# Equity in Transportation Asset Management: A Proposed Framework
## 4 Potential Limitation and Biases  
When proposing a framework that integrates social equity, environmental equity, and economic considerations into pavement management systems, it is crucial to recognize several significant limitations and potential biases. Firstly, the availability and quality of data related to social, environmental, and economic factors may vary across regions,  
Figure 4: Pavement condition variation by poverty rate.  
Figure 5: Pavement condition variation by air quality.  
potentially constraining the framework's generalizability. This introduces a possible bias, where regions with more robust data collection infrastructure might be overrepresented in assessing the framework's effectiveness.  
Another critical consideration involves the inherently subjective nature of assessing social and environmental equity. Divergent views among stakeholders on what constitutes equitable outcomes can introduce biases in decision-making. Additionally, economic conditions are dynamic, and the framework's reliance on economic factors may face challenges in adapting to changing circumstances. Economic biases may emerge if the framework lacks flexibility to accommodate variations in economic conditions.  
Furthermore, the framework may not fully account for cultural and regional differences, posing limitations in its applicability in diverse contexts and potentially introducing cultural biases. Temporal dynamics also pose a challenge, as the framework may not adequately address long-term changes in social, environmental, or economic factors, limiting its relevance over time.  
The effectiveness of the framework may also depend on stakeholder engagement, and challenges in obtaining meaningful participation could hinder successful implementation, potentially introducing biases in decision-making processes, especially if certain perspectives are underrepresented. Addressing these limitations through careful consideration, sensitivity analyses, and transparent decision-making processes is crucial for ensuring the robustness and applicability of the proposed framework in pavement management systems.  
Finally, it is important to note the potential for endogeneity in these types of analyses. Endogeneity can arise when there is a bidirectional relationship between the variables considered, meaning that the causal relationship may not be straightforward. In situations where endogeneity is present, the framework's ability to disentangle and accurately model the causal relationships may be compromised. This could impact the effectiveness of the decision-support system, as interdependencies among variables may introduce biases in the allocation of budgets. For example, improving the conditions in an underserved area may lead to changes in the social, economic, and environmental factors, influencing the overall dynamics of the pavement management system.  
To address the endogeneity concerns, advanced statistical techniques like instrumental variables (IV) or control function methods could be employed in. These techniques aim to extract the exogenous component of the endogenous variables, breaking the correlation between the endogenous variables and the error term. Additionally, sensitivity analyses using methods like the Hausman test or two-stage least squares (2 SLS) can be conducted to check for the presence of endogeneity and assess the robustness of the results. By employing these advanced statistical techniques and sensitivity analyses, the framework's ability to accurately model the causal relationships can be enhanced, mitigating potential biases introduced by endogeneity in the allocation of budgets.