The imported price, inflation and exchange rate pass-through in China

Liu, Hai Yue; Chen, Xiao Lan Article The imported price, inflation and exchange rate passthrough in China Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Liu, Hai Yue; Chen, Xiao Lan (2017) : The imported price, inflation and exchange rate pass-through in China, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 5, Iss. 1, pp. 1-13, https://doi.org/10.1080/23322039.2017.1279814 This Version is available at: https://hdl.handle.net/10419/194641 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Liu & Chen, Cogent Economics & Finance (2017), 5: 1279814 http://dx.doi.org/10.1080/23322039.2017.1279814 FINANCIAL ECONOMICS | RESEARCH ARTICLE The imported price, inflation and exchange rate pass-through in China Hai Yue Liu 1 and Xiao Lan Chen 2 * Abstract:This paper considers the effect of exchange rate (ER) level on China’s domestic prices during the period of 2003–2012. We examine China’s consumer price index (CPI), import price index (IPI) and producer price index (PPI) by using time series vector error correction analysis. The main finding of the paper is that ER passthrough has had a limited but growing effect on domestic prices and will continue to do so. ER regime change that was announced by Chinese government in 2005 led to an increased sensitivity of ER pass-through to domestic prices. Subjects: Economic Theory & Philosophy; Monetary Economics; International Finance Keywords: CPI; PPI; IPI; exchange rate JEL classification: F31; F41; E31 1. Introduction As China has been further embracing globalization, exchange rate becomes an important economic index to reflect the status of the macro economy. It is a key factor linking international trade and capital flow. Over the past twenty years, China has been moving from a managed exchange rate toward a fully floating exchange rate. Since 2005, China has reformed the exchange rate regime to a managed floating system referring to a basket of currencies. At present, China is urging further reform to improve exchange rate flexibility as part of plans to liberate China’s capital account. In the lead up to establishing a fully floating exchange rate, the fluctuation of the RMB will have more impacts on China’s economy, such as on the price levels in China. *Corresponding author: Xiao Lan Chen, School of Economics, Sichuan University, Chengdu 610064, China E-mail: [email protected] Reviewing editor: Caroline Elliott, Huddersfield University, UK Additional information is available at the end of the article ABOUT THE AUTHORS Hai Yue Liu is a associate professor in the Business School of Sichuan University, China. Her research interests are international economics, money and banking, Cross-Border Merger and Acquisition, etc. Xiao Lan Chen is a lecturer in the School of Economics of Sichuan University, China. She specialized in econometrics. Her research interests are environmental economics and international economics. PUBLIC INTEREST STATEMENT How does exchange rate pass-through affect domestic price level? It has long been a topic of interest in academia. In China, Scholars uses multiple theories and empirical methods only to prove that the imperfections of exchange rate pass-through channels. While in our research, we find that a shock on nominal effective exchange rate (NEER) has negative effect on CPI which depicts that RMB appreciation will cause CPI decrease. Exchange rate pass-through also proved to have limited but growing impact on domestic inflation since the foreign exchange rate regime reform in 2005. In light of these findings, China’s inflation can’t only be explained as “domestic economic imbalance”. More flexible exchange rate regime brings stronger transmission effect from exchange rate and imported prices to domestic inflation in China. Received: 23 October 2016 Accepted: 03 January 2017 Published: 02 February 2017 Page 1 of 13 © 2017 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 2 of 13 Liu & Chen, Cogent Economics & Finance (2017), 5: 1279814 http://dx.doi.org/10.1080/23322039.2017.1279814 The fluctuation of exchange rate will cause changes on the imported price and then pass on to the producer price index (PPI) and consumer price index (CPI) of a country. Traditional theory states that the appreciation of a country’s currency will relieve the pressure on the inflation in that country. Since 2003, the RMB has appreciated over 24% and this appreciation didn’t help bring down the price level while China was facing the high pressure of inflation(Yue, Qiang, & Kai, 2015). The real effective exchange rate (REER) increased 17% from 2003 to 2008, while CPI increased over 8% during that same time. It raised an interesting point regarding how effective the exchange rate pass-through is to the imported price and price level in China. This paper tries to study the effects of the exchange rate fluctuation on the price levels in China. Using monthly data from 2003 to 2012, the sensitivity of the imported price and consumer price in China responding to the exchange rate fluctuation was examined through the vector error correction (VEC) model. The rest of the paper is organized as follows: Section 2 reviews the relevant theoretical and empirical literature. Section 3 describes the theoretical background. Section 4 sets up the empirical model and explains the results. Section 5 concludes the paper. 2. Literature review Although exchange rate pass-through has long been of interest, the focus of this interest has evolved considerably over time. Beginning in the late 1980s, after a long debate over the law of one price and convergence across countries, exchange rate pass-through studies emphasized industrial organization theories. The role of segmentation and price discrimination across geographically distinct product markets was also heavily studied. More recently, pass-through issues have played a central role in heated debates over appropriate monetary policies and an optimal exchange rate regime in general equilibrium models. These debates have broad implications for the conduct of monetary policy, for macroeconomic stability, for international transmission of shocks, and for efforts to contain large imbalances in trade and international capital flows. 2.1. Explanations from theories of industrial organization Substantial work, both theoretical and empirical, has drawn on models of industrial organization to explain the links between exchange rates and prices in terms of market concentration, import penetration, the substitutability of foreign and domestic products, and the nature of oligopolistic competition. Dohner (1984) developed a dynamic model of pricing by forward-looking competitive profit-maximizing exporters in which consumers adjust slowly to price changes. The level of passthrough is determined by the speed of consumer adjustment and expectations of the duration of exchange-rate changes. Mann (1986) and Kreinin, Martin, and Sheehey (1987) all present some evidence suggesting that industry characteristics influence the extent to which exchange-rate movements are passed on to the dollar price of foreign goods. Krugman (1986) pointed out that the reason for incomplete exchange rate transmission to the imported price is the pricing-to-market (PTM) behavior of firms. Feinberg (1986, 1989) found that market concentration and import penetration, differing by industry, influenced the pass-through of changes in exchange rates to prices of West German domestic goods from 1977 to 1983. He also explained the pass-through into domestic prices for the United States over the 1974–1987. Froot and Klemperer (1989) provide both theory and evidence suggesting that expectations of exchange rates play a dominant role in price determination. Dornbusch (1987) developed several static models to explain pass-through with reference to the nature of competition between firms and the relative number of home and foreign firms. The results showed that incomplete pass-through can occur even in the case of perfect substitutes. Baldwin (1988) presented a model, with sunk market-entry costs, in which temporary exchange-rate fluctuations alter domestic market structure sufficiently to influence the pass-through from real exchange rates to real import prices. Fisher (1989) laid out a partial equilibrium model in which domestic and foreign firms set prices based on expected exchange rates; he showed that exchange-rate changes could imply changes in import prices, but the degree of pass-through depended on domestic and foreign market structures. As surveyed by Goldberg and Knetter (1997), most of the available evidence is from very narrowly defined export industries, with an emphasis often placed on the pricingto-market behavior of exporters. Marston (1990), Knetter (1992, 1993) and Goldberg and Knetter Page 3 of 13 Liu & Chen, Cogent Economics & Finance (2017), 5: 1279814 http://dx.doi.org/10.1080/23322039.2017.1279814 (1997) used export prices or export unit values from specific countries to multiple destinations with the intent of identifying price discrimination or pricing-to-market activity. Whereas import prices are by definition just the local-currency value of another producer’s export prices, the import price series aggregate across producers from all source countries and across a broader array of prices. 2.2. Explanations from monetary theories Most notably, theoretical works argue that volatility in monetary aggregates and exchange rates of countries should influence the choice of invoice currencies in trade (Devereux & Engel, 2001). In equilibrium, countries with low relative exchange rate variability or stable monetary policies would have their currencies chosen for transaction invoicing. The low-exchange-ratevariability countries would also be those with lower exchange rate pass-through. Taylor (2000) also has noted the potential complementarity between monetary stability and monetary effectiveness as a policy instrument. The idea is that if pass-through rates are endogenous to a country’s relative monetary stability, periods of more stable inflation and monetary performance will also be periods when monetary policy may be more effective as a stabilization instrument. For the purpose of the relevant macroeconomic debate, import price series with aggregation are the appropriate units for analysis. Taylor (2000) and Goldfajn and Werlang (2000), among others, have argued that pass-through rates may have been declining over time. 2.3. Some evidence found in Chinese literature Recent empirical research by some Chinese scholars has drawn controversial conclusions on the exchange pass-through to China’s inflation. Jixiang, Yancheng, Liping, and Yuanfeng (2011) found that RMB appreciation would increase the domestic price level through a Granger causality test by using monthly data of 2005–2010. Zhu and Liu (2012) studied the exchange rate pass-through and its relationship with aggregate domestic demand and world commodity price from the year 1980 to 2010. The result shows a positive relationship with RMB appreciation and domestic inflation by using an ARDL model. They drew a conclusion that China’s domestic inflation is a “domestic issue” rather than an “imported” one. Ha (2008)argued that RMB appreciation would restrain the domestic inflation. Shi, Fu, and Xu (2008) found the negative relationship between RMB appreciation and inflation by using quarterly data from 1994 to 2007. They also found that the exchange rate passthrough is imperfect in China. Jiang and Kim (2013)further proved their results. Bei and Zhu (2008)research showed that there is no direct relationship between exchange rate and domestic price. This conclusion is similar to that of Li and He (2007). This paper looks at China’ ER pass-through to domestic price levels by focusing on the exchange rate regime reform. Although many existed literature concluded that China does not have a strong exchange rate pass-through channel. We argue that a short-term pass-through channel from exchange rate to IPI and PPI and then to CPI is proved to be effective and smooth. Further the exchange rate reform in 2005 strengthened the sensibility of domestic price levels responding to exchange rate fluctuations. 3. Exchange rate pass-through and domestic price level According to Svensson (2000)’s inflation targeting model, exchange rate can affect domestic price level through direct and indirect channels hence having further impact on monetary policy. Firstly, exchange rate volatility affects the imported price level which is a part of the CPI calculation basket. Secondly, exchange rate volatility affects the relative price level of the importing and exporting countries, which in turn changes the demand level of the imported and exported products. Thirdly, taking foreign exchange rate as an asset, the fluctuation of the exchange rate expectations affects the domestic price level. Finally, the macroeconomic conditions of foreign countries would also affect the domestic price level. Built on Svensson’s framework, this paper set up a VAR model for the dynamic exchange rate pass-through to domestic price level taking Chinese economic reality into consideration. Page 4 of 13 Liu & Chen, Cogent Economics & Finance (2017), 5: 1279814 http://dx.doi.org/10.1080/23322039.2017.1279814 The transmission started from the foreign supply shocks which we use IM (China’s total import) as a proxy variable. TIM t represents the foreign supply. We use IM as a proxy variable. Mt is the broad money. We take M2 as a proxy variable. NEERt is the nominal effective exchange rate. ES t , EM t , ED t and EE t represent the impact from shocks other than to the supply level, the money supply, domestic demand or the exchange rate. We assume that the IPI, PPI and CPI have positive transmission relationships with each other. Expressed as flows: PIPI t , PPPI t and PCPI t represent the imported price index, producer’s price index and consumer’s price index, respectively. These three variables reflect the price level of a country. EIPI t , EPPI t and ECPI t represent the impact from shocks to IPI, PPI, and CPI, respectfully. We take these seven impacts as variables of the VAR model to evaluate their effect on inflation. 4. Empirical analysis Basically, the analysis starts from the reduced form of an unrestricted VAR model with k lags that can be written down as: where Dt is a (n×1) vector of all deterministic variables such as intercepts, trends, dummies etc., A1, ⋯ ,Ak are (n×n) matrix of coefficients, Zt = (Z1t, Z2t, ⋯, Znt)’ is a vector of n variables, ut is a (n×1) column vector of innovations, that is, serially uncorrelated disturbances that have zero mean and a variance-covariance matrix variance-covariance matrix E� u1,u� t � = ∑u , i.e. u t∼N � 0, ∑� . The estimation of a,A1, ⋯ ,Ak , are obtained by applying ordinary least squares (OLS) to each part of Equation (8) separately, and the estimate of ∑u is given by the sample covariance matrix of the OLS residuals. If co-integration between the variables exists, the VAR model can be rewritten in a VEC (vector error correction) model as: where Δ is a difference operator, is a (n×n) matrix of long-run multipliers and [ Γi ]k−1 i−1 is a (n×n) matrix of coefficients that contain the short-run responses among variables. (1) TIM t =E t+1( T IM t) +E S t (2) Mt =E t+1( M t) +𝛼 1 E S t +E M t (3) 𝛾 GDP t=Et+1 ( 𝛾GDP t ) +𝛽1ES t+𝛽2EM t+E M t (4) NEER t=Et+1 ( 𝛾GDP t ) +𝛾1ES t+𝛾2EM t+𝛾3ED t+E E t (5) PIPI t =E t−1( P IPI t−1) +𝛿 1 E S t +𝛿 2 E M t +𝛿 3 E D t +𝛿 4 E E t +E IPI t (6) PPPI t =E t−1( P PPI t−1) +𝜃 1 E S t +𝜃 2 E M t +𝜃 3 E D t +𝜃 4 E E t +𝜃 5 E IPI t +E PPI t (7) P CPI t=Et−1 ( PCPI t−1 ) +𝜂1ES t+𝜂2EM t+𝜂3ED t+𝜂4EE t+𝜂5EIPI t+𝜂6EPPI t+E CPI t (8) Z t=a0Dt+ k ∑ i=1 Ai+Zt−1+u t (9) Δ Zt=a0Dt+ k ∑ i=1 ΓiΔZt−i+∏Zt−k+u t Page 5 of 13 Liu & Chen, Cogent Economics & Finance (2017), 5: 1279814 http://dx.doi.org/10.1080/23322039.2017.1279814 In this study, Zt = ( LCPI,LIPI,LPPI,LM2, LIM,LNEER,LGDP )� , a (7×1) vector of I(1) variables are considered as endogenous. The data employed for the study are monthly series of CPI, IPI, PPI, NEER, M2, GDP, and IM, representing inflation, import price index, producer’s price index, nominal effective exchange rate, money supply, domestic demand and foreign supply, respectively. The time span chosen for analysis is from 2003 to 2012. Three major inflations happened during this period. The data sources of variables are listed in Table 1. Unless otherwise stated, all variables have been adjusted seasonally using X12 method and adopted the natural logarithm in order to eliminate the influence of seasonal interruptions and heteroscedasticity (Findley, Monsell, Bell, Otto, & Chen, 1998). Table 2 presents summary statistics on the key variables used in this analysis. The estimation process is organized as follows: firstly, it presents the unit root test result for the stability of all variables. Secondly, it shows the co-integration relationship examined by the Johansen co-integration test. At last, a VEC model will be set up to analyze the impulse response and variance decomposition. 4.1. Unit root test To determine the appropriate specification for the VAR estimate, the variables must be tested for the stationary process. The presence of non-stationary behavior in the autoregressive representation of the variable, i.e. the order of integration for each variable is determined using the Augmented Dickey-Fuller (ADF) unit root test. The results of the ADF tests for six key variables reveal that GDP, IPI, IM and PPI are stationary at level (I(1)process), while CPI, M2 and NEER are first difference stationary (I(2) process) at 10% critical value (Table 3). Table 2. Summary statistics of key variables Variables Observation Mean Std. dev. Minimum Maximum LCPI 120 4.6347 0.0227 4.5870 4.6886 LIPI 120 4.6642 0.0955 4.3770 4.8097 LPPI 120 4.6346 0.0407 4.5196 4.7014 LNEER 120 4.5521 0.0708 4.4308 4.6818 LM2 120 12.961 0.4929 12.155 13.789 LGDP 120 2.6473 0.2928 1.6864 3.1442 LIM 120 6.6628 0.4805 5.4714 7.4242 Table 1. Variable description and data source Variables Description Representation Source CPI Consumer price index (2005 = 100) Domestic inflation National Bureau of Statistics IPI Import price index (2005 = 100) Price levels of imported products National Bureau of Statistics PPI Producer price index (2005 = 100) Producer’s manufacturing cost National Bureau of Statistics NEER Nominal effective exchange rate (2005 = 100) Exchange rate level IMF International Finance Statistics M2 Broad money (in million US dollars) Domestic money supply National Bureau of Statistics GDP Gross domestic product (using industrial value-added as proxy variable) Aggregate domestic demand National Bureau of Statistics IM China’s total import from the world (in million US dollars) Foreign supply National Bureau of Statistics Page 6 of 13 Liu & Chen, Cogent Economics & Finance (2017), 5: 1279814 http://dx.doi.org/10.1080/23322039.2017.1279814 4.2. Co-integration test and VEC model setup Considering this study is to analyze the pass-through of the variables related to exchange rate volatility to the domestic price level, all these macro-economic variables might share the same long-run trend and affect each other in a certain way, or while they exhibit short run fluctuations around a long run equilibrium. The ADF test indicates that all the time series are not stationary at the same level. A Johansen co-integration test was employed in order to prove there is co-integration relationship between these variables. Table 4 shows that there are at least two co-integrating equations at the 5% level for both trace test and maximum eigenvalue test. Therefore, CPI, GDP, IPI, PPI, NEER, M2, and IM were included to establish a VEC model. The result shows that all the inverse roots are less than one, i.e. all the model roots are located in a unit circle (see Figure 1), and the optimal lag period is 2 according to AIC result (Table 5). The model is stable so that we can proceed to impulse response and variance decomposition analysis. 4.3. The dynamic behavior of the VEC model The dynamic behavior of the VEC model is analyzed using the impulse response function and variance decomposition. 4.3.1. Impulse response functions The impulse response function (IRF) traces the effect of a one-standard-deviation shock in a variable on current and future values of others in the system. It indicates the size and characteristics of the shock’s effects. It is well known that the results of the IRF and variance decomposition based on Cholesky’s decomposition are sensitive to the order of the variables and the lag length. A VEC model Table 4. Johansen co-integration test result Notes: Trace test indicates 2 co-integrating equation(s) at the 0.05 level. Maximum eigenvalue test indicates 2 co-integrating equation(s) at the 0.05 level. *Rejection of the hypothesis at the 0.05 level. Unrestricted co-integration rank test (trace) Unrestricted co-integration rank test (maximum eigenvalue) Hypothesized No. of CE(s) Eigenvalue Trace statistic 0.05 Critical value Prob. Hypothesized No. of CE(s) Eigenvalue Trace statistic 0.05 Critical value Prob. None* 0.4239 166.6119 125.6154 0.0000 None* 0.4239 62.3116 46.2314 0.0005 At most 1* 0.3087 104.3002 95.7537 0.0113 At most 1* 0.3086 41.7067 40.0776 0.0325 Table 3. ADF unit root test results Note: Lag length selection is automatically based on Schwarz info Criterion (SIC), maxlag=13. *Rejection of the null hypothesis (a variable has a unit root) at 1% significance level. **Rejection of the null hypothesis (a variable has a unit root) at 5% significance level. Variables At level 1st difference t-value (constant and trend) p-value (constant and trend) t-value (constant and trend) p-value (constant and trend) LCPI −2.3969 0.3793 LGDP −3.6875** 0.027 LIPI −4.2898* 0.0046 LPPI −2.5369** 0.0101 LM2 −2.5369 0.3101 LNEER −3.1075 0.1094 LIM −5.6911* 0.0000 LCPI −12.3134* 0.0000 LM2 −5.5843* 0.0000 LNEER −7.4046* 0.0000 Page 7 of 13 Liu & Chen, Cogent Economics & Finance (2017), 5: 1279814 http://dx.doi.org/10.1080/23322039.2017.1279814 can be used to test the Granger causality among variables of the model, i.e. to ascertain the direction of causality. Granger’s definition of causality is a widely used concept of causality (Granger, 1969). In order to analyze the causal relationships between CPI and the other variables, a Granger causality test within a VEC framework is used. The test estimates the χ2 value of the coefficient on the lagged endogenous variables. The null hypothesis is that the lagged explanatory variables of the model and also their joint significance do not Granger-cause the dependent variable. Table 6 reports p-values for F-statistics at lag three for the VEC Granger causality test results. The results presented in Table 6 indicate that among six variables, except dLM2 others do “cause” CPI individually and the six variables influence CPI jointly. Combined with the Granger Causality Test results and the economic reality, we can decide the Cholesky order as follows: GDP, M2, NEER, IM, PPI, IPI, CPI. Figure 2 shows a shock on NEER has negative effect on CPI. The impact from exchange rate to the CPI added up gradually and reached the highest point of 0.36% around the seventh month. A shock on PPI has positive effect on CPI. The effect is quick (reaches the highest point in the first months at about 0.36%) and then faded out to 0.1% after 12 months. A shock from imported price also has positive effect on CPI. It reaches the highest point after 3 month to 0.18% and slowly fades out. The results show a short-term pass-through channel from exchange rate to the CPI through imported price and PPI. This is the first empirical evidence that our hypothesis conforms to real-world trends. Figure 1. Inverse roots of AR characteristic polynomial. Table 5. VAR lag order selection criteria Notes: FPE: Final prediction error; AIC: Akaike information criterion; SC: Schwarz information criterion; HQ: HannanQuinn information criterion. *Lag order selected by the criterion: LR: sequential modified LR test statistic (each test at 5% level). Lag LogL LR FPE AIC SC HQ 0 1,006.884 NA 4.15E-17 −17.855 −17.685 −17.786 1 1,870.569 1,603.985 2.00E-23 −32.403 −31.044* −31.852 2 1,962.539 159.305 9.37E-24* −33.170* −30.622 −32.136* 3 1,996.708 54.914 1.25E-23 −32.905 −29.168 −31.389 4 2,044.992 71.565* 1.33E-23 −32.893 −27.965 −30.894 5 2,081.281 49.250 1.81E-23 −32.666 −26.549 −30.184 6 2,124.255 52.949 2.29E-23 −32.558 −25.252 −29.594 Page 8 of 13 Liu & Chen, Cogent Economics & Finance (2017), 5: 1279814 http://dx.doi.org/10.1080/23322039.2017.1279814 Figure 3 shows that a shock on NEER has negative effect on CPI, IPI, and PPI. The imported price index has been most affected by the changes in the exchange rate compared with the other two price indices. The effect increases gradually and reaches the highest point after 7 months (2.2%). It indicates the pass-through effect from exchange rate to imported price levels is relatively smooth. The NEER has the smallest impact on the CPI (0.27% at the highest). It explains the exchange rate pass-through to domestic inflation is imperfect and lagged-behind. 4.3.2. Variance decomposition Variance decomposition measures the percentage of the forecast variance in inflation. This variance can be attributed to shocks or changes to each explanatory variable over a series of time horizons. In addition, it shows how this proportion changes over time. The variance decomposition in CPI of the VEC model by using the Cholesky decomposition method is reported in Table 6 (the Cholesky order has been discussed earlier). The variance decomposition results support the empirical findings from the impulse response functions (Table 7). CPI itself caused 60% of its variability. Then outside supply shocks (IM) accounts for almost 16% of the CPI’s variance after ten lags, which is the highest among all these explainable variables. Exchange rate (NEER) claims 9.6% of the CPI’s variance at the end of 10 months which is supported by the IRF test as well. PPI has a significant contribution to CPI’s variance in the beginning (15.6%) but the percentage decreases gradually. IPI makes up 2% of CPI’s variance in the first month and keeps a steady percentage of 1.75% after 10 lags. M2 has immediate influence in the second Figure 2. Response of LCPI to Cholesky one S.D innovations. Table 6. VEC granger causality/block exogeneity wald test results-probabilities *Rejection of the null hypothesis (a variable has a unit root) at 1% significance level. **Rejection of the null hypothesis (a variable has a unit root) at 5% significance level. Dependent variables Excluded variables (df=3) dLCPI dLNEER dLIPI dLPPI dLM2dLGDP dLIM Block exogeneity (df=10) dLCPI 0.01* 0.01* 0.01* 0.34 0.00* 0.07* 0.00* dLNEER 0.48 0.63 0.01* 0.75 0.65 0.26 0.21 dLIPI 0.86 0.44 0.00* 0.64 0.84 0.35 0.00* dLPPI 0.03** 0.00** 0.14 0.77 0.05** 0.48 0.00 dLM2 0.02** 0.36 0.26 0.20 0.62 0.40 0.18 dLGDP 0.60 0.55 0.63 0.92 0.18 0.56 0.29 dLIM 0.25 0.77 0.13 0.23 0.00* 0.86 0.00*