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We developed the first global dataset of living wages and developed a model to account for the impact of wages on human capital, called the Health Utility of Income (HUI). Thanks to this dataset and method, companies can benchmark globally employees’ wages levels against the living wage baseline and assess their contribution to human capital with the HUI. This work has been supported by Novartis.


Download the Whitepaper here.

Download the Living Wages Global Dataset here.

Download the Health Utility of Income (HUI) global dataset here.

Sustainability, interpreted as the long-term growth of wealth relies on maintaining or increasing the stock of capital used to generate this wealth. Capital has different forms, i.e. natural capital, produced capital, social and human capital (Lange et al., 2018 – The changing Wealth of Nations).


The World Bank has shown that 64% of the current global wealth is represented by human capital, and that human capital is strongly correlated to GDP per capita figures. Human capital represents indeed 70% of the current wealth of high-income countries (OECD), while only 41% of the wealth in low-income countries. (Lange et al., 2018). There is a strong incentives to increase human capital in low-income countries in particular.


Human capital includes multiples aspects related to people including health, knowledge, skills and income. The World Bank formally measures it as the present value of the future earnings of the labor force. Income, as part of the employment conditions and environment, has also been recognized by the World Health Organization (WHO) as one among various social determinants of health. The WHO showed in particular that income inequalities within a country correlate more strongly with the quality of life and life expectancy than GDP per capita. More simply said, our level of income influences how long we live.


Indeed, income levels are highly unequal in our world. While inequalities are inevitable and even beneficial, the current level of wage inequalities negatively impacts our society and our capacity to grow our economy in the future. This is illustrated by the inequalities at global scale: 82% of the wealth created in the last year (2017) went to the top 1%, while the bottom 50% saw no increase at all (Oxfam, 2018). Actually, 42 individuals now own the same wealth as the bottom 3.7 billion people (Credit Suisse, Oxfam 2018).


Moving from these observations to analysis and action requires an understanding of what is a sustainable income. This is defined by the living wage (LW) concept which enables evaluating the quality of received income. It can be defined as the wage required to satisfy basic needs i.e. a decent living. LW is higher than the poverty wages. It is also higher than the legal minimum wages in many cases. As no living wages dataset exists for all countries, we developed it in this project (see associated downloads), using different data sources and models to fill the data gaps.


Receiving a wage below a living wage threshold will ultimately lead to a reduced quality of life and life expectancy, which is called health inequities. On the contrary, receiving a wage above the living wage threshold will positively influence the life quality and expectancy. Some authors, in the US (NAS, 2015) and in France (INSEE, 2018), have shown that differences in life expectancy can reach up to 13 years between the low- and high-income population group. This difference in life expectancy is influenced by many socio-economic factors as well as behavioral, genetic and environmental factors. In practice, we observe a high variability between different countries of health inequities, linked to income inequalities.


The Health Utility of Income (HUI) model accounts for the effect of those income inequalities per country on health inequities. A global dataset has been developed and can be used in association to the living wage dataset.


We used the living wages global dataset and the HUI model to account for the human capital value contribution of Novartis on their direct operations and in their supply chain.


According to our economic impact analysis for 2017 based on the World Input-Output database (Exiobase), Novartis spend generated 360´000 jobs in its supply chain, in addition to the approx. 120’000 direct employees. The Exiobase analysis suggests the following skills distribution for the supply chain employment results: 65% high-skilled jobs, 14% medium-skilled jobs and 21% low-skilled jobs. Jobs were identified per sector, but more importantly for our model, per country as this informs the reference levels of wages.


The direct employment impact of Novartis (i.e. direct operations) generated a positive impact value of USD 1.0bn, while seeing a very small negative impact of USD 1.4m (less than 0.2%) linked to the cases of salaries below the living wage threshold. These cases are considered temporary and were followed-up in accordance to the Novartis living wage policy.


Overall, the impact of Novartis supply chain (i.e. excluding direct operations) generated a USD 5.6bn net positive societal impact. The low-skilled jobs were estimated to generate a combination of a negative social impact value of USD 0.2bn and a positive social impact value of USD 0.2bn. The medium-skilled jobs created contributed negatively with USD 0.2bn and positively with USD 0.3bn. The high-skilled jobs contributed only positively with USD 5.5bn.


Interestingly, the top ten countries with the highest social impact in the Novartis supply chain were not all related to the magnitude of the spend. Those countries are, by order of impact (in bracket the spend ranking): USA (1), China (6), Singapore (16), Germany (3), Switzerland (2), Brazil (18), Indonesia (43), United Kingdom (4), Austria (5) and Spain (11).  There were 21 countries for which the social impact of living wages was only positive for both the Novartis supply chain and Novartis operations. All of them were in Europe.


Such model and data can provide important insights into the impact of employment supported by the private sector, both directly and indirectly. By sharing the whitepaper and the datasets (both living wages and HUI), we hope to support other organisations to replicate such analysis and develop further the model.


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