1. Discussion on “Female Labor Force Participation in Iran: A Structural Model Estimation” by S. Farahzadi and M. Rahmati
Murat Güray Kırdar
Boğaziçi University

2. Brief Review
This study examines the causes of low female labor force participation rate in Iran using a structural model.
The structural model allows for differences between men and women in job finding rate, job security, wages and search costs – in addition to the existing differences in educational attainment.
It uses a rich longitudinal dataset.
The key findings are that differences in the job finding rate plays a bigger role than differences in job security, wages and search costs. Yet, all together these factors can still explain a small fraction of the differences in labor force participation between men and women in Iran.
Murat G. Kirdar

3. This is a nice paper addressing an important topic and using sophisticated structural estimation methods.
The exposition of the paper could certainly be improved, but I suppose that this is an early draft of the paper.
My comments will be mostly about the modeling choices and the lack of clarity in the exposition of these modeling choices.

4. Utility Function
1) No curvature in the way consumption enters the utility function? It would be better to use a CRRA functional form instead to capture intertemporal substitution of consumption better.
2) We need to justify why leisure is interacted with schooling in the utility function.
3) Actually, we might expect interaction terms of N (number of children) with leisure.
4) Also, the marginal utility of consumption could depend on the number of children (N) and/or age of the couple.

5. Heterogeneity
It would be good to explain in what ways the couples differ at front. It appears in a piece by piece way and certain things never become clear.
As far as I understand, couples differ in terms of education and the number of children – when we start solving the problem at age 25.
“Age” is not clear. The problem is solved from age 25 to age 55. This is the age of the husband, I suppose. How is the age of the wife treated? This also generates heterogeneity in family type. A given 25-year-old men might have a wife at different ages.
Define time zero state variables. Everybody starts with no children, or with different numbers of children as they are observed in the data, in which case we need to solve the dynamic programming problem with different types of families.

6. Two Key Missing Elements:
Labor Market Experience: This is crucial in a life-cycle model addressing labor force participation decision. As discussed in the introduction, this would play a role in many ways (it changes wages in the future, it changes job finding probability, job security, etc.)
Permanent Unobserved Heterogeneity: The authors find that most of the observed differences in labor force participation between men and women cannot be explained by the elements in the model. One reason is that men and women are fundamentally different in their preferences for work or ability. It would be possible to introduce types (that differ in preferences, ability, etcs) ala Keane and Wolpin (1997).

7. Unclear Elements in the Model:
Search Cost: The job finding probability, lambda, is written as a function of capital S (which is schooling). Where is small s (job-search cost)?
Search Cost: Shouldn’t the search cost enter into the budget equation?
Job destruction only as a function of schooling. Why not other state variables? Also, specify what is low education, what is high?
Be clear in the way you define variables; for instance l (employment) takes the value of 1.
What is A in earnings equation (age or experience)?
Why does the number of children enter the earnings equation?

8. Other Issues:
The fit of the model is good, but a bit exaggerated; no child: data 18.21, model 13.95
Earnings as a Markov process is not clear until the empirical specification is given later.
Can we find a more systematic way to assess the goodness of fit?