper can be helpful in sorting out often 9As we discuss later, the difference in biases between IGRC and IGC becomes smaller as the coresidency rate increases.

> 10We focus on IGRC and IGC, as they are two of the most widely used measures of mobility. But

3
|(0.982)||||
||[0.000]|[0.003]|[0.009]||||
|Private||10.215|1.245||||
|||(2.751)|(2.377)||||
|||[0.000]|[0.600]||||
|_R-squared_|0.30|0.33|0.43||||
|# Observations<br># Schools|410701<br>15196|410701<br>15196|410701<br>15196|9890704<br>33148|9890704<br>33148|9890704<br>33148|
|Location FE*|Y|Y|Y|Y|Y|Y|
|School controls||Y|Y||Y|Y|
|Student controls|||Y|||Y|

Notes: Standard errors in parentheses, p-values in square brackets. OLS regressions for PISA were run with the student-level PISA dataset using the OECD’s repest Stata command. Standard errors clustered at the school level and use all 5 plausible values for each subject and _student final weights_ . Prova Brasil regressions run with standard OLS. Standard errors clustered at the school level and dependent variables are student learning outcomes on national tests at Grade 9 (the same exercise can be done with primary schools and tests at Grade 5). All specifications include location fixed effects (countries for PISA and states for Prova Brasil). **PISA controls** : School controls include school location, student-teacher ratio, log of the number of students, share of government funding relative to total school funding, and ratio of computers connected to the web as a proxy for school resources. Student controls include gender, grade, socio-economic status and immigration status. **Prova Brasil controls** : School controls include student-teacher ratio, log of the number
<!-- Start of picture text --> Risk of layoff/reduced hours by income from work<br>(measured as probability from 0 to 1)<br>0.45<br>0.40,<br>0.35<br>0.30 —}—_}—5 ¢ +<br>0.25 - + - o<br>020-4 = o i bd e +<br>0.15 4 4 - +3, . a *<br>rs = , ets $<br>0.10 + > ° -$ - 2 ¢ +<br>0.05 + + F + o<br>0 Ceo lie oe SS eS een ee eS eS ae ae ee ee ee<br>2235 €s Sst #322 E52 8 2 § EF ¢s 2468<br>= 2 a § 2° 3% 5 & GG ee * 2 2 8 o a<br>5 2<br>*high —middle + low<br>Data not available for Germany<br>Source: Eurostat eurostat@l<br><!-- End of picture text --> <!-- Start of picture text --> Risk of losing job by income from work<br>(measured as probability from0 to 1)<br>0.14<br>o12 7<br>0.10 ,<br>0.08<br>0.08 $—I— 5-5 —F<br>0.04 j 1} fo» 1;<br>e T Ps 7 3 4 el a : 6 ecw «5<br>0.00 © moe oe i e : ° os - ;<br>s= . —v2 . a hae i are = = ore<br>SETTER~g = £3 2 EGEs e 2 & 5STEER3 REGFesaE#<br
Policy Research Working Paper 9859

# **Abstract**

While the distributional impacts of the COVID-19 pandemic have been well-documented in high-income countries, studies in low- and middle-income countries have been relatively rare due to data limitations. This paper uses pre-pandemic household welfare data and high-frequency household phone survey data from seven middle-income countries in East Asia and the Pacific, spanning May 2020 to May 2021, to analyze the distributional impacts of the pandemic and their implications for equitable recovery. The results indicate that employment impacts at the extensive margin have been large and widespread across the welfare distribution during times of stringent mobility restrictions (low mobility). When mobility restrictions have been relaxed, however, employment impacts have been larger among poorer workers who have found it more difficult to return to employment. Data on the loss of labor income also suggests that the pandemic has exacerbated existing inequalities. In addition to being more susceptible to employment and income shocks, poorer households in East Asia and the Pacific are at higher risk of experiencing long-term scarring from the pandemic – due to rising food insecurity, increased debt, distress sale of assets, and fewer distance/interactive learning opportunities for their children. Taken together, the findings indicate that inequality has worsened during the pandemic, raising concerns about the prospects for an inclusive recovery in the absence of appropriate policy measures.

This paper is a product of the Poverty and Equity Global Practice and the Office of the Chief Economist, East Asia and the Pacific 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/prwp. The authors may be contacted at at ykim14@worldbank.org, mlugo1@worldbank.org, amason@worldbank.org, and iuochi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
# **1. Introduction**

This post-earthquake assessment of emergency assistance programs follows up on a previous assessment of the impact of assistance operations on food security<sup>2</sup> that used the first Rapid Emergency Food Security Assessment (EFSA I) survey which was conducted in midFebruary 2010 by the CNSA in collaboration with its main partners (ACF, FEWS-Net, Oxfam GB, FAO, UNICEF and WFP).<sup>3</sup> Three weeks after the earthquake, a random 933-household survey was conducted on the population in the disaster areas. The questionnaire examined food insecurity and livelihoods.<sup>4</sup> Food and non food emergency assistance was thus assessed in the wake of the earthquake.

Based on this unique survey, a first assessment examined how assistance programs were allocated in Haiti, looking at the households’ specific characteristics. The survey evaluated that between 30% and 50% of the houses were destroyed and that 31% of the households were experiencing limited or severe food insecurity (22% and 9% respectively), that is a nearly double the food insecurity prevalence observed before the earthquake.<sup>5,6</sup> At that early point, we also conducted an analysis of assistance allocation after the earthquake. Despite the fact that food assistance was not specifically targeted yet at the time of the survey, our results show that individuals staying in camps had a significantly greater probability of accessing assistance, especially food assistance. This entails that food assistance was likely to reach food insecure people, as the poorest households were either forced or had little choice but to move to these camps.<sup>7</sup> What is more, it appears that household food insecurity after the earthquake was more connected to pre-earthquake household wealth than to asset losses experienced by households after the quake.<sup>8</sup> Hence, it appears that households were able to partly buffer the impact of the shock. Nevertheless, it
in Uganda; while secondary education enrolls over 16 times more students as tertiary, it receives a budget allocation that is equivalent to only 80 percent (approximately) of the tertiary education budget allocation. These data come from the Ministry of Finance, Planning and Economic Development’s Annual Budget Performance Report (MoF, 2017) and the Uganda Ministry of Education and Sports’ Sector Annual Performance Report (Ministry of Education and Sports, 2017).

- <u>Health: Health expenditure in 2016/17 reached 1.3 percent of GDP, and mainly includes</u> expenditure in public primary health care and hospitals, which provide basic free health care services to the population. This expenditure mainly takes the form of grants to local governments, which include payments of wages to health workers, funding for service delivery operations, and construction and rehabilitation of health facilities (Jellema et al., 2017). Primary health care expenditure was close to 50 percent of total health expenditure and includes recurrent grants to local governments and medicines, while public hospitals received an allocation of 33 percent, including medicines. These data come from the Ministry of Finance, Planning and Economic Development’s Annual Budget Performance Report (MoF, 2017) and the Ministry of Health Annual Sector Report 2016/17 (MoH, 2017).

14
Policy Research Working Paper 9330

# **Abstract**

Using a high-frequency survey in the Republic of Yemen, this paper demonstrates how school attendance responds to a series of conflict-related shocks. First, there are plausibly exogenous changes in violence that have limited impacts on school attendance but do affect other dimensions of well-being. And second, consequences of conflict aside from living in close proximity to violence can impact attendance. The importance of a wide variety of conflict shocks suggests that an understanding of all shocks is needed before attributing the cause of attendance changes in such tumultuous settings, and these results have implications for the delivery of education assistance in conflict settings.

This paper is a product of the Education Global Practice and the Poverty and Equity Global Practice. 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/prwp. The authors may be contacted at standon3@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
across sectors unlike the DFE estimator. In the presence of long run homogeneity, therefore, our preference is for the use of the PMG estimator.

Finally, it is worth pointing out that a crucial advantage of the estimation approach of the present paper is that dynamics are explicitly modeled.

# **_The data_**

The summary statistics for the fully balanced (1,472 observations for each variable), 52country panel data for the period 1980-2002 from the World Bank World Development Indicators database are provided in Table 1. The list of countries is provided in Appendix A.

The dependent variables are (i) electricity production per capita (in kwh) (elepropc), and (ii) total telephone lines (fixed lines and cellular phone subscribers) per 1,000 population (telpc). The independent variables are: GDP per capita in PPP terms (constant US$, 2000) (gdppc), shares of agriculture (yag), manufacturing (yman) and services (yserv) in real GDP, and a dummy variable for a structural break arising from the emergence of the cellular lines (in 1996).

Demand for electricity production and telephone lines were estimated separately for each infrastructure service. Table 1 shows means, standard deviations, and other summary

> 3 See Pesaran and Smith (1995).

10
# **Table 1. Reported Larger Loan Use**

||All|Male|Female|P-val of<br>t-test<br>(2)-(3)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Household Durables|0.038|0.027|0.080|0.161|
|Food consumption|0.041|0.041|0.041|0.999|
|School Supplies|0.003|0.003|0.005|0.584|
|Festivals and Ceremonies|0.010|0.007|0.022|0.250|
|Household Repairs|0.070|0.081|0.027|0.293|
|Previous Loan Repayment|0.059|0.057|0.068|0.787|
|Savings|0.041|0.036|0.063|0.438|
|Inventories/ raw materials for main business|0.475|0.505|0.355|0.110|
|Equipment for main business|0.055|0.038|0.124|0.064|
|Inventories and Equipment for other household businesses|0.061|0.053|0.096|0.361|
|Other uses|0.146|0.153|0.118|0.606|
|Number of Obs.|125|100|25||

Note: Data come from the follow-up survey conducted in December 2008. Columns report the average fraction of loans used for each purpose.
firms whose operational links are well-documented through reliable sources like annual reports, filings, investor presentations, press releases, and company websites. This approach ensures that our data reflects the most complete and accurate representation of each firm’s network interactions.

Additionally, we explore the dynamics of external versus internal corporate relationships by examining the number of entities connected to a firm through ownership ties, which include subsidiaries, parent companies, and other affiliates within the same corporate family. This captures all other entities that share the same ultimate parent in a given year.<sup>15</sup> We also consider the relevance of these connections to the agribusiness sector by identifying corporate family entities whose primary industry aligns with agribusiness criteria, providing a more targeted estimate of industry-specific within-corporate family interactions in some of the analyses. We consider this metric a lower estimate of the number of entities that an agribusiness company engages with within its corporate boundaries.

# **3.2.2 Exit Dynamics**

In the final step of the analysis, we investigate the correlation between a firm’s network centrality and its exit probability from the agri-business network. Specifically, we apply a logit regression model to the subset of agribusinesses that are actively monitored and have available sales data:

> 15 Related firms can consist of a firm’s subsidiaries, a firm’s parent, its parent’s parent, or other subsidiaries of its ultimate parent. If a firm changes its ultimate parent in a given year, the firm family to which the firm belonged for the majority of the year is taken as a reference count. Non-firm entity types are excluded from the count: Aircraft, Commodity, Currency, ForEx, Index, Individual, Broadcast Call Sign, Asset-Backed, Bank Branch, Corporate Assets, Emp Stk Ownership Plan, Family of Fds (VC/Pvt EQ), Hedge Fund, Mutual Fd-Closed End, Mutual Fd-ETF, Mutual Fd-Open End, Pension Fund, Real Property, Port, Ship. The following entity types are included:
|(0.002)|(0.003)|(0.003)|(0.003)|
|Constant|4.205<sup>***</sup>|4.250<sup>***</sup>|4.106<sup>***</sup>|4.171<sup>***</sup>|
||(0.043)|(0.147)|(0.047)|(0.145)|
|Controls|No|Yes|No|Yes|
|Mean of dependent variable|4.216|4.223|4.099|4.106|
|R-squared|0.269|0.297|0.250|0.278|
|No. observations|1540|1529|1538|1527|

**Notes:** All plot sizes are natural logarithms of GPS measures, demeaned to center the data. SR indicates self-reported data. % over-/under-estimate is computed as percentage difference between self-reported and GPS measures for values above zero and zero otherwise. That is, for each over(under)estimated plot we compute percentage difference between self-reported and objective measures. Labor application includes labor allocations before or during planting (for land preparation, planting, and ridging). Standard errors, clustered at enumeration area level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

# **6. Conclusions**

Leveraging a unique experiment embedded into a national agricultural survey in Malawi, this paper investigates the extent, drivers, and persistence of NCME in farmer-reported plot areas. We also examine how farmers learn and adjust to demonstrably accurate information about plot size, both in terms of correcting