rafmacalaba/datause-encoder-data huggingface dataset:
loan or other sources of microfinance. Firms that survive the market selection process in the first years of existence are the ones the economy might actually benefit from.

With respect to the individual-specific determinants of self-employment survival, Cooper et al. (1994) find that firms with a male entrepreneur have a greater probability of high growth, but not of survival. Other studies have shown that the probability of survival increases with firm’s age, and further with owner’s age and education (Parker, 2004). Jovanovic (1982) has described that entrepreneurs with higher human capital might be faced with less uncertainty and learn faster about market conditions and adjusting capacity, therefore reducing the probability of exit.

With respect to financial variables, the evidence is often mixed: Using U.S. data, Holtz-Eakin et al. (1994) find that liquidity constraints play a key role in small firm viability; Astebrö and Bernhardt (2003) find a negative correlation between having a bank loan and small business survival and a positive correlation between having a nonbank loan and survival. In contrast, Faznzylber et al (2006) find that formal credit, such as bank loans, is significant in explaining firm survival in Mexico. Cressy (1996) uses U.K. data to assert that the correlation between access to finance and survival is spurious, with human capital being the true driving force.

Concerning past employment history, if the main incentive for starting a business is a “pushing” mechanism out of unemployment-inactivity or self-subsistence activities, there may not be time to look for good opportunities, make detailed plans and seek for advice. Thus, independent from productivity and/or learning effects that occur after startup, chances of survival may be affected by selection effects occurring prior to start-up. On the other hand individuals starting from employment are more likely to be in a position to raise more capital and set up larger and better equipped businesses, based on their higher earnings and more prestigious professional status as employees.

5.2 _<u>Results</u>_ In this section we examine the post-entry performance of individuals becoming self-employed in BiH during the period 2002-2003. In order to examine the determinants of survival of the new self-employed within their first year in business, we employ a probit model with sample selection (van de Ven and van Praag, 1981). Results in Table 8 18
In the above expression (3.2), **rij** stands for the share of group **j** in total enrollments at level **i** , while **si** represents the share of total of public spending on education going to level **i** .

One interesting approach to incidence analysis does not require an estimate of a unit subsidy. It notes only whether or not a service is used by a household. One can then define a dummy variable that takes the value of one if the service in question is used and zero otherwise. The analysis is thus based on the distribution of this dummy variable across socioeconomic groups.

One way of determining whether or not an expenditure program is progressive is to consider how the share of the benefits relative to the income of the recipients varies across income quantiles. If this share declines as we move from the poorest to the richest quantiles<sup>24</sup> , the underlying expenditure program is progressive. Table 3.1 shows an example based on the incidence of public spending on health in Ghana in 1992. According to these results, the underlying spending program is progressive because, for the poorest quintile, the subsidy represents 3.5 percent of total household expenditure compared to 1.8 for the richest quintile (Demery 2003).

Table 3.1 Incidence of Public Spending on Health in Ghana (1992)

|Quintile|Per Capita|Share of Total Subsidy|Subsidy as Share of Total Household|
|---|---|---|---|
||Subsidy|(%)|Expenditure|
||(Cedis)|||
|1|2296|12|3.5|
|2|3065|15|3.1|
|3|3692|19|2.8|
|4|4228|21|2.3|
|5|6515|33|1.8|
|Ghana|3959|100|2.4|

Source: Demery (2003) In general, one can compare the concentration curve of the imputed benefits with the Lorenz curve of the reference welfare indicator (e.g. income or consumption

> 24 Hence the distribution of benefits is regressive.

27
contrast, the JI market has long been dominated by public buyers (mainly the Netherlands, Denmark and Austria), representing 92% of those transactions in 2006 (up from 80% in 2004 and 2005).

By the end of first quarter 2007, EU governments had purchased 143 MtCO2e, about 30% of the assets identified for purchase from the flexible mechanisms (CDM, JI and AAUs).<sup>76</sup> 506 MtCO2e, about 45% of the expected demand for CDM and JI credits from EU ETS installations in Phase II, have already been contracted by European entities, either directly, by natural compliance buyers and the funds in which they are participants, or indirectly, by entities planning to sell back these credits on the secondary market.<sup>77</sup> As far as Japan is concerned, 266 MtCO2e credits purchased by Japanese entities so far account for around half of the expected shortfall for Japan (use of Kyoto Mechanisms by the Government and share of the burden borne by the private sector).<sup>78</sup> Together, these sources of demand could add up to at least one billion tCO2e in the next year or so. Even without factoring in any potential demand from Australia, Canada and the United States, there is still significant potential demand for CDM and JI from Japan and the EU before 2012.

The carbon market and associated emerging markets for clean technology and commodities have attracted a significant response from the capital markets and from experienced investors, including those in the United States. Analysts estimated that US$11.8 billion had been invested in 58 carbon funds as of March 2007 compared to US$4.6 billion in 40 funds as of May 2006, half of which is managed in the UK (Bulleid 2006; New Carbon Finance 2007).

# **Markets and the pricing of project credits**

Though nascent, formal market for pricing emission reduction units are quickly forming for Kyoto project-based offsets, CERs and ERUs. Exchange-traded futures and options contracts for CERs were launched in late 2007 on the Chicago Climate Futures Exchange and the Norwegian exchange Nord Pool and in March 2008 on the European Climate Exchange (ECX) and the European Energy Exchange (EEX). Similar contracts are in place for allowance under the EU ETS. When traded volumes are sufficiently large, exchange-based contracts offer the most transparent form of pricing. The contracts are of standard quality and the exchange stands behind delivery. For example, the exchange guarantees the delivery of any CERs purchased for future delivery. Behind the exchange

> 76Based on Fourth National Communications from EU Members States, the 2006 European Environment Agency report on GHG emissions trends and projections and updates from the NAPs, one may estimate a 450 MtCO2e demand for CDM and JI over 2008-12.

> 77Using a 1.25 billion tCO2e estimate for CDM and JI demand over 2008-12 by EU ETS installations, an average across assessments by Fortis, Merrill Lynch, New Carbon Finance, Point Carbon, Société Générale and UBS.

> 78This is based on estimates from the 4th National Communication in the “with existing measures” scenario.

48
2 international convergence. In addition, returns to education and work experience, i.e. the rates of human capital accumulation at work, may vary across sectors (Islam et al., 2019). Likewise, agglomeration economies may also vary by sectors (Venables, 2017; Burger et al., 2022). That may also be the case for congestion externalities. Finally, international trade affects endogenous innovation and growth (Melitz and Redding, 2021).

We first establish that, for a given population size and a given level of urban economic development, cities dramatically vary in their sectoral composition across the world. Using census data for about 65 countries, we obtain the sectoral composition of 7,000 agglomerations comprising three-fourths of the world’s urban population. We classify them as “production cities”, “consumption cities”, or “neutral cities”, depending on their employment share of manufacturing and tradable services. We also use our classification to highlight novel stylized facts related to the global distribution of urban employment and show that cities, not just countries, experience different patterns of structural change.

We extend the theoretical analysis of GJV16, who show that countries can urbanize because of _industrialization_ broadly defined (which includes tradable, or “industrialized”, services) or because they export _natural resources_ . An increase in resource export earnings raises incomes. The resource export earnings are used to import food and other tradable goods whereas the higher demand for urban non-tradables is met by an increase in labor in that sector. Since the rural sector contracts, the country urbanizes. However, urbanization generated by resource rents differs from industrialization-led urbanization in that cities will have different employment shares of tradables and non-tradables.

We consider two other paths leading to consumption cities. In GJV16, resources include fuels, mineral products, and a few high-rent cash crops, which require little labor. Yet, many countries export _agricultural products_ , including food crops. Their production generates rents. However, it also requires rural labor, leading to ambiguous urbanization effects. We show that, under a reasonable set of assumptions, the urban share should increase. The agricultural export earnings can indeed be used to import additional food and other tradable goods. Much like what can be observed for non-agricultural resources, cities in these countries will have high employment shares of non-tradables.<sup>2</sup>

> 2 Any significant source of foreign income could also promote urbanization through the rise of consumption cities. For our sample countries, during the period 1960-2020, remittances and foreign aid averaged just 1.7% and 1.8% of GDP per year, respectively, compared to 7.8% earned from natural resource and agricultural exports (source: World Bank (2021)).
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households from poverty “when it is directed outside the country and the migrants are men”.<sup>1</sup> Taken together, the conclusion of many rural households is that migration, and female migration in particular, is a “necessary evil”, separating women from their families and offering not much more than survival in return.

From the migrants’ perspective, a few general patterns emerge. Education is a primary motivation for migrating: younger women migrate after having to drop out because their families can no longer afford secondary school, and older women migrate to support their children’s education. Although they tend to move with short‐term goals in mind, most stay longer than they wish because their earnings do not allow them to accumulate savings or other assets, and their families grow dependent on their remittances to meet basic needs. A minority of migrants remains in urban areas out of choice (because they have grown used to the higher living standards and independence). Overall they find their working and living arrangements to conform to their expectations: they expect it to be hard work and it is.

**In Part II, in light of the overwhelming need for female migration to support rural households, we identify potential entry points for policy to better support the needs of migrant workers, particularly those in two primary employment sectors for women ‐ domestic and textile, and those of their families.** Specifically, we review the challenges faced by workers in five key areas and the opportunities for policy to better support them to obtain quality employment, find low‐cost and safe urban housing, protect their health, access financial services, and care for their children.

- _Quality employment:_ Our findings show that, as formal sector workers, textile workers have enjoyed and valued basic labor protections, even though the work still comes with many challenges. Policies to fill the gap left by the conclusion of the Better Work initiative<sup>2</sup> by continuing to improve the working conditions in textile factories as well as supporting migrants working in the informal sector would boost the welfare of workers and their families back home. Despite the recent 10‐year extension of AGOA, significant uncertainty regarding Lesotho’s continued eligibility in light of political and security challenges remains, as do fears of massive layoffs in the textile industry. Improving the quality of informal employment, where the majority of workers are concentrated, therefore remains critical. Organizations of informal workers, such as those that exist in South Africa, offer a way to extend some

> 1 World Bank  (2010).

> 2 The second phase of Better Work (2013‐2016) was still ongoing at the time of the data collection in March‐April 2015, but has been concluded by the time the report has been published.

5
bribe request or ignore an unsatisfactory payment. This measure is objective, which is preferred to perception based measures that have been used in many previous studies.

During survey collection, respondents have the option to refuse to answer or to say they don’t know the answer. When a firm’s response to the question if they were bribed is a refusal, the refusal is treated as an affirmative response to bribery. The rationale behind this is that if a firm refuses to answer a question about bribery, then they were likely bribed. We treat the cases where the participant doesn’t know the answer as missing.

Transparency International constructs Corruption Perception Index (CPI) which ranks more than 150 countries by perceived levels of corruption determined by expert assessments and opinion surveys. A lower CPI scores implies a more corrupt country. Data from the index and ES is available for 22 LAC countries. A comparison of the Bribery indicator with the CPI scored in 2009 shows that firm responses mirror the country scores from the CPI (Figure 1). There is a strong negative correlation between the CPI and the percent of firms bribed across countries, the R-squared of the best-fit line is 0.29.

The incidence of bribery in the regression sample is marginally higher than compared to the full sample (Table 4). This is reassuring that we are not altering the incidence of bribery in a significant way by restricting the sample; the loss of observations from non-response is also not altering the average levels of bribery.

Comparison of the incidence of bribery by country for the regression sample is presented in Table 5. There is significant variation across countries. The highest proportion of firms being bribed is found in Paraguay and the Bahamas; 31 and 27 percent of firms were bribed when conducting of the selected business transactions. In Paraguay, the incidence of bribery is almost three times higher than the average in the entire LAC region (10.3 percent). Among large countries, Chile and Colombia have very low proportions of firms who are bribed (2.8 and 4.7 percent). There are also a number of other countries with fewer than 5 percent of firms being bribed: Barbados, Dominica, Grenada, Panama, St Kitts and Nevis, St Vincent and the Grenadines, and Uruguay.

There are a total of 101 clusters in the region. No country is dropped out due to an insufficient number of firms in clusters. Within countries, there is large variation in the percent 12
Looking back at the estimates for the period 1981-2011, the trajectory for global poverty is largely similar for the $1.82 line at 2011 PPPs and $1.25 at 2005 PPPs. As with the estimates for 2011, the regional estimates for 1990 show a higher concentration of poverty in East Asia and the Pacific for the $1.82-line than the $1.25 line (Table 3). It is particularly noteworthy that the key indicator for the first Millennium Development Goal, halving the global poverty rate from its 1990 level, is estimated to have been reached between 2008 and 2010 under both the $1.25 and $1.82 lines and respective PPPs.

Figure 9 summarizes estimated poverty rates in 2011 and 2030 for a range of possible poverty lines in 2011 PPPs (ranging from 1 to 3 dollars per day), in effect tracing the global cumulative distribution functions for this segment of the global income distribution. The $1.82 and $1.92 lines are in the range where the global poverty rate in 2011 changes fairly significantly with small changes in the poverty line, as can be seen by the relative steepness of the 2011 line for global poverty estimates over all lines. Moving the poverty line by 10 cents (from $1.82 to $1.92), increases the poverty rate from 14.9 percent to 16.7 percent - or approximately 120 million people. In contrast, the change in 2030 poverty estimates from increasing the poverty line from $1.82 to $1.92 is relatively small, as illustrated by the relatively flat gradient of poverty estimates for both growth scenarios in this range. Estimates for any poverty line above approximately $1.60 will fall short of the World Bank’s 3 percent goal under both the 10 and 20 year growth scenarios.

# **V. Discussion**

An immediate response to some of the initial analysis of the 2011 PPP was that the change in the global poverty profile was in some way a statistical trick. The conversation was about how hundreds of millions of people became nonpoor overnight. This discussion led some to the view 27
higher. In other words, standards can plausibly be linked both to effects on export volume and the propensity to export (Chen et al., 2006; Baller, 2007; Chen and Mattoo, 2006).

Next, we consider introduction of an internationally harmonized standard in one potential export market.<sup>13</sup> This means that the same standard applies in that market as in a composite “rest of the world” region. By comparison with a standard-less benchmark, this scenario will still tend to reduce trade through the two mechanisms discussed in the previous paragraph. That effect will generally be weaker than if each harmonizing country implemented its own distinct standard: instead of paying one fixed and variable market access cost for the whole region, an exporter would have to pay multiple costs.

Consolidating the foregoing, we expect that standards will generally exhibit a negative impact on trade, but that such effects will be mitigated when these standards are aligned with de facto international standards. Given the data we have available, our working hypothesis is therefore the following: EU standards that are harmonized with international standards (proxied here by ISO standards) exert a less negative impact on African export volumes and propensity than those standards which are not.<sup>14</sup>

# **_4.1 Empirical Model_**

To examine this hypothesis, we use a standard gravity model of international trade applied to data on EU-15 imports of textiles and clothing from Sub-Saharan Africa (see Table 11 for

> 13 We assume that the fixed and variable costs of compliance do not vary too much across standards. We are therefore in the “horizontal” standards paradigm, in which differing norms reflect culturally influenced preferences and traditions rather than objective restrictiveness (Baldwin, 2000).

> 14 Since the policy question that motivates our research relates to the differential effect of harmonized versus nonharmonized standards, it is not strictly necessary for us to make any particular hypothesis as to the sign of the individual coefficients. It should still be possible to test our hypothesis even in the presence of the type of positive information effects found by Moenius (2004).

- 14 -
The analysis is based on a multi-country, multi-sector computable general equilibrium (CGE) model of global trade, called the GLOBE model.<sup>3</sup> As with the recent studies cited above, this type of modeling framework is extensively used for the analysis of the economy-wide implications of changes in trade policy.

Section 2 of the the scenarios. Section 3 describes the paper defines briefly methodology. Section 4 examines the results and Section 5 concludes.

# **2 Scenarios**

Since the beginning of 2018, the United States has introduced new tariffs on imports from several trading partners citing either national security concerns, unfair trade practices, or serious injury to domestic industries. Initially, protectionist measures targeted specific products such as washing machines, solar panels, steel, and aluminum and were imposed on imports from most trading partners. Additional tariffs were imposed on imports of certain products from China, which, following a tit-for-tat escalation of the dispute, now cover close to all bilateral trade between the two countries. If threatened tariffs on automobiles and parts materialize, they will cause serious disruptions to tightly integrated regional and global value chains (GVCs) and, together with previous measures, impact close to one-third of all U.S. imports.

Evidence suggests that newly introduced U.S. tariffs are comparable to “Column 2”, nonMFN tariffs (Lakatos 2018) of the U.S. harmonized tariff schedule (HTS). The HTS Column 2 tariffs average about 32 percent, and for the most part are the original Smoot-Hawley tariffs formalized under the Tariff Act of 1930. During subsequent rounds of negotiations and trade liberalization under the umbrella of the General Agreement on Tariffs and Trade (GATT) and the WTO, the United States significantly amended the column 2 rates over time. Figure 1 highlights how average U.S. tariffs have fallen since the 1930s and how recent increases compare using a long-term perspective.

Currently, Column 2 tariff rates apply to U.S. imports from Cuba and the Democratic Republic of Korea, countries that do not benefit from “normal trade relations (NTR).” At different points in time, the United States also revoked the NTR status of other countries, and their exports to the United States were subject to Column 2 tariffs. For instance, under the Trade Agreements Extension Act of 1951, the United States suspended the use of MFN tariffs on

> 3See McDonald et al. (2007) and McDonald and Thierfelder (2016) for a description of the model. The model uses data from the GTAP database. See Aguiar et al. (2016) for a description of the GTAP data.

4