rafmacalaba/datause-encoder-data huggingface dataset:
We also mapped the green job titles into major green economy sectors through clustering based on their semantic similarity. We used job title embeddings and applied Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) (McInnes, Healy, and Melville 2018), followed by Hierarchical Density-Based Spatial Clustering (HDBSCAN) (McInnes, Healy, and Astels 2017). Fine-tuning these techniques was essential to achieve meaningful results.

We configured UMAP with 10 neighbors to balance local and global structure, and a minimum distance of 0.1 to control the density of point packing, ensuring that local detail was preserved. This configuration maintains similarity among nearby points (local structure) while grouping clusters of similar points together (global structure).

For clustering, we used HDBSCAN with a minimum cluster size of 10 to ensure significance and a minimum sample size of four to define the number of points required to form a dense region.

# **3. Results**

# **Green Literature**

Our search yielded a total of 1,367 articles, with Scopus contributing 991 articles and Web of Science contributing 376 articles (table 1). We used the Digital Object Identifier and International Standard Serial Number to cross-check the uniqueness of the articles across the two databases. We found that 88 percent of the articles indexed in Web of Science were also indexed in Scopus. Consequently, we integrated the unique articles from Web of Science—those not found in Scopus—to arrive at our unique “analysis set” of 1,067 articles. In our ensuing analysis, we used the full texts of 567 articles for which we were able to retrieve Portable Document Format (PDF) files and the abstracts for the remaining 500 articles. While most of the publications are articles, with 915 from Scopus and 353 from Web of Science, we retrieved a diverse set of publications, including conference proceedings, book chapters, and other materials (table 2).

Both the number and geographical spread of the articles on green literature have expanded since 2009. In 2009, there were 44 articles, and in 2023, there were 162 articles (figure 2). Moreover, the articles in 2009 almost exclusively covered North America (table 3). However, the scope gradually diversified. Notably, by 2015, the number of articles covering European countries had surpassed those covering North American countries in cumulative terms; by 2022, the same had occurred for Asian countries.

In the early years of our analysis period, there were no articles focusing specifically on green jobs in South America and hardly any in Africa or Oceania. This situation has changed dramatically, with substantial increases in the number of articles covering these continents over time. This trend indicates that research on green jobs is becoming increasingly globalized, encompassing a broader range of geographical settings.

# **Green Job Titles**

We initially identified 799 potential green job titles using the RAG pipeline (see file in <u>GitHub</u> for the list of titles). We excluded 66 titles, as they were not job titles but referred to green activities.

7
who belong predominantly to lower-middle wealth classes at the expense of the richest 20 percent, who grants the usufruct.

<!-- Start of picture text --> Figure 1 - Gini concentration index - Adjusted and<br>unadjusted net wealth<br>0.650<br>0.640 Unadjusted<br>H1<br>0.630<br>H2<br>0.620<br>H3<br>0.610<br>0.600<br>0.590<br>0.580<br>0.570<br>1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->

# **5. Free residential houses in HFCS data**

The Household Finance and Consumption Survey (HFCS) is a comprehensive and harmonized survey conducted at the national level within the Eurozone. It aims to collect detailed data on household finances and consumption. The SHIW survey is the Italian part of the HFCS.

According to this source, the share of main residences used for free is a significant issue in several countries, including Austria, Croatia, Cyprus, Estonia, Greece, Poland, Portugal, Slovenia, and Spain, where the share of households is around 10 percent, close to or higher than in Italy (Table A6).

By examining the age of the reference person, we can gain evidence of the relative importance of this tenure mainly for the young and the old. In many countries (Austria, Cyprus, Czechia, Estonia, Germany, Hungary, Latvia, Poland, and the Slovak Republic), the age profile shows a U shape, and the shares of households whose reference person is below 30 years or above 65 years are the highest. In many other countries (Italy, Croatia, France, Greece, Lithuania, Portugal, Slovenia, and Spain) the share is generally decreasing with age.

Unfortunately, the harmonized survey's questionnaire collects information on the tenure status for the main residence, but it does not allow us to distinguish the forms that provide the occupants with a right to use the property (e.g., usufruct), that we have considered for the household wealth measurement, from other forms of free use (e.g. temporary use permit), that we have considered current transfers, and thus outside the wealth definition.

Still, in all countries, the share of free users is mainly concentrated in the bottom class of the wealth distribution (Table A7). It is thus presumable that even considering a small share of the households living for free in the main residence as holders of a right of use would significantly lower wealth inequality for these countries.

It is only a hypothesis. The lack of data prevents us from being more precise. The relevance of this aspect would suggest delving deeper into the issue, including questions in the questionnaires that can clarify whether the free use of these houses can be defined as a right held by occupants and
no evidence that the internal validity of the study is jeopardized by attrition bias (Dumville et al., 2006).

Annex Table A1 also provides a description of the short-term and medium-term samples. Overall, these samples consist of highly educated and typically unmarried individuals. Females represent about a quarter of them.<sup>28</sup> More than four in ten report belonging to socially and economically disadvantaged castes. Individuals in the sample hold mixed attitudes towards gender norms and VAW. For example, while a large majority believes that women should be able to wear clothing of their choice, almost a quarter thinks that women should be banned from the kitchen/shrine during menstruation or justifies VAW in cases of unfaithfulness.

# **4.2 Take-up rates and objective compliance**

Panel B of Table 1 shows viewership statistics of the media campaigns for the full baseline sample. The data suggests that the drama (treatment 1) and placebo movie experienced higher viewership rates compared to the documentary (treatment 2), potentially due to their higher entertainment content. While 65% of treatment 1 viewers self-reported watching half or more clips, only 47% of treatment 2 individuals reported doing so. The objective metrics, as measured by click data, confirmed higher take-up rates for treatment 1, compared to treatment 2, and provided overall insights into intervention compliance. These show that around 25% of people in any arm over-reported watching more than half of the videoclips (35% vs. 60%), though over-reporting was more-or-less similar across treatment arms.<sup>29</sup> Panels C and D of Table 1 provide information on compliance with the intervention for the two follow-up samples, using click data from the video “play” events. These data will also be used to estimate the Treatment-on-the-Treated effects, which thus will account for any differences in viewership across experimental arms. About 78% and 66% of respondents in the short-term and medium-term samples played half or more of the assigned series, respectively. Viewership patterns across treatment arms follow the general trends, with the docuseries being the least watched. In particular, information on mean duration of the intervention reported in panels C and D indicate that users assigned to watch this explicit format took

> differences across treatment arms.

> 28Despite our efforts in stratifying the Facebook recruitment by gender, we could not reach gender balance in the final sample. This mainly depends on the much higher costs to recruit females because of the widespread gender gap on social media presence, especially in Southeast Asia (Fatehkia et al., 2018).

> 29We cannot fully attribute the take-up difference to the entertainment format as the number of video clips for the documentary was 7 as opposed to 3 for the drama, and video clips were released every 2 hours (conditional on self-reporting that the previous one had been watched). Thus, the take-up differences may be partly explained by a larger number of episodes. On the other hand, the total time was longer for the drama compared to the documentary (25 and 20 minutes respectively).

15
EU membership as almost half of the countries in our sample are members of the EU and the agreement is arguably the deepest form of economic integration among states, including specific directives on government procurement. Two policy variables should account for trade liberalization specific to government procurement. One is an indicator for the presence of provisions on government procurement in TAs. We draw from the Deep Trade Agreements database (Mattoo et al., 2020), which includes a section on the presence of around 100 provisions specific to government procurement in each TA. In our baseline specifications, we use a simple dummy, _TAPROC_ for the presence of provisions on government procurement in an agreement. The other variable is a dummy for membership in the WTO GPA. Started in 1996 and revised in 2014, the agreement aims to ensure national treatment to foreign firms in government procurement markets, although each member defines the areas of commitments (e.g. different public entities, goods vs. service) that can thus vary substantially across countries. Importantly and unlike most of the WTO agreement, the GPA is “plurilateral”, meaning that it binds only its signatories having _de facto_ the same structure of a preferential TA.

To take into account how trade barriers around the world create a preference for local purchases, we next estimate an index of home bias, the CHB. Differently from the border effect, the CHB measures how trade frictions shift up the observed internal trade relative to a frictionless benchmark, where international transactions are thus predicted to be much greater. To estimate the index, we manipulate the gravity equation in eq (2) as follows (see Anderson and Yotov, 2010b): where the _φ_<sup>�</sup> term reflects the fact that we observe only an empirical estimates of trade costs (instead of the true ones). The estimated CHB is thus given by the predicted values of the gravity model rescaled by sectoral expenditures and incomes. The predictions are from the gravity specification in eq (10). We first obtain CHB for goods and services separately (and for each market _s_ ) and then aggregate those to the country level using the product of sales and expenditure shares as weights, similar to Anderson et al. (2014).

contrast to the ‘full’ general-equilibrium effect that takes into account the influence on trade flows through multilateral resistance terms, sales and expenditure.

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Reference Atkinson, Anthony B. 1970. "On the Measurement of Inequality." Journal of Economic Theory 2: 244-263.

> , 1987. "On the Measurement of Poverty". Econometrica 55:

749-64.

Bank Indonesia. 1988. Laporan Minaguan, Bank Indonesia, Jakarta. Booth, Anne. 1988. "Survey of Recent Developments." Bulletin of Indonesian Economic StudieR 24:3-36.

Booth, Anne and R. M. Sundrum. 1988. "Employment Trends and Policy Issues for Repelita V", Jakarta: International Labor Organization, Mimeo. Central Bureau of Statistics (Biro Pusat Statistik). 1984. "Indikator Pererataan Pendapatan Jumlah dan Persentase Penduduk Miskin di Indonesia". Biro Pusat Statistik, Jakarta.

. 1987. Statistik Indonesia, 1986. Biro Pusat Statistik, Jakarta.

. 1988. Statistik Indonesia. 1987. Biro Pusat Statistik, Jakarta. Cowell, Frank A. 1977. MeasurinR Inequality. Philip Allan, Oxford. Chernichovsky, D. and O.A. Meesook. 1984. "Poverty in Indonesia: A Profile." World Bank Staff Working Paper No. 671, Washington, D.C.

Collier, William L., Ir. Gunawan Wiradi, Soentoro, Makali, and Kabul Santoso. 1988, "Employment Trends in Lowland Javanese Villages", mimeo. Dapice, D. 1980. "Trends in Income Distribution and Levels of Living 1970-75". In G. Papanek (ed.), The Indonesian Economy, Praeger, New York.

Datt, Gaurav, and Martin Ravallion. 1989. "Regional/Sectoral Disparities and Poverty Alleviation in India". Paper prepared for the IFPRI/World Bank Poverty Research Conference, October.

Deaton, Angus. 1989. "Price Elasticities from Survey Data: Extensions and Indonesian Results", Woodrow Wilson School, Princeton University.
at different maturities are endogenously determined. The model illustrates the trade-off between cheaper short-term borrowing and safer long-term borrowing. On the one hand, investors are risk averse and have a short horizon, so they may need to liquidate the long-term bonds before maturity. As a result, they require a positive term premium to hold long-term bonds. On the other hand, it is costly for the country to generate large amounts of liquidity (or fi scal revenue) in a short period of time. Therefore, long-term debt is safer for the government because it reduces the expected cost associated with rolling over short-term bonds.

The model has several implications. First, it shows that the risk premium on long-term bonds is higher than the risk premium on short-term bonds, and that this difference increases during crises. Second, the model shows what types of shocks are consistent with the observed pattern of maturity choice and premia at different maturities. A negative shock to government resources leads to an increase in the risk premium on long-term bonds and an increase in the maturity structure of bond issues. This is analogous to a positive demand shock on the market for international loans, leading to higher prices and higher quantities of long-term bonds. A negative shock to investors’ wealth, on the other hand, leads to an increase in the risk premium on long-term bonds and a decrease in the maturity structure of bond issues. This is analogous to a negative supply shock, leading to higher prices and lower quantities of long-term bonds.

Empirically, the paper studies the behavior of bond prices and quantities. To do so, we assemble a new database on bond prices for several emerging economies. We use this database to estimate time series of the term premium for each emerging market. In the process, we also estimate spreads, bond returns, and risk premia at different maturities, relative to Germany and the United States (considered to be “safe” countries). We also study the characteristics of bond issuance by emerging economies to understand the relation between the cost of borrowing at different maturities and the maturity choice.

The main empirical results can be summarized in three stylized facts. First, when compared with Germany and the United States, the excess returns from holding emerging market long-term bonds are, on average, much higher than those from holding emerging market short-term bonds. In other words, there is a high “term premium” of around 4 percentage points per year, when comparing 3-year and 12-year maturities.<sup>3</sup> This high term premium re fl ects the high risk premium required by

> 3 To be precise, the term premium is equal to the difference between the risk premium an emerging economy pays

2
gathering data on household use of financial services that could be compared with that gathered in other countries via LSMS modules or other non- FinScope stand-alone surveys.

We should acknowledge at the outset that we do not focus on the distinction between access to and use of financial services in what follows. Access, the possibility to use financial services, and the actual use of financial services do not overlap perfectly, though both concepts are relevant to a discussion of financial sector outreach. Although our survey does have a series of questions on why respondents do not use financial services, and thus we do have information on voluntary exclusion from participation in the formal financial sector, we do not focus on that information in this paper. Rather, we conduct a methodological experiment about reported usage of financial services.

The rest of the paper is organized as follows. Section 2 describes the design of our experiment, while section 3 compares the characteristics of our sample with that of the full Ghana LSMS -- we re-visited only a subset of those households, though our sample was designed to be nationally representative. We also compare sample characteristics across treatment groups. Section 4 reports usage rates across financial products for product- versus institution-based questions and household usage rates provided via full enumeration versus an informant. In section 5, we introduce regressions to test whether certain types of individuals and households are responsible for the under-reporting of access that we find for some questionnaire formats in section 4. Section 6 offers concluding remarks.

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Table 1. **Economic policies and the economic rate of** return **(ERR) of projects:** single **policy** distortions

|snge**pocy**|stortons<br>||Averag|e ERR(%<br>|||
|---|---|---|---|---|---|---|
||All<br>|Public<br>||of which||Private<br>|
||projects|projects||||projects|
|._|_________|_________|agricul-<br>ture<br>__________<br>__________|industry<br>___<br>__________sectors|non-<br>tradable<br>||
|Overall averageERR|16.0|16.2|14.3|13.6|18.1|14.0|
|Policy Distortion<br>Index:|||||||
|1. Trade restrictions|||||||
|Highly restrictive|13.2|13.6|12.2|insf|14.6|9.5|
|Somewhat<br>restrictive|15.0|15.4|15.4|insf|16.0|10.7|
|Non-restrictive|19.0|19.3|14.3|insf|24.3|17.1|
|2. Exchange rate<br>overvaluation: black<br>market Premia:|||||||
|High (> 200%)|8.2|7.2|3.2|insf|11.5|insf|
|Medium (20-200%)|14.4|14.9|11.9|13.7|17.2|10.3|
|Low (< 20%)|17.7|18.0|16.6|16.6|19.3|15.2|
|3.Real interest rate|||||||
|Negative|15.0|15.4|12.7|12.7|17.9|11.0|
|Positive|17.3|17.5|17.0|17.8|17.9|15.6|
|4.Fiscal deficit|||||||
|High C 8% of GDP)|13.4|13.7|11.7|10.3|16.6|10.7|
|Medium (4-8%)|14.8|15.1|12.2|21.0|16.8|12.2|
|Low (< 4%)|17.8|18.1|18.6|14.1|18.2|14.3|
|5. Price distortion:<br>index of tradable|||||||
|High distortions<br>|15.6<br>|15.9<br>|13.1<br>|14.0<br>|18.4<br>|11.0<br>|
|Low distortions|17.5|17.5|17.0|16.5|18.1|17.2|

Notes: Average reestimated economic rate of return of public and private projects, classified by single policy distortion.

'Insf' denotes insufficient number of observations (less than 10) to make inferences.

Source: Authors' calculations.
**Parameters.** The labor share is set to match the average share of labor compensation in non-resource GDP over 2000-2019. This requires adjusting the PWT labor share, which applies to the whole economy, usually less labor intensive than the non-resource sector. Specifically: t ⁄ 0 PPWWPP where t is the share of labor compensation in GDP in period tt tt tt _t_ , taken from Penn ββ = ββ x RRDDPP RRDDPP PPWWPP 0 World Table 10 (PWT10), and is non-resource GDP in period _t_ . The baseline ββ calibration sets equal to the average value of tt over recent years. RRDDPP To calibrate the resource rents in resource industry tt , , we use information on ββ ββ natural resource rents shares, provided by the Global Trade Analysis Project ii ii γγ (GTAP), and averaged over 2004, 2007, 2011, and 2014.<sup>27</sup> The tax rate in the resource sector, , is calibrated using data on government natural resource revenues and resource GDP from the IMF’s World Commodity RR ττ Exporters Dataset (IMF-WCE). Specifically, is set to match the longest available historical average of resource revenues as a share of resource GDP since 2000. RR ττ

The fiscal rule in the Default submodel requires the calibration of the marginal propensity to invest, (see equation (13)). For BBR-HR and SSR, is set to one and zero, respectively. For BBR, is the average ratio of public investment to total θθ θθ expenditure over 2000-2019, taken from the IMF-WEO and the Investment and θθ Capital Stock Dataset provided by the IMF Fiscal Affairs Department (IMF-FAD). The External-Balance model requires five additional parameters: the private investment crowd-in parameter the average tax rate on the non-resource economy 0, the debt-elastic interest spread , the world real interest rate , and how the λλ, budget balance responds to debt . measures the response of private investment WW ττ ψψ ff to the fiscal balance (equation (19)). Its default value is set to 0.15 (a one-dollar φφ λλ increase in net government borrowing crowds in 15 cents of private investment) based on the average of estimates for both industrial and less developed countries (see Chinn et al. 2011). 0 is set to match the average ratio of non-resource revenue ττ

> 27 We can map parameter into the measure of natural resource rents from GTAP as it quantifies the total income that can be generated from the extraction of natural resources, less the cost of ii extraction, including the return on capital employed on the extractive activity. γγ

19
**Figure 1: Age and ethnic composition Sri Lanka provinces (Source: LFS 2015)**

a. Age groups by province b. Ethnic groups by province <!-- Start of picture text --> 5%9% 1%3% 1% 8%2%6% 7%7%3%<br>11% 37%<br>59% 55% 51% 62% 59%<br>93%<br>16% 18% 18% 15% 15% 75% 39% 84% 84%<br>25% 27% 30% 23% 26% 23%<br>3%<br>Sri Lanka Northern Eastern Western Other Sri Lanka Northern Eastern Western Other<br>Province Province Province Provinces Province Province Province Provinces<br><15 years 15‐24 years >24 years Sinhalese Tamil Moor Others<br><!-- End of picture text --> The Northern and Eastern regions also have lower levels of economic well‐being than the Sri Lanka average (Table 1). The GDP per capita of these regions is lower than that of the Western and all other provinces. The poverty headcount in 2012/13, according to the national definition, of these provinces is higher (10.9% and 11.0%) than in the country as a whole (6.7%). Poverty rates in some Divisional Secretariat Divisions, administrative areas smaller than districts, are estimated to exceed 30% (Doan and Deepawansa, 2015).

**Table 1: GDP, poverty, and consumption levels in Sri Lanka**

||Northern<br>Province|Eastern<br>Province|Western<br>Province|Othe<br> Provinces|Sri Lanka|
|---|---|---|---|---|---|
|GDP per capita 2014, '000 Rs.|345|391|732|420|503|
|Poverty rate|10.9%|11.0%|2.0%|8.1%|6.7%|
|Per capita consumption, 25th percentile|4,619|4,692|7,150|5,143|5,462|
|Per capita consumption, 50th percentile|6,325|6,255|10,542|7,211|7,805|
|Per capita consumption, 75th percentile<br>Notes: GDP data from Economic and Social Stat<br>Poverty and per capita consumption nu|9,501<br>istics of Sri Lan<br>mbers are co|8,542<br>ka 2016, Cen<br>mputed from t|15,800<br>tral Bank of Sr<br>he HIES 2012|10,760<br>i Lanka.<br>/13.|12,025|

4