rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9807

# **Abstract**

Using official COVID-19 death counts for 64 countries and excess death estimates for 41 countries, this paper finds a higher share of pandemic-related deaths in 2020 were at younger ages in middle-income countries compared to high-income countries. People under age 65 constituted on average (1) 11 percent of both official deaths and excess deaths in high-income countries, (2) 40 percent of official deaths and 37 percent of excess deaths in upper-middle-income countries, and (3) 54 percent of official deaths in lower-middle-income countries. These contrasting profiles are due only in part to differences in population age structure. Both COVID-19 and excess death age-mortality curves are flatter in countries with lower incomes. This is a result of some combination of variation in age patterns of infection rates and infection fatality rates. In countries with very low death rates, excess mortality is substantially negative at older ages, suggesting that pandemic-related precautions have lowered non-COVID-19 deaths. Additionally, the United States has a younger distribution of deaths than countries with similar levels of income.

This paper is a product of the Human Development Global Practice and the Development Research 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 gdemombynes@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
measures in residential and commercial buildings and also for natural gas consumption for heating. The demographic and economic projections (e.g., projections of population, household size and GDP) and projections of natural gas prices and average electricity grid emission factor are obtained from the MARKAL- Georgia model. All data sources used by the MARKAL-Georgia model are identified in Appendix B. National energy balance and statistical year book were used for fuel consumption values (National Statistics Office of Georgia, 2014)). Electricity prices were obtained from the local electricity distributor Telasi Ltd. (http://www.telasi.ge/en/customers/tariffs). The detailed data for Georgia Energy efficiency MAC analysis and their sources are presented in Appendix B.

# **3.3. Selection of Energy Efficiency Measures and Associated Assumptions**

The estimation of MAC curve for the residential sector depends on several factors including number of households, demolition rate of existing households, rate of new household construction, percentage of existing households with efficient appliances, percentage of newly built households with efficient appliances, energy consumption rates of both efficient and inefficient appliances, costs of those appliances and other variables. There are variety of energy utilizing devices and process which could be considered for GHG mitigation in the buildings, such as space heating devices (kerosene stoves, electric heaters), water heaters, refrigerators, washers and dryers, cooking appliances, electronic devices (TV, computers). Two options are normally available to reduce GHG emissions from these device or processes: improving their thermal efficiency and replacing more carbon intensive fuels used by these devices with less or none carbon intensive fuels. For space heating we considered the insulation improvement measures, such as increasing wall insulation, reducing heat leakage from windows and improving roof insulations. We did not consider energy efficiency improvements of space heating devices (e.g., kerosene stoves, electric heaters) because the efficiency of these devices is already relatively higher (> 87%) thereby leaving a small margin to further improve their thermal efficiencies. Central space heating units (a unit that can supply heat through out a building) could be an option, however a central heating system may not be feasible physically in the existing buildings as it require major remodeling of houses and it would be also too expensive. Moreover, the central heating systems are for heating the entire house or buildings, which would increase energy consumption and GHG emissions. The insulation improving measures we have considered here are also recommended by the energy audits (Sustainable Development Centre Remissia, 2014). The energy audit also show that ranges (cooking devices) and water heaters, which mostly use natural gas do not offer much room for efficiency improvements; fuel substitution, for 9
allows the Technical Secretariat to pay higher market remunerations than the public sector, to hire additional full time or part time professionals when needed, to commission external studies – what has not been necessary so far – to carry out _in situ_ visits, and in general, to access all the local and international statistical information that is necessary to conduct an investigation.

The Technical Secretariat is formed by a small group of professionals who are in charge of conducting investigations. These professionals also have other roles inside the Department of Trade Policy of the BCCH, which reports to the Department of International Exchanges and Trade Policy. The work of the Secretariat is defined as exclusively technical, of low profile and protected by the global institutionality of the BCCH. Thus, the political and public pressures that in many cases is excercisedduring investigations falls mainly on the members of the Commission, particularly on the representatives of Ministries, who adopt recommendations on the basis of the investigations performed by the Technical Secretariat. However, the law does not prevent Commission members from contributing additional evidence in the course of the investigation or during deliberations.

One of the functions of the Technical Secretariat is to explain to the interested parties the legal requirements of the WTO and of local laws that must be fulfilled to initiate and conduct an investigation. Another one is to analyze the evidence provided by the parties and to gather all the necessary information to prepare the technical report that is submitted to the Commission. The Technical Secretariat is also responsible for compliance with all the legal requirements of the WTO and Chilean legislation. The Technical Secretariat does not perform dissemination activities of its work, although it participates in seminars and other activities to which its members are invited to discuss aspects of Chilean law and of international legislations of general interest.

Throughout the years, the autonomy of the BCCH has been consolidated and its credibility in conducting monetary and exchange rate policies has been strengthened. Consequently, there have not been serious proposals to move the Secretariat out of this institution.

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# **Figure 7: Manufacturing Industries**

A. Manufacturing Exports by Product Type (% of Total Manufacturing Exports), BOT Classification, 1993 <u>to 2004</u> B. High-Tech Manufacturing Exports of East Asian Countries (% of Total Manufacturing Exports), WB <u>Classification, 1988 to 2003</u> <!-- Start of picture text --> 100% 80<br>90% 70<br>80%<br>60<br>70%<br>50<br>60%<br>50% 40<br>40% 30<br>30%<br>20<br>20%<br>10% 10<br>0% 0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br> High-tech products Labor intensive products Resource based products Indonesia Korea, Rep. Malaysia Philippines Thailand<br>C. Four-Way Breakdown of Manufacturing (% of D. Real Growth of Four-Manufacturing Sectors and<br>Manufacturing GDP), 1970 to 2003 Overall, 1971 to 2003<br>1971-1979 | 1980-1986 | 1987-1996 | 1997-1998 | 1999-2003 | 1971-2003<br>100% 18<br>16<br>14<br>80%<br>12<br>10<br>60% 8<br>6<br>4<br>40%<br>2<br>0<br>20% -2<br>-4<br>-6<br>0%<br>-8<br>-10<br>Heavy Industries High-Skill Labor-Intensive Industries Heavy Industries High-Skill Labor-Intensive Industries Traditional Light Industries<br>Traditional Light Industries Food and Related Industries Food and Related Industries Manufacturing<br>E. 12 Largest Manufacturing Industries in 2003 as F. Four-Way Breakdown of Manufacturing as Shares<br>Percentage Shares in Manufacturing Value Added, in Employment and Enterprise Numbers, 1997 and<br>1970 to 2003 2002 (Establishment Census)<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10%<br>10%<br>0%<br>0% 1996 1997 1998 1999 2000 2001 2002 2003 1997 2002 1997 2002<br>Rubber and Plastic ProductsOther Non-metallic Mineral Products Machinery and EquipmentChemicals and Chemical Products Workers | Enterprises<br>Wearing ApparelTextilesRefined Petroleum ProductsMotor Vehicles Office, Accounting and Computing MachineryFuniture; Manufacturing n.e.c.Radio, TV and Communication Equipment and ApparatusFood Products and Beverages Heavy IndustriesTraditional Light Industries High-Skill Labor-Intensive industriesFood and Related Industries<br>1970 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->

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Auctioning causes larger reduction of emissions than grandfathering in fossil fuels and electricity generation sectors. The reverse is true in the remaining sectors. For example, auctioning causes higher level of emission reduction than does the grandfathering in 10 provinces; the reverse is true in the rest. Five provinces (Shanxi, Inner Mongolia, Guizhou, Tianjin, Guangdong) would experience more than 30% of their CO2 reduction in 2030 from the baseline under both schemes of allowances allocation. This is because the economy in these provinces relies on coal mining or coal-intensive industries; meeting ETS means a reduction of more emissions from the coal sector and coal-intensive sectors.

When the CGE model uses a lower baseline consistent with the bottom-up TIMES model, emission reductions in all provinces drops by 50% under both free allocation and auctioning rules for allocation of emission allowances. The lower requirement of emission reduction from the baseline when the baseline itself shrinks would mean a lower level of market clearing allowance price.

**Table 5. Emission reductions under ETS in 2030**

**<u>(a)</u> By Sector**

|Sector|Free|Allowances|(Grandfathe|ring)||Aucti|oning||
|---|---|---|---|---|---|---|---|---|
||Without|linkage|With li|nkage|Witho|ut linkage|Wi|th linkage|
||Emission<br>based|Output-<br>based|Emission<br>based|Output-<br>based|Revenue<br>GOV|Revenue<br>HH|Revenue<br>GOV|Revenue<br>HH|
|AGRI|-20.8|-17.3|-8.9|-7.5|-16.7|-16.3|<br>-7.2|<br>-7.1|
|COAL|-43.7|-47.3|-22.5|-24.2|-47.8|-48.1|<br>-24.3|<br>-24.4|
|OILNG|-21.0|-20.4|-8.0|-8.5|-23.0|-22.6|<br>-9.8|<br>-9.7|
|MINE|-21.8|-19.2|-9.4|-8.4|-20.3|-19.9|<br>-8.9|<br>-8.7|
|FTPMF|-30.5|-25.0|-13.9|-11.3|-24.4|-24.3|<br>-11.0|<br>-11.0|
|PETRO|-29.0|-32.8|-13.1|-15.5|-32.5|-32.7|<br>-15.3|<br>-15.4|
|CHEMI|-28.3|-24.2|-12.9|-11.0|-24.8|-24.6|<br>-11.3|<br>-11.2|
|NMETA|-29.6|-25.8|-13.7|-11.8|-26.5|-26.3|<br>-12.1|<br>-12.0|
|METAL|-27.7|-22.9|-12.6|-10.4|-23.8|-23.5|<br>-10.8|<br>-10.6|
|OTHMF|-26.1|-21.6|-11.6|-9.7|-22.3|-22.0|<br>-10.0|<br>-9.8|
|ELECT|-27.4|-31.1|-12.6|-14.5|-31.0|-31.0|<br>-14.4|<br>-14.4|
|GAS|-34.1|-40.4|-16.2|-20.2|-40.0|-39.9|<br>-19.9|<br>-19.9|
|WATER|-22.2|-19.8|-9.8|-8.9|-19.8|-19.4|<br>-8.9|<br>-8.7|
|CONST|-24.8|-19.8|-11.0|-8.7|-20.4|-20.0|<br>-9.0|<br>-8.8|
|TRANS|-13.1|-11.3|-5.0|-4.6|-11.7|-12.3|<br>-4.8|<br>-5.1|
|SERVI|-18.7|-15.8|-7.6|-6.7|-14.2|-16.1|<br>-6.0|<br>-6.9|

45
with a negative e¤ect on benchmarks incidence, and volatility of domestic shocks, with a positive e¤ect on benchmarks incidence.

As mentioned in the introduction, we see this paper as a ...rst attempt to characterize the variations in the survey data on public debt management strategies across countries where establishing a regularly repeated survey would be incredibly bene...cial. The follow-up surveys on public debt management strategies could extend the coverage to all low income countries, and focus on distinguishing between implicit and formal strategies by structuring the applied questionnaire accordingly. Although the present work is intended to provide the opportunity for public debt managers to compare themselves to their peers or countries at a similar stage of development, some policy implications could be derived from a similar analysis in the future. In this respect inclusion of some institutional variables would be desirable. Examples could include the number of institutions responsible for public debt management in a given country, the degree of central bank independence, existence of a mediumterm expenditure framework, or the degree of transparency of measures concerning domestic macroeconomic policies.

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Table 4 Government Expenditure in Viet Nani, 1992

||Total<br>expendi-<br>turc (Bn.<br>Dongs)|Per capita<br>expcndi-<br>ture<br>(Dongs)|Per<br>cent or<br>GDP|Per<br>ce!nt<br>of<br>t otal|Provincial<br>govcrnment<br>expenditure|
|---|---|---|---|---|---|
|A. CLrrer.m Expenditure||||||
|I . General acministratioii|2404|34687|2.4|10.2|43.1|
|2. Economic services*|1495|21571|1.5|6.3|78.9|
|3. Social services|6240|90035|6.1|26.4|46.0|
|il Education|1495|21571|1.5|6.3|88.6|
|ii) Ilealtli|1136|16391|1.1|4.8|88.1|
|iii) Pensions|2369|34182|2.3|10.0||
|iv) Others|1240|17892|1.2|5.2|15.1|
|4. Interest|3218|46432|3.2|13.6||
|S. Others|5314|76674|5.2|22.5|18.9|
|Total Curremt Expenditure|18671|269399|18.3|79.0|35.9|
|B-Capital Expenditure|4956|71509|4.9|21.0|34.4|
|Total Expenditure|23627|390908|23 2|100.0|35.5|

* zsEconomic services" consists of agriculture and irrigation, forestry, industry, transport and communication and other economic services. The details of individual items are not available.

Source: Ministry of Finance, Government of Viet NamIn 1992, total government expenditure was D 23,627 bn, or 23 per cent of GDP, of which the provinces accounted for 35 per cent.<sup>6</sup> In per capita terms, provincial expenditure

- s Provincial expenditures include those of the district governments. The lowest level of local government, the conmmune, is , however, excluded from these estimates. Henceforth, the term "local governments" refers to provinces (including districts) and exclud2s communes, on which

9
whereas GDP and regulatory quality have a significant effect only in the resource sector. The finding that the regulatory quality measure has an opposite sign in the split sample analyses is noteworthy. Compared to political violence, low-quality investment regulations pose a continuous risk to MNEs because these institutions are very persistent, and the risk posed by them is predictable. Our results suggest that the regulatory environment matters for rent-seeking resource activities associated with large capital investments. None of the results changed meaningfully when a non-corrected LSDV estimator was used, suggesting that the Nickell bias is small.<sup>10,11</sup>

**Table 2 Effect of Political Conflict on FDI by relatively Undiversified and Diversified Firms**

||Dependent Variable:Log<br>Total FDI<br><br>|greenfieldFDI(inUSD mi<br>Resource-Related FDI<br><br>|llions),LSDVEstimation<br>Non-Resource-Related<br>FDI<br><br>|
|---|---|---|---|
||Undiversi-<br>fiedMNEs<br>Diversified<br>MNEs|Undiversi-<br>fiedMNEs<br>Diversified<br>MNEs|Undiversi-<br>fiedMNEs<br>Diversified<br>MNEs|
||(1)<br>(2)|(3)<br>(4)|(5)<br>(6)|
|BRDt-1(ln)|-0.129+<br>-0.015<br>(0.073)<br>(0.082)|0.060<br>0.088<br>(0.071)<br>(0.071)|-0.145*<br>-0.064<br>(0.061)<br>(0.061)|
|Constant|-50.651<br>-53.071|-106.398*<br>-108.520*|-51.979+<br>-54.134+|
||(34.898)<br>(34.912)|(49.570)<br>(49.577)|(30.738)<br>(30.729)|
|BRD (ln)*localizedt-1|-0.001<br>(0.079)|-0.122<br>(0.092)|0.084<br>(0.069)|
|Greenfield FDI-i, t-1(ln)|0.120**<br>(0.037)|0.037<br>(0.033)|0.137***<br>(0.036)|
|GDPt-1|-0.100<br>(0.460)|-0.933<br>(0.655)|0.293<br>(0.442)|
|Populationt-1(ln)|3.955|7.898*|3.761+|
||(2.417)|(3.316)|(2.155)|
|WGI regulatory qualityt-1|0.275|1.091|-0.454|
||(0.706)|(1.017)|(0.620)|
|Polity Indext-1|<br>-0.037|<br>0.008|<br>-0.050|
||(0.044)|(0.047)|(0.036)|
|Exchange Ratet-1(ln)|-0.400|0.050|-0.319|
||(0.295)|(0.459)|(0.295)|
|Exchange Rate Volatilityt-1|-13.326*|-6.163|-4.232|
||(5.404)|(7.057)|(3.556)|
|Control of Corruptiont-1|1.000+<br>(0.568)|0.361<br>(0.778)|1.117*<br>(0.542)|
|Inflationt-1|<br>0.001|<br>0.011|<br>-0.002|
||(0.008)|(0.010)|(0.007)|
|Observations|1,234|1,234|1,234|
|Number of Countries|90|90|90|
|Country FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|

Robust standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 Table 2 shows the results for hypothesis 5. For these regressions we aggregated greenfield FDI, distinguishing between investments made by relatively diversified and undiversified MNEs. Since this results in a three-dimensional data set (country - year - diversification dummy), it is impossible to use 24
||(-3.88)|(-3.65)|(-4.00)|(-3.70)|
|---|---|---|---|---|
|Living in a Metropolitan area|0.009|0.024|0.006|0.022|
||(0.18)|(0.49)|(0.11)|(0.44)|
|_Education (base = primary school)_|||||
|Secondary school|0.048|0.028|0.047|0.033|
||(0.81)|(0.49)|(0.80)|(0.58)|
|Higher Secondary School|0.145<sup>**</sup>|0.155<sup>**</sup>|0.134<sup>*</sup>|0.173<sup>**</sup>|
||(2.10)|(2.37)|(1.93)|(2.55)|
|Degree or above|0.311<sup>***</sup>|0.216<sup>***</sup>|0.308<sup>***</sup>|0.302<sup>***</sup>|
||(4.17)|(3.34)|(4.14)|(4.18)|
|_Type of employment (base = permanent full_<br>_time)_|||||
|Temporary full time|-0.035|-0.060|-0.040|-0.050|
||(-0.50)|(-0.92)|(-0.58)|(-0.76)|
|Part time|-0.178<sup>**</sup>|-0.126<sup>*</sup>|-0.158<sup>**</sup>|-0.115<sup>*</sup>|
||(-2.48)|(-1.87)|(-2.18)|(-1.67)|
|Day work|-0.412<sup>**</sup>|-0.416<sup>**</sup>|-0.417<sup>**</sup>|-0.457<sup>**</sup>|
||(-2.05)|(-2.15)|(-2.08)|(-2.39)|
|R2 /Log likelihood|0.3472|-679.5752|0.3527|-666.8276|
|Observations|415|978|415|978|

_Source:_ Original table for this publication.

_Note:_ OLS = ordinary least square. Y is log value of wage-earning. t-statistics in parentheses. All the estimates are also controlled for geographical divisions of respondents and the economic sectors where the respondents are working. The sample selection for Heckman models takes the same set of variables as model 4 of the employment outcome regressions. *** p<0.01, ** p<0.05, * p<0.1.

Next, we added an interaction term of the treatment and gender to see the heterogeneity of treatment effects by gender (models 3 and 4). Both the OLS and Heckman regressions show positive and statistically significant treatment effects on females. After control for all other variables, being in the treatment group is associated with a wage level increase of approximately 20 percent for female applicants. This increase strikes us as relatively modest compared to the large decrease caused by being females in the first place. However, it appears likely that the RPL assessment plays some role in mitigating such gender-based wage discrimination and giving them better economic opportunities.

Finally, we also look at how wages changed from the baseline to end-line among those in wage employment at both points to have a more microscopic view of wage level changes over time and difference by the treatment category if any. A total of 332 applicants meet this criterion, including 275 males and 57 females. Figure 3.4 reports the descriptive summary of the wage increase among those 332 applicants by the treatment group and gender. Those in the treatment group seem to have enjoyed larger wage increases over the six months between the two surveys. The difference is particularly stark for females.

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<!-- Start of picture text --> All Sectors<br>1<br>28<br>i=<br>:<br>gZo<br><!-- End of picture text --> <!-- Start of picture text --> oa<br>=<br>1960 1980 2000 2020<br>Year<br>Eas! Asia & Pacific = | atin America & Caribbean<br>——— Middle East & North Anca =————— South Asia<br>= Sub-Saharan Africa ee Eastern Europe & Central Asia<br><!-- End of picture text -->

<!-- Start of picture text --> Agriculture<br>2<br>Ea<br>5<br><!-- End of picture text --> <!-- Start of picture text --> é<br>~<br>1960 1980 Year 2000 2020<br>————— East Asia & Pacific —_——— Lalin America & Caribbean<br>——-_ South Asia —_——————"-_- Sub-Saharan Africa<br>een Easier Europe & Central Asia<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing<br>fi<br>28<br>i=<br>:<br>= Dy<br>§<br><!-- End of picture text --> <!-- Start of picture text --> 3°<br>oa<br>a<br>1960 1980 2000 2020<br>Year<br>ee East Asia & Pacific —_—————-_ Lalin America & Caribbean<br>—e South Asia ———_ Sub-Saharan Africa<br>eee Easter Europe & Central Asia<br><!-- End of picture text --> <!-- Start of picture text --> Commodities<br>22<br>5<br>Ee<br>5<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>©<br>1960 1980 Year 2000 2020<br>—— East Asia & Pacific ——— Latin America & Canbbean<br>== Middle East & North Africa =~ South Asia<br>—_—_ Sub-Saharan Africa ee Eastern Europe & Central Asia<br><!-- End of picture text -->
In the second stage I looked at external effects. If human capital has any external effects, industry wage premiums are expected to be higher in industries with average human capital. Proxies for human capital are taken to be average years of schooling and average age (or experience). No information of on-the-job training was available. Other control variables included are fraction male, fraction non-indigenous and fraction of workers in firms with less than 10 employees. Information on several other possibly relevant variables such as quit rates, industry concentration ratio and union density were not available.

Table 3: Wage Equations (Dependent Variable: Log Hourly Wage)

||(1)|(2)|(3)|
|---|---|---|---|
|Control variables"|19|19|No|
|Industry Dummiese|20|No|20|
|_N_|6,214|6,214|6,214|
|R<sup>2 </sup>(corrected)|0.60|0.587|0.353|

Source: ENS 1989.

a. Weighted regressions were used.

b. For control variables used see footnote 1.

c. Industry dummies were constucted by combining the one-digit industry variable and the occupation variable for: agriculture (control category), mining, four subgroups in manufacturing (professionals, administrators, office workers and manual workers), four subgroups in electricity, gs and water (same as above), £wo subgrodps in construction (white-colar and blue-collar works), two subgroups in commerce (professionals and administats, other), three subgroups in ansportaion (professionals, administrators and office workers, manual workers), two subgroups in finance (professional and administrators, other), and two subgroups in services (professionals and administrators, other).

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