rafmacalaba/datause-encoder-data huggingface dataset:
default probabilities, a more plausible explanation resides in investors of Indian origin viewing the risk of default with much less trepidation.<sup>6</sup> Table 2: Diaspora Bonds Issued by India

|**Bond Type**|**Amount**|**Year**|**Maturity**|**Minimum**|**Coupon**|
|---|---|---|---|---|---|
|India Development Bond<br>USD|$1.6 bn|1991|5 years|Not available|9.50%|
|GBP|||||13.25%|
|Resurgent India Bond<br>USD|$4.2 bn|1998|5 years|2,000*|7.75%|
|GBP||||1,000**|8.00%|
|DM||||3,000*|8.25%|
|India Millennium Deposits|$5.5 bn|2000|5 years|||
|USD||||2,000*|8.50%|
|GBP||||2,000*|7.85%|
|EUR||||2,000*|6.85%|

_* plus multiples of 1,000; ** plus multiples of 500 Source: State Bank of India_ The IDBs, RIBs and IMDs all had five-year bullet maturity. The issues were done in multiple currencies – US dollar, British pound, Deutsche mark/euro. Other relevant characteristics of the offerings are set out in Table 2. Unlike the Jewish diaspora, the Indian diaspora provided no patriotic discount on RIBs and only a small one on IMDs. When RIBs were sold in August 1998 to yield 7.75 percent on U.S. dollar-denominated bonds, the yield on BB-rated U.S. corporate bonds was 7.2 percent. There was thus no discount on the RIBs. As for the IMDs, the coupon was 8.5 percent while the yield on the comparably rated U.S. corporate bonds was 8.9 percent for a 40 basis points discount. In any case, Indian diaspora bonds provided much smaller discounts in comparison to Israel’s DCI bonds.

From a purely economic perspective, the SBI’s decision to restrict access to RIBs and IMDs to investors of Indian origin appears a bit odd. Why limit the potential size of the market? First, restricting the RIB and IMD sales to the Indian diaspora may have been a marketing strategy introduced in the belief that Indian investors would be more eager to

> 6   We take up this point again in explaining SBI’s decision to restrict the access to RIBs and IMDs to investors of Indian origin.

9
# **Figure B3: Natural gas**

<!-- Start of picture text --> Log scale<br>6<br>Log price Permanent component<br>5<br>4<br>3<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 44 —_
incentivized services. The Tajikistan PBF pilot increased by 18 percentage points the rate of women who received timely postnatal care. In addition, the program improved by three percentage points the rate of adults aged 40 and above who had their blood pressure measured in the preceding year. Coverage rates of family planning, child vaccination and growth monitoring and the timing and number of antenatal consultations were not impacted.

Several mechanisms linked to the PBF reform can explain the positive impacts in quality of care. First, providers might have responded to the financial incentives by exerting more effort to improve quality and engage with the communities. Second, the program ensured resources reached the rural facilities, and these facilities decided how to invest the 30% of the PBF bonuses in their infrastructure. Third, providers may have been motivated by the increased payment and increased satisfaction with their working conditions. Fourth, the program introduced a multi-layered system of supervision and verification. Facilities were monitored more closely, and the PBF tools and action plans may have provided quality improvement guidance towards better performance. Lastly, many providers were retrained in the context of the PBF project. Some of the improvements, especially with respect to clinical knowledge and quality, might be directly linked to these trainings.

The more modest impacts on health care utilization might be explained by several factors. One explanation may be that behavioral change might take time to materialize. Although we did not find increased utilization of many of the targeted services, the population in the PBF districts reported higher satisfaction with the local primary care facilities and were more likely to report general use of their services. These outcomes might represent changes in the right direction and that with more time, utilization of the targeted services will increase. It is also important to note that for some of the indicators, the overall coverage was already high in baseline. Close to 90% of women received any ANC and the same proportion of children aged 12-23 months received all basic vaccinations. With respect to some indicators, we find overall positive trends even if we do not find impact of the PBF pilot when we compare with the control districts. Between the baseline and follow-up surveys, the rate of women in the control districts who received any antenatal consultation increased from 85% to 95%. The rate of women who initiated their ANC during the first trimester increased from 57% to 74%. The relatively high baseline coverage rates and the 17
9 among advanced economies in Europe), East Asian crisis (late 1990s, emerging market economies), and Latin American crisis (early 2000s, emerging market economies).<sup>8</sup>

**Figure 2. Number of Recovery Episodes, 1965–2010**

<!-- Start of picture text --> Advanced Economies Emerging Market Economies<br>25<br>20<br>15<br>10<br>5<br>0<br>1965 1970 1980 1990 2000 2010 1965 1970 1980 1990 2000 2010<br>Credit-less Credit-with<br>Frequency<br><!-- End of picture text --> Note: Recoveries are first defined with the quarterly data and then aggregated by year. All the episodes identified in 2010 are based on private sector credit data for less than eight quarters.

Source: Authors’ calculations based on data from the IMF, OECD, UNECE, Haver Analytics, Datastream and country sources.

**Figure 3. Growth Performance, eight quarters before and after trough**

<!-- Start of picture text --> Advanced Economies Emerging Market Economies<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>t-8 t-6 t-4 t-2 t t+2 t+4 t+6 t+8 t-8 t-6 t-4 t-2 t t+2 t+4 t+6 t+8<br>Credit-less Credit-with<br>Real GDP Growth (%)<br><!-- End of picture text --> Note: A trough date is set _t_ . Median values of year-on-year real GDP growth rates within each country group are shown, and they are smoothed by calculating three-quarter moving averages ( _t-1_ , _t_ , and _t+1_ ). Due to this smoothing adjustment, the value at the trough date is not necessarily the lowest. Source: Authors’ calculations based on data from the IMF, OECD, UNECE, Haver Analytics, Datastream and country sources.

The paper also looks at how credit-less and credit-with events are different in terms of growth and investment performance. Under credit-less recoveries, output growth is found to be weaker, especially in emerging markets (Figure 3). At the trough _t_ , the difference in growth rates between credit-less and credit-with episodes is 3 and 8 percentage points for advanced and emerging market economies. In both country groups, though especially in the group of advanced

> 8 Due to the data availability, debt crisis in Latin America in the early 1980s is not fully captured.  The data are only available for Mexico (credit-with) and Peru (credit-less).
Thompson G, Swain J, Kay M, Forster C. F. 2001. The treatment of pulp and paper mill effluent: a review. Bioresource Technology, vol. 77, no3, pp. 275-286 (1 p.3/4).

- Williams, Rose Marie. Dioxina: Una toxina Universal. Revista de Medicina Medioambiental

Zentner, A. 2006. Measuring the Effect of File Sharing on Music Purchases. Journal of Law and Economics, April, p. 63-90 Zhang, Y. and Buongiorno, J. 1997. Communication media and demand for printing and publishing papers in the United States, Forest Science, Vol. 43, No. 3, pp.362–377.

26
## **Table 13: Biofuels Blending Mandates**

|Country|Ethanol|Biodiesel|
|---|---|---|
|Australia|E2 in New South Wales,<br>increasing to E10 by 2011; E5<br>inQueensland by2010||
|Argentina|E5 by2010|B5 by2010|
|Bolivia||B2.5 by 2007 and B20 by<br>2015|
|Brazil|E22-E25|B2by2008 andB5 by2013|
|Canada|E5 by 2010; E7.5 in<br>Saskatchewan and Manitoba;<br>E5 by2007inOntario|B2 by 2012|
|China|E10in9 provinces||
|Colombia|E10|B5 by2008|
|Dominican Republic|E15 by2015|B2by2015|
|Germany|E2by2007|B4.4by2007;B5.75 by2010|
|India|E10in 13 states/territories||
|Italy|E1|B1|
|Malaysia||B5 by2008|
|Paraguay||B1 by 2007, B3 by 2008, and<br>B5 by2009|
|Peru|E7.8 by 2010 nationally;<br>startingregionally by2006|B5 by 2010 nationally;<br>startingregionally by2008|
|Philippines|E5 by2008;E10 by2011|B1by2008;B2by2011|
|South Africa|E8-E10 (proposed)|B2-B5 (proposed)|
|Thailand|E10 by2007|3 percent share by2011|
|UnitedKingdom|E2.5 by2008;E5 by2010|B2.5 by2008;B5 by2010|
|United States|E10 in Iowa, Hawaii,<br>Missouri, and Montana; E20<br>in Minnesota; E2 in Louisiana<br>and WashingtonState|B5 in New Mexico; B2 in<br>Louisiana and Washington<br>State|
|Uruguay|E5 by 2014|B2 (2008-2011) and B5 by<br>2012|

Note: Targets with no dates are already in place except in some U.S. states where the targets are expected to be effective in future years. There are other countries with future indicative targets that are not shown here Source: Worldwatch Institute (2008).

# **6. Vehicle Inspection and Maintenance Programs**

Inspection and maintenance (I/M) programs are largely devised to identify primary “gross polluters” and ensure that they are retrofitted or retired. Be it developed or developing countries, vehicles that are not properly maintained are responsible for a large fraction of total transport sector emissions. Based on a cross country study of CO and HC 33
# **% of Respondents who Listen to Radio Every Day**

# **% of Households who are Aware of Local Council Projects**

<!-- Start of picture text --> under 20<br>20 − 40<br>40 − 60<br>over 60<br>(a) Year 2008<br><!-- End of picture text --> <!-- Start of picture text --> (b) Year 2008<br><!-- End of picture text --> <!-- Start of picture text --> under 20<br>20 − 40<br>40 − 60<br>over 60<br><!-- End of picture text -->

**% of Respondents who have Interacted with Councillor**

<!-- Start of picture text --> under 20<br>20 − 40<br>40 − 60<br>over 60<br>(c) Year 2008<br><!-- End of picture text --> Figure 3: These maps show the mean percentage of respondents who listen to the radio every day, who are aware of their local council’s projects and who have interacted with their local councillor over the past year. Only a very small percentage of respondents, 15 percent, are aware of councillors’ development projects. Councillors’ outreach efforts appear to be more common in the middle of the country, which has greater ethnic fractionalization.

32
Overall, domestic services have been growing, showing an annual growth rate of 1.5% between 2001 and 2013, with The Bahamas growing at 5.0%, and traffic between Trinidad & Tobago at 6.0%. On the other extreme of the spectrum is Jamaica, which lost 12.4 percent of seat capacity (Table 8). The loss of air transport connectivity within the island of Jamaica, however, should not be negatively viewed, since road transport has improved, and had become the preferred mode for intra-island travel.

**<mark>Table 8: Domestic Traffic for the Caribbean countries</mark>**

|**Route Type**<br>**Typology**|**Country**|**Seats**<br>**2013**|**Shares**|**g01-13**|**g08-13**|
|---|---|---|---|---|---|
||Antigua and Barbuda|Sche|duled service|s ceased 201|0|
|Small Island States|St. Kitts and Nevis|Sche|duled service|s ceased 201|2|
||St. Vincent and the Grenadines|Sche|duled service|s ceased 201|0|
||**Subtotal **|**-**|**-**|-|-|
||Bahamas,The|1,503,918|49.5%|5.0%|9.6%|
||Dominican Republic|Sche|duled service|s ceased 201|2|
|Hub Countries|Jamaica|156,442|5.2%|-12.4%|-2.2%|
|Domestic|Trinidad and Tobago|966,732|31.8%|6.0%|5.4%|
||**Subtotal**|**2,627,092**|**85.6%**|**1.4%**|**6.7%**|
||Belize|379,223|12.5%|-0.1%|-3.3%|
|Continental Caribbean|Guyana||None rep|orted||
||Suriname||None rep|orted||
||**Subtotal**|**379,223**|**12.5%**|**1.4%**|**6.7%**|
|Fraile States|Haiti|30,134|1.0%|||
|g|**Subtotal **|**30,134 **|**1.0%**|**1.4%**|**6.7%**|
|**Total **||**3,036,449**|**100%**|**1.2%**|**5.6%**|

_Source: Authors’ calculations on data provided by Diio (2014)._ The overall evolution of seat capacity is steady and growing slowly, but the number of airport pairs served also shows a steady, but slightly downward slope (Figure 10), suggesting that the concentration of the domestic markets is worsening.

**Figure 10: Domestic travel for the Caribbean countries.**

<!-- Start of picture text --> a. Estimated seat capacity b. Estimated airport pairs<br>90<br>4,000 80<br>3,500 70<br>3,000 60<br>2,500 50<br>2,000 40<br>1,500 30<br>1,000 20<br>500 10<br>0 0<br>Year Year<br>Domestic travel estimated seats Domestic travel airport pairs served<br> Seats, thousands<br>Airport Pairs<br>Estiamted<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> _Source: Authors’ calculation based on data provided by Diio (2014)._

# <u>Small Island States</u>

There are currently no scheduled domestic services for any of the small island states. Services have existed in the past for Antigua & Barbuda (for travel between the islands), St. Kitts & Nevis (between 34
-48-

## ANNE[X a BASIC **EARNINGs FUNCIONS**

# Argentina 1980

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.093|0.080|0.098|0.078|0.097|
|Experience (EX)|0.030|0.087|0.017|0.026|0.050|
|Experience-squared (EX<sup>2</sup>)|-0.0004|-0.0004|-0.0002|-0.0004|-0.0007|
|Log Hours (H) Worked|2.278|1.584|2.228|0.642|0.614|
|Constant Term|-3.191|-0.942|-3.096|3.231|3.056|
|R<sup>2</sup>|0.66|0.45|0.65|0.06|0.19w|
|N|6,156|2,745|3,411|2,030|-737|
|Means|||||||
|Earnings (Y)|735|1,234|333|1,207|1,309|
|S|7.9|8.1|7.8|7.9|9.5|
|EX|22|21|23|22|.21|
|H|31|43|22|47|40|
|Var (Ln Y)|3.621|2.554|2.673|1.543|1.024|

Source: _Encuesta Permanente de Hogares (EPH), CEPAL_ Notes: The earnings variable (Y) is in thousands of pesos per month. H is the weekly hours worked.

All coefficients are statistically significant at the _5%_ level or better.
adopt (Pautz, 2005).<sup>2</sup> Against this backdrop, it is hardly surprising that the refugee inflow 2015 was a controversial issue. At the initial stage, the government’s decision to welcome refugees and asylum seekers for humanitarian reasons received broad support in the German public. The public perception of the time is perhaps best summarized by German Chancellor Angela Merkel’s pledge from August 2015, “ _Wir schaffen das!_ ” ( _We can do it!_ ). In international media, the country was praised for its _Welcome Culture_ , with outlets showing footage of the local population cheering on the arriving refugees at Munich train station, or pictures of Angela Merkel posing for selfies with refugees (e.g. Deutsche Welle, 2020a). Nonetheless, the economic and social integration of asylum seekers posed a significant challenge for the country. From a practical perspective, it was challenging to host over one million people and gradually integrate them in the labor market. Data show that the asylum seekers were considerably less qualified than most Germans, and initially lacked the necessary language skills to gain easy access to the labor market or the education system (Br ̈ucker et al., 2016).

A second challenge was both political and cultural. Although large parts of German society initially supported the government’s decision, there was growing concern that one million people, many from a different cultural background and little knowledge about German society and values, would not be able to integrate into German society. These concerns were initially expressed by a small minority, but were shared by a larger number of people after a series of assaults against women on the New Year’s Eve 2015/16. Many of the perpetrators were immigrants, among them recently arrived asylum seekers. The most severe incident occurred in Cologne, where over 600 women were reportedly sexually assaulted. These events fundamentally changed the public’s perception of asylum seekers. It sparked anti-immigrant violence (Frey, 2020), fuelled right-wing movements like _Pegida_ , and led to the rise of the AfD, a right-wing party that first entered the German parliament in 2017 (Gedmin, 2019, Deutsche Welle, 2020b).

> 2For an example about the debate about islam, see Reuters (2018).

6
- 73 -

# TOM A.17: **Chane** In mral Conssption Patte, India 1967/68 - 1983

|Ave<br> <br>|rage Budget Sh<br>|are, Rural Hous<br>|eholds<br>|
|---|---|---|---|
|Item<br>1967/88|1972/73|1977/78|1983|
|Foods<br> <br>||||
|Cereals<br>45.4|40.6|32.8|32.3|
|Grain<br>0.8<br> <br>|0.6<br>|0.4|0.3|
|Cereal substitutes<br>0.8<br> <br>|0.5<br>|0.3<br>|0.2<br>|
|Pulses<br>4.4<br>|4.3|3.8|3.5|
|Mlk and milk||||
|products<br>7.4<br> <br>|7.3|7.7|7.5|
|Edible oil<br>2.9<br>|3.5|3.6|4.0|
|Meat, egg, fish<br>2.4<br>|2.5|2.7|3,0|
|Vegetables<br>3.3|3.6|3.8|4.7|
|Fruits and nuts<br>0.9|1.1|1.1|1.4|
|Sugar<br>3.2|3.8|2.6|2.8|
|Salt and sPlces<br>2.7<br> <br>|2.8<br>|3.0|2.5|
|Beverages<br>2.4<br> <br>|2.4<br>|2.5|3.3|
|Total Foods<br>77.3|72.9|64.3|65.6|
|NonFoods<br>||||
|Pan, tobacco,<br> <br>||||
|Intoxicants<br>2.9<br>|3.1|2.9|3.0|
|Fuel and light<br>5.6<br>|5.6|6.0|7.0|
|Clothing<br>5.5<br> <br>|7.0|8.7|8.6|
|Footwear<br>O0S<br>|0.5|0.7|1.0|
|Misc, goods and services<br>8.1<br>|8.7|10.3|12.5|
|Durables<br>0.1<br>|2.2|7.0|2.3|
|Total Nonfoods<br>22.7|27.1|35.7|34.4|
|Total Expenditure<br>100.0|1 C).0|100.0|100.0|

Soure: National Sample Survey, 38th Round as reponed in S n. Vol.9, No.3 (April 1986).
**Figure A.3. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to Target Survey Sample TA 1 for Different Sample Sizes, using 2020/21 Poverty Line, Normal Linear Regression Model, Tanzania**

<!-- Start of picture text --> Model 1 Model 2<br>10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100<br>(71) (142) (213) (284) (356) (427) (498) (569) (640) (711) (71) (142) (213) (284) (356) (427) (498) (569) (640) (711)<br>Model 8 Model 9<br>10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100<br>(71) (142) (213) (284) (356) (427) (498) (569) (640) (711) (71) (142) (213) (284) (356) (427) (498) (569) (640) (711)<br>% of target survey % of target survey<br>10% (464) 20% (930) 30% (1393) 100% (4644)<br>35 40<br>30<br>30<br>25<br>20<br>20<br>15<br>10<br>10<br>40<br>35<br>30<br>30<br>25<br>20<br>20<br>15<br>10 10<br><!-- End of picture text -->

**Note:** The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. The imputed poverty rates for TA 1 use the estimated parameters based on NPS 2020/21. 1000 simulations are implemented. All estimates are obtained with the population weights. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line from NPS 2020/21 deflated to 2022 prices. The sample size of TA 1 is selected as a percentage of the target survey varying from (randomly selected) 10% to 100% of the TA sample (with the sample size in parentheses). The sample size of NPS 2020/21 is selected as a percentage of the base survey sample, varying from (randomly selected) 10%, 20%, 30% to 100% of the NPS 2020/21 sample (with the sample size in parentheses). The total sample size of TA 1 is 711 households, and the sample size of NPS 2020/21 is 4,644 households.

70
**Figure 9: Source of South Asian FDI Inflows, 2003-11**

<!-- Start of picture text --> EU<br>32%<br>US<br>SAR<br>17%<br>4%<br><!-- End of picture text --> <!-- Start of picture text --> Other<br>5%<br>China<br>6% Japan<br>Korea 8%<br>7%<br><!-- End of picture text --> <!-- Start of picture text --> EAP(ex.China)<br>MENA 11%<br>9%<br><!-- End of picture text --> Source: Estmates from UNCTAD Statistics and fDimarkets Note: China includes Hong Kong SAR, China, Macau and Taiwan, China.

# **India contributes 70 percent of intra-regional South Asian FDI; however, total within-region FDI**

**represents just 3.7 percent of all inward FDI in South Asia.** The largest sources of inward FDI vary substantially across South Asian countries, but historical bilateral restrictions on FDI inflows from other South Asian countries have limited intra-regional flows. As shown in Table 5, FDI flows to India come primarily from developed economies, such as the EU, United States, Japan, and South Korea. In contrast, Pakistan’s inflows are dominated by capital from the Middle East, while Bangladesh’s and Sri Lanka’s FDI comes from a handful of countries, including the EU, United States, India, and China. The Maldives has perhaps the most diverse spectrum of countries contributing FDI, including Thailand, India, United States, the EU, and China. Landlocked Bhutan and Nepal depend heavily on India for investment in their countries. Chinese companies have made large investments in extraction businesses, which account for a large portion of the FDI in Afghanistan. China has also been active in Nepal’s renewable energy sector and in Sri Lanka’s transportation sector, specifically building ports, but also in constructing hotels and investing in Sri Lanka’s tourism sector.

**Table 5: FDI Recipient and Source Countries in South Asia**

<u>(percent of recipient countries’ total FDI inflow, 2003-11)</u>

|||Europen|||SARex|**So**<br>|**urce Countri**<br>EAPex|**es**|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||<br>Union|US|India|,<br>India|China*|,<br>China|MENA|ECA|LAC|SSA|Other**|
||Afghanistan|2.35%|1.57%|2.72%|0.97%|71.58%|0.00%|16.07%|4.73%|0.00%|0.00%|0.00%|
||Bangladesh|38.33%|11.95%|23.88%|0.49%|3.79%|10.02%|8.62%|0.00%|0.00%|0.40%|2.52%|
||Bhutan|0.00%|16.91%|48.75%|0.00%|0.00%|24.67%|0.00%|0.00%|0.00%|0.00%|9.67%|
|**Recipient**|India|36.40%|19.87%|0.00%|1.34%|4.10%|21.26%|5.09%|1.77%|0.56%|0.31%|9.31%|
|**Countries**|Maldives|4.71%|3.86%|29.35%|0.05%|3.86%|25.57%|13.13%|0.00%|0.01%|0.00%|19.47%|
||Nepal|22.05%|0.00%|53.63%|0.61%|11.20%|1.72%|10.68%|0.00%|0.00%|0.00%|0.12%|
||Pakistan|19.60%|9.92%|0.83%|0.30%|6.11%|7.15%|45.04%|1.52%|0.47%|0.00%|9.06%|
||Sri Lanka|18.09%|2.95%|37.41%|0.22%|9.19%|8.10%|2.71%|0.00%|0.15%|8.36%|12.82%|

Source: Estimates from UNCTAD statistics, fDiMarkets and World Bank staff calculations Note: (*) China includes Hong Kong SAR, China, Macau and Taiwan, China; (**) Other includes all advanced economies other than US and European Union.

15