Abstract
Volatility in output growth remains a genuine concern around the globe because of its detrimental effects on growth, poverty and welfare. In the realm of output volatility, the role of FDI and its consistency is particularly important and worth considering. This article examines the role of FDI inflows and specifically the instability in it on output growth volatility using a panel dataset of 141 world economies for the period 1971–2017. The study employs a variety of estimation techniques like pooled ordinary least squares (POLS), LS fixed effects (FE), LS random effects (RE), two stage least squares (2SLS) and generalised methods of moments (GMM). Findings of the study suggest that FDI acts as the volatility reducing factor, whereas uncertainty in it increases output volatility. On the policy front, this study recommends policies that not only encourage FDI inflows but also ensure the inflows to be more consistent and stable. Our results are robust corresponding to various above-mentioned estimation techniques and sensitivity analysis.
Introduction
Output volatility has emerged as a global challenge since many decades. The oil price shock of 1977 and the global financial crisis of 2008 advocate output growth to be highly volatile, subject to unpredictable fluctuations in growth rates. Economists and policymakers have central concerns about instability in output growth because it aggravates the business cycle. For poor and developing economies rise in output volatility has been documented extensively. This rise reflects underdevelopment and the inability to insulate from both internal and exogenous shocks. However, for emerging and developed economies (China and the US) decline in growth volatility has been observed in the last decade. This decline is also not well understood since economists are unable to identify what causes the absence of a gradual trend or a structural break in growth rates.
Frequent fluctuations in output growth signal an uncertain economic environment and increased probability of facing risk. Increasing literature on macroeconomic uncertainty has shown that aggregate instability in output growth is the combination of inconsistent macroeconomic policies, weak institution environment, country-specific characteristics (Chami et al., 2012; Loayza et al., 2007) and various external sources of instability; globalization measures, bilateral trade, exchange and other development assistance agreements, and increased capital inflows due to enhanced economic integration (Nicet-Chenaf & Rougier, 2014). Other than the growth deteriorating effect of output volatility, the vulnerability in growth rates also affects risk-averse economic agents. The direct effect on these agents is in a form of welfare loss (by limiting consumption) as they incurred certain costs against hedging the risk. Moreover, the indirect effect also prevails since output volatility disturbs overall income growth in the economy. Therefore, stability and high growth rates of output have always been central policy objectives of both developed and developing economies (Majeed & Noreen, 2018).
Many studies have investigated several causes of output volatility. These studies have linked output volatility with bank crisis (Hausmann & Fernandez-Arias, 2000), economic growth (Lensink & Morrissey, 2001), welfare (Loayza et al., 2007), FDI (Chung, 2010; Portes, 2007), foreign debt (Chung, 2010), remittance (Craigwell et al., 2010; Chami et al., 2012; Bugamelli & Paterno, 2011), finance (Gertler, 1992; Cermeno et al., 2012) capital flows (Federico et al., 2013; Nicet-Chenaf & Rougier, 2014) and financial development (Hakura, 2009; Majeed & Noreen, 2018). Among other important determinants of output volatility, fiscal and monetary policy (Bugamelli & Paterno, 2011; Chami et al., 2012; Loayza et al., 2007; Majeed & Noreen, 2018; Portes, 2007; Wang, 2017), country’s size (Chami et al., 2012) and institutional quality (Chami et al., 2012; Loayza et al., 2007) are also repeatedly explored.
Output volatility also depends on foreign capital flows and vulnerability in them. Existing literature recognizes that the nature of foreign capital matters in affecting output growth. On the other hand, when these foreign capital inflows are unpredictable and volatile (regardless of their nature) they have a positive impact on output volatility. Little attention has been put to linking FDI, its instability and output volatility together in the literature. Generally, studies extensively focus on the growth aspects of FDI mainly through its future prediction (Sidhu & Dhingra, 2009) or through its impact on macroeconomic variables like industrial production, exchange rates and foreign reserves (Srikanth & Kishore, 2012) however, there are also some studies that investigated the impact of FDI on output volatility. For instance, the study conducted by Portes (2007) analysed the impact of FDI inflows for the US economy only considering that the rapid growth of FDI is solely the reason for the steady growth rate in the economy. Moreover, Coric and Pugh (2013) in their study investigated the impact of the era of the Great Moderation mainly. They assume that the positive impact of FDI inflows on growth volatility is the cause of stable growth rates before the financial crisis. Another study conducted by Nicet-Chenaf and Rougier (2014) mainly focused on the Middle East and North African countries. This study analysed the volatility of source economies only assuming that the volatility in the source country is primarily the reason for FDI inflow. The most recent study by Ajide and Osode (2017) analysed the effect for the ECOWAS region only using a quantile regression approach. However, in the volatility of output growth, the role of FDI along with its instability has not been realised in any of these studies and is missing majorly.
Some studies pointed out that although the ‘bolted down’ argument of FDI is well-established even then unpredictability in FDI adversely affects output growth (Choong et al., 2011). This adverse effect can be viewed in a number of ways. Like, as mentioned by Choong (2011) volatile FDI discourages traditional knowledge transfer and technological spill-over effects, damping new innovations. Moreover, when viewed FDI volatility as a proxy for a country’s risk profile, it is also taken as a proxy for growth-retarding instability. Standard portfolio theory advocates that unpredictability in an investor’s portfolio depends on whether confronted with risk in underline investment (Choong, 2011; Federico et al., 2013). Hence, volatile FDI may be taken as an indication of deterioration in output growth.
This study contributes to the literature by exploring the links among FDI, its uncertainty and output volatility, unlike existing literature that mainly emphasised the role of FDI and its uncertainty on output growth. The most closely related study on FDI and output volatility nexus is conducted by Coric and Pugh (2013). But the study by Coric and Pugh (2013) does not incorporate the inherent volatility in FDI in their analysis. To put it differently, Coric and Pugh (2013) fail to realise that FDI inflows are volatile and inconsistent themself. Therefore, the potential role of FDI volatility is ignored by Coric and Pugh (2013). Additionally, the study is outdated and relatively has a small global panel.
To the best of our knowledge, this study is the first of its kind that empirically determines the effect of FDI and its uncertainty on output volatility using large panel data set of 141 countries from the period 1971 to 2017. Our study contributes to the existing literature in the number of ways: First, it addresses the relationship of FDI and FDI volatility in a single study. Second, this study analyses the impact of volatile FDI on volatility in output growth. The motivation behind this study is that though the FDI role on output volatility is well advocated but what if this capital inflow is volatile and inconsistent itself. What impact on output volatility would be in such circumstances and how FDI behaves when the role of its volatility is observed in an additive form? Third, this study also improves the validity of the findings by employing the number of estimation techniques like fixed effects, random effects, two-stage least squares and generalised methods of moments. Fourth, to the best of our knowledge, this study is an in-depth analysis in the selected field by analysing the nexus through a global panel, development as well as regional perspectives and also by excluding tax haven economies from the sample.
The study aims to test the following two hypotheses: (a) FDI tends to decrease output volatility; and (b) the instability of FDI contributes positively towards output volatility. The study finds that FDI inflows contribute significantly to deteriorating output volatility. The findings of the study imply that unpredictability in the FDI inflows needs to be controlled by adaptation of consistent and long-term policies that encourage inflows of FDI. Therefore, to materialise the volatility diminishing impact of FDI, the unpredictability in the incidence of these flows need to be managed. This management can be done by sticking to tailored policies that make domestic economies a favourable and safe place for foreign investment.
The remaining article is organised as follows: The second section presents the review of relevant literature. The third section depicts data, methodology and statistical analysis. The fourth section discusses the estimated results in detail based on empirical findings. Lastly, the fifth section concludes the study with some policy implications.
Brief Literature Review
Volatility in output growth reflects missing the basic underline objectives of macroeconomic stability through fluctuations in output. These fluctuations are caused either by changes in inputs like alterations in technology or change in derived demand. The literature on output volatility propagates the adverse consequences of it on economic growth and development beyond any doubt. In this regard, an increasing number of studies have been conducted over time, considering the situation to be prevalent in the majority of world economies. Neoclassical theorists in their model of the business cycle explained the initial dynamics of output volatility through uncertainty in borrower’s net worth. Incidence of irregular rise and fall in output have immediate and direct effects on prosperity of general public, particularly when constraints in smooth consumption are present in the economy (Ramey & Ramey, 1991).
Broadly categorising, there are mainly two types of factors affecting output growth volatility: institutional factors prevailing in an economy, exogenous unavoidable shocks. Therefore, among external sources of finance, the role of the global increase in FDI is fundamental in the analysis of output volatility. There are very few studies in the literature that have analysed the impact of FDI on output volatility (see Ajide & Osode, 2017; Coric & Pugh, 2013; Nicet-Chenaf & Rougier, 2014). However, none of them investigated the additional effect of inherent instability in FDI on output volatility. Apart from FDI as one of the determinants of volatility, the contribution of finance in this realm is also particularly important. Portes (2007) is the pioneer one who introduced FDI in the discussion of the financial accelerator framework, developed by Bernanke et al. (1999). According to Portes (2007), FDI is one of the ways of international diversification. This diversification introduces smoothness in economic agent’s earnings through the channel of stability in the capital market. Consequently, output volatility deteriorates. Empirically this concept is initially investigated by Coric and Pugh (2013) concluding stabilising role of FDI on output volatility. Three groups of sample countries are made; one having a trend in growth volatility, another with a break(s), and lastly, the one with no change (neither trend nor breaks) in the series. Moreover, the series is detected to have a downward slope since the authors documented that the output volatility decreases over time in its selected 85 economies. For consistency in the analysis, data from brackets of three time periods (1970–1981, 1982–1993, 1994–2004) are used. The variable of FDI is constructed as FDI assets and liabilities to the ratio of GDP. The authors concluded that there is a negative statistically significant impact of FDI on output volatility. Moreover, it is also established in the study that FDI enhances business cycle synchronisation by the reduction in output fluctuations.
Another study in this realm is conducted by Nicet-Chenaf and Rougier (2014) focused on MENA countries as hosts of European and non-European FDI flows. The authors used the gravity model in their analysis and considered the volatility of both host and source economies. The study concluded that FDI inflows tend to be higher when the source country is having higher volatility. In another world, when there is output volatility in an economy, the economic agents tend to move their investment out of the country by investing in a relatively stable economy. This results in much higher FDI inflows in the host economy than the expected ones leading to stable output growth. The findings of the study support the presence of a substitution effect between local and foreign investment for source (European) cooperations. Moreover, the authors also concluded that the element of risk-averseness is relatively less for non-traditional sources of FDI than the traditional (Western European economies) ones in the host sample economies.
The most recent and influential article by Ajide and Osode (2017) goes ahead in the analysis by categorising 11 ECOWAS countries in the quintiles of high and low volatility exposed economies. The authors used a quantile regression approach on analysing whether FDI has volatility reducing or inducing impact on output volatility. Empirical evidence of the study reveals that FDI plays the role of volatility damper only in the economies experiencing high output volatility. However, this impact is insignificant for relatively stable output growth countries. Therefore, the restrictive damping impact of FDI is re-confirmed by the authors.
So, from the above-mentioned channels, it is quite evident that the role of FDI is crucially important for understanding volatility in output growth. Theoretical approaches documented both positive and negative relationships between FDI and output volatility. However, the negative effect is more prominent as empirical literature for instance Bettin et al. (2012), Coric and Pugh (2013), Nicet-Chenaf and Rougier (2014) and Ajide and Osode (2017)—all highlighted the damping impact. On the other hand, a positive channel is also expected to prevail as an increase in diversification of agent’s worth also disturbs business cycle synchronisation (Bouoiyour et al., 2014). This increase in turn affects the diversifications and output stability consequently.
As far as volatility in FDI itself is concerned, the positive impact is advocated through the channel of growth retardation (Lensink & Morrissey, 2001). The volatility of FDI introduces uncertainty in the costs of R&D which resultantly discourages incentives to innovate. As the incentives lower down, investor’s interest undermines, decreasing investment and output growth both. The authors also labelled FDI volatility as representative of economic or political uncertainty. So, an increase in any of these uncertainties reflects enhanced unpredictability in FDI inflows and consequently a rise in output volatility. The opposite of FDI volatility is continuity in FDI flows and its channel of impacting output uncertainty is documented above through the number of studies like Coric and Pugh (2013), Nicet-Chenaf and Rougier (2014) and Ajide and Osode (2017).
So, summing up the literature on the focused variables we can say that the role of FDI as foreign capital inflows and its volatility is crucially important to analyse. This study contributes to the existing literature by analysing FDI as well as its volatility on output growth. Moreover, extensive econometric techniques are also employed so that availability of result accuracy is enhanced.
Methodology
The decision about the inflow of FDI in any host economy is strongly dependent on internal factors like economic environment, financial status, law and order and political condition of the country. Hence, the monetary, fiscal and real sectors along with sufficient development of the financial sector are important relatable factors to look upon (Majeed & Noreen, 2018; Mekonnon, 2017; Shah, 2016). In this article standard output volatility model is computed including FDI and its volatility as focused variables. This study employed data of 217 world economies, however, due to missing values only 141 countries (reported in Table A2 in the Appendix) are used in the analysis, over the period of 1971–2017. Data is acquired from the World Development Indicators database, however volatility is computed by five years moving standard deviation method. The detail of variables description and data source is provided in Appendix: Table A1.
Theoretical and empirical evidence from the literature mostly focused on the development of the financial sector as a key determinant of output volatility. FDI is viewed as linking it with finance and growth. Different measures of FDI are used in the literature such as annual GDP level, the ratio of FDI assets and liabilities to GDP and the rate of the flows. For the computation of volatility ARCH/GARCH, moving standard deviation and Hodrick–Prescott filter are commonly used. Among other determinants, persistent country-specific characteristics, exogenous shocks, policy distortions and institutional environment are looked upon for volatility of output growth.
In this study, we focus on FDI inflows percentage of GDP as a fundamental determinant of output volatility. There are contrasting two strands of views regarding the impact of FDI on the fluctuations. It is usually viewed that FDI inflows remove credit constraints in the economy during the Great Moderation era by decreasing randomness in output growth (Coric & Pugh, 2013). On the contrary, Nicet-Chenaf and Rougier (2014) pointed out that FDI inflows come at the cost of exposing external shocks from source to the host economy which in turn impose additional output volatility. Therefore, the role of FDI on output volatility is inconclusive in theoretical literature while empirical evidence heightened the negative impact predominantly.
In the light of the above discussion, it is evident that few studies are present on linking FDI with output volatility, while more on FDI and output growth. However, there are no attempts on linking the three (FDI, its uncertainty and output volatility) together through additive effect. This study fills the gap by including FDI and its uncertainty in the standard model of output volatility.
Model Specification
Keeping in view the literature on output volatility, we have used the most common measure of volatility, that is, the standard deviation of real per capita GDP (see Adeniyi et al., 2017; Ahamada & Coulibaly, 2011; Ajide et al., 2015; Chami et al., 2012; Coulibaly, 2015; Majeed & Noreen, 2018). We partially adopted the model from a closely related study to our analysis: Coric and Pugh (2013). The models estimated in our study are specified as follow.
For developing economies, internal in addition to external shocks are considered as volatility triggering in GDP growth. The effect of these shocks is viewed as changes in entrepreneurial and business activities. These changes also alter employment and trend in the country’s business cycle. Lensink and Morrissey (2001) specified that one of the outcomes of such inter and intra alia shocks are huge variations in the flows of FDI. Foreign investors repeatedly change their decision to invest by looking at the environment in the host economy for the fulfilment of their sentiments. So, considering all these aspects it is worth establishing that FDI inflows are inherently volatile and should be included in the analysis of output volatility whenever FDI is taken into account. The following specification introduces FDI volatility into the model as a focused variable along with FDI and is predicted to impact output volatility positively.
Where LOV is the log of output growth volatility measured by the standard deviation of real per capita GDP, constant 2010 US dollars. FDI is foreign direct investment, net inflows as a percentage of GDP, whereas VFDI is the representation of volatility in FDI. FD characterises financial development proxied by broad money growth annual percentage. LVINF is the log of volatility in inflation proxies for monetary sector volatility. Similarly, VTOT represents the volatility in terms of trade representing real sector uncertainty. Moreover, trade as a percentage of GDP and population growth as an annual percentage is also included in the model as crucial country-specific features. Additionally,μ is the error term, and i and t signify country and period, respectively. Pooled least square is employed in the baseline analysis. However, to cater the issue of endogeneity 2SLS and system GMM are also employed by using internal instrumental variables. Moreover, fixed and random effects are also employed.
Descriptive Analysis
Table 1 reports the descriptive statistics. For output volatility, the maximum value is 11,706.21 for UAE while the minimum value is 1.440 for Burundi. Therefore, in our study sample, UAE is the most output volatile economy and Burundi is the least. The maximum value of FDI inflows is 161.823 for the Cayman Islands and the minimum value is –55.242 for Suriname. Similarly, the largest value for FD is for Belarus as the value comes out to be 1,499,712 and the minimum value is –99.984 for Micronesia, Fed. As far as for VFDI the maximum value is 70.360 for the country Luxembourg, whereas the minimum value is 0.003 for Japan. For inflation and term of trade volatility, the maximum value is 9,803.13 for Congo, Dem. Rep. and 443.344 for Sierra Leone respectively. Whereas the minimum values for both of these volatilities are 0.11 for New Caledonia and 0.011 for Tuvalu, correspondingly. The population takes the maximum value of 16.331 for the UAE and the minimum value of –6.184 for Latvia.
Descriptive Statistics.
Correlation Matrix
Table 2 shows the results of a correlation matrix. All variables have a positive correlation with output volatility except FD, VINF and VTOT. Moreover, TRD has the highest correlation of 0.25, while the weakest correlation of 0.002 exists for FD.
Correlation Matrix.
Results of Granger Causality Test
Granger Causality test given by Granger (1969) is employed for the determination of the nature of the causal relationship between focused variables. Table 3 provide evidence that bidirectional causality exists between FDI and its volatility as both Granger causes each other. The result of the test in detail is reported as follows.
Results of Penal Granger Causality Test.
Empirical Result
Pooled OLS Results
Table 4 reports the results of Models 1 and 2 using pooled OLS technique. The results show that FDI has a negative sign signifying that an increase in FDI inflows mitigates output volatility. The effect of the inflows is relatively stronger (0.0061) when only FDI impact is taken into account. This relatively stronger impact gets weak (0.009) when the additive impact of FDI volatility is simultaneously observed. This indicates that higher FDI is associated high inflow of foreign capital. Resultantly, investment increases inducting certainty in output growth. FDI decreases output volatility by increasing capital stock, thereby reducing the cost of innovation and contributing towards technological change through technological diffusion (Lensink & Morrissey, 2001).
According to the endogenous growth literature, this technological change may occur in two possible ways. It may happen that technological diffusion results in an expansion in the variety of products through capital deepening. This deepening resultantly brings out an increased number of different varieties of intermediate goods. Consequently, the chance of variability in output growth decreases. The other strand of endogenous theories viewed technological progress through improvement in the quality of products. Here, the unpredictability in output growth is diminished by enhancing the quality of products thereby reaching high on quality ladders.
The negative impact of FDI on output volatility mainly channel through aggregate production. FDI enables investors to diversify their net worth resultantly to much riskiness in external finance premium lower down. This avoidance of risk translates to less volatility in the economy’s aggregate production due to which stability in output growth is ensured. Another channel through which FDI diminishes the strength of output volatility is through the degree of openness. The more open the economy, the greater would be the chance that part of the economic agent’s net worth is dependent on domestic GDP and the changes in it. So, in this situation, investors are effortless regarding their production decision. Consequently, producing the expected output and near to no variability in aggregate output production.
The volatility of FDI shows that a 1% increase in FDI volatility increases growth uncertainty by 0.012%. The literature pointed the volatility of FDI inflows as a reflection of political or economic uncertainty in the host economy (Lensink & Morrissey, 2001). Economic uncertainty is an important determinant of growth volatility affecting the productivity of investment. Moreover, the uncertainty also reflects the tendency of vulnerability to shocks. This vulnerability resultantly often has immediate effects through reducing income and constraining the economy to reach steady growth rates (Lensink & Morrissey, 2001).
The positive impact of FDI uncertainty on output volatility spread through the events of shocks. These shocks mostly constitute real and monetary sector volatility along with natural calamities. Terms of trade volatility and volatility in capital flows are commonly proxies for shocks related to the real and monetary sectors, respectively. Thus, a sudden change in the volume of FDI inflow channels through real and monetary sector shock representing unfavourable characteristics of an economy.
FDI volatility increases instability in output growth due to its growth retarding effect (Lensink & Morrissey, 2001). This instability increases expected costs of innovation which in turn makes the economy appear least attractive to foreign investors. To put it differently, FDI volatility induces instability in the expected cost of innovation. Resultantly, foreign investors get discouraged reflecting the economy unsuited for profitable investment ventures (Lensink & Morrissey, 2001).
The estimate shows a 1% increase in the lag of OV causes current output volatility to increase by 0.001% in the estimated Models 1 and 2. The results support the view that the previous year’s volatility in output growth causes today’s volatility to increase by 0.001%. It means that the volatility of output growth has a lag effect. Here, the magnitude of the effect is small but it is statistically significant. In other words, previous periods’ output volatility gets translated into the current period as one of the factors determining frequent fluctuations in output growth through the specialisation of different sectors in the economy. This finding is also in line with the results reported by Coulibaly (2009). These studies have also shown that an increase in the previous period’s volatility causes the current output growth to increase significantly through the lag effect.
The role of financial development is also focused upon since it is one of the important determinants of output volatility. The result for financial development is robust throughout the analysis with high statistical significance. Results in Table 4 shows that an increase in financial development causes output growth volatility to increase by 0.0003% and 0.0004%, respectively for equation 1 and 2. This volatility enhancing role is in conformation with the expectations. With an increase in financial development, the country’s exposure to adverse dynamic shocks also increases, which in turn raises output volatility. In other words, improvement in the county’s financial institutions leads the economy towards more vulnerable shocks. These shocks mostly channel through institutional framework, thereby enhancing output volatility.
Output Volatility: Pooled OLS Estimates of FDI and its Volatility.
Values in parenthesis represent probability values.
If financial markets are imperfect, the vulnerability towards positive as well as negative shocks gets intensified due to the inefficient absorptive capacity of the sector. The same goes for enhanced financial development. With a better developed financial market, the channels of international integration improved. This improvement increased the probability of getting affected by external shocks which resultantly elevate output volatility (Beck et al., 2000).
The parameter estimate also reveals a statistically significant impact of inflation uncertainty on output volatility. The coefficient of the log of inflation uncertainty shows that output volatility decreases by 0.122% and 0.124% in response to a 1% increase in inflation volatility. This is because a certain level of inflation is required for economic growth, so volatility in it represents a suitable environment to lower down frequent fluctuations in output. World economies that are using monetary policy as stabilising tool experience comparatively less frequent growth volatility. The association between inflation and output volatility depends on the nature of the shock. Like in the case when the economy is experiencing aggregate supply shock, increased abrupt fluctuations in inflation would be required to decline growth volatility. Whereas aggregate demand shocks move both of these volatilities in a similar direction. Therefore, it is the nature of shock on the response of which inflation uncertainty increases or decreases the volatility of output growth. Existing empirical studies have also proven that a rise in inflation uncertainty is associated with a decrease in output volatility (Bugamelli & Paterno, 2011; Coulibaly, 2009; Majeed & Noreen, 2018).
The coefficient of terms of trade volatility is also negative and statistically significant with the coefficient of 0.001 and 0.002 in Models 1 and 2, respectively. The impact is statistically significant at a 5% level of significance. This finding is in accordance with the results drawn by Coulibaly (2009).
Trade is a crucial factor affecting output volatility since it signals the extent of integration among economies. The coefficient is positive and statistically significant at 1% level of significance. In both the models, a 1% increase in trade causes output growth volatility to be increased by 0.004%. The results support the view that increased trade reflects the economy to be more internationally integrated by specialisation in specific products of comparative advantage only. Hence, the incidence of global commodity shocks gets easily transferred to large integrated economies connected through trade. Consequently, output growth becomes more volatile thereby raising output volatility. In other words, international shocks get transmitted easily with more integrated trade. This finding is in confirmation with the existing literature like Adeniyi et al. (2017).
Economy size represented by population is also another important determinant of output volatility. The population tends to lower down frequent fluctuations in output growth. The finding reveals that a higher population incline to decrease output volatility. The large population reflects a great degree of feasible diversification. This diversification consequently diversifies output production, reducing the volatility in output growth. Hence, population negatively contributes to output volatility through huge endowment and resource base resultantly help sustain growth. This result is in expectation to the model and also reported in the study, such as Furceri and Ribeiro (2008).
Moreover, the results show that the R 2 value comes out to be 0.54 indicating that around 55% variation in output volatility has been explained by explanatory variables. The results of Breusch–Pagan–Godfrey show that there is no problem of heteroskedasticity. Moreover, the Ramsey Reset test concludes that the functional form is correctly specified as the p-values lie under the significance level.
Results of Fixed and Random Effects
One of the drawbacks of pooled OLS is that it does not take into account significant country and temporal effects. Further, it is also based on restrictive assumptions. Thus, the problem of unobserved country-specific effects is addressed with fixed and random effects models. Table 5 illustrates the empirical estimates of fixed and random effects for both models. The results show that FDI tends to diminish output volatility, whereas FDI volatility inclines to augment it. Overall, the study results are not sensitive to fixed and random effects and the variable signs are also in accordance with the expectations. It is worth pointing out that the volatility effect of both FDI, as well as its volatility, turns out to be comparatively larger in fixed and random effects. This larger coefficient indicates that pooled OLS underestimated the effect of FDI and FDI uncertainty on output volatility. For choosing between fixed and random effects Hausman test is applied assuming that the fixed effects model is appropriate according to the null hypothesis. In both models, p-value is less than .1 suggesting that the fixed effects model is appropriate and the preferred one.
Output Growth Volatility: Fixed and Random Effects Estimates of FDI and its Volatility.
Values in parenthesis represent probability values.
Two Stage Least Squares Results
In an attempt to solve the problem of endogeneity in FDI flows, the commonly used method is the 2SLS estimation technique using instrumental variables. In the absence of heteroskedasticity in the data, the use of this method is applicable and most common. Table 6 shows the result of Models 1 and 2 employing two-stage least square. After the use of endogenous instruments, FDI is still statistically significant and has a detrimental effect on output volatility. The impact is relatively larger compared to OLS as the result suggests that 1% increases in FDI inflows cause output volatility to decrease by 0.007%. The significance level is 1%. Similarly, the positive impact of FDI volatility here also remains unchanged both in magnitude and sign. Concerning control variables, the sign of all remains consistent with FE and RE results except for terms of trade volatility, which is statistically insignificant, showing no impact on output volatility. Financial development, trade and lag effect of output volatility hinder stability in output growth for FDI receiving economies. However, inflation volatility and population aid the economy since they decrease output volatility. The coefficient of inflation volatility and population advocates, 1% increases in them causes uncertainty in output to decrease by 0.161% and 0.469% in Model 1 and 0.172% and 0.568% in Model 2, respectively. The adjusted R-square values also come out to be quite similar for both the models as they are 0.545 and 0.540. The results in detail are reported as follows.
Output Volatility: 2SLS Estimates of FDI and its Volatility.
Values in parenthesis represent probability values.
GMM Results
Like 2SLS, GMM is also used to cater to the problem of endogeneity. But according to Greene (1997), GMM yield consistent and efficient estimates in the presence of arbitrary heteroskedasticity. So, considering the possibility of arbitrary heteroskedasticity unlike no heteroskedasticity in 2SLS, system GMM is also employed in our analysis for increasing the authenticity of the results. The system GMM estimates have also been obtained for Models 1 and 2. Both results lie in close proximity to the results obtained from other techniques. The findings of system GMM are reported in Table 7.The estimates FDI suggests that if the inflows increased by 1%, this causes output volatility to diminish by 0.007. Moreover, when the role of FDI volatility is analysed along with FDI, this diminishing role further gets intensified by 0.044%. The effect of FDI volatility tends to be even stronger relative to the 2SLS estimate. The results show that a 1% increase in inconsistent FDI inflows augments output volatility by 0.035%. Unlike 2SLS, here among control variables, proxies for real sector volatility is statistically significant with a slight change in the magnitude to 0.002. Other control variables like trade suggest that 1% increase in trade causes uncertainty in output growth to rise by 0.004% and 0.006%, corresponding to Models 1 and 2. The adjusted R-square for the models is 0.533 and 0.515.
Output Volatility: System GMM Estimates of FDI and its Volatility.
Values in parenthesis represent probability values.
Sensitivity Analysis
The sensitivity analysis is conducted for checking the robustness of our empirical findings. Additional three variables, namely, government consumption expenditure, gross fixed capital formation and official exchange rate have been used in the analysis. The results are reported in Table 8 showing that the impact of FDI remains the same and highly significant across all three sensitivity variables. Likewise, the augmented impact of FDI uncertainty on output volatility also remains unchanged with the addition of all selected sensitivity variables. Overall, the results of sensitivity analysis advocate that the focused variables of the study are robust and not sensitive to further control variables.
Sensitivity Analysis of Variables.
Whereas value in parenthesis represents probability value.
Analysis Concerning Development Status
Table 9 reports reinvestigation of FDI and output volatility nexus for development categorisation of countries into developing and developed economies. The results show that FDI reduces output volatility in developing countries, however, for developed nations it enhances the volatility in output growth. The positive impact of FDI on output volatility for developed economies is justified on the ground that since the countries are usually on equilibrium growth paths so the inflows act as de-stabiliser and increase output volatility in their economies. This finding is consistent with the findings of Hwang et al. (2013) and Mirdala et al. (2015) who also reported FDI as a trigger to output volatility. As FDI is itself volatile so the inflows augment volatility in output growth (Hwang et al., 2013). Similarly, the inflows of FDI also come with increased exposure to external shocks which in turn raise output volatility in the host economy (Mirdala et al., 2015). Concerning the effect of FDI volatility on the uncertainty in output growth, the impact is the same (positive) in both developing and developed countries. Thus, it is re-affirmed that the volatility of FDI augments fluctuations in the output growth of a country regardless of its development status.
Estimates of Developing and Developed Countries.
Values in parenthesis represent probability values.
Analysis of ASEAN and East Asian Regions
The examination of FDI and output volatility nexus from a regional perspective, ASEAN and East Asian regions are incorporated in our study analysis. This investigation helps identify and implement region-specific policies for better management of output volatility. Table 10 reports the estimates obtained for the ASEAN region. The analysis of ASEAN countries is important since the economies (such as Singapore and Indonesia) of this region are one of the highest recipients of FDI inflows in the last few years. The results illustrated in Table 10 indicate that FDI inflows are output volatility diminishing. However, the uncertainty in FDI augments the growth volatility of ASEAN countries.
Analysis of ASEAN Region.
Values in parenthesis represent probability values.
Table 11 reports the results for the East Asian region. The findings indicate that an increase in FDI inflows reduces output volatility of East Asian countries. On contrary, the uncertainty in the FDI augments growth volatility in the region. These findings are consistent with our earlier findings of the ASEAN region and global panel analysis.
Analysis of East Asian Region.
Values in parenthesis represent probability values.
Analysis without Tax Haven Economies
Lastly, an analysis is conducted globally with the exclusion of tax haven economies from the panel. This analysis is particularly crucial to investigate since the the inflow of FDI is usually abundant in such countries where there is no or low-income tax liability on investors and businesses. Therefore, with the aim of investigating the nexus for non-tax heaven countries, this analysis is conducted. The estimates of Table 12 show that even with the exclusion of tax heaven economies from the global panel, the study findings are consistent. The analysis shows that FDI tends to reduce output volatility, whereas volatility in it tends to enhance uncertainty in output indicating that though FDI inflows help diminish global output volatility, the inherent fluctuations in it augment overall instability in output growth.
Analysis Excluding Tax Haven Countries.
Values in parenthesis represent probability values.
Conclusion and Policy Recommendations
Among foreign capital flows FDI has been increasing noticeably in the last few decades. It is marked as the second most abundant form of capital flows all over the world. FDI inflows are believed to have spillover effects on the host country’s economy. This effect is usually induced by technological innovation and development. Subsequently, enhanced output production and economic growth take place. However, the highly unpredictable nature of FDI due to variability in foreign investor decisions makes it reckless to depend upon. This variability is generated as a result of the decision whether to make an investment stance or not or delay it and wait and watch. Consequently, the impact of inconsistent FDI inflows on output volatility remains inconclusive and questionable. In this era of the global economic network, FDI tends to have a significant impact on output through financial markets. FDI when flow through the channels of public monetary entities, enhances capital availability in the economy. This availability resultantly improves the investment profile of the country, increases output production and ultimately output growth. Similarly, the incidence of volatility in FDI reverses its own negative impact on output fluctuations. The unpredictability in FDI leads to de-stability and vagueness in the decision about what production outcome would be thereby translating volatility in output growth. To put it differently, if irregularity in FDI is there, it gets transmitted inherently with the inflows resultantly augmenting output volatility. Therefore, this study has attempted to examine the impact of FDI and its uncertainty on output growth volatility for 141 economies over the period 1971–2017.
As output growth volatility is unobservable, so the very initial step constitutes computation of frequent fluctuations in output growth. In this regard, the uncertainty has been estimated by using five years moving standard deviation of per capita GDP growth. Similarly, the volatility in FDI is computed from FDI inflows. Followed by this pooled OLS, FE, RE, 2SLS, GMM techniques have been employed. Our findings suggest that FDI diminishes output volatility. However, this diminishing effect gets reversed when the inflows are volatile and inconsistent, thereby augmenting volatility in output growth. Moreover, among control variables previous year’s output volatility, financial development, trade and volatility in the real sector also significantly cause output volatility to be magnified. On the other hand, the volatility of inflation and population causes the volatility to be diminished. The positive impact of broad money growth (proxy of financial development) on output volatility has been justified earlier on the basis of theoretical grounds from existing literature.
The role of unpredictability in FDI on output growth volatility has been empirically investigated in Model 2 by introducing FDI volatility in the model. Our findings evident that the volatility is proved to be significant output volatility exaggerating determinant. The results show that frequent fluctuations in FDI when taken in additive form augment output volatility. This impact is opposite and simultaneous to FDI impact, where literature is missing out on this integral part empirically. Similarly, not only financial development, volatility in terms of trade, lag of output volatility itself and trade also enhances output uncertainty significantly. The behaviour of these variables remains the same even when the volatility in FDI is accounted for along with FDI itself. Likewise, the role of population growth is also consistently diminishing on output volatility in both the models of our study.
There are certain limitations of this study. For instance, the interactive term of FDI and financial development is not incorporated considering the underpinning of the financial accelerator framework. Future research on the subject matter could be made by using external IVs in the analysis so that more accurate conclusions could be drawn and included in this dominion of research.
Based on the findings of our empirical analysis, this study advocates policy measures regarding encouragement as well as stability in FDI inflows. In other words, such measures should be taken that encourage not only inward flows of FDI but also ensure stability in them. Doing so ensures that the detrimental impact of FDI inflows on output volatility can be materialised and does not get wasted. As the results have shown that not only does financial development positively affect uncertainty in output, rather trade and volatility in terms of trade also proved to have an encouraging impact on output volatility. So, these points justify that openness to the outside world has encouraging effects on output volatility. Therefore, essentially required trade openness has been mandatory in this realm. This may include opening the economy to considerable long-term less risky projects only and tariffs on trade so that external factors may have a controlled and limited impact on output volatility. Moreover, the government’s own involvement in various development and non-development projects should be focused upon so that crowding out of uncertain FDI gets discouraged. To conclude it can be said that policy measures regarding the encouragement of FDI as well as stability and smoothness in it are recommended in our study.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication of this article.