1 ESTIMATION OF A STRUCTURAL MODEL OF THE DETERMINANTS OF THE TIME SPENT IN PHYSICAL ACTIVITY AND SPORT: EVIDENCE FOR SPAIN Jaume Garcia (UPF) María José.

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1 ESTIMATION OF A STRUCTURAL MODEL OF THE DETERMINANTS OF THE TIME SPENT IN PHYSICAL ACTIVITY AND SPORT: EVIDENCE FOR SPAIN Jaume Garcia (UPF) María José Suárez (Universidad de Oviedo)

2 OUTLINE Objective Literature review Economic model Econometric specification Data and estimation procedure Results Conclusions

3 OBJECTIVE To analyse the determinants of time spent in physical activity and sports. We extend the neoclassical labour supply model to study the individual allocation of time to physical activity.

4 LITERATURE REVIEW Farrell & Shields (2002) Sporting participation in England. Random-effects Probit models. Humphreys & Ruseski (2006) Participation in physical activity. They develope an economic model of the decision to participate in physical activity. They estimate a reduced-form model using U.S. data and applying Heckman’s procedure. Lera-López & Rapún-Gárate (2006) Relationship between sports practice and spectatorship and the determinants of each. Spanish data Ordered Probit models.

5 ECONOMIC MODEL (I) Static neoclassical labour supply model: l= leisure time c= consumption

6 ECONOMIC MODEL (II) Our model: l 1 (time spent in physical activities) = f 1 (w,y) l 0 (leisure time) = f 0 (w,y) c = f c (w,y)

7 ECONOMIC MODEL (III) CES utility function: β>0, δ>0, γ>-1

8 ECONOMIC MODEL (IV) Demand for physical activity:

9 ECONOMIC MODEL (V) From FOC we obtain:

10 ECONOMETRIC SPECIFICATION (I) We allow both observable and unobservable factors to enter preferences:

11 ECONOMETRIC SPECIFICATION (II) The demand equation system to estimate is: SURE method

12 DATA AND ESTIMATION PROCEDURE (I) Spanish Time-Use Survey Sample: Individuals We drop workers who have more than one job or who have had an unusual working week. We split the sample by gender.

13 DATA AND ESTIMATION PROCEDURE (II) Estimation problems: Endogeneity of earnings and lack of information about this variable for non- working people. Sample selection bias.

14 DATA AND ESTIMATION PROCEDURE (III) Estimation steps: Probits for the probability of working. Log-linear hourly-earning equations using Heckman and interval estimation procedures. Probits for the probability of doing physical activity. Demand system by SURE and correcting selectivity bias.

15 RESULTS (I) PROBITS [Pr (l 1 >0)] Coefficients Females (N=14801)Males (N=12467) Age Age 2 /100 Health Married No. Children <6 No. Children st term 2nd term 3rd term Weekend Primary education Secondary education Higher education Degree of urb. 1 Degree of urb. 2 Region * 0.041* * * * 0.155* 0.215* 0.086* * 0.051* 0.053* S * 0.072* * * * * 0.108* 0.456* * 0.212* 0.133* 0.102* S

16 RESULTS (II) Females (N=5606)Males (N=4837) Structural parametersCoefficients γ 0.146*-0.168* α 1 Age Age 2 /100 Health Married No. Children <6 No. Children st term 2nd term 3rd term Weekend 0.019* * * 0.177* 0.176* * 0.070* 0.111* * 0.078* * * 0.181* * * * 0.151*

17 CONCLUSIONS Gender differences in determinants of the allocation of time to physical activity. Different effect of some variables on the probability of doing sports and the amount of time devoted to this activity.