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Ec423 Labour Economics

Ec423 Labour Economics. Alan Manning R451 a.manning@lse.ac.uk Office Hour: Tuesdays 11.30-12.30. Overview of Term. Primarily about the distribution of wages – what can explain why some people earn more than others

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Ec423 Labour Economics

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  1. Ec423Labour Economics Alan Manning R451 a.manning@lse.ac.uk Office Hour: Tuesdays 11.30-12.30

  2. Overview of Term • Primarily about the distribution of wages – what can explain why some people earn more than others • Interested in wage differentials that seem common to most countries at most times – by education, age, job tenure, gender, race • But these differentials do vary across countries e.g. gender pay gap bigger in some countries and over time e.g. wage inequality rose in US and UK in 1980s/1990s • Likely to be influenced by demand (e.g. technology, trade), supply (e.g. skills, immigration) and institutions (e.g. unions, minimum wages, welfare state)

  3. Models of Distribution of Wages • Will start with perfectly competitive model • Assumes labour market is frictionless so a single market wage for a given type of labour – the ‘law of one wage’ (note: this assumes no non-pecuniary aspects to work so no compensating differentials) • ‘law of one wage’ sustained by arbitrage – if a worker earns £6 per hour and an identical worker for a second firm earns £5 per hour, the first employer could offer the second worker £5.50 making both of them better-off

  4. The Employer Decision (the Demand for Labour) • Given exogenous market wage, W, employers choose employment, N to maximize: • Where F(N,Z) is revenue functions and where Z are other factors affecting revenue (possibly including other sorts of labour

  5. This leads to familiar first-order condition: • i.e. MRPL=W • From the decisions of individual employers one can derive an aggregate labour demand curve:

  6. The Worker Decision(the Supply of Labour) • Assume only decision is whether to work or not (sometimes called the extensive margin) – no decision about hours of work (the intensive margin) • Assume a fraction n(W,X) of individuals want to work given market wage W and there are L individuals. • The labour supply curve will be given by: • X is other factors influencing labour supply

  7. Equilibrium • Equilibrium is at wage where demand equals supply. This also determines employment. • What influences equilibrium wages/employment in this model: • Demand factors, Z • Supply Factors, X • How these affect wages and employment depends on elasticity of demand and supply curves (see exercize)

  8. What about unemployment? • As defined in labour market statistics (those who want a job but have not got one) does not exist in this model. • Anyone who wants a job at the market wage can get one • Failure of this model to have a sensible concept of unemployment is one reason to prefer models with frictions

  9. What determines wages? • Exogenous variables are demand factors, Z, and supply factors, X • Statements like ‘wages are determined by marginal products’ are a bit loose • True that W=MRPL but MRPL is potentially endogenous as depends on level of employment • Can use model to explain both absolute level of wages and relative wages • A simple model might help to explain this

  10. A Simple Two-Skill Model • Two types of labour, denoted 0 and 1. Assume revenue function is given by: • You should recognise this as a CES production function with CRS

  11. Marginal product of labour of type 0 is: • Marginal product of labour of type 1 is:

  12. As W=MPL we must have: • Write this in logs: • Where σ=1/(1-ρ) is the elasticity of substitution • This gives relationship between relative wages and relative employment

  13. A Simple Model of Relative Supply • We will use the following form: • Where ε is elasticity of supply curve. This might be larger in long- than short-run • Combining demand and supply curves we have that: • Which shows role of demand and supply factors and elasticities

  14. The Distribution of Wages in Imperfect Labour Markets • At end of last term, discussed a simple model of labour market with frictions – the Burdett-Mortensen model. In that model MPL=p so wages with perfect competition determined only by factors that affect MPL • But with frictions other factors are important

  15. Average wage is given by: • So the important factors are • Productivity, p • Reservation wage, b • Rate of job-finding, λ and rate of job-loss, δ i.e. a richer menu of possible explanations • But, also equilibrium wage dispersion (a failure of the ‘law of one wage’) so luck also important

  16. Institutions also important • Even if labour market is perfectly competitive institutions would be expected to affect wages/employment • Possible factors are: • Trade unions • Minimum wages • Welfare state (affects incentives to work)

  17. Stylized Facts About the Distribution of Wages • There is a lot of dispersion in the distribution of ‘wages’ • Most commonly used measure of wages is hourly wage excluding payroll taxes and income taxes/social security contributions • This is neither reward to an hour of work for worker nor costs of an hour of work to an employer so not clear it has economic meaning • But it is the way wage information in US CPS, UK LFS is collected though other countries are different

  18. Overall Distribution of Hourly Wages in the UK - Untrimmed

  19. Overall Distribution of Hourly Wages in the UK – trimmed (between £1 and £100 per hour)

  20. Comments • Sizeable dispersion • Distribution of log hourly wages reasonably well-approximated by a normal distribution (shown by the blue line) • Can reject normality with large sample sizes by a good working approximation • More interested in how earnings are influenced by characteristics

  21. The Earnings Function • Main tool for looking at wage inequality is the earnings function (first used by Mincer) – a regression of log hourly wages on some characteristics: • Earnings functions contain information about both absolute and relative wages but we will focus on latter

  22. Interpreting Earnings Functions • Literature often unclear about what an earnings function meant to be: • A reduced-form? • A labour demand curve (W=MRPL)? • A labour supply curve? • Much of the time it is not obvious – perhaps best to think of it as an estimate of the expectation of log wages conditional on x

  23. An example of an earnings function – UK LFS • This earnings function includes the following variables: • Gender • Race • Education • Family characteristics (married, kids) • (potential) experience (=age –age left FT education) • Job tenure • employer characteristics (union, public sector, employer size) • Industry • Region • Occupation (column 1 only)

  24. An example of an earnings function – UK LFS

  25. Education variables

  26. Family Characteristics

  27. Experience/Job Tenure

  28. Employer Characteristics

  29. Industry (selected relative to manufacturing)

  30. Region (selected relative to Merseyside)

  31. Occupation (relative to craft workers) – only 1st column

  32. Stylized facts to be deduced from this earnings function • women earn less than men • ethnic minorities earn less than whites • education is associated with higher earnings • wages are a concave function of experience, first increasing and then decreasing slightly • wages are a concave function of job tenure • wages are related to ‘family’ characteristics • wages are related to employer characteristics e.g. industry, size • union workers tend to earn more (?)

  33. The variables included here are common but can find many others sometimes included • Labour market conditions – e.g. unemployment rate, ‘cohort’ size • Other employer characteristics e.g. profitability • Computer use- e.g. Krueger, QJE 1993 • Pencil use – e.g. diNardo and Pischke, QJE 97 • Beauty – Hamermesh and Biddle, AER 94 • Height – Persico, Postlewaite, Silverman, JPE 04 • Sexual orientation – Arabshebaini et al, Economica 05

  34. Raises question of what should be included in an earnings function • Depends on question you want to answer • E.g. what is effect of education on earnings – should occupation be included or excluded? • Note that return to education lower if include occupation • Tells us part of return of education is access to better occupations – so perhaps should exclude occupation • But tells us about way in which education affects earnings – there is a return within occupations

  35. Other things to remember • May be interactions between variables e.g. look at separate earnings functions for men and women. Return to experience lower for women but returns to education very similar. • R2 is not very high – rarely above 0.5 and often about 0.3. So, there is a lot of unexplained wage variation: unobserved characteristics, ‘true’ wage dispersion, measurement error.

  36. Problems with Interpreting Earnings Functions • Earnings functions are regressions so potentially have all usual problems: • endogeneity e.g. correlation between job tenure and wages • omitted variable e.g. ‘ability’ • selection – not everyone works e.g. the earnings of women with very young children • Tell us about correlation but we are interested in and ‘correlation is not causation’

  37. Forward Look • Regularities found in UK earnings function found in most countries, most times • Will start this term by trying to understand them

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