Labour-incentive reforms at preretirement age in Austria

Abstract

Low participation rates of older workers in the labour market threaten the sustainability of the pension system in Austria. Given the current political debate on this issue, we try to shed light on employment and retirement behaviour of Austrian couples when income support is provided and pension benefits are reduced. Using a sample of married couples with both partners aged 50–65, we find that the proposed reform increases the labour supply of middle-income men whereas the effects on women are weaker. However, somehow surprisingly, we find that these reforms have an increasing effect on unemployment/inactivity probabilities which in turn is outweighed by a decreasing effect on the retirement probabilities in case of women. These findings emphasize the importance of a joint consideration of labour supply and retirement behaviour of married couples when introducing pension reforms and tax-benefit policies.

Appendices

Appendix 1

See Appendix Box 1, Tables 10, 11, 12, 13 and 14.

Box 1 Parametric reforms in Austria (2003)

Table 10 Pensions as percentage of GDP in 2004

Table 11 Expenditures as percentage of GDP in 2004

Table 12 Austrian Economically Inactive Population by Reason for Inactivity in 2006

Table 13 Earning equation for men and women (regression model with sample selection)

Table 14 Pension entitlement equation for men and women (regression model with sample selection)

Appendix 2: Prediction of earnings

Table 13 shows that labour market participation is lower for males with longer contributory period and residing in regions with high unemployment rates, while it is higher for those who are more educated and cohabitate. As for females, labour market participation is lower for those women with more years of contribution and is higher for the more educated ones. These features of labour supply behaviour reflect the attitudes of the working force close to the retirement phase. The estimates of earning equation show a significant and positive effect of education, experience and cohabitating status for both men and women pointing out that earnings possibilities improve with the increase of experience and higher education as shown in the human capital theory and labour market signalling.

Appendix 3: Prediction of pension entitlements

The Heckman selection model helps to predict the pension’s entitlement in case this alternative is not chosen by the individual. The imputation takes place as follows:

• (a) first we estimate the propensity to retire early based on a vector of characteristics $\Upphi (\lambda^{'} Z_{Pt} ) = {\text{Prob}}(\varepsilon_{pt} > - \lambda^{'} Z_{Pt} )$ where Z _Pt is a vector of characteristics, λ is a vector of parameters and ɛ _pt is a standard normal random variable

• (b) Pension entitlements specification used in this paper is based on a similar specification used by Colombino (2003). Pensions received at a certain time t depend on the initial value of pension after τ years of employment,

$$ \, P_{t} (\tau ) = A_{\tau } \vartheta \Uppsi (R_{\tau } ,R_{\tau - 1} , \ldots ,R_{\tau - n} )\tau e^{\rho (t - \tau )} \xi_{Pt} \, $$

(8)

$$ {\text{where}}\,\Uppsi (R_{t} ,R_{t - 1} , \ldots ,R_{\tau - n} ) = {\frac{{\sum\nolimits_{i = 0}^{n} {R_{\tau - i} } }}{n + 1}} $$

• ϑΨ (R_τ,R_τ-1, … R_τ−n) represents the proportion $\vartheta$ of the average employment earnings over the last n + 1 years

• τ represents the number of years of being employed (seniority)

• A _τ  = 1 when the individual is eligible for early retirement pension and 0 otherwise

• ρ represents the annual rate of increase of the pension

• $\xi_{\text{Pt}}$ is the stochastic component assumed to follow a lognormal distribution

If we ignore the stochastic component, we get

$$ \Uppsi (R_{t} ,R_{t - 1} , \ldots ,R_{\tau - n} ) = R_{\tau } \sum\limits_{i = 0}^{n} {{\frac{{e^{ - i} }}{n + 1}}} . $$

(9)

And therefore, the Eq. 8 can be expressed in a logarithmic way as follows:

$$ \ln (P_{t} (\tau )) = \ln (\vartheta ) + \ln \left( {\sum\limits_{i = 0}^{n} {{\frac{{e^{ - i} }}{n + 1}}} } \right) + \ln (R_{\tau } ) + \ln (\tau ) + \rho (t - \tau ) + \ln (\xi_{Pt} ) $$

(10)

where ln (R _τ ) represents labour income calculated using the Heckman selection regression for earnings (Table 13).

(c) Summing up the Eq. 10, we need to estimate the following equation on the sample of early retired individuals.

$$ E(\ln (P_{t} )) = \eta '{\text{Region}} + \ln (R_{\tau } ) + \ln (\tau ) + \rho (t - \tau ) + {\frac{{\text{cov} (\ln (\xi_{\text{Rt}} )n(\xi_{Pt} ))}}{{\sqrt {n(\xi_{\text{Pt}} )} }}}{\frac{{\phi (\lambda 'Z_{\text{PT}} )}}{{\Upphi (\lambda 'Z_{\text{PT}} )}}}. $$

(11)

As P _t represents the pension that the individual would receive at time t if he had to retire at the same time, t equals τ and therefore the term ρ(t − τ) cancels out.²⁵

We include in the vector Z _PT the following characteristics variables:

• 1. Education

• 2. Married or not

• 3. Income from self-employment

• 4. property income

• 5. income from investment

• 6. private pension

• 7. regional unemployment

Table 14 shows the estimates of the pension entitlement counting for the Heckman selectivity. The pension entitlements increase with wages and the number of contribution both for men and women. Income from self-employment and holding a private pension seem to be important for the retirement decision. In addition, the lack of job possibilities (signalled by a high unemployment rate) makes the retirement option more attractive. Lastly, the decision to opt for the retirement decision is more likely for those couples coming from small size households.

Appendix 4

4.1 A. Utility function specification

The specification is linear-in-parameters, which allows the use of potential estimation procedures available in most econometric or statistical packages. We chose a quadratic specification since it represents a good compromise between flexibility and ease of estimation:

$$ \begin{aligned} V(X,h_{F,} h_{M;} b) = & b_{x} X + b_{F} (T - h_{F} ) + b_{M} (T - h_{M} ) + b_{xx} X^{2} + b_{FF} (T - h_{F} )^{2} + b_{MM} (T - h_{M} )^{2} \\ + b_{xF} X(T - h_{F} ) + b_{xM} X(T - h_{M} ). \\ \end{aligned} $$

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Some of the above parameters b _s may depend on household or individual characteristics Z. A convenient choice might be to interact the disposable income and the leisure variables with the individual characteristics as follows:

$$ b_{F} = b_{F1} \left( {G\_60_{\text{wife}} } \right) + b_{F2} b_{F1} \left( {G\_65_{\text{wife}} } \right) $$

$$ b_{M} = b_{M1} \left( {G\_60_{\text{husband}} } \right) + b_{M2} (G\_65) $$

$$ b_{x} = b_{x1} \left( {{\text{Age}}_{\text{husband}} } \right) + b_{x2} \left( {{\text{Age}}_{\text{wife}} } \right). $$

4.2 B. Choice set specification and hours distribution

The choice set is composed of 6 alternatives for each individual by specifying the interval of hours of work and sample randomly within this interval which has a length of 16 h. The first alternative refers to zero hours of work, and the last to the pension choice. The actual observed hours will be rounded to the closest discrete value. The basic idea can be appropriately modified when one directly observes annual hours or weeks worked.

To capture the effect of each alternative on the utility, we use some alternative dummies and calling them with a common variable A, we express the probability function as follows:

$$ P^{n} (f,m;\vartheta ) = {\frac{{\exp \left\{ {\Uppsi^{n} (f,m;\vartheta ) + \gamma A} \right\}}}{{\sum_{f \in \Upomega } {\sum_{m \in \Upomega } {\exp \left\{ {\Uppsi^{n} (f,m;\vartheta ) + \gamma A} \right\}} } }}} $$

(13)

where the $\gamma {\text{s}}$ are parameters to be estimated.

The dummies can be interpreted as reflecting quantity constraints on the labour market (as in Aaberge et al. 1999) or specific utilities of full-time, part-time, extra-time jobs or maybe both (as in Van Soest and Das 2001, 2002).²⁶

The simulations are run under a neutral budget provided that this age group is treated differently from the others in terms of tax rates.