metadata: authors: 0: author: Hamilton,Kirk E.
1: author: Naikal,Esther G.
2: author: Lange,Glenn-Marie
count: World
docna: 0: docna: Natural Resources and Total Factor
            Productivity Growth in Developing Countries : Testing A New Methodology
docty: Policy Research Working Paper
subtopic: Global Environment,Energy and Natural Resources,Coastal and Marine Resources,Food Security,Oil Refining & Gas Industry,Inequality
teratopic: Energy,Water Resources,Agriculture,Industry,Poverty Reduction
abstracts: cdata!: Estimates of total factor productivity
            growth, a measure of increases in the efficiency of
            production, have traditionally been based on a two-factor
            model of labor and fixed capital. Because profits are
            measured residually in the System of National Accounts, they
            implicitly include rents on natural resource exploitation,
            with the result that the contribution of fixed capital to
            growth in the inputs to gross domestic product is misstated,
            particularly in resource dependent developing countries.
            This leads to incorrect measures of total factor
            productivity growth. Using data on natural resources from
            the World Bank's Wealth of Nations database and methods
            combining the Solow growth accounting model with recent work
            at the Organisation for Economic Co-operation and
            Development, this paper makes new estimates of total factor
            productivity growth for 74 developing countries over
            1996-2014. In the aggregate, including natural resources as
            a factor of production increases estimated total factor
            productivity growth across all country income classes and
            regions of the world when compared with the traditional
            two-factor approach. In addition, the estimated total factor
            productivity growth including natural resources is less
            volatile over time in the great majority of countries
            compared with the traditional approach. The availability of
            World Bank data on natural resource quantities and rents for
            a wide range of countries suggests that natural resources
            should be included in total factor productivity growth
            estimation going forward. Further research could focus on
            the distinctive roles played by different natural resource endowments.
repnme: repnme: Natural Resources and Total Factor
            Productivity Growth in Developing Countries : Testing A New Methodology
display_title: Natural Resources and Total Factor
            Productivity Growth in Developing Countries : Testing A New Methodology
listing_relative_url: /research/2019/01/30763939/natural-resources-total-factor-productivity-growth-developing-countries-testing-new-methodology
topicv3: Agriculture and Food Security m1326205 435,Natural Resources m1326446 1979,Poverty m1326687 695,Environment, Natural Resources and Blue
            Economy m1327391 566,Petroleum Industry m1583790 4710
publishtoextweb_dt: 2019-01-16T00:00:00Z
historic_topic: Energy,Water Resources,Agriculture,Industry,Poverty Reduction
no_of_pages: 37
author_keys: 0: author_key: 123840
1: author_key: 998534
2: author_key: 1041927
model_extractions: 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 increases in the efficiency of production, have traditionally been based on a two-factor model of labor and fixed capital. Because profits are measured residually in the System of National Accounts, they implicitly include rents on natural resource exploitation, with the result that the contribution of fixed capital to growth in the inputs to gross domestic product is misstated, particularly in resource dependent developing countries. This leads to incorrect measures of total factor productivity growth. Using data on natural resources from the World Bank’s Wealth of Nations database and methods combining the Solow growth accounting model with recent work at the Organisation for Economic Co-operation and Development, this paper makes new estimates of total factor productivity growth for 74 developing countries over 1996–2014. In the aggregate, including natural resources as a factor of production increases estimated total factor productivity growth across all country income classes and regions of the world when compared with the traditional two-factor approach. In addition, the estimated total factor productivity growth including natural resources is less volatile over time in the great majority of countries compared with the traditional approach. The availability of World Bank data on natural resource quantities and rents for a wide range of countries suggests that natural resources should be included in total factor productivity growth estimation going forward. Further research could focus on the distinctive roles played by different natural resource endowments.

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 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
# **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 OECDmember 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.

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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 A, and its growth rate as g�. Similarly, we denote GDP as Y and its growth rate as g�. We assume that the inputs to production can be categorized as fixed capital K, with growth rate g�, labor L with growth rate g�, and natural resources N, with growth rate g�. If we assume that there are corresponding shares of these factors in value added, denoted s�, s� and s�, 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
s� �1 �s� in the case with no explicit measure of natural resource rents, and s� �1 �s� �s� when such a measure does exist. s� 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,

# g� �g� ��s�g� �s�g��

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, s� and s�.

If we have measures of the natural resource rents and so can measure the share of rents in value added s� and the growth rate of resource inputs g�, then s� is correspondingly lower and we can write the formula for TFP growth measurement including natural resource inputs as, g� �g� ��s�g� �s�g� �s�g�� 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 g���� and with natural resources as g����, it will be shown below that Where s� is the GDP share of natural resource rents, while g� and g� 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
Y = AF(K,L) FN) (Fy— f’) AF(K,L,N) Y = A((F(K,L,N) — fN) = A((F(K,L,N) — fN)+AFe + AF, +A(Fy —f)N = ~~“~~ Y +AF,K + AF,L + A(Fy — f)N ~~Y~~ <u>Y</u> ~~y~~ o ~~A,~~ <u>A.A</u> A ~~PKK~~ <u>AFKKY</u> ~~K~~ <u>KK</u> ,| ~~AFiL~~ <u>AF,LY</u> ~~L~~ <u>LL</u> ~~AEn~~~ , <u>A(Fy-f)NY</u> ~~PN~~ **~~N~~** <u>WN AY AFKK K_ AF,L L_ A(Fy-f)N N</u> ~~AY~~ Y K Y L Y N
<u>fN N</u> Y N <u>AFKK</u> <!-- Start of picture text --> A<br><!-- End of picture text --> ~~A~~ A Gy —Sk k7~—S_L L—SN N x =1-s,-Sy AWNR _= gy —(1-s,-Sy)9x-S, 1 - SNN 1-s, AXNR _=Gy ~A-S1)9xK - SL 1 AWNR — ga”XNR _= Sn(9x — Jn) 1 2 Ny No Ny No N =SyNy +SNy N = ~~ten,(—+—SNy~~ Ni 4+ ~~—4—~~ <u>svi</u> ~~SNo~~ <u>+5Ny</u> N>)
<!-- Start of picture text --> dA<br>a nA) = A t—1<br>u(t) =<br>FL (t)L(t)(L(t 4) = Fult-DL(t-1 (t-1)L(t-1)<br>Y(t) L— 1) ¥(t—1)<br>1 ge. ( Fuct ect) , Fu (t-1)L(t-1)<br>= 0.5 ( y(t) + Y(t-1) )<br>In At) )_y (YO) =< 7 (KO) _- | L(t) ) = Ne)<br>(SS) = In ES) Sx «In (AS) 5, -In (ZS) SN ng»<br>A(t) _ (-¥®.) (_K® \SK (1m \ Ss (wy SN<br>A(t-1) (FS) (a5) (ZS) (<5)<br>A(t) __ A(t)—A(t-1) _<br>A(t-1).—A(t-1) +1=gat1<br>a= (Gy +1)- (on + Dg, + DE (gy +1)" - 1<br>x =1—S, —Sy<br><!-- End of picture text -->
_natural resources_ as s̅� �1 �s̅�. Applying expression (15), the ratio of the growth rates of TFP with and excluding natural capital reduces to, 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 n years we must calculate the geometric mean of g��t��1 over all years, then subtract 1. Here g��t� is the growth rate of TFP in year t: 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 <u>https://datacatalog.worldbank.org/dataset/wealthaccounting.</u>

# **Table 1. Data sources**

|**Indicator**|**Source**|
|---|---|
|**Resource rents**: oil, natural gas, coal,<br>minerals, cropland, pastureland, timber|_The Changing Wealth of Nations 2018_|
|**Production**: oil, natural gas, coal, minerals,<br>timber|_The Changing Wealth of Nations 2018_|
|Agriculture Gross Production Index|Food and Agriculture Organization of the<br>United Nations|
|Employed labor compensation|Penn World Table 9.0 (Feenstra et al 2015)|

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|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.<sup>4</sup>

# _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, s� 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.<sup>5</sup> 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.<sup>6</sup> 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 (g� in the second formula) by the returns to capital measured in the SNA (s� 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, g� 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.<sup>7</sup> 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 revisited 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.

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**Figure 3. Decomposition of GDP growth for 25 countries with the highest share of TFP growth with natural resources, ranked by GDP growth**

<!-- Start of picture text --> Contribution to GDP Growth<br>(long‐term averages, 1996‐2014)<br>Azerbaijan<br>China<br>Bosnia and Herzegovina<br>Mozambique<br>Rwanda<br>Chad<br>Iraq<br>Lao PDR<br>India<br>Mongolia<br>Nigeria<br>Armenia<br>Kazakhstan<br>Burkina Faso<br>Panama<br>Belarus<br>Tanzania<br>Georgia<br>Sierra Leone<br>Tajikistan<br>Sri Lanka<br>Dominican Republic<br>Malaysia<br>Botswana<br>Jordan<br>‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%<br>Labor Produced capital Natural capital TFP<br><!-- End of picture text --> 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**

<!-- Start of picture text --> GDP Growth and TFP<br>(long‐term averages, 1996‐2014)<br>‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%<br>Azerbaijan<br>China<br>Bosnia and Herzegovina<br>Mozambique<br>Rwanda<br>Chad<br>Iraq<br>Lao PDR<br>India<br>Mongolia<br>Nigeria<br>Armenia<br>Kazakhstan<br>Burkina Faso<br>Panama<br>Belarus<br>Tanzania<br>Georgia<br>Sierra Leone<br>Tajikistan<br>Sri Lanka<br>Dominican Republic<br>Malaysia<br>Botswana<br>Jordan<br>‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%<br>TFP GDP<br><!-- End of picture text --> 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**

<!-- Start of picture text --> Average TFP growth 1996‐2014<br>3.00%<br>2.50%<br>2.00%<br>1.50%<br>1.00%<br>0.50%<br>0.00%<br>Low income Lower middle Upper middle High income: non‐ High income: OECD<br>income income OECD<br>With natural resources Excluding natural resources<br><!-- End of picture text --> 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**

<!-- Start of picture text --> Average TFP growth 1996‐2014<br><!-- End of picture text --> <!-- Start of picture text --> 4.50%<br>4.00%<br>3.50%<br>3.00%<br>2.50%<br>2.00%<br>1.50%<br>1.00%<br>0.50%<br>0.00%<br>East Asia & Pacific Europe & Central Latin America & Middle East & South Asia (low Sub‐Saharan<br>(low and middle Asia (low and Caribbean (low North Africa (low and middle Africa (no NGA,<br>income) middle income) and middle and middle income) ZAF)<br>income) income)<br>With natural resources Excluding natural resources<br><!-- End of picture text --> 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**

<!-- Start of picture text --> Nigeria: Total Factor Productivity<br>35.00%<br>30.00%<br>25.00%<br>20.00%<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐5.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> 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 <!-- Start of picture text --> 6.00%<br>5.00%<br>4.00%<br>3.00%<br>2.00%<br>1.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐1.00%<br>‐2.00%<br>‐3.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> 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** **<u>growth over 1996-2014</u>**

||GDP<br>growth<br>rate|Natural<br>capital<br>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**

||GDP<br>growth rate|Natural<br>capital<br>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**

||<br>TFP growth<br>excluding<br>natural resources|TFP growth<br>Including<br>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 19962014**

<!-- Start of picture text --> Guinea Agriculture share of total wealth 55.2% TFP growth with natural resource ‐0.8%<br>TFP growth excluding natural resource ‐2.1%<br>Guinea: Total Factor Productivity<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐2.00%<br>‐4.00%<br>‐6.00%<br>TFP, with natural resources TFP, excluding natural resources<br>.<br>Centre‐Afrique Agriculture share of total wealth 44.0% TFP growth with natural resource ‐0.8%<br>TFP growth excluding natural resource ‐0.8%<br><!-- End of picture text --> <!-- Start of picture text --> Central African Republic: Total Factor Productivity<br>10.00%<br>5.00%<br>0.00%<br>‐5.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐10.00%<br>‐15.00%<br>‐20.00%<br>‐25.00%<br>‐30.00%<br>‐35.00%<br>‐40.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> 21
Agriculture share of total wealth <!-- Start of picture text --> Kyrgyz Rep<br><!-- End of picture text --> <!-- Start of picture text --> 40.9% TFP growth with natural resource 2.6%<br>TFP growth excluding natural resource 2.6%<br><!-- End of picture text --> <!-- Start of picture text --> Kyrgyz Republic: Total Factor Productivity<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐2.00%<br>‐4.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text -->

# _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 19962014**

<!-- Start of picture text --> Iraq Petroleum share of total wealth 67.3% TFP growth with natural resource 3.8%<br>TFP growth excluding natural resource 4.1%<br>Iraq: Total Factor Productivity<br>60.00%<br>50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br>‐10.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐20.00%<br>‐30.00%<br>‐40.00%<br>TFP, with natural resources TFP, excluding natural resources<br>Kuwait Petroleum share of total wealth 52.4% TFP growth with natural resource ‐0.1%<br>TFP growth excluding natural resource ‐1.6%<br><!-- End of picture text --> <!-- Start of picture text --> Kuwait: Total Factor Productivity<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐5.00%<br>‐10.00%<br>‐15.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> 23
Saudi Arabia Petroleum share of total wealth 48.5% TFP growth with natural resource 1.2% TFP growth excluding natural resource ‐0.1% <!-- Start of picture text --> Saudi Arabia: Total Factor Productivity<br>6.00%<br>5.00%<br>4.00%<br>3.00%<br>2.00%<br>1.00%<br>0.00%<br>‐1.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐2.00%<br>‐3.00%<br>‐4.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> _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**

<!-- Start of picture text --> Niger Total resources share of total wealth 73.0% TFP growth with natural resource 1.0%<br>TFP growth excluding natural resource 1.4%<br>Niger: Total Factor Productivity<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐2.00%<br>‐4.00%<br>‐6.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> 24
<!-- Start of picture text --> Mongolia Total resources share of total wealth 63.3% TFP growth with natural resource 3.9%<br>TFP growth excluding natural resource 4.0%<br>Mongolia: Total Factor Productivity<br>12.00%<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐2.00%<br>‐4.00%<br>‐6.00%<br>TFP, with natural resources TFP, excluding natural resources<br>Mauritania Total resources share of total wealth 59.8% TFP growth with natural resource 0.7%<br>TFP growth excluding natural resource ‐1.2%<br>Mauritania: Total Factor Productivity<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐5.00%<br>‐10.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> 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 lowincome 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 Excelbased 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 u (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, u�t��p�t��r�t��d�t����p�t��p�t�1�� Here p is the price of a capital asset, r is the return on capital and d is the rate of depreciation of the asset, and the final term measures capital gains.

While the OECD productivity database has detailed estimates of u 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
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# **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**|
|---|---|---|---|---|---|---|
|**Country**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**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<br>Herzegovina|9.0%|8.9%|-0.2%|-0.3%|5.0%|4.9%|
|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<br>Republic|1.2%|1.4%|-3.5%|-3.6%|-0.8%|-0.8%|
|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<br>Republic|1.0%|0.9%|1.9%|1.2%|1.4%|1.0%|
|Ecuador|-0.1%|-0.1%|1.0%|0.3%|0.4%|0.1%|
|Egypt, Arab<br>Rep.|0.3%|-0.2%|-0.3%|-1.0%|0.0%|-0.6%|
|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<br>|-0.7%|-1.0%|2.0%|1.6%|0.5%|0.1%|
|Iran, Islamic<br>Rep.|1.3%|0.7%|1.4%|-0.4%|1.3%|0.3%|

30
||**1996–**|**2006**|**2007**|**– 2014**|**1996**|**– 2014**|
|---|---|---|---|---|---|---|
|**Country**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|
|<br>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<br>Republic|2.7%|2.8%|2.5%|2.2%|2.6%|2.6%|
|<br>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,<br>FYR|0.5%|0.5%|-1.1%|-1.1%|-0.1%|-0.2%|
|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<br>Príncipe|1.1%|1.1%|1.7%|1.6%|1.3%|1.3%|
|<br>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%|

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||**1996**|**– 2006**|**2007**|**– 2014**|**1996**|**– 2014**|
|---|---|---|---|---|---|---|
|**Country**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**resources**|**TFP with**<br>**natural**<br>**resources**|**TFP excluding**<br>**natural**<br>**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<br>income|1.6%|1.4%|1.7%|1.2%|1.6%|1.3%|
|Upper middle<br>income|2.6%|2.4%|2.5%|2.1%|2.5%|2.3%|
|High income:<br>non-OECD|2.1%|2.1%|0.6%|-0.1%|1.5%|1.1%|
|High income:<br>|0.9%|0.8%|0.0%|0.0%|0.5%|0.5%|
|OECD<br>**Developing Regio**<br>E Ai &|**n (low and midd**|**le income)**|||||
|ast sa<br>Pacific|3.9%|3.5%|3.9%|3.5%|3.9%|3.5%|
|Europe &<br>Central Asia|2.8%|2.8%|0.1%|-0.1%|1.6%|1.6%|
|Latin America<br>& Caribbean|0.1%|0.1%|0.4%|0.2%|0.2%|0.2%|
|Middle East &<br>North Africa|2.2%|2.0%|0.6%|-0.2%|1.5%|1.1%|
|South Asia|2.3%|2.1%|2.0%|1.6%|2.2%|1.9%|
|Sub-Saharan<br>Africa (no<br>NGA, ZAF)|1.0%|0.9%|0.7%|0.1%|0.9%|0.6%|

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**Table A.2. Natural capital contribution to GDP growth (1996-2014)**

|**Country**|**GDP (average growth**<br>**rate, 1996 to 2014)**|**Natural capital**<br>**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%|

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|**Country**|**GDP (average growth**<br>**rate, 1996 to 2014)**|**Natural capital**<br>**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%|
|<br>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%|
|<br>Tunisia|4.1%|-0.6%|
|Turkey|4.0%|1.1%|
|Ukraine|1.6%|-1.1%|
|Zimbabwe|-0.4%|-16.9%|

34