rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Quality control_

Price comparisons would be much more credible if we could effectively control for differences in quality. However, in the absence of more information, we can only implement two implicit controls. First, as our selected procedures are routine and relatively uncomplicated, they do not require a relatively high level of surgical expertise. Hence, there is a presumption that there are not big quality variations for these procedures between the best hospitals in developing countries and those in developed countries. Second, since Vanbreda’s clients abroad are mainly international organizations that have citizens from industrial countries on staff, their claims data from foreign countries reflects prices of procedures in the best hospitals in that country, thereby mitigating the quality differentials concern. While these implicit quality controls are imperfect, no superior alternative was feasible given the lack of data on quality of care in developing countries.

# _Price comparisons_

The results of the cost comparison are illustrated in the last column of Table 4. The column contains an estimate of cost savings that would accrue if ten percent of the number of patients requiring a procedure in the US instead chose to undergo the procedure overseas.<sup>25</sup> Since the patient volumes are for 2002, while the prices pertain to the year 2004, our calculation of savings assumes that annual demand for procedures stays unchanged. In fact, studies have shown that the annual patient volumes for many of our selected procedures, e.g., knee and shoulder surgeries have displayed a rising trend over the last few years (see e.g., American Association of Orthopedic Surgeons).

As shown in Table 4, the gains from trade are large. If one in ten patients currently undergoing these fifteen procedures in the US instead were treated abroad, the savings would amount to $1.4 billion dollars annually. Based on the volume of Medicare patients in the H-CUP sample, we estimate that $690 million of these savings would accrue annually to the Medicare program alone. If we expand the list of tradable
210.

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) shows the point estimates of the Gini index calculated on the RuLIS ‘raw data sets’, that is on data as available from the data provider. Countries are ranked low (Kyrgyzstan) to high (Bolivia) accordingly. The bottom panel of Figure 6 shows the estimated Gini indices, for the same countries, after excluding outliers (as identified by `outdetect` ). Even a cursory inspection of the two graphs bring to light a number of rerankings: countries move up or down the ladder as a consequence of how outliers are treated.

30
Panel A in Table 8 shows the factors associated with the likelihood to have voted for Bolsonaro during the 2018 elections (see again Appendix E for definitions of the variables included in the logistic regression). Our results suggest that voting for the Partido Social Liberal in the second round of the elections is associated with resentment against the Partido dos Trabalhadores ( _antipetismo_ ), which encapsulates disapproval of leadership due to widespread corruption and economic deterioration. Our results also suggest that those who voted for the Partido Social Liberal tend to hold anti-immigrant, anti-redistribution, conservative, and pro-military viewpoints; there is no evidence that the groups holding such sentiments experienced larger declines in SWB.

Panel B of Table 8, however, shows that the factors that explain the decline in SWB are also the factors underlying the switch from voting for the Partido dos Trabalhadores in 2014 to voting for Partido Social Liberal in 2018. Thinking that the economy has worsened increases the likelihood of being a switch voter by 50%, while having resentment against the Partido dos Trabalhadores increases the chance of switching from Dilma to Bolsonaro by 12%. These results suggest that the economic crisis and corruption scandals affected not only experienced welfare, but also eroded support for the Partido dos Trabalhadores and for the social contract shaped by the party during the 2000s.

# **6. Concluding Remarks**

For decades Brazil’s SWB was rising and by the mid-2010s the country stood out with its relatively high level of experienced welfare, although inequality in subjective well-being also remained high. Brazil’s SWB considerably deteriorated in 2015, when the economy contracted, following an economic and political crisis. Using Gallup World Poll data for the period 2010-2020, we identify the factors underpinning the level of Brazil’s SWB and its change during this period, paying special attention to differences across groups. Our results suggest that having sufficient income, financial security, economic optimism, satisfaction with living standards and health services, social capital, tertiary education, and digital access are the main factors associated with SWB in Brazil
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Hadi, A. S., "Identifying Multiple Outliers in Multivariate Data," _Journal of the Royal Statistical Society,_ 54
more likely than those paid in cash to find employment. Women whose husbands were opposed to them working saw the largest treatment effects (Field et al., 2021).

These findings suggest that account ownership and having control over one’s income can impact cultural norms and empower women to make their own financial decisions. Importantly, there is evidence that digital government payments are also popular with women. A study from Bangladesh found that 80 percent of mothers preferred to receive education subsidy payments digitally, citing reduced wait times and no longer needing to travel as reasons for their preference (Gelb et al., 2019).

# **4.2 The benefits of digitalizing wage payments**

One-hundred-sixty-five million unbanked adults, including 50 million women, and 70 million adults in the poorest 40 percent of households in their economy, received private sector wage payments in cash (Global Findex 2021). Like government payments, digitalizing wage payments yields important benefits for both payment recipients and senders. As with digitalized government payments, digital wage payments are responsible for motivating increased account ownership. According to the Global Findex 2021, 32 percent of adults and 29 percent of women reported opening their first account to receive a wage payment.

A further benefit of digital wage payments relates to savings. Numerous studies find that digital wage payments are associated with increased savings and a greater tendency to keep payments stored in an account. Studies in Bangladesh and Afghanistan find that workers paid digitally saved more than those who were paid in cash (Breza, Kanz, and Klapper, 2020; Blumenstock, Callen, and Ghani, 2018). In Bangladesh, this increased saving and account usage was also associated with greater financial literacy—workers paid digitally developed the ability to use their accounts without assistance and to avoid illicit withdrawal fees (as compared to workers who received an account but continued to be paid in cash). Given that 62 percent of both unbanked adults and unbanked women in developing economies report being unable to use a financial institution account without assistance,
is technically robust to allow the initial work to begin and momentum and discussion to ensue.

_Box 1: Possible alternative approaches? – A discussion_ The method employed in the paper may not be the only approach possible in this context. Creating a PPMT that is limited to the overlapping proxies between the two PMTs may be restrictive in terms of number of available variables for model prediction. An alternative could be, rather than focusing the comparison between the two targeting models, comparing available information in the underlaying data sets of the two models may be more flexible. Potentially more variables will overlap between the data sets than between models. Then following the procedure described in the paper, one would estimate weights using the household survey that was used to devise the national PMT and create the probabilistic referral thresholds. One will even have an option to select an optimal set of variables and strike the balance between parsimony and goodness of fit when there are too may overlapping variables. With potentially more and/or better correlated covariates, the new pseudo-PMT may have more accurate referrals than the proposed PPMT when applied to the humanitarian database.

> 8 Data collected across several CCI and other CWG partners were harmonized for the 2016 “Beneficiary Vulnerability Analysis” report. The harmonized data formed a basis for the 2016 VM model. See Cash Consortium of Iraq (2016).

7
|29%|27%|22%|31%|47%|||30%|
||N|||||116|2810|2844|3020|3193|5853|2490||||
|**30-34**|Share(%)||||30%|28%|34%|30%|26%|36%|54%||||35%|
||N||||107|2052|2289|2673|2591|5221|2470|||||
|**35-39**|Share(%)|||25%|28%|34%|34%|23%|37%|57%|||||36%|
||N|||93|1684|1800|2253|2304|4313|2138||||||
|**40-44**|Share(%)||9%|26%|34%|30%|23%|36%|58%||||||34%|
||N||57|1234|1490|1874|1886|3924|1682|||||||
|**45-49**|Share(%)|7%|19%|25%|25%|22%|37%|56%|||||||32%|
||N|75|949|1336|1867|1820|3486|1662||||||||
|Weighte|d average|7%|19%|25%|29%|28%|31%|31%|31%|30%|29%|27%|25%|||

Source: DHS surveys for 1993, 1996/97, 1999/20, 2004,2007, 2011, 2014, and 2017.
of primary and secondary activities in the study areas, where many individuals engage in activities that require skills or livelihood assets as a primary occupation rather than a secondary occupation. For instance, about 7.6% of the individuals who have a primary occupation engage in salaried employment (comparable to 1.5% of individuals who have salary employment as a secondary occupation), which requires knowledge of the local working culture and some level of education (see Appendix D). Hence, people tend to keep their primary activity and either take on or diversify in a secondary activity. While less skilled and/or asset-intensive (secondary) occupations face fierce competition from immigrants or refugees as observed by Morales (2017) in Colombia and Ruiz and Vargas-Silva (2018) in Tanzania, such competition in host communities in Ethiopia is absent as refugees in Ethiopia, except for some Eritrean refugees, experience restricted movement out of camps and are not permitted to work according to the Ethiopian refugee law prior to 2019. This precludes the argument that households engage in more activities as a coping strategy to maintain their existing living standards.

> 24 The elasticities are calculated from the regression coefficients following Bellemare and Wichman (2020) to account for distortions in actual elasticities due to IHS transformation. If a percentage change in refugee presence leads to a more than a percentage change in our outcome variable of interest (i.e., the coefficient is greater than one), the observed effect is elastic. In contrast, if a percentage change in refugee presence leads to a less than a percentage change in outcome variables of interest (i.e., the coefficient is less than one), the observed effect is inelastic).

> 25 This could be for several reasons, but the major one is our main explanatory variable of interest – refugee inflow – is not randomized and hence, the estimates from OLS are less consistent and more biased.

15
on S3 plots is reduced in households with a greater dependency ratio, likely through labor channels; and that on the use of extension services. Uncertainty around rainfall patterns, proxied by the coefficient of variation in flowering season rainfall, has no significant relationship with technical efficiency in the majority of specifications. It does, however, exhibit a positive and statistically significant (at the 10 percent level) coefficient for AFSIS S2 plots, suggesting that increased uncertainty results in decreased technical efficiency.

The lack of predictive power of observable household and manager characteristics on technical inefficiency is consistent with the results of separate productivity analysis conducted via ordinary least squares regression (available in Annex Table A4), in which none of the household or manager characteristics has a statistically significant relationship with productivity, with the exception of agricultural asset counts. It is conceivable that the use of crop-cutting-based production measurement, rather than farmer-estimated production as is most commonly utilized in smallholder agricultural analysis, results in a reduced effect of these manager characteristics. By using objectively measured production data, we eradicate the noise and/or bias associated with the plot manager’s estimate of production, which may be correlated with manager’s education, experience, exposure to extension services, etc. However, there is little empirical evidence to support this theory, at least within this particular dataset, as Gourlay et al. (2019) find that observable manager characteristics have very little explanatory power in yield overestimation (measured as self-reported production-based yield minus crop-cutting-based yield).

Stochastic frontier analysis was also executed on subpopulations of interest, including plots with female managers, plots with male managers, and pure stand plots. Results of these analyses are available in Table 5. As the sample sizes of these subpopulations are small, it is difficult to draw conclusions on the S1 and S3 classifications so only the overall and S2 classifications are reported. Output elasticities across plot manager gender are similar, but male managed plots experience greater returns to soil suitability and only male managed plots have positive and significant returns to inorganic fertilizer
s relation. A positive (negative) value implies more (less) outward volume per capita relative to the world.

Second, we similarly residualize the log of the upward volume per capita (= total volume - outward volume). The resulting residuals capture to what extent a country has a lot of (low-rise + high-rise) heights per capita given its income level. In layman’s terms, we obtain for each country the absolute distance from the country’s value to the world’s relation. A positive (negative) value implies more (less) upward volume per capita relative to the world.

Third, countries with higher area residuals tend to have higher heights residuals (correlation _≈_ 0.7), implying that countries that are “good” in one dimension are also “good” in the other dimension. However, since we are interested in the comparative rather than absolute advantage of each country, we residualize the height residuals with respect to the area residuals (using country populations as weights). In layman’s terms, we obtain for each country the distance from the country’s value to the world’s relation. A positive (negative) value implies more (less) heights per capita (rather than area per capita) relative to the world. The final residuals thus indicate to what extent a country is relatively pro-height or pro-area for a given income level.

We proceed likewise for high-rises vs. low-rises, first residualizing high-rise and low-rise volumes with respect to income, then residualizing the high-rise residuals with respect to the low-rise residuals. The final residuals indicate to what extent a country is relatively pro-highrises rather than relatively pro-low-rises for a given income level.

## **C2. Relation between city economic development and city building stocks**

To study how city building stocks vary with income, we use the 2015 city GDP data of Zhou et al. (2022) who provide “real GDP” data (2017 dollars) based on unbounded night lights data
currently faces? Are there trade-offs, or can long-term climate adaptation be reconciled with nearterm development priorities? The paper offers three main propositions in this regard, and provides various supporting arguments and evidence to buttress them. First, it argues that for developing countries like Tajikistan, faster economic and social development is the best possible defense against climate change (i.e. rather than being a competing consideration). Second, it presents some key findings from a recent nationally representative household survey to illustrate the strong public support for more climate change related spending on agriculture, water management and infrastructure, disaster management, and public health—four key areas that the government’s latest poverty reduction strategy identifies as being especially important from a climate change perspective. Third, the paper argues that important as project-based adaptation measures may be, it is imperative that they be supported by an overall policy framework that provides a truly enabling environment to facilitate faster climate change adaptation.

5. This paper is organized as follows: Section 2 sets the stage for the rest of the paper by briefly reviewing the available evidence for Tajikistan on recent climate change trends and future projections. Section 3 then introduces the special climate change module added to a nationally representative household survey carried out in Tajikistan in February 2010, and presents the key findings related to public attitudes towards climate change gleaned from this survey. Wherever possible, the section contrasts these findings with observed patterns in Kazakhstan as well as EU27 countries where similar household surveys have also been recently carried out. Section 4 presents an overview of adaptation actions in priority areas identified by the survey respondents. Finally, Section 5 concludes by reviewing the main implications of these findings for Tajikistan, both for ongoing adaptation initiatives as well as the country’s future climate change adaptation strategy.

4
data on test scores<sup>5</sup> and finds that test scores worsen for children in early roll-out districts. They focus on gender based age specific differences in labor outcomes. Our study, on the other hand, uses school level data and highlights the heterogeneity in outcomes by school type, quality of private schools and age. There are two major differences that underscore the contributions of our paper. First, a novel result

> 5This data is collated by an NGO Pratham and is publicly available.

4
LICs Other non-overperformers (i) LICs<br>Other non-overperformers (ii) LMICs Other non-overperformers (ii) LMICs<br>Overperformer (IND) Overperformer (SSA: GHA, RWA)<br>Digital Infr. Digital Infr.<br>Human K. & Labor Mkt Human K. & Labor Mkt<br>Reg. & Ethics Reg. & Ethics<br>Innov. & Integr. Innov. & Integr.<br><!-- End of picture text --> <u>Source: The AIPI score and pillars for 2023 are obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI]. Notes: Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020).</u> Weighted average AIPI scores per group in parenthesis. Dotted yellow shows the weighted average pillar scores for non-overperforming LICs and dotted red shows them for non-overperforming LMICs. The “spider” package is taken from Naqvi (2024a).

> 33 See <u>https://www.mida.gov.my/mida-news/ai-digitalisation-and-green-economy-to-reshape-malaysian-job-market-sim.  “The report</u> highlighted 10 key sectors in the first phase of the upskilling initiative – aerospace, chemicals, electrical and electronics, energy and power, food manufacturing and services, global business services, information and communications technology, medical devices, pharmaceutical manufacturing, and wholesale and retail trade. 34 To support workforce upskilling and reskilling, KESUMA has allocated a RM3 billion fund (approximately US$670 million) to provide support through levies, credits, scholarships, and matching grants. These initiatives will be facilitated via the MyMAHIR platform and the forthcoming Akademi KESUMA. See <u>https://hr.asia/asean/malaysia-to-study-impact-of-ai-digitalisation-and-green-economy-onworkforce.</u> 35 The Concept is available online and has been translated into English using Google’s automatic translation tool: <