Prevención de riesgos laborales. Principios

Prior to carrying out unit root tests for the variables, this section first tests for the appropriateness of the logarithmic transformation of the non-linear equation (3.3) for each of the variables. For the ADF auxiliary equation in each variable, the study tests for the hypothesis that H0: _p 0 (in equation 3.25), which implies appropriate logarithmic transformation versus the alternative of Ha: _p 0, which indicates inappropriate logarithmic transformation. The results of the logarithmic transformation test are presented in Table 4.1.

Figure 4.1: Variables Used in This Chapter

a. China

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

1980 1985 1990 1995 2000 2005 LY

12.8 13.0 13.2 13.4 13.6

1980 1985 1990 1995 2000 2005 LL

2 3 4 5 6

1980 1985 1990 1995 2000 2005 LK

4.0 4.4 4.8 5.2 5.6 6.0

1980 1985 1990 1995 2000 2005 LO

b. India

3.2 3.6 4.0 4.4 4.8 5.2

1980 1985 1990 1995 2000 2005

LY

12.4 12.5 12.6 12.7 12.8 12.9 13.0

1980 1985 1990 1995 2000 2005

LL

3.0 3.5 4.0 4.5 5.0 5.5

1980 1985 1990 1995 2000 2005

LK

3.2 3.6 4.0 4.4 4.8 5.2

1980 1985 1990 1995 2000 2005

LO

c. Indonesia

3.2 3.6 4.0 4.4 4.8 5.2

1980 1985 1990 1995 2000 2005 LY

10.8 11.0 11.2 11.4 11.6

1980 1985 1990 1995 2000 2005 LL

3.2 3.6 4.0 4.4 4.8 5.2

1980 1985 1990 1995 2000 2005 LK

2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2

1980 1985 1990 1995 2000 2005 LO

d. Malaysia

3.2 3.6 4.0 4.4 4.8 5.2

1980 1985 1990 1995 2000 2005

LY

8.4 8.6 8.8 9.0 9.2 9.4

1980 1985 1990 1995 2000 2005

LL

3.2 3.6 4.0 4.4 4.8 5.2

1980 1985 1990 1995 2000 2005

LK

2.0 2.2 2.4 2.6 2.8 3.0 3.2

1980 1985 1990 1995 2000 2005

LO

e. Philippines

3.50 3.75 4.00 4.25 4.50 4.75 5.00

70 75 80 85 90 95 00 05

LY

9.2 9.4 9.6 9.8 10.0 10.2 10.4 10.6

70 75 80 85 90 95 00 05

LL

2.8 3.2 3.6 4.0 4.4 4.8

70 75 80 85 90 95 00 05

LK

1.8 2.0 2.2 2.4 2.6 2.8 3.0

70 75 80 85 90 95 00 05

LO

f. Thailand

2.5 3.0 3.5 4.0 4.5 5.0

75 80 85 90 95 00 05

LY

9.6 9.8 10.0 10.2 10.4 10.6

75 80 85 90 95 00 05

LL

3.0 3.5 4.0 4.5 5.0 5.5

75 80 85 90 95 00 05

LK

1.5 2.0 2.5 3.0 3.5 4.0

75 80 85 90 95 00 05

LO

Note: LY, LL, LK and LO stands for log of output, labour, capital and oil consumption.

98

Table 4.1: Test of Non-Linear Logarithmic Transformation of Output, Labour, Capital, and Oil Consumption

Country Series ˆ₄ ^{S. E.} T(ˆ₄) Prob. (ˆ₄) R^{2 Country Series} ˆ₄ ^{S. E.} T(ˆ₄) Prob.(ˆ₄) R²

China LY -5.382 4.155 -1.295 0.209 0.204 India LY 9.169 6.209 1.477 0.156 0.232 LL 0.0467 0.168 0.277 0.785 0.322 LL -112.770 168.488 -0.669 0.511 0.255 LK 0.654 1.049 0.624 0.539 0.042 LK -1.939 3.728 -0.520 0.609 0.413 LO -0.149 0.156 -0.958 0.349 0.309 LO 5.249 7.456 0.701 0.491 0.356

Indonesia LY 3.464 2.972 1.166 0.257 0.207 Malaysia LY 6.666 4.263 1.564 0.134 0.002 LL 0.178 0.187 0.943 0.357 0.108 LL 21.895 128.384 0.171 0.866 0.127 LK -0.181 1.235 -0.147 0.885 0.069 LK 1.024 0.773 1.325 0.201 0.013 LO 2.636 4.044 0.632 0.522 0.146 LO 2.204 2.992 0.737 0.470 0.051

Philippines LY -0.577 2.719 -0.212 0.834 0.236 Thailand LY -1.746 2.629 -0.664 0.512 0.413 LL -0.242 0.149 -1.638 0.112 0.172 LL -1.776 1.697 -1.047 0.304 0.096 LK -0.405 0.819 -0.495 0.624 0.044 LK -2.757 1.942 -1.421 0.162 0.2788 LO -0.861 1.409 -0.611 0.546 0.063 LO 0.116 0.163 0.709 0.484 0.228 Note: Ordinary least square estimation. Dependent variables are DLY, DLL, DLK and DLO. Lags order 3 is chosen for each of the equation.

It is apparent from Table 4.1 (opposite) that all of the variables for all the individual countries do not reject the null hypothesis ₄ 0 in equation 3.25. Hence, the test results indicate that the natural logarithmic transformation of equation 3.3 is appropriate for testing for a single unit root. The following sub-section discusses the results of unit root and structural break tests.

4.3.1 Unit root and Perron’s structural break tests

This study performs three different unit root tests, namely Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS). The results of these tests are presented in Appendix Tables 4.2, 4.3 and 4.4, respectively.

As mentioned in Chapter 3, the null hypotheses for both ADF and PP tests are that the series has a unit root. According to the results of both of the unit root tests the null hypotheses cannot be rejected; i.e. all the series for all the countries have unit root at their levels, while all these variables are stationary at their first differences.

For further clarification KPSS unit root test is undertaken as this test has the null that the series is stationary. For all the variables in respect of all countries the null cannot be accepted, thus the results of this test further confirm that all these variables are non-stationary at their levels.

The graphical representation of the variables reveals some spikes in the concerned variables for some countries during the time of Asian financial crisis and the traditional unit root test cannot be relied upon if the underlying series contains structural break(s). Therefore, this study uses Perron’s (1997) unit root test, which allows for a structural break and the test results are summarized in Table 4.2 (overleaf)²¹.

The Perron test results provide further evidence of the existence of unit roots in all series of different countries when breaks are allowed. However, for output and capital series of Indonesia the test reveals the existence of structural break in 1996, i.e. after this period the series experiences structural change. Hence, given the unavailability of long data series that could have permitted the study to divide the series; this study uses a dummy variable in the estimation process for Indonesia to deal with this change in overall structure. Prior to the break date the dummy takes

21 Perron (1997) is a unit root test which allows for multiple structural breaks. However, none of the tests identify more than one break point. The reason may be the test is not of high power.

the value of 0, while after the break date the dummy takes 1. For all the other countries when the underlying series is found non-stationary, the selected value of Tb

no longer yields a consistent estimate of the break point (Perron 1997). Therefore, it may be concluded that the underlying data are non-stationary at levels but stationary at their first differences.

Table 4.2: Perron Innovational Outlier Model with Change in Both Intercept and Slope for LY, LL, LK, and LO

Country Series T Tb k¹ t^ˆ t_ˆ t_ˆ t_ˆ ˆ t_ ^Infer_ence China LY 13 1989 0 2.29 1.67 -1.32 -3.01 0.732 -2.288 NS LL 13 1989 0 3.93 3.63 -4.17 3.39 0.522 -3.741 NS LK 15 1991 0 2.73 0.38 1.34 -0.89 0.498 -3.169 NS LO 26 2002 0 2.43 1.52 -1.47 -0.98 0.846 -1.845 NS India LY 21 2000 0 3.26 -3.32 3.46 1.18 0.431 -3.228 NS

LL 25 2004 0 1.54 8.42 -8.59 -4.60 0.920 -1.831 NS LK 19 1998 0 4.14 -4.09 4.17 2.79 0.383 -4.027 NS

LO 14 1993 0 1.93 3.35 -2.20 -1.71 0.712 -2.389 NS Indonesia LY 18 1996 0 6.34 0.24 -2.40 5.89 0.390 -6.228** S

LL 13 1991 0 3.03 2.63 -2.80 -0.50 0.237 -3.254 NS LK 18 1996 0 5.27 -1.44 -0.48 5.11 0.401 -5.468*** S

LO 13 1991 0 1.74 1.76 -1.04 -0.04 0.637 -1.761 NS Malaysia LY 13 1992 0 2.54 2.27 -2.16 -0.39 0.597 -2.273 NS

LL 19 1998 0 2.35 3.65 -3.87 -1.69 0.709 -2.312 NS

LK 17 1996 0 2.77 -0.94 -0.05 2.37 0.692 -2.425 NS LO 13 1992 0 2.64 2.67 -2.52 -0.39 0.597 -2.472 NS Philippines LY 13 1992 0 4.33 -2.29 -2.14 3.60 0.557 -4.986 NS

LL 13 1982 0 4.45 3.21 -3.13 0.36 0.138 -4.956 NS LK 13 1982 0 3.55 0.75 -2.74 2.81 0.496 -4.288 NS LO 16 1985 0 -3.06 1.20 1.51 -1.21 0.912 -1.139 NS Thailand LY 16 1986 0 2.65 3.70 -2.49 -1.56 0.765 -3.138 NS

LL 16 1986 0 5.25 4.69 -5.09 -1.17 0.306 -4.852 NS LK 26 1996 0 4.98 -2.79 0.95 2.61 0.571 -4.794 NS

LO 16 1986 0 -0.24 1.68 1.11 -1.22 0.807 -2.509 NS

Note: 1%, 5% and 10% critical values are -6.32, -5.59 and -5.29, respectively (Perron, 1997). The optimal lag length is determined by Akaike Information Criterion (AIC) with k_max 10. NS stands for Non-stationary at levels. . LY, LL, LK and LO stand for log of GDP, labour, capital and oil consumption, respectively. *, **, and *** indicate significant at 1%, 5%, and 10% level respectively.

The above unit root tests show that all the variables of all the concerned countries are found to be I(1) process. At this stage, tests for cointegration among the variables are

required as these tests enable the study to make inferences about the long-run relationships. The following section identifies cointegrating relationships among the variables.