rafmacalaba/datause-encoder-data huggingface dataset:
- _Factor-shift effect:_ Post-regulation production technologies may be more or less labor intensive, as more /less labor is required per dollar of output.

Morgenstern, Pizer and Shih (2002) empirically estimate a model incorporating these different effects of regulation on employment for four highly polluting/regulated industries using data for the period 1979-91. Importantly, they measure regulation by the plant’s expenditures on pollution controls, as self-reported in the Commerce Department’s survey on Pollution Abatement Control Expenditures (PACE).

While there are some differences across the four industries, Morgenstern et al. find that increased abatement expenditures generally do _not_ cause a significant change in employment at the industry level. Since these estimates do not consider the effect that regulation in one industry may have on other industries that make complementary or substitute products, the results should not be interpreted as relevant to the whole economy, but they do highlight the complex responses to the new regulations exhibited by plants in the four industries.

Interestingly, other studies have focused on air pollution control induced changes in employment, using measures of regulation tied to specific policy actions rather than to self-reported pollution control expenditures. Examples include the stringent air quality standards imposed in the South Coast Air Quality Management District in the 1990s, and the nationwide attainment/nonattainment designations used for criteria air pollutants, as well as the realignment of such designations following the 1990 Clean Air Act Amendments.

In an early study, Berman and Bui (1997) found small, statistically insignificant employment _gains_ associated with the more stringent South Coast air quality rules, consistent with the later findings of Morgenstern et al. In contrast, in the analyses of federal attainment/nonattainment designations, studies by Greenstone (2002) and Walker (2011) found adverse employment impacts associated with the regulations.

Importantly, all three of these studies use measures of regulation tied to specific policy actions rather than self-reported pollution control expenditures. The actual modeling is based on fixed effects, which only provides a relative indication of impact compared to the control group. Thus, even when employment is found to decline in a more stringently regulated area compared to a less stringently regulated one, it does not necessarily represent a net loss in jobs across all the areas. In fact, it may simply be a job _shift_ from one area to another. Without additional information on the nature of the impact of the regulation in the less stringently regulated area, it is not possible to determine if regulation caused a net loss or possibly even a net gain in employment across the different areas.

18
Yarn Market (India and the Rest of the Wor:id)

- _{(dSy/dPcc)/(Sy/Pcc) x_ (dPcc/Pwc)/(Pcc/Pwc) x Sy x (dPwc/Pwc)}

- **_+_** _{(dSy/dPcc)/(Sy/Pcc) x (dPcc/dtxc)l(Pcc/txc)_ x Sy x (dtxc/txc)}

- **_+_** _{(dSy/dPcc)/(Sy/Pcc) x_ (dPcc/tcc)/(Pcc/tcc) x Sy x (dtcc/tcc)}

- **_+_** _{(dSy/dPpy)/(Sy/Ppy)_ x (dPpy/dPwy)/(Ppy/Pwy) x Sy x (dPwy/Pwy)}

- **_+_** _{(dSy/dPpy)/(Sy/Ppy) x (dPpy/txy)/(Ppy/txy)_ x Sy x (dtxy/txy)}

- **_+_** _{(dSy/dPpy)/(Sy/Ppy)_ x (dPpy/dtpy)/(Ppyltpy) x Sy x (dtptltpt)}

- **_+_** _{(dSwy/dPwc)/(Swy/Pwc) x_ Swy x (dPwc[Pwc)}

- _+ {(dSwy/dPwy)/(Sw+y/Pwy) x_ Swy x (dPwy/Pwy)}

- _= {(dDy/dPcy)/(Dy/Pcy) x_ (dPcy/dPwy)/(Pcy/Pwy) x Dy x (dPwy/Pwy)}

- **_+_** _{(dDy/dPcy)/(Dy/Pcy) x_ (dPcy/dtxy)/(Pcy/txy) x Dy x (dtxy/txy)}

- **_+_** _{(dDy/dPcy)/(Dy/Pcy) x (dPcy/dtcy)/(Pcyltcy)_ x Dy x (dtcy/tcy)}

- **_+_** _{(dDy/dPpt)/(Dy/Ppt)_ x (dPpt/dPwt)/(Ppt/Pwt) x Dy x (dPwt/Pwt)}

- **_+_** _{(dDy/dPpt)/(Dy/Ppt) x_ (dPpt/dtxt)/(Ppt/tKt) x Dy x (dtxt/txt)}

- _+ {(dDy/dPpt)/(Dy/Ppt) x_ (dPpt/dtpt)/(Ppt/tpt) x Dy x (dtpt/tpt)}

- _+ {(dDwy/dPwy)/(Dwy/Pwy)_ x Dwy x (dPw%y/Pwy)}

- _+ {(dDwy/dPwt)/(Dwy/Pwt) x_ Dwy x (dPwl/ Pwt)}

# where

- _(dSy/dPcc)/(Sy/Pcc) (dSy/dPpy)/(Sy/Ppy) (dDy/dPcy)/(Dy/Pcy)_

- _(dDy/dPpt)/(Dy/Ppt)_

- _(dSy/dPwc)/(Swy/Pwc) (dSwy/dPwy)/(Swy/P wy) (dDwy/dPwy)/(Dwy/Pvwy) (dDwy/dPwt)/(Dwy/Pwt)_

- = Supply elasticity of yarn with respect to cotton consumer price, India

- = Supply elasticity of yarn with respect to yarn producer price, India

- = Demand elasticity of yarn with respect to yarn consumer price, India = Demand elasticity of yarn with respect to textiles producer price, India = Supply elasticity of yarn with respect to cotton consumer price, World = Supply elasticity of yarn with respect to yam producer price, World

- = Demnand elasticity of yarn with respect to yarn consumer price, World = Demand elasticity of yarn with respect to textiles producer price, World

# Textiles Market (India and the Rest of the World)

- _{(dSt/dPcy)/(St/Pcy)_ x (dPcy/dPwy)/(Pcy/Pwy) x St x (dPwy/Pwy)}

- **_+_** _{(dSt/dPcy)/(St/Pcy) x_ (dPcy/dtxy)/(Pcy/txy) x St x (dtxy/txy)}

- **_+_** _{(dSt/dPcy)/(St/Pcy)_ x (dPcy/dtcy)/(Pcy/tcy) x St x (dtcy/tcy)}

- _+ {(dSt/dPpt)/(St/Ppt)_ x (dPpt/dPwt)/(Ppt/Pwt) x St x (dPwt/Pwt)}

- _+ {(dSt/dPpt)/(St/Ppt) x_ (dPpt/dtxt)/(Ppt/txt) x St x (dtxt/txt)}

- _+ {(dSt/dPpt)/(St/Ppt) x_ (dPpt/dtpt)/(Ppt/tpt) x St x (dtpt/tpt)}

- _+ {(dSwt/dPwy)/(Swt/Pwy) x_ Swt x (dPwy/Pwy)}

- _+ {(dSwt/dPwt)/(Swt/Pwt) x_ Swt x (dPwt/Pwt)}

- _= {(dDt/dPct)/(Dt/Pct) x (dPct/dPwt)/(Pct/Pwt) x_ Dt x (dPwtlPwt)}

- **_+_** _{(dDt/dPct)/(Dt/Pct) x (dPct/dtxt)/(Pct/txt)_ x Dt x (dtxt/txt)}

- **_+_** _{(dDt/dPct)/(Dt/Pct) x (dPct/dtct)/(Pct/tct, x_ Dt x (dtct/ tct)}

- **_+_** _{(dDwt/dPwt)/(Dwt/Pwt) x_ Dwt x (dPwt/Pwt)}

# where

- _(dSt/dPcy)/(St/Pcy) (dSt/dPpt)/(St/Ppt) (dDt/dPct)/(Dt/Pct)_

- = Supply elasticity of textiles with respect to yarn consumer price, India

- = Supply elasticity of textiles with respect to textiles producer price, India

= Demand elasticity of textiles with respect to textiles consuner price, India 36
Figure A3: Specification Charts for Variance of Daily Rainfall <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.1 .1<br>0 0<br>-.1<br>-.1 -.2<br>* Weather Product * -.2 * Weather Product * -.3<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>1 .2<br>.5 .1<br>0<br>0<br>-.1<br>* Weather Product TAMSAT* -.5 * Weather Product TAMSAT*<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.05 .2<br>.15<br>0 .1<br>.05<br>-.05 0<br>* Weather Product * * Weather Product * -.05<br>TAMSAT -.1 TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

51
# **PATHWAYS FROM JOBS TO SOCIAL COHESION**

## **Frank-Borge Wietzke ***

JEL Codes I31, J01, 015 Keywords: labor markets, social cohesion, subjective wellbeing Acknowledgements: A previous version of this paper was prepared as a background document for the 2013 World Development Report. Earlier drafts of the paper received thoughtful comments and suggestions from Ambar Narayan, Jesko Hentschel, Dena Ringold, Kathleen Beegle, Emmanuel Jimenez and three anonymous reviewers. I am also grateful for research assistance from Catriona McLeod.

> ***** London School of Economics, Department of International Development, Houghton Street, WC2A 2AE, UK. Email: f.b.wietzke@lse.ac.uk

<mark>1</mark>
<!-- Start of picture text --> Figure 2: Incidence of Sovereign Default<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1800 1850 1900 1950 2000<br>Fraction of Non-OECD Countries in Default<br><!-- End of picture text --> 36
high technology goods. Evidence on this channel is, for example, provided by Caselli and Coleman (2001). They show that high levels of educational attainment are important determinants of adopting computer technology. Given the positive correlation between schooling and returns to experience as well as schooling and openness, controlling for the average level and the quality of schooling in the second stage regression is likely to reduce the coe¢ cient on openness.

Information on average years of schooling in the home country is taken from Barro and Lee (1993). This variable is only available for a subset of the sample. Regressing log Open on estimated returns to home country experience in the sample for which data on average years of schooling is available does not substantially alter the coe¢ cient on log Open, as can be seen in speci...cation (I) of Table 6.

As expected, returns to experience are higher in countries that have a higher level of average schooling. Controlling additionally for average years of schooling reduces the coe¢ cient on log Open. The coe¢ cient on average years of schooling is, however, only signi...cant when using the entire sample and as long as GDP per capita is not added to the set of explanatory variables. Similarly, other measures of the quantity of schooling, e.g. the log of average years of schooling and the percentage of the population with primary and secondary school completed, are not signi...cantly associated with estimated returns to experience when controlling for openness and GDP per capita. Average years of schooling is the variable that maximizes the R-square. Measures of school quality, such as the quality indices from Hanushek and Kim (1999) and measures such as the pupil-teacher ratio and real expenditure per pupil from Barro and Lee (1993) do not yield statistically signi...cant results. Since the coe¢ cients on measures of quality and quantity of schooling turn insigni...cant once GDP per capita and openness are controlled for and since adding them to the regression has only a negligible e¤ect on the R-square, the following results will be presented without controlling for measures of schooling.<sup>21</sup> Not only GDP per capita and educational attainment are likely to a¤ect the return to home country experience. On-the-job accumulation of human capital occurring in the manufacturing sector is likely to be more highly valued by the US labor market than skills acquired in the agricultural sector. Conditional on regional dummies, the partial correlation coe¢ cient between

> 21 The R-square of a regression of returns to experience on log Open, log of GDP per Capita and regional dummies in the sample that provides information on average years of schooling is 0.303 for all countries, 0.295 for the non-Oil countries and 0.133 for the non-OECD/non-Oil Countries.

14
ranked probability score (CRPS). Besides, the BCEF forecasts are compared with “naïve” density forecasts. This naïve forecasts are time-inexpensive and only require the standard deviation of historical forecast errors of world GDP growth predictions and a normality distribution assumption. Unweighted and weighted CRPS scores show that the BCEF fan charts are more accurate and reliable than the “naïve” density forecasts. In terms of forecast accuracy and reliability, the results show there is an incentive to invest in a method like the BCEF presented in this paper.

Furthermore, by construction, the BCEF results include variance and skewness decompositions of the density forecasts. For instance, the August 2015 density forecast indicates that around 68 percent of the world GDP growth uncertainty  measured via the  variance of the predictive distribution is directly explained with the four selected risk factors, whereas “other” risk factor components explain the rest of this predictive variance. In terms of the skewness decomposition, crude oil prices and “other” risk factor components are the elements that contribute more to the direction and magnitude of the world GDP growth predictive skewness across the three forecast horizons.

We describe the BCEF procedure in the following sections of the paper. Econometric considerations, unobserved parameters, distributional assumptions, prior information, and the BCEF minimization problem is explained in Section 2. The BCEF method is tested in Section 3, producing density forecasts to predict world GDP growth. The practical exercise targets world GDP growth, which is a variable that faces—ex-ante and ex-post—concerns of being incorrectly known, incorrectly quantified, or both. Risk factors affecting world GDP growth, prior values, the estimated density forecast series, and their evaluation scores are also discussed in Section 3. Finally, the main conclusions of this study are presented in Section 4.

# 2. THE MODEL

The introduced BCEF technique allows for the quantification of risks in the prediction of a studied variable. The BCEF procedure can be applied to predict economic variables; however, it is also suitable in other fields where unobserved information prevails. Examples of studied or 5
# **1. Introduction**

As is well known, the promotion of gender equality is one of the “Millennium development goals.” Female workers seem to be more exposed to vulnerability at work than males (e.g., United Nations, 2009; ILO, 2009). Despite recent improvements, women exhibit a higher involvement in unpaid (but productive and time consuming) domestic and care activities in developed and developing countries (Blau and Kahn, 2000). The main body of evidence suggests the persistence of a gender wage gap (i.e., ratio of female to male wages) after adjusted for worker characteristics (e.g., Aguallo-Tellez, 2011). However, the causes behind these statistics are complex and multifaceted and a variety of factors other than discrimination may bear to some extent on the different situations of female and male workers (Ganguli et al., 2011; Fernandez, 2007). Discrimination seems to exist but a precise estimation of its magnitude is difficult (Blau and Kahn, 2006). Although the first studies go back to Becker (1957) and Arrow (1971), analysis of discrimination remains a challenging topic from a theoretical and empirical perspective.

In this paper we extend previous analysis based on a computable general equilibrium (CGE) model for Tanzania (Jensen et al., 2010), in order to focus on its gender implications. Given the arguments in favor of a uniform tariff (Tarr, 2002), we analyze the effects of tariff reform, as well as the reduction of regulatory barriers to the provision of specialized services by domestic or foreign firms through foreign direct investment (FDI). How do foreign trade and the entry of new domestic or foreign firms affect female and male workers? To what extent would men and women be reallocated across sectors? What would be the effects on their wages? We aim to shed light on these questions. To this end, we first work on the data that are publicly available from the National Bureau of Statistics of Tanzania (NBS, 2002a). We produce an ambitious three-dimensional (sex-sector-skill) data set for 52 sectors with factors of production distinguished by four skill levels and by sex.

Our CGE methodology seems particularly well suited for in-depth analyses involving feedback effects across sectors of the economy. Another advantage is its general equilibrium perspective, which allows us to take into account the income and demand side of the economy, as well as the interplay of goods and factor markets. We extend the model of Jensen et al. (2010), which in turn builds on the more stylized theoretical model of Markusen et al. (2005), to include actual gender and factors of production data on the Tanzanian economy. The model therefore exhibits innovative features, compared to other CGEs, such as the presence of FDI and multinational enterprises (MNEs) in advanced services sectors. This, together with the incorporation of a Dixit-Stiglitz-Ethier mechanism in imperfectly competitive sectors, makes it possible to capture potential increases in wages and workers’ reallocation throughout the 2
<!-- Start of picture text --> 2013 2016<br>5<br>> 0<br>7)<br>5 2019<br>a 5<br>250 In<br>4 6 8 10 12 84 6 8 Oe10 12ae<br>LN TFP<br>= exiter ==" survivor<br>Top and bottom 1% and CIIU sectors O, P, Q are omitted Active firms are those reporting sales or labor differnte than zero.<br><!-- End of picture text -->
Table 6.5—Regression results of logarithm of monthly earnings from the latest job, for full sample and trimmed and weighted sample

||Full Sample<br>Model 1||Trimmed an<br>Model|d Weighted Sample<br>2|
|---|---|---|---|---|
|Years of schooling|0.0864***|(6.02)|0.0894***|(5.39)|
|Age|0.519***|(3.19)|0.557***|(3.66)|
|Age squared|-0.0125***|(-2.97)|-0.0139***|(-3.55)|
|TEEP cohort*TEEP province|0.0424|(0.15)|0.156|(0.59)|
|Years of schooling*TEEP cohort*TEEP province|-0.0204|(-0.62)|-0.0310|(-1.00)|
|TEEP cohort|-0.00543|(-0.02)|-0.230|(-0.85)|
|Years of schooling*TEEP cohort|-0.0463|(-1.52)|-0.0313|(-1.09)|
|Female*TEEP cohort*TEEP province|0.234**|(2.13)|0.272**|(2.26)|
|Female|-0.495***|(-3.20)|-0.333*|(-1.83)|
|Female*Years of schooling|0.0280*|(1.90)|0.0165|(0.99)|
|Female*TEEP cohort|0.122|(1.24)|0.0646|(0.58)|
|Mother’s years of schooling|0.00956|(1.39)|0.0165**|(2.02)|
|Mother’s age|-0.000262|(-0.12)|-0.00169|(-0.80)|
|Log(consumer durables)|0.0455***|(3.60)|0.0474***|(3.74)|
|Height|0.00569*|(1.92)|0.00613*|(1.78)|
|Number of students per classroom in high school|-0.000602|(-0.68)|0.000581|(0.52)|
|If phone interview|0.233***|(4.62)|0.242***|(3.18)|
|School fixed effects|yes||yes||
|Number of observations|1,716||1,592||

Source: TEEP Tracking Survey. Notes: t values are in parentheses. * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

41
level, and employment information is often difficult to obtain and potentially manipulatable by households. The impact of the decision to exclude this information is explored further in Section (5).

The final Sudan national PMT presented in Table 3 has an adjusted R-Squared just above 0.57, which is in the range of 0.40 to 0.65 commonly reported in other PMT studies (Brown, Ravallion, and Van de Walle 2018; Sebastian et al. 2018). The number of household members is the largest contributor to the PMT score, with larger households having a lower score. This is typical of PMT models because welfare is defined in per capita terms and, thus, household size tends to be an important predictor of welfare (Sebastian et al. 2018). Additional socioeconomic and demographic characteristics of household members contribute to the PMT score. For example, the dependency ratio, measured as the ratio of the sum of household members younger than 15 and 65-years-old and older to total household members, contributes negatively to the PMT score. Lower schooling levels among household members are also associated with a lower household PMT score and households that rely on external aid or farming have a lower PMT score. Households headed by a female also have a lower PMT score.

Housing and asset ownership contribute to the household’s PMT score in expected ways. Houses with more rooms per household member have higher PMT scores, while houses made of mud, wood, and straw, as well as with dirt floors, have lower PMT scores. Lack of access to household services, such as lighting, drinking water, and improved cooking fuels, was also associated with lower PMT scores. Additionally, not having assets, such as a motorcycle, motor vehicle, radio, computer, refrigerator, television, air conditioner (AC), or mobile phone, was also associated with a lower PMT score.

To evaluate the performance of the national PMT in identifying the poor for inclusion in welfare programs, we explore the PMT’s performance in identifying the bottom 20%, bottom 40% and the bottom 60% of the population. To do this, we set the PMT cutoff score equal to the observed per capita household consumption that identifies this group in the bottom of the consumption distribution, although in practice this PMT cutoff score could be set to any value based on program size and program objectives.

For comparison purposes, we evaluate model performance relative to randomly drawing beneficiaries from the sample of households surveyed in the NHBPS 2014/2015.<sup>18</sup> Table 4 below presents targeting results based on a random sample of household beneficiaries. In this case each household has a probability of being included in the social program equal to the household poverty

> 18 It may be the case that in practice a program not using a PMT could do better than randomly admitting applicants from the population, but a program could also do worse by admitting wealthy applicants not intended for the program.

14
- 30 -

return would increase from 28.8 percent to 29.6 percent. However, provided that climate changes affect hydrology from the beginning of the plant operation, the rate of return to the Thac Mo project would rise to about 35 percent. This may indicate a pitfall in assessing a hydropower project in economic terms; the future climate change impacts are generally underestimated in the IRR calculation, even though they are environmentally and socially significant.

In the Upper Kotmale case, a substantial increase in electricity production could be expected because of its margin of installed capacity, resulting in a higher IRR of 6.4 percent. The existing daily storage does not directly affect this result. These pieces of evidence suggest that having larger installed capacity and some storage capacity might be well worth consideration.

Table 12. Impact of Changes in Hydrology on Internal Rate of Return

||Or|iginalproject <br>Dynamicfore|cost<br>casts (2025)|50% addition<br>for a 6-month s|al project cost<br>torage capacity|
|---|---|---|---|---|---|
||Baseline|Without<br>storage|With current<br>storage|Baseline|Dynamic<br>forecasts (2025)|
|Vishnugad Pipalkoti (India)|15.8%|16.1%|16.1%|10.9%|11.9%|
|Upper Kotmale (Sri Lanka)|4.7%|6.4%|6.4%|1.2%|3.2%|
|ThacMo (Vietnam)|29.0%|28.8%|29.6%|21.9%|22.6%|

Source: Author's estimates. Note: The baseline scenario assumes hydrological series in a 90 percent dependable year without major storage capacity.

> Importantly, the above discussion _does_ ignore the likely implication on the cost side. Larger installed capacity must of necessity bring about higher construction costs. If a large-scale storage capacity is planned, the additional costs would be enormous not only financially but also socially. For example, consider a 50 percent increase in total project costs for

constructing a six month storage capacity. The optimal size of reservoir ranges from several hours to over a year, depending on geological and environmental conditions and operational objectives. The storage capacity selected here is roughly equivalent to the gross storage at the full reservoir level of the Thac Mo Reservoir. Note that in the Thac Mo case the project cost does not include any fraction of past investment in the original Thac Mo Reservoir; thus, this additional cost scenario may still be meaningful even in the Thac Mo case.