Uso actual de las tierras del enclave Cascadas los Frailes.

The next set of hypotheses examined relationships between measures of educational attainment, new economy employment, and geographic mobility, and indicators of inequality at the MSA-level. Results of the correlation tests used to test these hypotheses are reported in Table 4.2.

Hypothesis 2A examines the association between rates of educational attainment and the indicators of inequality. These tests revealed varying results. The proportion of workers in an MSA with at least a college degree demonstrates a moderate-to-strong, positive, and statistically significant correlation with both the Gini coefficient and the 90/20 earnings ratio. At the same time, this proportion is negatively associated with low earner rates. In 2000, the proportion of earners in an MSA with at least a college degree has a .338 correlation with the 90/20 earnings ratio and a .523 correlation with the Gini coefficient, both of which were statistically significant at the .01 level. In contrast, the

proportion of workers with at least a college degree in each MSA has a moderately strong negative relationship with MSA-level low earner rates in 2000.

Using the proportion of workers with degrees beyond a college degree, we see very similar results. This measure of educational attainment had a .361 correlation with the 90/20 earnings ratio and a .487 correlation with the Gini coefficient, both statistically significant at the .01 level. The proportion of individuals in an MSA with a degree

beyond the college degree has a correlation of -.252 with the low earner rate of employed individuals in the MSA.

From these results we can see that a higher MSA-level rate of educational attainment is associated with higher inequality when looking at employed workers in MSA’s. This pattern is moderately strong when using the 90/20 earnings ratio as the indicator of inequality, but is particularly pronounced when measured by the Gini coefficient. At the same time, these higher rates of educational attainment are associated with reduced low earnerrates among those who are employed in those MSA’s. For an individual MSA, having higher rates of educational attainment does seem to move employed workers out of poverty, but it is also associated with higher levels of overall inequality.

Next, we examine the tests of hypothesis 2B. These tests examine correlations between rates of new economy employment and these indicators of inequality. Using the standard definitions of high-technology industries and creative class occupations and the 2000 data, we find support for the fifth hypothesis. Rates of employment in high-

technology industries is positively associated with both the 90/20 earnings ratio and the Gini coefficient (.247 and .338, respectively, both statistically significant at the .01 level). Rates of employment in the creative class demonstrate even stronger correlations with

both indicators of inequality (.345 and .559, respectively, both statistically significant at the .01 level). These correlations are more moderate for rates of employment in the super- creative core, but still moderately strong (.217 and .386 respectively), positive, and significant at the .01 level. Consistently, these correlations are stronger for rates of employment in the creative class than for rates of employment in high-technology industries.

Interestingly, while these measures of inequality are associated with rates of new economy employment, we see very different results for tests involving the low earner rate. Each of these indicators of the new economy demonstrate moderate to strong correlations with low earner rates at the MSA-level. The proportion of workers employed in high-technology industries is has a statistically significant correlation of - .427 with low earner rates, and the proportion of workers employed in the creative class also has a statistically significant and negative correlation with low earner rates, at -.287. For all of these tests, very similar results are seen when using the variable definitions which are comparable between 2000 and 1990.

These results suggest several things. First, these correlation tests lend support to hypothesis five in that rates of employment in the new economy is associated with higher levels of inequality as measured by the 90/20 earnings ratio and the Gini Coefficient. At the same time, rates of employment in the new economy are also associated with reduced low earner rates. So, among those workers who are employed in these MSA’s, increases in new economy employment would seem to lift some of those workers out of poverty, but measures of overall inequality in these regions also increase.

Also, it should be noted that the correlations are higher for the Gini Coefficient than the 90/20 earnings ratio. As explained in Chapter 3, we can think of the Gini

Coefficient as an assessment of inequality with an eye towards the middle of the earnings distribution, while the 90/20 ratio measures inequality with a greater focus on how the earnings distribution is stretched by high-income earners. From this pattern, then, we may conclude rates of employment in the new economy is associated with greater inequality generally, but particularly, it is associated with income inequality due to having less middle-income earners in the region.

Hypothesis 2C examines correlations between rates of geographic mobility in MSA’s with these same indicators of inequality. No statistically significant linear correlation is found between rates of geographic mobility and the 90/20earnings ratio. A weak positive correlation is found between rates of geographic mobility and the Gini coefficient, with a correlation of .170, statistically significant at the .05 level. A very comparable correlation is also found between rates of geographic mobility and low earner rates. This evidence is inconclusive in regards to the sixth hypothesis. There is some weak support for the argument that rates of geographic mobility are associated with higher inequality, but it is very weak and inconsistent support.