rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 2: Sources of Household Data

|Country|Survey Name|Years|Original _n_|Final _n_|
|---|---|---|---|---|
|Ethiopia|Ethiopia Socioeconomic Survey (ESS)|2011/2012|3,969|1,689|
|||2013/2014|5,262|2,865|
|||2015/2016|4,954|2,718|
|||2018/2019|7,527|1,996|
|||2021/2022|4,999|1,406|
|Malawi|Integrated Household Panel Survey (IHPS)|2010/2011|3,247|2,250|
|||2013|4,000|2,472|
|||2016|2,508|1,845|
|||2019|3,178|2,330|
|Niger|Enquˆete Nationale sur les Conditions de Vie des|2011|3,968|2,223|
||M ́enages et l’Agriculture (ECVMA)|2014|3,617|1,690|
|Nigeria|General Household Survey (GHS)|2010/2011|4,916|2,674|
|||2012/2013|4,716|2,768|
|||2015/2016|4,581|2,783|
|||2018/2019|4,976|920|
|Tanzania|Tanzania National Panel Survey (TZNPS)|2008/2009|3,280|2,001|
|||2010/2011|3,924|2,013|
|||2012/2013|5,015|1,889|
|||2014/2015|3,352|2,127|
|||2019/2020|1,184|312|
|||2020/2021|
endogeneity of losses during WW2 to the death toll of COVID-19. One could argue that the most altruistic and socially and politically active people would be more likely to die during the war. A society with high levels of social capital might suffer disproportional losses. Then the argument that the exposure to war violence increases social capital, which, in turn, helps to reduce COVID-19 mortality is reversed. The fact that WW2 ended 75 years before the 2020 pandemic clearly reduces the concerns for such nonrandom selection. Nevertheless, to partially address this potential bias, we estimate our model on

> 8 Estimations of the cumulative COVID-19 infection rates on the same set of covariates produce no significant results. We also estimated specifications with other governance indicators from WGI dataset: Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. None of these variables shows significant coefficients in estimations. These results are available from the authors.

11
labor market effects from import competition differing from those of GVC participation. While the former type of trade directly competes with domestic production over consumer demand, the latter emphasizes the role of imported inputs for export production. Given that over half of international trade happens within the boundaries of GVCs (World Bank 2020), it is critical to not only focus on exports and imports, but also assess the role of GVC participation for labor market outcomes. In addition, renewed attention has been devoted to assessing the role of technology versus trade as both can create and destroy jobs. In a world characterized by GVCs, the trade and technology nexus is increasingly merged, as producers play off trade, investment, and technology to maximize profits (Farole et al. 2018).

Drawing on novel datasets with a good coverage of developing countries, this paper re-visits the relationship between trade and jobs across countries at the level of sectors and activities. This study reexamines the relationship between trade and jobs both indirectly, i.e., through econometric analysis applying an instrumental variables (IV) approach, and directly relying on new employment, income and input-output data that allow to characterize changing jobs and job specializations in exports. The combination of these methodologies and different datasets not only allows us to address endogeneity concerns, but also to provide new insights into the various trade and labor relationships, in particular (i) how the type of trade shapes within-sector job creation, (ii) how exports create employment directly in the export sector and indirectly in its supplying sectors, and (iii) which job activities embodied in exports are performed across countries, sectors, and occupations.

Importantly, the analysis aims to disentangle some of the complexities characterizing trade-and-jobs relationships, in particular the role of different types of trade, sectors, activities, and labor market

> 5 At the firm level, evidence for LMICs is less ambiguous, suggesting a positive association between a firm’s trading status (especially GVC participation) and employment. While the positive link between exporting
Fifth, we replicate our main analysis to different subsamples to investigate the sensitivity of our finding, as shown in Panels A and B of Table A5 (Appendix A). First, we show that our findings remain consistent when using a balanced sample in Column (2). Second, there are countries in our samples that contain only a small number of regions. We show in Column (2) that our results remain consistent when excluding these countries. The same finding is found when we exclude large countries that may drive our results such as United States, India, and Brazil (Columns 3 and 4). We also employ subsamples of countries without extremely cold weather (Column 5) and extremely hot weather (Column 6) using the 10 percent threshold. In Column (7), we cluster standard errors at the country level, while in Column (8) we use Conley standard errors that allow for spatial correlation in the error term. In overall, we find the estimated coefficients and significance levels are largely unchanged compared to our main finding.

Sixth, we exploit poverty and inequality data from alternative sources to check the robustness of our results. We exploit the annual (subnational/grid level) GDP data coming from Kalkuhl and Wenz (2020) and Kummu _et al._ (2018) to construct poverty measures. An advantage of these datasets is that we are able to use a longer period-average (10-year) in the long differences model compared to our analysis using GSAP data. Using both panel and long differences models, Tables A6 and A7 (Appendix A) show that our findings are not sensitive to the alternative datasets, and the results are consistent across different specifications. We then conduct a similar exercise using country-level data from the World Development Indicators (WDI) and the Standardized World Income Inequality Database (SWIID). The results presented in Table A8 confirm our expectation.

Finally, we conduct a placebo test of our study design. It is motivated by the fact that if estimating our chosen
The STEP survey includes background information of respondents including demographic characteristics, education, employment history, and earnings. It also includes a reading literacy assessment, administered along with the household survey to a representative sample of urban adults aged 15‐64 in participating countries. Respondents were asked to take a paper‐based literacy test that was designed to measure the level of proficiency in literacy with respect to word meaning, sentence processing and basic passage comprehension, in the language of their resident country. Scores were given based on accuracy (the number of correct answers) and rate (the time taken to answer correctly) on a scale from 0 to 500, as in the PIAAC (Pierre et al. 2014).<sup>10</sup> For analytical purposes, we standardize scores to have a mean of zero and a within‐country standard deviation of one in country‐specific analyses and standardize scores across countries when analyzing the pooled sample, following Hanushek et al. (2015) and Valerio, Sánchez Puerta, et al. (2016b).<sup>11</sup> There is a consistent sampling strategy (see Pierre et al. (2014)) across all STEP participant countries and this paper employs the sample weights within each country in the estimation. Pooled analyses give each country the same weight.

In both our descriptive learning trajectory model and the linear regression model, we limit the estimation sample to survey respondents who have 1‐12 years of schooling. This gives us sample sizes ranging from 1,558 in Bolivia to 2,503 in Vietnam, as Table 1 Panel A shows. The average age of the pooled sample across 5 countries is 32.9 years old, ranging from 28.5 years old in Kenya to 38.1 years old in Vietnam. The average years of schooling is 8.9 years, with about 21.2% still at school. Respondents in Vietnam achieve the highest reading proficiency score (234) while respondents in Ghana have the
regent). The council is a tripartite institution comprising members of government, labor unions, and employers. The members negotiate and set the standards of decent cost of living ( _Komponen Hidup Layak_ or KHL in Indonesian), which forms the basis of the minimum wage recommendation to the regency governor. This recommendation is a central element in the governor’s decision on the minimum wage level, along with political negotiations with employers and trade unions.

> 42 While minimum wages are set at the provincial level every year, regencies are allowed to have different rate of minimum wage, as long as it is above the provincial minimum wage. The regency-wage data was kindly shared by Chris Manning and Nurina Merdikawati, who manually collected the data from individual Governors’ decrees. The data is only available for Java and Bali, which however comprise the bulk of our sample.

> 43 The inclusion of province/district-year effects purges the nominal values of the minimum wages from the impact of price growth.

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shocks: an analysis of resilience cannot alone determine the desirability of such a policy. However, the policy cards can contribute to a discussion on a broad set of options to reduce natural risks and increase resilience, and ensure all options are discussed, from preventive actions like flood zoning to ex‐post options like insurance, contingent finance and social protection. The scorecard provides an integrated framework to discuss and compare these options, and could even help break the silos in governments and local authorities, where ministries or departments in charge of social protection, building norms and urban planning may not work well together or not even consider flood risks in their decisions.

Our analysis of Malawi and Sweden – and the 115 other countries – is a first‐round estimate using globally open data. It is a starting point for policy design and should be complemented by local studies (Aerts et al., 2014; Keating et al., 2014; Michel‐Kerjan et al., 2013). At the local or national level, for instance, the flood risks from the global model can be replaced by results from local analyses at higher resolution, including flash floods and small basins. Local data on flood protection and better exposure data can often be mobilized (Aerts et al., 2014). And socioeconomic characteristics can be refined, accounting for instance for the institutional capacity to scale‐up social protection beyond what a global database can reasonably aim at providing (Pelham et al., 2011). But in spite of all these limits, our global approach may contribute to the monitoring of country and global progress in terms of resilience, and our findings already provide insights into promising policy options, such as adaptive and well‐targeted social protection, and show that “good development” increases resilience, especially if it reduces poverty and improves social safety nets.

The comparison between Sweden and Malawi illustrates that our results are country‐specific: some factors are more important in some countries that in others. For instance, a low
but a 2% increase in per capita personal consumption, between 1997 and 1998 estimated from the national accounts (NSCB, 1999). The impact on the headcount index is of the order of about 9% (an increase from 29.1% to 3 1.7%), while the poverty gap index is about 11% higher due to the crisis, and similarly, the squared poverty gap index is indicated to be about 13% higher. The effects on living standards and poverty are of course larger for the crisis-affected population.

The measured magnitude of the impact of the crisis on poverty of course also depends on the poverty line. To examine this, we plot in Figure 2 how the entire cumulative distribution function (CDF) shifted as a result of the crisis. The Figure plots the difference between the actual ("post-crisis") and counterfactual ("pre-crisis") CDFs against log per capita consumption. Thus, for instance, at the poverty line, we can read off a value of 2.6, which confirms the result in Table 4 that the crisis induced an increase in the headcount index by 2.6% points. But the key point that Figure 2 shows is that the impact of the crisis could have been significantly higher or lower depending upon where the poverty line is drawn. This comes out even more strongly in Figure 3 where we plot the _ratio_ of the actual-to-counterfactual distribution functions. Again, while at the poverty line we have used, the crisis appears to induce about a 9% increase in the headcount index (as noted above), the _percentage_ impact could be much higher for lower poverty lines (up to 16% if the poverty line were halved), and much lower for higher poverty lines (falling down to 3% if the poverty line were doubled).

The crisis appears to have had little effect on measures of overall inequality (Table 4). With the exception of the third-degree generalized entropy measure, the impact ranges between less than 1
**FIGURE O.1a** With advanced and developing country growth rate differentials narrowing in the 2000s . . .

<!-- Start of picture text --> Growth rate 1990–2014<br>10<br>8<br>6<br>4<br>2<br>0<br>Developing countries growth<br>–2 Developing countries trend growth<br>High-income countries growth<br>High-income countries trend growth<br>–4<br>ercent<br>P<br><!-- End of picture text --> Source: World Development Indicators database (World Bank, various years).

**FIGURE O.1b** . . . the income gap has been closing more rapidly than in the 1990s

<!-- Start of picture text --> Developing countries GDP per capita as share of<br>high-income GDP per capita 1990–2014 (in 2011 PPP)<br>25<br>21.9<br>20 21.4<br>15<br>10<br>5<br>0<br>ercent<br>P<br><!-- End of picture text --> Source: World Development Indicators database (World Bank, various years). Note: The gross domestic product (GDP) trend was calculated using a Hodrick-Prescott filter with smoothing parameter equal to 6.25; PPP = purchasing power parity.

3
ensure that repeat data do not drive estimated property prices. More sophisticated duplicate removal would include the use of text analysis techniques to understand the extent of similarity of the title or description of the property to remove those observations that have only been slightly changed during re-submission. Given the significant time effort of this technique, we opted for the more stringent data removal as a robustness test for price estimations (Annex 2).

# Data Filtering

Next, we filter the data by excluding scraped data that are clearly not residential properties. These include storing units, garages, parking lots, undeveloped or agricultural land, or commercial properties. In addition, we truncate the data on price and size to exclude data entry errors and rental data. These include, for instance, rental data likely erroneously captured as sales price, particularly for those properties that include yearly rentals, spam or negotiable listings often detectable by “1” entered as the sales prices, and random data entries on square meter data. We apply a direct data filter to remove obvious errors and undesired data to obtain the _truncated data set_ . We assume that all observations of below 9 square meters (sqm) and above 3,000 sqm are either data entry errors or properties that cannot be considered residential properties (e.g., storing units; large farmland). Also, we assume that properties of less than 9 sqm are not habitable for one person, aligning with the definition of the UN (UN-Habitat 2007). Regarding the truncation on price data, we remove any properties below 5,000 US dollars and above 50 million US dollars to account for data entry errors, rental prices that are accidentally listed as sales, as well as – a very typical feature in EMDEs – entire apartment complexes that are sold in bulk as an investment project. The major issue with apartment complexes or several apartment units being sold in bulk is the mismatch between the size and price data. Often, the price reflects the price of the overall apartment complex while the size
package GAMS and the solver PATH.

# **2.1 The Tax Structure of South Africa**

Typical of a middle-income or more advanced developing country, the tax base of South Africa is derived more from manufacturing activities and formal services, which now account for more than two-thirds of the economy. The production structure of South Africa is reflected in Table 1, which is derived from a recent social accounting matrix (SAM). While the mining sector remains a significant source of export revenue, its share in the economy is less than 7 percent and, as an export sector, it does not contribute to VAT revenue.

> 6 Devarajan and Hossain (1995) analyze the combined incidence of taxes and public expenditure in the Philippines. They simulated the price effect of a particular tax, by eliminating the tax from the Philippines tax system and simultaneously replacing it with an increase in proportional income tax (Devarajan and Hossain,1995:10).

> 7 A detailed discussion on the mathematical structure of the standard model can be found in Löfgren et al (2001).

3
Policy Research Working Paper 10040

# **Abstract**

Obtaining representative information on hosts and displaced populations in a single survey is not straightforward. This paper demonstrates the value of combining traditional and nontraditional sampling frames, geospatial information, and listing exercises to design a representative survey of hosts and Rohingya displaced populations in Cox’s Bazar, Bangladesh. The paper applies innovative segmentation techniques using geospatial data to delimit enumeration areas in the absence of updated cartography. The paper also highlights the importance of listing exercises to inform stratification decisions and update population counts.

This paper is a product of 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 mgenoni@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
Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Observations|182|182|182|182|
|Log likelihood|−101|−100|−101|−100|
|Sample||2004–2|009||

_Source:_ Authors’ estimation using URSEA, ECH and National Meteorology Office data.

_Note:_ All columns report estimates using a zero inflated model. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include city and year fixed effects. Vuong tests indicate that the zero inflated models are appropriate. Treatment Units (six cities): Maldonado, Pan de Azúcar, Piriápolis, Punta del Este, Punta Ballena, and San Carlos. Control units (26 cities): Artigas, Canelones, Colonia, Dolores, Durazno, Florida, Fray Bentos, La Paloma, La Paz, Las Piedras, Melo, Mercedes, Minas, Montevideo, Pando, Paysandú, Progreso, Rivera, Rocha, Salto, San José, Atlántida, Tacuarembó, Toledo, Treinta y Tres, and Trinidad.

* indicates statistical significance at the 10 percent level.

32
Table 10: Effects on women’s time use

||(1)<br>Taking care<br>of children|(2)<br>Cooking/washing/<br>housework|(3)<br>At school|(4)<br>Doing<br>paid work|
|---|---|---|---|---|
|**Full sample**|||||
|ITT estimate|-0.280|-0.130|0.018|0.613***|
||(0.396)|(0.148)|(0.100)|(0.200)|
|Observations|1961|1964|1963|1963|
|_R_<sup>2</sup>|0.249|0.184|0.527|0.135|
|Control mean|4.800|2.810|0.402|3.461|
|**Have child aged 0-2**|||||
|ITT estimate|-0.069|-0.006|0.044|0.650**|
||(0.387)|(0.153)|(0.094)|(0.253)|
|Observations|1122|1125|1124|1124|
|_R_<sup>2</sup>|0.266|0.170|0.581|0.125|
|Control mean|4.765|2.723|0.418|3.266|

_Notes:_ Each column reports the number of hours spent on a given activity in the last 24 hours. Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

Table
007|
||(0.418)|(0.257)|(0.214)|(0.243)|(0.382)|(0.228)|(0.122)|
||||C. Old|plants (25 ye|ars and above)|||
|(0,1) Nodal district|1.142|1.054|1.198+|-0.036|0.064|0.036|0.278++|
||(0.809)|(0.787)|(0.651)|(0.364)|(0.359)|(0.166)|(0.126)|
|(0,1)District0-10kmfromGQ|0.335|0.197|0.342++|-0.013|0.012|0.157|0.048|
||(0.273)|(0.203)|(0.172)|(0.161)|(0.207)|(0.144)|(0.082)|
|(0,1)District10-50kmfromGQ|-0.540|-0.553+|-0.239|-0.507++|-0.374|0.150|-0.032|
||(0.415)|(0.288)|(0.260)|(0.233)|(0.314)|(0.192)|(0.099)|

Notes: Long-differenced estimations consider changes in the location and productivity of organized-sector manufacturing activity in 283 Indian districts from 2000 to 2009 from the Annual Survey of Industries (ASI). Explanatory variables are indicators for distance from the GQ network that was upgraded starting in 2001. Estimations consider the effects relative to districts more than 50 km
physical activity levels and the disease environment that may have lowered the demand for calories, but reach no firm conclusion.<sup>1</sup> This article presents estimates of Total Energy Expenditures (TEE) for the Indian population over the 1983-2012 period to provide insight into this puzzling finding. Total energy expenditures are the total calories required for maintaining one’s weight level given the body’s metabolic processes and physical activity levels. Anthropometric data from three waves of India’s National Family Health Survey (1998, 2005, 2015) are used to estimate Resting Energy Expenditures (REE) that capture metabolic requirements related to height, weight, age and gender for individuals at rest. Detailed time-use data from India’s 1998-1999 Time-Use Survey, matched to activity factors in (FAO/WHO/UNU Expert Consultation 2001), are used to estimate activity levels (AL) that capture the additional energy used for physically intensive activities. The large set of variables in common between these surveys and the NSS employment surveys (Schedule 10) that were conducted between 1983 and 2012 are used to estimate individual TEE. This quantification provides insight into the evolution of population TEE and the contributions of changes in demographics, occupation and industry, work status, and laborintensive domestic tasks.

The main finding is that population level TEE in India is fairly flat between 1983 and 2012 and actually increased slightly over the 1983 to 2005 period before declining slightly between 2005 and 2012. The quantification highlights two reasons for flat TEE over time. First, individual and household characteristics that predict lower Activity Levels (AL) also tend to predict higher Resting Energy Expenditures (REE) for a given demographic composition. For example, higher levels of education, higher levels of expenditure, and less labor-intensive occupations are all associated with lower AL but higher REE. Because TEE is computed as the product of REE and AL (FAO (2001)), as discussed later, the effect of changes in education, expenditures, and occupation on TEE tend to be
see that preferences are indeed near zero when MFN tariffs are low (note the y-axis records negative values, since we define preferences as bilateral applied tariffs minus MFN tariffs). In Panel (b), we see that variability in preferences is rising with mean MFN tariffs. Both these patterns are consistent with MFN-censoring constraining variation in the data.

> notified to the WTO, according to the WTO’s trade agreement database [ `http://rtais.wto.org/UI/ PublicMaintainRTAHome.aspx` ].

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