rafmacalaba/datause-encoder-data huggingface dataset:
# **3. The Severity of the Pandemic**

While the virus has spread rapidly and extensively, the reported death toll remains surprisingly concentrated in just a few geographies. As of May 23, the cut-off date for all data in this paper, the death toll has risen to about 340,000 people. That is roughly 2.2 times the number of deaths that we would expect to occur on an average day prior to the pandemic. As Figure 1 shows, five countries—the United States, Italy, the United Kingdom, Spain and France—account the bulk of the global death toll. While they only represent 7.5 percent of the world’s population, these five countries claim 65 percent of the total number of fatalities. The widening distance in the figure between the world and the top five also implies that the death toll in the rest of the world is now growing more quickly.

Figure 1. COVID-19 Death Toll: World and Top Five Countries <!-- Start of picture text --> World<br>300<br>Top 5: US, UK, Italy, Spain, France<br>200 Top 4: US, UK, Italy, Spain<br>Top 3: US, UK, Italy<br>100 Top 2: US, UK<br>Top 1: US<br>0<br>Mar 1 Apr 1 May 1<br>Number of COVID−19 deaths (thousands)<br><!-- End of picture text --> Source: Johns Hopkins University.

## **A New Indicator of Relative Severity**

In what follows, we will examine the unequal distribution and progression of the pandemic through the lens of the ‘relative severity ratio’. This indicator is defined as the ratio of (1) the total deaths attributed to COVID-19 over a given period to (2) the expected total deaths from all causes under the counterfactual assumption that the pandemic had not taken place over a base period of the same length. Depending on the length of the period, we consider two such indicators: a cumulative and a daily ratio. The daily ratio uses a single day as the basis for the period. The cumulative ratio uses the ‘outbreak-to-date’ (OTD) period, which is taken to be the countryspecific length of time between the occurrence of the first death and the cut-off date for the calculation of the indicator.

As noted earlier, different efforts have been made thus far to track the mortality burden of the COVID-19 pandemic. The ‘relative severity ratio’ adds to these efforts by explicitly relating COVID-19 mortality to the pre-pandemic mortality profile of individual countries. This is achieved not only with reference to all-cause deaths (the denominator of the indicator) but also 6
correlations between nutritional, health and other outcomes and wealth indices (Hong and Hong 2007; Zere and McIntyre 2003; Howe et al. 2009). Different data sets can tell different stories here, so a comprehensive look at the evidence across multiple countries is needed.

A strand of the literature has used the DHS wealth index to measure inequalities in child nutritional status, mainly using the concentration curve which gives the share of undernourished children living in the poorest x% of households based on the wealth index (Kakwani et al., 1997; Wagstaff and Watanabe 2000; Wagstaff et al., 2014; Bredenkamp et al., 2014).<sup>11</sup> A widely-used measure based on this curve is the concentration index, given by twice the area between the curve and the diagonal (analogous to the Gini index). A key finding from this literature of relevance here is that the concentration indices for child stunting and wasting in developing countries are almost invariably negative. A typical conclusion found in this literature is that “Unsurprisingly, in all countries, undernutrition is concentrated among the poor.” (Bredenkamp et al., 2014, p.1330). Such assessments appear to support the common, but often implicit, assumption among social policy makers that targeting poor households will be effective in reaching undernourished individuals. However, the concentration indices are rarely more negative than -0.3, with median values typically around -0.15 to -0.10 (depending on the measure of undernutrition).<sup>12</sup> While this confirms that children from wealthier households tend to be better nourished (given that the index is negative), it also suggests that there is quite wide dispersion of undernutrition across wealth strata. We study this dispersion, focusing on its implication in the context of efforts to use household poverty data to target undernourished individuals.<sup>13</sup> A number of recent papers review the existing evidence on the nutritional impacts of income growth and income support to poor households. On the first, as already noted, several papers find low income effects, particularly in the short-term (Grogan and Moers 2016; Haddad et al. 2003; Smith and Haddad 2015). With respect to the second, Alderman (2015) and Ruel and Alderman (2013) conclude that social safety nets targeting poor households with food or cash transfers (whether conditional or unconditional) have generally had limited impacts on children’s nutritional status. The papers speculate that this may be because the targeted households are not those that have young children in the right age range. They do not question the practice of targeting poor households to reach undernourished

> 11 There has been far less focus on inequalities in malnutrition among women.

> 12 The online addendum to Bredenkamp et al. (2014) provides concentration indices across 80 developing countries for child undernutrition using the wealth index as the ranking variable. The median for stunting is -0.15.

> 13 We do not use the concentration index here as there is greater interest in this context in points on the concentration curve.

6
20 41. Since the non-marginal costs (the difference between MSC and ASC) are fixed, the rectangle **EBP'F, EB.N',** is also equal to AC.N. The above inequality can thus be written as

**m < (1/2) AC*(N'-N)/(AC*N)**

or **m** < ( O2)(N'-N)/N or **m <** (1/2)AN/N where A denotes a finite charge: **AN** = (N'-N), **AP = (P-P').** Since the point elasticity e evaluated at point A is defined to be (AN/N)/(AP/P), it follows that: m < - (1/2)e AP/P The right-hand side of this inequality has been plotted in Figure 5 as a function of the proportional fall in price, **AP/P,** given the all-day price elasticities of demand for automobiles and buses, and the aggregate price elasticity of demand for trucks (from Annex 2).

<!-- Start of picture text --> D<br>4.)<br>A<br>o ~~~~~~~~~~~~~E<br>F ...-<br>tv B~1<br>83 I I MSC<br>l I<br>° N N'<br>Amount of travel<br><!-- End of picture text -->

**Figure 4:**

Effect of **Marginal** and **Averagc** Cost Pricing on **Total** Surplus
Despite the similarity of the upper-bound estimates, the corresponding lower bounds in each model are not so similar. Again, taking the example of Tanzania, the estimate for the school variance component is 0.12 standard deviation units under the (two-step) UBH model but is 0.25 for π = 0. Effectively, since the individual and residual terms are (on aggregate) also similar under these two models, the difference is found in the covariance term, which is systematically positive under the two-step estimates but is always much closer to zero under the extreme initializations of the modified partitioned iterative algorithm. This supports the contention that the latter approach produces systematically smaller estimates of the sorting component. Furthermore, estimates from these extreme choices do not appear very credible. Recall the UBS model includes proxies for the household terms, and this model yields estimates for the magnitude of sorting that are always positive and significantly greater than those based on the extreme initializations. For instance, the between-factor correlation ( _ρ_ ) in Table B4 (Kenya) is 1% when _π_ = 0 but is 9% under the two-step procedure (model UBH). That is, as the two-step upper-bound model points to the presence of positive sorting, the absence of this finding under the extreme initializations of the partitioned iterative algorithm support the contention such initializations suffer from a mechanical negative bias on the sorting (covariance) term.

Finally, estimates based on the BLUPs from the mixed-linear model closely resemble the results from the agnostic initialization across the various components. This is perhaps most stark for the sorting component, which is also larger in magnitude under this approach than found under either the UBH or UBS methods (as well as for the extreme initializations). Admittedly, the contribution of the school effects is somewhat larger under the mixed-linear model compared to the agnostic initialization, but this in part reflects the influence of the interaction terms. Thus, while we are not in a position to claim the agnostic initialization corresponds to the ‘true’ data generating process, it appears to provide the most reasonable and well-supported (conditional) variance decomposition in the present case.

# **4.1 Sub-group heterogeneity**

Having validated our proposed methodology, we now investigate the presence of heterogeneity in the variance components, focusing on the preferred agnostic initialization (upper bound sorting model) with the extended specification. To do so, we re-run the variance decomposition based on the earlier regression estimates but now, instead of reporting whole-sample results, we stratify individuals according to various individual characteristics (gender, age group, schooling level, attendance of public/private school, SES status, and maternal schooling).<sup>23</sup> Appendix Tables B7–B9 report these findings.

> 23 This is based on the same aggregate (regression) results reported previously, the difference being that the

21
**Table 4. Effects of Past Per Capita Agricultural Spending (2006) on Vulnerability to Food Insecurity** **<u>(2007)</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**VARIABLES**|Total exp|Restricted and<br>extended<br>definitions|Current<br>and<br>investment<br>categories|By function|
|**Total expenditure**|1.3166***||||
||(0.333)||||
|**Restricted expenditure**||2.3526***|||
|||(0.569)|||
|**Extended expenditure**||0.9140**|||
|||(0.396)|||
|**Current expenditures**|||2.0138**||
||||(0.938)||
|**Investment expenditures**|||1.2630***||
||||(0.353)||
|**Research and extension**||||13.2846***|
|||||(4.328)|
|**Infrastructure**||||1.2186***|
|||||(0.364)|
|**Support and development**||||-1.1690|
|||||(1.804)|
|**Administration and procedures**||||1.8195|
|||||(1.406)|
|**Chuquisaca**|0.3188***|0.3154***|0.3133**|0.3379***|
||(0.121)|(0.122)|(0.123)|(0.121)|
|**Cochabamba**|0.0950|0.0878|0.0948|0.1025|
||(0.092)|(0.092)|(0.091)|(0.090)|
|**Oruro**|0.1452|0.1214|0.1404|0.1290|
||(0.100)|(0.101)|(0.100)|(0.101)|
|**Potosí**|0.3194***|0.3205***|0.3184***|0.3235***|
||(0.096)|(0.096)|(0.096)|(0.096)|
|**Tarija**|-0.3482***|-0.3620***|-0.3537***|-0.3482***|
||(0.033)|(0.033)|(0.031)|(0.033)|
|**Santa Cruz**|-0.2887***|-0.2889***|-0.2889***|-0.2826***|
||(0.059)|(0.059)|(0.060)|(0.057)|
|**Beni**|-0.3143***|-0.3215***|-0.3197***|-0.3148***|
||(0.054)|(0.051)|(0.052)|(0.046)|
|**Pando**|-0.3957***|-0.3936***|-0.3985***|-0.3813***|

17
crisis countries tended to allow for less stringent definitions of capital, had more discretion in how banks calculated capital requirements, and exhibited lower actual capital ratios than the rest of the world. Both the 2007 and 2011–12 BRSSs show that crisis countries were more likely to allow the initial disbursement or subsequent injections of capital to be done with assets other than cash or government securities. At the same time, while 80 (100) percent of crisis countries allowed Tier 3 (Tier 2) in regulatory capital, only 28 (85) percent among non-crisis countries did so. Moreover, crisis countries were also more likely to allow hybrid debt instruments to be part of Tier 1. Also, the share of crisis countries that allowed banks to calculate their capital requirement for credit risk based on banks’ internal ratings models was 95 percent, about twice as a large as in the rest of the world. Finally, while the average level of the actual risk based capital ratio (Tier 1 capital to assets) of the banking system prior to the crisis was 16 (14) percent among non-crisis countries, this figure was 12 (9) percent among crisis countries. The same pattern is observed if we look at the statistics gathered in the 2011–12 Bank Regulation and Supervision Survey.

Second, banks in crisis countries faced fewer restrictions to engage in non-bank activities such as insurance, investment banking, real estate, as well as in non-financial activities. Using an index of 1 to 4 to measure restrictions imposed on bank activities, where higher values denote greater restrictions, we find that non-crisis countries were less likely to allow for a full range of non-bank activities to be conducted directly in banks.

Third, crisis countries were less likely to have in place non-performing loans and provisioning requirements and were more lax in the treatment of bad loans and loan losses. Close to 90 percent of non-crisis countries had an asset classification system under which banks had to report the quality of their loans using a common regulatory scale, while 57 and 65 percent of 8
# Contents

|**1 Methodology**|**2**|
|---|---|
|Measuring the quantity and quality of infrastructure|2|
|Principal components analysis (PCA)|3|
|The aggregate index of infrastructure stock|3|
|The aggregate index of infrastructure quality|4|
|Econometric methodology|5|
|**2 Infrastructure trends in Africa**|**6**|
|Aggregate indices of infrastructure development|7|
|Telecommunications|7|
|Electricity|8|
|Roads|9|
|**3 Infrastructure and productivity growth in African countries**|**9**|
|Regression analysis|10|
|Growth payoff from infrastructure development in 2001–5 relative to 1991–5|11|
|Infrastructure and other sources of growth in Africa|12|
|The contribution of infrastructure assets to growth in Africa|12|
|Potential growth payoff from infrastructure development among African countries|14|
|Scenario 1: Growth payoffs of reaching the leader in the African region|15|
|Scenario 2: Growth payoffs of reaching the representative country among the EAP7|16|
|Scenario 3: Growth payoffs of reaching the leader in South Asia|17|
|Scenario 4: Growth payoffs from reaching the representative country in Western Europe|18|
|**4 Conclusions**|**19**|
|**References**|**20**|
|**Appendix 1 Econometric methodology**|**22**|
|**Figures and tables**|**25**|
|**About AICD**|**61**|

## **About the author**

César Calderón is an economist in the World Bank’s Office of the Chief Economist for Latin America and the Caribbean.

iii
|Gold &<br>Livestock<br>Non-Housing|Silver<br>Wealth<br>(2)<br>(3)<br>(4)|(-0.56∗∗∗)<br>(-2.89∗∗∗)<br>(-0.38∗∗∗)|-(0.20)∗∗∗<br>-(0.47)∗∗∗<br>-(0.16)∗∗∗<br>(-2.59∗∗∗)<br>(-0.41∗∗∗)<br>(-0.07∗∗∗)|-(0.95)∗∗∗<br>-(0.93)∗∗∗<br>-(0.27)∗∗∗<br>(-0.43∗∗∗)<br>(-2.65∗∗∗)<br>(-0.69∗∗∗)|-(0.71)∗∗∗<br>-(0.83)∗∗∗<br>-(0.22)∗∗∗<br>(-0.96∗∗∗)<br>(-0.21∗∗∗)<br>(-0.89∗∗∗)|-(0.08)∗∗∗<br>-(0.08)∗∗∗<br>-(0.03)∗∗∗|-12060∗∗∗<br>-12060∗∗∗<br>-7804∗∗∗|-0.18∗∗∗<br>-0.32∗∗∗<br>-0.34∗∗∗<br>igh-confict if it experienced more terrorist attacks from 1973 to 2014<br>elmand and Kandahar which are the two most confict-ridden provinces.<br>e gender, age, marital status, education, household size, number of males<br>1.|
|---|---|---|---|---|---|---|---|---|
|GSL|(1)|(-2.41∗∗∗)|-(0.46)∗∗∗<br>(-0.08∗∗∗)|-(0.88)∗∗∗<br>(-1.09∗∗∗)|-(0.81)∗∗∗<br>(-0.48∗∗∗)|-(0.09)∗∗∗|-12060∗∗∗|-0.21∗∗∗<br>ct is classifed h<br>ds residing in H<br>controls includ<br>∗p<0.05, ∗p<0.|
|Variables||Variance of Income (log)|Confict×Variance of Income (log)|Confict|Income (log)||Observations|R-squared<br>The table shows the estimated coefcients of equation (2). A confi<br>compared to the mean of all provinces. The sample excludes househol<br>Robust standard errors are in parentheses. Household/Household-head<br>per household, and type of dwelling. Signifcance levels: ∗∗∗p<0.01, ∗|

28
**Table 4: Payment of minimum revenue guarantees, 2003-2021**

||**Payment Flow**|**s**|**Net contingent**|**debt**|**Maximum possibl**|**e value**|
|---|---|---|---|---|---|---|
|Year|Amount (UF)|GDP %|Amount (UF)|GDP %|Amount (UF)|GDP %|
|2003|||7,610,372|0.25%|||
|2004|||7,189,035|0.22%|||
|2005|||5,603,497|0.16%|||
|2006|||7,791,593|0.22%|||
|2007|||9,652,143|0.21%|159,293,178|3.50%|
|2008|||6,449,236|0.14%|167,362,390|3.72%|
|2009|403,914|0.01%|8,635,767|0.23%|156,617,577|4.16%|
|2010|467,603|0.01%|8,629,404|0.21%|167,703,662|4.11%|
|2011|608,797|0.01%|8,528,232|0.20%|176,679,755|4.08%|
|2012|551,356|0.01%|9,161,175|0.16%|169,282,523|3.00%|
|2013|657,329|0.01%|8,338,316|0.14%|158,830,123|2.67%|
|2014|717,094|0.01%|8,612,847|0.14%|148,956,861|2.49%|
|2015|983,734|0.02%|9,106,707|0.15%|153,173,869|2.53%|
|2016|945,906|0.02%|9,389,196|0.15%|144,710,605|2.29%|
|2017|1,252,638|0.02%|13,316,272|0.20%|109,796,369|1.64%|
|2018|1,445,460|0.02%|15,168,661|0.21%|100,432,433|1.40%|
|2019|1,758,179|0.02%|10,904,887|0.15%|86,030,778|1.19%|
|2020|2,420,254|0,04%|10,148,538|0,15%|97,208,601|1,43%|
|2021|821,362|0,01%|11,821,489|0,15%|88,751,039|1,12%|

Source: Informe de Pasivos Contingentes, Ministerio de Hacienda, years 2007 onwards. For previous years, from the yearly Informe de Finanzas Públicas, Ministerio de Hacienda. Amounts are in UF, an inflation indexed unit of account that usually fluctuates in the range USD38-44.

21
**Trade Finance in Crisis: Should Developing Countries Establish Export Credit Agencies?**

Jean-Pierre Chauffour, Christian Saborowski and Ahmet I. Soylemezoglu<sup>1</sup> The World Bank JEL Classifications: F1, G2, Keywords: International Trade, Financial Institution and Services.

> 1 We would like to thank Thomas Farole, Michael Gonter, Mona Haddad, Bernard Hoekman, Leora Klapper, Jean Le Cocguic, Fabrice Morel, Arun Sharma and Lennart Skarp for helpful remarks and suggestions, and to the Berne Union Secretariat for sharing their data on export insurance and guarantees. The remaining errors are solely our own. The views expressed in this paper are ours only and should not be attributed to the World Bank, its Executive Directors or the countries they represent. The authors can be contacted at <u>jchauffour@worldbank.org, csaborowski@worldbank.org</u> and asoylemezoglu@worldbank.org.
not forget those non-extremely poor children that are fully enrolled but hardly ever attend school.

# **Acknowledgements**

We wish to thank Soren Anderson, Fabrizio Carlevaro, Richard G. Newell and Marc-Alexandre Sénégas for the kind support in carrying out the econometric analysis. A special thank to Fabio Montobbio, Simona Iammarino, Dena Ringold, Robert Vos and the participants to the XV Network on Poverty and Inequality for providing us with important comments. Note that all mistakes and opinions expressed in the paper belong to the authors only.

# **References**

- [1] Al-Samarrai S. (2006), “Achieving education for all: how much does money matter?” _Journal of International Development_ , 18 (2), 179-206

- [2] Al-Samarrai S., Peasgood T. (1998), “Educational attainments and household characteristics in Tanzania”, Economics of Education Review, 17 (4), 395-417

- [3] Anderson S., Newell R. (2003), “Simplified marginal effects in discrete choice models” _Economics Letters_ , 81 (3), 321–326

- [4] Baland J. M. and Robinson J. (2000), “Is Child Labor Inefficient”, _Journal of Political Economy_ , 108 (4), 663-679

- [5] Basu K. (1999), “Child Labor: Cause, Consequence, and Cure, with Remarks on International Labor Standards”, _Journal of Economic Literature_ , XXXVII (September), 1083-1119

- [6] Basu K., Van P.H. (1998), “The Economics of Child Labor”, Am _erican Economic Review_ , 88 (3), 412-27

- [7] Bhalotra S. (2001), “Is child work necessary?”, The World Bank Social Protection Discussion Papers _,_ No. 0121, Washington, D.C.

- [8] Bonifaz A.., Ochoa M. (2002), “An Analysis of Dipsarities in Education: The Case of Primary School Completion Rates in Bolivia”, World Bank, mimeo

- [9] Canagarajah S., Coulombe H. (1997), “Child Labor and Schooling in Ghana”, World Bank’s Policy Research Working Papers No. 1844

- [10] Carlevaro F., Sénégas M. (2006), “Simplified marginal effects in discrete choice models: A correction” _Economics Letters_ , 92 (1), 44-46

- [11] Contreras D., Kruger D., Zapata D. (2006), “Child labor and schooling in Bolivia: Who is falling behind? The roles of gender and ethnicity”, Universidad de Chile, mimeo

- <mark>[12] Duflo E. (2001), “Schooling and labor market consequences of school construction in Indonesia: evidence from an unusual policy experiment”,</mark> _<mark>American Economic Review</mark>_ <mark>, 91 (4), 795-813</mark>

- <mark>[13] Glewwe P., Ilias N. (1996), “The determinants of school attainment in SubSaharan Africa: a case study of Ghana”, Journal of International Development, 8, 395–413</mark>

18
**Table A2.IR.A: Intergenerational Persistence in Schooling in Rural India -** **<u>(18 35 Years Age Cohorts)</u>**

|||**LINEAR CEF**<br>||**QU**|**ADRATIC C**<br>|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|0.45<br>(0.0064)***|0.52<br>(0.0071)***|0.075<br>(0.0095)***|0.52<br>(0.019)***|0.64<br>(0.021)***|0.12<br>(0.028)***|
||[0.011]***|[0.012]***|[0.012]***|[0.027]***|[0.030]***|[0.033]***|
||{0.017}***|{0.025}***|{0.024}***|{0.031}***|{0.053}***|{0.044}***|
|Quadratic Coefft.||||-0.007|-0.011|-0.007|
|||||(0.0014)***|(0.0018)***|(0.0014)***|
|||||[0.0019]***|[0.0022]***|[0.0019]***|
|||||{0.0020}***|{0.0034}***|{0.0020}***|
|Intercept|6.38|4.24|-2.14|6.32|4.16|-2.14|
||(0.044)***|(0.043)***|(0.062)***|(0.048)***|(0.046)***|(0.062)***|
||[0.12]***|[0.14]***|[0.097]***|[0.12]***|[0.14]***|[0.0097]***|
||{0.25}***|{0.38}***|{0.21}***|{0.25}***|{0.37}***|{0.21}***|
|No. Observations|16957|17022|33979|16957|17022|33979|
|||**Estima**|**tes of Mobility**|**from Quadrati**|**c CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|6.32|4.16||**_IGME0 _**|0.52|0.64|
|**_ES5 _**|8.75|7.09||**_IGME5 _**|0.454|0.533|
|**_ES10_**|10.82|9.46||**_IGME10_**|0.384|0.426|
|**_ES16_**|12.85|11.58||**_IGME16_**|0.300|0.298|

Notes: (1) The data used are IHDS 2012 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at statelevel in braces; *** p<0.01, ** p<0.05, * p<0.1.