rafmacalaba/datause-encoder-data huggingface dataset:
_Resul_ _13_ more than the total deaths.) The column of maximum estimates in Table 5 shows the results. In principle, the expert assessments could be attained if substantial numbers of deaths from il-defined causes were reclassified under these specific categories. This does not hold in one case: for pregnancy complications, the number of ill-defined deaths among women 15-44 is too small to allow the regression estimates to be adjusted upward sufficiently. In this case, the expert assessments could be correct only if deaths from complications of pregnancy are misclassified under other defined causes.

This does not establish that the expert assessments are correct-it merely shows one way most of them could be made consistent with the WHO database. Essentialy the assessments were based on estimates of numbers of cases, whether from survey data or from informed judgments. Different sources of error are attached to reports of cause of death and to estimates from surveys focusing mainly on morbidity. Surveys may allow greater care in identifying cases, but typicaUy often require additional assumptions, generaUy from inadequate data, about case-fatality rates. Therefore, choosing between the regression estimates and the expert assessments is difficult, and details of each are provided separately-the latter in Annex C, which discusses them further.

# _Trends in major causes of death_

Given uncertainties in figures for specific causes, only the major causes are projected into the future. Figures 4 and 5 shows how the actual numbers of deaths are expected to change for developed and developing countries. The two most important major causes show opposite trends, infectious diseases declining from causing 35 percent of deaths in 1970 to causing 16 percent of deaths in 2015, while diseases of the circulatory system rise, from causing 24 percent of deaths in 1970 to causing 39 percent of deaths in 2015. The infectious disease trend is mainly due to lower mortality from this cause in developing countries, whereas the circulatory disease trend is due to higher mortality from this cause in both developed and developing countries. A Figure 4. Deaths by cause, developed countries, 1970-2015 <!-- Start of picture text --> CL<br>In.<br>14.<br>M1170 II1BS M2000 lO015<br><!-- End of picture text -->
dollar coefficient is still lower than the pre-crisis level (though it has become larger in size and statistically significant in comparison to the mid-crisis period). Interestingly, the weight of the Japanese yen continues to be significant in Indonesia (0.31), Thailand (0.12), and the Philippines (0.10), even though the yen was never important in pre-crisis Indonesia or the Philippines.<sup>26</sup>

**_Rolling regressions._** Using 18-month period data for July 1997-December 1998 as a midcrisis sample may be problematic because exchange market developments were quite volatile, erratic, and dynamic during the crisis. Regional contagion, delayed currency attacks (Korea) and large exchange rate depreciations at times of political uncertainty (Indonesia) may have altered pattems of exchange rate movements over the course of events. Therefore, we next examine midcrisis exchange rate movements in more detail by dividing the sample into a series of 3-month periods."

Figure 3 reports the results of such rolling regressions for the period January 1990 - December 1999. The 10-year period is divided into a series of 3-month sub-samples, by rolling over the sample by one month each. More concretely, a total of 118 regressions are run, using the first sub-sample period as January-March 1990, the second as February-April 1990, and so onall up to the final sub-sample period of October-December 1999 (see the Appendix Table for a summary of the regression results).<sup>2 8</sup> Estimated regression coefficients of the US dollar, the Japanese yen, and the euro are plotted against time: a thick solid line for the dollar; a fine solid than a conscious policy shift to exchange rate stabilization, or the authorities' concem about too rapid an appreciation of the currency when growth momentum was about to pick up. Whatever the interpretation, it appears that the authorities continue to regard the US dollar as the most relevant reference currency for their exchange rate policies.

26 Again, the observed post-crisis importance of the yen may be the result of common shocks and news affecting Indonesia and the Philippines on the one hand and Japan on the other.

27 Our preliminary experiment indicated that one month (with about 20 observations) was too short to produce statistically significant results for coefficients other than the USD.

2S No observations have been removed in this regression analysis.

21
|P075502|2005|2009|11/10/2008|6 Closed|Venezuela,Republica|
|---|---|---|---|---|---|
|P075502|2005|2009|5/29/2009|7 Closed|Venezuela,Republica|
|P075502|2005|2010|12/19/2009|8 Closed|Venezuela,Republica|
|P075502|2005|2010|5/17/2010|9 Closed|Venezuela,Republica|
|P075523|2004|2004|5/24/2004|2 Closed|Samoa|
|P075523|2004|2005|6/20/2005|5 Closed|Samoa|
|P075523|2004|2006|2/7/2006|6 Closed|Samoa|
|P075523|2004|2007|3/30/2007|7 Closed|Samoa|
|P075523|2004|2008|5/29/2008|8 Closed|Samoa|
|P075523|2004|2009|4/19/2009|9 Closed|Samoa|
|P075523|2004|2010|5/28/2010|10 Closed|Samoa|
|P075523|2004|2011|6/28/2011|11 Closed|Samoa|
|P075523|2004|2012|4/10/2012|12 Closed|Samoa|
|P075523|2004|2013|4/3/2013|13 Closed|Samoa|
|P075523|2004|2014|10/19/2013|14 Closed|Samoa|
|P075523|2004|2014|5/20/2014|15 Closed|Samoa|
|P075528|2003|2003||0 Closed|Trinidad and Tobago|
|P075528|2003|2004|6/3/2004|2 Closed|Trinidad and Tobago|
|P075528|2003|2005|4/29/2005|4 Closed|Trinidad and Tobago|
|P075528|2003|2006|7/8/2005|5 Closed|Trinidad and Tobago|
|P075528|2003|2007|10/20/2006|6 Closed|Trinidad and Tobago|
|P075528|2003|2007|5/4/2007|7 Closed|Trinidad and Tobago|
|P075528|2003|2008|8/18/2007|8 Closed|Trinidad and Tobago|
|P075528|2003|2008|3/21/2008|9 Closed|Trinidad and Tobago|
|P075528|2003|2008|5/23/2008|10 Closed|Trinidad and Tobago|
|P075528|2003|2009|11/21/2008|11 Closed|Trinidad and Tobago|
|P075528|2003|2009|6/23/2009|12 Closed|Trinidad and Tobago|
|P075528|2003|2010|12/11/2009|13 Closed|Trinidad and Tobago|
|P075528|2003|2010|6/28/2010|14 Closed|Trinidad and Tobago|
|P075528|2003|2011|1/9/2011|15 Closed|Trinidad and Tobago|
|P075531|2006|2006||0 Closed|Lao People's Democra|
|P075531|2006|2007|11/20/2006|1 Closed|Lao People's Democra|
|P075531|2006|2008|1/24/2008|2 Closed|Lao People's Democra|
|P075531|2006|2009|6/9/2009|3 Closed|Lao People's Democra|
|P075531|2006|2010|5/29/2010|4 Closed|Lao People's Democra|
|P075531|2006|2011|4/13/2011|5 Closed|Lao People's Democra|
|P075531|2006|2012|9/27/2011|6 Closed|Lao People's Democra|
|P075531|2006|2020|6/10/2020|7 Closed|Lao People's Democra|
|P075534|2007|2007||0 Closed|Jordan|
|P075534|2007|2008|12/7/2007|1 Closed|Jordan|
|P075534|2007|2008|6/19/2008|2 Closed|Jordan|
|P075534|2007|2009|12/29/2008|3 Closed|Jordan|
|P075534|2007|2009|6/22/2009|4 Closed|Jordan|
|P075534|2007|2010|12/16/2009|5 Closed|Jordan|
|P075534|2007|2010|6/30/2010|6 Closed|Jordan|
|P075534|2007|2011|1/7/2011|7 Closed|Jordan|
|P075534|2007|2012|1/2/2012|8 Closed|Jordan|
In recent decades, PTAs have changed considerably in focus and content. Older agreements aimed largely to reduce formal barriers to trade and expand market access between member countries. Beginning in the 1990s they expanded considerably in ambition and coverage. This process began with the North American Free Trade Agreement (NAFTA) and numerous bilateral treaties between the European Free Trade Association and individual countries, such as Estonia, Latvia, and Mexico. This round of trade agreements expanded beyond tariffs to rules covering certain regulatory regimes, including intellectual property rights. A decade later, the EU announced its “new trade policy,” shifting its negotiating priorities toward rigorous protection of patents, copyrights, geographical indications and other IPRs in its many new PTAs in Eastern Europe and the Middle East, Latin America, Canada, and Japan.

NAFTA was among the first multi-country and economically large PTAs to set minimum standards for, and move toward partial harmonization of, nearly every aspect of IPRs as they existed then. Regarding patents, NAFTA required minimum patent duration, confidentiality for pharmaceutical clinical trials data, and extensions in patent length to compensate for administrative delays in granting protection, among other elements. These provisions became the foundation for expanding TRIPS-Plus patent standards in later PTAs. It also stipulated a minimum copyright length, the types of works that must be protected, and various neighboring rights. NAFTA required automatic recognition of internationally well-known trademarks, another TRIPS-Plus standard. And the accord called for protection of geographical names through systems effectively the same as those covering trademarks and collective marks. PTAs reached by the EU and EFTA in this era had similar requirements, though differing in particular areas of concern, most prominently in setting up specific systems for geographical indications (GIs).

NAFTA was preceded by the formation of the European Economic Area (EEA) in 1994, linking three members of EFTA to the EU’s single-market program. Central to the EEA is the adoption of largely harmonized intellectual property standards throughout the region.

The expanded coverage of PTAs sharply increased after 2000, with the United States and the EU demanding stronger IPR provisions, sometimes extended to new subject matters. To be sure, other newly created PTAs, not involving the United States or the EU, were reached by Mexico, Japan, Australia, South Korea, and Chile, among others. These PTAs also included IPRs 6
# **App. Table 2:  Distribution of oDesk job types and wage rates paid**

|||Comp|Non-ethnic<br>anies in Unite|d States|Non-ethnic|Companies|outside of the U<br>excludingIndia|Non-ethnic<br>nited States,|
|---|---|---|---|---|---|---|---|---|
|||Ethnic Indians|Indians who|Ethnic Indians|Indians who||Ethnic Indians|Indians who|
|Job category|Total|who are hiring<br>abroad|are hiring<br>abroad|who are hiring<br>in India|are hiring in<br>India|Total|who are hiring<br>in India|are hiring in<br>India|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|||||Panel A. Obs|ervation counts||||
|Total count|102,819|3,333|85,151|1,296|28,394|55,122|1,590|14,155|
|||Panel B|. Distribution o|f job types (orde|red by median wa|ge as shown in|Panel C)||
|Networking & inform. systems|2%|2%|2%|2%|1%|2%|1%|2%|
|Software development|7%|9%|7%|8%|8%|8%|37%|7%|
|Web development|40%|42%|42%|57%|63%|38%|41%|62%|
|Design & multimedia|9%|8%|9%|6%|6%|10%|4%|7%|
|Writing & translation|10%|9%|8%|6%|3%|11%|2%|4%|
|Business services|2%|1%|2%|1%|1%|2%|2%|1%|
|Customer service|1%|1%|1%|0%|0%|1%|3%|0%|
|Sales & marketing|10%|11%|10%|9%|7%|10%|4%|8%|
|Administrative support|20%|16%|20%|10%|10%|18%|6%|9%|
||||Pane|l C. Median hou|rly wage paid to w|orker|||
|Total|8.6|8.6|8.4|9.3|9.6|8.7|9.7|9.5|
|Networking & inform. systems|16.7|13.9|16.7|11.1|12.2|16.7|10.6|12.4|
|Software development|15.0|15.6|15.0|12.2|13.4<br>|15.0<br>|10.5|13.3|
|Web development|12.2|11.1|12.0|11.1|11.1|12.0|11.0|11.1|
|Design & multimedia|11.1|10.0|10.0|11.1|11.0|11.0|10.0|10.0|
|Writing & translation|5.6|5.6|4.4|5.3|5.0|5.6|4.4|4.4|
|Business services|5.0|3.5|3.3|5.5|4.4|5.6|6.0|5.6|
|Customer service|3.3|2.2|3.3|3.3|4.4|5.6|10.0|3.5|
|Sales & marketing|3.3|4.0|3.3|4.5|4.4|3.9|3.9|4.4|
|Administrative support|2.2|2.2|2.2|2.2|2.2|2.2|2.8|2.2|

Notes: Wage rates are calculated as the median wage paid to workers and are expressed in dollars. Sample includes contracts with ethnic name matches and identified job category classifications. Sample splits in columns 3-6 and 8-9 exclude company contacts for which a partial Indian ethnicity assignment is made.
Unfortunately, publicly available data on Covid-related deaths are not distributed by age-categories for most countries in the world, which prevents us from directly computing the number of lost-years. In order to overcome this problem, we assume that countryspecic infection probabilities are independent of age. Then, using estimates of Covid's age-specic infection-to-fatality ratios (IFR), we can infer the infection rate necessary to yield the number of deaths observed in the country, given its population pyramid. For developing countries, we use the age-specic IFR ratios estimated for China by Verity et al. (2020). For developed countries, we use analogous ratios estimated for France by Salje et al. (2020).

Formally, let _Naj_ denote the size of the population of age _a_ in country _j_ , _daj_ the number of Covid-related deaths at age _a_ in country _j_ , _dj_ the total number of Covid-19 deaths in country _j_ , and _μaj_ the _IFR_ at age _a_ in country _j_ . The proportion of people who are or have already been infected by Covid-19 in country _j_ is estimated as: The number of Covid-related deaths at age _a_ in country _j_ is given by Our global estimates of the poverty impacts of Covid-19 come from Lakner et al. (2020). There are two methodological dierences between these estimates and those reported in Section 3.1. First, rather than using growth data from Central Banks, GDP estimates now come exclusively from the January and June 2020 editions of the World Bank's Global Economic Prospects (GEP) (The World Bank, 2020), supplemented with the April 2020 and October 2019 editions of the IMF's World Economic Outlook (WEO) when countries are not in the GEP (which mostly applies to high-income countries). As before, the extrapolation assumes distribution-neutral (negative) growth. Figure 2 below plots this dierence in forecasts  the GDP shock due to Covid  against GDP per capita for the 150 countries in our sample. We continue to assume that this additional poverty lasts only for a single year, so that the number of poverty-years is equal to this additional number of poor people.

Second, instead of using national poverty thresholds, we use the World Bank's income class poverty thresholds, as derived by Jollie and Prydz (2016), namely $1.90 per person per day in low-income countries (LICs); $3.20 a day in lower-middle-income countries (LMICs); $5.50 a day in upper-middle-income countries (UMICs); and $21.70 a day in high-income countries (HICs). These lines were obtained as the median national poverty lines for each income category in a data set constructed by those authors.

Figure 3 shows the break-even _α_ ˆ for all 150 countries in our global sample, plotted 12
Jamkesmas. Recall that the SUSETI data was collected before these social programs had begun to use the UDB for selecting beneficiaries. Hence, these baseline figures indicate numbers of previous beneficiaries entering into the UDB and do not show the UDB’s anticipated effects on program targeting outcomes, which we explore next.

# **Evaluation of Expected UDB Targeting Performance**

In this section, we evaluate the changes in targeting accuracy that can be expected from the transition to using the UDB. Column 1 of Table 2 shows that at baseline, 44%, and 39% of all SUSETI households report having previously received Jamkesmas and BLT, respectively. The two programs exhibit similar baseline targeting errors, with leakage rates of 34% and 39%, respectively, and undercoverage rates of 45% and 51%. These targeting error rates for the six districts in SUSETI are in line with previous research analyzing the targeting performance across all of Indonesia (see World Bank, 2012).

In column 2, we report the expected targeting outcomes based on the use of the UDB beneficiary lists. In calculating targeting performance, program receipt equals one if the household's PMT score falls below the program-specific PMT eligibility threshold.<sup>15</sup> The first finding is that Jamkesmas and BLT coverage levels decrease significantly with the UDB compared to baseline.<sup>16</sup> This reduction in the number of beneficiaries leads to an increase in undercoverage for both Jamkesmas (from 45% to 53%) and BLT (from 51% to 58%). At the same time, the expected decrease in coverage also significantly reduces leakage to non-poor households. BLT baseline leakage of 34% is expected to decrease by 13 percentage points with use of the UDB. For Jamkesmas, baseline leakage rates are expected to fall from 39% to 25%. Similar patterns are observed for the severe measures of undercoverage and leakage, which are lower across all programs.

> 15 Nationally, these thresholds are the poorest 30% for Jamkesmas and the poorest 25% for BLT, but within our study areas, the thresholds result in coverage levels of 33% for Jamkesmas and 29% for BLT as seen in the table.

> 16 It is possible that these results overstate the extent of exclusion errors on account of misreporting actual program receipt due, for example, to social desirability bias (of saying yes) or confusion about the origin of the health insurance scheme as there are both regional (Jamkesda) and national (Jamkesmas) schemes. Such misreporting works against finding large improvements in undercoverage.

17
# **Figure 5. Physical climate change risks due to natural disasters**

- A. Annual number of disasters, 1981‐2017

<!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>C. Number of affected countries,<br>1981‐2017<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>E. Disaster damage by region, USD billion<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are<br>bottom and top of box, respectively<br>No. weather related disasters<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>No. Affected countries<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>USD billion<br><!-- End of picture text -->

- B. Share of disasters by type, 1981‐2017

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Drought Extreme Flood Landslide Storm Wildfire<br>temperat<br>D. Number of disasters by region,<br>1981‐2017<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are bottom<br>and top of box, respectively<br>No. weather related disasters<br><!-- End of picture text --> 30
## **Table 9. Log earnings returns to noncognitive skills, adults aged 25-54**

<u>Time preference 0.002 0.004</u> -0.025 -0.023 Notes: These results derive from the full specifications (Table 4, columns 2-5 and Appendix Table 4, columns 3 and 6 for men and women). Those specifications control for age, age-square, education (measured as spline variables), and cognitive skills (measured as plausible values of literacy assessments). Results for all countries pool the samples of Armenia, Bolivia, Colombia, Georgia, Ghana, Kenya, Ukraine and Vietnam and are estimated with country fixed effects. [1] No data on time preference for Colombia; no data on hostile attribution bias, risk-taking and time preference for Serbia. Statistical significance is denoted as * p < 0.10, ** p < 0.05, *** p < 0.01.

Data sources: STEP data for 9 countries, 2012-13

# **VI. Decomposing the Gender Earnings Gaps and the Role of Skills**

Finally, in this section, we draw upon the above results to explore three questions that focus on gender differences: Does estimating a model that allows the slope coefficients of the elements of human capital to differ by gender (that is, moving from a constrained model to an unconstrained model) change our estimates of the conditional gender earnings gap? Does expanding women’s fuller set of skills, beyond completed years of schooling, help to close the gender gap? What could be the effects on the gender earnings gap of policies that equalize observed covariates versus policies that equalize wage structure? We explore these questions here.

# **_Estimating gender disadvantage: Constrained and unconstrained models_**

Using the pooled sample of men and women in all countries to estimate the log-earnings functions, with gender as a dummy variable, we find that women’s log earnings are 31 percent lower than men’s in the basic model (only schooling, without skills), with selection bias correction, and 28 percent lower in the full model (with skills) (Table 4). Turning to individual countries, we also estimate a constrained model in which gender enters as a dummy variable, first with just schooling and then with both schooling and skills (columns 1 and 2, Table 10). Except in Serbia and Kenya, women are paid significantly less than men, whether or not we control for skills, with 51
Policy Research Working Paper 7983

# **Abstract**

This paper benchmarks the quality of project economic analysis in South Asia against other World Bank regions, using data on project exits between 1975 and 2015. The results show that the South Asia region performs on par with the other regions, in that the share of project documents that report estimated economic rates of return have declined from 70 to 36 percent for South Asia and the other regions. This finding suggests there is less attention to project economic analysis (especially for sectors where this has been a traditional practice, such as energy, transport, water, and agriculture). The finding also indicates that the incidence of reporting rates of return in project documents and the dispersion or difference between rates of return estimated at project appraisal and completion are significantly correlated with project performance (after correcting for country- and project-level variables). For the project-level variables, the task team effect is a key variable that explains project outcomes. The paper discusses the implications of the analyses for strengthening project performance and risk mitigation.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at KEzemenari@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team