rafmacalaba/datause-encoder-data huggingface dataset:
which shows that mobile broadband connectivity (3G/4G) is associated with a 2.1 pp increase in local economic growth.

The paper proceeds as follows. In Section 2, I describe and present the data used in the analysis. The empirical strategy and results are presented in Section 3. In Section 4, I examine the effects of mobile connectivity across heterogeneous groups. Also in Section 5 I explore the robustness of the main results to alternative hypotheses. Section 6 concludes the paper with a summary of findings.

# **2 Data**

This section presents an overview of the main datasets used in the analysis. The core datasets are mobile phone network coverage maps and nightlights data. The unit of analysis is the second administrative district in each country hereafter referred to as districts.

## **2.1 Global Mobile Phone Network Coverage**

Data on the global mobile network coverage comes from the Global System for Mobile Communications Association (GSMA) in collaboration with Collins Bartholomew, a digital mapping provider.<sup>4</sup> GSMA is an association of mobile network operators around the world. The data is compiled from network coverage submissions by mobile operators. In the recent past, additional coverage maps have been developed based on data from opencellid.org.<sup>5</sup> The data provides spatial coverage maps for three main technologies: 2G, 3G, and 4G mobile networks. From a technical standpoint, the main difference between these network technologies is that 2G mainly supports voice calls, SMS text messaging, and multimedia messages (MMS). However, in addition to these, 3G and 4G networks enable mobile broadband Internet services allowing users to utilize the Internet at a faster speed.<sup>6</sup> It is also important to emphasize that if a given location is covered by 3G, it is by definition covered by 2G, likewise 3G if covered by 4G. Therefore, in this paper, 3G and 4G coverage are hereafter referred to as 3G/4G: thus essentially focusing on mobile network coverage that supports broadband Internet (3G/4G) and those that only support voice and text messaging (2G). The data on 2G coverage used in the

> 4https://www.collinsbartholomew.com/mobile-coverage-maps/

> 5This is particularly important for countries without frequent updates from mobile operators

> 6Theoretically, the main differences between 3G and 4G is that the later is expected to be much faster than the former in theory, although in practice this is not always the case. See https://www.pcmag.com/news/ 3g-vs-4g-whats-the-difference

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abroad. The data set includes 63,767 domestic bond issuances and 20,994 cross-border bond issuances.

The literature on international banking typically compares the residence of banks and borrowers to distinguish between domestic and cross-border lending (Claessens, 2016; World Bank, 2018). The case of syndicated loans is not as straightforward because they can involve several financial institutions, both domestic and/or foreign ones. We follow Haselmann and Wachtel (2011) and compare the residence of the lead bank arranging the deal with the residence of the borrower to distinguish between domestic and cross-border syndicated loan issuances. The lead arranger establishes and maintains the relationship with the borrower, negotiates the terms of the loan, monitors compliance, and holds the largest share of the loan (Sufi, 2007; Haselmann and Wachtel, 2011; Bruche et al., 2017). Therefore, we define domestic loans as those with only domestic banks leading the syndication, whereas cross-border syndicated loans entail the participation of foreign banks as lead arrangers. The data set includes 56,589 domestic syndicated loans and 43,146 cross-border syndicated loans. For robustness, we considered two alternative definitions of cross-border syndicated lending: (i) when only foreign banks participate in the deal; or (ii) when more than half of the banks that participate in the deal are foreign. The results were robust to the ones reported in the paper.

To examine changes in debt issuance activity around crises, we focus on the GFC and domestic banking crises. The effects of the GFC were arguably different for advanced and emerging economies because (i) the crisis originated in advanced economies and (ii) advanced economies are more financially connected among themselves than with emerging economies (Claessens et al., 2010; Eichengreen, 2010; Didier et al., 2012). Therefore, for the purpose of studying the effects of the GFC, we classify economies as either advanced or emerging.<sup>6</sup> The final data set comprises 50 economies, 30

> 6 We follow the World Bank classification of countries as of 2012: advanced economies are those with a gross national income (GNI) per capita in 2011 above $12,476. All other economies are classified as emerging.

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to the temperature increase. No spatial patterns in Nepalese streamflow trends during 1965-1995 are apparent (Gautam and Acharya, 2012): observed trends in central and eastern Nepal are almost evenly divided between upward and downward; however, observed trends in western Nepal (Karnali-Mahakali River Basin) are mostly downward. Seasonally, a higher percentage of observed upward trends in pre-monsoon and winter average flow is noteworthy given potential snowmelt contribution to many studied sites in low flow periods; no trends are observed in the post-monsoon season (Gautam and Acharya, 2012).

On the Tibetan Plateau, Yao et al. (2007) note an increase of 5.5% in river runoff attributable to glacial melting, and an even higher increase (13%) in the surrounding Tarim River Basin. Significant increasing trends in streamflow over annual and wet season periods (MayOctober) are seen in Niyang River Basin, southeast part of Xizang province of China (Zhang et al., 2011). Zhang et al. (2011) attribute these trends to accelerated glacier melting based on both insignificant trends in annual precipitation and significant decreasing trends in wet-season water temperature. Streamflow in Lhasa River increases with two change points (1970, early 1980s) followed by an upward trend in the last 20 years due to increasing precipitation in summer and increasing temperature in winter (Lin et al., 2008).

In summary, increased contribution to streamflow of glacial and snowmelt in response to warming temperatures are noted as causes for the upward trend in streamflow in low flow periods and in areas of low precipitation (e.g. Tibetan Plateau and Indus basin).

# **2.6 Limited observations of ecosystem changes**

The Himalayan region is severely data-deficient in terms of observations of climate change impacts on ecosystem and biodiversity (IPCC, 2007). Globally, many endemic terrestrial, marine, and freshwater species are facing risk of extinction (Rosenzweig et al., 2007): the same should generally be valid for the Himalaya region as well.

Most local studies on trends and observed impacts of climate change on ecosystem and biodiversity are limited to perception studies. Tse-ring et al. (2010) document several observed impacts of climate change on biodiversity in the eastern Himalaya, but no literature is cited and many stated impacts appear to be subjects of further research. NBC (2011) describes perceptions of biodiversity change in Bhutan and relates them in the context of climate change. Related perceptions in Nepal (Tse-ring et al., 2010) largely point to changes in ecotone, reduced biodiversity, and new or proliferation of existing invasive species because of climate change.

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Table 14: IV estimates of the effect of ICT use on TFP: Services and Commerce

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Services|||Commerce||
|**Dependent variable:** ∆**ln(TFP)**|||||||
|∆Share of labor that uses computer|0.0080**|0.0178***|0.0274***|-0.0036***|-0.0011|0.0059***|
||(0.0038)|(0.0045)|(0.0100)|(0.0008)|(0.0008)|(0.0006)|
|F- frst stage|38.79|34.12|8.95|602.23|584.23|598.79|
|Sargan p-value|0.1345|0.5700|0.9562|0.3255|0.5137|0.5478|
|∆Share of labor that uses Internet|0.0052**|0.0146***|0.0096**|-0.0013**|-0.0002|0.0058***|
||(0.0024)|(0.0037)|(0.0046)|(0.0005)|(0.0005)|(0.0006)|
|F- frst stage|103.31|51.28|32.50|1251.50|1230.77|668.52|
|Sargan p-value|0.8628|0.2706|0.1693|0.2199|0.2833|0.6092|
|∆ln(stock of computer equipment/worker)|0.2834***|0.7115***|0.7792***|-0.2994**|-0.0340|-3.2149|
||(0.1020)|(0.1704)|(0.2448)|(0.1272)|(0.1733)|(2.1184)|
|F- frst stage|49.81|31.42|16.92|20.00|10.92|1.37|
|Sargan p-value|0.4660|0.4959|0.3778|0.1900|0.2703|0.1208|
|**Controls**|||||||
|∆ln(capital/worker)|No|Yes|Yes|No|Yes|Yes|
|Age dummies|No|No|Yes|No|No|Yes|
|Size dummies|No|No|Yes|No|No|Yes|
|Observations|6,241|6,241|6,241|8,333|8,333|8,333|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

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Having converted the international poverty line at PPP to local currency in 2005 we convert it to the prices prevailing at each survey date using the country-specific official Consumer Price Index (CPI).<sup>26</sup> The weights in this index may or may not accord well with consumer budget shares at the poverty line. In periods of relative price shifts, this will bias our comparisons of the incidence of poverty over time, depending on the extent of (utilitycompensated) substitution possibilities for people at the poverty line.

We started the series in 1981 and made estimates at three yearly intervals, up to 2005. For the 115 countries, 14 have only one survey; 17 have two surveys; 14 have three; while 70 have four or more surveys over the period, of which 23 have 10 or more surveys. If there is only one survey for a country then we estimate measures for each reference year by applying the growth rate in real private consumption per person from the NAS to the survey mean—assuming that the Lorenz curve for that country does not change.27 This seems the best option for dealing with this problem, though there can be no guarantee that the Lorenz curve would not have shifted or that a survey-based measure of consumption would have grown at the same rate as private consumption in the NAS. For example, growth in the latter might reflect growth in the spending by non-profit organizations— which are not separated from households in the NAS for most developing countries—rather than household spending (Ravallion, 2003).

Our benchmark estimates only use the annual NAS data for interpolation purposes given the irregular spacing of surveys; Chen and Ravallion (2004, 2008b) describe our interpolation methods. However, we provide sensitivity tests to the use of a Bayesian mixed method, combining the surveys and national accounts in estimating the mean, as a means of addressing the likely heterogeneity amongst surveys; we discuss this further below.

In the aggregate, 90% of the population of the developing world is represented by surveys within two years of 2005.<sup>28</sup> Survey coverage by region varies from 74% of the population of the Middle East and North Africa (MENA) to 98% of the population of South Asia. Naturally, the further back we go, the fewer the number of surveys—reflecting the

> 26 Note that the same poverty line is generally used for urban and rural areas. There are three exceptions, China, India and Indonesia, where we estimate poverty measures separately for urban and rural areas and use sector-specific CPIs.

> 27 For a few SSA countries, private consumption per capita is missing from the World Bank’s Development Data Platform; we use the series from _Africa Development Indicators_ 2007.

> 28 Some countries have graduated from the set of developing countries; we apply the same definition over time to avoid selection bias. In this paper our definition is anchored to 2005.

16
In this example, Thailand and China are engaged in GVC-trade that also involves Korea (backward) and Japan (forward). The bilateral backward and forward GVC measures for Thailand (T) and China (C) would be:

- _BackwardTC_ : Korean value-added in Thailand’s exports to China (i.e. the value of the tweeters and woofers)

- _ForwardTC_ : Thai value added in Chinese exports to Japan (i.e. the value of the frame)

The backward-GVC measure for Thailand and China captures the foreign value added in Thailand’s exports to China, while the forward measure captures the Thai value added exports to China that are re-exported. Both measures are elements of gross trade between Thailand and China but capture different aspects of GVC participation.<sup>27</sup> _Heterogeneous effects of services agreements_ As described in Section 2, the asymmetric and often non-discriminatory nature of services liberalization suggests that the effects of services trade agreements are likely heterogeneous. To allow for such heterogeneity, I include interactions variables with the SERV dummy: first with income pair dummies ( _SS_ , _NS_ , _SN_ ) and then with provision dummies ( _MFN_ , _NT_ , _NonEst_ , _Move_ ).

SS is a dummy variable equal to one if the exporter and importer are both developing countries (South-South), NS equals one if the exporter is highincome and the importer is a developing country (North-South), and SN equals one if the exporter is a developing and importer is a high-income country (South-North).<sup>28</sup> The benchmark (SERV) captures the effect of services trade agreements between developed countries (North-North).

> 27Note that the notion of “forward” and “backward” is relative for these bilateral indicators. The “forward” measure captures what is a forward participation from Thailand’s point of view but a backward participation from China’s point of view. Also, the value of Korean woofers exported to Thailand is captured by the forward-GVC measure between Korea and Thailand ( _ForwardKT_ ) as well as the backward GVC measure for Thailand and China ( _BackwardT C_ ).

> 28Income groups are defined according to the World Bank’s classification. North includes countries that were classified as high-income in 1995, and South includes countries that were classified as low- and middle-income in 1995. See Appendix A.1 for the of countries.

15
In order to solve these inconsistencies, we went through a process of “consolidation” among HS1996, HS2002, and HS2007 classifications.<sup>21</sup> A similar process was used by Schott and Pierce (2012) to concord 10-digit United States Harmonized System codes between 1989 and 2007 and by Wagner and Zahler (2011) to homologate among 6-digit HS1992, HS1996, and HS2002 classifications. The basic principle of consolidation is to identify the HS codes related to each other (e.g., codes that were split or merged with the modifications introduced by the HS2002 or the HS2007) and to replace them with a single code for the entire period. In the example above, this process results in the replacement of codes 030267 and 030268 by the code 030269 from year 2007 onwards. In this way, the products that are represented by these three codes are all included in code 030269 during the entire period.

As a result of this consolidation process 1104 codes are replaced by 402 codes that already exist in the HS lists but whose contents are altered as discussed in Cebeci (2012). Consequently, the number of unique potential HS 6-digit codes in the 1996, 2002, and 2007 classifications of 6065 declines to a final number of 4961 unique potential HS 6- digit codes in the consolidated classification.<sup>22</sup> In the cross-country raw dataset 4767 of those 4961 codes are present.

**e) Exclusion of oil exports:** We eliminate from the cross-country raw dataset observations

in HS Chapter 27 (hydrocarbons such as oil, petroleum, natural gas, coal etc.) given that

> 21 See Cebeci (2012) for the methodology used in the consolidation. The paper along with a list of consolidated codes and concordances are available at http://econ.worldbank.org/exporter-dynamics-database. 22 The number of HS 6-digit codes not affected by the consolidation process is 4559, obtained as the total number of codes 6065 minus the 1104 codes that disappeared and the 402 codes whose content changed. Despite this consolidation of HS codes, for the countries for which the data providers allow the sharing of the raw data, the country input database with the original HS 6-digit classifications is also available upon request.

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report significant warming in the eastern Indian Himalaya. For example, Jhajharia and Singh (2011) show a 0.2-0.8 °C/decade increase in Tav, 0.1-0.9 °C/decade in Tmax, and 0.10.6°C/decade in Tmin for stations exhibiting warming trend. Immerzeel (2008) reports a basinwide warming trend similar to global average Tav (0.6 °C/100 year for the 1901-2002 gridded dataset) for the Brahmaputra basin in the eastern Indian Himalaya and Tibetan Plateau. The Nepalese Himalaya also saw a warming trend in the last century. Shrestha et al. (1999) reports a trend varying between 0.4 and 0.9 °C/decade in the mean annual maximum temperature across different ecological belts of Nepal, with the high Trans-Himalayan region showing the highest and the Terai (lowland region) showing the lowest. While for the same regions for winter season they reported a trend varying between no trend in Terai to 1.2 °C/decade in the Trans-Himalaya. Tse-ring et al. (2010) report (Table 1) trend similar to that reported by Shrestha et al. (1999) for a slightly different time period. In Bhutan, average temperature in the Himalaya regions increased by 0.5 °C in the non-monsoon season from 1985-2002 (Tse-ring et al., 2010). Several studies report similar higher warming trends for the eastern Himalayas in China(Liu et al., 2006; Liu and Chen, 2000; Wang et al., 2008; Xu et al., 2008; Yang et al.,2011; You et al., 2008; Yunling and Yiping, 2005). Liu and Chen (2000) show a rate of 0.16 °C/decade for the annual mean temperature for 1955-1996. However, analyzing data from 90 stations recently Wang et al. (2008) found increased warming over the whole Tibetan Plateau with a rate of 0.36 °C/decade) during 1961-2007. Other studies with proxy temperature data reconstructed from tree ring width have also confirmed warming in the Tibetan Plateau. For example, Liang et al. (2009) reconstructed mean summer temperature (June-August) and found the last decade to be the warmest in the last 242 years (1765-2007) in southeast part of Xizang province of China. Yang et al. (2010) also observe distinct evidence of late 20<sup>th</sup> century warming in reconstructed annual mean temperature in southwest part of Xizang province of China, although within the range of natural climatic variability in the region.

The Everest (Qomolangma) region in China also exhibits warming at an average rate of 0.234 °C/decade in Tav from 1971-2004 (Yang et al., 2006). Higher warming rates of 0.28 °C/decade for annual average temperature are noted in the Chinese side of Brahmaputra basin (Yarlung Zangbo River Basin) during 1961-2005 (You et al., 2007).

6
Chart 8 Costa Rica: Ratio of Domestic to Foreign Debt <!-- Start of picture text --> 0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>85 86 87 88 89 90 91 92 93 94 95<br>- CRDDVVFO<br><!-- End of picture text --> Source: IMF Chart 9 Costa Rica: Foreign and Domestic Debt Ratios to GDP <!-- Start of picture text --> DD 0.35. 1.0 FD<br>0.30 %\ foreign debt domestic debt 0.9<br>- '- "0.8<br>0.25/ 0.7<br>0.20 0.6<br>% ' --<br>0.15 s's .<br>-0.4<br>__ _ __ _ __ _ __ _ _ __ _ __ _ __ _ _ 0.3<br>85 86 87 88 89 90 91 92 93 94 95<br>-CRDDVP ---- CRFODVP|<br><!-- End of picture text -->

Source: IMF 27
**Figure 12 Top 10 developing countries according to economic exposure to impacts of EU, US, and China policies on EV exports**

<!-- Start of picture text --> Economic exposure (LHS) Sector exposure (RHS)<br>0.07 100<br>0.06<br>80<br>0.05<br>0.04 60<br>0.03 40<br>0.02<br>20<br>0.01<br>0.00 0<br>Morocco Vietnam Türkiye Ukraine El Salvador Serbia Bosnia and Jordan Ecuador Argentina<br>Herzegovina<br>Percent Percent<br><!-- End of picture text --> _Source:_ Authors’ calculation based on 2022 data from the World Bank’s WITS. _Note:_ EV data is based on HS code 870380. Sector and economic exposure are export of EVs to three economies divided by exports of EVs to world and GDP, respectively. Mexico’s EV exports to three markets accounts for 82 percent of its EV exports to world and 24 percent of GDP, respectively.

## **3.2.3 Renewables and components**

**EU, US, and China policies on renewables are likely to increase demand for renewable energy tech and components.** The renewable energy tech and components sector (solar and wind products) is most exposed in Bosnia and Herzegovina, North Macedonia, Serbia, Tunisia, and Cambodia, with more than 80 percent of this sector’s exports going to the EU, US or China (Figure 13). In these countries the overall economy is exposed to the policies as renewable energy tech and components exports represent between 3 and 4 percent of GDP.

**Figure 13 Top 10 developing countries according to economic exposure to impacts of EU, US, and China policies on exports of renewable technology and components (solar and wind)**

<!-- Start of picture text --> Economic exposure (LHS) Sector exposure (RHS)<br>5 100<br>4 80<br>3 60<br>2 40<br>1 20<br>0 0<br>North Tunisia Cambodia Malaysia Vietnam Serbia Bosnia and Mexico Thailand Philippines<br>Macedonia Herzegovina<br>Percent Percent<br><!-- End of picture text --> _Source:_ Authors’ calculation based on 2022 data from the World Bank’s WITS and on the product list from Rosenow and Mealy (2024).

_Note_ : Sector and economic exposure are export of renewable technology and components to three economies divided by exports of renewable technology and components to world and GDP, respectively.

# **3.3 Impacts through prices**

23
themselves seem to charge higher fees than other banks, in foreign dominated banking systems, fees are lower and it is easier to open bank accounts and to apply for loans. On the other hand, in systems that are predominantly government-owned, customers pay lower fees but face greater restrictions in terms of where to apply for loans and how long it takes to have applications processed.

This paper is related to an emerging literature on access to financial services. Most of the existing research and the efforts underway focus on country case studies that aim at measuring and analyzing access to financial services at the household or firm level (see Claessens, 2006 and Claessens and Demirguc-Kunt, 2006). Few papers study this issue by focusing directly on banking services providers. Recently, Beck, Demirguc-Kunt and Martinez Peria (2007) present aggregate cross-country data on banking sector outreach (such as branch and ATM penetration, deposits per capita, and loans per capita) and show that these indicators closely track more difficult and costly to collect micro-level statistics of household and firm use of banking services. More directly related to our paper, Genesis (2005a) examines the costs of using bank accounts in seven countries - Brazil, India, Kenya, Malaysia, Mexico, Nigeria, and South Africa. However, in contrast to our study, this report focuses exclusively on deposit service affordability in a small number of countries.

While our paper is the first systematic effort to document and analyze banking barriers across countries, it has a number of limitations. First, our attempt to compare standard products across a broad sample of countries is limited by differences in financial practices. For example, while in some countries checking accounts are the prevalent form of transaction account, in other countries checking accounts might not be widely used and savings accounts might be preferred. Furthermore, even the same type of financial product, e.g., an SME loan, might have different definitions and features across banks and countries. We therefore assess barriers on somewhat 4