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REGIONÁLNÍ ROZDÍLY V KVALITĚ PRACOVNÍHO ŽIVOTA V ČESKÉ REPUBLICE
1Occupational Safety Research Institute, Czech Republic, firstname.lastname@example.org
This article reports on the regional differences in subjectively perceived quality of working life – measured using the Subjective Quality of Working Life index – in the Czech Republic. Using survey responses from 2,026 workers from 2020, representative of the working population, we show that variance in quality of working life is lower than in objective measures of social and economic development. At the same time, both importance and evaluation of the quality of working life domains are linked to macroeconomic characteristics, specifically unemployment and average gross wage.
Keywords: SQWLi, quality of working life, Czech Republic
Tento článek ukazuje regionální rozdíly v subjektivně vnímané kvalitě pracovního života – měřené pomocí indexu Subjective Quality of Working Life – v České republice. Na základě 2026 odpovědí z populačně reprezentativního dotazníkového šetření z roku 2020 ukazujeme, že variabilita kvality pracovního života je nižší než variabilita indikátorů sociálního a ekonomického rozvoje. Důležitost i hodnocení kvality pracovního života jsou nicméně propojené s makroekonomickými ukazateli, specificky mírou nezaměstnanosti a průměrnou hrubou mzdou.
Klíčová slova: SQWLi, kvalita pracovního života, Česká republika
Přijat k publikování / Received for publication 23. 10. 2022
Quality of working life is an indicator of broader quality of life and a key determinant of health at the personal level, as well as a critical component of productivity and sustainability of organisations. In the age of globalisation, digitalisation, hybrid models of work and more, working life is undergoing dramatic changes and workers’ safety, security and happiness are at constant risk. At the same time, with increasing dependence on quality rather than quantity of work, the importance of being content at work has been rising, as firms and policymakers realise that making employees happy can result in substantial return on investment through higher productivity and lower employee turnover.
Consequently, there has been increasing interest in measuring quality of working life in order to assess and improve it. In this regard, quality of working life is sometimes equated with job satisfaction, collectively referring to employee contentedness with the satisfaction of the needs through resources, activities and results stemming from the job (Sirgy et al., 2001). Such research projects include the Austrian Work Climate Index (Austria), Quality of Work in Flanders (Belgium), Job Quality Model (Canada), Gute Arbeit Index (Germany), Indicator of Quality of the Labour Market (Spain), as well as international projects such as the European Working Conditions Survey, European Labour Force Surveys, European Survey on Income and Living Conditions, and the International Survey Programme.
In the Czech Republic, quality of working life is measured using the Subjective Quality of Working Life index (SQWLi), developed by Vinopal (2011) (see also Vinopal, 2009, Vinopal, 2012, Vinopal and Pospíšilová, 2021, for details on the methodology). The index was developed as a standardised tool for long-term monitoring of quality of working life in the Czech Republic. It considers quality of working life in two dimensions – importance and evaluation – each consisting of six domains: remuneration, relationships, time, self-realisation, security, and conditions. Each domain is scored on an 11-point numeric scale and the total domain and dimension scores are calculated as simple averages of the individual scores, ranging from 0 to 100.
The index’s methodology is based on the theory of needs satisfaction; however, as the author argues, measuring satisfaction is not sufficient to assess the overall quality of working life as everyone’s perception of the individual elements – such as salary, interpersonal relations in the workplace, security, independence, prospects, etc. – will differ. As a result, the index tracks both the subjective evaluation and importance of the individual domains. The methodology has been certified by the Ministry of Labour and Social Affairs in the Czech Republic and has been used to quantify the quality of working life in the Czech Republic since 2011.
In this article, we utilise the certified methodology and population-representative data to discuss variance in the subjective quality of working life at the regional level, highlighting the links to other macroeconomic variables.
The Czech Republic is divided into 14 regions differing in number of inhabitants, industrial composition, economic output, unemployment rates and more. Following is a brief breakdown of the main indicators based on data from the Czech Statistical Office:
|Inhabitants (thousands)||Employees per 1000 inhabitants||Entrepren. per 1000 inhabitants||Unempl. rate (%)||Average gross monthly wage (CZK)||GDP per capita (thousand CZK)||Investment rate|
|Central Bohemia Region||1,394||299||106||1.1||41,825||476||39.0|
|South Bohemian region||637||345||97||1.9||36,377||433||25.8|
|The Pilsen Region||580||366||85||1.7||37,827||468||26.6|
|Karlovy Vary Region||283||291||99||4.2||34,725||332||28.3|
|The Ústí Region||797||303||77||3.4||36,866||371||26.7|
|Hradec Králové Region||542||359||99||3.0||38,712||481||20.1|
|The Pardubice Region||515||355||93||1.7||35,385||436||24.6|
|Southern Moravia Region||1,186||394||96||2.0||39,041||514||27.6|
|The Olomouc Region||623||350||84||3.6||36,012||419||28.4|
Table 1: Descriptive statistics of selected variables across the regions in the Czech Republic; latest available data (2020-2022)
Prague, the capital, outperforms the other regions in terms of economic activity: number of employees and entrepreneurs per 1000 inhabitants, average monthly wage, GDP per capita. As pointed out by OECD (2018, 2020), this is due to the fast economic growth in the region of Prague, which drove an increase in regional economic disparities. Some of the effects – average wage, investment rate, unemployment rate – spill over to the neighbouring Central Bohemia Region. Other regions are more similar in their characteristics, although particularly Karlovy Vary region, the Ústí Region, and the Olomouc Region show higher unemployment rates.
Working conditions and workers’ opportunities vary with economic performance and industrial composition. For instance, Prague has the highest potential for remote working (50% of jobs) compared to just 26% in Northwest and Central Moravia (OECD, 2020). People in Prague also rank at the very top of wellbeing scales in terms of access to education and jobs. Regions generally vary in the overall wellbeing indicators; the largest gaps are in perceived social network support (“Community”) and share of households with broadband access (“Access to services”) as shown in Figure 1.
Figure 1: Wellbeing regional disparities in the Czech Republic (source: OECD (2020)
We utilise data from a population-representative survey conducted in November 2020 by the Institute for Sociology of the Czech Academy of Sciences. The survey collected a total of 2,026 responses from the 6,095 initial survey requests using a computer-assisted web interviewing (CAWI) method. The collected data are representative of employees in the Czech Republic, 18-65 years of age, in terms of gender, age, education, region of residence and type of economic activity (employee/entrepreneur).
Quality of working life is assessed using the SQWL index described above. We present both importance and evaluation of the overall quality of working life, as well as with each of its six domains. Both variables are presented at a 0-100 scale, with higher values representing greater importance and higher evaluation. In addition to the questions assessing quality of working life across the six domains, information is obtained about respondents’ age, gender, region of residence and more. Following are key descriptive statistics of the dataset.
|Variable||Category||Freq./ Mean||SD||Min||Max||Missing obs.|
|Education||None or primary||2.2%||0|
|Secondary (GCSE equivalent)||33.1%|
|Secondary (A levels equivalent)||34.9%|
|Post-secondary or undergraduate||9.9%|
|Graduate or postgraduate||19.8%|
|Job position||Skilled agricultural, forestry and fishery workers||7.4%||80|
|Plant and machine operators, and assemblers||8.2%|
|Craft and related trades workers||12.1%|
|Service and sales workers||17.8%|
|Clerical support workers||19.7%|
|Technicians and associate professionals||15.9%|
|Organisation type||Private enterprise||70.2%||359|
|Public institution or NGO (school, hospital)||10.7%|
|Organisation size (number of employees)||1-9||23.2%||79|
Table 2: Descriptive statistics of the collected dataset
Tables 3 and 4 show the estimated differences in the subjectively perceived importance and evaluation of the six domains of and the overall quality of working life. We can see that, on average, there is very little upward variation in the importance of the individual dimensions (however, there are substantial differences across the dimensions following the national averages). That is, the regional averages tend not to be significantly higher than the national averages. The only exceptions are importance of time and working conditions, which are particularly important for workers in the Ústí Region. There is more downward variation; time is of lower importance to workers in Vysočina and Zlín regions, self-realisation in the Olomouc and Karlovy Vary regions, and job security in Prague.
|Central Bohemia Region||79.5||87.5||84.3||76.3||72.2||80.0||76.8|
|South Bohemian region||80.3||89.2||85.4||73.0||73.5||80.9||79.3|
|The Pilsen Region||77.8||85.7||83.5||73.0||69.9||79.7||74.6|
|Karlovy Vary Region||78.4||88.7||80.7||75.4||67.7||79.3||78.4|
|The Ústí Region||81.0||89.3||83.7||77.3||72.5||82.2||80.8|
|Hradec Králové Region||79.2||88.2||81.9||74.8||72.4||80.9||76.8|
|The Pardubice Region||78.2||87.3||84.2||72.4||70.5||76.5||78.4|
|Southern Moravia Region||79.5||88.4||83.8||74.7||71.8||81.0||77.6|
|The Olomouc Region||79.2||88.4||83.3||76.1||67.8||80.1||79.3|
Table 3: Regional differences in quality of working life: importance; differences greater than ±3 points from the overall average at the level of individual dimensions in bold
Similar picture is depicted in Table 4, showing evaluation of the domains. Here, workers in the Olomouc Region evaluate their quality of working life the lowest, showing negative difference of 3+ points in 5 out of 6 dimensions. Other regions are again close to the national averages, with Vysočina Region showing higher than average evaluation of remuneration, relationships and self-realisation.
|Central Bohemia Region||75.7||73.8||80.1||72.1||73.1||75.7||79.4|
|South Bohemian region||75.8||74.7||79.1||72.4||72.1||76.5||80.6|
|The Pilsen Region||75.6||74.7||78.7||72.1||69.8||76.8||80.2|
|Karlovy Vary Region||73.3||74.7||74.7||69.0||67.4||75.9||78.1|
|The Ústí Region||74.9||73.9||74.8||72.0||72.4||77.3||79.3|
|Hradec Králové Region||77.7||76.5||77.9||73.2||75.7||79.9||82.8|
|The Pardubice Region||75.0||73.9||77.6||71.6||71.5||75.0||79.4|
|Southern Moravia Region||74.9||74.1||77.2||69.8||71.7||77.2||80.2|
|The Olomouc Region||71.2||70.1||73.1||67.6||67.6||71.1||76.5|
Table 4: Regional differences in quality of working life: evaluation; differences greater than ±3 points from the overall average at the level of individual dimensions in bold
There is more variation in evaluation scores than in importance scores, as shown in Figure 2. Interestingly, despite the differences in economic performance, workers in Prague do not outperform those in other regions. On the contrary, their overall evaluation of the quality of working life is behind e.g. Karlovy Vary Region, which ranks high in unemployment rate and low in average gross wage.
Figure 2: Regional differences in quality of working life: overall importance and evaluation
More formally, we can assess correlation between quality of working life and the selected macroeconomic indicators (see Table 5). Importance of working life domains is positively correlated with unemployment rate (Pearson correlation coefficient r=0,39) and negatively correlated with average gross wage (r=-0,32), with the domains of remuneration and security showing substantially higher correlation (r=0,64 and r=-0,62, respectively), indicating that workers take the broader macroeconomic conditions in their perception of working conditions.
Correlation with the evaluation scores is lower and with opposite signs: r=-0,24 for unemployment rate and r=0,17 for average gross wage. The signs indicate that worse macroeconomic conditions lead to higher importance of quality of working life dimensions and, at the same time, lower evaluation. Investment rate, on the other hand, is effectively uncorrelated with both importance and evaluation.
|8||Average gross monthly wage||-0.32||0.17||-0.62||-0.49||-0.15||0.02||-0.45||-|
Table 5: Correlation coefficients for selected variables (analysis at the regional level)
Quality of working life is critical for personal wellbeing, organisational performance and long-term sustainability at the macroeconomic level. This article shows that in the context of the Czech Republic, variance in the subjective quality of working life at the regional level, assessed using the SQWL index, is lower than in objective measures of social and economic development. In particular, the averages for most regions are close to the national averages, with just the Olomouc Region evaluating their overall quality of working life significantly lower. At the same time, both importance and evaluation with the domains of quality of working life are linked to macroeconomic characteristics, specifically unemployment and average gross wage.
Quality of working life can therefore be improved at the individual level (e.g. by obtaining additional qualifications), at the organisational level (e.g. by providing more training and development opportunities for workers), and at the macroeconomic level (e.g. by lowering economic uncertainty and creating more jobs). The short- and long-term benefits can then equally be benefited from at multiple levels in terms of better health and work/life evaluation, higher productivity and lower turnover rates, and higher overall economic output.
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 Share of gross fixed capital to gross value added.