rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 1. Search Queries**

**Scopus query:** TITLE-ABS-KEY (“green job” OR “sustainable job” OR “green occupation” OR “sustainable occupation” OR “green work” OR “sustainable work” OR “green employment” OR (“green transition” AND “job”)) AND PUBYEAR > 2008 AND PUBYEAR < 2025 AND LANGUAGE (english)

**Web of Science query:** (AB=(“green job” OR “green occupation” OR “green employment” OR “sustainable job” OR “sustainable occupation”) OR TI=(“green job” OR “green occupation” OR “green employment” OR “sustainable job” OR  “sustainable occupation”) OR AK=(“green job” OR “green occupation” OR “green employment” OR “sustainable job” OR “sustainable occupation” (“green transition” AND “job”)) OR TS=(“green job”)) AND PY=(2008-2024) AND LA=(english)

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<!-- Start of picture text --> Figure 10: Internal Rates of Return on Investment in Education by Economic Sector<br> private sector public sector<br>Secondary + VT 9th grade + VT<br>9th grade + VT From 9th grade to Secondary<br>University + VT From primary to 9th grade<br>Primary + VT Secondary + VT<br>From 9th grade to Secondary From Secondary to University<br>From Secondary to University Primary + VT<br>From primary to 9th grade University + VT<br>Primary Primary<br>Aprentice (no primary) Aprentice (no primary)<br>0% 10% 20% 30% 40% 50% 0% 10% 20% 30% 40% 50%<br>Rate of return on marginal investment in education Rate of return on marginal investment in education<br> informal sector<br>9th grade + VT<br>From Secondary to University<br>From 9th grade to Secondary<br>Secondary + VT<br>University + VT<br>From primary to 9th grade<br>Primary + VT<br>Primary<br>Aprentice (no primary)<br>0% 10% 20% 30% 40% 50%<br>Rate of return on marginal investment in education<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations<br><!-- End of picture text --> What are the incentives for individuals to queue for jobs in the civil service? To answer this question we compute equation (15) for a 25 year old-individual having to decide whether to take a job in the private sector at time ‘t’, or wait for a job in the public sector that can occur with probability ‘p’. We simulate four values for p: 1%, 5%, 10% and 20%. We also assume that if the individual does not work he/she has to borrow an amount equivalent to 50% of the salary paid by the private sector job. The net gains from waiting for the public sector job are graphed in the left panel of Figure 11. Each line corresponds to one value of ‘p’. We observe that even in the case of a very low probability of finding a public sector job, the individual has an incentive to wait for one year. If the probability of finding a job is 10% then the individual would have incentives to wait for 8 years. If the probability is 20% then the individual would have incentives to wait for 12 years. With no borrowing needs (b=0) even with a 2% probability of finding a job in the public sector the individual would have incentives to wait for 10 years (see dotted line).

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The empirical analysis is performed in urban areas because rural areas are underrepresented in the PNADs, the law may not be fully enforced in rural areas, and because rural areas lack well-developed school systems and labor markets. According to the 1999 PNAD, around 82 percent of Brazil’s population lives in urban areas.

# **5.1. Descriptive Statistics**

This section presents the descriptive statistics of the main sample used for the short-term analysis and some stylized facts of the unaffected cohort that turned 14 immediately before the law change.

Our main sample for the short-term analysis consists of all male individuals living in urban areas and had turned 14 between April 1998 and September 1999. The basic descriptive statistics for the 1999 PNAD sample of males are presented in table 1. There are 4549 observations, 47 percent of boys are white, about two-thirds of boys working (19 percent) were in paid activities, 92 percent were in school, and 15 percent worked while in school. The average years of schooling of mothers and fathers suggest they did not hold a primary education degree. The average household size is about five members.

The figures 1.a and 1.b depict the incidences of time allocation in school and work for boys and girls separately. The sample consists of all individuals in the comparison group, that is, those who turned 14 in the last nine months of 1998. The outcomes examined are work incidence in paid and unpaid work,<sup>21</sup> and school attendance.<sup>22</sup> We also look at these outcomes in six exclusive combinations: working only in paid activities, working only in unpaid activities, only studying, paid work and studying, unpaid work and studying, and neither working nor studying. None of the definitions above include household chores. We excluded household chores because these are performed predominantly by girls and our estimates focus only on boys.

Figure 1.a shows that 90 percent of 14-year-olds were enrolled in school, but boys were more likely to work (either in paid or unpaid) than girls.

Figure 1.b presents the share of those only working, only studying, or combining both activities. Most boys and girls were only studying, but girls were 10 percentage points more likely to do so. Very few boys and girls were only working, but the

> 21 We use the PNAD question: Whether the individual worked last week (the week of reference)?

> 22 The PNAD question used is: Do you attend school?

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presented in Table 6. For Spanish, girls show a difference in test scores of 0.069 standard deviations while boys show a difference of 0.087 standard deviations. Neither of these estimates is statistically significant at conventional levels of significance.

Finally, Table 9 divides the sample by grade. Using the reported grade levels from the 2006 baseline survey, we report only the estimated treatment effects using equation (2) for the test scores normalized to the follow-up control distribution for the indicated subject. The results generally confirm the estimates presented earlier. In almost all grades, the estimated differences generally small and statistically insignificant at conventional levels of significance. The exception is students in grade eight and nine. Students in grade eight seem to perform worse due to the program while students in grade nine seem to benefit from the program. However, since these are the only statistically significant results, they are likely due to random variation. The fact that, as we will show below in Table 12, computers were not used in these grades further supports this contention.

Table 10 shows the results for outcome variables other than test scores. The table is organized similarly to Tables 6 and 8 with the differences estimated using equation (1). For most variables, the program seems to have little effect. There is no change in the probability that students like school or like the content of school. Similarly, there is no change in students reported grades or probability of failing a subject. And similarly, there is no change in students’ probability of talking to a teacher outside of class. The only statistically significant effects are the probability that students did not attend school in the previous week (reduction of 0.12 percentage points), the probability that a student

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INTRODUCTION Iiii Table 10 Annual Popu!ation Increase and Growth Rate by Geographic Region and by Income Group, 1985-2100

||Amual p|opulati|on incre|ase (mil|lions)|Ann|ual grow|th rate|(perce|nt)|
|---|---|---|---|---|---|---|---|---|---|---|
|Region or<br>income<br>group<br>|1985-90<br>2|<br>000-05<br>2|<br>025-30<br>2|<br>050-75<br>2|<br>100-25|1985-90<br>2|<br>000-05<br>2|<br>025-3<br>|0<br>2050-75<br>|<br>2100-25|
|World|88.2|91.7|'B.5|37.2|4.9|1.74|1.43|0.91|<br>0.35|<br>0.04|
|Less developed (a)|81.7|87.4|78.2|37.7|4.8|2.11|1.70|1.08|<br>0.41|<br>0.05|
|More developed (a)|6.5|4.3|0.3|-0.5|0.1|0.54|0.34|0.02|-0.04|0.01|
|Africa|18.2|26.0|30.0|19.4|2.3|3.02|2.79|1.81|0.77|0.08|
|EsstAfrica|7.9|11.6|14.6|9.7|1.1|3.15|2.94|1.98|0.84|0.08|
|West Africa|6.8|10.1|11.7|7.9|1.0|3.10|2.93|1.86|0.81|0.09|
|Nortn Africa|3.5|4.3|3.7|1.8|0.2|2.65|2.21|1.26|0.47|0.05|
|America|10.9|9.4|6.0|1.4|0.2|1.57|1.10|0.57|0.12|0.02|
|Latin America andCaribbe|an 8.5|<br>7.6|5.7|1.5|0.2|2.02|1.40|0.80|0.19|0.C2|
|Northern America|2.4|1.8|0.2|-0.1|0.0|0.87|0.59|0.07|-0.04|0.0C|
|Asia|55.4|53.9|41.9|16.5|2.2|1.a7|1.42|0.84|0.28|0.04|
|East andSoutheast<br>Asia|26.3|21.8|13.5|<br>3.0|0.5|<br>1.53|1.04|0.53|0.11|0.02|
|South Asia|25.6|27.7|23.7|10.7|'.5|<br>2.29|<br>1.82|1.09|0.39|0.05|
|Southwest<br>Asia|3.5|<br>4.4|4.7|<br>2.7|<br>0.2|<br>2.81|2.39|1.57|0.64|0.05|
|Europe and U.S.S.R.|3.3|2.0|0.4|-0.1|0.1|0.42|0.25|0.05|-0.02|0.01|
|Oceania|0.4|0.4|0.2|0.1|0.0|1.63|1.21|0.49|0.16|0.02|
|Income group (b)|||||||||||
|Low|58.7|63.1|56.4|27.8|3.8|2.05|1.67|1.06|0.42|0.05|
|Lower-middle|13.6|14.4|12.9|<br>5.7|0.6|2.23|1.74|1.09|0.38|0.04|
|Upper-middle|8.1|8.2|7.0|3.0|0.3|1.84|1.45|0.91|0.32|0.01|
|Nigh|4.4|3.3|0.2|-0.2|0.0|0.57|0.39|0.02|-0.02|0.01|
|Nonreportingionmember|3.3|2.7|2.1|0.9|0.1|0.89|0.65|0.44|0.17|0.03|

a. "Hore developed" coprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. "Less developed" comprises the rest of the world.

b. Criteria for income groups are given in World Bank 1989. See listing in Exhibit 1.
# **3. Empirical Strategy**

During the pandemic, WfH practices helped firms overcome constraints of social distancing through digital solutions that substituted for face-to-face interactions. So, the development of digital infrastructure should disproportionately help firms (or industries) more amenable to remote working arrangements. To assess this effect, we exploit the variation in firm sales across sectors with different amenability to remote work, and across countries with different levels of internet penetration. In other words, we assess whether firms in sectors with higher feasibility to work remotely were more resilient during the pandemic in countries with higher levels of internet penetration. In doing so, we examine the interaction between internet penetration by country and remote flexibility by sector in a regression explaining changes in sales by firm during the pandemic period. This is helpful because it looks for evidence of a specific mechanism by which digitalization affects firm resilience, thereby providing a stronger test of causality.

This approach was successfully applied in Rajan and Zingales (1998) - to show that industries dependent on external finance grow faster in countries with higher financial development - and in subsequent contributions to the financial development literature (Beck, 2003; Fisman and Love, 2003, 2007; Beck and Demirgüç-Kunt, 2008; Hur et al., 2006; Manova, 2008, 2013). This approach has also been applied to analyze topics as diverse as the linkages between property rights and growth, firm entry costs and firm creation, contract enforcement and comparative advantage, uncertainty aversion and growth, road networks and trade, and digital infrastructure and trade (Claessens and Laeven, 2003; Claessens and Klapper, 2005; Levchenko, 2007; Nunn, 2007; Jaimovich, 2019; Paunov, 2016; Fernandes et al., 2019).

Our specification is as follows: _Yisct_ denotes, for firm _i_ in sector _s_ and country _c_ , the reported percent change in sales, 30 days prior to the interview at time _t_ , relative to the same period in 2019. _Sizeict_ denotes the size of the firm (log of the number of employees); _mobilityct_ gauges the country-specific severity of the shock through the average mobility around transit stations 30 days prior to the interview as measured in the Google mobility reports (Google, 2021). The amenability of remote work at the 4-digit sector level is denoted by _remotes_ while _internetc_ denotes internet penetration at the country level.<sup>4</sup> We include fixed effects for 2-digit sectors _δs_ and country fixed effects _δc_ to correct for potentially omitted country and sector specific time-invariant covariates. We also include quarter fixed effects _δt_ (April-June 2020, July-September 2020, etc.) to capture the changing severity of the shock.

We estimate equation 1 by least squares for exporters and non-exporters, together and separately, to compare estimates of the coefficient _γ_ across samples. Since sampling weights are not consistently available across data sets, we weight the estimates using the inverse of the number of observations

> 4Both enter the regression as z-scores.

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We start by examining the prevalence of different types of employment in urban SSA and their dynamics. In particular, we quantify the share of jobs in each type of employment in the region and at the country level. We then briefly explore how the share of jobs correlates with GDP per capita, as well as how the change in the share of jobs correlates with changes in GDP per capita. We conclude that urban informality may decline in the long-run but it is a permanent part of the urban landscape in the short-run.

The informal sector comprises 56.3 percent (lower bound) to 64.6 percent (upper bound) of the urban informal labor market in Africa. Figure 3 presents the average distribution of urban workers by employment type for the 26 African countries in our GMD sample. We present the average using the weights available in the household surveys and an average giving an equal weight to each country. The two methodologies give broadly similar results.

The most prevalent employment type in urban Africa is low-educated self-employed workers, who we define as being in the informal sector, followed by informal wage employees. These two employment types comprise nearly half of urban employment and most of urban informal employment. An estimated 29.4 percent of urban workers in the 26country sample can be classified as LE-SE. Another 18.6 percent can be classified as informal wage employees. These shares are smaller than the national averages estimated in ILO (2018), reflecting the highly informal nature of rural jobs included in the national estimates.<sup>38</sup> The share of urban LE-SE is three times larger than the share of urban higheducated self-employed. Only 18.5 percent of the African urban workforce are formal sector employees in the private sector; the same share as who are informal sector employees.

# **Figure 3. Distribution of urban employment by employment types, 26 African countries in the SSA region**

<!-- Start of picture text --> Avg. (Pop. weighting) 8.3 18.6 29.4 9.3 10.6 18.5 5.3<br>Avg. (Equal-country weighting) 10.8 22.2 34.9 5.7 10.8 11.7 3.8<br>Unpaid employees Informal Wage employee<br>Low-educated Self-employed High-educated Self-employed<br>Formal Wage employee (Public/State owned) Formal Wage employee (Private)<br>Employer<br><!-- End of picture text --> Source: Own elaboration based on GMD data.

> 38 ILO (2018) finds that 28 percent of informal workers in SSA are informal employees, 52 percent are s-e, 18 percent are contributing family workers. Among formal SSA workers, 75 percent are employees, 21 percent are self-employed and 3 percent are employers.

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infection has been evolving over time.

At the initial round in 1989—1990, there is no robust or signi fi cant association between HIV prevalence and educational attainment. This is in sharp contrast with the situation 10 years later. For young individuals aged 18—29 at round 11–individuals who started their sexual life after the start of the prevention campaigns–there is a signi fi cant and robust negative gradient between education level and the risk of being HIV positive. This effect is concentrated among females. This fi nding suggests that more educated individuals reacted more to the arrival of information about the dangers associated with HIV/AIDS. A comparison between young individuals early in the 1990s whose sexual behaviors were not affected by the information about the epidemic and young individuals at the end of the same decade whose sexual life started after the arrival of this information, reinforces this conclusion by revealing that, over the decade, the HIV prevalence has decreased twice as much among more educated individuals, indicating a stronger reaction since the of the information.

Results from a study of HIV incidence in a duration framework con fi rm this fi nding by showing that, for young individuals, education reduces the probability of seroconversion. Moreover, this paper documents some of the mechanisms leading to this result by showing that changes in behavior and sexual practices, in particular condom use and visits to voluntary counselling and testing centers, have been more widespread among educated individuals.

The evidence from the variations in HIV prevalence and in HIV incidence and from the analysis of sexual behavior converges to establish that more educated individuals, especially among females, have been more responsive to the dissemination of the information about the HIV/AIDS epidemic. The responsiveness to the information is thought to be one of the channels through which education affects health. This paper can be read as a test of the importance of this channel.

In further research, it would be interesting to try to explain why the effect of education in reducing the risk of HIV infection is stronger among females than males. In this regard, the hypothesis that education increases the bargaining position of the women in the household deserves to be tested.

The results of this paper also have policy implications for future HIV prevention efforts. By showing that the very individuals that economic theory would predict to display the strongest behavioral response to the HIV/AIDS epidemic–educated individuals–are the ones who actually did, the analysis also reinforces the point that the declines in HIV prevalence and incidence observed 20
<!-- Start of picture text --> Spatial distribution of housing prices variation values<br>400<br>8<br>350<br>“0 Total housing annual price<br>300 E 6 increase € 250 millions<br>4 =<br>= 250 =<br>Ss o4<br>2 2<br>‘S 200 2<br>a=<br>5 =?<br>= 150<br>=<br>100 8% tsa2% svat4% ll6% ahMi8% sttts 10%atts a<br>t<br>By, A% 0% “1% 2% 3%. A%_ 5% 6% 7%_8% 9% 10% 1%<br>Real estate price variation, due to a 25% increase in RER B speed<br><!-- End of picture text --> Spatial distribution of housing prices variation values <!-- Start of picture text --> 70<br>12<br>60<br>*<br>— 10 Totalincrease housing€ 560 annualmillions price<br>,E 50 =Fe 8<br>= 3 6<br>2 40 a<br>° 2&4<br>a —<br>3* = 2<br>8% 2% 4% 6% 8%<br>° tT<br>Be 1% 0% 4%- 2% 3% 4% 5% 6% 7% 8%. 9% --~<br>Real estate price variation, due to a 25% increase in RER B speed<br><!-- End of picture text -->
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# **Intrahousehold Inequality and Child Gender Bias in Ethiopia**

# **Feridoon Koohi-Kamali** ∗ **June, 2008**

# **1-** **_Introduction_**

Child gender bias is often regarded as predominantly an Asian rather than an African phenomenon, though it is unclear whether this is due to a relatively greater absence of women in the job market, a problem common to old Asian cultures. Africa has received far less attention in this regard. Although in general, the greater role of the female labor force in African than Asian agriculture has been well documented, there are likely to be many African countries where this rather generalized comparison does not hold, as the case examined in this study will demonstrate. However, there is also a more general, second reason for the relative absence of studies on child gender bias in Africa, namely the lack of an effective approach for analyzing inequality in intra-household resource allocation due to gender of children. In particular, the Rothbarth model, most suitable for this purpose, has consistently failed to uncover such bias in all its applications undertaken over a long period.

This study applies the approach to the Rothbarth model advocated in KoohiKamali (2007) to Ethiopian household budget data and obtains similar results. Moreover, these results are significant beyond Ethiopia, in that the above employed wartime Iranian data in which measures had to be adopted to overcome the problems of involuntary purchases related to various forms of rationing and shortages, essential since the application of the Rothbarth model presupposes voluntary levels of adult goods expenditure. The present study is relatively free from the problems of involuntary expenditure levels, and, in that sense, provides the first extensive evidence for the effectiveness of the method and the approach advocated for its application.

In section 2, I shall discuss the issues required for an effective application of the Rothbarth method to an analysis of child gender bias in intra-household allocations;

> ∗ Research Scholar, Levy Economics Institute of Bard College, New York, U.S.A., `fkoohi@levy.org` <u>. This is a background paper for the Programmatic Poverty and Gender</u> Update for Ethiopia (see report "Unleashing the potential of Ethiopian women: trends and options for economic empowerment" ) which received generous funding from the Multi-donor Trust Fund for the Gender Action Plan Trust Fund (GAP TF) for its gender inequality diagnostics. The author also wishes to express his special thanks to Caterina Ruggeri Laderchi for her support and extensive comments on earlier drafts.
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