rafmacalaba/data-use-mentions-extended huggingface dataset:
also a panel of countries over time, Calderón and Servén (2010, 2012, 2014) argue persuasively that infrastructure can have positive effects on both growth and distributive equity. These beneficial effects, however, require a framework that regulates, organizes, and coordinates the governments and companies that build public infrastructure and provide its services. Moreover, as highlighted by Pritchett (1996) and Devadas and Pennings (2018), the amount of infrastructure spending is not necessarily an indication of effective infrastructure investment. The quality of spending matters, and this seems to be highly related to the strength of public institutions (World Bank 2003, 2017r).

# **Institutions**

_Public Institutions_ – in the regulatory, justice, policy, and political systems – can promote social and economic stability, provide a safe living and working environment, defend property rights, and safeguard basic civil rights. The environment and policies that public institutions provide have a large, fundamental impact on economic development (North 1990; Acemoglu, Johnson, and Robinson 2004). The evidence that good governance (reflected in political stability, the rule of law, the protection of property rights, bureaucratic quality, transparency and accountability, and the absence of corruption) has a positive effect on productivity and economic growth is large, comprehensive, and convincing.

– Barro (1991) shows in a study of around 100 countries for 1960 85 that economic growth is positively related to political stability and inversely to government-induced market distortions. Using ethnolinguistic fractionalization as an instrumental variable for measures of government corruption, Mauro (1995) finds that corruption has a statistically significant and economically large negative effect on economic growth. Knack and Keefer (1995) find that property rights, proxied by contract enforceability and risk of expropriation, has a substantial impact on economic growth, even after accounting for capital accumulation. Rodrik, Subramanian, and Trebbi (2004) show that the quality of institutions, measured by a composite indicator of the protection of property rights and the rule of law, has a
to increasingly large negative effects on earnings and employment in regions exposed to deeper tariff cuts (Kovak, 2013; Dix-Carneiro and Kovak, 2017). Since the more affluent regions, including the country’s leading areas such as São Paulo, Belo Horizonte, and Rio de Janeiro, experienced larger tariff cuts, liberalization contributed to a decline in inter-regional inequality. Costa et al. (2016) find that strong productivity growth in China led to the de-industrialization of Brazilian cities, but generated demand for Brazilian commodity exports. Hence, they also conclude that the “China shock” reduced regional inequality in Brazil. In Peru, Sotelo (2020) finds that increased global demand for grains and higher grain prices have had uneven rural-urban effects, benefiting farmers, but hurting urban consumers.

A collection of studies uses survey data from the late 1990s to the early 2000s to document territorial household income differences and dynamics in nine Latin American economies.<sup>4</sup> The analysis is conducted at the 2<sup>nd</sup> administrative level, except in Colombia, where it is at the 1<sup>st</sup> administrative level, and Ecuador, where the data are at the level of the parish, which is a sub-division of the 2<sup>nd</sup> administrative level. The synthesis of the results from these studies, provided by Modrego and Berdegué (2016), indicates slow, absolute convergence of mean household incomes in Brazil (Favareto and Abramovay, 2016), Colombia (Fernández et al. 2016), Ecuador (Larrea et al. 2016), Guatemala (Romero and Zapil Ajxup, 2009), and Mexico (Yúnez Naude et al., 2016),<sup>5</sup> but not in Chile (Modrego et al., 2016), and Peru (Escobal and Ponce, 2016). However, the results from these country studies are not directly comparable due to differences in the definitions used in each
Panel A: Percent of providers diagnosing correctly <!-- Start of picture text --> Diabetes<br>Pneumonia<br>Diarrhea<br>Neonatal asphyxia<br>Post-partum hemorrhage<br>Tuberculosis<br>Malaria<br>0 20 40 60 80 100<br>Providers diagnosing correctly (%)<br><!-- End of picture text --> Panel B: Percent of providers diagnosing and treating correctly <!-- Start of picture text --> Diabetes<br>Tuberculosis<br>Post-partum hemorrhage<br>Pneumonia<br>Diarrhea<br>Neonatal asphyxia<br>Malaria<br>0 20 40 60 80 100<br>Providers diagnosing and treating correctly (%)<br><!-- End of picture text -->

**Figure 1: Diagnostic and treatment accuracy**

Dots represent country-specific means, calculated using country-specific sampling weights, vertical bars indicate mean performance across countries, and boxes delineate the interquartile range. Panel A: Percent of providers diagnosing correctly. Panel B: Percent of providers diagnosing and treating correctly. Data are from clinical vignettes from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Senegal (2010), Sierra Leone (2018) Tanzania (2016), Togo (2014), and Uganda (2013). The malaria vignette was not used in the Kenya (2018) survey and the post-partum hemorrhage, neonatal asphyxia, and diabetes vignettes were not used in the Senegal (2010) survey.

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We combine the estimates from the regressions with the results of the GIS analysis to quantify the potential trade effects of the BRI. An upper‐bound estimate is that total BRI trade increases by 4.1 percent. This assumes that trade in all products can switch transportation modes relatively easily to take advantage of the improved transport links. A lower‐bound estimate, which assumes that products cannot switch transportation mode, is that trade increases by 2.5 percent. The trade effects of the BRI present a large variation across individual countries and across sectors. Largest improvements in trade are predicted for countries in Sub‐Saharan Africa, Central and Western Asia and for products that use time sensitive inputs such as chemicals. Complementing BRI infrastructure improvements with trade reforms such as deepening trade agreements or better market access would boost trade by respectively, 7.9 and 12.9 percent.

While there is much debate surrounding the Belt and Road Initiative, little rigorous economic analysis on its effects has been done so far. Exceptions include Villafuerte, Coron and Zhuang (2016) and Zhai (2018), who use a Computable General Equilibrium (CGE) model to assess the impact of the BRI on trade and economic growth.<sup>4</sup> Differently from the CGE approach, our paper builds on a number of recent econometric studies. First, there is a recent literature that aims at assessing the impact that reduction in time to trade, due to trade facilitation reforms or infrastructure improvements, has on trade flows (e.g. Djankov, Freund and Pham, 2010; Hummels and Schaur, 2013; and Baniya, 2017). Second, several studies analyze the trade effects of infrastructure projects (Donaldson, 2013; Duranton, Morrow and Turner, 2012; Alder, 2015). Finally, a subset of this literature also relies on georeferenced data and GIS analysis to assess the trade and spatial effects of transport infrastructure (e.g. Roberts, Deichmann, Fingleton and Shi, 2010; Roberts,
publicly available data alone is sufficient to establish that the market is being used in a significant way as a “crypto vehicle currency” to make fiat currency payments (at home or abroad) or to send capital abroad.

Of course, there is substantial anecdotal evidence on the use of crypto for transactions purposes, and it is well known that Bitcoin is the medium of choice on the Dark Web, not to mention ransomware. But systematic analysis has been lacking to support that claim. Our methodology allows us to systematically establish a lower bound on the use of Bitcoin as a medium of exchange. It is quantitative, rather than anecdotal. In principle, the same algorithm can potentially be applied to data from any P2P exchange, should the researcher or regulator obtain (or demand) similar data to ours. This includes not only P2P exchanges that mainly match buyers and sellers (and hold Bitcoins in escrow until both sides confirm the fiat money payment has cleared), but centralized P2P exchanges as well.<sup>9</sup> Table 1 provides some descriptive statistics of the core data set.

**Table 1: Descriptive Statistics**

|Number of Trades|45 528 193|
|---|---|
|USD Trade Volume|USD 11.0 billion|
|Average Trade Size (USD)|242|
|Average Trade Size (BTC)|0.03734 Bitcoin|
|Largest Trade Size Recorded|USD 2.3 million|
|Number of Fiat Currencies|135|

Source: LocalBitcoins.com API, authors’ calculations

> 9 In a centralized exchange, typically catering only to a single country, the exchange serves as an intermediary, buying and selling on its own account, with price moving to match demand and supply.  Coinbase is the largest such exchange in the United States. Again, it is important to emphasize that although “on-chain” transactions are public and therefore directly observable, the vast majority of off-chain transactions are proprietary information, making the LocalBitcoins
may be required to improve the outputs due to inadequate and poor-quality input datasets. The process is categorized into three main parts presented in the following sections.

# **3.2.1 Urban and Rural Classification**

People living in and around cities are referred to as the urban population, which usually has a fairly high population density. In contrast, a rural population is dispersed over vast areas of land and is found in developing regions. The overall population and geographic area are primary determinants of the size of the enumeration areas (EAs). A balance between maximum population and area constraints is needed to produce EAs of manageable size. Because of the disparities in population density and distribution between urban and rural administrative units, different criteria should be applied while forming EAs.

Armenia does not have well-defined boundaries to separate urban and rural areas. The following approaches have been used to establish the border between Armenia’s urban and rural classes as a first step in developing the national sampling frame:

1. Take the GHS-SMOD dataset and extract all urban classes; combine them into a single class.

2. Convert the raster dataset from the combined classes to polygon vector format. These polygons represent the urban area.

3. Extract the built-up area with values greater than 0 from the GHS-BUILT-S data.

4. Create polygons from the raster built-up area. The country’s settlement boundaries and extent are represented in this output.

5. Apply a 50-meter buffer to output 4 to account for recent urban growth and prevent cutting structures at the edge of the settlements.

6. Intersect output 5 with the administrative boundary in Armenia.

7. Make the necessary manual and automatic adjustments when necessary. For example, an administrative boundary should be regarded as urban if more than 90% of areas are urban (e.g., Yerevan).

8. Any polygon in output 7 that crosses the output 2 boundary is an urban area. The rural class encompasses the remaining portion of the administrative boundaries.

# **3
counties. Ten randomly sampled households were interviewed in each village.

The surveys included community, household and individual questionnaires. The community schedule collected data on natural conditions, infrastructure and access to services. The household survey collected data on ( _inter alia_ ) incomes, consumptions and assets. The individual questionnaires covered gender, age, education and occupation.

> 11 The attrition rate was 12% over the full period. Chen et al. discuss tests for attrition bias and for bias in selecting replacement sample households.

7
# **Figure 2: Individual equity investment performance by decade**

<!-- Start of picture text --> Panel A) Direct investments<br>0 1 2 3+<br>PME vs. S&P 500<br>Panel B) Investments in funds<br>0 1 2 3+<br>PME vs. S&P 500<br>1961-1969 1970-1979 1980-1989<br>1990-1999 2000-2009 2010-2019<br>1.5<br>1<br>Kernel density .5<br>0<br>1.5<br>1<br>Kernel density .5<br>0<br><!-- End of picture text --> _Source:_ IFC equity cash flows _Note:_ For the graph, values above 3 were recorded as 3. In Panel B, line for 1980-1989 includes one investment with vintage year 1978.

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-  31  -

unskilled labor products as the major levers of Lithuanian export expansion. In contrast, unskilled labor intensive products were the best performers in Bulgarian exports. In both periods, the shares of capital intensive and skilled labor intensive products in total exports of CEEC-8 increased whereas they fell in Bulgarian exports. Croatia’s exports, except for much slower overall growth, displayed similar change as CEEC-8.

While this should come as no surprise considering Bulgaria’s two, rather than one, transformational recessions, the comparison with Romania, which has also gone through them, raises concerns. Growth in Romania’s exports was much more robust in 1996-2003 exceeding in 2000-03 the increase in CEEC-8 exports. But more importantly, skilled labor intensive products replaced capital intensive and unskilled labor intensive products as main levers of impressive growth in 2000-03. Both Bulgaria’s and Romania’s performance, differences between them notwithstanding, diverged from that of SEE-4, where natural resource intensive products were behind their overall rebound in exports in 2000-03.

# **C** **_._** _What do developments in trade with the EU say about labor markets?_

While a comprehensive answer to this question would require a detailed analysis of the labor market itself, the evidence from developments in the factor intensity of Bulgaria’s trade with the EU suggests that the cost of labor relative to capital is low. On the other hand, however, the country’s double-digit unemployment rate would seem to reflect the relatively high labor cost. Since wage rates are low, this in turn would suggest high cost of compliance with labor market regulations, which effectively makes labor much more expensive than wage rates alone would suggest. Although Bulgaria’s export basket reflects the abundance of cheap labor, high unemployment rates would point to barriers preventing full exploitation of this potential. While some of them maybe related to distance and weaknesses in services linking domestic markets with international markets, other stem from domestic rigidities.

Labor market rigidities appear to account for the continuing
disappearance of the trend inflexion (apparent in Table 3 which included fixed effects by products but not by importing country because the unit of observation was allOECD imports) suggests that the inflexion resulted from a composition effect between importers as described above.

To sum up, the observed re-concentration of Table 2 is robust to the decomposition of OECD imports by importing country. However, as Tables 3 and 4 show, it does not occur at the extensive margin, all of the action being at the intensive margin.

Table 4 Regression results, OECD import concentration on time trend

# Herfindahl

||_(1)_|_(2)_|_(3)_|_(4)_|
|---|---|---|---|---|
|Time|-0.010|-0.009|-0.009|-0.007|
||(115.74)***|(86.33)***|(140.45)***|(92.51)***|
|Time, squared|0.000|0.000|0.000|0.000|
||(51.13)***|(27.11)***|(41.94)***|(3.42)***|
|Post 2000||0.005||0.008|
|||(14.76)***||(39.35)***|
|Constant|0.807|0.802|0.798|0.789|
||(932.13)***|(859.28)***|(1320.17)**|(1216.18)***|
|Observations<br>|1'154'420|1'154'420
2

# **I.** **<u>Introduction</u>**

The amount financial and economic news has increased dramatically over the past 20 years. The rise of real time news-wire such as Reuters and Bloomberg has also greatly increased the speed at which news travel across the world. Beyond providing information, journalists also choose to portray events related to asset market variations using tonal words that could intrinsically affect investors’ investment decisions. This paper explores the impact of such media sentiment on equity prices.

Using more than 4.5 million Reuters articles published across the globe between 1991 and 2015, we find that news tone – our measure of news sentiment - robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. However, not all news has the same impact. Local (countryspecific) news optimism (or pessimism) only affects equity returns temporarily, i.e. for a few days. By contrast, changes in global news sentiment have a larger impact on equity returns around the world that does not reverse in the short run, suggesting a novel and arguably stronger source of sentiment-driven asset price fluctuations. Combining our news sentiment indices with daily equity flows from mutual funds investing in sixteen EMs between 2007 and 2015, we find an effect strikingly close to that of stock returns: although local news optimism attracts equity flows for a few days only, global sentiment optimism attracts them permanently. However, this effect is driven by the (net) asset demand of foreign funds domiciled outside of the country rather than local funds domiciled in the country, suggesting that news tone affects prices mostly through its effect on foreign investors’ sentiment.

Our paper is constructed as follows. Using the fraction of positive and negative words in each article to capture the tone of news published, we first construct a daily news-based sentiment index for 25 advanced and emerging countries between 1991 and 2015. We then estimate the response of equity prices to sentiment shocks using Jordà’
World Bank. 2020-2024. Trade Watch series (May 2020 - First Quarter 2024) (English). Washington, D.C. : World Bank Group. https://www.worldbank.org/en/topic/trade/brief/trade-watch World Bank. 2023. “Connecting to Compete 2023: Trade Logistics in an Uncertain Global Economy - The Logistics Performance Index and its Indicators” (English). The International Bank for Reconstruction and Development/The World Bank, Washington, DC (2023) https://lpi.worldbank.org/ World Bank. 2022. “The Container Port Performance Index 2022: A Comparable Assessment of Performance Based on Vessel Time in Port“ (English). Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/099051723134019182/P1758330d05f3607f09690076fedcf4e71a World Bank. 2021. “The Container Port Performance Index 2021: A Comparable Assessment of Container Port Performance” (English). Container Port Performance Index Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/099125006072255739/P1758330dccd270a70b31e0619fee32eb41 World Bank. 2007. Port Reform Toolkit. Washington, DC: World Bank. World Bank. 2010. “Trade and Transport Facilitation Assessment: A Practical Toolkit for Country Implementation.” Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/2490 World Bank. 2003. Port Reform Toolkit. Washington, DC: World Bank. https://elibrary.worldbank.org/doi/epdf/10.1596/0-8213-5046-3 Bai, Xiwen; Jesús Fernández-Villaverde, Yiliang Li, and Francesco Zanetti, 2024. “The Causal Effects of Global Supply Chain Disruptions on Macroeconomic Outcomes: Evidence and Theory” (NBER Working Paper 32098), https://www.nber.org/system/files/working_papers/w32098/w32098.pdf 20
cost‐risk and qualitative factors (multilateral loans, commercial loans, domestic bonds, Eurobonds) 4. Identify baseline projections and risk in fiscal, monetary, and market indicators (projections of exchange rate, capital account, international reserves, expectations of domestic and global liquidity conditions, market rates, and likely pricing of non‐market instruments) 5. Review longer‐term structural factors (commodity price vulnerability, access to concessional financing, trends in real effective exchange rate and inflation) 6. Assess and rank alternative debt management strategies based on cost‐risk trade‐off (change in cost and risk indicators, feasibility of the strategy, and success of the strategy in meeting the public debt management objectives) 7. Review the implications of candidate strategies with fiscal and monetary policy, and financial market development (interactions with fiscal and monetary indicators, debt sustainability indicators, and the debt market) 8. Propose a preferred DMS, secure approval for and publish the DMS.

> 103 Directive No. NBE/TRE/001/2011.

> 104 World Bank (2018). Ethiopia Growth and Competitiveness Programmatic Development Financing. World Bank Group, Washington D.C.

> 105 This box draws on World bank (2015). Issuance Plan for Government Securities: Guidance Note. World Bank Group, Washington D.C.

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total treaty-shopping costs are linear in the avoided tax base. This assumption is easier to reconcile with empirical data when using bilateral FDI data, such as van’t Ried and Lejour (2018), than when using aggregate FDI data as we do.<sup>25</sup> The MNE seeks to maximize its global return after-tax and after treaty-shopping by choosing the investment magnitude and the place of financing subject to the constraint that the investment is fully financed ∑α� � �1. Formally, the MNE’s maximization problem reads: 23 For simplicity, we assume in the theoretical model that these costs are constant across all resident locations. In the empirical section, however, we allow them to vary between investment hubs and other locations.

24 Arguably costs of treaty shopping have escalated due to the recent emphasis in the OECD/G20 BEPS initiative on introducing anti-abuse provisions that require sufficient substance for an entity to qualify for treaty benefits.

> 25 In other words, it is easier to argue that all investments from country P to country S are rerouted through conduit country C, than to argue that all investments in country S are structured through country C.
the method of constructing ASI. More information on the Global Administrative Unit Layers (GAUL) is available at https://data.apps.fao.org/map/catalog/static/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654. At GAUL level 2, there are 63376 districts or counties.

> 12 The extreme heat hazard maps are available from

> <u>https://datacatalog.worldbank.org/search/dataset/0040194/Global%20extreme%20heat%20hazard?version=2.</u>

13