individuals are unable to progress to better employment (in the private or public sector) or successful entrepreneurship. However, if this is the case, then the returns to experience in these jobs should be lower than the returns to experience in full‐time government and private sector jobs. So the key question we seek to answer in this section is: What are the returns to years spent mostly in self‐employment for youth and for adults? In particular, how do these returns compare to the returns to private sector or government work? Consequently, the equation we are interested in estimating is:

# ��� � ��� ���� � ��� � ��� � �� � ���

Where ��� is the log monthly earnings (either wages or profit) of individual � in job � , �� is a vector of job characteristics, �� is a vector of education characteristics, �� is a vector of labor market experience variables (that are returns to experience for each � that is positive and an indication of scarring for each � that is negative. �� is a vector of observable time variant characteristics such as household characteristics and year fixed effects. Lastly, the structure of the data is reflected in the decomposition of the error term into two components, one of which is individual‐specific and time invariant ( �� ).

Particularly, we want to know if after controlling for individual heterogeneity (education and unobservables related to productivity), a scarring effect can be identified for some types of work experience. There are three key challenges involved in this. Firstly, there is the problem of the work history data not containing information about job search and unemployment. This means that we cannot investigate the long term scarring effects of unemployment. Secondly, another major shortcoming of using IFLS to measure the returns to experience is that only a variable for the approximate number of total years in each employment category can be created. As previously mentioned, the precise amount of experience for any given employment category is not known and so the resultant experience variables are “noisy”. Unfortunately, there is nothing we can do to reduce the measurement error in this variable. This means that if the work experience variables have no significant relationship with wages, it may simply be because they are noisy. While it may not be possible to confirm when an experience variable is truly unrelated to wages, the opposite is not the case. If there is a strong relationship between an experience variable and wages, this would be _in‐spite_ of measurement error, and not because of it. Thus it may be possible to confirm when there are significant positive or negative returns to certain types of work experience.

Lastly, there is the challenge of controlling for unobservable heterogeneity which may be quite important in the determination of wages. Not controlling for unobservable heterogeneity could lead to biased estimates. To control for individual time invariant heterogeneity ( �� ), we can use fixed effects (FE) estimation. To control for time variant heterogeneity (e.g. motivation) the best we can do is to include variables that measure a range of observable characteristics that are closely correlated to these unobservables. Of course, even with these measures there is no guarantee that our estimates will not be 26