Omeed Maghzian

Aggregate credit booms may induce firms to create jobs that bolster the long-run productivity of workers. Conversely, these jobs might be destroyed once the economy declines, displacing workers and impairing their human capital. We use administrative data from the U.S. Census Bureau to estimate the causal effects of loose credit conditions on firm employment and worker earnings. To obtain random variation in which firms borrow during booms, we exploit the segmentation of highyield (BB+ rated) versus investment-grade (BBB- rated) firms in credit markets. Loose credit conditions causally generate a boom-bust cycle in employment: high-default risk firms initially engage in heavy job creation, but then experience financial distress and destroy these jobs over the next five years. We show that these boom-bust dynamics are transmitted to workers. To obtain random variation in which workers take the jobs created during booms, we exploit the importance of parental connections in determining where labor market entrants first work. We find that recent high-school graduates with parents at high-yield (BB+) firms can more easily find high-paying jobs during credit booms, compared to graduates with parents at investment-grade (BBB-) firms. But ten years later, graduates with BB+ parents have substantially lower relative earnings. The magnitude of these negative long-term effects is comparable to the effect of entering the labor market during a recession. Overall, our results suggest that loose credit market conditions cause firms to create short-lived jobs that make workers more exposed to aggregate downturns and that stunt these workers' human capital accumulation.

We estimate the impact of household liquidity provision on macroeconomic stabilization using the 2020 CARES Act mortgage forbearance program. We leverage intermediation frictions in forbearance induced by mortgage servicers to identify the effect of reducing short-term payments with little change in long-term debt obligations on local labor market outcomes. Following statewide business reopenings, a 1 percentage point increase in the share of mortgages in forbearance leads to a 30 basis point increase in monthly employment growth in nontradable industries. In a model incorporating geographical heterogeneity in intermediation frictions, these responses imply a household-level marginal propensity to consume out of increased liquidity that aligns with existing estimates for direct fiscal transfers. The implied debt-financed fiscal multiplier effects of forbearance are sizable but depend on the repayment terms of deferred payments and the monetary policy stance.

We conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (rg = 0.70). We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)—to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.