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An Empirical Study into the Determinants of an Individuals Supply of Labour

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Introduction

An Empirical Study into the Determinants of an Individual's Supply of Labour James Allen Introduction Why does a person work the number of hours that they do? This has been a question of much research over many years by labour economists. It is a question which has important policy implications for government as to how much they should tax workers income, how high they should set the minimum wage and whether to impose a maximum working week. The information would also be useful to firms in the interests of setting suitable wages and other working conditions. I aim to answer the question of the determinants of the number of hours worked by individuals. The data I will use for analysis purposes is a condensed dataset of the survey results of wave 15 of the British Household Panel Survey [BHPS] (2005). This survey is conducted annually amongst over 15,000 individuals by ISER at the University of Essex. Background Figure 1: Work-Leisure Trade-Off In Figure 1 an individual faces a choice in the time they allocate for leisure and work day to day. Individuals are assumed in economics to be utility maximising hence the individual faces a constrained optimisation problem. Indifference curves are points which generate the same utility for the individual and the individual is indifferent between them. The aim of the individual is thus to be on the highest indifference curve because revealed preference theory states this is the highest level of utility for the individual. However this utility is limited by a budget constraint. Individuals are constrained by the twenty four hours of a day hence the feasible budget constraint does not extend beyond this point. The budget constraint also does not intercept the hours axis at twenty four. This is because at zero hours work a person will still receive unemployment benefits along with others from the government amounting to an income of YB. ...read more.

Middle

There appears to be more evidence of the substitution effect being greater at low levels of wage (w<w*) than the income effect than the evidence of the income effect being greater than the substitution effect at high levels of wage (w>w*) as reported by Fehr and Goette (2005). Empirical Analysis Model Descriptions Model 1: Original Model estimated using Ordinary Least Squares as outlined in the Model section Model 2: Model 1 with Married, Trade Union and Health removed as explanatory variables and with White Heteroskedasticity-Consistent Standard Errors & Covariance Model 3: Model 2 with all male observations removed and Married added as explanatory variable Model 4: Model 2 with all female observations removed and Married added as explanatory variable Model 1 Model 2 Model 3 Model 4 INTERCEPT 9.81** 9.37** 11.04** 16.88** (4.56) (4.15) (3.20) (5.28) Explanatory Variables AGE 0.62** 0.60** 0.52** 0.83** (6.88) (6.27) (3.64) (5.90) AGESQ -0.01** -0.01** -0.01** -0.01** (-7.87) (-7.19) (-4.67) (-6.01) CHILD12 -3.50** -3.80** -5.83** -0.79 (-6.89) (-8.81) (-7.72) (-1.44) EDUC 0.32* 0.31* 0.15 0.43* (2.31) (2.13) (0.71) (2.24) HEALTH -0.26 (-1.13) MALE 11.00** 11.05** (27.44) (25.92) MARRIED -0.63 -1.53* 0.15 (-1.28) (-2.10) (0.26) NONINEARN -0.0005** -0.0005** -0.0006** -0.0004* (-8.64) (-5.38) (-7.36) (-2.35) PERMANENT 3.88** 3.85** 4.75** 3.10* (4.74) (3.68) (3.02) (2.25) PROM 0.89* 0.93* 1.55** 0.03 (2.35) (2.50) (2.87) (0.07) SATISFY -0.72** -0.70** -0.75** -0.52** (-5.01) (-4.73) (-3.32) (-2.78) TRADEUN 0.53 (1.11) WAGE 1.52** 1.53** 2.13** 0.86* (5.86) (5.53) (4.64) (2.18) WAGESQ -0.06** -0.06** -0.09** -0.03 (-5.48) (-4.86) (-4.17) (-1.66) No Of Observations 2384 2384 1307 1077 R2 0.3988 0.3977 0.1802 0.1680 F Statistic 112.23 142.41 27.94 19.56 **/* = significant at the 1% / 5% level respectively T statistics are in parentheses The initial model regression generated 9 variables and the intercept significant at the 1% level and 2 variables significant at the 5% level. The R2 of 0.3988 suggests a high goodness of fit for panel survey data and a high f-stat of 112.23 indicated joint significance of the variables. ...read more.

Conclusion

The conclusion reached is that both women's and men's wage elasticities are quite wage inelastic with a woman's wage elasticity being slightly more elastic than a man's. Conclusion My analysis has brought me to the conclusion that the backward bending supply curve holds for female workers but not for male workers. This is not surprising as females tend to have more choice as to working more/less hours as males are still typically seen as the major breadwinner in households so their choice to work more or less hours is severely restricted. Thus a woman's wage elasticity will tend to be more elastic than a man's. Unfortunately due to the limited nature of the dataset I was unable to give a full analysis of the labour market. An individual's supply curve reflects an individual's desired hours of work, whereas the labour demand curve of firms represents the offered hours. Hence there may be divergence between the number of hours an individual is willing to work and the number of hours they are offered to work. This is demonstrated in Figure 4. Figure 4: Offered/Wanted Hours Divergence This is due to the inherent inflexibilities of the labour market. Many firms only offer full time contracts and part time contracts with little flexibility to work more or less hours. Overtime hours are an example whereby an individual wishes to work more hours than they are initially contracted. A useful question to be added to the BHPS dataset therefore could be "No of hours willing to work" to give a truer reflection of an individual's labour supply. An alternative investigation of number of hours worked can be achieved by recoding those in full time and part-time employment in terms of a binary dependent variable and running a regression using the Probit or Logit methods. This may eliminate the mis-specification suffered by the OLS model used in this study. A more correct wage variable might have also calculated using the marginal tax rate a person faces as this would be a more accurate reflection of the wage incentive to work. ...read more.

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