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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.
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Replication Data for: Teaching Voters New Tricks: The Effect of Partisan Absentee Vote-By-Mail GOTV Efforts
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  • Tabular Data
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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.
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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: • mixed_logit_RestTriang_20160620_T4_1.m • mixed_logit_RestTriang_20160620_T4_2.m • mixed_logit_RestTriang_20160620_T5_1.m • mixed_logit_RestTriang_20160620_T5_2.m • mixed_logit_RestTriang_20160620_TA4.m 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): • Data2012_2006.csv • Data2012_2007.csv • Data2012_2008.csv • Data2012_2009.csv • Data2012_2010.csv • Data2012_2011.csv • Data2012_af.csv • Data2012_afnosl.csv • Data2012_all.csv • Data2012_noaccidents.csv • Data2012_noaf.csv • Data2012_nofirst.csv • Data2012_paid.csv • Data2012_sl.csv 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): • sample2012af.m • sample2012afnosl.m • sample2012all.m • sample2012noaccident.m • sample2012noaf.m • sample2012nofirst.m • sample2012paid.m • sample2012sl.m • sample20122006.m • sample20122007.m • sample20122008.m • sample20122009.m • sample20122010.m • sample20122011.m Any comments or questions related to these replication files can be directed to Gianmarco Leon at Gianmarco.leon@upf.edu
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See README.pdf
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The 2004 General Social Survey (GSS) reported significant increases in social isolation and significant decreases in ego network size relative to previous periods. These results have been repeatedly challenged. Critics have argued that malfeasant interviewers, coding errors, or training effects lie behind these results. While each critique has some merit, none precisely identify the cause of decreased ego network size. In this article, we show that it matters that the 2004 GSS—unlike other GSS surveys—was fielded during a highly polarized election period. We find that the difference in network size between nonpartisan and partisan voters in the 2004 GSS is larger than in all other GSS surveys. We further discover that core discussion network size decreases precipitously in the period immediately around the first (2004) presidential debate, suggesting that the debate frames “important matters” as political matters. This political priming effect is stronger where geographic polarization is weaker and among those who are politically interested and talk about politics more often. Combined, these findings identify the specific mechanism for the reported decline in network size, indicate that inferences about increased social isolation in America arising from the 2004 GSS are unwarranted, and suggest the emergence of increased political isolation.
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Replication dataset and script
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Prior scholarship overlooks the capacity of other actors to raise the political costs of unilateral action by turning public opinion against the president. Through a series of five experiments embedded on nationally representative surveys, we demonstrate Congress’ ability to erode support for unilateral actions by raising both constitutional and policy-based objections to the exercise of unilateral power. Congressional challenges to the unilateral president diminish support for executive action across a range of policy areas in both the foreign and domestic realm and are particularly influential when they explicitly argue that presidents are treading on congressional prerogatives. We also find evidence that constitutional challenges are more effective when levied by members of Congress than by other actors. The results resolve a debate in the literature and suggest a mechanism through which Congress might exercise a constraint on the president, even when it is unable to check him legislatively.
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This folder contains the contents of a project examining the relationship between demographics and nearby early vote locations in the 2016 and 2012 elections in North Carolina. FILES: * derived: This folder contains data produced by nc_evote_analysis.do * maptile: This folder contains the files needed to define a “ncvtd” (North Carolina Voting Tabulation District) geography for the Maptile Stata program. Once Maptile is installed, drag these files to the same folder as the Maptile ado file in your personal ado folder. * nc_evote_analysis.do: The Stata DO-File that processes raw data and produces results * source: This folder contains the source data for the project. Some of these files are raw downloads, while others were created manually from a combination of inputs * tables_figures: This folder contains tables and figures produced by nc_evote_analysis.do
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This paper provides technical details and user guidance of the Research Infrastructure of Chinese Foundations (RICF), a database for non-commercial and academic research purpose on Chinese foundations, civil society, and social development in general. The database structure of RICF is deliberately designed and normalized according to the Three Normal Forms. The database schema consists of three major themes: basic organizational profile of foundations (i.e., tables of basic profile, board member, supervisor, staff, and related party), program information (i.e., tables of program information, major program, program relationship, major recipient), and financial information (i.e., tables of financial position, financial activities, cash flow, activity overview, and large donation). Data quality of RICF can be measured by four criteria: data source reputation and believability, completeness, accuracy, and timeliness. Data records are properly versioned, allowing verification and replication for research purpose.
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