rafmacalaba/datause-encoder-data huggingface dataset:
number of observations per demand equation fluctuates between 1,555 and 3,235. Data on per capita income were obtained from the World Bank’s World Development Indica‐ tors (WDI); data on population were taken from the United Nations; data on commodity consumption were taken from the BP Statistical Review (energy) and the World Bureau of Metal Statistics (metals); world commodity prices were taken from the World Bank’s Commodity Price Data and converted into real terms by using country‐specific GDP de‐ flators. Exchange rates were taken from both the WDI and the St. Louis Federal Reserve Bank’s database. Appendix B gives a detailed description of data, sources, and summary statistics.

# **4. Results**

This section presents results from the ARDL model, which include long‐run parameter estimates, adjustment coefficients, Hausman test statistics, and log‐likelihood values (short‐run parameter estimates are not reported). The BIC suggests an optimal lag of one in all specifications; hence the use of ARDL (1,1,1,1). In most cases the Hausman test sug‐ gests that the pool mean group specification is a more suitable choice than the mean group approach, as the _p‐_ values exceed the 5 percent level in nearly all model specifica‐ tions.

Results for commodity‐specific demand equations are reported in Table 1. Of the nine equations, only four (oil, aluminum, copper, and zinc) yielded parameter estimates for the two income and price variables that were both significantly different from zero at the 1 percent level as well as consistent with _a priori_ expectations—positive y� and nega‐ tive y�� and p�. Income elasticities, which were calculated at the median income of our sample according to (4), differ considerably among the four commodities, from a low of 0.3 for zinc to a high of 0.8 for aluminum. Indeed, the estimates exhibited a high degree of heterogeneity across commodities, a finding that is consistent with the literature as noted earlier. In order to examine the source of heterogeneity and also account for short‐ and log‐term substitutability, the next two sections discuss results from two extensions of the model: Aggregate demand equations and the inclusion of cross‐price effects in the commodity‐specific equations.

# _4.1 Aggregate demand equations_

The three energy and six metal commodities were combined into two group aggregates. Thus, the demand equation for each country features energy (instead of, say, oil) and _— 9 —_
# 5.7 Debt and Credit

Information on debt and credit is important for a full picture of an individual’s net worth and hence, their financial well being. In addition, knowing which individuals obtain credit allows for a gendered analysis of the credit market and who is able to access credit.

Since loans are typically made to individuals, rather than to households, it is relatively easy to collect disaggregated information on credit, and many surveys ask the minimal questions. In the Guatemala questionnaire, the respondent is asked whether any loans were received, paid off or are being paid off by any member of the household. Space is provided for the four most important loans and the ID of the member who received, paid off or is paying off the loan. The individuals are asked whether any guarantee was made (i.e. collateral) and what documents were signed. An additional set of questions is asked about whether any household member bought food or other items on credit, and the ID number of the individual household member is registered. These questions allow some gender analysis of credit markets. Many questionnaires do not ask about food and goods purchased on credit, and it would be interesting to see if gender patterns exist in this type of credit.

The Vietnam questionnaire asks both about borrowing and lending. It asks the ID of the household member who contracted or held any loan during the past 12 months. For unpaid loans, it asks the amount that is needed to repay the loan today. Thus, it provides individual-level information on debt. In addition, it asks about whether assets were provided as collateral and what type of assets, but does not ask whose assets were provided.

Information on agricultural credit is also collected in the Vietnam survey. For each type of input, seeds/seedlings, fertilizer, and insecticides/herbicides, the respondent is asked whether the input was received on credit and from whom the credit was obtained. An additional question could ask which household member received the credit.

The Ghana questionnaire currently asks about borrowing at the individual level. If anyone in the household has any outstanding loans, the questionnaire asks about which household member obtained the loan. However, no information is available on the outstanding balance.

None of the surveys ask whether anyone in the household has loaned out any money. The Vietnam survey solicits information on lending, but does not ask which household member provided the loan or the current amount that is owed to the respondent. (It asks about the amount that was lent, the purpose of the loan, the interest rate and collateral.) We recommend adding a question about the amount of the loan that is outstanding and which household member provided the loan.

39
# **4 Could an Increase in Green Trade Affect Workers?**

There are multiple mechanisms for why green goods trade could affect labor markets differently. Sector-specific skills needs for the production process involving green goods are not evenly distributed across genders and skill groups. Green industries often require specialized skills and knowledge related to sustainability and environmental technologies. The availability of these skills and the preferences of female workers towards these sectors can influence the share of female workers within green industries. If women possess the necessary skills and are more inclined towards environmentally focused sectors, the share of female workers in these industries may be relatively higher. For example, a study by International Renewable Energy Agency shows that renewable energy employs about 32% women, compared to 22% in the energy sector overall (IRENA, 2019).<sup>2</sup> Moreover, the skill level required in the production of green goods could be higher. The 2022 LinkedIn Global Green Skill Report (LinkedInEconomicGraph, 2022) found that in 2021 there were only 62 women for every 100 men considered green talent — a number that has been stagnant since 2015.<sup>3</sup> The sorting pattern of workers into industries with a higher share of green goods exports can be influenced by social norms and stereotypes that shape career choices and the representation of different genders and skill levels. For instance, the perception of green industries as environmentally conscious and socially responsible may attract a specific pool of workers, such as skilled college graduates (OCDE, 2014).<sup>4</sup> The expansion of green goods exports has the potential to increase the demand for labor across all types of industries, and it can be particularly advantageous for previously underrepresented groups. As the demand for labor in the green goods sector grows, the likelihood of employment opportunities for these underrepresented groups also increases like any other

> 2https://www.irena.org/publications/2019/Jan/Renewable-Energy-A-Gender-Perspective

> 3https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/global-green-skillsreport/global-green-skills-report-pdf/li-green-economy-report-2022.pdf

> 4https://www.oecd-ilibrary.org/sites/302d43f1-en/index.html?itemId=/content/component/302d43f1-en

23
- Keppo, I., Butnar, I., Bauer, N., Caspani, M., Edelenbosch, O., Emmerling, J., . . . Wagner, F. (2021). Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models. _Environmental Research Letters, 5_ (16), 053006.

- Lipsey, R., & Lancaster, K. (1956). The general theory of second best. _The Review of Economic Studies, 24_ (1), 11-32.

NGFS. (2021). Annual report.

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OECD. (2017). _Investing in Climate, Investing in Growth, A synthesis._ Paris, France: OECD.

- Pindyck, R. (2013, September). Climate Change Policy: What Do the Models Tell Us? _Journal of Economic Literature, 51_ (3), 860-72.

- Stern, N., Stiglitz, J., & Taylor, C. (2022). he economics of immense risk, urgent action and radical change: towards new approaches to the economics of climate change. _ournal of Economic Methodology, 29:3_ , 181-216.

- The New Climate Economy. (2018). _UNLOCKING THE INCLUSIVE GROWTH STORY OF THE 21ST CENTURY: ACCELERATING CLIMATE ACTION._ Washington, D.C.: The New Climate Economy.

- van der Mensbrugghe, D. (2020). The Mitigation, Adapation and New Technologies Applied General Equilibrium (MANAGE) Model, Version 2.0g. _GTAP Technical Paper, TP/20/xx. Center for Global Trade Analysis, Purdue University West Lafayette, IN_ .

- van der Ploeg, F., & Rezai, A. (2020). The risk of policy tipping and stranded carbon assets. _Journal of Environmental Economics and Management, 100_ (C).

25
The design of the mass media strategy focused on national and local channels of communication and included conventional media outlets such as local radio and TV, as well as billboards and other non-traditional and popular channels as well. Local DCC and IPC activities were rolled out in overlapping districts of the mass media communication campaigns and consisted of localized entertainment education and other entertaining events in roads, market places and schools, as well as visits to households, health centers and women’s groups. DCC & IPC activities were implemented in localities in urban and rural areas, randomly assigned among locality clusters.

The impact evaluation surveys conducted at baseline and follow up showed that recall of exposure to program elements is the same in treatment and control clusters, perhaps because of contamination between groups induced by the national mass-media hand washing campaign. Nevertheless, the rich data collected in the survey can be used to explore certain drivers and determinants of cognitive and socio-emotional development of children which, combined with DCC and IPC campaign program’s components, can shed light into those mechanisms that can make hygiene and behavioral change campaigns more effective. Thus, these linkages can contribute to better understanding of the conditions within households through which sanitation and hygiene information campaigns help induce changes in these important long-term indicators of early childhood development.

# **<u>Program Evaluation Design</u>**

In order to establish a causal linkage between handwashing and hygiene behaviors (treatment) and socio-emotional development (outcome), a counterfactual is required. That is, a group of comparison is needed to analyze what would happen to the target group in the absence of the intervention. A randomized experimental evaluation reduces the possibility that observed changes in outcomes of the intervention group are due to endogenous factors. This process ensures a robust counterfactual measure of the causal covered treatment areas. Therefore, the definition of the treatment of information campaigns refers to the intensity of those campaigns independently of the implementing agency that provided such information in treatment areas.

7
Olsen, Mancur (1971). _The Logic of Collective Action: Public Goods and the Theory of Groups._ Cambridge: Harvard University Press.

Organization for Economic Cooperation and Development (2004), “OECD Principles of Corporate Governance.” PAHO (Pan American Health Organization) (2008). “The Essential Public Health Functions as a Strategy for Improving Overall Health Systems Performance: Trends and Challenges Since the Public Health in the Americas Initiative 2000-2007.” Working document.

PATHS (2008), _Celebrating Success. PATHS in Nigeria (2002-2008). Final Programme_ - _Report_ , Partnership for Transforming Health Systems. http://www.lath.com/Our <u>Projects/12</u> Reid, Gary J. (2009), “Actionable Governance Indicators – Concepts and Measurement,” AGI Initiative, Washington, DC: World Bank. Mimeo.

Reinikka, Ritva and Jakob Svensson (2006), “Using Micro-Surveys to Measure and Explain Corruption,” _World Development,_ 34(2): 359–370. February.

Roberts, Marc J., William Hsiao, Peter Berman, and Michael R. Reich (2004). _Getting Health Reform Right: A Guide to Improving Performance and Equity_ . New York: Oxford University Press.

Saltman, Richard and Odile Ferrousier-Davis (2000). “The concept of stewardship in health policy,” _Bulletin of the World Health Organization_ , 78(6):732-739.

Savedoff, William D. and Pablo Gottret, eds. (2008). _Governing Mandatory Health Insurance: Learning from Experience_ . Washington, DC: World Bank.

Savedoff, William D. ed. (1998). _Organization Matters: Agency Problems in Health and Education in Latin America_ . Washington, DC: Inter-American Development Bank.

Solon, Orville, Kimberley Woo, Stella A. Quimbo, Riti Shimkhada, Jhiedon Florentino, and John W. Peabody (2009). “A Novel Method for Measuring Health Care System Performance: Experience from QUIDS in the Philippines,” _Health Policy and Planning_ 24:1-8.

Spiller, Pablo S. (1990). “Politicians, Interest Groups and Regulators: A Multiple Principals Agency Theory of Regulation, (or Let Them Be Bribed),” _Journal of Law and Economics_ , 33:65-101.

Spiller, Pablo S. and Santiago Urbiztondo (1994), “Political Appointees vs. Career Civil Servants: A Multiple-Principals Theory of Political Institutions,” _European Journal of Political Economy_ .

USP DQI (U.S. Pharmacopeia Drug Quality and Information Program) (2009), “Matrix of Drug Quality Reports Affecting USAID-assisted Countries,” Washington, DC: U.S. Agency for International Development and United States Pharmacoepia. <u>http://www.usp.org/pdf/EN/dqi/ghcDrugQualityMatrix.pdf</u> ______________________________________________________________________________________

-41-
|occ_father<br>p < 0.001<br>≤4<br>>4<br>father_edu<br>p < 0.001<br>{Medium, High}<br>{Unknown, None, Low}<br>supervisory_mother<br>p < 0.001<br>Not working<br>{No, Yes}<br>occ_father<br>p < 0.001<br>≤2<br>>2<br>n = 725<br>y = 29574<br>5<br>n = 1097<br>y = 26350<br>6<br>father_birth<br>p = 0.007<br>Country of residence<br>EU−27<br>n = 1952<br>y = 25718<br>8<br>n = 271<br>y = 22808<br>9<br>n = 708<br>y = 21390<br>10<br>mother_edu<br>p < 0.001<br>{Low, Medium, High}<br>{Unknown, None}<br>father_edu<br>p < 0.001<br>High<br>{Unknown, None, Low, Medium}<br>birth_area<br>p = 0.009<br>Native<br>Outside EU<br>n = 593<br>y = 26744<br>14<br>n = 48<br>y = 20041<br>15<br>tenancy<br>p < 0.001<br>Owned<br>Not owned<br>n_w_adult<br>p < 0.001<br>≤3<br>>3<br>father_edu<br>p = 0.003<br>{None, Low, Medium}<br>Unknown<br>n= 2869<br>y = 23265<br>19<br>n_w_adult<br>p = 0.004<br>≤1<br>>1<br>n = 87<br>y = 22615<br>21<br>n = 92<br>y = 17379<br>22<br>n = 126<br>y = 18501<br>23<br>n_w_adult<br>p = 0.007<br>≤3<br>>3<br>n= 2739<br>y = 21590<br>25<br>n = 95<br>y = 17176<br>26<br>tenancy<br>p = 0.004<br>Owned<br>Not owned<br>n = 511<br>y = 19784<br>28<br>n = 770<br>y = 17442<br>29<br>Father occ.<br>p<0.001<br>Father edu.<br>p<0.001<br>Mother Superv.<br>p<0.001<br>Father occ.<br>p<0.001<br>Father birth<br>p<0.007<br>Father edu.<br>p<0.001<br>Birth area<br>p<0.009<br>Mother edu.<br>p<0.001<br>House own.<br>p<0.004<br>House own.<br>p<0.001<br>Working adults<br>p<0.001<br>Working adults<br>p<0.007<br>Father edu.<br>p<0.003<br>Working adults<br>p<0.004<br>{Unknown, None,Low}<br>{Medium, High}<br>{Native}<br>{Outside<br>Europe}<br>{Native}<br>{EU-27}<br>{None, Low,<br>Medium}<br>{Unknown}<br>{Owned}<br>{Not Owned}<br>5.7%<br>8.6%<br>15.4%<br>2.1%<br>5.6%<br>4.7%<br>0.4%<br>22.6%<br>0.7%<br>0.7%<br>1%<br>21.6%<br>0.7%<br>6.1%<br>4%<br>{Service, Clerical, Technician, Professional, Manager}<br>{Professional,<br>Manager}<br>{Not working, Elementary, Plant operator, Craft/Trades,Agriculture}<br>{Service, Clerical,<br>Technician}<br>_Note_: Opportunity tree for Germany. White rectangular boxes indicate terminal nodes. The frst number inside the rectangular boxes indicates the<br>share of the population belonging to this group, while the second number indicates the predicted income. Occupation refers to ISCO-08 one digit codes.<br>All variables describing household characteristics refer to the period in which the respondent was about 14 years old. See Table 1 for details.|
|---|

19
Table A8: Relationship between management and energy intensity (quantity) (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||High EI|Moderate EI|Low EI|High KI|Low KI|
|General|0.358|0.416<sup>_∗∗_</sup>|0.975<sup>_∗∗∗_</sup>|0.296<sup>_∗_</sup>|-0.213|
||(0.347)|(0.169)|(0.286)|(0.158)|(0.274)|
|No. Employees|0.471<sup>_∗∗_</sup>|0.314<sup>_∗∗∗_</sup>|0.077|0.428<sup>_∗∗∗_</sup>|0.561<sup>_∗∗∗_</sup>|
||(0.213)|(0.117)|(0.178)|(0.117)|(0.178)|
|Capital Stock|0.362<sup>_∗∗∗_</sup>|0.159<sup>_∗∗∗_</sup>|0.250<sup>_∗∗∗_</sup>|||
||(0.082)|(0.044)|(0.081)|||
|Log(Sales)|-0.700<sup>_∗∗∗_</sup>|-0.630<sup>_∗∗∗_</sup>|-0.482<sup>_∗∗∗_</sup>|-0.631<sup>_∗∗∗_</sup>|-0.563<sup>_∗∗∗_</sup>|
||(0.142)|(0.075)|(0.128)|(0.083)|(0.118)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|381|1016|410|712|441|
|Adjusted _R_<sup>2</sup>|0.371|0.320|0.359|0.332|0.293|
|df<br>~~m~~|42|46|45|42|47|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 50
where ln yij is the log of annual earnings for immigrant i from source country j: Sij is a series of dummy variables for di¤erent degrees of schooling. Uij is measured as the di¤erence between the census year and the year at the midpoint of the year of immigration bracket.<sup>6</sup> As can be seen from equation (4), the coe¢ cient on Uij consists of the di¤erence between the return to post-migration and pre-migration experience. Potential experience Eij is de...ned as age minus years of education minus six.

The second step of the two-step procedure consists of regressing the estimated returns to experience on a measure of openness, that is Xj is a set of observed country-speci...c characteristics, such as GDP per capita, investment per worker, average years of education in the home country and governmental quality. These variables will be discussed in detail when presenting the results. Unobserved country-speci...c characteristics are captured by uj.

Under the assumption that openness a¤ects only the slope of the earningsexperience pro...le but not its curvature, the e¢ ciency of the estimator of �H could be improved by estimating the e¤ect of openness in one step. The two-step procedure, however, has several important advantages. First, it provides a straight-forward interpretation of the results by allowing to estimate the e¤ect of openness on returns to home country experience and not only on the slope of the earnings-experience pro...le. Second, being computationally less burdensome it facilitates the estimation of extremely ‡exible forms of the ...rst stage regression. In one of the speci...cations presented below returns to experience are, for example, allowed to vary with level of education. Third, it allows to illustrate the diversity in returns to experience across countries.

A special case of (2) is to assume that with the exception of �1j the coe¢ cients on the Mincerian equation do not vary across countries. This implies that openness can only a¤ect the slope of the experience-earnings pro...le but not its curvature.

> 6 Data on US immigrants is taken from the 1980 and 1990 US Censuses.

7
**Table 1:  Fraction of aggregate exports accounted for by sampled firms**

|Country|2002|2003|2004|2005|2006|2007|2009|2010 2011 2013|2014|2015 2016|2017 2018|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Angola|||||0.2|||0.1||||
|Benin|||51||||11|||15||
|Botswana|||||6|||2||||
|Burkina Faso|||||10||6|||||
|Burundi|||||3||||6|||
|Cameroon|||||28||21|||9||
|Cape Verde|||||0||45|||||
|CAR||||||||5||||
|Chad|||||||9||||19|
|Congo|||||||6|||||
|Côte d'Ivoire|||||||23|||18||
|DRC|||||9|||0<br>6||||
|Djibouti||||||||18||||
|Eritrea|3||||||5|||||
|Ethiopia|15||||?|||9||14||
|Gabon|||||||7|||||
|Gambia|||||5|||||||
|Ghana||||||7||10||||
|Guinea|||||15|||||1||
|Guinea-Bissau|||||1|||||||
|Kenya||22||||19||50||||
|Lesotho||100|||||43|||32||
|Liberia|||||||0.1||||4|
|Madagascar||||37|||14|36||||
|Malawi||||29|||12||24|||
|Mali||7||||11||2||9||
|Mauritania|||||26||||18|||
|Mauritius||||43|||70|||||
|Mozambique||||||2||||||
|Namibia|||||41||||30|||
|Niger||||84|||6||||5|
|Nigeria||||||4||?|4|||
|Rwanda|||||15|||6||||
|Senegal||39||||45|||37|||
|Sierra Leone|||||||0.3||||4|
|South Africa||17||||13||||||
|South Sudan|||||||||1|||
|Sudan|||||||||0.1|||
|Swaziland|||||33|||||20||
|Tanzania||11|||13|||3||||
|Togo|||||||37|||65||
|Uganda||24|||23|||34||||
|Zambia|51|||||12||24||||
|Zimbabwe||||||||9||8||

_Note:_ The statistics show the ratio of total exports across all firms sampled in a survey (calculated from the firmlevel World Bank Enterprise Surveys), relative to the aggregate national exports for the same year (calculated from the UN Comtrade data). Average exchange rates for the year from IMF are used to convert local currencies.

7
26

# **VIII.   APPENDIX**

# **Data Sources**

|Merchandise/Manufacturing exports|UN Comtrade|
|---|---|
|Total (goods plus services) exports|Duval_et al._(2014) based on OECD-WTO<br>TiVAdatabase|
|Value-added exports|Duval_et al._(2014) based on OECD-WTO<br>TiVAdatabase|
|Participation indices for manufacturing|Duval_et al._(2014) based on OECD-WTO<br>TiVAdatabase|
|Position indices for manufacturing|Duval_et al._(2014) based on OECD-WTO<br>TiVAdatabase|
|GDP in constant US dollar terms|World Development Indicators (WDI),<br>WorldBank|
|Real GDP growth of Foreign countries<br>weighted byExport Shares|IMF|
|Consumer PriceIndices|IMF, Nationalsources|
|Real effective exchange rate (REER)|IMF Information Notice System (INS), JP<br>Morgan, Nationalsources|
|Otherexchangerates (e.g. versus USD)|IMF|
|REER in Goods Value Added|Bayoumi_et al._(2013)|
|REER in Tasks ValueAdded|Bayoumi_et al. _(2013)|

# **List of Countries**

Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Korea, Republic of, Latvia, Luxembourg, Malaysia, Mexico, Netherlands, New Zealand, Norway, Philippines, Poland, Portugal, Romania, Russian Federation, Singapore, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, United Kingdom, United States.