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«February 28, 2011 Abstract So far, studies investigating the influence of income on individual well-being have used nominal income, adjusted for ...»

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Nominal versus real income: The impact of regional price levels on


Thomas Deckers∗ Armin Falk† Hannah Schildberg-H¨risch‡


Universit¨t Bonn

a Universit¨t Bonn

a Universit¨t Bonn


February 28, 2011


So far, studies investigating the influence of income on individual well-being have used nominal income, adjusted for inflation, as explanatory variable. According to economic theory,

however, it should be purchasing power of a given nominal income and not nominal income as such that matters. Therefore, this paper uses data on regional price levels to single out the effect of purchasing power on subjective well-being. We use a fixed effects model that controls for district heterogeneity other than the price level. The results show that higher prices significantly reduce well-being for individuals in the four lowest deciles of the income distribution. Our results provide a strong argument in favor of regional indexation of transfer payments such as social welfare benefits and contribute to our understanding of how people perceive nominal versus real terms.

Keywords: Subjective well-being, money illusion, redistribution, price index JEL-Codes: I31, D60, D63, D31, C23 ∗ Department of Economics, thomas.deckers@uni-bonn.de † Department of Economics, armin.falk@uni-bonn.de ‡ Department of Economics, schildberg-hoerisch@uni-bonn.de 1 Introduction Studies on the effect of income on subjective well-being are abundant, both for across country comparisons and within single countries.1 Due to lack of data all existing studies use nominal, usually inflation adjusted income to study its effect on individual well-being. According to standard economic theory, however, individuals derive utility from consumption of goods that they can afford with their income. Thus, the channel via which income affects well-being is purchasing power for which nominal income can at best be a rough proxy. From this perspective, real income that takes into account regional price differences is the appropriate variable for explaining individual well-being. In the light of this argument, we study whether different price levels at district level have an effect on individual well-being once we control for nominal income and other regional heterogeneity.

This paper uses novel and comprehensive data on price levels of all 393 German districts to obtain a precise measure of individual real income. The regional price levels are comprised in a price index at district level based on data from 2004 to 2009. The price index reveals that there is a price differential of 37% between the cheapest and the most expensive district in Germany. To get an intuition for the uniqueness of this data set note that the price index is based on roughly 7 million data points that measure prices of 205 distinct goods. Items included in the calculation of the price index are among many others rental rates, electricity prices, cinema tickets, fees for checking accounts, car prices, all kinds of food prices and dentist fees. We match the price index and data from the German Socio Economic Panel (GSOEP) covering the time span 2004-2008. The GSOEP is a very detailed, representative panel study of German households.

To explain individual well-being we use an individual fixed effects regression approach and control for regional heterogeneity. We find that a higher regional price level reduces subjective well-being for individuals in the four lowest deciles of the income distribution. Looking at the whole income distribution, the difference between real and nominal income does not seem to significantly inuence individual well-being. One plausible reason for this finding are decreasing marginal gains from an extra Euro on well-being. For richer individuals the difference between nominal and real For survey articles see e.g. Frey and Stutzer (2002), Di Tella and MacCulloch (2006) and Dolan, Peasgood and White (2008).

income that is driven by differences in the price level is not large enough to affect individual well-being. In contrast, for individuals in the lower part of the income distribution, the price level has an effect on individual well-being, since for them the difference between an extra Euro in nominal or real terms matters. The estimated effect of the price level on individual well-being is substantial: controlling for regional differences other than the price level, our results imply that, for a given yearly nominal income of 10,000 Euros, moving from the cheapest German district to Munich, the most expensive one, reduces subjective individual well-being by 1.7 points on a 10 point scale.

Besides providing new insights for the literature on individual well-being, our results have important policy implications and contribute to understanding how people perceive real versus nominal values.

On the one hand, our results do not question findings from former studies on well-being that used nominal inflation adjusted income as a proxy for real income if they aimed at analyzing well-being of the population as a whole. Our results show that measurement error due to using nominal instead of real income only marginally affects estimated coefficients of income. On the other hand, our results also imply that measurement error is substantial and significantly affects estimation results when analyzing well-being of individuals in the lower part of the income distribution. In our sample that is representative for Germany, the poorest 40% of the population are less satisfied with their life when living in a more expensive region.

In terms of policy implications, our results provide a strong argument in favor of regional indexation of transfer payments, in particular of those transfers which target needy groups such as the US Supplemental Security Income (SSI) or the German so called Arbeitslosengeld II, the lowest level of unemployment support. Our results also question country-wide uniform public sector wages. They show that not adjusting nation wide payments to regional price differences treats equals unequally in terms of individual well-being which seems hard to warrant in terms of justice.

Finally, our results promote the understanding of how people perceive real versus nominal terms, which is, among other things, crucial for determining optimal inflation rates to be targeted by the central bank. According to our results, people tend to perceive money values in real terms and thus, do not exhibit money illusion (only) if the difference between real and nominal values is large enough relative to their total income.

The paper is organized as follows: section 2 describes the data, section 3 presents our empirical strategy. Results are summarized in section 5. In the last section, we discuss implications of our results and conclude.

2 Data

This paper uses novel and comprehensive data on price levels in all 393 German districts (“Kreise”) to obtain a precise measure of individual real income. The data on prices at district level have been collected by the German Administrative Office for Architecture and Comprehensive Regional Planning. They are used to construct a price index that provides an overall price level for each district. The price index is based on roughly 7 million data points that measure prices of 205 distinct goods that are categorized in 57 classes of goods. In terms of classes of goods, this amounts to matching the basket of commodities used by the German Federal Statistical Office to calculate the Germany wide inflation rate to 73.2%. When constructing the price index the weight attached to each individual commodity is the same as the one used by the Federal Statistical Ofce. Items included in the calculation of the price index are, for example, rental rates, electricity prices, cinema tickets, fees for checking accounts, car prices, all kinds of food prices and dentist fees to name just a few. For a more detailed description of data collection and construction of the price index see Kawka, Beisswenger, Costa, Kemmerling, Mueller, Puetz, Schmidt, Schmidt and Trimborn (2009). We are not aware of any other data source from any other country that provides such a comprehensive price index below the national level.

Collecting such comprehensive data cannot be managed in a single year. The data were gathered in the years 2004 to 2009, with most of the data, roughly 85%, being collected from 2006 to 2008.

The data are used to build a single time-invariant price level for each district. Such a procedure implicitly assumes that the relative price level of each district stays constant over the period of study. To underline that this assumption is quite realistic Kawka et al. (2009) compare the relative rental prices at district level of 2004 with those of 2008 and find a correlation coefficient of 0.989. With a share of about 20%, rents are by far the most influencial component of the price index.

The original price index uses the district of the former German capital Bonn as baseline (100 points). The cheapest district is Tirschenreuth in East Bavaria with 83.37 points, while Munich, with 114.40 points the most expensive district, lies in Southern Bavaria. Hence, the most expensive district is around 37% more expensive than the cheapest, showing an extreme price differential within Germany. Figure 1 shows a map of Germany indicating the relative price level of each district. Three observations are worth mentioning: Price levels are lower in East than in West Germany and lower in Northern than in Southern Germany. Moreover, urban areas are unsurprisingly relatively more expensive than rural ones. To have a better interpretation of the estimates of our model (for details see section 3) we first rescale the price index to be always larger than or equal to 100 points and then divide it by 100 points. That is, we let the cheapest district be the base of 1 and rescale the other price levels accordingly.

We match the rescaled price index data and data from the GSOEP covering the years 2004-2008 by using district identifiers. The GSOEP is a representative panel study of German households that covers the years from 1984 on. In addition to household level information, individual information is available. Data cover a wide range of topics such as individual attitudes and health status, job characteristics, unemployment and income, family characteristics and living conditions. Haisken-DeNew and Frick (2003) provide a detailed description of the GSOEP.

The dependent variable is the answer to the question: ”How satisfied are you with your life, all things considered?”, which is answered on a ten point Likert scale. Frey and Stutzer (2002) list findings showing the validity of this question for measuring individual subjective well-being. In particular, the answers strongly correlate with real behavior such as smiling in social interactions or committing suicide.

Our explanatory variable of interest is real income. To our best knowledge, we provide the first study that uses a true measure of real income, i.e. one that takes regional price differences into account, as a determinant of individual well-being. The goal of our real income measure is to capture purchasing power of a given nominal income as precisely as possible: we start with household disposable nominal income, i.e. after tax household income including all kinds of government transfer income.2 We then form the corresponding per person equivalence income as suggested We adjust all income measures for inflation using 2004 as the baseline year. We use the Germany wide inflation

–  –  –

by the OECD, see Grabka (2008) for an application to GSOEP data. The idea of the equivalence income is to assign each household member the income that corresponds to the disposable income the household member would have if it were single. The equivalence income corrects household income for the number of persons living in a household by dividing through a factor. The factor takes a value of 1 for the first household member; 0.7 is added for each additional adult and 0.5 rate since there are no comprehensive data on regional inflation rates. The limited existing data on regional inflation rates suggest, however, that the inflation rate is quite uniform over Germany.

for each child. Last, to obtain our measure of real income, we divide the nominal equivalence income by the rescaled district specific price level.

Finally, we use a well established set of control variables similar to Frijters, Haisken-DeNew and Shields (2004), who also work with GSOEP data and investigate the influence of nominal income on happiness. These control variables are dummies for marital status (Married, Separated, Divorced, Widowed with being single as omitted category), dummies for employment status (Employed full time, Employed part time, Maternity leave, Non-participant with being unemployed as omitted category), the level of disability (Level of disability), the number of children in the household (Number of children), a dummy for whether a disabled person is living in the household (Invalid in household), and district dummies. Summary statistics of all variables can be found in Table A.1 in the Appendix. Moreover, we include year dummies. To have a representative sample of the German population we use all subsamples of the GSOEP data and use the cross-sectional weights provided in the GSOEP data, since the GSOEP oversamples certain population groups.

3 Empirical Strategy

The aim of our specification is to figure out whether, for a given nominal income, differences in purchasing power affect individual well-being. The identification is not hampered by reverse causality problems since it can be safely assumed that individual well-being does not influence regional price levels.

At first sight, it might seem natural to simply estimate a specification typically used in the literature on individual well-being and just substitute nominal by real income. Due to regional differences in price levels, an individual’s position in the German distribution of nominal income may differ substantially from the same individual’s position in the distribution of real income.

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