Impact of aging and underemployment on income disparity and professional networks and technology adoption

Abstract

The first paper examines how aging and underemployment affect household income and household income disparity between agricultural and non-agricultural sectors. A three-step regression analysis was conducted to estimate the aging and underemployment effects on household income and the income disparity between agricultural and non-agricultural households. First, we estimate aging and underemployment effects on household income from all households using a year fixed-effect longitudinal model. Second, our study investigates whether the marginal effect of aging and underemployment on household income differs between agricultural and non-agricultural sectors. Finally, we simulate using the estimated model to illustrate how government policies could help reduce the income disparity. Results from policy simulations suggest that the implementation of proper government policies to address aging and underemployment problems in agricultural households could significantly reduce the income disparity between agricultural and non-agricultural sectors.

The second paper examines the effect of information-intensive social networking on technology adoption decisions using 231 turfgrass professionals’ Twitter accounts data. To address the reflection problem in social networking analysis, we account for the networking heterogeneity that confounds in social networking process. The confounding effects are decomposed into individual- and group-level similarities, herd behavior, and clustering effects. To account for network structure-based heterogeneities (herd behavior and clustering effects), we employ the spatial autoregressive probit model that directly incorporates network structures into the model as a matrix system (i.e., adjacency matrix). A Bayesian estimation method is applied in our study to address the convergence problem that arises due to the complexity of model specifications. Empirical results show positive and significant information-intensive networking effect, observation-based networking effect (herd behavior) effect, and group-level similarity effects on turfgrass professionals’ decision-making process. The results also indicate that the information-intensive networking effect is larger than the observation-based networking effect. The interaction term between group-specific effects and observation-based networking effects explains the networking effect could significantly differ by each professional group. These results suggest policy and marketing strategy for new technology adoption should target to promote new technologies to individuals who are actively exchanging information through networking, while considering the networking behavior by groups to which the individuals belong.