University of Sussex, PhD Conference
5th of December 2014
Catalysts and Inhibitors of the
Trade Collapse
Mattia Di Ubaldo
Motivation & Context - 1
The Slovenian Experience
Growth of trade
Growth of GDP
29/10/2014
Mattia Di Ubaldo, University of Sussex
2
Motivation & Context - 2
• Causes of trade collapse:
• Supply side
• Credit crunch – working capital (Bricongne et al., JIE 2012; Chor & Manova, JIE 2012;
Behrens et al., RES 2013).
• Trade finance – bank or firm intermediated (Korinek et al., 2010; Malouche, 2011; Antràs
& Foley, JPE 2014).
• Demand side
• Decrease in expenditure – composition (Engel & Wang, JIE 2009; Eaton et al., 2011;
Petropoulou & Soo, 2011).
• Vertical linkages (Levchenko et al., IMF review 2010, Bems et al., AER 2011).
• Inventory adjustments (Alessandria et al., AER 2010).
29/10/2014
Mattia Di Ubaldo, University of Sussex
3
Contribution
My analysis: factors that amplified or dampened the reaction of trade.
• Most of the action was in GVCs! Intermediates:
• 2/3 of total trade (Bems et al., 2011).
• 11/15 most affected sectors (Bricongne et al. 2012).
1. Analyse the reaction of different inputs, depending on the cost-share in firms’ sales.
2. Explore the reaction of inputs along Intra-Firm vs Arm’s Length trade dimension:
one WP so far (Altomonte et al. 2012), but I add:
• Cost-share of inputs.
• Intensive and Extensive margins.
• Firm fixed effects and additional firm controls.
3. Detailed intensive/extensive (firm, destination, product) margins decomposition.
• New across Intra-firm vs Arm’s Length trade.
29/10/2014
Mattia Di Ubaldo, University of Sussex
4
Preview of findings
1. Cost-share of imported inputs in sales:
• Higher cost share, larger reaction (but Intra-Firm dampens).
2. Firm affiliation – Intra-firm (RP) versus arm’s length (AL) trade.
• No different performance.
3. Intensive vs Extensive margin
•
29/10/2014
70% Intensive margin; larger intensive margin share for RP.
Mattia Di Ubaldo, University of Sussex
5
Outline
• Data and sample.
• Hypotheses.
• Methodology.
• Results.
29/10/2014
Mattia Di Ubaldo, University of Sussex
6
Data and Sample
29/10/2014
Mattia Di Ubaldo, University of Sussex
7
Data
• Slovenia: why?
• small, open economy.
• strongly integrated with both eastern and western European countries: intermediates
72% of imports.
• Transaction-level trade data (SURS): monthly exports/imports at the CN-8 digits product
level (xickt); 2000-2011.
• Firm Balance Sheet data (AJPES): balance sheet and income statements of all Slovenian
firms; 2000-2011.
• Ownership data (Bureau Van Dijk): ORBIS allows to track all the proprietary network, on a
world scale, up to the 10th level of subsidiarity.
29/10/2014
Mattia Di Ubaldo, University of Sussex
8
Intra-firm (RP) trade proxy
• I need to infer whether shipments are RP or AL:
• I assume that transactions are RP when there is an affiliate in the destination they are directed to.
88% of trade by affiliates to a certain destination is either pure RP or pure AL (Bas & Carluccio 2009),
• This inflates RP trade proxy when firms adopt a mixed strategy; but it’s a classification mistake
working against me.
• 49% exports are RP in Slovenia in 2007:
• 47% in US (US Census Bureau data)
• 52.6% exports to US are RP – Slovenian data; 51.3% – Census Bureau data (Lanz & Miroudot, 2011).
29/10/2014
Mattia Di Ubaldo, University of Sussex
9
Timing and Sample
Timing
October 2008 – May 2010;
trough in November 2009.
Final sample
29/10/2014
Exporters
9,238
NACE-4dig sectors
462
Products
7,350
median 58; mean 136
Destinations
199
median 10; mean 16
Mattia Di Ubaldo, University of Sussex
10
Hypotheses
29/10/2014
Mattia Di Ubaldo, University of Sussex
11
Hypotheses – 1 and 2
• Inventory adjustments were observed to have amplified the reaction of trade (Alessandria et al.
2010). GVC ideal locus!
1st Hypothesis: a higher cost−share of imported intermediates in sales leads to a larger reaction of trade.
Why? Inventories of higher cost share inputs get adjusted more promptly, if inventories management costs
are proportional to the cost share of inputs.
Larger adjustments!
2nd Hypothesis: RP trade of intermediates is more resilient than AL trade.
Why? For RP trade:
• Lower uncertainty
• Shorter delivery lags
• Better communication
29/10/2014
Lower buffer of inventories
Mattia Di Ubaldo, University of Sussex
Lower adjustments!
12
Hypotheses – 3 and 4.
3rd Hypothesis: intermediates’ inventories adjustment generate a Bullwhip Effect: this is exacerbated
by the cost share in sales.
Why? Inventories accelerator mechanism!
• The reaction of RP and AL trade can differ across trade margins:
In a crisis, different sunk costs, market rigidities and hold-up problem would cause:
4th Hypothesis: intensive margin adjustments are more pronounced for RP trade than for AL
trade; vice-versa for extensive margin adjustments.
Why? Firms might more easily reduce the size of shipments to affiliates rather than to non-affiliates.
Extensive margin changes could happen more at AL.
29/10/2014
Mattia Di Ubaldo, University of Sussex
13
Methodology
29/10/2014
Mattia Di Ubaldo, University of Sussex
14
Methodology
Hypothesis 3
g ickt  α 0  α1Ω α 2Ω* recovery  γ i  ε ickt
Dep. var.: g ickt 
x ickt  x ick(t 12)
0.5(x ickt  x ick(t 12) )
Ω  β1Int ickt  β 2 RPikt  β3CS k
 δ1 Int ickt * CS k   δ2 Int ickt * RPikt   δ 3 Int ickt * CS k * RPikt   β4 X iy  β5 ExYug ickt
Hypothesis 1
Hypothesis 2
im ickt
1

t 1
CS k 
 Y
NY y  2000 i 1
iy
2007
29/10/2014
N
12
Mattia Di Ubaldo, University of Sussex
15
Results
Estimations
29/10/2014
Mattia Di Ubaldo, University of Sussex
16
RP trade and Cost Share (Hyp. 1 and 2):
(1)
Int.
(4)
(5)
(6)
(7)
0.0389***
0.0389***
0.0559***
0.0588***
0.0612***
(3.04)
(3.79)
(4.62)
(4.78)
(4.97)
-0.0448
-0.0465
-0.0482
-0.0480
-0.0616*
(-1.39)
(-1.25)
(-1.26)
(-1.26)
(-1.64)
-0.00391
-0.00156
-0.000922
-0.0104
(-0.70)
(-0.29)
(-0.17)
(-0.27)
0.00166
0.000918
-0.00934
-0.0104
(0.05)
(0.02)
(-0.24)
(-0.27)
-0.178
-0.287*
-0.306**
(-1.26)
(-1.95)
(-2.10)
0.380*
0.356
(1.62)
(1.54)
RP
(2)
CS
(3)
Int. * RP
Int. * CS
Int. * CS * RP
-0.106***
Ex Yug.
(-4.05)
Firm FE
yes
yes
yes
yes
yes
yes
yes
Firm Controls
yes
yes
yes
yes
yes
yes
yes
N
1,636,936
1,636,936
1,636,936
1,636,936
1,636,936
1,636,936
1,636,936
Note: t statistics arising from robust standard errors clustered at the firm level in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.
Results: sector-product CS variable
• Sectoral disaggregation of
the product level CS
variable:
(1)
im ijckt
1

t 1
CS k j 
 Y
NY y  2000 i 1
ijy
N
(5.31)
(5.48)
-0.0468
-0.0612
(-1.16)
(-1.55)
0.000588
0.000871
0.000858
(0.74)
(1.12)
(1.09)
-0.00944
-0.0108
(-0.23)
(-0.26)
-0.125**
-0.143**
(-2.20)
(-2.27)
RP
CS-SECT
2007
0.0607***
(3)
0.0633***
Int.
12
Int. * RP
Int. * CS-SECT
(2)
0.000236
Int. * CS-SECT * RP
(0.00)
29/10/2014
Firm FE
yes
yes
yes
Firm Controls
yes
yes
yes
N
1,554,771
1,554,771
1,554,771
Note:
t statistics
arising
Mattia
Di Ubaldo,
University
of from
Sussexrobust standard errors clustered at the firm level in
parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.
18
Hypothesis 3: Bullwhip
(1)
Int.
Downturn
(2)
(3)
0.0113
0.0234
(0.72)
(1.44)
CS
-0.00855
-0.00636
(-1.20)
(-0.90)
-0.294**
Int. * CS
(-2.05)
Int. * Rec
Recovery
0.0958***
0.0864***
(3.59)
(3.24)
CS * Rec.
0.0157
0.0167
(1.09)
(1.16)
0.401**
Int. * CS * Rec.
(2.31)
Firm FE
yes
yes
yes
Firm Controls
yes
yes
yes
N
1,636,936
1,636,936
1,636,936
Note: t statistics arising from robust standard errors clustered at the firm level in
parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.
Results
Margin decomposition
29/10/2014
Mattia Di Ubaldo, University of Sussex
20
Margin decomposition -2
Decomposition of growth of exports: net intensive and net extensive margin contributions
29/10/2014
Mattia Di Ubaldo, University of Sussex
21
Intensive/Extensive
margin decomposition
(in %):
RP vs AL trade
29/10/2014
Mattia Di Ubaldo, University of Sussex
22
Conclusion
• This work adds to the study of the trade collapse by finding:
• RP trade reacted more at the intensive margin, compared to AL trade; but no
significant difference overall.
• A higher cost-share of inputs induced a larger reaction of trade.
• Especially larger fall in downturn.
• CS heterogeneity allows to discover:
• Dampening impact of RP trade, relative to AL trade.
• Bullwhip effect.
29/10/2014
Mattia Di Ubaldo, University of Sussex
23
THANK YOU!
29/10/2014
Mattia Di Ubaldo, University of Sussex
24
RP trade and Cost Share (Hyp. 1 and 2):
• Impact of Cost Share of inputs and RP trade on growth rate of export of intermediates.
29/10/2014
Mattia Di Ubaldo, University of Sussex
25
Exports over the crisis
Growth of Exports, with “contributions” of RP and AL trade to overall variation.
29/10/2014
Mattia Di Ubaldo, University of Sussex
26
Hypotheses – 5 and 6. Trade finance.
• Trade finance (bank intermediated) became more expensive in the crisis:
5th Hypothesis: greater reliance on firm intermediated trade finance induced a better trade
performance.
6th Hypothesis: greater reliance on firm intermediated trade finance induced a better trade
performance for RP trade, relative to AL trade.
Why? Firm intermediated trade credit should be easier between related parties.
29/10/2014
Mattia Di Ubaldo, University of Sussex
27
Methodology
g ickt  α 0  α1Ω γ i  ε ickt
Dep. var.: g ickt 
x ickt  x ick(t 12)
0.5(x ickt  x ick(t 12) )
Ω  β1Int ickt  β 2 RPickt
 δ5 TC iy  δ6 TC iy * RPickt  β4 X iy  β5 ExYug ickt

Hypothesis 5

Hypothesis 6
TC iy 
29/10/2014
receivable s y 1
sales y 1
1  receivables ijy
TC j  n 1 y  2000
NY  sales ijy
N
Mattia Di Ubaldo, University of Sussex
2007




28
Results – hypothesis 4 and 5: trade finance
(1)
(2)
TC-SECT
0
0
-
RP
-0.0670**
TC-FIRM
(3)
(4)
(5)
0.0461*
0.0347
0.0350
(1.62)
(1.38)
(1.36)
-0.0843*
-0.0618
(-1.78)
(-0.77)
0.162
0.0827
(1.40)
(0.36)
RP
(-2.37)
TC-SECT * RP
0.00312***
0.00318***
(7.41)
(9.65)
-0.000752
Int. * TC-SECT * RP
TC-FIRM * RP
0.119
Int. * TC-FIRM* RP
(-1.11)
(0.56)
Firm FE
yes
yes
Firm FE
yes
yes
yes
Firm Controls
yes
yes
Firm Controls
yes
yes
yes
N
1,661,136
1,661,136
N
1,661,075
1,661,075
1,661,075
Note: t statistics arising from robust standard errors clustered at the firm level in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.
29/10/2014
Mattia Di Ubaldo, University of Sussex
29
Results – hypothesis 4 and 5: trade finance
• Impact of Receivables and RP trade on growth rate of exports.
Larger reliance on trade credit
was associated to a better trade
performance, with an additional
premium detected to RP trade.
29/10/2014
Mattia Di Ubaldo, University of Sussex
30
Results – all hypotheses together
Int.
(1)
(3)
0.0575***
Int.
(4.62)
RP
-0.0786***
(3.94)
RP
(-2.61)
CS
-0.00106
0
CS
-0.287**
TC-FIRM
0.381*
Int. * CS
0.00330***
Int. * CS * RP
-0.000947
0.386*
(1.65)
TC-FIRM * RP
(9.31)
Int.*TC-SECT*RP
-0.287**
(-1.96)
(1.63)
TC-SECT * RP
0.0321
(1.18)
(-1.95)
Int. * CS * RP
-0.000265
(-0.05)
(.)
Int. * CS
-0.0465
(-1.25)
(-0.20)
TC-SECT
(4)
0.0620***
0.0145
(0.06)
Int.*TC-FIRM*RP
(-1.38)
0.192
(0.81)
Firm FE
yes
Firm FE
yes
Firm Controls
yes
Firm Controls
N
1,636,936
N
yes
1,636,875
Note: t statistics arising from robust standard errors clustered at the firm level in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.
Margin decomposition -1
• Methodology (Bricongne et al., 2012).
I decompose mid-point growth rates: g ickt 
x ickt  x ick(t 12)
0.5(x ickt  x ick(t 12) )
Extensive margin: created (gickt =2) and destroyed flows (gickt =-2)
Intensive margin: increased (0< gickt <2) and decreased flows (-2< gickt <0)
Weight each flow by its share in total Slovenian exports: s ickt 
x ickt  x ick(t 12)
 x
i
c
k
ickt
 i c k x ick(t 12)
Aggregate subsets of the weighted mid-point growth rates to obtain the margins, since the
aggregate year on year total growth rate can be correctly approximated by:
G t  c i k g ickt * s ickt
29/10/2014
Mattia Di Ubaldo, University of Sussex
32
Margin decomposition -3
Decomposition of growth of exports: detailed extensive margin contributions
29/10/2014
Mattia Di Ubaldo, University of Sussex
33
Margin decomposition -5
29/10/2014
Mattia Di Ubaldo, University of Sussex
34
Margin decomposition -4
29/10/2014
Mattia Di Ubaldo, University of Sussex
35