2. The effects of automation in the Basque Country, Spain

Abstract

This chapter analyses the future of work in the Basque Country, Spain. The COVID-19 crisis has resulted in a labour market shock, reversing years of progressive job growth. COVID-19 is likely to accelerate automation in the region as firms look to technology as a way to pandemic proof their operations. Automation could put some jobs at-risk, including within the region’s historical industrial base. COVID-19 may also accelerate the automation of certain service-based occupations, such as sales workers or cleaning staff. The region, however, is in a strong position for this transition, as it benefits from a high share of jobs that can be carried out remotely, while Lanbide, the region’s public employment service, is taking steps to map the way occupations are evolving.

In Brief

The COVID-19 crisis is likely to accelerate automation in the Basque Country

The COVID-19 crisis will cause an unprecedented downturn in the Basque labour market, interrupting the prolonged recovery in employment since the 2008 crisis. Prior to COVID-19, unemployment had continued to decrease in the region, falling below 10% in 2019 – lower than the Spanish average of 14% but double the OECD average of around 5%. Employment protection measures put in place by the Spanish and Basque governments helped limit an initial surge in redundancies due to lockdown measures. The majority of requests for Short Time Work (STW) schemes came from industrial manufacturing and food services and accommodation, totalling 33.1% and 19.1% of all requests respectively (10 June 2020).
The Basque Country benefits from a relatively high share of jobs that can be carried out remotely compared to most Spanish regions, with 32% of jobs that can be carried out remotely, leaving 68% without the possibility of teleworking.
The COVID-19 crisis is likely to accelerate job automation in the region. Prior to the crisis, over 205 000 and 293 000 jobs were calculated to be at high or significant risk of automation respectively, representing 22% and 32% of employment in the Basque Country. The OECD calculates that a job is at high risk if over 70% of tasks within an occupation are likely to be automated, while ones at significant risk is where 50-70% of tasks could be automated, requiring workers to reskill.
Between 2008 and 2017, job loss in the Basque Country was concentrated in the region’s manufacturing and construction sectors. The region lost 50 300 construction jobs and 51 300 in industrial manufacturing, representing respective falls of 5 and 2.47 percentage points of total employment. Contrary to these trends, the COVID-19 crisis is likely to drive job loss and creation in sectors particularly linked to tourism and trade. Discounting employment protection measures, OECD calculations predict 10% of employment in retail and wholesale trade in the Basque Country to be at risk of suppression, compared to 12.4% in Catalonia and 10.9% in Madrid.
Within sectoral trends, the Basque Country tended to create jobs in occupations both at high and low risk of automation between 2008 and 2018. The region created over 12 300 stationary plant and machine operator positions and over 19 000 metal, machinery and related trades workers since 2008, occupations at high risk of automation. Meanwhile, the Basque Country lost 20 300 cleaners and helper positions and 23 800 building and related trades workers, also at high risk of automation.
Raising job quality is an opportunity to strengthen resilience and drive recovery in the Basque Country. However, following Spanish averages, temporary and involuntary part-time employment has proliferated in the Basque Country, with many contracts lasting less than six months, or even one week. In 2019, 92% of contracts signed in the Basque Country had a defined end date, while the OECD has ranked Spain among the lowest countries on job quality. Skill utilisation strategies offer an opportunity for the region to incite companies to invest in employee skills and move firms into higher value added products.

Introduction

Automation and digitalisation are likely to accelerate as the Comunidad Autónoma del País Vasco in Spain (onwards: Basque Country) recovers from the COVID-19 crisis. These trends will impact some sectors, places and groups more than others, raising the prospect of greater labour market inequalities (OECD, 2020[1]). To analyse these trends, this chapter is structured in three sections: section 1.1 provides an overview of the initial effects of the COVID-19 lockdown measures on the labour market in the Basque Country; section 1.2 presents OECD estimates of automation on jobs in the region; while section 1.3 looks at recent trends in job quality.

2.1. COVID-19 will have unequal impacts on workers, firms and places

2.1.1. Policy measures helped limit surges in unemployment due to the pandemic

In 2020, the COVID-19 pandemic prompted governments to put in place lockdown measures to slow the spread of the virus, saving lives and reducing strain on health systems. The measures halted economic activity across the globe, precipitating a recession across OECD countries. In 2020, the European Union’s (EU) and Spain’s real GDP are set to contract by over 8.3% and 10.9% respectively due to the COVID-19 crisis (European Commission, 2020[2]). This unprecedented situation comes at a time when the Basque Country was still recovering from the 2008 and 2010 economic shocks.

Lessons from the past can help inform how the COVID-19 labour shock could impact the Basque region over both the short- and long-term. In 2007, unemployment in the Basque Country reached 7.4%, compared to 8.2% in Spain and 5.8% in the OECD (Figure 2.1). The two waves of the 2008 economic crisis, however, pushed unemployment to a high of 16.7% and 26.3% in the Basque Country and Spain respectively in 2013. During this time, the OECD average was already recovering, reaching 7.9% in 2013. In 2019, the Basque unemployment rate was 2.6% higher than its 2008 level, sitting at 9.3%, showing the local labour market had not yet fully recovered. In 2019, this rate situated the region below the Spanish average of over 14.1%, but above the OECD average of 5.4%.

Between January and June 2020, unemployment rose from 11.0% to 13.5% in the Basque Country, showing the initial effects of lockdown measures and other disruptions (Figure 2.1). Although the lengthy employment shock that followed the 2008 crisis may indicate a prolonged recovery after the pandemic, other factors will also determine the duration and severity of the labour market crisis. For example, the “double-dip” nature of the 2008 crisis in Spain, with its second wave of consequences in 2010, suggests the effects of the COVID-19 crisis may be shorter or longer depending on a second shock. Increases in unemployment may also be set to continue through 2020 and 2021, particularly depending on how redundancy restrictions and other measures are loosened.

The Spanish government put in place a host of measure to help contain the economic consequences of the lockdown measures. In particular, the government implemented a EUR 138.2 billion fiscal package, including EUR 4.3 billion for health measures and EUR 19.2 billion to support employment, notably though Expedientes de regulación temporal de empleo (ERTEs), or Short Time Work (STW) schemes that allow workers to obtain unemployment benefits (OECD, 2020[3]). At the regional level, the Basque government has put in place a COVID-19 programme entailing EUR 841 million, including EUR 500 million to support self-employed workers and SMEs (Gobierno Vasco, 2020[4]). Outside of this main package, the Basque government is also supporting the teleworking capacity of Basque SMEs during the pandemic. The region put in place a EUR 2 360 000 fund to co-finance the acquisition of teleworking equipment for Basque companies and the EUR 390 000 INPLANTALARIAK programme to consult SMEs and self-employed workers on teleworking measures (Gobierno Vasco, 2020[4]).

Figure 2.1. Employment-support measures are helping limit surges in unemployment in the Basque Country, though the crisis has reversed years of recovery

2.1.2. A relatively high share of jobs can be carried out remotely in the Basque Country, an important factor for regional resilience

Facing a shutdown of all non-essential economic activity in March 2020, Basque enterprises adopted large-scale teleworking measures when possible to continue economic activity. Not all activities, however, can be performed remotely due to the nature of tasks performed, creating inequalities between occupations and sectors. The Basque Country appears as the Spanish region with the third-largest share of jobs that can be performed remotely. In the region, an estimated 32% of total jobs can be carried out remotely, compared to 31% average in Spain. The share of jobs that can be carried out remotely is only larger in Catalonia and the Madrid region, where 33% and 41% of jobs respectively can be done remotely (Figure 2.2).

The relatively high share of jobs amenable to remote work in the Basque Country may be explained by different factors. In particular, the Basque Country’s high level of educational attainment may support its teleworking capacity, as a strong statistical correlation has been found between educational attainmant and teleworking capacity (Özgüzel, Veneri and Ahrend, 2020[5]). The Basque Country’s teleworking capacity is also supported by its high urban density, as cities typically benefit from more developed internet infrastructure, enabling the region to leverage teleworking capacity (Özgüzel, Veneri and Ahrend, 2020[5]).

In a cross-national comparison, however, the share of jobs amenable to teleworking in the Basque Country is below averages in neighbouring countries. Across the OECD, an average of 34% of jobs can be carried out remotely, 2% above the proportion in the Basque Country. In France, Germany and Portugal the share of jobs that can be carried out remotely reaches national averages of 39%, 36% and 34% respectively, compared to 31% in Spain (Özgüzel, Veneri and Ahrend, 2020[5]).

Large-scale remote work has also revealed differences between sectors and occupations. Across the EU-27,the European Commission’s Joint Research Centre (JRC) estimated 40% of IT and communication service workers already worked remotely regularly or with some frequency before COVID-19, while this rate was low in sectors such as manufacturing (JRC, 2020[6]). This divide between higher and lower skill occupations can help guide policy decisions about employment protection and support for teleworking among occupations most vulnerable to lockdown measures.

Figure 2.2. The Basque Country is well positioned for teleworking among Spanish regions

2.1.3. Job loss was concentrated in construction and industrial manufacturing between 2008 and 2017

Driven by the collapse of a real estate bubble and a global decline in trade, after 2008 job loss in the Basque labour market were concentrated in construction and industrial manufacturing. Jobs in construction fell from representing nearly 10% of the Basque Country’s labour market in 2008 to under 5% in 2017, now lower than both the Spanish and OECD averages (Figure 2.3). The collapse of this sector has had a lasting influence on the composition of unemployed workers, as in 2016, 19.3% of the very long-term unemployed and 9.2% of the long-term unemployed in Spain still came from the construction sector (Bentolila, Jansen and García-Pérez, 2017[7]). The Basque Country also lost nearly 2.5 percentage points of its industrial manufacturing jobs over this period, compared to 3.2 percentage points in Spain. In the region, these losses represented 50 300 construction jobs and 51 300 in industrial manufacturing. Meanwhile, employment in public administration, defence, education, human health and social work activities grew, representing 27 200 jobs, or a growth of 1.4 percentage points.

As employment decreases sharply in manufacturing and non-tradeable sectors over the 2008-2017 period, productivity stayed positive. Between 2008 and 2011, labour productivity in manufacturing increased by 3.4 percentage points while it increased by 3 percentage points in non-tradeable services, including the region’s construction sector (Figure 2.3). These increases, however, were likely driven by large job suppression in these sectors, entailing a passive form of productivity growth (Orkestra, 2019[8]). In the manufacturing sector, employment loss and productivity rises showed signs of continuing through the 2008-2017 period, with a 0.8 percentage point loss of employment and an increase of 2.7 percentage points in productivity, indicating the sector was still experiencing the effects of the 2008 and 2011 downturns. In tradeable services, meanwhile, job loss also accompanied productivity decline between 2008 and 2011, as productivity fell by 1.2 percentage points and the sector lost 2 800 jobs. Between 2008 and 2017, a partial recovery in non-tradeable service employment accompanied by productivity growth of 1.4 percentage points may indicate a non-passive form of productivity growth driven by technology absorption, training or skill acquisition (OECD, 2018[9]).

Figure 2.3. The 2008 crisis hit employment hardest in the construction and industrial sectors

Figure 2.4. Productivity increases struggled to accompany employment growth following the 2008 crisis

2.1.4. Unlike 2008, employment in retail trade, food service and accommodation is particularly at risk from COVID-19

Initial evidence from the COVID-19 crisis shows that lockdown measures will effect employment disproportionately in the industrial manufacturing, tourism and retail sectors. The Spanish government lifted its initial lockdown measures from 10 May 2020 onwards, though has not lifted large-scale Spanish Short Term Work (STW) schemes. Indeed, Expedientes de Regulación Temporal de Empleo (ERTE), Spanish STW schemes, can serve as a gauge of the most distressed sectors.

Employment in food services and accommodation shows one of the highest signs of distress as lockdown measures prevented restaurants, bars and hotels from operating during March-May 2020. This sector, heavily driven by tourism in Spain, accounted for about one-fifth of STW requested in the Basque Country to Spain’s government as of 10 June 2020, for a sector that represents around 6.2% of the region’s jobs (Figure 2.5). This sector is likely to face a prolonged recovery as an uncertain recovery unfolds, especially in the prospect of new lockdown measures. Indeed, OECD analysis on the initial effects of lockdown measures show large differences in unemployment risk across regions may be partially accounted for by the size of the tourism sector (OECD, 2020[1]).

As in the previous crisis, the Basque Country’s large industrial manufacturing sector was also severely impacted. As of 10 June 2020, around one-third of STW requested in the Basque Country came from industrial manufacturing, for a sector representing 20.4% of total employment. As STW and other policy measures are lifted, employment in sectors such as retail and wholesale trade will also be at particular risk of destruction, as trade demand is set for a slow recovery in the face of uncertainty and possible new lockdown measures. According to OECD estimates, up to 10% of employment in the Basque Country involves retail and wholesale trade (Figure 2.6). Other key sectors at risk include art and entertainment, which the OECD estimates accounts for nearly 7% of employment in the region. Construction, on the forefront of the 2008 crisis, meanwhile, accounts for only 3.6% of STW requested as of 10 June 2020, for a sector that represents 6.1% of employment.

These sectors have been particularly vulnerable to COVID-19 as they have not have been able to turn to large-scale teleworking. Some have also been the most directly impacted by the shutdowns, as consumers have been unable to access services. As the government progressively lifts STW schemes, these sectors will require particular attention in terms of long-term support and reskilling opportunities for workers, particularly if demand does not return to pre-COVID levels.

Figure 2.5. Employment in industrial manufacturing, food services and accommodation showed the greatest signs of distress

Figure 2.6. OECD calculations show trade-related employment at particularly high risk in the Basque Country

2.2. What will be the impacts of automation in the Basque Country?

2.2.1. A higher share of jobs are at high risk of automation in the Basque Country than the OECD average

Automation has the potential to raise competitiveness and improve working conditions, though it can also supress jobs. The OECD considers a job at significant risk of change if 50% to 70% of the tasks within the job are vulnerable to automation, while those at high risk have more than 70% of tasks that could be replaced by a machine (Nedelkoska and Quintini, 2018[10]). When considering both jobs at high and significant risk, a smaller proportion of the overall labour market is vulnerable to automation in the Basque Country than all other Spanish regions, outside of Madrid, Ceuta and Melilla (Figure 2.7). Indeed, in Spain, 55% of jobs are at overall risk of automation compared to 54% in the Basque Country.

The Basque Country, however, has a significantly higher portion of jobs at high risk of automation compared to the OECD average. In the Basque Country, 22.2% are at high risk of automation compared to 14% across OECD countries, putting over 205 000 jobs in the region at high risk of suppression (Figure 2.7). Concerning high risk jobs, only Slovakia, Slovenia and Greece have a larger share of jobs at high risk of automation than Spain. The importance of industrial manufacturing as an employer in the Basque Country could be driving the region’s vulnerability to job automation. The sector tends to include a large share of jobs involving routine and non-cognitive tasks, at higher risk of replacement by technology. Moreover, evidence from past recessions indicate the region’s vulnerability to automation may increase with the COVID-19 crisis, as firms turn to automation to restructure production and cut costs (Box 2.1).

The region, meanwhile, has a nearly equal share of jobs at risk of significant change due to automation as the OECD average, with 293 000 jobs at risk, or 31.8% of total jobs, compared to 31.6% across the OECD. For those at high risk, this entails a risk of job suppression, while those at significant risk face change in the way these jobs are performed as many tasks becomes automated, calling for new skills to remain in the job (Nedelkoska and Quintini, 2018[10]) (see Box 2.2 for more information on the OECD methodology).

Indeed, the digitalisation of the workplace involves changes to the workplace beyond suppression that require the attention of policymakers. In Spain as in the Basque Country, evidence suggests these changes may be accelerating. Data on imports of multipurpose industrial robots indicates shipments increased by 12% between 2017 and 2018 in Spain, higher than all western European countries except Italy (International Federation of Robotics, 2018[11]). New technologies can harm the working conditions of manufacturing workers when algorithms send real-time information on their performance to centralised systems, damaging their autonomy and privacy, but may improve job quality when they reduce workplace accidents and decrease isolating and tedious tasks (Eurofound, 2018[12]). New workplace regulations will be required to ensure the working conditions of employees are upheld, while ensuring appropriate training measures are in place to ensure workers adapt and seize the productive advantages brought by technology.

Box 2.1. COVID-19 is likely to accelerate the uptake of labour-saving technologies

Evidence from pasts crises suggests automation could accelerate

Automation tends to accelerate during recessions, as enterprises replace human labour with cost-saving robots. Research, such as that by Hershbein and Kahn (2018), indicates routine job loss may explain the phenomenon of “jobless” recoveries in the United States, during which employment does not recover in certain sectors. According to this research, companies in the most heavily affected areas by the 2008 crisis in the United States tended to supress jobs in routine occupations, while hiring a greater share of higher skilled workers and increasing investments in capital. In particular, routine occupations involving manual tasks are found to have suffered the sharpest decline in employment share. The authors found that the hardest-hit US metropolitan areas were 5% more likely to contain education and experience requirements in job postings after the 2009 crisis, and 2-3% more likely to include cognitive or computer skill requirements. At the same time, firms increased investments in computers.

This risk of automation is compounded in the face of COVID-19, as routine occupations are particularly common in some of the hardest hit sectors such as industrial manufacturing, transportation and food services. In lead firms, particularly those who faced large supply chain disruptions due to COVID-19, companies may also automate production hand-in-hand with a reshoring global value chains as industry 4.0 technologies allow for more rapid adjustments to fluctuating demand.

Source: (Hershbein and Kahn, 2018[13]) (Muro, Maxim and Whiton, 2020[14])

Automation is also likely to generate inequalities between population groups within the Basque Country, as different groups tend to occupy jobs at higher or lower risk of automation. In the region, 51% of men and 57% of immigrants occupy jobs at risk of automation, making them particularly vulnerable (Figure 2.8). Men and immigrants tend to be overrepresented in the construction and the manufacturing sectors, where AI and other technologies may restructure production processes. 48% of women, meanwhile, occupy jobs at some risk of automation. Women may be at less risk as this groups tends to occupy less routine service jobs, in which less tasks that may be replaced by technology.

Multiple factors influence and structure job supression and change, determining the actual displacement of automatable jobs. Indeed, the effects of automation are determined by the rate at which technology is introduced, the way workers adapt as well as multiple differences in work organisation across countries and regions (Orkestra, 2019[15]). The social acceptability or the economic profitability of automation, also help weigh into the actual supression of a task or job at risk of automation (Le Ru, 2016[16]). Demand-side policies also play a role in the way automation unfolds. In the Basque Country, industrial and innovation strategies such as Basque Industry 4.0 and the region’s Smart Specialisation Strategy (RIS3) are encouraging this process of automation within firms, calling for attention on its effects on the workplace.

Figure 2.7. More jobs in the Basque Country are high risk compared to the OECD average, though less than all other regions in Spain

Box 2.2. How does the OECD calculate the risk of job automation?

The Programme for the International Assessment of Adult Competencies (PIACC)

Frey and Osborne (FO) estimated the number of occupations at high risk of automation in the United States using a two-step methodology. They conducted a workshop with a group of experts in machine learning, whom they provided with a list of 70 occupations and their corresponding O*NET task descriptions. Experts were asked “Can the tasks of this job be sufficiently specified, conditional on the availability of big data, to be performed by state of the art computer-controlled equipment?”. This allowed for the coding of each occupation as automatable or non-automatable. FO then used a machine learning algorithm to find out more about the links between the coding to automate and the list of O*NET variables. They were able to identify those variables (and their associated bottlenecks) with higher prediction power. High scores on these bottlenecks are likely to mean that an occupation is safe from automation. They could then compute a “probability of computerisation” for each occupation in the US, leading to the aggregate estimate that 47% of US jobs have a probability of automation of more than 70%.

Table 2.1. Automation bottlenecks
Computerisation bottleneck	O*NET variable
Perception and Manipulation	Finger dexterity Manual dexterity Cramped workspace; awkward positions
Creative intelligence	Originality Fine arts
Social intelligence	Social perceptiveness Negotiation Persuasion Assisting and caring for others
Note: Please refer to (Frey and Osborne, 2013[17]) for further details on the definition of automation bottlenecks.
Source: (Frey and Osborne, 2013[17])

Building on this approach, (Nedelkoska and Quintini, 2018[10])(NQ) calculated the risk of automation across 32 OECD countries. The approach is based on individual-level data from the OECD Survey of Adult Skills (PIAAC), providing information on the skills composition of each person’s job and their skillset. While drawing on FO, this methodology presents four main differences: (i) training data in the NQ model is taken from Canada to exploit the country’s large sample in PIAAC; (ii) O*NET occupational data for FO’s 70 original occupations were manually recoded into the International Standard Classification of Occupations (ISCO); (iii) NQ uses a logistic regression compared to FO’s Gaussian process classifier; (iv) NQ found equivalents in PIAAC to match FO’s bottlenecks. While PIAAC includes variables addressing the bottlenecks identified by FO, no perfect match exists for each variable. No question in PIAAC could be identified to account for job elements related to “assisting and caring for others”, related to occupations in health and social services. This implies that risks of automation based on NQ could be slightly overestimated.

Note: Please refer to (Nedelkoska and Quintini, 2018[10]) for further details on the definition of the PIAAC variables.

Source: (Nedelkoska and Quintini, 2018[10]). (OECD, 2018[18]).

Figure 2.8. Men and immigrants tend to occupy jobs with tasks more likely to be automated

2.2.2. Automation may accentuate longer term trends, putting industrial employment at risk in the region

The three occupations at high risk of automation with the greatest number of workers are all closely associated with the Basque industrial manufacturing sector (Table 2.2). These include:

Stationary plant and machine operators constitute the largest pool of workers at high risk of automation, representing over 23 000 workers in the region;
Metal, machinery and related trades workers represent the second-largest occupation group at high risk from automation in terms of number of workers, with 19 700 jobs at high risk.
Drivers and mobile plant operators represent the third-largest group, constituting 17 500 jobs at high risk of automation.

Automation of key industrial occupations may accelerate the relative and absolute decrease in industrial employment already underway in the region. This should be of particular concern in the Basque Country, as the region’s industrial base has been at the heart of its growth model since the 1980s, when the region leaned on industrial policies to redevelop its manufacturing sector (Box 2.3). Since 2000, industry, including the energy sector, has decreased from nearly 28% of total employment to 20% in 2017, compared to a smaller decrease in Spain, from 18% in 2000 to 12.3% in 2017 (Figure 2.9). During the 2008 economic crisis and its aftermath, between 2008 and 2015, the Basque Country lost 64 000 jobs in the sector, representing nearly 25% of sectoral employment.

The Basque Government has put industrial development at the heart of its new industrial strategy, seeing the sector a means to reduce unemployment, consolidate recovery, and raise social cohesion (Gobierno Vasco, 2017[19]). To do so, the region has put in place an Industry 4.0 policy, a process in which “the physical world of industrial production merges with the digital world of information technology – in other words, the creation of a digitized and interconnected industrial production, also known as cyber-physical systems” (UNIDO, 2017[20]). This involves supporting struggling firms and reinforcing financing instruments, particularly related to digitalisation, for example by promoting new industrial-technological projects and supporting digitalisation. The region, however, recognises the loss of lower-skilled industrial jobs as a major risk of the fourth industrial revolution in the Basque Country’s Employment Strategy 2020 (Gobierno Vasco, 2016[21]). In order for Industry 4.0 to reinforce its potential as a tool for job growth and social cohesion, regional innovation policies can work in tandem with employment policies to help workers prepare for a 4.0 setting. For instance, in the province of Ontario, Canada, the regional government has put in place a Second Career Program to help fund adult education of industrial workers who have lost their jobs (Box 2.4).

Box 2.3. Basque industrial reconversion in 1980-1990

The region inherits a tradition of industrial policy to steer economic change

The Basque economy has relied on industry since the start of the 20th century. Traditional industries range from iron and steel to machine tools and an auxiliary automotive sector (Gobierno Vasco, 2009[22]). In the late 1970s, the Basque economy entered a period of crisis as Spain liberalised its economy, toughening international competition (Gobierno Vasco, 2009[22]). As a response, in the early 1980s, the Basque Government put in place an industrial policy aimed at reconverting, reindustrialising and modernising the economy, particularly in sectors such as steel, shipbuilding and machine tools. The Basque Departamento de Industria (Department of Industry) and the Sociedad para la Promoción y Reconversión Industrial (SPRI) led this policy regionally, complementing the national Plan de Reconversión Industrial Sectorial (1980-1986). The Industry Department tended to lead more traditional industrial policies, associated with investments and restructuring, while the SPRI directed innovation programmes. Such innovation policies were varied, ranging from specific programmes for greater inclusion of technologies in companies, to efforts to internationalise Basque companies.

As part of this strategy, the Basque Country stimulated industry-business “clusters”. Ahedo (2004) defines clusters as: “a specific kind of sector or specialized field involving competing and cooperating firm concentrations, with a dense presence of small and medium‐sized enterprises (SMEs), in a proximate relational environment and with a supporting role of some public and semi‐public or civic institutions.” Within clusters, two types of relationships exist, those among firms and those between firms and institutions, such as chambers of commerce or public institutions. When first launched in the early 1990s, “cluster” policy entailed the creation of new tripartite institutions, such as an economic and social council, as well as industry-level working groups, tasked with assembling joint competitiveness programmes. Cluster development is marked by the region’s tradition of cooperation, worker-participation and incremental technology assimilation. The tradition of cluster-development continues to today, with the activity of partnerships and dialogue among firms and between firms and regional institutions. According to Ahedo (2004), cluster development has helped create a stronger system of industrial associations and collaboration between industry and the region’s government.

Source: Ahedo, M. (2004), “Cluster policy in the Basque country (1991–2002): constructing ‘industry–government’ collaboration through cluster‐associations”, European Planning Studies, Vol. 12/8, pp. 1097-1113; Del Castillo, J. and J. Paton (2010), “Política de promoción y reconversión industrial”, EKONOMIAZ. Revista vasca de Economía, Vol. 25/3, pp. 96-123, http://www.euskadi.eus/web01-a2reveko/es/k86aEkonomiazWar/ekonomiaz/abrirArticulo?idpubl=70&registro=1063.

Table 2.2. Industrial jobs in the Basque Country are at particular risk of destruction or change due to automation
Top 10 occupations with greater number of people with jobs at high risk of automation in the Basque Country, 2018
Occupation	Number of people at high risk, thousands	High risk, %	Number of people at significant risk, thousands	Risk of significant change, %	Total people at risk
Stationary Plant and Machine Operators	23 200	48	15 400	32	38 60
Metal, Machinery and Related Trades Workers	19 700	31	18 200	28	37 90
Drivers and Mobile Plant Operators	17 500	45	15 200	39	32 70
Cleaners and Helpers	15 100	32	26 900	56	42 00
Personal Care Workers	14 000	39	11 400	32	25 40
Sales Workers	11 700	17	24 300	34	36 00
Personal Services Workers	11 500	25	17 000	37	28 50
Numerical and Material Recording Clerks	10 400	31	9 700	29	20 10
Assemblers	8 400	44	10 500	56	18 90
Building and Related Trades Workers (excluding Electricians)	7 900	30	8 800	34	16 700
Note: The 100% estimate for Assemblers is due to statistical uncertainty. High rates of automation for this occupation correspond to prior OECD research.
Source: OECD calculations based on the European Labour Force Survey.

Figure 2.9. Automation may accelerate losses in industrial employment

Box 2.4. International good practice: Supporting mid-career workers in the industrial sector in Ontario, Canada

The Second Career Programme

The Province of Ontario, Canada has a labour force of 12.1 million people (15 years and older) with an unemployment rate of 5.7% before COVID-19. As of 2019, there were about 761 000 people working within the province’s manufacturing sector. The sector as a whole went through significant structural change over the last ten years. To help workers within the sector change jobs that were at risk of downsizing, the Ontario government introduced the Second Career programme. Second Career is for laid-off unemployed workers for which skills training is the most appropriate intervention to transition them into high-skill, in- demand occupations in the local labour market. Workers can receive up to up to CAD 28,000 for costs including: tuition, books, manuals, workbooks or other instructional costs, transportation, basic living allowance (maximum CAD 410 per week), and child care. The programme represents an intensive government investment in retraining workers for in-demand jobs, enabling them to go back to school for up to two years to retrain for a new job.

Individuals can apply to the programme if they receive employment insurance. Applications require candidates to describe how long they have been unemployed or holding a temporary job, positions they have applied for, level of education, prior positions, skills possessed and skills sought and information that shows how the skills and job are in-demand. The programme also asks candidates to submit personal financial information and submit details of relevant training institutions.

Source: OECD (2014), Employment and Skills Strategies in Canada, OECD Reviews on Local Job Creation, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264209374-en.; Ontario Ministry of Labour, Training and Skills Development (2020), Second Career, Toronto, Canada https://www.ontario.ca/page/second-career

2.2.3. Service jobs are also at risk of automation, particularly in sectors vulnerable to COVID-19

Although less concentrated by sector, multiple service-related occupations in the Basque Country face automation risks. Cleaners and helpers constitute the largest category of people at high risk of seeing their occupation disappear or change significantly, with 15 100 people at high risk and 26 900 at significant risk, representing 32% and 56% of helper and cleaner jobs in the region respectively (Table 2.3). These positions are associated with low skilled jobs in both the service and good-producing sectors, that can involve routine tasks such as watching, cleaning or washing. Such tasks may be particularly vulnerable to automation as risks related to COVID-19 continue. Given the need for increasing sanitary measures due to the COVID-19 crisis, there could be an acceleration of the use of machines in this occupational category to mitigate health risks.

Occupations requiring middle-level skills are also at risk. Such occupations include personal care or personal service workers, representing 14 000 and 11 500 jobs at high risk of automation in the region, and 11 400 and 17 000 at significant risk of change, respectively. These occupations often require care for individuals, such as personal assistance, travel or housekeeping, requiring both human contact and middle-level skills. Although societal, legal and cultural factors are likely to influence the automation of such human-facing tasks, particularly those involving personal care, elements of these occupations are likely to change, requiring the mastery of new tools and skills to perform the job. Sales workers, customer service clerks and food preparation assistants also constitute large groups of workers facing high risks of automation in the region, with 11 700, 7 500 and 4 100 jobs at high risk and 24 300, 22 200 and 1 200 at significant risk. Jobs such as clerks are also particularly at risk as secretarial tasks, word processing and other routine data manipulation often constitute a large part of their duties, tasks that are highly susceptible to digitalisation. Although these jobs cover a wide range of service sectors, they are particularly concentrated in sectors showing early and persistent signs of distress due to COVID-19, such as retail trade, accommodation and food services.

Table 2.3 Automatable service-associated jobs are common in sectors at risk from COVID-19
Top 8 occupations associated with services with the greatest number of people with jobs at high risk of automation in the Basque Country, 2018
Occupation	Number of people at high risk, thousands	High risk, %	Number of people at significant risk, thousands	Risk of significant change, %	Total people at risk
Cleaners and Helpers	15 100	32	26 900	56	42 000
Personal Care Workers	14 000	39	11 400	32	25 000
Sales Workers	11 700	17	24 300	34	36 000
Personal Service Workers	11 500	25	17 000	37	28 500
Numerical and Material Recording Clerks	10 400	31	9 700	29	20 100
Customer Service Clerks	7 500	16	22 200	46	29 700
Food Preparation Assistants	4 100	68	1 200	20	5 300
Health Associate Professionals	4 000	31	3 700	29	7 700
Source: OECD calculations based on the European Labour Force Survey.

2.2.4. The Basque Country is gaining industrial jobs, while it loses high-skilled service occupations at lower risk of automation

Three main trends can be identified concerning job creation and automation in the Basque Country between 2008 and 2018.

(1) First, the Basque Country has been creating jobs in a number of middle skill occupations which are at risk of automation, particularly occupations related to industrial manufacturing.

Job creation in the Basque Country’s industrial base is a welcome sign of recovery from the 2008 crisis, and may indicate the region’s recent industrial policies have helped restore industrial employment and slow long term trends concerning this sector. However, increases in jobs in these occupations may constitute a risk for these workers as they are among the highest risk occupations, all the more as the region’s industrial policies support the sector’s digitalisation. For instance, the region created over 12 280 stationary plant and machine operator positions and over 19 000 metal, machinery and related trades workers since 2008 (Figure 2.10).

The region has also created low skill service jobs, including over 13 000 customer service clerks, which are also at high risk of automation. The creation of low skill service jobs has been a pattern seen across Spanish regions, characteristic of Spain’s recovery from the 2008 crisis. These occupations are also at particularly risk due to the COVID-19, as sectors such as tourism, accommodation and food services may shed jobs as these sectors undergo a prolonged recovery.

(2) Second, the region lost employment in certain low and middle-skill occupations at high risk of automation, such as low-skill services.

The region lost 20 298 cleaners and helper positions and 23 845 building and related trades workers (excluding Electricians). These jobs also include occupations linked to industrial manufacturing at high risk of automation, such as drivers and mobile plant operators, an occupation which lost over 7 500 jobs. These changes can reflect structural, technological and policy changes in the Basque Country and Spain since the 2008 and 2010 crises, such as the sharp downturn in the construction sector. As many of the region’s unemployed workers may hold the skills from these occupations, this evidence could serve as a basis to tailor adult learning policies and can constitute an opportunity to upskill these parts of the Basque workforce.

(3) Third, the Basque Country has tended to lose high-skilled jobs at lower risk of automation, with the exception of jobs in education and health.

Principally, this concerns occupations such as business and administration associate professionals, business and administration professionals and legal, social and cultural professional lost employment. The region’s RIS3 specialisation strategy, with a volley of policies dedicated to energy and bio-health, may help reverse some of these trends, as growth in these areas may foster increases in high skill occupations at lower risk of automation.

Examples from regions across the OECD could help deepen strategic planning around skills development and demand-side policies to diversify the region’s economic base in ways that are complementary to its industrial core. For example, officials in the city of Southampton, United Kingdom, drafted an action plan on the future of work which involved mapping applicable international practices and meeting with city business leaders (Box 2.5). In the same way, in Washington-state United States, a task force made up of elected officials, labour and firms formulated policy recommendations related to the future of work. These involved a wide range of consultations among different stakeholders, ranging from online surveys and meetings to events and in-person consultations with different local actors.

Figure 2.10. The Basque Country has shed high skill occupations at lower risk of automation

Box 2.5. Finding synergies between local skills and specialisation strategies

Elected officials in Southampton, UK seek international examples and meet with business leaders

In Southampton, UK, a panel of elected officials inquired on the potential impact of artificial intelligence (AI), robotics and other digital technologies on the local economy. Between September 2018 and March 2019, the group of officials developed an action plan for the city on the future of work. In the inquiry process, the team identified good practices being implemented elsewhere and policies to potentially introduce in the city, including on the opportunities created by AI, automation and technological changes. The team also identified risk for workers in sectors that are most likely to see the greatest increase in the level of use of AI, automation and technological advancement. The action plan envisages the development of a city-wide skills strategy, a skills analysis of education providers, an update of education curricular to enrich the offer of digital skills and mapping of local lifelong learning platforms, particularly to find ways to improve access, improve rates of progression and increase job outcomes. As part of this inquiry, elected officials met with local practitioners in other cities, while a separate team developed a study of the city’s businesses. This team identified resources the city could mobilise to stimulate the technology sector, and institutions and partnerships that could help advance the city’s objectives.

Workers and firms articulate a local strategy for the future of work in Washington state, US

The US state of Washington recently assembled a 16-member task force made up of legislators, business, and labour leaders, which carried out a study between October 2018 and November 2019. The regional workforce board, an office connecting job seekers with the public workforce system, provided staff for the project. The task force performed a number of actions to compile their list of policy recommendations, paying particular attention to the input of local stakeholders:

Use of online surveys to understand the interests of representatives before a series of meetings;
Consultations with local stakeholders, such as municipal governments, labour unions or chambers of commerce;
Organisation of events related to the future of work;
Compilation of relevant research related to the future of work, particularly concerning the effects of technology on jobs;

Worker representatives highlighted low job quality as a challenge in the region, particularly those posed by digital monitoring tools, while businesses noted staffing difficulties and challenges to best train workers to use advanced technologies. The force formulated joint policy recommendations which included worker upskilling and access to lifelong learning, technology in the workplace, improved labour market data and credentialing transparency, modernisation of worker support systems and equal access to economic development resources across the region.

Source: OECD (2020 forthcoming), OECD Reviews on Local job Creation: Preparing Ontario, Canada for the Future of Work; Washington Workforce training and education coordinating board (2019), Future of work task force 2019 policy report, https://www.wtb.wa.gov/wp-content/uploads/2019/12/Future-of-Work-2019-Final-Report.pdf.

2.2.5. As employment starts to recover, the Basque Country can mobilise new tools to track sectoral change in the region

The Basque public employment service (PES), Lanbide, launched Futurelan, an innovative tool to track the evolution of occupations in the face of the future of work. Futurelan uses past labour market trends to make predictions about the future. This tool provides real and predicted data on evolution, the distribution of employment occupation and the evolution of contracts. Information can be selected according to occupation or sector. Although the COVID-19 crisis is likely to modify conjectures, Futurelan predictions set prior to the crisis foresee nearly 57 000 new jobs in food service, health and safety/security sectors, as well as over 41 000 jobs as accountants, administrative staff and other office employment (Figure 2.11). Meanwhile, the tool predicts a decrease of nearly 19 000 in machine and facility operators and assemblers. Other occupations, such as qualified workers in agriculture, livestock, forestry and fishing and technical and professional staff are also predicted to decrease over time, shedding nearly 7 000 and 5 500 jobs respectively between 2018 and 2030. Futurelan predictions correspond with OECD calculations which suggests a high likelihood of job suppression in machine and facility operators and assemblers.

The tool’s prediction model, however, may need to adapt to integrate predictions related to COVID-19. Indeed, Futurelan’s prediction of job growth in food services and sales occupations may change significantly as early estimates on the impact of COVID-19 show a significant, and likely prolonged, downturn in the sector. The region could also take this tool further to provide more data on skills trends, beyond sectors and occupations. In this way, tools such as Futurelan could help the region develop policies on how those holding those jobs can retrain for other jobs, putting in place appropriate training to assist job transition. For instance, in Wallonia, Belgium, the region has put in place a sectoral skill analysis that involves carrying out field and expert interviews to identify skills that could arise from emerging sectors (Box 2.6). Reinforcing Futurelan’s quantitative predictions with a wider qualitative approach would complement the tool’s predictions, receiving direct information from companies and key actors on how they see their sector evolving.

Figure 2.11. Futurelan is a helpful tool to evaluate occupational change of the future in the Basque Country

Box 2.6. International good practice: Industry skills mapping in Wallonia (Belgium)

Wallonia’s Public Employment Service (PES), Le Forem, undertakes yearly prospective analyses to identify local skills needs in specific sectors. In its Le Forem Study, the PES aimed to develop appropriate training offerings for Wallonia’s competitive and business clusters. The analysis first classifies future occupations and associated core skills in eight sectors. It then identifies a set of related or secondary skills that could subsequently arise from developing the sector. The approach follows a five-step qualitative process: (1) Analytical staff from the PES produce occupational reports by sector and (2) disseminate report to sectoral expert groups, composed of internal staff and industry specialists. Then, (3) a method called Abilitic2Perform is used to identify the skills required for each occupation or skills group. Wallonia adopted Abilitic2Perform methods from Interreg IV EU projects in Belgium. Four expert workshops, organised by occupation, identify key evolution factors and the potential evolution scenarios in a six step sub-process. At the end of the Abilitic2Perform process, (4) expert groups select the most likely (or desired) scenario, identifying associated skills needs. Finally, in a final step, (5) the local training department receives the results of the analysis in order to start designing appropriate training programmes, publishing them and forwarding them to relevant training authorities. A panel of experts answers a set of questions that are then included in the sector reports. The objective is to check the sectoral trends and particularly to detect the effects that these trends may have on occupations.

Sectors and associated industries value the programme since they do not have the capacity or resources to undertake such an extensive study, while job seekers and public administrators can benefit from a rich knowledge base about the labour market. Le Forem has built on this methodology to produce an array of yearly analyses that combine data work with qualitative evidence from expert groups, yielding knowledge not only on occupational change but also skills needs. For example, in 2020, Le Forem published Métiers en tension de recrutement en Wallonie which identifies in-demand occupations and the causes for hiring shortages, such as a skills mismatch or poor working conditions. The analysis leans on a three-part methodology, first through a statistical analysis of job offers received by the PES, followed by consultation with expert groups. In a final step, oriented towards implementation, Le Forem communicates the findings to Wallonia’s sectoral training funds, a body run by social partners that helps define training curricula.

Source: Wallonia (2018), “Peer Learning in Regions in industrial transition: Workshops good practice template”, Le Forem, the Public Employment Service, Prepared for the Peer Learning in Regions in Industrial Transition Workshop “Preparing Jobs of the Future”, 8-9 March 2018, Brussels, Belgium, Unpublished.; Le Forem (2020), Métiers en tension de recrutement en Wallonie - Liste 2020, https://www.leforem.be/MungoBlobs/1391501709248/202006_Analyse_metiers_tension_recrutement_wallonie_2020.pdf .

2.3. Job quality: a “high road” recovery can prepare the Basque Country for the future of work

2.3.1. Digitalisation may increase non-standard working arrangements

Technology-influenced changes in the workplace are likely to create more part-time, temporary or self-employed jobs as the possibility of new, remote or modified working arrangements becomes possible. Some of these changes may be accelerated by the COVID-19 crisis as remote work arrangements persist or become mainstreamed in the workplace. Such developments could support workers who require more flexible arrangements but they could also lead to increased employment insecurity (Orkestra, 2019[8]). In the same way, technology can improve job quality for some workers, by increasing earnings, improving safety, or reducing tedious tasks. New technologies in the workplace, however, can also reduce the well-being of workers by increasing monitoring, reducing autonomy and accentuating job strain (OECD, 2019[23]). Low job quality can harm labour productivity by lowering worker motivation and by discouraging firms from investing in the skill development of employees (Askenazy and Erhel, 2017[24]).

Recognising jobs as a central part of peoples’ well-being, the OECD has sought to place the quality of employment, and not only its quantity, at the centre of policy discussions on employment (OECD, 2014[25]). The OECD Job Quality Framework considers job quality can be broadly divided into earning quality, labour market security and the quality of the working environment, such as tasks performed (OECD, 2014[25]). According to these gauges, Spain records amongst the lowest job quality indicators in the OECD, a pattern followed by the Basque Country along key indicators (OECD, 2019[23]). In terms of earnings quality, Spanish and Basque wages fell sharply during the crisis and did not recover fully, recovering only partially in real terms in 2019 for the first time since 2015 (European Commission, 2020[26]). At the national level, raises in the minimum wage in 2019 and higher negotiated wages through social dialogue helped recover wages (European Commission, 2020[26]).

2.3.2. Temporary employment is pervasive in the Basque Country

Although the Basque Country benefits from a lower rate of temporary employment compared to most other Spanish regions, it remains much higher than EU and OECD averages. Indeed, in 2019, 92% of work contracts signed in the Basque Country were temporary, while in 2018, over 22% of employees were on temporary contracts in Spain, compared to just over 11% in the EU-28 (Lanbide website) (Figure 2.12). According to the European Commission, over 32% of those on temporary contracts in Spain have an agreement that lasts less than 6 months, while over 17% have a contract that last less than 1 month, highlighting the precariousness of working arrangements (European Commission, 2019[27]). Moreover, the number of very short contracts has increased in recent years in Spain. For example, 30% of all contracts signed in Spain in 2019 were less than one week long, compared to 17% in 2007 (European Commission, 2020[26]). The Commission also highlights that fixed-term contracts are not only pervasive in seasonal jobs, but also in education, health and manufacturing along with other higher-skilled occupations. Temporary contracts have also proliferated in the public sector, with over 27% of public sector employees on temporary contracts in Spain at the end of 2019, according to European Commission.

The high rate of temporary contracts in Spain causes difficulties attaining benefits and participating in training. Over 23% of workers on temporary contracts in Spain were at risk of poverty, while the share of workers suffering from in-work poverty in the overall economy increased from 10.8% in 2012 to 13.1% in 2017, higher than the EU-28 average of 9.4% (European Social Policy Network, 2019[28]). In Spain, young, low skilled and non-EU immigrant workers were disproportionately affected by temporary work and in-work poverty, compounding automation risks (European Commission, 2019[27]). The academic literature has put forth different possible causes of high rates of fixed-term work in Spain, ranging from labour market flexibility, or rigidity, to firm strategies and contract abuse. The Spanish Government has put in place the Plan director por un trabajo digno 2018-2020, a national strategy to curb to curb fixed-term work across the country (Box 2.7).In particular, the plan reinfroces the capacities of the Spanish labour inspectorate to identify abuses, while also opening avenues for cooperation between the national and regional labour inspectorates.

Figure 2.12. The share of temporary workers is well above the EU average in Spain, and has been on an upward trajectory

Box 2.7. The Plan director por un trabajo digno 2018-2020: the Spanish government’s strategy to reduce precarious work

National initiative presents an opportunity for regions to participate in curbing precarious work

The Spanish government has identified precarious work as one of the major challenges of the country’s economic recovery from 2008. This challenge is likely to remerge following COVID-19. Spain has put in place a labour inspection strategy known as the Plan director por un trabajo digno 2018-2020 to curb the abuse of short-term contracts and other aspects of labour law and regulation. The plan highlights that precarious work weighs on the country’s competitiveness while harming the rights of workers. Indeed, precarious work may be one of the channels for rising in-work poverty rates in the country. The plan sets the overarching objectives of improving job quality. The plan’s activities unfold along four lines:

Reinforcing the actions of the national labour and social security inspectorate, the Inspección de Trabajo y Seguridad Social;
Taking into account the competences of the country’s regions in terms of design, management, execution, evaluation and follow-up of measures. For instance, regions can participate in the plan through convenios de colaboración, or permanent collaboration agreements, and through campañas extraordinarias autonómicas, or target campaigns. It also opens participation to employer and worker organisations;
Combining short and long-term actions, including two immediate plans to fight abuses in part-time recruitment;
A cross-cutting approach, including 55 operative measures, 20 organisations measures and coordination from different public administrations.

The plan also takes into account a gender perspective, ensuring equality between men and women are part of the labour inspectorate’s activities. The plan also insists that it is solely aimed at enterprises that violate laws, and not those that meet their obligations. Some of the plan’s measures include increasing the resources and capacities of the labour and social security inspectorate, using big data on contracts to identify signs of fraud, reinforcing effectiveness of the labour and social security inspectorate, greater inter-institutional coordination and a communication plan, including a newsletter, improving website information and increasing presence on social media.

Source: Ministerio de trabajo, migraciones y seguridad social de España, Plan director por un trabajo digno 2018-2020, http://www.mites.gob.es/ficheros/ministerio/plandirector/plan-director-por-un-trabajo-digno.pdf.

2.3.3. Part-time employment rose more in the Basque Country than the Spanish average

Part-time employment increased significantly in Spain and the Basque Country after the 2008 downturn, suggesting part-time employment may rise again following the COVID-19 crisis (OECD, 2019[23]). Although part-time employment remained under the OECD average throughout the crisis in Spain, it increased from under 11% of total jobs in 2008 to over 14% in 2013-4, a share that fell to approximatively 13% in 2018 (Figure 2.13). Notably, part-time employment trends have been stronger in the Basque Country than in Spain, with a difference that rose throughout the crisis. Although the rate is lower than the EU average, the majority of part-time workers in Spain are involuntarily working part-time: over 55% of part-time workers were working part-time while wishing to work longer in 2018, relative to 26% in the EU the same year (Figure 2.14). Although Spain already registered a higher involuntary part-time unemployment rate than the EU-28 average in 2008, the gap between the EU-28 average and Spain has increased significantly. Positively, the share of involuntary part-time employment as a share of part-time employment began to decrease in Spain in 2014, though this trend may be at risk due to COVID-19. As Basque firms are faced with reduced margins as the COVID-19 crisis reduces demand, particularly in sectors such as tourism, the past reaction of Basque firms may indicate a renewed proliferation of involuntary part-time contracts. In the face of such prospects, encouraging “high road” firm strategies can serve as a way to improve productivity through job quality, while encouraging firms to pool training investments, move into value added products and develop other skills utilisation strategies (Box 2.8).

Figure 2.13. Part-time contracts are pervasive in the Basque Country

Figure 2.14. Involuntary part-time employment makes up the majority of part-time work in Spain

Box 2.8. “High road” firm strategies to support job quality and productivity

Better skills utilisation can encourage companies to curb the use of fixed-term contracts

In a “high road” development strategy, reinforcing competitiveness goes hand-in-hand with working conditions, particularly through a local competitiveness strategy that stress skills development and job quality. Recent research has shown that falling job quality may weigh on both working conditions and labour productivity. Studying France, Askenazy and Erhel (2017) explain the link between job quality and labour productivity through two main channels: low job quality restrains worker motivation and reduces firms’ incentives to invest in human capital.

Employment and training strategies can support productivity by shaping the choices made by firms in terms of workforce composition and work organisation. The OECD and ILO have identified multiple avenues for demand-side intervention to help encourage companies towards high road strategies. For instance, governments can set “good practice” standards for companies that do not abuse labour law loopholes, pay decent wages and invest in workers. Governments can also provide funding for employers to reshape their workplaces along higher quality work practices and staff training. Other interventions can include creating programmes targeted at sectors suffering from particularly low job quality or contract abuse, for instance supporting business clusters to move into higher value added products accompanied by better use of skills. Governments can also use labour market regulations to encourage better skills use, for instance through the involvement of unions or adequate regulations on temporary contract use. Finally, regional governments can have a particularly effective role in stimulating regional collaborations between firms, creating common marketing strategies or pooling investments for training.

Source: Askenazy, P. and C. Erhel (2017), Qualité de l’emploi et productivité, Éditions Rue d’Ulm/Presses de l’École normale supérieur, http://www.cepremap.fr/depot/2017/06/Opuscule_CEPREMAP43-Emploi_Productivite.pdf; OECD/ILO (2017), Better Use of Skills in the Workplace: Why It Matters for Productivity and Local Jobs, https://doi.org/10.1787/9789264281394-en. ILO (2014), Transforming economies: Making industrial policy work for growth, jobs and development, https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_242878.pdf.

Conclusion

The COVID-19 is likely to accelerate automation in the Basque Country. In 2020, the region will face steep economic challenges, requiring the region to transition short-term economic aid into targeted support for companies and workers most at risk, particularly for sectors reliant on tourism and trade. As the region recovers, a higher number of Basque jobs will face a high risk of automation, particularly those linked to the region’s historic industrial base and service jobs entailing risks related to COVID-19. These include not only occupations such as station, plant and machine operators, metal, machinery and related trades workers, but also sales workers or cleaners.

The Basque Country has already taken actions to mitigate these risks. Tools such as Futurelan can be expanded to lean on international skills mapping practices. The Basque Country can mobilise its historic cluster-based development to encourage better skills utilisation by Basque firms and acting early to train and upskill workers into quality jobs. These changes will call for effective support to displaced workers. In this way, Chapter 2 explores the way the region’s public employment service, Lanbide, plays a central role support these labour market transitions.

References

[24] Askenazy, P. and C. Erhel (2017), Qualité de l’emploi et productivité, Éditions Rue d’Ulm/Presses de l’École normale supérieur, http://www.cepremap.fr/depot/2017/06/Opuscule_CEPREMAP43-Emploi_Productivite.pdf.

[7] Bentolila, S., M. Jansen and J. García-Pérez (2017), Are the Spanish Long-Term Unemployed Unemployable?, http://ftp.iza.org/dp10580.pdf.

[12] Eurofound (2018), Automation, digitalisation and platforms: implications for work and employment, https://www.eurofound.europa.eu/publications/report/2018/automation-digitisation-and-platforms-implications-for-work-and-employment.

[26] European Commission (2020), Country Report Spain 2020, https://ec.europa.eu/info/sites/info/files/2020-european_semester_country-report-spain_en_0.pdf.

[2] European Commission (2020), European Economic Forecast, Summer 2020 (Interim), https://ec.europa.eu/info/sites/info/files/economy-finance/ip132_en.pdf.

[27] European Commission (2019), Country Report Spain 2019, https://ec.europa.eu/info/sites/info/files/file_import/2019-european-semester-country-report-spain_en.pdf.

[28] European Social Policy Network (2019), In-work poverty in Spain, Directorate General for Employment, social affairs and inclusion.

[17] Frey, C. and M. Osborne (2013), The future of employment: how susceptible are jobs to computerisation?, https://ora.ox.ac.uk/objects/uuid:4ed9f1bd-27e9-4e30-997e-5fc8405b0491/download_file?safe_filename=future-of-employment.pdf&file_format=application%2Fpdf&type_of_work=Journal%2Barticle.

[4] Gobierno Vasco (2020), EDIDAS programa COVID19, https://bideoak2.euskadi.eus/2020/03/24/news_61075/COVID19_Medidas_Neurriak.pdf.

[19] Gobierno Vasco (2017), Plan de Industrialización 2017-2020 “Basque Industry 4.0”, https://www.irekia.euskadi.eus/uploads/attachments/10018/Plan_de_Industrializacion.pdf?1500453186.

[21] Gobierno Vasco (2016), Estrategia vasca de empleo, https://www.euskadi.eus/contenidos/informacion/eve2020/es_def/adjuntos/EVE2020.pdf.

[22] Gobierno Vasco (2009), The Basque Country: Insight into its culture, history, society and institutions, http://www.euskadi.eus/gobierno-vasco/contenidos/informacion/ezagutu_eh/es_eza_eh/adjuntos/eza_en.pdf.

[13] Hershbein, B. and L. Kahn (2018), “Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings”, American Economic Review, Vol. 108/7, pp. 1737–1772, https://doi.org/10.1257/aer.20161570.

[11] International Federation of Robotics (2018), Executive Summary World Robotics 2018 Industrial Robots, https://ifr.org/downloads/press2018/Executive_Summary_WR_2018_Industrial_Robots.pdf.

[6] JRC (2020), Telework in the EU before and after the COVID-19: where we were, where we head to, European Commission, Joint Research Centre (JRC), https://ec.europa.eu/jrc/sites/jrcsh/files/jrc120945_policy_brief_-_covid_and_telework_final.pdf.

[16] Le Ru, N. (2016), L’effet de l’automatisation sur l’emploi : ce qu’on saitet ce qu’on ignore, https://www.strategie.gouv.fr/sites/strategie.gouv.fr/files/atoms/files/na-49-automatisation-emploi.pdf.

[14] Muro, M., R. Maxim and J. Whiton (2020), The robots are ready as the COVID-19 recession spreads, https://www.brookings.edu/blog/the-avenue/2020/03/24/the-robots-are-ready-as-the-covid-19-recession-spreads/?preview_id=791044.

[10] Nedelkoska, L. and G. Quintini (2018), “Automation, skills use and training”, OECD Social, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris, https://dx.doi.org/10.1787/2e2f4eea-en.

[1] OECD (2020), Coronavirus (COVID-19)From pandemic to recovery: Local employment and economic development, https://read.oecd-ilibrary.org/view/?ref=130_130810-m60ml0s4wf&title=From-pandemic-to-recovery-Local-employment-and-economic-development.

[3] OECD (2020), Country Policy Tracker, Spain (updated 1 June 2020), http://www.oecd.org/coronavirus/fr/.

[23] OECD (2019), OECD Employment Outlook 2019, OECD Publishing, Paris, https://dx.doi.org/10.1787/9ee00155-en.

[18] OECD (2018), Job Creation and Local Economic Development 2018: Preparing for the Future of Work, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264305342-en.

[9] OECD (2018), Productivity and Jobs in a Globalised World: (How) Can All Regions Benefit?, OECD Publishing, Paris, https://doi.org/10.1787/9789264293137-e.

[25] OECD (2014), Employment Outlook, https://www.oecd-ilibrary.org/docserver/empl_outlook-2014-en.pdf?expires=1576750060&id=id&accname=ocid84004878&checksum=08CD510F15872539BCF15C239C6F668D.

[15] Orkestra (2019), El futuro del empleo en la CAPV.

[8] Orkestra (2019), The Basque Country Competitiveness Report 2019: Are skills the panacea?.

[5] Özgüzel, C., P. Veneri and R. Ahrend (2020), Potential for remote working across different places, VOX EU CEPR, https://voxeu.org/article/potential-remote-working-across-different-places.

[20] UNIDO (2017), Accelerating clean energy through Industry 4.0: Manufacturing the next revolution, https://www.unido.org/sites/default/files/2017-08/REPORT_Accelerating_clean_energy_through_Industry_4.0.Final_0.pdf.

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