Evidence and determinants of consumer sentiment as aleading or coincident indicator of economic activity
November 2015
Jounghyeon Kim†
ABSTRACT: Based on the data from ten Organization for Economic Co-operation andDevelopment (OECD) countries, this paper explores the evidence as to whether a consumer
confidence indicator is a leading, coincident, or lagging measurement of economic activity,and examines factors that affect the consumer sentiment index (CSI). Determinants of CSI
and the causal relationship between CSI and economic activity vary across countries. In somecountries, however, evidence is found that CSI contains not only leading information but also
coincident information, which acts as a mood signal. Moreover, it is found that consumer
sentiment can be driven by both economic factors such as wealth and interest rate, as well
as non-economic factors such as emotional state.
JEL Classification: E21; C32; D12
KEY WORDS: consumer sentiment index; leading/coincident information; share price index

I. Introduction
Consumers are regularly surveyed in many countries to assess the past, current, andfuture economic and financial conditions and to publish the consumer sentiment index(CSI) by compiling survey data well before official data on consumer spending becomes
available. One of the advantages of the sentiment data is that it is “timely” (i.e.,
available monthly), making it a good measure for predicting economic activity.
In general, measures of consumer confidence or sentiment tend to be perceived
as barometers of the state of the economy. But do consumers anticipate or react tochanges in the state of the economy? The answer to this question is important because
leading information may be useful for forecasting purposes, while lagging or coincident†

Contact: Assistant Professor, Department of Economics, Chosun University, 309 Pilmun-daero,Dong-gu, Gwangju 61452 Korea. Tel: +82-62-230-6823. Email: jhyeon@chosun.ac.kr. This study is
supported by research funds from Chosun University, 2014.

1

information is more useful as a mood signal for assessing the changes in economic
activity.
In literature, a number of empirical studies have examined the role of informationand the effectiveness of consumer sentiment as sources of aggregate economic movements. The studies have stressed the role of consumer sentiment as a tool for predicting
economic activity. In particular, CSI is described as an important determinant of consumer spending as a leading indicator that can forecast consumption (for the U.S.,
Bram and Ludvigson, 1998; Carroll et al., 1994; Ludvigson, 2004; Fuhrer, 1993; Matsusaka and Sbordone, 1995, for the U.S. and the U.K., Easaw et al., 2005; for the U.S.
and Europe, Dees and Brinca, 2013). From a similar perspective, this paper explores
the causal relationship between movements in CSI and economic activity. Specifically,
the present paper seeks to find an answer to the following question: does an increase
in sentiment cause the gross domestic product (GDP) to increase because people feel
optimistic about the economic outlook and spend more, or does an increase in GDP
cause a rise in sentiment because people are (or feel) wealthier?
In previous analyses performed with data from multiple countries, the association
between CSI and GDP appears to vary substantially across countries. For example,
Golinelli and Parigi (2004) find that among the nine countries they singled out, CSI
possesses both coincident and leading information for output in Germany, the U.K.,
and the U.S.. Santero and Westerlund (1996) also find that among the eleven countries
they studied, there is a correlation between consumer sentiment and economic output,
but it is weaker than the correlation between business sentiment and economic output.1
Even after controlling for economic fundamentals, such as money supply, government
spending, and material prices, CSI is still identified as an important source of variationsin economic output (Matsusaka and Sbordone, 1995).
1

Christiansen et al. (2014) find that business confidence is a better predictive indicator of the
U.S. recession than consumer confidence.

2

Previous studies have focused primarily on investigating the role of sentiment as aleading indicator of economic activity. However, this paper focuses more on the roleof sentiment as a coincident or lagging indicator of economic activity and on finding
economic and non-economic determinants of CSI in various countries.
To distinguish this study from previous ones, I have taken a relatively large sample
consisting of ten Organization for Economic Co-operation and Development (OECD)
countries in various regions of the world: four in Europe (Germany, the U.K., France,and Italy), two in Oceania (Australia and New Zealand), three in North America (theU.S., Canada, and Mexico), and one in Asia (Japan). Based on the data from thesecountries, this study estimates a non-linear model using Marquardt’s nonlinear leastsquare algorithm to resolve the autocorrelation in the regression model presented in
Section 3.2
Estimation is performed with macroeconomic and financial variables such as CSI,
GDP, share price index (SHPI), short-term interest rate (STIR), inflation rate (INFR),
and unemployment rate (UEMR), as well as a non-economic variable of emotional state,proxied by smoothed probabilities for CSI, which are extracted from a simple Markov
switching model. It is found that CSIs of all countries except Australia and Mexico
measure leading economic activity. Moreover, in the univariate regression of CSI only
on GDP, or the multivariate regression with aforementioned other variables as control
variables, CSIs of all countries except Australia, New Zealand, and Italy are foundto contain coincident information that corresponds to the current economic activity.
These results suggest that consumers react to the changes in the state of economy.
Although consumer sentiment is usually driven by systematically varying economic
factors, it may be also driven by non-systematically varying non-economic factors, including psychological factors such as emotions and consumer expectations. Katona
2

Lovell and Tien (2000) also take into account autocorrelation in their regressions.

3

(1968 and 1975) argues that CSI measures attitudes and expectations of consumers
about financial and economic conditions and may not be a precise measurement of
economic evolution because it relies on the subjective nature of consumers. Doms andMorin (2004) and Starr (2012) find that news coverage of non-fundamental factors
about economic activity exerts a significant effect on changes in consumer sentiment.
Moreover, assuming that the residuals from regression of CSI on its economic determinants contain non-economic information, Garner (1981) finds that non-fundamental
unique events, such as Watergate and OPEC in 1973–1974, affect CSI. These studies
suggest that CSI is likely to contain non-economic information. In turn, this paper also
examines whether consumer confidence can be explained by non-economic factors suchas emotional state, in addition to standard economic determinants of consumption,such as wealth and interest rate.
Given a set of potential macroeconomic determinants of CSI in a vector autoregressive (VAR) model in multiple countries, Golinelli and Parigi (2004) find that thedeterminants are time- and country-specific. In the U.S., an increase in stock prices
causes the wealth effect on CSI and acts as a leading indicator of future income growth(Otoo, 1999). In Europe, stock prices have a less of wealth effect on CSI, whereas
the expenditure effect is greater and more dispersed, which implies that stock prices
play a dominant role as a leading indicator rather than producing the wealth effect(Jansen and Nahis, 2003). In general, an increase in stock prices makes consumers feel
wealthier and can lead to increase in their consumer sentiment, suggesting that thewealth effect plays a significant role in changing the consumer sentiment.
Similar to the findings of Golinelli and Parigi (2004), the present study also findsthat the determinants of CSI vary across countries. Nevertheless, SHPI and STIR are
identified as most significant factors in all countries, except for Mexico in the case of
SHPI and except for Germany, France, Italy, and Japan in the case of STIR, while

4

INFR is identified as such only in the U.S.. More importantly, both contractionary
economic state, proxied by a dummy variable for periods of negative GDP growth and acontractionary emotional state, proxied by smoothed probabilities for CSI in downturn
periods of GDP, are found to be important factors in Germany and Mexico. Thesefindings suggest that both economic and non-economic factors are partially responsible
for the changes in CSI.
The remainder of this paper is organized as follows. In Chapter 2, the variables
and data used in this study are explained. Chapter 3 presents the empirical analysis,
including prior evidence, regression model, estimation results, and the determinants of
CSI. Chapter 4 concludes the paper.

2. Variables and Data
The variables for the present study were selected based on previous empirical studies(Fuhrer, 1993; Garner, 1981; Golinelli and Parigi 2004; Jansen and Nahuis, 2003;
Lovell, 1975; Otoo, 1999). The variables include CSI, GDP, SHPI, STIR, INFR, andUEMR. In particular, the last four variables are singled out as potential forces driving
consumer sentiment since they are the determinants of consumption, which is known
to be highly correlated with CSI.
Share price index is used as a proxy variable of income (wealth), which has apositive relationship with consumption. Short-term interest rate is one of the importantfactors relevant to consumption. Inflation rate based on consumer price index (CPI) is
known to have a direct relationship with consumption. Unemployment rate contains
information as a lagging indicator about future economic conditions (Vuchelen, 2004).
A high UEMR indicates that economy has performed poorly, which implies a lower
future income level for consumers, presumably leading to decreases in current and5future consumption.
Data regarding all these variables come from OECD’s database OECD.Stat andthey are compiled from ten major OECD countries: four from Europe (Germany, theU.K., France, and Italy), three from North America (the U.S., Canada, and Mexico),
two from Oceania (Australia and New Zealand), and one from Asia (Japan). Amongthese countries, Mexico is the only emerging country, so my initial conjecture on empirical results is that the role and the determinants of CSI in Mexico would be different
from those of other countries.
The monthly data for some variables, such as CSI, CPI, and SHPI, are converted
into quarterly data by simply averaging the three-month values in each quarter. The
consumer sentiment index is calculated from the survey data related to the past, current, and future economic and financial situations. It is in general measured as abalanced score, which is the percentage of positive responses (or increases) minus thepercentage of negative responses (or decreases), plus 100.3 However, survey questions
and computing methods for CSI somewhat differ between European and non-European
countries, resulting in different index scales.4 Thus, to make the regression results ofvarious countries more comparable, the standardized CSI of OECD is used, which isseasonally adjusted.
Likewise, the seasonally adjusted and standardized UEMR is used, which is defined
as a percentage of the unemployed people to the civilian labor force, except in Germany.
German unemployment data are only available from the first quarter of 1991, causing
a reduced sample period in the empirical analysis. To extend the sample period, aseasonally adjusted UEMR for people aged 15 and over is used. Short-term interest
3

An index number above the threshold (100) indicates that the economy will improve because
the number of consumers with a positive outlook on the economy outnumbers those with a negative
outlook.
4
For the method of calculating the index, refer to Bram and Ludvigson (1998) and Ludvigson(2004) for the U.S. and Golinelli and Parigi (2004) for other countries.

6

Table 1
Description of country-specific samples for estimation
Countries

Sample period

Australia
New ZealandGermany
United Kingdom
France
Italy
United States
CanadaMexicoJapan

1974:Q4 - 2012:Q4
1988:Q3 - 2012:Q4
1973:Q1 - 2012:Q4
1974:Q1 - 2012:Q4
1973:Q1 - 2012:Q4
1981:Q2 - 2012:Q4
1978:Q1 - 2012:Q4
1980:Q1 - 2012:Q4
2001:Q2 - 2012:Q4
1982:Q3 - 2012:Q4

Variables{GDP, CSI, STIRSHPI, INFR, UEMR}

Data Frequency
Quartly
Quartly
Quartly
Quartly
Quartly
Quartly
Quartly
Quartly
Quartly
QuartlyNote: The U.K.’s STIR and France’s UEMR start from 1978:Q1, and Japan’s GDP and STIR start from 1994:Q1 and2002:Q2, respectively.

rate is the money market rate per annum, i.e., the short-term borrowing rate between
financial institutions, typically referred to as the call rate or the interbank rate. Inflation rate is defined as the quarterly percentage change in CPI measured by “all items”
with the OECD base year 2005 as 100. Share price index is the national share priceindex with the OECD base year 2005 as 100. The real GDP is also seasonally adjusted.
The initial sample period varies depending on the availability of data in each country, while the final period is 2012:Q4 for all countries. In some countries, sample periods
for some variables are not consistent due to the limitation of data. For example, theU.K.’s STIR and France’s UEMR start from 1978:Q1, and Japan’s GDP and STIR
start from 1994:Q1 and 2002:Q2, respectively. Therefore, the sample periods for thesecountries are reduced whenever the variables are included in regression analysis. The
description of variables and the sample periods for each country are summarized inTable 1.

7

3. Empirical Analysis
3-1. Prior Evidence
To observe the preliminary evidence of the causal relationship between CSI and GDP, I
first have to compare the time series of CSI with the historical quarter-to-quarter GDP
growth rate. The gray bars in Figure 1 indicate the periods of negative growth. The
figure shows that in all countries, negative GDP growth periods coincide with most of
CSI downturn periods. This presents evidence that there is likely to be a coincident andpositive correlation between CSI and GDP. However, the relationship between CSI and
one-quarter-lagged or -leading GDP growth rate, i.e., lagging or leading information of
CSI, is not clearly identifiable in the figure.
Even when CSI shows a downturn trend, the economy is not necessarily in contraction if the index is above the baseline of 100 (i.e., the number of positive consumer
responses is greater than the number of negative responses). To explicitly identify
true contractionary periods, the smoothed probabilities (SPs) for the contractionary
state are extracted from CSI using a simple Markov switching regression model andthe contraction periods are compared with the periods of negative growth.5 Smoothedprobabiliy is defined as the smoothed inference about the state at time t using information available regarding a future period.6
As shown in Figure 1, SPs describe the state of contraction in most of the periods
when CSI is below 100 and the state of expansion (i.e., non-contraction) in most of
the periods when CSI is above 100. In most countries, negative GDP growth periods,
in particular at least two consecutive quarters of negative GDP growth, coincide withthe periods of SPs for a contractionary state. Similar to the previous outcomes regarding the association between GDP growth rate and CSI, this also presents evidence
5
6

The model is specified as CSIt = βst + ut where ut ∼ N (0, σs2t ), st = states 1 and 2 for time t.
For further details, refer to Hamilton (1989 and 1993).

8

1986 Q4

1990 Q4

1994 Q4

1998 Q4

2002 Q4

2006 Q4

2010 Q4

1985 Q1

1989 Q1

1993 Q1

1997 Q1

2001 Q1

2005 Q1

2009 Q1

1991 Q3

1994 Q3

1997 Q3

2000 Q3

2003 Q3

2006 Q3

2009 Q3

2012 Q3

2000 Q3

2003 Q3

2006 Q3

2009 Q3

2012 Q3

1992 Q1

1996 Q1

2000 Q1

2004 Q1

2008 Q1

2012 Q1

0.0

96.0
1988 Q1

0.2

97.4

1984 Q1

0.4

98.8

0.8

1.0

0.6

1980 Q1

Canada

100.2

101.6

103.0

CSI
SP
100

1997 Q30.0

96.0
1994 Q3

0.2

97.4

1991 Q3

0.4

0.8

1.0

98.8

1988 Q3

New Zealand
0.6

CSI
SP
100

100.2

101.6

103.0

0.0

95.0
1988 Q3

0.2

96.6

1985 Q3

0.4

98.2

0.8

1.0

0.6

1982 Q3

Japan

99.8

101.4

103.0

CSI
SP
100

0.0

96.0
1981 Q1

0.2

97.4

1977 Q1

0.4

98.8

0.8

1.0

0.6

1973 Q1

Germany

100.2

101.6

103.0

CSI
SP
100

0.0

96.0
1982 Q4

0.2

97.4

1978 Q4

0.4

0.8

1.0

98.8

1974 Q4

Australia
0.6

CSI
SP
100

100.2

101.6

103.0

Figure 1
Consumer sentiment index (CSI) and smoothed probability (SP)

CSI

CSI

CSI

CSI

1985 Q1

1989 Q1

1993 Q1

1997 Q1

2001 Q1

2005 Q1

2009 Q1

Italy

2007 Q2

2009 Q2

2011 Q2

1990 Q1

1994 Q1

1998 Q1

2002 Q1

2006 Q1

2010 Q1

96.0

97.6

99.2

100.8

102.4

104.0

1978 Q1

1982 Q1

CSI
SP
100

1986 Q1

1990 Q1

1994 Q1

US1998 Q1

2002 Q1

2006 Q1

2010 Q1

0.0

0.2

0.4

0.6

0.8

1.0

0.0
1986 Q1

0.2
96.0
1982 Q1

0.4
97.6

1978 Q1

0.6

0.8

1.0

99.2

1974 Q1

UK
100.8

102.4

104.0

CSI
SP
100

0.0
2005 Q20.2

95.0
2003 Q2

0.4

96.8

0.8

1.0

0.6

2001 Q2

Mexico

0.0

0.2

0.4

0.6

0.8

1.0

98.6

CSI
SP
100

1981 Q2 1984 Q2 1987 Q2 1990 Q2 1993 Q2 1996 Q2 1999 Q2 2002 Q2 2005 Q2 2008 Q2 2011 Q2

CSI
SP
100

100.4

102.2

104.0

96.0

97.4

98.8

100.2

101.6

103.0

0.0
1981 Q1

0.2

97.0
1977 Q1

0.4

98.2

0.8

1.0

0.6

1973 Q1

France

99.4

CSI
SP
100

100.6

101.8

103.0

Note: Gray bars represent the periods of negative quarter-to-quarter GDP growth.

CSI

SP
SP
SP
SP
SP

CSI
CSI
CSI
CSI

CSI

SP
SP
SP
SP
SP

9

Table 2
Cross-correlation between CSI and GDP and Granger causality tests of CSI and GDP
Panel A: Cross Correlation
CSIt and GDPt±i for i= 0,1,2
Australia
New ZealandGermany
UK
FranceItaly
US
CanadaMexicoJapan-0.0480.022-0.067-0.125-0.0500.044-0.135-0.294-0.008-0.162

CSIt and GDPt±i for i= 0,1,2-0.031
0.049
0.157
0.019
0.139
0.180
0.000
-0.193
0.214
0.017

0.084
0.104
0.336
0.247
0.243
0.232
0.229
0.170
0.409
0.242

0.107
0.187
0.240
0.180
0.324
0.234
0.338
0.322
0.289
0.377

0.030
0.172
0.135
0.275
0.246
0.186
0.216
0.440
0.297
0.326

Panel B: Granger-Causality
Null Hypothesis

Australia
New ZealandGermany
UK
FranceItaly
US
CanadaMexicoJapan

CSI does not granger-cause GDP

GDP does not granger-cause CSI

0.016
0.002
0.002
0.001
0.002
0.031
0.000
0.000
0.464
0.004

0.547
0.299
0.344
0.860
0.015
0.075
0.791
0.267
0.371
0.622

Note: GDP and CSI are indicated in log values and the quarter-to-quarter growth rate (i.e., the difference in log values)
is shown in Panel A. Based on the Schwarz information criterion for the optimal lag length test, lag 2 is used in theGranger-Causality test. P -values are presented in Panel B.

that there is likely to be a coincident and positive correlation between the SPs for acontractionary state and negative GDP growth, (i.e., a positive correlation between
CSI and GDP), while the lagging or leading information of CSI is not still apparent.
These findings suggest that the current state of the economy, such as a contraction ora recession, is likely to contemporaneously affect the mood of consumers.
To further identify the causal relationship, the cross-correlation between CSI and
GDP is examined. Panel A of Table 2 shows that in all countries, CSI at time t has thehighest correlation with current or future GDP rather than past GDP.7 This indicatesthat CSI presumably contains coincident or leading information for economic activity.
Lastly, the Granger-causality test is performed. Variable X is said to Grangercause variable Y if and only if lagged variable X helps to predict Y. Panel B of Table
7

Cross-correlation is provided for up to two quarters, within which the highest correlation appears
in all countries.

10

2 displays the results of the test performed with optimal lag 2, based on the Schwarz
information criterion (SIC). Consumer sentiment index Granger-causes GDP in all
countries, except for Mexico, which implies that CSI may lead GDP. On the contrary,
GDP does not Granger-cause CSI in all countries, except for France and Italy. This
does not necessarily mean that GDP does not truly lead CSI, because Granger-causality
is neither a necessary nor sufficient condition for a genuine causal relationship between
two variables. Thus, to identify their relationship more rigorously, regression analysis
is performed in the next section.

3-2. Regression Model
It has been well documented in literature that CSI is capable of predicting economic
activity, i.e., it contains leading information. Nonetheless, it is also possible that there isa reverse causality between CSI and economic activity: current and past performancesin output may influence consumers’ mood. In such cases, CSI can possess coincident
and/or lagging information of economic activity.
To find this evidence, estimation models are set as in Equations 1 and 2, where error
terms are detected as autocorrelated AR(1)8 and further transformed into a nonlinear
model as in Equation 3. Coefficients of β > and µ are simultaneously estimated using
Marquardt’s nonlinear least square algorithm by correcting for the serial correlation inerror terms.9 To make the estimation results of each country comparable, the same setof explanatory variables is applied to all countries.
8

Ordinary least square estimation of Equation 1 produces a Durbin Watson statistic significantly
less than 2, which indicates that there is a positive autocorrelation in error terms.
9
When a lagged dependent variable is included in the model as a regressor, the conditional covariance matrices estimated from the Cochrane-Orcutt and Hildreth-Lu procedures of linear regression
are known to be invalid (Davidson and Mackinnon, 1993, pp. 339-340). This is why a nonlinear
estimation procedure is used.

11yt = β > xt + ut(1)

ut = µut−1 + εt , where − 1 < µ < 1(2)→ yt = µyt−1 + β > (xt − µxt−1 ) + εt(3)where xt is a k × 1 vector of economic variables described in Section 2, including lagged
dependent variable, and β is a k × 1 vector.
Before estimating the model, the augmented Dickey-Fuller unit root test is performed for all variables.10 In all countries, most of the variables are identified ashaving a unit root, so GDP and SHPI are first-differenced in log value, while STIR
and UEMR are only level-differenced because they are in the form of percentage. On
the contrary, INFR is not identified as having a unit root in all countries except forAustralia and France, so INFR is used at level. Consumer sentiment index is also not
identified as having a unit root in all countries, except for the U.S. and Mexico. Survey
data are known to be free from issues of trends and seasonality since they depend on
consumers’ subjective judgment regarding the economic condition (Santero and Westerlund, 1996). Nonetheless, CSI is first-differenced in log value in order to eliminate
this factor if mood is constant over time.11

3-3. Estimation Results
To scrutinize the association between CSI and GDP, a regression of GDP is performedon its own lagged variable and CSI, followed by a regression of CSI on its own lagged
10

The results will be presented upon request.
The use of CSI varies in empirical studies. Matsusaka and Sbordone (1995) and Lovell and Tien
(2000) use CSI at level since the index is a measure of the changes in consumer sentiment and istrendless. Jansen and Nahis (2003) and Vuchelen (2004) use first-differenced CSI at level due to its
non-stationarity and autoregressivity, respectively, while Otto (1999) and Carroll et. al. (1994) use
the first-differenced of CSI in the form of logarithm.
11

12

Table 3
Estimation results of GDP on CSICountry

VariableGDP(t-1)

CSI(t-1)

C

Australia

GDP-0.015(0.082)0.015(0.102)0.026(0.083)0.186**(0.081)0.477***(0.067)0.382***(0.085)0.318***(0.073)0.501***(0.072)0.260(0.156)0.107(0.111)

0.162(0.123)0.363*(0.198)0.371***(0.133)0.191*(0.115)0.354***(0.112)0.225*(0.129)0.379***(0.104)0.301***(0.092)0.271(0.232)
0.693***(0.220)0.008***(0.001)0.006***(0.001)0.004***(0.001)0.004***(0.001)0.003***(0.001)0.002***(0.001)0.004***(0.001)0.003***(0.001)0.004**(0.002)0.002(0.001)New ZealandGDP

Germany

GDP

UK

GDP

France

GDP

Italy

GDP

US

GDP

CanadaGDP

Mexico

GDP

Japan

GDP

Adj − R2

DW

-0.002

1.944

0.015

2.033

0.046

2.000

0.053

2.148

0.317

2.263

0.176

2.067

0.227

2.112

0.340

2.188

0.100

1.971

0.132

2.012

Note: CSI and GDP are the first-differences in log value. Standard errors are in parentheses. Adj − R2 and DW

represent adjusted R2 and the Durbin-Watson statistic, respectively. ***, **, and * represent p < 0.01, p < 0.05, andp < 0.1, respectively.

variable and GDP. The lagged dependent variable is included as an explanatory variable
not only to reduce the omitted variable bias, but also to capture a “mood formation”
similar to a habit formation in an economic consumption model because the current
mood of consumers is likely to be influenced by the consumer sentiment in the previous period. According to Lovell (1975), a lagged variable captures the contagious
disillusionment and the persistent mood of consumers.
Table 3 shows the results of the regression of GDP on GDPt−1 and CSIt−1 , which
are very similar to the findings from the Granger-causality test in Panel B of Table 2.12
In all countries except for Australia and Mexico, CSI exerts a lagged effect on GDP
with a statistical significance, which implies that CSI contains leading information foreconomic activity.
On the contrary, Table 4 shows the results of the regression of CSI on CSIt−1 ,GDPt , and GDPt−1 .13 In Germany, the U.K., France, the U.S., Canada, Mexico, andJapan, GDP has a contemporaneous effect on CSI with a statistical significance. This
12

Durbin-Watson statistics from OLS regression are close to 2 in all countries, so the results comefrom OLS estimation rather than non-linear least squares estimation that takes into account AR(1).
13
To be consistent with the sample period in the multivariate regression, the sample periods of the
U.K. and France and Japan start with 1978:Q3 and 2002:Q4, respectively.

13

Table 4
Estimation results of CSI on GDP
Country

variable

CSI(t-1)

GDP

GDP(t-1)AR(1)C

Australia

CSI

0.191
(2.683)0.181(1.054)0.044(0.052)0.049(0.052)-0.028(0.138)0.186(2.686)0.194(1.056)-0.0002(0.001)-0.0004(0.001)New Zealand

CSI

0.203
(3.234)0.205(3.089)0.019(0.052)0.016(0.051)

0.012(0.072)0.202(3.238)0.203(3.097)-0.0001(0.001)0.000(0.001)

GermanyCSI

0.266(0.379)0.340(0.307)0.139***(0.039)0.126***(0.038)0.049(0.055)0.398(0.369)0.336(0.326)-0.001(0.001)-0.001(0.001)

UK

CSI

0.268
(0.949)0.165(0.301)0.152***(0.053)0.164***(0.053)-0.082(0.156)0.210(0.966)0.307(0.303)-0.001(0.001)-0.001*(0.001)

FranceCSI

0.094(0.360)0.215(0.328)0.131*(0.068)0.16**(0.068)0.118(0.079)0.267(0.349)0.138(0.335)-0.001**(0.001)-0.001*(0.0005)

Italy

CSI

0.188
(0.698)0.229(0.564)0.076(0.059)0.088(0.059)0.064(0.084)0.223(0.696)0.185(0.575)-0.001(0.001)-0.0005(0.001)

US

CSI

0.119
(0.612)0.036(0.420)0.190***(0.071)0.164**(0.070)-0.093(0.131)0.027(0.623)0.120(0.422)-0.001(0.001)-0.001(0.001)

CanadaCSI

0.279(0.195)0.038(0.336)0.233***(0.079)0.145*(0.078)-0.311***(0.077)0.008(0.212)0.288(0.331)0.0005(0.001)-0.001(0.001)

MexicoCSI

0.115(1.989)0.133(0.415)0.210**(0.099)0.214**(0.095)0.023(0.451)0.122(2.001)0.110(0.444)-0.002(0.003)-0.001(0.001)

Japan

CSI

0.303
(1.081)0.294(0.546)0.110*(0.063)0.111*(0.061)-0.004(0.113)0.440(1.037)0.448(0.549)-0.0001(0.001)-0.0001(0.001)

Adj − R2

DW

0.100

1.896

0.104

1.892

0.092

1.803

0.101

1.802

0.411

1.783

0.410

1.816

0.227

1.919

0.223

1.877

0.196

1.912

0.188

1.955

0.164

1.897

0.163

1.913

0.065

1.959

0.063

1.927

0.190

1.989

0.106

1.891

0.128

2.045

0.149

2.058

0.416

1.812

0.432

1.807

Note: CSI and GDP are the first-differences in log value. Standard errors are in parentheses. Adj − R2 and DW

represent adjusted R2 and the Durbin-Watson statistic, respectively. ***, **, and * represent p < 0.01, p < 0.05, andp < 0.1, respectively.

presents evidence that current economic activity affects consumer sentiment, and suggests that CSIs contain coincident information of economic activity. However, only
in Canada does lagged GDP appear to be statistically significant with a negative coefficient. This suggests that in all countries except for Canada, past realization of
economic development does not contemporaneously affect the consumer sentiment. In
the regressions, GDPt , and GDPt−1 are likely to correlate with each other, which presumably leads to spurious results. In order to check the robustness of the results,
regression is re-run after dropping GDPt−1 . As shown in the next line, the outcome of14

estimation is consistent with previous results.14
In addition to GDP, macroeconomic information regarding financial and labor markets in relation to the state of economy may also influence consumer sentiment. Thus,
to control its effect and confirm the results on robustness, SHPI, which captures financial condition of consumers, as well as STIR, UEMR, and INFR, which capture
information about the current and future state of economy, are added to the previous
univariate regression model before regressions are re-run.
The estimation results in Table 5 show that, as in the univariate model in Table
4, the current GDP growth rates of Germany, the U.K., France, and Mexico are still
statistically significant. Accordingly, the GDP growths in these countries are very
likely to have an contemporaneous effect on CSI. Unlike in the univariate model, thecurrent GDP growths in the U.S., Canada, and Japan do not appear to be statisticallysignificant. Thus, the effect of GDP on CSI is ambiguous in these countries and their
CSIs should be cautiously regarded as coincident indicators. These findings confirm
that in some countries, CSI contains coincident information that can be useful as amood signal for assessing the changes in economic activity.

3-4. Determinants of CSI
A. Economic Factors

The control variables in Table 5 can also be regarded as potential determinants of CSI
in the countries. Thus, we shall examine which factors are important in determining
consumer sentiment. One conspicuous result is that SHPI exerts a significant andpositive effect on CSI in all countries except for Mexico. This implies that increased
income due to the wealth effect is a highly relevant factor in establishing consumer
14

Regression results only with GDPt−1 is also consistent with the previous ones except that inFrance. They will be available upon request.

15

16

0.191
1.926

0.246
(0.199)1.984***(0.543)-0.102**(0.045)-0.021(0.044)-0.001(0.150)0.096(0.218)-0.0002(0.001)

0.191
1.939

0.253
(0.192)0.055(0.054)
1.963***(0.544)-0.102**(0.045)-0.018(0.044)0.064(0.162)0.089(0.211)-0.001(0.001)

Australia

0.259
1.966

0.210
(0.192)
3.147***(0.872)-0.182**(0.078)-0.079(0.097)-0.165(0.151)
0.029(0.215)0.0003(0.001)

0.252
1.958

0.205
(0.203)0.020(0.052)
3.14***(0.882)-0.186**(0.079)-0.069(0.100)-0.155(0.154)0.036(0.227)0.0001(0.001)New Zealand

0.455
1.884

0.441***(0.122)2.269***(0.432)-0.082(0.058)-0.069(0.063)-0.195(0.163)0.136(0.145)0.0001(0.001)

0.487
1.903

0.444***(0.103)0.125***(0.039)2.072***(0.422)-0.13**(0.057)-0.058(0.060)-0.101(0.158)0.094(0.129)-0.001(0.001)

Germany

0.334
1.934

0.313**(0.143)2.749***(0.645)-0.171***(0.050)-0.044(0.037)-0.028(0.165)0.112(0.169)-0.0004(0.001)

0.359
1.908

0.284**(0.141)0.137**(0.055)2.296***(0.661)-0.172***(0.049)-0.020(0.038)0.122(0.176)0.156(0.167)-0.001*(0.001)

UK

Italy

0.239
1.924

0.192
(0.233)0.047(0.061)1.550***(0.414)0.017(0.054)-0.061(0.059)-0.050(0.158)0.241(0.245)0.0001(0.001)

0.206
1.950

0.252
(0.268)1.073***(0.331)
0.026(0.037)-0.055(0.037)0.0003(0.123)0.156(0.283)0.0001(0.0004)

0.241
1.923

0.199
(0.230)1.621***(0.406)0.025(0.053)-0.056(0.059)-0.066(0.157)0.245(0.244)0.0003(0.001)Panel B: CSI

0.227
1.964

0.228
(0.242)0.150**(0.074)0.929***(0.329)0.015(0.037)-0.064*(0.035)0.103(0.126)0.109(0.252)-0.0005(0.001)Panel A: CSIFrance

0.301
1.914

-0.036(0.134)4.341***(0.649)0.089*(0.048)-0.203***(0.057)-0.106(0.176)0.241(0.151)0.001(0.001)

0.296
1.914

-0.036(0.137)0.006(0.077)4.328***(0.669)0.088*(0.049)-0.202***(0.057)-0.098(0.196)0.241(0.152)0.001(0.001)

US

0.325
1.992

0.291**(0.120)2.959***(0.615)-0.194***(0.050)-0.042(0.058)0.021(0.136)-0.080(0.143)-0.0002(0.001)

0.331
1.993

0.270**(0.123)0.113(0.078)2.785***(0.624)-0.206***(0.051)-0.043(0.058)0.144(0.161)-0.074(0.146)-0.001(0.001)

Canada

0.118
2.176

0.283
(0.340)0.120(1.568)-0.378*(0.219)-0.114(0.205)-0.948(0.598)0.048(0.381)0.001(0.003)

0.184
2.122

0.192
(0.306)
0.213*(0.106)-1.246(1.626)-0.465**(0.219)-0.050(0.197)-0.552(0.591)0.160(0.331)-0.001(0.003)Mexico

0.622
1.983

0.442***(0.129)2.905***(0.675)-1.67(1.118)-0.228(0.166)
0.771**(0.367)-0.065(0.238)0.0005(0.001)

0.642
1.973

0.364**(0.151)
0.090(0.054)2.601***(0.683)-1.834(1.146)-0.259(0.164)0.785**(0.374)0.038(0.279)0.0003(0.001)Japan

represent adjusted R2 and the Durbin-Watson statistic, respectively. ***, **, and * represent p < 0.01, p < 0.05, and p < 0.1, respectively.

Note: CSI and GDP are the first-differences in log value. SHPI, STIRS, INFR and UEMR are divided by 100. Standard errors are in parentheses. Adj − R2 and DW

Adj − R2
DW

Constant

AR(1)

UEMR

INFR

STIR

SHPI

CSI(t-1)

Variables

Adj − R2
DW

Constant

AR(1)

UEMR

INFR

STIR

SHPI

GDP

CSI(t-1)variableCountry

Table 5
Estimation results of CSI on GDP and control variables and determinants of CSI

sentiment. In this case, we must also take into account that Mexico is an emerging
country and its stock market is not highly developed and therefore, consumer sentiment
may be less influenced by the changes in share prices compared to other developed
countries.
According to conventional wisdom, a rise in STIR has both negative and positiveeffects on consumption. On the negative side, it increases interest rate payments on
consumers’ short-term debts, having an adverse impact on their cash flow. On a
positive side, their interest incomes increase. In general, the negative effect is known
to be greater than the positive effect. Thus, an increase in interest rate can lead to
reduced overall consumption and subsequently a deline in consumer sentiment. In all
countries except for France, Italy, and Japan, STIR is statistically significant with anegative coefficient. However, its sign in the U.S. appears to be positive. It is likelythat the positive effect of higher interest rates outweighs the negative impact in the
U.S., resulting in a positive effect on CSI.
In France and the U.S., INFR is statistically significant at levels of 10% and 1%,
respectively. The result of the U.S. is consistent with the finding of Garner (1981).
Aggregate household consumption accounts for about 70% of GDP in the U.S., whichis the highest among all sampled countries and it has a high correlation with consumer
sentiment. Therefore, consumer sentiment appears to be very sensitive to INFR. Unemployment rate turns out to have a positive relationship with CSI only in Japan with astatistical significance. These results suggest that in most of countries macroeconomic
variables such as INFR and UEMR are not significant factors that affect consumer
sentiment.
To examine the robustness of these CSI determinants, regression is re-run after
dropping GDP and only with potential determinants. Panel B of Table 5 shows thatthe estimates are very similar to previous results: SHPI is statistically significant in all17

countries except for Mexico; STIR is significant in Australia, New Zealand, the U.K.,
the U.S., Canada, and Mexico; and INFR and UEMR are not statistically significant
in all countries except for the U.S. and Japan, respectively.15
In a nutshell, the determinants of CSI vary across countries. Nevertheless, there
are some common features: SHPI and STIR are most likely to be significant factorsof CSI in most countries, whereas INFR and UEMR may not be. Overall, we foundprevious outcomes to be quite robust.

B. Non-Economic Factors

It has been previously found that SHPI and STIR are the major driving economic
forces of CSI. Nevertheless, non-economic factors such as psychological sentiment may
also play a role. For example, when an economy is in a downturn, consumers are more
likely to be in a depressed mood and their responses to a sentiment survey would be
negative, causing the CSI to fall. To capture this emotional effect, a dummy variable
is generated, which is assigned a value of 1 for quarters of negative GDP growth, and0 otherwise. In addition, Okun’s economic misery index, which indicates the overalllevel of economic uncomfortability in a country, is generated by simply summing INFR
and UEMR.
The dummy variable (NGWR) and the misery index (MI) are added to the previous
regression equation instead of dropping INFR and UEMR. Regression results in Panel A
of Table 6 show that the negative GDP growth dummy variable (NGWR) is statisticallysignificant in Germany and Mexico. This suggests that an economic state reflecting
the contraction of economy exerts a significant effect on consumer sentiment.
To delve deeper into this contractionary state effect, the SPs previously extracted
15
There are minor differences among the results of interest rate and inflation rate: interest rate inGermany and inflation rate in France do not turn out to be statistically significant. These marginal
differences do not significantly change the overall outcomes.

18

19

0.195
1.915

0.240
(0.216)
1.893***(0.546)-0.103**(0.045)0.023(0.027)-0.072(0.181)0.120(0.238)-0.002(0.002)

0.205
1.910

0.232
(0.209)1.878***(0.542)-0.109**(0.044)0.026(0.026)-0.187(0.133)0.137(0.230)-0.002(0.002)Australia

0.246
1.927

0.228
(0.223)3.219***(0.852)-0.168**(0.077)0.014(0.029)-0.089(0.303)0.059(0.244)-0.001(0.002)

0.257
1.936

0.230
(0.196)
3.096***(0.855)-0.180**(0.073)0.012(0.028)-0.156(0.134)0.050(0.227)-0.0007(0.002)

New Zealand

0.464
1.906

0.457***(0.106)2.097***(0.426)-0.082(0.054)0.016(0.017)-0.320**(0.136)0.094(0.132)-0.001(0.001)

0.462
1.900

0.467***(0.105)2.077***(0.43)-0.083(0.055)0.011(0.017)-0.173**(0.078)0.092(0.133)-0.001(0.001)

Germany

0.327
1.934

0.318**(0.154)2.692***(0.660)-0.176***(0.048)-0.004(0.019)-0.0496(0.144)0.121(0.178)-0.0004(0.002)

0.336
1.920

0.288*(0.157)2.631***(0.656)-0.177***(0.047)-0.001(0.019)-0.167(0.12)
0.146(0.181)-0.0005(0.002)

UK

Italy

0.234
1.910

0.218
(0.251)1.616***(0.409)0.025(0.053)-0.010(0.032)0.0122(0.094)0.223(0.264)0.0006(0.003)

0.206
1.954

0.280
(0.265)0.893***(0.316)0.018(0.035)0.048(0.035)0.075(0.104)0.130(0.277)-0.005(0.003)

0.244
1.904

0.183
(0.243)
1.521***(0.411)0.010(0.054)-0.01(0.032)-0.206(0.178)0.234(0.252)0.001(0.003)

Panel B: CSI

0.206
1.955

0.284
(0.261)0.880***(0.315)0.020(0.035)0.051(0.034)0.063(0.086)0.125(0.273)-0.005(0.003)

Panel A: CSIFrance

0.255
1.892

0.021
(0.143)4.514***(0.695)0.119**(0.047)-0.010(0.031)0.410**(0.183)0.259*(0.153)-0.0004(0.002)

0.235
1.900

-0.003(0.152)
4.267***(0.704)0.102**(0.047)
0.002(0.030)0.200(0.166)0.240(0.162)-0.001(0.002)

US

0.326
1.983

0.261*(0.134)2.831***(0.637)-0.197***(0.048)0.019(0.025)-0.0424(0.190)-0.050(0.154)-0.002(0.002)

0.330
1.978

0.240*(0.132)2.703***(0.631)-0.203***(0.047)0.023(0.024)-0.135(0.143)-0.026(0.156)-0.002(0.002)

Canada

0.139
2.125

0.093
(0.349)-2.015(1.910)-0.398*(0.205)0.024(0.136)-1.691*(0.912)0.235(0.329)0.0001(0.007)

0.318
2.196

0.325
(0.217)-1.568(1.463)-0.297*(0.168)-0.055(0.104)-1.227***(0.319)-0.010(0.275)0.005(0.006)

Mexico

0.572
1.942

0.376*(0.189)2.659***(0.734)-3.354**(1.305)-0.029(0.132)0.178(0.252)
0.194(0.273)0.002(0.006)

0.583
1.934

0.319
(0.205)2.448***(0.736)-2.728**(1.253)0.004(0.132)-0.155(0.132)0.252(0.293)0.0007(0.006)Japan

Note: CSI and GDP are the first-differences in log value. SHPI, STIR, MI, NGWR, and PSI are divided by 100. Standard errors are in parentheses. Adj − R2 and DW
represent adjusted R2 and the Durbin-Watson statistic, respectively. NGWR and MI represent a dummy variable for quarters of negative GDP growth and a miseryindex, respectively. PSI represents an ad-hoc variable for the psychological indicator. ***, **, and * represent p < 0.01, p < 0.05, and p < 0.1, respectively.

Adj − R2
DW

Constant

AR(1)

PSI

MI

STIR

SHPI

CSI(t-1)

Variables

Adj − R2
DW

Constant

AR(1)

NGWR

MI

STIR

SHPI

CSI(t-1)

Variables

Country

Table 6
Estimation results of economic and psychological states’ effect on CSI

from CSI are used as a proxy variable for a psychological indicator. As seen in Figure
2, SPs indicate the likelihood that CSI falls into a contractionary state at any given
period t. Thus, it is assumed that the probability reflects consumers’ emotional state
about the economic condition for each period; the higher the probability, the worse theconsumer mood is likely to be. To further extract the information reflecting consumers’
gloomy mood only in downturn periods, SPs are multiplied by the dummy variable explained above. This new indicator is used as an ad-hoc variable for measuring consumer
sentiment during an economic contraction.
Panel B of Table 6 shows the results of a regression performed with the pseudo
psychological indicator (PSI). Similar to the results estimated for NGWR in PanelA, estimations of PSI have negative effects on CSI with statistical significances inGermany and Mexico.16 This presents evidence that consumers’ gloomy mood in an
economic recession can aggravate their sentiment, i.e., non-economic factors such as
psychological state can affect CSI. Furthermore, in both Panels A and B, SHPI is still
statistically significant in all countries except for Mexico, as are STIR in Australia,
New Zealand, the U.K., the U.S., Canada, and Mexico.17 The misery index is not
estimated to be statistically significant in both Panels A and B. This is not surprisingbecause in most of the countries, INFR and UEMR are not found to be significant
economic determinants of CSI, as shown in Table 5.
To summarize, although there are some variations of CSI determinants across thecountries, consumer sentiment can be driven by both economic forces such as wealtheffects and by non-economic forces such as emotional effects.
16

In the U.S., the estimated coefficient of PSI appears to be positive with a statistical significance.
The current contractionary state may cause the U.S. consumers to expect the economy to improve in
the near future, so PSI seems to have a positive effect on CSI.
17
In Japan, interest rate turns out to be significant, which contradicts the outcomes in Table 5, so
it is not considered a significant factor of CSI.

20

4. Conclusion
A number of previous studies have primarily focused on the role of information andthe effectiveness of consumer sentiment as sources of aggregate economnic movements;
consumers anticipate changes in the state of economy, so variation in CSI as a leading
indicator precedes changes in the overall economy. In this case, the leading information
is useful for predicting the changes that are likely to occur in the economy.
On the contrary, consumers may also react to changes in the state of economy; an
improvement in economic activity is likely to lead a rise in consumer sentiment. Thatis, it is possible that there is a reverse causality between CSI and economic activity.
If so, CSI can also be a coincident or lagging indicator that follows the changes in
economic activity. In this case, the coincident or lagging information is more usefulas a mood signal for assessing the changes in the economy. Based on the data fromten OECD countries, this paper explored the causal relationship between CSI and
GDP, and found some evidence that CSI contains not only leading information but
also coincident information in Germany, the U.K., France, the U.S., Canada, Mexico,and Japan.
When people feel wealthier they tend to increase their spending and their sentimentis more likely to be elevated. On the other hand, when the economy as a whole declines,
consumers feel worse and their sentiment about the economy tends to decline. These
economic and non-economic forces can influence consumer sentiment. Thus, this paper
also examined whether consumer confidence can be explained by non-economic factors
such as state of the economy and psychological state, as well as standard economic
determinants such as income and interest rate.
We found that in most of the countries, SHPI and STIR reflect the changes in
CSI very well, which suggests that wealth and financial effects play an important roleon consumer sentiment. Moreover, consumer sentiment depends on their judgment
21and expectations about the economy. That is, consumers seem to be sensitive to both
economic and psychological states. Therefore, CSI is also likely to be driven by noneconomic factors, such as emotional responses to economic realizations or uncertainty.
In a regression analysis of economic determinants, a contractionary state indicator,
defined as a dummy variable for the quarters of negative GDP growth, and an ad-hoc
psychological indicator, defined as the probabilities of the contractionary state of CSI
for economic downturn, were found to have a negative and significant effect on CSI in
Germany and Mexico, which suggests that non-economic factors also play an importantrole on consumer sentiment.
In conclusion, CSI is identified as a leading and/or coincident indicators of economic
activity. Both economic factors such as wealth and interest rate and non-economic
factors such as economic and psychological states are identified as driving forces ofconsumer sentiment.

22

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