rafmacalaba/datause-encoder-data huggingface dataset:
first step is an increase in parental involvement. We examine parental involvement in schools using four measures: whether parent associations organized school activities and events, whether parent associations met with teachers to discuss children’s academic progress, whether parent associations participated in school decision meetings, and the percent of parents attending parent association meetings.

In the next step, increase in parental involvement in schools should lead to changes in child inputs by parents and teachers. Parents have more information about their children’s behavior and performance in school, which allows them to adjust how they support their children at home. We measure two types of parental behavior at home: whether parents were aware of their children’s school assignments and whether parents helped with their children’s homework.

Increases in parental involvement at school also mean greater oversight over teachers in how they manage their classrooms. Teachers may be induced to exert greater effort given that parents are regularly participating in school activities and events. We measure two types of teaching behavior: recorded days of teacher absences in the past month and an index of student-centered instruction. The index of student-centered instruction is the first principal components from principal component analyses of four survey items: (i) teacher explains concepts clearly, (ii) teacher reviews homework assignments, (iii) teacher does not ask students to copy from textbooks/blackboard without any explanation, and (iv) teacher gives students exercises that apply concepts learned in class.<sup>7</sup> The last step in the theory of action is improvements in educational outcomes. Using administrative records from the school census data, we measure school-level failure, repetition, and dropout rates. We use the national standardized exam data for student-level test scores in Spanish and Math. Finally, we measure disciplinary action in schools by whether a student had been suspended, expelled, or involved in any other type of disciplinary action (i.e., referred to the principal) in the past academic year.

# **5 Empirical Strategy**

**Experiments.** For each experiment, we estimate the effect of being assigned to treatment at each year of data collection using the following model specification:

> 7To benchmark the effect size of student-centered instruction, we show the association between student-centered instruction and the items used to construct the index at baseline in Table A6.

10
Table 3: Regression Results for Cereal Equivalent Production

||(1)<sup>a</sup>|(2)<sup>~~b~~</sup>|
|---|---|---|
|_B0 _|0.24**|0.23**|
||(0.12)|(0.11)|
|_B1_|4.3 × 10<sup>-3</sup><br>|3.9 × 10<sup>-3</sup><br>|
||(5.4 × 10<sup>-3</sup>)|(4.3 × 10<sup>-3</sup>)|
|_B2 _|0.60***|0.62***|
||(0.11)|(0.10)|
|_B3_|0.33***|0.32***|
||(0.031)|(0.037)|
|R<sup>2</sup>|0.64|0.65|
|Observations|140|3762|

Source: Authors’ regression results Notes: ** and *** indicate that the coefficients are significant at the five and one percent level respectively. a Regression (1) is based on 2005-2009 averages of CE consumption and real GDP for 140 countries. The robust standard errors are given in parentheses. b Regression (2) shows the results using all the available data points for each year during the period 1980-2009. The standard errors in parentheses are based on heteroskedasticity-consistent estimates of the variance-covariance matrix and corrected for within country correlation (clustering).

Based on the parameter values reported in column 1 in Table 3, Figure 9 shows the estimated relationship between income levels and cereal equivalent production. To allow comparison in two dimensions, the CE production schedule for each country is adjusted so that it has the same land endowment as China (0.21 hectare per capita as an average of the period 19802009 per person).<sup>20</sup> The estimated CE production curve is visually close to linear: it rises in line with income, although less rapidly than income because of the secular decline in agriculture’s share of national income. From Figure 9, it appears that China has been an out-performer in terms of output. Agricultural output, which is slightly below the consumption level, is substantially above the global trend level. This may reflect the relatively high productivity of much of China’s agricultural land and the extraordinary efforts made in China to increase

> 20 Both sides of equations (2) are divided by � 0.21li �.33where _li_ is the average of land endowment of country _i_ and 0.21 is the average land endowment of China.

20
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which ignores any effect of the relative size of the public and private sectors (probably not a bad assumption for people who have already shown up for treatment. There could be scale effects due to differential severity of health problems if for some conditions people are nearly indifferent between seeking or not seeking treatment and less severe cases are seen when prices are low. This effect would have less to do with the public/private split, though, than with the treatment/non-treatment split).

If, in addition to free care and linear technology, there is either 1) no private sector _(Dv -_ 0 for all i) or 2) a useless private sector (i.e., one that can generate no improvement in health status:L,v - 0 for all i) or 3) there is no cross price elasticity of demand at all between public and private sectors (in the sense that private demand for services is completely unaffected by whether or not services are offered in the public sector: **_DjQ(I=1)_** _-_ **_Dv(I=O)_** _,_ for all i), then the rationing rule for the public sector becomes: <!-- Start of picture text --> B ><br>:-. <A<br><!-- End of picture text --> (A2.3) This is the rationing rule associated with standard cost effectiveness analysis of curative care options where the health improvement associated with a technology is divided by its resource cost (not the subsidy component only because of the assumption of free care) and this ratio is higher for any included procedure than for any excluded one.
women who have separated from their husbands. Single women tend to be younger, with an average age of 21.8 compared to 26.7 for widows and separated women. Unmarried women may be more likely to engage in transactional sex, as it is less costly to find additional partners when you do not have a spouse. This notion is supported by crosssectional data in the DHS which show unmarried women are more likely to engage in transactional sex (Chatterji, et al. 2012). Also, in addition to the transfers that transactional sex brings, women may be engaging in transactional sex to search for spouses (Luke 2003; Robinson and Yeh 2012).

Negative shocks should also have larger effects on women with lower socioeconomic status (SES). These women will on average have less savings and be less able to access a risk-sharing network. We measure a woman’s SES standing using the following question: “Think of a ladder in which people in your community are ranked with the highest status people on the top rung and the lowest status on the bottom round. On a ladder with seven steps, on which step would you place yourself?” Those who reported being on the last two rungs are classified as being in the lowest SES group.<sup>11</sup> Using the economic ladder has two advantages in our context. The first is that it might pick up unobserved characteristics that denote lower social economic status such as community level ties and access to credit. Second, the study did not collect comprehensive data on savings and assets from which we could build a monetary SES measure.

The distribution of shocks for both unmarried women and women in the lowest SES group are similar to the overall sample (see Appendix Figures A1 and A2).

# _Individual-level Panel_

In creating the panel, we use data from R2-R4, excluding R1 in order to have congruence with both the time periods and outcomes of interest. Data collected in R1 reflect shocks that occurred over the past year (vs. four months for R2-R4), and STIs at R1 may not reflect current sexual activity. Limiting our panel to R2-R4 generates

> 11 Our main results are robust to classifying low SES groups if they report being on the lowest 2 or 3 rungs of the ladder as well.

8
areas and defending economically important areas. These results appear aligned with certain key elements in the design of Tajikistan’s adaptation program under the Pilot Program for Climate Resilience. In particular, the results lend support to the program’s emphasis on agriculture and sustainable land management and on building climate resilience in major glacier-dependent river basins containing a large proportion of agricultural land, such as the Pyanj River basin in the Khatlon area (Climate Investment Funds, 2010).

<mark>The results indicate that to the extent that policy makers wish to direct funding toward areas with the highest vulnerability to climate change, they should avoid urban areas in favor of rural areas, in particular eastern RRS mountains, Southern Sughd hills, and Khatlon hills and lowlands. These are areas of varying elevation which share a high degree of sensitivity to climate change and weak adaptive capacity. These results do not tell policy makers</mark> _<mark>how</mark>_ <mark>to design adaptation. The results do suggest, though, that migration to urban areas and abroad for work might usefully form part of overall adaptation strategies.</mark> <mark>It is interesting to compare these results to a recent review of project proposals for community-based adaptation all over the developing world which found that most projects focused on rural areas, often in poor and remote parts, and rarely envisioned migration as part of adaptation (Heltberg, Gitay, and Prabhu, 2010). The results here suggest that while the rural focus is appropriate from the point of view of pro-poor adaptation, adaptation planners may want to consider the case for defending economically important rural areas and revisit the role of migration.</mark> <mark>15</mark>
**Figure 2: Average Monthly Earnings**

<!-- Start of picture text --> A. Less than College B. College<br>Year - Quarter<br>Primary or Less Junior High School<br>High School Year - Quarter<br>16000<br>8500<br>14500<br>7000<br>5500 13000<br>Monthly Labor Income 4000 11500<br>Monthly Labor Income<br>2500 10000<br>2000q2 2001q2 2002q2 2003q2 2004q2 2005q2 2006q2 2007q2 2008q2 2009q2 2010q2 2011q2 2012q2 2013q2 2014q2 2015q2 8500<br>2000q2 2001q2 2002q2 2003q2 2004q2 2005q2 2006q2 2007q2 2008q2 2009q2 2010q2 2011q2 2012q2 2013q2 2014q2 2015q2<br><!-- End of picture text --> Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2015. Earnings are in constant Mexican Pesos 2014:1. Earnings refer to full-time workers (at least 30 weekly hours of work) with a valid wage. It includes all workers (men and women): salaried, self-employment and owners. Earnings below and above the 1 and 99 percentile (within gender and education group) are dropped. Lines are smoothed by a moving average with a 0.4 weight to the current observation and 0.3 to the previous and post observation.

The average hourly wage for college-educated workers declined at a rate of approximately 2 percent per year between 2001 and 2014. Given that during this period the share of college graduates in the workforce rose from 14 to 18 percent, the decline in relative (and absolute) wages may be a simple matter of supply outpacing demand. An alternative explanation (and not mutually exclusive) could be that the fall in returns to college education is the outcome of a process intrinsic to a particular age group. Younger workers, for example, could be experiencing a decline in their average wage because—as a result of the large expansion of tertiary education institutions since 1995—their tertiary education is not of the same quality on average as that of younger workers in previous cohorts. For example, in the period 1984 – 1994, university enrollment increased by 1.95 percent per year, while during the 1994-2000 period, it increased by 5.4 percent per year. The skills acquired through tertiary education could have been of lower quality, either because the new post-secondary institutions are of lower quality and/or because the marginal student has lower abilities. We call this the “degraded tertiary effect”.<sup>4</sup> Another possibility (again, not mutually exclusive) is that older workers in recent cohorts receive lower wages than their counterparts in previous cohorts because their skills no longer match what the markets demand or because there was a skilled-labor-saving technical change. We call this the “skills-obsolescence effect.”

> 4 See Lustig, Lopez-Calva and Ortiz-Juarez (2013) and Camacho and Messina (2014).

1
Policy Research Working Paper 10142

# **Abstract**

Despite decades of economic growth, gender inequality in South Asia remains remarkably high. Although not the only one, social norms are a crucial driver of various gender outcomes, including differential economic participation. Using repeated cross-sectional data from nationally representative surveys, this paper explores the long term trends of gender outcomes and social norms (proxied by attitudes towards gender roles) in South Asia. The results corroborate the evidence that there has been almost no progress in gender equality in South Asia over the past half-century. There has been little progress on female labor force participation, marriage age, agency, intimate partner violence, and preference for sons, with education being the only exception. The lack of progress is apparent among all socioeconomic groups, including women who live in urban areas, are educated, and have higher incomes. Gender attitudes also remain unchanged, and in some cases, have become more conservative and have a negative relationship with gender outcomes. Better measurements of social norms and better understanding of how their constraining role can be loosened may be critical for achieving gender equality in the region.

This paper is a product of the Office of the Chief Economist, South Asia Region and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, jezebuihe@worldbank.org, amunozboudet@worldbank.org, s.poupakis@gmail.com, trahman4@worldbank.org, and nsarma@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
in the statistics literature, to offer extensions or alternative estimation techniques to the Elbers _et al._ (2003) method.<sup>20</sup>

# _Examples and Remaining Challenges_

Elbers _et al._ (2007) use “poverty maps” from three countries for an ex ante evaluation of the distributional incidence of geographic targeting of public resources. They simulate the impact on poverty of transferring an exogenously given budget to geographically defined sub-groups of the population according to their relative poverty status. They find large gains from targeting smaller administrative units, such as districts or villages. They also suggest that poverty map-based geographic targeting can be combined with within-community targeting mechanisms for better estimation results. Lanjouw, Marra, and Nguyen (2017) employ small area estimation techniques to estimate the poverty indexes of Vietnam's provinces and districts in 2009. They find poverty rates to become more spatially concentrated over time, which is consistent with agglomerationrelated growth processes. They offer simulation results suggesting that in both 1999 and 2009 geographic targeting for poverty alleviation improves upon a uniform lump-sum transfer, particularly for the more spatially disaggregated target populations.

We note that survey-to-census imputation shares a similar issue as with across-survey imputation methods: the variables used in the imputation in both the (base) survey and the (target) census should have the same distribution. Perhaps we cannot overemphasize the importance of this condition, given both the theoretical results and empirical evidence (offered by Tarozzi and Deaton (2007) and Dang _et al._ (2017b)). However, to our knowledge, few studies offer explicit

> 20 See, e.g., Bilton _et al._ (2017) for a proposal to use a classification trees technique for poverty mapping, and Das and Chambers (2017) for alternative standard error formulae with the Elbers _et al._ (2003) method. See also Pratesi (2016) for a recent collection of studies discussing various technical aspects of poverty mapping. Another study by Steele _et al._ (2017) applies machine learning techniques and big data (i.e., cell phone and satellite data) to evaluate poverty mapping.

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# **Tables and Figures**

Table 1: Number of Observations in each country by age, gender and income

|||**Argentina**|**Brazil**|**Mexico**|
|---|---|---|---|---|
||15-18|41,503|37,155,527|172,607|
|**All**|19-24|52,283|52,681,008|229,533|
||25-44|136,788|149,431,179|550,524|
||15-18|21,205|18,569,127|85,954|
|**Males**|19-24|25,799|26,028,747|111,725|
||25-44|64,292|70,599,971|255,291|
||15-18|20,298|18,586,399|86,653|
|**Females**|19-24|26,484|26,652,261|117,808|
||25-44|72,496|78,831,208|295,233|
||15-18|11,700|9,275,816|45,943|
|**Poor**|19-24|10,909|11,113,437|41,278|
||25-44|19,457|25,438,919|91,206|
||15-18|3,585|6,185,398|12,324|
|**Non poor**|19-24|5,332|9,266,013|26,032|
||25-44|30,603|31,464,169|103,288|

Note: The sum of “Poor” and “Non-poor” is not equal to the category “All” since “Poor” are only those in the 1<sup>st</sup> income quintiles and “Non-poor” are only those in the 5th income quintile while “All” are those in all five income quintiles.

Table 2: Propensity to move by income and gender, in percent

|**Argentina**|Poor|Nonpoor|Male|Female|All|
|---|---|---|---|---|---|
|15-18|27.0|9.2|25.4|18.5|22.0|
|19-24|43.7|28.8|41.5|34.0|37.7|
|25-44|40.4|17.0|28.3|27.5|27.8|
|**Brazil**|Poor|Nonpoor|Male|Female|All|
|15-18|17.6|12.3|17.8|12.8|15.3|
|19-24|23.6|19.6|21.7|18.1|19.9|
|25-44|19.6|13.9|14.9|15.2|15.1|
|**Mexico**|Poor|Nonpoor|Male|Female|All|
|15-18|28.2|20.9|29.8|24.0|26.9|
|19-24|32.8|28.3|32.1|26.8|29.4|
|25-44|25.7|20.0|24.0|21.8|22.8|

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# 1. Introduction

<mark>The COVID-19 pandemic has evolved into a defining event that shaped every aspect of our lives, including whether firms stay alive and how they perform. The pandemic has led to large gl</mark> obal variations in terms of government COVID-19 policy stringency and COVID-19 spread severity (Fang et al. 2021). The existential threat of the pandemic has resulted in unprecedented uncertainties for firms: what would be the demand for their products in the near future? Would their input supply be steady? Would banks continue to provide loans? Such uncertainties inevitably force every firm to employ whatever resources it has to meet the challenge.

<mark>The literature on the impact of the pandemic on firms has focused on the adverse effects of the pandemic on firm performance. Whether based on single-country studies on the United States and other countries,</mark><sup>1</sup> <mark>or the emerging literature examining the impact on developing countries, studies invariably and unsurprisingly find adverse and significant effects of the pandemic on firm survival and growth (C</mark> hundakkadan, Raj, and Sasidharan 2020, Liu, Wei, and Xu, 2021; Liu et al. 2021). Furthermore, favorable organizational resources as captured by state ownership and parentcompany affiliation tend to help firms in developing countries survive better and grow more strongly during the pandemic, and firm performance during the pandemic is further affected by pandemic containment policies and a country’s culture and governance (Liu et al. 2021).

Yet despite this emerging literature, on a _worldwide_ basis, much remains unknown. We remain ignorant about the mechanisms through which the pandemic has affected firms. There are many ways firms could be conceivably affected. The pandemic caused a large drop in demand: it resulted in a decline in income and rise in unemployment, all of which drastically reduced demand. The pandemic also drastically disrupts the supply of inputs: various policies that restrict domestic and international mobility, as well as financial difficulties facing firms, would surely disrupt both imports and domestic supplies. The pandemic also severely hampers the working of critical market-supporting institutions such as banks and other financial intermediaries, which play critical roles in maintaining economic prosperity (Levine 1997). The role of financial intermediations would play an even more critical role at the time of crisis, when otherwise healthy firms face risks

> 1 For studies on U.S. firms, see <mark>Albuquerque et al. (2020); A</mark> lfaro et al. (2020); Bloom et al. (2020, 2021); <mark>Bae et al. (2021);</mark> Demers et al. (2020); Fahlenbrach, Rageth and Stulz (2020); <mark>Li et al. (2020); P</mark> agano, Wagner and Zechner (2020); Papanikolaou and Schmidt (2020); and Ramelli and Wagner (2020).  For other single-country studies of the impact of the pandemic on firms, see Carletti et al. (2020) and Gu et al. (2020) on Italian and Chinese private firms.

2
# **Table A3: Robustness checks: Spillovers from alternative definitions of large EM**

||**On impact**|**1year**|**2years**|**3years**|
|---|---|---|---|---|
|**Sillovers from EM7 (baseline)**|**0.28**|**0.79**|**0.86**|**0.87**|
|**p**|[0.15 0.43]|[0.48 1.13]|[0.49 1.3]|[0.46 1.32]|
|**Spillovers from EM7 plus South Africa**|**0.29**<br>[0.16 0.44]|**0.80**<br>[0.48 1.14]|**0.85**<br>[0.47 1.3]|**0.84**<br>[0.44 1.31]|
|**Spillovers from EM7 plus Hong Kong**|**0.28**|**0.78**|**0.85**|**0.85**|
|**SAR, China**|[0.16 0.42]|[0.491.12]|[0.491.27]|[0.47 1.29]|
|**Spillovers from EM7 plus Singapore**|**0.28**|**0.79**|**0.87**|**0.87**|
||[0.16 0.42]|[0.5 1.11]|[0.51 1.28]|[0.48 1.3]|
|**Sillovers from EM7 lus Korea Re**|**0.28**|**0.77**|**0.82**|**0.80**|
|**p p p.**|[0.16 0.41]|[0.49 1.1]|[0.44 1.22]|[0.41 1.22]|

_Note:_ The table shows the cumulated impulse responses of growth in other EFM, for different horizons, due to a 1 percentage point increase on impact in EM7 growth. These are from the aggregate model using alternative definitions of large EM as spillover source. Bolded denote the medians and the numbers in the parentheses are the 16-84 percent confidence bands. **Spillovers from EM7 plus South Africa** : spillover source is the group of EM7 countries and South Africa. The spillover destination in this case is other EFM minus South Africa; **Spillovers from EM7 plus Hong Kong SAR, China** : spillover source is the group of EM7 countries and Hong Kong SAR, China; **Spillovers from EM7 plus Singapore** : spillover source is the group of EM7 countries and Singapore; **Spillovers from EM7 plus Korea Rep.** : spillover source is the group of EM7 countries and Korea Republic.

26