rafmacalaba/data-use-mentions-extended huggingface dataset:
well as liquidity hoarding by corporate customers as part of the “dash for cash.” The G-SIB deposit ratios increase from mean and P90 values of 78.18 and 85.31 percent in 2019:Q4 to 81.45 and 88.41 percent in 2020:Q4, respectively.

# **4.2 Additional Statistics That Do Not Depend on Accounting Data**

We evaluate whether bank failures during the crisis may have distorted the accounting data and masked significant problems in the U.S. banking industry. The FDIC reports only four of the over 5,000 U.S. banks failed in 2020, including one failure on February 14 before COVID-19 slowed the U.S. economy. The failed banks had combined assets of just $458 million in an industry with assets over $17 trillion.<sup>17</sup> These very mild failure data provide additional evidence that the banking system was not in significant distress, and also suggest that missing Call Reports from failing banks are not obscuring our view of any substantial industry problems.

We consider as well the possibility that accounting data may generally be less accurate indicators of bank financial distress than market indicators. The KBW Bank Index, which covers stock market values for 24 banking organizations, did fall from 113.4 to 97.9 from December 31, 2019 to December 31, 2020, and has since risen to 134.5 as of this writing on May 17, 2021.<sup>18</sup> Thus, there were some market losses during the calendar year, but the index is currently higher than before COVID-19, not revealing any market value indications of forthcoming financial distress.

Another market-based measure is SRISK for the U.S. financial system, an indicator of systemic risk that is employed in many research studies. It rose from 286 to 562 from December 2019 to December 2020, but then fell to 247 in May 2021.<sup>19</sup> Again, there was some
pursued, and how much treatment (if any) cost. Care was taken to ensure visits to the households were always on the same day of the week, which became the focal reference-point for the weekly recall questions. This design was followed to avoid the possibility of ―telescoping‖ (the reporting of events prior to the recall period) in the weekly surveys; it also had costs in terms of missing household observations in the weekly data, which are addressed below. Surveyors also attempted to always interview the same respondent in both weekly and monthly surveys; this person, designated as the primary respondent, was usually the head or the spouse of the head of the household. The primary respondent was chosen to partly reflect the belief that they may be better able to report on sicknesses of their children. These repeated visits followed a larger initial interview that covered a much wider range of household level information that was not likely to change much during the study period—household structure, total expenditures (using the standard ―thin round‖ set of questions for household income and expenditures from the National Sample Survey, which is India‘s representative household survey), education, and other household characteristics.

5
Until quality became a real issue for the middle and upper income classes, the most vocal unhappy user group tended to be the non-residential users. Not only did they not get the quality they needed to be competitive but in addition they were often subject to higher average and marginal tariff rates than residential users. Indeed, cross subsidies tended to be common—favoring mostly the middle class rather than the poor-- and generated significant economic inefficiencies since average tariff levels were increasing with decreasing production costs.

The poorest segment of the population were also quite unhappy because they were largely excluded from the delivery of public services by public enterprises, relying instead on alternative (often high costs) small and medium private providers. However, they had, and still have, little political clout. In a forthcoming study of the evolution of public service delivery in Africa, Estache (2004), reviewing the evidence, shows that for a large sample of African countries, the bottom two deciles of the population are essentially not connected and rely on alternative sources of energy and water. In other words, unless the reforms could generate affordable new access, the poor were not really concerned by the utility privatization wave. In fact, many were already dealing with private small scale providers in rural areas and secondary cities and there was no hope for a bigger public participation in the financing of their private provision.

Individually the various unhappy groups had little leverage on policymakers. Overtime, however, the inclusion in the coalition of the supporters of reform of the middle class--unhappy with the deterioration of service or increasingly demanding—made the demands for reform viable. Collectively, although with very different motivations, a fairly wide accumulation of frustrations of these heterogeneous groups of interests managed to provide the political support needed for reforms. A 1995 UN financed survey suggested that about 75% of the Latin American population actually supported privatization policies then.<sup>3</sup> The main residual vocal group against the reform were the public sector workers (and
Anecdotal reports suggest that this problem is more severe at private providers because they must worry about covering staff costs as well as medicines, supplies, and diagnostic tests.

Ghanaians who are not covered by NHIS must pay for healthcare on a fee-for-service basis. However, as we noted above, at public healthcare facilities, salaries and capital expenditures are covered by the central government budget, so the fees cover only nonsalary, noncapital costs.

# **3 Calculating Out-of-Pocket Healthcare Expenditures**

GLSS 2016/17 asks about healthcare expenditures for each individual if in the past two weeks she or he consulted a healthcare provider, purchased medicines, or was admitted to a hospital. For outpatient services, survey respondents can report what they paid for registration at the healthcare facility, consultation, diagnosis (laboratory fees, etc.), and medicines, or they can report the total amount they paid.<sup>12</sup> In our calculations, we use the larger of the sum of the individual items or the reported total. For inpatient services, survey respondents only report what they paid for “staying in a hospital or health facility.”<sup>13</sup> Survey respondents can also report “medicines and medical supplies” purchased outside the context of a healthcare visit.<sup>14</sup> Finally, they can report “total medical expenses.”<sup>15</sup> To calculate total OOP healthcare expenditures, we add the total of outpatient expenses, inpatient expenses, and medicine expenses and take the larger of this sum or the “total medical expenses” reported. In a few cases, respondents report exactly the same amount for medicines acquired in the context of a consultation and medicines acquired without a consultation.<sup>16</sup> For these, we set the value for OOP healthcare expenditures outside the context of a consultation to zero to avoid double counting these expenditures.

All the raw variables in GLSS 2016/17 have some very large
and savings products of supervised institutions and they lacked all product data for unsupervised institutions. The web was therefore used to fill in missing information, but since many of the terms were not available online either, they had to be requested directly from the institutions, particularly unregulated ones. The other problem was that financial institutions were not always able to disclose their product portfolio at the time of the study when asked a few months later. This resulted in a product dataset with 1,206 saving and 216 credit products. These data are used to generate Table 1 and to assess the total costs of the products offered to the auditors.

We use data from the questionnaire, and market products dataset to construct variables related to the amount of information provided by the institutions.<sup>15</sup> We first matched the product offered to the auditor during the visit to the actual product in the market product dataset using the commercial name of the product when available. Although auditors were instructed to gather the commercial name during their visits, the staff failed to provide it in more than half of the visits. For visits without recorded product commercial name matching was done based on other product attributes like the type of savings account (transaction vs. investment), the interest rate quoted, and the presence of several fees. Even when a product offered was found in the market products dataset, the match could not sometimes be used because the market products dataset contained missing terms for the product. The resulting sample of matched products consists of 119 savings and 59 credit products.

From the different product terms collected (see Footnote 15) not all are equally relevant or important, because they may not apply or may not be charged. For this reason we develop a “transparency index” that reflects the disclosed cost as a percentage of the total (annual) cost of the product. The index ranges between 0 and 100 percent, where 0 percent indicates that no information about the cost of the product is disclosed and 100 percent
Table 7 displays the results when using mean village consumption instead of the mean of the bottom half as the village welfare measure. Three main findings are clearly apparent. First, the partial registry approach continues to perform vastly better than the other alternatives when attempting to predict mean village welfare. Second, both the partial registry approach and the RWI suffer moderately when predicting the mean over all households rather than the mean of the poorest half of households, particularly when it comes to rank correlations. This may be because of idiosyncratic positive outliers in the upper half of the household predicted welfare distribution which are more difficult to predict using both geospatial data and predictions trained on asset indices.

**Table 7.** <u>Metrics of all the methods when using all households in the villages</u>

|<br>**Rank correlations**|<br>**All districts-all HH**|
|---|---|
|Partial registry (10% of the census sample)|0.61|
|PMT scores|0.02|
|IHS training sample|0.19|
|RWI|0.14|
|**AUC**||
|Partial registry (10% of the census sample)|0.77|
|PMT scores|0.50|
|IHS training sample|0.59|
|RWI|0.55|
|**R-squared**||
|Partial registry (10% of the census sample)|0.35|
|PMT scores|0.00|
|IHS training sample|0.01|
|RWI|0.02|

Third, the method that combined survey and geospatial predictors without a partial registry (IHS plus geospatial predictors) performs much better when using the full sample of households than when only using the bottom half for each village. The rank correlation increases from 0.13 to 0.19 and the AUC increases from 0.53 to 0.
greater influence on the achievement of rural and va-y poor children (Brazil, Wolff 1970; Iran, Ryan 19731 Indonosia, Haron 1977). This pattern may result from less variation in social class within rural **azdas** or to the fact that Western measures of class **are** even less valid in rural **areas** (compared to Third World urban areas).

In sum, family backgro appears to be more influential in developing countries when (&, **the** formal school is highly institutionalized, (b) class structures **are** more defined, and (c) in subjects that are familiar or linked to parentO' own knowledge. In contrast, when the school is foreign to the setting and teaches unfamiliar areas of knowledge (such **as,** a rural school providing science instruction), school characteristics appear to **be** more efficacious than family background.

# A Broader Conception of Pupil Background

Studies of family background effects on student achievement in Third World settings would be improved by the inclusion of two types of variables: (a) a broader range of family background indicators, and (b) more culturally valid indicators of social and material background.

Broader range of indicators. Most fundamentally, the construct of "pupil background" should be broadened to include other aspects of parents and the child that may operate independently of material aspects of class. We focus on two sets of factors in this regard. First, the value placed on staying in school and achieving well has been found to vary among parents within several societies, including the United States, Japan (Hess and Holloway 1984), Mexico (holloway et al. 1986), and Taiwan (Stevenson et al. 1985). Parents' own reports

-5-
(% of estate)|8.0|8.0|4.0|1.0|
|<br>|<br>Recoveryrate (cents onthe US$)|34.2|52.4|71.4|92.0|
|**Protecting investors**|Disclosureindex(0-7)|2|6|5|7|
|**Getting credit**|Cost to create collateral (% of income per<br>capita)|1.0|4.1|0.0|0.0|
||<br>Legal rights index (0-10)|6|4|9|<br>10|
|<br>|<br>Credit Information Index (0-6)|4|3|5|<br>6|
|<br>|<br>Public registry coverage (per 1,000 adults)|13|44|0|637|
|<br>|<br>Private Bureau Coverage (per 1,000<br>adults)|0|0|978|1,000|

Source: data from _Doing Business in 2005. Removing Obstacles to Growth_ . The World Bank and Oxford University Press, Washington DC, 2005.

Considering that Bulgaria has to compensate for its relative longer distance to EU markets than most other new EU member states, the frame of reference for Bulgaria’s regulatory reforms should be at least Baltic states. They are even more remotely located than Bulgaria from the center of EU economic gravity 8 It is interesting to note, however, that labor regulations are the fifth most problematic regulatory constraint, with almost 18 percent of respondents to the FIAS survey picking it up as a “major” or “very severe” problem. Yet, since in 2002 labor regulations were the second leading specific regulatory constraint, after tax administration, this suggests an improvement (FIAS 2004, p. 24).

> 9 As a
line data corresponding to the eight-digit tariff code in a manner analogous to equations 1 and 2. In general, there will be multiple ten-digit tariff lines corresponding to a single eight-digit tariff line; so we will need to average over multiple ten-digit tariff lines in such cases. Further, we continue to use data at the most disaggregated level available, i.e., the country level. So for any ten-digit tariff line we use the quantity and value of imports from all countries from which Russia had positive imports in all of the three time periods of our base period.

While we hope that the explanation above is clear and intuitive, the remainder of this section is devoted to the mathematical description, and this requires the introduction of considerable notation. The reader who is interested in the results should be able to skip the remainder of this section.

# **Definition of Matched Tariff Lines**

Define At = the set of all ten digit tariff lines in the Russian tariff code in year t, t= 2009, 2010, 2011*, 2011,..., 2020 (denote the first half of 2011 as 2011*);

> Define _A_ 2011* = the set of ten digit tariff lines in the Russian tariff code 2011*, the first half of 2011;

Define the set B = A2009  A2010  _A_ 2011* = the union of the three sets of tariff lines in the three periods comprising the base period; Define AMt = B  At = the intersection of the sets of all tariff lines in year t and the base period, t= 2011,..., 2020. We call this the set of matched tariff lines in the year t with the tariff lines for the base period. Note that for a ten digit tariff line to belong to the set AMt, it is sufficient for the tariff line in year t to match with one tariff line from any of the three periods in the base period.

The vast majority of tariff lines are matched with
0.25|0.79|
|Risk rationed|0.46|0.60||0.45|0.61||0.47|0.58||
|Quantity rationed<sup>a</sup>|0.14|0.11|0.21|0.17|0.13|0.28|0.11|0.10|0.12|
|Transaction cost rationed<br>|0.02|0.02||0.02|0.03||0.02|0.02||
|Uncategorized, yet constrained<sup>b</sup>|0.05|0.05|0.07|0.05|0.05|0.06|0.06|0.05|0.09|
|Number of households|1587|1223|364|741|544|197|846|679|167|

**Source:** Own computation from AAU/Gothenburg/WB survey 2006/07. **Note:**<sup>a</sup> For applicants, quantity rationed households include those whose applications were partially approved or fully rejected. Of the partially approved ones, only 16% (12 out of 75), 14.6% (8 out of 55), and 20% (4 out of 20) were quantity rationed in the pooled, Gojjam and Wollo samples, respectively. As shown in table 1, very few loans in the sample were fully rejected. b Uncategorized applicants are those who received the full amount they requested but still wanted to borrow more at prevailing loan terms.

17
paid regularly, who are eligible for recognition schemes, and who are in schools with better infrastructure are less likely to be absent (Table 4 - Column 2). However, none of these correlations are significant in multiple regressions or in specifications with state or district fixed effects suggesting that these measures are correlated with each other, and that states that have a longer history of investing in education may have better indicators of school and teacher quality and lower teacher absence, but that these metrics of teacher quality do not predict teacher absence within states or districts. Overall, there are few robust correlations across all specifications except that schools that have been inspected recently have significantly lower rates of absence. One important result in the correlations is that there appears to be no significant relationship between teacher salary and the probability of teacher absence. Since salary data were not collected in the 2003 survey, this variable is not included in the panel analysis below.

# **4.2 Correlates of Changes in Teacher Absence between 2003 and 2010**

The main identification challenge in the cross-sectional correlations presented in Table 4 (and in Kremer et al. (2005)) is that we cannot rule out the possibility that the results are confounded with village-level omitted variables. The use of panel data helps mitigate these concerns since our correlations are now identified using changes in village-level measures of school inputs. Table 5 (columns 4-6) presents results from the following regression: where ∆ _Absi_ is the change in the mean teacher absence rate in government schools in village _i_ between 2003 and 2010, **∆Ti** is the change in village-level means of measures of teacher attributes, **∆Si** is the change in village-level means of measures of school facilities, and **∆Mi** is the change in village-level means of measures of school monitoring and supervision. **Zi** represents different levels of fixed effects (state or district) and _εi_ is the error term. Since changes in the measures of school quality
associated with smaller shadow economies and lower perceptions of corruption (as measured by Transparency International), as fewer regulations, taxes, and tariffs reduce the opportunities for corruption available to public officials.

Notwithstanding the strong theoretical underpinnings and empirical evidence of the virtue of economic freedom,<sup>11</sup> a number of authors have questioned the causal relationship between economic freedom and economic growth, suggesting that it may have more to do with the tendency of rapidly growing economies to liberalize economically and politically than economic freedom per se causing growth. De Haan and Siermann (1998) find that the positive effect of economic freedom on economic growth is not robust, but depends on the indicator of economic freedom used. De Haan and Sturm (2000) conclude that while greater economic freedom will lead a country more rapidly to a steady state of economic growth, the level of this steady state of growth itself is unaffected by the degree of economic freedom. In a further analysis, de Haan, Leerouwer, and Sturm (2002) maintain that the various, largely ad hoc indices of economic freedom are not robustly related to economic growth.

Yet, overall economic scholars tend to support the notion of an overall positive causal relationship between economic freedom and economic growth.<sup>12</sup> In a survey of 33 empirical studies on the relationship between economic freedom and economic growth, de Haan, Lundström and Sturm (2006) conclude that, notwithstanding the various shortcomings of empirical studies using the EFW index, market-oriented institutions and policies are strongly related to economic growth. They found strong indications that liberalization, i.e. an increase in the EFW index, stimulates economic growth. Barro (1997) provides empirical evidence supporting the idea that free markets and maintenance of property rights foster economic growth. Gwartney and Lawson (2004) found that increases in economic freedom, as measured by the EFW index, led to more growth in the future (changes in economic freedom during the 1980s were associated with higher rates of economic
This paper evaluates the impact of a large-scale highway project on economic activity in the Indian manufacturing sector using establishment-level survey data from 1994-2007. The Golden Quadrilateral highway project of India upgraded the quality and width of 5,846 km of highways linking four major hubs in India. In the process, this upgrade improved the connectivity and market accessibility of districts lying close to the highway compared to those more removed. Non-nodal districts located within 0-10 km

> 12 For example, Duranton and Puga (2001, 2004), Rosenthal and Strange (2004), Ellison et al. (2010), and Gill and Goh (2010).

> 13 Henderson (2010) provides a broader review of the role of cities in development.

20
.83|1.34|1.21|
|Aryankor|INDEP|0.34|0.59|0.62|1.05|1.19|0.58|0.57|
|Életút|INDEP|0.00|0.53|0.74|0.25|0.20|0.21|0.29|
|Évgyűrűk|INDEP|0.16|1.15|0.58|0.65|0.54|0.57|0.50|
|Weighted Average|MKT|1.13|1.42|1.06|1.03|0.95|0.92|0.97|
|Weighted Average|EMP|0.05|0.62|0.44|0.37|0.32|0.23|0.28|
|Weighted Average|FIN|1.29|1.51|1.14|1.09|1.00|1.00|1.05|
|Weighted Average|INDEP|0.21|0.94|0.60|0.81|0.84|0.57|0.53|

Source: HFSA raw data 40. This segmentation in asset management fees can be ascribed to the governance structure of MPFs and weak transparency provisions in the regulation of the second pillar. As previously mentioned, MPF boards are _de facto_ controlled by their sponsors, which define the fees that will be charged by the service providers within the group. Members do not _de facto_ vote with their voices, as they typically do not go to general assemblies and vote for the boards, but could “vote with their feet”, i.e., switch to the funds generating higher rates of return and charging lower fees. However, the lack of information on critical data on rates of returns and fees have limited competition and promoted the