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Testing Endogeneity, Convexity and Congestion in a Matching Function: Evidence from Finland

Received: 9 September 2021    Accepted: 8 October 2021    Published: 21 October 2021
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Abstract

This study contributes to the literature on the efficiency of regional labor markets using matching function to model labor markets and nonparametric methods DEA and FDH to measure efficiency of those markets. DEA has been the most popular method in empirical studies measuring efficiency for an industry and there is also a literature applying DEA to study the efficiency of labor markets. However, this literature neglects two problems important for consistent estimation of a matching function: the possible endogeneity of inputs and non-convexity of the production set. Endogeneity manifests as correlation between inputs and efficiencies. In this paper, we first analyze whether the inputs of the matching function or unemployed jobseekers and open vacancies are exogenous. As our results do not reject exogeneity, we continue treating these inputs exogenous. Next, we evaluate convexity of production set. Testing convexity is an important prerequisite for the use of DEA, because DEA assumes convexity and supplies consistent efficiencies only when the production set is convex. However, convexity is rarely assessed when DEA is applied. In this paper, we evaluate convexity of the production set of the matching function. We use several tests including ones that are based on recently proposed central limit theorems for moments of DEA and FDH estimators. Out of ten tests performed, six ones reject convexity while four ones do not. The tests leave us with a strong belief in non-convexity, and this directs us to apply FDH instead of DEA in the sequel, when we study congestion of inputs. We find strong congestion of open vacancies concerning Helsinki travel-to-work area for several years. In 2017 the loss of matches due to congestion was more than 20 000, amounting to 2.5% of the labor force in Helsinki region, 0.8% in the whole country. Our research with data on 113 travel-to-work areas and 15 public employment (TE-) offices in 2007–19 in Finland, shows huge differences in labor market situation between regions, especially Helsinki and the rest of the country, calling attention from the decision-makers both in firms and government. Also, our study emphasizes the need to pretest data for exogeneity and convexity before applying DEA.

Published in International Journal of Business and Economics Research (Volume 10, Issue 5)
DOI 10.11648/j.ijber.20211005.14
Page(s) 187-202
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Matching Function, Regional Labor Markets, Efficiency, FDH, DEA, Convexity, Congestion

References
[1] Abbasi M., Jahanshaloo G. R., Rostamy-Malkhlifeh and Lofti H. (2014). Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis. The Scientific World Journal, Volume 2014, Hindawi Publishing Corporation.
[2] Althin, R. and L. Behrenz (2004). An Efficiency Analysis of Swedish Employment Offices, International Review of Applied Economics, 18, s. 471–482.
[3] Althin, R. and L. Behrenz (2005). Efficiency and productivity of employment offices: evidence from Sweden, International Journal of Manpower, 26, s. 196–206.
[4] Althin, R., Behrenz L. R., Färe R., Grosskopf S. and Mellander E. (2010). Swedish employment offices: A new model for evaluating effectiveness, European Journal of Operational Research, 207, s. 1535–1544.
[5] Andersson C., Månsson J. and Sund K. (2014). Technical efficiency of Swedish employment offices, Socio-Economic Planning Sciences 48, 57–64.
[6] Fahr R. and Sunde U. (2006). Regional dependencies in job creation: an efficiency analysis for Western Germany, Applied Economics, 38: 10.
[7] Banker, R. D., Charnes A., and Cooper W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30, 1078–1092.
[8] Blanchard O. P., Diamond P. (1989). The Beveridge-curve. Brookings Papers on Economic Activity, 1: 1989, 1–76.
[9] Boeri T., Burda M. C. (1996). Active labor market policies, job matching and the Czech miracle. European Economic Review 40, 805–817.
[10] Briec W., Kerstens K. and Eeckaut P. (2004). Non-convex Technologies and Cost Functions: Definitions, Duality and Nonparametric Tests of Convexity, Journal of Economics 81 (2), 155–192.
[11] Borowczyk M., Jolivet G. and Postel-Vinay F. (2013). Accounting for endogeneity in matching function estimation, Review of Economic Dynamics, 2013, vol. 16, issue 3, 440–451.
[12] Broersma L., van Ours J. C. (1999). Job searchers, job matches and the elasticity of matching. Labour Economics, 6 (1), 77–93.
[13] Calmfors L. (1994). Active Labour Marekt Policy and Unemployment – A Framework for the Analysis of crucial Design Features, OECD Economic Studies No. 22.
[14] Charnes, A., Cooper W. W. and Rhodes E. (1978). Measuring the efficiency of decision-making units, European Journal of Operational Research 2, 429–444.
[15] Cherchye L., Kuosmanen T. and Post T. (2001). Alternative treatments of congestion in DEA: A rejoinder to Cooper, Gu, and Li, European Journal of Operational Research, vol. 132, 75–80.
[16] Coles M. G. and Petrongolo B. (2008). A Test Between Stock-Flow Matching and the Random Matching Function Approach, International Economic Review, 49 (4), 1113–1141.
[17] Cooper W. W., Deng H., Huang Z. M. and Li S. X. (2002). A one model approach to congestion in data envelopment analysis, Socio-Economic Planning Sciences, vol. 36, no. 4, pp. 231–238.
[18] Cooper W. W., Seiford L. M and Tone K. (2007). Data Envelopment Analysis, A Comprehensive Text with Models, Applications, References and DEA-Solver Software, second edition. Springer Science and Business Media.
[19] Cordero, J. M., Santín, D. and Sicilia, G. (2017). Dealing with the Endogeneity Problem in Data Envelopment Analysis. Expert Systems with Applications 68, 127–184.
[20] Cordero, J. M, Santín, D, & Sicilia, G. (2015). Testing the accuracy of DEA estimates under endogeneity through a Monte Carlo simulation, European Journal of Operational Research, 244 (2), 511–518.
[21] Dauth W., Hujer R. and Wolf K. (2010). Macroeconometric Evaluation of Active Labor Market Policies in Austria, The Institute for the Study of Labor, Discussion Paper No. 5217.
[22] Daraio C., Simar L. and Wilson P. W. (2016). Nonparametric Estimation of Efficiency in the Presence of Environmental Variables, University of Rome, Technical Report n. 2
[23] Deprins, D., Simar L. and Tulkens H. (1984). Measuring labor inefficiency in post offices, in M. M. P. Pestieau and H. Tulkens, eds., The Performance of Public Enterprises: Concepts and Measurements, Amsterdam: North-Holland, pp. 243–267.
[24] Emrouznejad, A. (1995–2011). Data Envelopment Analysis Homepage, www.DEAzone.com, last viewed 05.06.2020.
[25] Fähre R. and Grosskopf S. (1996). Productivity and intermediate products: A frontier approach, Economics Letters 50, 65–70.
[26] Gregg P. and Petrongolo B. (2005). Stock-Flow Matching and the Performance of the Labor market, European Economic Review, 49, 1987–2011.
[27] Hillman K. (2009). Does the Hartz IV Reform have an Effect on Matching Efficiency in Germany? A Stochastic Frontier Approach. Munich Personal RePEc Archive, Paper No. 22295. http://mpra.ub.uni-muenchen.de/22295/. 20.11.2018.
[28] Hynninen S. M. (2007). Matching in Local Markets, Empirical Studies from Finland. Jyväskylä Studies in Business and Economics 56. University of Jyväskylä.
[29] Hynninen S. M., Kangasharju A. and Pehkonen J. (2009). Matching Inefficiencies, Regional Disparities, and Unemployment. Labour, 23 (3), 481–506.
[30] Ibourk A., Maillard B., Perelman S. and Sneessens H. R. (2001). Aggregate Matching Efficiency: A Stochastic Production Frontier Approach, France 1990–1994, IZA Discussion Paper No. 339.
[31] Ibourk A., Maillard B., Perelman S. and Sneessens H. R. (2004). Aggregate Matching Efficiency: A Stochastic Production Frontier Approach, France 1990–1994, Empirica 31, 1–25.
[32] Ilmakunnas P. and Pesola H. (2003). Regional Labor Market Matching Functions and Efficiency Analysis, Labour, 17 (3), 413−437.
[33] Jeruzalski T. and Tyrowicz J. (2009). (In)Efficiency of Matching - The Case of a Post-transition Economy, MPRA Paper No. 16598, http://mpra.ub.uni-muenchen.de/16598/, 03.06.2019.
[34] Khitri W., Emrouznejad A., Boujelbène Y., Nejib M., Ourtani A. (2011). Framework for performance evaluation of employment offices: a case of Tunisia. International Journal of Applied Decision Sciences 4, 16−33.
[35] Khodabakhshi M., Lotfi F. H. and Aryavash K. (2014). Review of Input Congestion Estimating Methods in DEA, Hindawi Publishing Corporation, Journal of Applied Mathematics, Volume 2014.
[36] Kneip A., Simar L. and Wilson P. W. (2008). Asymptotics and consistent Bootstraps for DEA Estimators in Nonparametric Frontier Models, Econometric Theory 24, 1663–1697.
[37] Kneip A., Simar L. and Wilson P. W. (2015). When bias kills the variance: Central limit theorems for DEA and FDH efficiency scores, Econometric Theory 31, 394−422.
[38] Kneip A., Simar L. and Wilson P. W. (2016). Testing Hypotheses in Nonparametric Models of Production. Journal of Business & Economic Statistics, 34: 3, 435-456.
[39] Lehmann, H. (1995). Active Labor Market Policies in the OECD and in Selected Transition Economies, World Bank Policy Research Working Paper No. 1502, Washington D.C.
[40] Orme, C. and Smith, P. (1996). The potential for endogeneity bias in data envelopment analysis. Journal of the Operational Research Society, 73–83.
[41] OSF, Official Statistics of Finland: Employment Service Statistics [e-publication]. Helsinki: Ministry of Economic Affairs and Employment [referred: 14.6.2019]. Access method: http://www.stat.fi/til/tyonv/index_en.html.
[42] Petrongolo B. and Pissarides C. (2001). Looking into the Black Box: A Survey of the Matching Function. Journal of Economic Literature XXXIX, 390–431.
[43] Pissarides, C. A. (1990). Equilibrium unemployment theory. Oxford: Oxford University Press.
[44] Politis D. N., Romano J. P. and Wolf M. (2001). On the asymptotic theory of subsampling. Statistica Sinica 11, 1105–1124.
[45] Ramirez J., Vassiliev A. (2007). An efficiency comparison of regional employment offices operating under different exogenous conditions. Swiss Journal of Economics and Statistics 143, 31–48.
[46] R Development Core Team (2008). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. URL: http://www.R-project.org.
[47] Santin D. and Sicilia G. (2017). Dealing with endogeneity in data envelopment analysis applications. Expert Systems With Applications, 68, 173–184.
[48] Recruitment of labor (2010–19), by several authors. Ministry of Economic Affairs and Employment, Analyses. https://tem.fi/julkaisulista?subject=TEM-analyysit (In Finnish).
[49] Riksrevisionen (2012). Effektivitetsmätning som metod för att jämföra arbetsförmedlingskontor. Granskningsrapport 2012: 9. (In Swedish).
[50] Sheldon G. (2003). The efficiency of public employment services: a nonparametric matching function analysis for Switzerland. Journal of Productivity Analysis 20, 49–70.
[51] Shephard R. W. (1970). Theory of Cost and Production Functions, Princeton: Princeton University Press.
[52] Sickles, R. C. and Zelenyuk, V., (2019). Measurement of Productivity and Efficiency, New York: Cambridge University Press.
[53] Simar L. and Wilson P. W. (2001). Testing restrictions in nonparametric efficiency models. Communications in Statistics –Simulation and Computation, 30: 1.
[54] Simar, L. and Wilson P. W. (2007). Estimation and inference in two-stage, semi-parametric models of productive efficiency, Journal of Econometrics 136, 31–64.
[55] Simar L. and Wilson P. W. (2008). Statistical Inference in Nonparametric Frontier Models: Recent Developments and Perspectives. In Fried H, C. A. Knox Lovell, and Shelton S. Schmidt: The Measurement of Productive Efficiency and Productivity Change. Published to Oxford Scholarship Online: January 2008, DOI: 10.1093/acprof:oso/9780195183528.001.0001.
[56] Simar, L. and Wilson P. W. (2010). Inference by the m out of n bootstrap in nonparametric frontier models, Journal of Productivity Analysis 36 (1), 33–53.
[57] Simar, L. and Wilson P. W. (2011). Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives, Foundations and Trends in Econometrics Vol. 5, Nos. 3–4 (2011) 183–337.
[58] Simar, L., and Wilson P. W. (2020). Hypothesis testing in nonparametric models of production using multiple sample splits, Journal of Productivity Analysis 53, 287–303. https://doi.org/10.1007/s11123-020-00574-w.
[59] Talonen Markku and Tuomaala Mika (1994). The Effectiveness of Employment Offices, Labour policy studies No. 79, Ministry of Labour Helsinki. (In Finnish).
[60] Talonen Markku (1998). Overall results of public employment offices, Labour policy studies No. 191, Ministry of Labour, Helsinki. (In Finnish).
[61] Talonen Markku (2020). Efficiency of Regional Labor Markets: Evidence from Finland, Finnish economic Papers no 2/2020. https://www.taloustieteellinenyhdistys.fi/finnish-economic-papers-2-2020/ 19.5.2021.
[62] Talonen Markku (2021). Efficiency of TE-offices: A Stochastic Frontier Analysis. Finnish Labour Review 1/2021, Ministry of Economic Affairs and Employment. (In Finnish).
[63] Vassiliev A., Ferro Luzzi G., Flückiger Y., Ramirez J. V. (2006). Unemployment and employment offices’ efficiency: what can be done? Socio-Economic Planning Sciences 40, 169–186.
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    Markku Talonen. (2021). Testing Endogeneity, Convexity and Congestion in a Matching Function: Evidence from Finland. International Journal of Business and Economics Research, 10(5), 187-202. https://doi.org/10.11648/j.ijber.20211005.14

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    Markku Talonen. Testing Endogeneity, Convexity and Congestion in a Matching Function: Evidence from Finland. Int. J. Bus. Econ. Res. 2021, 10(5), 187-202. doi: 10.11648/j.ijber.20211005.14

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    AMA Style

    Markku Talonen. Testing Endogeneity, Convexity and Congestion in a Matching Function: Evidence from Finland. Int J Bus Econ Res. 2021;10(5):187-202. doi: 10.11648/j.ijber.20211005.14

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  • @article{10.11648/j.ijber.20211005.14,
      author = {Markku Talonen},
      title = {Testing Endogeneity, Convexity and Congestion in a Matching Function: Evidence from Finland},
      journal = {International Journal of Business and Economics Research},
      volume = {10},
      number = {5},
      pages = {187-202},
      doi = {10.11648/j.ijber.20211005.14},
      url = {https://doi.org/10.11648/j.ijber.20211005.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20211005.14},
      abstract = {This study contributes to the literature on the efficiency of regional labor markets using matching function to model labor markets and nonparametric methods DEA and FDH to measure efficiency of those markets. DEA has been the most popular method in empirical studies measuring efficiency for an industry and there is also a literature applying DEA to study the efficiency of labor markets. However, this literature neglects two problems important for consistent estimation of a matching function: the possible endogeneity of inputs and non-convexity of the production set. Endogeneity manifests as correlation between inputs and efficiencies. In this paper, we first analyze whether the inputs of the matching function or unemployed jobseekers and open vacancies are exogenous. As our results do not reject exogeneity, we continue treating these inputs exogenous. Next, we evaluate convexity of production set. Testing convexity is an important prerequisite for the use of DEA, because DEA assumes convexity and supplies consistent efficiencies only when the production set is convex. However, convexity is rarely assessed when DEA is applied. In this paper, we evaluate convexity of the production set of the matching function. We use several tests including ones that are based on recently proposed central limit theorems for moments of DEA and FDH estimators. Out of ten tests performed, six ones reject convexity while four ones do not. The tests leave us with a strong belief in non-convexity, and this directs us to apply FDH instead of DEA in the sequel, when we study congestion of inputs. We find strong congestion of open vacancies concerning Helsinki travel-to-work area for several years. In 2017 the loss of matches due to congestion was more than 20 000, amounting to 2.5% of the labor force in Helsinki region, 0.8% in the whole country. Our research with data on 113 travel-to-work areas and 15 public employment (TE-) offices in 2007–19 in Finland, shows huge differences in labor market situation between regions, especially Helsinki and the rest of the country, calling attention from the decision-makers both in firms and government. Also, our study emphasizes the need to pretest data for exogeneity and convexity before applying DEA.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Testing Endogeneity, Convexity and Congestion in a Matching Function: Evidence from Finland
    AU  - Markku Talonen
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    PY  - 2021
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    T2  - International Journal of Business and Economics Research
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    AB  - This study contributes to the literature on the efficiency of regional labor markets using matching function to model labor markets and nonparametric methods DEA and FDH to measure efficiency of those markets. DEA has been the most popular method in empirical studies measuring efficiency for an industry and there is also a literature applying DEA to study the efficiency of labor markets. However, this literature neglects two problems important for consistent estimation of a matching function: the possible endogeneity of inputs and non-convexity of the production set. Endogeneity manifests as correlation between inputs and efficiencies. In this paper, we first analyze whether the inputs of the matching function or unemployed jobseekers and open vacancies are exogenous. As our results do not reject exogeneity, we continue treating these inputs exogenous. Next, we evaluate convexity of production set. Testing convexity is an important prerequisite for the use of DEA, because DEA assumes convexity and supplies consistent efficiencies only when the production set is convex. However, convexity is rarely assessed when DEA is applied. In this paper, we evaluate convexity of the production set of the matching function. We use several tests including ones that are based on recently proposed central limit theorems for moments of DEA and FDH estimators. Out of ten tests performed, six ones reject convexity while four ones do not. The tests leave us with a strong belief in non-convexity, and this directs us to apply FDH instead of DEA in the sequel, when we study congestion of inputs. We find strong congestion of open vacancies concerning Helsinki travel-to-work area for several years. In 2017 the loss of matches due to congestion was more than 20 000, amounting to 2.5% of the labor force in Helsinki region, 0.8% in the whole country. Our research with data on 113 travel-to-work areas and 15 public employment (TE-) offices in 2007–19 in Finland, shows huge differences in labor market situation between regions, especially Helsinki and the rest of the country, calling attention from the decision-makers both in firms and government. Also, our study emphasizes the need to pretest data for exogeneity and convexity before applying DEA.
    VL  - 10
    IS  - 5
    ER  - 

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  • Faculty of Social Sciences, University of Helsinki, Helsinki, Finland

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