rafmacalaba/datause-encoder-data huggingface dataset:
available. At the same time, the renewable power options considered in this paper have the advantage of permanently-available supply at a fuel source cost of zero. All things considered, our evidence suggests that the economics of decentralized renewable power may be compelling for large regions of rural Africa. Energy planners in Sub-Saharan Africa should therefore pay careful attention to opportunities for the expansion of renewable power now, not twenty years from now.

# **References**

- Barnes, Douglas F. 2007. _The Challenge of Rural Electrification. Strategies for Developing Countries_ . Resources for the Future and Energy Sector Management Assistance Program, Washington, DC.

- Baur, H., V. Meadu, M. van Noordwijk, and B. Swallow. 2007. Biofuel from Jatropha curcas: Opportunities, challenges and development perspectives. World Agroforestry Centre: Nairobi. <u>http://www.worldagroforestry.org/downloads/publications/PDFs/PO07207.PDF.</u>

- Bergey, Paul K., Cliff T. Ragsdale and Mangesh Hoskote. 2003a. A simulated annealing genetic algorithm for the electrical power distribution problem. _Annals of Operations Research_ , 121, 33-55.

- Bergey, Paul K., Cliff T. Ragsdale and Mangesh Hoskote. 2003b. A decision support system for the electrical power districting problem. _Decision Support Systems_ , 36 (1), 1-17.

- Bertani, R., 2005. World Geothermal power generation in the period 2001-2005. _Geothermics_ , 34, 65-69.

- Buys, Piet, Uwe Deichmann, Craig Meisner, Thao Ton That and David Wheeler. 2007. Country Stakes in Climate Change Negotiations: Two Dimensions of Vulnerability. World Bank Policy Research Working Paper No. 4300. August. Washington: DC.

- DHS (Demographic and Health Surveys). 2009. Reported by Measure DHS STATcompiler: <u>http://www.statcompiler.com</u> .

- Edjekumhene, Ishmael, Sefa Bonsu Atakora, Rodney Atta-Konadu and Abeeku Brew-Hammond. 2001. Implementation of renewable energy technologies – opportunities and barriers. Ghana Country Study. Kumasi Institute of Technology and Environment. Kumasi.

Ethiopian Rural Energy Development and Promotion Center. 2007. Solar and Wind Energy Resources Assessment (SWERA) Project: Country Background Information, Solar and Wind Energy Utilization and Project Development Scenarios, Final Report, Addis Ababa. Available at: <u>http://swera.unep.net/typo3conf/ext/metadata_tool/archive/download/EthiopiaSWERAre port_258.pdf.</u>

- ESMAP (Energy Sector Management Assistance Program, World Bank). 2007.  Technical and Economic Assessment of Off-grid, Minigrid and Grid Electrification Technologies. Technical Paper 121/07. December. World Bank, Washington, D.C.

24
# **4.1.1World Bank Services Trade Restrictiveness Indices Database.** The data for the

estimates of the barriers faced by foreign suppliers of services comes from the recently released World Bank database of survey information on these 11 sectors in 103 countries. The World Bank survey is a 169 page questionnaire of the regulatory regimes of these 11 sectors that was completed by law firms resident in each of the 103 countries.<sup>14</sup> Combining the data and methodology, they produced “Services Trade Restrictiveness Indices,” for all eleven sectors in all of the 103 countries.

# **4.1.2 Methodology based on the Australian Productivity Commission Methodology.**

Although we use the data of the World Bank database, we are unable to use the Borchert _et al_ . Services Trade Restrictiveness Indices, since they did not transform these into ad valorem equivalents. Our methodology builds on a series of studies supported by the Australian Productivity Commission, which develop alternate Services Trade Restrictiveness Indices and also convert these indices into ad valorem equivalents. We rely especially on the papers by Warren (2000) in telecommunications, Kalirajan _et al_ ., (2000) in financial services, Kang (2000) in transportation services and Nguyen-Hong (2000) in engineering services. For each of these service sectors, the authors first developed a matrix to evaluate and score the regulatory environment in the sector. They collected data and assessed the regulatory regimes of many countries. Evaluations of each criterion were transformed into a quantitative score and weights were assigned to each criterion so that the regulatory regimes of each country were transformed a “restrictiveness index.” They then regressed the price of services against their restrictiveness index and other relevant variables to determine the impact of the regulatory barriers on the price

> 14 The database is available at http://iresearch.worldbank.org/servicetrade/. See Borchert, Gootiiz and Mattoo (2012) for a guide to the database. They also developed a methodology for evaluating the restrictiveness of the services regimes against foreign suppliers.

11
|e||Income|||performancevs.<br>Benclonark|
|---|---|---|---|---|---|
|Deviation of-10%o or more from Benchmark||||||
|Energy: Access to ElectricityNetwork(% ofPopulation)|20|g2|15|60|Underperform|
|ICT: Cost ofPhone Call to the US (U5 cents/$ minutes)|428|ai4|497|335|Underperform|
|ICT: Mainlines Teledensity(mainlines subscribers/1000 people)|66|i226|31|213|Underperform|
|ICT:Phone Faults (reportedfaults/100 mairilines)|40|ae|SF|a7|Underperform|
|ICT: Teledensity (totaltelephone subscribers/1000 people)|182|sel|oo|S01|Underperform|
|Transport: PavedRoads (% oftotalroads)|13|47|25|50|Underperform|
|Transport: RailLines DensityinTerme ofLand (rail-kinw'l000 eqkin)|29|is|a7|23.1|Underperform|
|Transport: RoadDensityinTerms ofLand(road-km/1000 sq kn)|31|32%|156|S41|Underperform|
|Water: Access tolmprovedSanitation(% ofpopulation)|30|72|a7|64|Underperform|
|Deviation ofbetween -10% and +10% from Benchmark||||||
|Water: Access to ImprovedWaterSources (% ofpopulation)|30|as|64|30|Average|
|Deviation of +10% or more from Benchmark||||||
|ElectricityAverage End-UserPrices (US cente/kVWh) - (Residential‘N on-Energy: Residential)|4fa|RSF|6/3|of|Outperform|
|ICT: Cost ofCellularLocalCall(US cents/3 off-peakminutes)|30|44|42|49|Outperform|
|ICT: Cast of LocalPhone Call(U3 cente/3 minutes)|4|6|10|a|Outperform|
|Transport: RoadDensity inTerms ofPopulation (road-kmv/‘1000 people)|213|49|33|a7|Outperform|
|Transport:TravelTimetoWorkinMainCitiesGnirutes/one-wayworktrig)|20|dé|34|31|Outperform|
**Figure 12:  Incentives to Switch Jobs between the Public and Private Sectors**

<!-- Start of picture text --> 16% 49%<br>14%<br>Loss in wealth from 48%<br>12% switching<br>48%<br>10%<br>8% 47%<br>6%<br>47%<br>4%<br>46%<br>2% Loss explained by pension<br>system<br>0% 46%<br>25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59<br>Source: Authors’ calculations<br>Loss in wealth from switching<br>Loss explained by pension system<br><!-- End of picture text --> This section has analyzed incentives for labor mobility across main economic sectors. The analysis is based on the analysis of career paths and the calculation of three indicators: internal rates of return on education, net gains from queuing for jobs in the public sector, and the change in wealth resulting from switching jobs across sectors. These indicators can be easily computed on the basis of labor force or household survey data. When applied to Morocco the indicators show that a fragmented pension system distorts rates of return on education, reduces incentives to supply labor in the private sector, and discourages mobility between the public and private sectors. The corollary is that pension reforms that unify the pension system can contribute to facilitating labor mobility across sectors and therefore to facilitating economic restructuring and improved efficiency.

# **5. Conclusions**

There is ample evidence of distortions that pension systems can impose on labor markets by influencing the decision of whether to retire and become inactive; and whether to supply labor in the public, formal private, or informal sectors. Typical defined benefit pension system designs (lacking actuarial connection between contributions and benefits) constitute an especially high risk for labor market distortions since they tend to offer variable internal rates of return at various ages, and if the pension system is fragmented, they tend to offer different returns across sectors as well.

The standard indicators used to assess incentive problems are the internal rate of return (IRR) on contributions as a function of the retirement age and the change in pension wealth at a given retirement age. These indicators, however, do not capture the tradeoffs between consumption and leisure, which are at the core of the retirement decision, and they also ignore the interplay between benefit formulas, eligibility conditions and the individuals’ attitude towards risk. This is why we proposed a complementary indicator 22
_Table 3. Distribution of input subsidies relative to agricultural production value_

|Country|AUCproduction value|AUC input subsidy|Diff AUCs|
|---|---|---|---|
|Brazil|0.34|0.48|-0.14|
|Philippines|0.51|0.63|-0.13|
|EU_28|0.54|0.61|-0.07|
|Burkina Faso|0.48|0.52|-0.03|
|Malawi|0.56|0.59|-0.03|
|Kazakhstan|0.64|0.67|-0.03|
|Bolivia|0.45|0.46|-0.01|
|Ukraine|0.60|0.61|-0.01|
|South Africa|0.50|0.51|-0.01|
|Vietnam|0.47|0.47|0.00|
|Korea, Rep.|0.52|0.52|0.00|
|Mozambique|0.71|0.71|0.00|
|India|0.58|0.57|0.01|
|Türkiye|0.56|0.54|0.01|
|Canada|0.62|0.60|0.02|
|United States|0.52|0.50|0.02|
|Argentina|0.53|0.50|0.03|
|Colombia|0.57|0.53|0.04|
|Indonesia|0.69|0.63|0.06|
|China|0.64|0.58|0.06|
|Japan|0.44|0.36|0.09|
|Mexico|0.67|0.58|0.09|
|Kenya|0.83|0.68|0.15|

_Note:_ EU-28 is considered as a single unit. _Diff_ = difference between.

22
# Table 3. Incumbent Victories and Conflict Casualties: RD Design

|||Post-Elect|ion Period|||Pre-Elec|tion Peri|od|
|---|---|---|---|---|---|---|---|---|
||Casua|lties|Violent I|ncidents|Casu|alties|Violent|Incidents|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incumbent victory|1.22***|0.89***|0.40**|0.31***|0.29|0.059|0.023|–0.057|
||(0.44)|(0.29)|(0.16)|(0.12)|(0.62)|(0.44)|(0.24)|(0.19)|
|Margin|–17.3***|–10.1|–7.95***|–5.67**|1.24|4.73|–0.77|1.96|
||(6.52)|(6.56)|(2.79)|(2.45)|(16.8)|(11.0)|(7.53)|(6.09)|
|Margin _×_ incumbent victory|–17.5|–13.2|–0.43|–0.43|–22.4|–27.8|–5.66|–9.85|
||(26.4)|(16.9)|(7.95)|(6.30)|(26.8)|(20.1)|(10.6)|(8.59)|
|Mean of dep. var.|0.50|0.50|0.20|0.20|0.72|0.72|0.36|0.36|
|Control variables|No|Yes|No|Yes|No|Yes|No|Yes|
|No. of municipalities|153|153|153|153|153|153|153|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ The running variable of the RD design is the incumbent margin of victory. All regressions are weighted by a triangular kernel with a bandwidth of 0.05. Reported values are marginal effects from Poisson regressions, calculated at the sample means. Standard errors are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels. Control variables are: population, area, as well as share of households with electricity, piped water, indoor plumbing, roof made of strong building material, and wall made of strong building material. Following the classification of the 2000 Census of the Philippines, “strong” building materials for walls are defined as concrete, brick, stone, wood, galvanized iron or aluminum, asbestos and glass. “Strong” building materials for roofs are defined as concrete, galvanized iron or aluminum, clay tiles and asbestos.

21
**Figure 7: Price decomposition, 1900-2020**

<!-- Start of picture text --> Transitory shock Supercycle Trend<br>Share<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Lead Zinc Copper Tin Nickel Cocoa Coffee Palm oil Maize Wheat Rice Tea Cotton Natural rubber Aluminum<br><!-- End of picture text -->

**Source** : Authors’ calculations

**Figure 8: Supercycles, 1900-2020**

<!-- Start of picture text --> Log scale<br>0.70<br>Base metals Annual agriculture Perennial agriculture<br>0.35<br>0.00<br>-0.35<br>-0.70<br>1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 26 —_
to the European countries in our sample, fossil fuels account for 38% and renewables for 34%. Brazil and India are two countries that rely heavily on one energy type: hydroelectricity in Brazil (71% of electricity generation) and coal-fired generated electricity in India (73% of electricity generation). We gathered data about the price of oil, natural gas, and coal from the IMF Primary Commodity Prices. They are not available at a daily frequency, which prevents us from estimating a model of demand (by generators) and supply of the fuel inputs. Nevertheless, we can examine how these prices evolved during the pandemic using the monthly level fuel prices from major energy hubs.

# _2.1. Electricity generation and electricity mix_

As shown in the summary statistics in Table A. 1 and Table A. 2, the average daily generation in our sample was 953 GWh, with a max of 14,019 GWh. Capacity averaged 92 GW, with a max at 1,225 GW. Looking at the electricity generation by source, renewables saw a large increase after the pandemic started, led by solar and followed by hydropower and wind. Coal has seen the largest decrease in power generation.

Figure A. 2 presents daily generation in GWh. The figure shows that electricity generation is strongly seasonal. This is because the demand and generation profiles of renewables (hydro, solar and wind) are seasonal, depending on weather patterns and the more abundant availability of sunlight and wind at certain times of the year. Our econometric models (described below) must therefore control for month, temperature, and diffusion of air conditioning and various other types of heating/cooling equipment in the country.

Figure A. 2 also shows that all regions faced a significant drop in electricity generation during the first wave of COVID-19 – approximately starting in March 2020. All regions returned to their original level of electricity generation in the following months but recovered at different speeds. The United States recovered quickly, returning to its previous peak in July 2020. The European countries and Brazil recovered more slowly and reached their previous peak around March 2021. India was recovering slowly but plunged again in June 2021 due to the Delta variant of the novel coronavirus, reaching its previous peak around September 2021. On average, only the European countries continue to see reduced electricity generation in the post-pandemic world. The US, Brazil, and India experienced a slight increase in daily electricity generation (see Figure A. 3).

6
# Tax Holidays and Investment

<!-- Start of picture text --> by<br>Jk M. Mintz<br>Table of Coolants<br><!-- End of picture text --> <!-- Start of picture text --> I. Intduction ... ......... .. ....... 2<br>I. A Deaied Decrpio of Tax Holidays . 4<br>A. Tax Holiday Provisions..<br>B. Post-Tax Hoiday P vision .9<br>m. Theoretcal Analysis . . 10<br>A. Th Basic Theoord Model .11<br>B. Some ComnpliCaius .19<br>i Deferal of Depreciation .20<br>ii, Associed Non-Tax Holiday Frms .22<br>iii.Pemsonal Taxation and Debt Policy .26<br>IV. Empirical Analysis .32<br>V. Conclusins .38<br>Biblioahy .. 41<br><!-- End of picture text -->
the largest negative shock. Between 1996 and 2000, all integrated firms experienced a reduction in labor productivity as opposed to the non-integrated firms, that were catching up. This strongly suggests that the post NAFTA/devaluation trade expansion had asymmetric effects on firms based on their integration status, in particular in terms of their productivity performance.

Figure 5: Labor productivity performance by integration status <!-- Start of picture text --> 1992 1994 1996 1998 2000 2002<br>year<br>Non−integrated Fully Integrated<br>Exporters Importers<br>Data source: EIA<br>4.8<br>4.6<br>4.4<br>4.2<br>4<br>Value Added Labour Productivity<br>3.8<br><!-- End of picture text --> This Section shows that there is a great degree of heterogeneity in size, sector of specialization and productivity between firms with a different integration status. Exporting firms are similar in size to non-integrated firms although their level of labor productivity is higher with a level closer to the one exhibit by importing firms. Descriptive statistics also show that importers, as well as fully integrated firms, are concentrated in two capital intensive sectors: “machinery and equipment” and “chemical products”. Finally, the labor productivity trends show that NAFTA marked a turning point in productivity performance between firms with different integration status.

The rest of this paper will try to explore how much of the differential in labor productivity shown in Figure 5 is attributable to the increase in trade integration observed between 1993 and 2002. In our empirical strategy we take 1993 as the base year (period before NAFTA), compare the productivity growth rate between integrated and non-integrated firms (controlling for firm-level characteristics and allowing for heterogeneous effects across integration status) and attribute these difference to the reforms. Since many other factors can influence the productivity growth rate differentials, a formal econometric analysis is needed to control for other variables potentially influencing the patterns observed in Figure 5.

12
# TABLE 9

BRAZIL: REAL EXCHANGE RATE INDICES (1975=100)

|Years|RER/ USA<br>(1)|REER<br>(2)|IPE<br>(3)|
|---|---|---|---|
|1975|100.0|100.0|190.0|
|1976|95 6|96 5|102.0|
|1977|95.3|97.0|105.0|
|1978|95.1|1,2.5|106.2|
|1979|102.4|113.0|117.3|
|1980|110.3|121.9|107.5|
|1981|102.2|101.5|101.2|
|1982|104.6|96.5|101.1|
|1983|126.9|115.5|110.6|
|1984|123.7|108.6|98.9|
|1985|125.9|109.4|94.5|
|1986|112.2|113.0|97.6|
|1987|111.7|116.1|100.3|

Sources: iMartone and Braga, op.cit., Table II, and INPES/IPEA Notes: Col. (l): Real exchange rate (Cz$/ USS) based on wholesale price indices.

Col (2): Real effective exchange rate Countries and weights considered were: USA (41.5%), W. German!j (14.6X), Japan (11.3X), Argentina (7.1%), Italy (7.2X), Netherlands (6.8X), France (5.7X), and UK (5.7X).

- Col. (3): Index of profitability of exports (REER corrected by export subsidies).

- 32 -
Policy Research Working Paper 10964

# **Abstract**

This paper addresses the challenge of missing crop yield data in large-scale agricultural surveys, where crop-cutting, the most accurate method for yield measurement, is often limited due to cost constraints. Multiple imputation techniques, supported by machine learning models are used to predict missing yield data. This method is validated using survey data from Mali, which includes both crop-cut and self-reported yield information. The analysis covers several crops, providing insights into the importance of different predictors, including farmer-reported yields and geo-spatial variables, and the conditions under which the approach is valid. The findings show that machine learning-based imputations can provide accurate yield estimates, especially for crops with low intercropping rates and higher commercialization. However, survey-to-survey imputations are less accurate than within-survey imputations, suggesting limitations in extrapolating data across different survey rounds. The study contributes valuable insights into improving cost-efficiency in agricultural surveys and the potential of imputation methods.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at iyacouboudjima@worldbank.org; mtiberti@orldbank.org; and tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 5: COVID-19 and Electricity Consumption in Rwanda: Urban vs Rural

||Urb<br>|Resi<br>an<br>|dential<br>Ru<br>|ral<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|DID (Month> March=0)|0.047***<br>(0.001)|0.052***<br>(0.001)|0.025***<br>(0.001)|0.027***<br>(0.001)|
|DID (Month> March=1)|0.034***<br>(0.002)|0.032***<br>(0.002)|0.017***<br>(0.001)|0.017***<br>(0.001)|
|DID (Month> March=2)|0.049***|0.063***|0.019***|0.022***|
||(0.002)|(0.002)|(0.001)|(0.001)|
|DID (Month> March=3)|0.046***<br>(0.002)|0.043***<br>(0.002)|0.017***<br>(0.001)|0.017***<br>(0.001)|
|DID (Month> March=4)|0.043***<br>(0.002)|0.034***<br>(0.002)|0.025***<br>(0.001)|0.023***<br>(0.001)|
|DID (Month> March=5)|0.058***|0.084***|0.031***|0.038***|
||(0.002)|(0.002)|(0.001)|(0.001)|
|DID (Month> March=6)|0.087***<br>(0.002)|0.083***<br>(0.002)|0.053***<br>(0.001)|0.051***<br>(0.001)|
|DID (Month> March=7)|0.090***<br>(0.002)|0.083***<br>(0.002)|0.063***<br>(0.001)|0.062***<br>(0.001)|
|DID (Month> March=8)|0.056***|0.026***|0.047***|0.039***|
||(0.002)|(0.002)|(0.001)|(0.001)|
|DID (Month> March=9)|0.028|0.021|0.034|0.034|
||(0.002)|(0.002)|(0.001)|(0.001)|
|Climate Ctrls|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|
|R-squared|0.821|0.821|0.793|0.793|
|Mean dep. Var|37.415|37.415|11.670|11.670|
|Obs|5815537|5815537|11279386|11279386|

Notes: Dependent variable is the log of monthly electricity consumption. DID (Month> April= _ω_ ) represents the DID estimates for the _ω_ months since the start of COVID lockdown (April, 2020). The DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly</sup> average temperature and total precipitation . Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 31
|200|Figure A1. D|ecomposition of Chan|ges in US$2.50-a-DayPovertyRate—Al|l Countries||||
|---|---|---|---|---|---|---|---|
|-50<br>0<br>50<br>100<br>150<br>Percent Contribution to Total Change in Poverty||||||||
|-100|9<br>0<br>9<br>5|3<br>0<br>0|9<br>0<br>9<br>8<br>0<br>0|0<br>9|0|9|9|
||Romania 2001–0<br>Moldova 2001–1<br>Thailand 2000–0<br>Ghana 1998–200|Nepal 1996–200<br>Peru 2004–1<br>Bangladesh 2000–1|El Salvador 2000–0<br>Mexico 2000–1<br>Chile 2000–0<br>Costa Rica 2000–0<br>Argentina 2000–1<br>Ecuador 2003–1|Paraguay 1999–201<br>Brazil 2001–0|Colombia 2002–1|Honduras 1999–200|Panama 2001–0|
||Consumption-based|measure of welfare|Income-based measu|re of welfare||||
||Share of working-age|family members<br>Lab|or Income<br>Nonlabor Income<br>Consu|mption-to-incom|e ratio|||

**_Source:_** Authors’ calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA for Ghana and Nepal, and from household surveys for Bangladesh, Moldova, Romania, Peru and Thailand.

_Notes:_ “Labor income” refers to the change in employment and earnings per adult, “nonlabor income” refers to transfers, pensions, capital and other income not from labor.

36