rafmacalaba/datause-encoder-data huggingface dataset:
data, accounting for all the emissions of the firm (including subsidiaries), spanning from 2012 to 2021. The ICC includes detailed information on companies’ Scope 1, Scope 2, and Scope 3 emissions and emissions’ intensity, sourced from official reports such as Sustainability and ESG reports, the Carbon Disclosure Project (CDP), as well as estimated emissions from ISS ESG when official data is not available. For the analysis, we focus specifically on Scope 1 emissions since these are direct GHG emissions from sources owned or controlled by the firm.

# **2.4 List of green technologies and associated keywords**

In line with the approach of Bloom, Hassan, Kalyani, Lerner, and Tahoun (2021), we use the WIPO green patent data and the ECTs to identify biagrams or a set of two words that represent each green technology. We follow several steps. First, we extract the summary of the green technologies included in each patent in the WIPO database. Second, we transform the summary’s text into biagrams by applying a text recognition algorithm, removing numbers, special characters, and stop words (e.g. of, the, from, etc.) and converting each plural word into its singular form (eg. batteries, battery). Third, we drop bigrams that are not included in at least 20 patents and prior to the year 2000. The rationale behind this choice is to include not only the most influential but also the most novel technologies in our analysis. Fourth, we perform a human audit of the resulting biagrams. Fifth, we train a word embedding algorithm (word2ovec) with the patent’s summary to identify similar biagrams to each biagram and dropped nontechnical bigrams using Google Bigrams. Sixth, we perform a human audit of the new set of biagrams. Seventh, we train the word embedding algorithm with ECTs to capture business-like language referring to technical bigrams. Finally, we restrict the focus to technologies mentioned in more than 50 ECTs during the 2012-2021 period. Table A1 in the Appendix contains the final set of green technologies considered and the corresponding bigrams searched in the textual analysis of patents and earnings calls. Table A2 reports the number of ECTs that mention each technology in the period 2012-2021. As can be observed in this Table, the technologies that appear more frequently in ECTs include construction material, air conditioning, wind turbines, LED technology, hybrid/electric vehicles, Li-ion batteries, carbon capture technologies, transgenic crops, biofuels, and solar energy.

6
learning in a relevant context (purchase of a home). In this same light, the study participants in Gine and Mansuri (2011) were microfinance clients who could choose to participate in a business training course and loan lottery and then apply their learning directly to their microenterprises and loan behavior. On the other hand, Bruhn, Ibarra and McKenzie (2012) studied the effects of financial education programs that covered a broad range of financial topics in which the population did not experience an immediate circumstance like the purchase of a home or the financing of a business in which they could apply their learning. Bruhn, Ibarra and McKenzie (2012) evaluated a broad-based financial education program in Mexico, and among other factors, the diverse variety of topics covered and lack of a “teachable moment” to apply the learning may have contributed to the lower reported effectiveness of their program on loan default behavior. Similarly, while loan default was an outcome measured in Berg and Zia (2013), the topic was not central to their intervention which focused on source of borrowing (formal vs. retail credit) and gambling behavior.

The final meta-analysis relates to the impact of financial education on record-keeping. Record keeping and tracking expenditures are often cited as critical elements of gaining control of one’s finances, much the way that many fitness and diet programs focus on recording eating and exercise habits to control weight and improve health. This is a behavior that is fully under the control of the individual as compared to decisions to default or save money which may be influenced by factors outside one’s control such as unexpected illness (and medical fees), loss of a job or other problems that leads to financial distress.

The forest plot and regression results (Figure 14) combine data for record keeping related to both personal finance and to microenterprise or business records. This is necessary due to the small sample size but there are also reasons to believe that effects may be similar across these two types of interventions. Microenterprises frequently co-mingle finances between the business and individual owner so record keeping for a business is likely to include some common elements with personal financial management facilitating comparisons between these two types of interventions.

The meta-analysis presented in Figure 14 indicates that financial education may positively encourage record keeping behaviors although the meta-analysis results are not quite significant at the 90% confidence interval (p=0.134). The effects are weighted fairly evenly across the studies which involve an unusually diverse set of country cases (Ghana/Kenya, Vietnam, Australia / New Zealand, Indonesia and Mexico) but which use, with one exception, classroom methods.

23
|Table 5.2: Average Number o<br>|f Youthper Hous<br>|ehold Mem<br>|ber,2001<br>||
|---|---|---|---|---|
|Income(per capita)|Metropolitan|Urban|Rural|Haiti|
|Extremely poor (<$1 a day)|0.26|0.20|0.17|0.19|
|Poor (<$2 per day)|0.25|0.20|0.18|0.19|
|Nonpoor(>2$per day)|0.25|0.20|0.19|0.21|

_Source:_ Own calculations based on ECVH 2001.

The nuclear family with a wife, a husband, and two or three children is not a common phenomenon in Haiti. As in other countries, the division of labor is not as economically obvious as in the past since women’s opportunity cost of not working increases with an increasing level of education. Education of women and lack of jobs for men lead to marriage being less attractive. Furthermore, in Haiti men are typically involved in a number of unions or have several partners. Polygamy is widely accepted and 8 percent of married men have more than one wife. In fact, 2.4 percent of all married persons live in relationships where the husband has seven or eight wives. This, together with many households being affected by domestic or international migration as described in Section 4, changes the more traditional household into a more vulnerable one containing only one parent or no parents at all. Of all households in Haiti, 53 percent are headed by women. This is the case because husbands have migrated, died, or simply never taken part in the household. Table 5.3 reveals that only slightly more than one out of two children in rural areas live with both parents while just one out of three children is living with both parents in urban areas including Port-au-Prince. More than 30 percent of children are only living with their mother and despite stigma many fathers provide little economic support, leaving the household with a high probability of falling into poverty (World Bank 2002). Moreover, it is common to send children to other households for education and as domestic servants. Slightly more than 8 percent of children aged 5–7 are domestic workers; 59 percent of these are girls (Sommerfelt 2002). Child domestics are an important component in the household composition and social organization, but some children are exploited and exposed to hazardous work. Thus, the laws that protect children to allow them to attend school are not enforced and lack of human capital accumulation places domestic servants at risk. Naturally, domestic servants are not just affected in their childhood, but also disadvantaged as youth or adults. Therefore, measures also need to target children at risk and improve their opportunities later in life.

Table 5.3: Proportion Living with Parents, 2000 (percent)

||Urban|Rural|
|---|---|---|
|Both parents|38.9|53.8|
|Mother only|33.8|24.0|
|Father only|7.4|5.5|
|Neither|19.9|16.8|

_Note:_ Age group 0-14 years. Urban including metropolitan.

_Source:_ DHS 2000.

24
schools.

The pattern emerging from this description is that of large and increasing achievements gaps across schooling systems, with a divide between public regular and private or experimental schools at the primary and preparatory levels that is replaced by disparities between vocational and general schools at the secondary level.

If attending private schools allows children from advantaged backgrounds to perform better at the preparatory exam, allocation by school type, and attendance of a private school at the primary and preparatory levels, could contribute to educational inequities. However, rather than the school system, the differentials might just be explained by the selection of children with different characteristics into different school systems, such as the effects of other resources associated with a child’s background. To investigate this, **Table 10** reports the estimates of the equity-shares of learning achievements after controlling for the school system attended. The intuition underlying the analysis is that the controls should cancel out the indirect effects of a child’s circumstances through attendance of a different system. Thus comparing the share of achievements inequities in the uncontrolled and controlled analysis should inform on the differentials that stem from having benefited from a different system and associated schooling environment.<sup>15</sup>

> 15We rely here on OLS estimates of the effects of attending different school systems. An obvious limitation is that the estimates will be biased as soon as some unobservable variables that affect learning outcomes are associated with the school system attended.

31
# **1. Introduction**

In Jordan, recent university graduates face difficulties entering the labor market and firms seeking to fill entry-level positions complain that educated youth lack the appropriate interpersonal and technical skills required for the positions. In 2010, unemployment rates for men and women between the ages of 22 to 26 with a post-secondary degree were 19 percent and 47 percent, respectively. The mean duration of unemployment among this cohort was 10 months for males and 16 months for females (JLMPS 2010). The transition period from graduating university to stable employment for youth who do not immediately find a job is 33 months on average (Barcucci and Mryyan, 2014). In 2011, we surveyed 2000 firms in Amman, the capital city, that were looking to hire new employees. 60 percent of these firms said they experienced difficulty distinguishing between good and bad job candidates, and 64 percent said they experienced difficulty finding competent graduates in reasonably proximity to the firm.

Across the Middle East and North Africa, many countries face the same labor market issue for educated youth (Angel-Urdinola et al, 2010; Almeida et al, 2012). Youth unemployment across the Middle East and North Africa has been described as a “jobs shortage” and is currently a major policy issue (e.g. Reed, 2011; Sweis, 2014). But this raises the question of why the labor market does not clear. Theory offers at least three potential explanations. The first is that high minimum wages exceed the marginal product of workers. However, this seems less relevant for university graduates whose wages are well above the minimum wage. The second potential explanation is offered by search and matching theory, which explains persistent unemployment as the result of high search costs which prevent firms with vacancies linking with qualified job candidates. Search frictions may be particularly high for youth especially in Jordan where education may not be a good signaling mechanism for future work productivity. Finally, a third explanation is based on class consciousness and a trade-off between work and leisure; graduates refuse to work in jobs that they consider beneath them even at wages well above minimum wage due to _reservation prestige_ . The notion of reservation prestige may interact with gender norms, which restrict the set of jobs considered suitable for women.

Search frictions and reservation prestige both have intuitive appeal for helping to explain the high unemployment rates and duration among educated youth in the Middle East but there is 2
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Kenya’s power and water utilities are more efficient than those in other low-income countries**

a. Uncollected bills and unaccounted losses in the power sector, as a percentage of GDP <!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Kenya LIC<br>unaccounted losses collection inefficiencies<br>b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Kenya low-income countries<br>unaccounted losses collection inefficiencies<br>% of GDP<br>% of GDP<br><!-- End of picture text -->

- b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP

Source: Derived from Briceño-Garmendia, Smits, and Foster 2009.

Kenya’s infrastructure loses about $52 million each year because capital budgets are not fully spent. This problem affects the transport, WSS, and power sectors in almost equal degrees, leading to lost investments of the order of $15–20 million per year in each of these sectors. For example, capital-budget execution rates in Kenya’s road sector stood at only 60 percent in 2005–06. While this represents a substantial improvement from the 40 percent rate recorded in 2001–02, it still falls well short of the 80 percent level achieved elsewhere in Africa.

# Annual funding gap

Kenya’s infrastructure funding gap amounts to $2.1 billion per year, or about 11 percent of GDP. More than 80 percent of the country’s entire funding gap is found in the WSS sector, where the shortfall 30
<!-- Start of picture text --> Congo<br>oe<br>orl<br>© amt, :<br>wo<br>w<br>“<br>i) 10 20 30 40 Quantile50. 60 70 80 90 4100<br>—+— Education ——+— Enperience<br>—+*— Numberof Children ——*—\ Married<br>—*— Industry —+— Monthly Hours<br><!-- End of picture text --> <!-- Start of picture text --> Ghana<br>oe<br>wo<br>es<br>wo<br>uo<br>od<br>i) 10 20 30 40 Quantile50 60 70 80 90 100<br>—*— Education ——*— Enxperience<br>—+*— Numberof Children ——*#—\ Married<br>—*— Industry —+*— Monthly Hours<br>Saving<br><!-- End of picture text -->
**Model Schedule 1: Targeted Specific Commitments for IT and BPO Services**

|Sector or subsector|Limitations on<br>k|Limitations on<br>il|
|---|---|---|
|I. COMMITMENTS|maretaccess|natonatreatment|
|A. Professional Service<br>|||
|b. Accounting, auditing and bookkeeping services 862<br>c. Taxation Services 863|(1)+(2) None<br>(1)+(2) None|(1)+(2) None<br>(1)+(2) None|
|B.Computer and Related Services 84|(1)+(2) None|(1)+(2) None|
|C.Telecommunication services<br>n. on-line information and/or data processing (incl. transaction<br>processing) Part of CPC 843|(1)+(2) None|(1)+(2) None|
|F. All “Other Business Services”as defined as|||
|“Business Services N.E.C” 87 plus<br>b. Market research / public opinion polling services 864<br>c. Management consulting service 865<br>d. Services related to man. consulting 866<br>e. Technical testing and analysis serv.8676|(1)+(2) None<br>(1)+(2) None<br>(1)+(2) None<br>(1)+(2) None<br>(1)+(2) None|(1)+(2) None<br>(1)+(2) None<br>(1)+(2) None<br>(1)+(2) None<br>(1)+(2) None|
|m. Related scientific / technical consulting services 8675<br>r. Printing, publishing 88442|(1)+(2) None<br>(1)+(2) None|(1)+(2) None<br>(1)+(2) None|
|5.EDUCATIONAL SERVICES|||
|Adult education services n.e.c<br>.924<br>Other education services 929|(1)+(2) None<br>(1)+(2) None|(1)+(2) None<br>(1)+(2) None|
|7.FINANCIAL SERVICES<br>A.All insurance and insurance-related services<br> <br>d. Services auxiliary to insurance 8140<br>B.Banking and other financial services|(1)+(2) None|(1)+(2) None|
|l. Provision and transfer of financial information and financial data<br>processing and related software by providers of other financial<br>services 8131|(1)+(2) None|(1)+(2) None|
|and "Other services auxiliary to financial intermediation" 8133|(1)+(2) None|(1)+(2) None|
|10. RECREATIONAL, CULTURAL AND SPORTING SERVICES<br>C.Libraries, archives, museums and other cultural services<br> Library<br>and archive services 9631<br>**Plus from CPC 1.1**<br>“Other Business Service”complemented by<br>Support services as defined in CPC 1.1 85<br>Other commitments complemented by:<br>On-line information provision services CPC 1.1 843|(1)+(2) None<br>(1)+(2) None|(1)+(2) None<br>(1)+(2) None|
|Licensing services for the right to use non-financial intangible<br>assets CPC 1.1 733|(1)+(2) None<br>(1)+(2) None|(1)+(2) None<br>(1)+(2) None|

**_Option 2: A Horizontal Commitment to Liberalize Cross-Border Trade in a Wide Range of Services_**

The limitations of the previous option suggest that Members may wish to consider a more coherent and forward-looking approach. The approach is ambitious, but it has the potential to 24
Newman, M. L., Groom, C. J., Handelman, L. D., & Pennebaker, J. W. (2008). Gender differences in language use: An analysis of 14,000 text samples. _Discourse Processes_ , _45_ (3), 211-236.

<mark>Nolen-Hoeksema, S. (2001). Ruminative coping and adjustment to bereavement. In M. S. Stroebe, R. O. Hansson, W. Stroebe, & H. Schut (Eds.),</mark> _<mark>Handbook of bereavement research: Consequences, coping, and care</mark>_ <mark>(pp. 545–562). American Psychological Association. https://doi.org/10.1037/10436-023</mark> Nolen-Hoeksema, S. (2012). Emotion Regulation and Psychopathology: The Role of Gender. _<mark>Annual Review of Clinical Psychology, 8(1): 161-187.</mark>_ <mark>Nyhus, E. K., & Pons, E. (2005). The effects of personality on earnings.</mark> _<mark>Journal of economic psychology</mark>_ <mark>,</mark> _<mark>26</mark>_ <mark>(3), 363-384.</mark> <mark>Nyhus, E. K., & Pons, E. (2012). Personality and the gender wage gap.</mark> _<mark>Applied Economics</mark>_ <mark>,</mark> _<mark>44</mark>_ <mark>(1), 105-118.</mark> O’Brien Ed., Konrath S.H., Gruhn D., and Hagen A.L. (2013). Empathic concern and perspective taking: linear and quadratic effects of age across the adult life span. _The Journals of Gerontology: Series B, 68(2),_ 168–175. <u><mark>https://doi.org/10.1093/geronb/gbs055</mark></u> <mark>Organisation for Economic Co-operation and Development.</mark> (2021). Beyond Academic Learning: First results from the survey of social and emotional skills. _OECD Publishing._ <mark>Organisation for Economic Co-operation and Development.</mark> (2015). Skills for Social Progress: The Power of Social and Emotional Skills. _OECD Skill Studies._ <mark>Onyeizugbo, E. U. (2003). Effects of gender, age, and education on assertiveness in a Nigerian sample.</mark> _<mark>Psychology of women quarterly</mark>_ <mark>,</mark> _<mark>27</mark>_ <mark>(1): 12-16.</mark> <mark>Organisation for Economic Co-operation and Development. (2015). Skills for social progress: The power of social and emotional skills.</mark> _<mark>OECD Publishing.</mark>_ <mark>Paola, M., Gioia, F., and Scoppa, V. (2018).</mark> Teamwork, Leadership and Gender. _IZA Discussion Papers_ , No. 11861.

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38
|Nonmetal mineral products|3|0.40|
|---|---|---|
|Wood and wood products (other than furniture)|3|0.40|
|Furniture|2|0.27|
|Paper and paper products|6|0.80|
|Printing and publishing|4|0.53|
|Automobiles, other motor vehicles, and transport<br>equipment|67|8.89|
|Information technology and telecommunications<br>equipment|6|0.80|
|Machinery, and electrical and electronic equipment and<br>components|64|8.49|
|Other manufacturing|36|4.77|
|_Services_|**_336_**|**_44.56_**|
|Electricity, gas, and water|20|2.65|
|Alternative energy|19|2.52|
|Construction|53|7.03|
|Wholesale and retail trade|43|5.70|
|Hotels and restaurants|7|0.93|
|Other travel and tourism-related services|8|1.06|
|Logistics, transport, and storage|35|4.64|
|Telecommunications|13|1.72|
|Computer and software services|10|1.33|
|Financial services including insurance|44|5.84|
|Real estate|4|0.53|
|Business services|18|2.39|
|Professional, scientific, and technical services|32|4.24|
|Health services|8|1.06|
|Media and entertainment|7|0.93|
|Other services|15|1.99|
|_Other_|**_17_**|**_2.25_**|
|Total|754|100.00|

**Table A.3. Sectoral Distribution of Respondents Compared with Sample Frame**

|NA|CE Section|Respondents|Sample Frame|
|---|---|---|---|
|A|Agriculture, Forestry and Fishing|0.7%|0.7%|
|B|Mining and Quarrying|1.4%|1.7%|
|C|Manufacturing|37.8%|38.9%|
|D|Electricity, Gas, Steam and Air Conditioning|1.8%|0.8%|
|E|Water Supply, Sewerage, Waste Management and Remediation Activities|0.0%|0.2%|
|F|Construction|4.6%|3.4%|
|G|Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles|11.2%|8.9%|
|H|Transportation and Storage|1.1%|1.0%|
|I|Accommodation and Food Service Activities|0.7%|0.3%|
|J|Information and Communication|3.2%|3.7%|
|K|Financial and Insurance Activities|13.7%|17.1%|
|L|Real Estate Activities|0.6%|0.4%|
|M|Professional, Scientific and Technical Activities|8.8%|7.9%|
|N|Administrative and Support Service Activities|3.6%|3.8%|
|O|Public Administration and Defence; Compulsory Social Security|0.0%|0.0%|
|Q|Education|0.0%|0.0%|
|S|Human Health and Social Work Activities|0.0%|0.0%|
|(bla|nk)|9.8%|6.1%|

_Source_ : Computation based on sample frame drawn from Dunn and Bradstreet.

_Note_ : The D&B sample frame contains many missing values (28% of records) on the NACE code. This list is calculated by the authors, drawing from a field that contains “line of business” and mapping to a NACE code.

26