rafmacalaba/data-use-mentions-extended huggingface dataset:
disease itself. Abay et al. (2020) investigate the economic consequences of the pandemic by using Google search data to estimate the eects of the crisis on the demand for dierent services across 182 countries.

The remainder of the paper is organized as follows. Section 2 provides a simple conceptual framework that explicitly anchors the comparison between lost-years and povertyyears on standard social welfare analysis. Section 3 presents mortality and poverty estimates as of early June 2020, rst for a set of six countries (three developed and three developing) for which Covid-19 mortality data disaggregated by age are available (Section 3.1) and then for the rest of the world, using the age-distribution of deaths from population pyramids and infection-to-fatality ratios documented by Salje et al. (2020) and Verity et al. (2020) (Section 3.2). In both samples, the number of poverty-years is almost always at least 10 times larger than the number of lost-years. In many cases, the PY/LY ratio is above 100 or even 1,000. This suggests that, for most countries in the world, the welfare losses from the Covid-19 pandemic arise disproportionately from increases in poverty. This section also documents that the trade-o between human lives and poverty costs varies substantially with countries' GDP per capita, with developed countries facing both much larger mortality costs and much smaller poverty costs than developing countries - suggesting that the best policy responses may dier by country.

Section 4 then compares the estimated welfare consequences as of early June to a counterfactual no-intervention scenario in which contagion only stops when herd immunity is reached.<sup>11</sup> This permits a cleaner comparison of potential mortality burdens across dierent countries, since looking only at current mortality is hampered by the fact that countries are in dierent stages of the epidemic. In most countries, we nd that the number of lost-years under no-intervention are considerably larger than the sum of the lost-years and poverty-years generated
_Handbook of_

- _Regional and Urban Economics_ , edited by J. Vernon Henderson and Jacques-François Thisse, Volume 4:2481–2527. Cities and Geography. Elsevier.

- Gobillon, Laurent, Harris Selod, and Yves Zenou. 2007. “The Mechanisms of Spatial Mismatch.” _Urban Studies_ 44 (12): 2401–28. doi:10.1080/00420980701540937.

- Gulyani, Sumila, Debabrata Talukdar, and Darby Jack. 2010. “Poverty, Living Conditions, and

- Infrastructure Access: A Comparison of Slums in Dakar, Johannesburg, and Nairobi.” _World Bank Policy Research Working Paper Series_ 5388.

- Hamilton, Bruce W., and Ailsa Röell. 1982. “Wasteful Commuting.” _The Journal of Political Economy_ , 1035–53.

- JICA. 2006. “The Study on Master Plan for Urban Transport in the Nairobi Metropolitan Area in the Republic of Kenya.” Japan International Cooperation Agency.

- ———. 2013. “JICA Nairobi Personal Travel Survey.” Japan International Cooperation Agency.

27
businesses only modestly (e.g., Chetty, Friedman, Hendren, and Stepner, 2020; Hubbard and Strain, 2020), another concludes that PPP may have boosted employment on the order of about 3.5 percent at eligible firms (Autor, Cho, Crane, Goldar, Lutz, Montes, Peterman, Ratner, Villar, and Yildirmaz (2020), and a third suggests that PPP firms were nine percentage points less likely to reduce employment (Humphries, Neilson, and Ulyssea, 2020).

Turning to the community level, a number of studies link PPP with reduced unemployment (Barraza _,_ Rossi. Yeager, 2020; Bartik, Cullen, Glaeser, Luca, Stanton, and Sunderam, 2020; Granja, Makridis, Yannelis, and Zwick, 2020; Duchin and Hackney, forthcoming; Li and Strahan, forthcoming). One study also suggests that subprime consumer debt declined in local markets in which banks made more PPP loans (Berger, Epouhe, and Roman, 2021).<sup>28</sup> A key issue is whether PPP funds serve as substitutes versus complements for conventional loans from banks and other financial institutions. In theory, the PPP funds might have essentially substituted for credit from banks and other financial institutions, in which case other loans from these institutions would decline as a result of the program, with relatively little net total increase in small business funding. Alternatively, PPP funds may have made the recipient firms more creditworthy, and allowed them to borrow more conventional credit, resulting in a “multiplier effect” (Karakaplan, 2021). In addition, any subsidies to the banks from the fee income and rescues of their relationship borrowers may allow the banks to supply more credit to other borrowers. Distinguishing between these different potential outcomes is important for determining the extent to which the PPP program actually increased access to funds for small businesses and by how much.

Four studies using bank Call Report data find very strong multiplier effects supporting

> 28 This same study
18|15|n/a|0.02|0.02|0.02|4.12|3.16|2.44|227|155|149|
|_Bulgaria interms of _|_percent _|_of _||||||||||||||
|Average CEEC-8<br>of which:|103|106|n/a|213|285|1844|2|2|18|n/a|173|113|44|49|48|
|Hungary|92.3|95.8|102|542|1148|2380|2|2|15|n/a|242|185|63|84|73|
|Romania|205|196|189|127|90|77|n/a|n/a|18|n/a|121|80|94|99|95|
|Average SEE-3|239|194|189|229|155|n/a|13|9|109|n/a|75|59|17|28|32|

Notes: SEE-3 includes only Albania, Moldova and Serbia and Montenegro, as no data available for other SEE economies. Source: World Development Indicators, World Bank’s SimaWeb database.

Second, reforms of port management have barely begun. The state of the art arrangements call for the introduction of the landlord port, with the separation of operational and commercial functions, privatization of port services, concessioning of large container and bulk terminals to private operators. Bulgaria’s Ministry of Transport and Communications issued a port operator's concession program notice only in 2004 (see www.mtc.government.bg **)** . It has invited interested parties to participate in the future bidding process for operation and maintenance of ports’ terminals, as well as for the investment program for reconstruction and development of the existing and new terminals. The focus
By examining the effects of the CAR civil war on GDP per capita, NTL intensity, and various economic indicators such as industrial and manufacturing value added, as well as social indicators represented by the human asset index, it becomes possible to determine whether the observed shock to GDP is genuine or merely a statistical artifact. Additionally, this analysis helps evaluate the negative consequences of the civil conflict on crucial macroeconomic factors, ultimately affecting long-term development.

# **Data and variables**

As a proxy of economic development, we consider the GDP _per capita_ in purchasing power parity (in constant 2017 international US dollars) from the World Development Indicators (35) from 1990 (the first year available for CAR) up to 2017, as in Matta et al. (2019) (9).

When accounting for satellite NTL intensity, we use the Defense Meteorological Satellite Program (DMSP) data from the Earth Observation Group (EIG) at Colorado Schools of Mine’s (Ghosh et al., 2021) (22), as they provide original DMSP-OLS data from 1992 to 2013 but also an extended version of the DMSP NTL from 2013 to 2021. The regular DMSP NTL series has been discontinued after 2013 for two mains reasons. First the launch of a new generation of satellites equipped with more capable sensors, named Visible Infrared Imaging Radiometer Suite (VIIRS), providing more accurate NTL data. Second the orbital shifts from day/night to dawn/dusk of previously used DMSP satellites. The shift to NTL data collected by VIIRS prevented the use of NTL data for long temporal analysis and pushed researchers to produce harmonized data series combining DMSP and VIIRS data. Ghosh et al. (2021) provide consistent time series for the NTL from 1992 to 2021 by exploiting the constant orbital shift of satellites, which brought back older DMSP satellites (F15 and F16) into a usable day/night cycle to continue data collection with DMSP satellites. To have harmonized series for CAR, we select the period 2005-2017 as 2005 saw
would actually require them in their production process never get to come to life, precisely due to the lack of these factors. It is the typical chicken and egg vicious circle, as explained by Hausmann et al. (2008).

**Two categories of market failures are identified within this type of problems: Coordination failures and self‐ discovery.** Coordination problems arise when inputs and activities that are necessary to undertake other economic activities are underprovided or not provided at all, precisely because there is no demand from those yet‐to‐exist activities, or they have the character of public goods and there are hence limited incentives for an individual firm to start those activities. Self discovery problems arise from lack of _information_ about what goods or services, not yet locally provided, can be profitably produced; this is so because the process of “discovering” new goods entails initial costs for entrepreneurs, that may not be able to be recovered, because of high uncertainty on demand, or because the possibility that new entrants in the market (after the product has been found to be profitable) will, by means of competition, wipe away potential excess benefits for the entrepreneur (Hausmann and Rodrik, 2004). Thus way, new profitable products will be under‐provided ex‐ante, and over‐provided (in a sense that free entry will eliminate excess profits) ex‐post. Both, coordination failures and self discovery problems are said to justify some sort of public policy.

# **_6.9.1 Self­Discovery_**

**The task of identifying potential constraints to growth from lack of self‐discovery is a most difficult one, especially as it involves assessing activities that are** **_not_ in the country’s current production sphere.** However, as Hausmann et al. (2008) explain, one can think of a series of tests applicable to actual production data which refer to the degree of sophistication of existing activities; their relative position compared
that could not be interviewed despite several call attempts may introduce a potential bias in the selection of the sampled households. To overcome this potential bias and obtain statistics representative at the national level, a balanced sampling approach was adopted, leveraging on the extensive set of variables available in the Nigeria General Household Survey (GHS)-Panel 2018/19. Additionally, this study uses the publicly available phone survey weights, calculated by adjusting the GHS-Panel 2018/19 household weights to reflect the selection and interviewing process and calibrated according to the characteristics of the weighted sample included in the GHS-Panel 2018/19.

This study mainly relies on the information included in Round 6 of the COVID-19 NLPS. This survey round includes information at the individual level on school attendance and other education variables for household members aged 5-18.<sup>8</sup> A maximum of 6 household members were selected for each household to limit the randomly respondents’ burden, with a final sample of 4,006 members 5-18 aged of 4,325 eligible individuals (of the 1,762 households successfully interviewed in Round 6). Given the selection process of individuals aged 5-18 years, individual-level weights were calculated and calibrated to correspond to the sex and age distribution of the total weighted population aged 5-18 years as included in the GHS-Panel 2018/19.<sup>9</sup>

# **2.2.2 Measuring School Attendance**

To measure children’s school attendance just before the school closure and just after school reopening, we take advantage of the COVID-19 round 6 phone survey which, unlike previous rounds where schooling information was measured at the household level, provides information on schooling at the individual level for children aged 5-18.

In the survey, after schools reopened in Nigeria in October 2020, respondents were asked whether were school at the moment of the either in they attending survey person or remotely. Hence, we define school attendance after school reopening as a binary variable taking the value one if the individual answered "Yes"
-19-

# Procedures Used to Estimate the National Accounts Concept of Gross Value Added (contribution to GDP at factor cost) for Establishments Covered by the Mexican Industrial Census

# Mexico

- 1) Gross Value of Output = "Produccion Bruta Total";

- 2) US Value Added Concept = "Produccion Bruta Total" minus the following six items:

- a) "Materias Primas y Auxiliares Consumidas" (raw and intermediate materials used);

- b) "Envases y Empaques" (packaging);

- c) "Combustibles y Lubricantes" (fuels consumed);

- d) "Energia Electrica" (electric energy used);

- e) "Refacciones Accessorios y herriamientas" (repairs, accessories and tools);

- f) "Pagos por Maquila" (payment for contract work);

- Together these six items correspond to the US Cost of Materials Concept (see table 19 of Resumeii General);

- 3) In order to arrive at the present national accounts concept of value added (i.e. the contribution to GDP at factor cost before deduction for imputed financial services), we must further deduct three items which the census includes under the heading "Otros Insumos". These are:

- a) "Pagos por comisiones sobre rentas" (sales commissions);

- b) "Pagos por Servicios de Propaganda" (advertising costs);

- c) "Otros bienes y servicios" (other goods and service inputs);

- When these three items are deducted, we arrive at the Mexican census

- concept of value added ("Valor Aggregado censal bruto"), but this concept is grosser than what we want for national accounts purposes, so we must further deduct three items:

- a) "Gastos por Uso de Patentes y Marcas, Asistencia Tecnica y Transferencia de Technologia" (cost of patents, licences, technical

- assistance and transfer of technology);

- b) "Gastos por alquiler de maquineria y equipo" (costs of renting machinery and
area vis-à-vis what was planted (“planted area losses”).<sup>22</sup> However, losses may also occur if harvest could occur on part of the plot but plants in the harvested areas have some sort of damage that makes them less productive (e.g. pests destroying some cobs or some plants not developing properly due to poor weather conditions). The latter is what this paper calls “plant productivity losses”.<sup>23</sup> Consequently, it is necessary to find survey-based measures for reductions in the area harvested<sup>24</sup> _and_ reductions in productivity in harvested areas.<sup>25</sup> In relation to the general form expressed in Equation (1), we observe GPS measured plot area, h<sup>c</sup> , and realized production Y<sup>r</sup> . The two key unknowns to measure in the survey data are the reduction in the planted area and the reduction in productivity. We use a question on the percent share of the planted area that was harvested, si,j,t, to obtain the harvested area, h<sup>r</sup> , and the realized yield in harvested areas, y<sup>r</sup> . We then obtain the attainable yield, y<sup>c</sup> , from y<sup>r</sup> by accounting for plant productivity losses through a question on the percentage of damage on the crop, di,j,t. Therefore, in Ethiopia, the loss estimate built in this section will be a compound one consisting of a) planted area losses (the first term), and b) productivity (damage) losses (the second term).

We next take this approach to the data.<sup>26</sup>

> 22 One example may be the partial flooding of a plot which destroys the harvest
not all couples who practice son-preferred DSB end up with big families, some reach their desired number of sons early and stop early (Clark 2000). Finally, modern contraceptive use in Indonesia is important (54 percent of ever married women 15–49 years). Previous studies have found that use of contraceptive methods increases the enforcement of differential stopping and minimizes the effect of DSB on aggregate fertility (Basu and De Jong 2007).

# **5. Child-level evidence of sex-differential fertility stopping behavior**

One concern addressed in the couple-level analysis was that some women may have not completed fertility. In addition, two other factors may bias downwards the couple-level results. First, the outcomes and preferences of couples who started childbearing before the compilation and continued after the compilation reflect the effect of both old and new inheritance regimes. Second, they may be affected by changes in the extended family structure, as the men’s brothers who were alive when the first child was born may no longer be alive at the time of subsequent births. The combination of these influences would reduce a couple’s incentive to seek a son. Therefore, the coefficients obtained at the couple level should be considered a lower boundary of the actual effect of the inheritance law and of the difference between Muslims and non-Muslims.

As a different approach, in this section I perform tests of sex-differential fertility stopping behavior and estimate the effects of the Islamic inheritance exclusion rule using childlevel data. Son-targeting couples are more likely to continue childbearing if they have girls in the early parities. As a result, DSB has two important implications at the child level: boys have a smaller number of siblings and a higher relative order of birth than girls. Son and fertility preferences, child gender composition and complete family size cannot be examined using child-level data, but the effects of DSB at the child level have a bearing on fertility outcomes at the couple level.

21
9.2|7.6|4.1|6.3|9.3|6.6|
|Europe, Middle East & North Africa|0.1|5.4|13.5|6.2|2.1|4.8|10.9|5.9|
|Sub-Saharan Africa|0.0|3.9|12.9|5.5|1.3|1.3|12.5|4.9|
|**Memo:**|||||||||
|BRICS 5 countries|10.6|8.6|18.2|12.4|9.8|4.3|15.7|9.8|
|OPEC 12 members|-1.9|6.2|14.2|6.0|-0.6|5.0|12.1|5.4|

Source: Computations based on World Bank World Development Indicator database.
Given the inadequacy of safety-net support from government-run programs during monga, vulnerable households adopt a variety of coping mechanisms. As Table 7 suggests, some 57 percent of safety-net program recipients adopt some type of coping measure, compared to 49 percent among non-recipients. Among members of government safety-net programs, about 55 percent adopt informal coping mechanisms (e.g., advance sale of labor or crop) and 6.5 percent adopt formal means (e.g., borrowing from formal sources). Seasonal migration is the major informal coping mechanism for the poor in the greater Rangpur region: 38.6 percent of safety-net program recipients and 34.8 percent of non-recipients adopt out-migration as a coping measure, suggesting that both groups remain equally vulnerable to monga.

# **{TABLE 7 HERE}**

We next examine the extent of the hardship that households in the greater Rangpur region are subjected to during the monga period. The InM survey lacks data on household food consumption; rather, it has an indicator variable reporting the status of meal consumption of the sampled households, as follows: (i) starvation (households go without meals for a day or more), (ii) meal rationing (households consume less than what they normally would in a day), and (iii) full meal consumption (households consume desired quantity of meals, which is usually three a day). This information was collected from all households for both monga and non-monga periods.

A high percentage of the rural ultra-poor in the greater Rangpur region, with the exception of Nilphamari District, experienced starvation during the 2006 monga season (Table 8). Overall, about 47 percent of poor households experienced starvation during monga, while only 4.4 percent had full meals during the same period. Predictably, the rate of starvation during the non-monga period declined; however, it did not fall to zero; even during the non-monga season, 8.5 percent of households experienced starvation. On the other hand, the share of households that
performance for 775 cities, from 2000 to 2015. It includes capital cities and cities with at least 400,000 inhabitants. located in 140 countries across all regions and all income levels.<sup>4</sup> Second, we combine Oxford Economics Dataset with city centroids data from the Global Rural‐Urban Mapping Project data set (GRUMP) to geo‐reference the cities. Since we did not have the associated geographic limits for each of the cities in the original Oxford Economics Dataset, we chose to match each city with the corresponding city centroid to avoid using arbitrary limits. Since there was no common identification for the cities in both data sets, we use the name of the city (and country) for matching purposes. Because of this limitation, not all cities were perfectly matched. After manually fixing more than 200 city names to improve the matching, we end up with 769 geo‐referenced cities, distributed by region as follows: 27.5% in East Asia Pacific, 22.5% in OECD countries, 12.5% in Latin America, 11.5% in South Asia, 9.5% in Sub‐Saharan Africa, 9% in Europe and Central Asia, and 6.5% in Middle East and North Africa. The six cities in the Oxford Dataset that we were unable to match are: Baixada Santista and Norte Catarinense (Brazil), Erdos (China), Male (Maldives), Marseille (France), and Podgorica (Montenegro).

Third, we combine the city‐level data with PM 2.5 surface data estimated by van Donkelaar et al. (2016). They combine Aerosol Optical Depth (AOD) data from MODIS satellite (NASA), MISR, and SeaWIFS Instruments with the GEOS‐Chem Chemical transport model to obtain surface‐level measures of PM 2.5.<sup>5</sup> Their resulting data set is a global raster with a resolution of 0.1 degrees x
The end-use energy demand projected by the TIMES model and energy price indices provided by the CGE model are then used in TIMES to produce a mix of energy supply to meet the projected demands ensuring that the optimal energy supply mix represents the least cost scenario to deliver end-use energy services. Besides energy demands and prices the supply module of TIMES requires other input data including operational characteristics of energy supply technologies (e.g., power plant technologies, resource availability, such as solar irradiation in a given location for a solar power plant), and cost data (e.g., capital costs of photovoltaic cells, current fuel prices).

The projections of energy supply-mix produced by the TIMES model are supplied to the CGE model, where necessary parameters (e.g., share parameters, autonomous energy efficiency parameters) are adjusted in a way that the energy supply mix of the TIMES model is reflected in the CGE model. It implies that the growth rates of primary energy commodity (coal, oil, natural gas) in the CGE model are the same as in the TIMES model. The same should have been done for primary electricity. However, the CGE model has only one electricity commodity and does not explicitly represent the primary electricity. Therefore, growth rates of primary electricity in the TIMES model are not reflected in the CGE model. Moreover, new and renewable energy (solar, wind), which are explicit in the TIMES model, are embedded in the total electricity in the CGE model.

One would argue that why not disaggregate the electricity sector in the CGE model to various technologies in the same way several CGE models (e.g., Timilsina et al. 2018; Timilsina and Landis, 2014; Timilsina and Shretha, 2006) have done. However, the existing studies do not follow linking the CGE model with a bottom-up model. Instead, they refine their electricity sector modeling by disaggregating the electricity sector in their SAMs to various technologies (a kind of hard linking, as discussed earlier