Valuing improved productivity of new health innovations: partial vs. general equilibrium approach

When valuing new health technologies, we mostly focus on how these technologies improve patient health. However, there may be additional impacts. For instance, the treatment may improve your health such that you are able to return to work or become more productivity at work.

How can we measure this additional value? Two common approaches are the following:

Human capital approach (HCA). Productivity costs of an illness due to early mortality are generally calculated as the loss present value of future economic production over the expected remaining lifetime of an individual. An illness’s impact on morbidity are calculated as the value of lost production due to acute illness or short- to long-term disabilities. The production time lost is typically valued by gross hourly wages. Friction cost approach (FCA). This approach assumes that employees are largely replaceable on the labor market. Thus, the productivity impact is largely due to the need to find new employees and train them. Thus, disability, or death create cost during “friction period” to identify/train new employees but there are no costs after that.

A paper by Hafner et al. (2022) critiques these approaches noting that they represent a partial equilibrium approach for understanding the productivity impacts of illness. These approaches are problematic because they:

…omit the potential spillover effects that health problems and diseases have on the wider economy, for example, on other individuals, other firms, the government, other markets and international trade links. The illness of individuals can adversely affect the productivity of other laborers and capital, disrupt supply chains and ripples across to other markets and across borders.

The authors argue that general equilibrium (GE) macroeconomic models may better capture this ripple effect. To test this, they measure the productivity impact of nocturia, which is the frequent waking at night (>1 times) to pass urine during nighttime sleep. Clearly, a lack of sleep can impact your productivity the following day. They use data from the Vitality UK’s Britain’s Healthiest Workplace (BHW) survey which is administered to UK organizations with 20 or more employees. The final sample has 52,887 observations across 285 unique companies. Productivity was measured using the Work Productivity and Activity Impairment-General Health (WPAI-GH).

The authors use a computable general equilibrium (CGE) models have been used for other diseases (see list below). The authors explain the key model differences as follows:

A key difference between the GE and PE models is that the productivity level measure…(e.g., income)…is fixed in PE but adjusts endogenously in GE based on micro-founded economic theory (i.e., wages adjust to changes in the supply/demand of goods and the labor market). In summary, the GE approach quantifies the productivity costs beyond its direct effect (of multiplying the number of patients by their production loss) as in PE. Here, the prevalence of a health condition and its adverse effect on productivity levels ripples throughout the economic system, reaching indirectly other sectors and households. It would even spillover onto economies abroad, through trade linkages, indirectly adding additional costs to all countries.

How big a difference does using a general equilibrium approach make?

We find that the traditional PE approach underestimates the annual productivity cost of clinically relevant nocturia by around 16%. 

You can read the full paper here.

Other applications of GEE:

HIV/AIDS: Thurlow, J., Gow, J., & George, G. (2009). HIV/AIDS, growth and poverty in KwaZulu-Natal and South Africa: An integrated survey, demographic and economy-wide analysis. Journal of the International AIDS Society, 12(1), 18. https://doi.org/10.1186/1758-2652-12-18 Malaria: Yerushalmi, E., & Ziv, S. (2020). Imputing the social value of public health care: A new method with application to Israel. CAFE Working Paper 4. Centre for Applied Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University. Antimicrobial resistance: CCA. (2019). When antibiotics fail. Technical report. The Expert Panel on the Potential Socio-Economic Impacts of Antimicrobial Resistance in Canada, Council of Canadian Academics. Smith, R. D., Yago, M., Millar, M., & Coast, J. (2005). Assessing the macroeconomic impact of a healthcare problem: The application of computable general equilibrium analysis to antimicrobial resistance. Journal of Health Economics, 24(6), 1055– 1075. https://doi.org/10.1016/j.jhealeco.2005.02.003 Taylor, J., Hafner, M., Yerushalmi, E., Smith, R., Bellasio, J., Vardavas, R., Bienkowska-Gibbs, T., & Rubin, J. (2014). Estimating the economic costs of antimicrobial resistance. Technical report, RAND Report. Accessed: 2020-08-12. Pandemic influenza and non-communicable disease Smith, R. D., Keogh-Brown, M. R., & Barnett, T. (2011). Estimating the economic impact of pandemic influenza: An application of the computable general equilibrium model to the UK. Social Science & Medicine, 73(2), 235– 244. https://doi.org/10.1016/j.socscimed.2011.05.025
Keogh-Brown, M. R., Wren-Lewis, S., Edmunds, W. J., Beutels, P., & Smith, R. D. (2010). The possible macroeconomic impact on the UK of an influenza pandemic. Health Economics, 19(11), 1345– 1360. https://doi.org/10.1002/hec.1554(Keogh-Brown et al., 2010; Smith et al., 2011)COVID-19Keogh-Brown, M. R., Jensen, H. T., Edmunds, W. J., & Smith, R. D. (2020). The impact of Covid-19, associated behaviours and policies on the UK economy: A computable general equilibrium model. SSM – Population Health, 12, 100651. https://doi.org/10.1016/j.ssmph.2020.100651
Various health policy assessments Borger, C., Rutherford, T. F., & Won, G. Y. (2008). Projecting long term medical spending growth. Journal of Health Economics, 27(1), 69– 88. https://doi.org/10.1016/j.jhealeco.2007.03.003Hsu, M., Huang, X., & Yupho, S. (2015). The development of universal health insurance coverage in Thailand: Challenges of population aging and informal economy. Social Science & Medicine, 145, 227– 236. https://doi.org/10.1016/j.socscimed.2015.09.036Rutten, M., & Reed, G. (2009). A comparative analysis of some policy options to reduce rationing in the UK’s NHS: Lessons from a general equilibrium model incorporating positive health effects. Journal of Health Economics, 28(1), 221– 233. https://doi.org/10.1016/j.jhealeco.2008.10.002Yerushalmi, E., & Ziv, S. (2020). Imputing the social value of public health care: A new method with application to Israel. CAFE Working Paper 4. Centre for Applied Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University.