rafmacalaba/datause-encoder-data huggingface dataset:
26 Table 2: Contribution to national economic welfare that would result from removing cotton subsidies and tariffs,<sup>a</sup> by region and by policy instrument

|(equivalent variation in incomein 2001US$million)|
|---|

|||Byr|eformingr<br>All other|egion:<br>||By po|licyinstru|ment:|
|---|---|---|---|---|---|---|---|---|
|Benefitingregion:|US|EU25|high-<br>income<br>countries|Develop-<br>ing<br>countries|TOTAL,<br>WORLD|Tariff|Export<br>subsidy|Domestic<br>support|
|**High-income countries**|**374**|**109**|**0**|**-17**|**465**|**15**|**-7**|**457**|
|Australia|109|14|8|6|137|2|2|134|
|United States|401|8|1|19|429|18|1|411|
|EU25|-64|110|-5|-28|14|6|-9|18|
|Japan|0|-15|-2|-7|-24|-4|-1|-19|
|Korea-Taiwan|-52|-3|-2|-5|-61|-8|0|-54|
|**Developing countries**|**-224**|**22**|**-3**|**24**|**-182**|**13**|**7**|**-204**|
|**_E. Europe & C. Asia_**|**_-25_**|**_2_**|**_1_**|**_7_**|**_-14_**|**_1_**|**_0_**|**_-14_**|
|Turkey|-61|-17|0|-9|-86|-1|-2|-83|
|<br>Other ECA|38|24|1|7|72|2|2|69|
|**_East Asia_**|**_-54_**|**_-15_**|**_-6_**|**_-9_**|**_-83_**|**_-20_**|**_-2_**|**_-63_**|
|China|60|-12|2|-1|50|-13|0|62|
|**_South Asia_**|**_-71_**|**_-5_**|**_-3_**|**_-17_**|**_-96_**|**_5_**|**_-1_**|**_-99_**|
|Bangladesh|-11|-2|-1|2|-11|6|0|-16|
|India|-57|-6|-1|-20|-85|0|0|-84|
|Pakistan|-7|2|-2|0|-7|-1|-1|-5|
|**_M. East &North Africa_**|**_8_**|**_-4_**|**_1_**|**_14_**|**_19_**|**_1_**|**_8_**|**_10_**|
|**_Sub-Saharan Africa_**|**_72_**|**_33_**|**_4_**|**_39_**|**_147_**|**_32_**|**_2_**|**_112_**|
|South Africa|0|0|0|-1|-1|0|0|-1|
|Mozambique|1|1|0|0|2|0|0|1|
|<br>Zambia|0|0|0|0|0|0|0|0|
|Uganda|2|1|0|0|4|0|0|4|
|Other Sthn & E. Africa|10|5|1|2|17|2|0|16|
|Nigeria|0|-1|0|0|-1|0|0|-1|
|Other Sub-Sah. Africa|59|27|3|36|126|30|2|93|
|**_Latin America & Car._**|**_-154_**|**_9_**|**_0_**|**_-10_**|**_-155_**|**_-6_**|**_0_**|**_-150_**|
|Argentina|2|3|0|2|7|2|0|4|
|Brazil|6|6|0|1|13|0|0|12|
|Mexico|-120|-2|0|-6|-128|-2|0|-125|
|**World**|**149**|**130**|**-4**|**7**|**283**|**28**|**0**|**253**|

a Removal of those distortions left after the eventual phase-out of the quotas under the Multifibre Agreement at the end of 2004 Source: Authors’ GTAP model simulation results
adjust for large group sizes (there are 2,469,524 recorded grades in the fourth quarter), none of the normalized differences exceed the recommended threshold of 0.25 (Imbens and Wooldridge, 2009).<sup>18</sup> Hence, the number of grades recorded by teachers is balanced between groups, suggesting no systematic change in teacher grade-reporting behavior.

Finally, we collected data on students’ age, gender, and ethnicity, in order to routinely control for them throughout our regressions. Table 2 shows summary statistics and balancing properties for socio-emotional skills, GPAs, and student socio-economic characteristics at baseline, by experimental group.

_Table 2: Summary Statistics and Balancing Properties_

||Group<br>Control<br>(1)|Treatment 1<br>(2)|Treatment 2<br>(3)|T-Test<br>Difference<br>(1)-(2)|Difference<br>(1)-(3)|N|
|---|---|---|---|---|---|---|
|_Panel A: Outcomes, B_|_aseline_||||||
|Deliberate Prac-<br>tice Beliefs|16.580|16.640|16.740|-0.059|-0.156**|18,718|
||_(2.435)_|_(2.439)_|_(2.375)_||||
|Grit|29.590|29.600|29.470|-0.006|0.121|18,718|
||_(4.072)_|_(4.050)_|_(4.066)_||||
|Grit: Persever-<br>ance-of-Effort|||||||
|Facet|16.300|16.380|16.250|-0.074|0.049|18,718|
||_(2.499)_|_(2.471)_|_(2.518)_||||
|Grit: Con-<br>sistency-of-In-<br>terest Facet|13.290|13.220|13.220|0.069|0.072|18,718|
||_(3.077)_|_(3.060)_|_(3.073)_||||
|Frustration Re-<br>action|11.042|11.138|11.062|-0.096|-0.020|18,718|
||_(2.533)_|_(2.510)_|_(2.539)_||||
|Motivational|||||||
|Frameworks|20.836|20.895|20.912|-0.059|-0.076|18,718|
||_(2.900)_|_(2.886)_|_(2.817)_||||
|Locus of Con-|||||||
|trol|17.638|17.628|17.667|0.010|-0.029|18,718|
||_(2.314)_|_(2.332)_|_(2.317)_||||
|Present Bias|4.208|4.207|4.221|0.001|-0.013|18,718|
||_(0.987)_|_(1.015)_|_(0.999)_||||
|S/E Skills Index|0.0529|0.072|0.083|-0.019|0.031|18,718|
||_(0.997)_|_(1.005)_|_(1.010)_||||
|GPAs|3.324|3.304|3.311|0.019|0.012|33,454|
||_(1.150)_|_(1.141)_|_(1.157)_||||

18 Normalized differences are calculated as Δx = (xt - xc) / √(σt2 + σc2), where xt and xc is the sample mean of the covariate for the treatment and control group, respectively. σ2 denotes the respective variance. The normalized difference between our first treatment group (“student self-learning”) and our control group is -0.13, that between our second treatment group (“teacher delivery”) and our control group is -0.19.

9
“shifted” coefficients are smaller than the “true” coefficient, plotted at 0‐3, and are all statistically insignificant.

The second test involves replacing the upstream pollution variable with a falsified value using pollution data from the nearest off‐river region farther downstream—a location that is disconnected from river flow dynamics and from where the pollution cannot flow (upstream) to areas where the health outcomes are measured. In the case that the ‘falsified’ upstream pollution variable shows a significant impact on health, then it would be likely that our baseline results are capturing spurious spatial correlations. Table 4, however, reveals otherwise. There is no significant impact of the falsified value on health, suggesting that the upstream variable utilized in the analysis is indeed isolating quasi‐random variation in pollution.

In Table 5, in addition to the district fixed effect, we also include district time trends to address the concern that broad secular trends at the district level might be influencing our results. The results are of the same sign and magnitude as our baseline estimates, and remain significant at the 5 percent level.

In Table 6, we cluster the standard errors by state in DHS, as well as survey cluster in DHS, instead of district. Standard errors are more or less similar using either of these alternatives, and the results remain significant and unchanged.

# 6. Evidence from Other Regions

In Vietnam, home to one of the fastest‐growing and urbanizing societies in the world, agricultural growth and intensification have played significant roles in spurring development. But in parts of the country, the environmental footprint of the agricultural sector is deepening. In intensively farmed areas, agriculture has become a significant contributor to water pollution. This is particularly so in the intensively farmed Mekong delta region (Cassou, Jaffee, and Ru 2018; Chea, Grenouillet, and Lek 2016). To measure the consequences of nitrogen pollution, the analysis focuses on children aged four to twelve years surveyed in the latest Living Standards Survey of 1997–98. Table 7 shows that repeated exposure to nitrate pollution for the first three years of life substantially lowers height‐for‐age scores in childhood, with full exposure decreasing height‐for‐age scores by 0.7 standard deviation. These effects occur despite nitrate‐nitrogen concentrations being below the recommended safety thresholds of 10 mg/L and emerge even after accounting for exposures from other contaminants.

In Africa, although present‐day fertilizer usage is lower than in Asia, it is growing. Other sources of nitrate exposure include expanding urban centers that lack wastewater treatment facilities and increased livestock farming. The analysis is based on the entire universe of child records up to age 5 years across 33 countries in Africa from DHS records. The results in Table 8 show that 12
The last two columns in Table 4.1 report the estimates obtained on the cross-sectionally demeaned data. Accounting for time effects raises the magnitude of the PMG estimates of the long-run parameters **--** and, to a lesser extent, their standard errors too. The long-run coefficient on the return variable becomes now significant. The **MG** estimates are now of the correct signs, but only that on the return variable is significant; their values are again affected **by** a few large outliers (Costa Rica, Ireland and Saudi Arabia). As before, the long-run parameter restrictions are not rejected **by** the Hausman tests. Finally, the average speed of adjustment rises as well, especially in the case of the **MG** specification, and the model continues to account for a large portion of the variation in the de-meaned data..

Table 4.2 turns to the subsample of high and upper-middle income countris, for which the risk-return model might be expected to provide a better characterization of portfolio diversification than for the overall country sample. This expectation is bome out **by** the PMG estimates of the long run coefficients in the table, all of which are correctly signed and statistically significant. In particular, the return variable now carries coefficients much larger in absolute value than those in Table 4.1 above. Furthermore, the **MG** estimates carry also the correct signs and are fairly close to their PMG counterparts, although they remain imprecise.

The Hausman test statistics in Table 4.2 fail to reject the constraints on the long-run coefficients. The average speed of adjustment is somewhat lower than in the full-country sample when using the raw data, and higher when using the demeaned data, although the **SBC-** determined ARDL dynamics are still unstable in four countries, while some others (e.g., Singapore) display very slow adjustment. As before, the short-run coefficients show significant lagged effects of the foreign asset/wealth ratio and the foreign wealth /domestic wealth ratio. The estimated model fits the data fairly accurately. **By** way of example, Figure 4.1 plots the actual and fitted values of the dependent variable for **1996** (the last year with reasonably comprehensive sample coverage). The points cluster in a narrow region along the 45-degreee line. The pattern is very similar for other sample years.

Table 4.3 presents estimates for the sample of countries possessing low to moderate capital account restrictions, as earlier defined. **Of** the **33** countries included in this group, 22 belong also to the high and upper-middle income sample just analyzed. Like in the previous table, the PMG estimates of the long-run coefficients carry the correct signs and are statistically significant (although the return variable only reaches **10** percent significance in the specification 21
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Electrical power is very expensive to the end users in Sierra Leone. While the country made impressive progress reducing its production costs, power production in Sierra Leone remains more expensive than in most other parts of Africa due to relatively high costs of hydroelectric generation and continued reliance on thermal plants. High production costs translate into high end-user tariffs. Customers paid between $0.22 and $0.41 per kilowatt hour<sup>4</sup> during 2002–2008 (World Bank 2005, World Bank 2007). Since the availability of cheaper power through Bumbuna in 2009, electricity tariffs decreased to $0.31 per kilowatt-hour, which is still on the high side when compared to other African countries (figure 5).

**Figure 5.  Sierra Leone power tariffs are amongst the highest in Africa**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>0 5<br>US cents per KwH Liberia Sierra Leone (pre 2009) Chad Sierra Leone (2009) Cape Verde Senegal Burkina Faso Mali Uganda Kenya Congo, Rep. Rwanda Benin Niger Cote d'Ivoire Namibia Madagascar Cameroon Ghana Botswana Mozambique Lesotho Tanzania Zimbabwe South Africa Ethiopia Botswana Nigeria Malawi DRC Zambia<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and Shkaratan 2010.

Sierra Leone‘s NPA adds significant inefficiencies to the mix _._ Several factors have constrained the financial health of NPA (table 4). Increases in unscheduled outages of NPA‘s generating capacity reduced the total power produced and consequently sales. Technical and non-technical losses of 33–45 percent during the past decade have impacted revenues. In 2010, system losses drained $24.2 million from the revenue stream. Collection rates for the decade were on average 86 percent, but dipped precariously to 50 percent in 2008. In 2010, inadequate collection of bills deprived NPA of $8 million of revenues. Decrepit systems led to enhanced fuel consumption, escalating operating costs. Cash flow challenges precluded necessary upgrades and replacements of depreciated assets. Further, labor costs are burdening NPA. In 2009, NPA had only around 70 connections per employee, including non-active customers, while the benchmark for developing countries is around 400 connections per employee. Underpricing of the rather expensive power in Sierra Leone is not an issue at present, however, due to the already high end-user tariffs and cheaper cost of power production as a result of addition of Bumbuna hydro generation. But the current cost-recovery situation might be subject to change as the sector undergoes significant transformations and price adjustments.

Sierra Leone‘s power sector also suffers from substantial hidden costs. Total hidden costs amounted to 69 percent of revenue in 2010, driven by an increase in the amount of power available, which increased the amount of power lost. This, however, represents a decrease from the previous year, as the availability

> 4 The range is due to exchange-rate fluctuations.

17
also included some heterodox measures in the context of public prices and new rent regulations in the housing sector in line with the targeted inflation rate with an objective to reduce consumer price inflation to 25 percent by the end of 2000.

Although there was some success in reducing inflation—the CPI inflation declined to 33.4 percent on an annual basis by February 2001—weaknesses in the banking system, the severe terms of trade shock along with the deterioration of the macroeconomic vulnerabilities all contributed to the collapse of the program. As a result, a floating exchange rate regime was adopted on February 22, 2001.

In high-inflation countries like Turkey, the question of why prices increase lies at the heart of the debate over which policies should be adopted to stabilize the price level. As was pointed out earlier, the notion that inflation is ultimately a monetary phenomenon is widely accepted. There is strong evidence that in the medium and long term, there exists a very close correlation between the rate of growth of monetary aggregates and inflation, after changes in output and velocity are taken into consideration. This correlation has been corroborated both in the international<sup>8</sup> and the Turkish experiences (Figure 1).

At first blush, the above discussion might suggest that it would be relatively straightforward for the central bank to eliminate inflation in light of its influence on the behavior of monetary aggregates, the monetary base in particular. The close correlation between money and prices, however, does not reveal anything about the direction of causality.<sup>9</sup>

> 7 See the Inflation Report published by the Central Bank of Turkey (CBRT) in July 2000 for more on this.

> 8 See for instance Lucas (1996).

> 9 In fact, the results of Granger Causality tests show that the causality runs from prices to base money (Monetary Policy Report published by the CBRT in April 2002).

10
||World|US|Chn|Ind|Jap|WEU|EEU|FSU|LAM|MENA|SAFR|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_σY_|0.09|0.06|0.10|0.10|0.06|0.06|0.11|0.16|0.11|0.11|0.11|
|_σE_|0.06|0.04|0.07|0.06|0.03|0.04|0.06|0.07|0.10|0.08|0.06|
|_σY_|0.07|0.07|0.15|0.25|0.12|0.06|0.17|0.16|0.12|0.12|0.17|
|_σE_|0.10|0.10|0.12|0.19|0.11|0.11|0.20|0.16|0.16|0.11|0.22|
|_ρ_|0.63|0.40|0.78|0.87|0.50|0.42|0.25|-0.12|-0.33|0.89|0.67|

Table 3: Normalized standard deviations of the EIA (2005) scenarios for 2015 (top part), and normalized standard deviations and liner correlation coefficients of _E_ and _Y_ for 25 scenarios from various sources harmonized by Lecocq and Crassous (2003) (bottom part). Data refer to World, USA, China, India, Japan, Western Europe (WEU), Eastern Europe (EEU), Former Soviet Union (FSU), Latin America (LAM), Middle East and North Africa (MENA), and SubSaharan Africa (SAFR). For the EIA (2005), the latter two actually correspond to Middle East and Africa as a whole, respectively.

high scenarios, assuming all three as equiprobable. We find relatively small values for both _σY_ (between 0 _._ 06 and 0 _._ 16) and _σE_ (between 0 _._ 03 and 0 _._ 10). Uncertainty is in general higher for GDP than for emissions.

The bottom part of Table 3 presents estimates for the normalized standard deviation of cumulative GDP and BAU CO2 emissions for the period 2013 to 2017, based on 25 scenarios from multiple sources (IPCC, IIASA and US EPA) harmonized by Lecocq and Crassous (2003) and assumed equiprobable. We find values of _σY_ between 0 _._ 06 for WEU and 0 _._ 25 for India, while values for _σE_ lie in a slightly narrower band ranging from 0.10 (US) to 0.22 (SAFR). Uncertainty about _E_ and _Y_ , here, are of similar magnitude. Interestingly, parameter _ρ_ can take a very wide range, from a negative _−_ 0 _._ 33 for LAM to a strongly positive value of 0.89 for MENA, with most values, however, taking on positive values above or equal to 0.4.

We also carried out a limited assessment of the accuracy of past emissions and GDP forecasts, similar to Lutter (2000). To this end, we compared the reference 7-year forecasts of the 1995 issue of the International Energy Outlook (EIA, 1995) with actual data (EIA, 2005), and likewise the 1994 8-year ahead WEO forecasts with current data (IEA, 1994, 2002, 2004). Relative forecast errors are reported in Table 4.

As can be observed, forecast errors can be quite large (up to 67% for 8-year forecasts of East 21
data bases.<sup>3</sup> It is hard to imagine that the Bank could have emerged as the leading development agency without this portfolio of publications on development.

The Bank’s research is naturally skewed toward topics relevant to low and middleincome countries. If one focuses on publications addressing countries except the United States in the top 200 economics journals, the Bank has a similar output—about 1,000 papers—to the top five universities combined (Harvard, MIT, Chicago, Stanford and Princeton). The difference is that half of the Bank’s publications reported research findings on the poorest 40 percent of countries (ranked by GDP per capita), while those countries only accounted for about onequarter of the publications of the top five universities.<sup>4</sup> Data have long been an important research product. The Bank has been at the lead in expanding the volume, quality and accessibility of development data, as the foundation for development knowledge and well-informed policy making. These data are not simply economic; they include a range of sociological and political information. Early development thinking was often weak on facts about what was happening on the ground. Some of the major advances in development data emerged out of a growing awareness of that early weakness:

- When Robert McNamara, as President of the World Bank, saw the table of distributional data from household surveys in the first World Development Report, he was shocked to see so many holes.  For all except 17 developing countries there were no data in the 1979 WDR, yet macroeconomic aggregates (from national accounts) were available for virtually all countries.  McNamara told his research staff to go collect the data—and that was the beginning of the Bank’s still thriving _Living Standards Measurement Study_ , based in the research department Since the early 1990s, the Bank’s researchers have put considerable emphasis on collecting new micro data sets, on individuals, households, facilities and enterprises based on field work.

> 3  For an overview of the Bank’s publications and their scholarly influence, as revealed by citations, see Martin Ravallion and Adam Wagstaff, “The World Bank’s Publication Record.” Policy Research Working Paper 5374, World Bank, Washington, DC, 2010.

> 4  These calculations are based on the data set compiled by Jishnu Das, Quy-Toan Do, Karen Shaines and Sowmya Srinivasan, “U.S. and Them: The Geography of Academic Research.”  Policy Research Working Paper 5152, World Bank, Washington, DC, 2009.

7
# **Financial Globalization in Emerging Countries: Diversification vs. Offshoring**<sup>*</sup>

Francisco Ceballos Tatiana Didier Sergio L. Schmukler

## **JEL Classification Codes:** F36, G15, G20

**Keywords:** domestic financial markets, international financial markets, international financial integration, bond markets, equity markets

*All authors are with the World Bank. This paper derives from the background work prepared for the World Bank Flagship Report ―Financial Development in Latin America and the Caribbean: The Road Ahead.‖ We received very helpful comments from Augusto de la Torre, Alain Ize, Eduardo Levy Yeyati, Eswar Prasad, and Luis Servén, among many others, and participants at presentations held at the ADBI (Tokyo), American University (Washington), Casas das Garcas (Rio de Janeiro), the Central Bank of Brazil (Brasilia), the Central Bank of Colombia and the Global Development Network Annual Meeting (Bogota), the Chamber of Commerce (Lima), the IMF (Washington), the ITAM and the National Banking and Securities Commission (Mexico City), the NIPFP-DEA Workshop (Delhi), and the World Bank (Washington, DC). We are grateful to Luciano Cohan, Juan Cuattromo, Gustavo Meza, Julian Kozlowski, Lucas Nuñez, Paula Pedro, Virginia Poggio, Andres Schneider, Patricio Valenzuela, Luis Fernando Vieira, and Gabriel Zelpo for outstanding research assistance at different stages of this project. For help in gathering unique data, we thank the ADR Team from the Bank of New York, among many others. We thank the Office of the Chief Economist of the Latin American and the Caribbean Region, the SFLAP Trust Fund, and the Knowledge for Change Program (KCP) of the World Bank for financial support. The views expressed here are those of the authors and do not necessarily represent those of the World Bank. E-mail addresses: <u>fceballos1@worldbank.org, tdidier@worldbank.org and sschmukler@worldbank.org.</u>