rafmacalaba/datause-encoder-data huggingface dataset:
Mongolia and Norway are prominent examples of the opposite, with real GDP<sup>e</sup> at least 19% lower than real GDP<sup>o</sup> due to disadvantageous terms-of-trade.

# **5. Conclusions**

In this paper, it has been shown that there is a fundamental difference between real GDP measured from the output side or from the expenditure side in international comparisons. The difference between the two concepts is in the treatment of the terms of trade. Real GDP from the expenditure side represents the ability to purchase goods and services while real GDP from the output side measures the production possibilities of the economy. It is the latter concept of real GDP which is of interest to many studies of growth and convergence in the world economy. However, available data from the Penn World Tables are based on a mix of cross-country expenditure-side measures of real GDP for benchmark years, with national growth rates of real GDP based on output-side measures.

In this paper a clear-cut distinction between the two measures is made. It is shown that real GDP<sup>e</sup> can overstate real GDP<sup>o</sup> by as much as 100% (for Bermuda) and understate it by as much as 33% (for Hong Kong). In these two extremes and other cases, we would want to closely examine the export unit-values before treating the estimates of real GDP as reliable. For jo Bermuda, its exported sailboats may well be of higher quality than the world average, which indicates that we are overstating its high terms of trade. Conversely, the low export unit-values for Hong Kong may reflect it status as an entrepôt economy, processing and shipping goods from China and elsewhere in Asia. It is impossible to avoid using these trade unit-values as measures of prices, however, since no dataset exists for comparing actual trade prices across countries. So it is essential that we should attempt to correct the unit-values for differences in quality across countries. Recent papers along these lines include: Choi, Hummels and Xiang (2006), Feenstra and Romalis (2006), Hallak (2006), Hallak and Schott (2006), Hummels and Klenow (2005), and Timmer and Richter (2006); and we can hope that enough progress will being made on these methods to allow implementation over a wide set of countries, products and years. In our view, that is the key theoretical and empirical issue that must be resolved before applying the techniques described herein to obtain separate measures of real GDP on the output side, and the expenditure side, in the Penn World Tables.

19
Figure 3: Returns to Schooling by Level over Time (percent) <!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Primary Secondary Secondary University University<br>incomplete incomplete<br>1992 1997 2002<br><!-- End of picture text --> The estimate of the rate of return to schooling in 2002 is 12.0 percent for men and 10.8 percent for women (Table 3). These figures increased since 1992, when the rates of returns were 9.1 percent and 8.1 percent. The returns to all levels of education are much higher for men than for women. The coefficient on general experience did not change for men (4.6 percent) and experienced a slight increase for women (3.4 percent to 3.7 percent). As human capital theory suggests, the sign of the estimated coefficients for experience and experience-squared are of the correct sign. All these coefficients are statistically significant at the 1% level. The F-statistics for the significance of the joint regressors are higher than 119.

14
- Eastin, J., and K. Dupuy (eds.). 2021. Gender, Climate Change and Livelihoods: Vulnerabilities and Adaptations. CABI.

- Ebrahim, N. B., and M. S. Atteraya. 2021. "Polygyny and intimate partner violence (IPV) among Ethiopian women." _Global Social Welfare_ 8(3): 213-220.

- Ellena, M., et al. 2020. "Social inequalities in heat-attributable mortality in the city of Turin, northwest of Italy: a time series analysis from 1982 to 2018." _Environmental Health_ 19(1): 1-14.

- Elmallakh, N., and Q. Wodon. 2021. “Climate Shocks, Migration, and Labor Markets: A Gender Analysis from West Africa.” _GLO Discussion Paper_ 950.

- Epstein A, E. Bendavid, D. Nash, ED. Charlebois, and SD. Weiser (2020) Drought and intimate partner violence towards women in 19 countries in sub-Saharan Africa during 2011-2018: A population-based study. _PLoS Med_ 17(3): e1003064. https://doi.org/10.1371/journal. pmed.1003064

- Fafchamps, M., C. Udry, and K. Czukas. 1998. "Drought and saving in West Africa: are livestock a buffer stock?." _Journal of Development Economics_ 55(2): 273-305.

- FAO (Food and Agriculture Organization of the United Nations). 2011. _The State of Food and Agriculture_ .

- Feeny, S., A. Mishra, T. A. Trinh, L. Ye, and A. Zhu. 2021. “Early-Life exposure to rainfall shocks and gender gaps in employment: Findings from Vietnam.” _Journal of Economic Behavior and Organization_ 183: 533-554.

- Flatø, M., and A., Kotsadam. 2014. “Droughts and gender bias in infant mortality in sub-Saharan Africa.” _Memorandum of the Department of Economics_ 02/2014. Oslo University.

- Flor, L. S., J. Friedman, C. N. Spencer, J. Cagney, A. Arrieta, M. E. Herbert et al. 2022. “Quantifying the effects of the COVID-19 pandemic on gender equality on health, social, and economic indicators: a comprehensive review of data from March, 2020, to September, 2021.” _The Lancet_ .

- Gray, C., and V. Mueller. 2012. “Drought and population mobility in rural Ethiopia.” _World development_ 40(1): 134-145.

- Haile, K. K., Tirivayi, N., & Nillesen, E. (2019). “Climate shocks, coping responses and gender gap in human development.” _MERIT Working Papers_ 052. _Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT)_ .

- Hirvonen, K. 2016. “Temperature changes, household consumption, and internal migration: evidence from Tanzania.” _American Journal of Agricultural Economics_ 98(4): 1230-1249.

- Hoddinott, J. and B. Kinsey. 2000 “Adult Health in the Time of Drought” _IFPRI FCND DP No. 79._

23
29 So, are payments on these bonds subject, for example, to the Russian executive order requiring that creditors in _unfriendly_ nations be paid in rubles?<sup>99</sup> A literal read would suggest so. But the general rule of contract interpretation cited above clearly states that a literal read is not the court’s charge.<sup>100</sup> Additionally, boilerplate clauses are interpreted in the interest of “justice and commercial efficacy.”<sup>101</sup> In other words, they are interpreted with an eye to maintaining the stability of the market, which needs to rely on consistent interpretations over time.

Following the UKSC’s general rule and considering the United Kingdom’s strong interest in maintaining market stability, a narrower interpretation should be favored. First, the language appears under the header “Payments Subject to Fiscal Laws,” which is generally understood to mean taxation.<sup>102</sup> Second, the clauses often continue to describe other taxation to which payments may be subjected (for example in the U.S.) and cross-references the taxation condition, which contains Russia’s pledge to make payments free of any levies on its part.<sup>103</sup> Third, a major inconsistency would obtain from a broad reading that allowed for application of the issuersovereign’s laws when the parties bargained for the application of English law to what is the basic creditor right on a loan contract: receiving her money.<sup>104</sup> So, it is likely that the objective meaning of the clause refers to taxation and related laws.

As to the general context, consider the confusion that could follow if “hybrid law” sovereign bonds were suddenly birthed by a broad interpretation. At the London Commercial Court, “80% of cases involve a foreign claimant or defendant. Of course, that has a knock-on effect and the success of the legal services sector plays an unquantifiable role in helping London to

> 99 _See_ Morgan Lewis Lawflash, note 45 _supra_ .

> 100 _See Wood v. Capita_ , note 79 _supra_ .

> 101 Lam, note 52 _supra_ at 95.

> 102 _See e.g._ , Prospectus, note 81 _supra_ , at 30.

> 103 _Id_ .

> 104 A “substantive right” in legalese.
Figure 2: Abolishing fees had heterogeneous effects on per pupil funding

- (a) Average fees in public primary schools, before and after FPE, by socio-economic group

<!-- Start of picture text --> 80.0<br>Pre-FPE, 1997<br>Post-FPE, 2006<br>60.0 57.9<br>40.0<br>35.0<br>20.0 14. 5<br>3.4 4.6 5.3 8.9<br>0.0 1.2 2.3 1.5<br>Schooling of household head<br>Changes in pupil-teacher ratios, relative to initial<br>0 20 40 60 80 100<br>PTR 2001<br>(c) Net changes in funding per pupil<br>$16.69<br>$8.51 $8.01 $6.40<br>$4.40 $8.43 $.61<br>$.20<br>-$7.61 -$14.48<br>-$19.04 -$16.00<br>-$38.86<br>-$74.72<br>Scenario 1<br>Scenario 2<br>Scenario 3<br>-$111.94<br>Schooling of household head<br>None Some prim. Complete prim. Some sec. Some tert.<br>None Some prim. Complete prim. Some sec. Some tert.<br>US$ fees per pupil per annum<br>20<br>0<br>-20<br>Change in PTR 2001-2005<br>-40<br>50<br>0<br>-50<br>Change in US$ funding per pupil<br>-100<br><!-- End of picture text -->

- (b) Changes in pupil-teacher ratios, relative to initial level

23
- <mark>World Bank, Sustainable Department of the EAP region (2010). “A Strategic Approach to Climate Change in the Philippines”.</mark>

- <mark>Yang, Dean and HwaJung Choi (2007). “Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines”,</mark> _<mark>World Bank Economic Review</mark>_ <mark>, Vol. 21 (2), May 2007, pp. 219-248.</mark>

17
_Table 3. Results for Export Revealed Comparative Advantage (4-digit product level)_

|Group|Variable|Beta|Standard<br>deviation|Transformation<br>sign|
|---|---|---|---|---|
|1|GDP growth - 5Y forecast|4.103|0.619|1|
|1|Final consumption expenditure (% of GDP)|3.333|0.453|1|
|2|Employment to population ratio, 15+, total (%) (modeled ILO estimate)|2.150|0.698|1|
|2|Labor force participation rate, total (% of total population ages 15-64)|2.672|0.679|1|
|2|Labor force, total|2.148|0.610|1|
|2|Cooperation in labor-employer relations, 1-7 (best)|2.411|0.607|1|
|2|Country capacity to retain talent, 1-7 (best)|1.090|0.564|1|
|2|Flexibility of wage determination, 1-7 (best)|2.645|0.281|1|
|2|Hiring and firing practices, 1-7 (best)|2.126|0.681|1|
|2|Primary education enrollment, net %|0.609|0.356|1|
|2|Labor tax and contributions (% of profit)|3.532|0.507|-1|
|2|Pay and productivity, 1-7 (best)|2.260|0.409|1|
|3|Urban population (% of total)|1.213|0.556|1|
|3|Urban population growth (annual %)|5.803|0.960|1|
|3|Arable land (% of land area)|3.335|0.497|1|
|3|Water resources: total internal renewable water resources|1.154|0.451|1|
|3|Total renewable water resources per inhabitant (billion cubic meters)|1.605|0.499|1|
|4|Industry (including construction), value added per worker (constant 2010 US$)|1.755|0.532|1|
|4|Industry (including construction), value added (annual % growth)|1.802|0.554|1|
|4|Gross fixed capital formation (% of GDP)|2.757|0.648|1|
|4|Scientific and technical journal articles|1.128|0.490|1|
|4|PCT patents, applications/million pop.|2.378|0.436|1|
|4|Availability of scientists and engineers, 1-7 (best)|2.049|0.822|1|
|4|Company spending on R&D, 1-7 (best)|1.871|0.560|1|
|4|Number of persons engaged (in millions)|1.960|0.604|1|
|5|Getting electricity: Cost, % of income per capita|-0.835|0.255|-1|
|5|Total natural resources rents (% of GDP)|-0.556|0.288|-1|
|5|Coal rents (% of GDP)|0.314|0.167|-1|
|5|Mineral rents (% of GDP)|0.379|0.219|-1|
|5|Natural gas rents (% of GDP)|0.701|0.179|-1|
|5|Electricity Production (kWh/capita)|1.647|0.528|1|
|5|Crude Oil Proved Reserves (Billion Barrels)|2.750|0.651|1|
|6|Quality of roads, 1-7 (best)|2.094|0.532|1|
|6|% of population covered by a mobile-cellular network|0.460|0.274|1|
|6|% of population covered by at least a 4G mobile broadband network|1.854|0.471|1|
|6|Mobile network performance score|0.737|0.354|1|
|7|Domestic credit to private sector by banks (% of GDP)|1.737|0.806|1|
|7|Private credit bureau coverage (% of adults)|3.114|0.649|1|
|7|Getting credit - Distance to Frontier (DB15-19 methodology)|2.659|0.667|1|
|7|Financing through local equity market, 1-7 (best)|1.546|0.632|1|
|7|Soundness of banks, 1-7 (best)|1.803|0.967|1|

23
The Role of Officially Supported Export Credits in Sub-Saharan Africa's External Financing Asli DGmirgUc-Kunt and Refik Erzan* I. Introduction This paper addresses the question of how important officially supported export credits (OSECs) were, both in quantity and in quality, in SubSaharan Africa's (SSA) external financing during the last two decades, and examines the prospects for the 1990s.

OSECs comprise suppliers' and buyers' credits which are officially supported by way of direct credits, refinancing, eligibility for interest subsidies, guarantee or insurance. Official institutions which either directly extend export credits or provide some form of support or guarantee are generically called export credit agencies (ECAs).

It should be emphasized that OSECs were not designed to address the financial needs of developing countries. The surge in OSECs came about when most OECD countries perceived them as an economic policy tool, primarily to boost exports, in the wave of the new protectionism and mercantilism that swept over the industrial countries in the early 1970s. Consequently, OSECs soon became The authors are economists at the International Economics Department, the World Bank.

The authors gratefully acknowledge F. Ballarin and J.E. Ray for crucial data and useful inaights, and B. Balassa, G.G. Johnson, F.D. Levy, M. Pollock and W.S. Tambe for valuable comments.
# Table 4 Impact ofAid, Policies, Institutions on the Poor (Inequality; Level Regressions)

|Dependent Variable: Ln(Per Capi|taIncome oft|he Poorest|Quintile)|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||dQ)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|a)|(12)|(3)|
|Constant|-4.1807***-|4.1483*#*|-41572***-|3.9777T***|-41SOO***-|38242***|-5S2LO***|-5.6357***|-S.8752***|-2.9829***-|56432***|-5.7573***|-6.0209***|
||<br>(6.49)|<br>(6.10)|<br>(6.26)|<br>(4.20)|<br>(5.46)|<br>(5.49)|<br>(5.64)|<br>(5.81)|<br>(6.06)|<br>(3.45)|<br>(4.69)|<br>(4.82)|<br>(5.05)|
|Ln(Per Capita Income)<br>|1.1794***<br>|1.1763***<br>|1.1783***<br>|LASTI***<br>|1.1678***<br>|1.1590***<br>|1.3675***<br>|1.3772***<br>|1.4056***<br>|1.1606***<br>|1.3815***<br>|1.3912***<br>|1.4232***<br>|
|(real terms)#|(13.19)|(12.52)|(12.24)|(8.91)|(10.49)|(12.74)|(9.93)|(10.08)|(10.35)|(12.94)|(8.23)|(8.33)|(8.59)|
|Government Consumption/GDP|||-0.0015<br>(0.27)|||||||-0.0092<br>(1.34)||||
|Ln(1 + PeriodAverage||||-0.00099<br>(0.07)|||||||0.0779<br>(0.46)|0.0785<br>(0.47)|0.0892<br>(0.54)|
|Trade/GDP|||||0.0004<br>(0.37)|||||||||
|Institutional Quality||||||-0.0069*||||-0.2757*||||
|(ICRG Capacity)||||||(1.80)||||(1.96)||||
|EconomicAid/GDP|||||||-0.0023<br>(0.17)||||-0.0067<br>(0.38)|||
|SocialAid/GDP||||||||0.0188***<br>(3.39)||||0.0193***<br>(3.56)||
|AggregateAid/GDP|||||||||0.0152***<br>(5.43)||||0.0152***<br>(5.34)|
|Crises Dummy||-0.0185|-0.0163|-0.0233|-0.0109|-0.0052|-0.0199|-0.0232|-0.0194|-0.00006|-0.0250|-0.0268|-0.0234|
|||(0.87)|(0.71)|(1.00)|(0.52)|(0.25)|(0.80)|(0.92)|(0.79)|(0.003)|(0.93)|(1.02)|(0.91)|
|No. ofObservations|211|211|203|193|190|187|180|182|182|182|164|166|166|
|R-squared|0.99|0.99|0.99|0.99|0.99|0.99|0.99|0.99|0.99|0.99|0.99|0.99|0.99|

Notes: i) Absolute value of t-statistics calculated with White-corrected standard errors is in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. ii) Panel two-stage least squares methods is used for these analyses.

iil) # denotes variables are instrumented with the value of the previous year. Source: Authors' compilation
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.94*** 0.92*** 0.68***<br>H<br>Kendall tau-b 0.81*** 0.78*** 0.53***<br>Spearman 0.92*** 0.78*** 0.74***<br>A<br>Kendall tau-b 0.81*** 0.63*** 0.64***<br>Spearman 0.93*** 0.92*** 0.68***<br>M0<br>Kendall tau-b 0.79*** 0.77*** 0.54***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C13.1. FOSD: Counting Vector C13.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C13.3. SOSD: Social Sustainability Index C13.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.98*** 0.93***<br>H<br>Kendall tau-b 0.78*** 0.94*** 0.83***<br>Spearman 0.95*** 0.9*** 0.93***<br>A<br>Kendall tau-b 0.89*** 0.78*** 0.83***<br>Spearman 0.98*** 0.98*** 0.98***<br>M0<br>Kendall tau-b 0.94*** 0.94*** 0.94***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6 -.05<br>CCDF .4 Difference -.1<br>.2 -.15<br>0 -.2<br>.5<br>.4<br>.3<br>.2<br>Social Sustainability Index .1<br>0<br><!-- End of picture text --> _C14. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Satisfaction with democracy indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C14.1. FOSD: Counting Vector C14.2. Dominance Analysis 63
# **Table 3 Gross Government Debt, by Type of Economy, 2007–12** **<u>(Percent of GDP)</u>**

|_Type of economy_|_2007_<br>_(precrisis)_|_2008_|_2009_|_2010_|_2011_|_2012_<br>_(projected)_|
|---|---|---|---|---|---|---|
|Advanced economies|74.5|81.5|95.2|101.4|105.5|110.7|
|Germany|65.4|66.9|74.7|82.4|80.6|83.0|
|Japan|183.0|191.8|210.2|215.3|229.6|236.6|
|United States|67.2|76.1|89.7|98.6|102.9|107.2|
|Emergingeconomies|35.5|33.6|36.1|40.5|37.0|34.8|
|Brazil|65.2|63.5|66.9|65.2|64.9|64.1|
|China|19.6|17.0|17.7|33.5|25.8|22.2|
|India|75.5|74.1|74.2|68.0|67.0|67.6|
|Mexico|37.6|43.0|44.5|42.9|43.8|43.1|
|Low-income economies|44.0|41.1|44.1|42.8|41.1|42.5|

_Source:_ IMF 2012.

# **Comparison of Brazil, China, and India**

This section compares the experience of three of the largest developing countries, Brazil, China, and India. It begins with a brief overview of the framework of fiscal federalism. It then describes how the global crisis affected each country and how each country responded to the crisis, focusing on the role of subnational fiscal policy.

# **_Framework of Fiscal Federalism_**

Subnational governments have significant spending responsibilities in all three countries. However, taxing powers vary a great deal, as does the degree of dependence on central transfers. There are also marked differences in the political framework affecting the relations between the national and subnational governments.

The fact that subnational governments undertake the bulk of consolidated government spending is sometimes mistakenly interpreted as a high degree of decentralization in China. The degree of fiscal devolution depends primarily on taxing powers, which are highly centralized in China, which is a unitary state with some federal features (Bahl and MartinezVazquez 2003). In comparison, state governments in Brazil and India enjoy significant 14
indicator variable for being 0-10 km from the GQ network with indicator variables for whether the district’s work was completed by March 2003, March 2006, or later. Of the 70 districts, 27 districts were completed prior to March 2003, 27 districts between March 2003 and March 2006, and 16 districts afterwards. In almost every case in Table 3b, the relative sizes of the effects by implementation date are consistent with the project’s completion taking hold and influencing economic activity. Given that our final data survey comes from 2007, it is not surprising that we do not yet see substantial activity in the districts completed after March 2006. On the other hand, expansions in activity are generally strongest for districts that were completed by March 2003. Again, the timing of the GQ upgrades is consistent with the results we observe.

# **Comparison of GQ Upgrades with the NS-EW Highway**

Table 4 compares districts proximate to the GQ network to districts proximate to the NS-EW highway network that was not upgraded. The idea behind this comparison is that districts that are at some distance from the GQ network may not be a good control group if they have patterns of evolution that do not mirror what districts immediately on the GQ system would have experienced had the GQ upgrades not occurred. This comparison to the NS-EW corridor provides perhaps a stronger foundation in this regard, especially as its upgrades were planned to start close to those of the GQ network before being delayed.

The upgrades scheduled for the NS-EW project were to start contemporaneous to and after the GQ project. To ensure that we are comparing apples to apples, we identified the segments of the NS-EW project that were to begin with GQ and those that were to follow in the next phase. We use separate indicator variables for these two groups so that we can compare against both. Of the 76 districts lying with 0-10 km of NS-EW, 40 districts were to be covered in the 48 NS-EW projects identified for Phase I. The empirical appendix provides greater detail on this division.

The powerful result from Table 4 is that none of the outcomes we measure for the GQ system in the postupgrade period are observed for districts along the NS-EW corridor. The placebo-like coefficients from the interactions of post-GQ upgrades with districts lying between 0-10 km from the NS-EW highway are mostly negative and never statistically significant. The lack of precision is not due to too few districts along the NS-EW system, as the district counts are comparable and the standard errors are of very similar magnitude. In Appendix Table 1, we show that null results continue to hold when we combine the NSEW indicator variables and that the coefficients are well estimated. Said differently, with the precision that we estimate the positive responses along the GQ network, we estimate a lack of a change along the 17