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Odis Johnson Jr and Michael Wagner, University of Maryland; 2014-015

Governance Studies at Brookings paper

Wade Jacobsen, Criminology and Criminal Justice

$450k NSF grant awarded to Jessica Goldberg to expand PADE

Faculty Associate Melissa Kearney and Philip B. Levine find that inequality trumps location in predicting early childbearing out of wedlock

Increasing number of studies have investigated the impact of maternal exposure to air pollution during pregnancy and adverse birth outcomes, particularly low birth weight (LBW, <2,500 g at birth) and preterm birth (PTB, <37 completed weeks of gestation). We performed a comprehensive review of the peer-reviewed literature and a meta-analysis to quantify the association between maternal exposure to particulate matter with aerodynamic diameter 2.5 and 10 μm (PM 2.5  and PM 10 ) during pregnancy and the risk of LBW and PTB. We identified 20 peer-reviewed articles providing quantitative estimate of exposure and outcome that met our selection criteria. There was significant heterogeneity between studies, particularly for findings related to PM 10  exposure (LBW,  I -squared 54%,  p  = 0.01; PTB,  I -squared = 73%,  p  < 0.01). Results from random-effect meta-analysis suggested a 9% increase in risk of LBW associated with a 10-μg/m 3  increase in PM 2.5  (combined odds ratios (OR), 1.09; 95% confidence interval (CI), 0.90–1.32), but our 95% CI included the null value. We estimated a 15% increase in risk of PTB for each 10-μg/m 3  increase in PM 2.5  (combined OR, 1.15; CI, 1.14–1.16). The magnitude of risk associated with PM 10  exposure was smaller (2% per 10-μg/m 3  increase) and similar in size for both LBW and PTB, neither reaching formal statistical significance. We observed no significant publication bias, with  p  > 0.05 based on both Begg's and Egger's bias tests. Our results suggest that maternal exposure to PM, particularly PM 2.5  may have adverse effect on birth outcomes. Additional mechanistic studies are needed to understand the underlying mechanisms for this association.

Odis Johnson Jr., University of Maryland; 2013-004

A survey of models used for forecasting exchange rates and inflation reveals that the factor‐based and time‐varying parameter or state space models generate superior forecasts relative to all other models. This survey also finds that models based on Taylor rule and portfolio balance theory have moderate predictive power for forecasting exchange rates. The evidence on the use of Bayesian Model Averaging approach in forecasting exchange rates reveals limited predictive power, but strong support for forecasting inflation. Overall, the evidence overwhelmingly points to the context of the forecasts, relevance of the historical data, data transformation, choice of the benchmark, selected time horizons, sample period and forecast evaluation methods as the crucial elements in selecting forecasting models for exchange rate and inflation.

Faculty Associate Philip Cohen brings sociology research to the public eye by tackling thorny issues about race, gender, family, and inequality in an online public forum.

Meredith Kleykamp explores job market outcomes for military veterans