rafmacalaba/data-use-mentions-extended huggingface dataset:
of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes.

# 2. Benefits of economic participation of refugees

Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue. But, given the presence of thousands of refugees in the country, the constrained fiscal space the authorities face, and the likelihood that international assistance for refugees will taper in the future, an imminent policy question is how to ensure that refugees can be hosted in a sustainable manner, without becoming a fiscal burden in the future.

The fear that refugees are (or might become) a fiscal burden is driven by a broadly held perspective about forcibly displaced persons in general, and refugees in particular, namely that they are humanitarian subjects, vulnerable and worthy of public assistance (Betts and Collier 2017). This perspective is not universal, however. <mark>The economic contributions of refugees have been extolled for years, from posters</mark>

> 1 https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf.

> 2 Enquête sur la Consommation des ménages et le Secteur Informel au Tchad. The survey was carried out jointly with the National Statistics Office (Institut national de la statistique, des études économiques et démographiques, INSEED) and the UNHCR in Chad.

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_Acknowledgments_ : This paper is the result of a close collaboration with World Bank operational teams, the government of Cˆote d’Ivoire through BCPE, and IRC. We are very grateful to the World Bank team leaders (Steffen Janus and H. A. Wedoud Kamil), BCPE (Hermann Toualy, Adama Bamba, C ́esar G. Toassa, Boubacar Ndiaye, Ismahel A. Barry, Latif Doho, and Germain Kouadio) and IRC (K. Drissa Akou, Hubert K. Kassi, Akouli C. Seri, Mariame Kone, Louis Falcy, Cl ́ement Lorvao and Amaia Bessouet) for their many contributions throughout the program and RCT design and implementation. Horacio Vera Cossio, Eva Lestant and Daniel Corredor Vallejo provided excellent research assistance. We thank Lori Beaman, Christopher Blattman, Bruno Cr ́epon, Rema Hanna, William Parient ́e, Lore Vandewalle, a DIME reviewer, and seminar/conference participants at CREST, CSAE, IZA-WB, IPA, and Development Economics Network Switzerland for their helpful comments. The study was pre-registered in the AEA RCT registry (AEARCTR-0002737) and received IRB 2018-008 from J-PAL Europe. Financial support from the Knowledge for Change Program (KCP) at the World Bank is gratefully acknowledged. DIME analytics verified the computational reproducibility of the results. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the government they represent.

2
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measured by third-party inspectors at landing, I multiply each set-level percentage juvenile value by the ratio of landing-level percentage juvenile to average set-level percentage juvenile.

Finally, I shift the length distribution of each set up or down in half-cm increments until I minimize the absolute difference between the implied percentage juvenile (updated percentage of individuals that are less than 12 cm) and the corrected percentage juvenile. I use the resulting length distribution to calculate the corrected number of individuals caught by each set. The number of juveniles caught by each set is the corrected number of individuals times the corrected percentage juvenile. In Appendix C I describe the construction of these data in detail and demonstrate robustness to the assumptions I make in doing so.

Figure C1 displays the average uncorrected length distribution, the average corrected length distribution, the reported percentage juvenile values, and the corrected percentage juvenile values. If I did not have landings data, I would mismeasure juvenile catch because vessels seem to underreport percentage juvenile in the electronic logbook data. The individuals-weighted average percentage juvenile is 40% lower in the electronic logbook data than in the landings data (11% compared to 18.3%). Vesssels might underreport percentage juvenile to avoid triggering a closure in the area they are fishing.<sup>14</sup> This phenomenon also occurs in other settings where agents may be regulated as a consequence of the data they report, such as industrial plants in India and car owners in Mexico (Duflo et al., 2013; Oliva, 2015). Matching the electronic logbook and landings data preserves the resolution of the electronic logbook data while ensuring that the outcome variable in my main regression—juvenile catch at a given location and time—is not systematically manipulated.

There are 246,914 sets reported by 806 unique vessels in the electronic logbook data. 95% of sets occur within 80 km of the coast (Figure 2). During a fishing season there are 572 sets per day on average. The average set catches 570,103 juvenile anchoveta and
changes in their magnitudes. Therefore, both the main and auxiliary predictions of the model are veri fi ed and the magnitude of the stumbling block effect is similar, as noted in table 5.

As discussed in section 4.1, the data to construct an exact measure to capture the MFN externality is not available. Although we don’t think this seriously biases our results, as previously discussed, we create a proxy for this effect. For each HS-8-digit product, we calculated the share of the top 5 exporters in total exports to the EU in 1994 and 1989—the earliest year when the 8-digit hs data is reported. If a decrease in this share in a given product re fl ects a longer term decrease over the period between 1978 and 1994, then we can use it as a proxy. The model hence predicts a negative coefficient on this variable: products where the share increases have bigger decreases in tariffs.<sup>48</sup> When we include our proxy for the (inverse of) MFN externality we fi nd that it has the expected negative sign but it is insigni fi cant. This could either be because the effect is partially captured by the reciprocity variable, because the proxy is an imperfect one or MFN externalities were not signi fi cant. More importantly, for our purposes both the main and auxiliary predictions of the model are again con fi rmed and the growth effect is also statistically identical to the one in the baseline speci fi cation.

The theoretical model that guides our empirical estimation is parsimonious and therefore it is possible that we have not accounted for some determinants of the EU’s tariff changes. We do not expect that such omitted variables will bias our estimates because, as we argue above, even if they are correlated with the included regressors we instrument, test, and con fi rm the orthogonality of the excluded instruments relative to the error term. Nevertheless, there is one variable that does not appear in the theoretical model that we want to address explicitly
_HOTTER PLANET, HOTTER FACTORIES_ _15_

# **IV. Empirical Strategy**

We estimate a non-linear regression of the log of sales per worker and other measures of firm performance on area-level deviations in temperatures. Our identification strategy exploits the variation in area-level temperatures near the establishment. In a robustness exercise, we narrow the 30km radius of our main mode, to _≈_ 27.5km, which is the minimum grid size for both the temperatures and relative humidity data. The variation in area-level temperatures is plausibly exogenous as they are not the result of the firms’ activities. However, there are various sources of bias that we need to address, the main source arising from potentially omitted variables. In such cases, the deviations in area-level temperatures could be picking up variations in other observed and unobserved area-level factors, which are likely to be correlated with both temperatures and firm productivity. These include area-level sub-national patterns in economic activities, transportation activities, population growth, environmental degradation or restoration such as deforestation or afforestation, and so forth. Some of these factors are time-varying and potentially correlated with temperatures. The variation in these factors could also differ by administrative levels in that some could vary at the city level, while others may vary at the sub-national, country, or continental level. We control for these factors using country, sub-national, and World Bank global region-level fixed effects. We also interact the geographic fixed effects with year-fixed effects.

The basic linear regression model that estimates log sales per worker on deviation in WBT controlling for firm-level characteristics and sub-national, country, and regional factors is given by: where _yijkc,t_ denotes firm-level productivity measured by the log of sales per worker of firm _i_ in area _j_ of sub-national region _k_ , country _c_ , and year _t_ . ∆<sup>_WBT_</sup> _jk,t_ is the deviation in average annual near-surface WBT from the long-term average within a radius of 30km around the establishment (that is, area _j_ ) and sub-national region _k_ , country _c_ and
”trained” we then analyze how well they predict the test data set.<sup>13</sup> Our dependent variable, historical defaults, is sourced from The Bank of Canada (Beers and Mavalwalla (2017)). A default is defined as a late interest payment, an outright payment default, or when creditors incur losses owing to an extension of maturities or a reduction in rates. Using historical defaults helps us avoid having to define a vulnerability indicator and then trying to predict that. We are interested in predicting when a default occurs in time , i.e., when the 0 flips to a 1 in the sample. We test two cases for the timing of defaults, one where default occurs in period _T_ and data are expressed in period _T −_ 1, and the other where both default and data are contemporaneous. The results do not change much when we vary the timing. Admittedly the variables that lead to default might be different than the variables that keep a country in default. We are not separating out these effects and instead code the default data as equal to 1 when in default and 0 otherwise, and not only 1’s when the default occurs. This is partially related to the default data, where default states tend to be very persistent in the sample of countries.

Furthermore, all our data are stacked. We thus have _t_ annual observations from 2008 until 2020 for _k_ = 65 market access countries. We control for within country specific fixed effects via demeaning.

# **4.1 Econometric specification**

The standard logistic regression (with binary response equal to default (1) and no default (0)) can be expressed as: where _x_ is the input vector (i.e., the four indices from the DVA, or the three alternative models).

The parameters are estimated via maximizing the log-likelihood: An additional constraint is added for the Lasso. Lasso regressions are useful for model selection by shrinking coefficients to zero.
. Bendavid E, Boerma T, Akseer N, Langer A, Malembaka EB, Okiro EA, et al. The effects of armed conflict on the health of women and children. The Lancet. 2021;397: 522–532.

8. Armed Conflict Location and Event Data Conflict Index Mid-Year Update. 2023. Available: https://acleddata.com/acled-conflict-index-mid-year-update-2023 9. UNICEF. Progress on drinking water, sanitation and hygiene in Africa 2000–2020: five years into the SDGs. 2022.

10. Okun DA. The value of water supply and sanitation in development: an assessment. Am J Public Health. 1988;78: 1463–1467. doi:10.2105/AJPH.78.11.1463 11. Pickering AJ, Djebbari H, Lopez C, Coulibaly M, Alzua ML. Effect of a community-led sanitation intervention on child diarrhoea and child growth in rural Mali: a clusterrandomised controlled trial. The Lancet Global Health. 2015;3: e701–e711. doi:10.1016/S2214-109X(15)00144-8 12. Whittington D, Radin M, Jeuland M. Evidence-based policy analysis? The strange case of the randomized controlled trials of community-led total sanitation. Oxford Review of Economic Policy. 2020;36: 191–221.

13. Bekele T, Rawstorne P, Rahman B. Effect of water, sanitation and hygiene interventions alone and combined with nutrition on child growth in low and middle income countries: a systematic review and meta-analysis. BMJ Open. 2020;10: e034812. doi:10.1136/bmjopen-2019-034812 14. Wolf J, Hubbard S, Brauer M, Ambelu A, Arnold BF, Bain R, et al. Effectiveness of interventions to improve drinking water, sanitation, and handwashing with soap on risk of diarrhoeal disease in children in low-income and middle-income settings: a systematic review and meta-analysis. The Lancet. 2022;400: 48–59. doi:10.1016/S01406736(22)00937-0 15. Null C
with earlier ones (e.g., Pitt et al. 2006), which showed that microcredit, by helping to improve women’s empowerment, has in turn contributed to the social upliftment of girls in society.

# **3.4** **<u>Intergenerational Mobility</u>**

At an aggregate level, our long panel data also shows intergenerational mobility in terms of human capital investment. Overall, by 2010/11, 69.7 percent of boys and 69.9 percent of girls were 11
We follow the recent literature on SOE performance and use coarsened exact matching techniques to compare the performance of infrastructure firms in the World Bank database with firms the same size in a newly created database of private firms in the same sector (Lazzarini and Musacchio 2018). We compare firms by size, industry, and region, because finding a good private match for an SOE within the same country is hard, because many SOEs are large and usually have market power.

The paper is organized as follows. Section 2 reviews the literature. Section 3 describes the World Bank Infrastructure SOE Database. Section 4 presents the main patterns that emerge from the data. Section 5 presents the results of a matching exercise that compares the performance of SOEs with similar private firms. Section 6 summarizes the paper’s main conclusions.

# **2 Literature Review**

Financial performance in fully owned SOEs is weak, because of failures in monitoring (Shirley and Nellis 1991; Shirley and Walsh 2000), except when governments adopt corporate governance reforms (World Bank 2014; OECD 2018) and/or create holding structures to monitor their SOEs (OECD 2015; Musacchio and Pineda Ayerbe 2019). In the power sector, unbundling functions and allowing independent power producers to sell back their spare production and SOEs to recover costs can improve performance (Foster and Rana 2019).

Most progress in governance, monitoring, and regulating SOEs has been accompanied by partial privatization. The large body of literature on partial privatization finds significant improvements in monitoring, performance, and efficiency as government ownership falls. Most of this research uses fixed-effects regressions that exploit changes in ownership (Gupta 2005; Megginson 2005; Andrés, Foster, and Guasch 2006). Recent work compares the performance of partially privatized SOEs and private firms using matching techniques.

The results have been mixed. Using propensity score matching on a sample of 477 partially privatized SOEs, Lazzarini and Musacchio (2018) find that SOEs outperformed similar private firms. In their study of dozens of fully owned SOEs in Latin
# **1. Introduction**

Low female labor force participation can be an important barrier to economic growth. In Mexico, for example, women’s labor force participation is considerably lower than that of men and is also low in comparison to other countries. The female labor force participation rate was just 45 percent in 2019, compared to 77 percent for men, a gap of 32 percentage points. In the OECD, only Turkey and Italy have a lower rate of female labor force participation, while in LAC only Guatemala has a lower value than Mexico. This has important economic impacts. A recent World Bank analysis suggests that if women participated at the same rate as men, per capita income could be as much as 22 percent higher (Inchauste et al., 2021). The same analysis suggests that if Mexico could implement policies to increase the female labor force participation rate by 0.6 percentage points a year – in line with that observed in Spain, Ireland, and Chile – this would help eliminate the gender gap in labor force participation and increase economic growth by 0.4 percent a year, highlighting how increases in female labor force participation are not just an output of the development process, but a driver of it, as well.

Trends in female labor force participation are determined by several key factors, including the relationship between households’ economic conditions and female participation, the supply and demand for jobs deemed appropriate for more educated women, national growth strategies, and occupational gender segregation (Klasen, 2018). To better understand from a spatial perspective how policy can address these factors, it is useful to understand where pockets of low rates of female labor participation exist. However, most labor force surveys can only provide adequate estimates for larger areas of aggregation. Mexico’s labor force survey is no exception, as it can only provide estimates for urban and rural areas within states, as well as for select larger municipalities. Since Mexico has 32 states and 2,450 municipalities, reliable
Table 4: Decompositions of urban share changes, 1994-2005

|||Total Activity||O|rganized Sect|or|Un|organized Se|ctor|
|---|---|---|---|---|---|---|---|---|---|
||Plants|Employ.|Output|Plants|Employ.|Output|Plants|Employ.|Output|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|<br>A. Sector change in urbanization le|vel using bala|nced panel||||||||
|Urban share start|0.247|0.376|0.615|0.688|0.667|0.620|0.239|0.302|0.565|
|Urban share ending|0.289|0.395|0.528|0.595|0.567|0.514|0.279|0.347|0.589|
|Urban share change|0.042|0.019|-0.087|-0.093|-0.100|-0.106|0.041|0.045|0.024|
|B. Decomposition: Bailey et al. (19|92)|||||||||
|Within-district component|0.045|0.022|-0.074|-0.095|-0.086|-0.091|0.047|0.057|0.010|
|Between-district component|0.030|0.029|-0.001|-0.014|-0.009|-0.001
<!-- Start of picture text --> East Asia & Pacific<br>Cambodia Demographx and Health Survey (DHS) 2014<br>Fiji Population Census 2017<br>Phillipines Model Functioning Survey 2016<br>Samoa Labour Force and School-to-Work Transition Survey 2017<br>Timor Leste Demographx and Health Survey (DHS) 2016<br>Tonga Population Census 2016<br>Labor Force Survey (LFS) 2018<br>Tuvalu Population Census 2017<br>Europe & Central Asia<br>Moldova Population Census 2014<br>Serbia School-to- Work Transition Survey (SWTS) 2015<br>Tajikistan Survey of Water, Sanitation, and Hygiene (WASH) 2016<br>Latin America and Caribbean<br>Costa Rica National Disability Survey 2018<br>Haiti Demographx and Health Survey (DHS) 2016<br>Middle East and North Africa<br>Jordan Population Census 2015<br>South Asia<br>A fphanistan Living Conditions Survey (LCS) 2016<br>Bangladesh Household Income and Expenditure Survey (HIES) 2010, 2016<br>Pakistan Demographx and Health Survey 2017<br>Social and Living Standards Measurement Survey (PSLM) 2010<br>Sub-Saharan Africa<br>Benin Enquete sur la Transition vers la Vie Active (ETVA) 2011<br>Ethiopia Econom and Social Survey (ESS) 2011, 2013, 2015<br>Gambia, The Labor Force Survey (LFS) 2018<br>Lesotho Contmuous Multipurpose Household Survey/ Household Budget Survey 2017<br>Population and Housing Census 2016<br>Libena Core Welfare Indicators Questionnaire Survey (CWIQ) 2010<br>Household Income and Expenditure Survey (HIES) 2014, 2016<br>Makhwi Third Integrated Household Survey (IHS) 2010<br>Maldives Demographic and Health Survey (DHS) 2009<br>Mah Demographx and Health Survey (DHS) 2018<br>Namibia National Household Income and Expenditure Survey (NHIES) 2015<br>Nigeria General Household
10<br>40<br>−15<br>Informal Consumption Share 20 Informal Consumption Slope<br>−20<br>0<br>−25<br>5<br>100<br>80 0<br>−5<br>60<br>−10<br>40<br>−15<br>Informal Consumption Share 20 Informal Consumption Slope<br>−20<br>0<br>−25<br><!-- End of picture text --> This figure is the equivalent of figure 3 for the two alternative assignments of store types to formality. The Probability scenario uses the observed probability of VAT payment by store type in the Mexican census as the formality probability of each store type across countries (see Figure 1). The robustness scenario differs from the central scenario by assigning specialized stores to the informal sector, in addition to maintaining corner stores, non brick and mortar and self-production in the informal sector. Panel (a) and (c) show informal budget shares, on log per capita GDP. Panel (b) and (d) show the the slope of informality Engel curves on log per capita GDP. The bars correspond to the 95% confidence interval of the slope coefficient. GDP per capita is in constant 2010 USD (Source: World Bank WDI).

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