rafmacalaba/datause-encoder-data huggingface dataset:
Thus, even if small-scale experiments are unbiased within sample, they may be biased and also imprecise for population-level inference. In other words, there is likely to be a trade-off between the potential omitted variable bias in our panel-data estimates on one hand, and the advantages of greater precision, “as is” implementation, and unbiased site selection on the other. We do not attempt to quantify this trade-off in this paper (since we have no objective basis of doing so). However, one way of reconciling this trade-off is to conduct a substantial nationwide expansion of school inspections by hiring more staff in the context of a large experimental evaluation. From a decision-theoretic perspective, our results are strong enough to support such a policy even if there is only a 1% chance that our estimates are causal. In Appendix B, we formally show that, barring extreme priors, a policy-maker interested in lowering effective student-teacher ratio will find it cost-effective to invest in or scale-up monitoring of teachers.

# **7 Conclusions**

The central and state governments in India have considerably increased spending on primary education over the past decade. We contribute towards understanding the impact of these substantial nationwide investments in primary education in India by constructing a unique nationally-representative panel data set on education quality in rural India. We find that there has been a substantial improvement in several measures of school quality including infrastructure, student-teacher ratios, and monitoring. However, teacher absence rates continue to be high, with 23.6 percent of teachers in public schools across rural India being absent during unannounced visits to schools.

Using village-level panel data, we find two robust correlations in the panel data that provide external validity in nationally-representative data to results established in smallerscale experiments. First, reductions in student-teacher ratios are strongly correlated with increased teacher absence, suggesting that increased spending on hiring additional teachers was accompanied by increased inefficiency, which may limit the extent to which additional spending may improve outcomes. Second, increases in the frequency of inspections are strongly correlated with lower teacher absence, suggesting that of all the investments in improving school quality, the one that was most effective in reducing teacher absence was improved administrative monitoring of schools and teachers. We calculate that the fiscal cost of teacher absence is over $1.5 billion per year, and estimate that investing in improved governance by increasing the frequency of monitoring would be over ten times more cost effective at increasing student-teacher contact time than doing so by hiring additional teachers.

come from just one state, compared to the estimates in this paper that use panel data from 190 districts across 19 states.

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and Bruckner, 2012), with further examination and focus on Sub-Saharan Africa (SSA) (Konuki and Villafuerte, 2016).

The paper has seven sections. The next section describes the data and stylized facts of procyclicality over the period 2000-2016. Section III presents the empirical strategy. Section IV discusses the findings of procyclicality both on the taxation and spending sides. Section V presents a discussion on policy options to mitigate procyclicality, with a focus on the effect of fiscal institutions and fiscal rules on procyclicality. Section VI explains the cross-regional differences in procyclicality. Finally, Section VII presents concluding remarks.

# **II. Data sets and stylized facts**

To examine the correlation between a country’s fiscal policy stance and the business cycle, the standard procedure is to regress the cyclical real GDP on the cyclical real primary general government expenditures.<sup>2</sup> We run equation (1) below on a sample of 116 developing countries for the 2000-2016 period.<sup>3</sup> Cycl. rggpx�� �α�βCycl. rgdp�� �γ� �δ� �ε��, (1) where α refers to the intercept, and subscripts, i and t stand for country and year. γ� and δ� are country and year fixed effects (regional fixed effects are absorbed by country fixed effects). β, the coefficient of interest, captures the variation of cyclical spending (in local currency) due to changes in the cyclical GDP by _x_ units of local currency. Later sections explore cyclicality in the tax rates as in Aizenman et al. (2019), using the Vegh and Vuletin (2015) database on tax rates.

To examine how fiscal policy management evolved over time, we split the period into two subperiods: 2000-2008 (pre-global recession) and 2009-2016 (post-global recession) and compared how each country’s procyclicality coefficient changed over time. In the first subperiod, 64% of the countries had a procyclical fiscal stance, while in the second one the percentage fell to 60% (Figure 1).<sup>4</sup> Hence, only 40% of developing countries ran countercyclical fiscal policy in the second subperiod, but in SSA the proportion was only 20% (8 of 38) (Fig. 2). In LAC, 50% of the countries ran countercyclical fiscal

> 2 Alesina et al. (2008), Frankel et al. (2013). The cyclical components of GDP and expenditures are estimated using the Hodrick-Prescott (HP) filter with a smoothing parameter of 6.25, as done by Ravn and Uhlig (2002). Though correlation cannot be interpreted as causation, Ilzetsky and Vegh (2008) show that output causes government spending when properly instrumented. Konuki and Villafuerte (2016) also conclude that output shocks drive fiscal policy.

> 3 See Appendix A for a detailed description of the data set and the sample selection process.  To make results comparable with previous literature, Frenkel, et al. (2013), Vegh and Vuletin (2015), and Aizenman et al., (2019).

> 4 Figures 1 and 2 replicate the format employed by Frankel et al. (2013).

3
**_a_** , age groups 4-11 and 12-17

**_g_** _c,w_ , income shock projection of country _c_ , if and when available, the simulation can accommodate this parameter by welfare quintile (w)

# **_c_** , country

For simplicity, in each scenario, the Global simulation assumes the same sc for all countries within a particular scenario, and mc and pc vary only by country income level. We assume a uniform income shock across welfare quintile at the global level, since there is no better number available.

# _Simulation 2: Effect on mean (score)_

This analysis builds on scenarios used to estimate the learning losses from simulation 1 and provides an estimate of how much learning will be lost during school closures necessitated by COVID-19 in terms of PISA scores. Estimates are based on (i) typical test score gains from one grade to the next, (ii) data on availability of alternative schooling modalities, (iii) assumptions on the effectiveness of these alternative modalities, and (iv) assumptions on duration of school closures. Results are provided by country and are disaggregated by socio-economic status. The PISA and PISA-D databases are used for these calculations, 2018 or the latest available PISA year.

where,

**sc** , school closure (as a share of the school year) for country _c_

**mw** , mitigation effectiveness by welfare quintile (w)

**pw** , school productivity or learning gains by welfare quintile (w)

**w** , welfare quintile proxied by Social Economic Status (SES)

**c** , country

Note: For simplicity, the basic simulation assumes that, within a country, children have the same school productivity regardless of socio-economic status.

# _Simulation 3: Effect on share of students below a minimum proficiency threshold_

This analysis builds on scenarios used to estimate the learning losses from simulation 2, and provides an estimate of how the share of children performing below minimum proficiency (PISA Level 2) will change as a result of school closures.<sup>56</sup> Borrowing an analogy from poverty estimates – results are presented in terms of headcount of students (aka poverty rate of FGT0), a learning gap (or FGT1), and a learning gap severity (or FGT2). Results are provided for the following scenarios: (i) baseline, (ii) all children are effected identically (the whole distribution of test scores shifts to the left while maintaining its shape), (iii) inequality worsens (the distribution flattens with those at the top of the distribution moving ahead and those at the bottom falling behind), (iv) those who were already behind fall further behind while those at the top are unaffected (the distribution becomes left skewed). The PISA and PISA-D databases are used for these calculations, 2018 or the latest available PISA year.

> 56 UNESCO (2019a), Paper 4, Table 1.

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attritors reach 90 percent more likely to have obtained ICT jobs (represented by the value 1.9 on the x-axis of figure 1). However, the heterogeneous effects are highly robust, remaining significant for conventional levels of significance (p=0.05) even if the simulated non-attritors were three times more likely to get ICT jobs than the rest of the control sample (Figure 6, x-axis value of 3).

# **Discussion and conclusion**

Encouraging skilled workers to shift to a new industry can be a challenge, particularly given labor market frictions such as a mismatch between the skills required by the industry and the skill endowment in the existing labor force, and gender norms that limit women’s mobility in the labor market (including implicit attitudes harbored by women themselves). We studied the role that these constraints may play in the labor market for recent university graduates in urban Nigeria through a randomized control trial that experimentally assigned slots in a job training program to prepare participants for work in the nascent ICT industry. This sector is of particular interest given that it has, in other settings, been less affected by social norms prohibiting females from accessing these relatively desirable, formal sector office jobs. In some countries, certain subsectors such as business-process outsourcing have even been female-dominated.

Despite the training program’s coinciding with political violence and despite relatively low takeup of training offers, we find a modest average impact of the program. Those offered training were 1.7 percentage points or 26 percent more likely to be employed in the ICT sector two years later, with no significant impacts on overall employment. They were also almost 8 percent more likely to be actively searching for work at the time of the end line survey.

Heterogeneous treatment effects can shed light on the extent to which skills, location, and gender norms moderate these effects. Treatment impact on ICT employment appears completely driven by applicants who had scored above the median on an initial assessment modeled after the new certification examination for the industry; these participants were 4.8 percentage points (75 percent) more likely to work in the ICT sector. This population was already more likely to work in the sector, and the program augmented this skill premium in employment, suggesting relatively high returns to industry-relevant skills. Impacts also varied across cities suggesting a mismatch between the supply of ICT-relevant skills and firms’ demand for them. Some cities appear not to have absorbed program participants, even though after the training program, they experienced 21
# **2. Review of the Literature on the Relative Performance of Male and Female Entrepreneurs**<sup>**1**</sup>

In this section we briefly review the literature that evaluates the relative performance of male and female-owned firms by summarizing what we know about the types and sizes of gender gaps in entrepreneurial performance (Section 2.1) and what explanations have been offered for the gaps that have been found (Sections 2. 2). This literature spans a wide array of disciplines and journals. Since we focus on firm performance, we do not, for example, cover the vast labor economics literature on ‘selfemployment’ as a proxy for entrepreneurship. Most studies of women business owners are in managerial journals; they tend to be descriptive and based on small sample sizes in one country (see e.g., Brush, 1992 for a review). Our analysis is more rigorous, based on economic theory of the firm, and uses a large firm-level data set for numerous countries.

# **_2.1. Measures of Relative Performance_**

The evidence on the effect of gender on firm performance is mixed. Some studies provide evidence of female underperformance (e.g., Brush, 1992; Rosa et al., 1996), while others do not find gender-based differentials (e.g., Du Rietz and Henrekson, 2000; Bardasi, 2007). The findings depend in part on which measure of performance is used and what other factors are being controlled for.

A growing body of evidence consistently shows that female-owned enterprises are smaller -- in terms of sales, assets and employment -- than male-owned enterprises (Chagnati and Parsuraman, 1996; Coleman, 2007; Robb, 2002; Watson, 2002, Loscocco and Robinson, 1991). For example, Coleman (2007) finds that - as recently as 1998 in the US - among small firms (fewer than 500 employees), the average male-owned business was twice as large as the average female-owned business in terms of total sales and assets and had 50% more employees (without holding anything else constant). Similarly, Chagnati and Parsuraman (1996) find (using US data) that sales are twice as large in male-owned business as in female-owned business. However their sample (less than 400 firms) was much smaller than Coleman’s sample of 2,800 firms and covered smaller firms (4 - 99 employees). While these two studies only provide only ‘unconditional

> 1 This section was enriched tremendously by the chapter on ‘Female Entrepreneurship’ in the forthcoming new edition of Simon Parker’s textbook _Economics of Entrepreneurship_ . We are grateful to him for sharing it with us.

3
above-discussed biases. However, while there is a strong argument for women reporting less extramarital sex because of possible negative outcomes, it does not seem as likely that women would under-report condom use in the extramarital intercourse they have already reported. Once a woman has admitted having extramarital sex (despite strong societal pressures to not report it), admitting to condom use seems more likely. These tendencies may make the sample size smaller than they should be but condom use within these observations should be more or less accurate. This lends more credibility to the finding that condom use is low during sex among married women with extramarital partners.

These married females are acting like single females in that they have multiple sexual partners, however in terms of their condom use, they are acting more like married females, even though the level of risk of HIV is greatly increased by their extramarital relations. It is not clear why married women use condoms less frequently than unmarried women. It may be that it is more difficult for a married woman to obtain a condom without her husband’s or anyone else’s knowledge. She also may not have the financial resources at her discretion that a single female may have who is employed. Further research investigating the reasons for low condom use in women’s extramarital relations could help make prevention efforts more effective.

# **Section 6: Conclusion**

Based on nationally representative samples from 13 Sub-Saharan African countries, we reinforce and expand previous findings that condom use in general is low in this region, that men report using condoms more frequently than women, and that unmarried 24
that rural households are often disproportionally affected. Contrary to that, this paper illustrates that urban households can be as or more vulnerable when hit by extreme weatherrelated shocks. For these households, consumption per capita declined by 12.6% relative to the median consumption at baseline.

The negative effects of the shock span other areas of human welfare, particularly among urban households. The incidence of poverty increased by 5.5 percentage points, equivalent to 18% with respect to the poverty headcount that existed at baseline. Affected households cut back on expenditures on food by 10%, equivalent to a reduction of over 100 calories per day per household members. We also find evidence that households reduced expenditures on basic durables such as stoves or refrigerators.

Behind the limited ability of affected households to smooth consumption is a fall in income per capita of urban households on the order of 10%, driven mostly by a drop of labor income among salaried jobs. To cope with the shock, we find two different types of labor supply responses. First, male adults adjusted on the intensive margin by increasing the number of hours worked (1.9 hours more per week or 3.7%). This additional labor supplied occurred in tandem with a fall in the remuneration of workers, possibly signaling a sort of general equilibrium effect. Second, we find a sizable increase of 3.1 percentage points (10.8%) in child labor force participation –especially among boys in rural areas. Simultaneous to relying more on the labor force of their children, we find evidence that affected households were also more likely to withdraw them from school, raising the risk that they drop out.

A leading factor that helps to explain the sizable impact heterogeneity across areas is the strength of the shock itself. Impact studies often fail to quantify and map the intensity of natural shocks to the units of analysis. Our evidence suggests that part of the reason as to why the negative effects were more concentrated among urban households has to do with the fact that excessive precipitation was much stronger in urban areas. We also observe some evidence that food prices increased after the shock in parts of the region that saw the largest precipitation anomalies, especially in urban centers. At the same time, the relatively low sensitivity of rural households to the shock may be partly explained by the timing of Agatha with respect to local agricultural cycles. For the most part, the excessive precipitation fell in a period of the harvesting season that was not harmful for maize, beans, coffee and sugar cane, 22
national level that contain information on the environmental performance of regulated facilities across the US. Four are of particular relevance to the construction of pollution intensity indices: the Toxic Release Inventory, the Aerometric Information Retrieval System, the National Pollutant Discharge Elimination System, and the Human Health and Ecotoxicity Database.

# 2.1.1 The Toxic Release Inventory (TRI)

The TRI contains information on annual releases of toxic chemicals to the environment. It was mandated by the "Emergency Planning and Community Right-to-Know Act" (EPCRA) of 1986, also known as Title III of the Superfund Amendments. The law has two main purposes: to provide communities with information about potential chemical hazards; and to improve planning for chemical accidents.

The TRI reporting requirements cover all US manufacturing facilities that meet the following conditions:

- @e they produce/import/process 25,000 pounds or more of any TRI chemical or they use 10,000 pounds or more in any other manner;

- @® they are engaged in general manufacturing activities;

- @ they employ the equivalent of ten or more full-time _ employees.

The original TRI requirements, which applied for the 1987 reports, set a threshold of 75,000 pounds of TRI chemicals produced, imported or processed. This was lowered to 50,000 4
inequality may in fact be different from zero. This debate highlighted how different hypotheses about individual utilities could lead to very different optimal solutions for societies. It also introduced the notion of relativity intended as utility functions relative to individual tastes for commodities.

The economics profession had also accepted for long the notion of interdependent utilities, the idea that individual utilities also depend on the utility of others. Both Adam Smith and Karl Marx in different passages noted the importance of relative wealth as opposed to absolute wealth. In _The Wealth of Nations_ Adam Smith (1776) wrote that " _By necessaries I understand not only the commodities which are indispensably necessary for the support of life, but whatever the custom of the country renders it indecent for creditable people, even of the lowest order, to be without_ ". Marx (1847) himself wrote that " _A house may be large or small; as long as the neighboring houses are likewise small, it satisfies all social requirement for a residence. But let there arise next to the little house a palace, and the little house shrinks to a hut”._ Veblen (1898) noticed that people’s consumption is also functional in expressing status and position in society, a concept later developed by Frank (1985) in his theory of positional goods. Here the relativity concept acquires a new dimension. Not only people are different in their own evaluation of commodities but the value function also depends on the commodities of others. This greatly complicates the maximization problem of societal utility as utility becomes a function of both absolute income and relative income or rank.

The formalization of interdependent utilities did not find much traction in economics until Duesemberry (1949) proposed a relative income hypothesis to explain savings behavior in the US. Looking at data between 1869 and 1929, Kuznets (1942) had noticed that aggregate savings did not increase as a proportion of income over time while evidence from cross-sectional budget surveys during the period 1935-1942 showed that the household savings rate increased with income. In an effort to explain the mismatch between income and savings behavior in longitudinal studies, Duesenberry (1949) proposed the so called “relative income hypothesis”. His basic intuition was that households are concerned about community consumption standards and that this concern leads to savings rates being an increasing function of a household's position in the income distribution: “ _(...) for any given relative income distribution, the percentage of income saved by a family will tend to be unique, invariant, and increasing function of its percentile position in the income distribution._ ”(1949, pp.3). Duesenberry also noticed that social comparisons are not symmetric. People tend to discount or ignore downward comparisons while 4