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


2.3 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


3.3 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


extensive margin ........................................................................................... 77


intensive margin ............................................................................................ 78


country ever experienced a banking crisis for overall margin ..................... 79


country ever experienced a banking crisis for extensive margin ................. 80


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


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


2.30 Importer-year fixed effect and exporters’ banking crisis on intensive


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)


(0.026)

-0.021

(0.026)


(0.025)

-0.022

(0.025)


(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)


-0.015 (0.024)


(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)


-0.196*** (0.026)


(0.025)

-0.185***

(0.025)


-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)


-0.319*** (0.024)

0.305*** (0.025)

-0.332*** (0.024)


(0.024)

-0.301***

(0.024)


(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)



-1.108***

(0.006)

-1.110***

(0.006)

-1.111***

(0.006)

-1.110***

(0.006)

-1.111***

(0.006)



0.367***

(0.025)

0.371***

(0.025)

0.375***

(0.025)

0.373***

(0.025)

0.378***

(0.025)



0.575***

(0.011)

0.576***

(0.011)

0.577***

(0.011)

0.577***

(0.011)

0.579***

(0.011)


(0.002)

1.227***

(0.002)

1.227***

(0.002)

1.227***

(0.002)

1.228***

(0.002)


(0.002)

1.081***

(0.002)

1.079***

(0.002)

1.082***

(0.002)

1.082***

(0.002)


(0.027)


(0.026)


(0.026)

-0.020

(0.026)


(0.025)

-0.021

(0.025)


(0.025)

-0.023

(0.024)


(0.015)

0.007

(0.015)

0.005

(0.015)

0.004

(0.015)

0.002

(0.015)


(0.025)

-0.008

(0.025)

-0.008

(0.025)

-0.010

(0.025)


-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)


-0.013 (0.024)


(0.024)

-0.033

(0.024)



(0.026)


-0.228*** (0.026)


(0.025)

-0.216***

(0.025)


-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)


-0.355*** (0.025)

-0.349*** (0.026)

-0.394*** (0.026)


(0.025)

-0.405***

(0.025)

-0.389***

(0.025)

-0.440***

(0.025)


-0.337*** (0.024)

0.320*** (0.024)

-0.368*** (0.024)


(0.024)

-0.335***

(0.024)


(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)


R-square 0.635 0.636 0.636 0.636 0.636

No of obs. 379928 379928 379928 379928 379928


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


Table 1.5: Trade value and banking crises with lags

37

Dep. Var. ln(trade)


(0.004)

Contiguity 0.515***

(0.021)


(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


weighted language

Yes Yes Yes


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


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)


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


weighted language

Yes Yes Yes


weighted contiguity

Yes Yes Yes


R-square 0.156 0.156 0.159

No of obs 638716 638716 638716


Table 1.8: Importer-year fixed effect and importers’ banking crisis

40

Dep var ln(trade)


(0.004)

-1.383***

(0.004)

Contiguity 0.521***

(0.021)

0.521***

(0.021)


(0.009)

0.876***

(0.009)


(0.011)

1.043***

(0.011)


(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


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


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


effect from

Third column of Table 1.8

Coefficient of


effect from

Fifth column of Table 1.8


(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


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


weighted language


Importers’ GDP share weighted contiguity


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


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


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)


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



weighted language


Exporters’ GDP share weighted contiguity



R-square 0.253 0.285 0.254 0.285 0.265 0.286

No of obs 638716 638716 638716 638716 638716 638716


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.


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)



-1.166***

(0.006)

-1.166***

(0.006)

-1.166***

(0.006)

-1.166***

(0.006)

-1.165***

(0.006)



0.487***

(0.024)

0.492***

(0.024)

0.496***

(0.024)

0.494***

(0.024)

0.500***

(0.024)



0.668***

(0.011)

0.670***

(0.011)

0.672***

(0.011)

0.672***

(0.011)

0.675***

(0.011)


(0.002)

1.248***

(0.002)

1.248***

(0.002)

1.249***

(0.002)

1.249***

(0.002)


(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)


(0.026)


(0.026)


(0.024)

-0.029

(0.025)


(0.024)

-0.032

(0.025)


(0.023)

-0.034

(0.024)


(0.014)

0.000

(0.014)

-0.002

(0.014)

-0.003

(0.014)

-0.005

(0.014)


-0.018 (0.024)

-0.018 (0.024)

-0.020 (0.024)


(0.024)

-0.028

(0.024)

-0.027

(0.024)

-0.030

(0.025)


-0.008 (0.024)

-0.007 (0.024)

-0.010 (0.024)


(0.023)

-0.024

(0.023)


-0.042 (0.023)

5 years forward of banking crisis for importer -0.074**

(0.026)


(0.025)


(0.025)

-0.088***

(0.025)


-0.091*** (0.024)


(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)


(0.025)

-0.177***

(0.025)

-0.173***

(0.024)

-0.186***

(0.024)


-0.216*** (0.024)

-0.206*** (0.024)

-0.224*** (0.024)


(0.023)

-0.209***

(0.023)

-0.199***

(0.023)

-0.215***

(0.023)


-0.186*** (0.023)


(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)


R-square 0.586 0.586 0.586 0.586 0.587

No of obs. 379928 379928 379928 379928 379928


Table 2.2: Linear approximations for multilateral resistance and banking crises for overall

margin

77

Dep var ln(Extensive margin)



-0.622***

(0.004)

-0.622***

(0.004)

-0.622***

(0.004)

-0.621***

(0.004)

-0.620***

(0.004)



-0.186***

(0.017)

-0.179***

(0.017)

-0.173***

(0.017)

-0.174***

(0.017)

-0.167***

(0.017)



0.400***

(0.008)

0.404***

(0.008)

0.407***

(0.008)

0.408***

(0.008)

0.414***

(0.008)


(0.001)

0.682***

(0.001)

0.683***

(0.001)

0.684***

(0.001)

0.686***

(0.001)


(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)


(0.018)


(0.017)

-0.155***

(0.017)


(0.017)

-0.158***

(0.017)


(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)


-0.102*** (0.017)

-0.099*** (0.017)

-0.110*** (0.017)


(0.017)

-0.105***

(0.016)


-0.113*** (0.016)

5 years forward of banking crisis for importer -0.033

(0.018)



(0.018)

-0.085***

(0.018)


-0.112*** (0.017)


(0.017)

-0.119***

(0.017)


-0.170*** (0.010)

-0.181*** (0.010)

-0.178*** (0.010)

-0.192*** (0.010)


(0.017)

-0.186***

(0.017)

-0.181***

(0.017)

-0.196***

(0.017)


-0.223*** (0.017)

-0.212*** (0.017)

-0.232*** (0.017)


(0.016)

-0.247***

(0.016)

-0.234***

(0.016)

-0.254***

(0.016)


-0.230*** (0.016)


(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)


R-square 0.463 0.464 0.464 0.464 0.465

No of obs. 379928 379928 379928 379928 379928


Table 2.3: Linear approximations for multilateral resistance and banking crises for

extensive margin

78

Dep var ln(Intensive margin)



-0.544***

(0.005)

-0.544***

(0.005)

-0.544***

(0.005)

-0.545***

(0.005)

-0.545***

(0.005)



0.673***

(0.020)

0.671***

(0.020)

0.669***

(0.020)

0.669***

(0.020)

0.666***

(0.020)



0.267***

(0.009)

0.266***

(0.009)

0.265***

(0.009)

0.264***

(0.009)

0.261***

(0.009)


(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)


(0.021)


(0.021)

0.126***

(0.020)


(0.020)

0.126***

(0.020)


(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)


0.093*** (0.020)

0.092*** (0.020)

0.100*** (0.020)


(0.020)

0.080***

(0.020)


0.071*** (0.019)


(0.021)

4 years forward of banking crisis for importer 0.007 (0.021)


(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)


(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)


(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)


R-square 0.405 0.405 0.405 0.406 0.406

No of obs. 379928 379928 379928 379928 379928


Table 2.4: Linear approximations for multilateral resistance and banking crises for

intensive margin

79




-1.170***

(0.006)

-1.170***

(0.006)

-1.171***

(0.006)

-1.170***

(0.006)

-1.171***

(0.006)



0.476***

(0.024)

0.479***

(0.025)

0.482***

(0.024)

0.480***

(0.024)

0.483***

(0.024)



0.659***

(0.011)

0.660***

(0.011)

0.660***

(0.011)

0.660***

(0.011)

0.661***

(0.011)


(0.002)

1.249***

(0.002)

1.249***

(0.002)

1.249***

(0.002)

1.249***

(0.002)


(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)


(0.026)


(0.025)


(0.024)

-0.033

(0.025)


(0.024)

-0.036

(0.025)


(0.024)

-0.038

(0.024)


(0.014)

0.000

(0.015)

-0.003

(0.015)

-0.005

(0.015)

-0.009

(0.015)


-0.018 (0.025)

-0.019 (0.025)

-0.023 (0.025)


(0.025)

-0.029

(0.024)

-0.028

(0.024)

-0.033

(0.024)


-0.009 (0.024)

-0.008 (0.024)

-0.014 (0.024)


(0.024)

-0.027

(0.024)


-0.045 (0.023)


(0.026)


(0.025)


(0.025)

-0.146***

(0.025)


-0.150*** (0.024)


(0.023)

-0.154***

(0.024)


-0.162*** (0.015)

-0.181*** (0.015)

-0.178*** (0.015)

-0.208*** (0.015)


(0.024)

-0.229***

(0.025)

-0.223***

(0.024)

-0.255***

(0.025)


-0.270*** (0.024)

-0.255*** (0.024)

-0.292*** (0.024)


(0.024)

-0.262***

(0.024)

-0.247***

(0.023)

-0.282***

(0.023)


-0.250*** (0.023)


(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)


R-square 0.586 0.587 0.587 0.587 0.587

No of obs. 379928 379928 379928 379928 379928



country ever experienced a banking crisis for overall margin

80




-0.618***

(0.004)

-0.619***

(0.004)

-0.620***

(0.004)

-0.619***

(0.004)

-0.620***

(0.004)



-0.174***

(0.017)

-0.171***

(0.017)

-0.168***

(0.017)

-0.169***

(0.017)

-0.166***

(0.017)



0.410***

(0.008)

0.411***

(0.008)

0.411***

(0.008)

0.412***

(0.008)

0.414***

(0.008)


(0.001)

0.681***

(0.001)

0.681***

(0.001)

0.682***

(0.001)

0.683***

(0.001)


(0.001)

0.056***

(0.001)

0.057***

(0.001)

0.057***

(0.001)

0.058***

(0.001)


(0.005)

0.318***

(0.005)

0.317***

(0.005)

0.318***

(0.005)

0.316***

(0.005)


(0.018)


(0.018)


(0.017)

-0.180***

(0.018)


(0.017)

-0.185***

(0.017)


(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)


-0.121*** (0.018)

-0.128*** (0.018)

-0.152*** (0.018)


(0.017)

-0.114***

(0.017)

-0.116***

(0.017)

-0.142***

(0.017)


-0.115*** (0.017)

-0.115*** (0.017)

-0.140*** (0.017)


(0.017)

-0.133***

(0.017)


-0.141*** (0.017)


(0.018)


(0.018)


(0.018)

-0.063***

(0.018)


-0.090*** (0.017)


(0.017)

-0.097***

(0.017)


-0.140*** (0.010)

-0.155*** (0.010)

-0.148*** (0.010)

-0.168*** (0.010)


(0.017)

-0.158***

(0.017)

-0.150***

(0.017)

-0.171***

(0.017)


-0.195*** (0.017)

-0.181*** (0.017)

-0.207*** (0.017)


(0.017)

-0.219***

(0.017)

-0.204***

(0.017)

-0.229***

(0.017)


-0.206*** (0.017)


(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)


R-square 0.464 0.464 0.465 0.465 0.465

No of obs. 379928 379928 379928 379928 379928



country ever experienced a banking crisis for extensive margin

81




-0.551***

(0.005)

-0.551***

(0.005)

-0.551***

(0.005)

-0.551***

(0.005)

-0.551***

(0.005)



0.650***

(0.020)

0.650***

(0.020)

0.650***

(0.020)

0.650***

(0.020)

0.650***

(0.020)



0.249***

(0.009)

0.249***

(0.009)

0.249***

(0.009)

0.248***

(0.009)

0.247***

(0.009)


(0.002)

0.568***

(0.002)

0.568***

(0.002)

0.567***

(0.002)

0.566***

(0.002)


(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)


(0.021)


(0.021)


(0.020)

0.147***

(0.020)


(0.020)

0.149***

(0.020)


(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)


0.102*** (0.020)

0.109*** (0.021)

0.129*** (0.020)


(0.020)

0.085***

(0.020)

0.088***

(0.020)

0.109***

(0.020)


0.105*** (0.020)

0.107*** (0.020)

0.126*** (0.020)


(0.020)

0.105***

(0.020)


0.096*** (0.019)


(0.021)


(0.021)


(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)


(0.020)

-0.071***

(0.020)

-0.073***

(0.020)

-0.084***

(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)


(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)


R-square 0.407 0.407 0.407 0.407 0.407

No of obs. 379928 379928 379928 379928 379928



country ever experienced a banking crisis for intensive margin

82

Dep var. ln(Overall margin)


(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)


(0.009)

0.876***

(0.009)

0.876***

(0.009)

0.876***

(0.009)


(0.011)

0.361***

(0.011)

0.374***

(0.011)

0.383***

(0.011)


(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)


-0.111* (0.050)

-0.107* (0.050)

Two years lag of

two crises

-0.062

(0.049)

-0.056

(0.049)


-0.115* (0.048)

-0.112* (0.048)

Four years lag of

two crises

-0.046

(0.047)


-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


Table 2.8: Overall margin and banking crises with lags

83

Dep var. ln(Extensive margin)


(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)


(0.007)

0.329***

(0.007)

0.329***

(0.007)

0.329***

(0.007)


(0.009)

0.328***

(0.009)

0.346***

(0.009)

0.348***

(0.009)


(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)


-0.267*** (0.042)

-0.263*** (0.042)

Two years lag of

two crises

-0.197***

(0.041)

-0.192***

(0.041)


-0.216*** (0.040)

-0.214*** (0.040)

Four years lag of

two crises

-0.102**

(0.040)


-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


Table 2.9: Extensive margin and banking crises with lags

84

Dep var. ln(Intensive margin)


(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)


(0.008)

0.547***

(0.008)

0.547***

(0.008)

0.547***

(0.008)


(0.011)

0.032**

(0.011)

0.027*

(0.011)

0.035**

(0.011)


(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)


0.155** (0.049)

0.156** (0.049)

Two years lag of

two crises

0.135**

(0.048)

0.137**

(0.048)


0.100* (0.047)

0.102* (0.047)

Four years lag of

two crises

0.057

(0.046)


-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


Table 2.10: Intensive margin and banking crises with lags

85

ln(Overall margin) ln(Extensive

margin)

ln(Intensive

margin)


(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)


exporter’s crisis

0.001

(0.007)


-0.006 (0.007)


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)


0.050*** (0.007)

0.041** (0.007)

Three year lag of

exporter’s crisis

0.070***

(0.007)

0.061***

(0.007)


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)



Yes Yes Yes


weighted language

Yes Yes Yes


weighted contiguity

Yes Yes Yes


R-square 0.879 0.879 0.879

No of obs 823649 823649 823649


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


(0.000)

0.583***

(0.000)

0.583***

(0.000)


exporter’s crisis

0.001

(0.004)


-0.003 (0.003)


exporter’s crisis

-0.034***

(0.003)


-0.036*** (0.003)

One year forward of

exporter’s crisis

-0.031***

(0.003)


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)


0.025*** (0.003)

0.020** (0.004)

Three year lag of

exporter’s crisis

0.034***

(0.003)

0.030***

(0.003)


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)



Yes Yes Yes


weighted language

Yes Yes Yes


weighted contiguity

Yes Yes Yes


R-square 0.879 0.879 0.879

No of obs 823649 823649 823649


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


(0.000)

0.609***

(0.000)

0.609***

(0.000)


exporter’s crisis

0.001

(0.004)


-0.003 (0.004)


exporter’s crisis

-0.036***

(0.004)


-0.037*** (0.003)

One year forward of

exporter’s crisis

-0.032***

(0.004)


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)


0.026*** (0.004)

0.021** (0.004)

Three year lag of

exporter’s crisis

0.036***

(0.004)

0.031***

(0.004)


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)



Yes Yes Yes


weighted language

Yes Yes Yes


weighted contiguity

Yes Yes Yes


R-square 0.879 0.879 0.879

No of obs 823649 823649 823649


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)


(0.001)

0.005***

(0.001)

0.004***

(0.001)


-0.074*** (0.005)


importer’s crisis

-0.079***

(0.005)


-0.107*** (0.005)

Two year forward of

importer’s crisis

-0.104***

(0.004)


-0.093*** (0.004)

Banking crises for

impoter

-0.017***

(0.003)

-0.033***

(0.003)

-0.052***

(0.003)


-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)


weighted distance

Yes Yes Yes


weighted language

Yes Yes Yes


weighted contiguity

Yes Yes Yes


R-square 0.289 0.291 0.293

No of obs 638716 638716 638716


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


(0.001)

0.093***

(0.001)

0.095***

(0.001)


(0.001)

0.003***

(0.001)

0.002***

(0.001)


-0.046*** (0.003)


importer’s crisis

-0.050***

(0.003)


-0.067*** (0.003)

Two year forward of

importer’s crisis

-0.066***

(0.003)


-0.059*** (0.003)

Banking crises for

impoter

-0.011***

(0.002)

-0.021***

(0.002)

-0.033***

(0.002)


-0.047*** (0.003)

-0.058*** (0.003)

Two year lag of

importer’s crisis

-0.057***

(0.003)

-0.067***

(0.003)


-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)


weighted distance

Yes Yes Yes


weighted language

Yes Yes Yes


weighted contiguity

Yes Yes Yes


R-square 0.289 0.291 0.293

No of obs 638716 638716 638716


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


(0.000)

0.055***

(0.000)

0.056***

(0.000)


(0.000)

0.002***

(0.000)

0.001***

(0.000)


-0.027*** (0.002)


importer’s crisis

-0.030***

(0.002)


-0.040*** (0.002)

Two year forward of

importer’s crisis

-0.039***

(0.002)


-0.035*** (0.002)

Banking crises for

impoter

-0.006***

(0.001)

-0.012***

(0.001)

-0.019***

(0.001)


-0.028*** (0.002)

-0.034*** (0.002)

Two year lag of

importer’s crisis

-0.034***

(0.002)

-0.039***

(0.002)


-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)


weighted distance

Yes Yes Yes


weighted language

Yes Yes Yes


weighted contiguity

Yes Yes Yes


R-square 0.289 0.291 0.293

No of obs 638716 638716 638716


Table 2.17: Importer-year fixed effect and importers’ banking crisis on intensive margin

92



(0.004)

-1.383***

(0.004)

Contiguity 0.521***

(0.021)

0.521***

(0.021)


(0.009)

0.876***

(0.009)


(0.011)

0.379***

(0.011)


(0.007)

0.580***

(0.007)


(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)


(0.015)

0.057***

(0.015)


(0.015)

-0.040**

(0.015)

One crisis 0.054***

(0.010)

0.049***

(0.010)


(0.016)

-0.123***

(0.016)


-0.093*** (0.016)


(0.015)

-0.115***

(0.015)



(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)



Constant 2.884*** (0.037)

2.888*** (0.037)

R-square 0.688 0.688

No of obs. 420960 420960


Table 2.18: Overall margin and banking crises with forwards and lags

93



(0.003)

-0.767***

(0.003)

Contiguity 0.011

(0.018)

0.014

(0.018)


(0.007)

0.329***

(0.007)


(0.009)

0.354***

(0.009)


(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)


(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)


(0.013)

-0.232***

(0.013)


-0.152*** (0.013)


(0.013)

-0.128***

(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)


(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)



Constant 3.281*** (0.031)

3.280*** (0.031)

R-square 0.435 0.435

No of obs. 420960 420960


Table 2.19: Extensive margin and banking crises with forwards and lags

94



(0.004)

-0.616***

(0.004)

Contiguity 0.509***

(0.021)

0.507***

(0.021)


(0.008)

0.547***

(0.008)

One crisis ever 0.029**

(0.011)

0.025*

(0.011)


(0.007)

0.188***

(0.007)


(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)


(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)


(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


Table 2.20: Intensive margin and banking crises with forwards and lags

95


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)


two crises

0.092**

(0.035)

Four years forward of two crises

0.169*** (0.035)


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)


yes yes yes

Exporter year

fixed effect

yes yes yes


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


Table 2.21: Overall margin and banking crises with Importer-Exporter fixed effect

96



one crisis

-0.068***

(0.012)

Four years forward of one crisis

-0.038*** (0.012)


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)


crisis

-0.061***

(0.012)

-0.060***

(0.012)

Four years lag of one crisis

0.013 (0.012)

0.014 (0.012)


crisis

0.045***

(0.012)

0.047***

(0.012)


two crises

-0.027

(0.041)


two crises

0.006

(0.040)


two crises

0.040

(0.040)


two crises

0.014

(0.038)

One year forward of

two crises

0.011

(0.037)


(0.027)

-0.098***

(0.027)

-0.069*

(0.027)

One year lag of two

crises

-0.174***

(0.039)

-0.166***

(0.039)


crises

-0.123**

(0.038)

-0.111**

(0.038)


crises

-0.137***

(0.037)

-0.126***

(0.037)

Four years lag of two

crises

-0.048

(0.037)

-0.038

(0.037)


crises

-0.054

(0.036)

-0.042

(0.036)


yes yes yes

Exporter year

fixed effect

yes yes yes


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


Table 2.22: Extensive margin and banking crises with Importer-Exporter fixed effect

97



one crisis

0.094***

(0.014)

Four years forward of one crisis

0.089*** (0.014)


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)


crisis

-0.020

(0.013)

-0.023

(0.013)

Four years lag of one crisis

-0.070*** (0.013)

-0.072*** (0.013)


crisis

-0.158***

(0.013)

-0.160***

(0.013)


two crises

0.119**

(0.046)


two crises

0.163***

(0.045)


two crises

0.044

(0.045)


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)


crises

0.136**

(0.043)

0.144**

(0.043)


crises

0.100*

(0.042)

0.111*

(0.042)

Four years lag of two

crises

0.039

(0.042)

0.051

(0.042)


crises

-0.031

(0.041)

-0.022

(0.041)


yes yes yes

Exporter year

fixed effect

yes yes yes


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


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


Coefficient

of

importer-exporter

fixed

effect from Third

column of


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


Coefficient

of

importer-exporter

fixed

effect from Third

column of



(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


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)


exporter’s crisis

0.036***

(0.004)

0.042***

(0.004)


0.002 (0.004)

0.007 (0.004)


exporter’s crisis

0.060***

(0.004)

0.065***

(0.004)


-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)


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)


weighted distance



weighted language



weighted contiguity



R-square 0.428 0.429 0.430 0.430 0.438 0.438

No of obs 832649 832649 832649 832649 832649 832649


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


0.099*** (0.000)

0.099*** (0.000)

0.100*** (0.000)

0.101*** (0.000)

0.101*** (0.000)


exporter’s crisis

0.018***

(0.002)

0.020***

(0.002)


0.001 (0.002)

0.003 (0.002)


exporter’s crisis

0.029***

(0.002)

0.032***

(0.002)


-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)


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)


weighted distance



weighted language



weighted contiguity



R-square 0.428 0.429 0.430 0.430 0.438 0.438

No of obs 832649 832649 832649 832649 832649 832649


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


0.105*** (0.000)

0.105*** (0.000)

0.105*** (0.000)

0.106*** (0.000)

0.106*** (0.000)


exporter’s crisis

0.019***

(0.002)

0.021***

(0.002)


0.001 (0.002)

0.004 (0.002)


exporter’s crisis

0.031***

(0.002)

0.034***

(0.002)


-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)


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)


weighted distance



weighted language



weighted contiguity



R-square 0.428 0.429 0.430 0.430 0.438 0.438

No of obs 832649 832649 832649 832649 832649 832649


Table 2.27: Exporter-year fixed effect and exporters’ banking crisis for intensive margin


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)


(0.001)

0.017***

(0.001)

0.017***

(0.001)

0.018***

(0.001)

0.015***

(0.001)

0.018***

(0.001)


-0.043*** (0.003)

-0.056*** (0.003)


importer’s crisis

-0.087***

(0.003)

-0.100***

(0.003)


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)


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)


weighted distance



weighted language



weighted contiguity



R-square 0.013 0.013 0.019 0.019 0.023 0.025

No of obs 638716 638716 638716 638716 638716 638716


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)


(0.000)

0.003***

(0.000)

0.003***

(0.000)

0.003***

(0.000)

0.003***

(0.000)

0.004***

(0.000)


-0.009*** (0.001)

-0.011*** (0.001)


importer’s crisis

-0.018***

(0.001)

-0.020***

(0.001)


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)


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)


weighted distance



weighted language



weighted contiguity



R-square 0.013 0.013 0.019 0.019 0.023 0.025

No of obs 638716 638716 638716 638716 638716 638716


Table 2.29: Importer-year fixed effect and exporters’ banking crisis for extensive margin


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)


(0.001)

0.015***

(0.001)

0.014***

(0.001)

0.015***

(0.001)

0.012***

(0.001)

0.014***

(0.001)


-0.035*** (0.002)

-0.045*** (0.002)


importer’s crisis

-0.070***

(0.002)

-0.080***

(0.002)


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)


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)


weighted distance



weighted language



weighted contiguity



R-square 0.013 0.013 0.019 0.019 0.023 0.025

No of obs 638716 638716 638716 638716 638716 638716


Table 2.30: Importer-year fixed effect and exporters’ banking crisis for intensive margin


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.


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 îb 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, îb R .

If bidders have heterogeneous structure of the cost the optimal bidding price îb

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,

îb 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


Bidding price 1181.759 1620.926 50 7076


indoor design project



Bidding price 1014.807 1422.083 85 6877


infrastructure

project



Bidding price 1441.762 2071.752 60 11538


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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