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7/21/11

Unemployment in Italy

In this blog, we have reported empirical relationships approximating Okun’s law for many developed countries: the USA, France, Spain, Canada, Australia, the UK and Germany. Instead of the original form of Okun’s law we have applied a LSQ technique to its integral version:

u(t) = u(t0) + bln[G/G0] + a(t-t0) (1)

where u(t) is the predicted rate of unemployment at time t, G is the level of real GDP per capita, a and b are empirical coefficients.

For Italy, we have estimated a similar model with a possibility of a structural break somewhere between 1980 and 1990. The best-fit (dynamic) model minimizing the RMS error of the cumulative model (1) is as follows:

du = -0.13dlnG + 0.71, t<1990
du = -0.25dlnG + 0.00, t≥1990 (2)

This model suggests a significant increase in slope and a big fall in intercept around 1985.

Figure 1 depicts the observed and predicted curves of the unemployment rate, the latter is predicted by (1) with coefficients from (2). The agreement is not good, especially between 1985 and 2000. Figure 2 shows that when the observed time series is regressed against the predicted one, R2=0.84. Here we do not test both time series for stationarity but presume that the rate of unemployment has to be a stationary time series in the long run.

The integral form of the dynamic Okun’s law (1) is characterized by a standard error of 0.82% for the period between 1971 and 2009. The average rate of unemployment for the same period is 9.0% with a standard deviation of the annual increment of 0.63%.

Previously, we reported on the model linking the rate of unemployment in Italy to the change in labor force. It was shown that the change in labor force leads by 11 years and allows a prediction of the rate of unemployment with an accuracy of 1.5% for approximately the same period. Figure 3 depicts this model.

Figure 1. The observed and predicted rate of unemployment in the Italy between 1971 and 2009.

Figure 2. The measured time series is regressed against the predicted one. R2=0.84 with both time series likely to be stationary.

Figure 3. The rate of unemployment in Italy predicted from the change in labor force.

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