We assess the biases induced by measurement error in a study of correlations in educational attainment across two and three generations in the United States using linked data spanning 1940–2015. Although multigenerational regressions show an economically meaningful “grandparent effect,” we find that this effect is overstated and likely not statistically or economically significant after we correct for measurement error. However, that same measurement error means that inferences derived using only two generations of data understate persistence in educational attainment by roughly 20%. We find an 18% decline in the intergenerational correlation of educational attainment over the twentieth century.

This paper reviews the literature in historical record linkage in the U.S. and examines the performance of widely-used record linking algorithms and common variations in their assumptions. We use two high-quality, hand-linked datasets and one synthetic ground truth to examine the direct effects of linking algorithms on data quality. We find that (1) no algorithm (including hand-linking) consistently produces representative samples; (2) 15 to 37 percent of links chosen by widely-used algorithms are classified as errors by trained human reviewers; and (3) false links are systematically related to baseline sample characteristics, showing that some algorithms may induce systematic measurement error into analyses. A case study shows that the combined effects of (1)-(3) attenuate estimates of the intergenerational income elasticity by up to 20 percent, and common variations in algorithm assumptions result in greater attenuation. As current practice moves to automate linking and increase link rates, these results highlight the important potential consequences of linking errors on inferences with linked data. We conclude with constructive suggestions for reducing linking errors and directions for future research.