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Reporting and interpretation. Put these specifications into a well-organized table.

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“Enterprise Zone” (EZ) tax subsidy in Indiana cities affected employment, specifically looking at changes in their unemployment claims after a 10 cities were designated EZs in 1983 or 1984.

 1. Preparation. The data file, posted to Google Drive, is called ezunem2.dta.

Look at the variable labels and familiarize yourself with the data structure.

Use xtset to tell Stata that the data are structured as a panel

2. Panel Regressions. Consider the following panel-DD model:

ln(UnemploymentClaimsct) = αc + δt + βEZct + εit (1)

 

1. Use xtreg to estimate equation where αc is a city c fixed effect

2. Estimate again, but with αc treated as a random effect

3. Estimate the equivalent between estimator for 1

4. Estimated again, now handling the correlation within cities, not with αc

term, but by including a lagged dependent variable ln(UnemploymentClaimsc,t−1)

5. Estimated a multilevel model using mixed..., mle that includes a lagged dependent variable at level one and city random effects at level 2. 

3. Reporting and interpretation. Put these specifications into a well-organized table. How do they differ? What do they show about the association between EZs and unemployment claims?

gather large amounts data on from national patent offices to investigate how income tax rates affect international migration decisions of individual inventors. Their pol- icy variable of interest is the portion of income that inventors get to keep after tax, or one minus their tax rate (which the authors call “retention rate”).

In this section, we will replicate Akcigit, Baslandze and Stantcheva’s table 11, which looks at how differences in tax policy affect the decision of US and Canadian inventors to locate in the US or in Canada.

 

4. Migration dataset.

Load dataset inventor_data2.dta. This reports information for each inventor from US or from Canada for each year either is observed.

5. OLS baseline. Use OLS to estimate the specification

 

 

ResidUSit = β0 + β1c1{i ∈ c} + β2c ln(1 − τct) ∗ 1{i ∈ c} + Xictθ + εit (2)

c

 

where the variables are

ResidUSit is a dummy equal to 1 if i resides in the United States in year t, and 0 if they reside in Canada.

1 i c : a set of dummy variables equal to 1 if inventor i is in productivity group c and 0 otherwise

ln(1 τct)   1 i c : The log retention rate for group-c inventors in the US for year t, interacted with i’s productivity-group dummies

A large set of control variables Xict:

A dummy for whether i is native to the US

Log of US patents held by i

Age of i

A dummy for whether i works for a multinational firm

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