Attitudes Towards Large Income Risk in Welfare States: An International Comparison

Schroyen, Fred; Aarbu, Karl Ove

doi:10.1111/ecca.12267

Cited by 8 publications

(9 citation statements)

References 86 publications

(132 reference statements)

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“…With EZ, the CO 2 declines regardless of RA. We choose RA = 7 in our base-case calibration, a value roughly in line with attitudes toward large income risk across wealthy countries (59): RA in the United States alone is often higher (>8), while RA in European welfare states can be as low as 3. Similar patterns as those displayed in Fig.…”

Section: Methodsmentioning

confidence: 99%

Declining CO ₂ price paths

Daniel

Litterman

Wagner

2019

Proc. Natl. Acad. Sci. U.S.A.

108

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SignificanceRisk and uncertainty are important in pricing climate damages. Despite a burgeoning literature, attempts to marry insights from asset pricing with climate economics have largely failed to supplement—let alone supplant—decades-old climate–economy models, largely due to their analytic and computational complexity. Here, we introduce a simple, modular framework that identifies core trade-offs, highlights the sensitivity of results to key inputs, and helps pinpoint areas for further work.

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Section: Methodsmentioning

confidence: 99%

Declining CO ₂ price paths

Daniel

Litterman

Wagner

2019

Proc. Natl. Acad. Sci. U.S.A.

108

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“…Calibrations differ widely in the literature. See, e.g., Schroyen and Aarbu (2017) for a review of RA calibrations, showing significantly lower values in "welfare states" like Norway but higher values (>9) for countries like the United States. OECD averages for RA toward large income risks are close to 7 (Schroyen and Aarbu, 2017).…”

Section: Figure Xix-a Higher (Lower) Eis Goes Hand-in-hand With a Higher (Lower) Optimal Co2 Price In Early Yearsmentioning

confidence: 99%

Applying Asset Pricing Theory to Calibrate the Price of Climate Risk

Daniel

Litterman²,

Wagner³

2016

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Pricing greenhouse gas emissions involves making trade-offs between consumption today and unknown damages in the (distant) future. This setup calls for an optimal control model to determine the carbon dioxide (CO2) price. It also relies on society's willingness to substitute consumption across time and across uncertain states of nature, the forte of Epstein-Zin preference specifications. We develop the EZ-Climate model, a simple discrete-time optimization model in which uncertainty about the effect of CO2 emissions on global temperature and on eventual damages is gradually resolved over time. We embed a number of features including potential tail risk, exogenous and endogenous technological change, and backstop technologies. The EZ-Climate model suggests a high optimal carbon price today that is expected to decline over time as uncertainty about the damages is resolved. It also points to the importance of backstop technologies and to very large deadweight costs of delay. We decompose the optimal carbon price into two components: expected discounted damages and the risk premium.

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“…Such a comparison enables us to say something about the relative significance of risk-averse preferences for our sample relative to the national samples. The surveys were collected in 2002 for the USA ( n = 3,591) and Chile (n=11,475) and in 2006 for Norway ( n = 1,554) (for further details on the three surveys, see [ 37 ]). From Table 7 we observe that the distribution is skewed since 2 out of 3 households belong to category 4 (Strong).…”

Section: Resultsmentioning

confidence: 99%

The role of risk preferences: voluntary health insurance in rural Tanzania

Kagaigai

Grepperud

2023

Health Econ Rev

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Background Lower-middle-income countries (LMICs) have a common goal to achieve universal health coverage (UHC) through voluntary health insurance schemes. This is important to improve access to healthcare services and ensure financial protection for all by reducing out-of-pocket expenditures. This study aimed to examine the role of risk preferences on enrollment status (currently insured, previously insured, and never insured) into a Tanzanian voluntary health insurance scheme targeted at the informal sector. Methods Data were collected from households in a random sample of 722 respondents. The risk preference measure was based on a hypothetical lottery game which applies the BJKS instrument. This instrument measures income risk where the respondents are to choose between a certain income and a lottery. Both multinomial and simple logistic regression models have been used to analyze the relationship between risk aversion and enrollment status. Results On average, the respondents have a high degree of risk aversion, and the insured are more risk averse than the uninsured (previously insured and never insured). There is a weak tendency for the wealthiest, measured by household income or total household expenditure, to be somewhat more risk averse than the less wealthy. Logistic and multinomial logistic regressions show that risk aversion is strongly associated with enrollment status. A higher degree of risk aversion significantly increases the probability of being insured, relative to being previously insured, and relative to being never insured. Conclusion Risk aversion matters in a decision to enroll into the iCHF scheme. Strengthening the benefit package for the scheme, might increase the enrollment rate and hence improve access to healthcare services for people in rural areas and those employed in the informal sector.

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Attitudes Towards Large Income Risk in Welfare States: An International Comparison

Cited by 8 publications

References 86 publications

Declining CO ₂ price paths

Declining CO ₂ price paths

Applying Asset Pricing Theory to Calibrate the Price of Climate Risk

The role of risk preferences: voluntary health insurance in rural Tanzania

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Attitudes Towards Large Income Risk in Welfare States: An International Comparison

Cited by 8 publications

References 86 publications

Declining CO 2 price paths

Declining CO 2 price paths

Applying Asset Pricing Theory to Calibrate the Price of Climate Risk

The role of risk preferences: voluntary health insurance in rural Tanzania

Contact Info

Product

Resources

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Declining CO ₂ price paths

Declining CO ₂ price paths