rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: Prediction Accuracy for Tail Total Employment, Monthly Sales and Monthly Profits**

||||||Total Em|ploymen|t||Month|ly Sales|||Monthl|yProfits||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||Non-w|inners|Wi|nners|Non-w|inners|Wi|nners|Non-w|inners|Wi|nners|
|||Model|Predictors|Accu|Recall|Accu|Recall|Accu|Recall|Accu|Recall|Accu|Recall|Accu|Recall|
||1|Logit|Constant|80.7%|11.4%|80.8%|8.5%|81.8%|9.0%|81.3%|6.3%|80.8%|10.1%|80.5%|8.9%|
||2|Logit|Application Score|81.8%|15.7%|80.8%|8.8%|83.9%|19.1%|83.2%|15.7%|81.6%|13.2%|81.7%|<br>14.0%|
|Judges|3|Logit|Business Score|81.6%|14.3%|81.9%|13.8%|81.8%|9.1%|83.6%|17.6%|81.4%|11.8%|83.6%|<br>21.5%|
||4|Logit|SubScore|81.3%|12.6%|82.5%|17.0%|82.0%|10.2%|82.8%|14.0%|81.8%|14.1%|82.6%|17.9%|
||5|Logit|Gender|81.3%|12.9%|81.5%|12.5%|83.5%|17.5%|83.0%|14.8%|82.0%|14.4%|82.4%|<br>17.4%|
||6|Logit|Age|81.8%|15.3%|81.0%|9.4%|82.9%|14.8%|82.0%|10.1%|82.3%|16.0%|83.6%|<br>21.4%|
|Sil|7|Logit|Necessity Firm|81.6%|14.5%|81.2%|10.2%|81.8%|9.6%|82.2%|11.4%|81.6%|13.1%|81.1%|<br>11.7%|
|nge<br>Pdit|8|Logit|Grit|81.1%|11.7%|82.3%|15.2%|82.0%|10.1%|83.2%|15.9%|81.2%|11.3%|82.4%|<br>16.7%|
|recor|9|Logit|Digit-span Recall|81.1%|12.5%|81.2%|10.6%|83.9%|19.4%|82.4%|12.3%|82.7%|18.2%|82.4%|<br>17.4%|
||10|Logit|Raven Test|81.6%|14.6%|81.7%|12.5%|82.7%|13.7%|83.6%|18.2%|82.2%|15.2%|82.0%|<br>15.3%|
||11|Logit|Registration time|82.2%|15.7%|82.7%|17.1%|82.0%|10.2%|81.9%|9.2%|82.2%|15.4%|82.4%|17.4%|
||12|Logit|McKenzie|81.3%|13.4%|81.0%|9.9%|82.4%|21.3%|83.4%|17.4%|82.7%|17.7%|81.9%|<br>14.2%|
|Eit|13|Logit|FaWo|81.6%|14.6%|82.1%|15.1%|83.9%|19.2%|83.2%|16.3%|82.0%|14.7%|82.4%|<br>17.2%|
|conoms<br>Mdl|14|Logit|FaWo + BusScores|81.8%|15.6%|81.5%|12.2%|83.1%|15.9%|83.0%|15.2%|81.8%|13.8%|82.6%|<br>17.7%|
|oes|15|OLS|FaWo + BusScores|81.6%|14.9%|82.5%|16.0%|83.7%|19.0%|84.0%|20.1%|81.8%|13.7%|82.8%|<br>18.7%|
||16|Probit|FaWo+ BusScores|81.8%|15.6%|81.9%|14.1%|83.5%|17.7%|83.4%|17.2%|81.8%|13.9%|82.6%|17.9%|
|Mhi|17|Post-LASSO|All|83.3%|21.7%|84.0%|22.5%|83.1%|16.1%|84.1%|21.0%|82.0%|15.0%|81.9%|<br>14.5%|
|acne-<br>Li|18|SVM|All|80.5%|9.2%|81.0%|9.0%|83.3%|17.0%|84.1%|21.1%|81.8%|14.3%|80.5%|<br>9.4%|
|earnng|19|Boosting|All|84.7%|18.6%|85.3%|27.6%|83.1%|15.8%|83.8%|19.3%|82.9%|18.4%|81.5%|<br>12.6%|

Note: Accuracy (abbreviated as Accu) is the ratio of true positives and true negatives to all observations, and is the out-of-sample average over 5-fold estimation. Judges use scores from competition judges to predict the outcome. Single predictor models use a single survey measure. The economist models are the models of McKenzie (2015) and Fafchamps and Woodruff (2017) (abbreviated as FaWo) used in Tables 1-3. The three machine learning models are based on 393 predictors, along with interaction terms in the case of post-LASSO. All models apart from the constant model also include a dummy for whether the applicant was for an existing firm compared to a new firm. The recall rate is the percentage of businesses correctly predicted to be in the top 10% over the total number of businesses actually in the top 10%.

36
sectors, and the profile of green job holders in LAC. Section 4 discusses some caveats of the study and presents some robustness checks. Finally, section 5 draws some conclusions.

# 2. Methodology and Data

## Green occupations

There are different definitions of green jobs. This paper uses the Occupational Information Network (O*NET)<sup>7</sup> classification of occupations developed by Dierdorff et al. (2009, 2011) to define green and nongreen jobs.<sup>8</sup> Dierdorff et al. (2009) argue that the first step to classifying jobs as green or non-green is to define the green economy, which they consider involving economic activities related to reducing the use of fossil fuels, decreasing pollution and GHGE, improving energy efficiency, increasing recycling and adopting renewable forms of energy. Considering this, the “greening” of occupations takes place when the growth of the green economy _increases the demand for existing occupations, shapes the work and worker requirements needed for occupational performance, or generates unique work and worker requirements_ .

The classification is based on the US 2010 Standard Occupational Classification (SOC) System. It provides a list of 1,110 occupations, 204 of which are identified as green and divided into three mutually exclusive categories: Green Increased Demand (64 occupations), Green Enhanced Skills (62 occupations), and Green New and Emerging (78 occupations). Table 1 shows the definition of each group of occupations with examples of jobs falling into each category. The rest of the listed occupations (906) are considered nongreen and represent jobs that are not affected directly by the greening of the economy.

> 7 See also the Appendix for more detailed information on the data sources used in this paper.

> 8 In contrast, ILO (2018) defines green jobs based on whether “they reduce the consumption of energy and raw materials, limit GHGE, minimize waste and pollution, protect, and restore ecosystems and enable enterprises and communities to adapt to climate change. In addition, green jobs have to be decent”. Decent jobs are defined as “opportunities for women and men to obtain decent and productive work in conditions of freedom, equity, security and human dignity” (ILO, 1999). However, this definition combines several dimensions such as the tasks involved in the job, the “greenness” of the economic sector or firm, as well as characteristics of the job not clearly linked to the green economy (for example, social security benefits or wage levels). This paper does not follow ILO’s approach because its goal is to investigate the implications of the greening of the economy (based on the “greenness” of sectors of economic activity) on workers according to their green skills or tasks. Instead of using the “decency” of a job to characterize it as green or non-green, this paper explores the characteristics of jobs related to such dimensions (e.g., wage levels, informal status, etc.) according to their vulnerability to the greening of the economy (i.e., by whether they are in sectors with a high level of emissions and/or have the green skills needed in the new economy).

5
Table 5: Correlation between mean input counts and district traits, 2000

||Organized||Unorganized sec|tor|
|---|---|---|---|---|
||sector|Total|Male owned|Female owned|
||(1)|(2)|(3)|(4)|
|A. District traits from 2001 Population Census|||||
|Log population|0.072|-0.068|-0.112*|-0.049|
|Log population density|0.258*|-0.033|-0.044|-0.019|
|Age profile (demographic dividend)|0.082|0.190*|0.226*|0.126*|
|Share of population in scheduled caste|-0.091|0.121*|0.080*|0.094*|
|Share of population in scheduled tribe|0.029|-0.080|-0.046|-0.040|
|Educated worker share (% pop graduate)|0.315*|0.093*|0.112*|0.076|
|Literacy rate|0.196*|0.327*|0.370*|0.168*|
|Infrastructure: electricity access|0.063|-0.044|-0.016|-0.057|
|Infrastructure: paved roads|0.059|0.256*|0.282*|0.147*|
|Travel time to nearest of India's ten largest cities|-0.053|-0.003|0.025|-0.088*|
|Strength of household banking sector|0.303*|0.313*|0.274*|0.204*|
|Urbanization rate (% urban)|0.262*|0.216*|0.231*|0.156*|
|Log consumption per capita|0.277*|0.273*|0.302*|0.151*|
|B. District traits for manufacturing sector from ASI|/NSS in 2000||||
|Organized sector share of establishments|-0.094|0.026|0.025|0.053|
|Organized sector share of employment|0.133*|0.160*|0.160*|0.099*|
|Organized sector share of output|0.168*|0.170*|0.168*|0.093*|
|Female share of unorg. sector establishments|-0.005|0.034|0.135*|-0.038|
|Female share of unorg. sector employment|-0.065|-0.053|0.071|-0.107*|
|Female share of unorg. sector output|-0.124*|-0.069|0.024|-0.085*|
|HHI index of establishments across sectors|-0.106*|0.020|0.019|0.054|
|HHI index of employment across sectors|-0.018|0.018|0.017|-0.007|
|HHI index of output across sectors|0.020|0.038|0.034|-0.007|

Notes: Table documents correlations between input counts and district conditions. District traits in Panel A are from the 2001 Population Census. District traits are expressed in log values or percentage point values as indicated. District traits in Panel B are calculated from the ASI/NSS data. An asterisk denotes a correlation is statistically significant at the 10% level. Consumption per capita taken from NSS Round 55 (household survey).
(16a) and (16b) and the demand<sup>functions (9c) and (9d) respectively, the</sup> coefficients for sector shares<sup>(t)capsule the magnitudesPand aL. Relative</sup> values of coefficients for<sup>tL therefore depend only on aL the sectoral</sup> coefficients of health in<sup>the human capital earnings functions. Since we have</sup> assumed that a - a, for all<sup>i, the differences in these coefficients reflect</sup> only second-order effects<sup>(which are supressed by the assumption of a Cobb-</sup> Douglas functional form for<sup>the human capital earnings functions, but which</sup> are allowed for in the general<sup>formulation of the estimated demand functions).</sup> Differences in these coefficients<sup>could also reflect the impact of health</sup> investments on the time available<sup>(T), due to the longevity-increasing</sup> effects of these investments. This is discussed further in<sup>subsection (4) below.</sup> The two education intensive sectors<sup>(industry and services) are</sup> included in the regression, and<sup>the low education sector (agriculture)</sup> is excluded. If industry sector<sup>increases in importance relative to agriculture,</sup> _holding the share of services constant,_ schooling demand increases.<sup>Similarly,</sup> if the share of services in<sup>employment rises at the expense of agriculture.</sup> _with no change in the share of the Industrial sector,_ then the demand for education will unambiguously<sup>increase. However, if industry's share declines</sup> at the same time that the share<sup>of services rises, the effect on aggregate</sup> schooling demand is ambiguous.<sup>Hence agriculture must be the omitted class.</sup> Table 2 reports summary statistics<sup>for a sample of 91 countries in</sup> 1965, and 87 countries in<sup>1987. Table 3 lists correlations between the main</sup> variables used in the above<sup>regressions. Table 4 provides results for</sup> equations using primary school<sup>enrollment ratios as the dependent variable,</sup> 27
reflected in the choice of the inflation rate level to reduce the discrepancy between domestic inflation rate and inflation rates observed in the country supporting the peg (anchor country or economy – France).

# **4. Econometric Model Specification**

As CAMU and WAMU countries are in currency unions, we perform country-level and regional analyses, and country-level results are aggregated to derive a regional threshold. While CAMU and WAMU countries share the euro peg to the CFA franc, analyses are performed for each subregion because countries are structurally different, and the monetary policy is managed by two different regional central banks.

## **_Baseline Model_**

Based on Khan and Senhadji (2001), the following models are tested: Where is the growth rate of real GDP, the inflation growth rate, the population growth rate, the total investment in percentage of GDP, k the threshold level of inflation, tt tt tt Zt is a set of yy ππ pp additional control variables, and tt the random error term representing measurement error in ii the explanatory variables. All growth rates are computed as tt = 100 ∗∆l . The dummy εε variable D is defined as: tt tt xx llXX Page | 12
Table **11.4**

# Age **and** Education **of** SME Entrepreneurs

# **NUMBER** OF FIRMS

||Woven<br>**te3lle**|Automotive<br>part|**Eleronic**<br>**ut**|**FAct=**<br>**automaton**|**ToI**|
|---|---|---|---|---|---|
|**AGE**||||||
|30s|5|||5|10<br>(8.2%)|
|40s|15|_5_|9|21|50|
||||||(41.0%)|
|5Os|15|10|,|11|41|
||||||(33.6%)|
|60s|_5_|_5_|5|3|18|
||||||(14.7%)|
|70s|2||1|3|3|
||||||(2.5%)|
|AVERAGE AGE|50.38|54.20|53.40|47.45||
|EDUCATION**(years)**||||||
|**6**|1|||1|2|
||||||(1.6%)|
|9|1||||1<br>(.8%)|
|12|8|1|3|1|13|
||||||(10.7%)|
|14|1|||1|2<br>(1.6%)|
|16|28|18|17|28|91<br>(74.6%)|
|18|3|1||9|13<br>(10.7%)|
|AVERAGE|14.93|15.90|15.40|16.05||
|EDUCATION||||||
|TOTAL FIRMS|42|20|20|40||

4t
T **able 3. Quadratic AIDS Own-Price Elasticity Estimates, by Decile**

||**Scenario**<br>**1: no**<br>**response**|**Scenario**<br>**2: Nargis**<br>**et al.**<br>**(2014)**<br>**estimates**||**Scenario 3: Quadr**|**atic AID**|**S model**<sup>**a**</sup>|
|---|---|---|---|---|---|---|
|**Decile**|**Cigarettes**|**Cigarettes**|**Cigarettes**|**Cross-elasticity**<br>**bidis/cigarettes**|<sup>**Bidis**</sup>|**Cross-elasticity**<br>**bidis/cigarettes**|
|1.00|0.00|−0.75|−1.36|−0.11|−1.14|0.04|
|2.00|0.00|−0.75|−1.33|−0.11|−1.26|0.10|
|3.00|0.00|−0.75|−1.33|−0.10|−1.18|0.07|
|4.00|0.00|−0.52|−1.29|−0.09|−1.26|0.10|
|5.00|0.00|−0.40|−1.33|−0.11|−1.19|0.08|
|6.00|0.00|−0.40|−1.27|−0.08|−1.18|0.07|
|7.00|0.00|−0.39|−1.24|−0.07|−1.21|0.11|
|8.00|0.00|−0.36|−1.25|−0.08|−1.18|0.08|
|9.00|0.00|−0.36|−1.25|−0.08|−1.27|0.10|
|10.00|0.00|−0.36|−1.23|−0.08|−1.29|0.20|
|Average|0.00|−0.50|−1.29|−0.09|−1.22|0.10|

_Note:_ Consumption deciles are defined based on household consumption per capita deflated spatially to control for differences in prices across areas. The regressions control for household demographics and urban or rural location. The prices are approximated by the median unit values in a district across rural or urban areas. a. Calculations based on data of the HIES 2016/17.

The three scenarios provide upper- and lower-bound estimates of the impacts of a change in the price of cigarettes. Using these bounds allows one to assess the sensitivity of the results to errors in the estimation of the elasticity. Even though the model presented here is not dynamic, another way to motivate the bound analysis is to think about shorter- and longer-term responses from the short-run impact without response to a higher-response, longer-term impact.

# **4. Distributional impacts of a cigarette price increase**

This section summarizes the results at three levels of cigarette price increases: 25 percent, 50 percent, and 75 percent. These price changes reflect several types of tax reforms that could include eliminating the existing tiered structure and introducing a uniform, specific excise tax. Discussing specific changes in the tax structure and rates is outside the scope of this note.

Consumption changes that arise from an increase in cigarette prices are estimated for each decile based on the three elasticity scenarios presented in table 3: (1) the nonresponse case, in which price elasticities are assumed to be zero; (2) the medium-response scenario, in which the average elasticities of Nargis et al. (2014) are used; and (3) the high-response scenario in which quadratic AIDS elasticities 13
depending on the model, reduces the total infections<sup>4</sup> . In the intervention scenario, higher virulence may actually assist control as case detection becomes faster and easier<sup>5</sup> .

Since virulence during an epidemic of influenza is even more affected by personal characteristics of the individual, it should be discussed in terms of population‐level averages. In this context, the projected population‐level virulence of a future pandemic could compare to that in prior pandemics or seasonal epidemics. The data from individual human cases of the current avian H5N1 subtype, an overall CFR of about 60%<sup>6</sup> , are not as useful, since a pandemic capable virus may trade off some virulence for transmissibility.

The total cases and deaths in the historical pandemics are unknown, but they have been categorized by their estimated CFR, with ‘Category 5’ being the most devastating<sup>7</sup> . In a study, the ranges of CFR for each historical pandemic were shown as ‘lower, medium, and upper’ scenarios (Table 2) relevant for a future pandemic<sup>8</sup> . Another study used mortality from seasonal influenza epidemics in the US as a basis for projecting the CFR for a pandemic in the US<sup>9</sup> . From that study, the modeling estimates of CFR for two risk categories in the 0‐ 19 age group (based on the presence of a complicating comorbidity) are shown below.

3
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Source: Banerjee and others 2009.

At current tariff levels of $0.20 per kilowatt-hour the subsistence bill would be $5.00, which is affordable for almost 80 percent of the Kenyan population. Taking a cost-recovery tariff of $0.99 for water and a subsistence consumption of 4 cubic meters per month—which amounts to an absolutely minimal consumption of 25 liters per capita per day for a family of five—the monthly water bill would be $4. The graph indicates that purchasing a subsistence consumption bundle of this kind at cost-recovery prices would be affordable for the vast majority of Kenyan households. (Because the two bars reference the actual cost of the services, they are represented as vertical lines.) Indeed, even doubling physical consumption to less-modest levels, thereby raising the monthly utility bills to around $8, would result in affordability problems for only about 20 percent of the population. This analysis suggests that, given the level of household budgets in Kenya and the costs associated with utility services, affordability issues do not appear to be a major concern except for the very poorest Kenyans—and certainly not for the higherincome households that enjoy access to the service today.

Distribution losses of power and water utilities cost Kenya a further $70 million a year. Kenya’s power and water utilities are relatively more efficient than utilities in neighboring countries and lowincome peers (figure 18). Power utilities in Africa’s nonfragile low-income countries waste resources equal to almost 1 percent of GDP on average, about five times as much as in Kenya, where KPLC maintains cost-recovery tariffs and has achieved a high rate of collection of bills. Most of the resources wasted by Kenya’s utility are associated to distribution losses, likely an indication of an overstretched transmission network. KPLC reports transmission and distribution losses of 18 percent, almost double the best-practice benchmark of 10 percent. As a result, KLPC’s operational inefficiencies absorb 0.3 percent of GDP. In the case of the water sector, and in contrast to many other African countries, bill collection is not a conspicuous concern. However, at NWASCO, unbilled water use stands at 40 percent, more than double the best-practice standard of 20 percent. Thus, NWASCO’s operational inefficiencies absorb 0.1 percent of GDP.

29
16 such complex interaction between village-level inequality and group organization; perhaps most interestingly for our current discussion, in more unequal villages, individuals are less likely to report trust in the community, and more likely to report conflict of interest. There is more rigorous and detailed evidence from India on the role of historic institutions of inequality in influencing breakdowns in collective action for broad public goods (Banerjee and Iyer, 2005; Andersen, 2011), dissolution of social cooperation (Fehr, Hoff, et al, 2008), and weak performance of government poverty alleviation programs (Besley et al, 2004).

Ethnic fragmentation and polarization in many parts of Africa have been blamed for the lack of organization by civil society to generate political institutions in the public interest (Easterly and Levine, 1997; Alesina, Baqir and Easterly, 1999; Montalvo and Reynal-Querol, 2010). Miguel and Gugerty (2005) find that community contributions to public schools and quality of school infrastructure are lower in communities with greater ethnic diversity. In experimental work to examine why ethnicity matters in lowering contribution to public goods, Habyarimana et al (2007) conclude that it is not because of greater altruism towards co-ethnics, or greater sharing of common preferences, but rather because of greater power of social sanctions within ethnic networks. That is, social networks among co-ethnics make reciprocal exchanges easier to achieve.

Perhaps because it facilitates such reciprocal exchange, ethnic identity in Africa may be used to sustain clientelist political competition, enabling the type of quid-pro-quo interactions of provision of private benefits for political support that allow politicians to get away with larger rents and lower public goods. Consistent with this view of ethnicity as an instrument of clientelist strategies, Eifert et al (2010) find that self-reported ethnic attachment in the Afrobarometer surveys is greater when the question is asked nearer to elections.

Table 1 provides further data on reported participation in groups for 18 African countries covered by the most recent round of the Afrobarometer survey around 2008. On average, across all countries, 26 percent report being active members of a group, and 52 percent report frequently attending group meetings. Reported participation does not appear to be strikingly different across the countries classified as “full democracies” by Polity IV, versus those classified as “anocratic”. Indeed, there is significant variation in group participation within these categories across countries. Among the full democracies, the highest ranking (South Africa and Botswana) have active group membership reported by only 16 and 17 percent, compared to a Kenya and a Liberia lower down the “democracy” scale, with
# **Table A31: OLS; Effects of refugee presence on household total agriculture production by change in main source of income, with analysis at cluster level and the distance weighted refugee index -** **<u>at 50km from the clusters (Panel 2009 2012)</u>**

||Log of Tota|l Agricultural Pro|duction (UGX)|
|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|
|Log of refugees (50km)|-0.0012|0.0054|0.0089|
||(0.0258)|(0.0257)|(0.0284)|
|Log of refugees(50km)#To Commercial farming|0.2430**|0.2360*|0.2015**|
||(0.1172)|(0.1266)|(0.0824)|
|Log of refugees(50km)#To Wage employment|-0.0097|-0.0184|0.0128|
||(0.0367)|(0.0362)|(0.0314)|
|Log of refugees(50km)#To Non-Agric self-employment|0.0468|0.0397|0.0620*|
||(0.0365)|(0.0359)|(0.0344)|
|HH head age||0.0725***|0.0363***|
|||(0.0116)|(0.0116)|
|Age squared||-0.0007***|-0.0004***|
|||(0.0001)|(0.0001)|
|HH head sex (male)||0.5071***|0.0519|
|||(0.0658)|(0.0863)|
|Annual mean temperature||0.0791|0.0103|
|||(0.0791)|(0.1077)|
|Average 12 months total rainfall||0.0006<br>|0.0035**<br>|
|||(0.0014)|(0.0017)|
|Household size|||0.0516***|
||||(0.0136)|
|Experienced shocks|||-0.0263|
||||(0.0707)|
|**_HH head marital status_**||||
|Married monogamously|||-0.1104|
||||(0.2353)|
|Married polygamously|||-0.1336<br>|
||||(0.2498)|
|Separated (divorced/widowed)|||-0.4197|
||||(0.2603)|
|**_HH head education level_**||||
|Completed primary sch|||0.2889***|
||||(0.0653)|
|**_HH main source of income_**||||
|Change to Commercial farming|0.3538|0.3385|0.1476|
||(0.3001)|(0.2939)|(0.3275)|
|Change to Wage employment|-0.2378**|-0.2134*|-0.1889*|
||(0.1176)|(0.1170)|(0.1065)|
|Change to Non-agricultural self-employment|-0.0143|-0.0359|-0.1284|
||(0.1127)|(0.1086)|(0.1063)|
|**_Other source of HH income_**||||
|Property income and investments|||0.0689|
||||(0.1483)|
|Transfers and other benefits|||-0.0070|
||||(0.1082)|
|**_HH Asset_**||||
|Own > median size of land (2.5 acres)|||0.5042***|
||||(0.0689)|
|Observations|3,124|3,123|2,641|
|R-squared|0.3602|0.4005|0.4894|

51