Effects of Shocks in Demand on Employment Regarding Age and Gender Population Groups in Different Zones of Europe. Consequences of the Pandemic Lockdown

Volume 42-2, May 2024 // ISSN: 1133-3197 DOI: 10.25115/sae.v42i2.9416 Monographic Section Effects Of Shocks In Demand On Employment Regarding Age And Gender Population Groups In Different Zones Of Europe. Consequences Of The Pandemic Lockdown MARGARITA I. BARRERA LOZANO1, ALFREDO J. MAINAR-CAUSAPÉ2 1,2 Department of Applied Economics III, UNIVERSITY OF SEVILLE, SEVILLE, SPAIN. 1 ORCID: 0000-0003-2736-1132. E-mail: [email protected] 2 ORCID: 0000-0003-2032-9658. E-mail: a[email protected] ABSTRACT The effects of the COVID-19 pandemic on the economy have been devastating for several specific types of economic activities. Transport, Accommodation, and Food services, and travelling activities are closely linked to social distancing implications. This study reveals which activities are most affected and includes factors such as gender and age. To this end, different zones of the European Union have been analysed to determine the indirect, direct, and induced effects of a change in the final demand, using multi-sectoral models applied to social accounting matrices. The sensitivity to changes in demand and the effects on employment show specific patterns regarding age and gender. Keywords: Tourism employment; Labour market; Social Accounting Matrices; Linear multiplier analysis; COVID19 pandemic. JEL Classification: D57; J21; R19; Z30. Received: August 05, 2023 Accepted: January 28, 2024 Volumen 42-2, Mayo 2024 // ISSN: 1133-3197 DOI: 10.25115/sae.v42i2.9416 Sección Monográfica Efectos de los shocks en la demanda sobre el empleo según grupos de población por edad y género en diferentes zonas de Europa. Consecuencias del confinamiento por la pandemia MARGARITA I. BARRERA LOZANO1, ALFREDO J. MAINAR-CAUSAPÉ2 1,2 Departamento de Economía Aplicada III, Universidad de Sevilla, Sevilla, España. 1 ORCID: 0000-0003-2736-1132. E-mail: [email protected] 2 ORCID: 0000-0003-2032-9658. E-mail: [email protected] RESUMEN Los efectos de la pandemia de COVID-19 en la economía han sido devastadores para varios tipos específicos de actividades económicas. Los servicios de transporte, alojamiento y alimentación, y las actividades de viaje están estrechamente relacionados con las implicaciones del distanciamiento social. Este estudio revela qué actividades se ven más afectadas e incluye factores como el género y la edad. Para ello, se han analizado diferentes zonas de la Unión Europea para determinar los efectos indirectos, directos e inducidos de un cambio en la demanda final, utilizando modelos multisectoriales aplicados a matrices de contabilidad social. La sensibilidad a los cambios en la demanda y los efectos en el empleo muestran patrones específicos en función de la edad y el género. Palabras clave: Empleo turístico; Mercado laboral; Matrices de Contabilidad Social; Análisis de multiplicadores lineales; Pandemia de COVID-19. Clasificación JEL: D57; J21; R19; Z30. Recibido: 05 de Agosto de 2023 Aceptado: 28 de Enero de 2024 Margarita I. Barrera Lozano and Alfredo J. Mainar-Causapé 3 1. Introduction The limitations on social interaction and mobility since the spread of COVID-19 have revolutionised the way society is understood. Transport, accommodation, restaurant services, and travel activities have all been hit hard by the restrictions imposed to limit interpersonal contacts, due to the associated social and mobility component that differentiates them from other activities that have, in one way or another, been able to adapt to the current situation (Prades-Illanes and Tello-Casas, 2020). As a result, apart from the obvious social effects, the economic effects have been devastating, leading economies to recessionary situations typical in the context of war (OECD, 2020a). In such circumstances, an indepth study is necessary into how major demand shocks exert an influence on employment through their effects on these productive activities. However, not all sectors or population groups are equally affected by shocks of the nature of a confinement and stoppage of activity. Certain age and sex groups have a special vulnerability, both due to their own characteristics and the productive sectors in which they mostly carry out their professional and work activity. In this way, the main objective of this paper is to deepen the characterization of the effect, in terms of employment lost generated by the lockdown and drop in demand caused by the COVID-19 pandemic, distinguishing especially between age groups and gender due to the importance of these factors in gaining an understanding of the economic hysteresis consequence of the problem. The high degree of vulnerability of some age groups and female gender are usual issues in labour literature, basically from an empirical point of view. A variety of authors analyse the different behaviour in the labour market depending on the gender and age of the workers and their, with the general consideration that younger and elder groups and women are more vulnerable to shocks. Thus, Calvo-Gallego et al. (2016) analyse various points of view related to ageing in the labour market, and Gómez-Domínguez and Arévalo-Quijada (2016) analyses this issue in the context of policy makers. Castelli and Bogoni (2020) study how age and gender factors help in the identification of structural weaknesses of the labour market and show how the exit from and return to the labour market of the youngest and oldest members of the population are more complex than for those in the intermediate age groups and, following Autor and Dorn, (2009), the oldest and youngest members of the population are highly affected by displacements, the youngest segment shows both downward and upward occupational reallocation, while the oldest group moves primarily downwards, thereby showing age to be even more crucial than skill level. Methods for the analysis of the effects on the variation in employment have various perspectives. The direct analysis of shocks of demand indicates that there will be an obvious decrease in employment at the sectoral level and in the territory that has suffered the shock, but underestimates or does not consider effects caused by direct and indirect links. Thus, the circular flow of income causes sequential changes in final demand, which is reflected indirect and induced effects in output and employment. In this sense, the contribution of the approach presented in this article is the estimation of total effects, not only direct, but also indirect and induced, which reflect all the relations between sectors and agents of the economy providing interesting information on the different scope that the lockdown has had in European economies. In this context, the Input-output framework allows to analyse this chain of effects, especially the extension and improvement represented by Social Accounting Matrices (SAM). These databases expand the ability to analyse problems related to multisectoral issues, integrating them with the socioeconomic aspect, including the employment. The rest of this paper is structured as follows: Section 2 contains a brief revision of literature of issues analysed and related methods used in this paper. Section 3 describes the methods used in the analysis and Section 4 presents the results of the analysis. Section 5 provides the discussion, while Section 6 concludes the study. 2. Literature review Regarding the disruption triggered by the COVID-19 pandemic, many studies have faced the issue of stronger effects depending age or gender: Bluedorn et al. (2021), using a sample of 38 advanced Effects of Shocks in Demand on Employment Regarding Age and Gender Population Groups in Different Zones of Europe. Consequences of the Pandemic Lockdown 4 and emerging market economies, show early evidence on the pandemic’s effects pointed to women’s employment falling disproportionately, strongly related to COVID-19’s. Albanesi and Kim (2021) found evidence that employment losses were larger for women during the pandemic recession in USA, and Adams-Prassl et al. (2020), using real time survey evidence from the UK, US and Germany, show that the immediate labour market impacts of lockdown differ considerably across countries, being women and less educated workers the more affected by the crisis. Also, Alon, T. et al. (2020), show how in recession caused by the COVID-19 pandemic, unemployment is higher among women (opposite to most US recessions) and in Hupkau and Petrongolo (2020), they are analysed effects of that crisis and the associated restrictions to economic activity on paid and unpaid work for men and women in the United Kingdom, showing that women suffered smaller losses at the intensive margin, experiencing slightly smaller changes in hours and earnings. Caselli et al. (2020) test out the women and younger cohorts experienced a sharper drop in mobility in response to rising COVID-19 infections, warning about a possible widening of gender and inter-generational inequality. Bellotti et al. (2021) perform a review of contributions with work-related aspects across different age groups, between 2019 and March 2021, resulting in 36 papers pertinent to the scope of this review, grouped according to different topics, all linked to age. Pit et al. (2021) provide examples of how across the countries, the impact of COVID-19 on older workers is shown as widening inequalities. Finally, Simonson, Wünsche and TeschRömer (Editors) (2023) analyses ageing during the pandemic, examining the extent to which employed people in their mid-40s and older were affected by various changes to their work situation in the first months of the lockdown in Germany. In this context, the use of input-output models is revealed to be especially suitable for this type of analysis. However, as far as we know, not so much specific studies refer directly to different impacts by age or gender: Richarson and Dennis (2020) for gender gaps in Australian economy, and Farré et al. (2020) for gender inequality in Spain, are examples of this kind of approximation. Anyway, the inputoutput framework has indeed been used for lockdown effects on output and employment in another contexts. Thus, there are numerous examples in the literature that address the study of the impact of the pandemic and the lockdown on the economy through this type of models. In this way, Santos (2020) uses an Input-output model to explore the impact of the lockdown on labour, considering a variety of socio-economic groups. Bonet-Morón et al. (2020) use also this kind of model for labour impacts of lockdown. Fadinger and Schymik (2020) focus on different effects of changing work conditions in labour groups. Giammetti at al. (2020) investigate the role of the domestic value chain in transmitting the impact of COVID-19 lockdown measures, with effects on socioeconomic variables. Haddad et al. (2020) uses also input-output analysis to describe impacts of different lockdown scenarios. Mariolis et al. (2020) focus on impacts of COVID-19 on employment in tourism sector using Supply-and-use tables. In another context, Prades and Tello (2020) study with multiregional inputoutput tables the heterogeneity of the COVID-19 impact across regions and countries in the Euro area and demonstrate differences between and within regions. Reissl et al. (2023) also face the regional impacts, but specifically in Italy and extending impacts to demand and supply aspects. Pichler and Farmer also uses this perspective for Germany, Italy and Spain, but now using input-output networks. Finally, Febrero and Bermejo (2021) focus on impacts of COVID-19 on Spanish economy with a classical input-output model. 3. Data and methods This analysis employs a linear multi-sectoral model based on the social accounting matrices for a reference set of member states of the European Union. These countries (Austria, Germany, Spain, France, Italy, the Netherlands, and Sweden) have been selected in order to ascertain the behavioural patterns found in the context of the European Union. The inclusion of these seven countries incorporates a broad range of countries as member states of the European Union, and covers different population sizes and locations. We focus also on tourism sectors that have been the most severely punished by the pandemic situation, especially transportation, accommodation, and travel agencies, Margarita I. Barrera Lozano and Alfredo J. Mainar-Causapé 5 and organise the sectors into ten sectoral groups thereby making it possible to compare these sectors with the rest of the economy. The database used in this analysis consist of Social Accounting Matrices, referring to 2015 as the base year. A Social Accounting Matrix (SAM) is a comprehensive and economy-wide database that records data on transactions between all the economic agents within an economy in (usually) a year. A SAM is a square matrix representing economic activities, commodities (goods and services), factors, and institutional sectors by accounts in rows and columns. A SAM cell collects the payment by the account in the column to the account in the row. The income of accounts is described in its corresponding row and the expenditures are reflected in the corresponding column. The basic structure of a standard SAM is shown in Figure A1 in the Appendix. SAMs improve on traditional Supply-and-Use and Input-Output Tables (SUIOTs), because incorporate the relationships between the income and expenditure of institutional agents (not only the productive part of the economy). In this way, SAMs expand the explanatory capacity of I-O models, by explicitly introducing income, its primary and secondary distribution, and the final consumption of institutional agents, such as households and government. In this analysis, we employ SAMs for the selected countries for 2015 as previously used and presented in Mainar-Causapé et al. (2020). In order to develop the analysis proposed, it is necessary to obtain the so-called output multipliers (Pyatt and Round, 1979) for each selected member state considered. The starting point for the analysis (analogous to obtaining the Leontief inverse) is the following equilibrium equation: 𝐱 = 𝐀𝐱 + 𝐲 ⟺ 𝐱 = (𝐈 − 𝐀)−𝟏 = 𝐌𝐲 (1) where x is the vector of total gross output of endogenous accounts and y is the corresponding vector of total final demand. I stands for the identity matrix, and A is the well-known matrix of coefficients (but now in a SAM framework), with elements aij showing the share of the sector i in each unit produced by sector j. M represents the matrix of SAM multipliers. When M is pre-multiplied by the average direct employment ratio vector , e(j) with j representing different types of employment (here, following gender and age classification) per unit of output (in this case, millions of euros) for each activity, then a vector is obtained that represents the total employment generated, directly, indirectly, and induced, by each additional demand unit in each economic sector. In other words, this is a vector of total employment multipliers generated for each sector of the economy. 𝐦𝒆′(j)= 𝐞′(j)𝐌 (2) In order to disentangle the responsibility for demand, the calculations include the embodied employment due to final demand, thereby obtaining vectors of embodied employment (l), and hence: 𝐥 (𝐣) = 𝐦𝒆′(𝐣) 𝐝 (3) where d is a vector collecting the final demand for each commodity (it may include the whole final demand, or just one of its components: the household spending, public sector expenditure, or exports). An element i of l(j) indicates de employment of type j embodied in the final demand of commodity i. The data used for the analysis includes social accounting matrices with use-and-supply structures for each of the countries. The structure of these matrices comprises 65 activities and products, whose value added is divided into labour and capital, and whose final demand is divided into households, corporations, direct taxes, government, investment and savings, and the rest of the world. For further information regarding the structure of the SAMs, see Mainar-Causapé et al. (2020). For a better understanding, especially focused on tourism and its related sectors, the 65 economic activities have been grouped into 10 sectoral typologies: Agriculture, fishing, and food; Manufacturing; Construction; Trade; Land transport; Water transport; Air transport; Accommodation and food services; Travel agencies; and Other services. For clarity in the analysis, and to try to obtain an adequate representation of the different circumstances of different areas of Europe, as is established Effects of Shocks in Demand on Employment Regarding Age and Gender Population Groups in Different Zones of Europe. Consequences of the Pandemic Lockdown 6 in the introduction, the following countries have been selected: Austria, Germany, Spain, France, Italy, the Netherlands, and Sweden, thus including a representative range of member states of the European Union and covering different population sizes (large, medium-sized, and small) and locations (northern, southern, and central). The employment data is divided into five groups in terms of age and into two groups in terms of gender, thereby making it possible to better ascertain how the behaviour in demand affects various population segments. The five age groups present characteristics related to those included in each group: the youngest group (15-24) are people starting in the labour market, with a lower probability of having to take care of a family, with a lower level of educational attainment, and a higher probability of combining work and studies; the second group (25-39) are those people embarking on their professional and personal careers, with a goal to hold a position with higher expectations than those of younger workers; the third age group (40-49) of people are in the process of reaching the peak of their careers and with greater responsibilities and less dependency on others; the fourth (50-59) is a group with people that are, in general, at the peak of their careers, which makes it especially difficult for them to change their field of work; and the last group (+60) are those closest to retirement (OECD , 2021). A change in the level of employment in each of these different age groups could incur very different socio-economic effects. These effects depend on: (1) how easily people can reallocate to other jobs; (2) to what level workers’ families can be affected by a change in their labour situation; (3) how rejection from the labour market can affect future positioning in said market; (4) what flexibility workers have towards learning new procedures; (5) how much resilience they retain in adapting to the new global reality; (6) what experience the components in the labour market have in facing unconventional situations; (7) what the effects are on savings and investments in the economy. An analysis of the labour market in terms of age helps to answer all these questions. This analysis ascertains: how easily it is to reallocate a worker to another job; the repercussions on the family from losing a job and the future effects; the flexibility of the labour market for each group; the experience workers have in facing unconventional situations; and their level of savings/investments. All these questions influence the resilience of the economy when faced with an unexpected shock, such as a pandemic. We consider the youngest group to be more prone to changing their training and hence they can more easily change their type of job; the effect on the families is generally milder; a change in the labour market will affect their futures to a greater degree partly due to the damage caused by being out of the labour market in this period and therefore entering the next age group with no experience. These youngsters are more flexible since the opportunity cost of changing their situation is lower than for older groups and therefore the effect on their savings and investments is also lower. 4. Results Employment multipliers are presented 1 in Table 1, Table 1. Total employment multiplier for each group of sectors. AT DE ES FR IT NL SE G1 9.40 9.31 13.56 9.20 10.97 5.30 5.81 G2 5.00 6.77 8.70 6.10 8.51 3.54 4.46 G3 11.35 12.47 14.12 10.32 14.23 7.83 8.67 G4 9.18 12.35 14.86 10.68 13.98 8.34 10.02 G5 6.53 9.48 15.79 12.28 10.92 8.12 9.21 G6 0.41 7.17 12.92 6.91 9.42 3.83 4.39 G7 5.30 7.27 9.13 6.14 5.28 4.79 4.29 1 An aggregated classification is used for a clearer analysis in Table 1, but all calculations have been made with the maximum disaggregation. Margarita I. Barrera Lozano and Alfredo J. Mainar-Causapé 7 G8 10.59 17.42 16.60 12.87 15.81 13.46 12.18 G9 12.50 12.64 16.22 12.20 12.50 6.45 5.63 G10 10.09 11.75 14.47 10.50 11.43 8.80 9.80 Source: Authors’ own. Note: G1: Agriculture, fishing, and food industry; G2: Manufacturing; G3: Construction; G4: Trade; G5: Land transport; G6: Water transport; G7: Air transport; G8: Accommodation and food services; G9: Travel agencies; G10: Other services. Accommodation is the sector that represents the highest multiplier effect on average across all the countries, with a value of 14.13 2 . The other most dynamic sectors are Trade (G4: 11.34), Construction (G3: 11.29), Travel agencies (G9: 11.16), Other services (G10: 10.98) and Land transport (G5: 10.33). In contrast, Air transport (G7: 6.03) and Manufacturing (G2: 6.15) represent those sectors with the lowest employment multiplier effect on average, followed by Water transport (G6: 6.43). Most of the sectors present the highest maximum multipliers for Spain, which indicates that when a change in the final demand occurs, the most extreme change in employment occurs in Spain in most of the sectors. This is an indirect indicator of the low productivity as well as a warning regarding the higher dependence of employment on final demand when a negative shock occurs. Therefore, the contraction of final demand destroys a greater number of jobs in Spain than in any other country analysed. The variability of multipliers across countries differs depending on the sector considered. The sector with the most homogeneous behaviour is G7 (Air transport): 1.67, followed by G10 (Other services): 1.83, G2 (Manufacturing): 1.98, G3 (Construction): 2.51, G8 (Accommodation): 2.52, G4 (Trade): 2.46, G1 (Agriculture): 2.85, G5 (Land transport): 3.04, G9 (Travel agencies): 3.77, and G6 (Water transport): 4.06. This offers information on the similarities and therefore on the symmetry across member states when facing a similar shock. In other words, air transport faces similar issues in most of the countries, and overall measures influence each of them similarly. However, those sectors with more heterogeneous responses are targets of asymmetric shocks, and therefore policy measures should be designed in such a way that they can be adapted to countries or that they can be compensated with other mechanisms in order not to worsen their situation. Considering those sectors in which the pandemic has induced the greatest changes in final demand, Travel agencies and Water and Land transport are those for which the highest risk of asymmetric shock exist. The average employment multiplier across sectors differs between countries. Spain is the country in which a change in final demand implies the biggest change in the number of jobs in the economic activity. On average, a million euro change in final demand changes employment in more than 13 jobs (ES: 13.64), followed by Italy (IT: 11.31), Germany (DE: 10.66), France (FR: 9.72), Austria (AT: 8.03), Sweden (SE: 7.45), and the Netherlands (NL: 7.05). The highest employment multiplier in terms of countries corresponds to tourism-related sectors in all cases. Travel agencies is responsible for the highest level in Austria (G9: 12.50) and Accommodation and Food services are the highest level of employment multiplier in the remaining countries (G7: 17.42, DE; 16.60, ES; 15.81, IT; 13.46, NL; 12.87, FR; and 12.18, SE). The highest employment multiplier of all corresponds to Germany, where a million euro change in the final demand for Accommodation and Food services implies a change in more than 17 jobs. The employment multiplier is an indicator of the effects on employment of a one million euro change. Those countries with higher per capita income experience smaller effects, since the percentage in total final demand of one euro is also smaller; for this reason, it is important to analyse the 1% change in final demand instead of the employment multiplier. Table 2 shows the different composition of final demand by groups in each country: 2 An increase (decrease) in the final demand of one euro implies an increase (decrease) in employment of 14.13 jobs, considering direct, indirect, and induced effects. Effects of Shocks in Demand on Employment Regarding Age and Gender Population Groups in Different Zones of Europe. Consequences of the Pandemic Lockdown 8 Table 2. Total final demand shares for each group of sectors. AT DE ES FR IT NL SE G1 7,6% 7,6% 11,3% 9,9% 10,9% 11,5% 7,0% G2 40,3% 43,3% 30,9% 30,0% 35,6% 37,6% 34,4% G3 5,7% 5,3% 7,7% 8,1% 6,6% 4,6% 6,9% G4 1,1% 0,7% 1,1% 0,7% 1,2% 0,6% 0,6% G5 2,5% 0,8% 1,2% 1,0% 0,6% 1,1% 0,8% G6 0,1% 0,6% 0,2% 0,6% 0,2% 0,6% 0,5% G7 0,5% 0,6% 0,5% 0,7% 0,4% 0,8% 0,5% G8 5,3% 2,4% 8,0% 2,9% 5,2% 2,1% 2,1% G9 0,4% 0,2% 0,9% 0,1% 0,4% 0,8% 0,6% G10 36,6% 38,4% 38,2% 46,0% 38,8% 40,2% 46,5% Source: Authors’ own. The final demand is not equally shared by all the sectoral groups. The sectoral division is organised in terms of structural similarities, and of the damage produced by the pandemic, not to the size. Therefore, the importance in terms of monetary units presents major differences across sectors. On average, the largest final demands correspond to other Services 3 (41% of total), Manufacturing (36%), Agriculture (9%), Construction (6.4%), Accommodation (0.4%), Land transport (0.12%), Trade (0.09%), Air transport (0.06%), Travel agencies (0.05%), and Water transport (0.04%). It can be observed that those countries with the highest employment multiplier in sectors negatively affected by the pandemic are also those in which the final demand is the highest in relation to the sum of final demands of sectors. This is the case of Spain in Accommodation and Travel agencies, where they represent a greater share than in the rest of the countries. Table 3. Employment effects (‘000) of a change of 1% in the final demand of each sector. AUT Total 15-24 25-39 40-49 50-59 +60 Males Females G1 3.61 0.39 0.99 0.97 0.85 0.40 1.90 1.70 G2 10.16 1.29 3.83 2.69 1.99 0.36 6.16 3.96 G3 3.25 0.50 1.14 0.85 0.66 0.11 2.57 0.68 G4 0.49 0.08 0.17 0.13 0.09 0.01 0.35 0.14 G5 0.81 0.06 0.25 0.25 0.21 0.04 0.64 0.18 G6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 G7 0.13 0.01 0.06 0.04 0.02 0.00 0.08 0.05 G8 2.83 0.43 1.02 0.72 0.53 0.14 1.26 1.56 G9 0.28 0.02 0.12 0.08 0.05 0.01 0.12 0.16 G10 18.61 1.62 6.42 5.32 4.36 0.88 8.10 10.52 DEU Total 15-24 25-39 40-49 50-59 +60 Males Females G1 29.09 2.90 8.25 7.27 7.50 3.17 15.80 13.24 G2 121.16 11.76 37.41 31.07 30.53 10.39 77.32 43.74 G3 27.48 2.77 7.95 7.34 7.03 2.40 21.88 5.58 G4 3.73 0.51 1.19 0.90 0.84 0.29 2.83 0.90 G5 3.25 0.20 0.86 0.85 0.93 0.41 2.43 0.82 G6 1.77 0.12 0.57 0.46 0.45 0.17 1.28 0.49 G7 1.73 0.10 0.62 0.47 0.41 0.13 1.03 0.70 G8 17.39 2.70 5.69 4.09 3.46 1.46 8.57 8.81 3 The sectoral group of other services includes all services accounts in original SAMs used. Margarita I. Barrera Lozano and Alfredo J. Mainar-Causapé 9 G9 1.03 0.10 0.32 0.26 0.24 0.11 0.43 0.59 G10 186.44 15.70 57.95 45.82 48.39 18.58 77.56 108.83 ESP Total 15-24 25-39 40-49 50-59 +60 Males Females G1 20.98 1.10 7.72 6.28 4.53 1.34 12.93 8.05 G2 36.93 1.64 14.41 11.50 7.41 1.97 22.50 14.43 G3 15.00 0.46 5.78 4.82 3.15 0.79 11.93 3.08 G4 2.33 0.12 0.90 0.71 0.46 0.13 1.78 0.55 G5 2.71 0.07 0.82 0.99 0.66 0.17 2.18 0.53 G6 0.30 0.01 0.13 0.09 0.07 0.01 0.21 0.09 G7 0.57 0.02 0.23 0.20 0.10 0.02 0.35 0.22 G8 18.15 1.77 7.39 4.78 3.31 0.89 9.54 8.61 G9 2.02 0.12 0.80 0.63 0.38 0.09 1.03 1.00 G10 75.98 2.68 27.58 22.33 18.61 4.77 32.90 43.08 FRA Total 15-24 25-39 40-49 50-59 +60 Males Females G1 25.64 2.58 8.56 6.83 6.24 1.42 15.39 10.24 G2 51.62 4.54 18.42 14.81 11.86 1.98 31.83 19.79 G3 23.39 2.03 8.56 6.39 5.50 0.91 18.72 4.66 G4 2.09 0.21 0.75 0.57 0.49 0.07 1.51 0.58 G5 3.50 0.21 1.26 1.01 0.86 0.16 2.69 0.81 G6 1.08 0.08 0.39 0.32 0.23 0.05 0.66 0.42 G7 1.23 0.06 0.36 0.42 0.35 0.05 0.69 0.54 G8 10.47 1.84 3.60 2.61 2.01 0.40 5.72 4.75 G9 0.35 0.03 0.15 0.09 0.06 0.02 0.13 0.22 G10 136.00 8.58 46.60 38.01 34.26 8.56 53.92 82.08 ITA Total 15-24 25-39 40-49 50-59 +60 Males Females G1 24.50 1.16 8.00 7.47 5.71 2.15 15.80 8.66 G2 61.73 2.78 20.82 20.08 14.27 3.78 41.29 20.44 G3 19.09 0.81 6.62 6.04 4.46 1.18 15.79 3.31 G4 3.52 0.19 1.19 1.07 0.82 0.25 2.74 0.79 G5 1.38 0.04 0.43 0.45 0.36 0.10 1.10 0.28 G6 0.43 0.02 0.14 0.14 0.10 0.03 0.30 0.13 G7 0.48 0.02 0.18 0.16 0.10 0.03 0.31 0.17 G8 16.64 1.74 6.20 4.55 3.13 1.02 9.03 7.61 G9 0.96 0.05 0.37 0.28 0.20 0.06 0.49 0.47 G10 90.55 2.35 25.40 28.35 26.16 8.30 40.37 50.16 NDL Total 15-24 25-39 40-49 50-59 +60 Males Females G1 7.08 1.41 1.86 1.65 1.50 0.66 4.43 2.67 G2 15.46 2.84 4.42 3.77 3.21 1.22 9.96 5.46 G3 4.23 0.40 1.36 1.16 0.98 0.33 3.55 0.68 G4 0.57 0.09 0.18 0.15 0.11 0.05 0.43 0.14 G5 1.00 0.11 0.27 0.26 0.23 0.12 0.76 0.24 G6 0.28 0.04 0.09 0.06 0.06 0.03 0.21 0.07 G7 0.46 0.05 0.15 0.12 0.10 0.03 0.29 0.17 G8 3.33 1.39 0.83 0.49 0.43 0.18 1.71 1.62 G9 0.60 0.10 0.22 0.15 0.10 0.04 0.30 0.31 G10 41.15 3.92 13.32 10.30 9.76 3.85 18.15 22.99 SWE Total 15-24 25-39 40-49 50-59 +60 Males Females G1 2.49 0.33 0.79 0.53 0.50 0.35 1.55 0.94 G2 9.43 1.03 3.08 2.43 1.96 0.94 6.17 3.24 Effects of Shocks in Demand on Employment Regarding Age and Gender Population Groups in Different Zones of Europe. Consequences of the Pandemic Lockdown 16 37. Simonen J, Herala J, Svento R (2020) Creative destruction and creative resilience: Restructuring of the Nokia dominated high‐tech sector in the Oulu region. Reg Sci Policy Pract., 12: 931– 953. doi:https://doi.org/10.1111/rsp3.12267 38. Simonson J, Wünsche J, Tesch-Römer C (editors) (2023) Ageing in Times of the COVID-19 Pandemic. German Centre of Gerontology. Appendix Figure A1. Standard structure of a SAM. Source: Authors’ own from Mainar et al. (2018). Commodities Activities Factors Households Enterprises / Corporations Government Savings-Investment Rest of the World Total Commodities (C) Intermediate (inputs) consumption Household consumption Government expenditure Investment and stock changes Exports Demand Activities (A) Domestic production Gross output / Production (activity income) Factors (F) Remuneration of factors / Factor income Factor income from RoW Factor income Households (H) Factor income distribution to households (Inter-household transfers) Distribution of corporations income to households Government transfers to households Transfers to Households from RoW Household income Enterprises / Corporations (E) Factor income distribution to enterprises Government transfers to enterprises Transfers to Enterprises from RoW Enterprise income Government (G) Net taxes on products Net taxes on production Factor income to Government / Factor taxes Direct Household taxes / transfers to Government Direct Enterprise taxes and transfers to Government Transfers to Government from RoW Government income Savings-Investment (S-I) (Depreciation) Household savings Enterprise savings Government savings (Capital accounts transfers) Capital transfers from RoW (Balance of Payments) Savings Rest of the World (RoW) Imports Factor income distribution to RoW Household transfers to RoW Corporations income to Row Government transfers to RoW Payments to RoW Total Supply Costs of production activities Expenditure on factors Household expenditure Enterprise expenditure Government expenditure Investment Income from RoW