Working from home in developing countries

The spread of Covid-19 has led to the widespread adoption of social-distancing measures in countries across the world, be it in response to government mandates or on a voluntary basis. Since social distancing frequently involves the closure of workplaces to limit interpersonal contact, the ability to work from home (WFH) is a critical factor for determining its economic consequences.

Early in the pandemic, Dingel and Neiman (2020) measured the ability to WFH by occupation using US task data, and found that 37% of workers can. Their findingscomplement the literature measuring actual WFH prior to the pandemic (Mas and Pallais 2020, Hansvik et al. 2020). Mongey et al. (2020) extend these measures, stressing the fact that low-income workers are substantially less able to WFH and thus more vulnerable to job losses from social distancing. Bick et al. (2020) analyse US data during the pandemic and show that actual WFH largely lines up with predictions. Similarly, Adams-Prassl et al. (2020a) find that during the spring of 2020, roughly 40% of survey respondents reported being able to carry out work remotely during the pandemic months in Germany, the UK, and the US. This is consistent with other studies on the potential to WFH in several European countries (Alipour et al. 2020, Boeri et al. 2020). Furthermore, Adams-Prassl et al. (2020b) emphasise the considerable variation in the ability to WFH within occupations and industries.

The above measures cannot be directly extrapolated to developing countries, as the task content of occupations may vary significantly across contexts (Dicarlo et al. 2016, Lo Bello et al. 2019, Saltiel 2019). Berg et al. (2020) consult labour-market experts to define the occupational ability to WFH in different countries. Garrote Sanchez et al. (2020) argue that the US-based occupational WFH ability rates of Dingel and Neiman (2020) should be adjusted downward in developing countries due to more limited internet access. Hatayama et al. (2020) go a step further by using data from developing countries on tasks to characterise jobs that are amenable to WFH. 

In this column, we discuss the findings of a recent paper that contains two contributions (Gottlieb et al. 2021). First, we provide a measure of the ability to WFH using task data in developing countries and analyse its predictions. Second, we validate that measure with data on actual WFH and employment outcomes.                                      

A task-based measure of the ability to work from home

We develop a measure of the ability to WFH based on tasks performed by workers from the Skills Toward Employability and Productivity (STEP) survey. This covers a representative sample of workers in urban areas in ten low- and middle-income countries, ranging from Kenya to Macedonia. 

Table 1 provides the fraction of workers able to WFH, broken down by occupation, educational attainment, self-employment status, and gender. Overall, 9.3% of urban employment can be done remotely. The ability to WFH varies significantly across broad occupation groups. While close to one-quarter of jobs in managerial, professional occupations, and clerical support occupations could be done from home, fewer than 3% of jobs in elementary occupations, crafts, or occupations involving plant or machine operation can be done remotely.

The ability to WFH also varies across personal and job characteristics. The second and third columns of Table 1 show that educational attainment is a strong predictor of ability to WFH. The share for those who complete high school surpasses dropouts by 13 percentage points. High-school graduates have higher WFH ability than their counterparts across all occupations. Similarly, the ability to WFH for wage employees (12.4%) is far higher than for self-employed workers (3.3%). Finally, women have a slightly higher WFH ability than men. 

Our statistical analysis indicates that while a worker’s occupation is the main driver of the ability to WFH, individual characteristics (education, self-employment status, and gender) matter as well. Once these observables are taken into account, WFH ability is similar across countries in the data sample. This means that our estimates of WFH ability by occupation and observed characteristics are likely to hold across a large spectrum of developing countries spanning the income per capita range of STEP countries ($3,000–14,000 PPP USD). 

The existing literature on the ability to WFH largely builds on worker task data from developed countries. To compare these with the findings from STEP countries, we use US data from O*NET to compute for each three-digit occupation the fraction of workers able to WFH using a definition equivalent to ours. Figure 1 shows the results relative to our estimates from STEP. For virtually all occupations, the O*NET data predict a substantially higher WFH ability. This cautions against extrapolating from O*NET to developing countries, because the task content within occupations differs between rich and poor countries. At the aggregate level, developing countries have a relatively low ability to WFH for two reasons: (1) their employment is concentrated in low-WFH ability occupations; and (2) within occupations, tasks are less amenable to being executed remotely.

Predicted ability to work from home and actual outcomes

We then analyse the share of workers in Brazil and Costa Rica who actually worked from home during the pandemic. The estimated share of workers who actually worked from home in the second quarter of 2020 equals 10.6% in Costa Rica and 13.3% in Brazil, far below the corresponding shares in developed countries. Unsurprisingly, remote working varies enormously by occupation in both countries, remaining below 1% for plant operators and exceeding 40% for professionals. More educated workers, women, and wage employees are more likely to have worked from home during the pandemic, in line with our WFH ability predictions.

We propose various methods that validate our STEP-based measure of WFH ability on the Brazilian and Costa Rican data. Figure 2 visualises one such exercise. The horizontal axis portrays 20 equally sized bins of workers in the Brazilian (Costa Rican) sample whose likelihood of WFH is predicted using their occupation and observed characteristics following the methodology based on STEP. The vertical axis plots the corresponding share of workers who actually worked from home. The regression line is close to the 45-degree line, implying that our measure of the ability to WFH is a good predictor of actual WFH.

We also illustrate the usefulness of our measure of WFH ability for the case of Peru, a country with no information on actual WFH. We find that individuals with a low predicted ability to WFH are more likely to suffer employment losses during the Covid pandemic, especially if they have been employed in non-essential sectors. 

Conclusion                                          

We propose a measure of the ability to WFH for developing countries. Our findings indicate that fewer than 10% of urban jobs in developing countries can be done remotely. Various vulnerable groups are less likely to work remotely, including workers in low-wage occupations, high school dropouts, and the self-employed. Importantly, our results indicate that the low WFH ability in developing countries is driven not only by occupational composition, but also by the nature of tasks within occupations. 

We validate our measure with data on actual WFH from Brazil and Costa Rica, two countries that stand out among developing nations in gathering such information. For a broad range of other developing countries, the WFH ability can be computed using our measure of WFH ability by subgroups coupled with country-specific employment shares. Users can do so using our Work from Home simulator, which can be accessed at https://work-in-data.shinyapps.io/work_in_data/.

References

Adams-Prassl, A, T Boneva, M Golin and C Rauh (2020a), “Inequality in the Impact of the Coronavirus Shock: Evidence from Real Time Surveys”, Journal of Public Economics 189. 

Adams-Prassl, A, T Boneva, M Golin and C Rauh (2020b), “Work Tasks That Can be Done from Home: Evidence on the Variation Within and Across Occupations and Industries”, Cambridge Working Papers in Economics 2040, May.                                                                                               

Alipour, J-V, O Falck and S Schller (2020), “Germany’s Capacities to Work from Home”, IZA Discussion Papers 13152, Institute of Labor Economics (IZA).   

Berg, J, F Bonnet and S Soares (2020), “Working from Home: Estimating the Worldwide Potential”, VoxEU.org, 11 May.                                    

Bick, A, A Blandin and K Mertens (2020), “Work from Home After the COVID-19 Outbreak”, CEPR Discussion Paper 15000.                   

Boeri, T, A Caiumi and M Paccagnella (2020), “Mitigating the Work-Security Trade-Off while Rebooting the Economy”, VoxEU.org, 9 April.                              

Dicarlo, E, S L Bello, S Monroy-Taborda, A M Oviedo, M L Sanchez-Puerta and I Santos (2016), “The Skill Content of Occupations Across Low- and Middle-Income Countries: Evidence from Harmonized Data”, IZA Discussion Papers 10224.

Dingel, J I and B Neiman (2020), “How Many Jobs Can be Done at home?”, VoxEU.org, 7 April.

Gottlieb, C, J Grobovšek, M Poschke and F Saltiel (2021), “Working from Home in Developing Countries”, European Economic Review 133, 103679.

Garrote Sanchez, D, N Gomez Parra, C Ozden, B Rijkers, M Viollaz and H J Winkler (2020), “Who on Earth Can Work from Home?”, World Bank Policy Research Working Paper 9347.

Hatayama, M, M Viollaz and H Winkler (2020), “Jobs’ Amenability to Working from Home: Evidence from Skills Surveys for 53 Countries”, World Bank Policy Research Working Paper 9241.                                       

Hensvik, L, T Le Barbanchon and R Rathelot (2020), “Which Jobs Are Done from Home? Evidence from the American Time Use Survey”, IZA Discussion Papers 13138.                            

Lewandowski, P, A Park, W Hardy and Y Du (2019), “Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data”, IZA Discussion Papers 12339.                          

Lo Bello, S, M L Sanchez Puerta and H Winkler (2019), “From Ghana to America: The Skill Content of Jobs and Economic Development”, IZA Discussion Papers 12259.        

Mas, A and A Pallais (2020), “Alternative Work Arrangements”, Annual Review of Economics 12, 14 August.      

Mongey, S, L Pilossoph, and A Weinberg (2020), “Which Workers Bear the Burden of Social Distancing Policies?”, NBER Working Papers 27085.        

Saltiel, F (2019), “Comparative Evidence on the Returns of Tasks in Developing Countries”, Mimeo.

Lockdown accounting

Many countries have implemented social distancing and lockdown policies to tame the spread of Covid-19. This column discusses the potential GDP and employment effects of lockdown policies for a broad cross-section of countries ranging in income per capita from Niger to Luxembourg. It shows that the employment and GDP effects of lockdown policies are U-shaped in income per capita. While workers in rich countries have a substantially higher ability to work from home, which mitigates declines in employment and GDP, poor countries concentrate employment and value-added in essential sectors that are not shut down. Middle-income countries see the largest declines as they feature relatively large employment shares in non-essential sectors and relatively low work from home ability

In response to the outbreak of Covid-19, 114 countries have implemented policies that require either the closure of, or the implementation of working from home, for all but essential workplaces (Hale et al. 2020). In sectors required to shutter workplaces, work can only be conducted from employees’ homes. Cross-country differences in the ability to work from home (WFH) are therefore crucial in evaluating the economic implications of such policies. At the same time, countries differ substantially in their sectoral composition, implying that lockdowns targeting the same set of sectors may produce unequal outcomes in different economies.

Since the start of the pandemic, several papers have assessed the ability to work from home. Dingel and Neiman (2020) estimate that in the US, 37% of jobs are amenable to WFH. Projecting their measure of WFH ability by occupation across countries, the authors find that in the poorest economies, only around 5% of employment can be executed from home. However, this outcome hinges on the ability of self-employed farmers to work from home (Gottlieb et al. 2020). Adams-Prassl et al. (2020) estimate 45% and 43% of effective WFH employment for the US and the UK, respectively. Saltiel (2020) was the first to assess the feasibility of work from home using data sources from developing countries.1

Yet, as of now, the joint assessment of the ability to work from home and of the effects of lockdown policies on employment and output exists only for some rich countries. Barrot et al. (2020) projected a decline in GDP due to a lockdown of six weeks duration of 5.6% for France; Fadinger and Schymik (2020) project a decline of 1.6% of GDP per week of lockdown for Germany. There is no similar evidence on middle- or low-income countries, despite some very recent work on Covid-19 there (e.g. Djankov and Panizza 2020).

Based on a recent paper (Gottlieb et al. 2020), in this column we provide estimates on the ability to WFH and on the effect of realistic lockdown policies on employment and GDP for a broad cross-section of countries, ranging in income per capita from Niger to Luxembourg.  We show that the effect of specific policies depends both on a country’s aggregate WFH ability and on its sectoral structure.

Who can work from home?

We develop a measure of the ability to work from home using the first two rounds of the STEP household surveys. These cover a representative sample of workers in urban areas of ten lower and upper-middle-income countries in 2012-2013, ranging in income per capita (ppp) from $3,700 (Kenya) to $15,000 (Macedonia). The data contain information on workers’ job tasks, which we use to construct a measure of potential WFH ability.

Overall, 45% of urban employment could be done remotely in the STEP countries. At the individual level, WFH ability varies strongly with a worker’s occupation and demographics. Figure 1a shows the feasibility of WFH for nine major occupation groups. While the majority of jobs in managerial and professional occupations and in clerical support (groups 1-4) can be carried out from home, few jobs in elementary occupations, crafts, or occupations involving plant or machine operation (groups 6-9) can be done remotely. Figure 1b shows that mean WFH ability differs strongly across demographic groups: it is 20 percentage points lower for high school dropouts compared to graduates, and 15 percentage points lower for the self-employed compared to wage employees. Women have a far higher ability to WFH (51.5%) than men (37.4%). This assessment is based purely on job task characteristics. In practice, the ability to work from home depends also on household composition, in particular the presence and age of children (Alon et al. 2020a), and infrastructure (Brown et al. 2020). Yet, based on post-Covid surveys from Peru and the US, we validate our WFH measure and show that an increase in an individual’s WFH score from 0 to 100 is associated with a 91 percentage point increased likelihood of remaining employed through April in the US, and 71 percentage points in Peru.

Figure 1 Ability to work from home…

a) by occupation

b) by demographic group

Working from home across countries

By combining WFH rates for 72 detailed occupation/demographic groups with their employment shares, we construct a measure of the aggregate urban WFH ability for 57 countries across the entire development spectrum. Figure 2 shows that the work from home ability in urban areas is substantially lower in poor countries: it ranges from roughly 35% in the poorest countries to about 53% in the richest.

Much of this variation is due to differences in the occupational structure of countries, which differs systematically with development (Duernecker and Herrendorf 2016). As shown in Figure 3, poor countries have low employment shares in high WFH-ability occupations, such as managers and professionals, and high employment shares in low WFH-ability occupations. In addition, they have larger shares of high-school dropouts and self-employed workers. All of these factors reduce their WFH ability.

Figure 2 Aggregate work from home ability in urban areas across countries

Figure 3 Urban occupation composition by country income group

The effects of sectoral lockdown policies

Lockdown policies in practice do not shutter the entire economy but focus on non-essential sectors. Workplaces in essential sectors can still operate, even if workers cannot work from home (e.g. the health sector, groceries, agricultural activities). As a result, the effect of lockdown policies on aggregate employment and output depends not only on a country’s WFH ability, but also on its sectoral structure. We simulate various lockdown scenarios that shut down all workplaces in some sectors, a fraction of workplaces in others, and none in sectors considered essential. Employment that is shut down can be substituted with WFH, which we assume to be equally productive.

The main scenario we consider is a ‘hard’ lockdown policy, which we model based on measures implemented in Italy, Spain, and Germany.2 We also simulate an alternative ‘soft’ lockdown, which is designed to capture the situation as shutdowns are eased. It lifts most of the restrictions on industry and half of the restrictions in services. The latter are relaxed more slowly as they involve more interpersonal interaction, which fosters the risk of virus transmission. To compute the impact on GDP, we employ a simple multi-sectoral model that aggregates sectoral employment.3

Figure 4 Employment and GDP by lockdown scenario, relative to pre-trend

a) Employment, hard lockdown

b) GDP, hard lockdown

c) Employment, soft lockdown

d) GDP, soft lockdown

Figure 3 presents the fraction of employment and GDP by lockdown relative to pre-trend against countries’ real income per capita. The upper panel portrays the hard lockdown, and the lower panel the soft lockdown. Across countries, the hard (soft) lockdown generates an average employment drop of 25.5% (9.8%). GDP declines on average by 28.9% (8.9%) on an annualised basis. Under both scenarios, employment and GDP are U-shaped in income per capita. There are two forces at work. On the one hand, rich countries have a substantially higher WFH ability, which mitigates declines in employment and GDP. On the other hand, their sectoral composition favours poor countries, which concentrate employment and value-added in essential sectors that are not shut down. Middle-income countries see the largest declines as they feature relatively large employment shares in non-essential sectors and relatively low WFH ability.

The key driver of the sectoral composition effect is the agricultural sector, an essential sector that remains open. The low WFH ability in the sector is therefore less relevant. The sheer size of this essential sector in poor countries – on average, it accounts for 37.7% (23.2%) of employment (value-added) in the poorest quintile of countries, compared to only 2.1% (1.3%) in the richest quintile – mitigates the effects of the lockdown.

Conclusion

Lockdown policies have had large disruptive effects on economies across the globe. We document that the employment structure of economies across the income per capita spectrum is such that the potential employment and GDP effects of lockdown policies are of a similar magnitude for low- and high-income countries, and strongest for middle-income countries.

We also provide an online lockdown simulator that allows users to simulate user-defined sectoral lockdown policies, and foster the policy debate on the design of lockdown policies in low-, middle- and high-income countries (Alon et al. 2020, Barnet-Howell and Mobarak 2020).

References

Adams-Prassl, A, T Boneva, M Golin and C Rauh (2020), “Work tasks that can be done from home: Evidence on the variation within and across occupations and industries,” mimeo. 

Alon, T, M Doepke, J Olmstead-Rumsey and M Tertilt (2020a), “The Impact of COVID-19 on Gender Equality”, Covid Economics: Vetted and Real-Time Papers 4.

Alon, T M, M Kim, D Lagakos and M VanVuren (2020b), “How should policy responses to the Covid-19 pandemic differ in the developing world?”, Covid Economics: Vetted and Real-Time Papers 22.       

Baldwin, R and B Weder di Mauro (2020), Mitigating the COVID Economic Crisis: Act Fast and Do Whatever it Takes, a VoxEU.org eBook, CEPR Press.

Barnett-Howell, Z and A Mushfiq Mobarak (2020), “The Benefits and Costs of Social Distancing in Rich and Poor Countries”, arXiv preprint arXiv:2004.04867v1, 10 April.

Barrot, J-N, B Grassi and J Sauvagnat (2020), “Sectoral effects of social distancing”, Covid Economics: Vetted and Real-Time Papers 3.

Brown, C, M Ravallion and D van de Walle (2020), “Can the world’s poor protect themselves from the new coronavirus?”, NBER Working Paper 27200.

Delaporta, I and W Peña (2020), “Working from home under Covid-19: Who is affected? Evidence from Latin American and Caribbean countries”, Covid Economics: Vetted and Real-Time Papers 14.

Dingel, J and B Neiman (2020), “How many jobs can be done at home?”, NBER Working Paper 26948.

Djankov, S and U Panizza, U. (2020), COVID-19 in Developing Economies, a VoxEU.org eBook, CEPR Press.

Duernecker, G and B Herrendorf (2016), “Structural transformation of occupation employment”, mimeo, Arizona State University.

Fadinger, H and J Schymik (2020), “The costs and benefits of home office during the Covid-19 pandemic: Evidence from infections and an input-output model for Germany”, Covid Economics: Vetted and Real-Time Papers 9.

Fana, M, S Tolan, S Torrejón, C Urzi Brancati and E Fernández-Macías (2020), “The Covid confinement measures and EU labour markets”, Publications Office of the European Union.

Gottlieb, C, J Grobovsek and M Poschke (2020), “Working from home across countries”, Covid Economics: Vetted and Real-Time Papers 8.        

Gottlieb, C, J Grobovsek, M Poschke and M Saltiel (2020), “Lockdown Accounting”, Covid Economics: Vetted and Real-Time Papers 31.

Hale, T, S Webster, A Petherick, T Phillips and B Kira (2020), “Oxford Covid-19 government response tracker”, Blavatnik School of Government 25.

Hatayama, M, M Viollaz and H Winkler (2020), “Jobs’ amenability to working from home: Evidence from skills surveys for 53 countries”, Covid Economics: Vetted and Real-Time Papers 19.

Saltiel, F (2020), “Who can work from home in developing countries?”, Covid Economics: Vetted and Real-Time Papers 7.

Endnotes

1 More recently, Delaporta and Peña (2020) and Hatayama et al. (2020) have considered additional data sources.

2 We implement this based on the measures of Fana et al. (2020), who document the degree to which various sectors are deemed essential and therefore exempted from the March 2020 lockdown decrees in Italy, Spain, and Germany. The lockdown policies in New York State and in the Canadian provinces of Ontario and Québec were broadly similar.

3 This analysis abstracts from factors other than the lockdown that affect employment and output. Such factors could be, among others, reductions in labor supply (voluntary or for health reasons), financial frictions, or frictions in final or intermediate goods markets. It is also possible that lockdown policies trigger movements of economic activity to the informal sector. This would reduce their epidemiological effectiveness, while limiting their economic effects (Alon et al. 2020b). The model does capture adjustments in the demand and supply of final and intermediate goods.

Collateral damage: Cross-border fallout from pandemic policy overdrive

The onset of the COVID-19 pandemic meant governments faced their second systemic economic crisis in under 15 years. This year, policymaking went into overdrive as states rightly took steps to protect public health and to stabilise their national economies (BIS 2020, IMF 2020). The impact of those steps did not stop at national borders. Once more, the world trading system faced a major stress test.

When crises happen, overwhelmed officials and policymakers try stifling concerns about trade fallout with the following knee-jerk arguments:

  • Collateral damage to trading partners is inevitable at times like this.
  • Crisis policy response is temporary and so poses no long-term threat to the world trading system.
  • No across-the-border tariff hikes (like those witnessed in the 1930s) have occurred and so trade distortions are under control.
  • It is unrealistic to expect trade reform during crises.
  • Trade rules should not get in the way of national crisis response. 

Having documented and analysed information relating to over 2,000 policy interventions taken during the first ten months of 2020, in the 26th Global Trade Alert report we marshal evidence to reject every single one of these points (Evenett and Fritz 2020). We also compare the policy response this year to that in 2009, during the dark days of the Global Financial Crisis. Doing so reveals there is no single crisis playbook. Governments have a choice in how they respond to crises. Once again, states made dissimilar choices, with different repercussions for their trading partners. Collateral damage was not inevitable. In fact, we show the fallout across nations this year was very uneven.     

This report provides the most comprehensive account to date of the cross-border commercial fallout from government measures taken to tackle the COVID-19 pandemic. Not every element of pandemic response had consequences for trading partners. Of those that did, not all were harmful. Governments may see themselves as responsible solely for the wellbeing of their own citizens, but that doesn’t negate the fact that their actions can harm the health as well as the livelihoods of citizens of trading partners. This year has witnessed policy interventions that both sicken thy neighbour and beggar thy neighbour. There has also been a substantial amount of import reform.

Uneven global fallout

Key findings relating to global policy dynamics affecting cross-border commerce include:

  • Trade distortions implemented this year cover 13.6% of world goods trade. By contrast, trade reforms cover 8.2%.
  • By 31 October 2020, a total of 2,031 policy interventions affecting international commerce were imposed by governments around the world. That total is up 74% over the same period in 2019 and 147% higher than the average for 2015-2017, the years before the US–China trade war really kicked in.
  • Only 27% (or 554) of those 2,031 policy interventions benefited trading partners.
  • Thirty-seven nations saw their commercial interests benefit from 100 or more reforms in trading partners, whereas 58 nations saw their interests harmed 100 times or more so far this year. 
  • This year, 43 nations saw 10% or more of their goods exports face worse market access conditions. Only seven nations saw 10% or more of their goods exports enjoy better market access.
  • During the first ten months of 2020, 26 nations saw more of their goods exports exposed to better market access abroad than worse conditions. For the rest – over 170 economies – more of their goods exports faced impaired access to foreign markets than improvements.
  • Overall, policy intervention during the first ten months of this year generated a total of 10,546 positive cross-border effects for trading partners. Meanwhile, policy induced 17,252 negative spillovers.
  • A total of 110 export curbs on medical goods and medicines remain in force; 68 such curbs have no phase-out date raising the prospect of long-term scarring. 
  • This year, 106 nations implemented a total of 240 reforms to ease the importation of medical goods and medicines.  

Major differences across G20 members in pandemic-era crisis choice

As is the case before any G20 Leaders’ Summit, we put the track records of this group’s members under the spotlight. Here the main findings are:

  • In the first ten months of this year, together the G20 members undertook 1,371 policy interventions, 1,067 of which harmed trading partners. The harmful total is up 24% on 2019 and 117% higher than the years before the trade war (2015-2017).
  • G20 members were responsible for three-quarters of both the harmful and the beneficial knock-on effects for trading partners witnessed this year.
  • Three classes of G20 member can be identified: four nations that implemented over 125 trade-related policies in the first ten months of this year, three nations that implemented 33 or fewer, and the rest (see Figure 1).
  • The policy mix employed by G20 members varied markedly. For example, Brazil undertook 156 policy interventions this year, 47% of which harmed trading partners. For its part, the UK imposed 155 measures and 80% tilted the playing field in favour of domestic firms. Remarkably, the UK’s percentage was bested by Canada, Germany, Japan, Korea, and Saudi Arabia.
  • Resort to time-bound crisis intervention varies a lot too. Russia has already phased out 20% of harmful crisis intervention taken earlier this year. China is scheduled to phase out 29% of its harmful measures by the end of this year; the comparable percentages for Italy and Mexico are 32% and 26%, respectively. Overall, 47% of Mexico’s harmful crisis-era intervention is time-bound, just ahead of China (46%). In contrast, over 95% of Canada’s, Saudi Arabia’s, and South Africa’s policies imposed this year that harm trading partners are not time-bound.
  • This year, G20 members undertook 770 General Economic Support measures (WTO terminology that captures inside-the-border policy intervention that can affect global commerce). A total of 679 of such measures involved granting different types of trade-distorting subsidies, either to firms competing in home markets or in foreign markets. The G20 is responsible for substantial subsidy-related trade fallout, affecting competitive conditions for 9.4% of world goods trade this year.

Policy recommendations: A new approach to crisis management is needed

Coming at a time when the prospects for a revival of multilateral trade cooperation are improving, our evidence supports three recommendations to policymakers. 

First and foremost, a major shift in mindset is needed – away from the prevailing view that the world trade rule book must be effectively suspended for the duration of a crisis. This mindset has deep roots, going back to the origins of the post-war trading regime and manifests itself in what are euphemistically referred to as “flexibilities” in multilateral trade accords. In a world with extensive cross-border commercial ties, the current approach to crisis management is a recipe for the long-term scarring of the world trading system.  

Keeping goods – including medical kit, medicines, and hopefully soon vaccines – flowing across borders is essential during a pandemic. More generally, open trading regimes facilitate exports, which speed up national economic recovery. A crisis management protocol should be agreed by governments to shape how they respond to crises in a way that limits harm to trading partners and keeps commerce flowing. Temporary policy intervention should be prioritised and a mechanism included to encourage the unwinding of trade distortions introduced during crises. The World Health Organization has protocols that kick in when crises occur, so why can’t the World Trade Organization?

Second, governments and international organisations need to systematically compare the state responses to this year’s pandemic and to the Global Financial Crisis so as to identify those effective policy actions that inflicted little or no harm on trading partners. 

Third, developing such best practices requires systematic information collection on public policy responses and their cross-border commercial fallout. The new Director-General of the WTO should strengthen that body’s monitoring and analysis functions. That monitoring needs to pay particular attention to subsidies and other General Economic Support measures. Other international organisations and independent analysts should contribute too. 

References

Bank for International Settlements (2020), Annual Economic Report, June. 

IMF (2020), World Economic Outlook: A Long and Difficult Ascent, October. 

Evenett, S J and J Fritz (2020), Collateral Damage: Cross-Border Fallout from Pandemic Policy Overdrive, The 26th Global Trade Alert report, CEPR Press.