Structural and Exchange Components in Processes of Neighbourhood Change: A Social Mobility Approach
Structural and Exchange Components in Processes of Neighbourhood Change: A Social Mobility Approach
Tal Modai-Snir 0 1
Maarten van Ham 0 1
0 School of Geography & Sustainable Development , Irvine Building , University of St Andrews , North Street, St Andrews, Fife, Scotland KY16 9AL , UK
1 OTB - Research for the Built Environment, Faculty of Architecture and the Built Environment Delft University of Technology , PO Box 5043, 2600GA Delft , The Netherlands
Neighbourhood socioeconomic change is a complex phenomenon which is driven by multiple processes. Most research has focused on the role of urbanlevel processes, which lead to an exchange of relative positions among neighbourhoods of a single metropolitan area. Consequently, the effects of structural processes on neighbourhood socioeconomic change, such as overall income growth or decline, and increasing inequality, have been neglected. This is reflected in the standard methodological practices; the common measures of neighbourhood change exclude the effect of overall growth or decline and confound the effects of urban processes with the effect of increase in inequality. This paper proposes a method that was originally developed for understanding income mobility of individuals, to decompose total neighbourhood socioeconomic change measured in absolute terms into its contributing components. The approach enables to take account of all processes that generate neighbourhood socioeconomic change, while distinguishing between them. The method is demonstrated in an empirical analysis of neighbourhood socioeconomic change across 22 metropolitan areas in the US. The findings indicate that structural processes can be most substantial in generating change. Neighbourhood socioeconomic change in 'superstar cities' is mostly generated by the growth in overall incomes, with a relatively low contribution of increasing inequality. Conversely, in declining cities it is mostly driven by overall decline and increasing inequality. An additional finding relates to the interaction between urban processes and increasing inequality. These processes work in opposite directions such that any increase in positions of low-income neighbourhoods can be totally offset by an income decrease due to increasing inequality.
The socioeconomic status of urban neighbourhoods can change over time due to
multiple processes. Some of these processes operate at the urban level, which
cause neighbourhoods to change relative to one another within the urban area. The
movement of neighbourhoods up and down the urban socioeconomic hierarchy is
often related to their life cycle and stage of development; many neighbourhoods
decline as they age, and at some point might be subject to renewal processes
which lead to an upward trajectory
(Hoover and Vernon 1959)
. Much of this
cyclical movement is associated with the deterioration of the housing stock and its
consequent filtering down to lower-income households
(Muth 1973; Sweeney
. Social dynamics also drive these changes, for example,
households’ preferences for living among similar households generate sorting among
neighbourhoods, as demonstrated by
) segregation model.
Institutional actions at the urban level can change neighbourhoods’ socioeconomic
positions as well. Mainly, such actions include interventions in the housing stock
(e.g. Andersson and Musterd 2005)
, public investments in urban amenities (Van
Criekingen and Decroly 2003) or environmental improvements
(Meen et al. 2012)
Beyond the urban level, neighbourhood socioeconomic conditions are also affected
by structural processes that involve regional, national and global levels. These
processes can generate changes in the distribution of socioeconomic characteristics of the
population in an urban area, which can translate into neighbourhood change. Overall
socioeconomic levels can increase or decrease at the regional or national level, and
consequently they can increase or decrease at the neighbourhood level
(Galster et al.
2003; Zwiers et al. 2016a)
. Also changing levels of inequality in society can affect
(Andersson and Hedman 2016)
These various processes can be summarised into three distinct effects on
changes in neighbourhood socioeconomic conditions
(Collver and Semyonov
. Urban-level processes generate an ‘exchange’ effect which implies that
over time, neighbourhoods switch relative positions in their urban hierarchy. It is
a zero-sum process that simply circulates advantage and disadvantage among
urban neighbourhoods, within a given distribution of neighbourhood average
incomes. Two different ‘structural’ effects, on the other hand, change the
distribution of neighbourhood average incomes in the urban area: The ‘growth/decline’
effect which increases or decreases incomes among all neighbourhoods in the
urban area, and the ‘inequality’ effect which increases the disparities among
them. Globalization and re-urbanization processes in the last decades generated
increasing inequality within and between urban areas. Wealth has increasingly
concentrated in fewer ‘superstar’ cities while others have been left behind
(Florida 2017; Gyourko et al. 2006)
; this points to the importance of the
‘growth/decline’ effect on neighbourhoods. Urban areas have become more
polarized, with more rich and poor neighbourhoods and less middle-income
(Booza et al. 2006; Florida 2017; Hulchanski 2010; Tammaru
et al. 2015)
, which emphasizes the role of the ‘inequality’ effect in generating
neighbourhood upward or downward change.
The way neighbourhood change has been measured in most research on
neighbourhood socioeconomic change does not enable to distinguish between these
different components of change. The commonly used measures
(Choldin and Hanson
1982; Delmelle 2015; Fogarty 1977; Gould Ellen and O’Regan 2008; Landis 2016;
Logan and Schneider 1981; Owens 2012; Rosenthal 2008; Rosenthal and Ross 2015)
leave out the overall change in incomes throughout the urban areas. They do,
nevertheless, capture the effect of increasing inequality among neighbourhoods, but then
confound it with the ‘exchange’ effect related to urban processes. Ultimately, most
existing research mainly focused on changes that neighbourhoods experience relative to
others in the same urban area (what we call the ‘exchange’ effect). Neither the effect of
overall growth and decline, nor the effect of changing inequality levels gained enough
attention, despite their increasing role in understanding socio-spatial divisions in recent
decades. The processes of growth and decline and inequality have become so powerful,
that we believe that their effect on neighbourhood socioeconomic conditions can
overshadow that of urban processes, which have traditionally gained more attention
in the literature. Currently, however, we know very little about the relative roles of the
three processes in understanding the changing urban hierarchy.
The objective of this paper is to develop an approach to measure neighbourhood
socioeconomic change that can be used to investigate and distinguish all the
contributing processes. Such an approach will enable us to look at neighbourhood change
from a wider perspective, including not only the urban context but also higher-level
contexts which have become, we suspect, at least as important in determining
neighbourhood fortunes. The paper proposes a method to understand neighbourhood
change which comes from studies of income mobility.
Social and income mobility research have long been making the distinction between
exchange and structural components with regard to mobility measurement. Two
previous studies have already applied a similar approach in the neighbourhood and urban
(Collver and Semyonov 1979; Congdon and Shepherd 1988)
, partially based
on the work of McClendon (1977). This paper, however, challenges the conceptual
underpinnings of the components derived by these authors. This paper aims to advance
the approach by considering more recent methodological contributions in income
mobility research. Specifically, this paper uses a decomposition method presented by
Van Kerm (2004
). The paper ends with an empirical analysis of neighbourhood
socioeconomic change by using real data from US metropolitan areas.
The changing mosaic of neighbourhoods and their socioeconomic status hierarchy has
fascinated researchers for long, and has resulted in a large literature which reflects the
complexity of processes of change. One strand of the literature focuses on explaining
the sources of change through an individual-level approach. This approach examines
how residential mobility, social mobility and demographic changes alter the
socioeconomic compositions of neighbourhoods
(Bailey et al. 2017; C. Hochstenbach and
Musterd 2017; Cody Hochstenbach and van Gent 2015; Teernstra 2014)
Individuallevel studies focus, therefore, on how neighbourhood change is realized through the
aggregate changes in individual social and spatial positions. A different approach can
be characterized as a system-level one, which focuses on neighbourhoods as parts of an
urban socioeconomic hierarchy. This approach focuses on the underlying factors that
generate change through their effect on individuals. For example, how the evolution of
cities, economic and societal trends and institutional actions affect neighbourhoods.
This paper is located within the system-level perspective on neighbourhood and urban
change. The following section on theoretical background reviews diverse theories that
deal with the question of why neighbourhoods change from a systems perspective. We
sort the various factors into those pertaining to urban dynamics (termed ‘exchange’
processes) and those which are related to structural processes (‘growth/decline’ and
‘inequality’) involving the regional, national and global levels.
Neighbourhood Change and Urban-Level Processes
An influential class of urban models depicted neighbourhood socioeconomic change as
a cyclic process. The early ‘invasion-succession’ model developed by Chicago School
suggested that low-income households take the place of
higher-income households who gradually move outward to newer neighbourhoods at
the urban fringe. Two other models complement this view; the Life cycle model
(Hoover and Vernon 1959)
suggests that neighbourhoods move chronologically
through stages of development, characterized by gradual decline, until they reach a
point that reinvestment is economically worthy and go through a process of renewal.
The filtering model
(Muth 1973; Sweeney 1974a, 1974b)
emphasizes the role of the
deterioration of the neighbourhood’s housing stock in generating neighbourhood
decline. It drives away affluent households to newer neighbourhoods while the vacated
housing filters down to lower-income households. Empirical studies asserted, in
general, the life-cycle and filtering view
(e.g. Brueckner and Rosenthal 2009; Choldin et al.
1980; Choldin and Hanson 1982; Rosenthal 2008; Rosenthal and Ross 2015)
indicating a pattern of mean reversion; high-income neighbourhoods typically experience
decline while low-income ones experience increase. In a study that analysed unique
historical data of Philadelphia County, Rosenthal (2008) found that it took roughly
100 years for the city’s neighbourhoods to cycle back to their initial income level.
Other urban-development processes can also explain neighbourhood socioeconomic
change, regardless of the life-cycle stage. Transportation innovations such as commuter
networks, for example, have found to be one of the drivers of the historical flight of
high- and middle- income households to the suburbs
(Anas et al. 1998)
, with its
longlasting effects on city-centre decline in many metropolitan areas, especially in the US.
The suburbanization of employment has also contributed to that decline
Later evidence associated public investment in rail transit systems with gentrification
and socioeconomic increase of certain neighbourhoods
. Other public
investments can also generate gentrification and upgrading, for example, environmental
(Meen et al. 2012)
and public investment in historical areas
Criekingen and Decroly 2003)
. Also urban conservation practices and policies can
bring such impact
. Other urban policies aim at generating socioeconomic
upgrades through physical changes to the housing stock; the outcomes of such
restructuring policies are often the displacement of low-income households from
(Andersson and Bråmå 2004; Andersson and Musterd 2005;
Bolt and van Kempen 2010)
. Common criticism related to such policies is that
problems associated with poverty do not disappear due to such interventions, but move
to other places within the urban area
(Andersson and Musterd 2005)
. This depiction, of
‘moving disadvantage’ can be generalized to all income strata, as well as to other
driving mechanisms. As long as population characteristics do not change, urban-level
processes simply move advantage and disadvantage and cause the exchange of
positions among urban neighbourhoods.
The preference of people for living among people similar to themselves is central in
generating neighbourhood change, as demonstrated in
model. The model shows that even slight preferences for own-group presence can
drive such change and lead to segregation. Social dynamics are self-reinforcing as the
increasing presence of own-group households further attracts similar households; thus,
they can either accelerate the pace of socioeconomic change or make status persistent
(Rosenthal 2008; Rosenthal and Ross 2015)
. Housing market dynamics also play a role
in reinforcing the process of change, as changes are quickly manifested in housing
prices. The literature on gentrification, for example, describes how an initial inflow of
high-income households can increase housing prices and trigger the displacement of
existing low-income residents
(e.g. Atkinson 2000; Marcuse 1986)
. Some local
amenities, such as retail and public services, also take part in these dynamics; their location
reflects the presence of certain socioeconomic strata in the neighbourhood, but at the
same time they further attract other households of similar status
(Glaeser and Gyourko
2005; Rosenthal and Ross 2015)
Although socioeconomic change is most common, there are neighbourhoods that
persistently occupy a stable relative position in the urban neighbourhood hierarchy
. Rosenthal’s study (2008), indicated that a third of all
neighbourhoods remained in the same income quartile over a period of 50 years.
rather identified stability as the most frequent pathway among US
metropolitan neighbourhoods, but this finding relies on a different definition of
stability. Some urban features explain persistence in neighbourhood relative status.
(Lee and Lin 2013; Meen et al. 2012)
and historical city centres
(Brueckner et al. 1999), for example, represent fixed advantages that can be associated
with persistent affluence. Negative features, such as environmental problems or inferior
accessibility, can cause persistent deprivation.
Neighbourhood Change and Structural Processes
Regardless of the repositioning of neighbourhoods within the urban hierarchy,
various processes can drive changes in the absolute socioeconomic conditions of
neighbourhoods. These processes, which are termed hereafter as structural, operate
beyond the urban level and affect neighbourhood absolute conditions by changing
the socioeconomic makeup of the metropolitan population. One of them, is the
upward or downward change in overall socioeconomic conditions (termed
hereafter the ‘growth/decline’ effect). neighbourhood changes can result from overall
income growth or decline which follows from macro-economic and demographic
processes throughout the country or in specific regions. In rust-belt metropolitan
areas in the US, for instance, neighbourhood socioeconomic decline mirrored the
decline of whole cities due to the shrinking of the industrial sector
. Similarly, increasing poverty at the neighbourhood level was found to
be most dependent on the increase in poverty in the surrounding county
et al. 2003)
. Beyond the regional level, Zwiers et al. (2016b) illustrated how
global processes, such as the 2008 crisis, may translate into decline among
individual neighbourhoods. The ‘growth/decline’ effect means that the whole
distribution of neighbourhood average incomes shifts upward or downwards.
Another type of structural process that can affect socioeconomic conditions of
individual neighbourhoods is the change in the dispersion of the neighbourhood
income distribution within an urban area. Such change is likely to result from
changing economic inequality among individuals in the region or in society as a
whole (hence termed the ‘inequality’ effect). Increasing inequality among
individuals results in increasing disparities among neighbourhoods due to two different
(Andersson and Hedman 2016)
; first, when incomes of the rich and
poor diverge, the average incomes of their respective places of residence follow
the same path through an in situ process. Secondly, increasing income inequality
generates intensified selective mobility because of the increased disparities
between the rich and poor in the resources available to spend on housing. For
example, in the US, increasing income segregation has been associated with
increasing inequality among individuals
(Reardon and Bischoff 2011; Watson
. Also, the decline in the proportion of middle-income neighbourhoods
seems to correspond to a similar decline in the proportions of middle-income
families in the overall population (Booza et al. 2006).
To summarize, neighbourhood socioeconomic change is a result of distinct
processes operating at different levels: the urban level, which is associated with
the ‘exchange’ effect, and higher (inter-regional, national or global) levels which
are associated with two structural effects: The ‘growth/decline’ and ‘inequality’
effects. Figure 1 explains this distinction by illustrating the metropolitan
socioeconomic hierarchy of neighbourhoods as a ladder. Each echelon signifies a
certain socioeconomic position within the metropolitan hierarchy, occupied by a
certain neighbourhood at each point in time. Each pair of ladders denotes a
transition from one point in time to another, over which one can observe the
changes occurring to the whole array of neighbourhoods and to each individual
one. The left scheme illustrates the pattern of changes occurring among
neighbourhoods due to the exchange of relative positions. The socioeconomic
statuses incurred by each position on the ladders are identical across the two
observations, and neighbourhoods just swap places among themselves. The
middle scheme depicts the kind of change expected during a period of income
growth. The socioeconomic level entailed by each position is higher at the second
observation. During a period of overall decline socioeconomic levels among all
positions would be lower. The right scheme visualizes the effect of changing
inequality on neighbourhood change. In this example the distribution widens such
that high-positioned neighbourhoods experience an increase of socioeconomic
levels and low-positioned neighbourhoods experience a decrease. The opposite
could happen if the distribution became more equal; the ladder scheme would
depict positions which are closer to the average level, with smaller socioeconomic
gaps among positions.
Current Measures of Neighbourhood Change and their Limitations in Reflecting the Complexity of Processes
There are various ways to measure neighbourhood status change, and each captures a
different combination of the ‘exchange’, ‘growth/decline’ and ‘inequality’ processes of
change. Many studies measured neighbourhood change based on the status of
neighbourhoods relative to their respective metropolitan area
(Choldin and Hanson
1982; Delmelle 2015; Fogarty 1977; Gould Ellen and O’Regan 2008; Landis 2016;
Logan and Schneider 1981; Owens 2012; Rosenthal 2008; Rosenthal and Ross 2015;
. These measures eliminate the effect of metropolitan income growth or
decline. So, if a neighbourhood is located in an economically declining or growing
urban area, the absolute socioeconomic change implied by this process will not be
captured when a relative measure is used. Relative measures understate, therefore, the
upward or downward amount of change
(Gould Ellen and O’Regan 2008; Jun 2013)
and their use results in overlooking an important source of divergence in
neighbourhoods’ conditions across metropolitan areas.
Neighbourhood socioeconomic change has also been measured based on status
relative to other reference levels, for example, to the average of a
crossmetropolitan sample of neighbourhoods
(Jun 2013; Zwiers et al. 2016b)
. By using
this reference level, measures account for processes that affect the disparities in
growth or decline among the urban areas included in the sample. However, other
structural processes that lead to overall growth or decline may still not be
accounted for; for example, changing income disparities among metropolitan
and rural areas or among sampled and non-sampled metropolitan areas, and a
national growth/decline in incomes. Measuring neighbourhood change relative to
the average of a national sample of neighbourhoods may account for all structural
processes except a national increase or decline in incomes.
The higher the spatial scale used as a reference for neighbourhood status
measurement, the more processes of change can be captured. Figure 2 illustrates
that principle in three different cases (a,b,c). In each of the cases the outer
boundary represents a whole region or a country, smaller circles represent
metropolitan areas or cities and grey spots represent the smallest spatial units,
referring to neighbourhoods. In case a neighbourhood change is measured based
on status relative to the city- or metropolitan average; thus it only captures the
effect of processes operating within the respective boundaries. Case b represents
a situation where the reference level is the average of neighbourhoods across a
sample which includes several cities or metropolitan areas. Consequently it
captures processes that produce disparities among the sampled spatial units but
overlooks those that may produce spatial disparities among sampled and
nonsampled areas. Finally, case c shows that a reference level of a regional or
country average captures all processes within that boundary; however, processes
that operate beyond that level are still left out. Only a measure that is based on
absolute income values would capture the overall amount of neighbourhood
change associated with growth or decline processes. Using them, however,
cannot indicate whether neighbourhoods changed relative to other metropolitan
neighbourhoods or whether their change is related to the overall metropolitan,
regional, or national increase or decline, and this is why relative measures have
been used in the first place; they were assumed to isolate urban-level from
higher-level structural processes
(Logan and Schneider 1981)
However, the most common relative measures do not completely control for
higher-level structural processes. Measures that are based on computing the
ratio of neighbourhood average income to the average for all neighbourhoods
in the respective metropolitan area
(e.g. Fogarty 1977; Gould Ellen and
O’Regan 2008; Logan and Schneider 1981; Rosenthal 2008; Rosenthal and
, and to a lesser extent also those that are based on standardized
(Delmelle 2015, 2017)
do in fact capture the ‘inequality’ effect and
therefore confound it with the ‘exchange’ effect. This can lead to the
inconsistency of research designs with theoretical models, because the effect of
changing inequality on neighbourhoods is incorporated in the total observed change
which is attributed to urban-level processes. For example, increasing income
inequality is expected to increase the absolute socioeconomic levels of the
highest-positioned neighbourhoods and decrease those of the lowest status ones.
This pattern can counteract the typical mean-reversion pattern associated with
urban filtering, where high-income neighbourhoods move down and low-income
ones move up. In that case, the amount of change attributed to urban-level
processes can be understated.
It follows that most of the neighbourhood change literature neglected the
overall effect of higher-level structural processes and also confounded different
processes in their analyses. To fully account for all structural processes absolute
measures should be used. But at the same time, the contributions of different
processes of change have to be distinguished from each other to be able to
compare neighbourhood change between, for example, different cities, and to be
able to examine theoretical models that focus on specific sources of change. Two
previous studies, suggested approaches that comply with this strategy. They
decomposed total neighbourhood and urban change (measured in absolute terms)
into contributing components
(Collver and Semyonov 1979; Congdon and
. Although we have some reservations about the conceptual
implications underlying the derived components (which are discussed in the next
section), the approach appears beneficial. This methodological direction has,
nevertheless, not been further advanced.
This paper follows this abandoned route of neighbourhood change research; it
proposes the application of an alternative decomposition procedure of total
neighbourhood change to components reflecting ‘exchange’ and two different
‘structural’ effects: ‘growth/decline’ and ‘inequality’. The approach builds on
methodological advancements in decomposing total mobility to its contributing ‘exchange’ and
‘structural’ components from the field of individual income mobility.
A Social Mobility Approach to Decomposing Total Neighbourhood
The research field of social and income mobility of individuals also struggles with
the decomposition of total mobility into structural- and exchange-mobility
components. This paper proposes to use such a method and apply it to neighbourhood
Social mobility deals with the changes in individuals’ social and economic positions
through time. Sociologists have typically focused on transitions between parent’s and
offspring’s’ socio-occupational positions. In this context, structural mobility has been
referred to as the class mobility of individuals that is induced by the changing
availability of occupational positions across class categories, due to technological
development or other structural processes. Exchange mobility has been regarded as
the movement of individuals among positions within a given distribution of positions
among social classes
. Welfare economists are focused on the
evolution of economic well-being; with incomes at the centre of attention, the field is
more specifically termed ‘income mobility’. Here, structural mobility refers to changes
in individuals’ incomes which result from changes in the distribution of income, and
exchange mobility is referred to as the change in individuals’ relative positions within a
given distribution of incomes
In the social and income mobility research there is a lack of consensus as to whether
structural mobility matters. For example, welfare economists are divided by those who
consider the change in individuals’ incomes resulting from overall growth as mobility,
and those who do not. The latter, referred to as taking a relativist approach, would argue
that substantial mobility only occurs if individuals experience change in relative
positions across the income distribution [see Fields 2008 for a more detailed
explanation]. It is agreed, however, that exchange and structural effects have to be
distinguished from each other. Basically, this is done by quantifying the total amount of
mobility and by decomposing it to reflect the contributions of the different effects. Yet,
there are several distinctive conceptualizations of mobility
(Fields 2008; Fields and Ok
, and correspondingly, there are also different ways to measure it and to reflect
its underlying components.
, for example, presented a decomposition of
total distributional change to a component generated by inequality change and a
component reflecting the exchange of positions. The ‘total mobility’ decomposed in
this case adheres to a relative concept of mobility and thus it excludes the ‘growth/
decline’ component which we, in the context of this paper, do seek to account for.
Another procedure, proposed by
, decomposes the mobility
Chakravarty et al. (1985)
which is based on an ethical approach to mobility.
The concept underlying this measure does not suit, in our opinion, the analysis of
neighbourhood change. These two methods are therefore not applicable in the context
presented in this paper.
Two papers introduced variants of another decomposition strategy and applied
it in the context of neighbourhood and urban change
(Collver and Semyonov
1979; Congdon and Shepherd 1988)
; both are partially based on previous work of
McClendon (1977). They decompose the sum of squared differences between final
and initial neighbourhood indicator values, and derive three similar components of
change defined as 1) changes in the average over all neighbourhoods or areas 2)
changes in the dispersion of the distribution of indicator values and 3) changes in
the relative positions of neighbourhoods. These components can be regarded as
the equivalents of the ‘growth/decline’, ‘inequality’ and ‘exchange’ effects
respectively. The first component is expressed in both papers as the difference between
final and initial overall means. This statistic is inconvenient in a comparative
context, where a ratio statistic would be preferable. The second component is
expressed by Collver & Semyonov as the difference in standard deviations of final
and initial distributions. Congdon and Shepherd’s respective component is based
on the beta coefficient computed from regressing final on initial indicator values
and therefore it is also dependant on the relationship between final and initial
standard deviations. Standard deviations are scale-variant and
translationinvariant; scaling a variable changes the standard deviation proportionately, and
adding a constant amount to a variable does not change the statistic. This is
contrary to the axioms underlying the most commonly used inequality measures
(for example the Gini index). Finally, the exchange component is expressed by
Collver & Semyonov as the difference in standardized scores. The change in
standardized scores is not void of structural influences. 1 In case of significant
change in the shape of the distribution standardized scores are affected and
therefore do not ‘purely’ account for exchange processes. Our suggestion of
himself noted in footnote 3, p. 60.
applying an alternative decomposition rests therefore on the grounds of conceptual
perception regarding the derived components.
A Method for Decomposing Total Neighbourhood Change into Structural and Exchange Components
We propose the application of a decomposition presented by
Van Kerm (2004
procedure decomposes mobility measures to represent the relative contributions of
‘growth/decline’, ‘inequality’ (termed ‘dispersion’ by Van Kerm) and ‘exchange’
components of income mobility processes. It has the advantage of offering a general
framework that can be applied to different mobility measures which could be chosen to
conform to a particular research context. The method involves the analysis of two
observations of a vector of neighbourhood average incomes (given in absolute terms),
which represents a single, whole metropolitan system; the first observation is at time t
and the second at time t + 1.The procedure is based on the construction of
counterfactual income vectors, each representing the hypothetical effect of only one factor on the
initial vector of incomes, while the effect of the other two factors is neutralized.
) specified three functions that can be used to derive the three
counterfactual vectors. We explain the underlying rational, referring to the context of
neighbourhood change [refer to
Van Kerm 2004
for the technical overview and
formulas which relate to the income mobility context].
1) The ‘exchange’ counterfactual vector illustrates how neighbourhood average
incomes in the metropolitan area would look like if they were not affected by
growth/decline or increasing inequality and only affected by the changes in relative
positions. It is constructed by replicating the vector of neighbourhood average
incomes observed at time t, but reordered according to the rank orders of
neighbourhood average incomes at time t + 1.
2) The ‘structural’ counterfactual vector represents how neighbourhood average
incomes in the metropolitan area would look like if neighbourhoods didn’t
change relative positions and would only be affected by the growth/decline of
incomes and changes in inequality. It is constructed by replicating the vector
of neighbourhood average incomes observed at time t + 1, but reordered
according to neighbourhood original rank orders that was observed at time
t. While this counterfactual vector represents the combined impact of the two
‘structural’ components (‘growth/decline’ and ‘inequality’), the following two
vectors represent the isolated effect of each.
3) The ‘growth/decline’ counterfactual vector is constructed by ‘inflating’ (or
‘deflating’) the vector of neighbourhood average incomes observed at time t by the ratio
between the overall averages of t + 1 and t neighbourhood average incomes. Thus,
the procedure assigns each neighbourhood an income that reflects an identical
position to that it had in time t; also, the vector will maintain the same shape of
distribution (i.e. the same level of inequality) as the vector of incomes at time t.
4) The ‘inequality’ counterfactual vector applies the Lorenz curve of the vector of
incomes observed at time t + 1 to the vector of incomes observed at time t. This is
done by constructing a ‘structural’ counterfactual vector (as described in article 2
above), while eliminating the growth factor by also applying the inverse function
to that described in (3). This way, the vector of neighbourhoods will reflect the
same positional order as in time t, and the same overall level of incomes, and only
manifest the effect of inequality that occurred in the transition between t and t + 1.
The next step in this approach is to quantify the total amount of change, and the
amount associated with each counterfactual vector using a measure of mobility. Total
change is computed using the initial and final observed vectors of neighbourhood
average income. Component contributions are separately computed by using each
counterfactual vector instead of the observed vector of final incomes.
) listed several applicable mobility measures, which comply with required
axioms. Among them, we chose to use the measure proposed by Fields and Ok (1999b).
Its advantage is that it can be simply disaggregated to reflect the contributions of groups
of neighbourhoods. The measure is defined as:
mnðx; yÞ ¼ n i¼1
where (in the context of this application), yi and xi are neighbourhood average incomes
at the final and initial observation periods respectively, and n refers to the number of
neighbourhoods. This measure conforms to a concept of movement that focuses on the
distance between final and initial values given in absolute terms (referring to dollar
income). Due to the absolute value notation the measure would represent the average
movement of incomes among neighbourhoods regardless of the directions of change;
otherwise, the exchange component would sum up to zero. In computing contributions
of neighbourhood sub-groups, a directional measure should be used by omitting the
absolute value bars in the equation presented above. This variant of the mobility
measure would reflect both the magnitude and direction of change experienced by
each individual neighbourhood or group of neighbourhoods.
Van Kerm (2004
) referred to a problem that different sequences of eliminating
factors from the total mobility measure result in different component contributions. While
each computation represents the marginal impact of each factor, they do not sum up to the
total mobility computed. In order to derive additive components, Van Kerm proposed to
use the Shapley decomposition procedure [see Shorrocks
(2013, based on a previous
version from 1999)
for a comprehensive presentation]; this procedure averages the
contributions computed by applying each possible sequence of elimination.
Components of Neighbourhood Change in Metropolitan US – An
To demonstrate the decomposition of total neighbourhood change into its contributing
components, we use the Longitudinal Tract Data Base (LTDB) which provides data on all
US census tracts within constant 2010 boundaries for the years 1970–2010. The database
was processed to allow for geographical consistency in longitudinal analysis, by Spatial
Structures in the Social Sciences at Brown University
(Logan et al. 2014)
. We focus on
median household incomes for the years 1980 and 2010 which are based on sample
counts (the long-form questionnaire of the 1980 census and the American Community
Survey for 2010). The 1980 data is specified in real 2010 dollars. In order to offer a
comparative view on the components of socioeconomic change in different places, we
examined 22 of the largest metropolitan areas in the US as of 2010, after excluding those
for which data of more than 10% of neighbourhoods were missing at either 1980 or 2010.
Figure 3 shows the relative contributions of ‘exchange’ and ‘structural’ factors to the
average total change experienced by neighbourhoods of each metropolitan area. At a first
glance, one can notice the large variation among metropolitan areas in the relative roles of
change components. The ‘exchange’ component accounted for 74%–77% of total change
in Chicago, Minneapolis and Miami. In Boston, San Jose and San Francisco, however,
‘Structural’ components were dominant, accounting for 74%, 66% and 65% of change
respectively. A most substantial amount of change is driven, therefore, by higher-level
processes that affect the distribution of neighbourhoods within each metropolitan area.
These shares illustrate how by focusing exclusively on urban-level dynamics a notable
part of change experienced by neighbourhoods can be overlooked.
The effect of the two structural processes, ‘growth/decline’ and ‘inequality’, on the
average neighbourhood income also varies remarkably among different metropolitan
areas (Fig. 4). The first thing to notice is the negative association between the effects of
overall growth and inequality. Neighbourhoods in metropolitan areas which were most
affected by overall growth were the least affected by increasing inequalities. These
metropolitan areas encompass ‘superstar’ cities such as San-Francisco, New-York and
Boston, that are characterized by exploding demand coupled with a constrained supply
that make them the most expensive places
(Gyourko et al. 2006)
. In these cities,
San Jose-Sunnyvale-Santa Clara, CA
San Francisco-Oakland-Hayward, CA
New York-Newark-Jersey City, NY-NJ-PA
San Diego-Carlsbad, CA
Houston-The Woodlands-Sugar Land, TX
Sea le-Tacoma-Bellevue, WA M
Bal more-Columbia-Towson, MD
Tampa-St. Petersburg-Clearwater, FL
Riverside-San Bernardino-Ontario, CA
Cincinna -Middletown, OH-KY-IN
Los Angeles-Long Beach-Anaheim, CA
Phoenix-Mesa-Sco sdale, AZ
Miami-Fort Lauderdale-West Palm Beach, FL
Minneapolis-St. Paul-Bloomington, MN-WI
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
increasing inequality has not been a substantial factor of neighbourhood change,
because low- and middle income households have been gradually priced out of the
increasingly unaffordable metropolitan housing markets
(Florida 2017; Gyourko et al.
, and so the inequality among neighbourhoods within the metro area did not
increase much. This process, however, has most likely involved increasing disparities
between the metropolitan area and the surrounding region, such that the increasing
inequality was ‘absorbed’ at a higher spatial level.
Neighbourhoods in metropolitan areas at the middle range of Fig. 4, such as
Baltimore, Philadelphia and Sacramento, experienced a more moderate growth
of incomes but more significant changes driven by the inequality factor.
Following the same theoretical reasoning of
Gyourko et al. (2006)
, in those cities
the increasing demand was probably not high enough to spur gentrification
processes all over the metropolitan area; low-income households most likely
remained in previously low-income neighbourhoods and high-income
neighbourhoods absorbed the demand such that disparities among low- and
high- income neighbourhoods increased. Finally, there are metropolitan areas
whose neighbourhoods experienced overall income decline (or very little
growth), and were also very much affected by increasing inequality. These
characteristics, however, are shared by two different types of places; those
which were shrinking in population, such as Detroit, Cleveland and Cincinnati,
and those which were expanding such as Houston. In the former, the overall
decline in incomes reflects the direct effect of deindustrialization on
employment and income, and the plummeting demand to living in these cities. The
large role of inequality in neighbourhood change reflects the uneven spread of
decline across metropolitan neighbourhoods, for example in Detroit, where the
richest neighbourhoods were left almost intact
(Guerrieri et al. 2012)
the ‘growth’ and ‘inequality’ factors of neighbourhood change in Houston are
quite similar in extent to those of Rust-Belt metropolitan areas, Houston’s
underlying story is different. It is one of the fastest growing metropolitan areas
in the US, with a regulatory environment that enables an abundant supply of
affordable housing; this drew middle to low-income households, among which
(Center for opportunity urbanism 2016)
The role of inequality in neighbourhood socioeconomic change, which has
been practically ignored to date, is actually substantial. If we disregard the few
‘superstar’ cities in which the inequality factor does not significantly generate
neighbourhood change at the intra-metropolitan level, we see that the
contribution of that factor is at least 10% of the total. It can amount to a fifth of total
change in ‘middle-range’ metropolitan areas such as Miami and Chicago. It
accounts for a third of total change in Cincinnati neighbourhoods and more
than a half in Houston, Detroit and Cleveland. These figures demonstrate that
the effect of inequality is by no means negligent. If this effect is confounded
with the exchange effect by the use of relative measures, then empirical
analyses risk drawing the wrong conclusions about what drives neighbourhood
socioeconomic change. This becomes more evident when examining how the
exchange and inequality factors affect neighbourhoods that differ by their
socioeconomic starting position, that is their income decile in 1980 (Fig. 5).
Conforming to the expectations, exchange processes follow a mean reversion
pattern; high-income neighbourhoods generally decrease and low-income ones
increase. This pattern is evident in all metropolitan areas, four of which are
presented in the figure. Increasing inequality affects neighbourhoods in an
opposite manner; it makes high income neighbourhoods increase and
lowincome ones decrease. When the increase in inequality is substantial, it more
than offsets the changes neighbourhoods experienced due to the exchanges of
relative positions, as the case of Detroit demonstrates. In places like
SanFrancisco the effect of inequality is lower such that it only attenuates the
A final analysis shows exactly how the standard relative measure of neighbourhood
change underestimates the amount of change that can be attributed to urban-level
processes. Drawing on the Miami Metropolitan area as an illustration, we computed the
amount of change experienced by neighbourhoods of differing initial positions in 1980,
using the common relative measure and a corresponding measure that represents the
‘exchange’ factor exclusively (Fig. 6). The first measure, as for example used by
, is based on computing the 2010 ratio of neighbourhood average
income to the average for all neighbourhoods in the respective metropolitan area, and
dividing it by the respective 1980 ratio. This measure excludes the ‘growth’ component
but captures both the ‘exchange’ and ‘inequality’ components. The second measure is
computed by dividing each neighbourhood’s ‘exchange-driven’ income from the
‘exchange’ counterfactual vector by the initial observed income in 1980. So, it is essentially
the same ratio measure as the first, but it excludes the effect of increasing inequality
among neighbourhoods. The plot of the two measures illustrates how the inclusion of the
inequality factor in the common relative measure of change moderates the change
gradient; low-income neighbourhoods seem to have improved their positions much less
than in reality, and high-income neighbourhoods seem to have worsened much less. With
no increase in the inequality among neighbourhoods the two measures would coincide.
Fig. 6 Absolute change experienced by neighbourhoods of different income deciles (1 = lowest decile) in
Miami, FL metropolitan area: A comparison of a ‘standard’ measure of neighbourhood change which
incorporates the effect of increasing inequality and an ‘exchange’ measure which leaves out the ‘inequality’
This paper presents a new application to the measurement and analysis of
neighbourhood socioeconomic change. The application makes a distinction
between the contributions of different processes that drive these changes; we classify
them as ‘exchange’ processes which refer to urban-level dynamics that generate
change in neighbourhoods’ urban-relative statuses, and as ‘structural’ processes
that operate at higher levels and change neighbourhoods by affecting the overall
socioeconomic composition of urban areas. The two different structural processes
are overall income growth or decline (‘growth/decline’ effect) that can translate
into the growth or decline of the average income of neighbourhoods
and Hedman 2016; Galster et al. 2003; Zwiers, Bolt, et al. 2016)
, and changes in
the inequality among individuals (‘inequality’ effect) that can translate into
changing inequality among neighbourhoods. Most studies on neighbourhood change
have measured neighbourhood change based on their status relative to other
neighbourhoods in the respective urban area
(Choldin et al. 1980; Choldin and
Hanson 1982; Delmelle 2015; Fogarty 1977; Gould Ellen and O’Regan 2008;
Landis 2016; Logan and Schneider 1981; Owens 2012; Rosenthal 2008; Rosenthal
and Ross 2015)
. Relative measures neutralize the growth/decline effect, such that
the actual extent of change in neighbourhoods’ conditions is overlooked. Also,
these measures confound the effect of urban-level ‘exchange’ processes with
structural ‘inequality’ processes. This paper therefore suggests an alternative
approach in the measurement of neighbourhood change. An approach that on
the one hand, will enable to take account for the overall amount of change in
neighbourhood socioeconomic conditions, and on the other hand, will enable to
make a distinction between the different contributing processes.
The proposed approach is based on the distinction among similar process
components in social and income mobility research. The paper applies a method
Van Kerm (2004
) in the context of income mobility, which can be
applied to various mobility measures. By applying Van Kerm’s decomposition to
two different variants of a measure of mobility suggested by Fields and Ok
(1999b), the presented approach involves two levels of analysis which may
provide complementary insights in the context of neighbourhood change. The
first level of analysis is the ‘system’ level which refers to the urban or
metropolitan area as a whole. At this level, the total amount of change occurring
among metropolitan neighbourhoods is decomposed to reflect the amount of
change that can be attributed to ‘exchange’ and the two ‘structural’ effects
(growth/decline and inequality). The second level of analysis can provide insight
into the effect of different factor components on different parts of the
neighbourhood income distribution, different neighbourhood types or
neighbourhoods located at different places.
We demonstrated the approach in an empirical analysis of components of
neighbourhood socioeconomic change across 22 metropolitan areas in the US
during the period 1980–2010, using the Longitudinal Tract Data Base
et al. 2014)
. The analysis indicates that the relative roles of structural and
exchange processes vary remarkably. In almost half the cases, structural
processes accounted for half or more of the total income change experienced by
neighbourhoods. Also the ‘Growth/decline’ and ‘inequality’ factors interact very
differently across different metropolitan areas. In general, factor contributions
indicate that the larger the effect of growth, the lesser the effect of inequality
among metropolitan neighbourhoods. This finding can be explained by theories
that describe how demand coupled with constrained housing supply increase
incomes, decrease affordability and therefore decrease neighbourhood inequality
within the metropolitan area
(Gyourko et al. 2006)
. In addition, the analysis
emphasizes how increasing inequality and urban-level dynamics offset each
other. This implies that low-income neighbourhoods that improve their positions
in the urban hierarchy may not experience any actual income change because of
the inequality effect. From a policy perspective, this exemplifies the potential
role of people-based policies (as opposed to place-based policies) in tackling
urban deprivation. Finally, the analysis demonstrates how common relative
measures which include the effect of inequality underestimate, as consequence,
the amount of socioeconomic change that should actually be attributed to
By using the proposed method to account for exchange and structural processes in
neighbourhood socioeconomic change, empirical studies can disentangle the
complexity often observed among single urban areas and across them. As most cities struggle
with changing disparities among neighbourhoods, and with evolving spatial patterns of
these disparities, questions often arise regarding the underlying dynamics; for example,
is gentrification of inner city areas merely a changing centre of gravity, or is it a result
of the whole city becoming wealthier? Is increasing poverty incidence among
peripheral neighbourhoods related to the relocation of the poor due to a cycle of urban
development or is it related to changing overall inequality?
Such questions gain importance given the global trends in inequality.
Neighbourhoods, to a large extent now, reflect the story of increasing overall
inequality which translates into increasing disparities within and among cities.
In this context, it seems no longer plausible to ignore the role of inequality in
neighbourhood change processes. What happens to neighbourhoods within the
isolated context of each city or metropolitan area is a very incomplete picture
of change processes. It may be difficult to accept a conclusion that many
neighbourhoods are stable over the long run
(as was concluded for example
by Delmelle 2017)
with only their urban-relative positions are taken into
account, while neighbourhoods increase or decrease substantially because of
structural processes. The fact that neighbourhoods preserve their relative
standings does not necessarily render them stable. A neighbourhood in
SanFrancisco, Boston, New-York and other cities can keep a similar position within
the city’s hierarchy, but at the same time be twice as rich compared to a few
decades ago. Similarly, low-positioned neighbourhoods in Cleveland
experienced much extremer deprivation following structural transformations, even if
they maintained their relative status. The method and illustration presented in
this paper exemplify that neighbourhoods cannot be viewed as only part of
their respective urban systems; they are deeply embedded in higher-level
contexts and are greatly affected by the contemporary reality of increasing
inequalities at multiple spatial scales. This understanding should be reflected in how
neighbourhood change is measured and analysed.
Acknowledgements The research leading to these results has received funding from the European Union’s
Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.
702649; and from the European Research Council under the European Union’s Seventh Framework
Programme (FP/2007-2013) / ERC [Grant agreement No. 615159] (ERC Consolidator Grant
DEPRIVEDHOODS, Socio-spatial inequality, deprived neighbourhoods, and neighbourhood effects).
Compliance with Ethical Standards
Disclaimer This paper reflects the authors’ view and the Commission is not responsible for any use that
may be made of the information it contains.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
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