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Article

Income Expectations in Sustainability of Subjective Perception of Households’ Wellbeing

by
Marta Dziechciarz–Duda
Department of Econometrics and Operations Research, Wroclaw University of Economics and Business, 53-345 Wroclaw, Poland
Sustainability 2023, 15(5), 4325; https://doi.org/10.3390/su15054325
Submission received: 9 January 2023 / Revised: 24 February 2023 / Accepted: 24 February 2023 / Published: 28 February 2023
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
The knowledge of the sources of the sustainability of the subjective perception of households’ material wellbeing is essential in designing a country’s effective socio-economic policy to increase citizens’ satisfaction. The empirical goal of the study is to check the effectiveness and efficiency of statistical and econometric tools. The cognitive task of the analysis is describing material wellbeing and identifying the sources of satisfaction with life. The methods applied to measure and model emotions of interest include correspondence analysis, hierarchical clustering and panel data models (within estimator variants). The main findings are that Polish households declare a sustainable, increasingly high subjective perception of wellbeing. The author identified possible sources of wellbeing and self-perception sustainability. The main factors constituting the subjective perception of wellbeing are dwelling situation and the possession of durables, along with the absence of drastic differences in the material condition of families.

1. Introduction

In the present study, the author shows possible sources of wellbeing self-perception sustainability and attempts to measure and model emotions of interest. The importance of the basis of households’ material wellbeing perception manifests itself in the context of two phenomena. The first is political. National governments try to exercise socio-economic policy to increase citizens’ satisfaction with their life. One of the essential policy tools is social transfers. The policymakers want to know the most effective measures for socio-economic policy design [1,2,3].
The second aspect appeared as the result of international research on the relative level of household satisfaction. It turned out that the absolute bulk of income measured in monetary units does not play a decisive role in this respect. It turned out that differences in income levels between particular types of households play a more significant role than the absolute income level. The author presented empirical evidence from Central European countries [4,5,6].
The approach for describing households’ situations consists in gathering and analyzing information concerning the possession of durables following the problem formulation by E. Diener. He proposed tools to identify the factors influencing households’ subjective wellbeing perception [7].
The empirical goal of the study is to check the effectiveness and efficiency of the proposed methodological solutions in assessing the sustainability of the subjective perception of material wellbeing. Verifying the effectiveness of the proposed research techniques will consist of their application to real data from national sources.
The cognitive goal of the study is to describe material wellbeing using available measurement results.
The scientific contribution consists of a practical justification for using more accurate measurement techniques to assess the subjective perception of a household’s wellbeing. The assessment will include an analysis of satisfaction with the material situation of household types defined in official statistics [8].
The evaluation results provide more accurate knowledge of the perception of the population’s living conditions and identify factors that have a decisive impact on perceived wellbeing. The results could be the basis for designing an effective socio-economic policy at the national level. Such knowledge is crucial for decision-makers in every country. The results are essential for building and strengthening economic and social cohesion, social security policies and other vital elements of governance. Policymakers can use the results to determine the extent of poverty, design social benefit systems, calculate consumer price indices, determine the minimum wage, develop simulation models of household tax burdens and make international comparisons [9]. A. Jantsch et al. provide discussion on the comparative perception of wellbeing [4].
The expected scientific achievement of theoretical research and the methodological goal of the study is an improvement of analytical tools. A supplemental task of the current research is to indicate possible directions for creating new, upgraded analytical tools. Also, econometric methods suitable for available research data will be identified and tested.

2. Theoretical Background

2.1. Household: Household’s Wellbeing

Several authors developed the concept of households’ subjective wellbeing by assessing dwelling situation and the possession of durables. Key publications on the topic give the base for the description of the current state of the research field, i.e., sources and the sustainability of subjective wellbeing perception. Authors commonly agree that the number and the quality (value) of durables in a particular household are decisive determinants of the household’s subjective wellbeing. Following the theoretical indications, the author of the present study considers durables as the source of sustainable high-level satisfaction in life.
The conclusion is that the household’s large quantity and high quality of durables lead to the long-lasting experience of high-level positive emotions [1,10].
Additionally, researchers assume that the ownership of durables and durable replacement expenditure strongly correlate with self-perceived measures of social status and quality of life, which suggests a vital role in the household situation description [11]. The further development of E. Diener’s proposal includes an approach based on the quantitative measurement of household equipment and introducing a subjective indicator of household material wealth [12]. The difficulty is that ownership is not the sole factor. Equally influential characteristics are the quality and age of durables, which will affect consumption (satisfaction) [13].
In the general scheme, the description of a household’s material situation may concentrate either on poverty (lowest income decile(s), quintile or tertile), average situation (two medium quartiles, medium quintile or tertile) or wealth concentration (concentration indices, highest income decile(s), quintile or tertile). The works [14,15] contain a representative survey of problems and methods for measuring households’ material situation. Identifying the household situation (wellbeing) usually considers its multidimensionality [10,16,17,18].
In practical terms, the household’s wellbeing assessment means an attempt to capture three aspects: monetary measures (income and expenditure), subjective income evaluations and the possession of durables, including dwelling conditions. Unfortunately, income-based measures of wellbeing do not capture differences over time or across households in wealth accumulation, the ownership of durable goods or access to credit. The wealth assessment approach is one of the possible concepts of the assessment. The interested reader will find the detailed discussion in [19,20,21,22,23,24]. The concept of jointly estimating the structure of material wellbeing helps to measure other aspects of the sources of the sustainability of the subjective perception of the individual household’s situation.
By using the term “households’ subjective wellbeing perception”, the author understands the judgements formulated by respondents, usually by people, by household members, as a rule by the head of a household. The complex problem of how the term “household” is defined lies beyond the scope of this text. The author does not discuss it. Analogue restriction applies to household representation (head of household); comprehensive discussion on the topic interested readers will find in publications [25,26].
W. Tov and E. Diener summarized the discussion of particularities resulting from cultural differences between continents, nations or religions [27]. For the Nordic countries, P. Due with coauthors provided results of enlightening analysis [28]. An example for Mexico may be found in [6].
H. Cantril developed the related concept known as the Cantril life ladder [29]. In their opinion polls, the OECD, Gallup, Our WorldIn Data, Well Being International and others use the concept of the Cantril ladder to measure wellbeing [30,31,32,33].
The problem addressed in the present study is an attempt to specify, identify and clarify the factors influencing household wellbeing perception. The variety of proposals known from the publication on the subject include measures based on purchase prices (acquisition approach), equivalent measurements resulting from the estimation of the cost of rent and cost measures illustrating the level of end-user expenses [21,22,23,24,25,34,35,36].
The author of the present study tries to identify the conditions for the sustainability of subjective wellbeing perception. The analysis concentrates on the national policy of social transfers designed to diminish the differences among social groups. The author understands the social groups as household types in the present study. The list of household types consists of thirteen classes defined by official statistics [8].

2.2. The Family Life Cycle and Its Influence on Household Needs

The family life cycle phase is the vital factor influencing considerations on subjective wellbeing in a household. There is a strong correlation between the family life cycle phase, the reference person of a given household and the possession of durables and real estate, particularly their primary residence.
Specialists and researchers in social sciences, psychology, sociology and marketing widely use the family life cycle concept [37,38,39].
The material wealth of households also varies along with the family life cycle. The value of accumulated material resources in a ménage increases with the age of the household. Young families, where the head of the household is under 35 years of age, are less likely to own real estate. The phase of the family life cycle determines the accrued value of material resources. In this respect, the age of the head of the household is one of the crucial factors in analyzing household endowment with durables. An analogous statement describes the process of the accumulation of financial assets. Both features are closely related to a current family life cycle stage. Specific financial situations and interest in certain products and services characterize each phase of the family life cycle. The household head’s socio-economic characteristics, age, level of education, professional status, sources and income significantly influence the family’s wealth.
In the particular stage of a household’s life, the average wealth asset portfolio changes following the type of household. An important factor is the age of the head of the family (the household’s head).
Creating an asset position can be divided into four phases—initial, maturing, mature and end-stage (Figure 1). The first phase starts when a single adult leaves the family home. It entails two sub-phases, one when the family forms (through marriage or another type of cohabitation) and another when children appear. However, children are not a sine qua non for the needs typical for that stage to manifest. The mature phase begins when children become adults. Again, having children is not essential for this phase to occur. The end-stage begins when children move out of the house. Its second sub-phase occurs when only one older adult constitutes the household.
The first phase (initial) begins when the children start an independent life by taking up paid employment. As a rule, they are also leaving the family home then. A new household is beginning to create its property position by charging its current income with the costs of external capital (mortgage, loans).
The next phase begins when the younger generation becomes independent, and the initial household members (parents) end their active involvement in the functioning of the new household. The structure of household expenses is changing; health care and recreation expenditures play a more significant role. Assuming that the household material needs were satisfied in the first phase, the second phase primarily focuses on reducing household debt and repaying its essential part—the mortgage. In the meantime, the family may move to a larger house or flat and a better (more convenient) location. The length of the second (maturing) phase depends very much on the resourcefulness of family members. In reality, it depends on the ability to maximize income from work, minimize the cost of loans, properly manage available resources (the selection of tangible and financial assets) and propensity to save.
The mature phase of the household life cycle is the period of the expansion of material wealth and its diversification. During this time, the household’s ownership may grow by acquiring additional real estate, such as land ownership or second and subsequent flats/houses. Some newly purchased values are for investment purposes, i.e., to obtain supplemental rental income and profit from a resale. Financial assets also grow. Similarly to tangible assets, financial investments are made to obtain extra current income (interest, dividends) and for speculative purposes (resale income). Both parts of the material wealth may be expanded with credit support, with the expectation that additional revenue will service the debt.
The end-stage (empty nest and single senior citizen household sub-phases) is connected with the growing role of health care costs and expenditures financing free time activities—travel, hobbies, social events. The income, if any, comes from financial investments and real estate [40].

3. Materials and Methods

3.1. Data

The data used for analysis are from the Household Budget Survey, conducted annually by Statistics Poland [8]. The author used four survey waves (2017–2020) for the cross-sectional analysis and to model the dependencies. They included a representative sample of households. The sample comprised 36,655 households in 2017; 36,166 in 2018; 35,923 in 2019 and 33,529 in 2020.
The Household Budget Survey is executed annually by Statistics Poland, i.e., the Polish Central Statistical Office. The survey has a character of panel data collection that registers the actual structure of expenditures, income, socio-economic information on polish households and the demographic structure of families, e.g., place of residence and the number of members of households (Table 1). In the years 2017 to 2020, there were no considerable differences in survey size (the structure of the number of members included in the surveyed households list was very similar). The present place of residence structure slightly changed in favor of big cities (with more than 500 thousand inhabitants). B. Baltagi provided comprehensive characteristics of the econometric modeling of the panel data [41].
Around 30% of the households are married couples with dependent children (the biological type of household numbered: 2, 3, 4, 5, 8). The second biggest group are married couples without children (about 27%). The third most numerous household type is one-person households (about 22%). In general, the structure of all types of households is stable over time. In 2020 compared to 2017, there are around two per cent more married couples with two children. It is the only relatively substantial change in the structure (Table 2).
Table 3 contains structural information and the results of conducted descriptive analyses. It illustrates the households’ total expenditures, disposable income and income expectations in 2017, 2018, 2019 and 2020. Compared to 2017, in 2020 there is an increase in all levels of income expectations and household disposable income. In terms of expenditures per household, there is no noticeable change.
All households, especially married couples with dependent children (the biological type of household numbered: 2, 3, 4, 5, 8), show increasing income expectations (Figure 2c,d). Married couples without children (the biological type of household numbered as one) are relatively stable over time in terms of disposable income and total household expenditures in 2017, 2018, 2019 and 2020 (Figure 2a,b). However, some households reported decreasing spending (e.g., the biological household type number four).

3.2. Data Analysis

Correspondence analysis and hierarchical clustering were applied to analyze and illustrate the phenomenon’s structure and dynamics measured with the households’ budget data.
The relationships among the variables were analyzed and visualized using correspondence analysis. Correspondence analysis (CA) is a non-parametric statistical method appropriate for categorical data [42]. It reduces the dimensionality of data. Thus, it can be a valuable tool for visualizing relationships between two or more categorical variables. The interested reader will find detailed descriptions of CA in [43].
The hierarchical clustering technique confirms correspondence analysis results that show relationships between categories. The author used Ward’s minimum variance method of hierarchical classification analysis to cluster the households’ types [44,45,46].
The analyzed categories were grouped based on the calculated coordinate values (obtained from CA) and presented as a dendrogram. As a result, homogenous clusters of households were identified. Analysis was repeated for each of the four years. The task was to identify changes over time in households’ subjective evaluation of their material situation in each biological type of household.
The data were processed in R by the ca and hclust packages [47].
Econometric panel analysis served as a tool in modeling the dependencies in material wellbeing and the description of sustainability in Polish households’ subjective perception of wellbeing. The theoretical fundamentals are discussed in [41,48].
The pooled model and the fixed effects (FE) and the random effects (RE) model were estimated and tested with R using the plm package [47,48,49,50,51,52].
The specification of the fixed effects model is as follows:
y i t = α i + β x i t + u i t           i = 1 , … ,   N ; t = 1 , … ,   T ,
where β is a vector of coefficients, having individual specific intercepts α i ,   i = 1 ,   … ,   n , where each of these describes the fixed effect of entity i. The coefficients α i   are assumed to be constant over time.
Fixed-Effects Regression (within) estimator has the following specification:
y i t = α + β x i t + u i + ε i t ,
The arithmetic average of all observations for household i
y ¯ i = α + β x ¯ i + u i + ε ¯ i ,
by subtracting:
( y i t − y ¯ i ) = β ( x i t − x ¯ i ) + ( ε i t − ε ¯ i ) .
One receives so-called within transformation because it demeans all variables within their group.
The main disadvantage of the within estimator is that it does not allow or exclude estimating the effects of any time-invariant variables. The interested reader will find a detailed description of the panel models in [41] and the fixed effects regression in [49,50,51,52].

4. Results

4.1. Correspondence Analysis and Hierarchical Cluster Analysis

The conducted correspondence analysis (CA) of the subjective evaluation of the material situation of households by the biological type of household in the year 2017 (Figure 3a) shows that the groups that evaluate their material circumstances the best are marriages with one, two and three children. Types of households expressing the bad (or rather bad) material situation are one-person households and mothers with children. Confirmation for this result may be the dendrogram for 2017 (Figure 3b). Figure 3d, for 2018, indicates that the subjective evaluation of households’ material situation is worse for one-person households. In 2020, the subjective assessment of the material condition of households slightly improved; thus, groups with a good and very good assessment of their situation became greater (consisting of additional types of households) (Figure 4).

4.2. Panel Model Analysis

The analysis included households participating in four consecutive (2017, 2018, 2019 and 2020) waves. Thus, the panel was balanced (n = 10,635, T = 4, N = 42,540).
The dependent variable was a continuous variable describing the income level considered by households to be very good (D6_3_5).
Independent variables were the household’s available income (INCOME) and household expenditures (EXP). Moreover, the income level is considered by the household to be good (D6_3_4), barely sufficient (D6_3_3) and insufficient (D6_3_2). A dummy variable was the household assessment of the level of satisfaction of their needs concerning furnishing the apartment with furniture and durable goods (D6_5_5) on a 5-point scale from 1 (very unhappy) to 5 (very happy).
The descriptive statistics of all variables are presented in Table 4.
Firstly, the pooled model, the fixed effects (FE) and the random effects (RE) model were estimated (Table 5). Most of the independent variables were statically significant. In the estimated models, the goodness of fit (R2) value is 80.6%.
The pooled FE model and RE model characteristics comparison using statistical tests (Table 6) were tools for the most appropriate model determination. Since there was an indication for the rejection of H0 in the poolability Chow test (the null hypothesis is that the pooled model is more suitable for the data), the FE model proved more appropriate when applied to the analyzed data. The decision rule in the Hausman test: if the null hypothesis is that the preferred model is RE and the alternative hypothesis is that the fixed effects indicated that the FE model would be a better choice. For the analyzed data, both null hypotheses were rejected (Chow test, Hausman test). As a result, Fixed-Effects Regression (the within estimator) was chosen. The data preparation consists of the transformation of the values. Values for dependent and independent variables have been transformed to deviations from the subject-specific mean [41].
Table 7 shows model diagnostics for the FE model. The Breusch–Pagan test showed the presence of heteroscedasticity; the Pasaran CD test showed the presence of cross-sectional dependence and the Breusch–Godfrey/Wooldridge test showed the presence of serial correlation. Therefore, it is recommended to use a robust covariance matrix for estimation. The Arellano approach for fixed effects can be applied to heteroscedasticity and serial correlation [53].
The estimates for the final model are presented in Table 8. The coefficient, next to variable INCOME, indicates that the dependent variable (income level considered by household to be very good) changes over time by PLN 0.02, on average per object, when the household’s available income increases by one zloty (PLN). The coefficient, next to variable EXP, indicates that the dependent variable (income level considered by household to be very good) changes over time by PLN 0.08, on average per object, when household expenditures increase by one zloty. Households that assess their furnishing and durable goods possession at the highest levels compared to the least happy with their possession of durables set their income level considered to be very good lower by PLN 157 and 114, respectively.

5. Discussion and Limitations

The author used the available official statistical data. The quality of the data is beyond the author’s influence. The most significant imperfection of the available statistical data results from the method of constructing the research sample. The Central Statistical Office has a permanent panel with households divided into thirteen types. The sample’s representativeness considers the percentage share of individual classes among all households. The consequence of this construction is that the selection includes a small (absolute) number of particular households. Combined with the rotation of some households in the sample, this leads to instability of the characteristics of the least numerous family classes, e.g., single fathers.
The second limitation, independent of the author, also applies to the panel’s construction. The panel does not include extremely poor citizens and the wealthiest families. The first group is inaccessible for various reasons, mainly homelessness. For obvious reasons, representatives of the second group (the richest) are extremely difficult to recruit for statistical research.
One should remember that the data collected in the household budget survey are subjective in nature, reflecting the respondents’ current feelings about the family’s financial situation, usually formulated by the head of the household [54,55,56]. The way of building feelings and their conditions is complicated. They are not only a function of actual, tangible changes in the material situation. The issue of the differences in concepts of objective and subjective measurement in wellbeing research remains out of the scope of the current manuscript; the details may be found in work by B. Tibesigwa et al. [57]. Equally important are the emotions resulting from the general assessment of the economic situation in the country [58]. The comparisons in the circle of relatives and friends strongly influence formulated statements. They consider the pace and direction of changes in the material situation of the social environment and other households.
The subjectivity of judgements corresponds to the author’s interest because the subject of the study is the subjective assessment of the sustainability of the family’s material wellbeing.
Based on the discussed limitations of the present study, future research directions may include the highlighted issues. In particular, it seems advisable to aggregate the least numerous classes of families.
The observation that a monetary unit has an unequal value in families with different numbers of household members indicates the second direction of future in-depth research.
In large families with many children or generations, unit expenditure on durable household appliances, clothing, housing costs and other costs is spread over more people than in families with fewer members. Indicated observation leads to the conclusion that it is essential to examine the impact of family size on the subjective perception of wellbeing in households with a similar income level per capita [59].
Finally, since the available data do not cover the period of high inflation (starting in 2022), the situation needs to be re-researched.

6. Conclusions

The main conclusion from the analysis is that Poles and Polish households are generally satisfied with their socio-economic situation. The sustainable socio-economic growth of the Polish economy during the last three decades of profound political and socio-economic change manifests effects on the condition of the families. The analyses and econometric modeling results confirm the sustainability of the positive subjective perceptions of households’ material wellbeing.
The understanding of wellbeing sustainability conforms with the cognitive goal of the study. For policymakers, the most critical time horizon is the legislative period. Politicians have only this extent of decision-making power to determine socio-economic measures to limit poverty, offer social benefits and select and implement wage increase policies, at least in the institutions financed with governmental money.
The acquired knowledge is crucial for decision-makers and politicians at the national level. For policymakers, effective policies for building and strengthening economic and social cohesion, social security policies and other vital elements of governance are crucial for success in the next parliamentary elections. A. Moro-Egido et al. discuss the role of economic and social resources and provide substantiation of the importance of changes in subjective wellbeing over time [40].
The results could be the basis for designing an effective socio-economic policy at the national level.
In the international context, the obtained results approve the observation formulated in [5]. Some fast-growing economies, among them Poland, produce more subjective material wellbeing perception than established, traditionally more prosperous societies, e.g., Germany [59,60].

Funding

This research and publication was partly funded by the National Science Centre, grant number 2018/29/B/HS4/01420 in the framework of the research project entitled Households’ Equipment with Durable Goods in Statistical Analysis and Econometric Modelling of Material Wellbeing.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The author declares no conflict of interest.

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Figure 1. Family life cycle with representative types of household needs. Own study.
Figure 1. Family life cycle with representative types of household needs. Own study.
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Figure 2. Biological type of households in years 2017, 2018, 2019 and 2020 by the average value of: (a) Expenses of household—variable EXP, (b) Household’s available income—variable INCOME, (c) Income level considered to be: very good—variable D6_3_5, (d) Income level considered to be: very bad—variable D6_3_1. Own study.
Figure 2. Biological type of households in years 2017, 2018, 2019 and 2020 by the average value of: (a) Expenses of household—variable EXP, (b) Household’s available income—variable INCOME, (c) Income level considered to be: very good—variable D6_3_5, (d) Income level considered to be: very bad—variable D6_3_1. Own study.
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Figure 3. Correspondence analysis (CA) and hierarchical clustering (dendrogram) of subjective evaluation of the material situation of households by biological type of household in years 2017 and 2018: (a) 2017—CA, (b) 2017—dendrogram, (c) 2018—CA, (d) 2018—dendrogram. Own study.
Figure 3. Correspondence analysis (CA) and hierarchical clustering (dendrogram) of subjective evaluation of the material situation of households by biological type of household in years 2017 and 2018: (a) 2017—CA, (b) 2017—dendrogram, (c) 2018—CA, (d) 2018—dendrogram. Own study.
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Figure 4. Correspondence analysis (CA) and hierarchical clustering (dendrogram) of subjective evaluation of the material situation of households by biological type of household in years 2019 and 2020: (a) 2019—CA, (b) 2019—dendrogram, (c) 2020—CA, (d) 2020—dendrogram. Own study.
Figure 4. Correspondence analysis (CA) and hierarchical clustering (dendrogram) of subjective evaluation of the material situation of households by biological type of household in years 2019 and 2020: (a) 2019—CA, (b) 2019—dendrogram, (c) 2020—CA, (d) 2020—dendrogram. Own study.
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Table
Table 1. Sociodemographic characteristics of the households (HH): place of residence category, socio-economic group and household size in 2017, 2018, 2019 and 2020. Own study.
Table 1. Sociodemographic characteristics of the households (HH): place of residence category, socio-economic group and household size in 2017, 2018, 2019 and 2020. Own study.
2017 (n = 36,655)
n (%)		2018 (n = 36,166)
n (%)		2019 (n = 35,923)
n (%)		2020 (n = 33,529)
n (%)
The place of residence category
more than 500 k inhabitants4608 (12.6)4580 (12.7)4991 (13.9)4893 (14.6)
200–499 k inhabitants3109 (8.5)3038 (8.4)3115 (8.7)3034 (9.0)
100–199 k inhabitants2934 (8.0)2914 (8.1)2767 (7.7)2623 (7.8)
20–99 k inhabitants6451 (17.6)6335 (17.5)6124 (17.0)5397 (16.1)
below 20 k inhabitants3985 (10.9)3883 (10.7)3888 (10.8)3666 (10.9)
rural areas15,568 (42.5)15,416 (42.6)15,038 (41.9)13,916 (41.5)
The socio-economic group of household
employees17,708 (48.3)17,217 (47.6) 16,797 (46.8)16,269 (48.5)
farmers1658 (4.5)1555 (4.3) 1464 (4.1)1325 (4.0)
self-employed persons2508 (6.8)2467 (6.8) 2602 (7.2)2557 (7.6)
retirees and pensioners13,282 (36.2)13,657 (37.8) 13,797 (38.4)12,403 (37.0)
income received but not earned1499 (4.1)1270 (3.5) 1263 (3.5)975 (2.9)
Household size (number of household members)
one7823 (21.3)7919 (21.9)8149 (22.7)7551 (22.5)
two12,170 (33.2)12,300 (34.0)12,410 (34.5)11,454 (34.2)
three7021 (19.2)6574 (18.2)6336 (17.6)6037 (18.0)
four5995 (16.4)5719 (15.8)5510 (15.3)5228 (15.6)
five and more3646 (9.9)3654 (10.1)3518 (9.8)3259 (9.7)
Table
Table 2. The number of households by biological type in 2017, 2018, 2019 and 2020. Own study.
Table 2. The number of households by biological type in 2017, 2018, 2019 and 2020. Own study.
2017 (n = 36,655)
n (%)		2018 (n = 36,166)
n (%)		2019 (n = 35,923)
n (%)		2020 (n = 33,529)
n (%)
The biological type of household (TYPR)
1. a married couple without children *9582 (26.1)9808 (27.1)10,034 (27.9)9147 (27.3)
2. a married couple with a child *3476 (9.5)3174 (8.8)3208 (8.9)3086 (9.2)
3. a married couple with two children *3205 (8.7)3169 (8.8)3644 (10.1)3559 (10.6)
4. a married couple with three children *691 (1.9)726 (2.0)936 (2.6)920 (2.7)
5. a married couple with four (or more) children *139 (0.4)152 (0.4)263 (0.7)253 (0.8)
6. mother with children *665 (1.8)624 (1.7)653 (1.8)658 (2.0)
7. father with children *49 (0.1)46 (0.1)51 (0.1)63 (0.2)
8. a couple with at least one child * and other persons3551 (9.7)3441 (9.5)2502 (7.0)2282 (6.8)
9. mother with at least one child * and other persons839 (2.3)756 (2.1)728 (2.0)686 (2.0)
10. father with at least one child * and other persons88 (0.2)65 (0.2)57 (0.2)51 (0.2)
11. other persons with at least one child *260 (0.7)253 (0.7)244 (0.7)205 (0.6)
12. one-person households7823 (21.3)7919 (21.9)8149 (22.7)7551 (22.5)
13. other households6287 (17.2)6033 (16.7)5454 (15.2)5068 (15.1)
* Dependent child/children.
Table
Table 3. Descriptive analysis of the variables: level of income expectations, household disposable income and total household expenditures in 2017, 2018, 2019 and 2020. Own study.
Table 3. Descriptive analysis of the variables: level of income expectations, household disposable income and total household expenditures in 2017, 2018, 2019 and 2020. Own study.
2017 (n = 36,655)
Mean (SD)		2018 (n = 36,166)
Mean (SD)		2019 (n = 35,923)
Mean (SD)		2020 (n = 33,529)
Mean (SD)		2017 = 100%
(PLN (%))
Income level [PLN]
very bad (D6_3_1)1635.65 (769.17)1700.72 (791.57)1776.87 (825.37)1857.75 (846.50)222.10 (13.6)
insufficient (D6_3_2)2146.11 (931.17)2226.03 (956.66)2318.28 (989.66)2424.01 (1014.02)277.90 (12.9)
barely sufficient (D6_3_3)2773.82 (1194.71)2867.86 (1223.19)2987.91 (1271.51)3129.59 (1304.70)355.77 (12.8)
good (D6_3_4)4463.26 (1957.45)4581.89 (2004.38)4776.24 (2077.03)5052.77 (2135.72)589.51 (13.2)
very good (D6_3_5)6216.46 (2789.21)6359.02 (2844.66)6604.66 (2937.03)6941.55 (2989.13)725.09 (11.7)
Household’s disposable income (INCOME)4171.35 (2584.10)4378.13 (2725.57)4654.18 (2836.89)4890.56 (3032.29)719.21 (17.2)
Expenditures (total) of the household (EXP)3071.23 (1781.19)3043.86 (1766.59)3181.51 (1815.07)3102.32 (1715.48)31.09 (1.0)
Table
Table 4. Descriptive analysis of the dependent and independent variables (n = 10,635, T = 4). Own study.
Table 4. Descriptive analysis of the dependent and independent variables (n = 10,635, T = 4). Own study.
2017 2018 2019 2020
Income Level (PLN)Mean (SD)Mean (SD)Mean (SD)Mean (SD)
very bad (D6_3_1)1614.34 (750.55)1676.66 (780.52)1766.76 (825.99)1899.10 (853.05)
insufficient (D6_3_2)2108.04 (914.93)2202.69 (952.13)2303.26 (994.89)2475.91 (1022.97)
barely sufficient (D6_3_3)2714.76 (1167.79)2836.68 (1214.21)2964.31 (1270.29)3193.73 (1313.57)
good (D6_3_4)4406.87 (1921.25)4580.12 (2007.59)4759.57 (2076.38)5145.56 (2157.35)
very good (D6_3_5)6164.15 (2766.72)6369.83 (2838.20)6563.49 (2915.98)7068.46 (3005.47)
Household’s disposable income (INCOME)4137.70 (2535.73)4343.07 (2661.09)4658.46 (2777.38)4870.40 (3071.17)
Expenditures of the household (EXP)2924.87 (1569.77)2968.57 (1587.73)3061.57 (1599.80)3035.78 (1528.59)
D6_5_5 * (a dummy variable)n (%)n (%)n (%)n (%)
factor 1 (very unhappy)2647 (24.9)3192 (30.0)3235 (30.4)3950 (37.1)
factor 22741 (25.8)2812 (26.4)2921 (27.5)2866 (26.9)
factor 34153 (39.1)3808 (35.8)3770 (35.4) 3220 (30.3)
factor 4807 (7.6)629 (5.9)528 (5.0)455 (4.3)
factor 5 (very happy)287 (2.7)194 (1.8)181 (1.7)144 (1.4)
* assessment of the level of satisfaction of the households’ (HH) needs concerning furnishing with durables.
Table
Table 5. The pooled model, the fixed effects (FE) and the random effects (RE) parameter estimates. Own study.
Table 5. The pooled model, the fixed effects (FE) and the random effects (RE) parameter estimates. Own study.
Pooled ModelFixed Effects (within) ModelRandom Effects Model
VariablesEstimateStd. Errorp-ValueEstimateStd. Errorp-ValueEstimateStd. Errorp-Value
(Intercept)150.3528.600.00 158.4728.870.00
INCOME0.020.000.000.020.000.000.020.000.00
EXP0.080.010.000.080.010.000.080.010.00
good (D6_3_4)1.560.010.001.530.010.001.550.010.00
barely sufficient (D6_3_3)−0.270.020.00−0.240.020.00−0.270.020.00
insufficient (D6_3_2)−0.120.020.00−0.120.030.00−0.120.020.00
factor 2 * (D6_5_5)−100.4923.350.00−97.4827.070.00−100.0523.360.00
factor 3 * (D6_5_5)−76.0922.480.00−94.5726.470.00−77.9722.530.00
factor 4 * (D6_5_5)−118.6140.870.00−157.2047.750.00−122.6340.920.00
factor 5 * (D6_5_5)2.7566.040.97−113.9776.980.14−9.8266.110.88
* Wellbeing is measured by satisfaction (synonymous with happiness) on a 5-point scale from 1 (very unhappy) to 5 (very happy). D6_5_5: assessment of the level of satisfaction of the needs of HH with regard to furnishing with durables—is a dummy variable (reference: factor 1 (D6_5_5).
Table
Table 6. Testing for fixed effects and random effects. Own study.
Table 6. Testing for fixed effects and random effects. Own study.
TestHypothesis Test Statisticsp-ValueDecision
Test for Data Pooling: a Chow test for the poolabilityH0: OLS better than FEF = 1.18262.2 × 10−16, <0.05Reject H0. The FE model is a better choice
Fixed or Random: Hausman testH0: RE better than FEχ2(9) = 67.7364.225 × 10−11, <0.05Reject H0. The FE model is a better choice
Lagrange Multiplier Test: time effects (Breusch–Pagan)H0: no panel effectsχ2(1) = 0.67930.4098, >0.05Failed to reject H0; the RE is not appropriate
Table
Table 7. The fixed effects model diagnostics. Own study.
Table 7. The fixed effects model diagnostics. Own study.
TestHypothesis Test Statisticsp-ValueDecision
Testing for heteroscedasticity Breusch–Pagan testH0: homoscedasticityBP = 49,1012.2 × 10−16, <0.05Reject H0, the presence of heteroscedasticity
Testing for cross-sectional dependence. Pasaran CD testH0: residuals across entities are not correlatedz = 11.8212.2 × 10−16, <0.05Reject H0, the presence of cross-sectional dependence
Testing for serial correlation. Breusch–Godfrey/Wooldridge testH0: no panel effectsχ2(4) = 8726.72.2 × 10−16, <0.05Reject H0, the presence of serial correlation
Table
Table 8. Final model fixed effects (FE) parameter estimates (robust covariance matrix estimation: Arellano method). Own study.
Table 8. Final model fixed effects (FE) parameter estimates (robust covariance matrix estimation: Arellano method). Own study.
VariablesEstimateStd. ErrorStatisticp. ValueConf. LowConf. High
(Intercept)0.021 0.003 7.229 0.000 0.015 0.027
INCOME0.081 0.007 11.526 0.000 0.067 0.094
EXP1.527 0.008 203.264 0.000 1.512 1.542
good (D6_3_4)−0.242 0.025 −9.878 0.000 −0.290 −0.194
barely sufficient (D6_3_3)−0.124 0.028 −4.449 0.000 −0.179 −0.070
insufficient (D6_3_2)−97.483 27.075 −3.600 0.000 −150.549 −44.417
factor 2 * (D6_5_5)−94.574 26.473 −3.572 0.000 −146.461 −42.687
factor 3 * (D6_5_5)−157.200 47.750 −3.292 0.001 −250.788 −63.612
factor 4 * (D6_5_5)−113.966 76.977 −1.481 0.139 −264.838 36.905
factor 5 * (D6_5_5)0.021 0.003 7.229 0.000 0.015 0.027
* Wellbeing is measured by satisfaction (synonymous with happiness) on a 5-point scale from 1 (very unhappy) to 5 (very happy). D6_5_5: assessment of the level of satisfaction of the needs of HH with regard to furnishing with durables—is a dummy variable (reference: factor 1 (D6_5_5).
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Dziechciarz–Duda, Marta. 2023. "Income Expectations in Sustainability of Subjective Perception of Households’ Wellbeing" Sustainability 15, no. 5: 4325. https://doi.org/10.3390/su15054325

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