rafmacalaba/data-use-mentions-extended huggingface dataset:
enrolled students.<sup>15</sup> Finally, to measure the effects of group composition on academic performance, schools provided administrative records of math and science grades at the end of the academic year. Appendix 1 includes a description of all collected data and main outcomes. As shown in Appendix Table A5, the average matching rate of administrative data of enrolled children was 94% at baseline and 97% at follow-up. All matching rates were balanced between treatments and C groups except for the fraction of math grades at baseline between the HM and C groups, significant at 10%; and in absenteeism between both tracking groups, also significant at 10%. To account for this difference, I include the imputed grade for missing observations at baseline and a missing value indicator in all specifications for the academic outcomes. Additionally, the average matching rate of administrative data of nonenrolled students was 85% at baseline and 98% at follow-up.

The share of initially enrolled students that filled out the follow-up survey after the intervention was 92%, on average; for the HM and HT groups, it was 91% and 94%, respectively. There were no statistical differences between treatment and C groups in overall attrition rates. Therefore, results are not driven by the absence of follow-up survey data for any group. For the neurophysiological measures, after filtering electroencephalogram (EEG) recording data, the average attrition share was 49%. In Dinarte and Egana (2019), we present several checks to verify that this attrition rate was not correlated with the intervention. We argue that attrition was caused mainly by the quality of the data recordings. For example, long, dense, or dirty hair and/or “frozen” computers were the most common troubles that the Matlab toolbox encountered in reading the EEG recordings. As a summary, the time line of the study is shown in Figure 2.

> 15More details on the data-collection process for this third group of outcomes are discussed in Dinarte and Egana (2019).
, only measured at the 2-month survey; **Index of planning practices** is the proportion of 3 planning practices used; **Index of accounting practices** is the proportion of 10 accounting practices used; textbfIndex of marketing practices is the proportion of 9 marketing practices used; **Index of business practices** is the proportion of all 22 business practices used; **Sales in past month** is sales measured in Mexican pesos (winsorized at the 99th percentile); **Log sales** is log of sales in the past month for firms with positive sales; **Profits in past month** is profits in Mexican pesos (winsorized at the 1st and 99th percentiles). Regressions control for randomization strata, baseline value of outcome where available, and additional controls selected by pdslasso. Robust standard errors in parentheses. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.
and with their estimated signs according to theory. They include the terms of trade, domestic and foreign borrowing constraints, fiscal policy variables, and pension system variables. Regarding other determinants for which consumption theories either differ regarding their signs or point toward ambiguous signs, as in the case of income growth and interest rates, these empirical studies differ widely. They also differ in reported significance levels of variables for which theories tend to agree on expected signs, such as income level, inflation, and demographic dependency ratios.

# 3. Empirical strategy

The above empirical studies capture a number of factors relevant to saving decisions, but vary considerably in terms of data coverage and quality, empirical specification and econometric procedure. Our primary objective here is to extend this literature by providing a comprehensive characterization of the empirical association between private saving rates and a broad range of potentially important saving determinants using the best available data. To do this, we complement and extend previous work along three dimensions. First, we use the largest set of consistent macroeconomic data on saving assembled to date. Second, we adopt a reduced-form approach encompassing a variety of saving determinants identified in the literature — rather than adhere to one particular narrow structural model. Third, we employ a variety of estimation methods, but focus our attention
> The first stage is more precise in this specification (conventional p-value is 5 _×_ 10<sup>_−_7</sup> and randomization inference p-value is 0.057). We find that the elasticity of distance traveled with respect to fuel subsidy received is 1.746.

> 22Mismeasurement of subsidy days would bias our first stage and elasticity estimates if we differentially mismeasure subsidy days with respect to gross tonnage thresholds. We compare official subsidy days for 273 vessels from Putuo district, Zhoushan in Zhejiang province (see (Figure A1(a)) with our estimate of subsidy days using a t-test. We do not find evidence of differential mismeasurement. Vessels above their nearest gross tonnage threshold have a mean difference between official and estimated subsidy days of -13, while vessels below their nearest gross tonnage threshold have a mean difference of -9 (p-value = 0.625). As a point of comparison for interpreting the magnitude of these differences, the mean official subsidy days is 304. We report results from a t-test because re-estimating Equation 2 with these data results in an error, likely due to the small sample size.

23The reduced form coefficient differs slightly from the estimate in Column 3 of Table 3 for a similar reason. GFW calculates distance traveled from non-public, vessel movement point data. We use the public version of GFW’s AIS data when allocating subsidies to the monthly level. Due to the log transformation of the dependent variables, to remain in the data used to estimate Columns 4 to 6 of Table 4, the vessel-month-year must have positive distance traveled in the non-public data and positive AIS hours in the public data. Only the first condition must be true to remain in the data used to estimate Column 3 of Table 3.

23
decompose the average lending interest rate in 2017, we calculate the average value of each driver for that year across 64 countries and multiply them to the corresponding coefficient. Later, the results of that multiplication are added based on the three main driving groups (i.e., macro-fiscal conditions, banking sector characteristics, and business environment). We put together country-fixed effects and the regression estimated constant as part not explained by the drivers. These calculations determine the components of the lending interest rate (level).

**Decomposition of interest rate (changes)** : We calculate the difference between the average values of the drivers in 2017 vs. the average value of drivers in 2007. Later, we multiply that difference by the corresponding estimated coefficient and add the results using the three driving groups. The final result is the change in the interest rate attributed to changes in a particular driving group between 2007 and 2017.

**Regional decompositions:** We follow the same methodology but adjusting with country fixed effects associated with the countries that belong to the same region.

**Caveats** : The decomposition is based on a panel regression using data between 2005 and 2017. Consequently, non-linear relationships between variables, or regime structure changes are not captured by the model. The estimations use information at country level data so, some bank-level regressors are not included due to no existence of similar variable as a country-level information.

19
varname` as is), as well as “Trimmed” statistics (computed using just those observations of `varname` that are not flagged as outliers). A full description of all the available options is provided in the `outdetect` help file.

## **3.1 Basic usage**

In this example, the target variable is per capita annual household expenditure (here called `pce` ) in Malawi (monetary amounts are expressed in thousands of Malawian kwacha

> 9 The data set is part of the Rural Livelihoods Information System (RuLIS), and is also publicly available at the World Bank Microdata Library.

11
.

# **2.1 Reform chatter**

We make use of text in the form of newspaper articles as our main data source. The data are extracted from Dow Jones FACTIVA via the Dow Jones Data, News and Analytics Platform. Dow Jones FACTIVA is a global repository of newspaper articles covering over one billion articles published in 28 different languages. The full list of languages is listed in Appendix Table A1. Beyond the date of publication and the text, Dow Jones provides several classifiers to navigate searches. These classifiers include language codes, subject codes, country/region codes, industry codes, and publisher information.

The search in each language is conducted so that at least one of the words relating to the economy and at least one relating to reforms appear. For example, in English, the precise query is: _[(“economy” OR “economic”) AND (“reform” OR “reforms”)]_ . This search is performed for each country and each language, over the period from January 1, 1980 to December 31, 2018. The search yields 9.3 million (unique) reform articles. In addition, we obtain the number of total articles 3
</sup> = (x1, . . . , xl) is the market bundle and x<sup>nm</sup> = (xl+1, . . . , xm) is the non-market bundle. The non-market bundle corresponds to non-market endowment while the market bundle is optimally selected in the individual’s budget set. Individual i’s monetary aggregate corresponds to the monetary value of her market bundle, i.e., to �lj=1<sup>pjxij.</sup> Our main point is that the weights given to the observed dimensions should reflect how their achievements are related to achievements on the non-observed dimensions.

## **Identifying low well-being from monetary data**

We consider the case of a specialized survey covering the monetary domain. We show by means of an example that the weights of an achievement-LIM that minimizes mis-identification errors may deviate from market prices, at least when market behavior is non-separable from non-market endowment. This non-separability arise when the welfare function U cannot be written as U(x) = U(u(x<sup>m</sup> ), x<sup>nm</sup> ), where function u : R<sup>l</sup> +<sup>→Rrepresentsbehavior</sup> 28
in <u>University Technology Transfer: Questions and Answers, Council on Governmental Relations, November 30, 1993 (available at the web site http://www.cogr.edu/qa.htm</u> .  Another excellent source of information is available at the web site of the Association of University Technology Managers <u>http://www.autm.net</u>

> 69

> 1999 data are from The Association of University Technology Managers, Inc., report entitled, <u>AUTM Licensing Survey, FY 1999: A Survey Summary of Technology Licensing (and Related) Performance for U.S. and Canadian Academic and Nonprofit Institutions and Patent Management Firms.</u>

78
# **1. Introduction**

The world is undergoing the most extensive school closures ever witnessed. To combat COVID19, more than 180 countries mandated temporary school closures, leaving, at its peak in early April, close to 1.6 billion children and youth out of school. By the end of May 2020, 20 school systems had opened partially, and about 1.2 billion students remained out of school.<sup>2</sup> Most countries are projecting school closures to last through the summer (or winter break). The education system is witnessing an extraordinary twin shock: school closures needed to fight the pandemic and a widespread global economic recession.<sup>3</sup> Unemployment numbers are on the rise, family incomes are falling, and government fiscal space is shrinking. Moreover, this shock is being observed simultaneously across the planet<sup>4</sup> , and most likely, international aid budgets will also be affected.

This crisis is making a dire situation worse. Before COVID-19 shut schools down, the world was already in the midst of a global learning crisis that threatened countries’ efforts to build human capital – the skills and know-how needed for the jobs of the future. Data from the World Bank and UNESCO showed that 53% of children at the end of primary in low- and middle-income countries suffer from learning poverty (World Bank, 2019). And progress in reducing it was far too slow to meet the aspirations laid out in SDG4 – to ensure inclusive and equitable quality education.

At the rate of improvement that prevailed prior to COVID-19, about 43% of children will still be learning-poor in 2030. Figure 1 shows that prior to COVID-19, if countries were to reduce learning poverty at a more ambitious yet achievable pace, the global rate of learning poverty could drop to 27%. This would have meant on average nearly tripling the then-prevalent global rate of progress.

> 2 World Bank. Education Systems’ Response to COVID-19.
**OECD.** 2022 _b_ . “Employment and Labour Market Statistics.” `https://www.oecd-ilibrary.org/employment/data/ oecd-employment-and-labour-market-statistics_lfs-data-en` .

- **OECD.** 2022 _c_ . “Enterprises by Business Size.” `https://data.oecd.org/entrepreneur/ enterprises-by-business-size.htm` .

**Paradisi, Matteo.** 2021. “Firms and Policy Incidence.” _Mimeo_ .

- **Pissarides, Christopher A.** 1985. “Taxes, Subsidies and Equilibrium Unemployment.” _Review of Economic Studies_ , 52(1): 121–133.

- **Postel-Vinay, Fabien, and Jean-Marc Robin.** 2002. “Equilibrium Wage Dispersion With Worker and Employer Heterogeneity.” _Econometrica_ , 70(6): 2295–2350.

- **Rodrik, Dani, and Charles F Sabel.** 2022. “Building a Good Jobs Economy.” In _A Political Economy of Justice_ . , ed. Danielle Allen, Yochai Benkler, Leah Downey, Rebecca Henderson and Josh Simons, 61–95. University of Chicago Press.

- **Rodrik, Dani, and Stefanie Stantcheva.** 2021. “Fixing Capitalism’s Good Jobs Problem.” _Oxford Review of Economic Policy_ , 37(4): 824–837.

- **Rothstein, Jesse.** 2010. “Is the EITC as Good as an NIT? Conditional Cash Transfers and Tax Incidence.” _American Economic Journal: Economic Policy_ , 2(1): 177–208.

- **Rovigatti, Gabriele, and Vincenzo Mollisi.** 2020. “PRODEST: Stata Module for Production Function Estimation Based on the Control Function Approach.” Boston College Department of Economics Statistical Software Components S458239.

- **Saez, Emmanuel, Benjamin Schoefer, and David Seim.** 2019. “Payroll Taxes, Firm Behavior, and Rent Sharing: Evidence from a Young Workers’ Tax Cut in
Figure 20. Financial Result Compared before and after the Retirement Age Raise. Years 2018-2080 <!-- Start of picture text --> 2%<br>0%<br>-2%<br>-4%<br>-6%<br>-8%<br>-10%<br>-12%<br>Current Rise in retirement age<br>2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078<br><!-- End of picture text -->

# Source: author elaborations based on administrative data, IESS

The results obtained are the ones expected. From a financial perspective, the alternative scenario would imply a one-year delay in system deficit, though, considering that there would be gradual changes in retirement age, that allows approximate savings for one percentage point of the GDP during a whole deficit term, while savings were 2 percentage points in the last years.

A complementary idea that usually arises in public policy discussions relates to the inclusion of a funding pillar. In Ecuador's particular case, the option under analysis involves including an individual funding scheme targeted to managing workers' social security savings from the top income quintiles. The reason for this relates to a potential inequity resulting from a higher subsidy (given the 40% transfer of state expenditures) received by these workers as beneficiaries.

Thus, the impact on financial results from the traditional pay-as-you-go scheme managed by the IESS that would lead to an individual funding scheme has been simulated. Therefore, it is assumed that the total active population between 15 and 50 years of age, working and earning wages 2.5 times higher than the unified basic wages,<sup>9</sup> choose the funding scheme and contribute up to that cap on the payas-you-scheme and above it to the funding scheme. This accounts for approximately 15% of total contributors to the IESS (Figure 21).

> 9 In
vast sample of matched bank-firm data that merges the loan database with balance sheet information of the borrower. The specification is presented in Equation 4: where ��,� corresponds to the firm-level information including assets, liabilities, revenue and employment. The variables are available at the annual frequency and collected from Orbis, which includes a rich set of financial and ownership information for both public and private firms around the world. The variable of interest ���������,� indicates if the state _s,_ where the firm _f_ is located, has at least one senator as the chairman at the year _t_ . We cannot include firm*year fixed-effects as they would absorb the main independent variable. So we saturate the specification with fixedeffects at the firm level to control for time-invariant unobservable firm characteristics and also use year fixed-effects to exclude the overall effect of time. _β1_ is our main coefficient of interest and an estimate statistically significant from zero would imply that the preferential treatment to politically

> 32 Gan (2007) and Almeida et al (2012) document contraction in investment in affected borrowers but focus only on firms that have regulatory filings with borrower-level information. Chodorow-Reich (2014) relies on the 2008 financial crisis as shock to credit supply and show that it has a significant effect on employment at small and medium firms. Carvalho (2014) shows in the context of Brazil that firms expand employment near election in exchange for government bank loans.

19
# **3 Related Literature**

This paper relates to a number of literatures, in particular to the increased interest in cities and urban planning from the perspective of economic development. While urbanization already plays a central role in many of the seminal contributions in the early development economics literature (see for example Lewis (1954), Ranis and Fei (1961), Harris and Todaro (1970)), there have been a number of recent empirical and theoretical contributions studying the determinants of urbanization, as well as its effect on economic growth. Recent work by Henderson (2005) suggests that urban growth may be a necessary condition for GDP growth. Potts (2012) and Gollin et al. (2016) draw upon census data and economic theory to show the importance of natural resources as a determinant of city sizes in Africa, and raise questions about differences between urbanization in Africa and elsewhere.

We add to this literature by studying how the urban system in South Africa reacts to an exogenous population shock. The main policy question we address here is the degree to which population flows within a country can be managed in the medium to long run. To illustrate this policy question, consider a town of initial population _N_ 0, in a country with no population growth, that is given an exogenous population increase of ∆ to _N_ 0 + ∆people. There are only three ways in which the population of this town can respond in the long run. First, there could be mean reversion to the original relative population level, such that the long run population is smaller than _N_ 0 + ∆. Secondly, there could be a random walk process that generates path dependence, such that the long run expected value of the size of the town is now _N_ 0 + ∆. Thirdly, it could be that the additional population generates agglomeration effects and triggers a process in 11
0.38|0.49|0.80|
|Other loans|0.22|0.42|0.37|0.49|**0.01**|
|Government scholarships|0.45|0.50|0.38|0.49|0.26|
|HEI scholarships|0.64|0.48|0.75|0.44|**0.07**|
|_Link with productive sector_||||||
|Engagement with firms||||||
|Collect employment data for graduates more than once per year|0.44|0.50|0.52|0.50|0.22|
|Communicate with local firms about their needs more than once per year|0.38|0.49|0.39|0.49|0.79|
|Program has staff assigned to collect grads' employment data|0.71|0.46|0.76|0.43|0.33|
|Industry helps with student evaluation or curriculum design|0.68|0.47|0.61|0.49|0.24|
|Industry has agreements with HEI to hire program grads|0.30|0.46|0.45|0.50|**0.02**|
|Industry has agreements to train faculty|0.32|0.47|0.40|0.49|0.22|
|Industry lends or provides equipment to program for student training|0.54|0.50|0.50|0.50|0.53|
|Somebody in charge of industry relations|0.85|0.36|0.89|0.31|0.29|
|Job search assistance||||||
|HEI coordinates job interviews with firms|0.
in our paper shows that the enrollment falls in private schools which are expensive rather than in the public schools and there is heterogeneity even within private schools. Low quality schools drive the fall in enrollment in private schools. Second, from identification perspective, a clear advantage of our paper is that we have three years of pre-treatment data for a large subset of our schools. Thus, we can strengthen the confidence in our findings by demonstrating that there are no differential pre-trends. However, their study design is limited by only 1 year of pre-treatment data in the ASER sample.<sup>6</sup> Finally, our paper also complements the literature on child labor.<sup>7</sup> Basu and Van (1998) provide a theoretical model that examines conditions under which children work in the labor market. Edmonds (2005) uses data from Vietnam to examine whether improving standards of living reduces child labor. Edmonds and Pavnick (2005) examine the effect of international trade on children’s outcomes. Jacoby and Skoufias (1997) study the effects of financial market incompleteness on human capital accumulation and document that seasonal fluctuation in school attendance is a form of self-insurance by households. More recently Cascio and Narayan (2015) demonstrate that economic booms resulting from increased fracking in the US increase high school dropout rates due to an increase in the demand for unskilled labor. We contribute to this literature by examining the effects of employment guarantee schemes on child labor. Program induced increased labor opportunities for adults induce an increase in child labor.

Our findings have important policy implications: without adequate changes in incentives to attend school, large scale safety net programs designed to smooth household consumption may result in decreased school enrollment and worsening of performance in schools.

The rest of the paper is organized as follows: In Section 2, we offer more detailed information on the MGNREGA in India. Section 3 presents the data used, and Section

> 6Both these studies use the National Sample Survey Office (NSSO) employment and unemployment survey