rafmacalaba/data-use-mentions-extended huggingface dataset:
is available). Moreover, the latest available year for which data is available varies signi fi cantly across countries (see second column of Table 1). To enhance country and time coverage would allow to improve upon the precision of the estimates.

We obtained the entire UNCTAD TRAINS dataset through the World Bank’s WITS system. This dataset contains detailed information on various types of NTBs (more than 30 different types of NTBs are identi fi ed). As discussed earlier, we included in our measure of Core NTBs: Price control measures (UNCTAD Trains code 6100 6200 and 6300), Quantity restrictions (UNCTAD Trains code 3100 3200 3300), Monopolistic Measures (UNCTAD’s Trains code 7000) and Technical Regulations (UNCTAD’s Trains code 8100). The dataset was updated using information provided by the WTO’s Trade Policy Reviews and in the case of the European Union by the EU Standard’s Database built by Groupe d’Economie Mondiale at Science Po (Paris).<sup>14</sup> The Core NTB variable used in the estimation of equation (8) takes the value 1 when a given country imposes one of the Core NTB measures in a six digit tariff line, and zero otherwise. This obviously suffers from the drawback that we do not have a measure of the restrictiveness of a particular NTB in a particular country. However, instead of making ad-hoc assumptions regarding the restrictiveness of each NTB, we will rather rely on our estimation procedure to impute the restrictiveness of each measure in each country. Rather than the statistician deciding how restrictive is each measure we will let the data “decide” which type of NTB and in which country has the most restrictive impact on imports.

The second type of NTB included is agricultural domestic support. This was obtained from WTO members noti fi cations during the period 1995-1998 (see Hoekman, Ng and Olarreaga, 2004 for a discussion of the construction of this variable). Domestic support is measured in dollars and is a continuous variable
*<br>**with full income**<br>**statement data**|**Share of**<br>**Exporters in**<br>**the Sample**|
|---|---|---|---|---|---|
|Colombia|2017|2016|989|535|258|
|Greece|2018|2017|559|538|270|
|Italy|2019|2018|610|498|268|
|Jordan|2019|2018|380|169|166|
|Kazakhstan|2019|2018|1,377|640|139|
|Kenya|2018|2017|961|696|304|
|Morocco|2019|2018|755|458|345|
|Peru|2017|2016|976|415|275|
|Portugal|2019|2018|1,057|630|432|
|Russia|2019|2018|1,284|736|235|
|Turkey|2019|2018|1,506|894|501|
|Ukraine|2019|2018|1,305|688|417|
|**Total**|||11,759|6,897|3,610|

The estimation of survival times that we discuss is based on several survey questions (Table A1). Available liquidity is calculated using profits and external financing. In particular, the question on sales (question D.2) asks: “In the last completed fiscal year, what were this establishment’s total annual sales for all products and services?” External financing of working 4
the public market is doing well during the sample period. This issue is of particular concern in the literature because many papers have used data on private equity returns from the 1990s, when the public market was doing quite well. This issue is of less concern in the present paper because our sample period is 1961 to 2019.

## **3.3 Other Data**

We relate the PME to a variety of country-level covariates. Real GDP per capita, inflation, and local currency depreciation are taken from the World Development Indicators (World Bank 2019a). Legal origins (i.e., English, French, or Socialist) are measured using the classification of La Porta et al. (1999). A measure of political risk comes from the PRS Group, a measure of corruption perception from Transparency International, and a measure of economic freedom from the Heritage Foundation. Financial openness is measured by the index of Chinn and Ito (2006, 2008), which is the first principal component of dummy variables codifying capital controls reported in the IMF’s _Annual Report on Exchange Arrangements and Exchange Restrictions_ . Financial development is measured by the ratio of private sector credit by deposit money banks to GDP, as reported in the Global Financial Development Database (World Bank 2019b).

# **4 Results**

## **4.1 Performance of a Diversified Emerging Market Private Equity Portfolio**

As a first look, Table 2 reports the performance of the IFC portfolio, pooling cash flows from direct investments and fund investments. The evolution of portfolio returns over time is documented in the table’s columns, which report performance calculated on subsets of investments grouped by earliest vintage year beginning with all investments since the first in 1961, then all investments since 1970, since 1980, and so forth.

Table 2, Panel A reports the performance of the complete portfolio. The bottom row of this panel reports the number of investments in each vintage year group. Relative to the S&P 500
AlDS and safe sex practices in the general population<sup>2</sup> . Consequently sex workers who want to practice safe sex potentially face losing a considerable amount of income by doing so. Anecdotal evidence suggests that sex workers could face at least a fifty percent loss in income by practicing safe sex (Bhave et. al, 1995). There are, however, no direct estimates of the size of this disincentive for safe sex.

If effective policies to combat AIDS are to be devised it is, thus, very important to have some knowledge of the size of the compensating differential faced by sex workers who want to use condoms. If the income loss is large, this suggests that HIV/AIDS interventions among sex workers may need to ' International Institute of Population Studies, _National Family and Health Survey (NFHS),_ (1992-93).

2 The NFHS also found that only 36 per cent had heard of AIDS in 1993 (NFHS, 1993). A much smaller proportion were likely to have heard of HIV the virus that causes AIDS and therefore of the best methods of preventing it - though these questions were not asked in the NFHS survey. Another survey did ask more detailed questions and found that though 35 per cent had heard of AIDS only 4 per cent were aware of the HIV virus and of measures to prevent its transmission (Gupta and Mitra, 1998). A third survey of people attending an STD clinic in 1993-94, who would be expected to be more aware of STDs than the general population, found that only 14 per cent knew that condoms were effective in preventing HIV transmission (Grover et.al. xx).

3
, to which fourth variables were added one at a time, and so on. This process was stopped when no addition of an independent variable resulted in significant results for that variable.

**25** For some of the independent variables in the Table 14 regressions data were not available for all regions, leading to the drop in both the number of observation (N) and the adjusted R<sup>2</sup> in 1993 in the Table 14 regressions relative to Table 9.
2 modest level of 51 percent in 1992. Comparison of literacy rates for countries such as Ghana, Cote d'Ivoire and Kenya in SSA, and Indonesia, a country similar to Nigeria in many respects, however, indicate Nigeria has fallen far behind.

The primary manifestation of poverty occurs at the household (individual) level, the understanding of which requires data at the same level of disaggregation. Unfortunately, economic and social statistics in Nigeria are limited and unreliable particularly for such microlevel analysis. Furthermore, data on income and expenditures are absent or outdated. Although attemptsl/ have been made to fill the gap in household level information through a series of studies and household surveys, published government sources are inadequate due to the lack of proper accounting of sources and uses of income, imprecision in definition and the lack of household distribution by income (expenditure) groupings.

This paper's objective is to document the evolution of household welfare and poverty in Nigeria in the period of 1985-92. The present exercise is the first which investigates the changes in and implications for household welfare and poverty. The Nigerian economy achieved an impressive growth performance over the years for which survey data are available (around 5 percent GDP growth rate per annum on average), which implies that the average living standards rose over the survey period. However, the benefits of economic growth were not distributed equally. The extent to which the poor benefited from economic growth (about 1.13 million people rose above the poverty line) is therefore an issue of considerable policy interest. This paper focuses on the sub-groups of population by social and economic breakdown to understand which groups of people benefited from the economic reforms and which groups did not. The welfare changes that have taken place in each household is decomposed to facilitate the analysis of policy consequences of the economic measure introduced. However, unlike some other studies, this analysis does not adopt a formal general equilibrium model; instead it tries to understand each issue in a partial equilibrium setting. Although this might have disadvantages
6%|

Source: Author's calculations based on data from Maddison (1995).

To implement the comparative-advantage-defying strategy, developing country governments had to protect numerous nonviable enterprises in the priority sectors. Administrative measures to which governments resorted to reduce the investment and operation costs of nonviable enterprises included granting those enterprises a market monopoly, suppressing interest rates, overvaluing domestic currency, and controlling prices for raw materials. Such interventions caused widespread shortages in funds, foreign exchange, and raw materials. Consequently, governments also had to allocate resources directly to these enterprises through administrative channels, including national planning in the socialist countries and credit rationing and investment and entry licensing in nonsocialist developing countries. For ease of implementation, many countries also relied on state-owned enterprises to develop the targeted industries.

Protection also led to other types of costs. Prices of imports and of import-substituting goods increased relative to the world price, pushing the economy to consume the wrong mix of goods from the point of view of economic efficiency. Markets fragmented as the economy produced too many small-scale goods, again resulting in loss of efficiency. Protectionism also lessened competition from foreign firms and encouraged monopoly power among domestic firms whose owners were politically well connected. Moreover, it created opportunities for rents and corruption, which raised input and transaction costs.

In some cases (mainly in Eastern European and other socialist countries of the former Soviet Union) the industrial development brought about by the comparative-advantage-defying strategy appeared to be successful at the beginning because large-scale investment through massive state mobilization of resources increased the growth rate and improved productivity indicators. But firms in the capital-intensive sectors depended for their survival on the government‘s subsidies and protection, and when the state could no longer mobilize resources for further investment, the economy became stagnant. Moreover, the investment in the capital-intensive sectors generated little employment. The labor force remained mostly in the rural sector.
# **2. Accuracy of the Cross-Country Raw Dataset**

In order to have a sense of the reliability of the raw data in each of the countries included in the Database, we apply two filters: a) For the first filter we compare the total values exported (excluding HS 27) calculated from the cross-country raw dataset with the total values exported from the United Nations’ COMTRADE database (excluding HS 27) for every country and year.<sup>26</sup> This comparison yields quite different results for different countries. On the one hand, for Albania, Brazil, Cameroon, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Kenya, Lebanon Mexico, Morocco, Pakistan, Peru, South Africa, Tanzania and Turkey, the ratio of total values exported in the raw dataset to total values exported in COMTRADE is about 100 percent. On the other hand, for Mali and Yemen, the ratios indicate that total values exported in the raw dataset are as low as half of the total values exported in COMTRADE. On the opposite end, for Mauritius total values exported in the raw dataset are on average 30 percent above total values exported in COMTRADE.<sup>27</sup> Appendix 2 provides detailed results on these comparisons.

b) For the second filter, we focus on the countries and years that would have been left out because of a unfavorable match with COMTRADE (below 60 percent) and we keep those countries (and years) where we observe internal consistency within the export totals calculated from the corresponding exporter-level raw dataset over time. Although both sources of trade data, COMTRADE as well as the exporter-level raw datasets that we use, originate from customs authorities, we are aware of potential difficulties in the processing of the exporter-level

> 26 Given our understanding about which transactions are included in the raw files, for this comparison we consider gross export figures in COMTRADE (which include re-exports) for Albania, Kenya, Mauritius, Tanzania and Senegal and net export figures
)|9.|5%|WHO estimate for India,<br>Shibuya et al (2001)|
|Female COPD incidence rate(per 100 thousand)||79||
|ARI 2-week Prevalence in Children under 5years|22%|22%|NFHS-3,2006|
|Estimated annual cases of ARI per child under 5<br>years|1.0|1.0|Estimated from NFHS-3, 2006|
|Estimated annual cases of ARI per adult female (><br>30 years)|0.4|0.5|Estimated from a combination of<br>NFHS-3, 2006 and Krupnick et al,<br>2006|
|ARI mortality in children under 5 years (% of child<br>mortality)|2|2%|Office of Registrar General (2004)|
|DALYs per 100 thousand cases of ARI in children<br>under 5|165|165||
|DALYs per 100 thousand cases of ARI in female<br>adults(>30)|700|700|Estimated from WHO tables|
|DALYs per case of ARI mortality in children under<br>5|34|34||
|DALYs per case of COPD morbidity in adult<br>females|2.25|2.25||
|DALYs per case of COPD mortality in adult<br>females|6|6||

The results of the estimation of health losses associated with indoor air pollution are presented in Table 5.3. Estimated cases of ARI child mortality and ARI morbidity (children and female adults) from indoor air pollution represent about 38-53 percent of total ARI in India. Similarly, the estimated cases of COPD mortality and morbidity represent about 46-72 percent of total estimated female COPD from all causes.

20
This paper uses daily returns of stocks listed on the NYSE, AMEX and NASDAQ with share code 10 and 11 from July 1963 to December 2017 to compute downside betas. The data are obtained from the Center for Research in Security Prices (CRSP). Stocks with prices less than five dollars are excluded. In order to ensure enough observations, stocks must have at least 100 observations in the year that downside betas are constructed. All returns reported in this paper are excess monthly returns, where risk-free returns are approximated by the one-month Treasury Bill rate provided by Ibbotson Associates. The proxy for the market return is the value-weighted return of all stocks included in CRSP.

Firm characteristics are obtained from COMPUSTAT. Size is computed as the log of total assets. For computing liquidity beta, this paper obtains Pástor and Stambaugh liquidity risk factor from Professor Luboš Pástor’s website. To compute risk-adjusted returns, this paper also acquires the Fama-French three factors, five factors and the momentum factor from Professor Ken French’s website.

and respectively.

Panel A in Table 1 reports summary statistics of various betas, i.e., CAPM beta, usual downside beta " _βed_<sup>_−_",ES-impliedbeta"</sup><sup>_β_</sup> _es_<sup>_−_",relativeusualdownsidebeta"Relative</sup><sup>_β_</sup> _ed_<sup>_−_"andrelativeES-</sup> implied downside beta "Relative _βes_<sup>_−_".Columns "Mean", "SD", "Q1", "Median", and "Q3" present</sup> their mean, standard deviation, first quartile, median, and third quartile, respectively. The CAPM beta on average is less than 1, suggesting that the sample in this paper overall is less correlated with the market and the majority may be small firms. The usual downside beta and the ES-implied downside beta are larger and more volatile than
case, Google's estimate of the duration and distance on the most recommended and fastest routes was recorded and each trip was queried at multiple times of the day and days of the year.<sup>9</sup> To construct comparable<sup>10</sup> city-specific speed indices from trips _i_ in city c( _i_ ), Akbar et al. (2021) regress their trip-level measure of speed outcomes on observable trip characteristics _X,_ including trip length, distance to the city center, trip type, time of day, day of week, and weather, to estimate city fixed effects that serve as city-level mobility indices, _Mobilityc(i)_ : ′ log S S o o 0 + α M . (1) ii ii cc(ii) ii SS_o SS = ββ + MMooM ooMM + SS Real-time speed outcomes ( _Speed_outcome_ i) on recommended route are used to estimate a baseline index of city speed, or an overall mobility index. Then, because Akbar et al. (2018; 2021) observe the same trips at different times of the day, they conduct analogous estimations of (1) using speed outcomes in the absence of traffic. From these estimations they obtain city-level uncongested mobility indexes. The difference between the city-level overall and uncongested mobility indexes is the city-level index of congestion delay caused by traffic. Accordingly, the overall mobility index decomposes exactly into the congestion index and the uncongested mobility index, and each index can be related to city characteristics for cross-city comparisons. We use the three indexes to distinguish the role of congestion from the role of uncongested mobility in the relationship between mobility, density, and agglomeration economies. Descriptive statistics of the obtained speed indices are provided in Appendix A2.

The mobility data of Akbar et al. (2021) offers several advantages over alternative sources, such as INRIX and TomTom data. First, the mobility indices are based on millions of trips on Google Maps, simulated over the same period of time using the same trip
source is not suitable for analyzing effects by cause of death. The cause of death in the FEPB database is the official cause of death listed in their death certificate issued in the destination countries. The causes of death might have been tampered with to avoid insurance and other hassles (legal hassle for employers, detailed medical procedure to determine the exact causes, delays in dead body repatriation for the family of the deceased). The prevalence and variance of ‘natural’ cause of death of adult young male across different destinations is an evidence of the dubious quality of information on the cause of death of Nepali migrant workers (Figure 4). Hence, I do not pursue any analysis on the cause of death.

The data suggest very low and fairly constant mortality rate of Nepali workers abroad. I compute death rate by using the counts from the FEPB database and estimated migrant stock in each month in the destination countries. To estimate the migrant stock, I assume that migrants to the Persian Gulf countries return after 2 years and migrants to Malaysia return after 2.5 years. Using this rule in migrant outflow database from DoFE, I obtain net flow of migrants to each destination in each month. The census of 2011 provides a snapshot of the migrant stock for June of 2011 in each of these destinations. This, along with the net flow of migrants provides an estimate of the migrant stock in each destination for each month.<sup>7</sup> The estimated overall two-year mortality rate is 1.3 per thousand migrant workers in 2013, which is slightly higher than the rate of 1.16 per thousand worker in 2010.<sup>8</sup> Though the estimated magnitude of the rate is slightly higher in 2013 compared to 2010, the plot of smoothened death rates suggests that migrant death rates remained fairly constant throughout the study period (Figure 5). The figure also shows that death rates in Malaysia and Saudi Arabia are slightly higher than death rates in Qatar and the UAE.
different regions. The GTAP model estimated for this paper was explicitly run to assess the impact on average domestic prices in Mexico. The model produced estimates in changes in prices and export quantities for 13 product groups (4 manufacturing group, 8 agricultural group and 1 service group). The model also produces changes in returns for land, two types of labor, capital and natural resources. These product groups are matched to the household survey.

In matching and reconciliation the data from the household surveys with the GTAP data this paper follows the approach of Ianchovicina, Nicita and Soloaga (2001). In summary, the matching of the household survey classification to GTAP categories consists of two different exercises: consumption matching and income matching. On the expenditure side, the GTAP system has approximately 50 commodity categories, while the household data includes about 600 different categories. The matching of the expenditure side of the two data sets was facilitated by the use of concordance tables provided by the GTAP website (www.gtap.org). Regarding income, GTAP uses five different endowment categories, while in the household survey data there are more than 40. In addition, the two data sets adopt different systems in classifying income.

Therefore, they are more difficult to match and require some degree of arbitrariness. GTAP income is divided into land, capital, skilled labor, unskilled labor and natural resources. The attained level of education is the variable that allow us to distinguish between skilled and unskilled labor. An individual is considered skilled having completed secondary school or technical education. The household survey divides income into different categories, some of which are not univocally or clearly attributable to any single GTAP category. Many of those household income categories must be 29