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

Starting in the early 1990s, as developed and emerging countries became more integrated within the global financial system and especially as international transactions seemed to grow, interest in financial globalization increased substantially.<sup>1</sup> Since then, many have questioned the links between financial globalization and economic growth and have revisited the overall costs and benefits of financial integration.<sup>2</sup> In principle, financial globalization should increase access to capital and lower its costs.<sup>3</sup> But financial globalization may also expose countries to foreign shocks and crises, thus raising several new macro-financial challenges, such as the regulation and use of domestic financial markets and the conduct of macro-prudential policies.<sup>4</sup> Despite all the attention to financial globalization, its concept and extent have remained somewhat elusive. For example, different authors use, alternatively, net capital flows, gross capital flows, and country portfolios as measures of financial globalization.<sup>5</sup> Many also measure globalization through the participation of foreigners in domestic markets, while a number of other studies focus on the access of domestic firms and governments to foreign capital.<sup>6</sup> Moreover, while there is much discussion of increased financial globalization, little is known about whether the sharp globalization that took place during the 1990s continued during the following decade or whether the nature (or type) of financial globalization has changed.<sup>7</sup>

> 1. Obstfeld and Taylor (2004); Lane and Milesi-Ferreti (2007); Obstfeld (2012).

> 2. Rodrik (1998); Stiglitz (2002); Kose et al. (2010).

> 3. Foerster and Karolyi (1999); Stulz (1999); Errunza and Miller (2000); Errunza (2001).

> 4. Dornbusch, Goldfajn, and Valdes (1995); Calvo and Reinhart (2000); Allen and Gale (2000); Reinhart and Reinhart (2009); Kawai and Lamberte (2010); Calvo (2011).

> 5. Lane and Milesi-Ferretti (2001, 2007); Kraay et al. (2005); Devereux (2007); Gourinchas and Rey (2007); Reinhart and Reinhart (2009); Broner et al. (2010).

> 6. Forbes (2006); Kose et al. (2010); Henry (2007); Gozzi, Levine, and Schmukler (2010).

> 7. One exception is Levy Yeyati and Williams (2011).

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Table C.12: Effect of Years of Schooling on Sector of Employment: Job Different than Husband

||Secto|r of Current|Work|
|---|---|---|---|
||Skilled /<br>Professional|Service<br>/ Sales|Agriculture<br>/ Unskilled<br>Manual|
||(1)|(2)|(3)|
||A. No|n-MTI Region|s Only|
|�<br>Years of Schoolingizy|0.058|0.062|-0.027|
||(0.023)|(0.034)|(0.019)|
||[0.011]|[0.071]|[0.250]|
|First Stage F-Statistic|12.48|12.48|12.48|
|N|11,870|11,870|11,870|
||B. National|Estimates (F|PE + MTI)|
|�<br>Years of Schoolingizy|0.035|0.014|0.004|
||(0.015)|(0.024)|(0.029)|
||[0.019]|[0.569]|[0.879]|
|First Stage F-Statistic|8.22|8.22|8.22|
|N|21,242|21,242|21,242|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : MTI = mother tongue instruction; FPE = free primary education. The dependent variable is an indicator that equals 1 if employed in the denoted sec� tor and 0 otherwise. In panel A, Years of Schooling _izy_ is the predicted level of schooling, instrumented with only the FPE intensity measure, _Izy_<sup>FPE</sup> ; in panel B additional instruments include two MTI intensity measures, _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred. All samples include women in birth cohorts from 1970 to 1988 working in different jobs than their husbands. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Each estimate is from a unique regression. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

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the split between education and health, but has not done so specifically. The departmental and territorial government have a legal obligation to provide, in addition to the tax allowance, the same share of their ordinary income to finance primary education and health operating costs as they provided in 1972. According to the law, the funds provided through the tax allowance are to be administered through the Regional Education Funds (Fondos Educativos Regionales or "FERs") and Section Health Services (Servicios Seccionales de Salud or "SSSs"). Despite the seeming tightness of the legal provisions for implementing the tax allowance, there has been considerable room for flexibility in how they have been interpreted. There are a number of reasons for this: first the 7 -uirements of individual FERs and SSSs are considered and the amounts are totalled to make sure that they do not exceed the amount available to be distributed. Second, where the sum of the tax allowance and local resources in any jurisdiction is not sufficient to meet requirements, supplements can be added. Similarly where th?re are excesses, the excess may be diverted to the finance of investment with the apprcval of the Ministry of Finance. Third, the process of additions to the initial budget distorts the process still further since the additions do not follow the sharing formula precisely.

26. As a result, there is considerable variation in the proportion shared, the share going to each jurisdiction, and the split between education and health by jurisdiction or in total. These results show up clearly in statistical Appendix Tables A3 through A5. Table A3 shows that the share of the tax allowance in ordinary revenues (current revenues minus earmarked taxes) has mostly been above the 15Z level in the last decade. Starting from about 12% in 1975 it rose to a peak of 242 by 1984 before falling back sharply. While according to a strict interpretation of the sharing formula, each jurisdiction would obtain a constant share of the
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**Appendix 1: INPC Temporal Price Deflator, 1981-2004.**

|Year|September 2004 = 1|
|---|---|
|1979|7.430|
|1980|13.821|
|1981|27.760|
|1982|54.338|
|1983|144.115|
|1984|415.808|
|1985|1311.211|
|1986|2.611|
|1987|10.454|
|1988|84.815|
|1989|1.172|
|1990|34.729|
|1991|171.284|
|1992|2138.101|
|1993|44.534|
|1994|0.422|
|1995|0.526|
|1996|0.589|
|1997|0.605|
|1998|0.626|
|1999|0.658|
|2000|0.700|
|2001|0.753|
|2002|0.821|
|2003|0.942|
|2004|1|
|2005|1.047|

Source: Índice Nacional de Preços ao Consumidor (INPC); Instituto Brasileiro de Geografia e Estatística Note: The Corseuil and Fogel (2002) adjustment to the 1994 IBGE INPC index is applied.
sufficiently effective instrument for managing teachers and their performance, offering bonuses to head teachers may be a more efficient way to raise school performance than offering bonuses to all teachers in the school.

These views shaped the design of three alternative treatment variants to test in parallel. Bonus eligibility and size were varied according to whether or not the teacher is the school’s head teacher.<sup>7</sup> The treatment variants are:

- **_HT only_** : only head teachers were eligible for level-1 bonuses.

- **_all T_** : both head teachers and teachers were eligible for level-1 bonuses.

- **_HT+_** : head teachers were eligible for level-2 bonuses, while other teachers were eligible for level-1 bonuses.

The size of the level-2 bonus was set as twice the size of the level-1 bonus for a given level of school performance. Treatment variants and untreated status are randomly assigned across the evaluation sample by first blocking schools into quads based on 2010 school-level mean fifthgrade PEC exam scores.<sup>8</sup>

# _Bonus formula_

Table 1 presents the bonus formula, by treatment variant and teacher type (head teacher or other teacher). The slope parameters in the bonus formula were calibrated so that teachers in the average school at baseline in the evaluation sample face high-powered incentives to perform. Even if the average school does not increase enrollment and mean student exam scores but maintains its baseline exam participation rate of 97 percent, the minimum teacher bonus payout would be 14,550 Pakistani rupees (US$169).<sup>9</sup> If the average school maxes out the composite score in the first year, the minimum teacher bonus payout would be 86,200 Pakistani rupees (US$1,002).<sup>10</sup> These two minimum payouts represent 10 and 57 percent respectively of the

> 7 A teacher appointed as a head teacher on an acting basis is considered eligible for the head teacher bonus. A (head) teacher whose assignment to a program school is effective at the start of the school year is considered eligible for the (head) teacher bonus even if the (head) teacher is then transferred at any point during the school year to any another school.

> 8 The trial is unmasked: the experimental groups that schools are assigned to are known by the provincial and district education departments, program implementing partners, supporting international donor agencies, and us (the researchers).  Program schools are directly made aware of their assigned treatment variant.

> 9 We use the December 2010 exchange rate of 86 Pakistani rupees per US dollar.

> 10 Maxing out the composite score in the first year would imply raising the exam participation rate from 97 to 100 percent, doubling enrollment from 100 students, and raising the school mean exam score from 23 to 100 percent.

10
observed _within_ both the Early Taiping and Late Taiping areas, adding support to the distancebased stationary bandit hypothesis.

# **VI. Malthusian Transition with Evidence from Long-Term Data**

We have shown that the Taiping Rebellion permanently lowered the relative population level in Taiping areas, that the Late Taiping and Capital-Close areas had faster population recovery, and that _Likin_ intensity is associated with a lower population. However, since lower population growth could also indicate being in the modern growth regime and the transition from the Malthusian and/or the Post-Malthusian transition regime (Galor 2005), the interpretation of the Taiping effects is incomplete if not misleading without looking at the long-term consequences on the quality of long-term development. The incompleteness is illustrated by the negative association between Likin and population level, which is consistent with both the _Likin_ -astaxation hypothesis and the _Likin_ -helping-Malthusian-transition hypothesis (via the mechanism of facilitating modern sectors). We thus now examine how the rebellion and its various indicators are associated with indicators of modern development.

We measure the quality of modern development from two sources. Two measures come from the China Regional Economic Statistics Yearbook 2010. We use GDP per capita (in 2010) as a measure of the income level, and fiscal revenue per capita in 2010 as the measure of fiscal capacity. Several measures come from Census 2000 (China Data Center and Spatial Data Center 2017): the modern sector structure is represented by the share of GDP in manufacturing, and in non-agriculture; human capital is captured by average years of schooling and by the mortality rate. Using ArcGIS, we merge the modern outcome data set (2,876 counties in 348 cities in 2,000 administrative divisions) with the Taiping historical variable data set (266 prefectures in 1,820 Qing administrative divisions).<sup>49</sup> In the end, 272 modern cities are merged into 192 (Qing) prefectures. Additionally, several prefectures drop out in specific regressions due to missing values of a few variables.

We first examine the net effect of the rebellion on long-term outcomes. We present both the OLS and the IV results. The control variables are largely the same as before—except that we do not control for the prefecture and the year fixed effects because we now employ crosssectional data.<sup>50</sup> When using the 2SLS specification, as before, we use the longitude of the

> 49 There are 348 cities in the modern data set, of which 272 are in the territory covered by the Taiping data set (which consists of 266 prefectures). We first generate the center point of these cities, and then use the pointshape file to merge with the historical data set (polygon-shape files). When the central point of a city falls into one prefecture polygon, we call it a match.

> 50 Naturally the interactions between the time dummies and the time-invariant variables are replaced with the time-invariant variables.

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> SPECIAL ToPic II: DEMOGRAPHIC TRENOS IN FORMER SOVIET-UNION bocv

result in substantial refugee flows when ethnic frictions erupt, as in the Armenia-Azerbaijani conflict. Full freedom of movement was restricted in the former Soviet Union; to what extent new migration policies wili limit the movement of people among the new countries remains to be seen. In addition to economic, ethnic, and political factors, natural and environmental disasters may increase future migration. The Chernobyl nuclear accident has caused large outmigration In the areas of the Ukraine, Belarus, and Russia most affected by contamination.

# References

- Dutton, John C Jr. 1981. *Causes of Soviet Adult Mortalty Increases". _Soviet Studies,_ Vol 33, No 4: 548-559.

- Scherbov, Sergei, and Evgeniy Andreev. 1992. 'Population Prospects for the Republics of the Former USSR." Paper presented at the Annual Meeting of the Population Association of America, Denver, April 30-May 2, 1992.

- Zayanchkovskaya, Zh. A. 1991. 'Inter-Republic Population Migration in the USSR.' _SuUdies on Soviet Economic Development,_ Vol _2,_ No 3:228-242.
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Table 1 Continued: Descriptive Statistics: Manufacturing sector

||Mean|sd|p10|p25|p50|p75|p90|N|
|---|---|---|---|---|---|---|---|---|
|Change 2008-2013|||||||||
|**ICT variables**|||||||||
|∆Share of labor that uses computers|-2.55|38.95|-60.00|-15.00|0.00|11.00|40.00|11,438|
|∆Share of labor that uses Internet|0.01|36.15|-50.00|-10.00|0.00|12.00|40.00|11,438|
|∆ln(Stock of computer equipment/worker)|0.11|1.50|-1.69|-0.64|0.19|0.92|1.84|11,438|
|**Employment and wages**|||||||||
|∆ln(Number of white-collar workers)|0.03|0.81|-0.92|-0.41|0.00|0.47|1.01|11,438|
|∆ln(Number of blue-collar worker)|-0.02|0.75|-0.86|-0.36|0.00|0.35|0.80|11,438|
|∆ln(Annual real wage white-collar workers)|0.02|0.72|-0.86|-0.41|0.01|0.45|0.93|11,438|
|∆ln(Annual real wage blue-collar workers)|0.04|0.59|-0.66|-0.30|0.03|0.37|0.76|11,438|
|∆White/Blue-collar workers|1.21|22.81|-22.12|-8.05|0.23|9.88|25.83|11,438|
|∆White/Blue-collar wages|0.01|0.23|-0.22|-0.08|0.00|0.10|0.26|11,438|
|**Firm characteristics**|||||||||
|∆ln(Capital/worker)|0.18|1.49|-1.49|-0.47|0.23|0.92|1.78|11,438|
|∆ln(Total employment)|0.01|0.64|-0.69|-0.29|0.02|0.31|0.71|11,438|

_Source:_ Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI 15
supposed to apply a fair market price. Although governments are aware of these incentives and try to use their enforcement powers to ensure that royalties do not deviate from reasonable values, Hines (1995) and Grubert (1998) do find evidence of tax effects on reporting. Fortunately, these papers indicate that host country income tax rates and withholding tax rates can be used to control for the effects of tax incentives on reported intrafirm royalties.<sup>23</sup> BEA also collects data on royalty payments made by arm’s length foreigners to U.S. firms on the Annual Survey of Royalties, License Fees, and Other Receipts and Payments for Intangible Rights Between U.S. and Unaffiliated Foreign Persons. A sample covering 1987 forward is drawn from the results of this survey. Since these payments are between unrelated parties, they are not subject to concerns about manipulation for tax purposes. A large fraction of firms that file responses in this survey are also multinational firms that respond to the Survey of U.S. Direct Investment Abroad, thus providing firm level coverage of technology transfers to both affiliated and unaffiliated parties in individual countries through time.

Table I displays descriptive statistics for the benchmark years in which BEA collected the most extensive data on U.S. foreign affiliates. The sample includes firms that were active in countries that undertook the IPR regime changes described below. In the most recent benchmark year, 1999, the sample includes more than 5,000 affiliates of more than 1,000 parent companies.

> 23  Even when multinationals do not deliberately misrepresent the value of technology transfers for taxrelated reasons, one still may doubt the extent to which the recorded licensing payments reflect the full economic value of technology provided by the parent to the affiliate.  This is particularly true when there is no well-developed external market for the technology.  However, parent reporting does not pose a problem for our econometric inference as long as recorded payments consistently misestimate the full value of technology transferred.

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**Table 2** . Impacts of the “managed trade” scenario as compared to the “trade war” scenario (percent)

||Income||Total expo|rts|Total imp|orts|
|---|---|---|---|---|---|---|
||2020|2021|2020|2021|2020|2021|
|USA|0.92|1.22|4.20|5.18|3.24|3.97|
|CHN|0.17|0.06|3.47|4.12|3.74|4.43|
|EAP excl. China|-0.35|-0.43|-0.63|-0.80|-0.61|-0.73|
|SAR|-0.09|-0.14|-0.15|-0.22|-0.13|-0.20|
|Mexico|-0.30|-0.28|-1.01|-1.12|-0.94|-1.06|
|LAC|-0.11|-0.21|-0.40|-0.65|-0.29|-0.50|
|AFR|-0.10|-0.18|-0.24|-0.38|-0.19|-0.33|
|ECA|-0.09|-0.12|-0.20|-0.27|-0.16|-0.21|
|MENA (Egypt, Arab Rep.)|-0.07|-0.10|-0.16|-0.22|-0.14|-0.19|
|HICs|-0.21|-0.27|-0.47|-0.64|-0.45|-0.58|
|ROW|-0.14|-0.19|-0.35|-0.46|-0.31|-0.41|
|Global|0.12|0.13|0.51|0.60|0.52|0.62|
|World excl. USA and CHN|**-0.15**|**-0.20**|**-0.34**|**-0.45**|**-0.30**|**-0.39**|

Source: Linkage model simulations.

_Achieving the import targets of the China-U.S. agreement through multilateral liberalization by China rather than managed trade would leave all countries, other than the United States and Mexico, better off (Table 3)._ We next assess the impact of the China-U.S. agreement when import targets are met through multilateral liberalization rather than managed trade. Specifically, we compare the managed trade deal with the multilateral liberalization by China (15 percent reduction in tariffs and non-tariff barriers) that achieves the same gains in U.S. exports to China as targeted under the China-U.S. agreement.

Simulation results show that the United States is better off with China’s MFN liberalization relative to a trade war, but less so than with managed trade because it does not get preferential access to the Chinese market.<sup>16</sup> This is due to a negative terms of trade effect for the United States (i.e. the price of its exports declines relative to the price of imports): because China’s aggregate imports surge substantially more, the price U.S. exporters receive is lower when China opens multilaterally than when the United States receives preferential access. Due to the vertical linkages between the Mexican and U.S. economies, the fate of Mexico is closely tied to that of the United States and it experiences a small loss relative to the managed trade scenario. All other countries, including China, are better off with multilateral liberalization by China. Global income rises by the largest amount, more than 1⁄2 a percent in this scenario. The largest income gains would be registered in Latin America and other high-income countries (1.1 and 1.2 percent,

> 16 See Annex 4 for the impact of the “multilateral liberalization” scenario compared to the “trade war” scenario, which can be directly compared to the impact of the “managed trade” scenario as compared to the “trade war” scenario.

14
investment horizon. Moreover, providers have only held bonds issued by banks and corporations with very good credit standings – usually rated AA and higher and sometimes with specific credit enhancement features, thus maintaining credit risk at relatively low levels.

This pursuit of higher yields has been observed in other countries as well. For example, the TIAA-CREF pension fund, which is the largest annuity provider in the US, holds privatelyissued fixed-income instruments offering higher yields in order to match its fixed annuities, including a large share of less liquid instruments bought through private placements. The Chilean situation is different not because of the shift towards fixed income instruments issued by the private sector, but because these instruments are also inflation-indexed, allowing providers to match their indexed liabilities while extracting higher yields.

**Table 14: Portfolio of Life Insurance Companies, 1991-2008**<sup>**1**</sup>

<u>(as a percentage of the Total Assets)</u>

||1991|1995|2000|2003|2005|2008|
|---|---|---|---|---|---|---|
|Government Sector|38.3|40.3|28.7|17.6|15.0|9.0|
|Financial Sector|23|28.4|45.1|37.6|32.4|33.8|
|Mortgage Bonds|13.9|18.6|24.2|18.8|11.9|7.9|
|Mortgage-Backed Securities|3|6|10.1|10.1|9.6|11.9|
|Other Financial Instruments|6.1|3.8|10.8|8.7|10.9|14.1|
|Company Sector|29|22.1|15.3|33.4|38.4|35.5|
|Shares|8.9|10.2|3.4|2.9|3.5|2.0|
|Bonds|20.1|10.7|10.7|29.3|34.8|33.4|
|Real Estate|7.8|7.7|7.4|7.3|7.9|10.7|
|Other Assets|2|1.5|3.6|4.1|6.4|11.1|
|Total Assets|100|100|100|100|100|100|

Note: 1 December values in 1991-2003 and June values in 1995. Source: SVS Although this portfolio strategy has apparently succeeded in preserving positive financial spreads, the spreads have narrowed dangerously in the second half of the present decade. As shown in Figure 9, the marginal return on the fixed income portfolio, measured by the corporate bond rate, exceeded the annuity rate every year until 2005 (with the difference amounting to about 100 basis points after 2001), these margins have become extremely narrow in the past few years. This spread is thin considering the need to cover all costs and risks and still generate a positive return on equity.

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