Assessing the Sustainability of Economic Growth in South Asia: A Growth Accounting Perspective

Abstract

This study assesses the proximate sources of economic growth of South Asian economies and compares them with other Asian regions, particularly with Asian Tigers. Per capita GDP growth is decomposed into the components which are attributable to input accumulation, frontier shifting (innovation) and catch-up (technological diffusion) over the period 1960–2023. It employs the Malmquist Productivity Index, a DEA-based technique, to model the aggregate production frontier of the sample economies. The empirical results seem to indicate that unlike Asian Tigers, the per capita GDP growth of South Asian economies is entirely led by input accumulation, particularly by capital accumulation. The significant contribution of total factor productivity (TFP) growth to GDP growth is missing in the region, which is further exacerbated by the onset of COVID-19. Failure in technological diffusion (failure to catch up with the world technological frontier) and downward frontier shifting caused by bad economic policies are found to be the main hurdles on the path of TFP growth in South Asia. Therefore, the eventual capital-deepening diminishing returns, compounded with the failure in the diffusion of technology, casts shadow over the long-term prospects of economic growth and risks South Asia to fall in the middle-income trap.

Keywords

South Asia Malmquist Productivity Index DEA frontier shifting technological catch-up

I. Introduction

The economic growth in South Asia is improving at an impressive rate from the last two decades. In 2024–2025, the World Bank predicted 5.6% growth for South Asian economies. In 2023, it predicted 6.3% growth for India and 5.6% for Bangladesh. With the recovery in tourism, Maldives and Nepal start to perform well with the prediction of 6.3% and 3.9%, respectively. Pakistan and Sri Lanka also seem to recover from severe recession and are predicted to grow at 1.7% (World Bank, 2023). South Asia has undergone significant structural transformations, with the services sector taking a leading role. Over the past few decades, the proportion of the services industry has grown considerably. Given that most South Asian economies rely heavily on labour, high-skilled labour is crucial for driving economic growth, and these economies need to boost spending and collaboration to advance technologically, with India potentially playing a pivotal role.

The most pertinent questions that follow are: What drives this impressive output growth in South Asian economies? Will it be sustainable? Is there anything one can do to make it sustainable? To find answers to these questions, the first appropriate step would be to resort to the preliminary growth accounting exercise. In the growth accounting framework, the growth of output can usually be driven by following ways: (a) by increasing the inputs (capital and labour), (b) by improving the efficiency, (c) by improving the technology or (iv) by the combination of the above three ways. Following the growth accounting framework, empirically, the growth is generally decomposed into two mutually exclusive components—one is attributed to input accumulation, called ‘perspiration component’, while the other part is called ‘inspiration component’ (Arora & Kumar, 2013). The inspiration component is also called Solow’s residual or total factor productivity (TFP) growth. TFP growth specifically measures both technological change and the changes in the efficiency with which existing technology is utilized in production processes (Ahluwalia, 1991). The technological change component captures shift in the overall production frontier defined conceptually by state-of-the-art and potentially transferable technology, and improvements in this component are considered to be the evidence of innovation (Kumar & Russel, 2002). The second component, technological catch-up, represents movement towards or away from the frontier. This component is expected to capture the diffusion of technology, and as the study uses aggregate macro-data, changes in efficiency are also expected to reflect variations in capacity utilization and differences in economic structure, such as being highly regulated or competitive (Färe et al., 1994). The last component inputs accumulation accounts for movements along the production frontier. It is very important for the country or policymakers to know which of the above two components contributes most to their output growth. There is a consensus that due to diminishing returns to inputs, the growth dominated by input accumulation is not sustainable in the long run, whereas the growth dominated by TFP growth (TFPG) (technological progress and technological catch-up) is more sustainable in the long run (Arora & Kumar, 2013).

This article is outlined as follows. Section II reviews the literature and identifies the existing research gap. Section III provides the methodological framework for the decomposition of GDP growth into its various constituent. It illustrates the Malmquist Productivity Index (MPI) used to estimate TFPG. It also explains the DEA framework for the estimation of MPI. Section IV provides the data description. Section V is empirical in nature and provides the results related to the decomposition of per capita GDP growth into the frontier shifting, technological catch-up and input accumulation. Lastly, Section VI concludes the study and provides the relevant policy implications.

II. Review of the Literature and Research Gap

With the founding stone of Robert Solow’s (1957) seminal work, the empirical literature on sources of growth has proliferated. The famous debate about the sustainability of the high growth of East Asian economies is well documented in the economic literature. From Paul Krugman’s (1994) ‘The Myth of Asia’s Miracle’ to Alwin Young’s (1995) ‘The Tyranny of Numbers’, rich empirical literature reveals fascinating facts about the economic success of East Asian countries. There seems no consensus about the sources of high economic growth in East Asia. The studies of Krugman (1994), Young (1995), Collins et al. (1996), Senhadji (2000), Jerusalem (2019) etc. highlight the pivotal role of capital accumulation in East Asia’s economic growth. However, the studies of Chen (1997) and Singh and Trieu (1999) underline the importance of TFPG in their growth engine.

Although there is a vast amount of literature about the sources of output growth of various firms, industries, countries and different regions of the world, studies related to South Asian economies are very limited in number. The study of Srinivasan (2005) compares the experience of South Asia with China in terms of TFPG, using various empirical studies. The paper reiterates the finding of South Asia’s decline in TFPG between 1989 and 2003. Collins (2007) performs the growth-accounting exercise of South Asian economies by decomposing the growth into input accumulation and TFPG. The study finds that South Asia’s economic growth is mainly led by input accumulation rather than TFPG. Recently, Saha (2021) evaluated TFP growth in five South Asian countries—India, Pakistan, Nepal, Bangladesh and Sri Lanka—between 1991 and 2020. The findings reveal that TFP trends in these countries are highly cyclical and volatile. India’s TFPG, driven by technological progress, leads the region.

The above-mentioned studies are not comprehensive in nature in terms of both time line and sample size. Without further decomposing the TFPG to provide a better assessment of South Asia’s catch-up with the world technological frontier, these studies just give the general estimates of TFPG. This study not only further decomposes the TFPG to provide a better assessment of South Asia’s catch-up and innovation but also compares them with other Asian regions, including the then called newly industrialized economies (NIEs), known as Asian Tigers. This comprehensive comparison will help to unravel the factors, such as economic and human development policies and strategies, contributing to the differences in the sources of output growth. Therefore, keeping in view the above research gap, proximate sources of output growth are assessed by decomposing which components are attributable to input accumulation, technological progress and technological catch-up.

III. Methodological Framework

This section outlines the methodology to decompose the growth rate of GDP per capita into its various constituents. Initially, GDP growth is divided into two components: one is attributed to the growth of TFP, and another is attributed to the growth of inputs. Finally, TFPG is further divided into two components: changes in technological progress (frontier shifting) and changes in efficiency (technological catch-up or movement towards the frontier).

To get the estimate of TFPG, the conventional technique subtracts the contribution of labour and capital from the total output growth; the remaining part is called Solow residual. Usually, parametric methods are used to estimate the elasticity parameters of labour and capital. The usual assumptions of these methods are: known form of production function, optimizing behaviour, ruling out any inefficiency, neutral technical change and constant returns to scale. These assumptions may provide a biased estimate of TFPG (Arcelus & Arocena, 2000; Coelli & Perelman, 1998). Due to these limitations of conventional approach, this study uses the data envelopment analysis based–MPI approach, developed by Färe et al. (1994). The MPI is favoured for its ability to decompose productivity into technological change and catch-up, its independence from price data, its accommodation of multiple inputs and outputs, and its lack of requirement for pre-specified optimization criteria.

By using distance functions, Caves et al. (1982) proposed the MPI, which permits to describe multi-input, multi-output production without involving explicit price data and behavioural assumptions (such as cost minimization or profit maximization). It only requires data on input and output quantities. Following Färe et al. (1994), output-based MPI change for each period t = 1, …, T can be defined by the production technology S^t, which models the transformation of inputs, $x^{t} \in ℝ_{+}^{N}$ , into outputs, $y^{t} \in ℝ_{+}^{M}$ .

S^{t} = {(x^{t}, y^{t}) : x^{t} can produce y^{t}}

(1)

where production technology S^t consists of the set of all feasible input/output vectors. x^t and y^t are vectors of input and output quantities at time t, respectively.

An output distance function at time t, defined by Färe et al. (1994), is

\begin{array}{l} D_{0}^{t} (x^{t}, y^{t}) = \inf {θ : (x^{t}, y^{t} / θ) \in S^{t}} \\ = {(sup {θ : (x^{t}, θ y^{t}) \in S^{t}})}^{- 1} \end{array}

(2)

This function is defined as the inverse of the greatest proportional increase in the output vector y^t, given the input vector x^t. It fully defines the technology. D^t>₀( x ^t, y^t) ≤ 1 if and only if ( x ^t , y^t) ∈S^t . Moreover, D^t₀( x ^t , y^t) = 1 if the country is technically efficient and operates on boundary or the frontier of technology, whereas D^t₀( x ^t , y^t) <1 if it is technically inefficient and operates below the frontier (Färe et al., 1994).

Following Färe et al. (1994), two distance functions with respect to two different time periods are required to define the Malmquist index, such as

D_{0}^{t} (x^{t + 1}, y^{t + 1}) = \inf {θ : (x^{t + 1}, y^{t + 1} / θ) \in S^{t}}

(3)

This distance function quantifies the maximal proportional change in outputs necessary to make ( x ^t ^{+ 1}, y^t ^{+ 1}) feasible relative to the technology at t. This is demonstrated in Figure 1. As seen in Figure 1, production ( x ^t ^{+ 1}, y^t ^{+ 1}) takes place outside the set of feasible production in time t (which indicates the occurrence of technical change). The value of this distance function evaluated at ( x ^t ^{+ 1}, y^t ^{+ 1}) relative to period t technology is $\frac{o d}{o e}$ , which is greater than 1. Equivalently, another distance function D₀^{t + 1}( x ^t , y^t) can be defined, which quantifies the maximal proportional change in output necessary to make ( x ^t , y^t) feasible relative to technology at time t + 1.

Caves et al. (1982) define MPI as the ratio of two output distance functions as follows:

M^{t} = \frac{D_{0}^{t} (x^{t + 1}, y^{t + 1})}{D_{0}^{t} (x^{t}, y^{t})}

(4)

This productivity index takes technology at time t as a reference technology. However, it is also possible to construct productivity index, which takes technology at time t + 1 as the reference technology as follows:

M^{t + 1} = \frac{D_{0}^{t + 1} (x^{t + 1}, y^{t + 1})}{D_{0}^{t + 1} (x^{t}, y^{t})}

(5)

To avoid choosing an arbitrary reference technology, Färe et al. (1994) constructed the MPI as the geometric mean of the two indexes shown in Equations (4) and (5):

M_{0} (x^{t + 1}, y^{t + 1}, x^{t}, y^{t}) = {[(\frac{D_{0}^{t} (x^{t + 1}, y^{t + 1})}{D_{0}^{t} (x^{t}, y^{t})}) (\frac{D_{0}^{t + 1} (x^{t + 1}, y^{t + 1})}{D_{0}^{t + 1} (x^{t}, y^{t})})]}^{1 / 2}

(6)

According to Färe et al. (1994), Equation (6) can also be written as:

\begin{array}{l} M_{0} (x^{t + 1}, y^{t + 1}, x^{t}, y^{t}) = \frac{D_{0}^{t + 1} (x^{t + 1}, y^{t + 1})}{D_{0}^{t} (x^{t}, y^{t})} \times \\ {[(\frac{D_{0}^{t} (x^{t + 1}, y^{t + 1})}{D_{0}^{t + 1} (x^{t + 1}, y^{t + 1})}) (\frac{D_{0}^{t} (x^{t}, y^{t})}{D_{0}^{t + 1} (x^{t}, y^{t})})]}^{1 / 2} \end{array}

(7)

Färe et al. (1994) gives the following interpretation to the two terms on the right-hand side of Equation (7):

E f f i c i e n c y c h a n g e = \frac{D_{0}^{t + 1} (x^{t + 1}, y^{t + 1})}{D_{0}^{t} (x^{t}, y^{t})}

(8)

T e c h n i c a l c h a n g e = {[(\frac{D_{0}^{t} (x^{t + 1}, y^{t + 1})}{D_{0}^{t + 1} (x^{t + 1}, y^{t + 1})}) (\frac{D_{0}^{t} (x^{t}, y^{t})}{D_{0}^{t + 1} (x^{t}, y^{t})})]}^{1 / 2}

(9)

Efficiency change represents the technological catch-up, whereas technical change represents frontier shifting. In other words, the product of the change in relative efficiency occurred between periods t and t + 1, and the change in technology that occurred between periods t and t + 1 is simply the MPI.

Estimation

This study uses the following output-oriented DEA linear programming method, following Färe et al. (1994) and Kumar and Russel (2002).

For a given panel of k countries using inputs and outputs, $(x_{n}^{k, t}, y_{m}^{k, t})$ , four distance functions, $D_{0}^{t} (x^{t}, y^{t}), D_{0}^{t + 1} (x^{t + 1}, y^{t + 1}), D_{0}^{t + 1} (x^{t}, y^{t}) a n d D_{0}^{t} (x^{t + 1}, y^{t + 1}),$ are to be solved with linear programming to estimate the MPI change of country k’ between period t and t + 1. These four LP problems can be stated as follows:

\begin{array}{l} {(D_{0}^{t + 1} (x^{k^{'}, t}, y^{k^{'}, t}))}^{- 1} = \max θ^{k^{'}} \\ s u b j e c t t o \\ θ^{k^{'}} y_{m}^{k^{'}, t + j} \leq \sum_{k = 1}^{K} Z^{k, t + i} y_{m}^{k, t + i} m = 1, ..., M; \\ \sum_{k = 1}^{K} Z^{k, t + i} x_{n}^{k, t + i} \leq x_{n}^{k^{'}, t + j} n = 1, ..., N; \\ Z^{k, t + i} \geq 0 k = 1, ..., K, \end{array}

(10)

Where $\begin{array}{l} (i, j) = (0, 0) for solving for (D_{0}^{t} (x^{k^{'}, t}, y^{k^{'}, t}))^{- 1}; \\ (i, j) = (1, 1) for solving for (D_{0}^{t + 1} (x^{k^{'}, t + 1}, y^{k^{'}, t + 1}))^{- 1}; \\ (i, j) = (0, 1) for solving for (D_{0}^{t} (x^{k^{'}, t + 1}, y^{k^{'}, t + 1}))^{- 1}; a n d \\ (i, j) = (1, 0) for solving for (D_{0}^{t + 1} (x^{k^{'}, t}, y^{k^{'}, t}))^{- 1} . \end{array}$

In the given linear programming problems, Z^k,t serves as an intensity variable, signifying the degree to which a specific country is utilized in constructing the technology set’s frontier. The technology described here is non-parametric, assuming constant returns to scale and strong disposability of inputs and outputs. In this formulation, θ represents the efficiency score, ranging between 0 and 1.

IV. Data

The data for this study is sourced from the Penn World Table (PWT), specifically version 10.01 (refer to Feenstra et al. (2015) for detailed variable descriptions), covering the period from 1960 to 2019. For the post-COVID analysis covering the years 2020–2023, data is obtained from the World Development Indicators (WDI) database. The study uses the balanced panel of 14 countries over the period 1960–2023. As shown in Table 1, along with the five main South Asian countries, economies of Asian Tigers, South East Asia, China and USA are also included for the comparison purpose. The reasons to include these economies for the comparison purpose are: (a) Asian Tigers serve as an important reference point for South Asian economies due to their remarkable economic transformation from low-income to high-income status within a few decades (Amsden, 1989; World Bank, 1993). Their development path, which combined state-led industrialization, export-oriented policies, strategic human capital investments and institutional strengthening, offers valuable lessons for South Asia (Stiglitz, 1996; Wade, 1990). Moreover, Asian Tigers’ success in rapid poverty reduction and inclusive growth provides important policy insights for South Asian (Page, 1994; United Nations Development Programme (UNDP), 2023). (b) South East Asia’s developmental level is close to the development level of South Asia. In this article, developmental level refers to a combination of economic indicators (GDP per capita), social indicators (literacy rates, life expectancy) and structural indicators (share of industry and services in GDP). Considering these indicators, data from the World Bank (2023) and UNDP (2023) highlight that South Asia and Southeast Asia exhibit comparable levels of economic development in certain countries, particularly lower middle-income economies such as Bangladesh, India, Vietnam and the Philippines (e.g., According to World Bank (2023), South Asia’s average GDP per capita is around $2,000, while Southeast Asia’s is closer to $4,500, though there is significant intra-regional variation). On the Human Development Indicators, some Southeast Asian countries (e.g., Vietnam and Indonesia) are performing similarly to their South Asian counterparts (e.g., Bangladesh and India), though others such as Malaysia and Thailand are significantly ahead. (c) The USA is included in the analysis because it is a world technological leader as well as the major trading partner of most of the countries in the world (International Monetary Fund (IMF), 2023a; National Science Foundation, 2023; World Intellectual Property Organization, 2023; World Trade Organization, 2023). Through the channel of trade, technology which is embodied, particularly in the capital goods, is expected to flow from the USA to other regions; and (d) China has emerged as a leading economic power in the Asian region, driven by its rapid GDP growth, dominance in regional trade, extensive investments in infrastructure and expanding influence through strategic initiatives such as the Belt and Road Initiative (Asian Development Bank, 2023; IMF, 2023b; WTO, 2023). The literature typically recognizes four Asian Tiger economies. However, this study focuses on only three of them, excluding Hong Kong, due to the absence of data for key variables in the Penn World Table, which forms the primary data source for our analysis. Although Hong Kong’s data is available in the World Development Indicators database, including it would result in inconsistencies in variable definitions and measurements across the dataset. To ensure uniformity and comparability in the analysis, Hong Kong has therefore been excluded from this study.

Table 1.

Descriptive Statistics of the Data.

Data	Description	Average	SD	Source
GDP	Output-side real GDP at chained PPPs (in million 2017 US$)	1,473,766	3,317,885	PWT (10.01)
Population	Population (in millions)	156.7992	309.289	PWT (10.01)
Capital	Capital stock at current PPPs (in million 2017 US$)	5,260,444	1,266,0351	PWT (10.01)
Labour	Number of persons engaged (in millions)	70.16169	153.0364	PWT (10.01)
Sample Economies
South Asia	South East Asia	Asian Tigers	Others
Bangladesh	Indonesia	Singapore	USA
Sri Lanka	Malaysia	South Korea	China
Nepal	Philippines	Taiwan
Pakistan	Thailand
India

Note: Economies are included, based on the availability of data and existing literature.

This study uses GDP per capita as the output variable and labour and capital as input variables. The rationale for choosing these variables in the decomposition of GDP growth is rooted in standard economic growth theory, particularly the neoclassical growth framework and the Cobb–Douglas production function. Modern Western economics considers capital and labour as the most important production input elements. Besides, the data on these variables are generally available across countries and over time, ensuring comparability and reliability in empirical analysis. Therefore, following the empirical studies of Kumar and Russell (2002), Domazlicky and Weber (2006) and Delgado-Rodríguez and Álvarez-Ayuso (2008) on the decomposition of GDP growth, we use one output variable, GDP per capita, and two input variables, capital per capita and labour per capita. The output variable is real GDP at chained PPPs (measured in 2017 US$). The input variable capital is represented by the capital stock at chained PPPs (measured in 2017 US$), and the input variable labour is represented by the number of individuals aged 15 years and above engaged in production activities.

V. Empirical Results and Discussion

We start our analysis by plotting the log GDP per capita, in Figure 1, of various Asian regions to see its evolution. From Figure 1, it can be seen how rapidly Asian tigers have grown their per capita GDP to catch up with the USA. Although East Asia, Asian Tigers and South Asia almost start with the same low per capita income, as seen in Figure 1, Asian Tigers are now way ahead of them. The interesting case is of China, which started off with the lowest per capita GDP in 1960 but overtook South Asia and South East Asia as well.

Small differences in growth rates between countries can lead to striking outcomes over time. When compounded over a long period, even minor variations result in significantly divergent end results. According to Table 2, while the difference of the growth rate during the entire period between China and Asian Tigers is just 0.86%, Asian Tigers manage to reach to the per capita income levels of the USA.

Table 2.

Growth Rates of GDP per Capita.

Period	South Asia	South East Asia	Asian Tigers	China	USA
1960–1970	1.52	2.28	7.60	1.93	3.36
1970–1980	−0.39	5.65	10.09	3.57	2.32
1980–1990	1.79	2.74	5.91	4.10	1.98
1990–2000	2.24	2.72	4.49	4.60	2.13
2000–2010	4.59	4.30	3.82	8.56	0.85
2010–2019	5.18	4.11	2.58	4.69	1.70
Post-COVID	2.65	3.79	2.89	5.41	3.12
1960–2024	2.50	3.66	5.51	4.65	2.10
Period	South Asian Economies
Period	Bangladesh	India	Nepal	Pakistan	Sri Lanka
1960–1970	−0.44	2.38	0.76	3.88	1.08
1970–1980	−0.90	−0.98	0.58	1.69	−2.28
1980–1990	0.91	0.51	2.75	2.46	2.33
1990–2000	−0.02	4.21	2.71	0.31	4.07
2000–2010	4.61	6.74	2.61	3.48	5.53
2010–2019	7.06	5.33	6.60	2.21	4.76
Post-COVID	5.15	7.10	1.96	1.50	−2.20
1960–2024	2.07	3.26	2.64	2.26	2.26

Note: Post-COVID is the period between 2020 and 2024.

South Asia averages at the 2.50% growth, whereas South East Asia averages at the middle with 3.66%. During the first three decades, 1960–1990, Asian Tigers achieved very high growth rates, whereas for South Asia and China, it is a period of very low growth. Actually, it was after 1990 when South Asia started to pick up and showed impressive growth in the subsequent decades. China started to pick up one decade earlier than South Asia. South Asia seemed to be badly hit in the post–COVID period, with growth slowing down from 5.18% to 2.65%, whereas Chinese growth remained intact at 5.41% in the post–COVID period. The USA experienced impressive growth of 3.12% in the post–COVID period. Among the South Asian economies, India averages at the highest rate of 3.26% in the region, followed by Nepal, with 2.64%. It was after 1990, after the liberalization of its economy, when India started to pick up and thereafter grew at an impressive rate. Bangladesh has the lowest growth rate of 2.07% in the region. During the first four subsequent decades, it performed well and even achieved the highest growth rate of 7.06% in the region during the period 2010–2019. Pakistan and Sri Lanka grew at a medium rate of 2.26%. During the first two decades, 1960–1980, Pakistan relatively did well and almost caught up with Sri Lanka, as seen in Figure 1, but in the subsequent decades, it relatively worsened its position. Barring India and Bangladesh, the growth in Nepal, Pakistan and Sri Lanka seems to be collapsing in the post–COVID period, with Sri Lanka even experiencing negative growth. It is important to note that these economies seemed to be struggling even before the pandemic.

Thus, from the above analysis, it is apparent that there exist substantial inter-regional differences of economic growth. In recent times, the breakout of COVID-19 pandemic has disrupted growth patterns of world economies, particularly of Asian economies, and further exacerbated the already existing regional inequality. Therefore, to better understand the underlying causes of these regional differences in economic growth, it is important to understand the fundamental process of economic growth. The following section sheds some light on this fundamental process of economic growth by performing a preliminary growth-accounting exercise.

Decomposition of per Capita GDP Growth

As mentioned earlier, GDP growth is decomposed into two mutually exclusive components: (a) input accumulation, the part of growth which is attributed to the growth of capital per capita and labour per capita, and (b) TFPG. The TFPG component is estimated by an output-oriented DEA-based MPI. Using MPI, TFPG has been obtained using the following relationship:

TFPG = (MP1 - 1) \times 100

(11)

According to Table 3, the high 5.51% per capita GDP growth of Asian Tigers is supported by 1.84% TFPG, contributing almost 33% to the growth, whereas the rest is contributed by input accumulation. As shown in Table 4, it grows its capital per capita at a high rate of 6.14% and labour per capita at a rate of 1.05%. During the decade 1960–1970, the contribution of TFPG for Asian Tigers was dominating, contributing almost 67% to the high 7.6% GDP growth, and in the subsequent decade, it was about 43% to the whopping 10.09% GDP growth. For South East Asia, TFP grows at a rate of 0.23%, contributing almost 6%. Its growth is mainly supported by input accumulation, particularly by capital per capita, which grows at a good rate 4.41%. Like Asian Tigers, TFPG in South East Asia also dominated during the initial two decades. However, in the subsequent decades, South East Asia experienced deterioration in TFPG, particularly in the post-COVID period. China and South Asia experienced negative TFPG, decelerating at a rate of 1.08% and 1.11%, respectively. Chinese high growth rate is mainly supported by the high capital growth of 7.20%. South Asia also cushioned its GDP growth by increasing its capital arsenal at a rate of 4.38%.

The inter-economy analysis of South Asia reveals that the contribution of TFPG in South Asia is missing. Barring Pakistan, the rest of the economies indicate negative TFPG. India’s 3.26% growth is mainly led by input accumulation growing at a rate of 4.42%. The contribution of TFPG is negative to the growth. However, during the decade 2010–2019, there was some improvement in TFP in India, growing at a rate of 1.73% and contributing almost 32%. However, this significant TFPG of India was badly hit in the post-COVID period, decelerating at a rate of 3.72%. Nepal showed the highest TFP regress, at a rate of −3.14%, followed by Bangladesh at a rate of −2.21%. Although Nepal grows its capital at the highest rate of 6.08% in the region, its high negative TFPG retards its growth. Similarly, during the period 1980–2000, Bangladesh grew its capital close to 5%, but the high negative TFPG retarded its GDP growth as well. It is important to note that Bangladesh’s high growth of 5.51% in the post-COVID period was significantly supported by TFPG, contributing almost 21%. During the period 1960–1990, when Pakistan did relatively good in the region, the contribution of TFPG was positive. In fact, during 1980–1990, growth in Pakistan was dominated by TFP growth at a rate of 2.67%. However, in the subsequent decades, it failed to improve its TFPG and averaged at only 0.21%. Sri Lanka experienced negative TFPG, and its GDP growth was also led by input accumulation of 3.52%. During the period 1990–2000, its TFP grew at an impressive rate of 2.87% and dominated the high GDP growth of 4.07 by contributing almost 70%. However, during the subsequent decades, it deteriorated its TFPG. In sum, South Asia’s per capita GDP growth is almost entirely led by input accumulation, particularly by capital per capita, rather than TFPG. The picture gets clear when all the growth figures, TFP, GDP, labour and capital, are plotted in Figure 2. It is evident from Figure 2 that high growth rates of GDP per capita are accompanied by high growth rates of capital per capita. The significant contribution of TFPG is missing in the region. However, the good news for South Asia is that during the decade 2010–2019, barring Sri Lanka, all the economies showed some improvement in their TFPG and contribute positively to the per capita GDP growth.

To get a clear picture of the evolution of TFPG, cumulative TFPG of main Asian regions is plotted in Figure 3. As seen in Figure 3, it is evident that TFPG in the Asian region is dominated by Asian Tigers, followed by South East Asia. Initially, China’s TFPG was very low, but it picked very fast. However, soon after, it slid down to the TFPG levels of South Asia. It seems from Figure 3 that the differences in TFPG among the Asian regions got smaller over the period. Sri Lanka, India and Pakistan relatively have higher TFPG. Nepal, which had the worst TFPG, showed some upward trend in TFPG after the year 1999. During the early decades, Sri Lanka slid down but soon picked up and matched the levels of India and Pakistan.

In conclusion, the COVID-19 pandemic has undeniably disrupted economic growth patterns globally, with Asian economies bearing a disproportionate brunt of the impact. The ensuing regional inequalities have further highlighted vulnerabilities in the region’s development trajectory. Notably, South East Asia has experienced a marked deterioration in TFPG, particularly in the post-COVID era. India, despite its significant pre-pandemic strides, witnessed a sharp deceleration in TFPG, declining at a rate of 3.72%. In contrast, Bangladesh stands out as a resilient exception, achieving a remarkable post-COVID growth rate of 5.51%, with TFPG contributing a substantial 21% to this success. These disparities underscore the critical role of productivity-driven growth in fostering robust and equitable economic recovery across the region.

Decomposition of Total Factor Productivity Growth

Decomposition of TFPG enables us to know the sources of TFPG. TFPG is decomposed into two components: technological progress and technological catch-up. Technological progress (regress) represents the outward (inward) shifting of the production frontier, and technological catch-up (failure) represents the movement towards (away from) the production frontier. An index value exceeding unity signifies growth of the index, a value equal to unity indicates no change and a value below unity indicates deceleration of the index.

Before proceeding further, it must be noted that according to Färe et al. (1994), while the average results related to technical change are indicative, they do not enable us to pinpoint the specific countries driving the frontier’s shift over time. The technical-change component of the Malmquist index informs us about the changes in the frontier at each country’s input level and mix, but it does not indicate whether a particular country is responsible for this shift. To determine which countries act as ‘innovators’, we need to examine the component distance functions within the technical change index. If the following conditions are met, then

{TC}^{k} > 1

(i)

D_{0}^{t} (x^{k, t + 1}, y^{k, t + 1}) > 1

(ii)

D_{0}^{k, t + 1} (x^{k, t + 1}, y^{k, t + 1}) = 1

(iii)

that country shifts the production frontier between periods t and t + 1. The first condition implies that the value of technical change index must be greater than 1. According to the second condition, the technology in period t is unable to produce the output of period t + 1 with the same input vector of period t + 1, indicating the technological progress in period t + 1. The third condition assures that the country sits on the production frontier in period t + 1.

According to Table 5, which provides the list of innovative countries, the USA turns out to be the most innovative country by shifting the frontier 49 times, followed by Taiwan (shifted 30 times) and Singapore (shifted 23 times). Among the South Asian economies, Sri Lank and Pakistan provide some evidence of innovation. However, overall, it is the USA which produces the innovations and leads the world technological frontier. Other countries are expected to adopt these technologies and catch up with this world technological frontier.

Table 3.

Total Factor Productivity Growth and Its Contribution to per Capita GDP Growth.

Period		Bangladesh	India	Nepal	Pakistan	Sri Lanka	SA	SEA	AT	China	USA
	GDP	−0.44	2.38	0.76	3.88	1.08	1.52	2.28	7.60	1.93	3.36
1960–1970	TFPG	−0.39	−0.04	−1.82	0.25	0.20	−0.36	1.17	5.12	1.33	1.89
	%	−88	−2	−240	6	19	−24	51	67	69	56
	GDP	−0.90	−0.98	0.58	1.69	−2.28	−0.39	5.65	10.09	3.57	2.32
1970–1980	TFPG	−2.53	−0.10	−5.55	0.28	−3.18	−2.24	3.81	4.39	−0.98	0.85
	%	−280	−10	−957	16	−139	−574	67	43	−27	36
	GDP	0.91	0.51	2.75	2.46	2.33	1.79	2.74	5.91	4.10	1.98
1980–1990	TFPG	−3.48	1.79	−4.45	2.67	−0.28	−0.79	−0.27	0.61	−2.31	0.92
	%	−383	352	−162	109	−12	−44	−10	10	−56	46
	GDP	−0.02	4.21	2.71	0.31	4.07	2.24	2.72	4.49	4.60	2.13
1990–2000	TFPG	−4.51	−1.64	−6.88	−2.72	2.87	−2.63	−3.30	1.44	−4.07	1.76
	%	−22,535	−39	−254	−876	70	−117	−121	32	−89	82
	GDP	4.61	6.74	2.61	3.48	5.53	4.59	4.30	3.82	8.56	0.85
2000–2010	TFPG	−3.55	−1.56	−0.58	−0.93	−1.62	−1.65	0.24	−0.36	−0.37	0.99
	%	−77	−23	−22	−27	−29	−36	6	−9	−4	117
	GDP	7.06	5.33	6.60	2.21	4.76	5.18	4.11	2.58	4.69	1.70
2010–2019	TFPG	0.19	1.73	0.36	0.80	−0.55	0.50	0.75	0.75	−0.52	1.19
2010–2019	%	3	32	5	36	−11	10	18	29	−11	70
Post-COVID	GDP	5.15	7.1	1.96	1.5	−2.2	2.65	3.79	2.89	5.41	3.12
	TFPG	1.09	−3.72	−2.12	2.73	4.50	0.45	−1.67	0.72	0.81	−0.42
	%	21	−52	−108	182	205	17	−44	25	15	−13
	GDP	2.07	3.26	2.64	2.26	2.26	2.5	3.66	5.51	4.65	2.1
1960–2024	TFPG	−2.21	−0.22	−3.14	0.21	−0.15	−1.11	0.23	1.84	−1.08	1.15
1960–2024	%	−107	−7	−119	9	−6	−44	6	33	−23	55

Notes: (a) GDP and TFPG represent the average annual growth rates of per capita GDP growth and TFP growth, respectively.

(b) SA, SEA and AT stand for South Asia, South East Asia and Asian Tigers, respectively.

Table 4.

Input Accumulation and Its Contribution to per Capita GDP Growth.

Period		Bangladesh	India	Nepal	Pakistan	Sri Lanka	SA	SEA	AT	China	USA
	Capital	0.36	4.53	2.88	7.39	1.61	3.32	2.32	4.39	0.84	2.58
1960–1970	Labour	−0.86	−1.31	0.86	−0.51	−0.33	−0.43	−0.21	0.50	−0.56	0.63
	%	−12	102	340	94	81	124	49	33	31	44
	Capital	2.38	−1.20	6.97	1.84	1.17	2.20	2.25	8.31	5.89	2.14
1970–1980	Labour	−0.53	0.52	0.41	0.13	−0.16	0.07	0.41	2.37	1.02	1.07
	%	180	110	1057	84	39	474	33	57	127	64
	Capital	4.82	−1.46	7.54	0.07	3.18	2.78	3.72	7.99	6.78	1.88
1980–1990	Labour	0.58	0.46	0.18	−1.17	−0.22	−0.04	1.46	1.15	1.56	0.76
	%	483	−252	262	−9	112	144	110	90	156	54
	Capital	4.71	5.93	10.28	3.83	1.11	5.13	7.42	7.53	9.03	1.41
1990–2000	Labour	0.13	−0.20	0.26	−0.56	1.04	0.13	0.37	0.35	0.27	0.06
	%	22,435	139	354	976	30	217	221	68	289	18
	Capital	8.45	11.10	3.22	5.22	10.43	7.64	5.70	7.77	13.09	1.42
2000–2010	Labour	0.95	−0.13	1.34	1.41	1.49	1.01	0.51	1.06	0.05	−0.63
	%	177	123	122	127	129	136	94	109	104	−17
	Capital	9.35	5.53	6.22	1.79	7.97	6.14	4.45	2.47	8.20	0.61
2010–2019	Labour	1.25	−0.44	1.75	0.07	0.16	0.55	0.63	0.83	−0.25	0.43
	%	97	68	95	64	111	90	82	71	111	30
Post–COVID	Capital	4.09	11.25	4.19	−1.50	−6.42	2.15	5.69	2.02	4.87	3.59
	Labour	1.87	3.21	2.92	3.14	0.99	2.42	1.67	1.03	0.57	2.22
	%
	Capital	4.98	4.42	6.08	2.95	3.52	4.38	4.41	6.14	7.20	1.78
1960–2024	Labour	0.37	0.04	0.93	0.10	0.38	0.37	0.61	1.05	0.37	0.50
1960–2024	%

Notes: (a) Capital and Labour represent the average annual growth rates of capital per capita growth and labour per capita growth, respectively.

(b) SA, SEA and AT stand for South Asia, South East Asia and Asian Tigers, respectively.

To see whether other countries catch up with the frontier, we take a look at the technological change and technological catch-up indexes, listed in Table 6. According to Table 6, as expected, the USA’s TFPG is entirely led by technological progress. Asian Tigers’ TFPG growth of 1.8% is also dominated by technological progress with 1.5% contribution, whereas the contribution of technological catch-up is 0.4%. This result is also expected as most of the economies of Asian Tigers are innovators, as shown in Table 5. However, they also experience some catch-up with the frontier. On the other hand, South East Asia’s TFPG of 0.2% is mainly led by technological catch-up with 0.2% contribution. Technological catch-up contributed almost 3.5% to the 3.8% TFPG in 1970–1980, reflecting the good amount of technological diffusion during this period. China also showed some catch-up with 0.01% growth. The cause for the missing TFPG in South Asia is its failure to catch up with the technological frontier of the world, as shown by the regress of its catch-up index in Figure 4. South Asia also shows regression in its technological progress index, highlighting the policy failure. It should be noted that the regression in technological frontier does not necessarily mean deterioration in production technology. The frontier can also shift downward by the bad economic policies of the country. The impact of production environment also gets reflected in the frontier shifting index. Recently, South Asia has experienced improvement in both the indexes, especially catch-up index, with 0.4% growth.

Table 5.

List of Innovative Countries.

Countries	Number of Times the Frontier Shifted
USA	49
Taiwan	30
Singapore	23
Pakistan	14
Sri Lanka	11
Bangladesh	6
Nepal	3

Inter-economy analysis of South Asia reveals that India observes some catch-up, with 0.2%. However, the downward-shifting frontier, probably caused by some bad economic policies, retards these catch-up gains. The catching-up effect drives the positive TFPG of India during the period 1980–1990, probably because of the liberalizing efforts of its economy that were initiated in 1980. Pakistan, during the early decades, observed some catching-up and technological progress. The recent TFPG of Bangladesh seems to be caused by the frontier shifting of 2.7%. Since Bangladesh also takes some part in frontier shifting, as shown in Table 5, the rise in technological progress index is real. Sri Lanka’s cause for the TFP regression seems to be the downward frontier shifting, as shown in Table 5. The positive TFPG of Sri Lanka in the period 1990–2000 is mainly led by the technological catch-up index, highlighting the good amount of technological diffusion in this period. The poor performance of Nepal’s TFPG seems to be caused by the poor performance of both the indexes. Failure in technological diffusion and downward frontier shifting caused by bad economic policies are to be blamed in the case of Nepal. In sum, the failure to catch up supplemented by the downshifting frontier, as seen in Figure 4, is the main hurdle on the path of TFPG for South Asia.

Table 6.

Technological Progress and Technological Catch-up Index.

Period	Index	Bangladesh	India	Nepal	Pakistan	Sri Lanka	SA	SEA	AT	China	USA
	Catch-up index	1.000	0.991	1.000	0.996	0.990	0.995	0.989	1.017	1.020	1.000
1960–1970	Technology index	0.996	1.009	0.982	1.007	1.012	1.001	1.023	1.034	0.993	1.019
	TFPG index	0.996	1.000	0.982	1.003	1.002	0.996	1.012	1.051	1.013	1.019
	Catch-up index	1.000	1.010	0.989	1.016	0.978	0.998	1.035	1.013	1.014	1.000
1970–1980	Technology index	0.975	0.989	0.955	0.987	0.990	0.979	1.003	1.030	0.976	1.009
	TFPG index	0.975	0.999	0.944	1.003	0.968	0.978	1.038	1.044	0.990	1.009
	Catch-up index	0.972	1.005	0.964	1.009	0.993	0.988	0.988	1.002	0.981	1.000
1980–1990	Technology index	0.994	1.013	0.991	1.018	1.004	1.004	1.009	1.005	0.996	1.009
	TFPG index	0.965	1.018	0.956	1.027	0.997	0.992	0.997	1.006	0.977	1.009
	Catch-up index	0.988	1.018	0.964	1.000	1.039	1.002	0.986	0.994	0.993	1.000
1990–2000	Technology index	0.966	0.966	0.966	0.973	0.990	0.972	0.980	1.020	0.966	1.018
	TFPG index	0.955	0.984	0.931	0.973	1.029	0.974	0.967	1.014	0.959	1.018
	Catch-up index	0.981	0.999	1.011	1.000	1.000	0.998	1.029	1.012	1.012	1.000
2000–2010	Technology index	0.983	0.986	0.983	0.991	0.984	0.985	0.974	0.985	0.985	1.010
	TFPG index	0.965	0.984	0.994	0.991	0.984	0.983	1.002	0.996	0.996	1.010
	Catch-up index	0.999	1.016	0.999	1.000	0.992	1.001	1.001	0.997	0.988	1.000
2010–2019	Technology index	1.004	1.001	1.004	1.008	1.002	1.004	1.006	1.010	1.007	1.012
	TFPG index	1.002	1.017	1.004	1.008	0.995	1.005	1.007	1.008	0.995	1.012
Post-COVID	Catch-up index	0.984	0.934	0.948	1.000	1.011	0.975	0.954	0.982	0.989	0.965
	Technology index	1.027	1.030	1.033	1.028	1.033	1.030	1.030	1.026	1.019	1.032
	TFPG index	1.011	0.963	0.979	1.027	1.045	1.004	0.983	1.007	1.008	0.996
	Catch-up index	0.989	1.002	0.985	1.003	0.999	0.996	1.002	1.004	1.000	0.998
1960–2024	Technology index	0.989	0.997	0.984	0.999	1.000	0.994	1.001	1.015	0.990	1.014
	TFPG index	0.978	0.998	0.969	1.002	0.999	0.989	1.002	1.018	0.989	1.011

Notes: (a) These figures represent the averages of respective indexes by using the geometric mean.

(b) SA, SEA and AT stand for South Asia, South East Asia and Asian Tigers, respectively.

The gist of the overall analysis is that the factor accumulation, particularly capital accumulation, mainly drives the GDP growth in South Asia. This conclusion aligns with the findings of several studies that conducted growth-accounting exercises in East Asian and South Asian economies, including those by Kim and Lao (1994), Krugman (1994), Singh and Trieu (1999), Srinivasan (2005), Collins (2007), Jia and Chao (2016) and Saha (2021). To sustain this high growth, South Asia needs to further increase its investment rates. Although the investment rate in South Asia increased from approximately 10% in 1960 to almost 26% in 2023, as shown in Figure 5, it further needs to rise by a substantial amount. The increasing growth rate from 6% to 10% is consistent with at least 35% investment rate. According to Figure 5, Asian Tigers’ high growth periods are accompanied with high investment rates, and in 2023, China managed to increase its investment rate to about 43%, enabling it to grow at very high rates.

One lesson that we can learn from the success story of Asian Tigers is their adoption of advanced technology, as reflected in their high periods of TFPG. Capital accumulation cannot alone do the job unless it is accompanied with the improvements in efficiency through learning by doing and adoption of advanced technology through technological diffusion. It is important to note that South Asian economies are at a significant risk of falling into the middle-income trap, a phenomenon where nations experience a prolonged slowdown in growth before achieving a high-income status. This challenge stems from an over-reliance on capital accumulation to drive GDP growth, which, while essential, cannot sustain long-term economic advancement on its own. Although investment rates in South Asia have improved, the lack of substantial improvements in TFPG exacerbates the risk of falling into the middle-income trap. The experience of the Asian Tigers highlights that rapid and sustained growth requires not just capital investment but also significant gains in efficiency through technological adoption, innovation and skill development. Without addressing these structural inefficiencies, South Asian economies may struggle to escape the middle-income trap and achieve high-income status.

VI. Conclusion

This study tries to understand the proximate sources of per capita GDP growth of South Asian economies and compares them with other Asian regions, including the then so-called newly industrialized economies (NIEs), known as Asian Tigers. Per capita GDP growth is decomposed into the components that are attributable to input accumulation and TFPG. TFPG index is further subdivided into the frontier shifting index (serving as innovation index) and technological catch-up index (indicating the extent of diffusion of technology). DEA-based MPI is used to estimate the TFPG. Empirical evidence indicates that South Asia’s per capita GDP growth is almost entirely led by input accumulation, particularly by capital per capita, rather than TFPG. The significant contribution of TFPG is missing in the region, in contrast to Asian Tigers and South East Asia, where both TFPG and capital accumulation play important roles. Failure in technological diffusion (failure to catch up with the world technological frontier) and downward frontier shifting caused by bad economic policies are the main hurdles on the path of TFPG for South Asia. In recent times, the breakout of the COVID-19 pandemic has disrupted growth patterns of world economies, particularly of Asian economies, and further exacerbated the already existing stagnant TFPG.

Since in South Asia, an ample amount of labour is caught in lower productivity activities like agriculture, there is vast scope for productivity improvement. Reallocation of its labour from agriculture into higher productivity manufacturing and services is likely to improve its productivity performance. However, this reallocation must overcome various challenges of both physical and human capital stocks in South Asia. Appropriate physical infrastructure and human skills are key to this structural transformation in South Asia. In sum, the continuous deceleration of productivity growth casts the shadow over the long-term prospects of economic growth in South Asia and exacerbates the risk of falling into the middle-income trap. Even if South Asia maintains such high growth rates of investment to sustain its GDP growth, the eventual diminishing returns of capital deepening compounds the problem. To avert these problems, the region needs to upgrade its technology to catch up with the world technological frontier and effective utilization of its resources, particularly of labour.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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