rafmacalaba/data-use-mentions-extended huggingface dataset:
|_R_<sup>_2_</sup>|0.17|0.23|0.29|0.29|0.29|
|_Observations_|443253|443253|343252|343252|343252|

_Source:_ Authors’ analysis based on data described in the text.

_Notes_ : _p_ denotes six-digit HS product, _k_ denotes three-digit ISIC industry, _f_ denotes firm, _d_ denotes destination, and _t_ denotes time in years. Standard errors are in parentheses (clustered by firm-product combinations).<sup>***</sup> ,<sup>**</sup> , and<sup>*</sup> indicate the level of significance at 1%, 5% and 10%, respectively. All specifications include a constant term that is suppressed.

22
Policy Research Working Paper 9363

# **Abstract**

This paper analyzes bank stock prices around the world to assess the impact of the COVID-19 pandemic on the banking sector. Using a global database of policy responses during the crisis, the paper also examines the role of financial sector policy announcements on the performance of bank stocks. Overall, the results suggest that the crisis and the countercyclical lending role that banks are expected to play have put banking systems under significant stress, with bank stocks underperforming their domestic markets and other non-bank financial firms. The effectiveness of policy interventions has been mixed. Measures of liquidity support, borrower assistance, and monetary easing moderated the adverse impact of the crisis, but this is not true for all banks or in all circumstances. For example, borrower assistance and prudential measures exacerbated the stress for banks that are already undercapitalized and/or operate in countries with little fiscal space. These vulnerabilities will need to be carefully monitored as the pandemic continues to take a toll on the world’s economies.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ademirguckunt@worldbank. org, apedrazamorales@worldbank.org, and cruizortega@worldbank.org.

_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 views
# SECTION 1: CONTEXT & INTERVENTION

Lebanon’s political development system since Independence has been heavily influenced by its confessional system. While originally established to balance the competing interests of Lebanon’s diverse religious communities, it is seen as an impediment to inclusive growth and effective governance (World Bank, 2016), and has been closely tied to the economic and social inclusion challenges facing Lebanese youth today.

The confessional system of governance has heavily impeded the equitable and efficient distribution of investments and public services. Provision and targeting of public services tend to be guided by considerations of confessional quotas and electoral geography rather than needsbased service delivery that favors the poor. In the absence of effective state institutions, sectarian organizations have played a key role in the provision of social services such as education, health, and welfare support to the most vulnerable groups linked to their electorates, thus deepening a sense of discriminatory and inequitable system (World Bank, 2016; Kraft et al., 2008). Regional disparities are stark, with the bulk of the poor living in peripheral areas (particularly the North and the South), with visible inequality in access to and quality of social services. According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006.<sup>1</sup> A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large.<sup>2</sup> Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010).<sup>3</sup> In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and
|
|Missing Support for Government (=1)|-0.06|(0.05)|[0.84]|0.26|(0.44)|1,422|
|Missing Justify Human Rights Violations (=1)|-0.08|(0.07)|[0.84]|0.25|(0.44)|1,422|

**_Notes_** :

- The table shows the regression results on pre-specified political attitudes outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Support for Democracy (Z-Score) is defined as a z-score from whether the FR strongly agrees, agrees, disagrees, or strongly disagrees with "Democratic systems may have problems, yet they are better than other systems." Higher values indicate more support for democracy. Support for Government (Z-Score) is defined as a z-score from whether the FR strongly agrees, agrees, disagrees, or strongly disagrees with "Citizens must support the government’s decisions, even if they disagree with them." Higher values indicate more support for the government. Justify Human Rights Violations (Z-Score) is defined as a z-score from whether the FR strongly agrees, agrees, disagrees, or strongly disagrees with "The violation of human rights in Syria is justifiable in the name of promoting security and stability." Higher values indicate more justification for human rights violations. Interest in Politics (Z-Score) is defined as a z-score from the extent to which the FR is interested in politics (very uninterested, uninterested, interested, very interested), with higher values indicating more interest in politics.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls
alyzes pixels to classify facial expressions. The accuracy level of these algorithms (that is, success rate in predicting emotional state) is around 60%–80% (McDuff et al., 2015; Stockli, 2018). Affectiva’s metrics indicate when individuals manifest a specific emotion or expression (for example, a smile) along with the degree of confidence.<sup>43</sup> The Reactiva application, in particular, follows a protocol similar to that of EganadelSol (2016a), Egana-delSol (2016b), and Egana-delSol et al. (2023) to construct arousal (proxy of alertness and stress) and valence (proxy of emotional self-regulation) indices at the onset of positive and negative stimuli using emotionally laden videos from the Geneva Affective Picture Database (GAPED) (Dan-Glauser, 2011). Valence is a composite emotional metric that measures overall emotional experience. Valence values from 0 to 100 indicate a neutral to positive experience, while values from -100 to 0 indicate a negative to neutral experience. We interpret a reduction of valence, either positive or negative, as a proxy of a decrease in the emotional reaction to a stimulus or, put differently, an increase in emotion regulation.

Arousal is defined as the physiological and psychological state of being awake or reactive to stimuli. Arousal values range from 0 to 100. We interpret a reduction of arousal as a proxy of a decrease in emotional reaction to a stimulus, or, in other words, as an increase in emotion regulation. We present more details about the rationale for using these metrics, . our collection procedure for these measures, and how we analyzed them in Appendix 3 Both outcomes, valence and arousal, were coded by averaging the 10 highest arousal or valence scores recorded with the Reactiva application during the interval in which the student is watching the emotionally laden videos from GAPED (Dan-Glauser, 2011). Based upon the confidence variable provided by Reactiva with each video observation, we keep only measures that are above the 30th percentile
||x|x|x|x|

NOTES: Coefficients from specification (2) estimated using OLS regression on the mother-year sample. The dependent variable is an indicator for birth to a given mother in a given year. Sample includes all mothers who have ever given birth, for each year from their year of marriage to the year of interview. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

43
areas are listed so that a given number of households can be randomly chosen from the list to be interviewed. The advantage of this approach is that it only requires knowledge of any FDP residing in an enumeration area, so that this person can be included in the sampling frame. The more accurate the number of FDPs in an enumeration area before listing households, the more efficient is the resulting sample. This approach requires appropriate enumeration area maps that can be easily defined through satellite images for camps, but might not be available across the country for FDPs living in host communities. Implementation can be expensive especially with limited knowledge about the number of FDPs in enumeration areas. However, area-based sampling has the big advantage of not requiring FDPs to register. In contrast, list-based sampling uses an existing list of all FDPs and randomly chooses a sample among them. If additional characteristics are available, like location or country of origin, the sample can be stratified. However, the sample will only be representative of registered FDPs. Hence, it is rarely used in the context of IDPs unless a proper registration system is in place.

To partly address sampling issues, the UNHCR and the World Bank have been cooperating to use the UNHCR or national refugee registration systems as initial master samples to conduct consumption surveys, similarly to what is done with censuses of regular populations. Initial experiments in this respect have been conducted in Jordan, Lebanon, Iraq, Uganda, Kenya, Ethiopia, and South Sudan, resulting in consumption surveys and poverty analyses of refugees and IDPs. Both the UNHCR and the World Bank are now also working with statistical agencies in multiple countries across the Middle East and North Africa and SubSaharan Africa regions to include FDPs in national sampling frames with initial efforts conducted in countries such as Chad, Jordan, Kenya, Niger and Uganda (e.g., World Bank 2021b).

However, the political economy and data privacy can make these efforts challenging. For host countries, FDPs may be only one of the many marginalized groups
The SWIFT methodology has been tested extensively, not only using _within-sample tests_ —that is, testing how well it predicts poverty using the same data on which the model is trained—but also using _betweensample tests—_ that is, using two rounds of comparable household surveys with complete consumption data and using one round for model training and another round for model testing. Among other countries, the methodology was tested using data from Afghanistan in its development stages. The SWIFT-plus model, trained on NRVA 2011-12 data, predicted a poverty rate of 53.5 percent in 2016-17, outperforming traditional Proxy Means Testing (PMT) approaches (see Table 1). To date, the methodology has been applied in more than 75 countries, where SWIFT-plus predictions are proven to be within +/- 2 percentage points of those from actual consumption data, even when large changes in the poverty rate have occurred. The results are available in the annex A.

**Table 1: Past implementation of SWIFT-plus in Afghanistan to predict poverty changes, 2011 to 2016**

|||Afghanistan(2011 - 2016)|
|---|---|---|
||Official Estimates|Original PMT<br>SWIFT Plus|
|2011|38.3%||
|2016|54.5%|39.4%<br>53.5%|

**Note:** _PMT estimates exclude fast-changing consumption dummies._ **Source:** _Modified from_ Yoshida et al (2022).

# Model training in the IE-LFS 2019/20

The SWIFT-plus identifies variables that best predict household expenditure. Candidate variables include household characteristics, education levels, asset ownership, dwelling conditions, and fast-changing consumption variables. The SWIFT-plus approach relies on a regression model to estimate a welfaregenerating function on the data where both a welfare vector and household characteristics are available. In contrast with traditional PMT approaches, it implements several steps to control for the correlation and stability of the variables used to predict welfare
less control over whether they can work once they are married.

We delve deeper to understand the differences in LFP. Appendix Table 2 looks at the KOB decomposition by age. Columns (1), (2) and (3) show the decomposition results for those aged 15-29, confirming that here too, gender gaps are entirely driven by differences in returns. However, for adults aged 30-64—shown in columns (4), (5) and (6)—we note that 2.1 percentage points of the gap is driven by differences in endowments, in particular lower ownership of a vehicle and financial account.

We also look at LFP by urban/rural, and the results are illustrated in Appendix Table 3. In rural areas, differences in endowments are not correlated with the gender gap, but in urban areas, 3.1 percentage points of the overall 20 percentage point gender gap can be explained by differences in endowments. Similar to the results for older adults, we see that urban women’s lower financial account and vehicle ownership

> 12 One caveat is that areas denoted as urban/rural have changed for some enumeration areas in Burkina Faso over time. In the last LSMS done in 2014, the average urbanicity in the provinces was about 13 percent, and it rose slightly to 15 percent by 2019. Appendix Table 1 shows the proportion of urban population by province in 2014 and 2019. However, there are some provinces that faced a larger increase in urban population than other provinces. Both 2014 and 2019 surveys used a two-stage sampling method, but the enumeration areas picked were different in each of those samples, which does not allow us to distinguish whether the changes in LFP or earnings in urban and rural areas across time were due to more people moving to urban areas, or due to rural areas transforming to urban areas.

10
Jackson, C. K. (2010). A Little Now for a Lot Later A Look at a Texas Advanced Placement Incentive Program. _Journal of Human Resources_ , _45_ (3), 591–639.

- Kane, T. J. (1994). College Entry by Blacks since 1970: The Role of College Costs, Family Background, and the Returns to Education. _Journal of Political Economy_ , _102_ (5), 878– 911.

- Kane, T. J. (1995). _Rising Public College Tuition and College Entry: How Well Do Public Subsidies Promote Access to College?_ (Working Paper No. 5164). National Bureau of Economic Research.

- Kane, T. J. (2003). _A Quasi‐Experimental Estimate of the Impact of Financial Aid on College‐ Going_ (Working Paper No. 9703). National Bureau of Economic Research.

- Kerr, S. P., Pekkarinen, T., Sarvimäki, M., & Uusitalo, R. (2014). _Educational Choice and Information on Labor Market Prospects: A Randomized Field Experiment_ . Retrieved from Working Paper website: http://www.demm.unimi.it/extfiles/unimidire/100601/attachment/pekkarinen.pdf

- King, J. E. (2002). _Crucial Choices: How Students’ Financial Decisions Affect Their Academic Success._

- King, J. E. (2004). Missed opportunities: Students who do not apply for financial aid. _Washington, DC: American Council on Education_ .

- Kofoed, M. S. (2017). To Apply or Not to Apply: FAFSA Completion and Financial Aid Gaps. _Research in Higher Education_ , _58_ (1), 1–39.

- LaLumia, S. (2012). Tax Preferences For Higher Education And Adult College Enrollment. _National Tax Journal_ , (1), 59.

- Lavecchia, A., Liu, H., & Oreopoulos, P. (
that outward and inward FDI is diverted via a third country. Petkova and others (2018) control for treaty shopping opportunities in their assessment of the relevance of tax treaties for investment when adopting a network approach that builds on van’t Ried and Lejour (2018) and Hong (2018). Noticeably, they find that only the treaties which reduce tax obligations result in higher FDI, thus underscoring the potential importance of treaty shopping in driving treaty effects.

By quantifying the revenue implications of treaty-shopping, we add to recent assessments of the costs associated with tax treaties. Country-specific estimates have, for instance, been provided for Dutch treaties (McGauran, 2013), and the Ukraine (Balabushko et al, 2017). More recently, Jansky and Svidivy (2018) find that costs in terms of revenue foregone amount to up to 0.2 percent of GDP in some countries. Our assessment of the non-negligible cost of tax treaties concluded with investment hubs also raises more general questions on the decision-making processes in countries that are giving up some of their taxing rights. Looking at results of tax treaty negotiations, Hearson (2017) builds on earlier work by Rixen and Schwartz (2009) and Barthel and Neumeyer (2012) and finds that outcomes differ information and bilateral transaction information for Sub-Saharan African countries, our analysis is based on macroeconomic aggregate information, which is available for most countries, but tends to be less accurate when aiming at identifying tax driven effects.

> 8 A recently concluded global investor survey ranks taxation secondary to other factors such as political stability, legal and regulatory environment, and market size (WBG 2017).
companies with commercial interests. It follows that the general perception (some level of heterogeneity in responses notwithstanding) in Uruguay is that healthcare litigation does not pose major equity problems for the time being.

Unlike in the case of _equity,_ on the _efficiency_ dimension (including on questions referring to the overall threat to the financial sustainability of the Benefit Plan caused by potentially rising litigation), there is more variation of perceptions among respondents (see histogram below). A simple majority of 52.17% of stakeholders who responded to our quantitative survey perceive that there is very little effect overall. The histogram displayed in Figure 6 below plots the distribution of responses to Question 32 of the survey: “Do _recursos de amparo_ in healthcare affect any of the following categories?” Option 4 (“The financial sustainability of the system”) is a good additional measure of equity concerns.

**Figure 6: Healthcare litigation and efficiency**

<!-- Start of picture text --> 0 1 2 3 4<br>Effects of litigation on efficiency and financial sustainability of the system<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Likert scale: No effect Little effect Some Effect Significant effect Values: 1 2 3 4 Given this brief background of the baseline equity and efficiency perceptions among stakeholders, we now turn to specific tests of the hypotheses derived from the substantive literature on the impact of health litigation part of the formal model. To recap the major empirical implications, a change in the institutional context facilitating or inhibiting judicialization of healthcare from _S_ (  ,  ) to _S_ '(  ',  ') will generate predictable reactions from different actors. Generally, an institutional change will affect the welfare of a given group depending on how its marginal effect alters the perceived efficiency and equity of different actors.

31
*0.15**|**0.93**|**0.61**|**0.44**|**0.32**|**0.25**|**0.21**|**0.17**|**0.14**|**0.09**|**0.04**|
|**OTHER**|**0.18**|**0.36**|**0.34**|**0.26**|**0.28**|**0.26**|**0.23**|**0.2**|**0.18**|**0.18**|**0.13**|

_Source:_ World Bank estimates using ENIGH 2014. _Note_ : Deciles based on household expenditure per capita.

14
**Figure 7. Brain drain and remittances**

<!-- Start of picture text --> cM T+cM cL T+cL cn co c<br><!-- End of picture text --> In other words, for a bene...cial brain drain to obtain through remittances, the transfer received by each remaining resident must be relatively high so that a large share of the population gains access to education. This do not seem to portray the evidence from remittance data available in developing countries. Although remittances are generally positively correlated with donors’ incomes, meaning that skilled emigrants are presumably important remitters, the results from household surveys are mixed. At an aggregate level, Faini (2002) shows that migrants’ remittances decrease with the proportion of skilled individuals among the emigrants and concludes that ”this result suggests that the negative impact of the brain drain cannot be counterbalanced by higher remittances”. This does not imply that remittances by skilled migrants are negligible, especially if the proportion of temporary migrants increases; for example, Kangasniemi et al (2004) show that nearly half (45%) of Indian medical doctors working in the UK remit income to their home country and that remitters transfer on average 16% of their income.

Instead of sending remittances to relatives at home, migrants may return after they have accumulated savings abroad and use such savings for promoting investment projects (generally in small businesses). There is much evidence that low-skill workers migrate with the aim of accumulating enough savings so as to access to selfemployment and entrepreneurship (e.g., Mesnard, 2004, and Mesnard and Ravallion (2001) for Tunisia, Dustmann and Kirchkamp (2002) for Turkey, Ilahi (1999) for Pakistan, Woodru¤ and Zenteno (2001) for Mexico, or McCormick and Wahba (2001) for Egypt). The latter study o¤ers additional insights in that it shows that in the case of literate migrants, both the amount of savings and the