Government spending composition, technical change and wage inequality

Working out the equilibrium with this modification, reducing the system to one equation - as we did
in (A.1.1) - and substituting wh = θ0^σ_-γ into it we obtain a relation between the skill premium and
the composition of public spending (share of low-tech goods G(1) and share of high-tech goods G(2)):

(— + 1 - γ) (1 - — - γ]φ∕2= I n( ^wH ) ʌ (⅛+^γ ) (1 - Γ+ β - ψ) ,   (23)

∖WH       √ ∖   WH    √      μσ(ρ + n∕μ - n) ∖ Γ + Ψ J

where β = J1 β (ω) d_ω, which in our two-industry version implies β = 2 (G¹) + G(2θ ∙ and Ψ =
J1 λl(ω) d_ω becomes Ψ = 1 f G¹) + Gλ22) ) ' Table I below summarizes our calibration.

TABLE I

Summary of calibration

In our quantitative analysis we focus on two relevant 5-year periods: 1976-80, the period right
before both the skill premium and the technological bias of public spending start increasing rapidly,
and 1987-91, when the bulk of the shock has been consumed. To asses the effect of public spending on
wages we use BEA NIPA data on government investment in structure (G1), our low-tech aggregate,
and E&S (G2), our high-tech aggregate.²⁹ NIPA data on public expenditure shows the following
composition in the periods of interest: in 1976-80 average government investment in structure was 29
percent and in E&S was 7 percent of total private investment (G¹ = 0.29 and G² = 0.07); respectively,
in 1987-91 the low-tech expenditure share decreased to 26 percent and the high-tech share rose to 18

percent.

Table II presents the simulation results, the first entry in each columns shows the effect of the
public policy shock on the skill premium predicted by the model (∆w_H^m∕w_H^m), the second entry

shows how much of the change in the skill premium observed in the data is explained by our model

For the observed skill premium we use CPS data from Krusell et al.

(2000) on average wages of college graduates and high-school graduates - this is also shown in figure
1. Between the two periods considered this measure of the skill premium increased by 15.8 percent.

In the benchmark calibration the policy shock produces a 1.8 percent increase in the skill premium,
which accounts for 12 percent of the observed increase in wage inequality.

^{2 9} Notice that here we do not exactly use the fiscal policy rules specified in section 5. This is because in this simplified
version of the model those rules would not allow us to catch the entire effect of a change in the composition of public
spending on the skill premium. In fact, in the case of extreme asymmetric sp ending (α = 1) our rule predicts that the
low-tech sector gets a share of the public spending that is proportional to it’s quality jump. While, in the data the
extreme asymmetry would mean that the spending going to the low-tech sector would be zero (G1 = 0). Thus, to keep
the model closer to the data in the quantitative excercise we use directly government investment in the two sectors, as
a share of total private investment, as an index of spending composition.

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