This entry include the PDF of the published papers and the codes used for producing the results and tables for this article. Note that the data used is the CMS Medicare HEDIS data from 2003 to 2012 and 2007 Medicare Enrollment and claims files for the 20% sample.
this data is available from CMS through ResDAC (http://www.resdac.org/cms-data/request/research-identifiable-files) and it's not free
This paper tests the effects on the take-up of a preventative health product of two interventions based on behavioral models derived from psychology: varying the framing of the perceived benefits; and having people verbally commit to purchase the product. I find that none of these interventions had a significant effect (whether economically or statistically) on take-up, and that the gender of the household member targeted was also irrelevant. In contrast, I find that take-up is sensitive to price, as in Cohen and Dupas (2008), and is correlated with indicators of household’s wealth.
These data are associate with the following manuscript:
The Agassiz’s Desert Tortoise Genome Provides a Resource for the Conservation of a Threatened Species
Tollis M, DeNardo DF, Cornelius JA, Dolby GA, Edwards T, Henen BT, Karl AE, Murphy RW, Kusumi K
Both under- and over-treatment of communicable diseases are public bads. But efforts to decrease one run the risk of increasing the other. Using rich experimental data on household treatment-seeking behavior in Kenya, we study the implications of this tradeoff for subsidizing life-saving antimalarials sold over-the-counter at retail drug outlets. We show that a very high subsidy (such as the one under consideration by the international community) dramatically increases access, but nearly half of subsidized pills go to patients without malaria. We study two ways to better target subsidized drugs: reducing the subsidy level and introducing rapid malaria tests over-the-counter.
Contributors:Russo, Luana, Riera, Pedro, Verthé, Tom
Italian Parliamentary elections 2008 and 2013
Source: Ministero dell’Interno – Dipartimento degli Affari Interni e Territoriali – Servizi Elettorali
Contributors:Shen, Lu, Mickley, Loretta
The dataset and codes used in our paper "Influence of large-scale climate patterns on summertime U.S. ozone: A seasonal predictive model for air quality management".
Contributors:Alvarez, R. Michael, Levin, Ines, Nuñez, Lucas
This replication package contains data and R code that can be used to reproduce all tables and figures in "The Four Faces of Political Participation in Argentina: Using Latent Class Analysis To Study Political Behavior" by R. Michael Alvarez, Ines Levin, and Lucas Nuñez.
Replication Data for: Teaching Voters New Tricks: The Effect of Partisan Absentee Vote-By-Mail GOTV Efforts
Replication data and code for Rozenas, Schutte, and Zhukov: "The Political Legacy of Violence: The Long-Term Impact of Stalin’s Repression in Ukraine". Journal of Politics (forthcoming).
The replication data and code provided here can be used to replicate all the tables and figures in the main text and the supplementary information.
Contributors:Leon, Gianmarco, Miguel, Edward
Readme file to replicate tables and graphs in:
Leon and Miguel (2017) “Risky Transportation Choices and the Value of Statistical Life,” American Economic Journal: Applied Economics, Vol. 9(1): 202-228
The descriptive statistics, graphs, and conditional logit regressions were computed using stata, while all the mixed logit estimation was done using MATLAB, thus the replication files are grouped into two separate folders:
• Stata Replication
• MATLAB Replication
All the individual level information contained in the datasets comes from the survey applied in Freetown and Lungi in August and September 2012. The original questionnaire is in the replication files (Survey_Transp_Choices_General_2012FF.pdf)
To be able to replicate the results from the paper, you need to paste these two folders in your computer and change the path in the stata do file called: “TablesVSLReplication-20160615.do” This do-file contains specific instructions and pulls up the data sets necessary to replicate the indicated tables. Likewise, in this do-file, you will find details on which are the tables that were computed using MATLAB and the exact .m files that you need to run to replicate these results.
The folder “Replication STATA” contains the following files:
• TablesVSLReplication-20160615.do : Do file that generates all the descriptive statistics and graphs in the paper, with the exception of the mixed logit estimations.
• Transp-Regressions.dta: Dataset at the passengerXChoice Situation level, used to run conditional logit regressions, as well as to generate descriptive statistics of the choice situations.
• Transp-wide-final-Replication.dta: Dataset at the passenger level, used to generate tables that describe passenger characteristics and their choices.
• Trans_VSLEstimates.dta: Dataset containing the individual level VSL estimates from the mixed logit regressions, used to generate the correlates of the VSL in Table 6.
The MATLAB files are completely automatized, and were written based on the code provided by prof. Kenneth Train (publicly available at: http://eml.berkeley.edu/Software/abstracts/train1006mxlmsl.html). For these files to run, you need to copy to your computer all the files included in corresponding folder. To replicate the results, you just need to run the files named with the format “mixed_logit_RestTriang_20160620_XXX.m” In there, you need to un-comment the lines corresponding to the regressions you want to replicate (start in row 45 in all the files). After you have done that, the code will pick a sample, pull out the data set, and estimate the choice model. More precisely, the files in the folder “Replication MATLAB” contain the following files:
The main files that run the different regressions in the paper are the following:
These files call a set of ancillary files used for the estimation. Paraphrasing prof. Train’s readme file (downloaded from: http://eml.berkeley.edu/Software/abstracts/train1006mxlmsl.html):
• doit.m is a script (not a function) that is called at the end of mxlmsl.m. It checks the data, transforms the data into a more useful form, performs the estimation and prints results. It calls all the other functions either directly or indirectly.
• check.m is a function that checks the input data and specifications. It provides error messages and terminates the run if anything is found to be incorrect.
• loglik.m is a function that calculates the log-likehood function and its gradient. This funtion is input to Matlab's fminunc command (which is part of Matlab's Optimization Toolbox.) This function calls llgrad2.m.
• llgrad2.m is a function that calculates for each person the probability of the chosen alternatives and the gradient of the log of this probability.
• der.m is a function that calculates the derivative of each random coefficient with respect to the parameters of the model.
• makedraws.m is a function that creates the standardized (ie parameter-free) draws that will be used in the run, based on the specifications given by the user in mxlmsl.m.
• trans.m is a function that transforms the standardized draws into draws of coefficients. (EG, if coefficient c is normal with mean b and standard error w, then makedraws.m creates draws mu from a standard normal N(0,1), and trans.m creates the draws of coefficients as c=b+w*mu.)
• condmn.m generates the individual level parameters
• trirnd.m is a command that generates the restricted triangular distribution for the estimation of the model.
The following .csv files contain subsets of the main dataset used in the different regressions in the paper (the details are specified in the main .m files described above):
The following .m files call the datasets for each of the subsamples used in each regression and define the matrix sizes (specific details are provided in the main .m files describe above):
Any comments or questions related to these replication files can be directed to Gianmarco Leon at Gianmarco.email@example.com