rafmacalaba/data-use-mentions-extended huggingface dataset:
(Kling et al., 2007; Blattman et al., 2020). More specifically, we conjecture two scenarios: (i) estimate an imputed value of the outcome variable based on firm characteristics and past performance; and (ii) assume that all firms that did not answered the survey closed (i.e., impute a decrease in sales in 100%).

# **4. Six Stylized Facts on the Impact of COVID-19 on Impact and Recovery of Firms**

## **4.1 Stylized Fact 1: Months after the pandemic, some aspects of business performance show signs of recovery, albeit partial, thereby suggesting a possible ’U’ shaped pattern.**

The COVID-19 pandemic had a large and persistent impact on the operation and performance of firms. The first wave of the BPS and WBES COVID-19 showed that, on average, firm sales fall by 49%; almost 2 in 5 firms had already fallen or were expecting to fall into arrears in the following 6 month-period; 57% of the firms had reduced wages or hours worked of their employees, while 19% fired workers (Apedo-Amah et al., 2020).

The second wave of the survey data shows a positive trend of business recovery from this shock. We note the following key points on recovery: _First_ , more firms are now operational, in spite of modest changes in mobility.<sup>10</sup> Overall 93% of the surveyed firms were operational in the second

> 7In some countries, sampling weights are available in order to produce nationally representative results at the country level, but for cross-country comparison purposes, we do not include these weights. 8These additional results are available upon request.

> 9These methods are described in McKenzie (2021).

> 10See Appendix Figure C1. This could be explained by a decrease in mandatory closure measures that were less likely to be implemented in successive waves of the pandemic and replaced by other measures to restrict mobility and social

10
Figure 4. Drivers of Potential Growth A. Contributions to potential growth <!-- Start of picture text --> B. Contributions to potential growth in<br>EMDE<br><!-- End of picture text --> <!-- Start of picture text --> Percent TFP Percent TFP<br>10 10 Capital<br>Capital<br>8 Labor 8 Labor<br>Potential growth Potential growth<br>6 6<br>4 4<br>2 2<br>0 0<br>World AEs EMDEs EMDEs EMDEs excl.China<br>C. Contributions to regional potential growth D. Contributions to regional potential growth<br>Percent TFP Percent TFP<br>10 Capital 10 Capital<br>Labor 8<br>8<br>Potential growth 6<br>6<br>4<br>4<br>2<br>2 0<br>0 -2<br>EAP ECA LAC MNA SAR SSA<br>1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27<br>1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27 1998-2017 2003-07 2013-17 2018-27<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Notes: GDP-weighted averages of production function-based potential growth estimates. TFP growth stands for total factor productivity growth.

A.B. Sample includes 30 advanced economies and 50 EMDEs.

C.D. Regional samples include largest available sample for each region to ensure broad-based coverage. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

22
first component accounts for 32% of the overall variation in the 7 tests, and for 35% of the overall variation in the 4 subcomponents of the Denver. Table A2 reports the factor weights, showing that cognitive development tests all have relatively high weights (Denver language, TVIP, memory, fine motor skills).

The aggregate cognitive score constitutes our main ECD outcome. In addition, socioemotional skills are measured using the Strength and Difficulties questionnaire (SDQ) and a behavioral screening test consisting of questions to caregivers about a set of positive and

> 13The marshmallow test is a well-known test of delayed gratification, evaluating preschool-age children’s ability to wait before eating a marshmallow in exchange for being rewarded an additional one. Mischel et al. (1989) show that the test predicts outcomes later in life.

> 14Only a few items of the Denver are reported by caregivers.

11
globalization index (Dreher et al. 2000).<sup>5</sup> Political integration variable shows up as significant and positive while the social integration variable is insignificant.

Next we examine the impact of deregulation and financial liberalization on codependence. We use the database created by Abiad Detragiatche and Tressel (2010) that quantifies financial reforms over a thirty year time period. Results under models 6 and 7 show that reforms that have led to international capital liberalization and stock market liberalization have increased co-dependence. Reforms that have led to stronger bank supervision, however, have decreased co-dependence (model 8). We also examine the impact of bank concentration as measured by assets of 3 largest banks as a share of assets of all commercial banks. As mentioned earlier, there has been a substantial increase in concentration in both developing and developed countries. As the recent crisis has demonstrated, large complex financial institutions can cause systemic disruptions affecting all other financial institutions. We find concentration to increase co-dependence. This is in contrast to Beck, Demirgüç-Kunt, and Levine (2006) who show that countries with a more concentrated banking system are less likely to suffer a systemic banking crisis. Finally, we examine the impact of moral hazard on co-dependence. If there is an implicit guarantee provided by the State to cover losses stemming from a systemic crisis, banks will have incentives to take on correlated risks (Acharya 2005). Guaranteed banks will not have incentives to diversify their operations, since the guarantee takes effect only if other banks fail as well. We use the deposit insurance coverage ratio (Beck and Demirgüç-Kunt 2004) as a proxy for moral hazard. We find a positive and significant relationship between moral hazard and codependence. Overall, our results suggest that countries which are more integrated, and which have liberalized financial systems and weak banking supervision also have higher co-dependence in their banking sector.

# **4. Conclusion**

This paper examines time-series and cross-country variations in default risk co-dependence in the global banking sector. We compute weekly changes
No|No|No|No|Yes|Yes|No|No|

Source: own computations based on SEDLAC (CEDLAS and World Bank) and WDI (World Bank). Notes: Robust standard errors in brackets, clustered at the country level for models 3-10. * significant at 10%; ** significant at 5%; *** significant at 1%. The additional control variables include the log of GDP per capita, the log of the real exchange rate against the US Dollar (both from WDI), and the log of a tariff index. Column 8 includes the log of a minimum wage index compiled by ILO (available only for 1995-2009). The regressions in column 10 only include the nine countries for which there is a relatively long, continuous and homogeneous data source: Argentina, Brazil, Chile, Costa Rica, Mexico, Panama, El Salvador, Uruguay and Venezuela.

33
00|
|Highest price (pence per therm)|13.575|11.5|
|Smallest volume (therms per day)|18,000|15,000|
|Largest volume (therms per day)|250,000|300,000|
|Source: The Heren Index, July 1996|||

The most important issue in the further development of spolt markets in the U.K. is the need for a standardized gas contract. An efficient short-termn market should facilitate the pricing of gas according to the system short-run marginal cost of gas. A short-term gas contract has fewer parameters than a long-term gas contract, which includes a number of delivery characteristics in addition to price. A standardized short-term contract specifying a delivery to a major terminal or an interconnection in the pipeline system therefore improves the efficiency of trading short-term gas. The introduction of the "on-system" market at the National Balancing Point in BGT's pipeline system provides a "systemwide" trading point for such a contract. This is complemented by the activities of the International Petroleum Exchange (IPE) to launch the trading of standardized physical and financial gas contracts at the National Balancing Point. The IPE has succeeded in
preference, educational attainment for abilities, household income for family circumstances, and number of children for fertility. The proximity of these measures may vary across contexts and, even so, it may be difficult to accurately measure these proxies. We argue that using an individual fixed effect in longitudinal data that follow the same individual over time is a better approach, since we are not necessarily interested in estimating the effect of, say, fecundity on work participation; instead, we are interested in accounting for unobserved individual dispositions toward work. However, it is important to note that we can only control for time-invariant individual characteristics and preferences. We need to assume, for example, that fertility, career, and family preferences do not change over time.

We can estimate: y��� �α�β TK�� ⋅Eligible�� �γ TK�� �δ Eligible�� �μ� �φ� �θ� �ψ Xijt �ε��� �3� where y���, TK��, Eligible��, μ�, φ�, and Xijt are as described earlier. The inclusion of an individual fixed effect, θ�, helps account for important omitted variables such as career and family preferences, which thereby minimizes the estimates’ biases. It may also potentially relax the identifying assumption in the DDD setting. In Section 4.2, we have to assume that the employment gaps of _different_ mothers with and without any preschool-aged child in high- and low-growth districts are not systematically different from one another. In DDD-FE, we compare withinmothers and we assume that in the absence of differences in preschool access, the employment gap of the same mother with and without any preschool-aged child would have been the same. In other words, as the child ages from 2 to 3 and becomes eligible for preschools, if the mother is exposed to the same preschool density, then the difference in her likelihood to work (when the child was of age 2 and when
0</sup><sup>_._044.</sup>

# A.3 SMM estimator

The adjustment costs parameters are given by the vector Γ = ( _γ_ 1 _, γ_ 2 _, γ_ 3; _Ce, Cu_ ) , which we estimate by simulated method of moments (SMM). The SMM estimator is based on comparing a vector of empirical moments computed from rm and worker actual data, denoted by Ψ , with moments computed from rm and worker simulated data, denoted by Ψ<sup>_s_</sup> (Γ) . The simulated moments depend on the parameters Γ through the investment policy function and the employment transition probability policy function, given that the moments are based on rm-lelve investment and labor allocations across sectors. The estimator for the adjustment costs minimizes the weighted distance between the empirical and simulated moments. Formally, 43
|Legislative elections in non-democraciesi,t||-2.534<br>(2.004)||||-3.250<br>(2.562)|||
|Party of executive controls all houses in<br>democraciesi,t|||-2.227<br>(2.076)||||-1.674<br>(2.386)||
|Party of executive controls all houses in<br>non-democraciesi,t|||-6.183<sup>~~**~~</sup><br>(2.598)||||-6.188<sup>~~*~~</sup><br>(3.333)||
|Herfindahl Index of government party<br>seats inparliament in democraciesi,t||||-4.567<br>(3.427)||||-4.448<br>(3.940)|
|Herfindahl Index of government party<br>seats inparliament in non-democraciesi,t||||-17.345<sup>~~***~~</sup><br>(3.906)||||-17.831<sup>~~***~~</sup><br>(4.560)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R<sup>~~2~~</sup>within|0.644|0.644|0.678|0.647|0.606|0.607|0.645|0.619|
|R<sup>~~2~~</sup>overall|0.531|0.533|0.598|0.583|0.
estimated impact of domestic uncertainty (as measured by the EPU) on domestic investment is highly significant.

**C. Spillovers from the United States, the Euro Area, and China** . In order to quantify spillovers from an output slowdown in the United States and the Euro Area, a Bayesian structural vector autoregression is estimated for 1998Q1–2016Q2, using weighted average data for 18 EMDEs. The sample includes Brazil, Bulgaria, Chile, Costa Rica, Hungary, India, Indonesia, Malaysia, Mexico, Paraguay, Peru, Philippines, Poland, Romania, Russia, South Africa, Thailand and Turkey. The regression includes, in this Cholesky ordering: weighted average output growth in major advanced economies and China (excluding either the United States or the Euro Area), U.S. or Euro Area output growth, proxies for global financial conditions (U.S. 10‐year sovereign bond yield and JP Morgan’s EMBI index), and aggregate output growth or investment growth in EMDEs (excluding China). To conserve degrees of freedom, oil price growth is included as an exogenous regressor in the model. A similar estimation is applied to estimate the impact of a slowdown in China’s output or investment growth on EMDE output growth. The regression includes, in this Cholesky ordering: weighted average output growth in major advanced economies, proxies for global financial conditions (U.S. 10‐year sovereign bond yield and JP Morgan’s EMBI index), China’s output growth or China’s non‐investment growth and China’s investment growth, and output growth in EMDEs (excluding China). The oil price is again included as exogenous regressor.

**D. Crowding‐in of private investment by public investment** . A vector autoregression is conducted to estimate crowding‐in of private investment by public investment for eight EMDEs with available data for 1998Q1‐2016Q2. A decomposition of investment into private and public investment is only available for a restricted sample of
reduction of consumption of food and selling productive assets or land. We also analyze whether the scheme caused changes in the use of less costly coping mechanisms. Specifically, we estimate effects on the consumption of non-food goods, utilization of informal loans, receipt of remittances, and supply of labor by non-sick adult household members.

Our analysis exploits the introduction of the national health insurance scheme (NHIS) in Ghana, which was the first in Sub-Saharan Africa and served as a precedent for many countries of the continent (Alhassan et al., 2016). The main identification problem when analyzing the effects of a national policy is finding a suitable comparison group. In this paper, we exploit the fact that the staggered implementation of the NHIS overlapped with the rollout of the 5fth round of the Ghanaian Living Standards Survey. We are able to observe, within the same district, subdistricts interviewed right before the NHIS introduction, and subdistricts interviewed right thereafter. This allows us to use a regression discontinuity design with time and district fixed effects, where the running variable is months to the NHIS implementation at the district level. Because the timing of interviews of households was external to the timing of the NHIS adoption, we argue that, within the same district, whether a household was observed before or after NHIS implementation is as good as random. We indeed find no systematic discontinuities in the characteristics of households that were interviewed around the time of the NHIS introduction.

Our first finding is that the NHIS resulted in a reduction of out-of-pocket (OOP) payments for medical care of about 20 percent. To gauge the extent to which this effect is driven by NHIS cushioning households from the financial consequences of more severe health shocks, we split the sample by the length of a sickness episode. We show that this measure was not affected by the introduction of the NHIS in the period studied. Estimation results reveal that the drop in OOP payments is larger for households experiencing higher sickness intensity (35 percent).

Our second finding is that households experiencing higher sickness
our analysis, the time allocation gap is influenced by access to infrastructure, which affects the efficiency of time allocated to home production and child rearing.

# **IV. WOMEN’S TIME ALLOCATION AND GROWTH: EQUILIBRIUM**

An equilibrium of the economy requires the existence of a time sequence of prices, consumption in adulthood and in old age, physical capital stocks and female health status such that, given initial capital stocks _K_<sup>_P_</sup> (0) and _K_<sup>_I_</sup> (0) and initial health statuses _h_<sup>_C_</sup> (0) and _h_<sup>_f_</sup> (0), individuals maximize utility, firms maximize profits, markets clear, and the government budget is balanced. Individual and aggregate quantities must also coincide, so that _K_<sup>_P_</sup> = _K_<sup>_P,T_</sup> and _a_<sup>_f_</sup> = _A_<sup>_f_</sup> . In addition, in a balanced growth equilibrium, consumption and capital (both public and private), and output grow at the constant, endogenous rate γ, the rate of return on private capital is constant, and health statuses of children and adult females are either constant

> 31In the Global Gender Gap report (2010), the size of the gender gap is measured based on four dimensions of inequality between men and women: economic participation and opportunity (including salaries, participation levels, and access to high-skilled employment); education attainment (including access to basic and higher-level education); political empowerment (including representation in decision-making structures); and health and survival (including life expectancy and sex ratio).

24
survey used in this paper covered over 2,500 households and was conducted in May and early June 2021. The survey included questions about people’s willingness to get a COVID-19 vaccine and the factors that could be contributing to this, such as beliefs about the behavior of others and people’s most trusted sources of information about the vaccine. The results were weighted to match the general population based on the most recent nationally representative survey that included a measure of household welfare, which is the 2016–18 Demographic and Health Survey (DHS). The online randomized survey experiment was conducted in late June and throughout July 2021 with around 2,400 participants and trialed whether messages informed by the findings of the phone surveys could reduce people’s hesitancy about getting the COVID-19 vaccine. Specifically, the treatments that were tested referred to expert advice, social norms and the relative safety of the vaccine.

There is wide-ranging research about the potential drivers of vaccine hesitancy in general and existing work highlights that people’s beliefs, along with social norms, often play a crucial role in determining their willingness to get vaccinated (for reviews of existing work see: Brewer et al., 2017; Dubé et al., 2015; Larson et al., 2014). For example, people’s beliefs about the effectiveness of a vaccine in preventing illness (compared to the risk of negative side effects) and their own concerns about being infected are expected to influence whether they are willing to get vaccinated. Similarly, prior studies have shown that vaccine hesitancy appears to be related to the behavior of others and people’s most trusted information sources, particularly whether people trust misinformation on social media (Africa CDC, 2021; Ahearn, 2021). In addition, some evidence shows that a relationship exists between previously being vaccinated for another disease (which suggests having trusted a vaccine at some point in the past) and willingness to get a newly developed vaccine (Brewer et al., 2017). The vaccine hesitancy literature
yemen-humanitarian-response-plan-january-december-2019-enar.

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