rafmacalaba/data-use-mentions-extended huggingface dataset:
**Evidence of phasing out electricity subsidies is also strong.** Fig. 13a shows the domestic retail price for electricity from 2002 to 2010. Countries are ranked according to the level of tariff (in US$ cent per kWh) in 2010. The annualized rate of growth of tariffs (the blue bubbles) is measured on the right hand axis.  For our sample of countries the average electricity tariffs increased by 50% from US$ 6 cent in 2002 to US$ 9 cent per kWh in 2010. Net importer countries, including Chile, Dominican Republic and Turkey display the highest electricity tariffs. Net energy exporters, including Egypt, Yemen and Iran but also Argentina charge the lowest electricity tariffs. Only Yemen and Argentina recorded a decrease in tariffs. Among the countries that recorded the most dramatic increase in gasoline and diesel tariffs are both net energy exporters, such as Azerbaijan, Indonesia, Iran and Egypt, and net energy importers, such as Chile, Turkey, Moldova, and Dominican Republic.

**In our sample of countries, power consumption per capita increased from 1000 kWh in 1998 to above 1500 kWh in 2008 despite price increases.** Energy intensity in 2008, however, became less than half of the 1998 level, declining from 0.9 kWh to 0.4 kWh (in unit of GDP). These results are in line with the majority of the estimates found in the literature that indicate that income elasticity is higher than price elasticity for electricity. Also, in general, demand for electricity is more responsive to changes in price and income in the long run than in the short run (see Box 2).

**Box 2**

**Evidence from estimation of electricity price and income elasticity**

The results of Dahl (2011b), based on a global survey of electricity demand including more than 450 studies for about 60 countries for the period from 1951 to the present, indicate that the mean price elasticity in the short run is -0.21
sample it loses statistical significance.

# **4.1.2 Linear effects on height?**

So far, this paper has largely studied linear regression. A non-linear relationship between sanitation and height could be important for two reasons: first, fixed effects regression could be inconsistent; and second, a linear Blinder-Oaxaca decomposition could be inappropriate. Returning to figure 6, the relationship between sanitation and height appears approximately linear in the Indian data, but may not be among openly-defecating households in the African data.

Non-linearity can be tested by adding a quadratic term. Already, in table 6, a quadratic term was not statistically significant in the first two Indian DHS surveys. Panel A finds again that, in the Indian NFHS-3, there is no evidence for a quadratic term. In contrast, Panel B does find a quadratic term in the African sample. In light of the evidence for the importance of open defecation per square kilometer presented in section 2.2.3, one possible explanation for this negative quadratic term is that population density is relatively low in these African countries, so open defecation is not as important for health until there is more of it; unfortunately, geographic data such as population density is not generally available at the DHS PSU level.

Many sanitation policy-makers claim that there is a discontinuous increase in health when open defecation is fully eliminated. This belief was importantly popularized by Sanan and Moulik (2007), who assert that “public health outcomes can be achieved only when the entire community adopts improved sanitation behavior, the area is is 100 percent open defecation free, and excreta safely and hygienically confined” (4).<sup>22</sup> Whether the relationship between sanitation and health is linear or another shape, it is clear from figure 6 that there is no 22This conclusion was based on a study by an organization called Knowledge Links in three villages of Himachal Pradesh, which found a “prevalence of diarrhea” of 26 percent in a village where 95 percent of households used
presence of mineral wealth) as one key candidate among others (see Kim 2008; Venables 2005); second, a unilateral causation between mining and spatial inequality—the presence of mining deposits and activities influences spatial inequality, and not vice versa—as an identification strategy. With the aid of night-time lights data to measure inequality across space and time, and geocoded mine-level data, we confine our analysis at the district level, effectively allowing us to investigate the within-district inequality effects of mining activities across countries over time.

There were several reasons for our choice of districts as units of analysis. First, by focusing on districts we systematically examined the nexus between mining and spatial inequality in proximity to mines and over time. This is useful for untying the effects of mining on spatial inequality at the subnational level: understanding how mining affects economic activities at local scales, and identifying the drivers of spatial differences between mineral-rich and mineral-poor countries—a needed ingredient for eliciting appropriate policy responses. Second, the use of lower-level geographical administrative units allowed us to control for unobserved country and regional differences, which naturally cannot be handled in a setting where analysis is focused at the country level. In the context of our data, controlling for country and regional fixed effects is important if we are to understand the dynamic effects of mining

> 4 Producing regions can typically face forced displacement, population pressure due to in-migration, or environmental pollution and degradation (Akabzaa et al. 2007; Salami 2001), with little or no positive return in terms of higher incomes or better living standards.

> 5 Vertical inequality refers to inequality amongst individuals within countries or regions, while horizontal inequality refers to inequality between countries or regions. These inequalities can create frustrations and grievances in producing regions, leading to the so-called resource curse. In a worst-case scenario, attempts to gain more control over natural resource wealth can degenerate into violent regional conflicts or secessionist movements, as in the Democratic Republic of Congo, Indonesia, Myanmar, Nigeria, Sudan, or Yemen. For more
14.61|12.89|15.39|15.33|15.92|16.87|16.04|17.40|19.07|20.92|
|<br>Use fertilizer|0.29|0.33|0.35|0.38|0.39|0.37|0.43|0.41|0.40|0.43|0.43|
|Use impr. seed|0.03|0.02|0.03|0.03|0.02|0.03|0.01|0.04|0.06|0.03|0.03|

_Source:_ Own computation from 2003/4-2013/14 CSA smallholder farm surveys. _Note:_ Sample size is in terms of kebeles and, as explained in the text, a kebele is said to be growing a crop if at least 10% of the households in the kebele are growing the crop. Information on area and application of fertilizer and improved seed are collected at the farm level, but averages are calculated first within kebeles and then over all kebeles. Large farm area is in km<sup>2</sup> .

19
provide consistency checks across responses. Where gaps or discrepancies were noted, follow-up telephone calls were made to the source to resolve the discrepancy or fill in the missing data. The cost data presented particular difficulties, requiring substantial efforts to follow up with the original source as well as other sources in most countries to complete or correct the data. If several sources had different estimates of time and cost, the median value was used.

> 7 The world trade data are from the United Nations Statistics Division - UN Commodity Trade Statistics Database

9
January 2009 to August 2009.

I use monthly administrative data on employment at the industry level from the Mexican Social Security Institute (IMSS), covering nine years before and four years after program implementation, along with propensity score matching to construct groups of eligible and ineligible durable goods manufacturing industries that display statistically identical pre‐program trends in employment. I then estimate difference‐in‐difference equations and find a positive, but not statistically significant effect of the wage subsidies on employment during the program’s duration, ranging from 5.7 percent to 13.2 percent in magnitude, depending on the specification. The size of the effect is greater after the program ended: eligible industries had about 24 percent higher employment than ineligible industries, compared to the pre‐program difference in employment.

I then examine the time path of the program’s effect in more detail, estimating a separate effect for each year after the program (2009 to 2013) and find that the effect of the program increases over time – going from about 16 percent in 2010 to 31 percent in 2013. The findings thus suggest that employment after the program recovered faster in eligible industries than in ineligible industries. In fact, the data show that in eligible industries employment levels were back at pre‐crisis levels in 2011, while in ineligible industries employment levels were still not back at pre‐crisis levels in 2013. The impact estimates are robust to several alternative specifications and a falsification exercise.

> 1 Based on the ILO/World Bank Inventory of policy responses to the global financial and economic crisis of 2008 (http://www.ilo.org/empelm/projects/WCMS_158875/lang‐‐en/index.htm)

2
**Figure 4. Unemployment insurance benefit recipients and new monthly claimants**

<!-- Start of picture text --> 300,000<br>2017 2018 2019 2020<br>280,000<br>260,000<br>240,000<br>220,000<br>200,000<br>180,000<br>160,000<br>140,000<br>120,000<br>100,000<br>80,000<br>60,000<br>40,000<br>20,000<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>120,000<br>2017 2018 2019 2020<br>110,000<br>100,000<br>90,000<br>80,000<br>70,000<br>60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>Job seekers paid UIB<br>Initial Claims for Unemployment Benefits<br><!-- End of picture text --> Source: OAED Monthly Reports. Authors’ calculations.

# **5.3 Labor market flows**

The employment registry, ERGANI, provides a unique data source since employment levels and flows in and out of employment can be tracked on a daily basis. Figure 5 presents the day-by-day employment for 2020, from the start of the year to the end of June. The first COVID case and the introduction of the workplace restrictions are marked with vertical lines on the chart so that the employment trend can be observed with reference to these key dates. In order to provide perspective
|Fixed effects|Municipality|Municipality|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports placebo estimates of the effects of differential exposure to COVID-19. For this procedure, we drop data from 2021. Then, we assign the warning index values from 2020 to 2019 and the warning indices from 2021 to 2020, as if the pandemic began one year earlier. Each column presents results from a separate regression for each outcome. Clustered standard errors by municipality are shown in parentheses. Columns 1 and 3 estimates Equation (1) with no controls, Columns (2) and (4) include controls for gender, urban residence, level of schooling, grade, and whether the school provides bilingual instruction. We present q-values that adjust for multiple hypothesis testing in brackets, calculated using the method by Benjamini and Hochberg (1995) that controls for the false discovery rate described in Anderson (2008).

Significance levels: ***p<0.01; **p<0.05; *p<0.10.

28
.<sup>20</sup> This restructuring is common to state and privatized firms, and we view the resulting drop in revenues and employment as a necessary consequence of past distortions: we refer to it as the _transition_ or _marketization effect_ that all

**17** We analyze differences in the way state and privatized finns approach uncertainty and risks inherent in restructuring of their activities in a forthcoming paper.

**18** "It's the revenues, stupid," _The Financial Times,_ Dec. 27, 1996. The focus on revenues could distort our performance comparisons if the superior revenue.performance of privatized firms could be attributed to mergers or acquisitions. There is, however, no evidence of any mergers or acquisitions in our sample. We examined the annual revenue changes of all privatized firms in the sample, and for all those that increased their revenues by over 25% within a single year, we evaluated the employment changes. The _highest_ annual employment increase among those firms was about 18%, and it was a small firm that increased its employment from 176 to 209 employees -- hardly a merger-generated growth. The next highest annual employment increases among the same group of finms were 14.5% and 2.5%, respectively, indicating no major mergers or acquisitions in our sample. Another distortion could have been introduced if state firms were more likely than privatized companies to split or otherwise contribute a part of their assets to other entities. On this question we have direct evidence and it excludes such possibility: 20 privatized firms and only 11 state firms contributed some portion of their assets to other entities, and the effects of these contributions on employment and revenue measures used in our equations were approximately similar for state and privatized firms.

19 Enterprises in Eastern Europe tend, on average, to be larger than those in comparable developed market economies; see Komai (1992).

20 Balcerowicz, Gray and Hashi (1997) document the staggering decline of output and employment in the
<mark>Table 2b: Transition Matrices - Synthetic vs. Actual Panel Data Nicaragua 2001 and 2005</mark>

|Status in 2001, 2005|Lo<br>|wer Bou<br>Estimate|nd<br>s|Truth|Up<br>E|per Bou<br>stimate|nd<br>s|
|---|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|
|Poor,Poor<br>|40.63<br>|39.44<br>|37.34<br>|35.68<br>(32.08,39.26)|31.70<br>|31.17<br>|30.73<br>|
|Poor,Non-poor<br>|0.00<br>|0.00<br>|3.31<br>|3.35<br>(2.00,4.70)|11.86<br>|9.84<br>|10.52<br>|
|Non-poor,Poor<br>|21.17<br>|22.36<br>|24.46<br>|26.12<br>(22.82,29.41)|30.10<br>|30.63<br>|31.07<br>|
|Non-poor,Non-poor<br>|38.20<br>|38.20<br>|34.89<br>|34.85<br>(31.27,38.42)|26.34<br>|28.
Tankard, M. E. & E. L. Paluck. (2016) 'Norm Perception as a Vehicle for Social Change', _Social Issues and Policy Review,_ 10(1): 181-211.

UNICEF. (2021) 'Pre-Questions for Vaccine Hesitancy Survey'. https://png.ureport.in/opinion/5016/. (Accessed 09/09/2021).

United Nations Development Programme. (2020) 'Human Development Report: Papua New Guinea'. <u>http://hdr.undp.org/en/countries/profiles/PNG. (Accessed 09/09/2021).</u>

- World Bank. (2020). Papua New Guinea High Frequency Phone Survey on COVID-19: Results from Round 1. World Bank, Washington, DC.

- World Bank. (2021) 'World Bank World Development Indicators'. http://data.worldbank.org/data <u>catalog/world-development-indicators. (Accessed 12/03/2020).</u>

World Health Organization. (2021) 'Papua New Guinea Coronavirus Disease 2019 (COVID-19) Health - Situation Report #84'. https://covid19.info.gov.pg/files/Situation%20Report/PNG%20COVID <u>19%20Health%20Situation%20Report%2084.pdf. (Accessed 09/09/2021).</u>

# **<u>APPENDIX A - Additional Figures and Tables</u>**

29
for inventory, are challenging but are key step to accurately measuring the factors causing the food inflation of 2007-08, two additional scenarios were numerically simulated to further check the robustness of our conclusion.

# _7.3.1. Inelastic scenario_

Key parameters in our analysis and in simulation-based models in general are the elasticities, which are used to calibrate the demand and supply curves. The alternative specification, denoted the _inelastic scenario_ , assumes lower elasticities. The elasticities used in the baseline scenario were obtained from well-known and widely used sources such as the FAPRI elasticity database and the USDA elasticity database.<sup>14</sup> However, according to several other researchers, the elasticities of supply and demand for agriculture are more inelastic than those reported in the above databases (For instance, see Gardner [24] for a discussion of supply and demand elasticities for agricultural commodities). In order that the elasticities are on average lower than those in the baseline scenario and also conservative, we chose own-price supply elasticities in the range 0.2 to 0.3 and own- price demand elasticities in the range -0.3 to -0.2. Employing these elasticities, we find that the main qualitative conclusions regarding the importance of the different shocks from the baseline scenario hold.<sup>15</sup> Comparing the baseline scenario to the inelastic scenario results in the price changes summarized in Table 8. This comparison emphasizes the importance of obtaining good elasticity estimates. Elasticity matters, and the more inelastic scenario result in a larger impact.

> _14http://www.ers.usda.gov/Data/InternationalFoodDemand/_

> _15To this end, using world data on four major crops, namely, corn, soybeans, wheat, and rice from 1960 to 2007, Roberts and Schlenker estimate that short-term, own-price elasticity of supply and demand for calories from these crops is less than 0.15 and greater than -0.1, respectively [58]._

53
Peixoto and Sjoberg (2017) show the differences between the online and offline voters in the 2014 PB vote (using online and offline exit polls) closely track the digital divide. They found online voters skewed younger than both offline voters and the general population, and that online voters were more educated and male than either the general population or offline voters (see Figure 2 using the same data). We

> 1 The fieldwork for this study was conducted with the assistance of Aptivate.

12
long-run price differences that remain among them reflect trade costs only.<sup>11</sup> Assessing integration involves examining carefully the evolution of prices of specific products across different cities in South Sudan, and testing their short and long-run co-movement. When doing so, the drivers of different degrees of co-movement can also be explored, which allows us to understand how policy actions can help achieve greater market integration.

## **3.1. The Data**

We use consumer price series for seven food products for the period 2009-13: broad beans, groundnuts, maize (white), millet, sesame, sorghum and wheat flour, and for two energy products: diesel and gasoline. Five food products were dropped due to absence of data. These are: cassava and cassava meal for which the data available only covered the capital city of Juba, and red beans, maize in grains, and cooking oil for which data available covered less than 12 observations per city. For the remaining products, the period of analysis is heterogeneous, but typically covering 2009-2013 (diesel and gasoline prices, for example, are only available for 2012-2013). Appendix

> 10 This might change with the construction of the all year passable road to Kenya.

> 11 Varela, G. et al (2012).

7
countries, in percentage points, 2001–05 _Source:_ Calderón 2009.

Poor infrastructure is a handicap for business in Niger. Evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 35 percent of the productivity gap faced by Nigerien firms, the remainder being due to poor governance, red tape, and financing constraints (figure 2a). Firms’ perception of infrastructure as a bottleneck to growth in Niger is the worst among countries in West Africa. Customs clearance is the infrastructure constraint that weighs most heavily on Niger firms, with power deficiencies in second place (figure 2b). Niger is ranked 172 out of 181 economies on its ease of doing business indicators.

Looking ahead, Niger could grow by as much as 4.5 percentage points per capita (to the level of SubSaharan African MICs) if it expands and improves its infrastructure, particularly in the road sector (figure 1b).<sup>1</sup> Improving the condition of paved and unpaved roads would facilitate and increase trade within the country, boosting economic growth by 1.71 percentage points. Power would contribute around 1.51 percentage points to per capita growth if generation capacity and national access rates—which as of today are among the lowest in the region—were increased. ICT would also make an important contribution to economic prospects (1.31 percentage points), provided the mobile and Internet markets continue expanding.

1 The countries that are closest to Mauritius, whose infrastructure is the best in the region, will show the smallest infrastructure gap and will, therefore, have the smallest growth benefits.

3
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# Introduction

The proliferation of big data obtained from satellites and mobile devices, as well as research demonstrating that satellite and mobile phone records are strongly correlated with household welfare, has sparked great interest in statistical methods that combine big data with survey data.<sup>1</sup> A key motivation for complementing survey data with comprehensive big data is the potential for small area estimation, which can produce more precise and granular estimates of socioeconomic indicators by combining household-level survey data in surveyed enumeration areas with predicted values from all enumeration areas, whether sampled or not. Established methods for small area estimation typically utilize household survey data and contemporaneous household census data, by first estimating a prediction model of welfare in the survey data and then using the estimated model parameters to simulate welfare in the census.<sup>2</sup> Because traditional small area estimation links household survey with census auxiliary data through the estimated model parameters, the set of potential predictor variables is limited to those present in both the survey and the census. Furthermore, when the wording of questions or timing of the data collection differs significantly between the census and the survey, small area estimates using the standard method can suffer from bias. Finally, because this method relies on census data, a new census is required to credibly estimate welfare changes for small areas. Census data are typically dated and collected once per decade at best, which presents a significant hurdle to deriving timely small area poverty estimates.

To overcome these challenges, we link survey data with geospatial and remote-sensing auxiliary data at the _subarea_ level.<sup>3</sup> S _ubarea refers to_ the lowest geographic level at which spatial auxiliary data can be merged with household surveys containing data on welfare and poverty, while _area_ refers to the target level for the small area poverty estimates. In recent household surveys, GPS coordinates of households, enumeration areas, or villages are often available, which allows analysts to link survey data with other auxiliary sources of remote-sensing or geospatial data and improve