rafmacalaba/datause-encoder-data huggingface dataset:
output. We also find that an improvement of one position on the Getting Electricity index is associated with half a percentage point of sales lost by firms due to outages, and that a country’s income per capita has greater predictive power for its electrification rate than the efficiency of the electricity connection process.

(ii) Second, we examine if the overall bureaucratic environment in individual economies influences the efficiency of connection processes. To that end, we compare the new indicators with other measures of government efficiency such as the procedures, time and cost associated with starting a business, registering a property title or dealing with construction permits. With that we add to the business regulation literature by providing new results on how the overall regulatory environment in a country is related to provision of electricity services. We find that connection processes tend to be more lengthy and cumbersome in countries where other administrative processes are also burdensome. On whether majority publicly owned<sup>4</sup> or majority privately owned utilities are more likely to be affected by the general bureaucratic environment are not conclusive.

(iii) Third, we use firm level data from the Enterprise Survey data set to test whether the measures developed here are associated with firm performance. We find that total sales and sales per worker are higher where the provision of electricity connections is more efficient controlling for several firm and industry characteristics.

The next section describes the data sample. Section III presents descriptive statistics on the new data collected. Section IV presents the basic results on the relationship of the new data with other measures of public service efficiency and sector outcome indicators. Section IV discusses the firm level data and its relationship with the electricity variables. Finally, section V concludes.

# **II. Data**

# _Constructing the new indicators_

This paper is based on a new data set which summarizes the process of obtaining electricity connections for small-and medium sized companies across 183 economies<sup>5</sup> in 2010/2011. We are interested in all the procedures that an entrepreneur needs to carry out to legally connect to electricity. We also calculate the official costs and the time necessary for the completion of each procedure. The methodology developed in this paper follows closely the methodology for the Doing Business Starting a Business Indicators which, in turn, are based on the paper by Djankov et al (2002). The data set used here is the original data set developed for the getting electricity

> 4 For the rest of the paper we refer to utilities that are more than 50% government owned as public utilities and to utilities that are more than 50% privately owned as private utilities.

> 5 The unit of observation is an economy which in most cases is equivalent to the territory of a country. However, there are a few exceptions, such as, Puerto Rico and Hong Kong SAR, China.

4
Figure 1. Women’s share in total immigration <!-- Start of picture text --> 65,0%<br>60,0% 1990 2000<br>55,0%<br>50,0%<br>45,0%<br>40,0%<br>35,0%<br>30,0%<br>25,0%<br>20,0%<br>Poland Portugal Czech Rep. Japan Hungary UK Switzerland Austria Canada New Zealand Sweden USA Finland Italy Australia Netherlands Denmark Ireland Turkey Korea Norway Spain Luxemburg France Slovak Rep. Mexico Belgium Greece Germany Iceland<br><!-- End of picture text --> Figure 2. Women’s share in skilled immigration <!-- Start of picture text --> 60,0%<br>55,0%<br>1990 2000<br>50,0%<br>45,0%<br>40,0%<br>35,0%<br>30,0%<br>25,0%<br>20,0%<br>Portugal Greece Finland Italy Hungary Sweden Netherlands Japan Norway Spain Poland UK Korea Ireland USA Australia Canada New Zealand France Denmark Luxemburg Germany Belgium Austria Turkey Switzerland Mexico Slovak Rep. Czech Rep. Iceland<br><!-- End of picture text --> 12
|Debt Relief Grant Under the Enhanced HIPC Initiative|Chukwuma F. Obidegwu|
|---|---|
|Debt Relief Grant Under the Enhanced HIPC Initiative|Chukwuma F. Obidegwu|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Shanxi Coal Bed Methane Development and Utilization|XimingPeng|
|Institutional and Human Development in South Sudan|Sanjeev Ahluwalia|
|Institutional and Human Development in South Sudan|Sanjeev Ahluwalia|
|Institutional and Human Development in South Sudan|Sanjeev Ahluwalia|
|Institutional and Human Development in South Sudan|Sanjeev Ahluwalia|
|Institutional and Human Development in South Sudan|Sanjeev Ahluwalia|
|Institutional and Human Development in South Sudan|Sanjeev Ahluwalia|
|Institutional and Human Development in South Sudan|Sanjeev Ahluwalia|
|Institutional and Human Development in South Sudan|Sanjeev Ahluwalia|
|Liberia Reconstruction and Development|BarbryR. Keller|
|Liberia Reconstruction and Development|BarbryR. Keller|
|Liberia Reconstruction and Development|BarbryR. Keller|
|Liberia Reconstruction and Development|BarbryR. Keller|
|Liberia Reconstruction and Development|BarbryR. Keller|
|Public Sector Internal Audit IDF|Arman Vatyan|
|Public Sector Internal Audit IDF|Arman Vatyan|
|Public Sector Internal Audit IDF|Arman Vatyan|
|AGAIB Maritime - LICUS Ref. No. 18 C,TF056809|Setareh Razmara|
|AGAIB Maritime - LICUS Ref. No. 18 C,TF056809|Setareh Razmara|
|AGAIB Maritime - LICUS Ref. No. 18 C,TF056809|Setareh Razmara|
|AGAIB Maritime - LICUS Ref. No. 18 C,TF056809|Setareh Razmara|
|AGAIB Maritime - LICUS Ref. No. 18 C,TF056809|Setareh Razmara|
|Strengtheningof Revenue Administration and Collection Efficiency|Kim SyJacinto-Henares|
|Strengtheningof Revenue Administration and Collection Efficiency|Kim SyJacinto-Henares|
|Strengtheningof Revenue Administration and Collection Efficiency|Kim SyJacinto-Henares|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
|Climate Change Adaptation Program|Maurice Andres Rawlins|
_Hydrology data:_ These data were provided by the University of Colorado and the International Water Management Institute (IWMI) as part of the GEF Africa-wide study. Using a hydrological model for Africa, estimates were provided for flow and runoff for each of the sample districts.

# **4. The empirical model**

Alternative adaptation options for reducing the harmful effect of heat stress on farm revenue were explored by developing four Ricardian models. Model 1 is the standard Ricardian model, which includes weather, soil and socio-economic variables. Model 2 contains weather, soil and socio-economic variables, with the addition of the relevant hydrology variable. Model 3 contains the previous variables, with the addition of the quadratic term of the relevant hydrology variable. The rationale for using the quadratic term of the hydrology variable is that increasing the amount of irrigation might help reduce heat stress on growing crops and consequently could increase farm revenue. Model 4 contains the same variables as Model 2, with the addition of technology variables. For each model, a dummy variable for livestock activity was included to examine whether it could be used as an adaptation option. Farms without livestock activity were compared to farms with livestock.

In addition to the abovementioned models, a comparison was made between small- and largescale farms to assess their response to increased temperature. However, the differences between them were found to be not significant. Furthermore, Egypt was divided into three zones. Lower, Middle and Upper, to also assess their response to increased temperature, but again the differences were not found to be significant. This lack of significant difference could be attributed to the important role that irrigation may play in agricultural production all over the country and also to the different types of farming activity.

# **_4.1 Defining variables_**

# _4.1.1 Dependent variable_

The absence of a well-functioning land market in Egypt made it difficult for climate response functions to reflect the adjustments made by farmers to normal climate conditions, so net revenue (Kumar & Parikh 2001) was selected to be the dependent variable for the analysis. In addition to four measurements of net revenue, gross revenue was calculated from the household survey, with additional sets of costs being deducted from preceding net revenues. Gross crop revenue is the product of total harvest and price of the crop. The first one is nr1_3, which is gross revenue less the cost of fertilizer and pesticides per hectare of cropped area. The second one is nr2_3, where hired labor cost per hectare was deducted from nr1_3. The third one is nr3_3, where total machinery cost per hectare was also deducted from nr2_3. Other crop farming costs were deducted from nr3_3 to obtain the values of nr4_3. Table 3 summarizes the statistics for these four variables.

11
showed they were economically sound. Producing a feasibility study is a costly activity that can usually only be financed if the authorities have access to external funds, such as royalties. Hence, royalties may allow regional and local governments to hire specialists to produce sound feasibility studies that would increase the probability that their projects would be approved and that they would receive more public funds. In addition, investing in scientific and technological research may result in the development of sounder investment proposals with higher probabilities of being financed and, hence, higher regional public spending. If I run a simple regression of unanticipated mining revenues on changes in public expenditure using region-year data and only controlling for regional debt and a year fixed effect, I find that 100 million soles of additional mining revenues increase total spending by 2.8 million soles<sup>7</sup> .

The multiplier effect is even clearer in the size of the parameters found in the first-stage regression for both urban and rural areas, as Table 1 shows. An increase of 100 million soles in the amount of unanticipated mining revenues will increase the level of total expenditure by 1,095 and 635 million soles in rural and urban areas, respectively. Both coefficients are significant at a 1% level. The effect is especially large in rural areas, most likely due to the distribution rule within regions, which favored rural areas<sup>8</sup> . In addition, I assess the strength of the instrument using the first-stage F statistic. Estimators can perform poorly when instruments are weak, resulting in non-normal sampling distributions of IV statistics and unreliable standard IV point estimates, hypothesis tests, and confidence intervals<sup>9</sup> . The null hypothesis is that the instrument approximately 20 times the maximum spending in the rest of the regions.

> 7 The coefficient is significant at the 10% level.

> 8 See supplemental appendix section S4.3 for more detail on the distribution rule.

> 9 A set of instruments is defined as weak if the concentration parameter is small enough that inferences based on conventional normal approximating distributions are misleading. The concentration parameter is a unitless measure of the strength of the instruments (Stock et al. 2002; Stock and Yogo

8
# **1. Introduction**

A basic premise of representative democracy is that those who are subject to policy should have a voice in its making. Although women account for half of the world‘s population, they have historically lagged behind men in legal and political rights. In recognition of this, suffrage was extended to women in most western democracies in the early 20<sup>th</sup> century and the new democracies that emerged after WWII followed suit and granted women _de jure_ rights to political participation in all democratic institutions.<sup>1</sup> Despite these improvements, women are still far less likely than men to stand for public office, even in developed countries with older democracies. They are also less likely to participate in the electoral process as voters or to exercise independence in candidate choice when they do vote. Instead, women report voting in accordance with the preferences of the caste, clan or household head in contrast to men of all ages (CLRAE, 2002).<sup>2</sup> While women‘s relative absence from elected public office has received considerable policy attention in recent years<sup>3</sup> , there have been few attempts to reduce barriers to women‘s participation as voters, and even less attention has been paid to the use of women as passive vote banks, when they do participate. If preferences over the allocation of public resources vary by gender, this neglect could have implications for public policy, in addition to equity related concerns.<sup>4</sup>

> 1  These rights were also brought into international law by several important agreements to which most countries are signatories. These include the Universal Declaration of Human Rights (1948), the Convention on the Political Rights of Women (1952); the International Covenant on Civil and Political Rights (1966); and the Convention on the Elimination of All Forms of Discrimination against Women (1979).

> 2 See also Zia and Bari (1999) and Bari (2005) for Pakistan.

> 3 A number of countries have passed legislation requiring fixed quotas for women.  In South Asia, for example, India, Pakistan and Bangladesh have all instituted quotas for women in both local and national assemblies.

> 4 Chattopadhyay and Duflo (2004) exploit the quota introduced for women in the Indian Gram Panchayats and find that elected women leaders are more likely to provide public goods preferred by women. Lott and Kenny (1999) find that women‘s suffrage in the US increased overall government revenues and expenditures and has led to more liberal voting patterns. Edlund and Pande (2002) find that the decline in marriage has contributed to the shift by women voters in the US towards the Democratic Party. Several studies of intra-household resource allocation (e.g. Thomas (1990), Lundberg and Pollack (1997) and Hoddinott and Haddad (1995)) have also shown that women tend to make different choices over the allocation of household budgets, such as higher investments in health and the education of children. Croson and Gneezy (2009) review the experimental literature on gender differences in preferences related to risk, social attitudes and competition.

2
model. Despite similar aggregate effects, the relative importance of wage discrimination and gender norms reverses in a model with talent shocks, which aligns with the findings from Hsieh et al. (2019) and is further discussed in Section 7.2. Third, we show that nonhomothetic preferences _amplify_ the effects of declining gender barriers on aggregate and sectoral outcomes because falling barriers make economies richer, shifting consumption and labor demand away from agriculture and toward manufacturing and services. Lastly, we perform a robustness check that accounts for gender-specific comparative advantage across occupation-sector pairs, which we quantify from a benchmark economy – the – United States in 2015 which we assume to have no wage discrimination, following Lee (2024). After adjusting for these estimated gender productivity differences in all country-years, changes in gender barriers remain relevant, accounting for 21% of observed growth in GDP per capita, compared to 28% in the baseline.

**Related Literature.** A large literature documents gender disparities in the labor market, but only a few recent studies provide cross-country comparisons and examine the macroeconomic implications of gender roles. Cuberes and Teignier (2014), Albanesi et al. (2023), and Olivetti et al. (2024) provide excellent reviews. Several papers study the link between gender roles and structural transformation in the United States, including Ngai and Petrongolo (2017), Ngai et al. (2024), Moro et al. (2017), and Rendall (2018). Across countries, Gottlieb et al. (2023), Bridgman et al. (2018) and Bick et al. (2022) use rich time-use surveys to examine the implications of gender differences in hours worked. Our – quantitative approach which measures gender barriers as wedges and quantifies their – importance for aggregate outcomes aligns most closely with Hsieh et al. (2019), Lee (2024), and Chiplunkar and Goldberg (2024). Hsieh et al. (2019) study the aggregate implications of talent misallocation due to gender and racial barriers in the United States. They find, similar to us, that a reduction in barriers explains 20-40% of US economic growth over the last decades. Lee (2024) documents that gender barriers in poor countries are higher in non-agriculture sectors, also aligning with our findings.

While the literature on structural transformation emphasizes sectoral employment transitions, we further document transitions across occupations within sectors, with a focus on gender. We find that gender differences across sectors are more important in poorer countries, while occupational differences matter more in richer countries, in line with recent work by Caunedo et al. (2023) and Bandiera et al. (2022). We therefore develop one consistent framework to _jointly_ study the extensive margin of entering the labor force and the intensive margin of choosing a particular sector and 6
Table 3: Expected probability of enrollment into Secondary Education: Albanian Males and Females

|||Males<br>||<br>|Females<br>||
|---|---|---|---|---|---|---|
||Exp. Prob.of<br>Sec.Enrollment|Confidence<br>Interval|%Enrolledin<br>Sec.Educ.|<br>Exp.Prob.of<br>Sec.Enrollment|Confidence<br>Interval|%Enrolledin<br>Sec.Educ.|
|Cohortaged16-26|0.76|(0.75,0.77)|75.99|0.52|(0.51,0.54)|52.04|
|Cohort aged27-35|0.82|(0.80,0.83)|81.50|0.41|(0.39,0.42)|40.66|
|Counterfactual|||||||
|cohortaged16-26|0.85|(0.84,0.86)||0.45|(0.44, 0.47)||

Table 4: Expected Years of Education:Albanian Males and Females

|||Males<br>|||Females<br>||
|---|---|---|---|---|---|---|
||ExpectedYrs.<br>ofEducation|Confidence<br>Interval|Avg.Years<br>ofEducation|Expected Yrs.<br>ofEducation|Confidence<br>Interval|Avg.Years<br>ofEducation|
|Cohortaged16-26|11.58|(11.51,11.65)|11.68|11.62|(11.53,11.71|11.74|
|C6hdrtaged27-35|12.80|(12.75,12.85)|12.91|12.91|(12.84,12.98|13.02|
|Counterfactual<br>cohortaged16-26|12.91|(12.88,12.94)||12.97|(12.93,13.02)||

29
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# V. Conclusions, Discussion and Recommendations

# Conclusions

48. This review confirms and supports the findings of the 1982<sup>study of research</sup> within Bank education projects: research components do not<sup>produce the studies that</sup> are planned, and those that are produced are inadequately disseminated. We also find that the total number of studies per project has increased<sup>over time, but resources</sup> available for research components have declined since 1982,<sup>despite increases in Bank</sup> lending for education. A consistently small proportion of all studies<sup>address educational</sup> outcomes.

49. Studies are marginally more likely to be completed, though no<sup>more likely to be</sup> available, if they are included within project Conditions of Agreement.<sup>Completion rates</sup> are higher for studies embedded in larger research components.<sup>Studies that are fully</sup> described within a separate Annex of a Staff Appraisal Report<sup>are the most likely to be</sup> available. Study completion was also influenced by study design,<sup>and perceptions by</sup> national decision-makers and Bank staff of the benefits<sup>to be derived from research</sup> undertakings

## Discussion

_50._ What can be done to improve the completion and dissemination<sup>rate of studies</sup> financed by education projects? Two types of answers to this<sup>question must be</sup> considered. First, on methodology: are the data we collected<sup>sufficient to aniswer the</sup> questions posed, and, if not, how might it be improved?<sup>Second, on findings: if we</sup> accept our conclusions, how do we explain them, and what<sup>do we do about it?</sup> 51. Data sufficiency. The first issue is data sufficiency. Central questions<sup>regarding</sup> data sufficiency are: have all studies that were financed by the project<sup>been identified?</sup> Has information about all the studies identified been obtained? Overall, it is accepted that all studies financed by the Bank education projects, 1982-1989,<sup>may not have been</sup> listed and all the information that could be available about<sup>these studies has not been</sup> obtained. But what is missing .s unlikely to have a major effect on our conclusions.

52. To some extent the data may under-represent the total<sup>number of studies</sup> financed by the project. There are undoubtedly more studies planned<sup>and completed</sup> than are explicitly mentioned within SARs. It is beyond<sup>the scope of this paper to</sup> identify additional studies (some possibly hidden in colleague's<sup>drawers). However,</sup> quite a number of completed (available) studies would have<sup>to be identified before their</sup> inclusion would have a significant impact on our conclusions,<sup>as they would increase not</sup> only the number of completed (possibly available) studies, but would<sup>also increase the</sup> base upon which the percentages were computed.

53. Secondly, because of time constraints on the part of project<sup>staff, information</sup> received from the survey respondents may not be complete.<sup>However, a respectable</sup> 19