rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Total reduction<br>Market Income Net Market Income Disposable Income Consumable Income Final Income _ in poverty rate<br>By household type<br>couple without children -19.5%<br>couple with children -5.5%<br>couple w/children and elderly -39.8%<br>elderly -78.2%<br>By type of head:<br>out of labor force -37.3%<br>unemployed -12.3%<br>agriculture 7.8%<br>manufacturing 5.2%<br>services 6.9%<br>By location of household:<br>Ulaanbaatar -16.8%<br>Aimag cent “16.7%<br>Soum center “14.4%<br><!-- End of picture text -->

||MarketIncomeNetMarketIncomeDispo|ableIncomeConsumableIncome<br>FinalIncome_|Total reduction<br>inpovertyrate|
|---|---|---|---|
|Byhousehold type||<br>||
|couple without children|||-19.5%|
|couplewithchildren|||-5.5%|
|<br>couple w/children and elderly|||-39.8%|
|elderly|||-78.2%|
|By type ofhead:||||
|out oflabor force<br>|||-37.3%<br>|
|unemployed<br>|||-12.3%<br>|
|agriculture<br>|||7.8%<br>|
|manufacturing<br>|||5.2%<br>|
|services|||6.9%|
|By locationofhousehold:<br>||||
|Ulaanbaatar
are openly administering the territory, providing services and security, and running the local courts, respectively. LWJ may assess a district controlled by the Taliban or by the U.S. government if the district center frequently exchanges hands, and the Taliban or the government only controls a few buildings or villages in the district.

- Contested districts: places where the government is in control of the district center or buildings within the district center, or a base, but little else, while the Taliban controls large areas or all of the areas outside of the district center. Or, the Taliban may control several villages, mines and other resources, runs prisons in the district, or administers areas of the district.

With the classification of the LWJ, we construct the share of districts controlled by the Taliban, the U.S-Allies and the Contested territories in each of 31 provinces in Afghanistan.

# **B.C Social Desirability Bias**

Individuals tend to answer to how their will be viewed usually according responses by others instead of answering what they really believe, and this phenomenon is known as social desirability bias. For this purpose, we measure social desirability bias by using four questions from Marlone and Crowe’s social desirability scale (see Crowne (1964) for details). The questions assess whether or not respondents are concerned with social approval. A high number of socially desirable responses suggests the respondent is concerned with social approval.

Each question on the scale includes a statement to which the respondent has to answer true or false. The four questions we included are: ”It is sometimes hard for me to go on with my work if I am not encouraged (false corresponds to higher social desirability)”, ”There have been times when I was quite jealous of the good fortune of others (false corresponds to higher social desirability)”, ”I am always willing to admit when I make a mistake (true corresponds to higher social desirability)”, and ”I have never intensely disliked anyone
the degree to which the global response should include a development element. We find that the average stood at around 10.3 years at the end of 2015, with a median duration of 4 years, and significant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations.

To produce these numbers, we rely on the Population Statistics Database compiled and maintained by UNHCR. The database records the number of “persons of interest” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a _no-turnover_ assumption, whereby a decrease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such approach tends to over-estimate the true duration of exile, the lack of individual-level data on registration precludes refining the estimate further.

Attempts to estimate similar statistics have been limited. In a 2004 note to its Executive Committee, UNHCR established the average at 17 years at the end of 2003 (Executive Committee of the High Commissioner’s Programme 2004). This number has been widely quoted by media, activists, humanitarian agencies, and development institutions (Milner 2014; United Nations 2016; UNHCR 2015).

The rest of the paper is organized as follows. Section 1 gives some definitions and background information on the refugee population. In section 2, we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database. Section 3 describes the method followed to construct duration statistics and presents a few stylized facts. The results of our analysis are presented in section 4. Section 5 concludes.

# **1 Background: Definitions and Data**

Under the terms of the 1951 Convention Relating to the Status of Refugees –
combine data sets provided by the World Bank data on income poverty (Poverty and Inequality Platform (World Bank, 2023)) and an internationally comparable data set on mortality data (Global Burden of Disease Collaborative Network, 2020) from 1990 to 2019. We show that mortality is growing in relative importance and substantially affects global poverty comparisons: during the 2005-2019 period, at least 34% of PALE’s growth was due to the growth of life expectancy, as opposed to 17% from 1991 to 2004. For all possible values of _θ_ , _PALEθ_ is able to solve in 2019 about half of the between country and 40% of the within country comparisons when focusing on these comparisons for which life expectancy and headcount are conflicting.

**Literature review** Baland et al. (2021) proposed a way of integrating mortality into poverty measurement that is consistent with the minimalist view. They observe that an integrated indicator should at least meaningfully compare _stationary_ societies, for which natality, mortality and poverty are constant over time. (In stationary societies, the outcomes observed in a given year completely reflect the life-cycle effects of their mortality.) They show that such indicators satisfy a set of basic axioms when based on a weighted sum of a number of years of life prematurely lost and a number of years of life spent in poverty.

We improve on _GDθa_ ˆ, the main indicator proposed by Baland et al. (2021), along the following dimensions. First, given an age threshold ˆ _a_ , the death of a poor individˆ ual above the age of _a_ is considered by _GDθa_ ˆ as an improvement. The “minimalist” view taken by this measure therefore implies a form a mortality paradox. Second, _GDθa_ ˆ is not straightforward to interpret, preventing its widespread diffusion in pub-

> 6Again, this implies that a newborn is expected to be exposed throughout her lifespan to the poverty and mortality observed in the current period.

4
Table 1: Number of immigrants in GCC countries, 2020, in thousands

||||Destinat|ion coun|try|||
|---|---|---|---|---|---|---|---|
|Origin country|Saudi Arabia|UAE|Kuwait|Oman|Qatar|Bahrain|All|
|India|2,502|3,471|1,152|1,376|702|365|9,569|
|Bangladesh|1,278|1,095|380|317|262|115|3,446|
|Pakistan|1,484|996|339|250|236|105|3,409|
|Egypt,Arab Rep.|962|900|421|88|184|95|2,649|
|Indonesia|1,709|319|108|75|44|34|2,288|
|Philippines|645|565|197|46|170|59|1,682|
|Yemen, Rep.|770|206|71|-|36|22|1,103|
|Syria, Arab Rep.|823|53|23|-|25|6|930|
|Sri Lanka|529|121|40|30|154|13|886|
|Nepal|503|28|25|-|254|3|813|
|Other|2,249|963|355|191|161|121|4,040|
|Total migrants|13,455|8,716|3,110|2,373|2,226|936|30,816|
|(% of Population)|39%|88%|73%|46%|77%|55%|53%|

_Source:_ UNDESA International Immigrant Stock 2020 for immigrant stock; World Bank
Particulate Matter and Labor Supply: The Role of Caregiving and Non-Linearities<sup>_∗_</sup> Fernando M. Arag ́on<sup>_†_</sup> Juan Jose Miranda<sup>_‡_</sup> Paulina Oliva<sup>_§_</sup> _JEL Classification_ : Q52, Q53, J31, J24 _Keywords_ : Labor supply; Air pollution; Heterogeneous Effects

> _∗_ We thank the Comit ́e de Gesti ́on de la Iniciativa de Aire Limpio para Lima y Callao for sharing their pollution data. We are grateful to Thiemo Fetzer, Joshua Graff-Zivin, Rema Hanna, and seminar participants at UBC and University of Washington for useful comments and suggestions.

> _†_ Department of Economics, Simon Fraser University; Email: faragons@sfu.ca

> _‡_ Environment and Natural Resources Global Practice, World Bank; Email: jjmiranda@worldbank.org

> _§_ Department of Economics, University of California, Santa Barbara and NBER; Email:

paulina.oliva@ucsb.edu
Figure 8. Male-Female difference in work stoppages (percentage points) Note: Each bar represents the male coefficient in a regression by country and survey round of work stoppages on a male dummy and (most recent) employment sector fixed effects. Asterisks denote levels of statistical significance. Source: HFPS In sum, while periods of stringent government containment measures may have had similarly far-reaching impacts on workers across the welfare distribution, in areas or times of recovery or relative normalcy in terms of mobility, those at the top may have been more shielded from the economic impacts of the pandemic.

The partially mixed picture of work stoppages (with unequal impacts only at times of lower mobility restrictions) contrasts what has typically been observed in high-income countries. While beyond the scope of the present note, as mentioned in section 1, this result may be due to the tendency of the poor and vulnerable in middle-income EAP countries to reside in rural areas and be engaged in agriculture, which was relatively less affected during the pandemic. Moreover, limited scope for home-based work, as well as higher engagement in self-employment activities in middle-income EAP may have influenced our results. The next section looks at household labor income losses, complementing the analysis on work stoppages and providing a more comprehensive picture of the economic impacts of the pandemic.

# 3. Household labor incomes: Looking beyond work stoppages

HFPS data on labor income losses may paint a more comprehensive picture than work stoppages of the extent of the pandemic’s impact on livelihoods for two main reasons. First, the HFPS typically only asks about the employment status of one or two members of the household, and overwhelmingly the household head in most countries (Table 2). Thus, to the extent that employment patterns and job characteristics differ between the head and other members of the household or that additional household members begin to work as the main income earner loses employment (added worker effect), these surveys may not fully capture the pandemic’s impact on employment. Second, employment status is sensitive to type
|

20 composition and the relative strength of government and opposition coalitions. Currently, in order to characterize the internal structure of legislators, researchers have had to rely on measures of fractionalization of the legislature: the probability that any two legislators drawn at random would belong to the same party. Greater fragmentation is taken to imply a larger number of actors willing to act independently in the consideration of any given policy change. This variable has proven useful, but it treats as identical highly dissimilar cases. For example, the variable would record the same value for two legislatures: the first, in which a single opposition party controls 49 percent of the seats, 10 government parties share 49 percent of the seats, and a remaining government party controls 2 percent of the seats and a second, in which a single government party controls 49 percent of the seats with a coalition partner that controls 2 percent, while 10 opposition parties share 49 percent of the seats.

The data set introduced here allows for the calculation of the traditional measure of - legislative fragmentation. However, our sources also allowed us to identify which parties were inside and outside of the governing coalition. For coalition governments, the sources generally list the party affiliations of cabinet ministers. Otherwise, the country descriptions provided this information. Where the information was not available, for a small number of parties and countries, this is indicated in the DPI. With this information, variables were created to record separately the fragmentation of the government coalition and the opposition. The correlation of the government and opposition fragmentation measures is only 0.267, suggesting that average fragmentation for the entire legislature is likely to be misleading for many purposes. For example, the number of checks (parties in a government coalition) in parliamentary systems cannot be reliably inferred from fragmentation of the whole legislature. Similarly, in investigations of the relative influence of executives and
Policy Research Working Paper 6056

# **Abstract**

This paper provides an overview of the recent performance of the labor market in Vietnam during the Great Recession. The analysis uses data from the Labor Force Survey and the Vietnam Household Living Standard Survey. The author finds that, notwithstanding the global crisis and domestic volatility, job creation has been sustained in Vietnam, especially in the formal sector, but that the overall quality of employment has suffered. Gender differentials are found to affect older women especially, while educated women benefit from a skills wage premium. Reassuringly given the large youth share of the total workforce, the youth labor market is dynamic and outcomes for youths have improved. Meanwhile, participation in poverty alleviation programs and labor market programs has not changed, and few workers use the newly created employment services and unemployment benefits.

This paper is a product of the Poverty Reduction and Economic Management Unit and the Human Development Unit, East Asia and 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://econ.worldbank.org. The author may be contacted at gpierre@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
4

# 1. INTRODUCllON

The existence of reliable, disaggregated information on residential fuel consumption and supply is essential to the formulation of sound household energy strategie-s. Unfortunately, institutions and individuals concerned with data collection and management in many developing countries have often not considered household energy characteristics in the design of their information-gathering exercises. Thus, there is a serious need for carefullydeveloped aad maintained databases on residential fuel demand and supply so that planners and policymakers can identify, quantify and address the key issues relating to household energy. This paper reviews some of the important methodological, theoretical and practical issues that should be considered by organizations which are planning to gather dat;: on domestic energy use. It is based on the experiences of the joint UNDP/World Bank Energy Sectot Managment Assistance Program (ESMAP) in developing household energy strategies, as well as on a review oi the literature and advice from practitioners in the field.' Without comprehensive knowledge of how households acquire and use energy, attempts to develop policies and projects which purportedly seek to improve the welfare of households and/or protect the environment can be futile and even counterproductive. Solid information on fuel prices, quantities consumed and consumer preferences are necessary for interfuel substitution analysis and pricing policies. Knowledge of culinary practices, household decisionmaking on expenditures and stove/oven ownership are crucial in preparing the cooking efficiency component of a demand management program. Data on consumption patterns, biomass supply systems and household attitudes towards the natural resource base are essential to the development of a supply management strategy. Reliance on anecdotal evidence, unrepresentative statistics and theoretical extrapolations to create real policies will amount, at best, to random shots in the dark and, at worst, these shots might hit the wrong targets.

When policies and projects for the residential sub-sector have been implemented in thr nast, the subsequent development and analysis of a household energy database has led to the overturning of irR - a.e preconceptions and false paradigms upon which these real actions had been based. For example, the *ed promotion of
month period (R1); and (4) a recall-based data collection at the plot-level for a 12-month reference period, administered in a single visit (R2).

The paper provides evidence for the survey practitioners to move away from R2, given the significant underestimation of production and yield estimates, and asserts that the D2 households provided the most accurate information on annual cassava production. The latter is shown to be ensured through sustained participation in diary-keeping, which could have been connected to the relatively more valuable set of in-kind incentives these households received to be part of the experiment – though this hypothesis is not testable. However, as shown in Table 9, D2, while being significantly cheaper compared to the traditional diary operation, it is still more resource- and supervision-intensive compared to the recall variants.<sup>36</sup> The D2 unit cost per household is US$330, compared to US$469 for D1, US$186 for R2, and US$157 for R1.P35F<sup>37</sup> Thus, even if D2 may not always be an option for the survey implementing agencies, it would be more feasible if packaged within a broader effort to collect more frequent data through mobile phone calls on a wider range of topics, including but not limited to extended-harvest crop production, following baseline face-to-face interviews. In the case of Malawi NSO, an effort such as the existing Listening to Malawi (L2M) mobile phone survey, as part of the regional Listening to Africa initiative (Dabalen et al., 2016), could provide such platform. In the worst-case scenario, the analysis indicates that R1 is a second-best alternative to D2 and a clear improvement over R2. R1, at the mean, also performs as well as the traditional diary operation, and comes with less than US$30 additional cost per household compared to R2.P36F<sup>38</sup> On the whole, given the contribution of cassava farming to food security and agricultural commercialization
ago as a percent of mean<br>stuntingincidence|12.47%||||||

_Notes_ : Table presents estimates of equation (2) where the dependent variable is the stunting incidence for non-migrant children under the age of 5 between years 2004-2020. See notes to Table 5 for details on the variable construction. Fixed Effects include child’s-birth-year and month, and district fixed effects. Robust standard errors clustered at the district level and are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

# **5. Conclusion**

In this paper, we analyzed the complementarities between natural and human capital using a global analysis of 0.7 million rural non-migrant children across 46 countries. By linking high-resolution forest loss data with individual level health outcomes, our analysis shows that deforestation upstream has significant implications for DD incidence and stunting among children under 5 years old in downstream communities. Moreover, we provide novel evidence that these effects can have long lasting and significant impacts on human capital through stunting. This effect is particularly large if children are exposed to poor water quality in the first thousand days of life. Results for Peru confirm the sign and statistical significance of the result for DD, which is twice as large as the global results. However, the impact on stunting is less pronounced likely due to targeted government and donor health and sanitation campaigns.

Our analysis has both analytical and practical implications. From an analytical point of view, our analysis complements a growing literature on the complementarities between human and natural capital and provides further evidence of the importance of the water channel for short- and long-term health outcomes. On the practical side, our results suggest that maintaining natural capital has the potential to generate meaningful improvements in long-run human capital and that sustainable management of forests should be part of the menu of interventions targeting stunting in rural communities.

The study of the complementarities or substitutability between different forms of capital is a critical
# region.

Figure 7 displays the simulated impacts of AfCFTA on digital goods imports by country member, and for the whole region. The simulation results reveal that imports of digital goods are expected to increase only by 0.3% for the whole region. The small effect of AfCFTA on total digital goods imports of member countries is partly due to the absence of tariff reductions on extra-zone imports of digital goods, which constitute most digital goods imports, and the limited intra-regional trade in these products. Nevertheless, the impacts are higher for some countries in the region, including Zimbabwe, Zambia and Mozambique, which had relatively high tariffs, low imports of digital goods to begin with, and a greater reliance on digital goods imports from the region. Figure 8 depicts the simulated change in the ratio of intraregional digital goods imports to total digital goods imports as a result of AfCFTA. While some low-income countries are expected to experience a noticeable increase in the share of intraregional imports of digital goods, the whole AfCFTA region is expected to observe a minor increase between the ex-ante and ex-post scenarios.

For comparison, in Figure A4 we report the simulated change in total imports due to AfCFTA, drawing once again on the framework of Hoekman, Ng, and Olarreaga (2002), but considering now tariff and trade data on all goods, not just digital goods. We find that AfCFTA is expected to have a larger effect on total goods imports (0.8 percent) than on digital goods imports (0.3 percent). The effects are once again significantly heterogeneous across countries, with Zimbawe, the Central African Republic, Zambia, the Republic of Congo and Mozambique displaying the largest increases.

Besides the quantity of imports, AfCFTA might also be expected to impact the quality of digital goods imports. Since higher income countries tend to export higher-quality products, the moderate increase in the share of digital goods imports sourced from relatively lower-income African countries would be expected to lead to a decrease in the quality of imports. We approximate these expected