Mobility in the 1990s

Having looked at the transitions into and out of different employment states for

the three datasets and gained some insight into the problems of attrition with the NES, I now turn to look at mobility patterns using the NES and BHPS, the two datasets which

contain wage information. The earnings measure that I use is hourly earnings including

overtime pay. For the NES, this is defined as gross weekly pay divided by total weekly

hours. For the BHPS the definition is similar except that earnings are converted to a weekly measure from whatever the length of the individuals pay period.

It is well established that earnings rise over the life-cycle and there is an issue of

whether we should strip out these effects when looking at mobility (See Dickens ,1996a

or Gosling, Machin and Meghir, 1996b, 1996b). The earnings measure that I use for these transition matrices is unadjusted earnings. However, I have also experimented using age

adjusted earnings by taking residuals from yearly cross section regressions of earnings on

age and computing the transition matrices on these. The results are not substantially effected but, as we may expect, adjusting for age gives lower levels of mobility.

It is informative to look at mobility both within the wage distribution and into and out of the distribution to other employment states. It seems likely that those in the lower

part of the wage distribution are likely to be those that exit to unemployment more frequently. To look at this I have computed the deciles of the wage distribution and presented one year transitions both between deciles and to other employment states. Tables 4.2a and 4.2b presents these transition matrices for males between 1993 and 1994

from the NES/JUVOS and the BHPS datasets respectively. Tables 4.2c and 4.2b presents

the same information for females.

One of the striking things to come out of these matrices is the degree of inunobility

both in terms of deciles of the wage distribution and in states outside of employment. The diagonal elements of these matrices are all much higher than the off diagonal elements,

signifying a degree of persistence. Notice also that, as expected from the analysis of transitions above, the NES gives a higher degree of persistence than the BHPS.

be an artifact of computing mobility in terms of decile transition matrices. The range of wages at the bottom and top are much larger than in the middle so for a given wage

change it is more difficult to escape these deciles.

For males in the NES, some 48% of the bottom decile remain there one year later. This may sound like there are quite a large number of escapees, however many leave

employment altogether. In fact only 20 percent move up the wage distribution and two thirds of these only make it as far as the next decile. One finds greater mobility in the

BHPS, with 43% remaining in the bottom decile and 34% moving up the distribution. Once again however, of those that move up, 45% only make it to the next decile. Practically no individuals move beyond the median of the distribution.

Looking at deciles in the middle of the wage distribution one finds greater mobility, with about 40% staying on the diagonal in the NES compared to about 30% in the BHPS. However, there is still evidence of a concentration around the diagonal,

indicating that those individuals that do move over the year don’t move very far. When one looks at the top of the wage distribution one finds a very high level of persistence. Over 70% of the top decile remain there in the NES and 67% in the BHPS. Very few of those that do leave move any great distance down the distribution.

Another important point to note is that those in the lower deciles are more likely to enter unemployment, in both the NES/JUVOS and BHPS. Somewhere between 6.5%

and 9.5% of the bottom decile enter unemployment compared to about 1.8% of the top

decile. Similarly, those unemployed that make it into work are more likely to enter into

the lower deciles of the wage distribution. Between 4.6% and 6.3% of the unemployed enter the bottom decile, compared to 0.9-2.1% entering the fifth decile and practically

In the NES, it is evident that those in the bottom deciles are also more likely to go missing. This is surely related to the sampling problem associated with low wage

individuals. However, this pattern also emerges, to a lesser extent, in the BHPS and so is probably reflective of the low paid being more likely to enter inactivity. The matrices

portray a picture of persistence, with little mobility over a one year period. Many of the

low paid either remain in the bottom of the wage distribution or move out into unemployment or inactivity. Somewhere between 60% and 70% of the bottom decile

either remain there or move out of employment altogether. Many of the unemployed and inactive remain in this state, and those that do move into employment are more likely to enter in the lower deciles of the wage distribution.

The matrices for females from the NES and BHPS look very similar to those just described for males. There is a concentration on the diagonal of a similar order as that for males. However, it does look like females are slightly more likely to move up from the bottom decile than males. Nevertheless, between 55% and 65% either remain in the

bottom decile or move into non-employment. Women in the bottom deciles are also more likely to move into unemployment or out of employment than those further up the wage

distribution. Those who are non-employed that move into employment, do so into lower wage jobs.

So far I have only presented transition matrices for periods one year apart. It seems likely that mobility will rise the greater the time period over which the transition is

measured. Tables 4.3a-4.3d present three year transitions for the two datasets for males and females between 1991 and 1994. What is evident from these matrices is that mobility

measured over this longer time horizon is somewhat higher. The concentration along the diagonals is less than when measured over one year. Also, the differences observed

between the two datasets for the one year transitions are less apparent over this longer time horizon. Although the numbers leaving the NES are still much higher than those

entering the “other” state in the BHPS, the proportion remaining in each of the deciles look more alike than for the one year transitions.

Despite giving a higher degree of mobility, these three year transitions still point

to some signs of significant persistence. For example, 26-31% of those males in the bottom decile are still there three years later. A further 25-32% have moved out of

employment and around 9-10% have no observable wage. This leaves somewhere between 26% and 40% moving up the distribution in the space of three years. Of these

that do move up, only about half make it beyond the second decile and very few are above the median. At the top end of the wage distribution, a striking 56% of the top decile remain after three years. Most of those moving down only drop by one decile.

Movement into unemployment has a higher incidence in the lower deciles of the distribution, with 10-11% of the bottom decile becoming unemployed compared to 6%

of the fifth decile and 1-3% of the top. More of the unemployed move into employment over the three year period than the one year period. However, the entrants are concentrated in the bottom few deciles.

The transition matrices for females show a similar pattern (Tables 4.3c and 4.3d).

Once again the females display a higher degree of mobility, with a lower concentration on

the diagonals, at least at the extremes of the distribution. However, the number that leave for the “other” category which largely covers the inactive is much higher than for men,

particularly so for the lower deciles. Although only 24-27% of the bottom decile remain there, between 32% and 34% have left employment. Between 26% and 42% have moved

second decile, indicating slightly more mobility than for males. The bottom deciles display

a higher propensity to enter unemployment and inactivity, and individuals entering

employment from these states are more likely to enter in the lower deciles. Again there is a high level of persistence at the top of the wage distribution, with 49-52% of the top

decile remaining there three years later.

Tables 4.4a and 4.4b present five year transitions between 1989 and 1994, for males and females from the NES/JUVOS. Again, mobility is higher over this longer time

period for males and females. Nevertheless, although only somewhere between 20% and 22% stay in the bottom decile, many have moved out of the NES and only about 29-31% have moved up the distribution. Of those in the top decile, around 44-48% hold their position at the top five years later.