rafmacalaba/data-use-mentions-extended huggingface dataset:
educational persistence measures, intergenerational elasticity ( _β_<sup>ˆ</sup> ) and the correlation coefficient (ˆ _ρ_ ), in six stages. First, we present our baseline estimates at the country level using a pooled sample of all children in each country. Second, because our sample is comparable with the datasets used by Hertz et al. (2007) to rank 42 countries in five regions, we pool our data at the regional level and rank Sub-Saharan Africa in terms of intergenerational educational persistence among other regions. Third, we discuss the trend in intergenerational education mobility across five-year birth cohorts using both measures. Ranking each country among other nations on which comparable estimates are available follows. Fourth, we explore the potential differences in intergenerational education mobility across gender. Fifth, we explore the potential difference of paternal and maternal educational attainment in influencing child’s educational attainment across five-year birth cohorts in each country. The final section presents the estimates of the order probit model of children’s highest level of education.

## **5.1 Intergenerational education mobility at the country level**

Table 2 presents the estimates of the intergenerational elasticity and correlation coefficients for the pooled sample in each country. The results reveal two main findings of interest. First, for all specifications considered, parental education has a statistically significant effect on children’s educational attainment in all the countries. The estimates imply that, despite the increase in years of schooling in almost all the countries over the last 50 years, parental education plays a crucial role in children’s education attainment. There exists an intergenerational link in educational outcomes: for instance, a one year difference in parental schooling is associated with a 0.74-year difference in children’s education in Madagascar. In terms of the estimated intergenerational elasticity, Tanzania and the Comoros show the highest and the lowest intergenerational education mobility, respectively. On average, an additional year of parental schooling is associated with a 0.47 and a 0.91-year difference in children
9 these costs could amount to as much as US$1,500, although they could be significantly reduced if the household contributed its own labor.

As indicated in Table 3.3 below, access to potable water supply in the urban areas of Panama is close to universal, even among the poor. However, sewerage coverage is much lower, reaching only about 30% of poor households. Since increasing sewerage coverage was an important objective of the reform, it was important to establish the affordability of sewerage connections.

> **Table 3.3: Coverage of Water and Sewerage in Urban Areas in Panama** (%)

||**Non-poor**|**Poor**|**Total**|
|---|---|---|---|
|**Water**||||
|public utility service|96.8|87.2|100.0|
|community service|2.1|8.3|100.0|
|Sewerage|63.3|31.2|100.0|

_Source:_ Encuesta de Nivel de Vida, Panama, 1997.

Accordingly, the household survey conducted as part of the subsidy study also questioned households as to their willingness to pay for sewerage connections. The survey distinguished between the cost of the connection charge, which was set at an indicative level of US$850 per household, and that of the associated plumbing and sanitary installations, estimated at around US$1,500 per household.

Table 3.4 presents the results obtained from the poor households in the sample. It is striking that only 65% of households were willing to pay for a sewerage connection even when this was provided free of all cost. This suggests that households were concerned about the bills they would have to pay to use the sewerage service once connected. When households face positive connection costs, the proportion which are willing to pay declines dramatically.

The
Perceived inter-generational mobility is also correlated with estimates of actual inter-generational mobility from the World Bank’s Global Database of Intergenerational Mobility (GDIM) for the countries in the LiTS sample (Figure 6). Countries in which the share of adults who think that children will do better than the current generation is lower tend also to have higher estimates of inter-generational persistence, indicating lower inter-generational mobility.<sup>6</sup> The link between inequality of access to opportunities and inter-generational mobility is estimated based on the same model as before, where the dependent variable is instead a binary (above neutral) measure of inter-generational mobility that evaluates to 1 if the respondent agrees or strongly agrees that children will have a better life than the current generation, and zero otherwise. The estimates are reported in Table 9. The estimates reveal that when connections are unavailable, perceived unequal access to opportunities is associated with lower inter-generational mobility (Hypothesis 2, or _δ_ 1 + _δ_ 3 = 0 is rejected by the data). Similarly, in the overall sample, when access to opportunities is not equal, lack of connections is associated with lower perceived inter-generational mobility ( _δ_ 2 + _δ_ 3 = 0 is rejected).

It should be noted that the results are a bit noisier than in the case of intragenerational mobility. This is not entirely surprising since intra-generational mobility expectations are more immediate and for these, current assessments of inequality of opportunity may be more pertinent. Moreover, while intra-generational expectations are personal, in the sense that they are based on one’s own mobility expectations – for the future, the assessment of inter-generational mobility is general it refers to comparisons of children in general with the current generation (again, in general), rather than prompting respondents to report mobility expectations for their own children in particular. Given the reasonably strong relationship between assessments of intra- and inter-generational mobility in the data, it is quite possible that one’s

> 6The measure of intergenerational persistence is based on the coefficient from the regression of children
M, A and the national level.

- <u>`indicators():`</u> The `indicators` option is used to request the indicators to be estimated from the simulated vectors of welfare. The list of possible indicators is:

- The set of Foster Greer Thorbeck indexes (Foster, Greer, and Thorbeck 1984) _FGT_ 0, _FGT_ 1, and _FGT_ 2 ; also known as poverty headcount, poverty gap, and poverty severity respectively.

- The set of inequality indexes: Gini, and Generalized Entropy Index with _α_ = 0 _,_ 1 _,_ 2

- Set of Atkinson indexes

- `plinevar():` The `plinevar` option allows users to indicate a variable in the target data set which is to be used as the threshold for the Foster Greer Thorbeck indexes (Foster, Greer, and Thorbeck 1984) to be predicted from the second stage simulations. The user must have added the variable in the `sae data import` command when preparing the target dataset. Only one variable may be specified.

- `plines():` The `plines` option allows users to explicitly indicate the threshold to be used, this option is preferred when the threshold is constant across all observations. Additionally, it is possible to specify multiple lines, separated by a space.

- `area():` The `area` option is necessary and specifies at which level the clustering is done, it indicates at which level the _ηc_ is obtained. The only constraint is that the variable must be numeric and should

25
the large majority across countries. CGS can play an important role in supporting SMEs’ investment in climate change mitigation and adaptation.

**The transition to a low-carbon economy as well as the physical effects of climate change and environmental degradation are a source of financial risks for European CGSs.** There is growing global recognition that climate change, natural disasters and environmental degradation can lead to transition and physical risks for the financial sector (see, for example, NGFS 2020). CGSs are primarily vulnerable to climate risks through credit risk in their guarantee portfolio. Transition risks originate from efforts to mitigate climate change and improve local environmental conditions by decarbonizing the economy, which may create economic adjustment costs for SMEs in a broad range of sectors, ultimately resulting in stranded assets. Physical risks stem from both the gradual and abrupt impacts of climate change and natural disasters—such as droughts, floods, and hurricanes—on the value of assets and property owned by SMEs.

**Both transition and physical risks can have detrimental effects on the European economy and related financial instruments, including the SME loan portfolio of banks.** The European Central Bank (ECB) and European Banking Authority (EBA) have recently caried out initial climate risk assessments and exploratory stress tests of banks. For its stress testing exercise, ECB has investigated disorderly transition scenarios and a physical risk scenario in which temperatures

> 3 For an overview of European CGSs see, for example, Chatzouz et al. (2017) and Vienna Initiative (2014).

> 4 AECM Statistical Yearbook 2021. Available at https://aecm.eu/publications/statistics.

6
Thus, the accounting framework links the sectoral allocation of labor to the consumption shares, the trade shares, the intermediate shares, the wage gaps, and the value-added shares of the sectors. I exploit this relationship and stack the Latin American countries against the economies that have attained a high industrial share of employment to gauge the relative success of the factors. The input-output data are an essential ingredient of the analysis which I source from the OECD database (OECD (2017)) which contains this information for many countries – including seven countries in Latin America, for 1995–2010/11. To construct a comparator set for these Latin American economies, I pick countries in the GGDC 10-Sector database (Timmer _et al._ (2015)) in which the peak industrial share during 1995-2010/11 exceeds 30 percent. Fortunately, the OECD database has information on all these comparator countries barring Mauritius. I also add China to the comparator set as its peak industrial share falls just short of the 30 percent threshold.

On average, the share of employment in the broad industrial sector in a Latin American economy is 11 percentage points lower compared to the comparator set. The counterfactual exercise reveals that more than half of this gap is accounted for by differences in consumption shares. Cross-country differences in sectoral wage gaps and linkages are also significant and roughly account for 5 and 3.5 percentage points of the gap.<sup>2</sup> Differences in trade shares, on the other hand, play a muted role and can account for only a tenth of the gap in industrial employment shares. The findings for the manufacturing sub-sector which constitutes the bulk of the industrial gap, are somewhat different. Though differences in final consumption and wage gaps are still significant, they can only account for a quarter of the manufacturing gap. Differences in sectoral linkages emerge as the strongest factor and still account for a third of the employment share gap. The explanatory power of trade shares is more pronounced in manufacturing, but wanes compared to what
**Figure 14. Gross Exports and Value‐added Shares for Goods and Services, 2011**

<!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>0<br>BRA CHN IND RUS ZAF BRA CHN IND RUS ZAF<br>Machinery Business services<br>Gross exports Value-added exports<br>Share<br><!-- End of picture text --> Source: World Bank Trade Value Added Database and Francois et al, 2013 Note: Business services were chosen because in recent years this has been the most dynamic services category.

38
7|47.8|38.3|
|Sweden|24.1|30.0|37.0|36.2|33.7|
|Switzerland|31.6|35.3|35.6|36.9|35.0|
|United Kingdom|23.1|27.7|28.9|29.2|26.2|
|United States|5.8|10.0|7.5|7.0|6.9|

a/ Exports of goods and nonfactor services.

Source: The World Bank, World Tables 1987, 4th ed., 1988
- It can remove social and economic discrimination from the society, and

While the conventional microfinance industry has been globally growing at 13 percent per annum since early 1990s, only very limited information is available on micro finance institutions (MFI’s) operating under QH. A relatively small number of interest-free loans are operating in Pakistan (www.akhuwat.org.pk) and there are some small-scale micro-/rural banks in Indonesia. However, no organized institutions are known to be operating on the basis of QH except in Iran where QH has been utilized effectively to provide finance for the needy and where these institutions are widespread throughout the country.

Also QH funds have been operating in Iran since the Islamic Revolution they have enjoyed phenomenal growth and a successful track record. QH funds in Iran, by and large, provide small consumer and producer loans and, in some cases, engage in profit-sharing activities with small producers and firms, thus supplementing the fund’s capital. These funds are usually associated with local mosques or other religious organizations and sometimes with guilds or professional associations. The capital is contributed by the more well-to-do who are at liberty to withdraw their funds at any time. These funds operate with reasonably low administrative costs since most are managed on a voluntary basis by the people within the group.

A recent survey on QH based finance that has been operating with the objective of helping low-income group through short-run credit in Iran shows that there exited 1,229 _Qard-al-Hassan_ Funds (Kazem 2007). Total loans amounted to $169 mln, and deposits totaled to $227 mln with 60% of the total loans had been paid back (please see table 1 for a comprehensive survey on literature).

Conventional micro finance (MF) and Qard-al-Hassan Microfinance (QHMF) have both similarities and differences. MF charges interest, an abomination from an Islamic perspective, and MF schemes resort to collective punishments for defaulting, yet under QHMF, usually donors/subscribers introduce the borrowers and cosign for their loan, yet no collective punishment
Figure 3: Flowchart of study selection process for burden of malnutrition associated with diets

|**Identification of studies via database**|**Identification via other methods**|
|---|---|
|Records identified from:<br>Web of Science Core<br>Collection Database<br>(n = 2786)<br>Cochrane Database<br>(n = 305)<br>Records removed_before_<br>_screening_:<br>Duplicate records removed<br>(n = 451)<br>**Identification**|Records identified from:<br>Citation searching (n = 11)|
|Records screened<br>(n = 2335)<br>Records excluded<br>(n = 2124)||
|Reports sought for retrieval<br>(n = 211)<br>Reports not retrieved<br>(n = 0)<br>**Screening**|Reports sought for retrieval<br>(n = 11)|
|Reports assessed for eligibility<br>(n = 211)<br>Reports excluded:<br>Diet intervention, only<br>RCTs (n = 46)<br>|Reports assessed for eligibility<br>(n = 11)|
|No LMIC studies (n = 33)<br>No primary meta-analysis<br>(n = 26)<br>Diet exposure not relevant<br>(n = 17)<br>Outcome not relevant (n =<br>14)<br>Other (n = 3)<br>Total studies included in review<br>(n = 82)<br>**Included**<br>Studies included in review<br>(n = 71)||

<!-- Start of picture text --> Identification<br><!-- End of picture text --
the stream of past investments: where _K_ is the capital stock, _I_ gross fixed capital formation, _δ K_ the annual depreciation rate of the capital stock, and _t_ an index for time. The initial value of the capital-output ratio for 1950 is taken from the Nehru and Dhareshwar (1993) data

> 14 UNDP (2003a) reports a participation rate of 24.5 percent (2002). Based on INE data, as reported by Global Info Group (1999), this compares to 27.6 percent (1995), 29.9 percent (1990) and 28.2 percent (1985).

> 15 It should be emphasized that the reliance on IGSS data may understate the drop of the economically active population during the 1980s. This is because the working population in the informal and rural sectors—typically not captured by the social security system—was particularly affected by violence and displacement policies.

-23-
nationally representative sample of 15,000 Americans. They argue that citizen participation is the heart of a functioning democracy in which politicians respond to citizen concerns, interests, preferences and needs. This presumes that the voice of citizens is clear, understood, and loud so policymakers have an incentive to pay attention to what is being said. In the US, as dollars have substituted for time as inputs into the political campaigns, Verba et al show how participation in the political process becomes skewed to the wealthy and is responsive to their priorities and concerns. "When money replaces time as the principle form of political currency, the playing field is no longer level" (1997, p.21). The voice of the poor gets drowned out, which leads to their greater exclusion. The figures show that the top 3 percent of the sample with family incomes over $125,000 produce more than their proportionate share of the vote, protests, campaign hours and 35 percent of the money contributed. The top two income groups, with annual incomes over $75,000 account for more than half the money contributed to political campaigns, while the bottom 20 percent of the income distribution, with incomes less than $15,000 produce only 14 percent of the vote and 2 percent of the campaign dollars.

These distinctions are important because there is limited overlap of concerns and interests between these two groups. The disadvantaged, roughly one-sixth of the public with no education beyond high school, are more than twice as likely, and those receiving means-tested welfare benefits are four times as likely, to focus on basic human needs, poverty, jobs, housing and health. The concerns of the more advantaged are linked to economic issues, such as taxes, government spending, budget, and social/moral issues, such as abortion or pornography. In sum, public officials hear many more messages from the advantaged than from the poor because the former belong to many more organizations that mobilize voice and resources. The authors conclude that the
004|0.239|0.716|1.000|
|Other manu|7,117|2,616|152,026|257,025|418,784|0.017|0.006|0.363|0.614|1.000|
|Agriculture|33,340|96,683|64,962|328,230|523,215|0.064|0.185|0.124|0.627|1.000|
|Other good|5,804|20,070|100,720|410,055|536,649|0.011|0.037|0.188|0.764|1.000|

Source: Based on UN COMTRADE Statistics.

86
one regulatory-grade monitor functioning at any given time. Therefore, this data implies a uniform level of pollution across the city (Figure 8 Panel A). The PM 2.5 level from the regulatory-grade monitor aligns closely with that from the satellite data (Figure 8 Panel B), but both sources miss variation across space. A network of low-cost monitors captures significant spatial variation in pollution across the city, but the levels are substantially lower than those from the regulatory or satellite data due to the limitations in capturing dust (Figure 8 Panel C). The presence of at least one regulatory-grade monitor makes it possible to conduct a local calibration, which can then be used to adjust the PM2.5 data from the low-cost monitors to better align with the levels from the regulatory monitor, while preserving spatial variation across the city (Figure 8 Panel D). We can also calibrate the low-cost data using the satellite data, since there may not be high-quality regulatory data available for calibration in LMIC contexts. Calibrating the low-cost monitors using the satellite data shows potentially an overestimate of air pollution across the city, with the average value for the city higher than the regulatory one.

For the two calibrations, we use the values generated based on the hourly calibration and average the calibrated hours across the day. Alternatively, we can average the hours across the day and apply the daily calibration. The daily calibration, both with the regulatory and the satellite data, has a much higher out-of-sample R2, which may suggest that it could lead to better estimates (Tables A2). Applying the daily calibration for the same day, the daily local calibration looks quite similar to the one using the hourly calibration, but importantly the satellite calibration is now much more closely aligned with the local calibration and closer to the average regulatory value. These results suggest that investing in a combination of regulatory and low-cost monitors allows for a much richer dataset that can capture both magnitudes and variation in space. In the case where regulatory data is not available, though
rural-urban continuum and depend heavily on proximity to large population centers.

Improving access to health care for rural and urban populations, particularly when resources are limited, is essentially an optimization challenge, complicated by spatial contextual factors that preclude a “one size fits all” approach. The contextual factors that affect optimal resource utilization and should inform a research agenda include: (1) location-specific health needs defined using deaths and illnesses reported sub-nationally to national ministries of health; (2) geographic distributions of health care resources, including the number of staff employed, beds, functioning equipment, and other supplies; (3) constraints on the supply and demand for health care resources; and (4) potential indirect benefits associated with different resource allocation scenarios, as for example investing in roads to speed up transportation thereby making existing health care facilities more accessible. Addressing the challenge of reducing inequity in health care access along rural–urban gradients necessitates datadriven approaches that take into account where population in an URCA or FEA is located, while also incorporating local expertise on health care needs. In many cases, rural populations are well connected to an urban center, but access to services and opportunities can vary widely with the size of the urban center.

Recent developments that can shape a research agenda for defining health care priorities include global initiatives like the Global Burden of Disease project, which estimate morbidity and mortality for all causes of death in all countries (Vos et al., 2020); open-source initiatives such as OpenStreetMap provide a rich resource of health care facility information; and detailed information on population distributions and characteristics contained within national censuses and mapped at high-spatial resolutions by initiatives such as the WorldPop project (Tatem, 2017). By incorporating such 22