The Economics of Sports Participation: Some Longitudinal Analysis.
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The Economics of Sports Participation: Some Longitudinal Analysis. Paper presented to the GHS user group meeting 13 th March 2009 Paul Downward & Joe Riordan. Background:. Promotion of physical activity is now central to public policy concerns

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The Economics of Sports Participation: Some Longitudinal Analysis.

Paper presented to the GHS user group meeting 13th March 2009

Paul Downward


Joe Riordan

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Background: Analysis.

  • Promotion of physical activity is now central to public policy concerns

  • Relatively Little economic analysis and large-scale data testing

  • Builds upon Downward (2007); Downward and Riordan (2007) to analyse (and seek advice?)

    • Participation decisions

    • Social interactions

    • Over time

      THAT IS……………………………….

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Why do we do this? Analysis.

What has happened over time? Should we seek to promote this?

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Overview Analysis.

  • Policy Context

  • Literature; Theoretical Predictions

  • Data Variables

  • Empirical Strategy

  • Results

  • Discussion

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Policy Context Analysis.

In the UK a ‘Twin track’ approach

Increase mass participation

Enhance international success


Game Plan 2002

Increase quantity and quality

of participation

Creating a talent identification

and development pathway

A first class successful

sporting nation

A fit, active population

Sport England Analysis of Determinants of Participation (2004)

Sport England Strategy 2008-11

Buildon school provision → work with NGBs → develop community sport → Excel, Sustain, Grow

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Literature/Theory Analysis.

  • Heterodox:

    • Gratton and Tice (1991) explore the psychological foundations of consumer choice in sport and, in particular, learning by doing (Scitovsky, 1976; Earl, 1986, 1983).

    • Post Keynesian consumer analysis draws upon this concept and also insights from the studies of Leisure by Veblen (1925) and Galbraith (1958) and, by implication, Bourdieu (1984, 1988, 1991) that individual preferences are shaped by social values.

    • Prior experience in sports activities is likely to raise participation in any specific activity, and that social interactions, or lifestyles, will also affect participation.

      • Uncertainty, preferences evolve, social constraints

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Literature/Theory Analysis.

  • Neoclassical

    • Income-Leisure Trade off of Labour Supply (Gratton and Taylor, 2000)

    • Becker (1965, 1974).

      • The latter paper is directly concerned with the accumulation of personal-consumption capital and social interactions in consumption.

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Literature/Theory Analysis.

Marginal utility mediated through marginal productivity as

dUit = (δUit /δPit)(δPit /δxit)dxit + (δUit /δit)(δPit /δit)dit + (δUit /δPit)(δPit /δCit)dCit +

(δUit /δPit)(δPit /δSit)dSit

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Literature/Theory Analysis.

Can integrate time cost explicitly in a simple way e.g. add wst and wct to r.h.s. so that

economic shadow price includes time (If ws ≠wc then an element of ‘depreciation’ so that per period allocations are different).

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Literature/Theory Analysis.

In general this suggests that sports participation will be likely to vary directly with the acquisition of specific personal consumption and social capital, and also with the decline in any initial obstacles to participation through time (no reinvestment!).

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Empirical Work Analysis.

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Empirical Work Analysis.

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Empirical Work Analysis.

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Empirical Work Analysis.

*Studies used either Factor analysis of Cluster analysis to group activities.

**Study used cluster analysis to identify lifestyles

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Empirical Work Analysis.

  • Time (Investment; preferences shifting)?

  • Social Interactions (groups of characteristics)?

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Data\Variables Analysis.

The General Household Survey (GHS) was the data source for the research.

  • A continuous survey, which began in 1971, and is conducted by the Office for National Statistics. It collects data on a range of topics, by face-to-face interview, from private households in Great Britain. As well as core topics such as household and family characteristics, education, health, income and demographics, it also investigates other topics, such as Sport and Leisure, periodically.

  • Data from the 1980, 1986, 1990, 1996/7, 2002 are available.

    Some analysis done in Downward and Riordan (forthcoming, 2009)

  • But only 22 activities participated in or not over the last four weeks

  • Conformity?

    • Income a problem. Household and individual data, gross and net. A series of net income per week per individual identified by proportionate adjustment. This was deflated by the Retail Price Index for the year.

    • Some socio-economic; regional characteristics identifiable at more aggregate levels

    • Odd patterns of participation

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1980-2002 Analysis.

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1980-2002 Analysis.

Sample sizes?

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1996/7 -2002 Analysis.

  • 40 Activities

  • Easier to match variables

Pooled data, not a panel, from 5 different years of GHS Survey

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Empirical Strategy Analysis.

  • Cluster Analysis (Two-Step)

    • Personal Consumption capital; Social Characteristics

  • Regression analysis (Controlled for household selection; robust errors)

    • Individual factors, plus cluster membership variable.

    • Participation Decision

      • Ln(Pit/1-Pit) = β0 + ∑βjXjit + vit

    • Number of Sports

      • Numsportit = α0 + ∑αjXjt + uit

Adequate strategy?

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Empirical Strategy: Selection? Analysis.

Tobit Model: But:

  • Lacks robust SE corrections, cluster sampling and weighting options

  • Numsport*it = α0 + ∑ α jXjit + uit

    if Numsportit* ≤ 0 Numsportit = 0

    if Numsportit* > 0 Numsportit = Numsportit*

    Heckman Model to distinguish decisions/correct for sample bias:

  • (H1) Numsport*it = α0 + ∑ α jXjit + uitNumsporti > 0 only if Pit =1.

  • (H2) Pit = β0 + ∑βjXjit + vitPit = 1 and 0 otherwise

    Where u is N(0, σ)

    v is N(0, 1)

    Corr (u, v) = ρ

  • It could be the case that the choice set

  • comprises voluntary decisions to

  • participate on any number of independent

  • occasions which could include not at all

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Descriptives (1) Analysis.

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Cluster analysis Analysis.

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Regressions Analysis.

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Interactions? Analysis.

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Discussion Analysis.

  • Some standard drivers of participation receive large-scale empirical support

    • Income

    • Human capital – education; employment

    • Health

    • Minor impact of family (aggregate measure?)

    • Evidence that social and consumption characteristics matter

    • Age (-); Sex (M>F)

  • Time?

    • Cohort variable suggests declining general interest in sport

    • Interaction effects suggest reducing impact of traditional constraints/choices except BAME when allow for lifestyles

      • Access to a given sport seems to be easing (policy?)

    • Combined effects are decline.