1 Does More International Trade Results in Highly Correlated Business Cycle ? Presenter: Tao Xing Supervisors: Dr. A. Abbott & Dr. J. Easaw Third year PhD Student in Economics Department of Economics and International Development, University of Bath, U.K.

2 Motivations Both trade intensity and business cycle are criteria of OCA. More integration lead to more trade and more trade result in highly business cycle correlations, but the second part is not universally accepted. If we find the relationship between them, it is important in evaluating whether the present Eurozone was justified from OCA theory and also for other currency areas.

3 Hypothesis and Contributions Hypothesis: more trade intensity results in closer business cycles Contribution1: not only use a pooled sample but regress the model also for each country Contribution2: adapt a random effects panel estimation technique that allows for country- pair specific effects.

4 Data All data are from 1959 to 2003 and it is split into 5 periods for the 21 OECD countries plus China, HK, and Mexico. Trade Intensity Data Source: All trade data are taken from the IMF, Direction of Trade Statistics, ESDS International, University of Manchester, nominal GDP are from the OECD’s Economic Outlook

5 Data Business cycle correlations are measured by real GDP (By US$) converted to natural logarithms and de-trended with either a Hodrick-Prescott filter or first differences. Real GDP are from the OECD’s Economic Outlook. Convergence increase over time particularly for the European countries.

6 Methodology The relationship between trade intensity and business cycle (OLS): IV (Gravity Model) is better than OLS 1. From theory: TI is potential endogenous 2. From Econometrics: DHW test

7 Methodology Panel data model is better than OLS 1. Our data are panel data 2. Pooled OLS estimation Ignores potential important unobservable country-pair specific effects. 3. Econometrics: F-test & Breusch-Pagan Lagrangian Multiplier test suggest Panel data model is better 4. Instruments of language, distance and adjacent are time-invariant. Therefore two-step GLS estimator will be used.

8 Empirical Results All the results are based on 2-step GLS: 1. Instruments are important determinants of trade intensity 2. A positive and statistically significant relationship exists between the real GDP correlation and trade intensity for all de- trending and trade normalisation

9 Bi ­ lateral Trade normalise d by GDP Bi ­ lateral Trade normalised by total trade Const ­ 7.13 ( ­ 14.07) ­ 3.95 ( ­ 8.16) FIX ijt 0.14 (2.41)0.28 (4.95) LAN ij 0.33 (3.56)0.28 (3.16) dis ij ­ 0.36 ( ­ 10.01) ­ 0.33 ( ­ 9.46) ADJ ij 1.11 (7.82)1.30 (9.60) FTA ijt 0.62 (9.12)0.31 (4.69) GDP ij / Pop ij 0.18 (9.14)0.05 (2.63) De ­ trending method Bi ­ lateral Trade normalised by GDP Bi ­ lateral Trade normalised by total trade HP filterConstant 1.96 (18.24) 1.74 (15.43) ti ijt 0.24 (14.24) 0.24 (11.59) First diffeences Constant 2.08 (18.96) 1.83 (16.33) ti ijt 0.26 (15.37) 0.26 (12.79) First Step Second Step

10 For sub-periods, the coefficients are insignificant for the first period but significance and magnitude increases over time up until the 1986­94 period, when the maximum marginal effect occurs. For Individual countries analysis, a relatively strong and consistent relationship is found amongst the European countries. Variation in the size of  take place between countries or regions (European, Aisian, and NAFTA) of the world

11 Bi ­ lateral Trade normalised by GDPBi ­ lateral Trade normalised by total trade Countrycontti ijt CountryContti ijt Countrycontti ijt CountryContti ijt Australia 0.39 (1.10) 0.00 ( ­ 0.01) Italy 2.27 (6.00) 0.31 (4.90) Australia 0.28 (1.04) ­ 0.02 ( ­ 0.39) Italy 1.81 (5.23) 0.29 (4.02) Austria 1.90 (8.94) 0.20 (6.20) Japan 1.21 (1.96) 0.15 (1.58) Austria 1.71 (7.34) 0.20 (4.83) Japan 0.68 (1.56) 0.09 (1.02) Belgium 1.67 (9.46) 0.19 (6.14) Mexico ­ 0.46 ( ­ 1.16) ­ 0.06 ( ­ 1.23) Belgium 1.47 (7.78) 0.17 (4.65) Mexico 0.29 (0.86) 0.04 (0.78) Canada 0.25 (0.79) 0.00 (0.05) Netherlan ds 1.89 (9.27) 0.24 (6.57) Canada 0.17 (0.86) ­ 0.01 ( ­ 0.38) Netherlan ds 1.85 (7.70) 0.26 (5.41) China 0.64 (2.73) 0.06 (1.99) New Zealand 0.93 (2.47) 0.06 (1.13) China 0.41 (1.59) 0.04 (0.90) New Zealand 0.71 (2.09) 0.03 (0.61) Denmark 1.65 (9.51) 0.17 (6.25) Norway 2.14 (8.19) 0.25 (6.16) Denmark 1.51 (8.43) 0.17 (5.27) Norway 2.03 (4.89) 0.27 (3.60) Finland 1.60 (6.34) 0.16 (4.21) Portugal 1.95 (8.95) 0.21 (6.77) Finland 1.45 (6.79) 0.16 (4.26) Portugal 1.94 (7.39) 0.24 (5.57) France 1.77 (6.20) 0.20 (4.24) Spain 2.32 (5.95) 0.27 (4.61) France 1.56 (5.75) 0.21 (3.68) Spain 2.39 (4.94) 0.35 (3.85) Germany 1.94 (7.48) 0.27 (5.47) Sweden 1.51 (7.39) 0.17 (4.76) Germany 1.67 (7.08) 0.26 (4.85) Sweden 1.34 (6.15) 0.16 (3.67) Greece 2.06 (8.26) 0.21 (6.17) Switzerla nd 1.92 (6.63) 0.23 (4.73) Greece 1.98 (6.80) 0.23 (5.00) Switzerlan d 1.44 (4.89) 0.18 (3.02) Hong Kong 0.21 (1.03) 0.00 ( ­ 0.08) UK 1.81 (4.42) 0.24 (3.08) Hong Kong 0.39 (2.20) 0.03 (0.93) UK 1.14 (2.90) 0.14 (1.49) Ireland 2.11 (9.15) 0.23 (6.93) USA 0.13 (0.55) 0.00 ( ­ 0.10) Ireland 2.39 (8.03) 0.30 (6.30) USA 0.18 (1.06) 0.01 (0.17)

12 Conclusions Significant and positive relationship between business cycle correlations and trade intensity The marginal effect from more trade has increased over time A significant relationship is found for each European country but not for other non- European countries.

13 Further Analysis 1. Test the validity of our six instruments, especially for product of GDP pre capita 2. The causality test for bilateral trade intensity and business cycles

14 END