rafmacalaba/datause-encoder-data huggingface dataset:
It is possible to omit some destinations, international or local, to overcome data collection issues or other empirical problems. Overall, the ability to ease a pervasive model-selection problem and circumvent potentially prohibitive data constraints help to achieve a more complete inference about the welfare impact of wars.

To show these points formally, we first set up a general discrete choice framework with random utility, where agents choose among a finite number of locations. This decision is guided by (i) location-specific fixed utility common to all agents in a given location, (ii) an individual-specific utility for each location, and (iii) bilateral mobility costs associated with migration. For generality, the framework remains agnostic about the components of location-specific fixed utility, which can include wages, local amenities and, potentially, expected future utility, among others. We do not impose any strong assumptions on whether the people are myopic or forward-looking, how much they discount the future outcomes, or how they form their expectations about the future after conflict. Then, we derive sufficient statistics equations and mark the lower-bound estimates for the welfare impact of a war.

To estimate the main parameters of the model, we consider a case study: the conflict in Eastern Ukraine (i.e., the Donbas Region, covering Donetsk and Luhansk oblasts) between 2014 and 2019<sup>1</sup> . Specifically, we estimate the inter-region migration elasticity parameter using flows among Ukrainian regions prior to the conflict in 2014. This estimate is then used to back out the welfare impact of the war in Donbas as revealed by the flows of about 1.4 million internally displaced people (IDPs) after 2014. Our data includes yearly regional statistics and bilateral gross migration flows from the State Statistic Services (UkrStat) for 2008-2012 and IDP numbers from the Ukrainian Ministry of Social Policy for 2014-2019.

Our empirical analysis yields a migration elasticity parameter between 0.458 and 0.682 depending on the agents’ risk aversion and time preferences. Using these estimates, and following an inversion equation similar to Hotz and Miller, 1993, we then map moving probabilities onto welfare, and compute the welfare shocks implied by the post-conflict migration outflows from Eastern Ukraine. Finally, following the literature, we compute the equivalent income losses implied by these welfare shocks. Specifically, we answer the following question: what would be the rate of income loss that makes an average individual equally worse off as the conflict? Our estimates for Donetsk oblast show a 7.28 to 24.79 percent equivalent life-time income loss, depending on time preferences, vis-a-vis the average pre-conflict income when agents are risk neutral. This range is equivalent to 27.72 to 38.06 percent income-loss for a duration of 10 years. The magnitude of welfare loss is similar for

> 1Note that the data used in this paper covers a period before the war in 2022, which was not foreseen at the time of the analysis.

3
country-level and project-level factors that are associated with these outcomes. We discuss these factors next.

# _Country-Level Correlates of Project Performance_

We consider a small set of core country-level variables that have been identified in the literature as important correlates of project outcomes. We first include the logarithm of one plus the inflation rate as a crude proxy for macroeconomic stability ( _LNINFAV_ ). We also measure the quality of countrylevel policies and institutions using the Country Policy and Institutional Assessment (CPIA), ratings of the World Bank ( _CPIAAV_ ).<sup>6</sup> These variables have been emphasized in several earlier papers (notably Isham and Kaufmann (1999) and Dollar and Levin (2005)), and are intended to capture the effect of countrylevel policies and institutions on project performance. We also include real per capita GDP growth as a crude proxy for macroeconomic shocks ( _DYAV_ ). Finally, we consider the role of civil liberties and political rights as country-level correlates of project performance, as emphasized by Isham, Kaufmann and Pritchett (1997), for the particular economic rate of return outcome measure. We measure these using the sum of the Freedom House scores of civil liberties and political rights ( _FRHAV)_ .

Our unit of observation is a project, for which the execution and implementation typically lasts several years – the median length of a project in our sample is 6 years and 10 percent of projects last 9 years or more. For each project, we calculate the annual average of each of these country-level correlates of performance over the life of the project (from approval to completion). We will use these project-life averages as explanatory variables for project performance in the empirical work below.

# _Project-Level Correlates of Project Performance_

Our first set of project-level variables captures basic project characteristics. We use the logarithm (in millions USD) of the total amount of World Bank lending committed to each project as a basic measure of size ( _LNSIZE_ ). Larger projects tend to be complex, with multiple components, tranches and counterparts who implement individual components, all of which adds complexity to project implementation. We also have information on the start and end dates of each project, which

> 6 A potential concern regarding the CPIA measure is that it also is scored by Bank staff, leading to a possible mechanical relationship between country-level CPIA scores and project-level outcome ratings.  This however is less of a concern given that we average all the country-level variables over the life of the project, while the outcome is measured at or after the end of the project.  In any case, as a robustness check we also used the Worldwide Governance Indicators Rule of Law measure (www.govindicators.org) and found very similar results (not reported for reasons of space).

10
Borin, A., and Mancini, M. 2015. “Follow the Value Added: Bilateral Gross Export Accounting,” Economic Working Papers, Bank of Italy.

Borin, A., and Mancini, M. 2019. “Measuring What Matters in Global Value Chains and ValueAdded Trade,”Policy Research Paper 8804, World Bank, Washington, DC.

Broda, C., Limao, N., and Weinstein, D. 2008. “Optimal Tari¤s and Market Power: The Evidence,” American Economic Review 98(5): 2032-2065.

Brooks, D., and Ferrarini, B. 2012. “Vertical Gravity,”ADB Working Paper 303, Manila, Philippines.

Buelens, C., and Tirpak, M. 2017. “Reading the Footprints: How Foreign Investors Shape Countries’Participation in Global Value Chains,” Comparative Economic Studies 59(4): 561-584.

Caliendo, L. and Parro, F. 2015. “Estimates of the Trade and Welfare E¤ects of NAFTA,” The Review of Economic Studies, 82(1): 1-44.

Cheng, K., Rehman, S., Seneviratne, D., and Zhang, S. 2015. “Reaping the Bene...ts from Global Value Chains,”IMF Working Paper WP/15/204. Washington, DC: International Monetary Fund.

Chor, D. 2010. “Unpacking Sources of Comparative Advantage: A Quantitative Approach,”Journal of International Economics 82(2): 152-167.

Christ, N., and Ferrantino, M. 2011. “Land Transport for Exports: The E¤ects of Cost, Time, and Uncertainty in Sub-Saharan Africa,” World Development 39(10): 1749–1759.

Costinot, A. 2009. “On the Origins of Comparative Advantage,” Journal of International Economics 77: 255-264.

Desai, M., Foley, C., and Hines Jr, J. 2004. “Foreign Direct Investment in a World of Multiple Taxes,” Journal of Public Economics 88(12): 2727-2744.

Djankov, S., Freund, C., and Pham, C. 2010. “Trading on Time,” Review of Economics and Statistics 92(1): 166-173.

Eaton, J., and Kortum, S. 2002. “Technology, Geography, and Trade,”Econometrica 70(5): 17411779.

Ekholm, K., Forslid, R., and Markusen, J. 2007. “Export-Platform Foreign Direct Investment,” Journal of the European Economic Association 5(4): 776-795.

Feenstra, R. 1998. “Integration of Trade and Disintegration of Production in the Global Economy,” Journal of Economic Perspectives 12(4): 31-50.

Feenstra, R., and Hanson, G. 1997. “Foreign Direct Investment and Relative Wages: Evidence From Mexico’s Maquiladoras,” Journal of International Economics 42(3/4): 371-393.

Fernandes, A., Forero, A., Maemir, H., and Mattoo, A. 2019a. “Are Trade Preferences a Panacea? The African Growth and Opportunity Act and African Exports,”Policy Research Working Paper 8753, World Bank, Washington, DC.

28
Finally, in Panel D of Table 8 I revisit a potential source of heterogeneity in multipliers discussed in Kraay (2012), based on cross-country differences in the extent to which government spending is aidfinanced. An important component of the neoclassical mechanism for a positive government spending multiplier operates through wealth effects: when spending increases, private agents are poorer by the present value of the current and future tax obligations required to finance the increase in spending. According to this mechanism, private agents react by consuming less, investing more, and providing more labour input, so that output increases. The key point here is that the magnitude of this wealth effect depends on the size of the burden of current and future tax obligations associated with the increase in government spending. This burden is smaller in countries where a greater proportion of government spending is financed by aid flows from abroad that do not need to be repaid, and as a result, the neoclassical mechanism predicts a smaller spending multiplier in more aid-dependent countries.

To investigate this possibility in this large set of developing countries, I divide each sample in half at the median level of official development assistance (ODA) as a fraction of GDP, and then estimate spending multipliers separately in the two subsamples. The OLS estimates of the multiplier display the pattern suggested by the theory, and are consistently larger in the low-aid sample. However, the differences are not large. Turning to the 2SLS estimates, in the IDA sample of countries I do again find the same pattern, with a larger multiplier in the less aid-dependent subsample of these IDA countries (0.59 versus 0.43 in the more aid-dependent subsample). Unfortunately, however, in the full and high disbursement samples of countries, identification is extremely weak in the less aid-dependent groups. As a result, the multipliers are very imprecisely estimated, to the point of being meaningless, and it is not possible to draw any conclusion about differences in multipliers by the degree of aid dependence.

Summing up, the novel DRS loan-level data on which the instrument developed in this paper is based covers a very large set of developing countries. This makes it feasible to investigate a range of plausible hypotheses regarding various potential sources of heterogeneity in government spending multipliers in developing countries. This section has provided some suggestive (although not statistically significant) evidence that the short-run effects of government spending do vary with the state of the business cycle, the degree of trade openness, the exchange rate regime, and (to a limited extent) with sample. Consistent with the predictions of the Mundell-Fleming model with capital mobility, they find that multipliers are higher under fixed exchange rate regimes in their sample of countries. They also find higher multipliers in countries that are more open to trade, as I do here.

27
# Forced Displacement, Gender, and Livelihoods: Refugees in Ethiopia

## Yeshwas Admasu

The World Bank, Washington D.C. (E-mail: ayeshwas@gmail.com)

### _Acknowledgments_ **_:_**

The author is grateful for the guidance and comments received from Amalia Hadas Rubin, Diana Jimena Arango, Jeni Klugman, and Lucia Hanmer at the World Bank. We gratefully acknowledge Christian Wieser, Matthew Stephens and Mattias Lundberg for their comments and suggestions. We also thank participants of the Gender Dimension of Forced Displacement workshop. All errors remain our own.

### _Funding Disclaimer_ **_:_**

This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

### _Data availability statement:_

The data used in this paper is publicly available at the World Bank Microdata Library at - <u>https://microdata.worldbank.org/index.php/catalog/3445/study description</u>

### _Disclosure statement:_

There is no conflict of interest.
and fertilizer.<sup>26</sup> This is the most important substantive result which indicates that agriculture is capital-cost-intensive. The elasticity of fertilizers, 0.13, is considered to be high for several reasons. First, we deal with the aggregate agricultural production function, whereas fertilizer is used only on plant products. Thus the corresponding elasticity related to the plant products should be higher than that obtained for the aggregate product. Second, note that the dependent variable is value added, and in a competitive economy the elasticity of fertilizer, whose cost is allowed for in the computation of value added, should be nearly zero (an outcome of the envelope theorem). A higher value for the fertilizer elasticity is likely to signal constraints on the supply of fertilizer causing the shadow price to exceed the official price used in the national accounts to compute the cost of fertilizer. The labor elasticity appears low, and we return to this subject later on. In Mundlak, Larson, and Butzer (1999), the early literature on cross-country studies was reviewed, and it stands out that our results differ from those reported in that literature. In part, it may be due to the fact that we use a complete capital series, which was absent from the other studies, and in part it is due to the fact that we use the within-time-country estimates, whereas the reported results are mostly cross-country in nature and resemble the between-country regression in the present study.

Turning to the between regressions, we note that the values of R<sup>2</sup> are by far higher than that of the within equation, and it is particularly high for the time-series component as given in the between-time regression. The sum of the capital elasticities is 0.85 in the between-time regression and 0.32 in the between-country regression. The between-time elasticity is particularly high, and this suggests that the pace of the implementation of changes in the available technology was strongly constrained by the level of the capital stock in agriculture. Similarly, the land coefficient in the between-time regression is high, but its t-score is low. A high value for the land coefficient suggests an increase in the shadow price of land associated with the increase in productivity, while at the same time there was little increase in the time series of agricultural area (Table 1).

> 26 These values are of the same order of magnitude obtained in an earlier study (Mundlak, Larson, and Butzer 1999) for a different sample of countries and different time period.

19
# **Exports and Women Workers in Formal Firms**

**By: Mohammad Amin* and Asif M. Islam****

Keywords: Trade, Exports, Gender, Women workers, Globalization JEL: F15, F16, J21, F66 We would like to thank Norman Loayza, Jorge Luis Rodriguez Meza and participants at a seminar organized by Enterprise Surveys, World Bank for providing helpful comments. All remaining errors are our own.

* Corresponding author. Enterprise Analysis Unit, World Bank Country Office in Kuala Lumpur, Malaysia and Washington DC, USA. Email: mamin@worldbank.org. Phone: +1 202-412-5723. ** Office of the Chief Economist, Middle East and North Africa Region, World Bank, Washington DC. Email: aislam@worldbank.org. Phone: 202-458- 5246.
there is no significant heterogeneity, but for the export commodity price index there is significant heterogeneity, with the association being much stronger for primary and consumption goods.

Results for alternative measures of import concentration. To complement our baseline results using HHI, we present in Table 2 estimates of Equation 1 using as dependent variables alternative import market concentration measures. The results show that commodity exports result in higher concentration of imports in the top firm (column 1) or the top four firms (column 2). Focusing on different extensive margins of imports, the evidence implies that countries more dependent on commodity exports have significantly lower numbers of importing firms (column 3) and import sources (column 4). As a manifestation of import concentration, we consider unit values per market and find them to be higher for countries with higher commodity export shares or price indexes (column 5). Finally, higher commodity export quantities or prices may increase the import market share of firms that are also commodity exporters, and thus import concentration, though the effect is not statistically significant (column 6).

# 4 Trade policy mechanisms for import monopolization

Figure 1b showed that commodity export dependent economies have greater trade protection. There are multiple potential reasons for this, including industrial policy intended to develop infant industries and vested interests that gain commercial advantages from avoiding tariffs. Whatever the reason for this pattern, in this section we present evidence that trade protection can concentrate import markets. We consider two protectionist trade policies: tariffs and non-tariff measures imposed on imports.

Tariffs. Tariffs could cause concentration through three mechanisms:

1. <u>Demand decrease:</u> When passed through to consumers, tariffs raise prices and decrease

13
# **Appendix A:  Data Sources**

|Battle Death Data|UCDP/PRIO Armed Conflict Dataset version 4-2016|
|---|---|
|Presence of UN<br>Peacekeepingmissions|Coded from here|
|Refugees (by country or<br>territoryof origin)|UNHCR population statistics|
|Ethnic Income Inequality,<br>Spatial Inequality|Alesina, Michalopoulos, & Papaioannou (2016). Ethnic income<br>inequality combines satellite night light density -- a spatially-<br>disaggregated proxy for per capita income -- with maps delineating the<br>boundaries of different ethnic groups, to obtain a proxy for income<br>inequality across ethnic groups. Spatial Inequality measures inequality<br>across pixels within a country and therefore proxies for overall<br>inequality. Data is available only at decadal frequency, and we linearly<br>interpolate the intervening years to obtain annual data.|
|Natural Resource Rents (%<br>of GDP)|"The Changing Wealth of Nations: Measuring Sustainable<br>Development in the New Millennium" , World Bank (2011). It is based<br>on estimates of the difference between commodity prices and unit<br>costs of production, for a large number of commodities. These are<br>aggregated across commodities produced in a country, and expressed<br>as a share of GDP.|
|Ethnic and Religious<br>Fractionalization|Alberto Alesina, Arnaud Devleeschauwer, William Easterly,Sergio<br>Kurlat, and Romain Wacziarg. "Fractionalization"_Journal of Economic_<br>_Growth,_vol. 8, no. 2, June 2003, pp. 155-194.|
|Male 15-29 Pop. Share|Share of male population aged 15-29 in the total population. From UN<br>POPIN Database|
|History of Conflict|Fraction of years since 1970 that a country has been in conflict. It is<br>adjusted appropriately for countries that became independent after<br>1970|
|CPIA|World Bank's CountryPolicyand Institutional Assessment|
|Freedom House|Composite indicator (average) of civil liberties and political rights<br>comingfrom Freedom House|
|Political Terror Scale|Composed as an average indicator of the numerically coded indicators<br>from yearly country reports of Amnesty International, the U.S. State<br>Department Country Reports on Human Rights Practices, and Human<br>Rights Watch’s World Reports.|
|LogGDPper capita|Penn World Tables 9.0.|
|LogPopulation|World Development Indicators|
|GDPper capita Growth|Penn World Tables 9.0.|
|TOT change|Income effect of changes in the terms of trade, comes from Penn<br>World Tables 9.0. It is complemented with data from World<br>Development Indicators for countries with missing data|
|Natural Disaster|Incidence of natural disasters, constructed from EMDAT similar to<br>Besley and Persson (2011). This is a binary indicator of whether any of<br>the following types of natural disasters occur in a given country and<br>year: extreme temperature events,floods,landslides and tidal waves.|
|Neighbors in Conflict|Binaryindicator of whether a countryhas anyneighbors in conflict|

33
**Table 1. Fiscal Space in SSA Countries, 2010-13 vs. 2015-16: Fiscal Sustainability Indicators**

||Pri|mary Balan<br>(% GDP)|ce|F|iscal Balanc<br>(% GDP)|e|Gene<br>Gros|ral Governm<br>s Debt(% G|ent<br>DP)|Time to<br>deb|fully repay<br>t(in tax‐ye|public<br>ars)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Fiscal Indicators|2010‐13<br>Median|2015‐16<br><br>Median|Difference<br>(p‐value)|2010‐13<br>Median|2015‐16<br>Median|Difference<br>(p‐value)|2010‐13<br>Median|2015‐16<br>Median|Difference<br>(p‐value)|2010‐13<br>Median|2015‐16<br>Median|Difference<br>(p‐value)|
|Industrial Economies|‐1.9|0.1|(0.001)|‐3.7|‐1.4|(0.000)|80.4|69.6|(0.603)|2.9|3.0|(0.862)|
|Developing countries (excl. SSA)|‐0.9|‐0.9|(0.956)|‐2.5|‐2.7|(0.489)|38.8|45.9|(0.017)|2.4|2.9|(0.001)|
|Sub‐Saharan Africa (SSA)|‐1.6|‐2.5|(0.009)|‐2.6|‐4.2|(0.000)|34.4|49.7|(0.000)|2.8|3.6|(0.000)|
|SSA by Resource Abundance|||||||||||||
|Non‐resource rich|‐1.8|‐2.2|(0.421)|‐3.2|‐3.7|(0.148)|41.6|49.7|(0.006)|3.0|3.6|(0.001)|
|Non‐oil resource rich|‐1.7|‐5.4|(0.043)|‐2.4|‐5.3|(0.043)|26.9|43.6|(0.009)|2.5|3.8|(0.149)|
|Oil resource rich|1.1|‐3.0|(0.125)|0.2|‐4.5|(0.004)|21.4|47.0|(0.097)|1.3|2.5|(0.077)|
|SSA by Growth resilience|||||||||||||
|Bottom Tercile|‐1.4|‐3.2|(0.001)|‐2.4|‐4.7|(0.005)|32.6|50.8|(0.000)|2.2|3.4|(0.186)|
|Middle Tercile|‐1.6|‐1.8|(0.829)|‐2.4|‐3.3|(0.130)|33.5|47.3|(0.017)|3.0|3.8|(0.005)|
|Top Tercile|‐2.2|‐2.0|(0.743)|‐3.3|‐3.5|(0.743)|38.7|48.3|(0.743)|2.7|3.4|(0.003)|

_Note: The null hypothesis of the median equality test is that of no statistical difference across periods. GDP = gross domestic product; SSA = Sub-Saharan Africa. The p-value reported in this table is that of a two-tailed test. Source: Calculations based on data from Kose et al. 2017._