rafmacalaba/datause-encoder-data huggingface dataset:
# **Reference**

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- Adams, R.B., P. Funk. 2012. “Beyond the glass ceiling: does gender matter?” _Management Science_ 58(2) 219-235.

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- Amore, M.D., O. Garofalo. 2016. “Executive gender, competitive pressures, and corporate performance,” _Journal of Economic Behavior & Organization_ 131, 308-327.

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Aronson, R. 1991. Self-employment: A labor market perspective _._ Ithaca, NY: ILR Press.

- Ashraf, N., A. Delfino, E.L. Glaeser. 2019. “Rule of law and female entrepreneurship.” _NBER Working Paper No. 26366._

Barber, B., T. Odean. 2001. “Boys will bey boys: gender, overconfidence, and common stock investment.” _Quarterly Journal of Economics_ 116, 261-292.

- Beaman, Lori, Raghabendra Chattopadhyay, Esther Duflo, Rohini Pande, Petia Topalova. 2009. “Powerful women: does exposure reduce bias?” _Quarterly Journal of Economics_ 124(4), 1497-1540.

- Beck, Thorsten, Patrick Behr, Andre Guttler. 2010. “Gender and banking: Are women better loan officers?” _EBC Discussion Paper No._ 2009-19 _._

- Benouri, Moez, Tawhid Chtioui, Haithem Nagati, Mehdi Nekhili. 2018. “Female board directorship and firm performance: What really matters?” _Journal of Banking and Finance_ 88, 267-291.

- Bertrant, Marianne. 2011. “New perspectives on gender.” In _Handbook of Labor Economics_ , volume 4b.

- Bertrand, Marianne, Kevin F. Hallock. 2001. “The gender gap in top corporate jobs.” _ILR Reviews_ 55(1), 3-21.

- Bertrand, Marianne, Claudia Goldin, Lawrence Katz. 2010. “Dynamics of the gender gap for young professionals in the financial and corporate sectors.” _American Econ. J.: Applied Economics_ 2, 228-255.

- Bertrand, M., E. Kamenica and J. Pan. 2015. “Gender identity and relative income within households,” _Quarterly Journal of Economics_ 130 (2), 571-614.

24
# Magnitude and Distribution of Electricity and Water Subsidies for Households in Addis Ababa, Ethiopia<sup>1</sup>

## **Introduction**

In Addis Ababa, Ethiopia, increasing block tariff (IBT) structures have been used to calculate households’ monthly bills for both electricity and water services.<sup>2</sup> These IBT structures have two principal objectives. The first is to ensure the fair, equitable, and affordable provision of these utility services; the second is to use the higher prices in the IBT’s upper “blocks” to promote energy and water conservation. Our paper focuses on the first objective and analyzes the incidence of electricity and water subsidies delivered under these two IBTs to 366 households—with private connections to the electricity grid and to piped network water—for the exclusive use of their household members.

Our analysis is restricted to this 366-household subsample, which is derived from a large and representative household survey of Addis Ababa that includes 987 observations, because we have data for the subsample households—which have private electricity and water connections—from their monthly electricity and water bills; these data include the quantities of electricity and water used as well as the households’ socioeconomic status. We are thus able to calculate the magnitude of both the electricity and water subsidies that the 366 households received in 2015, and we can also examine the distribution of electricity and water subsidies by wealth groups within the subsample.

1 The authors express great thanks to Alemu Mekonnen, Tensay Meles, Abebe Damte Beyene, and Samuel Abera for their key contributions during the data collection fieldwork, to Gunnar Kohlin, Peter Martinson, and Mike Toman for their advice on the research design and survey instrument, and to Richard Spencer for useful comments on a draft of the paper. The authors also acknowledge with gratitude financial support for the research from the World Bank’s Knowledge for Change Trust Fund.

> 2 <mark>The electricity IBT was in force until November 2018, while the water IBT remained in force at the time this paper was issued. In December 2018, electricity tariff reforms were implemented in Ethiopia. One objective of the reforms is to achieve cost recovery in the medium term.  The IBT was replaced with a volume-based tariff that will increase i</mark> n over time.  The average tariff will increase from US$0.02 per kWh in 2018 to US$0.07 per kWh by 2021 in four annual increments.

2
14

**Figure 9. Contribution of Structural Change: Decomposition of Productivity Growth by Country Group, 1990–2005**

<!-- Start of picture text --> Productivity growth<br>due to structural change<br>High-Income Group<br>Asia<br>Latin America<br>Productivity growth<br>within sectors<br>Africa<br>-2 -1 0 1 2 3 4 5<br><!-- End of picture text --> Source: McMillan and Rodrik 2011.

The situation of African economies is of particular interest because they constitute the core of the development challenge today. They exhibit many signs of limited structural transformation that corroborate the empirical analysis by McMillan and Rodrik and explain why progress has remained slow since independence. In 1965 agriculture contributed 22 percent of Sub-Saharan Africa‘s GDP, services 47 percent, and industry 31 percent (of which manufacturing contributed 17.5 percent). In 2005 it was estimated that agriculture still contributed a healthy 15 percent of GDP, while services contributed 52 percent and industry 33 percent (of which manufacturing represented less than 15 percent; figure 10).

**Figure 10. Limited Structural Transformation in Sub-Saharan Africa: Sectoral Contributions to GDP (left axis) and Real GDP Per Capita (right axis), 1965–2005**

<!-- Start of picture text --> 100% 1,800 Services etc. val<br>add(% of GDP)<br>90% 1,600<br>80% 1,400 Manufacturing val<br>70% 1,200 add (% of GDP)<br>60%<br>1,000<br>50% Industry val add<br>40% 800 (% of GDP)<br>600<br>30%<br>20% 400 Agriculture, val<br>add(% of GDP)<br>10% 200<br>0% 0<br>GDP per capita<br>1965 1985 2005 (Maddison)<br>Khamis $)<br>GDP per capita (1990 Intl. Geary-<br>Share of sector in GDP (%)<br><!-- End of picture text --> Sources: For sectoral contributions to GDP, World Bank, World Development Indicators database; for GDP per capita, Maddison.

The sustained decline in the agricultural share of the labor force that is one of the stylized facts of economic development has not been observed in Sub-Saharan Africa. The region‘s economies
# Table 1: Access to Credit

<!-- Start of picture text --> Households' preferred sources of credit<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>All Marginal Farmers Small Farmer Large Farmer<br>PACS SHG Formal Institutions Informal Sources<br>Percentage of households that have at least one loan with...<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>All Marginal Farmers Small Farmers Large Farmers<br>PACS SHG Formal Institutions Informal Source<br>%<br>%<br><!-- End of picture text --> Source: Household survey conducted in three districts in AP, India in 2007.

11
to be finding the right policies, but instead making societies willing (or able) to adopt them. This goes back to having the right institutional environment.

Hence, the main recommendation from this literature is that resource-rich countries should improve their institutions in order to make the best use of a resource boom, and avoid its more deleterious effects. This is consistent with several efforts to improve governance in resourcerich economies and to understand how to build durable and effective institutions.

This recommendation is echoed by a vast literature in development economics that highlights the importance of institutions (especially the ones that improve property rights) for economic development (Acemoglu, Johnson, and Robinson 2005; Acemoglu, Robinson, and Woren 2012; Nunn 2009).

This literature, however, suggests that core institutions, such as the ones needed to avoid the resource curse, are in part shaped by profound historical events. Recent theoretical work also points to fundamental factors, such as external threats and distribution of political power, as important drivers of state building (Persson and Besley 2009). Given these antecedents, it is not clear whether, and which, short-term policies could significantly improve the quality of these core institutions. More research in this area is needed.

The use of country-level data has significantly advanced our knowledge on the impact of resource abundance. This literature, however, has several limitations. First, there are still relevant concerns regarding the causal interpretation of results. The presence of omitted variables, reverse causality, and measurement error are important empirical challenges in this literature (van der Ploeg 2011, 381). Scholars have tried to address them by including a richer set of covariates, exploiting panel data sets, and using instrumental variables. These solutions, however, still fall short relative to the experimental and quasi-experimental approaches currently used in applied economics.

Second, the impacts (positive or negative) of resource abundance are unlikely to be uniformly distributed in a country. For instance, many negative spillovers (such as pollution and population displacement) have a local geographic scope. Distribution of resource rents usually targets certain populations. Similarly, the impact of extractive industries’ demand for inputs may be felt more intensively in specific local markets. These local phenomena cannot be studied by looking at cross-country variation.

Finally, while informative on the importance of nationwide institutions, this literature is silent regarding other factors that may also exacerbate the negative effects of resource abundance or 13
<!-- Start of picture text --> Evolution of labor income inequality, Europe<br>Wage employees only<br>~<br>2<br>E<br>ionos .° ° 8 8 é i<br>me2 °e e ° : e e<br>80 e °<br>£% e ° :<br>= e e So<br>2 $ ee e 8 *<br>= e e e<br>e88e<br>=<br>oS 8<br>= ° e e e°®<br>a7 °e e . ° e °<br>3° e<br>=<br>)<br>N<br>1990 1995 2000 2005 2010 2015<br>Year<br><!-- End of picture text -->
Croke et al. (2016) infections, and also play an important role in spreading them in the local community (Hotez et al., 2006). The potential health consequences of worm infections are generally agreed to depend on the number of worms in the body (i.e., infection intensity), rather than a simple binary indicator of infection status. Infection intensity is highly skewed and is strongly correlated with disease prevalence: in low prevalence populations relatively few people have severe infections, while many more do in high prevalence populations (Anderson, Truscott and Hollingsworth, 2014).

Because of these health impacts, there is widespread acceptance that those who are known to be infected with intestinal helminths should be treated. Indeed, this is the standard of medical care (Horton, 2000; Keiser and Utzinger, 2008; Perez del Villar et al., 2012), and some consider not treating individuals known to be infected as unethical. New trials of this type are therefore typically not conducted, but TMSDG identify five trials of singledose treatment on what the authors term “infected children,”<sup>8</sup> and one trial of multiple dose treatment. The TMSDG meta-analysis found a near-term child weight gain effect of 0.75 kg across the single dose trials (95% CI: 0.24,1.26; p=0.0038).<sup>9</sup> These trials were largely conducted in settings with considerable infection intensity: for the 3 (out of 5) single dose studies that report infection intensities as measured by eggs per gram (epg), all three report mean epg values which are equivalent to a moderate intensity infection for at least one type of worm (Stephenson et al. 1989; Stephenson et al. 1993; Yap and Steinmann 2014).<sup>10</sup>

> 8 As described in more detail below, this terminology is slightly different than “screened for infection,” which was the classification used in the previous Cochrane Review (Taylor-Robinson et al., 2012).

> 9 The degree of confidence that TMSDG have in these findings is unclear. At one point in the text they note that the case for treating infected children is “obvious” (p. 30). However, they elsewhere describe the metaanalysis results as “low quality evidence” (Taylor-Robinson et al. 2015, p. 2).

> 10 These are our calculations, using the infection intensity thresholds from World Health Organization (2002).

8
|Journals: PC 4|(0.118)<br>-0.00168|(0.125)<br>-0.0394|(0.0622)<br>0.0466|(0.0645)<br>0.0615|
|---|---|---|---|---|
||(0.109)|(0.102)|(0.0617)|(0.0705)|
|No. SP papers published|0.0525|0.0708**|0.0350**|0.0486***|
||(0.0331)|(0.0322)|(0.0156)|(0.0146)|
|No. SP papers reviewed|-0.0175**|-0.0198***|-0.00297|-0.00370|
||(0.00815)|(0.00763)|(0.00802)|(0.00875)|
|Dummy: BSc|2.181***|2.193***|2.779***|2.609***|
||(0.323)|(0.286)|(0.193)|(0.226)|
|Dummy: MSc|0.327|0.458|1.147***|1.147***|
||(0.262)|(0.319)|(0.238)|(0.277)|
|Dummy: female|-0.126|-0.156|-0.264|-0.244|
||(0.230)|(0.248)|(0.257)|(0.274)|
|Country FEs?|No|No|Yes|Yes|
|Weighted regression?|Yes|Yes|No|No|
|Income elasticity|0.847|0.876|-|-|
|P-value for H0: elasticity = 1|0.533|0.538|-|-|
|_R_<sup>2</sup>|0.424|0.380|0.633|0.571|
|_F_statistic|14.1|11.7|11.6|8.95|
|Observations|403|403|403|403|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors  are in parentheses and were estimated jointly for the pair of equations (mean, median), with clustering by country.

Weights: inverse of number of respondents from a given country.

Comparing the results in the two leftmost columns of Table 8 to the corresponding columns in Table 6, we find that inclusion of these additional controls raised income elasticities somewhat, to 0.820 (mean WTP) and 0.829 (median WTP). Neither estimate was significantly different from unity. The estimates in Table 6 were therefore only mildly biased downward by correlations between national income and expert characteristics.

Results from the models in Table 8 could potentially be used to formulate more accurate predictions of mean or median WTP for a given country, than the simple average of the predictions by the experts from that country. Prior experience with Delphi exercises suggests that, while an estimate based on a group of experts is typically substantially better than that from any randomly chosen expert, giving more weight to experts with greater expertise is often preferable.<sup>24</sup> One straightforward way to do this is to estimate WTP as a function of expert

> 24 If expert characteristics do not have any systematic influence on estimates of WTP summary statistics then no issues arise. If this is not the case, however, then problems may arise that suggest the examination of alternative estimates. An example here is where lack of substantial valuation experience is associated with giving higher WTP

21
appeals from customary law courts, and vice versa,<sup>8</sup> the _Mmusi_ case, recently endorsed by the Court of Appeal<sup>9</sup> , could serve as the harbinger of a new era of legislative reform and constitutional oversight over discriminatory customary laws.

The case is indicative of a broader trend in the Sub-Saharan region. A 2013 study tracks legal constraints across 100 countries, including 33 countries in Sub-Saharan Africa, over the last 50 years. The analysis shows that countries in Sub-Saharan Africa, which had the highest number of constraints in 1960, have reformed more than fifty percent of these constraints.<sup>10</sup> The reform of these discriminatory laws can lead to transformative change. Inheritance law reform in India equalizing unmarried daughters’ rights to ancestral land increased investment in girls’ education and delayed the age of marriage.<sup>11</sup> In Ethiopia, reform of the family code, including removing the husband’s control over marital property, increased female labor force participation overall and in more productive sectors.<sup>12</sup> There are health consequences as well; equal inheritance and property law for women and girls can help to mitigate the economic and social burdens of HIV.<sup>13</sup>

# **_Post-Independence Gender Equality and Family Law_**

Since its independence in 1966, Botswana has consistently held unfettered elections, was fortunate to have discovered an abundance of diamonds and the average income has tripled. It has become one of the fastest growing economies in the world with an average growth rate of 9 percent, joining the ranks of upper middle-income countries. Yet Botswana’s track record of good governance and economic growth contrasts with its high levels of poverty and inequality and the generally low performance on human development indicators.<sup>14</sup> It ranks 119 of 187 countries in the 2012 Human Development Index and ranks 77 of 135 countries in the 2012 Global Gender Gap Index. Significant and persistent pockets of poverty remain especially in rural areas.<sup>15</sup> Many of Botswana’s poor are women and there is widespread poverty among rural women.<sup>16</sup> The first laws passed post-independence with respect to marriage set out a system of RomanDutch statutory law to regulate civil marriage. All civil marriages were presumed to be in community of property, unless the spouses agreed in writing before the marriage to opt out. Spouses owned the property jointly, but the husband had exclusive power over the administration of the estate. The wife had to obtain the consent of her husband before she could deal with family property. There was a minor exception to these broad provisions to allow the wife to buy

> 8 Kumar 2010

> 9 Southern Africa Litigation Center 2013

> 10 Hallward-Driemeier, Hasan and Rusu 2013

> 11 Deininger, Goyal and Najaran  2010

> 12 Hallward-Driemeier and Gajigo 2010

> 13 UN Global Commission on HIV and the Law  2012

> 14 World Bank 2012

> 15 World Bank 2012

> 16 CEDAW Committee on Botswana CEDAW/C/BOT/3 at para 39 ( February 2010)

4
socio-emotional skills in school, we run the same analysis as in Table 4 and estimate model (3) but now augmented with controls for transition years in educational attainment (in the form of dummies for ever entering lower secondary, senior secondary or higher education). In Appendix Table A11, we make the assumption that indicator variables for transition years account for any sorting mechanism which might be at play, and that our coefficients on years of education thus provide a conservative estimate of socioemotional skills building in formal education. Our main results largely remain. Women’s returns to education overall do not substantially change once we control for this sorting mechanism. Men get lower returns than before from interpersonal skills, but they are still positive and statistically significant.

Overall, men with higher socio-emotional skills are less likely to enter lower secondary but more likely to transition into senior secondary. We do not find any significant correlation between women’s level of socio-emotional skills and their transition into higher levels of education, which suggests that there is less sorting into education based on socio-emotional skills for women.

# **5.5 Heterogeneity across studies**

To uncover differential correlations between socio-emotional skills and economic outcomes across samples, we report estimates of correlations with earnings disaggregated at the study-level in Table 8. We find significantly lower aggregate levels of socio-emotional skills among women in all but three of the thirteen studies (Panel A). The three exceptions (Cote d’Ivoire PSAC, Facebook, and Togo PI) include samples restricted to working individuals – as farmers, business entrepreneurs, or employees, which may explain the statistically insignificant differences in socio-emotional skills levels among men and women in the selected samples. Altogether, gender differences in socio-emotional skills levels disfavor women across all studies and skills except for self-control, of which women display significantly higher levels in both the Mozambique and Togo PI studies (selecting farmers and entrepreneurs), and except for emotional regulation and PSDM being higher for women in the Facebook study. Turning to correlations with earnings (Panel B), we generally find insignificant gender differences except for two cases in which women display a significantly higher correlation between aggregate socio-emotional skills and earnings than men (in the Ghana GADCO and Kenya STEP studies). The overall female advantage in the correlation between interpersonal skills with earnings is driven by the Cote d’Ivoire Pro-Jeunes and Kenya STEP studies (both urban samples), along with positive although statistically insignificant gender gaps favoring women in most of the other studies. Most studies indicate high and statistically significant correlations of all types of socio-emotional skills with earnings for women. Taken together, these results indicate 27
Page 1 of 3 Table 13

# ZELLNER-PECK SYMMETRY AND LINEARITY<sup>MEASURES FOR MULTIMOD</sup>

1. Perturbations of Germany's Real<sup>Goverruent Expenditures</sup>

US US US US US US US US JA JA JA JA JA JA CUP A PUP IL aS IERN GOEF INVEST CNP PWP aS ER PXN CUSIAL STN(41,t0.0097 0.0093 0.0132 0.0086 0.0061 0.0041 0.0354 0.0086 0.0047 0.0166 0.0062 0.0277 0.0161 0.0302 STn(.S.-5) 0.2292 0.5879 0.1978<sup>0.2545 0.1899 0.0448 0.2868 0.6236 0.3063 0.0757 0.1498 0.2016 0.0367 o.6aso</sup> LlI*S,*I) 0.1436 0.1492<sup>0.0001o.oonZ</sup> 0.0019 0.0000 0.0489 0.0634 0.0133 0.0001 0.0017 0.0002 0.0001<sup>0.0045</sup> 0.3083 0.1656 0.1744 1.0286 0.0985 0.1079 LlIM-S,1 *Xa 0.3513 1.1093<sup>0.3924 0.7677 0.3303 0.0689 1.028S0.8299</sup> 2. Perturbations of the United States<sup>Target Monetary Base</sup> US US US US US US US US JA JA JA JA JA JA CP A PCNP IL aS HERN GOEF IMVST CUP PCNP aS ER PXN CURDAL W1N1,-1) 0.0096 0.0286 0.0286 0.0112 0.0097 0.0091 0.0115 _0.0372_ 0.0189 0.3516 0.04SO 0.0083 0.0266 0.0369 _sy(*s.-S)_ 0.0494 0.0460 0.0248 0.0740 0.0498 0.0983 0.0506 0.0530 0.0430 0.1052 0.0557 0.0068 0.07m 0.0124 SYNsO.- 10) 0.0690 0.0146 0.0165 0.3165 0.1298 0.2314 0.0199 0.0369 0.2530 o.9548 0.4662 0.o403 0.5618 o.oo09 LIU(10,1) 1.5415 1.7442 1.810? 0.8808 0.6063 1.5516 0.4313 1.5148 1.4992 1.628? 1.5730 1.5861 1.588 1.8785 LI-lO,A) _1.5728_ 1.7419 1.8051<sup>0.6310 0.4952 1.6332 0.4129 1.5258 1.6038 1.9010 1.7331 1.5978 1.7256 1.8824</sup> LIN(10.*5) 0.0161 0.052r 0.0184<sup>0.2572 o.0452 0.0030 0.0735 0.0373 0.11810.1171 0.0948 0.0430 0.0541 0.0332</sup> LIA(:5 $) 1.5535 1.7563 1.8073<sup>1.0770 0.5650 1.5528 0.4448 1.5305 1.4451 1.6022 1.5355 1.5698 1.5682 1.874S</sup>

***** SYR is symuetry measure.<sup>A value of ST near zero indicates</sup> high symmetry. ***** LIN is Linearity measure. A value of<sup>LIN near zero indicates</sup> perfect linearity.

- Notation: US (United States),<sup>JA (Japan), GR (Germany),</sup> LI (Other G-7), SI<sup>(Small Industrialized</sup> Countries, DC (Developing Countries). GNP (Gross National<sup>Product), PGNP (GNP deflator),</sup> RS (short term interest<sup>rate), RL (long term interest rate).</sup>

- A (domestic absorption), MERM (weighted effective exchange rate), INVEST<sup>(gross investment),</sup> GDEF (nominal goverrnment deficit) price)

- CURBAL (current account),<sup>PXM (mfg export prices), ER (exchange rate), IN (import volume), POIL (oil price), PCOM (Cominodity</sup>

**I-**