rafmacalaba/datause-encoder-data huggingface dataset:
conducted by Serra-Burriel et al (2020) of multi-drug resistant HAIs found 20 studies (19 OECD countries and China) with a 61% increase in excess mortality due to AMR. However, while deaths due to AMR in Africa are known, the marginal impact of AMR on the case fatality rate of an HAI is little known. Due to the seriousness of AMR and the expense and poor supply of second and third-line drugs in Africa, it is assumed that twice the number of patients with AMR die compared to those with HAI that is not AMR, i.e., 13.8%. This number is supported by Kariuki et al (2021) who conducted a later review for Sub-Saharan Africa. Drawing on Murray et al (2022), they show that 27% of deaths from an infection are associated with AMR and 6.5% are attributed to AMR.

# 11. Value of a premature death

The value of premature death has been widely evaluated in OECD countries, and less so in Africa. Saluja et al (2020) conduct an economic modeling study of the impact of physician migration on mortality in low and middle-income countries. Drawing on the recommendations by Robinson et al (2019), they use a baseline value-of-a-statistical life (VSL) of $9.4 million with a base GNI per capita PPP value of $57,900, and an income elasticity (IE) of 1.5, and extrapolate the VSL to developing countries. An IE above 1.0 implies people are willing to spend a lower proportion of their income on mortality risk reduction in lower income settings. More recently, Banzhaf (2022) conducted a meta-analysis of meta-analyses for VSL studies in the USA, and estimates the VSL to be US$8 million in 2019, with a 90% confidence interval of $2.4 million–$14.0 million. In this study, the latest value from Banzhaf (2022) of US$8 million is used to convert values to the nine African countries. In the baseline, an IE of 1.5 is used, while an IE value of 1.2 is used in sensitivity analysis. VSL used are shown in Table 10.

**Table 10. VSL values at different income elasticities**

|**Income**<br>**elasticities**|**Angola**|**Congo,**<br>**Dem.**<br>**Rep.**|**Ethiopia**|**Kenya**|**Madagascar**|**Mozambique**|**Sudan**|**Tanzania**|**Uganda**|
|---|---|---|---|---|---|---|---|---|---|
|IE = 1.5|$32,532|$5,224|$10,604|$35,782|$4,214|$4,113|$7,773|$13,733|$9,907|
|IE = 1.2|$97,828|$22,648|$39,902|$105,572|$19,072|$18,706|$31,123|$49,070|$37,789|
|IE = 1.0|$203,814|$60,216|$96,533|$217,172|$52,180|$51,345|$78,478|$114,691|$92,254|

Other ways of valuing human life include the human capital approach. This leads to different values depending on the age at death and the life expectancy. In the absence of data on age profile of patients dying, it is difficult to use the HCA in this study. Also, the value of a disability-adjusted life-year is also estimated and used to value premature death. For example, in Kenya, a DALY value has been estimated at I$ 4,918 in 2019 (Kirigia and Kubai, 2023). For this it is also necessary to know age profiles of patients dying to estimate the number of DALYs lost from HAIs. These data are not available.

38
the pattern of inequality during the crisis period cushioned the poverty impact of the crisis.

Table 3.6.Shapley Decomposition of Poverty Outcomes 1993-1996

|Measure|TotalChange|Growth|Inequality|
|---|---|---|---|
|Headcount|-11.04|-16.05|5.01|
|Poverty Gap|-5.70|-8.83|3.13|
|Squared PovertyGap|-3.14|-4.95|1.80|

Source: Computed Table 3.7. Shapley Decomposition of Poverty Outcomes 1996-1999

|Measure|TotalChange|Growth|Inequality|
|---|---|---|---|
|Headcount|4.79|10.00|-5.21|
|Poverty Gap|1.23|4.97|-3.74|
|Squared PovertyGap|0.47|2.61|-2.14|

Source: Computed Table 3.8. Shapley Decomposition of Poverty Outcomes 1999-2002

|Measure|TotalChange|Growth|Inequality|
|---|---|---|---|
|Headcount|-2.88|-5.99|3.11|
|Poverty Gap|-0.88|-2.97|2.09|
|Squared PovertyGap|-0.40|-1.55|1.15|

Source: Computed

# **_4.  Conclusion_**

This paper deals with the important issue of identification of pro-poor growth. This is essentially an exercise in social evaluation as it entails a comparison of social states characterized by various distributions of the living standards induced by the growth process. Starting with a general impact indicator defined as a weighted sum of individual impacts, the paper offers an integrative framework for a unified discussion of various concepts and measures of pro-poor growth emerging from the current literature. It is 27
terms, being zero-mean normally distributed, thus interpreting period and cohort effects as deviations or shocks.

Tables A5 and A6 present the parameter estimates and their standard errors for all the five APC models. While for PWA, HHDP, IE, ME these include the estimates for the age, period, and cohort dummies, for HAPC we have the coefficients for age and age-squared, along with the estimates of the variances for the random effects of period and cohort.

Table A5: Age-period-cohort effects using five different models for Bangladesh

|||**(1)**|**(**|**2)**|**(**|**3)**|**(**|**4)**||**(5)**|
|---|---|---|---|---|---|---|---|---|---|---|
||P|WA|H|HDP|I|E|M|E|H|APC|
||Coef.|(SE)|Coef.|(SE)|Coef.|(SE)|Coef.|(SE)|Coef.|<br>(SE)|
|**age**|||||||||0.0303|<br>(0.0013)|
|**age-squared**|||||||||-0.0004|(0.0000)|
|**age 22**|0.046|(0.022)|0.032|(0.023)|0.029|(0.010)|0.025|(0.015)|||
|**age 26**|0.099|(0.035)|0.087|(0.024)|0.065|(0.010)|0.058|(0.016)|||
|**age 30**|0.155|(0.049)|0.147|(0.025)|0.104|(0.010)|0.093|(0.017)|||
|**age 34**|0.205|(0.064)|0.202|(0.027)|0.137|(0.010)|0.123|(0.018)|||
|**age 38**|0.206|(0.079)|0.208|(0.029)|0.12|(0.010)|0.103|(0.020)|||
|**age 42**|0.228|(0.094)|0.236|(0.030)|0.126|(0.010)|0.105|(0.021)|||
|**age 46**|0.225|(0.116)|0.240|(0.032)|0.106|(0.010)|0.082|(0.023)|||
|**age 50**|0.225|(0.116)|0.233|(0.032)|0.088|(0.010)|0.061|(0.022)|||
|**period 1995**|0.192|(0.020)|0.139|(0.017)|0.209|(0.009)|0.212|(0.014)|||
|**period 1999**|0.020|(0.033)|-0.040|(0.016)|0.054|(0.009)|0.061|(0.014)|||
|**period 2003**|0.001|(0.047)|-0.064|(0.015)|0.053|(0.009)|0.063|(0.015)|||
|**period 2007**|0.080|(0.062)|0.010|(0.014)|0.148|(0.009)|0.162|(0.016)|||
|**period 2011**|-0.121|(0.077)|-0.196|(0.015)|-0.036|(0.009)|-0.019|(0.017)|||
|**period 2015**|0.075|(0.092)|-0.002|(0.016)|0.178|(0.009)|0.198|(0.018)|||
|**period 2019**|0.218|(0.108)|0.153|(0.017)|0.338|(0.010)|0.361|(0.020)|||
|**cohort 1945**|0.041|(0.038)|-0.057|(0.052)|0.024|(0.019)|0.020|(0.036)|||
|**cohort 1949**|0.106|(0.042)|0.020|(0.049)|0.072|(0.017)|0.065|(0.032)|||
|**cohort 1953**|0.155|(0.051)|0.078|(0.046)|0.104|(0.015)|0.094|(0.029)|||
|**cohort 1957**|0.188|(0.063)|0.119|(0.044)|0.12|(0.014)|0.106|(0.026)|||
|**cohort 1961**|0.206|(0.076)|0.143|(0.042)|0.121|(0.013)|0.103|(0.024)|||
|**cohort 1965**|0.228|(0.090)|0.170|(0.041)|0.126|(0.012)|0.105|(0.021)|||
|**cohort 1969**|0.245|(0.104)|0.190|(0.039)|0.125|(0.011)|0.101|(0.019)|||
|**cohort 1973**|0.259|(0.118)|0.206|(0.039)|0.123|(0.011)|0.095|(0.017)|||
|**cohort 1977**|0.260|(0.134)|0.217|(0.039)|0.106|(0.012)|0.075|(0.016)|||
|**cohort 1981**|0.271|(0.149)|0.232|(0.040)|0.1|(0.012)|0.066|(0.015)|||
|**cohort 1985**|0.272|(0.164)|0.237|(0.041)|0.084|(0.013)|0.046|(0.015)|||
|**cohort 1989**|0.273|(0.179)|0.240|(0.042)|0.068|(0.014)|0.027|(0.015)|||
|**cohort 1993**|0.276|(0.194)|0.244|(0.045)|0.054|(0.016)|0.010|(0.017)|||
|**Var(period)**|||||||||0.0108|<br>(0.0054)|
|**Var(cohort) **|||||||||0.0030|(0.0017)|

60
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics We then regress this earnings measure for each year _t_ on the regional tariff change ( _RTCr_ ) and the extensive set of controls described above. Figure 18 shows the results. The point estimate in 2009 is 0.86, implying that over the course of 20 years a worker whose initial region faced a 10 percentage point larger tariff decline lost 1.72 times their average pre-liberalization wage in the formal sector in relative terms.

Figure 19 shows the results for a closely related earnings measure, yearly earnings as a share of pre-liberalization mean earnings.

Rather than averaging over the sample period, this measure expresses earnings in each year. Once again we see positive and growing coefficients following liberalization, indicating that earnings of workers whose initial regions faced larger tariff declines steadily diverged from those initially in more favorable regions for more than a decade following liberalization.

# **6.5 Non-Formal Spells**

Above, we found that workers facing larger tariff declines spent more time out of formal employment. We complement that analysis by examining the length of non-formal spells. To implement this analysis, we need a representative sample of all job terminations that occur in a given region and year. We draw a 5% sample of workers who appear in the RAIS data between 1986 and 2005. We then record the length of all non-formal spells experienced by workers aged 25-55 and employed in the tradable sector at the time the spell began. Since our data end at December 2009, non-formal spells are right-censored at that point. We use a Tobit specification to relate the log non-formal spell length to the regional tariff change in the worker’s region of formal employment when the non-formal spell began and the full set of controls.<sup>39</sup> The results are shown in Figure 20. Workers losing formal employment in regions that faced larger tariff declines experienced relatively longer non-formal spells. The largest point estimate of -4.57 appears in 1998, implying that among spells beginning in 1998, those in regions facing 10 percentage point larger tariff declines are 45.7% longer, on average.<sup>40</sup>

# **6.6 Nontradable Sector Workers**

Recall that the earlier results in this section examine workers who initially worked in tradable goods industries. We also implemented all of the above individual-level analyses using an alternate group

> 39To calculate the pre-liberalization trend control, we estimate a parallel specification for non-formal spells beginning in 1986 and right-censoring spells extending beyond 1989, in order to avoid contaminating the pre-liberalization non-formal spells with the effects of trade reform.

> 40For reference, the average non-formal spell length among those starting in 1998 and not truncated (finalized before December 2009) is 21.4 months. The median length is 12 months.

28
public issues in the U.S. start in the 1970s, coverage of other markets starts later, with most regional databases starting in 1991. Therefore, we restrict our sample to the period 1991-2005.

SDC collects data on security issuances mostly from filings with local regulatory agencies and stock exchanges. These data are augmented with data from other sources such as offering circulars, prospectus, surveys of investment banks, brokers, and other financial advisors, news sources, trade publications, and wires. Although SDC constitutes the most comprehensive databases on security issuances around the world, SDC’s coverage may be less comprehensive for those regions for which it relies mostly on informal sources, instead of collecting data from filings with regulatory agencies and stock exchanges.

Since our analysis focuses on corporate capital raising activity, we exclude all public sector bond issuances, comprising debt issued by national, local, and regional governments, government agencies, regional agencies, and multilateral organizations. We also exclude security issuances by investment funds, investment companies, and real estate investment trusts (REITs), as well as mortgage-backed securities and other asset-backed securities. Moreover, since we focus on capital raising activity in public markets we exclude all private placements.<sup>4</sup> After these exclusions, we are left with a database covering 168,514 security issuances by 45,969 firms from 116 economies over the period 1991-2005. See Appendix Table 1 for a list of the economies included in our dataset and their regional and income level classification. Appendix Table 2 presents data on the number of observations and firms covered by region and income level.

> 4 Note that excluding private placements may affect the observed regional patterns of capital raising activity, as some regions may have more active private markets than others. This may be particularly relevant for debt markets, as private bond markets in some regions are as active, or even more active, than public bond markets.

9
strategy than countries with a medium level of indebtedness ranging from 50 to 100 percent. The second plot in the ...rst row of panels supports the ...nding of a humpshaped relationship between indebtedness and an existence of a strategy. However, the story coming from the tails of the distribution appears opposite to that from the ...rst plot. Namely, that countries with high levels of indebtedness have still higher probability of having a strategy than countries with small levels of indebtedness.

The distribution of the percentage of public strategies across levels of indebtedness is shown in the second row of panels in Figure (3). From the ...rst plot it appears that the relationship is rather non-linear and close to the U-shape. However, the second plot contradicts this pattern and shows a strong linear relationship between the percentage of public strategies and levels of indebtedness. This implies that the higher the indebtedness of a country the more likely is the country to be nontransparent about its debt management. We will investigate the latter relationship further using regression analysis later on in this paper to get more de...nite answers.

Finally, the second row of panels in Figure (3) also shows the distribution of benchmark strategies across levels of indebtedness. The left-hand side histogram suggests a hump-shaped relationship between benchmark strategies and the levels of indebtedness. The hump-shaped relationship implies that as countries are becoming more indebted they use benchmarks more often to express their strategies. However, as countries become highly indebted the use of benchmarks in debt management decreases. Although a non-linearity is also suggested by the plot on the right it follows a U-shape as opposed to a hump-shape thus completely contradicting the implications from the ...rst plot. Again, we hope to ...nd more conclusive insights into this relationship using regression analysis carried out in section 4.

# 3.4 Further Inspection of Strategic Benchmarks

In this section we further decompose the strategic benchmarks into three basic types of risk that the benchmarks can be addressing. These are namely foreign exchange (FX) risk, interest rate risk and re...nancing risk. Using graphical analysis, we examine the relative weight of the three types of risk in the existing benchmarks across 12
# Table 12: Program Impact on Child Participation in Non Farm Labor

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||6-15 years old|||6-12 years old||1|3-15 years ol|d|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School Meals|-0.006|-0.022|0.007|-0.006|-0.021|0.003|0.009|-0.025|0.044|
||[0.023]|[0.033]|[0.033]|[0.026]|[0.037]|[0.036]|[0.054]|[0.077]|[0.079]|
|Dry Rations|-0.074|n.a.|-0.090|-0.082|n.a.|-0.103|-0.043|n.a.|-0.031|
||[0.025]***|n.a.|[0.030]***|[0.028]***|n.a.|[0.033]***|[0.060]|n.a.|[0.071]|
|Follow-up|0.073|0.059|0.088|0.083|0.065|0.104|0.030|0.034|0.013|
||[0.015]***|[0.022]***|[0.022]***|[0.017]***|[0.024]***|[0.024]***|[0.035]|[0.052]|[0.049]|
|Girl|-0.028|||-0.028|||-0.030|||
||[0.011]**|||[0.013]**|||[0.026]|||
|Constant|0.126|0.112|0.109|0.128|0.111|0.115|0.327|0.369|0.258|
||[0.018]***|[0.027]***|[0.020]***|[0.019]***|[0.028]***|[0.021]***|[0.035]***|[0.050]***|[0.041]***|
|Observations|6047|2462|3585|4757|1931|2826|1290|531|759|
|R-squared|0.23|0.22|0.24|0.22|0.21|0.24|0.27|0.3|0.3|

## Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable is whether child participated in any non farm labor the week before the survey. Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

35
Data on the school construction program in each individual’s district of birth were needed to construct the instrumental variable used in this analysis. Data on the _actual_ number of schools built in each district between 1973/74 are not readily available from GOI or other sources. The Ministry of Education and Culture does, however, have data on the _planned_ allocation of schools to each district for each year between 1973/74 and 1978/79, which are used for this analysis<sup>1</sup> . IFLS migration modules contain information on each individual’s district of birth, which I have used to link each individual observation with corresponding SDIP data for her district of birth.

Two data issues related to district of birth data are worth mentioning. The IFLS migration module is only available from the first and third waves of the survey, as the module from the second (1997) wave has not been made publicly available yet. This was not a serious constraint as the majority of women in the sample were observed in at least one of the other two waves for which migration modules are available. However, 300 women who were only observed in the 1997 wave could not be matched with corresponding SDIP data as their district of birth was not known. A second issue is that it is common in Indonesia for district boundaries to be re-drawn and new districts created within districts from year to year, a process which accelerated around 2000, when all government administration in Indonesia was fully decentralized. IFLS migration modules verified changes in district names with respondents and recorded both current and former names for each individual’s district of birth. To ensure that birth districts reported in IFLS were

> 1 I am grateful to Esther Duflo for making this dataset available to me.

16
given level of expected quality, uncertainty about quality is likely higher in smaller countries, where donors have obtained relevant information from fewer projects and have undertaken fewer diagnostic and other analytic studies. If donors tend to be risk averse, this higher uncertainty can reduce use of country systems.

Use of country systems may be higher in more aid-dependent countries for two reasons. First, delivering higher volumes of aid may be accomplished in part by disbursing a greater percentage of it in the form of direct budget support, which by definition uses country PFM systems. Second, the impact of donor decisions to use (or bypass) country systems on strengthening (or undermining) government capacity and accountability is likely to be greater where aid volumes are higher. These potential impacts may also be greater in lower-income countries, perhaps explaining in part why governments of many middle-income countries receiving modest levels of aid show relatively little concern over donors’ choices of aid management practices (Wood et al., 2011). The net effect of income on use of country systems is therefore ambiguous.

Use of country systems is expected to increase with the quality of recipient countries’ national development strategies, as measured by PD Indicator 1. These qualitative assessments were based on three criteria (OECD, 2008; World Bank, 2006): (1) existence of an authoritative country-wide development policy; (2) realism of the development policy with clearly-identified priorities; and (3) explicit links with policies in the budget.<sup>8</sup> This “Strategy” indicator is scored on an A-E scale. Nearly half of the observations in the sample receive the middle score of C; only 2% have the highest grade of A and 1% have the lowest grade of E.

> 8 Scores were assigned by World Bank staff based in part on information provided by government officials responding to a PDMS questionnaire.  These assessments are not done by the same Bank staff responsible for the CPIA.  Clist, Isopi and Morrissey (2012) show that countries with an explicit national development strategy are more likely to receive aid in the form of budget support from the EC and World Bank, but they do not differentiate strategies by quality on the basis of the relevant PD indicators.

11
city experiencing a rise of over 50% in the per capita heat-related excess mortality rate. When factoring in urban expansion this results in more than 3,000 heat-related deaths per year for Lucknow and Surat, and approximately 6,500 annual deaths for Chennai (Figure 6).

_Figure 6: Projected rate of heat-related excess mortality for selected cities (present-day and 2050 under moderate and high global emissions scenarios)_ <u>Note</u> a moderate scenario for global greenhouse gas emissions while SSP3-7.0 refers to a high emissions scenario.

# _Labor productivity impacts_

Currently, about 20% of annual working hours in these cities exceed safe heat stress levels, measured by the WBGT over 31°C. Under a high-emission scenario, this could rise to 30%-40%, significantly reducing productivity and causing economic losses of billions of US dollars per year, affecting up to 4% of each city’s GDP. For example, Chennai currently loses about 1.9 billion USD annually, or 2.3% of its GDP, due to extreme heat. By 2050, these losses could increase by 50% under the high-emission scenario. Cities such as Surat and Lucknow, both projected to undergo significant demographic growth, could see their economic losses double (Figure 7).

_Figure 7: Projected economic output loss due to labor productivity effects under climate scenarios for selected cities (present-day and 2050 under alternate moderate and high climate change scenarios)_ 15
available for investments. Hence, total national income goes to government expenditures, household expenditures, and investments.

International trade is modeled by a system of Armington demands that give rise to flows of goods and services between the regions. At the national/regional level, import demand is driven by CES functions of domestic and imported components of demand for Armington commodities. Export supply is depicted by a two tier constant elasticity of transformation (CET) function, where, on the first tier, the total output of a sector is designated either to total exports or to domestic supply, and, at the second tier, total exports are partitioned according to their destinations.

The endogenous driver of the dynamics in the model is the vintage capital structure. The capital stock is composed of old and new capital, where new corresponds to the capital investments at the beginning of the period and old corresponds to the capital installed in previous periods. The ratio of new to old capital is also a measure of the flexibility of the economy as new capital is assumed to be perfectly mobile across sectors. Furthermore, a fraction of the old capital depreciates each period.

Population and productivity growth are exogenous drivers of the model’s dynamics. The former is taken from the projections of the United Nations Population Division, where labor force growth corresponds to the growth of the population aged 15-64 years. Productivity growth is modeled as factor neutral for agricultural sectors and labor augmenting for industrial and service sectors. Productivity of energy follows an autonomous energy efficiency improvement (AEEI) path so that there is no endogenous technological change in the model.

To ensure equilibrium in the model, three sets of market clearing conditions are met. First, total production of each commodity equals the sum of domestic consumption and exports so that the goods and services markets clear. Second, total investment equals total savings, where savings are composed of private (household) savings, public (government) savings and exogenously fixed foreign savings. Third, factor markets clear, which implies full employment.

5