rafmacalaba/data-use-mentions-extended huggingface dataset:
recovery. We find no evidence of problems in multiple statistics for bank conditions, credit performance, or portfolio allocations across any of the eight financial ratios examined for the distribution of all banks or for the distribution of large, systemically important banks.

Before proceeding, however, we provide three caveats regarding the regulatory accounting data due to easing actions by U.S. regulators and supervisors, and how they may affect our analysis. First, regulators made a number of changes to how the Basel III regulatory capital ratios were computed during the crisis to make it easier for banks to meet the requirements. For this reason, we view these regulatory capital ratios as potentially less informative during the crisis and exclude them from consideration. We focus attention instead only on a non-regulatory equity capital ratio that is measured consistently over time.

Second, supervisors allowed for forbearance of missed or late payments on some debt, which may affect the accuracy of our calculation of the nonperforming loans ratio based on credits that are past due at least 90 days or in nonaccrual status and/or charge-offs on these loans. While we keep these figures, we view them with additional caution and also include a ratio for loan and lease loss allowances, which should reveal whether banks are keeping sufficient reserves for eventual losses on these loans.

Third, supervisors temporarily reduced focus on bank examinations, which could result in some institutions taking advantage of lessened oversight and reporting inaccurately favorable Call Report data. While we do not expect substantial inaccuracies to occur as a result, this could affect the books of a small number of banks, and so we recommend overall caution in interpreting the data. Thus, while the statistics we report may be imperfect, they are the best that we have available and they tell a very consistent story.

As a further caveat, we certainly cannot claim that there will be no banking crises or other 17
an economic model. The model assumes no change in technology or organization of health services, and thus projects the demand for health care as if the current system of health care and technology remains in place in 2030. We then compare this demand projection with the supply and the “needs” projections based on WHO SDG threshold density of 4.45 health workers per 1,000 population (WHO 2016), and discuss the potential policy implications of the findings.

# **Methods**

# _Theoretical framework_

The demand for health workers reflects the willingness to pay of the purchasers of health care (e.g. government, private sector firms), which in turn drives the demand for employing health workers in clinics, hospitals, public health centers and other parts of the health system. The demand for health workers is influenced by factors including household income (i.e. the ability of consumers to purchase health services), the fiscal capacity of the government to support the health system and employ public sector workers, demographic and epidemiologic conditions of the population (e.g., aging and burden of disease that determine the relative types of health services consumers want), and the level of health coverage in terms of risk pooling and financial protection available to enable consumers to access and utilize health care in times of need.

The supply of health workers can be defined as the total number of health professionals with the appropriate skills and qualifications who are willing to enter the job market in the health 3
15.2|84.8|31.4|38.4|15.6|6.3|61.6|
||West Bank|5.1|94.9|5.7|8.4|1.7|0.8|91.6|
||Gaza<br>|47.6<br>|52.4<br>|46.2<br>|55.6<br>|23.5<br>|9.3<br>|44.4<br>|

_Note: (*) Indicates a refugee camp or settlement; (**) Refers to pre-assistance income poverty (***) Aid is expressed in percent of the poverty line (****) Survey data for Lebanon is available from the 2022 Vulnerability Assessment Panel. There are large variations in refugee poverty estimates for Lebanon ranging from 1 percent using the LMIC poverty line (PPP$ 3.65) and 16 percent using the UMIC poverty line (PPP$ 6.85) to 40 percent using the national poverty line. As Lebanon became a LMIC in July 2022, the first estimate should apply, even though, since Lebanon only recently became a LMIC a case could be made for using the UMIC poverty line._<sup>_4_</sup> 4 Yet even the poverty estimate derived for the UMIC line is on the low side, certainly in comparison to other countries in the region. Lebanon’s low PPP conversion factor, which leads to low poverty lines, is behind this. For instance, the UMIC poverty line is only 38 percent of the national poverty line, whereas it is about 70 percent of the 9
- Palmer, Margaret A, Catherine A Reidy Liermann, Christer Nilsson, Martina Flörke, Joseph Alcamo, P Sam Lake and Nick Bond. 2008. ―Climate change and the world‘s river basins: anticipating management options,‖ _Frontiers in Ecology and the Environment_ , 6(2): 81-89.

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(USD)|0.725|0.302|-1|
|10|Prevalence of trade barriers, 1-7 (best)|1.033|0.639|1|
|10|Tariff rate, most favored nation, simple mean, all products (%)|1.031|0.519|-1|
|10|General government final consumption expenditure (% of GDP)|2.423|0.853|-1|
|11|Inflation, consumer prices (annual %)|1.281|0.503|-1|
|11|General government net lending/borrowing, Percent of GDP|1.697|0.317|1|
|11|General government gross debt, Percent of GDP|0.656|0.352|-1|
|11|Political Stability No Violence|1.613|0.416|1|
|11|Business costs of crime and violence, 1-7 (best)|0.672|0.345|1|
|12|Foreign Direct Investment: Inward stock (USD per capita)|0.543|0.316|1|

Note: The table reports the results for variables with statistically significant estimated coefficients (at the 10% significance level). The first column profiles the variables with respect to groups outlined in Table 1. The results are obtained using the outlined methodology with country/sector panel data. The dependent variable is the binary indicator of FDI performance over 2015-19 period. The regressors are the policy indicators (average value over the 2010-14 period).

19
per private school is much larger in magnitude. The effect of the program on overall enrollment is small in magnitude. Using the average number of government, private and other schools per district in the sample period, our results indicate that 9,824 children per district are not attending school due to the program.<sup>20</sup> Our identifying assumption is that there are no pre-trends in enrollment in districts belonging to different phases prior to MGNREGA’s implementation. DISE data is not available for all states prior to 2005, although major states are covered since 2003. We use data from 2003 to 2005 to check if there are differential pre-trends in enrollment by phases of MGNREGA roll-out.<sup>21</sup> In Appendix Table S4, we control for district-specific changes in enrollment from 2003 to 2005 (pre-treatment years) and allow this to vary over time by interacting with year indicators for the states for which we have pre-program data.<sup>22</sup> The overall effect on enrollment and enrollment by primary and upper primary are similar to those reported in Table 3.

Prior to the program implementation, we observe that the Phase III districts are better in levels. But the growth rate in enrollment is similar. Appendix Figures S2 and S3 show that between 2003 and 2005, the growth in enrollment and number of schools looks similar across districts in different phases. These two tests together show that pre-trends in enrollment are not biasing our results.<sup>23</sup> As we discussed earlier, a concern with the DISE data is that there is measurement error in reporting. Since school headmasters provide the information, it could be inaccu-

> 20In results not shown, we do not find any differences in effects for girls versus boys.

> 21In the Appendix, we discuss that our main results are no different if we exclude or include the states for which we do not have DISE data prior to 2005, we are reassured that the sample for which we have
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44
recommendations such as marketization, privatization, and opening up (Yao 2015; Brandt et al. 2016; Megginson, 2017; Wu, 2018; Zhang 2019). Zhang (2019), for instance, offers evidence that _improvement_ in the level of marketization is strongly and positively associated with China’s provincial GDP growth rates. Relatedly, Brandt, Kambourov and Storesletten (2016) offered evidence that the reduction of the state sector contributed to narrowing regional disparity in the past two decades. Understanding the extent and effects of privatization is thus critically important for guiding other developing countries to achieve economic growth, and understanding what China needs to do next. Indeed, Rodrik (2006) implicitly interpreted China’s experience in the 1990s as evidence against the importance of privatization for economic success as follows: “Rapid economic growth in China, India, and a few other Asian countries has resulted in an _absolute_ reduction in the number of people living in extreme poverty. The paradox is that that was unexpected too! China and India increased their reliance on market forces, of course, but their policies remained highly unconventional. With high levels of trade protection, _lack of privatization_ (emphasis added), extensive industrial policies, and lax fiscal and financial policies through the 1990s, these two economies hardly looked like exemplars of the Washington Consensus. Indeed, had they been dismal failures instead of the successes they turned out to be, they would have arguably presented stronger evidence in support of Washington Consensus policies” (Rodrik 2006, p. 975).

Given the importance of sound policy recommendations based on the Chinese experience, the continued misunderstanding of China’s privatization, it is important to identify the causal effect of privatization in China. This requires using comprehensive Chinese SOE data and a credible identification strategy.

Importantly and fortunately, the manner in which Chinese SOEs were selected for privatization grants us a unique opportunity to identify the causal effects of privatization. The literature has focused on the selectivity of privatization from the perspective of investors, where private
|-6.27***|-2.14***|0.61|
|50th Percentile|3.20***|-6.36***|-2.06***|0.55|
|75th Percentile|2.91**|-6.55***|-1.88***|0.43|
|90thpercentile|2.71|-6.68***|-1.77***|0.35|
|||**2018-2019 Rec**|**ession**||
|Port Province|-0.97|-38.98***|-4.46***|-3.76***|
|25th Percentile|-3.14***|-37.81***|-4.02***|-3.41***|
|50th Percentile|-4.63***|-37.01***|-3.72***|-3.18***|
|75th Percentile|-8.01***|-35.20***|-3.04***|-2.65***|
|90thpercentile|-10.20***|-34.03***|-2.59***|-2.30***|

Notes: F-stat significance based on robust standard errors (clustered by province) in parenthesis *** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated combining port province and distance parameters (for each sector and time period separately) estimated in Table 3c. All regressions use data on all 81 provinces and employ province fixed effects. Distance to a major port is
that investing in better governance and monitoring of service providers may be an important component of improving state capacity for service delivery in low-income countries (Besley and Persson, 2009; Muralidharan, Niehaus and Sukhtankar, 2015).

Finally, recent research has pointed to ‘misallocation’ of capital and labor in low-income countries as an important contributor to lower total factor productivity (TFP) in these settings (Hsieh and Klenow, 2009), and has also documented that a plausible reason for this misallocation is that ‘management quality’ is poorer in low-income countries, and that publicsector firms are managed especially poorly (Bloom and Van Reenen, 2010). Our results provide a striking example of weak management and misallocation in publicly-produced primary education in India (a sector that accounts for over 3% of GDP in spending). In particular, our estimates suggest that reallocating a portion of the $5 Billion/year increase in education spending budgeted for hiring more teachers towards measures focused on reducing teacher absence (for instance, by hiring more supervisory staff) may be a much more cost effective way of increasing effective teacher-student contact time. Thus, misallocation is likely to be a first-order issue in this setting, and reallocating education spending towards better governance may substantially increase TFP in publicly-produced education.<sup>7</sup> The rest of this paper is organized as follows: Section 2 discusses our empirical methods and analytical framework. Section 3 reports summary statistics on school inputs and teacher absence. Section 4 presents the cross-sectional and panel regression results. Section 5 discusses the fiscal costs of weak governance and compares the returns to investing in better monitoring with that from hiring more teachers. Section 6 discusses policy implications, and Section 7 concludes.

# **2 Data and Analytic Framework**

The nationally-representative sample used for the 2003 surveys, which our current study uses as a base, covered both urban and rural areas across the 19 most populous states of India, except Delhi. This represented over 95 percent of the country’s population.
intensive in that constraint should be more likely to perform well, and vice versa.<sup>12</sup> Hence, for each potential constraint it is not enough to just conclude that the levels are low but it is also necessary to observe signs of scarcity, i.e. signs of a current need for the constraint to be released in the particular economic environment of the country in question.

**Benchmarking the performance of an economy is one way of identifying factors that hold investment and economic activity in a specific country back.** First of all, it is important to select an appropriate group of comparative countries. The core group of comparators should consist of countries that share similar characteristics with the country of interest, and are going through similar development states (see Box 1). More so this set of countries should also include good performers in the region (as well as regional and income groups’ averages) to see in what dimensions they differ from the studied country. Though the group of comparators may, in fact, vary depending on the question one needs to address.<sup>13</sup> While useful for identifying problem areas, benchmarking for growth diagnostics has limitations and should be used with caution and jointly with other methods to shed light on constraints to growth. For example, comparisons are meaningful only if the indicators are constructed following procedures that are consistent across countries, and a change in the set of comparator countries for an indicator may change the conclusions from a benchmarking exercise. Moreover, evidence that a country ranks low on a given indicator does not imply that the area is a ‘binding’ constraint to growth – it is only an indication that this might be the case. There might be worse constraints that are the binding ones, or the structure of the economy in the specific country of study is simply not sensitive to that potential constraint.

**Another popular source of information is enterprise survey data, but this type of data must also be
# Reference List

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Balcerowicz, Leszek. 11-18-2003. "Post-Communist Transition in a Comparative Perspective." Presented at Practitioners of Development Seminar Series, Washington, DC, November 18, 2003.

Baumol, William J. 2002. _The Free Market Innovation Machine_ . Princeton: Princeton University Press.

Cheung, Kui-yin and Ping Lin. 2004. "Spillover Effects of FDI on Innovation in China: Evidence From the Provincial Data." _China Economic Review_ 15(1): 25-44.

Chow, Gregory 1985. _The Chinese Economy_ . New York: Harper and Row Publishers. Djankov, Simeon and Peter Murrell. 2002. "Enterprise Restructuring in Transition: A Quantitative Survey." _Journal of Economic Literature_ 40(3): 739-792.

Dollar, David, Shi, Anqing, Wang, Shuilin, Xu, Lixin Colin. 2003. _Improving City_ _Competitiveness Through the Investment Climate: Ranking 23 Chinese Cities_ Washington, DC: World Bank.

Duckett, Jane 1998. _The Entrepreneurial State in China: Real Estate and Commerce_ _Departments in Reform Era Tianjin_ . London: Routledge.

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Feldman, Maryann and Roger Martin. 2004. "Jurisdictional Advantage." NBER Working Paper 10802. Cambridge, Mass.: National Bureau of Economic Research.

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Gore, Lance L. P. 1998. _Market Communism: The Institutional Foundation
# <u>B. Technology Commercialization</u>

The Latvian Technology Commercialization system is poorly developed. Indeed, it is fair to say that it is close to non-existent compared to systems in most EU countries, the US, or even what is being contemplated in Russia. Not surprisingly, as the charts below indicate, Latvia is near the bottom of all NMS’s in terms of the per capita volume of domestic and foreign patents issued to residents. This poor showing is not surprising. Why should Latvian enterprises go to the expense and bother of registering ownership of something that will not be commercialized and, therefore, will not generate revenues.

**Figure 30 A EPO patent applications (per million population)**

<!-- Start of picture text --> EU<br>Slovenia<br>Hungary<br>Czech Rep.<br>Estonia<br>Slovakia<br>Bulgaria<br>Latvia<br>Poland<br>Lithuania<br>Romania<br>0,0 20,0 40,0 60,0 80,0 100,0 120,0 140,0 160,0<br><!-- End of picture text --> _Source_ : Trendchart, 2003, <u>www.trendchart.org</u> Similarly, data from the Latvian patent office suggests that foreign firms also do very little patenting in Latvia. What little foreign patenting does occur is typically of a defensive nature – e.g., foreign pharmaceutical companies patenting drugs to ensure that Latvian firms do not begin producing generics or foreign consumer goods companies trying to protect themselves against locally-produced counterfeits. Foreign firms obtain almost no high tech patents in Latvia. This suggests that there is almost no concern that Latvian firms would even know how to use this knowledge for their commercial benefit. In this market, Latvia is more or less an afterthought.

55
|Sierra Leone|6,571|4,885|||||2018 SLIHS|
|---|---|---|---|---|---|---|---|
|Uganda|2,226|2,199|2,145|2,135|2,122|2,100|2016 UNHS|
|Zimbabwe|1,747|1,639|1,451||||2017 PICES|

A secondary source of data is the Global Monitoring Database (GMD), which is a collection of globally harmonized household survey data compiled by the World Bank. The GMD includes most recent household surveys used by national statistical agencies around the world, and subsequently, by the World Bank, to compute the official poverty statistics. In addition to the welfare aggregate, surveys in GMD include variables on demographics, assets, labor, education, and other topics that have been harmonized to a common set of specifications, making the GMD well-suited for cross-country analysis.

This analysis covers 14 countries in SSA (Table 1). Although more countries are included in both the GMD and HFPS, we were limited by three factors. First, the analysis could only be conducted in countries where the HFPS was conducted and which also has a survey available in GMD that was conducted prior to the pandemic. Second, we limited our analysis only to surveys in GMD that were conducted in recent years, using a cutoff of 2014. Third, we limited our analysis to countries that both had a minimum set of variables that could be used to model household per capita consumption.

# **<u>Survey-to-survey imputation</u>**

Survey-to-survey imputation (S2S) predicts household per capita consumption in a household survey that collects information only on selected covariates of consumption by using information from another survey that collected both the per capita consumption and its covariates. This literature builds largely on the poverty mapping literature