Working Capital, Trade and Macro Fluctuations Se-Jik Kim Hyun Song Shin

Transcription

Working Capital, Trade
and Macro Fluctuations
Se-Jik Kim
Seoul National University
May 2013
Hyun Song Shin
Princeton University
Se-Jik Kim and Hyun Song Shin: Working Capital, Trade and Macro Fluctuations
Working Capital
Stylized corporate balance sheet
Assets
Cash
Inventories
Receivables
Long-term assets
Liabilities
Equity
Short term debt
Payables
Long-term liabilities
(Net) working capital = Inventories + Receivables − Payables
1
2
Time Dimension of Production
Assets
date 1
Liabilities
Cash
Equity
Inventories (1 period old) (= v )
Inventories (2 periods old) (=2v )
date 2
Short-term Debt
date 3
3v
Stage
3
2v
Stage
2
v
Stage
1
Inventories (3 periods old) (=3v )
Receivables
Long-term Assets
Payables
Long-term Liabilities
Figure 1. Date 3 balance sheet of firm with three production stages. Older vintages of inventories reflect
greater inputs and hence greater financing needs
3
Boundary of the Firm
• When the production chain crosses the boundary of the firm, working
capital needs are reflected in receivables and payables
• Inventories are known to be procyclical
• Receivables and payables are also procyclical and reflect financial
conditions
Billion Dollars
4
800
600
Nonfinancial corporate business;
trade payables; liability
400
trade receivables; asset
200
0
inventories excluding IVA,
current cost basis
-200
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
-400
Figure 2. Trade receivables, trade payables and inventories of the US non-financial corporate business sector
(Source: Federal Reserve Flow of Funds, Table F102)
5
Percent
Treasury and Baa Corporate Rates
13.0
11.0
9.0
7.0
5.0
3.0
Baa Rate
Jan-10
Jul-08
Jan-07
Jul-05
Jan-04
Jul-02
10 yr Treasury constant Maturity rate
Jan-01
Jul-99
Jan-98
Jul-96
Jan-95
Jul-93
Jan-92
Jul-90
Jan-89
Jul-87
Jan-86
1.0
6
Percent
Treasury and Baa Spread
7.0
Baa-10 yr Treasury Spread
6.0
5.0
4.0
3.0
2.0
Jan-10
Jul-08
Jan-07
Jul-05
Jan-04
Jul-02
Jan-01
Jul-99
Jan-98
Jul-96
Jan-95
Jul-93
Jan-92
Jul-90
Jan-89
Jul-87
Jan-86
1.0
7
Panel regressions with firm fixed effects
Dependent variable
dln(sales)
dln(BD leverage)
constant
Observations
Firms
R-squared
(1)
dln(receivables)
(2)
dln(inventory)
(3)
dln(payables)
0.8645***
(87.76)
0.0510***
(3.04)
-0.00686
(-0.0)
0.7344***
(62.69)
0.0452***
(2.91)
0.0020**
(2.14)
0.5321***
(53.12)
0.0354*
(1.94)
0.02264***
(29.89)
61484
6377
0.5423
60169
6192
0.5156
64886
6583
0.3674
(Compustat US manufacturing firms, 1990 - 2012)
dln(BD leverage) = annual log difference of US broker dealer leverage
Clustered standard errors at firm level, t-statistic in parantheses
9
3.4
0.18
3.3
0.16
0.14
3.2
0.12
3.1
0.10
3.0
0.08
2.9
0.06
2.8
mfg value-added/GDP
mfg sales / value-added
0.04
mfg sales/va
mfg va/GDP
2.7
0.02
2.6
0.00
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
Figure 3. US manufacturing sales to value-added ratio and the ratio of manufacturing value-added to GDP
(Source: US Census Bureau)
Outline
• Model of production chain without offshoring (no financial sector)
• Model of production chain due to offshoring (no financial sector)
• Close the model with financial sector
10
11
Production Chain without Offshoring
• No fixed capital
• No savings decision
• No labor supply decision
• No distortions to product or labor markets
• Only “friction” on real side of economy is that production takes time
Financial shocks impact output and productivity through cost of working
capital
12
Time and Working Capital
1
date
1
2
..
−1

+1
..
−
()−
..
Production stages
2
···  − 1
−
−
−
..
···
···
···
···
−
−
−
−
−
..

−
−
−
−
−
−
..
cumulative
cashflow
−
−3
..
..
−(12)( + 1)
Wage cost  cannot be deferred (workers cannot be lenders to firm)
Cash transfer upstream is instantaneous
Constant hazard rate  of chain’s failure after date  + 1
13
Lender Cash Flow as Function of 
rwnn  1 / 2
rwn n  1 / 2
n
n
 nw
 nw
Figure 4. Lender cash flow as function of 
t
Model
 workers/firms
Chain length 
Number of chains = 
Output per chain (of length ) =  ()
Output per firm (= output per worker)
 ()
= 

(0    1)
14
An Austrian Theme
“Roundabout production” and the temporal dimension of production
“[t]he indirect method entails a sacrifice of time but gains the
advantage of an increase in the quantity of the product. Successive
prolongations of the roundabout method of production yield further
quantitative increases though in diminishing proportion.”
[Böhm-Bawerk (1884) Capital and Interest]
15
16
Aggregate Credit Demand
Firms finance working capital (inventories and net accounts receivable) with
credit
Aggregate credit demand:


+ 1)  ×

=
1
2  (
=
+1

2
Joint Profit Maximization
Abstract from incentive/bargaining within the chain
Solution as upper bound to joint payoffs with incentive problems
Ex ante, choose  to maximize
∞
X
=+1
(1 − )− ( −  − )
Equivalently, maximize per-period profit
Π =  −  − 
³
´
(+1)

=   −  1 + 2
17
First-order condition:
=
µ
2

1
¶ 1−
 determined by zero profit condition:
³
´
(+1)
 =  1 + 2
18
=
µ
¶ µ
¶ µ
¶

2
 1−
+

1+
1−


2+
µ
¶
2

1+
=
1−

=2
 =
µ
³  ´ µ 1 −  ¶1−

2+
¶ µ
¶
2


1+
1−

 1−

= +


2+
19
()
()
()
( )
( )
Ratio of Sales to Value-Added
 = sale by firm 
 =  + 
−1 =  +  ( − 1) + 
..
1 =  +  + 2
 =  (1 + )
−1 = 2 (1 + ) − 
..
1 =  (1 + ) −  (1 + 2 + · · · + ( − 1))
20
21
Sales to value-added ratio:
P
1
2

+

( + 2) ( +  (1 + ) + 3)

=1
3
3  + 1
= ( + 1)
=
1
 +  + 2
3 (1 − ) ( + 2)
SVA 14
12
10
8
6
4
2
0
0.00
0.01
0.02
0.03
0.04
0.05
0.06
r
Figure 5. Sales to value-adde ratio ( = 0033)
22
3.4
0.18
3.3
0.16
0.14
3.2
0.12
3.1
0.10
3.0
0.08
2.9
0.06
2.8
mfg value-added/GDP
0.04
mfg sales/va
mfg va/GDP
2.7
0.02
2.6
0.00
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
Figure 6. US manufacturing sales to value-added ratio and the ratio of manufacturing value-added to GDP
(Source: US Census Bureau)
23
Productivity
Y/L 1.7
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
r
24
Wage
w 1.7
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
r
25
Credit Demand
K/L 9
8
7
6
5
4
3
2
1
0
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
r
26
Working Capital and Fixed Capital Analogy
 ( ) = 
µ
¶
2
=
−1 

¶
µ

2
−
 1−
=
 
If working capital (e.g. inventories) is treated as factor of production
(Ramey (1989)), then TFP term has endogenous variables.
27
Working Capital and Fixed Capital Analogy
 ( ) =
µ

2
−
 
¶
 1−
Plot of TFP term in production function for  = 0033
TFP 1.018
1.016
1.014
1.012
1.010
1.008
1.006
1.004
1.002
1.000
0.998
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
r
Three Approaches to Trade
Technological
Spatial
Temporal
Temporal dimension implies role for financial conditions
28
29
After Offshoring
Before Offshoring
transport
stage
date 1
date 2
cash
date 1
date 3
date 2
date 4
date 3
cash
date 5
Stage 1
Stage 2
Stage 3
Stage 1
Stage 2
Stage 3
Figure 7. Left hand panel illustrates the inventories needed in a three-stage production process. The right
hand panel illustrates increased inventories from offshoring the second stage.
30
Offshoring entails greater working capital need
Assets
Cash
Inventories (1 period old) x 1
Liabilities
Equity
Inventories (2 periods old) x 2
Assets
Cash
Inventories (1 period old) x 1
Liabilities
Equity
Inventories (outward bound) x 2
Short-term Debt
Receivables
Long-term Assets
Payables
Short-term Debt
Inventories (inward bound) x 4
Receivables
Long-term Assets
Payables
Inventories are not buffer stock
“When our grandfathers owned shops, inventory was what was in the
back room. Now it is a box two hours away on a package car, or it
might be hundreds more crossing the country by rail or jet, and you
have thousands more crossing the ocean”
[Chief Executive Officer of UPS quoted in Thomas Friedman, The
World is Flat]
31
32
“On-shoring” or “Re-shoring”
Figure 8. Cover page from Accenture (2011) Manufacturing’s Secret Shift: Gaining Competitive Advantage
by Getting Closer to the Customer
“On-shoring” or “Re-shoring”
“Companies are beginning to realize that having offshored much of
their manufacturing and supply operations away from their demand
locations, they hurt their ability to meet their customers’ expectations
across a wide spectrum of areas, such as being able to rapidly
meet increasing customer desires for unique products, continuing to
maintain rapid delivery/response times, as well as maintaining
low inventories and competitive total costs.”
[Accenture (2011) Manufacturing’s Secret Shift: Gaining Competitive
Advantage by Getting Closer to the Customer]
33
34
Production Chain due to Offshoring

¯ stages to the production chain and 
¯ locations
Multinational firms with operations in all 
¯ locaitons
Each location has absolute advantage in precisely one stage of the production
Output of multinational firm (=production chain) is
Ã ¯
X
=1

!
(0    1)
with
 =
½
1+
1
if stage  is at high productivity location
otherwise
35
Output to offshoring  stages of the production chain to location with
absolute advantage
 () = (¯
 + )
Offshoring entails
• wage cost 
• time cost of 1 period added to production time
If offshoring takes place  times, the production process takes 
¯ +  periods.
36
World credit demand  is

=
1

2 (¯
=

¯++1

2
+ )(¯
 +  + 1) ×

( + )
Profit of multinational firm is
Π = (¯
 + ) −  − 
µ
¶

(¯

+

+
1)
= (¯
 + ) −  1 +
2
 is the proportion of the world workforce employed by firm
37
Firm’s Offshoring Decision
Optimal offshoring is
µ
µ
¶
¶
1
2

¯

1+
¯ 1−
+
−
=
1 − 



Wage rate determined by zero profit condition
2
=

Ã
1 − 
¡
¢
1
2+ 1+
¯ (1 −  )
!1−
⇒ Trade volumes, output and productivity decline with financing cost 
Value of International Trade
Total sales (as  → 0)
1
 + )) + 3) (¯
 +  + 1) 
 = ((1 + 2(¯
6
Value of international trade is



(¯
 + )
!
Ã
1
2
 + ) + 1
 (¯
 +  + 1) 3  + 3 (¯
= 
(¯
 + )
 + (¯
 + ) + 2
=
⇒ Ratio of trade to output   is declining in risk premium 
38
39
Two Sector Model
• Two sectors.
• Sector 1 is  = 0.
  0.
Sector 2 benefits more from offshoring as its  is
• Price  for the output of Sector 2 in terms of sector 1 good
• Take first-order condition for 1 and 2 and impose zero profit condition
for both sectors
— Labor takes all the surplus
— Labor is fully mobile, so that single wage rate  applies to both sectors
40
From zero profit conditions for the two sectors,
 =

¯
1 + 2 (1 + 
¯)
(sector 1)
(1)
 =
(¯
 +  2)
¡
¢


1 + 2 (1 + 
¯ ) + 2 2
(sector 2)
Then
µ
µ
¶¶1−
 
2
1

¯ 
¯ (1 −
)

¯+
1+ +
=
(2 + (1 + 
¯ )) 
1−

 
41
Proposition. Productivity of sector 2 (value of output per worker) is
decreasing in 
Ã
1
2 =

¯ 1 −
1−
1
2
¯)
 + (1 + 
µ

¯

¶!
Global Output
Cobb-Douglas preferences (1 2) =  ln 1 + (1 − ) ln 2
Relative value of output of the two sectors is

11
=
22 1 − 
Then
2 =
1 =
(1 − )1

2 + (1 − )1
2

2 + (1 − )1
42
Global output is:
 =
12

2 + (1 − )1
Since 2 is decreasing in , we have
Proposition. Global output is declining in risk premium .
43
44
Trade Growth Accounting
Notation:




GDP
Domestic manufacturing value-added
Domestic manufacturing sales
Imports
Suppose that
imported
+
intermediate goods
µ
¶
imported
= ×
intermediate goods
 =  ×

µ
domestically produced
intermediate goods
¶
Offshoring measure defined as:
≡
imported intermediate goods
imported
domestically produced
+
intermediate goods
intermediate goods
Then




 
×
× ×
= ×



=  ×  ×
45
46
So, import/GDP ratio is

  
×
=×
×




Growth of offshoring can be obtained as
 () = 
µ


¶
−
µ


¶
−
µ


¶
In long hand
Offshoring
growth
=
Growth of
−
Imports/GDP
Growth of mfg
−
sales/value-added
Growth of
mfg/GDP
47
3.4
0.18
3.3
0.16
0.14
3.2
0.12
3.1
0.10
3.0
0.08
2.9
0.06
2.8
mfg value-added/GDP
0.04
mfg sales/va
mfg va/GDP
2.7
0.02
2.6
0.00
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
Figure 9. US manufacturing sales to value-added and share of manufacturing in GDP
48
0.25
Annual Growth Rate of Offshoring and Import/GDP 0.20
0.15
0.10
0.05
0.00
‐0.05
Imports/GDP
‐0.10
q
‐0.15
mfg sales/va
‐0.20
mfg va/GDP
‐0.25
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
Figure 10. Annual growth rates of offshoring, imports/GDP, manufacturing sales to value-added and share
of manufacturing in GDP
49
Closing the Model with Credit Supply
Banks provide credit. Each chain defaults with probability . Loans rolled
over every period.
Correlated defaults across chains, but bank can diversify away idiosyncratic
risk (Vasicek (2002) credit risk model)
Chain  repays when ˆ  0, where Φ () is standard normal cdf
√
−1
ˆ
 = −Φ () +  +
p
1 − 
³
´
³√
´
p
−1
Pr ˆ  0
= Pr
 + 1 −   Φ ()
¡ −1 ¢
= Φ Φ () = 
50
Notation for Bank Balance Sheet
Bank
E
1 r
C
1 f
L
51
Bank diversifies away idiosyncractic risk
Conditional on  , defaults are independent.
Diversify across many borrowers and eliminate idiosyncratic risk. Realized
value of assets is function of  only
³
´
 ( ) ≡ (1 + )  · Pr ˆ ≥ 0|
´
³√
p
 + 1 −  ≥ Φ−1 () |
= (1 + )  · Pr
´
³ √
 −Φ−1 ()
√
= (1 + )  · Φ
1−
(*)
12
15
ρ = 0.3
ε = 0.2
8
density over realized assets
density over realized assets
10
ε = 0.1
6
4
ε = 0.2
2
0
52
12
ρ = 0.01
9
6
ρ = 0.1
3
ε = 0.3
0
0.2
ρ = 0.3
0.4
0.6
z
0.8
1
0
0
0.2
0.4
0.6
0.8
1
z
Figure 11. The two charts plot the densities over realized assets when  (1 + ) = 1. The left hand
charts plots the density over asset realizations of the bank when  = 01 and  is varied from 0.1 to 0.3.
The right hand chart plots the asset realization density when  = 02 and  varies from 0.01 to 0.3.
53
Contracting Problem between Bank and Depositors
Bank chooses portfolio of loans between:
• Good portfolio has probability of default   0,  = 0.
distribution (normalized) is
 () =
½
0
1
if   1 − 
if  ≥ 1 − 
Outcome
(2)
• Bad portfolio has probability of default +, with   0, and  =   0.
Outcome distribution is
 () = Φ
³
´
√
Φ−1(+)+ 1−Φ−1 ()
√

(3)
54
Bank default point 
Define  = (1 +  )  (1 + ) .
 is (1) (normalized) notional debt of bank and (2) strike price of embedded
put option from limited liability (Merton (1974))
Bank chooses  to maximize marked-to-market equity  ()
ˆ − [ −  ()]
subject to IC constraint:
ˆ − [ −  ()] ≥  ()
ˆ − [ −   ()]
 ()
Lemma 1. There is a unique solution ∗, where ∗  1 − .
Corollary 2. Bank borrows at the risk-free rate
(4)
55
State price density is second derivative of the option price with respect to
its strike price (Breeden and Litzenberger (1978)). Given risk-neutrality,
∆ () =
Z
0

[ () −  ()] 
or
∆ () =
⎧
R
⎪
⎪
⎪
 () 
⎪
⎪
⎨ 0
if   1 − 
⎪
1−
⎪
R
R
⎪
⎪
 ()  −
[1 −  ()]  if  ≥ 1 − 
⎪
⎩
0
1−
(5)
56
Thus ∆ () is single-peaked, reaching its maximum at  = 1 − .
Z
0
1
[ () −  ()]  =
Z
0
1
[1 −  ()]  −
Z
=  ()
ˆ −  ()
ˆ =
0
1
[1 −  ()] 
(6)
so ∆ () approaches  from above as  → 1.
  1 for any bank with positive notional equity. So, we have a unique
solution to ∆ () =  where the solution is in the range where ∆ () is
increasing.
Therefore ∗  1 − .
57
Supply of Credit by Bank
Credit supply  obtained from ∗ = (1 +  )  (1 + )  and balance sheet
identity  =  + 

=
1+
1 − 1+
· ∗
Funding rate  is risk-free rate
Lending rate  determined as equilibrium in credit market
=
58
Main Result
Proposition. Tighter bank credit conditions result in
(1) an increase in the borrowing rate 
(2) fall in output 
(3) fall in productivity per worker
(4) fall in the wage 
(5) fall in the offshoring activity of firms.
Corollary. Tighter credit conditions result in decline in trade volumes.
Decline is larger with more advanced vertical specialization.
(Bems, Johnson and Yi (2011))
59
Avenues for Future Research (1)
• Quantitative effects
— “Finance is statistically significant, but not economically significant”
Need more sophisticated model for quantitative predictions
— Spreads do the damage, even if credit quantity decline is small
(Gilchrist and Zakrajsek (2011), Adrian, Colla and Shin (2012))
• Boundary of firm determines allocation between inventories and net
accounts receivable
— Contracting approach
(Antras and Chor (2011))
— Details of the financing arrangements are important
(Antras and Foley (2011))
Avenues for Future Research (2)
• Connections with macro literature
— Can shocks to TFP be financial shocks, after all?
— Inventory cycles
(Blinder and Maccini (1991), Ramey and West (1999))
— Trade and inventories
(Alessandria, Kaboski and Midrigan (2009, 2010))
— Manufacturing demand and durables
(Eaton, Kortum, Neiman and Romalis (2010))
• Empirics of working capital over the cycle
— Literature on emerging market crises (Calvo et al.)
— Panel analysis of firm working capital
(Kalemli-Ozcan, Kim, Shin, Sorensen and Yesiltas (in progress))
60
61
How Does Leverage Affect Real Economy?
• Kahle and Stulz (2012)1 for 2007-9 crisis
— Firms with no debt cut capital expenditure as much (sometimes more)
as firms with debt
• Adrian, Colla and Shin (2012)2
— Sharp contraction in supply of intermediated credit
— Shortfall made up by increase in direct credit
— Cost of both types of financing rise sharply
1
Kahle and Stulz (2012) “Access to Capital, Investment and the Financial Crisis”
Adrian, Colla and Shin (2012) “Which Financial Frictions? Parsing the Evidence in the Financial Crisis
of 2007-9”
2
Trillion dollars
62
9.0
8.0
Corporate
bonds
7.0
6.0
Commercial
paper
5.0
Other loans
and
advances
4.0
3.0
Bank loans
n.e.c.
2.0
1.0
2012Q3
2011Q1
2009Q3
2008Q1
2006Q3
2005Q1
2003Q3
2002Q1
2000Q3
1999Q1
1997Q3
1996Q1
1994Q3
1993Q1
1991Q3
1990Q1
0.0
Total
mortgages
Figure 12. Credit to US non-financial corporate sector (US Flow of Funds, table L102)
63
210
180
150
Millions
120
90
Bond
change
60
30
0
-30
Loan
change
-60
-90
-120
-150
-180
2011Q2
2009Q4
2008Q2
2006Q4
2005Q2
2003Q4
2002Q2
2000Q4
1999Q2
1997Q4
1996Q2
1994Q4
1993Q2
1991Q4
1990Q2
-210
Figure 13. Changes in outstanding corporate bonds and loans to US non-financial corporate sector. Loans
are defined as sum of mortgages, bank loans not elsewhere classified (n.e.c.) and other loans (US Flow of
Funds, table F102)
64
other loans and
advances
4.0
depository
institution loans
n.e.c.
3.5
3.0
commercial
mortgages
2.5
2.0
multifamily
residential
mortgages
1.5
1.0
0.5
2012Q3
2011Q1
2009Q3
2008Q1
2006Q3
2005Q1
2003Q3
2002Q1
2000Q3
1999Q1
1997Q3
1996Q1
1994Q3
1993Q1
1991Q3
0.0
1990Q1
Non-corporate business sector total borrowing
(trillion dollars)
4.5
construction
loans on one-tofour family
homes
home
mortgages
Figure 14. Credit to US non-corporate business sector (US Flow of Funds, table L103)
65
Percent of liabilities
Percentage points
18
18
Expected year-ahead defaults at t-12 (left axis)
10-year high-yield corporate bond spread at t (right axis)
16
16
14
14
12
12
10
10
8
8
6
6
4
4
2
2
0
0
1997
1999
2001
2003
2005
2007
2009
2011
Figure 15. Risk premium versus expected default probability (Source: Egon Zakrajsek, based on Gilchrist
and Zakrajsek (2012))
Lending
rate
Bank
credit
supply
Bond
credit
supply
66
Aggregate
credit supply
Credit
demand
Total credit
Figure 16. Bank and bond credit supply before crisis
67
Lending
rate
Bank
deleveraging
Credit
demand
Total credit
Figure 17. Bank and bond credit supply following deleveraging by banking sector
Lending
rate
68
Bond
financing
Bank
financing
Total credit
Figure 18. Bank and bond credit supply following deleveraging by banking sector
69
Figure 19. Evidence from UK: Cumulative gross issuance of bonds by UK private non-financial corporations
(Source: Bank of England Asset Purchase Facility Quarterly Report, 2012 Q3)
70
How Does Leverage Affect Real Economy?
• Total quantity of credit (bank and bond) may not be good indicator of
financial conditions
• Composition of credit matters - i.e. relative size of banking sector to
bond market
— Bank deleveraging leads to spike in lending rates in both bank loans
and bonds
— Spike in spreads is followed by economic downturn (Gilchrist and
Zakrajsek (2012))
Meanwhile in Europe
• Bond markets have thawed
• But credit conditions faced by SME borrowers in Europe are still tight
• Conjunction of the two explained by subdued banks
71
Figure 20. Non-financial corporate lending rates (source: IMF document on Banking Union in Europe)
72

Working Capital, Trade and Macro Fluctuations Se-Jik Kim Hyun Song Shin

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