rafmacalaba/data-use-mentions-extended huggingface dataset:
estimates from a large set of key informants identified within a geographical unit that is small enough to be able to provide an estimate with low measurement error. In other words, quick population counts are usually done at the lowest feasible geographic unit, as informants are more likely to provide accurate information on units that cover smaller populations. The standard practice for implementing such a count in Bangladesh - followed both by the censuses and the IOM’s count of Rohingya displaced in camps – focuses at the village/camp-block level. Camps and blocks cover, on average, 359 and 109 households each in Cox’s Bazar district (Table 1). The IPA quick count, however, was implemented at the union level using a small set of union-level legislators as key informants. While the union is the lowest administrative level that has an office, the size of the population included in a union does not lend itself to an accurate quick count. This is particularly true for Rohingya displaced, for whom no administrative data are available.

**<u>Table 1: Average population by administrative and geographic units</u>**

||**Number of units**|**Average number of**<br>**persons**|**Average number of**<br>**households**|
|---|---|---|---|
|**Hosts**||||
|Unions|110|20,484|3,758|
|Mauzas|339|6,647|1,219|
|Villages|1,150|1,959|359|
|**Rohingya displa**|**ced**|||
|Camps|34|26,739|6,271|
|Blocks|1,953|465|109|
|Source: 2011 Ban|gladeshcensusandIOM NP|M Round12.Note:Averages|forCox’sBazardistrict|

On
across EU member states. The answers to these two guiding questions based on the latest and most granular analysis of the past ten years of the CAP, can inform if and where agriculture and the CAP can be one of the important drivers of social inclusion and territorial cohesion.

A main motivating question in this analysis is whether, and how, the CAP may complement other policies, or foster territorial cohesion on its own. A clear objective of the EU, as stated by the European Commission, is to “strengthen economic and social cohesion by reducing disparities between regions in the EU <mark>” (European Parliament, n.d.).</mark> In addition to economic and social cohesion, territorial cohesion was later included as a further objective. In a similar line as Crescenzi and Giua (Crescenzi & Giua, 2016), an important motivating question is whether sectorial policies like the CAP can contribute to or complement other policies’ objectives, specifically social cohesion in the EU. In the particular case of the CAP, we want to analyze if this program further complements other existing policies’ objectives by better channeling resources to socio‐economically deprived areas. This could potentially aid in the Cohesion Policy of the EU, as it contributes to reducing disparities between regions, based on poverty rates, across the EU.

The data used for the purpose of the various analyses in this working paper came from several sources; including the EU‐SILC survey from years 2003 to 2014 at the NUTS 1 and NUTS 2 levels, the

> 2 This work was produced as a background paper to the _EU Regular Economic Report : Thinking CAP ‐ Supporting Agricultural Jobs and Incomes in the_ EU.  (Brink,  Kordik, and Azevedo, 2018).

2
Table 3: Main economic activity of ICT firms interviewed

||ISIC 4|interviews|
|---|---|---|
|Computer programming, consultancy and related activities|62|29|
|Telecommunications|61|15|
|Information service activities|63|6|
|Architectural and engineering activities; technical testing and analysis|71|6|
|Civil engineering|42|2|
|Publishing activities|58|1|
|Motion picture, video and television programme production, sound<br>recording and music publishing activities|59|1|
|Total, excluding maintenance and trade||60|
|Repair of computers and personal and household goods|95|56|
|Retail trade, except of motor vehicles and motorcycles|47|9|
|Wholesale trade, except of motor vehicles and motorcycles|46|1|
|Total||126|

_Sources:_ Authors’ survey.

**Travel times.** A key ingredient of the analysis is the estimate of travel times. We borrow this part of the analysis from Fratto (2019). Travel times are computed using the shortest route between the centroid of two locations using ArcGIS. Table 4 summarizes the assumptions on the travel times and delays at each obstacle. Vehicles cannot pass through roadblocks, earthmounds, barrier gates, and across the Separation Wall. Due to an absence of information on road gates, they are assumed to be open.

For the checkpoints, there are three independent sources. First, Eklund and Martensson (2012) conducted an extensive survey in 2009 in which they asked questions on the shortest delays at a checkpoint (ranging from 0 to 90 minutes, with responses concentrated between 0 and 30 minutes) and the longest delays (between 0 and 1,080 minutes, with responses concentrated between 0 and 360 minutes). They find an average shortest delay of 15 minutes. Van Der Weide et al. (2018) provide a similar average delay using information from
-|---|---|
|**CEE**||||
|Bulgaria|33.1|19.4|8.8|
|Czech Republic|21.5|7.1|2.5|
|Estonia|18.6|13.3|8.0|
|Hungary|11.2|5.2|1.2|
|Latvia|21.0|13.0|5.5|
|Lithuania|23.1|21.7|10.1|
|Poland|23.9|19.4|5.6|
|Romania|4.0|8.6|3.9|
|Slovakia|42.5|18.7|5.2|
|Slovenia|8.9|5.5|2.3|
|**SEE**||||
|Croatia a)|13.7|16.7|7.6|
|Macedonia, FYR|-|-|-|
|Serbia and Montenegro a)|24.9|19.7|9.5|
|- Data not available||||
|Note: Labor Force Survey<br>data<br>a) 2002||||
|Source:||||
|CEE: Eurostat||||

Croatia: Central Bureau of Statistics, Labour Force Survey 2002 (second half), Bank staff calculations. Serbia and Montenegro: Republic of Serbia, Republican Statistical Office, Labor Force Survey 2002 (October), Bank staff calculations 25
use a monthly panel of RAIS created at the BCB, starting in 2013 and ending in 2019. In December 2019, RAIS included nearly 51 million employees.

# **_3.e. Timeline_**

Table 2 provides a timeline of the study. It first summarizes when the financial education intervention happened in high schools (2010 and 2011). It then shows the years for which we have data from the sources described above. The table also lists the corresponding average student age for each year. At the time of the intervention, students were 16 and 17 years old on average. In this study, we use data that follows the students until they were up to 26 years old on average. Most of the administrative data is only available for the post-intervention period, except for account ownership, which we have for 2008 and 2009.

# **4. Long-Term Results**

When analyzing the long-term effects of the financial education program, we use a monthly panel and estimate the following intent-to-treat OLS specification: yi,s,r,t = α + βTreatmenti,s,r + ∑γs,rds,r + ηfi,s,r r mrt + εi,s,r,t, (2) + ∑θθ where yi,s,t is a financial or labor market outcome of student in school pair , located in state , in month . The variables Treatmenti,s,r, ds,r, fi,s,r, and εi,s,r,t are defined as in equation 1. ii ss rr Following Bruhn et al. (2016), we show results for three different specifications: (i) no controls, tt (ii) with school pair dummies, ds,r, and (iii) with school pair dummies and gender, fi,s,r. We add fixed effects for state r in month t, mrt, to all specifications to account for regional trends in dependent variables.

We also estimate a specification
# **1 Introduction**

Households in developing countries face significant uncertainty about their future consumption. There are many sources of this uncertainty, for example job insecurity, ill health that affects the productive labor and income of a household, death of family members, drought that reduces crop yields and incomes and, more recently, price shocks that make purchasing basic food supplies more difficult for those with few resources.

The importance of risk for decision making and the consequences of realized shocks have become an ever-more important focus for policymakers as evidenced by the World Development Reports of 2001 and 2014 (World Bank, 2014). The increasing availability of data on shocks, particularly those that are experienced by entire communities at once (such as weather shocks and price shocks), allow for better estimates of the welfare impact of shocks. It also allows for new forward-looking quantitative research on the intersection of poverty and risk.

It is to this literature that this paper aims to add, by applying and extending lessons from both theoretical and empirical work on shocks and vulnerability to a newly compiled dataset that is nationally representative, and used to calculate official estimates of poverty.<sup>1</sup> The methodology we use is of general interest, as it combines several new ideas for estimating the impact of rainfall and price shocks on welfare using cross-sectional data, and combining these estimates with long-run data on rainfall and prices to generate estimates of household vulnerability. Although the availability of panel data surveys is improving for developing countries, they are not yet widespread, often are small in size, and not nationally representative. Vulnerability estimates have either relied on self-reported shocks and short, unrepresentative panel surveys (Calvo and Dercon, 2013; Klasen et al., 2015), or on the cross-sectional distribution of consumption (Chaudhuri, 2003)), neither of which are completely satisfactory. We show that measures of vulnerability that properly take covariate shocks into account can result in a very different understanding of welfare differences across groups than poverty measures at one
| POLICY RESEARCH WORKING PAPER 1751

# Summary findings

Bond presents a tariff index that uses constant-elasticityof-substitution aggregators of tariff line data to calculate how preferential tariff reductions affect both prices and average tariff rates.

A simple general-equilibrium model with sectorspecific factors of production can be combined with the tariff indices to calculate how a preferential trade arrangement affects sectoral output, factor prices, and general welfare. The general equilibrium model is simple enough that it can be calculated on an Excel spreadsheet, and is flexible enough to be used with different ranges of available domestic data.

Bond presents an example of the model, simulating the effects of free trade agreements between Chile and MERCOSUR countries and between Chile and NAFTA countries.

which allows large amounts of detailed tariff line data to be aggregated for use with domestic production data that is available only at a more aggregated level.

Bond finds that results using the tariff aggregators may differ substantially from those derived using simple averages of tariff reductions. Reductions in import prices using the index were 10 to 30 percent larger than those calculated using a simple average of tariffs. Ignoring the information available in tariff line data could lead to a substantial overestimate of the average tariff rate on imports after a preferential reduction.

The tariff index could be extended to incorporate the role of quantitative restructions. The general equilibrium nmodel could be used to consider the effects on domestic production of allowing reallocation of capital between industries over time.

Calculations for the case of Chile show that the index is simple to calculate because of its recursive structure,

- - This paper a product of the International Trade Division, International Economics Department represents the results of a study funded by the Bank's Research Support Budget, "An Operational Model for Evaluating Preferential Trading Arrangements" (RPO 679-77). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jennifer Ngaine, room NS-060, telephone 202-473-7947, fax 202-522-1159, Internet address trade@wworldbank.org. April 1997. (42 pages)

_The Policy
geocoordinates, can introduce mismeasurement when integrating them with remote sensing weather data.

In a new study based on a pre-analysis plan, Michler et al. (2022) employ 90 linked weather-household data sets that vary by the spatial anonymization method and show that, as the spatial resolution of most weather data produce is too coarse, spatial anonymization techniques have an overall small effect on the estimates of the weather-agricultural productivity relationship and do not introduce substantial mismeasurement. Depending on the specific type of weather data, however, measurement error can become significant, especially for higher-resolution data products. Importantly, Michler et al. (2022) also find that estimates of weather’s impact on agricultural productivity vary substantially in sign, significance, and magnitude, across different weather data sets for the same spatial anonymization technique. For these reasons, caution is in order when integrating household surveys such as the LSMSISA with external weather data, and the first-best would be to have high-resolution weather data already embedded in the survey data set.

# **4. LSMS-ISA data assessment**

To draw concrete operational implications from the review above, we start by outlining what the implications would be for one of the international survey programs that has been at the forefront of the methodological debate on data collection in low- and middle-income countries in the past 15 years or so. The LSMS-ISA program was launched in 2009 with funding from the Bill and Melinda Gates Foundation and the explicit aim is to fill the gaps in agricultural data through close collaboration with the national statistical offices (NSOs) of partner countries. The program is based on the implementation of multitopic, nationally-representative household longitudinal surveys and, to date, has been carried out in eight Sub-Saharan African countries, namely Burkina Faso, Ethiopia, Mali, Malawi, Niger, Nigeria, Tanzania, and Uganda.

LSMS-ISA survey panels are administered approximately every 1 to 3 years. In the LSMS-ISA program, not only original households are revisited each wave, even if they relocate within the country, but also individual household members who split
10: 1814

- Southerland, V., Brauer, M., Mohegh, A., Hammer, M., van Donkelaar, A., Martin, R., Apte, J., Anenberg, S. (2021) Global urban temporal trends in fine particulate matter (PM2.5) and attributable health burdens: estimates from global datasets. _The Lancet Planetary Health,_ Vol. 6(2): 139-146

- Tessum, C., Paolella, D., Chambliss, S., Apte, J., Hill, J., Marshall, J. (2021). PM2.5 polluters disproportionately and systemically affect people of color in the United States. Science Advances. Vol. 7(18).

- Thurston, G., Chen L.C., Campen, M. (2022) Particle toxicity’s role in air pollution. Science. 3 Feb 2022. Vol 375(6580): 506

- van Donkelaar, A., Hammer, M., Bindle, L., Brauer, M., Brook, J., Garay, M., Hsu, C., Kalashnikova, O., Kahn, R., Lee, C., Levy, R., Lyapustin, A., Sayer, A., Martin, R. (2021). Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty. _Environmental Science & Technology_ . Vol. 55(22): 15287–15300

- World Bank. (2021). Global Subnational Poverty Atlas (GSAP). Available at

- https://datacatalog.worldbank.org/int/search/dataset/0042041/international_poverty_line__subnational_p overty (Accessed: 5<sup>th</sup> November 2021).

- World Health Organization. (2005). _Air Quality Guidelines. Global Update 2005. Particulate Matter, Ozone, Nitrogen Dioxide, and Sulfur Dioxide_ . Geneva: World Health Organization.

- World Health Organization. (2018)
|Table A6: GO<br>**Category**|DI data categories covered in techni<br>**What we look at?**|cal survey<br>**Why we look at it?**|**Characteristics**|
|---|---|---|---|
|Budget|National government budget at a high<br>level. This is planned government<br>expenditure for the upcoming year, and<br>not the actual expenditure.To develop<br>this category the Index drew on work<br>fromOpen Spending.|Open budget data allows for<br>well-informed publics. It<br>showing what money is spent<br>on, how public funds develop<br>over time, and why certain<br>activities are funded. See<br>here a list of cases how<br>budget data has been used in<br>the past.|Following data must be online to qualify for<br>assessment:<br>•<br>Budget for each national<br>government department, ministry,<br>or agency<br>•<br>Descriptions for budget sections<br>•<br>Level of granularityBudget<br>separated into sub-department,<br>political program, or expenditure<br>type|
|Spending|Records of actual (past) national<br>government spending at a detailed<br>transactional level. Data must display<br>ongoing expenditure, including<br>transactions. A database of contracts<br>awarded or similar will not be<br>considered sufficient. Also, a database<br>only showing subsidies will not be<br>sufficient. To develop this category the<br>Index drew on work fromOpen<br>Spending.|Open spending data shows<br>whether public money is<br
# Long COVID: The Evolution of Household Welfare in Developing Countries during the Pandemic<sup>∗</sup>

Ben Brunckhorst<sup>†</sup> Alexandru Cojocaru<sup>†</sup> Ruth Hill<sup>†</sup> Yeon Soo Kim<sup>†</sup> Maurice Kugler<sup>‡</sup>

**Keywords:** COVID-19, welfare, inequality, labor market, phone surveys **JEL Codes:** E24, J15, J16, J21

∗ Corresponding author: bbrunckhorst@worldbank.org. This paper is a product of the Poverty and Equity Global Practice and the Umbrella Facility for Poverty and Equity, administered by the World Bank. We thank Benu Bidani, Tom Bundervoet, Margarita Isaacs, Joana Silva and Michael Weber for helpful comments and advice.

> †Poverty and Equity Global Practice, The World Bank.

> ‡School of Policy and Government, George Mason University.
(2018) is among the first studies to provide evidence that socioemotional skills may matter for employers when they evaluate candidates, although the evidence is collected using an unincentivized survey with hypothetical CVs. Even if employers would consider socioemotional skills during hiring, it remains unclear whether employers can get the relevant information on the candidate's socioemotional skills. While they can rely on educational attainment or technical certifications of prospective workers as signals of cognitive and technical skills, socioemotional skills are more difficult for employers to assess and for job seekers to signal. Furthermore, if socioemotional skill signals are not credible, they might hurt the applicant’s chances as they can be perceived by the employer as an attempt to oversell oneself.

The above complications are in part due to the vagueness in the definition of, and the difficulty in measuring, socioemotional skills. There is still no consensus on the definition or even the term: “soft skills", “personality traits", “non-cognitive skills", “non-cognitive abilities", “character", and “socioemotional skills" are all used to identify the personality attributes (Heckman and Kautz, 2012). In practice, the investigation of certain socioemotional skills in the literature depends heavily on data availability (Brunello and Schlotter, 2011). Perhaps the most used measure of socioemotional skills is the Big Five personality traits, and many studies find them important in career success (e.g., Boudreau et al., 2001; Seibert, Scott E. and Kraimer, Maria L., 2001; Gelissen and de Graaf, 2006) and earnings (e.g., Nyhus and Pons, 2005; Mueller and Plug, 2006; Heineck and Anger, 2010). However, the Big Five traits are too general to signal in a CV and some are not clearly applicable to the work context, so employers may not find such signals valuable or credible.

Furthermore, employers may value similar socioemotional skill signals of male and female candidates differently. This may be particularly important considering
interviews.<sup>25</sup> The woman known to us from the factory was always interviewed by the female enumerator, and the other interviewed household member by the male enumerator. The surveys were not conducted if privacy could not be maintained.<sup>26</sup> The survey response rates for the sampled women were high, above 90% in the first wave for the promotion analysis sample and above 80% for the role model analysis sample.<sup>27</sup> The response rate for the nominated operators dropped to 77% in wave 2 for the promotion analysis, compared to 91% of selected operators. In Section 4.4, I show that this attrition is not driving results. About 70% of both samples were found to be married and living with their husbands in the pre-surveys, who were then identified as the second respondent to be surveyed. Only for approximately 10% of respondents, other adult household members were targeted, primarily parents and siblings. For readability, I therefore use the terms “wife” and “husband” in the rest of the paper to refer to the women known to us from the factories and all second household members interviewed, respectively. Close to a fifth of respondents lived without another adult in the household, for example in factory dormitories.<sup>28</sup> Since I am interested in bargaining between household members, I exclude these individuals from one-person households from the main analysis.

# **3.3 Main outcome variables**

For both the promotion and the role model analysis, I use the survey data to implement the analysis in three steps.<sup>29</sup> In the first step, I investigate whether the wives report working as a supervisor during the home surveys (defined as any garment sector position with supervisory or managerial duties) and any possible effect on their personal and household income in Bangladeshi Taka. The personal and household income variables include income from all economic
current taka value for the 69,240 tenders.

2. Government Administrative Cost Savings

- a. Advertisement Cost Savings: Among the major savings that has resulted from the shift to the electronic procurement system was the decrease in the need for newspaper advertisements. All electronically administered tenders are published on the e-procurement portal and hence to need to advertise it decreases significantly. The advertisement cost savings is estimated by the difference of the product of the average manual advertisement cost and the predicted number of advertisements had the electronic tender been not done electronically and the product of the average e-procurement advertisement cost and the number of advertisements of that electronic tender. The number of advertisements of an

> 34 Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used. (International Monetary Fund, International Financial Statistics and data files.)

> 35 Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. (World Bank national accounts data, and OECD National Accounts data files.)

71