The relationship between wages and firm size

In both 1995 and 2005 we see a correlation between wages and firm size (Table 4-5), with wages rising monotonically with firm size category. The one exception to this relationship is that in 2005 wages in the medium-sized firms were slightly higher than wages in the largest enterprises.

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Table 4-5 Wages per enterprise group for employees in private enterprises (Int. USD)

1995 2005 Delta

Notes: The estimated proportion of the labour force in each group differs from that of Table 4-1 because this table covers only private sector employees.

Source: Author’s computations based on LFS 1995 and 2005; Purchasing Power Parity (PPP) conversion factors from World Development Indicators (World Bank, 2013).

These findings are put into perspective, however, by the importance of the correlation between wages and other variables, particularly education. Wages rise monotonically with education, in both years, with particularly sharp increases at the upper secondary level and post-secondary levels.⁹⁸ The regional differences are also pronounced – in 1995 mean wages in Bangkok were more than twice the level of those in the lowest wage region, the Northeast. Average real wages were lowest in agriculture and highest in services, in both years, although the

98 Returns to education are not calculated in this chapter, but these results suggest that they are high.

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differences were less pronounced than those between regions or educational levels.

We use regression analysis, with monthly wage in Int. USD as the dependent variable, to examine whether firm size has an effect on wages independent of region, sector and education, all of which are likely correlated. Results are reported in Appendix A-4. Using dummy variables for the six size groups described above, we find that firm size had a significant impact on wages in Thailand in both 1995 and 2005, even controlling for observable characteristics.⁹⁹ However, the coefficients on the firm size dummies are much small than the large coefficients on the upper- and post-secondary dummies, and also smaller than the coefficients on the regional dummies. Firm size has a significant impact on wage, but it does not influence wages as much as a worker’s education or of the area they live in.

Wages rose by about 30 percent in real terms between 1995 and 2005 (Table 4-5).

This implies that the average worker benefited from economic growth over this period. However, the pattern has varied by firm size. The medium-sized firms, with between 50 and 99 workers, saw an increase of around 52 percent. In the smallest ‘micro’ enterprises wages rose by 35 percent but in the slightly larger micro firms the increase was only 16 percent. As a consequence, wages in these size groups converged and in 2005 were roughly similar. Wages in the largest firms rose by 26 percent.

Wages grew fastest in services, and in Bangkok, the Central and Southern regions. Workers with elementary or less than elementary education experienced the fastest wage group. Those with secondary education or above saw slower growth. However, as noted above, the proportion with these levels of education rose.

The regression analysis in Appendix A-4 also allows us to quantify how the strength of the relationship between firm size and wages has changed over time.

The catch-up of employees of the smallest microenterprises is significant – by 2005 workers in enterprises with 5 to 9 employees were no better off, statistically, than this group. The coefficient on the dummies for the other small firm sizes is relatively stable, but the coefficient for the medium and large enterprises rose. So, while the convergence at the bottom of the distribution confirms our hypothesis that the relationship between firm size and wages would become weaker as Thailand developed, wage levels continued to diverge between the largest and smallest firms.

99 We used firms with fewer than 5 employees and workers with no education as reference categories.

119 4.3.5. Wage inequality in Thailand

Figure 4–1 plots the overall wage distributions for private employees for the two years analysed. In 1995 a large proportion of workers were clustered close to the mean value. In 2005 the mean wage had risen, and a larger number of workers were located above the mean, in the right hand tail of the distribution, with wages of 300 Int. USD or above. ¹⁰⁰ This suggests a rise in wage inequality, which is confirmed in the measures of inequality summarized in Table 4-6.

Table 4-6 includes a number of commonly used inequality measures. The Generalized Entropy (GE) class of measures is included: GE(0), or the mean logarithmic deviation (MLD), GE(1), or the Theil Index, and GE(2), which is equivalent to half of the squared coefficient of variation. The Gini coefficient, and measures of the Atkinson class with different levels of inequality aversion are also provided.¹⁰¹ A higher inequality aversion factor (represented by the number in brackets) makes the Atkinson and GE inequality measures more sensitive to differences at the bottom of the wage distribution.¹⁰²

Figure 4–1: Wage distributions for private employees in Thailand in 1995 and 2005

Source: Author’s computations based on LFS 1995 and 2005

100 These main results are not altered when we plot the wage distributions by firm size (see Figure 4-2 in the Appendix).

101 See Cowell (2000) and Atkinson (1970) for details on the different inequality measures.

102 For our further analysis we focus on the measures of the GE class, because these are decomposable.

0

.001.002.003.004.005

density

0 200 400 600 800

monthly wage in Int. USD

1995 2005

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The results in Table 4-6 show that the inequality of the wage distribution (for employees in private companies) increased between 1995 and 2005. This is in contrast to overall income inequality, which remained roughly constant, as noted above, and in contrast to our hypothesis in 4.2.3. This implies that other income sources, such as earnings from self-employment, social transfers or agriculture, compensated for the dis-equalizing effect of wages, or that the equalizing effect of the expansion of wage opportunities was enough to mitigate the rise in wage inequality.

The biggest proportional increase occurred using the measures with high inequality aversion factors. These measures are highly sensitive to changes in the lowest part of the distribution. This implies that the increase in the deviation from the mean was most pronounced for the workers at the bottom of the wage distribution. It appears that, as the mean wage rose, a certain group of workers fell further behind the average.

Table 4-6: Inequality measures for overall wage distributions

Source: Author’s computations based on LFS 1995 and 2005.