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Macroeconomics during the great lockdown: a discussion of recent coronavirus research

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COVID-19 has caused an economic shock of unprecedented scale and scope by shutting down sectors of the global economy. The consequences of this shock are likely to be particularly severe for poor households in developing countries , where social safety nets are more limited and supply chains may be less robust.

Understanding the COVID-19 shock is critical to determine the optimal policy response : What is the nature of the COVID-19 shock, and how does it differ from previous global contractions? What are the economic impacts of the COVID-19 shock, on macroeconomies and households? Given its nature and these impacts, how should policymakers respond?

Expanding the macroeconomic toolbox

A surge of recent research attempts to answer these questions, and the unique features of this global economic crisis, triggered by a pandemic and the resulting supply side shock, has required an expansion of the usual macroeconomic policy toolbox. These papers estimate impacts of the COVID-19 shock, develop new models that reflect different characteristics from past global economic contractions, and discuss implications for economic policy.

In this blog post we present highlights from these papers to support policymakers interested in these lessons. The discussion builds on resources including a COVID-19 seminar series organized by the Bank and working papers summarizing global social protection responses and lessons for macroeconomic policy.

Most of this research uses data from the United States and Europe where public high-frequency and high-granularity datasets are widely available. We complement the discussion of these papers by exploring how lessons from this research apply to developing countries.

While this blog post focuses on the macroeconomics of the COVID-19 shock, understanding these dynamics is key for microeconomists tasked with designing programs in response to this crisis; it also complements a discussion of optimal international policy, as opposed to national policy.

Estimating the size of the COVID-19 shock

Estimating the size of the shock is the first order of designing policy responses: What sectors are shut? What is the impact of the lockdown on workers, movement of people and goods, etc.?

Gender impacts: Alon et al. (2020) highlight that women disproportionately occupy jobs in sectors most affected by physical distancing measures, and will likely be overwhelmingly impacted by school closures.  They however note plausible positive long-term impact on gender equality, with a shift towards flexible work arrangements or childcare norms may result in positive long run impacts on gender equality.

Impacts on the stock market: Baker et al. (2020a) find that the fluctuations in the US stock market over the past month are the first major stock market fluctuations in US history that have been attributed in the media to economic fallout of or policy responses to a pandemic.  Further, by comparing this to impacts of past pandemics, they suggest that policy responses drive the unprecedented nature of the impacts of the COVID-19 shock on the stock market.

Impacts on household spending: Baker et al. (2020b) leverage transaction-level household financial data to study responses of consumer spending, and confirm that households initially stockpiled goods (particularly groceries) and reduced restaurant spending even before stay-at-home orders were issued.

Unemployment claims: “Nowcasting,” the process of using widely available high frequency data to predict outcomes that are measured with significant lag, is particularly valuable in crises when important economic variables can shift rapidly, especially in data poor contexts.Unemployment claim data for the US provides a useful validation exercise, as it is reported weekly so nowcasted predictions can be quickly verified.  Goldsmith-Pinkham & Sojourner (2020) applied this approach (with publicly available code) and accurately predicted the large spike in US unemployment insurance claims using internet search data.

Understanding the COVID-19 shock in developing countries

Surveys to quantify the shock: Unfortunately, many of the data sources that have been used in the United States are either unavailable or uninformative about the large cash-based informal sectors in developing economies. To compensate for this, a number of multi-country survey efforts (here’s one example survey from Bangladesh) are underway to systematically capture how households in poor countries are responding to the COVID-19 shock.

Nowcasting to quantify the shock: Other approaches are using high-frequency data sources that are available across countries to produce analytics informative about both the economic impacts and the spread of COVID-19. One World Bank initiative has begun using anonymized mobile phone metadata to produce estimates of mobility between regions, which can be used to measure changes in mobility and predict the spread of disease, and sharing these analytics with governments through informative dashboards (an illustration of the dashboard with randomly generated data is presented below).


Figure 1: Illustration of an interactive dashboard presenting aggregated mobility indicators for Zimbabwe generated using anonymized telecom metadata. The data for the image has been randomly generated and is for illustrative purposes only. Credit: Milusheva & Marty (2020).

Exploring the optimal policy response to the COVID-19 shock

The Great Lockdown: Two recent papers, Alvarez et al. (2020) and Eichenbaum et al. (2020), have attempted to quantify optimal lockdowns by embedding classic SIR (Susceptible-Infectious-Recovered) models of disease transmission into macroeconomic models. In these models, infected individuals impose a negative externality through infecting others when they work or spend. Lockdowns then can act as an optimal corrective tax or control.  The traditional intuition therefore holds: optimal lockdown strictness is proportional to this externality, which increases with the share infected and decreases with the share recovered.

They then work through some basic results. Some are intuitive: testing is valuable, with identifying infected individuals more valuable but potentially more costly than identifying recovered individuals, and the optimal lockdown is stricter the higher the mortality rate, the greater the effect of the lockdown on the spread of disease, and the higher the costs of overcrowding in health systems. Some are less so: speedier vaccine development can actually make the optimal lockdown stricter, as delaying cases until vaccines are available becomes less costly. Estimating these parameters is therefore crucial to determining the optimal response, and a growing number of efforts are attempting to do so with representative sampling.

Fiscal and monetary policy: Other recent work develops more realistic macroeconomic frameworks to evaluate the scope for fiscal and monetary policy in mitigating the economic impacts of a lockdown. Guerrieri et al. (2020) show that a lockdown, a supply shock in nature, can trigger a demand shock, once a multi-sector model with incomplete markets is considered.  Despite the supply shock causing a demand shortage, the effects of fiscal policy are muted because a large fraction of the economy is shut down. Monetary policy, on the other hand, can have magnified effects as long as complementary policy can prevent the spread of business shutdowns. Also in an incomplete market context with credit market imperfections, Buera et al. (2020) show that a realistic calibration of a one-quarter lockdown shock can ripple into persistent aggregate effects, through a large rise in the rate of unemployment and a protracted decline in total factor productivity (TFP).

Lockdowns in developing countries: While getting cash into the hands of the neediest households is a clear policy recommendation across rich and poor countries, there has been more debate over the costs and benefits of lockdowns in poor countries. Barnett-Howell & Mobarak (2020) note that in poor countries, the benefits of lockdowns may be lower  (flattening the curve may not help in countries where health systems cannot cope with status quo demand, and the younger demographics in developing countries implies lower mortality rates), while the costs of lockdowns may be higher (interrupting all economic activities while livelihoods depend on day-to-day wages presents a large public health threat of its own). As an alternative, they propose low cost interventions that limit the spread of disease without halting necessary livelihoods, such as universal cloth masks.

Fiscal policy in developing countries: While many lessons on responding to the economic shock also apply to developing countries, the details of implementation of optimal responses may be quite different. For example, Loayza & Pennings (2020) note that as poor households typically work in the informal sector in developing countries, direct transfers are likely better targeted and more effective than unemployment benefits or payroll tax cuts.  In addition, relatively larger expenditures on public health may be justified, to cover not only COVID-19 responses but also provide long needed boosts to primary care and disease monitoring.

Social protection in developing countries: Consistent with the recommendations above, many developing countries are expanding social protection programs to provide cash and in-kind support to vulnerable households through the COVID-19 shock. Recent work finds that over 600 million individuals are newly covered by social protection due to COVID-19 related responses.  As most of the world’s poor and extreme poor live in developing countries, this social protection response is particularly important in these countries. While it may seem surprising to some that direct intervention in fragile agricultural supply chains has not been prioritized, Sen (1981) famously highlighted that famine is almost always caused by poverty and rights and not by unavailability of food.



Roberto N. Fattal Jaef

Economist, Development Research Group

Florence Kondylis

Research Manager, Lead Economist, Development Impact Evaluation

John Loeser

Economist, Development Impact Evaluation

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