FY'19-RFQ-dfhv-for hire elasticity of demand study

The District of Columbia has issued a Request for Quotation (RFQ) for a For Hire Elasticity of Demand Study, identified by the procurement code FY'19-RFQ-DFHV. This solicitation appears to be seeking a comprehensive research study focused on analyzing the elasticity of demand within the for-hire transportation sector. The primary objective of the study is likely to understand how changes in pricing, service availability, or market conditions impact consumer demand for for-hire transportation services. Potential deliverables may include a detailed research report, statistical analysis, economic modeling, and recommendations for policy or regulatory considerations. The procurement was posted on June 4, 2019, with a firm submission deadline of June 21, 2019, indicating a relatively short response window of approximately two weeks for interested vendors. While specific evaluation criteria are not explicitly stated in the provided information, typical assessments for such a study would likely include methodological rigor, research expertise, analytical capabilities, and the proposed approach to data collection and interpretation.

The contract opportunity does not specify explicit set-aside designations or restrictions on respondent organizations. However, given the technical nature of the study, respondents would likely need to demonstrate significant expertise in transportation economics, market research, statistical analysis, and policy evaluation. Potential qualifications might include advanced degrees in economics, urban planning, transportation studies, or related fields, along with proven experience in conducting similar demand elasticity research. While no incumbent vendors are mentioned, the study suggests the District of Columbia is seeking an independent, specialized research team capable of providing objective, data-driven insights into the for-hire transportation market. Although no specific budget range is provided, elasticity studies of this complexity typically require substantial investment, potentially ranging from $50,000 to $250,000 depending on the depth of research, data sources, and analytical methodologies employed.