Local Technological Access, Income Disparities, and Job-Seeking in the United States Since 2010
Shaolong Wu
TL;DR
This study analyzes how local computing contexts and digital infrastructure influence wages and job-seeking in the United States using the NLSY97 cohort (2011–2017). It employs an OLS wage model $W_{i t} = \alpha + \beta_1 X_{i t} + \beta_2 Z_{i t}+e_{i t}$ and a logistic model for job-seeking probabilities, while exploring AR structures via $u_{i,t} = \rho u_{i,t-1} + e_{i t}$ and addressing unemployment endogeneity with two-stage least squares. Key findings show that education and regional disparities strongly predict wages; frequent Internet usage increases job-search propensity, particularly in earlier years, with marital status and income-to-poverty ratios also influencing outcomes. The results highlight that place-based digital infrastructure can either reduce or amplify labor-market inequalities, underscoring the need for localized policy efforts to promote equitable access to technology and opportunities in the digital economy.
Abstract
In the modern U.S. labor market, digital infrastructures strongly influence how individuals locate opportunities, build skills, and advance wages. Regional differences in computing access, broadband coverage, and digital literacy have significant labor implications for equity and sustainability. Drawing on longitudinal data from the NLSY97 (National Longitudinal Surveys of Youth) cohort, this study examines how place-based technological factors, personal demographics, household characteristics, and education shape income levels and decisions to seek new employment. The regression analyses reveal that educational attainment, marital status, and frequency of Internet usage strongly predict both wages and individuals' job-seeking intensity. Regional disparities in income underscore the need for more localized interventions to ensure equitable access to technology. This study raises key questions about how digital infrastructures can reinforce or challenge systemic inequalities in underserved communities.