rafmacalaba/datause-encoder-data huggingface dataset:
|Guinea|3.1|‐0.3|3.0|0.4|0.4|
|---|---|---|---|---|---|
|Burundi|3.3|3.5|4.1|4.7|‐2.3|
|South Sudan|..|5.0|‐8.0|3.4|‐5.3|
|_Equatorial Guinea_|22.7|‐4.5|‐0.7|‐0.3|‐9.3|
|_Sierra Leone_|7.3|3.2|11.5|4.6|‐20|

_Source: World Development Indicators, and Global Economic Prospects (June 2015)._ Even if we exclude Sierra Leone as a special case affected by the Ebola epidemic, the growth range in Sub‐Saharan Africa, from 10.2% in Ethiopia and 8.4% in Côte d’Ivoire, to ‐9.3% in Equatorial Guinea and ‐5.3% in South Sudan, is quite amazing. There are many factors behind this divergence, but one of the most important ones has to do with the country’s ability and preparedness to grapple with globalization.

In this brewing global challenge that has to do with labor, Africa will probably play a more major role than any other region in the developing world. This is because most of Africa will see a rise in its working‐age population—the so‐called ‘demographic dividend.’ China has already seen the high point of this; India still has more of this dividend to come but it is well on the way. It is Africa for which much of the demographic dividend still lies ahead. Without adequate global demand for this labor, the continent could get mired in conflict and forced migration. On the other hand, done right this is an opportunity to draw Africa into the mainstream of global growth.

Africa has some remarkable features that could power the economy. One of the most notable is the participation of women. Approximately 54% of African women aged 15+ are labor force participants, with the number rising to as much as 62% for Sub‐Saharan Africa as a whole and 76% in Eastern Africa. This is much higher than most other regions, such as the 40% in South Central Asia and 38% in Central America. This can be a great advantage, not just in promoting shared prosperity, but in helping with growth.

In keeping with the story in this paper, we can also see some remarkable divergence occurring within Africa. Continuing with the theme of female labor force participation, we see very low numbers in Northern Africa, the average being 27%, which contrasts sharply with other regions and nations, such as a remarkable 91% in Burundi, 87% in Tanzania, 85% in Rwanda, and 81% in Ethiopia. So, along with the divergence in performance that is happening because of short‐ term problems such as the drop in commodity prices and some slowdown from elsewhere, such as Japan and China, transmitting to Africa and more importantly, the Sub‐Saharan region, there is this demographic divergence that will contribute to Africa being differently positioned to take advantage of the rise of labor‐linking technology and labor demand from high‐income economies spilling over to the developing world. While for most of Africa it remains to be seen how this will play out, India has already had to contend with some of this and a retrospective view of what happened in India could inform India in the future, but also other economies that are beginning to feel the challenges and opportunities of globalization.

11
**FIGURE 1: Collapsing Output of Completed Dwellings**

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_Housing privatization_ has also been painfully slow, even when it is available to the current occupants at virtually no cost. This may reflect, at least in part, the fear that owners will be charged higher utility and maintenance costs than are renters. In any case, this slows down the overall process of privatization since housing generally represents a large part of any country's tangible capital. In the United States, for example, residential structures (excluding land) represent almost 30% of all tangible capital and all real estate (including land) represents almost 70% of all tangible capital (see Table 1). These ratios may be somewhat lower for the transition economies, but not by a large margin. This is illustrated in Figures 2 and 3 , which show the number of housing structures per 1000 population for groups of transition and developed economies respectively.

2
# **Changing Norms about Gender Inequality in Education:  Evidence from Bangladesh**<sup>**†**</sup>

Niels-Hugo Blunch _Washington and Lee University_ <u>blunchn@wlu.edu</u> Maitreyi Bordia Das _World Bank_ <u>mdas@worldbank.org</u>

**JEL Classifications:** D190, I290, J120, J160, J240.

**Keywords:** Gender education gap norms, externalities, norm production, human capital, spousal education, marriage, Bangladesh.

> † The findings and interpretations are those of the authors and should not be attributed to the World Bank or any of its member countries or affiliated institutions.
due to the large increase in online consumption of baby food. Baby food and formula are the top two package foods sold online in China.<sup>33</sup> Using the CFPS data, we find that breast feeding is negatively correlated with e-commerce development; the coefficient between food consumption and e-commerce z-score is larger for households with young children under three years old.<sup>34</sup> This is consistent with the Chinese culture and the strong willingness of parents to spend on children, while the judge is still out for the impact on nutrition.

The fast growth in food consumption may also stem from the online purchases of food varieties by higher-end consumers. In more developed areas, particularly tier one and tier two cities, people can purchase online specialty fresh produce, meat, or seafood (which are not available in traditional markets) with guaranteed fast delivery. End-to-end value chain digitalization, such as the Alibaba’s Hema store, which sells online high-end groceries and readymade food with guaranteed fast delivery, is one example that might be changing the pattern of online-offline retails in the future.<sup>35</sup> However, the contribution of such high-end online-offline grocery purchases to food consumption is likely to be limited for the period of our analysis given the limited coverage and the high requirement of logistics, although this might increase in the future, particularly in more developed areas.

As to personal services, such as education, medical, fitness, and health services for which personal interaction is more important, consumption growth is not associated with e-commerce development.<sup>36</sup> These services involve strong personal interactions. For example, high quality of service in education often requires instructor customization of teaching strategy, content, and speed according to students’ needs and feedback. As labor mobility is lower than the mobility of goods, it is costly to send service providers to distant customers.

# **VI Robustness**

> 33 See https://www.newyorker.com/magazine/2018/07/23/how-e-commerce-is-transforming-rural-china 34 Additional results on the relations between food consumption and whether the household has young children and those on the breastfeeding and e-commerce z-scores are available upon request. Forthcoming in another research paper. 35 https://www.pwccn.com/en/retail-and-consumer/publications/global-consumer-insights-survey-2018-china-report.pdf

> 36 Due to data limitation, we cannot examine the use of online education or health services, such as interactive online learning program for children and health monitoring apps, which may have a spillover effect over time for consumers to consume more of not just online consumption goods but also get exposed to the kind of goods and services that can potentially enhance human capital.

·· 21
<!-- Start of picture text --> Policy Reuah<br>Country OpwstlonJ<br>WPS 1108<br><!-- End of picture text --> This paper- a product of the Country Operations Division, Europe and Central Asia, Country Department II - is part of a larger effort in the department to develop background conceptual frameworks for opcrational activities. Ccpies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Anita Correa, room Hi 1-107, extension 38549 (February 1993,9 pages).

In this essay, Catsambas explores theoretical concepts behind the current debate on govemment growth, public sector inefficiency, and the role of fiscal policy with a view to raising the most important issues relevant for fiscal policy. He examines theories of public sector growth, the evaluation of benefits from government spending, and the response of the private sector to govenmment activities.

benefits, as perceived by citizens. Would a wellto-do citizen, who could afford private security gaards, make the same evaluation about public security that a poor citizen would make? In general, what considerations affect a person's desire for a given amount of public spending, and what are the important parameters that analysts should take into account in their investigation?

Three principal reasons have been suggested to explain public sector growth: conscious govemment choices, political pressure from interest groups, and the self-interest of bureaucracies. One may ask: Is the growth of the public sector a response to public demand or the result of govenmuent waste and inefficiency? In terms of the agent-principal theory, bureaucrats who are supposed to serve as agents for citizens may not necessarily do so - - which is where waste comnes in. If bureaucrats are interested in the nonpecuniary benefits of their bureaus, they will have an incentive to maximize their activities and budgetary allocation rather than their operating efficiency.

Catsambas also explores the issues behind the private sector's response to government activities and argues against a mechanistic approach to the interaction between the private and the public sector. Unless decisionmakers are relatively certain about how citizens evaluate govemment actions, citizens may respond in a way that nullifies the government action.

Catsambas concludes that more empirical work is needed on measuring citizens' response to public sector activities. And fiscal policy, especially on expenditures, should be modeled on a disaggregated basis to isolate hypotheses about potential private sector responses to individual public programs.

In discussing the evaluation of public programs, Catsambas focuses on the "true"

**The** Workmg PDserinestdisseates **Policy Research** thefndingsof workunderway itheBaAnobjectiveof ieesetes is to get these fidings out quickly. even if presenttions are less tha fully polishedL The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produoed by the Policy Research Dissenmination Center
<!-- Start of picture text --> Price of Agricultural Goods<br>05 N =2,J=1,9=1,d=10,d6=0.1, x = 0.3, a = 0.33333, A = 1<br>0.45<br>04<br>035 —Amazon Region<br>. —R.O.W<br>03<br>0.25<br>0.2!0 1 2 3 4 5 6 7 8 9 10<br>years<br><!-- End of picture text --> <!-- Start of picture text --> Price of Timber<br>19 N =2,J =1,S5 =1,d=10,¢=0.1, x = 0.3, a = 0.33333, \ = 1<br>1.85<br>18<br>= i:<br>—R.O.W<br>1.75>\<br>17<br>1.650 1 2 3 4 5 6 7 8 9 10<br>years<br><!-- End of picture text --> <!-- Start of picture text --> Price of Manufacturing Goods<br>06s N =2, J=1,5 =1,d=10, ¢6=0.1, x = 0.3, a = 0.33333, X= 1<br>0.645<br>0.64<br>0.635 —Amazon Region<br>0.63 —R.O.R.O.W<br>0.625<br>0.62<br>0.6150| 1 2 3 4 5 6 7 8 9 10<br>years<br><!-- End of picture text -->

<!-- Start of picture text --> Price Index of Consumption Goods<br>085 N =2, J=1,5=1,d=10, 6 =0.1, x = 0.3, a = 0.33333, A = 1<br>08<br>0.75<br>—Amazon Region<br>—R.O.W<br>0.7<br>0.65<br>0.60| 1 2 3 4 5 6 7 8 9 10<br>years<br><!-- End of picture text -->
# **6.3 Sectoral-level**

The empirical results suggest that the number of new firms increase when refugees are hosted in a province but they do not clarify the mechanism behind the increase. Furthermore, the increase in the number of new firms is not explained by new foreign firms in all estimations, which we might attribute more easily to entrepreneurial activity by the refugees themselves. The economic explanation would be that the increase in the supply of low-skilled labor increases the value of capital found by Del Carpio and Wagner (2015) leads to more firms entering the market. We can informally test this hypothesis by checking which sectors were most influenced by new firm entries. If low-skill employment is the main driver of new firm entries, we would expect sectors that utilize low-skill employment to be most strongly affected.

Table 7: Sectoral-level changes between 2013 and 2014

|||IV esti<br>|mates<br>|
|---|---|---|---|
||Average|Coef.|SE|
|Agriculture|375.2|0.0181***|(0.0059)|
|Mining|90.4|-0.0128*|(0.0056)|
|Manufacturing|5269.0|-0.0005|(0.0012)|
|Electricity|115.4|0.0366|(0.0180)|
|Construction|3208.8|0.0010|(0.0015)|
|Retail|101.1|0.0010|(0.0006)|
|Transportation|6927.3|0.0054**|(0.0027)|
|Hotel|3771.9|0.0038*|(0.0021)|
|Information|503.6|0.0072|(0.0050)|
|Financial|320.5|-0.0095***|(0.0027)|
|Real estate|634.6|0.0035|(0.0028)|
|Professional|2381.4|-0.0001|(0.0006)|
|Administrative|602.8|0.0019|(0.0021)|
|Education|355.8|-0.0004|(0.0021)|
|Health|528.9|-0.0144***|(0.0038)|
|Culture|425.4|-0.0129***|(0.0050)|
|Total|43574.6|0.0029***|(0.0011)|

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Source:_ Authors’ own analysis results from data described in text. _Note:_ All models include province and year fixed effects and a yearspecific control for distance to the closest border crossing with Syria. Data are from the years 2013 and 2014 for 81 provinces. The dependent variables are the log of the number of firms in the specified sector. Each OLS and IV coefficient is from a separate regression with 162 observations.

The data from the Chamber of Commerce and the Ministry of Science, Industry and Technology are insufficient to determine which sectors are most affected by the inflow of new firms. We use data from 2013-2014 reported by Turkish Statistics on the number 22
adults are increasingly unable to satisfy their basic needs. While the numbers have rebounded in recent years, in 2019, roughly 40 percent of adults still reported that they did not have enough money to buy food or provide adequate shelter or housing for themselves and their families in the last 12 months.

In 2019, roughly half of poorer adults did not have enough money at times to buy food or provide shelter in the last 12 months. Men and women were equally likely to report this. More specifically, among adults who report not having enough money to buy food in the last 12 months, the gap between those in the richest 60 percent and the poorest 40 percent of households was insignificant in 2010 (figure 1). But that gap has grown by more than 15 percentage points in the last decade.

**Figure 1: Did not have enough money to buy food for your family in the last 12 months (% of adults)**

<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>pre-ISIS ISIS control post-ISIS<br>poorest 40% richest 60%<br><!-- End of picture text --> Source: Gallup World Poll, 2019.

ISIS control has strongly influenced the regional food insecurity map today. At their peak in late 2014 and early 2015, ISIS occupied large parts of the Iraqi governorates of Anbar, Nineveh, Saladin and Kirkuk, all in the west and north-west of the country, including their major cities and population centers. They also had some control in the governorates of Diyala and Babylon in central Iraq. ISIS briefly reached Baghdad's outskirts before being pushed back by Iraqi armed forces and Popular Mobilization Forces as well as the international anti-ISIS coalition. These developments have been a key driver in the regional variation observed in the data.

5
19 “other incomes”, including any capital and rental incomes that are reported in the survey. This unsatisfactory state of affairs is in the process of being remedied, and the 2004 PNAD questionnaire already contains more detailed questions on transfer incomes than in the past, although not yet on specific amounts. In any case, for comparability with previous years, social assistance transfers must still be grouped within this residual category.

While this conflation prevents a confident assessment, there is some tentative evidence in Table 4 that recent increases in volume and better targeting of social assistance transfers are beginning to have some impact. From 1993 to 2004, mean “other” incomes have risen, and their inequality level has fallen dramatically from 49 to 23. The population share receiving incomes from this source has almost doubled, from 16% to 30%. Inequality amongst recipients has also fallen, from 7.4 to 6.6. Perhaps most tellingly, the correlation between this income source and total income has fallen from 40% to 30%. While it is possible that these changes reflect changes in the distribution (or the reporting) of capital or rental incomes, it is more likely that they reflect, at least in part, the substantial expansion of Brazil’s cash-based social assistance system, beginning with the Projeto Alvorada in 1994-95, the launch of the National _Bolsa Escola_ and _Bolsa Alimentação_ programs in 2000, and their integration into the _Bolsa Família_ in 2003. A more disaggregated analysis of the incidence of these transfers over the last ten or fifteen years is needed in order to form an assessment of their role in the decline in overall inequality observed in Brazil over the period.

# **4) The Dynamic Decomposition of Brazilian Inequality.**

Comparing static decompositions of inequality, whether by population subgroup or by income source, at different points in time may be informative about the changing structure of the income distribution. But dynamic decompositions of both inequality and poverty are a more direct approach to gaining insight into the factors associated with changes in those variables. In this section, we report on a dynamic decomposition of inequality (measured by E(0)) proposed by Mookherjee and Shorrocks (1982), and then on a decomposition of poverty changes into a growth and a redistribution component, due to Datt and Ravallion (1992).

Accounting for changes in an overall measure of inequality - such as E(0) - by means of a partition of the distribution into population subgroups must entail at least two components to the change: one
- Green, Amelia Hoover and Patrick Ball. 2019. “Civilian killings and disappearances during civil war in El Salvador (1980–1992).” Demographic Research 41:781–814.

- Henderson, J Vernon, Adam Storeygard and David N Weil. 2012. “Measuring economic growth from outer space.” American economic review 102(2):994–1028.

- Hijmans, Robert J, Susan E Cameron, Juan L Parra, Peter G Jones and Andy Jarvis. 2005. “Very high resolution interpolated climate surfaces for global land areas.” International <u>Journal</u> of Climatology: A <u>Journal</u> of the Royal Meteorological Society 25(15):1965–1978.

- Jablonski, Ryan S and Brigitte Seim. 2022. “What politicians don’t know can hurt you: The effects of information on politicians’ spending decisions.” Available at SSRN .

- Lauria-Santiago, Aldo A and Jeffrey L Gould. 2008. To Rise in Darkness. Duke University Press.

- Leon, Gianmarco. 2012. “Civil conflict and human capital accumulation the long-term effects of political violence in per ́u.” <u>Journal</u> of Human Resources 47(4):991–1022.

- Liu, Shelley X. 2022. “Control, Coercion, and Cooptation: How Rebels Govern after Winning Civil War.” World Politics 74(1):37–76.

- Lowes, Sara and Eduardo Montero. 2021. “The legacy of colonial medicine in Central Africa.” American Economic Review 111(4):1284–1314.

- Loyle, Cyanne E, Kathleen Gallagher Cunningham, Reyko Huang and Danielle F Jung. 2021. “New Directions in Rebel Governance Research.” Perspectives on Politics pp. 1–13.

- Mao, Zedong. 1966. Selected military writings of Mao Tse-tung. Foreign Languages Press.

- Mart ́ın Alvarez, Alberto. 2010. From Revolutionary War to Democratic Revolution: The Farabundo Mart ́ı National Liberation Front <u>(FMLN)</u> in El Salvador. Germany.

- Mart ́ın-Bar ́o, Ignacio. 1990. “La violencia en Centroam ́erica: una visi ́on psicosocial.” Salud, Problema <u>y</u> Debate <u>(Buenos</u> Aires) 2:53–66.

- Martinez-Bravo, Monica and Andreas Stegmann. 2022. “In vaccines we trust? The effects of the CIA’s vaccine ruse on immunization in Pakistan.” <u>Journal</u> of the European Economic Association 20(1):150–186.

- Michalopoulos, Stelios and Elias Papaioannou. 2013. “Pre-colonial ethnic institutions and contemporary African development.” Econometrica 81(1):113–152.

48
# Figure 14. Measures of Youth Demography for the France, Netherlands, United Kingdom, and United States, 1950-2050, United Nations Medium Variant Projections

**Panel A.  Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections**

<!-- Start of picture text --> 5.0%<br>United States of America<br>4.0% France<br>Netherlands<br>3.0% United Kingdom<br>2.0%<br>1.0%<br>0.0%<br>-1.0%<br>-2.0%<br>-3.0%<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year<br>Annual Growth Rate<br><!-- End of picture text --> <!-- Start of picture text --> Panel B. Ratio of Population Aged 12-24 to Population Aged 25-59, U.N. Medium Variant<br>Projections<br>0.7<br>United States of America<br>0.6 France<br>Netherlands<br>United Kingdom<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year<br>Ratio<br><!-- End of picture text --> 40