Shock Therapy! What Role for Thai Monetary Policy?
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund

Contributor Notes

Authors’ E-Mail Addresses: aharun@sas.upenn.edu; selekdag@imf.org

Thailand had to endure three major shocks during 2008–2011: the global financial crisis, the Japanese earthquake, and the Thai floods of 2011. Over this period, consistent with its inflation targeting framework, the Bank of Thailand (BOT) let the exchange rate depreciate and cut interest rates (to, for example, a historically low level of 1¼ percent by mid-2009). This paper seeks to uncover the role of monetary policy in softening the impact of these shocks. Specifically, it seeks to address the following question: if an inflation targeting framework underpinned by a flexible exchange rate regime had not been in place, how would the economic contractions associated with these shocks have differed? Counterfactual simulations based on an estimated structural model indicate that countercyclical monetary policy and exchange rate flexibility added up to a total of 4 percentage points to real GDP growth during periods when Thailand had to weather these three major shocks.

Abstract

Thailand had to endure three major shocks during 2008–2011: the global financial crisis, the Japanese earthquake, and the Thai floods of 2011. Over this period, consistent with its inflation targeting framework, the Bank of Thailand (BOT) let the exchange rate depreciate and cut interest rates (to, for example, a historically low level of 1¼ percent by mid-2009). This paper seeks to uncover the role of monetary policy in softening the impact of these shocks. Specifically, it seeks to address the following question: if an inflation targeting framework underpinned by a flexible exchange rate regime had not been in place, how would the economic contractions associated with these shocks have differed? Counterfactual simulations based on an estimated structural model indicate that countercyclical monetary policy and exchange rate flexibility added up to a total of 4 percentage points to real GDP growth during periods when Thailand had to weather these three major shocks.

I. Introduction

During 2008–2011, Thailand had to endure three major shocks. The first was the global financial crisis (GFC) which intensified with the collapse of Lehman Brothers. The second shock was associated with the spillovers from the earthquake in Japan and the ensuing disruption of supply chains which adversely affected Thailand in particular. The third major shock was the worst flooding in at least 50 years, which along with the devastating loss of life, dealt a severe blow to many of the primary sectors that form the backbone of the Thai economy. To mitigate the severity of these shocks, the Bank of Thailand (BOT) let the exchange rate depreciate and cut interest rates when appropriate. For example, starting in the third quarter of 2008, the BOT cut its policy rate by 250 basis points to a historically low level of 1¼ percent. But to what avail?

The focus of this paper is to assess the role of countercyclical interest rate cuts and exchange rate flexibility in mitigating the economic fallout from these three major shocks. Specifically we seek to address the following question: If an inflation targeting framework underpinned by a flexible exchange rate regime had not been in place, how would the economic contractions associated with these shocks have differed?

A structural model is used to provide a quantitative answer to this question. Specifically, we develop and estimate a small open economy dynamic stochastic general equilibrium (DSGE) model designed to capture the salient features of the Thai economy. The model contains a number of nominal and real frictions such as sticky prices, sticky wages, variable capital utilization, investment adjustment costs, habit persistence, and incorporates a financial accelerator mechanism à la Bernanke and others (1999) in an open-economy setup to better fit the data. The model is used to generate counterfactual simulations underpinning the main quantitative results and key policy implications of the paper.

The results indicate that without the adoption of the flexible exchange rate regime and active countercyclical monetary policy guided by an inflation targeting framework, the impact of the three major shocks on the Thai economy would have been substantially more severe. In particular, during the six quarters when output was most severely affected by the three shocks, results from the model indicate that countercyclical monetary policy and exchange rate flexibility contributed to growth by up to 1.6 and 2.1 percentage points, respectively, for a total of up to 3.7 percentage points. While exchange rate flexibility served as a shock absorber, countercyclical interest rate cuts consistent with the inflation target increased the resilience of the economy to these shocks. In fact, the latter result echoes the favorable output stabilization properties of exchange rate flexibility which can be traced back at least to the seminal contributions of Mundell and Fleming. Overall, the BOT’s monetary policy framework seems to have increased the robustness of the Thai economy to these three major shocks.

This paper builds on a tradition of small open economy DSGE models popularized by Mendoza (1991). Over time, these real models were augmented with nominal rigidities to motivate and then explore the implications of monetary policy (for example, Gali and Monacelli, 2002, among others). To capture financial frictions more appropriately, building on Bernanke and others (1999), a financial accelerator mechanism was also added on to these models (see for example, Cespedes and others, 2004; Devereux, and others, 2006; Gertler, and others, 2007; as well as Elekdag and Tchakarov, 2007).

With the growing feasibility and popularity of Bayesian method, building upon the closed economy studies of Smets and Wouters (2003, 2007), small open economy models were estimated (Lubik and Schorfheide, 2007; Teo, 2006; as well as Christensen and Dib, 2006). Then, Elekdag, Justiniano, and Tchakarov (2006) estimated a small open economy model with a financial accelerator for an emerging market, which later motivated others to use richer modeling structures (see, for example, Garcia-Cicco, 2010). Against this backdrop, as in Alp and Elekdag (2011), this paper takes Elekdag, Justiniano, and Tchakarov (2006) as a starting point, and augments their model with some of the features in Gertler and others (2007), Smets and Wouters (2007) to improve model fit and to facilitate the counterfactual simulations.

This paper is structured as follows. The next section begins by briefly providing the institutional backdrop of Thai monetary and exchange rate policies, and some background related to the three shocks which hit Thailand during 2008–2011. The paper then goes on to briefly describe the model used in this paper followed by a description of the estimation results for the case of Thailand. This is followed by a discussion of the main results and implications for monetary policy transmission. The final section concludes with some policy implications.

II. Three Major Shocks and Monetary Policy in Thailand

This section provides some context on the BOT’s inflation targeting framework, and the policy response to three major shocks that Thailand had to endure throughout 2008–2011: the global financial crisis of 2008–09, the Japanese earthquake of 2011, and the Thai floods of 2011.

Thailand formally adopted an inflation targeting framework in May 2000. Consistent with this target, the exchange rate is allowed to float freely. The main objective of the BOT is to ensure price stability in the economy, which is defined as low and stable inflation. However, the BOT also takes into careful consideration developments pertaining to economic growth and stability.2 Following the adoption of the new monetary policy framework, the Thai economy had average annual inflation of 2.6 percent during 2001–2007, and average annual GDP growth of 5.1 percent (Figure 1).

Figure 1.
Figure 1.

Thailand: Selected Macroeconomic Indicators

Citation: IMF Working Papers 2012, 269; 10.5089/9781475542851.001.A001

Source: IMF APDCore database and authors’ calculations.Note: All series in percent; year-over-year growth rates shown except for policy rates.

Strong headwinds from the global financial crisis which intensified with the downfall of Lehman Brothers reached Thailand at end-2008. Before these events transpired, however, Thailand had been in an investment slump since 2006. Nonetheless, export performance had been resilient with average growth of over 10 percent during 2006–07, and helped keep real GDP growth strong. In a rapid turnaround, exports plunged in the last quarter of 2008 in tandem with collapse in global trade. At the same time, the broad-scale pull back from foreign investors was associated with a marked rise in market volatility and an attendant sharp decline in the major stock market index. In parallel with a sequence of fiscal stimulus packages, the BOT cut its policy rate by 250 basis points to a historically low level of 1¼ percent.

Thailand then faced two additional major shocks in 2011:

  • The first was the earthquake which devastated Japan in March. Thailand was particularly affected because Japan is a key source of sophisticated intermediate and capital goods for Thailand (which account for over 90 percent of some components). Because of the specialization and concentration of upstream manufacturers, supply chains were particularly disrupted causing an abrupt production slowdown.

  • The second major shock was the worst flooding in at least 50 years, lasting from August to November. Throughout this period, over 800 people were killed and millions of residents were either left homeless or displaced as the floods inundated 66 of the country’s 77 provinces. Many of the primary sectors that form the backbone of the Thai economy—including manufacturing—were dealt a severe blow owing to the inundations. About one third of the country experienced flooding, including two key manufacturing provinces producing automobiles and electronic components, bringing down annual GDP growth from 7.8 percent in 2010 to 0.1 percent in 2011 after contracting by an astounding 11 percent in the last quarter of the year (which corresponds to an annualized rate of about 50 percent seasonally adjusted data). The Thai government responded to the floods with a broad set of policies, including fiscal stimulus, an infrastructure investment plan, and the BOT cut policy rates by 50 basis points to further support the recovery.

III. A Model for Thailand’s Monetary Policy Framework

This section presents an overview of the structural model underpinning our quantitative results. Readers primarily interested in the main policy implications of the paper could directly proceed to Section V and, in particular, Section VI. The goal here is to present the general intuition of the model, while the details are relegated to the Appendix.

The structural framework builds upon a core New Keynesian model. The model used is an open-economy variant of what the literature refers to as a New Keynesian dynamic stochastic general equilibrium (DSGE) model. However, to better fit the data, the model is augmented with a number of features including real and nominal rigidities (including, for example, investment adjustment costs and sticky wages), as well as a financial accelerator mechanism (to capture financial market imperfections) among several others.3

The model consists of several agents including households, producers, and the government. There are three types of producers: entrepreneurs, capital producers, and retailers. The government is responsible for implementing monetary and fiscal policy. A visual representation of the flow of goods and services across these agents is shown in Figure 2.

Figure 2.
Figure 2.

Model Schematic

Citation: IMF Working Papers 2012, 269; 10.5089/9781475542851.001.A001

Source: Authors’ calculations.

However, rather than elaborate on all aspects of the model, this goal in this section is to focus on the transmission of certain shocks and the role of monetary (and exchange rate) policy.

A. The Transmission of Shocks

Recall that this paper seeks to investigate the role of monetary policy in softening the adverse growth impacts on the Thai economy associated with the GFC, the Japanese earthquake, and the Thai floods.

To help foster intuition, it would be useful to discuss how these three shocks are likely to be captured within the modeling framework that is developed. These three major shocks would most likely be associated with a collapse in foreign demand, distress across international capital markets, heightened uncertainty, and abrupt declines in productivity. While the technical details are in the Appendix, an overview of how these shocks are propagated within our model is discussed below.

The export demand shock

The export demand shock, or perhaps equivalently, the foreign demand shock, propagates through the model via the market clearing condition below:

YtH=CtH+CteH+ItH+CtH*+Gt

Leaving aside differences in notation, this is basically the standard aggregate demand identity for home (domestically produced) goods, which posits that domestic output is equal to the sum of consumption of domestically produced goods (which is the sum of both household and entrepreneurial consumption, CtH+CteH), domestic investment good, ItH, government expenditures, Gt, and (gross) exports, CtH* Therefore, a collapse in export (foreign) demand is reflected in a decline in CtH*.

The sudden stop shock

Thailand’s experience during the global financial crisis was also associated with a reversal of capital inflows (a “sudden stop” in the parlance of Calvo and others, 2004), as well as a sharp depreciation of the exchange rate. To capture these interrelated disruptions, we (as in many other papers) augment the uncovered interest parity (UIP) condition with an exogenous shock:

it=it*Et[St+1St]Φt

where, it and it*, represent the domestic and international (gross) interest rates, respectively, St denotes the nominal exchange rate (Thai baht per US dollar—an increase represents a depreciation), Et is the expectations operator (conditional on information up to time t), and Φt is the sudden stop shock (also referred to an exchange rate shock or UIP shock in the literature). Therefore, as in Gerlter and others (2007), a shock that triggers large capital outflows is captured by this exogenous term which is appended to an otherwise standard UIP condition. When relevant, this sudden stop shock serves to capture the financial aspect of the three major shocks.4

The (financial) uncertainty shock

The description of this shock warrants some background. In this model, the real cost of capital departs from the standard representation in other studies because of the existence of an external finance premium. Consider the equation below:

Et[Rt+1K]=χt()Et[Rt+1]

where we have that the real cost of capital, RtK is equal to the real interest rate, Rt+1, augmented by the external finance premium represented by the term χt(·). The external finance premium depends on the leverage ratio (assets scaled by net worth) of the entrepreneurs:

χt=χt(QtKt+1Nt+1)

Note that total assets, QtKt+1, depends on the price of equity, Qt, which is not sticky (by contrast to goods prices or wages). This implies that the leverage ratio is quite sensitive to asset price fluctuations.5

The precise specification of the evolution of net worth (equity), Nt+1, is complex (and shown in the Appendix), so here an abridged version is used:

Nt+1=ϱtVt+Wte

where Wte and Vt, denote the entrepreneurial wage bill and the value of the firm, respectively. The (financial) uncertainty shock is an exogenous process, represented by the term, ϱt, which by construction has a direct impact on the level of aggregate net worth and therefore the external financial premium. Put differently, the net worth shock could be interpreted as a shock to the rate of destruction of entrepreneurial financial wealth (in line with several other studies). This shock directly affects entrepreneurial net worth and has been used in various forms by Elekdag and others (2006), Curdia (2007), Christiano and others (2010), and more recently by Alp and Elekdag (2011). Another way to think about this shock is that it could be thought of capturing counterparty risk—owing part to Knightian uncertainty—a key consideration during the global financial crisis. This heightened uncertainty regarding cash flows, for example, would impair assets and thus disrupt the financial system.

The natural disaster-related shocks

In the case of the Thai floods of 2011, it is clear that this was a disruptive supply shock to say the least. In terms of the model, temporary supply shocks are modeled in a standard fashion, and represented by a decrease in total factor productivity (TFP) consistent with the production function below:

Yt=AtKta(ZtLt)1-a

where Yt, Kt, and Lt denote output, the capital stock, and labor inputs, respectively. In addition, At and Zt, represent temporary and permanent technology (or productivity) shocks, respectively. The former is the benchmark supply shock which has been attributed a pivotal role in the real business cycle literature, because it has been documented to be an important source of aggregate business cycle fluctuations. The latter shock affects the growth trend of the economy, and as argued by Aguilar and Gopinath (2007), is a key determinant of business cycle fluctuations across emerging economies. Against this backdrop, if the decline in real GDP is primarily characterized by trend growth shocks, this would imply larger permanent output losses.

Another type of supply shock included in the model affects investment, rather than the stock of capital (in contrast to the temporary productivity shock). This investment-specific technology shocks, εti, is temporary and, as argued by Justiniano and others (2007), plays a potentially critical role in accounting for output dynamics. The evolution of capital (after taking into consideration adjustment costs, ψ(·) and the depreciation rate, δt) has the following properties:

Kt=(1-δt)Kt-1+[1-ψ(·)]Itεti

In sum, there are three types of supply shocks which directly affect productive capacity in the model economy, including one which allows for lower trend growth and thereby permanent output losses.6

B. What Role for Monetary Policy?

In our model, the central bank alters interest rates in an attempt to achieve certain policy objectives. Before proceeding to the details, note that the policy rule to be described below implies that the monetary authority sets the nominal interest rate, taking into consideration the inflation rate deviation from the time-varying inflation target, the output gap, the rate of exchange rate depreciation, and the previous period’s interest rate (policy smoothing).

A simplified version of the empirical interest rate rule takes the following (log-linear) form:

ι^t=ρiι^t-1+τπ(π^t-π^tT)++τyy^t+τsΔS^t+εti

where in this flexible specification, ι^t, π^t, y^t, S^t, denote the (short-term policy) interest rate, the (core CPI) inflation rate, the output gap, and the nominal exchange rate, respectively (see Appendix for further details). Note that εti denotes the monetary policy shock—interest rate changes that deviate from the (empirical) interest rate rule would be captured by this disturbances and could be considered discretionary monetary policy. The time-varying inflation target, π^tT, is assumed to evolve according to the following stochastic process:

π^tT=ρπ¯π^t-1T+εtπ

The time-varying inflation target has also been used in the literature to capture structural changes in the conduct of monetary policy that are not captured otherwise (see Adolfson and others, 2007, for further details). In the case of Thailand, the time-varying inflation target, allows the model to capture the change in the inflation target when the core inflation target range was narrowed to 0.5–3.0 percent in 2009, from 0–3.5 percent.7

Anticipating the results to follow, notice that when the output gap is negative—that is, output is below potential—strict adherence to the rule above would imply that the interest rate decreases by an amount dictated by the coefficient τy. However, the monetary authority might decrease interest rates by more than what the systematic component of the rule would imply. Recall that this deviation from the rule is capture by the error term, εti, which is the monetary policy shock—thereby capturing discretionary monetary loosening. As will be discussed in further detail below, interest rates decreased by more than the amount the empirical counterpart of the rule would have implied, helping soften the impact of the three major shocks.

IV. Estimation of the Model for Thailand

This section gives an overview of model estimation. It briefly reviews issues pertaining to data, parameter calibration, choice of prior distributions, resulting posterior distributions, model fit, and sensitivity analysis. An extensive discussion of these issues is covered in the Appendix.

A. Data

The log-linearized model is estimated using Bayesian methods primarily developed by Schorfheide (2000), and later popularized by Smets and Wouters (2003, 2007). The model is estimated using quarterly data from the third quarter of 2000 to the first quarter of 2012 using 12 standard time series, a few of which are shown in Figure 1. Specifically, in line with many other studies, we have chosen to match the following set of variables: the levels of the domestic policy and foreign interest rates, the inflation rates of domestic GDP deflator and core consumer price and foreign consumer price indices, as well as the growth rates of GDP, consumption, investment, exports, imports, foreign GDP, and the real exchange rate. The sample period used for estimation (2000–12) covers the period when the BOT was implementing inflation targeting (underpinned by a flexible exchange rate).

B. Model Parameters

We follow the literature and calibrate certain parameters (see, for example, Christiano and others, 2010), which could be thought of as infinitely strict priors. Many of the parameters are chosen to pin down key steady state ratios, while the remaining parameters are taken from the literature as summarized in Table 1.

Table 1.

Calibrated Parameters

article image
Source: Authors’ calculations.

The remaining 43 parameters, shown in Table 2, are estimated. These parameters determine the degree of the real and nominal rigidities, the monetary policy stance, as well as the persistence and volatility of the exogenous shocks. The table shows the assumptions pertaining to the choice of distribution, the means, standard deviations, or degrees of freedom. The choice of priors is in line with other studies (see Alp and Elekdag, 2011, for a selected review of the literature). The posterior estimates of the variables are shown in the same table, which reports the means along with the 5th and 95th percentiles of the posterior distribution of the estimated parameters obtained through the Metropolis-Hastings sampling algorithm. In general, the parameter estimates are in line with those found in other studies.8

Table 2.

Prior and Posterior Distributions

article image
Source: Authors’ calculations.Note: Log data density is 1,265. For inverse gamma distributions, mean and degrees of freedom are reported.

C. Sensitivity Analysis

To assess the robustness of the estimated model, we consider a few alternative specifications which include different monetary policy rules and alternative structural features. The results are summarized in Table 3, which depicts the log data density of the various models, and the posterior odd ratio contrasting the baseline and the alternative model specifications. While the details are discussed in the Appendix, the main takeaway is that we consider 7 alternative specifications, and the results are all decisively in favor of the baseline model.

Table 3.

Sensitivity Analysis

article image
Source: Authors’ calculations.Note: UIP and ΔS denoted uncovered interest rate parity and the change in the nominal (won/dollar) exchange rate, where an increase in S denotes a depreciation of the Thai baht.

V. The Monetary Transmission Mechanism

This section aims to explore the dynamics of the estimated model by investigating the monetary transmission mechanism. This is critical because the focus of the paper is to assess the role of monetary policy during the global financial crisis.

To this end, we consider the impulse responses to a one standard deviation monetary tightening shock as shown in Figure 5. To more openly communicate the degree of uncertainty regarding the monetary transmission mechanism in Thailand during a sample period which encompasses the global financial crisis, we present Bayesian impulse response functions for a selected set of variables along with their 90 percent bands which take into consideration parameter uncertainty.

Figure 3.
Figure 3.

Thailand: The Monetary Transmission Mechanism

Citation: IMF Working Papers 2012, 269; 10.5089/9781475542851.001.A001

Source: Authors’ calculations.Note: Bayesian impulse response functions to a contractionary monetary policy shock. Interest rates, inflation rates, and the external finance premium are shown as absolute deviations from their steady states, while the other variables are percentage deviations from their steady states.
Figure 4.
Figure 4.

Counterfactual Scenarios: The Role of Countercyclical Monetary Policy

Citation: IMF Working Papers 2012, 269; 10.5089/9781475542851.001.A001

Source: Authors’ calculations.Note: Figure denotes the level of real GDP as an index with 2008Q1=100. Baseline denotes the actual evoluation of Thai real GDP.
Figure 5.
Figure 5.

Counterfactual Scenarios: The Role of Exchange Rate Flexibility and Financial Reforms

Citation: IMF Working Papers 2012, 269; 10.5089/9781475542851.001.A001

Source: Authors’ calculations.Note: Figure denotes the level of real GDP as an index with 2008Q1=100. Baseline denotes the actual evoluation of Thai real GDP.

A one standard deviation contractionary monetary policy shock corresponds to a 80 basis point (quarterly) increase in the nominal interest rate (Table 2), which implies an annual increase in the policy rate of about three percent. The output gaps dips below the steady state by 31 basis points, whereas the year-over-year inflation rate reaches a trough of about 63 basis points below steady state after four periods.

The shock propagation is effected via three main channels:

  • The first channel operates as interest rates affect domestic demand, which primarily comprises consumption and investment. Working through the Euler equation, higher real interest rates foster an increase in saving as consumption is postponed to later periods. At the same time, higher real interest rates increase the opportunity cost of investment, decreasing the rate of capital accumulation. As a result, domestic demand decreases, putting downward pressure on inflation.

  • The second channel brings out the open economy features of the model as it works via the exchange rate. Because of the nominal rigidities, the increase in the nominal interest rate translates into higher real interest rates and is associated with an increase in the real exchange rate. In turn, this appreciation of the real exchange rate suppresses net exports (the expenditure switching effect), further decreasing aggregate demand.

  • The third channel is characterized by the financial accelerator mechanism. Higher interest rates depress asset prices (the real price of capital) bringing about a deterioration in net worth. Weaker balance sheet fundamentals cause an increase in the external finance premium thereby raising the opportunity cost of investment above and beyond the initial effect generated by the monetary tightening. This brings about an even sharper contraction in investment, which is the primary determinant of the deeper contraction. As discussed in further depth in other papers, the financial accelerator mechanism can amplify the effects of certain shocks (Bernanke, Gertler, and Gilchrist, 1999).

The model includes 15 structural shocks including the monetary policy shock discussed above. While we do not present the details here, in terms of our structural model, the dynamic implications of these shocks are in line with the literature, and therefore, in the interest of brevity, we refer the reader to other studies for further details (including, for example, Elekdag and others, 2006; Gertler and others, 2007; Curdia, 2007; Christiano and others, 2010; Alp and Elekdag, 2011).9

VI. Monetary Policy in Thailand and Three Major Shocks

In this section of the paper, we conduct counterfactual experiments with the goal of answering the following general question: If the BOT did not implement on inflation targeting framework underpinned by a flexible exchange rate regime, how much deeper would the output contractions have been?

As will be discussed below, that answer is that the output losses associated with the three major shocks (global financial crisis, Japanese earthquake, and the Thai floods) would have been significantly more severe. In fact, without countercyclical monetary policy, the cumulative growth losses associated with these three shocks would have been about 1.6 percentage points. Moreover, in another illustrative counterfactual simulation with financial fragilities mimicking those which existed during the mid-1995s, and with a fixed exchange rate regime in place, suggests that the cumulative growth losses would have been close to 4.1 percentage points.

A. Setting Up the Counterfactual Simulations

Though intimately related, the model allows us to separately investigate the contributions of countercyclical interest rate policy and exchange rate flexibility in terms of softening the impact of the three major shocks. Therefore in what follows, by altering the monetary policy response, we consider four counterfactual simulations and compare the output implications they imply with the actual realization under the baseline model specification. Under the baseline, the monetary policy framework (which is underpinned by a flexible exchange rate) operates in accordance with estimated baseline interest rate rule discussed above. In this context, the three counterfactual experiments are as follows:

  • No monetary policy shocks: this counterfactual posits strict adherence to the baseline empirical interest rate rule. It is a simulation which excludes the monetary policy shocks—that is, the monetary policy shocks, εti, are all set to zero in this simulation. It serves to address the following question: What would the dynamics of output have been if the BOT did not implement any discretionary loosening (deviations from the interest rate rule) when the shocks materialized?

  • No response to the output gap: under this counterfactual, the output gap coefficient in the empirical interest rate rule is set to zero (τy = 0). Furthermore, as these counterfactuals are “cumulative,” this scenario also sets the monetary policy shocks to zero. It serves to address the following question: What would the dynamic of output have been if the BOT did not implement any countercyclical policy? While interest rate smoothing is still allowed, in this case, a form of strict inflation targeting is implemented without any due regard to the output gap.

  • Peg: in this counterfactual, the BOT is assumed to implement a strict fixed exchange rate regime.10 Intuitively, there are no discretionary deviations from the rule (which solely focuses on stabilizing the nominal exchange rate). Here we seek to address the following question: What would the dynamic of output growth have been if the BOT was implementing a fixed exchange rate regime?

  • Peg with heightened financial vulnerability: under the last counterfactual, the BOT is presumed to operate under a fixed exchange rate regime as above, but the leverage ratio is calibrated to correspond to the case where it equals three (rather than the baseline of two under the baseline, see Alp and Elekdag, 2011, for further details). While not the main focus of the paper, our modeling framework allows us to construct such an illustrative counterfactual serving to address the following question: What would the dynamic of output growth have been if the BOT was implementing a fixed exchange rate regime and the economy was financially more fragile?

B. Results Based on the Counterfactual Simulations

The counterfactual simulations are summarized in Figure 4 and in Figure 5. These figures depict the level of real GDP with the third quarter of 2008 (the pre-global financial crisis peak) normalized to 100 to allow the reader to better distinguish the (cumulative) effects of each counterfactual. For presentation purposes and to really emphasize the main policy implications, the figures start in 2006:Q1, and only show the counterfactual simulations over selected periods.11 Figure 4 displays (1) the actual realization of real GDP (the baseline scenario), (2) the counterfactual scenario without the monetary policy shocks, and (3) the counterfactual scenario without any due regard to the output gap. These scenarios emphasize the role of countercyclical monetary policy. In contrast, Figure 5 shows (1) the actual realization of real GDP, (2) the counterfactual with a fixed exchange rate regime (peg), and (3) an another illustrative scenario with the peg combined with heightened financial fragilities. This latter figure only shows the counterfactual simulations over 2008:Q3–2009:Q4 because this is the period when the differences between the counterfactual simulations differs noticeably from the baseline.

The main message to take away from these simulations is that the inflation targeting framework underpinned by a flexible exchange rate regime implemented by the BOT clearly softened the impact of the GFC, the Japanese earthquake, and the floods which Thailand had to endure.12 More specifically, it is useful to discuss two main results:

  • The discretionary cuts in the policy rate helped soften the impact of the shocks throughout the period when the three major shocks hit as shown in Figure 4. This is most noticeable during GFC when the BOT cut policy rates 250 basis points to a historically low level of 1¼ percent. In addition, the results suggest that taking the output into consideration also help dampen the economic contractions that unfolded during 2008–2011. Consider the two most severe output contractions which were associated with the GFC and the Thai floods. Without these countercyclical monetary responses, results from the model indicate that the peak-to-trough decline in output would have increased to 8.7 percent from the actual of 7.3 percent during the GFC (for a difference of 1.3 percentage points), and increased to 12.3 percent from the actual of 11.1 percent in the aftermath of the floods (for a difference of 1.2 percentage points). To put these numbers in context, recall that the peak-to-trough real GDP decline during the 1990s U.S. recession was about 1.3 percent. Therefore, the additional total output loss of up to 2.5 percentage points implied by the simulations without countercyclical monetary policies is clearly quite substantial.

  • Along with the actual path of real GDP, Figure 5 presents two additional illustrative counterfactual simulations seeking to explore the role of exchange rate flexibility and the financial reforms implements since the late 1990s. The main differences occur during the GFC, which was characterized by a sharp slowdown in global trade and an episode of acute financial stress across international capital markets. The simulations indicate that the output losses would have been larger under a fixed exchange rate regime. The lack of the exchange rate to serve as a shock absorber decreases the resiliency of the economy to the shocks—particularly those transmitted via the trade channel—which ensued during the GFC. Nevertheless, in contrast to some other emerging economies, the real exchange rate in Thailand depreciated only by about 4 percent during the GFC, which is why the quantitative difference between the actual outcome and the simulation are visibly not that large. Intuitively, the illustrative counterfactual experiment with heightened financial fragilities leads to an even sharper decline in output. The acute episode of financial stress associated with the GFC is amplified because of a more pronounced balance sheets channel, and thereby characterized with larger output losses. In sum, and further discussed below, these counterfactual experiments highlight the importance of exchange rate flexibility and financial reforms in promoting macroeconomic stability and financial system soundness.

C. How Do the Results Compare with Those in the Literature?

This section starts by focusing on the contributions of the implemented countercyclical monetary policies to growth under the various counterfactuals during the three major shocks, which are shown in Table 4. It then compares these results to some related studies in the literature.

Table 4.

The Role of Monetary Policy and Three Major Shocks

article image
Source: Authors’ calculations.Note: In percent.

Before investigating the details, it would be useful to clarify the information contained in Table 4. Three episodes are considered: the GFC (2008:Q4–2009:Q3), the quarter when the spillovers from the Japanese earthquake were most forcefully felt (2011:Q2), and the quarter during which the Thai floods intensified (2011:Q4). Unless otherwise stated, the values under columns show the average year-over-year contributions to growth during these episodes under the four counterfactual simulations. After tabulating the number of quarters associated with each of the three shocks, columns [1] through [4] indicate the incremental contributions to growth owing to the consecutive implementation of each policy. For example, in the case of the GFC, under column [1], the results from the model suggest that the discretionary loosening by the BOT (owing to the monetary policy shocks which decreased policy rates) added ¼ percentage point to the average year-over-year real GDP growth during 2008:Q4–2009:Q3. Furthermore, the simulations suggest that because the BOT also took into consideration the widening of the output gap during this period, it supported growth by an additional 55 basis points as shown under column [2]. In other words, without the countercyclical monetary policy response by the BOT during the GFC, growth during the year under consideration would have been lower by ¾ percentage point. The other illustrative scenarios indicate that reduced financial fragilities and adopting a flexible exchange rate regime added up to 35 and 43 basis points to growth as depicted under columns [4] and [3], respectively.

The effectiveness of the policy response seems to be related to the nature of the shock confronted by the Thai economy. Consider columns [1] and [3]. In contrast to the external shocks, monetary policy seems to have contributed relatively more to growth during the Thai floods. On the contrary, when the aftereffects of the Japanese earthquake were felt in Thailand, results from the model suggest that exchange rate flexibility helped support growth by up to about 1¼ percentage points. These findings echo the seminal theoretical contribution of the textbook exposition of the Mundell-Flemming model, but also highlight the macroeconomic stabilization benefits of the BOT’s inflation targeting framework which is underpinned by a flexible exchange rate regime.

It would be useful to compare the results in Table 4 with the literature. A study by Christiano and others (2007) is somewhat related to ours in terms of conducting counterfactual experiments. Turning our attention to column [1], results from the model indicate that the total average contribution of the monetary shocks (discretionary deviations from the empirical interest rate rule) to output growth during the three episodes under consideration is about 70 basis points, which is in line with the values found by Chrisitiano and others (2007) for the U.S. (75 basis points) and the euro area (127 basis points). Moreover, accounting for the role played by actively responding to the output gap implies a total growth contribution of 91 basis points over the three shock episodes. Taken together, during the six quarters when output was most severely affected by the three major shocks, countercyclical monetary policy and exchange rate flexibility contributed to growth by up to 1.6 and 2.1 percentage points, respectively, for a total of up to 3.7 percentage points.

Table 5 summarizes our main findings. It tabulates the actual and simulated average year-over-year growth rates during the three episodes under consideration (columns [1], [2], and [4]). It also displays the differences in growth (columns [3] and [4]), and the totals for each of the three episodes. Overall, results from the model suggest that without countercyclical monetary policy, average growth across these three episodes would have been lower by a total of up to 1.6 percentage points. Without exchange rate flexibility (and a more financially fragile economy), growth would have been lower by an additional up to 2.5 percentage points, for a grand total of up to 4.1 percentage points. It would be useful to emphasize that these simulations only focus on the role of countercyclical monetary policy and exchange rate flexibility as defined above. Other countercyclical measures (for example fiscal policy and liquidity support) are not explicitly captured in this modeling framework and could be pursued in future research. In sum, without the adoption of the flexible exchange rate regime, and active countercyclical monetary policy guided by an inflation targeting framework, the impact of the three major shocks on Thailand’s economy would have been substantially more severe.

Table 5.

Summary of the Role of Monetary Policy

Real GDP Growth

(Year-over-year)

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Source: Authors’ calculationNote: In percent.

VII. Summary and Main Policy Implications

Over the course of 2008-2011, Thailand had to weather the aftereffects of the global financial crisis, the spillovers from the devastating earthquake in Japan which disrupted supply chains, and the most destructive floods in at least 50 years. The focus of this paper is to assess the role of countercyclical interest rate cuts and exchange rate flexibility in mitigating the economic fallout from these three major shocks. The results indicate that the monetary policy framework implemented by the Bank of Thailand (BOT) helped soften the impact of these three major shocks on the Thai economy.

Since May 2000, underpinned by a flexible exchange rate regime, the BOT has been an inflation targeting central bank. During the global financial crisis, for example, consistent with its inflation targeting framework, the BOT let the exchange rate depreciate and cut interest rates to a historically low level of 1¼ percent by mid-2009. In this context, this paper seeks a quantitative answer to the following question: If an inflation targeting framework underpinned by a flexible exchange rate regime had not been in place, how would the economic contractions associated with these shocks have differed?

The quantitative results emphasize the macroeconomic stabilization properties of the BOT’s inflation targeting framework which is underpinned by a flexible exchange rate regime. Specifically, results from the model indicate that during the six quarters when output was most severely affected by the three shocks, countercyclical monetary policy and exchange rate flexibility contributed to growth by up to 1.6 and 2.1 percentage points, respectively, for a total of up to 3.7 percentage points. Exchange rate flexibility and countercyclical interest rate policy served as shock absorbers and facilitated the recovery in the aftermath of these three major shocks. These finding are based on counterfactual simulations derived from an estimated structural model which captures the salient features of the Thai economy.

In sum, given the openness of the Thai economy through financial and especially trade channels, the flexibility and resilience of the economy are especially important when faced with severely disruptive exogenous shocks. In line with this, Thailand’s monetary policy framework, underpinned by a flexible exchange rate, is well suited to the characteristics of Thailand’s economy, as demonstrated through the counterfactual experiments discussed in this paper in the context of the three major shocks.

Shock Therapy! What Role for Thai Monetary Policy?
Author: Mr. Harun Alp and Selim Elekdag