BANKING CRISES AND THE VOLUME OF TRADE
Transcript of BANKING CRISES AND THE VOLUME OF TRADE
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BANKING CRISES AND THE VOLUME OFTRADEYifei MuClemson University, [email protected]
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BANKING CRISES
AND THE VOLUME OF TRADE
A Dissertation
Presented to
the Graduate School of
Clemson University
In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy
Economics
by
Yifei Mu
July 2013
Accepted by:
Dr. Scott L. Baier, Committee Chair
Dr. Robert F. Tamura Dr. Michal M. Jerzmanowski
Dr. Kevin K. Tsui
ii
Abstract
This dissertation consists of three chapters. The first two chapters investigate the
correlation between banking crises and bilateral trade flows. In the first chapter, we focus
on how banking crises may impact the bilateral trade flows over time. We attempt to
disentangle the financial shocks’ impacts on trade flow by factors that are related to
exporters and importers. The second chapter assesses how banking crises impact the
extensive and intensive margin of the trade. The third chapter attempts to use frontier
model to analyze the bidding behaviors and collusion in the different submarkets of low-
price, sealed-bid construction procurement.
Since 2007 banking crisis and the onset of Great Recession, there have been many
studies that have provided insights linking between the Great Recession and dramatic fall
in trade. The objective of Chapter 1 is to investigate the impact of banking crises more
generally. We use the data for 173 countries and across 32 years. We find relatively
robust results for the correlation between banking crises and bilateral trade flow
fluctuations. Most of the impacts from banking crises go through the importers. There
seems to be little evidence to support the hypothesis that banking crises directly lowers
the exports. There is a relative constant negative impact on import for the time periods in
iii
advance of the onset of crises. After the crisis is over, the decline in import tends to
intensified for another two years and starts to recover.
Chapter 2 decomposes the bilateral trade flow into extensive margin and intensive
margin. The results suggest different patterns of the financial shocks for exporters and
importers in different margins. For exporters, the insignificant result from Chapter 1 is
caused by neutralization of the opposite effects from extensive and intensive margin.
During banking crisis exporters tend to export fewer varieties of goods and increase the
volume for each variety. For importers, a banking crisis tends to have a larger negative
impact on extensive margin and relatively smaller impacts on intensive margin.
Chapter 3 adopts a frontier model to analyze the bidding behaviors and collusions
in low-price, sealed-bid procurement. In a market without collusion, the objective
function of each bidder is to maximize their own expect profit. In a market with collusion,
the objective function is to maximize profit than separated profit between colluders. The
bidding data from real world are usually the mixture of these two cases. Using frontier
model can avoid the explicit objective function and give a hint about whether there might
be collusion in a market.
iv
Dedication
I thank my family and friends for their support while in the graduate school.
v
Acknowledgements
I would like to thank all the members of my dissertation committee. I especially
thank Scott Baier for his help shaping first two chapters. I thank all the participants in
Macroeconomics Workshop and International Economics Workshop for their comments.
vi
Table of Contents
Title Page ................................................................................................................ i
Abstract ................................................................................................................... ii
Dedication ............................................................................................................... iv
Acknowledgements ................................................................................................ v
List of Tables .......................................................................................................... viii
List of Figures ......................................................................................................... x
1 Banking Crises and the Impacts on Bilateral Trade .................................... 1
1.1 Introduction .............................................................................................. 1
1.2 Literature Review ..................................................................................... 4
1.3 Model ....................................................................................................... 5
1.4 Data Source .............................................................................................. 12
1.5 Results ...................................................................................................... 14
1.6 Conclusion ............................................................................................... 26
Appendices .............................................................................................................. 28
A Robustness Check for Bilateral Results ................................................... 29
B Robustness Check for Two-stage Results ................................................ 31
2 Banking Crises and the Impacts on the Margins of Trade .......................... 47
2.1 Introduction .............................................................................................. 47
2.2 Literature Review ..................................................................................... 49
2.3 Model ....................................................................................................... 50
2.4 Data Source .............................................................................................. 58
2.5 Results ...................................................................................................... 60
2.6 Conclusion ............................................................................................... 69
Appendices .............................................................................................................. 72
A Robustness Check for Bilateral Results ................................................... 73
vii
B Robustness Check for Two-stage Results ................................................ 74
3 A Frontier Model Analysis on Bidding Behaviors and Collusions in Low-price,
Sealed-bid Procurement ................................................................................... 105
3.1 Introduction .............................................................................................. 105
3.2 Literature Review ..................................................................................... 107
3.3 Model ....................................................................................................... 108
3.4 Hypothesis Test and Results .................................................................... 114
3.5 Conclusion ............................................................................................... 119
Appendices .............................................................................................................. 121
A Traditional Analysis on Low-price, Sealed-bid Procurement .................... 122
Bibliography ........................................................................................................... 134
viii
List of Tables
1.1 Names of Countries and Districts ................................................................ 32
1.2 Summary Statistics for Chapter 1 ................................................................. 33
1.3 Linear approximations for multilateral resistance and banking crises .......... 34
1.4 Linear approximations for multilateral resistance and banking crises with
country ever experienced a banking crisis .................................................... 35
1.5 Trade value and banking crises with lags .................................................... 36
1.6 First stage of the regression ......................................................................... 37
1.7 Exporter-year fixed effect and exporters’ banking crisis ............................. 38
1.8 Importer-year fixed effect and importers’ banking crisis ............................ 39
1.9 Trade value and banking crises with forwards and lags .............................. 40
1.10 Trade value and banking crises with Importer-Exporter fixed effect ........... 41
1.11 Importer-Exporter fixed effect and time invariant bilateral variables ......... 42
1.12 Exporter-year fixed effect and exporters’ banking crisis for robustness
check ............................................................................................................ 43
1.13 Importer-year fixed effect and importers’ banking crisis for robustness
check .............................................................................................................. 44
2.1 Summary Statistics for Chapter 2 ................................................................. 75
2.2 Linear approximations for multilateral resistance and banking crises for
overall margin ............................................................................................... 76
2.3 Linear approximations for multilateral resistance and banking crises for
extensive margin ........................................................................................... 77
2.4 Linear approximations for multilateral resistance and banking crises for
intensive margin ............................................................................................ 78
2.5 Linear approximations for multilateral resistance and banking crises with
country ever experienced a banking crisis for overall margin ..................... 79
2.6 Linear approximations for multilateral resistance and banking crises with
country ever experienced a banking crisis for extensive margin ................. 80
2.7 Linear approximations for multilateral resistance and banking crises with
country ever experienced a banking crisis for intensive margin .................. 81
2.8 Overall margin and banking crises with lags ............................................... 82
2.9 Extensive margin and banking crises with lags ............................................ 83
2.10 Intensive margin and banking crises with lags ............................................ 84
2.11 First stage of the regression for different margins ......................................... 85
2.12 Exporter-year fixed effect and exporters’ banking crisis on overall margin 86
ix
2.13 Exporter-year fixed effect and exporters’ banking crisis on extensive
margin ........................................................................................................... 87
2.14 Exporter-year fixed effect and exporters’ banking crisis on intensive
margin ........................................................................................................... 88
2.15 Importer-year fixed effect and importers’ banking crisis on overall margin . 89
2.16 Importer-year fixed effect and exporters’ banking crisis on extensive
margin ............................................................................................................ 90
2.17 Importer-year fixed effect and exporters’ banking crisis on intensive
margin ............................................................................................................ 91
2.18 Overall margin and banking crises with forwards and lags .......................... 92
2.19 Extensive margin and banking crises with forwards and lags ...................... 93
2.20 Intensive margin and banking crises with forwards and lags ...................... 94
2.21 Overall margin and banking crises with Importer-Exporter fixed effect ...... 95
2.22 Extensive margin and banking crises with Importer-Exporter fixed effect .. 96
2.23 Intensive margin and banking crises with Importer-Exporter fixed effect ... 97
2.24 Importer-Exporter fixed effect and time invariant bilateral variables for
different margins ........................................................................................... 98
2.25 Exporter-year fixed effect and exporters’ banking crisis on overall margin
for robustness check ....................................................................................... 99
2.26 Exporter-year fixed effect and exporters’ banking crisis on extensive
margin for robustness check .......................................................................... 100
2.27 Exporter-year fixed effect and exporters’ banking crisis on intensive
margin for robustness check .......................................................................... 101
2.28 Importer-year fixed effect and exporters’ banking crisis on overall margin
for robustness check ....................................................................................... 102
2.29 Importer-year fixed effect and exporters’ banking crisis on extensive
margin for robustness check .......................................................................... 103
2.30 Importer-year fixed effect and exporters’ banking crisis on intensive
margin for robustness check .......................................................................... 104
3.1 Summary statistics of observations .............................................................. 124
3.2 Summary of FDH and DEA obs ................................................................... 125
3.3 Hypothesis test for different submarkets ...................................................... 126
3.4 Parametric Stochastic Model ........................................................................ 127
x
List of Figures
1.1 Import goods and services as a ratio of GDP for a select group of countries
with banking crises at duration of two years ................................................ 45
1.2 Interpretation of forward and lag time .......................................................... 46
3.1 FDH production possibility set ..................................................................... 128
3.2 DEA production possibility set ..................................................................... 129
3.3 Shephard output distance function for FDH and DEA ................................. 130
3.4 Box-plot for FDH and DEA estimation with four different submarkets ..... 131
3.5 Kernel density for FDH estimation with four different sub-markets ........... 132
3.6 Kernel density for DEA estimation with four different sub-markets .......... 133
1
Chapter 1
Banking Crises and the Impacts on
Bilateral Trade
1.1 Introduction
The financial crisis began in 2007 and intensified in 2008, pushing the global
economy into a large contraction: frequently referred to as the Great Recession. During
this time period1, world trade flows declined dramatically. For example, European trade
flows fell by nearly 16 percent from the fourth quarter of 2007 to the fourth quarter of
2008. The decline in trade flows was not particular to Europe, Asia’s exports declined by
5 per cent and North America’s by 7 percent. Trade within regions seemingly contracted
faster than trade between regions. Trade within Europe declined 18 percent. Trade within
Asia decreased at half this rate, while trade within North America fell 10 percent.
Could those large trade declines be explained by countries’ GDP decline during
this financial crisis? Most trade models predict that a country’s bilateral trade flows are
1 According to 2009 International trade statistics from the International Monetary Fund
2
proportional to its GDP2. However, during this crisis, trade declined more sharply than
GDP. For examples, year 2008 and 2009, the US export/GDP ratio decreased from 13.0%
to 11.4%, while the import/GDP ratio decreased from 18.0% to 14.2%. China, Japan and
Germany’s export/GDP ratio decreased from 35.0%, 17.7% and 48.0% down to 26.7%,
12.6% and 41.9%, respectively while their import/GDP ratio also decreased from 27.3%,
17.5% and 41.8% down to 22.3%, 12.3% and 37.0%. It seems the change in the volume
of trade following the Great Recession exceeded the decline in GDP.
As a result of the impact on trade from the Great Recession, there have been
several research papers that have provided insights linking the financial crisis to the
decline in trade. Manova (2013) provides a model of trade and financial credit constraints.
In this paper, she shows how trade can be impacted by the financial conditions.
Empirically, the research on the 2007 banking crisis by Char and Manova (2012) and
Amiti and Weinstein (2009) suggests that financial shocks impact international trade to a
greater extent than they do the domestic market.
While these papers provide insights into the relationship between The Great
Recession and the fall in trade, there is no study that looks at, how banking crisis, in
general, impacts trade flows. The focus of this paper is to investigate how banking crises
across time have a similar impact on trade flows, More specifically, the question we
investigate in this paper is as follows: “Is there a robust correlation between banking
crisis and trade flow fluctuations?” There are two channels that may induce the
correlation between banking crises and trade flows. One occurs through the producers:
2 Some works of Kaboski argues the decline in trade will be larger.
3
credit constraint, producers may have access to limited credit during a banking crisis.
This may cause a fall in production. In addition, if the producers (or agents in the source
country) need to finance trade, the impact of banking crisis on trade can be magnified.
This is supported by Manova’s (2013) theory. The other channel occurs through
consumers, it may be the case that trade is financed by the importing country. During a
banking crisis, this may limit the destination country’s ability to finance imports.
Additionally, if the tradable sector is comprised largely of durable goods and capital
goods, import demand may be relatively more income sensitive. In this paper, we will try
to disentangle whether declines in trade are robustly correlated with banking crises and
how much of the decline can be “attributed” to the source and destination countries. To
achieve this objective, this paper uses annual data from 1976 to 2008 and includes 173
countries3.
Finally, by introducing lags and leads into our empirical model, we can also
provide some insights regarding how long the typical banking crisis impacts trade. A
banking crisis that occurs in one period may impact trade well after the crisis is over. To
get a sense of the timing, Figures 1.1 shows the import goods and services as a ratio of
GDP for a select group of countries with banking crises at duration of two years. Banking
crises start at some time between two red lines and end between second red line and blue
line. It is not obvious that any discernible trend is present before or after the crisis. One of
the objects of this paper is to estimate, on average, whether a banking crisis impacts trade
and how large of an influence of it does to the trade volume on different time periods.
3 A complete list of countries is presented in Table 1.1
4
1.2 Literature Review
Several studies use high frequency data to analyze the influence of 2007 Great
Recession on trade. Levchenko et al. (2010) used disaggregated quarterly US trade data
and find dramatic declines in trade volume, especially for intermediate goods. Industries
with a larger reduction in domestic output also had larger reductions in trade. Chor and
Manova (2012) used monthly US import data to analysis the trade collapse after the 2007
crisis, finding that credit constraints had an impact on trade and that the exports of
industries with larger dependence on the external financial market will tend to be more
vulnerable and sensitive to financial shocks. Similarly, Bricongne et al. (2010) use
monthly data from France and also found out that the firms depending more on external
financing were more affected in the recent global crisis. Lacovone and Zavacka (2009)
use annual data to show that the industries which are more dependent on financial
markets in more financially developed countries saw larger declines in trade during the
banking crisis.
There is also some theoretical research on financial shocks and their effects on
trade. Eaton et al. (2008) developed a variation of the model from Melitz (2003), which
embeds an idiosyncratic shock for each firm and importer’s market, as well as a common
shock for importer’s market, thus leading to the changes in the cut-off productivities of
entry and export. When the Pareto distribution is utilized in this model, trade volumes
depend not only on the cost, economy size, inward and outward multilateral resistances,
but also on the variance of the entry shock, the variance of the demand shock and the
5
covariance of the two. In the context of banking crises, shocks arise from the financial
sectors. In Monova (2012), she adds a credit constrain to the exporter, embedding the
probability of default into the cut-off productivity of export. In this model, the financial
condition of one country could impact both extensive and intensive margins of trade. A
less financial development will lead to fewer foreign markets and lower the aggregate
trade volume.
These are not the only possible channels through which banking crisezs may
impact trade. Alessandria et al. (2010) studies the correlation between trade decline and
inventory adjustment. Gopinath et al. (2012) studies the trade price fluctuation in
different categories of goods during 07 banking crisis.
1.3 Model
1.3.1 Background context
Assume a world with N countries and M varieties of goods. All consumers have
identical constant-elasticity-of-substitution (CES) preference4:
. (1.1)
Where the utility of consumers in country j is, is the good consumed by people in
country j imported from country i, is the elasticity of substitution and 5 .
4 See Anderson and Van Wincoop (2003) for details.
5 The preference exhibits “love of variety”.
6
Maximizing utility subject to a budget constraint can solved out the demand for the
good consumed in country j import from country i, :
. (1.2)
Here is the price of the good’s price sold within the importer i. is the trade cost6 for
good shipped from country i to country j. is GDP of country j, and is the CES price
index that:
. (1.3)
Assume firms maximize profit and all markets clear, we can write an expression for
bilateral trade flow as:
. (1.4)
Where is the world gross GDP and
. (1.5)
. (1.6)
denotes , that is the share of country i’s GDP relative to the world. and are
usually known as multilateral resistance. is the outward multilateral resistance which
measures how difficult for country i to export goods relative to the rest of the world.
6 We can assume the trade costs as iceberg trade costs, the cost for goods were lost in transit.
7
is the inward multilateral resistance that measures how difficult for country j to import
goods relative to the rest of the world. Anderson and Van Wincoop (2003) noticed that
when influence trade is estimated, it is critical to include both inward and outward
multilateral resistances into the regression.
Taking the natural log of both side of equation 1.4, we can get:
. (1.7)
This measures relation between trade flow on the left side and trade cost, multilateral
resistance, and GDP on the right side with in some time period. When the trade cross
some time periods, equation 1.7 can be presented as:
. (1.8)
1.3.2 Bilateral effects estimation
In this paper, trade cost contains two components. One is traditional geography
variables, like distance, contiguity and common official language, which were wildly
used in lots of research. These variables are bilateral relations and are time invariant.
Other factors may be time varying including whether a country has a banking crisis.
More specifically, we assume the trade cost has the structure as follow:
. (1.9)
Where is the distance between country i and j, is the vector include other
geography information, like contiguity and language. is the vector contains the
8
information about financial shocks and can be presents in several different forms.
Replace equation 1.9 into equation 1.8, we get:
. (1.10)
is the error term with normal distribution.
The regression will include country-year fixed effects7. These fixed effects will
absorb the inward/outward multilateral effect effects, importer’s GDP, exporter’s GDP
and world GDP for the same year. We will also allow the financial shocks to have a
bilateral effect. The regression we are going to estimate is
. (1.11)
Where is the exporter-year is fixed effect and is the importer-year fixed effect.
We use the data from 173 countries across 32 years, so the number of dummy
variables for fixed effect will surpass 11000. If importer-exporter fixed effects are also
included, the number of dummy variables will surpass 25000, yield a large computational
burden. Also, the inverse a large matrix is usually imprecise. Previous method of
7Both importer-year fixed effect and exporter-year fixed effect.
9
reducing the computational burden has been to include only one out of every three years
of data with which to estimate regressions. Guimaraes and Portugal (2009) suggest a
method to estimate the models with high-dimensional fixed effects. This paper uses this
algorithm to make it possible to estimate the influence of banking crises while including
all data and all relevant fixed effects. The results are showed in Table 1.5.
1.3.3 Unilateral effects estimation
When the financial shock is treated as unilateral effect, trade costs are defined as:
. (1.12)
Baier and Bergstrand (2009) introduced the method to linear approximate the
multilateral resistances. For the bilateral trade costs, those resistances term in equation
1.5 and 1.6 can be presented as:
. (1.13)
. (1.14)
Plug equation 1.13 and 1.14 back to equation 1.8 and take the linear expansion to
, combine with equation 1.12, The regression changed into:
. (1.15)
10
Where:
. (1.16)
. (1.17)
. (1.18)
. (1.19)
. (1.20)
. (1.21)
is the measurement for contiguity, is the measurement for common
language. Since , and
are kind of the gross average of the
distance, contiguity and language of the world, these variables are constant for the same
year. Use year fixed effect will absorb all these variables and , which is the GDP
for the whole world at that year. The regression will be estimated is:
. (1.22)
Here is year fixed effect. Table 1.3 and 1.4 present the results for the unilateral
effects8.
8 For unilateral effect, The term
,
11
1.3.4 Unilateral effects estimation with two-stage model
Another way to think about the unilateral financial shock effects is that all these
effects have the same impact across all exporters (or importers). In this case, we employ a
two-step process to back out the effects of the banking crisis on exporters and importers.
In first-stage, we run the regress the log of bilateral trade on all trade costs except .
We then use the coefficients for and that captures all the country specific
information, include log of GDP and the multilateral resistance. For the exporter-year
fixed effect coefficient, it contains unilateral financial shock effects from exporter,
importers’ GDP share weighted bilateral effects9, some of the average trade cost cross the
world10, exporter’s GDP and some of the world GDP. So we can run the second stage
regression as follows:
.
(1.23)
From equation 1.8, the theory tells us the coefficient of log exporter’s GDP should be
equal to -1 when moved to left side. will capture all the information from bilateral
effects, world average trade cost effects and world GDP for the same year.
it actually becomes
.
Since the share of GDP sum up equal to one cross the world. and
is constant
for all the countries in the same year. Because of this unilateral effect cannot do the same expansion as
bilateral effects. We just include the unilateral effects by themselves. 9 Which is
.
10 Which is
.
12
Similar as previous, for importer-year fixed effect, we run the second stage
regression as:
.
(1.24)
Results are presented in Table 1.6 to 1.8, these also can be used a robustness
check compare to the results from equation 1.22.
1.4 Data Source
1.4.1 Value of the Trade
The value of bilateral trade for 173 countries for the years 1976-2008 is taken
from the UN Comtrade database11
. It is reported as 5 digit SITC level and aggregated as
each country’s import and export value. The value is measured in thousands of US dollar
in the current year. The inflation of the currency will be captured by importer-year fixed
effect and exporter-year fixed effect.
1.4.2 Geography Data
Geography data is used to measure the traditional trade cost. The paper uses
bilateral value of the distance, contiguity and common official language as measures of
the traditional trade cost. The data are from CEPII database12
. Both contiguity and
common language are dummy variables. Contiguity is equal to unity if two trade partners
11
The data can be obtained from http://comtrade.un.org/ 12
The data can be obtained from http://www.cepii.fr/CEPII/en/bdd_modele/bdd.asp
13
share the common border, and zero otherwise. Common official language is equal to
unity if two trade partners use the same official language and zero otherwise. CEPII
provides both simple great circle distance and population weighted distance between
countries. This paper uses the population weighted distance13
.
1.4.3 Banking Crises Data
Our data on banking crises is from the Leaven and Valencia banking crises
database provides annual banking crisis data for the year 1976-200814
. Leaven and
Valencia (2012) define a banking crisis as systemic if two conditions are met:
1) Significant signs of financial distress in the banking system (as indicated by
significant bank runs, losses in the banking system, and/or bank liquidations)
2) Significant banking policy intervention measures in response to significant
losses in the banking system.
Here, significant bank runs indicate a 5 percent or greater drop in deposits within
one month during the time period.
For policy interventions in the banking sector to be significant, at least three out of
the following six measures must have been used:
1) extensive liquidity support (5 percent of deposits and liabilities to nonresidents)
2) bank restructuring gross costs (at least 3 percent of GDP)
13
Use other measurement of distance will yield similar results. 14
The data can be obtained from http://www.imf.org/external/publications/index.htm
14
3) significant bank nationalizations
4) significant guarantees put in place
5) significant asset purchases (at least 5 percent of GDP)
6) deposit freezes and/or bank holidays.
In this paper, for bilateral effects, crises data are used if either one country or both
trade partners are experiencing a crisis. The crises lag variables capture the impact on
trade for the years after banking crises. In order to clarify whether bilateral trade is
impacted pre-crisis, forward crises variables will also be used.
In total, the panel dataset contains trade vales, banking, and geographic data for
173 countries for the years 1976-2008.
1.5 Results
As we mentioned in introduction, there may be two channels through which a
banking crisis might influence a trade flows. If the banking crisis’s impacts occur through
producers, we could expect that there should be a negative shock impact arising via
exports, and recovery from a financial shock may extend well after the crisis is over15
. If
the impacts occur through consumers, there should be a negative shock on trade flow at
current time when importer had a banking crisis. The negative impacts after importer’s
banking crises were ended also might be shown up in the results. For the similar reason,
there might be significant impact for the time period before crisis.
15
As mentioned in Manova’s (2013)
15
Since a trade might be financed by exporters and importers, both channels may be
important. We also think there might be a bilateral influence occurs there.
1.5.1 One-stage results for exporters and importers
In Table 1.3, the dependent variable is log of the bilateral trade value. The log of
the distance between country pairs is represented by Ln(distance). Contiguity and
common language are the dummy variables for two trade partners who share the same
border or the same official language. GDP Share weighted log-distance, contiguity and
common language is the linear approximation of the inward multilateral resistance and
outward multilateral resistance. The variables Banking crisis for exporter and Banking
crisis for importer are the dummy variables for exporter or importer has a banking crisis
in the current year. The variables N years16
forward of exporter/importer are the dummy
variables for the nth year’s period forward of the beginning year of the variable banking
crisis for exporter/importer, which means these variables are equal to unity if current year
is n years before the beginning of exporter/importer had a banking crisis. The variables N
years lag of exporter/importer are the dummy variables for the nth year’s period lag of
the ending year of the variable banking crisis for exporter/importer, which means these
variables are equal to unity if current year is n years after the ending of exporter/importer
had a banking crisis.
In the first column of Table 1.3, we can see for exporters’ country has a banking
crisis, there is no significant trade flow change, which is not the same as for the channel
16
N years represent one, two three, four and five years corresponding to the variables in the table.
16
through producers. For importers’ country, we can see there is a 21.9% decrease on
average at the current year. This decline is relatively large.
The effect of a banking crisis on trade flow may persist overtime, the second
column of Table 1.3 includes three years time lag to access this impact on trade. For
exporters, there is the lag of banking crisis seem to have no significant impact on export.
For importers, there is a 24.4% decline at the current year of crisis. After crisis was ended,
the trade value tend to decrease even more in the lag time period, it decreased by 30.5%
at the second year after banking crisis ended. After that, the decline become smaller and
back to 25.6% at third lag year.
The third column of Table 1.3 extends the time lag into five years. It shows
almost the same pattern as the second column. For exporter, there are no significant
impacts. For importer, the decline for the current year of crisis is 25.5%, and it still
intensified to the second year after crisis was ended, which is 32.1%. After that, the
contraction tends to recover slowly and back to 24.7% for the fifth year after ending of a
banking crisis. So the sticky effect showed up only for importer.
Column one to three in Table 1.3 provides information about the correlation
between bilateral trade fluctuation and banking crisis for the current year and the time
period after crisis. Trade may change before the onset of a banking crisis. Trade may
increase if there is a bubble in the banking sector or other financial sector which led to a
credit expansion. This expansion can impact the exporter or importer through producers
channel and consumer channel and led to a change in trade flow. Another potential case
17
is that the economy simply was in a recession and the recession caused the both banking
crisis and a decrease in trade value at the same time. Thus information about the trend of
trade flow before banking crisis is required. From fourth column of Table 1.3, for
exporters, there is still no significant influence for all the time, cross through three years
before crisis was begun to three years after crisis was ended. For importers, compare with
the countries don’t have banking crisis, there is average 16%17
decline for the time period
before crisis. This decline trend is relative constant. From these forward years’ results,
the second case that recession already impacted trade flow before crisis seems dominates.
The current year’s value and lag years’ values are almost the same as the second column.
The decline at the current year for crisis is 25.5%. Compare to the year before crisis,
there is an around 10% drop in trade value.
Similarly, the fifth column of Table 1.3 extends both forwards and lags into five
years and get the almost the same information from fourth column of Table 1.3. For
exporter, there is no significant influence on trade flow comes from banking crisis sector
for all the time period. For importer, there is a relative constant 18%18
decline trend.
When banking crisis begun, the decline extents to 27.2%, and it keeps intensified to 33.2%
at the second year after crisis was ended, after that, the decline tends to recover, at fifth
year after banking crisis, the decline is 25.2%
It seems almost all of the influence from banking crisis occurs through the
importers’ channel. For exporters, the financial shock tends to have no significant
17
Low point is 15.5% at two years before crisis. High point is 17.3% at the year right before crisis. 18 Low point is 16.8% at two years before crisis. High point is 18.9% at the year right before crisis.
18
economic impact on bilateral trade flows. For importers, there is constant decline before
banking crisis began. When crisis happened, there is around 10% drop more compare to
the previous decline. The negative impact trend keeps going down another 6% to the
second year after crisis was over, then it starts to recover slowly. From forward years’
results, it seems second scenario: recession before crisis, dominates.
Usually we expect that banking crisis will have a negative impact on bilateral
trade flows, especially focused on the short time period. That’s what we observed in this
great recession and the information from Table 1.3. However, there is another correlation
between banking crisis and financial development. By the fact that the probability of
having a banking crisis is also highly correlated with financial development, the baseline
of the trade value might be higher for a country ever experienced a banking crisis. Some
countries, like North Korea, never have a banking crisis, but also are less developed
financially and trade relatively less.
In Table 1.4, we use the same controls that were used in Table 1.3, and also
include dummy variables that exporter country and importer country ever had at least one
banking crisis cross year 1976 to 2008. We redo the same regressions as in Table 1.3.The
pattern of the banking crisis influence on trade flow on different time period is almost the
same as it was showed in Table 1.3.
For exporters, we see little impacts of a banking crisis on export. For importers,
on the other hand, trade flows appear to decline, on average, leading up to the recession.
For current year had a banking crisis, the negative impact ranges from 21.4% in first
19
column with no lag or forward to 29.7% in fifth column with 5 years forward and lag.
The trade flow suffered a 10% drop compare to the year right before crisis. After crisis
ended, the negative impact intensified to the second lag year, and then recovers slowly.
This pattern is similar to the pattern showed in Table 1.3. Also, the levels of the
coefficients are close to the results from Table 1.3.
From these results in Table 1.4, it seems majority of the influence from financial
shock goes through the importers’ channel, which is robust to the results from Table 1.3.
The persistence of impact for importers is also relatively robust compare to the similar
effect in Table 1.3. Also, Table 1.4 is consistent with the trade impacts on imports prior
to the banking crisis.
For exporters, if the country ever had experienced banking crisis does not seem to
influence bilateral trade flows. For importers, in first column, not includes any forwards
and lags, country ever had a banking crisis tend to trade 2.2% less, however only
significant at 5% level. When we include more information about banking crisis into the
regression, this coefficient tends to be positive and become higher. In third column, when
includes five lag years, a country ever had a banking crisis tends to trade 5.1% higher.
When includes five forward and lag years. The magnitude extends to 10.0%. This result
supports the previous assumption that a country ever experienced a banking crisis is
correlate with higher financial development, thus tends to trade more.
Overall, the results from Table 1.4 reflect that the time pattern for banking crisis
seems to be robust compare to Table 1.3. The countries which ever had experienced at
20
least one banking crisis tend to trade more for importer, but there is no impact for
exporter.
1.5.2 Bilateral results
In Table 1.5, we analyze the bilateral effects of a banking crisis, which captures
the average influence of financial shocks on importers and exporters. The control
variables are the same as previous tables. The variable one crisis ever is a dummy
variable if at least one country of the trade partners has experienced banking crises from
the year 1976-2008. The variable both crises ever equals one if both trade partners have
experienced at least one banking crisis in these years. These two dummy variables are not
mutually exclusive. The dummy variable one crisis equals one if either the importer or
exporter has a banking crisis in the current year, but not if both countries have crises.
Two crises is the dummy variable for if both importer and exporter have banking crises in
the current year. Thus one crisis and two crises are mutually exclusive. The variables N
years lag of one crisis are the dummy variables for the nth year lags of the ending year of
the variable one crisis, which means these variables are equal to unity if current year is n
years after the ending of one county’s banking crisis. The variables N years lag of two
crises are the nth year lags of the ending of two crises, which means these dummy
variables are equal to unity if current year is n years after the ending of the both counties’
banking crises. Since two crises is equal to unity only when both importer and exporter
have banking crises in the current year, there is a case that country A had a banking crisis
before country B’s banking crisis, however it ends during country B’s crisis’ period. In
21
this scenario, two crises is inside of the duration of the one crisis and it will separate the
time period of one crisis into two. However, n years lag of one crisis will not take into
account the ending year of this kind of gap. It only takes account the years after the one
crisis period. When both trade partners are have emerged from their banking crisis, that
year will be treated as the ending of the one crisis, and the lag years will start at that time.
For example in Figure 1.2, Country A had a banking crisis from time t-2 to t+1
and country B had a banking crisis from t to t+3. The overlap time of t to t+1 is
represented by variable two crises. The time period t-2 to t and t+1 to t+3 is represented
as one crisis. The lag year will be t+4, t+5 and so on. The forward year which will be
mentioned later is t-3, t-4 and so on. From the picture, two crises creates a gap inside of
the duration of one crisis, however, the time t won’t be treated as the ending of one crisis
period and t+1 won’t be treated as the beginning of one crisis period.
In the first column of Table 1.5, we can see there is a much higher value of trade
between country pairs if at least one has had a banking crisis. On average, if one trade
partner had banking crises ever, the trade value is around 180.7% higher. If both trade
partners had banking crises ever, the trade value will add another 241.8%. This result is
quite stable when this paper includes banking crisis dummy variables and the time lag
variables.
In the second column of Table 1.5, when one of the trade partners has a banking
crisis, the bilateral trade value is 6.8% higher during the year of the crisis. When both
trade partners have banking crises, the trade volume is 7.6% higher, but this only
22
significant at the 5% level. The signs are different from what we expected. The higher
level of trade in the current year for exporters might be caused by two reasons. One
reason is the regression is using annual data, so the results will capture the average
impact of the whole year. If a banking crisis happened in the second half of the year, then
at least 50% of the current year time was not directly impacted by the banking crisis.
Leaven and Valencia (2012) indicated that August, September and December have higher
frequencies of starting of banking crises as compared to other months. Another reason is
financial crises may impact the trade with a lag. When there is a shock in the financial
intermediary service, manufacture can sustain current production for a time by using
previous savings. The negative impact of the banking crisis will not influence the trade
immediately.
The third column of table 1.5 includes three years time lag of the banking crisis to
test if the financial shock had an impact on trade after it was ended. It shows if one trade
partners suffered a banking crisis, the trade value decreased by around 10%19
each year
after the crisis was over. The current impact of two crises is positive but not significant
for reasons mentioned before. The lag time impact of two crises is negative but also not
quite significant. This means that the presence of a banking crisis in the second country
will not tend to, but not necessarily intensify trade flows decreasing between the two
crises-stricken nations. As discussed previously, since two crises is usually in the gap
between two one crisis, the impact of lag of two banking crises and one banking crisis
might be overlapped.
19
Low point is 9.1% for the second lag year, high point is 12.0% for the first lag year.
23
The fourth column of table 1.5 extends the time lags into five years. It shows
when one of the trade partners suffered a banking crisis, the negative impact on trade
even last for five years after it was ended. The other results are similar to the results in
third column of Table 1.5.
For the lag trend in Table 1.3 and Table 1.4, there is an increasing decline in first
two years after crisis, then recover slowly. The coefficient in Table 1.5 shows the
negative sticky effect is still there, however, the magnitude seems to be relatively
constant.
1.5.3 Two-stage results for exporters and importers
Due to the insignificant results for exporters from previous Tables, this paper uses
two-stage model to test the previous results. In the first stage, Table 1.6 uses the same
control as before. The coefficients for Exporter-year fixed effect and Importer-year fixed
effect capture all the non-bilateral effect for importers and exporters. These unilateral
effects cross time contain the information of outward/inward multilateral resistance,
exporters/importers’ GDP and effect from financial shocks.
Table 1.7 uses coefficients of exporter-year fixed effect from Table 1.6 minus log
of exporters’ GDP as dependent variable to analyze the unilateral effect of banking crisis
on exporters’ side. Importers’ GDP share weighted log distance, common language and
contiguity is the linear approximation of outward multilateral resistance. Compare to the
exporter’s results from Table 1.3 and 1.4, the results in Table 1.7 are largely changed.
24
From column one of Table 1.7, when the exporter had a banking crisis in the
current year, there is a positive 2.7% increase in bilateral trade. When we include the
forward and lag time periods in column 3, the positive impact is around 3.5%. The
second column of Table 1.7 presents a positive impact in the lag time period. There is no
clear trend for this impact. On average, it is around 3%20
. In third column of Table 1.7,
regression includes the forward time period. The lag time period show the similar results
as in second column of Table 1.7. The forward time presents a 3%21
negative impact
from three years before banking crisis to the year right before. For exporters ever had a
banking crisis, the trade value tend to be around 15%22
higher than the courtiers never
experienced a banking crisis.
Overall, the results in Table 1.7 are significant. However, most of the impacts are
economically relatively small. Positive impacts at the current year and lag year are quite
small. The negative impact in the time period before crisis suggests the trade flow
declines prior to the banking crisis, which is consistent to the previous assumption. The
exporter ever had banking crisis used to be insignificant, now tend to be relative large.
Table 1.8 uses coefficients of importer-year fixed effect from Table 4 minus log
of importers’ GDP as dependent variable to analyze the unilateral effect of banking crisis
on importers’ side. In the first column of Table 1.8, there is a 3.7% decline at the current
year when the importer had a banking crisis. The third column of Table 1.8 shows the
20
Low point is 2.2% at five years after crisis. High point is 5.1% at four years after crisis. 21 Low point is 2.2% at one year before crisis. High point is 3.3% at two years before crisis. 22
Low point is 15.1% when include forward and lag time period, high point is 16.1% when not include
forward and lag time period.
25
impacts when we introduce forwards and lags of the banking crisis, we see and 8.0%
decline for the current year. Compare to the results from Table 1.3, Table 1.4, results
from Table 1.8 have same sigh but the magnitude of the decline is smaller.
The second column of Table 1.8 also shows that bilateral trade declines initially
and continues to decline for the two years following the end of a banking crisis. For the
second year lag period, the decline in trade is approximately 9.8%, for the fifth year lag,
the decline in trade is around 3.1%. Compared to the results of column three of Table 1.3
and column three of Table 1.4, the sluggishness of the trade to rebound follows a crisis
continues to hold, however, the magnitude is smaller in this specification.
In third column of Table 1.8, the regression specification includes leads as well as
lags. The lag time period show the similar results as in second column of Table 1.8. The
forward time period also exhibits a constant 14%23
negative trend, which is similar to the
forward trends in fifth column of Table 1.3 and fifth column of Table 1.4 but with a
smaller level. In Table 1.3 and 1.4, there is decline in bilateral trade value when the time
moves from the year before crisis to the crisis year. However, we cannot observe this
pattern in Table 1.8.
For importers, the coefficients of dummy variable for “ever had a banking crisis”
differs from the previous specifications. In the third column of Table 1.8, which includes
forwards and lags, countries that ever had a banking crisis tends to trade 2.9% less. This
23
Low point is 12.9% at four years before crisis. High point is 15.5% at three years before crisis.
26
is different from the positive results from Table 1.4 and bilateral effects from Table 1.5. It
is also against with our previous assumption.
Overall, results and most of the pattern for different time period of banking crisis
from Table 1.8 are similar to those in Table 1.3 and Table 1.4.
1.6 Conclusion
After the Great Recession, the researches focused on how the financial sector
impacts the bilateral trade and the magnitudes of these impacts. This analysis uses the
data that cover most of the countries involved in global trade across 32 years, and
produces relatively robust results, what is the correlation between banking crisis and
fluctuation in trade on average level.
One surprising result is almost all the influence from financial sector for trade
goes to importer’s side, even majority of the theoretical research are focus on the
exporter’s side. In two-stage analysis, there are some significant results for exporter.
However, almost all the effects are economically small24
.
On other side, there is a robust correlation between banking crisis and bilateral
imports. If trade contains a large amount of durable goods and capital goods, trade may
be more sensitive to income. Also almost all the impacts for the different time periods,
especially for the current year of banking crisis and the years after it, are significantly
negative.
24
Average is around 3%.
27
Combining two different influences from exporter and importer, there is one
potential explanation on the 2007 Great Recession in trade flow collapse. Previous
financial shocks, for countries with various income levels, occurred in different time
periods. The overall global market on demand side was only slightly disturbed leaving
the trade flow on stable average level. The Great Recession is wildly spread over the
world and impacted a lot of high income countries, which are also high demand
countries in global trade system. The large drop in demand causes the collapse in trade.
This paper also shows that there is a pattern for the impact of financial shock on
the trade flow for importers. Before the importer has a banking crisis, import is already
in a constant decline compare to other countries. It might be caused by the importer was
already in a recession before the onset of the banking crisis. When crisis begins, the
decline in import extends. Even after banking crisis is over, on average, the decline will
still be intensified to the second year after crisis, and then tends to recover slowly.
Most of the results support that, on average level, countries that had ever
experienced banking crisis tend to trade more. For bilateral effect analysis, this effect is
even larger. These results support the assumption that banking crisis is also correlated
with financial development, and countries with higher financial development tend to
trade more. In future research, the measurement of finical development could be used to
test this assumption.
28
Appendices
29
Appendix A Robustness check for bilateral results
Table 1.5 provides information about bilateral effects for the time period with
banking crisis and after banking crisis. Tables 1.9 include the time period before the
banking crisis.
From the first column of table 1.9, the coefficient for “one country had crisis” is
positive and significant. The coefficient for both countries had crises for the current year
is still positive but not significant. For the country pair that at least one has ever had a
banking crisis, the trade value is about 182.6% higher. If both trade partners had banking
crisis, the trade value will add another 244.5%. At the time when one of the country pair
had a banking crisis the trade value is 5.5% higher. When both countries had banking
crisis, the coefficient is positive but not significant. For the lag time periods, the trade
value is about 10% lower when one country had suffered a banking crisis. However, for
both countries had suffered banking crises, the negative impacts after crisis was over
seem won’t be intensified. The sticky effect is relatively constant. All these results are
quite close to the results from third column of Table 1.5.
In the first column of Table 1.9, estimates provide no support for a clear trend in
trade values in the year prior to a single country’s banking crisis. For the time period
before both country had banking crises, the trade value is around 10% lower, but
relatively not quite significant. In the second column of Table 1.9, the forward and lag
time periods are extend to five years. Almost all the conclusions are remained the same.
30
Even the magnitudes are quite similar to the first column of Table 1.9, which are also
similar to the results from fourth column from Table 1.5.
Overall, the bilateral effects are robust compare to the results from Table 1.5.
There is a relatively constant decline after banking crisis. When one side of the country
pair suffered a banking crisis, a banking crisis occurs on another side will not tend to
intensify the fluctuation of the trade flow between these two countries. For the years
ahead of banking crisis, there is no clear trend.
31
Appendix B Robustness check for Two-stage results
Table 1.10 replicates the regressions in Table 1.5 and include importer-exporter
fixed effect which will capture all the time invariant bilateral effects. The bilateral
banking crisis effects from Table 1.10 are consistent to the results from Table 1.5
The coefficients from Table 1.11 show that when one country of the trade pair
had ever experienced banking crisis, the trade flow tends to increased by around 65%. If
both countries of the trade pair had ever experienced banking crises, the trade flow tends
to increased by another 120%. These results are consistent to what we found in Table 1.5.
Compare to the results in Table 1.5, the magnitude is relatively smaller. However, they
are still higher than the corresponding results from unilateral effect estimations.
The results for exporter and importer from Table 1.12 to 1.13 seem to be puzzle.
The signs changed when include exporter/importer ever had experienced a banking crisis.
It might be correlated with the restriction that coefficient of log GDP is equal to -1 in the
dependent variables.
32
Afghanistan Ghana Pakistan
Albania Greece Panama
Algeria Greenland Papua New Guinea
Angola Guadeloupe Paraguay
Argentina Guatemala Peru
Armenia Guinea Philippines
Aruba Guinea-Bissau Poland
Australia Guyana Portugal
Austria Haiti Qatar
Azerbaijan Honduras Reunion
Bahamas Hong Kong Romania
Bahrain Hungary Russian Federation
Bangladesh Iceland Rwanda
Barbados India Saint Kitts and Nevis
Belarus Indonesia Samoa
Belgium and Luxembourg Iran Saudi Arabia
Belize Iraq Senegal
Benin Ireland Serbia and Montenegro
Bermuda Israel Seychelles
Bhutan Italy Sierra Leone
Bolivia Jamaica Singapore
Bosnia and Herzegovina Japan Slovakia
Brazil Jordan Slovenia
Bulgaria Kazakstan Somalia
Burkina Faso Kenya South Africa
Burma Kiribati Spain
Burundi Korea Sri Lanka
Cambodia Kuwait Sudan
Cameroon Kyrgyzstan Suriname
Canada Lao People's Democratic Republic Sweden
Central African Republic Latvia Switzerland
Chad Lebanon Syrian Arab Republic
Chile Liberia Taiwan
China Libyan Arab Jamahiriya Tajikistan
Colombia Lithuania Tanzania, United Rep. of
Comoros Macau (Aomen) Thailand
Congo Macedonia (the former Yugoslav Rep. of) Togo
Congo (Democratic Republic of the) Madagascar Trinidad and Tobago
Costa Rica Malawi Tunisia
Croatia Malaysia Turkey
Cyprus Mali Turkmenistan
Czech Republic Malta Uganda
Côte d'Ivoire Mauritania Ukraine
Denmark Mauritius United Arab Emirates
Djibouti Mexico United Kingdom
Dominican Republic Micronesia (Federated States of) United States of America
Ecuador Moldova, Rep.of Uruguay
Egypt Mongolia Uzbekistan
El Salvador Morocco Venezuela
Equatorial Guinea Mozambique Viet Nam
Estonia Nepal Yemen
Ethiopia Netherland Antilles Zambia
Fiji Netherlands Zimbabwe
Finland New Caledonia
France New Zealand
French Guiana Nicaragua
Gabon Niger
Gambia Nigeria
Georgia Norway
Germany Oman
Table 1.1: Names of Countries and Districts
33
No of obs. Mean Std. dev. Min. Max
Trade (1000 current US dollar) 420960 3.490e+5 3.376e+6 0.001 3.31e+8
Country’s GDP (exporter, 1000 current US dollar) 847008 1.67e+8 7.53e+8 2.057e+4 1.42e+10
Distance 987657 7653.3 4429.0 1.881 1.995e+4
Contiguity 987657 0.019 0.135 0 1
Common language 987657 0.137 0.344 0 1
Banking crisis (exporter) 987657 0.065 0.246 0 1
One country of the trade pair has banking crisis 987657 0.116 0.320 0 1
Both countries of the trade pair have banking crises 987657 0.007 0.084 0 1
One country of the trade pair ever had banking crisis 987657 0.863 0.344 0 1
Both countries of the trade pair ever had banking crises 987657 0.397 0.489 0 1
Table 1.2: Summary Statistics for Chapter 1
34
Dep var ln(trade)
Ln(distance) minus importers’ share weighted log distance
minus exporters’ share weighted log distance
-1.109***
(0.006)
-1.110***
(0.006)
-1.110***
(0.006)
-1.109***
(0.006)
-1.108***
(0.006)
Contiguity minus importers’ share weighted contiguity
minus exporters’ share weighted contiguity
0.363***
(0.025)
0.373***
(0.025)
0.379***
(0.025)
0.377***
(0.025)
0.386***
(0.025)
Common language minus importers’ share weighted
language minus exporters’ share weighted language
0.572***
(0.011)
0.577***
(0.011)
0.581***
(0.011)
0.581***
(0.011)
0.586***
(0.011)
Ln(exporter ’s GDP) 1.226***
(0.002)
1.227***
(0.002)
1.227***
(0.002)
1.227***
(0.002)
1.227***
(0.002)
Ln(importer’s GDP) 1.081***
(0.002)
1.082***
(0.002)
1.082***
(0.002)
1.084***
(0.002)
1.086***
(0.002)
5 years forward of banking crisis for exporter 0.023
(0.027)
4 years forward of banking crisis for exporter -0.008
(0.026)
3 years forward of banking crisis for exporter -0.020
(0.026)
-0.021
(0.026)
2 years forward of banking crisis for exporter -0.022
(0.025)
-0.022
(0.025)
1 years forward of banking crisis for exporter -0.024
(0.025)
-0.024
(0.024)
Banking crisis for exporter 0.005
(0.015)
0.004
(0.015)
0.002
(0.015)
0.002
(0.015)
0.001
(0.015)
1 years lag of banking crisis for exporter -0.010
(0.025)
-0.011
(0.025)
-0.011
(0.025)
-0.012
(0.025)
2 years lag of banking crisis for exporter -0.020 (0.024)
-0.021 (0.025)
-0.021 (0.024)
-0.023 (0.025)
3 years lag of banking crisis for exporter 0.001
(0.025)
-0.002
(0.024)
0.000
(0.024)
-0.003
(0.024)
4 years lag of banking crisis for exporter -0.013 (0.024)
-0.015 (0.024)
5 years lag of banking crisis for exporter -0.034
(0.024)
-0.035
(0.024)
5 years forward of banking crisis for importer -0.199*** (0.026)
4 years forward of banking crisis for importer -0.186***
(0.026)
3 years forward of banking crisis for importer -0.176*** (0.026)
-0.196*** (0.026)
2 years forward of banking crisis for importer -0.168***
(0.025)
-0.185***
(0.025)
1 years forward of banking crisis for importer -0.190*** (0.024)
-0.210*** (0.024)
Banking crisis for importer -0.247***
(0.015)
-0.280***
(0.015)
-0.294***
(0.015)
-0.295***
(0.015)
-0.317***
(0.015)
1 years lag of banking crisis for importer -0.321*** (0.026)
-0.337*** (0.025)
-0.334*** (0.026)
-0.357*** (0.026)
2 years lag of banking crisis for importer -0.364***
(0.025)
-0.387***
(0.025)
-0.374***
(0.025)
-0.403***
(0.025)
3 years lag of banking crisis for importer -0.296*** (0.025)
-0.319*** (0.024)
0.305*** (0.025)
-0.332*** (0.024)
4 years lag of banking crisis for importer -0.289***
(0.024)
-0.301***
(0.024)
5 years lag of banking crisis for importer -0.284***
(0.024)
-0.290***
(0.023)
Constant -40.292***
(0.083)
-40.294***
(0.083)
-40.300***
(0.083)
-40.322***
(0.083)
-40.511***
(0.083)
Year fixed effect Yes Yes Yes Yes Yes
R-square 0.635 0.636 0.636 0.636 0.636
No of obs. 379928 379928 379928 379928 379928
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.3: Linear approximations for multilateral resistance and banking crises
35
Dep var ln(trade)
Ln(distance) minus importers’ share weighted log distance
minus exporters’ share weighted log distance
-1.108***
(0.006)
-1.110***
(0.006)
-1.111***
(0.006)
-1.110***
(0.006)
-1.111***
(0.006)
Contiguity minus importers’ share weighted contiguity
minus exporters’ share weighted contiguity
0.367***
(0.025)
0.371***
(0.025)
0.375***
(0.025)
0.373***
(0.025)
0.378***
(0.025)
Common language minus importers’ share weighted
language minus exporters’ share weighted language
0.575***
(0.011)
0.576***
(0.011)
0.577***
(0.011)
0.577***
(0.011)
0.579***
(0.011)
Ln(exporter ’s GDP) 1.227***
(0.002)
1.227***
(0.002)
1.227***
(0.002)
1.227***
(0.002)
1.228***
(0.002)
Ln(importer’s GDP) 1.082***
(0.002)
1.081***
(0.002)
1.079***
(0.002)
1.082***
(0.002)
1.082***
(0.002)
5 years forward of banking crisis for exporter 0.024
(0.027)
4 years forward of banking crisis for exporter -0.007
(0.026)
3 years forward of banking crisis for exporter -0.018
(0.026)
-0.020
(0.026)
2 years forward of banking crisis for exporter -0.019
(0.025)
-0.021
(0.025)
1 years forward of banking crisis for exporter -0.022
(0.025)
-0.023
(0.024)
Banking crisis for exporter 0.008
(0.015)
0.007
(0.015)
0.005
(0.015)
0.004
(0.015)
0.002
(0.015)
1 years lag of banking crisis for exporter -0.006
(0.025)
-0.008
(0.025)
-0.008
(0.025)
-0.010
(0.025)
2 years lag of banking crisis for exporter -0.016 (0.024)
-0.019 (0.025)
-0.018 (0.024)
-0.021 (0.025)
3 years lag of banking crisis for exporter 0.004
(0.025)
0.001
(0.024)
0.003
(0.024)
-0.001
(0.024)
4 years lag of banking crisis for exporter -0.011 (0.024)
-0.013 (0.024)
5 years lag of banking crisis for exporter -0.031
(0.024)
-0.033
(0.024)
5 years forward of banking crisis for importer -0.228*** (0.027)
4 years forward of banking crisis for importer -0.217***
(0.026)
3 years forward of banking crisis for importer -0.188*** (0.026)
-0.228*** (0.026)
2 years forward of banking crisis for importer -0.181***
(0.025)
-0.216***
(0.025)
1 years forward of banking crisis for importer -0.203*** (0.024)
-0.243*** (0.024)
Banking crisis for importer -0.241***
(0.015)
-0.286***
(0.015)
-0.311***
(0.015)
-0.310***
(0.015)
-0.352***
(0.015)
1 years lag of banking crisis for importer -0.328*** (0.025)
-0.355*** (0.025)
-0.349*** (0.026)
-0.394*** (0.026)
2 years lag of banking crisis for importer -0.370***
(0.025)
-0.405***
(0.025)
-0.389***
(0.025)
-0.440***
(0.025)
3 years lag of banking crisis for importer -0.302*** (0.024)
-0.337*** (0.024)
0.320*** (0.024)
-0.368*** (0.024)
4 years lag of banking crisis for importer -0.306***
(0.024)
-0.335***
(0.024)
5 years lag of banking crisis for importer -0.301***
(0.024)
-0.323***
(0.023)
Exporter ever had a banking crisis -0.010
(0.009)
-0.009
(0.009)
-0.007
(0.009)
-0.007
(0.009)
-0.005
(0.009)
Importer ever had a banking crisis -0.022* (0.009)
-0.020* (0.009)
0.050*** (0.009)
0.043*** (0.009)
0.095*** (0.010)
Constant -40.287***
(0.083)
-40.295***
(0.083)
-40.307***
(0.083)
-40.328***
(0.083)
-40.529***
(0.083)
Year fixed effect Yes Yes Yes Yes Yes
R-square 0.635 0.636 0.636 0.636 0.636
No of obs. 379928 379928 379928 379928 379928
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.4: Linear approximations for multilateral resistance and banking crises with
country ever experienced a banking crisis
36
Dep var. ln(trade)
Ln(distance) -1.382***
(0.004)
-1.382***
(0.004)
-1.382***
(0.004)
-1.383***
(0.004)
Contiguity 0.520***
(0.021)
0.521***
(0.021)
0.520***
(0.021)
0.520***
(0.021)
Common language 0.876***
(0.009)
0.876***
(0.009)
0.876***
(0.009)
0.876***
(0.009)
One crisis ever 1.032***
(0.011)
1.025***
(0.011)
1.039***
(0.011)
1.048***
(0.011)
Both crises ever 1.229***
(0.006)
1.223***
(0.007)
1.234***
(0.007)
1.242***
(0.007)
One crisis 0.066***
(0.009)
0.051***
(0.010)
0.044***
(0.009)
One year lag of one
crisis
-0.128***
(0.016)
-0.127***
(0.016)
Two years lag of
one crisis
-0.095***
(0.015)
-0.095***
(0.016)
Three years lag of
one crisis
-0.112***
(0.015)
-0.117***
(0.015)
Four years lag of
one crisis
-0.092***
(0.015)
Five years lag of
one crisis
-0.135***
(0.015)
Two crises 0.073*
(0.036)
0.049
(0.035)
0.035
(0.035)
One year lag of two crises
-0.111* (0.050)
-0.107* (0.050)
Two years lag of
two crises
-0.062
(0.049)
-0.056
(0.049)
Three years lag of two crises
-0.115* (0.048)
-0.112* (0.048)
Four years lag of
two crises
-0.046
(0.047)
Five years lag of two crises
-0.107* (0.046)
Importer-year
fixed effect
yes yes yes yes
Exporter-year fixed effect
yes yes yes yes
Constant 17.946***
(0.037)
17.947***
(0.037)
17.947***
(0.037)
17.947***
(0.037)
R-square 0.74 0.74 0.74 0.74
No of obs. 420960 420960 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.5: Trade value and banking crises with lags
37
Dep. Var. ln(trade)
Ln(distance) -1.386***
(0.004)
Contiguity 0.515***
(0.021)
Common language 0.882***
(0.009)
Importer-year
fixed effect
Yes
Exporter-year
fixed effect
Yes
Constant 19.498***
(0.035)
R-square 0.74
No of obs. 420960
*** for p-value<0.001 ** for p-value<0.01
* for p-value<0.05
Table 1.6: First stage of the regression
38
Coefficient of Exporter-year fixed effect from Table 1.4 minus log of
Exporters’ GDP
Five year forward of exporter’s crisis
0.058*** (0.008)
Four year forward of
exporter’s crisis
0.051***
(0.008)
Three year forward of exporter’s crisis
-0.024** (0.008)
Two year forward of
exporter’s crisis
-0.034***
(0.007)
One year forward of exporter’s crisis
-0.022*** (0.007)
Banking crises for
exporter
0.027***
(0.005)
0.035***
(0.005)
0.034***
(0.005)
One year lag of exporter’s crisis
0.024** (0.008)
0.024** (0.008)
Two year lag of
exporter’s crisis
0.024**
(0.008)
0.024**
(0.008)
Three year lag of
exporter’s crisis
0.047***
(0.008)
0.045***
(0.008)
Four year lag of
exporter’s crisis
0.050***
(0.008)
0.049***
(0.008)
Five year lag of exporter’s crisis
0.022** (0.008)
0.022** (0.008)
Exporter ever had a
banking crisis
0.149***
(0.003)
0.141***
(0.003)
0.141***
(0.003)
Constant -20.075***
(0.035)
-19.958***
(0.034)
-20.606***
(0.035)
Importers’ GDP share
weighted log distance
Yes Yes Yes
Importers’ GDP share
weighted language
Yes Yes Yes
Importers’ GDP share
weighted contiguity
Yes Yes Yes
Year fixed effect Yes Yes Yes
R-square 0.131 0.131 0.131
No of obs 823649 823649 823649
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.7: Exporter-year fixed effect and exporters’ banking crisis
39
Coefficient of Importer-year fixed effect from Table 1.4 minus log of
Importers’ GDP
Five year forward of importer’s crisis
-0.148*** (0.007)
Four year forward of
importer’s crisis
-0.138***
(0.007)
Three year forward of importer’s crisis
-0.168*** (0.007)
Two year forward of
importer’s crisis
-0.149***
(0.007)
One year forward of importer’s crisis
-0.148*** (0.007)
Banking crises for
impoter
-0.038***
(0.004)
-0.053***
(0.004)
-0.083***
(0.004)
One year lag of importer’s crisis
-0.077*** (0.007)
-0.106*** (0.007)
Two year lag of
importer’s crisis
-0.103***
(0.007)
-0.127***
(0.007)
Three year lag of
importer’s crisis
-0.060***
(0.007)
-0.081***
(0.007)
Four year lag of
importer’s crisis
-0.040***
(0.007)
-0.061***
(0.007)
Five year lag of importer’s crisis
-0.032*** (0.007)
-0.049*** (0.007)
Importer ever had a
banking crisis
-0.075***
(0.003)
-0.062***
(0.003)
-0.029***
(0.003)
Constant -17.050***
(0.031)
-17.122***
(0.031)
-17.182***
(0.031)
Exporters’ GDP share
weighted distance
Yes Yes Yes
Exporters’ GDP share
weighted language
Yes Yes Yes
Exporters’ GDP share
weighted contiguity
Yes Yes Yes
Year fixed effect Yes Yes Yes
R-square 0.156 0.156 0.159
No of obs 638716 638716 638716
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.8: Importer-year fixed effect and importers’ banking crisis
40
Dep var ln(trade)
Ln(distance) -1.382***
(0.004)
-1.383***
(0.004)
Contiguity 0.521***
(0.021)
0.521***
(0.021)
Common language 0.876***
(0.009)
0.876***
(0.009)
One crisis ever 1.039***
(0.011)
1.043***
(0.011)
Both crises ever 1.237***
(0.007)
1.244***
(0.007)
Five years forward of one crisis 0.086***
(0.016)
Four years forward of one crisis 0.028
(0.016)
Three years forward of one crisis -0.030
(0.015)
-0.024
(0.016)
Two years forward of one crisis 0.056***
(0.015)
0.057***
(0.015)
One year forward of one crisis -0.040**
(0.015)
-0.040**
(0.015)
One crisis 0.054***
(0.010)
0.049***
(0.010)
One year lag of one crisis -0.124***
(0.016)
-0.123***
(0.016)
Two years lag of one crisis -0.094*** (0.015)
-0.093*** (0.016)
Three years lag of one crisis -0.112***
(0.015)
-0.115***
(0.015)
Four years lag of one crisis -0.091*** (0.015)
Five years lag of one crisis -0.134***
(0.015)
Five years forward of two crises -0.126* (0.052)
Four years forward of two crises -0.138**
(0.047)
Three years forward of two crises -0.153** (0.051)
-0.158** (0.051)
Two years forward of two crises -0.061
(0.048)
-0.065
(0.048)
One year forward of two crises -0.121* (0.047)
-0.122** (0.047)
Two crises 0.046
(0.035)
0.037
(0.035)
One year lag of two crises -0.107* (0.050)
-0.104* (0.050)
Two years lag of two crises -0.060
(0.049)
-0.054
(0.049)
Three years lag of two crises -0.114* (0.048)
-0.111* (0.048)
Four years lag of two crises -0.046
(0.047)
Five years lag of two crises -0.107*
(0.046)
Importer year fixed effect yes yes
Exporter year fixed effect yes yes
Constant 17.951*** (0.037)
17.957*** (0.037)
R-square 0.74 0.74
No of obs. 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.9: Trade value and banking crises with forwards and lags
41
Dep var ln(trade)
Five years forward
of one crisis
0.026*
(0.011)
Four years forward
of one crisis
0.051***
(0.011)
Three years forward
of one crisis
-0.046***
(0.010)
-0.056***
(0.011)
Two years forward
of one crisis
0.036***
(0.010)
0.026*
(0.010)
One year forward of
one crisis
-0.019
(0.010)
-0.028**
(0.010)
One crisis 0.063***
(0.006)
0.052***
(0.006)
0.046***
(0.006)
0.057***
(0.006)
0.058***
(0.006)
One year lag of one
crisis
-0.056***
(0.011)
-0.064***
(0.011)
-0.062***
(0.011)
-0.070***
(0.011)
Two years lag of
one crisis
-0.064***
(0.010)
-0.073***
(0.010)
-0.068***
(0.010)
-0.076***
(0.011)
Three years lag of
one crisis
-0.069***
(0.010)
-0.081***
(0.010)
-0.070***
(0.010)
-0.083***
(0.010)
Four years lag of
one crisis
-0.057***
(0.010)
-0.058***
(0.010)
Five years lag of
one crisis
-0.113***
(0.010)
-0.113***
(0.010)
Five years forward
of two crises
0.092**
(0.035)
Four years forward of two crises
0.169*** (0.035)
Three years forward
of two crises
0.076*
(0.035)
0.084*
(0.035)
Two years forward of two crises
0.155*** (0.033)
0.162*** (0.033)
One year forward of
two crises
0.125***
(0.032)
0.131***
(0.032)
Two crises 0.144*** (0.024)
0.134*** (0.024)
0.125*** (0.024)
0.170*** (0.024)
0.186*** (0.024)
One year lag of two
crises
-0.030
(0.034)
-0.031
(0.034)
-0.019
(0.034)
-0.011
(0.034)
Two years lag of two crises
0.012 (0.033)
0.012 (0.033)
0.022 (0.033)
0.033 (0.033)
Three years lag of
two crises
-0.034
(0.032)
-0.036
(0.032)
-0.023
(0.032)
-0.015
(0.032)
Four years lag of two crises
-0.008 (0.032)
0.013 (0.032)
Five years lag of
two crises
-0.085**
(0.032)
-0.064**
(0.032)
Importer year fixed effect
yes yes yes yes yes
Exporter year
fixed effect
yes yes yes yes yes
Importer-Exporter fixed effect
yes yes yes yes yes
Constant 7.668***
(0.002)
7.677***
(0.002)
7.688***
(0.003)
7.677***
(0.003)
7.682***
(0.003)
R-square 0.878 0.878 0.878 0.878 0.878
No of obs. 420960 420960 420960 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.10: Trade value and banking crises with Importer-Exporter fixed effect
42
Coefficient of
importer-Exporter fixed
effect from
First column of Table 1.8
Coefficient of
importer-Exporter fixed
effect from
Third column of Table 1.8
Coefficient of
importer-Exporter fixed
effect from
Fifth column of Table 1.8
Ln(distance) -1.158***
(0.004)
-1.158***
(0.004)
-1.160***
(0.004)
Contiguity 1.720*** (0.025)
1.722*** (0.025)
1.711*** (0.025)
Common
language
0.131***
(0.009)
0.131***
(0.009)
0.136***
(0.009)
One crisis ever
0.513*** (0.011)
0.520*** (0.011)
0.492*** (0.011)
Both crises
ever
0.823***
(0.007)
0.827***
(0.007)
0.792***
(0.007)
Constant 7.843*** (0.041)
7.829*** (0.041)
7.891*** (0.041)
R-square 0.139 0.139 0.138
No of obs. 846813 846813 846813
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.11: Importer-Exporter fixed effect and time invariant bilateral variables
43
Coefficient
of Exporter-
year fixed
effect from First
column of
Table 1.8 minus log
of
Exporters’ GDP
Coefficient
of Exporter-
year fixed
effect from First
column of
Table 1.8 minus log
of
Exporters’ GDP
Coefficient
of Exporter-
year fixed
effect from Third
column of
Table 1.8 minus log
of
Exporters’ GDP
Coefficient
of Exporter-
year fixed
effect from Third
column of
Table 1.8 minus log
of
Exporters’ GDP
Coefficient
of Exporter-
year fixed
effect from Fifth
column of
Table 1.8 minus log
of
Exporters’ GDP
Coefficient
of Exporter-
year fixed
effect from Fifth
column of
Table 1.8 minus log
of
Exporters’ GDP
Five year forward of
exporter’s crisis
-0.364***
(0.011)
-0.179***
(0.011)
Four year forward of exporter’s crisis
-0.406*** (0.011)
-0.216*** (0.011)
Three year forward of
exporter’s crisis
-0.321***
(0.010)
-0.124***
(0.010)
Two year forward of
exporter’s crisis
-0.410***
(0.010)
-0.203***
(0.010)
One year forward of
exporter’s crisis
-0.389***
(0.010)
-0.177***
(0.010)
Banking crises orientation
-0.278*** (0.006)
-0.092*** (0.006)
-0.292*** (0.006)
-0.063*** (0.006)
-0.347*** (0.010)
-0.118*** (0.006)
One year lag of
exporter’s crisis
-0.091***
(0.011)
0.145***
(0.011)
-0.111***
(0.011)
0.118***
(0.011)
Two year lag of exporter’s crisis
-0.086*** (0.011)
0.155*** (0.011)
-0.096*** (0.011)
0.131*** (0.011)
Three year lag of
exporter’s crisis
-0.061***
(0.011)
0.176***
(0011)
-0.063***
(0.011)
0.155***
(0.011)
Four year lag of exporter’s crisis
-0.080*** (0.011)
0.153*** (0.011)
-0.076*** (0.011)
0.135*** (0.011)
Five year lag of
exporter’s crisis
-0.066***
(0.011)
0.162***
(0.011)
-0.059***
(0.011)
0.145***
(0.011)
Exporter ever had a banking crisis
-0.527*** (0.004)
-0.552*** (0.004)
-0.482*** (0.004)
Constant -25.161***
(0.047)
-23.656***
(0.048)
-25.156***
(0.047)
-23.466***
(0.047)
-24.960***
(0.047)
-23.555***
(0.047)
Importers’ GDP share weighted distance
Yes Yes Yes Yes Yes Yes
Importers’ GDP share
weighted language
Yes Yes Yes Yes Yes Yes
Importers’ GDP share weighted contiguity
Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
R-square 0.256 0.274 0.256 0.274 0.260 0.272
No of obs 832649 832649 832649 832649 832649 832649
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.12: Exporter-year fixed effect and exporters’ banking crisis for robustness check
44
Coefficient
of Importer-
year fixed
effect from First
column of
Table 1.8 minus log
of
Importers’ GDP
Coefficient
of Importer-
year fixed
effect from First
column of
Table 1.8 minus log
of
Importers’ GDP
Coefficient
of Importer-
year fixed
effect from Third
column of
Table 1.8 minus log
of
Importers’ GDP
Coefficient
of Importer-
year fixed
effect from Third
column of
Table 1.8 minus log
of
Importers’ GDP
Coefficient
of Importer-
year fixed
effect from Fifth
column of
Table 1.8 minus log
of
Importers’ GDP
Coefficient
of Importer-
year fixed
effect from Fifth
column of
Table 1.8 minus log
of
Importers’ GDP
Five year forward of
importer’s crisis
-0.560***
(0.013)
-0.333***
(0.012)
Four year forward of importer’s crisis
-0.650*** (0.013)
-0.419*** (0.012)
Three year forward of
importer’s crisis
-0.645***
(0.013)
-0.413***
(0.013)
Two year forward of
importer’s crisis
-0.608***
(0.012)
-0.369***
(0.012)
One year forward of
importer’s crisis
-0.542***
(0.012)
-0.292***
(0.012)
Banking crises destination
-0.435*** (0.007)
-0.202*** (0.007)
-0.468*** (0.008)
-0.187*** (0.007)
-0.541*** (0.007)
-0.268*** (0.008)
One year lag of
importer’s crisis
-0.250***
(0.013)
0.036**
(0.012)
-0.293***
(0.012)
-0.019
(0.012)
Two year lag of importer’s crisis
-0.241*** (0.012)
0.053*** (0.012)
-0.269*** (0.012)
0.005 (0.012)
Three year lag of
importer’s crisis
-0.159***
(0.012)
0.127***
(0.012)
-0.180***
(0.012)
0.083***
(0.012)
Four year lag of importer’s crisis
-0.184*** (0.012)
0.096*** (0.012)
-0.203*** (0.012)
0.053*** (0.012)
Five year lag of
importer’s crisis
-0.100***
(0.012)
0.180***
(0.012)
-0.110***
(0.012)
0.142***
(0.012)
Importer ever had a banking crisis
-0.766*** (0.004)
-0.777*** (0.005)
-0.675*** (0.005)
Constant -24.304***
(0.051)
-22.422***
(0.053)
-24.449***
(0.053)
-22.362***
(0.052)
-24.297***
(0.052)
-22.529***
(0.052)
Exporters’ GDP share weighted distance
Yes Yes Yes Yes Yes Yes
Exporters’ GDP share
weighted language
Yes Yes Yes Yes Yes Yes
Exporters’ GDP share weighted contiguity
Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
R-square 0.253 0.285 0.254 0.285 0.265 0.286
No of obs 638716 638716 638716 638716 638716 638716
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 1.13: Importer-year fixed effect and importers’ banking crisis for
robustness check
45
Figure 1.1: Import goods and services as a ratio of GDP for a select group of
countries with banking crises at duration of two years
0.2
.4.6
0 5 10 15var1
BGR CAF
CRI DOM
GHA
.2.4
.6.8
1
0 5 10 15var1
HRV KOR
LTU LVA
NIC
.2.4
.6.8
1
0 5 10 15var1
PAN SLV
TGO TUR
TZA UKR
46
Figure 1.2: Interpretation of forward and lag time
47
Chapter 2
Banking Crises and the Impacts on the
Margins of Trade
2.1 Introduction
Since 2007 banking crisis and the onset of Great Recession, the investigation of
the collapse of bilateral trade has evolved into a cottage industry among trade economists.
Most of literatures provide a link between the Great Recession and trade fall in
international trade focusing on this event. In Chapter 1 of this dissertation, we focused on
how banking crisis may influence the bilateral trade flows over time. It attempted to
disentangle the financial shock’s impact on trade flows that seemingly originated on the
export side and those that originated on the import side.
Based on the research on financial shocks for both bilateral effect and unilateral
effect, the objective of this paper is to assess how the financial shocks impact the
extensive and intensive margins of trade. To this end, we decompose bilateral trade flows
into two parts: extensive margin, which reflects the information on a share weighted
48
count of the number of varieties of goods traded1, and the intensive margin, which
presents the volume of each variety. As a result of this decomposition, we can assess how
financial crises impact the number of goods shipped and volume of goods shipped.
Theoretically, it is not clear how the crisis will impact these margins. Hence, “Whether
there is a robust correlation between banking crises and extensive/intensive margins
fluctuations?” will be the main topic of this paper
As in Chapter 1, financial shocks may influence the extensive and intensive
margin through two channels. One occurs through the producers: when trade is financed
by exporters, a tightened credit constraint may force some producers to exit foreign
markets and cause a decline on extensive margin for export. The rising cost for financing
the trade will also have an impact on intensive margin from export side. The other
channel occurs through consumers when trade is financed by importers. There might be a
large decline in capital goods and durable goods, which are relatively income sensitive. It
might have a negative impact on both extensive margin and intensive margin for import.
However, when there is a decline on extensive margin and hence import less varieties of
goods caused by income effect, there might be a substitution effect and caused the change
in value of import for each variety and change the intensive margin in a positive direction.
In this paper, we will use Hummels and Klenow (2005)’s method to decompose the
margins and try to uncover the average impact for exporters and importers on both
extensive and intensive margin.
1 This paper defines variety at industry level.
49
In Chapter 1, we found that there was an impact for importers in advance of the
banking crises, and recovery extended well after the crisis was over. In order to capture
the timing of the effects and to be consistent with the modeling strategy in that
chapter ,we also include different time periods to find out whether there is any patterns
for impacts from banking crisis cross time on different margins.
2.2 Literature Review
Hummels and Klenow (2005) provide a method to decompose the bilateral trade
flow into extensive and intensive margin. They show that higher income countries tend to
export more varieties of goods. Their paper also shows that the majority of the bilateral
trade can be attributed to extensive margin. By adopting their method, the trading cost
can be estimated individually for both margins. Bernard et al. (2007) use U.S. firm level
data, research the distance effect on extensive and intensive margin.
The literatures on the trade collapse during Great Recession highlights factors that
contributed to our understanding of factors that are associated with the decline in trade
flows during the financial crisis. Levchenko et al. (2010) uses disaggregated quarterly US
trade data and finds a great decline in the volume of trade. Chor and Manova (2012) used
monthly US import data to analysis the trade collapse after the 2007 crisis, finding that
the exports of industries with larger dependence on the external financial market will tend
to be more vulnerable and sensitive to financial shocks. Lacovone and Zavacka (2009)
use annual data to show that the industries which are more dependent on financial
markets in more financially developed countries experienced larger declines in trade
50
during the banking crisis. Bricongne et al. (2012) uses French firm level data, found out
extensive margin and financial constraints played a minor role in the French export.
There are also studies on trade collapse and categories good via non-financial
channels. Gopinath et al. (2012) studies the trade price fluctuation in different categories
of goods during 07 banking crisis. Engel and Wang (2011) provide an insight to the links
between business cycle, trade volatility and durable goods.
2.3 Model
2.3.1 Background context
Assume a world with N countries and M varieties of goods. All consumers have
identical constant-elasticity-of-substitution (CES) preference2:
. (2.1)
Where the utility of consumers in country j is, is the good consumed by
people in country j imported from country i, is the elasticity of substitution and .
Maximizing utility subject to the budget constraint, we obtain the demand for the
good consumed in country j import from country i, :
. (2.2)
2 See Anderson and Van Wincoop (2003) for details.
51
Here is the price of the good ’s price sold within the importer i. is the trade
cost3 for good shipped from country i to country j. is GDP of country j, is GDP of
the world and is the CES price index that:
. (2.3)
Assumes firms maximize profit and all markets clear, we can write an expression for
bilateral trade flow as:
. (2.4)
Where is the world gross GDP, is the total value of goods export from i to j and
. (2.5)
. (2.6)
Where denotes , that is the share of country i’s GDP relative to the world. and
are referred to as a country’s multilateral resistance. is the outward multilateral
resistance which measures how difficult for country i to export goods relative to the rest
of the world. is the inward multilateral resistance that measures how difficult for
country j to import goods relative to the rest of the world.
We use the Hummels and Klenow (2005) decomposition to form the extensive
and intensive margin.
3 We assume the trade costs as iceberg trade costs
52
The extensive margin is defined as:
. (2.7)
Here is the total trade flow from world to country j. is the value of good
ship from world to country j. So the extensive margin between i and j is a share
weighted measurement of the varieties of goods that world export to country j,
meanwhile the same varieties are also exported from country i to country j. This
measurement is weighted by total imports of country j
The intensive margin is defined as:
. (2.8)
The intensive margin between i and j is the bilateral trade flow from country i to country
j and weighted by the trade flow from world to country j under the same categories.
The overall margin will be defined as:
. (2.9)
The overall margin is the product of extensive margin and intensive margin. It is the total
bilateral trade flow from country i to country j, weighted by total import of country j.
When multiply both side of equation 2.4 with
, it will be equal to overall margin.
Taking the natural log of both side of equation 2.9, we can get:
53
. (2.10)
When the trade cross some time periods, equation 2.10 can be presented as:
.
(2.11)
2.3.2 Bilateral effects estimation
We assume the trade costs have the standard structure, and are given as follow:
. (2.12)
Where is the distance between country i and j, is the vector include other
geography information, like contiguity and language. is the vector includes
bilateral effect of financial shocks and can that we represent differently in our
specifications. Substituting equation 2.12 into equation 2.11, we get:
54
. (2.13)
is the error term with normal distribution.
All regressions include country-year fixed effects4. These fixed effects will absorb
the inward/outward multilateral effect effects, importer’s GDP, exporter’s GDP, total
import for country j and world GDP for the same year. The estimating equations take the
form:
.
(2.14)
.
(2.15)
.
(2.16)
Here is the exporter-year is fixed effect and is the importer-year fixed effect.
Due to the large amount of fixed effect, this paper adopt Guimaraes and Portugal
(2009)’s method to estimate the models with high-dimensional fixed effects. The results
are showed in Table 2.7 to 2.9.
2.3.3 Unilateral effects estimation
4Both importer-year fixed effect and exporter-year fixed effect.
55
If we treat financial shock as a unilateral effect, the trade costs can be described
as:
. (2.17)
Baier and Bergstrand (2009) introduced the method to linear approximate the
multilateral resistances. For the bilateral trade costs, those resistances term in equation
2.5 and 2.6 can be presented as:
. (2.18)
. (2.19)
Substituting equation 2.18 and 2.19 into equation 2.11 and taking a log-linear expansion
around average trade costs, and combining with equation 2.17, the regression
specification become:
. (2.20)
Where:
. (2.21)
56
. (2.22)
. (2.23)
. (2.24)
. (2.25)
. (2.26)
is the measurement for contiguity, is the measurement for common
language. , ,
and will absorbed by year fixed effect. The
regression will be estimated is:
. (2.27)
. (2.28)
. (2.29)
Here is year fixed effect. Table 2.1 to 2.6 present the results for the unilateral effects5.
5 For unilateral effect, The term
, it actually becomes
=1 .
57
2.3.4 Unilateral effects estimation with two-stage model
Another way to treat these unilateral effects is that they are completely captured
by multilateral resistance. Hence we can do a two-stage regression to back out the
effects of a banking crisis.
In first-stage we regress as equation 2.14 to equation 2.16 without .
Coefficients of and will capture all the information about multilateral resistance
and other country-year specific effects. For the exporter-year fixed effect coefficient, it
contains unilateral financial shock effects from exporter, importers’ GDP share
weighted bilateral effects6, some of the average trade cost cross the world
7, exporter’s
GDP and some of the world GDP. So we can run the second stage regression as follows:
. (2.30)
. (2.31)
. (2.32)
Since the share of GDP sum up equal to one cross the world.
and is constant
for all the countries in the same year. Because of this unilateral effect cannot do the same expansion as
bilateral effects. We just include the unilateral effects by themselves. 6 Which is
.
7 Which is
.
58
Here, will capture all the information from bilateral effects, world average trade cost
effects and world GDP for the same year.
Similar as previous, for importer-year fixed effect, we run the second stage
regression as:
. (2.33)
. (2.34)
. (2.35)
Results are presented in Table 2.10 to 2.16, these also can be used a robustness
check compare to the results from equation 2.27 to 2.29.
2.4 Data Source
Data source will be the same as Chapter 1.
2.4.1 Value of the Trade
59
The value of bilateral trade for 173 countries for the years 1976-2008 is taken
from the UN Comtrade database8. It is reported as 5 digit SITC level. The value is
measured in thousands of US dollar in the current year. The inflation of the currency will
be captured by importer-year fixed effect and exporter-year fixed effect. We use industry
level data to construct the extensive margin and intensive margin as showed in equation
2.7 and 2.8.
2.4.2 Geography Data
The paper uses bilateral value of the distance, contiguity and common official
language as measures of the traditional trade cost. The data are from CEPII database9.
Both contiguity and common language are dummy variables. Contiguity is equal to unity
if two trade partners share the common border, and zero otherwise. Common official
language is equal to unity if two trade partners use the same official language and zero
otherwise. This paper uses the population weighted distance10
.
2.4.3 Banking Crises Data
The Leaven and Valencia banking crises database provides annual banking crisis
data for the year 1976-200811
. According to Leaven and Valencia (2012), a banking crisis
is defined as systemic if two conditions are met:
8 The data can be obtained from http://comtrade.un.org/
9 The data can be obtained from http://www.cepii.fr/CEPII/en/bdd_modele/bdd.asp
10 Use other measurement of distance will yield similar results.
11 The data can be obtained from http://www.imf.org/external/publications/index.htm
60
1) Significant signs of financial distress in the banking system (as indicated by
significant bank runs, losses in the banking system, and/or bank liquidations)
2) Significant banking policy intervention measures in response to significant
losses in the banking system.
Here, significant bank runs indicate a 5 percent or greater drop in deposits within
one month during the time period.
For policy interventions in the banking sector to be significant, at least three out of
the following six measures must have been used:
1) extensive liquidity support (5 percent of deposits and liabilities to nonresidents)
2) bank restructuring gross costs (at least 3 percent of GDP)
3) significant bank nationalizations
4) significant guarantees put in place
5) significant asset purchases (at least 5 percent of GDP)
6) deposit freezes and/or bank holidays.
In total, the panel dataset contains trade vales, banking, and geographic data for
173 countries for the years 1976-2008.
2.5 Results
61
Since the effects on the overall margin are the slight modifications of the
regressions specifications in Chapter 1, we would expect that overall margin results
should be close to the results what we found in Chapter 112
. Because the sum of the logs
of extensive and intensive margin equals the overall margin and because OLS is a linear
operator, the sum of the coefficients for the extensive margin and intensive margin will
add up to the coefficient on the overall margin for each of the right-hand side variables.
2.5.1 One-stage results for exporters and importers
All the variables are defined same as in Chapter 1. In table 2.2, for overall margin,
as in Chapter 1, most of the impact of a banking crisis goes through the importers’
channel. All the coefficients for exporters’ banking crisis variables within different time
periods are insignificant. For importers, results from column one to column five are quite
consistent. From fifth column of Table 2.2, we can see, there is constant decline around
8%13
before banking crisis began. At the time crisis begins. There is an additional 4.5%
decline on overall margin14
. After the crisis was over, on average, the decline in bilateral
trade for importers keeps increases through the second year15
. Then it begins to recover
slowly. These results show the same pattern as we found in Table 1.3 from Chapter 1
except the magnitude of the coefficients is relatively smaller.
12
For some years, there are only total trade value, do don’t include industry trade information, the results
are not identical 13
Low point is 7.1% at five years before crisis. High point is 8.8% at the year right before crisis. 14
The decline of overall margin is 13.3%. 15
The decline at second year after crisis was over is 20.1%.
62
From column 1 of Table 2.3, at the current year of banking crisis, the extensive
margin for exporters declines 7.5%. For importers, it declines around 13.8%. When
include the time periods before and after banking crisis, from fifth column of Table 2.3,
we can see at the current year of banking crisis, extensive margin declines 10.7% for
exporters and 17.5% for importers.
For exporters, there is a relative consistent 14% decline for exporter at the time
periods before banking crisis on extensive margin. After the crisis is over, there is a
relative consistent 10% decline trend on this margin. For importers, there is an intensified
decline trend before crisis starts. At the year right before the crisis, the decline is 11.2%.
Then there is an additional 6.3% decline when crisis begins. For the time period after the
crisis is over, the decline trend on extensive margin keeps increasing until the third year,
and then it starts to recover slowly.
In column one of Table 2.4, the contemporaneous effect of banking crisis on the
intensive margin for exporters is raised by 8.2%. For importers, it increased by 4.6%.
When include the time periods before and after banking crisis, from fifth column of Table
2.4, we can see at the current year of banking crisis, intensive margin increased by 11.4%
for exporters and 5.1% for importers.
For importers, there is no significant change on intensive margin for the time
periods before or after the banking crisis. For exporters, there is around 13%16
increase
16
Low point is 12.4% at the year right before crisis. High point is 15.4% at four years before crisis.
63
before banking crisis starts on intensive margin. After the crisis is over, there is around
8%17
increase on this margin.
From the information provided by these three tables, we can see that “no impact”
for exporter on overall margin is caused by neutralization of the negative impact from
extensive margin and positive impact from intensive margin. When there is a banking
crisis, credit constraint appears to force some firms or even industries exit from foreign
market. The rest firms and industries who can survive tend to export more. The total
export value is not significantly changed.
When there is a banking crisis, importers tend to import fewer varieties of goods
for all the time periods. This might be caused by shock on the income sensitive goods,
such as capital goods and durable goods. For the goods that still import, it seems there is
no influence on the value. This is consistent to what we found in Chapter 1. Also the
coefficients for the same variable from extensive and intensive margin added up equal to
the coefficient for that variable from overall margin.
When we include the variables that exporter/importer ever had banking crisis as
we did in Chapter 1. Overall margin results from Table 2.5 still hold the same pattern as
in Table 2.2. For exporters, if the country ever had experienced banking crisis does not
seem to influence the overall margin. For importers, in first column, not includes any
forwards and lags, country ever had a banking crisis tend to increase 10.6% on overall
17
Low point is 7.4% at five years after crisis. High point is 10.7% at four years before crisis.
64
margin. When includes five lag years, a country ever had a banking crisis tends increase
19.8% on overall margin. It is consistent to what we found in Chapter 1.
For extensive margin, coefficients for variables represent banking crisis with
different time periods tend to have the same pattern in Table 2.3. From fifth column of
Table 2.6, on average, exporters ever had a banking crisis tend to trade more varieties of
goods18
. Importers ever had a banking crisis tend to trade less varieties good goods19
.
For intensive margin, for exporters coefficients for variables represent banking
crisis with different time periods tend to have the same pattern in Table 2.4. For
importers, actually they tend to import less value of goods for each variety. On average is
around 6% less for each variety. From fifth column of Table 2.7, on average, exporters
ever had a banking crisis tend to trade less value of goods for each variety20
. Importers
ever had a banking crisis tend to trade more value of goods for each variety21
.
When we include country ever had banking crisis, most of the results are
consistent. For importer, it seems banking crisis make these countries import less
varieties of goods and less value for each variety. The more trade that for importer that
ever had a banking crisis tends to attribute more on intensive margin.
Coefficients from extensive and intensive margin for the same right-hand side
variable added up to the coefficient for that variable from overall margin. This is
consistent to our expectation. For exporters, a banking crisis tends to have negative
18
Extensive margin increased by 7.4% 19
Extensive margin decreased by 6.4% 20
Extensive margin increased by 6.3% 21
Extensive margin decreased by 21.8%
65
impacts on extensive margin and positive impacts on intensive margin. These two
opposite impacts tend to neutralize with each other. For importers, a banking crisis tends
to have a larger negative impact on extensive margin and relatively smaller impacts on
intensive margin.
2.5.2 Bilateral results
In Table 2.8, we analyze the bilateral effects of a banking crisis on the overall
margin. All the variables are defined same as in Chapter 1. For the dummy variable “one
country ever had a banking crisis”, the overall margin tends to increased by 44%22
. For
“both countries ever had banking crises”, the overall margin will increased by another
75%23
. This is consistent with what we found in Chapter 1 and the results from Table 2.5.
The other coefficients are almost identical to those in Chapter 1. When one of dyad has
banking crisis, the overall margin tends to increase slightly in the year of a banking crisis.
In subsequence year, the effect of the banking crisis tends to be negative. If both
countries have a banking crisis, the overall margin effect on bilateral trade is not
economically or statistically significant.
Table 2.9 presents the influence of banking crisis on the extensive margin. If one
of the countries ever had a banking crisis, the extensive margin tends to increased by
22
Low point is 43.5% when includes current year crises. High point is 46.7% when includes forward and
lag years. 23
Low point is 74.7% when includes current year crises. High point is 78.2% when includes forward and
lag years.
66
40%24
. If both countries had a banking crisis, the extensive margin tends to increased by
another 45%25
.
From fourth column of Table 2.9, we can see that when one country of the dyad
has a banking crisis, the extensive margin falls by 7.7% in the year of the crisis. The
impact of a banking crisis seems to be persistent. In the year following the end of a
banking crisis, the extensive margin declines by 20.3% and then starts to recover. When
both countries have a banking crisis, there is a 16.7% decline on extensive margin. In the
following year, the decline extends to 23.1% and then recovers slowly. All of the
negative effects on extensive margin are consistent to what we found from Table 2.3 and
2.6.
Table 2.10 presents the influence of banking crisis on intensive margin. For one
side of the trade pair had ever experienced a banking crisis, the intensive margin tends to
increased by 4%26
. For both sides of the trade pair had ever experienced a banking crisis,
the extensive margin tends to increased by another 22%27
.
From fourth column of Table 2.10, we can see that when one country of the trade
pair has a banking crisis, there is a 10.5% increase on intensive margin at the current year.
After crisis is over, the intensive margin tends to return to its previous level. When both
24
Low point is 38.0% when not include crisis time period. High point is 41.6% when includes forward and
lag years. 25
Low point is 41.9% when not include crisis time period. High point is 46.2% when includes forward and
lag years. 26
Low point is 2.7% when includes three forward and lag years. High point is 4.7% when not include
forward and lag years. 27
Low point is 21.2%when includes three forward and lag years. High point is 23.8% when not includes
forward and lag years.
67
countries experience a banking crisis, there is a 24.6% increase on intensive margin, and
tends to decline after crises are over. The results are also consistent with the results from
Table 2.4 and 2.7, that intensive margin tends to be positive for exporters and
insignificant or only slightly negative for importers.
The overall margin results for bilateral effects are quite consistent to the results
from Chapter 1. Both extensive margin and intensive margin results are also consistent
to the results from one-stage results we estimated previously. The coefficients from
countries ever had banking crises are also consistent to the assumption that history of
having banking crises is correlated with financial development and higher financial
development tend to trade more. For one country ever experienced a banking crisis. The
majority of the impact is attributed to extensive margin28
. For both countries of the trade
pair had ever experienced banking crises, the share of the impact from extensive margin
goes down29. The conclusion from overall margin “the presence of a banking crisis in the
second country will not tend to, but not necessarily intensify trade flows decreasing
between the two crises-stricken nations.” is the neutralization of the negative impact from
extensive margin and positive impact from intensive margin.
Same as in one-stage results, the coefficients from extensive and intensive margin
for the same variable added up equal to the coefficient for that variable from overall
margin.
2.5.3 Two-stage results for exporters and importers
28
Extensive margin takes around 90% of the effect. 29
Extensive margin takes around 65% of the effect.
68
Table 2.11 uses the same controls. The coefficients for exporter-year fixed effect
and importer-year fixed effect capture all the country-year specific effects. These
unilateral effects across time contain the information of outward/inward multilateral
resistance, exporters/importers’ GDP, total import and effect from financial shocks.
Results from two-stage estimation can offer a robustness check for what we have found
previously.
Table 2.12 uses coefficients of exporter-year fixed effect from Table 2.11 on
overall margin. Compare to the results from Chapter 1 for exporters, the coefficients for
variables of banking crisis with different time periods are almost the same. Results are
significant. However, they are relatively economically small. The coefficients for the
variable exporter ever had a banking crisis tend to be quite small or insignificant, which
is consistent to the result from 2.5.
Table 2.13 and 2.14 decomposes the effects for exporters into extensive margin
and intensive margin. All the influences of banking crisis have little impacts on extensive
and intensive margin. The overall margin seems evenly distributed into extensive and
intensive margin.
Table 2.15 uses coefficients of importer-year fixed effect from Table 2.11 on
overall margin. The coefficients for variables of banking crisis with different time periods
have the same sign as the results from Chapter 1. The magnitudes of the coefficients are
relatively small. Before banking crisis starts, there is a constant decline. After crisis is
over, the decline keeps intensified until the second year, and then starts to recover slowly.
69
This pattern is the same as in Chapter 1, and also consistent to the pattern in 2.2 and 2.5.
The coefficients for the variable exporter ever had a banking crisis are positive and
consistent to the results from 2.5.
Table 2.16 and 2.17 decompose the effects for importers into extensive margin
and intensive margin. During the different time periods of banking crisis, country tends to
import fewer varieties of goods and less value for each variety. When crisis is over, the
declines on both margins start to recover slowly. Roughly 60% of the impact on overall
margins occur through extensive margin.
Overall, the results from two-stage regression are consistent to the result in
Chapter 1. The pattern and coefficients on each margin are also close to the results from
Table 2.2 to 2.7
2.6 Conclusion
This paper investigates the statistical correlation between banking crisis and
fluctuations in bilateral trade flows using gravity model of international trade as the base
model. We decompose bilateral trade flows into the extensive margin and intensive
margin, which represent the varieties of the goods and value for each variety.
For the overall margin, the main results are consistent to what we found in
Chapter 1. As in Chapter 1, we find there is no significant impact for banking crisis on
exporters. For importers, there is a constant negative impact before the crisis. At the onset
70
of the crisis, there is an additional decline and starts to recover slowly after two years the
crisis is over.
The extensive-intensive margin decomposition reveals that banking crises have an
impact on both margins. For exporters, the lack of a significant impact of a banking crisis
on overall margin is caused by offsetting of the effects on extensive and intensive margin.
There is a negative impact on extensive margin and positive impact on intensive margin.
Exporters tend to export fewer varieties of goods and more value for each variety during
the banking crisis. It suggests that a limited credit constraint forces some firms or
industries quit from foreign markets. However, the survived firms and industries tend to
export more.
For importers, bilateral trade begins to fall before the onset of the banking crisis.
This effect is mostly through the extensive margin. At the starts of the banking crisis, the
extensive margin declines more. In subsequent period, the effect intensifie until the third
year, and then starts to recover slowly on extensive margin. There is also a relatively
small negative impact on intensive margin. So the importer tends to import fewer
varieties of goods and less value for each variety.
For exporters ever had experienced banking crisis, it seems there is no
economically large impact on both extensive and intensive margin. For importers ever
had experienced banking crisis, these countries tend to import fewer varieties of good and
more value for each variety.
71
If we treat the banking crisis as bilateral effects, we find that countries tend to
trade fewer varieties of goods and more value on each variety on bilateral trade flow
during banking crisis. These are consistent to what we found from unilateral effects.
72
Appendices
73
Appendix A Robustness check for bilateral results
Overall margin results from Table 2.18 are almost identical to the results from
Table 1.9
The results from Table 2.19 and 2.20 suggest that countries tend to trade fewer
varieties of goods and higher value of each variety during the banking crisis. These are
consistent to the results from Table 2.9 and 2.10. The bilateral effects for banking crisis
are robust to what we have found before. The negative impacts on extensive margin
intensified in the two to three years after crisis was over, and then it starts to recover
slowly. The positive impacts on intensive margin decline right after the crisis was over.
74
Appendix B Robustness check for Two-stage results
Table 2.21 replicates the regressions in Table 2.8 and include importer-exporter
fixed effect which will capture all the time invariant bilateral effects. The results are
consistent.
The results from Table 2.22 and 2.23 suggest that countries tend to trade fewer
varieties of goods and higher value of each variety during the banking crisis. The patterns
of the impacts after crisis on different margin are similar to the results from Table 2.19 to
2.21.
The coefficients from Table 2.24 show that when one country of the trade pair
had ever experienced banking crisis, the trade flow tends to increased by around 24%. If
both countries of the trade pair had ever experienced banking crises, the trade flow tends
to increased by another 77%. These results are consistent to the results in Chapter 1. The
impacts seem evenly distributed between extensive and intensive margin.
Tables 2.25 to 2.27 show that banking crises’ impacts on exporters are relatively
economically small. The impacts on overall margin are evenly distributed between
extensive margin and intensive margin.
Results from Tables 2.28 to 2.30 seem like puzzle for importers. The impacts are
positive and the majority of those impacts occur through intensive margin.
75
No of obs. Mean Std. dev. Min. Max
Trade (1000 current US dollar) 420960 3.490e+5 3.376e+6 0.001 3.31e+8
Overall margin 420960 0.009 0.036 2.63e-12 1
Extensive margin 420960 0.222 0.270 2.56e-8 1
Intensive margin 420960 0.030 0.081 4.48e-10 1
Country’s GDP (exporter, 1000 current US dollar) 847008 1.67e+8 7.53e+8 2.057e+4 1.42e+10
Distance 987657 7653.3 4429.0 1.881 1.995e+4
Contiguity 987657 0.019 0.135 0 1
Common language 987657 0.137 0.344 0 1
Banking crisis (exporter) 987657 0.065 0.246 0 1
One country of the trade pair has banking crisis 987657 0.116 0.320 0 1
Both countries of the trade pair have banking crises 987657 0.007 0.084 0 1
One country of the trade pair ever had banking crisis 987657 0.863 0.344 0 1
Both countries of the trade pair ever had banking crises 987657 0.397 0.489 0 1
Table 2.1: Summary Statistics for Chapter 2
76
Dep var ln(Overall margin)
Ln(distance) minus importers’ share weighted log distance
minus exporters’ share weighted log distance
-1.166***
(0.006)
-1.166***
(0.006)
-1.166***
(0.006)
-1.166***
(0.006)
-1.165***
(0.006)
Contiguity minus importers’ share weighted contiguity
minus exporters’ share weighted contiguity
0.487***
(0.024)
0.492***
(0.024)
0.496***
(0.024)
0.494***
(0.024)
0.500***
(0.024)
Common language minus importers’ share weighted
language minus exporters’ share weighted language
0.668***
(0.011)
0.670***
(0.011)
0.672***
(0.011)
0.672***
(0.011)
0.675***
(0.011)
Ln(exporter ’s GDP) 1.248***
(0.002)
1.248***
(0.002)
1.248***
(0.002)
1.249***
(0.002)
1.249***
(0.002)
Ln(importer’s GDP) 0.175***
(0.002)
0.180***
(0.002)
0.183***
(0.002)
0.183***
(0.002)
0.188***
(0.002)
Ln(total import) 0.011
(0.007)
0.005
(0.007)
0.002
(0.007)
0.003
(0.007)
-0.001
(0.007)
5 years forward of banking crisis for exporter 0.013
(0.026)
4 years forward of banking crisis for exporter -0.016
(0.026)
3 years forward of banking crisis for exporter -0.028
(0.024)
-0.029
(0.025)
2 years forward of banking crisis for exporter -0.031
(0.024)
-0.032
(0.025)
1 years forward of banking crisis for exporter -0.033
(0.023)
-0.034
(0.024)
Banking crisis for exporter 0.001
(0.014)
0.000
(0.014)
-0.002
(0.014)
-0.003
(0.014)
-0.005
(0.014)
1 years lag of banking crisis for exporter -0.016 (0.024)
-0.018 (0.024)
-0.018 (0.024)
-0.020 (0.024)
2 years lag of banking crisis for exporter -0.026
(0.024)
-0.028
(0.024)
-0.027
(0.024)
-0.030
(0.025)
3 years lag of banking crisis for exporter -0.006 (0.023)
-0.008 (0.024)
-0.007 (0.024)
-0.010 (0.024)
4 years lag of banking crisis for exporter -0.023
(0.023)
-0.024
(0.023)
5 years lag of banking crisis for exporter -0.041 (0.023)
-0.042 (0.023)
5 years forward of banking crisis for importer -0.074**
(0.026)
4 years forward of banking crisis for importer -0.076**
(0.025)
3 years forward of banking crisis for importer -0.077**
(0.025)
-0.088***
(0.025)
2 years forward of banking crisis for importer -0.082*** (0.024)
-0.091*** (0.024)
1 years forward of banking crisis for importer -0.082***
(0.023)
-0.093***
(0.023)
Banking crisis for importer -0.103*** (0.014)
-0.123*** (0.014)
-0.132*** (0.014)
-0.130*** (0.014)
-0.143*** (0.014)
1 years lag of banking crisis for importer -0.167***
(0.025)
-0.177***
(0.025)
-0.173***
(0.024)
-0.186***
(0.024)
2 years lag of banking crisis for importer -0.201*** (0.024)
-0.216*** (0.024)
-0.206*** (0.024)
-0.224*** (0.024)
3 years lag of banking crisis for importer -0.194***
(0.023)
-0.209***
(0.023)
-0.199***
(0.023)
-0.215***
(0.023)
4 years lag of banking crisis for importer -0.180*** (0.023)
-0.186*** (0.023)
5 years lag of banking crisis for importer -0.178***
(0.023)
-0.181***
(0.023)
Constant -41.315*** (0.080)
-41.311*** (0.080)
-41.313*** (0.080)
-41.324*** (0.081)
-41.329*** (0.081)
Year fixed effect Yes Yes Yes Yes Yes
R-square 0.586 0.586 0.586 0.586 0.587
No of obs. 379928 379928 379928 379928 379928
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.2: Linear approximations for multilateral resistance and banking crises for overall
margin
77
Dep var ln(Extensive margin)
Ln(distance) minus importers’ share weighted log distance
minus exporters’ share weighted log distance
-0.622***
(0.004)
-0.622***
(0.004)
-0.622***
(0.004)
-0.621***
(0.004)
-0.620***
(0.004)
Contiguity minus importers’ share weighted contiguity
minus exporters’ share weighted contiguity
-0.186***
(0.017)
-0.179***
(0.017)
-0.173***
(0.017)
-0.174***
(0.017)
-0.167***
(0.017)
Common language minus importers’ share weighted
language minus exporters’ share weighted language
0.400***
(0.008)
0.404***
(0.008)
0.407***
(0.008)
0.408***
(0.008)
0.414***
(0.008)
Ln(exporter ’s GDP) 0.682***
(0.001)
0.682***
(0.001)
0.683***
(0.001)
0.684***
(0.001)
0.686***
(0.001)
Ln(importer’s GDP) 0.040***
(0.001)
0.045***
(0.001)
0.048***
(0.001)
0.048***
(0.001)
0.053***
(0.001)
Ln(total import) 0.330***
(0.005)
0.325***
(0.005)
0.321***
(0.005)
0.323***
(0.005)
0.319***
(0.005)
5 years forward of banking crisis for exporter -0.127***
(0.018)
4 years forward of banking crisis for exporter -0.160***
(0.018)
3 years forward of banking crisis for exporter -0.144***
(0.017)
-0.155***
(0.017)
2 years forward of banking crisis for exporter -0.147***
(0.017)
-0.158***
(0.017)
1 years forward of banking crisis for exporter -0.139***
(0.016)
-0.151***
(0.017)
Banking crisis for exporter -0.078***
(0.010)
-0.088***
(0.010)
-0.093***
(0.010)
-0.102***
(0.010)
-0.113***
(0.010)
1 years lag of banking crisis for exporter -0.102*** (0.017)
-0.108*** (0.017)
-0.112*** (0.017)
-0.122*** (0.017)
2 years lag of banking crisis for exporter -0.093***
(0.017)
-0.101***
(0.017)
-0.100***
(0.017)
-0.112***
(0.017)
3 years lag of banking crisis for exporter -0.093*** (0.017)
-0.102*** (0.017)
-0.099*** (0.017)
-0.110*** (0.017)
4 years lag of banking crisis for exporter -0.098***
(0.017)
-0.105***
(0.016)
5 years lag of banking crisis for exporter -0.109*** (0.016)
-0.113*** (0.016)
5 years forward of banking crisis for importer -0.033
(0.018)
4 years forward of banking crisis for importer -0.083*** (0.018)
3 years forward of banking crisis for importer -0.073***
(0.018)
-0.085***
(0.018)
2 years forward of banking crisis for importer -0.103*** (0.017)
-0.112*** (0.017)
1 years forward of banking crisis for importer -0.108***
(0.017)
-0.119***
(0.017)
Banking crisis for importer -0.149*** (0.010)
-0.170*** (0.010)
-0.181*** (0.010)
-0.178*** (0.010)
-0.192*** (0.010)
1 years lag of banking crisis for importer -0.173***
(0.017)
-0.186***
(0.017)
-0.181***
(0.017)
-0.196***
(0.017)
2 years lag of banking crisis for importer -0.206*** (0.017)
-0.223*** (0.017)
-0.212*** (0.017)
-0.232*** (0.017)
3 years lag of banking crisis for importer -0.229***
(0.016)
-0.247***
(0.016)
-0.234***
(0.016)
-0.254***
(0.016)
4 years lag of banking crisis for importer -0.223*** (0.017)
-0.230*** (0.016)
5 years lag of banking crisis for importer -0.190***
(0.016)
-0.194***
(0.016)
Constant -25.368*** (0.057)
-25.363*** (0.057)
-25.366*** (0.057)
-25.391*** (0.057)
-25.402*** (0.057)
Year fixed effect Yes Yes Yes Yes Yes
R-square 0.463 0.464 0.464 0.464 0.465
No of obs. 379928 379928 379928 379928 379928
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.3: Linear approximations for multilateral resistance and banking crises for
extensive margin
78
Dep var ln(Intensive margin)
Ln(distance) minus importers’ share weighted log distance
minus exporters’ share weighted log distance
-0.544***
(0.005)
-0.544***
(0.005)
-0.544***
(0.005)
-0.545***
(0.005)
-0.545***
(0.005)
Contiguity minus importers’ share weighted contiguity
minus exporters’ share weighted contiguity
0.673***
(0.020)
0.671***
(0.020)
0.669***
(0.020)
0.669***
(0.020)
0.666***
(0.020)
Common language minus importers’ share weighted
language minus exporters’ share weighted language
0.267***
(0.009)
0.266***
(0.009)
0.265***
(0.009)
0.264***
(0.009)
0.261***
(0.009)
Ln(exporter ’s GDP) 0.566***
(0.001)
0.566***
(0.001)
0.566***
(0.001)
0.564***
(0.001)
0.563***
(0.001)
Ln(importer’s GDP) -0.135***
(0.001)
-0.135***
(0.001)
-0.135***
(0.001)
-0.135***
(0.001)
-0.135***
(0.001)
Ln(total import) -0.320***
(0.005)
-0.319***
(0.005)
-0.319***
(0.005)
-0.319***
(0.005)
-0.319***
(0.005)
5 years forward of banking crisis for exporter 0.140***
(0.021)
4 years forward of banking crisis for exporter 0.143***
(0.021)
3 years forward of banking crisis for exporter 0.116***
(0.021)
0.126***
(0.020)
2 years forward of banking crisis for exporter 0.116***
(0.020)
0.126***
(0.020)
1 years forward of banking crisis for exporter 0.107***
(0.019)
0.117***
(0.019)
Banking crisis for exporter 0.079***
(0.012)
0.088***
(0.012)
0.091***
(0.012)
0.098***
(0.012)
0.108***
(0.012)
1 years lag of banking crisis for exporter 0.085*** (0.020)
0.090*** (0.020)
0.093*** (0.020)
0.102*** (0.020)
2 years lag of banking crisis for exporter 0.067***
(0.020)
0.073***
(0.020)
0.073***
(0.020)
0.082***
(0.020)
3 years lag of banking crisis for exporter 0.087*** (0.020)
0.093*** (0.020)
0.092*** (0.020)
0.100*** (0.020)
4 years lag of banking crisis for exporter 0.075***
(0.020)
0.080***
(0.020)
5 years lag of banking crisis for exporter 0.068*** (0.019)
0.071*** (0.019)
5 years forward of banking crisis for importer -0.041
(0.021)
4 years forward of banking crisis for importer 0.007 (0.021)
3 years forward of banking crisis for importer -0.004
(0.021)
-0.003
(0.021)
2 years forward of banking crisis for importer 0.021 (0.020)
0.021 (0.020)
1 years forward of banking crisis for importer 0.026
(0.019)
0.026
(0.019)
Banking crisis for importer 0.045*** (0.012)
0.047*** (0.012)
0.049*** (0.012)
0.048*** (0.012)
0.050*** (0.012)
1 years lag of banking crisis for importer 0.007
(0.020)
0.009
(0.020)
0.008
(0.020)
0.010
(0.020)
2 years lag of banking crisis for importer 0.005 (0.020)
0.007 (0.020)
0.006 (0.020)
0.008 (0.020)
3 years lag of banking crisis for importer 0.035
(0.019)
0.037
(0.019)
0.036
(0.019)
0.038*
(0.019)
4 years lag of banking crisis for importer 0.043* (0.019)
0.044* (0.019)
5 years lag of banking crisis for importer 0.013
(0.019)
0.013
(0.019)
Constant -15.948*** (0.067)
-15.948*** (0.067)
-15.946*** (0.067)
-15.934*** (0.067)
-15.927*** (0.067)
Year fixed effect Yes Yes Yes Yes Yes
R-square 0.405 0.405 0.405 0.406 0.406
No of obs. 379928 379928 379928 379928 379928
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.4: Linear approximations for multilateral resistance and banking crises for
intensive margin
79
Dep var ln(Overall margin)
Ln(distance) minus importers’ share weighted log distance
minus exporters’ share weighted log distance
-1.170***
(0.006)
-1.170***
(0.006)
-1.171***
(0.006)
-1.170***
(0.006)
-1.171***
(0.006)
Contiguity minus importers’ share weighted contiguity
minus exporters’ share weighted contiguity
0.476***
(0.024)
0.479***
(0.025)
0.482***
(0.024)
0.480***
(0.024)
0.483***
(0.024)
Common language minus importers’ share weighted
language minus exporters’ share weighted language
0.659***
(0.011)
0.660***
(0.011)
0.660***
(0.011)
0.660***
(0.011)
0.661***
(0.011)
Ln(exporter ’s GDP) 1.249***
(0.002)
1.249***
(0.002)
1.249***
(0.002)
1.249***
(0.002)
1.249***
(0.002)
Ln(importer’s GDP) 0.163***
(0.002)
0.166***
(0.002)
0.167***
(0.002)
0.169***
(0.002)
0.172***
(0.002)
Ln(total import) 0.018**
(0.007)
0.013*
(0.007)
0.011
(0.007)
0.012
(0.007)
0.007
(0.007)
5 years forward of banking crisis for exporter 0.009
(0.026)
4 years forward of banking crisis for exporter -0.020
(0.025)
3 years forward of banking crisis for exporter -0.030
(0.024)
-0.033
(0.025)
2 years forward of banking crisis for exporter -0.033
(0.024)
-0.036
(0.025)
1 years forward of banking crisis for exporter -0.034
(0.024)
-0.038
(0.024)
Banking crisis for exporter 0.002
(0.014)
0.000
(0.015)
-0.003
(0.015)
-0.005
(0.015)
-0.009
(0.015)
1 years lag of banking crisis for exporter -0.016 (0.025)
-0.018 (0.025)
-0.019 (0.025)
-0.023 (0.025)
2 years lag of banking crisis for exporter -0.025
(0.025)
-0.029
(0.024)
-0.028
(0.024)
-0.033
(0.024)
3 years lag of banking crisis for exporter -0.005 (0.024)
-0.009 (0.024)
-0.008 (0.024)
-0.014 (0.024)
4 years lag of banking crisis for exporter -0.023
(0.024)
-0.027
(0.024)
5 years lag of banking crisis for exporter -0.041 (0.023)
-0.045 (0.023)
5 years forward of banking crisis for importer -0.129***
(0.026)
4 years forward of banking crisis for importer -0.133***
(0.025)
3 years forward of banking crisis for importer -0.119***
(0.025)
-0.146***
(0.025)
2 years forward of banking crisis for importer -0.126*** (0.024)
-0.150*** (0.024)
1 years forward of banking crisis for importer -0.126***
(0.023)
-0.154***
(0.024)
Banking crisis for importer -0.131*** (0.014)
-0.162*** (0.015)
-0.181*** (0.015)
-0.178*** (0.015)
-0.208*** (0.015)
1 years lag of banking crisis for importer -0.209***
(0.024)
-0.229***
(0.025)
-0.223***
(0.024)
-0.255***
(0.025)
2 years lag of banking crisis for importer -0.243*** (0.024)
-0.270*** (0.024)
-0.255*** (0.024)
-0.292*** (0.024)
3 years lag of banking crisis for importer -0.236***
(0.024)
-0.262***
(0.024)
-0.247***
(0.023)
-0.282***
(0.023)
4 years lag of banking crisis for importer -0.231*** (0.023)
-0.250*** (0.023)
5 years lag of banking crisis for importer -0.228***
(0.023)
-0.243***
(0.023)
Exporter ever had a banking crisis -0.004 (0.008)
-0.002 (0.009)
0.001 (0.009)
0.002 (0.009)
0.006 (0.009)
Importer ever had a banking crisis 0.101***
(0.009)
0.129***
(0.009)
0.151***
(0.009)
0.144***
(0.009)
0.180***
(0.010)
Constant -41.337*** (0.080)
-41.338*** (0.080)
-41.345*** (0.080)
-41.360*** (0.080)
-41.337*** (0.080)
Year fixed effect Yes Yes Yes Yes Yes
R-square 0.586 0.587 0.587 0.587 0.587
No of obs. 379928 379928 379928 379928 379928
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.5: Linear approximations for multilateral resistance and banking crises with
country ever experienced a banking crisis for overall margin
80
Dep var ln(Extensive margin)
Ln(distance) minus importers’ share weighted log distance
minus exporters’ share weighted log distance
-0.618***
(0.004)
-0.619***
(0.004)
-0.620***
(0.004)
-0.619***
(0.004)
-0.620***
(0.004)
Contiguity minus importers’ share weighted contiguity
minus exporters’ share weighted contiguity
-0.174***
(0.017)
-0.171***
(0.017)
-0.168***
(0.017)
-0.169***
(0.017)
-0.166***
(0.017)
Common language minus importers’ share weighted
language minus exporters’ share weighted language
0.410***
(0.008)
0.411***
(0.008)
0.411***
(0.008)
0.412***
(0.008)
0.414***
(0.008)
Ln(exporter ’s GDP) 0.681***
(0.001)
0.681***
(0.001)
0.681***
(0.001)
0.682***
(0.001)
0.683***
(0.001)
Ln(importer’s GDP) 0.054***
(0.001)
0.056***
(0.001)
0.057***
(0.001)
0.057***
(0.001)
0.058***
(0.001)
Ln(total import) 0.322***
(0.005)
0.318***
(0.005)
0.317***
(0.005)
0.318***
(0.005)
0.316***
(0.005)
5 years forward of banking crisis for exporter -0.151***
(0.018)
4 years forward of banking crisis for exporter -0.185***
(0.018)
3 years forward of banking crisis for exporter -0.157***
(0.017)
-0.180***
(0.018)
2 years forward of banking crisis for exporter -0.162***
(0.017)
-0.185***
(0.017)
1 years forward of banking crisis for exporter -0.154***
(0.011)
-0.178***
(0.011)
Banking crisis for exporter -0.081***
(0.010)
-0.095***
(0.010)
-0.106***
(0.011)
-0.118***
(0.011)
-0.142***
(0.011)
1 years lag of banking crisis for exporter -0.110*** (0.017)
-0.121*** (0.018)
-0.128*** (0.018)
-0.152*** (0.018)
2 years lag of banking crisis for exporter -0.100***
(0.017)
-0.114***
(0.017)
-0.116***
(0.017)
-0.142***
(0.017)
3 years lag of banking crisis for exporter -0.101*** (0.017)
-0.115*** (0.017)
-0.115*** (0.017)
-0.140*** (0.017)
4 years lag of banking crisis for exporter -0.111***
(0.017)
-0.133***
(0.017)
5 years lag of banking crisis for exporter -0.121*** (0.017)
-0.141*** (0.017)
5 years forward of banking crisis for importer -0.014
(0.018)
4 years forward of banking crisis for importer -0.062***
(0.018)
3 years forward of banking crisis for importer -0.047**
(0.018)
-0.063***
(0.018)
2 years forward of banking crisis for importer -0.076*** (0.017)
-0.090*** (0.017)
1 years forward of banking crisis for importer -0.080***
(0.017)
-0.097***
(0.017)
Banking crisis for importer -0.116*** (0.010)
-0.140*** (0.010)
-0.155*** (0.010)
-0.148*** (0.010)
-0.168*** (0.010)
1 years lag of banking crisis for importer -0.142***
(0.017)
-0.158***
(0.017)
-0.150***
(0.017)
-0.171***
(0.017)
2 years lag of banking crisis for importer -0.174*** (0.017)
-0.195*** (0.017)
-0.181*** (0.017)
-0.207*** (0.017)
3 years lag of banking crisis for importer -0.198***
(0.017)
-0.219***
(0.017)
-0.204***
(0.017)
-0.229***
(0.017)
4 years lag of banking crisis for importer -0.196*** (0.017)
-0.206*** (0.017)
5 years lag of banking crisis for importer -0.164***
(0.016)
-0.172***
(0.016)
Exporter ever had a banking crisis 0.009 (0.006)
0.022** (0.006)
0.034*** (0.006)
0.042*** (0.006)
0.071*** (0.007)
Importer ever had a banking crisis -0.118***
(0.006)
-0.097***
(0.006)
-0.080***
(0.006)
-0.089***
(0.006)
-0.066***
(0.007)
Constant -25.344*** (0.057)
-25.347*** (0.057)
-25.357*** (0.057)
-25.379*** (0.057)
-25.404*** (0.057)
Year fixed effect Yes Yes Yes Yes Yes
R-square 0.464 0.464 0.465 0.465 0.465
No of obs. 379928 379928 379928 379928 379928
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.6: Linear approximations for multilateral resistance and banking crises with
country ever experienced a banking crisis for extensive margin
81
Dep var ln(Intensive margin)
Ln(distance) minus importers’ share weighted log distance
minus exporters’ share weighted log distance
-0.551***
(0.005)
-0.551***
(0.005)
-0.551***
(0.005)
-0.551***
(0.005)
-0.551***
(0.005)
Contiguity minus importers’ share weighted contiguity
minus exporters’ share weighted contiguity
0.650***
(0.020)
0.650***
(0.020)
0.650***
(0.020)
0.650***
(0.020)
0.650***
(0.020)
Common language minus importers’ share weighted
language minus exporters’ share weighted language
0.249***
(0.009)
0.249***
(0.009)
0.249***
(0.009)
0.248***
(0.009)
0.247***
(0.009)
Ln(exporter ’s GDP) 0.567***
(0.002)
0.568***
(0.002)
0.568***
(0.002)
0.567***
(0.002)
0.566***
(0.002)
Ln(importer’s GDP) 0.109***
(0.002)
0.110***
(0.002)
0.111***
(0.002)
0.112***
(0.002)
0.114***
(0.002)
Ln(total import) -0.304***
(0.005)
-0.305***
(0.005)
-0.306***
(0.005)
-0.306***
(0.005)
-0.309***
(0.005)
5 years forward of banking crisis for exporter 0.160***
(0.021)
4 years forward of banking crisis for exporter 0.165***
(0.021)
3 years forward of banking crisis for exporter 0.127***
(0.020)
0.147***
(0.020)
2 years forward of banking crisis for exporter 0.129***
(0.020)
0.149***
(0.020)
1 years forward of banking crisis for exporter 0.119***
(0.020)
0.140***
(0.020)
Banking crisis for exporter 0.083***
(0.012)
0.095***
(0.012)
0.103***
(0.012)
0.113***
(0.012)
0.134***
(0.013)
1 years lag of banking crisis for exporter 0.094*** (0.021)
0.102*** (0.020)
0.109*** (0.021)
0.129*** (0.020)
2 years lag of banking crisis for exporter 0.075***
(0.020)
0.085***
(0.020)
0.088***
(0.020)
0.109***
(0.020)
3 years lag of banking crisis for exporter 0.095*** (0.020)
0.105*** (0.020)
0.107*** (0.020)
0.126*** (0.020)
4 years lag of banking crisis for exporter 0.087***
(0.020)
0.105***
(0.020)
5 years lag of banking crisis for exporter 0.080*** (0.019)
0.096*** (0.019)
5 years forward of banking crisis for importer -0.115***
(0.021)
4 years forward of banking crisis for importer -0.071***
(0.021)
3 years forward of banking crisis for importer -0.072***
(0.021)
-0.082***
(0.021)
2 years forward of banking crisis for importer -0.050* (0.021)
-0.060** (0.020)
1 years forward of banking crisis for importer -0.046*
(0.020)
-0.057**
(0.019)
Banking crisis for importer -0.014 (0.012)
-0.022 (0.012)
-0.027* (0.012)
-0.029* (0.012)
-0.040** (0.012)
1 years lag of banking crisis for importer -0.067***
(0.020)
-0.071***
(0.020)
-0.073***
(0.020)
-0.084***
(0.020)
2 years lag of banking crisis for importer -0.068*** (0.020)
-0.074*** (0.020)
-0.074*** (0.020)
-0.085*** (0.020)
3 years lag of banking crisis for importer -0.038*
(0.019)
-0.044*
(0.019)
-0.043*
(0.019)
-0.053**
(0.019)
4 years lag of banking crisis for importer -0.035 (0.019)
-0.043* (0.019)
5 years lag of banking crisis for importer -0.064***
(0.019)
-0.071***
(0.019)
Exporter ever had a banking crisis -0.013 (0.007)
-0.024*** (0.007)
-0.033*** (0.007)
-0.040*** (0.007)
-0.065*** (0.008)
Importer ever had a banking crisis 0.219***
(0.007)
0.226***
(0.007)
0.231***
(0.008)
0.233***
(0.008)
0.246***
(0.008)
Constant -15.993*** (0.067)
-15.990*** (0.067)
-15.988*** (0.067)
-15.981*** (0.067)
-15.973*** (0.067)
Year fixed effect Yes Yes Yes Yes Yes
R-square 0.407 0.407 0.407 0.407 0.407
No of obs. 379928 379928 379928 379928 379928
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.7: Linear approximations for multilateral resistance and banking crises with
country ever experienced a banking crisis for intensive margin
82
Dep var. ln(Overall margin)
Ln(distance) -1.382***
(0.004)
-1.382***
(0.004)
-1.382***
(0.004)
-1.382***
(0.004)
Contiguity 0.520***
(0.021)
0.521***
(0.021)
0.520***
(0.021)
0.520***
(0.021)
Common language 0.876***
(0.009)
0.876***
(0.009)
0.876***
(0.009)
0.876***
(0.009)
One crisis ever 0.368***
(0.011)
0.361***
(0.011)
0.374***
(0.011)
0.383***
(0.011)
Both crises ever 0.564***
(0.007)
0.558***
(0.007)
0.570***
(0.007)
0.578***
(0.007)
One crisis 0.068***
(0.009)
0.053***
(0.010)
0.045***
(0.009)
One year lag of one
crisis
-0.128***
(0.016)
-0.127***
(0.016)
Two years lag of
one crisis
-0.095***
(0.015)
-0.095***
(0.016)
Three years lag of
one crisis
-0.112***
(0.015)
-0.117***
(0.015)
Four years lag of
one crisis
-0.092***
(0.015)
Five years lag of
one crisis
-0.135***
(0.015)
Two crises 0.076*
(0.036)
0.052
(0.035)
0.038
(0.035)
One year lag of two crises
-0.111* (0.050)
-0.107* (0.050)
Two years lag of
two crises
-0.062
(0.049)
-0.056
(0.049)
Three years lag of two crises
-0.115* (0.048)
-0.112* (0.048)
Four years lag of
two crises
-0.046
(0.047)
Five years lag of two crises
-0.107* (0.046)
Importer-year
fixed effect
yes yes yes yes
Exporter-year
fixed effect
yes yes yes yes
Constant 2.878***
(0.037)
2.879***
(0.037)
2.879***
(0.037)
2.878***
(0.037)
R-square 0.688 0.688 0.688 0.688
No of obs. 420960 420960 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.8: Overall margin and banking crises with lags
83
Dep var. ln(Extensive margin)
Ln(distance) -0.770***
(0.003)
-0.768***
(0.003)
-0.766***
(0.003)
-0.766***
(0.003)
Contiguity 0.001
(0.018)
0.004
(0.018)
0.011
(0.018)
0.010
(0.018)
Common language 0.328***
(0.007)
0.329***
(0.007)
0.329***
(0.007)
0.329***
(0.007)
One crisis ever 0.322***
(0.009)
0.328***
(0.009)
0.346***
(0.009)
0.348***
(0.009)
Both crises ever 0.350***
(0.005)
0.357***
(0.006)
0.377***
(0.006)
0.380***
(0.005)
One crisis -0.061***
(0.008)
-0.079***
(0.008)
-0.081***
(0.008)
One year lag of one
crisis
-0.231***
(0.013)
-0.227***
(0.013)
Two years lag of
one crisis
-0.152***
(0.013)
-0.147***
(0.013)
Three years lag of
one crisis
-0.127***
(0.013)
-0.123***
(0.013)
Four years lag of
one crisis
-0.048***
(0.013)
Five years lag of
one crisis
-0.016
(0.013)
Two crises -0.141***
(0.029)
-0.179***
(0.029)
-0.183***
(0.030)
One year lag of two crises
-0.267*** (0.042)
-0.263*** (0.042)
Two years lag of
two crises
-0.197***
(0.041)
-0.192***
(0.041)
Three years lag of two crises
-0.216*** (0.040)
-0.214*** (0.040)
Four years lag of
two crises
-0.102**
(0.040)
Five years lag of two crises
-0.107** (0.039)
Importer-year
fixed effect
yes yes yes yes
Exporter-year
fixed effect
yes yes yes yes
Constant 3.304***
(0.031)
3.291***
(0.031)
3.274***
(0.031)
3.275***
(0.031)
R-square 0.434 0.435 0.435 0.435
No of obs. 420960 420960 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.9: Extensive margin and banking crises with lags
84
Dep var. ln(Intensive margin)
Ln(distance) -0.612***
(0.004)
-0.614***
(0.004)
-0.616***
(0.004)
-0.616***
(0.004)
Contiguity 0.520***
(0.021)
0.517***
(0.021)
0.510***
(0.021)
0.510***
(0.021)
Common language 0.548***
(0.008)
0.547***
(0.008)
0.547***
(0.008)
0.547***
(0.008)
One crisis ever 0.046***
(0.011)
0.032**
(0.011)
0.027*
(0.011)
0.035**
(0.011)
Both crises ever 0.214***
(0.006)
0.201***
(0.006)
0.192***
(0.006)
0.198***
(0.006)
One crisis 0.129***
(0.009)
0.132***
(0.009)
0.127***
(0.009)
One year lag of one
crisis
0.103***
(0.015)
0.100***
(0.015)
Two years lag of
one crisis
0.057***
(0.015)
0.052***
(0.015)
Three years lag of
one crisis
0.014
(0.015)
0.006
(0.015)
Four years lag of
one crisis
-0.044**
(0.015)
Five years lag of
one crisis
-0.119***
(0.015)
Two crises 0.217***
(0.034)
0.231***
(0.034)
0.220***
(0.034)
One year lag of two crises
0.155** (0.049)
0.156** (0.049)
Two years lag of
two crises
0.135**
(0.048)
0.137**
(0.048)
Three years lag of two crises
0.100* (0.047)
0.102* (0.047)
Four years lag of
two crises
0.057
(0.046)
Five years lag of two crises
-0.001 (0.045)
Importer-year
fixed effect
yes yes yes yes
Exporter-year
fixed effect
yes yes yes yes
Constant -0.426***
(0.036)
-0.412***
(0.036)
-0.395***
(0.036)
-0.397***
(0.036)
R-square 0.369 0.369 0.369 0.369
No of obs. 420960 420960 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.10: Intensive margin and banking crises with lags
85
ln(Overall margin) ln(Extensive
margin)
ln(Intensive
margin)
Ln(distance) -1.386***
(0.004)
-0.780***
(0.003)
-0.606***
(0.004)
Contiguity 0.515***
(0.021)
-0.001
(0.018)
0.516***
(0.021)
Common language 0.882*** (0.008)
0.312*** (0.007)
0.569*** (0.008)
Importer-year
fixed effect
Yes Yes Yes
Exporter-year fixed effect
Yes Yes Yes
Constant 3.513***
(0.035)
3.849***
(0.030)
-0.336***
(0.035)
R-square 0.688 0.434 0.368
No of obs. 420960 420960 420960
*** for p-value<0.001
** for p-value<0.01
* for p-value<0.05
Table 2.11: First stage of the regression for different margins
86
Coefficient of Exporter-year fixed effect from first column of Table
2.10
Ln(Exporter’ s GDP) 1.191***
(0.001)
1.191***
(0.001)
1.192***
(0.001)
Five year forward of
exporter’s crisis
0.001
(0.007)
Four year forward of exporter’s crisis
-0.006 (0.007)
Three year forward of
exporter’s crisis
-0.070***
(0.007)
Two year forward of exporter’s crisis
-0.073*** (0.007)
One year forward of
exporter’s crisis
-0.063***
(0.007)
Banking crises for exporter
0.024*** (0.004)
0.036*** (0.004)
0.024*** (0.004)
One year lag of
exporter’s crisis
0.049***
(0.007)
0.038***
(0.007)
Two year lag of exporter’s crisis
0.050*** (0.007)
0.041** (0.007)
Three year lag of
exporter’s crisis
0.070***
(0.007)
0.061***
(0.007)
Four year lag of exporter’s crisis
0.070*** (0.007)
0.062*** (0.007)
Five year lag of
exporter’s crisis
0.039***
(0.007)
0.033**
(0.007)
Exporter ever had a
banking crisis
0.010***
(0.002)
-0.002
(0.003)
0.009**
(0.003)
Constant -24.485***
(0.035)
-24.428***
(0.036)
-24.453***
(0.035)
Importers’ GDP share
weighted log distance
Yes Yes Yes
Importers’ GDP share
weighted language
Yes Yes Yes
Importers’ GDP share
weighted contiguity
Yes Yes Yes
Year fixed effect Yes Yes Yes
R-square 0.879 0.879 0.879
No of obs 823649 823649 823649
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.12: Exporter-year fixed effect and exporters’ banking crisis on overall margin
87
Coefficient of Exporter-year fixed effect from second column of
Table 2.10
Ln(Exporter’ s GDP) 0.582***
(0.000)
0.583***
(0.000)
0.583***
(0.000)
Five year forward of
exporter’s crisis
0.001
(0.004)
Four year forward of exporter’s crisis
-0.003 (0.003)
Three year forward of
exporter’s crisis
-0.034***
(0.003)
Two year forward of exporter’s crisis
-0.036*** (0.003)
One year forward of
exporter’s crisis
-0.031***
(0.003)
Banking crises for exporter
0.012*** (0.002)
0.018*** (0.002)
0.012*** (0.002)
One year lag of
exporter’s crisis
0.024***
(0.004)
0.019***
(0.004)
Two year lag of exporter’s crisis
0.025*** (0.003)
0.020** (0.004)
Three year lag of
exporter’s crisis
0.034***
(0.003)
0.030***
(0.003)
Four year lag of exporter’s crisis
0.034*** (0.003)
0.030*** (0.004)
Five year lag of
exporter’s crisis
0.019***
(0.004)
0.016**
(0.004)
Exporter ever had a
banking crisis
0.005***
(0.001)
-0.001
(0.001)
0.004**
(0.001)
Constant -11.973***
(0.017)
-11.945***
(0.017)
-11.958***
(0.017)
Importers’ GDP share
weighted log distance
Yes Yes Yes
Importers’ GDP share
weighted language
Yes Yes Yes
Importers’ GDP share
weighted contiguity
Yes Yes Yes
Year fixed effect Yes Yes Yes
R-square 0.879 0.879 0.879
No of obs 823649 823649 823649
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.13: Exporter-year fixed effect and exporters’ banking crisis on extensive margin
88
Coefficient of Exporter-year fixed effect from third column of Table
2.10
Ln(Exporter’ s GDP) 0.609***
(0.000)
0.609***
(0.000)
0.609***
(0.000)
Five year forward of
exporter’s crisis
0.001
(0.004)
Four year forward of exporter’s crisis
-0.003 (0.004)
Three year forward of
exporter’s crisis
-0.036***
(0.004)
Two year forward of exporter’s crisis
-0.037*** (0.003)
One year forward of
exporter’s crisis
-0.032***
(0.004)
Banking crises for exporter
0.012*** (0.002)
0.019*** (0.002)
0.012*** (0.002)
One year lag of
exporter’s crisis
0.025***
(0.004)
0.020***
(0.004)
Two year lag of exporter’s crisis
0.026*** (0.004)
0.021** (0.004)
Three year lag of
exporter’s crisis
0.036***
(0.004)
0.031***
(0.004)
Four year lag of exporter’s crisis
0.036*** (0.004)
0.032*** (0.004)
Five year lag of
exporter’s crisis
0.020***
(0.004)
0.017**
(0.004)
Exporter ever had a
banking crisis
0.005***
(0.001)
-0.001
(0.001)
0.004**
(0.001)
Constant -12.512***
(0.018)
-12.483***
(0.018)
-12.496***
(0.018)
Importers’ GDP share
weighted log distance
Yes Yes Yes
Importers’ GDP share
weighted language
Yes Yes Yes
Importers’ GDP share
weighted contiguity
Yes Yes Yes
Year fixed effect Yes Yes Yes
R-square 0.879 0.879 0.879
No of obs 823649 823649 823649
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.14: Exporter-year fixed effect and exporters’ banking crisis on intensive margin
89
Coefficient of Importer-year fixed effect from first column of Table
2.10
Ln(Importer’ s GDP) 0.149***
(0.001)
0.148***
(0.001)
0.151***
(0.001)
Ln(total import) 0.005***
(0.001)
0.005***
(0.001)
0.004***
(0.001)
Five year forward of importer’s crisis
-0.074*** (0.005)
Four year forward of
importer’s crisis
-0.079***
(0.005)
Three year forward of importer’s crisis
-0.107*** (0.005)
Two year forward of
importer’s crisis
-0.104***
(0.004)
One year forward of importer’s crisis
-0.093*** (0.004)
Banking crises for
impoter
-0.017***
(0.003)
-0.033***
(0.003)
-0.052***
(0.003)
One year lag of importer’s crisis
-0.075*** (0.004)
-0.092*** (0.004)
Two year lag of
importer’s crisis
-0.091***
(0.004)
-0.106***
(0.004)
Three year lag of importer’s crisis
-0.090*** (0.004)
-0.103*** (0.004)
Four year lag of
importer’s crisis
-0.060***
(0.004)
-0.073***
(0.004)
Five year lag of
importer’s crisis
-0.042***
(0.004)
-0.053***
(0.004)
Importer ever had a
banking crisis
0.064***
(0.002)
0.080***
(0.002)
0.099***
(0.002)
Constant -7.675***
(0.022)
-7.752***
(0.022)
-7.803***
(0.022)
Exporters’ GDP share
weighted distance
Yes Yes Yes
Exporters’ GDP share
weighted language
Yes Yes Yes
Exporters’ GDP share
weighted contiguity
Yes Yes Yes
Year fixed effect Yes Yes Yes
R-square 0.289 0.291 0.293
No of obs 638716 638716 638716
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.15: Importer-year fixed effect and importers’ banking crisis on overall margin
90
Coefficient of Importer-year fixed effect from second column of Table
2.10
Ln(Importer’ s GDP) 0.093***
(0.001)
0.093***
(0.001)
0.095***
(0.001)
Ln(total import) 0.003***
(0.001)
0.003***
(0.001)
0.002***
(0.001)
Five year forward of importer’s crisis
-0.046*** (0.003)
Four year forward of
importer’s crisis
-0.050***
(0.003)
Three year forward of importer’s crisis
-0.067*** (0.003)
Two year forward of
importer’s crisis
-0.066***
(0.003)
One year forward of importer’s crisis
-0.059*** (0.003)
Banking crises for
impoter
-0.011***
(0.002)
-0.021***
(0.002)
-0.033***
(0.002)
One year lag of importer’s crisis
-0.047*** (0.003)
-0.058*** (0.003)
Two year lag of
importer’s crisis
-0.057***
(0.003)
-0.067***
(0.003)
Three year lag of importer’s crisis
-0.057*** (0.003)
-0.065*** (0.003)
Four year lag of
importer’s crisis
-0.038***
(0.003)
-0.046***
(0.003)
Five year lag of
importer’s crisis
-0.027***
(0.003)
-0.033***
(0.003)
Importer ever had a
banking crisis
0.040***
(0.001)
0.050***
(0.001)
0.062***
(0.001)
Constant -4.820***
(0.014)
-4.868***
(0.014)
-4.900***
(0.014)
Exporters’ GDP share
weighted distance
Yes Yes Yes
Exporters’ GDP share
weighted language
Yes Yes Yes
Exporters’ GDP share
weighted contiguity
Yes Yes Yes
Year fixed effect Yes Yes Yes
R-square 0.289 0.291 0.293
No of obs 638716 638716 638716
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.16: Importer-year fixed effect and importers’ banking crisis on extensive margin
91
Coefficient of Importer-year fixed effect from third column of Table
2.10
Ln(Importer’ s GDP) 0.055***
(0.000)
0.055***
(0.000)
0.056***
(0.000)
Ln(total import) 0.002***
(0.000)
0.002***
(0.000)
0.001***
(0.000)
Five year forward of importer’s crisis
-0.027*** (0.002)
Four year forward of
importer’s crisis
-0.030***
(0.002)
Three year forward of importer’s crisis
-0.040*** (0.002)
Two year forward of
importer’s crisis
-0.039***
(0.002)
One year forward of importer’s crisis
-0.035*** (0.002)
Banking crises for
impoter
-0.006***
(0.001)
-0.012***
(0.001)
-0.019***
(0.001)
One year lag of importer’s crisis
-0.028*** (0.002)
-0.034*** (0.002)
Two year lag of
importer’s crisis
-0.034***
(0.002)
-0.039***
(0.002)
Three year lag of importer’s crisis
-0.034*** (0.002)
-0.038*** (0.002)
Four year lag of
importer’s crisis
-0.022***
(0.002)
-0.027***
(0.002)
Five year lag of
importer’s crisis
-0.016***
(0.002)
-0.020***
(0.002)
Importer ever had a
banking crisis
0.024***
(0.001)
0.030***
(0.001)
0.037***
(0.001)
Constant -2.855***
(0.008)
-2.884***
(0.008)
-2.903***
(0.008)
Exporters’ GDP share
weighted distance
Yes Yes Yes
Exporters’ GDP share
weighted language
Yes Yes Yes
Exporters’ GDP share
weighted contiguity
Yes Yes Yes
Year fixed effect Yes Yes Yes
R-square 0.289 0.291 0.293
No of obs 638716 638716 638716
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.17: Importer-year fixed effect and importers’ banking crisis on intensive margin
92
Dep var ln(Overall margin)
Ln(distance) -1.382***
(0.004)
-1.383***
(0.004)
Contiguity 0.521***
(0.021)
0.521***
(0.021)
Common language 0.876***
(0.009)
0.876***
(0.009)
One crisis ever 0.374***
(0.011)
0.379***
(0.011)
Both crises ever 0.572***
(0.007)
0.580***
(0.007)
Five years forward of one crisis 0.086***
(0.016)
Four years forward of one crisis 0.028
(0.016)
Three years forward of one crisis -0.030
(0.015)
-0.024
(0.016)
Two years forward of one crisis 0.056***
(0.015)
0.057***
(0.015)
One year forward of one crisis -0.040**
(0.015)
-0.040**
(0.015)
One crisis 0.054***
(0.010)
0.049***
(0.010)
One year lag of one crisis -0.124***
(0.016)
-0.123***
(0.016)
Two years lag of one crisis -0.094*** (0.015)
-0.093*** (0.016)
Three years lag of one crisis -0.112***
(0.015)
-0.115***
(0.015)
Four years lag of one crisis -0.091*** (0.015)
Five years lag of one crisis -0.134***
(0.015)
Five years forward of two crises -0.126* (0.052)
Four years forward of two crises -0.138**
(0.047)
Three years forward of two crises -0.153**
(0.051)
-0.158**
(0.051)
Two years forward of two crises -0.061
(0.048)
-0.065
(0.048)
One year forward of two crises -0.121* (0.047)
-0.122** (0.047)
Two crises 0.048
(0.035)
0.038
(0.035)
One year lag of two crises -0.107* (0.050)
-0.104* (0.050)
Two years lag of two crises -0.060
(0.049)
-0.054
(0.049)
Three years lag of two crises -0.114* (0.048)
-0.111* (0.048)
Four years lag of two crises -0.046
(0.047)
Five years lag of two crises -0.107* (0.046)
Importer year fixed effect yes yes
Exporter year fixed effect yes yes
Constant 2.884*** (0.037)
2.888*** (0.037)
R-square 0.688 0.688
No of obs. 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.18: Overall margin and banking crises with forwards and lags
93
Dep var ln(Extensive margin)
Ln(distance) -0.767***
(0.003)
-0.767***
(0.003)
Contiguity 0.011
(0.018)
0.014
(0.018)
Common language 0.329***
(0.007)
0.329***
(0.007)
One crisis ever 0.345***
(0.009)
0.354***
(0.009)
Both crises ever 0.379***
(0.006)
0.391***
(0.006)
Five years forward of one crisis -0.076***
(0.013)
Four years forward of one crisis -0.082***
(0.013)
Three years forward of one crisis -0.030*
(0.013)
-0.035**
(0.013)
Two years forward of one crisis 0.051***
(0.013)
0.041**
(0.013)
One year forward of one crisis 0.006
(0.012)
-0.004
(0.013)
One crisis -0.073***
(0.008)
-0.083***
(0.008)
One year lag of one crisis -0.229***
(0.013)
-0.232***
(0.013)
Two years lag of one crisis -0.152*** (0.013)
-0.152*** (0.013)
Three years lag of one crisis -0.126***
(0.013)
-0.128***
(0.013)
Four years lag of one crisis -0.053*** (0.013)
Five years lag of one crisis -0.018
(0.013)
Five years forward of two crises -0.190*** (0.044)
Four years forward of two crises -0.195***
(0.043)
Three years forward of two crises -0.143***
(0.043)
-0.154***
(0.043)
Two years forward of two crises -0.136***
(0.041)
-0.147***
(0.041)
One year forward of two crises -0.169*** (0.040)
-0.177*** (0.040)
Two crises -0.180***
(0.030)
-0.197***
(0.030)
One year lag of two crises -0.267*** (0.042)
-0.270*** (0.042)
Two years lag of two crises -0.196***
(0.041)
-0.198***
(0.041)
Three years lag of two crises -0.215*** (0.040)
-0.221*** (0.040)
Four years lag of two crises -0.112**
(0.040)
Five years lag of two crises -0.111** (0.039)
Importer year fixed effect yes yes
Exporter year fixed effect yes yes
Constant 3.281*** (0.031)
3.280*** (0.031)
R-square 0.435 0.435
No of obs. 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.19: Extensive margin and banking crises with forwards and lags
94
Dep var ln(Intensive margin)
Ln(distance) -0.616***
(0.004)
-0.616***
(0.004)
Contiguity 0.509***
(0.021)
0.507***
(0.021)
Common language 0.547***
(0.008)
0.547***
(0.008)
One crisis ever 0.029**
(0.011)
0.025*
(0.011)
Both crises ever 0.193***
(0.007)
0.188***
(0.007)
Five years forward of one crisis 0.162***
(0.016)
Four years forward of one crisis 0.110***
(0.015)
Three years forward of one crisis 0.000
(0.015)
0.011
(0.015)
Two years forward of one crisis 0.005
(0.015)
0.016
(0.015)
One year forward of one crisis -0.046**
(0.014)
-0.036*
(0.015)
One crisis 0.127***
(0.009)
0.132***
(0.009)
One year lag of one crisis 0.105***
(0.015)
0.109***
(0.016)
Two years lag of one crisis 0.058*** (0.015)
0.059*** (0.015)
Three years lag of one crisis 0.015
(0.015)
0.013
(0.015)
Four years lag of one crisis -0.039** (0.015)
Five years lag of one crisis -0.116***
(0.015)
Five years forward of two crises 0.063 (0.051)
Four years forward of two crises 0.057
(0.050)
Three years forward of two crises -0.010
(0.050)
-0.004
(0.050)
Two years forward of two crises 0.075
(0.048)
0.082
(0.048)
One year forward of two crises 0.049 (0.046)
0.055 (0.046)
Two crises 0.228***
(0.034)
0.235***
(0.034)
One year lag of two crises 0.159** (0.049)
0.166** (0.049)
Two years lag of two crises 0.136**
(0.048)
0.144**
(0.048)
Three years lag of two crises 0.100* (0.047)
0.065 (0.047)
Four years lag of two crises 0.065
(0.046)
Five years lag of two crises 0.004 (0.045)
Importer year fixed effect Yes Yes
Exporter year fixed effect Yes Yes
Constant -0.397*** (0.037)
-0.392*** (0.037)
R-square 0.369 0.369
No of obs. 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.20: Intensive margin and banking crises with forwards and lags
95
Dep var ln(Overall margin)
Five years forward of
one crisis
0.026*
(0.011)
Four years forward of
one crisis
0.051***
(0.011)
Three years forward of
one crisis
-0.056***
(0.011)
Two years forward of
one crisis
0.026*
(0.010)
One year forward of
one crisis
-0.028**
(0.010)
One crisis 0.037***
(0.006)
0.020***
(0.006)
0.028***
(0.006)
One year lag of one
crisis
-0.064***
(0.011)
-0.070***
(0.011)
Two years lag of one
crisis
-0.073***
(0.010)
-0.076***
(0.011)
Three years lag of one
crisis
-0.081***
(0.010)
-0.083***
(0.010)
Four years lag of one
crisis
-0.057***
(0.010)
-0.058***
(0.010)
Five years lag of one
crisis
-0.113***
(0.010)
-0.113***
(0.010)
Five years forward of
two crises
0.092**
(0.035)
Four years forward of two crises
0.169*** (0.035)
Three years forward of
two crises
0.084*
(0.035)
Two years forward of two crises
0.162*** (0.033)
One year forward of
two crises
0.131***
(0.032)
Two crises 0.093*** (0.024)
0.073** (0.024)
0.126*** (0.024)
One year lag of two
crises
-0.031
(0.034)
-0.011
(0.034)
Two years lag of two
crises
0.012
(0.033)
0.033
(0.033)
Three years lag of two
crises
-0.036
(0.032)
-0.015
(0.032)
Four years lag of two crises
-0.008 (0.032)
0.013 (0.032)
Five years lag of two
crises
-0.085**
(0.032)
-0.064**
(0.032)
Importer year fixed effect
yes yes yes
Exporter year
fixed effect
yes yes yes
Importer-Exporter fixed effect
yes yes yes
Constant -8.313***
(0.002)
-8.294***
(0.003)
-8.298***
(0.003)
R-square 0.854 0.854 0.854
No of obs. 420960 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.21: Overall margin and banking crises with Importer-Exporter fixed effect
96
Dep var ln(Extensive margin)
Five years forward of
one crisis
-0.068***
(0.012)
Four years forward of one crisis
-0.038*** (0.012)
Three years forward of
one crisis
0.000
(0.012)
Two years forward of one crisis
0.056*** (0.012)
One year forward of
one crisis
0.047***
(0.012)
One crisis -0.058*** (0.007)
0.066*** (0.007)
0.056*** (0.007)
One year lag of one
crisis
-0.150***
(0.012)
-0.151***
(0.012)
Two years lag of one crisis
-0.094*** (0.012)
-0.093*** (0.012)
Three years lag of one
crisis
-0.061***
(0.012)
-0.060***
(0.012)
Four years lag of one crisis
0.013 (0.012)
0.014 (0.012)
Five years lag of one
crisis
0.045***
(0.012)
0.047***
(0.012)
Five years forward of
two crises
-0.027
(0.041)
Four years forward of
two crises
0.006
(0.040)
Three years forward of
two crises
0.040
(0.040)
Two years forward of
two crises
0.014
(0.038)
One year forward of
two crises
0.011
(0.037)
Two crises -0.084***
(0.027)
-0.098***
(0.027)
-0.069*
(0.027)
One year lag of two
crises
-0.174***
(0.039)
-0.166***
(0.039)
Two years lag of two
crises
-0.123**
(0.038)
-0.111**
(0.038)
Three years lag of two
crises
-0.137***
(0.037)
-0.126***
(0.037)
Four years lag of two
crises
-0.048
(0.037)
-0.038
(0.037)
Five years lag of two
crises
-0.054
(0.036)
-0.042
(0.036)
Importer year fixed effect
yes yes yes
Exporter year
fixed effect
yes yes yes
Importer-Exporter fixed effect
yes yes yes
Constant -2.828***
(0.003)
-2.814***
(0.003)
-2.817***
(0.003)
R-square 0.508 0.508 0.508
No of obs. 420960 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.22: Extensive margin and banking crises with Importer-Exporter fixed effect
97
Dep var ln(Intensive margin)
Five years forward of
one crisis
0.094***
(0.014)
Four years forward of one crisis
0.089*** (0.014)
Three years forward of
one crisis
-0.056***
(0.014)
Two years forward of one crisis
-0.030* (0.013)
One year forward of
one crisis
-0.075***
(0.013)
One crisis 0.095*** (0.008)
0.086*** (0.008)
0.084*** (0.008)
One year lag of one
crisis
0.086***
(0.014)
0.081***
(0.014)
Two years lag of one crisis
0.022 (0.014)
0.017 (0.014)
Three years lag of one
crisis
-0.020
(0.013)
-0.023
(0.013)
Four years lag of one crisis
-0.070*** (0.013)
-0.072*** (0.013)
Five years lag of one
crisis
-0.158***
(0.013)
-0.160***
(0.013)
Five years forward of
two crises
0.119**
(0.046)
Four years forward of
two crises
0.163***
(0.045)
Three years forward of
two crises
0.044
(0.045)
Two years forward of
two crises
0.148***
(0.043)
One year forward of
two crises
0.119**
(0.042)
Two crises 0.177***
(0.031)
0.171***
(0.031)
0.195***
(0.031)
One year lag of two
crises
0.142**
(0.044)
0.155**
(0.044)
Two years lag of two
crises
0.136**
(0.043)
0.144**
(0.043)
Three years lag of two
crises
0.100*
(0.042)
0.111*
(0.042)
Four years lag of two
crises
0.039
(0.042)
0.051
(0.042)
Five years lag of two
crises
-0.031
(0.041)
-0.022
(0.041)
Importer year fixed effect
yes yes yes
Exporter year
fixed effect
yes yes yes
Importer-Exporter fixed effect
yes yes yes
Constant -5.485***
(0.003)
-5.479***
(0.003)
-5.481***
(0.003)
R-square 0.483 0.483 0.483
No of obs. 420960 420960 420960
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.23: Intensive margin and banking crises with Importer-Exporter fixed effect
98
Coefficient
of
importer-exporter
fixed
effect from First
column of
Table 2.20 (Overall)
Coefficient
of
importer-exporter
fixed
effect from Second
column of
Table 2.20 (Overall)
Coefficient
of
importer-exporter
fixed
effect from Third
column of
Table 2.20 (Overall)
Coefficient
of
importer-exporter
fixed effect
from First column of
Table 2.21
(Extensive)
Coefficient
of
importer-exporter
fixed effect
from Second
column of
Table 2.21 (Extensive)
Coefficient
of
importer-exporter
fixed effect
from Third column of
Table 2.21
(Extensive)
Coefficient
of
importer-exporter
fixed
effect from First
column of
Table 2.22 (Intensive)
Coefficient
of
importer-exporter
fixed
effect from Second
column of
Table 2.22 (Intensive)
Coefficient
of
importer-exporter
fixed
effect from Third
column of
Table 2.22 (Intensive)
Ln(distance) -1.131***
(0.004)
-1.131***
(0.004)
-1.133***
(0.004)
-0.549***
(0.002)
-0.548***
(0.002)
-0.549***
(0.002)
-0.583***
(0.002)
-0.582***
(0.002)
-0.583***
(0.002)
Contiguity 1.651*** (0.023)
1.653*** (0.023)
1.643*** (0.023)
0.801*** (0.011)
0.802*** (0.011)
0.797*** (0.011)
0.850*** (0.012)
0.851*** (0.012)
0.846*** (0.012)
Common
language
0.521***
(0.009)
0.520***
(0.009)
0.524***
(0.009)
0.253***
(0.004)
0.252***
(0.004)
0.254***
(0.004)
0.268***
(0.004)
0.268***
(0.004)
0.270***
(0.004)
One crisis
ever
0.223***
(0.010)
0.232***
(0.010)
0.209***
(0.010)
0.108***
(0.005)
0.112***
(0.005)
0.101***
(0.005)
0.115***
(0.005)
0.119***
(0.005)
0.107***
(0.005)
Both crises
ever
0.576***
(0.007)
0.581***
(0.007)
0.552***
(0.007)
0.279***
(0.003)
0.282***
(0.003)
0.268***
(0.003)
0.296***
(0.003)
0.299***
(0.003)
0.284***
(0.003)
Constant 8.345***
(0.038)
8.329***
(0.038)
8.384***
(0.038)
4.047***
(0.019)
4.039***
(0.019)
4.066***
(0.019)
4.298***
(0.020)
4.289***
(0.020)
4.318***
(0.020)
R-square 0.139 0.139 0.138 0.139 0.139 0.138 0.139 0.139 0.138
No of obs. 846813 846813 846813 846813 846813 846813 846813 846813 846813
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.24: Importer-Exporter fixed effect and time invariant bilateral variables for
different margins
99
Coefficient
of Exporter-
year fixed
effect from First
column of
Table 2.20
Coefficient
of Exporter-
year fixed
effect from First
column of
Table 2.20
Coefficient
of Exporter-
year fixed
effect from Second
column of
Table 2.20
Coefficient
of Exporter-
year fixed
effect from Second
column of
Table 2.20
Coefficient
of Exporter-
year fixed
effect from Third
column of
Table 2.20
Coefficient
of Exporter-
year fixed
effect from Third
column of
Table 2.20
Ln(Exporter’s GDP) 0.204*** (0.000)
0.205*** (0.000)
0.204*** (0.000)
0.205*** (0.000)
0.207*** (0.000)
0.207*** (0.000)
Five year forward of
exporter’s crisis
0.036***
(0.004)
0.042***
(0.004)
Four year forward of exporter’s crisis
0.002 (0.004)
0.007 (0.004)
Three year forward of
exporter’s crisis
0.060***
(0.004)
0.065***
(0.004)
Two year forward of exporter’s crisis
-0.048*** (0.004)
-0.042*** (0.004)
One year forward of
exporter’s crisis
-0.016***
(0.004)
-0.009*
(0.004)
Banking crises
orientation
-0.058***
(0.002)
-0.051***
(0.002)
-0.050***
(0.002)
-0.038***
(0.002)
-0.051***
(0.002)
-0.045***
(0.002)
One year lag of
exporter’s crisis
0.048***
(0.004)
0.060***
(0.004)
0.066***
(0.004)
0.073***
(0.004)
Two year lag of exporter’s crisis
0.052*** (0.004)
0.065*** (0.004)
0.068*** (0.004)
0.075*** (0.004)
Three year lag of
exporter’s crisis
0.085***
(0.004)
0.098***
(0.004)
0.097***
(0.004)
0.104***
(0.004)
Four year lag of
exporter’s crisis
0.076***
(0.004)
0.089***
(0.004)
0.088***
(0.004)
0.094***
(0.004)
Five year lag of
exporter’s crisis
0.095***
(0.004)
0.108***
(0.004)
0.107***
(0.004)
0.113***
(0.004)
Exporter ever had a
banking crisis
-0.020***
(0.001)
-0.031***
(0.001)
-0.015***
(0.002)
Constant -2.442***
(0.020)
-2.406***
(0.020)
-2.434***
(0.020)
-2.442***
(0.020)
-2.458***
(0.020)
-2.442***
(0.020)
Importers’ GDP share
weighted distance
Yes Yes Yes Yes Yes Yes
Importers’ GDP share
weighted language
Yes Yes Yes Yes Yes Yes
Importers’ GDP share
weighted contiguity
Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
R-square 0.428 0.429 0.430 0.430 0.438 0.438
No of obs 832649 832649 832649 832649 832649 832649
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.25: Exporter-year fixed effect and exporters’ banking crisis for overall margin for
robustness check
100
Coefficient
of Exporter-
year fixed
effect from First
column of
Table 2.21
Coefficient
of Exporter-
year fixed
effect from First
column of
Table 2.21
Coefficient
of Exporter-
year fixed
effect from Second
column of
Table 2.21
Coefficient
of Exporter-
year fixed
effect from Second
column of
Table 2.21
Coefficient
of Exporter-
year fixed
effect from Third
column of
Table 2.21
Coefficient
of Exporter-
year fixed
effect from Third
column of
Table 2.21
Ln(Exporter’s GDP) 0.099*** (0.000)
0.099*** (0.000)
0.099*** (0.000)
0.100*** (0.000)
0.101*** (0.000)
0.101*** (0.000)
Five year forward of
exporter’s crisis
0.018***
(0.002)
0.020***
(0.002)
Four year forward of exporter’s crisis
0.001 (0.002)
0.003 (0.002)
Three year forward of
exporter’s crisis
0.029***
(0.002)
0.032***
(0.002)
Two year forward of exporter’s crisis
-0.023*** (0.002)
-0.020*** (0.002)
One year forward of
exporter’s crisis
-0.008***
(0.002)
-0.005*
(0.002)
Banking crises
orientation
-0.028***
(0.001)
-0.025***
(0.001)
-0.025***
(0.001)
-0.018***
(0.001)
-0.025***
(0.001)
-0.022***
(0.001)
One year lag of
exporter’s crisis
0.023***
(0.002)
0.029***
(0.002)
0.032***
(0.002)
0.036***
(0.002)
Two year lag of exporter’s crisis
0.025*** (0.002)
0.032*** (0.002)
0.033*** (0.002)
0.037*** (0.002)
Three year lag of
exporter’s crisis
0.041***
(0.002)
0.048***
(0.002)
0.047***
(0.002)
0.051***
(0.002)
Four year lag of
exporter’s crisis
0.037***
(0.002)
0.043***
(0.002)
0.043***
(0.002)
0.046***
(0.002)
Five year lag of
exporter’s crisis
0.046***
(0.002)
0.052***
(0.002)
0.052***
(0.002)
0.055***
(0.002)
Exporter ever had a
banking crisis
-0.010***
(0.001)
-0.015***
(0.001)
-0.007***
(0.001)
Constant -1.184***
(0.009)
-1.167***
(0.009)
-1.183***
(0.010)
-1.152***
(0.010)
-1.197***
(0.010)
-1.182***
(0.009)
Importers’ GDP share
weighted distance
Yes Yes Yes Yes Yes Yes
Importers’ GDP share
weighted language
Yes Yes Yes Yes Yes Yes
Importers’ GDP share
weighted contiguity
Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
R-square 0.428 0.429 0.430 0.430 0.438 0.438
No of obs 832649 832649 832649 832649 832649 832649
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.26: Exporter-year fixed effect and exporters’ banking crisis for extensive margin
for robustness check
101
Coefficient
of Exporter-
year fixed
effect from First
column of
Table 2.22
Coefficient
of Exporter-
year fixed
effect from First
column of
Table 2.22
Coefficient
of Exporter-
year fixed
effect from Second
column of
Table 2.22
Coefficient
of Exporter-
year fixed
effect from Second
column of
Table 2.22
Coefficient
of Exporter-
year fixed
effect from Third
column of
Table 2.22
Coefficient
of Exporter-
year fixed
effect from Third
column of
Table 2.22
Ln(Exporter’s GDP) 0.105*** (0.000)
0.105*** (0.000)
0.105*** (0.000)
0.105*** (0.000)
0.106*** (0.000)
0.106*** (0.000)
Five year forward of
exporter’s crisis
0.019***
(0.002)
0.021***
(0.002)
Four year forward of exporter’s crisis
0.001 (0.002)
0.004 (0.002)
Three year forward of
exporter’s crisis
0.031***
(0.002)
0.034***
(0.002)
Two year forward of exporter’s crisis
-0.025*** (0.002)
-0.022*** (0.002)
One year forward of
exporter’s crisis
-0.008***
(0.002)
-0.005*
(0.002)
Banking crises
orientation
-0.030***
(0.001)
-0.026***
(0.001)
-0.026***
(0.001)
-0.020***
(0.001)
-0.026***
(0.001)
-0.023***
(0.001)
One year lag of
exporter’s crisis
0.024***
(0.002)
0.031***
(0.002)
0.034***
(0.002)
0.037***
(0.002)
Two year lag of exporter’s crisis
0.027*** (0.002)
0.034*** (0.002)
0.035*** (0.002)
0.039*** (0.002)
Three year lag of
exporter’s crisis
0.044***
(0.002)
0.050***
(0.002)
0.050***
(0.002)
0.053***
(0.002)
Four year lag of
exporter’s crisis
0.039***
(0.002)
0.046***
(0.002)
0.045***
(0.002)
0.048***
(0.002)
Five year lag of
exporter’s crisis
0.049***
(0.002)
0.055***
(0.002)
0.055***
(0.002)
0.058***
(0.002)
Exporter ever had a
banking crisis
-0.011***
(0.001)
-0.016***
(0.001)
-0.007***
(0.001)
Constant -1.258***
(0.010)
-1.239***
(0.010)
-1.251***
(0.010)
-1.219***
(0.010)
-1.261***
(0.010)
-1.245***
(0.010)
Importers’ GDP share
weighted distance
Yes Yes Yes Yes Yes Yes
Importers’ GDP share
weighted language
Yes Yes Yes Yes Yes Yes
Importers’ GDP share
weighted contiguity
Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
R-square 0.428 0.429 0.430 0.430 0.438 0.438
No of obs 832649 832649 832649 832649 832649 832649
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.27: Exporter-year fixed effect and exporters’ banking crisis for intensive margin
for robustness check
102
Coefficient
of Importer-
year fixed
effect from First
column of
Table 2.20
Coefficient
of Importer-
year fixed
effect from First
column of
Table 2.20
Coefficient
of Importer-
year fixed
effect from Second
column of
Table 2.20
Coefficient
of Importer-
year fixed
effect from Second
column of
Table 2.20
Coefficient
of Importer-
year fixed
effect from Third
column of
Table 2.20
Coefficient
of Importer-
year fixed
effect from Third
column of
Table 2.20
Ln(Importer’s GDP) -0.016*** (0.001)
-0.018*** (0.001)
-0.017*** (0.001)
-0.018*** (0.001)
-0.012*** (0.001)
-0.015*** (0.001)
Ln(total import) 0.016***
(0.001)
0.017***
(0.001)
0.017***
(0.001)
0.018***
(0.001)
0.015***
(0.001)
0.018***
(0.001)
Five year forward of importer’s crisis
-0.043*** (0.003)
-0.056*** (0.003)
Four year forward of
importer’s crisis
-0.087***
(0.003)
-0.100***
(0.003)
Three year forward of importer’s crisis
0.004 (0.003)
-0.008** (0.003)
Two year forward of
importer’s crisis
-0.079***
(0.003)
-0.093***
(0.003)
One year forward of
importer’s crisis
-0.003
(0.003)
-0.016***
(0.003)
Banking crises
destination
-0.008***
(0.002)
-0.012***
(0.002)
-0.002
(0.002)
-0.005**
(0.002)
-0.005**
(0.002)
-0.021***
(0.002)
One year lag of importer’s crisis
0.054*** (0.003)
0.051*** (0.003)
0.069*** (0.003)
0.054*** (0.003)
Two year lag of
importer’s crisis
0.038***
(0.003)
0.034***
(0.003)
0.052***
(0.003)
0.036***
(0.003)
Three year lag of
importer’s crisis
0.046***
(0.003)
0.042***
(0.003)
0.057***
(0.003)
0.042***
(0.003)
Four year lag of
importer’s crisis
0.066***
(0.003)
0.063***
(0.003)
0.076***
(0.003)
0.061***
(0.003)
Five year lag of
importer’s crisis
0.116***
(0.003)
0.112***
(0.003)
0.126***
(0.003)
0.111***
(0.003)
Importer ever had a
banking crisis
0.015***
(0.001)
0.010***
(0.001)
0.041***
(0.001)
Constant -0.151***
(0.014)
-0.175***
(0.014)
-0.137***
(0.014)
-0.156***
(0.014)
-0.206***
(0.014)
-0.283***
(0.014)
Exporters’ GDP share
weighted distance
Yes Yes Yes Yes Yes Yes
Exporters’ GDP share
weighted language
Yes Yes Yes Yes Yes Yes
Exporters’ GDP share
weighted contiguity
Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
R-square 0.013 0.013 0.019 0.019 0.023 0.025
No of obs 638716 638716 638716 638716 638716 638716
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.28: Importer-year fixed effect and exporters’ banking crisis for overall margin for
robustness check
103
Coefficient
of Importer-
year fixed
effect from First
column of
Table 2.21
Coefficient
of Importer-
year fixed
effect from First
column of
Table 2.21
Coefficient
of Importer-
year fixed
effect from Second
column of
Table 2.21
Coefficient
of Importer-
year fixed
effect from Second
column of
Table 2.21
Coefficient
of Importer-
year fixed
effect from Third
column of
Table 2.21
Coefficient
of Importer-
year fixed
effect from Third
column of
Table 2.21
Ln(Importer’s GDP) -0.003*** (0.000)
-0.003*** (0.000)
-0.003*** (0.000)
-0.003*** (0.000)
-0.002*** (0.000)
-0.003*** (0.000)
Ln(total import) 0.003***
(0.000)
0.003***
(0.000)
0.003***
(0.000)
0.003***
(0.000)
0.003***
(0.000)
0.004***
(0.000)
Five year forward of importer’s crisis
-0.009*** (0.001)
-0.011*** (0.001)
Four year forward of
importer’s crisis
-0.018***
(0.001)
-0.020***
(0.001)
Three year forward of importer’s crisis
0.001 (0.001)
-0.002** (0.001)
Two year forward of
importer’s crisis
-0.016***
(0.001)
-0.019***
(0.001)
One year forward of
importer’s crisis
-0.001
(0.001)
-0.003***
(0.001)
Banking crises
destination
-0.001***
(0.000)
-0.002***
(0.000)
0.000
(0.000)
-0.001**
(0.000)
-0.001**
(0.000)
-0.004**
(0.000)
One year lag of importer’s crisis
0.008*** (0.000)
0.008*** (0.000)
0.014*** (0.001)
0.011*** (0.001)
Two year lag of
importer’s crisis
0.006***
(0.000)
0.005***
(0.000)
0.010***
(0.001)
0.007***
(0.001)
Three year lag of
importer’s crisis
0.007***
(0.000)
0.007***
(0.000)
0.011***
(0.001)
0.008***
(0.001)
Four year lag of
importer’s crisis
0.010***
(0.000)
0.010***
(0.000)
0.015***
(0.001)
0.012***
(0.001)
Five year lag of
importer’s crisis
0.018***
(0.000)
0.017***
(0.000)
0.025***
(0.001)
0.022***
(0.001)
Importer ever had a
banking crisis
0.002***
(0.001)
0.002***
(0.000)
0.008***
(0.000)
Constant -0.024***
(0.002)
-0.027***
(0.002)
-0.021***
(0.002)
-0.024***
(0.002)
-0.041***
(0.002)
-0.057***
(0.003)
Exporters’ GDP share
weighted distance
Yes Yes Yes Yes Yes Yes
Exporters’ GDP share
weighted language
Yes Yes Yes Yes Yes Yes
Exporters’ GDP share
weighted contiguity
Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
R-square 0.013 0.013 0.019 0.019 0.023 0.025
No of obs 638716 638716 638716 638716 638716 638716
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.29: Importer-year fixed effect and exporters’ banking crisis for extensive margin
for robustness check
104
Coefficient
of Importer-
year fixed
effect from First
column of
Table 2.22
Coefficient
of Importer-
year fixed
effect from First
column of
Table 2.22
Coefficient
of Importer-
year fixed
effect from Second
column of
Table 2.22
Coefficient
of Importer-
year fixed
effect from Second
column of
Table 2.22
Coefficient
of Importer-
year fixed
effect from Third
column of
Table 2.22
Coefficient
of Importer-
year fixed
effect from Third
column of
Table 2.22
Ln(Importer’s GDP) -0.014*** (0.001)
-0.015*** (0.001)
-0.014*** (0.001)
-0.015*** (0.001)
-0.009*** (0.001)
-0.012*** (0.001)
Ln(total import) 0.014***
(0.001)
0.015***
(0.001)
0.014***
(0.001)
0.015***
(0.001)
0.012***
(0.001)
0.014***
(0.001)
Five year forward of importer’s crisis
-0.035*** (0.002)
-0.045*** (0.002)
Four year forward of
importer’s crisis
-0.070***
(0.002)
-0.080***
(0.002)
Three year forward of importer’s crisis
0.004 (0.002)
-0.006** (0.002)
Two year forward of
importer’s crisis
-0.063***
(0.002)
-0.074***
(0.002)
One year forward of
importer’s crisis
-0.002
(0.002)
-0.013***
(0.002)
Banking crises
destination
-0.007***
(0.001)
-0.010***
(0.001)
-0.001
(0.001)
-0.004**
(0.001)
-0.004**
(0.001)
-0.016***
(0.001)
One year lag of importer’s crisis
0.046*** (0.002)
0.043*** (0.002)
0.055*** (0.002)
0.043*** (0.002)
Two year lag of
importer’s crisis
0.032***
(0.002)
0.029***
(0.002)
0.041***
(0.002)
0.029***
(0.002)
Three year lag of
importer’s crisis
0.039***
(0.002)
0.036***
(0.002)
0.046***
(0.002)
0.033***
(0.002)
Four year lag of
importer’s crisis
0.056***
(0.002)
0.053***
(0.002)
0.061***
(0.002)
0.049***
(0.002)
Five year lag of
importer’s crisis
0.098***
(0.002)
0.095***
(0.002)
0.101***
(0.002)
0.089***
(0.002)
Importer ever had a
banking crisis
0.013***
(0.001)
0.009***
(0.001)
0.032***
(0.001)
Constant -0.127***
(0.012)
-0.148***
(0.012)
-0.116***
(0.012)
-0.132***
(0.012)
-0.165***
(0.012)
-0.226***
(0.012)
Exporters’ GDP share
weighted distance
Yes Yes Yes Yes Yes Yes
Exporters’ GDP share
weighted language
Yes Yes Yes Yes Yes Yes
Exporters’ GDP share
weighted contiguity
Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
R-square 0.013 0.013 0.019 0.019 0.023 0.025
No of obs 638716 638716 638716 638716 638716 638716
*** for p-value<0.001 ** for p-value<0.01 * for p-value<0.05
Table 2.30: Importer-year fixed effect and exporters’ banking crisis for intensive margin
for robustness check
105
Chapter 3
A Frontier Model Analysis on Bidding
Behaviors and Collusions in Low-price,
Sealed-bid Procurement
3.1 Introduction
As low-price, sealed-bid procurements are largely used in construction projects in
China, there is a tendency that collusions in bidding process are happened in
procurements. Bidders try to collude with each other and bid at a high price, and force
procurement agents to take high price offers. Governors have realized there are such
illegal behavior and tried to detect them through data mining. However, under the
condition with probability of collusion, because the objective function of bidders on
average level might been changed, the traditional ways to analyze the data could be
problematic. Another problem for traditional estimation could come from assumption of
106
homogeneity of the bidder. Even within the same category of one type of procurement.
For example, construction procurement, bidders’ characteristics based on their own cost
function and their estimation for other bidders’ might be sensitive to different submarkets.
When collusion happens, especially focused on some submarkets, the heterogeneity
problem might be intensified. These “outliers” from collusion might undermine the
results even with a large sample size.
This paper is focused on two questions: One is “Do different submarkets have
similar bidding strategies, Or do bidders perform differently in different submarket?”
Another one is that “By adopting frontier estimation, can we get some hints about
collusion?”
For the first question, this paper uses non-parametric frontier estimation to test the
hypothesis that bidders’ behaviors are different in different submarkets. This paper
assumes participants with heterogeneous characteristics. Instead of trying to find
Bayesian Nash Equilibrium of the procurement, it treats the bidding price as the
productions of reservation price and number of bidders. Utilizing the frontier estimation,
we try to find the characteristics of the market. This method is originated from Farrell,
(1957), as the measurement of productive efficiency. This paper will use Shephard,
(1970)’ method to measure the output distance function. Estimate the efficiency for each
bidder. Because this method avoids the relation between optimal bids and private
information in Nash equilibrium, it can largely reduce the complexity of the model and
computational burden.
107
For the second question, In a market without collusion, the objective function of
each bidder is maximizing their own expect profit. In a market with collusion, the
objective function is maximizing profit than separated profit between colluders. The
bidding data from real world are usually the mixture of these two cases. Using frontier
model can avoid the explicit objective function and give a hint about whether there might
be collusion in a market. This paper uses parametric frontier estimation to see whether
there is significant inefficiency in the procurement. In the context of procurement, the
inefficiency will become a proxy to measure collusion. When there is collusion, there
will be a large fraction of bidding data deviate from standard normal distribution, and this
can be captured by the inefficiency index from frontier model.
3.2 Literature Review
Vickrey (1961) analyzed market rules of auction and bidding and how to design
new rules to achieve better performance. In 1977, Robert Wilson’s paper “A Bidding
model of Perfect Competition” gave theory for bidding prices based on the distribution of
the bidder’s value. Since then, people realized that auctions with participators have
symmetrically distributed information about bidding object are different from those with
participants that have asymmetrically distributed information. A lot of attention is devote
to yield and test the models with asymmetrical information. In Hendricks and Porter,
(1988), they separate firms with two groups; one has more information than another. The
better informed firm will take advantage of the information and another group will also
adjust their strategy coordinately. After this, Laffont et al. (1995), separate firms by size,
108
Jofre-Bonet and Pesendorfer (2000), set capacity constraint for firms. All these settings
make the theory closer to the reality but also increase the complexity and estimation
burden.
The major difficulty for auction and procurement model with asymmetric private
information is that it is hard to find an explicit form for Bayesian Nash Equilibria1.
People try to estimate the private information without explicit equilibrium. In Guerre, et
al. (2000) paper, they proposed a non-parametric estimation procedure to ease the
computational burden, However, this still require the objective function was set as
everyone maximize their own expect profit and there is no collusion. When the data are a
mixture of collusion and non-collusion cases, results could still be biased.
3.3 Model
3.3.1 Non-parametric Model
In a low-price, sealed-bid construction auction and procurement, the procurement
agent will invite the construction companies to bid on the project. The agent will
announce the type and technique detail about the project and a reservation price R, which
is the highest price that could be paid to the construction companies. Each invited
company, as a bidder, will bid a price on the project. This bidding price is how much that
associated bidder will charge the agent. It must be smaller or equal to the reservation
price R or the bid will not be taken into account. All the bidders know only their own
1 See appendix
109
bidding prices. The company with the lowest bidding price will win the procurement
project.
In the traditional model, the symmetric Nash equilibrium bidding price ˆib will be
2:
2
1
( 1)[1 ( )] '( )ˆ( )
[1 ( )]
RN
c
i N
i
c N F c F c dcb c
F c
.
(3.1)
N is the number of bidders, and c is the lower boundary of the cost. F(c) is the
distribution of the cost for bidders. c and F(c) cannot be observed, ˆib R .
If bidders have heterogeneous structure of the cost the optimal bidding price ˆib
will be3:
1
( )ˆ( ( ) ) 1 ( , )(1 ( ))
Nk i
i i i
k k ik i
F cb c c c c R
F c
.
(3.2)
Here R is reservation price, N is number of bidders, c is the lower boundary of the cost.
( )kF c is the distribution of the cost for bidders k. c and ( )kF c cannot be observed,
ˆib R . There is no closed form for optimal bidding price.
From here, this paper will adopt the ideal form frontier estimation. Now assume
bidding price is the production of the reservation price and the number of bidders, which
are the only two observable parameters
2 See equation (3.17) from appendix
3 See equation (3.20) from appendix
110
( , )i ib G R N . (3.3)
iG is the production function that given reservation price R and number of bidders N.
each bidder i will produce a bidding price ib . The private information about cost is
embedded implicitly in iG . Each bidding price ib satisfies ib R . The bidder with lowest
ib will win the procurement project.
Let ig be:
( , ) ( , )i i i ig R b R G R N G R N . (3.4)
Where ig is the gap between bidding price and reservation price R.
Denote 2( , )x R N R as vector of input, denote ( )iy g R as vector of output.
Define the production possibility set as:
{( , ) can produce }p x y x y (3.5)
In the context of procurement, with similar inputs x, which is facing the same
reservation price R and number of bidders N, the construction company that produces
the highest output y will win the procurement. Because the highest y means largest gap
between bidding price ib and the reservation price R, since R is fixed, the company with
largest y will have lowest bidding price. According to the rules of low-price, sealed-bid
auction mentioned before, this company will win the procurement. All the winners will
form a production possibility frontier. By analyzing the relation between bidders and
111
this production possibility frontier, we can test the hypothesis of whether bidders
perform differently in different submarkets.
We use the Data envelopment analysis (DEA) production possibility set to
construct production possibility frontier and test the hypothesis. The DEA production
possibility set comes from the Free disposal hull (FDH) production possibility set.
Figure 3.1, is an example in a two-dimensional case, the FDH production possibility set
is the area that contains all the right below part for each observation:
,
{( , ) , for any observation ( , )}FDH
X Y
p x y x X y Y X Y . (3.6)
This means the area is dominated by observations of winners, which are located at
the corners of the line. Compared to the observation point, other points in this area at
most with same amount of inputs, yield less output. In context of procurement, this
means the area are dominated by observed bidder. With the same reservation price and
number of bidders, the winner has largest gap ig .
From figure 3.2, the DEA production possibility set is the convex hull of FDH
production possibility set.
Utilizing the Shephard output distance function to estimate the efficiency level for
each bidder:
1( , ) inf{ ( , ) }x y P x y P . (3.7)
112
Here P is DEA production possibility set. In figure 3.2, the curve line DEA that
envelops the FDH line is the production possibility frontier. Efficiency is defined as the
distance between a bidder and production possibility frontier4
. Here is a non-
parametric measurement of the output distance ratio between the observed bidder and the
corresponding point on frontier with same amount of inputs. From figure 3.3, in a two-
dimensional case, the DEA output distance function for observation A is:
1DEA
A
BA
BD . (3.8)
If 1DEA
A , then the observation A is on the DEA frontier5.
In a procurement context, one of the inputs is reservation price R, which contains
the information about the size of the project. It is also the upper boundary of the bidding
price. Another is the number of bidders, which contains information of how competitive
the bidding will be. For a specific project, a higher reservation price will give relativly
more potential room for bidders to cut down the bidding price. A larger number of
bidders will bring more competition and force bidders to cut down the bidding price.
From equation 3.8, for each bidder, there is an output distance estimator. The output
distance estimators reflect how far they are away from the winners. The private
4 It will be similar if the line FDH is used as a production possibility frontier.
5 the FDH output distance function for observation A is: 1FDH
A
BA
BC .
If 1FDH
A , then observation A is on the FDH frontier. This paper also provides the summary
statistics for using FDH as production possibility frontier.
113
information of cost structure c and ( )kF c is transferred into the shape of the production
possibility set frontier and the distribution of the output distance estimator.
3.3.2 Parametric Model
The Parametric stochastic frontier method to estimate the impact from the number
of bidders are implemented as follows. Assume the relation between inputs and output is:
i i i iY X v u (3.9)
Here Yi is the output, and Xi is the matrix for inputs. All the output and inputs are defined
as in the non-parametric model. For the error terms, 2(0, )i vv N and
2(0, )i vu N .Here iv is distributed normally and represent the noise from the
homogeneity characteristics of bidders. iu is distributed by the half-normal distribution
and represent the heterogeneity that might come from collusion. When there is collusion,
colluding bidders will bid high hence yielding a lower gap between the bidding price and
reservation price, which is a lower Yi. So it will deviate from the production possibility
frontier more than the regular bidders. This fraction of data from colluding bidding price
will be captured by the half-normal distribution iu . Hence, relatively how large is iu part
can give some information about collusion. Usually, in the parametric model, is used
for the measurement of inefficiency. It is defined as:
u
v
. (3.10)
114
The parameters u and v are the standard error for the half normal and normal
distribution in the respective error terms. In the context of procurement, inefficiency
means that a large amount of bidders bid high and yield a small gap between bidding
price and reservation price. This concentration of the irregularly low gaps can be captured
by u . It might give some information about collusion.
3.4 Hypothesis Test and Result
The data here used contains more than 300 construction projects in Shenzhen,
China, from 2001 to 2003, measured in 10,000 Yuan. All the projects are low-price,
sealed-bid procurements or auctions in the construction industry. The industry is
separated into four different types of submarkets: housing projects, government projects,
indoor design projects and infrastructure projects.
3.4.1 Hypothesis Test
Table 3.2 reports the summary of the FDH and DEA estimators for each type of
project. With two dimensions of inputs and one dimension of output, the convergence
rate of FDH estimator is1
3n
, the convergence rate of DEA estimator is 1
2n
. Figure 3.4 to
3.6 give some visual ideas about FDH and DEA estimators.
Now we construct a hypothesis test to determine if these four types of project
have the same frontier structure. In a procurement context, this means the bidders have
the same bidding behaviors in these four types of submarket.
115
Let i j denote the output distance function for observations in market i with
respect to the production possibility frontier of market j:
1( , ) inf{ ( , ) }i i j i i ji j i jx y P x y P . (3.11)
Using the DEA output distance function, the test function denotes as DEA
i jT :
1
1DEA
ni iDEA
i j DEAk i j
Tn
. (3.12)
DEA
i i is the measurement of inefficiency for bidders from submarket i relative to
the production possibility frontier of submarket i. DEA
i j is the measurement of
inefficiency for bidders from submarket i relative to the production possibility frontier of
submarket j. If bidders in submarket i and j have similar bidding behaviors, Bidders
should perform similarly, so DEA
i i and DEA
i j should be close to each other. Hence DEA
i jT
should include one in the confidence interval.
The hypothesis test is constructed as follows:
The null hypothesis is 0H : bidders in market i and j have the same bidding
behavior.
The alternative hypothesis is 1H : bidders in market i and j have different bidding
behavior.
116
We use the bootstrap to find the 95% confidence interval. As seen in table 3.3,
with 1000 replications of the bootstrap, none of the confidence interval contains one. So
we reject the null hypothesis at the 95% level of significance.
Bidders in different submarkets may have different bidding strategies be caused
by different submarkets having different cost distributions. Or the entry criteria could be
different for different submarkets when the heterogeneity of the firms in different
submarkets is significant. When there is collusion in some submarkets, this heterogeneity
can be intensified.
3.4.2 Results for Parametric Model
A procurement agent may be interested in how the number of bidders will impact
the bidding price. We expect that an increase in the number of bidders will increase the
competitions among bidders. As low-price, sealed-bid procurement, bidders will tend to
decrease the bidding price, hence increase the gap between reservation price and bidding
prices. The reservation price will reflect the size of the project. Larger projects will give
more room for bidders to bid down the price and yield a larger gap between the bidding
price and reservation price. Using the gap as an independent variable, we expect the
coefficients for both the number of bidders and the reservation price will be positive.
From Table 3.4, we find all the coefficients on reservation price are positive, all
of them are positive. For housing projects and indoor design projects, the coefficients are
relatively small. As the housing project’s reservation price increased by 10,000 Yuan, the
gap between bidding price and reservation price is increased by 1,540 Yuan. For indoor
117
design projects, the gap is increased by 1,670 Yuan. However, for government projects
and infrastructure projects, these coefficients are relative large. For government projects,
each 10,000 Yuan increased in reservation price, the gap between bidding price and
reservation price is increased by 5,460 Yuan. For infrastructure projects, the gap is
increased by 5,060 Yuan. It seems the potential profit from government project and
infrastructure project are much higher than other projects, which could possibly lead to
the potential motivation for collusion.
The coefficients for the number of bidders are quite different between different
submarkets. For the housing projects, the impact from number of bidders is insignificant.
For indoor design projects, the coefficient on the number of bidders is positive and
significant. On average, an increase in the number of bidders by one will force the
participants to cut down the bidding prices. The gap between bidding price and
reservation price will decreased by 92,760 Yuan. If more companies are invited to bid on
an indoor design project, the procurement agent will tend to pay less.
However for both government projects and infrastructure projects, the coefficients
are negative and significant. They are totally against the expectation that more
participators will bring more competition and decrease the bidding price, thus increasing
the gap between the reservation price and bidding price. For government projects, when
the number of bidders increased by one, the gap between the reservation price and
bidding price will decrease on average by 11,530 Yuan. For infrastructure projects, the
average decrease is 130,420 Yuan.
118
Although these results are against the theory of low-price, sealed-bid procurement,
they can be explained when collusion happens. If the illegal collusion is very severe in
government procurement markets and infrastructure markets, different companies will all
bid high and separate the profit from it even cannot win the procurement by itself. If
more companies collude with each other, it requires more profit to be separated between
each other. This violates the assumptions of low-price, sealed-bid procurement. The
assumptions require each participator only know his own bidding price and cost structure,
and requires each participant to maximize his own expected profit. However in an illegal
collusion case, participators know each other’s bidding price and try to maximize the
profit for the winner than separate the profit. So with one more bidders, more profit is
required for this additional colluder. Thus the bidding price will be even higher.
In the context of this paper, , which is used to measure the inefficiency of the
production, is used to measure the irregular low gap between the bidding price and
reservation price caused by high bidding price. In housing projects and indoor design
projects, is not significant. There is not enough evidence to suggest the bidders in
these two submarkets are trying to bid high. However, for government projects and
infrastructure projects, is quite large and significant. It seems there are a large
proportion of bidders trying to bid high in these two submarkets.
Overall, from the high potential profit from coefficient of reservation price,
opposite sign from coefficient of numbers of bidders and measure of inefficiency, it
119
suggests that there might be collusions happening in government project and
infrastructure project submarkets.
3.5 Conclusion
This paper used non-parametric FDH and DEA output distance estimators to
estimate the bidding possibility set and the relation between each bidder behavior and the
winner’s strategy. Since the relation between Nash equilibrium bidding price and private
information is already embedded into the production possibility frontier, the estimators
do not need the distribution of the private information explicitly. It largely reduces the
complexity of the model and computational burden.
From the hypothesis test, this paper suggests that in the same construction
industry, bidders bid differently in different submarkets. The heterogeneity between
different submarkets is needed to be take into account. When collusions are concentrated
in some submarkets, the heterogeneity problem could be intensified.
Using a parametric frontier model can give some hints about collusions. For
housing projects an indoor design projects, the impacts from the number of bidders and
reservation price follows the theory and expectation of low-price, sealed-bid procurement.
In submarkets of government projects and infrastructure projects, the impacts from the
reservation price are positive. But the large coefficients represents that potential profits in
these two submarkets are relatively higher than other submarkets. However, the finding
that an increase in the number of bidders tends to increase the bidding price, is totally on
the opposite side of the theory and our expectation. With more bidders participating in
120
the bidding, the bidding price tends to increase. The inefficiency measurement also shows
there are significant irregularly high bidding price in these two submarkets. Combining
the information from the reservation price, the number of bidders and the inefficiency
measurement suggests that in the government projects and infrastructure projects
procurement, there might be collusion inside the bidding process.
With collusion and moral hazard, the traditional model for Nash equilibrium will
require a different objective function. The problem has changed from maximizing the
winner’s expected profit into separated profit between colluders. However, the frontier
models still apply because it directly analyzes the relation between the bidder’s behavior
and the production possibility frontier.
121
Appendices
122
Appendix A Traditional Analysis on Low-price, Sealed-bid
Procurement
In traditional, symmetric information procurement, bidders are looking for the
Bayesian Nash Equilibrium. Assume that there are N bidders competing for procurement.
The reservation price is p , each bidder has the private information about his cost, and the
bidder who names the lowest price wins the procurement. Then the expected profit for
bidder i is:
( ) ( )Pr( )i i i i ib c b c i win . (3.13)
Bidder i wins if and only if i jb b for all i j .
Assume the bidding function b̂ is monotonic increasing, then ˆ( )i jb b c for any
i j .that is 1ˆ ( )i jb b c for any i j . Assume bidders’ costs have a distribution F from c
to p , (cannot pass the reservation price or it will not be profitable) the probability for i
wins the procurement is
1 1ˆPr( ) [1 ( ( ))]N
ii win F b b . (3.14)
Here, F(c) is the distribution for cost. From (13), expected profit is
1 1ˆ( ) ( )[1 ( ( ))]N
i i i i i ib c b c F b b . (3.15)
The first order condition for 3.15 is
123
2 1 ˆ( 1)[1 ( )] ( ) [(1 ( )) ( )]( )
N N
i i i i i
i
dc N F c F c F c b c
d c
. (3.16)
In the Nash Equilibrium, the bidding function b̂ is the best response for any
bidder i. Integrating both sides of equation 3.16, and the optimal bidding price is
2
1
( 1)[1 ( )] '( )ˆ( )
[1 ( )]
pN
c
N
c N F c F c dcb c
F c
. (3.17)
In an asymmetric information procurement, then probability for i to win the
procurement is
1
1
ˆPr( ) [1 ( ( ))]n
j i
jj i
i win F b b
. (3.18)
Expected profit is
1
1
ˆ( ) ( ) [1 ( ( ))]n
i i i i i j i
jj i
b c b c F b b
. (3.19)
The first order condition for equation 3.19 is
1
( )ˆ( ( ) ) 1 ( , )(1 ( ))
Nk i
i i i
k k ik i
F cb c c c c R
F c
. (3.20)
Both the symmetric Nash equilibrium and asymmetric Nash equilibrium depends
largely on the assumption of the cost distribution F(c), and usually will not have a closed
form.
124
Type of
projects
No of obs. Mean Std. Dev. Min Max
housing project 713 Numbers of bidders 6.873 2.090 2 12
Reservation price 4919.074 6116.975 131 29914
Bidding price 4190.783 5332.247 123 27820
Gap between two prices 728.292 1621.380 1 16094
government
project
378 Numbers of bidders 6.751 1.789 2 12
Reservation price 1586.357 2356.267 73 10718
Bidding price 1181.759 1620.926 50 7076
Gap between two prices 404.598 950.100 2 5360
indoor design project
223 Numbers of bidders 7.305 1.930 3 11
Reservation price 1255.682 1697.199 144 7554
Bidding price 1014.807 1422.083 85 6877
Gap between two prices 240.874 313.683 7 1966
infrastructure
project
231 Numbers of bidders 6.524 2.105 2 11
Reservation price 1993.658 3182.260 78 14717
Bidding price 1441.762 2071.752 60 11538
Gap between two prices 551.896 1262.128 1 7417
All prices are measured by 10,000 Yuan
Table 3.1: Summary statistics of observations
125
Summary of FDH and DEA observations
Type of projects Type of estimators
Number of obs.
Mean Median Std. deviation
housing project FDH 713 0.518 0.474 0.011
DEA 713 0.171 0.133 0.005
government
project
FDH 378 0.607 0.619 0.013
DEA 378 0.409 0.372 0.013
indoor design
project
FDH 223 0.659 0.670 0.016
DEA 223 0.552 0.548 0.015
infrastructure project
FDH 231 0.684 0.728 0.017
DEA 231 0.455 0.420 0.017
Table 3.2: Summary of FDH and DEA obs.
126
Bootstrap to test if different market have same bidding strategy
Market types Mean Variance 2.5% C.I. 97.5% C.I.
1 respect to 2 0.618 0.005 0.575 0.840
1 respect to 3 0.395 0.002 0.355 0.562
1 respect to 4 0.606 0.005 0.560 0.809
2 respect to 3 0.683 0.001 0.631 0.727
2 respect to 4 0.924 0.001 0.881 0.982
3 respect to 4 1.401 0.002 1.323 1.514
Here: 1 denotes housing project
2 denotes government project
3 denotes indoor design project 4 denotes infrastructure project
Bootstrap replication is 1000
Table 3.3: Hypothesis test for different submarkets
127
Dependent Var. : Gap between reservation price and bidding price
Types of project housing project government project
indoor design project
infrastructure project
Number of
bidders
-9.180
(23.615)
-1.153***
(0.001)
9.267*
(4.755)
-13.042***
(0.001)
Reservation price 0.154***
(0.008)
0.546***
(0.000)
0.167***
(0.005)
0.506***
(2.79e-07)
Constant 29.234 (1542.105)
-11.076*** (0.007)
-36.335 (304.856)
63.097*** (0.012)
Sigma v 1313.377 (34.780)
1.27e-05 (0.001)
136.311 (6.455)
2.44e-05 (0.002)
Sigma u 0.074
(1919.976)
813.728***
(29.595)
0.074
(379.245)
781.292***
(36.349)
lambda 5.61e-05
(1920.33)
6.39e+07***
(29.595)
5.41e-04
(379.374)
3.21e+07***
(36.349)
Number of obs. 713 378 223 231
Coefficient with * are significant at 10% Coefficient with ** are significant at 5%
Coefficient with *** are significant at 1%
Table 3.4: Parametric Stochastic Model
128
Figure 3.1: FDH production possibility set
129
Figure 3.2: DEA production possibility set
130
Figure 3.3: Shephard output distance function for FDH and DEA
131
Figure 3.4: Box-plot for FDH and DEA estimation with four different submarkets
Here: 1 denotes housing project
2 denotes government project
3 denotes indoor design project
4 denotes infrastructure project
132
Figure 3.5: Kernel density for FDH estimation with four different sub-markets
133
Figure 3.6: Kernel density for DEA estimation with four different sub-markets
134
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