Download INTERNATIONAL COMPETITION, RETURNS TO SKILL AND LABOUR MARKET ADJUSTMENT February 19, 2009

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Currency intervention wikipedia, lookup

Transcript
increased international competition on skill acquisition from that of industry relocation we
estimate a bivariate probit model. The underlying assumptions are that moving skill and
moving industry are two alternative routes for adjusting to increased competition, but they are
related.
We adopt the following baseline two equation system:
⎧⎪moveindit* = X itα 0 + θ 0 ∆ICKt + Z Jtν 0 + γ hhiKt + τ K + µt + Ψ it
⎨
*
⎪⎩ skillupit = X itα1 + θ1∆ICKt + Z Jtν 1 + γ hhiKt + τ K + µt + Λ it
(3)
where moveindit and skillupit are two observed binary indicator variables driven by the two
equation system of latent propensities to move industry ( moveindit* ) and skill upgrading
( skillupit* ). ∆ICKt is the percentage change in competition in industry K . Ψ it and Λ it are
exogenous disturbances (assumed to be correlated). The other variables are as before. The
observability criteria for the two sets of observed binary outcomes are
*
⎪⎧moveindit = 1(moveindit > 0)
⎨
*
⎪⎩ skillupit = 1(moveskillit > 0)
moveindit is equal to one if individual i in the subsequent observation is employed in a
different industry and skillupit is equal to one if individual i in the subsequent observation
has a higher level of skill. The main parameters of interest are θ 0 and θ 1 which reflect how
the propensities to move industry and skill vary with competition and we expect them to be
positive.
3. Data Description and Descriptive Statistics
3.1. The data set
Our data is from a longitudinal matched employer-employee dataset, “Quadros de Pessoal”
[QP], collected by the Portuguese Ministry of Employment, which covers virtually all
workers and firms in the Portuguese private sector over the 1986-2000 period, around
200,000 firms and more than 2 million workers each year. It provides comprehensive
information on worker’s demographic characteristics (age, gender, schooling), occupation
characteristics (occupational group, professional category, wage, hours worked) and plant
tenure, along with employing firm ID codes. Firm-level characteristics includes sales, number
of employees, equity, percentage of foreign capital, geographical location and date of
constitution, along with industry code. This code allows us to merge this data set with very
detailed trade data, disaggregated at the industry level with imports by country of origin,
9