rafmacalaba/data-use-mentions-extended huggingface dataset:
of the GDP per capita (valued at a daily value). Sensitivity analyses uses the value-added in industry for working adults, which is significantly higher than for agriculture.

# 9. Proportion of HAI patients participating in labor force

Participation in the labor force is based on three variables: whether the patient is of working age, whether a patient of working age is active in the labor force, and the gender of the inpatient. This analysis includes not only patients but also their caregivers, who will lose a similar amount of time from their work. The proportion of HAIs suffered by children is assumed to be 40% which is based on data from Kenya where under five children account for 38% of inpatient admissions. The proportion of women is assumed to be 65% compared to men (35%) due to the contribution of maternal conditions to hospital admissions, also based on data from Kenya. For adults, not all will be participating in the labor force. Female and male workforce participation is taken from the World Bank statistics for each country.

# 10. HAI case fatality rate

The mortality of patients that suffer an HAI is higher than those that do not suffer an HAI, even in developed countries with the best medical systems. From a long-term follow-up study in Norway, for example, patients with HAIs were 40% more likely to die within 1 year relative to those without HAIs (Koch et al, 2015). Evidence gathered by the American Thoracic Society suggests the mortality related to the healthcare-associated pneumonia or “attributable mortality” has been estimated to be between 33% and 50% (American Thoracic Society, 2005). There are only a few studies from Africa. An exploratory literature review with a focus on the nine countries of the present study found seven studies, shown in Table 9. Two studies presented case fatality of only HAI patients, while five compared HAI and non-HAI patients. Differences ranged from 4.2% to 14.6%, with two studies from Ethiopia with a
br>60<br>40<br>40<br>20 20<br>0 0<br>Baseline RCP 2.6 RCP 4.5 RCP 8.5 Baseline RCP 2.6 RCP 4.5 RCP 8.5<br><!-- End of picture text --> Source: World Bank estimates based on IADB (2019), MEF (2023), and INUMET (2023).

# 5.2 Flooding

**Data on damages from flooding and flooding frequency are taken from UNDRR 2015** (the GAR15 dataset). Capital stock in Uruguay that is exposed to flooding has a value of US$116 billion (2015 prices), or roughly twice the value of GDP. Uruguay has historically experienced moderate floods, with a 1 in 50year flood destroying US$642 million worth of assets (2015 prices). This is the equivalent of 0.34 percent of capital stock exposed to flooding, or 0.68 percent of GDP. Every year, with no further climate change, there is a 2 percent chance that a 1 in 50-year flood will occur. As suggested in Myhre et al. 2019, it is assumed that the frequency or probability of floods of any given size doubles with every degree increase

15
# Section 3: Data

Table 1 presents the 18 surveys included in our analysis. This list comprises 12 countries in West Africa including Nigeria, four countries in East Africa, Gabon in Central Africa, and South Africa. These countries account for 59 percent of the population of SubSaharan Africa in 2020 and were identified as those with a consumption survey between 2012-2019 with both region of birth and parental education available. As our outcome, we use per-capita household consumption rather than income given that consumption is a more direct measure of well-being and income measurement in surveys is subject to more error (Carletto et al. 2022). We include the following circumstance variables: birth region, birth urban-rural status, parental education, parental industry of work, religion, and ethnicity (race in the case of South Africa). Birth region and parent education are available in all countries, while other circumstances are available in most but not all countries. Father and mother industry and ethnicity are available in 12 countries, while religion and urban-rural birthplace are available in 15 countries (this is presented in Table A1 in the Appendix). Our study sample contains people aged 15 or over in each country.

**Table 1 : List of Countries and Surveys Included in Analysis**

|**Country**|**Year**|**Survey**|
|---|---|---|
|Benin|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Burkina Faso|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Côte d'Ivoire|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Ethiopia|2018-2019|Ethiopia Socioeconomic Survey<br>|
|Gabon|2017|Enquête Gabonaise pour l'Évaluation de la Pauvreté|
|Gambia, The|2015-2016|Integrated Household Survey|
|Ghana|2016-2017|Ghana Living Standard Survey|
|Guinea-Bissau|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Liberia
# 2. Methodology and data<sup>1</sup>

## 2.1. Bound estimates for poverty dynamics

This section summarizes the approach presented in Dang et al. (2014). Our aim is to estimate joint and conditional probabilities of poverty dynamics. For example, we wish to know the likelihood that an individual is poor in survey round 1 and non-poor in round 2, a joint probability which we can write as follows, denoting per capita income by and the poverty line by : ii yy zz 1 a 2). ii1 ii2 PP (yy ≤zz aa yy > zz Alongside evidence on joint probabilities, we are also interested in conditional probabilities. For instance, we might wish to estimate the likelihood of moving out of poverty, conditional on being poor initially. Such a conditional probability can be written as and can be computed straightforwardly from the joint probabilities, as the right-hand side of the equation shows. Panel data are not available in Malaysia. In the absence of such data, we do not observe incomes in round 1 and round 2 for the same unit and would normally not be able to estimate these probabilities. However, the method we describe below draws on cross-sectional data and yields ii both upper and lower bounds for each of the four possible outcomes of the dynamic process, in both conditional and unconditional terms.

We model the logarithm of per capita income in each round as a linear function of timeinvariant household characteristics and an individual error term. Schematically: and The basic idea of the method is to use time-invariant household characteristics to predict household per capita income for the year in which the actual observation is lacking. Such characteristics will be ii xx

> 1 This section is largely based on the description of methodology in Rongen et al. (2022).

1
to about 91.7 percent, with a mean of about 7 percent. Remittances are characterized by a low of US$9,980 and a high of US$37.2 billion, with an average of US$687 million. When expressed as a share of GDP, remittances receipts amounted to the tune of about 5 percent of GDP on average.

Table 4 presents correlation coefficients between remittances and GDP per capita growth across the five exchange rate regime classes. With the exception of regime 2, all other regimes classes show a positive correlation between remittances and growth, of which only regimes 1 and 3 are statistically significant. The estimates further show that when regime classes are combined into two groups only, then for regimes 1 through 3, there is a positive and statistically significant correlation between

> 2 The following are noteworthy: a. The choice of countries was guided by availability of data for remittance recipients, and particularly for exchange rate regimes. b. Although the data spans 38 years, the time dimension is in the range, 13 -15 years, across the various estimations due to missing observations. c. Annual observations are used primarily in the estimations, because the literature documents that remittances may affect growth by enhancing macroeconomic stability via consumption smoothing over the business cycle, in addtion to the potential impact through an investment channel. Refer to Lartey (2013) for further details.

5
declined.

## **3.3.2 Robustness 2: Catch-up to the United States**

Following Bernard and Jones (1996), another robustness check examines the accounting decomposition for each country relative to the United States. A measure of within and between-sector catch-up is computed by subtracting the productivity growth in the other countries for each sector as follows: where the notations are the same as in equation (1).

Figure 7 shows the between sector effects in EMDEs and LICs have contributed to convergence largely since 1995 while that was not the case between 1975 to 1995. This finding is line with the baseline. Figure 8 shows that both East Asia and Pacific (EAP) and South Asia (SAR) experienced both within- and betweensector catch-up whereas there has been divergence of the within sector effect in the Latin America and Caribbean (LAC), Middle East and North Africa (MENA) and Sub-Saharan Africa (SSA) regions before 2010. In those countries, the within sector effects in the manufacturing sector have not been converging to the U.S. (Kinfemichael and Morshed (2019) and Diao et al. (2021)).

# **4 Conclusion**

This paper investigates how the sector-specific source or the changing sectoral composition has contributed to aggregate productivity and convergence, constructing a new 8-sector database. The main findings are twofold. First, both within and sectoral reallocation have become important drivers of aggregate convergence in labor productivity. This reflects larger productivity improvements in many sectors in EMDEs (especially the LICs) compared to advanced economies as well the fact that many EMDEs experienced rapid sectoral shifts from agricultural sectors over the last few decades. Second, agricultural productivity growth has been a significant contributor to aggregate convergence. The strong growth of the agriculture sector has been the most important driver of aggregate productivity convergence even though agricultural productivity itself in LICs is weakly converging to advanced economies. Our result is in line with the literature that illustrates that the increase in 10
between OGD and certain aspects of health service delivery. As seen in the previous table, OGD and health provision are not significantly correlated; however, there is a significant association between OGD and sub-indexes related to immunizations, including DPT and Hepatitis B. No correlations exist for other health provision indicators including measles immunization and provision of anti-retroviral treatments. With respect to specific outcome indictors, it should be noted that the OGD index is significantly correlated with the index of health outcomes, as well as with child mortality outcomes and WHO disease prevention statistics.

28
Predicted probability of early childhood stunting (< 24 months of age) for simultaneous access to nutrition driver domains <!-- Start of picture text --> 19.2<br>Predicted baseline risk —o—<br>29.2<br>Adequate access to 0 domains ——. 0H<br>Adequate access to any 1 domain. —o—24.3<br>Adequate access to any 2 domains. —o——23.5<br>Adequate access to any 3 domains. —_o—19.5<br>Adequate access to any 4 or 5 domains. —o—13.3<br>0 10 20 30 40%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 3a (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown.
beating (and verse versa). However, the slopes of the relationship are almost flat. On the contrary there is a negative relationship between the two variables for Bangladesh and Nepal. An explanation for the opposing relationships may not be available but the clustering of the different cohorts leads us to the conclusion that domestic violence, its justification and incidence, is sticky and warrants effective interventions.

# **6. Conclusion**

This paper explores the evolution of gender-normative attitudes and gender norm-influenced outcomes in South Asia. Using repeated cross-sectional data from nationally representative surveys in the region, we examine the levels, trends, and relationship of these outcomes for women born across half a century. Our results highlight the presence of wide gender disparities and of conservative gender attitudes, which are a proxy for norms. In terms of trends, all gender outcomes relating to employment, marriage and children, and intrahousehold power/negotiation have remained stable or even regressed, with the exception of education. These trends were observed for all women, irrespective of location, education, 26
# **4. Sovereign bond returns, 1815–2016**

# 4.1. Sovereign bond pricing database: Sample and sources

This section presents our newly assembled, comprehensive dataset on sovereign bond prices and explains how we compute bond returns. Compared with Section 3, we thus move beyond sovereign default and restructuring situations and instead track the performance of foreign-currency sovereign bonds using bond prices. We start in 1815, during the decade in which London emerged from the Napoleonic Wars as the world’s dominant financial center (Michie 2001). While our data span until September 2017, we mainly show results through end-2016 to include only complete years.

To assemble the long-run bond pricing database, we include all external sovereign bonds for which we could find pricing information on the London or New York Stock Exchange (LSE and NYSE). In line with the above, we focus on bonds issued by central governments in foreign (USD and GBP) currency. We include bonds with a maturity of at least one year and those with a fixed coupon rate, thus dropping a small number of floating rate instruments in the modern sample. Throughout, we coded end-of-month price quotations.

We rely on several main sources of bond price data. For the pre-1870 period, we use prices from the Money Market Review, The Economist, Circular to Bankers, Course of the Exchange, and Banker’s Magazine. For the 1870-1930 period, we greatly benefitted from the work by William Goetzmann and Geert Rouwenhoorst, by using their digitized bond-level pricing data from the British Investor Monthly Manual.<sup>31</sup> We contribute to this collection by adding bond-level information on the timing and scope of default, that is, on missed or partial coupon and principal payments as well as the restructuring terms. We also expand that dataset forward, by 50 years, adding monthly price quotations for external sovereign bonds trading on the LSE from 1930 to 1980, as provided by The Economist and the Financial Times.

Importantly, for the interwar
about 45% if the cash subsidy increases to $90 (see Figure 1).

The spatial distribution of households who can afford SHS is also important as it provides useful insights into the potential for cost reduction in SHS dissemination from economies of scale. The district level affordability rate and the proportion of non-electrified households are presented in the Annex Table A2. Among the 42 districts in the sample, 17 districts have an affordability rate above 25%. More importantly, the 17 districts also have a relatively high concentration of households living in non-electrified villages at the level of 45% compared with the national average of 38%.

The spatial analysis indicates that the potential market for scaling up SHS indeed exists in many parts of the rural areas in Bangladesh if well-designed micro-finance schemes and delivery services can be implemented successfully. The cost of SHS dissemination, including distribution and post-purchase maintenance cost, can also be reduced substantially because of scale of the market.

# **5.3 Distributional impact analysis of SHS subsidies**

While the SHS programs financed through public resources have been implemented in many low-income countries over the past decade, the distributional consequences of SHS promotion programs are not well studied. The general belief based on anecdote evidence is that SHS

> 9 The study for housing affordability for California uses the budget share of 30% as the benchmark and households with a housing spending budget share above 30% are defined not able to afford (name)

11
.7|
|Total hidden costs as % of revenue|%<br>121.2|**15**|3.5|
|U.S. cents|**Kenya**<br>Predomina<br>gener|ntly hydro<br>ation|Other developing<br>regions|
|Power tariff (residential at 75 kWh)|**12.7**|10.27|5.0 – 10.0|
|Power tariff (commercial at 900 kWh)|**21.7**|11.73||
|Power tariff (industrial at 50,000 kWh)|**19.0**|11.39||

Source: Eberhard and others 2009, derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.**

# **Figure 8. Power outages continue to tax the Kenyan economy**

Economic cost of power outages in selected countries <!-- Start of picture text --> Tanzania<br>Uganda<br>Kenya<br>Senegal<br>Madagascar<br>Cape Verde<br>Niger<br>Benin<br>Cameroon<br>Burkina Faso<br>0.0 1.0 2.0 3.0 4.0 5.0<br>% of GDP<br><!-- End of picture text --> Source: Eberhard and others 2009.

As new capacity comes on stream, prices of power will eventually fall. Power tariffs in Kenya, currently at $0.20 per kilowatt-hour, are comparatively high. This is entirely appropriate at present, given that the country is able to meet current demand only by relying on emergency generation that costs 16
# **Methods**

## **Data**

We use data from High-Frequency Phone Surveys (HFPS) in five countries in Sub-Saharan Africa: Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda. The surveys were conducted by study countries’ national statistical organizations (NSOs), supported by the World Bank’s Living Standards Measurement Study (LSMS). Since May 2020, the LSMS-supported HFPS have collected cross-country comparable longitudinal data on a wide range of topics, focused on COVID-19 impacts on households and individuals.

Vaccine hesitancy related information was collected from in 2020, 2021, and recently in 2022, depending on the study country. This study uses four rounds of data for Malawi (Oct-Nov 2020, Apr 2021, Feb 2022, Jul-Aug 2022) and Uganda (Oct-Nov 2020, Feb 2021, Sep-Nov 2021, Aug-Sep 2022), three rounds in Burkina Faso (Dec 2020, May-Jun 2021, Apr-May 2022) and Nigeria (Oct 2020, Feb 2021, Dec 2021-Jan 2022), and two rounds of data for Ethiopia (Sep-Oct 2020, Feb 2021).

## **Sampling and sample representativeness**

The HFPS have national coverage and draw their samples from nationally representative samples of households interviewed in pre-pandemic face-to-face surveys of the LSMS-Integrated Survey on Agriculture (LSMS-ISA) series. In the settings at hand, mobile phone coverage is not universal, and the sample selection of phone surveys may not yield samples fully representative of the general population. Phone surveys also experience an extent (albeit limited) of non-response and attrition. The HFPS therefore use recalibrated sampling weights (based on propensity score and post-stratification methods), which mitigate sample selection biases. **[18–20]** In each household, one main respondent over the age of 15 was interviewed, selected to be knowledgeable of the household and its members. This purposive selection has been found to overrepresent certain population groups. **[20]** The sample sizes for each country are shown in Table A1.

##
_i_ in year _t_ . “Low-density smoke” and “high-density smoke” are the number of days during the growing season that each county was under a low-density or high-density smoke plume in the HMS data, averaged across corn or soy pixels. We control for the total growing degree days and extreme degree days in each county-year where the thresholds for extreme degree days varies by crop (29<sup>_◦_</sup> C for corn and 30<sup>_◦_</sup> C for soybeans). We also include quadratic controls for pre-season and growing season precipitation in each county-year. “Low cloud” and “high cloud” control for the number of days during the growing season in each county-year that have a cloud fraction between 50-75% (low) and 75-100% (high). Binning days by cloud cover allows for a non-linear response to cloud cover.<sup>22</sup> _αi_ and _χt_ are county and year fixed effects that account for county specific determinants of yield that do not vary over time (i.e. soil quality) and shocks 18
# **Tables**

Table 1: Descriptive Statistics for Employed

||All Employed|Informal|Formal|I/F Ratio|
|---|---|---|---|---|
|Sample Size|16,405|10,446|5,959|1.75|
|Population|7,974,924|4,799,786|3,175,138|1.51|
|E(_lnWs_)|-0.73|-1.11|-0.48|2.30|
|SD(_lnWs_)|2.01|1.85|2.06|0.90|
|E(_tes_)|88.51|92.33|82.77|1.12|
|SD(_tes_)|106.22|113.70|93.60|1.21|
|E(_tnes_)|6.90|7.34|6.33|1.16|
|SD(_tnes_)|13.13|13.63|12.45|1.09|
|E(_Y Ss_)|10.31|9.13|12.15|0.75|
|SD(_Y Ss_)|4.17|3.82|4.20|0.91|

Author’s calculations based on ECH, Second Quarter 2003, 13 Metropolitan Areas. All statistics weighted using expansion factors. _Y Ss_ : years of schooling. Employment and non-employment duration in months. Informality using SP(health and pension) definition. Log of Hourly Wages in current dollars of 2003. I/F ratio is different in the sample than in the population because of sample weights.

Table 2: Decomposition of Variance in Log Hourly Wages

||Informal Sector|
|---|---|
|Mean of Log Wages (_M_1)<br>
|Sorghum|ESS15|33.8%|74.5%|75.0%<br>49.8%|
|||ESS18|25.5%|53.3%|54.8%<br>53.3%|
|||IHPS10|56.5%||36.3%|
|Malawi|Maize|IHPS13|55.1%||44.6%|
|||IHPS16|65.3%||60.0%|
|||IHPS19|61.0%||51.8%|

Note: Crop loss incidences (percent) according to different proxies, by survey round and crop: area harvested < area planted (area loss), damage on the crop (damage), any area or damage-related loss, or expected production < realized production (overexpectation). ESS = Ethiopia Socioeconomic Survey, IHPS = Integrated Household Panel Survey.

For post-planting expectations to provide a realistic proxy of the attainable harvest, they need to be aligned with the actual management decisions on the plot.<sup>44</sup> Table A 4 and Table A 5 ascertain this through two equivalent yield regressions for each survey round and crop: one using realized harvest as the outcome variable and one using harvest expectations. They generally confirm that the alignment of expectations with production factors, based on the model’s R<sup>2</sup> , is generally equally good or better than for realized harvest. This increases confidence in farmers’ expectations’ alignment with their reported behavior. However, some notable exceptions in Ethiopia (ESS15 across both crops) and Malawi (IHPS10) exist.

> post-planting visit (not reported). This, again, suggests that some of the loss already occurred before expectations were elicited which will likely bias them downward.

> 43 For cross-country, longitudinal figures on crop and livestock losses in urban vs. rural areas see (Chaudhary, 2021)