Online Appendix
Asset Trading and Monetary Policy in Production
Economies
Guidon Fenig, Mariya Mileva and Luba Petersen
∗
Appendix A: Theoretical Model
The framework that we use to implement our experimental design is based on a representative agent dynamic general equilibrium model (DGE) with sticky prices and monopolistic competition. In order to have a constant fundamental value for the asset that we
introduce, we assume that the economy is subject to no shocks and therefore our model
is not stochastic. However, the framework can be easily extended to include various
shocks, in which case it will be a DSGE model. The choice of this particular framework
is motivated by the fact that DGE (and DSGE) models are widely used for monetary
policy analysis and forecasting among central banks. In this model households optimally
choose their consumption of final goods, labor supply, and savings. In addition, we introduce a tradable asset that yields a fixed dividend every period. This is because we
are interested in analyzing the consequences of monetary authorities’ intervention on the
asset market. In this economy final goods are produced by monoplistically competitive
firms that use labor as their only input. Firms set their prices based on the staggered
pricing mechanism à la Calvo (1983). Finally, the central bank sets the nominal interest
rate in response to fluctuations in inflation. We also consider an alternative monetary
policy rule in which the central bank also responds aggressively to asset price inflation.
Model
We begin with a description of the model and provide a characterization of the behavior
of households and their optimal decisions. Then we describe the production and price
setting decisions of firms, and finally we show how the central bank conducts monetary
policy.
Households
Households maximize the present discounted value of their utility associated with consumption and labor:
!
1+η
1−σ
∞
X
C
N
t+j
t+j
Ut =
βj
−
.
(1)
1−σ
1+η
j=0
Where
Z
Ct =
0
∗
1
θ−1
θ
Cit
θ
θ−1
di
.
Corresponding author: Luba Petersen: lubap@sfu.ca.
1
(2)
They obtain utility from the immediate consumption of a bundle of differentiated varieties, each variety denoted by Cit , and disutility from working Nt hours. The coefficient
of relative risk aversion is represented by σ, the elasticity of labor supply by 1/η, and
the elasticity of substitution between different varieties is given by θ.
Equation 3 is the household’s budget constraint that equates expenditures to income:
Pt Ct + Bt + Qt Xt = (Dt + Qt ) Xt−1 + (1 + it−1 )Bt−1 + Wt Nt + Tt .
(3)
Households may purchase a consumption good, Ct , at a price, Pt ; save or borrow through
a risk-free nominal bond, Bt ; and acquire shares of a risky asset, Xt , at a price, Qt .
They obtain income from dividends, Dt , on the risky asset; capital gains, Qt Xt−1 , from
last period’s asset shares; interest rate income, (1 + it−1 )Bt−1 , on bond holdings; wage
income from working, Wt Nt ; and a transfer, Tt , from the monopolistic firms. In both
our theoretical framework and in the laboratory experiment, we assume a constant value
for the dividend paid on each unit of the asset (the dividend is unrelated to economic
fundamentals). The representative household maximizes its utility stream (1) by making
optimal choices on Ct , Nt , Bt , and Xt subject to the budget constraint (3).
From the household’s first-order conditions the following equations are derived:
Ntη
Wt
−σ = P ,
Ct
t
"
β
Ct+1
Ct
"
Qt = β
(4)
#
(1 + it )
= 1, and
(1 + πt+1 )
#
Ct+1 −σ (Dt+1 + Qt+1 )
.
Ct
(1 + πt+1 )
−σ
(5)
(6)
Equation 4 describes the labor–leisure intratemporal trade-off taking the real wage as
given. Equation 5 represents the intertemporal tradeoff between current and future
consumption in terms of the risk-free bond. Equation 6 is an asset pricing equation for
the risky asset. Real interest can be defined using the Fisher equation:
1 + it
.
1 + rt =
(1 + πt+1 )
Firms
Firms possess a linear production function and operate in a monopolistically competitive
environment. They sell differentiated goods, Yi , using labor as the sole input in the
production process:
Yit = ZNit .
(7)
Here, Nit is the number of hours of work hired by the firms and Z is a productivity
parameter. Firms must decide what price to set for the output. Each period, only a
fraction 1−ω of the firms are allowed to adjust the price (Calvo mechanism). The prices
set by the firms determine the demand for each variety:
1
Yit =
I
Pit
Pt
2
−θ
Yt ,
where I is the number of firms in the economy. Pt is the aggregate price index and is
defined as
1
(" I
#) 1−θ
X
1
Pt ≡ I θ−1
.
(Pit )1−θ
i=1
The Calvo assumption about price stickiness can be also written as:
Pt1−θ = (1 − ω) (Pto )1−θ + ω (Pt−1 )1−θ .
(8)
Monetary Policy
The central bank sets the nominal interest rate on bonds according to the following
Taylor rule:
i1−γ
1 + it−1 γ h
(1 + it )
(1 + πt )δ
,
(9)
=
(1 + ρ)
1+ρ
where ρ is the natural nominal interest rate. Also important to notice is that when
γ > 0, the central bank exhibits interest rate smoothing behavior.1 Our decision to
incorporate interest rate smoothing was motivated by a desire to provide stability in
policy. We discuss alternative policies in the Treatments section.
Market Clearing
In order to close the model, we need to impose market clearing conditions on asset
markets. Note that in a dynamic general equilibrium, the net supply of bonds is zero
and the fixed supply of the shares of the risky asset is normalized to one:
Bt = 0, and Xt = 1.
We also impose that total demand for output is financed by income from output production and dividends:
Dt
Ct = Yt +
.
Pt
Steady State Equilibrium
To derive the steady state equilibrium, one can start by solving cost minimization problem for firm i (equation (7)):
Wt
Nit − ϕt (Yit − ZNit ) ,
min
Nit
Pt
where ϕt is the firm’s real marginal cost. From the first-order condition, the following
equation is obtained:
Wt 1
mct ≡ ϕt =
.
Pt Z
1
Our model introduces an asset whose dividend stream is constant and not directly related to firm
profits. Carlstrom and Fuerst (2007) note that if a central bank sets the nominal interest rate in response
to share prices, it implicitly weakens its response to inflation which leads to equilibrium indeterminacy.
This new source on indeterminacy hinges on the direct link between dividends and firm profits. Thus, a
monetary policy responding to asset prices would not imply indeterminacy in our model. Note that in
our model the no-arbitrage condition implies that interest rate smoothing is isomorphic to responding
to asset price inflation as 1 + it = (Dt /Qt + Qt+1 /Qt ).
3
It can be shown that the ratio of the price set by the firms when they are able to update
it, Pto , relative to the aggregate price index, Pt , is:
P∞
i i 1−σ
i=1 ω β Ct+i mct+i
θ
Pto
=
Pt
θ − 1 P∞
i=1 ω
i β i C 1−σ
t+i
Pt+i
Pt
Pt+i θ
Pt
θ−1
≡
θ S1t
,
θ − 1 S2t
where
S1t = Ct1−σ mct + βωEt S1t+1 , and
S2t = Ct1−σ + βωEt S2t+1 .
With no shocks in the economy, S1t = S1t+1 and S2t = S2t+1 . This implies that
θ
Pto
=
mct .
Pt
θ−1
(10)
Thus, in the steady state,
W
θ−1
=
Z.
(11)
P
θ
Using the market clearing conditions and equation (4), the steady state values for labor
and consumption are obtained:
1
θ − 1 1−σ η+σ
, and
Z
θ
1
θ − 1 1−σ η+σ
= Z
Z
.
θ
N SS =
C SS
From equation 5 the steady state for the interest rate can be obtained:
iSS =
1
− 1.
β
Finally, from equation 6 the steady state for the asset price can be derived:
QSS =
β
D̄.
1−β
Derivation of the Inflation Equation
Combining equations (4), (8), and (10), the following equation for inflation is obtained:
1
Πt = 1 − (1 − ω)
ω 1−θ
η σ
θ Nt Ct
θ−1 Z
1
1−θ 1−θ
− 1.
(12)
Linearizing (12), by using a first-order Taylor approximation around the steady state,
linearized inflation is obtained:
Πt = 1 + γ c (Ctmed − C SS ) + γ n (Ntmed − N SS ),
4
where,
γc =
γn =
σ−1
η
1
1
C SS
N SS σθ(ω − 1)ω 1−θ Ψ,
(1 − θ) Z
σ
η−1
1
1
C SS
N SS
ηθ(ω − 1)ω 1−θ Ψ,
(1 − θ) Z
and,

Ψ = 1 − (1 − ω)
σ
C SS
η
N SS
(θ − 1) Z
1
!1−θ −(1+ 1−θ
)
θ

σ
η !−θ
C SS
N SS θ
.
(θ − 1) Z
Appendix B: Parametrization
We choose parameter values for our environment based on two considerations. First,
we sought to have reasonable parameters consistent with empirical macroeconomic evidence. Second, our aim was to ensure a sufficiently interior steady state and steep
expected payoff hills. This would enable us to clearly observe actively chosen deviations from equilibrium behavior (these parameters are presented in Table 1). We set
the discount factor (framed in our environment as the probability of continuation of the
sequence) β equal to 0.965. This implies that a particular sequence of periods would
last for an average of 28 periods. The parameter of risk aversion, σ, is calibrated to be
0.33, while the labor supply parameter, η, is set to 1.5. The elasticity of substitution
between varieties, θ, is 15, implying a markup of 7 percent over marginal cost. The
Calvo parameter, ω, is 0.9, implying that 10 percent of firms have the ability to update
their prices each period. The interest rate smoothing parameter used by the central
bank is 0.5, while the Taylor rule parameter, indicating how responsive the nominal
interest rate is to inflation, is δ = 1.5. The per-period dividend paid on assets is 0.035,
which means that the asset is worth 1 after an average of 28 periods. We set a fixed
fundamental value to minimize subjects’ confusion. Each firm produces Z = 10 units of
output with 1 unit of labor. In the steady state, the selected calibration implies steady
state levels of individual consumption and labor of 22.4 and 2.24 units, respectively.
The steady state real wage is set to 9.35 and the steady state nominal rate of return is
0.036.
5
Table 1: Parameters and Steady State Values
Parameter
Z
1−ω
δ
α
γ
κ
θ
β
ρ
1/σ
1/η
µ∗
C∗
N∗
W∗
P∗
Parameter Description
Productivity level
Fraction of firms updating
Inflation target of CB
CB response to πtA
Interest smoothing parameter CB
Slope of NKPC
Measure of substitutability
Rate of discounting
Natural nominal rate of return
Elasticity of intertemporal substitution
Frisch labor supply elasticity
Steady state markup (θ/(θ − 1))
Steady state consumption
Steady state labor
Steady state nominal wage
Steady state output price
Value
10
0.1
1.5
0.15
0.5
0.07
15
0.965
0.0363
3.03
0.67
1.07
22.37
2.237
10
1.07
Appendix C: Asset market analysis including outlier sessions
In this section, we provide the summary statistics and non-parametric statistical tests
associated with our full data set that include NC5, C1 and AT1.
Visual inspection of the trading data suggests that assets are frequently traded and
usually above the constant fundamental value. On average and consistently across treatments, 62-65% of the time subjects are willing to engage in trade by submitting bids or
asks. Wilcoxon signed-rank tests reject the null hypothesis that subjects are unwilling
to engage in trade (N = 6 per treatment, p < 0.028 for all treatments).2
For each of the six sessions of the NI, C, and AT treatments, we compute the mean
trading price. In the No-Intervention treatment, the mean price is 5.14 (s.d. 6.4). The
introduction of a cash-in-advance constraint in the Constrained treatment results in a
mean price of 5.90 (s.d. 2.35), while an asset inflation targeting monetary policy lowers
the mean price to 3.73 (s.d. 1.52). Signed rank tests reject the null hypothesis that
asset prices are traded at the constant fundamental value of 1 (N = 6, p = 0.027 for
all treatments). We also consider the possibility that asset prices track the dynamic
fundamental value. For each session, we compute the mean period deviation of prices
from the dynamic fundamental value and conduct signed rank tests to determine whether
the values are different from zero. The mean deviation is 0.14 (s.d. 0.31, p = 0.753) in
the NI treatment, 0.26 (s.d. 0.21, p = 0.027) in the C treatment, and 0.07 (s.d. 0.07,
p = 0.046) in the AT treatment.
Table 2 presents measures of trading activity and mispricing at the session-level
with mean and standard deviation statistics. Two-sided Wilcoxon rank-sum tests are
calculated with average measures at the session level, and the associated p-values are
2
For reference, on the online appendix we plot the asset prices and trading volume for each session.
6
presented at the bottom of the table. We also provide box-plots of session-level statistics
in the Appendix.
Given that participants were paid primarily on their consumption and labor decisions, it seems reasonable to expect minimal trading activity in the asset market. Our
results indicate that this was not the case. In all sessions we observed P
considerable asset
trading. Turnover of the asset between traders is computed by T = t Vt /S, where V
is the total volume of trade and S is the total stock of outstanding assets in the market.
We observe little difference in turnover across treatments. Mean turnover is the lowest
in the NI treatment (0.71) and highest in the C treatment (0.78) with the differences
across treatments not statistically significant (p > 0.52 for all treatment comparisons
using two-sided Wilcoxon rank sum tests). More than two-thirds of the participants
submitted a bid during the experiment (76 percent in NI, 81 percent in C, and 79 percent in AT) and almost all participants submitted an ask (98 percent in NI, 92 percent
in C, and 87 percent in AT).
We hypothesized that the introduction of a leverage constraint in the C treatment
would reduce the market value of bids relative to the NI treatment. For each session,
we calculate the average market value of all bids (measured as bid price times quantity
demanded). However, a two-sided Wilcoxon rank sum test fails to reject the null hypothesis that the distribution of mean market value of bids are identical (N = 6, p = 0.4233)
in the two treatments. Neither the bid prices nor the quantities being demanded differ
significantly across the treatments. We obtain an identical result for mean market value
of asks.
We use a variety of measures to assess whether the policies influenced asset price
deviations. We first consider the amplitude of the asset price deviation, measured as
the trough-to-peak change in the market asset price relative to its fundamental value,
t (Pt −ft )
is computed as A = maxt (Pt −ft )−min
, where Pt is the market-clearing price of the
E
asset in period t and E is the expected dividend value over the lifetime of the asset.
The average amplitude in the NI treatment was 17.37 (s.d. 30). This result was driven
by one session that had a measured amplitude of 78.5, while the remaining five sessions
ranged from 1.92 to 6.33. Despite an inability to borrow for speculation, the measures
of amplitude decrease only modestly in the C treatment and are not significantly different from the NI treatment (p = 0.20 under both specifications of the fundamental
value). However, when we exclude the outlier NI session, we find that amplitudes are
significantly higher in the C treatment (p = 0.04 under both amplitude measures). Also
counter to our predictions, amplitudes in the AT treatment are significantly higher than
in the NI treatment under both specifications of the fundamental value (p ≤ 0.05 in
both cases). Weighting the amplitude by the volume of trade, we compute the market
value amplitude as M = maxt ((PEt −ft )Vt ) . The market value amplitude is higher in both
intervention treatments than in the baseline asset market environment, driven by more
frequent turnover.
The relative asset deviation measures the degree of mispricing in asset market experiments weighted by the number of periods of potential trade (see Stöckl, Huber and
P
t|
Kirchler (2010) for details). We compute this as RAD = T1 Tt=1 |Ptf−f
, where T is
t
the total number of periods in the asset market. The average RAD under a constant
fundamental value in the NI treatment was 4.38 (s.d. 6.45), and when we exclude the
outlier session, this measure drops to 1.61 (s.d. 1.13). Mean RAD in the C and AT
treatments are 5.12 (s.d. 2.44) and 2.85 (s.d. 1.57), respectively. The differences in
RAD across the NI and C treatments are statistically significant at the 5% level if we
7
exclude the outlier session. The results are qualitatively similar when we consider the
dynamic fundamental value.
Finally, we consider how volatility in asset markets may change in response to policy
interventions. We calculate the realized volatility of the asset prices as the standard
deviation of the asset’s log returns. The price of the asset in a period with no trade
is set equal to the last trading price. Under a leverage constraint, as the investors’
budget constraint fluctuates between binding and slackness, prices should adjust more
extremely. By contrast, under asset inflation targeting, nominal interest rates adjust
to provide or restrict liquidity to smooth asset price fluctuations. Our data fails to
support these prediction. We find that neither policy lead to significant changes in asset
price volatility. While volatility rises from 0.36 in the NI treatment to 0.41 in the C
and 0.44 in the AT treatments, the treatment differences are not statistically significant
(p = 0.873 in both pairwise comparisons).3
Table 2: Session-Level Statistics on Asset Market Variables (outliers included)
I
Treatment
Statistic
Turnover
Ampl.
S.S.
Ampl.
Dynamic
MV Ampl.
S.S.
MV Ampl.
Dynamic
RAD
S.S.
RAD
Dynamic
Volatility
NI
mean
s.d.
0.71
0.35
17.37
30.00
20.94
40.09
36.02
29.42
8.19
6.71
4.38
6.45
1.37
2.11
0.36
0.13
C
mean
s.d.
0.78
0.39
16.07
11.94
21.79
14.63
49.54
48.80
34.25
38.19
5.12
2.44
1.64
1.28
0.41
0.33
AT
mean
s.d.
0.77
0.34
19.94
25.47
31.06
46.19
49.70
34.30
24.05
16.31
2.85
1.57
1.41
1.58
0.44
0.25
NI vs. C
NI vs. AT
C vs. AT
p-value
p-value
p-value
0.57
0.52
0.87
0.20
0.05
1.00
0.20
0.04
0.75
0.52
0.42
1.00
0.26
0.02
0.87
0.15
0.52
0.15
0.20
0.34
0.63
0.873
0.873
0.749
(I) This table presents session-level statistics on asset market variables. N=6 for each treatment. Turnover measures
the trading activity in the market by the total volume of trade divided by the total outstanding stock in the
experiment and the number of periods. Amplitude measures the trough-to-peak change in market asset value
relative to fundamental value. Market value amplitude measures the normalized market value of trade by weighting
period amplitude by the volume of trade. RAD is the relative absolute deviation of asset prices from fundamentals,
weighted by the number of periods of trade. Volatility measures the historical volatility of the log-returns.
3
Computing asset price volatility as the standard deviation of log returns of only periods with
positive trade does not change our general results.
8
Table 3: Asset Prices - Comparison of Treatment Effects (outliers included)I
Dep.V ar.: logQt
C
AT
(1)
1.604***
(0.07)
1.099***
(0.09)
P ercEnteringW ithDebt
P ercEnteringW ithDebt × C
P ercEnteringW ithDebt × AT
(2)
1.581***
(0.09)
1.130***
(0.10)
0.728***
(0.20)
-0.743**
(0.31)
-0.962***
(0.34)
logQt−1
logYt−1
logYt−1 × C
logYt−1 × AT
πt−1
(3)
-0.679
(1.03)
2.159**
(0.95)
-0.071
(0.12)
0.025
(0.19)
0.053
(0.17)
0.905***
(0.02)
0.026***
(0.01)
0.124
(0.19)
-0.417**
(0.18)
0.542
(0.44)
it−1
9
it−1 × C
it−1 × AT
N
N g
g min
g max
g avg
χ2
775
18
24
59
43.06
665.3
775
18
24
59
43.06
500.8
723
18
22
56
40.17
21694.2
(4)
1.546***
(0.08)
1.138***
(0.09)
(5)
-0.961
(1.26)
0.499
(1.03)
3.421***
(1.03)
-2.609*
(1.35)
-4.837***
(1.21)
775
18
24
59
43.06
755.4
0.912***
(0.02)
0.019***
(0.01)
0.186
(0.24)
-0.084
(0.20)
0.877
(0.85)
0.044
(0.76)
-0.649
(0.81)
-2.801***
(0.90)
723
18
22
56
40.17
24906.2
(6)
-1.063
(1.40)
0.426
(1.06)
-0.046
(0.13)
0.023
(0.20)
-0.002
(0.18)
0.904***
(0.02)
0.023**
(0.01)
0.203
(0.27)
-0.070
(0.20)
0.787
(0.89)
0.148
(0.81)
-0.636
(0.88)
-3.048***
(0.92)
723
18
22
56
40.17
19735.4
(I) This table presents results from panel-data linear models fit using feasible generalized least squares,
to allow estimation in the presence of AR(1) autocorrelation with panels and cross-sectional correlations
and heteroskedasticity across panels. *p < 0.10, **p < 0.05, and ***p < 0.01.
Table 4: Asset Prices - By Treatment (outliers included)
Dep.V ar.: logQt
PercEnteringWithDebt
(2)
0.733***
(0.20)
llogassetprice
llogoutputproduced adjSub
loutputpriceinflation
(3)
-0.068
(0.12)
0.917***
(0.03)
0.023**
(0.01)
0.671
(0.55)
lir
N
N g
g min
g max
g avg
chi2
218
6
24
46
36.33
13.48
202
6
22
43
33.67
4668.3
logQt
PercEnteringWithDebt
(2)
0.500*
(0.30)
(3)
-0.006
(0.12)
0.893***
(0.03)
0.017**
(0.01)
-1.820*
(1.10)
llogassetprice
10
llogoutputproduced adjSub
loutputpriceinflation
lir
N
N g
g min
g max
g avg
chi2
297
6
43
59
49.50
2.782
277
6
39
56
46.17
4851.4
NI Treatment
(4)
(5)
0.907***
(0.03)
0.011
(0.01)
-1.001
(1.45)
1.495
(1.16)
202
6
22
43
33.67
4656.0
4.268***
(1.03)
218
6
24
46
36.33
17.04
AT Treatment
(4)
0.373
(0.77)
297
6
43
59
49.50
0.233
(5)
0.910***
(0.03)
0.034***
(0.01)
2.218
(1.41)
-3.520***
(0.78)
277
6
39
56
46.17
4285.6
(6)
-0.050
(0.12)
0.907***
(0.03)
0.014
(0.01)
-1.065
(1.46)
1.530
(1.18)
202
6
22
43
33.67
4436.1
(2)
2.058***
(0.33)
260
6
36
55
43.33
39.94
I
(3)
0.026
(0.16)
0.874***
(0.03)
0.031***
(0.01)
0.758
(0.70)
244
6
33
51
40.67
6735.8
C Treatment
(4)
(5)
9.130***
(1.00)
260
6
36
55
43.33
83.06
0.886***
(0.03)
0.030**
(0.01)
0.967
(1.46)
-0.157
(1.20)
244
6
33
51
40.67
8394.3
(6)
0.032
(0.18)
0.872***
(0.03)
0.031**
(0.01)
0.759
(1.59)
0.007
(1.42)
244
6
33
51
40.67
6337.5
(6)
-0.061
(0.13)
0.908***
(0.03)
0.038***
(0.01)
2.444*
(1.43)
-3.721***
(0.79)
277
6
39
56
46.17
4000.1
(I) This table presents results from panel-data linear models fit using feasible generalized least squares, to allow estimation in the presence of AR(1)
autocorrelation with panels and heteroskedasticity across panels. Total output produced, logYt−1 , is adjusted to account for decisions not submitted
on time and group size. *p < 0.10, **p < 0.05, and ***p < 0.01.
Production
In this section, we discuss findings associated with individual labour supply, output demand, and aggregate
production. Summary statistics computed at the session-level are presented in Table 5 and distributions of
labor supply and output demand decisions are presented in Figure ?? for each treatment. The distributions
include all decisions made by participants in the experienced phase of the experiment. The dashed vertical
line labeled SS refers to the steady state predicted individual labor supply of 2.24 hours and output demand
of 22.4 units. In the Benchmark (B) treatment, the median labor supply is 2.3 hours. Introducing an asset
market into the economy in the NI treatment reduces the labor supply across nearly the entire distribution
and median labor supply decreases to 2.1 hours. When a leverage constraint is imposed on subjects in the
C treatment, labor supply increases considerably across the entire distribution and median labor supply rises
to 2.5 hours. Finally, in the AT treatment, median labor supply is 2.25, with its distribution relatively more
centered around the steady state prediction than under the NI treatment. Mean labor supply, measured at
the session-level, is not significantly different from the steady state prediction in the NI and AT treatments
(signed-rank test, p > 0.60 in both cases), whereas it is significantly greater in the B treatment (p = 0.075)
and C treatment (p = 0.028).
Output demands, by contrast, are significantly greater than the steady state predictions in all treatments.
Median output demand is 40 units in the B treatment. Introducing asset markets and leverage constraints
in the NI and C treatments decreases the output demands across the majority of the distribution and we
observe quite large decreases in the number of participants demanding high levels of output. Median output
demands decrease to 30 and 32 units in the NI and C treatments, respectively. The median output demand is
40 units in the AT treatment. Mean output demands follow a similar pattern. Signed rank sum tests reject
the null hypothesis that mean output demands, measured at the session-level, are identical to the steady state
prediction (p = 0.028 in the B, C, and AT treatments, p = 0.075 in the NI treatment).
While every period of play involves some rationing, we observe that the majority of rationing takes place
in the output market. Labor rationing occurs most frequently in the C treatment in approximately 20 percent
of experienced periods while it never occurs in the experienced periods of the AT treatment.
As a result, production is usually determined by subjects’ labor supply. Mean production is 242.24 units in
the B treatment but is not significantly different from the steady state prediction at the 10% level (p = 0.116).
In the NI treatment, mean production is 208.39 units and is not significantly different from either the steady
state prediction or the baseline economy with no asset market. The imposition of the leverage constraint in
the C treatment results in a mean production of 237.55 units that is significantly higher than the steady state
prediction. Finally, under the asset inflation targeting policy in the AT treatment, mean production is very
close to the prediction at 200.29 units.
Output volatility is significantly greater than predicted by the non-stochastic model and signed-rank tests
consistently reject the null hypothesis that output volatility is zero (p = 0.028 in all treatments). Output
volatility is the lowest in the C treatment at 0.103 while the highest in the B treatment at 0.17. This difference
is statistically significant at the 5% level. Output volatility in the C treatment is also significantly lower than
in the NI treatment (p = 0.004). We attribute the increased and stable labor supply in the C treatment to
increased precautionary saving motives due to the imposed leverage constraint.
11
Table 5: Session-Level Statistics on Production (outliers included)I
Mean Labor
Supply
2.24
Mean Output
Demand
22.4
Total Output
Produced
201.6
Freq. Excess
Labor Supply†
0
Output
Volatility
0
mean
s.d.
2.76
0.76
49.91**
12.96
242.24
63.22
0.106
0.17
0.170**
0.05
6
mean
s.d.
2.34
0.39
39.77*
15.43
208.39
37.45
0.077
0.119
0.156**
0.032
C
6
mean
s.d.
2.70
0.28
40.53**
10.39
237.55**
19.38
0.196
0.255
0.103**
0.019
AT
6
mean
s.d.
2.25**
0.18
52.27**
9.52
200.29
16.71
0
0
0.136**
0.042
p-value
p-value
p-value
p-value
p-value
p-value
0.262
0.715
0.109
0.109
0.631
0.010
0.423
0.200
0.631
0.873
0.109
0.109
0.337
0.522
0.200
0.200
0.749
0.010
0.703
0.305
0.14
0.305
0.14
0.022
0.522
0.025
0.149
0.004
0.200
0.149
Treatment
Sessions
Statistic
B
6
NI
Steady State
B vs. NI
B vs. C
B vs. AT
NI vs. C
NI vs. AT
C vs. AT
(I) Session-level results for experienced participants are presented: total output produced, frequency of excess
aggregate labor supply and output volatility. Total output produced is adjusted for the number of participants
who submitted their decisions on time. Asterisks indicate the significance of the difference of the mean estimate
from its steady state prediction. *p < 0.10, **p < 0.05, and ***p < 0.01.
† All sessions experience rationing in every period.
Finding 4. The introduction of an asset market reduces both labor supply and output demand on average,
but the differences between the B and NI treatments are not statistically significant.
Finding 5. Leverage constraints increase labor supply and considerably increase the frequency of labor
rationing. Production volatility is significantly lower with leverage constraints in place as workers more consistently supply labor from one period to the next.
Finding 6. Asset inflation targeting policies slightly reduce average labor supply and lead to large increases
in average output demands. Average production is not significantly affected by the policy.
12
13
Table 6: Individual Labor Supply Decisions (outliers included)
S
logNi,t
Indebtedi,t
Benchmark Treatment
(1)
(2)
(3)
0.287***
(0.04)
it−1
4.433***
(0.21)
S
logNi,t−1
D
logCi,t
logW aget−1
logOutputP ricet−1
EnteringBankBalancei,t
(1)
0.034**
(0.02)
0.430***
(0.16)
0.521***
(0.02)
0.055***
(0.01)
0.070***
(0.01)
-0.041**
(0.02)
-0.000*
(0.00)
(2)
I
NI Treatment
(3)
(4)
0.303***
(0.03)
4.628***
(0.31)
logQt−1
0.358***
(0.02)
EnteringAssetHoldingsi,t
0.062***
(0.00)
BuyAssets
SellAssets
14
N
N g
g min
g max
g avg
χ2
S
logNi,t
Indebtedi,t
1581
54
15
46
29.28
58.60
1581
54
15
46
29.28
467.1
1508
54
8
46
27.93
34768.4
(1)
(2)
C Treatment
(3)
(4)
0.207***
(0.02)
it−1
7.417***
(0.17)
logQt−1
0.380***
(0.01)
EnteringAssetHoldingsi,t
0.068***
(0.00)
S
logNi,t−1
D
logCi,t
BuyAssets
SellAssets
logW aget−1
logOutputP ricet−1
EnteringBankBalancei,t
N
N g
g min
g max
g avg
χ2
2598
54
41
57
48.11
75.70
2598
54
41
57
48.11
1824.6
2270
54
33
54
42.04
1849.5
2598
54
41
57
48.11
1969.5
1926
50
22
47
38.52
84.14
1926
50
22
47
38.52
229.5
1704
50
19
45
34.08
558.1
1926
50
22
47
38.52
1182.8
(5)
(1)
(2)
AT Treatment
(3)
(4)
0.049***
(0.01)
0.131
(0.17)
0.015***
(0.00)
0.002***
(0.00)
0.577***
(0.02)
0.038***
(0.01)
-0.013
(0.01)
-0.017**
(0.01)
0.080***
(0.01)
-0.041**
(0.02)
0.000
(0.00)
2237
54
26
54
41.43
88636.9
0.101***
(0.03)
0.388***
(0.10)
0.254***
(0.01)
0.065***
(0.00)
2738
53
39
67
51.66
13.86
2738
53
39
67
51.66
16.24
2545
53
36
58
48.02
584.7
2738
53
39
67
51.66
4067.3
(5)
0.012
(0.02)
1.341***
(0.30)
0.358***
(0.02)
0.185***
(0.01)
-0.127***
(0.02)
0.082***
(0.03)
-0.000
(0.00)
0.068***
(0.01)
0.003**
(0.00)
0.015
(0.03)
0.022
(0.02)
1378
43
17
45
32.05
9577.0
(5)
0.021**
(0.01)
-0.131
(0.08)
-0.000
(0.00)
-0.000
(0.00)
0.616***
(0.02)
0.025***
(0.00)
0.018***
(0.01)
0.016***
(0.00)
0.091***
(0.01)
-0.045*
(0.02)
0.000
(0.00)
2516
53
32
58
47.47
147146.5
(I) This table presents results from panel-data linear models fit using feasible generalized least squares, to allow estimation in the presence of AR(1) autocorrelation
with panels and cross-sectional correlations and heteroskedasticity across panels. *p < 0.10, **p < 0.05, and ***p < 0.01.
15
Table 7: Individual Output Demand Decisions (outliers included)
D
logCi,t
Indebtedi,t
(1)
Benchmark Treatment
(2)
(3)
-0.022
(0.07)
it−1
3.484***
(0.43)
D
logCi,t−1
S
logNi,t
logW aget−1
logOutputP ricet−1
EnteringBankBalancei,t
(1)
-0.097***
(0.03)
-1.835***
(0.25)
0.809***
(0.02)
0.171***
(0.02)
0.288***
(0.03)
-0.216***
(0.03)
0.000
(0.00)
(2)
I
NI Treatment
(3)
(4)
0.047
(0.06)
3.498***
(0.59)
logQt−1
0.401***
(0.04)
EnteringAssetHoldingsi,t
0.201***
(0.01)
BuyAssets
SellAssets
16
N
N g
g min
g max
g avg
χ2
D
logCi,t
Indebtedi,t
1594
54
14
46
29.52
0.0968
1594
54
14
46
29.52
65.83
(1)
(2)
1520
54
7
46
28.15
156848.5
C Treatment
(3)
(4)
-0.066
(0.04)
it−1
11.804***
(0.51)
logQt−1
0.804***
(0.03)
EnteringAssetHoldingsi,t
0.182***
(0.01)
D
logCi,t−1
S
logNi,t
BuyAssets
SellAssets
logW aget−1
logOutputP ricet−1
EnteringBankBalancei,t
N
N g
g min
g max
g avg
χ2
2586
54
33
57
47.89
2.341
2586
54
33
57
47.89
532.6
2259
54
26
54
41.83
857.9
2586
54
33
57
47.89
1151.3
1915
50
22
47
38.30
0.591
1915
50
22
47
38.30
35.38
(5)
(1)
(2)
-0.061***
(0.02)
-2.456***
(0.25)
0.008
(0.01)
0.000
(0.00)
0.800***
(0.01)
0.183***
(0.02)
0.034*
(0.02)
-0.008
(0.01)
0.286***
(0.02)
-0.226***
(0.03)
-0.000**
(0.00)
2234
54
21
54
41.37
409012.2
-0.104**
(0.04)
1697
50
19
45
33.94
117.3
AT Treatment
(3)
1915
50
22
47
38.30
1138.1
(4)
1.352***
(0.32)
0.494***
(0.03)
0.233***
(0.00)
2754
53
40
67
51.96
6.375
2754
53
40
67
51.96
17.82
2561
53
39
58
48.32
298.6
2754
53
40
67
51.96
2502.2
(5)
-0.034*
(0.02)
-1.847***
(0.30)
0.825***
(0.01)
0.132***
(0.02)
0.268***
(0.02)
-0.171***
(0.03)
0.000**
(0.00)
-0.028***
(0.01)
0.002**
(0.00)
0.008
(0.03)
0.019
(0.02)
1653
50
17
45
33.06
215522.0
(5)
0.005
(0.02)
-0.570***
(0.16)
0.019***
(0.01)
0.000
(0.00)
0.906***
(0.01)
0.078***
(0.02)
0.031**
(0.02)
0.031***
(0.01)
0.117***
(0.02)
-0.147***
(0.04)
0.000
(0.00)
2528
53
35
58
47.70
514133.1
(I) This table presents results from panel-data linear models fit using feasible generalized least squares, to allow estimation in the presence of AR(1) autocorrelation
with panels and cross-sectional correlations and heteroskedasticity across panels. *p < 0.10, **p < 0.05, and ***p < 0.01.
Table 8: Individual Labor Supply and Output Demand Decisions - Treatment Comparisons (outliers included)I
C
AT
(1)
0.887***
(0.01)
0.753***
(0.00)
EnteringBankBalancei,t
(2)
S
Panel A: Labor Supply Decisions Ni,t
(3)
(4)
(5)
-0.000
(0.00)
0.001
(0.00)
0.001
(0.00)
EnteringBankBalancei,t × C
EnteringBankBalancei,t × AT
Indebtedi,t
0.160***
(0.03)
0.087**
(0.04)
-0.021
(0.04)
Indebtedi,t × C
Indebtedi,t × AT
it−1
4.119***
(0.31)
3.122***
(0.36)
-3.427***
(0.33)
it−1 × C
it−1 × AT
17
logQt−1
0.338***
(0.02)
0.029
(0.02)
-0.029
(0.02)
logQt−1 × C
logQt−1 × AT
EnteringAssetHoldingsi,t
EnteringAssetHoldingsi,t × C
EnteringAssetHoldingsi,t × AT
Controls
N
N g
g min
g max
g avg
chi2
No
7262
157
22
67
46.25
60768.0
No
7262
157
22
67
46.25
5.570
No
7262
157
22
67
46.25
140.2
No
7262
157
22
67
46.25
1879.6
No
6519
157
19
58
41.52
2914.4
(6)
-0.000
(0.00)
0.000
(0.00)
0.000
(0.00)
0.001
(0.02)
0.058***
(0.02)
0.029
(0.02)
-0.308*
(0.16)
0.519***
(0.16)
0.218
(0.17)
0.018**
(0.01)
-0.008
(0.01)
-0.013
(0.01)
0.003***
(0.00)
-0.002
(0.00)
-0.003**
(0.00)
Yes
6407
157
17
58
40.81
221298.0
(7)
0.014
(0.02)
0.061***
(0.02)
0.000
(0.00)
-0.000
(0.00)
-0.000
(0.00)
0.005
(0.02)
0.055***
(0.02)
0.018
(0.02)
-0.187
(0.17)
0.532***
(0.17)
0.142
(0.17)
0.023***
(0.01)
-0.009
(0.01)
-0.024**
(0.01)
0.004***
(0.00)
-0.003**
(0.00)
-0.006***
(0.00)
Yes
(1)
3.500***
(0.02)
3.704***
(0.02)
D
Panel B: Output Demand Decisions Ci,t
(2)
(3)
(4)
(5)
(6)
(7)
Yes
6517
157
19
58
41.51
1100.5
Yes
0.002***
(0.00)
-0.002***
(0.00)
0.002**
(0.00)
-0.099*
(0.05)
0.008
(0.07)
0.074
(0.08)
4.683***
(0.63)
5.605***
(0.81)
-3.347***
(0.71)
0.592***
(0.04)
0.018
(0.05)
-0.052
(0.05)
No
6407
157
17
58
40.81
215543.2
No
7255
157
22
67
46.21
70192.8
No
7255
157
22
67
46.21
112.5
No
7255
157
22
67
46.21
9.039
No
7255
157
22
67
46.21
493.9
(I) This table presents results from panel-data linear models fit using feasible generalized least squares, to allow estimation in the presence of AR(1) autocorrelation with panels and
heteroskedasticity across panels. *p < 0.10, **p < 0.05, and ***p < 0.01.
Appendix D: Convergence
Following Duffy (2014), we estimated the following equation for each session j:
yj,t = λj yj,t−1 + µj + j,t ,
where yj,t is either the median output demanded or the median labor supplied and j,t is
a random error term with mean zero. When the equations are estimated, it is possible
µ̂
to test for weak convergence if |λ̂j | ≤ 1 and strong convergence if j is not significantly
1−λ̂j
different from the steady state values. The results of the estimation are shown in the
following table:
Table 9: ConvergenceI
Median Labor Supply
Session
B1
B2
B3
B4
B5
B6
NI1
NI2
NI3
NI4
NI5
NI6
C1
C2
C3
C4
C5
C6
AT1
AT2
AT3
AT4
AT5
AT6
Median Output Demand
µ̂j
λ̂j
λ̂j
1−λ̂j
µ̂j
1−λ̂j
(Weak Conv.)
(Strong Conv.)
(Weak Conv.)
(Strong Conv.)
0.658*
0.569*
0.408*
0.497*
0.507*
0.317*
0.624*
0.443*
0.267*
0.583*
0.388*
0.405*
0.657*
0.191*
0.591*
0.464*
0.568*
0.328*
0.577*
0.482*
0.337*
0.44*
0.526*
0.092*
2.324*
2.334*
2.956
2.153*
2.482
4.003
2.789
2.868
2.218*
2.486
2.378
1.809
2.441
2.265*
2.84
2.48
3.151
2.781
2.129*
2.447*
2.273*
2.349
2.225*
2.127
0.553*
0.428*
0.073*
0.542*
0.674*
0.549*
0.462*
0.548*
0.017*
0.212*
0.269*
0.231*
0.611*
0.412*
0.852*
0.533*
0.119*
0.218*
0.669*
0.19*
0.222*
0.579*
0.26*
0.629*
45.469
72.234
41.552
32.757
44.975
41.938
44.418
44.789
24.315
49.514
33.043
20.465
34.491
39.157
50.043
46.154
31.388
35.049
53.813
51.076
42.411
51.519
30.636
38.054
(I) The results from the estimation of yj,t = λj yj,t−1 + µj + j,t are displayed in
the table for each session j. In columns 2 and 4 for each session, * indicates that we
can reject λ̂j ≥ 1 at a 5 percent level. For strong convergence (columns 3 and 5), *
implies that we cannot reject
µ̂j
1−λ̂j
= SS at a 5 percent level using a Wald Test.
18
Appendix E: Vector Autoregression (VAR)
Table 10: VARI
Dep Var.
Nt−1 −NSS
NSS
Nt−2 −NSS
NSS
it−1 −iSS
iSS
B1
B2
B3
B4
B5
B6
-0.06
0.04
0.4
-0.09
0.57
0.21
-0.34
0.02
-0.31
0.03
-0.06
-0.12
-0.12
-0.08
0.05
-1.44
-0.1
0
NI1
NI2
NI3
NI4
NI5
NI6
0
0.85*
0.1
0.33
0.19
0.05
0.3
0.29
-0.35
0.01
-0.06
0.41
C1
C2
C3
C4
C5
C6
0.44
0.42
0.44
0.22
0.29
0.38
AT1
AT2
AT3
AT4
AT5
AT6
0.06
0.35
-0.12
0.24
0.54*
0.76***
19
Sessions
Ind Var.
Nt −NSS
NSS
it−2 −iSS
iSS
A
πt−1
A
πt−2
Nt−1 −NSS
NSS
Nt−2 −NSS
NSS
it−1 −iSS
iSS
it −iSS
iSS
it−2 −iSS
iSS
-0.66
-0.23
2.12
-1.48
2.15
0.67
0.18
-1.19
-1.5
0.27
-0.61
0.43
1.49
3.79
0.87*
-3.83
-1.48
0.96*
πt−1
πt−2
-0.05
0.04
-0.05
0.66
0
0.02
2.69
3.19
-1.57
31.94
2.16
1.43
6.75
0.19
0.86
1.04
2
-0.33
3.07
-16.60*
-0.53
-2.95
2.29
-0.23
-1.62
8.22*
0.41
1.49
-0.98
0.14
-66.05
358.6*
11.08
62.43
-48.92
7.07
1.84
0.31
-0.85
0.06
-3.08
-2.16
0.08
0.17
0
0.07
0
0.01
-0.11
0.42
0.03
0.08
-0.04
0
-1.72
2.46
-1.84
2.39
1.23
-0.19
3.73
0.76
-0.08
-1.95
1.23
2.86*
0.27
0.18
-0.22
0.14
0.09
0.41
0.32
3.76
0.59
6.96
-2.92***
-2.24
-0.21
-1.88
-0.28
-3.41
1.46***
1.08
-6.22
-81.54
-11.9
-148.4
66.48***
49.62
-0.27
0.13
0.77
-2.04
-0.78
0.48
0.01
-0.33
-0.03
-0.03
0.07
0.12
0
-0.16
0.1
0.05
0.01
0.02
1.47
3.36
2.92*
-1.14*
1.77
1.59
-0.02
0.17
-0.02
0.18
0.04
-0.24
0
-0.07
-0.12
-0.06
0
-0.02
0
0.03
0.1
0.05
0.03
0.01
1.43
2.03
2.84
0.89
-1.46
-2.17
0.52
0.07
-1.26
-0.18
-0.16
3.24
-0.01
0.13
0.17
0.15
-0.06
0.02
0.01
-0.03
0.01
-0.06
-0.03
0
-6.28
2.02
-0.51
2.01
-2.81
-0.25
SS
SS
A
πt−1
A
πt−2
-4.55
11.68
6.3
6.65
-15.25
-20.60*
0.24
1.48
-0.16
*0.71*
0.06
0.14
-0.57
0.79
0.49
*0.73**
-0.2
-0.03
-815.3
41.03
219.3
-51.47
90.77
96.56
-12.06
5.24
3.69
2.95
7.25
-1.61
0.04
-1.67
-0.03
0.02
0.03
-0.07
0
-1.04
0.06
0.07
0.15
0.32
52.63
33.19
17.55
59.57*
-5.77
46.02*
6.89
13.73
-6.92
26.27
12.4
5.6
0.52
1.54
0.96
2.31*
-0.15
2.24
0.12
0.17
-0.18
0.06
-0.01
-0.48
πt−1
πt−2
-0.68
-1.43
-0.59
1.79
0.72
-0.13
-12.62
-60.09
-10.04
99.22
46.88
-11
6.52
-0.75
20.30*
6.53
8.48
-6.23
9.88
-20.34
-48.46*
-24.62
8.71
8.05
-4.68
9.51
23.97*
12.62
-3.65
-3.56
-193.8
443.6
1049.7*
533
-173.7
-139.3
-0.07
1.47
-0.31
1.16*
0.94
2.95**
38.97
-1.92
-9.44
3.54
-3.45
-4
-19.18
0.9
4.73
-1.55
1.67
2.26
-1.8
-3.45
-0.51
-0.88
-0.89
-1.73
-0.24
-0.54
-0.15
-1.62**
0.43
-1.02
0.24
0.38
0.54
-0.08
-0.13
0.92
−N
(I) *p < 0.10, **p < 0.05, and ***p < 0.01. N N
, i−i
, π, and π A are percentage deviation of total labor hired from the steady state, percentage deviation of interest rate from the steady state,
SS
iSS
output price inflation and asset price inflation, respectively.
Table 11: VAR (Continuation)I
Dep Var.
Nt−1 −NSS
NSS
Nt−2 −NSS
NSS
it−1 −iSS
iSS
πt
it−2 −iSS
iSS
πt−1
πt−2
B1
B2
B3
B4
B5
B6
-0.04
-0.01
0.07
-0.07
0.08
0.03
0.02
-0.06
-0.03
0.01
-0.03
0
0.05
0.16
0.02
-0.2
-0.07
0.02
-0.03
-0.07
-0.03*
0.08
0.03
-0.01
-0.55
-2.87
-0.44
4.52
1.83
-0.53
0.25
-0.04
0.59
0.32
0.36
-0.17
NI1
NI2
NI3
NI4
NI5
NI6
-0.08
0.11
-0.09
0.11
0.06
-0.01
0.17
0.04
0
-0.09
0.05
0.14*
0.46
-0.93
-2.29*
-1.17
0.39
0.37
-0.23
0.42
1.12*
0.59
-0.17
-0.17
-9.49
19.71
49*
24.73
-8.22
-6.78
C1
C2
C3
C4
C5
C6
0.07
0.16
0.13*
-0.05*
0.08
0.07
0
0.07
-0.01
0.05*
0.05
0.14**
1.78
-0.14
-0.45
0.13
-0.17
-0.22
-0.89
0.06
0.21
-0.07
0.07
0.11
AT1
AT2
AT3
AT4
AT5
AT6
0.02
0.05
-0.05
0
0.04
0.02
-0.03
-0.02
0.02
0.01
-0.04
-0.02
0
0
-0.05
0
0
-0.01
0
0
0.04
0
0
0.01
Sessions
Ind Var.
20
SS
SS
Nt−2 −NSS
NSS
it−1 −iSS
iSS
πtA
it−2 −iSS
iSS
πt−1
πt−2
A
πt−1
A
πt−2
A
πt−1
A
πt−2
Nt−1 −NSS
NSS
-0.2
0.54
0.3
0.31
-0.7
-0.99*
0.01
0.07
-0.01
0.03*
0
0.01
-0.03
0.04
0.02
0.03**
-0.01
0
2.7
-0.67
-0.98
0.87
-6.22***
1.27
-4.02*
-0.1
1.23
2.02*
-1.05
0.01
16.95
0.63
-10.02
3.63
-17.42
3.82
-8.71
0.04
4.88
-2.07
9.44
-2.29
-373
-9.98
215.9
-81.75
396.8
-84.33
20.02
-8.96*
-0.49
1.27
-8.1
12.38
0.12
-0.08
0.05
-0.09
-0.16
-0.32
-0.27
0.02
-0.24
-0.19
0.04
-0.31
-37.8
2.51
9.85
-2.24
3.84
4.62
-0.56
0.24
0.17
0.14
0.33
-0.08
0
-0.08
0
0
0
0
0
-0.05
0
0
0.01
0.02
4.12
-0.02
0.02
-0.17
0.03
0.34
-6.89
0.22
-0.65
-0.52
-0.35
-0.71
252.1*
4.35
13.21
-52.58
0.4
7.69
-127.5*
-2.11
-6.37
26.51
-0.05
-3.95
-5492.4*
-92.28
-284.1
1138.9
-9.34
-165.5
112.8
-3.39
-11.69
-12.09
-2.18
4.85
-0.08
0.01
-0.21
0.13
-0.15
-0.14
-0.02
-0.23
-0.75
-0.17
-0.08
0.02
0.3
0.09
1.4
0.12
0.18
0.54*
0.23
0.27
-0.38
-0.05
0.21
0.14
0
0.01
0.08
0
-0.01
0.02
0
0
-0.01
0
0.01
-0.02
-4.83
-0.2
0.3
1.02
-1.64
0.23
1.91
-0.58
-0.58
-0.53
-1.33
-1.24
-0.502*
-0.72***
0.4
-1.41***
0.03
-0.79**
0.24
0.36*
-0.21
0.06
-0.08
0.36*
20.4
18.58***
-10.42
35.62*
0.58
18.46**
-4.06
-0.39
0.81
17.05
11.46
7.81
0.29
1.03**
-0.66
1.86***
-0.1
0.98*
-0.09
0.07
-0.05
0.04
-0.02
-0.07
−N
(I) *p < 0.10, **p < 0.05, and ***p < 0.01. N N
, i−i
, π, and π A are percentage deviation of total labor hired from the steady state, percentage deviation of interest rate from the steady
SS
iSS
state, output price inflation and asset price inflation, respectively.
Appendix F: Asset Market
Figure 1: Asset Markets Activity, by Session (Left Axis: Asset Price, Right Axis: Units
of Trade)
NI1
C1
10
8
10
40
4
5
2
0
10
20
0
40
30
20
5
0
0
0
10
20
10
40
4
5
10
8
15
20
25
5
4
0
0
6
4
6
4
2
2
0
10
20
30
40
20
30
0
0
10
8
6
4
8
3
6
4
0
10
20
30
40
0
0
10
20
8
15
6
4
2
0
0
0
0
0
10
20
30
Period
NI5
3
80
60
40
20
40
50
30
40
10
20
2
4
10
1
10
2
5
0
0
0
0
0
10
20
30
40
5
10
40
0
10
0
5
0
10
20
30
Begin sequence
SS FV
0
20
10
0
10
20
30
40
50
0
10
5
0
10
20
30
40
50
15
0
8
6
40
50
0
4
5
0
2
0
20
Period
Period
Asset Price
50
10
5
30
40
Period
AT6
20
15
2
30
15
20
10
20
0
Period
C6
4
10
2
Period
AT5
30
Period
NI6
0
4
Period
C5
100
0
6
6
5
40
0
40
8
10
30
30
10
2
20
20
Period
AT4
4
10
10
4
1
0
0
8
2
0
5
10
2
20
10
4
2
0
0
15
8
4
10
40
6
5
2
0
30
10
Period
C4
10
0
20
Period
AT3
10
Period
NI4
0
10
Period
C3
8
10
0
2
Period
NI3
10
0
2
6
2
2
10
0
Period
AT2
6
6
0
30
10
8
5
4
40
Period
C2
10
0
6
20
Period
NI2
0
80
60
10
6
0
AT1
15
40
60
0
Period
Dynamic FV
Trade volume
Notes: Asset price is shown on the left axis. Trade volume is shown on the right axis
(grey bars). The red line is the dynamic fundamental value. The black dashed line is the
fundamental price of the asset. The vertical dotted black lines represent the beginning of
new sequences.
1
21
Figure 2: Asset Prices by Treatment (All sessions)
No Intervention
40
30
20
0
20
40
Period
60
0
AT1
AT2
AT3
AT4
AT5
AT6
FV
60
40
20
10
20
0
C1
C2
C3
C4
C5
C6
FV
Asset Price
22
Asset Price
60
40
Asset Price
NI1
NI2
NI3
NI4
NI5
NI6
FV
80
Asset Targeting
Constrained
0
20
40
Period
60
0
0
20
40
Period
60
Figure 3: Asset Prices Statistics by Treatment (All sessions)
23
Appendix G: Labor Supply and Output Demand per Session
Figure 4: Labor supply (left column) and output demand (right column), Benchmark
treatment
B1
B1
8
100
80
60
40
20
6
4
2
0
20
40
60
0
20
40
Period
B2
60
Period
B2
8
100
80
60
40
20
6
4
2
0
20
40
0
60
20
40
60
Period
B3
Period
B3
100
80
60
40
20
8
6
4
2
0
20
40
60
0
80
20
40
60
80
Period
B4
Period
B4
8
100
80
60
40
20
6
4
2
0
20
40
60
80
100
0
20
40
Period
B5
60
80
100
Period
B5
100
80
60
40
20
8
6
4
2
0
10
20
30
40
50
60
0
70
10
20
30
40
50
60
70
50
60
70
Period
B6
Period
B6
100
80
60
40
20
8
6
4
2
0
10
20
30
40
50
60
0
70
10
20
30
Median
Average
40
Period
Period
Begin Sequence
1
24
Experienced
Steady State
Figure 5: Labor supply (left column) and output demand (right column), NI treatment
NI1
NI1
8
100
80
60
40
20
6
4
2
0
20
40
60
80
0
20
Period
NI2
40
60
80
Period
NI2
8
100
80
60
40
20
6
4
2
0
20
40
60
0
80
20
40
60
80
Period
NI3
Period
NI3
100
80
60
40
20
8
6
4
2
0
20
40
60
0
80
20
40
60
80
Period
NI4
Period
NI4
8
100
80
60
40
20
6
4
2
0
20
40
60
80
100
0
20
Period
NI5
40
60
80
100
Period
NI5
8
100
80
60
40
20
6
4
2
0
20
40
60
80
0
100
20
40
60
80
100
Period
NI6
Period
NI6
100
80
60
40
20
8
6
4
2
0
20
40
60
0
80
Average
40
60
80
Period
Period
Median
20
Begin Sequence
25
Experienced
Steady State
Figure 6: Labor supply (left column) and output demand (right column), C treatment
C1
C1
8
100
80
60
40
20
6
4
2
0
20
40
60
80
100
0
20
40
Period
C2
60
80
100
Period
C2
8
100
80
60
40
20
6
4
2
0
20
40
60
80
0
100
20
40
60
80
100
Period
C3
Period
C3
100
80
60
40
20
8
6
4
2
0
20
40
60
80
0
100
20
40
60
80
100
Period
C4
Period
C4
8
100
80
60
40
20
6
4
2
0
20
40
60
80
100
0
20
40
Period
C5
60
80
100
Period
C5
8
100
80
60
40
20
6
4
2
0
20
40
60
80
0
100
20
8
6
4
2
20
40
60
80
100
80
60
40
20
100 0
Average
80
100
20
40
60
80
Period
Period
Median
60
Period
C6
Period
C6
0
40
Begin Sequence
26
Experienced
Steady State
100
Figure 7: Labor supply (left column) and output demand (right column), AT treatment
AT1
AT1
8
100
80
60
40
20
6
4
2
0
20
40
60
80
0
20
40
Period
AT2
60
80
Period
AT2
8
100
80
60
40
20
6
4
2
0
20
40
60
0
80
20
40
60
80
Period
AT3
Period
AT3
100
80
60
40
20
8
6
4
2
0
20
40
60
80
0
100
20
40
60
80
100
80
100
Period
AT4
Period
AT4
8
100
80
60
40
20
6
4
2
0
20
40
60
80
100
0
20
40
Period
AT5
8
100
80
60
40
20
0
100
6
4
2
0
20
40
60
Period
AT5
60
80
20
40
60
80
100
Period
AT6
Period
AT6
100
80
60
40
20
8
6
4
2
0
20
40
60
80
0
100
Median
Average
20
40
60
80
100
Period
Period
Begin Sequence
27
Experienced
Steady State
Appendix H: Aggregate Variables per Session
Figure 8: Average Output Produced
B1
NI1
C1
AT1
60
60
60
60
40
40
40
40
20
20
20
20
0
0
0
0
20
40
60
0
20
40
Period
B2
60
80
0
20
Period
NI2
40
60
0
80 100
60
60
60
40
40
40
40
20
20
20
20
0
0
0
20
40
60
0
20
Period
B3
40
60
80
0
50
Period
NI3
0
100
0
60
60
60
40
40
40
20
20
20
20
0
0
0
40
60
80
0
20
Period
B4
40
60
80
0
20
Period
NI4
40
60
20
80
0
100
0
60
60
60
40
40
40
20
20
20
20
0
0
0
40
60
80
100
0
20
Period
B5
40
60
80
100
0
50
Period
NI5
0
100
60
60
60
40
40
40
20
20
20
20
0
0
0
40
60
0
20
Period
B6
40
60
80
100
0
50
Period
NI6
100
0
60
60
60
40
40
40
20
20
20
20
20
40
Period
60
0
0
20
40
60
0
80
0
Period
Av. Output Produced
20
40
20
0
20
60
80
0
100 0
Period
Begin Sequence
1
28
80
40
60
80 100
40
60
80 100
40
60
80
Period
AT6
40
0
0
Period
C6
60
0
60
Period
AT5
40
20
20
Period
C5
60
0
80
Period
AT4
40
20
40
Period
C4
60
0
60
Period
AT3
40
20
40
Period
C3
60
0
20
Period
AT2
60
0
0
Period
C2
Experienced
50
Period
Steady State
100
100
Figure 9: Interest Rate (%)
B1
NI1
C1
AT1
40
40
40
40
20
20
20
20
0
0
0
0
−20
0
20
40
−20
60
0
20
40
Period
B2
60
−20
80
0
20
Period
NI2
40
60
−20
80 100
40
40
20
20
20
20
0
0
0
0
20
40
−20
60
0
20
Period
B3
40
60
−20
80
0
50
Period
NI3
−20
100
0
40
40
20
20
20
20
0
0
0
0
−20
−20
−20
−20
40
60
80
0
20
Period
B4
40
60
80
0
20
Period
NI4
40
60
80
100
0
40
40
20
20
20
20
0
0
0
0
20
40
60
80
100
−20
0
20
Period
B5
40
60
80
100
−20
0
50
Period
NI5
−20
100
40
40
20
20
20
20
0
0
0
0
20
40
60
−20
0
20
Period
B6
40
60
80
100
−20
0
50
Period
NI6
100
−20
40
40
20
20
20
20
0
0
0
0
20
40
Period
60
−20
0
20
40
60
−20
80
Period
Interest Rate (percent)
0
20
40
0
60
80
−20
100 0
Period
Begin Sequence
29
1
80
40
60
80 100
20
Experienced
40
60
80 100
20
40
60
80
Period
AT6
40
0
0
Period
C6
40
−20
60
Period
AT5
40
0
20
Period
C5
40
−20
80
Period
AT4
40
0
40
Period
C4
40
−20
60
Period
AT3
40
20
20
Period
C3
40
0
40
Period
AT2
40
0
20
Period
C2
40
−20
0
50
Period
Steady State
100
100
Figure 10: Inflation Rate (%)
B1
NI1
C1
AT1
40
40
40
40
20
20
20
20
0
0
0
0
−20
0
20
40
−20
60
0
20
40
Period
B2
60
−20
80
0
20
Period
NI2
40
60
−20
80 100
40
40
20
20
20
20
0
0
0
0
20
40
−20
60
0
20
Period
B3
40
60
−20
80
0
50
Period
NI3
−20
100
0
40
40
20
20
20
20
0
0
0
0
−20
−20
−20
−20
40
60
80
0
20
Period
B4
40
60
80
0
20
Period
NI4
40
60
80
100
0
40
40
20
20
20
20
0
0
0
0
20
40
60
80
100
−20
0
20
Period
B5
40
60
80
100
−20
0
50
Period
NI5
−20
100
40
40
20
20
20
20
0
0
0
0
20
40
60
−20
0
20
Period
B6
40
60
80
100
−20
0
50
Period
NI6
100
−20
40
40
20
20
20
20
0
0
0
0
20
40
Period
60
−20
0
20
40
60
−20
80
Period
Inflation Rate (percent)
0
20
40
0
60
80
−20
100 0
Period
Begin Sequence
30
1
80
40
60
80 100
20
Experienced
40
60
80 100
20
40
60
80
Period
AT6
40
0
0
Period
C6
40
−20
60
Period
AT5
40
0
20
Period
C5
40
−20
80
Period
AT4
40
0
40
Period
C4
40
−20
60
Period
AT3
40
20
20
Period
C3
40
0
40
Period
AT2
40
0
20
Period
C2
40
−20
0
50
Period
Steady State
100
100
Figure 11: Real Wage
B1
NI1
C1
AT1
60
60
60
60
40
40
40
40
20
20
20
20
0
0
20
40
0
60
0
20
40
Period
B2
60
0
80
0
20
Period
NI2
40
60
0
80 100
60
60
40
40
40
40
20
20
20
20
20
40
0
60
0
20
Period
B3
40
60
0
80
0
50
Period
NI3
0
100
0
60
60
40
40
40
40
20
20
20
20
0
0
0
40
60
80
0
20
Period
B4
40
60
80
0
20
Period
NI4
40
60
80
0
100
0
60
60
40
40
40
40
20
20
20
20
20
40
60
80
100
0
0
20
Period
B5
40
60
80
100
0
0
50
Period
NI5
0
100
60
60
40
40
40
40
20
20
20
20
20
40
60
0
0
20
Period
B6
40
60
80
100
0
0
50
Period
NI6
100
0
60
60
40
40
40
40
20
20
20
20
20
40
60
0
0
20
Period
40
60
0
80
Period
Real Wage
0
20
40
0
60
80
0
100 0
Period
Begin Sequence
31
1
Experienced
80
40
60
80 100
20
40
60
80 100
20
40
60
80
Period
AT6
60
0
0
Period
C6
60
0
60
Period
AT5
60
0
20
Period
C5
60
0
80
Period
AT4
60
0
40
Period
C4
60
0
60
Period
AT3
60
20
20
Period
C3
60
0
40
Period
AT2
60
0
20
Period
C2
60
0
0
50
Period
Steady State
100
100
Appendix I: Computer Interfaces
Figure 12: Main screen
32
Figure 13: Personal history screen
33
Figure 14: Market history screen
34
Figure 15: Asset market history screen
35
Appendix J: Instructions
The instructions distributed to subjects in all the treatments (B, NI, C and AT) are
reproduced on the following pages. Subjects received the same set of instructions except
that those in the B treatment did not get the last page with the title “Introduction of
Assets and Asset Market.” Subjects in the NI and AT received identical instructions.
In third paragraph of the “Introduction of Assets and Asset Market” for the NI and AT
treatments said, “You may also borrow money, at the current interest rate, to purchase
any assets.” This paragraph for the C treatment said, “You will not be able to borrow
money to purchase any assets.”
36
INTRODUCTION
You are participating in an economics experiment at the University of British Columbia. The purpose of this
experiment is to analyze decision making in experimental markets. If you read these instructions carefully and
make appropriate decisions, you may earn a considerable amount of money. At the end of the experiment all
the money you earned will be immediately paid out in cash.
Each participant is paid 5 CAD for attending. During the experiment your income will not be calculated in
dollars, but in points. All points earned throughout this game will be converted into CAD by applying the
exchange rates found on the whiteboard.
During the experiment you are not allowed to communicate with any other participant. If you have any
questions, the experimenter(s) will be glad to answer them. If you do not follow these instructions you will be
excluded from the experiment and deprived of all payments aside from the minimum payment of 5 CAD for
attending.
You will play the role of a household over a sequence of several periods (trading days). You will be interacting
with other human consumers. There will be also computerized firms and a central bank operating in this
experimental economy.
In this experiment, you will have the opportunity to work and purchase output in two markets. All transactions
in all markets will be conducted using laboratory money.
OVERVIEW
The objective of each player is to make as many points as possible. You will receive points for purchasing more
units of output in your bank account. You will lose points by working. You may borrow and save at the current
interest rate.
LABOR & OUTPUT MARKETS
At the top of the screen you’ll see a graph representing the different combinations of output (x-axis) and labor
(y-axis) you can choose. Each of the different combinations defines:


A current hourly wage
A current price for a single unit of output
This information will be located on the right hand side of the graph. Notice that these 2 pieces of information
are only potential outcomes. The actual outcomes will be computed based on everyone’s actual choices.
You may agree to trade none, some or all of your labor hours to firms in exchange for potential wage. You will
input the very maximum you would like to work. You may end up working less than your desired amount, but
you will never work more than that. You are able to work a maximum of 10 hours per period and may also
work fractions of an hour, up to 1 decimal place. eg. 4.3 or 7.2 hours. Each worker is able to produce 10 units
per hour and this will never change. Wage income will be deposited from your bank account.
You may also choose to purchase output. You will input the very maximum you would like to purchase. You
may end up purchasing less than your desired amount. Spending on output will be debited from your bank
account. You will also receive a dividend from firms that will also help you to pay for the varieties you will
purchase. This is an equal share of the positive or negative profits the firms earned in the current period.
To better understand how your labor and consumption decisions translate into points and how the balance on
your bank account changes, you will have the opportunity to move the red dot to your preferred point on the
payoff space. Notice that as you increase the amount of labor, you will lose points at an increasing rate. As you
increase the amount of output, you will gain points at a decreasing rate.
Actual wage, output price and the interest rate will be computed based on your choices and everyone else’s
choices. That’s why you will be able to move around 2 different dots, the red one that represents your own
decisions and the green one that characterizes the average of everyone else’s choices. This way you will
visualize different predictions on wages and prices for different combinations of aggregate consumption and
aggregate labor.
** You will have an initial balance of 10 experimental units of money on your bank account. Whenever your
bank account is negative, ie. you spent more than you earned, you will owe the bank the remainder PLUS
interest in the next period. So long as you pay the interest on your debt, you may continue to borrow. Any
money owing at the end of the experiment will be repaid through points. In particular, you will lose:
(
you will gain:
) . Similarly, If your bank account has a positive balance at the end of the experiment
(
)
.
**If your bank account is positive, you will receive interest on the saving in your bank account. This will be
credited to your account in the next period.
After all subjects submit their labor, consumption, and investment decisions, firms will decide how many
hours to hire. Wage and output price will be computed. There will be no unsold output. If the total number of
labor hours supplied in the economy is in excess of what is necessary to satisfy consumers’ output demands,
firms will hire fewer hours and you may find yourself working a fraction of the hours you requested. Similarly,
if the worker supplied hours is insufficient to cover consumer demand, you may find yourself able to purchase
only a fraction of the output you requested.
As you purchase more units, you will gain more points but at a decreasing rate. As you work more hours, you
will lose more points at an increasing rate. You do NOT obtain points from your holdings of cash.
Worker Points = (Points Gained from Consuming – Points Lost from Working)
The interest rate at which you spend or save will depend on inflation. Particularly, for every 1% that prices
increase from yesterday, the automated central bank will increase the borrowing and saving rate by more
than 1%. Over the long run, the central bank will aim to keep the interest rate around 3.5%, but it will
fluctuate as inflation on output occurs. Lower interest rates make it cheaper to borrow but more challenging
to accumulate savings, and vice versa.
Notice that interest rate might also be negative. In that case you will lose money by saving and gain money by
borrowing.
Each sequence will have a random number of periods determined by a continuation rate of 0.965. That is,
there is a 3.5% chance of a period ending at any period. To make the termination rule as transparent as
possible, the experimenter will carry a bag containing 200 marbles, 193 of them are blue and only 7 of them
are green. Each period a marble will be drawn. If a blue marble is drawn the sequence will end, otherwise the
sequence will continue. You will play multiple sequences. On average you will play 28 periods in each
sequence.
Screens
Throughout the experiment you will have a chance to flip back and forth between 4 different screens:
1) Action Screen. - This is the main screen. This screen is divided in two:
a) On the left hand side of the screen you’ll find a graph that represents all possible combinations of labor
and output. On the graph you’ll see two different dots. The red one represents your own choices. By
moving around the red dot you will be able to visualize the points you might earn by selecting different
combinations of labor and output.
The green dot denotes the average values of output and labor of the rest of the participants. By
moving around both dots you’ll have a better sense on how your choices as well as everyone else’s
decisions affect the potential wage and output price of the economy. Your predicted banking account
balance (without interest rate) will be also displayed.
Notice that by positioning the dots together you will be assuming that everyone else’s choices are the
same as yours.
b) On the right hand side of the screen (SUBMIT YOUR DECISIONS) you will have to enter your final
choices on output and labor. Immediately after everyone submits their decisions, the total amount of
output and labor will be computed.
2) Personal History.- You will find a summary of your previous decisions on consumption, labor, as well as the
points you earned and your bank account balance.
3) Market History. - On this screen you will be able to observe information on interest rates and inflation rate
from previous periods. Information on total output and labor is also included.
Some useful Information
(
)
(
)
INTRODUCTION OF ASSETS AND ASSET MARKET
All subjects will now receive 10 shares of an asset at the beginning of the next experiment. Each
period you will get 3.6 cent of lab currency per asset. That means that the average value of
each unit of asset is: 1. At the end of each sequence you will not receive money for the assets
you hold. You will incur no cost to holding the asset but no benefits either. Remember the only
way you will make points in this experiment is by purchasing the output. All other features of
the economy remain identical to the previous experiment.
All subjects may trade this asset costlessly in an asset market. You may specify how many units
you wish to buy or sell in the asset at a specified price. Note that you cannot sell more shares
that you currently own (ie. no short-selling). In a given period, you may either buy or sell, but
not both. All submitted offers to buy (bids) and offers to sell (asks) will be used to determine a
single market clearing price. All offers to buy at a price higher than the clearing price will be
transacted at the lower clearing price. Similarly, all offers to sell at a price below the market
clearing price will be transacted at the higher clearing price. Earnings from selling units of the
asset will be deposited to your bank account. Spending on the asset will be debited from your
bank account. You will retain any assets that you hold into the next period.
You may also borrow money, at the current interest rate, to purchase any assets.
The action screen will now include an option for you to preview your asset decisions:
1) Asset Market. - On this screen you will find the average number of assets and prices that
were offered to buy (bids) or to sell (asks) in each one of the previous periods. It is
important to notice that this information may not coincide with the actual information on
prices and traded assets, because the offers might not end up in trading.
Sequence of Events Instruction Phase: Subjects are walked through detailed instruction (included in the Appendix) regarding the tasks they will be completing. They are taught how to use the computer interface and obtain information in four practice rounds. During these practice rounds, we go over to each subject’s terminal and walk them through accessing information in the graphical interface. Inexperienced Experiment Phase: Figure 1. Flow chart for inexperienced subjects and benchmark treatment. We then begin the first repetition of the experiment. The flow chart above describes the sequence of events in a given period. Step 1: The repetition begins. Bank accounts are reset to 10 lab dollars and points are reset to zero. Last period’s prices are set to the steady state values, but wages, prices, and interest rates are free to adjust in response to subjects’ decisions. Step 2: At the beginning of Period 1, subjects submit their preferred maximum number of units to purchase and hours to work. They do not know what the market wage or price will be (that depends on the decisions that they make), but they can obtain that information by using the graphical interface. By moving markers representing their own decisions and the median decision of others, they can learn what wages, prices, their utility, and bank account balances will be under their assumptions of their behaviour and everyone else’s. Step 3: After all decisions are submitted, total output demand and labor supply are calculated. If total output demand exceeds what can be produced with the total labor supply, there is an excess demand for output. In this case, all workers will receive the number of hours of work that they would like. Output must be rationed. Subjects receive first priority on the units of output that they produced by working. If they do not wish to consume all the units, they will be made available, randomly, to other subjects who have a personal ‘excess demand for output’; that is, they want to consume more than they are producing from working. In cases where there is an excess supply of labor, workers will immediately receive the number of units they would like to purchase. Labor hours must be rationed. A subject is automatically given the hours associated with his or her output demand (for example, if Jill is willing to buy 80 units, she can immediately have 80/10=8 hours of labor if she so desires). If the subjects do not wish to work as much as they are offered, those excess hours are given randomly to subjects who have an excess demand for labor. The median labor and consumption decisions are used in the calculation of wages, prices, and the nominal interest rate. Specifically, if the economy has excess demand for output (labor), the median labor (consumption) decision will be used. Step 4: Subjects receive information about the number of hours they worked, units they were able to purchase and consume, their bank account balances, and their utility points earned. They also learn about the labor and output markets: how many hours were hired and how much people consumed, wages, prices, and the nominal interest rate. Step 5: A random draw occurs to determine whether the economy will continue onto the next round. With a probability of β, the economy continues for another period. The game returns to Step 2. Subjects carry over any cash balances from the previous period. Interest is accrued either on saving or debt, and the adjustment appears on their bank balance at the beginning of the next period. With a probability of 1-­‐ β, the economy ends and the game continues to Step 6. Step 6: Subjects’ points are adjusted based on the amount of cash or debt that they hold at the end of a repetition. If the subject’s bank account is positive, he or she will have to spend the remaining cash and will receive additional points -­‐-­‐ but at a decreasing rate -­‐-­‐ for each laboratory dollar held. If the bank account is negative, he or she will have to work to repay the debt, given the last period’s wages and prices, and will lose points at an increasing rate. After the repetition ends, a new repetition begins. Experienced Experiment Phase: After subjects have played for approximately 1 hour and a repetition ends, subjects will enter the experienced experiment phase. Now we classify subjects as ‘experienced’. They participate in one of three treatments for the remaining 1 to 1.5 hours: 1. Benchmark treatment (B) -­‐-­‐ They continue to play the same experiment that they were already participating in. 2. No Intervention treatment (NI) -­‐-­‐ Subjects are provided assets which they can now trade in an asset market in addition to making consumption and labor decisions. Subjects may borrow for speculative purposes. 3. Constraint treatment (C) -­‐-­‐ Subjects are provided assets which they can trade in addition to making consumption and labor decisions, but they may not borrow for speculative purposes. 4. Asset Inflation Targeting treatment (AT) – Subjects are provided assets which they can trade in an addition to making consumption and labor decisions. Subjects may borrow for speculative purposes. In contrast to the NI treatment, the nominal interest rate now responds to asset price inflation. The flow chart below depicts the sequence of events in the asset market treatments. Step 2: The only significant change is that subjects can submit bids and asks in a call market for the asset. They also have access to a calculator that shows them what will happen to market variables (wages and goods prices), their utility, and bank accounts when they make transactions at specific prices. Step 3: The assets are traded in a call market. The market clearing asset price is determined by the intersection of supply and demand. Step 4: In addition to learning about personal and real market outcomes, the subjects learn about activity in the asset market. In particular, they learn what the bid and ask volume were, the average bids and asks, the market clearing price, and number of units traded. Each period, subjects receive dividends of 3.5 cents for each unit of the asset they are holding. Step 5: Same as before. Subjects can also carry their asset balances over to the next period. Step 6: Same as before. Subjects do not earn anything at the end of the experiment for the assets that they are holding besides the period dividend payment. Figure 2. Flow chart for asset market treatments References
Calvo, Guillermo. 1983. “Staggered Prices in a Utility Maximizing Framework.” Journal of Monetary Economics, 12: 383–98.
Carlstrom, Charles T., and Timothy S. Fuerst. 2007. “Asset Prices, Nominal
Rigidities, and Monetary Policy.” Review of Economic Dynamics, 10(2): 256–75.
Duffy, John. 2014. “Macroeconomics: A Survey of Laboratory Research.” Chapter
prepared for the Handbook of Experimental Economics, Vol. 2.
Stöckl, Thomas, Jürgen Huber, and Michael Kirchler. 2010. “Bubble Measures
in Experimental Asset Markets.” Empirical Economics, 13: 284–98.
45