rafmacalaba/datause-encoder-data huggingface dataset:
the list of all villages with an existing private school and are therefore bigger and richer than the average village in these districts.<sup>4</sup> However, it should be noted that almost half of the children in rural Punjab now live in a village with at least one private school, like that in our sampling list-frame. Here, we focus on the crosssection data from 2003.

Our data cover 1,697 mothers with 4,331 children between the ages of 5 and 15 in these households.<sup>5</sup> In addition to demographic and educational data, the survey also collected detailed parental and child timeuse data, which forms the basis for a series of estimations in the paper. Given that these data are typically not available at the disaggregated level collected here (more on this below), a brief description of household inputs into education with an emphasis on time-use for a typical mother and a typical child in rural Pakistan is instructive. Table 1 provides summary statistics for the variables used in the regression analysis.

# **Mothers**

A majority of mothers in our sample—76 percent—report not having gone to school at all and less than 10 percent report any education beyond the primary grade-level. Consequently, the average number of years of education for mothers is 1.34 years, a number comparable to many other developing countries (Barro and Lee, 2000). Of particular interest are the detailed time-use questions asked of mothers. Time-use is computed from a question that reconstructs an “average day in the last week” and allows the respondent to flexibly specify activities and time-slots. For instance, a respondent may say that she woke up at 6am, prepared breakfast and then readied her children for school till 7am. After that, she cleaned from 7am to 9 am and so on for the remainder of the day, till she went to sleep. To code the activities reported we used 11 different codes. Table 2, Panel A shows time allotted to different activities for mothers with no education and for those with some education across the five main codes, aggregating the remaining (entertainment, prayer, shopping, sickness, religious activity and other) into the residual category of “other activities”.

Immediately striking is the remarkable similarity in time-use for most categories across mothers with some education and those with none. Apart from “rest”, “housework” is the single largest category with both

> 4 See Andrabi and others (2008) for more details on the rise of low cost, for-profit, secular private schools in rural Pakistan.

> 5 Birth village information was not available for all the mothers. Summary statistics are provided for the matched sample of 1437 mothers. We return to the matching issue below.

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Some of the obvious correlates like mother’s paid employment status, decision-making power of the adolescent, has children or not, separate earnings - do not have enough variation in the data or have far too many missing values to make their inclusion meaningful.

Thus, the key finding that appears to be emerging from Tables 2-4 is that self-efficacy appears to be one of the key determinants of aspiration among adolescent girls in Jharkhand, both in terms of education and employment. In terms of key correlates of self-efficacy, individual and household factors like age, previous training experience, and an “enabling” environment i.e. one where girls have family/social support, feel connected and have empowered parents and other successful role models to look up to, appear to be important. Consistent with our results, Darolia and Wydick (2011) find that actions such as parental praise designed to foster an increase in self-esteem result in academic achievement in university undergraduates above what natural ability would dictate.

## **5c. Discussion of Findings**

Our results have implications for the design and assessment of programs aiming to alter girls’ educational and economic trajectories, especially by boosting their aspirations. Psychological factors like self-efficacy and mental health appear to be important determinants of adolescent girls’ educational and employment aspirations, even after controlling for various objective individual and household characteristics. Hence these hidden forms of human capital could potentially serve as novel but critical targets in programs designed to boost the education and labor market outcomes of women. Importantly, an increasing evidence base shows the potential of interventions to increase youth self-efficacy and mental health in India as well as other countries (Barry et al., 2013; Krishnan & Krutikova, 2013; Leventhal et al., 2015). In Jharkhand, our analyses show that modifiable factors with strong correlations to self-efficacy include social supports, participation in training programs, and exposure to successful women.

However, these results also point to the challenges of evaluating any program aimed at altering girls’ life aspirations using non-experimental methods. Such interventions are gaining prominence as potential ways to impact actual life achievements (Schoon and Polek, 2011; Schoon and Parsons, 2002; Goodman and Gregg, 2010). However, as our results indicate, selection will play a key role in who participates in such programs and who is likely to benefit most from them. While some dimensions of skills and ability will surely play a role in participation decisions, certain additional non-measured dimensions are also likely to be at play. It is hoped that the findings of this paper, as well as future work with follow-up surveys, will further our understanding about the magnitude and direction of such selection bias in the process of evaluation of these programs.

# **6. Conclusion**

Evidence is accumulating on the importance of psychologically informed development policies and interventions, as underscored by the _World Development Report 2015_ 12
10 even though the cash component of earned income is usually the largest. Most unearned income on the other hand is in cash, with relatively few households receiving unearned income in kind, especially in Inner Mongolia. The China income data do not allow further disaggregation by gender.

# **3 A Theoretical Account of Mental Accounting**

When there is some pain associated with spending hard earned money on less basic or more frivolous goods or when there is less joy when spending unearned money on basic goods, households may mentally put earned and unearned income in different accounts, allowing them to evaluate the utilities derived from different consumption goods differently, depending on whether they are financed by earned or unearned income. The core intuition of a household’s consumption decision when facing such an (emotional) form of mental accounting is captured in the following one period optimization model: where are the amount spent on basic consumption goods (e.g. staples) using unearned and earned income respectively, and the amounts spent on non-basic goods using unearned and earned income respectively. , are unearned and earned income respectively. (>0) captures how the household weighs the utilities from consuming basic and non-basic consumption goods. Though not endogenized here, is likely also dependent on income with poorer households attaching more weight to the consumption of basic goods (smaller ) than richer ones, as per Engel’s and Bennett’s Laws.

The parameter reflects the loss in pleasure derived from spending unearned income on basic consumption goods ( ) following, for example, from the break in a person’s “feel good” mode after his acquisition of unearned income; reflects the additional pain/guilt derived from spending earned income on non-basic consumption goods ( ), due, for example, to the failure to mitigate the negative emotional tag associated with earned incomes. Differences in the parameters and could for example reflect the differential impetus for coping with negative and positive feelings, with the impetus for the former usually stronger than for the latter, implying a larger psychic cost associated with spending earned money on nonbasic goods than with spending unearned
# **2.2 Data and Data Sources**

We use the World Bank’s HFPS data for Ethiopia, conducted between April 2020 and May 2021 to monitor the impacts of the COVID-19 pandemic (World Bank, 2020).<sup>12</sup> The phone survey sample is a subsample of households drawn from both urban and rural areas in all regions of Ethiopia surveyed face-to-face in the Living Standards Measurement Study - Integrated Survey on Agriculture (LSMS-ISA) in 2019. The 2019 LSMS-ISA Ethiopia data are nationally representative sample of 6,770 households. Of these, 5,374 (79.3 percent) reported phone ownership and were used as sampling frame for the phone survey in 2020. The actual sample size for the phone survey was set at 3,300 households of which 3,247 of them were successfully interviewed in the first phone survey in April 2020 (Wieser et al., 2020). These households were re-interviewed every 3- 4 weeks, for eleven rounds, until May 2021, allowing high-frequency monitoring of changes in key outcomes of interest including labor market participation and food security. We combine the 2019 face-to-face survey data with these HFPS data.

The 2019 baseline data provide detailed characteristics of households, including GPS coordinates of household residence, which we used to merge these data with the ACLED database, which records conflict events at specific locations and which has been widely used to study the consequences of conflicts in different settings. The ACLED database provides event-based information for different types of conflicts, including battles, attacks against civilians, remote violence, and protests and riots. For our purpose, we focus on number of battles because the Ethiopian civil war that broke out on November 4, 2020, involved intensive battles. More than half of the incidents recorded by ACLED during our sample period (August 2019-May 2021) represent battles. We construct household-level measure of exposure to battles by counting the number of battles within a 10, 20, 30, 40 and 50 km radius around each survey household location. We compute cumulative number of battles in the previous months until the end of the survey month. Table A1 shows that the number of battles has tripled after November 2020.<sup>13</sup> The HFPS sample continually declined in follow-up rounds due to non-response to calls and attrition. Further, the Tigray sample was especially affected by the war itself and disruptions in telecommunication services in the region, further reducing the sample to 1,982 households in

> 12 World Bank. Ethiopia-COVID-19 High Frequency Phone Survey of Households 2020. Dataset downloaded from <u>www.microdata.worldbank.org.</u> 13 Before November 2020, such events consisted of limited-scale armed conflicts between state police or armed forces and ethnic militias, typically reflecting localized political grievances.

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The paper builds on some of the WB Principles for Public Credit Guarantee Schemes (PPCG) for SMEs.<sup>4</sup> While this paper does not attempt to assess the PCG schemes in the region based on the PPCG principles, it does build on some of them. Examples of this can be found in the areas of objectives, governance, and supervision, which reflect on principle 5 (“[PCGs] should have a clearly defined mandate supported by strategies and operational goals consistent with policy objectives.”), principle 1 (“[PCGs] should be established as an independent legal entity on the basis of a sound and clearly defined legal and regulatory framework to support the effective implementation of the CGS’s operations and the achievement of its policy objectives.”) and principle 4 (“[PCGs] should be independently and effectively supervised on the basis of risk-proportionate regulation scaled by the products and services offered.”).

This paper also benchmarks the characteristics and performance of PCGs in LAC vis-à-vis advanced countries during the COVID-19 pandemic. Advanced economies have also relied heavily on PCGs to provide support to their local enterprises as they dealt with the COVID-19 pandemic. While several advanced economies had well-established institutions in place, many had to introduce special programs, which included significant parametric changes, as they also brought in new public funding in order to maximize the impact of their policy measures. A presentation of these changes, along with an overview of their performance, are important for policy makers in LAC to explore future policy options for their own use.

# 4. PCGs in the LAC region

PCG schemes that support the SME sector serve different purposes in normal times and during market swings. While in normal times, PCG programs typically focus their efforts on supporting access to finance by SMEs, during market swings the programs become a valuable instrument to channel the government support for viable companies to weather the storm and de-risk bank portfolios to help in smoothing the credit flow towards the SME sector. Increasing the use of banking services by companies during crisis periods might be a difficult endeavor. Banks and other financial institutions protect their capital, by becoming more risk averse, that means curtailing their lending to riskier segments of the markets, such as SMEs. Notwithstanding, during market swings, PCG schemes may grow as a proportion of total lending to SMEs. This is because PCG schemes may help to de-risk banks’ loan portfolios and facilitate smoother lending to the SME sector, thus avoiding the possibility of a credit crunch.

# a. PCGs in the LAC region in pre-pandemic times

While PCGs in LAC have clear objectives, economic additionality is not one of them. In the absence of economic additionality as an objective, the incentive structure of PCG schemes runs the risk of misguidance. The lion’s share of surveyed PCGs (14)<sup>5</sup> has an explicit annual objective: either the volume

> 4 World Bank (2015).

> 5 Refers to the number of PCGs. The same notation is used henceforth.

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# **A Macroeconomic Perspective of Structural Deforestation in Brazil's Legal Amazon**

Joaquim Bento de Souza Ferreira Filho University of São Paulo – ESALQ Marek Hanusch World Bank

# **JEL classification:** Q15; Q24; C68

**Key words:** Macroeconomics and Growth; Structural Policy and Reform; Environmental Sustainability; Green Growth; Climate Change Mitigation

The authors extend their thanks for useful comments to José Gustavo Féres, Gerd Sparovek, and Alberto Barretto, as well as participants of a seminar organized by IPEA in October 2021.
ment of labor regulation across inter-state migrants, further reducing incentives to move even if wage gains are substantial.

Recognizing these issues, the central government passed a law, called the 1979 Inter State Migrant Workmen Act, specifically to regulate practices associated with the recruitment and employment of interstate migrant workers. The law requires middlemen who recruit interstate migrant workers and the firms that hire them to get a special license. It requires that migrant workers be paid in accordance with local minimum wage laws, be issued a passbook recording their identity, nature of work and remuneration, and be provided with accommodation and health care. However, as pointed out in Section 2, studies suggest that this law is not enforced: most firms hiring migrant workers do not carry the proper license and most migrant workers do not possess the required passbooks (Srivastava and Sasikumar 2003; Srivastava 2012).

We expect the lack of portability of PDS benefits and cards to contribute to the inertia of the unskilled who are likely to be most dependent on it. In Figure 4a, we plot the partial regression of the share of in-state unskilled emigration on participation in the PDS. The dependent variable (on the y-axis) is the number of unskilled emigrants who moved to destinations within the state of their origin, divided by the total number of unskilled migrants from the said state. This measure comes from the bilateral migration data in the 2001 Census, aggregated to the state level. The explanatory variable (on the x-axis) is the share of the unskilled population participating in the Public Distribution System (PDS).<sup>20</sup> The regression controls for the log average household income per capita and the share of agricultural households at the state level, both of which are also calculated from the NSS data. We find a positive and significant relationship between the two variables, i.e. the larger the share of unskilled population who rely on PDS, the higher the tendency for potential emigrants to choose home-state destinations over out-of-state destinations. This finding is con-

> 20We calculate this measure from the consumption module of the 55th round of National Sample Survey (1999-2000). The unskilled population refers to all members from households with a male household head who has completed primary education or below. Any household that reported a positive amount of PDS purchase is considered participating in the PDS, and consequently, so are all individuals from such households.

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# Sometimes More Equal Than Others

How Health Inequalities Depend on the Choice of Welfare Indicator

## Magnus Lindelöw

_The World Bank Centre for Study of African Economies, Oxford University_ Email: mlindelow@worldbank.org World Bank Policy Research Working Paper 3329, June 2004 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**_________________________________**

I am grateful to the National Statistics Institute (INE) in Mozambique for granting me permission to use the 1996/97 Household Survey. Ken Simler provided invaluable support in accessing and interpreting the data. I have also benefited from comments and suggestions from Marcel Fafchamps, Pieter Serneels, and Adam Wagstaff.
29 of other policy variables; and (4) the causal ties between<sup>exports and output</sup> arc ambiguous both theoretically and empirically.

# B. Fiscal Policy

One of the most important issues in economics is the<sup>role of government</sup> expenditures and taxation in economic growth. A large<sup>number of cross-</sup> country studies of growth have attempted to link<sup>aggregate measures of fiscal</sup> policy with average annual growth rates computed over<sup>long time periods [see:</sup> Table 1]. Thus far, the literature has been generally<sup>unsuccessful in</sup> identifying robust empirica_ relationships between<sup>growth and aggregate</sup> indicators of government expenditures or taxes. This<sup>section briefly reviews</sup> the theoretical relationships between fiscal policy<sup>and growth, the conceptual</sup> complexities associated with using macroeconomic theory to guide<sup>cross-</sup> country empirical investigations of growth and fiscal policy,<sup>and some</sup> statistical reasons why we have been unsuccessful in<sup>identifying consistent</sup> empirical relationships between existing measures<sup>of fiscal policy and growth.</sup> There are three basic themes in this review. First,<sup>the intuition underlying</sup> the theoretical linkages between fiscal policy<sup>and growth is intuitively</sup> appealing and fairly straightforward. Second, it is difficult to<sup>measure</sup> government provision of services because aggregate<sup>measures of expenditures do</sup> not delineate among categories of government expenditures<sup>that may have very</sup> different growth implications, and money spent<sup>may not accurately represent</sup> the actual delivery of services. Third, a country's<sup>"tax system" is difficult</sup> to represent using aggregate measures because the<sup>structure of taxes,</sup> enforcement, and the tax base differ internationally. Evaluating fiscal
where _i_ represents each household and _g_ the growth observed in per capita household final consumption expenditure reported in World Development Indicators.<sup>3</sup> For details on the interpolation methodology, see Prydz et al. (2019). We then collapse the 1 and C 20201 consumption vectors into 1,000 equally weighted quantiles in each distribution and estimate the growth for each quantile. Finally, we use FFCC2019 these quantile growth rates to grow the distribution (2019 FY distribution for India used in PIP, which his based on approach 2) to derive the C 2020c distribution for India. This ppiipp FFCC2019 paper reports estimates of inequality and poverty based on the C 2019 and C 2020c cciicc change in India. Our approach makes two assumptions: First, we need to interpolate to get from ppiipp cciicc fiscal years to calendar years. This is a standard PIP assumption whenever data do not line up with calendar years. Second, we assume that the growth rates estimated using approach 1 can be applied to the approach 2 distribution (using 1,000 quantiles). This seems to be a reasonable assumption given that Roy and van der Weide (2022) show that despite differences in poverty levels, the trend in the poverty rate is very similar across the two approaches. Once estimates from approach 2 become available, this assumption can be tested, and our estimates can be updated.

Table A.1 reports the 2020 estimates of poverty using various growth rates for 2020. Using the distribution-neutral per capita GDP growth from national accounts results in an extreme poverty rate of 11% which would mean a global poverty headcount rate of 8.8%.<sup>4</sup> Using unadjusted growth rates from the CPHS (as published by Gupta et al., 2021) instead would result in a poverty rate of 16.2% in India implying a poverty rate of 9.8% globally amounting to a net impact of 130 million additional poor. The preferred approach in this paper also employs the CPHS but with the adjustments suggested by Roy and van der Weide (2022). This results in a poverty rate in India of 13.4% implying a poverty rate of 9.3% globally and a net pandemic impact of 90 million additional poor in 2020 globally.

> 3 Given that 25% of the fieldwork for the 2019/20 round took place in 2020, we assign it the time 2019.25. To find the growth between the 2019/20 survey and 2020, 2019.25−2020, we first calculate per capita household final consumption expenditure (HFCE) at 2019.25 as a weighted average of the 2019 and 2020 HFCE, with gg 75% of the weight given to 2019. Upon that, we calculate the growth in HFCE from 2019.25 to 2020. A similar procedure is used with the growth from 2020 to the 2020/21 round.

> 4 In this comparison, we only switch the estimate for India to per capita GDP growth, while keeping the approach for all other countries as in the baseline. If instead we used per capita GDP growth for all countries, the global poverty rate would be 9%. For poverty at various poverty thresholds using distribution-neutral projections, see Table B.6.

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