rafmacalaba/datause-encoder-data huggingface dataset:
even with a prompt resolution of the pandemic in the form of a globally available vaccine so that we acquire effective herd immunity, significant damage has been inflicted on the global economy and the balance sheets of financial institutions. Given the emergency, these policies have provided a valuable stimulus tool beyond the conventional scope of fiscal and monetary policy. But as grace periods come to an end, more will be revealed on whether the problem facing countless firms and households is insolvency rather than illiquidity.

The challenge for many EMDEs extends beyond household and corporate balance sheets and encompasses the sovereign. Today, more than half of the low-income countries eligible for relief under the Debt Service Suspension Initiative (DSSI) are in debt distress or at high risk.<sup>6</sup> Several emerging markets have recently restructured (Argentina and Ecuador) or remain in default (Lebanon, Suriname, and Venezuela). The recent evolution of sovereign credit ratings is informative: while 15 percent of the advanced economies have had their rating cut since the start of 2020, the comparable share of downgrades for EMDEs that are rated is almost 40 percent.<sup>7</sup> The US Federal Reserve’s dramatic interest rate cut and other liquidity-enhancing measures in response to the pandemic have significantly eased global financial conditions for EMDEs and kept the incidence of new sovereign defaults from spiking.<sup>8</sup> This stands in stark contrast to the historic interest rate hikes that ushered in the developing-country debt crisis of the 1980s. Nonetheless, many EMDEs have recorded significant capital outflows and markedly weakening currencies (in some cases, outright currency crashes) during 2020. Furthermore, even before COVID-19, some large emerging markets (Argentina, Nigeria, and Turkey) as well as several of the low-income countries in debt distress have found it difficult to contain inflation to single digits.

The preceding narrative about current global fragilities places in context the topic I wish to address: Economic and financial crises in their various “guises or varieties.” Historically,

> 6 World Bank (2020c). The country risk ratings and other details are available at https://www.worldbank.org/en/topic/debt/brief/covid-19-debt-service-suspension-initiative.

> 7 Based on Fitch, Moody’s, and Standard and Poor’s sovereign ratings.

> 8 See Farah-Yacoub et al. (2021, forthcoming) for an encompassing database on sovereign defaults over 1800-2020.

6
Figure 1. Rise in Median Food Prices in Malawi, 2009–13 _(Kwacha)_ <!-- Start of picture text --> 400<br>350<br>300 <‐‐ IHS3<br>fieldwork ‐‐><br>250<br>200<br><‐‐IHPS<br>150 fieldwork ‐‐><br>100<br>50<br>0<br>Cassave prices Maize prices Rice prices Bean prices Soya prices<br>Malawian Kwacha<br>Mar‐09 May‐09 Jul‐09 Sep‐09 Nov‐09 Jan‐10 Mar‐10 May‐10 Jul‐10 Sep‐10 Nov‐10 Jan‐11 Mar‐11 May‐11 Jul‐11 Sep‐11 Nov‐11 Jan‐12 Mar‐12 May‐12 Jul‐12 Sep‐12 Nov‐12 Jan‐13 Mar‐13 May‐13 Jul‐13 Sep‐13<br><!-- End of picture text --> _Source:_ Authors based on the Agricultural Market Information System (AMIS) from the Ministry of Agriculture and Food Security.

Nationally, average per capita daily caloric intake increased slightly from 2,256 to 2,371 between 2010 and 2013 (Table 1). The prevalence of undernourishment (as measured by calorie deficiency) also dropped slightly from 53 to 50 percent during this period. We find similar improvements in individuals’ dietary diversity between 2010 and 2013. Table 2 shows there was a slight increase in the value of the FCS from 48.5 in 2010 to 50.5 in 2013. The data also reveal a substantial improvement in the distribution of FCS between waves. At the national level, the prevalence of ‘poor’ or ‘borderline’ FCS decreased from 27 to 19 percent; this same statistically significant trend is found separately in urban and rural areas. The observational measures of caloric intake and diet quality suggest that food security status improved overall (at the national level) between 2010 and 2013 with no evidence of deterioration in either urban or rural areas.

8
<!-- Start of picture text --> Total Production of L&A, in US$bil<br>$US bil $US bil<br>7 7<br>Crops<br>6 Dairy 6<br>Meat<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>1991 1996 2001 2006 2011 2016 2021<br><!-- End of picture text -->

**Figure 1: Agricultural production in Uruguay**

Total Production of main Crops, in thousand tons <!-- Start of picture text --> Thousands of tons<br>3,500<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>2016/17 2017/18 2018/19 2019/20 2020/21<br>Soy Rice Wheat Malting barley Maize Sugar cane<br><!-- End of picture text --> Source: Food and Agriculture Organization of the United Nations.

**Also linked to its natural capital, tourism, and more recently processed foods and wines, are an important source of export revenue** . Uruguay’s service exports have historically been dominated by tourism, mostly linked to inbound travelers from Argentina -and more recently Brazil- towards its beaches. Many resort cities and villages along its 220 kilometers of Atlantic coast become tourism hotspots during the summer months, notably Punta del Este, a high-end destination with a mix wide natural beaches, coastal forests, and modern amenities. Wineries are becoming a complementary touristic attraction, and more generally a staple export industry, making headway into international markets with the flagship Tannat reds. Software development and other ICT-related activities have become an increasingly important engine of export growth in recent years, reaching US$980 million in 2022.

**Figure 2: Uruguay merchandise export composition**

<!-- Start of picture text --> 9%<br>Other<br>12%<br><!-- End of picture text --> <!-- Start of picture text --> Livestock<br>31%<br><!-- End of picture text --> <!-- Start of picture text --> Industrial<br>Manufacturing<br>11%<br>Crop and<br>Paper and Animal and<br>Wood<br>Vegetable Oils<br>12%<br>25%<br><!-- End of picture text --> Source: Based on Uruguay XXI. Notes: Average from 2019-2022.

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depends on the extent of implicit liabilities, such as unfunded pension commitments for an aging population.

**Table 3.5. Government Debt, Percent of GDP**

||Country|2002|2007|2009|
|---|---|---|---|---|
|New Member States|Bulgaria|52|17|15|
||Czech Republic|28|29|35|
||Estonia|6|4|7|
||Hungary|56|66|78|
||Latvia|14|9|37|
||Lithuania|22|17|30|
||Poland|42|45|51|
||Romania|25|13|24|
||Slovak Republic|43|30|35|
||Slovenia|28|23|35|
|Candidates and Potential Candidates|Albania|65|54|60|
||Bosnia and Herzegovina|32|33|35|
||Croatia|40|33|35|
||Macedonia, FYR|49|32|21|
||Serbia||||
||Turkey|93|39|45|
|Eastern Partnership Countries|Armenia|37|16|41|
||Georgia|53|22|37|
||Moldova|73|27|28|
||Ukraine|34|12|35|

Source: Eurostat, Abbas et al (2010).

**Table 3.5a. Government Debt, Percent of GDP**

||2002|2007|2009|
|---|---|---|---|
|Euro Periphery|61|55|78|
|Emerging Latin America|75|46|44|
|Emerging Asia|47|38|42|
|New Member States|32|25|35|
|Candidates and Potential Candidates|56|38|39|
|Eastern PartnershipCountries|49|19|35|

Source: Eurostat, Abbas et al (2010).

In relation to private-sector indebtedness, a useful aggregate indicator is the ratio of private credit to GDP.<sup>5</sup> Table 3.6 shows the evolution of this measure, using World Bank data. For the new member states, this ratio grew quickly between 2002 and 2007 and continued to climb between 2007 and 2009. The Baltic States, Bulgaria, Hungary and Slovenia have the highest ratios but these remain well below the levels in more financially-developed advanced economies. The private credit ratio is far lower for the other emerging European economies but the rate of expansion over 2002 to 2009 has been broadly at the same pace.

In comparative terms, Table 3.6a shows that private credit in the new member states exceeds the level of financial development in emerging Asia and emerging Latin America but the other regions in emerging Europe are broadly similar to these other regions.

> 5 In addition to obtaining credit from the domestic banking system, domestic entities can also borrow directly from overseas banks or by issuing bonds domestically or internationally.  Table 3.4b shows the extent of cross-border borrowing by non-banks, while Table 3.4k shows the extent of international bond issuance by non-banks. However, in the other direction, some domestic credit might be redirected to other countries. For instance, the local affiliate of a multinational might borrow locally and make an inter-company loan to other units in the global enterprise.

19
**REFORMING HEALTH CARE**

# 7,eljko Bogetic and Dennis Heffleyl

# **I. BACKGROUND.**

'There is a pervasive feeling of imminent crisis with respect to the delivery of medical services in the United States. Expenditures on medical care ae rising without any offsetting decline in our mortality rates -- which compare poorly wvith tho " of many other countries. The costs of medical caro am increasing at an accelerating rate. Many big-city hospitals, which service the poor, are in a state of financial distress. Medicare and Medicaid, the programs developed to enable the disad-antaged to obtain medical care, set off the cwrent price spiral by creating a iapid increase in the demand for medical services while the supply of those ser-ices could be incmased only slowly. The middle class now finds it difficult to obtain the kind of medical care it has conwe to expe:t, namely that of an interested, warm physician who is familiar with the family history. There is a perceived over-all sho.tage of nurses and physicians. Moreever, many concerned individuals believe that the existing physicians are not distributed well. Th;y argue that there are relatively too few primary physicians and too many specialists, too few physicians in the inner cities and in rural areas, and too _miany_ in the affluent suburbs.' (Lave and LAve, 1970; pp.253-254).

Much attention is being given to possible health care reforms in the UJnited States<sup>2</sup> The preceding description of problems, written more than two decades ago, sug-. sts that the problerms of the health care system are not new. Nor are the problems unique to the United States. Efforts to rethink and reform health care systems are widespread in other developed countries, Eastern Europe, and less developed countries in general.<sup>3</sup>

1. Zeljko Bogetic is a Country Economist, Europe and Central Asia Region, Country Department 1, The World Bank. Dennis Heffley is a Professor of Economics at The University of Connecticut. The views expressed in this paper are the authors' and should not be construed as official views of their respective institutions. We gratefully acknowledge thorough and constructive comments from Alexander S. Preker, The World Bank.

2. Health care reform has emerged as a critical element of the Clinton agenda, but even the previous administration's economic advisors had come to view reform as an important issue. See Chapter 4 ('Tlhe Economics of Health Care') of the January 1993 _Economic Report of the President_ for a thorough overview of the issues and a discussion of various reform proposals.

3. Health has been selected as the primary focus of the 1993 _World Dwvelopment Report,_ reflecting the concen **jf** The World Bank ard other international agencies for improving health care in developing countries and ber understanding the role of health in rtimulating economic development. In a survey of the link between health and development and the contribution of health research, Birdsall (1990) identifies 'Analysis of altemnative financing
respondents grossly overestimated the number of deaths from both nuclear accidents. More than 75 percent of respondents think the number of deaths caused by the Chernobyl nuclear plant accident exceeded 10,000, with almost 17 percent believing that the accident caused a million or more deaths, but the actual estimates range from 50 deaths directly related to the accident to 4,000 total deaths from the radiation exposure over the lifetime.<sup>5</sup> About 92 percent of respondents think that the Fukushima accident resulted in fatalities much exceeding the actual estimates of no radiation-related deaths. Fifteen percent of respondents estimate the total number of fatalities in millions and almost 30 percent in hundreds of thousands.

We estimate a model that relates respondents’ perceptions of climate change and their willingness to pay for climate-change-mitigating policies with the aggregate index of their knowledge about fatalities in four global catastrophes. We use the instrumental variable (IV) approach to mitigate the effect of systematic non-response bias. Sensitivity analysis confirms the stability of our results to a wide range of alternative model specifications, aggregation methods, and variable definitions.

This paper presents evidence that challenges the assumption that more knowledgeable citizens invariably make better political decisions, particularly regarding climate change. It is widely believed that policies rooted in ignorance may lead to unjustified risks; our findings suggest that the opposite can be true in certain contexts. We show that individuals with _less accurate_ perceptions of catastrophe-related deaths tend to hold _more accurate_ beliefs about the societal risks of climate change and that people with more accurate beliefs about fatalities from catastrophes tend not to take climate change as seriously as scientists believe they should.

These findings challenge the conventional wisdom that better-informed citizens will make decisions that are in society’s best interests. In some cases, an inflated sense of risk from catastrophes may be more effective at instilling the urgency needed to address critical global challenges. Our findings suggest that a more nuanced understanding of the relationship between

> 5 These numbers correspond surprisingly well with the result of the 2012 All Russia Omnibus survey, which asked respondents to evaluate the number of deaths from the Chernobyl and Fukushima nuclear disasters using similar scales (Levada Center 2012). Thirty-three percent of Russian respondents thought that at least 100,000 people died in Chernobyl and 25 percent thought that as many people died in Fukushima; 10 percent of the respondents believed that millions for people died in Chernobyl, and 9 percent reported that millions died in Fukushima. A quarter of respondents responded “do not know” about Chernobyl and a third reporting not knowing about Fukushima.

3
in a given country on other countries. In a two-country setting, we conjecture that reserve accumulation in a country that is subject to learning-by-investing externalities may constitue a Pareto improvement if the second country is free of such growth externalities, as the second country benefits from a lower world interest rate and the first country from internalizing the externality. In turn, in a multi-country setting, all countries that exhibit learning-by-investing effects impose negative externalities on each other when they engage in reserve accumulation, whereas countries free of growth externalities benefit from other countries’ reserve accumulation.

# **References**

- Acemoglu, D., Aghion, P., and Zilibotti, F. (2006). Distance to frontier, selection, and economic growth. _Journal of the European Economic Association_ , 4(1):37–74.

- Aizenman, J. and Lee, J. (2008). The real exchange rate, mercantilism, and the learning-by-doing externality. _NBER Working Paper_ , w13853.

- Aizenman, J. and Marion, N. (2003). The high demand for international reserves in the far east: What’s going on? _Journal of the Japanese and International Economies_ , 17(Sept.):370–400.

- Arrow, K. J. (1962). The economic implications of learning by doing. _Review of Economic Studies_ , 29(3):155–173.

- Carroll, C. and Jeanne, O. (2009). A tractable model of precautionary reserves or net foreign assets. _NBER Working Paper_ , w15228.

- Charlton, A. and Stiglitz, J. E. (2006). _Fair Trade for All_ . Oxford University Press.

- Dooley, M. P., Folkerts-Landau, D., and Garber, P. M. (2003). An essay on the revived bretton woods system. _NBER Working Paper_ , w9971.

- Durdu, B. C., Mendoza, E. G., and Terrones, M. E. (2009). Precautionary demand for foreign assets in sudden stop economies: An assessment of the new mercantilism. _Journal of Development Economics_ , 89(2):194–209.

- Edwards, S. (1992). Trade orientation, distortions and growth in developing countries. _Journal of Development Economics_ , 39(1):31–57.

- Giles, J. A. and Williams, C. L. (2000). Export-led growth: a survey of the empirical literature and some non-causality results, part 1. _Journal of International Trade and Economic Development_ , 9(3):261–337.

- Gourinchas, P.-O. and Jeanne, O. (2007). Capital flows to developing countries: The allocation puzzle. _NBER Working Paper_ , w13602.

35
consistent with predictions by Carneiro and Arbache (2002), who use CGE simulations<sup>to</sup> explore the effects of export growth on Brazilian labor market. Third,<sup>the results suggest the</sup> contraction of employment in industries that experience an increase<sup>in theimport penetration.</sup> The coefficient on the import penetration in column 2 suggests that<sup>a.01increasein import</sup> penetration in a given industry reduces the industry's share in manufacturing<sup>employmentby</sup> .00 137. Since the import penetration increased on average by .076 from 1987<sup>to1998,an</sup> industry experiencing an average increase in import penetration would contract<sup>itsemployment</sup> share by .01 over the entire period. Finally, trade policy changes could potentially<sup>impact</sup> industries with greater exposure to international competition more. In column<sup>3,we check</sup> whether trade policy has differential impact on industry employment share in industries<sup>with</sup> higher import penetration by including the interaction of import penetration<sup>and tariffs.The</sup> results suggest that a tariff decline has a larger negative impact (in absolute<sup>terms)on industry</sup> employment in industries with higher import penetration. A 10 percentage<sup>point tariff declinein</sup> an industry (evaluated at the mean import penetration .08) is associated<sup>witha.002 declinein the</sup> industry's share in manufacturing employment.'<sup>4</sup> In sum, the employment patterns over 1984-1998 are not fully consistent<sup>withan</sup> explanation that would attribute the rise in the returns to skilled workers<sup>tochangesin trade</sup> policy, operating through Stolper-Samuelson effects. The high year-to-year<sup>correlationsof</sup> industry employment shares before and after trade liberalization suggest<sup>that thestructureof</sup> employment patterns across industries remain relatively stable over the<sup>11years surroundingthe</sup> trade liberalization episode. Moreover, we find no general association<sup>between employment</sup> contractions and tariff reductions. However, tariff reductions are associated<sup>with declinesin the</sup> industry share of manufacturing employment in industries with high<sup>import penetration. Also,</sup> 14 In our data, a tariff level of 10 indicates a 10% advalorem tariff (i.e. .1).

15
# **1. Introduction**

Colombia’s recent record of solid economic growth led to significant reductions in poverty and improvements in social indicators from 2002 to 2016. Moreover, during this period, the extreme poverty rate more than halved, falling from 17.7 percent to 8.5 percent; while moderate poverty fell from 49.7 percent to 28.0 percent, as shown in Figure 1. Consequently, the number of poor in Colombia declined from about 20 million in 2002 to approximately 13.2 million in 2016. In addition, during the 2008-2016 period the Gini index declined from 56.7 to 51.7. However, Colombia continues to be one of the most unequal countries in the region (see the results on the Gini index in Figure 1) with higher inequality than the average of the region (the Gini index for the Latin America and the Caribbean region was 51.4 in 2014 versus 53.8 in Colombia for the same year) and neighboring countries such as Ecuador (45.4 in 2014), Panama (50.7 in 2014) and Peru (44.1 in 2014).

Despite showing a downward trend in poverty from 2002 to 2015, monetary poverty did not show statistically significant changes from 2015 to 2016 (poverty increased 0.2 percentage points), while extreme poverty increased slightly during the same period (0.6 percentage points). This behavior is explained by the reduction in the growth rate experienced by Colombia, after the plunge in oil prices in mid-2014, followed by a devaluation of the Colombian peso in 2015 and high inflation in 2016. This raises concerns regarding the vulnerability of households to falling into poverty as well as on the evolution of the middle class during difficult economic environments. Thus, additional measures of welfare dynamics are necessary to obtain a richer picture of the evolution of movements across the distribution.

Ideally, a researcher would like to use a longitudinal survey or panel data to analyze welfare dynamics or income mobility. However, in many developing countries panel data sets are not readily available, span few periods, or suffer from “non-random” attrition issues, hindering the capacity of researchers to study elements such as the factors that help households escape or remain in poverty (Dang and Lanjouw, 2013; Bourguignon and Moreno, 2015). To overcome the absence of panel data or longitudinal surveys, authors such as Deaton (1985), Deaton and Paxson (1994) and Pencavel (2007) have proposed methodologies to construct pseudo-panels by following similar age cohorts across multiple crosssection surveys. Nevertheless, as argued by Dang _et al._ (2014), these methodologies typically rely on having several rounds of cross-section surveys, but do not allow to analyze mobility at a more disaggregated level than the cohort. In addition, Fields and Viollaz (2013) argue that pseudo-panel methodologies might not perform well in predicting income mobility in some cases.<sup>1</sup>

> 1 Fields and Viollaz (2013) analyze the performance of two pseudo-panels methods to predict income mobility only for Chile.

2
Table 10: NIS Efficiency LFM Estimator

|**Observations**|**Annual**<br>482||||||
|---|---|---|---|---|---|---|
|**Countries**|46||||||
|**Period**|1976-99||||||
|**Lagged Patents**|0.419 ***|0.400 ***|0.440 ***|0.479 ***|0.409 ***|0.478 ***|
||(0.07)|(0.06)|(0.07)|(0.08)|(0.07)|(0.08)|
|**ln R&D**|0.260 **|0.182 *|0.220 *|0.376 ***|0.260 **|0.350 ***|
||(0.13)|(0.11)|(0.12)|(0.12)|(0.13)|(0.11)|
|**ln US trade**|0.290 ***|0.330 ***|0.300 ***|0.222 ***|0.315 ***|0.260 ***|
||(0.08)|(0.08)|(0.11)|(0.07)|(0.11)|(0.08)|
|**Natural Resources**|-0.014|-0.037|-0.036|-0.046 *|-0.008|-0.044|
||(0.02)|(0.04)|(0.03)|(0.03)|(0.01)|(0.05)|
|**ln GDPcap**|0.029 ***|0.029 ***|0.026 ***|0.018 ***|0.027 **|0.013 **|
||(0.01)|(0.01)|(0.01)|(0.00)|(0.01)|(0.01)|
|**ln (Mean Patents)**|0.154 **|0.120 **|0.162 ***|0.097 **|0.147 **|0.098 **|
||(0.07)|(0.06)|(0.06)|(0.05)|(0.07)|(0.04)|
|**Quality**||0.034<br>(0.02)|||||
|**Colaboration**|||0.023<br>(0.02)||||
|**Education**||||0.049 **<br>(0.02)||0.058 ***<br>(0.02)|
|**IPR**|||||0.014 **<br>(0.07)|0.010 *<br>(0.01)|

**Note: The dependent variable is the count of Patents. Robust standard errors were estimated in all the specifications. All specifications include time dummies.**

32