rafmacalaba/data-use-mentions-extended huggingface dataset:
Refinitiv’s Security Data Corporation (SDC) Platinum, which provides transactions of new issuances of common and preferred equity, as well as publicly and privately placed bonds.<sup>3</sup> Because the analysis focuses on corporate financing, we exclude all public sector issuances, comprising issuances by national, local, and regional governments, government agencies, regional agencies, and multilateral organizations. We also exclude mortgage-backed securities and other asset-backed securities.

We classify bond issuances as domestic or international using the residence-based approach followed by the Bank of International Settlements (BIS). We compare the location of the issuance with the residence of the issuing firm (Gruić and Wooldridge, 2012). Domestic securities are those issued by residents in their local markets. International issuances are those issued by residents abroad.<sup>4</sup> Using this methodology, the data set includes 71,489 bond issuances in domestic markets and 9,703 bond issuances in international markets (Table 1, Panel A). Besides the residence-based approach, the literature uses two alternative criteria to classify international and domestic corporate bond issuances: the nationality-based and currency-based approaches (Gruić and Wooldridge, 2012; Avdjiev et al., 2014; Shin, 2014; McCauley et al., 2015). The nationality-based approach considers the nationality of a firm instead of its residence. Therefore, issuances by a subsidiary of a foreign-owned firm in the domestic market are considered as international, as the parent company resides outside the domestic market. Under the currency-based approach, debt issuances denominated in foreign currency are considered as international, and those in local currency as domestic. Because our paper focuses on the role of domestic markets for firms located in East Asia, we use the classification of issuances by the residence approach as our main results. But for robustness we also use the nationality-based and

> 3 Refinitiv’s SDC Platinum is one of the most widely used databases on transaction-level research (Henderson et al., 2006; Kim and Weisbach, 2008;
directly estimating the elasticity of quitting with respect to unemployment benefits, usually an impossible task given the lack of temporal variations in these benefits.

Our empirical estimates of the determinants of labor flows from the salaried to the selfemployed sector strongly support the specification suggested by the quitting theory: the individual probability ofjob separation is decreasing in the formal-sector wage (the expected payoffto staying) and increasing in benefits to self-employment and the probability of finding a formal-sectorjob (the expected payoff to leaving). Moreover, the above mentioned symmetry property of the quit-rate function cannot be rejected. When the microeconomic estimates are used to calibrate the macroeconomic model, we find the long-run effects of macroeconomic shocks on wages, labor turnover, and (formal-sector) employmen **t** o be substantial. The strong employment response found here stands in stark contrast to the disappointingly small unemployment effects reported by Danthine and Donaldson (1990,1995) and Kimball (1994) who calibrate an efficiency wage model with shirking (Shapiro and Stiglitz 1984) to US unemployment data.<sup>6</sup> This paper can be interpreted as providing a two-stage "test" of the real world relevance of efficiency wage models with labor turnover. In the first stage, microeconomic data are used to estimate and test what we believe to lie at the heart of this type of efficiency wage model, namely the quit-rate function. If the coefficients are found to be significant and of the correct sign, in a second stage the estimated quit-rate function is incorporated into the macroeconomic model and the calibrated model economy is used to assess the quantitative importance of efficiency wages. This second-stage check is important since there seems to be little value in having a macroeconomic theory of unemployment (underemployment) which is supported by microeconomic data but implies an almost constant unemployment (underemployment) rate. In this paper we present one fully worked out example of this two-stage procedure in the hope that it will spur interest in further 6Danthine and Donaldson (1990, 1995) explicitly consider aggregate uncertainty by solving a
), Socio-Emotional Skills was acceptable (0.771) and Classroom Culture was questionable/fair (0.651). These figures are similar, and slightly better, than reliabilities from a review of international Teach ECE data (Ding et al., 2023, 2024).

_Table 6. Cronbach’s Alpha of AIM-ECD Domain and Teach ECE Areas_

|**Tool**|**Domain/Area**|**Cronbach’s Alpha**|
|---|---|---|
|**AIM-ECD DA**|Early Literacy|0.938|
||Early Numeracy|0.964|
||Executive Functioning|0.943|
||Social Emotional|0.951|

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In the simple theoretical framework presented in Section 3, we emphasize the imported input channel as the main mechanism through which trade liberalization affects firms’ decision to upgrade foreign technology. For the sake of simplicity we did not take into account the export side of the story and the effects of trade liberalization through variations in trade variable costs affecting final goods that are already well-documented in the theoretical literature (Yeaple, 2005; Bustos, 2011).

Expansion of export opportunities due to foreign demand shocks might also increase the incentives for firms’ to upgrade foreign technology embodied in imported capital goods. Moreover, importing intermediate inputs might lead to higher exports as documented in the empirical literature (Feng et al., 2016; Bas, 2012; Bas and Strauss-Kahn, 2015). Higher export profits would allow to overcome the fixed cost of importing capital goods. If input tariff changes are positively correlated with export performance or with variations in output tariffs set by India’s trading partners, our previous empirical findings might be just picking up the effects of foreign demand shocks.

The industry-year fixed effects included in the estimations of the previous section already address this issue since they capture all unobservable shocks at the industry level varying over time. In this section, we provide additional evidence that foreign demand shocks at the sectoral level captured by export tariffs are not picking up our results. We control for this alternative explanation by including in the previous specifications the average effectively applied tariff at the 3-digit NIC industry level set by the rest of the world to India (export tariff) during the 1989-1997 period from WITS dataset (World Bank). Columns (1) to (3) of Table A5 in the appendix report the results. The effect of export tariff is negative but not significant. The coefficient of interest on the input tariffs remains robust and stable in all specifications when we take into account the role of foreign demand. This finding suggests that the supply side mechanism emphasized in this paper is also an important channel through which trade liberalization affects technology upgrading
). Data from six countries where reliable information could be gathered -often aggregated up from regional figures- highlight that the size of lands transferred recently is significantly above what was observed in the past. Total transfers in 2004-09, in millions of ha (table 2) amounted to 4.0 ha in Sudan, 2.7 in Mozambique, 1.2 in Ethiopia and 1.6 -mainly renegotiation of existing agreements- in Liberia.<sup>2</sup> The volume and nature of land transfers depends on policies; for example in Tanzania, where land rights are vested with villages, less than 50,000 ha were transferred to investors in the same time period. Virtually everywhere, local, rather than foreign investors, dominated. In most cases, expected job creation and net investment were either not recorded or very low. Often land was not fully used, as in Mozambique where a 2009 land audit found large shares of transferred land being either unused (34% of the total) or used in ways that did not comply with agreed investment plans (15%). Even if, as is the case

> 2 Comparison with figures on possibly available land in table 5 suggest that the land transferred accounts for 8.6%, 16.6%, and 25.4% of the total suitable non-forested non-protected area with a density of less than 25 inhabitants per km<sup>2</sup> for Sudan, Mozambique, and Ethiopia, respectively. 9
outcomes—even within the same school (Bruns and Luque, 2014). Evidence from Guatemala and Peru presented in Marshal and Sorto (2012) shows the importance of teachers’ content mastery: teachers who perform better on math tests have students who also perform better in that same subject. Using data for Mexico, De Hoyos, Espino, and García (2012) show that the proportions of teachers with a post-graduate degree and school directors assigned via a meritocratic process are both positively associated with higher test scores in math.

There is evidence from Argentina that also points to the importance of teachers within a context of school autonomy. Santos (2007) finds that giving teachers a higher degree of autonomy on decisions of budget allocation within schools, selection of textbooks, establishment of disciplinary policies, and involvement in student assessment policies is associated with better student test scores. According to Santos (2007), students performed better in schools where teachers had high expectations and where principals felt that there was a strong relationship between students and teachers. In contrast, Abdul-Hamid (2007) finds that teacher certification did not show a significant positive impact on learning outcomes. Finally, Fresoli et al. (2007) find that, among teachers’ years of education, their years of experience, and their amount of in-service training, only the number of years of education was significantly correlated with student learning (using data from LLECE). ONE data, meanwhile, shows a correlation between years of education as well as years of service (though only statistically significant in mathematics, not Spanish).

# _2.2 Institutional Setting_

There are a number of elements related to institutional settings that could influence student performance: pupil-teacher ratios, length of the school day, degree of school autonomy, and involvement of parents, to name a few. These institutional factors represent about one-quarter of the between-country variations in performance on PISA 2000, and account for a large portion of students’ success (Fuchs and Wößmann, 2004, and Fertig and Schmidt, 2002)
Such a channel would be consistent with psychology literature on behavioral activation, or the act of scheduling structured activities as a means of combatting depression (Cuijpers, van Straten, and Warmerdam (2007)). To explore this question, we supply a random subset of the employed with a calendar marking every date of work (Appendix Figure A5). The remainder receive a blank calendar and are instead informed weekly about their schedule. We find no impact of a schedule on respondent well-being or decision-making (Appendix Table A7). Despite this exercise, we cannot causally estimate the role of the structure alone on well-being, as the structure imposed by regular employment is coextensive with employment itself. Indeed, our measure of stability, which asks respondents how secure they feel at the moment and expect to feel in the future, increases substantially among the employed relative to both control and cash arms.

**Time use** Does employment improve well-being by allowing participants to substitute time away from unsavory or psychosocially costly activities? Appendix Table A8 presents how cash and work arms use their time. We document no significant difference between the two arms in the number of hours that respondents report spending across a variety of activities. Notably, while we cannot rule out that time outside due to employment may play a role (e.g., fresh air may boost one’s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable difference between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls.<sup>1718</sup> We also investigate whether those who were more idle prior to being employed benefit more from employment. We find no impact along this margin, suggesting that the elimination of boredom _per se_ is not the driving force behind the psychosocial value of
had an upper and a lower bound. For example, if 200 buildings are observed and two segments are needed, the method will use 99 as a lower bound and 101 as an upper bound as a first try, then proceed until a solution to the problem is obtained and the two segments are drawn. On average, the segments in that area will have the same number of buildings. For more details on the process, see <u>https://pro.arcgis.com/en/pro-app/tool-reference/geostatistical-analyst/generate-subset-polygons.htm.</u>

> 33 For example, Light House Para in the shapefile was located on the beach with no households, whereas in actuality, the area is in a moderately populated area much farther inside the city.

15
participate in these markets. This pattern could reflect exporters’ inability to pay the additional compliance costs and trade diversion to less costly product-destination markets. Our findings are consistent with firm-level evidence from France (Fontagné et al., 2015; Fontagné and Orefice, 2018).

Third, we extend the analysis of heterogeneity in the effects of NTM types across firm characteristics. Our results align with the predominant finding of the empirical literature, that small firms suffer disproportionate impacts of TBTs (e.g., Arkolakis, 2010; Curzi, 2020; Asprilla et al., 2019; Macedoni and Weinberger, 2022). Moreover, we show that GVC firms, defined as those that both import and export, benefit from the same muted trade effects of TBTs as big firms, even though the two firm characteristics differ substantially in definition. However, we also marshal novel evidence that quantity control measures make it less likely that small firms will exit export markets, with the opposite effect occurring for big firms. We argue that the quantity controls are more binding for big firms, increasing their likelihood of leaving markets and, conversely, helping small firms take over their space.

Superior data and methods underpin these three contributions. We combine a panel of Colombian firm-level trade data from 2007 to 2017 with time-varying information on NTMs in regional export markets. Employing a firm-level gravity model with a difference-in-differences research design, we estimate the relative importance of NTM types controlling for unobserved confounders at the firm-product-destination level. In doing so, we address empirical limitations in the literature. This concerns, on the one hand, the use of cross-sectional data to estimate NTM impacts (e.g., Bratt, 2017; Fugazza et al., 2018). On the other hand, many studies evaluate NTMs in isolation or bundled (Disdier and Marette, 2010 or Fontagné et al., 2015), not their joint or relative effects. Moreover, drawing from Kee and Nicita (2022), we also account for the endogeneity of NTMs by predicting
to make the additional employee profitable. Among these 40 percent, a majority (56 percent) say that a lack of capital is an important reason they did not hire anyone under the program. Note that the likelihood an owner reports that capital is a reason for not hiring is independent of either baseline profits or capital stock. Those with low profits and low capital stock are no less likely to hire under the program, though as we noted in Section 4.2 above, they are more likely to hire relatives and workers at very low rates of pay. In April 2010, we asked about the same issue with a different framing, asking how much capital would be required to make a new employee “as productive as (s)he could be.” Only 7 percent of respondents said “zero” to this question, but 43 percent said 15,000 LKR or less, a level which is approximately one month’s profit for the average firm. Only 22 percent said that more than 90,000 LKR – six months’ average profit – would be required. Though suggesting that a lack of capital plays a role in some cases, these data are consistent with the lack of any additional effect when the savings subsidy program is combined with the wage subsidy program.

The survey data also yield some information on several other constraints suggested by theory.

- _Newly-hired employees have lower productivity_ :  This, combined with either formal or informal minimum wages might require that wage payments exceed the marginal contribution of the employee for some period of time. In those circumstances, the subsidy may induce firms to hire when they are otherwise unable to pay an interim wage that reflects the lower marginal productivity of workers during the initial period working in the firm. In April 2010, we asked owners how long they thought it would take a hired worker to become fully productive. The median response was one month, and the mean 4.1 months; 86 percent said the period would be six months or shorter, suggesting that
(Agence Nationale de la Statistique et de la Démographie, 2023).

Dakar currently has 7 regulatory-grade air quality monitors or approximately one monitor per 580,000 residents, which is on the higher side of the typical range (one monitor per 100,000 to 600,000 residents) for urban areas of Europe and North America (Pinder _et al._ , 2019).<sup>3</sup> However, for long stretches of time only one or two regulatory-grade monitors, and for a period of approximately a year no regulatory-grade monitors, were reporting PM2.5 data (see Figure 2 for a visualization of all available data from the regulatory monitors from 2012 to 2024). In practice, this puts Dakar on par with other African urban areas, which typically have one monitor per 4.5 million residents (Pinder _et al._ , 2019).

Dakar experiences high levels of ambient air pollution, typically exceeding World Health Organization standards. The main sources of particulate matter in Dakar are traffic (49%), mineral dust (16%–25%), sea salts (15%–20%), and industries (10%–11%) (Dou). While a large share of particulate matter is generated by anthropogenic sources such as traffic (vehicular, two-wheel two-strokes and resuspended road particles), biomass (wood, charcoal and fuel) combustion, waste burning, and industries, Saharan dust storms are another major source of particulate matter (Dou). Particularly during the first few months of the year, Saharan dust storms can lead to extremely high levels of particulate matter (Dou). Relative to other African cities, the air pollution in Senegal is reported to be moderate-to-severe. Of 24

> 3The 2023 population of the Dakar region was reported at 3,896,563 according to the Agence Nationale de la Statistique et de la Démographie (2023).

5
the underlying national accounts growth (in terms of either GDP or HFCE) is reduced by 1.1 or 2.5 percentage points for the period 2015-2017, while assuming distribution-neutrality, we estimate a national poverty rate of 11.0 and 11.8 percent, respectively.

All these estimates are subject to strong assumptions; therefore, considerable uncertainty remains about poverty in India in 2017 and the trend in recent years, and this uncertainty can only be resolved if new survey data become available. Using leaked summary statistics of the withheld 2017/18 household survey, S. Subramanian (2019) estimates that poverty increased significantly between 2011/12 and 2017/18. Himanshu (2019) also finds a decline in average consumption using alternative recent survey data. In contrast, Bhalla and Bhasin (2020) claim that poverty declined significantly between 2011/12 and 2017/18. One additional complication is that different welfare aggregates give very different estimates of poverty levels and potentially also the trend. Using the data from the leaked report, similarly to S. Subramanian, we estimate a level of poverty (15.6 percent in 2017) that is still higher than all the estimates using our regular methods.<sup>36</sup> However, leaked data that cannot be verified are not an acceptable source of information for reliable poverty

> 36 As we explain in more detail in the Appendix (also see Section 2), this is explained by the different consumption aggregates being used. Our main analysis uses the URP aggregate which has been used historically in India and which gives higher levels of poverty than the MMRP aggregate, that is used in the leaked estimates. In other words, projecting a decline in the URP aggregate or an increase in the MMRP aggregate results in levels of poverty that are not too different. This of course does not answer the important question over the direction of poverty in recent years.

19
Natural resources play an important role in export baskets and economic activity for a large number of African countries. The importance of natural resources in export activity will be measured by the value of natural resource exports (in US$ at current prices) normalized by either the GDP or total merchandise exports. The data are collected from the World Integrated Trade Solution (WITS) database on the SITC, Revision 1.0. The definition of natural resource exports includes commodities exported in the SITC sections 0 – Food and live animals, 1 – Beverages and tobacco, 2- Crude materials, inedible except fuels (excluding 22 – oil seeds, oil nuts and oil kernels), 3 – mineral fuels, lubricants and related materials, 4 – Animal and vegetable oils and fats, and 68 - No ferrous metals (Lederman and Maloney 2007).

_Natural resources exports as a percentage of GDP_ . Natural resource exports in Sub-Saharan Africa declined, on average, from 11.8 percent of GDP in 1990-94 to 7.9 percent of GDP in 2010-14. The decline in this ratio also took place among resource and non-resource rich countries. In the case of resource rich countries, the (median) ratio of natural resources exports to GDP among resource rich countries dropped by almost half from 46.2% of GDP in 1990-94 to 23.6% of GDP in 2010-14. The (median) ratio also fell among non-resource rich countries (from 9.2% of GDP in 1990-94 to 6% in 2010-14). Finally, the share of natural resource exports in total economic activity in resource rich countries was nearly four times as large as that in non-resource rich countries.

_Natural resource exports as a percentage of total merchandise exports_ . Table 1 shows that the share of natural resource exports in total merchandise exports decreased over time in all country groups at different speeds. For the region as a whole, this share declined from 91.4 percent in 1990-94 to 77.3 percent in 2010-14; that is a reduction of nearly 15