rafmacalaba/data-use-mentions-extended huggingface dataset:
40

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# **1. Introduction**

To reduce the spread of the novel COVID-19, governments enacted mitigation strategies based on social distancing, national quarantines, and shutdown of non-essential businesses. The halt to the economy represented a large shock to the corporate sector, which had to scramble for cash to cover operating costs as a result of the revenue shortfall. The financial sector, and banks in particular, are expected to play a key role absorbing the shock, by supplying much needed funding (Acharya & Steffen, 2020; Borio, 2020).<sup>4</sup> Under these unprecedented circumstances, central banks and governments enacted a wide range of policy interventions. While some measures were aimed to reduce the sharp tightening of financial conditions in the short term, others sought to support the flow of credit to firms, either by direct intervention of credit markets (e.g., governmentsponsored credit lines and liability guarantees), or by relaxing banks’ constraints on the use of capital buffers.

While credit institutions are being called to play an important countercyclical role to support the real sector, these actions also have a series of implications for the future resilience of the banking sector. For instance, as lenders exhaust their existing buffers, they might also experience deterioration of asset quality, threatening the systems’ stability. As the crisis is expected to continue, even after the lockdowns are lifted and economies start to reopen, the net effect of these policy measures on the banking sector is largely unknown.

The contribution of this paper is twofold. First, we use bank stock prices around the world to assess the impact of the pandemic on the banking sector. Second, we combine bank stock prices with a global database on financial sector policy responses during the pandemic. Using an event

> 4 Using U.S. data, Acharya & Steffen (2020) provide evidence of the transmission mechanism, wherby firms convert commited credit lines from banks into cash, exerting significant pressure on lenders.

2
growth may have important aggregate effects on economies. This is particularly true with regard to employment growth. Compared with a larger literature on capital and entrepreneurship training, there is a paucity of research on the functioning of the informal labor markets most relevant to these firms. We use a shock to wages, implemented through temporary subsidies offered when enterprises hire new workers, to measure frictions in urban informal labor markets in Sri Lanka.

Data from the experiment suggest that labor markets function with modest frictions for these firms. The effects of the subsidy are consistent with well-functioning neo-classical labor markets. Firms respond to the subsidy by hiring additional workers, but the excess hiring dissipates completely within a year or so of the removal of the subsidies. Complementary experiments loosening credit constraints and providing entrepreneurship training do not change the effects of the temporary subsidies. We conclude that hiring frictions are not an important constraint to firm growth in our setting.

An obvious question is how much the results from this experiment generalize to the broader development context. We suspect that many characteristics of this labor market hold in most urban areas in developing countries. Workers remain unregistered and hiring is generally unregulated. The work performed by employees in small enterprises involves relatively more brawn and less creative energy. Employees are hired from local areas and there is usually a low degree of separation between employer and employee before hiring. There are other aspects of the context which are likely less usual. Our experiment coincided with the end of a long civil war in Sri Lanka, and a period of rapid growth. Compared with other low- and middle-income countries, our samples of both microentrepreneurs and wage workers show relatively high levels of generalized trust.<sup>15</sup> How much these more distinctive features of the context drive the results is difficult to say. We would hope this might be resolved by future work examining labor market frictions in different contexts.

# **References**

> 15 In April 2009 surveys. 32 percent of wage workers and 27 percent of entrepreneurs said
,924|4,520|5,571|
|**Average Annual Pop'n Growth Rates**||||||
|Gujarat State||2.47%|1.94%|2.06%|1.77%|
|Gujarat-Urban||3.53%|3.00%|2.88%|3.11%|
|Ahmedabad MC||3.30%|2.01% NA||2.11%|
|**Ratios**||||||
|Gujarat State Urban/Total|28.1%|31.1%|34.5%|37.4%|42.6%|
|Ahmedabad AMC/Gujarat Urban|23.1%|22.6%|20.5%|23.9%|21.7%|

Source: Census of India, various years Note: Growth of AMC population between 1991 and 2001 is not shown due to expansion of AMC area in 2001. Since population is measured on two different areas in the different years, the growth number reflects the jurisdictional shift not population growth.

Incomes are, however, growing rapidly. Per capita net state domestic product has grown at over 8.4 percent per annum in real terms between 2001 and 2010<sup>4</sup> , placing Gujarat amongst the most rapidly growing states in the country. Employment as reported in the Census of 2001 grew by 4 percent a year between 1991 and 2001 in the urban agglomeration of Ahmedabad, as against a population growth of 3 percent<sup>5</sup> . This is typical of the pattern for the state as a whole, with employment in urban areas outpacing both population growth in the cities and the rate of employment growth in the state as a whole. Table II.2 illustrates. Median household expenditure in Ahmedabad as measured by the National Sample Survey, has grown at
only up to four years between two data points. Finally, we check country-specific policy reports and scholarly studies to triangulate across data sources and to identify events which may explain discordance across sources.

Tax revenues are disaggregated as finely as possible by source, according to the OECD tax classification (OECD, 2020). To allocate taxes to capital and labor, we pay attention to three dimensions. First, we systematically separate income taxes into personal and

> 14The ICTD/UNU-WIDER data draws principally from the IMF Government Finance Statistics online data, which covers the past few decades well. Our use of the IMF data is restricted to the offline historical dataset, which covers 1972-89 and fills gaps from the OECD and historical archives data. The ICTD does not report pre-1980 data.

13
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coefficient, when calculated endogenously in the household survey, is also expected to decrease as the level of development of the country increases.

Figure 4 shows the _normative_ Engel coefficient of the most up-to-date urban poverty lines in LAC (scatter plot). All specifications of the poverty lines in the first four rows of Table 3 present an Engel coefficient of approximately 0.47, which is significantly lower than the _normative_ Engel coefficient of 0.62 determined by the currently-used poverty lines in LAC at the 2005 PPP values (i.e., $2.5 and $4 per person per day). There are two possible explanations for this result. On the one hand, it is conceivable that the currently-used poverty lines in 2005 PPP values might be overestimating extreme poverty and/or underestimating moderate poverty. In other words, the extreme and moderate poverty lines are relatively close to each other – in contrast to the empirical findings of this paper using more up-to-date poverty lines – which seems to be biasing the estimations of poverty. On the other hand, the difference in _normative_ Engel coefficients between the two sets of lines could be a reflection of the increasing development of the LAC region during the last decade (World Bank 2014b and World Bank 2015), which is manifested in the ratios of extreme and moderate poverty lines.

- _4.2 Regional poverty estimates in LAC in 2011 PPPs and comparison with currently-used poverty rates in 2005 PPPs_

The primary objective of this paper is to provide inputs for updating the currently-used extreme and moderate poverty lines in order to estimate the regional poverty rate in LAC. Figure 5 presents the sensitivity of extreme and moderate poverty headcounts to updates in the poverty lines and PPPs values. Using the set of extreme poverty lines shown in the first four rows of Table 3 (between $2.5 and $3.2 per person per day), the extreme poverty rate would have ranged from 7.7 percent to 11.9 percent in 2013. Similarly, using the moderate poverty lines (
While students in grade 4 were 1.8% more likely to be sampled in control schools than in treatment schools, we do not find systematic difference between the two groups in other student-level variables. The _p-_ values from the joint F-test are 0.477 (school) and 0.329 (student), which is consistent with successful randomization.

Similar to schools in the double grant experiment, schools in the information experiment also have large differences in the educational backgrounds of parent and teachers. The majority of parent association presidents report primary school as their highest level of education, while most teachers had completed a teaching college degree or a university degree. At the student-level, the surveys for experiment 2 were administered to students in grades 3, 4 and 5, and nearly half of the study sample is female. We perform the same procedure as Table 1 to construct the wealth index.

# **4 Conceptual Framework & Measures**

Parental involvement programs (also known as family engagement programs) aim to improve schooland-parent communication to support children’s overall learning environment. These interventions target parents and caretakers, as it is widely accepted that parents play an important role in shaping children’s educational experience and outcomes (Doepke, Sorrenti, & Zilibotti, 2019).

Parental involvement programs recognize that parents may not be able to fully engage with their children’s education because of biased beliefs (Barrera-Osorio et al., 2020; Bergman, 2016; Dizon-Ross, 2019; Rogers & Feller, 2018) and limited cognitive bandwidth, particularly for lowincome parents (Mani et al., 2013; Mullainathan & Shafir, 2013). Thus, the group-based information interventions in our study are aimed at overcoming these psychological and informational barriers that often impede school-and-parent communication.

Parental involvement programs also acknowledge that institutional discrimination hinders schooland-parent communication. Research suggest that schools can exclude parents whose culture or lifestyle differs from that of the dominant culture (J. S. Lee & Bowen, 2006). The power imbalance between schools and less
offset by the higher costs of operating in French and Colombian cities. Grover and Maloney (2022) find that the elasticity of physical total factor productivity with respect to density is negative in Colombia, Ethiopia, and Indonesia, but positive in Chile. The bulk of the literature on agglomeration economies, however, finds evidence of strong productivity gains of urban density in both developed and developing countries (see e.g., Melo et al., 2009; Beaudry & Schiffauerova, 2009; Combes & Gobillon, 2015; Duranton, 2015; Chauvin et al., 2017; Badr et al., 2019; Jones et al., 2020; Grover et al., 2021).<sup>2</sup> At the same time, there seem to be notable differences across countries and industries (De Groot et al., 2016; Ahlfeldt & Pietrostefani, 2019).

This mixed empirical evidence on the size of net agglomeration benefits in developing economies has attracted the attention of researchers in the field of regional science and urban economics (Duranton, 2015; Glaeser and Henderson, 2017; Grover et al., 2021) and motivates the empirical investigation in this paper. By exploring how these gains vary with firm characteristics and capabilities, the tradability of the sector in which the firm operates, and the mobility within the urban center in which the firm is located, we hope to inform the debate on “sterile” agglomeration economies in the developing economies. Specifically, the paper contributes to this literature in the following ways. It uses geo-coded data on more than 51,000 establishments in 649 metropolitan areas in 98 developing economies,<sup>3</sup> from the World Bank’s formal Enterprise Surveys and a multilevel estimation technique to assess the “pure” labor productivity gains of urban density, net of negative externalities such as crime, pollution, and limited mobility. We then test to what extent these estimated agglomeration economies differ across firms operating in the tradable versus nontradable sectors and
2 unit increase (35.4%) in the mental health score of their children (Column (3)). A one standard deviation increase in parental mental health scores also leads to a 1.6 percentage point (26.2%) increase in children's probability of having school performance below the average (Column (4)). This is consistent with previous findings for the general population showing that mothers’ mental health is a strong predictor of their children's human capital accumulation (Johnston et al., 2013). Overall, these findings suggest that 15
<mark>of the Ricardian framework to take into account a number of stylized facts of the African colonial period, most notably the surplus of land, the transition from subsistence production to the coexistence of food crops, export crops and resource extraction. Section IV discusses the implications of the underlying structure of gender production for women's economic empowerment and overall economic development in the post-colonial period. Section V provides the concluding remarks.</mark>

# **II Gendered production in SSA: Literature review and historical perspectives**

Although it is relatively more difficult to trace economic research focusing on intra-household distribution of resources and gender production in pre-colonial Africa<sup>8</sup> —owing in a large part to the preeminence of oral traditions, absence of household surveys, and distortions of knowledge in the colonial period<sup>9</sup> —there is a larger body of research devoted to gender production in the colonial period [Boserup (1970), Rodney (1982), Alesina et al. (2011), Akyampong and Fofack (2012)]. The majority of these studies highlight gender segregation with respect to economic activity. In particular, they point to gender bias in the non-agricultural sector and sexual division of labor in the agricultural sector, with some types of work strongly associated with women and some with men.

Reviewing the sexual division of labor in the African colonial period, Boserup (1970) established that African women performed mostly domestic duties. Outside the household in the realm of soil cultivation, women are primarily employed as workers, helping their husband in the process of food production and conservation. As the sole owner of the family enterprise and related production factors, men enjoyed the full control of household assets and inherent production of goods as the household head.<sup>10</sup> Thus, gender bias and sexual division of labor is largely driven by gender inequality in the distribution and ownership of assets.

However, the sexual division of labor is the part
begins after week 9 in 2020, corresponding to the first COVID-19 case, while, for separations, the exposure period is after week 12 when layoffs were restricted by the government.

We use a difference-in-differences (DiD) approach, with the identifying assumption being that control and treated weeks move on parallel trends before the exposure period. This is tested visually as well as by including leads of the treatment for a sufficient number of weeks before (Autor, 2003; Cookson and Laliotis, 2017). The model is the following: where m is the number of hires (or separations) in week t (t=1,...,21) for group i (i=0,1), Treat is a binary indicator equal to 1 if the week is observed in 2020 and 0 if observed before (average of weeks 2018-2019), Post is a dummy switched on during the exposure period for both groups (week 9 or 12) and e is an error term. In this quasiexperimental setup, the coefficient of interest is the one associated with the interaction of treatment and exposure period indicators, i.e., α3. As the lockdown was lifted on May 4, we restrict this analysis for weeks 1-21 so our DiD estimates are not affected by increased labor market activity due to relaxing restrictions. Under the pre-exposure parallel trends assumption, the estimated DiD coefficients will indicate the short-term labor market impacts of COVID-19. As in (1), the model is estimated using negative binomial regressions.

# **4.4. Online vacancies**

Daily data on the number of vacancies posted online are extracted from the two most popular job search portals in Greece. In order to achieve a wide coverage of the market, we use Alexa’s ranking, which is web traffic data-based metric.<sup>14</sup> An automated data acquisition mechanism was set up to scrape and store daily information on job postings from all selected portals. Extracted data were pre-processed (e.g
Table A.14: Birth spacing: Months between first and second birth

||No. of months|
|---|---|
|_Firstborn girl_|-1.094*<br>(0.596)|
|_Firstborn girl_ * _Post1_|1.342*<br>(0.757)|
|_Firstborn girl_ * _Post2_|1.901**<br>(0.726)|
|N|41,329|
|_Xi_|x|
|_Firstborn Girl_ x State FE|x|
|Year of frst birth x State FE|x|

NOTES: OLS regression. Sample include mothers who had exactly 2 births. The dependent variable is the number of months between first and second births. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

61
- **Credit Card Ownership** (Credit_Card_p): Ownership of a credit card Original variable name in dataset: q3ab Dummy variable equals 1 if respondent has a credit card, equals 0 if he/she does not own a card.

- **Money Borrowed from a Financial Institution** (Borrow_Bank): Any money borrowed from a bank, credit union (or another financial institution, where applicable – for example, cooperatives in Latin America), or microfinance institution

- Original variable name in dataset: q14a Dummy variable equals 1 if respondent has borrowed money from a financial institution, equals 0 if he/she has not.

- **Money Borrowed from a Store** (Borrow_Store): Any money borrowed from a store using installment credit or buying on credit

- Original variable name in dataset: q14b Dummy variable equals 1 if respondent has borrowed money from a store, equals 0 if he/she has not.

- **Money Borrowed from Family or Friends** (Borrow_Family): Any money borrowed from family or friends

- Original variable name in dataset: q14c Dummy variable equals 1 if respondent has borrowed money from family or friends, equals 0 if he/she has not.

- Reasons for not having a bank account:

- Too Far Away (Too_Far)

- Too Expensive (Too_Expensive)

- Not Possessing the Necessary Documentation (Lack_Doc)

- Not Trusting Financial Institutions (Lack_Trust)

- Not having Enough Money to Use the Account (Lack_Money)

- Religious Reasons (Religious)

- Someone Else in Family Has an Account (Family_Has)

Only respondents who do not have a bank account are asked to list their reasons.

Original variable names in dataset: Too_Far (q10a), Too_Expensive (q10b), Lack_Doc (q10c), Lack_Trust (q10d), Lack_Money (q10e), Religious (q10f), Family_Has (q10g) The Life in Transition Survey (LITS, 2010)

- **Bank Account Ownership** (Bank_Account_h): Ownership of a bank account
a country k are believed to foster trade between countries _i_ and _j_ then there might be unobserved bilateral factors between _i_ and _j_ , which encourage migrants from _k_ to select into migrating into those countries in the first place. In fact, controlling for pair-wise unobserved factors again drastically alters the results, with only 15 point estimates surviving. In the case of third-party effects however, the direction of the bias from excluding controls for the unobserved pair-wise factors is far from clear, since many estimates are both biased up and down. This result contrasts nicely with the estimates of the direct results. In that case, the direction of the bias was on average positive, which might be expected since international bilateral ties are more generally „good‟ as opposed to „bad‟ and in turn these good relationships might be expected to be positively correlated on average with migrant networks. In the case of indirect networks however, the results suggest a far more complex relationship. Arguably, these complexities can only be identified with richer more disaggregated data.

> 27 Felbermayr, Jung and Toubal (2009) are credited as having been the first to highlight the difference between direct and indirect links.

> 28 Furthermore they argue that these third-party effects must operate though the transaction cost channel.

28