I. Sources of Skill-Biased Wage Inequality

The wage differential between college graduates and high school graduates rose from 38 percent in 1980 to 53 percent in 1990 and the wage differential between college graduates and high school dropouts rose from 66 percent to 86 percent. Figure 1 presents this rise using the natural logarithm of average weekly earnings adjusted for experience from the annual demographic files of the Current Population Survey (U.S. Department of Commerce). The rise in the education premium during the 1980s contrasts with its decline during the 1970s when the baby-boom generation entered the labor force and the supply of college graduates increased rapidly.

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U.S. Wages

Citation: IMF Staff Papers 1996, 002; 10.5089/9781451947229.024.A002

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In the many recent papers that seek to explain the rising education premium, the most often-cited causes include declining manufacturing employment (Katz and Murphy (1992) and Murphy and Welch (1991)), loss of blue-collar wage premiums due to declines in both manufacturing employment and union power (Blackburn, Bloom, and Freeman (1990) and Bluestone and Harrison (1988)), the impact of technology (Bartel and Lichtenberg (1987)); Davis and Haltiwanger (1991); Krueger (1993); and Mincer (1989) and (1991)), and slower growth of the college-educated population in the 1980s (Katz and Murphy (1992)) and Murphy and Welch (1989)). The decline in manufacturing employment and blue-collar wages has generally been associated with increased import penetration in these sectors and the United States’ reduced global market share in traditional high-wage industries.

The relative strength of these four competing theories has been tested by Bound and Johnson (1992), who decompose changes in relative wages and determine technological change to be the principal cause of increasing education and age differentials and narrowing gender differentials. They find that economy-wide, technological change led to faster growth in the wages of the highly educated and of women and to slower wage growth for young workers with a high school diploma or less. The data also reveal a major shift in four premium-wage blue-collar industries (durables/mining, nondurables, transportation, and public utilities) toward employing more-educated workers and, among workers without college education, toward older workers. Bound and Johnson argue that turnover is low in these high-wage industries and the declining demand for less-educated workers was managed by hiring few young workers while experienced workers retained their jobs.¹

A related debate has arisen concerning the relationship between trade and wage dispersion, with opinion in the economics profession evenly divided as to whether a causal relationship exists. Krugman and Lawrence (1993) argue that under the hypothesis of factor price equalization, a skill-abundant country will shift its production toward skill-intensive sectors and away from labor-intensive sectors. This will induce a rising skill-biased wage differential and lead firms in all industries to reduce the ratio of skilled to unskilled workers. However, between 1979 and 1989 the ratio of skilled workers to unskilled workers rose in nearly all manufacturing industries and therefore Krugman and Lawrence discount any relationship between trade and wage dispersion. Leamer (1994) believes that the factor price equalization theorem does not hold in the United States and suggests using the Stolper-Samuelson theorem to analyze the relationship between trade and wage dispersion. The Stolper-Samuelson theorem states that a decline in the price of products made intensively by low-skilled workers lowers their relative wage but does not necessarily affect factor input ratios. Lawrence and Slaughter (1993) discount Learner’s assertion of a decline in the price of products made intensively by low-skilled workers by showing a rise in the relative import and export prices of unskilled-labor-intensive goods. Sachs and Shatz (1994) have carried out a similar analysis excluding the effect of computers, arguing that computer prices are difficult to measure. They find a negative—although statistically insignificant—relationship between relative import and export prices and skill intensity.

Borjas and Ramey (1994b) posit that increased international competition in durables is the dominant factor in explaining rising wage differentials. They show that the null hypothesis of noncointegration with the education differential can be rejected with 96 percent confidence for the durables deficit and, decomposing the durables deficit into the ratios of imports and exports to GDP, they find that imports have a stronger impact on the education differential. They also show that the null hypothesis of no cointegration cannot be rejected for any of the following variables, taken individually: the relative supply of college and high school graduates, the relative supply of college graduates and high school dropouts, the unemployment rate, the nonunionization rate among workers, the percentage of immigrants in the labor force, the female labor force participation rate, and spending on research and development per labor force participant. On this basis, Sodas and Ramey argue that these other factors are not significant causes of rising wage dispersion. In addition, Borjas and Ramey reject the importance of technological change as the residuals from their regressions are not autocorrelated and thus do not resemble rising technological levels.

Despite the strong empirical relationship between the durables deficit and the education differential, Borjas and Ramey’s analysis has two significant weaknesses. First, the durables deficit cannot directly cause rising differentials; rather, it must be proxying other trends in the economy such as changes in durables manufacturing wages or employment in durables manufacturing that would alter the relative wages of the groups. Borjas and Ramey cite two possible effects, both the result of increased international competition: declining durables employment and declining rents in durables manufacturing, both leading to lower wages. Real wage trends suggest, moreover, that the latter may be counterfactual: the real wage premium in durables manufacturing has risen over 1970–92, not fallen, although declining wages at the bottom of the distribution may have pulled the average down. Second, Borjas and Ramey do not control for other effects on the wage differential such as the supply of college graduates or technological change.

This paper seeks to address the two weaknesses of Borjas and Ramey’s approach as follows. First, the paper unbundles the effect captured by Borjas and Ramey in the durables deficit by identifying channels that directly affect wages. Second, the paper includes variables that proxy for the supply of college graduates and changes in technology. The long-run competitiveness of U.S. durable goods industries should be a function of relative prices in a common currency. Real exchange rate fluctuations influence movements in producers’ profit margins and eventually wages in durable goods industries adjust to enable firms to recover their profit margins, assuming no change in industry or labor market structure. Real exchange rate fluctuations also affect durables employment levels and the resulting displacement of workers puts downward pressure on wages in lower-paying industries, thereby indirectly increasing wage dispersion.

These effects can be seen in a simple model of three industries, one employing college graduates to provide services and the other two employing high school graduates to produce durable goods and services respectively. The two industries that employ high school graduates differ in terms of their exposure to foreign competition. We assume that the durable goods industry must compete with foreign suppliers so that its labor demand function depends on relative prices in a common currency. In contrast, we assume that the service industry does not trade internationally and therefore its labor demand depends only on its wage. Workers are substitutable between the durable goods and service industries but not between the high school and college graduate industries.

We now specify the demand and supply curves in each industry. Assume that the demand for the services provided by college graduates (D_It) can be expressed as

where α represents skill-biased technological change. L_d1 is a constant and $w_{1t}/p_t$ is the real wage in the high-skill service industry. We assume that the demand for college-educated labor and skill-based technological change are complements.

The supply of college-educated labor (S_It) is

where β indicates the growth of the college-educated population and L_sl is a constant. In equilibrium, supply equals demand and therefore the change in the log wage can be expressed as

The profile of the college-educated wage depends on the speed of technological change relative to the growth in the supply of college graduates.

The demand for durable goods industry workers (D_2t) is assumed to depend negatively on both the real effective exchange rate reer_t (a rise in reer represents appreciation) and the real wage in the durable goods sector $(w_{2t}/p_t)$:

The real effective exchange rate can be expressed as

where L_d2 is a constant and γ is the rate of change in the real effective exchange rate.

The supply of durable goods workers (S_2t) is

where λ is the elasticity of supply of durable goods workers. We assume that unions restrict entry into this industry so that a positive wage differential between durable goods wages and service wages may exist. The equilibrium change in the log wage is

and the equilibrium change in supply is

Assuming that low-skilled service workers provide nontraded services, the demand for service workers (D_3t) is based only on the real wage, $w_{3t}/p_t$:

The supply of low-skilled service workers (S_3t) is directly related to the change in the supply of durable goods workers $\left(\mathrm{\Delta }1n{S}_{2t}\right)$ because we assume that durable goods workers can obtain employment in the service industry:

The wage in the low-skilled service industry is market clearing and therefore fails when durable goods workers are laid off and seek work in the low-skilled service sector:

The model illustrates how three kinds of shocks to the economy may have affected the distribution of wages in the last 20 years: skill-biased technological change (α); an increase in the supply of college-educated workers β: and the extended appreciation of the dollar real exchange rate through 1985 γ. First, we assume that the economy has experienced a persistent technological bias in favor of college-educated workers, which has raised the relative demand for these workers. Equation (1) shows how an increase in α, skill-biased technical change, would increase demand for these workers. We use investment in computer, office, and communications equipment as a proxy for the rate of technical innovation and the impact of technology in workplaces, making the assumption that this is correlated with the introduction of computerized production machinery (for which separate data are not available). Generally, skill-biased technical change is associated with increased use of computers and information technology in the workplace (Krueger (1993) and U.S. Department of Labor (1994)). Prior to the advent of computers, Nelson and Phelps (1966) argued that the relative demand for highly educated workers should rise during periods of rapid technological change because of such workers’ ability to adapt to new methods.

The positive demand effect from skill-biased technological change may, however, be offset by the rise in the supply of college graduates, represented in the model as an increase in β (equation (2)). The net impact is therefore uncertain, as equation (3) shows. The supply effect was particularly strong in the 1970s when the education premium actually declined in response to a large increase in the supply of college graduates. We therefore expect a negative response of the education differential to the supply of college graduates.

The third shock that the model illustrates is the real appreciation of the U.S. dollar through 1985. Figure 2 presents a real effective exchange rate series produced by J.P. Morgan, which incorporates 22 OECD countries and 23 developing countries. The real exchange rate appreciated by more than 35 percent over 1980–85 alone, then declined nearly 30 percent to 1988. In terms of the model, the prolonged appreciation of the exchange rate lowered the competitiveness of U.S. products, putting downward pressure on the demand for labor in durable goods industries (equation (4)) and should have led to declines in both wages and employment in this sector. The displaced workers would find new employment in the service sector, lowering the wage in the latter sector.

The implications of the model lead us to emphasize changes in durables employment (demp) and the real exchange rate (reer) as inducing changes in the college-high school differential, w_hs, and college-dropout differential, w_dp. We control for the effects of changes in the supply of college-educated workers and technology with variables that measure the ratio of college- educated workers (cgrad) and real investment in computers and computerized office equipment (compi).² Section II will present estimates of the following specification:

and

The wage gap between high school dropouts and college graduates should be affected by the same factors as the gap between high school and college graduates. Skill-biased technological change would, however, be expected to hurt high school dropouts more than diploma holders because the former have lower skills and are less substitutable for college graduates. In addition, the range of high-wage jobs available to dropouts presumably is and has always been narrower than that for diploma holders. Thus, the loss of durable manufacturing jobs would likely hurt the average wage of dropouts more than that of high school graduates.

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U.S. Real Effective Exchange Rate and Education Wage Differentials

Citation: IMF Staff Papers 1996, 002; 10.5089/9781451947229.024.A002

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Our estimates of the determinants of the education differential use the Johansen-Juselius technique (Johansen (1988) and Johansen and Juselius (1990)), which permits the maximum likelihood estimation of all possible cointegrating relationships. The Johansen-Juselius technique also includes two likelihood ratio tests, with well-defined limiting distributions, to determine which cointegrating vectors are statistically significant and test linear restrictions on the parameters. These properties represent an improvement over other methods of cointeuration analysis.

II. Empirical Analysis of the Education Differential

Before arriving at our final specification,

we estimated a specification including only the durables deficit and the underlying durables employment and rent factors,

and a broader specification controlling for computer investment and the supply of college graduates,

All estimates use annual data for 1970–1990 and the Johansen-Juselius multi- variate maximum likelihood method. Phillips-Perron tests, shown in Table 1, were unable to reject the null hypothesis of a unit root for any of the variables.³

For equation (14), we find one significant cointegrating vector between the three variables, but no vector produces coefficient estimates with economically sensible coefficient signs. The broader specification (equation (15)), which includes the proportion of college-educated workers in the total labor force and real computer investment, yields three linear combinations that are cointegrated with the college-high school differential but, once again, no cointegrating vector produces estimates with economically sensible coefficient signs. The sign on the real exchange rate variable is economically correct (positive) in all of the cointegrating vectors with the highest eigenvalues, whereas the sign on the durables deficit is incorrect (negative rather than positive). It appears therefore that when we control for the effect of the supply of college graduates and computer investment on the college-high school wage ratio, the independent effect of the durables deficit disappears.

^{Table 1.}

Phillips-Perron Tests for Stationarity of Time-Series Data Phillips-Perron tests include a constant and a time trend. The Z_p and Z_t, statistics are shown; if the Z_p and Z_t statistics exceed their respective x percent critical values, then we reject the null hypothesis that the series is I(1) with 1−x percent probability.

^*Denotes that the null hypothesis of a unit root can be rejected at the 5 percent level $(Z_p<-17.9,Z_t<-3.6)$.

^{Table 1.}

Phillips-Perron Tests for Stationarity of Time-Series Data Phillips-Perron tests include a constant and a time trend. The Z_p and Z_t, statistics are shown; if the Z_p and Z_t statistics exceed their respective x percent critical values, then we reject the null hypothesis that the series is I(1) with 1−x percent probability.

Series	Zp	Zt
whs	0.28	0.16
wdp	0.61	0.44
Durables employment	0.24	0.22
College graduates	−1.40	−5.13 *
Computer investment	0.15	0.22
Real effective exchange rate	−7.71	−2.08
Durables deficit	−3.50	−1.36

^*Denotes that the null hypothesis of a unit root can be rejected at the 5 percent level $(Z_p<-17.9,Z_t<-3.6)$.

View Table

^{Table 1.}

Phillips-Perron Tests for Stationarity of Time-Series Data Phillips-Perron tests include a constant and a time trend. The Z_p and Z_t, statistics are shown; if the Z_p and Z_t statistics exceed their respective x percent critical values, then we reject the null hypothesis that the series is I(1) with 1−x percent probability.

Series	Zp	Zt
whs	0.28	0.16
wdp	0.61	0.44
Durables employment	0.24	0.22
College graduates	−1.40	−5.13 *
Computer investment	0.15	0.22
Real effective exchange rate	−7.71	−2.08
Durables deficit	−3.50	−1.36

^*Denotes that the null hypothesis of a unit root can be rejected at the 5 percent level $(Z_p<-17.9,Z_t<-3.6)$.

In light of the above results, we chose to eliminate the durables deficit from the analysis and test for a cointegrating relationship between the college- high school wage ratio and employment in durables, the real exchange rate, the supply of college graduates, and computer investment, as in equation (12). Using Johansen’s multivariate maximum likelihood method, we found that, according to the maximal eigenvalue test, the null hypothesis of $r\le 3$ against $r=4$ could not be rejected whereas the null hypothesis of $r\le 3$ against $r\ge 4$ could not be rejected using the trace test (see Table 2). Therefore the tests indicate that there are three significant cointegrating vectors, and we chose the vector with the correct economic signs for all variables.

The coefficient signs of the preferred long-run cointegrating vector indicate that a rise in durables employment relative to aggregate employment reduces the college-high school wage differential, as predicted by the model (Table 3). This effect is expected as durables manufacturing represents many of the highest-paid jobs for high school graduates. The rise in the supply of college graduates lowers the differential because it puts downward pressure on the college wage. Over the past 20 years this effect has been mitigated by the rapid rise in computer investment and the associated rise in the premium paid to college graduates, who are the most intensive users of computers. Finally, the appreciation of the real effective exchange rate in the early 1980s eroded the competitiveness of U.S. manufacturing, which has led to an erosion of the wage premium paid to durable workers and a rise in the education differential. All the variables in the long-run equation also help to explain short-run changes in the differential, although half of the coefficients are smaller in absolute value.

^{Table 2.}

Johansen Maximum Likelihood Tests and Parameter Estimates of the Determinants of the College Graduate-High School Graduate Differential

^{Table 2.}

Johansen Maximum Likelihood Tests and Parameter Estimates of the Determinants of the College Graduate-High School Graduate Differential

A. Cointegration likelihood ratio test based on maximal eigenvalue of the stochastic matrix
Hypothesis
Null	Alternative		Test statistic	95 percent critical value
r = 0	r = 1		70.2	33.32
r ≤ 1	r = 2		54.0	27.14
r ≤ 2	r = 3		22.3	21.07
r ≤ 3	r = 4		9.5	14.90
r ≤ 4	r = 5		1.5	8.18
B. Cointegration likelihood ratio test based on trace of the stochastic matrix
Hypothesis
Null	Alternative		Test statistic	95 percent critical value
r = 0	r ≥ 1		157.5	70.60
r ≤ 1	r ≥ 2		87.3	48.30
r ≤ 2	r ≥ 3		33.3	31.50
r ≤ 3	r ≥ 4		11.0	17.95
r ≤ 4	r ≥ 5		1.5	8.18
C. Estimated cointegrating vector
Whs	cgrad	compi	demp	reer
-1	-0.51	0.10	-0.69	0.07

View Table

^{Table 2.}

Johansen Maximum Likelihood Tests and Parameter Estimates of the Determinants of the College Graduate-High School Graduate Differential

A. Cointegration likelihood ratio test based on maximal eigenvalue of the stochastic matrix
Hypothesis
Null	Alternative		Test statistic	95 percent critical value
r = 0	r = 1		70.2	33.32
r ≤ 1	r = 2		54.0	27.14
r ≤ 2	r = 3		22.3	21.07
r ≤ 3	r = 4		9.5	14.90
r ≤ 4	r = 5		1.5	8.18
B. Cointegration likelihood ratio test based on trace of the stochastic matrix
Hypothesis
Null	Alternative		Test statistic	95 percent critical value
r = 0	r ≥ 1		157.5	70.60
r ≤ 1	r ≥ 2		87.3	48.30
r ≤ 2	r ≥ 3		33.3	31.50
r ≤ 3	r ≥ 4		11.0	17.95
r ≤ 4	r ≥ 5		1.5	8.18
C. Estimated cointegrating vector
Whs	cgrad	compi	demp	reer
-1	-0.51	0.10	-0.69	0.07

Using the estimated long- and short-run equations, we performed dynamic simulations to determine the factors that contributed to the rise in the college-high school wage differential from 1979 to 1990. Our simulations assume that the ratio of durables employment to aggregate employment was constant at its 1970–79 average, that the ratio of college graduates to total employment was constant at its 1970–79 average, and that both computer investment and the real exchange rate remained at their 1970–79 average.

^{Table 3.}

Estimated Determinants of the College Graduate-High School Graduate (w_hs) and College Graduate-High School Dropout (w_dp) Differentials

(All variables in logarithms)

^aA constant term is included in all equations, although its estimate is not shown.

^bt-statistics appear in parentheses.

^{Table 3.}

Estimated Determinants of the College Graduate-High School Graduate (w_hs) and College Graduate-High School Dropout (w_dp) Differentials

(All variables in logarithms)

Independent variable a	Long-run	whs Short-run b	Long-run	whs Short-run b
Durables employment	−0.69	–	−0.79	–
College graduates	−0.51	–	−0.59	–
Computer investment	0.10	–	0.14	–
Real effective exchange rate	0.07	–	0.08	–
Δ(Durables employment)	–	−0.38	–	−0.49
	–	(4.10)	–	13.98)
Δ(College graduates)	–	−0.25	–	−0.48
	–	(2.11)	–	(3.08)
Δ(Computer investment)	–	0.11	–	0.21
	–	(2.88)	–	(3.90)
Δ(Real effective exchange rate)	–	0.09	–	0.14
	–	(2.61)	–	(3.06)
Long-run error	–	−0.64	–	−0.71
	–	(−4.44)	–	(4.20)
Durbin-Watson	–	2.74	–	2.40
Adjusted R2	–	0.72	–	0.67

^aA constant term is included in all equations, although its estimate is not shown.

^bt-statistics appear in parentheses.

View Table

^{Table 3.}

Estimated Determinants of the College Graduate-High School Graduate (w_hs) and College Graduate-High School Dropout (w_dp) Differentials

(All variables in logarithms)

Independent variable a	Long-run	whs Short-run b	Long-run	whs Short-run b
Durables employment	−0.69	–	−0.79	–
College graduates	−0.51	–	−0.59	–
Computer investment	0.10	–	0.14	–
Real effective exchange rate	0.07	–	0.08	–
Δ(Durables employment)	–	−0.38	–	−0.49
	–	(4.10)	–	13.98)
Δ(College graduates)	–	−0.25	–	−0.48
	–	(2.11)	–	(3.08)
Δ(Computer investment)	–	0.11	–	0.21
	–	(2.88)	–	(3.90)
Δ(Real effective exchange rate)	–	0.09	–	0.14
	–	(2.61)	–	(3.06)
Long-run error	–	−0.64	–	−0.71
	–	(−4.44)	–	(4.20)
Durbin-Watson	–	2.74	–	2.40
Adjusted R2	–	0.72	–	0.67

^aA constant term is included in all equations, although its estimate is not shown.

^bt-statistics appear in parentheses.

The results indicate that the dominant factors driving the 15 percent rise in the college-high school wage differential between 1979 and 1990 are the rise in computer investment and the decline in durables employment (see Table 4.) For example, the combined rise in computer investment and the fall in durables employment were estimated to have increased the college-high school wage differential by about 37 percent. These effects more than outweighed the 22 percent decline in the college-high school wage differential caused by the rise in the supply of college graduates.⁴ The real effective exchange rate did not contribute anything to the rise in the wage differential because the real effective exchange rate was flat over the long run. These results suggest that most of the adjustment in the durables goods industry to the appreciation of the real effective exchange rate in the early 1980s took place through employment changes rather than through wage changes.

^{Table 4.}

Simulated Change in the College Graduate- High School Graduate Wage Differential Between 1979 and 1990

^aDifference between the wage differential obtained by holding the particular explanatory variables constant at their average level in the 1970s and the wage differential in 1990.

^{Table 4.}

Simulated Change in the College Graduate- High School Graduate Wage Differential Between 1979 and 1990

Change in the wage differential			15.4
	Owing toa
		Change in supply of college graduates	—22.3
		Change in computer investment	14.8
		Change in durables employment	22.0
		Change in the real exchange rate	0.0
		Residual	1.1

^aDifference between the wage differential obtained by holding the particular explanatory variables constant at their average level in the 1970s and the wage differential in 1990.

View Table

^{Table 4.}

Simulated Change in the College Graduate- High School Graduate Wage Differential Between 1979 and 1990

Change in the wage differential			15.4
	Owing toa
		Change in supply of college graduates	—22.3
		Change in computer investment	14.8
		Change in durables employment	22.0
		Change in the real exchange rate	0.0
		Residual	1.1

^aDifference between the wage differential obtained by holding the particular explanatory variables constant at their average level in the 1970s and the wage differential in 1990.

The wage differential between college graduates and high school dropouts behaves similarly to that between college and high school graduates.⁵ Employment in durables manufacturing, the supply of college graduates, computer investment, and the real exchange rate generate three cointegrating vectors with the wage differential between college graduates and high school dropouts and we chose the vector with correct economic signs (Table 5). As expected, the dropout differential is more sensitive to all shocks in both the long- and short-run equations (Table 3).

In the short-run equation, the most distinctive difference from the equation for college-high school differential lies in the coefficients on information investment and the supply of college graduates. Both coefficients are nearly twice as large (0.21 versus 0.11 for the information investment variable, and –0.48 versus –0.25 for the college graduate supply variable) in the dropout differential equation.

Simulations revealed a stronger combined effect of the rise in computer investment and the decline in durables employment on the college-dropout wage differential than on the college-high school differential (see Table 6). These positive effects were offset by a stronger downward pull on the wage differential from the rise in the supply of college graduates.

III. Conclusion

This paper finds that changes in employment in durable manufacturing industries and investment in computer equipment can explain rising wage dispersion in the United States, measured in terms of the education premium.

^{Table 5.}

Johansen Maximum Likelihood Tests and Parameter Estimates of the Determinants of the College Graduate-High School Dropout Differential

^{Table 5.}

Johansen Maximum Likelihood Tests and Parameter Estimates of the Determinants of the College Graduate-High School Dropout Differential

A. Cointegration likelihood ratio test based on maximal eigenvalue of the stochastic matrix
Hypothesis
Null	Alternative		Test statistic	95 percent critical value
r = 0	r = 1		78.6	33.32
r ≤ 1	r = 2		33.4	27.14
r ≤ 2	r = 3		26.3	21.07
r ≤ 3	r = 4		12.0	14.90
r ≤ 4	r = 5		1.8	8.18
B. Cointegration likelihood ratio test based on trace of the stochastic matrix
Hypothesis
Null	Alternative		Test statistic	95 percent critical value
r = 0	r ≥ 1		152.1	70.60
r ≤ 1	r ≥ 2		73.5	48.30
r ≤ 2	r ≥ 3		40.1	31.50
r ≤ 3	r ≥ 4		13.8	17.95
r ≤ 4	r ≥ 5		1.8	8.18
C. Estimated cointegrating vector
Wdp	cgrad	compi	demp	reer
- 1	-0.59	0.14	-0.79	0.08

View Table

^{Table 5.}

Johansen Maximum Likelihood Tests and Parameter Estimates of the Determinants of the College Graduate-High School Dropout Differential

A. Cointegration likelihood ratio test based on maximal eigenvalue of the stochastic matrix
Hypothesis
Null	Alternative		Test statistic	95 percent critical value
r = 0	r = 1		78.6	33.32
r ≤ 1	r = 2		33.4	27.14
r ≤ 2	r = 3		26.3	21.07
r ≤ 3	r = 4		12.0	14.90
r ≤ 4	r = 5		1.8	8.18
B. Cointegration likelihood ratio test based on trace of the stochastic matrix
Hypothesis
Null	Alternative		Test statistic	95 percent critical value
r = 0	r ≥ 1		152.1	70.60
r ≤ 1	r ≥ 2		73.5	48.30
r ≤ 2	r ≥ 3		40.1	31.50
r ≤ 3	r ≥ 4		13.8	17.95
r ≤ 4	r ≥ 5		1.8	8.18
C. Estimated cointegrating vector
Wdp	cgrad	compi	demp	reer
- 1	-0.59	0.14	-0.79	0.08

^{Table 6.}

Simulated Change in the College Graduate-High School Dropout Wage Differential Between 1979 and 1990

^aDifference between the wage differential obtained by holding the particular explanatory variables constant at their average level in the 1970s and the wage differential in 1990.

^{Table 6.}

Simulated Change in the College Graduate-High School Dropout Wage Differential Between 1979 and 1990

Change in the wage differential			20.8
	Owing to a
		Change in supply of college graduates	−25.9
		Change in computer investment	22.1
		Change in durables employment	25.8
		Change in the real exchange rate	0.0
		Residual	−1.6

^aDifference between the wage differential obtained by holding the particular explanatory variables constant at their average level in the 1970s and the wage differential in 1990.

View Table

^{Table 6.}

Simulated Change in the College Graduate-High School Dropout Wage Differential Between 1979 and 1990

Change in the wage differential			20.8
	Owing to a
		Change in supply of college graduates	−25.9
		Change in computer investment	22.1
		Change in durables employment	25.8
		Change in the real exchange rate	0.0
		Residual	−1.6

^aDifference between the wage differential obtained by holding the particular explanatory variables constant at their average level in the 1970s and the wage differential in 1990.

This finding represents a refinement of the Borjas and Ramey hypothesis that the trade deficit in durable goods explains changes in the education differential. Reduced employment opportunities in durables production drive down the average wage of workers with only high school education. thereby forcing up the premium for college education. An innovation in this paper is the inclusion of investment in equipment as a proxy for skill-biased technical change rather than treating it as a residual or with a time trend. The rise in the skill premium associated with the use of technology could alone explain all of the rise in the college premium since 1979 were there no offsetting effects. The real effective exchange rate also helps to explain the rise in the education differential in the short run by altering the competitiveness of the U.S. durable goods industry and thus affecting the industry’s optimal size and employment.