rafmacalaba/datause-encoder-data huggingface dataset:
industries, as a correlate of skill premiums. The argument is that product differentiation may matter. Perhaps firms in sectors with wide scope for product differentiation can exercise monopoly power, charge higher mark-ups, and perhaps pass-on those profits to their workers. Alternatively, product differentiation itself may require skills.

The calculation of unit values using data from the U.N. Comtrade database is not straightforward and inevitably brings measurement errors. We used three different measures for unit values in order to check for the robustness of the results.

First, in Comtrade, many recorded transactions for a single HS code appear with different quantity codes, making comparison between unit values for a single HS code impossible. To address this concern, for a given HS code, we pooled data from all countries and picked the quantity code that is reported more frequently. For the calculation of unit values, we only considered those transactions that were reported in the “most frequent quantity code,” to make sure that unit values for a given HS code are expressed in the same units across countries. Unit values are then aggregated at the ISIC Rev 3, 2-digit level by taking weighted averages (weights are given by the importance of a given HS code exports on total exports of the corresponding 2-digit ISIC industry). The measure for the dispersion of unit values is the variance of unit values across HS codes within a country and 2-digit ISIC industry.

Second, unit values are highly dispersed, and therefore we used the median unit values (without any weighting) as a second measure of unit values. The corresponding indicator of dispersion is still the variance of unit values.

Third, to account for outliers we trimmed the top and bottom five percent of the observations on unit values. In turn, we calculated the weighted average within countries and 2-digit ISIC industries as in the first approach.

The regression model is similar to equation (7). That is, we regress the skill premium in industry _j_ and country _c_ on the measures of unit values and the variance of unit values plus industry dummies and national characteristics instead of country dummies, namely the log of per capita GDP and the ratio of skilled to unskilled endowments.<sup>11</sup> Our main results are in Table 13. Each panel in the table corresponds to one of the three indicators of unit

> 11These results are not reported for the sake of brevity.

17
The impacts of burley clubs are gauged by using matching methods and instrumental variables estimators. The matching estimator is based on comparing outcomes for similar households in terms of observable characteristics. Our instrument is an indicator of default risk pertaining to a specific group of unmarried women. These individuals, especially younger ones, are risky for tobacco clubs because of a higher probability of marriage. Marriage can cause the bride to leave the village and renege on her debt, especially in the northern and central regions. Marriage can also overturn the farming decisions made by women and lead to default. Last, marriage can increase the risk of pregnancy. These are actually prevalent risks in Malawi. In their written “constitutions,” a large proportion of clubs explicitly prohibits young unmarried women from being members. While many others implicitly adopt this regulation, only a few are completely open to them.<sup>5</sup> The main source of data for our analysis is the tobacco module of the Malawi Integrated Household Survey of 2004/2005, a significant effort of the Malawi Statistical Office (with support from the World Bank) to gather comprehensive household data (World Bank, 2006). The survey collected typical demographic, expenditure, income, health, education, and employment information, together with a tobacco module with detailed data on tobacco farming. To complement these data, we deployed the Tobacco Clubs Survey (TCS) in the summer of 2007. This is a small-scale survey with supplementary information on the rules and regulations as well as on services and benefits of the burley tobacco clubs.<sup>6</sup> We find that the burley clubs are indeed an effective local institution for exports. Tobacco club members are much more productive than non-members—the tobacco club premium in yields (per acre) ranges from 40-74 percent. Members also earn between 45 and 89 percent more (per acre) than non-members via sales. This implies average income gains from burley membership of between 20 to 37 percent. These gains would be equivalent to increases in tobacco prices, for instance due to enhanced foreign market access, lower transportation Alliance (Uganda), Kenya Tea Development Authority, NERICA, New Rice for Africa (rice in Guinea), JIB (barley in the Jos Plateau in Nigeria).

> 5 Other reasons to bar unmarried women are land and labor supply constraints. See Section 3 for a thorough discussion on these issues.

> 6See Udry (2003) for a discussion on the role of complementary surveys in research on institutions in Africa.

3
# Appendix Figure 4: Local Inequality and Probability of FISE Project Receipt

<!-- Start of picture text --> .65<br>.6<br>.55<br>.5<br>.45<br>.05 .075 .1 .125 .15<br>expenditure share of top 1%<br>probability of any FISE project receipt<br><!-- End of picture text --> The results are obtained using the PLREG (partial linear regression) command in STATA, linearly controlling for all the explanatory variables used in the selection equation of the probit with selection model (Table 7).

34
_Table 3: Summary of investment scenarios_

|||||**Murrumbala**|||||
|---|---|---|---|---|---|---|---|---|
|||**Paving**|**Gravel**|**Partial Rehab**|**Bridge**|**Culverts**|**Initial**<br>**Capital**<br>**Cost**|**Life-**<br>**Cycle**<br>**Cost**<br>**(Median)**|
|**Investment 1**|RoadID|N322<br>(partial<br>–<br>45km)||N322 (rest),<br>R652, R1107,<br>R650 (rest),<br>R1110, R1109,<br>NC|||||
||Length|45|0|551|0|0|15|18|
|**Investment 2**|RoadID|R652<br>(partial<br>–<br>60km)||N322, R652<br>(rest), R1107,<br>R650, R1110,<br>R1109, NC|||||
||Length|66|0|530|0|0|15|19|
|**Investment 3**|RoadID|||N322, R652,<br>R1107, R650,<br>R1110, R1109,<br>NC|N322,<br>R652,<br>R1107,<br>R650,<br>R1110,<br>R1109,<br>NC|N322,<br>R652,<br>R1107,<br>R650,<br>R1110,<br>R1109,<br>NC|||
||Length|0|0|596|13|17|22|24|
|**Investment 4**|RoadID||R650 (partial<br>– 150km) and<br>R652 (partial<br>– 60km)|N322, R652<br>(rest), R1107,<br>R650 (rest),<br>R1110, R1109,<br>NC|||||
||Length|0|213|383|0|0|11|19|
|**Investment 5**|RoadID||N322, R652<br>(rest), R1107,<br>R650 (rest),<br>R1110,<br>R1109, NC|R650 (partial –<br>150km) and<br>R652 (partial –<br>60km)|||||
||Length|0|383|213|0|0|18|29|

We calculated the net present value for each of the five investment combinations in 2,000 scenarios following equation 6 above.

20
9 number of “migrants” who were actually born, raised and educated in that OECD (Europe) country. Another key difference between the OECD (Europe) data and the United States data has to do with the number of labor-exporting countries recorded. While the U.S. Census data can be used to count the number of “foreign-born” (or migrants) from about 50 different countries, the OECD (Europe) data only record the number of “foreign-born” (or migrants) in each European country coming from ten or fifteen countries. While this is not a significant problem for large-labor exporting countries (like Turkey), which send many migrants to Europe, it is a problem for smaller labor-exporting countries, like Brazil or Sri Lanka, where the actual number of migrants to any particular European country might not be recorded at all.

The final step in calculating the migration variable is to take the sum of the “foreign born” from each labor-exporting country that are living in either the United States or the OECD (Europe), and divide this sum by the population of each developing country. These “migration as share of population” figures are the ones which appear in Annex Table A1. In all likelihood, these figures seriously under-estimate the actual number of international migrants produced by any given labor-exporting country, because they do not include the large number of illegal migrants working in the United States and OECD (Europe). These figures also do not count the unknown number of international migrants working in other labor-receiving regions (like the Arab Gulf).

The process of calculating the remittances variable in Annex Table A1 is more straight-forward, but it also involves one heroic assumption. All remittance data comes from the IMF, Balance of Payments Statistics Yearbook. As noted above, the main problem with these data is that they count only remittance monies which enter through
Table 2: Refugee Waves and Attitudes Toward Immigrants

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||||+ Never treated||
|VARIABLES|Event FE only|+ Controls|+ Year FE|regions|Country*Year FE|
|Post-event: _≥_10,000 increase|0.008|0.014|0.031|-0.005|0.004|
||(0.016)|(0.023)|(0.025)|(0.020)|(0.020)|
|IHS refugee population||-0.001|-0.003|0.006|0.002|
|||(0.005)|(0.005)|(0.004)|(0.004)|
|IHS region population||-0.045|0.016|-0.030|-0.004|
|||(0.209)|(0.266)|(0.128)|(0.176)|
|Age||0.001|0.001|0.000|0.001|
|||(0.001)|(0.001)|(0.001)|(0.001)|
|Age<sup>2</sup>||-0.000|-0.000|-0.000|-0.000|
|||(0.000)|(0.000)|(0.000)|(0.000)|
|Male||0.016**|0.017**|0.004|0.004|
|||(0.007)|(0.007)|(0.005)|(0.005)|
|Completed secondary education||0.021**|0.020**|0.019***|0.017***|
|||(0.010)|(0.010)|(0.007)|(0.006)|
|Completed college education||0.060***|0.059***|0.050***|0.048***|
|||(0.013)|(0.013)|(0.011)|(0.011)|
|Lives in small town||0.017|0.013|0.044***|0.044***|
|||(0.015)|(0.014)|(0.011)|(0.010)|
|Lives in suburb of large city||0.066***|0.067***|0.084***|0.081***|
|||(0.017)|(0.017)|(0.015)|(0.014)|
|Lives in large city||0.063***|0.065***|0.069***|0.067***|
|||(0.016)|(0.016)|(0.011)|(0.011)|
|Constant|0.605***|1.214|0.297|0.968|0.610|
||(0.007)|(3.193)|(4.100)|(1.896)|(2.611)|
|Observations|71,313|71,313|71,313|216,051|216,051|
|R-squared|0.092|0.095|0.098|0.130|0.142|
|Event FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|No|Yes|Yes|No|
|Country*Year FE|No|No|No|No|Yes|
|Never treated Regions|No|No|No|Yes|Yes|
|Dep Var Mean|0.609|0.609|0.609|0.614|0.614|
|Events|113|113|113|113|113|
|Years|12|12|12|12|12|
|Regions|101|101|101|600|600|
|Countries|35|35|35|35|35|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the attitudes towards immigrants question, equal to 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?”, 0 for disagreement, and missing for blanks or refusals. Regional controls for the inverse hyperbolic sine transformation of region population and refugee population at the year of the event, and individual controls for age, age squared, sex, educational level and city size were used. The sample includes individual respondents to the Gallup World Poll in region-years (columns 1, 2, and 3) and countryyears (columns 4 and 5) who were surveyed within 4 years before or after an event. An event is defined as a region with an increase of at least 10,000 refugees in a calendar year. Results use the Gallup sampling weights and standard errors are clustered at the event level.

a recent literature has shown that the TWFE estimator problematic in the pres22
6. Natural language processing has advanced to real time, multi-lingual simultaneous translation... combined with AR, the technology can create virtual, global workplaces

# Sensors and communication technologies

7. Most consumer electronics and electric devices, and virtually all industrial machinery and equipment is built with sensor for Industrial Internet of Things (IIOT) connectivity and application, from power plants, water sanitation plants, to traffic lights and vehicles, to HVAC systems 8. Communication protocols that leverage bandwidth at speeds faster than 2018/2020 5G standards are available; 5G is the general minimum standard for true, real time IOT connectivity. (Given the population density and tower density needed to make 5G economic, 5G is not expected to be quickly deployed in rural areas.) 9. Low-earth orbit (LEO) communication satellites are commercially available, enabling extremely low latency two-way communications, at virtually every spot on the globe 10. LEO data collection satellites are common, allowing image collection and analysis if virtually the entire planet

# Manufacturing technologies

11. Additive manufacturing (3DP) is able to produce stable components at 2-3X current size and 10X its current speed 12. Mass production using 3DP allows dramatic light-weighting of vehicles and aircraft 13. 3DP is commonly employed to enable novel designs to improve energy efficiency of power plants and building materials 14. 3DP is readily commercially available for use in remote areas such as mines, and in hard-to-service areas 15. 3DP of parts of embedded chips and circuits are in most equipment and available for easy physical update 16. Advanced mfg techniques are commonly used in tandem, including 3DP, digital-enabled rapid prototyping, robotics systems trained by reinforcement learning or similar AI, virtual reality and augmented reality (VR/AR), flexible line machinery, and fluid “platform-based” supply chains

# Transportation

17. Electric Vehicles (EVs) are as available - or more available than - internal combustion engine (ICE) from all the major car manufacturers, globally 18. EV batteries are mass produced and sufficiently inexpensive to make EV cost fully on par with ICE cost 19. EV battery technology has evolved to a degree where driving range is equivalent to that of an ICE 20. Fully autonomous vehicles are readily available (see #1) 21. Much like pre-installed software on computers, cars can be sold as “MaaS-ready” with the ability to immediately integrate into a regional transportation network 22. Hydrogen vehicles are slightly more commonplace than in 2018 30
Peeples, L. (2020) News Feature: How air pollution threatens brain health. _Proceedings of the National Academy of Sciences (PNAS)._ June 23, 2020. 117(25): 13856-13860 Peters, R., Ee, N., Peters, J., Booth, A., Mudway, I., Anstey, K. (2019). Air pollution and dementia: a systematic review. _Journal of Alzheimer's Disease._ Vol. 70. 145-63.

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- Rao, N.D., Kiesewetter, G., Min, J., Pachauri, S., Wagner, F. (2021). Household contributions to and impacts from air pollution in India. Nature Sustainability. Vol. 4: 859–867

- Rentschler, J., Salhab, M. (2020). People in Harm’s Way Flood Exposure and Poverty in 189 Countries. _World Bank Policy Research Paper Series_ . WPS 9447

- Smith, A., Bates, P., Wing, O., Sampson, C., Quinn, N., Neal, J. (2019) New estimates of flood exposure in developing countries using high-resolution population data. _Nature Communications._ Vol. 10: 1814

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- 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

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

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# Moldova: A Stor of U ward Economic Mobilit <u>y p y</u><sup>1</sup>

María E. Dávalos and Moritz Meyer World Bank Keywords: poverty and inequality analysis, (socio) economic mobility, synthetic panel methodology JEL classification: C2, I3, O1

> 1 This study was prepared by María E. Dávalos (Senior Economist, Poverty GP, Europe and Central Asia) and Moritz Meyer (Economist, Poverty GP, Europe and Central Asia), under the guidance of Carolina Sánchez Páramo (Practice Manager, Poverty GP, Europe and Central Asia). The study is financially supported by the TFESSD under the program P127607. We thank Hai Ahn Dang, Peter Lanjouw, Ruslan Piontkivsky, Marcel Christuga, Mame Fatou Diagne and Elizaveta Perova for their comments and guidance.
In terms of implementing (3) one should note that _Vm_ is given by the storm track data described below, _Vh_ can be directly calculated by following the storm’s movements between locations along its track, and _R_ and _T_ are calculated relative to the point of interest _i_ . All other parameters have to be estimated or assumed. For instance, we have no information on the gust wind factor _G_ , but a number of studies (e.g. Paulsen and Schroeder, 2005) have measured _G_ to be around 1.5, and we also use this value. For _S_ we follow Boose et al. (2004) and assume it to be 1. While we also do not know the surface friction to directly determine _F_ , Vickery et al. (2009) note that in open water the reduction factor is about 0.7 and reduces by 14% on the coast and 28% further 50 km inland. We thus adopt a reduction factor that linearly decreases within this range as we consider points _i_ further inland from the coast. Finally, to determine _B_ we employ Holland’s (2008) approximation method, whereas we use the parametric model estimated by Xiao et al. (2009) to estimate _Rmax_ .

We used the local wind speeds to calculate the damage index of (1) and list in Table 2 those storms that produced a non-zero value in the Dominican Republic over the sample period 1992 to 2013 in Table 1. As can be seen, there were a total of 19 damaging storms according to this damage index. Of these Hurricane Georges [1998] was the most destructive, causing an average damage of 29 percent. This was followed by Floyd [1999], Irene [2011] and Ike [2008]. The remaining 15 storms, while sometimes damaging in some areas of the island, overall produced relatively little destruction.

# **Section IV: Econometric Specification and Results**

To measure the impact of tropical cyclones on local (logged) nightlight intensity the following specification is estimated: 11
_"Our understanding of the links of 1.5°C and 2°C of global warming to human migration is limited and represents an important knowledge gap.”_ Special Report of the Intergovernmental Panel on Climate Change (Hoegh-Guldberg et al., 2018)

# **1. Introduction**

The field of climate migration faces a paradox: climate mobility is increasingly under the media and political spotlights, yet scientific evidence on this phenomenon is mixed and inconclusive, resulting in a lack of clear-cut policy prescriptions. For instance: who are the climate migrants? To date, this apparently simple question lacks a definitive answer.<sup>1</sup> An even deeper layer of uncertainty envelops the mediating role played by rural development and adaptation policies: will they increase climate-induced mobility—by relaxing financial liquidity constraints that prevent people from leaving—or decrease it by enabling on-farm adaptation, thus making migration unnecessary? Answering such questions requires a deep understanding of farmers’ mobility dynamics under climate risk and of the transmission mechanisms that allow identification and targeting of different groups. While a better understanding of the dynamic relationship between local adaptation and migration is a central issue, there has been little research on the matter, resulting in a need for joint modeling of different mobility and adaptation outcomes (Cattaneo et al., 2019). In this work, we propose new ways to uncover the relationship between climate shocks, (im)mobility, and adaptation, and, in turn, inform policy making.

In a recent review, Letta et al. (2023) find that household responses to climate-related shocks are irreducibly heterogeneous, context-dependent, and driven by a host of socio-economic channels, to the extent that, depending on the role played by these intervening factors, a weather anomaly can equally lead either to an increase or to a decrease in household mobility. This implies that

> 1 This is particularly relevant for policy making as the concept of ‘climate migrant’ breaks the standard dichotomy between economic migrants and refugees from both a normative and a legal point of view. In this regard, the World Bank’s _World Development Report 2023_ acknowledges that there is no clear-cut distinction between economic migrants and refugees, but rather a continuum of complex patterns of movements (or lack thereof) corresponding to varying degrees of protection needs (World Bank, 2023).

2
# **Orphans and Ebola: Estimating the Secondary Impact of a Public Health Crisis**

David K. Evans World Bank Anna Popova World Bank Keywords: Ebola, orphans, epidemics, health economics JEL Codes: I15, O15, J13 Acknowledgments: This work benefited from valuable comments from Kathleen Beegle, Mapi Buitano, Jed Friedman, Camila Galindo Pardo, Markus Goldstein, and Eleni Yitbarek.