Assuming that the monthly wage is a function of age, education, and gender, whereas the probability of employment (the likelihood of the wage being observed) is a function of marital status and whether the individual in question is a household head or not, and wage (via the inclusion of age, education, and gender, which we use to determine wage), we will use the Heckman estimation model instead of the Ordinary Least Square regressions to fit a wage model, for the sample of observed wages could be biased upward.<sup>17</sup> To capture the non-linear effects of age on employment and earning, we introduce the variable “age square” into the equations.<sup>18</sup> Based on the pooled LFS data 2002-2004 for the entire country, we estimate the equations as follows: Selection equation (wage is observed if): 2 γ 0 + γ 1 _age_ + γ 2 _age_ + γ 3 _education_ + γ 4 _sex_ + γ 5 _married_ + γ 6 _household_ head_ + ∑θ _iregional_ dummyi_ + μ 2 > 0 where _i_ =2,3,4,5, considering Region 1 (Central Region) as reference; and _corr_ ( μ 1, μ 2 ) = ρ .

The estimation of regression 1 in table 8 shows that, at the national level, age plays a significantly positive role in employment and earning, but the positive effects decrease as age increases;<sup>19</sup> human capital, here measured by years of school, has positive effects on employment in a significant way, and the earnings level of an individual monotonically increases with his or her education endowment; women have a harder time to get a job than men, and face a lower level of earnings if they manage to get employed nonetheless, others being equal; being married or household head increases an individual’s probability to be employed, partly because of his/her stronger incentives to seek employments. The estimations of regression 2 show that all regional dummies are significant. It suggests that the probability of employment and the level of earnings significantly differ across regions for individuals with similar characteristics in age, education, and gender. More specifically, on employment, in comparison with the Central Region (where the employment rate is the highest), individuals with similar individual characteristics have lower chance to get employed in the other four regions – among these four regions, the individuals have the lowest chance to get employed in Adriatic South Region, and the highest chance in Adriatic North Region. On earning, in comparison with the Central

> 17 For further discussion on the methodology of the Heckman estimation, see Heckman (1976; 1979) and Greene (2003).

> 18 We had also tried to introduce the “years of school square” into the equations. However, the results show that effects of education on employment and earnings are not significantly non-linear.

> 19 Based on the results of table 8, the positive effects of age on employment reach the maximum level before those on earnings reach the maximum level. Instead of arguing the exact golden age for employment/earning, we would like to suggest that one’s earnings level (if he or she is employed) may continue to increase for a certain period after his or her age has the maximum positive impacts on employment, because experience counts. The studies at the regional level show that, given the difference in job market structure and in labor supply, the magnitude of the effects of experience varies across regions.

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