rafmacalaba/data-use-mentions-extended huggingface dataset:
2 Figure 1: Global food prices

# (A) Global food prices

# (B) Global food price volatility

<!-- Start of picture text --> Index, 100 = 2010 Nominal Real Coefficient of variation<br>200 0.49<br>180<br>160 0.48<br>140<br>120 0.47<br>100<br>80 0.46<br>60<br>40 0.45<br>20<br>0<br>(C) Undernourished people (D) Prevalence of undernourished<br>Millions Percent of population Percent 2014 2017<br>1000 Number 15 25 23.2<br>950 Prevalence (RHS) 14 20.7<br>13 20<br>900<br>12 16.1<br>850 14.8<br>11 15<br>800<br>10<br>750 9.7 9.8<br>9 10<br>700<br>8<br>5.3 5.4<br>650 7 5<br>600 6<br>0<br>EAP LAC SAR SSA<br>1970 1980 1990<br>2007 2011 2015<br>1960 1965 1975 1985 1995 2000 2005 2010 2017 Food Beverages Grains Oils Other food<br>2005 2006 2008 2009 2010 2012 2013 2014 2016 2017<br><!-- End of picture text --> Source: Food and Agriculture Organization of the United Nations, World Bank. A. Based on yearly commodity price indexes between 1960-2017.

B. Based on monthly nominal commodity price indexes between January 1960 – November 2017. C.D. Undernourishment is defined a state, lasting for at least one year, of inability to acquire enough food, defined as a level of food intake insufficient to meet dietary energy requirements.
similar signs, although the OLS estimates are smaller. Overall, the findings show that investment in infrastructure promotes job creation, and that the impacts are larger in more economically advanced African countries. This differential effect tends to validate the idea that the nature of jobs varies across countries with different levels of development, and that the expansion of roads and electricity provides fewer benefits to the sectors that predominate in less-developed economies, and greater benefits to sectors that predominate in more-developed countries.

# **6.2 Place of residence**

We examine how the employment impacts of expanding road and electricity access differ for urban and rural areas. Results are reported in columns 1 and 2 of Table 5 in the Appendix. There are net employment gains in response to road expansion in both urban and rural areas in the DHS sample. The marginal employment effect of roads is larger in rural areas. Estimates from the DHS sample shows that increasing proximity to the road network by 10 km marginally increases employment by 8.0 percentage points in urban areas, compared to 23.8 percentage points in rural areas along the grid. Similarly, in 26
are displayed in table 6. Receipt of PSG resources is estimated to have increased 2023 summer crop area significantly although with 0.17 ha, i.e., less than 2%, estimated effect size remains modest.

Regressions point to heterogeneity in three respects. First, PSG support seems to have been most effective within 50 km of the ‘front’, defined as the border to ineligible conflict-affected areas, where the estimated coefficient increases to 0.743 while coefficients on PSG access in areas mor distant from the front remain insignificant. PSG impacts also seem to vary by farm size and organization: Program grants are estimated to have been highly significant impact (coefficient of 0.081) for the smallest farm size group who may be most constrained in terms of accessing other sources of support and marginal (coefficient of 0.71, significant at 10%) for the largest farm size group. Estimated PSG impacts are highly significant for FOPs (coefficient of 0.38) while being less significant (0.09, significant at 5%) for small and marginally significant for farms in the 50-120 ha category (0.317, significant at 10%).

The timing of PSG resource disbursement in December of 2022 allows us to use the area cultivated with winter crops as a placebo treatment to check if there are any unobserved differences between the two groups that may have affected outcomes independently from the receipt of PSG funds. To do so, we run (1) replacing _Yit_ with area under winter crops and in i , include weather variables and their squares for three (planting, dormancy, and growing) instead of two phases of plant development, and set the cut-off for GDD XX at 0°C instead of 5°C. Table 6 shows all coefficients in the relevant regressions are insignificant, suggesting that treatment and control groups were not affected differently by the war in other systematic ways.

# **5. Conclusion and policy implications**

We find that mandatory use of a farmer registry
ions 2,5 an|d 7-9. Fi|gures in|italics use|updated data.|||HCI_min=|0.20||||

# **8. Conclusions**

This paper produces a new measure of the effect of closing gender employment gaps on long-run GDP: the Gender Employment Gap Index (GEGI). Both the basic and full variants of the GEGI are _equal_ to the percentage increase in long-run GDP from increasing female employment to close gender employments gaps, making them particularly easy to interpret. They are also transparent, and can be easily calculated via a simple closed-form expression for almost every country in the world (185 countries for the basic GEGI, 159 for the full GEGI). This is much larger than for estimates in other papers, allowing the production of estimates for many developing countries for which no other estimates exist (including most Pacific Islands). The GEGIs are highly correlated with other measures, which are quantitatively similar when defined using the same mechanims. On average across countries, the GEGIs indicate long-run GDP per capita would be around 19% higher if gender gaps could be closed by increasing female employment to that of men, though for individual countries with the largest gaps, GDP could be up to 85% larger. Long-run GDP per capita would be about 22% larger on average across 11 Pacfic Islands from closing gender employment gaps.

The basic GEGI is defined as the difference between male and female employment, as a share of total employment. This is equal to the precentage increase in employment from increasing female employment until it 17
growth is realized in the year of the disaster itself. Felbermayr et al. (2022) investigate how weather anomalies affect economic activity approximated by nighttime light emissions. They find significant effects on local growth and significant spatial spillovers, both driven by lower income regions. Studies differentiating the impact on growth in developing and advanced countries consistently show that developing countries are more sensitive and vulnerable to natural disasters (Noy 2009; Raddatz 2009; Loayza et al. 2012; Fomby, Ikeda, and Loayza 2013).

> 7They define large natural disasters using the distribution of number of people killed due to natural disaster from EM-DAT.

6
(2022) recommend considering three key factors—context, scale, and availability.  Additionally, we experiment with the potential use of big data, including satellite data. Subsequently, we compare these measures, highlighting their respective strengths and limitations. The main objective is to inform the Bulgarian government's efforts in measuring, operationalizing, and addressing energy poverty.

# **Additionally, the paper examines the correlation between energy poverty and income poverty,**<sup>**15**</sup>

**considering its policy implications.** Our analysis involves characterizing energy poverty and identifying overlaps with monetary poverty to understand potential intersections between these vulnerable groups. Recognizing these overlaps is crucial for designing effective instruments to mitigate energy poverty. This includes measures to protect the energy poor and vulnerable populations in the face of rising energy prices or other forward-looking policy interventions addressing energy inefficiencies.

# **Our results suggest substantial variations in the incidence rates of energy poverty depending on the**

**chosen definition.** Notably, the so-called 10 percent threshold, which defines the energy poor population as all those reporting an energy expenditure share in household income or expenditure of more than 10 percent, exhibits a significant deviation from other expenditure-based indicators, resulting in an incidence rate of approximately five times higher. This deviation could be attributed to measurement issues, and considerable criticism can be found in the existing literature on this indicator. Although this criticism is valid, the 10 percent measure is one of the few monetary measures of energy poverty that allow for comparisons across countries and over time. The remaining monetary-based measures show fluctuations ranging from 7.9 to 9.9 percent. Consensual measures, which rely on self-

> 14 EPAH is a leading EU initiative aiming to eradicate energy poverty and accelerate the just energy transition of European local governments, which replace the previous EU Energy Poverty Observatory.

> 15 Following the European Union standard, official poverty is measured by using relative-income poverty lines in Bulgaria. The at-risk-
they were living with one of these core members. This means that if no core member is found in the last known location, the household was not interviewed even if other previous household members still lived there.

In wave 4, the sample of the UNPS was refreshed. One-third of the original sampled households were rotated out as part of the panel refresh and were no longer tracked or interviewed.

In the current UNPS setting, tracking individuals requires the completion of an individual tracking form which contains all contact information for the split-offs and/or the individual movers. The information on their new location needed for the full tracking is generally gathered from their previous household members or any other knowledgeable person. For each core member that had moved away, a tracking form is completed. Based on the information filled in this form, the mover individuals are contacted and interviewed.

Although the tracking target sample comprises only the core members of each household, all persons living with these core members are interviewed and become part of the UNPS sample. Finally, if these individuals are core members of the new split-off household, they are interviewed in the subsequent waves of the UNPS, even if they move to different locations.

# 6.1. Empirical evaluation of the cross-sectional estimates

We computed the UNPS 2015/16 estimates. Data from the previous waves are included in the analysis to identify the household dynamics (e.g., movers, immigrants, and newborns).

We calibrated the base weights (expressed in Formula 3.8) to the known sex by age class population totals using UBOS official projections for 2015 and based on the population census of 2014. The projections used to calibrate the weights are presented in Table 6.1. Hereafter, we denote these weights as calibrated GWSM base weights.

We applied the calibrated GWSM base weights to a set of variables from UNPS 2015/16 data and compared them to Uganda official statistics to assess the functioning of the weights vis-à25
Given the data limitations we face, we focus our strategy to exploiting cross-sectional variation in the data and linking some welfare indicator (e.g. consumption per capita) or some component thereof (food versus non-food expenditure) to a climate-related shock defined on the basis of available rainfall data focusing mainly on rural households. As noted earlier, the yield of crops such as rice (staple food in Indonesia) and soybean can is very much affected by changes in precipitation patterns which are strongly influenced by ENSO.

Given the importance of rice farming in the rural economy of Indonesia, we define climate shocks with reference to this activity. Naylor et al. (2007) explain that El Niño events can cause a delay in monsoon onset of up to 60 days. The same authors define “ _onset_ ” as the number of days after August 1 when cumulative rainfall reaches 20 cm<sup>5</sup> , and “delay” as the number of days above the mean onset date over the 1979-2004 period. Since farmers will typically begin planting after monsoon onset, late onset may affect prospects for a second harvest later in the season and possibly change crop combinations (with potentially significant consequences on production and market prices).

While delayed onset is an important determinant of harvest, we also need to consider the amount of rainfall after the onset. After farmers plant the rice fields, 60-120 cm of rainfall are needed during the 3-4 month grow-out period (Naylor et al. 2002). Thus, the second dimension of our shock involves the deviation of the amount of post-onset rainfall from the 25 year mean for each weather station. We define the amount of post-onset rainfall as the total amount of rainfall during the 90 day period following the monsoon onset date.

**Figure 1: Timing** **<u>of typical climate events and the IFLS3</u>**

<!-- Start of picture text --> Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug
too it should be noted that (as measured in practice) private consumption in the NA includes sizeable and rapidly growing components that are typically missing from surveys (Deaton, 2005). However, aside from differences in what is being measured, surveys encounter problems of under-reporting (particularly for incomes; the problem appears to be less serious for consumptions) and selective non-response (whereby the rich are less likely to respond).<sup>19</sup> Survey measurement errors can to some extent be dealt with by the same methods used for addressing selection bias. For example, if the measurement problem affects the outcomes for treatment and comparison units identically (and additively) and is uncorrelated with the control variables then it will not be a problem for estimating _ATE_ . This again points to the importance of the controls. But even if there are obvious omitted variables correlated with the measurement error, more reliable estimates may be possible using the double-difference estimators discussed further in section 7. This still requires that the measurement problem can be treated as a common (additive) error component, affecting measured outcomes for treatment and comparison units identically. These may, however, be overly strong assumptions in some applications.

It is sometimes desirable to collect panel data (also called longitudinal data), in which both participants and non-participants are surveyed repeatedly over time, spanning a period of expansion in program coverage and over which impacts are expected. Panel data raise new problems, including respondent attrition (another form of selection bias). Some of the methods described in section 7 do not strictly require panel data, but only observations of both outcomes and treatment status over multiple time periods, but not necessarily for the same observation units; these methods are thus more robust to the problems in collecting panel data.

As the above comments suggest, NX evaluations can be data demanding as well as methodologically difficult. One might be tempted to rely instead on “short cuts” including less formal, unstructured, interviews with participants. The problem in practice is
due to differences in formats and platforms. See online Appendix A for more details on each remote sensing product and guidance for economists on merging these data with survey data.

## **2.1.1 Merged Gauge and Satellite Data**

In the past two decades, improvements in the accuracy of rainfall estimation in data scarce environments have been achieved by merging rain gauge data, which provide site-level observations, with data from meteorological satellites, which provide valuable indirect information at full coverage. We make use of three data products in this category.

First, the African Rainfall Climatology version 2 (ARC2) was developed to provide long-term daily rainfall monitoring for improved famine early warning systems over Africa (Novella and Thiaw, 2013). Input sources are daily Global Telecommunications System (GTS) rain gauge data and the Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) calculated from cloud-top infrared (IR) temperatures. Daily data are produced at 0 _._ 1<sup>_◦_</sup> resolution. A number

> 5Despite its lack of daily data, the product from the Center for Climatic Research at the University of Delaware remains popular among economists. Papers which rely on these data include Corno et al. (2020), Dell et al. (2012), Ito and Kurosaki (2009), Jayachandran (2006), Kaur (2019), Sarsons (2015), and Shah and Steinberg (2017).

8
those within the top 0.01% highest earners. We also document that the trends in capital vs. labor share in the top 1% fully drive total income shares - the share of labor in total income decreased from 70% in 2011-2014 to close to 62% by 2019, with barely any change in the shares of the bottom 99% of earners but a large shift toward capital income in the top 1%.

This contributes to the research on at the paper agenda measuring inequality top of the distribution in low- and middle-income countries. Many of the studies using tax registries, both in middle- and high-income countries, start from tabulated data of high-income earners provided by governments and use interpolation techniques to estimate income levels and composition at the top of the distribution. Working with SAR, the tax authority in Honduras, we use anonymized panel datasets at the taxpayer level, which lets us depict a very granular description of top earners. Furthermore, the panel data we use allows us to show that not only the level of income concentration remained stable over two decades in Honduras, but also the identity of individuals at the top is highly persistent: we show that more than half of the individuals belonging to the top 0.1% of the distribution in any given year will stay above that level in a three year horizon. Comparisons with the level of individual persistence at the top in other low- or middle-income countries is hard due to lack of data availability, but these rates are closer to 30-40% in high-income countries such as Norway and Australia (Alstadsæter _et al._ , 2016, H ́erault _et al._ , 2022). Additionally, we document the prevalence 2
# **4.2 Acute Illness: Care Received**

Table 3 combines information on health care utilization conditional on the child being ill (columns (1)-(3)) with data on the incidence of symptoms (columns (4)-(7)), obtained from a 7-day health calendar over the week preceding the survey. For diarrhea, we first asked respondents to report all days on which the child had diarrhea in the last week. If diarrhea was reported, we asked about days with more than three loose stools, and days with blood in the stool (a danger sign that may indicate dysentery). We also asked whether the respondent was concerned about the child’s health on any day. For care received, we recorded any visits to CSComs or associated reference hospitals (CSRef), any other formal provider visits (private or non-profit clinics and hospitals) and whether ORT was given.

We begin by analyzing curative care, and specifically acute care received for diarrhea. Note that the sample in columns (1)-(3) includes only children with diarrhea symptoms<sup>12</sup> Columns (1) and (2) show no significant effect of the subsidy on the use of formal care, either on its own or as part of the full program. While this may seem surprising in the light of the results of Sautmann et al. [2020], the same paper also provides a potential explanation for this finding. Using the more detailed data from the 2013 survey, Sautmann et al. [2020] find that the increase in health care utilization following the subsidy largely occurs on days in which care is required according to the WHO guidelines. Diarrhea on its own does not fall into this category, unless it has lasted for more than 5 days or is accompanied by other symptoms. It is likely that formal care is not the appropriate response to many of the diarrhea spells in the sample.<sup>13</sup
# **2 Data**

## **2.1 Nightlights**

We measure local economic activity using imagery from four weather satellites that are part of the United States Air Force Defense Meteorological Satellite Program. These satellites record daily cloud formation by measuring the amount of moonlight reflected by clouds at night. On nights with no cloud cover, these satellites measure the light emissions from the earth’s surface. Specifically, we use the annual composites produced by the National Oceanic and Atmospheric Administration (NOAA). These composites predominantly measure man-made lights because they only use information from cloudfree days and because NOAA’s methodology filters transient sources of light.<sup>1</sup> Nightlights provide a good proxy for economic activity because consumption of lights at night is likely to increase with income. Accordingly, nightlights have been extensively used to measure changes in economic activity (see Donaldson and Storeygard, 2016, for a literature review), including the downturns generated by hurricanes (Bertinelli and Strobl, 2013; Elliott et al., 2015; Bertinelli et al., 2016; Ishizawa et al., 2017; Del Valle et al., 2018).

Our nightlights data set is composed of 22 satellite-year composites for the period 20002013. Each composite covers Central America and contains information on 604,473 onekilometer square grid-cells. Each cell records the intensity of nightlights, on a scale that ranges from zero (no light) to 63 (maximum light). In years with overlapping satellite coverage, we aggregate nightlights by taking cell-level weighted averages across satellites, where the weights are given by the number of cloud-free days.<sup>2</sup> We restrict the sample to cells that have non-zero nightlights in at least one year during our sample period. The resulting cell-year panel is composed of 212,072 cells which we observe for 13 years. The average value of nightlights in 2000 is 5.1 with a standard deviation of 8.4.

> 1The procedure to produce composites is
Islamabad can go as high as US$ 6 million.

# 5.2 Cross-Validation

The predicted _BHP_ of the apartments obtained by both models were compared with the observed apartment prices in order to determine the predictive power of the different models. One standard measure often used in price estimation models is the quotient between the predicted price and the observed price for the property. The acceptable median ratio between predicted and observed price is 0.9-1.1 (cf. International Association of Assessing Officers 2014; Čeh et al. 2018).

Additionally, we evaluate the performance of the different models with the mean absolute percentage error (MAPE), which measures the average percentage deviation of predicted prices from actual property prices expressed as where p̂ is the predicted property price and _pi_ the actual property price of property _i_ . To understand differences in predictions for the BHP, we estimate a localized MAPE for apartments between 50 and 60 square meters. Comparing the predictive performances of the models based on the performance measures (ratio of predicted vs. actual value, MAPE, localized MAPE), we obtain more precise results in the RF models for all countries. All RF models are within the suggested range of the predicted/observed price ratio of 0.9-1.1, also for the estimations where all potential duplicates are rigorously removed (Annex 2) and where different splits are applied (Annex 5). In the main OLS estimation, South Africa exceeds the acceptable median ratio between predicted and observed price range. The MAPEs indicates that the percent deviation of the RF prediction from the actual property price ranges between 27 percent for Costa Rica and 31 percent for Pakistan. Across the board, while MAPE is relatively high in both RF-based and OLS-based 30