Evaluating US Fuel Economy Standards in a Model with Producer and Household Heterogeneity

2013 ◽  
Vol 5 (2) ◽  
pp. 148-187 ◽  
Author(s):  
Mark R Jacobsen
Keyword(s):  
Low Income ◽  
Fuel Economy ◽  
Welfare Cost ◽  
Corporate Average Fuel Economy ◽  
Household Decision ◽  
Gasoline Tax ◽  
Car Market ◽  
The Us ◽  
Used Car ◽  
Household Heterogeneity

This paper employs an empirically estimated model to study the equilibrium effects of an increase in the US corporate average fuel economy (CAFE) standards. I identify and model heterogeneity across firms and find that the profit impacts of CAFE fall almost entirely on domestic producers. The welfare analyses consider the simultaneous household decision of vehicle and miles traveled, allowing direct comparison with a gasoline tax. Finally, I consider dynamic impacts in the used car market. I find these comprise nearly half the gross welfare cost of CAFE and fall disproportionately on low-income households. Contrary to previous results, the overall welfare costs are regressive. (JEL H24, L51, L62)

scholarly journals A Quantitative Analysis of the Used-Car Market

2014 ◽  
Vol 104 (11) ◽  
pp. 3668-3700 ◽  
Author(s):  
Alessandro Gavazza ◽  
Alessandro Lizzeri ◽  
Nikita Roketskiy
Keyword(s):  
Quantitative Analysis ◽  
Willingness To Pay ◽  
Transaction Costs ◽  
Consumer Surplus ◽  
Welfare Effects ◽  
Secondary Markets ◽  
Car Market ◽  
The Us ◽  
Used Car ◽  
Aggregate Effects

Quantitatively, we investigate the allocative and welfare effects of secondary markets for cars. An important source of gains from trade in these markets is the heterogeneity in the willingness to pay for higher-quality (newer) goods, but transaction costs are an impediment to instantaneous trade. Calibration of the model successfully matches several aggregate features of the US and French used-car markets. Counterfactual analyses show that transaction costs have a large effect on volume of trade, allocations, and the primary market. Aggregate effects on consumer surplus and welfare are relatively small, but the effect on lower-valuation households can be large. (JEL D23, L62, L81)

scholarly journals The convergence of racial and income disparities in health insurance coverage in the United States

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
De-Chih Lee ◽  
Hailun Liang ◽  
Leiyu Shi
Keyword(s):  
Health Insurance ◽  
Low Income ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
The United States ◽  
Combined Effect ◽  
Medical Expenditure ◽  
The Us ◽  
Race Ethnicity ◽  
Vulnerability Measure

Abstract Objective This study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US. Data source The household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study. Study design Logistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively. Data collection/extraction methods We constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. Principal findings While income was a significant predictor of health insurance coverage (a difference of 6.1–7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health. Conclusion Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.

A model of the used car market with lemons and leasing

2010 ◽  
Vol 42 (28) ◽  
pp. 3619-3627 ◽  
Author(s):  
Arif Sultan
Keyword(s):  
Car Market ◽  
Used Car

Abstract 17309: Scope and Determinants of Nonfinancial Barriers to Healthcare Access in Individuals With Atherosclerotic Cardiovascular Disease

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Isaac Acquah ◽  
Javier Valero-Elizondo ◽  
Miguel Cainzos Achirica ◽  
Rahul Singh ◽  
Karan Shah ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Low Income ◽  
Barriers To Care ◽  
Multinomial Logistic Regression ◽  
The Elderly ◽  
Atherosclerotic Cardiovascular Disease ◽  
The Us ◽  
Barriers To Healthcare ◽  
Relative Prevalence

Introduction: Barriers to healthcare - financial and nonfinancial - may result in unmet health needs and adverse outcomes. Despite this, the nonfinancial barriers to care among adults with atherosclerotic cardiovascular disease (ASCVD) is poorly defined in the US. We aimed to explore the scope and determinants of nonfinancial barriers to care among individuals with ASCVD. Methods: We analyzed data from the 2013-17 National Health Interview Survey (NHIS). We included adults with self-reported ASCVD (heart attack, angina, and/or stroke). Nine key variables in the NHIS that represent nonfinancial barriers to healthcare were assessed as absent/present, and participants were classified as having 0-1, 2, or ≥3 barriers. Multinomial logistic regression (using 0-1 nonfinancial barriers as reference) was used to evaluate the relationship between various sociodemographic factors, and an increasing number of nonfinancial barriers. Results: Of all the 15,758 adults with ASCVD (8.1% annually in the US; representing 19.6 million), 23.4% reported having at least one nonfinancial barrier to care while 4.9% reported 3 nonfinancial barriers. In a multivariable multinomial logistic regression, after stratifying by age, individuals from low-income families had an almost 2-fold relative prevalence of 3 nonfinancial barriers ( Figure) . In the elderly, however, lack of insurance was the strongest predictor (relative prevalence ratio of 6.51 [95% confidence interval; 2.25, 18.87]) of having ≥3 barriers. Conclusion: Among adults with ASCVD, the relative prevalence of ≥3 nonfinancial barriers was low (4.9%) with low-income being the only modifiable predictor of reporting ≥3 nonfinancial barriers and lack of insurance being the strongest predictor in the elderly. Addressing financial barriers to healthcare may help alleviate these nonfinancial barriers.

Pakwheels.com—The Next Challenge!

2017 ◽  
Vol 14 (2) ◽  
pp. 137-159
Author(s):  
Farrah Arif ◽  
Sarah Suneel Sarfraz
Keyword(s):  
Business Model ◽  
Turning Point ◽  
Model Identification ◽  
Scaling Up ◽  
Car Market ◽  
Retail Channel ◽  
Start Up ◽  
Potential Sources ◽  
Used Car ◽  
Trade Channel

This case features the challenges of a start-up website in the used car market in 2015 as its founding partners assess potential sources of profitable growth and ways to maintain their competitive advantage. Founded in Pakistan, PakWheels.com was a vertically classified automotive portal. The case goes on to illustrate how PakWheels.com successfully revolutionized the used-car trade channel by bridging the gap between the traditional retail channel of car dealers and buyers. Key discussion points of the case revolve around the turning point of PakWheels business, namely retention of users while sustaining the current business model, identification of key strategies for scaling up and monetization, and response to competitive threats to safeguard its market position. Through assessing these options and discussing this case, students will learn about strategic problems faced by online portals as well as how these unique businesses create value for end users.

scholarly journals Mahindra First Choice

2021 ◽  
pp. 354-362
Author(s):  
Ben Jackson ◽  
Genevieve Joy
Keyword(s):  
Third Party ◽  
Repair System ◽  
Market Failures ◽  
System A ◽  
Car Market ◽  
System P ◽  
First Choice ◽  
Used Car ◽  
The Creation ◽  
Trust Information

Mahindra Firstchoice illustrates the process of ecosystem orchestration in the context of the second-hand car market in India. It describes how Mahindra Firstchoice mapped the ecosystem in relation to six key parties—consumers who were buyers, consumers who were sellers, car manufacturers, independent used-car dealers, independent car service workshops, and banks. It then identified the bottlenecks and ‘pain points’ that afflicted the six parties. The used-car market did not function properly because of lack of trust, information, and transparency and Mahindra Firstchoice worked with the parties to identify solutions to the market failures. These involved, amongst other things, the creation of third-party car inspection services, the establishment of a multi-brand car-dealer franchise, a warranty system, a bluebook of second-hand prices and transactions, and a car diagnosis and repair system.

Heuristic Thinking and Limited Attention in the Car Market

2012 ◽  
Vol 102 (5) ◽  
pp. 2206-2236 ◽  
Author(s):  
Nicola Lacetera ◽  
Devin G Pope ◽  
Justin R Sydnor
Keyword(s):  
Information Processing ◽  
Limited Attention ◽  
Product Markets ◽  
Car Market ◽  
Heuristic Information ◽  
Observed Information ◽  
Used Car ◽  
Heuristic Information Processing

Can heuristic information processing affect important product markets? Analyzing over 22 million wholesale used-car transactions, we find evidence of left-digit bias in the processing of odometer values, whereby individuals focus on the number's leftmost digits. The bias leads to discontinuous drops in sale prices at 10,000-mile odometer thresholds, along with smaller drops at 1,000-mile thresholds. These findings reveal that information-processing heuristics matter even in markets with large stakes and easily observed information. We model left-digit bias in an inattention framework and structurally estimate the inattention parameter. Empirical patterns suggest the results are driven by final customers rather than professional agents. (JEL D12, D44, D83, L81)

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