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Empirical econometric evaluation of alternative methods of dealing with missing values in Investment Climate surveys

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  • Escribano, Álvaro
  • Pena, Jorge

Abstract

The Investment Climate surveys (ICSs) are valuable instruments which improve our understanding of the economic, social, political and institutional factors determining economic growth, particularly in emerging and transition economies. However, at the same time, they have to overcome some difficult issues related with the quality of the information provided; measurement errors, outlier observations and missing data are frequently found in this datasets. In this paper we discuss the applicability of recent procedures to deal with missing observations in IC surveys. In particular we present a simple replacement mechanism—for application in models with a large number of explanatory variables—, which we call the ICA method, which in turn is a proxy of two methods: multiple imputation and EM algorithm. We evaluate the performance of this ICA method in the context of TFP estimation in extended production functions using ICSs from four countries: India, South Africa, Tanzania and Turkey. We find that the ICA method is very robust and performs reasonably well even under different assumptions on the nature of the mechanism generating missing data.

Suggested Citation

  • Escribano, Álvaro & Pena, Jorge, 2009. "Empirical econometric evaluation of alternative methods of dealing with missing values in Investment Climate surveys," UC3M Working papers. Economics we098750, Universidad Carlos III de Madrid. Departamento de Economía.
  • Handle: RePEc:cte:werepe:we098750
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    References listed on IDEAS

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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
    2. Escribano, Álvaro & Guasch, J. Luis, 2008. "Robust methodology for investment climate assessment on productivity: application to investment climate surveys from Central America," UC3M Working papers. Economics we081911, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Escribano, Álvaro & Guasch, J. Luis & Pena, Jorge, 2009. "Assessing the impact of infrastructure quality on firm productivity in Africa: Cross‐country comparisons based on investment climate surveys from 1999 to 2005," UC3M Working papers. Economics we098649, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Newey, Whitney K., 1984. "A method of moments interpretation of sequential estimators," Economics Letters, Elsevier, vol. 14(2-3), pages 201-206.
    5. Escribano, Álvaro & Guasch, J. Luis & Orte, Manuel De & Pena, Jorge, 2008. "Investment climate and firm’s economic performance: econometric methodology and application to Turkey's investment climate survey," UC3M Working papers. Economics we082113, Universidad Carlos III de Madrid. Departamento de Economía.
    6. Escribano, Álvaro & Guasch, J. Luis & Orte, Manuel De & Pena, Jorge, 2008. "Investment climate assessment based on demean Olley and Pakes decompositions: methodology and application to Turkey's investment climate survey," UC3M Working papers. Economics we082012, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Escribano, Alvaro & Guasch, J. Luis, 2005. "Assessing the impact of the investment climate on productivity using firm-level data : methodology and the cases of Guatemala, Honduras, and Nicaragua," Policy Research Working Paper Series 3621, The World Bank.
    8. Cameron,A. Colin & Trivedi,Pravin K., 2008. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9787111235767, December.
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    10. J. G. Ibrahim & S. R. Lipsitz & M.-H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non-ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
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    Keywords

    Random sampling;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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