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ECONOMIC GROWTH, AND DEVELOPMENT

The remaining dummy variables in equations (2) -(5) have the anticipated signs and they are statistically significant, while, as expected, the change in the terms of trade effect has a positive and statistically significant effect. The relative fit and efficiency of the EC regressions is quite good and, as the theory predicts, the EC terms are negative and statistically significant, suggesting, as in equation (4), that a deviation from long-run labor productivity growth in a given year is corrected by about 38 percent in the next year. Finally, stability tests were undertaken to determine whether the null hypothesis of no structural break could be rejected. The Chow breakpoint tests (available upon request) suggested that the hypothesis could not be rejected for the crisis years of 1973, 1975, and 1982. The EC models were also used to track the historical data on labor productivity growth in Chile. Table 5 below presents selected Theil inequality coefficients obtained from the historical simulations of the productivity growth equations. The Theil inequality coefficient measures the root-mean-square (RMS) error in relative terms; DOES FOREIGN INVESTMENT ENHANCE LABOR GROWTH IN CHILE 215 TABLE 4 Chile: Error Correction Model; Dependent Variable is: (ΔlnYt – ΔlnLt ), 1960-2000. OLS Regressions Variables (1) (2) (3) (4) (5) Constant -6.47 -2.59 -4.38 -5.39 -3.01 (-1.91)** (-1.39)* (-2.57)*** (-3.56)*** (-1.55)* ΔlnLt -0.55 -0.34 -0.28 -0.29 -0.33 (-2.03)** (-1.82)** (-1.75)** (-1.93)** (-1.92)** ΔlnKft-5 0.19 0.12 0.13 0.15 0.14 (2.12)** (1.91)** (2.30)** (3.00)** (2.35)** DUM4*(ΔlnKft-5)— — — — 0.18 (1.71)** ΔlnKpt 0.76 0.46 0.45 0.44 0.44 (5.43)*** (4.91)*** (4.68)*** (4.37)*** (4.30)*** ΔlnKgt-3 0.58 0.37 0.47 0.46 0.38 (1.85)** (2.04)*** (2.43)*** (3.03)*** (2.05)** Ectt-1 -0.45 -0.30 -0.31 -0.38 -0.30 (-2.25)*** (-2.73)** (-3.18)*** (-2.82)*** (-3.03)*** DUM1 — -6.15 -5.59 -5.38 -5.94 (-3.15)** (-3.02)*** (-2.95)*** (-3.09)*** DUM2 — — 2.99 3.56 — (2.82)*** (3.86)*** DUM3 — — — 2.73 — (3.20)*** TOT 0.17 0.18 0.22 0.19 0.20 (1.56)* (2.29)** (3.05)*** (2.24)** (2.45)** Adj R2 .60 .78 .81 .85 .80 S.E. 4.18 3.08 2.93 2.69 3.05 D.W. 1.74 1.80 1.76 1.84 1.85 AR(1) 0.54* 0.36* 0.42** 0.35* 0.36** Sample size 41 41 41 41 41 Note: Asterisks are defined as in Table 3. AR(1) refers to a first order auto-regressive specification and t-ratios are in parenthesis. i.e., it is a measure of the deviation between the simulated values of the variable and its actual value scaled so that it falls between 0 and 1. If the Theil coefficient is 1 then the predictive performance of the model is at its worst, while if it is equal to zero it is perfect. In general, the predictive power of the model is considered to be quite good if the coefficient is below 0.3 [Theil, 1966]. As can be seen from Table 5, the coefficients are well below the threshold value suggested by Theil, e.g., 0.210 and 0.202 for equations (3) and (4), respectively