rafmacalaba/data-use-mentions-extended huggingface dataset:
# **(c) New capacity or capacity added during 2000-2010 and 2010-2020 period (GW)**

<!-- Start of picture text --> 489 498<br>451<br>305<br>240<br>164<br>18 38 39 29 42 34<br><!-- End of picture text --> <!-- Start of picture text --> Oil<br><!-- End of picture text --> <!-- Start of picture text --> Nuclear<br><!-- End of picture text --> <!-- Start of picture text --> Hydro<br><!-- End of picture text --> <!-- Start of picture text --> Wind<br><!-- End of picture text --> Other RETs refer to other renewables, including biomass, geothermal, tidal, etc. Source: IEA online database. <u>https://www.iea.org/data-and-statistics/</u> The total investment in clean technologies, including renewables and energy efficiency (EE), has remained stagnant at around US$600 billion during the 2015-2019 period. It dropped to US$562 billion in 2020 due to the COVID pandemic (Figure 1). One key reason for the downward trend of investment despite continued expansion of clean technology adoption is falling unit costs for key renewable technologies (e.g., solar PV, wind, and electric vehicles).

The EE improvement means using technology that requires less energy to perform the same task. It has multiple benefits, including reducing energy bills<sup>3</sup> , mitigating climate change, reducing air pollution, improving energy security, and increasing energy access (IEA, 2020b). The IEA, (2020c) report estimates that the efficiency gains since 2000 in IEA member countries resulted in

>
20 in other provinces and counties.10 Furthermore, the central government retains considerable control over the activities of local governments, 11 including personnel appointments and monitoring of the budgetary process. This is a good example of the potential misleading conclusions that can be reached if simple proportions of total revenues derived locally are used as the sole measure of the amount of fiscal autonomy enjoyed by local jurisdictions.

Data from both Mexico and Ecuador reveal extremely large increases in the reliance of local governments on transfer revenues. The proportion of total revenues derived from transfers to both states and municipalities in Mexico increased from less than one-fifth to over three-fifths during the ten years from the mid-1970s through the mid-1980s. These changes resulted from the 1980 Ldw of Fiscal Coordination which provided additional shared revenues in exchange for changes or even elimination of certain previouslyused local revenue sources. Since the shared revenues were tied to relatively elastic revenue sources, they provided subnational governments with a more buoyant revenue base. The growth in shared revenues has, however, also resulted in a decline in the fiscal autonomy of local 10See Choong Yong Ahn, "Financing Local and Regional Development: The Case of the Republic of Korea," Paper present to the Workshop on Financing Local and Regional Development (Nagoya: United Nations Centre for Regional Development, 1984), p. 38.

1 lDong Hoon Chun, Hyung-Hwan Kim and Kyu Sik Lee, "Fiscal Performance of Local Governments in the Seoul Region: Implications for Urban Deconcentration Policies," Discussion Paper, Report No. UDD-88, Water Supply and Urban Development Department, Operations Policy Staff (Washington, DC: The World Bank, 1985).
# **Table 2: Variable definitions and sources**

|**Variable**<br>**Type**|**Data Type**|**Variable Name**|**Definition**|**Sources**|
|---|---|---|---|---|
|Dependent<br>Variable|Crypto-assets<br>activity|On-chain crypto-<br>assets volume|Log monthly on-chain crypto-assets volume (in<br>US$)|Chainalysis|
||Mi|Inflation|Year-on-year change in the monthly Consumer<br>Price Index (CPI)|IMF International<br>Financial Statistics|
||acroeconomc<br>factors (monthly<br>basis)|Exchange Rate<br>Change|Month-on-month change rate in exchange rates,<br>national currency per USD, end of period|IMF International<br>Financial Statistics<br>|
|||Broad Money|Year-on-year change in monthly broad money|IMF International<br>Financial Statistics|
|||Bitcoin Price<br>Change|Month-on-month bitcoin price change (in US$)|Federal Reserve|
|||Ether Price Change|Month-on-month etherprice change(in US$)|Federal Reserve|
|||VIX|CBOE Index of market’s expectation of future<br>S&P500 U.S. equity market volatility|Federal Reserve|
|Independent<br>Variables for<br>panel<br>|Global Financial|U.S. BBB<br>Corporate Spread|ICE BofA BBB US Corporate Index Option-<br>Adjusted Spread is a computed OAS index of all<br>bonds in a given rating category and a spot Treasury<br>curve|Federal Reserve|
|regressions|Factors (monthly<br>basis)|U.S. Inflation<br>Expectation Rate|5-Year, 5-Year Forward
Saharan African countries urbanization without growth characterized the 1990s, creating a new type of housing demand in a much more urbanized if poorer world.<sup>5</sup> For shelter policy in developing countries, perhaps the most notable change in recent years is with respect to the spatial dimension of poverty. This pattern had changed in rather fundamental ways. Though the majority of the world’s poor continue to live in rural areas, poverty is rapidly becoming an urban phenomenon. Today, unlike the situation in 1985, in many of the Bank’s biggest borrowers – e.g. Russia, Brazil, Mexico - most of the poor already reside in urban areas. Moreover, while in India most poverty remains in the countryside, in most of its larger states the poverty rate in urban areas is for the first time higher than it is in rural areas.<sup>6</sup> By some estimates, over 50 percent of the world’s poor will be living in urban areas by 2035, see Ravallion (2002).

In such an environment, it is not surprising that urban slums are not being absorbed into formal urban economies. Surveys in Brazil and India, for example, indicate that in many places slum dwellers are no longer participants in a temporary demographic transition process as was earlier thought to be the case. It is now recognized that many slum dwellers are not new immigrants who recently arrived from rural areas in search of better livelihoods. Today, many of the 100,000 pavement dwellers in Mumbai, for instance, are secondgeneration residents, see SPARC (2002), as is the case in Rio’s Favelas, see Perlman (1976 and 2002).

> 5 There is some debate about the finding that sub-Saharan Africa has experienced rapid urbanization without accompanying economic growth.  Since there have been few censuses available for recent years, the data used in many such studies have relied on projections of urban population growth from the 70s and 80s.  By some accounts,
# **6.    Directions for New Research**

- For the purposes of this research the countries in the sample were divided into different categories based on the specific threshold levels of system size and income. The selected thresholds are in line with the previous research (Besant-Jones, 2006). The alternative way of categorizing countries would be to apply a cluster analysis. This technique allows assigning a set of observations into subsets, such that observations in the same subset are similar in some parameters (for example, GDP per capita and power system size), but that the subsets differ between each other. The application of the cluster analysis allows obtaining the optimal number of subsets as well as the threshold levels based on the underlying data.

- Different definitions of the aggregate index of reforms can be used to examine the robustness of the results to the different specifications. More sophisticated dynamic error correction models and the  instrumental variable approach to allow for potential possible endogeneity biases can be used in future research.

- Finally, additional performance indicators, including indicators of quality (such as SAIDI and SAIFI), operational efficiency (including capacity utilization, load factor), financial efficiency (such as cost recovery index), and long run environmentally sustainability indicators (such as the share renewables) and energy endowment (such as reserve capacity, and self-sufficiency) can be studied in more detail with the help of the econometric models.

# **7. Bibliography**

Arizu, Beatriz, Defne Gencer and Luiz Maurer (2005) ―Centralized Purchasing Arrangements: International Practices and Lessons Learned on Variations to the Single Buyer Model‖ Energy and Mining Sector Board Discussion Paper Series, Paper No.10. Washington, D.C.: World Bank Arocena, Pablo, Tim Coelli and David Saal (2010) ―Measuring Economies of Integration in the US Power Industry: How Costly is Vertical Unbundling?‖, mimeo.

Besant-Jones, John E. (2006) " _Reforming Power Markets in Developing Countries: What Have We Learned?_
Table 12 re-estimates equation 3 but narrows down the way we define eligibility. Eligibility is no longer defined for any child being of preschool age, but it is defined for each child’s birth order. Therefore, column 1 defines eligibility relative to the first child, column 2 to the second child, and so on. If a mother only has 2 children, preschool eligibility status will only be defined for the first two columns pertaining to her first two children. Hence, the number of observations will fall as we go across the columns because the number of mothers with three children is fewer than the number of mothers with just one child.

Additional public preschool density has positive and statistically significant effects on the work participation of mothers with preschool-aged eligible first, second, and third children. The statistical significance of the third child is still robust to simultaneous inference with an FDR q- value of 0.095. The effects on the fourth child are positive but not statistically significant; on the fifth and sixth children they are negative and not statistically significant. Having better access to a private preschool does not seem to matter for work participation. We find a spurious positive and statistically significant effect for the second child; but the indicated positive effect does not stand the false discovery test.

# **_6.4 Heterogeneous effects by preschool enrollment level_**

Previous studies suggest that the effect of childcare services on maternal employment may depend on utilization rates; if the rate is already high then additional childcare services may no longer have an important effect on employment. It is important to note, however, that null findings in other contexts have 90 percent (Bauernschuster and Schlotter 2015) and 80 percent childcare utilization rates (Lundin et al. 2008). Meanwhile, average preschool enrollment rates among children aged 3- 6 per district between 2003 and 2014 in Indonesia is 15.6 percent; the 99th percentile is 49.8 percent.<sup>22</sup> As such, we may not expect preschools
describe changes in outlook due to the pandemic. The specific GDP measure that we use is the annual percent changes of constant price gross domestic product, where the base year is country-specific, according to WEO.

**Labor market projections.** To relate our findings to other labor market projections on the economic impact of COVID-19, we use data from the International Labour Organization’s (ILO’s) ILOSTAT, the employment-to-population ratio in percent, the number of persons who are employed as a percent of the total working age population, based on country labor force surveys, restricted to the group ages 15 years or older. The quarterly data for quarter 1 and quarter 2 of 2020 allow us to describe the change in employment at similar time points to the HFPS measure of the labor market.

# **4 Results**

## **4.1 Disruptions to Work in the High-Frequency Phone Surveys**

We start by examining the impact of the pandemic on work stoppages in different countries. As discussed in Section 3.2, we count a respondent as having stopped work if she was working pre-pandemic but no longer working the week preceding the interview. Figure 1 shows the share of respondents who report stopping work during the pandemic by country.

Panel (a) of Figure 1 groups countries by region, highlighting EAP countries in blue, ECA countries in red, LAC countries in green, MENA countries in yellow, and SSA countries in purple. Taking a simple average across countries, 34% of respondents reported stopping work. The average across countries in our data is 21% in the EAP region, 29% in the ECA region, 48% in the LAC region, 45% in the MENA region and 26% in the SSA region. (We note that the set of countries in our data is not representative of regions.) There is significant variation, even within regions. For example, within the LAC region, at the lower end 30% stopped working in Chile and 36
# <u>Abstract</u>

This paper uses a large, nationally representative household survey to analyze the impact of internal remittances (from Guatemala) and international remittances (from the United States) on poverty in Guatemala. With only one exception, the study finds that both internal and international remittances reduce the level, depth and severity of poverty in Guatemala. However, the paper finds that remittances have a greater impact on reducing the severity as opposed to the level of poverty in Guatemala. For example, the squared poverty gap – which measures the severity of poverty – falls by 21.1 percent when internal remittances are included in household income, and by 19.8 percent when international remittances are included in such income. This is true because households in the lowest decile group receive a very large share of their total household income (expenditure) from remittances. Households in the bottom decile group receive between 50 and 60 percent of their total income (expenditure) from remittances. When these “poorest of the poor” households receive remittances, their income status changes dramatically and this in turn has a large effect on any poverty measure – like the squared poverty gap – that considers the number, distance and distribution of poor households beneath the poverty line.
COVID-19. Job losses remained stark as well in _Wholesale and retail trade_ , which lost jobs for approximately35.6 percent of workers, compared to 10.1 percent in the construction sector.

5.5 Projected Job Losses by Demographic Group Next, we examine the rate of projected PFPS job loss by the demographic characteristics of pre-COVID job holders. Because our method can only reflect how shocks at the firm-, sector-, and production/nonproduction worker levels play out in terms of jobs lost (or at risk), our demographic projections would not capture any effects of differential individual or firm behavior that may result in divergent outcomes by gender, age, or nationality. As such, a comparison of our projections with actual outcomes provides a sense of the degree to which pre-pandemic employment patterns alone would produce demographic differences and how much of those differences reflect behavioral factors.

# **5.5.1 Jordan**

**Gender** : In both waves of the CFUWBES, our model projects a higher percentage loss of PFPS jobs for men than for women (see Table 5-14). For wave 2, the rate is 26 percent for men versus 19 percent for women. The survey-measured projection yields a more equal gender distribution of job losses than the model-based projection. However, the modeled prediction more closely aligns with the CMMHH data, which shows a 17.3 percent rate of job loss for females and 24.8 percent for men. Female PFPS job losses were lower because in

|_Table 5-14 Jordan: Co_<br>_due to firm closures),_|_mparison of PFPS J_<br>_by Demographic Gro_<br>CFUWBES1<br>|_ob Losses, Modeled, an_<br>_up_<br>CFUWBES2|_d Survey-Measured (_<br>(Cumulative)|_including jobs lost_<br>|
|---|---|---|---|---|
||Modeled|Model-predicted|Survey-measured|CMMHH
Economic Freedom, Heritage Foundation**|
||2|Low|Weighted average tariff rate greater than 4 percent but less<br>than or equal to 9 percent||Based on: The Economist Intelligence Unit, Country<br>Report and Country Commerce, 2002; International<br>Monetary Fund, Government Finance Statistics Yearbook<br>|
||3|Moderate|Weighted average tariff rate greater than 9 percent but less<br>than or equal to 14 percent||and International Financial Statistics on CD–ROM, 2002;<br>Office of the U.S. Trade Representative, 2002 National<br>Trade Estimate Report on Foreign Trade Barriers; U.S.<br>Department of State, Country Commercial Guide 3 and|
||4|High|Weighted average tariff rate greater than 14 percent but<br>less than or equal to 19 percent||Country Reports on Economic Policy and Trade Practices<br>for 2001 and 2002; World Bank, World Development<br>Indicators 2002; World Trade Organization, Trade Policy<br>|
||||||Reviews, 1995 to June 2001; and official government|
||5|Very high|Weighted average tariff rate greater than 19 percent.||publications of each country. For all the European Union<br>countries, the authors have based the score on data<br>reported by the World Bank.|
|i Hidden import|0 to 10|<br>(0= heavy|No barriers other than published tariffs and quotas.|1995,|**Economic Freedom of the World, The Fraser Institute**|
|barriers||regulation)||2000, and||
|(efw_bi)||||2001|From Section:|
|ii Costs of importing|||The combined effect of import tariffs, license fees, bank||4 Freedom to Exchange with Foreigners|
|<br
To facilitate estimation of the net gains to participants and to test the robustness of our results using the NSS, we will also use another household dataset collected in rural areas of the state of Bihar with support from the World Bank. Bihar is India’s poorest state by the government’s official poverty measures (Dutta et al., 2012a). Two rounds of survey data were collected for 3,000 randomly chosen households from 150 random villages spread across Bihar. The first round was between May and July of 2009 and the second during the same months one year later. A two-stage sampling design was followed, using the 2001 Census list of villages as the sampling frame. Data were collected through several survey instruments, including household surveys and individual surveys, the latter for one adult male and one adult female in each household. Dutta et al. (2012b, Chapter 3) contains a fuller description of the survey design.

In this paper we only report results for the first round of the Bihar survey, which is designed to be representative of rural Bihar. However, we did all our analysis for the second round as well and all qualitative findings reported for Round 1 were found to be robust. For comparison purposes we also provide results for the Bihar sub-sample of the NSS.

The World Bank’s Bihar Survey contains similar variables to the NSS data (including on consumption), but provides more detail on participation and other variables related to NREGS.<sup>9</sup> Importantly, the Bihar survey includes reported estimates of forgone work and earnings for NREGS participants. The Bihar Survey contains a block on time spent on casual work during the week ended, with total number of days and incomes for each activity (block 23). Thus we are able to estimate income gains (net of forgone earnings) from NREGS in Bihar, and impacts on poverty. Moreover, we know if household members had enough NREGS working days if they worked on NREGS, or if they did not want
(1992). While we could take a computable general equilibrium (CGE) modeling approach, this would require considerable aggregation of the tariff data, which might cause us to lose information in the dispersion of tariffs within the twenty or so aggregates routinely used in CGE analysis. Instead, we have used a more disaggregated, but simpler, model with around 5000 tariff lines identified separately, and identified as substituting in Armington fashion relative to a single, domestically produced good<sup>6</sup> .

We begin by representing the relevant features of the economic structure with the welfare evaluation approach of Lloyd and Schweinberger -- a distorted trade expenditure function: _<u>B = e(p, v) - g(p, v) – zp(p,v,u).(p-p*)</u>_ <u>(5)</u> where _B_ is the balance of trade, _e_ is the expenditure function in a vector of domestic prices and total utility, _u_ ; _g_ is revenue from production as a function of domestic prices and a vector, _v_ , of resources (we might generalize this by using a profit function with variable intermediate inputs); _zp_ is the derivative of _(e-g)_ and equal to net imports of each good; and _p*_ is a vector of world prices.

If we assume a standard Armington-type structure in a small economy, with imports differentiated from domestic goods, but domestic goods perfectly substitutable with exports, then we can simplify the analysis of tariff changes by focusing only on e(p,u) and the tariff revenue function. When tariffs change, there will be no change in the realized value of _g(p,v)_ and it can be ignored, unless there are distortions such as export taxes.

To make the approach easy to apply, we write the expenditure and revenue functions using a Constant-Elasticity of Substitution functional form for the expenditure function and a tariff revenue function based on
)|0.106|(0.116)|-0.188|(0.047)|-0.265|(0.1.26)|
|Fraction persistent||0.137|(0.047)|0.105|(0.034)|0.244|(0.029)|0.178|(0.075)|
|_Sensitivity of "Fraction_|_Persistent_|_" to the_|_assumpti_|_on that 7_|_7i, and eit a_|_re uncorr_|_elated._|||
|Frac. persist. if corr[rlitt,|e]=<br>1.0|<br>0.|081|0.0|65|0.1|84|0.1|01|
|<br>Frac. persist. if**corr[riit,**,,|<br> ]=<br>0.2|0.|120|0.0|93|0.2|25|0.1|54|
|Frac. persist. if corr[Tlit,|eq]= -0.2|<br>0.|159|0.1|19|0.2|68|0.2|10|
|Frac. persist. if corrhNi,|ej,l= -1.0|<br>0.|439|0.2|69|0.5|64|0.7|56|

Note: All measures are for equivalent adults. The equivalence scale is household size to the power of 0.75 (0=0.75). Data **consist of** balanced panels, 1993-1996 for Poland and 1994-1998 for Russia. Fraction persistent is the fraction of the shock between period 2 and **3 that** is persistent. **This** fraction is defined by
it does not collect information on labor market outcomes outside Germany, in particular after return to the home country.

Other available surveys in destination countries capturing temporary migrants are crosssectional. As for the GSOEP, the survey “Trajectoires et origines” in France released by Ined-Insee in 2009 includes questions on intentions to stay in the host country, and was used to analyze the relationship between remigration intentions and immigrants’ behaviors in the host and origin countries (Chab ́e-Ferret, Machado, and Wahba 2018). In addition to migration-dedicated surveys, some of the literature has also been using more traditional, nationally-representative data in destination countries to analyze temporary migrations. In the US, the American Community Survey (ACS), was for example used to reconstruct episodes of transit migration of current migrants (Artuc and Ozden 2018). The data includes information on the birth country of migrants as well as the country where they lived prior to moving to the US, which allows to identify transit migration. One advantage of destination country data is that they capture temporary migrants who moved overseas with their entire families, as opposed to surveys in origin countries. Survey data in host countries, however, 16