Updated: 25 October 2017.

Productivity and its Determinants:

Innovation, Education, Efficiency, Infrastructure, and Institutions

Young Eun Kim Norman V. Loayza

World Bank World Bank

Abstract

Based on an extensive literature review, we identify the five main determinants of economic

productivity as innovation, education, market efficiency, physical infrastructure, and institutional

infrastructure (institutions). We construct indexes representing each main determinant as a linear

combination of representative indicators, and assess the relative contribution of the indexes to the

variation of productivity across 65 countries for the period 1985−2011. We quantify the relationship

between the productivity growth and an overall determinant index. The results show that the variation of

productivity is the most sensitive to physical infrastructure, followed by education, market efficiency,

innovation, and institutional infrastructure. The overall determinant index has a positive relationship

with the productivity growth.

Keywords: Productivity, Innovation, Education, Efficiency, Infrastructure, Institutions, Growth

JEL: D24, O33, I25, G14, H54, O43, O47

Acknowledgement: We would like to thank Steven Michael Pennings, Ha Nguyen, and participants in a

conference and seminars at the World Bank for helpful comments. The views expressed herein are those

of the authors and do not necessarily reflect the views of the World Bank.

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1. Introduction

With the same amount of inputs, including physical capital and labor, some countries, sectors, and firms

produce more and others less. This difference depends on how efficient they are in allocating and using

resources to achieve high output. According to classical economic theory, productivity is defined as the

residual of output that is not explained by the direct contribution of input resources. This residual is

commonly referred to as total factor productivity (TFP) (Solow 1956).

In the 1950s, Solow and Swan developed a growth model in which changes in physical capital, labor,

and TFP determine a growth rate (Solow 1956; Swan 1956). A main drawback of this model is that TFP

that determines a long-term growth rate is assumed to be exogenous: that is, given outside the model. In

mid-1980s, theoretical economists attempted to resolve the main drawback with this assumption. For

example, Romer (1987, 1990) incorporated technological advances through research and development

efforts into the growth model as a driver of long-run growth. Lucas (1988) argued in a model that the

accumulation of human capital through education is a key factor for long-run growth. Rebelo (1991)

included human capital under aggregate capital in a model that suggests that continuous investment in

human capital leads to long-term growth. Barro (1990) incorporated tax-financed government goods into

a growth model, and suggested that public infrastructure could affect capital accumulation and growth in

the long run. In all these cases, the suggested mechanisms to increase productivity are ways of

explaining (or endogenizing) economic growth in the long run without resorting to exogenous

productivity changes.

As massive micro-level data became available in the 1990s, researchers began conducting empirical

studies using data at the industry and firm levels. This trend has confirmed the growth models developed

in mid-1980s and onward and has led to new findings that could not be attained using country-level data.

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For example, some authors showed that reallocation of inputs into innovating and growing firms

explains more than 50 percent of aggregate productivity in developed countries (Jorgenson, Ho, and

Stiroh 2005, 2008; Lentz and Mortensen 2008). Bloom, Draca, and Van Reenen (2016) showed that

Chinese import competition through a trade agreement increased productivity in European markets, as

less productive firms exited the markets and competitive firms innovated. Some studies examine how

regulations affect firms’ productivity. Bridgman, Qi, and Schmitz (2009) showed that regulations in the

U.S sugar market served as a disincentive to farmers and refiners from making their production more

efficient. Petrin and Sivadasan (2011) found that raising firing costs reduced allocative efficiency in

markets in Chile and decreased productivity.

Given the numerous studies on growth and productivity published in the past few decades, we want to

take stock of its main conceptual conclusions and synthesize its quantitative implications. Based on an

extensive literature review, TFP determinants are categorized into five components (Kim, Loayza, and

Meza-Cuadra Balcazar 2016):

1. Innovation: Creating new technologies leads to the development of high value-added activities

and improves the performance of existing economic activities. Historically, a small number of

countries have created new technologies, while many other countries have adopted the new

technologies through adaptation, trade, and foreign direct investment (Coe, Helpman, and

Hoffmaister 1997; de Mello 1999).

2. Education: Advancing knowledge and skills, with strong basic foundation and sufficient

specialization, is necessary for adopting, attaining, and spreading new and better technologies,

production processes, and outputs (Benhabib and Spiegel 1994; Erosa, Koreshkova, and

Restuccia 2010).

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3. Market efficiency: Timely and effective allocation of human and physical capital enhances

overall productivity, as inefficient firms exit markets, efficient firms grow, and new firms

emerge (Hsieh and Klenow 2009). The nature and quality of regulations are often related to

efficiency. Rigid regulations reduce flexibility in resource allocation in markets and decrease

productivity (Nicoletti and Scarpetta 2003).

4. Physical infrastructure: Transport, paved roads, telecommunication, stable electricity supply,

access to improved water and sanitation, and other tangible infrastructure provide timely and

cost-effective access to markets, and good physical environments for overall economic activities

(Straub 2008; Galiani, Gertler, and Schargrodsky 2005).

5. Institutional infrastructure: High-quality institutions provide friendly environments and policies

that lead to economic development. Governance and economic institutions are important

components of institutions and their quality is generally associated with productivity (Hall and

Jones 1999; Easterly and Levine 2003; Acemoglu, Johnson, and Robinson 2004; Rodrik,

Subramanian, and Trebbi 2004).

The objectives of the paper are identifying the main determinants of productivity, proposing proxies to

measure them, and assessing their relative importance as drivers of TFP growth at the country level. To

achieve these objectives, we first conduct an extensive literature review on productivity that focuses not

only on concepts and theories but also on empirical studies. Then, we propose and conduct a

straightforward way of measuring TFP for each country for the past three decades, from 1985 to 2011.

Third, we construct indexes representing the main determinants of TFP by the five categories described

above. And, finally, we measure both the relative contribution of each determinant category to the

variation of TFP and their overall effect on TFP growth, results of which are also used to build a TFP

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module in the extended Long-Term Growth Model software, a growth prediction tool developed by the

World Bank (Sinha 2017).

2. Literature review

To identify the main drivers of TFP, we conduct a literature review by focusing on papers published

from 1990 to 2015, starting with the reviews conducted by Isaksson (2007) and Syverson (2011). Key

search terms are “total factor productivity,” “economic growth,” and “determinants.” We filter papers

based on abstracts and main texts by choosing ones that focus on developing as well as developed

countries and present a quantitative relationship between productivity and its determinants of interest.

We select papers that examine time-variant determinants that a country can improve either through

market forces or by public policy decision and implementation (Appendix A for the list of the papers).

Based on the literature review, determinants of productivity are categorized into five components:

innovation, education, market efficiency, physical infrastructure, and institutional infrastructure.

Innovation

We classify papers on innovation into those related to creation of new technology and to adoption of

new technology.

Creation of new technology

A limited number of countries have created new technologies based on sufficient investment in research

and development (R&D) from the public and private sectors and an advanced level of human capacity

and capital intensity (Furman and Hayes 2004; Griffith, Redding, and Reenen 2004). Many studies show

that creating new technologies is positively associated with TFP, using investment in R&D and the

number of patents and scientific and technological journal publications as indicators for new

technologies (Nadiri 1993; Chen and Dahlman 2004; Guellec and van Pottelsberghe de la Potterie 2004;

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Abdih and Joutz 2006). An important role of new technology is well described in studies on information

technology (IT) in 1990s. For example, Jorgenson, Ho, and Stiroh (2008) and Oliner, Sichel, and Stiroh

(2008) show that IT played a central role in accelerating productivity in the United States (US) from

1995 to 2000 after the lackluster pace of productivity growth in the 1970s and 1980s. The comparison of

Europe and the US highlights the critical role of new technologies. Van Ark, O’Mahony, and Timmer

(2008) show that the productivity slowdown in Europe during the 1990s and 2000s is attributable to the

slower emergence of knowledge economy, the lower contribution of IT to growth, and the smaller share

of technology-producing industries as compared to the US.

Adoption of new technology

Most countries adopt technologies rather than create new ones. Technological adoption from frontier

countries can occur through foreign direct investment (FDI), trade, and knowledge diffusion. Studies

show that to enable FDI and trade to contribute to economic growth, host countries need to be prepared

with an advanced level of human capacity to learn new technologies, and have well-developed financial

markets to benefit from the inflows of foreign capital and increased imports and exports (Borensztein,

De Gregorio, and Lee 1998; Alfaro, Kalemli-Ozcan, and Sayek 2009; Chang, Kaltani, and Loayza

2009).

Studies on the relationship between FDI and productivity yield mixed conclusions, possibly because a

positive effect depends on having favorable business market conditions. Some studies suggest that FDI

is significantly associated with productivity improvement because foreign ownership leads to more

productive restructuring in investment outlays and employment, enhances integration into the global

economy, and fosters innovation activities (Arnold and Javorcik 2009; Fernandes and Paunov 2012;

Newman et al. 2015). However, other studies argue that FDI has negative impacts ─such as foreign

firms gaining markets and market share and crowding out domestic firms─ and thus leads to a decrease

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in the productivity of domestic firms (Aitken and Harrison 1999; de Mello 1999; Elu and Price 2010; Xu

and Sheng 2012; Girma, Greenaway, and Wakelin 2013).

Many studies show a positive relationship between trade openness and productivity. Various studies

show that globalization from the 1960s to 2000s led to faster growth in developing countries through a

remarkable increase in trade and a large decline in tariffs (Coe, Helpman, and Hoffmaister 1997; Miller

and Upadhyay 2000; Dollar and Kraay 2004; Maiti 2013). Meanwhile, studies for developed countries

yield mixed conclusions. For example, Coe and Helpman (1995) suggest that technical spillover through

trade is associated with a productivity increase more for countries that are not in the Group of Seven

(G7) (wealthy, industrialized) countries than for G7 countries (US, Japan, Germany, France, Italy,

United Kingdom, and Canada). Mendi (2007) shows that technology trade has a positive relationship

with productivity for member countries of the Organisation of Economic Cooperation and Development

(OECD), except for G7 countries.

Education

Studies indicate a positive relationship between education and productivity for developing and

developed countries. Some studies show that the number of schooling years or the completion rate of

secondary and tertiary education is important in explaining the improvement of TFP for many countries

(Benhabib and Spiegel 1994; Griffith, Redding, and Reenen 2004; Benhabib and Spiegel 2005; Bronzini

and Piselli 2009; Erosa, Koreshkova, and Restuccia 2010). Miller and Upadhyay (2000) show that

education attainment has a positive relationship with TFP in general; however, for low-income

countries, the effect of education on TFP is negative until trade openness is sufficiently large. This

implies that investment in education in low-income countries without market liberalization could lead to

underutilization of human capital. Barro (2001) shows in a study of around 100 countries that the quality

of education for male students is significantly related to economic growth ─but not for female students.

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One explanation of this result is that female workers are less incorporated in markets than male workers,

implying that many countries could increase productivity if they successfully include female workers in

labor markets.

Market Efficiency

Various studies indicate that market efficiency is associated with the variation in productivity across

countries and firms. Chanda and Dalgaard (2008) show that 85 percent of the variation of TFP among

around 40 countries in mid-1980s is explained by the variation in market efficiency. Jerzmanowski

(2007) shows that inefficiency in the allocation of human and physical capital is the main explanation

for a low-income level among around 80 countries from 1960 to 1995. Hsieh and Klenow (2009)

estimate that, if capital and labor had been allocated at the relatively efficient level of the US,

productivity in manufacturing sectors could have been 1.3 times higher for China and 1.6 times higher

for India in 2005. Some studies examine a relationship between regulations and productivity.

Haltiwanger, Scarpetta, and Schweiger (2008) and Bartelsman, Gautier, and De Wind (2016) show that

employment protection regulations preclude efficient labor allocation because they curb job flows or

discourage firms from adopting risky but highly productive technologies, and in turn reduce

productivity. Some studies for developed countries for the 1980s to 2000s show that strict market

regulations, or the lack of reforms for promoting private corporate governance and competition, caused

industries that use or produce IT to have meager productivity levels in several European countries, and

deterred firms from catching up to the international technology frontier (Nicoletti and Scarpetta 2003; J.

Arnold, Nicoletti, and Scarpetta 2008).

Physical infrastructure

Physical infrastructure ─such as telecommunication, paved roads, electricity supply, and water and

sanitation systems─ is positively associated with productivity and economic growth. Calderón and

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Servén (2010, 2012, 2014) argue in their studies that infrastructure has positive effects on growth and

distributive equity; however, weak regulatory frameworks and poorly designed privatization agreements

preclude efficiency and quality gains. Canning and Pedroni (2008) show that infrastructure is generally

associated with long-run economic growth; however, there is substantial variation across more than 40

countries. Hulten (1996) shows that 25 percent of the growth difference between East Asia and Africa

over 1970−90 is explained by the efficient use of infrastructure. Aschauer (1989) argues that public

capital stock, especially core infrastructure such as highways, airports, sewers, and water systems, was

critical in determining productivity in the US over the 1950s–1980s. Straub (2008) shows in a study for

140 countries over 1989–2007 that infrastructure stock has a positive external impact on growth, for

example, by allowing firms to invest in more productive machineries, decreasing workers’ commuting

times, and promoting health and education.

Institutional infrastructure

Studies for many countries show that the quality of governance ─represented by political stability, the

rule of law, bureaucratic quality, the absence of corruption, and the like─ is positively related to TFP

and economic growth (Barro 1991; Chanda and Dalgaard 2008). Some studies show that good

governance works as a channel for geographical endowments, such as temperate locations and good

growing environments for grains, to contribute to economic growth (Easterly and Levine 2003; Rodrik,

Subramanian, and Trebbi 2004). Ghali (1999) shows that government size can be positively associated

with growth via the increase of well-executed government investment. On the contrary, Dar and

AmirKhalkhali (2002) show that government size could be negatively associated due to policy-induced

distortions such as burdensome taxation, crowding-out effects for new capital, and the lack of market

forces to foster the efficient use of resources. Acemoglu, Johnson, and Robinson (2004) show that the

quality of economic institutions, more than geography and culture, contributes to growth, with evidence

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from the division of Korea into two parts and the colonization of countries by European powers starting

in the fifteenth century.

3. Methods

Based on an extensive literature review, we first identify the most relevant and available empirical

indicators for each broad category of TFP determinants. Second, we construct a set of indexes grouping

together these indicators by category. Third, we estimate TFP and TFP growth rates at the country level.

And fourth, we analyze the relationship between the proposed indexes of TFP determinants and the

estimated TFP.

Country and time horizon

We conduct the analysis across 65 developing and developed countries for the period 1985–2011 (table

B.1 for the country list). We exclude countries which have population lower than 2 million. Also,

countries that heavily depend on natural resources for gross domestic product (GDP) are excluded

because the contribution of natural resources to output could result in a large overestimation of TFP.1

Total factor productivity

To calculate TFP for each country, we use a Cobb-Douglas production function, which is often

considered to provide a reasonable and tractable description of production at the macroeconomic level.

Equation 1 shows that total production (𝑌) is a function of TFP and input resources, including physical

capital (𝐾) and human capital (𝐿). Following Hevia and Loayza (2012), we assume that L is a function

of productivity per worker with 𝐸𝑡 years of schooling (𝑒∅𝐸𝑡) and the number of workers (𝑁𝑡). A capital

1 Heavy dependence is defined as a reliance on natural resources for more than 36 percent of GDP on average over the period

2006—2015, which is 95th – 100th percentile among 207 countries (World Bank 2017n). The identified countries are Libya

(56%), Kuwait (52%), Congo, Rep. (47%), Iraq (45%), Saudi Arabia (44%), Liberia (43%), Mauritania (42%), Oman (39%),

Angola (39%), and Papua New Guinea (39%).

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share (𝛼) and a labor share (1 − 𝛼), which are the proportion of total income paid to rent capital and pay

wages, represent the responsiveness of total production to a change of physical and human capital,

respectively. For example, if the capital share is 0.3, a 1 percent increase in physical capital stock leads

to 0.3 percent increase in total production.

𝑌𝑐,𝑡 = 𝑇𝐹𝑃𝑐,𝑡𝐾𝑐,𝑡

1−𝛼𝑐,𝑡𝐿𝑐,𝑡

𝛼𝑐,𝑡 ,

𝑤ℎ𝑒𝑟𝑒 𝐿𝑐,𝑡 = 𝑒∅𝐸𝑐,𝑡𝑁𝑐,𝑡 (1)

𝑌: 𝑡𝑜𝑡𝑎𝑙 𝐺𝐷𝑃 (2010 𝑈𝑆$)

𝑇𝐹𝑃: 𝑡𝑜𝑡𝑎𝑙 𝑓𝑎𝑐𝑡𝑜𝑟 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦, 𝑇𝐹𝑃 > 0

𝐾: 𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑠𝑡𝑜𝑐𝑘

𝐿: ℎ𝑢𝑚𝑎𝑛 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑠𝑡𝑜𝑐𝑘

𝑒∅𝐸: 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑝𝑒𝑟 𝑤𝑜𝑟𝑘𝑒𝑟 𝑤𝑖𝑡ℎ 𝐸 𝑦𝑒𝑎𝑟𝑠 𝑜𝑓 𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑛𝑔

𝑁: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛

𝛼: 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 𝑝𝑎𝑖𝑑 𝑎𝑠 𝑤𝑎𝑔𝑒𝑠, 0 < 𝛼 < 1

𝑐: 𝑐𝑜𝑢𝑛𝑡𝑟𝑦

𝑡: 𝑦𝑒𝑎𝑟

We use data from the Penn World Table database for real GDP, physical capital stock, productivity of

education per worker, the number of employed population, and capital and labor shares (Feenstra,

Inklaar, and Timmer 2015). For missing capital and labor shares, we use data from the Global Trade

Analysis Project database (Aguiar, Narayanan, and McDougall 2016).

Main determinant indexes

We construct indexes that represent each of the five determinants: innovation, education, market

efficiency, physical infrastructure, and institutional infrastructure. First, we select indicators that can

represent current situations and environments of each determinant. Second, we impute missing values of

the selected indicators. Third, we construct an index as a linear combination of relevant indicators using

a factor analysis approach.

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Innovation. To construct an index for innovation (Innov), we choose public and private expenditure on

research and development (R&D)(% of GDP) as an indicator of environments for creating new

technology (World Bank 2017j). As indicators of the outcome of R&D, we choose the number of patent

applications by residents and nonresidents and the number of scientific and technological journal articles

(World Bank 2017h, 2017i, 2017l).

Education. To construct an index for education (Educ), we choose government expenditure on education

(% of GDP) as an indicator of public investment in foundational human capital (World Bank 2017d).

We choose the secondary enrollment rate (% of relevant population) as an indicator of the completion of

primary education (World Bank 2017k); a standardized international test score (a single average of

scores in math, science, and reading on the Programme for International Student Assessment, PISA) as

an indicator of secondary education (OECD 2016a, 2016c, 2016b); and the tertiary completion rate (%

of population aged 25 and above) as an indicator of tertiary education (Barro and Lee 2017).

Market Efficiency. To construct an index for market efficiency (Effi), we select the World Bank Doing

Business scores as indicators of output market efficiency, which measure regulatory environments in

terms of ease for firms to run their business, including such measures as the number of procedures and

days required to start a business, trade across borders, and get electricity installed in the workplace or

obtain credit (World Bank 2017a). As an indicator of financial market efficiency, we select the

International Monetary Fund (IMF) Financial Development Index, which measures the level of financial

development by including the size and liquidity of financial markets, ease for individuals and firms to

access financial services, and the ability of financial institutions to provide services at low costs with

sustainable revenues (Svirydzenka 2016). As indicators of labor market efficiency, we choose minimum

wage (% of value added per worker), severance pay for redundancy dismissal (weeks of salary), and the

share of women in wage employment in the nonagricultural sector (World Bank 2017f, 2017m).

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Physical infrastructure. For a physical infrastructure index (Infra), we select fixed telephone and mobile

subscriptions (per 100 persons) (World Bank 2017b, 2017g); the length of paved roads (km per 100

persons) (International Road Federation 2017b, 2017a); electricity production (kw per 100 persons)

(OECD/IEA 2017); and access to an improved water source (% of population) and access to improved

sanitation facilities (% of population) (WHO/UNICEF 2017b, 2017a).

Institutional infrastructure. An institutional infrastructure index (Inst) is assumed to consist of two

components: governance and macroeconomic environment. To construct a governance index, we select

the World Bank Worldwide Governance Indicators, which include voice and accountability (citizens’

participation in selecting their government, freedom of expression, and the like); control of corruption

(the extent to which public power is exercised for personal gain); government effectiveness (the quality

of public services and policy formulation and implementation); political stability (the absence of

politically motivated violence); regulatory quality (the ability of government to formulate and

implement regulations that promote private sector development); and the rule of law (the extent to which

citizens have confidence in and abide by laws) (Kaufmann and Kraay 2017). For the macroeconomic

environment, we select three indicators: government budget balance (% of GDP) (IMF 2017a); gross

domestic savings (% of GDP) (World Bank 2017e); and the distance of the inflation rate from an

assumed stable range of 0.5−2.9 percent (IMF 2017b).

We impute missing values of the selected indicators with different methods depending on the number of

available data and the characteristics of indicators. For a country that has data for more than 10 years out

of 27 years (1985−2011) for an indicator, we project a linear trend over years to impute missing values.

For a country that has data for less than 10 years, we replace missing values with a median value by

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income group2 except for minimum wage, severance pay, and institutional infrastructure. For minimum

wage and severance pay, we apply the oldest available data (2014) to the period 1985−2011, because

available data (2014−2017) are insufficient to evaluate a time trend and their values are difficult to

impute based on the income group. For institutional infrastructure, we do not impute because of the

difficulty to impute with the income level. For example, correlations of the income level with the

government budget balance and the inflation-distance indicator are lower than 5 percent.

Lastly, we build a determinant index as a linear combination of selected indicators using a factor

analysis approach. The factor analysis quantifies common variation in multiple variables as a single

index (or a small set of indexes) (Mulaik 2009). For example, to measure innovation which is

unobservable, we select R&D expenditure, the number of patent applications, and the number of

scientific and technological journal articles, assuming the common features of these indicators explain

the level of innovation in a country. The factor analysis enables to quantify common variation in the

three indicators as a single index.

Overall determinant index

We combine the five determinant indexes into an overall determinant index using a principal component

analysis approach. The principal component analysis constructs an index as a linear combination of

multiple variables by explaining as much of total variation in the variables as possible (Jolliffe 2002).

Unlike the five determinant indexes contain the common features in their indicators as described above,

an overall determinant index needs to represent the distinct feature of each determinant index. The

2 Classified based on GDP per capita, 2011 (constant 2010 US$) (World Bank 2017c). Low: <$1,000, low-middle:

$1,000−$4,000, upper-middle: $4,000−$12,500, high: ≥$12,500

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principal component analysis enables to capture as much of the distinct variation of each determinant

index as possible in a single index.

The relative contribution of the main determinants to total factor productivity

To measure the relative contribution of the five main determinants to TFP, we decompose the variation

of TFP to that explained by each determinant across countries over the period 1985−2011. We apply a

dominance analysis approach to a model which has a TFP as an additive function of the five determinant

indexes (equation 2). The dominance analysis decomposes the variance of a dependent variable into that

explained by each regressor incorporating covariance among regressors. In specific, it conducts the

variance decomposition for all possible subset models ─from a model with a single determinant index to

a model with an interaction term of all five determinant indexes─ and calculates the contribution of each

determinant index to the variance of TFP across the subset models (Azen and Budescu 2003). We

conduct the dominance analysis without and with country or year fixed-effects and compare results.

𝑇𝐹𝑃𝑐,𝑡 = 𝛽1𝐼𝑛𝑛𝑜𝑣𝑐,𝑡 + 𝛽2𝐸𝑑𝑢𝑐𝑐,𝑡 + 𝛽3𝐸𝑓𝑓𝑖𝑐,𝑡 + 𝛽4𝐼𝑛𝑓𝑟𝑎𝑐,𝑡 + 𝛽5𝐼𝑛𝑠𝑡𝑐,𝑡 + 휀𝑐,𝑡 . (2)

휀𝑐,𝑡: 𝑟𝑒𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙, 𝑎𝑠𝑠𝑢𝑚𝑒𝑑 𝑡𝑜 𝑏𝑒 𝑖𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡𝑙𝑦 𝑎𝑛𝑑 𝑖𝑑𝑒𝑛𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑑

The relationship between the overall determinant index and total factor productivity

To quantify the relationship between the overall determinant index and TFP, we build a model in which

TFP is a function of a lagged TFP and a lagged overall determinant index (equation 3). We use the

lagged index to take into account the likely endogeneity referring to Barro (2003) and assume the effect

of the index is realized after five years based on the finding by Giavazzi and Tabellini (2005) that

reforms affect economic growth after at least four years. The estimated variances are cluster-robust,

treating countries as clusters.

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ln (𝑇𝐹𝑃𝑐,𝑡) = 𝛽0 + 𝛽1 ln(𝑇𝐹𝑃𝑐,𝑡−5) + 𝛽2𝐼𝑛𝑑𝑒𝑥𝑐,𝑡−5 + 𝛿𝑡 + 휀𝑐,𝑡. (3)

𝛿𝑡: 𝑡𝑖𝑚𝑒 𝑓𝑖𝑥𝑒𝑑 − 𝑒𝑓𝑓𝑒𝑐𝑡

This model is equivalent to a model in which the annual growth rate of TFP is a function of a lagged

TFP and a lagged overall determinant index (equation 4).

ln (𝑇𝐹𝑃𝑐,𝑡)−ln(𝑇𝐹𝑃𝑐,𝑡−5)

5=

𝛽0+(𝛽1−1)∗ln(𝑇𝐹𝑃𝑐,𝑡−5)+𝛽2𝐼𝑛𝑑𝑒𝑥𝑐,𝑡−5+𝛽3𝐼(𝑡)+𝜀𝑐,𝑡

5. (4)

4. Findings

Total factor productivity by income group

As shown in figure 1, the median relative TFP (with US TFP =1) of the high-income group is at least

five times higher than the low- and middle-income groups throughout the period 1985−2011. The

median TFP of the high-income group decreases during 1985−2001 and increases afterward. The

median TFP of the upper-middle income group is at least 1.5 times those of the lower-middle and low-

income groups from late 1980s to mid-1990s, and decreases to the median level of the lower-middle

income group afterward. The median TFPs of the lower-middle and low-income groups do not change

much throughout the entire period.

17

Figure 1. Relative Total Factor Productivity (US=1) by Income Group, Median, 1985−2011

Main determinant indexes

In the innovation index, the indicators have similar weights (equation 5). The factor analysis shows that

a correlation of the innovation index is 0.95 with R&D expenditure (R&D), 0.78 with the number of

patents (patent), and 0.89 with the number of journal articles (article) in the sample across countries

over the period 1985−2011.

𝐼𝑛𝑛𝑜𝑣𝑐,𝑡 = 0.414 ∗ 𝑧(𝑅&𝐷𝑐,𝑡) + 0.341 ∗ 𝑧(𝑝𝑎𝑡𝑒𝑛𝑡𝑐,𝑡) + 0.386 ∗ 𝑧(𝑎𝑟𝑡𝑖𝑐𝑙𝑒𝑐,𝑡), (5)

where 𝑧(𝑋) 𝑖𝑠 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑋,X−mean(𝑋)

𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 (𝑋).

In the education index, the performance-related indicators have similar weights and the education-

expenditure indicator has a lower weight (equation 6). The factor analysis indicates that a correlation of

0

.2

.4

.6

.8

1

Tota

l F

acto

r P

rod

uctivity (

US

TF

P=

1),

Me

dia

n

1985 1990 1995 2000 2005 2010Year

Low Lower-middle

Upper-middle High

Source: Authors' calculation

18

the education index is 0.57 with education expenditure (eduexp), 0.87 with secondary enrollment rates

(secondary), 0.84 with PISA scores (pisa), and 0.78 with tertiary completion rates (tertiary). This

suggests that education expenditure has a low correlation with the three performance indicators.

𝐸𝑑𝑢𝑐,𝑡 = 0.236 ∗ 𝑧(𝑒𝑑𝑢𝑒𝑥𝑝𝑐,𝑡) + 0.363 ∗ 𝑧(𝑠𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦𝑐,𝑡) + 0.350 ∗ 𝑧(𝑝𝑖𝑠𝑎𝑐,𝑡) + 0.324 ∗

𝑧(𝑡𝑒𝑟𝑡𝑖𝑎𝑟𝑦𝑐,𝑡). (6)

In the market-efficiency index, the indicators are combined with similar weights (equation 7). The factor

analysis shows that a correlation of the efficiency index is 0.89 with Doing Business scores (business),

0.88 with the IMF Financial Development Index (financial), and -0.76 with the labor market index

(labor) across countries over 1985−2011. The labor market index consists of the indicators with similar

weights, and its correlation is 0.76 with minimum wage, 0.86 with severance pay, and -0.65 with the

share of women in wage employment.

𝐸𝑓𝑓𝑖𝑐,𝑡 = 0.415 ∗ 𝑧(𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝑐,𝑡) + 0.408 ∗ 𝑧(𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙𝑐,𝑡) − 0.355 ∗ 𝑧(𝑙𝑎𝑏𝑜𝑟𝑐,𝑡), (7)

𝑤ℎ𝑒𝑟𝑒 𝑙𝑎𝑏𝑜𝑟𝑐,𝑡 = 0.438 ∗ 𝑧(𝑚𝑖𝑛𝑤𝑎𝑔𝑒𝑐,𝑡) + 0.495 ∗ 𝑧(𝑠𝑒𝑣𝑒𝑟𝑎𝑛𝑐𝑒𝑐,𝑡) − 0.371 ∗ 𝑧(𝑤𝑜𝑚𝑒𝑛𝑐,𝑡)

In the infrastructure index, all indicators except the mobile subscription have similar weights (equation

8). The factor analysis shows that the infrastructure index has a correlation of 0.92 with telephone

subscription (tele), 0.50 with mobile subscription (mobile), 0.78 with paved road (road), 0.80 with

electricity production (elec), 0.82 with access to improved water source (water), and 0.87 with access to

improved sanitation facilities (sanit).

𝐼𝑛𝑓𝑟𝑎𝑐,𝑡 = 0.243 ∗ 𝑧(𝑡𝑒𝑙𝑒𝑐,𝑡) + 0.133 ∗ 𝑧(𝑚𝑜𝑏𝑖𝑙𝑒𝑐,𝑡) + 0.205 ∗ 𝑧(𝑟𝑜𝑎𝑑𝑐,𝑡) + 0.211 ∗

𝑧 (𝑒𝑙𝑒𝑐_(𝑐, 𝑡)) + 0.218 ∗ 𝑧(𝑤𝑎𝑡𝑒𝑟𝑐,𝑡) + 0.231 ∗ 𝑧(𝑠𝑎𝑛𝑖𝑡𝑐,𝑡). (8)

19

In the institution index, the sub-indexes of governance (gov) and macroeconomic-environment (macro)

are combined with the same weight (equation 9), because the institutional index has a very similar

correlation of around 0.79 with the sub-indexes across countries over 1985−2011.

𝐼𝑛𝑠𝑡𝑐,𝑡 = 0.634 ∗ 𝑧(𝑔𝑜𝑣𝑐,𝑡) + 0.634 ∗ 𝑧(𝑚𝑎𝑐𝑟𝑜𝑐,𝑡). (9)

The governance sub-index consists of the six indicators with similar weights (equation 10). A

correlation of the governance index is 0.91 with voice and accountability (va), 0.96 with the control of

corruption (cc), 0.97 with government effectiveness (ge), 0.84 with political stability (ps), 0.94 with

regulatory quality (rq), and 0.97 with the rule of law (rl).

𝑔𝑜𝑣𝑐,𝑡 = 0.175 ∗ 𝑧(𝑣𝑎𝑐,𝑡) + 0.184 ∗ 𝑧(𝑐𝑐𝑐,𝑡) + 0.185 ∗ 𝑧(𝑔𝑒𝑐,𝑡) + 0.161 ∗ 𝑧(𝑝𝑠𝑐,𝑡) + 0.179 ∗ 𝑧(𝑟𝑞𝑐,𝑡)

+0.185 ∗ 𝑧(𝑟𝑙𝑐,𝑡). (10)

In the macroeconomic-environment sub-index, government budget balance and gross domestic savings

have similar positive weights and the inflation-distance indicator has a negative weight, implying the

macroeconomic environment is considered stable when the first two indicators increase and the latter

indicator decreases (equation 11). The factor analysis shows that the index has a correlation of 0.83 with

government budget balance, 0.84 with gross domestic savings, and -0.14 with the distance of inflation

rates from the stable range; and it suggests that an inflation rate has a low correlation with other two

indicators.

𝑚𝑎𝑐𝑟𝑜𝑐,𝑡 = 0.585 ∗ 𝑧(𝑏𝑎𝑙𝑎𝑛𝑐𝑒𝑐,𝑡) + 0.592 ∗ 𝑧(𝑠𝑎𝑣𝑖𝑛𝑔𝑠𝑐,𝑡) − 0.102 ∗ 𝑧(𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑐,𝑡). (11)

Overall determinant index

An overall determinant index is constructed as a linear combination of the main determinant indexes

with similar weights using principal component analysis (equation 12).

20

𝐼𝑛𝑑𝑒𝑥𝑐,𝑡 = 0.444 ∗ 𝑧(𝐼𝑛𝑛𝑜𝑣𝑐,𝑡) + 0.466 ∗ 𝑧(𝐸𝑑𝑢𝑐,𝑡) + 0.469 ∗ 𝑧(𝐸𝑓𝑓𝑖𝑐,𝑡) + 0.463 ∗ 𝑧(𝐼𝑛𝑓𝑟𝑎𝑐,𝑡)

+0.390 ∗ 𝑧(𝐼𝑛𝑠𝑡𝑐,𝑡). (12)

The overall determinant index represents the main determinants with a correlation of 0.89 with the

innovation index, 0.94 with the education index, 0.94 with the market-efficiency index, 0.93 with the

physical infrastructure index, and 0.79 with the institutional infrastructure index.

Comparison of determinants of total factor productivity by income and regional group

We categorize high-income countries that have been members of OECD more than 40 years into the

OECD group and classify other countries by region. For the average main and overall determinant

indexes over 1985−2011, the OECD group has the highest values (figure 2). Among non-OECD

countries, the average overall index is the highest for East Asia & Pacific (0.55), followed by Middle

East & North Africa (0.37) and Europe & Central Asia (0.21). Specifically, the average innovation and

education indexes are the highest for Middle East & North Africa (0.28 and 0.30, respectively), followed

by East Asia & Pacific (0.28 and 0.26) and Europe & Central Asia (-0.16 and 0.24); the market-

efficiency index is the highest for Europe & Central Asia (0.46), followed by East Asia & Pacific (0.35)

and Middle East & North Africa (0.04); the infrastructure index is the highest for Middle East & North

Africa (0.57), followed by Europe & Central Asia (0.45) and East Asia & Pacific (0.13); and the

institution index is highest for East Asia & Pacific (0.21), followed by Middle East & North Africa (-

0.08) and Europe & Central Asia (-0.24). Appendix C shows the average values of all indicators by

group.

21

Figure 2. The Main and Overall Determinant Indexes by Income and Region, Average over 1985-2011

Correlation between total factor productivity and the main and overall determinant indexes

The average relative TFP (US=1) measure over 1985−2011 per country has a moderate correlation with

the average determinant indexes (figure 3): 0.59 with the innovation index, 0.64 with the education

index, 0.64 with the efficiency index, 0.65 with the physical infrastructure index, 0.44 with the

institutional infrastructure index, and 0.65 with the overall determinant index.

22

Figure 3. Relative Total Factor Productivity versus the Main and Overall Determinant Indexes, Average

over 1985–2011

The relative contribution of the main determinants to total factor productivity

The dominance analysis shows that the variation of TFP across countries for the last three decades is

explained the most by the physical infrastructure index, followed by the education index and the market

efficiency index at a similar level, the innovation index, and the institutional infrastructure index. The

results are similar with and without country or year fixed-effects (table 1).

Table 1. Relative Contribution of the Main Determinant Indexes to the Variation of Total Factor

Productivity Level

Variance of TFP level explained by each index

without country/year

fixed-effect

with country fixed-effects with year fixed-effects

Innovation 16% 17% 15%

Education 23% 23% 22%

Market Efficiency 22% 23% 25%

Physical infrastructure 30% 28% 29%

ALBARG

AUS

AUT

BEL

BWA

BRABGR

KHM

CMR

CAN

CHLCHNCOL

DNK

DOMECUEGYSLV

FIN

FRA

DEU

GRC

GTM

HND

HUN

IND

IDNIRN

IRL

ISRITA

JPN

JOR

KOR

MYS

MEXMNG

NAM

NLD

NZL

NOR

PAKPANPRY

PERPHL

POL

PRT

QAT

ROM

SGP

ZAF

ESP

LKA

SDN

SWE

TZATHATUR

GBRUSA

URYVNM

ZWE

0

.5

1

1.5

To

tal F

acto

r P

rod

uctivity (

US

=1)

-1 0 1 2 3Innovation

ALBARG

AUS

AUT

BEL

BWA

BRABGR

KHM

CMR

CAN

CHLCHNCOL

DNK

DOMECUEGY

SLV

FIN

FRA

DEU

GRC

GTM

HND

HUN

IND

IDN IRN

IRL

ISRITA

JPN

JOR

KOR

MYS

MEXMNG

NAM

NLD

NZL

NOR

PAKPANPRY

PERPHL

POL

PRT

QAT

ROM

SGP

ZAF

ESP

LKA

SDN

SWE

TZA THATUR

GBRUSA

URYVNM

ZWE

-2 -1 0 1 2Education

ALBARG

AUS

AUT

BEL

BWA

BRABGR

KHM

CMR

CAN

CHLCHNCOL

DNK

DOMECU

EGYSLV

FIN

FRA

DEU

GRC

GTM

HND

HUN

IND

IDN IRN

IRL

ISRITA

JPN

JOR

KOR

MYS

MEXMNG

NAM

NLD

NZL

NOR

PAKPANPRY

PERPHL

POL

PRT

QAT

ROM

SGP

ZAF

ESP

LKA

SDN

SWE

TZA THATUR

GBRUSA

URYVNM

ZWE

-3 -2 -1 0 1 2Market Efficiency

ALBARG

AUS

AUT

BEL

BWA

BRABGR

KHM

CMR

CAN

CHLCHNCOL

DNK

DOMECU

EGYSLV

FIN

FRA

DEU

GRC

GTM

HND

HUN

IND

IDN IRN

IRL

ISRITA

JPN

JOR

KOR

MYS

MEXMNG

NAM

NLD

NZL

NOR

PAKPANPRY

PERPHL

POL

PRT

QAT

ROM

SGP

ZAF

ESP

LKA

SDN

SWE

TZA THATUR

GBRUSA

URYVNM

ZWE

0

.5

1

1.5

To

tal F

acto

r P

rod

uctivity (

US

=1)

-2 -1 0 1 2Infrastructure

ALB ARG

AUS

AUT

BEL

BWA

BRABGR

KHM

CMR

CAN

CHLCHNCOL

DNK

DOMECU

EGYSLV

FIN

FRA

DEU

GRC

GTM

HND

HUN

IND

IDNIRN

IRL

ISRITA

JPN

JOR

KOR

MYS

MEXMNG

NAM

NLD

NZL

NOR

PAKPANPRY

PERPHL

POL

PRT

QAT

ROM

SGP

ZAF

ESP

LKA

SDN

SWE

TZA THATUR

GBRUSA

URYVNM

ZWE

-2 -1 0 1 2 3Institution

ALB ARG

AUS

AUT

BEL

BWA

BRABGR

KHM

CMR

CAN

CHLCHNCOL

DNK

DOMECUEGY

SLV

FIN

FRA

DEU

GRC

GTM

HND

HUN

IND

IDN IRN

IRL

ISRITA

JPN

JOR

KOR

MYS

MEXMNG

NAM

NLD

NZL

NOR

PAKPANPRY

PERPHL

POL

PRT

QAT

ROM

SGP

ZAF

ESP

LKA

SDN

SWE

TZA THATUR

GBRUSA

URYVNM

ZWE

-4 -2 0 2 4Overall Determinant Index

Source: Authors' calculation

23

Institutional infrastructure 9% 9% 9%

Total 100% 100% 100%

The relationship between the overall determinant index and total factor productivity

Table 2 shows the result of the linear regression for equation 3 in which TFP is a function of a lagged

TFP and a lagged overall determinant index. The coefficient of the lagged overall determinant index

(𝐼𝑛𝑑𝑒𝑥𝑐,𝑡−5) is significantly positive, indicating that an increase in the overall determinant index is

associated with an increase in the growth rate of TFP over the next five years (see equation 4). For

example, an increase of 𝐼𝑛𝑑𝑒𝑥𝑐,𝑡−5 by 1 is associated with an increase of annual TFP growth by 0.4%.

The coefficient of a lagged TFP [ln(𝑇𝐹𝑃𝑐,𝑡−5)] is lower than the unit (< 1), indicating that the growth

rate of TFP converges toward a steady-state level after the effect of the overall determinant index

reaches a peak during the period of 5−10 years. These results are robust when we use different time lags

of 1 year and 3 years.

Table 2. Linear Regression Results

Dependent variable 𝐥𝐧(𝑻𝑭𝑷𝒄,𝒕)

Number of observation 299

F(6, 64) 1211.93

p-value 0.000

R2 0.9615

Coefficient Standard error t-statistics p-value

Constant 0.151 0.085 1.78 0.080

𝐥𝐧(𝑻𝑭𝑷𝒄,𝒕−𝟓) 0.957 0.015 65.43 0.000

𝑰𝒏𝒅𝒆𝒙𝒄,𝒕−𝟓 0.020 0.009 2.13 0.037

Year 1996 -0.065 0.059 -1.10 0.276

Year 2001 -0.009 0.057 -0.15 0.878

Year 2006 -0.129 0.053 -2.42 0.018

Year 2011 0.045 0.056 0.79 0.430

Appendix D shows the predicted growth rate of TFP for a scenario in which non-OECD countries

increase the overall determinant index to the average level of OECD countries.

24

5. Conclusion

Based on a literature review, we select innovation, education, market efficiency, physical infrastructure,

and institutional infrastructure as the five main categories of determinants of TFP. For each of these

categories, we construct an index as a linear combination of representative indicators (or proxies) by

factor analysis, that is, by accounting for as much of the common variance in the indicators as possible.

We then combine the main determinant indexes into an overall index through a similar procedure. Using

dominance analysis for variance decomposition, we find that the variation of TFP during 1985−2011 is

most sensitive to the variation in physical infrastructure, followed by education or market efficiency,

innovation, and institutional infrastructure. The regression analysis shows that an increase in the overall

determinant index is significantly associated with an increase in the growth rate of TFP.

This study has some limitations that need to be considered when interpreting the results. When selecting

the main determinants of TFP, we exclude geographical conditions because they are impossible or very

difficult to change through policy. Some authors argue that the impact of geographical conditions on

growth is offset by that of institutional infrastructure. Others argue that geographical conditions still

matter for growth: for instance, by affecting health (through the spread of infectious diseases) and by

facilitating trade through proximity to core economies (after controlling for institutions) (Easterly and

Levine 2003; Gallup, Sachs, and Mellinger 1999; Sachs 2003).

The study’s focus on global patterns may pose another limitation. The relative contribution of the

determinant indexes to the variation of TFP and the impact of the overall determinant index on TFP

growth could be different for each country and region, generally depending on the level of economic

development and the nature of their political and social environment.

25

There is a limitation to consider when the regression model is used to predict a TFP growth rate

depending on the change of determinant indicators. The overall determinant index is a linear

combination of the main determinant indexes, in turn that of the indicators, without interaction terms

among them. This means that the potential spillover effect of a change in an indicator is not

incorporated. For example, the improvement in governance potentially leads to the enhancement in

market efficiency through effective market regulations. In the model, however, such an external effect is

not incorporated.

Taking the results at face value, however, this study renders several policy implications. For the

indicators of physical infrastructure, the most strongly associated determinant with the variation of TFP,

a large gap exists among income and regional groups. For example, the average electricity supply per

capita over 1985−2011 for developing countries in East Asia & Pacific and Latin America & Caribbean

is 34 and 25 percent, respectively, of that for OECD countries (Appendix C). The average access to

improved sanitation (% of population) for South Asia and Sub-Saharan Africa is 63 and 40 percent of

that for OECD countries. These gaps imply that, for developing countries, enhancing physical

infrastructure has a potential to increase productivity, considering that good-quality physical

infrastructure is a foundation for most if not all economic activities.

Education is the second (or third) most strongly associated determinant with the variation of TFP. The

gap between the OECD and non-OECD countries in terms of the indicators (Appendix C) suggests there

is a possibility to increase productivity through the improvement in education systems. Government

expenditure on education is not necessarily correlated with school enrollment rates, completion rates,

and test scores as shown by the results. These findings imply that developing countries have an

opportunity to increase productivity through efficient budget spending and effective policy

implementation with complementary efforts from schools, societies, and governments in the long term.

26

Market efficiency is the third (or second) most strongly associated determinant with the variation of

TFP. The gap of the index in comparison to the OECD group is the biggest for South Asia, followed by

Sub-Saharan Africa and Latin America & Caribbean (Appendix C). Improving market efficiency in

practice would require streamlining and modernizing the regulatory environment. The difficulties of

reform implementation, nevertheless, remain a challenge if interest groups (in the bureaucracy and the

private sector) can mobilize resources and even popular protests to oppose the reforms.

Innovation is the fourth strongly associated with the variation of TFP. The average R&D expenditure in

developing countries over the last three decades is below 60 percent of that of the OECD group, the

average number of patents is below 20 percent of that of the OECD group except East Asia & Pacific

and Middle East & North Africa, and the average number of scientific and technological articles for all

developing countries is below 40 percent of that of the OECD group (Appendix C). Creating new

technology requires intensive investment and an advanced level of human capital; however, a return on

investment could be significant as shown by high-income countries in technology frontier (Oliner,

Sichel, and Stiroh 2008). For countries that may appear to be caught in a middle-income trap (Larson,

Loayza, and Woolcock 2016), to invest in R&D and enact and implement policies to improve the

environments for creating and adopting new technologies has a potential to increase productivity and

lead to economic growth.

Institutional infrastructure is the least associated with the variation of TFP. This implies that especially

changing the quality of governance might require the most time among the main determinants. In

practice, enhancing the institutional infrastructure requires political, societal, and economic efforts over

the long term. Although it may take a long time, good institutional infrastructure would ultimately

provide a stable and friendly environment that leads to economic prosperity (North 1990).

27

28

Appendix A. Literature review

1. Innovation

Technology creation (13 studies)

Study Country/countries

and years Results Relationship

Nadiri (1993) 4 industrial

countries, 1970–90

The results suggest a positive and strong relationship between

research and development (R&D) expenditures and growth of

output or total factor productivity (TFP).

+

Coe and

Helpman (1995)

22 industrial

countries, 1971–90

International R&D spillovers have beneficial effects on

domestic productivity. The elasticity of TFP with respect to

foreign R&D expenditure is 0.02–0.08 for G7 countries and

0.04–0.26 for other small OECD countries.

+

Chen and

Dahlman (2004)

92 countries,

1960–2000

The number of patents and journal publications is statistically

significant in terms of real GDP growth via their effects on the

TFP growth rate.

+

Furman and

Hayes (2004)

29 countries,

1978–99

Innovation-enhancing policies and infrastructure need to be

developed to achieve leadership in innovation, but these are

insufficient unless coupled with ever-increasing financial and

human capital investments in innovation.

+/-

Griffith,

Redding, and

Reenen (2004)

12 OECD

countries, 1974–90

R&D is statistically and economically important in both

technological catch up and innovation. Human capital also

plays a major role in productivity growth.

+

Guellec and van

Pottelsberghe de

la Potterie (2004)

16 OECD

countries, 1980–98

R&D performed by the business sector, the public sector, and

foreign firms is a significant determinant of long-term

productivity growth.

+

Ulku and

Subramanian

(2004)

30 OECD

countries, 1981–97

The results suggest a positive relationship between per capita

GDP and innovation in both OECD and non-OECD countries.

However, the effect of the R&D stock on innovation is

significant only in the OECD countries with large markets.

+/-

Jorgenson, Ho,

and Stiroh (2005)

US, 1990s and

2000s

Industries that produce or use information technology

(IT)account for only 30% of U.S. GDP, but contributed to half

of the acceleration in economic growth in the 1990s and

2000s.

+

Abdih and Joutz

(2006) US, 1948–97

Long-run elasticity of TFP with respect to the stock of patents

is positive, but small. These results seem to suggest that while

research workers benefit greatly from “standing on the

shoulders” of prior researchers, the knowledge that they

produce seems to have complex and slowly diffusing impacts

on TFP.

+

Jorgenson, Ho,

and Stiroh (2008) US, 1960–2006

Information technology was critical to the dramatic

acceleration of U.S. labor productivity growth in the mid-

1990s.

+

Oliner, Sichel,

and Stiroh (2008) US, 1990s–2000s.

Authors confirm the central role for IT in the productivity

revival during 1995–2000 and show that IT played a

significant, though smaller, role after 2000.

+

29

van Ark,

O’Mahony, and

Timmer (2008)

US, Europe,

1980s–2000s

The slow-down in productivity in Europe can be attributed to

the slower emergence of the knowledge economy in Europe

compared to the US. Explanations include lower growth

contributions from investment in information and

communication technology in Europe, the relatively small

share of technology-producing industries in Europe, and

slower multifactor productivity growth (a proxy for advances

in technology and innovation).

+

Technology transfer

a. Channel: Foreign direct investment (FDI) (10 studies)

Study Country/countries

and years Results Relationship

Borensztein, De

Gregorio, and

Lee (1998)

70+ countries,

1970–89

FDI contributes to economic growth only when a host

economy has sufficient capability to absorb advanced

technologies.

+/-

Aitken and

Harrison (1999)

Venezuela, 4,000+

firms, 1976–89

The increase in foreign equity participation is correlated with

productivity increases in small plants. The increase in foreign

ownership negatively affects the productivity of domestically

owned firms in the same industry. No evidence is found to

support technology spillovers from foreign firms to

domestically owned firms.

+/-

de Mello (1999)

16 OECD and 17

non-OECD, 1970–

90

FDI has a positive relationship with TFP growth in OECD

countries, but a negative relationship in non-OECD countries.

+/-

Alfaro, Kalemli-

Ozcan, and

Sayek (2009)

60+ countries,

1975–95

Countries with well-developed financial markets gain

significantly from FDI via TFP improvements.

+/-

Arnold and

Javorcik (2009)

Indonesia, firms

from national

census of

manufacturing,

1983–99

Foreign ownership leads to significant productivity

improvements in the acquired plants. The rise in productivity

is a result of restructuring, as acquired plants increase

investment outlays, employment, and wages. Foreign

ownership also appears to enhance the integration of plants

into the global economy through increased exports and

imports. Productivity improvements and evidence of

restructuring are also found in the context of foreign

privatizations.

+

Elu and Price

(2010)

Ghana, Kenya,

Nigeria, South

Africa, and

Tanzania, 1991–

2004

Across firms and countries, there is no relationship between

productivity-enhancing foreign direct investment and trade

with China. Increasing trade openness with China has no

effect on the growth rate of TFP.

-

Fernandes and

Paunov (2012)

Chile, 4,913

manufacturing

firms, 1992–2004

FDI inflows in producer service sectors has positive on and

explains 7% of the observed increase in TFP among

manufacturing firms in Chile. This suggests that service FDI

fosters innovation activities in manufacturing firms and offers

opportunities for laggard firms to catch up with industry

leaders.

+

30

Xu and Sheng

(2012)

China, >150,000

firms from the

national enterprise

census, 2000–03

FDI has a significant positive impact on productivity of

domestic firms that purchase high-quality intermediate goods

with lower input prices, or equipment from firms receiving

FDI in the upstream industry. Negative horizontal effects are

found after controlling for firm-level market share. The

expected positive knowledge spillovers of FDI firms in the

same industry to domestic firms are counterbalanced by

competition effects from FDI firms. Firms do not benefit

uniformly from FDI.

+/-

Girma,

Greenaway, and

Wakelin (2013)

United Kingdom,

4,000

manufacturing

firms, 1991–96

Foreign FDI does not have any wage or productivity spillovers

to domestic firms, whether in levels or growth, on average.

-

Newman et al.

(2015)

Vietnam, 4,000

manufacturing

firms, 2006–12

Commonly used measures of spillovers do not capture all the

productivity gains associated with direct linkages between

foreign-owned and domestic firms along the supply chain.

This includes productivity gains through forward linkages for

domestic firms that receive inputs from foreign-owned firms.

+

b. Channel: Trade (11 studies)

Study Country/countries

and years Results Relationship

Coe, Helpman,

and Hoffmaister

(1997)

77 developing

countries, 1971–90

Based on data for 77 developing countries, R&D spillovers via

trade with 22 industrial countries are substantial.

+

Miller and

Upadhyay (2000)

83 countries,

1960–89

Higher openness benefits TFP. Outward-oriented countries

experience higher TFP, over and above the positive effect of

openness.

+

Alcalá and

Ciccone (2004)

138 countries,

1985 Trade has a positive relationship with total factor productivity.

+

Dollar and Kraay

(2004)

~100 developing

and developed

countries, 1960s–

90s

Large increases in trade and significant declines in tariffs lead

to faster growth and poverty reduction in poor countries.

+

Chang, Kaltani,

and Loayza

(2009)

82 countries,

1960–2000

The growth effects of openness may be significantly improved

if certain complementary reforms are undertaken in the areas

of investment in education, financial depth, inflation

stabilization, public infrastructure, governance, labor market

flexibility, ease of firm entry, and ease of firm exit.

+

Bernard, Jensen,

and Schott

(2006)

US, 1987–97 Industries experiencing relatively large declines in trade costs

exhibit relatively strong productivity growth.

+

Mendi (2007) 16 OECD

countries, 1971–95

Within OECD countries that are not in the G7, technology

imports increase the host country’s TFP. The effect is stronger

in the initial years of the sampling period. There is no

evidence on this positive effect of technology trade on

productivity among G7 countries.

+/-

Kim, Lim, and

Park (2009) Korea, 1980–2003

Imports have a significant positive effect on TFP growth, but

exports do not. The positive impact of imports stems not only

+/-

31

from competitive pressures arising from the imports of

consumer goods but also from technological transfers

embodied in imports of capital goods and from developed

countries.

Ferreira and

Trejos (2011)

12 developing

countries, 1985

Changes in the terms of trade cause a change of productivity.

That effect has an average elasticity of 0.73.

+

Maiti (2013) India, 1998–2005

Trade reform significantly raises mark-up and wage rent in the

sector competing with exports and reduces wage rent in the

sector that competes with imports. Trade openness has a

significant effect on productivity growth when the market

imperfections due to the trade reform are controlled.

+

Bloom, Draca,

and Van Reenen

(2016)

12 European

countries, 1996–

2007

Competition with Chinese imports led to increased technical

change within firms and reallocated employment between

firms toward more technologically advanced firms. These

within and between effects were about equal in magnitude,

and appear to account for 15% of European technology

upgrading over 2000–07.

+

2. Education (9 studies)

Study Country/countries

and years Results Relationship

Benhabib and

Spiegel (1994)

78 countries,

1965–85

Human capital is not significant in explaining per capita

growth rates. However, the growth rate of TFP depends on a

nation's human capital stock level.

+

Miller and

Upadhyay (2000)

83 countries,

1960–89

Human capital generally contributes positively to TFP. In poor

countries, human capital interacts with openness to achieve a

positive effect, on balance.

+/-

Barro (2001) 100 countries,

1965–95

Growth is significantly related to the years of schooling at the

secondary and higher levels for males and students’ test scores

(a proxy for the quality of education). The insignificant

relation between growth and years of schooling for females

implies that women are not well utilized in the labor markets

of many countries.

+/-

Griffith,

Redding, and

Reenen (2004)

12 OECD

countries, 1974–90

Human capital (percentage of higher school attained in the

total population) affects the rate of convergence of TFP

growth.

+

Benhabib and

Spiegel (2005)

27 countries,

1960–95

Elasticity of TFP with respect to years of schooling is positive

and statistically significant (0.008–0.018). +

Bronzini and

Piselli (2009) Italy, 1985–2001

Elasticity of TFP with respect to years of schooling is positive

and statistically significant (0.379). +

Coe, Helpman,

and Hoffmaister

(2009)

24 countries,

1971–2004

Elasticity of TFP with respect to years of schooling is positive

and statistically significant (0.513 – 0.756). +

Erosa,

Koreshkova, and

Restuccia (2010)

US, 1990–95 Human capital accumulation strongly amplifies TFP

differences across. +

Wei and Hao

(2011) China, 1985–2004

School enrollment has significant and positive effects on the

TFP growth of Chinese provinces. When education quality (as +

32

measured by the teacher-student ratio and government

expenditure on education) is incorporated, TFP growth

appears to be significantly enhanced by quality improvements

in primary education at the national level. TFP growth is

significantly associated with secondary education in the

eastern region; with primary and university education in the

central region; and with primary education in the western

region.

3. Efficiency (13 studies)

Study Country/countries

and years Results Relationship

Fagerberg (2000) 39 countries,

1973–90

While structural change on average has not been conducive to

productivity growth, countries that have managed to increase

their presence in the technologically most progressive industry

(electronics) have experienced higher productivity growth

than other countries.

+/-

Foster,

Haltiwanger, and

Krizan (2001)

US, 1977–87

The contribution of reallocation of outputs and inputs from

less productive to more productive establishments plays a

significant role in accounting for aggregate productivity

growth.

For the selected service industries considered, the contribution

of net entry (more-productive entering establishments

displacing less-productive exiting establishments) is dominant.

The contribution of net entry to aggregate productivity growth

is disproportionate. It increases in the horizon over which the

changes are measured because longer horizon yields greater

differentials from selection and learning effects.

The contribution of reallocation to aggregate productivity

growth varies over time (e.g. is cyclically sensitive) and across

industries. It is somewhat sensitive to subtle differences in

measurement and decomposition methodologies.

+

Nicoletti and

Scarpetta (2003)

18 OECD

countries, 1984–98

Productivity growth is boosted by reforms that promote

private corporate governance and competition. In

manufacturing, the productivity gains from liberalization are

greater the further a given country is from the technology

leader. Strict product market regulations – and lack of

regulatory reforms – appear to underlie the meagre

productivity performance in industries where Europe has

accumulated a technology gap.

+

Peneder (2003) 28 OECD

countries, 1990–98

Structural change generates positive as well as negative

contributions to aggregate productivity growth. Because many

of these effects net out, structural change on average appears

to have only a weak impact. Given that certain industries

systematically achieve higher rates of productivity growth and

expansion of output than others, structural change in favor of

specific industries might still be conducive to aggregate

growth.

+/-

33

Jerzmanowski

(2007)

79 developing and

developed

countries, 1960–95

Inefficiency appears to be the main explanation for low-

incomes throughout the world; it explains 43% of output

variation in 1995, and its importance has increased over time.

Countries with an inadequate mix of inputs are unable to

access the most productive technologies. The world

technology frontier appears to be shifting out faster at input

combinations close to that of the R&D leader.

+

Arnold,

Nicoletti, and

Scarpetta (2008)

OECD countries,

1985−2004

Tight regulation of services has slowed down growth in

sectors that use IT by hindering the allocation of resources

toward the most dynamic and efficient firms. Regulations

especially hurt firms that are catching up to the technology

frontier and that are close to international best practice.

+

Chanda and

Dalgaard (2008)

40+ countries,

1985

A development accounting analysis suggests that as much as

85% of the international variation in aggregate TFP can be

attributed to variation in relative efficiency across sectors.

+

Haltiwanger,

Scarpetta, and

Schweiger

(2008)

16 industrial and

emerging

economies, 1990s

Hiring and firing costs tend to curb job flows, particularly in

those industries and firm size classes that require more

frequent labor adjustment.

+

Lentz and

Mortensen

(2008)

Denmark, 4900

firms, 1992–97

The estimated model implies that more productive firms in

each cohort grow faster and consequently crowd out less

productive firms in steady state. This selection effect accounts

for 53% of aggregate growth in the estimated version of the

model.

+

Bridgman, Qi,

and Schmitz

(2009)

US, sugar

manufacturing

firms, 1934–74

Government’s enforcement on domestic and import sales

quotas significantly distorted sugar production at each factory

and the location of the industry.

+

Hsieh and

Klenow (2009)

China (1998–2005)

and India (1987–

95) vs. US (1977–

97)

When capital and labor are hypothetically reallocated to

equalize marginal products to the extent observed in the US,

manufacturing TFP gains are estimated at 30%–50% in China

and 40%–60%.in India.

+

Petrin and

Sivadasan (2011)

Chile,

manufacturing

firms, 1982–94

Comparing blue- and white-color labor in terms of the

marginal product and cost of an input suggests that the

increase in severance pay is associated with the decrease in

allocative efficiency.

+

Bartelsman,

Gautier, and De

Wind (2016)

European

countries, US,

1980s–2000s

Countries which have extensive employment protection

legislation (EPL) benefit less from the arrival of new risky

technologies than countries with limited EPL. The model is

consistent with the slowdown in productivity in the European

Union relative to the US since the mid-1990s.

+

4. Physical infrastructure (11 studies)

Authors Country/countries

and years Results Relationship

Aschauer (1989) US, 1949–85 There is a large return to public investment. +

Munnell (1992) Not applicable On balance, public investment has a positive effect on private

investment, output, and employment growth. +

34

Hulten (1996)

4 East Asian and

17 African

countries, 1970–90

25% of the growth difference between East Asia and Africa is

due to inefficient use of infrastructure. This result may partly

proxy for TFP differences.

+

Pritchett (1996) ~100 countries,

thought experiment

Pritchett presents theory and calculations to show that part of

the explanation of slow growth in many poor countries is not

that governments did not spend on investments, but that these

investments did not create productive capital. A variety of

calculations suggest that in a typical developing country, less

than 50 cents of capital were created for each public dollar

invested.

+/-

Galiani, Gertler,

and

Schargrodsky

(2005)

Argentina, 1990s Improved water services are associated with significant

reductions in deaths from infectious and parasitic diseases. +

Canning and

Pedroni (2008)

>40 countries,

1950–92

While infrastructure does tend to cause long-run economic

growth, there is substantial variation across countries. +

Straub (2008) 140 countries,

1989–2007

Good infrastructure allows firms to have more productive

investments in machinery, reduces time wasted commuting,

promotes better health and education, and so on. The analysis

obtains positive effects of infrastructure on growth when it

uses physical indicators of infrastructure. However, the effects

are not clear when infrastructure investment flows are used as

proxies for infrastructure.

+/-

Calderón and

Servén (2010)

>100 countries,

1960–2005

The estimates illustrate the potential contribution of

infrastructure development to growth and equity across Africa. +

Loayza and

Odawara (2010)

Egypt, Arab Rep.,

1971–2005

An increase in infrastructure expenditures from 5 to 6 percent

of gross domestic product would raise the annual per capita

growth rate of GDP by about 0.5 percentage points in a

decade’s time and 1 percentage point by the third decade.

+

Calderón and

Servén (2012)

Latin America,

1981–2005

Poor infrastructure is a key obstacle to economic

development. The experience of Latin America shows that

there is no question that private participation did deliver some

efficiency and quality gains. But they were held back by weak

regulatory and supervisory frameworks, and poorly designed

concession and privatization agreements, which led to

ubiquitous renegotiations and ended up costing governments

enormous sums.

+

(Calderón and

Servén 2014) Not applicable

Recent theoretical and empirical literature finds positive

effects of infrastructure development on income growth

and, more tentatively, on distributive equity.

+

5. Institutional infrastructure (10 studies)

Study Country/countries

and years Results Relationship

Barro (1991) 98 countries,

1960–85

Growth is inversely related to the share of government

consumption in GDP, but insignificantly related to the share of

public investment. Growth rates are positively related to

measures of political stability and inversely related to a proxy

for market distortions.

+/-

35

Przeworski and

Limongi (1993)

Review of previous

studies

Political institutions do matter for growth, but thinking in terms

of regimes, democracy, autocracy, or bureaucracy does not

seem to capture the relevant differences.

+/-

Sachs (2003) 60+ countries,

1995

The transmission of malaria, which is strongly affected by

ecological conditions, directly affects the level of per capita

income after controlling for the quality of institutions.

+/-

Hall and Jones

(1999)

100+ countries,

1986–95

Output is driven by differences in institutions and government

policies, which the authors call “social infrastructure.” The

authors treat social infrastructure as endogenous, determined

historically by location and other factors captured in part by

language.

+

Ghali (1999) 10 OECD

countries, 1970–94

A big government size causes economic growth with some

disparities, through the increase of government spending,

investment, or international trade.

+/-

Dar and

AmirKhalkhali

(2002)

19 OECD, 1971–

99

Total factor productivity on average is weaker in countries

where government size is larger due to policy-induced

distortions such as burdensome taxation, crowding-out effects

for new capital that embodies new technologies, and the lack

of market forces that could foster efficient use of resources.

+/-

Easterly and

Levine (2003)

64+ countries,

1995

Tropics, germs, and crops affect development through

institutions. No evidence is found that tropics, germs, and

crops affect country incomes directly other than through

institutions. Macroeconomic policies on development are not

significant once the factor of institutional quality is

controlled.

+

Acemoglu,

Johnson, and

Robinson (2004)

Korea, colonized

countries by

European powers

Differences in economic institutions, rather than geography or

culture, cause differences in per capita

incomes. Countries with more secure property rights (that is,

with better economic institutions), have

higher average incomes.

+

Rodrik,

Subramanian,

and Trebbi

(2004)

79+ countries,

1995

The study estimates the respective contributions of

institutions, geography, and trade in determining income

levels around the world, using recently developed instrumental

variables for institutions and trade. Results indicate that the

quality of institutions "trumps" everything else.

+

Chanda and

Dalgaard (2008)

40+ countries,

1985

The study compiles a Government Anti-Diversionary Policy

index (GADP), an average of five indexes capturing the

quality of government: rule of law, bureaucratic quality, risk

of expropriation by the government, government repudiation

of contracts, and corruption. The GADP is strongly related to

total factor productivity. Introducing geographical variables

reduces the impact of GADP considerably. The coefficient of

GADP falls in value by approximately half. Geographical

explanations seem to be at least as important as institutional

explanations.

+

36

Appendix B. Countries in the Sample

The sample covers 65 countries. We classify high-income countries that have been members of OECD

for more than 40 years as the OECD group and others by region. The country list is as follows.

OECD (20): Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece,

Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, United Kingdom,

United States

East Asia & Pacific (10): Cambodia, China, Indonesia, Korea, Rep., Malaysia, Mongolia, Philippines,

Singapore, Thailand, Vietnam

Europe & Central Asia (6): Albania, Bulgaria, Hungary, Poland, Romania, Turkey

Latin America & Caribbean (14): Argentina, Brazil, Chile, Colombia, Dominican Republic, Ecuador,

El Salvador, Guatemala, Honduras, Mexico, Panama, Paraguay, Peru, Uruguay

Middle East & North Africa (5): Egypt, Arab Rep., Iran, Islamic Rep., Israel, Jordan, Qatar

South Asia (3): India, Pakistan, Sri Lanka

Sub-Saharan Africa (7): Botswana, Cameroon, Namibia, South Africa, Sudan, Tanzania, Zimbabwe

37

Appendix C. Comparison of average determinant indexes and indicators over 1985−2011

Table C.1. The Main and Overall Determinant Indexes and Indicators by Income and Regional Group, Average over

1985−2011

OECD East Asia &

Pacific

Europe &

Central

Asia

Latin

America &

Caribbean

Middle East

& North

Africa

South Asia Sub-

Saharan

Africa

A. Overall determinant index 2.78 0.55 0.21 -0.72 0.36 -1.81 -1.31

B. Main determinant index and its indicators

1. Innovation index 1.64 0.28 -0.16 -0.59 0.28 -0.60 -0.62

Research and development

expenditure (% of GDP)

2.13 0.97 0.66 0.30 1.15 0.43 0.35

Patents per 100 people 0.11 0.12 0.01 0.02 0.07 0.01 0.01

Journal articles per 100 people 0.15 0.04 0.04 0.01 0.05 0.00 0.00

2. Education index 1.37 0.26 0.24 -0.30 0.30 -0.86 -0.67

Government expenditure on

education, total (% of GDP)

5.59 4.18 3.65 3.93 4.98 2.58 5.98

Gross enrolment ratio,

secondary, both sexes (%)

109.91 79.94 95.09 85.00 92.90 67.42 59.99

PISA, average of math,

science, and reading

503.97 451.24 454.73 399.91 407.95 384.65 398.27

Percentage of population age

25+ with tertiary schooling

18.98 13.17 11.02 8.14 14.19 7.08 1.35

3. Market efficiency index 1.47 0.35 0.46 -0.09 0.04 -0.69 -0.46

a. Doing business scores 77.33 64.17 67.76 60.02 59.17 48.95 51.25

b. Financial development

index

0.76 0.47 0.38 0.31 0.42 0.28 0.21

c. Labor market index -0.86 0.09 -0.41 0.31 0.45 1.19 0.35

Ratio of minimum wage to

value added per worker

0.27 0.36 0.32 0.56 0.23 0.50 0.55

Severance pay for redundancy

dismissal (weeks of salary)

4.54 21.63 9.01 20.68 17.79 29.49 18.87

Share of women in wage

employment in the

nonagricultural sector

47.92 41.17 41.41 40.96 22.45 22.56 37.53

4. Infrastructure index 1.36 0.13 0.45 0.08 0.57 -0.43 -0.68

Fixed telephone subscriptions

(per 100 people)

45.20 18.72 21.88 15.57 23.46 7.73 4.51

Mobile cellular subscriptions

(per 100 people)

117.07 111.79 114.36 118.58 106.60 74.19 87.25

Electricity production (kWh

per 100 people)

974858.80 336250.30 372237.70 246638.50 618458.60 66135.30 102341.80

Paved roads (km per 100

people)

1.15 0.16 0.38 0.17 0.32 0.13 0.13

Improved sanitation facilities

(% of population with access)

98.78 75.39 90.27 81.42 96.12 61.90 39.69

Improved water source (% of

population with access)

99.81 88.24 98.32 92.24 98.28 91.40 76.73

5. Institution index 0.56 0.21 -0.24 -0.37 -0.08 -1.06 -0.82

a. Governance index 1.10 -0.45 -0.09 -0.50 -0.55 -1.01 -0.87

38

OECD East Asia &

Pacific

Europe &

Central

Asia

Latin

America &

Caribbean

Middle East

& North

Africa

South Asia Sub-

Saharan

Africa

Voice and Accountability 1.33 -0.42 0.43 0.17 -0.76 -0.34 -0.45

Control of corruption 1.59 -0.21 -0.03 -0.18 0.06 -0.67 -0.43

Government Effectiveness 1.52 0.29 0.21 -0.11 0.24 -0.31 -0.47

Political Stability and Absence

of Violence/Terrorism

0.85 -0.18 0.16 -0.27 -0.68 -1.60 -0.32

Regulatory Quality 1.43 0.15 0.63 0.12 0.06 -0.36 -0.49

Rule of Law 1.53 -0.03 0.17 -0.40 0.15 -0.36 -0.52

b. Macroeconomic

environment index

-0.22 0.78 -0.29 -0.09 0.42 -0.66 -0.42

Government budget balance

(Percent of GDP)

-4.53 -0.36 -3.43 -1.49 -2.32 -7.27 -2.56

Gross domestic savings (% of

GDP)

23.12 32.87 19.26 18.73 30.37 20.77 14.91

Distance of inflation rate from

a stable rage

0.38 3.63 1.78 3.02 5.34 6.26 5.60

Note 1. OECD countries in the figure are high-income countries that have been members of OECD more than 40 years.

Other countries are grouped by region.

Note 2. Average values are calculated after imputing missing values of indicators.

39

Appendix D. Scenario analysis to assess the effects of the determinant index on the growth rate of

total factor productivity

With a scenario that developing countries increase their overall determinant index to the average level of OECD

countries over the period 2012–32, we predict the change in the growth rate of TFP for the developing countries

by region. The average overall determinant index in 2011 is 2.78 for the OECD group, 0.55 for East Asia &

Pacific, 0.21 for Europe & Central Asia, -0.72 for Latin America & Caribbean, 0.36 for Middle east & North

Africa, -1.81 for South Asia, and -1.31 for Sub-Saharan Africa. Using the regression model in table 2, we predict

the change in the growth rate of TFP per country, and calculate the average change across countries by region.

The result in figure C.1 shows that, if the developing countries increase the overall determinant index to the

average of OECD countries over 2012–32, the change in the growth rate of TFP is expected to increase to 1.7%

for South Asia, 1.5% for Sub-Saharan Africa, 1.3% for Latin America & Caribbean, 1.0% for Europe & Central

Asia, 0.9% for Middle East & North Africa, and 0.8% for East Asia & Pacific over 2012–42, and decrease

afterward.

Figure C.1. The Predicted Change in Total Factor Productivity Growth for Developing Countries with the

Determinant Index Change to the Level of OECD Countries

0

.003

.006

.009

.012

.015

.018

Cha

ng

e in

TF

P g

row

th r

ate

2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072 2077

Year

East Asia & Pacific Europe & Central Asia

Latin America & Caribbean Middle East & North Africa

South Asia Sub-Saharan Africa

Source: Authors' calculation

40

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