rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>400<br>PISA Math Average Score 2012<br>300<br><!-- End of picture text --> Source: OECD-PISA database The weighted average mathematics score of the seven developing countries is 383. It is helpful to understand the significance of the 128 point difference of the seven countries as compared with Vietnam. According to a recent OECD publication [OECD, 2013a], _“an entire proficiency level in mathematics spans about 70 score points –a large difference in the_ 2
and Bidani 1994, Ravallion 1998). This issue turns out to require special attention in the case of Bhutan: the process used to obtain consistent poverty lines is summarized in section 3.1. The second challenge concerns the absence of panel data: section 3.2 discusses the methodology employed for the construction of synthetic panels, following recent developments in the literature.

> 1 The 2017 sample counts 11,660 households (of 164,011 in the population), and the 2022 has 13,416 households (of 164,331).

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consistently larger than that of father's height (Barrera 1990, Thomas and Strauss 1992, Lavy _et al._ 1996). Note that the average height of mothers is highest in Jalapa and lowest in Totonicapan, probably reflecting the poorer living conditions found in the latter department.

Since the mother plays the central role in household domestic activities pertaining to childrearing, maternal education has usually been considered among the most important determinants of child health in developing countries. Mothers of children in the EGSF sample have on average 2.3 years of education. Note that half of the women in Totonicapan have no education.

Husband' s education is also likely to affect child anthropometry through the allocation of resources and the ability to use different types of health services more effectively. The majority of husbands in the EGSF sample have attended primary school, although few of them have completed the full six-year cycle. They have on average 3.1 years of schooling. As with mothers' education, average husbands' education is lowest in Totonicapan and highest in Chimaltenango.

Per capita expenditure (measured in quetzals per month) is highest and most variable among communities in Jalapa and lowest and least dispersed among communities in Totonicapan and Chimaltenango.

# **6.2 Community Characteristics**

We used two classes of variables to represent the community environment in which children grew up. First, _global_ variables are community indicators collected separately from the main individual questionnaire (Bilsborrow and Guilkey 1987). The global variables we use in our analysis come from the community questionnaire.]<sup>4</sup> '<sup>1 5</sup> Second, _contextual_ variables are obtained by aggregating data collected from the individual questionnaire (Bilsborrow and Guilkey 1987; Goldstein 1995). The EGSF design was such that households were selected randomly in each community. Surveyed households are therefore scattered randomly across the community so that contextual variables are likely to be representative of the whole community and not only of selected areas.

The community-level covariates in our study
immediately use in their firms. We see improvements in some of the areas that firms received specific instructions and homework on, such as using SEO.

Third, while the health concerns of the COVID-19 pandemic required all training and consulting to take place online, we believe that a mixed modality may be more effective in future programs. Our experiment shows that online live sessions do offer some advantages and can deliver some improvements. But complimenting these with some in-person sessions would allow more opportunities for firms to network with one another and to have more interactive and tailored discussions with trainers.

Finally, we note that these types of innovative SMEs are very reluctant to share their data, making tracking and measurement of firm outcomes difficult. Firms were required to sign a Memorandum of Understanding (MOU) that committed them to contribute their time to attending and to submitting data sheets. But in practice firms needed to be contacted very many times to obtain data from them, and the MOU acted more as a soft commitment device than something that they felt legal obligation to adhere to. From a methodological standpoint, in order to measure impacts of these programs in the future, and over longer time periods, it would be useful to require firms at the time of application to both legally commit to providing three years of annual financial data, along with written permission to have their sales and tax data released to the program implementers by the corresponding tax authorities. Offering firms some annual feedback or benchmarking based on the data they and other firms provide could help make this also a beneficial activity for firms.

18
defined high disruption villages to be at the 75th or higher percentiles of our overall Ebola-related disruption index. Clearly, some judgement must be made about how to define high intensity, and the quantitative results will always be a function of exact choice made. To show the qualitative robustness of our core results to small alternations in this definition, we examine how the results vary using different thresholds of the disruption index. Figure A5 shows the results, by age cohort and for two summary outcomes: (i) hrs/wk spent in productive activities of learning, income generation or ELA clubs (Panel A); (ii) hrs/week spent with men (Panel B). The omitted category are villages in the first six deciles of the disruption index. The impacts on time in learning activities and time spent with men both vary depending on the intensity of disruption, where we show cutoffs at the 60th, 70th, 80th, 90th percentile of the index. Reassuringly, for both outcomes, we observe impacts of Ebola-related disruption across deciles, and offsetting impacts of ELA clubs, as the measured intensity of the Ebola outbreak varies.

A second facet of the disruption measure used is that it is defined in absolute terms, and so measures the most disrupted villages in any of the four districts included in our evaluation sample. As Figure 5 shows, this inevitably leads to most high-disruption villages being defined in Port Loko, the district with the highest incidence of health related Ebola disruption. For our research questions, it is the absolute level of Ebola related disruption that we are concerned with. However, this raises the concern of whether the impacts of higher versus low disruption instead pick up something specific to Port Loko relative to other districts. To probe this further we redefine the high disruption dummy to be district specific, so that villages in each district that lie in the highest quartile of the continuous disruption index are classified as high disruption. This is the final row of results shown for our main outcomes in Table A6: of
between-group, and within-group) wage inequality, using harmonized information on wages across household surveys. For comparability, all trends depicted use the same sample of countries and years. Second, the paper provides new evidence on the importance of changes in wage inequality between and within sector-occupation-formality status, using the harmonized household data. Third, it extends the analysis on the role of firms in inequality trends, using longitudinal matched employer-employee administrative data from the formal sector in Brazil, Costa Rica, and Ecuador. Fourth, it provides an assessment of what we know and do not yet know about the potential drivers of the change in wage structure in Latin America, highlighting the most promising avenues for further research.

Our analysis yields three main empirical results. First, it finds that wage inequality started falling in 2002 in all countries in the region in which wages can be consistently measured over time, except Costa Rica. Despite the common trends, the wage inequality dynamics varied markedly across two groups of countries. Countries that benefited from the commodity boom in South America experienced stronger growth and much deeper reduction in inequality during the 2000s. Wages grew across the board, but growth was monotonically decreasing across each countries’ wage distribution. In virtually every country, wage growth was much faster at the bottom 10th percentile than at the median and 90th percentile. Net commodity importers in Central American economies and Mexico displayed more modest reductions. They also experienced a compression in the lower and upper tails of each countries’ wage distributions, but the compression at the top resulted from a combination of slow growth of median wages and virtual stagnation of real hourly wages among earners in the 90th percentile.

Second, the paper shows that these dynamics are associated with declining skill premiums and a reduction in within-group inequality during the 2000s. The college-to-primary earnings premium declined in all countries. By contrast, education premiums increased slightly during the 1990s (Galiani and Sanguinetti 2003; Pavcnik et al. 2004; Robertson 2004).<sup>2</sup> It also finds a
The variable “ **_Administration_** ” in Table A.1.5 takes on three values; one if the administration of the fund is official, two if it is joint, and three if it is private. If the EDIS of a country is administered by the central bank, it is considered to have an official administration. Moreover, some privately administered institutions have limited authorities. For example, in Italy and Croatia certain decisions need to go through the central bank approval, hence the EDISs of these countries are considered to have a joint administration in the database.

Finally, the variable “ **_Membership_** ” in Table A.1.5 takes the value one if the membership to the fund is compulsory and zero if it is voluntary. Majority of the countries have compulsory membership, whereas only ten percent of them employ a voluntary system.<sup>9</sup>

# **3.4 Barth, Caprio, and Levine (2004) survey questions**

We also incorporate the deposit insurance related survey results from Barth, Caprio, and Levine (2004) database on banking regulation and supervision. All of the data is coded for empirical use and presented in three different panels in Table A.1.6.<sup>10</sup> The variables in this section and the way they are coded are as follows: 1) _Does the deposit insurance authority make the decision to intervene a bank?_ The answer “Yes” is coded with one and “No” with zero (panel A). 2) _Does the deposit insurance authority have the legal power to cancel or revoke deposit insurance for any participating bank?_ The answer “Yes” is coded with one and “No” with zero (panel A). 3) _As part of failure resolution, how many banks closed or merged in the last 5 years?_ The number of banks is reported (panel A). 4) _Were depositors wholly compensated (
reports indicate that women’s response to the initiative was positive. For example, from July 2021 to March 2022, the percentage of women traveling by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). According to Goswami (2021), women made the of riders on Delhi buses March 2021 after the up majority Transport Corporation by program’s launch.

To identify the causal impact of the program on women’s labor outcomes, we collated data from several sources. Our main empirical analysis is based on the Consumer Pyramids Household Survey (CPHS) data maintained by the Centre for Monitoring the Indian Economy (CMIE). The rich CPHS data is a panel of about 160,000 households across all major Indian states after 2014. It includes comprehensive information not only on household expenditures, members’ demographic characteristics, and employment status but also on their time use patterns and allocation of time on various activities. We bolster the findings with a large primary survey of women from Delhi that was collected after the program’s launch in Delhi.

Our identification approach compares women in treated states (i.e., Punjab and Tamil Nadu) to their geographical neighbors, serving as our control group. The implementation of the in 2021 variation. To address con- policy provides temporal endogeneity cerns, we implement a synthetic differences-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) at the state level. This approach combines the synthetic control method (Abadie and Gardeazabal, 2003) with the difference-in-differences strategy.<sup>4</sup> We compare the pre-trends in our treated and the synthetic control group using event study models and do not discern differential trends.

We examine the effect of the treatment on on In first expenditure commuting. doing so, we determine if women’s travel demand is responsive to the cost of transportation. We find evidence consistent with elastic demand for transportation for women using the CPHS data. Overall expenditure on travel, specifically on a category
- Trujillo, L. and Nombela, G. _Privatization and regulation of the seaport industry_

of container traffic are located in Southeast Asia: Hong Kong, Singapore, Kaohsiung (Taiwan) and Pusan (South Korea).

**_Box 5._ Container world regional traffic (million TEUS)**

||**1980**|**1986**|**1990**|**1995**|**Change**<br>**1980-1995**|
|---|---|---|---|---|---|
|**Southeast Asia**|9.08|19.10|32.42|61.84|581%|
|**Europe**|11.49|17.76|23.25|33.06|188%|
|**North America**|9.92|13.42|16.49|21.85|120%|
|**Caribbean/Central America**|0.96|2.68|_3.56_|5.39|461%|
|**South America**|0.38|1.04|1.44|2.76|626%|
|**Middle East**|1.38|2.32|2.90|5.40|291%|
|**India**|0.26|1.08|1.83|3.17|1,119%|
|**Australia**|1.61|1.95|2.33|3.46|115%|
|**Africa**|1.27|1.74|2.42|4.66|267%|
|**Total**|**36.35**|**61
- P. A. Celhay, P. J. Gertler, P. Giovagnoli, and C. Vermeersch. Long-run effects of temporary incentives on medical care productivity. _American Economic Journal: Applied Economics_ , 11 (3):92–127, 2019.

- R. Chetty, A. Looney, and K. Kroft. Salience and taxation: Theory and evidence. _American Economic Review_ , 99(4), 2009.

- J. Das and P. J. Gertler. Variations in practice quality in five low-income countries: A conceptual overview. _Health Affairs_ , 26(Suppl2):w296–w309, 2007.

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- J. Das, A. Holla, V. Das, M. Mohanan, D. Tabak, and B. Chan. In urban and rural india, a standardized patient study showed low levels of provider training and huge quality gaps. _Health affairs_ , 31(12):2774–2784, 2012.

- J. Das, A. Holla, A. Mohpal, and K. Muralidharan. Quality and accountability in health care delivery: Audit-study evidence from primary care in india. _American Economic Review_ , 106 (12):3765–3799, 2016.

- J. de Quidt. Your loss is my gain: a recruitment experiment with framed incentives. _Journal of the European Economic Association_ , 16(2):522–559, 2018.

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- S. DellaVigna and D. Pope. What motivates effort? evidence and expert forecasts. _Review of Economic
between local housing prices and the cost discrepancy between the proposed approach and the PDNA (figure 4.b).

# **10. Conclusion**

This paper offers a tractable approach to rapidly estimate the overall cost of a disaster, its decomposition between damage to assets and indirect losses, and its spatial distribution. The proposed approach is light in terms of data collection but rigorous in its conceptual underpinnings. On the empirical front, it relies on data from household expenditure surveys predating the disaster and on some assessment of the housing destruction it triggered at the local level. On the analytical front, it combines insights from poverty analysis and urban economics.

A key tenet of the proposed approach is that the value of dwellings captures not only the comfort they directly provide, but also the access to earnings opportunities and non-monetary amenities they allow. A disaster affects the value of dwellings both by making them less livable – or not livable at all – and by undermining the opportunities and amenities around them. The second key tenet of the proposed approach is that this indirect impact can be captured by estimating hedonic pricing regressions for housing that explicitly incorporate neighborhood effects.

An application of the proposed approach to the 2015 Nepal Gorkha earthquakes shows that it is easy to implement, and yields results that in retrospect look eminently sensible. The overall disaster cost estimated this way is in line with that from standard assessments, whose main focus is on the physical destruction of assets. But the spatial distribution of the cost is substantially different, with potentially important implications for the allocation of post-disaster aid.

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meaning the increased relative proximity of firms allows them to profit from knowledge spillovers and a shared labor pool.

Transport infrastructure can also have positive effects outside the manufacturing industry even though evidence is scarcer for other key economic sectors such as tourism or agriculture. Focusing on the tourism sector, Khadaroo and Seetanah (2008) assess the importance of transport infrastructure in determining the ability of a country to develop as an international tourism destination. Based on a panel data set of bilateral tourism flows among 28 countries over the decade 1990–2000, the study finds that – besides tourism infrastructure and other classical determinants – stocks of transport infrastructure like roads, airport terminals, and ports are indeed significant determinants of tourism inflows.

# **Information and communication technologies (ICT) infrastructure is associated with increased productivity**

Linkages between ICT infrastructure and productivity are well documented at the country‐level. Analyzing OECD countries between 1996 and 2007, Czernich et al. (2012) find that a 10 percent increase in broadband penetration increases GDP growth by 0.9 to 1.5 percentage points. Albiman and Sulong (2016) analyze 45 Sub‐Saharan African countries from 1990 to 2014 and find that positive effects of ICT infrastructure on economic growth are evident once a penetration rate threshold of about 5 percent has been crossed for mobile phones, internet, and telephone lines.

This positive relationship between ICT infrastructure and performance holds also at the firm level. Paunov and Rallo (2015) use Enterprise Survey data for firms in 117 developing and emerging countries between 2006 and 2011 to quantify the link between ICT and productivity. Overall, an increase in an industry’s use of the internet by one standard deviation increases the labor productivity of an average firm in this industry from the 50<sup>th</sup> to the 55<sup>th</sup> percentile of the distribution. This effect is heterogeneous amongst firms, with the most productive firms showing three times the increase of the least productive firms. Using the same data
-- Start of picture text --> Percent Percent<br>Return on equity<br>25 Return on assets (RHS) 3<br>20<br>15<br>2<br>10<br>5<br>0 1<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

# F. EMDE-based banks operating in EMDEs

<!-- Start of picture text --> Number of branches and subsidaries<br>600 Within region<br>Other EMDEs<br>400<br>200<br>0<br>Before After Before After Before After<br>GFC GFC GFC GFC GFC GFC<br>SSA ECA EAP<br><!-- End of picture text --> Source: Institute of International Finance, International Monetary Fund, World Bank.

Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa, and GFC = global financial crisis, 2008/09.

A. Credit booms (crunches) are episodes when private credit to GDP ratio exceeds (falls below) its long-term trend by 1.65 times one standard deviation of a cyclical component obtained with the HP filter. Sample includes about 140 EMDEs with private sector credit data. Weights are based on nominal GDP measured in U.S. dollars at market exchange rates.

B. Unweighted averages. Based on the private credit by deposit money banks and other financial institutions to GDP (%) from the World Bank’s Financial Development and Structure Dataset.
the information recorded in LAIS.<sup>15</sup> Beyond highlighting differences across regions and transaction types that a regular performance monitoring system based on administrative data could help tracked and narrow, two observations are particularly pertinent.

With 42% (50% and 27% for purchases and non-monetary transfers, respectively) of households who failed to register transferred land in their name having taken action even at village level,<sup>16</sup> and a high share quoting lack of information as one reason for not taking any action, awareness of the need to register transactions to make them legally valid remains an issue. The challenge of maintaining the high level of gender-equality achieved in the first-time allocation of land holders (table 3) implies that campaigns to inform households of the need to register may also need to emphasize gender aspects.

The official valuation for parcels registered post-LTR is, with close to US$700, well above the US$365 for non-registered ones or the US$290 for plots registered free of charge during LTR (table 7), in line with households’ subjective valuation (US$666 vs. US$447 for registered/unregistered ones).<sup>17</sup> This suggests that, beyond information, registration fees may be an issue. To explore if high fees, relative to property value, make rural registration unaffordable, we combine data on sales values of transferred parcels from our survey with LAIS data on the value of transferred parcels for Kigali to compute the fee to be paid as a share of property value (table 8). Even if the 10% of rural parcels with values below the registration fee so that registration would not make economic sense are excluded, mean (median) rural parcel prices are US$ 438 and US$ 185. Thus, for the median rural property, registering a transaction would require a monetary outlay equivalent to 22.6% of property value, compared to urban properties in Kigali where the registration fee is