rafmacalaba/data-use-mentions-extended huggingface dataset:
enumerator would select the household next door to that one. If the second replacement was also unsuccessful, the enumerator would go on to the next original household (i.e., 10<sup>th</sup> household). viii LFP rates are from the 2013 Labour Force Survey (Bangladesh Bureau of Statistics, 2015).

ix Our definition of “working” does exclude individuals who were in the labor force but not working at the time of the screener. However, the unemployment rate in Bangladesh is low (4.3 percent in the 2013 Labor Force Survey).

> x The employment types were: Paid employee in government; Paid employee in a private entity; Apprentice/Intern/Trainee; Seasonal worker; Day laborer / casual worker; Domestic worker in a private household; Self-employed / business owner with no employees; Self-employed / business owner employing only paid or unpaid family members; Self-employed / business owner employing some non-family members; and Paid or unpaid family member working in a household business.

xi For women who could not be located, we replaced them with men, since we had already targeted all working women in the roster.

xii In the manuscript, we use the terms “written contract” and “contract” interchangeably. For employees who have a Provident Fund, both employers and employees make equal contributions (based on a percentage of the employee’s salary), and the fund is administered by a board of trustees.

xiii Although the survey enumerators emphasized that the respondents should assume that all other attributes were identical, we cannot completely address concerns about unobserved heterogeneity that may arise because of respondents’ prior beliefs about the correlation between included and non-included attributes; for example, if a respondent believes that working longer hours makes her a more likely candidate to get a contract in future, then our measure of the preference for working hours is picking up some preference for a (future) contract. We thank an anonymous referee for pointing this out.

xiv When we piloted
cognitive and socioemotional and that digital skills are associated to some degree with cognitive and socioemotional skills. Pairwise correlations among those subsets confirm those patterns. Similar patterns emerge for Vietnam, Cambodia, and Thailand.

Our estimations have three main limitations. First, we proxy a skills profile for 20 percent of occupations that are not sufficiently listed in the job postings data set used to create a skills profile. These occupations represent a sizeable share of employment in the four studied countries: from 14 percent in Malaysia to 43 percent in Cambodia. We address this shortcoming by imputing skills profiles for these occupations based on similar occupations. Second, we derive skills use from requirements in job postings, which may omit skills that employers implicitly require rather than explicitly request. Employers do not specify the level of skills proficiency or use, so we equally weight all skills listed in the job posting. As a result, our skills profiles may not be complete, particularly omitting lower-level skills or under- (or over-) weighting the importance of certain skills. Third, we may be over-estimating the degree of digital-ness of the 127 occupations. Since we draw our skills data from on-line jobs vacancy postings, the jobs within each of the 127 occupations that are listed on the internet – and used to construct a skills profile that is a proxy for all jobs within an occupation – may be those that are more inclined to use digital technologies.

Overall, our findings show that all occupations require digital, cognitive, and socioemotional skills, suggesting that policy makers may need to reform the “basic package” provided by education systems in response to digital technologies in the workplace. Education system still need to teach technical and language skills, since those are in very high demand by employers. However, they may also want to rethink the “basic package” of education, namely, to continue teaching cognitive skills, increasing emphasis on socioemotional skills, and adding basic digital skills to the general curriculum. Learning to learn will become increasingly important as new technologies enter the
mitigating disaster and conflict (Coskun et al. 2006). With increasing computer processing speeds and data storage, remote sensing and GIS methods to estimate runoff accommodate increasing complexity (Schultz 1994). Finally, many global models use precipitation data as a primary input, which show considerable variance and lack accuracy, as Fekete et al. (2004) demonstrated in the six monthly datasets they analyzed.

Applications of remote sensing to hydrology have improved dramatically over the past decade. Alsdorf et al. (2007) summarize the capabilities of remote sensing to calculate the spatial and temporal variability of water stored on and near the surface of all continents, illustrating this information can be measured reliably from space. A review of the literature notes several examples of specific basin runoff applications. Scipal et al. (2005) present a Basin Water Index to estimate runoff from the Advanced Scatterometer (ASCAT) ERS microwave sensor, with a 50km spatial resolution over the Zambezi River. Papa et al. (2007) apply a multi-satellite technique including Special Sensor Microwave/Imager (SSM/I) to the Ob river basin, which allows them to account for snow parameters that impact river runoff. Finally, the Basist Wetness Index (BWI), derived from SSM/I observations, is used to estimate surface wetness and runoff (Basist et al. 1998). The BWI takes advantage of the global coverage of the SSM/I satellite to observe most of the land surface twice a day, even under various weather and environmental conditions and time of day, because the wavelength of microwave radiation can penetrate cloud cover, dust, haze and rainfall. These observations are binned into 1/3 arc degree (approximately 30 km<sup>2</sup> ) monthly values.

Characteristics of hydrologic extremes vary and a variety of studies specify the distribution function in the analysis according to sample size, low or high flow, and distribution of the sample. Smakhtin (2001) and WMO (2008) provide a review of low flow hydrology
**Figures 18(a)-(b):  Net Lending by World Bank Since 1970**

<!-- Start of picture text --> (a) Disbursements of IBRD+IDA, % of GDP of Low and (a) Net Disbursements of IDA, % of GDP of<br>Middle Income Countries Low Income Countries<br>1.8<br>0.6<br>Net Disbursements Gross Disbursements 1.6<br>0.5<br>1.4<br>0.4 1.2<br>1<br>0.3<br>0.8<br>0.2 0.6<br>0.4<br>0.1<br>0.2<br>0 0<br>-0.1<br>970 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009<br>1 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Source: Authors‘ calculations based on data from the World Development Indicators.

Already, many loans to middle-income countries are sought after for an embodied knowledge function: why else would China continue to seek project finance from IBRD? It is highly likely that the main function of the WBG will increasingly shift to knowledge—whether that knowledge is produced as a stand-alone service, or bundled within the project preparation and supervision activities of a loan. This is also occurring to some extent in the regional development banks.

The Bank and other multilaterals have a long history of knowledge services bundled into lending activities. The concept of a project design involves Bank staff assessment of the technical and economic viability of the project. For example, Bank staff engineers examine the technical specifications and project economists assess the economic impact – in conjunction with government staff. The first developing country loan proposed
# Conflict and Poverty

Hannes Mueller<sup>1</sup> and Chanon Techasunthornwat<sup>2</sup> JEL codes: D74 I32 Keywords: Conflict; Poverty

> We are grateful to Samuel Freije-Rodriguez, Michael Woolcock, Alexandru Cojocaru, Minh Cong Nguyen, Patrick Barron, and participants of Poverty, Fragility and Conflict Webinar for the support, insightful comments and suggestions.

> 1 Institut d’Analisi Economica (CSIC), Barcelona GSE, CEPR and MOVE. Email: <u>h.mueller.uni@gmail.com</u>

> 2 Universitat Autònoma de Barcelona and Faculty of Economics, Thammasat University. Email: <u>chanon@econ.tu.ac.th</u>

The Poverty and Equity Global Practice| The project is the Poverty and Shard Prosperity Report, code is P172449 | Co-TTL s are Samuel Freije-Rodriguez and Michael Woolcock.
in the city than the “representative” migrant. By using a sampling frame based on a census of work-places, RUMiC avoids this bias.<sup>9</sup> Although RUMiC is designed as a longitudinal survey, the young and mobile nature of the migrant population leads to some attrition between annual survey waves. To maintain sample size, the RUMiC team randomly draws a refreshment sample for each wave, designated as a “New Household Sample.” Thus, the RUMiC survey has two sub-samples in each year: the non-attrited households from the previous year’s sample (“old-households”), and a new representative refresher sample (“new households”) (Meng, 2013). The implementation of the RUMiC surveys normally begins in late March to early April each year, after migrants return from visiting their homes during the Chinese New Year. Because tracking takes time and there is also a need to draw and enumerate refresher samples, the survey typically runs for 6-8 months and is completed by November. This paper uses RUMiC survey data from

> 9For detailed discussion of RUMiC sampling procedure, see Gong et al. (2008).

9
# **1 Introduction**

Limited opportunities for export-led growth and concerns over the unequal allocation of gains from trade have led policymakers and researchers to shift attention towards domestic supply chains and market integration to enhance economic development (Topalova, 2010; Atkin and Donaldson, 2015; Bustos et al., 2020; Grant and Startz, 2022; Goldberg and Reed, 2023). Progress in understanding the structure of domestic supply chains has been facilitated by the increasing availability of granular transaction-level firm network data, which are often sourced from tax records (see e.g. Panigrahi, 2022; Ad ̃ao et al., 2022; Alfaro-Ure ̃na et al., 2022; Boken et al., 2023).<sup>1</sup> These developments reflect a broader trend in the literature on development and structural transformation, where non-traditional novel micro-data such as credit registries, smartphone data, and matched employer-employee data enable new insights into classic questions (Bustos et al., 2020; Blanchard et al., 2021; Dix-Carneiro et al., 2024; Kreindler and Miyauchi, 2023). However, a potential drawback is that the data-generating process underlying such administrative records is often skewed toward particular segments of the economy, e.g. taxpayers in the case of firm-to-firm transaction data. This can leave us with a potentially incomplete view of economic activity due to the size and significance of the informal sector in many low and middle-income economies.

In this paper, we ask how observing only a selected segment of the economy, in our case, formal firms, might bias the inferences drawn from data on firm-to-firm trade networks constructed using tax records. Accounting for this bias is important because the structure of these networks can shape the aggregate and distributional impacts of infrastructure investments, industrial policies, and financial shocks (see e.g. Acemoglu et al., 2012; Liu, 2019; Demir et al., 2022; Balboni et al., 2023; Castro-Vincenzi et al., 2024;
the modeled area effect for each small area. The model was estimated using the fayherriot package in Stata (Halbmeier et al., 2019). The third column of Table A3 presents the coefficients from a post-LASSO OLS regression using the sample data, collapsed to the municipal (area) level. We provide more details on these alternative models in Appendixes C and D.

# 5. Main Results

## _5.1 Bias and uncertainty_

Table 2 displays evaluation metrics for overall bias and uncertainty for the two direct survey estimates (the Horvitz-Thompson approximation and AGEB-clustered variance estimates), the household model, the sub-area model, the area-level model, and the 2010 official poverty estimates produced by CONEVAL. For reference, the bottom row shows the mean poverty estimates for the official 2015 poverty estimates used as the benchmark.

The first column shows the simple average, across municipalities, of the estimated poverty rate prior to benchmarking, while the second and third column of Table 2 report two measures of the uncertainty of the point estimates – the mean estimated mean squared error and the median coefficient of variation. Remarkably, for sampled municipalities, the mean estimated poverty rate from the household model, prior to benchmarking, is only 0.1 percentage points lower than the direct estimate (28.1% vs 28.2%). This reflects the success of the ordered quantile normalization transformation at virtually eliminating overall bias in the household model, despite the slight deviations in the household error term from normality shown in Figure 1. It also suggests that any bias due to omitting household characteristics from the model in this context does not systematically bias the municipality estimates, at least on average. The mean poverty rate for the sub-area model matches the direct estimates to one decimal place, while the area-level model is substantially more downward biased.

There are four other striking findings in the table. The first is the difference in estimated uncertainty between the Horvitz-Thompson and standard clustered estimates of variance. This reflects the high degree of correlation in per capita income across AGEBs
Other factors such as remittance costs and migrants’ vintage also play a role in influencing remittance flows. In a survey of Tongan migrants in New Zealand, Gibson, McKenzie, and Rohorua (2006) find that remittances sent would rise by 0.22 percent if costs fell by 1 percent. Other studies have found that remittances are influenced by interest rate differentials, exchange rate premia (the difference between the official and black market exchange rates), and the duration of migration (Glytsos 1997; El-Sakka and McNabb 1999). Freund and Spatafora (2008) report that recorded remittances depend negatively on transfer costs and the parallel market premium, as migrants may prefer to send money through informal channels when transfer costs are high or when the official exchange rate is unattractive. Some authors argue that the skill composition of migrants matter for remittances, but the evidence of this phenomenon is mixed. While Niimi and Ozden (2006), Faini (2007) and Adams (2009) using cross-country data find that countries that have a larger proportion of high-skilled migrants receive less remittances— perhaps because these migrants are also more likely to settle in the host countries and reunite with their families—studies based on micro-level survey data find the opposite result. Bollard, et al. (2009) using survey data in 11 OECD destination countries find a positive relationship between education levels of migrants and the amounts remitted. Clemens (2009) finds that Nigerian migrant doctors in the United States sent more than $5,000 a year in remittances.

# 4. A simple model for forecasting remittances

While remittances are influenced by all of the above factors, their use in a forecasting exercise is constrained by the lack of reliable forecasts of the future evolution of these explanatory variables. The data on remittance costs are not easy to model, although we know that remittance costs are falling and causing remittance flows to increase. The migrants’ vintage, or the number of years lived in the destination country, is also a plausible determinant of remittance flows to the origin
37 World Bank, _World development report 1999/2000: Entering the 21st Century_ , The World Bank, 1999.

- , _World development report 2009: Reshaping economic geography_ , The World Bank, 2009.

- , “Convergence: Five Critical Steps toward Integrating Lagging and Leading Areas in the Middle East and North Africa,” 2020.

- , “World Development Indicators,” 2021.

- World Cement, _Global Trading Patterns in Cement_ , World Cement, Monday, 29 April 2013., 2013.

- Yang, Dennis Tao and Xiaodong Zhu, “Modernization of agriculture and long-term growth,” _Journal of Monetary Economics_ , 2013, _60_ (3), 367–382.
balloons and other inputs with a climate model, and therefore is less prone to station weather bias (Auffhammer _et al._ , 2013).

To explore the potential impacts of future climate change on poverty and inequality, we use climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This project offers a variety of scenarios, each depicting a different trajectory for global development, energy usage, policy implementation, and climate action, thus allowing a comprehensive assessment of future climate change impacts under diverse conditions. The scenarios we consider include SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Each of these scenarios represent distinct pathways reflecting varying levels of greenhouse gas emissions, socio-economic factors, and environmental developments. For instance, SSP1-1.9 represents a scenario where strong mitigation and sustainable development lead to a temperature rise below 1.4°C by 2100, illustrating a situation where global warming is limited to well below 2°C. In contrast, SSP5-8.5 portrays a scenario of high socio-economic growth coupled with high greenhouse gas emissions, leading to a temperature rise of 5.0°C, representing a trajectory with minimal climate mitigation efforts. By examining these diverse scenarios, we aim to gain nuanced insights into how differing future conditions and policy decisions can impact poverty and inequality, thereby providing a broad perspective on the societal implications of climate change trajectories.

# **B4. Other data**

To examine the role of agriculture as the mechanism, we utilize annual production of four major crops (maize, wheat, soybean, rice) available from Iizumi and Sakai (2020). The dataset records global gridded data of annual crop yields, measured in tonnes/hectare, at 0.5<sup>◦</sup> resolution and covers the period 1982–2015. The dataset was created by combining agricultural census data, satellite remote sensing and information on crop calendar and crop harvested area. Although the data include only four
primary studies and find that natural disasters have a significant negative effect on growth per capita, particularly strong for climatic disasters in developing countries. Felbermayr and Groeschl (2014) propose a new panel dataset and find robust adverse nonlinear effects on economic activity. On the other hand, Skidmore and Toya (2002) and Cuaresma et al. (2008) find an expansionary effect attributable to a productivity boost from new investments and technologies.

Research on how climate change affects the fiscal sector is relatively scarce. Most studies draw on the publicly available cross-country database on disaster losses called Emergency Events Database (EM-DAT). Lis and Nickel (2009) explore the implications of climate change for fiscal policy by assessing the impact of large-scale extreme weather events on changes in public budgets and conclude that the budgetary impact of such events ranges between 0.23 and 1.1 percent of GDP depending on the country group. Melecky and Raddatz (2011) examine the economic and fiscal consequences of a natural disaster for middle and high-income countries over the period 1975 through 2008. They divide the disasters into geological, climate, and a residual category, and find that on average budget deficits increase only after climate disasters, whereas for lower-middle-income countries, the increase in deficits is widespread across all events. Gerling (2017), however, finds that after weather-related disasters government primary balances remain surprisingly stable in a sample of 19 countries in Developing Asia from 1970 to 2015.

Koetsier (2017) applies a panel synthetic control method to compare the difference in government debt between the disaster countries and their respective control groups. His dataset comprises 163 countries for the period between 1971 and 2014 and finds a considerable increase in government debt for the most damaging and deadliest disasters. Government debt, on average, increases by 11.3 percent of GDP compared to the synthetic control group, with a median effect of 6.8 percent of GDP. Feyen et al. (2020) consider a large sample of countries and conclude that the debt of the general government is 2
Norway from the United States, Abramitzky, Boustan, and Eriksson (2019) also show that return migrants to rural Norway back in the late 1800s and early 1900s were more likely to become owner occupier farmers although they came from poorer backgrounds than non-migrants. Similarly, return migrants have been found to be more likely to become entrepreneurs than non-migrants, after accounting for selection in emigration and return migration in Albania (Piracha and Vadean 2010) and Egypt (Mah ́e 2019; Wahba and Zenou 2012; Wahba 2015). A causal relationship between temporary migration and financial investment back home has been estimated using natural experiments in the Philippines (Yang 2006; Yang 2008; Khanna, Theoharides, and Yang 2020), Mexico (Woodruff and Zenteno 2007) and Mozambique (Batista, McIndoe-Calder, and Vicente

> 17Consistent with this evidence, an analysis of a sample of workers from seven developing economies by Giambra and McKenzie (2021) finds that self-employed individuals are also less likely to emigrate.

27
a square rather than a line segment. We compare empirically for each of our four examples the single- and multiple-output results. In three of the four examples, allowing for multiple outputs makes a large difference. Section IV extends the analysis further to allow for exogenous factors that constrain a unit from reaching the frontier. We illustrate our approach of different groups of units having different frontiers on the California schools dataset, using poverty as the stratifying variable. By constructing separate frontiers for school districts with small and large fractions of poor children, we allow for the possibility that the latter are constrained to making do with a lower level of „home inputs‟ in the production of schooling outcomes. It turns out that allowing for separate frontiers makes less of a
*||||||||
|-<br>Microdata|Microdata avail|able|Microdata availa|ble|Microdat|a available both n|ationally|
|not collected|nationally only||internationally o|nly|and inter|nationally||

> 19 WB, IHSN microdata library as well as IPUMS, Eurostat, DHS and MICS data was accessed on 8th August 2021.

13