Lead Research Specialist (RDS-III)
Data & Analytics Division,
CHCQ| Informatics Branch
California Department of Public Health
Sacramento, CA, USA
Tis study employed a contingent valuation approach to estimate homeowners’ willingness-to-pay (WTP) for wildfire risk reduction in 35 WUI communities in Nevada. We presented respondents with two potential risk reduction programs: a private program focusing on individual home and surrounding vegetation modification, and a public program targeting community-wide risk through fuel management treatments. This research provides valuable insights into homeowners' WTP for wildfire risk reduction, which can guide policy making in communities where the benefits of such policies likely exceed their costs.
This paper presents the OG model and modification considering capital accumulation and bubble may burst. The Weil (1987) overlapping generations (OG) model, which contains an exogenous probability that the economy’s bubble may burst, is extended to allow capital accumulation as in Banerjee (2021). Like Weil (1987), we find that the rate of return on the bubble asset must generally be greater than the rate of return on the capital backed asset. Like Banerjee (2021), but in contrast to Weil (1987), a gap between the interest rate paid on the capital backed asset and capital rental rate must occur. Thus, we provide enhanced knowledge of how the rates of return earned on assets relate to the productivity of capital and the capital rental rate.
The third paper extends the Overlapping Generations model of Weil (1987) by adding capital accumulation and stock market clearing as introduced in Banerjee and Pingle (2023). The addition of the stock market clearing eliminates the indeterminacy and inefficiency from Weil's model, resulting in a unique, Pareto efficient equilibrium. Because the bubble may burst in our model, as in the Weil model, we like Weil find that the rate of return on bubbly assets must exceed that on capital backed asset to offset the risk of bubble bursts. In contrast to Weil, we find that the path for the bubble is not influenced by a change in the perception that the bubble will burst.
Paper 4
This paper investigates the relationship among poverty, inequality, and economic growth in Bangladesh through the lens of Marxian political economy, using Household Income and Expenditure Survey (HIES) data alongside Difference-in-Differences (DID) estimation, logistic regression, and machine learning models to test how structural and political factors shape poverty risk. The DID results show that although overall poverty declined from 2000 to 2022, urban and rural households benefited less than the national average, exposing persistent structural gaps in how economic shocks are absorbed. Logistic regression confirms that urban households generally face lower poverty risk but that political instability significantly raises the odds of being poor, supporting Marx’s view that the political superstructure protects elite interests and blocks redistributive change. A complementary Random Forest ranks urban status and political instability as the strongest poverty predictors, while a simple Neural Network highlights the challenge of detecting hidden poverty without rich structural data — reinforcing Marx’s point that exploitation often remains obscured beneath headline growth. Together, the findings show that Bangladesh’s rapid GDP growth has lowered absolute poverty but failed to dismantle deeper class divides and structural barriers that reproduce inequality, concluding that without stable, accountable governance and deliberate redistribution, surplus value continues to flow upward, validating Marx’s critique of capitalism’s inherent tendency to concentrate wealth and perpetuate exploitation.
