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«ENVIRONMENT AND DEVELOPMENT The Changing Wealth of Nations ENVIRONMENT AND DEVELOPMENT A fundamental element of sustainable development is ...»

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Stage 2. Schooling but no work:

–  –  –

where senr is school enrollment rate and subscript e+1 refers to the grade level of enrollment, the probability that an individual with educational attainment e is enrolled in education level e+1.

Stage 3. Both schooling and work:

–  –  –

Estimation is conducted in a backward recursive fashion, from those age 75, 74, 73, and so forth to those age 0. Expectations about future relative wage rates, enrollment, annual market income, and survival come from data on cohorts of older individuals alive in the year the estimates are constructed. Per capita estimates are multiplied by population estimates to derive total market human capital for each year. Divisia indexes are constructed using nominal human capital as weights and population growth rates to create estimates of real human capital.

Expected Lifetime Earnings of Individuals To measure lifetime earnings of all individuals in the population, future incomes are projected, discounted back to the present,5 and weighted for each individual by the age- and gender-specific probability of survival. This is done in two steps. First, imputed earnings equation parameters are used to estimate earnings for all individuals in a given year by applying the Mincer (1974) equation to micro-survey data.6 Mincer’s approach has been widely adopted in empirical research on earnings determination for numerous countries and

time periods:

exp2 in(inc) e exp u

where in(inc) is the logarithm of earnings; e is years of schooling; exp and exp2 are, respectively, years of work experience and experience squared; and u is a random error.

The coefficient is an estimate of the return to an extra year of schooling, and and measure the return to investment in on-the-job training. To ensure that income estimates are as accurate as possible, the parameters are estimated separately for the rural and urban populations by gender and year, using survey data in selected years. These are used to impute values for missing years over the period 1985–2007.

Second, earnings are derived for future years until retirement by assuming that real earnings grow at the same average annual rates of growth as labor productivity. Growth in labor productivity for the period 1978 to 2007 was 4.1 percent and 6.0 percent per year in the rural and urban sectors, respectively. It is assumed that labor productivities (and, hence, the real income) will continue to grow annually at these average rates in the future.


Annex 6.2: Recasting the Data to be Consistent with World Bank Methodology The work by Li et al. (2009) was recalculated to make it more consistent with the World Bank database in two ways. First, results are reported here in constant 2005 U.S. dollars, while Li et al. carried out their analysis in 1985 yuan. Some of the trends over time may differ depending on the currency used for analysis.

For example, Li et al.’s analysis in constant yuan found much higher growth in human capital than did the same analysis carried out in 2005 U.S. dollars.

More important, a social discount rate was derived using the same methodology used for the World Bank wealth accounts; this rate is considerably higher than the one used in the original report by Li et al. (2009). That study used a very low discount rate of 3.14 percent, based on the average real return on long-term government bonds from 1996 to 2007. However, there is little reason to think that the return on bonds reflects the real social discount rate, because financial markets are subject to control. A low discount rate leads to very high human capital estimates.

The discount rate used in World Bank calculations was derived from the Ramsey formula (see appendix A). Under the Ramsey formula, r, the discount rate, equals the pure rate of time preference,, plus the elasticity of utility with respect to consumption. The pure rate of time preference is assumed to be 1.5 percent, while the elasticity of utility with respect to consumption is assumed to be 1. The annual growth of real per capita consumption in China from 1970 to 2008 has been 6.76 percent. This results in a social discount rate for China of

8.26 percent. A high discount rate substantially lowers the present value of future earnings, resulting in much lower estimates of human capital.


Notes 1 See, for example, Cai and Wang (1999), Hu Angang (2002), Zhou Ya (2004), Hou and Cao (2000), and Hu Yongyuan (2005). Zhang (2000) and Qian and Liu (2007) calculated China’s human capital stock based on total investment (cost side); Zhu and Xu (2007) and Wang and Xiang (2006) estimated human capital from the income side. Zhou Delu (2005) and Yu (2008) used a weighted average of human capital attributes to construct a measurement.

2 See Stroombergen, Rose, and Nana (2002) for a comprehensive survey of methodologies.

3 This is the estimate for intangible capital.

4 There may also be differences in the working lifespan over which human capital is generated, but this effect is likely to be small relative to the expected rate of growth in earnings.

5 A discount rate of 3.14 percent was used, equivalent to the average real return on 10-year government bonds from 1996 to 2007.

6 Data used to estimate the Mincer equations come from two well-known household surveys in China: the annual Urban Household Survey and the China Health and Nutrition Survey (the latter covers both rural and urban households).

References Cai Fang and Wang Dewen. 1999. “The Sustainability of China’s Economic Growth and Labor Contributions.” Economic Research (in Chinese) 10: 62–68.

Démurger, Sylvie. 2001. “Infrastructure Development and Economic Growth: An Explanation for Regional Disparities in China?” Journal of Comparative Economics 29 (1): 95–117.

Fleisher, Belton, and Jian Chen. 1997. “The Coast-Noncoast Income Gap, Productivity, and Regional Economic Policy in China.” Journal of Comparative Economics 25 (2): 220–36.

Fleisher, Belton, Haizheng Li, and Min Qiang Zhao. 2010. “Human Capital, Economic

Growth, and Regional Inequality in China.” Journal of Development Economics 92 (2):


Graeker, Mads. 2008. “Sustainable Development and Changes in National Wealth for Norway in the Period from 1985 to 2007.” Statistics Norway, Oslo.

Gu, Wulong, and Ambrose Wong. 2009. “Human Development and Its Contribution to the Wealth Accounts in Canada.” Paper presented at the Canadian Economics Association Annual Conference, Toronto, May 29.

Hou Yafei and Cao Yin. 2000. “Analysis of the Quality of Human Capital Stock.” Chinese Journal of Population Science (in Chinese) 6: 43–48.

Hu Angang. 2002. “From the Most Populous Country to a Country with Great Power of Human Capital: 1980–2000.” Chinese Journal of Population Science (in Chinese) 5: 1–10.

Hu Yongyuan. 2005. “Human Capital and Economic Growth: A Co-integration Analysis.” Science and Technology Management Research (in Chinese) 4: 88–90.

Jorgenson, Dale W., and Barbara M. Fraumeni. 1989. “The Accumulation of Human and Nonhuman Capital, 1948–84.” In The Measurement of Saving, Investment and Wealth, ed.

R. Lipsey and H. Tice, 227–82. NBER Studies in Income and Wealth, vol. 52. Chicago:

University of Chicago Press.


———. 1992a. “Investment in Education and U.S. Economic Growth.” Scandinavian Journal of Economics 94, supplement: S51–70.

———. 1992b. “The Output of the Education Sector.” In Output Measurement in the Service

Sectors, ed. Z. Griliches, 303–41. NBER Studies in Income and Wealth, vol. 56. Chicago:

University of Chicago Press.

Keeley, Brian. 2007. Human Capital: How What You Know Shapes Your Life. Paris:

Organisation for Economic Co-operation and Development.

Li, Haizheng, Barbara M. Fraumeni, Zhiqiang Liu, and Xiaojun Wang. 2009. “Human Capital in China.” NBER Working Paper 15500, National Bureau of Economic Research, Cambridge, MA.

Mincer, Jacob. 1974. Schooling, Experience, and Earnings. New York: Columbia University Press.

Qian Xuya and Liu Jie. 2007. “Empirical Study of Human Capital in China.” Statistic Research (in Chinese) 3: 39–45.

Stroombergen, Adolph, Dennis Rose, and Ganesh Nana. 2002. “Review of the Statistical Measurement of Human Capital.” Statistics New Zealand, Wellington.

Wang Dejin and Xiang Rongmei. 2006. “Estimates of Human Capital Stock in China.” Statistics and Decision (in Chinese) 5: 100–102.

Yu Shujing. 2008. “Comprehensive Evaluation and Dynamic Analysis on China’s ProvincialLevel Regional Human Capital.” Modern Management Science (in Chinese) 4: 36–37.

Zhang Fan. 2000. “Estimates of Physical Capital and Human Capital in China.” Economic Research (in Chinese) 8: 66–71.

Zhou Delu. 2005. “Population-Based Indicators of Human Capital Accounting Theory and Empirical Study.” Chinese Journal of Population Science (in Chinese) 3: 56–62.

Zhou Ya. 2004. “Study on the Distribution Differences of China’s Human Capital.” Education & Economics (in Chinese) 2: 17–20.

Zhu Pingfang and Xu Dafeng. 2007. “Estimation of Human Capital in Chinese Cities.” Economic Research (in Chinese) 8: 84–95.


Linking Governance to Economic Consequences in

Resource-Rich Economies:

EITI and Wealth Accounting


wealth and is a principal source of income for many developing countries. At first glance, resource-rich economies appear to have an economic advantage over less-well-endowed countries because natural resources, especially oil, gas, and minerals (referred to hereafter as extractives), can provide funds to finance rapid development and poverty reduction. But the large incomes and foreign exchange generated by these exports must be carefully managed in order to avoid the “resource curse”—the paradox that such riches do not always lead to long-term, inclusive, and equitable prosperity and can even undermine development outcomes.

It has been argued that the resource curse is caused by several factors, some related to macroeconomic management, and others to political economy and governance.1 The major problems include (a) currency appreciation that can reduce the competitiveness of nonextractive exports, (b) more difficult macroeconomic management due to volatile commodity prices, (c) inefficient management of the extractive sector, (d) corruption and serious political conflicts over rent capture and management of revenues generated by the extractive sector, and (e) dissipation of rents on current consumption rather than investment. Evidence has shown that the economic performance of lessdeveloped countries is often inversely related to their natural resource wealth.


However, this relationship is not deterministic: some countries such as Chile and Botswana have done well with their natural capital. Having the right policy matters, and wealth accounting can enrich our understanding of the context and thereby improve policy making.

The overarching development challenge for resource-rich economies is to transform nonrenewable natural capital into other forms of productive wealth, so that once the extractive wealth is exhausted there are other income-generating assets to take its place. Mining is not sustainable, but the revenue from extractive sectors can be invested in other forms of wealth, such as infrastructure, human capital, renewable natural capital, and institutions (social capital), to build economies that are sustainable.

To achieve this transformation requires effective policy in three areas:

■ Policies to promote efficient resource extraction in order to maximize resource rent generated by the extractive sector ■ A system of taxes and royalties that allows government to recover equitable and proportionate shares of rent ■ A clear policy for investment of resource rent in productive assets This last point is especially important: the analysis of wealth accounts in earlier chapters shows that to achieve sustainable economic development, income from nonrenewable resources must be invested, not used to fund consumption.

Getting policy right in all three areas presents a considerable challenge, cutting across a broad swath of the economic and political landscape. There are areas where the best policy is relatively well understood but implementation is difficult. Regarding recovery of resource rent, for example, Hilson and Maconachie (2009) and Campbell (2003) show that African governments receive negligible shares of mining revenues compared to the shares of the (usually foreign) mining companies. For example, just 1.7 percent of the value of gold that companies mined in Ghana from 1990 to 2002 went to the Ghanaian treasury via royalties and corporate income taxes (Campbell 2003). This implies that policy makers should be just as concerned with ensuring that states receive a fair share of revenues as they are with setting macroeconomic policy.2 Regarding policies on investment of rents, the best path may depend on a variety of factors, and more analytical work is needed. For example, in a very poor country, should all rent be invested, or should some of it be used for current consumption to alleviate extreme poverty? Should the rent be managed entirely by a dedicated government investment fund, as in Norway, or should part of the rent be redistributed directly to citizens in order to promote private investment, as is done with oil revenues in the U.S. state of Alaska? If the revenues are managed by government, how should government balance investment in public infrastructure, support for domestic private sector development, and investment for the highest return even if that means investing abroad?


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