Intensive and Extensive Margins of Export Diversification as Strategies for Sustainable Economic Growth: Evidence from the Nigerian Economy

Abstract

Two opposite strands of literature analysing export diversification’s role in promoting sustainable growth have evolved in international economics and development, namely, the intensive and extensive margins of exports. This study empirically investigates which of the margin is more useful towards promoting sustainable growth using annual time series data of Nigeria for the period 1960–2021. Autoregressive distributed lag (ARDL) and innovative accounting procedure were employed. The ARDL results reveal that both margins significantly enhance growth in short and long run. However, importance of the extensive margin, in aggregate, dominates that of the intensive margin. Likewise, the results from innovative accounting procedures reveal that although both margins contribute positively to growth, the contribution to growth of extensive margin dominates over that of the intensive margin. These results, thus, lend credence to the extensive-margin exposition, which postulates that the export of extant commodities to new market destinations or export of new commodities to new and/or old market destinations plays a relatively more important role in export growth/diversification and, ultimately, sustainable growth. The study recommends that governments should develop and implement economic policies aimed at enhancing exports of value-added commodities—due to their relatively high income and price elasticities over primary commodities—to maximise the benefits in the extensive margin.

JEL Codes: F10, F14, O10, O12, O50, O55

Keywords

ARDL extensive margin intensive margin export diversification dualistic growth model VAR

Introduction

Two opposite strands of theoretical literature analysing export diversification’s role in promoting sustainable growth have evolved in international economics and development, namely, the intensive and extensive margins of exports (Veeramani et al., 2018). The former, whose notion is grounded in Armington’s (1969) model, postulates that export of extant products/commodities to old market destinations, at higher prices and/or higher volumes (Turkcan, 2014b), is the principal avenue for export growth/diversification and, ultimately, economic growth (Bista & Sheridan, 2021). The latter, whose standpoint is based on the monopolistic competitive model put forward by Krugman (1980), maintains that export of existing products to new destinations (Turkcan, 2014b) or export of new products to old and/or new destinations (Amurgo-Pacheco & Pierola, 2008; Vakataki‘Ofa et al., 2016) is instead more appropriate and plays the dominant role (Gao et al., 2014) in economic growth performance.

A large number of studies have analysed the relevance and macroeconomic implications of the two propositions. Empirical findings, however, have been mixed and inconclusive. Although some empirical studies (Adelan & Kakinaka, 2018; Bingzhan, 2011; Bojnec et al., 2021; Ekmen-Özçelik & Erlat, 2013; Gao et al., 2014; Islam, 2014; Jongwanich, 2020; Otamurodov et al., 2016; Sun & Xian-de, 2018; Tanasritunyakul, 2021; Tianhao & Jing, 2021; Turkcan, 2014a; Veeramani et al., 2018; Zhang et al., 2017; Zhou et al., 2013) lend credence to the intensive margin proposition, several others (Aldan & Çulha, 2016; Amarsanaa & Kurokawa, 2021; Banda & Simumba, 2013; Bista & Sheridan, 2021; Cho & Díaz, 2018; Dutt et al., 2013; Huchet‐Bourdon et al., 2018; Kehoe & Ruhl, 2013; Mora & Olabisi, 2021; Rahmouni, 2020; Turkcan, 2014b; Zhao et al., 2013) also validate the extensive margin exposition.

In Nigeria, ever since oil exploration and production activities commenced in 1958, oil export has continued to play dominant roles in the economy. Historically, it accounts for over 90% of export earnings and about 80% of federal government revenue, and generates more than 25% of the country’s gross domestic product (GDP; Agbaeze et al., 2015; Okotie, 2018). It also provides approximately 90% of foreign exchange earnings (Okotie, 2018) and about 70% of budget revenues (Olayungbo, 2019). Undoubtedly, since the discovery in 1956 (Emediegwu & Okeke, 2017; Ogbuigwe, 2018), Nigeria’s economy has benefited inestimably from oil exports. The country’s excessive reliance on oil revenues (Aigbedion & Iyayi, 2007) compounded by oil price volatility, however, has triggered macroeconomic challenges (Akinlo, 2012) and structural difficulties (Aigbedion & Iyayi, 2007) in the economy; deteriorating external debt situation (Pinto, 1987); severe perennial fiscal contraction; a persistently unhealthy domestic business environment; high level of poverty compounded by insurgency and acute unemployment rate (Njoku & Ihugba, 2011); and underdevelopment of manufacturing capacity for industrial exports aggravated by the neglect of non-oil sectors of the economy where potential remains great (Riti et al., 2016) but largely unexploited.

To resolve the dismal consequences of overreliance of the country on oil (Agbaeze et al., 2015) and, most essentially, diversify the economy’s productive base, a number of socio-economic programmes and policies, at different periods, were designed and implemented by successive Nigerian governments (Kolawole et al., 2015). Prominent among these are the 1982 Economic Stabilization Act; the 1986 Structural Adjustment Programme; the Rolling Plans of 1990–1992, 1993–1995, 1994–1996, 1997–1999; National Economic Direction of 1999–2003; the 2003–2007 National Economic Empowerment and Development Strategy; the Seven-Point Agenda of 2007–2009; the 2011–2015 Transformation Agenda; the 2012–2017 Subsidy Reinvestment and Empowerment Programme; Nigeria’s Vision 20: 2020; and the recent 2017–2020 Economic Recovery and Growth Plans. Although the content of these programmes appears feasible, the entire anticipated benefits are still far (Eko et al., 2013) from being realised as the economy is hitherto dominated by oil sector.

Several factors have been identified as possible causes of the failure of the policies (Obamwonyi & Aibieyi, 2014). Prominent among these are the incoherent implementation of the policies (Makinde, 2005) and neglect of country- specific circumstances which ought to, as a matter of necessity, be considered. Informed by the need to proffer solutions to the implementation problem and the country’s peculiar circumstances, the questions as to which priority sectors that Nigeria should target for export diversification policy and how to best promote the strategy have emerged in the literature. Empirically, a replete of studies (Ajibola et al., 2019; Eko et al., 2013; Ideh et al., 2021; Madichie et al., 2019; Omoju & Ikhide, 2020; Riti et al., 2016; Umeji, 2019) have advocated agriculture, industries, tourism, services sector and several other revenue-earning sectors as plausible options for diversifying the economy.

Motivated by the long-standing efforts of the government to diversify the economy’s productive base, coupled with the search for the best export diversification strategy, and unresolved debate, we empirically investigate whether intensive margin or extensive margin of diversification is more useful towards promoting sustainable growth in Nigeria. Although, as explicated in the literature cited above, empirical studies on the subject occupy a sizable portion of economic literature (Akinlo, 2009), nonetheless, an in-depth and exhaustive reading of the literature reveals that, apart from the ambivalence of the findings, most of the empirical studies focused on advanced (notably, European, Asian, American) and other industrialised economies. Very little, if any (Arize, 1996), has been reported exclusively for African countries (in particular, oil-producing African economies) and are mainly on growth effects of overall export diversification. Moreover, generalised studies across countries (Adedokun, 2013) utilising panel (sometimes, cross-sectional/cross-country) data appear to dominate the empirical inquiries. Additionally, as observed in the literature, empirical studies analysing the subject, especially at the country-specific level (in Africa), has received scant attention in economic literature. Specifically, no known study on Nigeria has been reported to date. Hence, this article fills the gap.

Utilising annual data from 1960 to 2021, the article employs the autoregressive distributed lag (ARDL) approach to cointegration and innovative accounting procedures to investigate which of the margin is more beneficial for sustainable growth in Nigeria. This article differs from the existing ones in several ways. First, it investigates the subject within the context of oil-producing African countries (OPAC), specifically with reference to Nigeria’s economy. To date, as shown in the next section, existing empirical studies based exclusively on OPAC/Nigerian data lack intuitive insights on the topic. Second, it contributes to the ongoing discourse on the design of future policies to enhance and sustain growth in OPAC countries, and offers a framework for analysing the nexus between sustainable growth and both margins in other countries that rely heavily on natural resources as the source of export earnings. Third, it complements extant studies via advanced econometric techniques. Essentially, apart from utilising the standard augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests (Tang & Tan, 2013), the study also employs ARDL cointegration procedure. This technique is able to handle any possible endogeneity issue. As argued in McNown et al., (2018), the ARDL bounds test has the advantage of solving endogeneity problems (Tong et al., 2020) and eliminating the possibility of inconclusive inference.

Finally, more detailed evidence are provided and analysed. Importantly, to enhance the depth and versatility of analysis, innovative accounting procedures—impulse response functions (IRFs) and forecast error variance decomposition (FEVD)—were used. While IRFs show the directions of response to a random shock of a variable (Tan & Tang, 2012), FEVD displays the information about the percentage of movement in a variable due to its own shocks (Tan & Tang, 2012) versus shocks due to other variables in the system. Both are out-of-sample tests that are useful in discerning the degree of exogeneity of the variables (Tan & Tang, 2012) and the dynamic responses of variables beyond the sample period. The rest of the study proceeds as follows. In the first place, a succinct review of empirical evidence are provided. Afterwards, econometric methodology and data are discussed. This is followed by techniques of estimation and empirical analysis. Conclusion, policy recommendations and possible directions for future work are presented thereafter.

Review of Related Studies

Empirically, over the past five decades, the role of intensive and extensive margins of export diversification in promoting sustainable growth has remained a subject of intense debate in international economics and development. A succinct list of previous studies on the theme is shown in Table 1. More importantly, to maintain brevity, we focus primarily on studies from the early 2000s. As the table illustrates, the growth implications of both margins have been extensively studied and occupy a substantial body of economic literature (Akinlo, 2004). A comprehensive reading of these studies, however, suggests that overall findings are hitherto inconclusive.

Apart from this, as Table 1 elucidates, most of the prior studies rely mainly on panel, cross-sectional, cross-country (and sometimes, pooled) data analysis. As noted in the literature, there is growing concern about the limitations of this approach in providing a sound empirical basis for informing the policy debate. Apart from the weak theoretical foundations as well as general methodological flaws (Deaton, 1989) pertaining to econometric procedure and model specification, there are numerous fundamental limitations that make results from panel/cross-country/cross-sectional regressions on this subject rather dubious (for details, see Keho, 2017; Naranpanawa et al., 2011; Sakyi et al., 2015; Siddiqui & Ahmed, 2019; Srinivasan, 1994; Srinivasan & Bhagwati, 2001).

Moreover, apart from the ambivalent results and generalised studies across countries, empirical literatures assessing the subject in the context of African countries (Odedokun & Round, 2001) are scarce. Most of the empirical inquiries (Arize, 1996) focused attention on industrial and advanced (particularly, European, Asian, American) economies. In addition, available extant studies based exclusively on African/Nigerian data (Abdullahi & Jibir, 2018; Doki & Tyokohol, 2019; Duru & Ehidiamhen, 2018; Hesse, 2008; Hodey et al., 2015; Huseyin & Shuaibu, 2020; Johnson, 2016; Lugeiyamu, 2016; Matezo et al., 2021; Nwosa et al., 2019; Odularu, 2009; Oyelami & Alege 2018; Owan et al., 2020; Vakataki‘Ofa et al., 2016; Yuni et al., 2020) that have examined the nature of the relationship between export diversification and economic growth have focused mainly on investigating the growth impacts of overall export diversification which by its nature have been far from being definitive on comparative analysis of the effects of export diversification at intensive and extensive margins on growth. Specifically, no known study has been reported exclusively for Nigeria. This article, thus, fills the gap.

Table 1.

Summary of the Extant Literature.

Author(s)	Region/Country	Period of Study	Results
Panel A: Intensive-Margin Analysis
Evenett and Venables (2002)	23 (developing and middle income) countries	1970–1997	The authors find that greater exports to long-standing trading partners of product lines traded since 1970 accounts for 60% of overall export growth in these economies.
Brunner and Calì (2006)	4 South Asian countries	1991–2002	They discover that South Asia’s export growth has been mainly driven by the expansion of the intensive margin.
Brenton and Newfarmer (2007)	99 developing countries	1995–2004	They establish that, in aggregate, the contribution to growth of intensive margin dominates over extensive margin.
Eaton et al. (2008)	Colombia	1996–2005	They observe that export growth is driven by changes in intensive rather than extensive margin.
Amurgo-Pacheco and Pierola (2008)	24 (developed and developing) economies	1990–2005	They establish that exports at intensive margin account for the most important share of overall trade growth.
Amiti and Freund (2010)	China	1992–2005	They discover that virtually all of China’s export growth is actually in existing varieties.
Besedeš and Prusa (2011)	46 (developing and developed) countries	1975–2003	They observe that developing economies will experience significantly higher export growth if they improve their performance with respect to two key components of intensive margin: survival and deepening.
Bingzhan (2011)	China	2001–2007	The author finds that China’s export is mainly driven by quantity (which accounts for approximately 70% of overall export growth).
Ekmen-Özçelik and Erlat (2013)	Turkey	1996–2006	The authors find that a far greater proportion of export growth is due to intensive.
Zhou et al. (2013)	China	1991–2000	They find that the rapid growth of China’s export is mainly along intensive margin.
Gao et al. (2014)	China		The authors discover that China’s export growth depends more and more on price increases, and less and less on quantity expansion.
Turkcan (2014a)	Austria	1998–2011	The finding reveal that intensive margin is a key driver of Austria’s export growth.
Otamurodov et al. (2016)	Kazakhstan	2004–2013	They find that most of the Kazakhstan export, for the period 2004–2013, is mainly driven by growth at intensive margin.
Zhang et al. (2017)	China	1995–2013	They find that contribution from intensive margin has reached and been staying at a high level since 2007.
Veeramani et al. (2018)	India and China	2000–2015	They observe that while China outperforms India at both margins, the gap is particularly wide at intensive margin.
Sun and Xian-de (2018)	China	2000–2015	They find that the main source of Chinese agricultural exports to the Association of Southeast Asian Nations countries has always mainly depended on intensive margin.
Adelan and Kakinaka (2018)	Philippines	1975–2012	The authors discover that the Philippines’s weak export can be traced primarily from its poor performance at intensive margin.
Jongwanich (2020)	Thailand	2002–2016	The author finds that expansion of intensive margins plays an important role in boosting growth in key industries within Thailand.
Bojnec et al. (2021)	11 Central and Eastern Europe (CEE) countries	2004–2019	They find that many CEE-11 countries achieved their export expansion along intensive margin.
Tianhao and Jing (2021)	China	1997–2019	China’s manufacturing export trade and export growth are both dominated by the intensive margin.
Tanasritunyakul (2021)	Developing Asian Countries	1990–2017	The author finds that intensive margin plays a crucial role in export success for total merchandise and its subcategories.
Panel B: Extensive-Margin Analysis
Funke and Ruhwedel (2001a)	19 member countries of the Organization for Cooperation and Development (OECD)	1989–1996	They find that GDP per capita, for the period 1989–1996, is significantly influenced by the ability to export new products.
Funke and Ruhwedel (2001b)	10 East Asian countries	1989–1997	They discover that producing highly differentiated export goods gives a competitive advantage.
Funke and Ruhwedel (2002)	15 OECD countries	1989–1997	They establish that export performance is significantly and positively influenced by products variety.
Addison (2003)	13 advanced and 16 developing countries	1979–1986	The author finds that product variety has a significantly positive effect on productivity.
Feenstra and Looi Kee (2004)	34 countries	1982–1997	They discover that countries with greater product variety in exports also have higher productivity.
Hummels and Klenow (2005)	126 exporting countries to 59 importing countries	1995	The authors discover that extensive margin accounts for nearly 60% of the increase in exports of larger economies.
Funke and Ruhwedel (2005)	14 East European transition economies	1993–2000	The finding reveals that export variety is beneficial for growth.
Feenstra and Kee (2008)	48 countries	1980–2000	The authors establish that extensive margin is the primary avenue for export growth.
Author(s)	Region/Country	Period of Study	Results
Panel B: Extensive-Margin Analysis
Martincus and Carballo (2008)	Peru	2001–2005	They find that export promotion actions are associated with increased exports, primarily along extensive margin, both in terms of markets and products.
Helpman et al. (2008)	158 countries	1970–1997	They find that extensive margin is especially important in shaping the response of trade flows across country pairs.
Bernard et al. (2009)	United States	1993–2003	They establish that variation in imports and exports across trading partners is mainly due to extensive margin.
Liapis (2009)	69 countries	1996–2006	The author finds that, in 44 of the 69 countries, extensive margin accounts for more than 50% of the overall growth.
Chen (2011)	China	1998–2005	The article discovers that export varieties, via export revenue, significantly affect productivity.
Cadot et al. (2007)	156 countries	1988–2004	The paper reveals that export diversification is mostly explained by extensive margin.
Dutt et al. (2013)	150 countries	1962–2006	The study reveals that the growth in extensive margin accounts for an important component of that country’s productivity growth over the period 1988–2006.
Zhao et al. (2013)	China	2000–2007	The authors find that extensive margin contributes more to trade expansion than intensive margin.
Kehoe and Ruhl (2013)	1,900 North American Free Trade Agreement (NAFTA) country pairs	1995–2005	They find that extensive margin accounts for 10% of the growth of the trade for NAFTA country pairs.
Banda and Simumba (2013)	Zambia	1999–2011	They find that extensive margin is the driver of export growth in Zambia.
Turkcan (2014b)	Turkey	1998–2011	The results suggest that the extensive margin, particularly geographic diversification, plays the most important role in Turkey’s total goods export growth.
Aldan and Çulha (2016)	Turkey	1995–2013	The results suggest that Turkey was quite successful in extending its extensive margin compared to other developing countries.
Huchet-Bourdon et al. (2018)	169 countries	1988–2014	The results reveal that extensive margin of export appears as a complementary strategy to increase the contribution of trade to economic growth.
Cho and Díaz (2018)	10 transition countries	1995–2008	The authors find a positive correlation between productivity growth and extensive margin across the transition economies.
Rahmouni (2020)	44 countries	1999–2018	The author finds that old product new destination strategy leads to an increase in export value.
Bista and Sheridan (2021)	162 countries	1950–2013	The results reveal that extensive margin is an important mechanism for export growth and, ultimately, economic growth.
Amarsanaa and Kurokawa (2021)	Mongolia	1997–2002	The author find large increases in extensive margin of Mongolia’s trade with 10 major trade partners.
Mora and Olabisi (2021)	69 countries	1997–2014	They find a positive relationship between extensive margin and economic development for least developed countries.
Kannan and Kumar (2022)	India	2001–2020	Broadly the study results point out that India’s exports along the extensive margin has not been fully exploited and that export diversification holds the key to higher export growth in agricultural products.

Source: The author.

Model and Data

Following Dunusinghe (2009) and Saleem and Sial (2015), we adopt and build on the dualistic growth framework developed by Feder (1983) to derive the econometric models employed in the empirical analysis. In line with Feder’s formulation, we assume that the overall economy (denoted by Y is composed of two distinct sectors: the non-export sector (represented by R ) and the export sector (depicted by Q ). That is,

Y = R + Q

(1)

Further, as in Dunusinghe (2009) and Saleem and Sial (2015), instead of an aggregate national production function, we assume that these sectors have different production functions depict mathematically as follows:

R = F (K_{r}, L_{r}, Q)

(2)

Q = G (K_{q}, L_{q})

(3)

where R and Q denote, respectively, output in non-export and export sectors; K_r and K_q depict respective physical capital stock; L_r and L_q signify respective labour force; and F and G represent the conventional production functions describing the respective sector’s technologies.

Moreover, as shown in Equations (2) and (3), respectively, we assume that the output of export sector Q is dependent on capital K_q and labour L_qwhereas the output of non-export sector R depends on the volume of export sector Q in addition to capital K_r and labour L_r employed in it. This formulation, as evidenced in Equations (2) and (3), represents the beneficial effects of export sector on non-export sector (such as the development of efficient and internationally competitive management, introduction of improved production techniques, steady flow of imported inputs, training of higher quality labour and so on). These effects are referred to as externalities as they are not reflected in market prices (Feder, 1983).

A total differentiation of Equations (1)–(3) respectively yields:

\overset{•}{Y} = \overset{•}{R} + \overset{•}{Q}

(4)

\overset{•}{R} = F_{K_{r}} . \overset{•}{K_{r}} + F_{L_{r}} . \overset{•}{L_{r}} + F_{q} \overset{•}{Q}

(5)

\overset{•}{Q} = G_{K_{q}} . \overset{•}{K_{q}} + G_{L_{q}} . \overset{•}{L_{q}}

(6)

where a variable with a dot over it indicates its growth rate; $F_{K_{r}}$ and $G_{K_{q}}$ denote, respectively, the marginal productivities of capital in the non-export and export sectors; $F_{L_{r}}$ and $G_{L_{q}}$ depict, respectively, the marginal productivities of labour in the non-export and export sectors; and F_q is the externality effect of export growth on non-export sector’s output.

Given that the overall output (GDP) of the economy (earlier by denoted by Y) is the summation of output of both sectors, incorporating Equations (5) and (6) into Equation (4) yields

\overset{•}{Y} = F_{K_{r}} . \overset{•}{K_{r}} + F_{L_{r}} . \overset{•}{L_{r}} + F_{q} \overset{•}{Q} + G_{K_{q}} . \overset{•}{K_{q}} + G_{L_{q}} . \overset{•}{L_{q}}

(7)

Furthermore, as in the Feder model, we assume that the ratio of respective marginal factor productivities in the two sectors, depicted in Equation (8), deviates from unity by d, that is,

\frac{G_{K_{q}}}{F_{K_{r}}} = \frac{G_{L_{q}}}{F_{L_{r}}} = 1 + δ

(8)

where the superscripts depict partial derivatives.

In the absence of externalities (Feder, 1983), and for a given set of prices, a situation where d = 0 would reveal an allocation of resources which maximises national output. However, due to a number of reasons, the marginal productivities of capital and labour are likely to be higher in the export sector Q than in the non-export sector R. An important reason, from economic intuition, is the more competitive environment in which export-oriented firms operate. Competition induces innovativeness (Feder, 1983), efficient management of firms’ resources, adaptability and so on. Other key reasons for the deviations between sectoral marginal factor productivities—other than the higher perceived uncertainty—are numerous regulations and constraints (Feder, 1983) such as credit and foreign exchange rationing associated with export enterprises.

Using Equation (8) in Equation (7) yields

\overset{•}{Y} = F_{K_{r}} . \overset{•}{K_{r}} + F_{L_{r}} . \overset{•}{L_{r}} + F_{q} \overset{•}{Q} + (1 + δ) F_{K_{r}} . \overset{•}{K_{q}} + (1 + δ) F_{L_{r}} . \overset{•}{L_{q}}

(9)

\overset{•}{Y} = F_{K_{r}} . \overset{•}{K_{r}} + F_{L_{r}} . \overset{•}{L_{r}} + F_{q} \overset{•}{Q} + F_{K_{r}} . \overset{•}{K_{q}} + (δ) F_{K_{r}} . \overset{•}{K_{q}} + F_{L_{r}} . \overset{•}{L_{q}} + (δ) F_{L_{r}} . \overset{•}{L_{q}}

(10)

\overset{•}{Y} = F_{K_{r}} . \overset{•}{K_{r}} + F_{K_{r}} . \overset{•}{K_{q}} + F_{L_{r}} . \overset{•}{L_{r}} + F_{L_{r}} . \overset{•}{L_{q}} + F_{q} \overset{•}{Q} + (δ) F_{K_{r}} . \overset{•}{K_{q}} + (δ) F_{L_{r}} . \overset{•}{L_{q}}

(11)

\overset{•}{Y} = F_{K_{r}} (\overset{•}{K_{r}} + \overset{•}{K_{q}}) + F_{L_{r}} (\overset{•}{L_{r}} + \overset{•}{L_{q}}) + F_{q} \overset{•}{Q} + δ (F_{K_{r}} . \overset{•}{K_{q}} + F_{L_{r}} . \overset{•}{L_{q}})

(12)

By defining total growth of capital $\overset{•}{K} \equiv (\overset{•}{K_{r}} + \overset{•}{K_{q}})$ and total growth of labour $\overset{•}{L} \equiv (\overset{•}{L_{r}} + \overset{•}{L_{q}})$ , Equation (12) is rewritten as

\overset{•}{Y} = F_{K_{r}} . \overset{•}{K} + F_{L_{r}} . \overset{•}{L} + F_{q} \overset{•}{Q} + δ (F_{K_{r}} . \overset{•}{K_{q}} + F_{L_{r}} . \overset{•}{L_{q}})

(13)

Recall that Equations (8) and (6) imply

\begin{array}{l} F_{K_{r}} . \overset{•}{K_{q}} + F_{L_{r}} . \overset{•}{L_{q}} = \frac{1}{1 + δ} G_{K_{q}} . \overset{•}{K_{q}} + \frac{1}{1 + δ} G_{L_{q}} . \overset{•}{L_{q}} = \frac{1}{1 + δ} \\ (G_{K_{q}} . \overset{•}{K_{q}} + G_{L_{q}} . \overset{•}{L_{q}}) = \frac{\overset{•}{Q}}{1 + δ} \end{array}

(14)

Substituting Equation (14) in Equation (13) yields

\overset{•}{Y} = F_{K_{r}} . \overset{•}{K} + F_{L_{r}} . \overset{•}{L} + F_{q} \overset{•}{Q} + δ (\frac{\overset{•}{Q}}{1 + δ})

(15)

\overset{•}{Y} = F_{K_{r}} . \overset{•}{K} + F_{L_{r}} . \overset{•}{L} + (\frac{δ}{1 + δ} + F_{q}) . \overset{•}{Q}

(16)

Following Saleem and Sial (2015), we assume in Equation (16) that the marginal productivity of capital $F_{K_{r}} = α$ , the growth rate of labour force $F_{L_{r}} = β,$ the productivity differential and the externality effect of the export sector $(\frac{δ}{1 + δ} + F_{q}) = θ$ ; accordingly, a more general version of Equation (16) is obtained as follows:

Y = α K + β L + θ Q

(17)

The formulation in Equation (17), which looks similar to the neoclassical production function, is the basis of our empirical model. According to this equation, the real GDP per capita is dependent on physical capital, labour force and exports.

To retain simplicity, we follow Bista and Sheridan (2021) when specifying exports and approximate it by means of three variables: total exports, intensive margins and extensive margins of exports. However, we use only the intensive and extensive margins of exports (which we denote by I and E) to avoid potential multicollinearity problems. Additionally, in line with the World Bank (2021), endogenous growth theories and augmented Solow model, we incorporated human capital (approximated by the human capital index, an international metric that benchmarks key components of human capital across countries) rather than the total labour force in the empirical analysis. The main reason for this, as noted in the economic literature, has been that the labour force performs major role in the determination of productivity level (Babatunde & Adefabi, 2005). This is because they speed up the adoption of foreign technology that is expected to balance the knowledge gap between the developed and developing countries. Similarly, they are more creative and inventive.

Keeping the above in view, following Saleem and Sial (2015), an econometric representation of Equation (17) is specified as follows:

l n Y_{t} = φ_{0} + φ_{1} l n K_{t} + φ_{2} l n H_{t} + φ_{3} l n I_{t} + φ_{4} l n E_{t} + ε_{t}

(18)

where Y is real GDP per capita; K, H, I, and E, denote, respectively, physical capital stocks, human capital index, intensive margin, and extensive margin; {₁, {₂, {₃, and {₄ are the respective parameters; {₀ and f_it are the intercept and white-noise error term; ln depicts natural logarithm; and the subscript t signifies time. To enhance the depth and versatility of analysis, an attempt is also made to explain Equation (18) with the help of various macroeconomic and structural policies. For a comprehensive analysis, we incorporated exchange rate, openness and foreign direct investment (FDI) inflows in Equation (18) as regressors (following Aurangzeb & Stengos, 2014; Burange et al., 2019; Razzaque et al., 2017; Sakyi et al., 2015; Sheikh & Malik, 2021).

From both a theoretical and an empirical perspective, two opposite strands of literature assessing the sustainable growth effects of exchange rate movement have evolved in development economics, namely, the classical and structuralist expositions. On the one hand, the classical economists argue that devaluation of a country’s currency—an increase in exchange rate—triggers an ‘expenditure-switch’ in domestic demand away from imports towards locally (Razzaque et al., 2017) produced import-competing goods. It also improves international competitiveness of domestic industries, which in turn, bolster exports. These two effects together exert an expansionary impact on overall growth (Razzaque et al., 2017). On the other hand, the structuralist contrarily argued and identified several factors why devaluation policy reduces an economy’s output growth. Paramount among these are the structural problems (notably in developing countries) and the phenomenon of foreign dependency (Razzaque et al., 2017). The bulk of the inputs used by these economies, mostly in their production processes, are provided through imports. Therefore, an increase in the exchange rate makes imported production inputs more expensive, which may, in turn, hamper growth (Karahan, 2020). Empirically, a sizable number of studies argue that country-specific inquiries appear to be the best plausible option. Hence, it is included in Equation (18) as a covariate.

Similar to the exchange rate, the debate on the role of openness to trade as a driver of sustainable growth has been a subject of controversy among economists and policymakers. In theory, there are two contentious views to the debate. On the one hand, beginning with the works of classical and neoclassical trade theorists (notably, Adam Smith and David Ricardo), are those who maintain that openness promotes the efficient allocation of resources through comparative advantage, allows dissemination of knowledge and technical progress, encourages competition in domestic (Gries & Redlin, 2012) and international markets (Chang et al., 2009). On the other hand, following the innovative works of Prebisch (1950) and Singer (1950), are those who contend that the effects of openness on growth is doubtful. If market or institutional imperfections exist, openness will lead to underutilisation of human and capital resources, concentration in extractive economic activities, or specialisation (Chang et al., 2009) away from technologically advanced, increasing-return sectors. Several studies have assessed the implications of the two propositions. Empirical findings, however, are far from being settled.

There are two opposite arguments on the growth impacts of FDI inflow in recipient countries. The first, whose idea is grounded in modernisation theory, holds the notion that FDI is beneficial for growth in the host economy. Proponents of this strand of literature emphasise that FDI inflow contributes to growth by stimulating capital accumulation and/or through positive externalities (Herzer, 2012) in the form of knowledge and productivity spillover to local firms. Advocates of the second view, whose standpoint is based on dependency theory, argue that FDIs, on average, have negative effects on growth in the recipient country. According to this strand of literature, the spillover effects of FDIs are often illusory, since domestic firms using unskilled workers and backward production technology (Herzer, 2012) are typically unable to learn from multinationals. Besides, it is argued that FDIs lessen capital accumulation when foreign investors claim scarce resources, such as import licences, credit facilities, skilled workers and so on, thereby crowding out investment from domestic sources. Also, FDI inflow creates an industrial structure in which monopoly is predominant (Adams, 2009a). Since multinationals have lower marginal costs due to some firm-specific advantage, competition from foreign companies can, paradoxically (Herzer, 2012), lessen the productivity of domestic firms, as some firm-level studies suggest. Given the conflicting theoretical views, it is included in Equation (18) as a determinant of growth.

In view the above arguments, to ascertain whether intensive or extensive margin of diversification is more useful towards promoting sustainable growth, Equation (18) is augmented as follows:

\begin{array}{l} l n Y_{t} = σ_{0} + σ_{1} l n K_{t} + σ_{2} l n H_{t} + σ_{3} l n I_{t} + σ_{4} l n E_{t} + \\ σ_{5} (\partial l n X_{t}) + σ_{6} l n T_{t} + σ_{7} l n M_{t} + ν_{t} \end{array}

(19)

where ln Y, ln K, ln H, ln I, ln E, ∂ ln X, ln T, and ln M denote, respectively, the natural log of real GDP per capita (measured in 2010 of constant US$), natural log of physical capital stocks (at current purchasing power parities in millions of 2011 US$), natural log of human capital index (based on years of schooling and returns to education), natural log of intensive margin (diversity within sectors), natural log of extensive margin (diversity across sectors), exchange rate (national currency/$, market + estimated), natural log of openness to trade (sum of imports and exports of goods and services as a share of GDP, current US$) and natural log of FDI inflow (net outflows BoP, current US$). The parameters v_i (where θ = 1, 2, 3, …, 7) are the corresponding numerical estimates. While subscript t depicts the time period (1960–2021), y signifies white-noise error term. With the exception of v₅, v₆, and v₇, which can be positively or negatively signed, the anticipated signs of all other coefficients (v₁, v₂, v₃ and v₄) are positive. All the variables (except exchange rate) were expressed in natural log (ln ()) to minimise the scale effect.

All the data were compiled from Penn World Table (PWT) 10.0, World Development Indicators (WDIs) of the World Bank (2021) and International Monetary Fund (IMF) export diversification and quality database (2014). Specific source and variable definitions are provided in Table 2. Data on stocks of physical capital, index of human capital and exchange rate were compiled from the PWT. Data on real GDP per capita and openness to trade were obtained from the WDIs. Although data on FDI from 1970 to 2021 were taken from the WDIs, those of 1960–1969 were derived using the linear forecasting technique. Data on intensive and extensive margins for the period 1962–2014 were taken from the IMF database, whereas those of 1960–1961 and 2015–2021 were, as in FDI, obtained using the linear forecasting technique. Table 3 presents the summary of descriptive statistics of the data. It is clear from the table that all the variables display a high consistency (as the mean and median are within maximum and minimum values). Moreover, the low standard deviation of almost all the variables suggests that the deviation of actual data from the mean is very small. The skewness, kurtosis and Jarque–Bera statistics show that, at 5% critical value, virtually all the variables are normally distributed. This is additionally buttressed by the nearness of median and mean.

Table 2.

Data Sources and Description of Variables.

Variables	Proxy	Sources
Economic growth (Y)	Real GDP per capita (constant 2010 US$)	World Development Indicators (2020)
Physical capital stocks (K)	Capital stock at current PPPs (in mil. 2,011US$)	Penn World Table, version 10.0
Human capital (H)	Human capital index, based on years of schooling and returns to education	Penn World Table, version 10.0
Intensive margin of export (I)	Diversity within sectors	International Monetary Fund (2014)
Extensive margin of export (E)	Diversity across sectors	International Monetary Fund (2014)
Exchange rate (X)	Exchange rate, national currency/USD (market + estimated)	Penn World Table, version 10.0
Trade openness (T)	Sum of exports and imports of goods and services as a share of GDP (current US$)	World Development Indicators (2020)
FDI inflows (M)	Foreign direct investment, net outflows (BoP, current US$)	World Development Indicators (2020)

Source: The author.

Table 3.

Descriptive Statistics of the Variables.

	LNY	LNK	LNH	LNI	LNE	dLNX	LNT	LNM
Mean	25.8042	12.6959	0.3155	1.5746	0.2498	2.2872	1.2385	20.5084
Median	25.7010	12.4577	0.2010	1.6414	0.2521	2.0842	1.1702	20.9282
Maximum	26.8747	14.9450	0.6923	1.7597	0.2284	6.1370	5.3345	21.5507
Minimum	24.8392	11.0847	0.1413	1.0735	0.2946	0.6037	1.9650	15.4183
Std. dev.	0.6005	1.1327	0.1876	0.1877	0.0146	2.5498	2.6340	1.1303
Skewness	0.3040	0.5506	0.7283	1.7762	0.5661	0.0760	0.0386	2.3225
Kurtosis	2.1715	2.1204	1.9812	4.8244	3.2413	1.2253	1.2184	10.0108
Jarque–Bera	2.6840	5.0480	8.0300	40.5328	3.4064	8.0641	8.0822	179.7657
Probability	0.2613	0.0801	0.1080	0.1500	0.1821	0.1077	0.1076	0.2200
Sum	1574.0560	774.4492	19.2444	96.0496	15.2385	139.5201	75.5477	1251.0110
Sum sq. dev.	21.6394	76.9759	2.1113	2.1130	0.0129	390.0906	416.2671	76.6537
Observations	61	61	61	61	61	61	61	61

Source: The author.

Note: Std. dev. and Sum sq. dev. denote, respectively, standard deviation and sum of squared deviation.

Techniques of Estimation and Empirical Results

Stationarity Tests

Before the study proceeded with the detailed estimation and analysis of Equation (19), ADF and PP unit root tests were employed to ascertain the order integration of the variables. Both statistics were performed at 1%, 5% and 10% level of significance. First, they were conducted with intercept only and, afterward, with trend and intercept. Estimated ADF and PP results presented in Tables 4 and 5, respectively, clearly reveal that (apart from extensive margin of export and FDI inflow which are stationary at level) every other variable becomes stationary at first difference.

Table 4.

Stationarity Tests of Variables: ADF Unit Root Test Results.

Variables	ADF (With Intercept Only)
		Level					1st Difference
	Test Statistics	Critical Values			Prob. Values	Remarks	Test Statistics	Critical Values			Prob. Values	Remarks
	Test Statistics	1%	5%	10%	Prob. Values	Remarks	Test Statistics	1%	5%	10%	Prob. Values	Remarks
lnY	–0.9993	–3.5461	–2.9117	–2.5936	0.7481	NS	–5.2128	–3.5461	–2.9117	–2.5936	0.0001	I(1)
lnK	–1.7503	–3.5461	–2.9117	–2.5936	0.4012	NS	–6.9304	–3.5482	–2.9126	–2.5940	0.0000	I(1)
lnH	–1.3931	–3.5461	–2.9117	–2.5936	0.5797	NS	–7.5073	–3.5482	–2.9126	–2.5940	0.0000	I(1)
lnI	–2.7545	–3.5441	–2.9109	–2.5931	0.0711	NS	–6.8379	–3.5461	–2.9117	–2.5936	0.0000	I(1)
lnE	–8.6990	–3.5482	–2.9126	–2.5940	0.0000	I(0)	***	***	***	***	***	I(0)
dlnX	–0.0589	–3.5461	–2.9117	–2.5936	0.1987	NS	–4.0677	–3.5461	–2.9117	–2.5936	0.0022	I(1)
lnT	0.1330	–3.5441	–2.9109	–2.5931	0.9657	NS	–3.6652	–3.5482	–2.9126	–2.5940	0.0072	I(1)
lnM	–4.8292	–3.5441	–2.9109	–2.5931	0.0002	I(0)	***	***	***	***	***	I(0)
Variables	ADF (With Intercept & Trend)
		Level					1st Difference
	Test Statistics	Critical Values			Prob. Values	Remarks	Test Statistics	Critical Values			Prob. Values	Remarks
	Test Statistics	1%	5%	10%	Prob. Values	Remarks	Test Statistics	1%	5%	10%	Prob. Values	Remarks
lnY	–2.1998	–4.1273	–3.4907	–3.1739	0.4805	NS	–5.1791	–4.1213	–3.4878	–3.1723	0.0004	I(1)
lnK	–1.7537	–4.1213	–3.4878	–3.1723	0.7144	NS	–6.8683	–4.1243	–3.4892	–3.1731	0.0000	I(1)
lnH	–1.4477	–4.1213	–3.4878	–3.1723	0.8361	NS	–7.5003	–4.1243	–3.4892	–3.1731	0.0000	I(1)
lnI	–2.1282	–4.1184	–3.4865	–3.1715	0.5198	NS	–7.1152	–4.1213	–3.4878	–3.1723	0.0000	I(1)
lnE	–4.9776	–4.1184	–3.4865	–3.1715	0.0008	I(0)	***	***	***	***	***	I(0)
dlnX	–1.9651	–4.1213	–3.4878	–3.1723	0.6078	NS	–4.0958	–4.1213	–3.4878	–3.1723	0.0107	I(1)
lnT	–2.1204	–4.1243	–3.4892	–3.1731	0.5237	NS	–3.6562	–4.1243	–3.4892	–3.1731	0.0336	I(1)
lnM	–6.2778	–4.1184	–3.4865	–3.1715	0.0000	I(0)	***	***	***	***	***	I(0)

Soure: The author.

NS and *** denote, respectively, non-stationary and not applicable.

Table 5.

Stationarity Tests of Variables: PP Unit Root Test Results.

Variables	PP (With Intercept Only)
	Level						1st Difference
	Test Statistics	Critical Values			Prob. Values	Remarks	Test Statistics	Critical Values			Prob. Values	Remarks
	Test Statistics	1%	5%	10%	Prob. Values	Remarks	Test Statistics	1%	5%	10%	Prob. Values	Remarks
lnY	–0.9993	–3.5461	–2.9117	–2.5936	0.7481	NS	–5.2128	–3.5461	–2.9117	–2.5936	0.0001	I(1)
lnK	–1.7503	–3.5461	–2.9117	–2.5936	0.4012	NS	–6.9021	–3.5482	–2.9126	–2.5940	0.0000	I(1)
lnH	–1.3984	–3.5461	–2.9117	–2.5936	0.5771	NS	–7.5073	–3.5482	–2.9126	–2.5940	0.0000	I(1)
lnI	–2.7310	–3.5441	–2.9109	–2.5931	0.0748	NS	–6.8839	–3.5461	–2.9117	–2.5936	0.0000	I(1)
lnE	–3.4854	–3.5441	–2.9109	–2.5931	0.0117	I(0)	***	***	***	***	***	I(0)
dlnX	0.1144	–3.5441	–2.9109	–2.5931	0.9643	NS	–4.0601	–3.5461	–2.9117	–2.5936	0.0023	I(1)
lnT	–0.1003	–3.5441	–2.9109	–2.5931	0.9443	NS	–7.7469	–3.5461	–2.9117	–2.5936	0.0000	I(1)
lnM	–4.8262	–3.5441	–2.9109	–2.5931	0.0002	I(0)	***	***	***	***	***	I(0)
Variables	PP (With Intercept and Trend)
	Level						1st Difference
	Test Statistics	Critical Values			Prob. Values	Remarks	Test Statistics	Critical Values			Prob. Values	Remarks
	Test Statistics	1%	5%	10%	Prob. Values	Remarks	Test Statistics	1%	5%	10%	Prob. Values	Remarks
lnY	–2.1998	–4.1273	–3.4907	–3.1739	0.4805	NS	–5.1791	–4.1213	–3.4878	–3.1723	0.0001	I(1)
lnK	–1.7537	–4.1213	–3.4878	–3.1723	0.7144	NS	–6.8360	–4.1243	–3.4892	–3.1731	0.0000	I(1)
lnH	–1.5418	–4.1213	–3.4878	–3.1723	0.8036	NS	–7.5003	–4.1243	–3.4892	–3.1731	0.0000	I(1)
lnI	–2.1315	–4.1184	–3.4865	–3.1715	0.5180	NS	–7.1110	–4.1213	–3.4878	–3.1723	0.0000	I(1)
lnE	–5.1526	–4.1184	–3.4865	–3.1715	0.0004	I(0)	***	***	***	***	***	I(0)
dlnX	–1.9082	–4.1184	–3.4865	–3.1715	0.6380	NS	–4.1040	–4.1213	–3.4878	–3.1723	0.0105	I(1)
lnT	–2.0172	–4.1184	–3.4865	–3.1715	0.5801	NS	–7.7552	–4.1213	–3.4878	–3.1723	0.0000	I(1)
lnM	–6.2778	–4.1184	–3.4865	–3.1715	0.0000	I(0)	***	***	***	***	***	I(0)

Source: The author.

NS and *** denote, respectively, non-stationary and not applicable.

Cointegration Tests

Having ascertained the stationarity properties of the variables, the bound testing approach was employed to examine the presence of cointegration relationship. The method is adopted and considered apposite for three reasons. First, as opposed to other cointegration techniques (the residual-based procedure advanced by Engle and Granger (1987), the fully modified ordinary least squares (OLS) method of Phillips and Hansen (1990), the full information maximum likelihood procedure of Johansen and Julius (1990) and Johansen (1992)), the ARDL technique is more robust and performs better for small (Kumar, 2010) and finite sample sizes. Second, given the nature of the interrelationship between physical capital stocks, human capital index, intensive margin, extensive margin, exchange rate, trade openness, FDI, and real GDP per capita, incorporated in the model, the method appears apposite to address any possible endogeneity issue (Samantaraya & Patra, 2014). Finally, the procedure allows the long-run and short-run coefficients of the model (Srinivasan et al., 2012) to be estimated simultaneously. In light of these, for this study, the ARDL model representation of Equation (19) is expressed as follows:

\begin{array}{l} Δ l n Y_{t} = ρ_{o} + \sum_{i = 1}^{w} λ_{i} Δ l n Y_{t - i} + \sum_{j = 0}^{r} λ_{j} Δ l n K_{t - j} + \sum_{k = 0}^{s} λ_{k} Δ l n H_{t - k} + \sum_{l = 0}^{t} λ_{l} Δ l n I_{t - l} + \\ \sum_{m = 0}^{q} λ_{m} Δ l n E_{t - m} + \sum_{n = 0}^{g} λ_{n} Δ (\partial l n X_{t - n}) + \sum_{p = 0}^{h} λ_{p} Δ l n T_{t - p} + \sum_{z = 0}^{f} λ_{z} Δ l n M_{t - z} + \\ θ_{1} l n Y_{t - 1} + θ_{2} l n K_{t - 1} + θ_{3} l n H_{t - 1} + θ_{4} l n I_{t - 1} + θ_{5} l n E_{t - 1} + θ_{6} (\partial l n X_{t - 1}) + \\ θ_{7} l n T_{t - 1} + θ_{8} l n M_{t - 1} + u_{t} \end{array}

(20)

where θ's, and λ's, depict, respectively, the long- and short-run estimates; D, t₀ and u denote, respectively, the difference operator, drift component and white noise error term; and w, r, s, t, q, g, h and f are the optimal lag lengths. Hence, to ascertain the existence of cointegration between sustainable growth and the explanatory variables, Equation (20) was first estimated by the OLS procedure. Afterwards, the study tested the null hypothesis θ₁ = θ₂ = θ₃ = θ₄ = θ₅ = θ₆ = θ₇ = θ₈ = 0 against the alternative hypothesis θ₁ ≠ θ₂ ≠θ₃ ≠θ₄ ≠θ₅ ≠θ₆ ≠θ₇ ≠θ₈ ≠ 0 employing the F-test. The computed Wald-test F-statistics value obtained from the test was compared with the upper bound I(1) and lower bound I(0) critical values of Narayan (2005) for small sample sizes, . However, prior to the bound test, the apt lag length of 2 included for each series in the model in Equation (20) was first of all determined using Akaike and Schwarz information criteria. Table 6 reports the results of the lag order selection.

Table 6.

Results of Lag Order Selection Criteria.

Lag	LogL	LR	FPE	AIC	SIC	HQ
0	116.1793	NA	3.53E–12	–3.667095	–3.385395	–3.55713
1	719.2422	1022.141	4.17E–20	–21.94041	–17.92222	–20.95073*
2	805.978	123.4883*	2.16e–20*	–22.71112*	–19.40511*	–20.84173

Source: The author.

Notes: * indicates lag order selected by the criterion.

LR: sequential modified LR test statistic (each test at 5% level).

FPE: Final prediction error.

AIC: Akaike information criterion.

SIC: Schwarz information criterion.

HQ: Hannan–Quinn information criterion.

Subsequently, with the optimal lag-length of 2, to test for cointegration, 4,374 distinct ARDL model representations of Equation (20) were examined and the most appropriate model (2, 0, 0, 0, 0, 0, 0) for this study was selected. The relative superiority of the chosen model over alternatives is depicted in Figure 1 (the Akaike information criteria graph of the top 20 models). As the figure shows, all the models use two lags of the endogenous variable. Table 7 lists the bound test results. Based on the results, at 5% significance level, the computed value (5.25) of the F-statistic is evidently larger than the upper bound value (3.21). Hence, the alternative hypothesis is accepted.

Figure 1.

Source: E-view 10 (2021).

Table 7.

Cointegration Results.

Level of Significance (% )	Bounds Values		F-Statistic
Level of Significance (% )	Lower 1(0)	Upper I(1)	F-Statistic
10	1.92	2.89	5.25
5	2.17	3.21	5.25
2.5	2.43	3.51
1	2.73	3.90

Source: The author.

Long-run and Short-run Estimations

Following the cointegration test, the short-run coefficient estimates and the long-run parameters associated with the selected ARDL are provided in panels A and B of Table 8, respectively. Beginning with the error correction model term, the estimated coefficient 0.3336 is statistically significant, in line with theoretical expectations, suggesting that the adjustment speed of the long-run equilibrium (Kalai & Zghidi, 2019) in response to the imbalance caused by short-run shocks in the previous year is 33.36%.

In relation to the growth effects of (physical) capital stocks, the estimated short-run and long-run parameters reveal that the accumulation of physical capital is an important driver of growth. Specifically, as Table 8 illustrates, a unit increase in the stock of physical capital increases growth by 0.2644 and 0.3939% in the short-run and long-run respectively. These significant positive effects suggest that enhanced and sustainable investment in infrastructure (such as road networks, railway lines, airlines, electricity, telecommunication, etc.) need to be accorded high priority. Hence, public–private partnerships should be initiated in such sectors as industries, building and construction to boost physical capital stocks. This result is consistent with the neoclassical and endogenous growth models and corroborates the findings of Bal et al. (2016), Iyke and Ho (2017) and Ahmed et al. (2020).

Regarding the effect of human capital on growth, the results suggest that human capital accumulation is indispensable and germane to short-run and long-run growth. Importantly, as Table 8 explicates, a 1% increase in human capital accumulation brings about 0.3336 and 0.5057% increase in sustainable growth, in the short run and long run, respectively. This implies that investment in human capital will not only accelerate the current growth but also help to sustain future growth. It also suggests that to bolster the short-run and long-run growth, other than focusing solely on diversification policies, policymakers should consider reforms relating to education, training and health by observing the successful human capital models adopted by developed countries (such as Singapore, Japan, Germany, Taiwan, Canada and Australia). Similar findings were observed by Akingba et al. (2018) and Toyosi (2020).

Table 8.

Short-Run and Long-Run Estimates.

Panel A: Estimated Short-Run Coefficients
Regressors	Coefficient	Std. Error	t-Statistic	Prob.
ECM(–1)	–0.3336	0.0621	–5.3693*	0.0001**
lnK	0.2644	0.1578	1.6751*	0.0006**
lnH	0.3336	0.0956	3.4911*	0.0010**
lnI	0.1647	0.0459	3.5834*	0.0007**
lnE	0.4882	0.2419	2.0177*	0.0493**
dlnX	–0.1617	0.0647	–2.4982*	0.0156**
lnT	0.0408	0.0311	1.3147*	0.1947**
lnM	0.2432	0.1210	2.0095*	0.0050**
Panel B: Estimated Long-Run Coefficients
Regressors	Coefficient	Std. Error	t-Statistic	Prob.
C	1.6584	0.5431	3.0538*	0.0000**
lnK	0.3939	0.1214	3.2458*	0.0022**
lnH	0.5057	0.1288	3.9262*	0.0003**
lnI	0.7289	0.3534	2.0626*	0.0445**
lnE	1.1720	0.1261	9.2912*	0.0000**
dlnX	0.6474	0.1861	3.4785*	0.0010**
lnT	0.5999	0.3737	1.6053*	0.1151**
lnM	0.1224	0.0824	1.4862*	0.1436**
Panel C: Goodness of Fit Measures
R-squared				0.8931
Adjusted R-squared				0.8734
F-statistic				242.8748
Prob (F-statistic)				0.0000
Durbin–Watson stat				1.5444
Panel D: Diagnostic Statistical Checking
Breusch–Godfrey Serial Correlation LM Test			4.5569***	0.1024**
Breusch–Pagan–Godfrey test for Heteroskedasticity			3.1272***	0.5572**
ARCH test for Heteroskedasticity			6.4145***	0.5197**
Jarque–Bera Normality Test			20.4077****	0.6943**
Ramsey RESET Specification Test			0.9067*	0.3691**

Source: The author.

Note: ****, ***, **, and * denote Jarque–Bera Statistic, Obs. R-squared, Probability, and t-statistic respectively.

With respect to the effects of diversification at intensive and extensive margins on growth, the estimated coefficients show that both margins significantly enhance sustainable growth in the long run and short run. However, the importance of extensive margin, in aggregate, dominates that of intensive margin. Specifically, as shown in Table 8, a 1% increase in intensive and extensive margin raises growth by 0.1647 and 0.4882% in the short run and 0.7289 and 1.1720% in the long run, respectively. These results, thus, lend credence to the extensive margin explanation, which postulates that export of extant commodities to new market destinations or export of new commodities to new and/or old market destinations plays a relatively more important role in export growth/diversification and, ultimately, sustainable growth. These results are in line with the findings of De Lucio et al. (2011), Turkcan (2014b), Aldan and Çulha (2016) and Matthee et al. (2016).

With respect to the relationship between exchange rate and growth, an insight from the estimates suggests that devaluation of currency is inimical and detrimental to short-run growth. As Table 8 reveals, a 1% increase in exchange rate lowers growth by 0.1617% in the short run. However, in contrast to its short-run negative significant impact, exchange rate devaluation has an expansionary effect in the long run. Explicitly, a 1% increase in the exchange rate leads to 0.6474% increase in growth in the long run. These findings imply that, in the short run, currency devaluation leads to certain resource allocation adjustments (Razzaque et al., 2017) besides inflationary pressure (comprising price change expectations), resulting in dwindling economic activity. However, in the long run, with the improvement in the incentive structure for resource allocation (Razzaque et al., 2017) in favour of the more productive tradable sector, and given its enhanced competitiveness (Razzaque et al., 2017), the expansionary effect sets in. This finding is consistent with Razzaque et al. (2017). However, it contradicts the results of Karahan (2020).

As regards the effects of openness on sustainable growth, in the short run and long run, the elasticity coefficients are positive as expected although statistically insignificant. As shown in Table 8, a unit increase in openness to trade results respectively in 0.0408 and 0.5999% increase in the short run and long run. These insignificant positive effects seem plausible and undoubtedly illustrates the nature and composition of Nigeria’s foreign trade. Most often than not, the import volume is heavily skewed in the direction of semi-finished goods unscrupulously packed as raw materials. Similarly, in the case of exports, the volume is dominated by low value-added and primary products (particularly, oil whose price and quantity are determined in the external market and has very little or no relationship with economic reality). Similar results were also observed by Olufemi (2004) and Sheikh et al. (2020) but contrasted by Nwadike et al. (2020).

With reference to the impact of FDI inflow on growth, estimated results in Table 8 suggest that an increasing level of FDI inflow contributes positively to growth. However, unlike its short-run positive significant impact, the estimated long-run coefficients are not statistically significant. As observed from Table 8, a 1% increase in FDI inflow increases growth by 0.2432% in the short run and 0.1224% in the long run. These findings have two intuitive implications. First, it suggests that, in the short run, FDI could be beneficial especially to increase the level of investment in the country, which (by multiplier effect) leads to increase in employment, income, savings, and by extension, sustainable growth. Second, in the long run, it indicates that FDI (by itself) although necessary cannot sufficiently guarantee sustainable economic growth. As such, for FDI-growth driven to materialise, a myriad of factors (e.g., sound macroeconomic policies that promote productive investment) are critical for FDI productivity to spill over into the whole economy of the recipient countries. The result is consistent with the findings of Kohpaiboon (2003), Adams (2009a, 2009b), Sakyi et al. (2015) and Raza et al. (2021). However, it contradicts the findings of Ahmad and Hamdani (2003), Herzer (2012) and Yusuf et al. (2020).

Finally, to ascertain that the chosen model is fit and appropriate for the empirical analysis, results of R-squared (a statistical measure of the proportion of variation in the endogenous variable explained by exogenous variables collectively), adjusted R-squared (a goodness-of-fit model accuracy measure), F-statistic (measure of an overall significance of the estimated model) and Durbin–Watson statistic (a test statistic employed to detect autocorrelation) are provided in panel C of Table 8. Furthermore, several diagnostic tests (presented in panel D of Table 8 and Figures 2 and 3) were carried out to establish the robustness and reliability of the selected ARDL model and the stability of the estimated coefficients. Results of the analyses revealed that the selected model and the estimates possess the apt best linear unbiased estimator properties.

Figure 2.

Source: E-view 10 (2021).

Figure 3.

Source: E-view 10 (2021).

Innovative Accounting Techniques

Thus far, the empirical analysis has been restricted to the in-sample tests. In order to provide further insight into which of the margin is more beneficial for sustainable economic growth beyond the sample period, IRFs and FEVD were applied. In doing so, we employed an augmented/overfitted multivariate vector autoregressive (VAR) framework, advanced by Toda and Yamamoto (1995) and Dolado and Lutkepohl (1996), to model the joint dynamics and causal relationship among the variables. The approach is preferred over other possible techniques for two reasons. First, the procedure is apt irrespective of the degree of integration (Guru-Gharana, 2012) or cointegration present in the system. Second, unlike other procedures (VAR model in the level/first-differenced data and vector error correction model), the method provides more robust and asymptotically reliable results (Guru-Gharana, 2012). Thus, following Guru-Gharana (2012), the Toda-Yamamoto-Dolado-Lutkepohl (TYDL)-augmented VAR representation of Equation (19) for the current study is expressed as follows:

[\begin{matrix} \ln Y_{t} \\ \ln K_{t} \\ \ln H_{t} \\ \ln I_{t} \\ \ln E_{t} \\ \partial \ln X_{t} \\ \ln T_{t} \\ \ln M_{t} \end{matrix}] = [\begin{matrix} δ_{0} \\ ϕ_{0} \\ φ_{0} \\ η_{0} \\ π_{0} \\ ϑ_{0} \\ ν_{0} \\ ψ_{0} \end{matrix}] + \sum_{i = 1}^{p} [\begin{array}{l} δ_{1 i} δ_{3 i} δ_{5 i} δ_{7 i} δ_{9 i} δ_{11 i} δ_{13 i} δ_{15 i} \\ ϕ_{1 i} ϕ_{3 i} ϕ_{5 i} ϕ_{7 i} ϕ_{9 i} ϕ_{11 i} ϕ_{13 i} ϕ_{15 i} \\ φ_{1 i} φ_{3 i} φ_{5 i} φ_{7 i} φ_{9 i} φ_{11 i} φ_{13 i} φ_{15 i} \\ η_{1 i} η_{3 i} η_{5 i} η_{7 i} η_{9 i} η_{11 i} η_{13 i} η_{15 i} \\ π_{1 i} π_{3 i} π_{5 i} π_{7 i} π_{9 i} π_{11 i} π_{13 i} π_{15 i} \\ ϑ_{1 i} ϑ_{3 i} ϑ_{5 i} ϑ_{7 i} ϑ_{9 i} ϑ_{11 i} ϑ_{13 i} ϑ_{15 i} \\ ν_{1 i} ν_{3 i} ν_{5 i} ν_{7 i} ν_{9 i} ν_{11 i} ν_{13 i} ν_{15 i} \\ ψ_{1 i} ψ_{3 i} ψ_{5 i} ψ_{7 i} ψ_{9 i} ψ_{11 i} ψ_{13 i} ψ_{15 i} \end{array}] [\begin{matrix} \ln Y_{t - i} \\ \ln K_{t - i} \\ \ln H_{t - i} \\ \ln I_{t - i} \\ \ln E_{t - i} \\ \partial \ln X_{t - i} \\ \ln T_{t - i} \\ \ln M_{t - i} \end{matrix}] + \sum_{j = p + 1} {}^{p + d_{m a x} \sum [\begin{matrix} u_{1 i} \\ u_{2 i} \\ u_{3 i} \\ u_{4 i} \\ u_{5 i} \\ u_{6 i} \\ u_{7 i} \\ u_{8 i} \end{matrix}]} [\begin{array}{l} δ_{2 j} δ_{4 j} δ_{6 j} δ_{8 j} δ_{10 j} δ_{12 j} δ_{14 j} δ_{16 j} \\ δ_{2 j} δ_{4 j} δ_{6 j} δ_{8 j} δ_{10 j} δ_{12 j} δ_{14 j} δ_{16 j} \\ φ_{2 j} φ_{4 j} φ_{6 j} φ_{8 j} φ_{10 j} φ_{12 j} φ_{14 j} φ_{16 j} \\ η_{2 j} η_{4 j} η_{6 j} η_{8 j} η_{10 j} η_{12 j} η_{14 j} η_{16 j} \\ π_{2 j} π_{4 j} π_{6 j} π_{8 j} π_{10 j} π_{12 j} π_{14 j} π_{16 j} \\ ϑ_{2 j} ϑ_{4 j} ϑ_{6 j} ϑ_{8 j} ϑ_{10 j} ϑ_{12 j} ϑ_{14 j} ϑ_{16 j} \\ ν_{2 j} ν_{4 j} ν_{6 j} ν_{8 j} ν_{10 j} ν_{12 j} ν_{14 j} ν_{16 j} \\ ψ_{2 j} ψ_{4 j} ψ_{6 j} ψ_{8 j} ψ_{10 j} ψ_{12 j} ψ_{14 j} ψ_{16 j} \end{array}] [\begin{matrix} \ln Y_{t - j} \\ \ln K_{t - j} \\ \ln H_{t - j} \\ \ln I_{t - j} \\ \ln E_{t - j} \\ \partial \ln X_{t - j} \\ \ln T_{t - j} \\ \ln M_{t - j} \end{matrix}]

(21)

where p denotes the true lag length of 2 (ascertained by Akaike and Schwarz information criteria); d_max depicts the maximum order of integration 1 (determined by ADF and PP); u_it represents the residuals {for θ = 1, …, 8}; and δ's, Φ's, φ's, η's, π's, ν's, ν's, and ψ's, signify the parameters. Other variables are as defined earlier.

Therefore, to implement the TYDL procedure, we first estimated the augmented VAR model in Equation (21) of order three using seemingly unrelated regression technique, and carried out the VAR Granger Causality/Block Exogeneity Wald test at 5% significance level. However, the Wald test result is not presented here to retain brevity. Nevertheless, it is available upon request. Afterwards, from the estimated model in Equation (21), FEVD and IRFs of sustainable growth over 10 years were conducted using Cholesky decomposition. The results of FEVD and IRFs, respectively, are depicted in Table 9 and Figure 5. Importantly, to retain conciseness, the FEVD of growth and the responses of growth to innovations in intensive and extensive margins are reported. Moreover, numerous diagnostic and stability checks were carried out to ascertain the appropriateness and reliability of the VAR model as well as the robustness of the estimated results. First, to establish the stability condition of the model in Equation (21), we employed the graph of AR inverse root of the VAR (presented in Figure 4). As the figure shows, all polynomial roots fall inside the unit circle, signifying that the model is stable. Afterward, autocorrelation, heteroskedasticity and non-normality tests were conducted. Estimated results suggest that the model’s residual is serially uncorrelated, homoscedastic and normally distributed. The results, however, are not presented here to maintain conciseness. Nonetheless, they are available upon request.

Table 9.

Forecast Error Variance Decomposition of Economic Growth.

Period	S.E.	lnY	lnK	lnH	lnI	lnE	dlnX	lnT	lnM
1	0.0693620	100.0000000	0.0000000	0.0000000	0.00000000	0.00000000	0.0000000	0.0000000	0.0000000
2	0.1120480	93.73356000	0.0579270	0.0670430	1.97755400	3.69480300	0.1041250	0.1409000	0.2240930
3	0.1363100	84.88356000	0.0695610	0.2073290	5.88204900	7.97880700	0.1073180	0.5183420	0.3530390
4	0.1496620	74.79100000	0.0578920	0.4322560	11.0317800	11.9026500	0.7455980	0.7070660	0.3317620
5	0.1581970	67.10266000	0.0770870	0.5957850	14.8365600	14.5373800	1.6634070	0.8897370	0.2973780
6	0.1638780	62.59823000	0.1123260	0.6318420	16.2554000	16.5574100	2.4484090	1.1158220	0.2805710
7	0.1676690	59.84958000	0.1138680	0.6228970	16.3590300	18.4145900	3.0367790	1.3349080	0.2683520
8	0.1705220	57.86676000	0.1295110	0.6138350	16.0574000	20.1745000	3.4500010	1.4481560	0.2598300
9	0.1730430	56.26939000	0.2742800	0.6191430	15.6808200	21.6986400	3.7329520	1.4706310	0.2541390
10	0.1754320	54.86082000	0.6406520	0.6469550	15.3041400	22.9081000	3.9338350	1.4510510	0.2544580

Source: The author.

Cholesky Ordering: lnY lnK lnH lnI lnE dlnX lnT lnM

Figure 4.

Source: E-view 10 (2021).

Figure 5.

Source: E-view 10 (2021).

Forecast Error Variance Decomposition of Growth

An insight into FEVDs of sustainable growth results presented in Table 9 revealed that own shock had the greatest impact. As the table illustrates, in the first year, the percentage was as high as 100% but slowly declined to 54.86% in tenth year. Aside own shock, extensive margin shock was second most important source of variation in sustainable growth. It grew from approximately 3.69 to 22.90% in second- and tenth-year respectively. This was followed by proportional contribution of intensive margin. It accounted for nearly 1.98% in second year but increased progressively to 16.35% in seventh year. However, it marginally decreased to 15.30% in tenth-year. The magnitude of the effects of other five regressors was quite minimal. The largest was by exchange rate, which contributed 1.66 to 3.93% respectively in the fifth-period and tenth-period. The next was openness, followed by human and physical capital (which were close in significance). FDI had the least contribution and virtually stable within the forecast horizons except for first, third and fourth, year.

Impulse Response Function of Sustainable Growth

Starting with the response of sustainable growth to shock caused by the intensive margin, the first notable impact (positive effect) on growth occurred after year 1. The effect peak is reached during the fourth year. However, it fell slightly from year 5 but remained positive up to the tenth year. As for the response of sustainable economic growth to the extensive margin, a 1 standard innovation shock in the margin only affects growth after the first year. The effect, as is the case in the intensive margin, peaks during the fourth year and falls marginally from year 5 but remains positive till the tenth-year period. The difference, however, is that the positive effect of the extensive margin is stronger as compared to the impact of intensive margin innovations. Overall, the results tend to confirm the conclusions found in ARDL and FEVD analyses.

Conclusion and Policy Implications

With Nigeria as a reference country, over the period 1960–2021, the study employs ARDL cointegration technique and innovative accounting procedures to empirically investigate whether intensive margin or extensive margin of export diversification is more useful towards promoting sustainable economic growth. The ARDL results reveal that both margins significantly enhance growth in the long run and short run. However, importance of the extensive margin, in the aggregate, dominates over the intensive margin. Similarly, the results from innovative accounting procedures reveal that although both margins contribute positively to growth, the contribution of the extensive margin to growth dominates over that of intensive margin.

These findings have two important policy implications for Nigeria. First, to realise the potential benefits inherent in product and geographic diversification (extensive margin) of Nigeria’s exports, economic policies and programmes aimed at enhancing exports of value-added commodities—due to their relatively high income and price elasticities over primary commodities—should be designed and implemented. Importantly, extant policies and initiatives (such as the Nigerian Industrial Revolution Plan of 2014) should be reviewed and strengthened, with a view to aligning the provisions with current economic realities, global aspirations and economic diversification. Second, there is a need to reassess the country’s export and trade policy with a view to identifying the best route to structural transformation and also tailor the policy to achieve the desired intensive and extensive margins of diversification. Experiences of China, Singapore, Malaysia, and South Korea provide very clear evidence.

One of the major limitations of time series analysis is the fact it is difficult to capture all variables influencing a particular variable of interest. Given that the study employed time series analysis, it bears the same limitation. Indeed, apart from physical capital stocks, human capital index, intensive margin, extensive margin, exchange rate, openness to trade and FDI inflow, there are several factors affecting sustainable economic growth. These include among others real effective exchange rate and current account balance variables. Hence, it would be of interest to find out the contribution of these variables as essential determinants of growth which is beyond the scope of the current study.

Finally, for future research, other areas of useful extension of the current study would be to investigate the interaction effects (of both margins) and the factors that drive diversification at both margins. This will provide not only the intuitive insights into what underpin the positive growth effects of diversification but also the channels through which both margins enhance economic growth.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

References

Abdullahi

, & Jibir

(2018). Export diversification and economic growth in Nigeria: An application of SVAR Model. Fourth International Conference on Social Sciences 2018, Research Centre for Social Sciences, Faculty of Social Sciences, University of Kelaniya, Sri Lanka. http://repository.kln.ac.lk/handle/123456789/19524

Adams

(2009a). Foreign direct investment, domestic investment, and economic growth in sub-Saharan Africa. Journal of Policy Modeling, 31(6), 939–949. https://doi.org.10.1016/j.jpolmod.2009.03.003

Adams

(2009b). Can foreign direct investment (FDI) help to promote growth in Africa? African Journal of Business Management, 3(5), 178–183. https://academicjournals.org/journal/AJBM/article-full-text-pdf/9C2487216158

Addison

(2003). Productivity growth and product variety: Gains from imitation and education (vol. 3023). World Bank Publications. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=636385

Adedokun

(2013). Forecasting remittances as a major source of foreign income to Nigeria: Evidence from ARIMA model. European Journal of Humanities and Social Sciences, 24(1). https://www.researchgate.net/profile/Adebayo-Adedokun/publication/267569045_Forecasting_Remittances_as_a_Major_Source_of_Foreign_Income_to_Nigeria_Evidence_from_ARIMA_Model/links/5acbcf6c4585151e80aa268b/Forecasting-Remittances-as-a-Major-Source-of-Foreign-Income-to-Nigeria-Evidence-from-ARIMA-Model.pdf

Adelan

D. B.

, & Kakinaka

(2018). Extensive and intensive margins of exports: The case of the Philippines. Asia-Pacific Journal of Accounting & Economics, 25(3–4), 404–418. https://doi.org.10.1080/16081625.2017.1293551

Agbaeze

E. K.

, Udeh

S. N.

, & Onwuka

I. O.

(2015). Resolving Nigeria’s dependency on oil—the derivation model. Journal of African studies and development, 7(1), 1–14. https://academicjournals.org/journal/JASD/article-full-text-pdf/D12F4AA49306

Ahmad

, & Hamdani

(2003). The role of foreign direct investment in economic growth. Pakistan Economic and Social Review, 29–43. http://www.jstor.org/stable/25825243

Ahmed

, Hossain

M. J.

, & Tareque

(2020). Investigating the roles of physical infrastructure, financial development and human capital on economic growth in Bangladesh. Journal of Infrastructure Development, 12(2), 154–175. 10.1177/0974930620961479

10.

Aigbedion

, & Iyayi

S. E.

(2007). Diversifying Nigeria’s petroleum industry. International Journal of Physical Sciences, 2(10), 263–270. https://academicjournals.org/article/article1380214499_Aigbedion%20and%20Iyayi.pdf

11.

Ajibola

A. A.

, Isiaka

M. A.

, Nnoli

I. T.

, & Abimbola

C. A.

(2019). Electricity supply and economic diversification in Nigeria (1981–2016). Valahian Journal of Economic Studies, 10(1), 7–19. https://www.researchgate.net/profile/Akinyemi-Ajibola/publication/336986757_Electricity_Supply_and_Economic_Diversification_in_Nigeria_1981-2016/links/5dc55c824585151435f42b8e/Electricity-Supply-and-Economic-Diversification-in-Nigeria-1981-2016.pdf

12.

Akingba

I. O. I.

, Kaliappan

S. R.

, & Hamzah

H. Z.

(2018). Impact of health capital on economic growth in Singapore: an ARDL approach to cointegration. International Journal of Social Economics, 45(2), 340–356. 10.1108/IJSE-12-2016-0376.

13.

Akinlo

A. E.

(2004). Foreign direct investment and growth in Nigeria: An empirical investigation. Journal of Policy Modeling, 26(5), 627–639. 10.1016/j.jpolmod.2004.04.011

14.

Akinlo

A. E.

(2009). Electricity consumption and economic growth in Nigeria: Evidence from cointegration and co-feature analysis. Journal of Policy Modeling, 31(5), 681–693. https://doi.org.10.1016/j.jpolmod.2009.03.004

15.

Akinlo

A. E.

(2012). How important is oil in Nigeria’s economic growth? Journal of Sustainable Development 5(4), 165–179. https://doi.org.10.5539/jsd.v5n4p165

16.

Aldan

, & Çulha

O. Y.

(2016). The role of the extensive margin in export of Turkey: A comparative analysis. Central Bank Review, 16(2), 59–64. 10.1016/j.cbrev.2016.05.002

17.

Amarsanaa

, & Kurokawa

(2021). The extensive margin of international trade in a transition economy: The case of Mongolia. Comparative Economic Studies, 63(4), 648–673. https://doi.org.10.1057/s41294-021-00168-x

18.

Amiti

, & Freund

(2010). The anatomy of China’s export growth. In Feenstra

R. C.

, & Wei

S.-J.

(Eds), China’s growing role in world trade (pp. 35–56). National Bureau of Economic Research, Inc. https://www.nber.org/system/files/chapters/c10451/c10451.pdf

19.

Amurgo-Pacheco

, & Pierola

M.D.

(2008). Patterns of export diversification in developing countries: Intensive and extensive margins [World Bank Policy Research Paper No. 4473]. Washington, DC, World Bank. https://openknowledge.worldbank.org/bitstream/handle/10986/6447/wps4473.pdf%3Bjsessionid%3DC4A89FF2DF66CCD2CA90943B16C5FE3F?sequence%3D1

20.

Arize

A. C.

(1996). The impact of exchange-rate uncertainty on export growth: Evidence from Korean data. International Economic Journal, 10(3), 49–60. https://www-tandfonline-com-s.web.bisu.edu.cn/doi/abs/10.1080/10168739600000004

21.

Armington

P. S.

(1969). A theory of demand for products distinguished by place of production. Staff Papers, 16(1), 159–178. https://doi.org.10.2307/3866403

22.

Aurangzeb

, & Stengos

(2014). The role of foreign direct investment (FDI) in a dualistic growth framework: A smooth coefficient semi-parametric approach. Borsa Istanbul Review, 14(3), 133–144. https://doi.org.10.1016/j.bir.2014.06.004

23.

Babatunde

M. A.

, & Adefabi

R. A.

(2005, November). Long run relationship between education and economic growth in Nigeria: Evidence from the Johansen’s cointegration approach. In Regional Conference on Education in West Africa. http://www.saga.cornell.edu/saga/educconf/babatunde.pdf

24.

Bal

D. P.

, Dash

D. P.

, & Subhasish

(2016). The effects of capital formation on economic growth in India: Evidence from ARDL-bound testing approach. Global Business Review, 17(6), 1388–1400. https://doi.org.10.1177/0972150916660403

25.

Banda

, & Simumba

(2013). The birth, death and survival of exports in Zambia 1999–2011 [Working Paper No. 12]. Zambia Institute for Policy Analysis and Research (ZIPAR). https://media.africaportal.org/documents/the_birth_death_and_survival_of_exports_in_zambia.pdf

26.

Bernard

A. B.

, Jensen

J. B.

, Redding

S. J.

, & Schott

P. K.

(2009). The margins of US trade. American Economic Review, 99(2), 487–493. https://doi.org.10.1257/aer.99.2.487

27.

Besedeš

, & Prusa

T. J.

(2011). The role of extensive and intensive margins and export growth. Journal of Development Economics, 96(2), 371–379. https://doi.org.10.1016/j.jdeveco.2010.08.013

28.

Bingzhan

(2011). Extensive margin, quantity and price in China’s export growth. China Economic Review, 22(2), 233–243. https://doi.org.10.1016/j.chieco.2011.01.007

29.

Bista

, & Sheridan

B. J.

(2021). Economic growth takeoffs and the extensive and intensive margins of trade. Review of Development Economics, 25(3), 1373–1396. https://doi.org.10.1111/rode.12777

30.

Bojnec, Š., Čechura

, Fałkowski

, & Fertő

(2021). Agri-food exports from central and eastern European member states of the European Union are catching up. EuroChoices, 20(1), 11–19. https://doi.org.10.1111/1746-692X.12307

31.

Brenton

, & Newfarmer

(2009). Watching more than the Discovery Channel to diversify exports. Breaking into new markets: Emerging lessons for export diversification (pp. 111–124). https://documents1.worldbank.org/curated/ru/821641468323336000/pdf/481030PUB0Brea101Official0use0only1.pdf#page=137

32.

Brunner

H. P.

, & Calì

(2006). The dynamics of manufacturing competitiveness in South Asia: An analysis through export data. Journal of Asian Economics, 17(4), 557–582. https://doi.org.10.1016/j.asieco.2006.05.006

33.

Burange

L. G.

, Ranadive

R. R.

, & Karnik

N. N.

(2019). Trade openness and economic growth nexus: A case study of BRICS. Foreign Trade Review, 54(1), 1–15. 10.1177/0015732518810902

34.

Cadot

, Carrère

, & Strauss-Kahn

(2007). Export diversification: What's behind the hump? (CEPR Discussion Paper No. 6590). Centre for Economic Policy Research, London. https://www.unil.ch/crea/files/live/sites/crea/files/Documents/Economie%20mondiale/Export_diversification_11.2007.pdf

35.

Chang

, Kaltani

, & Loayza

N. V.

(2009). Openness can be good for growth: The role of policy complementarities. Journal of Development Economics, 90(1), 33–49. https://doi.org.10.3386/w11787

36.

Chen

(2011). Export variety, revenue, and productivity in China. Review of International Economics, 19(2), 341–355. https://doi.org.10.1111/j.1467-9396.2011.00951.x

37.

Cho

S. W. S.

, & Díaz

J. P.

(2018). The dynamics of trade margins: Evidence from the European integration. Economics Letters, 167, 90–96. https://doi.org.10.1016/j.econlet.2018.03.014

38.

De Lucio

, Mínguez-Fuentes

, Minondo

, & Requena-Silvente

(2011). The extensive and intensive margins of Spanish trade. International Review of Applied Economics, 25(5), 615–631. 10.1080/02692171.2011.557051

39.

Deaton

(1989). Saving in developing countries: Theory and review. The World Bank Economic Review, 3(suppl_1), 61–96. https://doi.org.10.1093/wber/3.suppl_1.61

40.

Doki

O. N.

, & Tyokohol

Y. M.

(2019). Export diversification and economic growth in Nigeria. International Journal of Economics and Financial Management, 4(2), 16–28. https://www.iiardjournals.org/get/IJEFM/VOL.%204%20NO.%202%202019/EXPORT%20DIVERSIFICATION.pdf

41.

Dolado

J. J.

, & Lütkepohl

(1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369–386. https://doi.org.10.1080/07474939608800362

42.

Dunusinghe

(2009). On export composition and growth: Evidence from Sri Lanka. South Asia Economic Journal, 10(2), 275–304. https://doi.org.10.1177/139156140901000202

43.

Duru

I. U.

, & Ehidiamhen

P. O.

(2018). Empirical investigation of the impact of export diversification on economic growth: Evidence from Nigeria, 1980-2016. Journal of Economics, Management and Trade, 21(7), 1–24. https://www.researchgate.net/profile/Uchechukwu-Duru/publication/345224106_Empirical_Investigation_of_the_Impact_of_Export_Diversification_on_Economic_Growth_Evidence_from_Nigeria_1980-2016/links/5fa0f4d7458515b7cfb5c566/Empirical-Investigation-of-the-Impact-of-Export-Diversification-on-Economic-Growth-Evidence-from-Nigeria-1980-2016.pdf

44.

Dutt

, Mihov

, & Van Zandt

(2013). The effect of WTO on the extensive and the intensive margins of trade. Journal of International Economics, 91(2), 204–219. https://doi.org.10.1016/j.jinteco.2013.08.001

45.

Eaton

, Eslava

, Kugler

, & Tybout

(2008). Export growth in Colombia: Firm-level evidence (No. 446). Banco de la Republica de Colombia. https://doi.org.10.32468/be.446

46.

Ekmen-Özçelik

, & Erlat

(2013). What can we say about Turkey and its competitors in the EU market? An analysis by extensive and intensive margins. Topics in Middle Eastern and North African Economies, 15(1), 186–221. https://ecommons.luc.edu/cgi/viewcontent.cgi?article=1177&context=meea

47.

Eko

S. A.

, Utting

C. A.

, & Onun

E. U.

(2013). Beyond oil: Dual-imperatives for diversifying the Nigerian economy. Journal of Management and Strategy, 4(3), 81–93. https://doi.org.10.5430/jms.v4n3p81

48.

Emediegwu

, & Okeke

(2017). Dependence on oil: What do statistics from Nigeria show? Journal of Economics and Allied Research, 2(1), 110–125. https://www.research.manchester.ac.uk/portal/files/92683580/Dependency_on_Oil_.pdf

49.

Engle

R. F.

, & Granger

C. W. J.

(1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251–276. https://doi.org.10.2307/1913236

50.

Evenett

S. J.

, & Venables

A. J.

(2002). Export growth in developing countries: Market entry and bilateral trade flows [Working Paper]. https://users.nber.org/~confer/2002/si2002/venables.pdf

51.

Feder

(1983). On exports and economic growth. Journal of Development Economics, 12(1–2), 59–73. 10.1016/0304-3878(83)90031-7

52.

Feenstra

R. C.

, & Kee

H. L.

(2004). Export variety and country productivity [NBER Working Paper No. 10830]. National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w10830/w10830.pdf

53.

Feenstra

, & Kee

H. L.

(2008). Export variety and country productivity: Estimating the monopolistic competition model with endogenous productivity. Journal of International Economics, 74(2), 500–518. https://doi.org.10.1016/j.jinteco.2006.11.006

54.

Funke

, & Ruhwedel

(2001a). Product variety and economic growth: Empirical evidence for the OECD countries. IMF Staff Papers, 48(2), 225–242. https://doi.org.10.2307/4621668

55.

Funke

, & Ruhwedel

(2001b). Export variety and export performance: Empirical evidence from East Asia. Journal of Asian Economics, 12(4), 493–505. https://doi.org.10.1016/S1049-0078(01)00100-2

56.

Funke

, & Ruhwedel

(2002). Export variety and export performance: Empirical evidence for the OECD countries. Weltwirtschaftliches Archiv, 138(1), 97–114. https://doi.org.10.1007/BF02707325

57.

Funke

, & Ruhwedel

(2005). Export variety and economic growth in East European transition economies. Economics of Transition, 13(1), 25–50. https://doi.org.10.1111/j.1468-0351.2005.00206.x

58.

Gao

, Whalley

, & Ren

(2014). Decomposing China’s export growth into extensive margin, export quality and quantity effects. China Economic Review, 29, 19–26. 10.1016/j.chieco.2014.02.001

59.

Gries

, & Redlin

(2012, June). Trade openness and economic growth: A panel causality analysis. In International Conferences of RCIE, KIET, and APEA, March (Vol. 16, p. 18). https://faculty.washington.edu/karyiu/confer/sea12/papers/SG12-112%20Redlin.pdf

60.

Guru-Gharana

K. K.

(2012). Econometric investigation of relationships among export, FDI and growth in India: An application of Toda-Yamamoto-Dolado-Lütkepohl Granger causality test. The Journal of Developing Areas, 46(2), 231–247. https://doi.org.10.1353/jda.2012.0027

61.

Helpman

, Melitz

, & Rubinstein

(2008). Estimating trade flows: Trading partners and trading volumes. The Quarterly Journal of Economics, 123(2), 441–487. 10.1162/qjec.2008.123.2.441

62.

Herzer

(2012). How does foreign direct investment really affect developing countries’ growth? Review of International Economics, 20(2), 396–414. https://doi.org.10.1111/j.1467-9396.2012.01029.x

63.

Hesse

(2008). Export diversification and economic growth. Commission on Growth and Development [Working Paper No. 21]. World Bank. https://openknowledge.worldbank.org/handle/10986/28040

64.

Hodey

L. S.

, Oduro

A. D.

, & Senadza

(2015). Export diversification and economic growth in sub-Saharan Africa. Journal of African Development, 17(2), 67–81. https://doi.org.10.5325/jafrideve.17.2.0067

65.

Huchet‐Bourdon

, Le Mouël

, & Vijil

(2018). The relationship between trade openness and economic growth: Some new insights on the openness measurement issue. The World Economy, 41(1), 59–76. https://doi.org.10.1111/twec.12586

66.

Hummels

, & Klenow

P. J.

(2005). The variety and quality of a nation’s exports. American Economic Review, 95(3), 704–723. 10.1257/0002828054201396

67.

Huseyin

, & Shuaibu

U. A.

(2020). Investigating the role of diversification on economic growth in Nigeria: New evidence from SVARs. American Academic Scientific Research Journal for Engineering, Technology, and Sciences, 72(1), 230–250. https://www.asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/6300

68.

Ideh

A. O.

, Okolo

N. M.

, & Emengini

E. S.

(2021). Non-oil sector and economic growth in Nigeria: The national accounts perspective. European Journal of Sustainable Development, 10(1), 185–202. https://doi.org.10.14207/ejsd.2021.v10n1p185

69.

International Monetary Fund (IMF) (2014). Export diversification index. https://www.imf.org/external/datamapper/total_theil@SPRLU/OEMDC/ADVEC/WEOWORLD?year=2010

70.

Islam

(2014). Product varieties, extensive and intensive margins of trade: Evidence from Canada. Applied Econometrics and International Development, 14(2), 97–108. https://www.usc.es/economet/reviews/aeid1427.pdf

71.

Iyke

B. N.

, & Ho

S. Y.

(2017). Income inequality and growth: New insights from Italy. Economia Internazionale/International Economics, 70(4), 419–442. https://core.ac.uk/download/pdf/213994326.pdf

72.

Johansen

(1992). Determination of cointegration rank in the presence of a linear trend. Oxford Bulletin of Economics and Statistics, 54(3), 383–397. https://ideas.repec.org/a/bla/obuest/v54y1992i3p383-97.html

73.

Johansen

, & Juselius

(1990). Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210. https://digilander.libero.it/rocco.mosconi/JohansenJuselius1990.pdf

74.

Johnson

O. E.

(2016). Economic diversification and growth in Africa: Critical policy making issues. Springer. https://doi.org.10.1007/978-3-319-30849-4

75.

Jongwanich

(2020). Export diversification, margins and economic growth at industrial level: Evidence from Thailand. The World Economy, 43(10), 2674–2722. https://doi.org.10.1111/twec.12921

76.

Kalai

, & Zghidi

(2019). Foreign direct investment, trade, and economic growth in MENA countries: Empirical analysis using ARDL bounds testing approach. Journal of the Knowledge Economy, 10(1), 397–421. https://doi.org.10.1007/s13132-017-0460-6

77.

Kannan

, & Kumar

(2022). Intensive and extensive margins of India’s agricultural trade: Implications for export diversification and development [IFPRI Discussion Paper 2119]. International Food Policy Research Institute (IFPRI). https://doi.org.10.2499/p15738coll2.135900

78.

Karahan

Ö.

(2020). Influence of exchange rate on the economic growth in the Turkish economy. Financial Assets and Investing, 11(1), 21–34. 10.5817/FAI2020-1-2

79.

Keho

(2017). The impact of trade openness on economic growth: The case of Cote d’Ivoire. Cogent Economics & Finance, 5(1), 1–14. https://doi.org.10.1080/23322039.2017.1332820

80.

Kehoe

T. J.

, & Ruhl

K. J.

(2013). How important is the new goods margin in international trade? Journal of Political Economy, 121(2), 358–392. https://doi.org.10.1086/670272

81.

Kohpaiboon

(2003). Foreign trade regimes and the FDI–growth nexus: A case study of Thailand. The Journal of Development Studies, 40(2), 55–69. 10.1080/00220380412331293767

82.

Kolawole

B. O.

, Omobitan

O. A.

, & Yaqub

J. O.

(2015). Poverty, inequality and rising growth in Nigeria: Further empirical evidence. International Journal of Economics and Finance, 7(2), 51–62. https://pdfs.semanticscholar.org/9d3c/722da2034e9e4cb54956f944274750518ace.pdf

83.

Krugman

(1980). Scale economies, product differentiation, and the pattern of trade. The American Economic Review, 70(5), 950–959. http://purocihle.rrojasdatabank.info/krugman1980.pdf

84.

Kumar

(2010). Determinants of real exchange rate in India: an ARDL approach. Reserve Bank of India Occasional Papers, 31(1), 33–64. https://rbidocs.rbi.org.in/rdocs/Content/PDFs/2DRERAI160810.pdf

85.

Liapis

(2009). Extensive margins in agriculture. OECD Food, Agriculture and Fisheries [Working Paper No. 17]. Paris, OECD Publishing. https://read.oecd-ilibrary.org/agriculture-and-food/extensive-margins-in-agriculture_224422031753#page1

86.

Lugeiyamu

(2016). Is export diversification a key force to Africa’s economic growth? Cross-country Evidence [Unpublished M.Sc. economics thesis]. International Business School of Jonkoping University. https://www.diva-portal.org/smash/get/diva2:937117/FULLTEXT01.pdf

87.

Madichie

, Osagu

, & Eze

(2019). Economic diversification: Imperative for trade and industrial policies in Nigeria. Timisoara Journal of Economics and Business, 11(1), 67–86. https://www.researchgate.net/profile/Chekwube-Madichie/publication/331285073_Economic_Diversification_Imperative_for_Trade_and_Industrial_Policies_in_Nigeria/links/5c718baea6fdcc471595f556/Economic-Diversification-Imperative-for-Trade-and-Industrial-Policies-in-Nigeria.pdf

88.

Makinde

(2005). Problems of policy implementation in developing nations: The Nigerian experience. Journal of Social Sciences, 11(1), 63–69. https://doi.org.10.1080/09718923.2005.11892495

89.

Martincus

C. V.

, & Carballo

(2008). Is export promotion effective in developing countries? Firm-level evidence on the intensive and the extensive margins of exports. Journal of International Economics, 76(1), 89–106. https://doi.org.10.1016/j.jinteco.2008.05.002

90.

Matezo

, Makengo

, & Muhole

(2021). The Influence of Export Diversification on Economic Growth: A Case of Southern African Development Community (SADC). American Journal of Industrial and Business Management, 11(07), 829–845. https://hal.archives-ouvertes.fr/hal-03596577/document

91.

Matthee

, Farole

, Naughtin

, & Rankin

(2016). South African exporters and the global crisis: Intensive margin shock, extensive margin hangover. South African Journal of Economics, 84(2), 183–198. https://doi.org.10.1111/saje.12094

92.

McNown

, Sam

C. Y.

, & Goh

S. K.

(2018). Bootstrapping the autoregressive distributed lag test for cointegration. Applied Economics, 50(13), 1509–1521. https://doi.org.10.1080/00036846.2017.1366643

93.

Mora

, & Olabisi

(2021). Economic development and the margins of trade: Are the least developed countries different? Economic Inquiry, 59(2), 600–621. https://doi.org.10.1111/ecin.12937

94.

Naranpanawa

, Bandara

J. S.

, & Selvanathan

(2011). Trade and poverty nexus: A case study of Sri Lanka. Journal of Policy Modeling, 33(2), 328–346. https://doi.org.10.1016/j.jpolmod.2010.08.008

95.

Narayan

P. K.

(2005). The saving and investment nexus for China: Evidence from cointegration tests. Applied Economics, 37(17), 1979–1990. https://doi.org.10.1080/00036840500278103

96.

Njoku

A. C.

, & Ihugba

O. A.

(2011). Unemployment and Nigerian economic growth (1985–2009). Mediterranean Journal of Social Sciences, 2(6), 23–23. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.656.2529&rep=rep1&type=pdf

97.

Nwadike

G. C.

, Johnmary

A. K.

, & Alamba

C. S.

(2020). Impact of trade openness on Nigerian economic growth: An empirical investigation, 1970–2011. Foreign Trade Review, 55(2), 239–247. https://doi.org.10.1177/0015732519894153

98.

Nwosa

P. I.

, Tosin

F. O.

, & Ikechukwu

O. M.

(2019). Export diversification and economic growth in Nigeria. Signifikan: Jurnal Ilmu Ekonomi, 8(2), 227–234. https://www.researchgate.net/profile/Oluwadamilola-Fasina/publication/335097359_Export_Diversification_and_Economic_Growth_in_Nigeria/links/5ea7fc5092851cde0c902a9a/Export-Diversification-and-Economic-Growth-in-Nigeria.pdf

99.

Obamwonyi

S. E.

, & Aibieyi

(2014). Public policy failures in Nigeria: Pathway to underdevelopment. Public Policy and Administration Research, 4(9), 38–44. https://core.ac.uk/download/pdf/234669266.pdf

100.

Odedokun

M. O.

, & Round

J. I.

(2001). Determinants of income inequality and its effects on economic growth: Evidence from African countries (No. 2001/103) [WIDER Discussion Paper]. https://www.econstor.eu/bitstream/10419/52809/1/336657811.pdf

101.

Odularu

G. O.

(2009). Export diversification as a promotion strategy for intra-ECOWAS trade expansion. African Journal of Business Management, 3(2), 32–38. https://www.researchgate.net/profile/Gbadebo-Odularu/publication/239920094_Export_diversification_as_a_promotion_strategy_for_intra-_ECOWAS_trade_expansion/links/555af96a08ae980ca6119677/Export-diversification-as-a-promotion-strategy-for-intra-ECOWAS-trade-expansion.pdf

102.

Ogbuigwe

(2018). Refining in Nigeria: History, challenges and prospects. Applied Petrochemical Research, 8(4), 181–192. https://link-springer-com-443.web.bisu.edu.cn/article/10.1007/s13203-018-0211-z

103.

Okotie

(2018). The Nigerian economy before the discovery of crude oil. In The political ecology of oil and gas activities in the Nigerian aquatic ecosystem (pp. 71–81). Academic Press. 10.1016/B978-0-12-809399-3.00005-7

104.

Olayungbo

D. O.

(2019). Effects of oil export revenue on economic growth in Nigeria: A time varying analysis of resource curse. Resources Policy, 64, 1–10. 10.1016/j.resourpol.2019.101469

105.

Olufemi

S. M.

(2004). Trade openness and economic growth in Nigeria: Further evidence on the causality issue. South African Journal of Economic and Management Sciences, 7(2), 299–315. https://hdl.handle.net/10520/EJC31437

106.

Omoju

O. E.

, & Ikhide

E. E.

(2020). Harnessing the AfCFTA for economic diversification in Nigeria: The role of trade logistics and infrastructure. In Strategic Policy Options for Bracing Nigeria for the Future of Trade (pp. 159–171). Palgrave Macmillan. https://link-springer-com-443.web.bisu.edu.cn/chapter/10.1007/978-3-030-34552-5_6

107.

Otamurodov

, Shujin

Z. H. U.

, & Zhong

(2016). The role of extensive margin and intensive margin in Kazakhstan’s export growth. Ecoforum Journal, 5(2), 9–16. http://ecoforumjournal.ro/index.php/eco/article/view/394

108.

Owan

V. J.

, Ndibe

, & Anyanwu

C. C.

(2020). Diversification and economic growth in Nigeria (1981–2016): An econometric approach based on ordinary least squares (OLS). European Journal of Sustainable Development Research, 4(4), 1–10. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3606063

109.

Oyelami

L. O.

, & Alege

P. O.

(2018). Macroeconomic implications of trade diversiﬁcation in Nigeria. CBN Journal of Applied Statistics (JAS), 9(1), 23–45. https://dc.cbn.gov.ng/cgi/viewcontent.cgi?article=1053&context=jas

110.

Phillips

P. C.

, & Hansen

B. E.

(1990). Statistical inference in instrumental variables regression with I (1) processes. The Review of Economic Studies, 57(1), 99–125. https://doi.org.10.2307/2297545

111.

Pinto

(1987). Nigeria during and after the oil boom: A policy comparison with Indonesia. The World Bank Economic Review, 1(3), 419–445. https://doi.org.10.1093/wber/1.3.419

112.

Prebisch

(1950). The economic development of Latin America and its principal problems. United Nations Department of Economic Affairs. Also published in Economic Bulletin for Latin America, vol. 7, 1962, 1–22. https://repositorio.cepal.org/bitstream/handle/11362/10079/S6200129.pdf?sequence=1

113.

Rahmouni

(2020). The impact of the geographical diversification of Saudi exports: An intensive and extensive margin approach. Asian Economic and Financial Review, 10(10), 1077–1087. https://doi.org.10.18488/journal.aefr.2020.1010.1077.1087

114.

Raza

S. A.

, Shah

, & Arif

(2021). Relationship between FDI and economic growth in the presence of good governance system: Evidence from OECD countries. Global Business Review, 22(6), 1471–1489. https://doi.org.10.1177/0972150919833484

115.

Razzaque

M. A.

, Bidisha

S. H.

, & Khondker

B. H.

(2017). Exchange rate and economic growth: An empirical assessment for Bangladesh. Journal of South Asian Development, 12(1), 42–64. https://doi.org.10.1177/0973174117702712

116.

Riti

J. S.

, Gubak

H. D.

, & Madina

D. A.

(2016). Growth of non-oil sectors: A key to diversification and economic performance in Nigeria. Public Policy and Administration Research, 6(3), 64–75. https://irepos.unijos.edu.ng/jspui/handle/123456789/1819

117.

Sakyi

, Commodore

, & Opoku

E. E. O.

(2015). Foreign direct investment, trade openness and economic growth in Ghana: An empirical investigation. Journal of African Business, 16(1–2), 1–15. 10.1080/15228916.2015.1061283

118.

Saleem

, & Sial

M. H.

(2015). Exports-growth nexus in Pakistan: Cointegration and causality analysis. Pakistan Economic and Social Review, 53(1), 17–46. http://www.jstor.org/stable/26153247

119.

Samantaraya

, & Patra

S. K.

(2014). Determinants of household savings in India: An empirical analysis using ARDL approach. Economics Research International, 1–8. https://downloads.hindawi.com/archive/2014/454675.pdf

120.

Sheikh

M. A.

, & Malik

M. A.

(2021). The nexus of trade openness, institutions and economic growth: An empirical investigation of BRICS countries. Foreign Trade Review, 56(2), 206–215. 10.1177/0015732521995162

121.

Sheikh

M. A.

, Malik

M. A.

, & Masood

R. Z.

(2020). Assessing the effects of trade openness on sustainable development: Evidence from India. Asian Journal of Sustainability and Social Responsibility, 5(1), 1–15. https://doi.org.10.1186/s41180-019-0030-x

122.

Siddiqui

D. A.

, & Ahmed

Q. M.

(2019). The causal relationship between institutions and economic growth: An empirical investigation for Pakistan economy. International Economics and Business, 5(1), 1–19. https://mpra.ub.uni-muenchen.de/19745/1/MPRA_paper_19745.pdf

123.

Singer

H. W.

(1950). The distribution of gains between investing and borrowing countries. The American Economic Review, 40(2), 473–485. http://www.jstor.org/stable/1818065

124.

Srinivasan

, Kumar

P. S.

, & Ganesh

(2012). Tourism and economic growth in Sri Lanka: An ARDL bounds testing approach. Environment and Urbanization Asia, 3(2), 397–405. https://doi.org.10.1177/0975425312473234

125.

Srinivasan

T. N.

(1994). Data base for development analysis: An overview. Journal of Development Economics, 44(1), 3–27. https://doi.org.10.1016/0304-3878(94)00003-4

126.

Srinivasan

T. N.

, & Bhagwati

(2001). Outward-orientation and development: Are revisionists right? In Trade, development and political economy (pp. 3–26). Palgrave Macmillan. 10.1057/9780230523685_1

127.

Sun

Z. L.

, & Xian-de

L. I.

(2018). The trade margins of Chinese agricultural exports to ASEAN and their determinants. Journal of Integrative Agriculture, 17(10), 2356–2367. https://doi.org.10.1016/S2095-3119(18)62084-2

128.

Tan

B. W.

, & Tang

C. F.

(2012). The dynamic relationship between private domestic investment, the user cost of capital, public investment, foreign direct investment and economic growth in Malaysia. Economia politica, 29(2), 221–246. https://mpra.ub.uni-muenchen.de/27964/1/MPRA_paper_27964.pdf

129.

Tanasritunyakul

(2021). Asian export performance: Diversification, product variety, and productivity [Doctoral dissertation]. The Australian National University. https://openresearch-repository.anu.edu.au/bitstream/1885/227705/1/Tanasritunyakul_Thesis_2021.pdf

130.

Tang

C. F.

, & Tan

E. C.

(2013). Exploring the nexus of electricity consumption, economic growth, energy prices and technology innovation in Malaysia. Applied Energy, 104, 297–305. https://doi.org.10.1016/j.apenergy.2012.10.061

131.

Tianhao

, & Jing

(2021, March). Research on the four-dimensional decomposition of China’s manufacturing export growth. In Sixth International Conference on Financial Innovation and Economic, Development (ICFIED 2021) (pp. 367–370). Atlantis Press. 10.2991/aebmr.k.210319.067

132.

Toda

H. Y.

, & Yamamoto

(1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250. 10.1016/0304-4076(94)01616-8

133.

Tong

, Ortiz

, Xu

, & Li

(2020). Economic growth, energy consumption, and carbon dioxide emissions in the E7 countries: A bootstrap ARDL bound test. Energy, Sustainability and Society, 10(1), 1–17. 10.1186/s13705-020-00253-6

134.

Toyosi

A. N.

(2020). Human capital development and economic growth in Nigeria: An autoregressive distributed lag (ARDL) approach. Journal of Empirical Studies, 7(1), 41–51. https://doi.org.10.18488/journal.66.2020.71.41.51

135.

Turkcan

(2014a). Exports margins in Austria’s export growth. University Library of Munich. https://mpra.ub.uni-muenchen.de/53085/1/MPRA_paper_53085.pdf

136.

Turkcan

(2014b). Investigating the role of extensive margin, intensive margin, price and quantity components on Turkey’s export growth during 1998–2011. University Library of Munich. https://mpra.ub.uni-muenchen.de/53292/1/MPRA_paper_53292.pdf

137.

Umeji

(2019). Diversification of the Nigerian economy through agricultural sector transformation. SAU Journal of Management and Social Science, 4(1&2). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3760378

138.

Vakataki‘Ofa

, Spence

, & Mevel

(2016). Export diversification and intra-industry trade in Africa. In Inclusive growth in Africa (pp. 142–175). Routledge. https://www-taylorfrancis-com-443.web.bisu.edu.cn/chapters/edit/10.4324/9781315562179-17/export-diversification-intra-industry-trade-africa-siope-vakataki-ofa-malcolm-spence-simon-mevel

139.

Veeramani

, Aerath

, & Gupta

(2018). Intensive and extensive margins of exports: What can India learn from China? The World Economy, 41(5), 1196–1222. 10.1111/twec.12592

140.

World Bank. (2021). The human capital index 2020 update: Human capital in the time of COVID-19. 10.1596/978-1-4648-1552-2

141.

Yuni

D. N.

, Urama

, Ugwuegbe

, & Agbanike

(2020). When does export diversification improve economic growth? A comparative analysis of sub-Saharan African countries. Studies in Economics and Econometrics, 44(1), 129–141. https://journals.co.za/doi/abs/10.10520/EJC-1d4baac642

142.

Yusuf

H. A.

, Shittu

W. O.

, Akanbi

S. B.

, Umar

H. M.

, & Abdulrahman

I. A.

(2020). The role of foreign direct investment, financial development, democracy and political (in) stability on economic growth in West Africa. International Trade, Politics and Development, 4(1), 27–46. https://doi.org.10.1108/ITPD-01-2020-0002

143.

Zhang

, Zhou

, Geng

, & Tian

(2017). The intensive and extensive margins of China’s agricultural trade. Canadian Journal of Agricultural Economics/Revue Canadienne d’agroeconomie, 65(3), 431–451. https://doi.org.10.1111/cjag.12133

144.

Zhao

Y. L.

, Liu

D. X.

, Pu

H. L.

, & Yang

Z. H.

(2013). China’s export margins and their growth sources: An analysis of extensive margin and intensive margin. The Singapore Economic Review, 58(04), 1350029-1–1350029-19. https://doi.org.10.1142/S021759081350029X

145.

Zhou

, Tong

, & Sun

(2013). What’s special about the extensive and intensive margins in Chinese manufacturing exports? Journal of Chinese Economic and Foreign Trade Studies 6(1), 19–34. 10.1108/17544401311292655