Globalization and Export Concentration Chowdhury Nawsheen Farooqui

Outline of the Presentation Introduction Motivation and Research question Literature Review Methodology Empirical Framework Findings and discussion Conclusion

Introduction Joseph Stiglitz, a noble laureate economist defines Globalization as follows: Globalization "is the closer integration of the countries and peoples of the world ...brought about by the enormous reduction of costs of transportation and communication, and the breaking down of artificial barriers to the flows of goods, services, capital, knowledge, and people across borders." (from Globalization and its Discontents) Globalization covers a wide range of issues, economic, political, cultural, etc. Globalization does not only mean opening of trade opportunities only in terms of goods and services but it also refers to transfer or free flow of capital, labor and so on.

Introduction Export Concentration- Export concentration reflects the degree to which a country’s exports are concentrated on a small number of products or a small number of trading partners. A country that exports one product to only one trading partner has a perfectly concentrated export portfolio. Conversely, a country whose exports are comprised of a larger number of products and that trades with a larger number of trading partners has a lower export concentration ratio (ECR), i.e., has more diversified exports. Connecting the two in this paper .

Choosing Bangladesh to see the relationship between globalization and export concentration Bangladesh being a developing country and is a small open economy in international trade. Early 1980s the major export concentration was jute and jute products, which was overshadowed by the RMG sector in the end of 1990s. Bangladesh experienced vertical diversification of its exports (from primary to manufactures). By 2000, it became a unique LDC exporting predominantly manufactures (over 90%).The exports are largely dominated by readymade garments, whose share was 81.2 percent in FY2014. Around 96 percent of all exported goods are manufactured commodities. Bangladesh's export basket is heavily concentrated on one product that is the readymade garments and others include: jute goods, home textile, footwear and frozen shrimps and fish.

Motivation and Research question Bangladesh being a developing country still has restrictions on the free flow of capital, movement of labor and so on. Bangladesh has not yet enrolled in the super highway of globalization but still it has globalized to some extent and therefore we can expect some sort of impact of globalization on export scenario in the context of Riacrdian specialization theory. Research Question : Does globalization of Bangladesh leads to more export concentration or not ?

Literature Review Ricardo (1817) referred to the framework of two countries and two commodities when studying the notions of comparative advantage and efficient specialization in production. Studies by Simon Kuznets (1964) and A. 0. Hirschman (1945) suggest that country size and commodity export concentration are inversely related. Michael Michaely [1958] found that it is the level of development rather than country size that exerts a significant influence on the degree of export concentration. Johansson and Nilsson (2007) ,The results from the empirical analysis do not support the hypotheses of increasing trade globalization ,It is rather the case that export flows are becoming more internationally regionalized.

Methodology In this study I used the econometric methods such as Unit root tests  Johansen Cointegration test Granger Causality test Fully modified OLS Canonical OLS Dynamic OLS

Methodology Unit root tests are carried out to find out if the data series is stationary or non- stationary. Non-stationary data give rise to spurious regression results for which it is important for us to make our data stationary. We focus on the ADF and Phillips Perron unit test to check the stationarity of our data. In order to do so, we assume a hypothesis considering the data series to be non-stationary and integrated. If there is a clear proof of rejection, it is only then we reject the hypothesis. Cointegration means that a linear combination of different order 1-integrated variables I(1) is stationary (I(0)), and it implies the existence of an empirical long- run relationship between those variables. Johansen's cointegration test was performed to find out existence of any possible relationship between variables. Next, Johansen cointegration test is applied to investigate the relationship between the variables

Methodology It is pointed out by Granger (1986, 1988) and Engel (1987) that if two variables are cointegrated then a causal relationship must exist between them, at least in one direction. Therefore, once the cointegration is established, the next step is to investigate direction of causality existing between the variables. However cointegration test does not give the direction of relationship among variables so we took help of cointegrating regressions namely Fully Modified Ordinary Least Square (FMOLS), Canonical Cointegration Regression (CCR), and Dynamic Ordinary Least Squares (DOLS). These are single equation regression based methods and are variations of OLS method to avoid some problems that are common among cointegrating relationship

Methodology Fully modified ordinary least squares The FM-OLS regression is designed to provide efficient estimates of cointegrating regressions. The method modifies least squares to account for serial correlation effects and for the endogeneity in the regressor that results from the existence of a cointegrating relationship. In the same vein, CCR and DOLS estimators deal with the problem of second-order asymptotic bias arising from serial correlation and endogeneity.

Empirical Framework We used a linear regression model, where the HHI is the dependent variable (export concentration index measured by Herfrindal - Hirschman index) and the globalization index - the globalization index that covers the economic, social and political dimensions of globalization, is the independent variable. The KOF Index of Globalization was introduced in 2002 (Dreher, published in 2006) and is updated and described in detail in Dreher, Gaston and Martens (2008). The overall index covers the economic, social and political dimensions of globalization.

Empirical Framework Looked at the data between the periods 1985 to 2014. The final regression equation looks like (here the symbols have their usual meanings): Ran the regression keeping the dependent variable HHI and changing the globalization index , first did the regression with HHI and KOF Overall globalization index . The overall index covers the economic , social and political dimensions of globalization. Then ran the same tests using HHI and K0F political and HHI with KOF economic globalization index .

Deintions of variables Economic Globalization – characterized as long distance flows of goods, capital and services as well as information and perceptions that accompany market exchanges; Political Globalization – characterized by a diffusion of government policies. Social globalization , expressed as the spread of ideas , information, images and people.

Definitons of the variables Herfindahl Hirschman index is a flow-weighted concentration index which implies that it can be decomposed according to the shares of total flows of each group. Thus, the weight given to each group depends on the trade share of each group. The formula is as follows: HHI is ‘Herfindahl Hirschman Index’ of country i. Xik is the export value of commodity k or export destination k for exporter i. Xi is the total export value of all commodities or export destinations for exporter i. Then, it is converted into export concentration index

Results Obtained Group unit root test: Summary Series: HHI, KOFOVERALL   Series: HHI, KOFOVERALL Sample: 1986 2014 Cross- Method Statistic Prob.** sections Obs Null: Unit root (assumes common unit root process)  Levin, Lin & Chu t* -3.63540  0.0001  2   53 Breitung t-stat -2.39741  0.0083   51 Null: Unit root (assumes individual Im, Pesaran and Shin W-stat  ADF - Fisher Chi-square -2.18166  0.0146   2   53  11.3650  0.0228   2 53 PP - Fisher Chi-square  11.2887  0.0235

Cointegration Rank Test between HHI and kofoverall (Glob) Sample (adjusted): 1988 2010   Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None  0.364009  16.92435  25.87211  0.4206 At most 1  0.246683  6.515203  12.51798  0.3978  Trace test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Results of Granger Causality test between HHI and kofoverall (glob) Sample: 1986 2014 lag : 1    Null Hypothesis: Obs F-Statistic Prob.  KOFOVERALL does not Granger Cause HHI  25  11.8195 0.0023  HHI does not Granger Cause KOFOVERALL  0.47871 0.4962

Results of Granger Causality test between HHl and kofoverall Sample: 1986 2014 lags : 2   Null Hypothesis: Obs F-Statistic Prob.     KOFOVERALL does not Granger Cause HHI  23  2.74734 0.0909  HHI does not Granger Cause KOFOVERALL  0.50164 0.6138

Results of Granger Causality test between HHI and Kofoverall (Glob) Sample: 1986 2014 Lags:3    Null Hypothesis: Obs F-Statistic Prob.   KOFOVERALL does not Granger Cause HHI  22  1.03318 0.4061  HHI does not Granger Cause KOFOVERALL  0.64557 0.5977

Results showing the FMOLS between HHI and KOF0verall(Glob) Dependent Variable: HHI   Mehod: Fully Modified Least Squares (FMOLS) Included observations: 26 after adjustments Variable Coefficient Std. Error t-Statistic Prob.   KOFOVERALL 0.012172 0.001716 7.094716 0.0000 C 0.267179 0.054204 4.929113 R-squared 0.724842     Mean dependent var 0.641741 Adjusted R-squared 0.713377     S.D. dependent var 0.111848 S.E. of regression 0.059880     Sum squared resid 0.086056 Durbin-Watson stat 1.867044     Long-run variance 0.004531

Results of the CCR Regression Dependent Variable: HHI   Method: Canonical Cointegrating Regression (CCR) Included observations: 26 after adjustments Variable Coefficient Std. Error t-Statistic Prob.   KOFOVERALL 0.012149 0.001693 7.176186 0.0000 C 0.267894 0.052059 5.145930 R-squared 0.724849     Mean dependent var 0.641741 Adjusted R-squared 0.713384     S.D. dependent var 0.111848 S.E. of regression 0.059880     Sum squared resid 0.086054 Durbin-Watson stat 1.866951     Long-run variance 0.004531

Results of the DOLS Regression Dependent Variable: HHI   Method: Dynamic Least Squares (DOLS) Included observations: 21 after adjustments Variable Coefficient Std. Error t-Statistic Prob.   KOFOVERALL 0.015658 0.002093 7.481871 0.0000 C 0.038810 0.107735 0.360238 0.7249 R-squared 0.784616     Mean dependent var 0.628740 Adjusted R-squared 0.641027     S.D. dependent var 0.105769 S.E. of regression 0.063371     Sum squared resid 0.048191 Durbin-Watson stat 2.214063     Long-run variance 0.002892

Group unit root test Group unit root test: Summary   Series: HHI, KOFECON Sample: 1986 2014 Cross- Method Statistic Prob.** sections Obs Null: Unit root (assumes common unit rootprocess)  Levin, Lin & Chu t* -1.65792  0.0487  2  53 Breitung t-stat -0.02129  0.4915  51 Null: Unit root (assumes individual unit root process)  Im, Pesaran and Shin W-stat  -1.82311  0.0341 ADF - Fisher Chi-square 10.1618  0.0378 PP - Fisher Chi-square  10.0712  0.0392

Cointegration Rank Test between HHI and KOFECON Sample (adjusted): 1988 2010   Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None  0.371222  17.12641  25.87211  0.4056 At most 1  0.244707  6.454932  12.51798  0.4048  Trace test indicates no cointegration at the 0.05 level  * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Group unit root test Group unit root test: Summary   Group unit root test: Summary  Series: KOFPOLITICAL, HHI Sample: 1986 2014 Cross- Method Statistic Prob.** sections Obs Null: Unit root (assumes common unit root process)  Levin, Lin & Chu t* -3.61591  0.0001  2  53 Breitung t-stat -0.15566  0.4382  51 Null: Unit root (assumes individual unit root process)  Im, Pesaran and Shin W-stat  -2.32687  0.0100 ADF - Fisher Chi-square  11.9277  0.0179 PP - Fisher Chi-square  20.5828  0.0004

Cointegration test between HHI and kofpolitical Sample (adjusted): 1988 2010   Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None *  0.672016  35.55927  25.87211  0.0023 At most 1  0.350312  9.919062  12.51798  0.1309  Trace test indicates 1 cointegrating eqn(s) at the 0.05 level  * denotes rejection of the hypothesis at the 0.05 level  **MacKinnon-Haug-Michelis (1999) p-values

Results of Granger Causality test between HHI and Globalization (Glob) Sample: 1986 2014 Lags: 1    Null Hypothesis: Obs F-Statistic Prob.   KOFPOLITICAL does not Granger Cause HHI  25  8.36501 0.0085  HHI does not Granger Cause KOFPOLITICAL  1.66934 0.2098

Results of Granger Causality test between HHI and Kof political Sample: 1986 2014 Lags :2   Null Hypothesis: Obs F-Statistic Prob.   KOFPOLITICAL does not Granger Cause HHI  23  3.94859 0.0379  HHI does not Granger Cause KOFPOLITICAL  3.23338 0.0631

Results of Granger Causality test between HHI and KOF political Sample: 1986 2014 lags :3   Null Hypothesis: Obs F-Statistic Prob.   KOFPOLITICAL does not Granger Cause HHI  22  1.64427 0.2214 HHI does not Granger Cause KOFPOLITICAL  0.21018 0.8878

Results showing the FMOLS between HHI and kofpolitical (Glob) Dependent Variable: HHI   Method: Fully Modified Least Squares (FMOLS) Included observations: 26 after adjustments Variable Coefficient Std. Error t-Statistic Prob.   KOFPOLITICAL 0.007664 0.001342 5.711422 0.0000 C 0.140634 0.091134 1.543161 0.1359 R-squared 0.619124     Mean dependent var 0.641741 Adjusted R-squared 0.603254     S.D. dependent var 0.111848 S.E. of regression 0.070451     Sum squared resid 0.119119 Durbin-Watson stat 1.518938     Long-run variance 0.004190

Results of the CCR Regression Dependent Variable: HHI   Method: Canonical Cointegrating Regression (CCR) Sample (adjusted): 1987 2013 Variable Coefficient Std. Error t-Statistic Prob.   KOFPOLITICAL 0.007998 0.001151 6.949119 0.0000 C 0.117539 0.076691 1.532631 0.1384 R-squared 0.624968     Mean dependent var 0.641741 Adjusted R-squared 0.609341     S.D. dependent var 0.111848 S.E. of regression 0.069908     Sum squared resid 0.117291 Durbin-Watson stat 1.558981     Long-run variance 0.004190

Table 6.2: Results of the DOLS Regression Method: Dynamic Least Squares (DOLS) Sample (adjusted): 1988 2008 Included observations: 21 after adjustments   Variable Coefficient Std. Error t-Statistic Prob.   KOFPOLITICAL 0.019864 0.009638 2.060963 0.0617 C -0.790066 0.737303 -1.071562 0.3050 R-squared 0.721409     Mean dependent var 0.628740 Adjusted R-squared 0.535681     S.D. dependent var 0.105769 S.E. of regression 0.072072     Sum squared resid 0.062333 Durbin-Watson stat 2.057958     Long-run variance 0.005026

Conculsion From the regression analysis it can be concluded that globalization has positive association with increased export concentration . However the interesting observation from this study has been that the export concentration increased more with political globalization then with economic globalization . This therefore indicate that more political friendly or globalized we are as a nation it has impact on attracting our exports more by other nations.