rafmacalaba/datause-encoder-data huggingface dataset:
24 TABLE 4. PURCHASING POWER PARITIES (PPP), PRICE LEVELS, AND THE RELATIONSHIP OF IMPORTS TO GDP AT INTERNATIONAL PRICES.

|||RELATIVE|PRICES|(US-<br>100)|||
|---|---|---|---|---|---|---|
||1980||19|85|199|0|
|Japan<br>U. S.<br>EC|106<br>100.<br>116||9<br>100<br>7|3<br> <br>8|135<br>100<br>118||
||BILLIONS "|INTERNAZIONA|"REAL" C<br>L DOLLAR|DPS<br>S" AND [NOMINA|L DOLL|ARS)n|
|Japan<br>US<br>EC|999<br>2587<br>2781|(10401<br>125871<br>13125]|1432<br>3962<br>3259|(1343]<br>(39621<br>(25321|2180<br>5392<br>5081|(29431<br>(53921<br>(60121|
||RATIOS OF|ALL IMPORTS|TO "REA|L" GDP AND [NOM|INAL G|DP]|
|Japan<br>us<br>EC|0.141<br>0.098<br>0.140|(0.135]<br>(0.098]<br>[0.125]|0.090<br>0.091<br>0.096|[0.096]<br>(0.091]<br>[0.124]|0.108<br>0.096<br>0.117|(0.0801<br>(0.096]<br>(0.099)|
|RA|TIOS OF MANU|FACTURES IMP|ORTS TO|"REAL" GDP AND|(NOMI|NAL GDP]|
|Japan<br>U. S.<br>EC|0.025<br>0.048<br>0.052|(0.024]<br>(0.0481<br>(0.047)|0.022<br>0.063<br>0.043|(0.0241<br>(0.0631<br>(0.055]|0.046<br>0.070<br>0.074|(0.034]<br>[0.0701<br> <br>(0.062]|

> 2 PPP data for 1980 and 1985 are from _WORLD COMPARISONS OFPURCHASNG POWFA AND REAL PRODUCT FOR 1980,_ Pae IV of the ternmtiona Comparison Project, Buostat nd Umted Nations, New York, 1986 and 1989. Data for individual countdea were ggregated to form EC GDP, which wa then divided by the nominal GDP aregate in the same source to obtain the BC-wide price ntio. The aveage price ratio so obtined, and that directly obtained for Japan, wa then applied to the GDP data usd elewhere in this report (which are slightly different from thos of the ICP project) to obtain GDPs in linternationall dollars for 1980. Price and *reall GDP data for 1990 am preliminary resultu of the OECD intemational comparisom project.
# **Figure I2: Unconditional quantile regression of height-for-age z-scores on food security measures,** **<u>expanded controls (Uganda)</u>**

<!-- Start of picture text --> 1-SSEXR FCS<br>.05 .15 .25 .35 .45 .55 .65 .7 .05 .15 .25 .35 .45 .55 .65 .7<br>Quintile Quintile<br>95% Confidence Interval 95% Confidence Interval O<br>RIF-OLS RIF-OLS<br>DDS Per Capita Consumption<br>.05 .15 .25 .35 .45 .55 .65 .7<br>.05 .15 .25 .35 .45 .55 .65 .7 Quintile<br>Quintile<br>95% Confidence Interval<br>95% Confidence Interval RIF-OLS<br>RIF-OLS<br>.4 .4<br>.2<br>.2<br>0<br>0<br>-.2<br>-.4 -.2<br>.4 .4<br>.2<br>.2<br>0<br>0<br>-.2<br>-.2<br>-.4<br><!-- End of picture text --> 115
# **I. Introduction**

Although there is no agreement on the exact number and timing of armed conflicts around the world, there does seem to be a consensus regarding the time trends of the number of ongoing conflicts. The number of conflicts increased between 1945 and the late 1990s, regardless of the source or definition of armed conflicts. Figure 1 shows various estimates of the number of ongoing armed conflicts, based on six different definitions and sources. Although most of them suggest that the number of armed conflicts peaked in the mid‐1990s and declined during the first decade of the 21<sup>st</sup> century, it is likely that they rose again after 2011, coinciding with the Arab Spring. These episodes of conflict used to be circumscribed to developing countries (Besley & Persson, 2008), but in recent years spilled over to many European nations and other high‐income economies as the flow of refugees out of war‐torn economies intensified.

**Figure 1. Number of ongoing armed conflicts by selected authors**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>YEARS<br>Collier et al., 2004<br>Collier et al., 2009<br>Fearon, 2005<br>Chen, Loayza, reynal‐Querol, 2008<br>UCDP‐PRIO 17.2 (all conflicts)<br>UCDP‐PRIO 17.2 (conflicts with more than 1000 deaths a year)<br>Number of ongoing conflicts<br><!-- End of picture text --> Despite the human and economic pain brought by such confrontations, very little has been written about macroeconomic trends around the onset of civil conflict. The current literature studies civil wars mainly through the lens of their potential causes and determinants (Collier & Hoeffler, 2004; Collier et al 2009; Fearon & Laitin, 2003), or their relationship with poverty and economic growth (Kang & Meernik, 2005; 2
We further observe that approximately 22 percent of youths who are not working in their ideal job would like to work in STEM-related fields ( **Table A2 in the Annex** ). We explore aspirations to work in STEM-related fields comparing high-school youths aged 15-18 with college youths aged 19-25 in relation to their activity as of May/June 2020. As observed in relation to educational aspirations, **the inclination towards STEM-related careers is greater among youths aged 1518 compared to older youths** , regardless of their school enrollment, employment or NEET status. Furthermore, **we observe that the willingness to work in STEM-related jobs is the highest among young students aged 15-18 (Figure 3) and young NEETs aged 19-25 (Figure 4).** Being engaged in working activities, instead, is the weakest predictor of career aspirations in STEM.

Moreover, we observe gender disparities in career aspirations between female and male youths. **In general, female youths exhibit lower career aspirations than male youths, regardless their age and activity.** For instance, 37 percent of male students aged 15-18 would like to work in STEM-related jobs, compared to 26 percent of their female peers. Similarly, male NEETs and employed youths aged 15-18 report higher career aspirations compared to their female peers ( **Figure 3** ). Male youths continue to exhibit higher career aspirations than their female peers as they grow up. For instance, 21 percent of male NEETs aged 19-25 would like to work in STEMrelated jobs, compared to 11 percent of their female peers ( **Figure 4** ).

These findings are consistent at the country level, with only few exceptions. For example, in Ethiopia, female employed youths aged 15-18 show greater career aspirations than their male employed peers ( **Figure 3** ). Similarly, female students aged 19-25 also demonstrate higher career aspirations than their male peers in Malawi ( **Figure 4** ).

**Figure 3. Share of youths aged 15-18 who would like to work in STEM-related jobs, by sex and current activity.**

**Figure 4.  Share of youths aged 19-25 who would like to work in STEM-related jobs, by sex and current activity.**

<!-- Start of picture text --> 100%<br>100% Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>90%<br>90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 37% 40%<br>30% 26% 30%<br>20% 18% 16% 15% 11% 20% 21% 15%<br>11% 11% 10%<br>10% 8%<br>10%<br>0%<br>Male Female Male Female Male Female 0%<br>Male Female Male Female Male Female<br>NEETs Currently at school Working<br>NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity. Source: Own calculations based on Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024 11
**PREVIOUS MAIN JOB #1**

**[ENUMERATOR: ASK ABOUT PREVIOUS MAIN JOB #1 IF THE RESPONDENT HAS BEEN AT THE CURRENT JOB FOR LESS THAN 15 YEARS (IF THE START DATE FOR CURRENT MAIN JOB, C7a_startyear1, IS AFTER 2000. OTHERWISE GO TO SECTION D.]**

**ENUMERATOR READS: NOW I WOULD LIKE TO ASK YOU ABOUT THE MAIN JOB YOU HELD BEFORE YOUR CURRENT JOB.**

C2_emptype2 What describes the employment status of the MAIN job you held just before your current main job?

1. Paid employee in government

2. Paid employee in semi-government 3. Paid employee in a private entity 4. Apprentice/Intern/Trainee 5. Seasonal worker 6. Day laborer / casual worker 7. Domestic worker in a private household 8. Self-employed / business owner with no employees 9. Self-employed / business owner employing only paid or unpaid family members 10. Self-employed / business owner employing some non-family members 11. Paid or unpaid family member working in a household business 888. Don’t know 999. Refused C3_location2 Where did you mainly undertake your previous MAIN job?

**[DO NOT READ CHOICES BELOW. LISTEN TO SPONTANEOUS RESPONSE AND SELECT APPROPRIATE CODE FROM LIST.]**

1. At my home with no special work space

2. At my home with work space inside/ attached to the home 3. Factory, workshop, shop, kiosk, etc 4. Farm or individual agricultural plot 5. Home or workplace of client 6. Construction site 7. Market, bazaar stall, trade fair 8. Street pavement/ highway with fixed post 9. Office building 10. Employer’s home 11. Transport vehicle 12. No fixed location (e.g., mobile; door-to-door; street without fixed post) 13. Others, specify 888. Don’t know <u>999. Refused</u> C4_industry2 What was the industry of this previous MAIN job?

**[LISTEN TO SPONTANEOUS RESPONSE AND SELECT APPROPRIATE CODE FROM INDUSTRY LIST. IF NOT CLEAR THEN ASK FOR MORE DETAILS. IF OPTION IS NOT ON THE LIST THEN WRITE DOWN WHAT THE RESPONDENT SAYS.]**

**[If don’t know then put “Don’t Know”=888]** **<u>[If refused then put “Refused”=999]</u>**
_between actual and synthetic light series for control countries (normalized to 0 in 2012). The dark dash horizontal line indicates a null gap, and the two red vertical lines indicates the period of Séléka occupation in Bangui (2012, excluded, up to 2014, excluded). The subsequent period translates into localized conflicts areas in Northwestern and Eastern parts of the country, outside of major cities._

**Figure 9: Differences between manufacturing value added (percent of GDP) for CAR and control countries and their respective synthetic controls, 2009-17**

<!-- Start of picture text --> We keep all placebo countries<br>100<br>50<br>0<br>-50<br>-100<br>2009 10 11 12 13 14 15 16 17<br>Seleka occupation<br>Year<br>No pre-Manufacturing's MSPE five times higher than CAR's<br>100<br>50<br>0<br>-50<br>-100<br>2009 10 11 12 13 14 15 16 17<br>Seleka occupation<br>Year<br>Gap Manufacturing value added (index 100 =2012)<br>Gap Manufacturing value added (index 100 =2012)<br><!-- End of picture text --> _Source: Manufacturing value-added series are from the World Development Indicators_ (35) _, from 2009-17. Note: The dark dash line indicates the gap between actual and synthetic light series for CAR and the gray lines indicates_ 20
However, heat and floods shocks are associated with increases in poverty for the urban population living in households whose head is working in the secondary sector, with an average poverty increase of 3.8 and 10 percentage points, respectively. Given that the secondary sector includes the manufacturing industries and construction, among others, high temperatures and floods presumably impede workers to perform regular working hours, reducing their incomes, and pushing them and their dependents into poverty. Again, our results confirm that flooding is one of the most devastating weather shocks in urban Paraguay, with particularly distribution-sensitive effects for secondary-sector workers, as poverty increases, while income remains unaffected.

Finally, the population living in households whose head is engaged in the tertiary sector is practically unaffected by negative consequences of weather shocks in terms of poverty. Nevertheless, heat shocks lead to income declines of 4.6% in urban, and as much as 8.3% in rural areas, respectively, and drought shocks to a reduction in income by up to 6.8% on average in rural areas (Figure B.3 in Appendix B and Table A.15 in Appendix A).

# **5.3 Extensions: Heat days**

To extend our study of short-term weather shocks and poverty in Paraguay, we add an additional analysis in which we change our measure for heat shocks to the number of heat days during the 90-day time bin. The analysis shows that an increase in the number of heat days is strongly associated with rising poverty for rural households engaged in agriculture, while non-agricultural and urban households are less affected, except on days with extreme temperatures.

Measuring temperature shocks as days with extreme temperatures has been used widely literature and in various forms, and is considered an effective measure for labor productivity, health, and crop yields, amongst others (Aragón et al., 2021; D’Agostino & Schlenker, 2016; He & Chen, 2022; Schlenker, Hanemann, & Fisher, 2007). We use temperature thresholds of 25°C, 30°C and 35°C, respectively, which are considered as reference thresholds for ’summer days’, ’hot days’, and ’very hot days’ in the context of Paraguay (World Bank, n.d.-a), and thus capture absolute and short-term severe heat shocks. Figure 8 shows the impacts of the number of heat days on poverty disaggregated by whether the household is engaged in agriculture. In rural areas, there seems to be a strong association between the number of heat days and poverty for households in agriculture, with increases in poverty of nearly 0.3 percentage points per additional heat day, on average. The mechanisms here could be either a destruction of crop and reduction of yields, or reduction in physical labor hours due to the heat, as identified in Section 2. Non-agricultural households are less affected, although every additional very hot day (days with temperatures above 35°C) leads to large increases in poverty, by up to 0.4 percentage 21
17 Table 1: Selected food consumption and poverty variables, Mozambique 1997-2003-2009 and Ghana 2013 (weighted average<sup>5</sup> )

||**M**|**OZAMBIQUE**||**GHANA**|
|---|---|---|---|---|
||1997|2003|2009|2013|
|expenditure per capita per day (Metical or Ghana Cedi)|5.52|11.13|23.81|6.07|
|poverty line (Metical or Ghana Cedi)|5.34|8.47|18.41|2.74|
|food poverty line (Metical or Ghana Cedi)|4.08|6.02|13.55|1.65|
|food required per capita per day (1000 Kcal)*|2.154|2.132|2.113|2,258|
|food consumption per capita per day (1000 Kcal)|1.900|1.739|1.737|3.837|
|calorie cost (Metical or Ghana Cedi per 1000 Kcal)|2.20|4.48|8.27|0.72|
|household size|4.84|4.79|4.69|4.05|
|household adult equivalent scale*|0.68|0.71|0.70|0.76|
|food in total consumption (%)|56|47|45|49|
|homegrown in total food (%)|n.a.|46|48|18|
|children stunted (% of under-fives with HAZ<-2)**|49|41|44|28|
|household head can read and write (%)|48|49|52|61|
|household has safe drinking water (%)|7|17|14|59|
|house has toilet or latrine (%)|37|45|51|81|
|house has solid roof (%)|18|24|30|86|
|population below national poverty line (%)|70|60|56|25|
|urban population (%)|22|32|31|51|
|sample size (number of households)|7,912|8,461|10,516|16,014|

Source: authors computation from INE (1998, 2004, 2010) and GSS (2014).

* Requirement are computed from a gender and age specific equivalent scale for calorie needs of each household members, based on 3,000 Kcal for a 18 to 35 years old male (James and Schofield, 1990).

** Stunting figure for Mozambique 2003 is  taken from INE-MdS (2005)  and for Ghana from GSS-GHS (2009).

5 The sampling of the surveys follow a similar stratification of households with one stratum for each province (Mozambique) or region (Ghana) and a further rural-urban stratification within these strata. Application of Census weights (household or population weights) ensures that the sample averages are passable indications of the true average for the respective groups and for the population at large.
# 1 Introduction

Empirical research using longitudinal firm or plant level data has shown strong regularities in establishment and industry evolution. Recently, researchers have started to build structural models to explain these regularities. However, being so stylized has made it difficult for these models to confront the micro-level data in a more formal quantitative analysis. In this paper, I present a model that explains several salient features of the data that had eluded the existing models. Furthermore, using a panel of Chilean Manufacturers, I provide an estimation of the model parameters and show its quantitative success in fitting the data.

This paper builds on the stylized model of establishment and industry evolution presented by Klette and Kortum (2004). In their model, an establishment is defined as a collection of products and each product evolves independently. Every product owned by an establishment can give rise to a new product as a result of a stochastic innovation process or can be lost to a competitor. This birth and death process for the number of products is the source of establishment and industry evolution. Through this parsimonious model of innovation, they explain various stylized facts that relate R&D, productivity, patenting, and establishment growth. Their model also generates heterogeneity in establishments’ sizes and a skewed size distribution.

However, the Klette and Kortum (2004) model fails to capture several important features of the data. Through a parsimonious extension of their model, I succeed in explaining these features which are: the thick right tail of the size distribution, the independent relation between age, size and the hazard rate of exit, the relation between the variance of growth rate and size, and the pre-exit behavior of establishments in a birth cohort. At the root of the improvements over the Klette and Kortum (2004) model is the introduction of heterogeneity in producers’ efficiency levels<sup>1</sup> . As a result of this heterogeneity, producers differ from each other in both their innovation rates and the revenues generated from each product.

The improvements and how they emerge through this extension can be explained as follows. The first improvement is in fitting the size distribution. In the model, more efficient producers are more innovative and they generate more revenue per product. This complementarity between the innovation rate and the revenue magnifies the variation among establishments’ sizes

> 1Lentz and Mortensen (2008) also incorporates heterogeneity into Klette and Kortum (2004) model in a different way which I explain through the paper, is not sufficient for the improvements I specify here.

1
to 149). Another way to view the patterns by age, sex, and country, is to examine the mortality sex-ratio (M/F) for countries by tercile of GNI per capita, as shown in Figure 3. (See also Annex Table A1.) These figures show that the widening of the gender mortality gap driven by COVID-19 in 2020 was limited to higher income countries and principally among adults ages 45-54 and 55-65. Figure 4 displays the same data with individual country points. Annex Figure A2 shows the same figure with the subset of countries held constant across years.

# **Discussion**

This paper, using a global dataset covering a wide swath of middle- and high-income countries, confirms previous findings, based on more limited data, that for all age groups above age 45 and in all countries with few exceptions, men die at higher rates than women. It also identifies global variation in patterns of COVID-19 mortality by age and sex. The findings complement the finding from Demombynes et al. (2022) that the age curve of excess deaths in 2020 was flatter for middle-income countries and steeper in wealthier countries. The findings in this paper demonstrate that this difference is principally driven by the mortality patterns of men, resulting in COVID-19 amplifying the gender mortality gap in 2020 more in richer countries as compared to less-wealthy countries.

An important limitation of this study is that because it excludes excess mortality estimates not based on actual observed mortality, it does not include any low-income country.

<mark>Still, these findings point to the need to consider public policies during public health emergencies that offer more targeted protection for men over 45 with co-morbidities, for example those working in jobs exposing them to a higher risk of infections.</mark> The patterns found will hopefully inspire future country-level research using cause-of-death data to get a fuller understanding of relevant drivers of age-gender mortality patterns. It is worthwhile to explore structural socioeconomic conditions that vary with country income and sex, which would also interact with both infection exposure and fatality rates. This would include, for example, the structure of jobs and employment patterns (well documented to vary by country income and sex), especially the extent of remote work opportunities which may reduce exposure to infections during a pandemic. Another example is the extent of residency in long-term care facilities. Older persons, especially older women, have higher likelihoods of residing in nursing homes in wealthier countries. When these facilities are of poor quality and safety they result in greater exposure (Webster 2021). A third area could be the profile of co-morbidities pre-existing in the population. For example, women are relatively more obese than men in low- and middle-income economies but not in highincome countries (Ameye and Swinnen 2019).

<mark>Lastly, the analysis in this paper is also a reminder of the value of demographic data and the value of efforts by country governments and international organizations to promote and standardize vital statistics data.</mark> 6
# Out-of-Pocket Expenditures on Health: A Global Stocktake

## Adam Wagstaff<sup>a*</sup> , Patrick Eozenou<sup>b</sup> and Marc Smitz<sup>b</sup>

a Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA b Health, Nutrition and Population Global Practice, The World Bank, 1818 H Street, NW, Washington DC 20433, USA

**Keywords:** Out-of-pocket health expenditures; financial protection; health and poverty; sustainable development goals

**JEL codes:** I1, I3, J13

### **Acknowledgments**

The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.

- Corresponding author: Adam Wagstaff. Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA. Tel: +1 202 473 0566, awagstaff@worldbank.org