After the Pandemic: the economic impact on low-income countries, and what can be done?

How bad is the impact of the pandemic on low-income countries? How long will it last? And what can be done to accelerate recovery?


We have written a paper to answer three questions: How bad is the impact of the pandemic on low-income countries? How long will it last? And what can be done to accelerate recovery? The answers are that the impact is severe; and, depending on policy reform and the support of the international community, it will last for years, and recovery will be tough.[1]

Covid-19 has not erupted across Africa as elsewhere; at least, not yet. Among about one billion people there were just 628,000 recorded cases and 13,800 deaths, by mid-July.

But the economic impact of the pandemic is widespread because most of sub‑Saharan Africa went into lockdown from the second week in March. Lockdowns slow transmission of the virus but affect everyone. In Uganda, for example, more than half the population of 42 million stayed at home during lockdown (Figure 1, right-hand panel), and so far there have been 1040 cases and zero deaths.[2]

Figure 1: the pandemic and lockdowns

The referenced media source is missing and needs to be re-embedded.

Lockdowns have a dismal impact on people when they cannot sustain their livelihoods. They stop economic activity, reduce incomes and squeeze government tax revenues just when they seek to increase spending on health and social protection.

In our paper we look forward through the impact of pandemic and lockdowns to gauge the medium-term paths to recovery. The results are illustrated in Figure 2, which is constructed from numerical simulations generated by a dynamic general equilibrium macroeconomic model of a small open economy, calibrated to data from Uganda. The model describes the paths for production, consumption, investment, the fiscal balance and the balance of payments in response to national and global responses to the pandemic (the vertical bars) and how these paths are affected under alternative national and international policy responses (the lines).[3]

Figure 2: the impact of the pandemic and paths to recovery

The referenced media source is missing and needs to be re-embedded.

The impact is unprecedented outside war: The initial hit on consumption is a drop of one third. The legacy of lockdowns combined with the effects of a global recession is severe hardship. The domestic supply shock together with the international demand shock put at risk the development gains of African countries over the last 20 years.

Recovery depends on how the public finances are restored to sustainability. Just raising taxes and cutting spending makes for a slow recovery. Tax systems are narrowly based and plagued by ‘leakages’ and exemptions; they distort private investment and drag down growth. Fragile systems for delivering public services mean that cuts in investment and productive recurrent spending make things worse. There are no easy public policy options.

If ‘Official Development Assistance’ (ODA) supports the public finances, the recovery accelerates. External finance widens fiscal space, so that domestic policy choices are less awful; it relieves the need to raise distorting taxes and cut productive spending. The net increase in ODA needed is US$40-50bn, which matches estimates from the IMF (here). External finance at this scale will need leadership to catalyse innovative multilateral action.

Recovery would be further accelerated by effective reforms to strengthen systems for tax collection and public spending. Reform plus international support offers a return to trend after three years. A reformed, more dynamic, economy would sustain higher growth.

Read the full working paper from our Office of the Chief Economist here

[1] Adam, Henstridge, and Lee (2020) “After the lockdown: Macroeconomic adjustment to the Covid-19 pandemic in Sub-Saharan Africa”, Oxford Review of Economic Policy DOI: 10.1093/oxrep/graa023

[2] As at 15th July on the data from the European Centres for Disease Control, with the caveats due for data based on challenging variations in testing around the world.

[3] The details of the modelling are set out in full in the OPM working paper version of our paper here.

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