Inflation and the Rich After the Global Financial Crisis...regression will be run, where the change...

Inflation and the Rich After the Global

Financial Crisis

Branimir Jovanovic

National Bank of the Republic of Macedonia

[email protected]

February 13, 2014

VERY PRELIMINARY

ABSTRACT

We investigate the relationship between in�ation and the change in the top 10 percent

share of income after the global �nancial crisis of 2007-2008 in 42 countries. Contrary to

the recent literature, we �nd that higher in�ation is associated with greater reduction in the

top 10 share. We dismiss omitted variables or reverse causality as potential explanations for

the association. The main reason why would in�ation reduce inequality is that low-income

individuals have been protected from in�ation by minimum wage increases, di¤erently from

high-income individuals. Higher social transfers, due to higher revenues brought by in�ation,

may have also played a role in some countries.

JEL: D31, E31, G01

Keywords: in�ation, inequality, income distribution, top10 share, �nancial crisis, Great

Recession

1

I. Introduction

One of the elements of the consensus in macroeconomics before the global �nancial cri-

sis of 2007-2008 was that in�ation should be low and stable (Goodfriend (2007), Woodford

(2009)). In accordance with this, many central banks adopted in�ation targets of around 2

percent. With the emergence of the �nancial crisis, some challenged this consensus. Blan-

chard, Dell�Ariccia and Mauro (2010) questioned whether the distortions from higher in�ation

are really so high, and if they are higher than the potential bene�ts of avoiding the zero inter-

est rate bound. Rogo¤ (2008) proposed to use in�ation as one of the instruments for reducing

public debts, while Aizenman and Marion (2011) argued that raising in�ation to 6% could

erode US public indebtedness by 20 percent within 4 years.

Recent research argue that optimal in�ation in standard new Keynesian models remains

below or around 2 percent even when one allows for zero lower bound on the interest rate,

due to the low frequency of its appearance (Coibion, Gorodnichenko and Wieland (2012),

Schmitt-Grohé and Uribe (2010)). The situation is far from clear, however, because adding

certain features to the models easily increases the optimal rate of in�ation. For example,

Di Bartolomeo, Acocella and Tirelli (2013) demonstrates that when in�ation serves as a form

of taxation for �nancing social transfers, the optimal in�ation rate increases.

Another strand of literature that has attracted a lot of attention recently is income in-

equality. Many have pointed at rising income inequality in the U.S. as the fundamental cause

of the global �nancial crisis of 2007-2008 (see Stiglitz (2009), Milanovic (2009), Wade (2009),

Fitoussi and Saraceno (2010) and Rajan (2010)). Although the studies di¤er slightly in the

elaborations, the main idea is that the shift of income towards the rich and the fall of the

middle class in the U.S. created political pressures that were accommodated by easier credit.

This created a credit boom and eventually led to a �nancial crisis in the U.S., which spread

to the rest of the world. Ranciere and Kumhof (2010) develop a dynamic stochastic general

equilibrium model in which increase in inequality leads to a �nancial crisis.

Coibion et al. (2012) have linked these two strands of literature, analyzing the contribution

of monetary policy e¤orts for lowering the in�ation to income inequality. They have found

2

that the disin�ationary policies in the U.S. in the early 1980s have lead to a persistently

higher inequality in the 1990s.

Coibion et al. (2012) are not the �rst to analyze the relationship between in�ation and

income inequality. The in�ation-inequality nexus has attracted researchers� attention for

some time, and older studies have usually found that in�ation is likely to reduce inequality.

Bach and Ando (1957) analyze the redistributional e¤ects of in�ation in the US in 1939-1952,

while Bach and Stephenson (1974) in 1946-1971, �nding that, in general, in�ation has shifted

income from business pro�ts to wages and salaries, and from lenders to borrowers, and,

hence, has a¤ected equality favorably. Nordhaus (1973) attempts to determine the e¤ects of

anticipated in�ation on lifetime income, using three theoretical model frameworks - Classical,

Neoclassical and Keynesian. He �nds that in�ation is an equalizing factor in all the three

systems, although he notes that the simulation technique used is rather complicated and

sensitive to the assumptions.

More recent studies, on the other hand, have established a positive correlation between

in�ation and inequality across countries during 1960s-1990s (Beetsma and van der Ploeg

(1996), Dolmas, Hu¤man and Wynne (2000), Bulir (2001), Albanesi (2007), Crowe (2006)).

However, the arguments presented therein suggest that it is high inequality that leads to

higher in�ation. As Beetsma and van der Ploeg (1996) argue, in democratic societies with

high inequality, the median voter would prefer pro-poor policies, i.e. policies that redistribute

income from the rich to the poor, such as unanticipated in�ation, which would imply that

higher inequality results in higher in�ation. Similar mechanism is proposed by Dolmas,

Hu¤man and Wynne (2000), who argue that with higher inequality, the median voter prefers

higher in�ation as a way of �nancing higher government expenditures. Crowe (2006) and

Albanesi (2007) also provide models in which higher inequality raises in�ation, although for

di¤erent reasons. In their models, greater income inequality leads to greater inequality in

political in�uence, with the rich having greater political power. If the rich perceive that

in�ation will favour them, because the in�ation tax may hurt the poor disproportionately

more, then they may push for in�ationary policies.

3

This study will try to contribute to these debates, by investigating the relationship be-

tween in�ation and the top 10 percent share of income, during the Great Recession, on a

sample of 42 countries. Results suggest that higher in�ation during the Great Recession has

been accompanied by a decline in the top 10 share of income. The relationship is not likely

to be due to omitted variables or reverse causality. The main explanation is that countries

that have experienced higher in�ation during this period have also seen an increase in their

minimum wages, which has protected low-income individuals�income, but has not a¤ected

the income of the top 10 percent. In addition, higher in�ation may have brought "windfall"

revenues for some governments, which have been used for higher social transfers.

The paper proceeds as follows. Section II presents the methodology and the data used.

Section III investigates the relationship between in�ation and the change in the top 10 share

of income, using cross-country data, and assesses whether it can be interpreted causally.

Section IV delves more deeply into the underlying mechanisms that explain the relationship,

using both cross-country and household-survey data. Section V concludes.

II. Methodology and data

To assess the relationship between the change in inequality and the in�ation, simple

regression will be run, where the change in the income going to the top 10 share of population

after the �nancial crisis will be regressed on the average in�ation during the same period.

The change in the top 10 share after the crisis will be measured as the average annual change

between 2007 and the last data point (2010 or 2011). In�ation will be measured as the

average annual consumer price in�ation during 2008-2010. To gain some insights about the

underlying mechanisms driving the relationship, the same regression will be run for di¤erent

groups of countries.

To assess the underlying mechanisms in greater detail, data from household surveys will

be used. More precisely, it will be compared how income from the three main sources -

labour, capital and social transfers, changed after the crisis (2010 vs. 2007) for households

with di¤erent levels of income.

4

The macro analysis will cover the following 42 countries: Argentina, Austria, Belgium,

Bulgaria, Canada, Colombia, Cyprus, Czech Republic, Denmark, Dominican Republic, Ecuador,

Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Latvia,

Lithuania, Luxembourg, Malta, Moldova, Netherlands, New Zealand, Norway, Paraguay,

Peru, Poland, Portugal, Romania, Singapore, Slovakia, Slovenia, Spain, Sweden, United

Kingdom, United States, and Uruguay.

We chose to work with the top 10 percent (instead of the top 1 percent, for instance) since

data on the top 10 percent are available through several di¤erent sources, which increases the

number of observations. Three di¤erent sources will be used: the World Top Income Database

(WTID) of Alvaredo et al. (2013), Eurostat, and the World Development Indicators (WDI)

of the World Bank. For the countries that have data in more than one database, the primary

source is the WTID, and then Eurostat.1 The sources di¤er in their methodology: the

WTID compiles the data from tax records, the other two, from surveys. As a result, there

are signi�cant di¤erences across databases, for countries that are present in two of them. For

instance, 10 countries are accounted for both in the WTID and the Eurostat. For all of them,

the top 10 share is much higher in the former than in the latter (see Figure 1). However, it

can be also noted that the di¤erence between the two is relatively stable.2 Since our focus is

on the within-country change in inequality (i.e. we do not compare levels of inequality across

countries, but changes in inequality), the use of di¤erent methodologies should not represent

a signi�cant problem for our analysis.

1. Exceptions are countries on which the WTID has data only until 2009, and Eurostat until 2010 or 2011.These countries are represented by the Eurostat data. These are: Finland, France, Ireland, Italy, Norway,Switzerland, and the U.K.

2. Exception is Norway, for which the two data sources display some divergent movements in 2006 and2007.

5

Figure 1: Comparison of the top 10 percent share of income

in the two databases (%)

Table 1 shows average annual change in the top 10 share after the crisis, the last data

point and the data source for each country.

The micro analysis will be done using household survey data from the Luxembourg In-

come Study (LIS). Of the 42 countries analyzed in the macro part, these 15 countries are

present in the LIS both before the crisis (2007) and after it (2010): Colombia, Estonia, Fin-

land, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Poland, Slovakia, Slovenia,

Spain, United Kingdom, United States.

6

Table1:Changeinthetop10shareafter

thecrisisacrosscountries

Country

Change

Lastpoint

Source

Country

Change

Lastpoint

Source

Country

Change

Lastpoint

Source

Ecuador

-1.64

2010

WDI

Cyprus

-0.27

2011

Eurostat

CzechR.

0.00

2011

Eurostat

Peru

-1.08

2010

WDI

Spain

-0.27

2010

WTID

Latvia

0.00

2011

Eurostat

Iceland

-1.00

2011

Eurostat

Greece

-0.25

2011

Eurostat

Slovakia

0.03

2011

Eurostat

Romania

-1.00

2011

Eurostat

Canada

-0.21

2010

WTID

Bulgaria

0.05

2011

Eurostat

Argentina

-0.80

2010

WDI

Japan

-0.18

2010

WTID

Hungary

0.08

2011

Eurostat

Uruguay

-0.65

2010

WDI

Finland

-0.18

2011

Eurostat

Slovenia

0.08

2011

Eurostat

DominicanR.

-0.65

2010

WDI

Poland

-0.18

2011

Eurostat

Sweden

0.14

2011

WTID

Estonia

-0.50

2011

Eurostat

Luxembourg

-0.15

2011

Eurostat

UK

0.15

2011

Eurostat

Netherlands

-0.50

2011

Eurostat

New

Zealand

-0.11

2010

WTID

US

0.22

2011

WTID

Moldova

-0.48

2010

WDI

Colombia

-0.11

2010

WDI

Malta

0.25

2011

Eurostat

Lithuania

-0.38

2011

Eurostat

Italy

-0.05

2011

Eurostat

Denmark

0.29

2010

WTID

Portugal

-0.38

2011

Eurostat

Norway

-0.02

2011

Eurostat

Ireland

0.57

2010

Eurostat

Paraguay

-0.37

2010

WDI

Austria

0.00

2011

Eurostat

Singapore

0.60

2010

WTID

Germany

-0.30

2011

Eurostat

Belgium

0.00

2011

Eurostat

France

1.00

2011

Eurostat

7

III. Analysis

If one compares the change in the top 10 share of income after the global �nancial crisis

with the in�ation during the same period for these 42 countries, one can observe that there

is a clear negative correlation between the two: higher in�ation in the post-crisis period is

associated with a higher decline in inequality (see Figure 2 and Equation 1).

Figure 2: Inflation and change in inequality after the crisis

(1)

top10_ch = 0:21 �0:12��inflation

(0:10) (0:02)

n = 42;R2= 0:32

This association, however, cannot be interpreted as causal straightforwardly. It may

happen that some omitted variables, a¤ecting both the change in the top 10 share and the

in�ation, are driving the results. It may also happen that the causality �ows from inequality

8

to in�ation, as Beetsma and van der Ploeg (1996), Dolmas, Hu¤man and Wynne (2000),

Crowe (2006) and Albanesi (2007) have argued.

The literature on determinants of inequality identi�es several groups of factors that may

a¤ect inequality (see Roine, Vlachos and Waldenström (2009), Scheve and Stasavage (2009),

Afonso, Schuknecht and Tanzi (2010), Jovanovic (2014)). The �rst group refers to economic

activity. GDP growth may a¤ect inequality both positively and negatively, depending on the

conditions. Since GDP growth may also a¤ect in�ation, failure to control for its in�uence

is likely to bias the results. Economic activity in certain sectors may also a¤ect inequality,

like �nancial shocks. The second group refers to developments in inequality itself, like trends

or initial conditions. For example, if inequality is on a downward trend for a longer period

of time, for whatever reasons, and if the trend is somehow correlated with the in�ation,

the omission of the trend may bias the results. The third groups of factors is related to

public policy, like taxes, or social transfers, or government size. For instance, increase in

taxes may reduce inequality and lead to higher in�ation at the same time. The next group

comprises factors related to labour market characteristics, like labour marker regulations or

minimum wages. Increase in labour market regulations, or minimum wages, may reduce

inequality, and if they are related to in�ation, their omission may lead to wrong inference.

Monetary policy may also a¤ect both inequality and in�ation - monetary expansion may lead

to higher in�ation and to higher inequality, through higher �nancial income for the better-o¤

individuals. If one fails to control for this, one may wrongly conclude that in�ation a¤ects

inequality. The next group of factors refers to institutions and politics - political parties of

certain ideology may pursue in�ationary policies and push for higher or lower redistribution.

Finally, both in�ation and inequality may be a¤ected by productivity and openness.

We evaluate the e¤ect of these factors in turn, in Table 2. Each column presents the results

of one regression, with the column headline indicating which group of factors is included.

The exact de�nitions of the variables and the data sources are displayed in Table A1 in the

Appendix. As can be seen from Table 2, in all the cases the coe¢ cient on in�ation remains

intact, implying that the negative association between in�ation and the change in the top 10

9

share is not likely to be due to omitted variables.

Table 2: Adding variables to the basic regression-1 -2 -3 -4 -5 -6 -7 -8

Baseline Crisisandrecovery

Initialineq.andtrend

Govern-ment

Labour Monetary Institutions Productivityandopenness

in�ation -0.12*** -0.11*** -0.10*** -0.10*** -0.08*** -0.10*** -0.10*** -0.10***(0.02) (0.030) (0.021) (0.029) (0.028) (0.023) (0.027) (0.022)

fall_gdp -0.02(0.015)

recov_gdp 0.00(0.025)

fall_se -0.00(0.001)

recov_se 0.00(0.003)

top10_init -0.02(0.010)

top10_trend -0.11(0.116)

gov_size 0.01(0.026)

tax_top -0.00(0.009)

tax_top_ch -0.00(0.016)

bene�ts -0.00(0.006)

labour 0.09*(0.047)

participation -0.02**(0.009)

labour_ch -0.16(0.151)

min_wage -0.00(0.003)

ir 0.00(0.019)

m2 -0.00(0.003)

corrupt 0.07(0.068)

corrupt_ch -0.74(0.475)

left -0.15(0.113)

prod_ch -0.01(0.013)

open 0.00**(0.001)

Constant 0.21 0.04 0.63** 0.22 0.82 0.19 0.14 -0.03(0.10) (0.170) (0.278) (0.507) (0.583) (0.115) (0.150) (0.150)

Observ. 42 39 41 39 42 41 42 41R2 0.32 0.345 0.379 0.273 0.472 0.326 0.388 0.366

Dependent variable in all regressions is change in top 10 share. *** p<0.01, ** p<0.05, * p<0.1Robust standard errors in parentheses

10

To further assess the arguments for omitted variables, we conduct a Bayesian model av-

eraging exercise (BMA), including all these variables as potential explanatory variables for

the change in the top 10 share after the crisis. In this way, we basically investigate which of

the variables are most likely to be signi�cant determinants of the post-crisis change in the

top 10 share. BMA has gained prominence in recent years in applied research when there

is uncertainty regarding the appropriate theoretical model. BMA addresses the problem of

uncertainty by considering information from all available models (i.e. combinations of the

variables), instead of selecting only one model. The models are estimated using Bayesian

techniques, and then weighted by their goodness of �t, to obtain the desired statistics. Infer-

ences are usually based on the posterior inclusion probability (PIP), which acts as a measure

of the signi�cance of the variable, with PIP above 0.5 implying signi�cance, and on the

weighted averages of the posterior means and standard errors of the candidate variables.

It should be noted, though, that these are not directly comparable to the OLS coe¢ cients

and standard errors, because they include also models in which some of the variables are

excluded. A comprehensive explanation of BMA can be found in Hoeting et al. (1999). For

a brief introduction, see Jovanovic (2014).

The application of BMA usually requires the setting of priors for the model parameters,

the setting of priors for the models and the determination of how to choose from all the

available models. In our case, since the number of explanatory variables is rather low, 21,

and the number of potential models is therefore only 221 = 2097 152, instead of choosing only

a subset of models by Markov Chain Monte Carlo methods, we will estimate all the potential

models. Regarding the model prior, we will use two priors - the uniform prior, which assumes

that all the models have an equal prior probability of being correct, and the ��xed�prior of

Ley and Steel (2009), which sets the expected model size equal to 10 variables. Regarding

the prior for the model parameters, we will use the "hyper g" prior of Liang et al. (2008) and

the empirical Bayes (EBL) prior of Hansen and Yu (2001). We will report the PIP and the

posterior means for the coe¢ cients from the 100 best models. The results are presented in

Table 3, with each column heading denoting which combination the results refer to.

As can be seen, in�ation is always signi�cant, alongside the labour force participation

rate, the pre-crisis trend in the top 10 share, the change in corruption and the recovery of

11

the GDP and the stock exchange. We interpret these results as an additional support to the

claim that the identi�ed negative association between the change in the top 10 share after

the crisis and the average in�ation during the crisis is not likely to be driven by omitted

variables.

Table 3: BMA analysis of determinants of the change in

the top 10 percent share

Variables Coef. prior: hyper g Coef. prior: EBL Coef. prior: hyper g Coef. prior: EBL

Model prior: uniform Model prior: uniform Model prior: �xed Model prior: �xed

Post. Mean PIP Post. Mean PIP Post. Mean PIP Post. Mean PIP

top10_trend 0.67 0.99 0.69 1.00 0.67 0.99 0.69 1.00

participation -0.08 0.99 -0.09 1.00 -0.08 0.99 -0.09 1.00

corrupt_ch -3.91 0.95 -4.29 0.98 -3.28 0.84 -3.90 0.94

recov_se 0.02 0.88 0.02 0.89 0.02 0.89 0.02 0.88

recov_gdp 0.08 0.76 0.10 0.88 0.06 0.59 0.08 0.72

in�ation -0.12 0.70 -0.14 0.83 -0.09 0.53 -0.11 0.64

left -0.11 0.27 -0.12 0.30 -0.08 0.20 -0.10 0.25

tax_top_ch 0.01 0.16 0.01 0.22 0.00 0.09 0.00 0.12

m2 0.00 0.10 0.00 0.15 0.00 0.07 0.00 0.10

fall_se 0.00 0.10 0.00 0.13 0.00 0.08 0.00 0.08

fall_gdp 0.00 0.09 0.00 0.08 0.00 0.05 0.00 0.05

top10_init 0.00 0.09 0.00 0.08 0.00 0.05 0.00 0.07

labour 0.01 0.07 0.01 0.06 0.01 0.06 0.01 0.08

gov_size 0.00 0.06 0.00 0.07 0.00 0.04 0.00 0.06

bene�ts 0.00 0.06 0.00 0.07 0.00 0.06 0.00 0.06

tax_top 0.00 0.06 0.00 0.07 0.00 0.06 0.00 0.06

IR 0.00 0.05 0.00 0.07 0.00 0.06 0.00 0.05

labour_ch 0.01 0.05 0.01 0.06 0.02 0.05 0.01 0.05

corrupt 0.00 0.05 0.00 0.04 0.00 0.04 0.00 0.04

open 0.00 0.05 0.00 0.05 0.00 0.06 0.00 0.06

prod_ch 0.00 0.04 0.00 0.04 0.00 0.04 0.00 0.05

12

We next explore the arguments for reverse causality, i.e. we assess whether the observed

correlation between in�ation and the change in the top 10 share may be due to the latter

causing the former. The arguments for inequality causing in�ation are related to political

factors. As Beetsma and van der Ploeg (1996) argue, in democratic societies with high

inequality, the median voter would prefer pro-poor policies, i.e. policies that redistribute

income from the rich to the poor. Unanticipated in�ation may be one of those policies, which

would imply that higher inequality may result in higher in�ation. A similar mechanism is

proposed by Dolmas, Hu¤man and Wynne (2000), who argue that with higher inequality, the

median voter prefers higher in�ation as a way of �nancing higher government expenditures.

Crowe (2006) and Albanesi (2007) also provide models in which higher inequality leads to

higher in�ation, although for di¤erent reasons. In their models, greater income inequality

leads to greater inequality in political in�uence, with the rich having greater political power.

If the rich perceive that in�ation will favour them, because they may be insulated from the

in�ation tax, di¤erently from the poor, they may push for in�ationary policies.

One corollary of the explanation that the causality goes from inequality to in�ation is

that one would expect to see that the association between in�ation and the change in the

top 10 share is signi�cant only in countries with high inequality, or in countries with certain

political parties in power. Hence, if the relationship is present irrespectively of the level of

inequality or the political party in power, that could be treated as an argument against the

hypothesis that the causality goes from inequality to in�ation. We next test if there are

structural breaks related to the initial inequality and the political party in power.

Table 4 presents these results. In the �rst column, we allow for a structural break for

countries with high initial top 10 share, setting the median of the variable as a threshold

for high initial top 10 share. The coe¢ cient for the countries with high initial top 10 share

is virtually identical to the one for countries with low initial top 10 share. In the second

column, we use an alternative measure for the initial inequality - the Gini coe¢ cient in 2005-

2007 (data on Gini are from the Standardized World Income Inequality Database of Solt

(2009)). Results are very similar to the previous ones. Finally, we check whether in�ation

has reduced the top 10 share only in countries with leftist governments during the Great

Recession, which are considered to be more inclined to increases in public spending (column

13

3). It turns out that there are no di¤erences, again.

We interpret these results as arguments against the hypothesis that the causality in the

observed relationship between in�ation and the change in the top 10 share goes from inequal-

ity to in�ation.

Table 4: Reverse causality-1 -2 -3

High initial High initial Leftisttop 10 share Gini governments

in�ation -0.11*** -0.09** -0.11***(0.032) (0.037) (0.028)

hi_top10_init -0.19(0.197)

hi_top10_init*in�ation 0.00(0.043)

hi_gini_init -0.12(0.214)

hi_gini_init*in�ation -0.02(0.049)

left -0.12(0.199)

left*in�ation -0.01(0.051)

Constant 0.27** 0.22 0.25*(0.133) (0.135) (0.133)

in�ation+hi_top10_init*in�ation -0.10***(p value) 0.000in�ation+hi_gini_init*in�ation -0.11***(p value) 0.001in�ation+left_in�ation -0.12***(p value) 0.006Observations 42 42 42R-squared 0.354 0.360 0.352

Dependent variable in all regressions is change in top 10 share.*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors in parentheses

IV. Channels through which inflation has reduced

inequality

The literature identi�es several potential channels through which in�ation can reduce in-

equality (see Kane and Morisett (1993), Crowe (2006), Doepke and Schneider (2006), Coibion

et al. (2012)). The �rst one is through the di¤erent sensitivity to in�ation of the main source

of income of low-income and high-income individuals. Namely, the main source of income of

14

low income individuals is wages, which are sensitive to in�ation (unless they are indexed),

while high-income individuals realize big part of their income from capital, which is less sen-

sitive to in�ation. The second channel is through the asset holding. Low-income individuals

usually hold their assets in cash, whose real value is eroded by in�ation, while high-income

individuals hold most of their savings in in�ation-proof assets, such as equity, real assets,

commodities, or certain �nancial products. Another channel is through the redistribution

from lenders (i.e. the rich) to borrowers (i.e. the poor), when the in�ation in unanticipated,

which would make the poor bene�t from higher in�ation. The �nal channel is through the

government expenditure. Usually, in�ation serves as a windfall for the government, due to the

higher revenues and the erosion of the public debt, which may lead to higher social transfers,

bene�ting the poor.3

Thus, the �nding that in�ation has reduced the top 10 share after the global �nancial

crisis can be due to: 1) lower sensitivity of the lower-income individuals to in�ation, due to

increases in their wages; 2) low-income individuals holding more of their savings in in�ation-

proof assets, due to �nancial development; 3) redistribution from net-lenders to net-borrowers,

due to the low interest rates during the Great Recession; 4) higher government support to

the low-income individuals, due to the windfall from the higher in�ation. We next evaluate

these potential explanations, �rst, using macroeconomic, cross-country data, and then using

microeconomic data from household surveys.

IV.A. Cross-country data

If low-income individuals have been protected from in�ation, through wage-indexation or

wage increases due to the in�ation, one would expect to observe that the negative relationship

between in�ation and the top 10 share would be present only in countries which are more

likely to experience minimum wage increases, such as countries with strong trade unions, or

countries that have experienced higher increases in the minimum wages. Therefore, we next

estimate the in�ation-inequality regression, allowing for a di¤erential impact in countries with

strong trade unions, and in countries with increases in the minimum wages. We measure the

3. It should be noted that this relationship may turn around at very high levels of in�ation. If in�ation isvery high, government revenues may fall, due to time lags in collection, reduce real government spending (theOlivera-Tanzi e¤ect). This e¤ect is likely to bene�t the net tax payers (i.e. the rich). However, this e¤ect isunlikely to appear in our sample of countries, where the highest rate of in�ation is 10 percent.

15

strength of the trade unions in two ways. First, we use data on the trade union membership,

from the New Unionism Network. Second, we use data on the share of employees covered by

collective bargaining, from the International Labour Organization (ILO). Data on minimum

wages are obtained from the Doing Business Indicators of the World Bank. In each case,

we set the median of the variables as a cut-o¤ point between strong and weak characteristic.

The estimations are presented in Table 5, columns 1, 2 and 3..

It can be seen that the negative relationship between in�ation and the top 10 share is

present only in countries with strong trade unions and high minimum wage increases - the

coe¢ cient on in�ation is insigni�cant, but its sum with the coe¢ cient of its cross-product is

signi�cant at 1%. This supports the �rst hypothesis, that the observed relationship between

in�ation and the top 10 share is due to stronger trade unions protecting the low-income

individuals from the adverse e¤ects of in�ation through minimum wage increases.

The second channel, through asset holding, is more di¢ cult to assess, because there are

no data on investment in in�ation-proof assets. One proxy variable can be the degree of

inclusion of the �nancial system - in countries with more inclusive �nancial systems, low-

income individuals will be also able to hedge from in�ation. One often-used measure of the

inclusion of the �nancial system is the share of bank deposits to the GDP. The problem with

this variable is that it is determined by many variables, such as the level of development, or

the nature of the �nancial system (banking vs. stock-exchange). A better measure would,

therefore, be the rate of growth of the deposits - countries with higher deposit growth are

experiencing a wave of �nancial development, i.e. �nancial inclusion. This is the variable

that we will use, i.e. we will use the growth of the bank deposits before the crisis (2004-2007),

from the Financial Access Survey of the International Monetary Fund. As previously, we will

divide the countries in two groups - with high and low inclusiveness, using the median of the

variable as a cut-o¤ point, and will estimate the regression for the two groups of countries.

The results, shown in column 4 of Table 5, point out that there are no di¤erences in the

in�ation-change in inequality relationship for the two groups of countries, arguing against

the second channel.

To investigate the third hypothesis, that the relationship is due to redistribution from

net-lenders (i.e. the rich) to net-borrowers (i.e. the poor), we estimate the basic regression

16

for countries with low and high interest rates during the crisis. With low nominal interest

rates, higher in�ation results in low (sometimes even negative) real interest rates. If the

relationship is found only in countries with low nominal interest rates during the crisis, then

this would support the hypothesis that the negative relationship is due to redistribution.

We classify countries into �low-interest rates�and �high-interest rates�on the grounds of the

average money-market interest rate during 2008-2010, using the median as a cut-o¤ point.

These results are presented in Table 5, column 6. Somewhat surprisingly, the relationship is

signi�cant only for the countries with high-interest rates. The explanation for this may be

that countries with low interest rates actually had very low levels of in�ation. In any case,

the results do not support this hypothesis.

Finally, to assess whether the identi�ed relationship between in�ation and the change in

the top 10 share is due to the stronger government support to the low-income individuals,

due to the higher revenues from the in�ation, we estimate the basic equation separately for

countries with high and low government support through the bene�ts during the crisis. The

results are presented in Table 5, column 5. The relationship holds for the two groups of

countries, which implies that the underlying channel is the government support.

17

Table 5: Channels-1 -2 -3 -4 -5 -6

Union Collective Minimum Financial Government Low interestdensity bargaining wage inclusion bene�ts rates

in�ation -0.08 -0.03 -0.10 -0.10** -0.10*** -0.03(0.054) (0.040) (0.061) (0.040) (0.029) (0.057)

hi_union 0.25(0.228)

hi_union*in�ation -0.06(0.056)

hi_col_barg 0.23(0.225)

hi_col_barg*in�ation -0.11**(0.048)

hi_min_wage -0.16(0.192)

hi_min_wage*in�ation 0.00(0.065)

hi_inclusion 0.19(0.199)

hi_inclusion*in�ation -0.02(0.046)

hi_bene�ts 0.05(0.214)

hi_bene�ts*in�ation -0.03(0.050)

hi_ir_level 0.13(0.212)

hi_ir_level*in�ation -0.09(0.061)

Constant 0.06 0.00 0.23 0.11 0.19 0.07(0.217) (0.191) (0.161) (0.177) (0.119) (0.171)

in�ation+hi_union*in�ation -0.14***(p value) 0.000in�ation+hi_col_barg*in�ation -0.14***(p value) 0.000in�ation+hi_min_wage_in�ation -0.10***(p value) 0.000in�ation+hi_inclusion_in�ation -0.13***(p value) 0.000in�ation+hi_bene�ts*in�ation -0.13***(p value) 0.002in�ation+hi_ir_level*in�ation -0.12***(p value) 0.000Observations 42 42 42 42 42 42R-squared 0.342 0.393 0.340 0.337 0.332 0.366

Dependent variable in all regressions is change in top 10 share. *** p<0.01, ** p<0.05, * p<0.1Robust standard errors in parentheses

18

IV.B. Household-level data

The household-level analysis will be done using data from the Luxembourg Income Study.

Of the 42 analyzed countries, 15 are present in the LIS dataset with data both before and

after the crisis (Colombia, Estonia, Finland, Germany, Greece, Ireland, Italy, Luxembourg,

Netherlands, Poland, Slovakia, Slovenia, Spain, United Kingdom, United States). Ten of

them experienced a decline in the top 10 income share after the crisis - Colombia, Estonia,

Finland, Germany, Greece, Italy, Luxembourg, Netherlands, Poland and Spain. Only four of

these had at the same time above-average in�ation (i.e. average annual in�ation during 2008-

2010 above than 2.5 percent): Colombia, Estonia, Greece, Poland. These are the countries

on which we will focus. We will try to assess to what extent the decline in the top 10 share

that these countries experienced can be explained by the in�ation, through the alternative

channels that were explained above.

The pre-crisis data refer to 2007, while the post-crisis data to 2010. The data are from

the household-level surveys, not individual-, due to the better coverage. They are expressed

in per-capita terms and are bottom-coded (i.e. the negative values are replaced with zeros).

They are weighted by the provided weights, so the statistics can be interpreted as referring

to the total population. The values for 2010 are de�ated by the cumulative in�ation in

2008-2010.

The assessment of the relative merit of the alternative channels using micro data will

be done by comparing the income the low- and high-income households have realized from

di¤erent sources before and after the crisis. Households will be grouped in ten groups (deciles),

depending on the per-capita disposable household income (i.e. gross household income, minus

taxes and contributions). Three main sources of income will be analyzed: labour income,

capital income and income from social security transfers. The data on the di¤erent sources of

income are in gross terms, not net. Because of this, we will not try to assess the contributions

of the changes in the alternative sources of income to the change in total disposable income,

because the taxes for each category of income are not known, but will just compare the

changes in the di¤erent incomes before and after the crisis.

We begin with Colombia. Colombia had a decline in the top 10 share of income in 2010,

with respect to 2005 (no data for 2006 and 2007), of 0.54 percentage points. Average annual

19

in�ation in 2008-2010 was 4.5 percent. Table 6 presents the mean per capita income from the

three sources for households belonging to di¤erent income deciles before and after the crisis.

Table 6: Mean per capita household income

for different households, Colombia

Income decile Labour income Capital income Social transfers

2007

1 172,240 12,267 1,207

2 666,806 18,942 2,839

3 1,149,795 26,879 75,252

4 1,422,576 21,358 107,363

5 1,725,213 44,865 121,271

6 2,290,361 88,556 191,905

7 2,609,996 91,548 343,572

8 3,366,113 143,173 430,565

9 4,903,635 184,249 790,569

10 12,600,000 1,419,970 2,111,646

2010

1 257,199 17,609 553

2 841,822 35,816 21,701

3 1,280,106 34,737 111,161

4 1,567,708 34,328 121,756

5 1,877,156 70,596 148,222

6 2,363,258 77,773 187,907

7 2,704,567 123,265 347,103

8 3,573,100 139,300 505,333

9 4,964,984 234,313 761,473

10 11,455,950 1,335,398 2,503,636

In national currency. 2010 �gures are in 2007 prices (i.e. de�ated).

Bolded �gures point main changes.

Several things should be noted. First, poorer households (1st and 2nd decile) experi-

enced sizeable increase in their labour income between 2010 and 2007. Second, the top decile

households experienced a decline in both their labour and capital income. Hence, the pre-

vious explanation - that the poor households have been insulated from the adverse e¤ects

20

of in�ation, through minimum wage increases, di¤erently from the rich - seems to hold for

Colombia. Although trade union coverage is actually very low in Colombia, with only 2

percent of the workers being members, minimum wage has been increased between 2008 and

2010 by 18.7 percent in total. Regarding the decline in the capital income, it has been mostly

due to the decline in the rental income, not income from interest. It remains unclear why

the rental income declined.

Overall, it can be said that the micro data for Colombia support the previous explanation,

at least to some extent - in�ation has redistributed income from rich to the poor, mainly

through the labour income, as the former have been protected by the minimum wage increases,

di¤erently from the latter.

We next turn to Estonia. Estonia has seen a decline in its top 10 share of income of 2

percentage points in 2011, compared to 2007. Average in�ation in 2008-2010 has been 4.4

percent per annum. Table 7 presents its micro data developments.

21


for different households, Estonia


2007

1 4,351 111 26,130

2 15,744 36 26,723

3 26,542 171 23,066

4 34,858 141 20,646

5 48,848 313 13,984

6 62,951 320 10,448

7 68,523 303 9,642

8 75,140 188 8,266

9 98,970 538 8,505

10 148,012 3,563 7,855

2010

1 6,705 86 18,201

2 8,415 145 34,711

3 26,372 155 18,414

4 25,552 475 24,065

5 31,398 430 23,471

6 48,073 489 14,696

7 58,352 185 13,847

8 68,859 568 11,058

9 78,290 1,065 10,814

10 132,487 1,603 12,671



The situation in Estonia seems very similar to that in Colombia - the labour income of

the poorest (1st and 2nd decile) increased after the crisis, while labour income of the top

decile households declined. The top 10 percent also su¤ered a decline in their capital income,

but the magnitude of this decline is smaller. Union membership in Estonia is also rather low

(11 percent), but this did not preclude rise in the minimum wage, which was increased by 21

percent in 2008. At the same time, the labour income of the top 10 percent was not covered

22

by this, and has declined after the crisis. Regarding the capital income of the top decile, it is

hard to assess the reasons for its decline, because there are no data on income from interests

and dividends.


for different households, Greece


2007

1 1,649 65 1,785

2 1,645 122 3,052

3 2,789 144 2,580

4 3,695 123 2,684

5 4,735 237 2,675

6 6,061 333 2,293

7 7,345 309 2,295

8 8,855 515 2,371

9 12,194 530 2,176

10 20,449 1,790 2,939

2010

1 601 60 1,350

2 1,334 108 2,663

3 1,928 43 3,300

4 2,843 146 2,603

5 3,729 110 2,973

6 4,268 254 2,874

7 5,139 255 3,155

8 7,280 278 2,496

9 9,026 478 2,791

10 16,547 1,234 3,638



Next, we turn to Greece. In Greece, the top 10 share of income in 2011 declined by

1 percentage points vis-a-vis 2007, while average annual in�ation has been 3.4 percent in

23

2008-2010. The decline in the top 10 share, as can be seen from Table 8, was mostly due to

the decline in the labour income, which has been probably due to the crisis. Di¤erently from

Colombia and Estonia, the labour income in Greece declined for households from all deciles,

likely due to the severity of crisis. However, the decline for the lower-income households has

been milder (in absolute terms), possibly due to the minimum wage increase, which has been

increased by 13 percent in 2008-2010. It can also be noted that the capital income has also

declined for the richest households, but, again, it is not possible to assess if this is due to

lower interest income, due to unavailability of disaggregated data.

24


for different households, Poland


2007

1 867 8 4,313

2 2,060 13 5,464

3 3,093 18 4,600

4 3,877 8 4,108

5 4,738 15 3,761

6 5,745 21 3,545

7 6,783 8 3,194

8 8,273 26 3,164

9 10,524 40 2,838

10 19,856 259 2,325

2010

1 836 13 5,108

2 2,269 13 6,358

3 3,632 18 4,785

4 4,420 18 4,512

5 5,573 20 4,207

6 6,889 24 3,739

7 8,210 26 3,451

8 10,021 31 3,067

9 12,781 59 2,756

10 22,947 192 2,274



Poland, the last country we analyze, is somewhat di¤erent. In Poland, the top 10 share

of income declined in 2011, relative to 2007, by 0.7 percentage points, with average in�ation

in 2008-2010 of 3.6 percent per annum. The main reason behind the decline in the top 10

share in Poland seems to be the higher labour income for the other deciles, as well as the

higher social transfers for the households from the �rst two deciles of income. Regarding the

latter, it is possible that the increase in the social transfers is due to the higher government

25

revenues, due to the higher in�ation - both in�ation and government expenditure on social

bene�ts have been higher in 2008-2010 than in the previous four years, the former by 1.3

percentage points, the latter by 2.7 percentage points.

To summarize, the micro analysis supports, to some extent, the �ndings from the cross-

country analysis, that higher in�ation has some role to play in the decline in the top 10

share of income after the crisis. While the case for redistribution from lenders to borrowers

seems weak for the analyzed countries, there seems to have been some redistribution through

the labour income, since higher income earners have not been protected from the higher

in�ation, while lower income earners have been, through higher minimum wages. There is

also some evidence in favour of the hypothesis for higher government support through the

social transfers.

V. Conclusions

We have investigated the role of in�ation for the decline in the income of the rich (the top

10 percent) after the recent global �nancial crisis. Using both cross-country and household-

survey data, we �nd that higher in�ation has reduced the share of income going to the top

10 percent through several channels. First, in�ation has eroded the labour income of the

rich, but not of the poor, which have been protected by the trade unions, through minimum

wage increases. Second, higher in�ation has increased government revenues, and this has

been canalized towards higher social transfers.

The size of the e¤ect may seem small at �rst - increase in annual in�ation of one percentage

point decreases the share of income going to the top 10 percent by only 0.1 percentage points

per year. Still, the e¤ect can be sizeable if observed through a longer period of time - if

in�ation is increased from 2 to 5 percent, for a period of 15 years, that would reduce the top

10 share of income by 5 percentage points.

Given that the main channel through which in�ation has a¤ected inequality is the di¤erent

response of low- and high-income individuals�labour income to in�ation, the reduction in the

above scenario is likely to be smaller, because it seems unlikely that the labour income of the

rich would remain unchanged if in�ation averages 5 pecent for 15 years. Since the e¤ect of

in�ation on inequality reduction is modest, and since there are many uncertainties involved,

26

raising in�ation may not be a viable measure for decreasing income inequality. Perhaps a

better reading of the results is through the measures that should be taken during periods of

mild in�ation, in order to bene�t from it. In that sense, our results suggest that measures

aimed at protecting the most vulnerable individuals, through minimum wage increases, seem

to be the most e¤ective way to reduce inequality during in�ationary periods.

More research is needed in order to derive sounder conclusions. The �rst avenue for future

extension is to try to �nd household level data for the countries which had higher rates of

in�ation and higher decreases in the top 10 share during the Great Recession, like the Latin

American countries. This would allow us to gauge the underlying mechanisms more reliably.

Second area for improvement is to see if the results extend to other periods as well, or hold

only for the recent post-crisis period.

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Table A1: VariablesVariable name Variable de�nition Sourcetop10_ch Change in the top 10 share in 2011 (or 2010, analogously to the

last available data point) and the average for 2005-2007.WTID,Eurostat,WDI.

in�ation CPI in�ation, average for 2008-2010. WDIfall_gdp Annual change in real GDP per capita in 2009. WDIrecov_gdp Annual change in real GDP per capita in 2010, or average for

2010-2011, analogously to the last data point.WDI

fall_se Annual change in the market capitalization of listed companies(as a % of GDP) in 2008. The 2008 was selected instead of2009 because the �nancial shock happened in 2009.

WDI

recov_se Change in the market capitalization of listed companies, as a% of GDP, in 2010 (or 2011) with respect to 2009.

WDI

top10_init Initial top 10 share, i.e. top 10 share in 2005-2007. WTID,Eurostat,WDI.

top10_trend Pre-crisis trend in the top 10 share, i.e. average change in theperiod 2003-2007.

WTID,Eurostat,WDI.

gov_size General government consumption in 2010 (or 2010-2011), as apercent of GDP.

WDI

tax_top Top marginal tax rate in 2010 (or 2010-2011). EFWtax_top_ch Change in the top marginal tax rate in 2010 (or 2010-2011),

with respect to 2009.EFW

bene�ts Real change in subsidies and other government transfers in 2010(or 2010-2011), with respect to 2009.

WDI

labour Labor market regulation index in 2010 or 2010-2010. Higherindex=less regulated.

EFW

participation Labor participation rate, total (% of total population ages15+), 2010 or 2010-11

WDI

labour_ch Change in labor market regulation index in 2010 or 2010-2011,with respect to 2009. Higher value = made less regulated.

EFW

min_wage Minimum wage, change in 2010 with respect to 2007. DBIir Money market rate, 2010 (or 2010-2011) minus 2009. In real

terms. For the euro area countries, it�s the euro area data.IFS

m2 M2, % of GDP, 2010 (or 2011) vs. 2009. WDIcorrupt Control of corruption index, in 2010 (or 2010-2011). Higher

value = higher control.WGI

corrupt_ch Change in the control of corruption index, in 2010 (or 2010-2011) with respect to 2009. Higher value = improvement inthe control.

WGI

left Dummy if the country had a leftist government in 2010 or 2011 DPIprod_ch Di¤erence between productivity growth in 2000�s and 1990�s ILOopen Exports of goods and services, as a percent of GDP, 2010 or

2010-2011.WDI

WTID stands for the World Top Income Database of Alvaredo et al. (2013).WDI stands for the World Development Indicators of the World Bank.WGI stands for the Worldwide Governance Indicators of the World Bank.EFW stands for the Economic Freedom of the World dataset of Gwartney, Lawson and Hall (2013).DPI stands for the Database on Political institutions of Beck et al. (2001).IFS stands for the International Financial Statistics of the IMF.DBI stands for the Doing Business Indicators of the World Bank.30

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