Data and the American Dream

This book describes published economics studies which all use the same data source: individual responses to the American Community Survey (ACS), the nation's largest household survey. The author identifies intriguing studies that clearly illustrate core econometric methods--such as regression control and difference-in-differences--and the archived data and code for the book  replicates key statistics from the studies. The Appendix to this book contains a guide to using the free R software, downloading the ACS and other public-use microdata, and running the replication files, which assumes no background knowledge on the part of the reader beyond introductory statistics. 

By opening up the hood on how top scholars use core econometric methods to analyze large data sets, a motivated reader can use this book to learn not only how to replicate published research, but also to extend the analysis to create new knowledge about important social phenomena. 

A more casual reader can skip these online supplements and still gain data-driven insights into social and economic behavior. The book concludes by describing how careful empirical estimates can guide decision making, through cost-benefit analysis, to find public policies that lead to greater happiness while accounting for environmental, public health and other impacts.

With its accessible discussion, glossary, detailed learning goals, end of chapter review questions and companion resources, this book is ideal for use as a supplementary volume in introductory econometrics or research methods courses.

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Update: Since the book’s publication, I have written several articles that extend its themes and will be of interest to readers. In The American Economist, I describe the term paper assignment I use in my class, providing a concrete example of the “replicate and extend” pedagogical approach developed in the book. My article in Contemporary Economic Policy integrates the material from Chapters 2 and 7 by applying replication estimates from a regression model in a formal Cost-Benefit Analysis (CBA). In addition, two other recent studies—each with archived data and transparent R code—offer further opportunities for learning econometrics and working with the ACS. For all of these projects, I have followed the same open and transparent coding practices established in Data and the American Dream.

Prof Holian's recent publications using the ACS data (with data and transparent code in R):

•  "Replicate and extend: An approach for term paper assignments in introductory econometrics" by Matthew Holian.  The American Economist (2024): 05694345251334557.(preprint) (published version) (data and code) 

• ."Methods in open policy analysis: An application to California's building energy codes." by Matthew Holian.  Contemporary Economic Policy. (published version) (data and code.)

•  "Identity economics and the Russian-Ukrainian war: Evidence from the American Community Survey," by Matthew Holian and Oleh Wolowyna.  Journal of Economics, Race, and Policy (2025): 1-28. (preprint) (published version) (data and code)

• Earnings, Occupation and College Degree: A Focus on the Economics Major(with Kevin Chiu) (working paper) [data and code available upon request.]

More links and files related to studies that appear in Data and the American Dream can be found on this page below.

Below I include links to three studies, the published and working paper versions (when available), additional  replication files (beyond those available in the archived data and code package on Open ICPSR.) I also list the course topics and learning objectives that I use each study to illustrate.

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Study #1:

Winters, John V. "Is economics a good major for future lawyers? Evidence from earnings data." The Journal of Economic Education 47, no. 2 (2016): 187-191. 

Working paper: Available. 

Additional info on this replication: available.

Relates to the following Course Topics:

a. Descriptive statistics: counts, proportions, averages.

b. Hypothesis testing: the difference in means test

c. Regression: equivalence between difference in means tests and bivariate regression with a binary independent variable.

d. Best empirical practices: inflation adjustments and sample weighting

Additional links and files: 

Posit Cloud: Data and code here. Video on using this program here.

Stata files (Do File). Data File (DTA)

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Study #2: 

Costa, Dora L., and Matthew E. Kahn. "Electricity consumption and durable housing: understanding cohort effects." American Economic Review, 101, no. 3 (2011): 88-92.

Course Topics

a. Regression control: Using control variables to estimate causal effects 

b. Fixed effects 

c. Clustered standard errors

d. Logarithmic transformations

Stata files: do file, and data files

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Study #3:

Orrenius, Pia M., and Madeline Zavodny. "The Impact of Temporary Protected Status on Immigrants' Labor Market Outcomes." American Economic Review 105, no. 5 (2015): 576-80.

Working paper: Available

Course Topics

a. Basic difference-in-differences

b. Basic difference-in-differences with control variables

c. Polynomial models

d. Fixed effects

Stata files: do file, data file

 *New: R Studio Cloud program and data

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