WPS8704
Policy Research Working Paper 8704
Natural Resources and Total Factor
Productivity Growth in Developing Countries
Testing A New Methodology
Kirk Hamilton
Esther Naikal
Glenn-Marie Lange
Environment and Natural Resources Global Practice
January 2019
Policy Research Working Paper 8704
Abstract
Estimates of total factor productivity growth, a measure of countries over 1996–2014. In the aggregate, including nat-
increases in the efficiency of production, have traditionally ural resources as a factor of production increases estimated
been based on a two-factor model of labor and fixed capital. total factor productivity growth across all country income
Because profits are measured residually in the System of classes and regions of the world when compared with the
National Accounts, they implicitly include rents on natural traditional two-factor approach. In addition, the estimated
resource exploitation, with the result that the contribution total factor productivity growth including natural resources
of fixed capital to growth in the inputs to gross domestic is less volatile over time in the great majority of countries
product is misstated, particularly in resource dependent compared with the traditional approach. The availability
developing countries. This leads to incorrect measures of of World Bank data on natural resource quantities and
total factor productivity growth. Using data on natural rents for a wide range of countries suggests that natural
resources from the World Bank’s Wealth of Nations data- resources should be included in total factor productivity
base and methods combining the Solow growth accounting growth estimation going forward. Further research could
model with recent work at the Organisation for Economic focus on the distinctive roles played by different natural
Co-operation and Development, this paper makes new esti- resource endowments.
mates of total factor productivity growth for 74 developing
This paper is a product of the Environment and Natural Resources Global Practice . It is part of a larger effort by the World
Bank to provide open access to its research and make a contribution to development policy discussions around the world.
Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be
contacted at glange1@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
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names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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Produced by the Research Support Team
Natural Resources and Total Factor Productivity Growth in Developing
Countries: Testing A New Methodology
Kirk Hamilton, Esther Naikal, and Glenn-Marie Lange
Keywords: total factor productivity, economic efficiency, natural resources, growth accounting
JEL codes: E24, O13, O41, O47
Natural Resources and Total Factor Productivity Growth in Developing
Countries: Testing A New Methodology
Kirk Hamilton, Esther Naikal, and Glenn-Marie Lange
“Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to
improve its standard of living over time depends almost entirely on its ability to raise its output
per worker.”
Paul Krugman
1. Introduction
As the quote above suggests, in a world of decreasing marginal returns to factors of production,
the only way to ensure increasing growth and prosperity is to increase the efficiency of factor
use. Rather than focusing on a sole production factor such as labor, economists beginning with
Solow (1957) have developed methods of growth accounting which derive an indirect measure
of the rate of change in the efficiency of use of all factors of production, termed ‘total factor
productivity’ (TFP) growth or ‘multi-factor productivity’ (MFP) growth. With the exception of
recent work at the OECD (discussed below), estimates of TFP growth at the macroeconomic
level have excluded natural resources as a factor of production – a serious omission when
considering developing countries. Natural resources make up on average 47% of total wealth in
low-income countries, 36% in Sub-Saharan Africa, 27% in lower-middle-income and 17% in
upper-middle-income countries. A particular concern is that agricultural land makes up 62% of
natural wealth in low-income countries. The omission of natural resources was largely owing to
a lack of comparable cross-country data on natural resource stocks and flows, measured
according to System of National Accounts (SNA) standards.
The publication by the World Bank of The Changing Wealth of Nations 2018 (Lange, Wodon
and Carey 2018) has filled this gap, with data on the value of natural resource stocks and flows
across 141 countries and spanning the period from 1995 to 2014. The OECD pioneered
methodology to include natural resources (minerals and energy) in TFP estimations for OECD-
member countries and a few other countries (Brandt et al. 2017). This paper tests the OECD
approach for a broader set of natural resources and with some modifications described in section
2, for 74 middle- and low-income countries, exploiting the World Bank wealth database.
This study generalizes the preliminary ‘version 0’ estimates of TFP growth with natural
resources by Hamilton (2018) in The Changing Wealth of Nations 2018. It is important to
emphasize at the outset that the results reported here represent ‘version 1’ estimates and future
work will refine the estimates. The intent of this paper is to test the methodology for developing
countries, demonstrating the extent to which including natural resources in TFP growth measures
matters. We will not attempt, at this early stage, to provide in-depth analysis and policy
conclusions, which will be addressed in future work.
2
The work presented here builds on a large literature on macro-level growth accounting and TFP
measurement (Hulten 2010). However, it is important to note that there is also a rich literature
on measuring economic efficiency at the sector or firm level, using methods and data that are
much more fine-grained than what will be presented here. The focus on the macro scale has
advantages, in particular the ability to pick up, however indirectly, the effects of policy and
institutional reforms that can have macroeconomic consequences, in addition to technical
progress. Using the World Bank Wealth of Nations data also means that we can do broad
comparisons of country, regional and income-class performance on TFP growth over time.
The Wealth of Nations data include estimates of fixed capital stocks (based on the Penn World
Table), human capital, and stocks and flows of natural resources in both quantity and value
terms. To produce the estimates of TFP growth below, the World Bank data are augmented by
UN national accounts data on the composition of value added, total remuneration of labor from
the Penn World Table, as well as the number of persons employed aged 15 or more from the
ILO. We also employ indices of real agricultural output from the FAO.
We begin section 2 by considering the general approach to productivity measurement and the
specific issues associated with the incorporation of natural resources into growth accounting,
followed by a detailed specification of the methods and data used to measure TFP growth. While
it may seem at first glance that adding another factor to the calculation would reduce TFP growth
estimates, we will demonstrate that is not always the case. Results of the TFP growth
calculations and key findings are highlighted in section 3. The final section sums up the results,
identifies how new measures of TFP could be used for policy work in the World Bank, and
suggests areas for further work.
2. Methods and Data
TFP growth is not a particularly intuitive concept for many people, but a simple formalization
may help. If we compare the growth rate of production in an economy, which is measured by
GDP, with a weighted average of the growth rate of the factor inputs to production, we will
typically find a gap between the two. This gap is defined to be the growth rate of some
unmeasured input to production which we term Total Factor Productivity. Determinants of this
gap could include technological change, changes in economic policies and institutions, or
changes in the management practices of firms – to name just a few. This section derives the
equations used to estimate TFP including natural resources, then discusses the data used and how
certain data challenges were addressed.
To formalize the concepts, we denote total factor productivity as , and its growth rate as .
Similarly, we denote GDP as and its growth rate as . We assume that the inputs to
production can be categorized as fixed capital , with growth rate , labor with growth rate
, and natural resources , with growth rate . If we assume that there are corresponding
shares of these factors in value added, denoted , and , then these shares will sum to one.
Crucially, there is generally no direct measure of the returns to fixed capital in the SNA, so it is
defined as a residual after measuring wages (the contribution of labor to value added) and
resource rents (the contribution of natural resources to value added); formally we can say that
3
1 in the case with no explicit measure of natural resource rents, and 1
when such a measure does exist. is, in an important sense, too large when data on resource
rents are lacking.
If we assume that we have no direct measure of natural resource rents then the traditional
approach to TFP growth measurement has been to calculate,
This formalizes the statement above – TFP growth is measured as the difference between the
growth rate of GDP and a weighted average of the inputs to production. In this formula the
weights are, quite naturally, the share of the factors in value added, and .
If we have measures of the natural resource rents and so can measure the share of rents in value
added and the growth rate of resource inputs , then is correspondingly lower and we can
write the formula for TFP growth measurement including natural resource inputs as,
Now the weighted average of inputs to production includes the contribution to natural resources.
These two formulae are the basis for measuring TFP growth with and without natural resource
inputs in this study.
Recent work at the OECD has provided an approach to treating natural resources as production
factors, which they have applied to selected countries, mainly OECD member countries, using
World Bank data on minerals and energy (Brandt et al. 2017; See Annex I for the OECD
approach to TFP). An important finding, derived below and by Brandt et al. (2017), is that there
is a systematic relationship between TFP growth measured using only factor data on capital and
labor, and TFP growth that also measures natural resources as a factor of production. If we
denote TFP growth excluding natural resources as and with natural resources as , it
will be shown below that
. (1)
Where is the GDP share of natural resource rents, while and are the growth rates of
fixed capital and natural resources measured in volume terms. Given the high resource
dependence of developing countries, this formula suggests there are potentially significant biases
when measuring TFP growth in developing countries while ignoring natural resources as
production factors.
Analytical framework
The framework we use for estimating TFP growth with natural resources as a factor of
production is inspired by the recent work of the OECD. In what follows we adapt the approach
4
of Solow (1957) to include natural resources.1 The basic assumptions in Solow (1957) are that
production factors are valued at their marginal product, that production exhibits constant returns
to scale, and that technical progress is Hicks-neutral. Assuming that all variables are functions of
time unless stated otherwise, the equation for GDP when capital and labor are the only
production factors is,
, (2)
Here is the familiar index of efficiency. We introduce a natural resource input to production
which is costly to produce, with cost function . The economic value of a marginal unit of
resource is therefore given by , i.e. the marginal rental value. Aggregate production can
be consumed, invested or spent on resource extraction. The resource extraction cost is an
intermediate input to the economy.
As in Solow (1957), we assume that production , , exhibits constant returns to scale.2 If
̅ so that marginal cost
we assume that the marginal cost of resource extraction is constant at ,
equals average cost, then we can write GDP as,
, , ̅
(3)
In this formulation the efficiency factor affects GDP as a whole and it is straightforward to
show that GDP exhibits constant returns to scale in the production factors. GDP therefore equals
the sum of resource rents, wages and the returns to fixed capital.
GDP growth is derived using expression (3),
, , ̅
̅
̅ (4)
It follows that,
̅
⋅ ⋅ ⋅ (5)
and the TFP growth rate is therefore,
̅
⋅ ⋅ ⋅ (6)
1
The OECD approach builds on Jorgensen (1963) to measure the annual user cost of fixed capital and
derives TFP growth based on the decomposition of the costs of production. See Annex I.
2
Note that constant returns to scale is used in this section to simplify the presentation. However, constant
returns to scale are not strictly required to derive the empirical measures of TFP growth including natural
resources presented in this report – it is sufficient to assume that the relevant weights in calculating the
average growth rates of factor inputs are the corresponding shares of value added for each factor.
5
̅ increases TFP growth, this interpretation is
While expression (6) may appear to suggest that
̅ is an intermediate cost of production, some incremental amount of
not entirely correct. Since
̅
capital and labor and natural resource must be used in its production. The term ⋅ simply
offsets the weight of this incremental use of capital, labor and natural resource in measured TFP
growth.
In expression (6), is the factor share of fixed capital in GDP, which we denote , while
is the growth rate of TFP, denoted . Generalizing the notation, expression (6) can then be
written as,
(7)
As a result of the reapportioning of profits between fixed capital and natural resource rents
described in the subsection below on data sources, the factor share of fixed capital can be
measured residually as 1 (this assumption is also used in Brandt et al. 2017 for
countries where there are insufficient data to measure the annual user cost of fixed capital). For
countries where natural resource rent data exist, the TFP growth rate with natural resources
(WNR) is therefore calculated as,
1 (8)
For countries where no natural resource rent data exist, the traditional approach to calculating
TFP growth has been to start with the simple Solow model of expression (2). The factor share of
fixed capital in GDP and value added excluding natural resources (XNR) becomes 1 and
the calculated TFP growth rate becomes,
1 (9)
The difference in calculated TFP growth rates for the two approaches is therefore given by,
(10)
This is just expression (1), formally derived. The TFP growth rate estimated with natural
resources will exceed the growth rate estimated excluding natural resources if the growth rate of
fixed capital exceeds that of natural resources. The difference between the two TFP growth rates
is proportional to the natural resource rent share of GDP.
This result extends easily to the case where there are two natural resources and with factor
shares and , and growth rates and . The aggregate share of natural resources in
GDP and the weighted average growth rate of these resources are given by,
and ⋅ ⋅ (11)
6
With these definitions, the relationship between TFP growth with and excluding natural
resources is again given by expression (10), and this result generalizes to more than two natural
resources in the obvious way.
In terms of measurement, the growth rates of labor and natural resources are measured in volume
terms (number of persons employed3 and physical quantities of natural resources), while real
values are used to measure the growth rate of fixed capital.
Brandt et al. (2017) derive the discrete time version of expressions (6) and (7), starting with the
observation that ln . They take logarithms of the ratios of factors at times and 1
based on a Taylor series approximation, but note that the discrete time expression corresponding
to expression (6) can use factor shares for labor (for example) measured either as,
, which yields a Laspeyres index, or as 1 , which is a Paasche
index. Since neither approach is obviously better than the other, they opt for a Törnqvist index
which averages the factor shares. Denoting a simple average by a bar, we can therefore write the
factor share of labor at time as,
̅ 0.5 ⋅ . (12)
This obviously generalizes to the other factors of production and to shares of the overall factor
returns to natural resources as shown in expression (11).
Following Brandt et al. (2017), we can therefore write the logarithmic form of the discrete time
decomposition of TFP growth as,
ln ln ̅ ⋅ ̅ ⋅ ̅ ⋅ (13)
Taking exponents, this becomes
̅ ̅ ̅
⋅ ⋅ ⋅ (14)
Noting that 1 1, the rate of growth of TFP is therefore,
̅ ̅ ̅
1 ⋅ 1 ⋅ 1 ⋅ 1 1 (15)
Applying the re-apportionment of profits into returns to fixed capital and resource rents, the
factor share of fixed capital with natural resources is therefore given by ̅ 1 ̅ ̅ .
Paralleling the continuous time case, we calculate the factor share of fixed capital excluding
3
Ideally the labor input would be measured in hours worked or full-time-equivalent employees, but these
data are lacking for most developing countries. For OECD countries Brandt et al. (2017) use hours
worked.
7
natural resources as ̅ 1 ̅ . Applying expression (15), the ratio of the growth rates of TFP
with and excluding natural capital reduces to,
̅
(16)
In discrete time, the TFP growth rate with natural resources exceeds the growth rate calculated
excluding natural resources if fixed capital is growing faster than natural resources. The
difference in the growth rates increases with the share of natural resources in GDP. This result is
qualitatively exactly the same as the continuous time case. The generalization of this equation for
two or more natural resources is given in Annex II.
In presenting the empirical results of these calculations in the next section, it is important to note
that the calculation of the average rate of TFP growth for a given country is calculated as the
average annual rate of change (AARC) of TFP. To measure the AARC of TFP growth over
years we must calculate the geometric mean of 1 over all years, then subtract 1. Here
is the growth rate of TFP in year :
AARC = 1 1 ⋅ 2 1 … ⋅ 1 1 (17)
Because this is not a simple average of growth rates, we calculate the measure of variation in the
TFP growth rate as the root-mean-squared (RMS) of the differences between the annual
measures of TFP growth and the AARC. This notation signals that what is presented is not a
classic standard deviation.
For calculating aggregates of TFP growth, such as for low-income countries as a group, or
developing countries in South Asia, we measure a simple weighted average of the country
figures, with country GDP in constant US dollars providing the weights.
Data sources
Table 1 presents the data sources for the TFP growth calculations. For detailed information on
the measurement of the natural resources appearing in the Wealth of Nations database, the reader
is referred to Appendix A of The Changing Wealth of Nations 2018, or the detailed methodology
documentation which can be found at https://datacatalog.worldbank.org/dataset/wealth-
accounting.
Table 1. Data sources
Indicator Source
Resource rents: oil, natural gas, coal, The Changing Wealth of Nations 2018
minerals, cropland, pastureland, timber
Production: oil, natural gas, coal, minerals, The Changing Wealth of Nations 2018
timber
Agriculture Gross Production Index Food and Agriculture Organization of the
United Nations
Employed labor compensation Penn World Table 9.0 (Feenstra et al 2015)
8
Number of employed, 15+ International Labour Organization
Physical capital stock Penn World Table 9.0 (Feenstra et al 2015)
GDP World Bank
Resource rents
A major challenge to including natural resources in TFP has been the lack of information on
natural resources rents, their share in GDP and growth over time. The introduction of data on
rents derived from natural resource use and extraction, as appears in the Wealth of Nations
database, makes it possible to partition value added into the sum of wages, resource rents and
profits derived from fixed capital. Because unit resource rents are measured as price minus the
economic cost of extraction, this measure excludes both wages and the opportunity cost of
capital. It is therefore possible to neatly divide profits into a natural resource rent component and
a profit on fixed capital component. Since the three components (including wages) sum to total
value added, a defensible measure of TFP growth can be derived as the growth rate of GDP
minus the weighted average growth rate of the three factors of production, where the weights are
the shares of each factor in value added.
Considering the agriculture sector, the natural resource in question is the land used for crop and
livestock production. As a practical matter, however, the quantity of agricultural land in any
given country is more or less fixed – with the exception of countries where land is still being
converted from forest or grassland or wetlands to agricultural uses. In what follows we therefore
opt to treat agricultural produce, both food and non-food, as the natural resource in question, and
the associated land rents on agricultural production as part of the natural resource share of value
added. To measure the growth in agricultural output as a factor of production we use the FAO’s
index of agricultural production (both food and non-food) measured in real terms.
While this approach to agriculture may appear artificial, it exactly parallels how timber
production is treated in TFP growth estimation. The wood itself is considered to be the natural
resource, and the growth rate of the quantity of timber produced is calculated year on year based
upon timber production measured in cubic meters. The share of timber production in value added
is the associated resource rent on timber harvest – which, arguably, could also be viewed as a
type of land rent.4
Labor and the self-employed
In developing countries, particularly low-income countries, self-employment is a large share of
total labor inputs– in Sub-Saharan Africa, for example, the share of formal wages in value added
can be as low as 10%. For the share of labor in the equations above, as measured in the SNA
is too low because it includes only formal employment. The Penn World Table data on total
compensation of labor add other components of value added (part of mixed income in developed
countries, agricultural value added in low-income countries) to the SNA figures for the
compensation of employees. As a result, the sum of returns to capital, labor and natural resources
4
Note that there is some risk of double-counting the contribution of forests to TFP growth because,
according to SNA principles, plantation forest outputs are measured as part of production in the
agriculture sector.
9
exceed value added as measured in the SNA. We therefore normalize the returns to factors to
sum to 1 after the disentangling of resource rents and profits has been carried out.5 Another
wrinkle in the calculation is that, because data on the number of people self-employed are
lacking in many countries, the growth rate of labor inputs used in our calculation of TFP growth
is based on the number of people in formal employment, which may not be a good assumption in
many developing countries.
Fixed capital
Turning to fixed capital, the Wealth of Nations database uses capital stock estimates from the
Penn World Table, built on the Perpetual Inventory Model, converted to constant 2014 US
dollars.6 To reach the final value, however, the underlying stock of capital at constant local
prices and currency units (a measure of volume) is first converted to current international dollars
at purchasing power parities (PPPs), then to current US dollars at market prices, then finally to
constant 2014 US dollars. The various conversions involving prices and exchange rates lead
inevitably to volatility in the final estimates of fixed capital. To avoid this volatility, we use the
PWT figures for the volume measure – stock of capital at constant local prices – in our TFP
estimation.
One final issue arises, however. The PWT methodology measures the total capital stock, both
public and private. As a result of SNA conventions, however, public sector fixed capital is
assumed to have zero profits. As a consequence, when measuring the contribution of fixed
capital to GDP growth, we weight the growth rate of total fixed capital ( in the second
formula) by the returns to capital measured in the SNA ( in the second formula), which are
returns on productive capital. This approach introduces a potential bias to the extent that public
sector fixed capital may grow at a rate different from productive fixed capital (that is, may
have two components, one for productive fixed capital and another for public sector fixed
capital). We currently have no independent measure of public sector fixed capital, but there is
recent work on this at the IMF (IMF 2017).
Comparison with OECD estimates of TFP
We have compared the results of measuring TFP growth using the above methodology with the
OECD results published in OECD (2016) for specific countries.7 In some cases these estimates
are similar, in most cases they are not. The reasons for the disparity are many: OECD weights
fixed capital at the user cost of capital, rather than surplus less resource rents; OECD has a more
precise measure of labor input, based on hours worked; and the OECD adjustment for natural
capital is based on an earlier and more limited version of the Wealth of Nations database. As a
result, we do not publish the comparisons in this paper, but it will be an active point of
5
This approach is a heuristic which has the virtue of ensuring that the labor share does not
change when constructing the ‘with natural resource’ and ‘excluding natural resource’ measures
of TFP growth. The approach implicitly treats land rents in agriculture as part of the
compensation of employees in agriculture-dependent economies, however, and should be re-
visited in the next version of the Wealth of Nations database.
6
See the online supplementary material from Feenstra et al. (2015) for details.
7
We eliminate the adjustment made by OECD for pollution abatement expenditures to increase
comparability of the estimates.
10
discussion with OECD going forward. At any event, our focus in this paper is squarely on
developing countries, not high-income members of the OECD.
3. Empirical results
Based on the foregoing methodology, we derive a series of analytical results aimed at exploring
TFP growth in developing countries, with and without natural resources incorporated. Our goal
is to use the methodology to test whether it matters that natural resources are included in TFP
estimates, specifically, i) does TFP growth differ and ii) does volatility of TFP growth differ
when including natural resources, compared to traditional, 2-factor TFP that excludes natural
resources? Results for selected countries and groups of countries are discussed in this section.
The full set of results for all countries are given in Annex III.
TFP growth with and without natural resources: 25 top performers
Figure 1 ranks the 25 countries with the highest average annual growth of TFP including natural
resources (hereafter, TFP with NR) over the period 1996-2014. The impact of including natural
resources on TFP growth is mixed: in 16 of these 25 countries the estimated growth of TFP with
NR exceeds growth of TFP excluding natural resources (hereafter, TFP without NR), while in 9
other countries including natural resources reduces TFP growth. In a few countries the two
measures of TFP are very close. We remind the reader that adding natural resources to TFP
calculations will not necessarily reduce TFP growth: the growth rate of TFP with NR exceeds the
growth rate calculated excluding natural resources if fixed capital is growing faster than natural
resources.
While much more analysis is needed to fully understand the reasons behind TFP growth, it is
notable that many of the top performers are transition economies, 13 in all. African countries
also feature prominently in this list, with 7 countries in the top 25. Since the transition countries
of Eastern Europe and Central Asia suffered severe recessions immediately after the fall of the
Berlin Wall, our analysis of 1996 to 2014 coincides with a period where the reforms of policies
and institutions in these countries started to pay growth dividends. This is a possible explanation
for the strong TFP growth (including natural resources). In Sub-Saharan Africa 1996 represented
the start of debt relief under the Heavily-Indebted Poor Country initiative that led to substantial
debt reductions in 30 African countries. This, combined with macroeconomic reforms, may be an
explanation for the strong TFP growth performance (including natural resources) in many of
these countries.
11
Figure 1. TFP growth over 1996-2014: top 25 countries by TFP growth with natural
resources
TFP, average (1996‐2014)
Tajikistan
Georgia
Bosnia and Herzegovina
China
Armenia
Mongolia
Kazakhstan
Belarus
Iraq
Azerbaijan
Moldova
Rwanda
Nigeria
Mozambique
Kyrgyz Republic
Sri Lanka
Chad
Ukraine
India
Sierra Leone
Romania TFP excluding natural
resources
Burkina Faso
Swaziland TFP with natural resources
Tanzania
Tunisia
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0%
Volatility of TFP measures
How does including natural resources affect the volatility of TFP growth year-to-year? Turning
to a more technical measure, Figure 2 ranks countries by the RMS variation in growth of TFP
with NR. In 13 of the 25 countries shown the variability of growth of TFP with NR is lower than
the measure excluding natural resources. In five countries there is essentially no difference in
variation.
12
Figure 2. Variation in TFP growth, with and excluding natural resources,
top 25 countries ranked by RMS variation
RMS Variation (1996‐2014)
Bosnia and Herzegovina
Iraq
Central African Republic
Zimbabwe
Nigeria
Sierra Leone
Armenia
Ukraine
Mozambique
Lesotho
Botswana
Tajikistan
Indonesia
Azerbaijan
Burundi RMS, for TFP excluding
Paraguay natural resources
Romania
RMS, for TFP with natural
Mauritania resources
Moldova
Belarus
Georgia
Turkey
Chad
Togo
Mongolia
0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160 0.180 0.200
Note: These 25 countries have the highest variation in estimated TFP growth with natural resources.
Decomposition of GDP growth by factor input
Figure 3 ranks countries by GDP growth over 1996-2014 and presents the decomposition of this
growth into the contributions from fixed capital, labor, natural resources, and TFP growth
(including natural resources). Figure 4 complements Figure 3 by showing only GDP growth and
growth of TFP with NR. In eight of the 25 countries shown, growth of TFP with NR makes up
more than one-half of GDP growth.
13
Figure 3. Decomposition of GDP growth for 25 countries with the highest share of TFP
growth with natural resources, ranked by GDP growth
Contribution to GDP Growth
(long‐term averages, 1996‐2014)
Azerbaijan
China
Bosnia and Herzegovina
Mozambique
Rwanda
Chad
Iraq
Lao PDR
India
Mongolia
Nigeria
Armenia
Kazakhstan
Burkina Faso
Panama
Belarus
Tanzania
Georgia
Sierra Leone
Tajikistan
Sri Lanka
Dominican Republic
Malaysia
Botswana
Jordan
‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%
Labor Produced capital Natural capital TFP
Note that data on diamonds are not available for Botswana, which biases the TFP estimates
14
Figure 4. TFP growth with natural resources and GDP growth, top 25 countries ranked by
GDP growth
GDP Growth and TFP
(long‐term averages, 1996‐2014)
‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%
Azerbaijan
China
Bosnia and Herzegovina
Mozambique
Rwanda
Chad
Iraq
Lao PDR
India
Mongolia
Nigeria
Armenia
Kazakhstan
Burkina Faso
Panama
Belarus
Tanzania
Georgia
Sierra Leone
Tajikistan
Sri Lanka
Dominican Republic
Malaysia
Botswana
Jordan
‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%
TFP GDP
Note that data on diamonds are not available for Botswana, which biases the TFP estimates
Comparing TFP measures by region and income group
Figures 5 and 6 compare average TFP growth rates with and excluding natural resources for
countries grouped by income class and region (where regions include developing countries only).
In each income class we see that TFP growth with natural resources exceeds the measure
15
excluding natural resources. High-income OECD countries registered the lowest TFP growth by
either measure over 1996-2014, but recall that these figures are not estimated based on the
superior data available in these countries (see Annex I). Turning to developing regions, East Asia
and the Pacific clearly dominates TFP growth (thanks to China), while Sub-Saharan Africa is the
second lowest. The striking result is for Latin America and the Caribbean, where TFP growth
was extremely low over this time period.
Figure 5. TFP growth by income class
Average TFP growth 1996‐2014
3.00%
2.50%
2.00%
1.50%
1.00%
0.50%
0.00%
Low income Lower middle Upper middle High income: non‐ High income: OECD
income income OECD
With natural resources Excluding natural resources
Note that the averages reported for OECD countries may vary from OECD (2016) and Brandt
et al. (2017). High income non-OECD countries are largely petroleum exporters. China dominates
the figures for upper middle income countries, while India dominates lower middle income.
Figure 6. TFP growth by developing region
Average TFP growth 1996‐2014
4.50%
4.00%
3.50%
3.00%
2.50%
2.00%
1.50%
1.00%
0.50%
0.00%
East Asia & Pacific Europe & Central Latin America & Middle East & South Asia (low Sub‐Saharan
(low and middle Asia (low and Caribbean (low North Africa (low and middle Africa (no NGA,
income) middle income) and middle and middle income) ZAF)
income) income)
With natural resources Excluding natural resources
Note that the Sub-Saharan Africa figures exclude Nigeria and South Africa owing to their combined 70%
16
share of GDP in the region. Excluding these countries yields more representative estimates for the region. China
dominates the East Asia and Pacific figures, while India dominates South Asia.
Because Nigeria and South Africa were excluded from the Sub-Saharan Africa estimates in
Figure 6 (owing to their dominant GDP share), Figures 7 and 8 plot the two measures of TFP
growth for these countries separately. In the case of Nigeria there is a clear anomaly in the GDP
growth data in 2004, which boosts the TFP growth rate excluding natural resources in particular
– further work on Nigerian data will clearly be required. South Africa exhibits high volatility in
the TPF estimates, with modest average rates of TFP growth over the period.
Figure 7. Total factor productivity growth in Nigeria, 1996-2014
Nigeria: Total Factor Productivity
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐5.00%
TFP, with natural resources TFP, excluding natural resources
Note: Average TFP growth with natural resources was 3.11%, compared with 2.23% excluding natural
resources
Figure 8. Total factor productivity growth in South Africa, 1996-2014
South Africa: Total Factor Productivity
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%
‐1.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐2.00%
‐3.00%
TFP, with natural resources TFP, excluding natural resources
Note: Average TFP growth with natural resources was 0.94%, compared with 0.82% excluding natural
resources.
17
Tables 2 and 3 present a result from the growth accounting that underlies TFP estimation –
Figure 3 presents the full growth accounting for the fastest growing countries measured by GDP.
These tables isolate the effects of natural resource growth – Table 2 presents countries where
growth in natural resource extraction and harvest added more than 10% to GDP growth, while
Table 3 presents countries where the decline of natural resource inputs decreased GDP growth by
1% or more. To be clear, Table 3 shows that (other things being equal) the GDP growth rate of
Ukraine would have been 2.7% on average over 1996-2014 if natural resource inputs had been
constant.
Table 2. Countries where growth in natural capital inputs added more than 10% to GDP
growth over 1996-2014
GDP Natural
growth capital
rate contribution
Sierra Leone 5.8% 37.5%
Niger 4.5% 25.6%
Iraq 7.4% 22.7%
Togo 3.3% 17.0%
Azerbaijan 10.1% 16.9%
Chad 7.4% 16.6%
Kazakhstan 6.2% 15.0%
Suriname 3.7% 14.3%
Lao PDR 7.1% 14.0%
Mongolia 6.7% 13.9%
Bolivia 4.1% 12.1%
Burundi 2.3% 10.9%
Guinea 3.0% 10.5%
A note on interpretation: The 37.5% contribution that
natural capital growth makes to the GDP growth rate
of Sierra Leone (for example) amounts to 2.2% of the
total 5.8% GDP growth rate.
18
Table 3. Countries where decline in natural capital inputs reduced GDP growth by more
than 1% over 1996-2014
Natural
GDP capital
growth rate contribution
Ukraine 1.6% ‐1.1%
Georgia 5.9% ‐1.1%
Dominican Republic 5.4% ‐1.4%
Romania 2.6% ‐1.7%
Fiji 2.2% ‐1.9%
Moldova 3.0% ‐3.1%
Malaysia 4.8% ‐3.4%
Iran, Islamic Rep. 3.6% ‐5.3%
Gabon 2.0% ‐34.0%
See the note on interpretation for Table 2.
Table 4 returns to TFP growth measures and highlights the top 20 countries with the largest
boost to TFP estimates if natural resources are included. The list is, unsurprisingly, dominated by
resource-dependent countries, but it also includes China and India. Not shown in the table are
those countries where TFP growth with natural resources is less than growth estimated excluding
natural resources. There are 16 such countries, of 74 total in our sample, with the difference in
growth rates exceeding 1% only for Sierra Leone. In addition, there are 16 countries where
including natural resources in TFP growth estimation reverses the sign of TFP growth from
negative (excluding natural resources) to positive.
We would like to better understand how TFP growth varies systematically across groups of
countries with characteristics that may be particularly important for including natural resources.
It is not possible in this paper to fully analyze these variations, but we will take a closer look at
several natural resource dependent economies to shed light on this. We consider in turn, the top
three countries with i) the largest agricultural land share of total wealth, ii) the largest share of
petroleum assets in total wealth, and finally iii) the overall largest share of natural resources in
total wealth (excluding any overlap with the top agriculture and petroleum shares).
TFP growth in countries with high dependence on agriculture
Agriculture makes up a significant share of total wealth in many of the poorest countries in the
world and in Figure 9 we present the year by year estimates of TFP growth with and without the
inclusion of natural resources, as well as the average TFP growth over the 1996-2014 period.
The countries chosen are the top three countries by share of agricultural land in total wealth. In
the case of Guinea, TFP growth measured with or without natural resources shows a general
downward trend, but the trend is notably more moderate when natural resources are included.
Recalling expression (16), this tells us that fixed capital is growing more rapidly (or falling less
steeply) than the natural resource input. In the years where TFP growth by either measure is
negative, this indicates that the weighted average of factor inputs is growing faster (or falling less
steeply) than GDP.
19
Table 4. Top 20 countries where including natural resources in
TFP growth estimates from 1996 to 2014 leads to an increase
in estimated TFP growth
TFP growth TFP growth
excluding Including
natural resources natural resources Difference
Mauritania ‐1.2% 0.7% 1.87%
Gabon ‐0.4% 0.9% 1.33%
Guinea ‐2.1% ‐0.8% 1.26%
Iran, Islamic Rep. 0.3% 1.3% 1.04%
Mozambique 1.7% 2.7% 0.98%
Burundi ‐0.6% 0.3% 0.97%
Nigeria 2.2% 3.1% 0.88%
Chad 1.9% 2.6% 0.72%
Malaysia 0.4% 1.0% 0.67%
Egypt, Arab Rep. ‐0.6% 0.0% 0.60%
China 4.4% 4.9% 0.44%
Burkina Faso 1.4% 1.8% 0.41%
Indonesia 0.1% 0.5% 0.39%
Rwanda 2.9% 3.2% 0.35%
Dominican Republic 1.0% 1.4% 0.35%
Azerbaijan 3.4% 3.8% 0.35%
Ecuador 0.1% 0.4% 0.31%
India 1.8% 2.1% 0.29%
Tunisia 1.2% 1.5% 0.26%
Lao PDR 0.5% 0.7% 0.24%
Note: Botswana is excluded owing to the lack of data on diamonds
In the Central African Republic, the two measures of TFP growth nearly coincide in each year.
The trend in each case oscillates around 0, with the notable exception of 2013 where we see a
steep drop. This presumably coincides with the episodes of civil disorder in the CAR that year.
In the Kyrgyz Republic both measures show significantly positive growth over the chosen
period, perhaps reflecting the beneficial impacts of policy reforms and an opening up of the
economy after the fall of the Berlin Wall. The levels of the two measures of TFP growth largely
track each other except for sharp divergences in 2012 and 2013, suggesting that the growth rates
of factor inputs diverged strongly owing to local circumstances in the economy.
20
Figure 9. Agriculture-dependent countries, share of wealth 2014 and TFP growth 1996-
2014
Guinea Agriculture share of total wealth 55.2% TFP growth with natural resource ‐0.8%
TFP growth excluding natural resource ‐2.1%
Guinea: Total Factor Productivity
6.00%
4.00%
2.00%
0.00%
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐2.00%
‐4.00%
‐6.00%
TFP, with natural resources TFP, excluding natural resources
.
Centre‐Afrique Agriculture share of total wealth 44.0% TFP growth with natural resource ‐0.8%
TFP growth excluding natural resource ‐0.8%
Central African Republic: Total Factor Productivity
10.00%
5.00%
0.00%
‐5.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐10.00%
‐15.00%
‐20.00%
‐25.00%
‐30.00%
‐35.00%
‐40.00%
TFP, with natural resources TFP, excluding natural resources
21
Kyrgyz Rep Agriculture share of total wealth 40.9% TFP growth with natural resource 2.6%
TFP growth excluding natural resource 2.6%
Kyrgyz Republic: Total Factor Productivity
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐2.00%
‐4.00%
TFP, with natural resources TFP, excluding natural resources
TFP growth in countries with high dependence on petroleum
Turning to the petroleum producers, Figure 10, the general picture is of extreme volatility in TFP
growth measures, which is unsurprising considering the boom and bust nature of the petroleum
market. We present the top three most petroleum-dependent economies in our database. The case
of Iraq is particularly extreme given the advent of the Iraq War in 2003, which led to a steep
decline in economic activity followed by a sharp recovery. In Kuwait TFP growth is negative on
average, but growth measured with natural resources is generally higher (or less negative),
suggesting that growth in fixed capital is higher than growth in petroleum extraction. In Saudi
Arabia TFP growth is highly volatile and almost trendless, as the low estimates of average TFP
growth over 1996 to 2014 indicate.
22
Figure 10. Petroleum-dependent countries, share of wealth 2014 and TFP growth 1996-
2014
Iraq Petroleum share of total wealth 67.3% TFP growth with natural resource 3.8%
TFP growth excluding natural resource 4.1%
Iraq: Total Factor Productivity
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
‐10.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐20.00%
‐30.00%
‐40.00%
TFP, with natural resources TFP, excluding natural resources
Kuwait Petroleum share of total wealth 52.4% TFP growth with natural resource ‐0.1%
TFP growth excluding natural resource ‐1.6%
Kuwait: Total Factor Productivity
15.00%
10.00%
5.00%
0.00%
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐5.00%
‐10.00%
‐15.00%
TFP, with natural resources TFP, excluding natural resources
23
Saudi Arabia Petroleum share of total wealth 48.5% TFP growth with natural resource 1.2%
TFP growth excluding natural resource ‐0.1%
Saudi Arabia: Total Factor Productivity
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%
‐1.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐2.00%
‐3.00%
‐4.00%
TFP, with natural resources TFP, excluding natural resources
TFP growth in countries with high overall dependence on natural resources
In Figure 11 we present the top three natural resource dependent countries, with the provision
that we wanted to avoid overlaps with the top countries by share of agricultural land or
petroleum assets. Implicitly, therefore, we have chosen mineral producers. In Niger the two
measures of TFP growth oscillate, with a small upward trend. In contrast, Mongolia shows a
strong upward trend in TFP growth (aside from a sharp fall and rise in 2009) with very little
difference between the average TFP growth rates with and without natural resources. TFP
growth in Mauritania is almost trendless, with a slight positive growth rate for the measure
including natural resources and a small negative growth rate for the measure excluding natural
resources.
Figure 11. Resource-dependent countries, share of wealth 2014 and TFP growth 1996-2014
Niger Total resources share of total wealth 73.0% TFP growth with natural resource 1.0%
TFP growth excluding natural resource 1.4%
Niger: Total Factor Productivity
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐2.00%
‐4.00%
‐6.00%
TFP, with natural resources TFP, excluding natural resources
24
Mongolia Total resources share of total wealth 63.3% TFP growth with natural resource 3.9%
TFP growth excluding natural resource 4.0%
Mongolia: Total Factor Productivity
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
‐2.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐4.00%
‐6.00%
TFP, with natural resources TFP, excluding natural resources
Mauritania Total resources share of total wealth 59.8% TFP growth with natural resource 0.7%
TFP growth excluding natural resource ‐1.2%
Mauritania: Total Factor Productivity
15.00%
10.00%
5.00%
0.00%
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
‐5.00%
‐10.00%
TFP, with natural resources TFP, excluding natural resources
Finally, in Annex III Table A.1 we see the full listing by country of TFP growth over 1996-2006,
2007-2014, and 1996-2014. The sharp impact of the financial crisis is immediately obvious in
small or negative TFP growth rates over 2007-2014. Table A.2 lists the contribution of natural
resources to GDP growth for our sample of countries over the full period, highlighting the major
positive contribution that natural resources make to GDP growth.
4. Conclusions and next steps
We began this study by emphasizing the large reliance of developing countries, and particularly
low-income countries, on natural resources as a component of wealth. The traditional approach
to TFP growth estimation has, to date, excluded natural resources, with the exception of recent
25
work of the OECD. As the methodology for measuring TFP growth indicates, excluding natural
resources can result in misleading estimates of TFP growth. The methodology also shows that
introducing natural resources as a factor of production will not always boost the measure of TFP
growth compared to traditional measures.
Our results for 74 developing countries over 1996 to 2014 confirm that including natural
resources in TFP growth estimation does have significant impacts on estimated TFP growth. In
59 of these countries, including the developing giants – China and India – including natural
resources in estimates of TFP growth yields an increase in average TFP growth compared to
traditional estimates. But including natural resources in TFP growth estimation reverses the sign
of the average growth from negative using traditional methods to positive in 16 countries. When
natural resources are included in TFP growth estimation, the results show TFP growth to be an
important contributor to GDP growth in many countries – not only China and India, but the
transition economies as well as selected African countries including Nigeria and Mozambique. A
word of caution is in order for the overall picture of African TFP growth, however – some of the
countries highlighted are subject to fragility, conflict and violence, and the data quality may
therefore suffer.
Including natural resources in TFP growth estimation tends to reduce the variability of estimates
from year to year, at least over the 1996-2014 period presented in this study. The full set of
results for 74 developing countries in the Annex shows that in only 17 countries did the
variability of TFP growth estimates with natural resources exceed the variability excluding
natural resources. This could simply reflect the period chosen, but it may also be related to the
introduction of another source of variation in the calculation of the weighted average growth rate
of factor inputs.
Table 4 ranks the differences between TFP growth with and excluding natural resources,
highlighting the top 20 countries where the former dominates. Not surprisingly, a long list of
petroleum producers – Gabon, the Islamic Republic of Iran, Nigeria, Chad, Malaysia, the Arab
Republic of Egypt, Azerbaijan, and Indonesia – appear in the top 20. But it is notable that China
and India are also on this list.
Perhaps the most troubling result of our analysis is the very low TFP growth performance of
developing countries in Latin America and the Caribbean. As seen in Figure 6, the average TFP
growth of the region as whole over 1996-2014 amounted to 0.24% including natural resources
and 0.15% excluding natural resources. This result is certainly consistent with recent analysis at
the Inter-American Development Bank (2018).
In terms of how TFP including natural resources could inform policy work at the World Bank,
one of the examples is analysis with the Long Term Growth Model (LTGM; website:
www.worldbank.org/LTGM). TFP growth is used as an input into the LTGM which is used to
analyze what growth targets are feasible for a country, what combination of growth fundamentals
(TFP, human capital growth, investment, etc.) are required to reach the growth target, and what
growth paths would prevail if current trends continue. The model also allows us to look at the
implications of growth for poverty reduction. The calculation of trend TFP growth feeds into all
these calculations. That is, it affects what growth or poverty targets are feasible, what would be
26
achieved if current trends continue, implied poverty rates and also the best way to reach those
growth or poverty goals. For countries that are dependent on natural resources—all the low-
income countries and many middle-income countries—providing a measure of TFP growth that
includes natural resources could improve analysis with LTGM.
It is important to repeat the point we made in the beginning: these are early estimates of TFP
growth including natural resources. We foresee a program of work aimed at sorting out
remaining issues in the data as well as methodology. But this initial work provides a rich picture
of the potential biases that excluding natural resources in TFP measurement may introduce in
what is a profoundly important indicator of macroeconomic performance, especially in resource
dependent economies. The evidence is that traditional methods, which exclude natural resources,
may have under-estimated TFP growth in the majority of developing countries. That said, there
are significant data issues to be addressed, and much more work needed to fully understand the
reasons behind TFP growth estimates.
Priorities for further work include assessment of the robustness of the underlying data including
possible correlation among variables, understanding the drivers behind the volatility of TFP
trends over time, cross-sectional comparisons in order to aid benchmarking; and better
understanding the role of the types of natural capital. Further work on agricultural land could
draw on the extensive literature in that field, where sectoral TFP growth estimates have
traditionally included land.
We expect that this analysis will motivate the uptake of this work at the World Bank. We
encourage colleagues to further explore this topic by accessing the full data set and an Excel-
based tool to help analysis that will be made available on the Environment and Natural
Resources GP’s intranet site by August 2018.
27
Annex I. The OECD approach to TFP measurement presented in Brandt et al. (2017)
Brandt et al. (2017) highlight two broad approaches to TFP measurement depending upon
whether estimates exist of the user cost of capital (also termed the rental value of capital). The
OECD productivity manual (OECD 2001) employs the approach of Jorgensen (1963) to measure
this user cost in discrete time,
1
Here is the price of a capital asset, is the return on capital and is the rate of depreciation of
the asset, and the final term measures capital gains.
While the OECD productivity database has detailed estimates of for most OECD countries and
selected non-members, the estimates do not exist for the vast majority of developing countries. In
Brand et al. (2017) the authors fall back to an assumption of constant returns to scale in
production in order to measure TFP growth for the Russian Federation, Mexico, Chile and South
Africa – countries where estimates of the user cost of capital are not available.
In countries where user cost estimates exist for fixed capital, the OECD approach is to measure
TFP growth by subtracting the weighted average of the growth rates of factors of production
from the growth rate of GDP; in this instance the weights are the factor costs, including the user
cost of capital, normalized to sum to 1. Where these estimates do not exist then the weights
applied to the growth rates of factors of production are simply the factor shares in value added –
wages and profits derived from fixed capital, with the latter measured as a residual. These sum to
1 by assumption.
28
Annex II. Generalization of TFP calculations for multiple natural resources
If there are two natural resources and with factor shares ̅ and ̅ , then we can
generalize expression (16). As in previous equations, the bar over the factor shares indicates that
these are Tönqvist indices.
The factor share of fixed capital with natural resources is now ̅ 1 ̅ ̅ ̅ , while
the factor share without natural resources is ̅ 1 ̅ . Applying expression (15) again, the
ratio of the growth rates of TFP with and excluding natural capital reduces to,
⋅
̅ ̅
⋅
The denominator in expression (17) is the weighted geometric mean of the growth rate of the two
natural resources, where the weights are ‘within natural resource’ factor shares. If the growth rate
of fixed capital exceeds this weighted geometric mean then the growth rate of TFP with natural
resources will exceed the growth rate without natural resources. This weighted geometric mean
is in fact the aggregate growth rate of natural resources, 1. As in expression (16), the effect
of this difference in growth rates is magnified by the overall factor share of natural resources,
̅ ̅ ̅ . This result generalizes to the case of more than two natural resources in the
obvious way, and the weighted geometric mean growth rate can be derived for sub-categories of
natural resources such as minerals and fossil fuels.
29
Annex III. TFP Growth Results by Country
Table A.1. TFP with and excluding natural resources, by time periods
1996 – 2006 2007 – 2014 1996 – 2014
TFP with TFP excluding TFP with TFP excluding TFP with TFP excluding
natural natural natural natural natural natural
Country resources resources resources resources resources resources
Armenia 7.5% 7.5% 0.1% 0.2% 4.3% 4.3%
Azerbaijan 5.6% 6.3% 1.2% -0.5% 3.8% 3.4%
Belarus 6.1% 6.0% 0.9% 0.7% 3.9% 3.7%
Benin 1.7% 1.7% 0.9% 0.9% 1.4% 1.4%
Bolivia 0.2% 0.3% 0.7% 0.6% 0.4% 0.4%
Bosnia and
9.0% 8.9% -0.2% -0.3% 5.0% 4.9%
Herzegovina
Botswana -1.2% -1.4% -1.3% -2.1% -1.2% -1.7%
Brazil 0.2% 0.3% 0.8% 0.8% 0.5% 0.5%
Bulgaria -0.7% -1.0% -1.7% -1.9% -1.1% -1.3%
Burkina Faso 3.1% 2.8% 0.1% -0.5% 1.8% 1.4%
Burundi -1.3% -1.1% 2.5% 0.0% 0.3% -0.6%
Cameroon 1.5% 1.3% 0.7% 0.4% 1.1% 0.9%
Central African
1.2% 1.4% -3.5% -3.6% -0.8% -0.8%
Republic
Chad 3.0% 3.6% 2.0% -0.5% 2.6% 1.9%
China 5.1% 4.7% 4.5% 4.0% 4.9% 4.4%
Colombia -0.1% -0.2% 0.2% 0.2% 0.1% 0.0%
Costa Rica 0.6% 0.5% 0.6% 0.4% 0.6% 0.4%
Côte d'Ivoire 0.5% 0.8% 2.6% 2.3% 1.4% 1.4%
Djibouti -1.1% -1.1% 1.2% 1.0% -0.1% -0.2%
Dominican
1.0% 0.9% 1.9% 1.2% 1.4% 1.0%
Republic
Ecuador -0.1% -0.1% 1.0% 0.3% 0.4% 0.1%
Egypt, Arab
0.3% -0.2% -0.3% -1.0% 0.0% -0.6%
Rep.
Fiji 0.7% 0.6% 0.4% 0.3% 0.6% 0.5%
Gabon 0.3% -0.8% 1.8% 0.1% 0.9% -0.4%
Georgia 6.6% 6.4% 3.6% 3.5% 5.3% 5.2%
Guatemala -0.2% -0.2% 1.1% 1.1% 0.4% 0.4%
Guinea 0.0% -0.5% -2.0% -4.2% -0.8% -2.1%
Honduras 0.8% 0.7% -0.2% -0.4% 0.4% 0.3%
India 2.3% 2.1% 1.9% 1.5% 2.1% 1.8%
Indonesia -0.7% -1.0% 2.0% 1.6% 0.5% 0.1%
Iran, Islamic
1.3% 0.7% 1.4% -0.4% 1.3% 0.3%
Rep.
30
1996 – 2006 2007 – 2014 1996 – 2014
TFP with TFP excluding TFP with TFP excluding TFP with TFP excluding
natural natural natural natural natural natural
Country resources resources resources resources resources resources
Iraq 6.8% 6.9% -0.1% 0.3% 3.8% 4.1%
Jamaica -0.7% -0.7% -1.2% -1.3% -0.9% -0.9%
Jordan 1.9% 1.8% -0.7% -0.8% 0.8% 0.7%
Kazakhstan 5.1% 6.0% 2.4% 2.0% 3.9% 4.3%
Kenya 0.3% 0.3% 0.7% 0.3% 0.5% 0.3%
Kyrgyz
2.7% 2.8% 2.5% 2.2% 2.6% 2.6%
Republic
Lao PDR 0.9% 0.7% 0.4% 0.1% 0.7% 0.5%
Lebanon -0.1% -0.2% 0.5% 0.4% 0.1% 0.1%
Lesotho 0.5% 0.4% 1.5% 1.3% 1.0% 0.8%
Macedonia,
0.5% 0.5% -1.1% -1.1% -0.1% -0.2%
FYR
Malaysia 1.3% 0.6% 0.7% 0.1% 1.0% 0.4%
Mauritania 1.0% 0.6% 0.2% -3.6% 0.7% -1.2%
Mauritius 1.5% 1.5% 0.6% 0.6% 1.1% 1.1%
Mexico 0.1% 0.0% -0.7% -1.1% -0.3% -0.4%
Moldova 3.1% 2.9% 3.8% 3.8% 3.4% 3.2%
Mongolia 3.2% 3.0% 4.9% 5.4% 3.9% 4.0%
Morocco 0.8% 0.9% 0.5% 0.4% 0.7% 0.7%
Mozambique 5.2% 4.6% -0.7% -2.1% 2.7% 1.7%
Namibia 1.1% 1.0% -1.3% -1.6% 0.1% -0.1%
Nicaragua 0.8% 0.8% 0.7% 0.7% 0.7% 0.8%
Niger 0.7% 1.5% 1.4% 1.3% 1.0% 1.4%
Nigeria 3.8% 3.3% 2.2% 0.8% 3.1% 2.2%
Panama 0.9% 0.8% 0.2% 0.1% 0.6% 0.5%
Paraguay -1.2% -1.2% 2.1% 2.2% 0.2% 0.2%
Peru -0.3% -0.3% 0.7% 0.2% 0.1% -0.1%
Philippines 1.1% 1.1% 1.7% 1.9% 1.4% 1.4%
Romania 3.2% 3.0% 0.2% 0.0% 1.9% 1.7%
Rwanda 4.5% 4.7% 1.6% 0.5% 3.2% 2.9%
São Tomé and
1.1% 1.1% 1.7% 1.6% 1.3% 1.3%
Príncipe
Senegal 1.2% 1.0% -1.0% -1.2% 0.2% 0.1%
Sierra Leone 1.8% 2.9% 2.5% 4.3% 2.1% 3.5%
South Africa 1.6% 1.5% 0.1% -0.2% 0.9% 0.8%
Sri Lanka 2.0% 1.9% 3.5% 3.4% 2.6% 2.5%
Suriname 0.4% 0.2% 0.2% 0.3% 0.3% 0.3%
Eswatini 1.5% 1.5% 2.1% 2.1% 1.7% 1.7%
Tajikistan 5.2% 5.5% 6.6% 7.2% 5.8% 6.2%
31
1996 – 2006 2007 – 2014 1996 – 2014
TFP with TFP excluding TFP with TFP excluding TFP with TFP excluding
natural natural natural natural natural natural
Country resources resources resources resources resources resources
Tanzania 2.8% 2.8% 0.2% -0.3% 1.7% 1.5%
Thailand 0.7% 0.7% 0.9% 0.9% 0.8% 0.8%
Togo 0.5% 0.4% 0.4% 0.6% 0.5% 0.5%
Tunisia 1.8% 1.8% 1.0% 0.5% 1.5% 1.2%
Turkey 1.5% 1.4% -0.9% -0.9% 0.5% 0.4%
Ukraine 4.2% 4.2% 0.2% 0.2% 2.5% 2.5%
Zimbabwe -4.5% -4.5% 1.6% 1.6% -2.0% -2.0%
Income Group
Low income 1.4% 1.4% 0.6% -0.1% 1.1% 0.8%
Lower middle
1.6% 1.4% 1.7% 1.2% 1.6% 1.3%
income
Upper middle
2.6% 2.4% 2.5% 2.1% 2.5% 2.3%
income
High income:
2.1% 2.1% 0.6% -0.1% 1.5% 1.1%
non-OECD
High income:
0.9% 0.8% 0.0% 0.0% 0.5% 0.5%
OECD
Developing Region (low and middle income)
East Asia &
3.9% 3.5% 3.9% 3.5% 3.9% 3.5%
Pacific
Europe &
2.8% 2.8% 0.1% -0.1% 1.6% 1.6%
Central Asia
Latin America
0.1% 0.1% 0.4% 0.2% 0.2% 0.2%
& Caribbean
Middle East &
2.2% 2.0% 0.6% -0.2% 1.5% 1.1%
North Africa
South Asia 2.3% 2.1% 2.0% 1.6% 2.2% 1.9%
Sub-Saharan
Africa (no 1.0% 0.9% 0.7% 0.1% 0.9% 0.6%
NGA, ZAF)
32
Table A.2. Natural capital contribution to GDP growth (1996-2014)
GDP (average growth Natural capital
Country rate, 1996 to 2014) contribution
Armenia 6.5% 2.8%
Azerbaijan 10.1% 16.9%
Belarus 6.1% 0.2%
Benin 4.5% 8.8%
Bolivia 4.1% 12.1%
Bosnia and Herzegovina 9.2% 1.7%
Botswana 4.8% 0.3%
Brazil 3.0% 6.0%
Bulgaria 2.7% 0.8%
Burkina Faso 6.1% 8.6%
Burundi 2.3% 10.9%
Cameroon 4.0% 3.8%
Central African Republic -0.3% -36.9%
Chad 7.4% 16.6%
China 9.5% 3.0%
Colombia 3.5% 4.7%
Costa Rica 4.1% 1.4%
Côte d'Ivoire 2.6% 4.7%
Djibouti 3.0% 3.7%
Dominican Republic 5.4% -1.4%
Ecuador 3.6% 4.1%
Egypt, Arab Rep. 4.3% 0.4%
Fiji 2.2% -1.9%
Gabon 2.0% -34.0%
Georgia 5.9% -1.1%
Guatemala 3.6% 3.2%
Guinea 3.0% 10.5%
Honduras 3.7% 2.7%
India 6.8% 2.1%
Indonesia 4.1% 1.2%
Iran, Islamic Rep. 3.6% -5.3%
Iraq 7.4% 22.7%
Jamaica 0.4% 3.7%
Jordan 4.7% 1.8%
Kazakhstan 6.2% 15.0%
Kenya 4.0% 7.4%
Kyrgyz Republic 4.7% 7.5%
Lao PDR 7.1% 14.0%
33
GDP (average growth Natural capital
Country rate, 1996 to 2014) contribution
Lebanon 3.7% -0.4%
Lesotho 3.8% 2.5%
Macedonia, FYR 2.8% 5.0%
Malaysia 4.8% -3.4%
Mauritania 4.2% 1.9%
Mauritius 4.5% 0.2%
Mexico 2.9% -0.3%
Moldova 3.0% -3.1%
Mongolia 6.7% 13.9%
Morocco 4.5% 3.6%
Mozambique 8.8% 3.4%
Namibia 4.6% 0.8%
Nicaragua 3.9% 5.2%
Niger 4.5% 25.6%
Nigeria 6.6% 9.3%
Panama 6.1% -0.1%
Paraguay 3.0% 7.9%
Peru 4.7% 2.8%
Philippines 4.7% 3.8%
Romania 2.6% -1.7%
Rwanda 8.3% 7.5%
São Tomé and Príncipe 4.2% 3.8%
Senegal 4.0% 2.8%
Sierra Leone 5.8% 37.5%
South Africa 3.1% 2.2%
Sri Lanka 5.4% 0.2%
Suriname 3.7% 14.3%
Eswatini 3.3% 2.3%
Tajikistan 5.8% 4.9%
Tanzania 6.1% 6.7%
Thailand 3.2% 3.0%
Togo 3.3% 17.0%
Tunisia 4.1% -0.6%
Turkey 4.0% 1.1%
Ukraine 1.6% -1.1%
Zimbabwe -0.4% -16.9%
34
References
Brandt, N., P. Schreyer, and V. Zipperer. 2017. “Productivity Measurement with Natural
Capital.” Review of Income and Wealth 63 (s1): s7–s21. doi: 10.1111/roiw.12247.
Hulten, C.R., 2010. Growth Accounting. Chapter 23 in Hall, B. and N. Rosenberg, Handbook of
the Economics of Innovation. Amsterdam: Elsvier.
Feenstra, R. C., R. Inklaar, and M. P. Timmer. 2015. "The Next Generation of the Penn World
Table" American Economic Review, 105(10), 3150-3182, available for download at
www.ggdc.net/pwt.
Jorgenson, Dale. “Capital Theory and Investment Behavior.” American Economic Review 53, no.
2 (1963): 247-259.
Inter-American Development Bank, 2018. A Mandate to Grow. Co-ordinated by E. Cavallo and
A. Powell. Latin American and Caribbean Macroeconomic Report. Washington DC: IADB.
International Monetary Fund, Fiscal Affairs Department. 2017. “Estimating the stock of public
capital in 170 countries, Jan 2017 update.”
Lange, G., Q. Wodon, K. Carey (eds.). 2018, The Changing Wealth of Nations 2018, , (eds),
Washington DC: The World Bank.
OECD, 2016. Environmentally Adjusted Multifactor Productivity: Methodology and Empirical
Results for OECD and G20 countries. OECD Green Growth Papers 2016-04. Paris: OECD.
35