rafmacalaba/data-use-mentions-extended huggingface dataset:
Falling Short: A Global Survey of Electricity Tariff Design **_Vivien Foster_**<sup>**_1_**</sup> **_and Samantha Witte_**<sup>**_2_**</sup>

# **Acknowledgments**

This paper is based on data and tariff schedules collected as part of the World Bank/ESMAP project “Regulatory Indicators for Sustainable Energy (RISE), 2017”<sup>3</sup> co-led by Sudeshna Ghosh Banerjee, Alejandro Moreno, and Tanya Primiani, and implemented by Mallory LeeWong, Joonkyung Seong, Taylor Sloane, and Samantha Witte. A full list of acknowledgements for this project can be found at <u>http://rise.esmap.org. These data were supplemented by targeted desk-based research.</u> The authors are particularly grateful to the following colleagues for peer review throughout the development of the paper: Joern Huenteler; Ani Balabanyan; Sudeshna Ghosh Banerjee; Maria Vagliasindi; Juliette Besnard, Jevgenijs Steinbuks and Tu Chi Nguyen. The authors acknowledge the assistance with data collection by Sharmila Bellur.

# **Keywords:**

**JEL Classification** : L94 Electric Utilities

> 1 Vivien Foster is Chief Economist of the Infrastructure Vice-Presidency of the World Bank and is the corresponding author for this paper at vfoster@worldbank.org.

> 2 Samantha Witte is a doctoral student in economics at Oxford University.

> 3 2016 tariff and cost data were obtained from the 2018 iteration of the RISE project.
of firms that experienced at least one bribe payment request in any of the above six transactions is called the bribery incidence indicator of the WBES.<sup>4</sup> An additional separate question asks whether an informal payment or gift was requested if the manager attempted to secure a government contract. Since all these seven questions are prompted only if managers have recently undertaken these specific transactions, the bribery incidence indicator plus the bribery indicator on public procurement provide the overall occurrence of bribe demands on the enterprises that have engaged in the included transactions. On the contrary, the last question I use is asked to all the enterprises in the sample. It asks: “It is said that establishments are sometimes required to make gifts or informal payments to public officials to “get things done” regarding customs, taxes, licenses, regulations, services etc. On average, what percentage of total annual sales, or estimated total annual value, do establishments like this one pay in informal payments or gifts to public officials for this purpose?” Managers have an option to answer this question either in percentages or in local currency value. I convert answers in local currency to the percentages of sales using the annual sales information, also available from the survey.

The WBES interviewed 1,344 top managers or business owners in Türkiye in 2013. In order to achieve a representative sample of the country, the WBES follows a stratified sampling strategy along dimensions of location, sector, and size. The WBES provide public access to the raw enterprise-level data. Using these data requires some functional choices, including the treatment of missing questions due to item non-response. In this case, all instances where bribery rates exceeded total revenues, as well as “Don’t Know” or “Refusal” values for the key variables of the number of permanent full-time workers, the total value of annual sales, or the estimated bribery payment, were removed. This process yielded a sample of 812 enterprises.

Tables 1 and 2 report the weighted summary statistics
This led to the decision to make domestic violence (which was at the time not a topic studied much by economists) a focus of the analysis. In the process of trying to elicit responses on this sensitive issue, in the first week our respondents told us, basically, that life was tough but manageable. Their main problems were with the government – lack of good schools, lack of adequate drinking water, etc. After a week of staying in the village and continuing our interviews, one of the women finally opened up and said, “You have become our friends and we can’t lie to you anymore. Let me tell you the truth. We feel like we are in jail. Our husbands beat us all the time and no one helps when that happens. They spend all the family money on alcohol, and when they come home – no one can help us.” We then explored these issues further through in-depth interviews with men and women who outlined the cold rational calculation behind much of the violence.

Based on these discussions a few key questions on wife-beating were added to the quantitative survey instrument. The analysis of the qualitative and the quantitative data demonstrated the links of domestic violence to issues of control and power within the family, female sterilization (which led to an increased risk of being accused of infidelity), alcoholism, and dowry demands. Since the qualitative work demonstrated, among other things, that domestic violence was often a rational choice undertaken to forcibly extract money from the wife’s family, it also led us to rethink how economists modeled both intra-household and transfers behavior. Without the fieldwork, we would have never thought of studying domestic violence. Being in the field and engaging in many months of participant observation allowed issues to be probed in the field the moment they were observed. This permitted ‘surprises’ to be easily incorporated into the data gathering process. These surprises were often everyday, even mundane, experiences in the lives of the rural poor and only surprising to relatively
*|-86.216***|-0.001|36.538**|-0.116***|-123.587***|
||(0.019)|(30.892)|(0.004)|(16.085)|(0.019)|(33.826)|
|Mean in control|0.317|238.713|0.027|25.410|0.313|213.303|
|Adjusted R-squared|0.113|0.006|0.021|0.000|0.112|0.005|
|Observations|13464|13464|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. **The dependent variables in Panel A refer to transfers received from or given to households who live outside the village but are part of the first-degree family (parents, siblings, children, parents-in-law or siblings-in-law) network of the respondent. In contrast, the dependent variables in Panel B refer to transfers received from or given to households who live outside the village and are not part of the first-degree family network of the respondent’
departure (with the worker intercepted upon arrival in New Zealand and interviewed separately from their household).

Given these conditions, we used a rolling sampling methodology, adding samples as we received updates of when, where, and who employers were recruiting. In both countries the baseline survey was conducted between October 2007 and April 2008. In Tonga our survey has near national coverage, covering the islands of Tongatapu, Vava’u and ‘Eua. These three islands contain 90 percent of the population and 92 percent of the RSE workers in the first year. We worked closely with the Tongan Labour Ministry to identify villages supplying workers, and within those the village town officers identified households with RSE workers and households with members of the RSE work-ready pool who had not been selected yet. We additionally surveyed randomly selected households in the same villages where no one had applied for the program. In each village we aimed for approximately five households with an RSE worker, three households with a member of the work-ready pool who was not selected, and four households with non-applicants. Our resulting baseline survey covered 448 households containing 2,335 individuals in 46 villages.<sup>9</sup> Vanuatu’s rugged geography and high transportation costs meant it was not feasible to survey in all islands, so a decision was made to limit the evaluation to three islands from which we believed there was a high ex ante chance of workers coming. These islands were Efate (population 50,000), where the capital city, Port Vila, is located; and Ambrym (population 10,000) and Tanna (population 20,000). These latter two islands were chosen due to Seasonal Solutions hiring from these islands. In contrast to Tonga, not all villages in Vanuatu were initially participating in the RSE, and in addition to sampling non-applicant households from within villages with participating RSE workers, we also sampled households from nearby villages or communities which had not participated in the RSE. Ultimately our baseline survey covered 456 households containing 2,
Figure A.2. Refugee Characterization - Individual Characteristics <!-- Start of picture text --> 27 7<br>26.5 6.60<br>26.28 6.5<br>26<br>6<br>25.5 25.46 5.83<br>25 5.5<br>Female Male Female Male<br>.96 .31<br>0.95<br>.94<br>.3<br>.92<br>0.29<br>.29 0.29<br>.9<br>.88 .28<br>0.87<br>.86<br>.27<br>Female Male Female Male<br>.49<br>.48<br>0.47<br>.47<br>.46 0.46<br>.45<br>.44<br>Female Male<br>Age<br>Years of Education<br>Literate [=1] Student [=1]<br>Married or Engaged [=1]<br><!-- End of picture text --> _Notes:_ The data comes from the Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021. The Figure includes the 5,592 individuals using in the main regressions. Red lines represent 95% confidence intervals for the mean. Student is restricted to the population aged between 6 to 24 years old.

49
<mark>proportion of households that state they have some or complete trust in other people.</mark>

- <mark>Quality of public services such as police, courts, education, health, and social assistance contribute toward adaptive capacity. Areas with good services will find it easier to respond to climate risks and to craft the public-private collaboration required to prepare for climate change. We measure this variable using LiTS data on the average number of public services (out of 8 max) for which households declare that they are either satisfied or very satisfied. If all households in a region were satisfied with the quality of all 8 public service areas, the value becomes 8. If no household is satisfied with any of the services, the value becomes zero.</mark>

- <mark>Corruption in daily interactions measures a key hindrance for adaptive capacity and, vice versa, absence of corruption makes adaptation easier and proxies institutional strength. We measure corruption using LiTS data on the average number of public services (out of 8 max) for which households declare that they never or seldom find it necessary to pay bribes in order to obtain the service in question (the services are the same as above, namely police, courts, education, health, and social assistance services).</mark>

<mark>Compared to adaptive capacity, constructing indices of exposure and sensitivity proved relatively straightforward. We construct the exposure index from variables measuring temperature and precipitation variability and natural disaster frequency. We construct the sensitivity index from variables measuring agricultural, demographic, health, poverty, and disaster-related sensitivity to climate variability. This is described in the following section.</mark>

# **<mark>Variables in the index</mark>**

<mark>The exposure sub-index is comprised of the following six variables measuring exposure to variability and extreme values of temperature and precipitation as well as to natural disasters: (i) Standard deviation of the average monthly temperature 1950-90 (see formulas in Appendix
Following Graetz and Michaels (2018), we constructed an industry-level measure of replaceability using data from IFR on robot applications, the US census of occupational classifications and the distribution of hours worked across occupations and industries from the 1980 US Census. The IFR distinguishes between different applications of robots, including (among others) welding, processing, and assembling (International Federation of Robotics, 2012). Using the 2000 Census three-digit occupations, we assign a replaceability value of one to a three-digit occupation if the name and/or description of at least one of the five-digit occupations included in it contains at least one of the IFR application categories and zero otherwise. We assume that services occupations are not replaceable by industrial robots and remove occupations that our text analysis algorithm identifies as being potentially replaceable by robots but we believe are not, such as “agricultural inspectors” and “artillery and missile officers”. We then map our replaceability measure into the 1990 Census occupational classification, which is available for the 1980 and 2000 censuses. If several 2000 occupations map into one 1990 occupation, then we assign the 1990 occupation a replaceability value of one if and only if at least one of the corresponding 2000 occupations has a value of one. To measure replaceability at the industry level, we first assign these variables to each individual in the 1980 IPUMS Census based on their reported 1990 occupation. We then assign to each individual one of our 28 EU KLEMS industries based on a crosswalk to the 1990 Census industry classification. We compute the fraction of replaceable hours for each of the 16 industries by dividing the sum product of replaceability and annual hours worked by the total sum of hours worked (applying person weights both when computing the numerator and the denominator). The replaceability values represent an upper bound to the share of hours that may be performed by robots—occupations are classified as replaceable even if only a small proportion of their work can be replaced by robots.

We combined this information with product-level bilateral data on trade and import tariffs. Trade data come
...cation.<sup>43</sup> We then aggregate tari¤s (taking the simple average) and imports (taking the sum) to the EORA sector level. We then sum imports across countries of origin to obtain import weights which are used to compute an auxiliary measure of tari¤s on imports of BEC intermediates at the reporting country-EORA sector-year level. Then, based on the US Input-Output table for 2005 from WIOD which was aggregated to the eight EORA manufacturing sectors, we construct for each sector the share of its total intermediate inputs that originates in each of the eight EORA sectors. Finally, we combine these I-O table weights with the auxiliary tari¤s on imports of BEC intermediates to construct a weighted-average tari¤ on intermediate inputs at the reporting country-EORA sector-year level.

Third, to construct tari¤s imposed in destination markets on exports of the sector, we aggregate tari¤s (taking the simple average) and exports (taking the sum) to the EORA sector level. Then, we sum exports across destination markets to compute export weights at the reporting country-EORA sector-year level which allows us to construct a weightedaverage of the tari¤s the destination markets impose.

Finally, we also rely on the fDi Markets Database which allows us to construct a proxy for FDI at the country-sector-year for a limited sample period 2003-2015, following the approach of Hallward-Driemeier and Nayyar (2019). Speci...cally, we de...ne a measure of the number of cross-border green...eld FDI projects announced cumulating it from 2003 onwards for each country-sector (summing across the projects from any origin country).

# References

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Couharde, C., Delatte, A-L, Grekou, C., Mignon,V., and Morvillier, F. 2017. “EQCHANGE: A World Database on Actual and Equilibrium E¤ective Exchange Rates,” CEPII Working Paper 2017-14.

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Budjan, A., U. Pape, and L. Ralston. 2022. “Broken Promises: Evaluating an Incomplete Cash Transfer Program.” _Revise and resubmit at Journal of Public Economics_ . Available at: <u>https://bit.ly/3ddKHDb.</u>

- Buehren, N., M. Goldstein, S. Gulesci, M. Sulaiman, and V. Yam. 2017. “Evaluation of an Adolescent Development Program for Girls in Tanzania.” _World Bank Policy Research Working Paper_ 7961. World Bank: Washington, DC.

- Carlana, M. 2019. “Implicit Stereotypes: Evidence from Teachers' Gender Bias.” _Quarterly Journal of Economics_ 134(3): 1163–1224.

- Carlana, M., and E. La Ferrara. 2024. “Apart but Connected: Online Tutoring and Student Outcomes during the COVID-19 Pandemic”. _Manuscript_ . Available at: http://michelacarlana.com/wp- <u>content/uploads/2024/03/TOP_CarlanaLaFerrara_2024.pdf</u>

- Carlana, M., E. La Ferrara, and P. Pinotti. 2022. “Goals and Gaps: Educational Careers of Immigrant Children.” _Econometrica_ 90(1): 1–29.

- Carlana, M., and M. Fort. 2022. “Hacking Gender Stereotypes: Girls’ Participation in Coding Clubs”. _AEA Papers and Proceedings_ 112: 583–87.

- Campante, F. R., and D. Chor. 2012. "Why Was the Arab World Poised for Revolution? Schooling, Economic Opportunities, and the Arab Spring." _Journal of Economic Perspectives_ 26 (2): 167–88.

- Cecchi, F., A. Garcia, R. Lensink, B. Wydick. 2022. “Aspirational hope, dairy farming practices, and milk production: Evidence from a randomized controlled trial in Bolivia”. _World Development_ 160: 106087.

- Chiapa, C.,
# **5 Empirical results**

## **5.1 Descriptive statistics**

Table (2) presents a summary of our treatment variables (power constraints) in this study. With respect to power outages, 16,816 firms out of 30,403 firms faced power outages, or about 53.6% of the firms in our sample. In addition, for 19,551 firms, power is an obstacle, representing 62.3% of firms. Of these firms, 17.6% faced minor obstacles in the power sector, 15.1% faced moderate obstacles, 16.2% faced major obstacles, 13.2% faced severe obstacles, and 11.7% considered power to be the major challenge in their operations. Finally, while outages are one of the main manifestations of constraints in the power sector in these countries, there are a number of other constraints (high cost of power, voltage fluctuations, connection problems, etc.) that are not readily observable through the survey data. Indeed, we can see here that 32.7% of the firms that faced obstacles did not experience any outages. Similarly, 39.1% of firms that experienced minor obstacles, 28.3% of firms that experienced moderate obstacles, 28.9% of firms that experienced major obstacles, 33.8% of firms that experienced severe obstacles, and 27.2% of firms that reported power as their greatest challenge.

Table 2: Summary statistics on the treatment variables

||**No Outages**|**Outages**|**Total**|**No Outages**|**Outages**|**Total**|**No Outages**|**Outages**|**Total**|
|---|---|---|---|---|---|---|--
Pension Total<br><!-- End of picture text --> Source: author elaboration based on ENEMDU

> 5 At the same time, the government has implemented a non-contributory pension within the framework of program _Mis Mejores Años_ [My Best Years]. This program is different from the traditional social pension in the benefit amount that is USD100. This transfer was announced at the latest presidential campaign and it is being gradually introduced. The eligible beneficiaries for _Mis Mejores Años_ are vulnerable 65-aged and older people according to the welfare index based on social records.

14
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 21_ ing this, the low-cost of imputations, and the major gaps in primary data in the Pacific Islands, these results suggest that the methods could be deployed in the Pacific Island region to provide unique insight into local price dynamics.

The methods have wider applicability and could help to fill crucial data gaps in data scarce regions such as the Pacific Islands, especially in conjunction with specifically designed continuous surveys. Potential indicators include tracking broad price indices, but possibly the same strategies could be used for other development indicators. Sub-national estimates of prices may also improve smallarea estimates of poverty and welfare, which is an important topic actively being explored by Lange et al. (2021) and Newhouse et al. (2022).

The encouraging results set the stage for a real-time evaluation of the methods along with external validation. Below are a few directions for future efforts.

First, the high intra-month volatility in prices suggest that supply, demand and prices are fluid in PNG markets and economically meaningful price action may be observed in the higher frequency domains. The application showed that it worked well with a short time series of data gathered from 2017 onward. This suggests that a system that gathers data at a weekly interval may also work if the data exhibit meaningful variation. Similarly, a daily interval approach may generate enough survey data to process and yield results within weeks or months after deployment. Such high frequency systems may be particularly valuable to monitor trends after natural disasters. One approach could be to maintain such systems at low cost by sustaining a limited data gathering operation, but quickly ramp up data gathering during periods of heightened interest such as when inflation is high or once a disaster has emerged and intense price action is anticipated.

Market price data gathering can be inexpensive if enumerators can reach multiple markets in a short time. However, geographically highly dispersed markets as in many Pacific Islands can make such an effort unfeasible. At the same time, phone surveys are increasingly used to collect household information
In this paper, we take a different approach to estimating the impact of climate change on tropical cyclones by relying on a geographically detailed Tropical Cyclone Integrated Assessment Model (TCIAM) (Mendelsohn, Emanuel, and Chonabayashi 2010). The model begins with the A1B SRES global emissions trajectory that stabilizes greenhouse gas concentrations at 720 ppm (IPCC 2000). This path is one of the well established scenarios used by the IPCC in their Fourth Assessment Report (IPCC 2007a). There are consequently several climate model runs that were available using this scenario. We use this emission path in conjunction with four climate models to predict four global climate scenarios for both the present and the future (2100). These climate scenarios are then used to predict how tropical cyclones may change into the future using a newly developed tropical cyclone simulator (Emanuel et al. 2008). Potential storms are seeded into each ocean basin. Given both current climate and future climate conditions, the storms then either develop into tropical cyclones or they die off. A total of 3000 tropical cyclones are generated per basin<sup>2</sup> . This provides an extensive data set of tropical cyclones from which to examine whether current (2000) versus future (2100) storm patterns change. Normally, it would take three hundred years to observe this many tropical cyclones in nature.

The historic relationship between aggregate damages and the magnitude of each storm is estimated using data from storms that have hit the United States since 1960. Aggregate damages include the value of all lost structures and infrastructure. Damages do not include fatalities. Fatalities are estimated separately. Impacts in different years are updated to current dollars using the GDP deflator. The damages are matched with characteristics of the storm including minimum barometric pressure, maximum wind speed, and location at landfall (NOAA 2009). Estimates of county income and population density are inferred from Census data for the five counties closest to the point of contact. A regression estimates the relationship between storm intensity and damages. We then use