rafmacalaba/data-use-mentions-extended huggingface dataset:
migration is cost-prohibitive and that adverse weather shocks amplify liquidity constraints. Similarly, the study by Schutte et al. (2021) on the association between climatic conditions and asylum migration reveals that temperature anomalies are weak predictors of bilateral asylum migration to the European Union, and concludes that future asylum migration will mainly be driven by political changes rather than by climate change. This is in line with the findings of <mark>Martinez Flores, Milusheva and Reichert</mark> (2021) for West and Central Africa, who estimate that a standard deviation decrease in soil moisture leads to a 25-percent reduction in the number of international migrants, likely due to liquidity constraints. Bekaert, Ruyssen and Salomone (2021) leverage individual-level data from Gallup World Polls conducted in 90 countries to show that exposure to environmental stressors increases the probability of intending to migrate both domestically and internationally (but more so intra-regionally), especially in rural and less developed regions.

In short, there seems to be wide heterogeneity in the type and destination of migratory flows triggered by climatic shocks, depending on a variety of factors including, among others, the severity and frequency of the shock, the gender and resources of the affected individuals, and the context of the case study. Despite such heterogeneity, however, meta-analyses of the existing studies have found evidence that the case for weather-related migration is much more compelling for internal, within-country movements than for international cross-border flows (Hoffmann et al., 2020), a finding which contradicts the ‘conventional wisdom’ about large-scale international migration triggered by climate change. Lastly, an important missing piece, indeed also due to data gaps, is the paucity of studies reconstructing a potential ‘climate migration chain’ that might be triggered, directly or indirectly, by climatic changes in the original affected areas. It is not implausible to imagine a scenario in which a future increase in the frequency or intensity of weather shocks in a rural developing context will determine rural-urban internal migration which, in turn, gives rise to international, cross-border
real GDP per capita of the neighbor province _q_ in time _t_ , and _mi_ the number of the neighbor province of _i_ . In our case, _mi_ is an integer between 2 and 8.<sup>18</sup> The value of _GAWi_ , _t_ is conditioned by the growth rate of the neighbor provinces _q_ , _gaq_ , _t_ . The influence of the growth rate of each neighbor province _q_ is conditioned by its economic size relative to all the _mi_ provinces surrounding province _i_ .

In order to control for the effects of the labor supply, we introduce the variable _GPOPj_ , _t_ , which measures the population growth of the province _j_ in time _t_ .

Hence, for each industry _i_ , we estimate separately the following model in panel with fixed effects using the data of 1988-1997 at the provincial level:<sup>19</sup> We will study the geographic concentration of activities and the change in location of the non high-tech industries at the national level in section 3.3; and at the regional level in section 3.4.

# **3.3. Geographic concentration of activities at the national level**

Our econometric results are mixed. They suggest that the geographic concentration of activities differs across industries. For the sake of readability, we only present the determination of the concentration of some representative industries: two high-tech industries that are the most concentrated (“electronic and telecommunications equipment manufacturing” and “instruments, meters and other measuring equipment manufacturing”), one less concentrated labor intensive industry (“chemical material and products manufacturing”), and one relatively concentrated industry that is based on natural comparative advantages (“timber processing, bamboo, cane, palm bibber and straw products”)<sup>20.</sup> Table 4 shows that openess plays a positive role in the concentration of the labor intensive industry “chemical material and products manufacturing” and the primary material industry “timber processing, bamboo, cane, palm bibber and straw products”.<sup
20 large parts of their careers—in the US. Most (85) of the top-100 are also men. The US dominance is a little less pronounced in the top-50 (only 62 percent are Americans), but the male dominance is equally pronounced in the top-50 (88 percent of the top-50 are men).

# **Institutions**

Our dataset contains 738 institutions with five or more health economics publications in EconLit. (Recall the author‘s affiliation is at the time the publication was published.) Over half of these (420) have ten or more, and 30 percent have 20 or more.

Table 6 shows the top 100 institutions in terms of the _h_ -index. These 100 institutions account for 62% of health economics publications since the start of the 1980s when author affiliation data started to appear in EconLit records. Also shown in Table 6 are the other indices, the fraction of publications not found in Google Scholar, and the citations of the institution‘s most-cited publication. Harvard emerges the clear winner, beating others on the _h_ -index, the publication count, total citations and the _I_<sup>3</sup> index with  =0.5. Seven institutions come in the top 10 on total publications, total citations, the _h_ -index and _I_<sup>3</sup> with  =0.5, namely Harvard, the World Bank, UC Berkeley, U Chicago, U PA, U MI, and U York. Among the top-10, only MIT is not in the top-20 on all four measures. Eight of those ranked 11-20 are in the top-20 on all four measures, the exceptions being Cornell U, Princeton U and PA State U. Among the 80 institutions ranked 21-100 on the _h_ -index, 61 are in the top-100 on all four indices. Most of the 39 not in the top-100 on all four measures appear in the lower part of the top-100 on the _h_ -index. It is noteworthy
2 The Data Revolution in Justice 27 examine how legally relevant and legally irrelevant factors affect legal outcomes. For example, Elliott Ash and Daniel L. Chen (2018) uses judges’ writings to predict the average harshness and racial and sex disparities in sentencing decisions. That work finds that the information contained in written opinions can improve significantly on naive prediction of punitiveness and disparity.

This information could be used to aid decision makers in ways that reduce bias in the system. For example, training programs could be targeted toward biased judges, either with the goal of debiasing or to help them learn how to use the hearing process to better advantage. Simply alerting judges to the fact that their behavior is highly predictable in ways that may indicate unfairness may also be sufficient to change their behavior. Informing judges about the predictions made by a model decision maker could help reduce judge-level variation and arbitrariness. Potential biases that have been identified in prior decisions or writing could be brought to a judge’s attention, where they could be subjected to higher order cognitive scrutiny.

Thus, data not only makes it possible to measure and understand judicial bias, but also provides different avenues to address these biases and reduce existing discrimination. Leveraging such opportunities may be essential to ensure that not only de jure equality exists, but also that there is de facto equality in the interpretation and application of the law.

# **Applications of Text as Data**

Recent innovations have generated new opportunities for empirical research on the delivery of justice. Court proceedings and rulings are now increasingly digitized, allowing the construction of large-scale data sets. The increase in text availability is particularly promising in the legal field, where legal documents are meticulously documented and play an essential role in judicial decisions. Additionally, computer scientists have developed a slate of machine learning tools that can produce interpretable data from unstructured text–including written judicial opinions–making it possible to analyze a quantity of text that would be far too large for humans to read.

The question of how to analyze
across skill quantiles. Nevertheless, as a further check, we use the Survey of Overseas Filipinos to directly examine whether the observable characteristics of who is migrating varies over the destination business cycle.

The Survey of Overseas Filipinos is an annual survey which asks a nationallyrepresentative sample of households in the Philippines about members of the household who left for overseas in the past five years (see Yang, 2008). Since it is remaining members of the household who are reporting on the absent migrants, only basic details of the characteristics of 16
This paper marks the first effort to fill this information gap. The objective of the study was to test alternative approaches for the collection of FAFH with the ultimate goal of informing the identification of scalable best practices. To that end, we designed a survey experiment in urban Hanoi, Vietnam, in collaboration with the General Statistical Office. The performance of three different alternatives was tested against a ‘gold standard’ – a heavily supervised individual-level diary. The first arm was the status-quo in their current living standards survey (VHLSS), where one household member is asked to report total household FAFH consumption in only one line within the food consumption module. The design of the other two arms was informed by the survey methodology and behavioral sciences literatures and included not only the application of a separate (more detailed) module for the collection of FAFH but also followed different implementation protocols.

First, we tested the performance of an ‘individual-level recall’ arm, which asked every adult household member to report their own consumption of food away from home, under the rationale that each person is the best informant for their own consumption. Second, we tested a variant of a household-informant recall arm (‘bounding and salience’), with the objective of looking for an option that would still follow the most common field practice (i.e. interview one household informant) while minimizing the expected measurement error that comes from relying on the report of one person. More specifically, the distinctive elements introduced in this arm include: the implementation of the interview in two visits, with a 7-day period between visits; the administration of a 24-hour recall FAFH module in the first visit; and the handout of a simple worksheet. These changes were intended to: (a) reduce the information asymmetries associated with the fact that one person does not know everyone else’s consumption, (b) facilitate the tracking of consumption throughout the week, and (c) make the recall period more salient and therefore minimize the report of consumption that took place
. Developing a HE sector is therefore not a coping strategy, it is a growth strategy. _With 40-50 percent of households engaged in non-farm enterprises on average, and the share increasing in many countries, any investments which result in more household having a viable HE or higher incomes for even half of the HEs would have a substantial impact on GDP and poverty._ Our analysis of HEs and their owners shows that most HEs are engaged in non-tradable sectors such as retail trade, personal services, and processing of natural resources. Their competition is therefore internal. Earnings are higher for more educated owners, showing the importance of the expansion of educational opportunities for incomes in this sector. Despite being in a competitive sector, the majority of HEs appear to be viable enterprises, having been in operation for several years. In urban areas, it is common for HEs to operate full time as primary employment, but in rural areas HEs tend to operate less than 6 months per year and only a few days a week. If the owner is able to build the HE into a primary employment source, earnings and the contribution to household welfare tend to be higher. However, in all countries studied here, the vast majority of HEs continue to be self-employed even as the sector has grown, indicating that growth of employment in this sector will happen through the growth of new businesses, not through existing businesses taking on employees.

Contrary to popular belief, the majority of HE owners are registered, licensed, or in other ways known to the local authorities. Many report paying taxes or license fees to these subnational governments, and often the traders pay a fee to have a place in the public market. This demonstrates that the relationship between enterprise and the state cannot be summarized in a simple “formal/informal” dichotomy, as indicated by the variable “registration of enterprise” - the relationship is more complex, with variations across and within countries. Though from a research point of view a national register of all HEs would be helpful
<mark>We focus on issuances of corporate bonds in U.S. dollars, which is a prerequisite to being included in the bond indexes we analyze. We study issuances that take place only in international markets, defined as a firm issuing a bond in a market that is different from its country of origin. Additionally, we compare international dollar-denominated bonds issued by emerging market firms with a sample of investment grade bonds issued by firms from developed markets. In this way, we are able to compare yield and issuance outcomes for firms that are inherently riskier (emerging market firms) with a control group of firms that are considered relatively safe (investment grade developed market firms). This comparison is relevant because we hypothesize that investors’ search for yield leads them to increase their exposure to riskier firms around the world.</mark><sup>14</sup> <mark>We include firms from 68 developed and emerging economies (countries or markets) for the period 2000-2016. We use the nationality of the firm that is provided by SDC Platinum to classify firms into developed and emerging markets (as listed in Appendix Table 1).</mark><sup>15</sup> <mark>We include both financial and non-financial firms, because the market structure effects that we document affect issuances by any type of firms. However, our results are robust to excluding financial firms. Our sample includes 19,906 issuances from 4,965 firms.</mark> <mark>We complement these data with additional information, mainly from three different sources. We use injections/redemptions to emerging market debt funds from Emerging Market Portfolio Research (EPFR) Global to gauge changes in investor interest in emerging market debt. We use data from Morningstar Direct on the asset level portfolios of mutual funds to understand the different types of investors holding emerging market</mark>

> 14 In the Appendix, we provide additional results using jointly high-yield developed market firm bonds and emerging market firm bonds
our databases and thus make it an optimal indicator to assess the effect of GVC related policies in developing economies. In addition, domestic value added combines the gains for firms (gross profits) and workers alike (total compensation). Finally, it combines factor productivity improvements and factor employment changes into one variable.

Alternative measures that might capture other relevant aspects of economic upgrading, such as labor productivity and TFP, focus on specific subtypes of upgrading, such as skills or process upgrading (see section 1). Moreover, they would require us to limit the sample size considerably since they are only available for high-income countries or at a much higher sectoral aggregation. Domestic value added, by contrast, reflects economic upgrading via better skills, capital, and processes, and therefore represents a comprehensive upgrading metric.

However, we have to add, there is a structural relationship between _dva_ and our forward links GVC measure that might bias the results. By using the lagged relationship this bias should be reduced, though, and, most importantly, it should not affect the qualitative results obtained with the policy interaction terms as shown below in equation (2).

Our set of controls ( _C_ ) includes the amount of foreign value added for domestic processing ( _FVADP_ ).<sup>4</sup> Here, we are guided by a standard production function of the form:

> 4 Capital stock and employment data are available for the WIOD sample, which we use for robustness exercises to improve the reliability of the results. The regressions based on the OECD sample rely on the three two-dimensional fixed effects.

14
or the Green Card program. We can then use nominal household per capita expenditures to assess the targeting and coverage outcome of the Green card program.<sup>3</sup> Health expenditures are recorded in line with the international COICOP standards.

> 3 Ideally, we would prefer to use real consumption at the household level with both spatial and adult equivalence adjustments. But due to lacking regional identifiers in the household dataset, we can only compute nominal per capita expenditures to define population deciles.

5
# **1 Introduction**

Climate change is causing widespread impacts across the globe, and the latest UN Climate Change Report notes that risks will escalate exponentially, often causing irreversible impacts IPCC (2022). Afghanistan, which experiences an arid to semi-arid climate, has been facing extreme weather conditions in the recent decades.<sup>1</sup> Although less than 12 percent area is cultivable in the mountainous country, over two-thirds of the population depends on agriculture for their livelihood. The country has also been torn by incessant conflict for over 40 years, rendering its institutions fragile and its people highly dependent on humanitarian assistance. In 2019-20, 47 percent of the population was classified as poor, which represents a doubling of the poverty rate since 2006. Extreme weather events are likely to push people further into poverty, especially when most people can barely make ends meet and have little access to safety nets.

In this context, we study the effects of weather shocks in Afghanistan, mainly focusing on the effects of the 2018 drought on measures of consumption and poverty. We focus on the 2018 drought for two reasons. First, the 2018 drought was dubbed the worst drought in two decades and affected 22 of the 34 provinces in the country. About 13.5 million Afghan people faced heightened food insecurity in 2018, with lost livelihoods, livestock deaths, and forced displacement into urban areas (FAO, 2019).<sup>2</sup> Second, a nationally representative geo-coded household survey, the Integrated Expenditure and Labor Force Survey (IE-LFS), was administered right after the drought year, in 2019-20, enabling us to study the effects of the drought in the last growing season on household consumption.

There are multiple mechanisms whereby drought can affect household consumption in Afghanistan, where 76% of households live in rural areas and 46% of workers are in agriculture (NSIA, 2020). Grains and bread, primarily wheat, constitute 73% of calories consumed. Drought adversely affects farmers’ agricultural
**Figure 5. Employment-to-population ratio, % of population aged 15-64, 2012-2018 a. By age and gender b. By educational level**

<!-- Start of picture text --> 60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>2012 2013 2014 2015 2016 2017 2018 2012 2013 2014 2015 2016 2017 2018<br>All, 15-64 Female, 15-64 Male, 15-64 Age 15-24 Primary or less Secondary Post-secondary<br>Percent Percent<br><!-- End of picture text --> _Source: Own estimates using the 2012-2018 Kosovo LFS. Note: Secondary refers to upper secondary._

**Informality remained high in recent years and was more common among youth, males, the less educated and residents in rural areas.** As depicted in Figure 6 Panel a, 42.4 percent of overall employment was informal in 2018.<sup>16</sup> Among young individuals (aged 15-24), the percentage was 56.5. Informality was also negatively correlated with educational attainment, with larger increases observed among workers with less than primary school.

**Figure 6. Informality rate, % of employed population aged 15-64, 2012-2018**

**a. By age and gender b. By educational level**

<!-- Start of picture text --> 80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20<br>20<br>10<br>10<br>0<br>0<br>2012 2013 2014 2015 2016 2017 2018<br>2012 2013 2014 2015 2016 2017 2018<br>All, 15-64 Female, 15-64 Male, 15-64 Age 15-24 Primary or less Secondary Post-secondary<br>Percent Percent<br><!-- End
"working poor" (employed population below international poverty line) by sex component which is in Table 1 but not in the UN Women list. Relatedly, Open Data Watch (2019) refers to 32 SDG gender indicators and another 36 “additional” SDG gender indicators. The difference between their 68 and our 50 SDG gender indicators is that some of theirs are, in our assessment, gender neutral in terms of the present drafting of the indicator (such as 1.5.1 Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population).

2 Tier 1 indicators, according to the UN, are indicators that are conceptually clear, have an internationally established methodology and standards available, and data are regularly produced by countries for at least 50% of countries and of the population in every region where relevant. Tier 2 indicators are conceptually clear and have an internationally established methodology and set of standards, but are not regularly produced by countries. There are no gender equality indicators in the third category, Tier 3, which is defined as an indicator with no internationally established methodology or standards established, and, thus, these indicators are likely to have the lowest rate of coverage. (UNSD, 2022).

4
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