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: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:Velez, Yamil, Grace Wong
Replication files for "Assessing Contextual Measurement Strategies"
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.firstname.lastname@example.org
Readme file to replicate Tables and Figures in:
Hoffman, León and Lombardi. “Compulsory Voting, Turnout, and Government Spending: Evidence from Austria”. Journal of Public Economics, Vol. 145: 103–115. January 2017.
The zipped file contains all do files, raw data sets and working data sets. In order to replicate the results from our paper, you need to paste the contents of this folder in your computer and change the base directory in the 7th line of the STATA do-file “Master.do”. The entire analysis can be run from “Master.do”.
The folder “Raw Data Files” contains the following raw data sets:
- “1980-2012_expenditures.dta” holds the information on nominal spending for each of the nine Austrian states, broken down into ten spending categories for 1980-2012, as provided by the Austrian Statistical Agency.
- “cpi_index.dta” contains the Austrian Consumer Price Index (base year 2010), obtained from http://stats.oecd.org
- “population_unemployment_new2.dta” has the yearly population and unemployment rates of all Austrian states, provided by the Austrian Statistical Agency.
- “Election-Parliament.dta”, “Election-President.dta” and “Election-State.dta” contains voting data from the parliamentary, presidential and state elections held in Austria in 1945-2010, broken down by state. This data was obtained from the Austrian Federal Ministry of the Interior’s yearbooks.
The zip folder contains a do-file generating the working data sets. In particular, “Database_Construction_Elections_Expenditures.do” constructs the “Elections.dta” data set in lines 8-278, and the “Expenditures.dta” data set in lines 284-430. You will need to install the “carryforward” command in STATA by typing “ssc install carryforward”.
We construct our Tables and Figures from three working datasets:
- “Elections.dta” contains the data on turnout and election results needed to construct Figure 2, Panel A of Table 1, and Tables 3, 5, A.3, A.8 (columns 1 and 2), A.9, A.11, and A.12
- “Expenditures.dta” contains the data on state-level expenditure and additional variables needed for Figure 2, Panel B of Table 1, and Tables 4, A.5, A.6, A.7, A.8 (columns 3-6), and A.10.
- “ASS.dta” holds the relevant variables constructed from the 1986 and 2003 waves of the ASS, necessary for Tables 2 and 6. Due to data confidentiality reasons, we do not disclose the original files from the Austrian Social Survey, but we do disclose the Do File we used to construct it (“Database_Construction_ASS.do”).
Figures 2 and all Tables in the paper are built from the “Tables and Figures.do” do file. In order to calculate the wild-bootstrap p-values, it is first necessary to obtain the “cgmreg.ado” and “cgmwildboot.ado” ado-files from Judson Caskey’s website (https://sites.google.com/site/judsoncaskey/data). You also need to install the “unique” command in STATA by typing “ssc install unique”. The wild-bootstrap p-values featured in the paper were obtained from versions 12 or 13 of STATA (the random number generator differs slightly in STATA 14). If you are working in STATA 14, type “version 13.0”. To create Figure 2 you will have to install the grc1leg package in STATA by typing “ssc install grc1leg”.
Contributors:DE LUCA, MARINO, CIAGLIA, ANTONIO
Populism is being increasingly studied by political and social scientists. This article pays particular attention to the way in which 'people' can be approached and appealed to by their leaders. In particular, by undertaking a content analysis of the two most read daily newspapers in Italy, and by relying on the technique of correspondence analysis, this article shows that to fully understand the phenomenon of populism, the way in which 'the people' are approached by their leaders cannot be left aside. In doing so, this article empirically analyses and discusses three dimensions of populism and contributes to a more granular understanding of this phenomenon in established democracies.