rafmacalaba/datause-encoder-data huggingface dataset:
the speed and size of tariff reductions across countries and across products. This variation helps us to assess how preferential liberalization has affected trade and tariffs.

We first examine trade effects. We find that the formation of the trade bloc has had a meaningful positive impact on the trade flows among members. Interestingly, this does not seem to have happened at the expense of trade with outsiders. Growth of imports from nonmembers did not falter after the formation and the enlargements of AFTA. Nor is growth in imports from nonmembers significantly different from growth in imports from members subsequent to AFTA.

To examine the effect of AFTA on trade in more detail, we focus on the impact of preferential and multilateral tariff changes on intra-bloc import growth and import growth from excluded countries. For the analysis, we rely on detailed data on preferential and MFN applied tariffs at the product level for all ASEAN members, since the bloc was created in 1993 through 2007. This allows us to use a large set of fixed effects that control for a wide range of unobserved shocks. We find strong evidence that reductions in MFN tariffs have stimulated trade with nonmembers, but no evidence that preferential tariff reduction has reduced trade with nonmembers.

Next, we examine the effect of preferential tariff reduction on external tariffs. It is possible that preferential and MFN tariffs are related to each other, and that governments respond to changes in the preferential tariffs by adjusting MFN tariffs. We therefore proceed to analyze the reaction of the bloc members’ trade policies visà-vis outsiders. Specifically, we ask: Has the reduction of tariffs on within-ASEAN trade led its members to change their barriers on imports form excluded countries? If so, have they gone up or down as a result of ASEAN, and by how much?

Several theoretical forces have been advanced suggesting that the formation of a free trade agreement such as AFTA should induce changes in external tariffs. But just as in the trade creation/trade diversion debate, there are reasons supporting changes is either direction. Once again, the resolution of the debate must be empirical.

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highest debt/equity ratios -- building and civil engineering, engineering, mining, communication, domestic trade, retail sales, publishing, and other services. These are sectors that may<sup>have been</sup> forced to provide their output at subsidized prices and as a consequence incurred substantial debts in their operations. However, the average debt/equity ratio for the entire sample is well<sup>below a</sup> "normal" rate in most market economies (of course the 'normal' rate varies by the nature and riskiness of the enterprise's activities). The explanation for this may lie in the direct budgetary s"bsidies that these enterprises once received in the form of equity. As loss making enterprises increasing relied on banks, rather than the government budget, for financial support, these enterprises' debt levels may have risen dramatically since this data from 1991.

The sectoral distribution of Czech and Slovak enterprises was broadly similar as were debt/equity ratios. However, in terms of other characteristics, there were some najor differences. Czech enterprises tended to have higher book values on average and tended to be more capitalintensive in terms of book value (as evidenced by the capital/output and capital/labor ratios). Labor productivity was also higher on average in the Czech Republic. The major differencs was profitabiity. Profitability (as measured by profit/equity or profit/output) was on average over twice as high in the Czech Republic than in Slovakia.
Using bank-level panel data for countries in Central and Eastern Europe, East Asia, and Latin America, this paper analyzes the lending behavior of foreign banks during the recent global crisis. In particular, we explore the role of affiliate and parent financial characteristics (such size, solvency, liquidity, and funding structure), affiliate location, as well as the impact of parent origin (US, European or Asian) and geographic reach (global or regional) on foreign banks’ credit growth.

We find robust evidence that foreign banks curtailed the growth of credit relative to other banks, independently of what host region we focus on. US banks reduced loan growth less than other parent banks. Neither the global nor regional reach of parent banks (i.e., whether the parent bank operated in one or more regions) influenced the lending growth of foreign affiliates. On the other hand, foreign affiliates of well-capitalized parents experienced a significantly smaller decline in loan growth. Reliance on wholesale funding prompted more credit growth retrenchment by foreign banks, but only among those whose parents were non-financial institutions. This result suggests that wholesale funding is more volatile than parent funding. Albeit not the focus of our paper, our estimations also indicate that government-owned banks played a countercyclical role during the crisis, growing their loan portfolio faster than privately owned institutions.

Our paper is related to the rapidly growing literature providing evidence that foreign banks were conduits for the global propagation of the recent crisis. One strand of this literature has looked at how the crisis affected cross-border bank lending (i.e., direct lending from foreign banks outside a country to firms or consumers in the country). Within this strand of the literature, some studies have used aggregate country-level data collected by the Bank for International Settlements on foreign bank and cross-border bank claims (e.g., McGuire and Tarashev, 2008; 3
<!-- Start of picture text -->
1.14<br>Credit card ownership and use by account holders<br>Adults with a financial institution account by credit card use in the past year<br>(as % of all adults), 2014<br>Did not have card<br>Did not use card<br>Used card<br>53<br>20<br>15<br>12<br>2 3 2<br>East Asia Europe High-income Latin Middle South Sub-Saharan<br>& Pacific & Central OECD America & East Asia Africa<br>Asia economies Caribbean<br>Source:  Global Findex database.<br>THE GLOBAL FINDEX DATABASE  20<br><!-- End of picture text -->

> FIGURE 1.13

<!-- Start of picture text --> Debit card ownership and use by account holders<br>Adults with a financial institution account by debit card use in the past year<br>(as % of all adults), 2014<br>0 20 40 60 80 100<br>World<br>Developing economies<br>East Asia & Pacific<br>Europe & Central Asia<br>High-income OECD economies<br>Latin America & Caribbean<br>Middle East<br>South Asia<br>Sub-Saharan Africa Did not have card<br>Did not use card<br>Used card<br>Source: Global Findex database.<br><!-- End of picture text --> In developing economies 55 percent of adults with an account at a financial institution (or 27 percent of all adults) reported owning a debit card. But there are large variations across regions: while the share of account holders owning a debit card is 84 percent in Latin America and the Caribbean and 76 percent in Europe and Central Asia, it is only 37 percent in South Asia. Among those owning a debit card in developing economies, 46 percent (representing 13 percent of all adults) reported having used the card to directly make a purchase in the past 12 months.

# How many people own and use credit cards?

In many economies people use a credit card rather than a debit card to pay bills and make everyday purchases. While a credit card does not need to be linked to an account, less than 0.5 percent of adults around the world own a credit card but do not have an account at a financial institution.

> FIGURE 1.14

How extensive is credit card ownership? In high-income OECD economies 53 percent of adults reported owning a credit card in 2014 (figure 1.14). Credit card ownership in developing economies, despite recent growth, still lags far behind: on average in these economies only 10 percent of adults reported having one. But two developing regions stand out for high rates of credit card ownership: Latin America and the Caribbean and Europe and Central Asia.

Those who own a credit card are very likely to use it. Across both high-income OECD and developing economies the share of credit card holders who reported having used their card in the past 12 months typically exceeded 80 percent in 2014.
# Engineers, Innovative Capacity and Development in the Americas<sup>_∗_</sup>

William F. Maloney<sup>_†_</sup> Felipe Valencia Caicedo<sup>_‡_</sup> Keywords: Innovative Capacity, Human Capital, Engineers, Technology Diffusion, Patents, Growth, Development, History.

JEL: O1, O31, O33, O4, N1

> _∗_ Corresponding author Email: wmaloney@worldbank.org. We thank Ufuk Akcigit, Leo Feler, Claudio Ferraz, Christian Fons-Rosen, Oded Galor, Steve Haber, Lakshmi Iyer, David Mayer-Foulkes, Stelios Michalopoulos, Guy Michaels, Giacomo Ponzetto, Luis Serven, Andrei Shleifer, Enrico Spolaore, Uwe Sunde, Nico Voigtl ̈ander, Hans-Joachim Voth, Fabian Waldinger, David Weil and Gavin Wright for helpful discussions. We are grateful to William Kerr for making available the county level patent data. Our thanks to Carlos Andres Molina, Melissa Rubio, and Mauricio Sarrias for meticulous research assistance. This paper was partly supported by the regional studies budget of the Office of the Chief Economist for Latin America and Knowledge for Change (KCP) trustfund.

> _†_ Development Economics Research Group, The World Bank.

> _‡_ Department of Economics and Institute of Macroeconomics and Econometrics, University of Bonn.
Then, for the critical links exposed to floods and landslides, we assessed the disruption risk under many possible scenarios and compared alternative interventions to reduce this risk. We then identified the best intervention to reduce the risk associated with floods and landslides for these links, taking into account the uncertainties around future hazards and vulnerabilities. Here we applied state-of-the-art methodologies to address deep uncertainties related to future conditions and data gaps.

# 2.1. A network approach for measuring criticality

Ranking corridors based on their criticality can inform prioritization exercises and, ultimately, enable the analysis of the economic and development impacts of specific interventions and policy decisions.

Peru’s road network contains 164,411 kilometers of roads, of which we wanted to identify the most critical links. Several criteria can be used depending on the policy goal. For instance, policy makers might decide to prioritize export outlets for agricultural products, mining corridors, or increase access for lagging areas to main cities. Here we measured a link’s criticality in terms of the impacts that its removal would have on the overall network. This required building a model of the road network and calculating least-cost routes between Origin-Destination pairs (see annex for a description of the model and data).

Before calculating the impact of link removal, we applied two initial filters to identify the critical links. First, we narrowed the network down by selecting only the least-cost routes between our set of origins and destinations. We call these routes the indicative corridors.<sup>1</sup> Then, we applied a second filter which we used to capture the socio-economic importance of the different routes by looking at their traffic levels. We ranked the links by traffic levels and selected the 10 percent links with the highest traffic – that is with 4,000 or more vehicles per day<sup>2</sup> (Figure 1) **.** This step reduced the analysis to about 1.3 percent of the national network, or 2,274 kilometers – we call these 974 links candidate critical links.

> 1 Explicit criteria were imposed to define the corridors of interest to make sure cross-country comparisons were possible. To identify the indicative corridors, we used the Network Analyst, an extension of the ArcGIS software, which allowed us to analyze all routes via their HDM-4 estimated RUCs, which fully reflects the physical characteristics of the existing road network.

> 2 The median average daily traffic (ADDT) for Peru is 982 vehicles. Figure 1 is adapted from (Taylor et al., 2006) whose methodology we borrow. These authors first identify the candidate critical links as those that are part of the least-cost route between two locations -- then measure the change in cost or in accessibility associated with the loss of each of these links. For computation capacity reasons, in this study we modified the first step of their application by defining the candidate critical links as those links that are part of the least-cost routes and have the highest traffic.

4
Montgomery H and Weiss J., (2011). Can commercially-oriented micro-finance help meet the millennium development goals? Evidence from Pakistan, _World Development,_ 39 (1) 87-109 Nussbaum, M., 2000. Women's capabilities and social justice. _Journal of Human Development_ , 1(2), pp.219-247.

Pandey, V., Gupta, A. and Gupta, S., 2019. Labor and Welfare Impacts of a LargeScale Livelihoods Program. World Bank Group, Policy Research Working Paper 8883 Parida, P.C. & Sinha, A. 2010, "Performance and Sustainability of Self-Help Groups in India: A Gender Perspective", _Asian Development Review_ , vol. 27, no. 1, pp. 80-103.

Prennushi, G. and Gupta, A., 2014. Women's empowerment and socio-economic outcomes: impacts of the Andhra Pradesh rural poverty reduction program.

Qizilbash, M., 1997. A weakness of the capability approach with respect to gender justice. _Journal of International Development_ , 9(2), pp.251-262.

Quisumbing, A.R. and Pandolfelli, L., 2010. Promising approaches to address the needs of poor female farmers: Resources, constraints, and interventions. _World Development_ , 38(4), pp.581-592.

Rajiv Gandhi Charitable Trust (2017) Annual Report, New Delhi 30
26

- Docquier, F. and H. Rapoport (2005), “The Economics of Migrants’ Remittances”, _IZA Discussion Paper_ , no. 1531.

- Docquier, F. and A. Marfouk (2005), “International Migration by Educational Attainment (1990 – 2000),” Chapter 5 in C. Ozden and M. Schiff (eds.) International Migration, Remittances and the Brain Drain. Palgrave McMillan Press (for the World Bank). New York (October). Early version (August, 2004), entitled “Measuring the International Mobility of Skilled Workers (1990 – 2000): Release 1.0”, _Policy Research Working Paper_ , no. 3381, Research Program on International Migration and Development, International Trade Unit, Development Economics Research Group, World Bank.

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Faini, R. (2004), “Does the Brain Drain Boost Growth?”, mimeo.

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increasingly share neighborhoods while the highest‐income households—who are predominantly white—form enclaves (Reardon and Bischoff 2011).

Neighborhoods with high concentrations of African Americans and Latinos often have high poverty rates; few high‐poverty neighborhoods, conversely, are mostly white. Concentrated poverty—neighborhood poverty levels exceeding 30 percent or even 40 percent—has a host of negative outcomes for the people who live in it, especially for children who grow up amid such economic insecurity. These include, for example, a lower sense of subjective well‐being (Ludwig et al. 2012), lower birth weight (Osypuk et al. 2013), poorer physical health (Ludwig et al. 2011), worse educational outcomes (Sharkey and Elwert 2011), higher risks of adolescent parenthood (Wodtke 2013), weaker levels of labor‐force participation, higher rates of incarceration (Fagan et al. 2002), and dampened rates of civic participation (Stoll 2001). Moreover, key effects appear to be transmitted across generations (Sharkey 2013), contributing to “durable” inequalities. The incidence of high‐poverty neighborhoods—those whose poverty rate exceeds 30 percent—varies substantially across the U.S.; in Detroit, Cleveland, and Philadelphia, the probability that a person living below the poverty line also lives in a high‐poverty neighborhood exceeds 40 percent.<sup>2</sup> Poor people in Portland (OR), San Francisco, Seattle, and Tampa, by contrast, are less than 15 percent likely to live in a high poverty neighborhood. Although all comprehensive strategies for addressing the needs of low‐income children require a combination of community‐level institution‐building and family‐level interventions and supports, these contrasts in the intensity of concentrated poverty suggest different approaches are needed in various cities.

> 2 Authors’ calculations based on 2005‐2009 U.S. Census American Community Survey data.

20
18 costs to run these large utilities may outweigh the gains in the unit costs of service provision. Feigenbaum and Teeples (1983) notice that lack of economies of scale can be due to the fact that such utilities may offer a broader range of services which would raise unit costs. In the case of Brazil, the regional utilities offer both water and sewerage collection and increasingly sewerage treatment services. To see in how far this hypothesis can be tested, we estimated returns to scale for smaller (municipal) companies in Brazil. IBNET contains data on 426 Brazilian municipal WSS utilities over the period between 2000 and 2004. The average municipal WSS utility in Brazil serves a population of 174,000 persons (which is in between the size of the average Vietnamese and Colombian utilities, see table 2). We estimate returns to production density, returns to customer density, and returns to scale, at the mean, on the sample of regional WSS utilities and on the sample of municipal WSS utilities, over the 20002004 period. Yet, for both municipal and regional WSS companies, we cannot reject the hypothesis of constant returns to scale (see table A6 in Appendix).

Secondly, few utilities in developed countries are characterized by economies of scale. Brazil, which is the richest country in the sample of four case studies, does not exhibit economies of scale for both municipal and regional WSS companies, as the hypothesis of constant returns to scale can not be rejected. From the latter results, it seems that, not only the size of the utilities but also the country’s level of economic development, may explain that water and sewerage services do not necessarily operate under increasing returns to scale, similar to what was found in more developed countries (see the literature review in the introduction). Utilities in more developed countries may provide more water supply and wastewater collection and treatment services, services of better quality (universal coverage, 24-hour supply, quality of drinking water) and tend to operate in more regulated environments (possibly due to increased regulation of an environmental and economic nature, including labor regulation).<sup>24</sup> The provision of a greater number of higher quality water supply and sewerage services may increase administrative costs. High coverage levels that typically increase with development status will require the utilities to expand their network to more remote areas which may be reflected in lower network density of consumers, which is much lower in Brazil than in Colombia or Vietnam. The number of case studies in this study is limited to four, and hence a future area of research could be an analysis to investigate the

> 24 In Moldova (where we estimate the highest returns to scale), the quality of the water service is known to be quite deficient, in terms of population coverage and average duration of supply in particular (Moldova ApăCanal Association, 2004). Unfortunately, information on average duration of supply and number of pipe breaks are not available for Brazilian water utilities.
7 determined in part by the Z's and by some other set of variables, X's (the non-participation determinants of the W's). In summary, these multivariate equations state _that_ performance of a water project depends on: beneficiary participation; a set of inputs (Z) not related to participation (e.g., "adequacy of facilities", "availability of spare parts"); a set of performance determinants (W) that may in turn be determined by partcipation (e.g., "responsiveness of managers", appropriateness of technology) as well as by other inputs.

The distinction between the Z's and the W's is important for maintaining the distinction between the partial and the total impact of participation on project performance. In the multivariate regression (equation 2), the beta coefficient gives the direct impact of increasing participation, holding all included variables constant: <!-- Start of picture text --> 4) a0PE1 =z<br>aOBP 4Z%w<br><!-- End of picture text --> But participation may also influence performanace indirectly: the total impact of changing participation is the sum of the direct and indirect impacts: <!-- Start of picture text --> 5) dOPE =8OPE 1 + 8OPE * aw<br>dOBP aOBP aW 8FBP<br><!-- End of picture text --> or in this particular specification, _6) dOPE_ **_+_** _*y dOBP-_
<!-- Start of picture text --> Ed<br>8<br>&<br>&<br>i “<br>ry“ :te++ ° Tb 4 Aeennn--Vv wee eereee<br>° & 5 REO 00 osc<br>0 5000 10000 15000<br>Average EV Incentive $<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>rc}<br>SS<br>2<<br>aues<br>a * wee<br>8 wee +<br>m. ---77 +<br>wo pore in<br>° eae ~ *<br>0 50 100 150<br>Number of Charging Ports per thousand New Cars Sold<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>8<br>#<br>&<br>éi<br>Ff wee<br>c ees<br>~ wees 1<br>ol #A lee we detre i<br>o 20000 40000 ‘BODOD 80000<br>Real Income Per Capita in 2010 US $<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>mu<br>A<br>=<br>=<br>+<br>2 a<br>° aad we A iya ge 4 + ++rs<br>So a0 70 ao BO<br>Urban Population %<br><!-- End of picture text --> <!-- Start of picture text --> fe} © A oOo Vv x 4<br>Austria) Canada China France Germany Japan Netherlands<br>rd A a ° ros<br>Norway Spain Sweden Switzerland United Kingdom _ United States<br><!-- End of picture text -->