rafmacalaba/data-use-mentions-extended huggingface dataset:
born ethnic or linguistic minorities, second generation immigrants, as well as first generation immigrants (the highest rate is found among immigrants from CEE and former Soviet Union; in Spain, Portugal and Greece this rate exceeds 50%). In Eastern Europe, the picture is broadly similar: local born minorities feature the highest dependent informality rate of about 11%, followed by second generation immigrants and immigrants from CEE and former Soviet Union with 7%, whilst this rate is just 4% among the natives. Moreover, in Eastern Europe, as well as in Spain, Portugal, and Israel, ethnic and linguistic minorities are more likely to work informally also after controlling for a variety of characteristics (Table 9).

In Western Europe, the only minority group with above-average proportion of noncontracted employees consists of immigrants from CEE and former Soviet Union: 12% of employees of this origin works without contracts, whilst for the natives this rate is 7%. In the Nordic countries no clear pattern emerge, probably because the sub-sample of informal immigrant employees is too small.

Table 4 also compares informality rates of immigrants depending on whether they do have an ̳automatic‘ working right due to nationality (based on country- and year-specific rules on free movement of labor within EU). The differences by legal status are smaller than those by geographic origin. Somewhat surprisingly, informality rates are slightly higher among immigrants covered by the ―free movement of labor‖ provisions in all parts of Europe except the South. It appears that in other parts of Europe persons not covered by the provisions are either not likely to work as non-contracted employees or they are not captured by the ESS surveys. On the other hand, persons covered by the provisions are more likely to move without a job in hand, and hence more likely to end up with an informal job. The situation is strikingly different in Spain, Portugal, and Greece, where informality rate among non-covered immigrants is twice as big as among covered ones (33.7% vs.
(iii) cumulative voting or proportional representation of minorities on the board of directors is allowed, (iv) an oppressed minority mechanism is in place, (v) the minimum percentage of share capital that entitles a shareholder to call for an Extraordinary Shareholders' Meeting is less than or equal to 10 percent, and (vi) shareholders have preemptive rights that can only be waived by a shareholders' vote. In economies with higher values of Anti-Director, minority shareholder are better protected against expropriation by management and large shareholders and should therefore be more willing to provide the external resources that industries need.<sup>10</sup> _Rule of Law_ is an assessment of the law and order tradition of a country that ranges from 10, strong law and order tradition, to 1, weak law and order tradition. This measure was constructed by ICRG and is an average over the period 1982-1990. In countries with a higher law and order tradition, outside investors can more easily enforce their claims and rights and should therefore be more willing to provide external finance.

Table 2 provides descriptive statistics and the correlations for our measures of financial development and structure. There is a large variance in the measures of both financial development and structure. Japan shows the highest value for Finance-Activity and Bangladesh the lowest. Structure-Activity classifies Great Britain as the most market-based system and Bangladesh as the most bank-based system. We also note that most indicators of financial development are positively correlated with the indicators of financial structure, i.e. financially more developed economies tend to have market-based systems. Table Al lists all measures of financial development, structure and the legal indicators for the countries included in our study.

Wachtel (2000).

> 10 Instead of using the sum of the different creditor and minority shareholder rights, we also used principal component indicators, and obtained the same results.

17
# **1 Introduction**

The debate on the Dominican Republic - Central American Free Trade Agreement (DR-CAFTA) between the U.S., five Central American countries (Costa Rica, Guatemala, Honduras, Nicaragua, and El Salvador), and the Dominican Republic has been heated by the usual arguments surrounding recent trade deals. A seemingly persuasive argument against these deals is that although new jobs in Central America may be generated, this may be done at the expense of American jobs and to the detriment of local workers hired in jobs that do not comply with minimum labor standards.<sup>1</sup> The persuasiveness of this argument comes from its partiality; the argument considers only the distributional effects of trade reforms and the fact that these reforms create winners and losers, with the poor being most likely to be the latter. Similarly, the pro-trade-deals assumptions that free trade indisputably favors growth and that growth trickles down to the poor are partial and deserve scrutiny. A careful assessment of whether trade reform can be beneficial to poor people and what can be done in the short-term to correct potential anti-poor effects is needed to settle the debate, but it is also a difficult task.

There are various channels through which trade liberalization affects the poor as discussed in conceptual terms by McCulloch, Winters and Cirera (2001), although empirical evidence is rather thin, disparate and piecemeal. In this study, a numerical simulation model – a computable general equilibrium (CGE) model – in conjunction with a non-behavioral micro-simulation module based on household survey data for Nicaragua<sup>2</sup> is used to estimate _ex-ante_ the effects of a DR-CAFTA-like trade shock on poverty. The CGE model has the advantage of being a counterfactual analysis tool that can generate price effects which are directly and unequivocally linked to a trade reform. The changes in relative factor prices (particularly between labor and capital remunerations, and between skilled and unskilled labor wages) and relative goods prices (such as between food and non-food items) are
29,311 migrants from the Soviet Union in 1966 among the constituent republics to yield the bilateral numbers for Australia (table A3).

# **{Table A3 here}**

In this simple example, only the data for 2001 are available. Where data are available for more than one census, the shares across all decades are averaged before estimating the bilateral numbers.

In the absence of such data (disaggregated data for the _same_ destination country in _other_ census years), the world is disaggregated into destination subregions. Origin countries in the same subregion are then assumed to have a similar propensity over time to send migrants to a particular destination country in a subregion for which data are lacking as they do to other countries in that subregion. For example, assume that the census data for Morocco in a particular year include the origin category All West Africa but no individual data on migrants from Ghana and that there are no bilateral data on Ghanaian migrants in other Moroccan censuses. In this instance, migrants from Ghana are assumed to have a similar propensity to migrate to Morocco as they have to other countries in North Africa. Data from other countries in North Africa (Algeria, Egypt, Libya, and Tunisia) are then used to calculate the propensity of Ghanaians—relative to migrants from other West African countries—to migrate to each country in North Africa. These propensity shares, which sum to one, can be applied to the All West Africa aggregate category from the Moroccan census to disaggregate it into the constituent West African countries. Equation 2 expresses this propensity measure: In equation (2), _G_ denotes the set of comparable destination countries (Algeria, Egypt, Libya, and Tunisia in the example above); _R_ is the set of origin countries (All West Africa); _n_ is the number of census years for which data exist; and _f_ is the number of countries in region _G_ . In short, this is the relative propensity of origin country _o_ to send migrants to subregion _G_ relative to
that the capital share ranges from α =0.4 to α =0.6, this implies that estimates of returns to scale must range from 1.7 to 2.5 in order for a poverty trap to exist. When the scale factor depends on total capital, Equation (15) tells us that the sum of the estimated coefficients on capital and labor is equal to 1 +γ . This in turn means that estimated returns to scale must range from 1.4 to 1.6 over some range of the production function.

These required levels of returns to scale ranging from 1.4 to 2.5 are much higher than what is typically found in the literature. We know of only one study that approaches the lower end of this range. Levinsohn and Petrin (1999) look at data from Chilean plants, and estimate scale returns from value-added production functions that are above 1.20, and in some cases reach up to 1.44. However, most other available studies only find constant to moderate increasing returns. Using Mexican plant-level data, Tybout and West-brook (1995) obtained almost no evidence of scale for large plants. For rich countries, Harrigan (1999) found no evidence of scale in cross-country regressions. Many other OECD country studies point clearly to the existence of moderate scale returns. Paul and Siegel (1999) estimated industry-level scale returns in the range of 1.30 for many U.S. manufacturing industries, and Fuss and Gupta (1981) and Griliches and Ringstad (1971) also estimate moderately increasing returns to scale for Canada and Norway respectively.

The absence of empirical evidence of strong increasing returns casts doubt on these technology-based explanations for poverty traps. However, it is important to note that technology-based poverty traps could exist even in the absence of increasing returns in the aggregate production function. In the first example we discussed, we assumed that total factor productivity was a function of capital per capita. In this case, the aggregate production function has constant returns
increased, accompanied by a sharp decline in the rate of “inactivity.” Among youth workers, the trends in employment status and industry share are quite stable.<sup>8</sup>

# **3.2. Youth Employment and Adult Employment Compared**

In this section, I use the latest LFS data 2005/2006 and compare youth employment and adult employment by labor force participation (LFP) rate, unemployment rate, employment status, and industry allocation (Table1).

The LFP rate for male youth is significantly less than that for male adults. However, the male youth unemployment is almost triple the male adult unemployment rate. This is not particular to Pakistan, but very common in many developing countries. The overall LFP rate for women is very low, reflecting the fact that women are discouraged from participating in the labor force. The unemployment rate is high for both female youth and female adults, roughly 10 percent.

In terms of employment status, more adults than youth are formal workers (salaried workers or employers) or own-account workers. There are two possible reasons. First, adults are more competitive in the formal job market, and second, many youth begin their careers in the informal sector (as unpaid family workers, for example) and then gradually become own-account or salaried workers as they get older. Little difference exists in their industry allocations between youth and adult workers, except that male youth are more likely to be in the manufacturing sector while male adults are more likely to be in the service sector.

7
comparing a cross-section of beneficiaries to a matched set of control households with children in the age 2-5 range. The controls were selected by matching beneficiary children to other children from the same neighborhood, of a similar age and gender and whose mothers were working. Impact estimates were obtained by propensity score matching.

Because all mothers had to be working to be a part of the evaluation, it is impossible to estimate the effect of the program on mothers‟ rate of working. However, to get an idea of the potential effect, Ruel and Quisumbing (2006) compare the labor force participation rate for a random sample of mothers who have children aged newborn to 6 years with that of women who do not. The comparison suggests that the program could potentially increase labor force participation by 25 percent.

The program‟s impact on mothers‟ earnings and job choice is assessed by comparing beneficiary mothers to matched control mothers. This comparison indicates that beneficiary mothers had 30% higher earnings than mothers using alternative childcare arrangements and were more likely to be employed in the formal sector. The program had the largest benefits for younger and older women with low levels of education. A limitation of the analysis is that all mothers in the control group were restricted to be working. It is likely that some of the mothers in the treated group would not have been working had the program not been available to them. For this reason, the evaluation probably understates the effect of the program on mothers‟ incomes. The results from interviews with focus groups show that the program was very well received and much appreciated by the beneficiaries.

_A comparison of Guatemala and Ghana:_ Quisumbing, Hallman and Ruel (2003) analyze the determinants of mothers‟ joint labor supply and daycare utilization choices in Guatemala City and in Accra, Ghana. The analysis for Guatemala is based on a random sample of mothers with preschool age children from one zone of Guatemala City. The analysis for Accra is based on a household survey.

The urban settings of Guatemala and Ghana are
from the largest 40 industrial cities in the country, which were selected from 12 of the largest 15 states by picking the largest three or four industrial centers from each state. These 12 states were Andhra Pradesh, Delhi, Gujarat, Karnataka, Kerala, Haryana, Maharashtra, Madhya Pradesh, Punjab, Tamil Nadu, Uttar Pradesh, and West Bengal. These states account for more than 90 percent of India’s industrial GDP while the three or four cities covered in each state accounted for the bulk of manufacturing outputs of their respective states. In each city, samples were drawn from the population of firms with more than 10 workers and in one of the eight exporting or import competing manufacturing industries.<sup>4</sup> The total sample was allocated among states in proportion to a state’s share in the national employment total of the eight industries. The systematic sampling rule sets an establishment’s probability of selection proportional to the establishment’s number of employees.

In the China survey, 2,400 enterprises were sampled from 18 cities in 15 provinces considered to be representative of China five geographic divisions. The cities include in the northeastern region, Benxi and Dalian (Liaoning Province), Changchun (Jilin), and Harbin (Heilongjiang); in the coastal region, Hangzhou and Wenzhou (Zhejiang), Jiangmen and Shenzhen (Guangdong); in the central region, Changsha (Hunan), Nanchang (Jiangxi), Wuhan (Hubei), and Zhengzhou (Henan); in the southeastern region, Guiyang (Guizhou), Chongqing (Sichuan), Kunming (Yunnan), and Nanning (Guangxi); and in the northwestern region, Lanzhou (Gansu) and Xi’an (Shaanxi). The total GDP of these provinces accounted for almost exactly half of the national GDP in 2007.<sup>5</sup> Each of the selected cities was allotted a sample size of either 100 or 150 firms. These firms were then randomly drawn from an electronic database
br>30<br>25<br>25<br>20 20<br>15 15<br><!-- End of picture text -->

**Note:** The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed poverty rates for TA (TA1+TA2+TA3) use the estimated parameters based on the NPS 2020/21. 1000 simulations are implemented. All estimates are obtained with the population weights. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line from NPS 2020/21 deflated to 2022 prices. The sample size of TA (TA1+TA2+TA3) is selected as a percentage of the target survey sample varying from (randomly selected) 10% to 100% of the TA sample (with the sample size in parentheses). The sample size of NPS 2020/21 is selected as a percentage of the base survey sample varying from (randomly selected) 10%, 20%, 30% to 100% of NPS 2020/21 sample (with the sample size in parentheses). The total sample size of TA (TA1+TA2+TA3) is 2,110 households, and the sample size of NPS 2020/21 is 4,644 households.

43
to evaluate the impacts that changes in the price of energy can have on pollution or costs control (Pigott et al., 1992); (d) natural resource allocation or management models, whose objective is usually the efficient interregional or inter-sectoral allocation of multi-use natural resources—for example, allocation of water resources among agriculture, mining, industry, tourism, human consumption and ecological watersheds (Robinson and Gelhar, 1995; Ianchovichina et al., 2001); and (e) models focused on evaluating the economic impacts of environmental instruments, or of specific environmental regulations, such as the Clean Air Act in the USA (Jorgenson and Wilcoxen, 1990; Hazilla and Koop, 1990).

The CGE modeling in India with environmental links has mainly focused on reduction of carbon emissions and its implications for economic growth (Murthy, Panda and Parikh, 2000; Ohja 2005, 2008).

Source: Conrad (2002) 3. The CGE model used here is based on a framework developed and maintained by the Global Trade Analysis Project (GTAP) network<sup>4</sup> . GTAP model is built on a global trade database and reflects, among other indicators, India‘s performance in terms of export growth, which has increased dramatically during the last decade. With India emerging as a major producer and exporter of goods including pollution intensive commodities, the use of such a model to assess the environmental impacts of the country‘s development path was considered appropriate. The main environmental variable that has been included in the model is emissions of particulate matter of less than ten microns (PM10) as well as particles of sulfates and nitrates). These emissions are recognized among the most important in terms of their health effects. The standard GTAP model has been expanded to include emissions from all the key sectors, including PM10 and other small particles

> 4 <u>https://www.gtap.agecon.purdue.edu/</u> GTAP (1997), T. Hertel
to be most excluded from the general labor market: Black African workers with lower than matric qualifications. We attribute this result to the fact that the few mid-level and elementary occupations that can be classified as strictly green (e.g., refuse and garbage collectors), exhibit some of the highest GTIs and are responsible for a large share of employment.

Five policy areas emerge from the analysis. First, as the green economy grows, there will be a need to upskill the current work force and prepare the incoming work force with skills used in strictly and broadly green occupations. The 2022 critical skills list identifies 101 occupations that cannot be filled by South Africans; 40 are on our strictly green list, pointing to the current scarcity of local skills to fill (mostly) high-level green jobs.<sup>36</sup> Even more worrisome is that while most of the skills required for a green economy could be delivered by some slight adjustments to the education system, it is not happening on the required scale (Duncan 2023). A skills development strategy to carry out the tasks defined in the green jobs identified in this paper can create some order and efficiency in the scattered and ad hoc current offering of green skills programs (ILO 2019). It can be used to incentivize universities to expand curriculum that aligns with the green occupations identified in this paper to build the missing cohort of scientists and managers needed to spur the green revolution. It can also be a means for expanding and organizing the TVET system to prepare workers in mid-level occupations, particularly for broadly green jobs. Partnerships with the private training sector, which is already active in providing short courses in a range of green-related technologies, will allow for a rapid expansion of training opportunities to engage less-skilled youth in green occupations (Duncan 2023). Even workers in elementary occupations will need to upskill and reskill as new technologies and industries come on the market. This group is best served through practical, short courses provided through municipalities or other local private
# **Crypto-Assets Activity around the World: Evolution and Macro-Financial Drivers**

Erik Feyen, Yusaku Kawashima, and Raunak Mittal<sup>1</sup> _JEL classification:_ E42, F24, F38, G11, G12, G15, G28, O33 _Keywords:_ crypto-assets, digital assets, virtual assets, digital currencies, cryptocurrencies, crypto, stablecoins, blockchain, distributed ledger technology, Bitcoin, Ethereum, stablecoins, medium of exchange, payments, remittances, inflation, depreciation, global financial conditions, store of value

> 1 All authors are with the World Bank. We thank Rachel Alexandra Halsema, Ani Popiashvili, and Hamed Honari for significant contributions. We appreciate comments and suggestions from Raphael Auer, Parma Bains, Uzma Khalil, Stela Mocan, Martin Melecky, Alvaro Enrique Pedraza Morales, Cedric Mousset, Uli Ruch, Jean Pesme, Nobu Sugimoto, Mahesh Uttamchandani, Jeanne Verrier, William Zhang, and Peter Zhou. We thank Kim Grauer for discussions and the crypto-assets transaction volume data. This paper’s findings, interpretations and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank Group, their Executive Directors, or the countries they represent.
# **Contents**

|1. Growing but Unhappy: Making Sense of the Puzzle............................................................................... 3|
|---|
|2. Conceptual Framework for the Analysis of Perceptions of Mobility....................................................... 5|
|3. Individual and National Level Data........................................................................................................ 8|
|4. Perceptions of Economic Mobility and Their Correlates......................................................................... 9|
|a)<br>What are the perceptions of economic
of that for the United States). Thus, we scale back the local damage estimate by 60 percent.

Third, we did not make any adjustment for differences in automobile emissions rates. Our estimates of average miles per gallon for autos and microbuses in the GCMA are almost the same as those for Mexico City in Parry and Timilsina (2010). It is possible that vehicle emissions control equipment, for a given level of fuel economy, differs between the two cities, but we lack the data to reliably estimate the direction, let alone the magnitude, of any difference.

The net impact of the above adjustments is therefore a local pollution damage of 72 cents per gallon of gasoline. Following Parry and Small (2009), for public buses we assume local pollution costs per vehicle mile are three times those for auto.

_Marginal global pollution damages_ . Most estimates of the discounted (worldwide) damages from future global warming are in the order of about $10-$30 per ton of CO2, though studies using below market

> 17 Their estimate is also broadly in line with local pollution damage estimates for Mexico City in World Bank (2002) and also for Santiago discussed in Parry and Strand (2010).

31