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«SKILLS MISMATCH IN LATVIAN MANUFACTURING SECTOR Authors: Ilze Zumente and Kārlis Putriņš ISSN 1691-4643 ISBN 978-9984-842-28-8 November 2011 Riga ...»

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mismatch:

http://www.decowe.com/static/uploaded/htmlarea/files/A_methodological_contribution_to_the_measure ment_of_skill_mismatch.pdf Ilze Zumente and Kārlis Putriņš 40 Sloka, B. (2007). Compliance of Professional and Higher Education programmes with the requirements of labor market. Riga: University of Latvia.

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Appendix 1 Employees questionnaire Questionnaire for the employees This survey is a part of the Bachelor Thesis written by Ilze Zumente and Karlis Putrins.

The aim of this survey is to analyze the skills mismatch in the Latvian manufacturing sector.

The survey is anonymous and responses will not be analyzed individually. We thank you for your participation!

Part I

How much do you agree with the following statements (in scale 1- 5 where 1 means strongly disagree and 5 fully agree):

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1 2 3 4 5 Part III Choose the answer by circling the right option. If there are no options provided write the answer in the place provided.

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Appendix 2 Employers questionnaire Questionnaire for the employers This survey is a part of the Bachelor Thesis written by Ilze Zumente and Karlis Putrins.

The aim of this survey is to analyze the skills mismatch in the Latvian manufacturing sector.

The survey is anonymous and responses will not be analyzed individually. We thank you for your participation!

Please do critically evaluate how important the following skills are for the lower level employees working in your company(1 – not important, 5 – very important

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Source: Manufacturing industry distribution into sectors by number of people employed. Created by authors based on data by CSB 2010.

In order to create a representative sample from the whole manufacturing industry, the responses have been divided according to the individual shares of different manufacturing industries in the whole sector. The sample distribution is aimed to correspond to the distribution of the number of employees among various manufacturing industries. The distribution is based on the data by Central Statistics Bureau of Latvia and data stem from year 2008 which was the latest available.

Ilze Zumente and Kārlis Putriņš 46 Appendix 5 Sample normality testing Source: Created by authors based on the sample.

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Appendix 6 Regression results Regression Analysis- Skills mismatch impact on wages where the dependent variable logarithm of hourly net wage rate. Regressions (2) and (3) are modified by adding industry specific dummies. The model is estimated by OLS with heteroskedasticity robust standard errors. The constant is included in all regressions. Robust standard errors are reported in parenthesis. *, ** and *** denote significance at 10, 5 and 1 percent levels, respectively.

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Appendix 8 Variable description Variable Description lnwage Logarithm of hourly wage rate (after taxes). Rate is computed by dividing monthly wage rate by approximated 160 hours.

age Denotes person’s age at the time of doing the survey children Denotes the number of children person has in the family married Dummy variable, which takes value of 1, if the person is married and 0 otherwise man Dummy variable, which takes value of 1, if the person’s gender is man and 0 otherwise exp Years of experience in the field of current working place exp2 Square of years of experience in the field of current working place Wrong Dummy variable, which takes value of 1, if the person is characterized as wrong skills by Allen and Van der Velden (2001) and 0 otherwise Shortage Dummy variable, which takes value of 1, if the person is characterized as having skill shortage by Allen and Van der Velden (2001) and 0 otherwise Surplus Dummy variable, which takes value of 1, if the person is characterized as having skill surplus by Allen and Van der Velden (2001) and 0 otherwise Cosmetics Dummy variable, which takes value of 1, if the person is currently in employed in cosmetics sub-sector and 0 otherwise Food Dummy variable, which takes value of 1, if the person is currently in employed in food producing (including bakery, fishing, meat, beverages) sub-sector and 0 otherwise Constr Dummy variable, which takes value of 1, if the person is currently in employed in construction (including repairing) sub-sector and 0 otherwise Electrics Dummy variable, which takes value of 1, if the person is currently in employed in electricity production sub-sector and 0 otherwise Wood Dummy variable, which takes value of 1, if the person is currently in employed in wood (including timber) production sub-sector and 0 otherwise Metal Dummy variable, which takes value of 1, if the person is currently in employed in metal production sub-sector and 0 otherwise Textile Dummy variable, which takes value of 1, if the person is currently in employed in textile (including apparel and national apparel) production sub-sector and 0 otherwise Telecom Dummy variable, which takes value of 1, if the person is currently in employed in telecommunication sub-sector and 0 otherwise Ilze Zumente and Kārlis Putriņš 50

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Planning Communication Time Team Responsibility VarI Var2 Var I Var 2 Var I Var 2 Var I Var 2 Var I Var 2 Mean 4.03 4.01 3.97 3.99 3.50 3.91 3.90 4.11 4.80 4.32 Variance 1.34 0.80 1.76 0.76 1.84 0.96 1.96 0.78 0.23 0.71 Observations 30 201 30 201 30 201 30 201 30 201 Hypothesized Mean Difference 0 0 0 0 0 df 34 33 34 33 59 t Stat 0.11 -0.07 -1.58 -0.82 4.52 P(T=t) one-tail 0.46 0.47 0.06 0.21 0.00 t Critical one-tail 1.69 1.69 1.69 1.69 1.67 P(T=t) two-tail 0.92 0.94 0.12 0.42 0.00 t Critical two-tail 2.03 2.03 2.03 2.03 2.00 Source: Created by authors

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