rafmacalaba/datause-encoder-data huggingface dataset:
53.6 and 26.3 percent of skilled labor respectively. The majority of capital is employed in the two agricultural sectors, which employ 52.1 percent of the capital stock. The sectors mines and processed food employ 16.2 and 10.8 percent of the capital stock.

The mining, manufacturing, and processed food sectors have high value added shares for capital which accounts for over 80 percent of total value added in each of the sectors (see Table 12). Likewise, there is a high share of capital in value added in the construction sector (61.5 percent).

**Table 10: Structure of consumption, production, and trade (percent) in Niger, 2009**

||Consumption|Production|Imports|Exports|Import<br>share of<br>consumption|Export share<br>ofproduction|
|---|---|---|---|---|---|---|
|Food agriculture|19.9|23.9|10.2|26.2|13.7|16.3|
|Other agriculture|5.8|7.0|4.7|12.2|22.1|25.6|
|Mines|1.4|8.6|0.0|46.7|0.0|80.9|
|Processed food|8.2|9.0|2.0|0.6|6.6|1.0|
|Petroleum|4.1|0.0|15.3|0.0|100.0|0.0|
|Other manufacturing|18.7|3.3|65.1|12.8|93.5|57.3|
|Construction|5.4|6.3|0.0|0.0|0.0|0.0|
|Utilities|2.4|2.2|1.8|0.0|20.1|0.0|
|Services|20.7|24.0|1.0|1.5|1.3|0.9|
|Public services|8.0|9.4|0.0|0.0|0.0|0.0|
|Education/Health|5.4|6.3|0.0|0.0|0.0|0.0|
|Total|100.0|100.0|100.0|100.0|26.9|14.8|

_Source:_ Authors’ calculations using the microeconomic SAM for Niger, 2009.

**Table 11: Allocation of factors and value added by sector (percent) in Niger, 2009**

||Agricultural<br>Labor|Unskilled<br>labor (non-<br>agriculture)|Skilled<br>labor|Capital|Land|Value<br>added|
|---|---|---|---|---|---|---|
|Food agriculture|78.0|0.0|0.0|40.9|78.5|35.0|
|Other agriculture|22.0|0.0|0.0|11.2|21.5|9.6|
|Mines|0.0|2.9|1.2|16.2|0.0|6.1|
|Processed food|0.0|1.8|0.2|10.8|0.0|4.0|
|Other manufacturing|0.0|0.7|0.1|3.4|0.0|1.3|
|Construction|0.0|3.3|0.2|4.9|0.0|2.6|
|Utilities|0.0|0.5|3.9|2.4|0.0|1.3|
|Services|0.0|81.3|11.8|6.0|0.0|27.1|
|Public services|0.0|6.9|56.3|4.1|0.0|9.3|
|Education/Health|0.0|2.6|26.3|0.1|0.0|3.6|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

_Source_ : Authors’ calculations using the microeconomic SAM for Niger, 2009.

As seen in Table 13, consumption patterns vary by agent. Households, particularly rural households, have the highest expenditure shares for food agriculture, followed by services and processed food. Government purchases public services and education/health.

community service (2,8 percent). Given this composition of services, particularly the small financial services component, one does not expect a large share of skilled labor in the services sector.

14
|Post2012 Reform|2013||4.0|4.0|8.00||1.00||9.00||
|---|---|---|---|---|---|---|---|---|---|---|
|Reform|008-2012||4.0|4.0|8.00||1.00||9.00||
|7|2||||||||||
|Post200|2007||3.875|4.0|7.875||1.00||8.875||
|orm|2006||3.875|4.00|7.875||1.00||8.875||
|003 Ref|2005||3.75|4.00|7.75||1.00||8.75||
|Post2|2004||3.625|4.00|7.625||1.00||8.625||
|form|1996-2003||3.375|4.00|7.375||1.00||8.375||
|ost93 Re|1995||3.125|4.00|7.125||1.00||8.125||
|P|1994||2.875|4.00|6.875||1.00||7.875||
|orm|1993||2.17|2.33|4.50||1.00||5.50||
|90 Ref|1992||2.17|2.33|4.50||1.00||5.50||
|Post|1991||2.17|2.33|4.50||0.00||4.50||
|1985-1990|1985_−_1990||2.17|2.33|4.50||0.00||4.50||
|1984|1984||1.50|2.33|3.83||||3.83||
|||**SOCIAL SECURITY**|Pension|Health|Subtotal Social Security|**OTHERS**|Solidarity Fund|**TOTAL**|**EMPLOYEE NWLC**|**(AS percent OF WAGE)**|

42
**TABLE II. Results of calibration scheme for global parameters and sectoral characteristics.**

|Global parameters:|_r_ = 2 % yr<sup>_−_1</sup>|_B_ = 625 GtCO2|||
|---|---|---|---|---|
|Sector| ̄_a_<br>�<br>GtCO2 yr<sup>_−_1�</sup>|_δ_ [% yr<sup>_−_1</sup>]| ̄_c_|�<br>$ tCO<sup>_−_1</sup><br>2<br>GtCO2 yr<sup>_−_3</sup><br>�|
|Waste|0.82|3.3||12954|
|Industry|5.47|4||5566|
|Forestry|8.25|0.8||2259|
|Agriculture|4.07|5||7567|
|Transport|3.74|6.7||1942|
|Energy|11.99|2.5||895|
|Buildings|3.2|1.7||4122|

370 theoretically optimal policy later on. 371 We now design a set of numerical experiments to quantify the effects of Proposition IV.1 and Theo372 rem IV.5. Using (II.1) and (II.3), we simulate each policy suite outlined in Table I. These experiments 373 allow us to quantify the influence of each policy response on: (i) the decarbonization dates of all sectors 374 and the carbon prices they face; (ii) how changes in the decarbonization date and carbon price affects 375 the temporal distribution of abatement investment across each sector of the economy; and (iii) the 376 degree to which changes in the investment path impacts overall sectoral and aggregate policy costs.

377

# **V Numerical experiments**

## 378 **V.1 Design**

379 We follow Vogt-Schilb et al. (2018) and Bauer et al. (2024a) to design a set of numerical experiments 380 that evaluate the economic costs of PECs in each political economy policy suite in Table I. We con381 sider an economy with seven economic sectors ( _i.e._ , _|I|_ = 7), following the framework outlined in the 382 Intergovernmental Panel on Climate Change’s (IPCC) Sixth Assessment Report (AR6) (Intergovern383 mental Panel on Climate Change, 2022b). The economic sectors we consider are: waste management, 384 buildings, forestry, industry, energy, agriculture, and transportation.<sup>14</sup> In all of the results we show 385 below, the planner aims to decarbonize the world economy such that global average temperatures do

> 14For more information on what specific emissions sources and abatement technologies are included in each of these sectors, we refer readers to Intergovernmental Panel on Climate Change (2022b); see the Technical Summary, Chapter TS5, Section 9, “Mitigation Potentials Across Sectors and Systems”, p. 123-125 and figures therein.

18
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# **Appendix B. O*NET GEP Steps for classifying green occupations**

<!-- Start of picture text --> 1<br>Locate and review existing literature. About 60 reports<br>Identify and compile job titles. Accumulating a list of all job titles that were<br>2<br>mentioned in the reports<br>Review and sort collected job titles. Eliminate too molecular or too molar job titles<br>3<br>(2%). Kept 467 job titles<br>4<br>Cluster job titles to identify occupations. Similar job titles were clustered<br>5 Identify occupational sectors. A 12 schema was used to group job titles<br>6<br>Determine overlap with ONET. Determine if the occupation was existent in ONET SOC or<br>was a new occupation, and generate the 3 grouping: Green increased demand, green<br>7<br>Identify new and emerging. Group job titles into occupations<br>8<br>Research potential N&E occupations<br>9<br>Build consolidated evidence for final N&E determination<br>10<br>Compile and report N&E skills<br><!-- End of picture text --> _Source_ : Authors based on Dierdorff et al. (2009), Dierdorff et al. (2011) and O*NET (2010).

# **Appendix C. List of consulted material for the creation of the green dictionary**

## **Output approach**

BLS (2010). Green Goods and Services Survey Questionnaire. O.M.B. No. 1220−0181 BLS (2010). Industries where green goods and services are classified. Excel file with list of green industries at NAICS level. https://www.bls.gov/ggs/ BLS (n.d.) Survey Methods and Reliability Statement for Occupational Employment and Wages in Green Goods and Services . https://www.bls.gov/ggsocc/survey_methods.pdf BLS. (2012) Occupational employment and wages in green goods and services: November 2011. U.S. Bureau of Labor Statistics. News release: September 28, 2012 40
Quality considerations aside, the data indicate that the proportion of households not paying for services received in health posts or clinics and hospitals is both high and similar across socioeconomic groups, except for the richest quintile (see Figure 7). A significant share (40 percent of patients) from the richest quintile did not pay fees for services either in health posts or hospitals.

Figure 7: Percentage of patients who reported not making a payment for public health services by levels of consumption <!-- Start of picture text --> 90%<br>80% 76%<br>70% 68%<br>65%<br>61% 60% 60% 63%<br>60% 56%<br>50%<br>40% 42%<br>40%<br>30%<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Health Posts Hospitals<br>Percentage of beneficiaries who did not pay for public health services<br><!-- End of picture text --> _Source: LCMS VI_

# **5.1.3. Beneficiaries of Fertilizer Subsidies**

Beneficiaries of the FISP and FSP programs–that is, households that cultivate maize using fertilizers and/or seeds provided by the government or agricultural cooperatives—almost evenly distributed across the first four quintiles, with around one in ten agricultural households from each quintile participating in government maize programs. FISP/FSP participation decreases sharply to just one in twenty for the richest quintile. However, as consumption levels increase the share of agricultural households producing maize decreases markedly (see Figure 8). 72 percent of the poorest respondents identified as maize producers, while only 25 percent of respondents in the richest quintile grew maize. Consequently, as a share of _maize producers_ , respondents in the richest quintile are in fact significantly more likely to receive government support than respondents in the poorest.

This disparity is even more pronounced for all respondents who identified as farmers, whether or not they produced maize. 88 percent of respondents in the poorest quintile engaged in agriculture, while only 26 percent of the richest quintile did. Since 9 percent of the poorest and 5 percent of the richest received 26
**<u>Table 5: Structure of Second Pillars</u>**

|Country|Institutional<br>Structure|Type of Plan|Contribution<br>Rate|
|---|---|---|---|
|Australia|Occupational<br>& open funds|Mostly DC|9%|
|Chile|Open funds|DC|10%|
|Denmark|(1) Public scheme (ATP)<br>(2) Occupational funds|(1) DC/hybrid<br>(2) DC/hybrid|(1) 1%<br>(2) 11%*|
|Sweden|(1) Public scheme (PPM)<br>(2) Occupational funds|(1) DC<br>(2) DC|(1) 2.5%<br>(2) 3.5-4.5%|
|Switzerland|Occupational funds<br>operated by foundations<br>and insurers|DC with minimum<br>investment return and<br>annuity conversion<br>factor|Age-related 7-18% on<br>coordinated earnings<br>(between 8 and 9 %<br> ontotalearnings)|

* see text for elaboration.

Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

Second pillars are now mostly (Australia, Denmark, Sweden and Switzerland) or entirely (Chile) based on DC plans. In Australia, defined benefit plans and especially hybrid funds, which combine elements of DB and DC plans, still have a significant presence. In Denmark, the ATP and some of the occupational funds offer deferred group annuities, which change their character to that of hybrid plans. In Switzerland, the mandatory component of pension plans is stipulated as minimum defined credits in notional retirement accounts. A minimum contribution rate and a minimum interest rate (MIR) are specified as well as a minimum annuity conversion factor (ACF) on retirement. Both the MIR and ACF were held constant for 17 years after the creation of the mandatory system in 1985 but the MIR is now set annually in line with the level of market rates, while the ACF is set to decline gradually over the next few years until it reaches a more sustainable level.

Contribution rates vary within a rather narrow range. They are lowest in Sweden where they equal a combined 6 to 7 percent depending on the type of collective labor agreement (2.5 percent for the PPM and 3.5 to 4.5 percent for occupational plans). In Switzerland, the postulated minimum contribution rates, which vary by age, are calculated on so-called "coordinated earnings", which range between 30 and 120 percent of average earnings. The average contribution rate, calculated on total earnings, amounted to between 8 and 9 percent in 2005. In Australia, the contribution rate is a flat 9 percent, while in Chile it amounts to 10 percent. Chilean workers also pay an average of 2.1 percent of salaries to cover the cost of group term life and disability insurance and the operating costs and profit margins of pension fund administrators. In the other four countries, operating costs are either covered by employers or deducted from investment returns. Contribution rates vary by collective labor agreement in Denmark. The OECD Pensions at a Glance study assumes an average contribution rate of nearly 12 percent of earnings, based on 8
**x**<sup>_∗_</sup> US _,t_<sup>.Thismeansthat,fortheUS,theglobalvariable</sup><sup>**d**US</sup><sup>_,t_isempty.</sup>

For the rest of the sample economies, it is assumed that both the international commodity markets and the foreign financial markets influence their economies. Thus for economy _i_ , three commodity prices are included in **d** _it_ , and three foreign financial variables are included in **x**<sup>_∗_</sup> _it_<sup>.</sup> Lastly, regarding the real exchange rate, we include _eit − pit_ in **x** _it_ for the remaining economies. However, conversely, it implies that the value of US currency is determined outside the US economy, and thus _e_<sup>_∗_</sup> US _,t_<sup>_−p∗_</sup> US _,t_<sup>istreatedasapartofa“US-specificforeign”</sup> variable.

# **3.3 Several**

In order to appropriately measure the effect of China’s emergence in the global economy, we introduce several modifications to the standard GVAR model. They are:

1. making the weight matrix _ωij_ time-varying

2. adding the metal price index _p_<sup>_M_</sup> _t_ and the agricultural price index _p_<sup>_A_</sup> _t_<sup>to</sup><sup>**d**</sup><sup>_it_</sup> 3. detecting the structural breaks The importance of these aspects will be discussed below.

## **3.3.1 Making the weight matrix** _ωij_ **time-varying**

Previously, a set of “country-specific foreign variables” **x**<sup>_∗_</sup> _it_<sup>wasdefinedasEquation(1),</sup> which is constructed by using a constant weight matrix, _ωij_ . Now, it is modified as: so that the evolving between-countries closeness is measured by a sequence of time-varying weights, which satisfies _ωii_ ( _t_ ) = 0 and<sup>∑</sup><sup>_N_</sup> _j_ =1<sup>_ωij_(</sup><sup>_t_) = 1for</sup><sup>_i_= 1</sup><sup>_, . . . , N_.</sup> 12
14 may nave to be purchased, espedally when patents or license arc involved. An effective export strateV means that a network of distribution points and agents must be developed and maintained; advertising is another expenditure which requires foreign exchangc (Foster, 1989) The case for foreign exchange savings, therefore, has to go well beyond the import of raw materials and intermediates A third assumption was that employment would be created. Unfortunately, pharmaceutical manuhfcture is not labor-intensive; it rather requires small numbers of highly skied technical workers-the type of worker which is must scarce in developing countris. Management skills are also of capital importance and are vety scarce in Afric Creation of a few hundred jobs may in fact cause the average consumer to be wors off, in Aeat, a survey of the phamceutical industry, conduded that 'Subsdizing domestic firms through high drug prices can have negative socal effect In a ountry with ...drums accunting for about 40% of the peonal health budget, a uniformly high price for pharmaceuticals is a burden which fas heaviy on the poorer sectors of the population.' (.UNCTC, 1984.) If it results in higher drug prices, therefore, creation of a few poorly paid jobs can have a negative impact throughout the economy, and especially on those outside of the formal wage-earning sectors.

A fourth assumption was regarding the nature of the markLet. It u thought that it would be possible to become 'self-sufficient' on the one hand, and that it would be possible to export to neighboring countries on the other. The fact that only a small range of lbs than 40 products (in many ases even fewer) are produced by most factories meant that on the oe hand, they were not able to produce the ful range of 250-300 drugs needed for health care, and on the other, that their *neighbors' were to a **large** extent producing the same limited range of simple products The self-sufficiency argument largely ignores the fact that raw materials and intermediates are imported in any case.

A final assumption made by many firms is that they can become profitable by producing a wide range of products. Since the patents for most essential drugs have expired, profit margins are usually quite low and price competition is stif£ In order to overcome this constraint, a 'mixd-product' strategy is often adopted by local firms, which produce both esential drugs and other drugs (and possibly cosmetics) for which demand and profits margins are high. UNIDO has in the past recommended this strateV to countries planning to produce drugs locally. Unfortunately, however, the result is often that the non-essential products made are of little or no use; they may even be harmfuL Health goals are therefore sacrificed in favor of industrial development and commercial goals.

How can public health and commercial objective both be reached? The guiding principle should be a pharmaceutical version of tae Hippocratic oath. *do no harm'. This kads to several possible 'mies', ranging from the idal to the unacceptable, s follows:
to indicate local opportunities for improvement (Russia) or fellow feeling (South Africa). The reference group may be crucial. In rural China, the negative effect of being low in the village’s income distribution coexists with a positive effect of income inequality in the county in which it is located, as measured by the Gini coefficient (Knight et al. 2009). The former may represent relative deprivation, and the latter may represent perceived room for self-advancement.

Over the period from 1990 to 2010, China’s income grew substantially, yet life satisfaction was no higher in 2010 than it had been in 1990 (Easterlin et al. 2012). This finding is based on an examination of the six available time series of life satisfaction in China over that period (including, for instance, the World Values Survey and the Gallup World Poll). The explanation for the stagnation in life satisfaction can be found in the increase in the reference group’s income, which offsets the effect of the increase in one’s own income, as well as the powerful socioeconomic changes that accompanied China’s rapid growth (Knight and Gunatilaka 2011). These changes included higher unemployment and redundancy, greater economic uncertainty and insecurity, and changing reference groups.

Easterlin et al. (2012) found that inequality of income and inequality of the life satisfaction score rose over those two decades. This finding is consistent with the positive association between income and happiness found in the cross-section. However, it is plausible that subjective well-being becomes more sensitive to income inequality and that average subjective well-being decreases as income 16
Table 5: **Effects of ASP Social-Emotional Learning on Social-Emotional Skills and Emotion Regulation** _(Intention-to-Treat Estimates)_

||SES<br>Index|Fluid<br>Intelligence|Fluid<br>Intelligence<br>after<br>Negative<br>Stimuli|Fluid<br>Intelligence<br>after<br>Positive<br>Stimuli|Arousal|Valence|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Psychology-based curricula|0.003|-0.037|0.051|-0.027|-0.014|-0.245*|
||(0.013)|(0.088)|(0.086)|(0.094)|(0.117)|(0.129)|
||[0.845]|[0.700]|[0.600]|[0.830]|[0.910]|[0.050]|
|Mean pure club group|0.000|0.000|0.000|0.000|0.000|0.000|
|MDE|0.233|0.219|0.221|0.217|0.233|0.230|
|Observations|755|733|733|733|622|622|
|R-squared|0.043|0.045|0.032|0.030|0.034|0.024|

_Notes:_ This table shows the estimated impacts of the ASP’s social-emotional learning on social-emotional-skills (SES) and emotion-regulation outcomes. _Psychology-based intervention_ is a dummy equal to 1 if the student was enrolled in a school that was randomly assigned to the _Virtue_ or _Mindful_ intervention, and 0 if assigned to _Clubs_ . _Mean pure club group_ is the mean of the outcome for the group that only participates in _Clubs_ , without any extracurricular activity. We present the estimated coefficient _μ_ ˆ2 from specification (1). The description of dependent variables is available in Section 3.2. All dependent variables are measured in standard deviations relative to the comparison group. Sample size in each specification varies according to the number of observations available for each outcome. Estimations include all individual controls for which there is an imbalance at baseline. All regressions include randomizationblock (strata) fixed effects. Strata are defined as country and violence level (high or low) of the community in which the school is located. Wild bootstrap standard errors are shown in parentheses and adjusted _p_ -values in brackets. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01 _, p <_ 0 _._ 05 _p <_ 0 _._ 1 50
Figure 11: There is room for further liberalization <!-- Start of picture text --> a. Especially in lower income countries... b. ...in their trade with both developing and developed<br>nations<br>10.7<br>9.5<br>8.0 7.6<br>7.6<br>5.0 5.3 4.8 5.1 5.0<br>4.0<br>3.4 3.6<br>2.4<br>1.9<br>1.4<br>Low income Lower middle Upper middle High income South-South South-North North-South North-North<br>income income<br>MFN Preferential Tariff MFN Preferential Tariff<br>Average Preferential Trade Weighed (%) Average Preferential Trade Weighed (%)<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database What did preferences do to tariff peaks?

The analysis below focuses on “sensitive products,” defined as the subset of tariff lines that are subject to MFN rates above 15 percent.

Although preferential liberalization has targeted highly protected sectors, there remain pockets of protection in agricultural products, textiles and footwear. Preferential tariff lines with MFN rates over 15% are mostly concentrated in apparel and agroindustry goods. Around half of those tariff lines have been fully liberalized through preferential trade agreements (see Figure 12). While total liberalization efforts in these industries has been mostly granted by developed nations, developing nations are still reluctant to grant liberalization in multilaterally sensitive products (see Appendix Table A. 4 and Table A. 5). This trend is maintained when tariff rates are weighted by partner’s share of global trade at the product level,<sup>10</sup> to control for the fact that lower tariffs can be granted on non‐traded goods or to non‐trading partners (see Appendix Figure A 3).

> 10 We use the following formula to calculate the trade‐weighted tariff lines: wT�� �T�� ∗∑SX� �� WHERE T�� IS the total number of tariff lines of product k from country i. ( T�� �∑t� ��� ) and SX�� is the share of country j of global exports of product k ( SX�� � ∑∑�∑�� � � ������ ~~)~~ .

12