rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. Introduction

There is a growing body of literature investigating the labor market consequences of scaling up “green” policies.<sup>1</sup> However, most of the evidence is focused on the labor market implications for developed economies, and little is known about the consequences of greener growth on jobs for developing countries.<sup>2</sup> While rich countries account for a disproportionate share of Greenhouse Gas Emissions (GHGE; see IPCC, 2022), developing economies are expected to experience a disproportionate increase in GHGE in the future (Black et al., 2022). In addition, policies to promote a greener growth strategy may induce a skill-biased shift in labor demand, potentially creating bottlenecks in developing countries with lower stocks and adaptability of skills. This shift would raise concerns about the transition path of displaced workers to new jobs in the green economy.

This paper contributes to this literature by developing a new dataset on green occupations for 120 countries. It describes new statistics on green occupations across countries at different levels of economic development. This offers an initial picture of how the availability (or scarcity) of green occupations can affect the implementation of green policies and their impact on the labor market. The paper then focuses on Latin America and Caribbean (LAC) countries to provide more granular insights into the relationship between green occupations, green sectors, and the socio-economic profile of green job holders.<sup>3</sup> Green occupations are measured following the “Green Economy” program developed by the Occupational Information Network (O*NET).<sup>4</sup> It considers three types of occupations: new occupations that are emerging directly because of green policies (“ _new and emerging_ ”), existing occupations that are expected to experience changes in the tasks they entail (“ _enhanced skills_ ”), and existing occupations that will not change substantially but that will be in higher demand (“ _increased demand_ ”). A methodological contribution of this paper
The FEPBA score in Language, which presents an average of 448,<sup>20</sup> is also reported. We do not discuss the meaning of this score here, but use this variable with the sole intention of comparing student academic performance across our groups of CABA schools.

With regard to whether the experimental groups were similar with each other and representative, the differences in the means along with _p_ -values from two-tailed _t_ -tests of equality of means across experimental groups are shown (table 3). As seen, the treatment and Control Groups do not differ significantly in any observable dimension. The only variable with a statistical difference at the 95 percent level of confidence is student age, where Control Group students are slightly younger than those in the Sequence. However, this difference is very small and vanishes when considering seventh-grade repetition rate, which is balanced across the three experimental groups (see Appendix 6 for further differences in the means of other variables).

Only 14.5 percent, 15.3 percent, and 17 percent of students in the Control, Sequence, and Coaches Group, respectively, did not complete the Human Body test. These are relatively low non-response rates, and there is no statistically significant difference in the number of students who missed or omitted the test across the experimental groups (see table 4). Finally, only seven out of the 136 classrooms failed to teach the Human Body unit, which implies an attrition rate of 5 percent. Excluding classrooms not completing the Human Body unit carries no effect on the balance across the experimental groups (see Table 7.1 in Supplementary Appendix 7).

# 6. Identification Strategy

> 20 It is important to note that there is no school-specific measure of Science knowledge of sixth- or seventh-grade students available in Argentina or CABA. Therefore, to the best of our knowledge, FEPBA score in Language is the best approximation, based on administrative data, which we can make.

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its upward course during the 1990s, coinciding with the second episode of trade liberalization. Overall, there seems to be a correlation between tariff levels and the skill premium. We turn next to an econometric assessment of this relationship.

# 3 The Impacts of Trade

We begin with a simple econometric model in which sectoral tariffs affect industry wages. This would be consistent with a model of interindustry wage premiums (Dickens and Katz, 1986, and Krueger and Summers, 1989) whereby wage premia are attributable to compensating differentials, specific human capital, or more generally, to unobserved worker attributes that may be systematically correlated with industry affiliation. In our model, this wage premia depend on industry tariffs.

We regress the log of the wage of individual i, in industry j, at time t, (ln wijt), on the log of the tariff in industry j at time t, ln τtjt, an indicator of skill level dSigjt (where g indicates whether the worker is classified as skilled, semiskilled, or unskilled), and a number of other individual characteristics (xijt) such as age, gender, and marital status. The model is where Ij is an industry fixed effect, Yt is a survey-period effect, and μijt is the error term. As explained in section 2, we use data on sectoral tariffs at the 3-digit level.

We report findings from four different econometric models. In Model 1, the returns to schooling (δg) and tenure are constant across time; in Model 2, the returns to schooling are allowed to vary from survey to survey (δgt), but the returns to age are not time-varying; in Model 3, both the returns to schooling and the returns to age vary across surveys. In Model (4), we further allow for a sectoral linear trend in the model to control for possible trends in the change in wages that might be a confounding factor for the impacts of tariffs. A nice feature of our study is that the two episodes of trade liberalization that
the subjective assessments are based on checklists of rights and freedoms, reported in Table A1.6. A Freedom House team assigns a rating to each item on the checklist and produces an initial assessment for each country. The team then assess whether the checklists might have missed an important factor for a particular country. The scores are then reviewed to ensure quality and consistency across countries, and a final rating is produced.

# Nations in Transit (FHNT)

FHNT evaluates the progress in democratic and economic reform in post-communist countries. Country surveys are written by Freedom House staff or consultants and are reviewed by academics and senior Freedom House staff. Each report is divided into nine sections, ranging from the political process to progress in price liberalization. For each section, a preliminary rating is based on a checklist of issues. The academic oversight board establishes the final ratings by consensus following extensive discussions and debate, which are reviewed by the Freedom House rating committee.

40
well|972|3.69|1.18|1227|3.67|1.23|0.01|-0.13|0.15|
|Confidence committee will solve reported issue|1009|3.10|1.05|1227|3.12|1.03|0.02|-0.13|0.17|
|Satisfaction with water access (1-5)|1845|2.76|1.49|1438|3.38|1.43|0.56|0.27|0.85|
|Time spent collecting water (IHS)|1845|4.17|2.07|1438|4.04|2.05|0.03|-0.20|0.27|
|HHs pay for water (Dummy)|1845|10%||1438|23%||0.09|0.00|0.19|
|Water use expenditure (weekly)|137|153|217|303|254|351|114|-16|243|
|Hygiene and behaviour index|1845|0.00|1.00|1438|0.30|1.07|0.23|0.12|0.34|
|Handwashing score (0-10)|1845|2.79|2.22|1438|3.41|2.16|0.43|0.19|0.67|
|Open defecation (%)|1843|32%||1437|27%||-0.03|-0.08|0.01|
|Self-reported handwashing with soap/ash (%)|1843|67%||1437|82%||0.09|0.06|0.13|
|Frequency of latrine cleaning over past 2 weeks|761|4.89|4.26|
still be an issue. Oversampling is not common in LMICs but is undertaken in some surveys – for example measuring wealth in Chile and Uruguay (Gandelman and Lluberas, 2023).

## _Unit non-response_

We noted that a solution to sparseness is to oversample high-income individuals. But if there is a lack of high-income individuals because of unit non-response of high-income individuals then oversampling can also play a role in ameliorating possible unit non-response bias.

Unit non-response can also be reduced if survey protocols require enumerators to visit a certain number of times on different days before recording a non-contact (Ravallion, 2022). Another potential solution to unit non-response that has been discussed in the literature is to provide incentives to participate in the surveys. Ravallion (2022) points out that is a concern, especially for measuring the top tail of the income distribution, because the poor may be more likely to be persuaded than the rich, which could make non-response bias worse (Ravallion, 2022). The impact of incentives on response rates was investigated by Stecklov et al. (2018), who randomized a small monetary incentive in an Indian household survey, finding that whilst it did improve response rates it also resulted in those households receiving the incentive reporting lower consumption. The authors hypothesized that this was due to respondents exaggerating their poverty in the hopes of future payments from the surveyors.

## _Item non-response_

Lepkowski (2005) notes that one way of reducing item non-response to income or wealth questions asking for a monetary value a priori is to ask “unfolding bracket” questions if a respondent is unwilling or unable to give a monetary value, in which respondents are asked whether their income lies above or below a certain value. Depending on the answer, further questions about the range in which income lies are asked. This method was used in the South African National Income Dynamics Study. A simpler version of this is one question where individuals are asked which bracket their incomes fall into. This at least provides some indication of the level of income. It is
2 over time into changes due to growth and inequality (Datt and Ravallion, 1992; Ravallion and Sen, 1996; Wodon, 1995; Essama-Nssah, 1997). This is potentially more interesting, but it does rarely provide sufficient evidence for generalization since only a few observations are typically available using these decompositions (N-1 observations at the country level for N surveys).

Two of the rare countries for which time series data have been available for analyzing the relationships between growth, inequality, and poverty over time are the United States and India. Yet, panel data techniques could be used for many other countries with only a few surveys provided one is willing to carry the analysis at the regional rather than national level. This is shown in this paper using five cross-sectional surveys from Bangladesh spanning the years 1983 to 1996. By constructing a regional panel of consumption, poverty and inequality measures for fourteen areas and the five survey years, we are able to analyze not only the impact of growth and inequality on poverty, but also the impact of growth on inequality. The results differ strikingly between urban and rural areas, and they can be used by policy makers to promote faster poverty reduction. Section 2 of the paper describes our method for estimating poverty lines and obtaining measures of consumption, poverty, and inequality in real rather than nominal terms. Section 3 shows the insights and limits of standard methods of analysis used for empirical work on single countries. Section 4 analyzes the relationships between growth, inequality, and poverty using a regional panel. By combining the panel estimates of section 4 with the output of a consistent macro-economic model, section _5_ gives simulations of the reduction in poverty which could be achieved under alternative sectoral growth patterns over the next ten years. A conclusion follows.

# **II Poverty lines and welfare measures**

# _I. 1 Regional poverty lines_

To analyze the relationship between growth, inequality, and poverty one needs first to obtain good measures of these variables. Poverty lines must be estimated for obtaining poverty
with the 15‐percent sample of the 2009 Population and Housing Census as calculated by Lanjouw et al. (2013). In addition, household consumption is calculated from the VHLSS 2010, 2012, and 2014 based on detailed expenditure data in line with the methodology for determining the GSO‐World Bank poverty line. All consumption values are expressed in 2011 Purchasing Power Parity (PPP) values using data on the Consumer Price Index from the World Development Indicators.

These consumption estimates are used for the household‐level analyses differentiating between different socioeconomic groups and rural and urban areas. In rural areas 6,600‐6,750 households from about 2,200 communes in each year are included (‘Pooled’ cross section), 1,400 of them observed in all three rounds, 1,600 of them in 2010 and 2012, and 1,400 in 2012 and 2014 (‘Panel’ data set). In urban areas, the data set covers 2,650‐2,780 households in each year from 900 communes (‘Pooled’), 500 of them in all three rounds, 575 in 2010 and 2012, and 640 in 2012 and 2014 (‘Panel’).

The household and commune surveys in the VHLSS 2010, 2012, and 2014 also include a wide array of data on socioeconomic conditions. Data at the individual‐level include demographics, education, employment, health, and migration. At the household‐level data comprise information on durables, assets, production, income, and participation in government programs. The commune surveys collect information about the commune characteristics including access to land, infrastructure and services. Based on these data a number of socioeconomic controls are constructed for the analyses based on household and commune surveys (the summary statistics can be found in Table A.1).

# **2.2 Environmental risk data**

Based on geo‐spatial data sets, variables are constructed to measure environmental risk at district and commune levels representing 8 environmental risks.
This version: 15 Feb 2024 42 (2018) examined the puzzle of revolution in the Arab world at a time of low-income inequality. The authors traced this to falling happiness and perceptions of declining standards of living which they attribute to dissatisfaction with the quality of public services, lack of jobs, and rising corruption. These perspectives also apply in Malaysia.

However, one crucial element of Hirsch’s argument does not seem to apply. Urbanized areas of Malaysia seem to be less concerned by social status and position than rural areas. In the next section we analyze the responses to the question on relative versus absolute income.

# **4a. Relative versus Absolute Income**

We asked respondents to express a preference between two scenarios: Scenario 1: You earn RM300 and the average person earns RM200 Scenario 2: You earn RM600 and the average person earns RM800 Scenario 2 was clearly superior to Scenario 1 in providing a higher income, but in Scenario 1 the person earned more than the average person and thus had a higher relative income. They were then asked to justify their response in the FGD, which allowed us to probe Duesenberry’s (1949) relative income hypothesis which argued that utility may be determined both by a higher own income, and one’s income relative to others. We collated responses for individual participants in the FGDs to this question, which we summarize in Table 6.

# **Table 6:  Survey of Relative versus Absolute Income**

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or expenditure item determine its marginal effects on inequality and poverty measures. For example, a highly progressive but small government transfer may have a small marginal effect on inequality and, therefore, small redistributive impacts. We first show how taxes and transfers affect household income across the distribution to illustrate their distributional impact. We then analyze the marginal effect of each individual tax and benefit component on the Gini coefficient and the poverty headcount.

> 13 Based on the data from the CEQ data center: https://commitmentoequity.org/datacenter/.

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High levels of political influence may determine the lower governance levels of the Board vis-àvis the CEO. Whereas the selection and appointment of the directors to the Board may not be based on strict meritocratic rules, the selection of CEOs is restricted in the majority of the cases to pre-determined criteria. This includes, but is not limited to, the need for a professional degree and a given level of sector expertise.

Similarly to other cases, T2 levels in both Board and CEO show more homogeneity than companies in T3. Companies such as ESSAP, ANDE, and EMCALI do not have a Board of Directors. Its decision-making body is individual, a President or Director appointed by political authorities.

<!-- Start of picture text --> Board Competitiveness CEO Competitiveness<br>Source: LAC SOEs Governance Database, The World Bank, 2009. Source: LAC SOEs Governance Database, The World Bank, 2009.<br>CEO BOARD<br>25 40<br>35<br>20<br>30<br>15 25<br>20<br>10<br>15<br>10<br>5<br>5<br>0 0<br>T1 T2 T3 T1 T2 T3<br>THRESHOLD LEVELS THRESHOLD LEVELS<br>NUMBER OF COMPANIES NUMBER OF COMPANIES<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2<br>.2<br>0<br>0<br>T1 C R PER T2 D R ECU BZL ARG BOL URU COL JAM T T MEX PAR T1 COL BOL ECU JAM URU MEX D R T2 PER T T ARG PAR C R BZL<br><!-- End of picture text -->

# **COMPONENT 3: MANAGEMENT/STAFF**

This index measures the composition and characteristics of the enterprise’s staff by levels of education, type of training,
, Sørensen (2007, 2010) argues that a lack of elasticity information, combined with administrative and political economy considerations, suggests that “uniform taxation should be the main guideline for indirect taxation” (Sørensen, 2010; p241). Nevertheless, he also adds that, following the recent home production literature, there is a good case for applying relatively low indirect tax rates to substitutes for home production activities .

In summary, consideration of the above literature provides support for a uniform VAT rate in two ways. First, and most clearly, the optimal indirect tax literature provides the strong message that rate differentiation should not be used to achieve distributional goals when other more direct instruments are available. Second, while the optimal indirect tax literature does provide a potential efficiency rationale for differentiated rates, such an approach is not practically implementable due to the extremely high data requirements. Therefore, from an efficiency perspective, we fall back on a single rate structure to avoid distorting consumption decisions (while also reaping the administrative benefits of a single-rate structure, as discussed subsequently in this paper). The one potential exception on efficiency grounds is regarding substitutes for home production activities – although, as discussed below, this also has practical limitations.

22 Additionally, they find capital income to be significantly negatively related to hours of work. Gordon and Kopczuk (2014) make a similar finding based on United States data. Both papers argue therefore that capital income should be taxed. They also argue that current direct tax concessions (e.g. mortgage interest deductibility) for housing should be removed.

23 Bastani et al. (2015) argue that Mirrlees et al. (2011) are effectively considering a situation where all goods are separable from leisure with the exception of one good needed for work. They develop a model to show that in such a case there remains an equity argument to apply differential tax rates to the goods that are separable from leisure. The rationale is that, because a family needs to pay for childcare in order to work
0.307)|(0.362)|(0.193)||(0.548)|(0.442)|
|Debt crisis DV|-0.197|1.889|-0.119||1.504***|1.390**|
||(1.179)|(1.270)|(0.552)||(0.555)|(0.567)|
|Debt restructuringDV|1.561***|2.397|-0.635||-0.131|2.061***|
||(0.232)|(1.490)|(0.617)||(0.845)|(0.607)|
|Constant|8.923***|31.116***|36.723***|12.784***|28.276***|28.834***|
||(1.782)|(2.616)|(8.987)|(0.081)|(2.934)|(2.852)|
|Year FE|yes|yes|yes|yes|no|no|
|5-year period FE|no|no|no|no|yes|yes|
|Country FE|no|no|yes|yes|no|no|
|Observations|249|539|537|599|130|171|
|Adj. R-sq|0.596|0.591|0.922|0.859|0.603|0.581|

## _Source:_ Authors’ estimates.

_Note:_ Columns 1-4 report estimations with annual-frequency data. Columns 5-6 report results with non-overlapping 5-year averages (the following periods: 2002-06; 2007-11; 2012-16; 2014-21). Column 5 shows results
Nepal, Kenya, Philippines) or the percentage living under a dollar-a-day in Zimbabwe two-dollars-a-day in Brazil.1<sup>0</sup>

**9** An attemp **t** o do this would require benchmarking the index derived in each country to an international standard, or pooling the data to derive weights. Attempting this interesting work is left for a separate research endeavor and the interpretation of the present analysis is limited to relative gaps within a country. **10** We do not simply include the list of variables that make up the index to "control" for wealth in the regression as advocated by Montgomery et al, 1997, as for a substantial part of the paper we will interested in the effect of wealth per-se. More applications of this "asset index" approach using the DHS can be found

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38 326–342.

- Rossi, Federico, “Human Capital and Macroeconomic Development: A Review of the Evidence,” _The World Bank Research Observer_ , 04 2020, _35_ (2), 227–262.

- Schoellman, Todd, “Education Quality and Development Accounting,” _Review of Economic Studies_ , 2012, _79_ (1), 388–417.

- and Bart Hobijn, “Structural Transformation by Cohort,” Technical Report 2017.

- Shi, Shouyong, “A Divisible Search Model of Fiat Money,” _Econometrica_ , 1997, _65_ (1), 75–102.

- The Atlantic, _Minorities Are More Likely to Believe Education Will Help Their Careers._ , The Atlantic, November 13, 2013.

- Trostel, Philip, Ian Walker, and Paul Woolley, “Estimates of the economic return to schooling for 28 countries,” _Labour Economics_ , 2002, _9_ (1), 1–16.

- Unesco, _International Standard Classification of Education: ISCED 2011_ 2012.

- UNESCO, _UNESCO COVID-19 education response: how many students are at risk of not returning to school?_ , Paris: Unesco, 2020.

- , _What Have We Learnt? Overview of findings from a survey of ministries of education on national responses to COVID-19_ , Paris: Unesco, 2020.

- , _Global Monitoring of Schools Database_ 2021.

- United Nations, _World Population Prospects: The 2019 Revision_ 2019.

- , _COVID-19 and its economic toll on women: The story behind the numbers_ 2020.

- Uzawa, Hirofumi, “Optimum Technical Change in An Aggregative Model of Economic Growth,” _International Economic Review_ , 1965, _6_ (1), 18–31.

- World Bank, _International Income Distribution Database Harmonization Manual_ , Washington DC: The World Bank, 2013.

- , _World Development Report 2019: The Changing Nature of Work_ , World Bank Publications, 2018.

- , _COVID is hurting women economically, but governments have the tools to offset the pain_ 2020.

- , _World Development