Related Experiment Video
Updated: Feb 5, 2026

Electrophysiological Measurements and Analysis of Nociception in Human Infants
Published on: December 20, 2011
Impact of federal transfers upon US infant mortality rates: a secondary analysis using a fixed effects regression
Michael McLaughlin1, Mark R Rank1
1George Warren Brown School of Social Work, Washington University, St. Louis, Missouri, USA.
Objectives:
In order to improve health outcomes, the federal government allocates hundreds of billions of annual dollars to individual states in order to further the well-being of its citizens. This study examines the impact of such federal intergovernmental transfers on reducing state-level infant mortality rates.
Setting:
Annual data are collected from all 50 US states between 2004 and 2013.
Participants:
Entire US population under the age of 1 year between 2004 and 2013.
Primary And Secondary Outcome Measures:
State-level infant mortality rate, neonatal mortality rate and postneonatal mortality rate.
Results:
Using a fixed effects regression model to control for unmeasurable differences between states, the impact of federal transfers on state-level infant mortality rates is estimated. After controlling for differences across states, increases in per capita federal transfers are significantly associated with lower infant, neonatal and postneonatal mortality rates. Holding all other variables constant, a $200 increase in the amount of federal transfers per capita would save one child's life for every 10 000 live births.
Conclusions:
Considerable debate exists regarding the role of federal transfers in improving the well-being of children and families. These findings indicate that increases in federal transfers are strongly associated with reductions in infant mortality rates. Such benefits should be carefully considered when state officials are deciding whether to accept or reject federal funds.
Related Concept Videos
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Regression Toward the Mean
Microsoft Excel: Regression Analysis
To perform regression...
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Correlation and Regression
Framing Effects

