rafmacalaba/data-use-mentions-extended huggingface dataset:
,159|

Note: Figures in red are estimated based on 4 admissions per 100 population.

For more detailed future analysis, breakdowns between different hospital wards would be possible if specific HAI rates are available. In Kenya, for example, admissions data are commonly provided by hospital level (HC III level, General Hospitals, HC IV, and Regional Referral Hospitals), by region, by age category (typically under five years of age and over five years of age), and by cause.

# 4. Average hospital cost per day

Recently, a cost categorization of HAI and AMR has been comprehensively provided by Morel et al (2020). However, their framework contains a large number of variables and there are no reports yet of studies that have applied their framework.

More than ten years ago, a review of economic studies that assessed the costs of illness (18 studies) and the cost-effectiveness of interventions (12 studies) for patient safety was 32
number of firms surveyed. The sample for each economy is stratified by industry, firm size, and geographic region. Stratification by industry follows a different level of detail depending on the size of the economy. Stratification by size follows three levels: 5 to 19 (small), 20 to 99 (medium), and 100 or more (large); firms with fewer than five employees are ineligible for the survey. Regional stratification covers the main economic regions in each economy. Through this methodology estimates for the different stratification levels can be calculated on a separate basis and inferences can be made for the non-agricultural private economy.

**<u>Table 3. List of developing East Asian countries covered by the Enterprise Survey</u>**

|**Country**|**Survey year**|**# of firms**|
|---|---|---|
|Cambodia|2016|373|
|Indonesia|2015|1320|
|Lao PDR|2016|368|
|Malaysia|2015|1000|
|Myanmar|2016|607|
|Philippines|2015|1335|
|Thailand|2016|1000|
|Vietnam|2015|996|

11
state, and local government. The table uses the simple sum of questions by each item as dependent variables. The unit of observation in all Columns is the census tract. The information was obtained from the Latin American Public Opinion Project survey (LAPOP). Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.14. Effects of Guerilla Territorial Control on Distance to Police Stations and Incarcerations

||Distance to|Incarcerations|
|---|---|---|
||Police Stations|(1992-1999)|
||(1)|(2)|
|Guerrilla control|0.0198|0.0193|
||(0.0614)|(0.0137)|
|Observations|3,652|3,681|
|Bandwidth (Km)|2.266|2.266|
|Dependent mean|1.850|0.0580|

_Notes:_ This table shows the effects of guerrilla control on the distance to the closest local police station (Column 1) and the number of incarcerations per segment between 1992 and 1999 (Column 2).

87
**Figure 4: Awareness of Climate Change Causes and Mitigation Measures**

**Personally, do you think you are well informed or not about...**

<!-- Start of picture text --> (a) The different causes of climate change<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>9 8<br>13<br>90<br>80<br>33 36<br>70<br>60 51<br>50<br>40<br>48<br>30 54<br>20 31<br>10<br>9<br>0 2 4<br>EU27 countries Kazakhstan Tajikistan<br>Very well informed Fairly well informed Not very well informed Not at all informed<br>(b) Ways in which we can slow down climate change<br>100<br>10<br>90 18<br>26<br>80<br>70 36<br>60<br>54<br>50 50<br>40<br>30 45<br>20<br>22<br>10 23<br>0 8 1 5<br>EU27 countries Kazakhstan Tajikistan<br>Very well informed Fairly well informed Not very well informed Not at all informed<br>Percent of respondents<br>Percent of respondents<br><!-- End of picture text --> Source: Authors calculations based on P-LITS 2 data. Special EU Barometer 300/wave 69.2

# **Climate Change Impacts of Greatest Public Concern**

18. Respondents were presented several possible adverse impacts of climate change, and were asked to choose (a) which of these were of concern to them, as well as (b) which of these was of greatest concern. “Increase in natural disasters
|<br>5054 J|U.N. 1988 revision<br>(prelim.)|Celade<br>printout<br>2/87|Celade printout<br>2/87|
|Noly See|Apr 48|<br>1 F|UNPVSR 4/87 (official<br>est.)|||
|Honodras|Mar 74|<br>2657 J|CELADE<br>printout<br>2/87|Celade<br>printout<br>2/87|Celade printout<br>2/87|
|Hong Kong|Mar 86|<br>5396 F|U.N. 1988 revision<br>(prelim.)|Bank est.|Based on U.N. 1988 revision<br>(prelim.)|
|Hungary|Jan 80|10709 F|UNPVSR 4/87 (official<br>est.)|Based on INED 10/87|Based on UNPVSR 4/87|
|Iceland|Dec 70|<br>**205**J|UNPVSR 4/87 (official<br>est.)|Based on INED 10/87|Based on UNPVSR 4/87|
|India|Mar 81|685185<br>F|Bank projection<br>from census,<br>adjusted<br>for 1.83S uridercoun|Bank assessment<br>of Ssmple<br>t Registration<br>System<br>data|Based on Sawpte Registration<br>System<br>data|
|ionYesia|Oct80|147490<br>F|U.N. 1984 assessment|1987 DHS|Bank assessment<br>of 1985<br>intercensal survey|
|an, Islamic|Nov 76|33709 F|U.N. 1984 assessment|Based on U.N. 1984|Based on U.N. 1984|
|Rep. of||||
findings are robust to controlling for a range of country-level institutional and policy variables, such as the strength of bank regulation and supervision, exchange rate flexibility and restrictions on capital flows.

Our main contribution in this paper is to investigate the role of the structure of the international banking market in the international transmission of monetary policy. The literature on the effect of monetary policy on cross-border lending builds on several papers investigating the bank lending channel domestically. Bank balance sheet strength (Jiménez et al., 2012a; Jiménez et al., 2014b; Gambacorta, 2005) and bank risk (Altunbas et al., 2010) have been shown to affect the impact of monetary policy on bank credit supply. Further, low monetary policy rates induce risk taking (e.g. Jiménez et al., 2014a; Ioannidou et al. 2015), and there is evidence of a portfolio rebalancing channel as well (den Haan et al., 2007).

The paper most closely related to ours is Cerutti, Claessens and Ratnovski (2014), who study drivers of cross-border bank flows using aggregate, bilateral credit flow data published by the BIS. They find that these flows are largely driven by global factors (e.g. VIX volatility and the slope of the US yield curve). These authors also find that the transmission of monetary policy to cross-border credit is affected by certain borrowing country policies, such as exchange rate flexibility, capital controls and bank regulation. Our paper differs from Cerutti et al. (2014) in several ways. First, we control for credit demand at the borrower firm level, so our findings are more likely to reflect supply side conditions. Second, our main interest is the effect of monetary policy in the relevant lender countries on cross-border lending. Finally, we provide a more comprehensive picture of cross-border lending by also studying non-volume loan terms.

Our paper is also related to the literature showing how banks reduce cross-border lending in response to non-policy funding shocks at home. Peek and Rosengren (1997
innovative.<sup>32</sup> This suggests that Latvia’s national innovation strategy should include policies to help firms improve their management structures and establish more extensive linkages with global markets.

**Figure 25 Strategic and organizational changes in innovative and non-innovative enterprises in 1999-2001, %**

_Source_ : Latvia Survey of Innovation Activities, 2002.

<!-- Start of picture text --> Significant changes in the aesthetic appearance or<br>design<br>Changes in the enterprise's marketing concept<br>Introduction of new organisational structures<br>Introduction of advanced management techniques<br>Strategic changes<br>0 10 20 30 40 50 60<br>Innovative enterprises Non-innovative enterprises<br><!-- End of picture text -->

> 32  Domestic and international competition should, in theory, motivate firms to become more innovative. However, anecdotal evidence suggests that many Latvian firms may be overwhelmed by competition. They simply do not have the organizational, managerial and financial capacity to innovate, absorb and adapt new technology, and respond to rapidly changing market demands.  If so, entrepreneurial and managerial skill – even more so than greater access to modern technology -- may be the main factor inhibiting innovation.  Thus, from a public policy perspective, greater attention to management and entrepreneurship may be as important as R&D or technology commercialization policy.

39
Lewis-Beck & Stegmaier, 2007). Job losses related to automation and globalization are also often mentioned as important reasons for the election of Donald Trump in the United States in 2016 (e.g., Autor et al., 2017). Second, voters can punish or reward the incumbent based on the standing of the national economy, irrespective of their own personal economic situation.

However, economic conditions are not the only factors linked to voting preferences. Several psychological studies have focused on other factors explaining voting behavior such as cultural backlash against progressive policies, demand for law and order, and status threat. Knuckey and Hassan (2020) associate the vote for Trump with anti-immigration sentiment and racial prejudice. Norris and Inglehart (2016) attribute the rise of populism in the West not to economic insecurity but to a cultural backlash and a reaction of once-dominant parts of the population to progressive viewpoints about ethnic minorities, women, and the LGBTQ+ community. In this regard, status threat is also perceived as an important explanation for the Trump vote (Mutz, 2018).

There is also a literature on the links between voting behavior and SWB. Most studies on voting behavior mention a strong sense of discontent in parts of the population as a driver of voting the incumbent out of office (Ward et al. 2020). The explanation for this is simple: voters punish the incumbent party if their experienced welfare worsens as they blame the incumbent party for this deterioration; voters may also reward the incumbent party if they experience improvements in their quality of life. Several empirical studies find a strong positive association between SWB and voting for the incumbent party (e.g., Bravo, 2016; Liberini et al., 2017; Ward et al., 2020).<sup>21</sup> Bravo (2016), who studied this issue in Latin America, finds a strong correlation between SWB and the likelihood that the incumbent will win the elections using data from the Latinobarómetro. In elections held in
Andr ́ee

# 6.2.5 Linear analysis of case hospitalization rates

The main results provided evidence for increased COVID-19 incidence in areas where populations are more exposed to air pollution. However, it is possible that the estimated association between PM2 _._ 5 concentrations and COVID-19 incidence can be attributed to worse respiratory health in polluted areas, which then leads to more severe symptoms and higher case detection. The analysis tried to control for this using health data and the percentage of confirmed cases that resulted in hospitalization as controls. This did not impact the results. A second way to further test this theory is by analyzing the association between PM2 _._ 5 and the case hospitalization rate because worse respiratory health would lead to more severe COVID-19 disease (Ruan, 2020). The suspect correlation is investigated below using step-wise AIC variable selection keeping PM2 _._ 5 in the variable set, followed by the full spatial specification. Model 6 uses March 31 confirmed COVID-19 hospital admissions as a percentage of March 22 confirmed COVID-19 cases, model 7 uses March 30 confirmed cases. The analysis finds that age, male gender, and the share of population with overweight are positively associated with increased case hospitalization rates which follows earlier identified risk groups (Ruan, 2020).

Table 8: Dependent Variable: Confirmed COVID-19 related hospital admissions as a percentage of COVID-19 cases.

|**Variable**|**Model 6**|**Model 7**|
|---|---|---|
|(Intercept)|-587.36*|-93.71***|
||(296.95)|(35.24)|
|Population density||-2.08*|
|||(1.26)|
|Share of male population|935.03**||
||(487.40)||
|Share 45 to 64|9.17***|1.05*
22, 2022.

Vaccine hesitancy refers to delay in acceptance or refusal of vaccination despite availability of vaccination services (MacDonald et al. 2015). It is not a new phenomenon, and some level of hesitancy exists for most vaccines, usually revolving around concerns about efficacy or safety. Even before the COVID-19 pandemic began, the World Health Organization (WHO) identified vaccine hesitancy as one of the top threats to global health (WHO 2019). The 2019 Wellcome Global Monitor from Gallup assessed general attitudes about the importance, safety, and effectiveness of vaccines across the globe prior to the onset of COVID19. Overall, on average, globally 7 percent of respondents either somewhat or strongly disagreed that vaccines are safe, and 5 percent either strongly or somewhat disagreed that vaccines are effective (Gallup 2019). Levels of vaccine hesitancy were lower in low- and middle-income countries compared with highincome countries. Across regions, countries in <mark>Eastern Europe and former Soviet states were among the least likely in the world to believe vaccines are safe and effective. A</mark> lthough these beliefs were expressed prior to the COVID-19 pandemic, they suggest that higher levels of COVID-19 hesitancy might be expected in Central and Eastern Europe as compared to other regions of the developing world. Furthermore, they suggest that concerns about safety are greater than concerns about efficacy.

Despite the importance of vaccinations against COVID-19, little is known about the prevalence of COVID19 vaccine hesitancy, and even less about the extent to which concerns about the COVID-19 vaccine are similar or different to longstanding concerns about other vaccines. Anecdotally, several factors likely contribute to COVID-19 vaccine hesitancy: for example, the accelerated speed of development of the COVID-19 vaccine, combined with its newness, has intensified concerns about unknown longer-term sideeffects. There are also reports of specific effects, such as of myocarditis in men under the age of 40 (Husby et al. 2022). Uncertainty about the duration of the vaccine’s effectiveness raises the possibility of needing annual vaccination. The availability of
7 the individual lives, and a vector of variables capturing employment status (not just whether employed but also type of job) which should proxy eligibility for GIS/LIS. Both our instruments can plausibly be argued to be correlated with the probability of the individual being insured (for example, a peasant farmer is most unlikely to be insured with GIS or LIS), but neither seems likely to influence the probability of the individual having unduly high health spending, once we have controlled for health insurance and our other covariates, such as household income, education, and so on.

Our surveys are listed in Table 1. They vary in several important dimensions. One is geographic coverage: the Gansu Survey of Children and Families (GSCF) covers just Gansu province; the China Health and Nutrition Survey (CHNS) covers mostly central and eastern provinces; and the World Bank Health VIII project baseline survey (H8BS) cover mostly the poorer central and western provinces. Another difference is the rural-urban focus within provinces: the GSCF and the H8BS are rural-only surveys, while the CHNS covers urban and rural areas. A third difference is the panel dimension: the CHNS and GSCF are both panels, while the H8BS is a cross-section. There are other less obvious differences, as will become apparent below.

Variable definitions and notes on the variables are provided in Table 2. Out-of-pocket health spending is for a 4-week window in the case of the CHNS, and for one-year windows in the two other surveys. In the CHNS, out-of-pocket expenses are explicitly net of any reimbursement; in the other surveys, respondents are not explicitly asked about reimbursement. Information on income is most complete in the CHNS, the GSCF has the second most detailed income information, while income in the H8BS is based on just one question. The low per capita income in the H8BS undoubtedly reflects in part the fact that the sample was drawn from
# **5.4 Areas for future research**

At least three areas for future research emerge from this study. First, developing a better understanding of how formal firms have adjusted their capital–labor mix during this time of crisis is key as this will provide an indication as to whether the apparent ‘decoupling’ of sales and labor is transitory. Second, there is obvious value in generating causal evidence (our study does not do this, nor does it claim to) about what is driving the recovery of firms. Finally, more in-depth country studies in Sub-Saharan Africa, drawing on multiple data sources, could help to build a comprehensive picture of the recovery of formal firms in the region.

32
# **3.1 PROFILE OF THE UNBANKED**

7. **Being unbanked is clearly associated with lower incomes.** The majority of the unbanked are poor, but the unbanked are not just the poor. As Figure 3.3 shows, the percentage of unbanked persons increases as incomes fall. Slightly more than half (51%) of bank account holders belong to the top three deciles of income distribution, and about a third (34%) belong to the group earning less than the median income. Of the unbanked, over half (53%) earn less than half the minimum wage and 70% earn less than one minimum wage. On the other hand, of those earning over 4 minimum wages, 84.22% are banked and 15.78% unbanked. At the other end of the income scale, it is notable that no less than a quarter of the families in the lowest 40% of income distribution (those earning less than half a minimum wage) are banked. This data shows that even very poor households have the capacity to be banked.

**Figure 3. 3  Use of banking services in relation to income**

<!-- Start of picture text --> 100%<br>90% 81%<br>80% 74% 75% 73% 76%<br>70%<br>70% 64%<br>59%<br>60% 55% 56%<br>50% 41% 45% 44%<br>36%<br>40%<br>30%<br>30% 26% 25% 27% 24%<br>19%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Population distribution by income level<br>Banked Unbanked<br>Source: DANE household study of Bogotá, 2003
ha)|0.29|0.89|0.00|0.00|
|<br>Area of leased in land (ha)|0.31|0.00|0.00|0.85|
|**Assets and social capital**|||||
|Value of household assets (birr)|300.32|264.51|274.26|354.72|
|House value (birr)|7382.48|6560.25|8901.85|6827.77|
|No. of male adult children living in the village|0.41|0.61|0.41|0.22|
|<br>No. of female adult children living in the village|0.33|0.48|0.32|0.20|
|<br>No. of head's brothers living in the village|1.03|0.80|1.02|1.24|
|<br>No. of spouse‟s brothers living in the village|0.65|0.54|0.69|0.72|
|<br>No. head's sisters living in the village|0.69|0.56|0.68|0.81|
|No. spouse's sisters living in the village|0.51|0.41|0.54|0.57|
|<br>No. of adult children living in the village|0.74|1.09|0.73|0.42|
|No. of brothers and sisters livingin the village|2.88|2.31|2.94|3.33|
|Number observations|2487|813|770|904|

_Source:_ Own computation from AU/WB/UG land tenure survey.

17
212|369|433|615|
|||Lower Bound|87|120|216|55|
|Four Times|3400 to 3600|Upper Bound|125|216|259|862|
|Poverty Line||Mean|-48|-137|-7|274|
|||Lower Bound|-185|-568|-249|-345|

Note: All values are in 2000 RMB Yuan per capita. The nutrition-based poverty line is equal to 875 RMB Yuan per capita. We use the RCRE Rural Household Surveys for 1995 to 2003 from Anhui, Henan, Jiangsu and Shanxi and the matched RCRE 2004 Supplemental Rural Household Social Network, Labor Allocation and Land Use Survey.

37
found in CEE (Central and Eastern Europe) rather than CIS countries (i.e., Commonwealth of Independent States, countries formerly affiliated with the Soviet Union). A thorough understanding of privatization effects thus requires us to account for the selectivity of privatization, and the political and economic contexts in which privatizations take place.

Surveys agree that a glaring shortcoming in the literature surrounding the aftermath of privatization is a lack of convincing studies on the causal effect of privatization on firm performance in China. Most studies on privatization focus on CEE and CIS countries, with occasional exceptions on other large countries such as the United States, Mexico, and India (Megginson and Netter 2001; Djankov and Murrell 2002; Estrin et al. 2009). Much less work is done on privatization in China. Estrin et al. (2009) concludes, “There are as yet no TFP studies using data from China that employ robust methodologies and, perhaps because of this, the available papers find diverse results, with the effect of nonstate ownership being mostly positive but sometimes statistically insignificant and sometimes negative” (Estrin et al. 2009, p. 702).

This deficiency has not been successfully addressed in the past decade, even though this period has witnessed strong interest in, and more studies on, privatizations in China. In the most recent survey on privatization around the world, Megginson (2017) finds that in the new literature on privatization, papers on China accounted for the largest share among all regions. However, most of the papers are on share issue privatizations (SIPs), that is, partial privatization for publicly listed SOEs with the government retaining strong control,<sup>2</sup> and these account for a tiny share of privatizations in China. Under the slogan of “grabbing the big and letting go the small,” the vast majority of the privatized SOEs under the SOE restructuring program near the turn of this century were much smaller and non-listed, and they were afterwards truly private firms (Xu
legislation and then federal), there were not specific rules regulating RMs until 2012, as part of the enhanced responsible lending obligations for reverse mortgages which were introduced into the _National Consumer Credit Protection Act 2009_ (National Credit Act).The market regulator ASIC reviewed the market in 2018, examining data on 17,000 RMs.<sup>7</sup> The review found that borrowers had a poor understanding of the risks and future costs of their loan – including the fact that depending on house prices and interest rates, they may not have enough equity remaining in the home for longer-term needs (i.e. elderly care). Under the new Code of Banking Practice, approved by ASIC in 2018, banks will be required to take extra care with customers who may be vulnerable, including those who are experiencing elderly abuse.

# **_Countries in Asia_**

Estimates provide an upbeat account of the RM market potential in Taiwan, China. Using Taiwanese census data and estimation methods derived from the US market, Chan & Liu (2015) estimate that a tenure payment for 65-year old single homeowner would amount to NTD 280,000, ca. 64% of average income for this age group. The authors recommend the Taiwanese authorities to promote the introduction of RM in the country (Chan & Liu, 2015).

Empirical research in Korea casts a more skeptical light on the benefits of RM **.** Koreans in their 40s and 50s start increasing their savings rate as they prepare for higher costs of children’s education, home purchase, and to assume greater responsibility for their own retirement income than previous generations. In this context, RM may enhance welfare and contribute to macroeconomic growth because it allows homeowners to consume excess savings when they are older. Yoo & Koo (2008) approach the subject from a novel perspective, asking 290 children (aged between 25-35) of homeowners whether they would support their parents’ RM application: only 37% of the respondents do. This lack