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The rising cost of college tuition and the accompanying investment parents often make have received considerable attention recently. While classic models in economics make important predictions about the magnitudes of these investments, their distribution across children, and their relationship with later cash transfers, there has been little empirical work examining these predictions, especially with regards to the differential treatment of siblings. Using unique data from a supplement to the Health and Retirement Study, we find that parents typically invest differentially in the schooling of siblings, but we find no evidence that these investments are offset by later cash transfers.
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The purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research, weighting is called for when it is needed to make the analysis sample representative of the target population. In the latter type, the weighting issue is more nuanced. We discuss three distinct potential motives for weighting when estimating causal effects: (1) to achieve precise estimates by correcting for heteroskedasticity, (2) to achieve consistent estimates by correcting for endogenous sampling, and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does. We recommend diagnostics for assessing the advisability of weighting, and we suggest methods for appropriate inference.
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Motivated in part by the dramatic changes in the United States economy and public assistance policies, many researchers have examined the changes in the resources of the low-income population over the last two decades, with particular attention paid to income from earnings and public assistance programs. One source of income that has received comparatively little attention is income from private transfers. However, private transfers may be a key source of support for low-income individuals, especially for those who have had little attachment to the labor force or who have experienced reductions in public assistance. In this paper, we provide a conceptual discussion of private transfers drawing on several related literatures and provide new empirical evidence regarding the significance of private of transfers as a source income. We find that private transfers are an important source of income for many less-skilled households, the contribution of private transfers to total income has increased over time, and shared living arrangements are a common mechanism for providing assistance.
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The purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research, weighting is called for when it is needed to make the analysis sample representative of the target population. In the latter type, the weighting issue is more nuanced. We discuss three distinct potential motives for weighting when estimating causal effects: (1) to achieve precise estimates by correcting for heteroskedasticity, (2) to achieve consistent estimates by correcting for endogenous sampling, and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does. We recommend diagnostics for assessing the advisability of weighting, and we suggest methods for appropriate inference.
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