rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

The urbanization process is efficient when land can be assembled for urban development at no cost and both capital and labor can move freely across local jurisdictions. This well-known proposition from the literature on city formation stems from the assumption that entrepreneurial local governments can compete to attract the number of firms and workers that maximizes the local surplus. Through land zoning and incentives, they overcome the coordination failures that would arise if firms and workers gathered spontaneously across locations (Henderson 1974, Henderson and Venables 2009).

However, the necessary conditions for the efficiency of the urbanization process may not be met in developing countries. For starters, the assembly of relatively unencumbered land with clear titles can be challenging. Property rights are not always well defined, and they may involve a high degree of state ownership. Cadastral records and titling are incomplete, and regulations cumbersome.

Local governments in these countries are also less capable to maximize local surplus than their counterparts in advanced economies. In the words of Henderson and Becker (2000): “not all agglomerations have single pro-active autonomous local governments. Local autonomy may not be part of the national constitution; many local governments may not or cannot be pro-active; and not all agglomerations are governed by a single local government or by a set of township governments that coordinate well.” With the availability of urban land and the capacity of local governments being constrained, there may not be enough cities. And a few of those that emerge – capital cities in particular – may become oversized. Consistent with this distorted spatial equilibrium, a majority of the megacities of the world can by now be found in developing countries (United Nations 2018). Instead of efficiency, there tends to be excessive primacy combined with overall under-urbanization.

Not surprisingly, cities in developing countries are often described as messy, crowded and disconnected. In South Asia, the widespread existence of slums and sprawl constrains the potential of agglomeration forces to bring about faster improvements in prosperity (Ellis and Roberts 2016).
(English origin). Examination of the long-term debt ratio reveals a weak relation between legality and long-term debt. French and German legal origin firms seem to have roughly the same amount of long-term debt which is substantially more than English legal origin firms. Chinese firms have an overall reliance on debt and equity similar to firms from German legal origin but exhibit a higher (lower) reliance on short-term (long-term) debt.

To test our hypotheses more precisely we estimate regression models using firm-level data with equity or debt ratios as the dependent variables, legality as an explanatory variable and a set of control variables to account for other probable determinants of external finance levels. The first two columns of Table 2 show results from tests with the market capitalization and total debt ratios as the dependent variables using all firms in our sample. As suggested by the findings in Table 1 legality is strongly positively related to the amount of firms’ external equity capital and strongly negatively related to the amount of total debt. The positive relation for external equity is consistent with both Hypothesis 1 and its alternative. However, the negative relation for total debt is inconsistent with Hypothesis 1 (that total debt and legal quality are positively related). Instead, it is consistent with the alternative (that total debt and legal quality are negatively related). Thus, these results support the monitoring models of Diamond (1991, 1993).

It is interesting to note the relations between external capital and the other variables included in the regression. Larger firms with less tangible assets tend to have both less (or less valued) external equity and less debt. In contrast, more profitable firms have more (or more highly valued) equity but less debt which is consistent with the pecking-order theory and the results of many prior empirical studies (see, for example, Rajan and Zingales, 1995).

The remainder of Table 2 presents results from similar tests that examine firms based on their use of foreign currency debt. The third and fourth set of results repeat the analysis in
however is not readily available. Although these are important limitations that require further work, the available data can provide some first insights into the relationships between environmental risks and poverty.

# **3. Environmental risks and poverty across districts**

This section performs a spatial analysis to explore whether the incidence of poverty is higher in districts with high environmental risk levels.

## **3.1 Methods**

National maps are produced that show the extent of environmental risks and poverty at district‐level. First, all environmental risk variables are calculated at district level and all districts are then categorized into three groups with an equal number of districts in each group: high, medium and low risks.<sup>5</sup> Similarly, all districts are classified into high‐medium‐low poverty categories based on the 2010 poverty rate maps produced by Lanjouw et al. (2013). In addition, we calculate the absolute number of poor people in each district, also from Lanjouw et al. (2013) and classify districts as high, medium and low poverty according to the number of poor people. These relative and absolute poverty maps are then overlaid with the environmental risk maps (Figures 1 and A.1). In addition, the poverty rate and number of poor people in each risk category is calculated (Tables 1 and 2).

> 5 For land degradation, due to the large number of districts experiencing no loss (0), the low risk category includes all districts with 0 loss (243 districts), and the rest of the districts are split into the medium and high categories.

6
estimated. Therefore, restricting the weight to be identical over time should also not affect the results substantially. Another implication of the small changes over time in the weights is that they did not contribute signi fi cantly to the EU’s tariff reduction in the UR.<sup>62</sup> To conclude, the assumption of constant weights is reasonable in this sample and does not appear to affect our main results signi fi cantly. This is further supported by noting that the sign and signi fi cance of the main parameters in table 6 are identical to those we found in table 2. The main predictions of the model are still con fi rmed and the only difference is a small change in the magnitude of the point estimate of the growth effect, but even that is not signi fi cantly different from table 2.<sup>63</sup> Nonetheless we did fi nd evidence of signi fi cant determinants that generate different political economy weights across industries. This suggests that in general the variation in these weights should be modeled empirically and theoretically.<sup>64</sup>

# 5 Conclusion

We analyze the effects of preferential trade agreements on multilateral trade liberalization—a controversial issue where the evidence has been scarce. The model we develop captures key features of the current trading system and provides a rich set of predictions regarding the impact of PTAs on MTL. We derive and estimate the structural equations of protection using detailed tariff data for the EU during the last two multilateral rounds and fi nd evidence that its PTAs slowed down MTL. As the model predicts, this occurred only in products with a zero preferential tariff and was not present in agreements with a common external tariff and transfers. Our model also incorporates domestic political economy motives for setting trade policy and we fi nd a negative relation between import penetration and tariff levels working through the extra weight that governments place on producer surplus. We estimate that these industry weights are increasing in regional concentration and employment shares. We also fi nd evidence
# **Annex A: World Bank Enterprise Survey**

Table A1: Firm distribution and frequency World Bank Enterprise Survey Database by country

|**Country**|**Freq. **|**Percent**|**Cum.**|
|---|---|---|---|
|Cambodia|845|5.30|5.30|
|China|2,700|16.93|22.23|
|Indonesia|2,763|17.32|39.55|
|Lao PDR|970|6.08|45.63|
|Malaysia|1,000|6.27|51.90|
|Mongolia|722|4.53|56.43|
|Myanmar|1,239|7.77|64.20|
|Philippines|2,661|16.68|80.88|
|Thailand|1,000|6.27|87.15|
|Vietnam|2,049|12.85|100.00|
|Total|15,949|100.00||

Source: World Bank Enterprise Survey Database. Note: Data is over the years 200‐2016 Table A2: Firm distribution and frequency World Bank Enterprise Survey Database by year

|**year**|**Freq. **|**Percent**|**Cum.**|
|---|---|---|---|
|2009|4,184|26.23|26.23|
|2012|2,970|18.62|44.86|
|2013|832|5.22|50.07|
|2014|632|3.96|54.03|
|2015|4,651|29.16|83.20|
|2016|2,348|14.72
aged 15 to 65 would be willing to work more, especially men in Uganda (51% versus 47%) and women in Malawi (49% versus 44%). Because questions on ‘availability to work more hours’ are missing, we do not know whether the respondents could indeed work more when offered the opportunity.

The LSMS‐ISA surveys in Burkina Faso, Nigeria, Niger and Mali, instead, only capture the availability to work more hours, but use different reference periods or response options (next month, immediately, yes/no). Figure 5 shows that few respondents in Burkina Faso, Nigeria, Niger and Mali are available to start a new job. Thus, just including questions on ‘desire to work’ without considering who is actually available to work more is not informative. For this reason, Benes and Walsh (2018b) recommend asking a question about availability only if the respondent wants to work more hours. This would facilitate measuring time‐related underemployment.

> 21 The Guatemala LFS starts with the question _‘In addition to the number of hours that you actually work in all your activities, would you like to work more hours per week?’_ , followed by _‘If you were offered an additional job or to work additional hours in your actual employment: 1.Would you be available immediately?; 2.Would you prefer starting in a month?’_ (original version in Spanish). Similarly, the South Africa LFS first asks _‘Do you want to work more hours at your current rate of pay?’_ and then _‘If extra work became available, would you be able to start such work in the next four weeks?’._

> 22 The Guatemala and South Africa LFS include the question _‘Are you usually available to work more hours?’._ In addition, the Nicaragua LFS includes the question _‘If extra work became available, would you be able to start such work in the
This lack of municipality- and year-specific variations can be highlighted by a simple regression analysis. Table 7 shows the result of a simple regression of the binary variable indicating receipt of Colombia Mayor—taken from the four rounds of the ENCV during 2010–13—on the proportion of age-eligible residents enrolled based on administrative data. The first column runs the regression using all age-eligible observations, and the second column only shows those observations that also fulfill the Sisben criterion (which will be the sample in our analysis below). In the first group, the municipality proportion of beneficiaries is at least a statistically significant predictor of the actual receipt of the benefit (at the 5 percent level, the standard errors are clustered on the municipality), but it ceases to be a statistically significant predictor if we focus on the actual population of interest. Moreover, even in the former group, a 1 percentage point rise in the proportion of beneficiaries would only raise the likelihood of actual receipt by 0.173 percent. Among the second group, the corresponding likelihood is only 0.09 percent.

**Figure 5: Rollout of Colombia Mayor across 298 ENCV 2009–13 Municipalities**

**Table 7: Regression of Program Receipt on the Municipal Rollout Level, Controlling for Municipal and Year Fixed Effects**

|_Binary dependent variable_|_(1)_|_(2)_|
|---|---|---|
|Beneficiaries, share|0.173**<br>(0.073)|0.090<br>(0.109)|
|Observations|48,271|20,172|
|R-squared|0.003|0.010|
|Municipalities, number|298|294|
|F|16.87|25.09|

18
to shocks and less able to cope with those shocks. Thus, we control for livelihood zone fixed effects in our regressions of TA-level aid distribution.<sup>14</sup>

# **3.4. Additional Explanatory Variables**

<u>Explicit Household Targeting Characteristics</u> At the local level, aid distribution should be targeted to less wealthy, female-headed households, households with babies and infants (we use a dummy for households with children under three years old), households where all adults are elderly (defined as all adults older than 59), and households with any disabled adult member (Babu et al., 2018; MVAC, 2016; ALNAP, 2003).

To proxy wealth, we use an index of household durables, which is created from a principal component factor analysis of consumer durables and household dwelling characteristics<sup>15</sup> ; the number of mobile phones owned by family members; an index of agricultural implements, created from a principal component factor analysis<sup>16</sup> ; and the natural logarithm of total landholdings.<sup>17</sup> Additional household demographics include size; the maximum number of years of education completed by any household member; the proportion of adult household members who are literate in English; and a dichotomous variable for whether the household had moved during survey periods.

In addition, we include a series of dichotomous variables to control for whether the household faced other idiosyncratic shocks such as having any member that lost employment or facing high food

> 14 - Livelihood zones are constructed by FEWS NET, and were accessed at <u>https://fews.net/southern africa/malawi/livelihood-zone-map/july-2015.</u>

> 15 The index is based on (i) the dichotomous variables for whether the household has any bed, table, chair, or other living room furniture; any of fan, air conditioner, clock, or solar panel; any of radio
# **4 Generative AI User Profile and Usage Patterns**

This section examines the demographic characteristics and usage patterns of generative AI tool users. Drawing from clickstream data aggregated by Semrush, we analyze key user attributes including age, gender, education, and employment status, alongside usage metrics such as daily traffic fluctuations and device preferences. This analysis offers a nuanced view of who is using generative AI tools and how they’re being integrated into daily routines and workflows. By exploring these factors, we aim to illuminate current trends in AI tool adoption across various population segments.

The gender gap in digital tool usage remains a significant aspect of the digital divide, with females representing 48% of global internet users. Figure 5 illustrates the share of female users of five popular generative AI tools across each category, as well as five other widely used websites. Generally, female users are underrepresented in generative AI tools compared to male users, with video generation tools showing the lowest female participation. ChatGPT has a female user share of 33%, lower than Google (48%), Quora (54%), and Wikipedia (52%).<sup>4</sup> Runway, a popular video generation tool, has an even lower female user share at 21%. Midjourney, the most popular image generation tool in our sample, bucks this trend with 52% female users.

> 4The gender disparity in ChatGPT adoption is also found in Humlum and Vestergaard (2024). In their Danish survey, female white-collar workers are 20.6% less likely to use ChatGPT compared to their male counterparts within the same occupation categories.

17
stringency of the harmonized standards and the restrictiveness of the rules of origin. We contribute to this literature by identifying through a firm-level analysis regulatory convergence in deep trade agreements (and, specifically, the presence of SPS and TBT provisions in RTAs) as a source of spillover effect.

The paper is organized as follows. The next section describes the data and provides some descriptive statistics. Section 3 presents the empirical specification and results for the firm-level analysis. Section 4 explores the differential effects for regulation-intensive products. Section 5 provides robustness analysis and Section 6 concludes.

# **2 Data and descriptive statistics**

To identify the impact of trade-related institutions in destination markets on Costa Rican firms, we combine data on regional trade agreements, the universe of export transactions at the firm level, and product-level information on regulation sensitivity. First, we use the World Bank Deep Agreements Database to capture the depth of trade agreements (Hofmann et al., 2017). The data set provides information on the detailed content of 279 RTAs, signed by 180 countries between 1957 and 2015. Following Mattoo et al. (2017), we measure the depth of an agreement as the count of strictly enforceable provisions included in the RTA.<sup>8</sup> Other dimensions of the content of RTAs used in the analysis include the presence of regulatory provisions and the preferential or nondiscriminatory nature of provisions. Appendix A.1 provides the details. In a robustness test, we use an alternative measure of RTA depth from Mario Larch’s Regional Trade Agreements Database (Egger and Larch, 2008) which categorizes RTAs into partial scope agreements, free trade agreements, and customs unions.

For exporter transactions, we use the Exporter Dynamics Database described in Fernandes, Freund, and Pierola (2016). The underlying data for Costa Rica cover the universe of exporting firms over 1998–2012 and provides information on export value and quantity at the firm-product-destination-year level. The data cover 14,253 firms exporting to 211 destinations. We limit our
WTP survey presents an important form of experimentation which lies somewhere along the spectrum of laboratory experiments and observational studies. Such survey based ‘field experiments’ represent a practical mix of control and realism. Control comes from the design of the survey sample and the structure of the survey instrument. Contrasted to the laboratory settings of experimental economics, realism comes from interviewing people in their homes about goods and services that are important to their daily life, and considering the infrastructure programs that are familiar to respondents.

What can we get from WTP surveys for improved piped water services? It is important to review some of the qualifications regarding the typical product – mean WTP – because the economic concept measured by WTP can be difficult to grasp, leading to misinterpretations and misuses of results. Analysis and interpretation of survey data usually generates a measure of 3
8 entire period to the replacement year not covered by the other phases. All countries with initial TFR under 3.0 exhibit only this phase. All other countries also go through this phase, though it may begin later.

The age pattern of fertility was allowed to change with its level. The age patterns when TFR is 7.6, 4.1, or 2.8 are shown in Figure 4. The - mean age at childbirth declines from 28.86 years when TFR 7.6, to 28.45 years when TFR - 4.1, to 27.98 years when TFR - 2.8. For intermediate TFRs, the age pattern falls between these curves.

# Migration level and trend

Estimates of international migration come from the U.N. 1984 assessment and the U.N. 1988 revision, documents prepared by the U.N. for the 1984 International Conference on Population, the U.S. Bureau of the Census (1985), Eurostat (1987), and Bank sources. To deal with the volatility of refugee movements in various parts of the developing world, only those are counted as migrants who are believed likely to remain permanently in the receiving countries.

Available data seldom include age-sex distributions of migrants, which were instead chosen in this manner. For each country, some estimate was made of the sex ratio among migrants. Based on this sex ratio, an appropriate age-sex distribution was chosen from among alternative models. This distribution was assumed to be constant over time, regardless of the total number of migrants.

Trends in the volume of migration are based on past trends, but are still essentially speculative. For most countries, net migration is assumed to decline to zero by the year 2000; for the remainder, net migration declines to zero by the year 2025.

# Summary of Results

Summary projection results are provided in Tables 2-12. (Tables 4-12 appear at the end of this Introduction.) In discussing these tables, we will summarize world population
14 Just under 11 percent (74) of the 694 publications allocated for topic assignment were not in the event assigned to a topical group. The most-cited items amongst these were textbooks (led by Paul Feldstein) which, by definition, paint on a much broader canvas. Others included the quite extensive reference made by economists to the literatures of demography and epidemiology written by scholars of those disciplines. The most frequently cited excluded source that was not a textbook was Scott and Meyer‘s book on organization theory and application ―Institutional environments and organizations: Structural complexity and individualism‖: a pioneering and hugely influential sociological work whose impact on economists is indicated by its being the 63rd most cited source.

Some titles were found to be superficially misleading. For example, Kremer‘s excellent ―The Illusion of Sustainability‖ (Kremer and Miguel, 2007) may appear superficially to belong to category 5 (Health and the economy) but is in fact about the evaluation of public health options in Kenya (category 12, Economic evaluation). Some authors excel at the invention of intriguing but slightly baffling titles. Mark Kamstra‘s ―Losing Sleep at the Market: The Daylight Saving Anomaly‖ (Kamstra et al., 2000) turned out to about equity returns in international capital markets in consequence of sleep disruption, and so was excluded. Contoyannis et al. on ―The Dynamics of Health in the British Household Panel Survey‖ (Contoyannis et al., 2004) could on the face of it have been assigned either to category 1 (Health and its value) or 3 (Determinants of health and ill-health) but on closer inspection was assigned to category 3 given its concentration on the impact of health status in a previous period on the probability distribution of current health status, and on the impact of socio-economic determinants. Articles that were about both supply and demand were assigned according where the balance of emphasis seemed to lie or, where the balance was even, to category 11 (Markets in health care). Siciliani
, Percentage of Pre-COVID Jobs_

||Lost job because of<br>job or business losses<br>due to COVID-19|Stopped<br>working to avoid<br>exposure to the<br>virus|Reduced income<br>because of job or<br>business losses to<br>COVID-19|Reduced income<br>due to reduced<br>workinghours|
|---|---|---|---|---|
|Industry/Manufacturing|16.3|0.0|60.7|11.8|
|Construction|10.1|0.0|53.6|27.3|
|Wholesale and Retail (Commerce)|35.6|7.2|67.5|22.2|
|Transport Services (Taxi, Bus, Truck)|19.7|0.0|55.6|25.2|
|Communications|14.7|0.0|41.2|22.5|
|Restaurants,Hotels,Bars,Cafes|55.8|1.5|81.7|11.6|

Source: GHFPS.

Notes: “Individuals who lost jobs because of job or business losses due to COVID-19”: Those who had a job in March 2020 but don’t have a job in December 2020 and answered that they stopped working because they lost jobs or no business because of COVID 19. “Individuals who stopped working to avoid exposure to the virus”: Those who had a job in March 2020 but didn’t have a job in December 2020 and answered that they stopped working because they don’t want to be exposed to the virus. ”Individuals who reduced income because of job or business losses to COVID-19” Those who answered they decreased income due to job loss or closure of business. “Individuals who reduced income due to reduced working hours:” Those who answered