Governments in both advanced and developing economies have responded to the COVID-19 pandemic with unprecedented lockdowns and transfers aimed at keeping individuals at home. However, it has quickly become clear that the unique economic and demographic landscape of developing economies poses challenges not shared by more advanced economies. Widespread informality and an inability to administer large income-replacement programmes (Hevia and Neumeyer 2020) clearly make Western-style lockdowns more challenging. Allowing business to go on as usual is clearly not a palatable option either. So how should policy responses to the pandemic differ in the developing world?

Key differences between advanced and developing countries

Although developing and advanced economies differ in countless ways, recent research has highlighted four key differences that influence the effectiveness of COVID-19 responses. Figure 1 summarises these differences – which form the basis for our analysis – defining advanced economies to be those in the top quartile of the world income distribution, and developing economies to be the bottom quartile. In short, developing economies have weaker healthcare systems, younger demographics, less fiscal capacity and much larger informal sectors. We elaborate on each of these below.

Figure 1 Advanced vs developing economies: Key differences for the pandemic

Healthcare systems

It is well known that developing economies have substantially weaker healthcare systems than advanced economies. To take one metric, advanced economies have about 48 hospital beds per capita on average, while developing economies have just 12. Of course, those with critical cases of COVID-19 do not need only beds, but also ventilators. El-Sadr and Justman (2020) report that many African countries possess just a handful of ventilators – or none at all.

Age demographics

All available evidence suggests that COVID-19 is much more deadly for older people (e.g. Ferguson et al. 2020). On average in the world’s advanced countries, 15% of the population is above 65; in developing countries, this figures is just 3%. This younger demographic is the one characteristic that bodes favourably for their health outcomes during the pandemic.

Fiscal capacity

Most developing economies have inefficient tax administrations. As a result, they collect just 16% of GDP in taxes on average, compared to 32% in advanced economies. This lower fiscal capacity limits the ability of governments to institute large-scale income replacement programmes for furloughed workers during lengthy lockdowns. These problems are being confounded by lower natural resource revenues (Arezki and Nyguen 2020) and inability to issue more sovereign debt (Arellano et al. 2020).

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In contrast to advanced economies, the majority of workers in developing countries are engaged in the informal sector. As a crude proxy, 71% of workers in developing countries are self-employed compared to 13% in advanced economies. By definition, informal activities are beyond the purview of the government to tax or regulate and make implementing lockdowns more difficult (Dhingra 2020, Koczan and Plekhanov 2020).

A model of lockdowns in developing countries

Our recent research (Alon et al. 2020) analyses the effects of various lockdown policies in both advanced and developing countries. To do so, we construct a quantitative heterogenous-agent macroeconomic model with uninsurable income risk and epidemiological dynamics as in the SICR (Susceptible-Infected-Critical-Recovered) model. Our model expands on the model of Glover et al. (2020) to allow for the four key differences described above. In our model, the disease’s path is endogenously determined by both biological factors as well as individuals’ economic activities and government policies.

To understand how policy outcomes differ in developing and advanced economies, we allow each of the above channels to vary with a country’s level of development consistent with the differences we observe in the data. These channels include uninsurable idiosyncratic health and income risks, age heterogeneity, fiscal capacity constraints, an informal sector, and healthcare capacity constraints. An especially salient feature of our model is the differential effectiveness of containment policies between the formal and informal sector. Reflecting the low level of compliance to lockdowns in the informal sector, we assume that lockdowns can lower the rate of infection in the formal sector but are ineffective in doing so in the informal sector. Consequently, the spread of disease depends in part on how many workers choose to work in the informal sector, creating a dynamic feedback between the economic and epidemiological state of the aggregate economy.

Takeaways for developing countries

We simulate a variety of different lockdown scenarios in advanced and developing countries and analyse the effect on both lives and livelihoods by calculating the impact on welfare, GDP, and deaths per 100,000 people. We simulate lockdowns that apply to the entire population, which we refer to as blanket lockdowns, as well lockdowns that allow the young population to work while requiring only the older population remain at home. We refer to these latter regimes as age-targeted policies, following Acemoglu et al. (2020) and Bairoliya and İmrohoroğlu (2020), who explore age-targeted policies in the United States.

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The first takeaway from our analysis is that blanket lockdowns are less effective in developing countries both at preventing the outbreak of disease (i.e. ‘flattening the curve’) and at saving lives. Due to low fiscal capacity, developing countries can only provide small transfers to help replace income lost during lockdown. As a result, many workers turn to the informal sector to make up the income difference and so continue to spread the disease.  Consequently, a 28-week blanket lockdown in a developing country saves about 70 lives per 100,000 people, while the same lockdown in an advanced country saves about 320.

Blanket lockdowns are also less efficient in developing countries as measured by lives saved per unit of lost GDP. For instance, a 28-week blanket lockdown saves 320 lives per 100,000 people in advanced economies and reduces GDP by 16%, resulting in about 20 lives saved per 100,000 people for each unit of GDP. The same policy in developing economies saves only about 10 lives per 100,000 people for each unit of GDP lost. In other words, saving a given number of lives costs more output in developing countries under blanket lockdowns.

The second takeaway is that age-targeted policies are more potent in developing countries. Table 1 displays the potency of blanket and age-targeted policies for both the advanced and developing economies according to our simulations. For each unit of lost GDP, an age-targeted policy saves 95 lives per 100,000 people in the developing economy. This is roughly 10 times more than a blanket lockdown. Additionally, unlike blanket lockdowns, the age-targeted policy saves more lives per unit of GDP in the developing economy than in the advanced economy.

Table 1 Lives saved per 100,000 people per unit of GDP lost

Why are age-targeted policies so much more effective in developing economies? The answer stems from their dramatically younger populations, and how age-targeted policies leverage this demographic difference to mitigate the deleterious effects of weaker fiscal capacity and widespread labour market informality. Weak fiscal capacity normally constrains the ability of developing countries to provide sufficiently large transfers to keep workers out of the informal sector, where they continue to spread the disease. However, the vulnerable old population is sufficiently small that large enough transfers can be sustained to keep them from turning to the informal sector. Since the risks of COVID-19 increase dramatically with age, encouraging compliance among the most vulnerable elderly population proves especially effective at reducing mortality during the pandemic.

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Developing countries face a unique set of challenges that limit the effectiveness of the blanket lockdowns adopted by the west. Our analysis suggests that the weaker fiscal capacity and widespread labour market informality in developing countries pose especially salient challenges in implementing blanket lockdowns successfully. Age-targeted lockdown policies – which focus on shielding elderly populations – appear to be a much more effective option for developing economies, as they leverage their younger and less-susceptible populations to focus limited resources on the most vulnerable parts of their populations.


Acemoglu, D, V Chernozhukov, I Werning and M D Whinston (2020), “Optimal Targeted Lockdowns in a Multi-Group SIR Model”, NBER Working Paper No. 27102.

Alon, T M, M Kim, D Lagakos and M VanVuren (2020), “How Should Policy Responses to the COVID-19 Pandemic Differ in the Developing World?”, NBER Working Paper No. 27273.

Arellano, C, Y Bai and G P Mihalache (2020), “Deadly Debt Crises: COVID-19 in Emerging Markets”, NBER Working Paper No. 27275.

Arezki, R and H Nguyen (2020), “Coping with a Dual Shock: COVID-19 and Oil Prices”,, 1 April.

Bairoliya, N and A İmrohoroğlu (2020), “Macroeconomic Consequences of Stay-At-Home Policies During the COVID-19 Pandemic”, NBER Working Paper No. 27102.

Dhingra, S (2020), “Protecting Informal Workers in Urban India: The Need for a Universal Job Guarantee”,, 02 May.

El-Sadr, W M and J Justman (2020) “Africa in the Path of Covid-19”, New England Journal of Medicine.

Ferguson, N, D Laydon, G Nedjati-Gilani et al. (2020), “Impact of Non-Pharmaceutical Interventions (NPIs) to reduce COVID-19 mortality and healthcare demand”, March. 

Glover, A, J Heathcote, D Krueger and J-V Rios-Rull (2020), “Health versus Wealth: On the Distributional Effects of Controlling a Pandemic”, NBER Working Paper No. 27046

Hevia, C and P A Neumeyer (2020), “A Perfect Storm: COVID-19 in Emerging Economies”,, 21 April.

Koczan, Z and A Plekhanov (2020), “The COVID-19 Shock: Employment in Middle-Income Economies”,, 22 April.