Human Capital, Consumption and Human Capital, Consumption and Housing Wealth in Transition Housing Wealth in Transition

Jarko Fidrmuc ZU Friedrichshafen, Comenius University Bratislava, IOS

Regensburg

Matúš Senaj National Bank of Slovakia and Comenius University

Bratislava

Bratislava Economic Meeting

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The views expressed are those of the authors and do not necessarily reflect those of the National Bank of Slovakia or the Eurosystem.

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MotivationMotivation

Microeconometric analysis: households surveys on income, expenditures, and housing.

What is the impact of age, experience and human capital on disposable income, consumption and housing value.

The relationship between income, real estate wealth and private consumption (welfare).

Political economy implications with regard to support of economic reforms in Eastern Europe. Who are the winners and losers of economic reforms?

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Human Capital and TransitionHuman Capital and Transition It is generally acknowledged that the CEECs

have a relatively efficient education system especially in field of natural and technical sciences.

„Conventional wisdom holds that if there is one area where the countries of Eastern Europe and the former Soviet Union are well served, it is with respect to their stocks of human capital.” (Campos and Coricelli, 2002, JEL, p. 808)

However, education of economics, business administration, marketing, and foreign languages were often insufficient.

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Our ContributionOur Contribution

We estimate the returns to human capital in Slovak economy.

We augment this discussion by an analysis of real estate wealth and consumption.

We show that cohorts entering the labor market before or after 1989 have highly different equipments of human and physical capital, which corresponds to differential institutional settings in both periods.

Through the estimation of the consumption function we can get a proxy for the welfare effects.

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Data DescriptionData Description We merge two data sets in our paper. (1) we use the household budget survey (HBS)

conducted annually by the Statistical Office. (2) we calculate the value of real estates owned

by the households using the database of housing prices published by the NBS.

The final data set representative data for more than 4500 households for every year in all Slovak regions.

Households are available only for 1 year. The recent data set covers ranges from 2004 to 2009,

which reflect the change of methodology of HBS in 2004.

Weights ensure that the results are representative.

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Income Profile Income Profile 20

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Consumption Profile Consumption Profile 30

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Home Ownership Home Ownership .5

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Housing Wealth Housing Wealth 20

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Income EquationIncome Equation We estimate the income equation with the

following explanatory variables: Age and squared age of the household head; Household size: number of adults, number of

children, dummy for single households; Gender variable of the household head; Education: secondary, tertiary for household head

and the partner; Cohort variable – labor market entrance (age 26)

before 1990 Cohort education variable (dummies & years of

education) Regional and time dummies.

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Income Equation – OLS I Income Equation – OLS I Income 1 Income 2 Income 3

Age 0.095*** 0.161*** 0.163***

Age squared -0.016*** -0.020*** -0.021***

No of adults 0.271*** 0.276*** 0.275***

No of children 0.058*** 0.052*** 0.053***

Female head -0.048*** -0.048*** -0.049***

Single parent -0.230*** -0.226*** -0.227***

Education primary -0.122*** -0.122*** -0.328***

Education tertiary 0.185*** 0.185*** 0.187***

Partner’s ed. primary -0.131*** -0.131*** -0.128***

Partner’s ed. tertiary 0.137*** 0.136*** 0.135***

Cohort 90 -0.071***

Cohort 90 x primary education 0.148***

Cohort 90 x secondary education -0.074***

Cohort 90 x tertiary education -0.076***

Constant 9.115*** 8.958*** 8.961***

Sample All households All households All households

No. of obs. 27,650 27,650 27,650

R2 0.641 0.642 0.643

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Income Equation – OLS II Income Equation – OLS II Income 4 Income 5 Income 6

No of adults 0.244*** 0.292*** 0.275***

No of children 0.030*** 0.064*** 0.049***

Female head -0.076*** -0.019 -0.039***

Single parent 0.090 0.119*** -0.227***

Years of education 0.488*** 0.265***

Years of partner’s education 0.261*** 0.304***

Work experience 0.377*** -0.242**

Work experience squared -0.103*** -0.050

Years of education before 1990 0.415***

Years of education after 1990 0.494***

Work experience before 1990 -0.173***

Work experience after 1990 0.096***

Work exp. before 1990 squared 0.041***

Work exp. after 1990 squared 0.036***

Constant 8.135*** 8.197*** 8.604***

Sample Cohort of

younger h. Cohort of

older h. All

households No. of obs. 8,674 19,288 27,962

R2 0.464 0.665 0.637

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Income - Summary of ResultsIncome - Summary of Results Very standard results. Concave age profile. Negative gender differential. Positive returns to education, especially for higher

education. Returns to education are also gender specific. Moreover, they differ between the cohorts. Labor

market entrants from before 1990 receive lower returns to education.

The difference for basic education is not significant. High regional and time effects, slower growth after

the financial crisis.

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Household WealthHousehold Wealth

About 95% of household owns a house or an apartment.

For the cohort entering labor market before 1990, 97% of households own a real estate.

The main explanatory variables is income and the cohort variable.

There may be a truncation or selection problem. Therefore we use Heckman selection model.

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Heckman Selection ModelHeckman Selection Model

We estimate a linear Heckman selection model. In the first step, we estimate the probability that a

respondent has house In the second step, we estimate the value of his

property Age variables are used as instrumental variables

for the identification of the selection equation.

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Housing Wealth - Heckman Selection Housing Wealth - Heckman Selection Model Model

Housing 3 Housing 4 Housing 5 Housing 6

Disposable income 0.061*** 0.043*** 0.061*** 0.054***

No of family members 0.050*** 0.056*** 0.063*** 0.062***

Single-parent -0.058*** -0.045*** -0.060*** -0.058***

Cohort 90 0.133*** 0.093***

Constant 13.434*** 13.619*** 13.286*** 13.395***

Homeownership – selection model

Disposable income 0.370*** 0.336*** 0.360*** 0.336***

Single parent -0.199*** -0.238*** -0.206*** -0.238***

No of family members 0.042 0.019 0.034 0.019

Age 1.023*** 1.093*** 1.058*** 1.093***

Age squared -0.067*** -0.076*** -0.071*** -0.076***

Cohort 90 0.041 -0.000 -0.031 -0.000

Constant -5.301*** -4.931*** -5.178*** -4.931***

No. of observations 27,965 27,965 27, 965 27,965

No. of censored obs. 1,674 1,674 1,674 1,674

rho -0.447 -1.000 -0.275 -0.701

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Consumption EquationConsumption Equation

Income and consumption are endogenous variables.

We instrument income with gender and small town, because these variables are not correlated with residuals from the consumption equation, as confirmed by Hansen J-test.

Housing ownership may also influence consumption trough lower savings and better access to credits.

We keep housing ownership as exogenous variable, although it could be endogenous.

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Consumption Function – IV Consumption Function – IV Results Results

Consumption 1 Consumption 2 Consumption 3 Consumption 4

2SLS 2SLS GMM GMM

Disposable income 0.874*** 0.862*** 0.872*** 0.860***

Housing wealth -0.001 0.001 -0.001 0.001

Primary education -0.113*** -0.106*** -0.114*** -0.107***

Tertiary education 0.015* 0.017** 0.014* 0.016**

Constant 1.149*** 1.280*** 1.170*** 1.296***

Cohort 90 -0.047*** -0.046***

No of observations 27 965 27 965 27 965 27 965

Centered R2 0.620 0.623 0.620 0.624

Hansen J statistic 0.164 0.157 0.164 0.157

Hansen p-value 0.686 0.692 0.686 0.692

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ConclusionsConclusions

Employees with education gained before 1990 have significantly lower returns to education.

By contrast, they have accumulated significantly higher stock of physical capital (housing).

In sum, the impact on household welfare (consumption) is ambiguous.

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Thank you for your attentionThank you for your attention