rafmacalaba/data-use-mentions-extended huggingface dataset:
M. C., Masterson, D. T. R., 2020. Does Aid Reduce Anti-Refugee Violence? Evidence from Syrian Refugees in Lebanon. Am Polit Sci Rev. 2020/06/30. 2020;1–8.

Maxwell, D., Caldwell, R., 2008. The Coping Strategies Index Field Methods Manual, Second Edition.

Oxford Policy Management, 2018. Evaluation of the Directorate-General for European Civil Protection and Humanitarian Aid Operations-funded Emergency Social Safety Net (ESSN) in Turkey.

<u>Özler, B., 2016. Definitions in RCTs with interference. Development Impact Blog, June 6, 2016. https://blogs.worldbank.org/impactevaluations/definitions-rcts-interference.</u> Penglase J. Consumption Inequality Among Children: Evidence from Child Fostering in Malawi*. Econ J. 2020 Aug 20; Available from: https://doi.org/10.1093/ej/ueaa104 Refugees Association Turkey, 2020. Numbers of Syrians in Turkey June 2020.

Regional Refugee and Resilience Plan /3RP. 2020. 3rp Regional Refugee and Resilience Plan in Response to the Syria Crisis, Turkey Country Chapter 2019/2020. pp. 5-7.

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# **II.III – Uzbekistan L2CU Baseline Survey 2018**

Official HBS data for Uzbekistan had not been made available by the National Statistical Agency to the World Bank until recently. However, the L2CU activity, which is conducted by a private firm on behalf of the World Bank and in cooperation with Ministries and the Statistical Agency, included a comprehensive baseline survey appropriate to the needs of poverty mapping. The survey design closely followed the Living Standards Measurement Study (LSMS) surveys and was conducted using a standard two-stage sampling design, in which 200 clusters (administrative territories called mahallas, in the case of Uzbekistan) were randomly selected with probability proportionate to size. The national sample was stratified by region and by urban areas.

Though a national household census would be a preferable sample frame for the first selection step; no such census has been conducted in Uzbekistan since independence from the Soviet Union (a census is currently scheduled for 2022). Instead, the universe of mahallas, the smallest official jurisdiction in Uzbekistan, was used. These data include official population statistics provided by the National Mahalla Committee from official registration data (current as of the first quarter of 2018). The data were re-weighted based on observed population totals within the mahallas at the time of the survey fieldwork. The second stage procedure was conducted using simple random selection with equal probability within selected mahallas. A separate stratification level for households that receive social assistance was included, totaling 4 households per mahalla. The final sample included 4,000 households in total (20 households per mahalla), 800 of which were social protection recipients by design.

The baseline survey included a full consumption and expenditure module using a list/recall approach. The resulting estimates are representative for 12 regions, 1 autonomous republic, and 1 independent city (Tashkent), crossed with their urban areas (except for the City of Tashkent, which is entirely urban). The survey was conducted entirely on tablet devices (CAPI), enabling validation using crossreferencing and other techniques to
For 2020, the pandemic-induced shutdowns implied that traditional household surveys were largely absent and, where available, were switched to phone-based surveys.<sup>5</sup> At the same time, average growth in national accounts may be a poor proxy of growth in household income or consumption during 2020. For example, the wide-ranging public spending policies may not be fully reflected in the national accounts growth rates or the lockdowns had a very heterogeneous impact across sectors. Therefore, for 2020, we rely on the following sources of data, ranked by order of preference.

First, we use the household survey micro data when available in PIP, which applies to only 20 countries. Second, for 8 countries, we rely on tabulated income statistics that report average income for various quantiles of the income distribution available from national statistical offices (NSOs). Third, in 37 countries, we use the High-Frequency Phone Surveys (HFPSs) that the World Bank has collected in collaboration with NSOs.<sup>6</sup> These surveys are conducted over the phone and are hence less comprehensive than traditional household surveys. In many countries, phone surveys are some of the only national surveys available from 2020. Most of the phone surveys have been reweighted, to at least partially address problems of representativeness (Ambel et al. 2021; Brubaker et al. 2021).<sup>7</sup> Fourth, we use distributional changes available in the literature or made available to us by World Bank teams. Fifth, we use sectoral growth rates from national accounts to capture at least the limited heterogeneity across sectors.<sup>8</sup> Sixth, when none of the above sources are available, we use per capita GDP growth rates from national accounts 5 Castaneda et al. (2022) discuss the available 2020 surveys and how COVID-19 led to changes in survey methodologies.

6 At the time of writing, such surveys have been conducted in 85 countries across all developing regions. We only use a
in 1979. In addition, the whole distribution shifted downwards with respect to 1974. There was also a further reduction in as of 1982.

The trend in trade reforms is broken in 1982; there was a slight increase in average tariffs starting in 1982 and continuing all throughout the eighties. In contrast, tariffs were reduced again in 1990 and 1991, remaining below 20% throughout the decade. In 1994, Mercosur was adopted and tariffs were further reduced. However, in an attempt to prevent the fiscal crisis, there was a slight increase in tariffs in 2001.<sup>4</sup> We turn now to the labor force data. The standard source of individual data on labor earnings and worker characteristics in Argentina is the Permanent Household Survey (EPH, Encuesta Permanent de Hogares). This is a typical labor force survey with information on wages, employment status, and individual and family characteristics (age, gender, family size, etc.). The data are usually collected twice a year, in May and October. The labor force surveys EPHs of the 1990s have been already used in the literature but, for our purposes, we needed to track the surveys back into the 1970s and 1980s. We were able to compile 40 EPH surveys. Table A3 in the appendix provides a brief description of the different data sets used here and their sample sizes. We have data for all years, except for 1979, 1983, and 1984. For years 1974, 1976, 1977, 1978, 1981, 1985, 1986, and 1992, we only have information for October (and thus not for May). In contrast, in 1980 and 1982, we use data on May but not on October.

To compute the skilled wage premium, we define at this stage three educational categories: skilled labor, which comprises workers who have finished college, semiskilled labor, which comprises workers who have finished secondary school (and may have incomplete college education), and unskilled labor, which comprises workers
- Javorcik, Beata S., and Mariana Spatareanu. 2008. “To Share or Not to Share: Does Local Participation Matter for Spillovers from Foreign Direct Investment?” Journal of Development Economics 85 (1–2): 194–217.

- <mark>Jiménez, A. (2011). Political risk as a determinant of Southern European FDI in neighboring developing countries.</mark> _<mark>Emerging Markets Finance and Trade</mark>_ <mark>,</mark> _<mark>47</mark>_ <mark>(4), 59-74.</mark>

- <mark>Durán, J. J., De la Fuente, J. M., & Jiménez, A. (2008). Political risk as a determinant of investment by Spanish multinational firms in Europe. Is there an East-West structure?. In</mark> _<mark>Communication presented at the 34th EIBA International Annual Conference in Tallin (Estonia)</mark>_ <mark>.</mark>

- Jun K. and Singh H. (1996). "The Determinants of foreign direct investment in developing countries", Transnational Corporations, Vol. 5, p. 67-105.

- Kearney, A. T. 2017. _Foreign Direct Investment Confidence Index_ . Global Business Council, New York.

- Kinda, T. (2010). Investment climate and FDI in developing countries: affiliate-level evidence. World development, 38(4), 498-513.

- Kowalski, P., Gonzalez, J., Ragoussis, A., and Ugarte, C. (2015). Participation of Developing Countries in Global Value Chains: Implications for Trade and Trade-Related Policies. _OECD Trade Policy Papers_ 179.

- Krifa-Schneider, H., & Matei, I. (2010). Business climate, political risk and FDI in developing countries: Evidence from panel data. International Journal of Economics and Finance, 2(5
in high and lower income settings with magnitudes ranging from 12 to 38 percent (Kleven, 2022; Kleven et al., 2019, 2023). However, it can plausibly be argued that women have a comparative advantage in childcare relative to their husbands. We show that even before childbirth, marriage itself might already affect female labor force participation in settings with deeply entrenched gender norms. Our work provides some of the first evidence on the magnitudes of marriage penalties, rather than child penalties, and proposes a method for distinguishing between the two in cross-sectional data. We also directly link marriage penalties to skill levels, outside options, and social norms.

The remainder of the paper is organized as follows. Section 2 provides an overview of the data and empirical strategy. Section 3 presents our findings, followed by an exploration of the determinants of the marriage penalty for women in Section 4. Section 5 concludes with policy implications.

# **2 Data and Empirical Strategy**

## **2.1 Data**

We use data from the Demographic and Health Survey (DHS) from Bangladesh, India, Maldives, and Nepal in the analysis (Table A1).<sup>1</sup> The surveys were conducted approximately every 5 years starting in the 1990s. These nationally representative surveys form a repeated cross section of reproductive age women between the ages of 15 and 45. The more recent surveys also include data on the men in the households, so there are fewer rounds of data for men. The final sample covers 1,780,854 women and 389,313 men residing in around 650,000 households in all level 1 administrative areas of the 4 countries.

The surveys include age, education, urban residence, employment status, and marital status. For women, the surveys include age at cohabitation, which we use as the age at marriage, and birth history, which includes the timing of the first birth. Importantly, these surveys contain complete household rosters with data on employment
below those provided by aggregate models (Deininger _et al._ 2023).<sup>8</sup> Moreover, less than one-fifth of the total effects is attributable to direct conflict-induced field damage while the remainder comes about through indirect effects, including damages to the electricity grid and logistics infrastructure including road transport and grain elevators (Khoshnood _et al._ 2022); limited availability of key inputs including fuel, labor, and agro-chemicals; and reduced demand as a result of higher cost for transport and marketing.

To link data on area cultivated and yield to welfare and prices or profitability, surveys at household or farm level are needed. Household surveys conducted by FAO highlight the importance of home production on garden plots dating from Soviet time as a safety net to increase resilience (FAO 2022b). In a nation-wide survey of 5,230 rural households, drops in income were reported by 55% of respondents, especially for IDPs. For the population who are not displaced three salient characteristics emerge, namely (i) 25% of the surveyed rural population reported reducing or stopping agricultural production due to the war; (ii) 72% of crop and 64% of livestock producers reported increased production costs; and (iii) more than half and around 20% of respondents reported spending more than 50% and 75% of their total expenditure on food, respectively (FAO 2022c).

While these figures illustrate the breadth and depth of war impacts, disaggregated data on small and medium scale farm production is important to understand the extent to which the invasion may deplete the productive capacity of rural areas and to design responses on the continuum between humanitarian and productive support to maintain and expand productive capacity, diversification, and employment opportunities in rural areas. This is particularly relevant as experts agree that a significant part of Ukraine’s agriculture sector operates in informality, but little is known on either its size or the nature and profitability of its operations.

# **2.2 Sample construction and level of informality*
and Development (OECD) in 1989–98. They show that the main drivers of political budget cycles were the levels of government transparency and political polarization. Using data from 32 European countries in 1990–2010, <u>Enkelmann and Leibrecht (2013) find that political budget</u> cycles are typical in newly democratized countries. In contrast, voters in mature democracies penalize politicians for increasing public expenditure to enhance their chances of re-election. Using a panel of 71 countries over 1972–2009, Brender and Drazen (2013) show that politicians’ tenure may be another driver of political budget cycles and argue that to win votes, experienced politicians are more likely to change the composition of government expenditures than newcomers. A new leader might need several years to make changes in the budget composition, probably because it takes time to understand legislative procedures and bureaucratic rigidities after taking office.

<u>Castro and Martins (2018)</u> analyze government expenditures in 18 European countries in 1990– 2012. They show that governments increase public spending during election years regardless of ‐ the political party they belong to. Kyriacou, Okabe, and Roca <u>Sagalés (2022) analyze electorate</u> myopia and show that voters in a diverse sample of 67 countries prefer the immediate benefits to their consumption rather than longer-term fiscal sustainability, incentivizing politicians to implement opportunistic policies to meet voters’ demands.

The literature on the political business cycles, discussed above, does not address, however, whether political budget cycles persist in the presence of systemic shocks like a pandemic, war, or natural disaster. To our knowledge, no prior studies have analyzed whether the maturity of democracy, political competition, and the rule of law in a country remain driving factors of political budget cycles in such contexts. Also, much of the cross-country literature has not exploited an exogenous variation in the national electoral cycle across countries, which would allow for clearer identification of the effects of the political budget cycle. The shock of
# **2. Data**

The data used for our analysis are derived from multiple sources. We introduce a novel dataset that provides a granular perspective on poverty and inequality at the subnational level. In particular, we draw on the Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021), a collaborative effort among different teams at the World Bank over a period of time. The GSAP is built on countries’ official household income (consumption) surveys, covering over 1,594 subnational units across 134 countries, with more than 90 percent of the data ranging from 2010 to 2019. In most cases, a subnational unit refers to a province or state (i.e., firstlevel administrative boundaries – ADM1) but can also be a group of regions determined by the specific sampling strategy of household surveys.

For the main outcomes, we utilize the (headcount) poverty rate at US$1.90 a day, as estimated by the percentage of the population living on less than $1.90 a day at the 2011 purchasing power parities (PPP) prices.<sup>4</sup> For richer analysis, we also employ other poverty lines of $3.20 and $5.50 a day. As alternative sources of poverty data, we also utilize two other sources: i) country-level poverty data from the World Bank World Development Indicators (WDI), which is a widely used database for global poverty measures, and ii) subnational GDP from Kalkuhl and Wenz (2020) and Kummu _et al._ (2020), which we further convert to poverty data. Panel A of Figure 1 shows that Sub-Saharan Africa currently has the highest poverty rates and the poorest countries include Tanzania (51.3 percent), Mozambique (54.7 percent), and the Democratic Republic of Congo (72.9 percent).

For inequality, we mostly employ the Gini index and Theil index, which are the most commonly used measures of income
for the younger children even though the overall impacts when the older ages are included are mixed. The more detailed presentation of the estimates for Group II in Table D.2 indicates that among two year olds, the proportions with diarrhea are significantly reduced for all three durations and for two and three year olds the proportions with worms are significantly reduced for those with 17+ months of duration.

**Table 8. Summary of Estimated Intent-to Treat Positive and Negative Program Impacts** **<u>for Nearest Neighbor Matched Estimates with Control for Attrition, Philippines ECD Study</u>**

||Number o<br>wi<br>|f Significant<br>th Program<br>|Coefficients<br>Effect<br>|Mean M<br>Significant E<br>ofsam<br>|agnitude of<br>ffects (in terms<br>ple SDs)<br>|
|---|---|---|---|---|---|
||Positive|Negative|%Positive|Positive|Negative|
|**Group I: Predominately positive**|**program i**|**mpacts**||||
|Cognitive skills|9|0|100.0|0.553|na|
|Expressive language|7|0|100.0|1.085|na|
|Gross motor skills|7|0|100.0|0.791|na|
|Self-help|7|0|100.0|0.334|na|
|Fine motor skills|5|0|100.0|0.647|na|
|Social-emotional skills|10|1|90.9|0.545|–0.468|
|Weight-for-height z Score|7|1|87.5|0.234|–0.370|
|Receptive language|
= 0.1). The figure shows the percentage of workers in each bin of the pre-pandemic and wave 3 occupations. Most workers remained in similar occupations in terms of WFH. In the case of women, 78 percent remained in the same bin —84 percent if we also consider changes to adjacent bins. The proportions are similar for men. Of those that moved from the pre-pandemic occupation, the number of cases for which the WFH index increased or decreased is similar. The latter seems counterintuitive, but it could be associated with the increase in certain essential occupations during the pandemic that are not compatible with remote work, such as delivery services.

Columns 3 and 4 of Table 11 complement this analysis by showing the results of a model of the change in the WFH index for those who changed occupation. The dependent variable is defined as the difference between the WFH index of the occupation in wave _w_ and the WFH index of the pre-pandemic occupation of individual _i_ in country _c_ . Again, keeping everything else constant, women were more prone to move into occupations more compatible with remote work. An increase in the WFH index is also associated with the presence of children at home, with a high educational level, and with having been a salaried worker in the pre-pandemic occupation. We also find a negative and significant partial correlation between the WFH content of the pre-pandemic job and that of the new job. Although this might seem counterintuitive, it reflects a somewhat mechanical mean reversion

# **7 Concluding Remarks**

The COVID-19 pandemic has been a huge unexpected shock on the lives of people around the world. Many have lost their jobs, suffered income reductions or had to change occupations to cope with the new situation. In this paper we explore these issues in Latin America by exploiting an unusual rich survey carried out immediately after the onset of the pandemic: the World Bank’s high-frequency phone household surveys (HFPS). In particular, by codifying the occupation variables in these surveys we are able to construct a variable
to real-time anchoveta catch data from all vessels, and there is only anchoveta catch in an area if anchoveta are sufficiently abundant in the area. This information may be particularly valuable because reducing search costs is the main margin by which fishers can increase profits within a fishing season.

Fishers are largely unable to adjust output quantity, output prices, or input prices. First, individual vessel quotas limit the tons of anchoveta that vessels can land each season. This constraint on output quantity is typically binding. Second, output price is exogenous because firms pay fishers a fixed percentage of the international price of fishmeal for each ton of anchoveta they land.<sup>25</sup> Individual fishers or fishing vessels cannot affect the international price of fishmeal. Input prices are also not affected by fishers’ decisions. The price of fuel is exogenous and wages equal a fixed percentage of the international price of fishmeal.

Fishers can adjust input quantities, namely the time and fuel they spend searching for anchoveta. Fishers spend more than 20% of their time on fishing trips searching for anchoveta; fuel comprises about one-third of variable costs; and maintenance is about one-fifth of total costs (Kroetz et al., 2016; Joo et al., 2015).

Fishing near closures could also reduce costs by increasing tons caught per set of the fishing net. Tons caught per set is a measure of fishing productivity conditional on finding anchoveta because fishers only perform a set when they see anchoveta in the water.<sup>26</sup> An increase in tons caught per set would be valuable to fishers because each set requires about one and a half hours of physically demanding labor and increases the cost of maintaining the net (e.g., by causing wear and tear). Moreover, an increase in tons per set would be indirect

> 25Firms pay fishers per ton of anchoveta they land. The price per ton is a fixed percentage of the average monthly free-on-board (FOB) price of fishmeal in Hamburg (Fr ́
Table 5: Present-Day Population Controls

||World B<br>All|ank aid 1<br>All|995–2014<br>PrC|
|---|---|---|---|
|Mission|0.10<sup>_∗∗∗_</sup>|0.07<sup>_∗∗∗_</sup>|0.18<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.05)|
|Population in 1995 (4th-order polynomial)|Yes|Yes|Yes|
|Populated place dummy|No|Yes|No|
|Ethnic dummies|Yes|Yes|Yes|
|Mean dependent variable|0.25|0.25|0.62|
|_R_-squared|0.40|0.43|0.64|
|Observations|6,876|6,876|698|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A.

_Note_ : “PrC” denotes a sample of cells that contain the capital of a province. Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with
<sup>_◦_</sup> F are suppressed for parsimony but all regressions include the full set of temperature bins and the full set of precipitation bins. 100 _×_ the coefficient estimates indicate the percent change in the impact of a day in each temperature bin on arrests relative to a day in the omitted bin after 2016 relative to pre-2016. “Gun charges” refers to all charges that we categorize as involving guns based on there National Crime Information Center (NCIC) and Texas Uniform Offense Classification codes. “Narrow gun charges” refer to those charges that are specifically related to possessing, discharging, or displaying a gun. Assault and aggravated assault charges are categorized based on their NCIC and Texas Uniform Offense Classification codes.

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