The public release data, Gelman, Kariv, Shapiro, Silverman, and Tadelis, "Harnessing Naturally-Occurring Data to Measure the Response of Spending to Income, Science (2014), are stored in the facilities of the University of California, Berkeley’s Econometrics Lab (EML). They are protected by password and you must register to acquire the credentials. When you register to gain access, you are agreeing to the Conditions of Use for these data.

After registering you will obtain a link to download the Gelman, Kariv, Shapiro, Silverman, and Tadelis public release data, as well as replication code for Gelman, Kariv, Shapiro, Silverman, and Tadelis (2014, Science). Before downloading the public release data and replication code you will be asked to agree to the following:

By downloading these Materials, I agree to the following:

I will not use the Materials:

 - For any commercial purpose.
 - To obtain information that could directly or indirectly identify individuals whose data have been aggregated in these Materials
 - To obtain information about, or further contact with, individuals known to me and whose data have been aggregated in these Materials.

I agree not to download any Materials where prohibited by applicable law.

I agree not to use the Materials in any way prohibited by applicable law.

I agree not to redistribute the Gelman, Kariv, Shapiro, Silverman, and Tadelis (2014) public release data or replication code to any third parties.

I agree to secure the Gelman, Kariv, Shapiro, Silverman, and Tadelis (2014) public release data on a password protected machine to prevent its accidental redistribution.

I agree that any books, articles, conference papers, theses, dissertations, reports, or other publications that I create which employ these data will include bibliographic citation to the data.

EML makes no warranties, express or implied, by operation of Law or otherwise, regarding or relating to the dataset.

Contact EML Manager Rowilma del Castillo <rowilma@econ.berkeley.edu> with questions or concerns about the Gelman, Kariv, Shapiro, Silverman, and Tadelis (2014) public release data files.