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Income-‐based and consumption-‐based measurement of absolute poverty: insights from Italy
Andrea Cutillo (ISTAT – Istituto Nazionale di Statistica)
Michele Raitano (Sapienza University of Rome)
Isabella Siciliani (ISTAT – Istituto Nazionale di Statistica) ♦
Centro Interuniversitario di Ricerca –
Interuniversity Research Center
“Ezio Tarantelli”
♦ The opinions expressed in this publication are those of the authors. They do not purport to reflect the opinions or views of the ISTAT or its members.
Centro Interuniversitario di Ricerca ..“Ezio Tarantelli”
CARATTERI E PROSPETTIVE DEL LAVORO PUBBLICO
16 dicembre 2015
SNA, Aula 5 Roma, Via dei Robilant 11 – primo piano
PROGRAMMA
14.30 REGISTRAZIONE DEI PARTECIPANTI
15.00 SALUTO DI APERTURA
Sandro Mameli, Capo Dipartimento Management, organizzazione e risorse umane, SNA; CIRET
Maurizio Franzini, Sapienza Università di Roma; Direttore CIRET
RELAZIONI TEMATICHE
Sergio Destefanis, Università degli Studi di Salerno; CIRET
Paola Naddeo, ISTAT Premio salariale pubblico-privato ed eterogeneità: un’analisi di sei paesi europei
Lucia Rizzica, Banca d’Italia Lavoro precario e selezione (avversa) dei lavoratori del settore pubblico
Leonello Tronti, Docente SNA; CIRET Economia della conoscenza, innovazione organizzativa e partecipazione cognitiva: un nuovo modo di lavorare
Madia D’Onghia, Università degli Studi di Foggia La formazione dei dipendenti pubblici ancora cenerentola tra esigenze di razionalizzazione e contenimento della spesa
Anna Rita Scolamiero, Counselor, CNCP
Massimo Tomassini, Università di Roma Tre
Pietro Trentin, Counselor, Segretario Generale CNCP “Counseling di gruppo” in un’azienda pubblica
18.00 CONCLUDE
Angelo Mari, Capo Dipartimento Istituzioni, Autonomie e politiche pubbliche e dello sviluppo, SNA; CIRET
Centro Interuniversitario di Ricerca ..“Ezio Tarantelli”
CARATTERI E PROSPETTIVE DEL LAVORO PUBBLICO
16 dicembre 2015
SNA, Aula 5 Roma, Via dei Robilant 11 – primo piano
PROGRAMMA
14.30 REGISTRAZIONE DEI PARTECIPANTI
15.00 SALUTO DI APERTURA
Sandro Mameli, Capo Dipartimento Management, organizzazione e risorse umane, SNA; CIRET
Maurizio Franzini, Sapienza Università di Roma; Direttore CIRET
RELAZIONI TEMATICHE
Sergio Destefanis, Università degli Studi di Salerno; CIRET
Paola Naddeo, ISTAT Premio salariale pubblico-privato ed eterogeneità: un’analisi di sei paesi europei
Lucia Rizzica, Banca d’Italia Lavoro precario e selezione (avversa) dei lavoratori del settore pubblico
Leonello Tronti, Docente SNA; CIRET Economia della conoscenza, innovazione organizzativa e partecipazione cognitiva: un nuovo modo di lavorare
Madia D’Onghia, Università degli Studi di Foggia La formazione dei dipendenti pubblici ancora cenerentola tra esigenze di razionalizzazione e contenimento della spesa
Anna Rita Scolamiero, Counselor, CNCP
Massimo Tomassini, Università di Roma Tre
Pietro Trentin, Counselor, Segretario Generale CNCP “Counseling di gruppo” in un’azienda pubblica
18.00 CONCLUDE
Angelo Mari, Capo Dipartimento Istituzioni, Autonomie e politiche pubbliche e dello sviluppo, SNA; CIRET
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Working Papers Series -‐ Num. 2/2019
Abstract
Despite the debate about the introduction of an official absolute poverty line is growing in the EU, at the moment Italy is the only EU country providing an official measure of absolute poverty. Absolute poverty is estimated in Italy with reference to household consumption using the Household Budget Survey (HBS), but it can be estimated also relying on incomes. Focusing on the Italian experience, this article contributes to the literature about poverty measurement in three ways. First, a detailed review of the methodology adopted in Italy to compute absolute poverty is presented. Second, the article investigates what changes when absolute poverty is assessed relying on income (using EU-‐SILC data) instead than on consumption (using HBS). Third, a comparison between income-‐based absolute poverty and the two main indicators of poverty and social exclusion used at the EU level – at risk of poverty rate, AROP, and severe material deprivation index, SMD – is shown. Main findings are: i) the level and the characteristics of the poor change when absolute poverty is measured with reference to income rather than to consumption, especially when we refer to individuals rather than to households; ii) as expected, the incidence of income-‐based absolute poverty has risen more steeply than the incidence of the AROP since the upsurge of the economic crisis in 2008; iii) a very low correlation at the individual level between the income-‐based absolute poverty status and the SMD status emerges, thus strongly questioning the idea of using SMD as a proxy of absolute poverty.
Keywords: absolute poverty; income; consumption; relative poverty; material deprivation; Italy
1. Introduction
The research about poverty differs in many methodological aspects (Lemmi et al., 2019): the definition of the poverty line, i.e. absolute vs. relative (Rowntree, 1901; Townsend, 1979; Sen, 1983); the measure of the individuals’ wellbeing, i.e. objective vs. subjective (Goedhart et al., 1997); the dimensions to be considered to capture a poverty status, i.e. unidimensional vs. multidimensional (Sen, 1991); the proxy of the living standard chosen when an unidimensional approach is followed, e.g. income, consumption, wealth or indicators of economic distress (Garner and Short, 2010; Kuypers and Marx, 2016); the time period to be considered for identifying poverty, i.e. static vs. dynamic (Addison et al., 2009; Chen and Ravallion, 2013; Jenkins and Van Kerm, 2014). Furthermore, on the empirical side, differences in the measurement of poverty might be due to the adopted statistical tool (Buhmann et al., 1988; Balcázar et al., 2017; de Vos and Garner, 1991), or to the features of the used database, according to the data quality and the proxy variables’ definition (Angel et al., 2017; Hansen and Kneale, 2013; Lemmi et al., 2019).
Despite these several possible methodological choices, which might engender large differences in the features of the observed phenomenon, the preliminary main issue in poverty studies concerns how to identify the poor, hence, how to define a line (a threshold) distinguishing poor and non-‐poor individuals and households in a population.
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As pointed out by Ravallion (2016), poverty has almost exclusively been estimated through a relative approach in the last decades in developed countries, where the poverty line is usually defined with respect to the living standard of the reference population (usually fixing the line at a certain proportion of the mean or the median of the distribution of income or consumption). The absolute approach – where the poverty line is theoretically based on the identification of basic goods and on the cost to buy these goods – has been instead adopted, apart from very few exceptions, for developing countries only.
However, in recent years, the importance of relying also on an absolute approach is growing in developed countries, due to some well-‐known limits of the relative approach (e.g. Ravallion, 2016). Actually, relative poverty – especially when the value of the line is rather high – captures more the inequality in the bottom tail of the distribution than the actual spread of social exclusion (Sen, 1983). Furthermore, comparisons across countries are biased by the level of the national relative line,1 and, mostly – especially if one is interested in the evolution overtime of a poverty index in a certain country –, relative poverty may fail to capture the evolution of living standards in periods of recession or high growth since the median/mean income might move disproportionately with respect to incomes at the bottom of the distribution (Jenkins, 2018). Hence, relative poverty might be characterized, paradoxically, by a sort of pro-‐cyclicity with economic growth when the growth is not “pro poor” (or a recession damages relatively more the middle class) and is not evenly spread along the income distribution.
However, it has to be pointed out that absolute poverty – especially when it refers to high and middle income countries – cannot be considered as a mere concept of serious resources deprivation or extreme poverty that puts individuals at risk of survival, as is instead, for instance, the 1.9$ per day threshold set by the World Bank (Cruz et al., 2015). Rather, for developed countries the absolute poverty threshold should be considered as a sort of "acceptable minimum" of living standard in the social context in which individuals live. According to this view, also an absolute poverty line should be implicitly set following relative considerations. In other terms, the value of an absolute poverty line should be country and time specific, even if – differently from the relative approach – it is set independently on the income/consumption distribution and, therefore, it should not merely capture how many individuals are far from the others in a given society. To identify an absolute poverty line in middle-‐ and high-‐income countries as the EU member States – instead of simply looking at the cost of a basket of goods needed for the mere subsistence – researchers should then focus on essential requirements to live in dignity, what we can summarize through the concept of basic needs. Once identified these requirements, they should be “translated” in a basket of goods and services to be evaluated in monetary terms.
To deal with these issues, the European Union has recently started the project “Measuring and monitoring absolute poverty -‐ ABSPO” to facilitate data collection for measuring and monitoring absolute poverty at EU, national and regional levels. Such a project follows a previous pilot project for the development of a common methodology on reference budgets in Europe (Goedemé et al., 2015). However, to the best of our knowledge, among developed countries only Italy and the US officially provide an absolute poverty measure.2 The Italian case is particularly important for a couple of reasons: i) Italy is the only EU country where absolute poverty is computed on an official base; ii) a great policy attention has been devoted to the increasing trend of absolute poverty in 1 Goedemé and Rottiers (2011) underline that many of the poor in the high-‐income countries may have more purchasing power than the majority of population (not classified as poor) in the least wealthy countries. Consistently, other authors (Guio, 2005a and 2005b; Beblavy and Mizsei, 2006; Juhász, 2006) argue that poverty figures generated through a relative approach can lead to an underestimation of poverty in the less wealthy countries. 2 Absolute poverty is also calculated in Canada by the Fraser Institute, but this is not an official measure.
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Italy since the upsurge of the economic crisis,3 and new means tested minimum income benefits – i.e. a safety net provided to all households with an equivalized economic conditions below a certain threshold – have been introduced in 2018 (Inclusion Income, Reddito di Inclusione) and 2019 (Citizenship Income, Reddito di Cittadinanza) to deal with the increase in absolute poverty (Jessoula et al., 2018 and 2019).
Ax explained in detail in Section 2, absolute poverty lines are defined in Italy identifying basic needs concerning food, housing and basic non-‐food needs, and computing the cost of the basket of goods and services needed for satisfying these needs, where that cost (i.e. the poverty line) changes according to the household composition (number and age of the members) and to the area of living (the geographical area of residence and the demographic size of the municipality). The diffusion of absolute poverty is officially computed by using the Italian Household Budget Survey (HBS). Hence, an household is considered poor when the monthly expenditure is lower than the absolute poverty line attributable to that household.4
In this article, we focus on the Italian case and contribute to the literature about poverty measurement providing three main novelties.
First, we review the methodology adopted in Italy to measure absolute poverty since, to the best of our knowledge, it is not available to an international audience (Section 2).5 This review can be of great relevance for the mentioned attempts to develop an absolute poverty approach in mid-‐ and high-‐income countries and at the EU level.
Second, we carry out some analyses on the Italian distribution of both income and consumption to observe what changes – in terms of dynamics of the phenomenon and characteristics of the poor – when absolute poverty is assessed by looking at incomes instead than at consumption (Section 3, where, after a short review of pros and cons of income and consumption as a proxy of economic wellbeing, we compare the incidence of absolute poverty measured by using HBS and the Italian component of the European Union Statistics on Income and Living Conditions – EU-‐SILC; the Italian component is named IT-‐SILC). Despite the Italian official measure of absolute poverty is based on households’ expenditure, absolute poverty may be indeed computed with reference to incomes as a proxy of living standard, since the definition of the absolute poverty line is exogenous to the distribution of both income and consumption. Computing absolute poverty according to incomes is also important from a policy perspective, since eligibility requirements and benefit amount of the recently introduced means tested minimum income benefits are based on household income. To the best of our knowledge, no other studies have so far computed an income-‐based absolute poverty for Italy.
Third, in Section 4, to better inform the debate at the European level, we make use of IT-‐SILC data and compare the extent of absolute poverty (computed with reference to incomes) with the two main indicators of poverty and social exclusion used at the European Union Level, i.e. the AROP and the “Severe Material Deprivation” (SMD) indexes (Atkinson et al., 2017). On the one hand, this comparison allows us to assess the different extent and trend of poverty observed when the
3 As shown in Section 4, because of the aforementioned limits of the relative poverty approach in periods characterized by a sudden change of the business cycle, the at-‐risk-‐of poverty indicator (AROP) – where the poverty line is set at 60% of the median of the equivalised income distribution – has remained rather constant since the upsurge of the economic crisis in 2008. 4 Absolute poverty is computed in the US by comparing pre-‐tax cash household income with a threshold set at three times the cost of a minimum nutritionally complete food diet yearly adjusted for inflation, differentiated for family size and composition. 5 The methodology is deeply described, but in Italian, in ISTAT (2009).
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phenomenon is assessed according to a relative or an absolute concept. On the other hand, comparing the incidence of absolute poverty and SMD, we can evaluate the capacity of the SMD concept to capture the diffusion of absolute poverty when an absolute poverty line is not available (Section 4).
2. The Italian methodology to measure absolute poverty
The Italian National Institute of Statistics (ISTAT) designed in 2005 a methodology for calculating an “acceptable minimum expenditure” of the household which can lead to the measurement of the absolute poverty (ISTAT, 2009). Consistently with what argued in the Introduction about the logic behind absolute poverty in developed countries, the concept of acceptable minimum expenditure is clearly far from a mere concept of survival or subsistence, i.e. a lack of resources that might seriously endanger life itself. The concept of absolute of poverty relates to the fact that it is possible to identify a threshold (a poverty line) which is independent of the fact that other individuals in a society lack or not the same minimum requirement (Sen, 1983).
Poverty lines are defined in Italy on the basis of basic needs, identified through of a minimum food basket, plus housing needs and an allowance for basic non-‐food basic needs. Once these needs have been identified, the poverty line is defined as the cost of buying the basket of goods and services needed to satisfy the basic needs. In other terms, absolute poverty lines are defined as the monetary value, at current prices, of a fixed basket of goods and services considered essential for each household – according to the number and age of its members, the geographical area of residence and the municipality demographic size – to attain the minimum acceptable standard of living. Therefore, since, in general, needs vary according to the household composition while the cost of the basket changes according to the area of living, ISTAT calculates a set of absolute poverty thresholds, instead than a single threshold. The detailed methodology to define the poverty lines and compute absolute poverty, extensively described in Italian in ISTAT (2009), follows four steps and is summarized as follows.
The first step is the identification of individual and household essential requirements, referring to the idea of acceptable minimum standard of living: a household that cannot afford to purchase goods and services essential to meet these basic requirements (or needs) cannot attain an acceptable standard of living, although modest, in the social context in which it lives. This could imply severe forms of social exclusion, thus the impossibility to adequately take and make the various social roles one should be able to take as a member of a particular society (Storms, 2012). The second step is the identification, for each essential requirement, of the specific goods and services to be included in the basket summarizing basic needs. The third step is the identification of the sources for evaluating costs of goods and services in the basket. Finally, the fourth step concerns the final definition of the thresholds, i.e. the minimum value of economic resources necessary to a household for not being defined as absolute poor.
As mentioned, basic needs are supposed to be homogeneous throughout the national territory, except for differences due to external factors, such as weather conditions influencing heating demand. Instead, costs to meet the basic needs differ in different geographical areas of the country, since they reflect the cost of living in the various areas, also related to the size of the municipality where the households resides, and the different variations overtime in the various areas of the prices of goods and services included in the basket.
The basket is composed by three components: i) food and drink, which refer to the concept of adequate nutrition; ii) housing, which refers to the availability of a dwelling of adequate size
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according to household size and equipped with heating and main services, durable goods and accessories; iii) a residual component, which includes the minimum necessary amount to dress, communicate, be informed, move, be educated and in good health.
As clarified below, some of the components of the poverty threshold are estimated through coefficients obtained by models run on HBS data (through a pooled sample on the 2003-‐2005 surveys). However, once estimated these coefficients, the methodology makes the thresholds exogenous to the survey results. The poverty lines have been calculated for the year 2005. To adjust the lines for price changes over time, specific price indexes for each good and service in the basket are yearly used. Under the assumption that prices trends may differ spatially, the inflation rate is considered by territorial domains.
The food and drink component
The food and drink basket was identified through a nutritional model defined by ISTAT and the National Nutritional Institute. The food and drink need of the individuals (by sex and age groups) were defined translating the recommended daily intake levels of food (LARN -‐ Livelli di assunzione raccomandati di nutrienti per gli Italiani) into combinations of average daily food quantities at the individual level, expressed in average daily grams for each type of food. These needs are supposed to be independent from individual preferences. To determine the monetary value of individual food combinations, the data from the consumer price survey conducted by ISTAT were used, thus obtaining:
𝑞!! = 𝐶𝑜𝑚𝑏! ∗ 𝑃𝐶! (1)
where the combination of foods needed by an individual of the jth age group (𝐶𝑜𝑚𝑏!) multiplied by the set of unitary prices of the foods in the k geographical area (𝑃𝐶!) gives the monetary value of the individual nutritional needs (𝑞!!).
By summing the individual monetary food needs, the monetary value of the basket of the family is obtained:
𝑝𝑎!!….!!! = 𝑞!!!
!!! ∗ 𝑧! (2)
where 𝑧! is the household number of components for each jth age group.
To evaluate the minimum cost of the basket of foods, specific “saving coefficients” are applied to consider the effect of possible saving actions: larger households can save money purchasing bigger quantities of food or, conversely, smaller households might pay more being forced to buy the minimum packaging. Thus, finally, including saving coefficients, 𝑝𝑒!!….!!
! represents the monetary value of the food and drink component:
𝑝𝑒!!….!!! = 𝑝𝑎!!….!!
! ∗ 𝑐! (3)
where 𝑐! is the specific saving coefficient for a household of size z.
The housing component
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The housing component takes into account both the availability of the accommodation (i.e. the rent cost) and the facilities the house should contain (i.e. electricity, heating, durables). The housing (minimum) requirement is defined through a ministerial decree, which defines parameter for granting the habitability (Ministerial Decree 5/7/1975).
The rental subcomponent represents the major part of the housing component. The estimation model relies on a suitable dwelling dimension which varies according to the household size and a price per square meter. Such variables differ by the type of municipality and the geographical area of residence.
The monetary value of the rental component for a household of size z, residing in the geographical area k and in a municipality of type c is defined as:
𝑎𝑐!!" = 𝑠𝑝! ∗ 𝑐𝑚!" (4)
where spz is the suitable surface for household of size z (as defined by the law) and cmkc is the monthly expenditure per square meter for rent of families residing in the municipality of type c of the geographical area k.
The parameter cmkc is estimated through the following model:
𝑐𝑚!" = 𝑏!! ∗ 𝑒𝑥𝑝 −𝑠𝑝!!!!!!!!" (5)
where sp is the surface of the dwelling and ds is a dummy variable which takes value 1 if the household is resident in the South or Islands and 0 otherwise.
The other housing sub-‐components consider the facilities the dwelling should contain (electricity, heating, durables). The minimum energy consumption threshold was defined by the Authority for electricity and gas, differentiated by household size. This threshold is expressed in kilowatt hours and the monetary value is based on the application of the tariffs in force. It was assumed that the expenditure for electricity refers, in addition to lighting, to the use of television, washing machine and refrigerator. The heating component was estimated through a model based on HBS data, by geographical area, dwelling size and household typology. The expenses that a household affords for purchasing basic durable goods (refrigerator, cooker, washing machine, TV) was based on the calculation of depreciation quotas, obtained on the basis of consumer price and average duration.
The residual component
Food and housing alone do not give a complete picture of individuals’ and households’ needs. A residual component which includes the minimum necessary goods and services to dress, communicate, be informed, move, be educated and be in good health was calculated as a percentage of the expenditure on food and beverages.
The residual component re is a function of the monetary value of the food basket and takes into account the age and the number of household members:
𝑟𝑒!!….!!! = 𝑝𝑒!!….!!
!!∗ 𝑒𝑥𝑝 𝛽!!
!!! ∗ 𝑧! (6)
where 𝛼 and 𝛽! were estimated through the model:
𝑙𝑛 𝑟𝑒 = 𝛼 ∗ 𝑙𝑛 𝑠𝑎𝑝 + 𝛽! ∗ 𝑧!!!!! (7)
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where re and sap are the expenditures for good and services considered in the residual component and food expenditures, respectively.
The poverty lines
The monetary value of the basket was obtained in 2005 by summing the monetary values of the different components. Each component is yearly revaluated, differentiating the trend of consumer prices with respect to specific indexes of goods and services and to the territory of residence.
Absolute poverty is officially estimated in Italy focusing on household consumption (then, by using the Italian annual HBS). Hence, a household is considered poor when the monthly expenditure is lower or equal than the threshold.
Therefore, following the standard assumption in inequality studies, the poverty status is assessed at the household level: the poverty status of the members of the household thus depends on the household status, under the hypothesis that all members have the same chance of accessing household economic resources.
As remarked, no single poverty line exists, since the monetary value of the basket of absolute poverty varies according to the number and age of household members, the geographical area of residence (distinguishing North, Centre and South) and the demographic size of the municipality (distinguishing metropolitan cities, large and small municipalities). Following this approach, 342 absolute poverty lines were published in 2005 according to the household types included in the HBS. However, an algorithm allows ISTAT researchers to compute the threshold for every possible type of household.
3. Income-‐based versus consumption-‐based approach for measuring absolute poverty
3.1 Theoretical framework
The official source for computing absolute poverty is the Italian HBS. Therefore, the poverty status is defined according to household’s expenditures. However, the methodology described in Section 2 makes the poverty lines exogenous to the distribution of expenditures. More in general, it has to be remarked that, consistently with the theoretical “absolute approach”, poverty lines are identified in Italy independently of the distribution of variables proxying living standards. This implies that, instead than looking at households’ expenditure (henceforth “consumption based absolute poverty”), absolute poverty might be also analysed by focusing on households’ income (henceforth “income based absolute poverty”).
The literature on inequality and poverty has extensively discussed pros and cons of the various variables to be used as a proxy of individual wellbeing. In most cases, when a monetary unidimensional approach is followed, the focus has been placed on income or consumption.
Following the concept of full income (Simons, 1938)6 or extended income (Atkinson, 2015), the literature about economic inequality, (e.g. Canberra Group, 2011) has usually pointed out that the best proxy of economic wellbeing is the disposable equivalised income, that is made by all incomes earned in the market by household members from every source (employment, self-‐employment, capital, land), net of taxes and including welfare state transfers, equivalised by
6 Simons (1938) argues that the best proxy of economic wellbeing should be expressed as consumption plus change in net worth in a certain time period.
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dividing total income by the so-‐called equivalence scale to take into account differences in households’ sizes. However, disposable income is an exhaustive proxy of economic wellbeing if all income sources – e.g. earnings from employment and self-‐employment, financial incomes, fringe benefits, imputed rents for houseowners, home production, monetary values attributed to ink-‐kind welfare transfers, as health care and education – and all types of social contributions and direct and indirect taxes on income and wealth and tax expenditures (e.g. for private welfare schemes) are correctly measured in the available dataset. However, the more some income sources are badly measured (e.g. due to income underreporting or to the methodological complexity for imputing monetary values for health care and homeownership), the less measures of inequality become reliable, especially in cross-‐country comparisons. This is the reason why empirical studies usually make use of datasets where the definition of the various income sources is homogenous across countries (the EU-‐SILC or the Luxembourg Income Study – LIS), and almost all possible income sources are recorded (apart from the monetary value in-‐kind welfare transfers which might be estimated through different approaches). Therefore, income is often suggested as the best proxy for analysing income inequality in developed countries, where these datasets are available.
However, income has a further drawback for proxying individuals’ wellbeing since it may be affected by temporary fluctuations that do not seriously change the economic wellbeing if the individual may save or dissave. Correctly, the full income concept proposed by Simons (1938) also considers the change in the value of wealth for measuring income in a certain period (e.g. a year), but correctly measuring variations in the value of wealth is extremely complex.7
It has then been argued that consumption – usually proxied by the expenditure in a certain period – might be a proxy of wellbeing better than income, since it is more stable overtime, independently of short-‐term income fluctuations, and may capture also the living standard associated with incomes hidden in surveys or in administrative tax files.
Actually, the underlying conceptual model foresees that economic well-‐being derives from consumption, which in turn depends on income. However, since the seminal studies of the Nobel prizes Milton Friedman and Franco Modigliani about permanent income and the life-‐cycle hypothesis (e.g. Friedman, 1957; Modigliani, 1966), it has been agreed that consumption reflects, to a certain extent, households’ long-‐run resources rather than the mere current income. Indeed, especially where proper capital markets work, current levels of consumption depend, over current income, also on expectations of future incomes and on saving and dissaving along the life course (Meyer and Sullivan, 2011; Meyer and Sullivan, 2013; Brewer and O’Dea, 2012). Furthermore, consumption might be considered a better proxy of wellbeing in empirical studies about developing countries where reliable income data are more rarely available. However, consumption is affected by individual preferences, thus biasing comparisons across individuals. Nevertheless, limits due to individual preferences seem less serious when one focuses on the bottom tail of the income distribution, since, consistently with the arguments made in previous Sections, less-‐well off individuals should mostly satisfy basic needs which, by definition, should not change according to preferences.
Therefore, since both income and consumption provide useful insights for assessing the distribution of economic wellbeing, analysing poverty using both proxy variables might shed more light on the characteristics of the poor.
7 For instance, some types of wealth are easily hidden (e.g. cash money, paintings), and attributing a value to wealth is a bit arbitrary when some types of wealth are not sold/bought (e.g. houses or unrealized capital gains on shares).
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3.2 Income-‐ and consumption-‐based absolute poverty: results about Italy
In this Section we compare estimates of incidence and intensity of absolute poverty carried out by using the 2017 waves of the Italian HBS (henceforth, consumption-‐based poverty) and of the IT-‐SILC (henceforth, income-‐based poverty).8 As mentioned, even if the official absolute poverty is computed in Italy relying on data on household consumption, the exogenous poverty lines can be easily applied to IT-‐SILC data, according to the dimensions defining these lines (household’s size and age composition, plus the geographical area of living and the size of the municipality).
The comparison between the consumption-‐based and the income-‐based absolute poverty is strengthened by major similarities between the Italian HBS and IT-‐SILC: the two surveys are carried by the same Institute, the ISTAT; samples are extracted from the same population in the same year; sample designs are almost the same (a two stage sampling with stratification by demographic size of the municipality within the regions); the same calibration procedure with almost the same auxiliary variables is applied to calculate survey weights (Deville and Sarndall, 1992; ISTAT, 2008; ISTAT, 2015)9.
To compare the two approaches, in the following Tables we present indicators distinguishing the same population groups considered in the official report on absolute poverty (ISTAT, 2018).
In Table 1 we show the incidence of absolute poverty considering both the household and the individual as the unit of analysis, referring to total household monthly expenditure and to household monthly disposable income as the proxy of living standard.10 When focusing on households, the consumption-‐based absolute poverty incidence is 6.9% (about 1,778,000 households), and a comparable figure (6.8%, about 1,759,000 households) is obtained when we focus on incomes using IT-‐SILC. Instead, when focusing on individuals, large differences emerge, suggesting that the size of poor households surveyed in HBS and IT-‐SILC differs: the consumption-‐based value (8.4%) is indeed much higher than the income-‐based value (6.7%). In absolute values, poor individuals according to the income distribution are about one million less than poor individuals observed according to their expenditures (4.047 versus 5.058 millions).
Tab. 1: Absolute poverty incidence, at the individual and the household level -‐ 2017 Consumption-‐based Income-‐based
Absolute values (thousands) Poor households 1,778 1,759 Resident households 25,865 25,817 Poor individuals 5,058 4,047 Resident individuals 60,220 60,322 Poverty incidence (%) Households 6.9 6.8 8 Given the IT-‐SILC design, income refers to 2016. To obtain the 2017 value, we applied the growth rate of households’ disposable income at current prices, differentiated by NUTS-‐1 level, as disseminated by the National Accounts. However, note that the results do not change if we do not apply this correction. 9 Very slight differences in the overall number of households and individuals are due to the fact that HBS disseminate data earlier than IT-‐SILC, which can use more updated demographic information. Note that in all analyses shown in this article we make us of sample weights provided in the surveys. 10 Monthly disposable income is obtained by dividing by 12 annual income, that is recorded in IT-‐SILC. Note that expenditure and income do not have to be equivalised, since absolute poverty lines change according to household size.
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Individuals 8.4 6.7 Source: elaborations on HBS and IT-‐SILC
Apart from 2017 levels, it also interesting to compare the trend of consumption-‐based and income-‐based absolute poverty since 2005, the first year when ISTAT computed absolute poverty according to the methodology reviewed in Section 2 (Figure 1, where light and dark grey lines refer to consumption-‐ and income-‐based poverty, respectively, whereas dashed lines refer to individual-‐level poverty). As expected, all four indexes of absolute poverty incidence have highly increased from 2005, even if a higher relative increase from 2005 to 2017 characterized consumption-‐based poverty (+154.5% and +91.7% at the individual-‐ and the household-‐level, respectively, while the corresponding increases for income-‐based poverty amounted to 60.1% and 72.0%).11
Fig. 1: Trend of absolute poverty incidence, at the individual and the household level – 2005-‐2017
Source: elaborations on HBS and IT-‐SILC However, consistently with the theoretical discussion reported in Section 3.1, the different trends of income-‐ and consumption-‐based poverty in the starting phase of the economic recession have to be pointed out. The sudden drop in household income due to the crisis started in 2009 engendered a sudden steep increase in income-‐based absolute poverty from 2009, while the increase in consumption-‐based poverty was delayed because, very likely, in the first phase of the economic crisis (until 2011) poor households were able to sustain their basic needs by using their savings of getting into debt.
In what follows, unless where different specified, we refer to figures referring to households instead than to individuals, even because the individual condition is determined by the household condition, but differences according to the two units of analysis should not be neglected. 11 Looking at the whole population, mean household expenditure (at constant prices) decreased by almost 17% in the period 2005-‐2017, and the decrease exceeded 9% in the period 2011-‐2013. In the same period, mean household income (at constant prices) decreased by almost 11%, and the decrease mainly emerged in 2007-‐2011 (around -‐6%) and 2011-‐2013 (around -‐5%).
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Households -‐ Consumption based Individuals -‐ Consumption based Households -‐ Income based Individuals -‐ Income based
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Tab. 2: Absolute poverty incidence by households’ characteristics -‐ 2017
Consumption-‐based Income-‐based Household size 1 5.3 8.9 2 4.9 4.7 3 7.2 5.4 4 10.2 6.7 5 and over 17.8 11.8 Household typology Single member under 65 5.9 12.8 Single member 65 and over 4.6 4.3 Couple with household head under 65 5.0 5.0 Couple with household head aged 65 and over 2.6 1.0 Couple with one child 6.3 4.2 Couple with 2 children 9.2 6.4 Couple with 3 or more children 15.4 12.3 Single parent 9.1 11.0 Other typologies 15.7 6.1 Household members’ citizenship Nationals only 5.1 5.4 At least one non-‐national 25.6 20.0 Tenure status Tenants 17.5 18.2 Owners 3.9 3.1 Rent-‐free dwelling 10.5 9.3 Source: elaborations on HBS and IT-‐SILC
As expected from the different figures obtained for household and individual poverty, we find that the greatest differences between the consumption-‐ and the income-‐based approach emerge when the incidence of absolute poverty is assessed by household size (Table 2). While for a single member household income-‐poverty is higher than consumption-‐poverty (8.9% vs 5.3%), for large size households the situation overturns, with a poverty incidence for households with at least 5 components equal to 17.8% or 11.8% when expenditures or incomes are considered, respectively. Consistently, different figures also emerge when poverty incidence is assessed by household type: actually, single persons aged below 65 years old are more frequently poor when considering income rather than consumption (12.8% versus 5.9%, respectively), while in all household types with children the consumption-‐poverty is higher than the income-‐poverty. These results are also consistent with the spread of consumption-‐ and income-‐poverty by age of the household head (see Table 3, below). As expected, the incidence of absolute poverty is much higher within immigrants than within households with all members with the Italian citizenship, even if the gap between the two types of household enlarges when consumption-‐based poverty is computed, maybe due to different consumption habits between immigrant and native households. Finally,
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absolute poverty is highly associated with the tenure status of the dwelling in both approaches, and, as expected, the highest incidence of absolute poverty emerges among the tenants.
Tab. 3: Absolute poverty incidence by characteristics of the household head -‐ 2017
Consumption-‐based Income-‐based Age Until 34 years 9.6 12.7
35 -‐ 44 years 8.8 10.4
45 -‐ 54 years 8.4 8.9
55 -‐ 64 years 6.7 7.5
65 years and over 4.6 2.7
Education
At most primary 10.7 5.7
Lower secondary 9.6 10.1
Upper secondary 4.6 6.1
Tertiary of higher 1.4 3.7
Professional condition
Employed 6.1 5.9
Employee 6.6 5.1
-‐-‐-‐-‐-‐ Executive, Middle Management and White-‐collar 1.7 1.9
-‐-‐-‐-‐-‐ Blue-‐collar 11.8 8.3
Self-‐employed 4.5 8.4
-‐-‐-‐-‐-‐ Entrepreneur and freelance 1.3 3.9
-‐-‐-‐-‐-‐ Other self-‐employed 6.0 10.6
Not Employed 7.7 7.8
-‐-‐-‐-‐-‐ Seeking for job 26.7 33.8
-‐-‐-‐-‐-‐ Retired 4.2 2.2
-‐-‐-‐-‐-‐ Other conditions 11.9 17.2 Source: elaborations on HBS and IT-‐SILC When the incidence of absolute poverty is assessed according the characteristics of the household head (Table 3), we find that the size of the gap between consumption-‐ and income-‐based poverty changes when we distinguish households by the age of the household head. Income-‐based poverty is higher than consumption-‐based poverty among the households headed by individuals aged less than 35 (12.7% vs 9.6%), because of the serious income constraints due to unemployment and low-‐paid jobs often characterizing young workers, whereas consumption-‐based is higher than income-‐based poverty among the elderly (4.6% vs 2.7%), maybe because, due to their habits, older households have a high saving propensity, even if poor households are entitled at receiving means-‐tested minimum and social pensions. Interestingly, both consumption-‐ and income-‐based poverty steeply reduce when the age of the household head increases even if
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the reduction is much steeper when poverty is assessed looking at incomes, consistently with the aforementioned greater stability of household consumption along the life cycle.
When distinguishing households by education and professional condition and occupation of the household head, some non-‐negligible differences between the consumption-‐ and the income-‐based approach emerge (Table 3). The higher consumption-‐based poverty for households whose head attained at most a primary education attainment is due to the large presence of elderly persons – with higher savings propensity – in this group. Conversely, higher level of income-‐based poverty within tertiary graduates is attributable to the higher share of professionals, whose income is often very volatile, within this group. Consistently with this interpretation, income-‐poverty is considerably higher than consumption-‐poverty among all self-‐employed categories, while the opposite emerges among the blue-‐collar employees.
When we look at the area of residence (Table 4), differences between the two approaches are likely related to the household composition in the various areas. As concerns the size of the municipality where the household lives in, in big cities the incidence of absolute poverty is lower when it is based on expenditures (6.3%) than on incomes (9.7%). Concerning the geographical area of residence, both approaches confirm that absolute poverty is much more spread in the South than in the other areas and only slight differences between the incidence computed according the two approaches emerge.
Tab. 4: Absolute poverty incidence by geographical areas -‐ 2017
Consumption-‐based
Income-‐based
Municipality demographic size Metropolitan area – centre 6.3 9.7 Metrop. suburbs and municipalities with at least 50,001 inhab. 7.6 7.5 Other municipalities (non-‐metrop. suburbs) until 50,000 inhab. 6.7 5.5
Geographical area North 5.4 5.0 Centre 5.1 6.0 South and Islands 10.3 10.0
Source: elaborations on HBS and IT-‐SILC
Finally, we cast a fast glance at the absolute poverty intensity, measured through the poverty gap (the average percentage distance from the threshold for poor households). We notice that the gap almost doubles when poverty is based on income than on consumption (40.0% vs 20.9%; Table 5). Being the intensity of poverty related to the distribution of the proxy variable, this result is due to the well-‐known fact that the income distribution is much more unequal than the consumption distribution, as confirmed by the Italian data. In absolute terms, the mean monthly household income of poor households is around 600 Euros, much lower than the corresponding figure on expenditures (around 900 Euros). At the opposite, the mean monthly income of non-‐poor households (around 3,150 Euros) is higher than the corresponding figure on expenditures (around 2.700 Euros).
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Tab. 5: Intensity of absolute poverty (%) and mean household expenditure and income (Euros) Consumption-‐based Income-‐based Poverty gap 20.9 40.0 Mean monthly household expenditure/income Poor households 896 594 Non poor households 2,687 3,156 Total 2,564 2,982 Source: elaborations on HBS and IT-‐SILC
4. Intersections between absolute poverty, relative poverty and severe material deprivation
The aim of this Section is to test whether the incidence of absolute poverty and of the two indicators used at the EU level, the AROP and the SMD,12 have similar patterns or, on the contrary, trends of the various indexes and the groups considered at risk according to the various concept differ. To this aim, we refer to income-‐based absolute poverty since we make use of IT-‐SILC data, that is the data source used for the official measure of both AROP and SMD.
We first compare in the period 2007-‐2017 the trend of income-‐based absolute poverty (computed at the individual level to be consistent with AROP and SMD definitions) with the trend of the SMD index and of the AROP (considering 40% or 60% of the national median of the distribution of equivalised disposable income as the relative poverty line; Figure 2).
As expected, trends of the various indicators largely differ. Absolute poverty and SMD are characterized by a steep increase since the upsurge of the economic crisis (the increase from 2007 to the year when the highest value occurred amounted to +101% and 107.1% for absolute poverty and SMD, respectively), whereas the AROP pattern shows a much lower increase when the 40% threshold is considered (+42.0% is the maximum increase over the period) and is even almost constant when the 60% poverty line is used (+5.6% is the maximum increase over the period). The different patterns of the various curves clearly confirm that the various indicators capture different phenomena and, mostly, that relative indexes are not well grounded to observe trends of social exclusion during downturn macroeconomic phases.
Fig. 2: Trend of income-‐based absolute poverty, AROP and SMD – 2007-‐2017
12 The AROP is a typical relative poverty measure where individuals are considered poor when their equivalised disposable income is below a threshold defined looking at the distribution of the proxy variable, while the concept of SMD is based on the affordability of a selection of items and, thus gives an indication of the proportion of people whose living standards are affected by a lack of resources. SMD is measured through the share of households which cannot afford to have at least four out of nine items (goods or services) considered to be necessary or desirable for people to have an “acceptable” standard of living in the country where they live.
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Source: elaborations on IT-‐SILC
Apart from the spike observed in 2012, SMD has a trend rather similar to the income-‐based absolute poverty. However, before arguing that SMD is a good proxy of absolute poverty, one should investigate whether the two concepts identify the same households as those at risk.
Tab. 6: Intersection between absolute poverty and SMD -‐ 2017
Row percentages SMD 0.0 1.0 Total
Absolute poverty 0 91.0 9.0 93.2 1 69.1 30.9 6.8
Total 89.5 10.5 100.0
Column percentages SMD 0 1 Total
Absolute poverty 0 94.7 79.9 93.2 1 5.3 20.1 6.8
Total 89.5 10.5 100.0 Source: elaborations on IT-‐SILC To this end, using IT-‐SILC 2017 we cross-‐tabulated the absolute poverty and the SMD status of all households according to the idea that the higher the association between the two concepts at the household level the better SMD works as a proxy of absolute poverty. Unfortunately, this exercise shows discouraging results since the intersection between absolute poverty and SMD is extremely low (Table 6, where marginal, row and column percentages of the two-‐way table between
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
22.0
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Income based individuals' absolute poverty AROP (line as 40% of the median)
AROP (line as 60% of the median) SMD
17
absolute poverty and SMD is shown): only 30.9% of households who are below the income-‐based absolute poverty line are also in a SMD status, while only 20.1% of those in SMD are also absolutely poor.
5. Discussion and conclusions
This article contributed to the literature about poverty measurement, first, by presenting a detailed review of the methodology adopted in Italy to compute absolute poverty, and, secondly, carrying out computations about the incidence of poverty. The aim is assessing what changes, on the one hand, when absolute poverty is computed with reference to income instead than to consumption, and, on the other hand, when income-‐based absolute poverty is compared to the two indicators used by the EU for proxying poverty and social exclusion, i.e. the AROP and the SMD.
We find that the methodological choices about the poverty concept and the proxy variables of the economic wellbeing largely affect both the extent of the observed phenomenon and, mostly, the identification of the population subgroups who are considered as poor according to the various choices. Actually, we find three main results.
First, the level and the characteristics of the poor change when absolute poverty is measured with reference to income rather than to consumption, signalling that the choice between the possible proxies of the household economic wellbeing is not trivial. Second, trends in the incidence of AROP and income-‐based absolute poverty largely differ since the upsurge of the economic crisis in 2008, thus confirming that relative poverty indicators are not well-‐suited to capture the decrease in the economic wellbeing during recession periods. Third, an extremely low association between the income-‐based absolute poverty status and the SMD status emerges at the micro-‐record level, thus strongly questioning the idea of using SMD as a proxy of absolute poverty; according to our results, SMD and absolute poverty, even if following similar trends, seem capturing different concepts of hardship and social exclusion.
These results have further implications for EU and national policies.
At the EU level the debate about the introduction of an income-‐based absolute poverty line is quickly growing and the Italian experience might represent a cornerstone to define this line and to fully understand the implications of the various methodological choices needed to obtain an absolute poverty line. To this aim, the revision of the Italian methodology on absolute poverty scheduled for next years (ISTAT, 2009) – with a further inquire about possible intersections between income and consumption to obtain a better proxy of household living standard – might provide crucial insights also for the EU debate.
At the Italian level, the so-‐called “Citizenship Income” (Reddito di Cittadinanza) – a minimum income safety net paid, according to conditionality rules, to all households with an income below a certain threshold – was introduced in April 2019 with the explicit aim by the proponents to fight poverty. However, a careful look at the several details about the economic requirements for being entitled to this benefit and about the formula for computing the benefit amount for different households brings us to put the attention on some details, that are often underrated in the debate but are actually crucial to define an effective safety net. This could lead to an increase of the effectiveness of such policy measure in reaching its aim. For example, the entitlement condition is based on both income and wealth, while the benefit tops up incomes, but with an equivalence scale disadvantaging large households (Jessoula et al., 2019). Moreover, the benefit is not
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differentiated by area of residence, while the concept of absolute poverty is properly based in the different cost of living in the different areas of the country, and this aspect should be at least debated. Finally, the results based both on income and on consumption distribution show that absolute poverty heats much more tenants than owners. Even though the benefit differs, with an adjunctive amount for tenants, this amount should be evaluated and, eventually, increased or decreased.
More in general, our findings clearly show that groups of beneficiaries of a means tested minimum income benefit might highly differ according to the economic variable considered for the means testing conditions, thus confirming that the identification of the poor is not robust to the way the poverty concept is modelled. This aspect is instead often neglected in the policy debate, where it is implicitly assumed that poverty is a sort of objective condition. However, all the possible (and not unanimous) methodological choices about poverty measurement might represent a sort of hidden danger of targeted social programmes, since the identification of contributors and beneficiaries and the amount paid/received by them might be in some sense arbitrary and not transparent (Granaglia and Raitano, 2017).
The empirical research should then move further steps towards a more robust definition of the groups of individuals more at risk of social exclusion. To this aim, it would be crucial to have at disposal microdata where both consumption and incomes were recorded, in order to analyse in detail the characteristics of those individuals who would be considered poor independently of the used proxy variable or, conversely, enter or exit out of poverty according to the chosen variable. Unfortunately, at the moment no micro-‐datasets record both consumption and income at the household level in Italy, even if statistical matching techniques for building a unique dataset are currently being exploited. A well suited dataset could permit to compare the distributions of income and consumption, especially at the bottom tails, thus arriving at a joint distribution of income based and consumption based absolute poverty, and, more in general, could be of great relevance to policy purposes regarding inequalities and fiscal policies.
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