Luxembourg Income Study
Working Paper Series

Working Paper No. 513

Redistribution as an Income Mobility Process:
The Identification and Measurement ofRedistribution
Juan Rafael Morillas
May 2009

Luxembourg Income Study (LIS), asbl

Redistribution as an Income Mobility Process:
The Identification and Measurement of Redistribution

Juan Rafael Morillas
University of Durham
j.r.morillas@durham.ac.uk

Abstract.
How can we evaluate the redistributive effect of welfare states? Do tax and transfer
systems reduce the level of inequality generated in the market? In order to answer thesequestions, we need to be equipped with adequate measures of redistribution. Current
measures employed in the sociology and politics of redistribution are seriously flawed.
This paper elaborates the reasons why we cannot rely on those measures. Furthermore,
it develops a framework to statistically identify redistribution as a micro-level incomemobility process, and develops an index to measure it. Using data from the Luxembourg
Income Study, the paper shows that the new measure of redistribution developed in thispaper is empirically distinct from the conventional measures that have been used in theliterature. The paper concludes by demonstrating the importance of the new measure of
redistribution for improving our understanding of the social and political determinantsof redistribution. In particular, it shows that the conventional wisdom in the political
economy of redistribution does not hold in the light of the new measure developed inthis paper.
Keywords: redistribution, inequality, income mobility, political economy, power
resources, partisanship, median voter.

1

Introduction
One key question in the sociology of the welfare state is the role that it plays in the
reduction of inequality and poverty. By means of tax and transfer systems, welfare
states modify the income stratification that is generated in the market. Typically,
households at the bottom of the distribution of market income obtain a higher share of
final or disposable income, while those at the top experience a substantial reduction in
their share of disposable income -as compared with that of market income. Therefore,
the measurement, description and explanation of redistribution are critical for an
evaluation of the ‘true’ effect of the welfare state on the stratification of households’
opportunities for income acquisition.

In spite of the importance of redistribution for the assessment of theconsequences of different welfare states, our knowledge about it is very limited. This isdue mainly to two reasons. First, until recently, reliable and comparative data on market
income (income derived from the market) and disposable income (market income,minus direct taxes plus welfare cash transfers from the state) have not been available formost of countries. As an attempt to compensate for the lack of data, the literature has
used proxies for redistribution such as social spending as a proportion of GDP (e.g.
Hicks, 1999; Huber and Stephens, 2001). However, it is well-known that not all social
spending is redistributive (vid. Hacker et al., 2004; Lambert, 1993). Social spending as apercentage of GDP does not measure and is not a substitute for redistribution. Indeed,social spending benefits not only the poor but also, and sometimes to a larger extent, themiddle-class (Goodin and Le Grand, 1987; Le Grand, 1982).

2

Second, more recent attempts at measuring the redistributive consequences of
welfare states, although have used the adequate data, compare aggregate measures ofinequality for the distributions of market income and disposable income (e.g. Bradley etal., 2003; Mahler and Jesuit, 2006; Korpi and Palme, 1998) and have assumed that thedifference between the two or the proportional reduction of the Gini coefficient is ameaningful measure of the effect of redistribution on the reduction of inequality.
However, and this is the main point of this paper, the comparison of aggregate measuresof the inequality of market income and disposable income is inappropriate and does notprovide a real measure of redistribution. The reason for this, explained in a nutshell, isthat current measures are based on the comparison of aggregate figures of inequality,
which, by definition, are unable to analyze the incidence of taxes and transfers at theindividual level and, as a consequence, are unable to measure redistribution1.

The main aim of this paper is to develop a framework for the identification andmeasurement of redistribution. In order to do this, in section 1, building on theaxiomatic theory of inequality measurement, as well as on the characterization of
ecological fallacies, I discuss the reasons why that the measures used in very recent
1

There is an additional reason why this approach may provide a biased evaluation of the extent to which

tax and transfer systems influence the level of redistribution. As noted by Bergh (2005), this approachassumes that the distribution of market income is not influenced by the level of taxation and transfers.
However, individuals and households may adjust their market participation by anticipating how taxes andtransfers will influence their final or disposable income. In other words, market income needs to be
exogenous to the welfare state. However, households’ market income may be endogenous to tax andtransfer systems. For this reason, an evaluation of the effect of the welfare state on redistribution requires
the construction a counterfactual for the distribution of market income, i.e. what would have been thelevel of market income for each household if a welfare state had not existed.

3

papers (Bradley et al., 2003; Kenworthy and Pontusson, 2005; Mahler and Jesuit, 2006)
are unable to identify and measure the redistributive consequences of welfare states.

Once we know the reasons why redistribution cannot be evaluated by comparing
aggregate figures of market and disposable income inequality, I discuss in section 2 theempirical content of the concept of redistribution and characterize it in a way that will
enable us to statistically identify and measure it. The point of this section is tocharacterize redistribution as an income mobility process. This will allow us to identify
it and, in a more practical vein, measure it by relying on the available income mobility
indexes.

Once the process of redistribution is identified and we have the tools to measure
it, in section 3, using comparative micro-data from the Luxembourg Income Study, thelevel of redistribution is evaluated for different OECD countries at different years. This
provides a systematic description and comparison of the similarities and differences ofthe process of redistribution across different advanced industrial societies. Furthermore,this section shows that the correlation between the index of redistribution that measure
it at the micro-level and the conventional measures of aggregate inequality reductionused by Bradley et al (2003), Iversen and Soskice (2006), and Korpi and Palme (1998)
is very low. This is a first step towards showing that our knowledge of redistribution israther limited due to the fact that it has not been properly measured as of yet.

Equipped with the new measure of redistribution introduced above, I analyse thepolitical determinants of the variability of redistribution across countries in Section 4. In
order to do so, the new measure of redistribution developed in this paper is merged with4

country-specific measures of political institutions, partisanship, constitutional structure
and other macro economic, social and political variables. The findings from theseanalyses will become crucial for an accurate understanding of the political economy of
the welfare state. Using this new measure of redistribution provides results that go
against some of the existing interpretations of the politics of redistribution. In particular,
these calculations supply a different result on the relationship between partisanship andredistribution to the one offered by proponents of the power-resources model, as shownin Bradley et al. (2003), and against the predictions of the median-voter theorem(Meltzer and Richard, 1981; Kenworthy and Pontusson, 2005).

1. ‘Redistribution’ in sociology and politics: why they fail to identify it?
How can we statistically assess the extent of redistribution in a society? Do
welfare states reduce inequality and poverty? As I wrote in the introduction, theliterature in the political economy of redistribution has provided an answer to thesequestions. According to such a literature, we can assess the extent of redistribution bycomparing the gini coefficients of two distributions of income: the gini coefficient of
the distribution of market income and the gini of the distribution of disposable income.
The actual measures of redistribution consist of the computation of either the
proportional reduction (Bradley et al. 2003; Iversen and Soskice, 2006; Korpi andPalme, 1998) or the difference between the two gini indexes (Mahler and Jesuit, 2006;
Kenworthy and Pontusson, 2005). The logic underlying this approach is very simple.
The inequality of the distribution of disposable income is lower than the one of the
distribution of market income. Therefore, the reduction in the gini coefficient, either inabsolute or in proportional terms, provides an account of redistribution.

5

Does the comparison of the aggregate measures of inequality –market income
and disposable income- really account for redistribution? Can we evaluate the reductionof inequality effected by redistribution on such a comparison of aggregate figures? The
answer to this question is “no”2. The conventional measures used in the literature do not
actually measure the effect of redistribution on the reduction of inequality. And they do
not do so because they fail to properly identify redistribution as a statistical process. The
comparison of aggregate measures of the inequality of market income and disposable
income, by calculating either the proportional reduction (Bradley et al. 2003; Iversen
and Soskice, 2006; Korpi and Palme, 1998) or the difference between the two (Mahler
and Jesuit, 2006; Kenworthy and Pontusson, 2005), is inappropriate and does notprovide a real measure of redistribution. The reasons for this are two. First, we cannot
obtain conclusions about micro-processes from aggregate variables such as overall
inequality -trying to do so amounts to an ecological fallacy. And second, astraightforward comparison of the inequality of market income with that of disposable
income may not fulfil the Lorenz dominance criterion, making measures based on suchcomparison inappropriate for the analysis of redistribution. Let me elaborate these two
reasons.

2

Do note that the counterfactual problem of redistribution is independent of the methodological problem

identified in this paper. Certainly, the index of redistribution that is going to be formulated in this paper
can be applied both to the actual level of market income or to the value of the latter calculated in acounterfactual exercise.

6

1.1. The comparison of income distributions: the Lorenz dominance criterion.
Since Atkinson (1970), it is a well-established criterion in welfare economics toregard one distribution of income F as more equal that another one, F’, when the firststrictly Lorenz dominates the latter. In other words, if we want to compare two income
distributions, the Lorenz curves associated to each of them have to fulfill one property:
one of the two has to dominate the other –alternatively, one curve has to be dominatedby the other. More formally, an income distribution F is said to Lorenz-dominate
another distribution F’ when the Lorenz curve of the first is above the one of thesecond:LF(p) > LF’(p) for all p ∈ [0, 1](1)

The graphical implication of the Lorenz dominance criterion is that the
distribution of market income and that of disposable income should not cross each other–one should always lie below the other (Cowell, 1977: 34-9). Figures 1 and 2 are theLorenz curves of hypothetical income distributions to illustrate this. Figure 1 represents
the Lorenz curves derived from two distributions of income that can be unambiguously
characterized as displaying different levels of inequality. If these curves represent the
distribution of market and disposable income, we can affirm that the inequality ofmarket income is more unequal than that of disposable income because the former lies
below the later.
[Figure 1 about here]

7

Figure 2 represents the case of two distributions of income that intersect.
Because of this, we cannot establish which distribution is more or less unequal. In thissituation, the comparison of the Lorenz curves of the two distributions, and the same
applies to the gini coefficient, does not unambiguously tells us which of the two
distributions is more unequal. In such a case, the comparison of gini indexes is what
Cowell (1977: Chapter 1) calls a ‘non-decisive’ approach to compare the inequality of
two distributions.

[Figure 2 about here]

In the papers cited above that measure redistribution in what I have called theconventional way, we find no analytical assessment of whether this property, the Lorenz
dominance, is satisfied when we compare the distributions of disposable and market
income inequality in the cases that they analyze -observations for pairs of country-years.
More seriously, even if it is fulfilled in the cases that they analyze, there is no guaranteethat this property will always be satisfied when comparing other income distributions,making this tool, the comparison of the gini coefficients of disposable and market
income distributions, a ‘non-decisive’ measure of redistribution. If no analyticaldemonstration is provided that the Lorenz curves of market and disposable income
distributions for the same society will never intersect, then there is no guarantee that theconventional measures of redistribution provide a valid tool to measure redistribution.

Even if he Lorenz dominance criterion is satisfied, the conventional measures ofredistribution face another even more serious challenge. Such measures are based on
aggregate or summary indexes of the dispersion of the distribution of incomes.

8Therefore, by definition, these measures do not tell us who improves (worsen) her
position in the distribution of final or disposable income, and to what extent. They justtell us that, on average, inequality diminishes, but keeps anonymous the identity of
those who benefit (suffer) from redistribution –something crucial if we want to give apolitical economy of redistribution. Let me elaborate this point in more detail in the next
section.

1.2. Ecological fallacy: aggregate snapshots vs. micro-level income trajectories.
The recent comparative literature on redistribution has established that the levelof inequality of disposable income is lower that the inequality of market income. For
example, Mahler and Jesuit (2006: 497) show that the mean value of the level of market
inequality in Sweden for the years under analysis is 0.335, while that of disposable
income inequality is 0.220. In the US, the mean value of market income inequality is
0.404 while that of disposable income inequality is 0.339. In all advanced industrial
societies, the distribution of disposable income is more compressed than the one
generated in the market. Does this imply that the (market) poor are better off? Or does itmean that the rich are worse off? What happens to individuals in and around the middleof the distribution of market income? By comparing the level of aggregate inequality,
we simply cannot offer an answer to the former questions. We do not know who arethose who improve (worsen) their rank in the distribution, or what share of the total
market and disposable income that they receive.

This is especially troublesome for our ability to provide a political economy
explanation of redistribution. Indeed, as Harold Lasswell famously remarked, politics isall about Who Gets What, When, How (Lasswell, 1936). The comparison of aggregate9measures of inequality, by definition, abstract away the who from the analysis. Standard
measures of redistribution do not allow us to identify who the winners and losers ofredistribution are. Additionally, if we cannot identify their identity, we cannot assess
what share of one source of income or another, market or disposable, goes to theirhands.

For these reasons, we need to analyze redistribution at the micro-level. By sodoing can we identify the who and the how of the process of redistribution. Analyzed
from such perspective, we identify that the process of income attainment takes place in
two steps. In the first place, individuals engage in market activities, for example in thelabor and financial markets, and obtain an income. This income is what the variable‘market income’ measures. In a second step, this initial income is transformed into thefinal or disposable income. After paying taxes and receiving cash transfers from thestate, the market income that each household initially obtains becomes mapped into alevel of disposable income. The mapping rule is defined by the combined effect of cashtransfers received by the family and the taxes that it pays. The state collects (direct)
income taxes and transfers cash to individuals. These roles of the state taxing andtransferring income are not the same for all individuals. Indeed, some individuals andhouseholds pay income tax, while some others do not pay. Similarly, some receive
income transfers from the state, while others do not receive any.

The crucial issue in the assessment of the consequences of redistribution is that it
is a non-homothetic process. This is so because the position that individuals andhouseholds occupy in the distribution of market income does not parallel the one thatthey occupy in the distribution of disposable or income. Indeed, many individuals

10change their ranking in the distribution of it as compared to the one they had in the
distribution of market income. In other words, the poorest individual in terms of market
income needs not be the poorest in terms of disposable income. This is what technicallyis called as re-ranking or rank reversal (Lambert, 1993). Similarly, the shares of total
income for each individual needs not to be the same in the two distributions of income,market and disposable. For example, a person who is unemployed and does not obtainany market income commands a 0% share of the total market income. If she receives
unemployment benefits, she will have a non-zero share of the overall disposable incomein her society.

In summary, if we analyze incomes at the micro-level, we observe that
individuals experience changes in both their rank in the distribution of disposable
income, as well as in the share of the total income that they receive. By looking at
aggregate figures, we are unable to identify how redistribution works. Certainly, a given
level of overall inequality reduction is compatible with multiple, in fact infinite,combinations of individual income trajectories from market- to disposable-income.
Aggregate data do not enable us to identify the process of redistribution. It isdissagregated or micro-data what we need in order to evaluate how redistribution affects
individuals’ income.

Let me illustrate why with the help of a stylized example why we need to adopt
a micro-perspective in order to identify the process of redistribution. Let us consider the
distributions of market and disposable incomes in a society with two individuals. In
Table 1 we find a matrix describing the market and disposable incomes of these two

11

individuals. The rows corresponds to the two individuals, i= {1, 2}, and the columns
represent market income (first column) and disposable income (second column).

[Table 1 about here]

In the society represented in Table 1, the first individual obtains an income of 1
unit in the market, while the second obtains a market income of 10 units. It is clear thatthe second individual is ranked first if we consider the distribution of market income.
However, if we analyze the distribution of disposable income we find that their rank isreversed. After adding cash transfers and deducting taxes, the first individual gains 9
units and the second one looses the same amount, making their disposable incomesequal to, respectively, 10 and 1. The political economy implications of the redistribution
effected in this society are quite obvious. The first individual will support a policy withthe redistributive consequences described in the mobility matrix represented in Table 1,
while the second one will oppose it. Such a policy has a winner and a looser. However,
if we rely on the conventional measures of redistribution, we fail to identify this.
Certainly, as shown in Table 2 below, the gini coefficient of the two distributions ofincome, market and disposable, is the same; consequently, conventional measures tellus, no redistribution has taken place in this society.

[Table 2 about here]

Therefore, if we rely on the conventional aggregate measures of redistribution,
we will be unable to identify the evident process of redistribution summarized in Table
1. It is very obvious that this society has experienced a radical rank-reversal, and a12change of the shares of income that go the hands of each member of society.
Nevertheless, such a massive redistribution is obscured by the conventional measuresused in the literature (e.g. Bradley et al., 2003; Kenworthy and Pontuson, 2005; Mahler
and Jesuit, 2006). Because such measures rely on aggregate indexes of inequality, by
definition, they abstract away the micro-level consequences of redistribution.
Consequently, they fail to identify the identity of the winners and losers. This isparticularly troublesome for the political economy of redistribution. Certainly, if wewant to know who supports (opposes) redistributive policies, we need to identify who isbetter off (worst off) after redistribution. Only if we analyze it as a process will we beable to identify the social basis of support and opposition to redistributive policies.

2. Redistribution as an income mobility process: the identification andmeasurement of redistribution.
How can we evaluate the redistributive consequences of tax and transfer
systems? How can we measure redistribution? The first step that we need to take inorder to provide a measure is to identify the process of redistribution. The main insightunderlying this section is to characterize redistribution as just a particular case of an
income mobility process. Indeed, as over-time income mobility is a process that maps
individuals’ incomes at two or more points in time, redistribution, analogously, is aprocess that, for each individual, maps pre-tax and transfers (market) income into posttax and transfers (disposable) income. This simple characterization of redistribution
allows us to build on the literature on income mobility and obtain a micro-level measureof redistribution.

13

The conventional measures of redistribution used in the social sciences
described in the previous section are all based on a comparison of two aggregate‘snapshots’: the overall level of aggregate market income inequality and that ofdisposable income inequality. Such a static and aggregate evaluation of market income
and disposable income provides an incomplete and, as will be shown in the two latter
empirical sections of this paper, misleading evaluation of redistribution. Indeed, such an
approach to redistribution abstracts away the micro-level dynamics that map at theindividual level the initial income that individuals obtain in the market (market income)into the final income that they have at their disposition (disposable income) following
the receipt of cash transfers from and the payment of taxes to the welfare state.

In order to overcome this shortcoming, we can conceive the effect of transfers
and taxes as an income mobility process. Certainly, in the first place, individuals obtainan income in the market. In the second place, the state collects taxes and transfers cash
benefits to citizens. The income obtained at time 1 is the market income (MI), while theone obtained at time 2 is the disposable income (DI). Consequently, redistribution canbe defined as the process mapping the original income obtained in the market, MI, into
the final or disposable income. Formally, m= (m1, m2, … mn) represents the distributionof market income of a n-individuals society, where mi represents the income that
individual i obtains in the market (time 1). Such a market income, mi, becomes mapped
into a final or disposable income di in the second place. For the n individuals in thesociety under study, the final distribution of disposable income is represented by d= (d1,d2, … dn). More formally, redistribution can be defined as the dynamic process mappingm into d, and can be denoted as: m Æ d. It can also be represented, for each individualin society, as pairs of initial and final income: {m1, d1}, {m2, d2}, … {mn, dn}.

14

[Table 3 about here]

A redistribution process can be described in a mobility matrix such as the one in
Table 3. The information contained in such a redistribution matrix can be analyzed in
two different ways: as the comparison of two static snapshots or as a dynamic microlevel process. Graphically, this corresponds to two different readings of the mobility
matrix: a horizontal and a vertical reading. If we decide to analyze redistribution as adynamic individual-level process, we will read horizontally the mobility matrix thatsummarizes the process of redistribution. By definition, this allows us to assess theeffect of redistribution at the individual level and therefore establishes the identity of thewinners and losers of redistribution3.

The measures of redistribution currently used in the literature have a different
take. These measures are based on a vertical reading of the table above. ‘Redistribution’is thought to be uncovered by comparing the aggregate measures of market anddisposable income inequality. In this approach, the micro-data provided by national
surveys on individual and household incomes are aggregated and replaced by an overall3

The substantive interest in redistribution of the social sciences arises from its (hypothesized)

equalizer effect over the distribution of income. This interest is shared between the conventional measuresused in the sociology and the political economy of inequality and the measure of redistribution formulated
in this paper. The difference between the conventional measures of redistribution (difference or
proportional reduction of the gini coefficient of the distribution of market and disposable incomes) andthe mobility-based measure of redistribution is that the former only compares two “snapshots”, the giniindexes of the distributions of market and disposables incomes, and does not analyze the incidence oftaxes and transfers at the micro-level. The mobility-based measure of redistribution, by providing a micro
and dynamic analysis of redistribution, overcomes such a serious shortcoming.

15

summary of the inequality in each of the columns. The obvious first negative
consequence of this approach is that we are loosing very valuable information. By using
just a summary measure of the dispersion in the distribution of market and disposable
income, we loose extremely valuable information to evaluate the consequences of tax
and transfer systems at the individual- or household-level. A further consequence of thisis that, by relying of aggregate measures, we may come up with a wrong
assessment/characterization of the redistributive efforts of different welfare states4.

In other words, the conventional measures of redistribution (hereafter C-MOR)
fail to identify the micro-level process of redistribution. In order to identify it, we needto take advantage of all the information available to us in a redistribution matrix such as
the one in Table 3.

The characterization of redistribution as an income-mobility process provides
such a micro-level identification. Therefore, in order to measure the consequences of theprocess of redistribution, we have to ‘de-codify’ all the relevant information containedin the law or rule that maps market income is mapped into final/disposable income:
R: m= (m1, m2, …, mn) Æ d= (d2, d2, …, dn)

4(2)

Analyzing redistribution with disaggregated or micro-data is specially appropriate because oftheoretical reasons. Indeed, political economy theories of redistribution are formulated by specifying the
choices that individuals make in reaction to taxes and transfers that affect not the overall or aggregate
pattern of inequality, but their own personal income. For this reason, we need a measure of redistributionthat assesses the micro-level consequences of taxes and transfers.

16How can we ‘de-codify’ the law mapping market into disposable income at theindividual level? How can we evaluate the reduction of inequality effected byredistribution? The characterization of redistribution as an income mobility process, inaddition to accurately identify it, supplies the natural toolkit to measure it. Since
redistribution is one particular class of mobility process, the natural way to measure it isby calculating measures of an income mobility. Indeed, the concern of income mobility
indexes is to assess the extent to which overtime income mobility reduces the initiallevel of inequality. In other words, income mobility indexes measure whether societies
characterized by an unequal distribution of income at a point in time display high levelsof (future) income mobility that reduce such an initial inequality5.

The parallel of income mobility with redistribution is quite straightforward.
Indeed, the substantive concern of redistribution and income mobility is the same,
namely the extent to which the mobility from market income to disposable income
equalizes individuals’ opportunities for income acquisition. Given this substantive
concern of redistribution, then the natural way to evaluate how redistribution reduces
the initial inequality generated in the market is the computation of the Shorrocks index
of mobility (Shorrocks, 1978) applied to the process of redistribution, i.e. the income
mobility process mapping market into disposable income.

5

It is well known that income mobility provides a superior evaluation of welfare than the one

provided by the cross-sectional analysis of inequality (e.g. Gangl, 2005). As Atkinson, Bourgnignon andMorrison (1992) argue, mobility is of interest because it may reduce the inequality in the lifetime sum of
earnings relative to that in a single point in time. For this reason, cross-sectional inequality provides anincomplete and potentially misleading picture of welfare.

17

As the Gini index is the most commonly used measure of cross-sectional
inequality, the Shorrocks index is the most common index of income mobility. This
makes this index a very suitable candidate to become our mobility-based measure of
redistribution (hereafter M-MOR). Furthermore, there is a substantive reason that makes
it the most suited measure for becoming our M-MOR. Shorrocks (1978) developed thisindex in the substantive aim of evaluating how over time income mobility reduces thelevel of initial inequality. Therefore, it is an index particularly designed to assess theextent to which mobility equalizes incomes over the time period under analysis.

The interpretation of the values of the Shorrocks index (R) is very simple. R lies
between 0 and 1 and represents the proportion of inequality that is persistent in the
period under analysis. Equivalently, M= 1–R measures the relative reduction of crosssectional inequality when the accounting period is extended from one cross-section to T
periods. The following example helps to interpret it. Imagine that we obtain a value of
R= 0.9. This means that the process of redistribution contributes to a 10% reduction ofmarket inequality. Alternatively, we could say that in spite of tax and transfer made by
the welfare state a 90% of market inequality remains unchanged.

18

3. Redistribution in advanced industrial societies.
Once we have identified redistribution as an income mobility process and haveobtained an derived a measure consistent with such an identification, we are ready toevaluate how different welfare states, by taxing market income and providing cashsocial transfers, reduce the inequality generated in the market. In this section, I calculate
the mobility-based measure of redistribution (M-MOR) for a number of advanced
industrial societies. In addition to it, I calculate the conventional measures ofredistribution (C-MOR) used in the literature and show that the latter and the newmeasure of redistribution formulated in this paper, what I call the mobility-based
measure-of-redistribution (M-MOR) are empirically distinct -indeed, they are very
weakly correlated.

The data used in the calculation of the indexes of redistribution presented in thissection is provided by the Luxembourg Income Study (LIS). The LIS collects microdata
on market and disposable income obtained from national statistical authorities, which
are harmonized –‘lissified’, allowing for cross-country over-time comparisons. The LIS
provides nationally representative individual-level data on market and disposable
income for the countries listed in Table 4. The period of time for which data are
available starts in the late 1970s, although for some countries historical data are
available going back to the 1960s (for example, for Sweden and the UK; see Table 4 forfurther details). Overall, the number of surveys used in the calculations reported in thissection is 84 surveys.

[Table 4 about here]

19

The measure of income used in the calculations in this section, as is the case invirtually all the studies of inequality and poverty, is the equivalent household income.
The reason for this is that individuals live in families, and the latter constituteeconomies of scale. Since households vary in terms of size (the total number ofmembers of a family) and structure (the mix of adults and children in the family), thetotal family income needs to be adjusted by calculating the equivalent householdincome. This is the figure obtained by dividing the total family income into theequivalent size of the household. The equivalent household size is calculated using
equivalence scales. The equivalence scale that I have used in the calculations reported inthis paper is the square root of the family size. Therefore, the equivalent householdincome is calculated as the total family income divided into the square root of thefamily size. This figure, the equivalent household income, allows for comparisons ofincome at the individual level, taking into consideration that individuals live inhouseholds –which constitute economies of scale.

The following choices have been made in order to calculate the measures ofincome, inequality and redistribution reported in this paper:

1. Zero income values. I have included in the analyses households which reported zero
market income, but have disregarded households reporting zero disposable income. The
rationale for this is that while it is possible that some households do not obtain any
income at all in the market, it is very reasonable to expect that household receive somepositive level of income from unreported sources.
2. Confidentiality issues and top coding. For reasons of confidentiality, national surveys
top code the highest income values at some maximum value. Furthermore, there may be20national variations in the extent of underreporting high incomes. In order to account for
this, I have applied the top-coding rule suggested by the LIS: ten times the median of
non-equvalized income. Similarly, I have also bottom-coded household at 1% of
equalivalized mean income.
3. The population under analysis. Likewise Bradley et al (2003), Kenworthy andPontusson (2005), and Iversen and Soskice (2006), the population under analysis in theempirical analyses in this section is the working-age population (the 25–59 age group).
The reasons for this choice are two: i) the variability of pension systems acrosscountries, and ii) allowing the comparability of the evaluation of redistribution provided
by the C-MOR with the one supplied when using the new M-MOR. I elaborate thesereasons in more detail in the next section of the paper.

The value of the C-MOR can be found in Table 5. The columns in Table 5
report, for each country, the mean value (across the time points available from the LIS
database) of i) the inequality of aggregate market income (MI) (column 1), ii) the
inequality of aggregate disposable income (DI) (column 2), iii) the difference between
aggregate market income and disposable income inequality (MI-DI) (column 3), and,
finally, iv) the proportional reduction ([MI-DI]/MI) (column 4).

[Table 5 about here]

The measure of inequality used in the calculations summarized in Table 5 is thegini coefficient. This index is the most commonly used measure of inequality. It is avariable that ranges from zero to one. The interpretation of this index is very

21

straightforward: the higher the value of the gini coefficient, the greater the level ofinequality in a society. The left extreme value of the gini coefficient, G= 0, accounts fora society where all individuals have the same level of income. The right extreme value,
G=1, is the one of a society where one individual owns all income and the rest ofmembers of the population have zero income.

The measures of ‘redistribution’ reported in Table 5 are what I have called the
C-MOR. In Table 6 I report the value of the new measure of redistribution developed inthis paper: the M-MOR formulated in the previous section. Let us recapitulate the logic
and basis of this measure. Thanks to our identification of redistribution as an income
mobility process, we can measure its consequences in terms of the reduction of market
inequality by calculating the Shorrocks index. Since the concern of redistribution is to
do with the extent to which it reduces the initial inequality generated in the market, thenatural way to evaluate the redistributive consequences of welfare states requires thecalculation of the Shorrocks index of mobility for the process mapping, for eachindividual, the initial market income into her final disposable income. The M-MOR
reported in Table 6 is the Shorrocks index of income mobility for the process {market
income Æ disposable income}. To recapitulate, this index measures the extent to which,the individual mobility of income has as a result a reduction in inequality. For example,
a value of M= 0.05 indicates that redistribution reduces the level of inequality in a 5%.
Table 6 reports the mean value (across the surveys carried at different points in time foreach country) of the M-MOR for each country.
[Table 6 about here]22

As I argued in sections 1 and 2 in this paper, there are substantive and technicalreasons why the conventional measures of redistribution, based on a straightforward
comparison of aggregate measures of inequality, do not provide a valid evaluation ofredistribution. A measure of redistribution needs to evaluate the micro-level effects ofredistribution on individuals’ income. From a theoretical point of view, therefore, the
new measure of redistribution is absolutely different and distinct from the other two.
But, what is the empirical relationship between the three measures of redistribution?
Are the M-MOR and the two C-MOR empirically distinct? This question is justified
because, after all, it could be that –to my despair!- the new measure of redistribution,
M-MOR, is highly correlated with the other two. Is this the case? In order to answer thisquestion, I explore the relationship between the three measures of redistribution. Fromthe correlations presented in Table 7, it is clear that the answer is negative. The
correlation between the measure of redistribution that I have put forward, M-MOR, andthe other two measures is weak. Obviously, these two measures are highly correlated
among themselves –the correlation coefficient is higher than 0.9. However, thecorrelation of each of them with the M-MOR is very weak: about 0.20 for the difference
between the inequality of disposable and market income, and about 0.37 when using the
proportional reduction in inequality as a measure of redistribution.

[Table 7 about here]

Therefore, it can be concluded that the M-MOR formulated in this paper
provides a new assessment of the extent to which welfare states, by collecting taxes andproviding cash social transfers, reduce inequality. This assessment is distinct form theone provided by the current measures of redistribution used in the literature on the23social and political determinants of redistribution. This measure, as argued in sections 1
and 2 above, is based on an accurate identification of the process of redistribution.
Furthermore, this measure is empirically different from the ones currently used in theliterature on the sociology and politics of redistribution in advanced industrial societies.

4. The political economy of redistribution in advanced industrial societies.
To this point, the article has provided a new measure of redistribution –the MMOR. This new measure is based on the identification of redistribution as an income
mobility process. By analyzing redistribution as such, we have obtained an evaluationof the extent to which redistribution reduces inequality. This measure avoids the
problems of conventional measures of redistribution, which are based on thecomparison of two aggregate ‘snapshots’ and, as a consequence, abstract away the
micro- or individual-level dynamics of mobility experienced by individuals’ incomes asa consequence of taxes and transfers. In addition to provide a measure of redistribution
based on the right identification of it, this new measure is empirically different from theconventional ones used in the specialized literature. Indeed, we have seen above that thecorrelation between the M-MOR and the conventional measures of redistribution is as
low as 0.37 (for the proportional reduction in inequality) and 0.20 (difference between
the gini of market income distribution and the gini of the disposable income
distribution).

The intention of this final section of the article is twofold. In the first place, itaims at showing that issues of measurement are particularly crucial for the evaluation
and assessment of the political economy of redistribution. In particular, I shall analyze

24whether our knowledge about the determinants of redistribution is contingent on theway redistribution has been (conventionally) measured. Our knowledge of thedeterminants of redistribution is based on models that use as dependent variables what I
have called conventional measures of redistribution, derived from the comparison of
two aggregate variables (measures of the inequality of market income and of disposable
income), suffering from the statistical and substantive problems described in the firstsection in this paper. Do these findings hold when we use the new measure of
redistribution developed in this paper? Does our understanding of the political economy
of redistribution changes when we measure it with a proper measure? The second aim of
this paper is of a more substantive nature. By assessing the conventional wisdom of thedeterminants of redistribution when using the M-MOR, this paper will identify the main
variables describing the socioeconomic, political and institutional characteristics ofadvanced industrial societies that account for the variability of redistribution acrosscountries. This will provide a first exploration of the social and political determinants ofredistribution.

What do we theoretically know about the socioeconomic, political andinstitutional fabric of redistribution? Why some democracies redistribute more thanothers? To explain this variability, we need to adopt a comparative perspective andsearch for economic, political and institutional differences among advanced industrial
societies. The major explanations that have been formulated in the literature can besummarized in three main groups: economic explanations, explanations in terms ofpolitical partisanship and public policy, and explanations emphasizing the role ofpolitical institutions such as electoral rules and the organizational structure of the state.

25

The basic logic of the economic explanations of redistribution is provided by the
median voter theorem (Roberts, 1977; Romer, 1975; Meltzer and Richard, 1981).
According to this theorem, market inequality and redistribution are related as follows:
the greater the inequality of market income, the higher the level of redistribution. This isthe basic conclusion of Meltzer and Richard (1981). The underlying argument on which
it is based departs from the empirical fact that the distribution of income is skewed to
the right; in other words, the mean income is higher than the income of the medianvoter. As a consequence, in democracies, the median voter pushes for redistributive
policies. If political competition follows the logic of the Downs’ model, the medianvoter imposes redistributive policies. Although this theorem has provided the basis forthe analysis of several economic issues, most notably the relationship between
inequality and economic growth (Alesina and Rodrik, 1994; Perotti, 1993; Persson andTabellini, 1992), the theorem, for very long, has not been tested with the right data. It
has not been until very recently that Kenworthy and Pontusson (2005) have provided asystematic cross-country over-time analysis of the relationship between market
inequality and the variability of redistribution. They show that the inequality of market
income, as measured by the gini coefficient, is positively associated with redistribution.
They estimate a model with market income and voter turnout as independent variables
and show that both are significantly associated with the difference in the gini coefficient
as a measure of redistribution.

A second broad of group of explanations of redistribution is built around theinterrelationship between the political color of governments and public policy. There isa long tradition in political sociology and political economy that establishes a link
between political partisanship on the one hand, and economic outcomes and welfare

26state performance on the other hand. In this tradition, political parties are conceived as
the agents of the different economic interests in society. In particular, left or labor
parties advance the interests of the working-class, while right-parties advance pro-richor pro-well-off social and economic policies (Hibbs, 1977; Hibbs and Dennis, 1988;
Hicks and Swank, 1984, 1992; Huber and Stephens, 2001).

Within this broad second group of explanations, there is a very well articulated
and influential explanation of the variability of welfare state outcomes across countries:
the power resources theory (Korpi, 1983; Stephens, 1979). This theory has been applied
mainly to explain the origin and the development of the welfare state (vid. Korpi, 1989;
Esping-Andersen and Korpi, 1984; Esping-Andersen, 1985). More recently, Bradley etal. (2005) have argued that the right test of the theory requires an evaluation of theconsequences of left parties for levels of income redistribution. In their own words:“to consider this work on welfare state development as test of power resources
theory misses the mark because the theory is really about the causes of
distributive outcomes […] different working-class power-resources aremobilized at two points in the distributive process: union strength reduces pretax and transfer income inequality while leftists government redistribute income
by increasing the size and affecting the distributive profile of taxes andtransfers” (Bradley et al, 2003: 195).

Bradley et al. (2003) carry out a rigorous empirical analysis in which they asses
the determinants of both the level of market inequality and the level of redistribution
across advanced industrial societies. Using as a measure of the latter the proportional27

reduction of the gini coefficient of the distribution of disposable income as comparedwith that of the distribution of market income, they show that left parties are associatedwith higher levels of redistribution. The political color of the government in officematters for the level of redistribution. In particular, i) the cumulative presence of leftparties in cabinets is positively and significantly associated with higher levels ofredistribution, and ii) the cumulative presence of Christian-democratic parties incabinets is associated with lower levels of redistribution. This evidence, in addition to
the fact that measures of union strength are associated with low levels of market
inequality, according to Bradley et al. (2003) offers support to the power-resources
theory.

Finally, there is a long tradition of explanations of the development of thewelfare state based on the role played by state structures and institutions (Skocpol andAmenta, 1986; Immergut, 1992). Also, there is a growing literature on the role playedby electoral rules on welfare policies and economic performance (e.g.: Persson andTabellini, 2003; Austen-Smith, 2000; Rogowski and Kayser, 2002). Building on thisliterature, Iversen and Soskice (2006) provide an explanation of the variance ofredistribution across democracies as a consequence of the type of electoral system usedin each democracy to translate votes into representation. They find that proportional
systems are statistically associated with the level of redistribution. Their argument isthat proportional representation systems increase the likelihood of left parties holding
office and, as a consequence, higher levels of redistribution.

To summarize, within what Palme (2006: 388) calls the third generation of
welfare state studies, namely comparative research using income data to explicitly28evaluate the variability of redistribution across countries, a number of complementary
explanations have been provided of the variability of redistributive outcomes acrosswelfare states. Kenworthy and Pontusson (2005) find that market inequality and voter
turnout are associated with higher levels of redistribution: those economies that produce
more inequality in the market also redistribute more. Bradley et al. (2003) find thatpartisanship matters for redistribution: left cabinets redistribute more, and centerChristian cabinets redistribute less. Finally, electoral systems play a role inredistribution: proportional representation systems, by increasing the probability of leftparties to reach the executive power, are associated with higher levels of redistribution(Iversen and Soskice, 2006).

The conclusions above are established on the basis of the estimation of rigorous
statistical models that use as dependent variables the C-MOR described above. In
particular, among others, Bradley et al. (2003) and Iversen and Soskice (2006) use theproportional reduction in inequality, while Kenworthy and Pontusson (2005) use thedifference between the gini coefficient of market income and the one for disposable
income inequality. Do these findings hold when we use the M-MOR formulated in thispapers? Are the conclusions above contingent on the measure of redistribution used?
How does our understanding of the socioeconomic and political factors of redistributionchanges when we use the M-MOR instead of the C-MOR?

In order to answer the previous questions, this section employs the data
introduced in section 3, and combines the indexes of redistribution presented there withvariables describing the economic, political and institutional characteristics of advanced
industrial societies. A dataset providing information on welfare states, macro economic,

29

demographic, political and institutional variables is Huber, Ragin and Stephens (1997) –updated by Brady, Beckfield and Stephens (2004). Combining the two sets of variables
provides rich database with a panel of countries with time-series cross-sectional data.

In exploring empirically whether the conventional wisdom about redistribution
holds, several time-series cross-sectional models, also know as panel data models, areestimated in what follows. Because of the availability of market and disposable income
from the LIS surveys, the panel data against which models are to be estimated constitutean unbalanced pool –i.e. the years at which surveys are conducted are neither constant
nor the same across countries. An additional issue to consider when estimating timeseries cross-sectional models is the likely autocorrelation of time-observations for eachcountry (Beck and Katz, 1995). Because of this, standard errors obtained from theestimation of a model by OLS (ordinary least squares) may be inaccurate. In order toobtain robust results, three estimation techniques are going to be used: OLS with robust
standard errors, panel corrected standard errors, and panel corrected standard errors
assuming the existence of an autocorrelation of order one between the time-observations
for each country.

Let us remember that the double aim of this final section of the paper is: i) to
uncover the socioeconomic, political and institutional determinants of redistribution; ii)
to assess the extent to which our understanding of redistribution is contingent on the useof ‘conventional measures’ of ‘redistribution’. The modus operandi that I adopt in orderto achieve these aims is as follows. In the first place, I estimate a set of models using the
M-MOR as a dependent variable. These models are reported in Table 8. This battery ofmodels identifies the variables that drive the heterogeneity of redistribution found in30

different societies. In other words, by estimating the models presented in Table 8, we
will uncover the political, social and economic variables that are associated, as well as
those that are not, with the extent to which different welfare states redistribute income.

Once we identify the determinants of redistribution, we can assess how ourknowledge of the political economy of redistribution has been limited as a result of theuse of ‘conventional measures’ of redistribution as a dependent variable –which sufferfrom the serious problems described in the previous sections of the paper. In order tofulfill this second aim, three models sharing the same independent variables are going tobe estimated, each of them using a different measure of redistribution as dependent
variable. These models are reported in Table 9.

As I wrote above, likewise Bradley et al (2003), Kenworthy and Pontusson(2005), and Iversen and Soskice (2006), the population under analysis in the empiricalanalyses in this section is the working-age population –the 25–59 age group. The main
reason for this has to do with the variability of pension systems across countries.
Because some countries enjoy generous public pension systems, private savings are
discouraged and therefore, if we include the non-working age population in theanalyses, the degree of redistribution would be exaggerated. There is also a second
reason for this choice. Since one of the aims of this section is to show that our
understanding of redistribution has been limited by the use of inadequate measures ofredistribution, I will be showing that the conventional wisdom on the sociopolitical
determinants of redistribution, established in influential papers that have beenpreviously cited, does not hold when we use the right measure of redistribution. In orderto make the results in this paper comparable to Bradley et al. (2003), Kenworthy and31

Pontusson (2005), and Iversen and Soskice (2006) I need to focus on the same
demographic group of the population.

In order to find out the determinants of redistribution, I estimate a model toaccount for its variability across countries, measuring redistribution with M-MOR as
dependent variable. In the models in Table 8, I use different explanatory variables tocontrol for the socioeconomic, political and institutional factors that different theories
and arguments in the literature put forward as relevant to account the variability oflevels of redistribution across countries. I also control for other variables that arecommonly assumed to be associated with redistribution. All these variables, together
with the rationale for why they are expected to be associated with redistribution, aredescribed next.

Dependent variables.
M-MOR: mobility based measure of redistribution.
Difference: Gini(market) – Gini(disposable).
Proportional: [Gini(market) – Gini(disposable)]/Gini(market).

Independent variables.
Inequality of market income. This variable measures the inequality in the
distribution of market income and is included in order to capture the logic of themedian-voter theorem. If Meltzer and Richard (1981) have got the logic ofredistribution right, we should expect that the higher the level of market
inequality, the higher the level of redistribution.

32

Wage coordination. In addition to have consequences for the distribution ofmarket income, wage coordination may have consequences for the generosity ofsocial policies (e.g. Bradley et al. (2003): 200). The measure of wage
coordination used in the analyses is taken from Brady et al. (2004), and its
original source is Kenworthy (2001). The variable has five values, indicating
different degrees of wage coordination.
Unemployment. The effect of unemployment is open to empirical assessment.
In the first place, unemployment may increase the level of market income
inequality. In the second place, such an increase will be translated into higher orlower levels of redistribution depending on the level of generosity of
unemployment benefits.
Industrial Employment. The level of employment in the industrial sector of theeconomy is included in the analyses. This variable is used as an indicator of theextent of deindustrialization –which according to Cusack and Iversen (2000) is a
key driver of the expansion of the welfare state in the last decades.
Voter turnout. A number of empirical studies have found an association
between electoral participation and the provision of welfare benefits (e.g. Hicks,
1999; Mahler, 2002; Kenworthy and Pontusson, 2005). Because there is acorrelation between citizens’ socioeconomic status and their probability to turnto vote (Lijphart, 1997), one can expect a positive correlation between levels ofvoter turnout and redistribution.
Left cabinet. This variable is the cumulative (since 1946) % of seats held by leftparties among all government parties in cabinets. In the account of redistribution
provided by Bradley et al. (2003) this variable is positively and significantly
associated with the level of redistribution.

33

Center Christian cabinet. This variable is the cumulative (since 1946) % ofseats held by center Christian parties among all government parties in cabinets.
In the account of redistribution provided by Bradley et al. (2003) this variable isnegatively and significantly associated with the level of redistribution.
Right Christian cabinet. This variable is the cumulative (since 1946) % of seats
held by right Christian parties among all government parties in cabinets. In theaccount of redistribution provided by Bradley et al. (2003) this variable isnegatively and significantly associated with the level of redistribution.
Federalism. In federal states, it is more difficult to pass progressive legislation.
Consistent with this logic, I expect a negative association between federalism
and redistribution. The variable of federalism available in Brady et al. (2004) has
three values: 0= central state, 1= weak federal state, 2= strong federal state.
Electoral systems. Iversen and Soskice (2006) find an association between
electoral institutions and the level of redistribution. Specifically, the find that
democracies with proportional electoral systems are more redistributive thanthose that have majoritarian electoral systems. The variable of electoral systems
used in the analyses that follow has three values: 0= proportional representation,
1= modified proportional representation, 2= single-member systems.
Globalization. In order to find out whether globalization is related to the levelof redistribution effected in democracies, I include two variables in the analyses:
trade openness and capital market liberalization. The first variable is simply thevalue of export and imports as a % of GDP. The second variable ranges fromzero to four and is taken, again, from Brady et al. (2004).

***

34

In Table 8, I estimate a set of models with M-MOR as dependent variable. In Model1, all the independent variables described above are included as explanatory variables.
This model is estimated by OLS with robust standard errors (cluster: country). The mostremarkable results of the estimation of this model are highlighted below:[Table 8 about here]

1. Inequality of pre-tax and transfers income is negatively associated with the levelof redistribution. This is statistically significant at a 0.003 level. This finding
goes against an expectation based on the Meltzer-Richard model. For thepredictions of the M-MOR to be confirmed, we should have find that the level ofmarket inequality is positively associated with the extent of redistribution –justthe opposite to the findings of Model 1.
2. The cumulative presence of left parties in the government is not associated to thelevel of redistribution. Neither left nor center Christian nor right Christian
governments account for the variability of redistribution across countries. This
result goes against what the power resources theory (Korpi, 1983; Stephens,
1979) would have predicted. This is specially relevant because it shows that,when using M-MOR as dependent variable, the results of Bradley et al. (2003),
according to which left governments are positively related to levels ofredistribution, do not hold any more. This result will be more carefully exploredwhen comparing the results of statistical models using three different dependent
variables in Table 9.
3. Political institutions matter for redistribution. In particular, there is a negative
association between federalism and redistribution which is statistically35significant. Another political institution, namely the type of electoral system,
does not appear to be associated in a statistically significant way with the levelof redistribution according to Model 1. Although the coefficients of modified
proportional representation and that of majoritarian systems are negative –showing that democracies with any of the two electoral systems are lessredistributive than those with proportional systems, the differences are notstatistically significant. The reason for this, if Iversen and Soskice (2006) arecorrect, may be that the prevalence of governments of one or another politicalcolor is dependent on the type of electoral system. Their argument is that leftparties are more likely to hold office in democracies with proportional
representation. This possibility is further explored in Model 4, Table 8.
4. The idea much formulated by economic and political commentators that
globalization reduces the capacity of governments to redistribute income andreduce income inequality does not obtain empirical support from Model 1, Table
8. In particular, the measures of commercial and capital market openness showthat redistribution is not related to globalization.
5. Other variables in Table 8 that are not statistically significant to account forredistribution are wage coordination6, unemployment, industrial employment
and voter turnout.
6

In models not reported here, I have explored two reasons why wage-coordination is not associated toredistribution. The first reason may be that more open economies, like Scandinavian countries, also
display high levels of wage-coordination. The second reason is that wage coordination andunemployment are associated: were there is more wage coordination unemployment is lower. I haveestimated models including neither openness nor unemployment and wage coordination is still
statistically non-significant. Given that these results are not important for the point of this paper, I do notreport them here. Nevertheless, they are available from the author upon request.

36

In Model 2, I have estimated a new model that includes as explanatory variables
those that are statistically significant in Model 1, as well as measures of the politicalcolor of cabinets and the type of electoral system. Again, the two latter sets of variables
are not statistically significant to account for redistribution. Likewise Model 1,
federalism and the level of market income inequality are negatively associated withredistribution at 0.034 and 0.006 significance levels. This reinforces the idea obtained
from the previous model according to which the political color of governments is not
related to the level of redistribution in advanced industrial democracies. Politics matters,
but it is throughout political institutions, not partisanship.

In Model 3 I further explore the role of political institutions to account forredistribution. Once we do not control for the political color of cabinets, the type ofelectoral system is related to the level of redistribution. Both modified proportional
electoral systems and majoritarian institutions are associated with lower levels ofredistribution, as compared to electoral systems based on proportional representation.
The first difference is significant at a 0.10 and the second at a 0.037 level.

To summarize the findings of the models in Table 8, we can conclude from themthat market inequality is negatively associated with the level of redistribution in all theestimated models. Politics is important to account for redistribution, but not because of
the ideology of cabinets as partisanship or power resources theories would anticipate,but because of the nature of political institutions. Indeed, federalism is negatively
associated with the level of redistribution in all models. Electoral systems display signsof association with redistribution. When not controlling for partisanship, like in Model 337

above, as compared to proportional systems, both modified proportional andmajoritarian electoral systems are less redistributive.

As I wrote at the beginning of this section, one of its aims is to show that our
understanding of the political economy of redistribution has been limited by the use of
inadequate measures of redistribution. In order to fulfill this aim, I have estimated threemodels that share the same independent variables. The three models, also, are estimatedusing the same technique –OLS with robust standard errors. The only difference
between the three models is that each uses a different dependent variable: M-MOR,
proportional reduction in inequality (hereafter ‘proportional’), and the difference
between market inequality and disposable income inequality (hereafter ‘difference’).
The independent variables in the three models are the same ones of Model 2 in Table 8
above. Given that the three models are estimated against the same number ofobservations (84 observations from 16 countries), we can readily compare thecoefficients of each explanatory variable across the three models. The models arereported in Table 9.
[Table 9 about here]

The remarkable differences between the three models in Table 9 have to do with therole of market inequality, the cumulative presence of left parties in the cabinet andfederalism. According to the models using the conventional measures of redistribution(Models 2 and 3 in Table 9), the relationship between market income inequality andredistribution is positive –the one expected on the basis of the Meltzer-Richard model:
more market inequality is related to higher levels of redistribution. The sign of the38coefficient is positive in Models 2 and 3 and significant at 0.114 and 0.000 respectively.
This finding was already advanced by Kenworthy and Pontusson (2005) using thedifference between the inequality of market income and that of disposable income as ameasure of redistribution. With regards to the role of federalism, both models using CMOR fail to establish a relationship between federalism and the level of redistribution.
Finally, Models 2 and 3, the ones using the C-MOR as dependent variables, establishthat left governments are related to the level of redistribution. The relationship between
the two, according to both models, is positive and statistically significant at a 2% level:
the longer the cumulative presence of left parties in the executive power, the higher thelevel of redistribution.

When we examine the determinants of redistribution with the M-MOR formulated
in this paper, we find that the political color of governments is not relevant for the
explanation of redistribution. Also, we find that the Meltzer-Richard model gets thelogic of redistribution the other way around: the association between market inequality
and redistribution is negative. This is consistent with theoretically more sophisticatedand realistic models of redistribution like the one formulated by Moene and Wallerstein(2001), that show that under certain very realistic conditions greater inequality is related
to less support for redistributive policies. These conclusions also contradict theempirical findings of Kenworthy and Pontusson (2005), who find a positive relationshipbetween the extent of market inequality and the level of redistribution.

In showing the soundness of the former conclusions, we need to rule out thepossibility that estimation results are not robust to different techniques. In order to ruleout this possibility, the three models in Table 9 have been re-estimated using two

39

additional estimation techniques: i) panel corrected standard errors, and ii) panel
corrected standard errors assuming that errors follow an autoregressive process of order
1 (AR1). The results are reported in Tables 10, 11 and 12.
[Table 10 about here]

Table 10 reports the estimation of a model in which the dependent variable is the MMOR by OLS with robust standard errors, panel corrected standard errors and panel
corrected standard errors with AR1.
[Table 11 about here][Table 12 about here]

Table 11 and 12 proceed analogously with the C-MOR as dependent variables. We
can see that the similarity in the size of coefficients and significance levels allow us toconclude that the conclusions formulated above are not an artifact of the estimation
technique used7.

In conclusion, we can be confident that if the findings of this section depart from theconventional wisdom, it is because the process of redistribution has been correctly
identified as an income mobility process and measured accordingly with the help of the
Shorrocks mobility index.

7

There is only one important difference: the significance levels of the type of electoral system. The

significance levels are lower when estimating the model by PCSE and even lower when estimating it withPCSE assuming that the errors follow a first-order autocorrelation process. In the later case, compared toproportional representation systems, majoritarian electoral institutions are less redistributive and thisdifference is statistically significant at a 0.05 level.

40

Conclusions.
To explain the redistributive consequences of tax and transfer systems, it isessential to be equipped with appropriate measures. Existing measures of redistribution,used in recent research papers such as, among others, Bradley et al (2003), Iversen andSoskice (2006), Kenworthy and Pontusson (2005), and Mahler and Jesuit (2006),
assume that we can assess the extent of redistribution by comparing two aggregate
figures: the inequality of market income and the inequality of disposable income. This
paper has argued that such an approach to measure redistribution is seriously flawed.
The reason for this is that such measures are based on a wrong identification ofredistribution. Certainly, current measures are based on the comparison of aggregate
figures of inequality, which, by definition, are unable to identify the incidence of taxes
and transfers at the individual level and, as a consequence, are unable to provide
meaningful measures.

In order to overcome this problem, the paper has established that when analyzed
at the micro-level, redistribution can be identified as an income-mobility process. It is aprocess that for each individual maps a level of market income into one of disposable
income. This characterization of redistribution plays a twofold role. In the first place, it
supplies us with the proper identification of redistribution. In the second place, it
provides us with a natural index to measure it. Certainly, the measure of redistribution
developed in this paper assesses the extent to which, by taxing income and transferring
cash to households, welfare states reduce the level of inequality. Since the substantive
interest of redistribution coincides with the one of the Shorrocks index of income
mobility, the M-MOR developed in this paper consists of the calculation of the41

Shorrocks index to the income mobility embodied in the process of redistribution –themobility from market to disposable income.

Using data from the LIS, I have evaluated the distribution effected by different
welfare states. The first important empirical finding of this paper is that the M-MOR
provides an evaluation that is empirically different from the one supplied by C-MOR.
Indeed, the correlation between the former and any of the latter C-MOR is very weak.
Furthermore, in the last part of the paper I have explored whether our understanding of
the social and political determinants of redistribution has been misrepresented as aresult of using an inadequate variable as a proxy of redistribution –i.e. the C-MOR. The
findings of this last section confirm that our understanding of the political economy ofredistribution does not hold when measuring redistribution properly. To summarize,
based on the models that use as dependent variables conventional measures,
redistribution is driven by market inequality in a Meltzer-Richard fashion and by thepresence of left parties in governments as expected by the power-resources theory. The
point of this paper is that precisely those conventional measures are inadequate to assess
the impact of redistribution on the reduction of inequality because the do not consider
the micro dynamics of income mobility embodied in the process of redistribution. In
section 3, we have seen that the correlation between conventional measures ofredistribution and the one formulated in this paper is rather low. Furthermore, ourunderstanding of the political economy of redistribution has been limited due to the useof inadequate measures of redistribution.

42

Figures and Tables.

Figure 1. Non Intersecting Lorenz Curves of Market and Disposable Incomes.
Non Intersecting Lorenz Curves of Market Disposable
Incomes
120
100
80
45º
60

disposable
market

40
20
0
1

2

3

4

5

6

7

8

9

10

Figure 2. Intersecting Lorenz Curves of Market and Disposable Incomes.
Intersecting Lorenz Curves of Market and Disposable
Income12010080Perfect Equality
Disposable Income

60

Market Income
40
20
0
1

2

3

4

5

6

7

43

8

9

10

Table 1. Mobility Matrix of a Stylized Example.

1

10

10

1

Table 2. Inequality Comparison and the Evaluation of Redistribution.

Household 1
Household 2
Gini
Redistribution
Gini(M)– G(D)Market Income
1
10
0.41

Disposable Income
10
1
0.41

0 (No redistribution at all)Very considerable redistribution from the rich tothe poor
Rank reversal

Reality

Table 3. Mobility Matrix Summarizing the Process of Redistribution.
m1

d1

m2

d2

..

..

..

..

mn

dn

44

Table 4. Surveys of the Luxembourg Income Study Analyzed.
Country
AustraliaYears
1981, 1985, 1989, 1995, 2001, 2003

Austria
Belgium
Canada1994, 1997
1985, 1988, 1992, 1997
1971, 1975, 1981, 1987, 1991, 1994,
1997, 1998, 2000
1987, 1992, 1995, 2000, 2004
1987, 1991, 1995, 2000, 2004
1979, 1981, 1984, 1989, 1994
1981, 1984, 1989, 1994, 2000
1986, 1987, 1989, 1991, 1993, 1995
1987, 1994, 1995, 1996
1983, 1987, 1991, 1994, 1999
1979, 1986, 1991, 1995, 2000
1967, 1975, 1981, 1987, 1992, 1995,
2000, 2005
1982, 1992, 2000, 2002
1969, 1974, 1979, 1986, 1991, 1994,
1995, 1999, 2004
1974, 1979, 1986, 1991, 1994, 1997,
2000, 2004

Denmark
FinlandFranceGermany
Italy
IrelandNetherlands
NorwaySwedenSwitzerland
United Kingdom
United States

45

Table 5. Inequality and Conventional Measures of Redistribution.
Country

Australia
Austria
Belgium
CanadaDenmark
FinlandFranceGermany
Italy
IrelandNetherlands
NorwaySwedenSwitzerland
UnitedKingdom
UnitedStatesGini ofmarketincome.3899733

Gini ofdisposable
income.28913C-MOR(difference)

C-MOR(proportional).1008433.256901.34745.34472.3638978.331966.329332.379838.331316.3502283.44212.361544.302686.3361062.3172925.3998789.270395.2222075.2848322.204946.208842.28228.251562.3158933.3242075.251578.21545.2055987.2813925.3063389.077055.1225125.0790656.12702.12049.097558.079754.034335.1179125.109966.087236.1305075.0359.09354.2217327.353954.2162051.3808628.3642804.2561768.2383235.0983053.2658619.3027255.2858195.3853714.1133109.2305354.406865.33439.072475.1784025

46

Table 6. Mobility Measure of Redistribution.
Country
Australia

M-MOR.0052467

Austria
Belgium
CanadaDenmark
FinlandFranceGermany
Italy
IrelandNetherlands
NorwaySwedenSwitzerland
UnitedKingdom
UnitedStates.01631.0194925.0077789.030704.0149.015442.017132.0144467.00843.020086.017052.0273225.01398.0116756.0070575

Table 7. Correlation Among Conventional and Mobility-Based Measures ofRedistribution.
M-MOR
C-MOR
C-MOR
(difference)(proportional)
1
M-MOR
0.20
1
C-MOR
(difference)
0.37
1
C-MOR
(proportional)

47

Table 8. The Determinants of Redistribution.
Model 1Model 2Model 3Market inequality-.0822717***(.0235059)-.0731501**(.0313856)-.0766063**(.0342973)

Wage coordination.0004845(.0018934)--

Industrial
employment

1.49e-07(4.60e-07)--

Unemployment

3.83e-07(1.81e-06)--

Openness of…market.0000263(.0000647)--

Capital…

.0008839(.0018295)--

Voter turnout.0001486(.0001205)--

Left cabinets(cumulative).0001218(.0001628).0001325(.0001344)-

Center Christiancabinets(cumulative)
Right Christiancabinets(cumulative)
Federalism-.0002802(.0002965)-.0002222(.0003004)--.0000311(.000191).0001004(.000101)--.0060927**(.0021875)-.0061868***(.0019307)-.0064553**(.0017801)

Single member
district-.0019674(.0047151)-.0028263(.0027852)-.0036947*(.0021583)

Single member
district-.0014847(.0043686)-.0027931(.0026423)-.0040878***(.0017853)

Constant.0274389*(.0157473).0433893***(.012336).0469086***(.0133271)

N

84

84

84

0.34570.3288R2
0.3645*p= 0.10, **p=0.05, ***p=0.01

48

Table 9. The Measurement of Redistribution and the Political Economy ofRedistribution.

M-MOR

Market inequality-.0731501**(.0313856)

Proportional
reduction Gini
coefficient.2850972(.1698541)

Left cabinets(cumulative).0001325(.0001344).0048124**(.0018812).0016707**(.0006572)

Center Christiancabinets(cumulative)
Right Christiancabinets(cumulative)
Federalism-.0002222(.0003004).0029771(.00371).0011138(.001275).0001004(.000101).0009709(.0019736).0003548(.0006479)-.0061868***(.0019307)-.0217812(.0395586)-.0068633(.0131521)

Single member
district-.0028263(.0027852)-.0010576(.0311785)-.0006235(.0106421)

Single member
district-.0027931(.0026423)-.0110555(.0499909)-.0056419(.0165972)

Constant.0433893***(.012336).0992694(.0892425)-.0558453*(.0304626)

N

84

84

84

R2

0.34570.45500.503049

Difference in theGini coefficient.3587432***(.062338)

Table 10. The Political Economy of Redistribution when Measured as M-MOR:
Results from Three Estimation Techniques.

M-MOR(Robust std errors)

M-MOR(panel corrected
errors)M-MOR(pcse with ar1)

Market inequality-.0731501**(.0313856)-.0731501***(.0159894)-.0692306***(.0169791)

Left cabinets(cumulative).0001325(.0001344).0001325(.0001236).0001088(.0001286)

Center Christiancabinets(cumulative)
Right Christiancabinets(cumulative)
Federalism-.0002222(.0003004)-.0002222(.0003085)-.0002704(.0003095).0001004(.000101).0001004(.0002119).000087(.0002116)-.0061868***(.0019307)-.0061868***(.0012761)-.0064106***(.0013274)

Single member
district-.0028263(.0027852)-.0028263(.0021964)-.0034469(.0023091)

Single member
district-.0027931(.0026423)-.0027931(.0017858)-.0035982**(.0018653)

Constant.0433893***(.012336).0433893***(.0065429).0428311***(.0070138)

N

84

84

84

R2

0.34570.34570.377650

Table 11. The Political Economy of Redistribution when Measured as the
Proportional Reduction of Inequality: Results from Three Estimation Techniques

Proportional
reduction Gini
(Robust std errors)

Proportional
reduction Gini
(panel corrected
errors)

Proportional
reduction Gini
(pcse with ar1)

Market inequality.2850972(.1698541).2850972*(.1518706).2953089*(.1650585)

Left cabinets(cumulative).0048124**(.0018812).0048124***(.0006565).0047176***(.0006968)

Center Christiancabinets(cumulative)
Right Christiancabinets(cumulative)
Federalism.0029771(.00371).0029771(.0025253).0026989(.0024921).0009709(.0019736).0009709(.0010388).0009776(.0010355)-.0217812(.0395586)-.0217812(.0179592)-.0246183(.0181828)

Single member
district-.0010576(.0311785)-.0010576(.023723)-.0030563(.0237481)

Single member
district-.0110555(.0499909)-.0110555(.0190472)-.0131635(.0192978)

Constant.0992694(.0892425).0992694*(.0571033).0995253*(.0618724)

N

84

84

84

R2

0.45500.45500.490651

Table 12. The Political Economy of Redistribution when Measured as the
Difference in Inequality: Results from Three Estimation Techniques

Difference in Gini
(Robust std errors)

Difference in Gini
(panel corrected
errors)Difference in Gini
(pcse with ar1)

Market inequality.3587432***(.062338).3587432***(.0539435).3626004***(.0586127)

Left cabinets(cumulative).0016707**(.0006572).0016707***(.0002283).0016342***(.0002445)

Center Christiancabinets(cumulative)
Right Christiancabinets(cumulative)
Federalism.0011138(.001275).0011138(.0009321).0010102(.0009165).0003548(.0006479).0003548(.0003372).0003554(.0003357)-.0068633(.0131521)-.0068633(.0059175)-.0078885(.0059926)

Single member
district-.0006235(.0106421)-.0006235(.0083743)-.0013964(.0083817)

Single member
district-.0056419(.0165972)-.0056419(.0064451)-.0064814(.0065578)

Constant-.0558453*(.0304626)-.0558453***(.0201085)-.0557452***(.0217608)

N

84

84

84

R2

0.50300.50300.520852

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