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Expert Q&A; Done 3. For this problem, consider the following research question:

ID: 2936519 • Letter: E

Question

Expert Q&A; Done 3. For this problem, consider the following research question: "How does immigration in a county affect the overall unemployment level in that county? 3a. Write down a simple regression model that you could use to answer this rescarch question. 3b. Suppose collected data on immigration and unemployment, then used Stata to estimate y equation from 3a. Do you think your estimate is causal? If so, explain why. If not, describe at least one factor that could ruin the causal interpretation of your estimate.

Explanation / Answer

3a.
The simple regression model can be written as,
Unemployment = a + b Immigration

where Unemployment is the Unemployment level of the country
Immigration is Number of Immigrants in the country
a is the intercept (Unemployment level when Immigration is 0)
b is the coefficient of Immigration (Increase in Unemployment level with 1 unit change in Immigration)

3 b.
The estimate is not causal as the model only establishes the relation between Unemployment and Immigration and not causation of Unemployment by Immigration.

Let us consider another variable - global_economic (defines the global economic situation)
global_economic plays an important role and may be related to both the immigration and unemployment. If a causal estimate is desired, simple regression on unemployment across different immigration levels that ignore global_economic variable will be misleading because the effect of the immigration will be “confounded” with the effect of global_economic (Bad economy of neighbouring countries led to increase in immigration and also increase in the level of unemployment in the country) . For this reason, such predictors are sometimes called confounding covariates.

So, a simple predictive comparison is not necessarily an appropriate estimate of a causal effect.However, there is a simple solution, which is to run the regression on unemployment levels for immigration levels keeping global_economic variable constant to nullify the effect global_economic and examine the causality relationship between Unemployment and Immigration.

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