Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T06:20:54.089Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatial interdependence and instrumental variable models

Published online by Cambridge University Press:  30 January 2019

Timm Betz ,
Scott J. Cook  and
Florian M. Hollenbach
Timm Betz
Affiliation:
Department of Political Science, Texas A&M University, College Station, TX77843, USA
Scott J. Cook
Affiliation:
Department of Political Science, Texas A&M University, College Station, TX77843, USA
Florian M. Hollenbach*
Affiliation:
Department of Political Science, Texas A&M University, College Station, TX77843, USA
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments: rainfall, natural disasters, economic shocks, and regionally- or globally-weighted averages. Because the biases due to spatial interdependence and predictor endogeneity can offset, addressing only one can increase the bias relative to ordinary least squares. We demonstrate the extent of these biases both analytically and via Monte Carlo simulation. Finally, we discuss a general estimation strategy – S-2SLS – that accounts for both outcome interdependence and predictor endogeneity, thereby recovering consistent estimates of predictor effects.

Type
Original Article
Information
Political Science Research and Methods , Volume 8 , Issue 4 , October 2020 , pp. 646 - 661
Copyright
Copyright © The European Political Science Association, 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahmed, FZ (2012) The Perils of Unearned Foreign Income: Aid, Remittances, and Government Corruption. American Political Science Review 106(1), 146–165.CrossRefGoogle Scholar
Anselin, L Lozano-Gracia, N (2008) Errors in Variables and Spatial Effects in Hedonic House Price Models of Ambient Air Quality. Empirical Economics 34(1), 5–34.CrossRefGoogle Scholar
Ashraf, Q Galor, O (2011) Dynamics and Stagnation in the Malthusian Epoch. American Economic Review 101(5), 2003–2041.CrossRefGoogle ScholarPubMed
Bartels, LM (1991) Instrumental and “Quasi-Instrumental” Variables. American Journal of Political Science 35(3), 777–800.CrossRefGoogle Scholar
Beck, N, Gleditsch, KS Beardsley, K (2006) Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy. International Studies Quarterly 50(1), 27–44.CrossRefGoogle Scholar
Betz, T (2013) Robust Estimation with Nonrandom Measurement Error and Weak Instruments. Political Analysis 21(1), 86–96.CrossRefGoogle Scholar
Betz, T, Cook, SJ Hollenbach, F (2018) On the Use and Abuse of Spatial Instruments. Political Analysis 26(4), 474–479.CrossRefGoogle Scholar
Boix, C (2011) Democracy, Development, and the International System. American Political Science Review 105(04), 809–828.CrossRefGoogle Scholar
Bound, J, Jaeger, DA Baker, RM (1995) Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogeneous Explanatory Variable Is Weak. Journal of the American Statistical Association 90(430), 443–450.Google Scholar
Büthe, T Milner, HV (2008) The Politics of Foreign Direct Investment into Developing Countries: Increasing FDI through International Trade Agreements? American Journal of Political Science 52(4), 741–762.CrossRefGoogle Scholar
Cooperman, AD (2017) Randomization Inference with Rainfall Data: Using Historical Weather Patterns for Variance Estimation. Political Analysis 25(3), 277–288.CrossRefGoogle Scholar
Drukker, DM, Egger, P Prucha, IR (2013) On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors. Econometric Reviews 32(5-6), 686–733.CrossRefGoogle Scholar
Fingleton, B Le Gallo, J (2008) Estimating Spatial Models with Endogenous Variables, a Spatial Lag and Spatially Dependent Disturbances: Finite Sample Properties. Papers in Regional Science 87(3), 319–339.CrossRefGoogle Scholar
Franzese, RJ Jr. Hays, JC (2007) Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data. Political Analysis 15(2), 140–164.CrossRefGoogle Scholar
Franzese, RJ, Hays, JC Cook, SJ (2016) Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes. Political Science Research and Methods 4(1), 151–173.CrossRefGoogle Scholar
Gleditsch, KS Ward, MD (2006) Diffusion and the International Context of Democratization. International Organization 60(4), 911–933.CrossRefGoogle Scholar
Hansford, TG Gomez, BT (2010) Estimating the Electoral Effects of Voter Turnout. American Political Science Review 104(02), 268–288.CrossRefGoogle Scholar
Kelejian, HH Prucha, IR (2004) Estimation of Simultaneous Systems of Spatially Interrelated Cross Sectional Equations. Journal of Econometrics 118(1), 27–50.CrossRefGoogle Scholar
Kirby, AM Ward, MD (1987) The Spatial Analysis of Peace and War. Comparative Political Studies 20(3), 293–313.CrossRefGoogle Scholar
LeSage, JP Pace, RK (2014) The Biggest Myth in Spatial Econometrics. Econometrics 2(4), 217–249.CrossRefGoogle Scholar
Liu, X Lee, L-F (2013) Two-Stage Least Squares Estimation of Spatial Autoregressive Models with Endogenous Regressors and Many Instruments. Econometric Reviews 32(5-6), 734–753.CrossRefGoogle Scholar
Neumayer, E Plümper, T (2016) W. Political Science Research and Methods 4(1), 175–193.CrossRefGoogle Scholar
Plümper, T Neumayer, E (2010) Model Specification in the Analysis of Spatial Dependence. European Journal of Political Research 49(3), 418–442.CrossRefGoogle Scholar
Ramsay, KW (2011) Revisiting the Resource Curse: Natural Disasters, the Price of Oil, and Democracy. International Organization 65(3), 507–529.CrossRefGoogle Scholar
Simmons, BA, Dobbin, F Garrett, G (2006) Introduction: The International Diffusion of Liberalism. International Organization 60(4), 781–810.CrossRefGoogle Scholar
Siverson, RM Starr, H (1990) Opportunity, Willingness, and the Diffusion of War. American Political Science Review 84(1), 47–67.CrossRefGoogle Scholar
Sovey, AJ Green, DP (2011) Instrumental Variables Estimation in Political Science: A Readers’ Guide. American Journal of Political Science 55(1), 188–200.CrossRefGoogle Scholar
Starr, H (1991) Democratic Dominoes: Diffusion Approaches to the Spread of Democracy in the International System. Journal of Conflict Resolution 35(2), 356–381.CrossRefGoogle Scholar
Stasavage, D (2005) Democracy and Education Spending in Africa. American Journal of Political Science 49(2), 343–358.CrossRefGoogle Scholar
Ward, MD O’Loughlin, J (2002) Spatial Processes and Political Methodology: Introduction to the Special Issue. Political Analysis 10(3), 211–216.CrossRefGoogle Scholar
Ward, MD Gleditsch, KS (2002) Location, Location, Location: An MCMC Approach to Modeling the Spatial Context of War and Peace. Political Analysis 10(3), 244–260.CrossRefGoogle Scholar
Wooldridge, JM (2002) Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.Google Scholar
Supplementary material: Link

Betz et al. Dataset

Dataset

https://doi.org/10.7910/DVN/CZYUJH
Link
Supplementary material: PDF

Betz et al. supplementary material

Online Appendix

Download Betz et al. supplementary material(PDF)
PDF 211.9 KB