rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Introduction**

There is a learning crisis (World Bank 2017). It existed before COVID-19 (Angrist et al. 2021, 2022) and it is global. It has been exacerbated since the school closures began in March 2020 (McKinsey 2022).

COVID-19 caused significant disruption to the global education system. Early in the pandemic, the World Bank (Azevedo et al. 2021) presented simulations of the potential effect of school closures on learning outcomes. It forecast significant declines in the global level of schooling and learning outcomes. School closures could result in a loss of 0.3-1.1 years of schooling and students from the current cohort could, on average, face a reduction of $366 to $1,776 in yearly earnings. Globally, a school shutdown of 5 months could generate learning losses that have a present value of $10 trillion.

Early reviews of the first wave of lockdowns and school closures suggested significant learning loss in a few countries (see, for example, Engzell et al 2021; Haelermans et al. 2022; Jack et al. 2022; Maldonado and De Witte 2022). More recent and thorough analyses of recorded learning losses documented since the beginning of the school closures in March 2020 find even more evidence of learning loss (Donnelly and Patrinos 2021). Robust studies suggest learning losses on average amounting to 0.17 of a standard deviation, or at least one-third of a year's worth of learning (Betthäuser et al. 2023; Hammerstein et al. 2021; Patrinos et al. 2023; Zierer 2021). Most studies observe increases in inequality where certain demographics of students experienced learning losses that were more significant than others. But there are also outliers, countries that managed to limit the amount of loss.

The Progress in International Reading Literacy Study (PIRLS) is the largest international test of 4<sup>th</sup> -grade students’ reading achievement. It is the largest international assessment of reading skills at the primary
that one of the most likely explanations of the difference is that households fail to respond to the survey, and that the probability of non-response plausibly

> 43 Household income (net of taxes and transfers) in Russia exceeds household consumption for almost all households. Part of the reason is savings for investment. But in the case of Russia, an important part of the reason is that Russia has a large current account surplus. Consistency between the macro balances and the household data in construction of the Social Accounting Matrix implies that household factor income must be larger than household consumption for most households to allow for the transfer of capital to foreigners. It follows that the change in factor income as a percent of consumption will be larger than the change in factor income as a percent of household income.

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|43.4||43.4|
|Downwardmobility|23.3|40.5|23.6||41.0|

**Note** : Estimation results are based on per adult equivalent income. The OECD equivalence scale assigns a value of 1.0 to the first adult, a value of 0.7 to each additional adult (age 17 or older), and a value of 0.5 to each child (age 0-16) but does not account for the economies of size in large households. We set the economies of size equal to 0.8. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 4941 panel individuals from the 5th and 13th rounds of the RLMS, 3722 and 3719 panel individuals from the 13th and 24th rounds of the RLMS and 2477and 2475 panel individuals from the 5th and 24th round of the RLMS respectively.

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for salary|0.359|0.404|0.261|***|
|**Rural/Urban and provinces:**|||||
|Rural|0.779|0.758|0.827|***|
|Central|0.084|0.075|0.105|***|
|Copperbelt|0.109|0.127|0.069|***|
|Eastern|0.123|0.156|0.050|***|
|Luapula|0.095|0.050|0.193|***|
|<br>Lusaka|0.083|0.100|0.045|***|
|Muchinga|0.095|0.091|0.106|**|
|Northern|0.114|0.107|0.127|**|
|North Western|0.091|0.078|0.120|***|
|Southern|0.107|0.116|0.087|***|
|Western|0.100|0.100|0.098||
|Number of households|7,113|4,888|2,225||

_Notes_ : Own computation from 2017/18 Quarterly Labor Force Survey conducted by Zambia’s Central Statistical Office. Stars indicate t-tests for significance of differences in mean values for the two categories: * for sig. at 10%, ** for sig. at 5% and *** for sig. at 1%. To deal with non-response bias, we exclude the households that are not reporting owning or using land and are renters or obtaining free dwelling from companies.

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To study how dynamic incentives affect borrower behavior, Online Appendix D develops a simple model that incorporates both moral hazard and adverse selection. We provide here some

> where the follow-up survey was administered, but as we discuss below in Section 5.C. (see Online Appendix Table 3), there is no evidence of selective attrition related to treatment status. For the full sample as well as the borrower subsample, in no regression is fingerprinting or fingerprinting interacted with predicted repayment statistically significantly associated with attrition from the survey.

> 16 It will turn out, however, that the regression results to come are not substantially affected by the inclusion or exclusion in the regressions of a large set of control variables (including the “male” indicator).

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share of the lowest quintile, may be more appropriate. Data availability, however, dictates the choice.<sup>12</sup> The growth determinants come from Loayza, Fajnzylber, and Calderon (2002).

Our regressions are conducted using non-overlapping five-year periods spanning the years 1960-2000. Income and inequality data are taken from the latest available date within the given period. Growth and inequality determinants are averages for the fiveyear period in question. These transformations would reduce the sample to 291 observations for 87 countries for which we have at least 2 consecutive Gini coefficients. If we also eliminate countries with less than 3 observations (note that as implied by (16) and (17) we need a minimum of three observations per cross-section unit to run the GMM estimator outlined above), the sample is further reduced to 194 inequality observations for 61 countries.

The original sample of growth determinants is higher and includes data for 78 countries and the total number of observations is 350. When both databases are merged, however, the number of observations available for estimation purposes is reduced to 134 cases and 41 countries. Working with five-year averages reduces the problems associated with the original panel where 24 countries would account for more than half of the 953 observations. Further, the use of at least three observations for each country (i.e. 15 years) gives a long-run perspective to the problem that is lost with the original panel, where more than 30 countries have only one observation.

Admittedly, using five-year periods also brings some complications. For example, the sample size is notoriously reduced from the original inequality database. The reduction in the number of observations is particularly important when one considers the estimator proposed above, since we are working with a large number of variables and a small crosssection dimension (41 countries in some cases). For example, if we were to consider the explanatory variables as predetermined, even limiting the maximum number of lagged levels to be used as instruments to two, we would still end up with
salaried workers fell by 10.5 percentage points and unemployment fell by 7.5 points. This decline in labor informality is particularly puzzling because, during the same period, the minimum wage increased by 61 percent in real terms and changes in other labor regulations were negligible.<sup>18</sup> Haanwinckel and Soares (2020) develop a search model of informal labor markets with worker and rm heterogeneity, intra-rm bargaining with imperfect substitutability across types of workers, and a comprehensive set of labor regulations. They estimate the model using Brazilian data and use it to understand the main determinants of the reduction in labor informality. The model accounts for 57 percent of the decline in informality and 58 18 See Haanwinckel and Soares (2020) . Barbosa Filho and Veloso (2016) provide a detailed discussion of the decline in labor informality in Brazil during the 2000s.

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Figure A.3: Regional Level Protected Water Deprivation a) b) <!-- Start of picture text --> Urban Protected Water Source Deprivation - by<br>Geopolitical Zone - GHS<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC<br>Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE<br>Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW<br>Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE<br>Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS<br>Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW<br><!-- End of picture text --> <!-- Start of picture text --> Wave 2_SW<br><!-- End of picture text --> <!-- Start of picture text --> Wave 3_SW<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4.

Note: Individuals with missing age were dropped from the analysis. Estimates are based on children aged 0-17.

<!-- Start of picture text --> Figure A.4: State Level Protected Water Deprivation by Region<br>a) b) c)<br>Protected Water Deprivation - by Protected Water Deprivation - by Protected Water Deprivation - by<br>State in North Central - MICS State in North East - MICS State in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<
**Table 2. Standardized Height-for-Age and Incidence of Stunting: Children between 6 and 60 months in 1997 & 2000**

||1997|2000|Diff|
|---|---|---|---|
|**_6-12 Months_**||||
|Mean|-0.96|-0.90|0.06|
||(0.12)|(0.07)|(0.13)|
|Stunted (%)|20.29|16.67|-3.62|
||(0.03)|(0.02)|(0.03)|
|Observations|207|318||
|**_12-24 Months_**||||
|Mean|-1.66|-1.62|0.04|
||(0.06)|(0.05)|(0.08)|
|Stunted (%)|36.32|28.05|-8.28**|
||(0.02)|(0.02)|(0.03)|
|Observations|468|574||
|**_24-60 months_**||||
|Mean|-1.60|-1.56|0.04|
||(0.03)|(0.03)|(0.04)|
|Stunted (%)|<br>35.12|<br>32.33|<br>-2.79*|
||(0.01)|(0.01)|(0.02)|
|Observations|1495|1726||

Notes: Calculated from IFLS data. Standard errors are in parentheses. (*) is significant at 10%; (**) is significant at 5%; (***) is
% 8.8 58.4 60%<br>50% 50% 25.8 33.2<br>40% 20.3 33.9 40% 18.2 17.8<br>30%<br>30% 57.4<br>20% 33.0 13.8 49.9 20% 35.4 35.4 29.2 45.6 30.1<br>27.6 10%<br>10%<br>14.0 0%<br>0%<br>Brazil Bosnia and Turkey Belarus United Arab Brazil Bosnia and Turkey Belarus United Arab<br>Herzegovina Emirates Herzegovina Emirates<br>Age Education Gender Income<br>Notes:. FINDEX is an individual level data base. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage<br>rates are shown. See Appendix for all countries. The variable control list for LITS and FINDEX varies.<br>Sources : FINDEX 2011 and 2014<br><!-- End of picture text --> Using the LITS, which is a household level database, Turkey’s coverage rate of bank account ownership decreases by over 17 percentage points; and the contribution of gender in the dissimilarity index is greatly reduced (Figure 9). The reduction in the role of gender can be explained by the low number of households with female head of household as well as the presence of male household members with bank accounts. In the Shapley decomposition of the dissimilarity index, it is also important to note that the effect of gender, at almost 18 percent, is still an important part of dissimilarity at the household level.

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Total|1.5%|454,936|<br>100.0%|1.4%|440,764|<br>100.0%|

_Notes: Author’s Calculations. Reported estimates derived from mahalla records and adjusted using population weights for the selected L2CU Baseline locations. Estimates are subject to sampling and measurement error. The share of the population that is officially registered is imperfectly measured due to the combination of a lack of census data, and the illegality of non-registration status._ There have been several changes to the propiska system since 2017 (see Appendix G for more details), and temporary registration has become more accessible and less bureaucratic according to many qualitative interviews conducted as part of this study. However, as of this writing, a person without a local propiska (either permanent or temporary) in Uzbekistan is not permitted to apply for identification documents, register a marriage, obtain pensions or other social benefits, send their children to public schools, obtain legal employment, or register a business, among other restrictions.<sup>15</sup> Purchasing housing in the city of Tashkent (with recent exceptions first for high-value newly constructed housing, later expanded to newly constructed housing more generally) is illegal for all

> 15 There is ambiguity about the use local hospitals or clinics for non-emergency purposes. Legally and according to most qualitative interviews, health care facilities are open to residents even if they are unregistered. However, some sources (including Tukmadiyeva, 2016) claim there are de facto many access restrictions.

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</mark> **<mark>[</mark> 1]** <mark>Selby, J., Dahi, O. S., Fröhlich, C., & Hulme, M. (2017). Climate change and the Syrian civil war revisited.</mark> _<mark>Political Geography</mark>_ <mark>,</mark> _<mark>60</mark>_ <mark>, 232-244.</mark> **<mark>[</mark> 1]** <mark>Spyratos, S., Vespe, M., Natale, F., Weber, I., Zagheni, E., & Rango, M. (2019). Quantifying international human mobility patterns using Facebook Network data.</mark> _<mark>PloS one</mark>_ <mark>,</mark> _<mark>14</mark>_ <mark>(10), e0224134.</mark> **<mark>[</mark> 2]** <mark>Taylor, L. (2016). No place to hide? The ethics and analytics of tracking mobility using mobile phone data.</mark> _<mark>Environment and Planning D: Society and Space</mark>_ <mark>,</mark> _<mark>34</mark>_ <mark>(2), 319-336.</mark> **<mark>[</mark> 2]** <mark>Tol, R. S. (2018). The economic impacts of climate change.</mark> _<mark>Review of Environmental Economics and Policy</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>(1), 4-25.</mark>
# **I. INTRODUCTION**

Which is more important for businesses’ investment decisions – the magnitude of the regulatory burden placed upon them or the consistency with which regulations are enforced? On the one hand, a large body of work built on Ronald Coase’s (1960) theory of transaction costs suggests that excessive regulation can serve as a barrier to investment. From this perspective, high levels of regulation complicate the ability of firms to enter and exit markets, while also seriously hampering factor allocation of both capital and labor (c.f. Cooper and Haltiwanger 2006, Bentolila and Bertola 1990, Joyanovich 1982, Hopenhayen 1992, Erickson and Pakes 1998). On the other hand, an emerging body of work has pointed to regulatory uncertainty – uncertainty about how regulation will change or be enforced – as a key barrier to investment. This literature builds on the intuition that firms invest based on expected returns, which are discounted as the probability of negative shocks increases net costs (Pindyck 1991, Dixit and Pindyck 1994). Work on regulatory uncertainty argues that a lack of predictability in regulation and its enforcement increases the risks of negative shocks, thus decreasing firms’ incentives to invest.

Thus far, the literatures on regulatory burdens and regulatory uncertainty have remained separate, making it difficult to understand which has a more profound influence on firms’ investment decisions. More problematically, however, much of the literature on regulation has focused on formal, written regulations and institutions. For example, work on regulatory uncertainty has used proxy measures such as the number of antitrust cases brought over time (Bittlingmayer 2001), the number of offices responsible for permitting (Feiock and Jeong 2002), history of passage and repeal of regulation (Fabrizio 2012), retroactive changes in investment regulation (Lyons and Mayo 2005), and survey responses on the anticipated direction of policy (Barrodale 2010; Engau and Hoffmann 2009). Although some work relies on measures that may capture informal institutions ( _c.f._ Beazer 2012),
in the following manner:

- 32 departments and 5 districts (including the _Distrito Capital de Bogotá_ ).

- 20 sub-departmental entities ( _corregimientos departmentales)_

- 1,101 municipalities

- 8,059 villages (smaller than municipalities)

The GDP in Colombia in 2009 (last year for which this information is available) was $234,045 million (current international $ or 392,446 million $ PPP).<sup>16</sup> This represents a per capita income of $4,990.<sup>17</sup> The life expectancy at birth in 2009 was 73 years;<sup>18</sup> the adult literacy rate (15-years-old and above) was 93%.<sup>19</sup>

> 15 Censo General de Colombia (www.dane.gov.co). 2009.

> 16 World dataBank. World Development Indicators (WDI) & Global Development Finance (GDF) (http://databank.worldbank.org).

> 17 The World Bank Open Data. GNI per capita, Atlas method (current US$) (2009)

> (http://data.worldbank.org/indicator) (Atlas method).

> 18 The World Bank Open Data. Life expectancy at birth, total (years) (2009)

> (http://data.worldbank.org/indicator).

> 19 The World Bank Open Data. Literacy rate, adult total (% of people ages 15 and above) (2009)

> (http://data.worldbank.org/indicator).

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We control for several firm and country characteristics to further raise our confidence against omitted variable bias problem. For this, we draw on the broader literature on gender gaps in the labor markets. We provide a brief description of the variables below and a more detailed description is provided in table A2 in the Appendix.

# 3.5.1 _Baseline_ c _ontrols_

We begin with controlling for all time invariant country specific factors such as social norms and culture and slow-moving institutions. We do so by using a set of dummy variables indicating the country where the firm operates ( _Country fixed effects_ ). Industry specific factors and global annual shocks to the dependent variable are accounted for by dummy variables indicating the industry (2digit ISIC Rev. 3.1) of the firm ( _Industry fixed effects_ ) and by dummy variables for the year the WBES round was administered ( _Year fixed effects_ ). The data source is WBES.

At the macro-level, gender gaps in the labor market are found to be correlated with income levels or GDP per capita (see Coric 2018, Gaddis and Klasen 2014), gender parity in education (see Klasen et al. 2021, Bussemakers et al. 2017, Islam and Amin 2016), and macroeconomic growth (see Klasen 2019). Country size and demographics may also matter. Smaller countries tend to rely more on international markets that are more competitive. This incentivizes cost cutting via less discrimination against women. Demographics matter as ceteris paribus, more women relative to men in the age group relevant for managers implies proportionately more women managers. We account for all these factors using the following controls (all lagged by 1 year): log of GDP per capita (PPP adjusted and at constant 2017 int’l dollars); ratio of gross primary enrollment rate of women to men ( _Primary enrollment gender parity index_ ); log of total population in the country; 12
mobility indicators and regions except the upward mobility gap.

There could be multiple reasons for the endogeneity of mobility to country income levels. For example, richer countries may spend more on public education and targeted transfers to the poor, which, all else equal, may increase intergenerational mobility. While, theoretically, such endogeneity could be addressed through convincing instrumental variable estimates, in practice, finding valid instruments for growth regressions at a country level is notoriously difficult (e.g., <u>Durlauf et al. 2005). So, despite the battery of tests and estimations of alternative specifications,</u> we cannot claim to have estimated the causal effect of intergenerational educational mobility on economic development.

> 22 The dynamic panel bias (Nickell 1981) is especially acute in situations when the time dimension of the panel dataset is small and the number of cross-sectional units is large. The dynamic panel bias typically caused underestimation of the coefficients on the lagged dependent variables.

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Table 1: Preschool breakfast program timeline

|Date|Activity|
|---|---|
|2000||
|February-March|Baseline survey|
|January-March<br>March-April<br>April<br>May-November|School attendance check in preschools and primary grades 1-8<br>Parent-teacher meetings to announce the program in all schools<br>Start of the implementation of the breakfast program<br>School attendance checks in preschools (6 rounds)|
|2001||
|January-February|School attendance check in preschools and primary grades 1-8|
|February-November|School attendance checks in preschools (7 rounds) and grade 1 (1<br>round)|
|November|Snack programs survey in comparison schools|
|2002||
|January-March<br>February-March<br>May-July|School attendance checks in preschools, grades 1-2 (2 rounds)<br>Cognitive and attainment tests, anthropometric measurements<br>Elder siblings survey, socio-economic survey<br>School attendance checks in preschools and primary grades 1-2 (2<br>rounds)|
|June-July|Parent-teacher meetings in 25 feeding schools, announcement of cost<br>sharing|
|September-December|Introduction of cost sharing, attendance checks in preschools, grades<br>1-2|

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Overall, as seen in Figure 2, the employment impact of the pandemic and its gendered implications were not uniform across job types. As previously discussed, the bulk of job losses for both male and female workers was concentrated in the informal sector. What is of particular interest is the gendered impact of the pandemic among salaried workers in the private sector. In fact, while male employment increased, albeit marginally, during the first year of the pandemic, the opposite trend was observed among women.<sup>11</sup>

**_Figure 2: Changes in employment 2019-20/2020-21, by gender and type of job_**

<!-- Start of picture text --> 200000<br>100000<br>0<br>Informal Salaried private Salaried public Employer<br>-100000 sector sector<br>-200000<br>-300000<br>-400000<br>-500000<br>Men Women<br># of jobs<br><!-- End of picture text --> In order to better investigate whether the differential gendered impact of the pandemic is due to differences in pre-pandemic characteristics of employment, we take advantage of the rotating panel component of the LFS and restrict the sample to individuals who were interviewed at least one time pre and post March 2020 and who were employed before the pandemic.<sup>12</sup> In particular, we estimate the following model: where the dependent variable is the probability of being employed in 2020-21 for individual _i_ conditional on him/her holding a job _j_ before the start of the pandemic. The main variable of interest is the interaction term between a dummy indicating female workers and a categorical variable indicating the job type before the pandemic, which can be either informal (omitted category), salaried worker in the private sector or salaried worker in the public sector. Controls include a set of individual characteristics which includes age, age squared, education, marital status urban residence and province