rafmacalaba/data-use-mentions-extended huggingface dataset:
are actively monitored by local communities, and those charged with ineffectiveness or corruption are swiftly removed from their posts by the center, and replaced with new officials charged with implementing the GTP more efficiently (Plummer 2012).<sup>7</sup> Compared to Ethiopia’s neighbors, local despotism is a relatively unsuccessful strategy for woreda officials to pursue.

Since 2006, external donors have supported the IGFT system through the Promotion of Basic Services (hereafter simply PBS) program. During these periods, donors contributed about half the budget of PBS, with the government financing the rest. Donor finance amounted to some 20-30% of the cost of the total block grant system during this period, and is conditioned on transfers meeting key criteria, including equity, additionality, and efficiency, all of which are actively monitored.

As envisaged at its design, PBS has contributed to large increases in certain types of staff financed through regional and especially woreda budgets, which has in turn led to substantial improvements in basic service delivery indicators. Primary and secondary enrolment rates have shown a steep upward trajectory. Under-5 mortality rate has dropped from 109.5 in 2005 to 88 in 2011. Table 2 shows changes in key indicators since 2006. Starting from a very low base, Ethiopia’s improvements over the past decade represent a significant achievement.

# **3.2 Data**

Our data source for horizontal or geographic equity analysis is the Poverty and Social Impact Assessment (PSIA) database (Khan et al. 2014). This includes data from five federal government ministries and agencies including Health, Education, Agriculture, Water and Energy, Finance and Economic Development, and the Central Statistical Agency (CSA). Woreda-level poverty rates are based on small area estimation of poverty levels.<sup>8</sup> Data has been collected for all of Ethiopia’s woredas,<sup>9</sup> as delineated at the time of the 2007 census. The database includes information on expenditures by sector
# **3 Empirical Strategy**

We estimate the impact of conflict exposure on firms’ economic performance by combining longitudinal firm-level data with geolocalized information on conflict events. Our main specification reads as follows: where _Yit_ represents the outcome of interest for firm _i_ in fiscal year _t_ . The analysis considers several firm-level outcomes as dependent variables, including _Sales_ , _Total Costs_ , and _Profits_ . _Conflict exposure it_ denotes the firm-specific measure of conflict exposure. In our baseline specification, it is computed as the logarithm (log of 1+) of the number of conflict events that occurred within a 20 km radius of the firm’s location during the 12 months preceding the end of the last fiscal year.<sup>13</sup> The vector _Xit_ represents a set of time-varying firm characteristics, including size, age, and export status. _μi_ denotes firm fixed effects, accounting for all time-invariant observed and unobserved characteristics of the firm that could potentially influence the outcome of interest. _θt_ represents year fixed effects, controlling for overall trends in economic activity and common shocks. As a robustness check, we also include the full set of area-year interacted fixed effects to account for all time-varying location-specific determinants of firm performance, such as infrastructure availability and local labor market characteristics. Finally, _εit_ is the error term. In all specifications, we report robust standard errors clustered at the firm level, which is the unit of observation used to measure the intensity of conflict exposure. All variables are defined in Table C1.

Our identification strategy relies on the assumption that, conditional on our full set of fixed effects, conflict events occurring near the firm’s location are uncorrelated with any latent determinant of its economic performance. Under this assumption, _β_ captures the reduced-form effect of the firm-specific conflict exposure on economic performance.<sup>14</sup> In what follows, we present our core results and examine in detail potential threats to the validity of our identification strategy.

> 13We also employ alternative measures of conflict exposure constructed
average tariff (US$c per KWh)|
|<br>**Carbon Emission Index**<br>**(CO2 ton/Kwh)**|**ENVIRONMENTAL SUSTAINABILITY**<br>= energy generated from each energy source weighted with the following<br>coefficients of CO2 emission intensity by weight per unit of electricity<br>generated divided by total amount of energy generated (coal = 1.00;<br>petroleum fuels = 0.94; natural gas = 0.63; renewable energy and nuclear<br>power = zero)|

Tables 2 and 3 report the explanatory variables that our model will use to determine the power sector performance. Table 2 includes a comprehensive list of policy variables, such as the degree of vertical unbundling together with the degree of concentration and private sector participation of each segment of the market, as well as the introduction of an autonomous regulator.

8
<!-- Start of picture text -->
1.00<br>G<br>2007 G<br>G<br>0.75<br>2010 G<br>0.50<br>Displacement<br>0.2 0.5 1.0 2.0 5.0<br>G Ever Displaced<br>0.25<br>2007 G<br>0.00<br>2006 2007 2008 2009 2010 2011 2010 G<br>Year<br>G % Ever Displaced<br>Variable % Good/Very Good Relationship with Community<br>% Has Ability to Contribute to Events in Community 0.2 0.5 1.0 2.0 5.0<br>Odds ratio (log scale)<br>(a) General Trends in Uganda Data (b) Temporal Variation in Relationships<br>Relationship with Community<br>Proportion<br>Year<br>Contribute to Events in Community<br><!-- End of picture text -->

Figure 3: Uganda Temporal Analysis communities. When we re-sampled the same area in 2010, experience with displacement became positively correlated with perceptions of being able contribute to the communities. In a span of three years, therefore, the relationship between experience with displacement and perception of empowerment within their community flips when sampling the same area.

While there is some risk of conflating group attachment with empowerment as northern Uganda has high community identification/attachment in general (as highlighted by Figure 8), we focus on an explicit measure of perceived empowerment. We direct ask respondents whether they perceive themselves as able to contribute to their communities in the bottom graph of Figure 3b. We do not believe that the variation in responses that we highlight – the change between survey waves – is likely driven by a community pressure mechanism that changes over time. Instead, we find it much more plausible that changing dynamics in the conflict
2011 4 5 6 7 8<br>Year log(Earnings).2005 Reals<br>Average Earnings Median Earnings Minimum Wage 1995 2003 2012<br>1995=1 kdensity<br><!-- End of picture text --> Notes.

Panel A: Median and average labor monthly earnings are calculated with the estimating sample (formal, informal and self-employed of ages 18-65). Negative earnings and the 99th and 1st percentiles of earnings distribution are trimmed. The minimum wage is the national monthly minimum wage. All series are deflated using 2005 Consumer Price Index (CPI) from the World Development Indicators. Panel B: Kernel density functions for all years use the same bandwidth, 0.7. Source: Pesquisa Nacional por Amostra de Domicílios PNAD.

28
countries for which there are two or fewer census data points, it is impossible to meaningfully interpolate missing census totals or bilateral numbers. In these cases the census totals detailed in the Trends in International Migrant Stock are used. This has the advantage of ensuring consistent totals for the number of migrants in each of the five census periods. The average bilateral shares from the censuses with data are then applied to these totals to derive bilateral data for each census round.

Finally, there are six destination countries for which bilateral data are completely lacking.<sup>22</sup> In these cases, data for all the other countries in the subregion are used to 19 The 2008 revision includes data only for 1990–2010. To ensure consistent figures over time, the 2005 revision, which covers 1960–2005, was used instead.

20 Taiwan, China, and Norfolk Island pose an additional problem, since the United Nations does not provide data for these locations, so migrant totals in other years cannot be calculated. For these two areas, therefore, the numbers of migrants are set to zero in the earlier decades for which data are lacking.

21 In the case of Palestine, for which the UN totals consist entirely of refugees, these totals are not removed. It is possible to calculate migrant totals for Palestine in other decades.

22 The six countries are China, Eritrea, Maldives, Qatar, Somalia, and Democratic People‘s Republic of Korea. Of these, , Eritrea, and Somalia have been affected by conflict. China has conducted censuses over the period, but their definition of migration is not compatible with the definitions used throughout the article.

14
FAO. 2013. “The State of Food and Agriculture 2013: Food System for Better Nutrition.” Rome.

- ———. 2021. “Minimum Dietary Diversity for Women: An Updated Guide to Measurement - from Collection to Action.” Rome.

- FAO, IFAD, UNICEF, WFP, and WHO. 2020. “The State of Food Security and Nutrition in the World 2020. Transforming Food Systems for Affordable Healthy Diets.” Rome. https://doi.org/10.4060/ca9692en.

Gassara, Goudja, and Jihua Chen. 2021. “Household Food Insecurity, Dietary Diversity, and Stunting in SubSaharan Africa: A Systematic Review.” _Nutrients_ 13 (12): 4401. https://doi.org/10.3390/NU13124401/S1.

GBD 2017 Diet Collaborators. 2019. “Health Effects of Dietary Risks in 195 Countries, 1990–2017: A Systematic Analysis for the Global Burden of Disease Study 2017.” _The Lancet_ 393 (10184): 1958–72. https://doi.org/10.1016/S0140-6736(19)30041-8.

Global Diet Quality Project. 2022. “Measuring What the World Eats: Insights from a New Approach.” Geneva and Cambridge, MA. https://doi.org/10.36072/dqq2022.

Grantham-McGregor, S., and Charles C Ani. 2001. “A Review of Studies on the Effect of Iron Deficiency on Cognitive Development in Children.” _The Journal of Nutrition_ 131 (2S-2). https://doi.org/10.1093/JN/131.2.649S.

- Guedes, Larissa Fernanda Fonseca, Mariana Santos Felisbino-Mendes, Aline Siqueira Fogal Vegi, Adriana Lúcia Meireles, Mariana Carvalho de Menezes, Deborah Carvalho Malta, and Ísis Eloah Machado. 2022. “Health Impacts Caused by Excessive Sodium Consumption in Brazil: Results of the GBD 2019 Study.” _Revista Da Sociedade Brasileira de Medicina Tropical_ 55 (suppl 1
)|
|Lag Rain in cm|0.0292|0.0294|0.0294*|0.0300*|0.0269|
||(0.0189)|(0.0188)|(0.0177)|(0.0177)|(0.0181)|
|Lag 2 Rain in cm|0.0381**|0.0372**|0.0376***|0.0382***|0.0319**|
||(0.0158)|(0.0155)|(0.0138)|(0.0141)|(0.0156)|
|Constant|-0.0381|-0.0396|-0.0487**|-0.0621**|-0.0688**|
||(0.0294)|(0.0317)|(0.0237)|(0.0264)|(0.0290)|
|Month FE|Yes|Yes|Yes|Yes|Yes|
|Health Post Area Controls|No|No|No|Yes|No|
|Health Post x Month Obs|432|396|432|432|432|
|R-squared|0.509|0.512||||
|Hausman Test Comparing Co|lumn 1 and|Column 4|p-|value=0.008|3|

Testing Predictions

|Test Lag Imported =Lag Non-Imported|p-value=0.650|
|---|---|
|Test Imported=1|p-value=0.792|
|Test Lag Incidence=0.273|p-value=0.0247|
|Test Imported + Lag Incidence=1|p-value=0.0428|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.
. As shown in Figure A1, the fraction of those who did not migrate and who are self-employed in rural and semi-urban areas (the areas covered by the BRMS) is fairly stable over time. In short, we do not observe any noticeable association between the share of self-employment in Bangladesh and large fluctuations in oil prices over time.

As the instrument primarily operates through its effect on the duration of stay abroad, another potential threat is that duration of stay may affect self-employment odds after return in ways that extend beyond effects on total earnings abroad. This could occur, for example, if a longer duration abroad reduces the likelihood of finding a wage job back in Bangladesh, due to a loss of social networks and contacts with local employers. However, as we have previously discussed, self-employment is typically preferred by workers in Bangladesh. Therefore, it is unlikely that workers who spent a longer time abroad would turn to self-employment because they encounter more frictions in finding a wage job. To provide further evidence on this,

> 23The ratio is around 1.2 percent in agriculture and 0.2 percent in retail.

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)|(0.305)|
|Observations|1,579|1,574|1,574|601|601|601|
|_R_<sup>2</sup>|0.024|0.485|0.486|0.045|0.115|0.115|
|Number of frm|223|223|223|152|152|152|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

**_Source:_** Data for the study comes from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII).

**_Note:_** Table report further robustness check using the average ex-ante trade liberalization tariff instead of the initial tariff to replicate the main results. Robust standard errors clustered at three digit industry level in parentheses. _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1.

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|0.10|1.01|
|Tunisia|0.95|2.91|0.14|0.01|0.14|
|Turkey*|0.92|24.68|1.91|0.02|2.04|
|Uganda|0.51|9.98|4.93|0.11|6.49|
|Ukraine|0.91|17.22|1.52|0.03|1.37|
|Uzbekistan|0.94|7.06|0.35|0.01|0.39|
|Vietnam|0.88|25.40|3.00|0.03|3.03|
|Zambia|0.52|3.48|1.65|0.09|2.15|

**Note** : Number of inadequate households extrapolated from the AHI, average HH size, and overall number of population (Source: Authors’ calculation based on countries’ available household consumption and expenditure survey and WDI (2019)). * Due to absence of rural / urban differentiation in HCES, stock deficits are calculated based on overall AHI, total population, and total average HH size for these countries.

# **_5.2 Flow Deficit_**

Given unprecedented population growth rates that are projected to add millions of people by 2030, many more additional HHs will require adequate housing within the next decade. Based on projected urbanization rate and population growth, we estimate that at least 40 million _additional_
<!-- Start of picture text --> 0.6<br>0.5 predictions based on district market prices<br>observations based on trader survey<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 1 2 3 4 5 6 7<br>unit transaction costs in constant Malawi kwacha<br>12<br>10 prediction based on district market prices<br>observations based on trader survey<br>8<br>6<br>4<br>2<br>0<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%<br>% share of transaction costs in sales price<br>kernel density<br>kernel density<br><!-- End of picture text --> _Source_ : Author’s calculations;

* The figure shows kernel density estimations of predicted and observed unit transaction costs in Malawi kwacha per kilogram of maize (constant 2000 prices), in levels (upper panel) and expressed as a percentage of sales prices of the supplying district (lower panel). Predictions are based on district market prices, and observations are obtained from a trader survey (Fafchamps et al., 2005).

|**Table 2**<br>|**Comparison of **|**Predicted an**|**d Observed **|**Transaction **|**Costs* **||
|---|---|---|---|---|---|---|
|**Rural region,**<br>**urban areas**||**level**|||**Share**||
||predicted|observed|mean test
Figure 8: Explaining change in sales: Country-level versus firm level attributes <!-- Start of picture text --> (a) Shapley decomposition of change in sales (b) Elasticity of change in sales to severity<br>80 0<br>72.1 72.5<br>60<br>-.1<br>40<br>-0.161<br>-.2<br>20 17.9 14.4<br>0 3.7 6.4 0.1 4.9 8.0 -.3<br>Wave 1 Wave 2<br>-0.342<br>Firmcharacteristics Country fixedeffects Severity of the crisis(weighted Googlemobility index) -.4<br>Change in saleswave 1 Unexplained variance Wave 1 Wave 2<br>R2 explained by group<br>Shapley Value (%) - Change in sales<br>Change in sales elasticity<br>1 point increase in severity of the crisis<br><!-- End of picture text --> _Third_ , part of this heterogeneity in recovery is related to firm size and sector. The gaps observed in improvements across firm sizes and sectors, documented by Apedo-Amah et al. (2020) are still relevant in the recovery process. Micro and small firms have seen their sales improve but still are 36% and 32% below 2019 levels (Figure 9, panel a). More importantly the gap with medium and large firms increased, especially in relation to large firms. Thus, the gap in sales recovery between smaller and larger firms has widened, and micro and small firms are recovering at a slower pace.<sup>15</sup> Likewise, the gap in recovery for the hospitality sector is still large and the drop in sales is far below its pre-COVID-19 levels in sales, -45%.<sup>16</sup> Other services, while improving is also -
The increase in CPS enrollment is fueled in large part by a large substitution from IPS to CPS (IPS enrollment was 24.7 pp lower at midline and 20.2 pp lower at endline) which was by design as the new CPS replaced the IPS (Table 6). There was also a more moderate reduction in SPS enrollment (SPS enrollments were 5.6 pp lower at midline and 5.2 pp lower at endline). This reduction in SPS enrollment may affect how to interpret our estimates. First, since SPS are expected to outperform IPS, the substitution would have the effect of driving down the ITT estimates of the impact of the program on child development, particularly in T2. Second, as detailed in Section 4.3, the bounds exercise relies on the assumption that _LATEac ≥_ 0. The reduction could be driven by two mechanisms: first, parents actually switching to SPS as a response to the program; second, the slight baseline imbalance in the availability of SPS (significant only in T2). Although we cannot empirically distinguish between these mechanisms, the consistency between the SPS availability and enrollment (Table 5 and Table 6) suggests that it is the imbalance that explains the result. In addition, the fact that SPS have higher quality than CPS makes it less likely that parents would switch. Both mechanisms would have the effect of driving down the ITT estimates of the impact of the program on child development, especially in T2.

Using the preschool enrollment data, we can calculate the share of _h-_ and _acompliers_ ( _Sac_ in Section 4.2). At midline, 52% of the sample are compliers, and 28% are _a-compliers_ . Therefore, the share of _a-compliers_ (among the compliers) is _Sac_ = 54%(=28/52) at midline. At endline, the total share of compliers goes down to 32% and is composed of a large majority of _a-compliers_ (81%).

In addition to enrollment on the day of the survey visit
rights index, Recovery rate and Quality of judicial process index, representing legal framework, process efficiency and quality of civic and community justice, respectively. We calculate an average percentile rank of these three indicators, where a higher percentile rank indicates weaker corporate insolvency institutions/higher corporate insolvency vulnerabilities.

**Exhibit 2. Full sample coverage by region (2019Q4)**

||**Number of**<br>**Firms**|**Number of**<br>**Countries**|**Total**<br>**Corporate**<br>**Debt**<br>**(Billions**<br>**US**<br>**dollars)**|**Total**<br>**Corporate**<br>**Asset**<br>**(Billions**<br>**US**<br>**dollars)**|**Median**<br>**Country**<br>**Corporate**<br>**Debt to**<br>**GDP,**<br>**percent**|**Median**<br>**Country**<br>**Corporate**<br>**Asset to**<br>**GDP,**<br>**percent**|
|---|---|---|---|---|---|---|
|**EAP**|6,992|9|2,459.3|7,943.2|13.4|44.5|
|**ECA**|3,138|20|526.2|2,009.4|4.2|16
concentrated in one particular geographic area, but rather firms like these are present throughout the region.

The GPS mapping also helps allay a second concern of knowledge spillovers to control group businesses from businesses in the treated groups. Note that such spillovers will only undermine treatment effects, nevertheless, our study design minimizes the potential for such effects. Appendix Figures 2a and 2b zoom in on two street level locations and show that businesses in our sample

> 10 The FinScope survey did not elicit interest in business training, nor did it ask whether firms were operating out of an independent physical structure. Hence, it is difficult to precisely replicate the screening process of our study sample.

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In this paper, we examine the relationship between e-commerce development and household consumption growth in China using three data sources: Alibaba e-commerce data, household survey data, and official statistics. First, we construct a series of indicators to measure the development of e-commerce at the county level using online sales and purchase information provided by the Alibaba Group, gross domestic product (GDP) from the China Statistical Yearbook, and population drawn from the Population Census 2010. Second, we merge the county-level e-commerce development measures with household consumption data obtained from the CFPS survey administered by Peking University.

# _<u>Indicator construction</u>_

We measure county-level e-commerce development on three dimensions: penetration, intensity, and market size (as a share of the national online market). For each of the three dimensions, we construct two indicators measuring, respectively, the online sales and purchase using the amount of online sales and online purchases (Gross Merchandise Value, or GMV), numbers of online sellers and online purchasers, and numbers of packages sent and received associated with online transactions. The three indicators are comparable over time. Therefore, compared with Alibaba’s three official e-commerce indicators, Alibaba e-commerce development indicators (aEDI), OBI, and OSI, which use different weights across years, our three indicators are comparable over years.

We constructed the indicators in three steps. First, we extracted the original indicators for each index. Second, we normalized the indicators and transformed them into z-scores for easy comparison.<sup>13</sup> The number of buyers, annual online purchase GMV, national online purchase GMV, and total number of national online buyers are from Alibaba. GDP is obtained from China Statistical Yearbooks. Residential population is from the Population Census 2010.<sup>14</sup> The three sets of indicators are defined as follows:

# _E-commerce penetration indexes:_

> 13 See Appendix 1 for a detailed description of the construction of the indicators.

> 14 Measures using different sources