rafmacalaba/data-use-mentions-extended huggingface dataset:
requirements are associated with greater corruption. Typically, the analyses suggest this to be the case. We explore this further by confronting the DB data with data from the ES.

It is clear that what is “on the books” need not be the way the world actually works. In fact, the claim of the DB project is not to characterize the way the world works, but to characterize what governments make – regulations in this case. Where, for example, complex regulations lead to lengthy procedures firms may have an incentive to pay “speed money” to cut through the process. The 150 days it took in Sao Paolo, Brazil, until 2009 to register a business the official way, was not a good predictor of the actual time taken, for example. “Facilitators” would help firms get registered much faster. Still the market for facilitation was created or sustained by regulation. The impact of official regulation cannot be determined by measuring whether people behave in accordance with the law, but by whether and how they respond to the law.

Doing Business complements the above mentioned enterprise surveys (ES) that capture the world as experienced by firms. The ES were expanded to cover not only Africa, Eastern Europe and Central Asia and a few extra countries, where they originated, but also other regions. Contrary to DB the ES are not available every year for all the countries covered. Nevertheless it is now possible to start confronting the data about official regulation with actual practice using panel data sets.

Hallward-Driemeyer and Pritchett (2010) have recently explored in detail the difference between data on official requirements for business registration, construction licenses and import licenses with the actual time to obtain these licenses. They found complex relations between the two data sets.<sup>2</sup> What seems to be the case is that as official requirements become more burdensome, some firms are able to cope much better than others. For example, an increase in official time from 77 to 601 days for construction licenses
margins. Simple averages of the average number of OECD suppliers by SITC4 product category are rising monotonically over time.<sup>4</sup> <!-- Start of picture text --> Figure 2<br>Average number of exporters to OECD, 1963-2006<br>Extra-OECD suppliers only 5<br>(a) Simple average (b) Import-weighted average<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Year Year<br>120<br>100<br>80<br>60<br>40<br>60<br>50<br>40<br>30<br>20<br><!-- End of picture text --> Note: a/ Simple averages of number of exporters to OECD at the product (SITC4) level. b/ Import-weighted averages (weights = shares of each SITC4 product in OECD imports in given year) Import-weighted averages are leveling out after 2000, but this is not very surprising. The numbers on the vertical axis show that on a trade-weighted basis, the average number of suppliers per product was over 100. For many products, this is likely to exhaust the pool of potential exporters, so a leveling off is to be expected.

Table 3 reports the results of pooled and fixed-effects regressions of the number of exporters to the OECD on time, its square, and a specific time trend for the post2000 period.

In the latter period, as expected from Figure 2, there is a decline in the rate of increase in the number of exporters to the OECD. This is reflected by the negative coefficient on Post 2000. This inflexion is however not strong enough to reverse the

> 4 Bas and Strauss-Kahn (2010) provide evidence of this increased number of exporters serving the OECD market. Focusing on French firms, they found that the number of imported inputs varieties from developing countries increase in average by 48% over the 1995-2005 period.

> 5
country and the size of the country‘s shadow economy, using a sample of transition countries for which estimates of the informal sector as a percent of official GDP are available from Schneider and Klinglmair (2003). _Figure 1_ measures the values based on data sets for the years 1999 and 2000. The results indicate a strong negative correlation between both variables (-0.657), significant at the 0.01 level. In order to increase the number of observations, in _Figure 2_ we include countries that are not included in the WVS 1999-2000 but that were part of WVS 1995-1997 (19 observations in total). We observe also in _Figure 2_ a strong negative correlation (Pearson _r_ = -0.551), significant at the 0.01 level. Thus, transition countries with low tax morale show a clear tendency to have a large shadow economy. Indeed, a simple linear regression in both figures suggests that a decrease of tax morale by 1 unit would lead to an increase of the shadow economy of roughly 20 percentage points. Moreover, the variable tax morale can explain more than 30 percent of the total variance of the variable size of shadow economy.

However, these analyses give only information about the raw and not the partial effects. The observed correlation might be explained in terms of factors that affect the size of the shadow economy. It is important to investigate the causes as a whole with their interdependencies. An investigation that focuses on a simple correlation has a somewhat limited validity. Thus, multiple regressions help us to disentangle the effects of other factors from a possible tax morale effect. Multivariate studies indicate that tax morale has a very strong impact on the size of the shadow economy using a crosssectional and a panel approach (see Torgler and Schneider, 2007a, 2009).

# **III. HOW IMPORTANT ARE INSTITUTIONS AND GOVERNANCE QUALITY?**

## _1. Theoretical Considerations_

As Cowell (1990) notes, ―... the issue of evasion is, unlike other illegal
also finds that parent participation only works on test scores through autonomy and that the impact of autonomy (as well as the enhancing effect of participation) increases for poor and public sector schools. These results suggest that: (a) school autonomy, even if limited, is effective in promoting higher quality; (b) school autonomy effectiveness increases with the level of participation, or, the strength of the “client power” relationship; (c) this “client power” relationship only becomes effective when there is school autonomy, which; and (d) school autonomy can be useful in increasing the performance of poor schools (and promote convergence with richer schools).

The complementarity between school autonomy and parent participation, and the need for more specific analysis of other determinants of the impact of school autonomy on educational performance, is illustrated in a recent study of Paes de Barros and Mendonca (1998) on Brazil. This study, using an education production function analysis which exploits the variation in space and time of educational innovations to test the impact on test scores and other educational achievement indicators (such as school repetition, out-of-school-youth and age-for-grade distortion) of three measures of school autonomy (financial autonomy, election of principals and the adoption of school councils)<sup>35</sup> , finds a generally positive and substantial impact of the three innovations on school repetition. It also finds, however, that, even if still significant, the three

> 34 The study, which covers 20,000 students drawn from a representative sample of private and public Argentinian schools, also attempts to address the issue of the potential endogeneity of autonomy and participation.

> 35 The model, run on the 18 states between 1981 and 1993,  takes particular care to control for variables like the quality of family environment and teachers which could be correlate with the innovative States.

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overall effect of an increase of one unit in the stringency index is 0.2 and 0.3 percentage points respectively – lower than the 0.4 percentage point result in the longer term. In any case, column 6 suggests that there are diminishing marginal effects of stringency on vaccine acceptance as its squared term is negative and significant. Further, prolonged and very stringent measures seem to have an adverse effect on vaccine acceptance. Moving from the 25<sup>th</sup> percentile in the distribution of days above a stringency score of 80 (equal to 0 days in our estimation sample) to the 75<sup>th</sup> percentile (equal to 80 days, incidentally) is associated with a 9.6 percentage point _decrease_ in individual vaccine acceptance.<sup>9</sup> This phenomenon, or diminishing effect of non-pharmaceutical interventions, is witnessed also with respect to actual spread of the disease as well and has been termed as _lockdown fatigue_ in other work (Goldstein, Levy Yeyati and Sartorio, 2021).

An important aspect of government policy response to the pandemic that is not captured by the stringency index -which is focused on non-pharmaceutical interventions- is the deployment of a vaccination campaign. The first country to launch a COVID-19 vaccination campaign was the United Kingdom on December 8, 2020. In the weeks that followed, several countries -including the 18 countries in our sample- started their vaccination campaigns. The different specifications of Table 2 all show that the presence of an active vaccination campaign is associated with an increase in vaccine acceptance of around 9 percentage points, a magnitude slightly higher than the effect of personally knowing a COVID-19 positive case or the effect of an increase in the stringency index from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution.

> 9 This is the product of the number of days implied by moving from the 25th percentile to the 75th percentile (80 days
</sup> Incorporating both the dimensions of the extent of GVC participation and the primary participation mode within a unified framework provides a coherent and easily comprehensible method for a thorough evaluation of a country’s or a sector’s engagement in GVCs. Additionally, the fact that the forwardness index is bounded between -1 and 1 and neutral at the world level streamlines assessments when moving from a global perspective to specific country-sector interactions. Collectively, the set of indicators we present constitutes a straightforward and comprehensive toolbox of GVC descriptive statistics, readily applicable for policy analysis.

# **4 Results: Assessing GVC Participation Through the New Measures**

We used the GVC participation measures from Section 3 on four popular Inter-Country InputOutput (ICIO) datasets: EORA, the Asian Development Bank MRIOT, OECD TiVA, and WIOD. This helped us create a detailed and up-to-date set of indicators for 189 countries and 26 to 56 industries (depending on the dataset) from 1990. The industry classification and time coverage vary based on the raw data available in each dataset. These measures are available in the World Bank’s WITS Platform, which also provides details on industry classification and time coverage. Using these measures, we can answer two main questions: First, how much

> 14See Section S2 in the supplementary online appendix for more details.

> 15See Section S3.4 in the supplementary online appendix.
the second half of 2020, and the most recent survey collected in 2021 for the third period.<sup>11</sup> For employment outcomes, the sample is restricted to households in which the respondent was between 18 and 64 years old, since employment questions were only asked of the respondent. Survey waves were only included in the calculation of summary statistics if the indicator had a response rate of at least 50 percent. Table 2 describes welfare outcomes of interest related to employment, income, food security, assistance, and coping strategies. Annex 2 provides more detail on the construction of these indicators.

> challenging to use these for cross-country comparisons with countries without individual level weights.

> 10The OxCGRT stringency index records the strictness of ‘lockdown style’ policies that primarily restrict people’s behavior (Hale et al. 2021). It is calculated using 9 indicators including school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stayat-home requirements; public information campaigns; restrictions on internal movements; and international travel controls.

> 11The main findings from the descriptive analysis are consistent if we instead pool surveys within each period. The regression analysis does not compare across time periods and uses all HFPS rounds (countries are weighted equally).

9
implementation phase were important in changing management practices. The control firms typically did not adopt the more complex practices like daily quality meetings, formalizing the yarn monitoring process or defining roles and responsibilities.

Fifth, the non-experimental plants in the treatment firms also saw a substantial increase in the adoption of management practices. In these 5 plants the adoption rates increased by 17.5%. This increase occurred because the owners of the treatment firms copied the new practices from their experimental plants over to their other plants.

# **V. THE IMPACT OF MANAGEMENT ON PERFORMANCE**

Previous work has shown a strong correlation between management practices and firm performance in the cross-section, with a few papers (e.g. Ichniowski et al. 1998) also showing this in the panel.<sup>16</sup> Our unique panel data on management practices and plant level performance, coupled with the experiment, enables us to examine the extent to which these relations are causal. We begin with a panel fixed-effects specification: OUTCOMEi,t = αi + βt + θMANAGEMENTi,t+νi,t (2) where OUTCOME will be one of the key performance metrics of quality, inventory and output. The concern is that management practices are not exogenous to the outcomes that are being assessed, even in changes. For example, a firm may start monitoring quality only when it starts to experience a larger than usual number of defects, which would bias the fixed-effect estimate towards finding a negative effect of better management on quality. Or firms may start monitoring product quality as part of a major upgrade of workers and equipment, in which case we would misattribute quality improvements from better capital and labor to better management.

To overcome this endogeneity problem, we instrument the management practice score with log(1+weeks since the implementation phase began)<sup>17</sup> . We use this logarithmic form because of the concave adoption path of management practices shown in Figure 2, with the results robust to alternative functional form
WPS3632

# **Corporate Governance and Bank Performance: A Joint Analysis of the Static, Selection, and Dynamic Effects of Domestic, Foreign, and State Ownership**

Allen N. Berger Board of Governors of the Federal Reserve System Wharton Financial Institutions Center George R.G. Clarke World Bank DECRG Robert Cull World Bank DECRG Leora Klapper World Bank DECRG Gregory F. Udell Indiana University

## **Abstract**

We jointly analyze the static, selection, and dynamic effects of domestic, foreign, and state ownership on bank performance. We argue that it is important to include indicators of all the relevant governance effects in the same model. “Nonrobustness” checks (which purposely exclude some indicators) support this argument. Using data from Argentina in the 1990s, our strongest and most robust results concern state ownership. State-owned banks have poor long-term performance (static effect), those undergoing privatization had particularly poor performance beforehand (selection effect), and these banks dramatically improved following privatization (dynamic effect). However, much of the measured improvement is likely due to placing nonperforming loans into residual entities, leaving “good” privatized banks.

JEL Classification Numbers: G21, G28, G34, F36 Keywords: Bank, Governance, M&A, Foreign Acquisition, Privatization World Bank Policy Research Working Paper 3632, June 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The authors thank the journal referee; the conference discussants, Andrew Powell
Petrou, Stavos, and Emil Kupek. 2010. “Poverty and Childhood Undernutrition in Developing Countries: A Multi-National Cohort Study,” _Social Science and Medicine_ 71: 1366-1373. Pitt, Mark. 1983. <mark>“Food Preferences and Nutrition in Rural Bangladesh,”</mark> _<mark>Review of Economics and</mark>_ _<mark>Statistics</mark>_ <mark>LXV: 105-114.</mark> Ravallion, Martin. 1990. “Income Effects on Undernutrition,” _Economic Development and Cultural Change_ 38: 490-515.

Ravallion, Martin. 1992. “Does Undernutrition Respond to Incomes and Prices?: Dominance Tests for Indonesia,” _World Bank Economic Review_ 6: 109-124.

Ravallion, Martin. 2009. “How Relevant is Targeting to the Success of the Antipoverty Program?” _World Bank Research Observer_ 24(3): 205-231.

Richard, Stephanie, Robert Black, Robert Gilman, Richard Guerrant, Gagandeep Kang, Claudio Lanata, Ka ̊re Mølbak, Zeba Rasmussen, Bradley Sack, Palle Valentiner-Branth, William Checkley, and Childhood Infection and Malnutrition Network. 2012. “Wasting is Associated with Stunting in Early Childhood,” _Journal of Nutrition: Nutritional Epidemiology_ 142: 1291–1296.

Rose, Elaina. 1999. “Consumption Smoothing and Excess Female Mortality in Rural India,” _Review of Economics and Statistics_ 81(1): 41-49.

Ruel, Marie, Harold Alderman, and Maternal and Child Nutrition Study Group. 2013. "Nutritionsensitive Interventions and Programmes: How can they Help to Accelerate Progress in Improving Maternal and Child Nutrition?" _The Lancet_ 382: 536-551.

Sahn, David and David Stifel. 2003. “Exploring Alternative Measures of Welfare in the Absence of Expenditure Data.” _Review of Income and Wealth_ 49: 463–489.

Sahn, David and Stephen Younger. 2009. “Measuring Intra-Household Health Inequality: Explorations Using the Body Mass Index.” _Health Economics_ 18: S13-S36.

Smith, Lisa and Lawrence Haddad
. Wacziarg. 2020. “Understanding spatial variation in COVID-19 across the United States.” NBER Working Paper No. 27329

- Ding, W, R. Levine, C. Lin, W. Xie. 2020a. “Corporate immunity to the COVID-19 pandemic.” NBER Working Paper No. 27055.

- Ding, W., R. Levine, C. Lin, W. Xie. 2020b. “Social distancing and social capital: Why U.S. counties respond differently to COVID-19.” NBER Working Paper No. 27393.

- Friedson, A.I., D. McNichols, J.J. Sabia, D. Dave. 2020. “Did California's shelter-in-place order work? Early Coronavirus-related public health effects.” NBER Working Paper No. 26992.

- Fairlie, R.W. (2020). “The impact of COVID-19 on small business owners: Evidence of early-stage losses from the April 2020 Current Population Survey.” NBER Working Paper No. 27309.

- Fang, H., L. Wang, and Y. Yang. 2020. “Human mobility restrictions and the spread of the novel Coronavirus (2019-NCoV) in China.” _Journal of Public Economics_ 191 (191): 104272.

- Fattorini, D., F. Regoli. 2020. “Role of chronic air pollution levels in the COVID-10 outbreak risk in Italy.” _Environmental Pollution_ 264, 114732.

- Frey, C.B., C. Chen, G. Presidente (2020). “Democracy, culture, and contagion: Political regimes and countries responsiveness to COVID-19.” Oxford University, working paper.

- Fukuyama, F. 1995. _Trust_ . New York: Free Press.

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- Galasso, V., V. Pons, P. Profeta, M. Becher, S. Brouard, M. Foucault. 2020. “Gender differences in
education reduces the probability a household uses destructive fishing gear, although the coefficient on this variable is rather small. Female education is not a significant determinant of choice of fishing gear. Wealthier households, in terms of value of physical assets, are less likely to use destructive fishing gear. Access to credit, on the other hand, increases the probability of using destructive fishing gear. Land ownership and the amount of land cultivated are not significant determinants of the use of destructive fishing gear.

> 15 Individuals may rent fishing gear for a fee, or, as it is more often the case, use fishing gear provided by a fish trader subject to an agreement to sell all or most of its catch to the trader at an agreed below market price. 16 From our survey questionnaire, we cannot determine which household member uses a particular type of fishing gear. Therefore the finding that female households are more likely to use destructive fishing gear does not imply that females are more likely to use destructive gear. In Tanzania, females are generally not directly involve in fishing, other than collecting seashells, sea cucumbers, and octopus for a few hours a day during the spring tides.

27
early interest in (and the data required for) constructing a poverty map. Second, we use national, rather than international, poverty lines. This has the advantage that poverty is measured in the terms which each particular country’s residents feel is appropriate. But it has the disadvantage that poverty does not mean the same living standard across the eight countries. Third, we do not systematically adjust for cost-of-living differences across different cities. These differences may be expected to be

> 5 One exception was a poverty profile of Brazil by Ferreira, Lanjouw and Neri (2003).

> 6 The World Bank divides the developing world into sub-Saharan Africa (AFR), East Asia and the Pacific (EAP), Eastern Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), the Middle-East and North Africa (MENA); and South Asia (SAR).
Measures taken to ensure human subject protection over the course of this experiment are discussed in Appendix C.

# **Recruitment**

A total of 363 women commuters and 51 men commuters were recruited to participate in the study through online social media and networks, referrals, and flyers distributed at the train stations. The recruiting material invited respondents to download a smartphone application and respond to survey questions regarding their experience with the SuperVia.<sup>9</sup> None of the recruitment material mentioned gender, harassment or the reserved space.

Recruitment occurred in two waves starting August 2015 and August 2016. After signup, riders were offered both a demographic survey task and the crowdsourcing task. Of those riders, 72.5 percent completed the demographic survey (Table A1). Riders reside along the SuperVia network all around the metropolitan area , spanning a mix of rich and poor areas (Figure A1). Women riders were offered to participate in ride tasks, while men riders were asked to collect platform observations. We now describe these two work streams.

# **Women riders’ tasks**

Each woman rider is offered a series of tasks which entail riding the SuperVia and answering questions before, during and after each ride. Data were collected between September 2015 and February 2017. Figure 1 shows how the ride task is presented in the app and broken down into three sub-tasks: check-in ( _Check-in na esta ̧c ̃ao_ ), ride either the reserved or public space ( _Escolhi viajar no vag ̃ao feminino/carro comum_ ) and check-out ( _Check-out da esta ̧c ̃ao_ ). Total pay-out to complete a ride varies from $4.50-$4.70 per ride.<sup>10</sup><sup>_,_11</sup> These rides can be completed any weekday between 6-9AM or 5-8PM (rush hour window when the reserved space policy is in effect), up to twice per day (once in the morning, once in the evening