Inclusive wealth

Trends in inclusive wealth, produced, human, and natural capital.

Produced capital (also referred to as manufactured capital) includes investment in roads, buildings, machines, equipment, and other physical infrastructure. Human capital comprises knowledge, education, skills, health and aptitude. Natural capital includes forests, fossil fuels, fisheries, agricultural land, sub-soil resources, rivers and estuaries, oceans, the atmosphere and ecosystems, more generally. Social capital is also acknowledged as critically important to a nation's wealth, and includes trust, strength of community, transparency of institutions, and the overall ability of societies to overcome problems. An economy's institutions and politics are factors that determine the social value of its assets because they influence what people are able to enjoy from them. In the present methodology of IWI, social capital is not directly measured but embedded in other capital types. Not all components of capital that are conceptually components of wealth are currently included in the Inclusive Wealth methodology. This is due to difficulties in measuring certain assets, as well as data availability and comparability constraints.

The conceptual framework looks at the change of inter-temporal well-being at:

${\displaystyle V(t)=\int _{t}^{\infty }U(C_{\tau })e^{-\delta (\tau -t)}d\tau }$

Assuming equivalence between wealth and well-being, it is measured by wealth in practice. Denoting produced, human, and natural capital as 𝐾, 𝐻, and 𝑁, the change in inclusive wealth 𝑊 is expressed by:

${\displaystyle dW=(K,H,N,t)/dt=p_{k}(dK/dt)+p_{H}(DH/dt)+p_{N}(dN/dt)\delta V/\delta N}$

where 𝑝_𝐾, 𝑝_𝐻 and 𝑝_N are the marginal shadow prices of produced, human, and natural capital, respectively. They are formally defined by,

${\displaystyle p_{K}\equiv \delta V/\delta K,p_{H}\equiv \delta V/\delta H,p_{N}\equiv \delta V/\delta N}$

given a forecast of how produced, human, and natural capitals, as well as other flow variables, evolve in the future in the economy in question.

Practically, shadow prices act as a weight factor attached to each capital, resulting in the measure of wealth, or:

${\displaystyle IWI=p_{K}(K)+p_{H}(H)+p_{N}(N)}$

In practice, W and IWI can be used interchangeably, although they can differ in that IWI also uses shadow prices on the margin. In addition, the unit of IWI is in (monetary) terms, rather than utility units.

This does not affect the sustainability assessment overall.

In the current Inclusive Wealth methodology, the components of natural capital include renewable resources (agricultural land, forests, and fisheries) and nonrenewable resources (fossil fuels and minerals).

The inclusion of fossil fuels within an indicator that tracks sustainability may appear counterintuitive because fossil fuels are often considered liabilities or stranded assets. The mechanism assumed in the IWI framework is the business-as-usual scenario currently pursued by the imperfect economies that form the basis of our societies. The shadow price of any type of natural capital represents the marginal contribution towards social wellbeing. In this context, the prevailing discourse is that the potential benefit of fossil fuels for driving investment in other types of capital, outweighs the drawbacks in terms of social costs of carbon.

In the IWI, non-renewable natural capital resources are oil, coal, natural gas, minerals and metals. To measure any given fossil fuel, data is taken for the current stock and compared to data on production from previous years, in order to develop a consistent time-series dataset which reflects accurate extraction (or flow) variables. The unit shadow price for non-renewables is the price net of extraction cost, also called the rental price. The rental rate of the total price is assumed constant and follows the methods of Narayanan et al. (2012).[8] Ideally, the marginal cost of extraction should be used for corresponding remaining stock, but it is notoriously hard to obtain. The methodology for accounting for minerals is similar to that used for fossil fuels, and for rental rates, the sectoral rental rates of different mineral industries from Narayanan (2012)[8] are used, as well as data from the U.S. Geological Survey (2015).[9]

The timber stocks included in IWI estimates are those which are commercially available. To calculate the quantity of timber available, the total forest area, excluding cultivated forest[3], is multiplied by both the timber density per area and percentage of total volume that is commercially available. The exclusion of cultivated forest from this category may be debatable, as it is regarded as contributing to timber and non-timber values. It is because the activity of cultivating forest is categorized as a production activity in the System of National Accounts.

Following the estimation of physical stocks, the shadow prices are computed to determine the wealth in monetary terms. The World Bank’s approach (2006)[10] is taken, in which a weighted average price of two different commodities was used for industrial roundwood and fuelwood. Country-specific GDP deflators are used for converting prices from current, to constant, and regional rental rates for timber (estimated by Bolt et al. (2002)[11] are applied, which are assumed to be constant over time. To obtain the proxy value for the shadow price of timber, the average price over the entire study period (1990 to 2014) is taken. Taking these calculations, wealth corresponding to timber value is taken as the product of quantity, price and average rental rate over time.

Aside from the provisional ecosystem service of timber production, forests yield many other benefits. These additional ecosystem services are accounted for in the following manner: Firstly, total forest area in the country under analysis (again, excluding cultivated forest) is retrieved from FAO (2015).[12] Secondly, the fraction of the total forest area which is accessed by individuals and contributes to human well-being is assumed 10%, following World Bank (2006).[10] Third, the unit benefit of non-timber forest to inter-temporal social well-being is obtained from the Ecosystem Service Valuation Database (ESVD) database of van der Ploeg and de Groot (2010).[13] This is expressed as USD/ha/year. Finally, to translate this benefit into capital asset value, we take its net present value, using the discount rate of =5%.

Fishery stocks are much harder to estimate than other resources for various reasons. They cannot be estimated based on the habitat area, unlike forest or agricultural lands, and furthermore a marine fishery habitat is usually not contained within national borders. Global fish stocks are often assessed using trends in catch or harvest data, simply because the only data available for most fisheries are the weight of fish caught annually (Pauly et al. 2013).[14] With a country's harvest and effort data, along with catchability coefficient, stocks can be estimated using the Schefer production function (Yamaguchi et al. 2016).[15] For estimating fishery stocks in countries that lack sufficient effort data, a resource dynamic approach is taken, using an algorithm developed by Martell and Froese (2013).[16]

Agricultural land in IWI natural capital accounts is composed of cropland and pastureland, and the methodology for assessing both types is much the same. Data from the Food and Agricultural Organization (2015)[17] is employed for quantifying the permanent area of cropland and pastureland. Then, as there is usually no market price for agricultural land, a shadow price is computed as the net present value of the annual flow of services per hectare that a parcel of land yields, in line with World Bank (2011).[18] It is difficult to link rents to a particular amount of land involved in the production process, so we assume the shadow price of pastureland is equal to that of cropland.

The calculation of shadow prices is central to developing the IWI, particularly natural capital. Shadow prices are the estimated price of a good or a service that does not have a market price. Various non-market valuation techniques can be used to estimate these prices and these methods of valuation are generally considered more comprehensive than using un-adjusted market prices. The use of shadow prices for natural capital attracts a large amount of critique, mainly regarding the knowledge gap surrounding how to represent production functions of life-supporting ecosystems. Nevertheless, although the ‘right’ shadow prices of natural capital or ecosystem services may not be achieved or the thresholds of ecosystems be sufficiently captured, shadow prices based on willingness to pay measures are currently considered the best available approach for estimating their value (Dasgupta et al. 2012[19]) (Farley 2012).[20]

The two main components of human capital are health and education. Aspects of human capital recognised as important but rarely quantified include parenting, on-the-job training, informal education and migration, among others.

Human health is affected in 3 main ways: directly affecting well-being, raising productivity and extending life years. The latter is computed as the proxy for health-related human capital, largely due to there being limited options for accurately quantifying the first and second contributions. Then, the shadow price of health capital is simply the value of statistical life year (VSLY) (Arrow et al. 2012).[21] The rate of return on education, as well as value of statistical life (VSL) year, is derived from market transactions and thus can deviate from the marginal impact on well-being.

Education pays off later in life in terms of lifetime income and well-being. Consistent with the literature, the Inclusive Wealth methodology used focuses on the return on formal education, whilst acknowledging that other non-formal education like early childhood learning and vocational training also contribute to wealth. Using data from Barro and Lee (2013),[22] educational attainment is proxied by the average years of total schooling per person. The rate of return on education is assumed at 8.5%, and then multiplied by the population who have received an education. In the frontier approach of calculation, we determine shadow prices of education and health-induced human capital by employing a non-parametric method.

Produced capital, also referred to as manufactured capital, includes physical infrastructure, land, property, and facilities of private firms, houses, etc. Upon calculation, we follow the method originated by Harberger (1978)[23] and applied by King and Levine (1994)[24] and Feenstra et al. (2013).[25] In particular, the perpetual inventory method (PIM) is employed, which is a simple summation of gross investment net of depreciation that occurs in each period.

Three adjustments are included that influence wealth and social well-being, but are not covered by familiar capital assets: carbon damage, oil capital gains, and total factor productivity.

Carbon damages can be regarded mostly as an exogenous change in social well-being, and calculation involves the following key steps: (1) Obtain the total global carbon emissions for the period under analysis, 1990 to 2014; (2) Derive the total global damages as a function of the emissions; and, (3) Allocate the global damages to the countries according to the potential effect of global warming in their economies.

Oil prices are notorious for experiencing fluctuations within relatively short periods of time. Even if the quantity of oil in an oil-rich nation doesn't change, a spike in oil price translates into better opportunities for the country, as it can turn its oil wealth into investments in other capital and increased consumption. Conversely, rising oil prices may result in reductions in social well-being for net oil importing countries. For this reason, oil capital gains are calculated. In the current methodology, an annual increase of 3% in the rental price of oil is assumed, corresponding to the annual average oil price increase during 1990–2014 (BP 2015),[26] implying that even if no oil is withdrawn, the nation can enjoy 3% growth in wealth.

Total factor productivity (TFP) measures all the residual contributions to social well-being and is formally regarded as shadow value of time as a capital asset (UNU-IHDP and UNEP, 2012).[27] The IWI methodology therefore includes TFP as an adjustment term. A non-parametric analysis called Malmquist productivity index is employed, which in turn is based on the concept of data envelopment analysis. This approach enables TFP adjustment in the context of natural capital, as well as produced and human capital.