rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Since the early 1990s, emerging economies have been integrating rapidly with the international financial system. Financial integration has manifested in many ways, including financial liberalization of previously closed economies, larger cross-border capital flows, entry of foreign banks, and participation of domestic firms in international markets. In particular, as firms go abroad, part of the domestic market activity has migrated to international markets: Capital is being raised in international markets and securities are traded in international stock exchanges, in addition to domestic ones.<sup>1</sup> This process of financial integration has been driven by an expectation that it fosters a better allocation of resources and risk around the world, and ultimately promotes higher growth.<sup>2</sup> Two factors have emerged to threaten this financial integration. First, as countries opened up to capital flows, a series of crises erupted, leading many to question the net benefits of outright financial liberalization.<sup>3</sup> Second, capital controls have emerged as a way to mitigate financial integration.<sup>4</sup> During bad times, controls on capital outflows have been used as a way to stem reserve losses and currency devaluations, and eventually the collapse of the banking sector: Two well-known cases are those of Malaysia during

> 1 See Levine and Schmukler (2006 and 2007) and Gozzi, Levine, and Schmukler (2008a and 2008b), and references therein.

> 2 See Obstfeld (1994), Acemoglu and Zilibotti (1997), Fischer (1998), Rogoff (1999), and Summers (2000) for arguments in favor of financial integration.

> 3 See Henry (2006) and Kose et al. (2006) for comprehensive surveys on the literature of financial integration. See Bhagwati (1998), Rodrik (1998), Soros (2002), and Stiglitz (2002) for arguments against financial integration.

> 4 See Eichengreen et al. (1998), Cooper (1999), and Stiglitz (2000) for arguments against free capital flows. See Kawai and Takagi (2008) for a survey on the literature on managing capital inflows. Also, see Grenville (2008) for a discussion on the macroeconomic consequences of capital inflows and Schadler (2008) for an analysis of over 90 recent episodes of large capital inflows.

1
**Table 4. Exporting and Foreign ownership:** **<u>The SADC sample</u>**

|foreign|**exporter**<br>0.055***<br>(0.021)|
|---|---|
|largest owner|-0.061***<br>(0.024)|
|ln(firm age)|0.012|
||(0.009)|
|loss_power|0.000<br>(0.001)|
|ln(GDPpc)|0.000<br>(0.016)|
|fire_cost|-0.000<br>(0.000)|
|interest_spread|-0.001<br>(0.001)|
|government effectiveness|-0.008|
||(0.021)|
|industry dummies|yes|
|lnS|0.037***|
||(0.004)|
|Number of observations|6,248|
|Adjusted R2|0.189|

standard errors in parentheses, clustered at the country level. *, ** and ***: statistical significance at the 10, 5 and 1 percent levels.

26
reasons. First is the well-known phenomenon of urban heat islands, whereby urban environments become substantially hotter than the neighboring lands due to the increased absorption of energy (in the form of solar radiation) and reduced cooling through evapotranspiration. Second, higher population density centralizes the demand for cooling during extreme heat events in locations where air conditioning is available, thereby eliminating the natural process of smoothing demand spikes via more spatially distributed demand nodes that are unlikely to all peak in cooling demands simultaneously.

Having established that the costs of power system blackouts are considerable, and that anthropogenic warming is increasing the magnitude and frequency of heat waves, the goal of this paper is to use the latest generation of climate projections to estimate the propensity of urban power systems to eclipse their design capacity as a result of extreme cooling demands into the 21st century in major urban centers globally, with a special focus on the urban centers of Southeast Europe (Figure 1). Secondarily, this paper provides estimates of the economic costs associated with these urban power system disruptions. Lastly, this paper discusses the implications of increasing extreme heat events on critical power (and energy) system infrastructure and what adaptive responses can help ensure system reliability into the coming decades.

# **Urban Centers of Southeast Europe**

Power system disruptions can be particularly salient in urban areas because of the high population density, the potential for cascading failures due to interconnected and interdependent systems, and the concentration of economic and industrial activities in and around urban centers. Higher population densities mean that more humans are impacted by power system disruptions. The interconnected and interdependent nature of many critical systems in urban settings, such as transportation and water systems, mean that power system interruptions can spark cascading failures and disruptions to multiple systems. Lastly, due to the high concentration of businesses and industries that rely on a stable power supply to operate in and around urban centers, urban power system disruptions can result in substantial lost revenue, reduced productivity, and increased costs for businesses.

6
_<mark>Table 3. Number of Moves within a Host Country after Leaving Syria and Household Characteristics</mark>_

|Dependent Variable=Number of Moves After Initial Move|All|Jordan|Lebanon|KRI|
|---|---|---|---|---|
|Kurdistan|0.48<br>(5.208)||||
|Lebanon|-0.792||||
||(5.202)||||
|Household Size|-0.030***|-0.072**|-0.006|-0.110***|
||(0.011)|(0.031)|(0.018)|(0.029)|
|Dependency Ratio|0.041***|0.062|0.027|0.072**|
||(0.014)|(0.041)|(0.023)|(0.036)|
|Working Age Male in Household|0.076|0.035|0.045|-0.144|
||(0.065)|(0.158)|(0.097)|(0.263)|
|Home Owner|-0.002|-0.01|0.036|-0.002|
||(0.038)|(0.121)|(0.060)|(0.101)|
|Home Destroyed|-0.042|0.260**|-0.046|0.099|
||(0.038)|(0.125)|(0.058)|(0.115)|
|Vehicle Owner|0.177***|0.198|0.222***|0.037|
||(0.053)|(0.124)|(0.086)|(0.128)|

22
59

- Florida, Richard L. and Kenney, Martin. "Venture Capital, High Technology and Regional Development." Regional Studies. Vol. 22.1, 1988.

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- Fung, Victor. Prudential Asia. Hong Kong. Interview, November 1989.

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- Hoy, Frank and Carland, James W. Jr. "Differentiating Between Entrepreneurs and Small Business Owners in New Venture Formation." In Babson College Entrepreneurship Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1983.

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- Jennings, D.F. and Sexton, D. L. "A Contextual Analysis of Venture Activities: Implications for New Research Directions in Venture Capital." In Babson College Entrepreneurship Research Conference Proceedinas. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1985.

- Jensen, M. and Meckling, W. "Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure." Journal of Financial Economics 3, 1976.

- Khan, Arshad M. "Assessing Venture Capital Investments with Noncompensatory Behavioral Decision Models." Journal of Business Venturing, 1987.

- Knight, Russell M. and Ferguson, W. Ker. "The Success of Ontario's SBDC Program." In Babson Collece Entreoreneurshio Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1984.

- Knight, Russell M. "Bank Financing Experioence of Canadian Entrepreneurs." In Babson College Entrepreneurship Research Conference Proceedinas. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1982.
- 12 -

Table 2: THE WORLD MERCHANT FLEET Ownership and Registration Patterns Status: December 31, 1991

|Country of<br>Domicile|National<br>Flag<br>dwt|Foreign<br>Flag<br>dwt|Total<br>dwt|Foreign<br>Flag as<br>percentage<br>of Total|Total as<br>percentage<br>of World<br>Total|
|---|---|---|---|---|---|
|Greece|41 859998|52549313|94409311|55.7|14.8|
|Japan|32951196|49 836 719|82 787 915|60.2|13.0|
|U.S.A.|18 307 576|43 108154|<br>61 415 730|70.2|9.7|
|Norway|37 838 056|18 934 850|56 772 906|33.4|8.9|
|Hong Kong|3 887 664|28 258 783|32 146 447|87.9|_5.1_|
|FSU|27 233 783|2 579 203|29 812 986|8.7|4.7|
|China|19_8S5_948|6_058_<br>735|25 914 683|23.4|4.1|
|United Kingdom|5M70529|17 570 273|23 440 802|75.0|3.7|
|South Korea|11 414 224|6 361 036|17 775 260|35.8|2.8|
|Germany|6 555 611|8 967 829|15 523 440|57.8|2.4|
|Denmark|7 763652|4 927 169|12 690 821|38.8|2.0|
|Italy|9 900 320|1 663 541|11 563 861|14.4|1.8|
|Taiwan|7 368 914|4 1085 53|11 477 467|35.8|1.8|
|India|10 203 623|610 144|10 813 767|5.6|1.7|
|Brazil|9 356 082|443 112|9 799 194|4.5|1.5|
|Sweden|3 342 995|5 717 387|9 060 382|63.1|1.4|
|Iran|8 274 363|18 863|8 293 226|0.2|1.3|
|Turkey|7 017 974|561 281|7 579 255|7.4|1.2|
|Singapore|4 972 367|2 066 801|7 039 168|29.4|1.1|
|France|2 971 030|3 201 033|6 172 063|51.9|1.0|
|**_TOTAL_**|_276,945,905_|**_257,542,779_**|**_34,488,6_**|_48.2_|**_84.0_**|

Source: Lloyd's Maritime Infornation Services. London
|Table 14: Decentrali<br>momicIndicators|zation and|Closeness|Indexes:|Relationsh|ip with S|elected Ec|o-||
|---|---|---|---|---|---|---|---|---|
|<br>Tight hand side var's|||left hand <br>|side var's<br>|||descriptiv<br>|e statistics<br><br>|
||[E-2<br>|xpdec<br><br>|DT <br><br>|main<br><br>|TT <br><br>|adj.<br><br>|mean in-<br>dep-var.|st.<br>dev.<br>indep.<br>Var|
||no other<br>cont.|cont.<br>for<br>GDP;<br>|no<br>other<br>cont.|cont.<br>for<br>GDP/<br>|no<br>ot!<br>cont.|cont.<br>for<br>GDP;<br>|||
|||cap||cap||cap|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|politic|20.51<br>(3.02)|14.43<br>(3.87)|7.29)<br>(1.29)|44)<br>(1.05)|8.1<br>(1.63)|5.65)<br>(1.34)|ei|2d|
|DI fiseal|33.539**<br>(4.00)|20.387+*<br>(4.35)|12.90***<br>(1.87)|11.58***<br>(1.62)|13.63***<br>(1.98)|12.07***<br>(1.68)|ad|35|
|DI administrative|27.254"<br>(3.02)|26.187**<br>(3.39)|10.26***<br>(1.65)|&.71***<br>(1.33)|11.135***<br>(1.76)|O.43*4*<br>(1.41)|=]|28|
|OGI|31.60)<br>|980)<br>|22.92*<br>|10.97<br>|27.57T*<br>|13.77<br>|a5|08|
||(24.96)|(29.64)|(11.84)|(8.17)|(14.89)|(10.30)|||
|GDP capita,PPP|0.26***<br>(0.01)||0.18***<br>(0.01)||O.21***<br>(0.01)||G5|14.6|
|GG cons.,=GDP|0.05<br>|0.10<br>|-0.08***<br>|0.00<br>|-0.09"*<br>|0.01<br>|18.5|||
||(0.11)|(0.10)|(0.03)|(0.02)|(0.03)|(0.03)|||
|Start of bus., # proc.<br>|-0.68***<br>(0.15)<br>|-0.44***<br>(0.15)<br>|-0.29***<br>(0.07)<br>|-0.13**<br>(0.08)<br>|-0.29*4*<br>(0.06)<br>|-0.11**<br>(0.05)<br>|7.4<br>|6.4<br>|
|#civilconflicts|-0.36*<br>|0.09<br>|~0.08<br>|0.10<br>|0.08<br>|0.12<br>|a|om]|
||(0.21)|(0.26)|(0.08)|(0.08)|(0.07)|(0.08)|||
|Corruption pere. ind.|21 0*+*<br>(0.53)|1.10<br>(0.95)|1.317<br>(0.28)|Lar**<br>(0.36)|Lager<br>(0.28)|1.5<br>(0.37)|4|21|
|Democracyscore|0.00**|0.03|0.049**|O.01|0.04**|0.01|aT|18.8|
||(0.04)|(0.04)|(0.01)|(0.01)|(0.01)|(0.01)|||
|Durab.ofregime|0.08**|-0.00|0.07***|0.04**|O.08***|0.05**|22.6|30.4|
||(0.03)|(0.03)|(0.02)|(0.02)|(0.02)|(0.03)|||

Note * - significant at 10% level, ** - significant at 5% level, *** - significant at 1% level. Abbreviation: DI - decentralization index. Corruption perception index (source - Transparency International), GDP per capita (source - WDD), GG consumption (source - Penn World Tables correspondingly), Start of business, # proc. (source - WE Doing Business), Democracy score (source - Polity IV), Durability of regime (source - Polity IV) are from 2005. CGI - citizen-centric gowermance indicators (source vanyna and Shah (2009)), 4 civil conflicts (source -WDI) are averages in 2000-2005.
enters in the county-level US analysis.

# **2.2 Sub-national Income per Capita**

Income in 2005 PPP US Dollars is drawn from a highly disaggregated spatial data set on population, income and poverty constructed on the basis of national census data by the World Bank (2009) for the World Development Report on _Reshaping Economic Geography_ .<sup>13</sup> Modern county level US income in dollars is mean household income taken from the 2000 US Census.

# **2.3 Controls**

As controls in our regressions, we also employ data on: _Literacy_ : Aggregate literacy rates we take from (Mariscal & Sokoloff, 2000; N ́u ̃nez, 2005). Sub-national literacy rates for Argentina, Chile, Mexico, Venezuela, and the US taken from same census data as above. County level from 1880 census.

_Secondary Schooling_ : Similar to Goldin & Katz (2011); Goldin (1999) for the US we construct a proxy for secondary schooling by the share of 14-17 years olds who report that they are attending school (1880 US census).

_Higher Level Non-Engineering Human Capital_ : For the US, this is measured as number of lawyers and medical doctors per 100,000 inhabitants (US Census 1880). We also aggregate across occupational categories the EDSCOR50 measure that indicates the percentage of people in the respondent’s occupational category who had completed one or more years of college. Together these allow us to ensure that our engineering measure is not proxying for the availability of higher order human capital in general.

> 13For further detail see Maloney & Valencia (2015)

8
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- In the _Czech Republic,_ the minimum wage has been increased with the aim to reduce under-declaration of wages. This policy appears to have not achieved its objective.

- In _Slovakia_ , the increase in the minimum wage may have acted as an incentive for undeclared work, particularly in small firms and low-wage sectors, although there is no clear empirical evidence for such an impact.

- In _Estonia, Lithuania, and Slovenia_ , there appears to be no evidence for a major impact of the minimum wage on undeclared work; in these countries the minimum wage is relatively low and relatively few workers are on the minimum wage.

- In _Bulgaria_ , minimum wage earners are exempt from social insurance  contributions. While this reduces the costs of transition from informal to formal work, it provides incentives to underdeclare wages in order to benefit from this exemption.

- In _Romania_ , the minimum wage is regarded as helpful in reducing under-declaration of earnings. A higher minimum wage has been introduced for workers with high educational attainment. This measure is aimed to reduce under-declaration of earnings by higher skilled workers.

# **13.3 Regulatory burden for doing business**

101. Countries that are reforming their tax systems with the aim to combat undeclared work are unlikely to be successful if at the same time the regulatory burden and administrative costs on business remain high and the quality of government institutions remains poor. Strict regulations on business activity, such as barriers to entrepreneurship including administrative burdens on start-ups and restrictive labor codes, constrain job creation in the formal sector and push firms and workers into informality. The new EU member countries have made much progress in improving conditions for doing business. For example, Estonia has reduced administrative burdens with the help of e-services for taxpayers. In Slovakia, the procedures for starting new business have been simplified. According to the 2009 World Bank ́s Doing Business report, which covers 10 indicator sets in 181 countries, the regulatory environment for business is - among our country group - most favorable in Estonia, Lithuania, and Latvia. In some of the other countries, notably Croatia, Poland, the Czech Repbulic, and Slovenia there are still important barriers for business activity (Table 14).

**Table 14: Ease of Doing Business (rank among 181 countries)**

|NewEUcountries andCroatia|Bestperformers:|
|---|---|
|Estonia(22)|Singapore(1)|
|Lithuania(28)|NewZealand(2)|
|Latvia(29)|United States(3)|
|Slovakia(36)|Selected European countries:|
|Hungary (41)|Denmark(5)|
|Bulgaria(45)|United Kingdom(6)|
|Romania(47)|Finland(14)|
|Slovenia(54)|Sweden(17)|
|Czech Republic(75)|Germany (25)|
|Poland(76)|Spain(49)|
|Croatia(106)|Greece(96)|

_Source:_ World Bank, Doing Business 2009.

43
they are both higher in the vicinity of mines. As indicated earlier, this too can be interpreted as indicative of proto-urbanization.

To verify the robustness of our findings, we start by estimating Figures 5 and 6 with the 2000 data. Results, shown in the online appendix, are similar but less pronounced. Next we control for proximity to urban centers. A look at the maps in Figures 2 and 3 indeed suggests that distance to mines may be correlated with distance to large cities. There are two major mining areas: the large mine near Obuasi, accounting for more than 50 percent of all mining output in the 1990s; and the complex of mines near Bogoso-Prestea-Tarkwa. There is a town close to each of them. Right next to the Obuasi mine is the town of Obuasi proper, Ghana’s eighth largest city, and Obuasi itself is located about 50-60km from Kumasi, Ghana’s second largest city. There is no large town in the immediate vicinity of the Bogoso-Prestea-Tarkwa mining complex, but it is located 50-60km from Sekondi-Takoradi, Ghana’s third largest city. Since localities where much of Ghanaian gold production originates happen to be located 50-60 km from a major urban center, failing to control for urban proximity may affect our estimates.

We reestimate Figures 5 and 6 with the 2000 and 2010 data using the regression model (5). It is not clear whether city population in 2000 or in 2010 is the appropriate control for pre-existing cities.<sup>10</sup> Therefore we also estimate a mixed version of model (5) where we control for lagged city population together with 2010 mining and outcome data. Results are again very similar to those in Figures 5 and 6. They are available in the online appendix. Finally, we also include district dummies in the regression in an effort to control for geographical, institutional, and other features that are common to a district and may be correlated both with proximity to gold mines and drive urbanization. However, in our spatial analysis, districts might also pick up part of the effect of mining otherwise captured in _γk_ , because locations close to mines fall within only a few districts (see Figures 1 and 2).<sup>11</sup> Comparing locations located at different distances to mines within the same district, a small geographical area, is therefore an especially conservative test of the effects of mining. Results are summarized in Figures 7 and 8 for the year 2010. For locations in the immediate vicinity of gold mines, the patterns are qualitatively similar to those

> 10Our mining variable is measured as a flow over the decade up to time _t_ but the outcome variables are stocks at _t_ .

> 11Imagine a district that spans across locations situated 10-30 km from a mine. The dummy coefficient for this district will then pick up the average effect of mining at distances 10-30 km, while _γ_ 2 and _γ_ 3 will pick up deviations from this average for locations at distances 10-20 km and 20-30km, respectively.

19
e¤ects cancel each other out.) In addition, trade intensi...es these e¤ects. It increases the incentive to screen assiduously at high-productivity ...rms due to the extra volume of sales that will come from exports. It decreases the incentive to screen at marginal surviving ...rms, which reduce their output and do not export. As a result, trade unambiguously increases wage inequality (and in a much stronger sense than in the Egger and Kreickemeir model, since it actually produces a new wage distribution that dominates the autarky one by second-order stochastic dominance). In addition, trade increases unemployment by increasing the market share of large ...rms, and then making those large ...rms more picky about hiring. Thus (as in Egger and Kreiemeier) trade further increases income inequality by increasing the fraction of workers receiving zero income.

# 2.3 New approaches to comparative advantage and inequality.

The heterogenous-...rms literature has provided a number of channels in which trade can a¤ect income inequality even between identical countries. Beyond that, a number of recent papers have re-examined and extended the comparative-advantage approach in ways that allow for a more nuanced view of trade and income inequality than was available before.

## 2.3.1 Trade in tasks, revisited.

Grossman and Rossi-Hansberg (2008) explore the implications of trade in tasks, earlier examined by Feenstra and Hanson (1996). Their emphasis is the possibility of productivity bene...ts from o¤shoring, which can in principle make o¤shoring a Pareto-improving phenomenon. In the simplest version of the model, there are two goods, X and Y , both of which can be produced in Home by completing a given set of tasks. Some of the tasks need to be performed by high-skilled labor but others can be performed by unskilled labor. Consider ...rst the production technology if only domestic labor is used. For good j there is a continuum of 16