rafmacalaba/data-use-mentions-extended huggingface dataset:
- Deaton, A., Zaidi, S. (2002) Guidelines for Constructing Consumption Aggregates for Welfare Analysis. World Bank, Washington, USA.

- Duflo, E. (2003) Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa. _The World Bank Economic Review_ 17(1), 1–25.

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- Filipski, M., Taylor, J., Thome, K., Davis, B. (2015) Effects of treatment beyond the treated: a general equilibrium impact evaluation of lesotho’s cash grants program. _Agricultural Economics_ 46(2), 227–243.

- Fitzgerald, J., Gottschalk, P., Moffitt, R., Gottschalk, P., Moffitt, R. (1998) An Analysis of Sample Attrition in Panel Data The Michigan Panel Study of Income Dynamics. _The Journal of Human Resources_ 33(2), 251–299.

- Gertler, P., Martinez, S., Rubio-Codina, M. (2012) Investing Cash Transfers to Taise Long-Term Living Standards. _American Economic Journal: Applied Economics_ 4(1), 164–192.

- Gilligan, M., Mvukiyehe, E., Samii, C. (2013) Reintegrating Rebels into Civilian Life: QuasiExperimental Evidence from Burundi. _Journal of Conflict Resolution_ 57(4), 598–626.

- Glennerster, R., Takavarasha, K. (2013) Running Randomized Evaluations: A Practical Guide. Princeton University Press, Princeton, USA.

- Haggblade, S., Hammer,
- Inability indicates the percentage of the total population who cannot keep their homes adequately warm.

- Arrears is the percentage of the total population with arrears on utility bills (heating, electricity, gas, water) on time due to financial difficulties.

- Leakages capture the percentage of the population living in a dwelling with a leaking roof, damp walls, floors, or foundation, or rot in window frames or floors.

In this case, the higher the value of the EP index, the higher the degree of energy poverty. The researchers give the inability to keep the home adequately warm a higher value as they argue that this indicator has greater importance, as proposed in the literature (Thompson and Neil, 2013; Bouzarovski and Tirado Herrero (2017)). According to this indicator, nearly half of the population is affected by at least some dimension of energy poverty (Figure 15). In this case, a significant share (seven out of 10) of the poor based on the at-risk of poverty are also energy poor (Figure 16).

<!-- Start of picture text --> Figure 15: Multidimensional energy poverty rate Figure 16: Overlap of at-risk of poverty and<br>(2019/20) multidimensional energy poverty rate (2019/20)<br>45.0 42.8 100%<br>40.0 0.504<br>35.0<br>50%<br>30.0 0.258<br>0.068<br>25.0<br>0.170<br>0%<br>20.0<br>15.0 Neither nor<br>10.0 Only energy poor<br>5.0<br>Only monetary poor<br>0.0<br>Energy and monetary poor<br>Energy poverty index<br><!-- End of picture text --> _Figure 16: Overlap of at-risk of
WPS3800

# **Is Formal Lifelong Learning a Profitable Investment for All of Life?**

## **How age, education level, and flexibility of provision affect rates of return to adult**

## **education in Colombia**

Thomas Pave Sohnesen and Andreas Blom World Bank

## JEL: I2; J24

## **Abstract**<sup>**1**</sup>

_Lifelong learning is increasingly being recognized as a primary factor for knowledge diffusion and productivity growth. However, little economic evidence exists on the economic value of lifelong learning for the individual, especially in developing countries. This paper contributes to remedy this shortfall. It investigates one aspect of lifelong learning: returns to formal education across ages. In the absence of long-term longitudinal data, the paper estimates rates of return for simulated re-entry into the education system. The estimations use the method of internal rate of return and are based on observed education-age-earnings profiles from the Colombian national household survey. We find that rates of return to all levels of education are only slightly smaller for 35 year olds than for young people, thus confirming the profitability of investment in adult education. Tertiary education continues to attract a positive return until late in life, 45-50 years, whereas the economic value of reentering primary and secondary education is positive up till the age of 40-45. Thus, formal lifelong learning seems to remain a profitable investment for at least half of life. However, lack of part-time work, high tuition fees, and prolonged study time reduce the return. The findings suggest that adult formal education initiatives should focus on the 20 to 40- year olds and be designed flexibly in order to allow learners to work part time._

## World Bank Policy Research Working Paper 3800, December 2005

_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
</sup> , one-third of its current stock. Globally, bridging the gap would increase stocks by _∼_ 14%.

However, other factors could affect the economic, social, and environmental “optimality” of construction patterns. Bridging the gap could have dramatic effects on the environment, given the building sector accounts for 39% of carbon emissions worldwide (UNEP, 2022). Meeting the estimated global needs would increase global carbon emissions by _∼_ 5%, implying trade-offs between promoting housing affordability and ensuring livability/sustainability.

# **4.3. Quantifying Future Global Construction Needs**

**4.3.1. Forecast until 2025**

The forecast of future, growth-driven construction needs is a crucial tool in view of a fasturbanizing world and unaffordable cities (UN Habitat, 2020; The World Bank, 2023b). With estimates of the future population and income of a country, we can quantify by how much the country’s volume must increase to match global construction trends. From the estimated quadratic fit in Figure 4, we obtain coefficients _α_ (2,209***), _β_ (-580***) and _γ_ (39***) in eq. (6): _V olume/Population_ = _α_ + _β ∗ log_ ( _GDP/Population_ ) + _γ ∗_ ( _log_ ( _GDP/Population_ ))<sup>2</sup> (6) Using data from the World Bank (2019), we obtained the predicted real GDP growth rate of each country in 2019-2022. We consider countries from the viewpoint of 2019, thus ignoring the unrelated economic effects of the COVID-19 pandemic. Using the average annual growth rates in 2019-2022, we estimate each country’s GDP in 2025. From the United Nations (2019), we obtained each country’s predicted population in 2025 (ignoring COVID-19 mortality).<sup>5</sup> Equation (6) is used to estimate how Volume
Models 1 and 3 have a value of 0.42, whereas Model 2 has an RMSE of 0.41. When we turn to the overall consumption aggregate, we note a small difference between the three models. The RMSE values range from 0.53 to 0.58, with Model 3 and Model 1 having the smallest and highest RMSE, respectively. The MAE is quite similar across the three models, within a range from 0.39 (Model 3) to 0.41 (Model 2). These results suggest that no model consistently outperforms the other models.

Tables 3 and 4 apply the model to the three data sets described in the data section (ProGres 2, Targeting 2, and ProGres Targeting data), using the normal linear regression model and the Empirical Error Model and three poverty lines: (i) a poverty line of US$1.9 a day (PPP), which represents the international poverty line for extreme poverty (panel A); (ii) the national poverty line, which corresponds to around US$2.6 (World Bank 2013), represented in panel A; and (iii) the national food poverty line of US$1.8 (PPP), shown in Table 3. These three poverty lines are among the set of the arbitrary poverty lines considered in the general simulation above to evaluate the quality of the prediction.<sup>6</sup> With one exception, the predicted poverty rates are not statistically different from the poverty rates obtained from survey consumption data (henceforth, “survey estimates”). For the case of the food and international poverty lines, this is partly due to the large standard errors of the prediction estimates, but these findings hold for the national poverty line where the standard errors of the predicted values are much smaller.<sup>7</sup> Figure 1 repeats the exercise of Table 5 for the ProGres Targeting data and all the poverty lines between the 66th and 99th
|13.4692<br>|-15.7323|
|Observations|91<br>|91<br>|91|
|R-squared|0.4629<br>|0.4925<br>|0.4756|

Dependent variable is domestic credit to the private sector (% GDP). Linear regression with robust standard errors. Domestic credit as a percentage of GDP is taken from the World Development Indicators for the year 2008. For Kyrgyztan the corresponding 2007 figure and for Zimbabwe the 2006 figure is used for want of missing 2008 values in both countries.

# **7. CONCLUSION**

Our primary contribution is collecting and making available information on a key dimension of policy in an unprecedented range of countries, sectors, and modes. We then take some first steps towards measuring policy and its impact. First, we construct a services trade restrictions index (STRI) which simply and transparently captures the restrictive effect of the entire set of policies applied by a country in a given service sector and mode of supply. This measure helps us depict the broad pattern of policy across countries and sectors. Second, we 44
# 2.2 Sources of Governance Data

The 35 WGI data sources used in the 2024 update are summarized in Table 1, with a more detailed description of each data source in Annex 1. The WGI data sources fall into two broad categories: surveys of firms and households, and expert assessments. The former includes several major surveys of firm managers: the World Economic Forum’s Executive Opinion Survey that informs their annual Global Competitiveness Report, the Institute for Management and Development’s Executive Opinion Survey that informs their annual World Competitiveness Report, and the World Bank’s Enterprise Survey Program, which has recently been greatly expanded to achieve near-global coverage on a rotating threeyear cycle to inform the World Bank’s new Business Ready report series. The WGI also include data from several regional household survey programs (Afrobarometer, Latinobarometro, Americas Barometer, and European Quality of Governance Survey), as well as one global household survey (the Gallup World Poll). In addition, one of our data sources, the World Justice Project Rule of Law Index, is a hybrid combining findings from an expert assessment with a cross-country household survey with near-global coverage. These firm and household survey data sources are particularly salient because they directly capture survey respondents’ perceptions of the quality of governance in the countries in which they live. In total, household and firm surveys make up 11 of the 35 data sources in the WGI.

The remaining 24 WGI data sources are expert assessments, reflecting the perceptions and views of experts affiliated with a range of NGO, private sector, and government sector organizations. All of them share a broadly similar approach in which experts employed and/or recruited by the organization provide numerical scores of various dimensions of governance, following a standardized methodology and set of definitions developed by the organization producing the data source. In many cases, the experts providing the assessments are based in the country or region they assess. These data sources based on expert assessments have advantages and disadvantages relative to surveys. One advantage is that they
Straus 1979).

Events like changing neighborhoods or cities and pregnancy contributed to deepened social isolation. By staying at home to take care of the house and children, women lost contact with the outside world and with their social support systems. In these cases, restrictive social norms that naturalize the role of women as primary caregivers within the home and reinforce the stereotype of the “good wife” as the submissive, passive and accepting “homemaker” further remove women from protective social networks and access to information (see also Ellsberg et al. 2000). Social isolation became a conducive context for the escalation of the violence towards more physical forms, as the survivor lost financial and emotional autonomy. Many women cited the intensification of violence as a motivation to consider seeking help to put an end to the situation - either with family members or friends. Notably, it is also when they reported facing the first challenges to exit the abusive relationship in the attempt to break out of social isolation.

# 3.2 **The decision to reach out for some form of support**

The experiences shared by the women who were interviewed demonstrate that women tended to reach out first to family and friends. Interactions with formal services generally occurred after interacting with informal support networks. This finding is in line with population-based surveys (RHS/DHS data available for Latin America) that show that, while help-seeking behaviors by women who experience violence vary widely by country, women in the region tend to seek help from informal institutions (social networks, family and friends) at much higher rates than formal institutions (PAHO 2012).<sup>13</sup> It is commonly the immediate social network rather than formal services that serve as a first point of contact for women in

> 13 Among those women who experienced violence from a partner within the past 12 months, one-third (29.3 percent) in Honduras 2005/6 to almost two-thirds (65.5 percent) in El Salvador 2008 sought the help of family or
<!-- Start of picture text --> A. IPV Often Index<br>Boys, Partner p=.055 / b=-0.398<br>Goal, Partner p=.006 / b=-0.495<br>Boys, No Partner p=.240<br>Goal, No partner p=.622<br>-1 -0.5 0 0.5 1<br><!-- End of picture text --> <!-- Start of picture text --> B. IPV in last year Index<br>Boys, Partner p=.498 / b=-0.103<br>Goal, Partner p=.017 / b=-0.381<br>Boys, No Partner p=.054 / b=-0.090<br>Goal, No Partner p=.945<br>-1 -0.5 0 0.5 1<br><!-- End of picture text -->

**Figure A1** Impact of treatments on IPV, heterogeneity by had partner in past two years at baseline

_Notes._ This figure presents estimates of _β_ 1 and _β_ 2 and from equation 1. Panel A presents estimates for the IPV often index and Panel B presents estimates for the IPV in last year index. The indices are centered on females in the ELA only communities who had a partner in the past two years at baseline and were not assigned to the _Goal_ treatment. Bolded markers are statistically significant at _p <_ 0 _._ 1 _. p_ -values and coefficient estimates are displayed beside each marker. _Source._ Female baseline and endline data, balanced panel. The sample in the top half of each panel is restricted to females who had a partner in the past two years at baseline. The sample in the bottom half of each panel is restricted to females who did not have
Therefore, it is unlikely that changes in foreign demand are correlated with the _distribution of unobserved factors_ that _differentially_ drive changes in local labor markets.

As shown in the bin-scatter below, a visual representation of the first-stage regression, changes in exposure to exports growth strongly correlate with changes in exposure to foreign demand shocks. The F-statistic is 1,545, so the instrument is extremely relevant. If the instrumentation strategy is valid, then the instrument satisfies the exclusion restriction. In that case, we can interpret the results in this section as the causal dynamic effect of exports on local labor markets in Indonesia.

**Figure 4.1: Indonesian Districts’ Labor Market Exposure to Merchandise Exports** (binscatter that partitions the support of the x-axis into 100 bins; x-axis: regional exposure to foreign demand growth, in pct change; y-axis: regional exposure to export growth, in pct change)

<!-- Start of picture text --> .6<br>.4<br>.2<br>0<br>-.2<br>-.4 -.2 0 .2 .4<br>Regional Exposure to Foreign Demand Growth, pct change<br>The underlying regression has n = 8,558, with unconditional ß = 0.697 and t-stat = 39.31<br>Regional Exposure to Export Growth, pct change<br><!-- End of picture text --> Source: Authors’ calculations with data from Sakernas and UNCOMTRADE.

# **5. Main results**

The results in this section should be interpreted in **relative terms,** as in a difference-in-differences estimator **.** Below we plot the results of a given region being relatively more exposed to exports growth (as instrumented by foreign demand shocks) over time, in other words, the causal dynamic treatment effect of exports on those outcome variables.

In each Figure we present here, the red line shows the coefficients βh
meta-analysis of the psychological treatment of anger: developing guidelines for evidence-based practice. _The journal of the American Academy of Psychiatry and the Law 37_ (4), 473–488.

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- Wood, E. J. (2008). The social processes of civil war: The wartime transformation of social networks. _Annual Review of Political Science 11_ , 539–561.

45
non-financial data – and they also review public information (produced by the company or by other sources of information). Such direct, full assessment of the ESG practices of each counterpart is not always feasible given the large amount of information to be processed and the large number of firms. The spectrum of firms covered by the main rating agencies is very wide, as each agency may cover from 1,000 to 10,000 companies (Moody’s, 2021). However, the coverage of smaller companies, less regulated industries and emerging countries by the main rating agencies is particularly patchy.

To address the data constraints issues related to SMEs, Moody’s is complementing its firm-level approaches with another methodology, namely with a “deductive” top-down approach (“ESG Score Predictor”). It developed a model of ESG ratings for a set of companies whose metrics are known, which it then applies to “unscored” companies (Moody’s, 2021). By this method, it enlarges the spectrum of companies with an ESG rating (potentially to an infinite number). It does so by leveraging on only 3 firm-level dimensions: company size (based on assets or turnover), location and industry (615 NACE4 industries). The firm-level information (size, location and industry) is complemented with numerous country level data (more than 50 criteria) on socioeconomic indicators and scores. If size, location and industry are known, the model leverages on these three inputs to generate an ESG rating for any company. Hence the Moody’s ESG Score Predictor methodology, which is very partial (with less information available, it generates an output covering by far more firms compared to “traditional” ESG ratings), does not rely on any firmlevel ESG-related information, as the full assessment of each company is not possible. This is different from our methodology, which is entirely based on firm-level information of the Enterprise Survey and does not leverage any country data, despite the availability of ESG country data for numerous
This paper contributes to the growing literature of migration's impacts of non-income based outcomes and, in particular, this is among the rst papers to investigate the impact of international migration on time allocation and leisure consumption by gender in Nepal. To my knowledge, Lokshin and Glinskaya (2009) is the only other study that explores this topic in the Nepalese setting. However, they limit their analysis to women's labor supply for wage-employment and exclusively on the extensive margin. Using the 2003/04 round of Nepal Living Standard Survey (NLSS II) data, Lokshin and Glinskaya (2009) nd that men's migration from Nepal has discouraging eect on women's labor force participation. In contrast, this paper uses the 2010/11 round of NLSS, when work-related international migration was at record high, and incorporates both extensive and intensive margins by gender. This is especially important given the traditional roles of rearing children and household chores women assume within the household and society, the left-behind women are expected to be aected dierently than men. One of the most salient features of the employment related emigration in Nepal is that it is predominantly a male phenomenon. With males migrating abroad, the authority over household decision making may shift to women. However, the shift may be accompanied by extra responsibilities requiring extra hours of work or it might compel women to give up their jobs to assume the new roles. An additional important distinction between the papers is the timing of the surveys: while the NLSS II was implemented during a critical phase of the Maoist insurgency in the country, the NLSS III data were collected well after the end of the civil conict.

The primary identication strategy of the paper relies on an instrumental variable (IV) approach using a popular instrument in migration literature, that is, historical migration networks. Specifically, using the 2001 Nepal Census, I compute the percentage of international migrants from a village as an instrument for migration in 2010/11. A decade
**Figure 3.1 Green dictionary capturing green tasks**

<!-- Start of picture text --> a. Green dictionary, total terms b. Total green and green potential task<br>statements captured<br>Total terms Green task statements<br>0 25 50 75 100 125<br>0 10 20 30 40 50 60 70 80<br>Agriculture, forestry, and fish...<br>Agriculture, forestry, and fish production<br>Clean Energy Clean energy<br>Climate change Climate change terms<br>Energy efficiency<br>Energy efficiency<br>Environmental Certifications<br>Environmental Knowledge GHG reduction and pollution...<br>Environmental Regulations and... Low-carbon mobility<br>GHG reduction, Pollution reduction &... Natural Resource Conservation<br>Low-carbon mobility<br>Recycling and reuse of waste and...<br>Low-polluting construction<br>Natural Resource Conservation Multiple<br>Recycling and reuse of waste and... Agriculture, forestry, and fish...<br>Agriculture, forestry, and fish production Energy efficiency<br>Energy efficiency<br>Low-carbon mobility<br>Environmental Knowledge<br>Low-carbon mobility Natural Resource Conservation<br>Low-polluting construction Recycling and reuse of waste and...<br>Natural Resource Conservation<br>Repair<br>Recycling and reuse of waste and...<br>Multiple<br>Repair<br>Terms present in the occupational database<br>Terms not present in the occupational database<br>Source : authors<br>Table 3.3 Select examples of green and green potential task captured by the green dictionary<br>Task type Task statement<br>Green providing environmental engineering assistance in network analysis, regulatory analysis,<br>tasks and planning or reviewing database development;<br>assisting in the development of environmental pollution remediation devices under the