rafmacalaba/datause-encoder-data huggingface dataset:
BLS. (2013) Green Goods and Services survey: results and collection. U.S. Bureau of Labor Statistics. Monthly Labor Review. September 2013.

BLS. (2013) The Green Goods and Services Occupational survey: initial results. Warren, Zack. U.S. Bureau of Labor Statistics. Monthly Labor Review. January 2013.

Canada Survey of Environmental Goods and Services, 2012. Environment Accounts and Statistics Division. Statistics Canada Environmental Goods and Services Sector (EGSS): list of products and activities. Excel. Version as of 6/1/2016 Eurostat (2009). The environmental goods and services sector. doi 10.2785/31117 Federal Planning Bureau (2014). The Environmental Goods and Services Sector in Belgium 2010-2011. Report for Eurostat OECD, Eurostat (1999). The environmental goods and services industry: manual for data collection and analysis.

# **Process approach**

BLS (2012). Green Technologies and Practices Questionnaire. O.M.B. No. 1220−0184 BLS. (2011) Counting Green Jobs: Developing the Green Technologies and Practices (GTP) Survey. Conference paper: Stang, S. and Jones, C. Section on Survey Research Methods : JSM 2011 BLS. (2012) Green technologies and practices: August 2011. U.S. Bureau of Labor Statistics. News release: June 29, 2012.

Oyunbileg, D., Stoevska, V. (2017) Employment in the environmental sector and green jobs in Mongolia. Pilot Study. International Labour Organization (ILO) Stoevska, V., Elezi, P., and Muraku, E. (2014) Report on the pilot project towards developing statistical tools for measuring employment in the environmental sector and generating statistics on green jobs. Albania. Intenrational Labour Organization (ILO)

# **Task approach**

Dierdorff, E.; Norton, J.; Drewes, D. W.; Kroustalis, C.; Rivkin, D.; and Lewis, P. (2009) Greening of the World of Work: Implications for O*NET® -SOC and New and Emerging Occupations. The National Center for O*NET Development.

Dierdorff, E.; Norton, J.; Gregory, C.; Rivkin, D.; and Lewis, P. (2011) Greening of the World of Work: Revisiting Occupational Consequences. The National Center for O*NET Development.

Excel shared by Janser with green jobs terms Janser, Markus (2018) The greening of jobs in Germany: First evidence from a text mining based index and employment register data, IAB-Discussion Paper, No. 14/2018, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg Janser, Markus. (2018b) The greening of jobs in Germany: Online Appendix ‘text Mining & Descriptives’ Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg 41
-4.

# **_C. Mahodoeg_**

1. **The** study drew on data sets for 1985 to 1989 in order to permit a comparison of trends before and after the Nairobi conference.

2. The data were examir _Ld_ for characteristics that would identify SM components.

3. Where there were no specific components within a health loan or primary health care program, the respondent was asked to estimate the amount of financing for SM. Information from interviews was supplemented by the official annual reports of bilateral and miultilateral agencies.

4. For purposes of comparison, each financial source was asked to use common definitions and to include all major categories of ODA for the health sector as follows:

- Bilateral financing (regional or country proJects or programs)

- Official contributions to multilateral sources (omitting assessed contributions, for instance. to WHO)

- Official contributions to nongovernmental and private organizations.

_5._ Interviews were held with the following countries and other international organizations' from January tc March 1989: Austria Belgium Denmark DAC/OECD European Community Finland Federal Republic of Germanv (2) France Italv (2) Japan Netherlands OPEC Fund for International D<velopment PAHO Sweden Switzerland United Kingdom United States UNDP For governmental organizations, the responsible international development aid authority was interviewed. The bracketed number indicates the number of official aid-related agencies. Development agencies in Australia, Canada, Japan, Norway and New Zealand were canvased by mail cr telephone. As of April 22 only two organizations had responded.
( 1997)). Similarly, Bartel and Sicherman (1998) provide empirical evidence that the probability of receiving training increases monotonically with education. Psacharopolous and Layard (1979) show that experience pro...les are steeper for individuals with higher educational attainment. Using US panel data, Altonji and Pierret (1997) reach a similar conclusion. Based on data of 11 European countries, Brunello and Comi (2000) ...nd that employees with tertiary education have steeper experience pro...les then employees with upper secondary or compulsory education.

The same applies to US immigrants. Returns to home country experience of US immigrants are higher for immigrants with more years of education. Table 11 shows that the slope of the home country experience pro...le is steeper for US immigrants with at least a high school degree as compared to immigrants with a lower level of schooling. At the same time the coe¢ cient on the square of experience is higher for better schooled immigrants indicating a faster decrease in returns to experience. The largest gap between skilled and less skilled US immigrants is reached after approximately ten years of labor market experience. Controlling for ...xed e¤ects decreases the slope of the log earnings pro...le slightly. It leaves the fact unchanged that the log earnings pro...le is steeper for younger workers with higher educational attainment.

If returns to home country experience vary with education then the OLS estimator of the homogenous US return to home country experience may be written as a variance weighted average of the returns to home country experience of the di¤erent educational categories. As US immigrants are on average better educated then home country residents, heterogeneity in returns to experience implies that the average US return to home country experience estimated on a sample of US immigrants exceeds the average US return to home country experience in the home country population.<sup>31</sup> If education a¤ects returns to experience and if educational di¤erences between home country and immigrants population are related to source country characteristics, then the estimated e¤ect of openness on returns to experience will di¤er from the e¤ect of openness on US returns to home country experience of the average source country resident. Consider, for ex-

> 31 The concavity of the log earnings-experience pro...les implies that the return to experience decreases with years of experience. There exists empirical evidence that this decrease is more pronounced for workers with a higher level of schooling (see for example, Brunello and Comi (2000)). Workers with a long labor market experience and a high level of schooling may therefore have lower returns to experience then comparable less schooled workers. Consequently, the claim that returns to experience are higher for more schooled workers refers in what follows to workers with few years of labor market experience.

23
expected because most of the cross-listing firms from these nations do not have a concentrated shareholder and Table VII reveals very few changes from a widely held ownership structure to concentrated ownership on cross-listing. The extent to which a government protects private property and enforces laws to protect private property also seems to be determining factor of control changes. Cross-listing firms are less likely to sell their control blocks to a new controlling owner on cross-listing if their home market is protective of property rights. So control changes following ADR issuance are more common in countries that have poor property rights protection. I also find the interesting result that a unit increase in capital control restrictions decreases the probability of a control change by almost 8.8%. Therefore probability of a control change on issuing an ADR is decreased if the home country’s economy is not very open and is subject to severe capital control restrictions. Firms from such economies are not very attractive to investors even after they issue an ADR.

# _4.4. Emergence of a new foreign controlling shareholder_

This section investigates if control changes result in a foreign controlling shareholder. Multinomial logit is used to predict whether a company is more likely to experience a control change resulting in a new domestic owner, or in a foreign owner, or becomes widely held or does not experience a control change at all. The regressors are lagged as before and standard errors are adjusted to allow for clustering.

Table X reports the relative risk ratios when control change results in a new domestic owner, a new foreign owner, or in the firm becoming widely held. The base line case is when there is no control change. The set of independent variables used are the same firm and countrylevel characteristics used in Table IX with one change. I substitute capital control regulations with the level of foreign direct investment, FDI, taken from the IMF’s Balance of Payment Statistics and YEARS_LIBER which is the number of years since the official date of 30
<!-- Start of picture text --> 100<br>90<br>80<br>70<br>= 60<br>xs<br>< 50<br>8<br>oO 40<br>a<br>30<br>20<br>10<br>0<br>95 96 97 98 99 O00 O01 O02 O38 O04 O5<br>—*— SHO .<br>—_— SSO 10 Years period [1995-2005]<br>—e— SMO<br><!-- End of picture text -->
Africa a great deal but it does increase the magnitude of the losses in districts that are damaged. Reducing precipitation (Figure 8c) has a much larger harmful effect on the wetter parts of Africa. The central humid band of the continent bears the brunt of the damage in this scenario. Doubling the precipitation loss to 14% (Figure 8d) increases the magnitude of the losses and their extent near the humid zone and equatorial Africa.

# **_AOGCM scenarios_**

We also examined a set of climate change scenarios predicted by Atmospheric-Oceanic Global Circulation Models (AOGCMs). We relied on three scenarios consistent with the range of outcomes in the most recent IPCC (Intergovernmental Panel on Climate Change) report (Houghton et al. 2001). Specifically, we used the A1 scenarios from the following models: CCC (Canadian Climate Centre) (Boer et al. 2000), CCSR (Centre for Climate System Research) (Emori et al. 1999), and PCM (Parallel Climate Model) (Washington et al. 2000). In each of these scenarios, climate changes at the grid cell level were summed to predict climate changes by country. We then examined the consequences of these country level climate change scenarios for 2020, 2060, and 2100.

For each climate scenario, we added the predicted change in temperature from the climate model to the baseline temperature in each district. We also multiplied the predicted percentage change in precipitation from the climate models by the baseline precipitation in each district or province. This gave us a new climate for every district in Africa. Table 9 presents the mean temperature and rainfall predicted by the three models for the years 2020, 2060 and 2100. In Africa in 2100, PCM predicts a 2 ° C increase, CCSR a 4 ° C increase and CCC a 6 ° C increase in temperature. Rainfall predictions are noisier: PCM predicts a 10% increase in rainfall in Africa, CCC a 10% decrease, and CCSR a 30% decrease. Even though the mean rainfall in Africa is predicted to increase/decrease depending on the scenario, there is substantial variation in rainfall across countries.

Examining the path of climate change over time reveals that temperatures are predicted to increase steadily until 2100 for all three models. Precipitation predictions, however, vary across time for Africa: CCC predicts a declining trend; CCSR predicts an initial decrease, and then increase, and decrease again; PCM predicts an initial increase, and then decrease, and increase again. However, it should be noted that because the AOGCMs make geographically detailed predictions the predicted changes for individual countries vary.

17
# **1. Introduction**

**Despite recent economic growth, Burundi faces serious economic challenges.** Between 2001 and 2012, the country's real GDP grew on average by 3.5 percent a year (Figure 1). Although this was a significant improvement over the previous decade of recurrent civil conflict, the recovery in growth was much less pronounced than in other post-conflict countries (Figure 2). Given Burundi’s rapid population growth—the fifth highest in the region at around 3 percent per year—growth in per capita GDP averaged only around 1 percent a year—not enough to make a significant dent in poverty among its 10 million people.<sup>1</sup>

**Figure 1. GDP and per capita GDP growth, 1980–2012**

<!-- Start of picture text --> % Per capita real GDP growth<br>15<br>Real GDP growth<br>10<br>5<br>0<br>-5<br>-10<br><!-- End of picture text --> _Source_ : World Bank World Development Indicators and IMF World Economic Outlook

> 1 Based on World Bank World Development Indicators in 2012.

2
|(3.0 to 7.6)|(2.8 to 5.8)|(6.1 to 14.1)|(10.9 to 17.7)|(2.3 to 8.9)|(3.7 to 6.5)|
|---|---|---|---|---|---|
|_N_<br>483|1,261|366|1,077|477|2,021|

Note: Most trusted information sources by vaccine acceptance. All values in percent. 95% confidence intervals in parentheses.

|**Ambassador**|**Table A12**<br>Burkina Faso|**: Vaccine amba**<br>Kenya|**ssadors**<br>Malawi|Nigeria|Tanzania|
|---|---|---|---|---|---|
|At least some ambassador|42.1|44.0|36.1|58.2|77.3|
||(35.8 to 48.3)|(25.2 to 62.9)|(27.2 to 45.0)|(52.3 to 64.1)|(73.1 to 81.4)|
|Family|28.3|17.6|29.9|42.7|12.1|
||(21.5 to 35.1)|(8.2 to 27.1)|(21.2 to 38.6)|(36.9 to 48.6)|(8.9 to 15.4)|
|Religious leader|23.4|11.2|25.4|44.0|20.4|
||(17.1 to 29.7)|(3.9 to 18.6)|(17.6 to 33.2)|(38.0 to 50.0)|(16.2 to 24.6)|
|Medical professional|34.5|34.8|26.6|40.1|31.9|
||(28.1 to 40.9)|(15.3 to 54.3)|(19.1 to 34.2)|(34.5 to 45.7)|(27.2 to 36.5)|
|Community leader|22.4|7.3|19.3|35.5|9.5|
||(16.1 to 28.7)|(3.2 to 11.4)|(12.4 to 26.2)|(29.9 to 41.1)|(6.4 to 12.6)|
|Traditional healer|19.2|5.8|11.0|24.4|2.8|
||(13.0 to 25.3)|(2.2 to 9.4)|(5.9 to 16.2)|(19.8 to 29.0)|(1.2 to 4.3)|
|Scientist and epidemiologists|21.8|23.1|16.2|32.3|13.4|
||(15.5 to 28.1)|(4.0 to 42.1)|(10.1 to 22.3)|(27.7 to 37.0)|(10.1 to 16.8)|
|Celebrities and influencers|16.9|5.8|13.5|22.6|4.2|
||(10.8 to 23.0)|(2.2 to 9.4)|(8.0 to 19.0)|(18.4 to 26.8)|(2.2 to 6.2)|
|Other ambassador|2.3|5.9|0.0|1.1|37.2|
||(0.5 to 4.2)|(1.9 to 9.8)|(0.0 to 0.1)|(0.1 to 2.2)|(32.3 to 42.2)|
|_N_|526|313|366|609|769|

Note: People whose recommendation would make the hesitant more likely to get vaccinated. All values in percent. 95% confidence intervals in parentheses.

25
2. **Entry and exit patterns.** This model naturally generates entry and exit by the same firm. Exit is especially likely after the first attempt. This yields a positive correlation between entry and exit. Weaker entrepreneurs (small entrants) are more likely to exit. Weaker firms will enter into exporting with small trials in order to avoid high entry costs if they receive a bad cost draw. As a result of entry and exit, in the first year of a given cohort, there will be more different quality types of firms in the export sector. This implies that the variation with respect to the mean of exporters’ size should decrease with age of the firm. Many of the lower quality exporters will exit, while some that receive a good cost draw will expand.

3. **New products and new markets.** Firms that pioneer new products or new markets tend to be high productivity (large) firms, and have a lower exit rate than later entrants. After a product or market is discovered and discovery costs disappear, there is more entry (herding).

# **III. Empirical Evidence from Transactions Data**

In this section, we examine whether the predictions of the model are consistent with the Peruvian experience in the nontraditional agricultural sector. This is a particularly dynamic sector (Figure 3), which began a period of rapid growth in the mid to late 1990s. The product that mainly explains this surge is asparagus, but there is also considerable growth in the exports of other nontraditional crops (in particular, prepared/preserved artichokes, avocados, paprika, grapes and mangos) in recent years (Figure 4).

20
# **3 Empirical model**

## **3.1 Household model**

We separately model two components of the household energy consumption decision: the choice of which fuel to use for lighting and, conditional on that choice, the quantity of energy consumed.

The household consumption of kerosene or electricity is given by the estimation equation (1): The dependent variable _qit_ is household _i_ ’s consumption of the fuel (either kerosene or electricity) during month _t_ , measured in kWh. Other household characteristics are captured by the vector **_zit_** . These include the number of adults and the number of children in the household, the size of the dwelling, the type and ownership status of the dwelling, the nature of any improvements to the dwelling, and an urban/rural indicator.

The variable _pit_ is the price per kWh of the fuel in the region of household _i_ during month _t_ . For electricity, one challenge is that the regulated tari↵for Umeme is a two-part, increasing block tari↵. Households pay a low subsidized price for the first 15 kWh of consumption each month, then a higher price for each additional unit of consumption. The quantity on the first price tier is much smaller than the consumption of most households in the sample. For simplicity, we do not explicitly model the nonlinearity in the tari↵. Instead, we assume that _pit_ is the regulated price per kWh on the highest consumption tier.

The variable _yit_ is the annual expenditure of household _i_ , as measured at the survey date _t_ .<sup>10</sup> The choice to use expenditure from the household survey, rather than income, reflects the considerable difficulty of measuring self-reported income on a household survey. This challenge is especially relevant for a rural setting in a developing country where few people have a stable salary. We make an implicit assumption that income and expenditure are fixed and do not depend on the choice of fuel source. If access to modern energy services increases household productivity, it is possible that expenditure might change based on the fuel choice. We assume that energy services provide consumption utility and do not enter as inputs into household production, so there is no feedback from fuel choice to household income or expenditure.

> 10In both the consumption and discrete choice models, we deduct the annualized capital cost of the fuel choice from household expenditure _yit_ , using an annual discount rate of 15 percent.

8
# **Table 5. Effect of preschool availability on maternal employment with different comparison groups**

||||**Work p**|**articipation**|||
|---|---|---|---|---|---|---|
|**Eligible Child**||Oldest|||Youngest||
|Comparison Cohort|0-2|7-12|All|0-2|7-12|All|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: Public**|||||||
|DDD|0.063**|-0.022|0.041*|-0.020|0.001|0.035|
||(0.028)|(0.027)|(0.025)|(0.029)|(0.022)|(0.025)|
|DDD-FE|0.075***|0.023|0.075***|-0.037|0.023|0.055**|
||(0.026)|(0.026)|(0.020)|(0.027)|(0.018)|(0.023)|
|**Panel B: Private**|||||||
|DDD|0.004**|-0.002|-0.000|0.006***|-0.004***|-0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|DDD-FE|0.004*|-0.001|0.002|0.004**|0.000|0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.001)|
|Observations|49,067|69,329|226,400|54,661|58,653|226,400|
|Mean|0.445|0.520|0.520|0.548|0.640|0.520|

Note: Sample includes eligible mothers whose first/last child are aged 3-6 (preschool-aged) and comparison mothers whose first/last child are aged as indicated in the column heading. Ages 0-2 are too young for preschools and ages 7-12 are primary school ages. The ‘All’ columns include mothers of children aged 0-2, 7-12, 13+, and non-mothers in the comparison group. Column 1-3 define eligibility pertaining to the oldest child only and column 4-6 to the youngest child only. All columns regress work participation on preschool density, preschool eligibility dummy, and their interaction; only interacted coefficients are reported. All regressions include district and year fixed effects, and an urban residence dummy. Estimation strategy used is indicated at each row. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Number of observations and means of work participation for each column are indicated in the last two rows.

50
# **1 Introduction**

Faced with climate change, countries use both carbon pricing and green bonds to finance their transition to low-carbon economies. This paper studies the economic interaction effects and the optimal combination of those two types of climate policies.

Carbon pricing, in the form of carbon taxes or emissions trading schemes, has been used since the 1990s as an incentive for abating greenhouse gas (GHG) emissions, and since then spread to 46<sup>1</sup> jurisdictions, presently raising 43 billion in revenues.<sup>2</sup> Green bonds (or climate bonds) represent a more recent development in the policy toolkit for financing climate change mitigation, adaptation or conservation of natural capital. First created by the European Investment Bank and the World Bank together with the Swedish SEB in 2007/2008, green bonds are today issued by government agencies, multilateral institutions, and private businesses. Despite their exponential uptake since 2011-12 (see figure 1 in section 2), both instruments are still far too small for containing climate change: less than 5 percent of emissions covered under explicit carbon pricing initiatives is currently priced at a level that would be consistent with the Paris Agreement;<sup>3</sup> and the financing needs for the low-carbon climate-resilient transitions are estimated in trillions per year rather than the cumulative 500 billion<sup>4</sup> issued over the last decade. This paper therefore reviews the roles of both instruments, and their optimal combination in scaling up mitigation ~~a~~ nd adaptation.

The paper is organized as follows. Section 2 reviews theory on the roles and interaction effects of carbon pricing and green bonds. In Section 3, we theoretically model these interaction effects for two lowcarbon transition scenarios using a new numerical solution method, called nonlinear modeling predictive control (NMPC). Section 4 concludes. We find that there are strong synergies from combining green bonds with carbon pricing for sustainability outcomes, investor returns and debt sustainability. These synergies are stronger if carbon is priced through taxes than through emissions trading.

# **2 Combining Carbon Pricing and Green Bonds**

The fundamental purpose of carbon pricing is to make consumers and producers of polluting goods take into account the costs imposed by this pollution on society as a whole. Carbon pricing policies, such as carbon taxes or emissions trading systems (ETS), can also be used in addition to green bonds to achieve both greater environmental effectiveness and lower overall cost of mitigation. Here, we review the efficiency benefits from including such carbon pricing in a joint policy with green bonds.

> 1Counting both countries that have already implemented carbon taxes or emissions trading systems and countries where implementation plans are far advanced, based on (World Bank 2019a). 2Ibid.

> 3World Bank (2019a).

> 4World Bank (2018).

2