rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 2**

**Sources of Support for China’s Rural Elderly in 2005, by Age Category** ( Percent Reporting as Most Significant Source of Support )

|Age Group|60+|60-64|65-69|70-74|75-79|80-84|85+|
|---|---|---|---|---|---|---|---|
|Labor Income|37.9|64.3|45.1|24.4|12.0|4.3|1.7|
|Pensions|4.6|4.7|5.1|4.7|4.4|3.8|2.6|
|Dibao|1.3|0.8|1.2|1.5|1.9|2.0|2.1|
|Insurance and||||||||
|Subsidy|0.1|0.1|0.1|0.1|0.1|0.1|0.1|
|Property Income|0.2|0.1|0.2|0.2|0.2|0.1|0.1|
|Family Support|54.1|28.6|46.6|66.9|79.1|87.2|91.1|
|Others|1.8|1.2|1.7|2.2|2.3|2.4|2.3|

Source: National Bureau of Statistics, 2005 One Percent Population Sample Data, China Statistics Press, 2006.

34
the generally low savings capacity in Colombia compared to other countries<sup>48</sup> . A simultaneous survey of banked and unbanked people in Mexico City revealed that 45% of the total population held financial savings and 47% of the unbanked were savers. The considerable difference in savings capacity between Bogotá and Mexico City can be attributed to the generally low savings capacity in Colombia. However, the numbers need to be treated with caution due to certain limitations of the Bogotá survey<sup>49</sup> .

19. **Informal savings instruments are used by both banked and unbanked individuals.** Informal savings are used by 13% of the population in Bogotá, compared to 39% of the population in Mexico City. In Bogotá and in Mexico City, banked individuals outnumber the unbanked in their use of informal savings services. As Table 3.2 shows, 19% of banked individuals in Bogotá (7.5% of the total sample) use informal savings, twice as much as the unbanked, with 9% usage of informal savings services (corresponding to 5.4% of the total sample).

20. **The majority of the unbanked saves at home.** Home savings is used three times more frequently by unbanked persons than by the banked population, while other forms of informal savings are as likely to be used by persons with bank accounts as without. There was no notable difference between unbanked and banked regarding the use of “ _tandas_ ” (savings clubs with friends), savings clubs at stores and other organizations and loans to friends and families.

21. **The cost of informal savings is high.** Informal savings systems do not offer interest, so that savings lose value with inflation. In the case of savings clubs and “lay-away plans” at stores, informal savings can be shown to lose a margin in excess of the inflationary loss, since savings are frequently traded in for certain items sold above list price. In addition
stratum fixed effect, _φenumerator_ is a survey enumerator fixed effect, _ζmonth_ is a fixed effect for the month the survey was administered, _Xi_ is a vector of individual controls, and _εi_ is a conditionallymean-zero error term.<sup>14</sup><sup>_,_15</sup>

> 14In our main specifications, we include controls for baseline household size, education level, and indicators for having given birth, having received any vocational training, or having any paid work experience prior to the baseline survey. Results are similar in magnitude and significance when these controls are omitted.

> 15We do not correct for the false discovery rate in our analysis. In our analysis of medium-term impacts, we consider a relatively small set of labor market outcomes (because the midline survey did not collect data on a broader range of outcomes), none of which can be treated as statistically independent. As will become apparent in the subsequent discussion, most of these outcomes are impacted by the treatments over the medium-term; so the overall pattern of findings is unlikely to be explained by multiple testing. In our analysis of longer-term impacts, we look at a broad range of outcomes; however, as almost none are

16
the tens or low hundreds, making comparisons between host and refugee GERs unreliable. As such, while refugee GERs are higher in some countries, these differences are typically small or the result of extreme circumstances, such as state fragility or significant constraints to state education.

Among those countries that provided gender disaggregated data, average enrollment rates were similar for both boys and girls at primary and secondary level. The average GER for refugees at primary level was 63 percent for males and 61 percent for females, compared to GERs of 36 percent and 35 percent for males and females at secondary level, respectively (UNHCR, 2023c). While average GERs appear to indicate gender parity, large gender disparities persist in some countries. Most notably, GERs are far higher for boys than girls in Gabon, Ethiopia and Kenya by 22, 13 and 12 percentage points, respectively. In contrast, GERs are higher among the female refugee population in Senegal, the Philippines, and Iraq, by 17, 8 11
at the bank level if no asset class data are available or skipped altogether if relevant data are not retrievable. With this information, the minimal set of bank-level variables can be compiled so that the proposed approach can be deployed at a reasonable degree of granularity, where it is assumed that the required (country-specific) macroeconomic data are available either from country authorities or IFIs.

# **8. Conclusion**

This paper proposed a macroprudential stress testing approach and illustrated its application and properties on an empirical data for a set of Eastern European banks. The innovative features of the proposed macroprudential stress tests were inspired by the effects of the global financial crisis on Eastern Europe. We demonstrated in the empirical application section how the proposed approach can be effectively used to identify systemic and idiosyncratic risk factors to gauge the relative financial soundness and resilience of individual banks to economic risks. The proposed approach is also useful for monitoring and assessing banking-sector wide systemic risks via aggregate measures of outcome indicators under the three different macroeconomic scenarios that are analyzed.

The direction of future research should focus on the functions estimating response of the banking sector and the policy makers to changing balance-sheet solvency conditions of the Future research should also focus on these re- banking system. connecting sponse functions to the real economy so that dynamic stress tests can be constructed which would enable stress testers to study the response pattern of some key macroeconomic and bank specific variables to various shocks that hit the economy and the financial system. Nonetheless, it is important to highlight here that bank-level granularity, which is valuable to policy makers, should be preserved to ensure the practical usefulness of any newly 67
a broader regional level (see Valente, 2014, 2015; Pivovarova and Swee, 2015; Akresh et al., 2012). Moreover, this study extends the literature on legacies of war by documenting the long-term eect of exposure to conict on women's nal adult height. Along with Akresh et al. (2018), which examines the impacts of the Biafran war using the variation in exposure to the war by ethnicity, this is among the rst papers to document the intergenerational transmission of the impact of early childhood conict exposure on second generation health.

Nepal experienced a decade-long violent civil conict between 1996 and 2006, which resulted in more than 13,000 fatalities, destroyed considerable infrastructure, hindered delivery of basic services, and generated pervasive and strong feelings of fear, insecurity, and stress among its citizens. I use the Informal Sector Service Center's (INSEC) records of conict victims to create a casualty-level data set with exact geographical locations (villages) and dates of incidents. I merge the villagelevel conict intensity data with the 2016 Nepal Demographic Health Survey (NDHS), which is a nationally representative survey of the female population aged 15 to 49. I limit the analytical sample to women who were either born before or who were in utero at the start of the war in February 1996 to assess the lasting impact of conict on height. In 2016, these women were old enough (20 to 49) to be sampled in the ever-married NDHS 2016 women sample and had documented survey responses about their children that can be used for second-generation analysis. Limiting the sample to women born before the conict's start date also reduces potential confoundedness through selective fertility and migration (further discussed in empirical strategy section 4). I rely on the biomedical literature that height development in human beings is characterized by rapid growth during the rst three years of life, followed by lower level of constant growth and then a secondary growth spurt during adolescence (Figure A.5
can help identify suitable regions for pro-poor adaptation measures, as well as other policies that require accurate information on the location of poor households ( _e.g._ cash transfers). The third, and most important, concern relates to their conceptualization and quantification of welfare impacts. H2016 only model a single channel through which shocks affect welfare, which is damage to assets resulting in income and consumption losses, assuming that income and consumption are suitable metrics to contrast the disproportionate welfare impacts.

A growing body of literature has emphasized that income and consumption are imperfect metrics of poverty. For example, the global Multidimensional Poverty Index (MDP), developed by the Oxford Poverty and Human Development Initiative (OPHI), captures the acute deprivations in health, education and living standards household face. Such multidimensional poverty measures better capture a households’ ability to achieve worthwhile goals ((Stiglitz et al., 2009; Alkire and Foster, 2011), and are therefore aligned with Amartya Sen’s idea of capabilities (Sen, 1989) and directly related to the UN’s Sustainable Development Goals. The data required for such a framework are also generally available at a more granular level allowing more detailed analyses of the affected households’ characteristics. Hence, specifying a modeling framework that can capture alternative channels of disasters impacting household welfare (in this case MDP) can help complement existing disaster risk modeling studies. Moreover, it allows decision-makers to evaluate the wider benefits of adaptation options in terms of asset risk reduction, reduce the time that people face additional deprivations that can negatively affect wellbeing, and prevent people from falling temporarily into poverty.

In this study, we extend the H2016 model and aim to construct a new spatial risk framework to quantify the welfare implications of present-day and future climate extremes to households. The methodology is applied to a case study in the coastal zone of Bangladesh, an area inhabited by around 43 million people and considered to be among the most climate vulnerable regions globally. Using a recently developed synthetic household survey dataset that
we incorporate this imprecision into our simulation analysis. Section III describes the different targeting schemes that are assessed in the simulation stage. In this section we also demonstrate how one particular targeting scheme can be viewed as optimal in terms of ensuring the maximum possible gains from geographic targeting. Section IV presents the results from our simulation analysis, and Section V presents a concluding discussion.

# **II. Producing Local Estimates of Poverty**

The methodology we implement here has been described in detail in Elbers, Lanjouw and Lanjouw (2002, 2003). We estimate poverty based on a household per-capita measure of consumption expenditure, _yh._ A model of _yh_ is estimated using household survey data, restricting explanatory variables to those that are also found in, and strictly comparable to, the population census. The regression models consumption on a set of household-level demographic, occupational and educational variables as well as census variables calculated at the level of the census-tract or other level aggregation above the household level.<sup>8</sup> Letting _W_ represent an indicator of poverty or inequality, we estimate the expected level of _W_ given the observable characteristics in the population census and parameter estimates from model estimated on the household survey data _._ We model the observed log per-capita expenditure for household _h_ as: where **x** **_h_** β is a vector of _k_ parameters and _uh_ is a disturbance term satisfying E[ _uh_ | _xh_ ] = 0. The model in (1) is estimated using the survey data. We use these estimates to calculate the welfare of an area or group in the population census. We refer to our target population as a ‘region’.

10
essence, providing them more information on _how_ to help students stay in school. A letter signed by the Minister of Education accompanied the guidance manual, emphasizing the importance of every Guatemalan child completing at least nine years of basic education (primary and lower secondary) and the responsibility of schools to support them in doing so. The second treatment arm adds to the first a list of sixth-grade students at high risk of dropping out based on a prediction model using administrative data– providing them more accurate information on _who_ needs the most help. The third treatment arm adds to the first two small behavioral nudges to try and keep dropout prevention top-of-mind for principals and teachers. A fourth, control arm included schools that received no intervention or information about ENTRE.

4
to stay in office. The<br>risk rating assigned is the sum of three<br>subcomponents: government unity, legislative<br>strength, and popular support|PRS Group (database)|
|Law and order|“Law” assesses the strength and impartiality of the<br>legal system, while the “Order” element is an<br>assessment of popular observance of the law.|PRS Group (database)|
|Capital account openness|The Chinn-Ito index (KAOPEN) measuring a country’s<br>degree of capital account openness; available from<br>1970-2019 for 182 countries.|Chinn and Ito (2006)|
|Exchange rate regime|A dummy indicator, where “1” is a floating exchange<br>rate regime and “0” is fixed. Classification codes 1-3<br>in the database have been classified as “0” and codes<br>4-6 as “1.”|Ilzetzki, Reinhart, and Rogoff<br>(2021)|
|Openness|Sum of exports and imports (percent of GDP)|World Integrated Trade<br>Solution (World Bank)|
|Fiscal rules|Covers four types of rules: budget balance rules, debt<br>rules, expenditure rules, and revenue rules, applying<br>to the central or general government or the public<br>|Davoodi et al. (2022)|
|SWF (sovereign wealth<br>fund)|sector.<br>Dummy variable: “0” if a country does not have an<br>SWF and “1” if it has an SWF.|https://www.swfinstitute.org/<br>fund-rankings/sovereign-<br>wealth-fund|

45
competences assessed by EGRA and EGMA modules. The combined intervention ( _T_ 2) improved scores for almost all literacy and numeracy competence levels, with statistically significant increases ranging from 0.16 and 0.67 _σ_ . The largest impacts are observed in foundational skills for both literacy and numeracy. For literacy, the largest impact is observed in ‘letter recognition’ and ‘letter dictation’, where children living in _T_ 2 school catchment areas scored 0.42 and 0.41 _σ_ higher than the control group, respectively. This is followed by ‘listening comprehension’ (0.23 _σ_ ), ‘oral passage reading’ (0.18 _σ_ ) and ‘word dictation’ (0.16 _σ_ ).

> 14Using data from the Uwezo literacy assessments, which are simpler tests designed for low-literacy populations, we find qualitatively similar results in terms of literacy gains. These results are not shown in the paper.

> 15Our learning outcomes have been standardized using the mean and the standard deviation of the control group at baseline as in Banerjee et al. (2007) and Muralidharan et al. (2019) to facilitate the comparison of treatment effects across studies. See cost-effectiveness analysis in Section 6.6.

22
adoption. For implicit countries. income, inflation, deficit, and M2 stability are averaged over all years for which data were available. Bank crisis data are for the five years leading up to the last decision to retain an implicit program (i.e., 1990-94). For both implicit and explicit countries, the ICRG Law Index (a six-point scale with higher values indicating a stronger legal tradition) is averaged over 1985-91. M2 instability is the coefficient of variation in M2/GDP (standard deviation/mean). Bank crisis is a dummy variable equal to one if a bank crisis occurred.

* Means for explicit and implicit are different from one another at the t = 0.05 level. (results similar when variance for each sample assumed equal or unequal).

Cross-country regressions for the level of financial depth in 1992 indicate that the positive link between explicit deposit insurance and financial development suggested by the summary statistics in Table I is closely associated with income levels. Controlling for income and the average level of inflation from 1990-92, the partial correlation between explicit deposit insurance and financial depth is actually _negative._<sup>_9_</sup> The estimated coefficients for the explicit deposit insurance variable imply large reductions in financial

- 9 Similar qualitative results apply when deficits as a percentage of GDP are included in the regression and when the coefficient of variation in M2/GDP replaces inflation as a measure of instability. The instability measures enter the regressions with the expected negative sign, but they never approach conventionally accepted levels of significance, perhaps because they are averaged over a relatively brief time period (1990-92).

8
SWB, social unrest (e.g., Arampatzi et al., 2018; Witte et al., 2020; Abi-Nasif et al., 2021), and voting behavior (e.g. Liberini et al., 2017; Ward et al., 2021) and using a nationally representative survey, collected for the Brazilian Electoral Study (Meneguello, 2019), we show that the decline in SWB and the switch in voter support in the 2018 general elections from one end of the political spectrum to the other were both associated with

> 7 A recent paper also provides evidence of considerable differences in preferences over policy areas across space and demographic groups in Colombia (Burger et al. 2021).

> 8 Here we define a social contract as ‘ _the entirety of explicit or implicit agreements between all relevant societal groups and the sovereign (i.e., the government or any other actor in power), defining their rights and obligations toward each other’_ (Loewe et al., 2021).

3
</sup> The absence of diverse actors from the LPA peace process did not appear to affect immediate conditions for safe IDP return, with contextual indicators giving mixed results. Of the three LPAs that did not engage diverse actors, both Tikrit and Anbar witnessed high rates of IDP return<sup>97</sup> and both locations showed marked contextual changes: in Tikrit, there were no reports of discrimination or conflict between IDPs and host communities post-signature, a notable finding given the high rates of tension and threat of violence reported pre-signature,<sup>98</sup> and in Anbar, threats, blocked returns and ‘returnees feeling unwelcome’ all reduced to zero within 6 months of signature in the provincial capital of Ramadi, with no concerns over revenge attacks or tribal tension were reported. Of the five LPAs that did involve women, youth or civil society actors in the peace process, conduciveness of safe return remained low in Ayadhiya, where threats towards IDPs, physical attacks, mistrust and concern over tribal tension remained relatively high in the post-agreement period despite the LPA

> 95 Interview with Tribal Leader #1, Yathrib, 26 April 2021.

> 96 Interview with political representative, Mahalabiya, 12 May 2021.

> 97 In Tikrit, 1,500 IDPs returned within 2 months of the LPA signature, and 63,360 IDPs returned within 6 months, according to IOM’s DTM Master List. For Anbar, 405,696 IDPs had returned to Ramadi by roughly the same time as the LPA was signed, and this increased incrementally, with 478,596 IDPs returning within 12 months of signature, according to IOM’s DTM Master List.

> 98 See footnote 88 above.

22
-2-

It should be stressed that the ICP evaluation of a country's relative standing can be very different from one derived from exchange rate comparisons, and the difference is usually bigger, the poorer a country happens to be. It is for this reason that this topic has more than academic interest. For Brazil, Mexico and India, ICP evaluation of per capita ODP performance in 1975 was 25.2, 34.7 and 5.6 per cent of US levels whereas exchange rate comparisons showed 16.0, 20.4 and 2.0 per cent respectively (see Kravis, Heston and Summers, 1982, p. 22).

# The Alternative "Industry-of-Origin" Approach

The expenditure approach is useful for analysis of macro economic performance, but cannot be directly used for sectoral analysis as it does not show real product by industry. This handicaps comparative structural analysis, -fork on labour or total factor productivity, growth accounting. and studies of technological performance. It does not help in deriving weights for world production indices for sectors such as agriculture or manufacturing, nor does it make a clear breakdown between tradeable and nontradeable goods and services which is needed for analysis of competitiveness. The industry of origin approach, which is used here. promises to yield solutions to these problems, as well as providing a crosscheck on ICP results which are sti'll a subject of controversy.

One way of illustrating the difference between the expenditure and the industry of origin approaches is presented in table 1.1. This is derived from the Mexican input-output table for 1975. Ideally, the industry of origin approach should derive PPPs (purchasing power parities) for column (1) of table 1.1 for GDP ac factor cost. For agriculture (see Van Ooststroom and Maddison, 1985) this is possible, but for manufacturing most of our price (unit value) information refers to column (3).

What the expenditure approach does, is to estimate PPPs for the last column of table 1.1
# **1. Introduction**

Nepal depends on imports for its fossil fuel supplies. This implies that almost all fuel-based CO2 emissions and most of the local air pollutants are caused by imported fuels. Coal, the most carbonintensive fuel, currently accounts for 36% of the total national CO2 emissions from fossil fuel combustion.<sup>3</sup> Brick making is an energy-intensive process due to the high-temperature heat requirement and coal is the most common fuel used for heat in Nepal. The brick industry alone consumes more than one-third of the total coal supply in Nepal (ICIMOD, 2019a), and this industry accounts for 27% of the total national CO2 emissions from coal consumption (Sadavarte et al., 2019). Several cities in Nepal are suffering from local air pollution as the pollution levels are many times higher than standards set by the World Health Organization (WHO). The capital city Kathmandu was the world’s most polluted city with its Air Quality Index (AQI) 265 at 11:06 am on April 10, 2024. The fine particulate matter pollution (PM2.5) level was 34 times the WHO’s annual air quality guideline value at that time.<sup>4</sup> Brick kilns are one of the sources of PM2.5 emissions in the city. The 2023 average PM2.5 concentration in Nepal was 8.5 times the WHO annual air quality guideline value.<sup>5</sup> Nepal is experiencing rapid urbanization and post-earthquake reconstruction of buildings with rising demand for bricks. For example, between the last two census years (2011 and 2021), the number of housing units built with brick walls increased by 21% (CBS, 2014; NSO, 2023). If the current practice of brick manufacturing continues, CO2 emissions from the brick industry will increase, thereby challenging Nepal’s ability to meet its nationally determined contribution (NDC) by 2030<sup>6</sup> and net-zero emission