Evaluación financiera: VAN, TIR, B/C, PR

One way to measure the consequences of female participation is to focus on the supply of experience. Due to interruptions of labour market activity that are more frequent for women than for men, women necessarily accumulate less labour market experience as men. However, measures of labour market experience from individual surveys usually have low quality, since the exact number of years worked is typically not known. Experience is often approximated with the so-called potential experience, i.e. age minus education. If this proxy is

considered as a good instrument in the estimates of wage equations, the measurement error is rather large: O’Neill and Polacheck estimate that actual experience of women is about 2/3 of potential experience, with an increase in this fraction over time and cohorts. For this reason, we prefer to use aggregate data to approximate experience of men and women.

The idea is as follows: let pat be the participation rate of a cohort of age a at time t. For

simplicity, we ignore the gender dimension in the notation. At time t-1, the cohort had a participation rate of pa-1,t-1, and at the age of entry to the labour market, say 15, that cohort had

a participation rate of p15,t-(a-15). It follows that the average experience of the cohort of age a at

time t can be written:

∑

= − − − = a a a a a a t a t a p E 15 ' ' ) ' ( , ' , δ

where d<1 is a discount factor characterizing the yearly depreciation rate of the accumulated experience at rate 1-d. Since we use participation rates and not employment rates, this measure of experience is experience of the labour market, not of employment.32 Rewriting the above formula by changing the index and defining by e=a-a' the potential experience of the labour market, one obtains

∑

− = − − = 15 0 , , a e e e t e a t a p E δ .

This formula is equivalent to the usual formula defining the capital stock of a plant of age a, i.e. as the sum of depreciated past investments in physical capital. There is one difference though: the initial stock of experience is known as it is simply zero at the age of 15.

The total stock of experience of men and of women is the sum across all cohorts, with weights the share of the cohorts in the male (resp. female) active population (denoted byα , _a,K_t with K=m,f for male and female). We thus obtain:

∑

= = 64 15 , , a K t a t a K t E E α , K=m, f

which also gives the average experience of labour market participants, with α_tf the share of women in the labour force, as

f t f t m t f t t E E E =(1−α ) +α 32

This measure of experience is less accurate than employment experience as far as the issue is to measure human capital (although, young graduates usually learn a lot when looking for jobs). Labour market experience however captures better the idea of attachment to the labour market: unemployment spells then matter. There was not enough employment data per age and gender available to estimate employment experience. One can use this constraint to one’s advantage: as argued later on, labour market experience is a good instrument for employment experience in an unemployment model.

The OECD and the ILO provide participation data and population by gender and age intervals for several countries for the period 1960 to 1998. A logistic function of participation rates by age, gender and country are regressed on lagged values, a trend and its square. The series are then dynamically forecasted on the basis of each model to obtain retrospective data. Series of forecast errors are computed to provide confidence intervals. This method ignores the break due to the war period, with the implication that forecasts (in the present case, a more accurate word would be backcasts) of participation before 1945 have a systematic error. The weight of those years in the average experience across cohorts is however rather small between 1970 and 1998, which is the period considered hereafter. See Wasmer (2001a) for more details about this method. We set δ to 1, i.e. do not consider human capital depreciation.

Figures 6.1-6.4 illustrate the time series evolution of experience by gender in France, Germany (western Länder), Sweden, Finland, the Netherlands, the US. and the United Kingdom. They establish the following facts:

1. In all countries, the average experience level of men is between 18 and 20 years, with a decline in the seventies as the size of the cohorts of new entrants in the labour market peaks 16 years after the baby-boom.

2. The average experience of women differs much more across countries. In the early 70’s, it was about 5 years in the Netherlands, 6 years in Spain, 8 years in the UK, 10 years in France, the US and Germany, and 12 or more in Sweden and Finland.

3. There is a general trend towards gender convergence, although it is especially slow in Spain, Germany and the Netherlands.

4. The level of average experience, being by definition a weighted combination of both series, is closer to male experience in low participation countries. In most countries, average experience declined in the late 70’s, especially in countries where female participation was low and grew fast.

The low experience levels of women in the 70’s are explained both by low investments in the labour market from older cohorts of women and by the over-representation of young women in the female labour force. The latter is a pure cohort-effect, presumably a transitory one given gender convergence. This indicates that countries with historically low levels of female participation, which faced a large demographic impact of the baby-boom and a change in social norms for recent cohorts had to cope with a strong reduction in average experience in the first instance. Subsequently, in the late 80’s, these factors have reverse consequences: the population becomes older, and higher female participation is rewarded by

higher average experience levels. Both factors, the ageing and the higher propensity of women to participate to the labour market, contribute to a U-shape pattern of experience in the labour market. One can isolate formally the effect of female participation on this U-shape evolution: for E_tf <E_tm, an increase in female participation raises α_tf and initially contributes to a decline in E , while _t E_tf is an increasing function of past values of α_tf implying, with a lag, that both E and _t f

t

E will increase.

In document ESTUDIO DE PREFACTIBILIDAD PARA LA INSTALACIÓN DE UNA ENOTECA (página 97-0)