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Linear Probability Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators

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  • James J. Heckman
  • James M. Snyder, Jr.

Abstract

This paper formulates and estimates a rigorously-justified linear probability model of binary choices over alternatives characterized by unobserved attributes. The model is applied to estimate preferences of congressmen as expressed in their votes on bills. The effective dimension of the attribute space characterizing votes is larger than what has been estimated in recent influential studies of congressional voting by Poole and Rosenthal. Congressmen vote on more than ideology. Issue-specific attributes are an important determinant of congressional" voting patterns. The estimated dimension is too large for the median voter model to describe congressional voting

Suggested Citation

  • James J. Heckman & James M. Snyder, Jr., 1996. "Linear Probability Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators," NBER Working Papers 5785, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:5785
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    Cited by:

    1. Sergio de Nardis & Massimo Mancini & Carmine Pappalardo, 2003. "Regolazione del mercato del lavoro e crescita dimensionale delle imprese: Una verifica sull'effetto soglia dei 15 dipendenti," ISAE Working Papers 38, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    2. Tuttle, Charlotte & Beatty, Timothy K. M., 2017. "The Effects of Energy Price Shocks on Household Food Security in Low-Income Households," Economic Research Report 260484, United States Department of Agriculture, Economic Research Service.
    3. Goolsbee, Austan & Klenow, Peter J, 2002. "Evidence on Learning and Network Externalities in the Diffusion of Home Computers," Journal of Law and Economics, University of Chicago Press, vol. 45(2), pages 317-343, October.
    4. Arianna Degan & Antonio Merlo, 2011. "A Structural Model Of Turnout And Voting In Multiple Elections," Journal of the European Economic Association, European Economic Association, vol. 9(2), pages 209-245, April.
    5. Joseph F. Hair & Christian M. Ringle & Siegfried P. Gudergan & Andreas Fischer & Christian Nitzl & Con Menictas, 2019. "Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 115-142, April.
    6. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    7. Signe Krogstrup & Sébastien Wälti, 2008. "Do fiscal rules cause budgetary outcomes?," Public Choice, Springer, vol. 136(1), pages 123-138, July.
    8. Arianna Degan & Antonio Merlo, 2006. "Do Voters Vote Sincerely?," PIER Working Paper Archive 06-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. Bohara, Alok K. & Camargo, Alejandro Islas & Grijalva, Therese & Gawande, Kishore, 2005. "Fundamental dimensions of U.S. trade policy," Journal of International Economics, Elsevier, vol. 65(1), pages 93-125, January.
    10. Keith Poole, 2007. "Changing minds? Not in Congress!," Public Choice, Springer, vol. 131(3), pages 435-451, June.
    11. Bart J. Bronnenberg & Jean-Pierre H. Dube & Matthew Gentzkow, 2012. "The Evolution of Brand Preferences: Evidence from Consumer Migration," American Economic Review, American Economic Association, vol. 102(6), pages 2472-2508, October.
    12. Edward López & Carlos Ramírez, 2004. "Party Polarization and the Business Cycle in the United States," Public Choice, Springer, vol. 121(3), pages 413-430, February.
    13. William C. Horrace & Ronald L. Oaxaca, 2002. "New Wine in Old Bottles: A Sequential Estimation Technique for the LPM," Econometrics 0206002, University Library of Munich, Germany, revised 11 May 2003.
    14. Beatty, Timothy K.M. & Tuttle, Charlotte, 2014. "The Effect of Energy Price Shocks on Household Food Security: Do Federal Assistance Programs Mitigate the Effects of Price Shocks," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170546, Agricultural and Applied Economics Association.
    15. James Adams & Thomas Brunell & Bernard Grofman & Samuel Merrill, 2010. "Why candidate divergence should be expected to be just as great (or even greater) in competitive seats as in non-competitive ones," Public Choice, Springer, vol. 145(3), pages 417-433, December.
    16. Ronald Goettler & Ron Shachar, 2000. "Estimating Product Characteristics and Spatial Competition in the Network Television Industry," Econometric Society World Congress 2000 Contributed Papers 1691, Econometric Society.
    17. Atif Mian & Amir Sufi & Francesco Trebbi, 2010. "The Political Economy of the US Mortgage Default Crisis," American Economic Review, American Economic Association, vol. 100(5), pages 1967-1998, December.
    18. Arianna Degan & Antonio Merlo, 2004. "Do Citizens Vote Sincerely (If They Vote at All)? Theory and Evidence from U. S. National Elections," PIER Working Paper Archive 04-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

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    More about this item

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior

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