Monetary policy efficiency and macroeconomic stability: Do financial openness and economic globalization matter?

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

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North American Journal of Economics and Finance journal homepage: www.elsevier.com/locate/najef

Monetary policy efficiency and macroeconomic stability: Do financial openness and economic globalization matter? Helder Ferreira de Mendonçaa,

⁎,1

a b

, Natalia Cunha Nascimentob

Fluminense Federal University – Department of Economics, National Council for Scientific and Technological Development (CNPq), Brazil Fluminense Federal University – Department of Economics, Brazil

ARTICLE INFO

ABSTRACT

JEL classification: E52 E58 F41 F62

We provide indicators for monetary policy inefficiency and macroeconomic instability by the use of efficiency frontier approach to measure the performance of the monetary authority. In particular, based on panel data analysis which takes into account information from forty-two countries for the period 1990 to 2014, we analyze the impact of an increase in financial openness and economic globalization on both monetary policy inefficiency and macroeconomic instability. The findings denote that both financial openness and economic globalization are important tools to improve monetary policy efficiency and macroeconomic stability. Furthermore, we observe that countries with inflation targeting, higher level of development, low risk of political pressures (such as socioeconomic pressures and democratic accountability), as well as the absence of international financial crisis, have a better performance in terms of monetary policy efficiency and macroeconomic stability.

Keywords: Monetary policy efficiency Macroeconomic stability Financial openness Economic globalization

1. Introduction The growing interdependence between the economic systems of different countries resulting from the process of globalization has led to changes in macroeconomic policy, especially monetary policy. Some studies have pointed out that financial openness represents a useful tool for avoiding the inflation bias of discretionary monetary policy. In other words, greater freedom for capital flow is capable of reducing the time inconsistency problem in monetary policy.2 There is evidence that greater financial openness and globalization stimulate economic growth (see Gozgor, 2017; Potrafke, 2015; Quinn & Toyoda, 2008; Ying, Chang, & Lee, 2014) and are associated with lower inflation (see, Aizenman, Chinn, & Ito, 2011; Binici, Cheung, & Lai, 2012; de Mendonça & Veiga, 2017; Trabelsi, 2016; Zhang, Song, & Wang, 2015). In brief, because most central banks make use of a loss function that considers deviations of inflation and output from their targets, it is possible to conjecture that financial liberalization and economic globalization can bring benefits to the management of the monetary policy as well as to the macroeconomic stability. The main contribution of this study is to provide empirical evidence regarding the performance of the monetary policy and macroeconomic stability in a context of financial openness and economic globalization. The findings indicate that financial openness and economic globalization contribute significantly to improve the monetary policy efficiency and macroeconomic stability. In particular, we observe that economic globalization has a greater impact on the variables of interest (monetary policy inefficiency and macroeconomic instability) than financial openness. In addition, countries with inflation targeting, as well as a low risk of political pressures (such as socioeconomic pressures and democratic accountability) have a better performance in terms of monetary policy Corresponding author. E-mail address: [email protected] (N.C. Nascimento). 1 Rua Dr. Sodré, 59 – Vila Suíça, Miguel Pereira – Rio de Janeiro CEP: 26900-000, Brazil. 2 See, Bartolini and Drazen (1997), Gupta (2008), Badinger (2009), and Spiegel (2009). ⁎

https://doi.org/10.1016/j.najef.2018.10.018 Received 31 May 2018; Received in revised form 20 October 2018; Accepted 29 October 2018 1062-9408/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: De Mendonca, H.F., North American Journal of Economics and Finance, https://doi.org/10.1016/j.najef.2018.10.018

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efficiency and macroeconomic stability. Studies concerning the impact of financial openness and economic globalization on the conduct of monetary policy and economic instability are scarce. However, some studies suggest that there is a positive effect of openness on output, as well as for low and stable inflation (see, for example, Angkinand, Sawangngoenyuang, & Wihlborg, 2010; Campos, Karanasos, & Tan, 2012; Spiegel, 2009). Analogously, the literature points out that economic globalization has a positive influence on output (see Chang & Lee, 2010; Lurong, 2017; Ying, et al., 2014), and can also help in controlling inflation (see Binici et al., 2012; Trabelsi, 2016). Although financial openness and globalization can be useful to economic policy management, we cannot neglect the possibility of adverse effects. For example, in the case of emerging economies, increased openness and globalization make these economies more vulnerable to external shocks, which in turn can undermine economic policy management for economic growth with price stability (see Bumann, Hermes, & Lensink, 2013; Demetriades & Luintel, 2001; Eichengreen & Leblang, 2003; Schmukler, 2004; Stiglitz, 2000). An increase in financial openness and globalization can increase the commitment of central banks to the inflation target, thus contributing to the convergence between inflation expectations and the target (de Mendonça & Veiga, 2014). The main idea is that openness creates an environment where there is a punishment for central banks using inflationary policy through an increase in the possibility of substitution of the domestic currency for foreign currency (Gupta, 2008).3 Furthermore, openness and globalization promote the efficiency and development of financial intermediation, and thus contribute to financial markets can transform saving into investment and growth (Hermes & Lensink, 2008; Klein & Olivei, 2008). In addition, globalization spurs economic growth because it enables countries to exploit comparative advantages, to gain from specialization, to foster innovation and efficient production (Potrafke, 2015).4 Therefore, there exist channels through which financial openness and economic globalization can induce to monetary policy efficiency and macroeconomic stability. We develop measures for monetary policy inefficiency and macroeconomic instability using inflation-output variability trade-off, or efficiency frontier (see, for example, Cecchetti, Flores-Lagunes, & Krause, 2006; Cecchetti & Krause, 2002) for forty-two countries from 1990 to 2014. The measure of macroeconomic performance is associated with the idea that the monetary authority is capable of eliminating losses arising from variability in output and inflation. In this context, better macroeconomic performance is a result of lower weighted average of output and inflation variability. On the other hand, the measure of inefficiency represents how much the performance of the monetary policy deviates from the target (inflation-output variability frontier). Therefore, the greater the stability of inflation and economic growth, and the smaller the deviation of output and inflation from the target, the better the situation of the economic agents are. In order to observe how financial openness and economic globalization affects monetary policy inefficiency and macroeconomic instability, we provide evidence based on panel data analysis (Ordinary Least Squares – OLS, Feasible Generalized Least Squares – FGLS, and Method of Generalized Moments Systemic – S-GMM). The article is organized as follows. Section 2 summarizes the main idea in literature regarding efficiency and performance and presents the measures that we use in this study. Section 3 discusses the structure of the analysis. Section 4 describes the data used in this analysis. Section 5 presents the empirical evidence based on panel data analysis for different models. Section 6 concludes the article. 2. Monetary policy inefficiency and macroeconomic instability: Concepts and measures In the literature, influenced by the seminal studies of Farrell (1957) and Debreu (1951), there are several definitions for efficiency. Although there are many concepts, there is a convergence that efficiency is achieving an objective in the best way as possible. Regarding performance, a good definition is offered by Lebas (1995, p. 23) “the potential for future successful implementation of actions in order to reach the objectives and targets”.5 Overall, we can divide efficiency measurement techniques into two groups: nonparametric (Data Envelopment Analysis and Free Disposal Hull) and parametric (Stochastic frontier and distribution free).6 Because we assume that central banks choose a point on a trade-off between output variability and inflation variability (see Cecchetti & Ehrmann, 2002), we make use of frontier efficiency technique. Based on a sample of twenty-four countries, Cecchetti and Krause (2002) were one of the first to present measures for efficiency of central banks and macroeconomic performance. These authors measured macroeconomic performance and monetary policy efficiency by using the inflation-output variability trade-off, or efficiency frontier. However, one of the limitations of this study is that the authors do not analyze the changes in the management of policies and their effects on macroeconomic outcomes. Another limitation is the period under consideration (1991–1998) which excludes important events from the world economy such as the 2008 global financial crisis. In order to overcome the first limitation Cecchetti et al. (2006), also based on a sample of twenty-four countries and two periods (1983–1990 and 1991–1998), developed a methodology to measure how an improvement of monetary policy changes macroeconomic performance. Although the study developed by Cecchetti et al. (2006) represents an advance in comparison to the previous, important changes in the conduct of monetary policy as the use of inflation targeting is undervalued. Using close methodology of the above-mentioned studies, that is the performance of monetary policy can be assessed through the inflation and output variability trade-off faced by the policymaker, Mishkin and Schmidt-Hebbel (2007) provide empirical evidence 3

For empirical evidence regarding financial openness as tool for reducing inflation see: de Mendonça and Veiga (2017), and Aizenman, Chinn, and Ito (2010). 4 For empirical evidence about positive relationship between globalization and economic growth, see Dreher (2006), and Dreher, Gaston, and Martens (2008). 5 Some studies do not make difference between efficiency and effectiveness (see, for example, Lin, 2015; and Kliber et al., 2015). 6 For a review of the standard techniques of efficiency measurement, see Mester (2003). 2

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based on a sample of twenty-one inflation targeters and thirteen nontargeters. In general, the findings denote that the adoption of inflation targeting improves the efficiency of the monetary policy, although nontargeters present better macroeconomic performance.7 For our purpose of measuring monetary policy inefficiency and macroeconomic instability, we consider an economy subject to two types of disturbances, both of which may demand policy responses as suggested by Cecchetti and Krause (2002): (i) aggregate demand shocks – move output and inflation in the same direction; and (ii) aggregate supply shocks – move output and inflation in opposite directions. Hence, “increases or decreases in the variability of supply shocks shift this frontier, while movements toward or away from the trade-off arise from improvements or declines in policy efficiency” (Cecchetti et al., 2006, p. 411). Moreover, we assume that policymakers choose a point on a trade-off between output variability and inflation variability. Therefore, the location of the economy on the efficiency frontier depends on the policymaker’s preferences for inflation and output stability. In order to measure both movements in the performance point and shifts in the policy efficiency frontier, we assume that the objective of policymakers is to minimize a weighted sum of inflation and output variability through the following loss function (see, for example, Cecchetti & Krause, 2002):

L = Var ( ) + (1

(1)

) Var (y ) 8

where π is inflation, y is output, and ϕ is the policymaker’s preference parameter (0 ≤ ϕ ≤ 1). Based on Cecchetti and Ehrmann (2002), the optimal police rule is that interest rate moves offset demand shocks and thus both output and inflation depend only on the variance of aggregate supply shocks. In addition, changes in the volatility of aggregate supply shocks shift the variance of output and inflation in the same proportion. Hence, as pointed out by Cecchetti, McConnell, and PerezQuiros (2002) the ratio between the variances of output and inflation, measured as deviations from their desired levels, reveals important implications in relation to the policymaker’s preference parameter (ϕ):

Var (yt Var ( t

y ) = )

2

(1

(2)

)

where θ is the ratio of the responses of output and inflation to a policy shock and can be thought of as the inverse of the slope of the aggregate supply curve. Based on idea that central banks set monetary policy in order to achieve some combination of inflation and output stabilization (Svensson, 2000), varying ϕ between 0 and 1 permit us to trace out the entire output-inflation variability frontier. Furthermore, from Eq. (2) the policymaker’s preference parameter is given by:

=

[Sd (

t

Sd (yt y ) ) + Sd (yt

*

(3)

y )] *

where Sd(yt − y ) and Sd(πt − π ) are the standard deviations of output and inflation measured as deviations from their targets, respectively. It is important to note that the preferences of monetary authorities may differ from one country to another, even if these countries belong to the same group (for example, advanced or developing economies). Therefore, these differences are crucial to explain the behavior of economies, and especially to distinguish between performance and efficiency. In addition, one point that is undervalued in previous studies is that the events that occurred after the 1990s (for example, global financial crisis of 2008) can imply some change in the preferences of monetary authorities. Using a sample of forty-two countries from 1990 to 2014, we compute the policymaker’s preference parameter (ϕ) for each country.9 In order to quantify the monetary transmission mechanism, as suggested by Ehrmann (1998), we make use of Structural Vector Autoregression (SVAR) framework. Following Cecchetti, McConnell and Perez-Quiros (2002), the idea is to extract a measure of preferences from a model that employs an identical set of identifying restrictions for each country. Hence, the models take into account the following variables (quarterly data): monetary policy interest rate, real industrial production, inflation rate, nominal exchange rate, and a monetary aggregate. Moreover, in order to control the specificities of each country, we make use of dummy variables (value equal to “1” regarding the main shocks on the economy, and value equal to “0” otherwise).10 After to compute the policymaker’s preference parameter (ϕ), we gauge the monetary policy efficiency taking into account how close the actual performance (Var(π) and Var(y)) is to the performance under optimal policy (Var(π)* and Var(y)*), while macroeconomic performance is a weighted average of variances of output and inflation. Hence, the measure of monetary policy inefficiency (INEF) and macroeconomic instability (INST) for each period corresponds, respectively, to:

INEFt = | [Var ( t )

Var ( ) ] + (1

)[Var (yt )

Var (y ) ]|, and

(4)

7 Briec et al. (2012) proposed an alternative method for measuring the movements toward the frontier by the Farrell measure (firm’s performance) and the Malmquist index (bilateral index, which compares the production technology of two economies) but the authors do not provide empirical evidence. 8 Cecchetti and Krause (2002), suggests ϕ = 0.8 for the most countries in their analysis and ϕ = 0.3 for countries with high inflation rate in the s. 9 The number of countries in our analysis is limited by information available from different data sources for building the monetary policy inefficiency and macroeconomic instability indicators. 10 The use of dummies for each county is based on the following studies: Lee, Lin, and Zeng (2016), Kleimeier, Sander, and Heuchemer (2013), Laeven and Valencia (2013), Barkbu, Eichengreen, and Mody (2012), Eijffinger and Karataş (2012), Reinhart (2010); and Cecchetti and Ehrmann (2002).

3

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Table 1 Monetary policy inefficiency and macroeconomic instability (1990–2014). Countries

INEF

INST

Countries

INEF

INST

Australia Austria Bangladesh Belgium Brazil Bulgaria Canada Chile Colombia Croatia Czech republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel

0.045 0.020 0.107 0.017 0.094 0.115 0.022 0.120 0.133 0.047 0.101 0.032 0.209 0.157 0.015 0.213 0.038 0.479 0.278 0.143 0.091

0.078 0.048 0.390 0.043 0.195 0.291 0.057 0.228 0.287 0.114 0.206 0.067 0.361 0.224 0.039 0.252 0.120 0.840 0.592 0.395 0.197

Italy Japan Latvia Malaysia Mexico Netherlands New Zealand Nicaragua Norway Poland Portugal Republic of Armenia Romania Russia Slovenia South Korea Spain Sweden Switzerland Turkey United Kingdom

0.041 0.097 0.449 0.023 0.097 0.063 0.032 1.138 0.045 0.183 0.108 0.894 1.052 0.726 0.150 0.228 0.035 0.061 0.030 1.072 0.040

0.110 0.107 0.643 0.070 0.216 0.096 0.057 1.271 0.090 0.557 0.214 1.631 1.661 1.131 0.264 0.485 0.115 0.086 0.049 4.419 0.075

Note: INEF is the monetary policy inefficiency; INST is the macroeconomic instability. Values correspond to the average for the period (1990–2014) – values multiplied by 100.

INSTt = Var ( t ) + (1

(5)

) Var (yt ), t = 1, 2, ...,n; periods

In brief, more efficient policymakers have INEF close to zero, while an improvement in macroeconomic performance is a result of INSTt−1 − INSTt > 0. Table 1 shows the results on monetary policy inefficiency (INEF) and macroeconomic instability (INST) for forty-two countries from 1990 to 2014.11 3. Framework of analysis This section presents our empirical specification and estimation strategy. 3.1. Empirical specification In order to observe the impact of the main independent variables of the models (financial openness and economic globalization) on monetary policy inefficiency and macroeconomic instability, our baseline specification is given by:

Xi, t =

0i

+

1 OPENi, t

+

2 Xi, t

+

(6)

i, t

where Xi,t correponds to monetary policy inefficiency (INEF) or macroeconomic instability (INST) – see Eqs. (4) and (5), respectively; i = 1,…, 42 is the cross-section unit (countries); t = 1,2, …, 25 is the time index (annual frequency); εi,t is the stochastic error term; β0i represents a vector of country specific factors. OPEN is a measure of financial openness (KAOPEN – see Gozgor, 2017) or economic globalization (EGLOB – see Binici et al., 2012). X is a vector of control variables gathered from the literature on monetary policy and macroeconomic fluctuations: the use of some control over the exchange rate (FER – see Chow, Lim, & McNelis, 2014; de Mendonça & Tiberto, 2017) and the real output (GDPR, in logs – see Adema & Pozzi, 2015; Cover & Mallick, 2012). Since the 1990s, many countries have adopted inflation targeting as a strategy to achieve a low and stable level of inflation. In order to observe whether the adoption of the inflation targeting represents an institutional change capable of affecting monetary policy inefficiency or macroeconomic instability, we introduce in the model a dummy variable (IT) that assumes a value equal to “1” if country i is an inflation-targeter in period t and “0” otherwise. Therefore,

Xi, t =

3i

+

4 OPENi, t

+

5 Xi, t

+

6 ITi, t

+

(7)

i, t

Regarding the literature on monetary policy performance and macroeconomic stability, there are divergences of results when advanced and developing economies are considered (see, for example, Jha & Dang, 2012; Kandil, 2014; Mishra, Montiel, & Spilimbergo, 2012). Therefore, in order to consider the possible effect from advanced and developing economies on monetary policy inefficiency and macroeconomic instability, we add to the model a dummy variable DEV, which consists of a dummy variable with value equal to “1” for developing economies based on classification made by International Monetary Fund, and value equal to “0” for the case of advanced economies (see Apergis, 2017). Another factor to be considered is that the global financial crisis of 2008 led to 11

Tables A1 and A2(appendix) present monetary policy inefficiency and macroeconomic instability indices, respectively (annual frequency). 4

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changes in the orientation of macroeconomic policies, especially in the conduct of monetary policy in several countries (see, for example, Joyce, Miles, Scott, & Vayanos, 2012; Robinson, 2015; Silva & Vieira, 2017; Tavman, 2015). Hence, with the intention to observe a possible impact of the international financial crisis on monetary policy inefficiency and macroeconomic instability, we introduce in the model a dummy variable (CRISIS) which assumes value equal to “1” for 2008 and 2009 and “0” for the remaining years (see, for example, Montes, Oliveira, & de Mendonça, 2016). Thus:

Xi, t =

7i

+

8 OPENi, t

+

9 Xi, t

+

10 ITi, t

+

11 DEVi, t

+

12 CRISISi, t

+

(8)

i, t

An environment of low political stability is capable of affecting monetary policy performance and macroeconomic stability (see, for example, Aisen & Veiga, 2006; Acemoglu, Johnson, Robinson, & Thaicharoen, 2003; Carmignani, 2003; Sangnier, 2013). As a consequence, we observe a possible influence of political stability on macroeconomic instability and monetary policy inefficiency through the introduction of the following variables in the model: SOC – this is an assessment of the socioeconomic pressures at work in society that could constrain government action or fuel social dissatisfaction; COR – this is an assessment of corruption within the political system; and DEM – this is a measure of how responsive government is to its people. Hence:

Xi, t =

13i

+

14 OPENi, t

+

15 Xi, t

+

16 ITi, t

+

17 DEVi, t

+

18 CRISISi, t

+

19 SOCi, t

+

20 CORi, t

+

21 DEMi, t

+

(9)

i, t

3.2. Empirical strategy We provide empirical evidence based on panel data analysis which takes into account information from forty-two countries for the period 1990 to 2014. Besides usual Ordinary Least Square (OLS) method for panel data analysis, due to the fact that the period of analysis is long (25 years), the use of fixed-effects model is a good choice. However, as a consequence of cross-dependence which is common in long macroeconomic panel data, this method can have problems of an inconsistent and biased estimator (see de Hoyos & Sarafidis, 2006; Reed & Ye, 2011; Sarafidis & Wansbeek, 2012; Wooldridge, 2002). Hence, to attempt to deal with this issue, we perform test for cross-section dependence of the models such as proposed by Pesaran (2004).12 The rejection of the null hypothesis of cross section independence makes us to estimate models using Feasible Generalized Least Squares (FGLS) with country weights. In order to generate robust standard errors in the presence of heteroskedasticity, serial correlation, and cross-dependence, the covariance matrices are adjusted following Arellano (1987) and White (1980).13 In order to deal with potential endogeneity which generally occurs due to omission of relevant variables, simultaneity, and measurement errors, we also provide evidence from the application of the system Generalized Method of Moments (S-GMM) method (see Arellano & Bover, 1995; Blundell & Bond, 1998) in our robustness analysis. It is important to highlight that the use of S-GMM overcomes the weakness of the instruments as presented in first-difference GMM (D-GMM). As pointed out by Bond, Hoeffler, and Temple (2001, p. 9), the S-GMM estimator “combines the standard set of equations in first-differences with suitably lagged levels as instruments, with an additional set of equations in levels with suitably lagged first-differences as instruments”. A point of concern is that when the instruments are too many, they tend to over-fit the instrumented variables and bias the results. In this sense, with the objective of prevent an excessive number of instruments in the regressions, the number of instruments/number of cross-sections ratio is less than 1 in each regression (see, de Mendonça & Barcelos, 2015). Moreover, as suggested by Arellano (2003), we perform tests of over-identifying restrictions (J-test) in order to confirm the validity of the instruments in the models. In addition, we perform tests of first-order (AR1) and second-order (AR2) serial correlation. 4. Data We use several data sources to build our panel data set. Data include information regarding monetary policy inefficiency, macroeconomic instability, financial openness, economic globalization, and other control variables. The dependent variables in the models, monetary policy inefficiency (INEF) and macroeconomic instability (INST), based on Eqs. (4) and (5), respectively, are constructed from the data gathered from International Financial Statistics (IFS) and Organisation for Economic Co-operation and Development (OECD). We use information from Chinn and Ito (2006), Dreher (2006), and Dreher, Gaston, and Martens (2008) for our independent variables of interest, financial openness (KAOPEN) and globalization (EGLOB). Regarding the control variables, we utilize: (i) Ilzetzki, Reinhart, and Rogoff (2017) for classifications of countries on use of some control over the exchange rate (FER); (ii) Penn World Table 9.0 to gather data on real GDP (GDPR); (iii) Samarina and de Haan (2014), and de Mendonça and de Guimarães e Souza (2012) for identifying the date of adoption of inflation targeting (IT); (iv) World Economic Outlook and Aggregates Information for classifications of countries based on their level of development (DEV); (v) Kleimeier, Sander, and Heuchemer (2013), and Reinhart (2010) for the global financial crisis (CRISIS); and (vi) International Country Risk Guide for the variables associated with political stability (SOC, COR, and DEM). 12

Cross-section dependence test statistic (CD) is based on the average of pair-wise correlation coefficients ( ij ) of the OLS residuals, resultant from

the individual ADF regressions (Bakas, Panagiotidis, and Pelloni, 2013). Hence, CD = 13

2T N (N 1)

(

To test the presence of serial correlation in the estimations we followed Wooldridge (2002). 5

N 1 i=1

N j = i + 1 ij

) , CD ∼ N(0, 1) for T

ij

> 3 and

6

Devised by authors, based on Eq. (4) Devised by authors, based on Eq. (5) Chinn and Ito (2006) http://web.pdx.edu/~ito/Chinn-Ito_website.htm Dreher (2006) https://www.kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html Gygli et al. (2018) https://www.kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html Gygli et al. (2018) https://www.kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html Penn World Table 9.0 https://www.rug.nl/ggdc/productivity/pwt/ International Country Risk Guide https://www.prsgroup.com/about-us/our-two-methodologies/icrg International Country Risk Guide https://www.prsgroup.com/about-us/our-two-methodologies/icrg International Country Risk Guide https://www.prsgroup.com/about-us/our-two-methodologies/icrg Devised by authors, based on de Mendonça and de Guimarães e Souza (2012), and Samarina and de Haan (2014) Devised by authors, based on de Mendonça and de Guimarães e Souza (2012), and Samarina and de Haan (2014) Devised by authors, based on World Economic Outlook Database – International Monetary Fund Devised by authors, based on Kleimeier, et al. (2013), and Reinhart (2010)

INEF INST KAOPEN EGLOB EGLOBDF EGLOBDJ GDPR SOC COR DEM IT1 IT2 DEV CRISIS

Note: S.D. means standard deviation and C.V. means coefficient of variation (Pearson).

Source

Variables

Table 2 Variables, sources of data, and descriptive statistics.

0.186 0.404 0.745 69.489 61.040 72.525 12.558 6.967 3.895 5.228 0.345 0.312 0.380 0.080

Mean 0.050 0.111 1.000 73.848 62.579 77.797 12.513 7.000 4.000 6.000 0.000 0.000 0.000 0.000

Median

6.918 11.048 1.000 97.254 93.304 94.130 15.344 11.000 6.000 6.000 1.000 1.000 1.000 1.000

Maximum

0.001 0.003 0.000 8.999 18.398 16.393 8.923 1.000 0.083 1.000 0.000 0.000 0.000 0.000

Minimum

0.457 1.081 0.325 16.581 17.571 15.812 1.496 2.063 1.348 1.067 0.476 0.464 0.486 0.271

S. D.

2.462 2.675 0.436 0.239 0 0 0 0.296 0.346 0.204 1.379 1.484 1.000 3.000

C.V.

H.F. de Mendonça, N.C. Nascimento

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Fig. 1. Correlations: KAOPEN × INEF, EGLOB × INEF, KAOPEN × INST, EGLOB × INST.

Table 2 provides descriptive statistics for all variables used in the different specifications. Besides the dependent variables INEF and INST, which we built as presented in Section 2, the variables regarding financial openness and economic globalization (OPEN) corresponds to: KAOPEN – financial openness – index developed by Chinn and Ito (2006) measuring a country’s degree of capital account openness. This index considers information regarding restrictions in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) and is the first standardized principal component of the variables that indicate the presence of multiple exchange rates, restrictions on current account transactions, restrictions on capital account transactions, and the requirement of the surrender of export proceeds. Higher values of this index reflect greater financial openness. According to de Mendonça and Veiga (2017), and Klein and Olivei (2008) financial openness can induce central banks to adopt better practices with positive effect on economic growth. EGLOB – economic globalization – index extracted from KOF Index of Globalisation, it takes into account as long distance flows of goods, capital and services as well as information and perceptions that accompany market exchanges (Dreher, 2006). Higher values of EGLOB indicate greater economic globalization. According to Gözgör and Can (2017), and Gurgul and Lach (2014), besides globalization having a positive relation with economic growth, it can change the objective function of the monetary authorities. In order to observe, in a preliminary way, a possible effect of financial openness and globalization on monetary policy inefficiency and macroeconomic instability, Fig. 1 shows a scatter diagram between these variables. The results show a negative correlation. In other words, there are indications that both financial openness and economic globalization can lead to a reduction in monetary policy inefficiency and macroeconomic instability. We also include several controls identified in the literature as possible determinants of monetary policy inefficiency and macroeconomic instability:14

14 In order to check for the presence of unit root, we perform Levin-Lin-Chu, Im-Pesaran-Shin, Fisher-ADF, and Fisher-PP tests (see Table A.3). The results do not indicate non-stationarity of the series.

7

8 0.098 25.204*** 896

Model 4

Model 5

Model 6

Model 7

0.129 45.599*** 902

−0.080*** (0.011)

−0.078** (0.032)

−0.011*** (0.001)

0.137 36.758*** 902

−0.090*** (0.030)

−0.082*** (0.011)

−0.103*** (0.033)

−0.011*** (0.001)

0.140 37.743*** 902

−0.108*** (0.031)

−0.082*** (0.011)

−0.109*** (0.033)

−0.011*** (0.001)

0.470 19.057*** 896 24.584***

−0.086*** (0.022)

−0.019* (0.011)

−0.111 (0.035)

***

0.497 20.616*** 896 20.908***

−0.088*** (0.017)

−0.070*** (0.017)

−0.014 (0.011)

−0.105 (0.034)

***

0.503 21.097*** 896 21.304***

−0.084*** (0.012)

−0.061*** (0.017)

−0.016 (0.010)

−0.091 (0.032)

***

Model 9

0.490 20.681*** 902 18.987***

−0.046*** (0.013)

−0.036*** (0.009)

−0.005*** (0.001)

Model 10

0.502 21.167*** 902 16.893***

−0.065*** (0.019)

−0.025* (0.015)

−0.026** (0.010)

−0.005*** (0.001)

Model 11

0.508 21.647*** 902 17.904***

−0.070*** (0.013)

−0.019 (0.014)

−0.025*** (0.009)

−0.005*** (0.001)

Model 12

Note: Marginal significance levels: (***) denotes 0.01, (**) denotes 0.05, and (*) denotes 0.1. White’s heteroscedasticity consistent covariance matrix was applied in regressions. Robust standard errors between parentheses. OLS – Ordinary Least Squares. FGLS – Feasible Generalized Least Squares with country weights and correction of standard errors for heteroscedasticity (using the methodology of White). The sample is an unbalanced panel of 42 countries from 1990 to 2014. CD-test is the cross-section dependence test as proposed by Pesaran (2004). Constant is included in the models.

0.095 24.546*** 896

Adj. R2 F-statistic N. Obs. CD-Test

0.084 28.484*** 896

−0.118*** (0.031)

−0.062*** (0.012)

−0.077** (0.033)

−0.390 (0.050)

IT2

−0.106*** (0.031)

−0.061*** (0.012)

−0.059*** (0.012)

GDPR

IT1

−0.073** (0.033)

−0.401 (0.050)

−0.043 (0.032)

−0.386 (0.050)

***

Model 3

Model 8

***

Model 2

Model 1

***

FGLS

OLS

FER

EGLOB

KAOPEN

Regressors

Table 3 Monetary policy inefficiency – baseline model and IT.

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Table 4 Monetary policy inefficiency – baseline model + IT + DEV + CRISIS. Regressors

KAOPEN

OLS

FGLS

Model 1

Model 2

−0.187*** (0.058)

−0.168*** (0.058)

EGLOB

Model 3

Model 4

−0.007*** (0.001)

−0.007*** (0.001)

Model 5

Model 6

−0.109*** (0.032)

−0.093*** (0.031)

Model 7

Model 8

−0.005*** (0.001)

−0.005*** (0.001)

FER

−0.117*** (0.033)

−0.124*** (0.033)

−0.128*** (0.033)

−0.135*** (0.033)

−0.014 (0.010)

−0.016* (0.010)

−0.025** (0.010)

−0.025*** (0.009)

GDPR

−0.055*** (0.011)

−0.055*** (0.011)

−0.070*** (0.011)

−0.070*** (0.011)

−0.080*** (0.018)

−0.071*** (0.018)

−0.036** (0.016)

−0.030* (0.015)

IT1

−0.126*** (0.030)

−0.113*** (0.030) −0.145*** (0.031)

IT2

−0.086*** (0.017) −0.133*** (0.031)

−0.065*** (0.019) −0.083*** (0.012)

−0.070*** (0.013)

DEV

0.261*** (0.037)

0.267*** (0.037)

0.201*** (0.038)

0.205*** (0.038)

0.239** (0.103)

0.243** (0.103)

0.225** (0.101)

0.229** (0.101)

CRISIS

0.020 (0.049)

0.024 (0.049)

0.018 (0.048)

0.023 (0.048)

0.035*** (0.008)

0.034*** (0.008)

0.031*** (0.008)

0.031*** (0.008)

Adj. R2 F-statistic N. Obs. CD-Test (Pesaran)

0.142 25.591*** 896

0.146 26.529*** 896

0.162 30.026*** 902

0.166 30.980*** 902

0.505 20.450*** 896 13.581***

0.511 20.918*** 896 13.758***

0.508 20.780*** 902 10.456***

0.514 21.265*** 902 11.173***

Note: Marginal significance levels: (***) denotes 0.01, (**) denotes 0.05, and (*) denotes 0.1. White’s heteroscedasticity consistent covariance matrix was applied in regressions. Robust standard errors between parentheses. OLS – Ordinary Least Squares. FGLS – Feasible Generalized Least Squares with country weights and correction of standard errors for heteroscedasticity (using the methodology of White). The sample is an unbalanced panel of 42 countries from 1990 to 2014. Constant is included in the models.

FER - some control over the exchange rate – as in de Mendonça and de Guimarães e Souza (2012), is a dummy variable that assumes a value equal to “1” when the country has some control over the exchange rate (e.g., currency board, crawling peg, de facto peg, etc.) and value equal to “0” otherwise (e.g., managed floating, freely floating, etc.). There exists evidence that some control over the exchange rate is associated with lower inflation, which, in turn, can improve macroeconomic stability (see, for example, Cavoli, 2008; Chow et al., 2014). GDPR – Real GDP at constant 2005 national prices (converted to international dollars using purchasing power parity rates). This indicator represents the size of the economies (see, Gattini, Pill, & Schuknecht, 2012). According to Furceri and Karras (2007), the larger economies have a diversified production structure, which gives them better economic performance. IT – inflation targeting – is a dummy variable that assumes a value of “1” during the adoption of inflation targeting and “0” otherwise. We take into account two different dates of inflation targeting adoption (see Corbo, Landerretche, & Schmidt-Hebbel, 2002; Roger & Stone, 2005). The first set of dates (IT1 – soft IT) corresponds to the period when the country announces a numerical target. The second set of dates (IT2 – full-fledged IT) refers to complete adoption of IT. The justification for the use of this variable is the evidence that the adoption of inflation targeting is associated with lower and more stable inflation rates, as well as higher and more stable output growth (see, Abo-Zaid & Tuzemen, 2012; Hartmann & Roestel, 2013; de Guimarães e Souza, de Mendonça, & Andrade, 2016). DEV – developing economies – is a dummy variable that assumes value equal to “1” for developing economies and value equal to “0” otherwise. It is expected, for example, that the use of populist policies is greater in developing economies than developed countries, which in turn suggest greater probability of bad management of monetary policy (see, Didier, Hevia, & Schmukler, 2012; Ibarra & Trupkin, 2016). CRISIS – global financial crisis – is a dummy variable that assumes value equal to “1″ for the years that correspond to the peak of the crisis (2008 and 2009) and value equal to “0” otherwise. According to Iley and Lewis (2011), for example, the global financial crisis caused changes in the conduct of macroeconomic policies, especially monetary policy. Regarding political pressures, which can increase difficulty in the management, and thus worsening macroeconomic stability (see, Barugahara, 2015; Campos et al., 2012; Misati & Nyamongo, 2012), we include as control variables: SOC – socioeconomic conditions – index that varies from “0” to “12”. Higher values of SOC denote lower risk of pressures that could constrain government action or incite social dissatisfaction. COR – corruption within the political system – such corruption reduces the efficiency of government and business by enabling people to assume positions of power through patronage rather than ability. The index varies from “0” to “6”. Lower values of COR indicate higher risk in such corruption at some time it will become so overweening resulting in a fall or overthrow of the government, a major reorganizing or restructuring of the country’s political institutions. DEM – democratic accountability - less responsive government is to its people, the more likely it is that the government will fall. This indicator varies between “0” and “6”, and larger values correspond to societies more respected by their governments. 9

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Table 5 Monetary policy inefficiency – baseline model + IT + DEV + CRISIS + SOC + COR + DEM. Regressors

KAOPEN

OLS

FGLS

Model 1

Model 2

−0.110* (0.060)

−0.099* (0.059)

EGLOB

Model 3

Model 4

−0.004*** (0.001)

−0.004*** (0.001)

Model 5

Model 6

−0.118*** (0.035)

−0.101*** (0.035)

Model 7

Model 8

−0.005*** (0.001)

−0.005*** (0.001)

FER

−0.138*** (0.033)

−0.143*** (0.033)

−0.139*** (0.033)

−0.143*** (0.033)

−0.019** (0.009)

−0.019** (0.008)

−0.028*** (0.009)

−0.027*** (0.007)

GDPR

−0.050*** (0.011)

−0.051*** (0.011)

−0.059*** (0.012)

−0.060*** (0.012)

−0.051*** (0.018)

−0.045*** (0.017)

−0.027 (0.021)

−0.022 (0.019)

IT1

−0.093*** (0.031)

−0.095*** (0.030) −0.108*** (0.031)

IT2

−0.078*** (0.016) −0.111*** (0.031)

−0.063*** (0.018) −0.075*** (0.011)

−0.066*** (0.012)

DEV

0.153*** (0.048)

0.158*** (0.048)

0.157*** (0.045)

0.160*** (0.045)

0.243** (0.101)

0.246** (0.101)

0.224** (0.099)

0.229** (0.100)

CRISIS

0.034 (0.049)

0.036 (0.049)

0.029 (0.048)

0.032 (0.048)

0.034*** (0.008)

0.034*** (0.008)

0.033*** (0.008)

0.033*** (0.008)

SOC

−0.054*** (0.009)

−0.052*** (0.009)

−0.044*** (0.010)

−0.042*** (0.010)

−0.008*** (0.002)

−0.007*** (0.002)

−0.004** (0.002)

−0.004 (0.002)

COR

0.002 (0.014)

0.001 (0.014)

0.005 (0.014)

0.004 (0.014)

−0.004 (0.003)

−0.003 (0.003)

−0.005 (0.004)

−0.005 (0.004)

DEM

−0.013 (0.019)

−0.013 (0.019)

0.001 (0.020)

0.001 (0.020)

−0.004 (0.006)

−0.005 (0.006)

−0.003 (0.006)

−0.004 (0.006)

Adj. R2 F-statistic N. Obs. CD-Test (Pesaran)

0.174 21.748*** 890

0.176 22.134*** 890

0.179 22.670*** 896

0.182 23.105*** 869

0.506 19.224*** 890 12.405***

0.511 19.557*** 890 13.154***

0.500 18.905*** 896 11.106***

0.506 19.352*** 896 11.919***

Note: Marginal significance levels: (***) denotes 0.01, (**) denotes 0.05, and (*) denotes 0.1. White’s heteroscedasticity consistent covariance matrix was applied in regressions. Robust standard errors between parentheses. OLS – Ordinary Least Squares. FGLS – Feasible Generalized Least Squares with country weights and correction of standard errors for heteroscedasticity (using the methodology of White). The sample is an unbalanced panel of 42 countries from 1990 to 2014. Constant is included in the models. Table 6 Effect of shocks on monetary policy inefficiency and macroeconomic instability. Variables

INEF INST

Table

Table 5 Table 9

KAOPEN

EGLOB

10%

1 s.d.

10%

1 s.d.

−4.1%* −4.7%*

−17.5%* −20.5%*

−17.4%* −16.3%*

−41.2%* −38.5%*

Note: Effects computed from the mean values of the variables using the coefficients in F-GLS estimation (models 6 and 8). (*) Denotes statistical significance in estimation.

5. Empirical results This section first conducts an estimation of the relationship between monetary policy inefficiency or macroeconomic instability and financial openness or economic globalization. In addition, we explore four aspects that may be relevant to explain the inefficiency of monetary policy and macroeconomic instability: adoption of inflation targeting; the level of development of the countries; the global financial crisis of 2008; and political pressures. The first two subsections present the results of regressions based on OLS and FGLS methods. In order to give robustness to these results, in the third subsection we re-estimate the models presented in the previous subsections using S-GMM method. 5.1. Evidence for monetary policy inefficiency Based on Eqs. (6) and (7), the results in Table 3 indicate that the coefficients on the independent variables of interest (KAOPEN and EGLOB) have statistical significance and they are negative in all models. This result is in line with the view that both financial openness 10

11 0.140 37.456*** 896

Model 4

Model 5

Model 6

Model 7

0.115 40.184*** 902

−0.135*** (0.027)

−0.246*** (0.075)

−0.026*** (0.002)

0.130 34.596*** 902

−0.284*** (0.071)

−0.141*** (0.027)

−0.326*** (0.077)

−0.026*** (0.002)

0.133 35.492*** 902

−0.317*** (0.073)

−0.141*** (0.027)

−0.337*** (0.077)

−0.026*** (0.002)

0.601 31.678*** 896 18.475***

−0.164*** (0.041)

−0.006 (0.020)

−0.383 (0.098)

***

0.622 33.729*** 896 15.082***

−0.240*** (0.031)

−0.113*** (0.020)

−0.001 (0.009)

−0.274 (0.031)

***

0.618 33.218*** 896 15.800***

−0.181*** (0.022)

−0.109*** (0.021)

−0.002 (0.011)

−0.262 (0.034)

***

Model 9

0.606 32.472*** 902 14.891***

−0.132*** (0.028)

−0.044*** (0.010)

−0.012*** (0.002)

Model 10

0.637 36.093*** 902 13.883***

−0.222*** (0.030)

−0.088** (0.034)

−0.045*** (0.014)

−0.010*** (0.002)

Model 11

0.633 35.464*** 902 14.839***

−0.183*** (0.019)

−0.064** (0.030)

−0.031*** (0.009)

−0.009*** (0.001)

Model 12

Note: Marginal significance levels: (***) denotes 0.01, (**) denotes 0.05, and (*) denotes 0.1. White’s heteroscedasticity consistent covariance matrix was applied in regressions. Robust standard errors between parentheses. OLS – Ordinary Least Squares. FGLS – Feasible Generalized Least Squares with country weights and correction of standard errors for heteroscedasticity (using the methodology of White). The sample is an unbalanced panel of 42 countries from 1990 to 2014. CD-test is the cross-section dependence test as proposed by Pesaran (2004). Constant is included in the models.

0.140 37.469*** 896

Adj. R2 F-statistic N. Obs. CD-Test

0.119 41.403*** 896

−0.345*** (0.073)

−0.095*** (0.027)

−0.291*** (0.077)

−1.257 (0.114)

IT2

−0.340*** (0.071)

−0.095*** (0.027)

−0.089*** (0.027)

GDPR

IT1

−0.288*** (0.077)

−1.295 (0.115)

−0.191** (0.075)

−1.246 (0.116)

***

Model 3

Model 8

***

Model 2

Model 1

***

FGLS

OLS

FER

EGLOB

KAOPEN

Regressors

Table 7 Macroeconomic instability – baseline model and IT.

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Table 8 Macroeconomic instability – baseline model + IT + DEV + CRISIS. Regressors

KAOPEN

OLS

FGLS

Model 1

Model 2

−0.844*** (0.134)

−0.792*** (0.134)

EGLOB

Model 3

Model 4

−0.015*** (0.003)

−0.015*** (0.003)

Model 5

Model 6

−0.282*** (0.030)

−0.270*** (0.032)

Model 7

Model 8

−0.010*** (0.002)

−0.009*** (0.001)

FER

−0.381*** (0.077)

−0.390*** (0.077)

−0.400*** (0.077)

−0.414*** (0.077)

−0.003 (0.008)

−0.004 (0.010)

−0.046*** (0.014)

−0.031*** (0.009)

GDPR

−0.082*** (0.026)

−0.082*** (0.026)

−0.109*** (0.027)

−0.108*** (0.027)

−0.139*** (0.016)

−0.136*** (0.018)

−0.111*** (0.032)

−0.085*** (0.030)

IT1

−0.378*** (0.07)

−0.343*** (0.071) −0.398*** (0.072)

IT2

−0.238*** (0.030) −0.381*** (0.072)

−0.222*** (0.03) −0.179*** (0.022)

−0.183*** (0.019)

DEV

0.539*** (0.086)

0.553*** (0.086)

0.556*** (0.089)

0.565*** (0.089)

0.328*** (0.094)

0.330*** (0.094)

0.307*** (0.087)

0.307*** (0.088)

CRISIS

−0.067 (0.114)

−0.057 (0.114)

−0.110 (0.114)

−0.097 (0.114)

0.069*** (0.017)

0.071*** (0.019)

0.057*** (0.009)

0.058*** (0.015)

Adj. R2 F-statistic N. Obs. CD-Test (Pesaran)

0.174 32.515*** 896

0.176 32.865*** 896

0.164 30.534*** 902

0.168 31.385*** 902

0.632 33.705*** 896 7.119***

0.628 33.192*** 896 7.531***

0.645 35.759*** 902 7.611***

0.639 34.996*** 902 8.091***

and economic globalization can be useful in reducing monetary policy inefficiency (see, for example, Jafari Samimi, Ghaderi, Hosseinzadeh, & Nademi, 2012, and Spiegel, 2009). In addition, we observe that the adoption of the inflation targeting regime is relevant for the reduction of monetary policy inefficiency (the coefficients related to IT1 and IT2 are significant and negative). The results of the estimates based on Eq. (8), which add control variables regarding developing economies and global financial crisis to the baseline model, present coefficients on KAOPEN and EGLOB that are significant and negative in all models (see Table 4). Therefore, this result reinforces the idea that financial openness and economic globalization can contribute to the reduction of monetary policy inefficiency. In addition, the results indicate that developing economies are associated with an increase in monetary policy inefficiency (coefficients on DEV are significant and positive). Furthermore, the results from FGLS estimates show that the coefficients related to the global financial crisis of 2008 contribute to an increase in monetary policy inefficiency (coefficients are significant and positive). Table 5 presents the results that take into account the introduction of the control variables, which captures the effect of political pressure on monetary policy inefficiency (Eq. (9)). Once again, the coefficients on KAOPEN and EGLOB are significant and negative, which in turn, permit us to conjecture that an increase in financial openness and economic globalization can improve the conduct of monetary policy. Moreover, regarding the variables concerning political pressure, the highlight is for SOC. The coefficient on socioeconomic conditions is significant and negative in almost all models. This result regarding monetary policy provides support for the fiscal results in Pastén and Cover (2015). In order to observe how an increase in financial openness or economic globalization leads to an improvement in monetary policy (reduction in INEF), we consider the effect of a shock of 10% and 1 standard deviation of the value relative to the average of KAOPEN and EGLOB on INEF (see Table 6). Using as reference the models (6) and (8) in Table 3 because they have the highest coefficients of determination (adjusted R2 of 0.511 and 0.506, respectively). We observe that that a 10% increase in financial openness and globalization produces a reduction of 1.9% and 8% (respectively) in the average inefficiency of monetary policy.15 In other words, although both financial openness and economic globalization are beneficial to the management of monetary policy, the effect of economic globalization is greater than that observed for financial openness. 5.2. Evidence for macroeconomic instability As in previous subsection, Table 7 shows the results of regressions using OLS and FGLS based on Eqs. (6) and (7). The findings denote that the coefficients on KAOPEN and EGLOB are significant and negative. Therefore, this evidence is in agreement with the view that economic globalization can contribute to reduce macroeconomic instability (see, for example, Kimakova, 2009; Potrafke, 2012). Furthermore, the coefficients on the variables regarding the adoption of inflation targeting are significant and negative and thus are in 15 Because each variable has a standard deviation which corresponds to a different proportion of their respective means, which can be verified by the Pearson coefficient of variation, a consequence is that the shock of one standard deviation has magnitudes different from the linear shock of 10%.

12

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Table 9 Macroeconomic instability – baseline model + IT + DEV + CRISIS + SOC + COR + DEM. Regressors

KAOPEN

OLS

FGLS

Model 1

Model 2

−0.660*** (0.138)

−0.621*** (0.138)

EGLOB

Model 3

Model 4

−0.008** (0.003)

−0.007** (0.003)

Model 5

Model 6

−0.268*** (0.025)

−0.258*** (0.030)

Model 7

Model 8

−0.010*** (0.002)

−0.009*** (0.002)

FER

−0.440*** (0.077)

−0.444*** (0.077)

−0.448*** (0.077)

−0.456*** (0.077)

−0.008 (0.012)

−0.012 (0.013)

−0.031*** (0.009)

−0.031*** (0.010)

GDPR

−0.072*** (0.026)

−0.073*** (0.026)

−0.079*** (0.028)

−0.080*** (0.028)

−0.100*** (0.028)

−0.093*** (0.029)

−0.078* (0.046)

−0.069 (0.042)

IT1

−0.306*** (0.071)

−0.291*** (0.071) −0.314*** (0.073)

IT2

−0.193*** (0.024) −0.317*** (0.073)

−0.177*** (0.024) −0.159*** (0.020)

−0.159*** (0.019)

DEV

0.325*** (0.110)

0.330*** (0.111)

0.462*** (0.106)

0.464*** (0.106)

0.342*** (0.093)

0.346** (0.093)

0.303** (0.085)

0.312*** (0.086)

CRISIS

−0.027 (0.113)

−0.021 (0.113)

−0.065 (0.113)

−0.057 (0.113)

0.067*** (0.016)

0.069*** (0.017)

0.066*** (0.014)

0.065*** (0.015)

SOC

−0.122*** (0.022)

−0.119*** (0.022)

−0.121*** (0.024)

−0.117*** (0.024)

−0.005 (0.004)

−0.006 (0.004)

0.002 (0.005)

0.001 (0.005)

COR

0.033 (0.033)

0.029 (0.033)

0.052 (0.033)

0.047 (0.033)

0.006 (0.006)

0.007 (0.005)

0.006 (0.007)

0.007 (0.007)

DEM

−0.053 (0.044)

−0.055 (0.044)

−0.048 (0.046)

−0.050 (0.046)

−0.030*** (0.011)

−0.033*** (0.012)

−0.030*** (0.009)

−0.034*** (0.010)

Adj. R2 F-statistic N. Obs. CD-Test (Pesaran)

0.205 26.404*** 890

0.205 26.430*** 890

0.188 24.094*** 896

0.191 24.407*** 869

0.637 32.247*** 890 6.538***

0.641 32.706*** 890 6.899***

0.639 32.624*** 896 7.684***

0.646 33.621*** 896 8.243***

Note: Marginal significance levels: (***) denotes 0.01, (**) denotes 0.05, and (*) denotes 0.1. White’s heteroscedasticity consistent covariance matrix was applied in regressions. Robust standard errors between parentheses. OLS – Ordinary Least Squares. FGLS – Feasible Generalized Least Squares with country weights and correction of standard errors for heteroscedasticity (using the methodology of White). The sample is an unbalanced panel of 42 countries from 1990 to 2014. Constant is included in the models.

consonance with the perspective that this monetary regime can improve macroeconomic stability (see, de Guimarães e Souza et al., 2016). Table 8 provides the results based on Eq. (8). As observed in the previous case, the coefficients on both KAOPEN and EGLOB are significant and negative for all models. This observation confirms the idea that financial openness and economic globalization are elements that contribute to an improvement in the macroeconomic environment. The results regarding the coefficients on DEV and CRISIS are similar to those observed for the case of monetary policy inefficiency. In other words, the statistical significance and positive sign for the coefficients on DEV and CRISIS (in FGLS estimations) indicate that both developing economies as well as global financial crisis of 2008 are associated with an increase in macroeconomic instability. In Table 9, we report the results based on Eq. (9). As observed in the previous cases, the coefficients on KAOPEN and EGLOB are significant and negative. Therefore, there exists evidence that openness as well as globalization is associated with lower macroeconomic instability. In relation to the coefficients of control variables associated with political pressure, the highlights are the negative sign of SOC (significant in OLS) and DEM (significant in FGLS). In other words, we cannot neglect the negative effect on macroeconomic instability due to a worsening in the indicators regarding socioeconomic conditions and democratic accountability. As made in previous subsection, we perform a shock of 1 standard deviation and 10% of the value relative to the average of KAOPEN and EGLOB on INST (see Table 6 – previous subsection). The main idea is to observe how an increase in the financial openness or economic globalization reduces macroeconomic instability. Based on the coefficients present in the models with highest coefficients of determination in Table 6, models (6) and (8), we verify that a 10% increase in financial openness and globalization implies a decrease in 4.7% and 16.3% in the average macroeconomic instability, respectively. In brief, as observed for the case on monetary policy inefficiency, although both financial openness and economic globalization are beneficial, the effect of economic globalization is greater than that observed for financial openness. 5.3. Robustness analysis We extend our analysis based on Eq. (9) providing new empirical evidence for monetary policy inefficiency and macroeconomic instability based on S-GMM method. Although, the results presented in previous sections are robust to many alternative specifications 13

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Table 10 Monetary policy inefficiency and Macroeconomic instability – baseline model + IT + DEV + CRISIS + SOC + COR + DEM (S-GMM). Regressors

KAOPEN

Monetary policy inefficiency Model 1

Model 2

−1.850*** (0.207)

−2.242*** (0.180)

EGLOB

Macroeconomic instability Model 3

Model 4

−0.069*** (0.006)

−0.033** (0.014)

Model 1

Model 2

−6.287*** (0.406)

−4.481*** (0.469)

Model 3

Model 4

−0.136*** (0.016)

−0.160*** (0.026)

FER

−1.197*** (0.168)

−1.197*** (0.128)

−1.213*** (0.135)

−2.152*** (0.162)

−2.776*** (0.447)

−3.238*** (0.450)

−1.989*** (0.251)

−5.061*** (0.585)

GDPR

−0.490*** (0.053)

−0.619*** (0.064)

−0.688*** (0.115)

−0.704*** (0.111)

−0.070 (0.145)

−0.836*** (0.236)

−0.001 (0.173)

−1.308*** (0.270)

IT1

−0.738*** (0.089)

−0.597*** (0.066) −0.270*** (0.061)

IT2

−2.159*** (0.127) −0.727*** (0.040)

−3.905*** (0.138) −2.204*** (0.187)

−2.153*** (0.246)

DEV

3.270* (1.886)

3.758* (2.100)

3.637*** (0.789)

3.731*** (1.003)

7.548** (3.783)

2.716*** (0.806)

1.381** (0.630)

1.441 (1.626)

CRISIS

0.121*** (0.020)

0.124*** (0.018)

0.050*** (0.018)

0.007 (0.025)

0.288*** (0.035)

0.330*** (0.025)

0.092** (0.047)

0.108** (0.047)

SOC

−0.047* (0.028)

−0.042** (0.019)

−0.043* (0.022)

−0.016 (0.019)

0.037 (0.029)

−0.074*** (0.020)

−0.058** (0.028)

−0.039 (0.047)

COR

−0.113** (0.045)

−0.085* (0.049)

−0.152*** (0.056)

0.033 (0.089)

−0.230*** (0.088)

−0.014 (0.093)

−0.424*** (0.134)

0.228 (0.145)

DEM

−0.089*** (0.030)

−0.103*** (0.032)

−0.05* (0.026)

−0.090 (0.056)

−0.292*** (0.061)

−0.168*** (0.042)

−0.229*** (0.051)

−0.364*** (0.097)

N. Obs. N. inst./N. cross-sec. J-Statistic (p-value) AR(1) (p-value) AR(2) (p-value)

704 0.762 24.710 (0.365) −0.057 (0.095) −0.029 (0.372)

707 0.738 25.684 (0.265) −0.069 (0.041) −0.027 (0.398)

705 0.929 32.163 (0.360) −0.070 (0.047) −0.050 (0.149)

652 0.857 29.270 (0.348) −0.087 (0.017) −0.041 (0.134)

627 0.786 22.772 (0.533) −0.077 (0.027) −0.038 (0.201)

686 0.714 27.035 (0.170) −0.082 (0.028) −0.057 (0.108)

656 0.857 31.804 (0.239) −0.067 (0.076) −0.054 (0.112)

663 0.714 21.550 (0.426) −0.072 (0.057) −0.047 (0.110)

Note: Marginal significance levels: (***) denotes 0.01, (**) denotes 0.05, and (*) denotes 0.1. White’s heteroscedasticity consistent covariance matrix was applied in regressions. Standard errors between parentheses. S-GMM – uses two-step of Arellano and Bover (1995) without time period effects. Tests for AR (1) and AR (2) check for the presence of first order and second-order serial correlation in the first-difference residuals. The sample is an unbalanced panel of 42 countries from 1990 to 2014. Constant is included in the models.

regarding the benefits from financial openness and economic globalization, we cannot completely rule out the risk of endogeneity affecting the results. As we know, monetary policy as well as inflation and output are subject to several types of shocks (economic, political, institutional, etc.). Hence, the use of lags in the regressors can have a relevant role. In this sense, our S-GMM estimation results are robust to using more lags as internal instruments.16 The results in Table 10 indicate that all S-GMM regressions accept the null hypothesis in the Sargan’s J-tests and thus the overidentifying restrictions are valid. Furthermore, both serial autocorrelation tests (AR(1) and AR(2)) reject the hypothesis of the presence of serial autocorrelation. In a general way, the use of S-GMM did not change the statistical significance and the signs of the coefficients on monetary policy inefficiency and macroeconomic instability observed in the previous subsections. The same observation is valid for the results regarding the control variables in the model. In brief, the findings confirm the view that an increase in financial openness and economic globalization are important drivers for reducing monetary policy inefficiency and macroeconomic instability. Because the revised version of the economic globalization index provided by KOF introduces a distinction between de facto (EGLOBDF) and de jure measures (EGLOBDJ), we re-estimate models 3 and 4 from Table 10 for both monetary policy inefficiency and macroeconomic instability. According to Gygli, Haelg, and Sturm (2018), in the revised version of the economic globalization index, de facto measure comprehend variables that represent flows and activities, while de jure measure consider variables that represent policies that, in principle, enable flows and activities (see Table 11). The results in Table 11 show overall that the instruments as a group are exogenous (J-statistics with p-values > 0.10) and there is no autocorrelation in levels (AR(2) tests with p-values > 0.10). Regarding the variables of interest in the models (EGLOBDF and EGLOBDJ),

16 Besides the lags of regressors, we use the following instruments in the regressions (data provided by ICRG): bureaucracy quality, internal conflict, external conflict, government stability, and law and order.

14

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

H.F. de Mendonça, N.C. Nascimento

Table 11 Monetary policy inefficiency and Macroeconomic instability – economic globalization (de facto and de jure measures). Regressors

EGLOBDF

Monetary policy inefficiency Model 1

Model 2

−0.022*** (0.008)

−0.015*** (0.005)

EGLOBDJ

Macroeconomic instability Model 3

Model 4

−0.010*** (0.002)

−0.004* (0.002)

Model 5

Model 6

−0.014*** (0.005)

−0.159*** (0.023)

Model 7

Model 8

−0.023*** (0.008)

−0.016** (0.007)

FER

−1.689*** (0.208)

−1.904*** (0.093)

−1.621*** (0.093)

−2.571*** (0.174)

−1.873*** (0.647)

−8.332*** (1.510)

−0.556 (0.963)

−0.645 (0.585)

GDPR

0.066 (0.227)

−0.177* (0.098)

−0.148*** (0.052)

−0.289*** (0.065)

−0.023 (0.167)

−1.010*** (0.362)

−0.003 (0.362)

0.135 (0.137)

IT1

−1.231*** (0.142)

−1.262*** (0.029) −1.187*** (0.044)

IT2

−3.322*** (0.107) −0.883*** (0.026)

−4.147*** (0.453) −0.707** (0.319)

−3.308*** (0.177)

DEV

4.246 (2.617)

3.836*** (0.978)

5.542*** (1.040)

8.434*** (2.920)

13.965* (7.170)

4.669 (2.836)

10.339 (7.156)

1.979 (1.976)

CRISIS

0.064*** (0.024)

0.124*** (0.014)

0.102*** (0.011)

0.079*** (0.010)

0.170*** (0.019)

0.259*** (0.060)

0.152*** (0.034)

0.238*** (0.028)

SOC

−0.044* (0.026)

−0.042** (0.020)

−0.021** (0.009)

−0.012 (0.008)

0.016 (0.015)

−0.086 (0.059)

0.029 (0.040)

−0.044* (0.025)

COR

−0.325*** (0.108)

0.038 (0.074)

−0.131*** (0.040)

−0.082*** (0.025)

−0.095 (0.065)

0.100 (0.147)

−0.321** (0.146)

−0.181* (0.072)

DEM

0.026 (0.033)

0.006 (0.024)

−0.083*** (0.014)

−0.026*** (0.010)

−0.254*** (0.050)

−0.860*** (0.162)

−0.466** (0.196)

−0.095 (0.061)

N. Obs. N. inst./N. cross-sec. J-Statistic (p-value) AR(1) (p-value) AR(2) (p-value)

595 0.786 20.910 (0.644) −0.071 (0.051) −0.046 (0.182)

621 0.786 27.595 (0.278) −0.050 (0.098) −0.022 (0.404)

600 0.810 27.480 (0.332) −0.099 (0.007) −0.028 (0.412)

588 0.857 30.962 (0.273) −0.106 (0.006) −0.062 (0.110)

561 0.690 21.760 (0.354) 0.002 (0.960) −0.064 (0.113)

623 0.690 19.034 (0.520) −0.062 (0.037) −0.032 (0.214)

592 0.643 17.742 (0.473) −0.021 (0.588) −0.040 (0.196)

595 0.714 23.676 (0.309) 0.031 (0.376) −0.040 (0.130)

Note: Marginal significance levels: (***) denotes 0.01, (**) denotes 0.05, and (*) denotes 0.1. White’s heteroscedasticity consistent covariance matrix was applied in regressions. Standard errors between parentheses. S-GMM – uses two-step of Arellano and Bover (1995) without time period effects. Tests for AR (1) and AR (2) check for the presence of first order and second-order serial correlation in the first-difference residuals. The sample is an unbalanced panel of 42 countries from 1990 to 2014. Constant is included in the models.

we observe that the coefficients on economic globalization are negative and significant in all models. In brief, the findings confirm the previous results that economic globalization is important to improve the efficiency of monetary policy and macroeconomic stability. 6. Conclusion Based on a panel data analysis for forty-two countries over the period from 1990 to 2014, this study examined the effect of financial openness and economic globalization on monetary policy inefficiency and macroeconomic instability. In order to build indicators of monetary policy inefficiency and macroeconomic instability, we use efficiency frontier approach to measure the performance of the monetary authority. In particular, this study investigated whether increased financial openness and economic globalization are capable of promoting a more efficient conduct of monetary policy and a more stable macroeconomic environment. The findings provide robust evidence that both financial openness and economic globalization are beneficial to improve the monetary policy efficiency and macroeconomic stability. Although both openness and globalization are associated with good results for the economy, we observe that the effect from economic globalization measured through a shock of 10% and one standard deviation is greater for the case of economic globalization. In addition, our results permit us to conjecture that the adoption of inflation targeting, the level of development, international financial crisis, and political stability represent important factors in the explanation of the monetary policy inefficiency and macroeconomic instability. In brief, the main result of this analysis indicates that the degree of openness and integration of the economies have an important role to promote greater efficiency in the conduct of monetary policy and greater macroeconomic stability. Appendix Tables A1–A3. 15

16

n.a. 0.26 n.a. n.a. 0.05 n.a. n.a. 0.09 n.a. n.a. n.a. n.a. 0.10 n.a. 0.28 0.08 0.06 n.a. 0.35 n.a. 0.24 n.a. 0.23 0.09 n.a. 0.05 0.22 0.08 0.17 n.a. 0.15 n.a. 0.69 n.a. n.a. n.a. 0.06 0.23 0.30 0.09 4.27 0.17

Armenia, Rep.b Australiaa,c Austriaa,c Bangladeshb Belgiuma,c Brazilb Bulgariab Canadaa,c Chileb,c Colombiab Croatiab Czech Republica,c Denmarka,c Estoniaa,c Finlanda,c Francea,c Germanya,c Greecea,c Hungaryb,c Icelanda,c Irelanda,c Israela,c Italya,c Japana,c Latviaa,c Malaysiab Mexicob,c Netherlandsa,c New Zealanda,c Nicaraguab Norwaya,c Polandb,c Portugala,c Romaniab Russiab Sloveniaa,c South Koreaa,c Spaina,c Swedena,c Switzerlanda,c Turkeyb,c United Kingdoma,c

n.a. 0.08 n.a. n.a. 0.05 n.a. n.a. 0.10 n.a. n.a. n.a. n.a. 0.02 n.a. 0.27 0.05 1.63 n.a. 1.57 n.a. 0.08 n.a. 0.17 0.07 n.a. 0.05 0.04 0.08 0.07 n.a. 0.06 n.a. 0.46 n.a. n.a. n.a. 0.16 0.15 0.36 0.14 2.39 0.20

1991

n.a. 0.14 n.a. n.a. 0.01 n.a. n.a. 0.02 n.a. n.a. n.a. n.a. 0.02 n.a. 0.14 0.01 2.09 n.a. 1.94 n.a. 0.02 n.a. 0.05 0.04 n.a. 0.06 0.03 0.09 0.10 n.a. 0.01 n.a. 0.11 n.a. n.a. n.a. 0.16 0.03 0.13 0.08 0.94 0.08

1992

n.a. 0.11 0.08 n.a. 0.01 n.a. n.a. 0.04 n.a. 0.34 n.a. n.a. 0.01 n.a. 0.07 0.01 0.71 n.a. 0.82 n.a. 0.04 n.a. 0.03 0.05 n.a. 0.03 0.07 0.07 0.08 n.a. 0.01 n.a. 0.03 n.a. n.a. n.a. 0.09 0.03 0.24 0.03 0.58 0.12

1993 n.a. 0.03 0.03 n.a. 0.01 n.a. n.a. 0.02 0.60 0.12 n.a. n.a. 0.01 n.a. 0.20 0.01 0.20 n.a. 0.45 n.a. 0.05 n.a. 0.03 0.03 n.a. 0.02 0.05 0.05 0.02 n.a. 0.02 0.65 0.03 n.a. n.a. n.a. 0.12 0.04 0.15 0.07 1.50 0.14

1994 n.a. 0.03 0.01 n.a. 0.01 n.a. n.a. 0.04 0.14 0.02 n.a. n.a. 0.05 n.a. 0.23 0.00 0.02 n.a. 0.67 n.a. 0.20 0.28 0.02 0.01 n.a. 0.01 0.11 0.05 0.03 n.a. 0.04 0.36 0.06 n.a. n.a. n.a. 0.29 0.01 0.04 0.04 3.26 0.05

1995 n.a. 0.05 0.02 n.a. 0.01 n.a. n.a. 0.01 0.33 0.01 n.a. 0.30 0.02 n.a. 0.04 0.01 0.02 n.a. 0.26 n.a. 0.29 0.17 0.05 0.02 n.a. 0.00 0.34 0.05 0.03 n.a. 0.06 0.12 0.18 n.a. n.a. 0.18 0.33 0.01 0.02 0.01 1.62 0.01

1996 n.a. 0.02 0.02 0.04 0.00 n.a. n.a. 0.02 0.24 0.02 0.10 0.09 0.04 n.a. 0.03 0.01 0.01 0.12 0.10 n.a. 0.13 0.04 0.02 0.03 n.a. 0.01 0.46 0.04 0.01 n.a. 0.04 0.15 0.10 n.a. n.a. 0.25 0.09 0.04 0.03 0.00 0.89 0.00

1997 n.a. 0.04 0.01 0.03 0.00 0.07 n.a. 0.01 0.15 0.06 0.11 0.09 0.11 n.a. 0.09 0.00 0.01 0.05 0.17 n.a. 0.11 0.11 0.04 0.04 n.a. 0.02 0.29 0.04 0.02 n.a. 0.04 0.04 0.08 6.92 n.a. 0.17 0.07 0.04 0.01 0.01 0.12 0.01

1998 n.a. 0.01 0.00 0.01 0.01 0.06 n.a. 0.01 0.12 0.37 0.07 0.04 0.05 n.a. 0.07 0.00 0.01 0.01 0.25 n.a. 0.25 0.10 0.02 0.06 n.a. 0.02 0.02 0.04 0.02 n.a. 0.03 0.12 0.11 2.70 3.09 0.07 0.20 0.01 0.00 0.01 0.37 0.01

1999

Note: “a” – advanced economies; “b” – developing economies; and “c” – OECD countries.

1990

Countries

Table A1 Monetary policy inefficiency (1990–2014).

0.12 0.04 0.01 0.09 0.01 0.04 n.a. 0.03 0.09 0.42 0.02 0.15 0.03 n.a. 0.11 0.01 0.01 0.02 0.53 n.a. 0.11 0.06 0.01 0.04 n.a. 0.03 0.04 0.05 0.01 n.a. 0.04 0.07 0.07 2.24 3.34 0.03 1.22 0.02 0.00 0.02 1.81 0.01

2000 0.10 0.10 0.04 0.13 0.03 0.14 n.a. 0.03 0.03 0.18 0.01 0.05 0.04 0.16 0.12 0.02 0.02 0.02 0.38 0.31 0.32 0.12 0.04 0.05 0.89 0.04 0.01 0.07 0.03 n.a. 0.05 0.15 0.07 1.25 1.15 0.12 0.78 0.05 0.02 0.03 1.91 0.00

2001 0.24 0.05 0.02 0.27 0.02 0.06 n.a. 0.01 0.02 0.07 0.03 0.04 0.01 0.05 0.05 0.02 0.02 0.02 0.22 0.41 0.26 0.09 0.03 0.07 0.24 0.02 0.00 0.08 0.03 1.08 0.03 0.07 0.07 0.60 1.64 0.04 0.14 0.03 0.02 0.01 1.00 0.00

2002 0.55 0.03 0.01 0.22 0.01 0.08 0.07 0.01 0.02 0.04 0.03 0.06 0.00 0.03 0.03 0.02 0.00 0.02 0.15 0.09 0.05 0.08 0.02 0.05 0.08 0.00 0.01 0.06 0.01 0.73 0.03 0.10 0.06 0.78 0.76 0.09 0.11 0.01 0.02 0.01 0.93 0.00

2003 1.80 0.03 0.01 0.08 0.01 0.11 0.09 0.00 0.02 0.02 0.01 0.03 0.01 0.01 0.06 0.01 0.00 0.00 0.13 0.10 0.06 0.02 0.01 0.04 0.11 0.00 0.03 0.03 0.01 0.67 0.03 0.36 0.08 0.58 0.12 0.11 0.11 0.00 0.01 0.01 1.64 0.00

2004 1.96 0.02 0.01 0.06 0.01 0.04 0.12 0.00 0.01 0.02 0.02 0.04 0.01 0.07 0.08 0.01 0.01 0.00 0.23 0.09 0.06 0.07 0.01 0.03 0.17 0.02 0.04 0.04 0.01 0.91 0.03 0.27 0.09 0.34 0.04 0.07 0.13 0.01 0.01 0.01 1.15 0.01

2005 0.69 0.01 0.01 0.09 0.02 0.05 0.11 0.01 0.03 0.08 0.05 0.05 0.01 0.16 0.14 0.00 0.02 0.01 0.33 0.43 0.11 0.10 0.01 0.02 0.26 0.05 0.06 0.05 0.03 1.17 0.04 0.07 0.07 0.14 0.03 0.03 0.13 0.02 0.00 0.02 0.34 0.01

2006 0.11 0.01 0.01 0.09 0.00 0.05 0.20 0.01 0.05 0.37 0.05 0.15 0.01 0.18 0.19 0.00 0.04 0.00 0.48 0.34 0.18 0.12 0.01 0.04 0.21 0.02 0.06 0.06 0.01 1.55 0.03 0.33 0.05 0.21 0.09 0.14 0.11 0.01 0.01 0.04 0.17 0.02

2007 0.52 0.02 0.02 0.31 0.02 0.07 0.34 0.01 0.14 0.34 0.07 0.18 0.01 0.27 0.16 0.01 0.04 0.00 0.37 0.41 0.15 0.16 0.03 0.04 0.24 0.03 0.02 0.08 0.01 1.49 0.04 0.25 0.05 0.30 0.18 0.19 0.13 0.02 0.04 0.04 0.17 0.02

2008 1.05 0.01 0.02 0.17 0.02 0.16 0.17 0.00 0.13 0.11 0.08 0.22 0.07 0.54 0.43 0.02 0.11 0.07 0.91 0.89 0.10 0.05 0.07 0.57 1.07 0.02 0.11 0.08 0.01 1.30 0.06 0.03 0.13 0.09 0.37 0.40 0.11 0.03 0.03 0.02 0.37 0.02

2009 1.19 0.01 0.00 0.07 0.01 0.20 0.11 0.00 0.07 0.10 0.03 0.18 0.09 0.37 0.44 0.01 0.11 0.14 0.84 0.65 0.19 0.05 0.05 0.56 0.92 0.02 0.12 0.08 0.01 1.07 0.07 0.06 0.07 0.08 0.30 0.34 0.31 0.01 0.01 0.01 0.40 0.01

2010 2.45 0.01 0.02 0.04 0.02 0.24 0.08 0.02 0.08 0.08 0.03 0.09 0.03 0.63 0.21 0.01 0.10 0.10 0.51 0.10 0.19 0.08 0.02 0.29 0.94 0.01 0.11 0.08 0.01 1.21 0.08 0.30 0.03 0.16 0.18 0.09 0.69 0.01 0.01 0.01 0.58 0.02

2011 1.68 0.00 0.04 0.02 0.04 0.06 0.02 0.03 0.02 0.08 0.02 0.08 0.02 0.37 0.17 0.02 0.07 0.04 0.18 0.06 0.13 0.06 0.04 0.07 0.65 0.01 0.10 0.09 0.01 1.24 0.06 0.18 0.08 0.11 0.12 0.03 0.14 0.03 0.02 0.02 0.30 0.03

2012

0.78 0.01 0.01 0.04 0.01 0.03 0.01 0.01 0.02 0.02 0.05 0.02 0.01 0.05 0.18 0.01 0.01 0.03 0.05 0.02 0.03 0.03 0.03 0.05 0.25 0.00 0.04 0.08 0.02 1.22 0.05 0.01 0.05 0.03 0.10 0.01 0.02 0.02 0.01 0.02 0.06 0.01

2013

0.29 0.00 0.01 n.a. 0.03 0.02 0.00 0.01 0.01 0.01 n.a. 0.01 0.02 0.08 0.20 0.01 0.00 0.03 0.12 0.02 0.22 0.01 0.01 0.04 0.21 0.01 0.01 0.05 0.02 n.a. 0.05 0.02 0.00 0.11 0.13 0.00 0.01 0.02 0.01 0.02 0.01 0.04

2014

H.F. de Mendonça, N.C. Nascimento

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

17

n.a. 0.37 n.a. n.a. 0.06 n.a. n.a. 0.12 n.a. n.a. n.a. n.a. 0.14 n.a. 0.33 0.10 0.08 n.a. 0.98 n.a. 0.40 n.a. 0.29 0.12 n.a. 0.07 0.66 0.09 0.24 n.a. 0.23 n.a. 0.85 n.a. n.a. n.a. 0.37 0.30 0.39 0.11 6.40 0.22

Armenia, Rep.b Australiaa,c Austriaa,c Bangladeshb Belgiuma,c Brazilb Bulgariab Canadaa,c Chileb,c Colombiab Croatiab Czech Republica,c Denmarka,c Estoniaa,c Finlanda,c Francea,c Germanya,c Greecea,c Hungaryb,c Icelanda,c Irelanda,c Israela,c Italya,c Japana,c Latviaa,c Malaysiab Mexicob,c Netherlandsa,c New Zealanda,c Nicaraguab Norwaya,c Polandb,c Portugala,c Romaniab Russiab Sloveniaa,c South Koreaa,c Spaina,c Swedena,c Switzerlanda,c Turkeyb,c United Kingdoma,c

n.a. 0.26 n.a. n.a. 0.09 n.a. n.a. 0.16 n.a. n.a. n.a. n.a. 0.07 n.a. 0.33 0.10 1.66 n.a. 2.25 n.a. 0.24 n.a. 0.31 0.09 n.a. 0.09 0.42 0.10 0.20 n.a. 0.17 n.a. 0.73 n.a. n.a. n.a. 0.50 0.31 0.60 0.20 6.10 0.33

1991

n.a. 0.09 n.a. n.a. 0.07 n.a. n.a. 0.11 n.a. n.a. n.a. n.a. 0.04 n.a. 0.23 0.08 2.33 n.a. 2.56 n.a. 0.21 n.a. 0.28 0.07 n.a. 0.14 0.32 0.12 0.06 n.a. 0.12 n.a. 0.51 n.a. n.a. n.a. 0.55 0.27 0.41 0.20 6.63 0.32

1992

n.a. 0.02 0.11 n.a. 0.06 n.a. n.a. 0.05 n.a. 0.58 n.a. n.a. 0.04 n.a. 0.16 0.06 1.05 n.a. 1.25 n.a. 0.26 n.a. 0.21 0.08 n.a. 0.13 0.16 0.12 0.01 n.a. 0.10 n.a. 0.40 n.a. n.a. n.a. 0.31 0.22 0.15 0.13 6.80 0.15

1993 n.a. 0.02 0.10 n.a. 0.05 n.a. n.a. 0.06 1.06 0.46 n.a. n.a. 0.05 n.a. 0.29 0.04 0.14 n.a. 0.76 n.a. 0.30 n.a. 0.17 0.06 n.a. 0.11 0.10 0.11 0.02 n.a. 0.09 1.45 0.29 n.a. n.a. n.a. 0.33 0.18 0.08 0.07 8.13 0.05

1994 n.a. 0.06 0.07 n.a. 0.04 n.a. n.a. 0.09 0.76 0.37 n.a. n.a. 0.08 n.a. 0.35 0.04 0.07 n.a. 1.05 n.a. 0.61 0.56 0.18 0.02 n.a. 0.10 0.21 0.11 0.05 n.a. 0.10 1.49 0.24 n.a. n.a. n.a. 0.68 0.18 0.08 0.04 11.05 0.04

1995 n.a. 0.09 0.05 n.a. 0.03 n.a. n.a. 0.06 0.49 0.34 n.a. 0.47 0.06 n.a. 0.17 0.04 0.04 n.a. 0.84 n.a. 0.82 0.51 0.19 0.03 n.a. 0.10 0.48 0.09 0.06 n.a. 0.10 1.25 0.31 n.a. n.a. 0.60 0.70 0.15 0.04 0.02 10.82 0.05

1996 n.a. 0.05 0.03 0.27 0.03 n.a. n.a. 0.03 0.35 0.31 0.14 0.36 0.10 n.a. 0.15 0.03 0.03 0.23 0.70 n.a. 0.65 0.35 0.13 0.03 n.a. 0.08 0.71 0.08 0.04 n.a. 0.08 0.92 0.22 n.a. n.a. 0.33 0.43 0.09 0.01 0.02 9.03 0.04

1997 n.a. 0.01 0.03 0.21 0.02 0.19 n.a. 0.04 0.25 0.34 0.20 0.45 0.17 n.a. 0.23 0.02 0.04 0.21 1.09 n.a. 0.81 0.20 0.07 0.04 n.a. 0.11 0.64 0.07 0.02 n.a. 0.07 0.78 0.18 8.88 n.a. 0.28 0.27 0.06 0.01 0.02 9.73 0.03

1998 n.a. 0.01 0.02 0.18 0.01 0.07 n.a. 0.06 0.16 0.63 0.20 0.33 0.11 n.a. 0.19 0.01 0.03 0.18 1.15 n.a. 0.95 0.12 0.05 0.06 n.a. 0.12 0.32 0.07 0.01 n.a. 0.06 0.40 0.18 6.44 3.74 0.21 0.54 0.04 0.01 0.02 8.71 0.02

1999

Note: “a” – advanced economies; “b” – developing economies; and “c” – OECD countries.

1990

Countries

Table A2 Macroeconomic instability (1990–2014).

0.60 0.06 0.03 0.28 0.02 0.11 n.a. 0.08 0.10 0.62 0.16 0.13 0.08 n.a. 0.21 0.01 0.02 0.15 1.35 n.a. 0.75 0.17 0.05 0.04 n.a. 0.06 0.17 0.08 0.01 n.a. 0.08 0.37 0.12 2.82 5.19 0.26 1.66 0.06 0.01 0.03 6.56 0.01

2000 0.54 0.13 0.05 0.39 0.04 0.22 n.a. 0.09 0.09 0.31 0.14 0.17 0.09 0.33 0.20 0.03 0.04 0.09 1.03 0.45 0.84 0.19 0.07 0.05 1.36 0.02 0.12 0.11 0.04 n.a. 0.09 0.40 0.11 2.47 2.36 0.36 1.22 0.09 0.02 0.04 5.10 0.01

2001 0.55 0.12 0.05 0.62 0.04 0.17 n.a. 0.05 0.08 0.15 0.11 0.17 0.05 0.28 0.12 0.03 0.03 0.07 0.27 0.60 0.66 0.12 0.07 0.08 0.74 0.02 0.07 0.13 0.06 1.16 0.08 0.15 0.11 1.74 0.92 0.27 0.24 0.09 0.03 0.02 4.60 0.01

2002 1.60 0.07 0.03 0.63 0.03 0.24 0.23 0.04 0.06 0.04 0.06 0.06 0.04 0.24 0.09 0.03 0.02 0.06 0.27 0.32 0.32 0.11 0.07 0.06 0.48 0.02 0.06 0.12 0.04 0.83 0.07 0.19 0.10 0.89 0.74 0.17 0.25 0.08 0.04 0.02 3.57 0.01

2003 3.12 0.05 0.02 0.51 0.02 0.33 0.28 0.05 0.04 0.10 0.05 0.10 0.02 0.23 0.11 0.04 0.02 0.05 0.46 0.13 0.26 0.06 0.06 0.05 0.49 0.02 0.07 0.09 0.03 0.78 0.06 0.64 0.13 0.52 0.71 0.11 0.43 0.07 0.02 0.01 1.69 0.01

2004 3.27 0.04 0.04 0.50 0.04 0.30 0.31 0.04 0.04 0.18 0.07 0.15 0.02 0.26 0.12 0.04 0.03 0.05 0.52 0.30 0.18 0.13 0.05 0.04 0.43 0.04 0.08 0.08 0.04 1.03 0.05 0.60 0.13 0.32 0.62 0.10 0.44 0.07 0.01 0.02 0.81 0.02

2005 1.71 0.06 0.04 0.40 0.05 0.18 0.31 0.05 0.08 0.26 0.10 0.15 0.03 0.30 0.17 0.03 0.04 0.05 0.55 0.71 0.18 0.20 0.05 0.03 0.40 0.07 0.09 0.08 0.07 1.30 0.05 0.42 0.11 0.32 0.51 0.13 0.41 0.09 0.01 0.03 0.58 0.03

2006 0.73 0.06 0.04 0.51 0.03 0.11 0.43 0.04 0.10 0.54 0.11 0.24 0.03 0.30 0.20 0.03 0.06 0.05 0.61 0.72 0.23 0.24 0.05 0.04 0.27 0.06 0.09 0.07 0.07 1.69 0.05 0.66 0.08 0.52 0.47 0.20 0.37 0.08 0.02 0.05 0.61 0.04

2007 0.95 0.07 0.05 0.87 0.06 0.17 0.67 0.03 0.22 0.45 0.14 0.24 0.04 0.42 0.18 0.04 0.07 0.06 0.42 0.90 0.21 0.27 0.06 0.05 0.27 0.10 0.04 0.09 0.07 1.65 0.06 0.50 0.10 0.52 0.47 0.23 0.35 0.09 0.05 0.06 0.46 0.06

2008 1.40 0.07 0.05 0.77 0.06 0.23 0.59 0.03 0.29 0.17 0.17 0.28 0.11 0.74 0.46 0.05 0.13 0.16 0.93 1.51 0.16 0.13 0.10 0.59 1.11 0.10 0.11 0.10 0.08 1.49 0.09 0.17 0.18 0.24 0.64 0.45 0.31 0.11 0.06 0.03 0.61 0.07

2009 1.63 0.05 0.03 0.35 0.03 0.26 0.25 0.02 0.10 0.14 0.10 0.23 0.13 0.54 0.47 0.03 0.13 0.26 0.85 1.38 0.21 0.12 0.09 0.57 0.96 0.04 0.13 0.10 0.05 1.27 0.11 0.19 0.11 0.20 0.57 0.39 0.51 0.07 0.04 0.02 0.63 0.07

2010 3.09 0.05 0.05 0.07 0.04 0.30 0.12 0.04 0.03 0.11 0.05 0.13 0.07 0.75 0.24 0.03 0.12 0.22 0.52 0.57 0.20 0.15 0.05 0.30 0.98 0.04 0.13 0.10 0.07 1.38 0.13 0.43 0.06 0.30 0.44 0.12 0.87 0.04 0.03 0.02 0.80 0.08

2011 2.54 0.05 0.07 0.03 0.08 0.13 0.11 0.05 0.08 0.10 0.06 0.11 0.05 0.49 0.19 0.04 0.08 0.15 0.21 0.38 0.14 0.12 0.06 0.08 0.71 0.04 0.12 0.11 0.07 1.37 0.10 0.30 0.10 0.27 0.33 0.05 0.28 0.06 0.04 0.02 0.53 0.10

2012

1.72 0.04 0.05 0.04 0.05 0.09 0.06 0.03 0.06 0.03 0.09 0.04 0.03 0.11 0.21 0.03 0.03 0.06 0.08 0.24 0.05 0.07 0.06 0.05 0.32 0.03 0.07 0.11 0.02 1.32 0.07 0.09 0.08 0.18 0.25 0.03 0.07 0.05 0.01 0.02 0.28 0.07

2013

1.22 0.04 0.03 n.a. 0.01 0.09 0.04 0.02 0.06 0.01 n.a. 0.03 0.01 0.06 0.23 0.01 0.02 0.03 0.16 0.15 0.26 0.04 0.02 0.04 0.28 0.04 0.04 0.08 0.01 n.a. 0.06 0.08 0.03 0.30 0.26 0.02 0.05 0.02 0.00 0.02 0.25 0.04

2014

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Table A3 Unit root tests Levin-Lin-Chu, Im-Pesaran-Shin, Fisher-ADF, and Fisher-PP. Levin-Lin-Chu

KAOPEN EGLOB GDPR SOC COR DEM

Im-Pesaran-Shin

ADF-Fisher

PP-Fisher

Satistic

Prob

Satistic

Prob

Satistic

Prob

Satistic

Prob

−10.395 −8.893 −4.334 −3.110 −3.926 −6.407

0.000 0.000 0.000 0.001 0.000 0.000

−16.492 −4.337 −3.147 −2.925 −3.681 −7.086

0.000 0.000 0.001 0.002 0.000 0.000

902.412 151.372 150.017 115.565 135.844 157.370

0.000 0.000 0.000 0.013 0.000 0.000

435.754 206.188 137.209 243.996 94.272 139.015

0.000 0.000 0.000 0.000 0.167 0.000

Note: Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. Automatic lag difference term and bandwidth selection (using the Schwarz criterion for the lag differences, and the NeweywWest method and the Bartlett kernel for the bandwidth). With expection of GDPR that includes trend and intercept, all series include intercept. FER, IT1, IT2, DEV, and CRISIS are dummy variables and thus we do not performed unit root tests for this case.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.najef.2018.10.018.

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