Relationships between Average Wages in the Manufacturing Sector and Economic Indicators of the Manufacturing Sector in the Region of Visegrad Group Countries

Abstract

The role and position of the manufacturing sector changes over time. Its importance in the sustainable growth of the economy, innovations, trade, reducing energy demand, and environmental problems is currently being shown again. The study underlines the significance and importance of the manufacturing sector in the economy of countries, and the generally applicable economic principles are explicitly examined in regard to the manufacturing sector. It examines whether selected economic indicators of the manufacturing sector in the region of the Visegrad Group countries can affect the level of average wages in the sector. Wages represent a key determinant of attractiveness, as well as the potential to increase the standards of living and the long-term sustainability of a given sector. The selected economic indicators for the period 2008–2019 concerning average wages in the manufacturing sector were: FDI Flow, GDP, labour productivity, employment, and the number of hours worked in the manufacturing sector. The source of secondary data was the OECD database. A multiple regression model was used and tested. The suitability of the proposed model was tested using the ANOVA method. A significant effect was shown in the case of two of the examined variables, namely the GDP and employment in the manufacturing sector. Based on the findings of the study, it can be assumed that the sectoral GDP can positively affect average wages in the sector and the level of employment in manufacturing can negatively affect them. The summary of implications and proposals indirectly supports the need to develop and introduce innovations, new technologies, automation, and robotization, as well as for further implementation and support of Industry 4.0 and 5.0.

1. Introduction

At the outset, it is necessary to emphasize the significance and importance of the manufacturing sector in the economies of countries. From a historical point of view, the manufacturing sector has been the driver of economic growth, structural changes, and catch-up [1]. It is well known that the manufacturing sector has long been a mainstay of many national economies, as it is a key sector that creates productive jobs and sustainable economic growth [2]. This sector has multiplier effects, being closely related to the other sectors of the economy with backward and forward links [3]. Its importance is also recognized within the EU. The European Commission has stated that the manufacturing sector has a strong spill-over effect on other sectors—additional final demand in manufacturing generates around half as much additional final demand elsewhere in the economy [4].

This article examines the average wages in the manufacturing sector based on secondary data. It attempts to follow generally applicable macroeconomic principles of relationships between economic variables and examine them explicitly in the manufacturing sector. Wages in the manufacturing sector are a key determinant of the industry’s attractiveness in the labour market. However, it should be borne in mind that the composition of the labour market of each sector has its specifics, whether in terms of age structure, educational structure, experience requirements, etc. These specifics also affect the level of wages compared to other sectors. On the other hand, there may be links between wages in a specific sector of the economy and economic indicators in this sector. These may be those which are examined in this paper: the flow of foreign direct investment in the manufacturing sector, labour productivity in the manufacturing sector, GDP/capita in the manufacturing sector, the number of employees in the manufacturing sector, or the number of hours worked within the manufacturing sector. The paper aims to determine how the selected economic indicators of the manufacturing sector could affect the level of average wages in the manufacturing sector in the Visegrad Group (V4) region. For this purpose, a multiple regression model was used and tested, with the suitability of the proposed model being tested using the ANOVA method. The necessary data were obtained from the OECD database.

There is a lack of recent studies and evidence that examine this specific region within Central Europe and, especially, that focus on the economy of the manufacturing sector separately. This study is intended to contribute to filling this gap. The study underlines the significance and importance of the manufacturing sector in the economies of countries, and the empirical part explicitly examines the manufacturing sector in the context of generally applicable economic principles. The authors of this study were limited by the availability of indicators and data for the specific manufacturing sector; however, the results of the studies presented in the following parts of the paper justify and confirm the validity of the selected indicators as variables.

When examining the relationship between labour productivity in the manufacturing sector and wages in the manufacturing sector, it is possible to draw on the recent study of Stundziene and Baliute [5]. The authors found that in the manufacturing sector, a long-term relationship exists between wages and apparent labour productivity. The research was carried out on a sample of 27 European countries. The relationship between wages and productivity has garnered a great deal of attention from researchers who were attempting to empirically justify the links between these two indicators in European countries [6,7,8].

Regarding the relationship between wages and GDP in a country’s economy, a recent study on a sample of European countries (including the V4 countries) [9] confirmed that the relationship between GDP and wages is positively correlated, varying in the same direction for all analysed European countries. Several studies have examined the relationship between wages and GDP in the European area empirically [10,11,12], with similar results for entire countries, as this study attempts to apply individually to the manufacturing sector.

The authors [13] examined the relationships between the inflow of foreign direct investments (FDI) and economic indicators, including the average wages at the level of countries’ economies. However, the flow of FDI is also dealt with by empirical studies at the level of the manufacturing sector [14,15,16] in the European area. In general, FDI should have a positive effect on the level of wages, especially in low-income sectors of the economy [13,17].

The theoretical approaches to the relationship between the level of wages and the level of employment are presented in the literature review section; however, there are also recent studies in the European area dealing with the topic [18,19,20,21]. Most studies examine the relationship in the direction of wages influencing employment. The novelty of this study is the opposite approach, which can occur precisely in cases such as the manufacturing sector in European regions, where there is a high potential for the introduction of new technologies [22,23]. Digitization and the introduction of new technologies (in the context of Industry 4.0 but also Industry 5.0, which also includes a human-centred approach [24]) result in the need for more skilled employees in industry sectors. As recent studies show, more skilled employees need to work fewer hours for wages, while the wages of skilled employees are also generally higher [25,26,27]. For the above reasons, it is worth examining whether average wages increase in the manufacturing sector with a decreasing number of hours worked.

The content of the paper includes the literature review, which contains an overview of the theoretical background and research results of the topic concerning the ties explored in the article. The next chapter specifies the research problem, hypothesis, and research methodology aimed at fulfilling the established research objective. The research results are shown in the next chapter, which is followed by their critical discussion and laying out the possible policy implications, practical implications, and proposals. The last part of the paper is the conclusions, which contain the summarization of the results, implications, and proposals. Also, it sets out the limitations of the presented research and further research ambitions.

2. Literature Review

As mentioned in the Introduction, the manufacturing sector occupies an important position in the economies of countries. The literature review was mainly processed concerning generally applicable economic principles and relationships of the manufacturing sector with the economy as a whole. This study is intended to apply them in examining the manufacturing sector itself as a separate part of the economy.

Kaldor [28,29] uncovered a set of long-run relationships at the sectoral level using cross-sectional data for OECD countries. Kaldor’s Law created the traditional Kaldorian approaches which consider the manufacturing sector as the main source of growth of countries’ economies, as it is characterised by the development of technical progress, capital accumulation, economies of scale [30], technological spillovers, and strong backward and forward linkage effects [31]. However, over the last decades, the share of manufacturing in employment and value added is shrinking to much lower levels of income in comparison with early industrialists [32]. Services sector growth represents a qualitatively new stage in the social structure of production and division of labour and, in the absence of sizable manufacturing industries, whether services activities can lead to the economic growth of countries is a matter of concern [33]. The McKinsey Global Institute points out that the role of manufacturing in the economy changes over time and it differs according to the economic development stage of the country [34]. According to the claims of the mentioned study, empirical evidence shows that, as countries’ economies become wealthier, the share of manufacturing in a country’s GDP rises to a certain point and, as economies mature, the manufacturing sector becomes more important for attributes such as its ability to drive country’s productivity growth, innovation, and trade. Furthermore, the manufacturing sector plays an essential role in reducing energy and resource consumption, and limiting greenhouse gas emissions [35]. The above reasons for the important position of the manufacturing sector in the economy lead the authors of this study to examine it in this paper.

The common research area for the manufacturing sector in this paper is the territory of the Visegrad Group (V4) countries. Czech Republic, Hungary, Poland, and Slovakia are countries that share geographical proximity, as well as cultural and historical development. The V4 countries also share similar experiences in the field of economic transformation and the challenges of joining international structures.

This region has its specifics and may have them in the manufacturing sector as well. After all, it is even possible to find differences between the production plants themselves. In a model with heterogeneous plants and quality differentiation, more productive plants produce higher-quality goods than less productive plants, and they pay higher wages to maintain a higher-quality workforce [36].

Foreign capital can be one of the factors which determines the level of wages in individual companies. Foreign direct investments (FDI) are an integral part of an open and efficient international economic system and a major catalyst for the economic development of countries [13]. A large empirical section of the literature documents that companies with foreign investment are larger, more productive, more capital intensive, pay higher wages, and have a more skilled workforce compared to firms with exclusively domestic capital [17,37]. Based on the research of Breau and Brown [38], performed within the Canadian manufacturing sector, the results from wage regression models reveal that foreign-controlled plants do pay higher wages than domestic-controlled plants and these results hold even after controlling for other plant and worker characteristics. Cross-sectional studies by Lipsey and Sjöholm [39] made on Indonesian manufacturing industries and provinces imply that a higher foreign presence raises the general wage level in the economy. The FDI can also affect the level of wages individually in separate sectors of the economy. The results of Li et al. [40] showed that FDI in the generalised virtual economy industry could promote the increase of average wage level in the way that every 1% increase in FDI increases the average wage level in the industry by 0.88%. From the other perspective, the authors Damijan and Kostevc [41] examined the role of FDI in the adjustment pattern of regional wages, and the results showed that, in most cases, the FDI had contributed significantly to faster adjustment of relative regional wages.

This suggests that foreign investors may have a positive effect on the welfare of the workforce of the host economy [17]. This argument has been used to justify national industrial policies to attract and secure foreign investment.

On the other hand, [42] concluded in his research that there is a correlation between FDI and market size (as measured by per-capita GDP); however, he also indicated that the relation with wages is highly sensitive to small alterations in other variables used in the research. The research was based on the examination of 16 variables on a sample of 135 countries only for the year 1994, which may limit the results.

As Krugman and Wells [43] point out, in countries with high labour productivity, the average wages are at a high level as well. The European Commission highlighted that “growth in real wages, as a result of increased productivity, is crucial to reduce inequalities and ensure high standards of living” [44]. Other authors say that the differences in the level of wages between countries are caused by different labour productivity and the economic development of a particular country [45,46,47]. The labour productivity–wages relationship is bi-directional [6]. Findings for whole countries could be applied to individual sectors of the economy, such as the manufacturing sector examined in this article. According to Efficiency Wage Theory, wages positively influence labour productivity, a fact that suggests that increasing wage levels encourage workers to boost productivity in response to high incentives offered by their firms [48,49,50]. On the other hand, other studies (neoclassical theory) [51,52,53] have pointed out that higher worker productivity increases the general level of wages in the economy. Ozturk et al. [54] examined whether the labour productivity index affects the labour wage in New Zeeland’s construction sector (which is the fourth-largest contributor to their national economy). They concluded that the more productive the labour was, the more wages were earned. There are also examples, such as the study of Tadjoeddin [55], showing a decoupling trend between real wages and productivity in the overall manufacturing sector.

The authors Stojanova and Trnkova [56] researched the relationship between GDP and wages in a sample of the following countries: the Czech Republic, Germany, Ireland, Italy, Austria, and the United Kingdom. They found that there is a direct dependence of average gross hourly earnings on GDP per capita in all selected countries. Also, the authors Suhányi et al. [57] concluded in their research that the increase in wages is dependent on the economic development of the country. They also found that there is an evident labour migration to countries with better economic results and subsequently higher wages.

Information on wage levels is essential when evaluating the living standards of a country, and in the assessment of the working and living conditions of the workers. The relation between wages and level of employment can be also a valuable indicator in the analysis of business cycles [58]. Keynes [59] viewed cyclical movements in employment along a stable labour demand schedule, thus indicating counter-cyclical real wages. His deduction is in line with the ideas of sticky wages and sticky expectations, which augments models such as the Phillips curve. In these models, the real wages behaved as counter-cyclical. However, Long and Plosser [60] and Kydland and Prescott [61] highlight the technology shocks which lead to pro-cyclical real wages, which follow from their study of real business cycle models. There are also authors arguing against the counter-cyclical nature of wages concerning employment. Even a study conducted on a sample of European Union countries showed stronger pro-cyclicality in the annual data [62], which is consistent with Dunlop’s [63] claims in his research.

The simple neoclassical model of labour supply assumes that employees earn a specific individual wage rate that determines the number of working hours. However, the assumption of constant wages is often criticized and most questionable [64]. There are several reasons, e.g., (1) lower wage rates are paid for part-time than for full-time jobs [65]; (2) the number of working hours may also influence productivity [66,67,68]; (3) wage differentials may also be caused by spatial constraints on the supply of labour [69]; (4) work that employees consider undesirable (but that saves company costs) increases wages to compensate for unpleasant conditions.

3. Materials and Methods

Based on a detailed examination of published works, it can be concluded that the issue of average wages is paid considerable attention on a global scale. Probably one of the most researched wage topics in research studies is the issue of the minimum wage. However, this does not apply to wages in the manufacturing sector of the Visegrad Group region (V4), where the issue of average wages is unexamined, or poorly examined. Thus, the lack of scientific and academic output in this area opens many possibilities and perspectives that can and should be explored in connection with the phenomenon of average wages.

As can be seen on the graph (Figure 1), in addition to other mentioned similarities, the analysed four countries also share very similar characteristics concerning average wages in the manufacturing sector. The levels of these wages are very similar in the mentioned region. All four countries that are part of the examined region operate on the principle of open economies. The graph presented below shows the annual average wages in the manufacturing sector recalculated according to the current conversion rate in the respective year to the reference currency. This approach also offers a comprehensive picture concerning foreign direct investments and the international attractiveness of the industry and allows comparing countries by individual years. However, it is worth mentioning that nominal wages expressed in national currencies in the manufacturing sector have grown continuously since 2009 in all four countries (between the years 2008 and 2009, in all four cases, there was a decrease in average wages, even when converted to the national currency).

It has already been mentioned that the specificity of the approach to average wages in this article consists of their conversion with the current annual conversion rate of the reference currency (US dollar) based on data from the OECD database. It is not a simple indicator available in the database, and was calculated in the following way:

The average annual wage of an employee in the manufacturing sector in a given year = total annual wage paid in the manufacturing sector in the country/

number of employees in the manufacturing sector/

a conversion rate of the national currency to the US dollar for the relevant year

On the other hand, when comparing the average wages in the manufacturing sector and the average wages in the economy of the examined countries (Figure 2), significant differences can be noted. The manufacturing sector appears to be significantly undersized compared to the average wages in the economies of the countries, namely in the entire region of the V4 countries. Hungary seems to be the closest to equalizing wages, which, in actuality, is due to the reduction in average wages in other sectors of the economy rather than the progress of wages in the manufacturing sector.

Given the theoretical knowledge and the results of the scientific studies presented in this article, the manufacturing sector is one of the most significant sources of economic growth from a long-term perspective. This is also confirmed by Kaldor’s Law and other Kaldor approaches. It follows that countries’ economies cannot rely only on the growth of the service sector, which has become a phenomenon of the past period. The manufacturing sector has its irreplaceable place in the long-term sustainability of economic growth, which has been recognized also by the European Commission (EC). A prosperous sector of the economy must also ensure adequate remuneration for its employees. As this article states, the consumer behaviour of manufacturing sector employees has another multiplier effect on the remaining sectors of the economy.

Therefore, the problem to be solved in this paper (i.e., determining whether selected economic indicators of the manufacturing sector in the region of V4 countries can affect the level of average wages in the manufacturing sector) has its clear justification for further setting the policy implications and the practical implications of V4 countries.

The consequent aim of the paper is to determine how selected economic indicators of the manufacturing sector could affect the level of average wages in the manufacturing sector in the V4 region. The evaluation of the influence of the selected identifiers (indicators) in this study is based on the validation of the following working hypothesis:

H1: 

The level of average wages in the manufacturing sector in the region of the V4 countries can be affected by the FDI flow in the manufacturing sector per capita (positive relationship assumed), the GDP in the manufacturing sector per capita (positive relationship assumed), the employee productivity in the manufacturing sector (positive relationship assumed), the level of employment in the manufacturing sector (negative relationship assumed), and the number of hours worked in the manufacturing sector (negative relationship assumed).

The research sample consisted of the region of the V4 countries, and the rationale is given in the following text.

The examined available economic indicators of the manufacturing sector (independent variables) were those that are supported by theoretical knowledge and scientific studies in the first part of the paper. The composition of selected economic indicators of the manufacturing sector together with the mentioned approach to the expression of the average wage in the manufacturing sector constitutes a significant novelty of the presented scientific contribution. The selected economic indicators (data on an annual basis) were:

• The flow of foreign direct investments (FDI Flow) in the manufacturing sector, which were examined from the point of view of their net inflow (i.e., based on the inbound principle). The variable used in the model was:

• The inflow of the FDI to the Manufacturing Sector per Capita in the relevant V4 country = ‘FDI_Flow_manuf_t’;

• GDP in the manufacturing sector—measured by the output method. The variable used in the model was:

• GDP in the Manufacturing Sector per Capita in the relevant V4 country measured by the Output Method = ‘GDP_output_manuf_t’;

• Growth of labour productivity in the manufacturing sector—as the growth rate of productivity in the manufacturing sector (an indicator in relative terms) is originally calculated from the GDP of the manufacturing sector per one person employed in the manufacturing sector, there is no need to adapt the indicator to country size specifics. This also reduces the risk of collinearity with the GDP in the Manufacturing Sector per Capita. The validation showed that the correlation coefficient between the two variables had a non-significant value of −0.027157 when tested. The variable used in the model was:

• Growth of labour productivity in the manufacturing sector = ‘Productivity_manuf_t’;

• Employment in the manufacturing sector. The variable used in the model was:

• Employment in the Manufacturing Sector per 1000 inhabitants in the relevant V4 country = ‘Employment_manuf_t’;

• The number of hours worked in the manufacturing sector. The variable used in the model was:

• The total Number of Hours Worked in the Manufacturing Sector per one person employed in the Manufacturing Sector in the relevant V4 country = ‘Hours_worked_manuf_t’;

Average wages and salaries in the manufacturing sector, characterized in the previous text, appear as the dependent variable in the study. The original data come from the OECD database, specifically from SDBS Structural Business Statistics (ISIC Rev. 4). The data were available in national currencies and were converted to US dollars. The exchange rates were also available in the OECD database in the ‘PPPs and exchange rates’ dataset, from which current exchange rates were applied—period-average on an annual basis, and these were used for the purpose of this study.

Within the scope of the study, the region of the V4 countries was examined, which includes Slovak Republic, Hungary, Czech Republic, and Poland. It is an active regional structure of four member countries of the EU and NATO, which share similar political, socioeconomic, economic, and cultural-historical values. On the official website of the Visegrad Group [70], it is stated that these grouped countries in the Central European region aim to cooperate in several areas of common interest within the framework of pan-European integration. All V4 activities are aimed towards strengthening stability in the Central European region. The regional cooperation of the V4 is successfully developing even in the current period. This primarily takes the form of sectoral policies, such as the economy, infrastructure, energy, digitization, and innovation.

According to the OECD definition taken from ISIC Rev.4 [71], the manufacturing sector consists of: manufacture of food products, manufacture of beverages, manufacture of tobacco products, manufacture of textiles, manufacture of wearing apparel, manufacture of leather and related products, manufacture of wood and of products of wood and cork (except furniture), manufacture of articles of straw and plaiting materials, manufacture of paper and paper products, printing and reproduction of recorded media, manufacture of coke and refined petroleum products, manufacture of chemicals and chemical products, manufacture of pharmaceuticals, medicinal chemical and botanical products, manufacture of rubber and plastics products, manufacture of other non-metallic mineral products, manufacture of basic metals, manufacture of fabricated metal products (except machinery and equipment), manufacture of computer, electronic, and optical products, manufacture of electrical equipment, manufacture of machinery and equipment n.e.c., manufacture of motor vehicles, trailers, and semi-trailers, manufacture of other transport equipment, manufacture of furniture, other manufacturing, and repair and installation of machinery and equipment.

The examined period was the period from 2008 to 2019, for which the available data were reliable at the time of their collection. The period in question was limited by the availability of data for determining the values of the selected variables. Adding more recent data (data for 2020) could distort the results due to the pandemic and crisis, which particularly affected the manufacturing sector in the region (wages, employment, productivity, and temporary government measures).

A multiple regression model was used to verify the mentioned hypothesis. The software product Statistica 13 was used for processing.

The model reflects the effect of all mentioned explanatory (independent) variables on the explained (dependent) variable: Average wage in the manufacturing sector (Average_wage&salary_manuf_t). The explanatory economic variables of the manufacturing sector in the V4 region were: the flow of foreign direct investments into the sector–inbound (FDI_Flow_manuf_t), GDP of the manufacturing sector–output approach (GDP_output_manuf_t), productivity per employee (Productivity_manuf_t), employment in the manufacturing sector (Employment_manuf_t), and the total number of hours worked in the manufacturing sector (Hours_worked_manuf_t). The goal of using the regression model was to quantify the effect of the selected explanatory variables on the explained variable. It was necessary to find and create a linear model expressing the dependencies between the selected newly designed variables. In this article, the model and the predicted effects of the explanatory variables on the explained variable were inspired by the approach of Meixnerová and Krajňák [72] on a sample of V4 countries; however, their study also took into account the minimum wage, the price index, and the implicit tax rate. The mentioned approach was adapted based on the above-mentioned facts, the availability of data, the presented theoretical knowledge, and the results of scientific studies—the new approach to solving the problem was formulated as follows:

$$\begin{gathered} Average\_wage\&salary\_manu{f}_t= \\ f\left(FDI\_Flow\_manu{f}_t,GDP\_output\_manu{f}_t,Productivity\_manu{f}_t,Employment\_manu{f}_t,Hours\_worked\_manu{f}_t\right) \end{gathered}$$

(1)

$$Average\_wage\&salary\_manu{f}_t=f\left(+,+,+,-,-\right)$$

Based on the compilation and calculation of a multiple regression model containing all of the proposed variables, a multiple regression model with a reduced number of independent variables was subsequently used. It was compiled on the basis that some of the variables could not be considered significant based on p-values exceeding the set significance level of 0.05. Therefore, the mentioned variables were excluded from further calculations. This also increased the number of observations, since the original model lacked available data for some of the excluded variables. The modified multiple regression model with reduced variables has the following form:

$$Average\_wage\&salary\_manu{f}_t=f\left(GDP\_output\_manu{f}_t,Employment\_manu{f}_t\right)$$

(2)

$$Average\_wage\&salary\_manu{f}_t=f\left(+,-\right)$$

The ANOVA method was used to test the suitability of the proposed model. To test whether the means differ, the ANOVA test compares the explained variance (caused by the input fields) to the unexplained variance (caused by the source of the variance). If the ratio of explained variance to unexplained variance is high, it means that the mean values are statistically significant. The tested null hypothesis is the statement that the model that was chosen to explain the effect is not suitable (the alternative hypothesis states the opposite). The F-test is used to evaluate the statement. If the resulting level of significance in the F-test is lower than 0.05, the null hypothesis is rejected, which means that the model was chosen correctly.

4. Results

The following section of the paper presents the results of the multiple regression analysis. Based on the tested model described in the methodological part, it was examined whether there is a relationship between the average wage in the manufacturing sector, which was considered as a dependent variable (based on the assumptions), and five economic indicators of the manufacturing sector, considered as independent variables (

Table 1. The summary output of the multiple regression model with a dependent variable: Avg_wage&salary_manuf–Full number of Independent Variables.

Summary Output
Regression Statistics
Multiple R	0.843741926
R Square	0.711900437
Adjusted R Square	0.670743357
Standard Error	930.6711742
p	0.000000013
No. of cases	48
Observations	35

).

As shown, the Multiple R-value (correlation coefficient) is equal to 0.8437. The closer this value is to 1, the stronger the dependence. In the examined case, there is a high degree of intensity of dependence between the variables. The value of R square (value of the coefficient of determination) is at the level of 0.7119. This value, after multiplication by 100, indicates that the selected regression line explains the variability of average wages in the manufacturing sector in the V4 region to approximately 71%, and the remaining part represents unexplained variability, the influence of random variables, and other unspecified influences. The adjusted R square (adjusted coefficient of determination) also takes into account the number of estimated parameters and the number of measurements, after which the values are still at the level of 67%. The Standard Error (standard error of the mean) should be as small as possible. It was possible to evaluate 35 observations out of 48 cases, for which the data were available for every single examined variable. This was caused by the character of the data that were collected for four countries of the V4 region in the case of six variables. If any variable was not available for a country in a certain year, that case was excluded from the regression model.

Based on the given data, it can be concluded that, in the case of three of the five examined independent variables, a significant relationship has not been proven concerning the average wage in the manufacturing sector in the V4 region. It is not possible to confirm that the flow of foreign direct investments in the manufacturing sector has an impact on average wages. At the same time, it has also not been proven that the year-on-year change in the productivity of workers in the manufacturing sector has an impact on them, and according to the statement, we cannot even confirm the link between the total number of hours worked in the sector and the average wage.

The results of the regression summary (

Table 2. Regression summary for dependent variable: Avg_wage&salary_manuf–Full number of Independent Variables.

	b*	Std. Err. (of b*)	Coefficients (b)	Standard Error (of b)	t Stat (35)	p-Value
Intercept			7134.514921	2359.145865	3.024194063	0.004646301	**
FDI_flow_manuf	0.056927316	0.093922902	209260.6441	345253.7088	0.606106868	0.548354864
GDP_output_manuf	2.338042615	0.383001314	2517837.561	412454.0281	6.104528965	0.000000562	***
Productivity_manuf	−0.015820167	0.094254086	−3.586393665	21.36717352	−0.167845956	0.867670596
Employment_manuf	−1.77437723	0.387263978	−72255.23248	15769.95484	−4.581828751	0.000056343	***
Hours_worked_manuf	0.154600339	0.129622198	0.002379821	0.001995323	1.192699562	0.241010368

Note: ***, ** indicate that the p-value is equal to or less than 0.001, and 0.01.

) show that it is possible to assume the presence of a link between the average wage in the manufacturing sector in the V4 region and the GDP of the sector as determined by the output method, which indicates a positive relationship between the variables; in the case of employment in the manufacturing sector, the results indicate a negative relationship between the variables. The following output from the Statistica 13 software (

Table 3. Coefficient of determination and partial correlations for independent variables with the dependent variable: Avg_wage&salary_manuf.

	b* in	Partial (Cor.)	Semipart (Cor.)	Tolerance	R-Square	t Stat (35)	p-Value
FDI_flow_manuf	0.056927316	0.101917286	0.05499035	0.933107213	0.066892787	0.606106868	0.548354864
GDP_output_manuf	2.338042615	0.718103977	0.553846526	0.056114362	0.943885638	6.104528965	0.000000562	***
Productivity_manuf	−0.015820167	−0.028359733	−0.015228186	0.926561358	0.073438642	−0.167845956	0.867670596
Employment_manuf	−1.77437723	−0.612310046	−0.415696273	0.054885844	0.945114156	−4.581828751	0.000056343	***
Hours_worked_manuf	0.154600339	0.19762687	0.108210234	0.489909421	0.510090579	1.192699562	0.241010368

Note: *** indicate that the p-value is equal to or less than 0.001.

) also shows the individual values of the coefficient of determination for individual variables compared to the above table, and also includes the partial correlation values.

The achieved values for the individual coefficients of determination (Table 3) in the case of two independent variables, which turn out to be significant based on the reported p-value, indicate that, in the variable GDP_output_manuf explains more than 94% of the cases and the variable Employment_manuf explains almost 99% of them.

Furthermore, testing of the suitability of the used model using the ANOVA method is also presented.

In the ANOVA section (

Table 4. Testing the suitability of the proposed model—analysis of variance.

ANOVA
	Sums of (Squares)	df	Mean (Squares)	F	Significance F (p-Value)
Regression	74,909,557.2	5	14,981,911.44	17.2971554	0.000000013	***
Residual	30,315,209.21	35	866,148.8345
Total	105,224,766.4

Note: *** indicate that the p-value is equal to or less than 0.001.

), the null hypothesis is tested, which states that the regression model which was chosen to explain the dependence was not suitable (the alternative hypothesis states the opposite). To evaluate this statement, the F test was used, the p-value of which was lower than 0.05 (the chosen level of significance α), i.e., H0 was rejected, which means that, according to the results, the model was chosen correctly.

Since three of the five examined independent variables were found to be non-significant in the model, it was decided to exclude them from further examination and a new regression model was built, which considers only the variables that have been shown to affect the dependent variable—the average wage in the manufacturing sector in the V4 region. At the same time, the new model caused a slight increase in the number of examined observations, as in the cases of some excluded non-significant variables the data in the observations were missing.

In this case (

Table 5. The summary output of the multiple regression model with a dependent variable: Avg_wage&salary_manuf–Reduced number of Independent Variables.

Summary Output
Regression Statistics
Multiple R	0.847021994
R Square	0.717446259
Adjusted R Square	0.703663149
Standard Error	905.4953669
p	0.000000000
No. of cases	48
Observations	41

), the value of Multiple R (correlation coefficient) is equal to 0.847. The closer this value is to 1, the stronger the dependence and, compared to the first model, there was a very slight increase in value. This is a high degree of intensity of dependence between the variables. The R square value (value of the coefficient of determination) is at the level of 0.7174. According to this, the chosen regression line explains the variability of average wages in the manufacturing sector in the V4 region to more than 71%, while the other part represents unexplained variability, the influence of random variables, and other unspecified influences. The adjusted R square (adjusted coefficient of determination) also takes into account the number of estimated parameters and the number of measurements and, compared to the first model, its value increased by more than three per cent. After rounding, it is at the level of 70%. A positive aspect of the new model is that a slightly lower Standard Error (standard error of the mean) was achieved. It was possible to evaluate 41 observations out of 48 cases, for which the data were available for every variable examined in this modified model. As previously mentioned, the number of observations was caused by the character of the data, which was collected for four countries of the V4 region in the case of six variables. If any variable was not available for a country in a certain year, that case was excluded from the regression model.

The results of the Regression summary (

Table 6. Regression summary for dependent variable: Avg_wage&salary_manuf–Reduced number of Independent Variables.

	b*	Std. Err. (of b*)	Coefficients (b)	Standard Error (of b)	t Stat (41)	p-Value
Intercept			9909.396068	366.608995	27.02987707	0.000000000	***
GDP_output_manuf	2.371563971	0.320158765	2,693,751.984	363,653.8241	7.407462277	0.000000004	***
Employment_manuf	−1.707955091	0.320158765	−73,888.51557	13,850.51398	−5.3347129	0.000003800	***

Note: *** indicate that the p-value is equal to or less than 0.001.

) show that, regarding the link between the independent variables and the average wage in the manufacturing sector in the V4 region, it can be said that the GDP per capita of the manufacturing sector, determined by the output method, indicates a positive relationship between the variables. In the case of employment in the manufacturing sector (calculated as the number of employees per 1000 inhabitants), the results indicate a negative relationship with the dependent variable.

Based on the data, it can be concluded that the examined independent variables had a significant effect on the average wage in the manufacturing sector in the V4 region.

The regression function in this case has the form:

$$Average\_wage\&salary\_manu{f}_t=9909.39+\mathrm{2,693,751.98} x GDP\_output\_manu{f}_t-\mathrm{73,888.51} x Employment\_manu{f}_t$$

(3)

Purely mechanically, based on the regression function, it could be assumed that, if we consider unchanged employment in the manufacturing sector, then the growth of GDP in the manufacturing sector will be accompanied by an increase in average wages in the manufacturing sector. Analogously, in the case of the considered unchanged GDP in the manufacturing sector, employment growth in the manufacturing sector will be accompanied by a decrease in average wages in the manufacturing sector.

In both cases, the null hypothesis assuming the insignificance of the respective regression coefficients is rejected. These statements were evaluated using p-values, which in all cases were lower than the established significance level of 0.05.

The suitability of the applied model was again tested using the ANOVA method.

When testing the suitability of the proposed model with reduced variables using the ANOVA method (

Table 7. Testing the suitability of the proposed model with reduced variables-Analysis of Variance.

ANOVA
	Sums of (Squares)	df	Mean (Squares)	F	Significance F (p-Value)
Regression	85,358,079.44	2	42,679,039.72	52.0525703	0.000000000	***
Residual	33,616,796.24	41	819,921.8594
Total	118,974,875.7

Note: *** indicate that the p-value is equal to or less than 0.001.

), the null hypothesis was tested, which states that the regression model chosen to explain the dependence is not suitable (the alternative hypothesis states the opposite). To evaluate this statement, the F-test was used, the p-value of which was lower than 0.05 (the chosen level of significance), i.e., H0 is rejected, which means that, according to the results, the model was chosen correctly.

5. Discussion

The results of this study show that, from the examined economic indicators in the manufacturing sector, which could have an impact on average wages in the manufacturing sector, it was not possible to confirm the significance of the effect of the flow of foreign direct investments (FDI), labour productivity, or the number of hours worked. However, GDP in the manufacturing sector (measured by the output method) and employment in the manufacturing sector proved to be significant in the examined case. Based on the findings of this study, it can be concluded that sectoral GDP could positively affect average wages in the sector and the level of employment in manufacturing could negatively affect them.

The share of wages in GDP, or in other words, the share by which labour participates in the created output, describes the structure of the economy. Therefore, developed countries with a higher added value of labour have a higher share of wages in their GDP. Countries in which the qualification of workers is still low on average, and a decisive part of the added value is created by capital goods, have a lower share of wages in their GDP. Translating theory into practice, where a substantial portion of employees work in services and not in the industry, wages have a higher share of the GDP [32,33,34]. This study examined, individually, the manufacturing sector in the region of the V4 countries, and it examined whether it is even possible to assume that the GDP of the mentioned sector could affect the level of wages of employees in the sector, as some studies suggest [9,10,11,12]. The findings of this study on the manufacturing sector are also consistent with the general findings of Stojanova and Trnkova [56], according to which there is a direct relationship between GDP/capita and wages. They did not conduct a specific study in the manufacturing sector but focused on whole countries. Here, it is shown that their findings about the general economy are also consistent with our sector-specific findings. Based on the results of the research, and the findings and results of other authors presented in this article, it is necessary to consider the fact that, despite all of the well-acknowledged advantages of the manufacturing sector [5], Europe has been engaged in a process of deindustrialisation for several decades [73]. The deindustrialisation is illustrated by the constant reduction in the manufacturing share in the gross domestic product (GDP) and employment, and the rise in the share of the service sector [32,33]. In developed countries, this has not been perceived as a negative phenomenon, but it has rather been seen as a natural consequence of the economic development process [74]. Some authors state that deindustrialisation began too early in developing countries [30,75,76]. This deindustrialisation process can be attributed to policy shifts, or radical economic reforms, rather than to the economic structure’s maturity (transfer to the tertiary sector) [76], as was also the case of the V4 countries. Currently, deindustrialisation is no longer perceived as a natural process of economic development. The manufacturing sector is seen as an important source of growth, both in Europe and the USA’s economies [2,77].

Considering wages and employment in the manufacturing sector, the findings of this study support the findings of some recent studies [18,19,20,21] and the claims of Keynes [59] which were made from a macroeconomic point of view, from which it follows that a decrease in employment will be accompanied by an increase in wages. The mentioned approach also confirms the principle of constructing the Philips curve in theory. Based on the above, wages should have a countercyclical nature relative to employment. On the other hand, some studies do not support the results obtained in this study or the Keynesian view itself, such as the research study of Dimelis [62], whose evidence suggests stronger procyclical behaviour of wages relative to cyclical employment in the annual data for most of the EU countries. Her findings are also confirmed in the theoretical and practical findings regarding the historical literature by the author Dunlop [63]. Also, the data of Makridis and Gittleman [78] point toward highly procyclical wages among performance pay jobs. However, it is also necessary to realize that, while the prevailing view among many macroeconomists (dating back to Keynes [59] and Bewley [79]) was in the favour of countercyclical nature and assuming that nominal wages are downward rigid, there is increasing evidence from a combination of studies using surveys [80], administrative data [81,82], and payroll data [83,84] that wages are more flexible. Thus, the assumption deduced from the research results of this study and other recent studies in the European area [18,19,20,21], that the growth of employment should be accompanied by a decrease in the average wages, has its justification. It is possible to work with this information further and to develop this line of thinking.

In the light of recent research [5,6,7,8,9,10,11,12,13,14,15,16,18,19,20,21,25,26,27], the novelty of this study lies in examining a very specific region within Central Europe and especially focusing on the economy of the manufacturing sector separately. The composition of the selected indicators and the results of their investigation indicate that they create space for further investigation of the issue.

The policy implications of the results of this study should support the manufacturing sector within countries’ economies. Governments should attempt to ensure that the country’s economic prosperity will not be based only on the services sector, as is the case in some developed countries [32,33,74]. They should ensure that emphasis will also be placed on other areas of the economy, for example, the manufacturing sector and its support. The manufacturing sector can be an important engine of economic development and GDP growth, and it does not have to originate from the growth of the manual productivity of workers or increased employment in the sector, but from the possibility of developing and implementing innovations and technological development [22,23,24]. It turns out that GDP growth in the manufacturing sector can increase the standard of living of employees in that sector, as their average wages rise. The sustainable development of the manufacturing sector in the V4 region is conditioned by the creation of measures that can accelerate economic development, increase the rate of added value by increasing the appreciation of production-consumption, and increase the competitiveness of the manufacturing sector. This is true not only of the European market but also of the global market, thereby contributing to efficiency. However, in this global setting, the producers in the examined region are still negatively affected by the aspects of environmental protection. Pressure should be created on suppliers from third countries to ensure comparable standards of environmental protection, and to prevent environmental dumping.

On the other hand, it turns out that there is no need to push for increasing employment in the manufacturing sector [18,19,20,21]. Rather, there is a need to focus on the support of new technologies, automation, robotization, and the human-centred approach that Industry 5.0 requires [22,23,24]. This creates the potential for cooperation between manufacturing companies and scientific research organizations. It is important to selectively support new investments in high-tech areas with a high level of added value, as well as to diversify the manufacturing sector. It is also necessary to focus on building a pro-innovation infrastructure, and intensively support the cooperation of companies with organizations that are focused on science and research and implement measures to stabilize a high-quality workforce in manufacturing companies.

One of the significant possible practical implications of the results of this study is strong support for the implementation and use of Industry 4.0 [23]. The term Industry 4.0 has been a part of the practice and has been used in manufacturing in the developed world for more than a decade. The essence of Industry 4.0 does not have to be a new and revolutionary technological invention, but rather a deep systemic change in the production process, the task for which is to make the production process more efficient, transparent, and connected. The basis for this is the use of artificial intelligence in the production process. Such progress in manufacturing ultimately requires less labour for the manufacture of products or employees needed for the manufacture of products [25,26,27], and according to the results of this study, it creates opportunities for the growth of average wages in the manufacturing sector.

From a microeconomic point of view, the results of this study also indicate practical implications for the companies that are operating in the manufacturing sector. Similarly, as in the case of policy implications for the support of the sector, the companies operating in manufacturing should also focus on a long-term increase in production through the increasing of people’s qualifications and skills, and the introduction of new technologies, technological procedures, and innovations. It most likely requires long-term investments instead of recruiting new employees.

A big challenge for the manufacturing companies in the V4 region in increasing the consumption of their products and thus creating a market space for production growth (and thus ultimately the growth of the GDP of the manufacturing sector) is how they can deal with reducing the costs of complying with environmental standards and requirements for the manufacturing process. Considering the current state of environmental pollution and the resulting consequences, it is not possible to expect a lowering of environmental standards.

Changes in manufacturing technologies and work procedures are necessary to ensure sustainable environmental conditions [35] and for the sustainable development of companies in the manufacturing sector in the region of V4 countries. At the same time, for the companies in the manufacturing sector, it can be concluded that, in the labour market, it is not only necessary to pay attention only to the unemployed people and job seekers who often have an income at the level of the subsistence minimum [20,85], but also to people who already have a job but who can only keep it by improving their qualifications.

6. Conclusions

The research carried out in the study aimed to discern whether the examined economic indicators of the manufacturing sector affect the level of average wages in the manufacturing sector. The region of the V4 countries was examined for the period 2008–2019. Czech Republic, Hungary, Poland, and Slovakia are countries that share geographical proximity, as well as political, cultural, and historical development. The V4 countries also share similar experiences in the field of economic transformation and the challenges of joining international structures. The examined economic indicators that were considered as independent variables concerning average wages in the manufacturing sector (dependent variable) were: (1) flow of foreign direct investments (FDI Flow) in the manufacturing sector, which was examined from the point of view of the net inflow, i.e., based on the inbound principle; (2) GDP in the manufacturing sector measured by the output method; (3) growth of labour productivity in the manufacturing sector; (4) employment in the manufacturing sector; (5) the number of hours worked in the manufacturing sector. Based on the research, no significant effect was found in the case of three of the five independent variables (on the average wages in the manufacturing sector in the V4 region). It was not possible to confirm that the flow of foreign direct investments in the manufacturing sector would affect average wages, it was not proven that the year-on-year change in the productivity of workers in the manufacturing sector would affect them, and it was not even possible to confirm the link between the number of hours worked in the sector and the average wages. On the other hand, the results of the study show that it is possible to discuss the connection between the average wage and the economic indicators of GDP and employment in the sector. Based on the results, it is possible to assume that, in the case of the GDP in the manufacturing sector measured by the output method, there is a positive relationship with average wages in the sector, and in the case of employment in the manufacturing sector, the results indicate a negative relationship with the dependent variable.

Within the discussion of the achieved results, the paper also presents policy implications and practical implications and proposals linking the theoretical knowledge, the results of this study, the results of studies realised by other authors, and the knowledge resulting from knowing the real environment. From the summary of implications and proposals, it follows that both the policymakers and the actors of the manufacturing sector themselves should strive to support the use of new technologies, artificial intelligence, automation, and robotization, as well as supporting systemic changes and efficient production procedures within the framework of Industry 4.0 as well as human-centric solutions within the framework of Industry 5.0. At the same time, efforts should be made to increase the appreciation of product consumption and the effective application of environmental standards. Cooperation with organizations focused on science, research, and education is proving to be the right method for the manufacturing sector. All of these aspects could have an impact on increasing the GDP of the manufacturing sector and saving in the number of necessary jobs, which could ultimately increase average wages in the sector. The study does not prove this directly. There may be multiple influences on the results. However, the study shows a possible negative relationship between the employment level in the manufacturing sector and the level of the average wage in the sector, which is worth further investigation. Mainly from the point of view of possible further reasons.

The research conducted in this study has several limitations that need to be considered when applying its results. It is necessary to be careful when generalizing the results since the data forming the basis of the study are for the V4 region, which includes four countries that are geographically, character-wise, politically, historically, and developmentally close. At the same time, the factor of countries was not taken into account in the analysis, which, despite their similar characteristics, could play a role in the evaluation. The analysis of panel data planned in the next period could bring new knowledge to the issue. A potential source of limitations is that the study works with average nominal wages in the manufacturing sector, which may affect the interpretation regarding the increase in the real purchasing power and living standards of employees when wages rise. One of the dominant limitations is in the data itself, or in the method of calculating average wages in the manufacturing sector, which are relativized by the annual exchange rate of the reference currency, the US dollar. The mentioned approach has its advantages, which are mentioned in the article; however, these also come with the pitfalls of fluctuations in the exchange rate of the currency of one of the world’s largest economies.

Further research ambitions in this area will be directed towards examining the relationships of factors that could affect the increase of the GDP of the manufacturing sector, as a dependent variable. At the same time, the ambition of the authors is to collect primary data on a sample of employees in the manufacturing sector as well as representatives of the companies themselves—employers in the manufacturing sector—to create a set of specific recommendations for the effective setting and planning of the number of necessary jobs, as well as the adaptation and the possible qualification growth of employees. The impact of crisis periods on the situation in the manufacturing sector could also be a future area of research focus.