Covid-19 has had a staggering adverse impact on lives and livelihoods, disproportionately affecting the poor and the vulnerable. The IMF (2021) cautions that, while global growth is expected to recover, divergent recoveries are occurring across and within countries, with a high risk of persistent economic damage for many. Recent research suggests that past pandemics have increased income inequality and hurt the employment prospects of people with low educational attainment (Furceri et al. 2020). These effects could be stronger this time around, given the considerably broader reach of Covid-19.
Could policies help? Furceri et al. (2021) show that fiscal policy played a significant role in reducing or amplifying income inequality during past pandemics. Ma et al. (2020) find that the negative effects on GDP and unemployment were less in countries with larger first-year responses in government spending, particularly in healthcare. We carry forward this discussion in our recent work (Cuesta Aguirre and Hannan 2021).
Past pandemics had detrimental macroeconomic and distributional effects
Using a sample of 55 countries over the period 1990-2019, we estimate the impact of five recent modern pandemics (SARS in 2003, H1N1 in 2009, MERS in 2012, Ebola in 2014, and Zika in 2016) on key economic indicators. We employ local projections to estimate the dynamic impulse response functions of pandemic events (dummy variable representing the pandemic year) on our variables of interest. The regressions include time- and country-fixed effects and relevant control variables (see note in Figure 1).
The results confirm that pandemics have lasting negative effects on the economy (Figure 1). Output declines by 2.2% three years after a pandemic and does not return to pre-pandemic levels within five years, underscoring that the scarring effects of pandemics tend to persist. Similarly, the unemployment rate – which tends to increase by one percentage points after four years – remains above pre-pandemic levels after five years. Pandemics are also associated with a 1.1 percentage point increase in the poverty rate one year later, and this effect persists after five years. The poverty gap – a measure of poverty intensity (mean shortfall in income or consumption from the indicated poverty line) – also increases following a pandemic and persists over the medium term (suggesting that pandemics not only increase the share of population in poverty but also intensify the hardships of those in poverty). Finally, inequality, as measured by the net Gini index, increases by 1.7% after five years.
Figure 1 The effect of past pandemics
Note: Impulse response functions of the relevant variable on pandemics are estimated using a sample of 55 countries over the period 1990-2019. The solid line indicates the response while the dotted lines correspond to 90% confidence bands using standard errors clustered at the country level. The x-axis denotes time: t=0 is the year of the change. The y-axis denotes the change in the variable of interest at time t, compared to the year before pandemic. Poverty refers to WDI’s poverty headcount ratio at $1.90 a day (2011 PPP; as a share of population), while inequality is represented by net Gini index from SWIID database. All equations include a dummy variable (and two lags) to capture the pandemic, two lags of the dependent variable, two lags of output, and country- and time-fixed effects. In addition, the equations for output and unemployment rate control for two lags of: log of income per capita, trade-to-GDP ratio, private credit-to-GDP ratio, and banking crisis.
Policies and strong structural features help
So, how can these scarring effects be mitigated? We investigate the role of fiscal policy (using fiscal impulse – the negative of the change in the structural primary balance) and initial conditions in informality (economic activities hidden from official authorities), family benefits (component of social expenditure from OECD; public spending on family benefits, including financial support that is exclusively for families and children), and health spending per capita. For each policy/structural feature studied, the baseline regressions were augmented by interacting the pandemic shock with ‘high’ and ‘low’ dummy variables, where high and low represent countries above or below/equal to the median of the respective policy/structural feature across the sample during the pandemic year (fiscal policy), the year before pandemic (informality and heath), or using decade average (family benefits).
The results suggest that countries that had provided relatively higher fiscal support had comparatively better output outcomes, with an output decline of 1.5% after three years, compared to 3.4% for those with relatively low support (Figure 2). We also find that countries with higher fiscal support and better initial conditions had relatively better outcomes in unemployment, poverty, and inequality. For example, the poverty rate increases by 1.3 percentage points after the first year for countries with relatively higher informality, compared to an increase of 0.7 percentage points in the same timeframe for those with lower informality. Five years after the pandemic, the poverty rate remains one percentage point above pre-pandemic levels for countries with higher informality, compared to a 0.4 percentage point increase for those with lower informality. Similarly, the increase in inequality is 2.2% after five years for countries with lower family benefits, compared to 1.5% in the same period for those with higher family benefits. Finally, inequality increases to 1.2% after five years for countries with relatively lower health expenditure per capita, compared to 0.6% increase for the high expenditure group over the same period. These results are robust to a battery of alternative specifications (discussed in our paper).
Figure 2 The differential responses three years after a pandemic shock
Note: The bars show the coefficient of impulse functions three years after pandemic shock: the estimated change in the variable of interest three years after pandemic shock, compared to the year before pandemic. Filled bars represent variables that are statistically significant (90%) for at least one year of the t=0 to t=5 time horizons.
Several channels are at play. For example, fiscal measures provide resources to vulnerable segments of the population, such as those at risk of poverty and those disproportionately affected by pandemic-induced challenges (e.g. relatively less access of quality healthcare, loss of job in contact-intensive industries, and less personal savings to support livelihoods in case of job loss). The World Bank (2021) documents in detail the channels through which informality aggravates the effects of Covid-19 – workers in the informal sector tend to be lower-skilled and lower-paid, with limited access to social safety nets and finance, while informal firms tend to have labour-intensive production and are more widespread in the services sector (which is more likely to be hit given the contagious factor related to Covid-19).
This time round, policies are even more crucial
Certain Covid-specific effects point to higher detrimental effects for countries with weaker initial conditions. For example, the social distancing during the pandemic meant reliance on online schooling across many countries. To the extent that countries with higher informality are characterised by lower internet access, the already comparatively lower school enrolment rates could be exacerbated, resulting in persistent loss of human capital and deterioration in poverty and inequality outcomes (Figure 3). Higher informal economies also tend to have a lower share of the population with current account ownership at a financial institution or with a mobile money service provider (which would affect access to financial resources). Similarly, countries with lower social expenditure have less internet and financial access, which could exacerbate poverty and inequality outcomes. These characteristics further underscore the need for supportive policy actions. Adequate fiscal support, higher health spending, and targeted family benefits should be part of the package. Higher policy support and complementary policies might be required in countries with high informality who might – as our findings suggest –witness more negative impacts on unemployment, poverty, and inequality.
Figure 3 Informality versus development indicators
Source: WDI and Medina and Schneider (2020).
Authors’ note: The views expressed herein are those of the authors and should not be attributed to the IMF, its Executive Board, or its management.
Cuesta Aguirre, J P and S A Hannan (2021), “Recoveries After Pandemics: The Role of Policies and Structural Features”, IMF Working Paper Series WP/21/181.
Furceri, D, P Loungani, J D Ostry and P Pizzuto (2021), “Fiscal austerity intensifies the increase in inequality after pandemics”, VoxEU.org, 03 June.
Furceri, Da, P Loungani, J D Ostry and P Pizzuto (2020), “Covid-19 will raise inequality if past pandemics are a guide”, VoxEU.org, 08 May.
IMF (2021), “Managing Divergent Recoveries”, World Economic Outlook, April.
Ma, C, J Rogers and S Zhou (2020), “Modern Pandemics: Recession and Recovery”, International Finance Discussion Papers 1295. Washington: Board of Governors of the Federal Reserve System.
Medina, L and F Schneider (2020), “Shedding Light on the Shadow Economy”, World Economics 21(2): 25-82.
World Bank (2021), The Long Shadow of Informality, Challenges and Policies, F Ohnsorge and S Yu (eds), Washington DC.