Reader’s guide

This publication contains three thematic chapters focusing on the impact of COVID-19 on local labour markets, how other labour market transitions could change as a result, and the local dimension of rebuilding better. It is accompanied by online only country profiles, available at the publication’s website.

The majority of data is presented at the OECD Territorial Level 2 (TL2), which typically represents the first administrative tier of subnational government. Often, functional local labour markets can operate on a scale smaller than the OECD’s TL2 regional classification, but span several TL3 regions. This publication predominantly uses TL2 data to ensure as broad a coverage as possible, as data availability is limited across countries and time for TL3 regions. For many analyses, the regional variation at the TL2 level within a country should be considered the lower bound of the actual variation across local labour markets. For more information, see OECD (2018[1]) and OECD (2020[2]). Where included in graphs, the numbers in parenthesis after the country name/label indicate the number of regions included in the analysis. 

Given the rapidly evolving situation in relation to COVID-19, all efforts were made to provide the most up-to-date and relevant data possible. However, lags in the availability of internationally comparable subnational data present limitations. For some analyses, national or other data sources have been used to help compensate. It is important to note, however, that COVID-19 has impacted data collection procedures in some countries, and accordingly the quality of statistics produced. Thus, these statistics may be subject to revisions. Differences in how countries classify different types of workers (e.g. those on furlough) may also limit international comparability.

The remainder of this section provides further details on the methodology and sources for specific analyses.

The estimates of the share jobs at risk by region are based on the analysis undertaken in the OECD’s COVID-19 policy note, “From pandemic to recovery: local employment and economic development”, published in April 2020 (OECD, 2020[3]). Given the lack of comparable and timely official subnational data, the approach followed in the note required making hypotheses on the sectors hardest hit by containment measures. The OECD note “Evaluating the initial impact of COVID-19 containment measures on economic activity” (OECD, 2020[4]) provides a reference framework for identifying specific sectors considered at risk. Using the standard ISIC-4 classification of economic activities, the sectors considered as most affected include manufacturing of transport equipment, construction, wholesale and retail trade, air transport, accommodation and food services, real estate services, professional service activities, and arts, entertainment and recreation. According to the above-mentioned OECD note, decline in output in those activities was expected to range from 50% to 100%. For this analysis, the same expected decline rates are assumed, with the exception of manufacturing, for which the immediate expected decline has been halved (from 100% to 50%). The resulting classification assumes that transport manufacturing and “other personal activities” (e.g., hairdressers fall within this category) face a 50% output decline, similarly to construction and other professional services. Output in the other above-mentioned sectors is expected to face a 75% output decline.

The selection of the above-mentioned sectors as “high risk” is broadly consistent with the sectors receiving the largest number of claims under the French short-time work scheme as of April 1st, 2020, as reported in DARES (2020[5]). The note reported that the five sectors receiving the largest shares of claims were trade, accommodation and hospitality, construction, professional service activities and other professional services.

The estimates of the share jobs amenable to teleworking are based on the analysis undertaken in the OECD’s COVID-19 policy note, “Capacity for remote working can affect lockdown costs differently across places” published in June 2020 (OECD, 2020[6]) and Regions and Cities at a Glance 2020 (OECD, 2020[7]).

The assessment of regions’ capacity to adapt to remote working is based on the diversity of tasks performed in different types of occupations and is structured in two steps. The first step requires classifying each occupation based on the tasks required and according to the degree to which those tasks can be performed remotely. For example, occupations requiring workers to be outdoors (e.g., food delivery person) or to use heavy equipment (e.g., a vehicle) are considered to have a low potential of remote working. In contrast, occupations requiring only a laptop and an internet connection (e.g., an accountant, finance specialist, etc.) will have a high potential to work remotely. This classification is based on a recent study by Dingel and Neiman (2020[8]) which is built from the O*NET surveys conducted in the U.S. These surveys include targeted questions that make it possible to assess the potential of remote working of occupations in a systematic way.

The second step relies on data from labour force surveys and consists of assessing the geographical distribution of different types of occupations and subsequently matching those occupations with the classification performed in the first step. Combining the two data sets allows for an estimate of the number of workers that can perform their task from home as a share of the total employment in the region.

While other authors have used the US Standard Occupational Classification system (SOC) to classify occupations, this analysis uses the International Standard Classification of Occupations (ISCO), requiring a crosswalk between the two schemes for associating each occupation to a level of remote working potential in other countries. It is worthwhile noting that this work assumes that task content of occupations is consistent across countries, as in Saltiel (2020[9]) or Gottlieb, Grobovsek and Poschke (2020[10]). Other studies focused on specific countries have categorised the remote working potential of occupations based on subjective, expert judgement, such as OFCE (2020[11]) and Magrini (2020[12]) for France and the United Kingdom, respectively.

The geographical concentration of total and high-skill employment is measured by using the Herfindahl-Hirschman Index (HHI). The index is calculated by squaring the total and high-skill employment shares of each region in a country and then summing the resulting numbers. It varies between 0 and 1, where 0 indicates that jobs are not geographically concentrated and 1 indicates that jobs are highly concentrated in one region. This analysis is only relevant for countries with more than one TL2 region.

In order to assess whether concentration has increased or decreased over time, the index has been computed in two periods of time, as shown in Table 1. Data for France, Hungary and Poland should be interpreted with caution as a change in the regional classification over the period of analysis could impact the results.

The analysis of job polarisation is based on the evolution of employment by occupation over time at the subnational level. It follows on previous OECD analysis undertaken at the national level, e.g. OECD (2017[13]). In order to classify occupations by skill levels, the following categories have been used:

  1. 1. High-skill occupations include jobs classified under the ISCO-88 major groups 1, 2, and 3. That is, legislators, senior officials, and managers (group 1), professionals (group 2), and technicians and associate professionals (group 3);

  2. 2. Middle-skill occupations include jobs classified under the ISCO-88 major groups 4, 6, 7, and 8. That is, clerks (group 4), skilled agricultural workers (group 6), craft and related trades workers (group 7), and plant and machine operators and assemblers (group 8);

  3. 3. Low-skill occupations include jobs classified under the ISCO-88 major groups 5 and 9. That is, service workers and shop and market sales workers (group 5), and elementary occupations (group 9).

Employment data beyond 2010 was mapped from ISCO-08 to ISCO-88 using a many-to-many mapping technique. Data from different classification systems is mapped to ISCO-88 classification.

The change over time is calculated as the percentage point change in the share of jobs at each skill level. Table 2 indicates the years of analysis and the data sources by country.

The share of jobs at risk of automation is computed by adapting the methodology to produce national level estimates undertaken by Nedelkoska and Quintini (2018[14]). This approach uses individual-level data from the OECD Survey of Adult Skills (PIAAC), which provides information on the skills composition of each person’s job and their skillset. For the subnational estimates provided in this report, data on regional employment by occupation is combined with the estimated probabilities of automation from Nedelkoska and Quintini (2018[14]). These subnational estimates assume that jobs within each job category have the same risk of automation across all regions of a country.

“High risk of automation” refers to the share of workers whose job faces a risk of automation of 70% or above. “Significant risk of change” reflects the share of workers whose job faces a risk of automation between 50% and 70%. Further information on the methodology can be found in OECD (2018[1]) and Nedelkoska and Quintini (2018[14]).

Table 3 indicates the years of analysis and the data sources by country. All analysis was undertaken at the TL2 level unless otherwise indicated.


[5] DARES (2020), Tableau de bord hebdomanaire - Situation sur le marché du travail durant la crise sanitaire au 1er avril 2020,

[8] Dingel, J. and B. Neiman (2020), “How many jobs can be done at home?”, Journal of Public Economics, Vol. 189,

[10] Gottlieb, C., J. Grobovšek and M. Poschke (2020), “Working from home across countries”, Covid Economics, Vol. 1/8.

[12] Magrini, E. (2020), How will Coronavirus affect jobs in different parts of the country?, Centre for Cities, (accessed on 20 September 2020).

[14] Nedelkoska, L. and G. Quintini (2018), “Automation, skills use and training”, OECD Social, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris,

[6] OECD (2020), “Capacity for remote working can affect lockdown costs differently across places”, OECD Policy Responses to Coronavirus (COVID-19),

[2] OECD (2020), Delineating Functional Areas in All Territories, OECD Territorial Reviews, OECD Publishing, Paris,

[4] OECD (2020), “Evaluating the initial impact of COVID-19 containment measures on economic activity”, OECD Policy Responses to Coronavirus (COVID-19),

[3] OECD (2020), “From pandemic to recovery: local employment and economic development”, OECD Policy Responses to Coronavirus (COVID-19),

[7] OECD (2020), OECD Regions and Cities at a Glance 2020, OECD Publishing, Paris,

[1] OECD (2018), Job Creation and Local Economic Development 2018: Preparing for the Future of Work, OECD Publishing, Paris,

[13] OECD (2017), OECD Employment Outlook 2017, OECD Publishing, Paris,

[11] OFCE (2020), “Evaluation au 20 avril 2020 de l’impact économique de la pandémie de COVID-19 et des mesures de confinement en France”, No. 66, Sciecnces Po, OFCE, (accessed on 20 September 2020).

[9] Saltiel, F. (2020), “Home working in developing countries”, Covid Economics: Vetted and Real-Time Papers, No. 6, (accessed on 15 September 2020).

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