infl inequality
TRANSCRIPT
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Is There Ination Inequality Across HouseholdTypes in Europe?
Roberta Colavecchio & Ulrich Fritsche & Michael Graff
Hamburg University & KOF ETH Zurich
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Outline
IntroductionInation Inequality across Households: why should we care?Literature Review
Our ApproachResearch QuestionsData Set
Empirical AnalysisStationarityConvergence IssuesPooled Analysis- preliminary
ConclusionCountry-by-country analysisPanel analysisTo do list
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Introduction
Ination as a macroeconomic phenomenon (general rise inthe overall price level)
Typically ination is measured on the national level, using arepresentative household concept (HICP - basketsharmonized throughout Europe)
Extensive literature on price level/ ination rate and businesscycle convergence/ divergence across nation states in Europe(EU/ EMU)
However, the literature on the distribution/ structure of inationrates within countries (or within Europe) faced by householdsacross different socio-economic categories is rather limited
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Introduction
In this paper , we analyse cross-household ination dispersionin Europe using ctitious monthly ination rates for several types of households (grouped according to income levels,household size, socio-economic status, age)
The data set covers the period from 1997 to 2008 (update:2010)
Panel of 23 (up to 27) household-specic ination rates percountry
15 European countries and the euro area aggregate
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Introduction
The paper consists of two parts:1. In the rst one, we employ time series and non-stationary
panel techniques to shed light on cross-country differences inination inequality with respect to the number of driving forcesin the panel ( Focus : the degree of persistence of the household-specic ination rates and their adjustment behaviour towards the ination rate of a representative household );
2. In the second one, we pool the full sample of all countries andtest if and by how much certain household categories across
Europe are more prone to signicant ination differentials andsignicant differences in the volatility of ination.
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IntroductionInation Inequality across Households: why should we care?
1. Poverty reduction and income redistribution measures aremostly aimed at stabilizing real income at low income levels knowing the features of the ination rates faced by thosehousehold categories might improve the effectiveness of themeasures
2. Elderly people (whose relative importance is constantlyincreasing in our ageing society) often show a quite differentconsumption pattern compared to the median household.
3. Savings rates differ across, e.g., age and income groups;
ination rates might differ as well. As households areconcerned about their real consumption and savingspossibilities, differing ination rates give raise to a possibleamplication of wealth effects in the economy as a whole
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The KOF/ UHH-Report for the EU Commission, DGECFIN (2009)Ination Dispersion
Figure: Differences with respect to HICP in EU-15
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The KOF/ UHH-Report for the EU Commission, DGECFIN (2009)Weight Dispersion (1999)
Figure: Differences in weights in EU-15
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IntroductionLiterature Review
United StatesMichael (1979), Hagemann (1982): after the rst and secondoil price shock, low income households, households with lowereducation, older-aged households face higher than averageination. However, within group differences are typically morepronounced than differences between groups;Amble and Stewart (1994): found higher ination for the elderlydue to above-average increases in medical costs in the US;Hobijn and Lagakos (2005): elderly are more prone to ination,poorer households as well. Differences to median ination are,
however, not very persistent;Idson and Miller (1997): ination in the US is falling with thelevel of education (due to fuel, energy)
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IntroductionLiterature Review
CanadaChiru (2005): higher ination for elderly and low incomehouseholds
EuropeLivada (1990) for Greece: Childless couples and high-income
households face highest ination;Crawford and Smith (2002) for the UK: persistent differences ination rates (opposite to the ndings of Hobijn and Lagakos,2005): non-pensioners, mortgage-payers, childlesshouseholds are more prone to ination;Noll and Weick (2006) for Germany: conrm Engels law,signicant but small differences in ination and consumptionpatterns;Rippin (2006) for Germany in the 1998-2003 period: lowestination among the youth (telecommunication)
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Our ApproachResearch Questions
Questions:1. Do household-specic ination rates deviate from the ination
rate faced by the representative household?2. Are these deviations persistent ? If not, how long do these
deviations last? How large are they? Are they signicant ?3. Does the volatility of household-specic ination rates differ
across categories compared to the representative householdination?
4. Can we identify clusters of households which feature(statistically) similar rates of ination?
We construct a data set of cticious household-specicmonthly ination rates
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Our ApproachData Set
Source: Eurostat (Household Budget Surveys and HICPs)Countries:1. EMU, i.e. 12 countries2. Plus Sweden, UK, Denmark and the euro area aggregate
Time span: January 1997 December 2008
Categories of prices: COICOP 1-12What socio-economic categories can we refer to?
By employment status (manual, non-manual, self-employed,unemployed, ...)By number of active personsBy income quintileBy household type (single, single with dependent children, twoadults, ...)By age
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Our ApproachData Set
HICP has annually changing weights (chain index), the HBSsare conducted every 5 years (data frequency mismatch!)
We selected a base year (where we have both types of data),calculate the distance of the weights and keep the relativedistance constant
Reference ination rate slightly differs from HICP, we use theaverage over all households in the Consumer survey
(consistency issues)
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Our ApproachData Set
Table: Description of COICOP Categories
Category Description
cp1 Food, and non-alcoholic beveragescp2 Alcoholic beverages and tobaccocp3 Clothing and footwearcp4 Housing, water, electricity, gas and other fuelscp5 Furnishings, household equipment and maintenance of housecp6 Healthcp7 Transportcp8 Communicationcp9 Recreation and culturecp10 Educationcp11 Hotels, cafes and restaurantscp12 Miscellaneous goods and services
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Our ApproachData Set
Figure: Weights for the 12 COICOP categories in HICP (1996-2008) in
Euro area
0
200
400
600
800
1,000
96 97 98 99 00 01 02 03 04 05 06
EA_CP1 EA_CP2 EA_CP3EA_CP4 EA_CP5 EA_CP6EA_CP7 EA_CP8 EA_CP9
EA_CP10 EA_CP11 EA_CP1215/39
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Our ApproachData Set
Figure: Household specic ination rates, pooled data, 1997m01 to2008m11 (n = 52,910)
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Our ApproachData Set
Figure: Deviations of household specic ination rates from countrymeans, pooled data, 1997m01 to 2008m11 (n = 52,910)
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Empirical Analysis
Time series and non-stationary panel techniques are employed toexplore cross-country differences in the persistence ofhousehold-specic ination rates and in their adjustment behaviour towards the representative household ination.In particular, we assess:
Stationarity of ination rates (panel unit root tests)Convergence issues :
PANIC 1 approach (Bai and Ng, 2001, 2004);Panel cointegration tests (on a country level);Bivariate error correction models (special focus on adjustment
speed )
1 Panel Analysis of Nonstationarity in the Idiosyncratic and Commoncomponents.
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Empirical AnalysisStationarity
Question : From a country-specic perspective , are thehousehold-specic ination rates stationary or not, i.e. dohousehold-specic ination rates show some persistence?Panel unit root tests , 2 assumptions:
1. common unit root process , i.e. the persistence parameters are
common across cross-sections (household categories) (Levinet al. (2002));2. individual unit root , i.e. the persistence parameters are allowed
to vary freely across cross-sections (Maddala and Wu (1999)and Choi (2001))
For the majority of the countries of our panel (and irrespectiveof the deterministic assumptions):
1. the tests fail to reject the hypothesis of a common unit rootprocess;
2. the hypothesis of an individual unit root process is rejected
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Empirical AnalysisStationarity
On the basis of the outcome of this rst set of tests, we couldconclude that:
Persistence over time is expected in our datasetThe persistent component in each countrys household-specic ination rates is likely to be driven by asingle common source
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E i i l A l i
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Empirical AnalysisConvergence Issues - PANIC approach
Question : Are the different household-specic ination ratesdriven by one or more common trends?PANIC approach (Bai and Ng, 2001, 2004)
Idea : Decompose the model in the driving common factor(s)(F t ) and the idiosyncratic components (e it )
X it = c i + i F t + e it (1)
where:X it are the household ination rates;
The common factor is interpretable as the ination rate sharedby all types of households ( not necessarily HICP );The idiosyncratic components are measures ofhousehold-specic parts in their respective ination rates
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E i i l A l i
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Empirical AnalysisConvergence Issues - PANIC approach
PANIC approach (Bai and Ng, 2001, 2004)
Step 1 : Determine the number of common factors accordingto information criteria
Step 2 : Test for stationarity of the common factor andidiosyncratic components (is the common factor the onlysource of non-stationarity in the panel of household-specicination rates?)
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E i i l A l i
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Empirical AnalysisConvergence Issues - PANIC approach
Table: Determining the number of factors (PANIC approach)
Variance proportion of it explained by...First principal component Second principal component
Austria 0.990 0.006Belgium 0.991 0.007
Germany 0.987 0.006
Denmark 0.983 0.011Euro area 0.994 0.005Spain 0.987 0.009
Finland 0.978 0.018France 0.993 0.004Greece 0.984 0.010Ireland 0.981 0.016
Italy 0.987 0.010Luxembourg 0.996 0.003Netherlands 0.983 0.013
Portugal 0.976 0.017Sweden 0.982 0.014
United Kingdom 0.976 0.017
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Empirical Analysis
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Empirical AnalysisConvergence Issues - PANIC approach
Results:
Step 1 : One main common factor the panel ofhousehold-specic ination rates in each country seems to bedriven by one single factor;Step 2 : The hypothesis of a unit root in the common factor canbe rejected for several countries (Germany, Denmark, Euroarea, Spain, Italy, Luxembourg, Portugal and Sweden) thereis a signicant proportion of non-stationarity remaining in the idiosyncratic components (implying persistent deviations of theidiosyncratic parts from the common component);The remaining part of the cross-sectional variance in the panelis driven by stationary idiosyncratic components (UK excluded) , i.e. the part not explained by the single commonfactor in each country is mean-reverting with a constantvarianceGood news : individual household ination rates do not divergepermanently without bounds from the common factor
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Empirical Analysis
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Empirical AnalysisConvergence Issues - Panel co-integration tests
Question : Do household-specic ination rates featuremean-reversion towards the representative household ination ? If not lasting or permanent gap between theination rates experienced by the representative consumerand the ones faced by specic household categories.
Panel co-integration tests(country-panel analysis: are the household-specic inationrates cointegrated with the respective representativehousehold ination?)
Kao (1999) test: strongly rejects the null of no cointegration in
all the country panels (i.e. suggests the presence of at least one cointegrating relationship );Maddala and Wu (1999) test: validates that a single cointegrating vector exists in the ination rate panel of all theconsidered countries (except Luxembourg)
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Empirical Analysis
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Empirical AnalysisConvergence Issues - bivariate ECMs
Question : do household-specic ination rates adjust towards
the ination rate faced by the representative household?Individual adjustment behaviour (bivariate ECMs)
y t = a 0 y (y t 1 bx t 1 ) +n x
j = 0a xj x t j +
n y
j = 1a yj y t j + u yt
x t = b 0 x (y t 1 bx t 1 ) +k x
j = 1b xj x t j +
k y
j = 0b yj y t j + u xt
where:y t indicates the household-specic ination seriesx t indicates the representative household ination series
The speed and the direction of the adjustment processbetween y t and x t are mirrored in the behaviour of y and x (ECM loading coefcients )
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Empirical Analysis
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Empirical AnalysisConvergence Issues - bivariate ECMs
Results:Different convergence assumptions (i.e. absolute or relative convergence) deliver different pictures of the behaviour of theloading coefcients .Under the assumption of absolute convergence , only theination rates of households
featuring unemployed and inactive memberswith no active personformed by a single componentformed single parents with dependent children
adjust towards the representative household ination(signicant y )
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Empirical Analysis
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Empirical AnalysisConvergence Issues - loading coefcients
Under the assumption of relative convergence ,The number of signicant loading coefcients under relativeconvergence increases ;Households with one active person display, on average, thelargest loading coefcient together with households belongingto the fourth quartile of the income distribution;For the majority of the socio-economic categories theadjustment speed towards equilibrium is low thehousehold-specic ination rates deviate persistently from the
representative household ination
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Empirical Analysis
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Empirical AnalysisSystematic patterns - pooled analysis
In differences: Ination for households at the lower end seems
to be 0.05 percentage points lower, ination for households atthe higher end seems to be 0.09 percentage points higherthan the average
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Empirical Analysis
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Empirical AnalysisCluster analysis
Euro area/ EU 15 data
Hierarchical Ward algorithm, applied to the squared Euclidiandistance
Algorithm focuses on the within-group homogeneity ratherthan on the dissimilarity between clusters, and hence isappropriate to explore whether there are clusters ofhouseholds sharing common household-specic ination
rates
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Empirical Analysis
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Empirical AnalysisCluster analysis
Figure: Cluster algorithm result
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Empirical Analysis
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Empirical AnalysisCluster analysis
Table: Cluster membership
Variable 5 Clusters 4 Clusters 3 Clusters 2 Clusters
socWork 1 1 1 1socFree 1 1 1 1
actPers2 1 1 1 1actPers3 1 1 1 1
hh2AduCh 1 1 1 1
hh3Adu 1 1 1 1hh3AduCh 1 1 1 1age30 44 1 1 1 1age45 59 1 1 1 1
socInact 2 2 2 2actPers0 2 2 2 2
hhSing 2 2 2 2age60 2 2 2 2
actPers1 3 1 1 1
quint3 3 1 1 1quint4 3 1 1 1hh2Adu 3 1 1 1
quint1 4 3 3 2quint2 4 3 3 2
hhSingCh 4 3 3 2quint5 5 4 1 1
age0 29 5 4 1 1
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Empirical Analysis
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p yCluster analysis
Clusters in differences? Five clusters :1. Young and rich2. Low socio-economic status3. Middle-classe income earners4. Economically inactive and elderly5. Classical role models: households with children, mostly
middle-aged, actively earning incomes
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Empirical Analysis
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p yDriving forces: Principal component analysis
Step 1: varimax rotation (orthogonality imposed, makesinterpretation easier)
Step 2: promax rotation (orthogonality relaxed, less restrictivedecomposition)
Results: In both cases, two factors stand out as driving forcesof the bulk of variance
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Empirical Analysis
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p yDriving forces: Principal component analysis
Figure: 1st and 2nd PC (varimax and promax rotation)
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Empirical Analysis
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p yDriving forces: Principal component analysis
Common driving forces in differences? Mainly two principal forces :According to loading factor analyis:
1. The rst one is associated with low income households (versus high income households);
2. the second one is associated with households with children (versus households without children)
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Conclusion
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Country-by-country analysis
On the national level :The panel of household-specic ination rates in each country seems to be driven byone single factor (not necessarily coinciding with the HICP ination rate);
The remaining part of the cross-sectional variance in the panel is driven by stationary idiosyncratic components , i.e. the part not explained by the single common factor in
each country is mean-reverting ( good news : household ination rates do not diverge permanently without bounds from the common factor);
Evidence for a single co-integration vector (mean-reversion of the household-specicination rates towards the representative household ination rate);
The adjustment speed towards the representative household is low persistence of deviations is high ;
Even if there is little concern about a long-run stable distribution, atleast in the short- to medium run deviations tend to last
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Conclusion
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Pooled panel analysis
On the pooled level Small but signicant differences in the deviations ofhousehold-specic ination rates from the reference ratemainly along income and education levels.
We can separate ve clusters and we identify two main driving forces for the differences in the overall panel.
These driving forces are related to low-income households and households with children .
Uncomfortably, our results suggest that some of the economically
more vulnerable parts of the population may be subject togroup-specic ination dynamics resulting in systematichigher-than-average ination.
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To-do-list
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Data update
How sensitive are the results with respect to 1999 HBS waveversus 2005 HBS wave?
Identify economic factors for dispersion in ination rates.
House prices, supply shocks, oil price, demand shocks
Income effects and substitution effects
Link ination experience with ination perception/ expectations of different groups
Link ination experience with consumption/ savings data
Decompose the paper in different parts
Check differences in ination volatility
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