rafmacalaba/datause-encoder-data huggingface dataset:
Table 6a: Care and Domestic Responsibilities - Correlation with Gender Equality Norm: Attitudes and Norms

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Main<br>Caregiver:<br>Preschool<br>or School<br>Children|Hours<br>spend on<br>care<br>activities<br>(Main<br>Caregiver<br>Children)|Hours<br>spend on<br>household<br>chores<br>(Main<br>Caregiver<br>Children)|Main<br>Caregiver:<br>Preschool<br>or School<br>Children|Hours<br>spend on<br>care<br>activities<br>(Main<br>Caregiver<br>Children)|Hours spend<br>on household<br>chores (Main<br>Caregiver<br>Children)|
||**_Gender Eq_**|**_uality Norm_**|**_: Men and w_**|**_omen shoul_**|**_d have equa_**|**_l opportunities_**|
|Female|0.07***|2.95***|1.52***|0.11***|1.89***|1.53***|
||[0.02]|[0.43]|[0.15]|[0.02]|[0.38]|[0.13]|
|Personal Belief:Agree(No=0/Yes=1)|0.00|-0.12|0.13||||
||[0.01]|[0.21]|[0.08]||||
|Personal Belief:Agree*Female|0.01|-0.24|-0.32**||||
||[0.02]|[0.44]|[0.15]||||
|Community Norms (0/1)||||0.02|-0.07|0.35***|
|||||[0.02]|[0.36]|[0.12]|
|Community Norms (0/1)*Female||||-0.04|1.44***|-0.44**|
|||||[0.03]|[0.55]|[0.19]|
|Constant|0.31***|5.00***|3.84***|0.29***|4.09***|4.11***|
||[0.05]|[0.95]|[0.35]|[0.07]|[0.98]|[0.50]|
|Observations|22327|9564|10131|15506|6820|7162|
|Adjusted R-squared|0.21|0.15|0.17|0.23|0.17|0.18|
|Mean Female|0.47|9.41|4.26|0.48|9.52|4.26|
|Mean Male|0.41|6.20|2.94|0.42|6.17|2.93|
|Norms+Fem(Coeff)|0.02|-0.36|-0.20|-0.03|1.36|-0.08|
|Norms+Fem(SE)|0.02|0.39|0.13|0.02|0.43|0.15|
|Norms+Fem(P-value)|0.31|0.36|0.14|0.17|0.00|0.56|
|Sample|ALL|ALL|ALL|ALL|ALL|ALL|
|CountryFE|YES|YES|YES|YES|YES|YES|
that have been public for at least 10 years). Young firms are typically more dependent on external finance than older firms. Their capital expenditures in their first years of existence tend to be larger than in subsequent years and they have limited internal funds. New FDI projects are likely to share with young firms this heavy reliance on external finance. Hence, in a given sector, the ED of young firms (ED _young_ ) may provide a better picture of the external financial dependence of firms engaging in FDI that the ED of mature firms (ED _mature_ ). Indeed, columns (2) show a strong relationship between FDI and the interactions of financial development with the ED _young_ measure whereas in column (3) the coefficients on the interactions of financial development with the ED _mature_ measure are not statistically significant at the 5% level. However, the coefficients on the financial development-ED _young_ interaction terms are smaller than those we previously found in Table 2 using the standard ED variable.<sup>66</sup> A potential explanation for this is that the ED _young_ measure misrepresents average reliance on external finance because a fraction of firms engaging in FDI in each sector are mature firms, with better access to internal funds than young firms. The standard ED variable, by taking into account the external financial needs of both young and mature firms, largely avoids this issue. It is therefore a preferable ED measure than ED _young_ .

# _Ability to access external finance_

In columns (4) and (5), we take into account that sectors not only vary in firms’ reliance on external finance but also in firms’ ability to access external finance. Raising outside finance is likely to be easier for firms in sectors characterized by a high level of tangible assets, as they can pledge these assets as collateral. In column (4), we interact our sector-specific measure of asset tangibility, the TANG variable, with the financial development variables. As expected, we find that on both source and destination sides, higher financial development increases relatively less bilateral FDI in more TANG sectors. In column (5) we include both the financial development-ED interaction terms and the financial development-TANG interaction terms to capture simulta-

> 66Using this measure of ED, the estimates reported in column (2) suggest that if the Philippines improved its level of financial development to that of Finland, i.e. financial development was doubled, its outward FDI in a typically high ED sector would increase by about 12% more than its outward FDI in a typically low ED sector. Likewise, its inward FDI in a typically high ED sector would increase by about 17% more than its inward FDI in a typically low ED sector.

29
conflict count across departments, the dummy conflict indicator may not adequately explain the variation in average years of education across departments. As a robustness check, the last two columns report the estimated coefficients of years of education when the war intensity variable is measured as the actual number of conflict events. The outcome suggests that an increase in the war intensity by one additional event of conflict lowers the average years of education for the young age-cohort (aged 10 to 22 years) by .01 years compared to old age-cohort (aged 23 to 32).

Table 4.1.2 Effect of War on Education using 2008 household survey data <u>(Dependent variable = Years of Education)</u>

||**War int**<br>**dummy (=**<br>**was at lea**<br>**eve**|**ensity =**<br>**1 if there**<br>**st one war**<br>**nt)**|**War intens**<br>**number**<br>**eve**|**ity = actual**<br>**of conflict**<br>**nts**|
|---|---|---|---|---|
|War intensity× Young Cohort|-0.940***|-0.499***|-0.011***|-0.008***|
|Control variables|||||
|Birth fixed effects (department)|Yes|Yes|Yes|Yes|
|Birth fixed effects (Age Cohort)|Yes|Yes|Yes|Yes|
|Household controls|No|Yes|No|Yes|
|Constant|8.355***|4.677***|8.477***|4.651***|
|Observations|16,345|16,017|16,345|16,017|
|R squared|0.235|0.423|0.241|0.426|

Notes: The household level controls include log per capita consumption expenditure, gender, gender of household head, average years of education in the household, ethnic groups and religious groups; *** implies significant at 1%, ** implies significant at 5% and * implies significant at 10%.

The estimated coefficients of the causal effect of war on education show an expected sign. However, underlying assumptions of the difference-in-difference model in the present context deserve more careful consideration. First, using department of birth as an identification strategy may not reveal the heterogeneous impact of war victimization on education for children from different socio-economic groups. In other words, there exists a possibility of selection into victimization across individuals which could be largely hidden by the total number of conflict events in a department. Second, the proximity to a war zone dummy variable may fail to identify the true impact of war on education because the intensity of war measured as the count of war events varies significantly across departments. Third, due to a large number of internally displaced people, it is often hard to track their movements between 2004 and 2008. If many 13
Policy Research Working Paper 11039

# **Abstract**

The lack of a workable and accurate national sampling frame is one of the methodological constraints in conducting representative national surveys. It undermines policy and research efforts in many developing countries, particularly those experiencing significant internal displacement and relocation due to territorial and military conflicts. This paper addresses this issue by constructing the first digitized national sampling frame in Armenia, a conflict-affected developing country, where reliable and accessible national sampling frames for household and individual surveys are severely limited. The first-ever sensible digitized urban and rural classification boundaries in the country have been created for the sampling frame. The paper constructs a national sampling frame that meets international standards based on the pre-census enumeration areas tool, leveraging a semi-automatic and resource-efficient approach and the most recent data available. The generated national sampling frame offers several advantages over the existing potential sampling frames in the country, which are based on census settlements, electoral precincts, grid sampling, and old census enumeration areas. This new frame is applied to conducting the World Bank’s “Listening to Armenia” survey, demonstrating its potential for other socioeconomic surveys in the country. The method can also be employed to generate and update national sampling frames in other countries more efficiently, given that manually generating both the digitized and non-digitized national sampling frames requires substantial financial and non-financial resources.

This paper is a product of the Poverty and Equity Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sismailakhunova@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# 1. Introduction

The global labor market is undergoing a fundamental transformation with important consequences on global inequality. In the decades ahead, younger and better-educated cohorts will enter the global workforce while older, less educated ones leave. With better education, workers have better skills (hereafter, skilled workers in this paper refer to those having nine or more years of education, and the two terms, skilled workers and better-educated workers, are used synonymously.) The new entrants of better-educated workers will come mainly from developing countries (figure 1). This paper analyzes the effects of this forthcoming demographic and education transition on global inequality using a general-equilibrium macro-micro simulation framework that covers harmonized household surveys for almost 120 developing countries. Our findings suggest that a more educated labor force in developing countries will contribute to a reduction in global income inequality by 2030.

Figure 1 Future sources of educated and skilled working-age population, new entrants w.r.t 2015 <!-- Start of picture text --> 600<br>500 High income countries<br>Eastern Europe & Central Asia<br>400<br>Middle East & North Africa<br>300<br>East Asia & Pacific<br>200 Latin America & Caribbean<br>Sub-Saharan Africa<br>100<br>South Asia<br>0<br>2020 2025 2030 2035 2040 2045 2050<br>-100<br>millions<br><!-- End of picture text --> Source: Authors’ projections.

Notes: Skilled is defined as workers with more than nine years of education. Projections are based on UN (2015) and education information from harmonized household and labor surveys from 117 countries.

The impending change in the skill composition of labor is the latest wave of demographic and education trends that have been shaping the global labor market. In the first recent wave, a truly global labor market took shape almost all at once in the 1990s, when China, India, and the former Soviet bloc joined the global economy, increasing the size of the labor pool from 1.46 billion workers to 2.93 billion workers. This “great doubling” of global labor, as Freeman (2008 and 2007) called it, came from new entrants who were low skilled and low wage. The impact of this great doubling on global inequality is not immediately clear, as it depends on changes in the dispersion of incomes within as well as between countries. Freeman argued that China, India and the former Soviet bloc would not contribute much to the global capital stock, and therefore the 2
Use of the term “underclass” rose through the 1970s and 1980s, but peaked in the mid1990s. The idea of a “culture of poverty” started to fall out of favor earlier, from about 1970 (Figure 9).<sup>35</sup> References to “welfare dependency” rose in the 1980s and (particularly) 1990s, coinciding with the (highly politicized) debate on welfare reform in the US in the mid-1990s. References to the “working poor” were more common, and also rose from the 1970s (Figure 9). This was happening while attention to the “working class” as a concept was abating.

The English Poor Laws back to the early 17th century had been premised on the idea that there were both “deserving” and “undeserving poor,” usually defined according to the ability to work, and that public relief should focus on those unable to work. (Indeed, according to the Peel <u>Web, under Elizabethan Poor Law, the able-bodied paupers were “severely beaten until they</u> realized the error of their ways”.) Much later in the 20th century, there were concerns about “welfare dependency”—that the provision of welfare itself left little incentive for poor people to take actions that would lift them out of poverty.<sup>36</sup> Social surveys have suggested that whether welfare recipients are considered “deserving” influences public support for welfare payments.<sup>37</sup> While antipoverty policies have tried often to target the “deserving poor,” the meaning of this distinction is unclear in the models of poverty that show how the behaviors that make one seem “undeserving” have deeper structural roots; then the label risks adding insult to injury.<sup>38</sup> Figure 10 plots both terms. (There were no references to either before 1790.) References to the “deserving poor” peaked around 1900, but there has been a recent resurgence of it use since 1960, which was sustained alongside a marked increase in references to the “undeserving poor.” The steep increase in references to the undeserving poor in the 1990s came alongside increasing emphasis on individual responsibility in welfare-policy discussions.

> 35 Small et al. (2010) describe a renewed interest since 2000 in cultural factors underlying poverty.

> 36 As already noted, incentive effects have long been an issue for critics of direct interventions against poverty, ranging from Thomas Malthus to Charles Murray.

> 37 See, for example, Will (1993) and Applebaum (2001).

> 38 This point was made well by James Boswell (1791, p.217) in his _Life of Samuel Johnson_ , “A particular kind of manufacture fails; those who have been used to work at it, can, for some time, work at nothing else. You meet a man begging; you charge him with idleness; he says, „I am willing to labour. Will you give me work?' 'I cannot.' 'Why, then you have no right to charge me with idleness.'”

27
13

# **5.  Estimating the challenge**

―Growth with equity‖ is a contemporary development goal espoused by governments and international organizations (for example, World Bank 2005). In theoretical terms one can assume that social welfare ( _SW_ ) consists of two components: efficiency ( _EF_ ) and equity ( _EQ_ ), as in the following social welfare function:

# (10) _SW = f (EF, EQ)_

This function could be specified as:

# (11) _SW = (Y/P)_<sup>_α_</sup> _(1- GINI)_<sup>_β_</sup>

where ( _Y_ / _P_ ) is per capita income, _GINI_ is the common inequality index and α and β the value of the weights one puts at the efficiency and equity components. Assigning such weights implies a value judgment that is best left to voters and politicians. The education challenge in this framework is how particular education policies (for example, greater coverage of primary education) are conducive or not to changing the values of per capita income and income distribution in a way that promotes social welfare.<sup>4</sup>

# **Data and sources**

The following data were used in our simulations:

- Historical national accounts data from Maddison (2010)

- Investment in education data from the World Bank on line indicators (EdStats)

- Education data from Barro and Lee (2010) and World Bank indicators

- Micro returns to education from Psacharopoulos and Patrinos (2004)

- Gini coefficients from the Word Bank indicators

Although all measures of education are used in this paper, our central education variable is the average years of schooling of the population aged 15-plus. This choice is dictated both by the availability of a solid comparable database spanning 60 years (1950-2010), and the fact this measure is a summary of educational development encompassing all levels (Barro and Lee 2010). This measure of education also links to an extensive body of recent theoretical and empirical economic growth literature from which we can borrow parameters or compare our estimates. It also happens that the educational level of the population, rather than the work force, is more appropriate in a macro analysis of this kind in order to catch the wider or external effects of education on per capita income, for example, by lowering fertility or improving health conditions of the population at large.

It should be mentioned at the outset that years of schooling sometimes or often masks the quality of schooling; for example, four years of education in Finland does not equal four years of schooling in Colombia. Hanushek and Woessmann (2008) have shown that education quality affects economic growth. Increases in wages from a one standard deviation increase in cognitive test scores range from a low 5 percent in Ghana (Jolliffe 1998) to a high of 48 percent in South Africa (Moll 1998). In South Arica, Gustafsson, van der Berg, Shepherd and Burger (2011) using a grade- and race-specific educational 4 For an application of this framework to an actual country case, see Psacharopoulos (1977).
# **Wage/profit equation for skilled**

Number of obs = 15960009 F( 16, 6514) = 995.20 Prob > F = 0.000 R-squared = 0.453 Root MSE = 0.749

||_Ro_|_bust Std._<br>|||||
|---|---|---|---|---|---|---|
||_Coef._|_Err._|_t_|_P>|t|_|_[95% Co_|_nf. Interval]_|
|edu|-0.060|0.007|-9.140|0.000|-0.073|-0.047|
|edu2|0.009|0.000|30.520|0.000|0.009|0.010|
|exp|0.063|0.002|39.830|0.000|0.060|0.066|
|exp2|-0.001|0.000|-22.990|0.000|-0.001|-0.001|
|gend|-0.515|0.009|-55.410|0.000|-0.533|-0.497|
|preta|-0.277|0.024|-11.570|0.000|-0.324|-0.230|
|parda|-0.214|0.011|-18.600|0.000|-0.236|-0.191|
|dun|-0.153|0.023|-6.760|0.000|-0.198|-0.109|
|drn|-0.520|0.099|-5.260|0.000|-0.714|-0.326|
|dune|-0.408|0.018|-22.390|0.000|-0.443|-0.372|
|drne|-0.796|0.044|-18.130|0.000|-0.882|-0.710|
|drse|-0.276|0.055|-5.030|0.000|-0.384|-0.169|
|dus|-0.146|0.017|-8.600|0.000|-0.179|-0.113|
|drs|-0.317|0.052|-6.030|0.000|-0.419|-0.214|
|ducw|-0.122|0.023|-5.210|0.000|-0.168|-0.076|
|drcw|-0.108|0.098|-1.100|0.270|-0.301|0.084|
|_cons|5.278|0.043|122.680|0.000|5.194|5.363|

Source: Authors’ calculations. Note: edu: years of schooling edu2: edu squared exp: age minus schooling exp2: experience squared gend: female 1 preta: black parda: mixed black nnonrem: number of non-remunerated household members d*: regional dummies with r (u) for rural (urban), n north, ne northeast, s south, cw centerwest.

61
_41_ _WATER WHEN IT COUNTS_ Too.” _The Guardian_ .

- **World Bank.** 2007. “Mozambique Country Water Resources Assistance Strategy: Making Water Work for Sustainable Growth and Poverty Reduction.” 56855, World Bank Group.

- **World Bank.** 2010. “Mozambique Public Expenditure Review for the Water Sector.” 54692-MZ, World Bank Group.

- **World Bank.** 2011. “Mozambique: Analysis of Public Expenditure in Agriculture.” 59918-MZ, World Bank Group.
not contribute appreciably to longer-term potential growth in SAR.

# 4. Prospects for Potential Growth: What Could Happen?

Factors weighing on potential growth over the past five years are likely to persist over the next decade. Demographic trends are expected to become less favorable. This will weigh on potential growth even if trend improvements in human capital and female labor force participation (and, for advanced economies, migration) continue. Although investment growth is expected to recover from its recent weakness, it is unlikely to return to elevated pre-crisis levels. Short of unexpected surges in productivity growth—perhaps as a result of dissipating crisis legacies or unanticipated technological breakthroughs— these trends imply an outlook for mediocre potential growth. This is also reflected in repeated downgrades to global growth forecasts (Kose, Ohnsorge, and Sugawara forthcoming).

The forward-looking scenario presented here applies the production function approach to assumed paths for capital, population statistics, and education and health outcomes (Kilic Celik, Kose and Ohnsorge 2020). The baseline scenario is one of “follow the same path” in that it assumes that all policy variables follow their long-term average trends.

The scenario assumes that all population-related variables (including age and gender structure of the population, fertility, and life expectancy) evolve as in the UN Population Projections under the assumption of median fertility, normal mortality, and “normal” (i.e. trend) migration.<sup>14</sup> Past trend improvements in each economy’s education and health outcomes are expected to continue. Secondary and tertiary enrollment rates by gender are assumed to grow through the forecast horizon at their average growth during 1998-2017 but are capped at 100 percent. Economy-wide averages are calculated as the population-weighted (2000-16) average of these gender-specific rates.

Secondary and tertiary education completion rates by gender and age group are assumed to grow at their average rate during 1998-2017. Economy-wide averages are calculated as the population-weighted (2000-16) average of these gender and group-specific rates.

In line with historical experience, investment growth is assumed to remain constant at its long-term average.<sup>15</sup> Under this baseline scenario, the fundamental drivers of potential growth all point to continued softening. A slowing pace of capital accumulation, especially in China, will be offset by growing capital accumulation in advanced economies. In EMDEs other than China, the pace of capital accumulation will remain broadly steady as investment growth keeps pace with recovering output growth after its pronounced weakness of 2015-16. Subdued investment and less room for catchup productivity growth as per capita income

> 14 Cohort effects are assumed to stay constant at their latest level throughout the forecast horizon, starting in 2018. 15 Considering the policy-driven rebalancing away from investment in China, investment growth rates are assumed to be constant at their last five-year average (2013-17).

10