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Unemployment and liquidity constraints

Author

Listed:
  • Yannis M. Ioannides

    (Department of Economics, Tufts University, Medford, Massachusetts, USA)

  • Vassilis A. Hajivassiliou

    (Department of Economics, London School of Economics, London, UK)

Abstract

We present a dynamic framework for the interaction between borrowing (liquidity) constraints and deviations of actual hours from desired hours, both measured by discrete-valued indicators, and estimate it as a system of dynamic binary and ordered probit models with panel data from the Panel Study of Income Dynamics. We analyze a household's propensity to be liquidity constrained by means of a dynamic binary probit model. We analyze qualitative aspects of the conditions of employment, namely whether the household head is involuntarily overemployed, voluntarily employed, or involuntarily underemployed or unemployed, by means of a dynamic ordered probit model. We focus on the possible interaction between the two types of constraints. We estimate these models jointly using maximum simulated likelihood, where we allow for individual random effects along with an autoregressive process for the general error term in each equation. A novel feature of our method is that it allows for the random effects to be correlated with regressors in a time-invariant fashion. Our results provide strong support for the basic theory of constrained behavior and the interaction between liquidity constraints and exogenous constraints on labor supply. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Yannis M. Ioannides & Vassilis A. Hajivassiliou, 2007. "Unemployment and liquidity constraints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 479-510.
  • Handle: RePEc:jae:japmet:v:22:y:2007:i:3:p:479-510
    DOI: 10.1002/jae.953
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    5. Hajivassiliou, Vassilis & Savignac, Frédérique, 2007. "Financing constraints and a firm's decision and ability to innovate: establishing direct and reverse effects," LSE Research Online Documents on Economics 4774, London School of Economics and Political Science, LSE Library.
    6. Jean-Marie Dufour & Joachim Wilde, 2018. "Weak identification in probit models with endogenous covariates," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 611-631, October.
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    8. Lee, Jeong-Joon & Sawada, Yasuyuki, 2010. "Precautionary saving under liquidity constraints: Evidence from rural Pakistan," Journal of Development Economics, Elsevier, vol. 91(1), pages 77-86, January.
    9. Manuela Deidda, 2014. "Precautionary saving under liquidity constraints: evidence from Italy," Empirical Economics, Springer, vol. 46(1), pages 329-360, February.
    10. António Afonso & Pedro Gomes & Philipp Rother, 2006. "What “Hides” Behind Sovereign Debt Ratings?," Working Papers Department of Economics 2006/35, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    11. Sònia Muñoz, 2006. "Habit Formation and Persistence in Individual Asset Portfolio Holdings; The Case of Italy," IMF Working Papers 2006/029, International Monetary Fund.
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    13. V A Hajivassiliou & Frédérique Savignac & Frédérique Savignac, 2019. "Novel Approaches to Coherency Conditions in Dynamic LDV Models: Quantifying Financing Constraints and a Firm's Decision and Ability to Innovate," STICERD - Econometrics Paper Series 606, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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    More about this item

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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