rafmacalaba/data-use-mentions-extended huggingface dataset:
Callum, 1995; Feenstra et al., 2001; Feenstra, 2002; Evenett and Keller, 2002; Anderson and van Wincoop, 2003; Santos Silva and Tenreyro, 2006), enforcing its theoretical base and narrowing the gap between theoretical and empirical findings.

> 5 The lists of countries and sectors included in the data set are included in Table A.4 and A.5 in Appendix 2.

10
a k-mean algorithm was implemented to create 10 groups of similar localities based on four characteristics: i) proportion of females aged three years or older who spoke an indigenous language in 2010; ii) marginalization index by locality in 2010; iii) number of CONAFOR grants for forest activities received by the locality between 2007 and 2017; and iv) proportion of adult women in the total adult population of the locality in 2010. Second, one-third of each group was randomly assigned to each of the three experimental groups, ensuring a more balanced sample. Prior CONAFOR grants help account for existing forest management capacity, the marginalized locality index (CONAPO, 2010) accounts for socio-economic level, the adult population for the number of potential applicants, and the proportion of adult women and indigenous language speakers account for prioritized groups for this call for proposals.

Finally, localities that were part of an agrarian unit (see footnote 1 for definitions) and that were assigned different experimental groups were re-randomized as a cluster to the same experimental group. The 113 localities were clustered in 84 agrarian units. Most components of the intervention were implemented at the locality level, but some aspects utilized the infrastructure of the agrarian unit, including rooms to have the informational meetings or to hang posters. The re-randomization

> 9 For example, localities that are not part of an agrarian unit are dropped when looking for forest coverage. Also, localities without data for the number of women are dropped when looking for more than 10 adult women. (The census does not include subgroup information of the population for small localities with one or two houses.)

> 10 It should be noted that the call for proposals that this RCT leverages is not part of CONAFOR. However, CONAFOR grants are similar in objective and were the best past data available.

8
Overall, Nigeria’s figures in terms of both child labor and child marriage underscore the importance of schooling as a protective strategy for children against vulnerability to harmful practices known to undermine children’s schooling outcomes (Edmonds, 2006; Field and Ambrus, 2008). It is against this background that we estimate the effect of temschool closures due to COVID-19 on the school attendance of porary Nigerian children aged 5 ́ 18.

# **2.2 Data and Measurement of Variables**

In this section, we present the data, the construction of relevant variables, and summary statistics.

## **2.2.1 Data**

This uses data from the COVID-19 National Phone paper Nigeria Longitudinal Survey (COVID-19 NLPS), implemented by the National Bureau of Statistics of Nigeria with support from the World Bank. The COVID-19 NLPS collects monthly data to monitor the socioeconomic effects of the evolving COVID-19 pandemic in nearly real time. The sample of the COVID-19 NLPS is a subsample of households that had been interviewed face-to-face prior to the COVID-19 pandemic as part of the Nigeria General Household Survey (GHS) - Panel 2018/19, designed to be representative at the national level as well as at the zonal level. Linking the near real time information of the COVID-19 NLPS data with the very extensive set of information collected just over a year prior to the pandemic in the GHS - Panel 2018/19 (January/February 2019) allows exploring heterogeneous effects of school closure on school attendance.

The study selected 3,000 households from the frame of 4,934 households with a phone number contact for at least one household member or a reference individual. Of these, 1,950 households were successfully interviewed in Round 1 of the COVID-19 NLPS and the same households were called about every four weeks for the subsequent round interviews. Excluding from the final sample households with no access to a mobile phone and 11
of macroprudential policy is to minimize systemic risk by limiting boom-bust credit cycles. Several new instruments have been developed under the Basel III framework specifically to promote this objective, such as: countercyclical capital requirements and dynamic provisioning to build up capital or liquidity buffers during good times; maximum leverage ratios to capture both on- and off-balance sheet exposures; and capital surcharges on systemically important financial institutions.

Macroprudential instruments have increasingly become an integral part of the toolkit of many central banks and other financial regulators since the crisis. Macroprudential indexes derived from a dataset for 36 advanced economies and 124 EMDEs suggest that all advanced economies, and about 70 percent of EMDEs, have used these instruments to strengthen the resilience of their financial systems (Cerutti, Claessens, and Laeven 2017). EMDEs have more **— —** actively used macroprudential instruments often on an ad- hoc or experimental basis partly reflecting the fact that they are more exposed to volatile capital flows and have less liberalized financial systems (Claessens 2014). These instruments have been used to reduce the growth of credit to nonfinancial corporations and households, and to help restrain asset price inflation, especially in the housing sector (Budnik and Kleibl 2018; Kuttner and Shim 2013, Vandenbussche, Vogel, and Detragiache 2015; Zhang and Zoli 2016).

In ECA, more than four-fifths of EMDEs have increased the use of macroprudential tools, while in SSA the share is only about one-half (Figure 4). The use of different tools has reflected different structural characteristics among countries. ECA has had relatively high financial integration internationally with a large presence of foreign banks slower lending in U.K. banks, and dynamic provisioning has been associated with smoother credit cycles in Spain (Aiyar, Calomiris, and Wieladek 2016; Jiménez et al. 2017). In contrast, foreign exchange and liquidity policies, such as limits on foreign currency loans and foreign exchange countercyclical reserve requirements, have been more often used in EMDEs in efforts to reduce exposures to volatile capital inflows (Figure 4).
**o,Pbcrlnft and R~rch**

# **Sodoooonom** Date

The most direct way of comparing output in different countries is to use the official exchange rate to convert GDP in one country's prices into the prices of another country - and in multicountry comparisons to convert it into a common currency, such as the U.S. dollar. But exchange rates mainly indicate the purchasing power of currencies over tradables, not the average purchasing power of currencies over aU goods and services. And even for tradables, the use of exchange rates is problematic because of currency fluctuations and capital movements.

With the measurement of comparative real output across countries intertwined with assessments of purchasing power, the question becomes: what is the best way to make those assessments? Most purchasing power parities have been developed for the components of final demand - for consumption, investment, and so on. This expenditure approach is useful for looking at an entire economy, but it cannot be used directly for analyzing individual sectors because it does not show real product by industry.

The method essentially takes the value of output in national prices and uses unit values from census data to standardize output unifonrly and consistently. So, rather than use a common conversion factor (the exchange rate) it uses a standardized basis of valuation.

Applied to the manufacturing sectors of Brazil, Mexico, and the United States, the revaluation of output in national and U.S. prices provides a sounder basis for constructing relative indicators of roductivity. It also reveals much about trade protection policies and their incidence on different sectors of the economy.

The approach shows, in addition, which data are anomalous and which analytically useful in industrial census. It thus provides important lessons for the development of the Bank's data base - on how to increase its reliability and improve its relevance to operations. It also shows how new insights might be gained by exploiting some official sources which, though rich in detail, often remain untapped by intemational agencies.

The production
4.3|
|Land<br>productivity<br>of<br>feedstock|Tonne /ha.|20.55<sup>a</sup>|45-75<sup>b</sup>(60)|21.47<sup>c</sup>|
|Starch or sugar content|%|25|15|20|
|Assumed<br>conversion<br>efficiency|Tonne<br>(ethanol)/tonne<br>(feedstock)|0.14|0.625|0.112|
|Land<br>productivity<br>of<br>ethanol|Tonne /ha.|2.88|2.81-4.69 (3.75)|2.4|
|Ethanol yield potential on<br>unutilized land|Million tonne|0.36|14.93-24.91(19.93)|3.13|
|Ethanol yield potential on<br>low-productivity arable land|Million tonne|20.14|19.69-32.81(26.25)|7.21|
|Ethanol yield potential on<br>marginal land|Million tonne|20.5|34.63-57.72(46.18)|10.35|

Notes: a. the 2008 value; data from Fang, et al (2010); b. Data from EB (2009); c. the 2008 value; data from DRST, et al. (2009) ; d. the value is not available; e. data from Tian et al.(2010); f. the 2008 value with other 15% of sweet potato was rotting; data from Jin et al. (2011); g. data from EB (2009), but because North China is suitable for both sweet sorghum and sweet potato, unused land here is assumed to be divided evenly between both crops; h. data from Shi (
dependence of real GDP growth, for the full sample (left panel) and the advancedcountry sample (right panel). The CD test statistic is above 40 for both samples, overwhelmingly rejecting the null. Table 1 also reports the exponent of cross-sectional dependence _α_ along with 95% confidence bands, for both the advanced and full country samples. The estimated value of _α_ is 1 in the advanced-country sample (with a confidence region reaching well above 1) and .94 in the full sample. In both cases, the results point to the presence of strong common factors in the output growth data, consistent with the findings of, e.g., Kose, Otrok and Whiteman (2003).

Table 1: GDP growth: cross-sectional dependence

||All countries|Advanced countries|
|---|---|---|
|Pesaran CD statistic|42.343|41.661|
|Exponent of CSD|0.943|1.004|
||(0.906, 0.979)|(0.905, 1.102)|
|Number of countries|117|21|

Notes: GDP growth is the first difference of the log of real GDP. ’Exponent of CSD’ is the exponent of cross-sectional dependence of Bailey, Kapetanios and Pesaran (2015), and values in parenthesis are its 95% confidence bands. The sample period is 1970-2016.

# **4.1 Model estimation results**

In order to estimate the factor-augmented dynamic spatial model (1), we first need to determine the number of unobserved common factors. To do so, we compute the _IC_ , _BIC_ and _HQ_ information criteria proposed by Choi and Jeong (2018) setting the maximum number of factors to 5.<sup>11</sup> The results are shown in Table 2. The upper panel of the table reports results for the full sample, and the bottom panel reports results for the advanced-country sample. For the full sample
separately by the degree of how routine the tasks are within a given occupation, and for samples of workers who stay within an industry, switch between industries, and specifically exit manufacturing.

# **III. Data Description, Trends, and Empirical Strategy**

- A. Data Description

Our sample of US workers is taken from the Current Population Survey Merged Outgoing Rotation Groups for 1983-2002, which provides data for over 3.4 million workers who are assigned a consistent classification of their industry and occupation.<sup>6</sup> Offshore activity in each industry is measured by the total employment of foreign affiliates among multinational US firms, separated into high and low-income locations, stored by the Bureau of Economic Analysis (BEA).<sup>7</sup> The BEA sample of multi-national firms accounted for 80 percent of total output in manufacturing in 1980, suggesting that the coverage is fairly extensive. Our data on import penetration and export shares are taken from Bernard at al. (2006), which we recalculated and updated through 2002. Since relative price series for imports and exports are incomplete, we generally substitute for prices by using the share of exports in production and import penetration at the four-digit SIC 1987 level. Results using prices instead of quantities are available in the appendix materials. As a control for productivity changes that could also affect labor demand as well as wages, we use the NBER’s calculations of total factor productivity provided by Wayne Gray. This data source also provides us measures of the price of investment goods and the real price of shipments by industry and year, which we use to control for technology and industry level demand, respectively. We also match our workers to information on computer use rates by industry and occupation from CPS computer supplements

> 6 Special thanks to David Autor for providing us with concordances that provided a consistent coding scheme of industries and occupations for the period. The CPS occupation and industry codes were reclassified in 2003 to correspond to the North American Industrial Classification System
but might be in-demand as part of the green transformation. Lastly, some institutions like the ILO add the "decent job" layer to the definition: a green job not only needs not to damage the environment, but it also needs to pay adequate wages and provide worker protection.

The lack of convergence stems from the unfeasibility of translating the concepts into a single measurement methodology that suits existing data (or cost-effective, collectible new data) and different policy questions. Many policy makers prioritize supporting workers moving out from brown jobs to other green jobs. Researchers instead have a broader set of questions, for which they use different definitions and deploy very different specialized surveys. These surveys are usually a good tool for specific questions at hand, but generalizing their use to other policy questions creates important biases.

As a single definition may not be possible—or even desirable—in view of the multiple policy questions, this paper seeks to identify which occupations and skills should be the focus of skills development policies needed to support the greening of the economy. Put simply, it aims to identify which occupations may require new skills training to support the green economy. With this objective in mind, this paper shows that green jobs should be understood as those that have specific tasks assigned to lessen consumers' and/or firms' environmental impact. These green tasks can either be assigned with the goal of producing greener outputs or of reducing the firm's environmental footprint. The proposed methodological approach helps to identify the skills that will be needed to carry out such tasks. Some of these jobs will require core skills, some of which will be transversal, while others would be specific 2
of multi-unit housing stock reflects the role of quality and location of housing in the households' readiness to privatize.

**Table 1.12.** Vladimir and Gorodets, housing stock byt.e of ownership,,1996 **(%).**

|NType<br>of housing|Vladimir|Gorodets|
|---|---|---|
|Total privately owned|48|66|
|o/w: Privatized housing|42|33|
|o/w: Already private single-family houses|6|33|
|State & municipal|52|35|
|Total|100|100|

Source: Vladimir and Gorodets Longitudinal Household Survey, December 1996.

**Table 1.13.** Vladimir and Gorodets, share of private housing by type of building, 1996 __%/ of total_in each grou)

|Type of**housing**|Vladimir|Gorodets|
|---|---|---|
|Separate apartments, average|47|50|
|of which:|||
|Old low-rise stock|43|36|
|Khrushchev-era buildings|62|64|
|Modem brick buildings|47|50|
|Modem panel buildings|38|60|
|Old single-family private houses|100|100|
|Average|48|66|

Source: Vladimir and Gorodets Longitudinal Household Survey, December, 1996.

In Vladimir, the quality factor proved insignificant: even in the worst dwelling category-the old low-rise apartment stock-43% was privatized. Location was more important: the rate of apartment privatization

> 9 Lehmann and Ruble (1994). Tihe sample contained 1,430 respondents.

'<sup>0</sup> In most Russian towns, the share of such privately.owned houses is higher than in Vladimir and lower than in Gorodets.
# **3.3. How Does This Paper Analyze Cost Recovery and Financial Viability?**

19. The paper analyzes the power sector reform journey from the late 1980s and 1990s through the period 2010-17 of 17 jurisdictions<sup>8</sup> (14 countries and 3 Indian states) that represent a wide diversity of geographies, income levels, and approaches to reform. The analysis consisted of three steps:

1. **Post-reform cost-recovery level of electricity tariffs (2010-17) at the country/state and utility levels.** Data on tariff levels during 2010-17 (or a subset of years) were collected for all 17 jurisdictions on the country / state-level and the utility level and compared to corresponding estimates of six cost recovery levels (A1-A3 and C1-C3<sup>9</sup> ). The total sample of the utility-level analysis includes 18 majority publicly-owned and 7 privately-owned utilities. Where possible, the analysis included actual cash collected besides the billed revenue. The data was compiled from the financial statements of all utilities in the sector to obtain a full picture of cost and revenues in the jurisdictions, complemented with information on indirect government support (e.g., in the form of subsidized fuels). Exceptions were made for independent power producers and small power producers, the cost of which were approximated by the electricity purchase cost of the off-taking utility. The only two cases where such a holistic picture of costs and revenues in the sector was not possible were the Dominican Republic, where the analysis focused only on the utility Edesur, and Morocco, where the analysis relied on a previous study by The World Bank (2017) for information on full-cost recovery levels in the sector. All data presented in the paper is expressed in 2017 US$ to adjust for inflation. An overview of data compiled for the case studies, the years covered, and the data sources is provided in Table 2.

2. **Pre-reform cost-recovery level of electricity tariffs (1980s-1990s).** Data
br>28.1|<br>_50.0_|52.0|-3.4|<br>13.3|
|1996|17.4|<br>10.2|<br>8.4|14.0|<br>42.3|44.0|-5.4|<br>9.1|
|**1997d**|31.6|<br>-|37.1|<br>38.4|<br>40.4|33.8|3.7|-|

Source: Ministry of Finance, Bank of Albania, and the IMF's Intemnational Financial Statistics.

Notes: (a) Domestically financed deficit as a % of GDP. As of September 1997, the budget deficit was 40 % higher than the corresponding period of 1996. (b) Year-on-year depreciation against USD and minus sign indicates appreciation. (c) The real exchange rate defined as the ratio of the exchange rate against the USD times the US Whole Sale Price Index (WPI) to Albanian CPI, and minus sign indicates real appreciation. (d) Figures for 1997 are as of September and represent percentage changes from September 1996 to September 1997.

A careful look at Table 2 reveals the unique combination of strong economic growth and low inflation that the country enjoyed during 1994 and 1995; interestingly, strong growth and low inflation took place in the presence of high monetary growth and high fiscal deficits. This phenomenon can be attributed to three factors. First, vigorous economic growth can, to a large extent, be explained by the convergence hypothesis, 7
or to request just a marginal one) of the customs declared value. The resulting tax savings are presumably shared with inspectors. Although we do not directly observe bribe payments, our findings are consistent with extensive circumstantial evidence collected during repeated field visits, IT audits, and a survey of customs inspectors. Based on our findings, Madagascar’s customs management sanctioned inspectors for corruption, suspended the head of the IT department and reformed inspector assignment by divesting it to a third party outside customs. This delegated randomization made the third party responsible for inspector assignment. Using its own software the third party randomly assigned declarations. It was so successful in eliminating deviations from random inspector assignment that delegated randomization became standard practice.

Explanations other than corruption are difficult to reconcile with the totality of the observed patterns. They also fail to explain why the delegated randomization intervention virtually eliminated the prevalence of deviations from random inspector assignment. IT manipulation resurfaced after a few months, however, albeit in a different guise. Customs IT staff figured out a new way to manipulate inspector assignment

> 1As discussed in Section 4, our approach does not capture all forms of corruption, hence these estimated losses reflect only the tax revenue losses associated with the specific corruption scheme we document.

2
paper, November 2005.

De Barros, Gillberto (2005). Sector note to the Lesotho CAS on telecom and electricity sectors. World Bank, AFTPS.

Easterly, W. and Servén, L. (2002). _The Limits of Stabilization: Infrastructure, Public Deficits and Growth in Latin America_ , Stanford University Press and the World Bank.

Esfahni, H.S., and Ramirez, M.T., 2003, “Institutions, Infrastructure and Economic growth,” _Journal of Development Economics_ , 70, 443-477.

Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” _The World Bank Policy Research Paper No. 3643_ , The World Bank, Infrastructure Network, Office of the Vice President (June), Washington, D.C.

Leipziger, Danny (2001). “The Unfinished Agenda: Why is Latin America Lagging Behind?” _Finance & Development_ , International Monetary Fund, vol. 38, 38-41.

U.K. Department of Transport website: <u>http://www.dft.gov.uk/stellent/groups/dft_control/documents/homepage/dft_home_page.hcsp. In</u> particular, see TRACS surveys of the principal roads network.

World Bank (1994). _World Development Report 1994: Infrastructure for Development_ . New York: Oxford University Press.

11
37 <u>Table 5: Data sources for the financial protection part of the 2018 HEFPI database</u>

||Inaccessible|Key variable(s)|missing<br>Analyzed – dro|pped<br>Analyzed – kept|Total|
|---|---|---|---|---|---|
|CWIQ|1|6|||7|
|E123||||2|2|
|EAPPOV|1|||3|4|
|ECAPOV|20|7|14|221|262|
|EUROSTAT|||24|23|47|
|HBS|109|136|1|3|249|
|HEIDE|||6|2|8|
|HIES|164|277|77|158|676|
|LIS|||98|54|152|
|LSMS|2|20|18|11|51|
|MCSS|1||11||12|
|MNAPOV||3|2|11|16|
|SARLF||3|||3|
|SARMD||5|||5|
|SEDLAC||7|||7|
|SHES|1|1|96|58|156|
|SHIP|||20|26|46|
|WHS|||50||50|
|Total|299|465|417|572|1,753|
|-0.0467***<br>(0.0141)|
||Utilities|-0.0547***<br>(0.0172)|-0.0555***<br>(0.0156)|

We see similar trends among the high-yield bond indices. Focusing on column 4 (regressions with lags), we find that the Bloomberg Energy high-yield bond index stands out as the most affected high-yield sector. A 10% decline in WTI oil price causes the Energy high-yield bond index to drop by 0.90%. Interestingly, high-yield bonds of most other sectors also suffer, ranging from Materials (0.51%) to Industrials (0.18%).

Cheap oil improves investment-grade corporate bonds, except those in Energy and Materials sectors. The signs for almost all sectors are negative, implying a negative relationship between oil prices and the investment-grade corporate bonds’ indices: when oil prices are lower, the corporate bond indices are higher. However, we do not find evidence for a negative relationship between cheap oil and prices of investment grade bonds in the Energy or Basic Materials sectors. This suggests that 22