rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2d.  Sector Value Added in Rwanda  (Percentage unless otherwise indicated)**

||<br>La|<br>bor|||<br>G|<br>DP|
|---|---|---|---|---|---|---|
||Skilled<br>labor|Unskilled<br>labor|Capital|Natural<br>Resources|USD in<br>billion|% of total|
|**Business Services**|||||||
|Air Transport|13.5|58.7|27.8|0.0|0.0|0.3|
|Communication|23.5|34.3|42.2|0.0|0.0|1.6|
|Insurance|32.1|46.7|21.2|0.0|0.0|0.8|
|Business Services nec|32.6|47.6|19.8|0.0|0.1|3.0|
|Financial Services nec|30.3|44.2|25.4|0.0|0.1|4.2|
|Transport nec|14.1|61.4|24.5|0.0|0.1|2.9|
|Water Transport|13.1|57.0|29.9|0.0|0.0|0.1|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|3.2|17.3|79.6|0.0|0.0|1.2|
|Energy and Minerals|2.9|18.0|38.0|41.1|0.2|6.0|
|Food Products|5.5|34.7|59.8|0.0|0.1|4.7|
|Petroleum and Coal Products|7.1|33.0|59.9|0.0|0.0|0.0|
|Other Manufacturing|4.7|24.6|70.8|0.0|0.0|0.7|
|Textile and Apparel|7.9|53.4|38.7|0.0|0.0|0.3|
|Wood and Paper Products|5.0|33.2|61.8|0.0|0.0|0.3|
|**CRTS**|||||||
|Agriculture and Forestry|0.6|70.0|28.6|0.9|1.2|39.8|
|Other Services|26.3|32.6|41.2|0.0|0.7|23.9|
|Trade|9.9|42.8|47.3|0.0|0.3|9.4|
|Utilities|12.2|23.0|64.9|0.0|0.0|0.9|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

43
## **High-Density Urban Clusters Based on Grid-Level Population Data**

To check the robustness of the main result using other population data, we conducted the same estimation using the alternative population measure based on the grid-level WorldPop data. While the significance level di↵ers, the rest of the subpopulation analysis shows a similar relationship and magnitude as the analysis in the main text (Appendix E).

## **Using Data from Di↵erent Years**

Finally, we run the regressions for the 2006 and 2015 UEUS data separately to examine whether the associations between the town population size and labor outcomes hold in di↵erent years. The overall results remain unchanged (see Appendix F for the results).

## **Sectoral Shifts: Excluding Agricultural Employment**

Although agricultural employment is a small share of urban employment, it could contribute to a negative relationship between city size and self-employment (because agriculture is less prevalent in larger cities). To eliminate the possibility that the estimation result is driven by changes in sector (which tends to be self-employed), this subsection provides an estimate excluding the workers in the agriculture sector. The overall results remain unchanged (see Appendix G for the results).

# **6 Discussion and Conclusion**

Among the channels of urbanization for economic growth and poverty reduction, the transition in the mode of labor is a critically important factor. Focusing on urban Ethiopia, this study empirically examines the nexus between urban agglomerations and employment changes from selfemployment/informal jobs to wage/formal jobs. Overall, our IV regressions based on individual workers in the 2012/13 LFSs confirm such link, providing several important findings. First, town population size is positively correlated with the probability of individuals engaging in wage/formal jobs (as opposed to self-employment/informal jobs). According to our preferred speci- fication, the probability change in an individual’s chance (or the town-level share) of wage employment with respect to the town population size is 4.4 percentage points. Similarly, positive probability change is estimated for formal employment (5.1 percentage points). As emphasized in the literature on 22
A study commissioned by Restaurant Opportunities Center of New York (ROC-NY) and the New York City Restaurant Industry Coalition in 2005 gives evocative accounts of the perception of discrimination that are attributed to poor English skills among workers. Filters or screens such as language seem to matter more when rationing of “good jobs” is in evidence; that is, when there are fewer good jobs and a larger pool of applicants. In such markets, Desai et al (2008) find based on Indian data, that knowledge of English has a statistically significant association with getting “good jobs”. Once in employment, nonpreferred workers may face discriminatory attitudes that prevent them from being integrated into the labor market on equal terms as the dominant group. ILO (2011) cites an example from Flanders, Belgium, where an internal rule of an automobile components company in which 70 per cent of the employees are of foreign origin, stated that workers using languages other than Dutch on three consecutive occasions were liable to be dismissed. The justifications of this rule were “security reasons” and “respect for other workers”.

I do not for a moment imply that lack of language skills is a mechanism for discrimination. Both language and non-cognitive skills may be essential for a job and for that reason may exclude a certain category of applicants. In general, unilingual applicants do worse than bilingual or trilingual applicants in many labor markets and this is the nature of their human capital endowments. But like other aspects of human capital, there may be a pattern in stronger or weaker language skills. Upadhyaya (2007) finds from a study of the Indian outsourcing industry, that English-speaking graduates who also have western social skills fare much better in the burgeoning market. These criteria manage to exclude Scheduled Castes from the pale of this growing opportunity. She shows that, for the most part, threequarters of the workers in jobs in information technology are from higher castes, while the remainder is from the Other Backward Class (OBC) category – individuals who are placed higher in the caste hierarchy than the Scheduled Castes. Deshpande and Newman (2007) interviewed postgraduate university students and found that Scheduled Castes in degree programs show lower expectations about jobs than their high caste counterparts and see themselves as disadvantaged because of their caste and family backgrounds. Because they 19
# **2.1 GDP and Geographic Data**

Historic data on GDP per capita come from INEGI for 2000, 2005 and 2010. The geographic location of the municipality affects productivity, which in turn impacts poverty. Therefore we account for each municipality s latitude, longitude and elevation above sea level in the analysis. Distance from the municipality to the nearest federal and state road are also used as a proxy measure of connectivity.

# **2.2 Climate and Weather Data**

Historic weather data were imputed to municipalities using a Variable Infiltration Capacity Model (VIC) for Mexico, developed by Liang et al. (1994)<sup>6</sup> . The VIC model interpolates data using grids by: i) creating correlation matrices between existing rainfall stations and radar stations that indicate the presence of rain, but cannot capture its amount; ii) computing the corresponding means for both types of stations from the grids within the municipality; and iii) using those values multiplying the values of the core stations by the means ratio to scale them, through the reconstruction of a climatic model.

The climatic variables employed for the study are annual average precipitation (in millimeters) and temperature, both estimated from daily records.

# **2.3 Climate Change Scenarios**

Climate change modifies the mean and variance of rainfall and temperature. Different scenarios proposed by IPCC were tailored for different regions in Mexico to predict climate change. We assembled one global scenario of rainfall and temperature obtained for 22 climate models assembled by the World Bank Climate Change Knowledge Portal (2011)<sup>7</sup> . Global climate change

> 6VIC as a semi-distributed macroscale hydrological model, VIC balances both the water and surface energy within the grid cell; and its sub-grid variations are captured statistically. Distinguishing characteristics of the VIC model include: subgrid variability in land surface vegetation classes; subgrid variability in the soil moisture storage capacity; drainage from the lower soil moisture zone (base flow) as a nonlinear recession; and the inclusion of topography that allows for orographic precipitation and temperature lapse rates resulting in more realistic hydrology in mountainous regions. VIC uses a separate routing model based on a linear transfer function to simulate the streamflow. Adaptations to the routing model are implemented in VIC to allow representation of water management effects including reservoir operation and irrigation diversions and return flows. Since its existence, VIC has been well calibrated and validated in a number of large river basins over the continental US and the globe. Applications using the VIC model cover a variety of research areas.

> 7Future climate data was provided by the WB Climate Change Knowledge Portal (http://climateknowledgeportal.worldbank.org). All climate models are credited to the Coupled Model Inter-comparison Project Phase 3 (CMIP3) of the World Climate Research Programme (WCRP) produced a multi-model dataset, referenced in the Intergovernmental Panel on Climate Change’s (IPCC) Third and the

6
business in the previous 12 months. Trainees are also significantly less likely to migrate away in search of employment, which is again consistent with trainees making some longer-run investments, often in collaboration with the master craftsmen trainer’s at their location of origin.

# **5.3. Effects of Training on Well-being and Health Behaviors**

In Table 9 we investigate the impacts of training on non-market outcomes including psychosocial well-being, self-esteem, and sexual behavior. Subjective measures of well-being are a useful complement to the time use and labor market data we collect to paint a more comprehensive picture of the overall effects of the training intervention. Such measures are increasingly used in the economics and evaluation literatures (Ashraf, Field, and Lee 2010; Devoto et al. 2012).

Participating in training had strong positive effects on subjective measures of well-being. Specifically, random assignment to training increases the share of respondents happy and satisfied with life and agreeing that life has improved during the last year by 5 and 7 percentage points respectively (22 and 29 percentage point effect in the IV – TOT specification). These are sizeable increases relative to the control group means. Additionally, related to the prior discussion on skills acquisition, trainees report being more confident in their ability to switch away from agriculture and earn money in other sectors. We don’t observe any statistically significant effects on health behaviors. However, there is important heterogeneity by gender across all these outcome variables, which we will explore below.

# **5.4. Gender differences in outcomes**

Both the drop-out and the attrition analysis indicated that women are significantly more constrained in their decision-making than men (unlike men, women participate in training when other opportunities disappear, drop out due to illness or injury, and attrite when they are not assigned to treatment). Table 10 reports results disaggregated by gender to explore whether these apparent constraints lead to differential gender incidence of benefits and costs of training. The sub-sample analysis also helps to establish the robustness of our results in the male sample, where attrition bias is less of a concern.

Trained men and women report very similar gains in self-reported skills, but that is where their similarity ends. Men spend more time in training (probably due to the occupations they select into), and this extra time comes from men’s hours in self-employment. In the full sample, 16
# **1.  Introduction**

This paper describes an approach to local climate forecasting that integrates local weather history and future projections in simple display formats that are useful for policymakers. It draws on historical climate data from weather stations and satellites; projections from climate models (GCMs) linked to global emissions scenarios from the Intergovernmental Panel on Climate Change (IPCC); and a methodology for summarizing stochastic variation in the data. The methodology can translate very large, multidimensional datasets into easily-interpreted formats for all countries at a high level of spatial disaggregation. In this pilot application for Ethiopia, we develop formats for 372 grid squares that cover the whole country; three time periods (1960-2000, 20012050, and 2051-2100); eight GCMs<sup>1</sup> ; and one IPCC future emissions/climate scenario (A1B). However, we have designed our approach for an arbitrary number of grid squares, periods, GCMs and IPCC scenarios. It can produce a fine-gridded database for multiple periods that covers all countries and incorporates predictions from all available GCMs and IPCC scenarios.

This exercise is primarily designed to serve policymakers, public investment planners and other actors whose decisions must necessarily balance a host of technical, economic, political and social concerns. For these people, the future impact of global warming is one factor among many that must be considered. At the same time, appropriate decisions require an approach that captures all the relevant dimensions of future climate forecasts, at least to a first approximation. This paper describes and implements such an approach through a step-by-step process. The remainder of the paper is organized as follows. In Section 2, we provide a general introduction to the

> 1 See Appendix 1 for documentation of the eight GCMs.

2
# PREWorkingPaper Series

||Iie|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS596_|The Mexican Sugar Industry:<br>Problems and Prospects|Brent Borrell|February 1991|P. Kokila<br>33716|
|WPS597_|Rent Sharing in ‘he Multi-Fibre<br>Arrangement: Theory and Evidence<br>from U.S. Apparel Importsfrom<br>Hong Kong|Refik Erzan<br>Kala Krishna<br>LingHuiTan|February 1991|G. llogon<br>33732|
|WPS598|Africa Region Population Projections:<br>1990-91 Edition|Patience W. Stephens<br>Eduard Bos<br>MyT. Vu<br>Rodolfo A. Bulatao|February 1991|O. Nadora<br>31091|
|WPS599_|Asia Region Population Projections:<br>1990-91 Edition|EduardBos<br>Patience W. Stephens<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|O. Nadora<br>31091|
|WPS600|Latin America and the Caribbean<br>Region Population Projections:<br>1990-91 Edition|My T. Vu<br>Eduard bos<br>Patience W. Stephens<br>Rodolfo A. Bulatao|February 1991|O. Nadora<br>31091|
|WPS601_|Europe, Middle East, and North<br>Africa Region Population Projections:<br>1990-91 Edition|Eduard Bos<br>Patience W. Stephens<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|O. Nadora<br>31091|
|WPS602_|Firm Output Adjustment to Trade<br>Liberalization: Theory with<br>Application to the Moroccan<br>Experience|Mark A. Dutz|February 1991|S. Fallon<br>38009|
|WPS603_|The Role of Officially Supported<br>Export Credits in Sub-Saharan<br>Africa's External Financing|Asli Demirgiig-Kunt<br>Refik Erzan|February 1991|G. llogon<br>33732|
|WPS604|Foreign Trade and Its Relation to<br>Competition and Productivity in<br>Turkish Industry|Faezeh Foroutan|February 1991|S. Faiion<br>37942|
|WPS605|Overview of Contractual Savings<br>Savings Institutions|Dimitri Vittas<br>Michael Skully|March 1991|W. Pitayato-<br>nakarn<br>37668|
|WPS606|Adjustment Policies and Investment<br>Performance in Developing<br>Countries: Thoory, Country Experienc<br>andPolicyImplications|Luis Serven<br>Andrés Solimano<br>es,|March 1991|E. Khine<br>39361|
**Table 3:** Benchmark sector output, trade, and emissions

||Y|||D||X||M|C|O2|Int|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Name|abs|pct|abs|pct|abs|pct|abs|pct|abs|pct|abs|
|**Non-EITE sectors**||||||||||||
|Natural gas|146|0.3%|157|0.5%|29|1.4%|39|1.4%|101|1.8%|692|
|Electricity|456|0.9%|460|1.4%|1|0.0%|5|0.2%|2286|41.0%|5013|
|Coal|89|0.2%|71|0.2%|20|1.0%|1|0.0%|10|0.2%|112|
|Crude oil|318|0.6%|603|1.9%|6|0.3%|290|10.6%|41|0.7%|129|
|Rest of economy|27762|55.8%|28184|86.8%|1477|70.6%|1899|69.2%|1725|31.0%|62|
|**EITE sectors**||||||||||||
|Refned petroleum products|778|1.6%|698|2.2%|152|7.3%|72|2.6%|119|2.1%|153|
|Pulp and paper|439|0.9%|436|1.3%|31|1.5%|28|1.0%|38|0.7%|87|
|Non-metal minerals|181|0.4%|194|0.6%|12|0.6%|25|0.9%|59|1.1%|326|
|Iron and steel|227|0.5%|254|0.8%|26|1.2%|53|1.9%|42|0.8%|185|
|Non-ferrous metals|209|0.4%|214|0.7%|58|2.8%|64|2.3%|12|0.2%|57|
|Chemicals|1201|2.4%|1188|3.7%|281|13.4%|268|9.8%|94|1.7%|78|
|**Final demand sectors**||||||||||||
|Consumption|12011|24.1%|NA|NA|NA|NA|NA|NA|1045|18.8%|87|
|Government|2575|5.2%|NA|NA|NA|NA|NA|NA|NA|NA|NA|
|Investment|3403|6.8%|NA|NA|NA|NA|NA|NA|NA|NA|NA|

_Note: Y refers to production, D is domestic consumption, X is exports, and M is imports in billions of dollars. CO2 is domestic emissions of carbon dioxide in millions of metric tonnes. Int is CO2 intensity in tonnes of CO2 emissions per $1,000 of production. abs is the value in dollars or tonnes as described above. pct is the sector share of the total economy in percentage terms._ rebating options based on empirical data.<sup>14</sup> With the United States as the country for investigation, we aggregate the 65 sectors in GTAP to 11 sectors reflecting the specific requirements of our research question. In the composite data set, we distinguish energy-producing and transforming sectors (coal, gas, crude oil, electricity, and oil refining), energy-intensive and trade-exposed industries (iron and steel, nonferrous metals, nonmetallic minerals, chemicals, pulp and paper), and an aggregate sector (rest of the economy) reflecting the remainder of the economy. In the results below, we group these 11 sectors into two composite segments: an aggregate of emission-intensive and trade-exposed industries (EITE) and an aggregate non-EITE sector (NEITE).<sup>15</sup> Investment and government demand are exogenous and held fixed across the different simulations that we conduct. Key economic variables describing the economy are presented in Table 3.

In our simulation, we introduce each type of policy described above sequentially. In

> 14Of course, the model is a general equilibrium model, whereas the theory focuses on partial equilibrium outcomes, so some potential for discrepancy does exist.

> 15While electricity and refined petroleum products are not always considered as highly trade exposed, they are often still singled out for special treatment. The EU has refineries on its carbon leakage list. Canada includes electricity in its industrial output-based performance standards.

24
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25
The link between health and human capital investment has been examined intensively in previous research and at different points in the life-cycle. Shocks to fetal or infant health have been shown to lower educational investments or returns. These include maternal malnutrition (Field et al. 2009; Maluccio et al. 2009; Almond and Majumder 2011); low birth-weight (Behrman and Rosenzweig 2004); excessive or insufficient rainfall during the first year of childhood (Maccini and Yang 2009; Shah and Steinberg 2014); exposure to diseases (Almond 2006; Currie and Vogl 2013); and famine during early childhood (Almond and Currie 2011; Gorgens et al. 2012). Exogenous shocks from exposure to environmental pollution or hazards (Chay and Greenstone 2003; Foster et al. 2009; Jayachandran 2009; Almond et al. 2009), and exposure to violence or civil war (Akresh et al. 2011; Camacho 2008; Blattman and Annan 2010; Leon 2012; Yuksel 2014) have also reduced educational attainment and labor market earnings.

While the link between these shocks and education might be due to health or related income shocks, past studies have been able to isolate the effects of health on human capital investments. Exploiting variation in the timing and intensity of hookworm and malaria eradication in the American South and in developing countries, Bleakley (2007, 2010a) and Lucas (2010) demonstrated improved education and earnings outcomes for populations with early, compared to later, exposure to the public health interventions. Miguel and Kremer (2004) found that children who received treatment for intestinal worms in Kenya were absent 25% less frequently than students in the schools that were randomly assigned to receive the treatment 2-3 years later. Follow-up surveys of these students reveal that those students from the treatment schools worked 13% more hours and earned 20%-29% more than those from the control schools (Karlan and Appel 2011). Bhalotra and Venkataramani (2012) found that the availability of 3