Bob Barsky
Miles Kimball
Noah Smith

• Two Polar Views of Consumer
Confidence in
Macroeconomics
–Information
–Animal Spirits
• Issues in Cognitive Psychology
–Effect of affect on judgment
–Degree of persistence in
“happiness shocks”

• Important movements in confidence that are exogenous to the macroeconomy
• “Confidence shocks” have causal effects on economy

• Do changes in measured confidence reflect information or fluctuations in pure sentiment?
• Is the relationship between confidence and subsequent economic outcomes causal?
• What might “explain” variation in confidence?
– Time-series variation
– Cross-sectional variation at a point in time
– Individual innovations in panel data

1. Pago : “Would you say that you (and your family living there) are better off or worse off financially than a year ago?”
2. Pexp
: “Do you think that a year from now you (and your family living there) will be better off financially, or worse off, or just about the same as now?”
3. Bus12 : “Do you think that during the next 12 months we’ll have good times financially, or bad times, or what?”
4. Bus5
: “Which would you say is more likely – that in the country as a whole we’ll have continuous good times during the next 5 years or so, or that we will have periods of widespread unemployment and depression, or what?”
5. Dur : “Generally speaking, do you think now is a good time or a bad time for people to buy major household items?”

“
Now turning to business conditions in the country as a whole:
Do you think that over the next five years we will have mostly good times, or mostly bad times with periods of widespread unemployment and depression, or what?”

• Time series co-variation in happiness measures and the responses to the various confidence questions
• How is variation in confidence answers across individuals related to the happiness individual effect?
• How do changes in each individual’s answer to the confidence questions between the initial interview and the reinterview covary with happiness?

Now think about the past week and the feelings you have experienced. Please tell me if each of the following was true for you much of the time this past week:
• Much of the time during past week, you felt you were happy.
(Would you say yes or no?)
• (Much of the time during the past the week,) you felt sad.
(Would you say yes or no?)
• (Much of the time during the past week,) you enjoyed life.
(Would you say yes or no?)
• (Much of the time during the past week,) you felt depressed.
( Would you say yes or no?)
(average survey time=36 seconds)

HAP_INDEX
20
16
12
8
4
0
100 200 300 400 500 600 700 800 900

.8
-.4
-.8
.4
.0
5 10 15
Residual
20
Actual
25 30
Fitted
35
14.4
14.0
13.6
13.2
12.8

2.50
2.25
2.00
1.75
1.50
1.25
1.00
0.75
0.50
5
(Green is Happiness)
10
BUS5
15 20
BUS12
25 30
HAP_INDEX
13.8
13.6
13.4
13.2
35
13.0
14.6
14.4
14.2
14.0

HAPPINESS_Z
.100
.075
.050
.025
.000
-.025
-.050
-.075
-.100
1975 1980 1985 1990 1995 2000 2005
• Negligible variation even over this long period
• Strikingly, national mood was not depressed during 1970s “malaise” period nor ebullient in late 1980s or 1990
• Celebrated relationship between happiness and inflation, unemployment apparently not first order

. reg bus5_2 hap_index_1 hap_index_2
Source SS df MS Number of obs = 5980
F( 2, 5977) = 115.73
Model 454290.534 2 227145.267 Prob > F = 0.0000
Residual 11731062.6 5977 1962.70079 R-squared = 0.0373
Adj R-squared = 0.0370
Total 12185353.2 5979 2038.02528 Root MSE = 44.302
bus5_2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
hap_index_1 .1887032 .0239293 7.89 0.000 .1417932 .2356133
hap_index_2 .1988577 .0238007 8.36 0.000 .1521999 .2455156
_cons 12.26128 2.289796 5.35 0.000 7.772454 16.75011
Interpretation:

To get intuition about the likely size of causal effects of changes in expectations on happiness at the 6 month frequency, let’s look at the effect of objective circumstances on happiness at the
6 month frequency.
Effect of household income on happiness
Table 2: Effect of Log Income on Happiness
Dependent Variable
Happiness
Change in happiness over 6 mo.
Regressor Effect size P-value
Average income over 6 mo.
Change in income over 6 mo.
Average income over 6 mo.
Change in income over 6 mo.
0.226
0.052
-0.002
0.069
0.000
0.087
0.889
0.038

• Discreteness of the components of the happiness index.
• Differential sensitivity of the components of the happiness index to the underlying latent variable.
• Plan: Use an approach similar to Kimball,
Sahm and Shapiro, “Imputing Risk Tolerance from Survey Responses” JASA (forthcoming) and NBER 13337

x itq
 x t
N  x i
P  x it
T  s i
 u it
 v iq
 w itq x t
N , x i
P , x it
T , s i
, u i
, v it
, w itq
orthogonal and i.i.d
x it 1
 HAPPY x it 2 x it 3
 NOTSAD
 NOTDEPRESSED x it 4
 ENJOYLIFE

Component Impulse Responses
(National News)
Impulse Response of Happiness Components to
National News
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Day
"Feeling happy" "Not sad" "Feeling good" "Not depressed"

Component Impulse Responses
(Personal News)
Impulse Response of Happiness Components to
Personal News
4.0
3.0
2.0
1.0
0.0
-1.0
7.0
6.0
5.0
-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Day
"Feeling happy" "Not sad" "Feeling good" "Not depressed

x itq
 x t
N
 x i
P  x it
T  s i
 u it
 v iq
 w itq
LATENT HAPPINESS: x t
N  national, x i
P  persistent personal, x T it
 transitory personal.
IDIOSYNCRATIC RESPONSE ERROR: s i
 persistent, u it
 transitory,
ALL IDIOSYNCRATIC
QUESTION-SPECIFIC ELEMENTS: v iq
 persistent, w itq
 transitory

y it
   
N x t
N  
P x i
P  
T x it
T   s s i
  u u it
  it y it
 Expectation or Evaluation
 it
uncorrelated with x i
P , x it
T , s i
and u it
for National y
(possibility of reverse causality y t
 x t
N )

y it
   
N x t
N  
P x i
P  
T x it
T   s s i
  u u it
  it


P
N
: includes social construction of expectations
: includes (1) habitual rose-colored glasses
(i.e., effects of personality-type) and
(2) effect of a good mood over extended time

T
 s
 u
: effect of being in a good mood right now
 effect of persistent global response style
 effect of transitory global response style

x it
 x t
N
 x i
P  x it
T  s i
 u it
 v i
 w it where x v i it w it
 ( x it 1
 ( v i 1
 x
 v i 2 it 2

 v i 3 x it 3
 v
 i 4 x it 4
) / 4
) / 4
 ( w it 1
 w it 2
 w it 3
 w it 4
) / 4

x
 it
 where x t
N x
 it

 j  i x jt m t
 1
 x P
 it
 x T
 it
 s
 it
 u
 it
 v
 it etc .
 w
 it
NOTE: All components depend on the date because the set of “others” depends on the date.

N
Cov ( x it
, x
 it
)  Cov ( x t
N  x i
P x t
N
 Var ( x t
N )
 x P
 i
 x it
T
 x T
 it
 s i
 s i
 u it
 u
 it
 v i
 v

 i w
 it
, w
 it
)
Cov ( y it
, x
 it
)  Cov (   
N x t
N

 
P x i
P  
T x it
T x t
N

N
Var ( x t
N
 x P
 i
 x T
 it
)  Cov (  it
 s
 i
, x t
N )
 u
 it

(y  x causation)
  s s i
 v
 i
  u u it
 w
 it
)
  it
,

N
Cov ( y it
, x it
)
Cov ( x it
, x
 it
)
 
N

Cov (  it
, x t
N
Var ( x t
N )
)

0.015
Estimated covariance of happiness with its own lags
0.01
0.005
0
-0.005
-0.01
0 15 5 10
Lag
Covariance of happiness with lagged happiness

Table 5: Estimates of covariance of happiness with its own lags
Lag Covariance t-statistic 95% (low)
0 0.0073531
1 0.0050124
2 0.0006685
3 0.0061494
4 -0.0008634
5 -0.0002865
6 0.0007713
7 -0.0034011
8 -0.0010418
9 -0.0012532
10 -0.0027007
11 -0.0015549
12 -0.0008071
13 -0.0022994
14 0.0071094
2.3
2.18
95% (high)
0.0010768 0.0136293
0.28 -0.0040431 0.0053801
2.49
0.0005042 0.0095207
0.0013059 0.0109928
-0.34 -0.0057708
0.004044
-0.11 -0.0053285 0.0047555
0.3 -0.0043453 0.0058879
-1.3 -0.0085169 0.0017147
-0.38
-0.006384 0.0043003
-0.46 -0.0066251 0.0041187
-0.95 -0.0082713 0.0028699
-0.54 -0.0071569
0.004047
-0.28 -0.0064879 0.0048738
-0.79 -0.0079699 0.0033711
2.44
0.001396 0.0128227

Table 6: Effect of national happiness on national expectation levels and vice versa
Variable pago pexp bus12 bus5 dur inexq1 v228 hom car homeval bago bexp govt unemp ratex gaspx1 gas1px1 px1q1 px5q1
Effect of happiness on variable P-value Effect of variable on happiness P-value
-0.350
0.430
-0.104
0.597
-0.864
0.175
-0.232
0.387
0.325
0.470
0.238
0.334
-0.093
0.047
0.657
0.284
0.563
0.765
-0.341
0.209
0.213
-0.487
0.004
0.081
-0.381
0.625
0.249
0.425
0.583
0.889
0.946
0.677
0.691
0.461
0.078
0.629
0.498
0.651
0.283
0.992
0.859
0.118
0.135
0.536
0.053
0.508
0.567
124.249
0.046
-0.019
0.026
0.085
0.215
-0.260
0.003
-0.513
-0.016
0.258
-0.476
0.201
0.324
0.755
0.364
0.179
0.995
0.836
0.664
0.605
0.739
0.731
0.454
0.918
0.104
0.849
0.217
0.395

P
1=1st obs date, 2=2d obs date; cross-question covariance: r
 q
Cov ( y i 2
, x i 1
 x
 i 1
)  Cov (   
N x
2
N

 
P x i
P  
T x T i 2
  s s i
  u u i 2
 x i
P

P
Var ( x i
P
 x P
 i 1
) 
 x T i 1
 x T i 1
 ( s i
 ( s
 i 1
 u
 u
 i 1 i 1
 v
 v
 i 1 i
 w i 1
)
 w
 i 1
))
 s
Var ( s i
)
 i 2
,
Cov ( x i 2 q
, x i 1 r
 x
 i 1 r
)  Cov ( x
N
 x i
P  x T i 2
 ( s i
 u i 2
 v iq
 w i 2 q
), x i
P  x P
 i 1
 x T i 1
 x T i 1
 ( s i
 ( s
 i 1
 u
 u
 i 1 i 1
 v ir
 v
 i 1 r
 w i 1 r
 w
)
 i 1 r
))
 Var ( x i
P )  Var ( s i
)

P
Cov ( y i 2
, x i 1
Cov ( x i 2 q
, x i 1 r
 x
 i 1
 x
 i 1 r
)
)
 (1   ) 
P
  s
, where  =
[ Var ( x
Var ( s i i
P
)
)  Var ( s i
)]
.

P
Cov ( y i 2
, x i 1
Cov ( x i 2 q
, x i 1 r
 x
 i 1
 x
 i 1 r
)
)
 (1   ) 
P
  s
, where  =
[ Var ( x
Var ( s i i
P
)
)  Var ( s i
)]
.

P
(1   ) 
P
  s

Cov ( y i 2
, x i 1
Cov ( x i 2 q
, x i 1 r
 x
 i 1
 x
 i 1 r
)
)

Cov ( x i 2
, x i 1
Cov ( x i 2 q
, x i 1 r
 x
 i 1
 x
 i 1 r
)
)
Cov ( y i 2
, x i 1
Cov ( x i 2
, x i 1
 x
 i 1
 x
 i 1
)
)
 1.25
Cov ( y i 2
, x i 1
Cov ( x i 2
, x i 1
 x
 i 1
 x
 i 1
)
)

P
Var ( x i
P )  Var ( s i
Var ( x i
P
)  Var ( v i
)  Var ( s i
)
)

Cov ( x i 2
, x i 1
Cov ( x i 2 q
, x i 1 r
 x
 i 1
 x
 i 1 r
)
)
 1.07
since
Cov ( x i 2
, x i 1
 x
 i 1
)  Cov ( x
N
 x i
P  x T i 2
 ( s i
 u i 2
 v i
 w i 2
), x i
P  x P
 i 1
 x T i 1
 x T i 1
 ( s i
 ( s
 i 1
 u
 u
 i 1 i 1
 v i
 v
 i 1
 w i 1
)
 w
 i 1
))
 [ Var ( x i
P )  Var ( s i
)  Var ( v i
)] and

Covariances of Happiness
Responses across Contact Dates
[Average of All]/[Average off Diagonal] = 1.07

T
1=1st obs date, 2=2d obs date
Cov ( y i 2
 y i 1
,[ x i 2
 x i 1
]  [ x
 i 2
 x
 i 1
])
 Cov ( 
N
[ x
2
N

 x
1
N
 u
[ u i 2
]  
 u i 1
T
[ x T i 2
]  [ 
 x T i 2 i 2

]
 i 1
],
[ x T i 2
 x T i 1
]  [ u i 2
 u i 1
]  [ w i 2
 w i 1
]
 [ x P
 i 2
 [ s
 i 2
 [ v
 i 2
 2 
T
Var ( x i
T
 x P
 i 1
]  [ x T
 i 2
 x T
 i 1
]
 s
 i 1
]  [ u
 i 2
 v
 i 1
]  [ w
 i 2
 u
 i 1
]
 w
 i 1
])
)  2  u
Var ( u i
)

T
1=1st obs date, 2=2d obs date; cross-question covariance: r q
Cov ( x i 2 q
 x i 1 q
, [ x i 2 r
 x i 1 r
]  [ x
 i 2 r
 x
 i 1 r
])
 Cov ([ x
2
N  x
1
N ]  [ x T i 2
 x T i 1
]  [ u i 2
 u i 1
]  [ w i 2 q
 w i 1 q
],
[ x T i 2
 x T i 1
]  [ u i 2
 u i 1
]  [ w i 2 r
 w i 1 r
]
 [ x P
 i 2
 [ s
 i 2
 [ v
 i 2 r
 x P
 i 1
]  [ x T
 i 2
 s
 i 1
]  [ u
 i 2
 v
 i 1 r
]  [ w
 i 2
 x T
 i 1
]
 u
 i 1
]
 w
 i 1 r
])
 2[ Var ( x it
T )  Var ( u it
)]

T
Cov ( y i 2
Cov ( x i 2 q
 y
 x i 1 q i 1
, [ x i 2
, [ x i 2 r
 x i 1
 x i 1 r
]  [ x
 i 2
]  [ x
 i 2 r
 x
 i 1
 x
])
 i 1 r
])
 (1   ) 
T
  u
, where  =
[ Var ( x
Var ( u it
T it
)
)  Var ( u it
)]
.

T
(1   ) 
T
  u

Cov ( y i 2
Cov ( x i 2 q
 y
 x i 1 q i 1
, [ x i 2
, [ x i 2 r
 x i 1
 x i 1 r
]  [ x
 i 2
]  [ x
 i 2 r
 x
 i 1
])
 x
 i 1 r
])



Cov ( x i 2
Cov ( x i 2 q
 x
 x i 1 q i 1
, [ x i 2
, [ x i 2 r
 x i 1
 x i 1 r
]  [ x
 i 2
]  [ x
 i 2 r
 x
 i 1
 x
])
 i 1 r
])






Cov ( y i 2
Cov ( x i 2
 y i 1
, [ x i 2
 x i 1
, [ x i 2
 x i 1
 x i 1
]  [ x
 i 2
]  [ x
 i 2
 x
 i 1
 x
 i 1
])
])



 1.91
Cov ( y i 2
Cov ( x i 2
 y i 1
, [ x i 2
 x i 1
, [ x i 2
 x i 1
 x i 1
]  [ x
 i 2
]  [ x
 i 2
 x
 i 1
 x
 i 1
])
])

T
Var ( x T it
)  Var ( u it
Var ( x T it
)  Var ( w it
)  Var ( u it
)
)

Cov ( x i 2
Cov ( x i 2 q
 x
 x i 1 q i 1
, [ x i 2
, [ x i 2 r
 1.40
 x i 1
]  [ x
 i 2
 x i 1 r
]  [ x
 i 2 r
 x
 i 1
])
 x
 i 1 r
]) since
Cov ( x i 2
 x i 1
, [ x i 2
 x i 1
]  [ x
 i 2
 x
 i 1
])
 Cov ([ x N
2
[ x T i 2
 x
1
N ]  [ x T i 2
 x T i 1
]  [ u i 2
 x T i 1
]  [ u i 2
 u i 1
]  [ w i 2
 u i 1
]  [ w i 2
 w i 1
]
 [ x P
 i 2
 [ s
 i 2
 [ v
 i 2

 s x P
 i 1
 i 1
]  [ x
]  [ u
T
 i 2
 i 2
 v
 i 1
]  [ w
 i 2
 x T
 i 1
]
 u
 i 1
]
 w
 i 1
])
 2[ Var ( x it
T )  Var ( u it
)  Var ( w it
)]
 w i 1
], and

Covariances of 6-month changes in individual responses to happiness questions
Average on Diagonal/Average off Diagonal = 1.400
(Omitting ENJOYLIFE: 1.421)

Bus5 (national, future, ρ=.446)
“Which would you say is more likely – that in the country as a whole we’ll have continuous good times during the next 5 years or so, or that we will have periods of widespread
(1unemployment and depression, or what?”
 ) 
P
  s
 .521 (t=11.26)
(1   ) 
T
  u
=.05 (t=.014)
Bus12 (national, future, ρ=.422)
“Do you think that during the next 12 months we’ll have good times financially, or bad times, or what?”
(1 ) 
P
(1   ) 
T


 s
 u
 .393 (t=8.42)
=.050 (t=3.24)

Why is (1  than (1 ) 
P
 ) 
T
 
 s
?
 u
so much smaller

P
: includes (1) habitual rose-colored glasses
(i.e., effects of personality-type) and
(2) effect of a good mood over extended time

T
 s
 u
: effect of being in a good mood right now
 effect of persistent global response style
 effect of transitory global response style

• People may not reevaluate their expectations every day.
• However, being interviewed may trigger the reevaluation of an expectations.
• If being interviewed does not trigger reevaluation, people can respond from explicit or implicit memory.
• Today’s happiness should only matter if the expectation is reevaluated, pushing down

T
in relation to 
P
.

The ratio
(1   ) 
T
(1 ) 
P


 u
 s
should be smaller for expectations and other judgments that are likely to be reevaluted less often.

Bus5 (national, future, ρ=.446)
“Which would you say is more likely – that in the country as a whole we’ll have continuous good times during the next 5 years or so, or that we will have periods of widespread
(1unemployment and depression, or what?”
 ) 
P
  s
 .521 (t=11.26)
(1   ) 
T
  u
=.05 (t=0.01)
Bus12 (national, future, ρ=.422)
“Do you think that during the next 12 months we’ll have good times financially, or bad times, or what?”
(1 ) 
P
(1   ) 
T


 s
 u
 .393 (t=8.42)
=.050 (t=3.24)

Px5q1 (national, future, ρ=.312)
“Do you think prices will be higher, about the same, or lower 5 to 10 years from now?”
(1-
(1 
 ) 
P
 ) 
T


 s
 u
  .102 (t=  2.23)
=-.003 (t=  0.02)
Px1q1 (national, future, ρ=.263)
“Do you think that during the next 12 months, prices will go up, or go down, or stay where they are now?”
(1 ) 
P
(1   ) 
T


 s
 u
 .057 (t=1.26)
=.020 (t=1.25)

Gaspx1 (national, future, ρ=.318)
“Do you think that the price of gasoline will go up during the next 5 years, go down, or stay
(1 ) 
P
 about the same?”
 s
 .032 (t=0.75)
(1   ) 
T
  u
=.004 (t=0.27)
Gas1px1 (national, future, ρ=.231)
“Do you think that the price of gasoline will go up during the next 12 months, go down, or stay about the same?”
(1 ) 
P
(1   ) 
T


 s
 u
 .029 (t=0.65)
=.015 (t=0.85)

Bexp (national, future, ρ=.323)
“A year from now, do you expect that in the country as a whole business conditions will be better, or worse than they are at present, or just about the same?”
(1-
(1 
 ) 
P
 ) 
T


 s
 u
 .145 (t=4.27)
=.005 (t=0.23)
Ratex (national, future, ρ=.297)
“What do you think will happen to interest rates for borrowing money over the next 12 months
– will they go up, stay the same, or go down?”
(1 ) 
P
  s
 .145 (t=4.02)
(1   ) 
T
  u
=.015 (t=0.69)

Unemp (national, future, ρ=.285)
“How about people out of work during the coming 12 months – do you think that there will be more unemployment than now, about the
(1 ) 
P
 same, or less?”
 s
 .132 (t=3.93)
(1   ) 
T
  u
=.057 (t=2.71)
Dur (national, present, ρ=.274)
“Generally speaking, do you think now is a good time or a bad time for people to buy major household items?”
(1 ) 
P
(1   ) 
T


 s
 u
 .148 (t=4.11)
=.050 (t=2.22)

Govt (national, present, ρ=.539)
“As to the economic policy of the government – I mean steps taken to fight inflation or unemployment – would you say the government is doing a good job, only fair, or a poor job?”
(1-
(1 
 ) 
P
 ) 
T


 s
 u
 .338 (t=9.91)
=.049 (t=2.89)
Bago (national, past, ρ=.345)
“Would you say that at the present time business conditions are better or worse than they were a year ago?”
(1 ) 
P
(1   ) 
T


 s
 u
 .155 (t=4.64)
=.036 (t=1.77)

1. The variance and persistence of national happiness are small.
2. Happiness affects expectations causally.
3. Suggestive evidence for sticky expectations
expectations that people would plausibly reevaluate less often seem less affected by changes in happiness.

• Upper bound on the size of business cycle fluctuations that are caused by structural shocks to the emotion of happiness.
• Bad for the “animal spirits” view
• Happiness does seem to affect expectations, but happiness is mostly idiosyncratic.
• Leaves plenty of room for the empirical successes of the information view based on aggregate data.

Inexq1 (personal, future, ρ=.416)
“During the next 12 months, do you expect your
(family) income to be higher or lower than
(1 ) 
P during the past year?”
  s
 .176 (t=5.17)
(1   ) 
T
  u
=.067 (t=3.54)
Pexp (personal, future, ρ=.419)
“Do you think that a year from now you (and your family living there) will be better off financially, or worse off, or just about the same as now?”
(1 ) 
P
  s
 .124 (t=3.67)
(1   ) 
T
  u
=.013 (t=0.70)

Pago (personal, past, ρ=.462)
“Would you say that you (and your family living there) are better off or worse off financially than a year ago?”
(1-
(1 
 ) 
P
 ) 
T


 s
 u
 .402 (t=11.87)
=.047 (t=2.60)
V228 (personal, past, ρ=.429)
“Compared with 5 years ago, do you think the chances that you
(and your husband/wife) will have a comfortable retirement have gone up, gone down, or remained about the same?”
(1 ) 
P
(1   ) 
T


 s
 u
 .275 (t=8.22)
=.084 (t=4.51)

Homeval (personal, past, ρ=.580)
“Do you think the present value of your home – I mean what it would bring you if you sold it today – has increased compared with a year ago, decreased, or
(1 ) 
P stayed about the same?”
  s
 .090 (t=2.14)
(1   ) 
T
  u
=.013 (t=.069)
Hom (personal, present, ρ=.361)
“Generally speaking, do you think now is a good time or a bad time to buy a house?”
(1-
(1 
 ) 
P
 ) 
T


 s
 u
 .247 (t=7.22)
=  .001 (t=  0.03)

Car (personal, future, ρ=.286)
“Do you think the next 12 months will be a good time or a bad time to buy a vehicle?”
(1-
(1 
 ) 
P
 ) 
T


 s
 u
 .208 (t=5.81)
=.000 (t=0.00)