rafmacalaba/datause-encoder-data huggingface dataset:
9 litigation experience embodies some of the most salient lessons reflected in the legal structure of current Eurobonds.

Ten years before Elliott Management’s saga with Argentina, there was the Noga litigation episode with Russia.<sup>23</sup> The Swiss firm signed a deal with the Russian government in 1991 to export goods like medicine and pesticide in exchange for oil.<sup>24</sup> The contracts contained a waiver of sovereign immunity and a forced arbitration clause.<sup>25</sup> This is important because it allowed for faster access to court adjudication, and arbitration award enforcement actions generally enjoy weaker immunity defenses from both suit and attachment.<sup>26</sup> After the deal fell apart, Noga began legal actions across Europe in 1993, claiming a loss of approximately US$100 million.<sup>27</sup> In 199394 Noga assigned portions of the proceeds for its receivables from the Russian government to the banks that financed the initial deal.<sup>28</sup> In 1996 Noga filed for bankruptcy, which committed the rest of its claims on Russia to the same creditors.<sup>29</sup> These banks agreed to finance the costs of the arbitration proceedings and established a collection mandate for Noga in the final bankruptcy compact.<sup>30</sup> An arbitral award was obtained in 1997.<sup>31</sup> The supplier, at the time reduced to a collection shell, found myriad ways to pressure Russia for restitution through legal actions: frozen bank accounts in Switzerland and Luxembourg; and threats of seizure for goods as varied as the

> 23 _See_ R. Merle, _<u>How one Hedge Fund made $2 billion from Argentina’s Economic Collapse</u>_ <u>, The</u> Washington Post, March 26, 2016; Robert W. Kolb, _Sovereign Debt: From Safety to Default,_ John Wiley & Sons, Inc. (2011) at 10-11.

> 24 _Id_ . at 11; Memorandum Order S.D.N.Y. 00 Civ. 0632 at 3, Aug. 15, 2008. Note that the initial deal was signed with the Government of the Federative Socialist Soviet Republic of Russia, a predecessor of the Government of the Russian Federation. In defending against Noga’s quest for restitution, Russia attempted to differentiate between “the Government” and “the Russian Federation” arguing that under the Russian constitution they were separate legal entities. The Second Circuit rejected that argument in _Compagnie Noga D’Importation et D’Exportation S.A. v. The Russian Federation_ , 361 F.3d 676, 685 (2d Cir. 2004).

> 25 _Compagnie Noga D’Importation et D’Exportation_ , 361 F.3d 676, 677-680 (2d Cir. 2004).

> 26 _See e.g._ , 28 U.S.C. Sec. 1610(a)(6); State Immunities Act of 1978 (UK) Sec. 9, Sec. 13(4)(b).

> 27 Kolb, _supra_ note 23, at 11.

> 28 Memorandum Order S.D.N.Y. 00 Civ. 0632 at 3, Aug. 15, 2008.

> 29 _Id_ . at 4

> 30 _Id_ .

> 31 _Compagnie Noga D’Importation et D’Exportation_ , 361 F.3d 676, 677-680 (2d Cir. 2004).
Perceived inter-generational mobility is also correlated with estimates of actual inter-generational mobility from the World Bank’s Global Database of Intergenerational Mobility (GDIM) for the countries in the LiTS sample (Figure 6). Countries in which the share of adults who think that children will do better than the current generation is lower tend also to have higher estimates of inter-generational persistence, indicating lower inter-generational mobility.<sup>6</sup> The link between inequality of access to opportunities and inter-generational mobility is estimated based on the same model as before, where the dependent variable is instead a binary (above neutral) measure of inter-generational mobility that evaluates to 1 if the respondent agrees or strongly agrees that children will have a better life than the current generation, and zero otherwise. The estimates are reported in Table 9. The estimates reveal that when connections are unavailable, perceived unequal access to opportunities is associated with lower inter-generational mobility (Hypothesis 2, or _δ_ 1 + _δ_ 3 = 0 is rejected by the data). Similarly, in the overall sample, when access to opportunities is not equal, lack of connections is associated with lower perceived inter-generational mobility ( _δ_ 2 + _δ_ 3 = 0 is rejected).

It should be noted that the results are a bit noisier than in the case of intragenerational mobility. This is not entirely surprising since intra-generational mobility expectations are more immediate and for these, current assessments of inequality of opportunity may be more pertinent. Moreover, while intra-generational expectations are personal, in the sense that they are based on one’s own mobility expectations – for the future, the assessment of inter-generational mobility is general it refers to comparisons of children in general with the current generation (again, in general), rather than prompting respondents to report mobility expectations for their own children in particular. Given the reasonably strong relationship between assessments of intra- and inter-generational mobility in the data, it is quite possible that one’s

> 6The measure of intergenerational persistence is based on the coefficient from the regression of children’s years of education on the education of their parents, using ten-year age cohorts. The estimates in Figure 5 are for the most recent, 1980s birth cohort. For more details, see Narayan, Van der Weide et al. (2018).

23
# Figure A.1: Financial Inclusion and Tax Compliance Uruguay in a Cross-Country Comparison

<!-- Start of picture text --> A. Financial Inclusion Indicators<br>A.I: Account Ownership A.II: Credit/Debit Cards in Circulation<br>Uruguay<br>Uruguay<br>5 6 7 8 9 10 11 12 6 7 8 9 10 11 12<br>Log GDP per capita, 2011 Log GDP per capita, 2011<br>B. Size of the Informal Economy<br>Uruguay<br>7 8 9 10 11 12<br>Log GDP per capita PPP 2012<br>100.00 500<br>400<br>50.00 300<br>200<br>Account Ownership (% 15+)<br>100<br>0.00<br>Debit or Credit Cards in Circulation (% of Population) 0<br>50<br>40<br>30<br>a Share of GDP 20<br>Informal Economy as<br>10<br>0<br><!-- End of picture text --> Notes: As discussed in the Introduction and in Section 3.1, Uruguay lagged behind peer countries in terms of financial inclusion. Panel A plots the cross-country relationship between financial inclusion and GDP per capita. Panel A.I display data on account ownership, as measured by the percentage of the population (15 years +) with an account at any formal financial institution in 2011. Panel A.II display data on debit and credit card circulation, as measured by the percentage of the population (15 years +) with ownership of a debit and/or credit card in 2014. The GDP data is from the World Bank World Development Indicators Database. The account ownership data is from the World Bank Global Findex Database. The credit/debit card data is from the Global Payments System Survey. Panel B plots the cross-country relationship between the size of the informal economy (measured as a share of GDP) and GDP per capita for 158 countries in 2012. The measure for the size of the informal economy is from Medina and Schneider (2018). The GDP data is from the World Bank World Development Indicators Database.

35
it is representative of the consumption sector of any economy. However, since it includes both traded and nontraded goods, it is not the ideal index for measuring competitiveness of traded goods. In Western and Central Africa, due to a weak statistical base, it is frequently the only inflation indicator available for economic analysis and hence, was widely used in the exercise. However, for trade with Western Europe and Northern America, while the CPI’s were used, a set of simulations was conducted using the WPI (wholesale price index), which is more heavily weighted towards tradeables.

# **4. CHOICE OF TRADE WEIGHTS**

Country weightings were chosen based on shares of total trade for both regional imports and regional exports averaged over two years (2000 and 2001). In the CEMAC and UEMOA zones, many of the trading partners are similar for exports and imports so it was not necessary to disaggregate the REER’s. The sum of the weights was adjusted to equal 1. Furthermore, since the analysis was for only five years, there was no need to provide annual updates of the trade weights. However, a set of simulations were run using trade shares including unrecorded trade obtained from survey data, especially from Nigeria and Ghana, and the weighting schemes were adjusted accordingly. Fortunately, for the sake of the analysis, much of this unrecorded trade did not occur in parallel markets, since both Nigeria and Gabon have reformed their exchange rate regimes, and differentials between parallel and market rates in these countries is minimal. Thus, for the simulations using informal trade, one can safely use measures of official exchange rates. Finally, third-country competition used in several weighting schemes was not used because of data limitations.<sup>2</sup>

# **5. CHOICE OF CURRENCIES**

Currencies were chosen from countries which had a share of no less than 0.5% of total UEMOA exports and imports. The currency of the country or area which had the share of no less than 1% among UEMOA or CEMAC total imports and exports on its destination basis in 20002001. Under this criterion, more than 10 currencies are included in the study - the euro, the British pound, the US dollar, the Algerian dinar, the Ghanaian cedi, the Chinese yuan, the Korean won, the Indonesian rupiah, the Taiwan dollar, the Nigerian naira, the Canadian dollar, the Japanese yen, the Moroccan dirham, the India rupee, the Thai baht, and the Brazilian real. Monthly nominal bilateral rates were obtained over a span of five years for each of the currencies, and bilateral and effective exchange rate indexes were calculated from these monthly numbers.

> 2 The International Monetary Fund’s Information Notice System  (INS) is the only official source that uses a model of competition in international markets to obtain trade weights that reflect third country competition. (Zanello and Desruelle, 1997)

10
- 30 -

**<u>Table 6. OLS, 2SProbit and IV estimation with metering assumed to be exogenous</u>**

||OLS|Hewlett OLS<br>estimator 1/|Terza two-<br>stage probit<br>estimator 2/|IV using tariff<br>structure|
|---|---|---|---|---|
|_I_|–0.163 ***<br>|–0.040 ***<br>|–0.003<br>|–0.583 ***<br>|
||(0.017)|(0.014)|(0.026)|(0.117)|
|ln_MP_|0.565 ***<br>|1.023 ***<br>|–0.886 ***<br>|0.048 ***<br>|
||(0.037)|(0.065)|(0.205)|(0.005)|
|ln_D_|0.016 ***|0.008 ***|0.009 ***|–0.382 ***|
||(0.001)|(0.002)|(0.002)|(0.032)|
|ln_TEMP_|0.145 **|0.041|0.204 ***|0.087|
||(0.057)|(0.054)|(0.049)|(0.062)|
|ln_PREC_|–0.038 ***|–0.023 ***|–0.039 ***|–0.022 **|
||(0.009)|(0.009)|(0.008)|(0.010)|
|ln_Y_|0.020 **|0.021 **|0.031 ***|0.027 ***|
||(0.008)|(0.008)|(0.007)|(0.009)|
|ln_HH_|0.057 ***|0.058 ***|0.086 ***|0.066 ***|
||(0.021)|(0.021)|(0.016)|(0.022)|
|D(_Power for lighting_)|0.144 **|0.152 **|0.140 *|0.120 *|
||(0.059)|(0.062)|(0.076)|(0.068)|
|D(_Power for heating_)|0.068 ***|0.067 ***|0.069 ***|0.052 ***|
||(0.015)|(0.015)|(0.013)|(0.017)|
|D(_Power for cooking_)|0.037 ***|0.043 ***|0.011|0.046 ***|
||(0.014)|(0.014)|(0.013)|(0.018)|
|D(_Power for water heating_)|0.030 *|0.024|0.035 **|0.046 ***|
||(0.016)|(0.016)|(0.016)|(0.017)|
|D(_Power for others_)|0.023|0.030|–0.004|0.032|
||(0.028)|(0.028)|(0.019)|(0.030)|
|D(_Gas for lighting_)|0.016|0.032|–0.041|–0.033|
||(0.042)|(0.041)|(0.049)|(0.050)|
|D(_Gas for heating_)|0.003|–0.001|0.003|–0.002|
||(0.014)|(0.014)|(0.012)|(0.015)|
|D(_Gas for cooking_)|–0.037 **|–0.029 *|–0.054 ***|–0.015|
||(0.017)|(0.017)|(0.014)|(0.019)|
|D(_Gas for others_)|0.090|0.099|0.129 **|0.106|
||(0.097)|(0.097)|(0.063)|(0.100)|
|ln_TV_|0.0064 *|0.0068 **|0.0001|0.0076 **|
||(0.0033)|(0.0034)|(0.0024)|(0.0036)|
|ln_Video_|0.0022 ***|0.0021 **|0.0020 ***|0.0025 ***|
||(0.0008)|(0.0008)|(0.0007)|(0.0009)|
|ln_Refrigerator_|0.0043 *|0.0040 *|–0.0013|0.0051 **|
||(0.0022)|(0.0022)|(0.0019)|(0.0023)|
|ln_Freezer_|0.0042|0.0038|0.0030|0.0043|
||(0.0031)|(0.0030)|(0.0030)|(0.0033)|
|ln_Washer_|0.0031 ***<br>|0.0027 ***<br>|0.0012<br>|0.0042 ***<br>|
||(0.0010)|(0.0010)|(0.0010)|(0.0012)|
|ln_Dishwasher_|0.0111 ***|0.0112 ***|0.0137 ***|0.0106 ***|
||(0.0026)|(0.0026)|(0.0023)|(0.0027)|
|ln_Stove_|0.0040 ***|0.0036 ***|0.0029 ***|0.0048 ***|
||(0.0010)|(0.0010)|(0.0011)|(0.0011)|
|ln_AirConditioner_|0.0103***|0.0098***|0.0097***|0.0119***|
54 in 1980 to appraise estimates of schooling returns subject to alternative methods for dealing with sample selection bias. He estimates a nonmarket (reservation) wage function and a function for market wage offers. The reservation wage function determines the shadow **value** of nonmarket time of the individual, and hence what the individual requires to inducc him or her to enter the market labor force. **The** selection-corrected model of Heckman's is plausibly identified within **the** context of **the** family or bargaining labor supply model. A conventiona **l** og linear specification of the earnings function is estimated where returns to schooling **are** constant across schooling levels. 22 The selection-corrected maximum likelihood estimate of schooling returns is 16 percent, compared with **the** conventional estimate of 14 percent (based on only the third of the sample who earn wages). In this case the estimated returns to schooling increases for womn with the adoption of a sample selection procedure, and the selectivity term is statistically significantly different from zero, showing that the sample of wage earning women are not a random sample of the population with regard to their wage rates. King (1986) analyzed the earnings of women in the 1985/86 Peruvian Living Standard Survey. A probit equation for women participating in paid employment (i.e. wage earner or self-employed) is used to **estimate** an hourly earnings function with Heckean's (1979) two-stage procedure. When **the sample** selection correction is employed, the rate of return for women decreased trivially from 12. 2 to 12. 0 percent for primary schools, from 8. 0 to 7. 8 percent for secondary school, but from 6.8 to a -1.7 **percent** return **at the** university level (if a diploma is received after four **years** of study) (King, 1986; Table 10, 11 and 12). As in Griffin's study, the family's nonearned income and husband's characteristics **are** included only in **the** paid-participation equation, along
|**Country**|**HV (110 kV and**<br>**above)**|**MV to HV (66**<br>**kV–109 kV)**|**MV (1–66 kV)**|**Total above 1kV**|
|---|---|---|---|---|
|Sudan_2014|3,427|0|63,719|**67,146**|
|Swaziland_2014|296|970|10,034|**11,300**|
|Tanzania_2015|4,288|579|22,396|**27,263**|
|Togo_2013|0|75|2,647|**2,722**|
|Uganda_2014|1,592|35|11,572|**13,199**|
|Zambia_2014|3,014|1,771|3,137|**7,922**|
|Zimbabwe_2012|5,532|1,742|65,965|**73,239**|
|**Total**|**112,600**|**16,870**|**665,203**|**794,674**|
|**Total excluding South Africa**|**81,493**|**16,870**|**616,499**|**714,863**|

_Sources_ : Utility annual reports and other documents. _Note_ : The year following each country name is the reference year for countries included in the QFD analysis, or the latest year with available data for T&D assets for countries not included in the QFD analysis.

90
<!-- Start of picture text --> 4.00<br>0.75<br>faa8& 050<br>0.25<br>0.00 025 050Peak Ground Acceleration075 1.00 125<br>Vulnerability Curve for Building- Quality —o—2| 4—6<br><!-- End of picture text -->

<!-- Start of picture text --> 4.00<br>0.75<br>a&8e& 050<br>0.25<br>0.00 025 050Peak Ground Acceleration075 1.00 125<br>Vulnerability7 Curve for Building. Quality. —0—2 4—6<br><!-- End of picture text -->
Many agronomic modeling studies have assessed the impacts of climate change on several grain crops (e.g., rice, maize, wheat) in various regions of China. The general findings of these studies are that crop yields will fall in China like those in other developing countries (e.g., Matthews and Wassmann, 2003; Parry et al., 2004; Tao et al., 2006; Wu et al., 2006; Xiong et al., 2007; Yao et al., 2007). These and other crop modeling studies have the same caveat in that they assume the same crops are grown in the same places as climate changes. Further, crop modeling studies in China do not include any economic values attached to the estimated yield reductions. And, there are no agro-economic models (such as Adams et al., 1995) that convert crop modeling results into economic outcomes for China.

The only economic study in China to date of the effect of warming on agriculture is a Ricardian analysis (Liu et al. 2004). Curiously, this study finds that warming will increase average farm net revenue, not reduce it. However, this Ricardian study is based on county level data with severe data limitations. Therefore, it is difficult to weigh the results of this study in comparison to the results of the host of crop studies that suggest that warming is harmful. Thus, there is not sufficient evidence to determine whether China can continue feeding itself given global warming.

To answer this question, this paper reports the results of a new study that measures the sensitivity of Chinese agriculture to warming, employing farm level data. Like the Liu et al., (2004) study, the analysis in this paper relies on the Ricardian method (Mendelsohn, et al., MNS 1994). The analysis is conducted on 8,405 farms sampled across 28 provinces. The data include information on each farm’s economic operations, locational data, and other farm characteristics. Net revenue per hectare is regressed on climate and a number of other exogenous control variables. Matching the location to climate data (rainfall and temperature) and soils, it is possible to examine the effect of climate on net revenue controlling for many other factors.

The available data allow us to measure econometrically the direct effects of temperature and precipitation on crop net revenues. Unfortunately, the amount of irrigation water a farmer uses is not available in the dataset. Although we know whether each farm is irrigated or not, we do not know water availability or cost. If future climate scenarios reduce available water supplies, this is likely to have an important harmful effect on China’s agriculture that this study does not take into account. The analysis does not capture the indirect effect of climate change on 3
# **Co-Location, Socioeconomic Status and Perceptions of Environmental Change in the Indian Sundarbans**

Susmita Dasgupta Lead Environmental Economist, The World Bank Bansari Guha Consultant, The World Bank David Wheeler Senior Fellow, World Resources Institute _Key Words:_ Commons, Collective Action, Environmental Perceptions, Sundarbans, India _JEL Classification_ : Q01; Q23. Q57

- Authors’ names in alphabetical order.

Corresponding Author: Susmita Dasgupta. Lead Environmental Economist, The World Bank, Address: MSN: MC-3-308, The World bank, 1818 H Street, Washington DC, 20008, USA; Email: <u>sdasgupta@worldbank.org;</u> Tele phone: 1-202-473-2679; Fax: 1-202-522-2714 _Acknowledgment:_

**This research was conducted under the South Asia Water Initiative - Sundarbans Landscape administered by the World Bank.**

Authors are thankful to Swastika Bhattacharya for research assistance.
_Table 7: Effects of BMI changes on food and non-food consumption_

|||Food consu|mption|||Non-food c|onsumption||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Change in average|0.173|0.120|0.231|0.169|0.432|0.322|0.654|0.533|
|BMI among|(1.83)|(1.10)|(1.98)|(1.27)|(3.03)|(1.97)|(3.70)|(2.64)|
|household|||||||||
|members aged|||||||||
|18+ in 1992/93|||||||||
|Health insurance||0.013||0.024||0.070||0.089|
|||(0.82)||(1.50)||(2.90)||(3.73)|
|BMI x Health||0.186||0.164||0.335||0.272|
|insurance||(0.90)||(0.80)||(1.08)||(0.88)|
|Poor 1992|||0.116|0.118|||0.178|0.186|
||||(8.40)|(8.51)|||(8.52)|(8.85)|
|BMI x Poor 1992|||-0.115|-0.084|||-0.508|-0.445|
||||(0.62)|(0.45)|||(1.81)|(1.57)|
|R-squared|||||||||
|Estimates of BMI|||||||||
|effects by|||||||||
|insurance and|||||||||
|income group|||||||||
|Poor uninsured||||0.085||||0.088|
|||||(0.56)||||(0.38)|
|Poor insured||||0.249||||0.360|
|||||(1.10)||||(1.05)|
|Non-poor||||0.169||||0.533|
|uninsured||||(1.27)||||(2.64)|
|Non-poor insured||||0.333||||0.805|
|||||(1.83)||||(2.91)|

Note: _t_ -statistics in parentheses. Regressions include all covariates listed in Table 2 and commune fixed effects, which were jointly significant.

10
greater amount of evidence needs to be built, and ideally we would like to have the expected effectiveness of the alternative modalities offered through G.

# Hence, _m = G * A * E_

In the context of our global simulations, the parameter _m_ is used as a single parameter which combines all three elements described above:

- γ, families are losing income. The income loss is an exogenous parameter, as is determined by existing GDP projections, from the World Bank and IMF.

- _d_ , countries have age group specific income elasticities to schooling<sup>55</sup> , which will lead

- some children to drop out.

- Learning, measured in terms of Harmonized Learning Outcomes (HLO); PISA score; and PISA Level.

- Schooling, measured in Expected Years of Schooling (EYS).

- LAYS,  Learning Adjusted Years of Schooling.

_Simulation 1: Effect on LAYS (years)_ This analysis examines the impacts of school closures on the stock of earning Adjusted Years of Schooling (LAYS) as well as on Harmonized Learning Outcomes across country income groups. Additionally, we combine data on the projected GDP per capita change provided by the MPO (Macro Poverty Outlook, WBG) with the Global Monitoring Database (collection of globally harmonized household survey data, WBG) to estimate how much dropout is likely to occur as a result of COVID-19. The HCI 2017 database is used as the baseline for these calculations.

∆ _LAYSc = f(_ ∆ _HLOc ,_ ∆ _EYSc)_ changes in the LAYS of country _c_ is a function of changes in both the HLO and EYS of country _c_ , where,

**HLO** , Harmonized Learning Outcomes of country _c_

**EYS** , Expected Years of Schooling of country _c_

Hence, we simulate the impact of COVID-19, both in terms of school closures and household income, on both the HLO and EYS as per the equations below: where,

**sc** , school closure (as a share of the school year) of country _c_

**_mc_** , mitigation effectiveness of country _c_

**_pc_** , learning gains (school productivity) of country _c_

**_dc,a,w_** dropout-income elasticity of children that have attended school by age group (a) and welfare quintile ( **w** ) from country _c_

> 55 For more information regarding this parameter please see the discussion on school enrollment-income elasticity in annex A.2.

17