rafmacalaba/datause-encoder-data huggingface dataset:
6 In South Asia, the ratio of deposits to GDP grew about 40 percent over the decade and in the Middle East and North Africa about 28 percent, while in Latin America, which had the lowest overall ratio, deposits to GDP grew nearly 25 percent between 1990 and 2000.<sup>7</sup> The slowing of deposit growth after 1993 occurred in all regions except the Middle East and North Africa, where the growth rate was roughly the same before and after 1993. The countries where the deposit to GDP ratio did not increase over the 1990s were Brazil, Egypt, and Venezuela.

Table 1 Deposit Mobilization 1990-2000 (percent of GDP, averages by Region **)**

||Twenty-five<br>Countries|East Asia (excl.<br>China, 5)|Transition<br>(4)|Latin<br>America (7)|M. East, N.<br>Africa (3)|South<br>Asia (3)|China|
|---|---|---|---|---|---|---|---|
|1990|34.2|43.9||22.8|48.8|26.1|61.3|
|1991|35.5|45.2||24.0|48.5|26.9|67.4|
|1992|38.8|50.1||26.5|49.3|28.9|72.8|
|1993|41.6|53.8|38.6|31.0|52.0|30.3|83.7|
|1994|40.6|55.2|37.7|26.1|50.6|31.6|81.4|
|1995|40.7|58.7|37.7|24.6|48.6|29.9|86.4|
|1996|41.4|60.9|37.5|24.3|46.4|31.6|94.4|
|1997|45.3|67.1|38.4|27.7|51.0|33.5|108.0|
|1998|47.7|73.6|39.2|28.7|51.9|33.9|117.8|
|1999|50.1|75.1|40.3|29.9|54.9|34.2|132.8|
|2000|50.4|76.3|41.3|28.4|57.7|36.4|137.3|

# **B.   Foreign Sources of Funding for the Banks**

Despite globalization, the stock of net foreign borrowing of banks in the 25 countries declined over the 1990s. In fact, average net foreign funding of banks was actually slightly negative in 2000, -0.5 percent of GDP. In other words, by the end of the decade, banks in the 25 countries had placed more funds overseas than they had received from abroad, on average. In contrast, in 1990, net foreign funding was slightly positive, 0.15 percent of GDP on average.<sup>8</sup> Banks’ gross foreign liabilities were only about 1 percentage point of GDP higher in 2000 than in 1990, despite globalization (Figure 2). Gross foreign funding (liabilities) of banks actually declined in seven countries between 1990 (or 1993) and 2000, another 10 countries had a rise of 2 percent of GDP or less. Note that in percentage terms the declines in dollar term were generally even larger. Many countries experienced real currency depreciations over the decade (especially after 1997) and a real depreciation

> 7 Latin American figures are very influenced by the rise and fall of deposits in Brazil. See footnote 4. Indian data probably reflect a shift from non-banks after 1997.

> 8 These data do not reflect off-balance sheet guarantees by banks of direct corporate borrowing off-shore; such contingent liabilities enter the balance sheet only when the direct borrower cannot pay.
of more and more disaggregated data on poverty. We show that a given impact on poverty can be achieved at considerably less expense with detailed spatial targeting than with a uniform transfer. The gains are generally more muted when the targeting scheme makes only crude use of the local level poverty estimates. However, we find that overall targeting performance with fine geographic targeting, remains far from the ideal of perfect targeting. This implies that there may be scope for combining geographic targeting with other targeting methods in order to reduce errors of inclusion and exclusion even further.

In the next section we briefly summarize the methodology and data underpinning the poverty map estimates in Morocco. We emphasize that these spatially disaggregated data are estimates, with confidence bounds, rather than actual measures of poverty. Section III describes the different targeting schemes that are assessed in the simulation stage, and characterizes how one such scheme can be designated as optimal in the sense of ensuring the maximum possible gains from geographic targeting. Section IV describes the simulation procedures and presents results, and Section V discusses budget allocations across provinces. Section VI provides a concluding discussion.

# **II. Producing Local Estimates of Poverty**

We employ a methodology for producing local-level estimates of poverty that has been described in detail in Elbers, Lanjouw and Lanjouw (2002, 2003).<sup>6</sup> Let _W_ be a welfare indicator based on the distribution of a household level variable of interest, _yh_ . Using a detailed household survey sample, we estimate the joint distribution of _yh_ and observed correlates _xh_ . By restricting the explanatory variables to those that also occur at the household

> 6 Elbers, Lanjouw and Leite (2008) present evidence for Minas Gerais, Brazil, in support of the underlying assumptions of the methodology.

5
25,000 businesses. In this paper we restrict the sample to the 50 countries that have sufficient sample size to conduct gender-disaggregated analysis to form a total sample size of 154,458 businesses. The target population of the Future of Business Survey are Facebook Business Page administrators that identify themselves as business owners and managers who take the survey on their Facebook mobile application.<sup>16</sup> In order to look at the impact of school closure, we use school closure policies related to COVID-19 extracted from data collated by UNESCO that indicates whether a country was: fully open, partially open, on an academic break, or fully closed due to COVID-19 across time measured in daily increments (UNESCO, 2020 and 2021).<sup>17</sup> In this paper we create a dummy variable to indicate whether schools were partially or fully closed in a country at the time of each survey wave (School Closure = 1 if schools fully or partially closed due to COVID-19, and 0 otherwise). Appendix B provides a table of the COVID-19 related school closure policies in place for all countries included in the analysis at the time of each survey wave throughout 2020 and in September 2021.<sup>18</sup> Fears about virus contagion that were often coupled with lockdown measures to contain the COVID-19 virus reduced mobility and social interactions leading to a large shock to business demand, while also restricting the ability of firms to perform key business activities such as in-person sales. To account for this in our analysis we measure mobility in a country at the time of each survey wave by utilizing Google’s Community Mobility Reports (Google, 2020 and 2021). The Google mobility data are created with aggregated, anonymized sets of data from users who have turned on the ‘Location History’ setting on their phone and show how visits and length of stay at different types of places change compared to the median value, for the corresponding day of the week, during the 5-week period from January 3, 2020 to February 6, 2020. That is, mobility is indexed relative to a “baseline” level

> Wave 7 was conducted in the last 2 weeks of September 2021. Appendix C includes further methodological details of the Future of Business survey.

> 16Over 90 million businesses estimated to be represented in this sampling frame (Facebook, 2019).

> 17UNESCO records the evolution of national school closures and re-openings on a daily basis.

> 18School closure status was fixed according to the first day of data collection for each survey wave e.g. for wave 7 on September 15, 2021.

11
collect individual and household income for these various sources changed significantly beginning with the November 1998 survey. With this caveat in mind it should be noted that a serious effort was made to maintain comparability of income by source across the survey rounds. The various sources of income (excluding the PROGRESA cash transfers) were transformed into monthly income and then aggregated into 4 main sources of income: labor income; income from self employment (such as income from sewing, food preparation, construction or carpentry, commerce, produce transportation, repairs and laundry or cooking); other income (such as pensions, interest income, rents and community profits); and government transfers (such as educational scholarships from _Niños de Solidaridad,_ benefits from _Instituto_ _Nacional Indigenista_ (INI), PROBECAT, _Empleo Temporal_ and Procampo).

The income data by source are useful at quantifying the direct costs associated with participation in the PROGRESA program. Children can contribute income to families by working for wages or by being recipients of cash transfers from other government transfer programs excluding PROGRESA. Figure 1 presents how the income contributed to families by children between ages 8-17 evolved across different survey rounds in the treatment and control villages (using only eligible households E=1). Panel (a) in Figure 1 reveals that the total (labor + other) income (excluding PROGRESA cash transfers) beneficiary families received from children declined in both treatment and control localities since the initiation of PROGRESA in November 1998. The mean total income contributed by children in November 1998 is slightly lower among treatment villages compared to the control villages and the gap gets even bigger by the June 1999 round. By November 1999, this gap is completely eliminated as control households are already incorporated into PROGRESA.<sup>8</sup> Panels (b) and (c) in the same figure

> 8 Note that control households started receiving cash benefits in December 1999. Households are first incorporated into PROGRESA, meaning that they are given all the necessary forms and informed of all the program requirements. A few months later, the cash benefits are sent out by the PROGRESA

8
Table 11: Robustness Check: Alternative Connectivity Indices

|Dependent Variable|(1)<br>DVAR|(2)<br>DVAR|
|---|---|---|
|ICI|0.001<br>(0.002)||
|FCI||0.039***<br>(0.012)|
|Control|Yes|Yes|
|Firm FE|Yes|Yes|
|City-Year FE|Yes|Yes|
|Observations|213,331|213,313|
|Adjusted R<sup>2</sup>|0.722|0.722|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation). In column (1), we further control for the share of products imported by firms in the initial year that accounts for their total imports in subsequent years.

# **6 Theoretical Analysis**

In this section, we introduce a simple two-sector model to explain the previous empirical findings, illustrating how HSR expansion may enhance the DVAR of exporting firms. The core mechanism is driven by the anecdotal evidence highlighted earlier, where HSR enables employees to travel between cities for business meetings with domestic suppliers. In theoretical terms, this implies that HSR expansion increases the likelihood of exporting firms connecting with domestic suppliers at lower communication costs. This, in turn, reduces input prices and expands the variety of domestic materials available, allowing firms to substitute imported materials with domestic alternatives, ultimately raising their DVAR.

The economy consists of two sectors. The upstream materials sector, _M_ , supplies inputs for the downstream final goods sector, _Y_ . Sector _Y_ operates in a monopolistically competitive market, where an infinite number of firms, each with a unique productivity level _φ_ , produce differentiated varieties _ω_ . The materials sector, _M_ , is perfectly competitive, featuring an infinite number of identical firms, each producing a distinct variety of material, _η_ . The economy is endowed with a large labor supply, _L_ , and includes a numeraire sector employing _L_ 0, which allows us to normalize the wage rate to one.

# **6.1 Final Goods Sector**

We begin with sector _Y_ , the final goods sector, following the standard framework of a heterogeneous firm model with constant elasticity of substitution (CES) preferences across the differentiated final goods varieties: 26
especially in the case of antidumping, it may also signal the existence of an informational barrier that hinders developing countries from enforcing their market access rights in other situations where WTOillegal measures are less observable.

# **3. The Advisory Centre on WTO Law and Its Involvement in WTO Disputes**

To help developing countries get maximum benefits from WTO membership, a group of nations established the Advisory Centre on WTO Law (ACWL) in 2001; developed countries in the group provide the bulk of the financing. The ACWL assists developing countries in their enforcement through the DSU of WTO market access rights by providing a variety of subsidized legal services to developing country governments.<sup>10</sup> Although these services include free or low cost legal advice on WTO issues and training programs for officials who carry out WTO-related functions on behalf of their governments (capacity building), the ACWL’s most prominent role is to supply low-cost legal support to developing countries when they act as complainants, respondents, or third parties in WTO dispute-settlement proceedings. Under its charter, the ACWL can provide its services to developing countries, customs territories, or economies in transition. Specifically, ACWL services are available to any developing country that is a member of the Centre as well as any WTO member designated by the United Nations as a least developed country (LDC)—i.e., any WTO member with very low per capita income.

In this section we analyze data on ACWL activities from 2001 through 2008. We begin by examining how developing countries have actually used the ACWL litigation services: which countries the ACWL has represented in DSU cases, how frequently, and against whom. Then we use economic theory to identify various mechanisms through which introduction of the ACWL in 2001 may have affected the ways developing countries participate in WTO dispute settlement and the resulting DSU caseload. Finally, we look for patterns in the data that are consistent with these channels.

> 10 Earlier theoretical analyses of the ACWL include Van der Borght (1999), Hoekman and Mavroidis (2000), Jackson (2002), Bown and Hoekman (2005), and Shaffer (2006).

13
primary school enrollment rates, a measure of educational inequality<sup>11</sup> (as emphasized by De Gregorio et. al. (2002)), and the share of agriculture in GDP (as emphasized for example in Datt and Ravallion (2002)).<sup>12</sup> Appendix B provides a detailed description of the definitions and sources of all of these variables.

In each model, Xj(i, t) includes one particular combination of these 13 variables, and we will consider all 2<sup>13</sup> = 8192 possible combinations of these variables. We rely on Bayesian Model Averaging (BMA) as a tool to summarize the results from this large number of regression models. In particular, BMA allows us to assign a posterior probability to each model, P[Mj] , which indicates the relative likelihood of model j compared with all of the other models considered. Intuitively, these posterior probabilities reflect a tradeoff between goodness of fit and parsimony: for two models with the same number of explanatory variables, the model that delivers the higher R-squared receives a higher posterior probability. Similarly, for two models with the same R-squared, the model with fewer explanatory variables receives a higher posterior probability.

We calculate the posterior model probabilities for the regression for overall social welfare growth in Equation (9), and then use these to construct probability-weighted estimates of the slope coefficients across all models, i.e.

This expression summarizes the estimated relationship between each of the explanatory variables and growth in social welfare, averaging across all models. Moreover, it allows us to separate the effects operating through growth and through increases in equality. Finally, for each variable we also calculate a posterior inclusion probability (PIP). This is simply the sum of the posterior probabilities of each model in which the variable appears, and is a useful summary of the relevance of that variable for growth in

> 11 Specifically, we use data on educational attainment by different levels of attainment from the Barro-Lee dataset to construct a (grouped) Lorenz curve summarizing the distribution of the total number of years of education across individuals, and from this calculate a corresponding Gini coefficient.

> 12 We also considered several other variables found to be significant correlates of inequality in some papers in the literature, but did not include them in our analysis because data coverage was very poor for many of the developing countries in our sample.  These included indicators of labour market regulation and progressivity of tax systems (Checchi et. al. (2008)), public sector employment (Milanovic (2000) ), and social transfers (Milanovic (2000), De Gregorio et. al. (2002)).

22
4 It is important to recognize that for measuring the ordinary price elasticities for freight demand, the freight demand system must be estimated simultaneously with the shippers' output decisions, i.e., treating output as endogenous. Ignoring the endogeneity of shippers' output decisions is equivalent to assuming that changes in freight rates do not affect output levels. This, in turn, is equivalent to ignoring the secondary effect of a freight rate change on input demand caused by the induced change in the level or scale of output. Because most of the freight demand models reviewed in this survey do not treat this secondary effect properly, the price elasticity values reported here may be biased. Since the mid-1970s, many economists have estimated neoclassical input demand systems by deriving them from the firm's or industry's cost (production) function, often specified in a translog or other flexible functional form (for example, Oum 1979b and 1979c, Friedlaender and Spady, 1980, and Spady and Friedlaender, 1978). However, most of these models are derived by minimizing the input costs (including freight transport costs) for transporting a given (exogenously determined) output, and thus yiea.d compensated demand elasticities rather than ordinary demand elasticities. Therefore, the elasticities reported in such studies are not directly comparable with those of other studies. The freight demand study by Oum (1979c) is an exception in that he computes the ordinary price elasticities by adding the effects on demand of the changes in output scale induced by a freight rate change to the compensated price elasticities computed from the neoclassical freight demand system. Therefore, his ordinary price elasticities are comparable with those of others. Because virtually all freight demand studies report something close to ordinary demand elasticities, that is the appropriate interpretation of the results in this survey.

Passenger demand models normally are derived by maximizing, explicitly or implicitly, the utility function subject to the budget constraint. These give ordinary price elasticities, i.e., they include both ince-.a and substitution
Policy Research Working Paper 6600

# **Abstract**

Relying on census data collected in 2002 and historical weather data for Uganda, this paper estimates the impact of weather-induced internal migration on the probability for non-migrants living in the destination regions to be employed. Consistent with the prediction of a simple theoretical model, the results reveal a larger negative impact than the one documented for developed countries. They further show that this negative impact is significantly stronger in Ugandan regions with lower road density and therefore less conducive to capital mobility: a 10 percentage points increase in the net in-migration rate in these areas decreases the probability of being employed of non-migrants by more than 10 percentage points.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at marie-anne.valfort@univ-paris1.fr.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Portugal|0.520|0.512|0.530|RUS|
|---|---|---|---|---|
|Spain|0.507|0.501|0.517|CZE, DEU, LVA|
|**Turkey**|**0.426**|**0.420**|**0.435**|KAZ|
|**Western Balkans**|**0.406**|**0.395**|**0.419**||
|Albania|0.401|0.393|0.414|BGR, GEO, KGZ|
|Bosnia and Herzegovina|0.362|0.352|0.373|MKD|
|Kosovo|||||
|Montenegro|0.415|0.396|0.428|ALB, BGR, GEO, KAZ, KGZ, TUR|
|North Macedonia|0.359|0.353|0.368|BIH|
|Serbia|0.443|0.429|0.457||
|**Western Europe**|**0.541**|**0.530**|**0.556**||
|Austria|0.533|0.522|0.549|BEL, FRA, ISL, LTU, NOR, POL, RUS,<br>SVN|
|Belgium|0.548|0.536|0.566|CHE, CYP, DNK, EST, FIN, FRA, ISL,<br>LTU, NOR, POL, SVN|
|France|0.537|0.526|0.555|AUT, BEL, CHE, FIN, ISL, LTU, NOR,<br>POL, SVN|
|Germany|0.501|0.491|0.512|BLR, CZE, ESP, LVA|
|Ireland|0.587|0.575|0.607|GBR, NLD, SWE|
|Luxembourg|0.487|0.475|0.503|BLR, DEU, GRC, ITA, UKR|
|Netherlands|0.581|0.568|0.597|GBR, IRL, SWE|
|Switzerland|0.553|0.542|0.570|BEL, CYP, DNK, EST, FIN, NOR, POL|
|United Kingdom|0.580|0.570|0.596|IRL, NLD, SWE|
|**Simple average**|**0.481**|**0.471**|**0.496**||
|**Population-weighted average**|**0.496**|**0.484**|**0.511**||

_Source_ : Authors’ calculations.

# **_6. Concluding Remarks_**

This paper provides an extension of the Human Capital Index that makes it more relevant for the education and health challenges faced by countries in Europe and Central Asia. Specifically, the extension incorporates two elements that are particularly important for the region. First, there is an additional focus on quality adjusted years of tertiary education, in addition to basic education. Second, health status is captured by including risk factors such as obesity, smoking and heavy alcohol consumption, all of which are prevalent in the region. This exercise highlights the importance of investing in tertiary education for many countries in the region, as well as the importance of preventing risk factors for noncommunicable and infectious diseases in the aging societies of the region.

As in any cross-country benchmarking exercise, there are limitations. When analyzing the contribution to productivity from higher education, the ECA-HCI does not distinguish between types of disciplines and the 31
Table 1: Statistics of poverty estimates in the focus countries.

|**Country**|**Burkina**<br>**Faso**|**Chad**|**Guinea**|**Mali**|**Niger**|
|---|---|---|---|---|---|
|Year of most<br>recent<br>census|2018|2009|2014|2009|2012|
|**Regions**||||||
|Number of<br>regions in<br>sample|13|22|8|9|8|
|Number of<br>regions in<br>census|13|23|8|9|8|
|Median cve<br>of sample<br>estimates of<br>headcount<br>poverty rates<br>for regions|0.114|0.123|0.069|0.085|0.079|
|**Target area**||||||
|Name of<br>target area|Commune|Department|Subprefecture|Commune|Commune|
|Number of<br>target areas<br>(population)|351|112|343|704|266|
|Number of<br>target areas<br>(sample)|234|99|251|244|228|
|Median cve<br>of sample<br>estimates of<br>headcount<br>poverty rates<br>for target<br>areas|0.435|0.271|0.370|0.415|0.425|

Notes: Sample estimates are obtained from the 2018 round of the EHCVM in each country. Median cve of the sample estimates refers to the median estimated coefficient of variation across target areas. Departments in Chad are defined using an unofficial shapefile provided by