Households’ (In)Security in the European Union: From Principal Components to Causality Analysis
Department of Economics and International Relations, Faculty of Economics and Business Administration, “Alexandru Ioan Cuza” University of Iasi, 700505 Iasi, Romania
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Author to whom correspondence should be addressed.
Economies 2025, 13(2), 33; https://doi.org/10.3390/economies13020033
Submission received: 11 December 2024
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Revised: 23 January 2025
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Accepted: 24 January 2025
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Published: 31 January 2025
Abstract
:Economic security is considered one of the primary aspects of well-being and it is usually closely associated with poverty. This article intends to explore the main determinants of economic (in)security in European Union countries and to investigate the relationship between the level of economic insecurity and the degree of economic freedom. In order to achieve our goal, we used Principal Component Analysis (PCA) to stipulate a composite and standardized index of economic insecurity in the European Union and, afterwards, we utilized panel data regressions to analyze its correlation with the variables of the Index of Economic Freedom.
1. Introduction
Historically, both policy-makers and socio-economic scientists have constantly devoted considerable attention to well-being, traditionally understood as the qualities of a good life or of a good society (Diener & Suh, 1997). Such a wide concept has been historically related to an increased number of variables, either objective or subjective (Livingston et al., 2022; Beltramo et al., 2024). The objective approach to well-being largely originates from Sen’s works in welfare economics regarding how to measure poverty and inequality (Sen, 1973; Sen, 1976). Following this objective approach, subsequent studies regarding well-being have been particularly focused on assessing inequality and poverty in order to propose solutions to reduce them (Böhnke & Kohler, 2010). If income was the predominant objective well-being indicator used in most of the studies conducted during the 20th century (Western & Tomaszewski, 2016), the literature from the late 1990s started to highlight the prominent role of economic security in the objective measurement of well-being (Andersen, 2002). Subsequently, the two major global crises from the beginning of the 21st century, the 2007–2008 financial crisis and the COVID-19 pandemics, increased the concerns not only of the researchers but also of the political authorities about the role of economic insecurity, and thus various studies were conducted in order to capture its dimensions and evolution and to find ways to measure it. For example, the Rockefeller Foundation is one of the institutions that estimates the economic security of the American society and releases reports about it (Hacker et al., 2010). This economic security index is mainly focused on household income, measuring the share of the Americans who experience at least a 25 percent decline in their inflation-adjusted “available household income” from one year to the next and who lack an adequate financial safety net to replace this lost income (Hacker et al., 2010). Researchers from the Institute for Women’s Policy Research (McMahon et al., 2018) have also stipulated a multidimensional method for calculating economic security. Other studies proposed different approaches to the Economic Security Index (Hacker et al., 2012), some of them having a particular focus on household income volatility. Bossert and D’Ambrosio (2013) developed an indicator of economic insecurity that represents a weighted combination of the wealth levels of individuals and the past wealth dynamics. Osberg (2009) proposed an Economic Security Index based on four major aspects: unemployment, family breakup, medical costs, and poverty in old age.
Despite the fact that, up to now, several measures have been proposed to assess economic security, the precise definition and measurement of this concept remain under discussion. Therefore, it is legitimate to ask ourselves what would be the essential characteristics of economic security, and which variables and in what proportions should be included in an index of economic security in order to clarify how analysts, policy-makers, and statistical agencies could better assess a critical feature of individuals’ lives with a fundamental influence on their well-being? However, economic security can become difficult to quantify due to a lack of data, which is why a valid alternative is to study the phenomenon from the opposite direction, more specifically by analyzing economic insecurity. Considering these aspects, the purpose of the present paper is to explore the main determinants of economic (in)security in the European Union countries and to investigate the relationship between the level of economic insecurity and the degree of economic freedom. In order to achieve our goal, we firstly intend to propose an aggregate index of economic insecurity for the European Union countries based on the indicators offered by Eurostat. The considered variables were as follows: Inability to face unexpected financial expenses, Arrears (mortgage or rent, utility bills or hire purchase), Inability to make ends meet, Inability to keep home adequately warm, Inability to afford a meal with meat, chicken, fish (or vegetarian equivalent) every second day, Inability to afford paying for one week annual holiday away from home, At-risk-of-poverty rate by poverty threshold, Children aged 0–17 living in jobless households, Housing cost overburden rate, and the Overcrowding rate. We used Principal Component analysis to identify the interdependence between these variables and also the weight they may have in the economic insecurity index. After defining the composition of our economic insecurity index, we will test the correlation between the stipulated index and economic freedom variables (which we will select from the Economic Freedom Index, elaborated by the Heritage Foundation).
In order to achieve our purpose, we start our study from the following research question: Is there a correlation between our economic insecurity index and the degree of economic freedom in the European countries?
2. Literature Review
As noted above, the fields of economic security and economic insecurity are strictly correlated. Economic security can be related to aspects regarding financial stability, even though they are essentially different concepts. Financial stability may deal with household issues (Smythe, 1968; Pricop & Maxim, 2024; Pricop, 2023), although in essence it is more concerned with the stability of the banking system and markets (Albulescu, 2012; Carbó-Valverde & Sánchez, 2013; Čihák, 2007; Bundesbank, 2023). A differentiation between the two areas is therefore necessary. Economic security is concerned with the economic fragility of households (Hacker et al., 2010; Hacker et al., 2012) and their ability to cope with unexpected expenditures (Bağlıtaş & Atik, 2023; Boboc et al., 2019; Romaguera De la Cruz, 2019), with a particular focus on citizens, rather than banks and markets. Even though both aspects can be related to the macroeconomic area (especially if we compare countries), their focuses are different: markets and banks on the one hand (financial stability) and citizens on the other (economic security). In this study, we are interested in the well-being of European Union citizens from an objective perspective.
A closer look at the concept of economic security highlights various definitions that point out different forms of savings that citizens can obtain (Kosny & Piotrowska, 2013), the educational level and internet access (Rysova & Kazansky, 2021), and also the democratization level (Mogyorósi et al., 2022). Some of the most known definitions of economic security belong to Berton et al. (2012), who considers the importance of stable resources in shaping an adequate standard of living (in shaping economic security in this sense), and to Hacker et al. (2012), where economic security is seen as the level up to which individuals are protected against any difficulty that may lead to economic losses. The definition given by the International Committee of the Red Cross (2020) is also important, considering economic security as vital for the well-being not only of the people and households but also of communities affected by different downturns because it offers them the ability to withstand shocks and risks. We reiterate the link between economic security and financial stability, as this capacity to “cope with risks” and “withstand shocks” is often mentioned by Central Banks when defining financial stability (European Central Bank, 2023). Osberg and Sharpe (2005), in their turn, address the subject of economic security by taking into consideration the risks from unemployment, illness, single parenthood, and old age. In a subsequent study, conducted in 2009, their major hypothesis was that changes in the subjective level of anxiety about a lack of economic safety are proportionate to the changes in the objective risk (Osberg, 2009).
In a globalized world, Tang (2015) considers that the concept of economic security should embrace issues beyond poverty and consider the emergent threats affecting the non-poor. The same idea was expressed by Inglehart and Abramson (1994), which argued that people living in rich countries may experience a stronger sense of economic security than those in poor nations. Therefore, they considered that a high gross national product per capita could be a rough indicator of a country’s level of economic security.
By contrast, the “economic insecurity” was defined as “the anxiety produced by a lack of economic safety”, considered, in its turn, to be the “inability to obtain protection against subjectively significant potential economic losses” (Osberg, 1988). An alternative definition of “economic insecurity” was offered by R. G. Anderson and Gascon (2007). They stated that it represents “an individual’s perception of the risk of economic misfortune” (R. G. Anderson & Gascon, 2007). Therefore, we can say that while economic insecurity puts the accent on the economic losses, economic security is usually associated with certain conditions which could ensure the well-being of the individuals. Yet there have been studies which argued that the economic security for the national community (in terms of national economic growth) is attained through processes that depend to some degree on the economic insecurity of the individual (Liew, 2000). The argument consists of the fact that the presence of economic insecurity for individuals provides incentives for people to seek work. Therefore, the individuals’ economic insecurity is a necessary by-product of the ‘creative destruction’ from the Schumpeterian model of capitalist progress.
Hernando De Soto (2000) suggests another important element that has an impact on the individuals’ economic security: property rights. He argues that property rights have been denied to vast segments of the poor in parts of Asia, Africa, and Latin America. These individuals have little or no access to land and other economic resources that could help them earn an income. Secure property rights are, thus, vital not only for individual economic security but also for the welfare of the entire economy. Rodrik (2001) identifies four other types of market-supporting institutions that are critical for the economic security at the macro-level: regulatory institutions, institutions for macroeconomic stabilization, institutions for social insurance, and institutions for conflict management. Moreover, the complementary nature of these institutions could enhance the economic security of individuals since, for instance, social insurance mechanisms help mitigate the insecurities people face (Nesadurai, 2005).
From all these previous studies, those of Bağlıtaş and Atik (2023), Boboc et al. (2019), and Romaguera De la Cruz (2019) have a particular importance for our study since they focus on the “inability to make unexpected financial expenses”. We consider this variable as defining for the field of economic insecurity, since “surviving” unforeseen economic situations is one of the central elements of a household that manages to be in a state of security. The first two mentioned studies, those of Bağlıtaş and Atik (2023) and Boboc et al. (2019), utilized Principal Component Analysis (PCA) in order to define the state of economic security in the European Union countries.
Bağlıtaş and Atik (2023) measured economic security by using 14 variables from the Eurostat “Quality of Life Indicators-Economic Security and Physical Safety Statistics”. Their study was structured in two parts: the first one included a PCA, while the second one was based on a cluster analysis, using two time periods: 2008 and 2021. The results of their analysis highlighted how Greece would score the worst in terms of economic and physical security, with Germany, Denmark, Italy, Slovenia, Finland, Portugal, Czechia, Austria, Estonia, Poland, and Slovakia being at the opposite pole.
Boboc et al. (2019) used the Inability to make unexpected financial expenses indicator and other eight indicators (Material deprivation rate, Employment rate, Population with tertiary education, Life expectancy, Social life, Trust in the political system, Pollution, and Life satisfaction) to describe the economic security, conducting both a PCA and a Cluster analysis. They divided the analysis into two dimensions: social and material, and found that Eastern European countries tend to rank low in both, while Western and Northern European countries rank high, especially in the material dimension.
Romaguera De la Cruz (2019) based her analysis on three European Union countries, namely France, Spain, and Sweden, developing three subjective indicators of insecurity: Household’s incapacity of facing unexpected expenses, Household’s financial dissatisfaction, and Changes in the ability to go on a holiday. The study demonstrates how the incidence of insecurity diminishes as income increases, being significantly present in the middle-income households from Spain and France, but not in Sweden.
Considering these previous results, we have developed our first research hypothesis:
H1.
Northern and Western European countries have a high level of economic security than the Eastern states.
Regarding the link between the economic security and the economic freedom, the vast majority of the studies suggests that, in order to materialize this relationship, it must first “pass” through the filter of institutions (Bergh & Bjørnskov, 2021), as they de facto decide the degree of economic freedom. Acemoglu et al. (2005) suggest that economic freedom would determine the space in which different economic agents shape their economic security, in this sense, the “freedom to choose” (Friedman & Friedman, 1980) being the means to avoid poverty. It is important to mention that institutions are changeable (North, 1990) and their changes affect the link between economic freedom and economic security.
The benefits of economic freedom are mentioned by Carter (2007) in a study involving 39 countries and 104 observations, in which the results showed how economic freedom would reduce inequality. Yet Stankov (2017) affirms that there would be a general non-linear association between the economic freedom and the citizens’ welfare. Apergis and Cooray (2015) consider that the impact of increasing freedoms on the inequality degree depends on the existing level of economic freedom. Therefore, in the case of those states with low levels of economic freedom, increasing freedom may lead to an increase in inequality. Meanwhile, introducing new reforms in countries with high levels of freedom makes the economies more equal.
The correlation between economic freedom and welfare is also reiterated by Frimpong et al. (2023) in a study conducted on 39 African countries in which they use regression equations with panel data for the period 2004–2017. Following the statistical analysis, the authors confirm the hypothesis that a higher level of economic freedom will lead to a stronger correlation between financial inclusion and financial stability in Sub-Saharan African countries. A different opinion is stated by Arestis and Karakitsos (2013), who would rather opt for increased control of the financial sector in an attempt to enhance the economic security of the citizens. Investigating the financial sector and banking practices in the Malaysian economy, Abdullah (2015) concludes that economic security requires free pricing and monetary stability. Caldara and Herbst (2019) also found that monetary policy shocks significantly suppress real economic activity and the financial climate, thus leading to economic insecurity. Reducing the level of nonperforming loans and increasing the stability of the banking system were considered to be important monetary strategies for enhancing economic security in Ukraine (Kovalenko et al., 2023).
Apart from free pricing and stable monetary policy, another important component of economic freedom is related to market openness, and more precisely to the freedom of trade and investment. The relationship between market openness and economic security has led to vivid debates among researchers. By offering the example of Singapore, a country that lacks natural resources and largely relies on imports, Dent (2001) argues that the open-investment regime and open foreign trade policy enhanced the economic security of the people living in this country. Yet the advocates of economic security make a distinction between developed and developing states when exploring the consequences of market freedom. They argue that while free trade itself fosters economic security, in some developing states, this does not occur because the free trade is not entirely free, as trade restrictions unfairly favor the already-developed nations (Horrigan et al., 2008). Therefore, in these cases, the problem is not related to the insecurity brought about by free trade but by an unfair trading system.
Taking into account all these results of the previous studies, we developed our second research hypothesis:
H2.
A higher level of economic freedom, involving higher monetary freedom and stability, higher investment, and trade freedom, increases the economic security degree of the analyzed states.
3. Methodology
In order to achieve our goal, and considering the results of previous studies, we will first develop a composite index of economic insecurity (we decided to analyze economic insecurity because, as shown in Table 1, the variables provided by Eurostat are synonymous with economic insecurity rather than economic security) with the help of PCA. Subsequently, we will proceed with panel data regression analysis.
To stipulate a proper index of the economic insecurity, we will use Principal Components Analysis (1), which is an alternative to methods previously used to construct an aggregate index of the economic security (Rohde & Tang, 2018). The formula of the method is as follows:
PCn = bn1X1 + bn2X2 + …… + bnnXn
PCn = Principal Components Analysis
bn = coefficients for principal components
X = variables of the Principal Components Analysis
The main advantage that PCA offers is that this type of method is not just about reducing the variables in an aggregate index but rather about eliminating variables that are insignificant for the composite index we want to create (Khatun, 2009). If there are no correlations, it means that these variables measure other dimensions of the phenomenon under investigation (Manly & Navarro Alberto, 2017). These variables have been selected and collected in Table 1.
When choosing the variables, the following aspects were taken into account:
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- “Children in jobless households” was added to our indicator by taking into account a revised version of Osberg and Sharpe’s considerations (Osberg & Sharpe, 2005): they considered single parenthood, but we preferred to focus on children whose parents are not working, perhaps being in an even more disadvantaged economic situation than single-parent families.
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- We selected the variable “Inability to afford to pay for a one-week annual vacation away from home” for the same reasons as Romaguera De la Cruz (2019) did.
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- For “Inability to make ends meet”, we considered the percentage of households making ends meet with great difficulty.
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- In the case of “Persons at risk of poverty”, we considered “At risk of poverty rate (cut-off point: 60% of median equalized income after social transfers)”.
We preferred not to standardize the variables (with values between 0 and 1) because we noted that all the variables have the percentage of the population as the study item. In Table 2, Table 3 and Table 4 can be seen the score for each variable for all 27 countries of the European Union in three important time frames for the economic insecurity at the beginning of the 21st century: 2010, 2017, and 2023.
We selected the year 2010 for two main reasons: first of all because in 2008 and 2009, the effects of the global financial crisis were not fully felt in all the European Union, especially in the Central and Eastern parts, when the negative consequences appeared on a large scale in 2010, and secondly, because Croatia has data for these variables only after 2010. The year 2023 is important in our study because it is the most recent year for the data and it is very close to the current economic and political situation, marked by the effects of both 2020’s COVID-19 crisis and the post-2022 energy and security crisis surrounding the Russia-Ukraine war. The year 2017 was selected because it could indicate the European economic security situation in the post-2015–2016 migration crisis period.
Further, we will proceed with subjecting the indicator to a regression equation, the independent variables being various variables obtained from the Index of Economic Freedom (see Table 5) for the last ten years (period 2014–2023) and including 26 out of 27 EU countries (Luxembourg being excluded due to lack of data).
4. Results and Discussions
4.1. Main Determinants of the Economic Insecurity in European Union States
Economic insecurity was defined having as a reference PCA for three time frames: 2010, 2017, and 2023 (see Table 6, Table 7, Table 8, Table 9, Table 10 and Table 11). As mentioned above, the analysis covered three different periods, reporting roughly the same results.
To stipulate the index, we utilized the results for 2023 (Table 10 and Table 11), using the first two principal components, since they would have a validity of 65.38%. The Benzécri criterion (Benzécri & Benzécri, 1980) suggests a validity of minimum 70%. Still, on the other hand, the Kaiser criterion (Kaiser 1960) forces us to have a root greater than one, thus implying that only the first two principal components can be used (Principal components 3 has a root of 0.93 and a coverage of only 9.31%). Also, the Kaiser–Meyer–Olkin (KMO) Test (see Table 10) does not have values below 0.6 (Dehisat & Awang, 2020) and is also statistically valid, which allows us to state that the PCA meets the preliminary requirements.
We used the eigenvectors with larger values (in our case, greater than 0.4), as was done in previous studies that utilized PCA (Morenikeji et al., 2017; Vijaya, 2010). Six variables were included into the index to give it its final form, as seen in Table 12, presenting two main dimensions: the first, being composed of Inability to face unexpected financial expenses and Inability to afford paying for one-week annual holiday away from home, shall be called “Lack of savings and leisure time (LSL)”, and the second, being composed of Inability to make ends meet, Housing cost overburden rate, Arrears (mortgage or rent, utility bills or hire purchase), and Children aged 0–17 living in jobless households shall be called “Household’s predisposition to risk (HPR)”.
It is important to note that the index stipulated in this research can be considered similar to the AROPE (At risk of poverty or social exclusion) index adopted by European Union. However, we note that the index proposed in this analysis could be a reduced and improved version of the Eurostat index, having at least the following differences:
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- The AROPE index is composed of three large dimensions: Risk of poverty, Severe material and social deprivation, and Low employment intensity (Eurostat, 2024), thus focusing on the poverty rate (which was excluded from our indicator by the PCA analysis), material deprivation, and unemployment rate (our indicator interprets the unemployment rate differently). The indicator presented in this analysis reduces everything to just six variables.
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- The present indicator focuses more on households than on individuals, thus using only household-level variables (the AROPE Index uses both household and individual depravation). We consider such an approach to be more appropriate because households are a better reflection of the population than individuals.
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- The only variable that is negatively correlated in our indicator is that of the CJH (Children aged 0–17 living in jobless households), which leads us to infer that families with unemployed parents would not be in a less severe state of economic insecurity than the unemployed without children, which can be explained by the substantial aids that European countries grant for raising children (Bradshaw & Finch, 2023). Also, EU spending on families with children is often considered an efficient antidote in reducing child poverty (Sánchez & Navarro, 2021), which reiterates our point.
Considering the Eurovoc division of Europe (Eurovoc, 2024), we note the following:
As noted in other studies (Bağlıtaş & Atik, 2023) (Pricop, 2023), we also see how Greece ranks last, Greek citizens having a very high degree (25.97 for 2023) of economic insecurity in their own households, meaning that in unforeseen economic downturns, they would suffer more than other EU citizens. However, this was also visible when the 2008 financial crisis hit the European area. Similar to Greece, Romania is the only country in the fourth tier of Table 13 (countries with a high degree of economic insecurity). On the other hand, European citizens from Denmark, Sweden, Finland, Luxembourg, Austria, Belgium, Netherlands, Czechia, Slovenia, and Malta have an economic status in their own household that is more resilient to external shocks.
At the regional level, a double dichotomy can be observed: North and West, on the one hand, with low levels of economic insecurity, and East and South (except for Malta), with high levels of economic insecurity. Explanations for these differences can be many. Some authors, such as Mussida and Parisi (2019), note the importance of the post-2008 economic recession as a major factor in increasing poverty in Greece, Italy, and Spain, while other authors add the factor of political instability on top of the economic insecurity (Zamora-Kapoor & Coller, 2014). The heightened levels of economic insecurity in Eastern Europe, on the other hand, are broadly explained by various problems involving the transition from communist to capitalist regimes, perpetuated over decades, and by the proximity to the Russia-Ukraine conflict. As noticed by Golinowska (2009), even the adhesion to the European Union of the Central and Eastern European states was marked by different problems resulting from the economic restructuring process. These problems had deeper roots in the very beginning of the transition process from a centralized to a market economy, when the old-age security systems from Central and Eastern European states were defined through a combination of the conservative-corporatist and social democratic regimes (Golinowska, 1999).
It has to be said that, in addition to the 2007–2008 financial crisis, the pandemic crisis from 2020 also played an important role in deepening the dichotomy outlined above, with France, Italy, and Spain being the worst affected (Pillai et al., 2020). According to Chen et al. (2020), the declining consumption proves a negative sensitivity to daily variations in the intensity of a public health emergency as the state of public health deteriorates. Meanwhile, as Brodeur et al. (2021) stated, confidence in the market and financial transactions may reduce under uncertain circumstances. Job losses played an important role during the COVID-19 pandemic (Jiménez, 2021), allowing us to conclude that economic insecurity was an important factor in Southern and Eastern European states even before the 2020 pandemic, but the increasing inequality and poverty of the last years worsened the situation (Antriyandarti et al., 2024). Analyzing the food insecurity in V4 countries (Czechia, Hungary, Poland, and Slovakia), Dudek (2022) concluded that, in 2018, Hungary and Slovakia had a higher share of people living in households making ends meet with difficulty than Poland (results that are similar to ours for the year 2017—see Table 3). However, she mentions that the COVID-19 pandemic may exacerbate these insecurity problems in the analyzed states. The idea that the pandemic worsened economic insecurity in Central and Eastern European states can also be seen in the study of Kalinowski and Łuczak (2021). Their findings suggest that the insecurity of the households in Poland would increase in the post-pandemic period due to the fact that people fear that they will lose the financial stability because of unemployment and, implicitly, the family’ poverty will rise.
Considering these results, our first hypothesis (H1) is accepted.
In order to reduce the economic insecurity generated by the pandemic, the governments could adopt various measures, considering both the recommendations and the lessons learned from previous crises. For instance, as noticed by Antonucci et al. (2024), both active and passive labor market policies could have a positive effect in reducing job insecurity across the skill groups from the European Union states. The positive role of passive labor market policies in enhancing job security, aimed at providing replacement income during periods of joblessness or job search, have also been previously underlined in studies conducted after the 2008 financial crisis (van Oorschot & Chung, 2015).
4.2. The Relationship Between Economic Insecurity and Economic Freedom
In order to test the correlation between the aggregate index on economic insecurity and the variables of the Index of economic freedom it is necessary to present the descriptive statistics (see Table 14). It can be noticed that the values of the Economic Freedom index variables range from 0 to 100, while the economic insecurity index ranges from 5.85 (the lowest degree of economic insecurity) to 30.89 (the highest degree of economic insecurity). The mean of economic insecurity is 14.52, a result similar to that of Lithuania (14.50), meaning that Lithuanian citizens have the average degree of economic insecurity in the European Union.
4.2.1. Panel Root Test
Panel root tests are mostly used in time-series regression analysis and are necessary to make sure that the set of data is stationary (Petrică et al., 2017). We decided to undertake stationarity tests such as Levin, Lin, and Chu and PP-Fisher to confirm the stationarity at level 0. Table 15 shows that we can use our economic insecurity index without further differentiation.
4.2.2. Correlation Matrix
The correlation matrix (Table 16) shows how the different variables relate to each other (Gujarati, 2003), in this case how the economic insecurity index relates to the IEF variables. The validity of these correlations will also be confirmed by the Granger test, as we will see in Table 18.
4.2.3. VIF Test
In order to avoid redundancy in the information, it is necessary to perform VIF tests for all the variables. A VIF value greater than 10 indicates the presence of multicollinearity (Büyükuysal & Öz, 2016), which is not the case in our analysis, as can be seen in Table 17.
4.2.4. Granger Causality Test
As previously mentioned, we are interested in the validation of the correlation between the economic insecurity index and the economic freedom variables, but, at the same time, we are also interested in the direction of such correlations, which is why it is necessary to stipulate a Granger test, as can be seen in Table 18. Looking at the p-values, we can elaborate various correlations between economic insecurity and the various economic freedom variables. Firstly, we can observe a unidirectional causality between Investment Freedom and Economic Insecurity. Economic insecurity can also be the dependent variable for Monetary Freedom or Trade Freedom.
The formula of a panel data regression model is as follows:
where Yit (I representing countries and t time) will be the dependent variable (in our case economic insecurity), α0 the country-specific constant, β1 × Xit the regressor, and µ the error term (Gujarati, 2003).
Yit = α0 + β1 × Xit + µ
Previous research, such as that undertaken by Sabău-Popa et al. (2020), had used the same methodology, creating an aggregate indicator using the PCA method and then proposing the indicator created to independent variables, thus creating a statistical model.
Considering this, the model we will propose is a model in which the three variables will be the independent variables (2).
EIit = α0 + β1MONETARYit + β2INVESTMENTit + β3TRADEit + µ
Economic insecurity can also be the independent variable, as the Granger test shows us in various forms such as in correlation with Monetary Freedom, Government Spending, Government Integrity, Business Freedom, or Trade Freedom. All these models in which economic insecurity actually causes the variations of these variables are summarized in Table 22. However, we are interested in having economic insecurity as the dependent variable in the present analysis, because the aim of this research is to identify which variables could increase it and which variables could decrease it.
Table 18.
Granger causality test.
| Null Hypothesis | Obs | F-Statistic | Prob. | Conclusion |
|---|---|---|---|---|
| FINANCIAL FD does not Granger Cause EI | 208 | 2.38464 | 0.0947 | No Causality |
| EI does not Granger Cause FINANCIAL FD | 1.77075 | 0.1728 | No Causality | |
| INV FD does not Granger Cause EI | 208 | 4.76750 | 0.0095 | Unidirectional Causality |
| EI does not Granger Cause INV FD | 0.36622 | 0.6938 | No Causality | |
| TAX BURDEN does not Granger Cause EI | 208 | 0.29224 | 0.7469 | No Causality |
| EI does not Granger Cause TAX BURDEN | 0.41445 | 0.6613 | No Causality | |
| MONETARY FD does not Granger Cause EI | 208 | 5.30205 | 0.0057 | Bi-directional Causality |
| EI does not Granger Cause MONETARY FD | 12.3785 | 8 × 10−6 | ||
| GOV SPEN does not Granger Cause EI | 208 | 0.94310 | 0.3911 | No Causality |
| EI does not Granger Cause GOV SPEN | 3.14785 | 0.0450 | Unidirectional Causality | |
| GOV INTEG does not Granger Cause EI | 208 | 0.44410 | 0.6420 | No Causality |
| EI does not Granger Cause GOV INTEG | 13.9559 | 2 × 10−6 | Unidirectional Causality | |
| BUSIN FD does not Granger Cause EI | 208 | 1.56942 | 0.2107 | No Causality |
| EI does not Granger Cause BUSIN FD | 8.72311 | 0.0002 | Unidirectional Causality | |
| LABOR FD does not Granger Cause EI | 208 | 1.12082 | 0.3280 | No Causality |
| EI does not Granger Cause LABOR FD | 0.38731 | 0.6794 | No Causality | |
| TRADE FD does not Granger Cause EI | 208 | 11.8206 | 1 × 10−5 | Bi-directional Causality |
| EI does not Granger Cause TRADE FD | 3.18292 | 0.0435 | ||
| PROPERTY R does not Granger Cause EI | 208 | 0.44094 | 0.6440 | |
| EI does not Granger Cause PROPERTY R | 2.71910 | 0.0683 |
Source: own elaboration using EViews 12 SV.
4.2.5. Regression Models
Regression models are necessary in order to determine how the independent variables (the selected ones being Monetary Freedom, Investment Freedom, and Trade Freedom) influence the dependent variable. The results are represented in Table 19, showing how a decrease in Monetary and Investment Freedom scores can cause an increase in economic insecurity, while an increase in Trade Freedom causes an increase in economic insecurity.
As for the variables that correlate negatively with increased economic insecurity, there are several explanations. In Table 5, we note how Monetary Freedom is calculated by The Heritage Foundation utilizing both inflation and subsidies, while the scores for Investment Freedom are points entirely proposed by the Heritage researchers. Excessive state control of the money supply has always been a fundamental concern for economists such as Rothbard (1963, 1994) and Friedman (1962), advocating awareness of the damage caused by the central banks’ monopoly of the money supply and on understanding the differences between real and “inflated” economic growth. This analysis, therefore, confirms their hypothesis, underlining that economic growth is not always synonymous with greater economic security for citizens precisely because of inflation. On the other hand, lower Investment Freedom scores mean greater economic insecurity. Investments are the main way a country can achieve economic growth (D. Anderson, 1990), whether we are talking about foreign investment (Ozawa, 1992) or intra-national investment (Popescu & Diaconu, 2021). It therefore directly influences economic security.
The most interesting aspect of the regression equation, however, is that the Trade Freedom variable is positively correlated with economic insecurity, showing how more trade freedom would cause more insecurity. An important note needs to be made, namely that the Heritage Foundation calculates Trade Freedom based on trade tariffs (see Table 5). This correlation, therefore, reiterates the idea that the protectionist policy of the European Union, which has become increasingly important after the Crimean war in 2014 and the COVID-19 pandemic (Stanojevic, 2021), would translate into more economic security, bearing in mind, however, that the positive effects of European protectionism may only be in the short term (Baur & Flach, 2023). Another important aspect to mention is that it would be more accurate to say that the European Union is not protectionist but rather prefers to prioritize its market (Pelkmans, 2024).
Considering our results, our second hypothesis (H2) is only partially accepted.
4.2.6. Robustness Test
Robustness tests are needed to test the validity of the model, which is why we perform two different robustness tests to observe whether the fixed-effects regression equation shows substantial changes. The first robustness test will aim at excluding the countries with the highest economic insecurity scores, namely Romania and Greece. As can be seen from the third column of Table 20, the results of the FEM Model are unchanged.
In Table 21, on the other hand, Romania and Greece have been reinstated, but the ten countries with very low economic insecurity scores have been excluded. Here again, we see that the values of the fixed effects model remain unchanged.
Table 22.
Economic insecurity as independent variable.
| Government Spending | Variable | OLS Model | REM Model | FEM Model |
| Economic insecurity | 0.803 (0.192) *** | −0.753 (0.159) *** | −0.886 (0.165) *** | |
| Constant | 25.451 (3.023) *** | 48.069 (4.177) *** | 50.000 (2.439) *** | |
| Adj R | 0.059 | 0.073 | 0.879 | |
| Observations | 260 | 260 | 260 | |
| Hausman test | Chi-Sq. = 32.279628 (Prob. 0.0016) Chi-Sq. d.f. = 5 | |||
| Monetary Freedom | Variable | OLS Model | REM Model | FEM Model |
| Economic insecurity | −0.080 (0.033) ** | −0.088 (0.049) * | −0.098 (0.067) | |
| Constant | 83.010 (0.526) *** | 83.127 (0.821) *** | 83.278 (0.997) *** | |
| Adj R | 0.018 | 0.008 | 0.303 | |
| Observations | 260 | 260 | 260 | |
| Hausman test | Chi-Sq. = 0.050634 (Prob. 0.8220) Chi-Sq. d.f. = 1 | |||
| Government Integrity | Variable | OLS Model | REM Model | FEM Model |
| Economic insecurity | −2.055 (0.128) *** | −1.375 (0.174) *** | −1.087 (0.201) *** | |
| Constant | 95.164 (2.018) *** | 85.284 (3.180) *** | 80.970 (2.970) *** | |
| Adj R | 0.497 | 0.186 | 0.784 | |
| Observations | 260 | 260 | 260 | |
| Hausman test | Chi-Sq. = 8.645991 (Prob. 0.0033) Chi-Sq. d.f. = 1 | |||
| Business Freedom | Variable | OLS Model | REM Model | FEM Model |
| Economic insecurity | −0.637 (0.082) *** | 0.168 (0.094) * | 0.354 (0.102) *** | |
| Constant | 85.565 (1.305) *** | 73.862 (1.908) *** | 71.164 (1.514) *** | |
| Adj R | 0.183 | 0.007 | 0.782 | |
| Observations | 260 | 260 | 260 | |
| Hausman test | Chi-Sq. = 21.110658 (Prob. 0.0000) Chi-Sq. d.f. = 1 | |||
| Trade Freedom | Variable | OLS Model | REM Model | FEM Model |
| Economic insecurity | 0.072 (0.033) ** | 0.084 (0.030) *** | 0.652 (0.070) *** | |
| Constant | 83.878 (0.531) *** | 83.708 (0.484) *** | 75.453 (1.038) *** | |
| Adj R | 0.013 | 0.018 | 0.255 | |
| Observations | 260 | 260 | 260 | |
| Hausman test | Chi-Sq. = 80.277066 (Prob. 0.0000) Chi-Sq. d.f. = 1 | |||
Source: own elaboration using EViews 12 SV. Notes: Significance levels are *** for 1%, ** for 5% and * for 10%.
5. Conclusions
Countries all around the world have been facing many ups and downs during the last two decades, in a global economy which has become more and more integrated, stimulated interest in economic security, and forced its redefinition. Therefore, our study aimed to redefine the concept of economic insecurity by proposing a new index that we tested on European Union states.
Our research proposes a statistical approach slightly different from the traditional ones because the aggregated index of economic insecurity was developed by utilizing, in the first phase, PCA, proposing an index based on PC1 and PC2, with a total significance of 65.83% (we chose the Kaiser criterion rather than the Benzécri one), which comprises six different variables.
Moreover, when trying to identify the level of economic insecurity for the year 2023, our index shows the existence of a double regional dichotomy within the European Union: Northern and Western Europe are the regions with the lowest degree of economic insecurity, while the countries of Southern and Eastern and Central Europe remain the states with the highest degree, with the Mediterranean area (except Malta) being the most sensitive point of the European Union (even more than the Central–Eastern part). Many reasons could be found for such large differences between the Northwestern Europe and the Southeastern region. One of them could be related to the historical path followed by states. While some countries have enjoyed decades of market economy, others have been forced to conform to the Soviet model and, even after the collapse of the communism, in certain states, the reforms were incoherent and implemented at a very slow pace during the transition period. Since these results are for 2023, another possible explanation could be related to the different ways in which countries dealt with the negative consequences of the pandemic. However, this aspect requires further investigation.
Aiming at investigating the relationship between economic insecurity and economic freedom in European Union states, our results showed both positive and negative correlations. The regression results may suggest that an anti-inflationary monetary policy, doubled by the full freedom of investment and a prioritization of the European Union internal market, would allow citizens to achieve a higher degree of economic security in their households. Robustness checks confirmed the proposed model.
The limits of our study could be related to the fact that our index was developed by taking into account only objective indicators, and, thus, we conducted our research based on secondary data. We intend, in a future study, to extend our index by also including subjective factors, such as individuals’ perception of economic security. This will involve a survey, based on a questionnaire, which will allow us to collect primary data in order to have a more complete representation of the economic (in)security phenomenon.
Starting from this new index, a second future research direction could be related to investigating further relations with other indexes (such as, for example, the human development index) or indicators reflecting economic growth.
A third future research direction will focus on specific policy interventions after the COVID-19 pandemic, especially in the Central and Eastern European Union countries, where the risk of increasing economic insecurity was higher. More precisely, we intend to investigate the relationship between our economic insecurity index and the institutional variables.
Author Contributions
Conceptualization, I.-A.P. and L.D.; methodology, I.-A.P. and L.D.; software, I.-A.P.; validation, I.-A.P. and L.D.; formal analysis, I.-A.P. and L.D.; investigation, I.-A.P. and L.D.; resources, I.-A.P. and L.D.; data curation, I.-A.P.; writing—original draft preparation, I.-A.P. and L.D.; writing—review and editing, L.D.; visualization, I.-A.P. and L.D.; supervision, L.D.; project administration, L.D. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data utilized in this research can be found at https://ec.europa.eu/eurostat and https://www.heritage.org/index/ (accessed on 2 December 2024).
Conflicts of Interest
The authors declare no conflicts of interest.
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Table 1.
Description of variables with abbreviations and Eurostat codes.
| Definition | Abbreviation | Eurostat Code |
|---|---|---|
| Arrears (mortgage or rent, utility bills or hire purchase) | Arrears | ilc_mdes05 |
| Children aged 0–17 living in jobless households | CJH | lfsi_jhh_a |
| Housing cost overburden rate | HCO | ilc_lvho07a |
| Inability to afford paying for one-week annual holiday away from home | IAH | ilc_mdes02 |
| Inability to afford a meal with meat, chicken, fish every second day | IAM | ilc_mdes03 |
| Inability to make ends meet | IMEM | ilc_mdes09 |
| Inability to face unexpected financial expenses | IFUFE | ilc_mdes04 |
| Overcrowding rate | Overcrowding | ilc_lvho05a |
| Inability to keep home adequately warm | IWH | ilc_mdes01 |
| At-risk-of-poverty rate by poverty threshold | RPR | ilc_li02 |
Table 2.
European Union countries’ scores for each of the ten variables—year 2010.
| Arrears | IFUFE | IMEM | PRP | CJH | IWH | IAM | IAH | HCO | Overcrowding | |
|---|---|---|---|---|---|---|---|---|---|---|
| Belgium | 7.8 | 25.4 | 7.7 | 14.6 | 12.2 | 5.6 | 5.0 | 26.9 | 8.9 | 4.2 |
| Bulgaria | 33.8 | 65.0 | 29.0 | 20.7 | 13.6 | 66.5 | 43.2 | 62.4 | 5.9 | 47.4 |
| Czechia | 6.0 | 37.9 | 8.4 | 9.0 | 7.8 | 5.2 | 9.7 | 39.5 | 9.7 | 22.5 |
| Denmark | 6.2 | 23.7 | 3.7 | 13.3 | 8.2 * | 1.9 | 2.1 | 11.8 | 21.9 | 7.3 |
| Germany | 4.9 | 33.7 | 2.8 | 15.6 | 9.5 | 5.0 | 8.6 | 23.7 | 14.5 | 7.1 |
| Estonia | 13.3 | 43.6 | 8.5 | 15.8 | 12.9 | 3.1 | 10.1 | 50.6 | 6.0 | 39.7 |
| Ireland | 16.7 | 49.1 | 15.2 | 15.2 | 19.7 | 6.8 | 3.0 | 41.7 | 4.9 | 3.4 |
| Greece | 30.9 | 28.2 | 24.2 | 20.1 | 6.3 | 15.4 | 7.9 | 46.3 | 18.1 | 25.5 |
| Spain | 11.7 | 38.7 | 15.5 | 20.7 | 10.1 | 7.5 | 2.6 | 42.6 | 9.7 | 5.0 |
| France | 10.8 | 33.0 | 4.4 | 13.3 | 9.6 | 5.7 | 6.9 | 28.7 | 5.1 | 9.2 |
| Croatia | 30.1 | 62.3 | 18.3 | 20.6 | 9.7 | 8.3 | 15.7 | 67.3 | 14.1 | 43.7 |
| Italy | 13.5 | 33.8 | 17.4 | 18.7 | 8.2 | 11.6 | 7.0 | 40.5 | 7.7 | 24.3 |
| Cyprus | 28.0 | 49.9 | 23.3 | 15.6 | 5.0 | 27.3 | 4.4 | 47.8 | 3.1 | 3.5 |
| Latvia | 25.2 | 78.1 | 23.5 | 20.9 | 12.0 | 19.1 | 26.8 | 62.7 | 9.8 | 55.7 |
| Lithuania | 11.9 | 62.3 | 12.0 | 20.5 | 14.8 | 25.2 | 23.5 | 63.1 | 10.6 | 45.5 |
| Lux | 3.3 | 24.4 | 1.9 | 14.5 | 2.8 | 0.5 | 0.9 | 13.6 | 4.7 | 7.8 |
| Hungary | 24.3 | 73.9 | 25.3 | 12.3 | 16.7 | 10.7 | 27.6 | 64.9 | 11.3 | 47.2 |
| Malta | 7.8 | 28.2 | 19.7 | 15.5 | 9.7 | 14.3 | 10.8 | 60.8 | 3.7 | 4.0 |
| Net | 4.9 | 22.2 | 3.8 | 10.3 | 6.2 | 2.3 | 2.6 | 15.8 | 14.0 | 2.0 |
| Austria | 7.0 | 25.0 | 5.9 | 14.7 | 5.8 | 3.8 | 8.7 | 22.3 | 7.5 | 12.0 |
| Poland | 15.3 | 50.6 | 14.1 | 17.6 | 8.7 | 14.8 | 15.5 | 59.9 | 9.1 | 47.5 |
| Portugal | 8.6 | 27.2 | 20.3 | 17.9 | 7.1 | 30.1 | 3.3 | 64.6 | 4.2 | 14.6 |
| Romania | 29.0 | 44.8 | 21.1 | 21.6 | 9.9 | 20.1 | 21.4 | 77.4 | 15.8 | 52.0 |
| Slovenia | 19.5 | 45.1 | 8.9 | 12.7 | 3.9 | 4.7 | 8.5 | 31.4 | 4.3 | 34.9 |
| Slovakia | 12.1 | 38.2 | 11.5 | 12.0 | 10.2 | 4.4 | 23.0 | 55.7 | 7.6 | 40.1 |
| Finland | 10.3 | 28.1 | 2.4 | 13.1 | 4.4 | 1.4 | 2.9 | 14.7 | 4.2 | 6.1 |
| Sweden | 7.7 | 18.8 | 3.6 | 14.8 | 9.4 | 2.1 | 2.7 | 11.0 | 7.8 | 13.1 |
* For Denmark, we utilized the score of 2011 for Children in Jobless Households because there were no data for 2010. Source: Eurostat.
Table 3.
European Union countries’ scores for each of the ten variables—year 2017.
| Arrears | IFUFE | IMEM | PRP | CJH | IWH | IAM | IAH | HCO | Overcrowding | |
|---|---|---|---|---|---|---|---|---|---|---|
| Belgium | 5.4 | 25.5 | 8.6 | 15.9 | 12.3 | 5.8 | 5.7 | 25.3 | 9.4 | 4.8 |
| Bulgaria | 33.3 | 53.2 | 28.0 | 23.4 | 10.9 | 36.5 | 31.7 | 52.6 | 18.9 | 41.9 |
| Czechia | 3.2 | 28.1 | 7.4 | 9.1 | 6.2 | 3.1 | 7.1 | 25.0 | 8.7 | 16.0 |
| Denmark | 6.0 | 25.1 | 3.4 | 12.4 | 8.9 | 2.7 | 2.1 | 13.8 | 15.7 | 8.6 |
| Germany | 4.4 | 29.3 | 2.1 | 16.1 | 9.4 | 3.3 | 7.0 | 15.3 | 14.5 | 7.2 |
| Estonia | 7.3 | 36.3 | 3.7 | 21.0 | 6.4 | 2.9 | 5.3 | 27.9 | 4.8 | 13.5 |
| Ireland | 13.0 | 41.6 | 8.7 | 15.6 | 11.8 | 4.4 | 1.7 | 35.5 | 4.5 | 2.8 |
| Greece | 44.9 | 52.7 | 39.9 | 20.2 | 9.2 | 25.7 | 13.2 | 50.9 | 39.6 | 29.0 |
| Spain | 9.3 | 36.6 | 9.5 | 21.6 | 8.8 | 8.0 | 3.7 | 34.3 | 9.8 | 5.1 |
| France | 9.1 | 29.6 | 4.1 | 13.2 | 11.9 | 4.9 | 7.1 | 23.1 | 5.0 | 7.7 |
| Croatia | 21.9 | 56.2 | 15.5 | 20.0 | 8.4 | 7.4 | 10.5 | 58.2 | 5.8 | 39.9 |
| Italy | 6.1 | 38.3 | 8.6 | 20.3 | 9.6 | 15.2 | 13.4 | 43.0 | 8.2 | 27.1 |
| Cyprus | 24.8 | 50.1 | 22.2 | 15.7 | 9.5 | 22.9 | 3.8 | 52.3 | 2.8 | 2.8 |
| Latvia | 14.0 | 59.9 | 13.5 | 22.1 | 7.5 | 9.7 | 13.0 | 37.3 | 6.9 | 41.9 |
| Lithuania | 8.7 | 50.6 | 7.1 | 22.9 | 9.8 | 28.9 | 16.5 | 41.8 | 7.2 | 23.7 |
| Lux | 3.0 | 20.4 | 5.1 | 16.4 | 7.6 | 1.9 | 2.2 | 10.9 | 7.1 | 8.3 |
| Hungary | 15.7 | 31.5 | 15.7 | 13.4 | 7.5 | 6.8 | 16.4 | 48.2 | 10.7 | 40.5 |
| Malta | 6.5 | 15.6 | 4.6 | 16.7 | 7.7 | 6.3 | 5.6 | 33.9 | 1.4 | 3.0 |
| Net | 4.6 | 20.7 | 3.2 | 13.2 | 6.5 | 2.4 | 1.9 | 15.2 | 9.4 | 4.1 |
| Austria | 5.9 | 20.6 | 4.5 | 14.4 | 6.7 | 2.4 | 5.5 | 14.2 | 7.1 | 15.1 |
| Poland | 10.3 | 34.8 | 6.8 | 15.0 | 8.3 | 6.0 | 6.3 | 38.4 | 6.7 | 40.5 |
| Portugal | 7.7 | 36.9 | 15.2 | 18.3 | 5.9 | 20.4 | 3.0 | 44.3 | 6.7 | 9.3 |
| Romania | 17.3 | 52.5 | 14.7 | 23.6 | 9.4 | 11.3 | 19.2 | 65.0 | 12.3 | 47.0 |
| Slovenia | 15.2 | 37.1 | 6.5 | 13.3 | 3.0 | 3.9 | 6.5 | 23.1 | 5.2 | 12.8 |
| Slovakia | 7.4 | 34.6 | 8.1 | 12.4 | 8.0 | 4.3 | 14.8 | 42.3 | 8.4 | 36.4 |
| Finland | 10.8 | 28.5 | 2.3 | 11.5 | 5.1 | 2.0 | 2.6 | 15.4 | 4.3 | 6.1 |
| Sweden | 5.1 | 19.7 | 2.9 | 15.8 | 5.8 | 2.1 | 1.8 | 8.8 | 8.4 | 13.5 |
Source: Eurostat.
Table 4.
European Union countries’ scores for each of the ten variables—year 2023.
| Arrears | IFUFE | IMEM | PRP | CJH | IWH | IAM | IAH | HCO | Overcrowding | |
|---|---|---|---|---|---|---|---|---|---|---|
| Belgium | 4.6 | 21.4 | 6.0 | 12.3 | 11.9 | 6.0 | 4.2 | 21.5 | 7.7 | 5.7 |
| Bulgaria | 18.8 | 46.7 | 10.1 | 20.6 | 8.8 | 20.7 | 19.9 | 44.2 | 11.1 | 34.9 |
| Czechia | 2.9 | 19.7 | 3.7 | 9.8 | 4.4 | 6.1 | 6.8 | 20.3 | 9.1 | 15.9 |
| Denmark | 7.8 | 23.1 | 4.5 | 11.8 | 6.0 | 6.9 | 3.8 | 15.4 | 15.4 | 8.7 |
| Germany | 8.3 | 35.0 | 3.1 | 14.4 | 9.2 | 8.2 | 13.3 | 22.8 | 13.0 | 11.4 |
| Estonia | 5.8 | 30.4 | 2.7 | 22.5 | 7.8 | 4.1 | 5.7 | 23.0 | 7.6 | 17.0 |
| Ireland | 10.6 | 34.3 | 6.4 | 12.0 | 6.5 | 7.2 | 1.6 | 23.7 | 4.7 | 3.9 |
| Greece | 47.3 | 44.3 | 36.7 | 18.9 | 4.7 | 19.2 | 10.9 | 43.1 | 28.5 | 26.9 |
| Spain | 13.6 | 37.2 | 9.4 | 20.2 | 8.0 | 20.8 | 6.4 | 33.2 | 8.2 | 7.6 |
| France | 10.0 | 29.4 | 7.8 | 15.4 | 10.0 | 12.1 | 12.2 | 25.1 | 6.5 | 9.9 |
| Croatia | 12.7 | 41.4 | 6.7 | 19.3 | 4.6 | 6.2 | 5.5 | 39.4 | 4.0 | 31.3 |
| Italy | 5.0 | 28.8 | 5.5 | 18.9 | 9.1 | 9.5 | 8.4 | 32.3 | 5.7 | 25.4 |
| Cyprus | 14.3 | 37.6 | 7.1 | 13.9 | 4.5 | 16.9 | 1.3 | 36.0 | 2.6 | 2.2 |
| Latvia | 7.9 | 44.8 | 7.7 | 22.5 | 7.5 | 6.6 | 7.7 | 31.4 | 7.2 | 40.9 |
| Lithuania | 7.1 | 40.5 | 2.5 | 20.6 | 8.0 | 20.0 | 11.1 | 34.0 | 5.2 | 26.0 |
| Lux | 8.7 | 24.1 | 2.2 | 18.8 | 5.1 | 2.1 | 3.3 | 10.6 | 22.7 | 7.4 |
| Hungary | 10.8 | 31.5 | 7.9 | 13.1 | 4.7 | 7.2 | 14.7 | 43.3 | 8.7 | 15.6 |
| Malta | 5.7 | 15.9 | 5.6 | 16.6 | 9.3 | 6.8 | 9.4 | 30.0 | 6.0 | 2.4 |
| Net | 2.4 | 15.3 | 1.6 | 15.0 | 4.6 | 6.9 | 2.8 | 12.6 | 11.1 | 3.7 |
| Austria | 6.9 | 22.8 | 5.0 | 14.9 | 6.0 | 3.9 | 4.6 | 19.7 | 6.0 | 14.5 |
| Poland | 5.1 | 25.7 | 3.8 | 14.0 | 4.2 | 4.7 | 3.5 | 27.6 | 5.9 | 33.9 |
| Portugal | 5.2 | 30.5 | 10.0 | 17.0 | 5.8 | 20.8 | 2.3 | 38.9 | 4.9 | 12.9 |
| Romania | 14.4 | 46.4 | 9.8 | 21.1 | 13.4 | 12.5 | 23.3 | 59.5 | 9.1 | 40.0 |
| Slovenia | 7.3 | 22.7 | 4.4 | 12.7 | 2.7 | 3.6 | 3.3 | 16.4 | 3.7 | 10.3 |
| Slovakia | 8.8 | 29.3 | 10.4 | 14.3 | 6.0 | 8.1 | 17.8 | 36.2 | 5.9 | 30.5 |
| Finland | 9.5 | 26.0 | 2.0 | 12.2 | 6.9 | 2.6 | 3.9 | 12.9 | 5.5 | 8.8 |
| Sweden | 6.7 | 21.8 | 3.5 | 16.1 | 3.9 | 5.9 | 2.8 | 11.2 | 10.9 | 16.4 |
Source: Eurostat.
Table 5.
Description of the independent variables.
| Variable Name | Symbol | Composition and Calculation | Source |
|---|---|---|---|
| Financial Freedom | FINANCIAL FD | The extent of government regulation of financial services, The degree of state intervention in banks and other financial firms through direct and indirect ownership, Government influence on the allocation of credit, The extent of financial and capital market development, and Openness to foreign competition. | (The Heritage Foundation, 2024) |
| Government Integrity | GOV INTEGRITY | Perceptions of corruption, Bribery risk, and Control of corruption including “capture” of the state by elites and private interests. Sub-factor Scorei = 100 × (Sub-factorMax − Sub-factori)/(Sub-factorMax − Sub-factorMin) | (The Heritage Foundation, 2024) |
| Investment Freedom | INVESTMENT FD | Points system elaborated by The Heritage Foundation | (The Heritage Foundation, 2024) |
| Monetary Freedom | MONETARY FD | The weighted average rate of inflation for the most recent three years and A qualitative judgement about the extent of government manipulation of prices through direct controls or subsidies. Monetary Freedomi = 100 − α √Weighted Avg. Inflationi − PC penaltyi | (The Heritage Foundation, 2024) |
| Labor Freedom | LABOR FD | Minimum wage, Associational right, Paid annual leave, Notice period for redundancy dismissal, Severance pay for redundancy dismissal, Labor productivity, Labor force participation rate, Restrictions on overtime work, and Redundancy dismissal permitted by law. Sub-factor Scorei = 50 × (Sub-factoraverage/Sub-factori) | (The Heritage Foundation, 2024) |
| Property Rights | PROPERTY RIGHTS | Risk of expropriation, Respect for intellectual property rights, and Quality of contract enforcement, property rights, and law enforcement. Sub-factor Scorei = 100 × (Sub-factori − Sub-factorMin)/(Sub-factorMax − Sub-factorMin) | (The Heritage Foundation, 2024) |
| Tax Burden | TAX BURDEN | The top marginal tax rate on individual income, The top marginal tax rate on corporate income, and The total tax burden as a percentage of GDP. Tax Burdenij = 100 − α (Sub-factorij) | (The Heritage Foundation, 2024) |
| Trade Freedom | TRADE FD | The trade-weighted average tariff rate and A qualitative evaluation of nontariff barriers (NTBs). Trade Freedomi = 100(Tariffmax − Tariffi)/(Tariffmax − Tariffmin) − NTBi | (The Heritage Foundation, 2024) |
| Government Spending | GOV SP | GEi = 100 − α (Expendituresi) | (The Heritage Foundation, 2024) |
Source: The Heritage Foundation.
Table 6.
PCA—year 2010.
| Number | Value | Difference | Proportion | Cumulative Value | Cumulative Proportion |
|---|---|---|---|---|---|
| 1 | 5.583378 | 4.437744 | 0.5583 | 5.583378 | 0.5583 |
| 2 | 1.145634 | 0.091478 | 0.1146 | 6.729012 | 0.6729 |
| 3 | 1.054156 | 0.354733 | 0.1054 | 7.783168 | 0.7783 |
| 4 | 0.699423 | 0.216434 | 0.0699 | 8.482591 | 0.8483 |
| 5 | 0.482989 | 0.039639 | 0.0483 | 8.965580 | 0.8966 |
| 6 | 0.443350 | 0.120662 | 0.0443 | 9.408929 | 0.9409 |
| 7 | 0.322688 | 0.177676 | 0.0323 | 9.731617 | 0.9732 |
| 8 | 0.145012 | 0.073654 | 0.0145 | 9.876628 | 0.9877 |
| 9 | 0.071358 | 0.019344 | 0.0071 | 9.947986 | 0.9948 |
| 10 | 0.052014 | --- | 0.0052 | 10.00000 | 1.0000 |
Source: own elaboration using EViews 12 SV.
Table 7.
First three principal components for the year 2010.
| Variable | PC1 | PC2 | PC3 |
|---|---|---|---|
| PRP | 0.282 | −0.033 | 0.478 |
| IWH | 0.313 | −0.390 | 0.166 |
| Overcrowding | 0.339 | 0.280 | −0.121 |
| IFUFE | 0.360 | 0.097 | −0.298 |
| IMEM | 0.366 | −0.212 | 0.196 |
| IAM | 0.361 | 0.103 | −0.233 |
| IAH | 0.373 | −0.055 | −0.024 |
| HCO | 0.027 | 0.805 | 0.419 |
| CJH | 0.219 | 0.224 | −0.548 |
| Arrears | 0.351 | −0.023 | 0.262 |
Source: own elaboration using EViews 12 SV.
Table 8.
PCA—year 2017.
| Number | Value | Difference | Proportion | Cumulative Value | Cumulative Proportion |
|---|---|---|---|---|---|
| 1 | 5.694415 | 4.490551 | 0.5694 | 5.694415 | 0.5694 |
| 2 | 1.203864 | 0.195179 | 0.1204 | 6.898279 | 0.6898 |
| 3 | 1.008685 | 0.285103 | 0.1009 | 7.906965 | 0.7907 |
| 4 | 0.723582 | 0.186970 | 0.0724 | 8.630547 | 0.8631 |
| 5 | 0.536612 | 0.144077 | 0.0537 | 9.167158 | 0.9167 |
| 6 | 0.392535 | 0.193496 | 0.0393 | 9.559693 | 0.9560 |
| 7 | 0.199039 | 0.068168 | 0.0199 | 9.758732 | 0.9759 |
| 8 | 0.130871 | 0.058370 | 0.0131 | 9.889604 | 0.9890 |
| 9 | 0.072501 | 0.034607 | 0.0073 | 9.962105 | 0.9962 |
| 10 | 0.037895 | --- | 0.0038 | 10.00000 | 1.0000 |
Source: own elaboration using EViews 12 SV.
Table 9.
First three principal components for the year 2017.
| Variable | PC1 | PC2 | PC3 |
|---|---|---|---|
| PRP | 0.290 | −0.279 | 0.264 |
| IWH | 0.346 | 0.079 | 0.205 |
| Overcrowding | 0.286 | −0.367 | −0.440 |
| IFUFE | 0.357 | −0.203 | −0.002 |
| IMEM | 0.369 | 0.344 | −0.080 |
| IAM | 0.336 | −0.232 | −0.125 |
| IAH | 0.356 | −0.252 | −0.001 |
| HCO | 0.236 | 0.616 | −0.127 |
| CJH | 0.164 | 0.026 | 0.797 |
| Arrears | 0.353 | 0.348 | −0.139 |
Source: own elaboration using EViews 12 SV.
Table 10.
PCA—year 2023.
| Number | Value | Difference | Proportion | Cumulative Value | Cumulative Proportion |
|---|---|---|---|---|---|
| 1 | 4.597151 | 2.655976 | 0.4597 | 4.597151 | 0.4597 |
| 2 | 1.941175 | 1.009800 | 0.1941 | 6.538326 | 0.6538 |
| 3 | 0.931374 | 0.023984 | 0.0931 | 7.469700 | 0.7470 |
| 4 | 0.907390 | 0.177193 | 0.0907 | 8.377091 | 0.8377 |
| 5 | 0.730198 | 0.403495 | 0.0730 | 9.107289 | 0.9107 |
| 6 | 0.326703 | 0.080048 | 0.0327 | 9.433991 | 0.9434 |
| 7 | 0.246654 | 0.070318 | 0.0247 | 9.680646 | 0.9681 |
| 8 | 0.176337 | 0.062836 | 0.0176 | 9.856983 | 0.9857 |
| 9 | 0.113501 | 0.083984 | 0.0114 | 9.970483 | 0.9970 |
| 10 | 0.029517 | --- | 0.0030 | 10.00000 | 1.0000 |
| KMO = 0.6 Sig. = 0.000 | |||||
Source: own elaboration using EViews 12 SV and R 4.4.2.
Table 11.
First two principal components for the year 2023.
| Variable | PC1 | PC2 | PC3 |
|---|---|---|---|
| PRP | 0.301 | −0.182 | 0.159 |
| IWH | 0.322 | 0.034 | −0.537 |
| Overcrowding | 0.308 | −0.216 | 0.230 |
| IFUFE | 0.405 | −0.090 | −0.175 |
| IMEM | 0.338 | 0.424 | −0.061 |
| IAM | 0.327 | −0.246 | 0.349 |
| IAH | 0.400 | −0.180 | −0.238 |
| HCO | 0.139 | 0.515 | 0.560 |
| CJH | 0.166 | −0.410 | 0.322 |
| Arrears | 0.341 | 0.450 | −0.014 |
Source: own elaboration using EViews 12 SV.
Table 12.
Economic insecurity of European households.
| Country | LSL (PC1) | HPR (PC2) | Final Score | Ranking |
|---|---|---|---|---|
| Greece | 35.18 | 49.6 | 25.79 | 27 |
| Romania | 42.59 | 9.8 | 21.48 | 26 |
| Bulgaria | 36.59 | 14.9 | 19.70 | 25 |
| Croatia | 32.52 | 8.7 | 16.64 | 24 |
| Hungary | 30.07 | 10.8 | 15.91 | 23 |
| Latvia | 30.70 | 7.5 | 15.56 | 22 |
| Cyprus | 29.62 | 8.9 | 15.35 | 21 |
| Spain | 28.34 | 11.0 | 15.17 | 20 |
| Lithuania | 30.00 | 3.7 | 14.50 | 19 |
| Portugal | 27.91 | 6.7 | 14.13 | 18 |
| Slovakia | 26.34 | 8.9 | 13.84 | 17 |
| Germany | 23.29 | 8.0 | 12.25 | 16 |
| Ireland | 23.37 | 7.2 | 12.14 | 15 |
| Italy | 24.58 | 3.8 | 12.03 | 14 |
| France | 21.94 | 7.1 | 11.45 | 13 |
| Poland | 21.44 | 5.2 | 10.87 | 12 |
| Estonia | 21.51 | 4.5 | 10.75 | 11 |
| Malta | 18.43 | 4.2 | 9.29 | 10 |
| Denmark | 15.51 | 10.9 | 9.25 | 9 |
| Luxembourg | 14.00 | 14.4 | 9.24 | 8 |
| Austria | 17.11 | 5.9 | 9.00 | 7 |
| Belgium | 17.26 | 3.7 | 8.65 | 6 |
| Czechia | 16.09 | 5.8 | 8.51 | 5 |
| Slovenia | 15.75 | 5.9 | 8.39 | 4 |
| Finland | 15.69 | 5.1 | 8.20 | 3 |
| Sweden | 13.30 | 8.5 | 7.77 | 2 |
| Netherlands | 11.23 | 5.6 | 6.25 | 1 |
Notes: The first column represents scores for Lack of savings and leisure time (PC1) but with only variables with values over 0.4 being selected, comprising 45.97% of the analysis (score for country = 0.400 × AH + 0.405 × IFUFE). Household’s predisposition to risk (PC2) is calculated in the second column, utilizing loadings with over 0.4 and comprising 19.41% (score for country = −0.410 × CJH + 0515 × HCO + 0.450 × Arrears + 0.424 × IMEM). In the third column, we have the total Z-scores, calculated with the formula PC1 × 45.97% + PC2 × 19.41% (covering, in this sense, 65.38% of the analysis). We decide to not add PC3 because it has a covering of just 9.31%, and even with 65.38%, we do not reach the 70% Benzécri criterion, while the Kaiser rule imposes us to not consider eigenvalues with root under 1 (PC3 has 0.93). However, we do not exclude the possibility of including PC3 in future analyses, preferring the Benzécri criterion.
Table 13.
Profile of households’ economic insecurity.
| Europe Region | Very Low (00.00–10.00) | Low (10.01–15.00) | Medium (15.01–20.00) | High (20.01–25.00) | Very High (25.01–30.00) |
|---|---|---|---|---|---|
| Northen Europe | Denmark, Sweden, Finland | Lithuania, Estonia | Latvia | ||
| Western Europe | Luxembourg, Austria, Belgium, Netherlands | Germany, Ireland, France | |||
| Eastern and Central Europe | Czechia, Slovenia | Slovakia, Poland | Bulgaria, Croatia, Hungary | Romania | |
| Southern Europe | Malta | Portugal, Italy | Cyprus, Spain | Greece |
Source: own elaboration.
Table 14.
Descriptive statistics.
| Variable | N | Mean | Max | Min | Standard Dev |
|---|---|---|---|---|---|
| EI | 260 | 14.526 | 30.899 | 5.854 | 6.096 |
| Financial | 260 | 66.576 | 90.000 | 40.000 | 10.407 |
| Investment | 260 | 79.326 | 90.000 | 55.000 | 8.756 |
| Tax Burden | 260 | 66.999 | 94.400 | 37.200 | 14.931 |
| Monetary Fr | 260 | 81.845 | 91.700 | 74.800 | 3.309 |
| Gov Spending | 260 | 37.127 | 81.100 | 0.000 | 19.429 |
| Labor Fr | 260 | 60.679 | 92.100 | 34.600 | 10.237 |
| Business Fr | 260 | 76.311 | 98.100 | 53.600 | 8.998 |
| Gov int | 260 | 65.308 | 99.500 | 33.200 | 17.732 |
| Trade fr | 260 | 84.936 | 88.000 | 78.600 | 3.333 |
| Property R | 260 | 75.748 | 100.00 | 30.000 | 14.731 |
Source: own elaboration using EViews 12 SV.
Table 15.
Panel unit root test.
| Variables | Levin, Lin, and Chu | PP—Fisher Chi-Square |
|---|---|---|
| Economic Insecurity | −8.16207 *** | 136.624 *** |
Source: own elaboration using EViews 12 SV. Notes: Significance levels are *** for 1%, ** for 5% and * for 10%.
Table 16.
Correlogram.
| EI | Financial | Investment | Tax Burden | Monetary FD | Gov Spending | Labor Fr | Business Fr | Gov Int | Trade Fr | Prop R | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| EI | 1.000 | ||||||||||
| Financial | −0.638 | 1.000 | |||||||||
| Investment | −0.603 | 0.612 | 1.000 | ||||||||
| Tax Burden | 0.511 | −0.224 | −0.304 | 1.000 | |||||||
| Monetary F | −0.147 | 0.099 | 0.217 | −0.087 | 1.000 | ||||||
| Gov Sp | 0.252 | −0.153 | −0.028 | 0.732 | 0.048 | 1.000 | |||||
| Labor Fr | −0.133 | 0.199 | 0.234 | 0.135 | 0.203 | 0.201 | 1.000 | ||||
| Business Fr | −0.431 | 0.406 | 0.358 | −0.489 | 0.186 | −0.399 | 0.093 | 1.000 | |||
| Gov int | −0.706 | 0.572 | 0.512 | −0.534 | 0.098 | −0.351 | 0.098 | 0.697 | 1.000 | ||
| Trade fr | 0.133 | 0.105 | 0.246 | 0.027 | 0.264 | 0.099 | 0.022 | −0.072 | −0.257 | 1.000 | |
| Property R | −0.764 | 0.517 | 0.499 | −0.446 | 0.130 | −0.255 | 0.131 | 0.569 | 0.808 | −0.369 | 1.000 |
Source: own elaboration using EViews 12 SV.
Table 17.
VIF test.
| Variables | VIF |
|---|---|
| Financial | 2.124704 |
| Investment | 2.408889 |
| Tax Burden | 3.161964 |
| Monetary Fr | 1.230744 |
| Gov Sp | 2.608080 |
| Labor Fr | 1.183566 |
| Trade Fr | 1.968572 |
| Business Fr | 2.189030 |
| Gov Int | 4.543470 |
| Property R | 3.830326 |
Source: own elaboration using EViews 12 SV.
Table 19.
Models with economic insecurity as dependent variable.
| Independent Variable | OLS Model | REM Model | FEM Model |
|---|---|---|---|
| Monetary FD | −0.163 (0.089) * | −0.222 (0.052) *** | −0.221 (0.052) *** |
| Investment FD | −0.461 (0.033) *** | −0.214 (0.042) *** | −0.155 (0.046) *** |
| Trade FD | 0.585 (0.089) *** | 0.516 (0.045) *** | 0.501 (0.045) *** |
| Constant | 14.767 (8.882) * | 5.830 (5.462) | 2.338 (5.560) |
| Adj R-squared | 0.449 | 0.337 | 0.873 |
| Observations | 260 | 260 | 260 |
| Hausman Test | Chi-Sq. = 9.377065 (Prob. 0.0247) Chi-Sq. d.f. = 3 | ||
Source: own elaboration using EViews 12 SV. Notes: Significance levels are *** for 1%, ** for 5% and * for 10%.
Table 20.
Robustness checks—Greece and Romania excluded.
| Independent Variable | OLS Model | REM Model | FEM Model |
|---|---|---|---|
| Monetary FD | −0.059 (0.086) | −0.200 (0.053) *** | −0.207 (0.054) *** |
| Investment FD | −0.364 (0.036) *** | −0.228 (0.045) *** | −0.198 (0.049) *** |
| Trade FD | 0.572 (0.086) *** | 0.514 (0.046) *** | 0.506 (0.046) *** |
| Constant | −0.911 (8.954) | 4.672 (5.697) | 3.541 (5.783) |
| Adj R-squared | 0.340 | 0.348 | 0.828 |
| Observations | 240 | 240 | 240 |
Source: own elaboration using EViews 12 SV. Notes: Significance levels are *** for 1%, ** for 5% and * for 10%.
Table 21.
Robustness checks –top countries excluded.
| Independent Variable | OLS Model | REM Model | FEM Model |
|---|---|---|---|
| Monetary FD | −0.079 (0.093) | −0.264 (0.062) *** | −0.272 (0.063) *** |
| Investment FD | −0.405 (0.038) *** | −0.222 (0.049) *** | −0.176 (0.054) *** |
| Trade FD | 0.649 (0.103) *** | 0.627 (0.058) *** | 0.619 (0.059) *** |
| Constant | −0.263 (9.570) | 2.517 (6.609) | 0.303 (6.760) |
| Adj R-squared | 0.411 | 0.394 | 0.827 |
| Observations | 180 | 180 | 180 |
Source: own elaboration using EViews 12 SV. Notes: Significance levels are *** for 1%, ** for 5% and * for 10%.
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Pricop, I.-A.; Diaconu, L. Households’ (In)Security in the European Union: From Principal Components to Causality Analysis. Economies 2025, 13, 33. https://doi.org/10.3390/economies13020033
AMA Style
Pricop I-A, Diaconu L. Households’ (In)Security in the European Union: From Principal Components to Causality Analysis. Economies. 2025; 13(2):33. https://doi.org/10.3390/economies13020033
Chicago/Turabian StylePricop, Ionuț-Andrei, and Laura Diaconu (Maxim). 2025. "Households’ (In)Security in the European Union: From Principal Components to Causality Analysis" Economies 13, no. 2: 33. https://doi.org/10.3390/economies13020033
APA StylePricop, I.-A., & Diaconu, L. (2025). Households’ (In)Security in the European Union: From Principal Components to Causality Analysis. Economies, 13(2), 33. https://doi.org/10.3390/economies13020033
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