rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Total consumption expenditure<br>6 months 12 months 18 months<br>Y<br>8 ‘8 Z 8<br>nN / N / N<br>/ /<br>a a \V ene a<br>/ yo Ne / yrs e-<br>si= 7 S= 3= V J<br>-~-7- sTN “<br>fr i N<br>o / Nog / o 7 ~>s<br>/ / / / \<br>/ / / / \ \<br>/ / \ ‘<br>8 8 gy /<br>Le<br>0 2 4 6 8 1 O 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Food Consumption Score (standardized)<br>6 months 12 months 18 months<br>,<br>* = jo<br>~ ‘ /<br>SY oy oD \ /<br>-” SLL? ~= \ / aeof<br>” \ 7 ” Fa 7 ~K =<br>\ Y --<br>\_/7<br>x “ n<br>“4 L--eo \ \ = ~~ yy _ TM = ~-= ~‘\<br>\ \ 7 / \<br>° \ \ NUZ Z 4 / \<br>\ ° ° “ \<br>a<br>02 4 6 8 1 O 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!--
for Child<br>Mortality Estimation.Third on accuracy of debt reporting as classified by the World<br>Bank (Source: World Bank WDI metadata). Fourth, on availability of safely managed<br>drinking water data for use by WHO/UNICEF Joint Monitoring Programme. Fifth, on<br>labor force participation data for use by ILO. While these data sources provide only a<br>partial coverage of data used by international organizations, they do provide an<br>indication of the performance of the national statistical system.|
|Dimension 2.1: Data<br>releases|SDDS/e-GDDS subscription. This indicator is based on whether the country subscribes<br>to IMF SDDS+, SDDS, or e-GDDS standards. The source is the IMF Dissemination<br>Standards Bulletin Board. This is a reliable data source but we recognize that it is a<br>proxy for the concept we are seeking to capture rather than a direct measurement.|
||ODIN Open Data Openness score (Jamison Crowell et al. n.d.). This is a well-<br>established data source with good country coverage, which scores countries based on|
|Dimension 2.2: Online<br>access|whether indicators are available online in a format that is machine readable, in a non-<br>proprietary format, downloadable, with metadata available and terms of use. Scores<br>range from 0-1. For more details, consult theODIN technical documentation|
|Dimension 2.3:|Not included because of lack of established methodology. This could be a new<br>indicator of the number of non-recurring products on NSO website (ad|
|Advisory/ Analytical<br>Services|<br>hoc/experimental rather than regular releases). The indicator is the number of products<br>found. No established source exists for this indicator.|
|Dimension 2.4: Data<br>
(2006). Cruces and Trebesch (2013) estimate a 37% average haircut for the sample period 1978– 2010 and a 50% average haircut during 1998–2010.

# 3.5. Strong association between financial stress and banks’ total exposure to the public sector

Regression estimates suggest that financial sector and banks’ total exposure to the public sector are strongly associated, even after controlling for global financial conditions (i.e., the VIX index). Spreads on sovereign dollar‐denominated debt appear to price this in (Table 2). To explore this link, we run panel regressions with country‐ and year‐fixed effects (with standard errors clustered at the country level) to explain the median probability of default (PD) of financial institutions within the next 60 months.

- In a sample of 52 EMDEs for months in the period 2000 ‐ 2017, we find a strong association with banks’ total exposure to the public sector to GDP (this includes central, state, and local government and public non‐financial entities). Regressions 1 and 2 suggest that a one percent increase in public exposure raises the PD by 0.16%, even after controlling for the VIX, a widely used proxy for global financial stress and risk appetite. This effect is economically relevant since the sample means for banks’ total claims and the PD are 11.7% and 2.2%, respectively, and both exhibit substantial cross‐country variation.

- In a smaller sample of 36 countries, Regressions 3 and 4 show that the EMBI spread is associated with the PD as well, although the effect diminishes once the VIX is controlled for. This may also in part be explained by the fact that the PDs are estimated from equity prices which themselves depend on global financial conditions.

- Regressions 5, 6, and 7 show that banks’ total exposure is no longer significant in the presence of the EMBI spread and an interaction. This suggests that international investors already price in the
were first introduced in the May 2020 round, dropped in rounds 8-11, and then reintroduced in round 12 (May 2021). Thus, in Table 6, the third round serves as a base period and round 12 as the post-war-onset period. The key measure of the impact of the war on food security outcomes is thus the interaction term of round 12 and the Tigray dummy.

Relative to the base period, food insecurity has been declining in most of Ethiopia. Prior to the outbreak of the war, the overall trends in food security outcomes had been similar across Tigray and the rest of Ethiopia, as shown by the statistically insignificant time trends in Table 6. More specifically, the coefficients associated with the interaction terms between the Tigray dummy and pre-war survey rounds confirm that the parallel trend assumption holds for almost all cases. However, after the outbreak of the war, Tigray experienced a statistically differential trend in food security. Compared to the May 2020 round, households in Tigray reported 37 percentage points higher probability of experiencing moderate or severe food insecurity in the May 2021 round. This is almost identical to the aggregate results discussed in Section 5.1, despite the different base (prewar) outcome to which this is compared. Similarly, households from Tigray report 20-22 percentage points higher probability of experiencing food insecurity after the outbreak of the war.

29
# **References**

- **Abel, Joshua and Andreas Fuster** , “How Do Mortgage Refinances Affect Debt, Default, and Spending? Evidence from HARP,” _American Economic Journal: Macroeconomics_ , 2021, _13_ (2), 254–291.

- **Agarwal, Sumit, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet, and Douglas D. Evanoff** , “The role of securitization in mortgage renegotiation,” _Journal of Financial Economics_ , 2011, _102_ (3), 559–578.

- **, , , , Tomasz Piskorski, and Amit Seru** , “Policy Intervention in Debt Renegotiation: Evidence from the Home Affordable Modification Program,” _Journal of Political Economy_ , June 2017, _125_ (3), 654–712.

- **, , Souphala Chomsisengphet, Tomasz Piskorski, Amit Seru, and Vincent Yao** , “Mortgage Refinancing, Consumer Spending, and Competition: Evidence from the Home Affordable Refinance Program,” _Review of Economic Studies_ , 2022.

> **Aydin, Deniz** , “Forbearance, Interest Rates, and Present-Value Effects in a Randomized Debt Relief Experiment,” _Working Paper_ , 2021.

**Barboni, Giorgia and Parul Agarwal** , “How do Flexible Microfinance Contracts Improve Repayment Rates and Business Outcomes? Experimental Evidence from India,” _Working Paper_ , 2021.

- **Battaglia, Marianna, Selim Gulesci, and Andreas Madestam** , “Repayment Flexibility and Risk Taking: Experimental Evidence from Credit Contracts,” _Working Paper_ , 2021.

- **Bolton, Patrick and Xavier Freixas** , “Equity, Bonds, and Bank Debt: Capital Structure and Financial Market Equilibrium under Asymmetric Information,” _Journal of Political Economy_ , 2000, _108_ (2), 324–351.

- **, , Leonardo Gambacorta, and Paolo Emilio Mistrulli** , “Relationship and Transaction Lending in a Crisis,” _The Review of Financial Studies_ , 06 2016, _29_ (10
benefit of 200 Pakistan rupees (PRs) a month ($3.40) for each girl enrolled in grades 6–10 in a government school, conditional on an

> 6 The first distribution of cash benefits was initiated in January 2004, based on enrollment and attendance information for the October–December 2003 quarter.

> 7 Nankana Sahib was formed from the division of Sheikhupura in 2005, and Chiniot from the division of Jhang in 2009.

10
the state level. The survey reports information on the year of marriage, age at marriage, age at cohabitation, age at the consummation of marriage, and age at first birth of ever-married women between ages 15 and 49. We construct a retrospective state-year-level dataset of around 415,000 marriages between 1990 and 2014 across Indian states using this information. This allows us to compare outcomes for women across Indian states married before and after the Gujarat riots of 2002. For a sub-sample of women, the survey also reports information on their socioeconomic statuses, such as their employment status, exposure to intimate partner violence, and their decision-making power, which we use to analyze the effect of the Gujarat riots on women’s post-marital outcomes.

The variables used for analyzing women’s socioeconomic outcomes post riots include women’s years of education and indicators for whether they experienced physical and emotional violence by their husbands. We define physical violence as an indicator for whether the woman was slapped, pushed, kicked, dragged, burned, threatened using weapons, beaten up, physically injured in other ways, or sexually abused by her husband. Emotional violence is an indicator of whether the respondent was humiliated, insulted, or threatened by her spouse. We also include indicators for whether women are employed, have autonomy over their own income, participate in decisions related to household purchases, and make their own decisions about visiting relatives and friends. The sub-sample used for analyzing socioeconomic outcomes consists of around 70,700 women married during 1990-2014.<sup>1</sup> Table 1 reports the sample means of key variables for women married before 2002 across Gujarat and other Indian states. There are some differences between the characteristics of women married before 2002 in Gujarat and other Indian states. For example, in terms of religion, Gujarat has a higher fraction of the Hindu population and a lower fraction of Muslims. Gujarat is also wealthier on average than the other states. The average marriage age for women from Gujarat or other Indian states is approximately
percent state mosque donations, remittances, and government and social services payments as main source of income.

Despite low incomes and low per capita consumption (which nevertheless exceeds incomes), food consumption scores (FCS) on average, across the sample, are acceptable. The mean is 40.5 (>35 is acceptable), dropping to a just-acceptable 36.1 among female-headed households with high standard deviation of 21.8 (Annex C). However, in certain districts households more often have borderline scores (25.5 – 35), for example in Ana, Tikrit and Qa’im (34.6, 30.2, and 26.7, respectively). One explanation for generally acceptable FCS might be the frequent incurrence of debt, whether as cash loans or goods on credit, which smooths food consumption. However, this needs additional data to verify.

Most households in the sample (77 percent) resided in standard housing, with the remainder living in unfinished, abandoned, or public buildings, or informal tent settlements. Just under half (49 percent) share their housing with other families, with an average of 3 families per shared shelter. Over half (51 percent) of the sample have access to private hygiene and water facilities, with just under half (46 percent) saying they shared these with relatives; less than 2 percent said they had no access to water, sanitation, or hygiene facilities.

_Figure 5: Unemployment and per capita consumption (IQD) over the assessment period._ <!-- Start of picture text --> 100% IQD 80,000<br>90%<br>IQD 70,000<br>80%<br>IQD 60,000<br>70%<br>IQD 50,000<br>60%<br>50% IQD 40,000<br>40%<br>IQD 30,000<br>30%<br>IQD 20,000
2020). These results show that various population segments encounter distinct aspects of energy poverty. Efficient targeting strategies must consider which part of the population is affected by what type of energy poverty.

# 3.3. Multidimensional Measures

**Given the multidimensional nature of energy poverty, one approach would involve using a comprehensive array of indicators to construct a thorough understanding of energy.** Among the wide range of multidimensional measures identified in the literature, we construct a multidimensional energy poverty index following the methodology developed by Bouzarovski and Tirado Herrero (2017) and Rodriguez-Alvarez et al. (2021). The advantage of this index is its simplicity and the fact that all the dimensions can be measured with one data source, the EUSILC, and, therefore, can easily be constructed and tracked over time. Tirado Herrero (2017) emphasizes that the suggested index operates under the assumption that relying solely on consensual measures (like self-reported inability to maintain warmth) may not adequately grasp the intricate nature of energy poverty. They suggest integrating indicators describing the population's housing and financial circumstances to comprehensively understand them.

The multidimensional energy poverty index is constructed as a composite index using three indicators from the EU-SILC and weighting them accordingly as follows: E E E P I Where: PPE PPEE IIEEIIEEII= (0.5 ∗IIEEI IIPPEE+ 0.25 ∗AAE EEIIEEAA+ 0.25 ∗LLEEIILLIIE AA) ∗100 27
) provide a spatial estimate of GDP in 2015 USD PPP terms by distributing national GDP estimates from the World Development Indicators at 5 arc-min (0.0833 degrees or 9.3km at the equator) resolution using subnational value-added estimates from Gennaioli et al. (2013) and population from the HYDE 3.2 database.

Using Eq. 4, I compute MA maps using both measures of economic mass ( _Lj_ ), and trade elasticities of _θ_ = 1 (Peng & Chen, 2021) or _θ_ = 3 _._ 8 (Jedwab & Storeygard, 2022). As Eq. 4 excludes cell _i_ itself, the centers of several large cities have a lower MA measure than adjacent cells. To remedy this, I add _i_ ’s GDP/wealth to the MA measure using a distance _δij_ = 25km/ _κi_ , where _κi_ is cell _i_ ’s RE/TE measure from Figure 4. Figure 5 shows the resulting maps with _σ_ = 1 and Appendix Figure A4 with _σ_ = 3 _._ 8.

Evidently, _θ_ = 3 _._ 8 yields a much stronger concentration of MA around major cities and a high correlation of the MA measure with cell GDP. Both _θ_ values elicit northern Egypt, Nigeria, and, to a lesser extent, greater Johannesburg as high MA areas. Cairo has the greatest MA.

# **3 Barriers to Cross-Border Trade**

Africa has ’thick borders,’ and any evaluation of continental roads should at least be mindful of them. Trade barriers can broadly be distinguished into traditional trade tariffs and regulatory obstacles and frictions at the border. This paper will concentrate on the latter as tariffs and other non-tariff barriers tend to be product- and quantity-specific. But, for the sake of completeness, I begin with a brief overview of tariffs applied by African countries in international comparison.

## **3.1 Trade Tariffs**

The World Development Indicators provide aggregate estimates of the extensive and intensive margins of trade tariffs applied by each country.
# **1 Introduction**

Accurate measurement in agricultural survey data is key to official agricultural statistics and central to tracking progress towards national and international development goals. Recent work has shown that there is systematic measurement error in agricultural survey data on a range of topics, including cultivated area, crop production, yields, and crop variety, among others (Carletto et al., 2017; Abay et al., 2019; Kosmowski et al., 2019; Lobell et al., 2020; Gollin and Udry, 2021; Kilic et al., 2021). Such mismeasurement creates challenges for generating unbiased point estimates, making valid inferences, and, ultimately, for providing sound policy recommendations.

Lacking from this strand of empirical research is an exploration of the consequences of measurement error in remote sensing weather data. The goal of a remote sensing weather product is to document an objective fact: that is, the volume of precipitation or the temperature in a given location at a given time. Inaccuracies introduced by either the sensor (e.g. infrared, microwave, optical), the algorithm used to convert sensor data into rainfall or temperature (e.g. reanalysis, interpolation), or the resolution of the data (e.g. spatial, temporal) means remote sensing products may mismeasure the objective fact. Simply with respect to the “raw” weather data, there can be substantial variation in what a remote sensing product reports as the actual rainfall or temperature in a given location. Figures 1 and 2 show this variation across six remote sensing precipitation products and three temperature products. One precipitation product reports rainfall of 0-5mm in the southeast corner of the grid cell while a different product reports 47-64mm for the same location on the same day. Temperature also varies by remote sensing product, with one product reporting a maximum temperature of 23 degrees Celsius while another reports the maximum temperature that day as 27 degrees Celsius.

In this paper, we quantify the significance and magnitude of the effect of measurement error in
# **3 Data and methodology**

### **The objective of this fiscal incidence analysis is to analyze the impact of taxes and social expenditure on**

**households’ welfare, poverty, and inequality in the West Bank and Gaza.** Although designing and implementing fiscal policies should consider different dimensions—including fiscal sustainability, economic efficiency, externalities, and equity—the motivation for performing our analysis is fourfold:

- (i) There has not been a comprehensive distributional assessment of the country’s fiscal system.

- (ii) The current government is operating in an environment of increasing fiscal pressure, with an urgent need to mobilize revenue and increase fiscal space.

- (iii) Given the above, findings from the fiscal incidence analysis create an evidence base for how to promote tax and expenditure reforms consistent with both fiscal sustainability and fiscal equity.

- (iv) In addition to helping assess distributional repercussions of existing fiscal policies, developing detailed microsimulation models for fiscal incidence analysis also creates a platform to simulate distributional impact of potential policy reforms.

## **3.1 Data sources**

### **The fiscal incidence analysis in the Palestinian Territories uses the following data sources:**

- The most recent, nationally representative household survey, the Palestinian Expenditure and Consumption Survey (PECS) 2016/17, is a multi-purpose survey on household budget and living standards used to estimate official poverty statistics for the Palestinian Territories. PECS was implemented by the Palestinian Central Bureau of Statistics (PCBS). The PECS 2016/2017 is a 12month survey covering 3739 households and implemented from October 2016 to September 2017. The survey has information on income, employment, consumption, health, and education.

- Tax legislation and comprehensive administrative data on direct and indirect taxes, tax rates, tax collection, and executed government expenditure data from MoF and World Bank teams.

- Social protection data from the main social programs on budget, beneficiaries, and allocation rules from the MoSD
Furthermore, we demonstrate how our algorithmic accuracy is affected based on 1) the type of satellite data used (optical only, radar only or both) - given the considerable differences in the complexity and costs of imagery processing across the various options, and 2) whether plots under specific area thresholds are excluded from the training data - given the potential concerns around the mismatch between the relatively small scale of farming in Malawi and Ethiopia and the Sentinel-2 imagery used in our analysis.

The analysis leverages three national multi-topic household surveys that have been implemented by each country’s national statistical agency over the period of 2018-2020 with financial support from the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative. The surveys include detailed, plot-level data on crop farming and georeferenced plot locations. Each data set offers a representative snapshot of the smallholder production system in each country for a given reference season. Linking georeferenced plot-level survey data to publicly available Sentinel-2 imagery and other ancillary geospatial data for the reference agricultural season, we conduct a rich array of sensitivity analyses to assess how crop type prediction accuracy changes when trained on different subsets of plot observations in the survey data - not only in terms of the plot observation count but also the approach to georeferencing plot location. Each data subset was designed to simulate a specific ground data collection scenario. We simulate conditions where, for example, only a certain amount or quality of data is available to train a model, and then compare the out-of-sample prediction accuracies across the scenarios. The results of 26,250 _in silico_ experiments shed light on the ground data needs that should be met for household surveys to plan a more enabling role in satellite-based crop type mapping. After identifying the best available model, we apply it to map areas cultivated with maize across Malawi and Ethiopia at 10-meter spatial resolution.

There are six headline findings that emerge from our analysis. First, a simple machine learning workflow can classify pixels with maize cultivation with up to 75 percent accuracy - though the predictive accuracy varies
By using the interdiction technique on thousands of links, this paper shows how to select the most critical links. Ideally this phase would be conducted in close collaboration with policy makers, who are generally well aware of the most critical links and can bring nuances to the selection. Analysts can easily take into account additional qualitative information on the strategic or economic relevance of some links to improve the decisions. The combination of quantitative information on disruption costs and knowledge of the local challenges allows the identification of the most important areas of the network for further analysis.

This paper then demonstrates how to identify the most exposed of these links to natural hazards by overlaying the network with hazard data and factoring in different parameters of their vulnerability – such as flood depth and duration, traffic rerouting, and structural damage. Finally, by running hundreds of scenarios of possible events and their impacts, it applies a robust decision-making approach to guide an analysis of policy options available when a road network is exposed to unpredictable climate events.

More data are needed to evaluate the full economic and social costs of disruptions and improve the economic analysis on the most vulnerable links of Peru’s road networks. In addition, other nonengineering solutions exist to reduce the vulnerability of the critical links that could be as effective, if not more, as the options explored in this paper. They include reforestation, traffic management, and multimodality. Reforestation, for instance, could significantly reduce the risk of landslides. Finally, traffic management, particularly if linked to early warning systems, could reduce losses during a disaster, by redirecting traffic to alternative routes and lowering the costs of a disruption afterwards by avoiding congestion.

# 5. References

Bonzanigo, L., Brown, C., Harou, J., Hurford, A., Karki, P., Newmann, J., Ray, P., 2015a. South Asia Investment Decision Making in Hydropower. Decision Tree Case Study of the Upper Arun Hydropower Project and Koshi Basin Hydropower Development in Nepal (GEEDR South Asia