rafmacalaba/datause-encoder-data huggingface dataset:
predictors of nonperforming loans, reflecting the relatively low level of bank equity. Section VI provides some concluding remarks.

# **II. Nonperforming Loans in Sub-Saharan Africa**

During the late 1980s and most of the 1990s, a large number of countries underwent episodes of systemic and borderline banking and financial crises across the developing world.<sup>16</sup> These crises, varying in scope across countries, were largely characterized by the poor performance of banking and financial institutions. They were largely associated with a rapid accumulation of nonperforming loans in the banking industry and a deterioration of asset quality in the face of increased credit risks. Over the decade spanning most of the 1990s, the scope of these loans increased significantly to reach over 30 percent of total loans in Sub-Saharan Africa. The peak was achieved in 1993, when they reached 32 percent of total loans, the highest in the developing world. Indeed, even during the height of the Asian Financial crisis in 1997, the aggregated share of impaired loans never exceeded 25 percent of total loans (see Figure 1).

**Figure 1: Trend of Non-Performing Loans in percent of Total Loans by Region**

<!-- Start of picture text --> 35<br>AS EAN Middle East S ub-S ahara n Afric a<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Figure 1 compares the trend of nonperforming loans in three regions of the developing world: Asia, Middle East and Sub-Saharan Africa. Solid dark line refers to Sub-Saharan Africa, light gray line refers to Asia and doted dark line refers to Middle East. The graphs show a massive accumulation of nonperforming loans in Sub-Saharan Africa, especially in the early 90s, an indication of poor asset quality and high credit risks. Expressed as a percentage of total loans, Sub-Saharan Africa recorded the highest share of these loans throughout the first half of the

> 16 However, while much has been written about these crises in Latin America and Asia, the literature on banking crises in Sub-Saharan Africa has remained scant, see for instance Gonzalez-Hermosillo et al. (1997) for the financial crises in Latin America, and International Monetary Fund (1999) for the Asian financial crisis.

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averages mask significant variation across countries. Concerns about vaccine efficacy were greater or equal to concerns about safety in five Latin American countries (Argentina, Chile, Dominica, Dominican Republic, and Mexico). Respondents in the Democratic Republic of Congo and the Republic of Congo were hesitant because of dislike for vaccines in general (40% and 44% in March 2021 of hesitant respondents, respectively) and distrust for government, pharmaceutical industry, and the international community (40% and 22% in March 2021, respectively).

Supply-side concerns for not getting vaccinated such as “health facility too far or too hard to get to,” “there is shortage of vaccines in the country,” and “I don’t know how to access the vaccine” (see Appendix Table 2b for full list) only accounted for 6% on average across all surveys with the single answer option. However, over 10% of respondents in several Latin American countries reported the concerns “Health center too far or hard to reach” or “I don't have time to go to get vaccinated.” These included Antigua and Barbuda (22%), St. Lucia (18%), Jamaica (16%), Guyana (13%), Belize and Nicaragua (both 12%). The relatively incidence of supply-side concerns in the HFPS data may be influenced by the framing of the question, however, such as the access issues being queried in the context of other concerns, or because respondents were asked if they would get vaccinated “when the vaccine becomes available”, and access constraints may be equated in people’s minds with vaccines not being available presently, and thus vaccines becoming available signifying the alleviation of existing supply constraints. We are not able to test this formally, however.

Among the seven countries with multiple answer options, a similar pattern was observed: concerns about safety were the most common concern cited in five countries (Mali, Nigeria, Kenya, Sudan, Uganda). In The Gambia, dislike of vaccines in general was the most cited reason, followed by preference for natural immunity (which includes perceiving self as low risk and already having had COVID-19 illness). In Georgia, the only country in Europe and Central Asia with information on reasons for hesitancy, the most cited reason was “not eligible,” followed by lack of knowledge or access and preference for natural immunity.

Only one country, the Republic of Congo, reported reasons for two waves. Between December 2020 and March 2021, only small changes in reasons were seen: the share of respondents who cited distrust of the international community declined from 30% to 22%, and the share who cited concerns about safety and dislike of vaccines in general increased (from 27% to 31% and 42 to 44%, respectively).

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Assessing the Global Economic and Poverty Effects of Antimicrobial Resistance Syud Amer Ahmed, Enis Barış, Delfin S. Go, Hans Lofgren, Israel Osorio-Rodarte, and Karen Thierfelder<sup>1</sup> _Keywords: health (pandemics), economic growth of open economies, measurement and analysis of poverty, computable general equilibrium modeling._ 1 Syud Amer Ahmed: The World Bank; Enis Barış: The World Bank; Delfin S. Go: The World Bank; Hans Lofgren: The World Bank; Israel Osorio-Rodarte: The World Bank; and Karen Thierfelder: U.S. Naval Academy. The paper is part of the background analysis for the World Bank report, _Drug Resistant Infections – A Threat to Our Economic Future,_ a discussion draft presented by Jim Kim to the United Nations in September 2016, and the final report issued in March 2017. The paper was prepared collaboratively between DEC Prospects Group and the Global Practice on Health, Nutrition, and Population (HNP) at the World Bank. It benefitted partly from funding from HNP and the Strategic Research Program on Infectious Diseases. We thank Olga Jonas, Henning Tarp Jensen, Csilla Lakatos, Maryla, Maliszewska,Donald Edward Shriber, Jason Nassios and other participants at the 20<sup>th</sup> Annual Conference on Global Economic Analysis on June 8, 2017 and the World Bank seminar on June 13, 2017 for suggestions and suggestions. The corresponding authors are Delfin Go (dgo@worldbank.org) and Karen Thierfelder (thier@usna.edu).
_BOND VALUES FOR DIFFERENT CAPS_ <!-- Start of picture text --> 100<br>14I<br>so<br>70<br>0<br>0~~6<br>4 °0 -f0<br>50 0<br>5 40- 3- .<br>o o<br>n<br>o 30<br>0~~£<br>N<br>10 -<br>0.1 0.3 0.5 0.7 0.9<br>(Thousauds)<br>CAP VALUES<br>0 BOND + COUPON O TOTAL<br><!-- End of picture text -->
<!-- Start of picture text --> Mean accessibility gains per NPR<br>Municipal HO<br>(monsoon)<br>Municipal HO<br>(dry)<br>Health facilities<br>(monsoon)<br>Health facilities<br>(dry)<br>Financial institutions<br>(monsoon)<br>Financial institutions<br>(dry)<br>0.00 0.02 0.04 0.06 0.08<br>Mean accessibility (person-hour) improvement (per NPR)<br><!-- End of picture text --> <!-- Start of picture text --> Mean accessibility gains vs. dispersion<br>Municipal HO<br>(monsoon)<br>Municipal HO<br>(dry)<br>Health facilities<br>(monsoon)<br>Health facilities<br>(dry)<br>Financial institutions<br>(monsoon)<br>Financial institutions<br>(dry)<br>0.00 0.05 0.10<br>Mean accessibility improvement (per NPR)<br>relative to service and population dispersion<br><!-- End of picture text -->
# Figure 15. MNA: Output growth and drivers of potential growth

A. GDP growth B. Contributions to potential output growth <!-- Start of picture text --> Percent MNA Median Percent TFP Capital Labor Potential growth<br>10 8<br>8 6<br>6 4<br>2<br>4<br>0<br>2<br>0<br>-2<br>2000-21 2000-10 2011-21 2022-23 EMDEs MNA<br>C. Working-age population growth D. Political stability<br>Percent Index, 0-12, 12 = best<br>3 MNA Median 11 EMDEs<br>Oil importers<br>10 Oil exporters<br>2<br>9<br>8<br>1<br>7<br>6<br>0<br>2000-2021 2000-2010 2011-2021 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: International Monetary Fund; Penn World Tables; PRS Group; UN Population Prospects; World Bank. Note: EMDEs = emerging market and developing economies; MNA = Middle East and North Africa. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates). A.C. Bars show period averages of annual GDP-weighted averages. Markers show median GDP-weighted averages of the six EMDE regions; orange whiskers show max-min ranges.

B. Period averages of annual GDP-weighted averages. Estimates based on the production function approach. Sample includes 53 EMDEs, of which 5 are from MNA.

- C. Working-age population refers to population ages 15-64. Sample includes 53 EMDEs (5 from MNA).

D. Based on the government stability subindex of the International Country Risk Guide. Unweighted average of 10 MNA oil exporters, 6 MNA oil importers, and 102 EMDEs.
transition countries.<sup>2</sup> Figure 1 plots nonparametric estimates of the distribution, in 1996 and 2004, of World Bank scores of adherence to the rule of law for 27 transition countries and the world as a whole.<sup>3</sup> The rule of law indicator is scaled so that the values of the world average and median are zero and the standard deviation is one. Higher scores represent greater adherence to the rule of law. In 1996, the transition countries had, on average, slightly less adherence to the rule of law than the world as a whole, but showed a low level of dispersion. Between 1996 and 2004, a twin peaks pattern emerged in the transition countries: Some achieved a good measure of rule of law by world standards, while the majority had scores that remained low or even deteriorated.

When it became clear in Russia’s privatization that many individuals were obtaining equity stakes through rigged bids and that controlling shareholders were stripping assets, even then some people argued that businessmen would, sooner or later, settle down to value-building. The following passage attributed in 1996 to the first head of Russia’s privatization program (Anatoly Chubais) is suggestive:

> 2 See, for example, Pistor (1999) and the symposium “Demand for Law” in the _East European Constitutional  Review,_ Hellman (1998, 2002), Aslund and Dmitriev (1999), Black et al. (2000),  Nagy (2000, p. 88), Sperling (2000, pp. 16-17), Kolodko (2001), and Graham (2002, esp. p. 49).  Other scholars have argued that in many countries, the cause of the absence of the rule of law lies on the supply side—for instance, the inability to collect taxes to finance a market-oriented system (Johnson, Kaufmann, and Shleifer (1997) and Roland and Verdier (1999)).  But financial problems reflect endogenous decisions of essentially the same kind as the demand side that we analyze here.  In Russia, it was clear that the state was giving away assets of value an order of magnitude greater than the cost of administering a rule of law system.  The lack of finance was a reflection of the same lack of demand for the rule of law on which we focus here.

3 The governance indicators are weighted composite scores constructed by Kaufmann, Kraay, and Mastruzzi (2005) from indicators produced by 31organizations, including Freedom House and the European Bank for Reconstruction and Development.

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# WPS3569

## **From Efficiency-Driven to Innovation-Driven Economic Growth: Perspectives from Singapore**<sup>*****</sup>

Kim-Song Tan (kstan@smu.edu.sg) and Sock-Yong Phang (syphang@smu.edu.sg)<sup>1</sup>

## Singapore Management University

### World Bank Policy Research Working Paper 3569, April 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* This paper was prepared for the East Asia Prospect Study being conducted in DECRG at the World Bank.  The authors thank Shahid Yusuf, Anjum Altaf, and Kaoru Nabeshima for their suggestions and guidance.  The authors also are grateful for the helpful comments provided by the participants of the workshop organized for this study held in Bangkok, February 22-23, 2004.

1 We would like to thank Lee Lay Pheng for her very able and patient research assistance.
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Two, the extremely-poor may not be able to respond to transitory real wage declines by working more hours<sup>79</sup> . Downturns cannot be offset by working more if households are already working all they can<sup>80</sup> . This might be particularly relevant for rural households which may also have lower mobility (because walking to distant work consunies too many scarce calories, because they live in remote regions with little transportation, or because they cannot afford the transportation) and fewer alternative opportunities in any given location. Thus, temporary downturns in the labor market may have direct 81 nutritional repercussions Three, the extremely-poor appear to have higher age-specific participation rates, which affects their demand for education. The children of the extremely-poor may participat **e** arly in economic activities in both urban (begging, shoe shining) and rural (working on the family farm, household activities) settings. Independently of the supply of educational facilities, the opportunity cost to the household of having children in school is too high, so that the extremely-poor, as opposed to the moderately-poor, may not be in a position to beneflt from educational programs. "In low income households, investments in the human capital of children, which provide lifetime returns to the child but possibly not to the parents making the investments, may be sacrificed to more immediate household needs" (Birdsall and Griffin, 1988, p. 34). Conversely, households with higher incomes can increase investments per child, in a sense engaging in a trade-off of quantity for quality. With larger number of children such investments may be deterred if parents face the risk of losing their investmen **t** hrough child death.

79The 'unemployment rates' registered in table 6 for the lowest income groups would seem to contradict this statement. But recall that the IES only inquired about employment status on the previous month, so that there is a large element of seasonality. A more detailed study of labor participation rates by income groups is required. 80Contrast this with the usual response for higher income groups, where as income falls the cost of leisure is higher; an increase in hours worked can then partly offset the income fall. At the level of the household this can imply that members that were previously not working can temporarily join the labor force (as probably happened during the 83-88 crisis).

810A survey carried out in 1982 in Oaxaca by the National Institute of Nutrition indicates that seasonal variation in food intake is widespread, wlth a pattern that falls between 1,900 calories per capita per day during harvest time, to close to about 1,400 in the perlod immediately before harvest. Many infants between 8 and 18 months are not able to survive the drastic decline in food availability" (Torche, 1990, p. 13; emphasis in the original).
The comparison for the state of Rio de Janeiro reveals that the state’s improvement in the IDEB score is larger than the average change of other states in the region. The state is converging fast with the regional averages. Because there are only two observations after the introduction of the bonus system, it is not yet possible to determine whether there is statistical significance. On the other hand, the homicide rate for the years 2008 and 2011 has significantly decreased in the state of Rio de Janeiro, but on average the rest of the states in the region did not register a significant change. The gap between the state and the region is quickly closing, and the difference is no longer statistically significant.

# **V. EMPIRICAL ANALYSIS**

Using an interrupted time-series model of the state health, security, and secondary education indicators data from 2002 to 2011, this section further analyzes if any systematic change in outcome measures can be attributed to the introduction of results agreements linked to variable pay. Fixed effects are added to control for unobserved time-invariant factors associated with particular states.

Using “state-year” as the unit of analysis, the following regression model is used: Where i = 1,2,....; N; refers to a cross-sectional unit; t = 1,2,....; T; refers to a time period; and k = 1,2,....; K; refers to each specific explanatory variable. Yit and Xit refer respectively to the dependent and independent variables for unit i and time t; and eit is a random error and β0 and βk refer, respectively, to the intercept and the slope parameters. The variable T is the treatment dummy in this case scored 0 for all periods in the before series and 1 for all periods after the intervention, β1 is the treatment effect, in this case the difference in intercept t, on the vertical space between the lines repressing the before and after series. The variable M is a dummy for state-years in which government-wide management reforms, known in Brazil as ‘management shocks,’ have been implemented. The disturbance term is assumed to be randomly distributed to the periods in the series.

Using this technique to estimate the pay-related effects on key outcomes, child mortality, the homicide rate, and the IDEB score are regressed on a dummy variable for the state-years in which the model was in place. The panel comprises ten yearly observations for 26 states and the Federal District. Each observation represents the aggregate state measure.

Partial models are used to testing the explanatory power of the treatment and control for some relevant factors (Table 5). The control variables include sector expenditure per capita (to proxy for the resources allocated to the sector), the poverty rate and GDP per capita (to proxy for the level of economic development), and GDP per square km and the population density (reflecting density effects).

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# **5.2.1. Schooling and Employment Shocks**

The size of the schooling shock amounts to an estimate of the value of the parameter _ν_ in the education shock equation: _hit_ = _hi,t−_ 1 + (1 _− ν_ ) _rs_ ( _sit_ ). For each group, we obtain _ν_ in the following manner. First, we obtain estimates of the school closure rate _s_ for each year (1, 2). Students at schools that were closed or partially closed continued their education in some form or another – through the internet, via TV or the radio, or by receiving work packs at home – with relative reach _c_ and effectiveness _e_ . Thus, the share 1 _−s_ children attend school as normal, and the share _s_ of children participate in distance learning with reach _c_ and effectiveness _e_ . Finally, we set _ν_ = _s ×_ (1 _− ce_ ). **School closures** UNESCO (2021) provides data on daily school closures globally. For each country and school day, we know the share of schools that are _fully open_ , _partially open_ or _closed_ . The data cover the period 02-16-2020 until 10-31-2021. For days that schools are supposed to be open but are _closed_ , we assume online instruction. For days that schools are _partially open_ , we assume a hybrid system where half the students are present and half are online (see Web Appx. Section C for details on our assumptions).

A school year lasts 9.5 months on average globally. We thus examine the share of _not fully open_ school days (using the formula “ _closed_ + 0.5 _partially open_ ”) for the first 9 _._ 5 school months (starting 02-16-2020) against the last 5-8 school months (which depends on the country’s school year schedule). We think of the last 5-8 school months as being representative of the second year of disruption. This provides estimates of _s_ .<sup>38</sup> **Reach and Effectiveness** To obtain estimates of the reach ( _c_ ) _×_ effectiveness ( _e_ ) rate of school not in-person in each group, we first discuss data on the reach and effectiveness rates of different remote learning modalities in each group. However, as these rates are likely misestimated, we also rely on direct estimates of the reach _×_ effectiveness rate.

UNESCO, UNICEF and the World Bank recently conducted a survey of ministries of education on national responses to COVID-19 (UNESCO, 2020b). Four remote learning modalities have been provided: _online platform_ (mostly in HICs and MICs), _take-home packages_ (HICs, MICs), _television_ (MICs, LICs), and _radio_ (LICs) (Web Appx. Table A.1). While take-home packages are accessible to all, we find lower reach rates of online platforms (as proxied by internet access) and television and radio programs (television and radio ownership) in LICs and MICs than in HICs (Web Appx. Table A.2).

38Web Appx. Fig. A.3 shows for each school year and each income group the share of schools that are _fully open_ , _partially open_ or _closed_ (using the child population of each country c. 2018 as weights).