rafmacalaba/data-use-mentions-extended huggingface dataset:
for example by catering to the two languages of instruction in the country.<sup>9</sup> Adaptations to language and details of featured characters (e.g. names and ethnicities) could be easily tailored and transferred to other contexts.

Third, our results are not affected by selective school buy-in. Almost all schools in the country were included in the program and were randomly assigned to one of the two treatment groups or the control group, again strengthening external validity. Within each school, there was no bias in the selection of teachers, classrooms, or students. Therefore, any positive effects of the intervention cannot be attributed to students’, teachers’, or even administrators’ pre-intervention interest in the curriculum. In the field of social-psychological interventions, nearly all randomized controlled experiments in school contexts require school-level buy-in, and impacts could thus be specific to those schools who want or choose to participate, and not necessarily representative of all schools in a country.

This paper is organized as follows. In Section 2, we describe our intervention, including the different treatment arms and the key mechanism of behavior change. Section 3 gives an overview of our survey data on socio-emotional skills and administrative data on GPAs from official records in the country. Our empirical model is outlined in Section 4. The impacts of our intervention on socio-emotional skills and GPAs, on average and by different student sub-groups, as well as robustness checks are presented in the Section 5. Section 6 discusses them against findings in the literature, including cost-effectiveness, and then concludes.

# **2. The Intervention**

The objective of our intervention was to cultivate grit among sixth and seventh-grade students in North Macedonia. Past research highlights the long-term benefits of working with this age group: in early adolescence, motivated behaviors have been shown to have long-term effects on outcomes such as high school retention, college enrollment, or workforce earnings (Allensworth and Easton, 2005; Benner and Graham, 2011; Crosnoe, 2011; Heckman et al.
bring down their total power purchase cost; and (iv) developing a complex tariff structure made up of several increasing block-tariff (IBTs) rates, fixed costs, surcharges and duties.

Recent developments indicate that policy attention is now turning towards addressing issues of pricing electricity. The central Ministry of Finance through its annual Economic Survey 2015-16 has recently proposed a strategy to make residential price schedules equitable and easier for consumers to comprehend and respond to. The Power Ministry has proposed a draft amendment to simplify tariff categories and rationalize retail prices.<sup>3</sup> At the state level, other regulators, such as Andhra Pradesh’s State Electricity Regulatory Commission (APERC), have undertaken studies to design better residential electricity pricing.<sup>4</sup> Like APERC, Rajasthan’s Electricity Regulatory Commission is also evaluating efficient strategies to better price their residential electricity consumers.

A better understanding of the relationship between socioeconomic and demographic characteristics of households and their willingness to pay for electricity is an important input into the process of designing tariff schedules that can achieve revenue sufficiency for the discoms. Understanding the economic determinants of a household’s monthly electricity consumption, especially for low-income consumers, is also essential for meeting the affordability and equity objectives of the government, regulator and the utility.

This paper uses a structural demand model to propose an alternative tariff structure that can achieve these three objectives. Even though the demand model and welfare analysis outlined in this paper can be used to set prices for any discom in India, the paper uses data from the state of Rajasthan to illustrate the methodology to set tariff levels. The average revenue generated by domestic consumers of a utility in Rajasthan in FY2016-17 was Rs. 6.31 per kWh as against an average revenue requirement of Rs. 7.74 per kWh of energy for the utility. This suggests that domestic consumers of the utility were undercharged by Rs. 1.43 per kWh on average, a shortfall that is cross-subsidized by commercial and industrial consumers of the
send them for child labor. This adds an element of uncertainty (on top of ability dierences) for the children born into disadvantaged households, resulting in a higher conditional variance in completed schooling. The conditional variance of children's schooling attainment is likely to decline with the education of parents when such economic shocks (income or health shocks) are the primary sources behind the observed variance in the data. In this case, children born to higher educated parents not only have higher expected years of schooling (as found in numerous studies of intergenerational educational mobility), but also a lower variance in schooling attainment. Under the plausible assumption of risk aversion, this implies being born to higher educated parents brings double advantages for children part of which is ignored by the existing measures of intergenerational mobility.

We analyze the relationship between family background and conditional variance of children's outcome in the context of intergenerational educational mobility. We make two contributions to the literature. First, using data from three large developing countries (China, India, and Indonesia, with 42 percent of world population in 2000 (2.56 billion)), we provide the rst empirical evidence that the conditional variance of children's schooling is systematically related to his/her family background as captured by father's education.<sup>3</sup> Second, we develop a methodology that combines the eects of father's education on both the mean and conditional variance of children's schooling. We propose new measures of relative and absolute mobility that adjust the standard mean eects by the risk premium associated with the conditional variance in educational outcomes faced by children. With risk neutrality, our proposed measures reduce to the canonical measures of intergenerational educational mobility widely used in the current literature (see, for example, Hertz et al. (2008), Azam and Bhatt (2015), and Narayan et al. (2018)). But, under the more plausible assumption of risk aversion, the
**|**2014**(Est.)|**2015**(Prog.)|
|---|---|---|---|
|1. Fiscal deficit|10.5|10.2|7.2|
|2. Current account deficit|11.9|9.6|8.2|
|3. Nominal external debt|35.2|48.5|56.0|
|_4. PPG share(% of 3.)_|_87.2_|_88.5_|_92.9_|

**_Source:_** Lines 1 and 2 are from Table 1, p.25, of IMF (2016), while 3 and 4 are from the external DSA table.

The table shows that the current account deficit (CAD)—which equals the fiscal deficit plus the gap between private investment and saving—is substantially accounted for by the fiscal deficit. Hence, it is not a surprise that PPG external debt accounts for the bulk of total nominal external debt. For LICs, the idea that the CAD is largely explained by the fiscal deficit is not surprising because it is usually difficult for the domestic private sector to borrow from abroad, either because lenders consider it too risky or because of legal restrictions (once again, Zambia is an exception). In this case, the private sector is restricted to borrowing at home and could be “crowded out” even in this space by the government if large deficits push up real interest rates or if banks are statutorily required to hold government securities.

7
to transfer what they know to students? (4) To what extent does teacher content and pedagogical knowledge matter?

Averaging across countries, we find that students receive about two hours and fifty minutes of teaching per day—or just over half the scheduled time. This is largely because teachers, even when in school, are not teaching. Furthermore, teachers’ subject knowledge is strikingly low. Only about one in ten fourth-grade teachers master their students’ language curriculum, and about a quarter of the teachers fail simple tasks (such as subtracting two-digit numbers for math teachers, or choosing the correct pronoun or conjunction to complete a sentence for language teachers). With regard to pedagogy, few teachers are able to assess children’s abilities and evaluate their students’ progress, and few exhibit practices that are typically associated with good teaching (e.g. regularly checking for students’ understanding and giving feedback).

Exploiting the linked student-teacher data across countries, and within-teacher withinstudent variation, we find significant, and importantly large, positive effects of teacher content and pedagogical knowledge on student achievement.

Our findings have two main sets of implications: the first are for education systems and education policy reform; the second are for the experimental and quasi-experimental research agenda on ways to improve education quality. On the former, rapid expansions in school enrollment in developing countries have put substantial pressure on education systems’ ability to provide quality education—and rapid hiring of teachers to limit student–teacher ratios may have contributed to our findings of underprepared teachers. At the same time, the high rates of teacher absenteeism from school, as well as absenteeism from the classroom while at school, point to systemic governance, accountability, and management issues. The magnitude of the problem highlighted here suggests that there is an urgent need to tackle these underlying causes of poor service delivery. Failure to do so not only represents a waste of the considerable resources that countries spend on education, but more importantly, condemns generations of students to subpar education and consequently diminished
# **2. Life cycle wealth accumulation in developing countries**

Underlying this research is the life cycle hypothesis (LCH), originally proposed by Modgliani and Brumberg (1954, 1979), which posits that individuals will save and dissave to smooth consumption over their lifetime.<sup>2</sup> One recent approach, used in the Findex surveys, consists in asking households directly whether they save for old age. Demigurc-Kunt et al. (2016) show that in South Asia this fraction maxes out at around 10%-15% when individuals are in their 50s. However, larger fractions report saving more generally, and much of those savings may be multipurpose, with old age consumption constituting a secondary, less urgent motive. Some components of household net worth such as housing may not be identified as savings by respondents, even though they are indirectly consumed in old age in the form of saved rent payments and, while relatively illiquid, could be sold as a last resort.

A much larger literature has taken a different approach and analyzed the main prediction of the LCH, which is that household consumption should be disconnected from income fluctuations (see Deaton, 1997, for a review). Comparing consumption and income over the life cycle, the literature finds that consumption generally tracks income more closely than would be implied by the theory (Carroll and Summers 1991, Attanasio and Davis 1996). Some studies have examined the extent to which liquidity constraints (Deaon, 1991), precautionary savings (Carroll, 1997), and cohort effects (Deaton and Paxson, 1994) can explain this puzzle.

When wealth is analyzed directly, households appear to save too little (Deaton, 1997) and concerns abound that households do not adequately prepare for retirement (e.g., Mitchell and Moore, 1998 for the U.S.). These findings prompted theoretical developments in behavioral economics aimed at capturing departures from rationality or time-consistency such as hyperbolic discounting (Laibson, 1997, Harris and Laibson,
productivity-based ones. The former considers compliance with labor regulation and the right to social protection (e.g., pension, health care). The latter considers the type of job, such as being unskilled self-employed, salaried in a small firm, casual/irregular worker or a zero-income worker. However, this measure has been shown to introduce large measurement errors in transition economies and is deemed less relevant (Lehmann H. , 2014). We draw from multiple sources of data and find a broad congruence across surveys -despite the type of definition used- in the evolution of informal employment in the last decade.

The rest of this section proceeds as follows: we briefly summarize findings from the previous literature on the causes and consequences of informality, both globally and specifically for the case of Russia. Then we describe in more detail the various data sources, the specific definitions of informal employment used, and the evolution of trends that are uncovered from different data sources.

# **Findings of the previous literature**

The literature has approached the causes of informality from two main viewpoints: “exit” and “exclusion”. The exclusion view maintains that workers and firms are excluded from the formal sector, due to cumbersome and expensive regulations, and formal employment is somehow rationed so many workers, despite wanting to have a formal job, are not able to get one. While these factors are important, there is a second perspective, the exit view, which presumes that informality is an outcome preferred by workers and firms as the optimal level of engagement with the state, especially when the perceived costs of formalization outweigh the benefits. In other words, informality is a large-scale opting out of formal institutions by firms and individuals as a result of their valuation of the net benefits from formality and the state’s service provision and enforcement capability. In other places, this labor market divide is explained in terms of segmented (in contrast to integrated) or dual labor markets.<sup>4</sup
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1692 Regulating Market Risk in Banks:<br>A Comparison of Alternative<br>Regulatory Regimes|Constantinos Stephanou|December 1996|P. Infante<br>37642|
|WPS1693 Famines and Economics|Martin Ravallion|December 1996|A. Ramirez<br>85734|
|WPS1694 What Can New Survey Data Tell Us<br>about Recent Changes in Distribution<br>and Poverty?|Martin Ravallion<br>Shaohua Chen|December 1996|**A.**Ramirez<br>85734|
|WPS1695 Are There Dynamic Gains from a<br>Poor-Area Development Program?|Jyotsna Jalan<br>Martin Ravallion|December 1996|A. Ramirez<br>85734|
|WPS 1696 South-North Migration and Trade:<br>A Survey|Maurice Schiff|December 1996|M. Patena<br>39515|
|WPS1697 Strategies to Develop Mortgage<br>Markets in Transition Economies|Dwight M. Jaffee<br>Bertrand Renaud|December 1996|R. Garner<br>37670|
|WPS1698 Regulatory Structure and Risk and<br>lnfrastrcuture Firms: An International<br>Comparison|Ian Alexander<br>Colin Mayer|December 1996|R. Schneiderman<br>30191|
not change their deposit insurance coverage till the recent financial crises, we use the deposit insurance coverage in 2003 for years 2003 to 2007.<sup>9</sup> Finally, we also use data on the investor protection index ( _investor_protection_ ), depth of credit information sharing ( _credit_info_depth_ ), and the existence of a private information bureau ( _private_bureau_ ) from Djankov, McLiesh, and Shleifer (2007) and the World Bank Doing Business Survey.<sup>10</sup> These variables measure the strength of private monitoring and information

> 9 See Demirguc-Kunt, Kane, and Laeven (2008) for a detailed analysis of various features of deposit insurance mechanisms.

> 10 Details on how these variables are constructed are available on World Bank’s Doing Business Survey website at <u>http://www.doingbusiness.org/methodology.</u>

10
Botswana, data collection began later. Therefore most of the data for Botswana are from the 2004–08 period. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Since the period post 2008 has been particularly eventful for Botswana’s infrastructure sectors, key changes are noted in the text even if they cannot be reflected directly in the data which for the sake of international comparisons must retain a consistent time period.

Second, to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level. In the specific case of spending needs, achievement of the Millennium Development Goals (MDGs) has been assumed as the social targets. These targets look indeed relatively modest for middle-income countries like Botswana, and therefore the spending needs of the water and sanitation sectors represent a lower bound yet a cross-country comparable spending targets for the country.

2
sup> ) = λλ<sup>2</sup> σσ + (1 −λλ)<sup>2</sup> ccY (yy�−yy<sup>∗</sup> 2 2 = 2 <u>y2</u> λλ2<sup>∗</sup> 2 = <u>φ ρ</u> , (A.7) where is the correlation between λλ<sup>∗</sup> _l_ and σσyy<sup>2</sup> _y_ (ββ. In our context, we also allow for region-specific<sup>2</sup> σσyy∗σσll+σσσσ lly∗)+σσll σσyy∗ 1−ρρφ−<sup>22</sup> when calculating the optimal . ρρ ρρ Based on Eqs. (A.1) and (A.2), we can obtain three sample moments from the observed data: + 2 λλ<sup>∗</sup> 2 2 2 2 ∗ , ∗ and ∗ . However, we still need one more equation to solve for the above 2 ccY (yy) = σσyy<sup>2</sup> <u>y</u> unknown parameters. To achieve this, we specify a value for σσyy ccY (ll) = ββ σσyy + σσll YYc (ll, yy) = ββσσyy 2 ~~,~~ i.e., the ratio of signal to the total variance σσ y∗ in measured GDP growth. If we divide the countries into two groups based on the levels of measurement error in φφ= σσyy∗+σσyy2 GDP following HSW, by assuming a specific value for _φ_ for the countries with high quality of GDP measurement (developed countries), e.g., 0.9, we can derive the corresponding value of _φ_ for the other group (developing countries like China), i.e., 0.594 in this case
**Figure 3.1 Wage premiums and relative supplies over time, net of country effects. 16 LA countries, 1989-2009**

<!-- Start of picture text --> 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>.3<br>.2<br>.2<br>.1<br>0 .1<br>-.1 0<br>-.2 -.1<br>Wage Premium, Skilled-Unskilled -.3 -.2<br>Wage Premium, Highschool-Dropouts<br>.4 .5<br>.2<br>0<br>0<br>-.5<br>-.2<br>-.4<br>-1<br>Relative Supply, Skilled-Unskilled<br>Relative Supply, Highschool-Dropouts<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

# **Figure 3.2**

**Relative demands over time (residual assuming σ=3 for both elasticities), net of country effects. 16 LA countries, 1989-2009**

<!-- Start of picture text --> 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>1<br>.5<br>.5<br>0<br>0<br>-.5<br>-.5<br>Residual Relative Demand, S-U Residual Relative Demand, H-D<br>-1 -1<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

25
facilitates the consistency of estimates with variables defined at the individual level and those constructed with variables at the household level. ′ = �X , ... , X , ... , X � from administrative data, the latest census, or demographic tt tt,1 tt,pp′ tt,PP To introduce this estimator, let XX be a column vector of PP auxiliary = �X , ... , Xtt , ... , X � household k such that known totals for the population UU tt,kk tt,1,kk tt,pp,kk tt,PP,kk statistics. Let XX be a vector of auxiliary PP variables of the . �MMtt tt,kk tt Let X kk=1XX = XX are two possible situations regarding the relationship between the auxiliary variables defined tt,pp,kk,ii at the household level, X be the auxiliary variable defined for the ii-th individual of the kk-th household. There , and those defined at the individual level, X . Suppose the variable X can only be defined at the household level (e.g., the household's state of poverty). tt,pp,kk tt,pp,kk,ii In that case, all individuals in the household will have the same value of the household variable, tt,pp,kk 14
, a new vector of earnings is generated using estimates from a set of Mincer equations. To account for unobserved characteristics, each individual residual is brought into the new sectors and scaled accordingly. This latter process assures that an individual switching sectors can carry his unobservable personal characteristics - beyond age, experience and years of schooling.

**The third step adjusts average wages between groups of workers and sectors** . While the second step operates at the individual level, the third step operates at the group level scaling the average wages for each type of worker and sector. In practical terms, one group is selected as numeraire, i.e. unskilled agricultural, and average wages for each group are scaled relative to the numeraire. Within a group, all earnings are scaled with respect to the numeraire group. Operating through changes in relative wages guarantees internal consistency between macro and micro results considering that in the CGE-GIDD linkage, initial relative wages were obtained from the micro data and feed into the global CGE model. It is important to highlight that until this point the microsimulation has operated only in relative terms.

**Lastly, GIDD adjusts the average income/consumption per capita to guarantee that it changes exactly in line with the CGE results** . After creating new earnings for workers, a vector of per capita household income is constructed considering new earnings and household size. When information about the relationship between incomes and savings exists, if not, a one-to-one passthrough from per capita household income to consumption is assumed. In this regard, **GIDD constructs a household-specific deflator to adjust for changes in relative prices** . The price deflator is constructed using initial and final price indexes of food versus non-food expenditure from the macro model and household-specific budget consumption shares for food and non-food expenditure observed in the micro data. Food and non-food shares can be adjusted estimating a Lorenz’s curve of budget share expenditure on food and total per capita household consumption. These steps are explained in detail in the annex of this paper.
from the 2001 census there are many cities that have achieved a population of 100,000 since 2001 and have not been included in the study. This can be updated in the future. .

Pollution data for all cities, where available, was taken from the Central Pollution Control Board’s (CPCB) Environmental Data Bank website for the year 2008. Health damage estimates for PM10 were calculated based on observations for the year 2008. The study included 96 cities with monitoring stations and 223 cities with no monitoring stations (254 million people in total). The population for 96 cities with monitoring stations amounts to 186 million, or about 16% of the country’s population. In addition there are about 225 cities with an average population of 69 million for which there are no data on PM concentrations. Since excluding them from the estimation of health impacts would be a serious omission, annual average PM10 levels were assigned to these cities based on scaling up of the World Bank modeling PM10 concentrations (taken from the World Bank Internal Research Database), using an average factor for the major cities.

The age distribution of the urban population was estimated using urban population parameters from the 2001 India Census. PM10 were transformed into PM2.5 to obtain values for the latter using a ratio of 0.5 based on evidence from India (CPCB, 2011). This ratio reflects the mean of the PM2.5/PM10 ratio for large Indian cities reported in this paper

> 7 Also called total suspended particulates or TSP.

11