rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 51%<br><!-- End of picture text --> <!-- Start of picture text --> 18%<br><!-- End of picture text --> <!-- Start of picture text --> 31%<br><!-- End of picture text --> <!-- Start of picture text --> 58%<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br>2%<br><!-- End of picture text --> <!-- Start of picture text --> 44%<br><!-- End of picture text --> <!-- Start of picture text --> 48%<br>8%<br><!-- End of picture text --> <!-- Start of picture text --> 87%<br>13%<br><!-- End of picture text --> Fig. 5: Insurance and government assistance for selected disasters as a percentage of direct losses (Source: Linnerooth-Bayer and Mechler, 2007) 3. Physical vulnerability describes the degree of damage to the capital stock due to a natural hazard event. The method commonly used here are fragility curves setting the degree of damage in relation to the intensity of a hazard.

Based on data on the return period and losses in percent of capital stock, CATSIM generates loss frequency distributions describing the probability of specified losses occurring, such as a 100-year event causing a loss of 200 million USD, a 50-year event causing a 40 million USD loss, and so on.<sup>6</sup> It should be kept in mind that top-down estimates at this broad scale are necessarily rough. Since most disasters are rare events, there is often little in terms of historical data; furthermore it is difficult to include dynamic changes in the system, for example, population and capital movements and climate change. To improve the data information, bottom-up assessments can be undertaken that involve a detailed analysis of the occurrence of hazards in certain areas, the exposed elements and vulnerabilities of structures on a more microscale level.

As already indicated risk and potential losses are summarized by means of loss-frequency distributions, which relate probabilities of loss to assets destroyed. For example, figure 6 shows a cumulative loss-frequency distribution for flood risk in a hypothetical country. The horizontal axis shows the fraction of capital stock destroyed by a disaster, and the vertical axis represents the probability that losses will _not_ exceed a given level. For example, with a probability of 0.9 (90%) flood losses will not exceed 250 million LCU; inversely, there is a 0.1 (10% chance) that such a loss and larger will occur.

> 6 It is standard practice to refer to 20-, 50-, 100-, 500- and 1000-year events.

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<!-- Start of picture text --> GTP and long-term targets translate into a transition of the CRGE implementation could ensure a low-carbon economic<br>Ethiopian economy development pathway, decreasing per capita emissions by 60%<br>GOP, billion USD Emissions per year', Mt CO,e @® 'CO.e/capita Agriculture Ml Power Ml Industry<br>Oe @ Forestry Ml Transport [1] Others<br>Agriculture<br>BH industry 400<br>a Services= 185 90<br>~10% p.a. Key transitions 64%)<br>* Diminishing weight of Additional<br>32% agriculture from 42% to abatement<br>54 29% of GDP 150 90 2) potential of<br>a=30 | * Migration from agriculture 75 |__40” ~19 Mt CO,eZ<br>—_ ee 38% jobs to services and 5 from exporting<br>2010 ee2015 GTP 2025 projectedeee; |. industrypreaching of middle- 58a”a5 Fe10 . - 4markets regional<br>Population income status before 2025 2010 2030 Agri: Forestry Trans- Industry Buil- — Green<br>min (mid-year) QED &> C116) BAU Culture port dings 2030Economy<br>coricap.in ED Qa Ee Dm =><br>21 CurrentlyRounded estimatednumbers emissions form buildingsand waste<br><!-- End of picture text -->
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low and uncertain, and dependent on a wide range of complementary actions such as the availability of basic infrastructure and services. In such cases, sharp price adjustmrents may only be inflationary and have particularly adverse impacts on net purchasers. Awareness of the trade-offs between different groups and between different time periods, in the early stages of SAL design, would allow designing appropriate long and short run poverty reducing policies.

Box 4 Macroeconomic Policy Choices and Income Distribution: Philippines A study of the effects of macroeconomic variables on the income **shares** of the rich **and** poor, based on quarterly surveys of households Income for 1980-86 in the Philippinos, found that the poor are relatively hard-hit by overall recession and a contraction in the labor market. Accelerating Inflation is also rogressive because of the stickiness in nominal wages. The poor gain from real exchange rate depreciation because exports **are** Intensive in unskilled labor (and often produced by peasants) and tradeables account for a small part of the consumption basket of the majority of the Philippine poor. Since benefits from government sponding largely go to the middle classes, cutbacks hurt the poor lose. Finally, the effects of real interest rate increases are largely borne by the rich, who **are** the principal debtors In the economy. In 1985, only 8 percent of the poor farmers reported using formal credit In the previous fifteen years. The stabilization path chosen was not favorable to the poor, with the exception of cuts in some forms of public spending and the liberalization of interest rates. Although some fiscal and monetary contraction wse necessary, greater reliance on relative price changes would have moderated **the** extent of the recession and provided relative protection for the poor. Drastic changes in the composition of public expenditures (a large part of which were benefitting higher Income groups) could have moderated the costs of economic decline for the poor.
Another difference between this paper and Metzler and Woessman (2012) is that we also estimate the persistence parameter _γ_ based on variation across subjects only. The size of the point estimates for _γ_ tend to decrease from the level model to the school fixed-effects model to the student-teacher model.

# **4.2.1 Projected compounding effects of improvements in teacher subject-matter knowledge**

The primary interest of this research is to measure the counterfactual learning effects of improvements in teacher subject-matter knowledge. The parameter estimates presented in table 3 measure the input effects on a year-to-year basis. But nation-wide efforts to increase teacher subject-matter knowledge will imply that students have better teachers for two, three years in a row, or throughout their entire schooling career. The overall effect on student-learning outcomes, therefore, depends on the strength of the input effect but also on the rate at which past achievement gains persist into the future.

Within the context of our model we predict what would happen to student outcomes in response to an increase in teachers’ subject-matter knowledge of 1.0 and of 2.0 standard deviations across-the-board. Figure (5) shows projections based on the results from the studentteacher fixed-effects model. For the projections I took the average of the parameter estimates of column (5) and (6), i.e. _β_ ˆ _≈_ 0 _._ 175, and _γ_ ˆ _≈_ 0 _._ 3. Students benefit most (as measured relative to the distribution of performance in the current population) in the first year after an improvement in teachers’ subject-matter knowledge. The increase is smaller in the second year as the second year’s teacher receives the student already at a higher starting level. Students who benefit year-after-year from teachers with higher levels of subject-matter knowledge practically reach the new equilibrium level at the end of grade 3. (Note that you need teachers with higher levels of subject-matter knowledge also in grade 4, 5 and 6, to maintain higher relative test scores. Otherwise the student would fall back again.) At the end of the primary cycle, test scores have increased by 0.25 and 0.50 student-level standard deviations respectively. These effects are substantial. PISA reports for example that the standard deviation of PISA scores for Indonesian 15-year-olds is roughly 70 PISA points in 28
**Figure 1. Wealth Distribution among Mexican Population**

<!-- Start of picture text --> 45 v .<br>40 -- * - --<br>- 35 4 g:U_ _<br>C 30-- Botom20%l<br>25 -- *-Lower 40%<br>v 25 - ~-E--Middle 30%<br>0 20 - - - As --. = --t-n --Top 10%<br>X10<br>0 - ,<br>1984 1989 1992 1994 1996 1998<br><!-- End of picture text --> _Source:_ Author's calculations based on ENIGH.

At the height of liberalization, Mexico underwent a severe currency crisis. In December 1994, Mexico sharply devalued the peso causing a deep economic recession. In 1995, real gross domestic product (GDP) fell by 6.2 percent, demand fell by 14.4 percent, and investment fell by 43 percent. This market contraction shifted trade as exports increased by almost 31 percent and imports fell by 9 percent. Nevertheless, a cheap peso bolstered exports and offered new markets to firms whose domestic sales had collapsed. Domestic demand quickly recovered, and by 1997 real GDP had returned to its pre-crisis level.

This paper investigates the skill-biased technological change (SBTC) hypothesis for Mexico, using panel data from the National Survey of Employment, Salaries, Technology, and Training (ENESTYC) and the Annual Industry Survey (EIA). The panel has observations for 1992, 1995, and 1999 (for a description of these surveys and the panel see Appendix A and B, and for a list of variables Appendix C).

Section 2 reviews Mexico-specific circumstances. Section 3 asks whether technological change is biased towards particular skill groups. Section 4 analyzes the relationship of skilled labor employment to the use of technology. Section 5 discusses productivity gains from TA and training. Section 6 presents conclusions.

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<!-- Start of picture text --> Figure 46: Foreign-owned Bank Sector Liabilities<br>200%<br>180%<br>160%<br>140%<br>120%<br>100%<br>Ireland<br>80%<br>Iceland<br>60%<br>Greece<br>40%<br>20% Portugal<br>0%<br>1996 1998 2000 2002 2004 2006 2008 2010<br>Percentage of GDP<br><!-- End of picture text --> Sources: Bank for International Settlements; International Monetary Fund, World Economic Outlook.

93. To summarize, U.S. housing, monetary and fiscal policies were at the core of the U.S. low savings rates and the asset bubble in the real estate market. This asset bubble fuelled an extended consumption boom and rising current account deficits, which could be financed as a result of the reserve currency status of the U.S. dollar and were the key driver for global imbalances. Housing policies were influenced by the financial sector, which realized that the subprime market offered extensive opportunities for higher profits. The financial deregulation and low interest rates brought about by the Federal Reserve‘s monetary policy fostered expansion and innovation of the financial sector. Increasingly complex financial derivatives allowed the risks associated with the new instruments to be underestimated. When the housing bubble burst, this had significant implications for the U.S. financial system as a whole.

94. The so-called global savings glut brought about by economic policies of China and other East Asian countries was not the cause of low interest rates and the financial innovation boom. In fact, it was the excess liquidity created by countries with a reserve currency, in particular the United States through its expansionary monetary and fiscal policies, as well as housing policies that led to the U.S. current account deficits and to global imbalances. With the U.S. dollar being the main reserve currency, the reserves held by the central banks of surplus countries will primarily be invested in U.S. dollar assets. But what has been the role of China‘s policies and its savings rates in the evolution of the global economic crisis?

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Our finding that cross-border banks face higher interest expenses, especially if they are performing badly, is consistent with a reluctance on the part of national authorities to contribute to the bailout of a cross-border bank. Freixas (2003) provides a theoretical modeling of incentives to engage in fiscal burden sharing to resolve a cross-border bank failure. Recapitalizing a distressed international bank is taken to be costly relative to simply liquidating the bank. The benefits of recapitalizing the bank, however, are dispersed over the countries where the bank operates. For the bank to be recapitalized, the concerned countries have to collectively share the cost. Contributing to this cost is an international public good. Underprovision of this public good may result in liquidation of the bank in equilibrium, despite the fact that the summed international benefits of recapitalization exceed its cost.<sup>3</sup> Possible international coordination failure at the recapitalization stage in case of a cross-border bank, as modeled by Freixas (2003), may cause bank liability holders to charge higher interest rates to cross-border banks. As a related issue, national authorities may have limited legal options to resolve distress at an international bank in a quick and cost-effective manner to contain the joint losses of national financial safety nets and bank liability holders, resulting in higher expected losses for bank liability holders.

Alternatively, international banks may have higher interest expenses due to a lack of international cooperation in the regulatory treatment of solvent banks. Bank regulators, in particular, may fail to exchange relevant supervisory information, making it difficult to monitor international banks properly. Also, some countries engage in ring-fencing, i.e. they impose restrictions on profit or capital transfers among a parent banks and its subsidiaries located in

> 3 Building on Freixas (2003), Goodhart and Schoenmaker (2009) consider whether some simple international burden sharing rules would, if adhered to, lead countries to recapitalize banks with a particular international structure. 3
Sindh Government’s Annual School Census (ASC) (2010/11), we ranked districts by size as measured by the number of schools and villages in each district. In consultation with the Government of Sindh, we then chose one large, one medium-sized and one small district, ensuring representativeness of the selected districts along two key education measures.<sup>8</sup> The selected districts were Matiari, Mirpurkhas and Sanghar.

In the absence of an updated population census, the ASC was used to set the population frame for selecting villages.<sup>9</sup> Using probability-proportional-to-size (PPS) sampling, we selected a sample of 550 schools drawn from 377 villages. The administrative data appeared to be unreliable regarding the exact location of schools and the inclusion of schools that are permanently closed. To address this, we mapped 300 of the 377 villages in a village-census exercise. The maximum of 300 villages was reached using a randomized ranking procedure.<sup>10</sup> The process through which treatment schools were selected from the village mapping exercise is outlined in **<u>Figure 2</u>** .

The school sampling strategy for the baseline covered public primary schools that were open on the day of the visit or closed for a period of less than one year prior to the day of the visit. We sampled all such schools, identified by the local community, in the main settlement of our sampled

> 8 Matiari was ranked the third smallest district, Mirpurkhas was ranked 12th, and Sanghar was ranked 18th, according to the number of schools and villages in each district. In terms of education indicators, Mirpurkhas had one of the lowest levels of education outcomes, Matiari was in the middle, while Sanghar was among the highest. They were deemed relatively safe by the government for field teams to visit.

> 9Given the heterogeneity in village size, we employed Probability Proportional to Size (PPS) sampling to create selection probabilities proportional to the number of students enrolled in primary education in a village. Virtually every village in Sindh has a government public primary school.

> 10 In order to select 300 villages from 377 (per our evaluation design), a village mapping firm identified and listed all schools in each village in the rank-order specified, until they hit the maximum sample of 300 villages. They were allowed to skip villages only when i) schools were not found in listed revenue villages due to noisy administrative data; or ii) field-teams were denied permission by local residents to enter the village. However, the number of such cases was small, with total replacements amounting to fewer than 20 villages.

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districts experiencing more casualties and that this effect was more robust across specifications for females.

Next, I investigate whether specific aspects of the conflict had different effects on primary schooling completion (table 3). First, I use INSEC data on the total number of school destructions per district to test whether these destructions had a negative effect on primary schooling completion despite the overall positive impact of the insurgency (columns (1) and (2)). For both genders, I find a statistically insignificant effect, which is likely because a district-level analysis lacks the power to identify the effect of school destructions. School destructions were a rare and isolated aspect of the conflict<sup>11</sup> that could be expected to have had a large effect on schooling at a disaggregated level but not at the district level. Second, I use INSEC data on the total number of abductions by Maoists per district to test whether a larger number of abductions, often targeting school children, had an adverse effect on schooling. The results in columns (3) and (4) indicate that abductions had a negative effect on female primary schooling. An increase in the number of abductions (per 1,000 inhabitants) by one standard deviation of the district-level distribution (16.82) decreases female primary schooling attainment by 3.7 percentage points. In other words, the effect of a move from the 5th to the 75th percentile of the district-level distribution of total abductions yields a 1.6 percentage point decline in female primary completion. Third, I test whether primary schooling completion improved more in districts controlled by Maoists where the insurgents were better able to affect schooling provision according to their ideology (columns (5) to (8)). There is no clear-cut definition of insurgent control, with discrepancies between the classifications used by the People’s Army and the government (Hatlebakk 2007). Therefore, I use two alternative classifications. The choice of definition affects the magnitude and significance of estimates, but the overall message is that primary schooling has tended to become more prevalent over time for both genders in areas controlled by the Maoists.

Tables S4 and S5 replicate the analysis in table 3 with birth year fixed effects restricted to be identical across development regions of Nepal (tables S4 and S5) and the control cohort replaced 21
Bank, 2021). High inequality rates have been attributed to persistent disparities in labor market outcomes, inadequate coverage of the poor by the social protection system, and a fiscal system characterized by limited progressivity (Vaughn & Cabrera, 2022).

**Figure 2. Rising inequality in recent years, reaching the highest inequality in the EU**

<!-- Start of picture text --> a. Gini Index of equivalized disposable income, Bulgaria,<br>2009-2021<br>43.0<br>41.0<br>38.4<br>39.0<br>37.0<br>35.0<br>33.0<br>29.6<br>31.0<br>29.0<br>27.0<br>25.0<br>Bulgaria EU-27<br>c. At-risk-of-poverty (AROP) rate, EU27, 2021<br>25.<br>20.<br>15.<br>10.<br>5.<br>0.<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Bulgaria Estonia Latvia Romania Lithuania Spain Italy Greece Croatia Luxembourg Malta EU27 Portugal Sweden France Austria Germany Netherlands Ireland Cyprus Poland Slovakia Belgium Finland Denmark Hungary Slovenia Czechia<br><!-- End of picture text --> <!-- Start of picture text --> b. Gini Index of equivalized disposable income, EU27,<br>2021<br>45.<br>38.4<br>40.<br>35.<br>30.<br>25.<br>20.<br>15.<br>10.<br>5.<br>0.<br>Slovakia Slovenia Czechia Belgium Netherlands Poland Finland Hungary Sweden Denmark Austria Ireland Croatia Germany Cyprus Luxembourg EU-27 France Malta Greece Estonia Spain Portugal Romania Italy Latvia Lithuania Bulgaria<br><!-- End of picture text --> _Source: World Bank estimates using Eurostat. These estimates represent the reference year, not the survey year. For example, estimates for 2021 reflect data collected in 2022, whose reference period is 2021._ _<u>https://ec.europa.eu/eurostat/databrowser/view/ILC_LI02/default/table?lang=en</u> Note: Estimates are based on adult equivalents of disposable income. More recent estimates are not yet available._

**Some groups continue to be left behind, reflecting in part uneven service delivery across regions and groups, consistent with the recent deterioration in inequality indicators.** Public service provision is adequate in the capital city and several other well-developed areas, but in other regions, especially the Northwest region, more limited access to quality social and municipality services has undermined both the inclusiveness and strength of growth (World Bank, 2021). There is a significant variation of poverty by district. In the 2021 income year, the districts with the most significant proportion of individuals facing a risk of poverty are Stara Zagora at 33.9%, Dobrich at 27.0%, Lovech at 26.8%, and Pernik at 25.5%. Conversely, the districts with the lowest percentage of people at risk of poverty are Shumen at 12.9%, Veliko Tarnovo at 15.8%, Silistra at 16.1%, and Targovishte at 16.2%. In the 2021 income year, about 81.6 percent of Roma were at risk of poverty and social exclusion, compared to only 32.2 percent among the overall population, and more than half of the Roma could not afford to keep their home adequately warm,

4
TALE 6 AUIL: REAL PRICE INDICES OF PUDLICLY PRODUCED GOODS AND SERVICES (193.100)

|Y-ears|Electrici|ty<br>Telecouuunicat|ionfs<br>Flat<br>Stee|<br>Postal<br>lServices|Gasoli<br>|ne<br>Diese<br>Oil|l Fuel<br> <br>Oil|Liquid<br> **Gas**|
|---|---|---|---|---|---|---|---|---|
|1970|<br>124|-|141|<br>28|**39**|53|31|114|
|1990|<br>**100**|**1to**|**1to**|**100**|<br>1H|I|100|10|
|1981|<br>14||109|<br>103|<br>9|119|<br>141|<br>98|
|1982|<br>98|82|**108**|**10N**|<br>**88**|119|<br>126|<br>H|
|1983|<br>95|68|96|<br>78|81|121|<br>136|<br>9|
|1984|<br>79|59|103|<br>57|77|122|<br>142|105|
|985|<br>8|48|1#5|<br>69|67|108|<br>132|<br>91|
|1986|<br>76|51|69|<br>76|73|74|Be|<br>61|
|1987|<br>92|44|70|<br>85|60|81|100|<br>76|

Sources: Verneck (1987), page 45 for 1970-85, and Central Dank of Brazil, Brazil Economic Program, tor 1986-87.

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