rafmacalaba/data-use-mentions-extended huggingface dataset:
while Lopez-Calva et al. (2021) find absolute convergence from 1992 to 2014 using a unique five wave panel dataset at the municipal level. The convergence process in both cases stemmed from a combination of positive developments in poor municipalities and stagnation or negative growth in richer ones.

> 6 While the synthesis in Modrego and Berdegué (2016) confirms the role of human capital in the convergence process in most Latin American countries, they conclude that the relevance of other factors is country specific.

5
wages, from -7.5 to -4.7 percent, and the effect on hours of work from 1.4 to 0.53 hours per week, while the DID coefficient on probability of working in the formal is not statistically different from zero. We interpret this changes in the magnitude of the estimates as evidence that OLS estimates are upwardly biased due to the omission of unobserved individual worker characteristics.

Finally, it is worth acknowledging that the DID estimates do not control for another potential source of omitted-variable bias: the quality of the match between employers and workers. That is, it is possible that the probability of a plant closing is systematically correlated with the quality of the match between the employer and its workers. Since the survey data, which nonetheless covers both informal and formal workers, does not offer information on the employer-worker relationships, we are unable to control for this potential source of omitted-variable bias.

Future research should attempt to re-estimate the DID while controlling for employer-employee fixed effects, which requires administrative data, which would unfortunately leave out the informal labor market. This said, the fact that we did not find strong evidence of plant-closings being associated with higher probabilities of being informal after job separations could be interpreted as there being little remaining bias in our DID estimates since poor quality matches between employers and workers would presumably also be associated with informal employment of said workers.

29
: Little Data Book on Financial Inclusion 2018 The above comparison reveals mixed results. East Asian achievers like Thailand and Malaysia fare better on both access to, as well as usage of, formal finance. While Sri Lanka seems on par or slightly ahead of the East Asian average (as well as the upper middle-income countries’ average) in terms of account ownership, it is lower in terms of usage of credit and digital payments. This indicates that opportunities to scale up usage of transactional services through more efficient

> 12 World Bank Findex 2017.

> 13 Little Data Book on Financial Inclusion 2018.

> 14 The latest Findex available is for 2017, hence data used are from 2017 for comparison purpose.

8
# (b) IMPACTS ON SOFT SKILLS

Improvements in soft skills are measured through three brief indicators taken from the literature and that were seen during discussions with government counterparts as the most aligned with the project’s theory of change. The first indicator is leadership/teamwork, which was constructed by adapting the American Camp Association (2012) youth survey.<sup>15</sup> It consists of 16 different questions that can be ranked on a five-point scale. The scale’s maximum value is 80 and its minimum value is 16. The second indicator is communication skills, constructed based on Barkman and Machtmes (2002),<sup>16</sup> and which assesses youth’s ability to communicate well by examining how frequent youth use the skills included in effective communication practices.<sup>17</sup> The indicator consists of 23 questions that can each be ranked from 0 to 5, which makes the total possible score range from a maximum value of 92 to a minimum value of 0. The third indicator is confidence skills, which is adapted from Rosenberg’s (1965) self-esteem measure.<sup>18</sup> The indicator consists of 10 questions that can each be ranked on a four-point scale, which makes the total possible score range from a maximum value of 40 to a minimum value of 10. Similar to social cohesion measures, soft skills indicators are internally standardized. They have also been piloted ahead of data collection to test their validity and reliability.

Results in **table 4** indicate that assignment to NVSP has no impact on the three indicators for soft skills among selected youth for specifications 1 & 2 (see the δ estimate for columns 1, 2, & 3). The leadership skills measure appears to have worsened for both selected and non-selected youth over time, which is somewhat puzzling given that both groups are active volunteers and members in their communities (see the β� δ estimate and the β estimate for column 1
Policy Research Working Paper 5466

# **Abstract**

This paper investigates the impact of rising wheat prices—during the 2007/08 global food crisis—on food security in Afghanistan. Exploiting the temporal stratification of a unique nationally-representative household survey, the analysis finds evidence of large declines in real per capita food consumption and in food security (per capita calorie intake and household dietary diversity) corresponding to the price shocks. The data reveal smaller price elasticities with respect to calories than with respect to food consumption, suggesting that households trade off quality for quantity as they move toward staple foods and away from nutrient-rich foods such as meat and vegetables. In addition, there is increased demand in the face of price increases (Giffen good properties) for wheat products in urban areas. This study improves on country-level simulation studies by providing estimates of actual household wellbeing before and during the height of the global food crisis in one of the world’s poorest, most food-insecure countries.

This paper—a product of the Economic Policy and Poverty Team, South Asia Region—is part of a larger, programmatic effort in support of the Afghanistan Poverty Assessment activities. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at djolliffe@worldbank.org.

_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
trivial choice, and in some cases might not be warranted if only the focus axiom is needed.

For this simulation, initial conditions are radically different, as some regions have much higher levels of learning poverty than others. The simulation needs both simplicity and the ability to generate the correct incentives so different stakeholders will act on the results. Hence, simulation results will be presented based on three measures—the headcount rate, which is suitable for countries and regions with lower levels of learning poverty, and the learning poverty gap and learning poverty severity, which are the most relevant for Africa and the low-income countries aggregate.

However, one important empirical question remains. Are all these complementary different measures empirically relevant? The extend in which will depend if we find that countries with same learning poverty level have different learning poverty gaps (figure 3, panel A), or do countries with the same learning poverty gap have different learning poverty severity (figure 3, panel B). Figure 3 illustrates those points using the latest available data from 99 countries in the learning poverty database for which learning poverty gap and learning poverty severity indicators are available.<sup>49</sup> The figure shows a wide range of

> 48 See for example Alkire et al. (2015) and Cameron et al. (2019).

> 49 All learning assessments used are anchored in a standard deviation of 100 points, this should be sufficient to have the FGTclass of measures to be minimally comparable. Of course, within-country temporal comparisons, assuming temporal comparability of the assessments, are the ideal case. All gap measures are relative to the test-specific MPL. One interesting aspect is that once the gap conversion is made, the measure becomes test independent, and can be presented independently of any scale. One important assumption however, which is shared with the global poverty monitoring, when doing cross-country comparisons, is that the learning (income) marginal sensitivity of the cardinal variable is the same. Meaning, improving one learning point is equally hard
as indices calculated on the levels reached in the final year, with the final year for the BaU equal to 100. In the case of the full liberalization scenario and across all variables, own liberalization accounts for a large share of the total shock. Imports in agriculture actually increase more in the partial own liberalization shock than in the combined shock, given that the external shock drives international agricultural prices up. The reduction of the mentioned anti-(agriculture-)export bias implicit in the initial protection, is also explaining the large share of export and real GDP effects accounted by the own liberalization shock.

Decomposition results for the Doha scenarios are less clear cut. The magnitudes of the shocks are much smaller, however even the very low levels of domestic tariff abatement seem to matter for the final result. A relevant policy lesson emerges from the comparison of the two partial shocks panels of Table 10: a passive non reciprocating attitude may bring some advantages; however these are quite limited, even in the extreme case where every one in the world but Brazil implements full liberalization. In fact, these externallyinduced benefits may be greatly enhanced by an active domestic liberalization reform.

For their poverty and income distribution implications, changes in factors’ markets are the most important aspect of the structural adjustment caused by trade reform. Changes in wages and sectoral employment are linked to changes of goods prices through the production technology and the functioning of the factor markets. Different production technologies are approximated by different factor’s intensities across sectors, as shown in Table 4, and labor markets function so as to mimic realistic adjustment possibilities: 40
, are clustered at the account level. Estimates in panel B are based on endline survey data. The omitted group consists of respondents in the control who continued to be paid in cash and did not receive an account. Controls include stratification dummies, marital status, number of dependents, dummies for savings, formal savings, years of work experience, and years with current employer. Robust standard errors in parentheses, sharpened q-values in brackets. * _p <_ 0.1, ** _p <_ 0.05, *** _p <_ 0.01.
which the advanced economy had a comparative advantage, and that this contributes to narrow the productivity gap within industries.

This paper complements and extends several strands of literature. A number of cross-country studies have identified systematic empirical links between foreign direct investment and growth, including early work by Blomstrom et al. (1996) and Borensztein et al. (1998). The current paper contributes to this literature by providing microeconomic evidence on these links, exploiting Poland’s accession to the EU as a source of variation in the degree of market integration with a high-income country. In doing so, this paper also relates to recent research using firm-level data to document effects of foreign acquisitions on firm performance, including Arnold and Javorcik (2009), Bloom et al. (2012), Guadalupe et al. (2012) and Bastos et al. (2018). In contrast to this strand of literature, we emphasize the key role of initial specialization patterns in the advanced economy in driving the impacts of deeper 3
improved or build new links between locations. We assume trade costs to be iceberg costs, such that the costs between the origin location _o_ and destination _d_ are given by _dod_ = _max_ (1 _,time_<sup>_τ_</sup> ). Border costs are also added to travel costs, as detailed below.

We calibrate the model using spatial data for land, population, and sectoral shares from the sources used earlier. Because of the complexity of a three-sector model to converge in order to recover the initial sectoral productivities, we reduce the spatial disaggregation to fewer locations. Such aggregation also smoothed measurement issues of sectoral employment based on the DHS data. We divide the Horn of Africa into 32 regions, including 11 first-order administrative divisions in Ethiopia, 5 in Djibouti, 8 groupings of first-order administrative divisions in Somalia, and 8 groupings in Kenya. Figure A11 shows the share of agricultural employment and the population for each subnational region.

# **5.3.1 Productivity shocks from electrification in the Horn of Africa**

The counterfactual consists in providing access to electricity for all in all regions based on the current coverage measured by night time lights as described in previous sections. We calibrate the increase in productivity using the estimated productivity increase of 28.4 percent for Ethiopia from Moneke (2020) and the current coverage of electricity per location. The lower the existing coverage, the higher the productivity increase in the counterfactual. The new productivity levels _A_<sup>∗</sup> are given by : _A_<sup>_i_</sup> _n_<sup>∗=</sup><sup>_Ai_</sup> _n_<sup>+</sup><sup>_Ai_</sup> _n_<sup>×(1−</sup><sup>_Elecn_)×0</sup><sup>_._284</sup><sup>_,_</sup> _n_ = _M,S_
using the sinusoidal distribution as suggested by d'Agostino and Schlenker (2016).</mark> Data on cumulated GDD>0 days computed based on daily values of _Tmin_ and _Tmax_ in figure 4 illustrates a marked seasonal pattern and clear differences across years.<sup>18</sup> Most relevant for our analysis are a warm winter and spring in 2020; a mild winter followed by a cool spring that provided favorable growing conditions in 2021; and 2022 starting with a cold fall and remaining the coolest of the four growing seasons thus far, leading to delayed crop development and harvest dates.

Data on precipitation in figure 5 highlights that, in addition to having been unseasonably warm, the 2020 season was very dry, with abnormally low winter rainfall followed by a spring with long dry spells that contributed to a cumulative water deficit of 70-100 mm compared to 2019. This resulted in widespread winter crop failure, especially in the South and Center due to insufficient soil moisture. By contrast, in the 2021 season, ample spring and subsequent rainfall provided favorable growing conditions, contributing to what was widely considered a record harvest. Fall precipitation (i.e., until Dec. 1) was even lower in 2022 than in 2020 and, by end May, had approached levels of total rainfall similar to those observed in 2020.

> 16 Dhillon _et al._ (2020) report that the best fit is obtained most to the Feekes scale (Large 1954), i.e., between 97 and 112 GDD>0

> 17 Due to a change in Sentinel 2 data processing that was introduced with version 4.0.0. end of January 2022, GEE’s S2_SR_HARMONIZED collection was used for 2022 NDVI values.

> 18 In line with the empirical pattern, we define fall as the period between Oct 1 and Dec 1; winter as the period between Dec. 1 and Mar. 1; and spring as the period thereafter.

10
by expanding their coverage. In the end, using the methodology of Kendall _et al._ , we arrive at two sets of predictions: one for 2008, and the other for 2009, both complemented with additional data from a variety of sources (see Table 3) and for the same sample of 191 countries.

Kendall _et al_ . (2010) use a variety of econometric models to predict the number of commercial bank deposit accounts around the world. Table 4 provides the estimation results for the two sets of predictions mentioned above, which are almost identical to the results reported by Kendall _et al._ using _Financial Access 2009_ data. We then use these models to predict the number of commercial bank deposit accounts for those countries for which data are unavailable. For the rest of the non-bank financial institution types, no estimation was made as the number of comparable observations at the country level is not sufficient. In addition, not all countries have different types of non-bank financial institutions. Instead, _Financial Access_ data for both years are complemented by other data sources (see Table 3), and the number of deposit accounts for each institution type for each reporting country is counted. The total number of deposit accounts across all institutional types within each country is then calculated by adding up the numbers for each institutional type. This prediction exercise yields the total number of deposit accounts in each country. However, the indicator of interest is the total number of banked (or unbanked) adults (see Table 3). Hence, following Kendall _et al._ , we convert the number of deposit accounts

> 3 See Map 1.1 in CGAP and World Bank (2010), pp.4.

5
2.2. Choice of Fiscal Instrument and Fiscal Position: Our selection of government consumption as the fiscal instrument reflects several considerations. Government consumption is a subset of the much broader measure of government spending that strips out the automatic stabilizers component, and hence represents discretionary fiscal policy. We recognize, however, that government consumption plays a modest role in large discretionary fiscal stimulus programs, such as those implemented in 2008-10, or discretionary consolidation packages. Such large packages, on the spending side, are often predominantly based on government investment and transfers (OECD 2009). Government investment could be an ideal fiscal instrument, in principle, to model large discretionary policies but, in practice, available quarterly data is insufficient to establish robust results. In addition, in our sample, government investment is relatively small, averaging only about one-quarter of government consumption. Hence, we choose government consumption as a fiscal instrument that is quantitatively large over the full sample period and available for many economies.<sup>7</sup> On the tax side, the ideal measure to represent discretionary fiscal policy would be tax rates. However, consistent measures of tax rates across countries are not readily available. Tax revenues, even though they are easily available, are highly procyclical and less suitable for our study.

Our selection of government debt as the conditioning state of fiscal position is motivated by the fact that in theoretical dynamic models that feature a fiscal sector, government debt is a state variable that enters as a lagged variable due to the flow budget constraint of the government (Galí, López-Salido, and Vallés 2007; Forni, Monteforte, and Sessa 2009; Davig and Leeper 2011). Moreover, in these models, nonlinear dynamics would depend on the initial state of government debt (and other state variables in the model). By conditioning on debt, our empirical approach attempts to match this theoretical aspect, and measures the effects of fiscal shocks conditional on the initial level of government debt.

The IPVAR model does not allow for endogenous feedback
level. Official poverty figures are consumption-based, and the official consumption aggregate does not include rent or imputed rent. It is important to note that we were not able to access to the methodology for determining the official poverty line. Therefore, in the following, Albania does not appear in some parts of the analysis. Data for Bangladesh come from the 2005 and 2009/10 Household Income and Expenditure Survey (HIES). The surveys are representative by divisions at urban/rural/statistical metropolitan area. Official poverty statistics are based on a consumption aggregate that includes imputed rents, although is not clear which imputation method is adopted in the official methodology. The data sets used for Iraq are the 2007 and 2012 rounds of the Iraq Household Socio Economic Survey (IHSES). IHSES is representative by governorate at urban/rural/statistical metropolitan area. The consumption aggregate for official poverty statistics includes rents following the self-assessment approach. Finally, figures for Peru are based on the 2010 and 2013 rounds of the Peruvian Household Survey on Living Conditions and Poverty ( _Encuesta Nacional de Hogares sobre Condiciones de Vida y Pobreza_ - ENAHO). Official distributional statistics are based on consumption and the official consumption aggregate includes rents following the self-assessment approach with adjustments for nonresponse.<sup>8</sup> Typically, the housing section of household budget surveys contains a question about occupancy status of households, allowing selection of different categories, broadly referable to (i) owners (sometimes specifying whether outright or still paying mortgage), (ii) tenants, (iii) tenants living in subsidized dwellings, (iv) individuals living for free in dwellings provided by relatives or employer. These categories sometimes are more finely

> 7 The World Bank classifies countries by region (East Asia and Pacific; Europe and Central Asia; Latin America & the Caribbean; Middle East and North Africa; South Asia; Sub-Saharan Africa) and by development stages (low-, lower middle-, upper middle-, high-income). The four countries analyzed in this paper
1482|
||(0.2583)|(0.3309)|
|Private Domestic|0.0211*||
||(0.0120)||
|Government owned|0.0126<br>(0.1057)||
|Government-influenced|0.0525<br>(0.0362)||
|Constant|0.2064|0.1274|
||(0.1348)|(0.1541)|
|Observations|327|327|
|Number of banks|45|45|
|AdjustedR2|0.5634|0.7295|

Source: Own calculations, based on CBK data The Table 10 results show that the significant results from our base models are not a reflection of the banking sector as a whole, but are rather driven by specific subsets of banks. To see this, we re-run the base models in Table 9 with interaction terms for each of the three ownership categories that appeared in the original regression (private domestic, governmentowned, and government-influenced). In this way the association between each explanatory variable and the interest spread is allowed to differ by ownership type. The coefficients on the non-interacted variables therefore summarize the effects for foreign banks, our omitted ownership category. Rather than present the full specification, we present the effect of each 21