Panel Corrected Standard Errors (PCSE)

In order to deal with heteroscedasticity and/or serial correlation issues, as the second alternative, the wage equation is estimated using the Panel Corrected Standard Errors (PCSE) estimate. In practice, the PCSE, which is recommended by Beck and Katz (1995), allows for the presence o f the first-order autocorrelation AR(1) parameter within the panel. The standard model o f PCSE also allows the presence of

heteroscedastic disturbances and shows a consistent estimate. Compared to the simple fixed effects model, PCSE is therefore relatively corrected from the presence o f serial correlation and heteroscedasticity.

Table 5.4 Wage Equation using Panel Corrected Standard Errors (PCSE) Estimate

Log of Real

Minimum Wage Fraction Affected Fraction At Fraction Below

(1) (2) (3) (4)

Coef. P value Coef. P value Coef. P value Coef. P value MW Measure 0.1669 0.000 0.2881 0.009 -0.8040 0.042 -0.1766 0 .0 9 8

Urban 0.6289 0.000 0.2699 0.020 0.5900 0.000 0.5682 0 .0 0 0

Youth -0.2656 0.477 -0.3703 0.329 -0.3965 0.292 -0.4710 0 .2 0 9

Women 0.2699 0.446 -0.0880 0.849 0.2359 0.501 0.3085 0 .3 8 6

Industry -0.8475 0.015 -0.8778 0.038 -0.7214 0.050 -0.7194 0.051

Trade -1.4159 0.000 -0.5848 0.124 -1.3359 0.000 -1.2702 0.000

Services -0.1850 0.627 -0.0783 0.857 -0.2019 0.583 -0.1288 0 .7 2 6 Construction 0.2269 0.771 -0.2186 0.777 0.3992 0.615 0.3933 0 .6 2 5 High School 0.0366 0.902 0.3448 0.152 -0.0713 0.817 -0.0489 0 .8 7 5

University 1.4424 0.049 3.1051 0.001 1.6471 0.021 1.6320 0.021

Unemp Rate (-1) -0.7675 0.063 -0.4424 0.363 -0.8596 0.027 -0.8808 0 .0 2 8

Year 1990 0.0765 0.000 0.1131 0.000 0.0914 0.000 0.0995 0.000

Year 1991 0.0591 0.006 0.0230 0.000 0.1284 0.000 0.1389 0.000

Year 1992 0.0942 0.000 0.0609 0.000 0.1735 0.000 0.1825 0.000

Year 1993 0.1888 0.000 0.1097 0.000 0.2858 0.000 0.2976 0.000

Year 1994 0.1836 0.000 0.0153 0.067 0.3150 0.000 0.3371 0.000

Year 1995 0.2251 0.000 0.0746 0.007 0.3714 0.000 0.3952 0.000

Year 1996 0.2586 0.000 0.0287 0.391 0.4109 0.000 0.4297 0.000

Year 1997 0.3208 0.000 0.0719 0.000 0.4784 0.000 0.4928 0.000

Year 1998 0.1260 0.001 -0.2332 0.000 0.2318 0.000 0.2417 0.000

Year 1999 0.1949 0.000 0.0577 0.000 0.2903 0.000 0.2979 0.000

Year 2000 0.3251 0.000 0.1686 0.000 0.4470 0.000 0.4581 0.000 include province dum m ies and are estim ated assum ing a A R(1) error structure.

Table 5.4 provides a set o f results using the PCSE estimate. Most o f the control variables are significant with the expected signs. The proportion o f labour force in urban areas shows a significantly positive effect on the average wage in different regressions. This positive effect is potentially stimulated by a higher economic growth rate in urban areas compared with rural areas. In practice, this effect is bigger when using the log o f real minimum wage as the minimum wage measure compared to using the other measures. Using the log o f real minimum wage, it is suggested that an increase in the proportion o f workers in the urban areas by 10% increases the average monthly wage paid to the workers by 6.3%.

The effects o f the proportion o f workforce in the manufacturing and trade sectors are significantly negative to the average wage. A deep economic recession at the end o f the 1990s seems to be the main reason for this negative effect o f the manufacturing and trade sectors especially in the highly import-dependent manufacturing sector. As pointed out by Islam (2002), the real wages o f workers in the manufacturing and trade sectors in Indonesia declined by 7% per year during the 1997-2000 period.

In terms o f educational attainment, the proportion o f the workforce with university qualifications is strongly and positively associated with the average wage. As pointed out by Feridhanusetyawan and Gaduh (2000), this is due to the scarcity of skilled labour in Indonesia. Using the log o f real minimum wage measure, the result suggests that an increase in the proportion o f the workforce with university qualifications by 10% increases the average monthly wage paid to the workers by 14.4%. In addition, relating to the demand side, the unemployment rate shows a significant negative effect, although it is only significant at 10% level. Regarding unemployment rate

variable, I have tried to use the log o f the lagged unemployment rate instead o f using the lagged unemployment rate, as suggested by the wage curve literature, but the result is insignificant.

The log o f real minimum wage and the fraction affected consistently show a positive and significant effect on the average wage. On the other hand, similar to the results from the simple fixed effect estimates, the fraction below and the fraction at measures show unexpectedly negative effects o f the minimum wage. These results are consistent with the simple fixed effect estimates, suggesting that the fraction below and the fraction at measures are less effective in measuring the effects o f minimum wage on average wage.

As mentioned above, the PCSE estimate theoretically provides a well-suited specification. However, the estimated coefficients o f the minimum wage measures are relatively similar. Using the log o f real minimum wage, it is suggested that a 10%

increase in minimum wage increases the average wage by 1.67%, while using the fraction affected, it is suggested that an increase in minimum wage by 10% increases the average wage by 1.07%21. Similarly to the simple fixed effect estimate, we can conclude that the coefficient o f the log o f real minimum wage is higher than the fraction affected measure, suggesting that the log o f real minimum wage measure will potentially provide a stronger effect on employment than the fraction affected.

Simple fixed effects and PCSE estimates discussed above generally assume that the minimum wage measures are exogenous. The potentially biased estimate will arise if

21 S im ila rly to th e p re v io u s s e c tio n , th is c o e ffic ie n t is o b ta in e d a fte r m u ltip ly in g th e ra w c o e ffic ie n t o f th e fra c tio n a ffe c te d an d th e frac tio n a ffe c te d e la s tic ity w ith re s p e c t to th e lo g o f real m in im u m w ag e, as s u g g e s te d b y L em o s (2 0 0 4 d ).

the minimum wage measures are endogenously determined with the labour market condition. If the minimum wage measures are endogenous, the OLS estimate (simple fixed effects and PCSE estimates) will not only biased but also provide an inconsistent estimate.

Although Rama (2001) argued that the index o f minimum living needs (KHM), as the main consideration to set the Indonesian minimum wage, is not directly affected by labour market conditions, Suryahadi et al (2001) found that the real minimum wage in Indonesia is endogenously determined with real wages because in practice the existing wage rate are also used as one o f the considerations for regional government to set their minimum wage level. In addition, Lemos (2004d) also found that the spike measure (the fraction at) tends to be endogenously determined with wages in the case o f Brazil because in practice wage bargaining decides which workers are paid at (around) the minimum wage level. If there is evidence o f endogeneity, then it is important to control for this bias using the instrumental variable method.