1
Emerging Market response to Developed Countries over recent
periods of US recessions and crises: What changed? 1
Regan Deonanan2
University of Notre Dame, Department of Economics,
424 Flanner Hall, Notre Dame, IN 46637, USA
Preliminary - not for publication
May 31st, 2011
1
Many thanks are expressed to the Department of Economics at the University Notre Dame for helpful suggestions
and comments. All errors are my own.
2
The author is currently a 4th-year Economics PhD (ABD) Candidate at the University of Notre Dame, Indiana, USA.
Questions/comments may be emailed to rdeonana@nd.edu. The author may also be reached at mobile number
202-299-7505.
2
Emerging Market response to Developed Countries over recent
periods of US recessions and crises: What changed?
Abstract
Emerging markets have responded very differently towards developed countries over the 2007 financial
crisis as compared to the 1981, 1990 and 2001 US recessions. Was this simply the result of a bigger crisis
resulting from bigger US shocks or did the transmission mechanism change due to changes in the
economies of emerging markets? This paper documents the difference in behavior between these two
groups and then investigates which is a more significant explanation of the change: size of the shock or
propagation? Additionally, if it is propagation, what factors are associated with the underlying structural
change? Three main results are contributed. First, structural change within emerging markets played a
bigger role than the size of the US shocks. Second, the structural change was oriented particularly towards
the US. Third, the structural change was associated with trade factors and not financial factors.
Collectively, these findings have important policy implications for countries seeking to diversify
economic growth risks through particular trade partners, as well as, investors managing investment risk
through portfolio diversification.
Keywords: Financial Crises; Emerging Market Growth, Shocks
3
1. Introduction
This paper is motivated by the observation that emerging markets (EMs) have responded very differently
to developed countries (DCs) during the most recent US disturbance as compared to the previous three
US disturbances. As Figure 1 shows, there was a significant increase in the modal pair-wise correlation of
quarterly real GDP growth between EMs and DCs over the 2007 financial crisis versus the US recessions
of 1981, 1990 and 2001. In contrast, there was little change in this EM-DC relationship over the previous
three US recessions.
The picture that emerges therefore is that something changed markedly in moving from the 2001 US
recession period to the 2007 financial crisis period. The primary issue is then, “What’s driving this recent
change in EM response?” Within this context, this paper empirically investigates the direct impact of US
growth and US shocks on EM growth over the 2001 US recession and 2007 financial crisis through two
main questions. Firstly, was it the size of the US shock (impulse) or change in propagation of the US
shock within EMs (indicating structural change) that is driving our observation? Secondly, if the
propagation mechanism is responsible, what factors underlie the structural change within EMs?
This paper adds to the literature examining the impact of the 2007 financial crisis on EMs. In terms of the
most closely related paper, Blanchard, Das and Faruqee (2010) seek to explain the heterogeneity of EM
growth over the 2007 crisis in terms of pre-crisis macro-variables. Using 29 EM countries and a crosssectional regression of unexpected nominal gross domestic product (gdp) growth (averaged over 2008Q4
and 2009Q1) on 2007 pre-crisis variables, they conclude that the diverse pattern observed can be
explained by ‘differences in trade and financial exposure’ and ‘differing growth performances of
countries’ trading partners.’
The approach of this paper is very different from the current related literature. Fundamentally, it seeks the
root cause of the recent different EM response from a very elementary level (impulse versus propagation)
which is important because each carries different policy prescriptions for EMs. It also attempts to
understand the underlying factors from both cross-sectional and time-dimensional points of view using a
panel of real gdp growth of 24 EM countries over the 2001 US recession period and 2007 US crisis
period.
In addition to documenting the change in response behavior of EMs towards DCs over various US
disturbance periods, this paper also contributes the following three main results. Firstly, structural change
within EMs was the main driver of the difference in recent EM behavior. Secondly, the structural change
4
within EMs was US driven. Thirdly, the structural change enabled the US to have a bigger impact on
EMs through trade channels; greater impact through financial channels was not associated with the
structural change.
The rest of the paper follows with Section 2 describing the data sources, Section 3 discussing the
methodology employed, Section 4 interpreting the results and Section 5 providing concluding remarks.
2. Data
Quarterly nominal gdp and gdp deflator (local currency) for EMs and DCs were obtained from the IFS
database for the period 1980q1 through 2009q3, or as available. Quarterly real gdp (2005 base year, local
currency) was calculated and seasonally adjusted as needed (using Eviews X12 function), and then used
to construct real gdp growth.
The panel consisted of real gdp (rgdp) growth for 24 EM countries over the period 1999Q1 through
2009Q3. 1999Q1-2004Q4 was defined as period 1 and corresponded to the 2001 US recession period.
2005Q1-2009Q3 was defined as period 2 and corresponded to the 2007 financial crisis period. The MSCI,
FTSE and Dow Jones Emerging Market listings were used to identify EM countries (39 in total) – the 24
EMs included in the panel represented those with data over the periods of interest.
Most of the cross-sectional data for the 24 EMs was also obtained from the IFS database but were annual
series (nominal gdp, exports, imports, gross fixed capital formation, household consumption and
government consumption). The pre-crisis periods used were 1999-2000 and 2005-2006. The financial
openness variable used was the Chinn-Ito index (available on the websites of these authors) for the same
period. Both FDI inflows and bank capital to asset ratios were obtained from the WDI database for the
same periods.
The aggregate G6 rgdp growth rate was calculated using an arithmetic average of the individual quarterly
rgdp growth, weighted by real gdp in 2005.
5
3. Methodology
3.1 Intuition
Structural VAR is the usual approach employed to identify shocks and perform impulse responses to
shocks, in an effort to uncover causal factors driving crises. However, given the short periods under
consideration, identification of the shocks poses a problem.
To address this limitation, two assumptions are made: (1) DCs are the most important trading partners of
EMs and consequently, developments in DCs are the most important drivers of EM growth rates; and, (2)
EMs have no power to influence DC behavior.
As a result, we can think of EM growth as being influenced in either of the following ways during US
disturbance periods (subsequently referred to as ‘Case 1’ and ‘Case 2’ respectively):
EM growth = Prior EM growth
+
US shocks + Other DC shocks
+
EM shocks
EM growth = Prior EM growth
+
US growth + Other DC growth
+
EM shocks
Given that the periods being examined are limited to the last two US disturbances, the US can be thought
of as the source of a global disturbance to EMs. As such, the ‘US growth’ (or ‘US shocks’) term
constitutes the direct influence of the global disturbance on EMs whereas the ‘Other DC growth’ (or
‘Other DC shocks’) represents the indirect/spillover of the global disturbance to EMs.
The intuition gained from the equations above is that the direct influence term became relatively bigger
over the 2007 crisis, resulting in EMs becoming more correlated with the US. At the same time, as noted
by other papers in the crisis literature (Perri and Quadrini (2010)), other DCs have also become
significantly more correlated with the US. These two factors combined would then account for the recent
higher correlation between EMs and DCs.
The primary interest of this paper is therefore identifying the most important factor behind the direct
influence term becoming bigger. The two possible candidates are:

Change in size of the US disturbance (impulse)

Change in propagation of the US disturbance (due to structural change within EM economies)
6
3.2 Determination of the shocks
Since the time periods being considered are short and identification of the shocks emanating from the US
and other DCs (henceforth taken to be the G6 countries) poses a problem, the following method was
utilized to proxy for the shocks. Consider the regression of the US growth rate on lags of itself:
k 4
US
YtUS  cons    kUS YtUS
 k  et
k 1
Thinking about a shock as the unpredicted change in the growth rate, the error term is used as a proxy to
the shock from the US (henceforth referred to as the US shock). This regression is run separately for two
periods: 1990q1-2004q4 and 2004q1-2009q3 (to allow for different variances of the shocks) and the error
terms are recovered. Each error term is then taken as the US shock over period 1 and period 2 defined in
the data section. The procedure was repeated for G6 growth to recover the G6 shocks. Table 1 and Table
2 in the Appendix give the results of these regressions respectively.
3.3 Impulse or propagation?
The equations described in the ‘Intuition’ section motivate the following regressions performed on a panel
of EM growth of 24 countries over 1999q1 through 2009q3 (periods 1 and 2) and correspond respectively
to Case 1 and Case 2 mentioned earlier:
k 4
US
Yi ,EM
 consi    kEM Yi ,EM
  2etUS * dumt  3dumt   4etG 6  5etG 6 * dumt  i ,t
t
t  k  1et
k 1
The LHS is EM rgdp growth. The RHS consists of a constant for each EM country, 4 lags of EM rgdp
growth, the US shock, the US shock interacted with the period dummy, the period dummy (‘0’ for period
1 and ‘1’ for period 2), the G6 disturbance, the G6 disturbance interacted with the period dummy and the
error term. For Case 2, the US and G6 shocks are replaced with US and G6 growth rates.
k 4
US
Yi ,EM
 consi    kEM Yi ,EM
  2 YtUS * dumt  3dumt   4 Yt G 6  5 Yt G 6 * dumt   i ,t
t
t  k  1Yt
k 1
The null hypothesis being tested here is that  2 , the additional effect in period 2 of the US growth
(shock) on EM growth as compared to period 1, is statistically equal to zero. Under the null we would
conclude that the US shock (or growth) in period 2 did not have a significantly different effect on EM
7
growth as compared to period 1. If the standard deviation of the shock (growth) in period two was
statistically different from period 1 then the direct influence of the US shock (growth) on EMs would
have been bigger because of the size of the shock (growth). The impulse argument would be favored. On
the other hand, rejecting the null would imply a different effect exerted by the US on EMs and
consequently interpreted as structural change within EMs (propagation argument). A statistically different
US shock (growth) would also mean that the impulse argument is possible. In this case, counterfactual
analyses would have to be performed to delineate the two possibilities. The results of both cases are
reported in Table 3.
3.4 Impulse or propagation? – Counterfactual analysis
In the above section, the determination of structural change was contingent upon observing a value of  2
statistically different from zero. In the context of those regressions, the  2 found represented a pooled
value over all countries. To gain further insight on the impulse versus propagation argument as the main
driving force behind recent EM behavior, both regressions in 3.3 were re-run under the following
alternative specification (Case 1 and Case 2 respectively):
4
23
k 1
i 1
US
Yi ,EM
 consi    kEM Yi ,EM
  2etUS * dumt  3dumt   4etG 6  5etG 6 * dumt   i 5etUS * dumt * dumi 1  i ,t
t
t  k  1et
4
US
Yi ,EM
 consi   kEM Yi ,EM
  2 YtUS * dumt  3dumt   4 Yt G 6  5Yt G 6 * dumt 
t
t  k  1Yt
k 1
23

i 1
i 5
YtUS * dumt * dumi 1   i ,t
23
The difference here was the inclusion of

i 1
US
i 5 t
e * dumt * dumi 1 in the Case 1 regression and the
corresponding term in the Case 2 regression (where dumi 1 equals ‘1’ for the (i+1)th EM country and
zero otherwise). The purpose of this regression was to isolate the additional effect, in moving from period
1 to period 2, of the US shock (growth) on EM growth by EM country (in effect, we’re splitting up  2 by
country to obtain the “individual  2 s”). The results for these regressions are shown in Table 4. Table 5
shows a ranking of the most affected by structural change to the least for both cases.
8
With the “individual  2 s” estimated the counterfactual exercise described below was then carried out
over period 2:
1. EM growth was calculated by feeding in the actual data into the above estimated equations.
2. EM growth was calculated assuming the US shock (growth) had no additional effect in period 2
(that is, the corresponding ‘  2 ’ for each country was made zero in the above estimated
equations) holding everything else fixed.
3. EM growth was calculated assuming the US shock (growth) had the same standard deviation in
period 2 as it did in period 1 (that is, the volatility of only the US shock (growth) term was
adjusted to match the previous period’s volatility) holding everything else fixed.
A graphical comparison of the three alternatives is meant to provide a relative measure of how EM
growth was affected by the increase in impulse size as compared to the increase in effect over the 2007
crisis for each country. Figure 2 and Figure 3 show these results under Case 1 and Case 2.
3.5 Factors associated with the Structural change within EMs
If structural change within EMs was deemed to be an important factor in accounting for their recent
response to DCs, what exactly was the change they underwent?
The “individual  2 s” estimated in the previous section represented the additional effect the US exerted on
each EM as a result of the structural change within each EM. Said differently, EMs that had the biggest
‘  2 s’ underwent the most structural change. Given this interpretation, the  2 s were considered an index
of the degree of structural change among EMs and used as the dependent variable in cross-sectional
regressions aimed at identifying possible factors associated with the structural change that occurred.
Given that only 24 observations at most were available, any investigation of associated factors would
have to be very simple. As such, three broad hypotheses were tested: (1) the structural change was trade
related, (2) the structural change was associated with financial factors, and (3) the structural change was
related to both trade and financial variables.
For hypothesis 1, the independent variables used were
Imports
Exports
,
NGDP
NX
,
NGDP
Trade
,
NGDP
GFCF
,
NGDP
NGDP
.
For hypothesis 2, the independent variables used were the Chinn-Ito index of financial openness, FDI
inflows as a share of nominal GDP and bank capital to assets ratio.
9
For hypothesis 3, the independent variables used in hypothesis 1 and the Chinn-Ito index of financial
openness were utilized.
The independent variables were all annual observations averaged over 2005 and 2006. Results for both
Case 1 and Case 2 are reported in Tables 6-11.
4. Discussion of Results
The primary question raised was: ‘Impulse or propagation?’ The empirical strategy employed in Section
3.3 constituted the first attempt to gain some insight into this problem. However, as Table 3 shows, these
regressions actually uncover the richest results of this paper. Four main results stand out and are reflected
in both the shock case and the growth case (Case 1 and Case 2). First, prior to period 2 the US had a very
small and insignificant effect on EM growth. Second, G6 behavior was highly influential on EM growth
prior to period 2, as reflected in the large size and significance of  4 . Third, the relationship between
EMs and the G6 did not change over the period 1 and period 2, shown by the small and insignificant  5
coefficient. Fourth, and most interesting, the US played a dramatically more influential role on EM
growth over period 2 and even surpassed the influence of the G6. These results point to the conclusions
that EMs certainly did undergo some form of structural change and that the EM structural change was
oriented specifically to the US.
A two-sided t-test of the null hypothesis that the standard deviation of the US shock (and US growth) was
the same across the two periods led to the rejection of the null (with a lower one-sided F-statistic of 0.004
in the case of the US shock and 0.000 in the case of US growth). Not surprisingly, the takeaway here was
that the shocks were indeed bigger over the 2007 crisis as compared to the 2001 recession. Hence, either
explanations of impulse or propagation being the more important driver were certainly plausible based on
these results alone.
As a means of delineating these two possibilities, the counterfactual exercises undertaken in Section 3.4
were indeed informative. In looking at Figures 2 and 3, two observations appear to define the range of
individual EM experiences. In period 2 prior to the onset of the 2007 crisis, EM growth was higher as a
direct consequence of the larger effect of the US on their economies. Once the crisis hit however, EMs
10
experienced larger declines because of this elevated US effect. In the case of the US shock, deviations
from original growth were much smaller. These observations point towards structural change as the
underlying cause behind the change in EM behavior.
In terms of identifying the actual structural change, the cross-sectional regressions utilized in Section 3.5
yielded the overall result that the structural change was associated with trade variables – on the other
hand, no significant relationship could be found linking the structural change to financial factors. As can
be seen from Table 6, the proportion of trade, imports, exports and gross fixed capital formation (as a
proxy for capital accumulation) in GDP were all positively and significantly related to how much of an
effect was felt from the US. The results for trade and export share reinforce the findings of previous
papers examining the channels through which emerging markets and other developing countries were
affected. However, the result obtained for GFCF (gross fixed capital formation) share has not been
articulated explicitly in the current literature. From the viewpoint of the previous findings of this paper,
this result is not surprising – the structural transformation within EMs meant greater investment in capital.
5. Conclusion
This paper has documented the fact that the response of EMs to DCs over the recent crisis changed
significantly when compared to their response over the 1981, 1990 and 2001 US recession periods. It then
proceeded to investigate the underlying cause of this change in behavior. Through the perspective that the
shocks emanating from the US during US downturns represent global shocks that directly impact EMs,
the hypothesis that ‘the change was driven by bigger shocks arising from a bigger crisis versus some
fundamental change in the economic landscape of EMs that left them more vulnerable to US
disturbances’ was tested (shock versus effect).
The empirical analysis yielded three main results. First, structural change within EM economies played a
greater role in explaining the change in behavior observed. Second, the structural change was particularly
oriented towards the US. Third, structural change left EMs more vulnerable to the US through trade and
not through financial channels.
In interpreting the results, the following story is advanced. Prior to the 2000s, EM growth activity was
more broadly focused. They relied on a wider group of countries including the G6 countries and as such,
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were not very dependent on the US. However, in the early to mid-2000s, a period over which the US was
experiencing strong and positive rgdp growth, EMs focused their own growth efforts on the US with the
objective of faster growth. Given that exports already played an important role in EM economies, this
‘focusing of growth efforts on the US’ naturally took the form of greater export activity particularly
geared towards US demand. The structural change they underwent was therefore directly associated with
this increased export-led growth activity. The plan worked - before the 2007 crisis, EMs experienced
higher growth as a direct result of their closer relationship with the US and as such, had little reason to
change their growth strategy. Quite the opposite, they continued to invest heavily in capital for instance.
When the crisis hit in 2007, EMs were heavily dependent on the US, certainly much more than during any
previous US disturbance period. It was particularly this increased orientation of their economic activity
towards the US that left them vulnerable to the direct impact of these global shocks emanating from the
US. Unable to offer sufficient resistance as they may have in the past, EM growth rates closely followed
the decline in US growth. Given that many other DCs also closely followed the US downturn the net
effect was this drastic increase in growth correlations being observed.
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Table 1: Determination of US shocks
Dep Var: US rgdp growth
1990q1 – 2004q4
2004q1-2009q3
Lag 1
0.18
0.68**
0.267
0.037
0.28*
-0.05
0.059
0.907
-0.11
-0.13
0.399
0.759
0.05
0.02
0.699
0.955
0.50***
0.12
0.003
0.600
0.152
0.391
Lag2
Lag 3
Lag 4
Constant
R-squared
Notes:
(1) p-value provided below coefficients.
(2) ***, ** and * indicates significance at the 1, 5 and 10 percent levels of testing.
(3) This convention applies to all subsequent tables
Table 2: Determination of G6 shocks
Dep Var: G6 rgdp growth
1990q1 – 2004q4
2004q1-2009q3
Lag 1
0.26*
0.54*
0.074
0.064
0.003
0.24
0.979
0.696
0.19
0.08
0.166
0.912
-0.12
-0.90
0.169
0.220
0.29***
0.36
0.001
0.234
0.104
0.367
Lag2
Lag 3
Lag 4
Constant
R-squared
13
Table 3: Impulse or propagation?
Dep Var:
EM rgdp growth
Case 1:
US and G6 shocks
0.25
Case 2:
US and G6 growth
0.12
0.000
0.12
0.027
0.08
0.009
0.01
0.056
0.02
0.762
0.09
0.672
0.08
0.241
-0.15
0.219
-0.07
0.366
1.16***
0.659
0.83**
3
0.002
-0.20*
0.017
-0.20
4
0.077
0.85***
0.333
0.77***
5
0.000
-0.17
0.000
-0.09
dumt
0.429
-0.20*
0.689
-0.20
Constant
0.077
0.61***
0.333
0.51***
R-squared overall
Obs
Countries
0.000
0.272
933
24
0.001
0.328
933
24
1EM
 2 EM

EM
3

EM
4
1
2
Yi ,EM
 consi 
t
Yi ,EM
 consi 
t
i 1, k  4

i 1, k 1
i 1, k  4

i 1, k 1
EM
US
iEM
 2etUS * dumt  3dumt  4etG 6  5etG 6 * dumt 
, k Yi ,t  k  1et
i  24, k  4

i  2, k 1
iEM
, k Yi ,t k   i ,t
EM
US
iEM
 2 YtUS * dumt  3dumt   4Yt G 6  5Yt G 6 * dumt 
, k Yi ,t  k  1Yt
i  24, k  4

i  2, k 1
iEM
,k Yi ,t k   i ,t
14
Table 4: Regression results - structural change by country
Dep var: EM gr
Case 1: US and G6 shocks
Case 2: US and G6 growth
Coef
P-value
Coef
P-value
1EM
0.27***
0.000
0.09*
0.096
 2 EM
0.12**
0.011
0.09*
0.054
3EM
0.00
0.992
0.00
0.936
 4EM
0.07
0.299
0.06
0.358
1
2
3
-0.16
0.368
-0.05
0.741
2.25***
0.000
1.86***
0.000
-0.19*
0.084
-0.18
0.381
4
0.84***
0.000
0.77***
0.000
5
-0.16
0.455
-0.07
0.749
6
-1.65***
0.000
-1.57***
0.000
7
-2.26***
0.000
-1.83***
0.000
8
-1.40***
0.000
-1.12***
0.000
9
-1.77***
0.000
-1.50***
0.000
10
-2.14***
0.000
-2.19***
0.000
11
-2.26***
0.000
-1.84***
0.000
12
-1.21***
0.000
-1.45***
0.000
13
-0.48***
0.000
-0.79***
0.000
14
-1.05***
0.000
-0.74***
0.000
15
-1.81***
0.000
-2.15***
0.000
16
-1.26***
0.000
-1.11***
0.000
17
-1.72***
0.000
-1.65***
0.000
18
-1.36***
0.000
-1.40***
0.000
15
19
-1.79***
0.000
-1.58***
0.000
 20
-1.33***
0.000
-1.38***
0.000
 21
-1.37***
0.000
-0.81***
0.000
 22
-1.28***
0.000
-1.08***
0.000
 23
0.14
0.213
0.17
0.199
 24
-1.45***
0.000
-1.70***
0.000
 25
1.43***
0.000
1.06***
0.000
 26
0.16***
0.007
0.26***
0.000
 27
-0.20***
0.000
-0.30***
0.000
 28
-0.39***
0.000
-0.25***
0.000
Constant
0.62***
0.000
0.57***
0.000
0.316
0.387
Obs
933
933
Countries
24
24
R-squared overall
16
Table 5: Ranking by degree of structural change experienced in going from period 1 to period 2
Case 1: Using US & G6 shocks
Case 2: Using US & G6 growth
1. LAT
3.20 KOR
2.44
2. LIT
2.55 MAL
2.17
3. EST
2.39 THAI
2.13
4. RUS
2.26 MOR
1.97
5. SLO
1.79 TUR
1.94
6. ROM
1.79 RUS
1.94
7. MAL
1.57 CHI
1.83
8. KOR
1.34 INDON
1.80
9. MEX
1.14 ROM
1.76
10. ARG
1.10 SLO
1.75
11. THAI
0.98 JOR
1.73
12. PER
0.95 COL
1.72
13. TUR
0.88 PER
1.47
14. JOR
0.85 POL
1.46
15. CZE
0.83 ISR
1.43
16. BRA
0.70 MEX
1.40
17. CHI
0.65 CZE
1.29
18. HUN
0.60 EST
1.23
19. POL
0.59 SAFRICA
1.17
20. SAFRICA
0.57 ARG
1.06
21. MOR
0.44 LIT
0.96
22. COL
0.31 HUN
0.94
23. INDON
0.24 BRA
0.87
24. ISR
0.03 LAT
0.69
17
Table 6: What is the structural change? – Hypothesis 1, Case 1
Dep var: Degree
of structural
change,  2
Exp
NGDP
CS reg 1
CS reg 2
CS reg 3
CS reg 4
CS reg 5
0.010*
0.082
0.009*
Imp
NGDP
0.091
0.005*
Trade
NGDP
0.079
-0.001
NX
NGDP
0.947
0.054*
GFCF
NGDP
Constant
Adj r-squared
Obs
0.600
0.054
0.096
23
0.625
0.040
0.088
23
0.589
0.057
0.099
23
1.07
0.000
-0.047
23
0.093
-0.168
0.817
0.087
23
18
Table 7: What is the structural change? – Hypothesis 1, Case 2
Dep var: Degree
of structural
change,  2
Exp
NGDP
CS reg 1
CS reg 2
CS reg 3
CS reg 4
CS reg 5
0.008
0.191
0.006
Imp
NGDP
0.246
0.004
Trade
NGDP
0.207
0.003
NX
NGDP
0.841
0.027
GFCF
NGDP
Constant
Adj r-squared
Obs
0.412
0.175
0.036
23
0.461
0.126
0.019
23
0.422
0.169
0.031
23
0.764
0.000
-0.046
23
0.402
0.147
0.843
-0.012
23
19
Table 8: What is the structural change? – Hypothesis 2, Case 1
Dep var: Degree of
structural change,
CS reg 1
CS reg 2
CS reg 3
2
FO
0.078
0.482
0.023
FDI inflow
NGDP
0.498
0.021
Bank Cap
Bank Asset
Constant
Adj r-squared
Obs
0.991***
0.000
-0.023
23
0.922***
0.001
-0.026
22
0.767
0.866
0.197
-0.045
22
Table 9: What is the structural change? – Hypothesis 2, Case 2
Dep var: Degree of
structural change,
CS reg 1
CS reg 2
CS reg 3
2
FO
0.059
0.588
0.004
FDI inflow
NGDP
0.902
0.042
Bank Cap
Bank Asset
Constant
Adj r-squared
Obs
0.705***
0.001
-0.033
23
0.735***
0.007
-0.049
22
0.543
0.380
0.552
-0.030
22
20
Table 10: What is the structural change? – Hypothesis 3, Case 1
Dep var: Degree
of structural
change,  2
Exp
NGDP
CS reg 1
CS reg 2
CS reg 3
CS reg 4
CS reg 5
0.010
0.114
0.009
Imp
NGDP
0.133
0.005
Trade
NGDP
0.113
0.002
NX
NGDP
0.908
0.052
GFCF
NGDP
FO
Constant
Adj r-squared
Obs
0.033
0.764
0.586*
0.066
0.055
23
0.017
0.884
0.622**
0.046
0.044
23
0.022
0.845
0.585*
0.066
0.056
23
0.082
0.488
0.989***
0.000
-0.073
23
0.116
0.055
0.610
-0.171
0.816
0.054
23
21
Table 11: What is the structural change? – Hypothesis 3, Case 2
Dep var: Degree
of structural
change,  2
Exp
NGDP
CS reg 1
CS reg 2
CS reg 3
CS reg 4
CS reg 5
0.007
0.236
0.006
Imp
NGDP
0.312
0.004
Trade
NGDP
0.261
0.005
NX
NGDP
0.731
0.025
GFCF
NGDP
FO
Constant
Adj r-squared
Obs
0.025
0.819
0.404
0.198
-0.009
23
0.018
0.878
0.459
0.137
-0.029
23
0.019
0.866
0.418
0.185
-0.016
23
0.069
0.549
0.698***
0.001
-0.078
23
0.449
0.048
0.667
0.144
0.849
-0.053
23
22
Figure 1: Pair-wise correlations of rgdp growth among EM and DC over US recession and crisis
periods
23
Figure 2: Growth Counterfactuals – Case 1 [yellow=orig, red=lower sd US shock,blue=no P2 effect)
Col
2005q3
2006q3
2007q3
2008q3
2
0
-2
-1
-4
0
-2
1
0
2
4
2
3
6
Chi
4
Bra
2009q3
time
BRA_Orig
BRA_Shock
2005q3
BRA_Effect
2006q3
2008q3
2009q3
2005q3
2006q3
2007q3
Indon
2008q3
2009q3
2008q3
2009q3
2008q3
2009q3
2008q3
2009q3
3
Hun
2
time
-4
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
time
Isr
Kor
Mal
0
2
0
-5
-4
-1
0
-2
1
2
5
4
2006q3
3
2005q3
0
-6
-4
-2
1
0
-2
2
0
2
4
Cze
2007q3
time
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
time
Mex
Mor
Per
-2
-2
-6
-4
0
0
-2
0
2
2
2
4
4
2006q3
4
2005q3
2005q3
2006q3
2007q3
time
2008q3
2009q3
2005q3
2006q3
2007q3
time
2008q3
2009q3
2005q3
2006q3
2007q3
time
24
2007q3
2008q3
2009q3
1
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
time
Thai
Tur
Arg
2008q3
2009q3
2008q3
2009q3
2008q3
2009q3
2008q3
2009q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
Est
Jor
Lat
0
-10
-20
1
-15
-5
2
-10
3
-5
0
4
0
5
time
5
2006q3
5
2005q3
-2
-6
-5
-4
0
-2
0
0
2
2
5
4
2006q3
4
2005q3
-2
-15
-1
-1
-10
0
0
-5
1
0
2
2
SAfrica
5
Rus
3
Pol
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
time
Lit
Rom
Slov
2005q3
2006q3
2007q3
time
2008q3
2009q3
-10
-10
-15
-10
-5
-5
-5
0
0
0
5
5
5
2005q3
2005q3
2006q3
2007q3
time
2008q3
2009q3
2005q3
2006q3
2007q3
time
25
Figure 3: Counterfactuals – US and G6 growth (Case 2)
Col
2006q3
2007q3
2008q3
2009q3
-4
2005q3
-4
-4
-2
-2
-2
0
0
0
2
2
2
4
Chi
4
Bra
time
BRA_Orig
BRA_Shock
2005q3
BRA_Effect
2006q3
2008q3
2009q3
2005q3
2006q3
2007q3
Hun
Indon
2008q3
2009q3
2008q3
2009q3
2008q3
2009q3
2008q3
2009q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
Isr
Kor
Mal
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
time
Mex
Mor
Per
-6
-4
-4
-4
-2
-2
-2
0
0
0
2
2
2
4
4
2005q3
-10
-6
-4
-4
-3
-5
-2
-2
0
-1
0
2
0
5
time
4
2006q3
1
2005q3
-4
-6
-6
-3
-4
-4
-2
-2
-2
-1
0
0
0
1
time
2
2
Cze
2007q3
time
2005q3
2006q3
2007q3
time
2008q3
2009q3
2005q3
2006q3
2007q3
time
2008q3
2009q3
2005q3
2006q3
2007q3
time
26
SAfrica
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
Thai
Tur
Arg
2008q3
2009q3
2008q3
2009q3
2008q3
2009q3
2008q3
2009q3
-10
-10
-4
-2
-5
-5
0
0
0
2
4
time
5
2006q3
5
2005q3
-6
-4
-15
-10
-4
-2
-5
-2
0
0
0
5
Rus
2
Pol
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
time
Est
Jor
Lat
10
2006q3
5
0
-5
-10
-4
-10
-2
-5
0
0
2
5
4
2005q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
2008q3
2009q3
2005q3
2006q3
2007q3
time
time
time
Lit
Rom
Slov
10
2006q3
2005q3
2006q3
2007q3
time
2008q3
2009q3
-10
-10
-10
-5
-5
-5
0
0
0
5
5
5
2005q3
2005q3
2006q3
2007q3
time
2008q3
2009q3
2005q3
2006q3
2007q3
time
27
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