rafmacalaba/datause-encoder-data huggingface dataset:
# <u>Table A14. Socioemotional skills factors and earnings for Women in Pakistan</u>

||**Co**<br>_M_<br>|**nscientiousness**<br>(1)<br>_onthly earnings_<br>|<br>|**Open**<br>_M_<br>|**ness to experien**<br>(2)<br>_onthly earnings_<br>|**ce**<br>|**A**<br>_M_<br>|**greeableness**<br>(3)<br>_onthly earnings_<br>|<br>|
|---|---|---|---|---|---|---|---|---|---|
||_All_|_Working_|_women_|_All_|_Working_|_women_|_All_|_Workin_|_g women_|
||_Mean_|_Mean_|_Median_|_Mean_|_Mean_|_Median_|_Mean_|_Mean_|_Median_|
|Years of schooling (a1)|1.36***|8.10*|6.63**|1.32***|7.74*|4.92*|1.39***|8.17**|5.75**|
||(0.43)|(4.14)|(3.06)|(0.43)|(4.28)|(2.84)|(0.43)|(4.09)|(2.83)|
|Cognitive skills (a2)|3.22*|2.17|-6.60|3.14*|1.53|-0.96|3.05*|1.97|-3.51|
||(1.81)|(18.5)|(13.2)|(1.79)|(18.1)|(13.1)|(1.81)|(18.1)|(11.2)|
|Socioemotional skills (a3)|-0.024|-5.46|-0.94|1.30|8.71|1.84|-1.92**|1.81|1.93|
||(0.78)|(9.96)|(8.65)|(0.79)|(8.57)|(7.07)|(0.83)|(8.67)|(7.92)|
|Constant|-8.00*|-20.6|-11.0|-8.35*|-19.5|2.52|-6.81|-22.7|7.62|
||(4.23)|(47.6)|(40.1)|(4.50)|(49.2)|(49.6)|(4.12)|(49.7)|(48.6)|
|Observations|1927|111|111|1927|111|111|1927|111|111|
|R-squared|0.070|0.25|0.22|0.070|0.25|0.22|0.070|0.25|0.22|
|Median/Mean dependent|7.84|107.43|57.69|7.84|107.43|57.69|7.84|107.43|57.69|

||_M_|**Extraversion**<br>(4)<br>_onthly earnings_||**Em**<br>_M_|**otional stabilit**<br>(5)<br>_onthly earnings_|**y**<br>|_M_|**Grit**<br>(6)<br>_onthly earning_|_s_|
|---|---|---|---|---|---|---|---|---|---|
||_All_|_Working_|_women_|_All_|_Workin_|_g women_|_All_|_Workin_|_g women_|
||_Mean_|_Mean_|_Median_|_Mean_|_Mean_|_Median_|_Mean_|_Mean_|_Median_|
|Years of schooling (a1)|1.36***|8.19**|6.76***|1.35***|7.55*|6.44***|1.36***|8.15*|6.20**|
||(0.43)|(4.04)|(2.27)|(0.42)|(4.11)|(2.43)|(0.43)|(4.14)|(2.43)|
|Cognitive skills (a2)|3.26*|1.90|-4.63|3.24*|4.33|-3.40|2.98|1.75|-6.24|
||(1.81)|(18.1)|(10.5)|(1.80)|(18.5)|(11.2)|(1.84)|(18.5)|(10.5)|
|Socioemotional skills (a3)|-0.38|0.49|-1.82|1.24|13.8|3.41|1.40*|2.52|-8.92|
||(0.72)|(7.28)|(8.28)|(1.34)|(11.9)|(12.8)|(0.76)|(8.99)|(6.54)|
|Constant|-7.90*|-24.8|-9.61|-8.09*|-0.76|-8.19|-7.83*|-24.1|-23.5|
||(4.15)|(46.0)|(48.0)|(4.72)|(46.0)|(51.3)|(4.10)|(47.3)|(49.2)|
|Observations|1927|111|111|1927|111|111|1925|111|111|
|R-squared|0.070|0.25|0.23|0.070|0.26|0.22|0.070|0.25|0.23|
|Median/Meandependent|7.84|107.43|57.69|7.84|107.43|57.69|7.84|107.43|57.69|

* p<.10  ** p<.05  *** p<.01

_Notes_ . This table reports estimates of the relationships between schooling, skills, and earnings for women in Pakistan. The dependent variable “monthly earnings” is the raw monthly earnings for median regressions; they are top-coded at 100,000 PKR per month (961.5 USD) for mean regressions. The cognitive index is the mean of the Urdu, Mathematics, and 97
O.A.M.D.G.

0 0 _E_  _R C_ , _V_   _E_  _R C_  . Treatment assignment _V_ and average response ( _R_<sup>_1_</sup> , _R_<sup>_0_</sup> ) are conditionally mean independent, given _C_ . The role of the relevant covariates _C_ is to ‘match’ those treated and not treated with violence, in order to identify the effect of violence on the household response.<sup>17</sup> In this context, the matching covariates are not given a causal interpretation for the household behavior; nonetheless their association with the outcomes will be interesting to observe. It is important that none of the control variables are caused directly by violence, because this would take away from the ‘pure effect’ in the coefficient on the violence variables. When specifying our approach below, we discuss for each selected variable why it fulfils this criterion. However, including variables that are potentially endogenous to the response (but not violence) will not bias the coefficients on the violence variables, as long as the assumption of conditional independence holds.

Regarding exogeneity of violence, we do not find any support in the Colombian literature that households of SISBEN 1 (poorest sixtile) may be attractive to criminals because of their savings. There is a link from household wealth to property crime, as shown by Gaviria and Pages (1999) and Gaviria and Vélez (2001) for urban households, but it is unlikely to hold for the poor rural households of our sample. As we assume that migration is relevant for savings we also have to take into account that displacement and migration may already have influenced the mix of households we observe in the Familias survey, and thereby their savings preferences. We apply Heckman’s method for selection correction in the OLS approach. Unfortunately our data does not allow us to apply a weighted likelihood after Boyes, Hoffman and Low (1989) to correct the non-linear

> 17 Other papers using the matching assumption for identification are for example Heckman, Ichimura and Todd (1997) and Blundell, Costa Dias, Meghir and Van Reenen (2004).
Figure 1 <!-- Start of picture text --> Relationship Between Infant Mortality Rate and GNP per capita, 1994<br>Infant mortality rate<br>(per thousand live births)<br>140<br>OBhutan<br>120 -<br>100 ONepal<br>* Lao PDR<br>Bangladesh<br>80 - Myanmar<br>ndia<br>60 - 0 Papua New Guinea<br>0 Mongo Indonesia<br>40 - Viet Nam * Philipl s<br>OChina OThailand<br>20 0 Sr Lanka a<br>0<br>0 500 1,000 1,500 2,000 2,500 3,000 3,500<br>GNP per capita (US$)<br><!-- End of picture text --> Figure 2 <!-- Start of picture text --> Relationship Between Life Expectancy at Birth and GNP per capita, 1994<br>Life expectancy at birth (year)<br>80<br>Sri Lanka mtys<br>70 Chinailand S-<br>Viet Nam ° China P____ThJi _____<br>60 _ r ia P rinesia<br>O Ngidepal * Papua Now Guinea<br>50_ Lao PDR<br>so p cBhutan<br>40-<br>30-<br>20-<br>10<br>0<br>0 500 1,000 1,500 2,000 2,500 3,000 3,500<br>GNP per capita (US$)<br><!-- End of picture text -->
# **Figure 4**

Marginal Effect of Mobile Cell Subscriptions by Intensity of COVID <!-- Start of picture text --> 2 3 4 5 6 7 8 9 10 11 12 131415<br>COVID Growth Shock (2020)<br>.4<br>.3<br>.2<br>.1<br>Change in Digital Payments from<br>Change in Mobile Cell Subscriptions<br><!-- End of picture text --> Notes. Figure derived from Table 5, model 4. Horizontal scale is logarithmic. The line reflects average marginal effects, the whisker endpoints mark the 90% confidence intervals.

43
**Policy Research Working Paper Series**

|**Titlb**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS**1**64 Power, Distortions,<br>Revolt, and<br>Reform in Agricultural<br>Land Relation|Hans P. Binswanger<br>s Klaus Deininger<br>Gershon Fader|July 1993|H. Binswanger<br>31871|
|WPS**1**65 Social Costs of the Transition to<br>Capitalism:<br>Poland, 1990-91|Branko Milanovic|August 1993|R. Martin<br>39026|
|WPSI 166 The Behavior of Russian Firms in<br>1992: Evidence<br>fromaSurvey|Simon Commander<br>Leonid Liberman<br>Cecilia Ugaz<br>Ru. an Yemtsov|August 1993|0. del Cid<br>35195|
|WPS**1**67 Unemployment<br>and Labor Market<br>Dynamics in Russia|Simon Commander<br>Leonid Liberman<br>Ruslan Yemtsov|August 1993|0. del Cld<br>35195|
|WPS1 168 How Macroeconomic<br>Projections<br>in Policy Framework<br>Papers for the<br>Africa Region Compare with Outcom|Rashid Faruqee<br>es|August 1993|N. Tannan<br>34581|
|WPS**1**69 Costs and Benefits of Debt and<br>Debt Service Reduction|Eduardo Fernandez-Ari|as August 1993|R. Vo<br>33722|
|WPS**1**70 Job Search by Employed<br>Workers:<br>The Effects of Restrictions|Avner Bar-Ilan<br>Anat Levy|August 1993|D. Ballantyne<br>37947|
|WPS117'i Finance and Its Reform: Beyond<br>Laissez-Faire|Gerard Caprio, Jr.<br>Lawrence<br>H. Summers|August 1993|P. Sintim-<br>Aboagye<br>38526|
|WPS**1**72 Liberalizing<br>Indian Agriculture:<br>An Agenda for Reform|Garry Pursell<br>Ashok Gulati|September<br>1993|D. Ballantyne<br>37947|
|WPS**1**73 Morocco's<br>Free Trade Agreement<br>with<br>the European Community:<br>A Quantitative Assessment|Thomas F. Rutherford<br>E. E. Rutstr6m<br>David Tarr|September<br>1993|N. Artis<br>38010|
|WPS1 174 Asian Trade Barriers Against Primary<br>and Processed<br>Commodities|Raed Safadi<br>Alexander Yeats|September<br>1993|J. Jacobson<br>33710|
|WPS**1**75 OECD Trade Barriers Faced by the<br>Successor<br>States of the Soviet<br>Union|Bartlomiej<br>Kaminski<br>Alexander Yeats|September<br>1993|J. Jacobson<br>33710|
|WPS**1**76 Cash Social Transfers,<br>Direct Taxes,<br>and Income Distribution<br>in Late<br>Socialism|Branko Milanovbc|September<br>1993|R. Martin<br>39065|
|WPS**1**77 Environmental<br>Taxes and Policies<br>for Developing<br>Countries|Neil Bruce<br>Gregory M. Ellis|September<br>1993|C. Jones<br>37699|
Turning from participation to the nature of work, workers in the Northern and Eastern provinces on average participate in more productive sectors than workers in provinces other than Western. Compared to workers outside Western province, workers in the North and East are less likely to be in agriculture, which is the least productive sector. A common measure of productivity is value added per worker. According to data from the Central Bank of Sri Lanka, the value added per worker in 2014 was about SL Rp 40,000 in agriculture, compared with SL Rp 144,000 in industry and SL Rp 285,000 in services. The service sector is the largest employer in the Northern and Eastern provinces. The industrial sector employs less than a quarter of the population in the Northern and Eastern provinces, similar to provinces other than Western.

**Figure 4: Workers in the Northern and Eastern provinces are less likely to be in agriculture and in paid employment than provinces other than Western**

<!-- Start of picture text --> a. Industry of employment b. Type of employment<br>29%<br>47% 48% 38% 36% 41% 46%<br>61%<br>23%<br>20% 23%<br>71%<br>64% 59% 54%<br>33%<br>39%<br>33% 29%<br>6%<br>Northern Province Eastern Province Western Province Other Provinces Northern Province Eastern Province Western Province Other Provinces<br>Agriculture Industry Services Paid employee/employer Self‐employed/non‐paid employee<br>Source: Labor Force Survey 2015 (Sample: 15+ year olds) Source: Labor Force Survey 2015 (Sample: 15+ year olds)<br><!-- End of picture text --> Paid employment is more prevalent in the Northern and Eastern provinces than in the provinces other than Western, with 64% and 59% of employed individuals in the Northern and Eastern provinces reporting to be paid employees or employers, compared to 54% in provinces other than Western (Figure 4). Self‐ employment in the service sector includes jobs such as retail trade that do not provide regular income or are not as reliable as paid employment. The fact that these types of jobs are most common in the Northern and Eastern provinces suggests that although the level of employment in the Northern and Eastern provinces is poorer than the national average, those who are working in the North and East are generally working in more productive sectors of the economy than workers in other provinces besides Western. As discussed below in section 3, the occupations of workers in the Northern and Eastern provinces are largely similar to those in provinces other than Western. Roughly half and two‐fifths of the working population in the Northern and Eastern provinces work in elementary occupations (such as farming or fishery); in provinces other than Western, the comparable share is about half. Finally, among wage workers, wage earnings in the Northern province are similar to the other provinces besides Western Province, with wage earnings in Eastern province slightly higher (Table 2). This pattern holds throughout the wage distribution.

6
# Ghana Cocoa-Maize price ratio Ratio of domestic to world price ratios

<!-- Start of picture text --> percentage<br>100<br>80<br>60<br>40<br>20 -<br>1953 1958 1963 1968 1973 1978 i983<br><!-- End of picture text --> year <!-- Start of picture text --> 9A dom/S price ratios<br>Figure<br><!-- End of picture text --> Ratio to 3yr **MA world** price ratio
Table A.3: Baseline Parameters (continued)

|Parameter|Description|Units|Value|
|---|---|---|---|
|_γ_<sup>_g_</sup>|AIDADS Subsistence Parameter for Processed<br>Grain Products||0.33|
|_γ_<sup>_l_</sup>|AIDADS Subsistence Parameter for Processed<br>Livestock Products||0.003|
|_γ_<sup>_e_</sup>|AIDADS Subsistence Parameter for Energy Ser-<br>vices||0.03|
|_γ_<sup>_w_</sup>|AIDADS Subsistence Parameter for Processed<br>Timber Products||0.03|
|_γ_<sup>_r_</sup>|AIDADS Subsistence Parameter for Ecosystem<br>Services||0.03|
|_γ_<sup>_o_</sup>|AIDADS<br>Subsistence<br>Parameter<br>For<br>Other<br>Goods and Services||0.35|
|_A_|AIDADS Utility Function parameter||1|
|_δ_|Social Discount Rate||0.015|
|_π_1|Scrap Price of Unmanaged Forests||50|
|_π_2|Scrap Price of Commercial Forests||10|
|_δ_|Social Discount Rate||0.015|

12
In general, we may expect a great deal of heterogeneity in the relative qualities Ric across goods within a country’s export basket. To evaluate the central tendency of the relative quality of a country’s export basket, while minimizing the role of outliers, we take the median of the values of Ric for that country:

# III.1 Quality Differences across Countries

For most of our analysis, we compare the relative unit value distributions of OECD (pre-1995 definition) with non-OECD countries, with the two groups denoted by the index G. For any group (OECD/Non-OECD)’s export basket we have where R<sup> ̃</sup> G denotes the median relative quality of the basket of exports by the group. Values of R<sup> ̃</sup> G may be obtained as coefficients in a median regression of the group-good averages described above on group fixed effects: where DOECD is an indicator variable denoting membership in the OECD. The first column of Table 1 presents these regression results. As expected, OECD countries are at the higher end of the relative quality distribution, R<sup> ̃</sup> G = 0.58, compared to non-OECD countries, R<sup> ̃</sup> G = 0.37 (results raised to 10<sup>2</sup> in the table for clarity). This is not too surprising and is consistent with earlier work, including Schott (2004). Quantitatively, however, the median 6
<u>Table 1. Competition, Bids and Bundling</u>

|||Num|ber of bidde|rs|||Bid /|Estimated c|ost||
|---|---|---|---|---|---|---|---|---|---|---|
||Obs.|Mean|Std.Dev.|Min|Max|Obs.|Mean|Std.Dev.|Min|Max|
|All|69|5.04|3.13|2|16|301|1.27|1.19|0.13|11.30|
|Only treatment plant work|21|5.43|3.37|2|14|90|1.19|0.32|0.53|2.28|
|Only distribution network work|35|5.23|3.29|2|16|167|1.08|0.39|0.51|2.56|
|Both|7|3.29|1.11|2|5|22|2.36|3.42|0.13|11.30|

Source: Author’s calculation.

Figure 2. Probability Distribution of Normalized Bids <!-- Start of picture text --> 0 .5 1 1.5 2 2.5<br>Bid / Engineering cost estiamte<br>.2<br>.15<br>Fraction .1<br>.05<br>0<br><!-- End of picture text --> Source: Author’s calculation.

There are two possible reasons for high costs of the bundled approach, though related to one another. First the bundling might increase the entry cost for potential bidders, thus limiting participation and deteriorating auction efficiency (i.e., increasing the equilibrium bid). According to the simple statistics in Table 1, the number of firms that can implement both seems to be small. This would obviously explain the relatively high bids, but maybe partly. For technical reasons, second, there might be no significant cost advantage of engaging the two different activities together. Rather, it could be more costly to internalize and coordinate them within a single company (i.e., diseconomies of scope). This might be the case if the required knowledge, skills and workers are not substitutable between the two works. Conversely, if there are economies, it means that the observed bids would be rather higher without scope economies.

To estimate the (adverse) entry effect of bundling separately from the effect of bundling on the equilibrium bid through the underlying firm cost structure, a two-stage approach is used. Following the earlier empirical auction literature (e.g., Porter and Zona, 1993; Gupta, 2002;
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## **3.2.1 A statistical validation of the instrument**

We test the validity of our instrument by assessing the probability that labor productivity performance has led to the installation of coal-fired power stations. This is done by assessing the effect of labor productivity on coal-fired stations. We include lagged and lead effects to rule out endogeneity. Lead effects are included to rule out the anticipated productivity effects on an expansion in electricity provision by building new plants, while lagged effects are used to rule out the impact of previous growth effects on the supply of electricity from coal-fired power plants. We also control for other effects, such as city sales and wages. We use a standard logit regression: The vector _X_ contains fixed effects, as well as the various lags and leads of labor productivity, while our dependent variable is a dummy ( _Dj,k,t_ ), taking the value of 1 in the year the power plant was built in city _j_ of region _k_ at time _t_ , and 0 otherwise.

Table 1 summarizes the results for our orthogonality test. Both lead and lagged labor productivity do not significantly impact the choice of building power plants. We conclude that our narrative regarding the exogeneity or the validity of the instrument is supported by the standard F-stat statistic (see IV first-stage estimates below), as well estimates from labor productivity on building power plants.

**Table 1:** Impact of labor productivity on building power plants

||Dummy|Dummy|
|---|---|---|
|log <sup>_Yi,t−_1</sup><br>_Ni,t−_1|-0.06<br>(0.03)||
|log <sup>_Yi,t_+1</sup><br>_Ni,t_+1||-0.06<br>(0.03)|
|FE (Year)|Yes|Yes|
|FE (Sub-Sector)|Yes|Yes|
|FE (Province)|Yes|Yes|
|Observations|35,197|35,197|
|Pseudo_R_<sup>2</sup>|0.32|0.33|

*** Notes: Robust standard errors in parenthesis. _p <_ 0 _._ 01,** _p <_ 0 _._ 05, * _p <_ 0 _._ 1

# **4 Structural estimates of pollution on labor productivity**

In order to analyze the impact of pollution on labor productivity, we estimate the following panel regression, where _Yi,j,k,t_ is the log of labor productivity in sector _i_ in city _j_ and in region _k_ , with _n ×_ 1 observations. The set X _i,j,k,t_ gives the control variables, with size _n × p_ , _POL_ ˆ _j,k,t_ stands for log pollution (mean PM2.5 concentrations), and _εi,j,k,t_ is the residual, which is assumed to be normally distributed _εi,j,k,t ∼ N_ (0 _, σ_<sup>2</sup> ). Finally, _t_ stands for the time indicator for region _k_ . The parameter _φ_ should be interpreted as an elasticity. In Appendix A we illustrate how the elasticity is mapped to an otherwise standard macroeconomic model.

In (2) we control for unobserved sector specific labor productivity fixed effects ( _αi_ ), unobserved city effects ( _αj_ ), and time fixed effects ( _αt_ ), capturing yearly trends, as well as region-time fixed effects ( _αk,t_ ). As 6
Policy Research Working Paper 5904

# **Abstract**

This is the first paper to build a comprehensive empirical picture of power pricing practices across Sub-Saharan Africa, based on a new database of tariff structures in 27 countries for the years 2004–2008.

Using a variety of quantitative indicators, the paper evaluates the performance of electricity tariffs against four key policy objectives: recovery of historic power production costs, efficient signaling of future power production costs, affordability to low income households, and distributional equity.

As regards cost recovery, 80 percent of the countries in the sample fully recover operating costs, while only around 30 percent of the countries are practicing full recovery of capital costs. However, due to the fact that future power development may be based on a shift toward more economic technologies than those available in the past, existing tariffs look as though they would be consistent with Long Run Marginal Costs in nearly 40 percent of countries and hence provide efficient pricing signals.

As regards affordability, today’s average effective tariffs are affordable for 90 percent of today’s customers. However, they would only be affordable for 25 percent of households that remain unconnected to the grid. Tariffs consistent with full recovery of economic costs would be affordable for 70 percent of the population. As regards equity, the highly regressive patterns of access to power services, ensure that subsidies delivered through electricity tariffs are without exception also highly regressive in distributional incidence.

The conclusion is that achieving all four of these policy objectives simultaneously is almost impossible in the context of the high-cost low-income environment that characterizes much of SSA today. Hence most countries find themselves caught between cost recovery and affordability.

This paper is a product of the Sustainable Development Unit, the Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cbricenogarmendi@worldbank.org and mshkaratan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team