rafmacalaba/data-use-mentions-extended huggingface dataset:
own children or children from one’s reference group whose conditions may be similar, probably receive greater weights in one’s evaluation of children’s future chances in general, how exactly respondents frame their answers to this survey question is not clear. Still, the results can be cautiously viewed as reinforcing the main results of the analysis, as well as the predictions of theoretical models discussed in section 2, that link inequality in access to key opportunities in life to lower expectations of future mobility; moreover, these lower expectations may be relevant not only in the context of the immediate future, but rather may persist over time, and across generations, thus perpetuating existing inequality traps.

# _7.2. Inequality in access to opportunities and redistributive preferences_

If access to key opportunities in life is perceived to be unfair, on account to whom you know, then resulting inequalities in society are likely to be similarly perceived to be unfair. This is especially likely to be the case for those who do not have connections that may facilitate access to opportunities such as jobs or university education. When inequalities are perceived to be unfair, individuals may be more likely to desire a greater amount of redistribution in society to compensate for such unfair distribution of rewards (see Cojocaru, 2014 for empirical evidence of the link between fairness and inequality aversion based on LiTS data). It is possible to explore with LiTS data whether the “Rawlsian” inequality of opportunity examined in this analysis is associated with stronger redistributive preferences. The latter are elicited in the survey questionnaire by way of the following statement “The gap between the rich and the poor in our country should be reduced”, with which the respondents can either agree or disagree on a 5-step Likert scale that ranges from “strongly disagree” to “strongly agree”. Using responses to this question I construct a binary variable that evaluates to 1 for above neutral responses (either agree or strongly agree) and zero otherwise. The model is re-estimated with the preference for reducing the gap between
include proxies for international liquidity and for contagion in financial markets. Indeed, given that there are investors in emerging market bonds that are common across countries, it is natural to expect that a crisis in one country should be associated with higher spreads in other markets, if they both are the result of a changed attitude to risk or liquidity.

A third improvement relative to the current literature is our use of more recent data (until June, 2004)—and longer time series; this may help to distinguish between hypotheses. In particular, we use monthly data for individual country emerging market Bond Index Plus (EMBI<sup>+</sup> ) spreads, available from JP Morgan which is a major dealer in emerging bond markets, and extending back for some countries to 1991. The bonds are issued in US dollars, so that spreads reflect credit risk—the probability that the borrower will not repay. The set of countries includes all the major sovereign borrowers, and the data are based on trading in secondary markets of Brady bonds and Eurodollar issues. Our sample includes the following 17 countries: Argentina, Brazil, Bulgaria, Colombia, Ecuador, Mexico, Morocco, Nigeria, Panama, Peru, the Philippines, Poland, Russia, South Africa, Turkey, Ukraine, and Venezuela. We estimate an unbalanced panel, with data availability varying from country to country. While the data on spreads are based on secondary market data, they provide many more data points and allow a finer appreciation of the effects of interest rate increases than primary market data. Moreover with transaction volumes in secondary markets surpassing those in primary markets by several fold, spreads based on secondary market prices are more informative and less contaminated by supply effects than the spreads for new issues.

4
Figure 2.3 Employment polarization is increasing in China <!-- Start of picture text --> 0 20 40 60 80 100 0 20 40 60 80 100<br>Occupation's Percentile in 2005 Wage Distribution Occupation's Percentile in 2000 Education Distribution<br>2000 - 2005 2005 - 2010 2000 to 2015 2000 to 2005<br>2010 - 2015 2005 to 2010 2010 to 2015<br>.2 .15<br>.15 .1<br>.1<br>.05<br>.05<br>0<br>0<br>-.05<br>-.05<br>-.1 -.1<br>100 x Change in Employment Share 100 x Change in Employment Share<br>-.15 -.15<br><!-- End of picture text --> _Source_ : Du and Park 2017.

_Note_ : The figure shows the employment share for five-year periods between 2000 and 2015 by two measures of initial skills. The left panel shows it using initial levels of wages as proxies for the skill distribution, and the right panel using initial levels of education.

The new or transformed jobs require a fundamentally different set of skills. As most of these jobs involve nonroutine tasks, they may not be readily filled by the displaced workers. Regardless of whether labor markets are showing signs of polarization in developing countries, the tasks and skills that individuals use at work are changing. Work is becoming increasingly intensive in nonroutine cognitive and interpersonal skills. Tasks that are difficult to automate—intensive in the latter skills—are becoming a larger share of tasks performed by the workforce in the developed and developing world (Figure 2.4). The steady increase in demand for nonroutine cognitive and interpersonal tasks suggests that these skills are highly valued in the face of technological changes, globalization, and structural changes.

Figure 2.4 Nonroutine cognitive and interpersonal skills are highly valued everywhere <
mix. Hence product creation and destruction can be tracked at firm level. This unique feature of the data allows me to evaluate the relationship between firms’ adjustment of their product lines and policy changes.

There are several innovation strategies that firms can follow. They can introduce process innovation by significantly improving their production processes or they can introduce product innovation by introducing a new product variety that they did not produce before. Studies like Bertschek (1995), Parisi et al. (2006), and Fritsch and Meschede (2001) highlight the differences between these two innovation strategies. The model introduced in this study and the data from Prowess are appropriate for analyzing product innovation strategy which will be measured by the change in product mix of firms.<sup>5</sup> The database contains information on income statements and balance sheets of relatively large Indian firms from 1989 to 1995. This dataset is well suited for the particular purpose of the study as large firms contribute more to aggregate product creation compared to small firms. As discussed in Goldberg et al. (2010b) the dataset is not well suited to analyze entry and exit dynamics because firms are not obliged to report to the data collecting agency. Prowess database accounts for 60% to 70% of the economic activity in the organized industrial sector. The panel is unbalanced and the number of firms included in the data increases from 752 in 1989 to 2293 in 1995.

In the data product level information is available for around 85% of the firms in manufacturing sector. Products are defined according to CMIE’s internal product classification. Goldberg et al. (2010a) present a detailed description of the data, product classification, and product mix change. They identify 1,886 products linked to 108 four digit 1998 version of National Industrial Classification (NIC) industries in manufacturing sector and they find that products that can be mapped to four or five digit NIC codes account for 99 percent of total output. They also show that the number of products classified in India
(7.9% annually). Minerals exports, which propelled export growth as well as wage growth, are not job intensive and as a result supported far less job growth. Our data also show that exports in South Africa have become less labor intensive, where the quantity of jobs and the labor value added per unit of exports has diminished overall, and more capital intensive. The composition of exports also matters for the impact of trade on jobs and wages in South Africa. While minerals have increasingly become an enclave sector with few backward linkages to the domestic economy, the labor impact of manufacturing exports in South Africa is increasingly felt in input-providing sectors, particularly services sectors, which had distributional implications by skill. Studies that only consider the jobs and wages to directly produce exports will underestimate the impact of trade on labor.

Our analysis is enabled by a newly developed World Bank database—The Labor Content of Exports (LACEX) (Calì et al. 2016)—that measures the contribution of labor to a given country’s domestic production and exports, measured as employees’ compensation (wages) or the number of jobs. Using inputoutput tables, exports and production data from the Global Trade Analysis Project (GTAP) and employment data from the International Labour Organization (ILO), the LACEX database represents a methodological innovation for developing countries. We consider aggregate exports as well as sectoral exports for South Africa and compare them to other emerging and middle-income countries and the other BRIC (Brazil, the Russian Federation, India and China) in particular. We also consider how exports contribute to labor market outcomes separately through their direct and indirect linkages (including both forward and backward linkages), and distinguish between skilled and unskilled labor. By considering the fuller labor implications of exports for the economy, our data allow us to evaluate the extent to which traditional versus nontraditional exports indeed support incomes and jobs.

The paper has three key findings. First, the sectoral composition as well as the labor intensity of exports in South Africa
# **<u>Table 3.2: DALYs for Different Health Endpoints</u>**

|**Health Effect**|**DALYs lost per**<br>**10,000 cases**|
|---|---|
|Mortalityadults|75,000|
|Mortalitychildren under 5|340,000|
|Chronic Bronchitis(adults)|22,000|
|Respiratoryhospital admissions|160|
|EmergencyRoom visits|45|
|Restricted activitydays(adults)|3|
|Lower respiratoryillness in children|65|
|Respiratorysymptoms(adults)|0.75|

Note: DALYs are calculated using a discount rate of 3% and full age weighting based on WHO tables.

Urban air particulate pollution is estimated to cause around 109,000 premature deaths among adults and 7,500 deaths among children under 5 annually. Adult mortality estimated above is consistent with Cropper et al’s (2012) estimate of the annual mortality associated with coal electricity generation in India (about 60,000 people calculated as about 650 deaths per year with 92 coal burning power plants in India). Electricity generation is responsible for a fraction of PM pollution analyzed in this study<sup>8</sup> . Estimated new cases of chronic bronchitis are about 48,000 per year. Annual hospitalizations due to pollution are estimated at close to 370 thousand and emergency room visits/outpatient hospitalizations at 7,300 thousand per year. Cases of less severe health impacts are also presented in the Table. In terms of annual DALYs lost mortality accounts for an estimated 60 percent, chronic bronchitis around 5 percent, restricted activity days (RADs) for 7 percent, and respiratory symptoms for 25 percent.

.

# **<u>Table 3.3: Estimated Health Impact of Urban Air Pollution</u>**

|**Health end-points**|**Total**<br>**Cases**|*
Table 4. Smoking-Attributable Direct Medical Expenditures, 2016

|_Country_|_National_<br>_NCU(Million)_|_aggregate_<br>_PPP(Million)_|_Per adult (_<br>_NCU_|_+15 yr)_<br>_PPP_|_Per smoker (_<br>_NCU_|_+15 yr)_<br>_PPP_|
|---|---|---|---|---|---|---|
|Bangladesh|28,105|881|243|<br>8|1,055|33|
|Bosnia and Herzegovina|188|236|62|<br>78|160|201|
|Chile|271,209|596|19,061|<br>42|50,427|111|
|Indonesia|18,794,853|3,769|99,491|<br>20|252,515|51|
|Moldova|896|131|299|<br>44|1,237|181|
|Russian Federation|403,028|16,202|3,376|<br>136|8,590|345|
|South Africa|20,207|3,218|509|<br>81|2,509|400|
|Ukraine|16,296|2,757|427|<br>72|1,478|250|

_Sources:_ Adapted from Goodchild, Nargis, and Tursan d’Espaignet 2018 and Fuchs and Matytsin 2018. Population, smoking rates, consumer price index and PPP conversion factors are taken from WDI (World Development Indicators) (database), World Bank, Washington, DC, http://data.worldbank.org/products/wdi. _Note:_ PPP = purchasing power parity exchange rate. NCU = national currency units. The consumer price index is used to account for
In (7), denotes the joint distribution of _y_ and _X_ , _g_ denotes the conditional distribution of _y_ on _X_ , and denotes the joint density of the covariates in the vector _X_ .<sup>12</sup> If the joint density of the observable covariates _X_ in a particular survey for country _j_ is written , then our first proposed estimate for a test-score distribution (density) corrected for sample selection on observables is given by: Where (9) Equation (9) is simply the ratio of the density of fifteen year-olds whose observed characteristics _X_ take certain values, in the ancillary household survey (HH), to the density of fifteen year-olds with the exact same observed characteristics in the PISA survey. is a re-weighting function exactly analogous to that used by DiNardo, Fortin and Lemieux (1996) to construct counterfactual income densities in their study of inequality in the US. Whereas DiNardo et al. use the ratio of densities across different years (of the same survey), we use the ratio of densities across different surveys (for the same year). To the extent that test-taking (i.e. being in the PISA sample) is correlated with observed covariates in X, the counterfactual distribution in (8) should correct for the corresponding selection bias.<sup>13</sup> In practice, this procedure was implemented by partitioning both the PISA and the ancillary household survey into cells with identical values for three observed covariates: gender, mother’s education, and father’s occupation, with the latter two variables classified as in Section 2.<sup>14</sup> The ratios of densities in each cell in these partitions were used to construct the reweighting function (Equation 9), and both the S.D. and the IOp measures were computed over the counterfactual density of scores given by (8).

This procedure assumes that selection into the PISA sample is fully explained by observable variables, such as gender and family background. While such
identify the causal effect on exports in response to previous policy changes. Both methods have their own advantages and disadvantages and, hence, provide complementary evidence. Third, we exploit the relatively recent market access liberalization for African exports to the EU, through the expansion of the various GSP schemes, and to the United States, following the implementation of AGOA. Our estimates will quantify the magnitude of the export elasticity with respect to full or partial trade liberalization and the extent to which these elasticities vary across product categories. Fourth, we summarize the remaining market barriers for textile and apparel exports that African countries face on EU and US markets. Together with the estimated elasticities this will provide a sense of how much potential for output growth through further market access liberalization remains.

The remainder of the paper is organized as follows. In Section 2 we provide some supporting evidence on the initial industrialization potential of textiles and apparel exports and the relative performance of sub-Saharan Africa. In Section 3, we provide an overview of the two market access liberalization episodes that we exploit to estimate the potential export response. In Section 4 we describe our empirical framework and the logic behind the identification, followed by a description of the data in Section 5. In Section 6 we discuss the estimation results and in the concluding Section 7 we address a few caveats, and we draw implications for the remaining growth potential through the removal of remaining trade barriers.

# **2. Focus on textile and apparel industry**

One reason it is interesting to focus on the benefits of improved market access in textile and apparel is that the many aspects of the value chain of this industry has already been investigated through case studies. Starting with Gereffi (1999), many authors in the GVC literature have mapped the role of suppliers in developing countries in the global textile industry and the barriers they face to integration in the global production network. That work draws mostly on case studies and is supplemented by firm surveys in several countries.

The web site www.globalvaluechains.org
3,003 displaced Syrian families in Lebanon, Alrababa’h et al. (2020) weigh the influence of “pull” factors, which draw individuals back to their former homes, and “push” factors which encourage them to leave their host communities. In doing so they find that conditions in a refugee’s home country and access to information about those conditions are a primary driver in decision making (Alrababa’h et al. (2020)). Indeed, economic factors may be even more vital to return choices than sentiment towards or connection with their home country (Camarena and Hägerdal (2020)). Refugees with strong ties to their home country may still choose to return as regular visitors rather than permanently if their new residence offers more attractive economic opportunities. Additionally, refugees are less likely to return to areas with ethic mixing when ethic tensions were a factor in initial violence and displacement (Camarena and Hägerdal (2020)).

Refugees have complex relationships with their host communities as well as with their home locations. Displacement can be part of a strategy of war, with militants manipulating refugee and IDP movement to gain access to resources or ethnically cleanse certain areas (Lischer (2008)). As part of these strategies refugees may be militarized, spreading conflict to their new locations (Lischer (2008)). More positively, in areas with low public service provision the introduction of refugees to a community may bring goods and services which benefit all the residents (Zhou et al. (2021)). Such community benefits mitigate negative sentiment towards refugee settlements.

Identifying effective ways to measure return practices is not straightforward as there are no systematic data sources on refugee return, and many current reports rely on surveys to test the underlying assumptions governing return decisions (Ghosn et al. (2021)). Given the importance of information for decisions around return (Alrababa’h et al. (2020)) and gauging living conditions in the home country, leveraging social media sources is of critical
# 1 Introduction

Trade in agricultural products is perhaps one of the most promising instruments for poverty alleviation in developing countries. This is because agriculture is a key productive activity in poor countries and because world markets can provide new opportunities for rural producers. However, international markets for agricultural products have long been distorted by the policies of developed countries. Policies of domestic support, such as subsidies to production or exports, and policies of market access, such as tariffs and non-tariff barriers, are examples. Understanding and measuring the impacts of WTO reforms on poor households in developing countries is therefore critical to the current trade and poverty debate.

When price reforms are implemented, particularly in agriculture, households are affected both as consumers and as income earners. The standard proposition of the literature is that, after an increase in the price of a good, net consumers will be worse off but net producers will be better off (Deaton, 1989). In this static context, the welfare effects of a price change can be assessed by comparing budget shares and income shares. This argument, however, fails to consider dynamic household responses. Consumers may respond by substituting away from the more expensive goods. In rural areas, farmers may increase agricultural production, farm employment and wages, and purchases of inputs and services in local markets. Importantly, thus, the net position of the household becomes endogenous: sufficiently large consumption and income responses may cause an ex-ante net consumer to become an ex-post net producer, thus benefiting from the price increase.

The measurement of the total household welfare effect, one that jointly considers first order effects in consumption and production as well as consumption and income responses, is the objective of this paper. I propose a joint estimator of demand price-elasticities and agricultural wage price-elasticities from survey data. The empirical method relies on a two-step strategy, first estimating the responses of household behavior to prices and, second, linking prices to the reforms. The method requires the estimation of structural models: household responses are linked to prices through structural
4 In the early stages of structural transformation there typically exists a substantial gap between the share of the labor force employed in agriculture and the share of GDP generated by that workforce. In SSA, agriculture’s share of GDP has declined to 17 percent in 2013, according to World Bank data. Meanwhile, agriculture still occupied more than half (52%) of the region’s economically active workforce in 2013, according to FAO data.<sup>5</sup> This suggests substantial differences in annual average labor productivity among sectors within SSA. Further analysis (see below) indicates that this is mainly because of larger underemployment within agriculture and rural areas, rather than intrinsically low average or marginal productivity of agriculture. It also signals the existence of barriers to more efficient allocation of factors of production.

Once agricultural productivity growth yields agricultural surpluses and rural incomes rise, intersectoral linkages typically enhance agriculture’s contribution to economic growth through factor markets. Rising agricultural labor productivity and the relative decline in food spending as incomes rise (i.e. relative to nonfood spending, even though food expenditures increase in absolute terms)<sup>6</sup> holds down growth in food prices and agricultural incomes, while the demand for non-farm goods and thus off-farm labor increases. Given intersectoral competition for factors of production, this releases labor and capital from the rural farm sector. With average output per person (not necessarily per hour worked) typically lower in the rural farm sector than in the rural non-farm and urban sectors, the intersectoral reallocation of labor further adds to growth and poverty reduction, largely because labor approaches full employment. Identifying and addressing factor market imperfections to facilitate such movement is therefore equally central to the task of stimulating agricultural and structural transformation for the low-income rural economies of SSA. If financial and labor markets worked perfectly, there would be few productivity gains from intersectoral factor migration.

After robust agricultural growth has been established and sustained for some period and the nonagricultural sector has become significant, economies move