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The U.S. Macro model contains more than 1,800 variables, including unpublished intermediate variables, and is designed to produce forecasts that run thirty years. In addition to producing good cyclical near-term forecasts and stable long-run equilibrium, the model is designed to allow for scenario construction. To quantify the impact of the higher completion scenario on the U.S. economy, the Moody’s Analytics model of the U.S. economy was simulated incorporating the estimated direct effect of greater educational attainment on the following variables: earnings, employment, and federal government spending (see Table 1).

As with the direct effects, the overall effect of higher completion on the economy accumulates slowly but has a significant and positive long-run impact. Greater productivity and employment translate to faster GDP growth. From 2036 to 2046, GDP growth will be 2 percent compared with 1.9 percent under the baseline. By 2046, total real GDP will be 2.5 percent larger than under the baseline. In addition, more productive workers translate to greater income, with median household income 3.5 percent higher in 2046 than under the baseline.

The labor market is also positively affected, with the unemployment rate at 4.9 percent, a 0.1-percentage point decline compared with the baseline. The effect on total employment in the short run is negative because more students are enrolled in school instead of working, which reduces labor supply. By 2046, however, total employment is 0.5 percent higher, as more-educated workers have a higher employment rate.

On the cost side, greater spending on higher education increases the deficit in the near term, with the negative impact peaking at $137.8 billion in 2025 (see Chart 11). Higher debt means greater interest payments, which also contribute to the deficit. However, the deficit begins to decline in 2026 as the employment effects turn from negative to positive and wage gains gradually accumulate, both of which increase federal government tax revenues. As a result of higher wages and employment and a larger economy, by 2043 the deficit is lower than it otherwise would be. The effect of higher costs of college is to increase total U.S. debt by a peak of $1.9 trillion in 2041. However, the debt begins to decline after this and ends up $1.6 trillion higher by 2046, an increase of 2.6 percent compared with the baseline.

Chart 11: Effect on Budget Deficit

Sources: U.S. Bureau of Labor Statistics, U.S. Department of Education, and Moody’s Analytics

The importance of the cost of increasing completion is made clear in an alternative scenario: the average cost of an additional year of school is equal to 125 percent of current costs instead of 150 percent. This does not appreciably change real GDP and employment. However, it does reduce the fiscal impact (see Table 2). By 2046, the federal debt grows by $942 billion instead of $1.6 trillion. The lower costs also reduce the debt to GDP ratio, which is projected to be 105 percent by 2046 instead of the 106.3 percent under the higher cost scenario. The peak increase in total debt is also lower, at $1.4 trillion in 2041 instead of $1.9 trillion.

Increasing educational attainment entails both costs and benefits. If the actual costs and benefits approximate the assumptions modeled in this analysis, the U.S. economy would benefit from higher wages and larger GDP, which eventually begins reducing the deficit. The size of the effect on debt depends on the cost of increasing completion for marginal students.

Table 1: Annual Difference Between Scenario and Baseline

Sources: U.S. Bureau of Economic Analysis, U.S. Bureau of Labor Statistics, S&P, Federal Housing Finance Agency, U.S. Department of the Treasury, and Moody’s Analytics

Table 2: Annual Difference Between Scenario and Baseline with 125% Costs

Sources: U.S. Bureau of Economic Analysis, U.S. Bureau of Labor Statistics, S&P, Federal Housing Finance Agency, U.S. Department of the Treasury, and Moody’s Analytics

Table 3: Baseline Forecast

Sources: U.S. Bureau of Economic Analysis, U.S. Bureau of Labor Statistics, S&P, Federal Housing Finance Agency, U.S. Department of the Treasury, and Moody’s Analytics