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Other estimates are smaller, but whatever the actual figure is, it's a large one, as indicated by the slow growth in the labor force — people working or actively looking for work — and a sharp decline in the labor-force participation rate.
The DD estimates are smaller and somewhat noisier, but possible spillover effects may render non-residents an inaccurate counterfactual.
This is confirmed in Table 4, where the point estimates are smaller in magnitude than they are for year 1 flagship enrollment and not statistically significant.
If the differences between the two types of estimates are smaller than the differential thresholds, then such differences are not significant to the perception and can be ignored.
This pattern, which is consistent with greater endogeneity of black than white labor supply, explains why the coefficient estimates are smaller when the equation is estimated in ratios: they represent a weighted average of the black and white coefficients.
When 1970 is added to the sample, the coefficient estimates are smaller in absolute value (between −.13 and −.16), indicating a larger elasticity of substitution between black and white labor during the 1960s.
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Still, even the largest estimates are small by world standards.
[End Page 162] More important, the estimates are small and not robust.
First, we note that the standard errors of the estimates are small which makes the 95percentt confidence interval rather narrow.
That is, most standard deviation estimates are small and actual distances to the motif site tend to be small as well.
Moreover, even observational studies with "state-of-the-art" performance face an important methodological problem if the observed risk estimates are small.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com