IDEAS home Printed from https://ideas.repec.org/p/otg/wpaper/1611.html
   My bibliography  Save this paper

Does institutional quality resolve the Lucas Paradox?

Author

Listed:
  • Muhammad Akhtaruzzaman

    (Toi Ohomai Institute of Technology, Rotorua, New Zealand)

  • Christopher Hajzler

    (Bank of Canada, International Economic Analysis Department, Ottawa, Canada)

  • P. Dorian Owen

    (University of Otago, Dunedin, New Zealand)

Abstract

The Lucas Paradox observes that capital flows predominantly to relatively rich countries, contradicting the neoclassical prediction that it should flow to poorer capital-scarce countries. Alfaro, Kalemli-Ozcan, and Volosovych (2008) (AKV) argue that cross-country variation in institutional quality can fully explain the Paradox, contending that if institutional quality is included in regression models explaining international capital inflows, a country’s level of economic development is no longer statistically significant. We replicate AKV’s results using their cross-sectional IFS capital flow data. Motivated by the importance of conducting inference in statistically adequate models, we focus on misspecification testing of alternative functional forms of their empirical model of capital flows. We show that their resolution of the Paradox relies on inference in a misspecified model. In models that do not fail basic misspecification tests, even though institutional quality is a significant determinant of capital inflows, a country’s level of economic development also remains a significant predictor. The same conclusions are reached using an extended dataset covering more recent IFS international capital flow data, first-differenced capital stock data and additional controls.

Suggested Citation

  • Muhammad Akhtaruzzaman & Christopher Hajzler & P. Dorian Owen, 2016. "Does institutional quality resolve the Lucas Paradox?," Working Papers 1611, University of Otago, Department of Economics, revised Dec 2016.
  • Handle: RePEc:otg:wpaper:1611
    as

    Download full text from publisher

    File URL: https://deptcontrib.otago.ac.nz/economics/otago631038.pdf
    File Function: First version, 2016
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Carmen M. Reinhart & Kenneth S. Rogoff, 2004. "Serial Default and the "Paradox" of Rich-to-Poor Capital Flows," American Economic Review, American Economic Association, vol. 94(2), pages 53-58, May.
    2. David F. Hendry & Bent Nielsen, 2007. "The Bernoulli model, from Econometric Modeling: A Likelihood Approach," Introductory Chapters, in: Econometric Modeling: A Likelihood Approach, Princeton University Press.
    3. David F. Hendry & Bent Nielsen, 2007. "Preface to Econometric Modeling: A Likelihood Approach," Introductory Chapters, in: Econometric Modeling: A Likelihood Approach, Princeton University Press.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Olufemi A Aluko & Muazu Ibrahim, 2019. "Does institutional quality explain the Lucas Paradox? Evidence from Africa," Economics Bulletin, AccessEcon, vol. 39(3), pages 1687-1693.
    2. MULOWAYI, Francis K. & PINSHI, Christian P., 2023. "Lucas Paradox, Institutional Quality and Corruption: Evidence from D.R. Congo," MPRA Paper 117370, University Library of Munich, Germany.
    3. Alba Del Villar Olano, 2018. "The Lucas Paradox in the Great Recession: Does the type of capital matter?," Economics Bulletin, AccessEcon, vol. 38(2), pages 1052-1057.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    2. Paul Plummer & Michael Taylor, 2011. "Enterprise and Competitive Advantage in the Australian Context: A Spatial Econometric Perspective," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(3), pages 311-330, January.
    3. Paweł Kaczmarczyk, 2017. "Ekonometryczne modelowanie i prognozowanie rozwoju polskiego sektora ICT z uwzględnieniem wskaźników makroekonomicznych," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 45, pages 259-272.
    4. W H Boshoff, 2012. "Gasoline, Diesel Fuel And Jet Fuel Demand In South Africa," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 36(1), pages 43-78, April.
    5. P. Dorian Owen, 2017. "Evaluating Ingenious Instruments for Fundamental Determinants of Long-Run Economic Growth and Development," Econometrics, MDPI, vol. 5(3), pages 1-33, September.
    6. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    7. Mrs. Swarnali A Hannan, 2015. "If the Fed Acts, How Do You React? The Liftoff Effect on Capital Flows," IMF Working Papers 2015/256, International Monetary Fund.
    8. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    9. Monique Reid & Gideon Rand, 2015. "A Sticky Information Phillips Curve for South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 83(4), pages 506-526, December.
    10. Takamitsu Kurita, 2019. "A Recursive Monte Carlo Study of Structural-Break Sensitivity of Adjustment Coefficients in Cointegrated VAR Systems," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 251-270, June.
    11. Matthew Greenwood-Nimmo & Daan Steenkamp & Rossouw van Jaarsveld, 2022. "A banklevel analysis of interest rate passthrough in South Africa," Working Papers 11027, South African Reserve Bank.
    12. Francesco Grigoli & José M. Mota, 2017. "Interest rate pass-through in the Dominican Republic," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-25, December.
    13. Steven Lehrer & Tian Xie, 2017. "Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 749-755, December.
    14. Jonas Harnau, 2018. "Log-Normal or Over-Dispersed Poisson?," Risks, MDPI, vol. 6(3), pages 1-37, July.
    15. Antonia Lòpez-Villavicencio & Luis Antonio Reyes Ortiz, 2018. "Is globalisation taking away jobs? An empirical assessment for advanced economies," Working Papers halshs-01895223, HAL.
    16. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    17. Clements Michael P. & Hendry David F., 2008. "Economic Forecasting in a Changing World," Capitalism and Society, De Gruyter, vol. 3(2), pages 1-20, October.
    18. Fullerton, Thomas M., Jr. & Ceballos, Alejandro & Walke, Adam G., 2015. "Short-Term Forecasting Analysis for Municipal Water Demand," MPRA Paper 78259, University Library of Munich, Germany, revised 04 Aug 2015.
    19. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Post-Print halshs-00917797, HAL.
    20. Mr. Andrew J Swiston, 2011. "Official Dollarization As a Monetary Regime: Its Effectson El Salvador," IMF Working Papers 2011/129, International Monetary Fund.

    More about this item

    Keywords

    Lucas Paradox; capital flows; foreign direct investment; institutions; misspecification testing;
    All these keywords.

    JEL classification:

    • F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements
    • F34 - International Economics - - International Finance - - - International Lending and Debt Problems
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics
    • E02 - Macroeconomics and Monetary Economics - - General - - - Institutions and the Macroeconomy
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:otg:wpaper:1611. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Janet Bryant (email available below). General contact details of provider: https://edirc.repec.org/data/etotanz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.