metadata: authors: 0: author: Bussolo,Maurizio
1: author: Karver,Jonathan George
2: author: Lokshin,Michael M.
3: author: Lopez-Calva,Luis-Felipe
4: author: Torre,Ivan
count: Europe
docna: 0: docna: Growing Vulnerability: What Happened to
            Europe’s Middle Class in the Course of a Decade ?
docty: Policy Research Working Paper
abstracts: cdata!: This paper uses a vulnerability-based
            approach to analyze the evolution of the middle class in
            Europe between 2005&#x2013;08 and 2015&#x2013;18.
            The analysis reveals that, on average, the income level
            needed to ensure a low probability of falling into
            poverty&#x2014;also understood as the vulnerability
            threshold&#x2014;increased between those periods in real
            terms. This increase correlates with decreases in the size
            of the middle class in many European countries. In parallel,
            the composition of the middle class changed, with an
            increased share of tertiary-educated household heads and a
            larger share of household heads with managerial and
            professional occupations. Lastly, the households that were
            not poor, but not yet middle class, were further from
            becoming middle class in the second period than in the first period.
display_title: Growing Vulnerability: What Happened
            to Europe’s Middle Class in the Course of a Decade ?
listing_relative_url: /research/2025/01/34450062/growing-vulnerability-happened-europe’s-middle-class-course-decade
topicv3: Shocks and Vulnerability to Poverty
            m1326691 1160,Poverty Measurement and Analysis m1326941 707,Poverty and the Middle Class m1327183 1153
publishtoextweb_dt: 2025-01-27T00:00:00Z
dois: 10.1596/1813-9450-11047
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 38
author_keys: 0: author_key: 669228
1: author_key: 1129567
2: author_key: 144523
3: author_key: 435254
4: author_key: 1521219
model_extractions: Policy Research Working Paper 11047

# Growing Vulnerability

What Happened to Europe’s Middle Class in the Course of a Decade?

_Maurizio Bussolo Jonathan Karver Michael Lokshin Luis-Felipe López-Calva Iván Torre_ Europe and Central Asia Region & Poverty and Equity Global Department January 2025
Policy Research Working Paper 11047

# **Abstract**

This paper uses a vulnerability-based approach to analyze the evolution of the middle class in Europe between 2005– 08 and 2015–18. The analysis reveals that, on average, the income level needed to ensure a low probability of falling into poverty—also understood as the vulnerability threshold—increased between those periods in real terms. This increase correlates with decreases in the size of the middle class in many European countries. In parallel, the composition of the middle class changed, with an increased share of tertiary-educated household heads and a larger share of household heads with managerial and professional occupations. Lastly, the households that were not poor, but not yet middle class, were further from becoming middle class in the second period than in the first period.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region and the Poverty and Equity Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, jkarver@worldbank.org, mlokshin@worldbank.org, lflopezcalva@worldbank.org, and itorre@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Growing Vulnerability: What Happened to Europe’s Middle Class in the Course of a Decade?**

Maurizio Bussolo, Jonathan Karver, Michael Lokshin, Luis-Felipe López-Calva, and Iván Torre JEL codes: I32, D31, O15 Keywords: vulnerability, middle class, poverty, Europe
# **I. Introduction and motivation**

A large social science literature suggests that the larger and more established the middle class, the better off society is, or will be (Easterly 2001; Banerjee and Duflo 2008). This suggestion is not new: Aristotle argued, in _Politics_ , that the middle class serves as a mediator between the rich and the poor, preserving values conducive to stability and prosperity. Among academics but also in the public discourse, a narrative of increasing inequality and a crisis of the middle class has emerged (Fukuyama 2012; Besharov and others 2016; Sitaraman 2018; Bussolo and others 2018). Remarkably, the most common way in which the crisis of the middle class has been represented is by its shrinking.

This paper examines the _health_ of the middle class by using on a novel definition of the middle class. In this new definition, introduced by López-Calva and Ortiz-Juarez (2014), being in the middle class requires absence, or a low risk, of vulnerability to poverty. The risk of experiencing poverty –defined as having income levels below a given poverty line– is what makes an individual _vulnerable_ or insecure in an economic sense. Since economic security is a hallmark of the middle class, being vulnerable means not being in the middle class. So rather than focusing on what happens to the size of the middle class, this paper investigates changes in the risk of vulnerability to poverty. In essence, this vulnerability is assessed by estimating a level of household income associated with a low probability (defined as less than 4 percent over four years) of falling into poverty. By using European panel data covering 22 countries during the period from the 2008 global financial crisis to just before the onset of the COVID-19 pandemic, this paper assesses, for the first time, whether the crisis of the middle class manifested itself as an increase of the risk of falling into poverty or, equivalently, as an increase of the minimum income needed to be (almost) immune from this risk.

Our results show that to maintain this low risk between 2008 and 2018, household incomes had to increase, on average, by about 11 percent in real terms. This value, however, is an average of a heterogeneous picture at the country level: in many countries, the income associated with this low probability of falling into poverty has increased substantially – above 20 percent – while in some countries, the associated level of income has decreased. Interestingly, the increase in the level of the vulnerability threshold is related to an increase in the magnitude of the shocks which households have been facing between the two periods. The average income shock experienced by a household that fell into poverty in the most recent period is about 21 percent larger than the shock in the earlier period. To _insure_ against these larger shocks, the minimum income needed to maintain the middle class status had to increase.

2
There is a correspondence between the changes in vulnerability and changes in the size of the middle class. We find that the size of the middle class for Europe as a whole (all countries pooled together) has remained stable between 2008 and 2018, with around 28 percent of households categorized as middle class. However, this stability hides considerable cross-country heterogeneity and countries experiencing a substantial increase in the vulnerability income threshold -above 10 percent- have seen their middle classes reduce by eight percentage points. The increase in the vulnerability threshold has also been associated to an increase in the size of the vulnerable group which, as a share of the population below the vulnerability line and above the poverty line, grew from around 38.7 percent in 2008 to 44 percent in 2018.

This paper finally highlights that the increase of the vulnerability threshold is linked to structural changes rather than to a short term rise of income volatility. The analysis shows that the composition of the middle class changes and that, at least in part, these changes reflect the polarization of labor markets due to long term trends of technology, demography and globalization as characterized by a recent literature (Acemoglu and Autor, 2011; and Goos and Manning, 2007). We find that the characteristics and assets of middle class households have changed between 2008 and 2018, pointing to an increased share of tertiaryeducated household heads and a larger share of household heads with managerial and professional occupations, further highlighting the economic security nature of the middle class (i.e., to safely be middle class over time, steady jobs and steady incomes are necessary). Importantly, these compositional changes in the middle class are different from the changes in the overall population, pointing out the reshuffling of the positions of the households in the overall income distribution.

The paper is organized as follows. Section II reviews the literature on the measurement of the middle class. Section III describes the methodology and data used to measure vulnerability in Europe. Section IV presents the results of the analysis. Section V summarizes the paper’s conclusions.

# **II. The literature on the middle class: A short review**

Analysis of the evolution of the middle class is a central topic of public discussion because of its strong influence on many spheres of societal life. Studies note that the middle class constitutes the backbone of democracy (Birdsall 2010; Stewart 2005; Alesina and Rodrik 1994) or helps produce economic benefits and foster economic development (Easterly 2001). The entrepreneurial activities that often originate in the middle class lead to employment creation and productivity enhancements, incentivizing investments in education and health, contributing to human capital accumulation and growth in consumption and 3
savings, and creating a virtuous circle that expands the middle class (Szymańska 2019; Banerjee and Duflo 2008; Doepke and Zilibotti 2007; Acemoglu and Zilibotti 1997). Middle-class consumption patterns may lead to market diversification and help mitigate shocks (Murphy, Shleifer, and Vishny 1989). A robust middle class also demands adequate public services and good governance (OECD 2019). As such, it may act as a buffer for potential social conflicts emerging from horizontal inequalities between racial and ethnic groups and from vertical inequalities between the poor and the rich (Barro 1999; Stewart 2005; Bussolo and others 2018; Bosco and Poggi 2020).

In sum, there is a consensus about the fact that countries benefit from having a large and stable middle class. However, the definition of the middle-class varies across and within disciplines (Ricci 2020) and no consensus has been reached on how to measure it and its evolution. This is clearly an issue because without a clear definition, without a firm understanding of whether the middle class denotes homogeneous or heterogeneous groups of people or whether it varies in different regions, countries, and at different stages of development, it becomes difficult to design policies that are effective at strengthening and fostering the middle class.

From a simple notion to make tax collection more effective, the concept of class became something much more complex and, at least in sociology, it involved more than levels of income. In the Weberian tradition, the concept of stratification contains three intertwined notions: (i) class, or the strictly economic aspect of stratification; (ii) status, or the identity and prestige associated to membership (cultural consumption); and (iii) party (power) which is related to the notion of power in social relations. Sociologists are thus more likely to focus on occupational status and education (Reeves, Guyot, and Krause 2018) while economists are likely to rely on income levels (Banerjee and Duflo 2008; Ravallion 2010).

More in detail, in the economics literature two approaches are used to define and measure the middle class: a relative and an absolute approach. In the relative approach, the middle class is identified as the population located at the middle of the income distribution. Different authors set intervals for the middle class constructed in relation to the median income or average income, or based on specific quintiles of the distribution of wellbeing. For example, Graham, Pettinato, and Birdsall (2000) choose 75% to 125% of median income as their interval; Castellani and Parent (2011), 50% to 150% of median income; Song and others (2016), 100% to 250% of average income; Barro (1999) and Easterly (2001), the middle three quintiles; Alesina and Perotti (1996), the third and fourth quintiles; and Partridge (1997), the third quintile. The theoretical framework used to define the middle class in this relative approach is related to the idea 4
of polarization. The middle class is defined as the set of individuals whose feelings of identification within the group and alienation with respect to the set of poor and rich individuals are both contemporarily maximized (Esteban, Gradin and Ray 2007). The second or the absolute approach uses instead boundaries that are fixed exogenously, and that have the important advantage of allowing for international comparisons. So for example Banerjee and Duflo (2008) define the middle class as those people whose income is withing the $2 to $10 interval; Ravallion (2010) uses the $2 to $13 range; Castellani and Parent (2011), $2 to $20; Milanovic and Yitzhaki (2002), $10 to $20; Ferreira et al. (2013), $10 to $50; Kharas (2010 and 2017), $10 to $100; and Clementi et al. (2023), $5.5 to $ 10. In this absolute approach, the theoretical framework is one of vulnerability, and the middle class is defined as the set of individuals who are able to insure against idiosyncratic shocks, and therefore are able to reach a level of economic security that makes them safe from becoming poor.

In this paper, following the recent work of Lopez-Calva and Ortiz-Juarez (2014), we opt for a definition of the middle class in terms of absolute monetary thresholds. However, at least for the lower bound of the middle class, we do not set it arbitrarily and we proceed by analogy with well-established approaches to setting poverty lines. We identify income levels associated with the “functionings” that define the class. In the case of the poor class, the functioning corresponds to sufficient nutrition or a given minimum level of calories. A poor person is a person whose income is not sufficient to purchase such a minimum level. In the case of the middle class, the functioning is one corresponding to economic security, which in turn is defined as low vulnerability to falling back into poverty. In this definition, the lower threshold for the middle class is absolute in the sense of the functioning that defines it, relative in terms of the specific context. Note also that this concept of middle class encompasses more than being able to afford a particular living standard. Its key feature is a sense of stability and economic security, often achieved by owning assets, having high level education and skills, or working in certain professions (Atkinson and Brandolini, 2013).

Vulnerability is typically measured by poverty dynamics—transitions from one level of vulnerability to another (Dercon 2011; Elbers and Gunning 2003). Some studies define vulnerable households as households with income between the poverty line and a higher vulnerability line that puts them at high risk of falling into poverty (Pritchett and others 2000). Dang and Lanjouw (2017) propose a non-parametric method for establishing the vulnerability line, and Schotte and others (2018) apply a method in which they model the poverty transition of households as a first-order Markov process. More recent analysis by Prieto (2024) follows this method and estimates two vulnerability lines – one higher, which focuses on 5
households in the central part of the income distribution, and one lower, which focuses on households in the upper part of the income distribution.

Many studies indicate that global vulnerability and economic and social inequalities increased in recent decades (Pleninger, Haan, and Sturm 2022), despite the rapid decline in absolute poverty (Kharas 2017; Kochar and Oates 2015; Burrows 2015).

Globally, the middle class has been affected by the simultaneous trends of globalization, automation, and job polarization (Pleninger, Haan, and Sturm 2022; Derndorfer and Kranzinger 2021; Salido and Carabaña 2020). A shrinking middle class implies that individuals may experience more difficulties reaching or maintaining their middle-class status and are more vulnerable to falling into poverty (OECD 2019).

Historically, Europe has been a middle class society with low levels of inequality (Ranci et al. 2021). However, in recent years, between-group inequality has been growing (Callan, Doorley, and Savage 2018). Estimates on the size of the middle class using different absolute and relative definitions show little movement in size, despite growing perceptions of inequality (Bussolo et al. 2018). There has not been, however, an assessment of the evolution of the middle class in the region using a vulnerability approach. This paper is the first to apply such approach used, for instance, in Latin America (López-Calva and OrtizJuarez 2014), to the case of Europe.

# **III. Methodology**

The vulnerability approach identifies members of the middle class using three variables: a poverty line, a low probability of falling into poverty, and a permanent income threshold that guarantees that households earning at least such an income face such a low probability risk of becoming poor.

The poverty line is set at $24.35<sup>1</sup> per day.<sup>2</sup> A household in the European Union is considered middle class if its permanent income is equal to or above the level required to maintain the specified low risk of falling

> 1 All dollar figures in this paper are in 2017 purchasing power parity (PPP).

> 2 The vulnerability-based approach to the middle class relies on an absolute poverty concept and thus on an absolute poverty line. Eurostat has established a relative poverty measure (“at-risk of poverty”) which relies on a relative poverty line equal to 60 percent of the median household equivalized income in a given year. Since this poverty line is a function of the income distribution, it is not fixed over time or across countries. An at-risk of poverty rate of 20 percent in one country does not mean the same thing in another (and a change in at-risk of poverty might reflect a change in the income distribution rather than a change in the share of the population living under a certain level of

6
into poverty and below the level that makes it affluent.<sup>3</sup> Using data from four-year panels of the EU Statistics on Income and Living Conditions (EU-SILC), an annual harmonized income and living standards survey conducted in EU countries, we can observe transitions into and outside poverty and thus specify a low probability of falling into poverty. Clearly, the vulnerability to falling into poverty is negatively correlated with the lifetime or permanent income. This, in turn, is a function of household and individual assets and characteristics. Focusing on income generation characteristics, such as the assets and skills of household members, we link vulnerability to poverty to permanent income rather than to the more transient ups and downs of annual income flows.

# **_Data_**

The EU-SILC survey is based on a rotating panel that allows for cross-sectional and longitudinal estimations for most EU and three other countries—Iceland, Norway, and Switzerland. Our analysis focuses on the European Union (excluding Germany<sup>4</sup> ), Iceland, and Norway<sup>5</sup> (Table 1). Panels in two- to four-year increments are defined from 2005 through 2021. We use the four-year panels corresponding to reference years 2005–08 (just before the global financial crisis) and 2015–18 (the post–global financial crisis period).<sup>6</sup> We exclude the panels that span the COVID-19 pandemic (2020 and 2021).<sup>7</sup> The choice of the two periods—the first characterized by inclusive growth throughout Europe, the second characterized by recovery but still plagued, in many cases, by scarring from the global financial crisis—was motivated by our desire to assess changes in vulnerability.

income). We therefore use the high-income poverty lines of $24.35 per day. Note that this poverty line is the poverty line for high-income countries and that that all countries in the European Union except Bulgaria (an upper-middleincome country) are high-income countries. Another option would have been to use the upper-middle-income line for upper-middle-income countries. But as this value ($6.85) is substantially lower than the high-income line ($24.35), we use the same line for all countries. For a discussion of how the poverty line for high-income countries was established, see Joliffe and others (2022).

3 For symmetry with the lower threshold, we define the upper threshold also in terms of probability of falling into poverty – but at an even lower level (of 1 percent). Alternative definitions can be to use the income that puts a household in a certain top percentile of the income distribution. We discuss these alternative definitions in section 4 and table A.4.

4 Eurostat requires permission from each participating country to access microdata. Unfortunately, Germany does not authorize the access to microdata pertaining to that country for researchers that are based in non-EU institutions.

5 Switzerland is excluded because some years of data were not available.

6 The analysis excludes Croatia and Romania, because data for these countries are not available for the 2005-08 period.

7 As of 2024, there is not yet a 4-year panel that completely excludes the pandemic shock of 2020 and 2021.

7
# **_Estimation_**

To identify the middle class, we use a regression-based approach which is organized in three stages (López-Calva and Ortiz-Juarez 2014).

In the first stage, we classify households in different groups depending on their transitions in and out of poverty. In this stage, we use the four-year intervals panel (the longest panel available) of the EU-SILC datasets. For each country and four-year interval and depending on their transitions, households are classified into four groups: poor-to-poor, poor-to-nonpoor, nonpoor-to-nonpoor, and nonpoor-to-poor (Tables A.1 and A.2 in the Appendix). Across all countries, the average share of households that become poor was around 4 percent, that means that 4 percent of households move from being non-poor to being poor.<sup>8</sup> We thus specify this 4 percent chance of falling into poverty in a four-year period as the “low” probability of falling into poverty.

In the second stage, using a logistic probability regression, we estimate the probability of being poor at the end of each panel (year _t2_ ) as a function of characteristics measured at the beginning of the panel (year _t1_ ).<sup>9</sup> Formally, this can be written as follows: where: _pic_ is the probability of household _i_ in country _c_ of being poor in year _t2_ ( _pooric,t2_ ) conditional on observable household characteristics in year _t1_ ; **X** is the vector of such household characteristics; is the associated vector of country-specific model parameters for the logistic probability function _F(.)_ . ββ The vector of household characteristics includes measures of human capital and occupational status of the household head, as well as demographic characteristics, the quality of housing, a subjective measure of welfare, and observed health and labor shocks at the household level. We refer to these characteristics, which include tangible and intangible variables, as _assets_ of households.

> 8 For middle-income countries, this average is almost 10 percent (Cruces and others 2011).

> 9 Note that this estimate is different from the probability of becoming poor between _t1_ and _t2_ . In terms of the matrices in tables A.1 and A.2, it is the sum of both being poor at time _t1_ and remaining poor at time _t2_ plus not being poor at time _t1_ and becoming poor at time _t2_ . Following López-Calva and Ortíz-Juarez (2014), we are interested both in those who enter poverty and those who remain in poverty. Additionally, restricting the analysis to only those that enter poverty shrinks the estimation sample to the point where estimates become statistically unreliable.

8
We order all households by their predicted values of _pic_ and define 50 quantiles of predicted probabilities of falling or remaining into poverty. For each quantile _q_ , we define as the average value of the characteristics **X** of the households in quantile _q_ evaluated at time _t1_ . qqiitt1 xx̅ In the third and last stage, we estimate an income equation as follows: i i i , where l i is the logarithm of per capita household income at the start of the panel ( _t1_ ), **X** is the vector of household characteristics evaluated in the initial year tt _t1_ , and **γ** is the vector of associated countryYY specific returns. We calculate _Yqct1_ , the predicted permanent income for each quantile _q_ , by multiplying the average value of **X** in each quantile ( ) on the respective return **_γc_** obtained from the estimation of equation (2).<sup>10</sup> The permanent income of the quantile qqiitt1 _q_ is defined as xx̅ � 1 , (3) kk<sup>qq1γγxxqq1</sup> qqiitt1 YY = ee<sup>∑xx̅q</sup> ee<sup>εεııq</sup> for all _x_ = 1 _,... k_ independent variables, where is the quantile mean and the coefficient associated with each _x_ from equation (2). We multiply the first term by the mean (exponentiated) error in equation qqiitt1 xxii xx̅ γγ (2)( � ) to obtain the Smearing-corrected estimate. ııiitt1 εε In a graph where permanent income levels are measured on the vertical axis and probabilities of falling into poverty on the horizontal axis, it is possible to plot a line which connects the levels of _Yqct1_ and the corresponding quantiles of the probabilities _pic_ of falling into poverty. This line, labeled the vulnerability line, is shown in Figure 1 as an example for Latvia in 2015-18. It clearly highlights the inverse relationship between poverty probabilities and permanent income levels. High risk of falling into poverty is associated with low levels of income or, vice versa, to reduce vulnerability to this risk income has to rise. The specific vulnerability threshold is defined as the value associated with the quantile _q_<sup>_V_</sup> that has a predicted probability of falling into poverty of 4 percent.<sup>11</sup> It corresponds to the lower threshold of the middle class. qqiitt1 YY<sup>VV</sup> YY

> 10 As López-Calva and Ortiz-Juarez (2014, p.33) point out, “it is important to use the predicted income - a mean, conditional on characteristics - instead of the observed average income, because the predicted income has lower volatility and becomes an index related to stocks (assets), as an income generation capacity of the households.” 11 As there may not be a quantile that has a predicted probability of falling into poverty of exactly 4 percent, we select the quantile that has the predicted probability closest to this value.

9
Households with incomes _below_ this threshold but above the poverty line are classified as vulnerable, they are neither poor nor middle class. The upper threshold of the middle class, , is defined as the value associated with the quantile _q_<sup>_U,_</sup> that has a predicted probability of falling into poverty of 1 percent. YY<sup>UU</sup> Households with an income between qqiitt and are classified as middle class. YY YY<sup>VV</sup> YY<sup>UU</sup>

# **IV. Results**

The main objective of the approach outlined above is to identify the thresholds that define the middle class, the most relevant of which is the vulnerability threshold, i.e. the level of permanent income above which the probability of falling into poverty is lower than 4 percent.

Applying this approach separately to the panels for 2005–08 and 2015–18 highlights the fact that the vulnerability threshold increased in Europe between these two periods. This is the specific manifestation of the crisis of the middle class emphasized by this paper. Between these two periods, the vulnerability threshold rose, for the average country, by 10 percent in real terms. The average, across all countries, of the permanent incomes associated with the 4 percent probability of falling into poverty was $50.5 per day in 2008 and became $55.5 in 2018 (Table 2).<sup>12</sup> This 10 percent increase could be interpreted as an insurance premium to maintain the same level of protection against the risk of poverty. Households that did not “pay” this premium, and thus remained at the same level of income of the earlier period, faced an increased risk of falling into poverty which meant that they were no longer part of the middle class. The values reported in table 2 show that in 10 countries, the middle-class lower threshold increased by more than 10 percent between 2008 and 2018. The largest increase was in Bulgaria, one of the poorest countries in Europe, where the predicted income associated with the lower middle-class threshold soared 70 percent, increasing from $30 to $51 per day. Within Central Europe and the Baltic countries, the lower threshold increased from $42 to $52 per day in Latvia and from $44 to $50 per day in in Estonia. In the rest of Europe, substantial increases were seen in Austria, Denmark, Italy, the Netherlands, and Norway. Only in Cyprus did the threshold drop by more than 10 percent. That drop likely reflects the sharp decline in incomes after the global financial crisis, which disproportionately affected the bottom half of the

> 12 In Table A.5 we report the change of the vulnerability threshold between 2015-18 and 2017-20, that is, including the effect of the COVID-19 pandemic. In most countries for which data is available there was a decrease in the vulnerability threshold in that time period, most probably explained by the social protection mitigation measures implemented during 2020. There is not yet a 4-year panel in EU-SILC that completely excludes the years of the pandemic, so it is still early to know whether this change is permanent or not.

10
income distribution. In five other countries, the vulnerability threshold decreased less than 10 percent. It remained unchanged in Lithuania.

A useful way to show the change of the vulnerability threshold is to compare the vulnerability curves estimated for the two panels. As an example, Figure 2 plots these vulnerability curves for Bulgaria and Latvia for the two panels. The upward shift in the vulnerability curve indicates that the permanent incomes needed to prevent a household from falling into poverty at any given probability were higher in 2015–18 than in 2005–08. The relevant shift in terms of impact on the middle class is that of the income levels associated with the 4 percent probability of falling into poverty, shown in figure 2 by the vertical distance between the two vulnerability curves at the 4 percent probability.

An important corollary to these findings is that, since the poverty line has not changed between these two periods, an increase in the level of the vulnerability threshold means that the size of the shocks driving households into poverty must have increased. Indeed, the average income shock experienced by a household that fell into poverty in the latest period was larger than the shock in the pre-crisis period. In our sample, on average, the per capita income of a household that became poor between 2005 and 2008 fell from $46 to $17 per day. The corresponding figures for the 2015-18 period were $48 and $12. This means that the average cross-country shock produced a loss of $29 per day in 2005-08 and a loss of $35 per day in 2015-18, or about 21 percent larger. To _insure_ against these larger shocks, the minimum income needed to remain in the middle class must increase.

To assess the sensitivity of our main results to different vulnerability thresholds, we provide estimates corresponding to a probability of falling into poverty of 8 percent (see appendix table A.3). Using this higher probability does not change the main finding. In nine countries, the lower middle-class threshold increased by more than 10 percent between 2008 and 2018. The most significant increase was in Bulgaria, where the income associated with the lower middle-class threshold increased from $24 to $43 per day.<sup>13</sup> Within Central Europe and the Baltic countries, Estonia and Latvia saw increases. In the rest of Europe, substantial increases were seen in Austria, Denmark, Italy, Luxembourg, and the Netherlands. Only in Cyprus, Hungary, and Lithuania did the value of the threshold by this definition drop more than 10 percent. In six other countries, the vulnerability threshold decreased by less than 7 percent.

> 13 The lower middle-class threshold can be below the poverty line because the measure is based on predicted, rather than observed, income. In countries where the probability that an individual is poor is high, the predicted income level at low probabilities of poverty can be below the poverty threshold.

11
Between 2008 and 2018, the size of the middle class, expressed as the share of the population of the sample countries (pooled together) whose incomes fall between the middle class thresholds, remained stable at around 28 percent. This result does not change when alternative definitions of the thresholds are used. For example, as shown in appendix table A.4, at the 8 percent probability of falling below the poverty line threshold, the middle class accounted for a larger share of the European population, but this share does not change much as it moves from 36.8 percent in 2008 to 39.8 percent in 2018. However, this stability at the Europe-wide level of aggregation masks the fact that there is significant heterogeneity in the evolution of the size of the middle class across countries: there was an increase in 14 countries and a decrease in 8 countries (Figure 3). As expected, there is a correspondence between the change in the vulnerability threshold and the change in the size of the middle class as illustrated by Figure 4. In this scatter plot, increases of the vulnerability threshold are mostly accompanied by decreases in the size of the middle class. For those countries where there was a substantial increase in the vulnerability threshold -above 10 percent-, the average decrease in the size of the middle class was about 8 percentage points. Likewise, the few reductions of the vulnerability threshold observed during this period are coupled with increases of the middle class.

# **_Composition of the middle class: Assets and returns_**

Up to this point, we have highlighted the shifts in the minimum _level_ of income that guarantees a low exposure to the risk of falling into poverty. In most countries, households had to increase this minimum level, so the next relevant question is about _how_ a typical middle class household generates this minimum income. Households are heterogeneous in terms of their income-generation capabilities because they have different characteristics and own different assets. Analyzing the shift in the assets and their returns for households at the vulnerability threshold provides insights into the incidence of the increase in vulnerability between the two periods and whether the composition of the middle class is changing. This detailed analysis is more policy relevant than one focusing only on the changes of the size of the middle class.

Consider first the change in the assets’ returns by analyzing the estimates of equations (1) and (2) across the two periods. In these equations, parameters **β** can be interpreted as the ‘returns’ of households’ assets related to the probability of falling into poverty; while parameters **γ** represents the ‘returns’ related to the permanent income. The estimates of **β** for the 2005–08 and 2015–18 panels are displayed in <u>Figure 5; note that the coefficients’ signs are easily interpreted, however their magnitude cannot be interpreted</u> 12
in a straightforward way given the use of a logistic regression. The main result is that the pattern for the coefficient of these assets remained stable across the two periods, indicating that the relative returns did not appear to change much. In both periods, households with children have a high probability of falling into poverty, while households owning a car or whose head has tertiary education or is employed in a managerial of professional occupation have a low probability of falling into poverty. The gender and marital status of the head appear uncorrelated with the probability of falling into poverty.

The estimates of **γ** —the ‘Mincerian returns’ associated with household characteristics—are shown in <u>Figure 6. The signs of the coefficients display the expected pattern and they do not change between the</u> two periods. Households with a married head or children have lower household per capita income; households headed by a person with tertiary education or a professional or managerial occupation and households that own a car or live in urban areas have higher per capita income.

These estimates, obtained with a linear regression model, are comparable between two periods. <u>Figure 7</u> plots the differences between the coefficients **γ** of the two periods and demonstrates that premia for higher education over basic education increased (with the notable exception of tertiary education). The returns for professional and managerial occupations increased over that of more elementary occupations. Households with dependents (children and elderly people) were also better off in the second period.

The assets composition of the middle class households also shifted between 2005-08 and 2015-18. In the post-crisis period of 2015-2018, higher levels of education and professional/managerial occupations became more common among households in the middle class. Households with lower levels of education and skills were more likely to fall into poverty in the later period than in the earlier period (table 3). In 2005–08, for example, nearly one in four middle class households had a head with primary education; in 2015–18, this share dropped to less than one in seven, and nearly one in three heads at the threshold had tertiary education.

This shift in the educational composition of the middle class can be thought of as the result of two trends. The first is the change in education of the _overall_ population, a simple quantity effect where population becomes better educated over time. The second is related to reshuffling, or reranking, of different households in the overall distribution. This is due to changes in relative returns. An increase in the premia for higher education favors households with better-educated heads which will move up in higher income ranks, while households with less educated heads will move down the ranks. While a general equilibrium analysis is beyond the scope of this paper, the rising premium of education observed in conjunction with 13
a secular improvement of education in the region is hinting that demand for highly educated workers is outpacing supply. These two components, the increases in quantity and in relative returns for education, determine that the changes in the educational composition of the whole population are not as marked as for the households in the middle class. In the whole population, the share of households with a household head with only primary education dropped by 7 percentage points which is less than the 10-percentage point drop observed for middle class households. Similarly, the share of households with a tertiary educated household head increased, for the overall population, by about 7 percentage points, while for middle class households that increase was of 9 percentage points. These differential compositional changes reflect the reshuffling of the positions of specific households in the distribution.

A similar compositional change linked to reshuffling is observed when considering the occupations of the household heads. Among the middle class, the share of heads employed in a managerial or professional jobs increased by almost four percentage points (or 8 percent of the 2005-08 value) while the share of heads working in sales or services occupations declined by almost three percentage points (or 11 percent of the 2005-08 value), and the share of heads in elementary occupations decreased by two percentage points (or 11 percent of the 2005-08 value). There was also an increase in the share of heads working as plant and machine operators at one percentage point (or 15 percent of the 2005-08 value). The occupational composition of the overall employed population evolved slightly differently than that of the middle class. For the total population, managerial and professional occupations became more prevalent, but so did sales and service occupations, while the latter became less common in the middle class. Sales and services occupations, though more prevalent in the overall employment, have become less of a guarantee of middle-class status. The changes in the “mincerian” returns to different occupations, a shift in relative returns favoring professional occupations vis-à-vis elementary and services ones, are behind the reranking of households and these different compositional shifts.

These findings about the changing composition of the middle class confirm conclusions in the literature on job polarization that shows that middle-income workers who are displaced by automation should sort out into low-paid or high-paid jobs based on their skill levels (Acemoglu and Autor 2011). In this sense, middle skilled workers who are better substitutes of low skills workers move “down” and become service workers, while those who are better substitutes of high skilled worker move “up” and become managerial and professional workers. In the past, having a medium level of skills seemed to ensure that an individual would not fall into poverty and be part of the middle class. These figures suggest that that may no longer be the case.

14
# **_Changes in the vulnerability gap_**

As the vulnerability threshold has increased, it is relevant to understand what happened to the income of households below that threshold but above the poverty line. Has reaching the middle class status become more difficult? In many countries, the vulnerability gap—defined as the average distance between a vulnerable household’s income and the lower threshold for the middle class— significantly widened. This widening means that, for non-poor but not yet middle class households, it has become more difficult to belong to the middle class.

For the countries studied (except Bulgaria<sup>14</sup> ), the average vulnerability gap increased by about 13 percent between 2008 and 2018, though it decreased in nine countries (Figure 8). Among countries in which the vulnerability gap increased, Norway stands out. Its gap rose from $14.4 per day in 2008 to $24.0 per day in 2018. Greece followed, with an increase from $12.5 to $19.6. Austria, Denmark, Estonia, Latvia, and the Netherlands saw increases exceeding 30 percent. Among countries in which the vulnerability gap narrowed, Czechia saw the largest decrease, with a 30 percent drop (from $7.7 per day in 2008 to $5.2 per day in 2018). Hungary and Poland saw decreases exceeding 20 percent.

# **V. Final remarks**

Being middle class can be understood as not being vulnerable to falling into poverty. Using a vulnerabilitybased approach, we find that the size of the middle class in Europe has barely changed between 2008 and 2018. However, the income required to become middle class has increased. To be safely above the highincome country poverty line of $24.35 per day in 2018, households needed a higher income than they needed in 2008, before the global financial crisis. On average, to ensure a low probability of falling into poverty (defined as 4 percent) and be considered middle class, European households required a daily per capita income of $50.5 in 2008 and $55.5 in 2018. The change was heterogeneous, with some countries registering increases in the lower middle class or vulnerability income threshold above 20 percent and others witnessing decreases of 10 percent or more. The vulnerability gap—the distance of vulnerable households, above the poverty line but below the middle class threshold, to that same threshold— increased by about 13 percent.

> 14 In Bulgaria, the vulnerability gap increased by 343 percent (from $3.8 in 2008 to about $16.8 in 2018). In 2008, the vulnerability threshold was very close to the poverty line, so the number of vulnerable households was very small.

15
The increase in the income threshold to become middle class has consequences for who makes up the middle class. Between 2008 and 2018, the composition of households in the middle class shifted to include more households headed by a person with tertiary education and a greater prevalence of households headed by a person in a professional or managerial occupation. Many formerly middle-class households with lower levels of education and households engaged in elementary occupations no longer qualified as middle class. Education and occupation now play a vital role in defining middle-class status.

The results of the analysis show that focusing only on the size of the middle class hides changes in key aspects of middle-class identity, such as lack of vulnerability, education, and type of job. Households that previously could aspire to be middle class can no longer do so, and a sizeable share of them are now vulnerable to falling into poverty.

# **References**

- Acemoglu D., and D. Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” _Handbook of Labor Economics_ 4B: 1043–171.

- Acemoglu, D., S. Johnson, and J.A. Robinson. 2005. “Institutions as a Fundamental Cause of Long-Run Growth.” _Handbook of Economic Growth_ , vol. 1, 385–472.

- Acemoglu, D., and F. Zilibotti. 1997. “Was Prometheus Unbound by Chance?” _Journal of Political Economy_ 105 (4): 709–51.

- Alesina, A., and D. Rodrik. 1994. “Distributive Politics and Economic Growth.” _Quarterly Journal of Economics_ 109 (2): 465–90.

Alesina, A. and R. Perotti. 1996. “Income distribution, political instability, and investment.” _European Economic Review_ 40 (6): 1203-1228

- Atkinson, A. B., & Brandolini, A. 2013. “On the identification of the middle class.” In _Income inequality: Economic disparities and the middle class in affluent countries_ , ed. J. C. Gornick and M. Jäntti, Stanford University Press.

- Banerjee, A.V., and E. Duflo. 2008. “What Is Middle Class about the Middle Classes around the World?” _Journal of Economic Perspectives_ 22 (2): 3–28.

Barro, J. R. 1999. “Determinants of Democracy.” _Journal of Political Economy_ 107 (6): S158–S183.

- <mark>Besharov, Douglas J., Antonio López Peláez, and Sagrario Segado Sánchez-Cabezudo. 2016. "The Decline Of The Middle Classes Around The World?."</mark> _<mark>Journal of Policy Analysis and Management</mark>_ <mark>vol. 35 (1): 245-251.</mark>

- Birdsall, Nancy. 2007. “Reflections on the macro foundations of the Middle Class in the Developing World” Available at SSRN: https://ssrn.com/abstract=1101658

- ———. 2010. “The (Indispensable) Middle Class in Developing Countries; or, the Rich and the Rest, Not the Poor and the Rest.” In _Equity in a Globalizing World_ , ed. Ravi Kanbur and Michael Spence.

16
- Birdsall, N., F. Ferreira, L.F. López-Calva, and J. Rigolini. 2011. “The Middle Class in Developing Countries: Concept, Measurement and Recent Trends.” Unpublished paper, World Bank, Washington, D.C.

- Bosco, B. and A. Poggi. 2020. “Middle class, government effectiveness and poverty in the EU: A dynamic multilevel analysis.” _Review of Income and Wealth_ 66 (1): 94-125.

Burrows, M. 2015. “The Emerging Global Middle Class—So What?” _Washington Quarterly_ 38 (1): 7–22.

- Bussolo, M., M. E. Davalos, V. Peragine, and R. Sundaram 2018. _Toward a New Social Contract: Taking on Distributional Tensions in Europe and Central Asia._ World Bank, Washington, DC.

Castellani, F. and G. Parent. 2011. “Being ‘middle-class’ in Latin America.” OECD Development Center Working Paper 305, Paris, France.

Chaudhuri, S. 2003. “Assessing Vulnerability to Poverty: Concepts, Empirical Methods and Illustrative Examples.” Department of Economics, Columbia University, New York.

- Clementi, F., Molini, V., Schettino, F., Khan, H. A., and M. Fabiani. 2023. “Polarization and its discontents: Morocco before and after the Arab Spring.” _Journal of Economic Inequality_ vol 21: 105–129

- Cruces, G., L.F. López-Calva, and D. Battiston. 2010. “Down and Out or Up and In?” In _Search of Latin America‘s Elusive Middle Clas_ s, Inclusive Development Series 3, <mark>Bureau for Latin America and the Caribbean</mark> (RBLAC) and United Nations Development Programme (UNDP), New York.

- Dang, H.-A., and P. Lanjouw. 2017. “Welfare dynamics measurement: two defnitions of a vulnerability line and their empirical application.” _Review of Income and Wealth_ vol. 63: 633–660

- Dercon, S. 2001. “Assessing Vulnerability to Poverty.” Paper prepared for the Department for International Development, University of Oxford, Oxford.

- ———. 2006. “Vulnerability: A Micro Perspective.” QEH Working Paper, University of Oxford, Oxford.

Derndorfer, J., and S. Kranzinger. 2021. “The Decline of the Middle Class: New Evidence for Europe.” _Journal of Economic Issues_ 55 (4): 914-938.

- Desai, R.M., and H. Kharas. 2017. “Is a Growing Middle Class Good for the Poor?” In _Social Policy in a Time of Globalization._

- Doepke, M., and F. Zilibotti. 2007. “Occupational Choice and the Spirit of Capitalism.” CEPR Discussion Paper DP6405, Center for Economic Policy Research, Washington, DC.

Easterly, W. 2001. _The Middle Class Consensus and Economic Development_ . Washington, DC: World Bank.

- Elbers, C., and J. Gunning. 2003. “Vulnerability in a Stochastic Dynamic Model.” Discussion Paper 2003– 070/2, Tinbergen Institute, Rotterdam.

Esteban, J., Gradin, C. and D. Ray. 2007. “An Extension of a Measure of Polarization, with an application to the income distribution of five OECD countries.” _Journal of Economic Inequality_ , vol. 5: 1-19.

- Ferreira, F.H., J. Messina, J. Rigolini, L.F. López-Calva, M.A. Lugo, and R. Vakis. 2012. _Economic Mobility and the Rise of the Latin American Middle Class_ . World Bank, Washington, DC.

- <mark>Fukuyama, F. 2012. “The future of history: Can liberal democracy survive the decline of the middle class?”.</mark> _<mark>Foreign affairs</mark>_ <mark>, vol. 91 (1): 53-61.</mark>

Goldthorpe, J.H. 1987. _Social Mobility and Class Structure in Modern Britain_ . Oxford: Clarendon Press.

17
- Goldthorpe, J.H., and A. Mcknight. 2004. “The Economic Basis of Social Class.” CASE Paper 80, London School of Economics, London.

- Goos, M., & Manning, A. (2007). Lousy and lovely jobs: The rising polarization of work in Britain. The review of economics and statistics, 89(1), 118-133.

Graham C., Pettinato, S. and N. Birdsall. 2000. "Stuck in the Tunnel: Is Globalization Muddling the Middle Class?," Luxembourg Income Study Working Paper 277, LIS Cross-National Data Center in Luxembourg.

- Hertova, D., L.F. López-Calva, and E. Ortiz-Juarez. 2011. “Bigger. . . but Stronger? The Middle Class in Chile and Mexico in the Last Decade.” Inclusive Development Series 2, <mark>Bureau for Latin America and the Caribbean</mark> (RBLAC) and United Nations Development Programme (UNDP), New York.

- Hoddinott, J., and A. Quisumbing. 2003. “Methods for Microeconometric Risk and Vulnerability Assessments.” Social Protection Discussion Paper 0323, World Bank, Washington D.C.

- Joliffe, D., D. Mahler, C. Lakner, A. Atamanov, and S. Tetteh Baah. 2022. “Assessing the Impact of the 2017 Ppps on the International Poverty Line and Global Poverty.” Policy Research Working Paper 9941, World Bank, Washington DC.

Kharas, H. 2010. “The emerging middle class in developing countries.” OECD Development Center Working Paper 285, Paris, France.

Kharas, H. 2017. “The Unprecedented Expansion of the Global Middle Class: An Update.” Global Economy & Development Working Paper 100, Brookings Institution, Washington D.C.

Kochhar, R., and A. Oates. 2015. “A Global Middle Class Is More Promise Than Reality.” Luxembourg Income Study Working Paper 641, LIS Cross-National Data Center in Luxembourg.

Ligon, E., and L. Schechter. 2004. “Evaluating Different Approaches to Estimating Vulnerability.” Social Protection Discussion Paper 0210, World Bank, Washington D.C.

- López-Calva, L.F., and Ortiz-Juarez, E. 2014. “A Vulnerability Approach to the Definition of the Middle Class.” _Journal of Economic Inequality_ 12 (1): 23–47.

- López-Calva, L.F., J. Rigolini, and F. Torche. 2011. _Is There Such Thing As Middle Class Values? Class Differences, Values and Political Orientations in Latin America_ . World Bank, Washington, DC.

Milanovic, B. and S. Yitzhaki. 2002. “Decomposing World Income Distribution: Does The World Have A Middle Class?” _Review of Income and Wealth_ 48 (2): 155-178.

Murphy, K.M., A. Shleifer, and R. Vishny. 1989. “Income Distribution, Market Size, and Industrialization.” _Quarterly Journal of Economics_ 104 (3): 537–64.

- OECD (Organisation for Economic Co-operation and Development). 2019. _Under Pressure: The Squeezed Middle Class_ . Paris _._

Partridge, M.D. 1997. “Is Inequality Harmful for Growth? Comment.” _American Economic Review_ 87 (5): 1019-1032.

Pleninger, R., de Haan, J., and J.-E. Sturm. 2022. “The ‘Forgotten’ middle class: An analysis of the effects of globalization.” _The World Economy_ 45 (1): 76-110.

Prieto, J. 2024 “Degrees of vulnerability to poverty: a low-income dynamics approach for Chile.” _Journal of Economic Inequality_ . https://doi.org/10.1007/s10888-023-09611-8 18
- Pritchett, L., A. Suryahadi, and S. Sumarto. “Quantifying Vulnerability to Poverty: a Proposed Measure, Applied to Indonesia.” Policy Research Working Paper 2437, World Bank, Washington, DC.

- Ravallion, M. 2010. The Developing World‘s Bulging (but Vulnerable) Middle Class.” _World Development_ 38 (4): 445–54.

Reeves, R. V., Guyot, K., and E. Krause. 2018. “Defining the middle class: cash, credentials, or culture?” Middle Class Memo Series, Brookings Institution, Washington D.C. Available at: https://www.brookings.edu/articles/defining-the-middle-class-cash-credentials-or-culture/ Ricci, C. A. 2020. “How to measure the middle class: approaches from economics.” DaSTU Working Paper 4/2020. Laboratorio di Politiche Sociali, Politecnico di Milano, Milan, Italy.

Ridao-Cano, C., and C. Bodewig. 2018. _Growing United: Upgrading Europe’s Convergence Machine_ . World Bank, Washington, DC Salido, O. and J. Carabaña. 2020. “On the Squeezing of the Middle Class: Overview and Prospects for the EU-15.” _European Review_ 28 (2): 325-342.

Schotte, S., Zizzamia, R., and M. Leibbrandt. 2018. “A poverty dynamics approach to social stratifcation: the South African case.” _World Development_ , vol. 110: 88–103.

Sen, A. 1983. “Poor, Relatively Speaking.” _Oxford Economic Papers_ 35 (2): 153–69.

Sitaraman, G. 2018. _The crisis of the middle-class constitution: Why economic inequality threatens our republic_ . Vintage.

Song, J., Cavusgil, E., Li, J., and R. Luo. 2016. “Social stratification and mobility among Chinese middle class households: An empirical investigation.” _International Business Review_ 25 (3): 646-656.

Stewart, F. 2005. “Horizontal Inequalities: A Neglected Dimension of Development.” World Institute for Development Economics Research (WIDER) Annual Lecture 5, United Nations University.

Wright, E O. 1979. _Class Structure and Income Determination_ , vol. 2. New York: Academic Press.

19
# **<u>Table 1. Number of observations in panels, by country</u>**

|_Country_|_2005–08panel_|_2015–18panel_|
|---|---|---|
|_Eastern Europe_|||
|Bulgaria|735|3,249|
|Czechia|3,052|2,028|
|Estonia|1,226|1,344|
|Hungary|1,844|1,529|
|Lithuania|1,337|1,145|
|Latvia|999|1,268|
|Poland|3,175|2,439|
|Slovenia|1,583|1,645|
|_Continental Europe _|||
|Austria|1,102|1,258|
|Belgium|1,266|2,106|
|Cyprus|761|1,015|
|Denmark|880|990|
|Spain|2,650|2,376|
|Finland|1,461|2,078|
|France|4,989|5,415|
|Greece|1,268|5,006|
|Italy|4,276|5,614|
|Luxembourg|349|823|
|Malta|674|841|
|Netherlands|1,286|2,672|
|Norway|2,616|1,216|
|Portugal|911|3,272|

20
**<u>Table 2. Lower threshold of the middle class: income level associated with 4 percent probability of</u>**

|<br>**falling into poverty**<br>_Income level(2017_<br>|<br>_PPP dollars)_|<br>|
|---|---|---|
|Country<br>2005–08|2015–18|% change<br>between<br>2005–08 &<br>2015–18|
|_Eastern Europe_|||
|Bulgaria<br>30|51|69|
|Czechia<br>38|36|–6|
|Estonia<br>44|50|14|
|Hungary<br>48|43|–9|
|Lithuania<br>45|45|0|
|Latvia<br>42|52|23|
|Poland<br>50|46|–8|
|Slovenia<br>45|44|-1|
|_Continental Europe_|||
|Austria<br>56|72|29|
|Belgium<br>48|51|6|
|Cyprus<br>62|51|–17|
|Denmark<br>61|73|19|
|Spain<br>57|65|13|
|Finland<br>52|54|5|
|France<br>50|50|0|
|Greece<br>48|56|16|
|Italy<br>53|63|19|
|Luxembourg<br>69|67|–2|
|Malta<br>44|47|7|
|Netherlands<br>49|59|20|
|Norway<br>63|85|35|
|Portugal<br>50|46|-8|
|Europe(average)<br>50.5|55.5|10|

_Source_ : Authors’ calculations using data from the EU-SILC UDB-L. _Note_ : Europe is average of countries listed in the table.

21
# **<u>Table 3. Shifts in education level and profession for middle class households in 2005–08 and 2015–18</u>**

||<br>Shares<br>education<br>type of oc<br>|<br>(%) by<br>level and<br>cupation<br>|<br>%|
|---|---|---|---|
||2005–08|2015–18|<br>change|
|_Education of household head_||||
|Primary or less|24|13|–43|
|Lower-secondary|17|15|–14|
|Upper-secondary|35|40|13|
|Post-secondary|2|1|–49|
|Tertiary|22|31|41|
|_Occupation of_ _household head_||||
|Managers, professionals, technicians|48|52|8|
|Support, service, sales workers|23|21|–11|
|Crafts, trades, elementary occupations|18|17|–11|
|Plant/machine operators/assemblers|7|8|15|
|Skilled and unskilled agricultural/etc.|2|2|–10|

_Source_ : Authors’ calculations using data from the EU-SILC UDB-L.

_Note_ : This table the composition in terms of education and occupation of the household head for the European households that belong to the middle class under the baseline definition (lower threshold = income associated to a 4 percent probability of falling into poverty; upper threshold = income associated to a 1 percent probability of falling into poverty). Values are pooled for all countries in the sample, population weighted.

22
<!-- Start of picture text --> 80 4% probability of falling into poverty<br>|<br>I<br>|<br>~|<br>& |<br>a 60 |<br>a |<br>3MN 52+- _Permanentincome associated to 4% probabilityof falling into poverty§ ==<br>p=< |<br>£a |<br>= 40 |<br>a }|<br>2 I<br>c=fe)Ee[s} ||<br>> 20 |<br>% |<br>a<br>|<br>|<br>|<br>0 I<br>0 20 40 60 80 100<br>Probability of falling into poverty (percent)<br><!-- End of picture text -->
<!-- Start of picture text --> a. Bulgaria b. Latvia<br>f=] So<br>co ie)<br>a8&©<br>aa<br>a)Qaa aaa)<br>~ ~<br>So P=)<br>N Nv<br>2ao Oo 2aesSo<br>g~ 8<br>aa<br>oo<br>==<br>33<br>££<br>=2<br>88 BR<br>Oo o<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Probability of falling into poverty (percent) Probability of falling into poverty (percent)<br>—e— 2005-08 ——*— 2015-18<br><!-- End of picture text -->
<!-- Start of picture text --> oO<br>ico}<br>Cc<br>2<br>oO<br>=O<br>at<br>fe)<br>Qa<br>oO<br>s<br>—°<br>c<br>Bo<br>oN<br>oO<br>oO SY WEA LY ZA BMS EH LO LYS KK KO »<br>WS FNMA S FE EFNM PGMS SYN S CPP L<br>HS 2008 2018<br><!-- End of picture text --> <!-- Start of picture text --> e he<br>e<br>\ @C ) @<br>C ) C ) e<br>Cd<br>@e®<br>a) @ @ e<br>e @<br>e<br><!-- End of picture text -->
<!-- Start of picture text --> Age HH ones ” Significant coefficient<br>Age squared HH +' +° Cross-countryInsignificant coefficientmedian value<br>Female HH we 0° ce ode cod0 mo ° °<br>Married HH ° ° em mofo me oo 0<br>Children and no elderly i oe a<br>Children and elderly $e ote oe<br>Only elderly oe © mo © Ode $ 0 °<br>Urban . + ce compe © 0 wove<br>Has car Co<br>Detached house oo $ © cape + # °<br>Lower secondary HH eo oo f° omopo $ co eee<br>Upper secondary HH ie wo 8 +e ' ° ° °<br>Post-secondary HH ° eo coo bo ° eo<br>Tertiary HH oo © «hoo eo 0 0 }<br>Manager and professional HH . @ 0+ 000 4 °<br>Support and service worker HH o 6 6e 06 oF cc woo<br>Trades and elementary occup. HH wo + a °<br>Plant/machine operator HH ee tooo ° wod °o<br>Agricultural worker HH oe © 00 cf of e © moe °<br>-2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00<br>Value of the regression coefficient<br><!-- End of picture text --> <!-- Start of picture text --> Age HH ° cmpeseo ¢ Significant coefficient<br>Age squared HH +' +© Cross-countryInsignificant coefficient median value<br>Female HH © © © 0 cm deeiaco wo<br>Married HH eo a eC<br>Children and no elderly ° ° a od<br>Children and elderly ° o} ot bee 6<br>Only elderly 4 © ° ° cece of * ° eo @ ©<br>Urban ° 000 oho @ 0 oo .<br>Has car] @ © © © amo coo @<br>Detached house ° ° ° ° 40 ° oo o<br>Lower secondary HH ° we ow cots 200 @o o 8<br>Upper secondary HH . © ce om ompo oon coo oe e<br>Post-secondary HH © 000 oo eo + coo<br>Tertiary HH oe wbooo 0 4 °<br>Manager and professional HH] *# #¢ ¢ @+¢o oo boo<br>Support and service worker HH ° 2° t600 ¢ the~D @ . °<br>Trades and elementary occup. HH4 # ¢° e400 0 0 F °<br>Plant/machine operator HH owes cote 0 che °<br>Agricultural worker HH ooo ° © 0 oH n—o—o—o—0 °<br>-2.00 -1.50 -1.00 -050 0.00 0.50 1.00 1.50 2.00<br>Value of the regression coefficient<br><!-- End of picture text -->
<!-- Start of picture text --> Age HH op ° ° Significant coefficient<br>Age squared HH +' +° Insignificant coefficientCross-country median value<br>Female HH co edfeam oo<br>Married HH © conene = °<br>Children and no elderly o camper @ ° 4<br>Children and elderly ° Oo +090 000 $<br>Only elderly o e400 woo + «<br>Urban ° oe seco e<br>Has car 10 © dame<br>Detached house secefel o °<br>Lower secondary HH ° oo og cates 0 0 °<br>Upper secondary HH . oe ote Pew +0 °<br>Post-secondary HH . cpoofo 0m 0 0 °<br>Tertiary HH ° 00 0 0 Oh OMe oo 0<br>Manager and professional HH ° + eee mee 6<br>Support and service worker HH ° 00 @ omee ooo<br>Trades and elementary occup. HH $0.c0e Mie eee 0<br>Plant‘machine operator HH 4 ao eate cone @<br>Agricultural worker HH od o 00 @ © boo © 00¢0 om<br>-0.80 -060 -040 -0.20 0.00 020 040 060 £0.80<br>Value of the regression coefficient<br><!-- End of picture text --> <!-- Start of picture text --> Age HH odes * Significant coefficient<br>Age squared HH +' +° Cross-countryInsignificant coefficientmedian value<br>Female HH © weber o<br>Married HH + ¢ ejeceose<br>Children and no elderly onememe 46 0 |<br>Children and elderly 6 © 0000-6 0 comm 0 $0<br>Only elderly ° odo open + °<br>Urban conten +<br>Has car [coche +<br>Detached house o ewocheen<br>Lower secondary HH °o 0 do 4o e0om ¢<br>Upper secondary HH ° ° ee eget + °<br>Post-secondary HH ©} @e of H0e ° °<br>Tertiary HH ° ' © 0 MOO Home o ey<br>Manager and professional HH $ © 0 Seelewee ooo<br>Support and service worker HH : © dmteloeoeereoo<br>Trades and elementary occup. HH fo Co;e pee &<br>Plant/machine operator HH $ we oo oo<br>Agricultural worker HH ° eo} ookee-fow oom of<br>0.80 -060 -040 -020 000 020 040 060 0.80<br>Value of the regression coefficient<br><!-- End of picture text -->
<!-- Start of picture text --> Age HH +<br>Age squared HH +<br>Female HH +<br>Married HH +)<br>Children and no elderly +<br>Children and elderly i +<br>Only elderly +<br>Urban +<br>Has car +<br>Detached house i +<br>Lower secondary HH +<br>Upper secondary HH +<br>Post-secondary HH +<br>Tertiary HH +<br>Manager and professional HH +<br>Support and service worker HH +<br>Trades and elementary occup. HH +:<br>Plant/machine operator HH + i<br>Agricultural worker HH +<br>-0.15 -0.12 -0.09 -0.06 -0.03 0.00 0.03 0.06 0.09 0.12 0.15<br>Difference in median OLS coefficient between 2008 and 2018<br><!-- End of picture text -->
<!-- Start of picture text --> °<br>oa)<br>= 24.0<br>S<br>2S 19.6<br>g 217<br>oO 16.1 18.4<br>ro) 19.3<br>2+ 154 38<br>q<br>so 14<br>ON 24.9 21.2<br>FyZoO 10.6u [| 1<br>c<br>3:To) 17.4TELE13 117 99 10.4 11.9<br>x) .<br>8 12.0<br>So | 52 10 —<br>oY<br>c=<br>Qa<br>°<br>9<br>PS~ SokKE +SSES POSSKLKEFEILYQe FO FESSESKA SK OS<br><!-- End of picture text -->