Infl Inequality

8/3/2019 Infl Inequality

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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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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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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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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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Figure: Household specic ination rates, pooled data, 1997m01 to2008m11 (n = 52,910)

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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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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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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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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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Figure: Cluster algorithm result

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Empirical Analysis


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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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Figure: 1st and 2nd PC (varimax and promax rotation)

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Empirical Analysis


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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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Infl Inequality