rafmacalaba/data-use-mentions-extended huggingface dataset:
# - 38 -

of provisioning in a rather more thorough and systematic way.

In this section we approach the question by looking at the implications for the various actors involved in the Internetional debt problem; the banks, the debtor countries and the official sector. We also briefly examine the implications of provisioning for relations between these various actors.

# (a) For the banks

As already noted, provisioning has the effect of bringing the banks' actual balance sheets more in line with the market perception of what they should look like. Any discrepancy of this kind between the market view and the banks' view, as illustrated by tJeir published balance sheets, will be a cause of concern within the market and will damage confidence in the banks. Removal of this discrepancy will tend to restore a measure of confidence. It might have been anticipated, as bankers themselves clearly did, that provisioning would improve the banks' market position. Data given in Table 10 confirm the positive short-run effect of provisioning on banks' stock prices. The Table also suggests, when read in conjunction with Table 5 showing the dates of provisioning, that it was the earlier provisioners that enjoyed the greatest stock price increase.

However, it is perhaps more doubtful how durable this improvement will be. After all, provisioning does nothing to strengthen the basic position of those banks which have suspect developing country loans on their books. Neither, as will be seen below, does it really do anything to raise the probability that debtors will be able to service their debt obligations.
medium-sized Indian firms. As previously mentioned this dataset was already used in several studies on the performance of Indian firms.<sup>22</sup> The dataset covers the period 1989-1997 and the information varies by year. It provides quantitative information on sales, capital stock, income from financial and non financial sources, consumption of raw material and energy, compensation to employees (wage-bill) and ownership group.<sup>23</sup> This dataset allows us to estimate firm total factor productivity (TFP) using the Levinsohn and Petrin (2003) methodology. The Prowess database provides detailed information on imports by category of goods: finished goods, intermediate goods and capital goods. In our main empirical specification, we use imports of capital goods (machinery and equipment) to measure foreign technology. Although we are not able to test directly for the impact of imported capital goods depending on the country of origin since the Prowess dataset does not include the origin country of imported goods (e.g developed vs. developing countries), one realistic assumption for the case of a developing country like India is that most imports of capital goods are sourced from more advanced economies and thus, they are a good proxy of a modern and high-technology. Looking at imports of capital goods at HS6 product level of India by country of origin reveals that about 70% of their imports came from developed countries in the period 1989-1997.<sup>24</sup> Input-trade liberalization might also allow firms to access to high-quality inputs. Using detailed firm-product level data for Colombia, Kugler and Verhoogen (2009) compare the price of domestic and imported inputs and provides evidence that higher-quality inputs may be relatively more available internationally. Due to data constraints, we are not able to look at the effects of input-liberalization on quality up-grading. The Prowess database does not provide any information on quantities to compute unit values as a proxy of quality of intermediate goods. Despite that we can not observe the quality of intermediate goods, for imported capital goods
procurement also involves decisions over potentially much larger sums of money than tax returns, thereby increasing the opportunities and incentives for corruption. Given that far fewer firms bid for government contracts than file taxes, it is more susceptible to collusive relationships between bureaucrats and firms. In addition, more transparent information about government contracts may itself encourage collusion (Center for Global Development, 2014). Another explanation is that implementation of relatively unsophisticated informational or transactional e- procurement systems might not be enough to improve public procurement competitiveness. Only four high-income countries have adopted connected e-procurement systems, but they are not considered in our firm-level analysis given lack of data for our outcome variables. Finally, it is possible that firms’ reports of securing or attempting to secure a government contract from the WBES database may not adequately capture public procurement competitiveness. More research is therefore needed to assess the impact of e-government on public procurement competitiveness.

# **_4.3. Development and E-government_**

11
present new evidence on how mutual funds behave and explore how the organization of financial intermediaries can affect the investment decisions they make across countries and regions.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 studies how U.S. mutual funds allocate their portfolios internationally. Section 4 analyzes the factors behind the degree of international diversification. Section 5 studies whether there are potential costs and gains to the international diversification strategies of global funds. Section 6 concludes.

# **2. Data**

To conduct the analysis, we use data on U.S. equity mutual funds established to purchase assets around the world.<sup>16</sup> The U.S. mutual fund industry is very large (in 2005 there were 8,044 mutual funds with a market capitalization of US$ 8 trillion, or 69% of U.S. GDP), has a strong international presence (according to some estimates, U.S. mutual funds represent more than 70% of the assets held worldwide by all mutual funds), channels a significant share of retirement savings (mutual funds captured 24% of retirement savings in the U.S. in 2004), and is a relatively mature and sophisticated industry. We use two types of data: mutual fund holdings and mutual fund returns.

Mutual fund holdings are available from Morningstar, a company that collects mutual fund data. We analyze reports from March 1992 (when they became available) until June 2006. While some mutual funds provide monthly reports, most do so quarterly or semiannually (depending on the reporting SEC rules at the time). Given this heterogeneity in the release of new information, we construct our database with the last reported portfolio information for each fund in any given year. For example, our sample of mutual fund holdings for 2005 contains portfolio data for the Fidelity Worldwide Fund as of October 2005 and portfolio data for the Scudder Global Fund as of December 2005. In sum,
proxy for access to the fiber backbone that is equal to one if there is a node of the backbone in the location of interest. Each node has a year attribute, which allows us to build a panel for access to the backbone. We assume that access before 2008 was null everywhere, an assumption that is supported by World Bank data on access to Internet, which reports that less than 4 percent of individuals in SubSaharan African countries (including high-income countries) had access to Internet in 2008. We confirmed our figures by cross-checking them against World Bank indicators reporting the percentage of the population using Internet.<sup>8</sup> Figures A9, A13, A19 in the Appendix shows the access to Internet in Nigeria, Cameroon, and Chad.

**Employment** We are interested in structural transformation, which we interpret as changes in sectoral employment, in line with the literature (Herrendorf et al., 2014). We derive sectoral employment shares from Demographic and Health Surveys (DHSs), which produce harmonized

> 6The World Bank reports access to electricity (percent of population) for most countries for a long period at https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS. 7More details can be found on the blog https://blogs.worldbank.org/energy/using-night-lights-map-electricalgrid-infrastructure and in the paper Arderne et al. (2020)

> 8The World Bank reports access to Internet (percent of population) for most countries for a long time period. See https://data.worldbank.org/indicator/WeT.NET.USER.ZS.

7
- Incentives for cost efficiency.

- Incentives for transparency and competitive service provision.

Under such an accountable governance framework, grant-financed budget allocations support contracts and work program agreements, which are based on pre-specified outputs and performance targets. The grant recipient‘s flexibility in input selection— including hiring and firing of personnel and implementation of programs—is fully respected, but there is strict accountability for achieving results. The incentive and accountability regime created by output-based transfers is expected to create responsive, responsible, and accountable governance without undermining local autonomy. In contrast, traditional conditional (earmarked) grants with input conditionality undermine local autonomy and budgetary flexibility while reinforcing a culture of opportunism and rent seeking where such culture exists (table 8). Even where such a culture of opportunism may not be a concern as in most OECD countries, citizen empowerment engendered through such transfers would be invaluable in strengthening citizen trust and confidence in their governments.

**<u>Table 8. Features of Traditional Earmarked and Output-Based Conditional Grants</u>**

|_Feature_|_Traditional Earmarked grant_|_Output-based grant_|
|---|---|---|
|Grant objectives|Spending levels|Quality and access to public<br>services|
|Grant design and administration|Complex|Simple and transparent|
|Eligibility|Recipient government<br>departments/agencies|Recipient government provides<br>funds to all government and non-<br>government providers|
|Conditions|Expenditures on authorized<br>functions and objects|Outputs -service delivery results|
|Allocation criteria|Program or project proposals<br>approvals with expenditure<br>details|Demographic data on service<br>population (potential clients)|
|Compliance verification|Higher level inspections and<br>audits|Client feedback and redress,<br>comparison of baseline and post-<br>grant data on quality and access|
|Penalties|Audit observations on financial<br>compliance|Public censure, competitive<
5 was the total value of food (your) household received from this migrant during the last 12 months: (4) What was the total value of non-food items (your) household received from this migrant during the last 12 months? and (5) Where does this migrant live?<sup>8</sup> Lacking data on current migrants and their characteristics, the focus of this study will be on remittances and how the welfare – or poverty status – of households changes with the receipt of remittances.

Since the focus is on remittances, it is important to clarify how these income transfers are measured and defined. Each household that is recorded as receiving remittances -- internal or international -- is assumed to be receiving exactly the amount of remittances measured by the survey. This means that households which have migrants who do not remit are not recorded in this study as receiving remittances; rather these households are classified as non-remittance receiving households. This assumption seems sensible because migration surveys in other countries generally find that many migrants do not remit. Since no data are available on the number of current remitters per household, each household that is recorded as receiving remittances is assumed to be receiving remittances from just one migrant. Since the survey data also contain no information on the characteristics of current migrants, households may be receiving remittances from different people: family members or relatives. Because of data limitations, the focus throughout this study is on the receipt of remittances by the household rather than on the type of person sending remittances. Finally, remittances in this study include both cash and inkind remittances. The inclusion of in-kind remittances (food and non-food goods) is important because it leads to a more accurate measure of the actual flow of remittances to households in Ghana.
Figure 1. Female labor force participation rate and GDP per capita in 2018. Cross-country perspective <!-- Start of picture text --> 90<br>80 LAV...<br>PE<br>70<br>60 PYGERS<br>DO<br>HN<br>50<br>40<br>LK<br>SN<br>30<br>GT<br>20<br>10<br>0<br>6 7 8 9 10 11 12<br>Log of GDP per capita, PPP (constant 2017 international $)<br>Female LFP<br><!-- End of picture text --> <!-- Start of picture text --> Sources: World Development Indicators (World Bank, 2021).<br>Notes: FLFP of women ages 15 to 64.<br><!-- End of picture text --> Figure 2. Female labor force participation rate and GDP per capita in 2018. Within-country perspective <!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0<br>Log of GDP per capita (US dollars)<br>Female LFP<br><!-- End of picture text --> Sources: 2018 National Census of Guatemala (INE) and FUNDESA. Notes: FLFP of women ages 15 to 64.

21
0|-0.3|0.0|-1.2|-0.5|
|Unemploymentrate(%)|7.7|10.4|8.7|7.9|8.7|8.4|13.3|8.9|9.6|10.1|
|Inflation (% per year)|4.7|4.5|4.9|5.8|5.0|7.0|9.6|10.5|10.6|9.4|
|Exchange rate (Peso/$)|26.3|26.4|29.8|35.4|29.5|40.7|39.4|42.9|40.6|40.9|
|Net foreign reserves<br>(million $)|10268|10065|8261|7456|9013|6990|8418|7970|8004|7845|

_Note:_ The figures for inflation, exchange rate and net foreign reserves are quarterly mean calculated from of monthly data.

_Source:_ National Statistical Coordination Board and Bangko Sentral ng Pilipinas (BSP).

**Table 2: The incidence of crisis-related economic shocks**

|_Crisis-related shocks_|_Percent of sample_<br>_households affected_|_Percent of population_<br>_affected_|
|---|---|---|
|Price shock|89.9|91.4|
|Domestic employment shock|18.4|20.3|
|Overseas/migrant employment shock|4.32|4.93|
|Wage shock|15.5|16.7|
|DroughtlEl-Nifto shock|56.6|59.8|
|Price shock only|28.2|26.0|
|Domestic employment shock only|0.16|0.16|
|Overseas/migrant employment shock only|0.02
employment by sector and region. This drives changes in poverty and income distribution. Using the POF (the expenditure survey) data mentioned below, we extend the CGE model to cover 270 different expenditure patterns, composed of 10 different income classes in 27 regions. In this way, all the expenditure side detail of our micro-simulation dataset is incorporated within the main CGE model.

There are two main sources of information for the household micro-simulation model: the Pesquisa Nacional por Amostragem de Domicílios –PNAD (National Household Survey – IBGE, 2001), and the Pesquisa de Orçamentos Familiares- POF (Household Expenditure Survey, IBGE, 1996). The PNAD contains information about households and persons, and shows a total of 331,263 records. The main information extracted from PNAD were wage by industry and region, as well as other personal characteristics such as years of schooling, sex, age, position in the family, and other socio-economic characteristics.

The POF, on the other hand, is an expenditure survey that covers 11 metropolitan regions in Brazil. It was undertaken during 1996, and covered 16,014 households, with the purpose of updating the consumption bundle structure. The main information we drew from this survey was the expenditure patterns of 10 different income classes, for the 11 regions. We assigned one such pattern to each individual PNAD household, according to each income class. As for the regional dimension, the 11 POF regions were mapped to the larger set of 27 CGE regions. Here it must be stressed that the POF survey just brings information about urban areas (the metropolitan areas of the main state capitals).

# **2.1 Model running procedures and highlights**

As mentioned before, our model consists of two main parts: a Computable General Equilibrium model (CGE) and a Household Model (MS). The models are run sequentially. To 8
47|1.45|0.83|0.14|1.14|
|Hong Kong|250|0.02|0.02|0.41|1.10|0.86|0.14|0.83|
|South Korea|453|0.05|0.02|0.59|1.82|0.64|0.03|0.91|
|Greece|157|0.05|0.05|0.47|1.80|0.92|0.01|4.45|
|France|726|0.06|0.04|0.62|1.76|0.76|0.04|1.36|
|Germany|706|0.02|0.02|0.64|1.81|0.53|0.06|1.35|
|Japan|3138|0.03|0.01|0.6|2.14|0.75|0.17|0.99|
|United Kingdom|1428|0.08|0.05|0.53|1.48|0.79|0.11|1.47|
|United States|462|0.11|0.06|0.6|1.81|0.46|0.28|1.95|
|Average (excl. Egypt)||0.06|0.04|0.57|1.77||0.10|1.43|

Source: Egypt—CMA data (own calculations), other countries—Worldscope. Reported are medians. TA stands for total Assets. Tobin’s Q is defined as market value of equity +book value of liabilities over total assets. Interest coverage is defined as earnings before interest and taxes over interest payments.
- Armand, Alex et al. (2017). Do public health interventions crowd out private health investments? Malaria control policies in Eritrea. Labour Economics 45, pp. 107115.

- Balcan, Duygu et al. (2009). Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences 106.51, pp. 21484 21489.

- Barofsky, Jeremy, Tobenna D Anekwe, and Claire Chase (2015). Malaria eradication and economic outcomes in sub-Saharan Africa: evidence from Uganda. Journal of health economics 44, pp. 118136.

- Bjorkegren, D and Darrell Grissen (2018). Behavior revealed in mobile phone usage predicts credit repayment. World Bank Economic Review. Accepted.

- Bleakley, Hoyt (2010). Malaria eradication in the Americas: A retrospective analysis of childhood exposure. American Economic Journal: Applied Economics 2.2, pp. 145.

- Blumenstock, Joshua (2016). Fighting poverty with data. Science 353.6301, pp. 753754. (2018). Don't forget people in the use of big data for development.

- Blumenstock, Joshua, Gabriel Cadamuro, and Robert On (2015). Predicting poverty and wealth from mobile phone metadata. Science 350.6264, pp. 10731076.

- Blumenstock, Joshua, Nathan Eagle, and Marcel Fafchamps (2016). Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters. Journal of Development Economics 120, pp. 157181.

- Bogoch, Isaac I et al. (2016). Anticipating the international spread of Zika virus from Brazil. Lancet (London, England) 387.10016, pp. 335336.

- Buckee, Caroline O, Andrew J Tatem, and Jessica Metcalf (2017). Seasonal population movements and the surveillance and control of infectious
to winsorize the data in somewhat of an arbitrary fashion so as to remove observations whereby the percentage change in headcount poverty is enormous (e.g., Bourguignon, 2003). To further illustrate this issue, in Appendix C, we re-estimate Equations (7)-(10) using an elasticity framework as opposed to a semi-elasticity framework (i.e., we use ∆ _H_ as our left-hand-side variable as opposed to _dH_ ). We show how are results are very sensitive to including observations where headcount poverty is close to 0.

## _III.A Data_

The data used to estimate Equations (7), (8), (9), (10), and (13) are taken from PovcalNet for all countries that have at least two surveys where measurement of our key variables (e.g., headcount poverty rate, Gini coefficient, mean income) remains comparable across the two surveys.<sup>1213</sup> This leaves us with a total of 135 countries with surveys spanning 1974-2018. For each country, we calculate the difference in our key variables across each pair of consecutive surveys for which measurement of these variables remained comparable. This gives us a total of 1,307 observations on changes in our keys variables or, equivalently, 1,307 “growth-spells”. See Table IV in Appendix A for a list of all 135 countries with the range of survey years for each country.

# IV Empirical Results

In this section we present the results from our four empirical models, and, in turn, explore in detail how well our preferred specification does at explaining observed changes in headcount poverty. First, using our entire sample of 135 countries over 1974-2018, we estimate Equations (7), (8), (9), (10), and (13). Results are presented in Columns (1)-(5) 12. PovcalNet is the on-line tool for poverty measurement developed by the Development Research Group of the World Bank
spread; and the death rate among the COVID-19 infected, to measure the pandemic mortality rate. The pandemic mortality rate captures pandemic severity, and it sheds light on the amount of consumption a society is willing to give up to contain the pandemic (Hall, Jones and Klenow 2020).

The dataset on government policy responses to the COVID-19 outbreak is the Oxford COVID-19 Government Response Tracker (OxCGRT),<sup>4</sup> which tracks government policy responses to COVID-19 across 147 countries and time. We use it to define international and domestic mobility restriction delay (denoted as _DelayI_ and _DelayD_ ), respectively. _DelayI_ is delay in controlling the international travel, equals to the number of days, divided by 100, from the day

> 4 Data available at https://github.com/OxCGRT/COVID-policy-tracker, see Hale et al. (2020).

11
Next, we perform an analogous analysis for each IEA sub-region. The results from these exercises are presented in panels B to F. Nearly all sub-regions are expected to see an increase in FHD, with sub-regions like the Caribbean and Southern South America particularly hard hit. One notable exception is the Andean Region (Colombia, Ecuador, and Peru), which is expected to see a reduction in the FHD of roughly 25 percent. Given the location of the Andean Region and the anticipated evolution of hydrological droughts, this result highlights the potential that cross-border trade may hold in mitigating drought-induced air pollution.

In the second exercise, we project the evolution of premature deaths in LAC up to 2059. Specifically, we extend the premature deaths counterfactual calculation presented in Section 4.1 but compute excess air pollution using the runoff projections from the climate models instead of the observed runoff. We also allow for demographic changes using the demographic projections of the SSPs. The SSPs do not include projections on the evolution of electricity generation. However, because we expect the retirement schedule of combustion power plants to have significant implications for this exercise, we include information on possible retirements of combustion power plants by taking advantage of the region’s IEA electricity generation scenarios and attaching the most closely aligned scenario to each SSP.

For SSP1-2.6, we use the Announced Pledges Scenario (APS). This scenario is described in IEA (2023) and is consistent with an expected temperature rise of 1.7 _◦_ C by 2100. This scenario outlines how total electricity generation by fuel type will evolve if countries fully honor existing pledges. This scenario is optimistic as it does not mirror what governments are doing but rather governments’ aspirations. In this scenario, gas, coal, and oil power generation are expected to decline, while bioenergy generation is expected to increase. For the exercise, we omit the increase in bioenergy generation (no new plants are included) and use the information on the decline in generation from coal, gas, and oil