rafmacalaba/datause-encoder-data huggingface dataset:
|Rural Electrification Phase I Project of the Rural Electrification(APL|)Veasna Bun|
|---|---|
|Rural Electrification Phase I Project of the Rural Electrification(APL|)Veasna Bun|
|Rural Electrification Phase I Project of the Rural Electrification(APL|)Veasna Bun|
|Rural Electrification Phase I Project of the Rural Electrification(APL|)Veasna Bun|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|VN-GEF-RURAL ENERGY 2|KyHongTran|
|ECUADOR - Hydro SIBIMBE|David Reinstein|
|ECUADOR - Hydro SIBIMBE|David Reinstein|
|ECUADOR - Hydro SIBIMBE|David Reinstein|
|ECUADOR - Hydro SIBIMBE|David Reinstein|
|MEXICO - Wind Umbrella|Claudia Croce|
|MEXICO - Wind Umbrella|Claudia Croce|
|MEXICO - Wind Umbrella|Claudia Croce|
|MEXICO - Wind Umbrella|Claudia Croce|
|MEXICO - Wind Umbrella|Claudia Croce|
|MEXICO - Wind Umbrella|Claudia Croce|
|MEXICO - Wind Umbrella|Claudia Croce|
|MEXICO - Wind Umbrella|Claudia Croce|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|MX-Decentralized Infrastructure Reform and Development Loan|GregJ. Browder|
|Education-For-All in Vietnam(CIDA BETF TF053231 RETF TF053232)|An Thi MyTran|
|Education-For-All in Vietnam(CIDA BETF TF053231 RETF TF053232)|An Thi MyTran|
|Education-For-All in Vietnam(CIDA BETF TF053231 RETF TF053232)|An Thi MyTran|
|Health CareQualityImprovement Project|Amr Elshalakani|
|Health CareQualityImprovement Project|Amr Elshalakani|
|Health CareQualityImprovement Project|Amr Elshalakani|
# Acknowledgments

Converting and Transferring Currency (CTC) is part of the FDI Regulations database of the Global Indicators and Analysis Department (GIA) of the World Bank Group (WBG). The lead author, John Anderson, is a Private Sector Development Specialist working in GIA. The author is grateful for valuable peer review comments received from Jeff Chelsky and Swati Ghosh (WBG PRMPI); Stephan Dreyhaupt and colleagues in MIGA; Pablo Furche (Central Bank of Chile); Pierre Guislain, Christine Qiang, and colleagues in the WBG Investment Climate Department; Barry Johnston (consultant); Annamaria Kokenyne (IMF MCM); and Sarah Sanya (IMF SPR). Additional valuable comments were received on earlier drafts of the paper from Carolin Geginat, Melissa Johns, Augusto Lopez-Claros, Tanya Primiani, and Federica Saliola from GIA. The author is also grateful for contributions to the analysis and writing made by consultants Diane Pallez and Zeynep Caglar.

Two groups of people provided pro-bono contributions to CTC without which the data and indicators could not have been gathered and developed.

The first is the group of lawyers, accountants, bankers, and government regulators in each of the 98 economies covered by the report who responded to the CTC legal questionnaire. Their input about the foreign exchange regime for foreign direct investment in their economy was the fundamental source of data for the analysis. Grateful acknowledgment of the contributors across all topics of the FDI Regulations database is made at iab.worldbank.org. Particular acknowledgment is made of the contribution of KPMG’s Global Tax Practice, which responded to questionnaires in more than 40 economies.

The second is the expert consultative group formed to provide technical input to the development of the CTC topic. This group of global experts in foreign exchange, investment regulation, and political risk provided input on the focus of the CTC legal questionnaire and on the methodology to develop the CTC indicators. Members of the expert consultative group include:

|Paul Barbour, MIGA, Washington DC|Annamaria Kokenyne, IMF, Washington DC|
|---|---|
|Alick Bruce-Brand, formerly with South African|Peter Kusek, IFC, Washington DC|
|Reserve Bank|Sebastien Lieblich, MSCI, Geneva|
|Stephan Dreyhaupt, MIGA and IFC, Washington DC|Paulo Marcelo Cavalcanti Muniz, Central Bank of|
|Pablo Furche, Central Bank of Chile|Brazil|
|Richard Gordon, Case Western Reserve University<br>School of Law, USA|Julie Martin, Marsh, Washington DC<br>Latifah Merican-Cheong, Malaysian Securities|
|Hannah Ha, Mayer Brown JSM, Hong Kong SAR,|Commission|
|China|Hoda Moustafa, MIGA, Washington DC|
|Laura Hills, Hills Stern & Morley, Washington DC|Kleddao Sungnet, Bank of Thailand|
|Rick Jenney, Morrison Foerster, Washington DC|Emilio Wills, Lewin & Wills Law, Colombia|
|BarryJohnston,formerlywith IMF,Washington DC||
The remainder of the paper is structured as follows. Section 2 discusses our data sources, section 3 details our methodology, section 4 introduces a theoretical framework, section 5 presents our empirical results, section 6 conducts robustness checks, and section 7 concludes.

# **2 Data**

Our data source of academic articles is the Semantic Scholar Open Research Corpus (S2ORC) (Lo et al. 2020). The corpus contains more than 130 million English language academic papers across multiple disciplines. The papers included in the Semantic Scholar corpus are gathered directly from publishers, from open archives such as arXiv or PubMed, and crawled from the internet.

We placed some restrictions on the articles to make them usable and relevant for our purposes. First, only articles with an abstract and parsed PDF or latex file are included in the analysis. The full text of the abstract is necessary to classify the country of study and whether the article uses data. The parsed PDF and latex file are important for extracting important information like the date of publication and field of study. This restriction eliminated a large number of articles in the original corpus. Around 30 million articles remain after keeping only articles with a parsable (i.e., suitable for digital processing) PDF, and around 26% of those 30 million are eliminated when removing articles without an abstract. Second, only articles from the years 2000 to 2020 were considered. This restriction eliminated an additional 9% of the remaining articles. Finally, articles from the following fields of study were excluded, as we aim to focus on fields that are likely to use data produced by countries’ national statistical system: Biology, Chemistry, Engineering, Physics, Materials Science, Environmental Science, Geology, History, Philosophy, Math, Computer Science, and Art. Fields that _are_ included are: Economics, Political Science, Business, Sociology, Medicine, and Psychology. This third restriction eliminated around 34% of the remaining articles. From an initial corpus of 136 million articles, this resulted in a final corpus of around 10 million articles.

Due to the intensive computer resources required, a set of 1,037,748 articles were randomly selected from the 10 million articles in our restricted corpus as a convenience sample. Summary statistics of the final sample of 1 million articles are available in Table 1.

_Table 1. Summary Statistics of Article Corpus. 2000-2020_

|Field|Published in Journal<br>(1=yes)|Data Use<br>(1=yes)|Country Identified<br>(1=yes)|Articles|Share of<br>Articles|
|---|---|---|---|---|---|
|Business|0.56|0.64|0.30|28,571|2.8|
|Economics|0.79|0.68|0.28|62,241|6.0|
|Medicine|0.96|0.85|0.10|840,920|81.0|
|Political Science|0.42|0.33|0.34|26,185|2.5|
|Psychology|0.75|0.70|0.14|44,191|4.3|
|Sociology|0.90|0.33|0.25|35,640|3.4|

4
# **3.2 Exposure to disasters**

The EM-DAT data provide insight into the Caribbean’s exposure to major natural disasters. In the countries included in this study, the disaster categories captured in the EM-DAT database combined to a total of 188 events that were large enough to overwhelm local capacity since 1950 (Figure 1a). Meteorological events and hydrological events occurred most frequently, while geophysical events and climatological events were less common. Haiti stands out as the most frequently affected of the five countries. It experienced a total of 126 events, 44 of which were meteorological and 68 of which were hydrological in nature. It is also the only country to experience geophysical and climatological events on a somewhat regular basis. Meteorological events are also common in the other countries in the Caribbean hurricane belt; Belize, Dominica, and Saint Lucia respectively experienced 17, 13, and 15 meteorological events. Belize and Saint Lucia respectively also experienced five and four hydrological events. Suriname, which is not located in the Caribbean hurricane belt, recorded the lowest number of disasters with five hydrological events.

A clear increase in the number of major events can be observed over time. Figure 1b shows the number of events experienced per country in 10-year intervals (11-year period from 1950 to 1960 and 13-year period from 2021 to 2023). Especially the number of meteorological and hydrological events amplified visibly over the period from 1950 to 2023. For instance, before the 2000s the number of events experienced by Haiti in each category tended to be in the low single digits. By the 2000s, the number of meteorological and hydrological events was in the double digits. Likewise, before the 2000s, Suriname experienced only one hydrological event. In the 2000s, it experienced four. Moreover, a comparison with other regions (not displayed) makes clear that the prominence of meteorological events sets the Caribbean apart. In contrast, in each of the main regions in the EM-DAT database (Oceania, Europe, Africa, Americas, Asia), the data show a predominance of hydrological events.

The latter is important because meteorological events tend to impact a high number of people in the countries we study. In Belize, Dominica, Haiti, and Saint Lucia, the average meteorological event affects about 31,000, 15,000, 163,000, and 19,000 people respectively. Figure 1c expresses the average number of people affected by individual natural disasters as a share of the total population in the country at the time of the event. In Dominica and Saint Lucia, the average meteorological event affects nearly one in three people. In Belize it affects nearly 8
Table A3 : Summary of eligibility criteria for provincial water social tariff schemes

|**Area**|**Eligibility**|**Benefit**|**Implementation**|
|---|---|---|---|
|Metropolitan<br>Area|Eligible households have<br>Income below poverty line (<US$172.8)<br>No more than three months payment backlog|Fixed sum discount, debt<br>and<br>disconnection<br>amnesty|Resources are allocated to<br>municipalities,<br>who<br>are<br>responsible<br>for<br>identifying<br>beneficiaries|
|Chaco|Eligible households are<br>Poor households led by children, pensioners or<br>disabled<br>Pensioners with incomes <US$33.8/month|50%<br>discount<br>on<br>the<br>minimum charge|Beneficiaries receive the AIPO<br>voucher that can be used to pay<br>water or electricity bills|
|Formosa|Concessionaire applies subsidy to low income<br>neighborhoods|||
|Mendoza|Eligible households have<br>Small dwellings <60m<sup>2</sup><br>Live in shanty towns<br>Score Level 8 or 9 in health register<br>Appropriate social development classification<br>Furthermore<br>If unmetered, pay minimum charge<br>If metered, consume <15m<sup>3</sup>|50%<br>discount<br>on<br>the<br>minimum charge||
|Salta|According to ENRESP score based on<br>Per capita income<br>Type of housing<br>Overcrowding<br>Geographic area<br>Educational attainment<br>But disqualifying if<br>Monthly bill>$10<br>Electricity consumption >20kWh/person/mo.<br>Possess more than one dwelling<br>Possess a motor vehicle<br>Receive TV cable service<br>Receive telephone service|Variable<br>according<br>to<br>ENRESP score|A database was created based<br>on<br>national<br>health<br>system<br>(APS)<br>and<br>social<br>benefits<br>system (SISFAM) databases|
|Santa Fe|Eligible households are<br>Low income pensioners<br>Lacking telephone service|70% discount on bill|Interested<br>candidates<br>must<br>apply to the authorities|
|Santiago del<br>Estero|Eligible households<br>Lack sewerage service<br>Lack telephone service<br>Have incomes <US$101.6/mo.<br>Live in deprived zones (0.9<Z<1.0)<br>Have unsatisfied basic needs|50% discount on bill|Concessionaire responsible for<br>identifying beneficiaries|
|Tucumán|Pensioners who<br>Are family breadwinners<br>Possess a single dwelling<br>Receive income US$94.8/mo.|50% discount on bill||

55
voted signi fi cant attention to identifying di ff erent factors that can strengthen the farm-nonfarm inter-linkages (see, Lanjouw and Feder, 2001 for a survey). Empirical evidence from this early literature demonstrated the presence of a positive and signi fi cant correlation between farm and nonfarm income and employment, and highlighted the importance of rural roads in augmenting these inter-linkages (Hazell and Haggblade, 1990; Hazell et. al, 1991; Lewis and Thorbecke, 1992).<sup>1</sup> A pair of recent studies by Foster and Rosenzweig (2004 a and b), however, raised some doubts about the universality of a positive correlation between farm productivity and non-farm employment/income. Utilizing a rigorous empirical methodology and a panel dataset from India, these studies fi nd that only non-tradeable nonfarm activities such as services are positively in fl uenced by agricultural productivity growth. In contrast, tradeable nonfarm activities such as small manufacturing move into areas with lower wages implying a negative relationship with agricultural productivity growth.<sup>2</sup> This particular pattern of nonfarm growth, according to Foster and Rosenzweig (2004a), had contributed signi fi cantly to reducing spatial wage inequality and overall poverty in India. Although these studies mentioned possible in fl uence of rural towns on non-farm activities, none has explored e ff ects of larger urban centers on the location of such activities.

The second insight into the evolution of nonfarm activities comes from the literature on urban-rural linkages, particularly from traditional and so-called new economic geography. According to this work, urban demand exerts a distinct in fl uence on the types of activities that take place in rural areas (von Thünen, 1842; Fuijta, Krugman and Veneables, 1999; Henderson, Shalizi and Venables 2001; Renkow, 2006). The inter-play of agglomeration economies and congestion dis-economies, according to this view, determines location of activities over space re-

> 1 It should be noted that because of their reliance on primarily cross-section, and to some extent pooled data, and of their inability to address the targeting of road infrastructure, the positive correlation between farm and non-farm income and employment found in these studies do not necessarily establish any causal relationship.

> 2 Harris (1987) reported similar fi ndings with respect to manufacturing located in rural towns in India.

2
**Figure 1: Relative bias of simulation results using Rapid Consumption estimation.**

<!-- Start of picture text --> 8%<br>6.4%<br>6%<br>4.2%<br>4%<br>1.6%<br>2%<br>0.6%<br>0.1% ‐0.6%<br>0%<br>‐2%<br><!-- End of picture text -->

**Figure 2: Relative standard error of simulation results using Rapid Consumption estimation.**

<!-- Start of picture text --> 100%<br>88.0%<br>80%<br>60%<br>40%<br>16.3%<br>20%<br>1.8% 0.2% 0.6% 1.0%<br>0%<br><!-- End of picture text -->

**Source: Authors’ own calculations based on NBHS 2009 data.**

# Durable Consumption Flow

The consumption aggregate includes the consumption flow of durables calculated based on the user‐cost approach, which distributes the consumption value of the durable over multiple years (Amendola and Vecchi, 2014). The user‐ cost principle defines the consumption flow of an item as the difference of selling the asset at the beginning and the end of the year as, this is the opportunity cost of the household for keeping the item. The opportunity cost is composed of the difference in the sales price and the forgone earnings on interest if the asset is sold at the beginning of the year. The current price of the durable is _pt_ . If the durable item would have been sold one year ago, the household would have received the market price for the item twelve months ago plus the interest on the revenue for one year. The market price from 12 months ago is calculated by adjusting for inflation π� and annual physical or technological depreciation rate δ arriving at<sup>28</sup> with the nominal interest rate denoted as _it_ . Alternatively, the household can use the durable and sell it after one year of usage for the current market price _pt_ . The difference between these two values is the cost that the household is willing to pay for using the durable good for one year. Hence, the consumption flow is: By assuming that δ�π� ≅0 , the equation simplifies to where r� is the real market interest rate i� �π� in period _t_ . Therefore, the consumption flow of an item can be estimated by the current market value p� , the current real interest rate r� , the inflation rate π� and the depreciation rate δ . Assuming an average annual inflation rate π , the depreciation rates δ can be estimated utilizing its relationship to the market price<sup>29</sup> :

> 28 Assuming a constant depreciation rate is equivalent to assuming a “radioactive decay” of durable goods (see Deaton and Zaidi, 2002).

> 29 In particular π solves the equation ∏�������1 �π�� ��1 �π�<sup>�</sup> .

11
However, our projections also highlight the unnecessarily high cost of regulatory inaction. If economic reform is not supplemented by tighter regulation, most of China's waterways will remain heavily polluted and many thousands of people will die or suffer serious respiratory damage from air pollution. Continuing recent trends in tightened regulation for water pollution will result in sharp improvements; adopting an economically-feasible policy of much stricter regulation will restore the health of many waterways. In the case of air pollution, the stakes are even higher because regulatory enforcement has apparently weakened in many areas during the past half-decade. Reversal of this trend will save many lives at extremely modest cost. Adoption of NEPA's recent recommendation for a tenfold increase in the air pollution levy would produce a major turnaround in most cities, and our results suggest that even NEPA's recommendation is very conservative. For a representative Chinese city, Zhengzhou, we find that a fiftyfold increase in the levy appears warranted from an economic perspective.

Finally, our microeconomic analysis of abatement alternatives has highlighted the costeffectiveness of a pollution control policy targeted on large sources of particulate and S02 emissions. Abatement of particulates from large non-SOE plants is so cheap that even an extremely conservative economic analysis affrms the benefits of very high abatement levels. Our analysis suggests that high social rates of return can be obtained from regulation which induces at least 70% abatement of S02 and even greater abatement of particulates from large urban industrial facilities. At present, lax regulation of such facilities is causing so much health damage that reform seems imperative. Inaction amounts to valuing a Chinese worker's life at less than $US 500, a figure which is tragically low by any standard.

39
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31