Government spending composition, technical change and wage inequality

for our time unit in table II.²⁴ Autor, Katz, and Kruger (1998) show that the relative supply of skills
(college and above over non-college) rises from 0.138 in 1970 to 0.25 in 1990. We follow this evidence
by choosing the threshold γ to bound the relative supply of skilled workers below 25 percent of the
workforce.

The crucial parameters of the calibration are the R&D difficulty index μ, and the quality jumps of
the low and high-tech sectors, λ1 and λ2 respectively. We calibrate the quality jumps using estimates of
the sectorial markups for 2-digit US manufacturing industries. We use the revised OECD classification
of high-tech and low-tech sectors as in Hatzichronoglu (1997). Martins, Scarpetta, and Pilat (1996)
provide the most conservative estimates for markups in US manufacturing industries for the period
1970-92, and they also break down the industries according to their R&D intensity²⁵ . For the R&D-
intensive industries (our high-tech group) the estimated average markup is 33 percent, while for the
medium and low-tech industries the average markup is 13 percent.²⁶ In our 5-year time frame this
implies setting λι = (1 + 0.13 * 5) = 1. 65 and λ2 = 1 + 0.33 * 5 = 2. 65. We also explore the sensitivity
of our results to setting the difference between the two markups to the maximum obtained by Martins
et al. (1996), that is 5 percent low-tech and 54 percent high-tech (λ1 = 1.25 and λ2 = 3.7), and
the minimum positive distance, 22 percent for low-tech and 29 percent for high-tech (λ1 = 2.1 and
λ2 = 2.45).

Once we have calibrated the two quality jumps we can use the equation for the growth rate to
obtain the difficulty index parameter μ:

u^{.         1}                   n 1

g = —= I log λ (ω) dω =--(ln λ(1)÷ln λ(2)).

^u Jo               μ-

From the Penn World tables we take an average GDP growth rate for the period 1976-1991 in the
US of 2.3 percent and using the quality jumps, calibrated as explained above, we obtain μ equals to
0.47²⁷.

To account for the weight of public investment expenditure in the economy we consider government
investment as a share of total private investment.²⁸ Therefore we set β(ω) = G(C^ω) and the demand
in (8) becomes

cN (t)   N(t)β(ω)c   N (t)c

Ж ⁺ -(λ(ωτ- = W^(1+β(^ω)) = '

²⁴ Dinopoulos and Segerstrom (1999) use a training time of four years, we stretch it to five to match our time unit of
five years.

²⁵ These estimates are more conservative than those in previous estimates such as Hall(1990) and Roeger (1995) in that
they provide substantially lower values. Lower values are more plausible b ecause they reflect more closely the observed
profit rates.

²⁶The four high-tech industries are drugs and medicines, office and computing machineries, electrical machineries,
and . This is in line with the OECD classification. We are aware of using different sector classifications for markups
and for public investment. This is due to lack of estimates of markups for the aggregates equipment and software and
strucutures, and to lack of data on goverment spending by industry.

²⁷We use equal weights for the two sectors for simplicity. We have also performed the exercise using some measure of
the weights of the high-tech and low-tech sectors in the real economy and we get similar results. Using sectoral output
shares, for instance, we obtain a 51 percent high-tech share and a 49 percent low-tech share.

^{2 8} Private spending in the model, labeled c, is consumption. In the calibration, since we work with investment data,
private spending is private investment. Notice that since we do not have data on the structure of public consumption
we cannot use GDP as the measure of the size of this economy.

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