Macroeconomic data project

1

ECON 2 Fall 2021

Prof. Lint Barrage

Data Project #2 [Due: Monday November 15 at 5PM PST]

Note: For ALL graphs in the project, you must include (i) a proper title, (ii) a trendline, (iii) a y-axis label, and (iv) an x-axis label. The axis labels should specify the units for each variable as well.

1) Unemployment across States

 Make a vertical bar graph of unemployment rates across states in September 2021. Sort the states by the value of the unemployment rate, with the highest value first. Make sure that every state’s name is visible.

 Hint: Sort the data in reverse order (smallest unemployment rate first) before making the graph.  An analogous example to what your graph should look like is as follows:

 Which state had the highest unemployment rate in September 2021? Report its name and the value rounded to 1 decimal point (e.g., 10.5%).

 Which state had the lowest unemployment rate in September 2021? Report its name and the value rounded to 1 decimal point (e.g., 10.5%).

 What was California’s unemployment rate in September 2021? Report its value rounded to 1 decimal point (e.g., 10.5%).

2) Potential Drivers of Unemployment: Your Analysis!

Investigate the relationship between state unemployment rates in September 2021 and ONE of the following variables: (i) minimum wage levels, (ii) unemployment insurance (UI) duration, OR (iii) unionization rates (percent of workers represented by a labor union).

 Create a scatter plot of state unemployment (y-axis) and the variable of your choice (x-axis).

0 10 20 30 40 50

Other

Cathy Murillo

James Joyce III

Randy Rowse

Vote Share (%)

Ca nd

id at

e

Santa Barbara 2021 Mayoral Election Results

2

 In two to four short bullet points, describe: o Based on what we learned in the class, how would you expect your variable to correlate

with unemployment rates, and why? o In the data, what do you see? How are your variable and unemployment rates

associated (positively, negatively, not at all)? Are the data consistent with your prediction?

3) Unemployment and COVID Infections

 Now create a scatter plot of the unemployment rate in September 2021 (y-axis) and Covid infection rates in September 2021 (daily new cases per million people) on the x-axis.

 Describe the association you find. Was there a positive, negative, or neutral correlation between new Covid infections and unemployment across states in Sept. 2021?

 Summarize the correlation in words: “States with higher Covid infection rates had ____ unemployment rates in September 2021.”

 Provide at least one possible explanation for the relationship you find. (1-2 sentences max.)

4) Unemployment and COVID Vaccination Rates

 Now create a scatter plot of the unemployment rate in September 2021 (y-axis) and the Covid vaccination rate in September 2021 (number of single doses given per capita).

 Describe the association you find. Was there a positive, negative, or neutral correlation between Covid vaccinations and unemployment across states in Sept. 2021?

 Summarize the correlation in words: “States with higher vaccination rates have ____ unemployment rates.”

5) Omitted Variable Bias: Your Analysis!

 Your results in Problem 4 are likely an example of “omitted variable bias,” which refers to a situation where two variables A and B appear correlated but are not actually causally linked. That is, A does not cause B, but rather a third (omitted) variable C is correlated with both A and B. For example, ice cream sales (A) and homicides (B) are positively correlated, but this is not because A causes B but rather because a third variable, hot weather (C), triggers both higher ice cream sales and more homicides.

 To demonstrate the likely presence of omitted variable bias in the data, draw a scatter plot between whichever variable you picked in Problem 3 (minimum wage, unionization, or unemployment duration) on the x-axis and covid vaccination rates on the y-axis.

 Describe the association you find. How is your variable associated with vaccination rates?  Explain how your finding could account for the association you found in Problem 4.