Flattening Indonesian Phillips Curve

Globalization and the Flattening Indonesian Phillips Curve.

An Empirical Study

Background and Motivation

It was around 50 years ago, AW Phillips plotted nominal wage rate and unemployment of UK and found there is tight and negative relationship between those two variables based on data 1862-1957. This relationship accommodates a wide variety of inflation theories. It argues that inflationary pressure stem from excess demand for goods. This is in turn creates excess demand for labour bidding up wages and prices. However, it does not discuss the sources of the excess demand.

Excess demand as a main source of inflation is focus of attention of authority which control inflation such as central bank. The relationship of the two variables over time change induces monetary policy taken by the authority. Identifying the relationship whether it is positive or negative, or it is more or less positive or flattening is crucial for the aspect of policy implication.

Bank Indonesia, as central bank who has mandate to control domestic price has great concern to this relationship. Given the structural changes in Indonesia, especially the financial crises 1997-1998, Indonesia experiences significant changes of behaviour of economic variables. In addition changes of monetary policy regime such as entering inflation targeting regime probably creates different relation between inflation and excess demand. Or even the question whether the relationship still exists or disappears becomes valid.

Against this backdrop, the purpose of this research will try to answer the significant questions regarding inflation process in Indonesia from the Phillips Curve perspective. The research question will be as follow:

1. Which Phillips Curve can represent the Indonesian economy better?

2. Is that Phillips Curve flattening? How does it evolve given the structural changes experienced by the economy?

3. If the above hypothesis is right, why does the change happen? Is there any role of globalization in this case?

Literature Review

Estimation of the Phillips Curve

This research will focus on New Keynesian Phillips Curve which is combination of the two approaches, Keynesian and Neoclassical. It begins to be explored since Taylor (1980) and Calvo (1983) introduced staggered contract setting. The idea is monopolistically competitive individual firm set its price using derivation of an explicit optimisation problem. At any given time, a firm has fixed probability of maintaining or changing its price which is independent from time to time and this probability becomes a fraction of firms that fix their price and another fraction change their price. Earlier combination of monopolistic competition and staggered price, though are static, are Svensson (1986), Blanchard and Kiyotaki (1987), Blanchard and Fischer (1989), Akerlof and Yellen (1991). Their model are not based on optimization problems of all agents in the economy.

Comparing the model with the data creates critics on this type of Phillips Curve. As mentioned on Olafsson (2009) there are three main issues on Phillips Curve: 1) the choice of variables to include in relationship, 2) modeling its microfoundation (price setting, expectation), and 3) optimal estimation approach. Mankiw (2001) notes there are three aspects that this model is not in line with the empirical data. Firstly, this model cannot explain the endogenous trade off between inflation and activity. The model with rational expectations shows a fully credible disinflation can cause an economic boom in a new Keynesian model, which is contrary to empirical facts. Secondly, the model cannot explain inflation persistence in empirical data well. Thirdly, the delay between monetary policy shock and inflation which happen empirically cannot be explained by this model.

To respond the critics, development of Phillips Curve comes to what Gali and Gertler (1999) proposed as a hybrid New Keynesian Phillips Curve. In this model, inflation depends on current and expected future economic condition. By optimization firm set its price based on its marginal cost. The coefficient of the hybrid Phillips Curve are explicit function of three model structural parameters: the degree of price stickiness q, the degree of backwardness w and the discount factor b.

When coefficient of forward looking inflation gf is large, Berg et all (2006) refer this as “speedboat economy” since only small hike can simultaneously have large effect on current inflation, and if it is small Berge et all (2006) refer this to “aircraft economy”, it needs many periods and accumulation of interest rate adjustment to direct inflation to its target.

Again, the model is not without criticism, especially when it comes to empirical data. Even Gali and Gertler note “reconciling the new Keynesian Phillips Curve with the data, has not proved to be a simple task”, (Gali and Gertler, 1999:201). Some researches try to analyse this model. In terms of the importance of forward looking versus backward-looking variables in the relationship, some research can be recorded. Paloviita (2006) uses survey based expectation for European data to estimate the model and finds backward looking is dominant. By using survey he avoids the problem of simultaneously testing the specification of PC and formation expectation. The use of survey data gives empirical result that are more reliable than those obtained from the rational expectation GMM approach. Henzel, S., Wollmershauser, T., (2008) also use data from survey and find purely forward looking Phillip curve can be rejected in favor of the Hybrid New Keynesian Phillips Curve. The estimated coefficient on past inflation are higher when using survey expectation than when using the rational expectation GMM approach.

In terms of estimation approach there are some insights. In their famous paper Gali and Gertler (1999) use GMM approach and uses lag variables as instrumental variables. The disadvantage is simultaneously test specification of the Phillips Curve and the Rational Expectation theory. Rudd and Whelan (2005) criticize the GMM estimation approach used by Galí andGertler (1999) and state that “their procedure is likely to suggest that forward-looking behaviour is very important even if the true model contains no such behaviour” (Rudd and Whelan, 2005: 1179). Rudd and Whelan (2006) elaborate further on this and note that other model that rely on nonrational, backward-looking expectations can also mimic inflation data if include lag inflation. Hence, incorporating lagged inflation into the inflation equation should allow the hybrid model to fit the level of inflation relatively well.

Lindé (2005) estimates a macroeconomic model with a hybrid Phillips curve using FIML and find where coefficient of backward looking is equal or more than forward looking the model provides a reasonable approximation of US inflation dynamics. Kiley (2005) argues for maximum likelihood (ML) techniques as their estimation offers more efficient and less-biased estimates of structural parameters, even in case of misspecification, and provides an explicit measure of fit through the likelihood function enabling assessment of alternative non-nested models. On the other hand, Barkbu and Batini (2005) use an application of the method by Johansen and Swensen (1999) to estimate a hybrid new Keynesian Phillips curve when inflation is non-stationary using Canadian data and emphasize that GMM and FIML methods are invalid under these circumstances. In addition, Rabanal and Rubio-Ramírez (2005) argue for Bayesian estimation procedure outperforms GMM and maximum likelihood in small samples. And even in the case of misspecified models Bayesian estimation is consistent. To respond the criticisms by Rudd and Whelan (2005) and Lindé (2005), Galí, Gertler and López-Salido (2005) and Sbordone (2005) emphasize that their results are robust across a number of different estimation methods, including the closed form specification stressed by Rudd and Whelan (2005) and maximum likelihood methods uphold by Lindé (2005).

Real activity in Gali and Gertler (1999) is represented by marginal cost that comes from derivation of Phillips Curve. Output gap or labour share can be a proxy of marginal cost. Wolman (1999) is critical on the relationship between marginal cost and inflation in this case. He underlines the measurement problem that should be considered, which unlike GDP or inflation, marginal cost is not a data series measured by a government statistical agency. He also argues that using labour share as a proxy for real marginal costs is an oversimplification since it assumes that all firms use Cobb-Douglas production technologies and that there is an economy-wide competitive labour market. Rudd and Whelan (2005) also oppose the use of labour share as a proxy for real marginal cost due to its countercyclical pattern, with the series spiking upward during each postwar recession in the US. In contrast, Batini, Jackson and Nickell (2005) show that using UK data, those two variables, inflation and labour share, have positive correlation. In terms of output gap, Neiss and Nelson (2005) argue that if the output gap is measured in a theory-consistent manner, an output gap-based Phillips curve will fit the data about as well as a marginal cost-based Phillips curve.

Flattening Phillips Curve and possible explanation

Policy-makers have recently noted an apparent flattening of the Phillips curve. The IMF in its World Economic Outlook reports that in many countries, inflation is less sensitive to business cycle in 1990's than before IMF (2006). There are some methodologies to assess the relationship between real activity and inflation in Phillips Curve.

Ormerod et all (2009) use fuzzy logic clustering to identify each year observation whether it belongs to specific cluster or group. In their paper, they evaluate three countries (US, UK and Germany) for period 1871-2009 annually and find the result are quite similar for the three countries. For all countries, they conduct 500 separate solution of the fuzzy cluster algorithm and divide the cluster into three categories: steady, where both inflation and unemployment are low; weak, where inflation is low/deflation and high unemployment and disruption where high inflation and moderate unemployment. Most of the time the observations belong to steady group. And their transition matrix shows the persistent of each regime or the high probability each regime will end up at the same regime. If there is movement, the probability of moving is the highest from disruption and weak to steady regime.

Beaudry and Doyle (2000) run 15 year moving window rolling regression starting from 1978 for US and Canada. For instance, the estimation of 1983, is derived from observation over period from 1969-1983. They regress the change in inflation on the lag of output gap as baseline. They show that the peak of the slope happen in 1982 and decline over 1980s and 1990s in both US and Canada. To check robustness they conduct the same rolling but with additional regressor, perform weighted rolling regression and pool the US and Canadian data to increase the number of the sample.

For stability of the Phillips curve in Germany and the euro area Barkbu et al., (2005) assess it including structural break using recursive estimation tests. They indicate that substantial parameter instability is present in the early 1980s and also around the time of reunification. It is suggested that the re‐unification in Germany increased the instability of inflation‐unemployment relationship in the Euro area in the early 1990s. Simpler method use scatter plot of inflation and proxy of real activity. Iakova (2007) use scatter plot between inflation and deviation of unemployment from NAIRU to see the correlation between inflation and economic slack in UK.

While the stylized fact of a flatter Phillips curve has been reasonably well established, the precise reasons for this change are not well understood. Firmer anchoring of inflation expectations is one possibility, advanced by Roberts (1998) and Mishkin (2007), among others. As discussed in Bean (2006) and Mishkin (2007), flattening Phillips Curve has consequence in monetary policy. A benefit is higher output or lower unemployment is less inflationary, but once established, inflation would be difficult to bring it down. Iakova (2007) employ a New Keynesian open economy macroeconomic model with rational expectation to assess the flattening Phillips Curve. She suggests a central bank that cares both about the inflation target and deviations of output from potential should respond relatively less to deviations of inflation from target and relatively more to deviations of output from potential in a world with a flatter Phillips curve.

The role of monetary policy to flatten Phillips Curve is strengthened by Beaudry and Doyle (2000). They employ the model based on the monopolistically competitive model of Blanchard and Kiyotaki (1987) in manner that allows for real disturbance and imperfect information. Using this model, they have result that as Central Bank becomes more aware of and sensitive to real development in the economy, the slope will tend to zero.

Others, such as Borio and Filardo (2007) and Razin and Binyamini (2007), cite the effects of globalisation. Borio and Filardo (2007) investigate the hypothesis that greater economic integration or globalization has role in effecting the domestic inflation observed over the past decade. The author treat this hypothesis not as an alternative of determinant of lower inflation but rather as a complement to the widely accepted view that better monetary policy performance has significant role on that. They test the hypothesis using two approach country-centric and globe-centric approach. To sum up, the stylised country-centric approach explains inflation in a bottom-up fashion, plays down international and global factors and, when explicitly considered, treats them as exogenous. By contrast, the globe-centric approach explains inflation in a more top-down fashion, focuses on global factors, with domestic ones seen as providing an incomplete picture of the inflation process, and treats many influences on country-specific developments as endogenous. Using data of 16 developed countries they find (1) the sensitivity of inflation on domestic output gap is declining, (2) By estimating country by country, it is apparent that foreign gap statistically significant across countries, (3) To test the robustness of their mode, they estimate augmented globe-centric PC where the additional regressor included other domestic and external specific factor. The result is it is not only strengthen the fit of the model but also doesn't weaken the significance of the global slack, (4) Due to the structural changes experienced by China economy, there is probability its output gap data is not reliable. Concerning the China factor, especially the measurement of global gap given China's structural change, the estimation is added by variable of China speed limit which is measured by the change in its estimated output gap. However the result provide weak confirmation, it is only significance for few countries.

Razin and Binyamini (2007) build a New Keynesian model to assess the effect of globalization. Using simulation of their model, it is shown that labor, goods, and capital mobility are the factors that flatten the Phillips curve, where the tradeoff between inflation and activity become lessen. The simulation also demonstrates that the same globalization forces lead monetary policy to be more aggressive with regard to inflation fluctuations rather than to the output-gap fluctuations. In addition, when the economy opens up, the equilibrium response of inflation to supply and demand shocks is more moderate, on the other hand the response of the output gap to these shocks is more pronounced.

Other researches that also test this hypothesis, among others are, Tootell (1998) who uses an augmented Phillips curve specification for the United States, with a trade-weighted foreign output gap. The foreign gap, however, is limited to the G-7 trading partners. He finds no relationship between foreign measures of slack and US inflation. Finally, his paper focuses on the direct trade channel of inflation transmission. Ciccarelli and Mojon (2008), use a dynamic factor model to first identify global inflation for 22 OECD countries; then they search for domestic and global (real and nominal) variables that are correlated with the global inflation factor. Their evidence concerning the correlation between global inflation, on the one hand, and commodity prices, the global business cycle and global liquidity, on the other, suggests that global factors may be important. Mumtaz and Surico (2006) corroborate and extend this empirical analysis by exploring the temporal cross-country variation in inflation volatility. They find evidence that inflation volatility appears to have become increasingly driven by a common global factor, especially when compared to the 1970s and early 1980s.

Morimoto et al (2003) look at this issue from yet another perspective. They estimate a global supply shock by extracting the first principal component of the residuals from a set of New Keynesian Phillips curve regressions for seven countries (which include South Korea and Taiwan, China). They find that this component shows a systematic pattern since the mid-1990s, which is consistent with rough measures of globalisation, such as global import penetration by emerging market economies. In addition, based on a structural VAR, they find further statistical support for the view that the global supply shock could be interpreted as a proxy for the rapid expansion of production capacity in emerging market economies. The authors' interpretation of the results emphasises the role of direct trade channels from the emerging market economies to industrialised economies to explain the disinflationary pressures.

Kuttner and Robinson (2008) show that the flattening is evident in the baseline ‘structural' new-Keynesian Phillips curve using data for the United States and Australia, and using the standard new-Keynesian Phillips curve. They consider a variety of reasons for this structural flattening, such as data problems, globalisation and alternative definitions of marginal cost, none of which is entirely satisfactory. Their estimates suggest that there has also been a flattening in this ‘structural' model, that is, there has been a change in the price setting behaviour of firms. In particular, it appears that the duration between price resetting may have lengthened. Many of the common explanations for changes in the price-setting behaviour of firms are related to globalisation. While globalisation may alter the relationship between the output gap and marginal costs, it is unclear why it would alter the link between marginal costs and inflation in a way that corresponds to a flattening of the Phillips curve. In a structural model, the deep parameters in the Phillips curve should be invariant to changes in the conduct of monetary policy. However, one potential explanation is that lower-trend inflation resulting from the improved conduct of monetary policy may account for the more infrequent price resetting and hence the flattening Phillips curve, a possibility which is not accommodated in the benchmark new-Keynesian model.

In the case of Indonesia, there are some researches that directly asses Phillips Curve or indirectly as additional result of other research. Among others are Solikin (2004) and Affandi (2007). Solikin (2004) uses quarterly data (1974.1-2002.4) to estimate three type of PC (traditional, hybrid and non linear backward looking PC) to test its existence. Method used are Ordinary Least Squares (OLS), GMM, and (MLE)-Kalman Filter (KF) Algorithm. To measure output gap, this study used HP filter, unobserved component (Clark, 1987), and unobserved component with Markov-Switching process (Hamilton, 1989), (Lam, 1990), and (Kim, 1994). This study concludes that PC is exist in Indonesian economy, output gap pressure after crises tend to greater than that of pre crises, in terms of parameter, the coefficient of output gap is greater after the crises and the relationship is non linear. However, this research does not answer why the structural change of the Phillips Curve happen. Affandi (2007) estimates a New Keynesian model following Pesaran and Smith (2006) using Indonesia data. The main goal of this paper is not to assess the Phillips Curve per se but to investigate whether the better performance of inflation in Indonesia due to better monetary policy or not. The simulation result of his model shows no evidence that the good luck factor has played more dominant role than the good policy response, given the fact that after the crisis, the Indonesian economy is subject to higher supply and demand shocks. These higher uncertainties have certainly affected the output to grow as rapid as previously achieved. Therefore, it would not be correct to conclude that recent performance of macroeconomic stability was mainly due to good luck factors. Related to the above topic, it can be seen that the coefficient of NKPC in his model changes after the crises. In particular, the coefficient of output gap after the crises is less than that before the crises or the Phillips Curve is flattening. It is interesting since it is the contrary of the previous research. The same as previous research, it is unclear why the flattening happens.

Based on the literature review above, this research will try to answer research questions in first heading. The main contribution of this paper is it will give stronger confirmation whether Phillips Curve in Indonesia is flattening or not, given contradiction result between both two previous researches. In addition to that, the explanation of the changes will be answered. The results of this research will contribute significant views to the Indonesian monetary authority to produce better and comprehensive monetary policy.

Data and Methodology

This research will mainly use data recorded by Bank Indonesia and Statistical Bureau. Range of the data is from 1990 to 2000. In estimating Phillips Curve of the Indonesian economy, usage of labour share or output gap will be evaluated in the estimation. For the output gap, two output gaps will be evaluated. One is generated from simple filtering of GDP and another from Cobb Douglass production function. To generate output potential using production function, this research will use data of capital stock and labour force that are maintained respectively by Bank Indonesia and Statistical Bureau. Due to the data of the two variables are annual data, extrapolation into quarterly is needed. Due to availability of the data, some data need to be interpolated, such as GDP from quarterly to monthly. A methodology that is attached in some software package such as X11, X12 or other will be employed.

For inflation expectation, result of survey of expectation conducted by Bank Indonesia will be used to test whether subjective expectation or rational expectation can explain the inflation better. Other sources of international data needed are IFS of the IMF and CEIC.

There are three main works that will be conducted in this research. Firstly, to estimate the Phillips Curve, that can follow methodologies used in Solikin (2004). The new thing in this work is an alternative of using result of expectation survey, output gap from production function and use more updated data. To confirm the flattening of Phillips Curve, this research will use some methodologies such as fuzzy logic clustering, Markov switching, rolling window regression and recursive estimation. Those methodologies are employed simultaneously to produce stronger confirmation whether the flattening really happen or not.

In order to test the role of globalization, this paper will employ the methodology used by Borio and Filardo (2007) who introduce the variable of foreign output gap with some alternative measurements. The difference is, unlike what Borio and Filardo (2007) has done by estimating inflation and foreign output gap only, this paper will attach that variable into the Phillips Curve that has been judged as the best Phillips Curve in previous work. In this way, there will be a consistency in the sequence of work.

Birmingham, 15 Februari 2010


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