I have a question as follows and the answer is c). Can you please help to explain why? The Spanish Army used to have a 2-year forced-conscription military service at 21 years of age. Because each year there were more conscripts that were needed, recruits could apply for military service exemption. Not surprisingly, there used to be more applications for exemptions than exemptions available, and the army used to make a lottery to randomly choose which applicants were awarded the exemption (and which applicants were forced to serve). A comparison of the earnings of exempted applicants and unsuccessful applicants some years after the service would allow us to evaluate: a) The average earnings effect of serving in the army. b) The average earnings effect of serving in the army for those who served c) The average earnings effect of serving in the army for those who did not serve
This is a really good question. You need to think about what the two groups are. You have everyone who did not serve, but only a subset of those that did. Therefore you cannot generalize to the full population or those that did serve. Here is a GPT4 generater explanation: The correct answer is c) "The average earnings effect of serving in the army for those who did not serve." This might initially seem counterintuitive, but it's based on a fundamental concept in econometrics known as the "Local Average Treatment Effect" (LATE). In the scenario described, there is a random assignment of military service exemptions through a lottery. This randomization ensures that, on average, the groups of exempted (treatment group) and non-exempted (control group) applicants are similar in all respects except for the treatment-here, serving in the military. This similarity is crucial because it mimics the conditions of a randomized controlled trial, allowing us to infer causality from the comparison between the two groups. Now, let's break down why each answer choice is what it is: a) "The average earnings effect of serving in the army" suggests we are looking at the impact on all individuals, regardless of their inclination or circumstances that led them to apply for the exemption. This is not what the comparison would reveal since the analysis only includes individuals who applied for the exemption and were subjected to the lottery system. b) "The average earnings effect of serving in the army for those who served" might seem like a reasonable answer, but it's not the focus of this comparison. This is because the analysis isn't solely focused on those who served; it also includes those who were exempted. The aim is to understand the impact of not serving (being exempted) versus serving. c) "The average earnings effect of serving in the army for those who did not serve" is correct because the analysis effectively compares individuals who wanted to be exempted (and thus, by extension, did not want to serve). Those who win the lottery (and are exempted) serve as the treatment group, and those who lose (and thus serve) are the control group. The comparison then reveals the effect of not serving on the group that applied for exemptions but had to serve due to losing the lottery. In essence, this setup allows us to estimate the impact of military service on those who would have preferred not to serve but were compelled to do so because they did not win the exemption lottery. It's a subtle but important distinction in understanding the causal effects in this scenario.
No, I meant that we measure the characteristic of each individual after the treatment. We cannot measure the effects of treatment. We can only measure the characteristics of interest and then use these measured characteristics to calculate an estimate for the average treatment effect.
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You saved me for my Quantitive Theory Methods exam. Thanks!
You are welcome!
I have a question as follows and the answer is c). Can you please help to explain why?
The Spanish Army used to have a 2-year forced-conscription military service at 21 years of age. Because each year there were more conscripts that were needed, recruits could apply for military service exemption. Not surprisingly, there used to be more applications for exemptions than exemptions available, and the army used to make a lottery to randomly choose which applicants were awarded the exemption (and which applicants were forced to serve). A comparison of the earnings of exempted applicants and unsuccessful applicants some years after the service would allow us to evaluate:
a) The average earnings effect of serving in the army.
b) The average earnings effect of serving in the army for those who served
c) The average earnings effect of serving in the army for those who did not serve
This is a really good question. You need to think about what the two groups are. You have everyone who did not serve, but only a subset of those that did. Therefore you cannot generalize to the full population or those that did serve.
Here is a GPT4 generater explanation:
The correct answer is c) "The average earnings effect of serving in the army for those who did not serve." This might initially seem counterintuitive, but it's based on a fundamental concept in econometrics known as the "Local Average Treatment Effect" (LATE).
In the scenario described, there is a random assignment of military service exemptions through a lottery. This randomization ensures that, on average, the groups of exempted (treatment group) and non-exempted (control group) applicants are similar in all respects except for the treatment-here, serving in the military. This similarity is crucial because it mimics the conditions of a randomized controlled trial, allowing us to infer causality from the comparison between the two groups.
Now, let's break down why each answer choice is what it is:
a) "The average earnings effect of serving in the army" suggests we are looking at the impact on all individuals, regardless of their inclination or circumstances that led them to apply for the exemption. This is not what the comparison would reveal since the analysis only includes individuals who applied for the exemption and were subjected to the lottery system.
b) "The average earnings effect of serving in the army for those who served" might seem like a reasonable answer, but it's not the focus of this comparison. This is because the analysis isn't solely focused on those who served; it also includes those who were exempted. The aim is to understand the impact of not serving (being exempted) versus serving.
c) "The average earnings effect of serving in the army for those who did not serve" is correct because the analysis effectively compares individuals who wanted to be exempted (and thus, by extension, did not want to serve). Those who win the lottery (and are exempted) serve as the treatment group, and those who lose (and thus serve) are the control group. The comparison then reveals the effect of not serving on the group that applied for exemptions but had to serve due to losing the lottery.
In essence, this setup allows us to estimate the impact of military service on those who would have preferred not to serve but were compelled to do so because they did not win the exemption lottery. It's a subtle but important distinction in understanding the causal effects in this scenario.
Thank you, Professor Mikko.
How can we build an operational definition of causality to approach SEM models in research in the social sciences?
You do it with a solid research design. The question is a bit too broad to answer in a TH-cam comments, but there are good books that address it.
@@mronkko Thank you!
Thank you! Great job. I now understand counterfactual.
With “characteristic” you mean “the effects of treatment”? Thanks in advance!
No, I meant that we measure the characteristic of each individual after the treatment. We cannot measure the effects of treatment. We can only measure the characteristics of interest and then use these measured characteristics to calculate an estimate for the average treatment effect.
Thanks..how do we go for the regression plz
I am sorry, but I do not understand the question.
Fantastic video, thank you
Good that it helped.
Thank you, now is clear
You are welcome