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The Impact of the December 2004 Tsunami: An Empirical Investigation of Indonesia,
Sri Lanka and Thailand
Shamila A. Jayasuriya *
*Associate Professor, Department of Economics, Ohio University, Athens, OH 45701, USA.
Contact information: phone: +1 740-593-2094, fax: +1 740-593-0181, email: jayasuri@ohio.edu.
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ABSTRACT
This paper tests the hypothesis that the December 2004 tsunami has had a significant short term
impact on the equity markets and trade behavior of the three worst affected economies of
Indonesia, Sri Lanka and Thailand. Using monthly data from February 1985 to July 2007, we
estimate pooled cross-section and time-series regression models to examine direct and indirect
effects of the tsunami. Our findings suggest that neither equity markets nor net exports are
significantly different in the post- versus pre-tsunami time periods. However, results indicate
that an appreciation of the U.S. dollar exchange rate in the post-tsunami period is significantly
associated with increased stock market returns.
Also, a real exchange rate appreciation is
significantly correlated with lower net exports particularly in the post-tsunami era.
JEL Classification: F14; G15
Keywords: Tsunami; Exchange rates; Net exports; Stock market returns; South Asian economies
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INTRODUCTION
The December 2004 Indian Ocean earthquake and tsunami had a devastating impact on
several Asian and African economies. In particular, Indonesia, Sri Lanka and Thailand were
three of the worst affected countries that experienced extensive damage to human life,
infrastructure and private property, and economic activity. The negative impact on economic
activity was expected to be felt especially in sectors such as fisheries and tourism. However, as
the January 2005 Bulletin of the Central Bank of Sri Lanka also points out, part of the negative
impact was expected to be dampened by the reconstruction and rehabilitation efforts in the
affected areas. There has been an outpouring of funds from foreign donors ranging from private
individuals and organizations to national governments that has greatly facilitated the
reconstruction and rehabilitation activities in the aftermath of the tsunami. In a September 2007
report by the United Nations’ Humanitarian Affairs division, the total funding received by
affected countries is approximately 1.2 billion U.S. dollars and total humanitarian assistance
received is approximately 6.2 billion U.S. dollars.1 Private donors consisting of individuals and
organizations are the leading donors and Japan, U.K. and U.S. are among the six leading donor
governments.
This paper is based on the idea that the December 2004 tsunami had a significant short
term impact on equity markets and trade behavior of Indonesia, Sri Lanka, and Thailand. It is
also partly based on the idea that the receipt of large foreign inflows in the post-tsunami period
would have affected the domestic exchange rate with respect to foreign currencies in the aid
recipient countries.2 In particular, the domestic currency is expected to have appreciated in value
following the massive foreign capital inflows. Subsequently we investigate whether the tsunami
has had a direct impact, and an indirect impact via the exchange rate on the aggregate equity
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markets of the three countries. We also examine the impact on overall net exports and on the
exports of a primary commodity for which data is available.
Our empirical analysis uses monthly data for the time period from February 1985 to July
2007, which may vary based on data availability. We believe that a period of two and a half
years from December 2004 to July 2007 reasonably captures the short term period immediately
following the Tsunami. In the existing literature, an econometric investigation of the economic
impacts of the tsunami has not yet been done for these three economies. Our study therefore
takes steps in this direction with a focus on testing the hypothesis that the tsunami has had a
significant effect on the equity markets and trade performance of Indonesia, Sri Lanka and
Thailand.
Figure 1 motivates the analysis by plotting the U.S. dollar exchange rates for the 12
months before and after the month of December 2004. For Indonesia and Sri Lanka, we observe
an appreciation of the exchange rate immediately after December 2004 whereas the exchange
rate continues on a path of increasing value for Thailand. Based on estimation results that we
will discuss later in the paper, our findings suggest that equity markets have not been directly
affected by the tsunami. However, we do observe an indirect effect via the U.S. dollar exchange
rate. Specifically, an appreciation of the U.S. dollar exchange rate in the post-tsunami period is
associated with increased stock returns. In addition, overall net exports are not significantly
different in the post- versus pre-tsunami periods. But there is clear evidence that an appreciation
of the real exchange rate in the post-tsunami phase, regardless of whether the domestic prices are
increasing relative to those in Japan, U.K., or the U.S., is linked with lower net exports.
The remainder of the paper is organized as follows. In Section 2 we provide a brief
review of the existing literature on the December 2004 tsunami. It will also contain a general
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review of existing work on equity market returns and net exports in developed and emerging
markets. Section 3 describes the estimation methodology and the data used in the analysis.
Empirical results are provided in Section 4. And Section 5 concludes.
LITERATURE REVIEW
Athukorala and Resosudarmo (2005) provide a comprehensive analysis of the December
2004 tsunami with a focus on Indonesia and Sri Lanka. The nature and extent of the disaster are
discussed first. The authors then provide an analytical discussion of the economic impact and
the disaster management process immediately following the tsunami.
A key focus is the
international donor response and the limited aid absorptive capacity of affected countries.
Nidhiprabha (2006) discusses the impact of three different shocks including the impact of the
tsunami on the Thai economy. The author documents that the tsunami’s impact on the Thai
tourism industry has not been as severe as initially expected. However, the long term negative
impact on the environment has been underestimated. This includes the damages inflicted on the
coastal ecosystems and the socioeconomic activities of local communities.
Kurien (2005)
discusses the devastating impact of the tsunami specifically on the fishing communities of Asia.
The author raises several questions that deal with the safety and welfare of coastal fishing
communities. A set of policy proposals that address the immediate rehabilitation of survivors
and the future growth of fishing communities is also presented. Thorburn (2009) provides a
qualitative study on how effective the livelihood recovery programs in Aceh, Indonesia have
been since the 2004 tsunami. Based on survey results, the author finds that the degree and type
of follow-up guidance and support that program beneficiaries received have been the main
reasons for a successful livelihood recovery program in the Aceh communities.
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Rajasingham-Senanayake (2005) analyzes the politics of representation that is set in the
international development and reconstruction process following wars and natural disasters. With
an application to Sri Lanka, the author emphasizes the importance of politically and culturally
sensitive reconstruction policies without which renewed conflict and violence may be inevitable.
On a similar note, Liddle (2005) also examines political interests and responses that emerge in
the wake of natural disasters such as the tsunami and peace agreements in Aceh, Indonesia.
Bandara and Naranpanawa (2007) use a computable generalized equilibrium (CGE)
model to analyze the effects of the tsunami and reconstruction aid package on various
macroeconomic variables and industry level output in Sri Lanka. Based on simulation results,
the authors conclude that it is important to consider the combined effects of the tsunami and the
reconstruction aid package. For example, despite the clear negative economic effects due to the
tsunami there was evidence that the reconstruction package would in fact stimulate the economy.
Using a quantitative approach, Lee et al (2007) examine whether the December 2004 tsunami
affected the correlation structure in stock and foreign exchange markets for 26 countries. The
authors first compute the conditional correlation coefficients that account for heteroskedasticity
in market returns for both a stable and turmoil time period. They then compare the difference in
correlations between the two periods and measure contagion as the significance of adjusted
correlation coefficients for pair-wise countries. The authors find that the international stock
markets did not suffer contagion. However, several foreign exchange markets experienced
limited contagion effects up to three months after the tsunami. Our paper is also quantitative in
nature and contributes to the existing work by providing an econometric investigation of the
impact of the tsunami on two aspects of the economy, the equity markets and trade behavior, for
the three most severely affected countries of the tsunami.
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In the existing literature, studies of aggregate stock market returns in both developed and
emerging markets are abundant.
Many models of equity returns are built primarily on
autoregressive and moving average (ARMA) terms especially if the empirical analysis is based
on high frequency data. Some, especially the emerging market studies, have included other
potential determinants of stock return behavior such as foreign stock market returns, growth rates
of domestic macroeconomic fundamentals, and variables that account for stock market
liberalization and development policies to name a few. See Bekaert and Harvey (1997, 2000),
De Jong and De Roon (2005), Henry (2000), and Jayasuriya (2005, 2006) for select studies.
Evans and Speight (2006) study the impact of real time macroeconomic data on the U.K. stock
returns and find that unexpected inflation and economic uncertainty are key determinants of U.K.
stock returns over the business cycle. Also, Cauchie et al (2004) examine the developed stock
market of Switzerland and find that both global and domestic economic conditions such as
unexpected changes to industrial production and inflation in the G7 countries and the Swiss term
structure influence stock market returns. Madsen (2006) finds a link between factor shares, in
particular the labor share of income, supply shocks and technological innovations as important
determinants of stock returns for a group of sixteen industrialized economies.
There is an extensive literature on the determinants of bilateral trade between countries
since the inception of the gravity model by Tinbergen (1962) modified later by others including
Anderson and Van Wincoop (2003) in more recent years. While there is still debate on the
theoretical appropriateness of the gravity model, it has been used widely and generated accurate
results in empirical analyses over the years. See Deardorff (1998) for a good review of the
existing works that provide a theoretical justification for the gravity model. In this paper, our
focus is not on bilateral trade but on total trade of countries. Studies of overall trade typically
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follow a partial equilibrium analysis based on the hypothesis of imperfect substitution between
domestic and foreign goods. In this type of set up, the key determinants of export demand
identified are relative prices and a measure of economic activity in the trading countries. See for
example Arize (1990), Reinhart (1995), and Senhadji and Montenegro (1999). A good survey of
related early empirical work is provided by Goldstein and Kahn (1985). Many studies have also
investigated the role of exchange rate volatility on trade and found largely inconclusive evidence.
See McKenzie (1999) and Choudhry (2005) for good discussions of the existing work. In recent
years, the link between foreign direct investment (FDI) and trade has received much attention too
and FDI has been increasingly incorporated into general equilibrium trade models such as those
of Markusen and Venables (1998). The empirical literature on the role of FDI on trade is also
inconclusive. In a comprehensive study, Sharma (2003) examines the link between FDI flows
and export performance for India and is unable to detect a significant link between the two
variables.
METHODOLOGY AND DESCRIPTION OF DATA
Methodology
We use a pooled cross-section and time-series estimation methodology with White crosssection standard errors and covariances for all estimations. Fixed effects are used to account for
country specific effects as appropriate. Equations (1) and (2) are estimated in order to analyze
the equity market and trade behavior, respectively.
Ri,t = i + Ri,,t-1 + ForeignStockReturnt + InterestRatei.t + InflationRatei,t
+ ExchangeRatei,t + TsunamiDummyt + InteractionDummyt + i,t
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(1)
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NXi,t = i + RERi,t +  TsunamiDummyt +  InteractionDummyt
+ DomesticStockPricei,t +  ForeignStockPricei,t + Trendt + i,t
(2)
The dependent variable Ri,t in regression equation (1) is the real stock return for country i in
month t. The intercept specification i allows for individual country effects that do not vary over
time. Following previous work we add relevant ARMA terms to the model. We find that a
parsimonious AR(1) term or, in other words, a one-month lagged stock return provides the best
fit based on the Schwarz Information Criterion (BIC).
Co-movements with foreign stock markets could largely explain the behavior of
emerging market returns. The estimation therefore includes stock returns for Japan, U.K., and
the U.S. which are three main developed markets of the world.3 The U.K. and U.S. equity
market returns are somewhat highly correlated with a correlation coefficient of 0.70 and we
include only the one that better fits the model. Growth rates of domestic macroeconomic
fundamentals such as the interest rate, inflation rate, and exchange rate are also considered
because they may be good indicators of prevailing economic conditions. We look at three
different exchange rates in the analysis, which are the national currency per U.S. dollar, U.K.
pound, and the Japanese yen. The rationale here is that Japan, U.K. and the U.S. are three
leading donors in the reconstruction and rehabilitation process following the tsunami and there is
reasonable expectation that large inflows of these currencies may have affected the value of the
domestic currency of aid recipient countries. The levels and growth rates of the three exchange
rates are highly correlated with the correlation coefficient estimates ranging anywhere from 0.85
to 0.99. As a result, separate regressions are estimated for each exchange rate.
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A dummy variable that accounts for the tsunami is also included in the estimation. This
variable is constructed by setting equal to 0 the months preceding the tsunami and setting equal
to 1 all remaining months starting from December 2004. A significant negative coefficient on
the dummy variable would suggest that equity market returns were considerably lower in the
post tsunami era. We also create an interaction variable of the tsunami dummy and the relevant
exchange rate variables. By construction, we obtain three interaction variables based on the
three different exchange rates. The interaction variables as constructed are able to capture the
effect of exchange rate changes on equity market returns specifically in the post tsunami period.
This would allow us to investigate whether equity markets have been especially sensitive to
exchange rate changes in the post tsunami time period.
We then repeat regression estimation (1) by replacing the exchange rate variable with the
real exchange rate (RER), which is defined as the ratio of foreign to domestic prices measured in
the same currency. A decrease in RER therefore indicates an appreciation of the real exchange
rate. A decreasing real exchange rate lowers the external competitiveness of the economy via the
trade balance and has a negative impact on aggregate income. To that extent, a real exchange
rate appreciation may be reflected in lower equity returns. The use of three exchange rates in the
model necessitates the computation of three different real exchange rates and a new set of
interaction variables as well. The levels and growth rates of the real exchange rates are also
highly correlated with the correlation coefficients ranging from 0.81 to 0.99. Therefore, as was
done earlier, separate regressions will be estimated for each real exchange rate.
The dependent variable NXi,t in regression equation (2) is the overall net exports in
millions of U.S. dollars for country i in month t. Here, too, the intercept specification i allows
for individual country effects that do not vary over time. Following previous work, we add the
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real exchange rate as a key determinant of net exports. A real exchange rate appreciation is
expected to have a negative impact on net exports because it would make exports more
expensive and imports less expensive, which will lead to a decrease in the trade balance. In this
estimation, too, we add the tsunami dummy variable to examine net exports in the post- relative
to the pre-tsunami era. A significant negative coefficient estimate would suggest a decline in net
exports following the tsunami. As before, the interaction variables would capture the effect of
real exchange rate changes on net exports particularly in the post-tsunami time period.4
Several proxy variables for economic activity are also added to the estimation model. In
the absence of a monthly measure of production, the aggregate stock price index of an economy
may in fact be a good proxy. For example, the aggregate stock price indexes for Indonesia, Sri
Lanka, and Thailand are used as approximate measures of domestic economic activity. A well
performing stock price index can be a reasonable indicator of good economic conditions. Better
economic conditions would also mean a higher demand for imports and, therefore, a negative
impact on net exports. The aggregate stock price indexes for the U.S., U.K. and Japan are also
used to proxy the economic conditions of the trading partners.5 The conjecture here is that better
performing stock prices in the foreign countries would imply better economic conditions abroad
that would in turn lead to a higher demand for exports. The aggregate stock price indexes for the
U.S. and U.K. are highly correlated with a correlation coefficient of 0.98 and we, therefore, use
only one of the two that provides the better fit for our estimations.
We also add a time trend to our estimation equation (2). A visual inspection of the net
exports data series raised the potential issue of non-stationary data. As a result, we conducted
the Augmented Dickey Fuller (ADF) test to formally detect a unit root in the series. The
hypothesis of a unit root is rejected at the one percent critical level when a time trend is included
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in the alternative hypothesis of the test.
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Adding a time trend to the estimation therefore
eliminates a potential spurious regression problem in our model.6
Lastly, we repeat regression estimation (2) by replacing net exports with the exports of a
primary commodity measured in millions of U.S. dollars for which monthly data is available.
Overall net exports may not effectively isolate the impact of the tsunami on the sectors that were
affected the most. For example, the fisheries and tourism sectors are undoubtedly two key
sectors that were affected the most. And we can reasonably expect the export earnings related to
these two industries to have decreased dramatically following the tsunami even if there was no
apparent decline in overall net export earnings.7 In the absence of data for such sectors, we can
still raise an interesting question. That is, could the export earnings of a primary commodity
have been affected especially via real exchange rate changes?
Primary commodities have
traditionally played a major role in the export performance of emerging market economies and,
therefore, of growth and development prospects. Here, we consider petroleum exports for
Indonesia, tea exports for Sri Lanka, and rice exports for Thailand. In the early 1980s the export
earnings of these three commodities were about 60, 35, and 20 percent of total export earnings
respectively but have decreased to about 10 percent or even less in the most recent years. 8 The
Europa World Yearbook 2005-2006 indicates that machinery and transport equipment are in fact
among the principal exports for Indonesia in 2003. Data also indicates that textiles and garments
are the main commodity exports for Sri Lanka in 2005, which made up approximately 43.30
percent of total export earnings. On the other hand, machinery was the primary export for
Thailand in the year 2005 making up about 44.70 percent of total export earnings.
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Description of the data and preliminary statistics
All estimations are based on monthly data from February 1985 to July 2007, which may
vary based on data availability.9 The equity prices are obtained from the Datastream database for
all the markets.10 All returns are measured as the log difference of equity prices denominated in
the local currency. All returns are deflated by the relevant Consumer Price Index (CPI) in order
to obtain real returns.
The CPI data is from the International Monetary Fund’s (IMF’s)
International Financial Statistics (IFS) database. Data for macroeconomic fundamentals such as
the domestic interest rate, inflation rate, nominal and real exchange rates are also from the IFS
database.11 The net exports and primary commodity exports data in millions of U.S. dollars are
from the IFS database as well.
As discussed earlier, the motivation of our paper stems partly from the idea that foreign
aid flows after the tsunami are expected to have appreciated the domestic currency with respect
to the relevant foreign currencies. Table 1 presents some informal evidence to this regard
especially for Sri Lanka and Thailand. In particular, Table 1 documents the national to the
foreign, which is the U.S., U.K., and Japanese, currency exchange rates for Indonesia, Sri Lanka,
and Thailand for selected months before and after the tsunami. For both Sri Lanka and Thailand,
all three exchange rates indicate a relative increase in value of the domestic currency for at least
12 months following the December 2004 tsunami. This, however, is not the case for Indonesia.
We observe that the Indonesian Rupiah to the U.K. pound exchange rate appreciated in value
during the one month immediately following the tsunami relative to the one month preceding it.
All other cases indicate a depreciation of the Indonesian currency relative to the foreign
currencies. We are nevertheless able to test the hypotheses that changes to the nominal and real
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exchange rates, despite an appreciation or depreciation, have had a significant impact on equity
markets and trade behavior for the three economies in the post tsunami time period.
EMPIRICAL RESULTS
Table 2 presents the results for the impact of the tsunami on real stock returns. 12 We
observe that real returns are not significantly different in the post- versus the pre-tsunami period.
We observe however that an appreciation (depreciation) of the U.S. dollar exchange rate is
associated with increased (decreased) real returns in the post-tsunami era. That is, equity market
behavior appears to be sensitive to the U.S. dollar exchange rate in the aftermath of the disaster
such that an increase in the value of the domestic currency relative to the U.S. dollar is linked
with a positive response of the equity markets. Interestingly, the exchange rate does not appear
to have a significant effect on equity returns unless the tsunami is accounted for. The domestic
interest rates, on the other hand, indicate a significant negative impact on equity returns. That is,
higher interest rates are associated with lower equity returns, which is a phenomenon that is also
observed in the U.S. stock markets. For example, an interest rate hike by the Federal Reserve
Board often triggers a decrease in stock prices because of the contractionary economic
conditions a higher interest rate typically entails. In addition, the lagged stock returns and
foreign stock returns also appear to be significant determinants of equity market behavior in
general. In particular, the domestic equity markets react positively to better past performance of
their own as well as better concurrent performance of major developed markets of the world.
Table 3 presents results for the impact of the tsunami on real stock returns while
replacing the nominal with the real exchange rate in the estimation model. As before, results do
not indicate an apparent distinction in equity returns in the post- relative to pre-tsunami periods.
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We also observe that changes to the real exchange rate do not significantly affect equity returns
in the post-tsunami time period or otherwise. The interest rate however is still a significant
determinant as are the lagged stock returns and foreign stock market movements. A possible
interpretation of our results is that a strengthening of the domestic currency against the U.S.
dollar is observed immediately in the currency markets and perceived as positive to the economy
perhaps due to the implications on short term capital inflows. The impact of a real exchange rate
appreciation on the economy via the trade balance however is of a longer term nature and is not
felt immediately in the equity markets.
Table 4 documents the impact of the tsunami on net exports.13 Clearly, net exports are
not significantly different in the post- versus pre-tsunami time periods. The interaction variable
suggests however that a real exchange rate appreciation (depreciation) in the post-tsunami period
is significantly associated with lower (higher) net exports. The impact of the real exchange rate
on net exports is visible even if the tsunami is not accounted for but, interestingly, the magnitude
of the real exchange rate effect is greater following the tsunami. That is, net exports have
become more sensitive to real exchange rate changes after the disaster. Also, the domestic stock
prices have a significant negative impact on net exports. To the extent that increasing domestic
stock prices imply improving economic conditions, there would be a higher demand for imports
leading to a decrease in net exports. On the other hand, increasing stock prices in foreign equity
markets may reflect better economic conditions of trading partners that would lead to a higher
demand for domestically produced goods and, therefore, increased exports. For the most part,
we do observe a significant positive link between foreign stock prices and net exports.
Although, the significant negative link observed between the Japanese Nikkei index and net
exports is contrary to what we expect. A possible explanation is that the import demand for
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some goods originating in Japan is now being met by exports from emerging markets other than
Indonesia, Sri Lanka and Thailand as the Japanese economy continues to become wealthier.
This could be due to changes in consumer tastes and preferences and possibly due to new
developments in trade agreements.
Lastly, Table 5 shows the impact of the tsunami on the exports of a primary commodity.
In general, primary commodity exports are lower in the post- versus the pre-tsunami time period.
Given that the production capacities of petroleum, tea, and rice in the respective countries were
not substantially affected by the tsunami, it is difficult to attribute the decline in these exports to
the tsunami alone. An alternative explanation is that there have been structural changes to the
composition of exports of these countries with primary commodities decreasing as a percentage
share of total exports over time. And such structural changes may have coincided with the time
around the December 2004 tsunami. Data from the International Trade Statistics database show
that fuel exports were the leading merchandise exports for Indonesia in the mid 1980s.
However, manufactures especially machinery and transport equipment have been the main
exports for Indonesia in the recent years. For Sri Lanka, agricultural products were the primary
merchandise exports in the mid 1980s whereas manufactures especially clothing have been the
key exports in the last few years. For Thailand, too, agricultural products were the leading
merchandise exports in the mid 1980s but these have been replaced by manufactured items
including machinery/transport equipment and office/telecommunications equipment in the recent
years.
The interaction variable suggests that primary commodity exports are sensitive to real
exchange rate changes in the post-tsunami period.
In particular, a real exchange rate
appreciation (depreciation) is associated with decreased (increased) exports. Unless the tsunami
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is specifically accounted for, a real exchange rate depreciation is in fact associated with
decreased primary commodity exports. It is very likely that we are observing a crowding-out
effect in which a real exchange rate depreciation benefits exports other than primary
commodities. For example, manufacture exports such as machinery/transport equipment and
textiles/garments have gained momentum for these countries in recent years and, as a result, the
export demand for primary commodities may continue to decline even in the midst of a relative
fall in domestic prices. The negative coefficient estimates on the domestic and some of the
foreign stock prices provide further evidence that the commodity exports considered here are no
longer the primary exports as they may have been in the past. The Japanese stock prices are
however mostly consistent with our expectations. Therefore, better economic conditions in
Japan appear to be linked with higher primary commodity exports from the three countries.
CONCLUSION
In this paper, we examined the impact of the December 2004 Indian Ocean earthquake
and tsunami on the three most severely affected economies of Indonesia, Sri Lanka and Thailand.
Specifically, we investigated the research hypothesis that the tsunami has had a significant short
term impact on the equity market and trade behavior of the three Asian economies. Using
monthly data from February 1985 to July 2007 in a pooled cross-section and time-series analysis,
we found that equity market returns and net exports are not significantly different in the postversus pre-tsunami time periods. However, we did observe an indirect effect via exchange rates.
In particular, an appreciation of the U.S. dollar nominal exchange rate in the post-tsunami period
is associated with increased stock returns. Also, an appreciation of the real exchange rate is
associated with lower net exports especially in the post-tsunami era. In addition, we found
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evidence that primary commodity exports are lower in the aftermath of the tsunami, which is a
result that we have argued may not be attributed to the devastating impacts of the tsunami alone.
Therefore, even though a direct impact of the tsunami was not felt on the equity markets
or net exports, we did observe an indirect effect via exchange rate changes. Provided that
sufficient data is available, an investigation of the fisheries and tourism sectors is likely to reveal
a direct negative impact of the tsunami on the production capacities of these sectors. The
indirect effects on overall net exports that we observed in the post-tsunami period could very
well reflect a drop in export earnings in the fisheries and tourism industries on the one hand and
an increase in import expenditures for emergency relief goods needed for reconstruction and
rebuilding efforts on the other hand.
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Deardorff, A. V. (1998). Determinants of bilateral trade: Does gravity work in a neoclassical
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Fund.
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Table 1: Exchange rates (national per foreign currency) for selected months before and after the tsunami
Month
1-mo
2-mo
3-mo
4-mo
5-mo
6-mo
12-mo
18-mo
24-mo
National Currency to the US Dollar
Indonesia
pre
post
9179.80
9109.33
9104.15
9117.28
9147.10
9130.25
8963.14
8796.13
8736.92
National currency to the UK Pound
Indonesia
pre
post
9240.67
9229.05
9255.55
9307.60
9355.49
9381.34
9686.13
9562.81
9468.59
17630.46
17176.76
16924.42
16799.65
16806.67
16780.46
16407.04
15696.97
15218.93
pre
post
pre
post
pre
post
104.84
104.68
104.36
104.06
103.89
103.76
101.21
99.42
98.79
100.80
99.99
99.76
99.67
99.67
99.67
100.22
100.87
101.52
201.28
197.32
193.94
191.70
190.88
190.71
185.29
177.46
172.12
190.72
188.47
188.99
188.56
188.70
187.67
183.49
182.23
185.01
1.01
1.00
0.98
0.97
0.96
0.96
0.93
0.91
0.89
0.97
0.96
0.96
0.95
0.95
0.95
0.92
0.91
0.90
pre
post
pre
post
pre
post
39.30
39.89
40.36
40.62
40.79
40.83
40.32
40.22
40.72
38.88
38.68
38.57
38.72
38.87
39.09
40.11
39.85
39.33
75.48
75.18
74.96
74.77
74.89
75.00
74.85
72.39
71.29
73.66
72.96
73.11
73.29
73.63
73.62
73.39
71.99
71.64
0.38
0.38
0.38
0.38
0.38
0.38
0.37
0.37
0.37
0.38
0.37
0.37
0.37
0.37
0.37
0.37
0.36
0.35
Sri Lanka
1-mo
2-mo
3-mo
4-mo
5-mo
6-mo
12-mo
18-mo
24-mo
8952.15
8898.46
8892.95
8878.16
8903.51
8891.62
8883.76
8580.26
8419.84
Sri Lanka
Thailand
Thailand
Source: Datastream database.
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8830.66
8703.84
8581.02
8515.99
8494.89
8450.46
8269.44
8057.54
7847.69
Sri Lanka
Thailand
1-mo
2-mo
3-mo
4-mo
5-mo
6-mo
12-mo
18-mo
24-mo
17500.97
17396.29
17535.87
17605.66
17713.00
17658.66
17713.65
17278.10
17249.90
National currency to the Japanese Yen
Indonesia
pre
post
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Table 2: The impact of the tsunami (direct, and indirect via the nominal exchange rate) on real stock returns
Variables
(1)
National currency per
US dollar exchange rate
(2)
National currency per
UK pound exchange rate
(3)
National currency per
Japanese Yen exchange rate
0.1703***
(0.0436)
0.0984
(0.0616)
0.1393***
(0.0554)
0.2716**
(0.1290)
0.3322***
(0.1228)
Lagged Stock Returns
Stock Returns_US
Stock Returns_UK
0.3573***
(0.0719)
Stock Returns_Japan
0.1341*
(0.0699)
0.2597**
(0.1111)
0.1548
(0.1076)
Interest Rate
-0.0448***
(0.0170)
-0.0723***
(0.0274)
-0.0552**
(0.0217)
Inflation Rate
-0.0001
(0.0006)
0.0001
(0.0007)
0.0002
(0.0008)
Exchange Rate
-0.1717
(0.1652)
-0.1844
(0.1513)
-0.0949
(0.1173)
Tsunami Dummy Variable
-0.0025
(0.0072)
0.0063
(0.0086)
0.0051
(0.0082)
Interaction Dummy Variable
-0.9344**
(0.4322)
0.1133
(0.2472)
-0.1727
(0.2360)
R-squared
Log Likelihood
Observations
0.1145
803.3260
761
0.1419
436.7270
405
0.0954
506.5028
490
Note: White-cross section standard errors are given in parentheses. Significance at the 1%, 5% and 10% levels are indicated
by ***, **, and * respectively.
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Table 3: The impact of the tsunami (direct, and indirect via the real exchange rate) on real stock returns
Variables
(4)
National currency per
US dollar exchange rate
(5)
National currency per
UK pound exchange rate
(6)
National currency per
Japanese Yen exchange rate
0.1758***
(0.0443)
0.1364***
(0.0510)
0.1443**
(0.0564)
0.3751***
(0.1194)
0.3254***
(0.1228)
Lagged Stock Returns
Stock Returns_US
Stock Returns_UK
0.3554***
(0.0723)
Stock Returns_Japan
0.1425**
(0.0704)
0.1339
(0.0947)
0.1626
(0.1072)
Interest Rate
-0.0455***
(0.0170)
-0.0500***
(0.0184)
-0.0554**
(0.0217)
Inflation Rate
-0.0003
(0.0006)
0.0001
(0.0008)
0.0001
(0.0008)
Real Exchange Rate
-0.1324
(0.1501)
-0.1433
(0.1249)
-0.0629
(0.1049)
Tsunami Dummy Variable
-0.0005
(0.0082)
0.0064
(0.0083)
0.0076
(0.0083)
Interaction Dummy Variable
0.0605
(0.4026)
0.2943
(0.2110)
0.0815
(0.2082)
R-squared
Log Likelihood
Observations
0.1070
800.1052
761
0.1032
567.9568
542
0.0914
505.4263
490
Note: White-cross section standard errors are given in parentheses. Significance at the 1%, 5% and 10% levels are indicated
by ***, **, and * respectively.
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Table 4: The impact of the tsunami (direct, and indirect via the real exchange rate) on net exports
(7)
National currency per
US dollar exchange rate
(8)
National currency per
UK pound exchange rate
(9)
National currency per
Japanese Yen exchange rate
0.0950***
(0.0189)
0.0384***
(0.0139)
0.0343
(0.0284)
50.6549
(123.3941)
-137.4104
(144.4793)
-87.9421
(153.6488)
Interaction Dummy Variable
0.1091***
(0.0192)
0.0432***
(0.0109)
0.1036***
(0.0288)
Domestic Stock Prices
-0.5774***
(0.0620)
-0.5740***
(0.0639)
-0.5598***
(0.0872)
Foreign Stock Prices_US
0.5080***
(0.0993)
0.3329***
(0.0971)
0.3683***
(0.1027)
Foreing Stock Prices_Japan
-0.0056***
(0.0020)
0.0172**
(0.0077)
0.0133
(0.0085)
1.0514*
(0.5867)
4.5909***
(1.0851)
3.9004***
(1.2789)
0.7000
-5972.0390
798
0.7093
-4316.0740
569
0.6604
-3923.0000
514
Variable
Real Exchange Rate
Tsunami Dummy Variable
Trend
R-squared
Log Likelihood
Observations
Note: White-cross section standard errors are given in parentheses. Significance at the 1%, 5% and 10% levels are indicated
by ***, **, and * respectively.
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Table 5: The impact of the tsunami (direct, and indirect via the real exchange rate) on primary commodity exports
(10)
National currency per
US dollar exchange rate
(11)
National currency per
UK pound exchange rate
(12)
National currency per
Japanese Yen exchange rate
-0.0130***
(0.0032)
-0.0102***
(0.0016)
-0.0267***
(0.0044)
Tsunami Dummy Variable
-2.0258
(8.3283)
-24.3551**
(9.4741)
-16.7627*
(9.1784)
Interaction Dummy Variable
0.0310***
(0.0068)
0.0170***
(0.0038)
0.0337***
(0.0086)
Domestic Stock Prices
-0.0063
(0.0044)
-0.0115***
(0.0041)
-0.0083*
(0.0044)
-0.0720***
(0.0138)
-0.0624***
(0.0134)
Variable
Real Exchange Rate
Foreign Stock Prices_US
Foreign Stock Prices_UK
-0.0074**
(0.0031)
Foreign Stock Prices_Japan
-0.0014**
(0.0006)
0.0040***
(0.0006)
0.0038***
(0.0007)
Trend
0.5030***
(0.1047)
1.1447***
(0.1405)
0.9943***
(0.1415)
0.8912
-4315.3570
759
0.9359
-2857.9550
533
0.9395
-2569.6530
478
R-squared
Log Likelihood
Observations
Note: White-cross section standard errors are given in parentheses. Significance at the 1%, 5% and 10% levels are indicated
by ***, **, and * respectively.
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Figure 1: Exchange rates (national currency to the US dollar) for 12 months around the tsunami
Indonesia
10400
10000
9600
9200
8800
8400
8000
2004M01
2004M07
2005M01
2005M07
Sri Lanka
105
104
103
102
101
100
99
98
97
96
2004M01
2004M07
2005M01
2005M07
Thailand
42
41
40
39
38
2004M01
2004M07
2005M01
2005M07
Note: The solid line in all three graphs indicates the month of December 2004 when the Indian
Ocean tsunami occurred.
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Endnotes
1
The relevant data are available at http://www.reliefweb.int/fts (Table ref: R5 and Table ref:
R24).
2
The 2005 and 2006 Annual Reports on the Exchange Arrangements and Exchange Restrictions
by the International Monetary Fund (IMF) indicate that all three countries had at the time of the
December 2004 tsunami an exchange rate system where exchange rates are determined by
supply and demand conditions in the foreign exchange markets. The exact classifications are
managed floating with no pre-determined path for the exchange rate for both Indonesia and
Thailand, and an independently floating exchange rate for Sri Lanka.
3
The choice of Japan, U.K., and the U.S. is appropriate also because they are among the leading
donors of tsunami relief to the affected countries.
4
Here, too, separate regressions will be estimated for the different real exchange rates and their
respective interaction variables.
5
The International Trade Statistics database made available at www.wto.org documents that the
U.S. and Japan are among the top three destinations for merchandise exports for Indonesia and
Thailand. For Sri Lanka, the main destination for its merchandise exports is the U.S. with the
European Union and India ranking second and third, respectively.
6
Some measure of trade policy would also be a good choice of an explanatory variable to the
model. Because of the lack of data, however, we are not able to account for the impact of trade
restrictions on net exports. Data for import tariffs made available by the International Trade
Statistics database provide an indication of the extent of trade restrictions in the three countries.
For example, the simple average ad-valorem duties applied on all goods in the year 2007 were
6.9, 11.2, and 10.5 percent for Indonesia, Sri Lanka, and Thailand respectively. Also, exports to
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the U.S. faced simple average duties of 4.8, 6.6, and 4.3 percent for non-agricultural products in
the year 2007.
7
One may argue that even the most affected sectors can have only a minimal impact on overall
production and export capacities if production in these sectors constitutes only a small
percentage of aggregate production in the economy.
8
The relevant commodity export earnings as a percentage of total export earnings is based on
data that is obtained from the International Financial Statistics (IFS) database.
9
For example, the monthly exchange rate data for Japan and the U.K. are available starting only
in mid 1995.
10
The equity price data is based on the Jakarta SE Composite index for Indonesia, the Colombo
SE All Share index for Sri Lanka, and the Bangkok S.E.T. index for Thailand. The equity prices
for Japan, U.K., and the U.S. are based on the Nikkei 225 Stock Average, FTSE 100, and the
S&P 500 Composite index respectively.
11
For the interest rate, we use the 3-month deposit rate for Indonesia and the money market rates
for both Sri Lanka and Thailand.
12
Based on likelihood ratio tests, we find that the individual country effects are not necessary for
all equity market estimations. Table 2 and Table 3 results therefore do not include fixed effects.
13
Likelihood ratio tests indicate that the individual country effects are significant to the model
when the dependent variable is a measure of trade. Subsequently, Table 4 and Table 5 take into
account fixed effects in the estimations.
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