This chapter identifies industry characteristics that can help explain observed differences in resilience to the COVID-19 economic shock. It provides a framework for policy makers to analyse the ability of industries to withstand the shock and rebound after the crisis, aiding in understanding the heterogeneity of the impact and facilitating a targeted policy response. The chapter focuses first on industries’ ability to continue operating in the short run, based on the extent that their functioning is essential or can be done through telework. It then evaluates pre-existing characteristics that determine the extent that firms and workers in different industries are affected beyond the direct consequences of restrictions on economic activity, distinguishing between factors relating to demand, liquidity and credit. In an annex, a dashboard presents the identified channels as an indicator set, for direct comparison of the potential extent industries are affected by – and their resilience to – the economic shock.
Strengthening Economic Resilience Following the COVID-19 Crisis
3. Resilience across industries
Abstract
Key findings
Industry characteristics can help to explain the propagation of economic shocks, and consequently to deliver targeted and more effective economic support measures – both during a crisis, and to foster long-term resilience. The COVID-19 crisis impacted firms and workers differently depending on their industry, and the degree to and ways in which they are vulnerable to economic shocks like the one resulting from the pandemic depends significantly on industry characteristics. An industry perspective can therefore help governments anticipate concerns affecting firms and workers in specific parts of the economy.
The way in which firms produce and supply, demand patterns and consumer behaviour, and firms’ financial positions are key determinants for the type of support needed to keep viable businesses afloat. Policy should consider the extent to which firms and industries are affected across these three broad dimensions, both in the immediate term and during the recovery period. These factors can determine what type of support is needed to keep viable firms in an industry afloat – for example, governments may have to address constraints related to the financing of intangible investments, differing to a large extent across industries, to stimulate the recovery.
Whether firms in industries deemed non-essential can conduct business through telework – and for how long – is a major determinant for their survival. Around 31% of workers across the OECD could do their jobs via telework, but telework rates varied widely by industry, with 70% of jobs being teleworkable in IT and financial services but fewer than 20% in the hospitality, agriculture, and construction sectors. The ability to shift to telework is also often only a partial solution. For instance, if firms are able to convert employees to telework, but cannot supply their product or service remotely. Portions of a firm’s supply chain may also be cut off, even if the firm itself can function remotely.
Introduction
The COVID-19 crisis is different from previous economic crises and shocks in several ways, as outlined in Chapter 2. Importantly, it affected economic players differently – not only between countries, but also between industries and sectors. The pandemic triggered a severe supply-side shock through the immediate and severe containment response, with partial or complete shutdowns of certain types of economic activity implemented in most countries.1 However, most of these shutdowns exempted industries deemed as essential, and other industries could largely continue operating through digital technologies such as telework or e-commerce. In addition to these immediate impacts, recessionary effects and changes to consumer behaviours as a result of restrictions and health concerns alter demand patterns – again affecting industries differently. Lastly, firms vary substantially in their ability to cushion the financial shocks induced by the COVID-19 crisis; this ability can also be related to underlying industry characteristics.
Different types of economic activity may thus be more vulnerable or more resilient to a crisis like the COVID-19 outbreak, and the ensuing containment response and restrictions on activity. Pre-existing characteristics of industries may amplify or mitigate the impact of the economic shock. The goal of this chapter is thus to identify industry characteristics that affect resilience to different aspects of the COVID-19 shock, in order to provide an industry-based assessment of the potential affectedness of firms and workers in industries with different underlying structural characteristics.
Industry characteristics that are relatively similar across countries can help explain the propagation of the COVID-19 economic crisis, and accordingly, in developing targeted and effective policies to tackle it. An industry perspective provides a framework of influencing channels, which also serves as a basis for the analysis in the subsequent topical chapters. How shocks might play out at the country level, and across different types of firms and workers that are differently exposed to the impacts of the pandemic, depending on their industry and the nature of production (e.g. remote work, supply through e-commerce, or due to being essential) will be the focus of these chapters.
The industry perspective is also relevant for policy, as different types of policy and forms of support are likely to be relevant to varying degrees for different industries, depending on the exposure to the shocks and the types of risk that industries face. Through an industry-centred analysis, this chapter identifies a range of pre-existing factors (e.g. the tangibility of the asset base to bridge liquidity constraints, or the extent to which supplying goods and services relies on face-to-face customer contact) that vary to a great extent between industries and have affected how firms and governments responded to the unprecedented shock induced by COVID-19. Many of these factors continue to be relevant for further adjustments as the pandemic continues and economies move into the recovery phase.
The chapter is organised by three broad mechanisms through which the pandemic has affected industries. First, the initial impact on production and provision of goods and services (i.e. the ability of firms in different industries to produce and supply in the short run). Second, the indirect demand implications (i.e. exposure to demand changes). Third, liquidity and credit constraints (i.e. the likelihood of firms in different industries encountering financial difficulties, both in terms of immediate liquidity needs and longer-term investment funding).
This chapter refrains from providing an aggregate assessment of how the industry effects will combine and play out at the country level, acknowledging the fact that many other factors will shape the overall impact in different countries. The country perspective is covered in the topical chapters in the remainder of this volume, which aim to incorporate the most important of these factors, focusing on issues related to the business sector (comprising both workers and firms) such as business dynamics, innovation, productivity, technology, and skills.
Nevertheless, to facilitate a comparison – at the level of the aggregate economy – of factors that are important in shaping the effects of the crisis across industries, Annex A provides a dashboard of the identified channels. They are presented in the form of indicators at the industry level, with the aim of comparing their importance for single industries. Annex B also provides an overview of the relative size of industries across countries, both in terms of value added and employment, to allow policy makers to gauge the importance of individual channels depending on the importance of the affected industries in single economies.2 Summarising the industry channels in a dashboard also enables an easier assessment of how industries are likely to be affected differently, which may indicate the need for different types of policy support in different sectors.
Ability to produce and supply in the short run
Two of the most critical and unique features of the COVID-19 pandemic have been the immediacy of the containment response, with many countries moving from normal operating conditions to a state of lockdown almost overnight, and the supply-side shock this induced, with large variations across sectors. As many governments moved to rapidly close borders and restrict mobility, sectors and firms with heavy reliance on mobility (e.g. through tourism inflows) experienced a sharp reduction in demand. Confinement measures in domestic labour markets in turn prevented activities that could not be undertaken by teleworking in all but a few designated essential industries. Even where lockdown measures were weaker, changes in consumer behaviour still occurred in many countries, as individuals acted to protect their own health by altering how they accessed and purchased essential goods and services (e.g. moving to online ordering and interactions), and purchasing fewer goods that were considered non-essential, thereby lowering demand (Andersen et al., 2020[1]; Goolsbee and Syverson, 2020[2]).
This section provides an overview of the ability of industries to produce and supply goods and services, and potential disruptions to supply chains due to restrictions on the production of intermediate inputs. This reflects both the specific measures put in place by governments to protect public health, and changes in behaviour adopted by individuals and firms, such as encouraging remote work beyond the confinement period or reducing consumption of activities requiring physical proximity.
Essential industries
As governments shut down entire sectors of the economy during the most serious periods of the health emergency, they explicitly authorised economic activities deemed essential3 to continue operating with weaker restrictions (e.g. workers were permitted on-site but needed to meet new safety requirements). While both the severity of confinement measures and the specifics of essential industry definitions varied across countries, the latter tend to be faced with a similar set of defining conditions. Economic activities deemed essential and legally authorised to continue operating during shutdowns can be placed into three categories:
activities forming part of the health response to the crisis (e.g. health services, R&D, pharmaceutical manufacturing and retail pharmacies)
activities forming part of the supply chain for basic necessary goods and services (e.g. farming, food processing and grocery retailers)
activities related to critical systems and infrastructures whose incapacity would have a debilitating impact on security, safety or health (e.g. energy production; public administration).
All else equal, these activities are expected to be relatively sheltered from the direct impact of measures aimed at containing the spread of COVID-19, making them structurally more resilient in the short run. In some cases, output and employment have even risen in essential industries such as healthcare and food retail, as demand shifted from other areas.4 However, the COVID-19 crisis can still create significant disruptions within such essential industries even though they are relatively sheltered overall (e.g. in parts of the retail industry) (see Box 5.3).
Figure 3.1. Level of economic essentiality, by industry
Note: Published NACE two-digit indicators have been aggregated to SNA A38 level for each country based on employment shares taken from the OECD Structural Analysis (STAN) Database (OECD, 2020[3]). The cross-country median of the resulting country-industry value is then taken as the Essential Industry indicator. This indicator is a continuous variable between zero and one, where higher values indicate higher essentiality, and thus less direct impacts from the initial shocks. The analysis is based on three countries: Germany, Italy and Spain.
Source: OECD calculations based on Fana et al. (2020[4]), “The COVID confinement measures and EU labour markets”, https://doi.org/10.2760/079230.
Figure 3.1 provides an overview of the extent to which industries are deemed essential in the context of the COVID-19 pandemic,5 based on an index constructed by the European Commission’s Joint Research Centre (JRC) (Fana et al., 2020[4]). The JRC list assigns an “essentiality” index for each NACE6 two-digit industry, based on a review of COVID-19-specific legislation passed by Italy, Spain and Germany during the first wave of the pandemic. While the degree, focus and implementation of containment measures has differed across countries and over time (see Chapter 2), this index is taken as a broad indicator of the likelihood that different sectors were permitted to also continue operating in other countries.
Differentiating between essential and non-essential industries is useful for helping to understand and contextualise the role of other factors in mediating the impacts of the crisis. For example, remote work has been critical to continued operations throughout the economy, but the impact of this mitigating factor is most relevant in the group of industries that are restricted from their usual operations (i.e. those that are less essential). For example, transport equipment manufacturing is likely to have been affected more strongly than real estate, even though both were deemed to be largely non-essential in the short term, as reported in Figure 3.1. This is because many of the tasks involved in the real estate industry can be performed remotely while the ability of manufacturing to continue producing is more limited.
Figure 3.2 shows the share of employment and value added accounted for by essential industries at the country level. Values for employment range from 42.9% in Norway to 27.6% in Italy, and the combined share of value added in the same industries is quite similar in most countries.
Figure 3.2. Share of employment and value added in essential industries
Note: Essential industries defined as having an index value above 0.8 on the essential industry indicator. These include Agriculture, Food products, Coke and petroleum, Pharmaceuticals, Electricity, gas and steam, Water, sewerage and waste, Transportation and storage, Finance and insurance, Media, Telecommunications, Public administration and defence, Health services, Care and social work. Average refers to the unweighted average across the reported countries. Data refer to 2018 except for: Australia, France, Germany, Latvia, Lithuania, Luxembourg, Norway, Portugal, Switzerland and the United Kingdom (2017); Chile, Greece, Israel and Slovenia (2016); Sweden and Turkey (2015); Canada and Ireland (2014).
Source: OECD calculations based on OECD (2020[3]), Structural Analysis (STAN) Database, http://oe.cd/stan (accessed in December 2020).
Ability to work remotely
One aspect of economic resilience that has been crucial throughout the pandemic is the extent to which activity can shift rapidly from on-site to remote work. As business premises had to close their doors and workers were confined to their homes, digital tools have become indispensable for continuing work and connecting people. The ability to work remotely depends on a number of different factors at the worker, firm, and country levels. These include the existence and availability of appropriate technology and communications infrastructure, as well as worker skills and the types of tasks and activities required on the job, as discussed in more detail in Chapter 5.
This argument is supported by recent OECD analysis (OECD, 2021[5]) that uses disaggregated sectoral data to investigate the link between the decline in firm entry and structural sectoral characteristics. It shows that industries with a higher information and communication technology (ICT) task content of jobs have experienced significantly lower declines in business registrations during the second quarter of 2020. The latter result may be related to the higher propensity to telework in industries that require more ICT tasks from workers, as ICT content and ability to telework are empirically strongly correlated (as explored in more detail in Chapter 5). The importance of the industry dimension is also evidenced by a lower decline in business activity in high-telework industries, compared to other industries (OECD, 2020[6]).
OECD analysis using a task-based approach to evaluate data from the Programme for the International Assessment of Adult Competencies (PIAAC) estimates that an average of 31% of workers in the OECD could work from home (Espinoza and Reznikova, 2020[7]). However, the cross-sectoral disparity of telework potential is very high, with more than 70% of jobs in digital intensive services sectors such as IT and Finance being considered viable for telework, compared to fewer than 20% in Hotels and Restaurants, Agriculture, Construction, and a number of manufacturing sectors (Figure 3.3).7 This implies that the industry composition of the economy is an important factor in how well countries can be expected to maintain production through the crisis.
Figure 3.3. Task-based indicator of telework potential vs. pre-crisis observed teleworking
Note: Espinoza and Reznikova (2020[7]) task-based measure of potential telework, aggregated to A38 industries. This measure classifies an individual job as teleworkable if the worker reports that their job organisation is highly flexible (six questions on flexibility in organising and planning their own activities), involves daily use of ICT (five questions on specific tasks including e-mail, use of word processors and spreadsheet software), and seldom or never involves long periods of physical work. Jobs are classified as telework compatible if they have at least one indicator within each of the three domains which is compatible with teleworking. An analysis of the sources of variation in the potential telework measure finds that industry dummies account for 63% of the total variation in the indicator across OECD countries, with country dummies accounting for a further 9%, implying that the variation in the index is indeed structural, in the sense of relating to the production methods of specific industries. Share of people reporting that they worked from home at least several times a month in Eurofound (2017[8]).
Sources: Espinoza and Reznikova (2020[7]), “Who can log in? The importance of skills for the feasibility of teleworking arrangements across OECD countries”, https://doi.org/10.1787/3f115a10-en; OECD calculations using Eurofound (2017[8]), European Working Conditions Survey, 2015 (data collection), https://doi.org/10.5255/UKDA-SN-8098-4.
The cross-industry patterns in telework potential predicted on the basis of work tasks are closely correlated with actual observed telework activity in Europe in 2015. However, actual telework experience before the pandemic was far below the estimated potential level.8Besides required physical presence, factors that are associated with lower levels of telework at the firm level include a lack of relevant technical skills and management practices (OECD, 2020[9]), as further analysed also in Chapter 5 of this report.
Finally, it is important to note that even in industries where a large proportion of workers can work remotely, the extent to which remote work can wholly substitute for normal operations remains limited in most cases. An example of this is legal services that deal with highly confidential issues, which cannot be conducted remotely for security reasons. Another example is the real estate sector: while it is possible to close an existing real estate sale through online communications, it is less likely that individuals or firms will be willing to enter into a contract based solely on digital inspection of a property. This suggests that in some instances, only a subset of the tasks critical to an occupation or to an industry can be performed remotely. As such, while the ability to telework has been key to reducing the spread of the virus and maintaining economic activity in the short run, even industries with high telework potential may be unable to sustain functional operations in the long term, and may also require significant reorganisation to conduct even limited activity while many staff work remotely.
While the share of employment in non-essential but “teleworkable”9 industries is relatively low in most countries (ranging from 9.2% in Mexico to 23.0% in Israel, Figure 3.4), these industries tend to account for a higher share of value added due to their relatively high levels of labour productivity (see also Chapter 7 on differential exposure and inclusiveness across skill groups).
Figure 3.4. Share of employment and value added in non-essential industries with high telework potential
Note: Non-essential industries defined as having an index value below 0.8 on the essential industry indicator. High telework potential defined as more than 40% of jobs classified as able to be worked remotely. These include IT services, Real estate, Legal and accounting, Scientific R&D, Other business services, Education. Average refers to the unweighted average across the reported countries. Data refer to 2018 except for: Australia, France, Germany, Latvia, Lithuania, Luxembourg, Norway, Portugal, Switzerland and the United Kingdom (2017); Chile, Greece, Israel and Slovenia (2016); Sweden and Turkey (2015); Canada and Ireland (2014).
Source: OECD calculations based on OECD (2020[3]), Structural Analysis (STAN) Database, http://oe.cd/stan (accessed in December 2020).
Ability to supply remotely
Although many industries have a high percentage of jobs that can be done remotely at least in part, this does not automatically imply that goods and services produced remotely can also be supplied to customers. The ability of firms to continue operating during periods of confinement depends not only on whether employees can continue to work, but also on whether customers can continue to purchase and obtain the produced goods and services.
E-commerce has played an important role in allowing firms to shift to contactless modes of sale. Recent OECD work (OECD, 2020[10]) finds that the COVID-19 crisis has led not only to an increase in overall e-commerce, but also to an expansion to products which were previously not typically purchased online, in particular everyday staples such as groceries.10 However, shifting to e-commerce is not an option for all types of products and firms. In particular, in many service sectors, the production and supply of a service are closely intertwined.
Figure 3.5. Share of employment in occupations involving regular face-to-face contact with customers
Note: Share of jobs in each industry that involve face-to-face contact with customers. A job is defined as involving face-to-face contact if job tasks include tasks such as dealing with external customers, assisting and caring for others, or providing consultation and advice to others, and face-to-face communication occurs at least several times a week. Indicators constructed by matching the tasks associated with different occupations in O*NET, then matching these to the occupation structure of NAICS17 three-digit industries using the US Bureau of Labour Statistics industry-occupation matrix for February 2020. Koren and Petö’s three-digit industry-level measures have been aggregated to SNA A38 industries to match the book definition. The reported Agriculture industry does not include Agriculture, but does include Forestry and logging; Fishing, hunting and trapping; and Services to agriculture. Three A38 industries could not be matched to the three-digit NAICS information: Pharmaceuticals, Scientific R&D, Public administration and defence.
Source: Measures from Koren and Petö (2020[11]), “Business disruptions from social distancing”, https://cepr.org/sites/default/files/news/CovidEconomics2.pdf, aggregated to A38 industry classification.
One important determinant of the ability to switch to e-commerce is the extent to which the purchase of a good or service relies on face-to-face contact. Figure 3.5 shows the extent to which different industries usually rely on face-to-face interaction with customers (Koren and Petö, 2020[11]), which can be compared with the proportion of jobs that can be done remotely. Even in service industries where remote work is feasible, such as IT services, Finance, Marketing and other business services, and Education, a substantial number of activities rely not only on the ability of firms and workers to switch to online modes of production, but also on the ability of consumers and households to shift towards online modes of identifying, ordering, and consuming goods and services (an issue elaborated on in Chapter 5). Moreover, as restrictions on economic activity are relaxed, industries that deal directly with customers, as well as the upstream industries supplying them, face a delayed and gradual recovery in demand. This is due to being subject, directly and indirectly, to longer or repeated periods of restrictions in many countries and because consumers may continue to limit activity in order to protect their own health.
Recent evidence (OECD, 2021[5]), focusing on a subset of five OECD countries (Belgium, Finland, the Netherlands, Portugal, and the United States), supports this hypothesis: the decline in entry during the first period of national lockdowns – in the second quarter of 2020 – was more pronounced in industries in which occupations involving more regular face-to-face contact with customers account for a larger share of total employment, based on the same data as the paragraph above (Koren and Petö, 2020[11]).11
The remaining industries that are neither essential nor teleworkable, and do not rely heavily on face-to-face contact with customers, are those in which production requires a physical on-site presence (e.g. Manufacturing industries, Mining, Construction, Wholesale and retail trade).
Both types of non-essential industries with low telework potential – those relying on face-to-face contact and those requiring on-site presence – were heavily affected by the initial impacts of the crisis, and make up a sizeable share of employment in most economies (Figure 3.6).
Importantly for policy making in the post-COVID-19 recovery, these industries are also particularly vulnerable over the medium to long term, as they are unlikely to be able to return to normal activities for the duration of the pandemic or beyond. Thus, a careful evaluation of longer-term impacts is required, whereby it is important to distinguish between effects stemming from continued restrictions on production due to social distancing rules, recessionary effects due to demand drops through reduces incomes, and structural changes in demand due to crisis-induced changes in preferences.
Figure 3.6. Share of employment and value added in non-essential industries with low telework potential
Note: Essential industries defined as having an index value above 0.8 on the essential industry indicator. Low telework potential industries defined as having an estimated share of employment less than 40% in jobs which can be worked remotely. These include Mining, Textiles and apparel, Wood and paper, Chemicals, Rubber, plastics and minerals, Metal products, Computer and electronics, Electrical equipment, Machinery and equipment, Transport equipment, Other manufactures, Construction, Wholesale and retail trade, Hotels and food services, Administrative and support services, Arts and entertainment, Other services. Average refers to the unweighted average across the reported countries. Data refer to 2018 except for: Australia, France, Germany, Latvia, Lithuania, Luxembourg, Norway, Portugal, Switzerland and the United Kingdom (2017); Chile, Greece, Israel and Slovenia (2016); Sweden and Turkey (2015); Canada and Ireland (2014).
Source: OECD calculations based on OECD (2020[3]), Structural Analysis (STAN) Database, http://oe.cd/stan (accessed in December 2020).
Potential supply chain disruptions
Another aspect of production affected by prolonged and repeated periods of confinement and lockdown across countries is the ability of firms to source intermediate inputs. The role of supply chains in the transmission of the crisis is complex and depends on a range of factors, including the extent of the pandemic and associated restrictions on activity in source countries, transportation methods (air vs. surface freight), and the degree of substitutability of inputs. Many of these factors are discussed in more detail at the country level in Chapter 6.
One simple proxy for exposure to potential supply chain disruptions is the extent to which different industries rely on intermediate goods from other industries.12 Given that some industries continued to operate while others had to shut down completely, the potential for disruptions is higher when intermediate inputs are sourced from a different industry than the one in which the downstream firm is active. A measure of the degree of backward and forward linkages in the economy is provided by the Hirschman-Rasmussen index. This measure is normalised within each country, such that an index value above one implies that the industry has above average reliance on other sectors for providing intermediate inputs.
Figure 3.7. Hirschman-Rasmussen index of total and foreign backward linkages
Note: “Total” = foreign and domestic combined. The Hirschman-Rasmussen indexes (HR) provide information about the backward and forward linkages in the economic system considering its productive structure. The HR index is normalised to the average industry in each country, such that index value above one implies that the industry has above average reliance on other sectors for providing intermediate inputs. An analysis of the sources of variation of the index finds that industry dummies account for 76% of the total variation in the index across OECD countries between 2005 and 2015, implying that the variation in the index is indeed structural, in the sense of relating to the production methods of specific industries. The measure reported is averaged over years at the country level for 2013-15, then reported as the unweighted average across 37 OECD countries.
Source: OECD calculations based on OECD (2018[12]), Inter-Country Input-Output (ICIO) Database, http://oe.cd/icio (accessed in May 2020), following the methodology of Guilhoto, Sonis and Hewings (2005[13]).
Figure 3.7 shows the Hirschman-Rasmussen index for each industry, distinguishing between all linkages (domestic and foreign) and foreign linkages only, indicating the extent to which the industry relies on intermediates from abroad. While manufacturing industries and construction tend to rely quite heavily on intermediate inputs from other industries, the opposite is true for many service industries. The “benchmark” group of industries, with close to average level of backward linkages, includes services such as Water, sewerage and waste, Telecommunications, and Media, as well as Transportation and storage and Hotels and food services. In most industries, looking specifically at foreign linkages accentuates the existing relationships and ranking, with a few exceptions. In particular, while Food products and Wood and paper manufacturing tend to be highly reliant on other industries for their inputs, these links are mainly domestic. This may provide some degree of protection from global supply chain disruptions, especially those related to cross-border transport bottlenecks or to difficulties accessing inputs from abroad due to mismatch in the timing of lockdowns across countries.13
Chapter 6 provides a more in-depth discussion of international connectedness, including through global value chains. The chapter complements the industry perspective presented here, and also highlights country-level factors – such as economic centrality – that play a role in the extent to which economies are interconnected as a whole.
Indirect demand implications
The COVID-19 crisis will continue to affect the demand side of the economy beyond the initial impacts of the health crisis and associated containment measures, through decreased household income and wealth and increased economic uncertainty. The global nature of the crisis, and ongoing restrictions on international travel, imply that export volumes will continue to be affected alongside domestic consumption. The extent to which industries are affected differently by these indirect effects depends on the magnitude of the fall in the different components of demand, and industries’ direct and indirect exposure.
This section provides an initial assessment of the relative strength of indirect demand shocks across industries, by disentangling some of the main components of demand – investment, household consumption, government consumption and exports – that can be expected to evolve differently and may be targeted by different policies. This approach complements OECD work estimating fluctuations in demand for specific industries in direct response to measures aimed at addressing the health crisis (OECD, 2020[14]), and provides a sectoral lens to complement macro-economic scenarios.
Cyclicality of demand
Beyond the initial impact of the containment measures on the ability to produce and supply, industries also differ in terms of the extent to which the demand for their products varies in response to changes in current and expected incomes. In response to an actual or expected decrease in income, households cut back on spending. This is especially relevant for purchases of luxury goods and consumer durables, which typically have a high elasticity of demand based on income. However, even beyond this drop, firms that produce basic items will still be affected, due to a fall in business confidence and increased uncertainty about the future. These factors reduce firms’ willingness to invest, even if they have escaped relatively unscathed from the direct impacts of the crisis.
A simple proxy measure of the sensitivity of demand at the industry level is the correlation between industry value-added growth and national gross domestic product (GDP) growth, shown in Figure 3.8. Essential services to the household sector, such as Health, Education, and Care and social work (all of which have a significant degree of government funding in most countries), core utilities such as Electricity and gas, and Water, sewerage and waste, and the production of food and related items (Agriculture, Food and beverage manufacturing) tend to be relatively insulated from fluctuations in aggregate demand.14 In contrast, most other manufacturing industries, and particularly those that produce durable investment goods (such as Machinery and equipment), experience much stronger fluctuations through the business cycle and therefore tend to suffer more during recessions.
Figure 3.8. Correlation between industry value-added growth and national GDP growth
Note: The cyclicality indicator is constructed by using the industry-specific coefficients from a regression of growth in industry-level value added on growth in aggregate GDP, controlling for country-industry fixed effects, and therefore controlling for the industry relative size in the country’s economy. Public administration and defence is the reference industry – its coefficient is zero by definition and other industries are expressed relative to it. A larger coefficient indicates greater cyclical variation in industry output associated with changes in aggregate production. While this indicator provides a simple proxy for the relative demand effects of recessions on different industries, it may also be reflective of the industrial structure of OECD countries, with supply shocks in large sectors having a greater influence over aggregate GDP than in smaller sectors. This indicator is computed based on 35 OECD countries (Australia, Austria, Belgium, Canada, Costa Rica, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Lithuania, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, the United Kingdom and the United States) over the period 1970-2017.
Source: OECD calculations based on OECD (2020[3]), Structural Analysis Database (STAN), http://oe.cd/stan (accessed in May 2020).
Destination of industry output
A second factor determining aggregate demand for products at the industry level is the final destination of those products; that is, whether the industry primarily serves households, other businesses, government, or foreign demand. This is particularly relevant in the case of COVID-19, as governments step in to provide different forms of support. For example, while demand for investment goods can be expected to drop relatively more than for consumption goods, policies that ease firms’ borrowing constraints may help to mitigate the reduction in investment spending that is due to financing constraints rather than reduced profitability of investment projects and increased uncertainty. Similarly, where industries are primarily serving private demand, government support to employment and household incomes can dampen the strength of the demand shock. Figure 3.9 shows the share of value added accounted for by the final destination of each industry’s output.
Fluctuations in output of investment- and export-heavy industries are more closely linked to the business cycle than those that serve governments or households. This includes investment both in physical goods (e.g. the Construction sector) and also in intangible assets (e.g. IT services, Legal and accounting, and Scientific R&D that feed into final investment). Trade flows tend to contract more strongly than GDP, and the global nature of the COVID-19 pandemic suggests that export-reliant sectors – particularly those supplying investment or durable consumer goods (e.g. Electrical equipment, Machinery and equipment manufacturing) – may also be strongly affected. In contrast, industries which primarily serve the public sector are expected to be relatively less affected by medium-term demand fluctuations, as governments step in to support core services and infrastructure spending.15
Figure 3.9. Share of industry value added by final destination
Note: Share of household consumption, exports, gross fixed capital formation (“investment”), and government consumption in the final destination of value added for the industry. For example, the share of exports – just like the other components of demand – captures both products which are directly exported, and value added which is embodied in export goods. Data available for 2005-15. Reported data is averaged over the period 2013-15 for each country, then an unweighted average is taken across 37 OECD countries. Industry composition accounts for 77% (household), 62% (exports), 73% (GFCF), and 84% (government consumption) of the total variation in these shares across OECD countries between 2005 and 2015, suggesting that cross-time and cross-country variation is moderate compared to industry variation.
Source: OECD calculations based on OECD (2018[12]), Inter-Country Input-Output (ICIO) Database, http://oe.cd/icio (accessed in July 2020).
Liquidity and credit constraints
Industries differ in the extent to which firms can cope financially with a period of inactivity or reduced demand. There are two types of financial vulnerability to consider. First, the extent to which industries structurally need more liquidity in the short run to operate, due to the characteristics of the production process. Second, the likelihood of financial constraints arising in the longer run due to borrowing constraints, as firms attempt to access credit to fund future investment and growth, which are likely to affect the speed of the recovery as economic activity resumes.
Short-term liquidity needs: Cash conversion cycle
In the short run, firms are exposed to a liquidity risk resulting from a collapse in cash inflows in the wake of reduced demand or restrictions on their operations imposed to reduce the spread of the virus. Some liquidity needs may be reduced as firms downscale their production and variable costs (e.g. firms reducing their labour inputs and their purchase of raw materials, adjusting inventories, etc.), but this might not be enough to offset the effect of the drop in sales revenue entirely. Additionally, firms also incur expenses such as rents or interest payments that are fixed (at least in the short term) and therefore inelastic to demand and supply conditions.
This section sheds light on the industry dimension of liquidity risks, complementing OECD work on modelling short-term liquidity risks that is discussed in more detail in Chapter 4 (OECD, 2020[15]). It presents a measure of liquidity needs – the cash conversion cycle (CCC) – that is related to the nature of the activity and the production process of firms in particular sectors, rather than to aggregate financial stability, debt-servicing levels, or financial sector development.
The CCC refers to the average length, in days, between the moment a firm pays for its raw materials and intermediate inputs and the moment it receives payment for the sale of the final output. As such, it measures the time it takes for a company to convert resources used into cash flows from sales. As activity resumes following lockdowns, industries that typically experience a long delay between incurring expenses and realising the value of those expenses (a long CCC) may face difficulties if their activity is liquidity intensive, as most firms have experienced a reduction of their liquidity cushions.
As shown in Figure 3.10, manufacturing industries tend to have a relatively longer CCC, with high-tech manufacturing experiencing a particularly long delay between incurring expenses and receiving payment. In contrast, services industries and utilities tend to have a rapid turn-around between expenses and receipts, such that liquidity constraints may ease relatively quickly once production picks up.
Figure 3.10. Cash conversion cycle
Note: Average length in days within an industry between the moment a firm pays for its raw materials and the moment it is paid for the sale of its final output during the normal course of operations. The CCC used here is based on non-deflated firm-level data for active firms with more than ten employees. For reasons of data availability, unconsolidated accounts are used by default. Consolidated accounts used when unconsolidated accounts are not available. For each indicator, a minimum number of non-missing observations is required: 10 observations per country-industry, and 4 000 per country (irrespective of industry distribution). The industry coverage is adjusted to match the SNA A38 classification; however, data for some industries are not available for the financial indicators (SNA A38 industries Public administration, Education, Health, Social work, Arts and entertainment, and Other service). Data is averaged over 2011-15, and the median is taken over the following countries: Austria, Belgium, Finland, France, Hungary, Italy, Japan, Norway, Portugal, Spain and Sweden.
Source: OECD calculations based on ORBIS (2017) (accessed in May 2020).
Longer-term borrowing constraints and the tangibility of assets
Policy makers reacted swiftly to prevent the dramatic effects of liquidity shortages, but firms also face longer-term financial challenges that continue in the recovery period, and beyond. That is, besides immediate financing needs to ensure day-to-day operations, in the longer term, firms will likely also face financing constraints for new investment. For instance, banks may be reluctant to extend new loans, either because they are vulnerable due to a prolonged crisis with a surge in non-performing loans, or simply due to added economic uncertainty.
At the sectoral level, a commonly used indicator of potential borrowing constraints relates to the extent to which firms rely on tangible vs. intangible assets. Industries that have a highly intangible asset base may struggle more to secure loans, as these assets are less widely accepted as collateral (Braun, 2005[16]; Manova, 2008[17]; Demmou, Franco and Stefanescu, 2020[18]). Industries with particularly heavy reliance on intangible assets include a number of professional services industries, such as Media, IT services, Legal and accounting, and Other business services, as well as Wholesale and retail trade, and several high-tech manufacturing industries. Over the short-term, these industries may have been less affected by the direct effects of COVID-19 (as confirmed by recent OECD work (Demmou et al., 2021[19])) due to relatively high telework potential (from lower reliance on physical capital during lockdowns), better management and skills, and a reliable customer base. However, they may still face challenges over the longer term in accessing finance for recovery and expansion. Government policies that address constraints related to the financing of intangible investments can stimulate the recovery and future productivity growth.
Figure 3.11. Tangibility of assets
Note: The graph depicts the share of tangible fixed assets over total assets. Indicators are financial ratios from non-deflated firm-level data, for active firms with more than ten employees. For reasons of data availability, unconsolidated accounts are used by default. Consolidated accounts used when unconsolidated accounts are not available. For each indicator, a minimum number of non-missing observations is required: 10 observations per country-industry, and 4 000 per country (irrespective of industry distribution). The industry coverage is adjusted to match the SNA A38 classification; however, data for some industries are not available for the financial indicators (SNA A38 Public administration, Education, Health, Social work, Arts and entertainment, and Other services). The financial indicator based on firm-level micro-data refers to the industry fixed effect from a regression of the financial variable on industry and country-year fixed effects, but also gives consistent results computed as median by industry. An analysis of the sources of variation finds that industry fixed effects alone explain 30% of variation in asset tangibility at the firm level, while including also country-year fixed effects raises this only slightly to 33%. Braun (2005[16]) shows that asset tangibility is fairly stable across industries over time. Reported data is averaged over 2011-15 and the median is taken across the following countries: Austria, Belgium, Brazil, Finland, France, Hungary, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden and Turkey.
Source: OECD calculations based on ORBIS (2017) (accessed in May 2020).
Conclusions and policy implications
The COVID-19 crisis has affected firms and workers differently not only between countries, but also between industries. The initial impact of the pandemic set this crisis apart from many previous economic crises, due to the immediacy and severity of the containment response and the supply-side shock it induced. Shutdowns to all but essential industries disrupted production and cut off the flow of services that could not be provided remotely, and changes to consumer behaviour as a result of restrictions altered demand patterns.
Different sectors of activity are more vulnerable or more resilient to crises like the COVID-19 pandemic, in part because of structural characteristics. These include how goods and services are produced, procured and supplied; how firms and industries are interconnected, including with other parts of the economy like the financial sector; and the structure of demand for their outputs. Importantly for policy, different forms of restrictions and support measures are likely to have different impacts based on these characteristics.
For example, focusing on the ability to work remotely, only around 31% of workers across the OECD could do their jobs via telework, but telework rates varied widely by industry. Roughly 70% of jobs in IT and financial services can be done remotely, whereas in industries such as Hospitality and food services, Agriculture and Construction, fewer than 20% of jobs can be done through telework. The continuation of normal functioning also depends on whether customers could continue to purchase and receive goods and services remotely through digital modes of supply, such as e-commerce. Highly customer-facing industries such as Hotels and food services, and Arts and entertainment, are more at risk of suffering from containment restrictions.
The COVID-19 crisis has continued to affect the demand side of the economy beyond the impacts of the initial containment measures (e.g. due to changes in household income, investment, liquidity, economic uncertainty and travel restrictions). Industries more sensitive to changes in demand (e.g. manufacturing, luxury retail), and those that are heavily dependent on foreign exports, are subject to much stronger declines or fluctuations than industries with more inelastic demand, or that produce essential goods (e.g. utility services, healthcare, food).
Industries also differ in the extent to which firms can cope with financial difficulty. The abrupt collapse of cash inflows led to immediate concerns, but in the long term, firms may face borrowing and investment constraints that leave them unable to survive – let alone grow – through the recovery period. An industry perspective can help governments anticipate these concerns in particular parts of the economy, and implement targeted measures to help viable firms access finance during and beyond the crisis. Chapter 4 complements this industry perspective on financial constraints, drawing on more fine-grained analysis at the firm level.
While analysing the potential role of structural characteristics of industries can provide valuable insight for policy targeting and design, this chapter does not assess how the differential impacts at the industry level combine and play out on a more aggregate level, acknowledging the fact that many other factors will shape these effects. Instead, the topical chapters that follow discuss some of these and provide the basis for more specific policy recommendations.
Annex A provides a structured overview of the different industry characteristics discussed in this chapter, summarising the individual dimensions into indicators and presenting them in different ways, to enable comparisons across indicators as well as across industries. Annex B also contains an overview table of the relative size of industries across countries to allow a case-by-case assessment of the relative importance of single industries – and hence the identified vulnerabilities and potential channels of impact of the crisis – for individual countries.
Subsequent sections of this volume explore in further detail the many factors shaping the economic impacts of the crisis beyond structural industry characteristics, and relate these to individual countries. These factors are organised into the overarching topics of Business dynamics and financial vulnerabilities (Chapter 4), Supporting productivity through digital technologies (Chapter 5), Industrial and international connectedness (Chapter 6), and Inclusiveness across gender and skill groups (Chapter 7).
References
[1] Andersen, A. et al. (2020), “Consumer Responses to the COVID-19 crisis: Evidence from Bank Account Transaction Data”, CEBI Working Paper, No. 18/20, Center for Economic Behaviour and Inequality, University of Copenhagen, https://doi.org/10.2139/ssrn.3609814.
[24] Arriola, C. et al. (2020), “Efficiency and risks in global value chains in the context of COVID-19”, OECD Economics Department Working Papers, No. 1637, OECD Publishing, Paris, https://doi.org/10.1787/3e4b7ecf-en.
[20] Baldwin, R. and R. Freeman (2020), “Supply chain contagion waves: Thinking ahead on manufacturing ‘contagion and reinfection’ from the COVID concussion”, VOX, CEPR Policy Portal, https://voxeu.org/article/covid-concussion-and-supply-chain-contagion-waves.
[16] Braun, M. (2005), “Financial Contractability and Asset Hardness”, SSRN Electronic Journal, https://doi.org/10.2139/ssrn.2522890.
[19] Demmou, L. et al. (2021), “Insolvency and debt overhang following the COVID-19 outbreak: Assessment of risks and policy responses”, OECD Economics Department Working Papers, No. 1651, OECD Publishing, Paris, https://doi.org/10.1787/747a8226-en.
[18] Demmou, L., G. Franco and I. Stefanescu (2020), “Productivity and finance: the intangible assets channel - a firm level analysis”, OECD Economics Department Working Papers, No. 1596, OECD Publishing, Paris, https://doi.org/10.1787/d13a21b0-en.
[25] Dingel, J. and B. Neiman (2020), “How Many Jobs Can be Done at Home?”, NBER Working Paper, No. 26948, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w26948.
[7] Espinoza, R. and L. Reznikova (2020), “Who can log in? The importance of skills for the feasibility of teleworking arrangements across OECD countries”, OECD Social, Employment and Migration Working Papers, No. 242, OECD Publishing, Paris, https://doi.org/10.1787/3f115a10-en.
[8] Eurofound (2017), European Working Conditions Survey, 2015 (data collection), UK Data Service. SN: 8098, https://doi.org/10.5255/UKDA-SN-8098-4.
[4] Fana, M. et al. (2020), “The COVID confinement measures and EU labour markets”, JRC Technical Report, Publications Office of the European Union, Luxembourg, https://doi.org/10.2760/079230.
[2] Goolsbee, A. and C. Syverson (2020), “Fear, Lockdown, and Diversion: Comparing Drivers of Pandemic Economic Decline 2020”, NBER Working Paper, No. 27432, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w27432.
[13] Guilhoto, J., M. Sonis and G. Hewings (2005), “Linkages and Multipliers in a Multiregional Framework: Integration of Alternative Approaches”, Australasian Journal of Regional Studies, Vol. 11/1, pp. 75-89.
[11] Koren, M. and R. Petö (2020), “Business disruptions from social distancing”, Covid Economics - Vetted and Real-Time Papers 2, pp. 13-31, https://cepr.org/sites/default/files/news/CovidEconomics2.pdf.
[17] Manova, K. (2008), “Credit constraints, equity market liberalizations and international trade”, Journal of International Economics, Vol. 76/1, pp. 33-47, https://doi.org/10.1016/j.jinteco.2008.03.008.
[5] OECD (2021), Business dynamism during the COVID-19 pandemic: Which policies for an inclusive recovery?, OECD Publishing, Paris, https://dx.doi.org/10.1787/f08af011-en.
[29] OECD (2021), Global value chains: Efficiency and risks in the context of COVID-19, OECD Publishing, Paris, https://dx.doi.org/10.1787/67c75fdc-en.
[26] OECD (2021), OECD COVID-19 Hub, OECD Publishing, Paris, https://www.oecd.org/coronavirus/en/.
[22] OECD (2021), OECD Economic Outlook, Interim Report March 2021, OECD Publishing, Paris, https://doi.org/10.1787/34bfd999-en.
[21] OECD (2020), “Business as usual or fundamental shift? Resilience and reallocation at the time of COVID19 - Evidence from online job vacancies”, mimeo.
[15] OECD (2020), Corporate sector vulnerabilities during the Covid-19 outbreak: Assessment and policy responses, OECD Publishing, Paris, https://dx.doi.org/10.1787/a6e670ea-en.
[30] OECD (2020), COVID-19 and global value chains: Policy options to build more resilient production networks, OECD Publishing, Paris, https://dx.doi.org/10.1787/04934ef4-en.
[10] OECD (2020), “E-commerce in the times of COVID-19”, OECD Policy Responses to Coronavirus (COVID-19), OECD Publishing, Paris, https://www.oecd.org/coronavirus/policy-responses/e-commerce-in-the-time-of-covid-19-3a2b78e8.
[14] OECD (2020), “Evaluating the initial impact of COVID-19 containment measures on economic activity”, OECD Policy Responses to Coronavirus (COVID-19), OECD Publishing, Paris, https://www.oecd.org/coronavirus/policy-responses/evaluating-the-initial-impact-of-covid-19-containment-measures-on-economic-activity-b1f6b68b/.
[28] OECD (2020), Food Supply Chains and COVID-19: Impacts and Policy Lessons, OECD Publishing, Paris, https://dx.doi.org/10.1787/71b57aea-en.
[27] OECD (2020), “Key Issues Paper”, 2020 Ministerial Council Meeting, OECD Publishing, Paris, https://www.oecd.org/mcm/Key-issues-paper-MCM2020.pdf.
[6] OECD (2020), OECD Economic Outlook, Volume 2020 Issue 1, OECD Publishing, Paris, https://doi.org/10.1787/0d1d1e2e-en.
[23] OECD (2020), OECD Economic Outlook, Volume 2020 Issue 2, OECD Publishing, Paris, https://doi.org/10.1787/39a88ab1-en.
[9] OECD (2020), Productivity gains from teleworking in the post COVID-19 era: How can public policies make it happen?, OECD Publishing, Paris, https://dx.doi.org/10.1787/a5d52e99-en.
[3] OECD (2020), Structural Analysis (STAN) Database, http://oe.cd/stan.
[12] OECD (2018), Inter-Country Input-Output (ICIO) Database, http://oe.cd/icio.
Notes
← 1. Many of the general features of the crisis have been covered in the various contributions on the OECD COVID-19 Hub (OECD[26]), as well as in OECD (2021[22]) and OECD (2020[23]).
← 2. Where cross-country data is available at the firm or industry level, simple diagnostic regressions have been performed to confirm that observed variations are largely explained by industry characteristics, rather than by country-specific or idiosyncratic variations. Details of these assessments are included in the notes below the relevant figures.
← 3. Note that essential industries are different from what are often considered essential goods, which include pharmaceutical products such as diagnostic tests, respiratory devices, or personal protective equipment (masks). Previous OECD work has looked at policies to ensure the supply of essential goods, e.g. OECD (2021[29]; 2020[30]; 2020[27]).
← 4. For example, ongoing OECD work based on Burning Glass Technologies data indicates that vacancies – taken as a proxy for hires – increased in the UK healthcare sector during the lockdown period (OECD, 2020[21]). Similarly, the rapid increase in demand for some specific goods such as medical and personal protective equipment led to shortages in many countries, with producers moving rapidly to ramp up supply despite temporary disruptions and transport constraints (OECD, 2020[27]).
← 5. Note that this is different from what are often considered essential goods, which include pharmaceutical products such as diagnostic tests, respiratory devices, or personal protective equipment (masks) (OECD, 2021[29]; 2020[30]; 2020[27]).
← 6. Statistical classification of economic activities in the European Community.
← 7. An alternative measure of potential telework is that of Dingel and Neiman (2020[25]) who estimate the number of jobs that can be done entirely at home in the United States. These authors link information from O*NET on regular tasks performed in different occupations with information on the occupational composition of the United States workforce. Aggregated to the A38 industry level, Dingel and Neiman’s estimates of telework potential for the United States are strongly correlated with the cross-country average from Espinoza and Reznikova (2020[7]) (correlation coefficient of 0.9).
← 8. One exception is the Agriculture, Forestry and Fishing sector, in which surveys show that 39% of workers in the United Kingdom (2019) and 48% in Europe (2015) reported that they sometimes worked from home, and 40% of workers in Europe reported that they worked from home on a regular basis (at least several times per month). This is in stark contrast to the predicted level of remote work based on tasks, and may reflect instead a tendency for farm owners and workers to live on the premises.
← 9. “Teleworkable” and “teleworkability” are terms used throughout the report to describe jobs and tasks that are able to be done through telework. Originating mainly in the COVID-19 crisis, given extensive literature and research arising from the rapid global uptake of telework, the terms are now of demonstrated accepted and common use in many official documents. As such, they are used within this context in this document.
← 10. In addition to this expansion to new product types, new firms and new consumer groups have also shifted to e-commerce, as discussed in more detail also in Chapter 5.
← 11. More specifically, a one percentage point increase in the share of employment in occupations involving regular face-to-face contact with customers is associated with a 0.4 percentage point additional decline in entry.
← 12. For further discussion of supply chain issues in COVID-19, see for example OECD (2020[28]), OECD (2020[27]), Baldwin and Freeman (2020[20]), Arriola et al. (2020[24]).
← 13. While relying on foreign inputs through global value chains may increase risks on the production side, integration in international trade networks can also be a source of resilience for consumption, as domestically produced final products can be replaced by foreign ones in case of lockdowns.
← 14. Specifically, value added growth in these industries varies by about half as much as the annual change in aggregate GDP growth. The majority of industries see around a one-to-one relationship between industry value-added growth and aggregate GDP growth, while in Construction and durable goods manufacturing, a 1.0 percentage point change in GDP is associated with a 1.5 percentage point change in industry value added, on average.
← 15. The distinction between household and government consumption depends in part upon the model of service used in different countries. For example, while in some countries residential care (for example of elderly people) is directly operated by public institutions (government consumption), in others it is operated by private institutions, with governments directing funding to households through pension systems (household consumption).