WPS5452
Policy Research Working Paper 5452
Comprehensive Wealth, Intangible Capital,
and Development
Susana Ferreira
Kirk Hamilton
The World Bank
Development Research Group
Environment and Energy Team
October 2010
Policy Research Working Paper 5452
Abstract
Existing wealth estimates show that in most countries successful in explaining the variation in output per
intangible capital is the largest share of total wealth. worker when they use intangible capital instead of
Intangible capital is calculated as the difference between human capital as a factor of production. This suggests
total wealth and tangible (produced and natural) capital. that intangible capital captures a broad range of assets
This paper uses new estimates of total wealth, natural typically included in the total factor productivity residual.
capital, and physical capital for a panel of countries to Human capital is an important factor, both in statistical
shed light on the constituents of the intangible capital and economic terms, in regressions decomposing
residual. In a development-accounting framework, intangible capital.
the authors show that factors of production are very
This paper--a product of the Environment and Energy Team, Development Research Group--is part of a larger effort
in the department to extend the national accounts to include more comprehensive measures of the wealth of nations.
Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted
at khamilton@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Comprehensive Wealth, Intangible Capital, and Development
Susana Ferreira and Kirk Hamilton
Keywords: Comprehensive Wealth, Intangible Capital, Human Capital
JEL Classification: Q56, E21, E24
Ferreira: Department of Agricultural and Applied Economics, University of Georgia, 313 Conner Hall, Athens GA
30602, USA; +1 706 542 0086; sferreir@uga.edu. Hamilton: The World Bank, 1818 H St NW, Washington DC
20433, USA; +1 202 473-2053; khamilton@worldbank.org. This paper has benefited from comments received at the
World Congress on Environmental and Resource Economics, Montreal, June 29-July 2, 2010, and from Jon Strand.
The usual caveats apply.
Comprehensive Wealth, Intangible Capital, and Development
1. Introduction
It has been understood since at least the time of Irving Fisher (Fisher 1906) that income is the
return on wealth. But if we scale this idea up to the level of the national economy, we arrive at a
puzzle. If we measure wealth only as produced capital, we see from the national balance sheet
accounts of countries such as Canada that wealth is only a small multiple of gross national
income, implying unrealistically high rates of return on wealth.
Table 1 shows Canadian figures for 2009. The value of produced capital is less than three times
GNI, while net worth (the sum of produced capital, commercial land and net financial assets) is a
bit less than four times GNI the implicit rates of return on wealth are correspondingly high,
35.9% and 25.4%, respectively. Canadians appear to be very productive.
The `solution' to this puzzle, of course, is that the national balance sheets of the system of
national accounts (SNA) exclude many intangible1 asset values, such as human capital and the
value of social / institutional capital. Moreover, the Canadian balance sheets highlighted in Table
1 exclude the value of commercial natural resources.2 Since a `normal' rate of return on assets
should be on the order of 5%, a comprehensive measure of national wealth should be on the
order of 20 times national income.
Hamilton and Hartwick (2005) show how to estimate a comprehensive measure of national
wealth for a competitive economy with constant returns to scale. For production F F ( K , L, R )
with production factors K (produced capital), labor L, natural resource flow R, and interest rate r
(equal to the marginal product of capital), comprehensive wealth is given by
s
W K H S C ( s) e t
r ( z ) dz
ds (1)
t
That is, comprehensive wealth can be measured either by adding up asset values K, H (human
capital) and S (natural resource stock)3 or by measuring the present value of consumption C
along the competitive development path. The intuition behind expression (1) is that in the long
run a country must consume within its possibilities, which are given by the sum of all its assets.
1
The SNA has precise definitions for intangible fixed and intangible non-produced assets, which include items such
as mineral exploration expenditures and the value of patents. In this paper we use the term `intangible' to include all
non-physical, non-financial assets.
2
While SNA 1993 (United Nations 1993) requires the inclusion of the value of commercial natural resources in the
balance sheet accounts, to date only Australia has published such accounts.
3
Note that L and R are flows of inputs, measured in worker-hours or barrels of oil, whereas H and S are asset values
measured in dollars.
2
It is then possible to derive the following result from Hamilton and Hartwick (2005): if interest
rate r is constant, is the depreciation rate for produced capital, and FR R is the value of resource
depletion, then net income is just equal to the return on total wealth, i.e.
C K K FR R rW r C ( s) e r ( s t ) ds .
(2)
t
Given the difficulty in obtaining monetary estimates for intangible assets, in this paper we
compute intangible capital (IC) as a residual by subtracting the values of produced capital,
natural capital and net financial assets from the value of comprehensive wealth. Our estimates of
comprehensive wealth are calculated according to the RHS of (1) as the present discounted value
of future consumption. Human capital is therefore implicitly included in the intangible capital
residual along with institutional, social capital and other missing asset values, e.g. diamonds and
fisheries, for which data are not widely available.
For most of the countries in our sample, IC is the biggest contributor to total wealth; it
represents, on average, more than 60 percent of comprehensive wealth. The finding of a large
intangible capital residual is reminiscent of the finding in the development accounting literature
of large cross-country differences in total factor productivity (TFP), after controlling for physical
and human capital (see e.g. Klenow and Rodriguez Clare, 1997; Hall and Jones, 1999). The
conventional wisdom is that "more than half of the variation in income per capita results from
differences in TFP. And the same applies to differences in growth rates of income per capita:
more than half of the variation results from differences in TFP growth. Students of economic
growth have concluded from this evidence that, in order to understand the growth of nations, it is
necessary to develop a better understanding of the forces that shape total factor productivity"
(Helpman, 2004, p34).
In Section 3, we bridge the growth/development accounting literatures and the wealth accounting
literature. First, in the tradition of the development accounting literature, we show that
differences in physical capital, natural capital, and human capital per worker explain only
between 20 and 43 percent of the variation in output per worker in our sample. However, if we
use intangible capital instead of human capital, variation in factors of production explains 97
percent of the variation in output per worker. This is precisely what we would expect if
intangible capital is indeed measuring a wide range of assets (human, social, institutional, etc.).
The contribution of TFP to explaining the variation of output per worker should be small - zero
in the limit. This result confirms what we know from the definition of intangible capital: it
encompasses not only human capital but any other assets, such as institutional or social capital,
which constitute the residual left when produced and natural capital are subtracted from total
wealth. Second, in the tradition of the growth accounting literature, we estimate a production
function from which the shares of produced, natural and intangible capital in production can be
derived. Although both these approaches shed light on the contribution of intangible capital to
3
explaining output per worker, they do not directly address the question of what constitutes
intangible capital.
In Section 4, we directly investigate the composition of intangible capital, by analyzing the
relative contributions of human capital and institutional/social capital using regression analysis.
As in World Bank (2006), potential correlation between the regressors and the error term
(arising, for example, from measurement error or omitted variable bias) is an issue in the
empirical estimation. The intangible capital residual includes, by construction, (i) any assets not
accounted for in the tangible capital estimates, for example, some minerals (e.g. diamonds,
platinum), fisheries and groundwater, not accounted for in the calculation of natural capital, and
(ii) any errors in the estimation of tangible capital and/or of total wealth.
Unlike World Bank (2006), we have a panel dataset with observations for 115 countries for the
years 1995, 2000 and 2005. The use of country and time fixed effects helps us to mitigate
omitted variables bias as long as the unobserved variables are constant over time and/or across
countries. In addition, we take a number of steps to reduce measurement error in the variables
used in the analysis. Our indicator of intangible wealth accounts for net foreign financial assets;
we use several indicators of human capital that are a function of health status in addition to years
of schooling; we analyze the robustness of the results to alternative measures of institutional
capital and to the exclusion of outliers. Finally, we estimate an instrumental variables regression
in which differences in European settler mortality rates (from Acemoglu, Johnson and Robinson,
2001), distance from the equator, and the percentage of the population speaking a Western
European language as their first language today (from Hall and Jones, 1999), are used as
instruments for current institutions and human capital. Overall, our results show that human
capital is an important factor, in both statistical and economic terms, in explaining intangible
capital.
2. Data
2.1 Intangible capital
We compute intangible capital for over 100 countries for the years 1995, 2000 and 2005 by
subtracting produced capital, natural capital and net foreign financial assets from total
(comprehensive) wealth. Our estimates of comprehensive wealth and its "tangible" components
are from World Bank (forthcoming).
Applying expression (1), World Bank (forthcoming) calculates comprehensive wealth as the
present discounted value of future consumption.4 To test whether these comprehensive wealth
4
The computation is performed for a time horizon of 25 years, which roughly corresponds to a generation.
Assuming that the elasticity of utility with respect to consumption is one and that consumption grows at a constant
4
estimates make sense, we use data from World Bank (2010) to calculate net income, and then
apply expression (2) in order to derive the implicit rate of return on comprehensive wealth in
each country. The distribution of rates of return is plotted in Figure 1, which shows that 80% of
the rates lie between 4% and 6%.
Produced capital stocks are derived from historical investment data using a perpetual inventory
model. Most natural resources are valued by taking the present value of resource rents--the
economic profit on exploitation--over their assumed lifetime. Resources considered include
energy, mineral, timber and non-timber forest resources, cropland, pastureland and protected
areas.
Finally, the wealth estimates in World Bank (forthcoming) account for ownership of capital. The
interest payments derived from foreign financial assets or obligations will affect future levels of
consumption of the country's residents and, by construction, total wealth. The adjustment to the
wealth estimates to account for ownership is particularly important in the light of the acceleration
in cross border asset trade observed in the last decades.5
By definition intangible capital includes any asset other than physical capital, natural resources
and net foreign assets. It thus includes human capital--the sum of knowledge, skills, and know-
how possessed by the population. It includes the institutional and social infrastructure of the
country. It also includes resources omitted in the natural capital calculations such as subsoil
water, diamonds, and fisheries.6
Table 2 presents descriptive statistics of total wealth and its components. Our sample includes
an unbalanced panel of 115 developed and developing countries for years 1995, 2000 and 2005
(total of 315 observations). All the numbers in Table 2 are expressed in per capita terms in
constant 2005 US$. For most countries in our sample, intangible capital is the largest component
of total wealth; on average it constitutes 64 percent of total wealth, although it varies from a
minimum of 5 percent in Uganda to a maximum of 90 percent in St. Lucia. Figure 2a shows that
intangible capital is positively correlated with income. This is not surprising. As shown in Panel
B of Table 2, except for net foreign assets, richer countries have, on average, more capital than
rate, the expression for total wealth can be simplified to W t
C (t ) e ( s t ) ds , where , the pure rate of time
preference, is assumed to equal 1.5 percent. For more details please see World Bank (forthcoming).
5
An indicator of financial integration used by Lane and Milesi-Ferretti (2007), the sum of external assets and
liabilities over GDP, has risen steadily in both developing and developed countries, more markedly in the latter,
where it increased by a factor of 7, from 45% in 1970 to over 300% in 2004, with a clear acceleration in the mid
1990s.
6
Owing to data limitations no explicit value for ecosystem services is estimated. However, the services provided by
ecosystems, such as the hydrological functions of forests and the pollination services of insects and birds, are
indirectly captured in the natural wealth estimates through the values of cropland and pastureland.
5
poorer countries for each capital category.7 The share of intangible capital in total wealth is also
positively correlated with income, as shown in Figure 2b. For the OECD countries, this share is
on average 78 percent and always larger than 60 percent. Panel B in Table 2 also shows IC
increasing over time, in absolute value and as a percentage of total wealth. The z-statistic for a
test of a difference between the means in the IC shares in years 2005 and 1995 is 1.12.
2.2 Human capital
The analysis in World Bank (2006) suggests that the value of the stock of human capital is a
large component of a country's wealth. This fits well with the intuition that an important asset of
a country is its people. Not surprisingly, the statistical agencies in some developed countries are
starting to systematically compile human capital accounts to monitor the evolution of the stock
of human capital. A method typically employed to value the stock of human capital in dollars is
the lifetime income approach of Jorgenson and Fraumeni (1989, 1992a, 1992b). Under this
approach, human capital is valued as the net present value of the income flow it produces over an
assumed lifetime. In practice, the application of the method requires information on survival,
enrollment and employment probabilities as well as earnings, by sex, age and educational
attainment. Although these are relatively modest informational requirements, applying this
method to a cross section of countries is impracticable.
Most of the empirical studies studying the role of human capital on growth and development
accounting have focused instead on a cost-based approach to measure human capital. In order to
increase future labor productivity and future income, people forgo consumption and invest in
education. The human capital embodied in the labor force can then be seen as a function of
education. Even accepting education as a valid proxy for investment in human capital, the
relationship between education and human capital needs to be correctly specified.8 The current
consensus relies on a log-linear specification between earnings and years of schooling first
formulated by Mincer (1974), to express the human capital per worker as an exponential function
of the years of schooling, h e ( N ) , where the function (N ) represents the efficiency of a unit of
labor with N years of schooling relative to one with no schooling (for reviews see Krueger and
Lindahl, 2001; Woessman, 2003). We follow common practice and use ( N ) re N , where re is
the rate of return to education.
We made different assumptions regarding the returns to education. Our benchmark is re=8.5
percent return on years of schooling as in Arrow et al. (2010) and Klenow and Rodriguez-Clare
7
This is also true for aggregate natural resource stocks; the simple correlation between GNI per capita and the
natural resource stock per capita for the countries in our sample is 0.44.
8
For example, Klenow and Rodriguez-Clare (1997) show that using secondary school enrolment rates as proxies for
human capital in Mankiw, Romer and Weil (1992) exaggerates the role of human capital in explaining differences in
output per worker, as secondary enrolment varies more than other (preferable) measures of human capital. The
results of Mankiw Romer and Weil (1992) are not robust to expanding the human capital estimator to include
primary and terciary education or to using an alternative human capital estimator based on Mincerian regressions.
6
(1997, 2005) -this is the average of returns to education in Psacharopoulos and Patrinos (2004).
The results did not change when we adjusted the returns to education to account for differences
in educational quality across countries. In this case, ( N ) re QN , where Q is a country-specific
educational quality index, as in Hanushek and Kimko (2000).9
We also used two alternative data sources for years of schooling, s, as an indicator of the level of
educational attainment in the labor force: Barro and Lee (2001) and Cohen and Soto (2007).10 In
this paper we present the results for the human capital indicator based on Barro and Lee's years
of schooling. This reduces our sample to 112 countries and 309 observations. When the human
capital indicator was based in Cohen and Soto's N, the estimates were robust, if a bit less precise,
but the sample size further dropped, by 20 percent (from 112 to 88 countries, or from 309 to 253
observations).
Finally, we augment our indicator of human capital to account for the health of the population
and of the working force;
h Ah e ( N ) (3)
Ah can be expressed as Ah e ASR ASR as in Caselli (2005), where ASR is the adult survival rate.
Shastry and Weil (2003) argue that differences in health status proxied by adult mortality rates
map into substantial differences in energy and capacity for effort. Caselli (2005) finds that
correcting the standard human capital measures to account for health, proxied, as in our case, by
adult survival rates (ASR), increases the percentage of cross-country income variance attributed
to physical and human capital by almost one third. In Weil (2007) accounting for health
differences reduces the variance of log GDP per worker by 9.9 percent.
9
Hanushek and Kimko (2000) combine the results of cognitive achievement tests in mathematics and science into
two educational quality indexes. We take an average of the two indexes and, as Woessman (2003), normalize the
educational quality index for each country relative to the US. Woessman argues that the US is a good reference
country as the returns to schooling should be relatively undistorted in the competitive US labor market. Data are
available for 80 countries in our sample.
10
From both data sets we take the years of schooling in the population aged 15 and over because this age group
corresponds better to the labor force for most developing countries (the majority in our sample) than the population
aged 25 and over (Woessman 2003, p.256). The Barro and Lee dataset contains the average years of schooling in 5-
year intervals from 1960 to 1995 and projections for 2000. Values for 2005 were predicted by using a linear trend
fitted from the observations between 1960 and 2000. When a country-specific growth rate in the years of schooling
could not be calculated, the regional average growth rate was used. A visual inspection of the data suggests that a
linear trend is a good approximation. A caveat, however, is that educational attainment is asymptotically a constant,
and by fitting a linear trend we may overstate the educational attainment for the countries that have reached the
asymptote. This would affect mainly OECD countries. Our second source for schooling data (Cohen and Soto,
2007) suggests that this may not be a problem. Virtually all the developed countries with the exception of Norway
and Sweden exhibit more years of schooling in this second dataset for all the years considered, including 2005. For
Norway and Sweden, the largest difference between the two datasets is 0.52 years of schooling. Cohen and Soto
(2007) report educational attainment for the years 1990, 2000, and a projection for 2010. Since we are interested in
the years 1995 and 2005, averages of 1990 and 2000 values were taken for 1995 and of 2000 and 2010 for 2005.
7
Adult survival rates are available in consistent form for a large cross-section of countries from
11
the WDI. The ASR has the advantage of measuring survival during working years (15-60), and
thus seems likely to be a good measure of health during working years, which is what should be
most relevant for determining the level of output per worker (Weil 2007). Weil (2007) estimates
a value of ASR =0.653.
Table 2 reports descriptive statistics for years of schooling and human capital for all the
countries in the sample (Panel A) and subsamples according to level of economic development
and year (Panel B). Notice that while other capital assets in Table 2 are expressed in dollars,
human capital is the 'physical' human capital embodied in a worker as expressed in (3).
2.3 Institutional capital
As in World Bank (2006) our indicator for institutional capital is a rule of law index from
Kaufmann, Kraay and Mastruzzi (2009). It measures the extent to which agents have confidence
in and abide by the rules of society. In particular, it measures the quality of contract enforcement,
property rights, the police, and the courts, as well as the likelihood of crime and violence.12
In their Worldwide Governance Indicators (WGI) research project, Kaufmann, Kraay and
Mastruzzi, provide data on five additional dimensions of governance: voice and accountability,
political stability and absence of violence, government effectiveness, regulatory quality and
control of corruption. These indicators are highly correlated -the lowest pair-wise correlation
coefficient in our sample is 0.76 between political stability and regulatory quality.
An argument employed in World Bank (2006) to use the rule of law indicator, is that it captures
well some of the features of a country's social capital, in particular trust. The correlation between
a generalized trust indicator in Paldam and Svendsen (2006) and rule of law, 0.51, is larger than
for other indicators of governance.
As an alternative, we averaged the six governance indicators. Many previous studies have
averaged or summed governance indicators, with the rationale being that averaging reduces
measurement error if the indicators pertain to similar underlying concepts of governance and
have independent errors (e.g. Wheeler and Mody, 1992; Mauro, 1995; Chong and Calderon,
2000). In Section 4 we report results for the rule of law index but the results were robust to the
use of an aggregate indicator.
11
Adult mortality rates (AMR) are calculated as the weighted average of male and female adult mortality rates
(series SP.DYN.AMRT.MA and SP.DYN.AMRT.FE, respectively) available from the WDI (2008). For Australia,
Canada, Germany, Italy and the US, 2005 rates are missing so 2004 values are used instead. ASR=1-AMR.
12
In the original dataset, the rule of law index ranges between -2.69 and 2.12; we rescaled it between 1 and 100.
8
3. Bridging the development accounting and wealth accounting literatures
There is an obvious parallel between our finding of a large intangible wealth residual and the
finding in the growth and development accounting literatures that a residual, TFP, accounts for
more than 50 percent of the variation in income per capita.
Development accounting tries to identify the basic determinants of income levels. Conceptually,
it can be thought of as quantifying the contributions of factor inputs and productivity to
economic performance:
Y=F(Factors, Productivity).
More specifically, development accounting calculates the relative contributions of measurable
input quantities physical and human capital- and the total factor productivity (TFP) residual in
explaining cross-country differences in income levels.
In performing a development accounting exercise, the starting point is a Cobb-Douglas
production function, in which we include natural resources, S, as a factor of production, in
addition to physical capital, K, and human capital (H=hL). If we assume Hicks-neutral
productivity, total output can be expressed as:
Y AK S (hL) (1 ) , (4)
and output per worker, y Y / L , as:
y Ak s h (1 ) (5)
where s S / L is the natural resources to labor ratio, k K / L is the physical capital to labor
ratio, h is the 'physical' human capital per worker, and L is the number of workers.
3.1 Explaining variation in output per worker with factors of production
The typical question in the literature is how much of the variation in y can be explained by
variation in the observables k, s and h, and how much is attributed to differences in A. As in
Caselli (2005), if we define y factors k s h (1 ) , we can rewrite (5) as
y Ay factors . (6)
We can measure the "success" of the factors-only model at explaining cross-country income
differences using two standard indicators based on the tradition of variance decompositions:
var[log( y factors )] var[log( y factors )] cov[log( A), log( y factors )]
SuccessC , and SuccessKR .
var[log( y )] var[log( y )]
9
The first expression, assumes that all the countries have the same level of TFP, so that A is a
constant. The second expression, proposed by Klenow and Rodriguez-Clare (1997), allows
countries to have different levels of efficiency, and splits the contribution from the covariance
term evenly between A and yfactors.
We have data on k, s, and h.13 We use the WDI GNI series to compute output per worker. For the
factor shares, most of the previous empirical studies take =1/3 and ignore natural resources as a
factor of production (i.e. =0). A notable exception is the study of Tzouvelekas, Vouvaki and
Xepapadeas (2007). They introduce the environment, proxied by CO2 emissions, into their
computations of TFP. The implicit shares of the environment in output in their analysis can be as
high as 14%. For our analysis we take a more conservative value of =10%, but the results in
Table 3 are robust to taking =0.
The first column of Table 3 shows the results of the estimation of the two measures of success,
and their components. The fraction of the variance of income explained by observed
endowments (k,s, h) is 0.20 if we use the first standard measure of success, and 0.43 if we use
Klenow and Rodriguez-Clare's. The Klenow and Rodriguez-Clare measure assigns a greater role
to factors of production, but their contribution is still under 50 percent.
Compare equation (5), with equation (7):
y Ak s ic (1 ) (7)
Equation (5), even including natural resources, is more or less conventional. Only produced,
natural and human capital are represented in the equation, so that the TFP factor A picks up the
contribution to income of all `missing' assets.
For equation (7) we have included intangible wealth as the third production factor. If ic actually
is measuring a wide range of assets (human, social, institutional, etc.), then the contribution of A
to explaining the variation of output per worker should be small zero in the limit.
So one way to bring together the wealth accounting and growth accounting literatures would be
to estimate the two success indicators and examine the estimated contributions of yfactors, (with ic
instead of h), and A to see if the contribution of A is indeed small. The second column of Table 3
shows that to be case. When ic is the third factor of production instead of h, both measures of
success of the factors-only model are 97 percent.14
13
We take the estimates of total physical capital and natural capital wealth and divide them by the labor force series,
(SL.TLF.TOTL.IN), to obtain estimates of k and s.
14
We repeated the calculations in Table 3 for each of the years, 1995, 2000, and 2005, independently, and for the
subsamples of OECD and non-OECD countries. The results, not reported, are available from the authors. The
relative contributions of A and yfactors are constant over time and similar to those in Table 3. The differences between
OECD and non-OECD countries are minimal when factors of production are conventionally defined, but if ic is used
10
These measures of success are certainly large, but it must be recalled that intangible capital
includes, by construction, all missing factors of production. The result therefore makes sense. By
comparison, the traditional development accounting literature invokes TFP as the factor which
explains all of the residual variation in output across countries after capital and labor factors have
been taken into account.
3.2. Explaining output per worker
By taking logarithms of (5), we obtain:
ln( y ) ln( A) ln(k ) ln(s) (1 ) ln(h) . (8)
In the tradition of the development accounting literature, equation (8) can be estimated
econometrically.
ln( yit ) i t k ln(kit ) s ln(sit ) h ln(hit ) it . (9)
The parameters k , s and h represent the contributions of physical capital, natural capital and
human capital to aggregate productivity, gamma allows for differences in total factor
productivity across countries and lambda for differences in TFP over time. Notice that unlike the
approach in the previous section, equation (9) treats k , s and h as free parameters, which adds
a layer of testability to the theory. Equation (9) is similar to equation (7) in Topel (1999).
We estimate (9) using country and time fixed effects. Thus, we do not need to assume that
capital intensities are orthogonal to productivity differences across countries or over time. A
widespread criticism to the estimation of (9) with cross-section data (as is done in Mankiw,
Romer and Weil, 1992) is that if more productive (higher A) countries are also more intense
users of capital, the causal contribution of observed inputs will be overstated. By using country-
specific fixed effects, we avoid this potential bias.
Tzouvelekas, Vouvaki and Xepapadeas (2007) also estimate a function similar to (9). They note
that an additional problem when estimating a production function is the potential endogeneity of
the inputs which results in inconsistent estimators. However, they also note that, as shown by
Mundlak (1996), under constant returns to scale, OLS estimates of a k-input Cobb-Douglas
production function in average productivity form, with regressors in input-labor ratio form (as
we have in equation 9), are consistent.
The estimated elasticities from the estimation of equation (9) are reported in the first column of
Table 4. The share of physical capital is 0.4 and statistically significant. However, the shares for
instead of human capital, the measures of success are larger in non-OECD countries than in OECD countries (0.94
and 0.82, respectively, for SuccessC).
11
human capital and natural resources are not statistically significant. In column (2) of Table 4, we
use intangible capital as the third factor of production instead of human capital. The estimate for
the share of physical capital, 0.32, is similar to that reported in Tzouvelekas, Vouvaki and
Xepapadeas (2007). Our estimate for the share of natural capital, 0.07, is larger than theirs
(0.04). An explanation could be that their indicator of natural capital, which only refers to CO2
emissions, is narrower than ours. Finally, our estimated share of intangible capital, 0.18, is also
larger than their estimated share for human capital, 0.07. This is what we would expect. Our
intangible capital variable is broader and arguably encompasses their indicator of human capital.
The results in column (2) seem to be driven by non-OECD countries. In column (3) we report
estimates for the subsample of non-OECD countries. The results are similar to those in column
(2) for the whole sample. The results for the subsample of OECD countries, reported in column
(4), are strikingly different. The share of physical capital is 0.21 and statistically insignificant,
suggesting that physical capital plays a smaller role than in non-OECD countries, while the share
of intangible capital increases to 0.5. The estimated share of natural capital is reduced and is
statistically insignificant. These results are consistent with the poor performance of savings
measures that exclude human capital (or other intangible assets) accumulation in OECD
countries reported in Ferreira and Vincent (2005).
4. Investigating the composition of intangible capital
The previous section suggests that intangible capital is indeed measuring a wide range of assets:
human capital and other forms of intangible capital grouped under the heading of total factor
productivity. In this section, we estimate the relationship between intangible capital and our
proxies of human and institutional capital econometrically to analyze their relative contributions.
We assume that
ic=f(h, g), (10)
where ic is intangible capital per capita, h is our indicator of 'physical' human capital per worker,
and g is an indicator of institutional capital (a rule of law index).
Compared to World Bank (2006), we do not consider foreign remittances. Workers' remittances
received by a country represent compensation to its human capital residing abroad, but they are a
flow (yearly payments) rather than a stock.15
By definition, intangible wealth is the sum of sources of wealth other than physical and natural
capital. We thus estimate a linear specification,
15
It could be argued that even though they are a flow, remittances are correlated with the stock of human capital of a
country. We repeated all the regressions including remittances per capita as an additional explanatory variable and
the results we report below did not change; remittances were not significant in any specification and the coefficients
on the other variables were robust to its inclusion.
12
icit i t hit g it it , (11)
in which is the price of a unit of h, is the price of one point in the rule of law index, g, i is
a country-specific intercept and t is a time dummy for t=2000 and 2005. We also estimated the
relationship in (10) using a log linear model, and a log-log model, but (11) was our preferred
specification.
4.1 Econometric issues with the estimation of equation (11)
Intangible capital is, by definition, a broad concept, and our specification in (11) is very
parsimonious with only two regressors, h and g. If h and g are correlated with the regression
error term, then our estimators would be inconsistent. This potential correlation between
regressors and error term can stem from various sources including omitted variables, errors in
variables (measurement errors in the regressors) and simultaneous causality.
In order to deal with the first issue, omitted variable bias, we introduce (country- and time-) fixed
effects in the regression. The combined time and country fixed effects regression model
eliminates omitted variable bias arising from both unobserved variables that are constant over
time, and from unobserved variables that are constant across countries. The country dummies
( i ) capture time-invariant country-specific traits while the time dummies ( t ) capture common
shocks to the intangible capital across countries.
Measurement error, on the other hand, typically results in attenuation bias, so, provided this were
the only source of correlation, we can interpret our coefficients for h and g as lower-bound
estimates of their true effects.
The third source of correlation, simultaneous causality, is causality running from our dependent
variable, intangible capital, to h and g as well as from h and g to intangible capital. An advantage
of our analysis is that intangible capital, by construction, is forward looking. Total wealth is
calculated as the present discounted value of future consumption flows (see Section 2.1 for
details on its derivation) and by definition cannot determine past educational and institutional
outcomes.
The discussion above should go some way to allaying endogeneity concerns. Alternatively, we
could use instrumental variables (IV). In practice, finding good instruments, i.e. exogenous
sources of variation that affect intangible capital only through their effect on h and g and not
directly, is difficult.
Acemoglu, Johnson and Robinson (2001) use differences in European settler mortality rates as
an instrument for current institutions. We can use this variable as an instrument for the rule of
law index, but we need at least an additional instrument for human capital. Hall and Jones
13
(1999) propose using the geographical and linguistic characteristics of an economy as
instrumental variables for their social infrastructure. We focused on distance from the equator
and the percentage of the population speaking a Western European language as their first
language today.
Another problem with the IV strategy is that it limits the analysis to a cross-section as the
instrumental variables are available only at a point in time. We chose the year 1995 since the
data for years of schooling for 1995 are arguable measured with less error than for 2000 and
2005, where extrapolations were used. The first instrumental variable, logarithm of settler
mortality rates, further limits the analysis to 59 countries that are ex-colonies. Thus, the results
from the IV analysis should be interpreted with caution and considered only tentative.
4.2 Panel estimation results
Table 5 shows the results from the estimation of equation (11). The columns differ in terms of
the estimation technique and the variable measuring human capital per worker. For column (1)
we replicate the results in World Bank (2006); that is, we just pool the observations over the
years 1995, 2000, and 2005 and use OLS so that i i and t 0 t . The indicator of
human capital is simply years of schooling from Barro and Lee (2001). In column (2) we also
use OLS on the pooled sample, but the indicator of human capital is given by equation (3). Both
the coefficients on our indicator of institutional quality and years of schooling exhibit positive
signs and are highly significant. An additional point in the rule of law index is associated with an
increase in intangible wealth per capita of around $3,000. An additional year of schooling is
associated with an increase in intangible wealth per capita of over $11,000 and a unit increase in
human capital per worker with an increase in intangible wealth per capita of around $45,000.
The regressions in columns (1) and (2) also include income dummies relative to high income
countries. All of them are negative and statistically significant. This means that countries in
each income group have a lower level of intangible capital than high income countries which is
consistent with the positive relationship between income and intangible capital shown in Figure
2. The coefficients are large in magnitude; around $100,000 for all income categories.
In columns (3) and (4) we introduce country fixed effects in the model. The null hypothesis of
country-specific intercepts equal to zero is rejected for both regressions. Compared to columns
(1) and (2) the coefficients on rule of law are reduced and, although still positive, they are no
longer statistically significant. The estimated coefficients of human capital in both columns
increase in size and remain highly significant. Note that of the three components of h in (3)years
of schooling, returns to education and adult survival rates, returns to education does not vary
over time or across countries. Thus, estimation of (11) using country fixed effects relies on the
within-country variation of years of schooling and adult survival rates.
14
In columns (5) and (6), we introduce time fixed-effects in addition to country fixed effects in the
regression, as we also reject the joint null hypothesis that the time dummies are equal to zero, in
this case at a 2 percent significance level or better. This is our preferred specification, since, as
discussed, time and country fixed effects address omitted variable bias arising from both
unobserved variables that are constant over time, and from unobserved variables that are constant
across countries. Compared to column (3), in column (5) years of schooling becomes
insignificant, suggesting that the time dummies capture the variation of years of schooling over
time. This is not surprising; a linear trend fits years of schooling well for most countries (see
footnote 8). h, which accounts for adult survival rates, remains significant when we include both
types of fixed effects.
The estimated coefficient on human capital in column (6) is large; a one-point increase in the
human capital indicator is associated with an increase in intangible wealth of over $92,000. This
implies a difference of intangible wealth of over $320,000 per capita between the country with
the lowest human capital (Mozambique with an average value of 1.54 over the sample period)
and the country with the highest human capital (Norway with an average value of 5.08). Finally,
the estimates for the time dummies are positive and statistically significant indicating that
intangible capital per capita has increased over time, as shown in Table 2 Panel B.
The results in Table 5 are robust to a number of changes. First, we repeated the regressions using
alternative measures of human capital. In particular, we considered an indicator that accounted
for differences in the quality of schooling across countries (see footnote 7). We also repeated the
regressions with human capital measures based on Cohen and Soto's (2007) years of schooling.
Second, we used an average index of governance instead of the rule of law index as indicator of
institutional capital. Third, we excluded outliers and influential observations as identified by
Cook's distance. Fourth, we introduced interaction terms between the indicator of institutional
capital and human capital.
We used the estimates in column (6) to compare actual intangible capital with the in-sample
predictions from the model. Results of the calculations are available upon request. The positive
and large contributions to intangible wealth from human capital and from common shocks to all
the countries (from the time dummy coefficients) contrasted with the negative (and statistically
significant for non-OECD countries) country-specific intercepts.
4.3 IV estimation results
Table 6 reports the results of the IV analysis. The first panel in Table 6 shows the association
between the three instruments (log of settler mortality rates, distance from the equator and
fraction of a country's population whose first language is Western European) and the two
independent variables, h and g. Distance from the equator and the percentage of the population
whose first language is a Western European language have the expected positive signs in both
15
regressions and are highly statistically significant in the human capital regression. Settler
mortality rate has the expected negative sign and is statistically significant at a 1 percent level in
both regressions. Overall, the instruments are strongly associated with the rule of law and human
capital variables. Together the three instruments explain approximately 52 percent of the
variation in our institutions variable and 62 percent of the variation in our human capital
variable.
A second desirable property in the instruments is that they affect the dependent variable, in our
case, intangible capital, only through their impact on the rule of law index and human capital; i.e.
the instruments are not correlated with the error term in the equation explaining intangible
capital. By employing three instruments, we can test this assumption.16 Panel B in Table 6
reports the results from two tests, the Sargan and Basman tests. In both cases we cannot reject
the null that the instruments are exogenous.
Both rule of law and human capital have positive coefficients in the second stage regressions in
Panel C Table 6. For rule of law, its magnitude, around $2,300, is similar to that in the OLS
regressions in Table 5, while for human capital, at around $80,000, it is between the OLS and
fixed effect estimates in Table 5. They are estimated less precisely, however, and neither is
statistically significant at conventional levels; human capital is significant at a 32 percent
significance level.
5. Conclusions
For most countries the sources of wealth reported in the standard system of national accounts
(SNA) are just a small percentage of their comprehensive wealth. Based on economic theory, in
this paper we use a measure of comprehensive wealth computed as the present discounted value
of future consumption to analyze what conventional accounting systems are leaving out. Our
estimates of comprehensive wealth are on the order of 20 times net national income; the implicit
rate of return on comprehensive wealth for 80 percent of the countries in our sample lies between
4 and 6 percent.
We calculate intangible capital as a residual, by subtracting the values of assets that the SNA
measures (produced capital and net financial assets), and estimates of the value of the stock of
natural capital, from the value of comprehensive wealth. For most of the countries in our sample,
16
This assumption, known as the exclusion restriction, is not testable in exactly identified models (i.e. models in
which the number of instruments is equal to the number of endogenous covariates). If the model is overidentified
(so that there are more instruments than endogenous covariates, as in our case with 3 instruments and 2 endogenous
covariates), there is information available which may be used to test this assumption.
16
and especially for OECD countries, intangible capital is the largest constituent of total wealth.
For the whole sample, it represents on average more than 60 percent of total wealth.
Our measure of intangible capital therefore captures a broad range of assets not only human
capital, but also social/institutional and other forms of capital. Intuitively, we would expect
intangible capital to be related to total factor productivity (TFP), and part of the analysis in this
paper is concerned with verifying this intuition. Using a development accounting framework, we
applied variance decomposition techniques to measure the extent to which factors of production
explain the variation in output per worker. When the factors of production are physical capital,
natural capital, and (physical) human capital per worker, they explain only between 20 and 43
percent of the total variation in output per worker in our sample the balance of the variation is
assumed to be explained by TFP. When the factors of production are physical capital, natural
capital, and the intangible capital residual (instead of human capital), the variation in factors of
production explains 97 percent of the total variation in output per worker the balance of the
variation explained by TFP must therefore be extremely small.
There is strong evidence, therefore, for a link between the non-human capital portion of
intangible capital and TFP. This is important given the emphasis given to TFP growth, versus
increases in factor inputs, in the growth literature. Ultimately, growth in factor inputs will be
subject to diminishing returns. But this can be overcome through growth in TFP, which
economists have generally equated with knowledge more efficient technologies, more effective
management, and increased quality of institutions. Our analysis suggests that wealth accounting
can begin to put an asset value on this knowledge.
To better understand the role played by intangible capital in production, we also estimated a
Cobb-Douglas production function where produced, natural and intangible capital are the factors
of production. Using fixed effects estimation for all countries in our sample (115 countries over
the years 1995, 2000 and 2005), we find that the shares of produced, natural and intangible
capital in production are 32%, 7% and 18% respectively; if we limit our sample to developing
countries the shares are quite similar. However, when we limit the sample to OECD countries,
we find the striking result that the only statistically significant factor of production is intangible
capital, with a 50% share. This finding supports the conjecture in Ferreira and Vincent (2005)
that intangible factors, rather than produced or natural capital, are the principal sources of
consumption growth in high-income countries.
We also attempted to decompose the constituents of intangible capital using data on institutional
quality (as measured by a rule of law index) and human capital. Panel regression analysis
suggests that the contribution of human capital is indeed important. A one-point increase in our
human capital indicator is associated with an increase in intangible wealth of over $92,000 for
the average country in our sample. This implies a difference in intangible capital of over
17
$320,000 per capita between the country with the lowest human capital (Mozambique with an
average value of 1.54 over the sample period) and the country with the highest human capital
(Norway with an average value of 5.08). In contrast, we find that the rule of law index is not a
significant contributor to intangible capital. When we decompose the predicted levels of
intangible capital using our estimated model we observe large negative (and statistically
significant) fixed effects for all developing countries. It is certainly conceivable that the quality
of institutions, captured only imperfectly with our rule of law index, and the legacy of geography
and history for developing countries can explain these large negative fixed effects.
18
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21
Table 1: National wealth and income in Canada, 2009 ($bn CDN)
Net financial assets -109,452
Land assets 1,846,753
Produced capital (K) 4,191,919
Net worth 5,929,220
GNI 1,505,817
Implicit rate of return
K / GNI 2.78 35.9%
Net worth / GNI 3.94 25.4%
22
Table 2: Descriptive statistics
Panel A Full Sample
Mean Std. Dev. Min Max
Total wealth 163,582 215,857 2,152 902,960
Produced capital 30,625 40,887 132 183,078
Natural capital 11,925 20,730 2 169,150
Net foreign assets -24 12,515 -49,818 74,280
Intangible capital 121,056 170,061 278 799,123
Intangible capital/
total wealth 0.64 0.18 0.05 0.90
Years of schooling 6.52 2.87 0.76 12.59
Human capital 3.06 0.91 1.46 5.34
Rule of law 56.17 23.19 6.96 100.00
Non- Year Year
Panel B OECD OECD Year 1995 2000 2005
Means (standard deviations in parentheses)
Total wealth 486,177 61,000 157,722 162,365 169,751
(183,435) (84,088) (204,314) (213,149) (229,400)
Produced capital 90,562 11,566 30,153 30,237 31,410
(34,932) (17,580) (40,364) (40,081) (42,447)
Natural capital 17,656 10,102 12,050 12,947 10,806
(19,540) (20,805) (21,709) (22,212) (18,360)
Net foreign assets -3,469 1,072 273 -165 -135
(15,814) (11,083) (11,537) (12,799) (13,120)
Intangible capital 381,427 38,260 115,246 119,347 127,669
(147,274) (52,810) (158,600) (167,351) (182,924)
Intangible capital/ 0.78 0.59 0.63 0.63 0.66
total wealth (0.06) (0.18) (0.17) (0.19) (0.19)
Years of schooling 9.64 5.53 5.99 6.61 6.88
(1.59) (2.44) (2.83) (2.84) (2.88)
Human capital 4.14 2.71 2.92 3.08 3.16
(0.57) (0.71) (0.88) (0.90) (0.95)
Rule of law 86.64 46.48 57.74 56.04 54.96
(10.12) (16.92) (24.25) (22.52) (23.06)
Notes: Panel A: full sample includes an unbalanced panel of 115 developed and developing countries for years
1995, 2000 and 2005 (total of 315 observations), except for human capital for which sample is 112 countries and
309 observations. All variables except years of schooling, human capital and rule of law are expressed in per capita
terms in constant 2005 US$. Years of schooling in population aged 15 and over from Barro and Lee (2001); human
capital h Ah e ( s ) (see text for details); rule of law index (1-100 scale) from Kaufmann, Kraay and Mastruzzi
23
(2009). Panel B: repots means with standard deviations in parentheses for subsamples of OECD countries (76
observations); non-OECD countries (239 observations); year 1995 (94 observations); year 2000 (110 observations);
year 2005 (111 observations).
24
Table 3: Fraction of output variance explained by factors of production
Factors considered
k,s,h k,s,ic
var (log(y)) 2.80 2.80
var (log(yfactors)) 0.56 2.71
cov(log(A), log(yfactors)) 0.65 0.003
SuccessC 0.20 0.97
SuccessKR 0.43 0.97
Notes: sample includes an unbalanced panel of 112 developed and developing countries for years 1995, 2000 and
2005 (total of 309 observations).
25
Table 4: Development accounting regression; implicit shares
Implicit shares
(1) (2) (3) (4)
ln(k) 0.398*** 0.320*** 0.313*** 0.205
(0.073) (0.058) (0.062) (0.128)
ln(s) -0.022 0.068** 0.072* 0.030
(0.037) (0.033) (0.039) (0.037)
ln(h) 0.356
(0.348)
ln(ic) 0.176*** 0.169*** 0.502***
(0.054) (0.055) (0.010)
Country Fixed Effects Yes Yes Yes Yes
(p-value F.E =0) (0.00) (0.00) (0.00) (0.00)
Time Fixed Effects Yes Yes Yes Yes
(p-value F.E =0) (0.01) (0.00) (0.00) (0.00)
Sample All All Non-OECD OECD
N 311 311 234 76
n 112 112 86 26
Notes: Dependent variable is ln(output per worker). Independent variables are ln(capital-labor
ratio), ln(natural resources-labor ratio), ln(intangible capital-labor ratio), ln(human capital per
worker) ***,**, * denote significance at 1% , 5% , and 10 percent levels, respectively.
26
Table 5: Explaining intangible capital
(1) (2) (3) (4) (5) (6)
Rule of law 3,000 2,819 308 37 354 188
(434)*** (433)*** (383) (388) (385) (380)
Years 11,025 36,568 2,480
schooling (2,817)*** (7,877)*** (13,893)
h 46,178 124,450 92,899
(10,280)*** (24,078)*** (27,590)***
li -107,214 -92,272
(28,644)*** (29,568)***
lmi -153,354 -141,295
(22,928)*** (23,375)***
umi -163,090 -153,567
(18,925)*** (19,352)***
dy2000 12,954 6,470
(4,264)*** (2,337)***
dy2005 25,356 10,013
(8,588)*** (4,137)**
Constant -22,502 -90,246
(42,663) (50,148)*
Country FE No No Yes Yes Yes Yes
(F-test p-value) 0.00 0.00 0.00 0.00
Time FE No No No No Yes Yes
(F-test p-value) 0.01 0.02
Observations 315 309 315 309 315 309
Number of id 115 112 115 112 115 112
R-squareda 0.77 0.78 0.21 0.30 0.25 0.31
Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. a.
R-squared in fixed-effects regressions corresponds to within-R-squared.
27
Table 6: IV analysis
Panel A: 1st stage regressions
Rule of law Human capital
Distance 36.57** 1.53***
(17.48) (0.58)
***
Log (settler -7.75 -0.24***
mortality) (2.04) (0.07)
European language 7.11 0.59***
(%) (5.37) (0.18)
***
Constant 74.18 3.26***
(12.20) (0.40)
R-squared 0.52 0.62
N 59 59
Panel B: Tests of overidentifying restrictions
Sargan Basmann
chi2(1) 0.37 0.34
p-value (0.55) (0.56)
Panel C: 2nd stage regression
Intangible capital
Rule of law 2,326
(3,190)
H 77,921
(78,161)
Constant -266,511***
(64,854)
R-squared 0.58
N 59
28
Figure 1. Distribution of implicit rates of return on comprehensive wealth, 2005
35
30
25
20
15
10
5
0
Source: Author's calculations, based on data from World Bank (forthcoming).
Vertical axis is country count, horizontal axis is ranges of rates of return.
29
800000
Intangible capital pc (constant 2005 US$) ISL
ISL
600000
USA
GBR DNK
USA DNK
ISL NOR NOR
SWE
DNK NOR
GBR
USA CHE
FRANLDIRL CHE
SWE
FIN
AUT
400000
BEL
FRA NLD CHE
GBR DEUAUT
DEU
IRL
SWE CAN
ITAFIN JPN
FRA BEL
AUT
DEU AUS
ITACAN
NLD
BEL JPN
CAN AUS
ITA JPN
IRL
ESP
GRC
FIN
NZLAUS
CYP ESP
ISR
PRT GRC
ISR NZL
ISR ESP
200000
CYP
PRTNZL
GRC HKG
HKG
KOR HKG
PRT
SYC KOR SGP
SYCCZE
HRV
HUN SGPSGP ARE KWT
HRV KOR BHR
SVK
POL
LTU
TUR CZE BHR
MEX
MEX
HUN KWT
LCA KNA
LVA
POL
KNA
MEX
HUN ARE
JAMSVK
TUR
LCA
URYTTO
CHL
ZAF
URY
LTU
VCTCHL
CHL
PAN
MUS
CRI
ROM
ARG
ARG BHR
DOM
DMA
SLVTTO TTO
BRA
JAM
BGR
PAN
BRA
ZAF
ARG
CRI
DOMLVA
DMA
JOR
VCT
COL
MUS
PAN
TUN
ROM
DOM
MUS
COL
PER MYS
MYS
JORVEN
LKA VEN
NIC MYS
EGY BWA
LSOBWA
TUN
SEN RUS
HTI VEN
PHL
THA
PHL
EGY
SYR
MDA
LKA
CMR
HND
CMR
MRT
IDN
BOL
CHN
IND
NIC
ZMB
PAK
KEN
SLERUS
BGD
GHA
VNM
CHN
GMB
IND
SDN
TGO
PNG
BEN
LBR
TUN
BGR
PER
BWA
FJI
FJI
SWZ
GTM
THA
GTM
ECU
DZA
ECU
SWZ
CHN
IRN
ECU
IRN
RWA
NER
ZWE
MLI
MDA
BDIDZA
GNB
MWI
MOZ
TJK
UGA
NPL
CAF
ZAR
0
0 20000 40000 60000 80000
GNI pc (constant 2005 US$)
1995 2000 2005
Figure 2a: Intangible capital is positively correlated with income
1
JOR LCA LCA ISL
SYC ISR GBR ISL GBR
SLV
SLVVCT TUR SYC ISR GRCISL
JORDOMURY HRV HUN PRTIRLFIN
DOM URYLTU
VCT ZAF MEX HRV ISR GBR
PRT FRA USA
CYP SWE
USA
USA
SWE
SWE
Intangible Capital/Total Wealth
LSO FRA
ESPAUT
POL PRTGRCNLD DNK DNK
FRA
ITANLD
IRL
IRL
.8
SEN URY
SLV ARG MEX
TUR
PAN ITA
CYPCAN DNK
BEL
ESPDEU
ESP DEU
DEU
AUT
ITABEL
AUTFIN
NLD
TGO LSO TUNTUN PAN POL KORKOR FINBEL
TUN ARG
LKA JOR JAM MEXSVK
DOM BGR LVA CAN
CAN
GMB
GMB SEN LSO COL CRI GRCAUS
PERJAMLTU KNACZE HKG AUS
CRI
PERDMA ZAF HUN KOR NZLAUS
LBR GMB HTI SEN EGY
BGD ZMB LKA CRI CHL
PHL PERPAN CHL CZE
COL ROM
JAMMUS
ARG
JPN
NZL JPN NOR
JPN
MOZ HTI MRT
TGO MRT PHL
BGD HTI BRA HUN
BRA TTO CHE
SLE BGD PHL
EGY COL ZAF SVK
DMA KNA NZL
HKG CHE
CHE NOR
ZWEBEN LKA EGY
ZMB NIC
NER GHAKENNICIDN
ZMBPAK MYS CHL
ROM TTO
MUS LVA NOR
MLI
SLE MWI TJKGHA SGPHKG
.6
BGR MUS
SLE ZWE MRT CMR
TGO MDA
IND HND
CMR
IDN THABWA
GTM BWA
GTM
SWZFJI MYS
THA
CMR SYR SGP
SGP
MOZ
GNB PAK BOL SYR ECU MYS
PAK
LBR RWA ZWE
NER NIC
KEN HND
IND GTM
DZA FJI
NER
RWA CHN CHN BWA BHR
MOZ MLI GHA VNM IDN
MWI
GNB MLI KEN CHN SWZ
THA VEN TTO BHR
BEN BOL
NPL IND
.4
SYR DZA FJI VEN
RWA
NPL
UGA BEN SDN HNDBOL KWT
ECU
BDI GNB NPL RUS
VEN
ARE
ZAR MWI UGA SDN SWZ
IRN BHR
TJK PNG KWT
ARE
ECU
.2
BDI
ZAR MDA
IRN
CAF RUS
DZA
UGA
0
4 6 8 10 12
Ln(GNI pc)
1995 2000 2005
Figure 2b: Share of intangible capital in total wealth is positively correlated with income
30