Econ 440 - Problem Set 1

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Feb 20, 2024

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Labor Economics Fall 2024, Problem Set 1 Name: Navya Ahuja 1 Correlation vs. Causation (10 points, 5 points each.) 1. This conclusion may be incorrect because the researcher found a correlation between couples who live together before marriage and divorce, but didn’t provide more reasoning as to why she accessed that this was a causal relationship. Correlation is not equal to causation, thus there is more information needed to determine if it’s causal. There could be other factors, such as perhaps sharing financial stress for a longer time as a cohabitating couple, that leads to higher divorce in couples who lived together before marriage. That is to say, there could be bias. 2. Quasi random means that the data is naturally separatable into treatment and control groups. A balance table compares the pre-intervention characteristics of the treatment and control and tests for differences in their means. In this case, if there is a lack of significant differences between the treatment and control, then it can be concluded that cohabitation does have a causal relationship with divorce. 2 Labor Demand in a Competitive Market (10 points.) 3 Labor Demand Elasticity (30 points, 5 points each.) 1. In the short run, capital is modelled to be as fixed. A wage change generates a small response, relative to the long term, as a firm accumulates or draws down capital slowly.

As shown in this example, labor demand elasticity is quite small, at -0.2, so as the wage per hour increases, there will be a small decrease in capital. 2. In the long term, capital can be adjusted, which allows for significant changes in the firm’s inputs. As a result, there is higher labor demand elasticity, so a change in wages would lead to a significant change in labor. 3. Labor demand elasticity the percent change in quantity of labor demanded over the percent change in wage rate. So, the percent change in wage is: -0.2 * (- 0.1) = 2%. This means that a 10% decrease in hours worked would lead the wage to increase by 2%. 4. In the long term, as there is higher labor demand elasticity, if labor changes by 10%, we could expect the wage to change by a proportional or larger amount. 4 Difference-in-differences Analysis (10 point, 5 points each.) 1. Difference-in-difference compares the outcome before and after an intervention. I would assume that in this case, if the pattern of spending increasing by USD 100 each year would continue, then spending in 2016 would have been 1400, so the change is: $1400 – $1200 = $200 decrease in spending. 2. From the Indiana data, we can now have treatment and control groups. Illinois would be the treatment group, as the retirement income tax exemption ended, and Indiana would be the control, as there was no change in its retirement income tax exemption policies. The difference in difference calculation, if we account for 2010 to 2016, is: (1200-1400) – (1100 -1000) = -200 – 100 = -300 This number depicts the causal effect of the treatment if, absent the policy change, the difference between Indiana and Illinois would stay the same. Based on this, in 2016 the total spendings have decreased by USD 300.

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