Introduction
In the last few decades both the developed and developing nations have gained significant economic benefits from trade liberalization and outward-oriented policies. Specially, the remarkable performance from the Asian Tiger economies has brought attention towards the outward-oriented policies over inward-oriented policies. Other Asian nations like India and Bangladesh have also become successful in achieving significant growth after adopting more liberal stance. Inward oriented policies were prevailing economic strategy during the 1950’s and 1960’s. But outward oriented policies are gaining importance as the globalized world is propelling countries to go outward for economic growth. Further, international organizations like World Bank, IMF and WTO are also encouraging their member states to adopt more liberal and open trade positions.
A recent paper by Huseyin Erpek (2014) investigated into the relationship between trade and income analyzing international trade and income data of eighteen countries in Western Asia region for a period of 1950 to 2010. Using panel data regression the analysis shows that there is a positive relationship between trade and income. The paper also finds that, 1% increase in international trade causes increase in income of the countries by 1.5% on average. I propose to replicate this study with 19 countries of ASEAN and SAFTA regions. Though several studies (Michaely, 1977 ; Helpman, 1988 ; Rodrik, 1995 ) show that there are exact relationship between trade and other variables, the author only examines the relationship between trade and income.
In this paper I propose to replicate the same panel data regression methodology used by Erpek to assess whether the same positive relationship exists between ASEAN and SAFTA. Established in 1992, the Association of South-East Asian Nations (ASEAN), is gaining considerable importance as a trade bloc. After the European Union and the North American Free Trade Agreement (NAFTA), ASEAN is now the third largest trading bloc in the world. With 11 countries (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam) from the East Asia and Pacific region ASEAN is home to some 600 million people. As of 2013, it has a combined GDP of $2.4 trillion. On the other hand, operational since 2004, the South Asian Free Trade Area (SAFTA) is yet to become a full-fledged trading bloc. Eight South Asian countries (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka) agreed to develop this trade bloc with an aim to reduce custom duties of all traded goods to zero by 2016. However, after 10 years of its establishment, the South Asian nations see trade among them making up a meager five per cent of their total transactions.
I also propose to add another independent variable Foreign Direct Investment (FDI) in my replication. Past studies show that FDI and trade have a positive impact on economic growth. But the size of such impact may vary across countries depending on the level of domestic investment, human capital, macroeconomic stability, infrastructure, and trade policies. However, for a developing country FDI is considered as an important catalyst for economic growth. FDI can stimulate domestic investment, thereby triggering more economic activities which in turn raise the national income in a developing country. According to UNCDAT, in 2015 the total amount of FDI inflow into ASEAN countries were $125.688 billion at current price, whereas total amount of FDI inflow into SAFTA were $48.435 billion at current price . In the last two decades, ASEAN member countries have been pursuing intra-regional market liberalization in order to promote the region as a competitive production platform. Although FDI flows to ASEAN countries suffered after the Asian Crisis but have picked up since 2005. Plummer and Cheong (2009) find that ASEAN countries suffered a fall in total FDI but experienced an increase in intra-regional FDI after 1998. There are ongoing debates that whether the inflows of FDI to developed host countries affect their economies and what is the interaction between FDI and growth. Moudatsou and Kyrkilis (2011) find that economic growth of the host country motivates inward FDI in both developed and developing economies. However they also find that, relation between growth and FDI in case of ASEAN is path dependent and country-specific.
The replication paper hypothesizes that, there is a positive relation between trade and income in ASEAN and SAFTA regions. Further, the paper also expects that there is a positive relationship between FDI and income in these two regions as well. For the study, the paper utilizes panel data regression on the data for a period of 2001-2015.
Literature review
During the last 30 years, there had been a debate on the links and causality between trade openness, growth and income distribution which is still open today (Rodriguez and Rodrik, 2001) . Empirical evidence tends to show that in the long run more outward-oriented countries register higher economic growth (Sachs and Warner, 1995 ; Edwards, 1998 ; Frankel and Romer, 1999 ; Dollar and Kraay, 2004 ; Lee et al., 2004). More recently, using broader databases and cross-section or panel-data estimations, Freund and Bolaky (2008) and Chang et al. (2009) show that trade openness has a positive impact on income and that this positive relationship is enhanced by complementary policies.
Such studies have supported the notion that developing countries need to change trade policies in order to enhance standard of living and overall economic prospect, although there are debates about appropriate methodological approaches. Theories suggest that whether and to what extent countries benefit from trades depend on various country-specific factors, degree of factor mobility between sectors, type of specialization, and the ability of a country to invest in physical or human capital or adopt foreign technology. So, the effect of trade on income must be different or heterogeneous across countries (Herzer, 2013) . In this regard, some scholars argue that panel data regression is the appropriate approach for measure the relationship between trade openness and growth. Harrison (1996) argues that the strength of this relationship depends on whether the analysis is carried out by panel data or cross-sectional data. Using the panel data framework Greenaway (2002) finds that trade liberalization appeared to affect economic growth in developing countries. However, using the same panel data methodology Ugurlu (2010) reports a weak negative relationship between these two variables. Dollar and Kraay (2003) used instrumental variable regression and found that cross country variation in institution and trade were not significant on long run growth. Frankel and Romer (1999) addressed the endogeneity of trade openness by using gravity model and find a positive and significant relationship between trade openness and per capita GDP. However, Rodriguez and Rodrik (2000) identify flaws in the instrumental strategy used by Frankel and Romer (1999) and argue that their results are affected by non-trade effects of geography on income. The authors also argue that, previous studies on the positive relationship between trade openness and income suffer at least two serious shortcomings that make their results to be questioned: the way trade openness is measured and the retained estimation methods.
Nevertheless, cross-country regression or homogenous panel data models are widely used in studying relationship between trade and income. Impact of trade on income is due to many country-specific factors that usually remained uncontrolled in a cross-country analysis, which in turn creates omitted variable bias. With panel data, it is possible to control for some types of omitted variables even without observing them, by observing changes in the dependent variable over time. However, traditional homogeneous panel estimators, such as the one used in the existing trade-income literature, produce inconsistent and potentially misleading estimates of the average values of the parameters in dynamic panel data models when the slope coefficients are heterogeneous.
The debate is still evident in recent studies and has remained inconclusive. Dava (2012) concludes that the argument on the trade-growth relationship is still unsettled. Further, Ulasan (2012) mentions that the debate is controversial and available literatures have not provided any convincing and robust evidence. The author also argues that, theory provides very little evidence and therefore the relationship between trade openness and growth is more theoretical than empirical.
On the other hand, based on the neoclassical approach Solow (1957) shows that FDI only affects income and have no impact on growth. The author based this remark on the idea that long-run growth can only arise because of technological progress and/or population growth, both are considered as exogenous. Therefore, according to neoclassical models of economic growth, FDI will only have effect on growth if it affects technology positively and permanently. However, a few recent endogenous growth models imply that FDI can affect growth endogenously if it generates increasing returns in production via externalities and spillover effects (Makki et al., 2004) .
Applying endogenous growth theory, Balasubramanyam et al. (1996) show that effect of FDI on growth is stronger in countries that pursued an outward orientation policy. Their analysis finds that the elasticity of output with respect to FDI exceeds that of domestic capital investment, which implies that FDI is the driving force in the growth process. Borensztein et al. (1998) also utilize the same endogenous growth model to investigate the role of FDI in promoting economic growth. Analyzing FDI flows from industrial countries to 69 developing countries during 1970-1989, they find that FDI is an important vehicle of technology transfer, contributing more to economic growth than domestic investment. They make a case for a minimum threshold stock of human capital necessary to absorb foreign technologies efficiently.
The relationship among FDI, trade and economic growth is still an unresolved issue. Past studies have either examined the impact of trade and FDI on economic growth (Borensztein et al., 1998; Balasubramanyam, 1996) or analyzed the effects of economic growth on FDI (Barrel and Pain, 1996 ; Lipsey, 2002 ). Positive effect of FDI and trade on economic growth may interprets that countries expecting faster growth and following open-trade policies will pursue more FDI. Therefore, it is, important to understand the interrelationships among FDI, trade, and economic growth. Since theory is unclear, this issue has been the subject of empirical studies.
Data and Model specification
In the original paper Erpek uses only two variables, trade and income. In estimating the functional relationship between these two variables the author uses the following model
ln〖(Y_i)= α+ βln(T_i)+ε_i 〗
Where,
Y_i: Total income
T_i: International trade, and
ε_i: The other influences on income.
In my replication study the model remains the same, only a new variable FDI enters the equation. So, the new equation is:
ln〖(Y_i )= α+ β_1 ln(T_i)+〖β_2 ln(F_i )+ ε〗_i 〗
Where,
F_i: FDI inflow
The original study examines the model with two options, Ordinary Least Squares (OLS) regression and Fixed Effects (FE) regressions. All variables are in natural logarithm form. On an empirical level, evidence suggests that trade and FDI are positively related with income in developing countries. For this reason, the selected countries in the replication study are all considered as developing economies. Therefore, using the same methodology in this paper, I aim to find the evidence of the same hypothesis in ASEAN and SAFTA regions, together and separately.
The dependent variable in this study is the yearly GNI per capita data measured in Atlas method. One of the independent variable FDI is measured as yearly net FDI inflow on Balance of Payment (BoP) basis. The other independent variable total trade is calculated adding yearly export and import data. Both the export and import data include goods and services. All data are taken from a secondary source World Bank for a period of 2001 to 2015. The base currency used in this model is US dollar at current price. Additionally, all the series are transformed into natural log form. Log transformation can reduce the problem of heteroscedasticity because it compresses the scale in which the variables are measured, thereby reducing a tenfold difference between two values to a twofold difference (Gujarati 1995) . The following panel data series are analyzed in this study:
1. LNINC = Log of GNI per capita
2. LNTRADE = Log of Total trade (Export and Import)
3. LNFDI = Log of FDI (Foreign Direct Investment)
The prefix 'LN' stands for the natural logarithm of the concerned series and all the econometric estimations in this paper have been carried out using Stata.
Replication
As the study is dealing with panel data of 19 countries of ASEAN and SAFTA regions, at first it is to be decided that whether Fixed Effect or Random Effect model should be used in estimating the data. For this purpose the study applies the Hausman test which is shown as follows
—- Coefficients —-
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fixed random Difference S.E.
————-+—————————————————————-
lntrade | .2793863 .2745999 .0047864 .0018606
lnfdi | .2024539 .2005171 .0019367 .
——————————————————————————
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 6.36
Prob>chi2 = 0.0416
(V_b-V_B is not positive definite)
The Hausman test shows a p-value (0.0416) which is less than 0.05. So, we reject the null hypothesis and should use Fixed Effect for the study. A comparative analysis of using Ordinary Least Squares (OLS) and Fixed Effect regression models is shown in the table 1. The table also presents results applying robustness of the two models.
Table 1: OLS and Fixed Effect regression results for ASEAN and SAFTA countries
—————————————————————————-
(1) (2) (3) (4)
OLS19 OLS19R FIXED19 FIXED19R
—————————————————————————-
lntrade 0.184*** 0.184*** 0.279*** 0.279*
(3.46) (5.40) (11.31) (2.27)
lnfdi 0.0456 0.0456 0.202*** 0.202**
(0.88) (1.16) (10.49) (3.41)
_cons 2.202** 2.202** -3.282*** -3.282
(2.96) (3.24) (-6.81) (-1.72)
—————————————————————————-
N 272 272 272 272
adj. R-sq 0.157 0.157 0.672 0.694
—————————————————————————-
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
OLS estimation assumes that all the independent variables across the countries are homogenous. But theories suggest that whether and to what extent countries benefit from trades depend on various country-specific factors, degree of factor mobility between sectors, type of specialization, and the ability of a country to invest in physical or human capital or adopt foreign technology. So, the effect of trade on income must be different or heterogeneous across countries . That is why Fixed Effect or Random Effect model is preferable than the OLS model while dealing with panel data. Table 1 shows that, all the coefficients of the variables LNTRADE and LNFDI in the Fixed Effect model and the Fixed Effect model with robustness option are statistically significant. Between these two models the paper chooses the Fixed Effect model with robustness as it is showing the lowest standard errors.
The Fixed Effect model with robustness option confirms the hypothesis that there is a positive relationship between trade and income of ASEAN and SAFTA regions. The study finds that, 1% increase in trade increases income by 0.279%. That means there is a positive relationship between trade and income in these two regions together. Positive relationship is also evident between income and FDI. According to the estimation, 1% increase in FDI increases income by 0.202%.
The study also applies the same methodology in case of 11 countries of ASEAN and 8 countries of SAFTA. For the ASEAN region, the result of the Huasman test is presented in the table below,
—- Coefficients —-
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fixed random Difference S.E.
————-+—————————————————————-
lntrade | .2027614 .2022375 .0005239 .0025843
lnfdi | .2642871 .2624525 .0018347 .0014643
——————————————————————————
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 1.81
Prob>chi2 = 0.4048
According to the test with 11 countries of ASEAN data, the p-value (0.4048) is greater than 0.05. Therefore, we fail to reject the null hypothesis and should use the Random Effect. The comparative analysis of using Ordinary Least Squares (OLS) and Random Effect regression model is shown in the table 2. As usual, the study prefers the Random Effect model over OLS model.
Table 2: OLS and Random Effect regression results for ASEAN countries
—————————————————————————-
(1) (2) (3) (4)
OLS11 OLS11R Random11 Random11R
—————————————————————————-
lntrade 0.300*** 0.300*** 0.202*** 0.202*
(4.90) (9.63) (8.18) (2.43)
lnfdi -0.00626 -0.00626 0.262*** 0.262***
(-0.09) (-0.11) (11.76) (4.80)
_cons 0.766 0.766 -2.504*** -2.504*
(0.78) (0.86) (-3.79) (-2.11)
—————————————————————————-
N 159 159 159 159
adj. R-sq 0.269 0.269
—————————————————————————-
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
As shown in the table 2, the coefficients are all statistically significant in the Random Effect model and Random Effect models with robustness option. It shows that, in ASEAN region 1% increase in international trade increases income by 0.202%. Also, 1% increase in FDI causes 0.262% increase in income. Both of the hypotheses are confirmed in case of 11 ASEAN countries. Income is positively related with trade and FDI.
Now, the paper examines these hypotheses on the 8 countries of SAFTA region. The Huasman test in the table below confirms that we can reject the null hypothesis and can use the Fixed Effect model with the SAFTA data.
—- Coefficients —-
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fixed random Difference S.E.
————-+—————————————————————-
lntrade | .8081248 .7910783 .0170466 .0054805
lnfdi | -.007346 -.0060036 -.0013423 .0007729
——————————————————————————
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 10.06
Prob>chi2 = 0.0065
The result in the table 3 shows that, 1% increase in international causes 0.808% increase in income in 8 countries of SAFTA region. That means, trade is also positively related with income in case of SAFTA region. However, the study shows that 1% increase in FDI rather decreases income by 0.00735%. That is trade is negatively related with FDI in SAFTA region. The study also finds that, this relationship is not statistically significant at any level in Fixed Effect model. This implies that, though the effect of FDI on income is negative, it is statistically significant for SAFTA.
Table 3: OLS and Fixed Effect regression results for SAFTA countries
—————————————————————————-
(1) (2) (3) (4)
OLS8 OLS8R FIXED8 FIXED8R
—————————————————————————-
lntrade -0.547** -0.547*** 0.808*** 0.808***
(-2.84) (-3.44) (15.59) (6.64)
lnfdi 0.396** 0.396** -0.00735 -0.00735
(2.79) (3.36) (-0.28) (-0.13)
_cons 12.48*** 12.48*** -11.74*** -11.74**
(5.62) (7.32) (-12.24) (-4.42)
—————————————————————————-
N 113 113 113 113
adj. R-sq 0.053 0.053 0.792 0.806
—————————————————————————-
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
Conclusion
The concluding remark of the original paper by Erpek states that there is a positive relationship between international trade and income. Erpek finds that 1% increase in trade brings 1.57% increase in income. The paper finds this positive relationship effective for 18 countries in Western Asia region including OPEC and non-OPEC members. However, the study does not find any evidence of this hypothesis in case of six OPEC members. The author concludes that, the positive relationship between trade and income loses its effectiveness when countries are divided into regional groups.
This paper replicates the same methodology on 19 countries of ASEAN and SAFTA regions together and finds the same positive relationship between trade and income. However, unlike the original paper, this replication study finds the same positive relationship in these two regions separately. It asserts that trade is influential on income in all of the countries in ASEAN and SAFTA regions. The positive relationship between these two variables doesn’t lose its effectiveness when countries are divided into groups. According to the study, 1% increase in trade increases income for these two regions by 0.279%.
In addition to trade, the replication paper also examines the effect of Foreign Direct Investment (FDI) on income for these regions. The study finds positive relationship between income and FDI for all the 19 countries of these regions together. However, when investigating separately, the study finds negative relationship between FDI and income for SAFTA region. Moreover, this result is found to be statistically insignificant in all significance level. That means, he effect of FDI on the income of SAFTA region is not significant.