The Contemporaneous Impact of Unemployment and Income Inequality on Violent vs Property Crime Rates in New England - A Panel Data and Machine Learning Approach

Aug 6, 2026

Upload by Maeve

Investigates how unemployment and income inequality relate to property and violent crime rates in New England (2003–2022) using panel data across Connecticut, Maine, Massachusetts, Rhode Island, New Hampshire, and Vermont. The analysis incorporates unemployment, measures of income inequality, and demographic factors such as race and gender to assess whether economic distress drives criminal behavior. Employs machine learning decision trees and cluster graphs for county-level prediction in Massachusetts (2008–2010). Finds significant effects for unemployment rate and race, with some results differing from regression patterns.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Introduction

Crime, economic stress, and inequality

Study objectives and research design

Background and prior findings

Unemployment and crime relationships

Income inequality and crime incentives

Data and methods

Panel data regression

Machine learning decision trees and clustering graphs

Policy relevance

Interpreting crime-rate reductions and uncertainty

The Contemporaneous Impact of Unemployment and Income Inequality on Violent vs Property Crime Rates in New England: A Panel Data and Machine Learning Approach Cora Sousa a Abstract: This paper investigates the possible impact of unemployment and income inequality on property and or violent crimes in the New England region in the United States from 2003 to 2022 . Using a panel data analysis looking at unemployment rates and factors of income inequality in New England states, Connecticut, Maine, Massachusetts, Rhode Island, New Hampshire, and Vermont, along with demographic factors like race and gender aim to find a relationship between unemployment and income inequality with crime rates. Economic distress, usually caused by unemployment, can cause an individual to commit crimes due to hard times. Using machine learning decision trees and cluster graphs, we then investigate the connection within different Massachusetts counties from 2008 to 2010 aiming to see if the independent variables can be used as predictors. Overall, there was significant evidence that unemployment rate, race being non-white and minimum wage affect whether someone is likely to commit a property and violent crime. The machine learning findings did find that demographic factors like race being Hispanic can be used as predictors but also found certain results to contradict regression findings. JEL Classification: K42, J64, E24, R23 Keywords: Unemployment, Crime Rate, Income Inequality, New England, Property Crime, Violent Crime. a Department of Economics, Bryant University, 1150 Douglas Pike, Smithfield, RI02917. Phone: 774-283-1151. Email: [csousa4@bryant](csousa4@bryant.edu)[.](csousa4@bryant.edu)[edu](csousa4@bryant.edu). 1.0 INTRODUCTION Crime can be influenced by one’s cognitive function as well as social surroundings. Though, it has been questioned whether economic factors can influence crime behavior. Unemployment and income inequality tend to have a negative impact on how someone behaves within society. This can be due to the stress they could be facing or just a means of survival. While sociodemographic data can be a contributing factor, this research along with the past explores the economic factors contributing to crime behavior. Unemployment specifically has been known to have a sporadic relationship with crime (Krohn, 1976) . There has also not been one unanimous explanation of crime rates based off employment status and income levels. Income inequality can put individuals in a place of financial hardship causing them to be more tempted into committing a crime of any kind. The term crime can be broken down into two subcategories, property and violent crimes. Property crimes data may include burglary, arson, motor vehicle theft, and larceny-theft while violent crimes can include murder, rape, robbery, and assaults. While it is expected that economic factors will have a stronger relationship with property crimes than violent crimes as property crimes are more heavily influenced by stealing another’s property rather than harming another. Unemployment and Income Inequality have been known to play a key role influencing personal behavior. The extent that is taken to, does depend on the person, situation, and area they live in. Results from past research have not shown one unanimous explanation of crime rates based off employment status and income levels as some find a positive relationship for one over the other, or even no relationship at all. (Gao et al., 2017) found a negative relationship between unemployment and violent crime along with a null relationship between unemployment and property crime. (Schleimer et al., 2022) found a statistically significant increase between the relationship between unemployment and violent crimes. This study aims to enhance understanding by using regression tools to explore the relationship in New England States: Connecticut, Maine, Massachusetts, Rhode Island, New Hampshire, and Vermont. For a

FAQs

What time span and geographic scope does the study cover?

The paper analyzes New England states in the United States from 2003 to 2022, including Connecticut, Maine, Massachusetts, Rhode Island, New Hampshire, and Vermont. It also focuses on Massachusetts counties for machine learning analysis from 2008 to 2010.

How does the paper model the relationship between economic factors and crime?

It uses panel data to examine unemployment rates and measures of income inequality alongside demographic controls like race and gender. It then applies machine learning methods, including decision trees and cluster graphs, to explore predictive patterns at the county level.

What key findings are reported about unemployment and crime types?

The paper reports significant evidence that the unemployment rate affects the likelihood of both property and violent crime. It also finds that race (including non-white and Hispanic indicators) and minimum wage relate to which type of crime a person is more likely to commit, with some outcomes that contradict regression results.

Aug 6, 2026

Upload by Maeve