79424 AUTHOR ACCEPTED MANUSCRIPT FINAL PUBLICATION INFORMATION Total Factor Productivity and Macroeconomic Instability The definitive version of the text was subsequently published in The Journal of International Trade & Economic Development, 20(5), 2011-05-24 Published by Taylor and Francis THE FINAL PUBLISHED VERSION OF THIS ARTICLE IS AVAILABLE ON THE PUBLISHER’S PLATFORM This Author Accepted Manuscript is copyrighted by the World Bank and published by Taylor and Francis. It is posted here by agreement between them. Changes resulting from the publishing process—such as editing, corrections, structural formatting, and other quality control mechanisms—may not be reflected in this version of the text. You may download, copy, and distribute this Author Accepted Manuscript for noncommercial purposes. 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(3) You must attribute this Author Accepted Manuscript in the following format: This is an Author Accepted Manuscript of an Article by Berument, Hakan; Nergiz Dincer, N.; Mustafaoglu, Zafer Total Factor Productivity and Macroeconomic Instability © World Bank, published in the The Journal of International Trade & Economic Development20(5) 2011-05-24 http://creativecommons.org/licenses/by-nc-nd/3.0/ © 2013 The World Bank Total Factor Productivity and Macroeconomic Instability* Hakan Berument Department of Economics Bilkent University 06900 Ankara, Turkey Phone: + 90 312 290 2342 Fax: + 90 312 266 5140 e-mail: berument @bilkent.edu.tr homepage: http:\\www.bilkent.edu.tr/~berument N. Nergiz Dincer† Economic Modeling Department State Planning Organization Necatibey cad. No:108 Room: 1016 06100 Yucetepe, Ankara, Turkey Phone:+90 312 294 6036 Fax: +90 312 294 6077 e-mail: ndincer@dpt.gov.tr and Zafer Mustafaoglu† The World Bank 1818 H Street, NW Washington, DC 20433-0001 e-mail: zmustafaoglu@worldbank.org Phone: + 1 202 248 9541 Fax: + 1 202 522 2119 * We would like to thank Anita Akkas and Rana Nelson for their helpful comments. † The views presented here are those of the authors; they do not necessarily reflect the official position of the State Planning Organization of Turkey or of The World Bank. Total Factor Productivity and Macroeconomic Instability Abstract Total factor productivity (TFP) is an important component of growth for most countries. This paper assesses the role of macroeconomic instability on TFP growth. We consider volatility in inflation, openness of an economy and financial market deepness as measures of macroeconomic instability. Empirical evidence provided from Turkey suggests that volatility of openness and financial market deepness reduce TFP growth, whereas volatility of inflation increases TFP growth. JEL Codes: D24, O49 and E22. Key Words: Macroeconomic instability, TFP growth and VAR-GARCH models. 1 I. Introduction Total factor productivity (TFP) has long been a variable of interest in economics growth literature. Empirical evidence suggests that it is TFP, rather than the accumulation of production factors, that accounts for most cross-country differences in the level and growth rate of per capita income. Abromovitz (1956) and Solow (1957) both argue that only a small fraction of output growth can be attributed to factor input accumulation, but that 88-90% of growth is attributed to TFP increases. 1 Finding an answer to the question of what determines TFP has become the main goal of a growing body of research. Subsequent studies have argued that it is important to account for changes in the quality of factors of production that are otherwise attributed to TFP. There is a set of studies that suggests that TFP is affected by factors such as investments in education, training and human capital development 2, the openness of the economy to international trade and foreign direct investment 3, lower inflation rate 4, financial development 5 and investments in machinery and equipment 6. The investigation of the determinants of TFP is not limited to the studies mentioned above. Table 1 reports some other variables that are considered in the literature. However, to the best of our knowledge, no study discusses the effects of volatility measures of macroeconomic variables on TFP, except Miller’s and Upadhyay’s (2000), who suggest that openness volatility decreases TFP. 7 The purpose of this paper is to assess the role of macroeconomic instability on TFP growth by using a reduced-form analysis. Based on the data availability of various variables, we choose to explore the volatilities of three variables as possible indicators of macroeconomic instability: inflation, openness of an economy and 1 Although later work has indicated a lesser role of TFP in growth, it still remains the major factor accounting for growth (see for example Kendrick, 1961; Denison, 1985; Jorgenson, Gollop and Fraumeni, 1987; Maddison, 1995; Mankiw, Phelps and Romer, 1995; Klenow and Rodriguez-Clare, 1997a; Jones 1997 and Abramovitz and David, 2000). 2 Miller and Upadhyay, 2000; Aiyar and Feyrer, 2002; Black and Lynch, 1996; Schultz, 1961; Becker, 1962; Becker, Murphy and Tamura (1990) . 3 Edwards, 1998; Harris, 1999; Cororatan and Zingapan, 1999; Miller and Upadhyay, 2000; Alcala and Ciccone, 2004. 4 Miller and Upadhyay, 2000; Clark, 1982. 5 Kugler and Neusser, 1998; Levine, 2003; Tadessa, 2005; Jeong and Townsend, 2004; Beck, Levine and Loayza, 2000. 6 Harris, 1999. 7 Volatility measures are the proxy of second moments such as moving variance or conditional variance of macroeconomic variables. 2 deepness of financial markets. In order to assess the roles of these volatilities we employ a reduced-form analysis within a vector autoregressive (VAR) model framework to determine how the conditional variability of these three factors affects total factor productivity growth. We decide to use a VAR model to form our analyses because these models are successful at capturing the dynamics of a series with relatively few parameter estimates. This is especially vital for countries where data span availability is limited. Moreover, for developed economies, using reduced-form analyses to capture macroeconomic volatilities and their effects on economic performance is common in the literature; for example, Cogley (2005) and Cogley and Sargent (2005) use time-dependent variances in VAR contexts. It is important to note that the volatility measures of the three variables that we use come from a reduced-form specification. Thus, a caution should be given about interpreting the estimated coefficients of volatility variables for the specifications. The estimated coefficients assess how the volatilities in the three variables as a measure of macroeconomic instability affect TFP growth. They do not capture the structural shocks that may occur to these three variables after other factors that affect the variables are accounted for. I.a Inflation Volatility Inflation volatility is the first macroeconomic instability measure that we consider. Friedman (1977) argues that inflation volatility adversely affects allocative efficiency by increasing unemployment and decreasing growth. To be specific, inflation uncertainty hampers the allocative efficiency of the price system. He discusses that unanticipated changes in inflation will cause systematic errors of perception on the part of employers and employees that will initially lead unemployment to deviate from its natural rate. Moreover, Lucas (1973) shows that inflation uncertainty can obfuscate the distinction between real and nominal shocks that economic agents suppose will respond differently. Studies such as Froyen and Waud’s (1987), Holland’s (1986) and Hafer’s (1986) also analyze the hypothesis empirically and provide supporting evidence. Moreover, Dotsey and Sarte (2000) show that inflation uncertainty increases precautionary savings due to lower output and this lowers the nominal interest rates. On the other hand, Hahn (1970), Juster and Wachtel (1972a, b), Juster and Taylor (1975) and Cukierman and Meltzler (1986) argue that inflation variability increases savings and creates the incentive to loosen 3 monetary policy and thus decrease interest rates, which stimulates investment. If new investment is likely to increase capital stock that uses more advanced technologies, TFP will then increase. I.b Openness Volatility We use openness volatility, which captures the instability of a country’s volume of transactions with the rest of the world, as a second macroeconomic instability measure. To be specific, openness volatility captures the ability of the economy to provide imported raw materials for the production process as well as machines and equipment for investment purposes and spare equipment for existing capital stock. Higher openness variability may discourage firms from adopting more efficient foreign technologies due to the difficulties they might experience in the future regarding spare parts or input requirements; instead, they adopt lower, domestically available technologies; this decreases TFP. Rodrick (1998) sets a macroeconomic model where an increase in the riskiness of exports calls for a reallocation of the economy's resources towards the safe activity, even when the return to safer activities lies below the (mean) return of other activities. This ultimately decreases TFP growth. Montalbano, Federici, Triulzi and Pietrobelli (2005) provide empirical evidence that trade vulnerability in 1990s adversely affected the well being of Eastern European countries. I.c Financial Market Deepness Volatility Lastly, volatility of the deepness of financial markets measures instability in the financial sector. The perception of higher vulnerability affects the behavior of financial intermediaries as well as firms that may claim credit. To be specific, higher vulnerability in the financial system discourages financial intermediaries from giving long-term loans despite that doing so might enhance TFP; they tend to concentrate on giving short-term loans. Firms also are less willing to receive credit from financial intermediaries, which decreases new investments. Moreover, in times of higher vulnerability (or the perception of it), companies tend to use internal resources to finance their investments. Lower external financing may also suggest that without financial intermediaries, investments are allocated less efficiently. Hence, productivity enhancement is lower at a given level of investment. 4 Angeletos (2006) argues that incomplete markets, which are associated with a lack of financial intermediation, reduces TFP by shifting resources from more risky but also more productive projects. However, Evers, Nieman and Schriffbauer (2008) argue that financial intermediation increases the qualitative composition of investment rather than the amount of investment, and this improves TFP. Acemoglu and Zilibotti (1997) offer a model that links financial development to economic growth. Levine et al. (2000) provide empirical evidence for this. This paper provides empirical evidence for the effects of inflation, trade openness, and financial market deepness volatilities on TFP growth by using Turkish data, which has various advantages. Firstly, Turkey has had volatile growth and high and persistent inflation along with an unstable economic and political environment for more than three decades. All these factors decrease Type-II errors – the probability of not rejecting the null when it is true 8. Secondly, Turkey has relatively well-developed and liberal financial markets without heavy regulations that might prevent the market mechanism from working properly. In thin markets, financial variables could change at the initiation of a few speculators (or manipulators) rather than because of the dynamics of the economy itself. Thirdly, studying Turkey is an interesting exercise because although Turkey is an important emerging country on the way to membership in the European Union, it has so far failed to achieve income convergence with European countries. Recent high growth rates have facilitated some convergence with average income levels in the EU countries, but the sustainability of these growth rates and the future course of income convergence depend critically on the achievement of higher TFP. Therefore, modeling and understanding the determinants of TFP growth and analyzing the factors that increase its variability are vital not only for Turkey but also for other countries trying to close the income gap. Table 1 summarizes the literature on the determinants of TFP to be human capital development, openness of the economy, volatility of exports, inflation, taxes, 8 Type-II error is defined as not rejecting the null although it is false. The way to reduce Type-II errors would be to either increase the level of significance denoted by (a) or decrease the confidence coefficient denoted by (1-a). Apart from these conventional methods, another suggestion is to increase the dispersion of the collected data as per Netter, Wasserman and Kutner, 1985, p.71). They argue that increasing spacing results in an increase in the t-statistics of a given estimated parameter, the sample size and the variance of the errors. In addition, increasing spacing also decreases the standard errors of the parameters of interest. Concerning the fact that Turkey is the only country experiencing high and sustainable inflation, the result is that spacing is higher and Type-II error is lower (see also Berument et al., 2005 for further discussion). 5 labor market flexibility, research and development (R&D) expenditures, institutional settings, foreign direct investment (FDI), financial deepening, innovation and exchange rate volatility. In this study we do not cover all of these determinants. The lack of useful data is one of the constraints. For example, it is not easy to capture human capital development or labor market flexibility on a quarterly basis for Turkey. Likewise, quarterly data on institutional settings, R&D expenditures and innovation are not available. We include the exchange rate variable in our econometric specification. The estimates were sensitive to initial values and excluding (or including) additional observations changed the estimates too much. One reason for these changes is that Turkey adopted its monetary policy such that it stabilized the real exchange rate until 2000. The Central Bank of the Republic of Turkey (CBRT) depreciated the local currency parallel to the expected inflation (see Berument, 2007 for details). Thus, the (expected) inflation and depreciation were highly collinear for most of the samples that we considered. Because of this, we exclude exchange rate from our analyses and limit our study to the key macroeconomic variables of inflation, openness and financial market deepness The outline of this paper is as follows: Section II describes the modeling strategy. Section III presents the data. Section IV explains the model and Section V concludes the paper. II. Modeling In order to capture how macroeconomic instability measures affect TFP, we employ an ARCH (Autoregressive Conditional Heteroskedasticity) class of model known as Exponential Generalized ARCH (EGARCH) 9. To be specific, we estimate the following specification tfpt = xt'α + λhtfpt + utfpt (Eq. 1) , where ~(0, ℎ ) and 9 This paper assesses the determinants of TFP growth, however, we will call this TFP in the text. 6 p q u tfpt − j u tfpt − j u tfpt − j log htfpt = τ + ∑ Pj log htfpt − j + ∑ Qi { −E +ϕ } (Eq. 2) j =1 j =1 htfpt − j htfpt − j htfpt − j , where xt' is the vector of explanatory variables for tfpt at time t. and the error term of the tfpt equation is utfpt , The effect that the higher perceived variability of utfpt has on the level of tfpt is captured by the parameter λ. If P1 is greater than 1 and if when the lag order of the EGARCH specification is 1 (p=1), then the process of conditional volatility to be explosive. Thus P1 should be less than 1 in absolute value. utfpt − j The ϕ parameter allows the effect of on log htfpt to be asymmetric. If htfpt − j ϕ =0, then a positive surprise has the same effect on volatility as a negative surprise. If 0> ϕ >-1, a positive surprise increases volatility less than a negative surprise does. If ϕ >-1, a positive surprise actually reduces volatility, while a negative surprise increases volatility. The EGARCH model can be estimated by maximum likelihood, which utfpt − j specifies a density for . Nelson (1991) proposes using the generalized error htfpt − j distribution, normalized to have zero mean and unit variance 10 In this study, we also employ a multivariate GARCH when we consider the n equation system form Yt = Axt + ΓH t + ut , (Eq. 3) where xt is a vector of explanatory variables and ut is a vector of white noise residuals. Let Ht denote the (n×n) conditional variance-covariance matrix of the residuals: H t = E (ut ut' | yt −1 , yt −2 ,...xt , xt −1...) . Engle and Kroner (1995) suggest the vector generalization of the GARCH (r,m) specification as: 10 See Hamilton (1994, pp. 668-70) and Berument, Coskun and Sahin (2007) for the advantages of EGARCH specifications against other types of ARCH models and estimation using General Error distribution. 7 H = κ + ∆ 1 H t −1 ∆'1 + ∆ 2 H t − 2 ∆' 2 + ... + ∆ r H t − r ∆'r + Λ 1u t −1u t' −1 Λ 1 + Λ 2 u t − 2 u t' − 2 Λ 2 + ... + Λ m u t − m u t' − m Λ m . (Eq. 4) Here, κ, ∆s and Λs for s=1, 2,… denote (n × n) matrices of parameters. III. Data The data used in this paper covers the 1987Q1-2007Q3 period. Total factor productivity used in the model is the usual Solow residual from a Cobb-Douglas production function with constant returns to scale 11. The two components of the production function are capital stock and employment. Capital stock is calculated from the investment data by using the methodology of the OECD (see Saygili, Cihan and Yurtoglu, 2005). Employment statistics used in the TFP calculation are taken from the Turkish Statistical Institute. Inflation is the logarithmic first difference of the consumer price index (CPI). Deepness is measured by the ratio of M2Y to gross domestic product (GDP), where M2Y is the sum of M2 and foreign-exchange denominated deposits. Finally, openness is calculated as the ratio of the sum of exports and imports to GDP. The data on investment and GDP is gathered from the Turkish Statistical Institute and the remaining variables are obtained from the data delivery system of the Central Bank of the Republic of Turkey. All the data used in the analysis is seasonally adjusted by using the X11 procedure. In order to assess in which form these variables will enter into the analyses we perform a set of unit root tests. If these series have a unit root and they enter into the analyses in levels, then the econometric analyses could give spurious estimates. Table 2 reports the Augmented Dickey and Fuller (ADF), Phillips and Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) unit root tests. The null hypothesis is the unit root for the ADF and PP tests, but the non-unit root for the KPSS test. Rejecting the null for the first two tests means stationarity and rejecting the null for KPSS means the presence of a unit root in the series. At least two of three tests suggest a unit root for all the series with constants. One may consider inflation as trend 11 The estimated coefficients of the Cobb-Douglas production function for capital is 0.42 and for labor is 0.58. 8 stationary. However, all the series are difference stationary. Thus, we perform the analyses in their first difference form. IV. Model Specification(s) a. Univariate Model In order to assess the presence of the ARCH effect on TFP growth, we perform Engle’s (1982) ARCH-LM test. Thus, first we regress TFP growth on a constant term and its first four lags 12. Later, squared residuals were regressed on their first four and eight lags, along with a constant term, the number of observations times R2 are calculated as 18.962 and 19.846, respectively. They are distributed χ2 with four and eight degrees of freedom. The p-values for these statistics are 0.008 and 0.000, respectively. Thus we cannot reject the presence of the ARCH effect on TFP growth even at the 1% level. Because of these results, we modeled TFP growth with a class of ARCH models. The existing literature assesses the relationship between TFP growth and its volatility by assuming that TFP growth volatility captures macroeconomic stability. As discussed above, we use the EGARCH specification for TFP growth’s conditional volatility. After modeling the conditional variance of TFP growth with an EGARCH specification, we assess the effect of TFP growth variability on TFP growth itself. We model the TFP growth equation with a constant term, four lag values of the dependent variable and the conditional variance of TFP growth. The estimates are reported in Table 3. Panel A of Table 3 reports the estimates of the mean equation (Equation 1). The table suggests that the estimated coefficient for the conditional variance of TFP growth for TFP growth is negative but not statistically significant 13. We could not find statistically significant evidence that TFP growth variability adversely affects 12 The lag order of 4 is determined by the final prediction error (FPE) criteria that set the lag length such that the residuals are no longer autocorrelated. Casimano and Jansen (1988) suggest that autocorrelated errors imply the presence of the ARCH effect even if the ARCH effect is not present. 13 The level of significance is at 5% unless otherwise mentioned. 9 TFP growth itself 14. Secondly, in Panel B of Table 3, we observe a negative leverage effect, φ, and the effect is less than 1 in absolute value. This suggests that a negative shock to TFP growth increases its variability more than a positive shock, reflecting the asymmetry of the effects of positive and negative shocks on TFP growth’s conditional volatility. Moreover, the estimated coefficient for the lag value of the logarithm of the conditional variance is less than 1. This satisfies the non- explosiveness of the conditional variance requirement. Panel C of Table 3 reports the p-values of the specification tests. We consider a non-parametric sign-biased test, Ljung-Box Q, at four and eight lags for autocorrelation on squared standardized residuals and ARCH-LM tests at four and eight lags for heteroskedasticity on the standardized residuals (see Berument et al. 2007 for the calculations of and elaborations on these tests). None of the test statistics was statistically significant; this supports the TFP growth specification. Various reasons can be postulated as to why we could not find statistically significant relationships. The first one is that TFP growth is affected by factors other than its history. Including other sets of variables may help to explain the behavior of TFP growth and its own innovations. Secondly, TFP growth might be affected by macroeconomic instability but TFP growth volatility may not capture the macroeconomic instability. Note that the specification we use to measure macroeconomic instability is of a non-linear system. It is possible that the inclusion of a large number of statistically insignificant coefficients will increase the variability of the model’s forecast. Therefore, our concern is to fit a parsimonious model and use a nonstructural approach such as VAR to capture the dynamic relationship among these variables. VAR models are often used in reduced-form specifications and are considered successful in capturing a rich array of dynamic relationships among a set of variables with relatively few parameter estimates. Here, we also assume that the conditional variance of TFP growth is constant because we could not detect the effect of TFP growth variability on TFP growth. Introducing TFP growth volatility to the system would create more difficulties for convergence and the estimates would be more sensitive to initial values. 14 We used various ARCH and GARCH specifications but the basic evidence on the mean effect is robust. 10 b. The Multivariate Model Specification(s) Table 4 reports the estimates of three different VAR-GARCH specifications. Specification 1 includes three variables: inflation, openness and tfp. Equation 1 of Specification 1 in the table models inflation with its two lags, two lags of openness, two lags of tfp and the constant term. Equation 2 is for openness, which is modeled with two lags of inflation, openness and tfp. The last one (Equation 3) is for the tfp, which is modeled as a function of two lags of inflation, openness and tfp; tfp is also explained by the conditional variances of inflation and openness to assess the role of these instabilities in explaining the behavior of tfp 15. Moreover, we model the conditional variances of inflation and openness with a GARCH (1,1) specification 16. Specifically, we estimate the following set of equations: 2 2 2 Inft = α 0Inf + ∑ α iInf Inf t − i + ∑ β iInf opent − i + ∑ γ iInf tfpt − i +u Inf t , (Eq. 5a) i =1 i =1 i =1 where u Inft ~ (0, hInft ) ; hInft = κ Inf + δ Inf hInft −1 + µ Inf u Inf 2 t −1 and (Eq. 5b) 2 2 2 opent = α 0 open + + ∑ α iopen Inf t − i + ∑ β iopen opent − i + ∑ γ topentfpt − i + uopent , (Eq. 6a) i =1 i =1 i =1 where uopent ~ (0, hopen t ) ; hopent = κ open + δ open hopent −1 + µ open u open 2 t −1 and (Eq. 6b) 2 2 2 tfpt = α 0 tfp + ∑ α itfp Inf t −i + ∑ β itfp opent −i + ∑ γ ttfp tfpt −i + φInf hInft + φopen hopent + utfpt , (Eq. 7) i =1 i =1 i =1 where utfpt ~ N(0, htfpt ) and htfpt = κ tfp . The covariances of the equations are presented as follows: κ Inf ,open = cov(u Inf , uopen ) t t κ Inf ,tfp = cov(u Inf , utfp ) t t κ open ,tfp = cov(uopen , utfp )t t . 15 The lag order of 2 is selected by the final prediction error criteria when we consider a class of models with the same lag order across equations. 16 We also estimate the model with the EGARCH specification. The estimated coefficient for the leverage effect and the estimated coefficient for the conditional variances were not statistically significant (possibly due to over-parameterization), thus we did not elaborate on them here. 11 The estimated coefficient for the inflation variability ( hInft ) in the tfp specification is positive and statistically significant, as reported in the Equation 3 section of Table 4. This suggests that inflation volatility increases TFP growth. The estimated coefficient for the openness variability is negative and statistically significant. It is plausible that exports and imports could be affected by different factors and have different dynamics. Thus, in order to partially account for this, we define two new openness measures: export-GDP ratio and import-GDP ratio. When these two openness measures are included jointly in the econometric specification without including any other volatility measures, the estimated coefficients of openness volatility measures were jointly statistically significant and negative. Next, we examine the validity of the variance specification estimates in Panel B. Note that all the estimated coefficients for the VAR-GARCH specifications for the inflation and openness variabilities are positive. This satisfies the non-negativity of the conditional variance specification. It is also important to highlight that the sum of 2 the coefficients of hInf and u Inf t −1 , as well as the sum of the coefficients of hopent −1 and t −1 2 uopent −1 , are less than 1. This also satisfies the non-explosiveness property of the conditional variances. Lastly, cov(u Inft , utfpt ) , cov(u Inft , uopent ) and cov(utfpt , uopent ) are the time-independent covariances. The deepness volatility of the financial market is an additional potential determinant of TFP growth. Thus, we perform the analyses by incorporating deepness and its volatility into the VAR-GARCH specification. Here, we do not include deepness as an additional variable of the three-variable VAR-GARCH specification, but rather substitute it with openness or inflation to avoid overparameterization. Column 2 of Table 4 presents the coefficients of the VAR-GARCH model that is composed of inflation, financial deepness and TFP growth. The sign of inflation variability in the tfpt equation is positive and statistically significant. This result is consistent with the former specification. Financial deepness variability negatively affects TFP and this effect is also statistically significant. 12 The last set of estimates, shown in Column 3 of Table 4, is for analyzing whether the openness and deepness volatilities affect TFP. The results support our previous analyses: both variables decrease TFP growth. However, the negative effect of the openness variable on TFP growth is not statistically significant, which may suggest that these two volatilities might be working from the same channel or that a high colinearity of these two variables exists. 17 Lastly, the estimated coefficients for the VAR-GARCH specifications reported in Panel B are all positive. This satisfies the non-negativity condition of the conditional variances. Moreover, the sums of the slope coefficients for each of the VAR-GARCH specifications are all less than one. This supports the non- explosiveness of the conditional variances 18. Figures 1 to 3 report the actual and fitted values for the three TFP growth specifications from Table 4. The solid lines indicate TFP growth figures and the grey lines indicate the fitted values for the three specifications that we estimate. All these three specifications capture the dynamics of TFP growth well, which further supports our specifications. The empirical evidence provided in Table 4 suggests that inflation volatility increases TFP growth, whereas the volatilities of openness and deepness reduce TFP growth. Even if the positive effect of inflation volatility on TFP growth does not suggest the presence of the allocation inefficiency due to higher inflation uncertainty (as Friedman, 1977 argues), Hahn (1970), Juster and Wachtel (1972 a, b) and Juster and Taylor (1975) argue the presence of a negative relationship between inflation uncertainty and interest rates. The latter three studies suggest that consumers seek to protect themselves against inflation. If variability of income does not match inflation volatility, the latter will affect actual income variability because of loss of consumer 17 In this paper, we explore the behavior of TFP growth by using three-equation VAR-GARCH specifications. We also estimate a model that incorporated deepness, TFP, openness and inflation simultaneously: a four-variable VAR-GARCH specification. The estimated coefficients for inflation volatility, openness volatility and deepness volatility were too small, even if they were statistically significant, and the results were sensitive to initial values. This might be due to the highly nonlinear nature of the specification and the high colinearity of deepness, TFP and openness measures. We did not report and elaborate on these estimates here, but they are available for interested readers upon request. 18 Conducting specification tests on the multivariate GARCH models is not an easy task, and we tried various classes of them. Since the tests are so extensive, we did not report them all, however, sign- biased, Ljung-Box-Q and ARCH-LM tests mostly passed for the tfpt specification (Equation 3 of Table 4). 13 confidence. Thus, consumers will increase their savings, which will cause consumption and interest rates to decrease. Moreover, Cukierman and Meltzer (1986) argue that unanticipated inflation can be generated by governments by decreasing short-term interest rates in order to stimulate their economies. Lower interest rates stimulate investment; new investment is more likely to increase capital stock that uses more advanced technologies, which may increase TFP growth. Secondly, openness volatility decreases TFP growth. In the literature, there are various studies that analyze the relationship between the level of openness and TFP, but to the best of our knowledge, the only study discussing the effects of openness variability on TFP is that of Miller and Upadhyay (2000). Similar to our study, they find a negative relationship between openness variability and TFP growth; in other words, the less volatile openness is the higher total factor productivity. Regarding the relationship between openness and TFP growth, they suggest that greater openness facilitates the economy’s adoption of more efficient technologies for production, leading to a faster growth of production. Thirdly, the adverse effect of financial market deepness volatility on TFP growth is in line with the hypothesis that financial intermediaries and firms that need credit to finance their operations are both influenced by the higher vulnerability of the financial system. In an economy where there is higher deepness volatility, financial intermediaries prefer short-term lending to long-term credit, which ultimately affects a country’s TFP growth. Moreover, there are lower levels of new investment as financial firms are less willing to provide credit regardless of credit maturity. Furthermore, firms use more of their internal resources for investment. This likely decreases TFP growth because the liquidity provided by financial intermediaries allows for more efficient allocation of resources. V. Conclusion This paper assesses how a set of macroeconomic instability measures contributes to total factor productivity growth. The results suggest that inflation volatility increases TFP growth, and that openness and deepness volatilities reduce TFP growth. As Turkey is a developing country that is also a candidate for full membership to the European Union, lowering macroeconomic instability may help Turkey catch 14 up with European Union countries, as noted above. Thus, we perform an exercise as to how much GDP growth would change if the three volatility measures were 16% lower. (We choose 16% because Z1-0.16=1 from Standard Cumulative Normal distribution 19). Table 5 reports the calculations of this exercise. Note that the estimates reveal that inflation volatility actually increases TFP growth, and thus GDP growth. Therefore, decreasing inflation volatility only decreases output growth by 4.77% in Specification 1 and by 0.97 % in Specification 2. Decreasing inflation volatility with lower deepness volatility decreases output by 0.32% annually but if inflation volatility decreases with openness volatility output is increased by 0.33%. However, decreasing openness and deepness volatilities increases output growth. Lower openness volatility increases annual GDP growth by 5.29% for the first specification and by 0.28% in the third specification. Lowering deepness volatility increases growth 0.65% in the second specification and 0.13% in the third specification. When both openness and deepness volatility are lowered by 16% each, output growth increases by 0.57%. One needs to be cautious about 4.77% lower growth for lower inflation volatility or 5.29% higher growth for lower openness volatility from Specification 1 because these magnitudes are quite high, but consider them simultaneously. Thus, we may claim that 0.33-0.57% higher annual growth is feasible, which means 7-11% higher growth for the 19-year period that we consider. Turkey has failed to achieve convergence with EU per capita income levels in the past. The low per capita income growth (at about 2.25% between 1988 and 2006) was not sufficient to allow for income convergence since it was just above the growth of per capita income in the EU. However, our estimations indicate that a 16% decline in the volatility of openness and financial deepness would increase the annual average GDP growth by around 0.33-0.57% 20. Such an increase in the GDP growth rate would result in a meaningful difference between per capita income growth rates in Turkey and the EU, and thereby significantly accelerate income convergence. Based on historical averages on population and GDP growths, a simulation exercise shows that 19 Introducing one-standard deviation shocks is a common exercise in empirical simulation. See for example Sims and Zha (1999). 20 Since increasing inflation volatility may have other adverse effects, we did not pursue this avenue further. 15 Turkey could reach about 34-35% of the EU25 per capita income levels in 15 years from a base of 29% with a 16% reduction in the volatility of openness and financial deepness. Furthermore, without any decline in volatility, Turkey could reach 32% of the EU25 income level, which would be almost 10% lower, without changing the historical growth rates. 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Warren, Ronald (1985). “The Effect of Unionization on Labor Productivity: Some Time- Series Evidence,” Journal of Labor Research, 6(5): 199-207. Williams, Martin and Ronald Moomaw (1989). “Capital and Labor Efficiencies: A Regional AnalysisFinancial Development and Technology,” Urban Studies 26(6): 573-585. 20 Table 1. Summary of the Literature Subject Direction of effects Studies Kendrick (1961); Denison (1985); Jorgenson, Gollop and Fraumeni TFP-Growth Accounting (1987); Maddison (1995); Klenow and Positive Methodology Rodriguez-Clare (1997); Jones (1997); Abramovitz and David (2000); Solow (1957); Jeong and Townsend (2004). Determinants of TFP Growth Positive Harris (1999); Aiyar and Feyrer (2002); Black and Lynch (1996); Human Capital Development Schultz (1961); Becker (1962), Becker, Murphy and Tamura (1990) Condition on openness: For low levels of openness, the relation between human capital development and TFP Miller and Upadhyay (2000) growth is negative. It reverses when openness is larger. Positive Harris (1999); Cororatan and Zingapan (1999); Miller and Upadhyay (2000); Openness of the Economy Edwards (1998); Alcala and Ciccone (2004). Volatility of Exports Negative Miller and Upadhyay (2000) Exchange Rate Positive Miller and Upadhyay (2000) Negative Harris (1999); Miller and Upadhyay Inflation (2000); Clark (1982). Taxes Negative Harris (1999) Positive Harris (1999); Scarpetta and Tressel Labor Market Flexibility (2002) Negative Warren (1985); Williams and Moomav Unionization (1989); Haskel (2005). Positive or negative, depending on Cororatan and Zingapan (1999); Miller Research & Development market structure and technology and Upadhyay (2000); Singh and Trieu regimes. (1996); Scarpetta and Tressel (2002) One of the factors explaining TFP Institutional Settings growth differences is institutional Scarpetta and Tressel (2002) differences. Positive Cororatan and Zingapan (1999); Ferrett (2004); De Mello (1999); Sadik and Foreign Direct Investment Bolbol (2001); Liu and Wang (2003);Aitken and Harrison (2004). Positive Kugler and Neusser (1998); Tadesse Financial Deepening (2005); Jeong and Townsend (2004); Beck, Levine and Loayza (2000). Innovation Positive Harris (1999). Note: * Exchange rate is the domestic currency value of foreign currency. Thus, an increase in exchange rate equals depreciation. 21 Table 2 Unit Root Tests ADF PP KPSS1 Total Factor Level Constant -0.42 -7.53** 1.50** Productivity Constant & -1.88 -8.08** 0.14* trend First Constant -2.81 -17.93* 0.11 difference Inflation Level Constant -1.20 -2.01 0.83** Constant & -4.21** -4.02* 0.28** trend First Constant -8.81** -19.42** 0.39 difference Export + import Level Constant -0.61 -0.59 1.03** GDP Constant & -2.57 -2.53 0.10** trend First Constant -9.31** -9.31** 0.07 difference M2 Level Constant -0.15 -0.10 1.04** GDP Constant & -2.91 -2.91 0.14* trend First Constant -9.96** -9.95** 0.18 difference Note: ** denotes significance at 5% and * denotes significance at 10% 1 Null hypothesis of KPSS test assumes stationarity, whereas ADF and PP assume unit root. 22 Table 3. EGARCH in Mean Specification for the TFP Growth† Coefficients Panel A: Mean Equation Dependent Variable tfpt Constant 1.8072 E x (0.00) tfpt-1 -0.0449 (0.72) tfpt-2 0.0177 (0.82) tfpt-3 -0.1407 (0.08) tfpt-4 -0.2681 (0.01) httfp -0.2196 (0.08) Panel B: Conditional Variance Constant 1.2035 (0.00) Explanatory Variables of log htfpt −1 -0.1515 Conditional Variance (0.43) tfp tfp tfp |vt-1 |-E|vt-1 |-φ vt-1 0.9326 (0.01) ”φ -0.1077 (0.59) Panel C: Specification Tests: p-values The Sign Bias Test (0.76) Ljung-Box Q-Stat. [4] (0.61) Ljung-Box Q-Stat. [8] (0.88) ARCH-LM [4] (0.90) ARCH-LM [8] (0.78) † p-values are reported under estimated coefficients in parentheses for the corresponding variables. The lag orders for the specifications are reported in brackets next to the specification tests. Note: tfpt denotes total factor productivity growth, inft denotes inflation, opennesst denotes the ratio of sum of export and imports to GDP, and deept is for M2Y to GDP −1 ratio. vt-1tfp denotes for . �ℎ−1 Sample includes quarterly observations from 1987Q1 to 2007Q3 for a total of 83 observations. 23 Table 4. Determinants of TFP Growth: Multivariate Analyses† Panel A: Mean Equation Specification 1 Specification 2 Specification 3 Dependent Variables of Equation 1: inft inft deept constant 0.2102 0.0735 3.0148 (0.00) (0.00) (0.36) inft-1 -0.4539 -0.2199 Explanatory Variables of Equation 1 (0.00) (0.00) inft-2 -0.2605 -0.0664 (0.00) (0.00) opennesst-1 2.8669 -0.2826 (0.00) (0.12) opennesst-2 5.0420 0.1579 (0.00) (0.76) tfpt-1 2.2592 1.8832 -0.3447 (0.00) (0.00) (0.62) tfpt-2 5.6044 4.0346 0.2431 (0.00) (0.00) (0.52) deept-1 2.9772 -0.0470 (0.00) (0.86) deept-2 1.5168 -0.0631 (0.00) (0.67) Dependent variables of Equation 2 opennesst deept opennesst constant 0.3008 0.0255 0.2201 (0.00) (0.00) (0.54) inft-1 0.0012 -0.0004 Explanatory Variables of Equation 2 (0.87) (0.38) inft-2 0.0043 0.0161 (0.00) (0.00) opennesst-1 -0.0961 -0.1275 (0.01) (0.85) opennesst-2 -0.0642 1.6246 (0.07) (0.58) tfpt-1 -0.0786 -0.2666 -0.7352 (0.01) (0.00) (0.54) tfpt-2 -0.0923 -0.1600 1.1661 (0.00) (0.00) (0.59) deept-1 -0.3316 0.2201 (0.00) (0.54) deept-2 -0.0399 -0.6147 (0.00) (0.00) 24 Table 4. Determinants of TFP Growth: Multivariate Analyses (cont.) Dependent variables of Equation 3 tfpt tfpt tfpt constant 0.0110 0.0088 1.2410 (0.00) (0.00) (0.00) inft-1 -0.0023 -0.0467 (0.00) (0.00) inft-2 0.0030 0.0170 (0.00) (0.00) opennesst-1 -0.2911 -0.0185 Explanatory Variables of Equation 3 (0.00) (0.46) opennesst-2 -0.0400 -0.0381 (0.00) (0.15) tfpt-1 0.0749 -1.3281 -0.0317 (0.00) (0.00) (0.20) tfpt-2 0.0061 -0.8402 -0.0415 (0.00) (0.00) (0.00) deept-1 -0.6936 -0.0935 (0.00) (0.00) deept-2 -0.1540 -0.0571 (0.00) (0.00) hInf t 0.0967 0.0580 (0.00) (0.00) hopen t -0.4039 -0.0085 (0.00) (0.79) hdeep t -0.7956 -0.6060 (0.00) (0.00) 25 Table 4. Determinants of TFP Growth: Multivariate Analyses (cont.) Panel B: Conditional Variances Dependent variables of (conditional) variance hinf t hinf t Constant 0.0299 0.3204 Variables of CV (0.00) (0.00) Explanatory 2 uinf t −1 0.0001 0.2109 (0.98) (0.00) hinf t −1 0.9648 0.0100 (0.00) (0.00) Dependent variables of (conditional) variance hopent hopent Constant 0.1570 0.1853 Variables of CV (0.00) (0.00) Explanatory 2 uopent −1 0.0078 -0.5870 (0.00) (0.00) hopent −1 0.2437 0.2882 (0.00) (0.00) Dependent variables of (conditional) variance hdeept hdeept Constant 0.7467 0.7807 Variables of CV (0.00) (0.42) Explanatory 2 u deept −1 0.0112 0.1769 (0.00) (0.58) hdeept −1 0.1003 -0.1378 (0.00) (0.16) Var(tfpt) 4.0725 7.4912 4.6590 (0.00) (0.00) (0.00) Panel C: Time-Independent Covariances cov(u inf t , u opent ) 1536.67 (0.00) cov(uinft , u deept ) 21.8605 (0.00) cov(u deept , utfpt ) 20.2241 -5.0514 (0.00) (0.00) cov(uinft , utfpt ) -3.5370 -4.4942 (0.00) (0.00) 26 cov(u deept , uopent ) 341.66 (0.46) cov(utfpt , uopent ) -7.7569 -0.5766 (0.19) (0.98) † p-values are reported under estimated coefficients for the corresponding variables. Note: tfpt denotes total factor productivity growth, inft denotes inflation growth, opennesst denotes the growth rate of the ratio of sum of export and imports to GDP, and deept is for M2Y to GDP ratio growth. hinf t −1 , hdeept −1 and hopent −1 denotes the conditional variances of inflation, deepness and openness variables, respectively. Sample includes quarterly observations from 1987Q1 to 2007Q3 for a total of 83 observations. 27 Table 5. Growth Impact of Lower Volatility Specification Specification Specification 1 2 3 0.16 lower inflation and openness volatility 0.33 0.16 lower inflation and deepness volatility -0.32 0.16 lower openness and deepness volatility 0.57 0.16 lower inflation volatility -4.77 -0.97 0.16 lower openness volatility 5.29 0.28 0.16 lower deepness volatility 0.65 0.13 28 Figure 1. Actual and Fitted Values of TFP Growth: Specification 1 6 4 2 0 -2 -4 -6 -8 -10 -12 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 Note: Solid line indicates TFP growth and grey line indicates the fitted values. Figure 2. Actual and Fitted Values of TFP Growth: Specification 2 7.5 5.0 2.5 0.0 -2.5 -5.0 -7.5 -10.0 -12.5 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 Note: Solid line indicates TFP growth and grey line indicates the fitted values. 29 Figure 3. Actual and fitted Values of TFP Growth: Specification 3 6 4 2 0 -2 -4 -6 -8 -10 -12 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 Note: Solid line indicates TFP growth and grey line indicates the fitted values. 30