rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 1: Key Data and Sources**

|**Data Requirement**|**Source**|
|---|---|
|Energy Balances of Romania|<br>National Institute of Statistics, Romania; International<br>EnergyAgency (IEA)|
|Resource Potential,<br>including imports/exports|<br>Various sources published by Romanian Regulatory<br>Authority for Energy (ANRE), Ministry of Economic<br>Affairs|
|Installed capacity and<br>characterization of new<br>technologies for electricity<br>generation, heating and CHP<br>plants|<br>ANRE, Electricity Utilities (Electrica, Hidroelectrica,<br>Nuclearelectrica)<br><br>IEA Clean Coal Centre Database, Energy Information<br>Administration of USDOE<br><br>World Bank: Private Participation in Renewable Energy<br>Database|
|Load Profile|<br>European Network of Transmission System Operators for<br>Electricity|
|Fuel prices projections|<br>IEA World EnergyOutlook(2013)|

**Table 2. Definition of scenarios considered in the study**

|**Scenario**<br>**Name**|**Scenario Definition**|
|---|---|
|Baseline|It is an extrapolation of the current state of the energy sector including already<br>planned or implemented mitigation measures, in particular ongoing implementation<br>of the current EU 2020 climate and energy package, which sets an EU-wide target to<br>reduce GHG emissions by 21 percent in energy-intensive sectors, which participate<br>in EU emissions trading, compared to 2005. However, it does not include broader<br>reforms that the energy supply system needs to implement in line with the EU’s long-<br>term plan to reduce carbon emissions.|
# **5. Conclusion**

In this paper, our ultimate objective was to uncover the impact of weather shocks and aid receipt on household consumption outcomes. To do so, we first looked at where weather shocks had the largest impacts on maize yields, as we expect that negative impacts will occur primarily through lower agricultural outcomes. Point estimates give substantial negative impacts of droughts ranging from 33 to 40 percent across the two samples, and even greater negative impacts of floods ranging from 47 to 54 percent across the samples. We next considered the relationship between predictors of maize yields and WFP distribution rates, since we expect that well-targeted distribution would occur where maize yields suffered most. There are a number of different GIS-based data sources and specific variables on which to construct measures of droughts and floods, and our analysis suggests that using different variables from different sources matters when defining droughts and floods. This is important because geographic targeting of aid distribution is also based in part on specific GIS-based weather shock variables. Our results show that weather shock variables that explain maize yields do not explain aid distribution. Even more disconcerting is the fact that two rainfall variables that explain aid distribution also have significant impacts on maize yields, but exactly in the opposite direction vis-à-vis the expected impacts. These results suggest that there were significant geographic targeting errors.

Subsequently, we estimated the households’ probability of aid receipt, and aid receipt interacted with a weather shock. Those living in areas subject to a weather shock and with higher WFP distributions were more likely to receive food aid, as we expect. However, the interaction between aid distribution and the weather shock was not significant. The targeting errors also explained household aid receipt, indicating errors of exclusion (negative impact on aid receipt for households subject to weather shocks in poor districts with limited aid distribution) and errors of inclusion (positive impact on aid receipt for households not subject to weather shocks in non-poor districts with positive aid distribution). At the household-level, we documented that households headed by women and
the EU can also provide small Balkan economies with greater stability and financial security, facilitating their ability to invest in long-term capital (i.e. investments in infrastructure, education, research and development, and other productive sectors of the economy) that ultimately promotes sustainable development and contributes to reducing poverty.

Against this background, this paper quantifies the potential benefits of more international integration. It uses various sources of microdata and simulates different scenarios using recent advancements in quantitative spatial models.

The paper is structured as follows. First, it describes the different data sources used in the analysis. The paper uses various sources of information, such as the I2D2 database, which reports employment, earnings, place of residency, industry variables, and aggregate information from the Working Time Statistics database held by the International Labour Organization (ILO). In doing so, this paper combines datasets specific to the Western Balkans with other sources for the EU. Second, the paper presents a range of descriptive statistics relevant to the discussion. Finally, it uses a quantitative spatial model (QSM) to understand the impact of market integration policies in the region. Using this QSM model, the paper simulates different counterfactual analyses to understand the impact of interventions such as reducing transit time at border crossings, resulting from the implementation of the National Single Window for Trade, investments in border management systems, and upgrading of border crossing points; accession of the Western Balkan economies to the EU; and the provision of improved road infrastructure. Section 4 describes the model and section 5 explains the counterfactual analyses.

Overall, the paper finds that market integration policies can effectively generate economic gains in the region, especially if cross-border coordination exists in the policy reform agenda and across policies. For example, reducing transit times at border crossings generates gains in terms of real income of around 2.5% in the region. Both cross-border coordination in the trade facilitation reform agenda and EU accession amplify these gains significantly. Similarly, EU accession boosts the benefits of road infrastructure improvements. In
are categorized as ranking “high” in WASH. **However, it is important to note that a perfect score of 1 indicates relatively better WASH but not necessarily adequate WASH based on JMP standards. Therefore, it is most useful to read these results to compare across upazilas keeping in mind that bad scores indicate very poor WASH in CCs but good scores do not necessarily reflect adequate WASH.** Similarly, the upazila level poverty probabilities and stunting estimates are also rescaled to range between 0 and 1, and then categorized into “low” or “high” poverty/stunting based on the 0.5 score cut‐ off. Again, “low” and “high” should be read in relative terms only.

Using the WASH, poverty and stunting indices, we identify and map upazilas in Bangladesh that rank high in poverty and in stunting in relation to their WASH status. The base maps for the upazilas are from the Global Administrative Areas database.

The Status of WASH in Community Clinics: A Snapshot

**The community clinics in Bangladesh still have a long way to go to achieve adequate WASH facilities that are critical to providing safe, hygienic environments that help promote proper treatment and well‐ being of patients.** Overall, the rapid assessment data shows that adequate handwashing facilities at CCs are becoming near universal, whereas adequate water and sanitation are far from adequate.

**A large majority of the community clinics had limited WASH facilities only.** Table 3 shows the number of CCs that met individual WASH category criteria. Less than 36 percent of the community clinics had basic water supply, i.e. a functional improved water source. Around 30 percent did not have any functional sanitation facility. Only 16 percent had two functional latrines on premises. According to the 2008 WHO guidelines, HCFs must have at least 4 functional, _Table 3:HCFs with limited/ basic WASH, by category_ improved sanitation facilities on premises.  Less than 2 percent of the community clinics met this WHO _WASH
, while none are attributable to Barisal. The reason for missing data for Barisal is unclear. While we had to exclude several PBS Zones in Barisal due to reporting and date issues, it is unlikely that Barisal did indeed not experience any storm-related outages in the included zones. Anecdotal evidence – e.g. reported by local newspapers – suggests that Barisal did indeed experience storm-related outages in the period covered by the data.<sup>20</sup> Thus, this division is excluded in the rest of this study.

On average, he longest storm-induced outages are experienced by Rangpur’s population while in Dhaka and Sylhet such disruptions are shorter but more frequent power disruptions: In other words, a storm in

> 18 The World Bank Doing Business measure for instance estimates that firms in the USA experience 0.5 power outages per year on average. In Bangladesh, no such number is reported –the Doing Business methodology does not report a number if outage frequency exceeds 100 times per year, as no accurate estimate is possible.<sup>18</sup> In other words, disruptions in the electricity network in Bangladesh are far more frequent than serious storms.

> 19 The analysis that follows will be conducted at the division level, to accommodate the climate data.

> 20 https://www.thedailystar.net/backpage/hailstorm-rain-take-country-unawares-660337

15
But there is also a temporal aspect to fear. Asset prices can be used to show how expectations change with political violence. Zussman and Zussman (2006) and Willard et al. (1996) show, for example, that asset prices during conflict react to important conflict events like battles or cease fire agreements. Besley and Mueller (2012) show that house prices seem to react to changes in expectations rather than violence itself. This implies that even once violence is over it can be that asset prices stay suppressed. Parts of the economy will then only recover when peace is regarded as stable. This generates huge problems in fragile states which, as the word suggests, are not regarded as stable. Poverty reduction in these countries might rely as much on changing perceptions of political stability as on economic help.

This fear effect also has a strong impact of how the effect of local violence should be captured at the macro level. If the effect of violence is only contemporaneous then we do not expect conflict to affect poverty beyond the conflict period. However, if expectations are important, we expect economic effects to persist as with a debt that needs to be repaid after conflict has ended. Expectations will then also lead to the sort of patterns we find in the poverty data.

# Poverty and the Cost of Crime

There is no robust empirical relationship between homicide rates on growth rates at the macro level, neither for regions within counties nor across countries. This is puzzling given the extreme levels of violence associated with crime in some regions. A possible reason for this lack of a relationship is that crime does not affect expectations in the same way as political violence. Also, crime rates move much more slowly and are therefore not causing the same dramatic disruptions or refugee waves as political violence.

This does not mean that violent crimes might not have a profound impact on the economy. There is a large related literature that calculates the cost of crime (See Soares (2009) for a review). The standard accounting approach
their recent experiences with conflict. Imputation techniques could be invaluable in fragile and conflict-affected regions, offering a way to circumvent the challenges and practical difficulties of extensive crop-cutting surveys. These techniques provide a more efficient and cost-effective method for estimating crop yields, enabling timely and reliable data analysis. Moreover, they can address data collection disruptions caused by conflict or political instability. By imputing missing values, we can maintain the continuity of data series, leading to more complete and consistent datasets for analysis. This is crucial for assessing agricultural trends and evaluating interventions in situations where direct data collection is challenging.

Another practical application of survey-to-survey imputation of crop yields is seen in the WAEMU harmonized survey program. Here, national statistics offices in West Africa collect plot-level agricultural information while conducting parallel farm surveys with crop-cut operations either in the same or the previous year. An across-survey imputation model could effectively fill these crop-cut data gaps.

In this study, we focus on evaluating the validity of the imputation procedure by forecasting (and backcasting) mean crop-cut yields in the 2018 (2017) dataset using the 2017 (2018) cropcut sample as the training sample.

# **3 Data**

We have access to data from two nationally representative surveys of rural areas in Mali, conducted during two successive agricultural campaigns. The Enquˆete Agricole de Conjoncture Int ́egr ́ee aux Conditions de Vie des M ́enages 2017 (EACI-2017), administered in the 2017-2018 season, covered 8,398 households, while the Enquˆete Agricole de Conjoncture 2018 (EAC2018), carried out in the 2018/2019 season, involved 8,225 households. These datasets were collected by the Statistics Unit of the Ministry of Agriculture, in collaboration with the LSMSISA team of the World Bank. This collaboration resulted in repeated cross-sectional surveys of households (in the same Enumeration Areas, or EAs) that gathered comprehensive data on household characteristics and living conditions. The surveys, with a focus on agriculture, provided detailed and precise plot-level information. For each edition of the survey, households were
82

# **Appendix C: Sources of Raw Data**

|**Variable**|**Source**|
|---|---|
|GDP (constant LCU)|W orld Bank W orld Development Indicators (W DI)|
|GDP is the sum of gross value added by all resident producers in<br>the economy plus any product taxes and minus any subsidies not<br>included in the value of the products. It is calculated without<br>making deductions for depreciation of fabricated assets or for<br>depletion and degradation of natural resources. Data are in<br>constant local currency.||
|GDP per capita (constant LCU)|W DI|
|GDP per capita is gross domestic product divided by midyear<br>population. GDP is the sum of gross value added by all resident<br>producers in the economy plus any product taxes and minus any<br>subsidies not included in the value of the products. It is calculated<br>without making deductions for depreciation of fabricated assets or<br>for depletion and degradation of natural resources. Data are in||
|Population, total|W DI|
|Total population is based on the de facto definition of population,<br>which counts all residents regardless of legal status or citizenship--<br>except for refugees not permanently settled in the country of<br>asylum, who are generally considered part of the population of<br>their country of origin.||
|Average Years of Schooling of Population 15 years and older|Barro and Lee (1993, 2000); Cohen and Soto (2001)|
|Average Years of Schooling of Population 25 years and older|Barro and Lee (1993, 2000); Cohen and Soto (2001)|
|Rates of Return to Education|Psacharopoulos (1994); Bils and Klenow (2000)|
|Educational Quality<br>USPTO Patent
# **Figure 1**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>.2 .4 .6 .8 1<br>Gini head years of education<br>Head years of education<br><!-- End of picture text -->

# **6. Conclusion**

We estimated the effect of income and education inequality on growth, using imputed data on expenditure inequality and growth for small administrative units in Uganda (sub-counties), along with census data for education inequality. Analysing this relation for a specific country has important benefits: first, it avoids data comparability problems that typically affect cross-country growth regressions. Moreover, by identifying the effects of inequality on growth for a given country, country specificity is taken into account. This enhances the relevance of our results for local policy makers.

In the empirical section we adjusted the standard errors of variable coefficients for the fact that some regressors are imputed–in our case initial expenditure levels and expenditure inequality, and therefore associated with a standard error. The 21
three separate component measures, from surveys covering different sets of donor countries and using somewhat different question wording. Coefficients are positive and significant using GI (equation 5.2) and WVS (equation 5.3), but not for Eurobarometer (equation 5.4). Each 10-point increase in the percentage of GI respondents (or 5-point increase for WVS respondents) is associated with an increase in use of country PFM systems of more than 4 percentage points.

Equation 6.1 shows that results for the index of public support are robust to the inclusion of sector shares and other donor-level control variables from Table 4. Public support is weakest for the USA, providing a possible explanation for its extremely low usage of country systems as shown in equation 4.4. The USA dummy coefficient remains strongly significant, however, when it is tested together with public support for aid in equation 6.2. The coefficient magnitude of -34 is not so different from the -37 in equation 4.4, so the outlier status of the USA is largely unexplained by the substantive variables in the analysis.<sup>19</sup> The USA is an influential observation however in testing the link between public support for aid and use of country systems. The public support index is significant only at the .06 level in equation 6.2, with a coefficient only half as large as in equation 6.1.

Public support for aid is potentially endogenous to its effectiveness, which may in turn be influenced by donor‟s use of country systems. The opinion surveys were all conducted between 1995 and 2004, prior to the 2005-2010 period for which the PDMS measures use of country systems. This timing sequence reduces but does not eliminate the potential for reverse causation. We therefore instrument for public opinion in equation 6.3, with first-stage results shown in

> 19 The large negative USA coefficient reflects its relatively tepid support for some of the PD principles.  Due to U.S. opposition the PD omitted
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https://
Table A4. Contributions of global shocks to alternative measures of global inflation: Robustness checks

||||Struc<br>|tural Shocks<br>||
|---|---|---|---|---|---|
|Sensitivity|Proxy variables|Oil<br>Price|Global<br>Supply|Global<br>Demand|Global<br>Interest rate|
|Core CPI|Baseline<br>Global Factor Measures<br>(Simple average of G7)<br>Global Factor Measures|6.9<br>[0.5 14.5]<br>11.9<br>[1.7 25.0]<br>17.9|41.1<br>[8.3 77.3]<br>41.9<br>[11.7 65.9]<br>38.5|25.7<br>[1.9 55.6]<br>25.5<br>[2.9 55.8]<br>23.2|26.4<br>[2.5 54.3]<br>20.7<br>[1.9 45.4]<br>20.4|
||(US variable)<br>Alternative narrative approach|[0.8 32.9]<br>13.7<br>[1.8 28.7]|[8.0 71.3]<br>40.9<br>[8.7 73.4]|[4.6 43.1]<br>18.1<br>[1.0 35.5]|[1.0 44.6]<br>27.3<br>[3.5 54.6]|
level. The online GIS-based mapping tool enables an easy access to different types of indicators including poverty, demographics of the population, children’s health and nutrition, education, employment, and household access to energy, water, and sanitation services.<sup>15</sup> These maps (see maps in supplementary Figure S1) were constructed by combining three different data sources all of which are publicly available: (i) 2010 Bangladesh Poverty Maps, (ii) 2011 Bangladesh Census of Population and Housing, and (iii) 2012 Undernutrition Maps of Bangladesh. Upazila-level GIS data with a geographic projection (WGS 1984 Web Mercator coordinate system) have been used in this study.

Upazila-level total population and percentage of poor population (i.e., percentage of the population that lives below the official national upper poverty line, which is based on household's poverty status assessed using per capita consumption) are shown in Figure S1.

27