rafmacalaba/datause-encoder-data huggingface dataset:
30. Jones, R. L., Guha-Sapir, D. & Tubeuf, S. Human and economic impacts of natural disasters: can we trust the global data? _Sci. Data_ **9** , 572 (2022).

31. Rice, X. Severe drought causes hunger for 10 million in west Africa. _The Guardian_ (2010).

32. World Bank. Niger Takes Action to Reverse 50 Years of Food Insecurity. _World Bank News_

- https://www.worldbank.org/en/news/feature/2011/09/01/niger-takes-action-to-reverse-50-yearsof-food-insecurity (2011).

33. Munro, T. & Wild, L. As drought hits Ethiopia again, food aid risks breaking resilience. _The Guardian_ (2016).

34. Jeffrey, J. Ethiopia drought: How can we let this happen again? _Al Jazeera_ (2016).

35. Chonghaile, C. N. Malawi floods devastation far worse than first thought. _The Guardian_ (2015).

36. Africa drought fears grip Malawi and Mozambique. _BBC News_ (2016).

37. Gall, M., Borden, K. A. & Cutter, S. L. When Do Losses Count?: Six Fallacies of Natural Hazards Loss Data. _Bull. Am. Meteorol. Soc._ **90** , 799–810 (2009).

38. Moriyama, K., Sasaki, D., Ono, Y., & International Research Institute of Disaster Science (IRIDeS), Tohoku University 468-1-S302 Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-0845, Japan.

- Comparison of Global Databases for Disaster Loss and Damage Data. _J. Disaster Res._ **13** , 1007–1014 (2018).

39. Panwar, V. & Sen, S. Disaster Damage Records of EM-DAT and DesInventar: A Systematic Comparison. _Econ. Disasters Clim. Change_ **4** , 295–317 (2020).

40. Wirtz, A., Kron, W., Löw, P. & Steuer, M. The need for data: natural disasters and the challenges of database management. _Nat. Hazards_ **70** , 135–157 (2014).

41. Osuteye, E., Johnson, C. & Brown, D. The data gap: An analysis of data availability on disaster losses

- in sub-Saharan African cities. _Int. J. Disaster Risk Reduct._ **26** , 24–33 (2017).

19
POLICY RESEARCH WQORKING PAPER 2102

# Summary findings

Maloney uses cross-country data from Latin America and OECD countries to test the predictions of a simple efficiency wage model (Krebs and Maloney 1998) about the share of the workforce in self-employment and the rate of labor turnover across the process of development and demographic transition.

The model is supported, with numerous demographic, economic, and labor market institutions appearing as important determinants of both self-employment and turnover. Social security taxes on firms and barriers to firing workers appear to reduce the size of the formal sector, and barriers to firing do appear to reduce turnover. But the level of formal sector productivity, real interest rates, and education levels generally have a greater impact.

turnover as indicators of distortion or rigidity without first adjusting for these factors. Somewhat speculatively, Maloney offers adjusted measures that suggest that Latin American labor markets are not especially distorted and are about average in flexibility, with important exceptions.

Central to the theoretical framework is the view that self-employment is a desirable destination for many salaried workers rather than the disadvantaged sector of a labor market segmented by union- or governmentinduced rigidities. To prevent the loss of investment in training to the informal sector, firms will pay abovemarket-clearing "efficiency wages," in the process creating unemployment or segmentation that may cut across lines of formality.

A central lesson is that it is misleading to use the size of the informal self-employed sector and the rate of labor This paper -- a product of the Poverty Reduction and Economic Management Unit, Latin America and the Caribbean Region is part of a larger effort in the region to understand the functioning of developing country labor markets and the impact of labor legislation. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Tania Gomez, room 18-102, telephone 202-473-2127, fax 202-522-0054, Internet address tgomez qworldbank.org. Policy Research Working Papers are also posted on the Web at http://ww-w.worldbank.org/html/ dec/Publications/Workpapers/home.html. The author may be contacted at wmaloney@worldbank.org. April 1999 (37 pages).

_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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
**Table 1.A. Literature review of forecasting methods for crude oil prices (continued)**

|**Authors and**<br>**year**|**Crude oil**<br>**prices**|**Methods**|**Main findings**|**Real or**<br>**nominal**<br>**prices**|**In-sample or**<br>**out-of-sample**<br>**forecasts**|
|---|---|---|---|---|---|
|Fernandez<br>(2007)|Arab Gulf<br>Dubai|ARIMA, artificial<br>neural networks,<br>support vector<br>regression|ARIMA model forecasts<br>outperform artificial neural<br>networks and support<br>vector regression<br>approaches only in the<br>short term.|Nominal<br>prices|Out-of-sample<br>forecasts|
|||Time series<br>models, dynamic|NARX model is more|||
|Godarzi et al.<br>(2014)|Crude oil<br>price|Nonlinear Auto<br>Regressive model<br>with eXogenous<br>input (NARX)|accurate than the time<br>series models in predicting<br>oil prices.|Nominal<br>prices|Out-of-sample<br>forecasts|
|He (2018)|WTI|ARIMA, simple<br>exponential<br>smoothing,<br>moving average,<br>support vector<br>regression|Support vector regression<br>and ARIMA models have<br>similar forecasting<br>accuracy.|Nominal<br>prices|Out-of-sample<br>forecasts|
|Jin (2017)|WTI|Futures, no-<br>change,<br>unobserved<br>components model|A futures-based unobserved<br>components model<br>outperforms no-change and<br>futures prices.|Real prices|Out-of-sample<br>forecasts|
||||No-change is inferior to<br>f i f|||
|Kaboudan<br>(2001)|Crude oil<br>price|Genetic<br>programming, no-<br>change|orecasts derved rom<br>genetic programming<br>models but outperforms<br>those from artificial neural<br>networks.|Nominal<br>prices|Out-of-sample<br>forecasts|
|Lalonde, Zhu<br>and Demers<br>(2003)|WTI|SVAR, VAR, AR<br>(1) model, no-<br>change|SVAR models outperform<br>other models in out-of-<br>sample oil price<br>forecasting.|Real prices|Out-of-sample<br>forecasts|
|||Time series,<br>ensemble|Ensemble empirical mode<br>|||
|Li et al.<br>(2018)|Brent;<br>WTI|<br>empirical mode<br>decomposition,<br>sparse Bayesian<br>learning|decomposition with sparse<br>Bayesian learning and<br>addition outperforms other<br>forecasting methodologies.|Nominal<br>prices|Out-of-sample<br>forecasts|

24
time. Redoing these calculations in 2015, the PPP exchange rate was 1.62 Pa’anga per New Zealand Dollar, and a time when the market exchange rate varied from 1.43 to 1.54. As a result, using the market exchange rate would slightly undervalue the gains to migration.

The second adjustment was to put everything into Auckland prices, based on a spatial cost of living index driven by rental cost differences and a housing share of 44 percent. One of the big movements over the 10 years appears to have been a movement of migrants out of the Auckland metro area, towards locations with cheaper living costs (see Figure 2). The proportion of our sample located outside Auckland rose from 14 percent in the first survey in 2005/06 to 39 percent in 2013/14. These are cross-sectional estimates and our samples differ over time but our surveying methods are the same (the figures are for ‘long’ surveys requiring face-to-face interviewing), with the same lead interviewer in all years, and similar resources available for tracing people and traveling to interview them, so we believe that this dispersion reflects a genuine trend.

The final point to note about the movement of the migrants out of Auckland is that the regional destinations overlap with the main locations for Tongan seasonal workers. Just onesixth of the migrants outside of Auckland were found in the two other main cities of Wellington and Hamilton. More were in the Bay of Plenty, Gisborne, and Hawkes Bay, which are all horticultural regions and include two of the main destinations for Tongans under the seasonal worker scheme. It was not the case that PAC migrants had adopted an itinerant lifestyle and moved to regions in search of seasonal work (a multitude of tasks are carried out by seasonal workers so there is no well-defined ‘season’ anyway). Instead, in our follow-up qualitative interviews most said that they only became aware of opportunities in these regions through the experience of the seasonal workers, often mediated by extended family in Tonga. Several of the PAC migrants who moved to the regions also noted that they had been employed in elementary occupations in Auckland, such as factory workers and cleaners, and did not expect 16
# **References**

Aftab, Khalid and Eric Rahim (1988) “’Barriers’ to the Growth of Informal Sector firms: A case study” _The Journal of Development Studies_ 25(4): 490-507 Allcott, Hunt, Allan Collard-Wexler, and Stephen D. O’Connell (2016) “How Do Electricity Shortages Affect Industry? Evidence from India” _American Economic Review_ 106(3): 587-624 Amin, Mohammad and Asif Islam (2015) “Are Large Informal Firms More Productive than the Small Informal Firms? Evidence from Firm-Level Surveys in Africa” _World Development_ 74: 374-385 Alterido, Reyes, Mary Hallward-Driemeier, and Carmen Pages (2011) “Big Constraints to Small Firms’ Growth? Business Environment and Employment Growth across Firms” _Economic Development and Cultural Change_ 59(3): 609-647 Ashraf, Nava, Edward L. Glaeser, Abtaham Holland and Bryce Millett Steinberg (2017). “Water, Health and Wealth” NBER Working Paper 23807 Ashraf, Nava, Edward L. Glaeser and Giacomo A.M. Ponzetto (2016). “Infrastructure, Incentives, and Institutions.” _American Economic Review: Papers and Proceedings_ 106 (5): 77-82.

Bais, Brian, Lucas W. Davis, Stephen W. Salant, William Wilcox (2010). “The Welfare Costs of Unreliable Water Service” _Journal of Development Economics_ 92:1-12.

Bagachwa M.SD. and A. Naho (1995) “Estimating the Second Economy in Tanzania,” _World Development_ 23(8): 1387-1399 Benjamin, Nancy C. and Ahmadou Aly Mbaye (2012), “The Informal Sector, Productivity, and Enforcement in West Africa: A Firm-level Analysis,” _Review of Development Economics_ 16(4): 664-680.

Bogetic, Zeljko, and Olasupo Olusi (2013), “Drivers of Firm-Level Productivity in Russia’s Manufacturing Sector” World Bank Policy Research Working Paper 6572 Chaudhuri, Kausik. Friedrich Schneider, and Sumana Chattopadhyay (2006), “The Size and Development of the Shadow Economy: An Empirical Investigation from States of India,” _Journal of Development Economics_ 80:428-443 Commander, Simon and Kan Svejnar (2011) “Business Environment, Exports, Ownership, and Firm Performance” _Review of Economics and Statistics_ 93(1): 309-337 Coulibaly, Housseini D., and Manuel J. Rodriguez (2004), “Development of Performance Indicators for Small Quebec Drinking Water Utilities,” _Journal of Environmental Management_ 73: 243-255 Davis, Jennifer, Alice Kang, and Jeffrey Vincent (2001) “How Important Is Improved Water Infrastructure to Microenterprises? Evidence from Uganda.” _World Development_ 29, no. 10: 1753–67.

De Giorgi, Giacomo, Mathew Ploenzke, and Aminur Rahman (2017) “Small Firms’ Formalisation: The Stick Treatment” _The Journal of Development Studies_ forthcoming doi.org/10.1080/00220388.2017.1327660 De Soto, Hernando (1989). The Other Path: The Invisible Revolution in the Third World. New York: Harper and Row.

21
<!-- Start of picture text --> Policy Research<br>Country Operations<br>WPS 993<br><!-- End of picture text --> This paper-a product of the Office of the Director, Westem Africa Department-is part of a larger effort in the department to examine the issues of debt, macrostability, and resource flows in the region. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Ofelia Miranda, room J6-255, extension 34877 (October 1992, 78 pages).

part of a larger effort Unlike Latin America, there have been no detailed estimates of capital flight or its determinants in Africa. Ajayi addresses this problem and, using several concepts, provides "bands" or a "range" for capital flight in Nigeria. A significant proportion of capital flight can be estimated from recorded data in the balance of payments and debt statistics - but these estimates are only as good as the data are reliable.

Significant amounts of capital flight, relative **to** external debt, took place between 1970 and 1989. Trade-faking was an important vehicle: exports were underinvoiced to the tune of about US$8. i billion and imports were overinvoiced about US$6.0 billion.

Econometric analysis shows the culprit to be domestic macroeconomic policy - in the form of inflation, exchange rate misalignment, fiscal deficit, and the lack of opportunities for profitable domestic investments - combined with the relative attractiveness of foreign investments.

Eliminating distortions in the economy could minimize substantially extemally held foreign claims and minimize capital flight. Among things that need to be done:

* Ensure that the nation's currency is not overvalued.

* Establish an integrated, unified tariff structure to reduce the rewards for trade-faking.

* Establish fiscal discipline, to maintain macroeconomic stability and reduce inflation.

* Ensure a positive real rate of interesthigh enough to attract funds but not so high as to stifle investment initiatives.

* Adopt a realistic exchange rate determined by market forces.

* Foster attitudinal changes that contribute positively to honest govemment.

**The Policy Research Working PaperSeriesdissemiinates theridingsofworkunder way in theBank. Anrjectiveof the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent of ficial Bank policy.**

**Produced by the Policy Research Dissemination Center**
cents. The universe of blocks (“manzanas”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To ensure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process.<sup>8</sup> As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children’s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communities, which account for 80% of refugees worldwide (Climate Center 2022). We address these all available information on Venezuelan settlements across difficulties, leveraging the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit organization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically.<sup>9</sup> Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found. This led to the of a list process compilation containing approximately 7,000 Venezuelan families with children and adolescents in Medell ́ın. From this sampling frame, families were randomly chosen to participate in our study, ensuring representation across different age, gender, and socioeconomic strata.

_Sample size._ The study covered 2,556 households, gathering information from a total of 3,169 children and adolescents, with 44% being Venezuelan migrants. The sample comprised 834 Colombian children between the ages of 5 and 10, and 944 adolescents aged 11

> 8After identifying the sampling blocks, the field team followed a protocol that (i) randomly identified the first house which they should go. Then they would roll a dice to identify the next house to go. Once a block was finished, the team started sampling the next block at the house across the last house they interviewed.

> 9For more information see: https://immap.org.

16
14 and Roraima.<sup>16</sup> Sample sizes varied somewhat from year to year, around approximately 300,000 individuals per year.

For our wage analysis, we considered all workers aged 15-65 who reported positive earnings during the survey’s reference week. Since we are interested in quantifying the importance of trade and openness-related changes in explaining overall changes in the wage distribution, we include all workers, in agriculture, industry and services, regardless of formality or own-account status. Effective sample sizes for this analysis were 107,195 workers in 1998 and 123,455 workers in 1995. This is an important difference between our analysis and those of Gonzaga et al. (2006), or Pavcnik et al. (2004), who focus exclusively on workers in manufacturing.<sup>17</sup> The wage definition is hourly wages, calculated as a quarter of the monthly wage, divided by the number of hours worked on the average week. All monetary values are inflated to September 2004 prices, using the INPC deflator with the Corseuil and Fogel (2002) adjustment to the 1994 index. See Ferreira, Leite and Litchfield (2006) for the full deflator in each PNAD reference month.

As all PNAD-based studies, we use years of schooling as our measure of a worker’s skill.<sup>18</sup> In earnings regressions, we group workers into nine schooling groups: zero years; 1-3 years; 4 years; 5-7 years; 8 years (completed primary); 9-10 years (some high school); 11 years (completed high school); 12-14 years (some university); 15+ years (completed university). We also use this variable to construct a dichotomous skill indicator, classifying workers with 0-10 years of schooling as unskilled, and those with 11 or more (completed high school and above) as skilled. Earnings regressions were also estimated with an alternative indicator, which classified only workers with 15 years of

> 16 These rural areas broadly correspond to the Amazon rainforest, which was excluded from PNAD sampling until 2003. Census data suggests that these areas account for 2.3% of the Brazilian population. 17 The sample in Pavcnik et al. (2004) is, in addition, based on the Pesquisa Mensal de Emprego (PME) and is thus only representative of the country’s six main metropolitan areas.

> 18 Use of education to define a worker’s skill is in fact common to most household or labor-force surveybased studies. Articles relying on firm-level data typically classify production workers as unskilled, and non-production workers as skilled. Although Slaughter (2000) shows that the two definitions do not seem to lead to very different conclusions in the US, Gonzaga et al. (2006) show that the distinction does matter for Brazil. Like the latter authors, we feel that the education classification is more appropriate in the Brazilian case, since trade liberalization and outsourcing of support activities led to considerable changes in the employment levels of non-productions workers during the period.
1

# Spite and Development

ERNST FEHR, KARLA HOFF AND MAYURESH KSHETRAMADE<sup>∗</sup>

**The disparity in the performance of economies and the persistence of disparate economies through time have not been satisfactorily explained by development economists. ... what has been missing is an understanding of the nature of human coordination and cooperation (Douglas North, 1990, p.11).**

Effective institutions for contract enforcement and collective action are probably among the most important conditions for successful economic and social development. However, in many developing countries these institutions are weak and agreements lacking formal third party enforcement are ubiquitous. Under these conditions, endogenous contract enforcement and the ability of private parties to solve the cooperation and coordination problems inherent in collective action are key. A large body of experimental evidence suggests that social preferences are vital to solving such problems.<sup>1</sup> In particular, the willingness to punish cheaters in informal agreements even at a net cost to the punisher substantially reduces cheating (Fehr, Gachter, and Georg Kirchsteiger 1997), and the willingness to altruistically punish free riders in public goods greatly enhances the scope of private parties to solve collective action problems (Fehr and Gächter 2002). Likewise, many people exhibit a propensity to cooperate conditional on others’ cooperation even when free-riding would maximize their material payoff (Urs Fischbacher, Gachter, and Fehr 2001).

In this paper we document, however, that social preferences may also constitute important obstacles to development. We show that spiteful preferences – the desire to reduce another’s material payoff for the _mere_ purpose of increasing one’s _relative_ payoff – are surprisingly widespread in experiments we conducted in one of the least developed regions in India (Uttar Pradesh). Our results suggest that spitefulness diminishes the propensity to cooperate even in those situations in which mutual cooperation is an equilibrium for selfish players. Finally, we

> ∗ We gratefully acknowledge support from the Research Priority Program on the “Foundations of Human Social Behavior” at the University of Zurich (Fehr) and from the World Bank (Hoff). We thank Sonal Vats for excellent research assistance.

> 1See Elinor Ostrom, James Walker, and Roy Gardner 1992; Ernst Fehr and Simon Gächter 2000; Christopher M.. Anderson and Louis Putterman 2006; Ozgur Gürerk, Bernd Irlenbusch, and Bettina Rockenbach 2006; and Jeffrey Carpenter 2007.