Downward nominal wage rigidity and gender

The gender aspect of downward nominal rigidity has received little attention.

Nevertheless, there are reasons to believe that men and women behave diffe-rently when faced with a proposed cut in wages. For example, Agell & Benn-marker (2007) find from a survey of Swedish firms that managers in companies with a large share of female workers are less inclined to believe that workers who feel underpaid respond by reducing effort than are managers in male-dominated firms (Agell & Bennmarker, 2007, p. 366). The reasons for this could be linked with the more risk adverse behaviour of women compared with men (e.g. Croson & Gneezy (2009), Holt & Laury (2002)).

When looking at pooled distributions of the yearly personalised social tax changes from 2002–2008, it seems that wage freezes are more common for men than for women (see Figure 21). This could be taken as the first sign of greater rigidity in the men’s wage. However, it is important to keep in mind that the spike at zero is not a very useful tool for identifying rigidities if there is no comparison through a counter-factual distribution. Another interesting obser-vation is that secondary spikes are missing. The explanation for this is a technical one – as the minimum wage and the minimum base of social tax grow at different rates in different years, pooling of the histograms serves as a smoothing mechanism.

In constructing the histogram bins that serve as the basis for the later analysis, the 35^th percentile was used as the location of the distribution, while for the standardisation of wage growth figures the difference between the 95^th and 35^th percentiles of the wage change distribution was used³⁶. The bin width was

36 The use of different measures of location for employer and employee subgroups which can be seen in the following sub-chapters is motivated by the attempt to include in the analysis as few histogram bins as possible. The lower the location, the fewer the number of bins that must be estimated. However, the location must be sufficiently high to not be affected by DNWR itself, so it must always be above zero wage growth.

Figure 21. Wage change distributions for men and women 2002–2008 Source: Estonian Tax and Customs Board, author’s calculations

points. Altogether three different estimations were performed with equation systems (5–1), (5–2) and (5–3). First, the model (5–1) was estimated. This specification does not allow the rigidity coefficient to vary over time, and the results are presented in Table 23.

The table shows that all the coefficients are statistically significant and 11.5% of the wage cuts were censored for men, while the same estimate for women was only 9.1%. This indicates that there is a difference between the DNWRs of men and women. According to the Wald test for the equality of rigidity coefficients (See Appendix 3), this difference is also statistically significant.

Table 23. Rigidity coefficients from WLS estimates of equation system (5–1), by gender

Gender Coefficient Std. Error t-Statistic Prob.

Woman 9.1% 0.0035 25.6154 0.0000

Man 11.5% 0.0039 29.5869 0.0000

Source: Estonian Tax and Customs Board, author’s calculations

Now we can turn to the model that allows a test of whether the business cycle has an influence on rigidity coefficients. As an indicator of the economic climate the change in the annual unemployment rate is used at first. If worsening labour market conditions do make workers less resistant to pay cuts, then ρ_ΔU should be statistically significant and have a negative sign.

Table 24. Time-varying rigidity coefficients from the WLS estimates of equation system (5–2), by gender

Gender Coefficient Std. Error t-Statistic Prob.

Woman ρ 7.9% 0.0053 15.0815 0.0000

ρΔU –0.8% 0.0034 –2.4378 0.0165

Man ρ 10.5% 0.0060 17.6501 0.0000

ρΔU –0.8% 0.0037 –2.2702 0.0253 Source: Estonian Tax and Customs Board, author’s calculations

All the coefficients listed in the table are statistically significant at the 5%

significance level, meaning that for both men and women DNWR is influenced by the labour market situation³⁷ (see Figure 22). However, as was already

37 This result is relatively sensitive to the estimation method. Anspal & Järve (forthcoming) used on the same data the SUR estimator and found that for men the

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mentioned in the previous chapter, the specification of model (5–2) tends to give results that are not intuitively appealing, as despite the decreasing unemployment the wage rigidity also seems to decline. The same problem is evident also from the Figure 22. The next logical step is to obtain the estimates from model (5–3) as well.

Figure 22. Rigidity coefficients for men and women (model (5–2)) Source: Estonian Tax and Customs Board, author's calculations

Table 25. Time-varying rigidity coefficients from the WLS estimates of equation system (5–3), by gender

Gender Coefficient Std. Error t-Statistic Prob.

Women ρ 10.2% 0.0119 8.5800 0.0000

ρΔU –1.0% 0.0031 –3.2763 0.0015 ρU –0.3% 0.0016 –2.1103 0.0374

Men ρ 14.9% 0.0136 10.8939 0.0000

ρΔU –1.1% 0.0038 –2.9205 0.0043 ρU –0.6% 0.0018 –3.3813 0.0010 Source: Estonian Tax and Customs Board, author’s calculations

Again all coefficients are statistically significant at the 5% level of confidence.

There are several conclusions from this sub-chapter. Firstly, men’s wages are cyclical component of DNWR (coefficient ρΔU) was insignificant while for women this was not the case. Here weighted least squares estimator is preferred, as it gives more stable results on small samples (Beissinger & Knoppik, 2001, pp. 407-408).

0%

2%

4%

6%

8%

10%

12%

14%

2002 2003 2004 2005 2006 2007 2008

Men Women

more downwardly rigid than women’s wages. Secondly, the DNWR of men is more volatile over the business cycle than that of women.

Figure 23. Rigidity coefficients for men and women (model (5–3)) Source: Estonian Tax and Customs Board, author’s calculations