rafmacalaba/data-use-mentions-extended huggingface dataset:
% of the individuals who save for old age around the world are financially included, with the remaining 15.3% being financially excluded. The low figures in certain regions such as ECA and MENA could be indicative of the usage of alternative means of

> 10 The survey is conducted in the major languages of each country. The target population is the entire civilian, non-institutionalized population aged 15 and above. Data weighting is used to ensure a nationally representative sample for each economy. Final weights consist of the base sampling weight, which corrects for unequal probability of selection based on household size, and the poststratification weight, which corrects for sampling and nonresponse error. Poststratification weights use country-level population statistics on gender and age and, where reliable data are available, education or socioeconomic status. In some large economies, such as China and the Russian Federation, sample sizes of at least 4,000 are collected. Detailed country-level information about the data collection dates, sample sizes, excluded populations, and margins of error can be found at http://go.worldbank.org/IGRTPHK660

> 11 We do not exclude from the sample individuals older than 65 years old, following the evidence in Alessie et al. (1999), who find only some evidence of decumulation among the elderly, but only at an advanced age. The authors find that precautionary savings, bequest motives, and health are among the reasons why some elderly do not decumulate in the pattern predicted by the life-cycle model.

> 12 All summary statistics are weighted country averages.

8
|---|
|2 inputs (public expenditure, literacy of adult) – 2<br>outputs (life expectancy,immunization DPT.)|.81|.91|.97|.93|.97|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization DPT.)|.81|.91|.97|.94|.97|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization measles.)|.80|.91|.96|.94|.98|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 3 outputs (life expectancy,<br>immunization DPT.,DALE)|.82|.91|.97|.95|.98|.97|

Tables 9 and 12 show that, on average, developing nations score between .85 and .95 in output efficiency in the multiple input-output framework. This figures imply that developing countries could raise their output levels by an average of 10 percent with the same input consumption, if they were as efficient as the comparable benchmark countries. This figure is simply indicative, as the precise estimate varies with the country and with the selected indicator, and has a large variance across countries: for instance, the bottom decile of (output) efficiency scores is about .66, implying that the scope for increasing health and education attainment levels is between 3 or 4 times higher than for the whole sample average.

23
following section. The second part of the literature review presents the available qualitative and quantitative empirical literature, which highlights the current understanding of the relationships between OGD, transparency, accountability, and service delivery.

# **_Theoretical Perspectives, Definitions, and Differentiations_**

The primary topic of this research—Open Government Data (OGD)—touches on a very active and ongoing contemporary debate on the boundaries of a number of concepts which have emerged over the past decade. While not entirely scholarly in its nature, numerous governments, international institutions, and private sector entities have proffered differing definitional propositions with respect to terms such as “Big Data,” “Open Data,” and “Open Government Data,” albeit with significant overlap and interconnection. Accordingly, as a first step, it is necessary to disentangle the various concepts to best specify a clear, working definition of OGD, which is the main topic of this research.

Distinctions in terminology have been provided by various sources, with differing levels of conceptual specificity. For instance, McKinsey and Co., refer to “Big Data” as data sets that are “voluminous, diverse, and timely,” with modifier “big” referring to the size and complexity of these sets. “Open,” in McKinsey’s definition, describes how liquid the data are—that is, how “transferable” these data are across platforms and users.<sup>22</sup> Relatedly, the World Bank’s research on the topic posits that the term “Big Data” describes very large and complex data sets that must be processed using advanced analytic techniques, whereas the key defining characteristic of “Open Data” is that it is made available as a public good.<sup>23</sup> These distinctions are related to those of other authors, such as those of the Open Data Barometer, who describe knowledge “as ‘open’ if anyone is free to access, use, modify, and share it—subject
as non-sensitive and 3,750 are classified as sensitive products. The tariff preferences offered by the general arrangements differ according to the sensitivity of the products concerned: nonsensitive products enjoy duty free access to the EU market, while sensitive products benefit from a tariff reduction. These arrangements provide, as a rule, for a reduction of MFN _ad valorem_ duties by a flat rate of 3.5 percentage points. An important exception to this rule of a flat rate reduction is granted to the textiles and clothing sectors which enjoy a percentage reduction of 20%. For specific duties a percentage reduction of 30% is the general rule. Where duties include _ad valorem_ and specific duties, only the _ad valorem_ duties are reduced.

A special arrangement under the Everything but Arms (EBA) initiative, which is incorporated into the GSP preference scheme, is provided for least developed countries (LDCs). The EBA scheme provides duty-free access for all products covered and originating in the beneficiary country, with the exception of imports of fresh bananas, rice, and sugar.<sup>10</sup> This scheme is more generous in terms of duty reduction than the Cotonou scheme; however in some other aspects, such as cumulation rules, it is less generous.

# **Importance of preferences for ACP countries**

To examine the relationship between trade and preferential treatment we use information of EU member states' imports eligible for preferences under the Cotonou agreement for the period 2001 from non-least developed ACP countries at the 8-digit level. This raw trade data was obtained from Eurostat and includes both total imports from ACP countries and imports requesting preferential treatment. Using information on tariffs and preferential quota applicable on each 8- digit product for the year 2001 we then identified those products which were eligible for preferences and calculated the _ad valorem_ tariff rates.

Figure 1 shows the developments of EU imports from non-least developed ACP countries during the last decade.<sup>11</sup> While the value of the imports had been increasing during the period
)|
|IPMD|0.043||
||(0.19)||
|An Giang|-0.118<br>(-0.33)||
|Can Tho|-0.821**|-0.628**|
||(-2.49)|(-2.39)|
|Long An|-0.449<br>(-1.36)||
|Tien Giang|-0.841***|-0.605**|
||(-2.58)|(-2.41)|
|Observations|187|187|

* - significant at the 1% level; ** - significant at the 5% level *** - significant at the 10% level

> 8  We also estimated a regression for pesticide use, with the following right-hand variables: farm size, IPM and pesticide training, actual use of IPM, farmer’s age, education, and income and provincial dummies. The significant variables were farm size, farmer’s age, and province dummies for An Giang, Long An and Tien Giang.  Farm size has the largest impact (ε =0.93), suggesting that pesticide use is primarily driven by scale.

10
category, so that we bundle exports and imports as “net imports”. We denote energy produced by Pakistan (in ktoe) as with indicating the category (coal, oil, gas, renewable). Net imports are denoted as , while energy commodities consumed in the production kk tt E E QQ kk of electricity is denoted as . MM kk,tt QQ Historical market price data for energy is sourced from the World Bank’s commodity sheets (pink sheets). kk,tt QQ These are denoted as . The units of the prices are USD per metric ton (USD/mt) for coal, USD per barrel (USD/BBL) for oil and USD per one million British thermal units (USD/MMBTU). The following UUSSDD kk,tt PP conversions are applied to make the units between prices and quantities compatible: = 1.429 ~~,~~ B s sstt = 7.4 and = 39.683 . The International Renewable Energy Agency (IRENA) provides ββ<sup>EECCbbEE</sup> ttCCEE LL bbttss data on renewable energy. Given that historically the largest energy source in the “renewable” category ββ<sup>CCiiEE</sup> ttCCEE ββ<sup>EEbbff</sup> ttCCEE is hydropower, we use the price of hydropower as a proxy for the category historically.<sup>13</sup> For local currency conversion we use the rupee- U.S. dollar exchange rate, . Electricity prices are usually denoted as cents per Kwh, however, for consistency here they are scaled to reflect the price per ton of oil equivalent. tt ffxx In recent years, fossil fuels have been sold below their respective world market prices in Pakistan. While there are plans to reduce or abolish subsidies, fuel subsidies still amounted to a total of 3.4 USD billion in 2017 or 1.14% of GDP according to IEA data. In the model, subsidies on fuel are represented as negative carbon taxes
- Pollitt, E., K. Gorman, P. Engle, J. Rivera, and R. Martorell (1995, 05). Nutrition in early life and fulfilment of intellectual potential. _The Journal of nutrition 125_ , 1111S–1118S.

- Powell, C. A., H. Baker-Henningham, S. M. Grantham-McGregor, S. P. Walker, T. J. Cole, and J. M. M. Gardner (2005, 08). Zinc supplementation and psychosocial stimulation: effects on the development of undernourished Jamaican children. _The American Journal of Clinical Nutrition 82_ (2), 399–405.

- Prado, E. L. and K. G. Dewey (2014). Nutrition and brain development in early life. _Nutrition Reviews 72_ (4), 267–284.

- Ravallion, M. and Q. Wodon (2000, March). Does Child Labour Displace Schooling? Evidence on Behavioural Responses to an Enrollment Subsidy. _Economic Journal 110_ (462), 158–175.

- Rockoff, J. E. (2004, May). The impact of individual teachers on student achievement: Evidence from panel data. _American Economic Review 94_ (2), 247–252.

- Rodriguez Herrero, H. (2005). Qualitative evaluation of the programa de apoyo alimentario. final report. _CIESAS, Available at www.diconsa.gob.mx_ .

- Roland G. Fryer, J. (2017, May). Management and Student Achievement: Evidence from a Randomized Field Experiment. NBER Working Papers 23437, National Bureau of Economic Research, Inc.

- Santibañez, L., J. F. Martinez, A. Datar, P. J. McEwan, C. M. Setodji, and R. BasurtoDavila (2007). Breaking ground: Analysis of the assessment system and impact of mexico’s teacher incentive program" carrera magisterial." technical report. _RAND Corporation_ .

- Skoufias, E., M. Unar
a binary explanatory variable, _D_est_ , is related to the implied percentage difference, _D_%_ , through the following function _D_%_ = 100 * ( _e_<sup>_D_est_</sup> – 1).

> 17 Our sample goods that are produced and consumed locally on both sides of the river are cassava flour, cement, eggs, green beans, mineral water, palm oil, plantain, potato, sugar cane, sweet potato, tomato and wheat flour.

> 18 These products are body lotion, butter, cement, cooking oil, mineral water, palm oil, plastic chairs, sugar cane, washing powder and wheat flour. Four of these goods also appear on the list of locally-produced-and-locally-sold products (cement, mineral water, palm oil, sugar cane, wheat flour). This is possible because the Brazzaville price data distinguish between locally produced varieties and imported varieties. Hence, for instance, separate prices are recorded for cement produced in Loutete (RC) and cement imported from Lukala (DRC).

17
cyclone wind maps for six return periods (2, 5, 10, 20, 50, 100-years), which were created by fitting an extreme value distribution to modeled wind speeds per grid (Steptoe and Economou, 2021). Examples of the cyclone maps are shown in Figure D2. Future wind speed maps are derived by adding an 8% increase in the maximum wind speed to the return period maps, making them consistent with the coastal flood scenario.

# **_3.2.2 Exposure, vulnerability and risk_**

The exposed assets and land-use we include in our analysis are housing, roads, educational facilities, health facilities, hospitals, electricity substations, wells, latrines and agricultural land. Commercial and industrial land-use are not considered in this study, given the lack of data. However, evidence from Cyclone Sidr (2007) suggests that damages to commercial and industrial assets only contributed 3% to the total disaster damages and are thus small.

The location and type of housing (e _.g.,_ jhupris, kutcha, semi-pucca, pucca) per household are taken from SHSD derived for the coastal zone of Bangladesh. For critical infrastructure assets (roads, schools, health facilities, hospitals, electricity substations), we collected various geospatial layers of the different asset types, with an overview of the data sources included in Table D1. For tube wells and latrines, we do not have asset data on the location of wells and latrines. However, we do know the number households that are dependent on tube wells and pit latrines using the SHSD. Hence, we use the exposure of those households that use tube wells and pit latrines as an indicator of the location of assets. The rationale for this is that pit latrines are commonly located on the household premises at a distance of 10 meters or less from the house (personal communication DPHE, 2022), while tube wells are generally co-located with housing within a relatively small radius ( _e.g.,_ tube wells serve a
a consent form signed by their parents or tutor and to complete a registration form. This instrument collected personal and family information such as age, gender, mother’s education, and average commuting time, among others. These were used to estimate the IVV. I also collected school records of academic grades, absenteeism, and behavior reports for all children.

According to the ASP’s theory of change, the program can directly affect behavioral and neurophysiological outcomes such as children’s violence, misbehavior at school, and emotional regulation. It may also have some indirect effects on academic performance, since changes in noncognitive outcomes can affect cognitive skills (Cunha and Heckman, 2008). Since this paper investigates differences in those outcomes by group composition, I study the effects of tracking and integration in the same three categories of variables.

First, short-term follow-up data on attitudes toward school and violent behaviors were collected from enrolled participants in school facilities. Students completed a follow-up survey in classrooms set up especially for this purpose at the end of October 2016, after all clubs had finished implementing their curricula. Each survey took approximately 45–60 minutes. Most surveys were self-administered, with assistance from staff trained in the survey methodology. To increase statistical power to detect effects for outcomes within a family and to reduce the number of hypothesis tests, I construct indexes of variables that are expected to move in a similar direction (Haushofer and Fehr, 2014; Heller et al., 2017). Since I do not necessarily trust self-reports, I attempted to recheck and validate these behaviors and attitudes using proxies for these outcomes obtained from administrative data such as absenteeism figures and reports of misbehavior at school.

For the analysis of group composition on stress and emotional regulation, I use neurophysiological recordings collected for our work (Dinarte and Egana (2019)) from a random subsample of 15
strongly capitalized following the GFC as in the U.S. as discussed in Section 6 varies significantly across the rest of the world (e.g., Anginer, Bertay, Cull, Demirguc-Kunt, and Mare, 2021). We encourage future research on both the impact of the various stimulus programs and the post-GFC prudential reforms on banking systems performance during and after COVID-19 using international banking data.

Third, the problems noted above for the U.S. with the potential to create near-term economic and banking problems – such as economic dislocations, inflation, and financial market issues created by the crisis and policy responses – are likely to be even more threatening of nearterm problems in other countries. For example, some countries relaxed their banking regulations on nonperforming loan provisioning and classification much more than in the U.S. (e.g., Feyen, Gispert, Kliatskova, and Mare, 2021). Moreover, borrower assistance and prudential measures may have exacerbated the bank stress significantly in countries that operate with little fiscal space due to strongly expansionary fiscal and monetary policies (e.g., Demirgüç-Kunt, Pedraza, and Ruiz-Ortega, 2021; Reinhart, 2021). When these forbearance policies come to an end and fiscal and monetary support are reduced, the extent of financial sector problems will become clearer. If the crisis has indeed seriously damaged financial institutions’ balance sheets, repairing such damage will take time, possibly leading to deleveraging and credit crunches that can hamper recovery in these countries for many years. We suggest that both policy makers and researchers pay close attention to these issues around the world.

We strongly encourage more international banking research for the same reason brought up in the introduction for why crises are so informative to research. The best way to learn what works is to study what happens when things do not work. It is likely that future research will uncover a significant number of things not working across the world during COVID-19.

27
are also those with relatively lower enrollments, it is unlikely this will alter substantive conclusions – the lowest preforming countries will be revealed to be even lower performing. In addition, data at the primary level should be largely unaffected, since at this level students are being taught basic skills, such as reading “the name of the dog is Puppy.” Thus, even if marginal students enter the system, these students should still be expected to attain basic skills by the time they are tested in later primary school grades. Of note, in future work, we aim to include household-based learning data to sign and quantify the 31
which asylum seekers are randomly allocated. They find that inclusive labor market access regulations substantially increase the employment chances of asylum seekers, in particular if the language distance is short. Zetter and Ruaudel (2018) and Aiyar et al. (2016) argue that, for refugees, the right to work and access to labour markets are key for becoming self-reliant and maximizing their net contribution to the public finances in the longer term.

The literature on the link between policies and public preferences is relatively scarce. Bansak et al. (2016) find that public preferences over asylum seekers are shaped by sociotropic evaluations of their potential economic contributions as well as humanitarian concerns about the deservingness of their claims. Zhou (2018) finds that citizens who live near refugees in their country are substantially more likely to support restrictions on citizenship access compared to fellow citizens farther away. She finds that the effect is stronger for more recent arrivals but does not address whether the effect varies by government and humanitarian policies. Blair et al. (2021a) construct an original dataset of _de jure_ asylum and refugee policies covering more than 90 developing countries that are presently excluded from existing indices of migration policy. They find that unlike in the Global North, forced displacement policies in the Global South have become more liberal over time. Betts et al. (2021) explores the role of inter-group interaction in shaping social cohesion between refugees and host communities in East Africa. The authors find mixed results: host community attitudes towards refugees (and vice versa) are likely to be shaped by a combination of intra-group attitude formation at the neighbourhood level, and inter-group interaction, with different mechanisms of interaction likely to be more salient for attitude formation in particular contexts (e.g. urban versus camp-based).

Our data and empirical setting provide some unique advantages that allow us to provide new evidence in several dimensions. First, we use sub-national data on refugee populations covering almost all low- and middle-income countries, which enables us to provide large-scale, cross-country evidence on forced migration
caseload using the SDI data than the WHO data at the country level. The comparison confirms that our computations based on crude averages are likely an overestimate of the true caseload in primary care. The most plausible reason for this overestimate is that the number of PHC visits per person per year is likely much lower than the OECD average of 6, but we cannot rule out that there are other systematic differences in the way that health care workers are counted in WHO statistics versus the on-the-ground surveys used here.

We also investigated if patient loads could be much higher for doctors under the assumption that in facilities where they were present, all patients would be allocated to that doctor rather than the nurse. The estimated outpatient caseload per doctor increased from 9.5 to 19.3 patients per day ( **Supplemental Appendix Figure 4** ). However, 63% of clinics were exclusively staffed by health care professionals who were _not_ doctors; as a result, average caseloads per provider remained low for the majority of providers. Overall, the facility-level surveys supported the conclusion that PHC providers across these 10 countries experienced substantial excess capacity and underutilization during the workday.

# **3.2 Variation in Caseloads across Facilities**

A key feature of these data is that even though the mean and median caseloads are low, there is enormous variation across providers, as shown in **Figure 1** . At the 75th percentile of the caseload distribution, for example, providers see on average 2.5 times as many patients as the median provider does; and 5 times as many at the 90th percentile of the caseload distribution. One question is whether this variation largely reflects differences in facility levels and/or differences across urban and rural areas.

We therefore examined the variation in caseloads and providers across facilities using multiple regression analysis in a specification with country-level fixed effects ( **Table 2** ). Column 1 shows that at the facility level, indeed urban hospitals carried the largest caseload, followed by rural hospitals
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