rafmacalaba/data-use-mentions-extended huggingface dataset:
changes in credit supply versus credit demand.

For the unused commitment ratio, we ignore the mean values, which may mainly reflect some extreme outliers, given that they are always many times greater than the P90 values. Focusing instead on the P50 and P90 values, they are both remarkably steady and suggest no significant increases or decreases. The P50 value remains in a tight range between 9.16 and 9.69 percent and the P90 statistic varies between 18.29 and 19.85 percent.

The deposit ratios for the industry as a whole show only some very modest increases. The mean and P90 deposit ratios each increase less than one percentage point and stay near 81 and 89 percent, respectively. However, as above for the loan ratios, the industry statistics may disguise some significant heterogeneity. One study using weekly branch-level deposit data finds substantial movements of deposits and the rates paid on these deposits that vary with COVID-19 infection rates (Levine, Lin, Tai, and Xie, forthcoming).

The loan ratios for the G-SIBs are much lower than for the industry, and again show substantial reductions in lending and possibly credit risk. The mean loan ratio declines from 36.80 to 31.49 percent, and the maximum ratio similarly declines from 54.80 percent in 2019:Q4 to 49.84 percent in 2020:Q4.

Unused commitments for the G-SIBs decline very considerably during the COVID-19 crisis, likely primarily due to the “dash for cash” by corporations drawing down their commitments at the largest banks described in some of the research discussed later in this paper. The P50 and P90 values fall from 48.48 and 69.29 percent in 2019:Q4 to ranges of 38.35 to 42.18 percent and 60.20 to 62.46 percent, respectively.

Deposits at the G-SIBs increase more than for the industry as a whole, which may reflect in part a flight to safety by some depositors that believe these institutions are “too big to fail,” as 24
(e.g., subsidies being increased when the industry experiences a negative shock) and other sources of omitted variables bias (e.g., subsidizing less-profitable producers). Third, the reform we analyze was in effect for six years, which allows us to interpret the derived elasticities as long-term elasticities. Long-run elasticities are the benchmark necessary to measure the economic and ecological costs of

> 1Vessels have no incentive to manipulate their AIS activities in response to the subsidy program because the regulator does not use AIS data to calculate fuel subsidies.

3
Table A1. Definition of Variables (cont.)

|Variable|Definition|
|---|---|
|**_Knowledge of current events and the politic_**|**_al process_**|
|Aware of imposition of Emergency Rule<br>(1=Yes)|Dummy variable equal to 1 if respondent had heard about the imposition of the emergency rule, the removal of Chief Justice of the Supreme Court and the<br>house arrest of various lawyers.|
|Index of knowledge of current events|Average score of the following knowledge questions: "Aware of imposition of emergency/house arrest of lawyers and removal of Chief Justice" and "Knows<br>name of newly elected Prime Minister".|
|Share of political party signs correctly<br>identified|Proportion of signs (outof 7) that respondent were able to correctly match to a political party contesting for a National Assembly seat.|
|Share of political party names correctly<br>identified|Proportion of party's names contesting for National Assembly that the respondent was able to recall perfectly out of 2.|
|Recalls winning candidate (1=Yes)|Dummy variable equal to 1 if woman perfectly recalled the name of the candidate that won the National Assembly seat in her constituency|
|Recalls names of main candidates (1=Yes)|Dummy variable equal to 1 if woman perfectly recalled the names of the candidates from the two main parties that contested a National Assembly seat|
|Knows the gender of main candidates<br>(1=Yes)|Dummy variable equal to 1 if woman perfectly identified the gender of the candidates from the two main parties that contested a National Assembly seat|
|**_Voting behavior and perceptions_**||
|Checked voter list after February 5th<br>(1=Yes)|Dummy variable equal to 1 if after February 5th woman or a family member checked to see if she was on the voter list
tariff peaks, tariff escalation or<br>simplification of tariff schedules<br>Customs modernization and reform,<br>harmonization of procedures and<br>cooperation across borders|<br>•<br>MFN 0 tariff lines / Import value (WTI 1.7)<br>•<br>Tariff bounds / Overhang (WTI 1.8)<br>•<br>Non-AV tariffs (WTI 1.9)<br>•<br>Non-tariff measures (WTI 1.10)<br>•<br>Customs duties (WTI 1.11)<br><br>|
|•<br>•|<br>Simplification of customs procedures,<br>including SPS, TBT, and other<br>certifications, rules of origin,<br>valuation, etc. to conform with<br>relevant agreements or international<br>best practices<br>Implementation of WTO or<br>regional/bilateral commitments (e.g.<br>common external tariff)|•<br>Export restrictions (WTI 1.13)<br>•<br>Logistics performance index and its indicators - efficiency of customs and other border<br>procedures (LPI, WTI 4.1)<br>•<br>Trading across borders – Doing business (IFC, WTI 4.2)<br>•<br>Trade Enabling and Global competitiveness indexes – goods market efficiency: burden of<br>customs procedures, prevalence of trade barriers, trade tariffs, efficiency of customs<br>administration, efficiency of import-export procedures, transparency of border<br>administration (WEF GCI 6.10, 6.11, 6.13, ETI 1.01-4.02)<br>•<br>Average time to clear exports through customs / time to export/import (WDI)<br>•<br>Documents to export/import(
there are additional complications; we present a full description of this process and results robust to the exclusion of these facilities in **Supplemental Appendix Tables 2–4** .

Third, in terms of the implications for quality, multiple studies have found a large gap between medical competence and clinical practice.<sup>21, 25–29</sup> Any gains that we compute here (either from reallocation or upskilling) are based on provider competence rather than clinical practice. The data used here are also based on a sample of the health facilities and providers in each country. While previous studies have documented similar variation in caseloads and quality even at the level of the village,<sup>30, 31</sup> we would require a substantial increase in the scale of data collection to accurately compute the gains to reallocation within local areas. These data limitations arguably imply that our results are a lower bound of the gains that could actually ensue from allocative strategies. Larger samples and data on clinical quality (as opposed to knowledge) may further increase the gains from such approaches if the variation in clinical quality is larger than that in knowledge, and larger samples could increase this variation even further.

In contrast, measurement error, either in caseloads or in competence, will overstate the gains from better allocative efficiency. The SDI surveys do not take repeat measurements, which are required to establish the reliability of these measures. For caseloads, the measures track well with other official statistics, but for facilities like central hospitals, where caseloads are very high, small errors can lead to large changes as the reassignment of just a few providers can lead to disproportionately large gains. In both these

**15/30**
%).

# Insert Table 4

To the extent that data from September 2009 can point to the impacts of the 2009 global financial crisis, the results suggest that this shock appears to have had limited impact in Bangladesh. The two main channels which were of concern were a drop in remittance income and job loss in export industries (especially garments). The household survey data suggests that these concerns did not materialize. When households were asked whether they had experienced a crisis due to a set of reasons, less than 1% of households said they had experienced a shock 6
.

For the case of Colombia, Allen et al. (2021) conduct a survey experiment investigating the preferred policy preferences of native Colombians in response Venezuelan mass immigration. On the one hand, their results suggest that natives favor the use of numerical immigration limits and limiting the length of the residency permit. On the other hand, natives support granting conditional labor market access to Venezuelan immigrants (i.e., only in certain occupations) as well as unrestricted location choice and complete access to public services. In terms of effect heterogeneity, natives who have less contact with Venezuelans, who put more emphasis on economic matters, and who perceive the situation in Venezuela as mainly an economic problem, have more restrictive policy preferences.

Additionally, Albarosa and Elsner (2021) study the example of refugee immigration into Ger-

> 4On the effects of immigration on crime, see also: Bianchi et al. (2012) and Bell et al. (2013).

7
# **1. Introduction**

Starting in the early 1990s, as developed and emerging countries became more integrated within the global financial system and especially as international transactions seemed to grow, interest in financial globalization increased substantially.<sup>1</sup> Since then, many have questioned the links between financial globalization and economic growth and have revisited the overall costs and benefits of financial integration.<sup>2</sup> In principle, financial globalization should increase access to capital and lower its costs.<sup>3</sup> But financial globalization may also expose countries to foreign shocks and crises, thus raising several new macro-financial challenges, such as the regulation and use of domestic financial markets and the conduct of macro-prudential policies.<sup>4</sup> Despite all the attention to financial globalization, its concept and extent have remained somewhat elusive. For example, different authors use, alternatively, net capital flows, gross capital flows, and country portfolios as measures of financial globalization.<sup>5</sup> Many also measure globalization through the participation of foreigners in domestic markets, while a number of other studies focus on the access of domestic firms and governments to foreign capital.<sup>6</sup> Moreover, while there is much discussion of increased financial globalization, little is known about whether the sharp globalization that took place during the 1990s continued during the following decade or whether the nature (or type) of financial globalization has changed.<sup>7</sup>

> 1. Obstfeld and Taylor (2004); Lane and Milesi-Ferreti (2007); Obstfeld (2012).

> 2. Rodrik (1998); Stiglitz (2002); Kose et al. (2010).

> 3. Foerster and Karolyi (1999); Stulz (1999); Errunza and Miller (2000); Errunza (2001).

> 4. Dornbusch, Goldfajn, and Valdes (1995); Calvo and Reinhart (
# **<mark>A1. Appendix 1 - Variable descriptions</mark>**

|Variable Name|Description|Number of observations|Source|
|---|---|---|---|
|GDP pc|Real GDP per capita<br>constant prices (2011 US<br>dollars)|Unbalanced panel from<br>1800 to 2018 (219 years) with<br>a minimum of 20 countries to<br>a maximum of 169|Maddison Project<br>Database, version 2020|
|Sovereign default|Binary variable taking<br>the value 1 if the country is in<br>sovereign default and 0<br>otherwise|Panel from 1800 to 2020<br>(221 years) of 195 countries|Farah Yacoub et al.<br>(2021)|
|Infant mortality|Number of children who<br>die before reaching their first<br>birthday per 1000 live births.<br>Some of the data points are<br>extrapolated using uses the<br>past trend in a country and the<br>global trend.|Unbalanced panel from<br>1960 to 2020 (61 years) with<br>a minimum of 79 countries to<br>a maximum of 199|United Nations Inter-<br>Agency Group for Child<br>Mortality Estimation|
|Population|Number of inhabitants<br>measured mid-year in<br>thousands|Unbalanced panel from<br>1900 to 2018 (119 years) with<br>a minimum of 53 countries to<br>a maximum of 169|Maddison Project<br>Database, version 2020|
|Life expectancy|Life expectancy at birth<br>in years for the total<br>population|Panel from 1960 to 2020<br>(61 years) of 208 countries|World Development<br>Indicators|
|Energy supply|Basic supply and
analysis covers cities with population over 500,000 as well as the three most populous cities in the country. The network is generated by solving for each city pair the routing determined by the shortest time. The limitation of the methodology is that it fails to include road and air transport features in the current application. Other limitations are also discussed in the study by de Soyres et. al. (2018) such as lack of information on rail gauge, service frequency port facilities etc. Another key challenge was posed by the identification of BRI-related projects where timelines and specific locations were not always available. Nevertheless, the authors generate a consistent data set of BRI-related trade costs reductions in rail and maritime transport costs including switching across the two transport modes. These trade costs reductions are then applied to ad valorem equivalents of value of shipment day generated based on updated estimates of Hummels and Shaur (2013). The time in trade estimates are based on US imports data and variations in the premium paid for air shipping versus maritime shipping. The average delay of one day in shipping is estimated to be associated with ad valorem tariff of 4.9% in the baseline. However, the data is available at the HS2 digit level and aggregated to GTAP sectors and country pairs based on 2014 trade flows.

In the baseline, the trade among BRI countries air transport accounts for nearly 12% of total transport cost, land is around 31% and sea is around 58%. These shares vary dramatically across exporting countries—with high sea shares for island economies such as Indonesia and the Philippines. Therefore, our analysis covers a significant share of total transport services. In the baseline, the highest time in trade estimates are estimated in products of animal origin, cereals, fruits and nuts, pulp and paper; while products the least sensitive to time in trade covered special woven fabrics, silk, man-made fibers and filaments.

- 4 -
33 The available regional data on infant mortality for 1994-96 demonstrate unusually large and unstable (with almost chaotic fluctuations from year to year) changes in this variable after the beginning of transition.27 This may be explained by a scale and nature of impact of transition on social development, which was massive and quite uneven across regions and across different time periods. Also, the first years of transition were accompanied by major migration flows and deterioration in a quality of demographic statistics.

Even after controlling for regional differences in the standard of living, economic mismanagement and geographic location, the administrative type of regions remains an important predictor of the degrees of fiscal decentralization. On the revenue side, the OLS coefficients of the dummy variables Autonomous Okrugs and Republics are (-10.6) and (-8.7), while on the expenditure side the corresponding OLS estimates are (-6.8) and (-8.6). These results imply that the predicted values from the two regressions of the shares of local governments in consolidated budget revenues and final expenditures are significantly lower in autonomous okrugs and republics than in oblasts and krais.

Finally, the regressions suggest that regional location is another significant variable to explain variation in fiscal decentralization. Regions located in three geographic areas (Urals, Western and Eastern Siberia) on average have much higher degree of decentralization when it is measured as a share of local governments' expenditures. There are two possible explanations of this phenomenon. First, regions located next to each other tend to have similar budget arrangements and fiscal policy. This similarity derives from horizontal inter-government interactions that are supported through activities of local associations of regional governments. Second, on average regions in both Western and Eastern Siberia are the regions with the largest territory. The size of region's territory might be in itself an important determinant of decentralization: larger territory requires more delegation of authority to local governments and thus decentralization.

To test the latter hypothesis, we re-estimate the two decentralization regressions adding the relative size of region
affected by the presence of ‘‘new” donors (such as China) by<br>using panel data for 54 African countries over the 1980–2013<br>period.|Empirical results indicate that the World Bank delivers loans with significantly fewer<br>conditions to recipient countries which are assisted by China. In fact, these receive 15%<br>fewer conditions for every percentage-point increase in Chinese aid.|
|Honig, 2020|Yes (Table<br>A1.2 and<br>Text)|To investigate the relationship between staff presence in<br>recipient countries and aid project performance.|Merely placing WB staff in developing countries has little effect on the success of<br>development projects. However, in the most fragile states, the presence of senior<br>personnel (WB Country Director) is associated with greater project success after the<br>"Strategic Compact" increased CD's power. In short, the impact of WB staff<br>decentralization is mixed and appears to be driven primarily by the power of senior<br>personnel in the field, not the ability of field staff to gather local information.|
|Ika et al., 2012|Yes (Text<br>only)|What is the relationship between critical success factors (CSFs)<br>for World Bank projects and projects’ success?|<br>There is a statistically significant and positive relationship between project success and<br>each of the five CSFs: monitoring; coordination; design; training; and institutional<br>environment. The most prominent CSFs for project supervisors are design and<br>monitoring.|
|Isham et al., 1997|Yes (Table<br>A1.2 and<br>Text)|Is there a link between WB project success and country<br>governance indicators?|There is a strong statistical link between a country’s civil liberties and WB project<
, who examined 702 detailed occupations in the United States, about 47 percent of those – including taxi drivers, accountants, paralegals and loan officers – could be replaced by robots in the next decade or two. These fears may be somewhat exaggerated; while automation may indeed eliminate entire categories of work, it is likely to generate indirect effects that lead to more jobs being created in other sectors and higher demand, generating employment growth across the economy as a whole. In particular, new technologies may generate employment opportunities and better salaries in sectors that use robot‐produced products as inputs; And the demand for new robot‐produced products may increase the need for upstream products, and the new technology may boost overall demand (Autor and Salomons (2018), World Development Report (2019)).<sup>3</sup> Thus, although the net effect in the short and medium term may be to destroy jobs, the longer‐term effects on employment may well be positive. Nevertheless, the potential effect of automation and rapid technological advances on jobs means that for Morocco – as for many other countries – a growth model based solely on labor‐intensive manufacturing is highly risky and may not help to boost employment sufficiently to absorb new entrants in the labor market and keep unemployment low.

> 2 As noted in Cattaneo _et al._ (2013), global value chains driven by large buyers ( _buyer‐driven chains_ ) have become relatively complex in labor‐intensive consumer goods sectors (clothes, shoes, toys, furniture, fruits and vegetables, and cut flowers).

> 3 Autor and Salomons describe these indirect effects in more detail. They look at 28 industries in 19 OECD countries between 1970 and 2007 and find that while employment has fallen by 8.2 percent in industries affected by technological advances, the indirect effects have led to overall increases in employment of 17.8 percent.

2
on the historical cross-country crisis experience

> 16 To provide an example of such a scenario, consider Lebanon which is identified by the Laeven and Valencia (2008) database to have gone through a Systematic Banking and Currency Crisis in 1990. However, (annual) real GDP growth from 1989 to 1991 was _−_ 42%, 26% and 38%, respectively for these three years. So GDP growth was an astonishing 26% and 38% for the years from 1990 to 1991 (the year of the crisis and the following year) while GDP growth was negative before the financial crisis. This example of the actual historical experience of Lebanon is clearly an exception that does not follow the typical scenario of a deep downturn in economic activity during a financial crisis. Including such a country experience in the Stress scenario construction would thus not be very informative.

21
of GenAI does not bypass those who are most in need of its advantages. Focus on informality in the transition will also be of key importance. We find that workers with formal jobs are more exposed to GenAI automation than their counterparts in the informal sector. Even though formal jobs typically offer some coverage of the social protection systems, eventual automation of these occupations does not guarantee that the same workers would easily find new formal employment. Since workers in the informal sector from developing countries rarely move to better paying and formal jobs (Donovan et al., 2023), allowing automation to slowly erode the existing formal sector would simply expand informality. Policies aimed at reducing the segmentation of the labor market along the formal-informal lines would help improve the chances of displaced workers’ transitioning back to the formal sector. In this context, it is important to recognize that technological transformation can actually offer opportunities for job e-formalization through innovative government services (Chacaltana Janampa et al., 2024).

Finally, while this study provides a detailed overview of the LAC region, it is not without limitations, some of which can be considered as open avenues for future inquiry. The first of those concerns data on the use of computers and internet at work, for which the imputation from PIACC was the best available strategy in our case. Obtaining new survey data for at least the most exposed occupations would surely offer more precision to future estimates in that regard. In the absence of such data, imputation based on the second cycle of OECD’s PIAAC, covering 2022-23, might be a viable option, as soon as such data become publicly available. Second, more could be done to understand how the task content of occupations varies across countries and in systematizing such differences according to income groups and possibly regional characteristics beyond LAC. Third, future studies could try to obtain more fine-grained characteristics of individuals’ internet access, since network latency, reliability or type of devices used may affect the adoption and impacts of GenAI by workers.<sup>42