The Value of Nature in Thailand: Implications for Social Policy

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Chapter 14
The Value of Nature in Thailand: Implications for Social Policy

Appendix A: Macroeconomic aggregates
Appendix B: Disaggregation of PKN into its components

Dodo J. Thampapillai, Shandre M. Thangavelu, and Euston T. Quah


The preservation and enhancement of nature is an essential ingredient for the welfare of any society. This chapter will assess the economic status of natural endowments in Thailand by asking the following questions: how much environmental capital (KN) does the Thai economy utilize each year? Does the economy display efficiency gains with respect to the utilization of KN? What is the monetary value of KN, and has it appreciated or depreciated over time?

To this end, use is made of a framework first presented in Thampapillai and Thangavelu (2003a, 2003b) for assessing the state of the environment in the Australian economy. This framework involves a simple method for deriving point estimates for the price and quantity of environmental capital stock in an economy and rests on the assumption that natural endowments can be aggregated as analogues of the stock estimates of manufactured capital (KM) that are presented in national accounts. The point estimates of KN so derived permit the assessment of efficiency gains by recourse to information, such as the amount of KN utilized for the formation of a unit of national income (Y) and KM—that is, time trends of ratios such as [KN/Y] and [KN/KM]. As illustrated below, this framework also enables the estimation of the price of KN, namely PKN, on the same scale as PKM, the price of KM, which is a composite measure of the interest rate and the depreciation rate.


The main features of the framework can be summarized as follows. The valid descriptor of national income (Y) is a Cobb-Douglas (C-D) factor utilization function, as in Solow (1986), where (Y) is distributed between three factors, namely, manufactured capital (KM), labor (L), and environmental capital (KN). Thus,

where θs, λ s, and ηs are shares of Y accruing respectively to KM, L, and KN, and a represents a measure of total factor productivity. Furthermore, (θs + λs + ηs) = 1 in the context of constant returns to scale.1 However, the description of Y in the income approach to the national accounts (IANA) conforms to the standard C-D function that is used in most texts, such as Dornbusch and Fischer (1999), namely:

Where θI and λI are shares of Y accruing respectively to KM and L, and (θI + λI) = 1 in the context of constant returns to scale. This is because the IANA is based on the following identity, which excludes KN:

Where OS represents operating surplus, which is the sum of payments accruing to KM, and SW is the sum of wages (compensation to employees), or payments accruing to L. Since equation (3) forms the basis for the IANA, it follows that in equation (2):

Hence, for a set of estimates in the IANA, denoted by Y(t), KM(t), L(t), OS(t), and SW(t), the distribution of Y(t) can be illustrated by conceptualizing the existence of marginal value product (MVP) functions of KM and L (equations 6 and 7) that are derived from equation (2), as follows, and are illustrated in Figures 14.1a and 14.1b:

1 The subscript s is used for the coefficients describing the relative factor shares of Y in order to distinguish the C-D function in equation (1) from the standard C-D function that excludes KN in equation (2) below, where the subscript I is employed.

However, the distribution of Y(t) as depicted in Figure 14.1 is invalid owing to the exclusion of KN—that is, the MVP functions that define the distribution of Y(t) need to be based on equation (1) which includes KN. The direct implication of assuming the validity of equation (1) is the acknowledgement that the returns to KM and L are over-stated in the IANA and that these returns, [OS(t)] and [SW(t)], also include within them the returns that are owed to KN.

Thus, if the returns that accrue to KN in a given year is [CEM(t)], then this value, [CEM(t)], needs to be subtracted from [OS(t)] and [SW(t)]. If an estimate for [CEM(t)] can be found, it is then possible to estimate the factor share of Y(t) accruing to KN in equation (3) as:

Hence, in the context of constant returns to scale, the factor shares of income accruing to KM and L can be estimated as follows:

If it is assumed that the relative factor shares of KM and L are the same in both equations (1) and (3); that is,


It is now possible to define the true social returns to KM and L, namely, [OSS(t)] and [SWS(t)] as follows:

The distribution of Y(t) when KN is recognized can now be described with reference to the MVP functions of KM and KN which are derived from equation (1), the premised valid descriptor of Y. These are illustrated in Figure 14.2, in which is included the MVP function of KM from equation (3) for comparison. Thus, the greater the size of [CEM(t)], the greater the deviation of [OS(t)] and [SW(t)] from their true social values, and hence the greater will be the opportunity cost of KN and the price of KN, namely PKN(t).

Note that in the formulations given in Figure 14.2 there are two sets of coefficients, [θS, λS, and ηS] and [θI and λI], such that:

Hence, it follows that:

From equation (14), the size of KN(t) can be defined as:

From here it is possible to define the price of KN for a given period, namely, PKN(t) as:

The point estimates of KN(t) and PKN(t) for Thailand can now be derived.


The data was taken from a survey report of the Asia Productivity Organization and the annual database of the Asian Development Bank (2002). The size of KM is computed in constant 1995 prices using the perpetual inventory method. This method makes use of the data on Gross Capital Formation and the Consumption of Fixed Capital. However, owing to the absence of data on capital consumption, it is assumed the fixed depreciation rate is 3.3 percent per year. Furthermore, because the income accounts are not presented in the various databases that are accessible, OS and SW were estimated from the publication of the Asia Productivity Organization (2001). This limitation meant that the point estimates could only be shown between 1980 and 1996. The pertinent macroeconomic aggregates that have been used here are displayed in Appendix A. According to the discussion above, the estimation of KN and PKN rests on the estimation of [CEM(t)]. Because the proxy data is easily available for estimating the costs of air pollution abatement at the national level, [CEM(t)] can be equated with these costs so that the definition of KN is confined to the airshed of the economy.

A proxy for the valuation of air quality in airsheds can be drawn from the work of Hartman et al. (1997) as part of the Industrial Pollution Project of the World Bank. Using scientific data and a range of approximations, it is possible to estimate the quantities of key pollutants that would emerge from the energy consumption of economies that are typically dependent on fossil fuels. These pollutants, namely, nitrous oxide (NOX), sulphur dioxide (SO2), carbon monoxide (CO), carbon dioxide (CO2), and particulates, are regarded as important because of their impact on the productivity of the economic system. The World Bank project also identified abatement costs for the emission of these pollutants. This information is used to estimate the value of CEM(t) as the sum of the pollution abatement expenditure for each year from 1980 to 1996 (Table 14.1).

Table 14.1 Point estimates of CEM(t) from energy consumption data
YearEnergy (KWH)NOXSO2COParticulatesCO2CEM
Note: All monetary values are in billions of constant 1995 baht.
19801.504E + 110.2370.1860.0990.01184.09284.624
19811.610E + 110.2530.1990.1060.01290.03590.605
19821.574E + 110.2480.1940.1030.01287.99388.550
19831.676E + 110.2640.2070.1100.01393.72394.317
19841.861E + 110.2930.2300.1220.014104.079104.738
19852.071E + 110.3260.2560.1360.016115.786116.519
19862.123E + 110.3340.2620.1390.016118.697119.448
19872.323E + 110.3660.2870.1520.018129.898130.720
19882.661E + 110.4190.3280.1740.020148.762149.703
19893.144E + 110.4950.3880.2060.024175.808176.921
19903.682E + 110.5800.4540.2420.028205.897207.200
19914.030E + 110.6340.4970.2640.030225.354226.780
19924.313E + 110.6790.5320.2830.033241.130242.657
19934.924E + 110.7750.6080.3230.037275.307277.050
19945.482E + 110.8630.6770.3600.041306.511308.451
19956.589E + 111.0370.8130.4320.050368.413370.746
19967.173E + 111.1290.8850.4700.054401.063403.602
19977.585E + 111.1940.9360.4970.057424.092426.776
19987.140E + 111.1240.8810.4680.054399.214401.741
19997.748E + 111.2190.9560.5080.059433.187435.929
20008.056E + 111.2680.9940.5280.061450.444453.295
20018.511E + 111.3401.0500.5580.064475.854478.866

Environmental Capital Efficiency and Scarcity

The information on CEM(t), together with the macroeconomic aggregates Y(t), OS(t) and SW(t), is used to obtain point estimates for the two sets of coefficients [θS, λS, and ηS] and [θI and λI]. These in turn enable the application of equations (16) and (17) above for the display of KN(t) and PKN(t) as point estimates (Table 14.2). Following Jorgenson's (1967) theory of capital pricing, PKN has been disaggregated into two components, namely, an interest rate (ιKN) and a depreciation rate (δKN). The underlying method of disaggregation is presented in Appendix B.

Table 14.2 The size and price of KN and the coefficients for estimation

The main contention of neoclassical economists, such as Grossman and Krueger (1991, 1995), Nordhaus (1973, 1992), and the World Bank (1992) concerning the usage of KN in the context of economic growth is twofold. First, economic growth is associated with the development of new technologies that conserve scarce environmental resources; and secondly, economic growth as a consequence is accompanied by a decline in the real price of natural resources.2

To ascertain the existence of environmental capital efficiency, the time-trends of the following ratios, are presented in Table 14.3 and Figure 14.3:

Each of these ratios shows the amount of KN that is utilized by a unit of Y, KM, and L, respectively. For comparative purposes, also included in Figure 14.3 is the time-trend on the amount of KM and L that is utilized by a unit of Y, that is,

The gradient of the time-trends of these ratios explains the rate of improvement in the utilization of KN, KM, and L over time (Figure 14.3).

2 According to Grossman and Kreuger (1995), “Economic growth is associated with evidence of remarkable ingenuity in harnessing new technologies to conserve scarce resources,” while the World Bank (1992, p. 37) states: “Declining price trends also indicate that many non-renewables have become more, rather than less, abundant.”

Table 14.3 Measures of factor productivity and relative prices

It is evident that the rate of improvement in the utilization of KN with respect to Y has been far better than that of KN with respect to KM; that is, approximately 7.6 percent per year versus 4.8 percent. These trends clearly support the existence of technological improvements in the utilization of KN in Thailand. A similar observation was made in the Australian studies by Thampapillai and Thagavelu (2003, 2004). However, there was a loss in efficiency in the utilization of KM with respect to Y and that of KN with respect to L. For example, the amount of KM utilized per unit of Y increased over time. Thus, the rate of utilization of KM per unit of Y improved in Australia but not in Thailand.

Table 14.3 also displays the measure of total factor productivity, namely, α Note that α is eliminated in the estimation of KN in equation (16). However, once KN is estimated, the value of α can be elicited from equation (1) as a point estimate, since it is the only unknown in the equation. The point estimate of α derived from equation (2) will also be the same as that derived from equation (1). It is evident from Table 14.3 that, on average, the Thai economy has displayed a total factor productivity improvement of nearly 14 percent between 1980 and 1996. However, this has not altered the evidence of scarcity, as observed above.

The estimates of PKN derived here display a consistently increasing trend in the past twenty years (Figure 14.4). This trend does not sit well with the generalizations of Nordhaus (1973, 1992), who estimated the real relative price of minerals with respect to labor. His observation was that the relative price of minerals had fallen over a seventy-year period (1900–1970). As indicated earlier, similar observations of declining trends were made by the World Bank (1992) on the long-run prices of non-ferrous metals between 1900 and 1991. However, the price analyses of Nordhaus and the World Bank suffer from at least two shortcomings. First, the price estimates do not account for the value of environmental amenities and services. For example, the extraction of a tonne of coal does not merely involve labor and capital but also the clearing of forests, the loss of ecological systems, and the possible contamination of ground and surface water systems. Had the costs of these environmental effects been included, the trend in relative prices may have been different. It is this shortcoming that is addressed in this chapter. This analysis shows that over a sixteen-year period the real price of Thailand's airshed increased from approximately 1.2 percent in 1980 to nearly 2 percent in 1996.

The second shortcoming is that these price trend analyses assume the world market for natural resources to be competitive. This is hardly the case. Most resource producers are developing countries, and most resource buyers are the industrial countries. Michael Todaro's (2001) cogent discussion reveals that nearly 90 percent of resources are used up by either the industrial countries or powerful corporations within such countries. In other words, the decline in resource prices is also due to the monopsony purchasing behavior of the industrial countries. The declining price trends might not have been as dramatic as perceived by the World Bank (1992) had the imperfect competition aspect been recognized.


This chapter has presented a simple analytic framework for the valuation of environmental capital at the aggregate level. As indicated, these values can be estimated directly from the national accounts with the aid of some simplifying assumptions. This is clearly

advantageous in the context of sparse data for environmental variables. At the same time, the analysis provides useful information for economic planning at the national level in at least two ways. First, it is possible to determine the relative importance of KN in terms of its size and price. Thailand's airshed was worth at least 10 percent of national income in 1996. Secondly, the evidence of real natural resource scarcity, despite possible technological advances, implies that the mitigation of resource scarcity should form an important component of natural resource management. Because KN is an airshed, the trends of PKN and its associated relative price ratios show clearly that Thailand's airshed is becoming increasingly scarce. Technology might assist people to exist with less of an airshed but it might not be able resolve how they could exist without one.


Asian Development Bank. 2002. National accounts: Volume I. Manila.

Dornbusch, R., and S. Fischer. 1999. Macroeconomics. New York: McGraw-Hill. Grossman, G., and A. Krueger. 1991. Environmental impact of a North American free trade agreement. Working Paper 3914. Cambridge, MA: National Bureau of Economic Research.

Grossman, G., and A. Krueger. 1995. Economic growth and the environment. Quarterly Journal of Economics 110: 353–77.

Hartman, R., D. Wheeler, and M. Singh. 1997. The cost of air pollution abatement. Applied Economics 29: 759–74.

Jorgenson, D. 1967. The theory of investment behavior. In Determinants of investment behavior, ed. R. Ferber. New York: Columbia University Press.

Nordhaus, W. 1973. World dynamics: Measurement without data. The Economic Journal 83: 1156–83.

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Solow, R. 1986. On the intergenerational allocation of natural resources. Scandinavian Journal of Economics 88: 141–49.

Thampapillai, D., and S. Thangavelu. 2003. The environmental Kuznets curve effect and the scarcity of natural resources: A simple case study of Australia. Canberra: Australian Agricultural Resource Economics Society (Australian Capital Territory Branch), September 24.

Thampapillai, D., and S. Thangavelu. 2004. Towards unitary economics: Valuation of environmental capital for environmental stewardship. In International Journal of Social Economics 31 (4): 430–42.

Todaro, M. 2001. Economic development. New York: Addison-Wesley Longman. World Bank. 1992. World development report. New York: Oxford University Press.

Appendix A: Macroeconomic aggregates

Table 14.3 Measures of factor productivity and relative prices
YearY(t) (106)KM(t) (106)L(t) (103)OS(t) (106)SW(t) (106)
Note: All monetary aggregates are in 1995 baht.

Appendix B: Disaggregation of PKN into its components

In order to disaggregate PKN into its interest rate (ιKN) and depreciation rate interest rate (δKN) components, first the price of KM and its components is estimated. Then the principle of equivalence is applied, as outlined below. Because equation (1) is used as the basis for this, the price of KM is denoted as PKMS and this can be estimated from the adjusted value of the operating surplus, OSS. Thus,

The IANA also provides data on the consumption of fixed capital [KMC(t)]. Hence, the depreciation rate of KM for any given year can be directly estimated from the IANA as:

It is now possible to estimate the interest rate component of PKMS as a rate that is implied from the IANA:

It can now be assumed that the interest rate of KN is proportionately adjusted by the ratio [PKN(t)]/[PKM(t)]. Thus,

Hence, the depreciation rate of KN can be found by using the estimate for PKN: