rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The Millennium Development Goals (MDGs) consider income poverty and malnutrition indicators of poverty that should, relative to their 1990 levels, be halved by 2015. This raises the question whether nutrition and income poverty measure different dimensions of the same development strategy. In particular, would a growth strategy that might achieve a significant increase in GDP as well as a reduction in income poverty, also be sufficient to attain the nutrition MDGs? Or does malnutrition respond differently to income growth than does income poverty?

Increases in income are clearly important for reducing malnutrition. Greater incomes at the household level allow families to spend more on food, clean water, hygiene and preventive and curative health care. It allows them to have a more diversified diet and to obtain more effective childcare arrangements. At the community level, greater income will eventually lead to better access to and higher quality health care, improved water and sanitation systems and greater access to information.

Empirical evidence supports the importance of income growth for the reduction of malnutrition. A recent study using household survey data from 12 countries (including three African countries: Kenya, Mozambique, and South Africa) estimates the magnitude of the response of weight-for-age to income growth (Haddad et al. 2003). Under a scenario of sustained per capita income growth of 2.5% per annum, the average reduction in the fraction of underweight children would be between 27% and 34%. This average hides substantial inter country variation, even within Africa. The reduction could be as high as 42% (Kenya) or as low as 14% (South Africa).

Thus, Haddad et al. (2003) suggest that complementary instruments would be needed. However, while there is a body of clinical and programmatic evidence on which interventions are most promising (Allen and Gillespie, 2001), there is comparatively little data that indicates how nutrition interventions could augment income growth, especially in Africa, the one region in which malnutrition rates have not yet begun to decline (de Onis et al. 2004). Papers that do assess the impact of interventions on nutritional status, often deal with programs such as food aid and food for work in the aftermath of a drought shock (Yamano et al. forthcoming; Quisumbing 2003). Others such as Christiaensen and
The countries in our sample are drawn from all World Bank regions. While the coverage reflects heavily the availability of surveys which include detailed information on the income sources—as well as the expenditure patterns—of the poor, we sought to improve the coverage of our country sample in the countries that contain the largest numbers of poor people: for example, in terms of population our sample represents 98 percent of the South Asia region, 32 percent of Sub-Saharan Africa, 41.8 percent of middle income countries and 34.5 percent of the population of lowincome countries.

Where available, we used country-level data on actual changes in domestic food prices from _Food Price Watch_ (World Bank, 2011) presented in Table 2. Comparison of Table 1 and Table 2 suggests that the transmission of global price increases to domestic prices has been high in many countries. For instance, between June 2010 and December 2010, the 68% increase in the international price of wheat was associated with large price increases in Kyrgyzstan (54%), Bangladesh (45%), Tajikistan (37%), Sri Lanka (31%), Azerbaijan (24%), and Pakistan (16%). The domestic price of rice rose broadly in line with the 21% increase in global prices in Indonesia (19%), Bangladesh (19%), and Pakistan (19%) during this six month period. Higher sugar prices have fed through to domestic prices quickly in many countries—for instance, sugar prices doubled in Cambodia between June and December 2010 and edible oil prices increased by 15% between September and December in Afghanistan. Several countries have intervened to temper this pass-through. In Algeria, taxes and import duties on sugar and edible oil were sharply reduced in January 2011 due to double-digit price rises. In Indonesia, the government reduced taxes on sugar and increased subsidies to local cooking oil producers.

In cases, where no domestic price data were available, we used import shares reported in version 7 of the GTAP database (Hertel 1997) to link global prices (Table 1) with domestic consumer prices. This approach is consistent with imported goods being imperfect substitutes for domestically-produced goods and the changes in domestic prices being a weighted average of the prices of imported and domestic goods.

# **Results**

In Table 3, we first present the results on the poverty impact of the price changes experienced by households for the standard international extreme poverty headcount of people living below 7
Table 2 examines the elasticity of a region’s exports to global income. The intuition for looking at regional trade and global income (instead of regional income) is to see how tied a region’s exports are to the global economy. To the extent that the increase in the world elasticity of trade to income is a result of greater fragmentation we should see especially large numbers for East Asia. Indeed, East Asia records the largest elasticity in the recent period, as well as the largest change in elasticity, suggesting that fragmentation is in part responsible. It also implies that East Asia may be the most affected region as a result of trade linkages during the current downturn. The growth in the elasticity in this region is also likely connected to China’s phenomenal growth in assembly of manufactures over the period. Still, as the OECD is responsible for the bulk of world trade, the global elasticity also closely mirrors that of the OECD. In contrast trade in the low income countries does not significantly respond to world income.

Trade may respond differently to GDP during global downturns. Figure 2 shows real trade growth and real GDP growth in the years around the previous global crises. We report the mean and the median to ensure that results are not driven by an outlier. While income growth falls to 1.5 percent, trade growth becomes negative, declining by about one percent. (Real trade growth was negative only in 1975 and 1982.) In addition, the decline in growth from the previous year to the crisis year is much larger for trade. GDP growth declines on average by 1.5 percentage points from previous year, while real trade declines on average by 7.2 percentage points, nearly 4.7 times as much. If during this downturn, we see a similar ratio there would be a decline of about nearly 20 percent in real trade this year, given the World Bank’s current GDP forecast.<sup>4</sup>

> 4 Using an elasticity of trade to income during the downturn of between 4.7 and a deceleration in real world income growth of 4.8 percentage points (the World Bank estimate), the deceleration is real trade growth

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# SIMULATION WITH CONSTANT FISCAL POLICY

<!-- Start of picture text --> (Simulations: 1978 - 1988)<br><!-- End of picture text --> <!-- Start of picture text --> 0.11 -<br>0.1 ,<br>0.09<br>0.0<br>0-<br>0<br>-~0.08<br>0.0<br>0.05-<br>1978 1980 1982 1984 1986 1988<br><!-- End of picture text --> 0 Actual (fitted) + 1978 Credit & Infro

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and other countries in the periphery experienced major withdrawals of foreign-owned funds in the course of the crisis. The crisis exposed Ireland's vulnerabilities to financial contagion.<sup>13</sup> <!-- Start of picture text --> Figure 13: Foreign-owned banking sector liabilities<br>30000%<br>25000%<br>20000% Ireland<br>Spain<br>15000% Iceland<br>Switzerland<br>10000%<br>5000%<br>0%<br>Percent GDP<br><!-- End of picture text --> Sources: BIS; and IMF, World Economic Outlook database.

35. The housing boom was also the crucial factor obfuscating the soundness of Ireland‘s fiscal position. On the surface, Ireland had pursued conservative fiscal policies and in fact had been running fiscal surpluses since the mid-90s, except in 2002. However, these surpluses were increasingly driven by tax revenue boosted from the property bubble. In fact, during 2000-2007, driven largely by the revenue boom, revenue from capital taxes and stamp duties increased from 2 percent in 2000 to 4 percent of GDP, while VAT increased by one percentage point of GDP to 7 1⁄2 percent.<sup>14</sup> At their peak in 2007, these revenues represented over a third of total current revenue, as opposed to one-fifth of current revenue in Spain and the UK, two other countries with large asset booms. This asset-related revenue encouraged increases in structural expenditure and masked a rising structural deficit. When adjusting for the impact of asset prices, the Irish structural deficit reached 8 percent in 2007 (as opposed to a measured surplus of 0.9 percent of GDP), spiking to 12 percent of GDP in 2008.

> 13 Lenihan and Honohan (2010).  Also, Lane (2011), which discusses how Ireland was able to refinance their debt through the ECB-IMF.  Iceland, on the other hand, was not able to do so, and had to negotiate directly with UK and Dutch governments in order to refinance, paying higher interest rates in the process. 14 IMF (2010).

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substantial share of the population appears to be influenced by this IV. For example, distance to the family planning center remains statistically significant and has similar estimated coefficients even when the first-stage regressions control for various commune characteristics, including infrastructure and occupation (Table 1.1 in the Appendix). The IV impacts of family size on different outcomes remain similar (and are even stronger for certain outcomes) in the case of households with larger extended families (row 2, Table 8), households that perceive their children’s school as being of good quality (row 5, Table 8), and those that are better-off (that is, that belong to the richest three consumption quintiles—results not shown). A conservative estimate shows that together, these groups of households make up half or more of our estimation sample.

_Outcomes for younger cohorts in 2008_ Even if the quantity-quality tradeoff exists, it will not necessarily remain the same for different cohorts over time. Recent studies find that this tradeoff holds for younger but not older cohorts in Norway (Black, Devereux, and Salvanes, 2010), turns from positive to no effect and then negative during the 1977-2009 period in Brazil (Marteleto and de Souza, 2012), and changes from positive for older cohorts to negative for younger cohorts in urban areas in Indonesia (Maralani, 2008).

To investigate whether the same quantity-quality tradeoff applies to younger cohorts in the same age range (6-18) in Vietnam, we rerun the same regressions on available indicators in the 2008 round of the VHLSS.<sup>39</sup> While the 2008 data collect fewer variables on tutoring and include information only on tutoring expenditures, our estimation results on the available indicators provide broadly qualitatively similar results (row 6), except that the effect

> 39 As in a previous regression in Table 7, because the distances to school variables are not significant in this specification, we left them out to allow larger sample sizes and greater precision of estimates.

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Respiratoxy Hospital Admissions. Current evidence indicates that ozone may be associated with - hospital admissions related to respiratory disease (RHA)3<sup>5</sup> This possibility is supported by findings from panel studies of asthmatics indicating that exacerbations occur in response to ozone?34-' Clearly, some of these exacerbations may result in either emergency room visits or hospital admissions. Unfortunately, because of the high covariation between ozone and other pollutants in the summer when most of the studies have been undertaken, it is difficult to determine the effect on RHA attnrbutable to ozone alone. However, by using information from several studies, it is possible to begin to apportion the effects of the different pollutants. Thurston et al.53 found a significant association between RHA and both ozone and sulfates in New York City (the Bronx) and Buffalo in the summer 1988- In this analysis, correccions for autocorrelation and day-of-week effects were made. At the mean, the effect of ozone was approximately twice the effect observed for sulfates. Burnett et al.4 also reported a staistcally significant association between hospital admissions and both ozone and sulfates in Ontario, Canada for the years 1983 through 1988. Their findings suggest that the ozone effect was approximately 3 times tbat of sulfates, based on a regression equation that included both pollutants. Therefore, it is reasonable to apportion the effects of RHA based on the relative coefficients in Thurston et al. The average of the coefficients for the two cities in that paper is 2L .3 RHA per day/ million/ppm ozone. which becomes the central estimate, with a standard error of 10.9. Thus, after annualizing, the functions for RIHA become: Upper change in RHA per person 0.012 * change in daily 1-hour max ozone (ppm) Central change in RHA per person = 010077 * change in daily 1-hour max ozone (ppm) Lower change in RHA per person -0.0038 * change in daily 1-hour max ozone (ppm) Restrictions in Activitv. Portney and Mullahy<sup>1</sup> ' used the 1979 Health Interview Survey conducted by the National Center for Health Statistics. to examine the relationship between ozone and
its cross-country average, and the _Success_ ratio. The second column shows that human capital per worker as measured by (5) is higher in the United States (which represents the 95<sup>_th_</sup> percentile the income distribution) compared to most other countries. For the poorest countries, however, the human capital gap is substantially smaller than the gap in terms of GDP per worker, reported in the first column. As a consequence, _Share_<sup>_US_</sup> _c_ is relatively low for those countries. The pattern is different for countries above the 60<sup>_th_</sup> percentile, for which human capital can account for the majority of the income gap with respect to the United States.

Overall, the limited variation in human capital between the richest and poorest countries leads to a _Success_ ratio of 8.05%. Indeed, most early contributions to the development accounting literature found a limited role for educational attainment which, combined with similar results for physical capital, lead to the conclusion that Total Factor Productivity, _A_ in equation (4), accounts for most of the cross-country dispersion in economic performance (Hall and Jones, 1999; Caselli, 2005). More recent work has challenged this conclusion by considering broader measures of human capital.

# **4.2 Measure of Cognitive Skills**

In this section I consider extensions of the human capital formulation that include direct measures of cognitive skills. A recent strand of the literature incorporates the average performance in standardized tests of either school-age children (Hanushek and Woessmann, 2012b; Hanushek et al., 2017) or working-age adults (Hidalgo-Cabrillana et al., 2017) in development accounting exercises. This approach partially bypasses the problem of identifying the key _inputs_ for human capital accumulation by measuring directly an _output_ of that process. Differences in cognitive skills, as measured by test scores, reflect a combination of school quality (Hanushek and Woessmann, 2012a), parental influence (De Philippis and Rossi, 2016) and characteristics of the institutional environment (Woessmann, 2016).

I adopt the standard assumption that human capital per worker is given by where _tc_ is an average measure of cognitive skills, while _δ_ is a parameter governing the conversion from this measure to human capital.

For cognitive skills, I use the dataset constructed by Hanushek and Woessmann (2012a), who combine results from various standardized tests and express them on a common metric. I integrate this database with additional test results for Latin American countries that the same authors provide in Hanushek and Woessmann (2012b). This leaves me with a sample of 81 countries. While the tests used for the construction of the dataset were administered 13
# 2.2 Sources of Governance Data

The 35 WGI data sources used in the 2024 update are summarized in Table 1, with a more detailed description of each data source in Annex 1. The WGI data sources fall into two broad categories: surveys of firms and households, and expert assessments. The former includes several major surveys of firm managers: the World Economic Forum’s Executive Opinion Survey that informs their annual Global Competitiveness Report, the Institute for Management and Development’s Executive Opinion Survey that informs their annual World Competitiveness Report, and the World Bank’s Enterprise Survey Program, which has recently been greatly expanded to achieve near-global coverage on a rotating threeyear cycle to inform the World Bank’s new Business Ready report series. The WGI also include data from several regional household survey programs (Afrobarometer, Latinobarometro, Americas Barometer, and European Quality of Governance Survey), as well as one global household survey (the Gallup World Poll). In addition, one of our data sources, the World Justice Project Rule of Law Index, is a hybrid combining findings from an expert assessment with a cross-country household survey with near-global coverage. These firm and household survey data sources are particularly salient because they directly capture survey respondents’ perceptions of the quality of governance in the countries in which they live. In total, household and firm surveys make up 11 of the 35 data sources in the WGI.

The remaining 24 WGI data sources are expert assessments, reflecting the perceptions and views of experts affiliated with a range of NGO, private sector, and government sector organizations. All of them share a broadly similar approach in which experts employed and/or recruited by the organization provide numerical scores of various dimensions of governance, following a standardized methodology and set of definitions developed by the organization producing the data source. In many cases, the experts providing the assessments are based in the country or region they assess. These data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they lend themselves well to cross-country comparisons, as their methodologies are explicitly designed for this purpose. Expert assessments can also provide more granular technical assessments, for example on the quality of specific types of public institutions, that would be more difficult for a typical household or firm survey respondent to provide and informed view on. Expert assessments also are less likely to be affected by respondent reticence, a concern in household and firm surveys where 6
functions. However, the set of large farm interaction parameters are significant. The hypothesis that the set of large farm interaction parameters is zero is rejected at 1% significance level according to the F test statistics of 6.3 in the rainfed regression and 5.5 in the irrigated land regression. In other words, large and small farms have significantly different climate coefficients.

In order to test this more formally, we compute the marginal impacts of climate in Table 5. The marginal temperature effects of rainfed and irrigated lands are quite different. Rainfed farms are very sensitive to summer temperatures. Small farms have a negative temperature elasticity of -2.8 and large farms of -1.7. Small irrigated farms have no response to summer temperature and large irrigated farms actually benefit from warmer summer temperatures. Warmer winter temperatures benefit small rainfed farms whereas warmer winter temperatures are harmful to both small and large irrigated farms. The results for summer precipitation are more consistent across farm types although large irrigated farms have surprisingly high elasticities. The results for winter precipitation follow a mixed pattern being harmful to large rainfed farms and small irrigated farms but beneficial to large irrigated farms. The positive effect of an increase in winter precipitation on small rainfed farms and the positive effect of an increase in winter temperature on large rainfed farms are not significant. Most of the marginal climate effects for irrigated farms are not significant. However, an F test of whether rainfed and irrigated farms have the same climate response coefficients is rejected at the 1% level (F=9.16) for small farms and at the 5% level (F=2.25) for large farms.

# **5.  Climate Scenarios**

In order to predict climate change impacts for the coming century, we examine climate 14
increases across rounds in our outcome variables of interest, with some exceptions. The fraction of firms that reduced the total number of temporary workers is unchanged across the two rounds in El Salvador. In Georgia, the average percentage change of permanent full-time workers increases almost four percentage points between two rounds, but this change is not statistically significant. The fraction of firms that anticipate falling in arrears on outstanding liabilities in the near future decreases considerably across the survey rounds in El Salvador but increases slightly in Georgia.

# **4.3. Main Explanatory Variable**

COV-ES includes a question on whether firms received any national or local government support in response to the COVID-19 crisis, with three response categories: Yes, No but expect to receive it in the next three months, and No. In the first round, this question is asked with the reference period of “since the outbreak of COVID-19”. In the second round, the firms that participated in the previous round were asked the same question but with the reference period “since the previous round month”. We create a dummy variable taking value 1 for firms that responded Yes, and 0 otherwise to create our main explanatory variable. Importantly, for the second round, this variable is created cumulatively, meaning that if the firms responded Yes, either in the first or the second round are counted as having received government support.

Table 3 reports summary statistics of this variable too, for each country and round. The reach of government support increases considerably across rounds in both countries. While only 1.3% of the firms in El Salvador received government support in R1 (June-August 2020), 42.1% of them received some type of government support by R2 (October 2020 through January 2021). Government support in Georgia reached firms earlier than in El Salvador. By the time of R1 interviews (June 2020), the fraction of firms that received government support was 31.5%, and it increased to 48% in R2.

COV-ES also collects information about the types of government support. Firms that received or expect government support are further asked a battery of Yes/No questions on the type of support received. In particular, each firm was asked whether or not they received support in the form of (i) cash transfers, (ii) deferral of payments, (iii) access to new credit, (iv) fiscal relief, (v) wage subsidies, or (vi) some other form. Importantly, many firms in both countries received more than one type of government support, with a wide variety of combinations of the support types 13