Related Experiment Video
Updated: Jan 4, 2026
11:51
Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
Published on: March 2, 2011
15.6K
The PSID and Income Volatility: Its Record of Seminal Research and Some New Findings
1Johns Hopkins University, Baltimore, Maryland, USA.
The Annals of the American Academy of Political and Social Science
|November 1, 2019
Summary
The Panel Study of Income Dynamics (PSID) is crucial for understanding income volatility. New findings show income volatility increased significantly during the Great Recession.
Area of Science:
- Economics
- Sociology
- Demography
Background:
- The Panel Study of Income Dynamics (PSID) is a cornerstone dataset for U.S. income volatility research.
- Its longitudinal data has facilitated seminal studies, offering unparalleled insights into economic fluctuations.
Purpose of the Study:
- To explain the PSID's significant contributions to income volatility research.
- To review key studies utilizing the PSID and compare their findings with other datasets.
- To present updated analyses of income volatility trends in U.S. men through 2014.
Main Methods:
- Analysis of longitudinal data from the Panel Study of Income Dynamics (PSID).
- Comparative analysis of research findings across different datasets.
- Examination of gross income volatility and variance of transitory shocks.
Main Results:
- The PSID's design and data quality are key to its research impact.
- Income volatility among U.S. men shows a distinct three-phase trend: increase (1970s-1980s), stability (1990s-early 2000s), and a sharp rise during the Great Recession.
- Gross volatility and the variance of transitory shocks followed this trend.
Conclusions:
- The PSID remains an indispensable resource for studying economic instability.
- Income volatility experienced a notable surge coinciding with the Great Recession.
- Understanding these trends is vital for economic policy and social welfare analysis.
Related Concept Videos
01:52Regression Toward the Mean
6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K
01:08Outliers and Influential Points
5.8K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.8K
01:11Testing a Claim about Mean: Known Population SD
3.1K
A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
3.1K
01:09Empirical Method to Interpret Standard Deviation
9.2K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
This rule is used widely in statistics to calculate the proportion of data values...
9.2K
01:26Estimating Population Standard Deviation
3.3K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.3K
01:20Evolutionary Psychology
869
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
869