rafmacalaba/data-use-mentions-extended huggingface dataset:
after successively replacing the 1984 distribution of wage rates, hours worked and schooling attainment by its 1994 counterpart. The second column under each income concept reports the value of each Gini index calculated in this manner, while the third column reports the percentage difference of these values relative to the original 1984 Gini index.

Table 3: Sources of Increased Earnings Inequality

|**Wage**|**s & Salaries**<br>|**Monet**|**ary Inc**<br>|**ome**<br>|
|---|---|---|---|---|
||Level %change over<br>Gini-84||Level|%change over<br>Gini-84|
|Gini 1984|0.2281<br>0.0|Gini 1984|0.1712|<br>0.0|
|Gini 1994|0.3034<br>33.0|Gini 1994|0.2812|<br>64.3|
|Gini-Awage|0.2962<br>29.9|Gini-Awage|0.2543|<br>48.5|
|Gini-Ahours|0.2208<br>-3.2|Gini-Ahours|0.1696|<br>-0.9|
|Gini-Aeducation|0.2222<br>-2.6|Gini-Aeducatio|n 0.1710|<br>-0.1|

Source: Own calculations based on data from Tables I and 2.

Note: the %-changes in the lower half of each column do not add up to the %-difference between Gini-94 and Gini-84 due to large rounding errors.

Table 3 indicates that the "synthetic" Gini index increased by 33 percent between 1984 and 1994 (under the Wage and Salary income concept) and that over 90

> 5 These "synthetic" Gini indexes are constructed under the assumption that all the individuals
adjustment costs include a fixed cost, then firms may react to a demand shock only if it is sufficiently large. In that case we expect the parameter β to be zero for small demand shocks, and to be positive for larger shocks. We have therefore also run the above regressions including quadratic terms for the demand shock variables (results not reported). We do not find them to be significant, however, suggesting that fixed costs do not drive the adjustment pattern in labor inputs.

Then we also check whether the above results suffer from an endogeneity bias because firms that reduce labor inputs will also tend to reduce sales because of a reduction in output. However it should be noted that we are primarily interested in the _relative_ impact of demand shocks – comparing temporary versus permanent shocks or demand shocks in credit constrained firms versus in firms that are not credit constrained. Although endogeneity bias will lead to an overestimate of the absolute impact of demand shocks, there is no a priori expectation whether and in which direction there would be a bias in the relative size of the impact across different shocks and firms due to endogeneity. Moreover, because we analyze the impact of (first) differences in sales on differences in inputs, all time-invariant firm-level (and labor type-level) factors affecting the level of sales and inputs are controlled for. Most interestingly, however, we expect endogeneity bias to be of even a less concern in case the firm applies input smoothing after a sales shock. In this case, if inputs are unexpectedly low for an unobserved reason, this will not affect total sales because the production constraint is non-binding (λ2 = 0 in the model of section 3). We therefore expect that endogeneity bias, if present, will be more of a concern for credit-constrained firms than for firms that are not credit constrained.

We therefore also estimate the model with instrumental variables in order to control for possible endogeneity bias. As instruments for the demand shock, we use the information on exporting behavior from the pre-crisis ICS 2007 survey as
<mark>in the late 1990s, finding that Internet job search is ineffective in reducing unemployment duration. Using comparable data from a decade later, K</mark> uhn and Mansour (2014) replicated this study and found that Internet job search significantly reduced unemployment durations.<sup>2</sup> Kroft and Pope (2014), meanwhile, exploited the geographic and temporal variation in the expansion of the classifieds advertisement website Craigslist in the United States and found no effect on the unemployment rate. <mark>Atasoy (2013), on the other hand, examined the effects of the expansion of broadband Internet access in the United States from 1999 to 2007 on labor market outcomes and found that gaining access to broadband services in a county is associated with an increase in employment rates.</mark> <mark>Another strand of the literature examines the effect of Internet use on wage growth. It includes the work of DiMaggio and Bonikowski (2008), who found a positive association between Internet use and earnings growth at the end of the “Internet Boom” in the United States. While focusing on the dimension of “intermediaries” in the online hiring process, S</mark> tanton and Thomas (2015) found that workers affiliated with outsourcing agencies had substantially higher probabilities of finding a job and higher wages, relatively to those without an agency affiliation.<sup>3</sup> Offering evidence from 12 African countries, Hjort and Poulsen (2019) more recently exploited the gradual arrival of submarine Internet cables from Europe to African coastal cities and found that Internet penetration had large positive effects on employment in Africa and that the impact on job creation was also associated with an increase in average wages.

<mark>Offering evidence from Egypt, this paper builds on the aforementioned literature on the impact of Internet job search by the unemployed on their employment prospects. Even though the cited literature spans several studies related to these questions, these studies focused for the most part on the
# **Measuring Empowerment in Practice:**

# **Structuring Analysis and Framing Indicators**

Empowerment—that is, enhancing an individual’s or group’s capacity to make choices and transform those choices into desired actions and outcomes—is an increasingly familiar term within the World Bank and many other development agencies.<sup>1</sup> Targeting practitioners engaged in the analysis of projects and policies that have empowerment components, this paper provides guidance on how to unpack the concept in order to measure related processes and outcomes.<sup>2</sup> First recognized by the Bank in its _World Development Report 2000/2001_ (World Bank 2000b) as one of the three pillars of poverty reduction, empowerment is now found in the documentation of over 1,800 World Bank-aided projects, and it is the subject of debate and analytic work within the development community (see annex 1 for a summary of efforts to measure empowerment).<sup>3</sup> Despite growing interest and increased investments in empowerment, the development of instruments and indicators with which to monitor and evaluate empowerment processes and outcomes is still at an early stage. Project teams and governments still lack the tools necessary for determining whether and how projects and policies aimed at empowering stakeholders reach their intended goals. This paper presents such an analytic framework. Rooted in both the theory and the practice of measuring empowerment, the framework demonstrates how practitioners can structure their approach to gathering and analyzing empowerment data. This paper also provides examples of indicators useful for tracking empowerment at both the project level and the country level.

The first section of this paper presents and explains components of the measuring empowerment (ME) framework. Section 2 illustrates how this framework has been interpreted and applied to projects in four countries. Corresponding empowerment indicators are presented in annex 2. The section also discusses using the framework to

> 1 The term empowerment is commonly used to indicate both a process (of empowering groups or individuals) and an outcome
the teachers from doing so, especially during the monsoon; for they usually live elsewhere. This is confirmed by the survey data: in _kharif_ 2009, primary school children in villages with an all-weather road each lost, on average, 1.9 days involuntarily, in those without, 8.6 days, a difference that is not only substantial, but also statistically highly significant. In contrast, only one of the 30 villages has a secondary or high school, so that the connection will normally affect the attendance of pupils and teachers alike. The corresponding average numbers of days lost involuntarily were 2.9 and 9.4, respectively, a difference that is not only statistically highly significant, but also leads to the suspicion that it is the teachers’ attendance, at all levels, that is most furthered by an all-weather road. Secondary and high school pupils enjoyed virtually no savings in time making the daily round trip according to the cross-section comparisons, but between 17 and 30 minutes a day (depending on the definition of the cohort) in ‘before’ and ‘after’ comparisons in the villages receiving a PMGSY road after January 2007. The respondents in the 35
followed by Ghana, Ethiopia, and Cˆote d’Ivoire. As points of reference, the degree of misallocation measured by the dispersion in revenue total factor productivity (TFPR) is on average larger than in India and China, and is similar to those of the worst performing countries in Latin America in terms of allocative efficiency, such as the Rep ́ublica Bolivariana de Venezuela and Colombia.<sup>1</sup> Besides significant dispersion, we find a tight correlation between the distribution of distortions, TFPR, and the distribution of physical productivities across firms, TFPQ. Controlling for other firm characteristics, we estimate a regression coefficient for the relationship between the logarithm of TFPR and the logarithm of TFPQ to be between 0.42 and 0.53. This statistic is an important determinant of the extent to which the estimated distribution of distortions creates a decline in aggregate productivity in the economy. As shown in Restuccia and Rogerson (2008), when resources are diverted away from high productivity firms to relatively unproductive ones, distortions carry a larger drag on TFP. Our estimate shows that such perverse misallocation, the one “taxing the good”, is evident in the four economies that we study.

Taken together, these findings imply that, the distortion and productivity-dependence of the distribution of distortions create a substantial decline in manufacturing productivity in the four countries. Had resources been allocated according to the output-maximizing rule, productivity would have been

> 1The magnitudes of TFPR dispersion in India are our own calculations based on the Prowess database. These values, in turn, are very close to those reported by Hsieh and Klenow (2009) from which we take the dispersion in TFPR in China. For Latin America, our reference is Busso et al. (2013).

2
have stable access, which is crucial for delivering health services, even in a broader scope than those analyzed here. In addition to the shown relationship between access to electricity and provider’s knowledge, the availability of drugs, vaccines and medical inputs, a stable electricity connection is also key to perform tests (e.g., laboratory test), treat patients (e.g., use ventilators), be able to perform medical procedures from the simplest to surgeries, among others. Therefore, providing the health facilities with a nationwide access to stable electricity access remains a priority to be addressed in Kenya. Needless to say, this would be an investment that would benefit many other sectors and households directly.

Likewise, it is equally important to invest in and improve the accessibility to roads and enhance their quality. Our findings suggest that more and better roads might help facilities eliminate access barriers that allow health facilities to access inputs, patients, and providers with more ease. Making facilities more accessible, however, may not contribute to addressing these issues if other complementary measures are not put into place. For instance, the 2018 SDI data suggests that urban-based health care workers had similar levels of diagnostic accuracy scores as their rural counterparts. Therefore, just increasing access will not necessarily translate into better providers if the pool of qualified providers is not large enough.

Moreover, the evidence presented suggests that ICT may be a valuable ally for improving the efficiency of supply chains and distribution of inputs to their end users. Enabling facilities to access and use ICT may positively impact the availability of medicines and inputs and also save time and resources. There is a substantial potential to increase the use of ICT for supply chain management in Kenya since only 15% of the facilities reported using it for this purpose.

Overall, these results show that investments in infrastructure might have significant positive effects for the quality of health service delivery and might be a relevant factor for improving health (and possibly many other) outcomes in the population as a
- i) _py/c_ : Computing the net (cereal) consumption ratio _p_ ( _y − x_ ) _/c_ requires the value of cereal production for each household, yet there is no farm production data in the 61st round. Using 59th round data, which has both value of production and consumption expenditure data, we estimate a tobit regression of the form _py/c_ = _β′X_ + _u_ , where _X_ includes linear splines in the amount of land the household cultivates, household demographic composition and age of head, proportion of household members with different education levels, social category of household (scheduled caste/tribe), and district dummies. Using the corresponding _X_ variables, we then impute _py/c_<sup>�</sup> = _β_<sup>�</sup> _′X_ for each household in the 61st round expenditure survey. From this imputation we subtract the _actual_ value of _px/c_ , the share of cereals in total consumption in NSS61, to obtain the household’s net consumption ratio.

- ii) _κ_ : The proportion of a household’s labor allocated to its own farm can be calculated from data available in the 61st round employment-unemployment survey and predicted based on observable characteristics _X_ using a procedure analogous to that described in (i). Imputation of a value of _κ_ � to each household in the NSS61 consumption expenditure survey follows accordingly. Households owning no land (aside from their homestead) receive _κ_ = 0 by default.

- iii) _e_ : Whereas in our theoretical development we assumed two sectors, in practice we allow for three (rural) wage sectors: (1) agriculture ( _A_ ), in which wages are affected by climate; (2) non-agricultural ( _NA_ ), in which wages are also affected by climate but to a lesser degree; and (3) public sector ( _P_ ), as described in fn. 26, in which wages are assumed to be unaffected by climate. Based on a sample
much larger than the positive effect on REE. Both expenditure and education reduce _TEE_ holding constant demographics and appear to have independent effects.

Columns 7 and 8 of Table 2 estimate the same specification but replace TEE with caloric intake estimated for the 1993-94 NSS 50th round Schedule 1 (consumption survey) in the same states (see Appendix C). The coefficient is similar in magnitude (-0.029 using households sized 2 to 8) suggesting that scale economies in energy expenditures can potentially explain much of the apparent decline in caloric intake with household size.<sup>5</sup> Note that the

> 5The decline is “apparent” because (Gibson 2002) and (Gibson and Kim 2007) show that much of the puzzling decline first documented in (Deaton and Paxson 1998) is reduced when using survey instruments less subject to recall errors, implying that a substantial part of the measured decline in most household surveys is simply measurement error that

9
Two kinds of data are needed in our process: i) vector data of roads, natural boundaries and/or settlement boundaries with which to split the country area into sub-units and ii) high-resolution (100 m) estimates of population density. Our process first combines all sources of vector data to split the country into small sub-areas, then merges them into units matching constraints such as population size or area, a process we termed the split and merge algorithm. Details of data requirements and processing are discussed in a later section. In regions where data on geographic and manmade features are too sparse to obtain small enough regions after the splitting stage (either nonexistent e.g. desertic areas or unmapped), a quadtree algorithm is used to further split the country. In this case, not all EAs follow visible boundaries.

In general, using our automated delineation method being developed into a tool we expect that a solution can always be found where no EA falls outside of the limits set. As long as space is partitioned in sufficiently small areas the region merging algorithm can have enough flexibility to create EAs that meet all the targets. Where there is a lack of geo-referenced features to obtain small enough areas, then the automated tool will use the quadtree algorithm to split. A flowchart of the process is given in Figure 1.

4
participation), market entry might be driven by the attributes of individuals rather than by their interaction with peers inside the classroom.

To deal with this concern, we test whether students without trading background in courses with more (above-median) and less (below-median) precourse trading experience display significant differences in any characteristics. We also run the same stratification test, splitting the sample in courses where experienced students have returns above and below the median. The raw results of all six characteristic variables in our data set are presented in Table 2. Only gender is significant, although the difference is small in magnitude; courses with below-median experienced classmates have 3.3% more women than men. Other characteristics, such as age, students’ earnings, and education are similar for the two groups. Also, both groups have instructors with similar teaching experience and trading returns.

Another potential concern with our class setting is that friends or acquaintances might register together for a CSE course. If an inexperienced student enrolls in a course with a friend who has a trading background, interactions about trading strategies and past performance might occur outside the classroom before the start of the course. According to our electronic survey, while 22% of students reported registering for a stock trading class with a friend, among experienced students, only 5% said they had taken the course with an acquaintance. In other words, most experienced students did not know their classmates before the program began.

# **4. EMPIRICAL METHODOLOGY**

To analyze peer effects and the role of selective communication in stock market participation, we estimate several variations of the following baseline empirical model: E (3) ii,cc,tt cc cc tt cc ii tt pp ll ii,cc yy = αα+ ββEThe subscriptEEiiEEaaccEE RRaaRREE _i_ refers to an individual, + γγPPE EE RREER _c_ indexes each course,EEaass + QQ′Ω + MM′Ψ _l_ + ZZ′Γ indexes the location (city), + μμ + δδ + γγ + εε _t_ is the
**The RRS data show that when couples have children, husbands deliver more labor supply and,**

**conditional on labor status, husbands and wives increase the home production time.** The fifth functional form assumption is that children affect the flow utility directly through changes in household type-specific tastes for spouses’ leisure �α � and for household production (α3). To identify those six parameters, we use changes in time allocation, comparing couples with and without children as well as CC CC AA change information in all the steady states (see figure 3 and table A2 in the appendix).

**Flabbi and Mabli** ( **2018) established that for unitary models with leisure and consumption, the flow utility’s risk aversion parameters are identified from the interdependence of spouses` decisions in the labor market.** The model introduces time in household production as another identification source for couples’ simultaneous choices. Figure 3 shows that males’ home production time allocation can range from almost 27 percent when NP or UE to 6 percent when males have a FT job. The observed degree of reduction of home production and leisure that spouses exhibit across different labor market states provides the identification for the risk-aversion parameters. The intuition is as follows: if the household is risk averse for male leisure, then the parameter will tend to zero. Thus, males will demand almost the same leisure in any labor market state. On the contrary, if the risk aversion parameter tends to 1, males will reduce the consumption of leisure significantly when changing the labor market state. The same will apply to home production, but as home production is a public good for the household, identification is trickier.

**Defining the household production function is vital for the model to capture the trade-off between working and home production, and the substitution/complementarity that wives’ and husbands’ times exhibit in the data** . If the production function is linear, spouses’ times in home production would have large
<!-- Start of picture text --> 10h. Pakistan 14 10i. Sri Lanka 40<br>14 40<br>12 35<br>12 35<br>30<br>30 10<br>10 25<br>25 8<br>8 20<br>20 6<br>6 15<br>15 4<br>4 10<br>10<br>2 5<br>2 5<br>0 0<br>0 0<br><!-- End of picture text --> Source: Authors’ estimates using CPAT and GTAP-10 data (see also Section 3.2).

# **4.5 Comprehensive energy price reform**

The previous results look solely at the effects of a carbon tax. However, Figure 3 makes it evident that to achieve the optimal price for all fuels, governments will need to remove both explicit and implicit subsidies (the latter achieved mostly through the introduction of the carbon tax). It would defeat the purpose of rationalizing prices if governments introduced a carbon tax, on the one hand, while spending on fossil fuel subsidies on the other. Only the complete removal of fossil fuel subsidies would provide clear price signals, given their distortionary nature. We, therefore, show a ‘comprehensive energy price reform’ (CEPR) scenario, under which all fossil fuel subsidies, price controls and exemptions are gradually phased out along with the introduction of the US$25/ton CO2e carbon tax by 2030. Section 5 below discusses how to overcome some policy implementation challenges.

In terms of net monetized welfare co-benefits, the results for South Asia of implementing the CEPR are still resoundingly positive (Figure 11.a). The larger portion of these net welfare co-benefits comes from the carbon tax, with just phasing out fossil fuel subsidies coming in at a non-negligible 0.31 percent of GDP in 2030. The welfare co-benefits (net of efficiency costs)