Does economic liberalization promote economic growth in Pakistan? An empirical analysis

Qual Quant DOI 10.1007/s11135-013-9882-9

Does economic liberalization promote economic growth in Pakistan? An empirical analysis Qazi Muhammad Adnan Hye · Wee-Yeap Lau · Marie-Aimée Tourres

© Springer Science+Business Media Dordrecht 2013

Abstract The study aims to examine the short and long term impacts of economic liberalization on economic growth in case of Pakistan from 1971 to 2011. Economic liberalization consists of reforms in both trade liberalization and financial liberalization. This study contributes to the existing literature by constructing an economic liberalization index using principal component analysis. Our results show, firstly, that economic liberalization reforms have a positive impact on economic growth in the short run. However, trade liberalization is negatively associated with economic growth in the long-run. Secondly, the estimated coefficients through rolling window show that impact of economic liberalization on real GDP is unstable during the selected period of sample. This study recommends to policy makers to enhance human capital by having more expenditure on education sector. In addition, financial reforms by way of a sectoral credit allocation should be introduced to further promote the economic growth. Keywords Economic liberalization · Economic growth · Trade liberalization · Financial liberalization · Pakistan

Q. M. A. Hye (B) Department of Economics, Faculty of Economics and Administration, University of Malaya, Kuala Lumpur, Malaysia e-mail: [email protected] W.-Y. Lau Department of Applied Statistics, Faculty of Economics and Administration, University of Malaya, Kuala Lumpur, Malaysia e-mail: [email protected] M.-A. Tourres Department of Development Studies, Faculty of Economics and Administration, University of Malaya, Kuala Lumpur, Malaysia e-mail: [email protected]; [email protected]

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1 Introduction The link between economic liberalization (EL) and economic growth (EG) has drawn significant attention from researchers after the emergence of new growth theories. In 1980s, many developing countries have put into practice the endogenous growth theory model and started the process of liberalization in order to achieve EG. The complete liberalization of economy means the liberalization of financial sector and trade sector. However, empirical evidence on the results of financial and trade liberalization are inconclusive. This study presented here is motivated by the fact that Pakistan has embarked in a liberalizeation process in order to achieve a sustainable EG. The liberalization focused on both the financial and trade sectors reforms which we briefly explain as follows: Pakistan started the process of financial sector liberalization in late 1980s. The objectives were to improve the efficiency of financial markets, to formulate the market-based and relatively more efficient monetary and credit policies, and lastly to strengthen the capital and market based financial institutions. The number of modifications which aimed at strengthening financial market, macroeconomic stability with institutional development: • In 1991, approval was granted to open the private domestic banks, while the privatization of banking sector started to take place with the Muslim Commercial Bank (MCB), Allied Bank Limited (ABL) and Habib Credit & Exchange Bank as the first ones (23.2 % share of the National Bank of Pakistan (NBP) was off-loaded in 2004–2005). The same year, stock market was restructured. The system of credit ceilings was replaced by credit deposit ratio (CDR) in 1992. After 3 years the system of CDR was stopped and replaced by a market based mechanism. • To enhance competition in the banking sector, the entry barriers in financial sector were removed in 1993. • In 1994 a prudential regulation system was introduced. The same year Pak-Rupee was made convertible and, in 1998 a New Exchange Rate Mechanism was introduced (dual exchange rate system). This was later replaced by a market-based unified exchange rate system within a narrow band in 1999. This replaced the managed floating exchange rate system. • The system of Open Market Operation was introduced in 1995 and now it is a key tool of monetary policy. Caps on minimum lending rates of banks and NBFIs for trade and project related modes of financing were removed in 1997. To strengthen the legal framework of loan recoveries, banking courts were also established in 1997. The same year, interest rate was completely liberalized. • The unofficial cap on the exchange rate was finally removed on July 21, 2000 to make it purely market based. Similarly, a serie of trade reforms as stated below were also started in the late 1980s: • The major policy was the shift from import substitution to export promotion. • In 1987 the broad tariff reforms were started. The number of tariff rate was reduced from 17 to 10 %. The maximum tariff rate was reduced from 225 to 125 %. • In order to attract the foreign direct investment, the government allowed a 100 % ownership with the exception of a few industries. Although previous studies have shown that EL in financial and trade in a country can lead to EG, yet improper management will cause the country to fall into crisis. For example, Diamond and Dybvig (1983) stated that bank runs in the traditional model causes real economic loss. Singh (1997) stated that financial liberalization in terms of expansion of stock markets

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Does economic liberalization promote economic growth in Pakistan?

in developed countries hamper development. Rodriguez and Rodrik (1999) presented a literature survey on trade openness and EG, and report that there is little evidence that trade openness reforms like reduced tariff rate and removal of non-tariff barriers on trade is linked by economic progress. This study aims to examine the short and long run impacts of EL on EG in the case of Pakistan by using the data from 1971 to 2011. This study contributes to the literature by developing an economic liberalization index (ELI). The short run and long run relationship are estimated by using the error correction model, JJ cointegration and full modified OLS method. The paper is organized as follows: the Sect. 2 presents a literature review of the topic. It is followed by a presentation of our methodology and various estimations. The empirical results are presented in Sect. 4. The final part concludes with some policy recommendation.

2 Literature review The review of literature is divided into theoretical and empirical point of views. 2.1 Review theoretical literature 2.1.1 Financial liberalization and economic growth Three majors approaches can be found in the theoretical literature on the link between financial liberalization on development and EG. The first approach was by Schumpeter (1911) who declared that a proper financial development structure channels the country’s savings to the most innovating entrepreneurs. Later Gerschenkron (1962) stated that countries can understand the important role of financial organizations to lead the direction of capital towards the most advanced technological sectors. Mckinnon (1973) and Shaw (1973) agreed with Schumpeter’s and Gerschenkron’s view to endorse financial development through financial liberalization for EG. They condemned the financial repressionist’s observation, and explained that government limitations on banking system such as interest rate ceiling, higher reserve requirements and directed credit control hamper financial development and reduce output. According to the second approach, finance is not the main driver for EG. The EG leads to financial development (Robinson 1952). The financial markets develop as a consequence of EG (Lewis 1955). The physical capital, human capital and technological changes are the only factors influencing EG (Lucas 1988). These findings suggest that EG creates the demand for various financial services that will be met through financial sector. Derived from the precedent approach, the third approach was emulated by neo-Keynesian and neo-Structuralists. They further argued that financial liberalization is negatively linked with EG. These economists state that the financial liberalization model increases interest rate and manufacturing costs that consequently impede EG as well as increase inflation (Diamond and Dybvig 1983; Singh 1997). 2.1.2 Trade liberalization and economic growth The theoretical literature shows that trade liberalization impacts EG by the following three channels: through efficient distribution of resources, through allocation of local resources,1 1 It is negatively linked with EG if the local resources of the country are unable to effectively use the

technology generated by the trade openness (see Romer 1986).

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lastly via learning by doing effect that is developed countries innovate while developing countries imitates (Grossman and Helpman 1991; Young 1991; Rivera-Batiz 1995). 2.2 Review of empirical literature After the emergence of new endogenous growth theories, researchers have started to investigate the impact of EL on EG. The empirical literature indicates that the EL and EG nexuses are usually tested on the following ways: finance–trade–EG relationship, finance-growth link, and trade-growth link. Soukhakian (2007) investigated the long run relationship between trade openness, financial development and EG in the case of Japan. His findings supported the supply leading and growth-driven trade hypothesis. The long run relationship among trade openness, financial development and EG was also studied by Katircioglu et al. (2007) in the case of India. They also suggested unidirectional causality from real income to exports and imports; bidirectional causality between real income and M2, and between real income and domestic credits. Similar conclusions were found by Khan and Qayyum (2007) in the case of Pakistan where both financial development and trade openness are vital elements for economic growth. The positive links were not supported however by more recent studies. The link between finance, trade and EG has been investigated in case of 13 Latin American and Caribbean countries2 by the Gries et al. (2008a). They did not find any empirical link between financial development, trade openness and EG. Furthermore, Gries et al. (2008b) estimate the financetrade-growth link hypothesis in the case of 16 Sub-Saharan Africa countries.3 They found a long run relationship only three countries. Yucel (2009) used JJ cointegration and Granger causality test to examine relationship between trade openness, financial development and EG. He suggests that financial development is negatively and trade openness positively affected EG in the case of Turkish economy. Other researchers investigated the finance–growth and trade–growth nexuses separately. The link between financial development and EG has been widely covered in the literature. In an empirical work covering 80 countries, King and Levine (1993) tested the Schumpeter’s observation according to which financial system leads EG. They concluded financial development is positively related to real GDP per capita growth. Similarly, Levine and Zervos (1998) used banking sector development as an indicator of financial development, and tested the association between financial development and EG of the developed and less developed countries. They have reported a positive correlation between financial development and EG. In another study, Ang and Mckibbin (2007) using Malaysian data from 1960 to 2001 on the issue of whether financial liberalization increases EG, concluded “financial liberalization, through removing the repressionist policies” is stimulating financial sector development, and financial development is positively related to EG in case of Malaysia. The importance of financial development is empirically proved by Hye and Dolgopolova (2011a) in case of china. They concluded that financial development and real interest rate are positively related to EG, and shocks in financial development and real interest rate are highly explained by EG. Financial development has a positive impact on growth as found by Calderon and Liu (2003), Chang and Yuan-Hong (2002), Mazur and Alexander (2001). Patrick (1966) proposed two hypotheses: supply-leading hypothesis and demand-leading hypothesis. If the financial 2 Mexico, Venezuela, Costa Rica, Ecuador, Honduras, El Salvador, Paraguay, Guatemala, Dominican Repub-

lic, Colombia, Chile, Suriname, Jamaica. Burundi, Cameroon, Cote d’Ivoire, Ethiopia, Gabon, Ghana, Kenya, Madagascar, Mauritius, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, The Gambia, Togo.

3

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Does economic liberalization promote economic growth in Pakistan?

development causes EG, it is supply-leading hypothesis, and if EG causes the financial development, it is demand-leading hypothesis. Mckinnon (1973); Neusser and Kugler (1998), Levine and Beck (2000) and Fase and Abma (2003), supported the supply leading hypothesis while the demand leading hypothesis has been recognized by Gurley and Shaw (1955), Goldsmith (1969); Jung (1986), Yildirim et al. (2008). Hye (2010) estimated the impact of financial liberalization on overall growth and sectoral growth. They found that financial liberalization has a positive impact on GDP growth only in the short run. The financial liberalization positively affects agricultural growth in the short run and long run. On the other hand, financial liberalization negatively causes industrial and services sector growth in the long run and positively in the short run. Keho (2010) estimated the finance–growth nexus in case of seven African countries and concluded that financial development has no significant impact on EG. In contrast, in case India and Bangladesh, Hye (2011b), Hye and Islam (2013) found that the financial liberalization is negatively linked to economic growth. These studies recommended the policy makers to improve financial risk management system. As discussed by Lee (1991), lack of risk management system in developing countries like Argentina, Chile, Columbia, Brazil, Mexico and Uruguay can be the source of financial distress. The case of Nigeria was studied by Maduka and Onwuka (2013) who found negative coefficients of the financial market variables and concluded that the supply of financial assets is not sufficient to increase the EG at the desired level. With regard to trade liberalisation, Dollar (1992) show that trade liberalization, devaluation and stability in real exchange rate could radically increase EG in poor developing countries. Villanueva (1994) suggested trade openness and human capital accumulation impact positively EG in the case of 36 countries. Frankel and Romer (1999) investigated the impact of international trade on standard of living by using the data of 63 countries. They found trade enhances income by stimulating the cumulation of physical and human capital. Sonmez and Sener (2009) concluded that human capital and trade openness affect EG in developing and developed countries but at different rates. Using the data of 60 countries, Kim et al. (2011) found that greater trade openness is positively related to EG in the case of developed countries but it is negatively linked to EG in developing countries. Ghatak et al. (1995) investigated the effect of trade openness on economic development. They found a long run connection among trade openness, human capital, physical capital and real GDP in the case of Turkey. Abdulhamid and Ramakrishna (2002) stated that the countries which liberalize their international trade can grow faster relatively to closed economies. Dutta and Ahmed (2004) examined the connection among trade openness and industrial sector growth in Pakistan. He found a long run affiliation among the trade policies and industrial sector growth. Khan and Qayyum (2007) found that trade openness and financial development both positively related to EG and recommend further liberalization. Chaudhry et al. (2010) tested the relationship between trade liberalization, human capital and EG. They concluded that trade policies and human capital both positively determine EG. Klasra (2010) concluded trade openness derives EG in the case of Pakistan and on the other hand EG derives the exports in case of Turkey. 3 Model, data and estimation methodology 3.1 Theoretical framework The ‘Human Capital Model of Endogenous Growth’ (Lucas 1988) is employed to test our empirical relationship. This model considers human capital accumulation through education

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as an instrument of EG. The dual effect of human capital accretion on EG is that it accelerates the productivity of workers at the individual level. Second it enhances the competence at all stages of production. The model is written in a functional form as follows: β

Yt = At K t (μqt L t )1−β qαδ

(1)

where Yt indicates the total output; At represents the level of technology (which is assumed to be constant); K t and L t correspondingly physical capital and total numbers of workers. The qt shows the average value of human capital, and the externalities of average human capital is symbolized by qαδ . In this model Lucas has assumed that all labor force is at the same skill level (qt = qα ). Thus this model is rewritten as follows: β

1+δ−β

Yt = At K t (μL t )1−β qt

(2)

Lucas model indicated positive stable EG due to the increasing returns to scale (2 + δ − β > 2 − β > 1). The steady growth depends on the value of δ. For simplification Lucas has supposed that the workers have used a portion (μ) of their non-leisure time to present production, dedicating the remaining (1 − μ) to human capital accumulation thus qi /qi = γi μi where γi indicates the positive coefficient showing workers skill creation in sector i. The internal and external skill of workers is enhanced under the EL. Our empirical work examines the link between liberalization and EG by using the Lucas production model, where the EL indicators are used as a separate factor input while the other input factor represent physical capital and human capital. Y = F(L skill , K , E L I )

(3)

In the below equation we rewrite function-3 as follows: Ln (Y ) = β0 + β1 Ln (L skill ) + β2 Ln (K ) + β3 Ln (E L I ) + εi

(4)

where Y, L skill , K and E L I respectively refer to the real GDP, skill labor force/ human capital, physical capital, and economic liberalization indicator (i.e financial liberalization index (FLI), trade openness index and ELI). The Ln shows the sign of natural logarithm, and βs represents the slope coefficients of respectively variables. The εi is the error correction term. The real GDP is used as a proxy of economic growth. The physical capital shows the real gross fixed capital formation and primary school enrollment is used as a skill labor force/human capital. 3.2 Data The FLI, trade openness index and liberalization index have been constructed using the world development indicators data published by the World Bank. 3.2.1 Construction of financial liberalization index The literature shows that researchers computed financial indicators by applying two different approaches. Along the first approach, Bandiera et al. (2000) constructed FLI by utilizing various financial institutional reforms and regulations like interest rate deregulation, procompetition measures, reserve requirements, directed credit, bank ownership, prudential regulations, stock market reform and international financial liberalization. Following Bandiera

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et al. approach are Laeven (2003),4 Nair (2004),5 Shrestha and Chowdhury (2007),6 Ahmed (2007)7 and Hye and Wizarat (2011c). 8 The second approach focuses on the banking based financial system. Kelly and Mavrotas (2003) and Ang and Mckibbin (2007) explained that it is a difficult application to measure government deregulationn policies. Ang and Mckibbin (2007) computed financial development index for Malaysia by utilizing the three indicators: liquid liabilities as a ratio of nominal GDP, domestic credit to private sectors as a ratio of nominal GDP, and the ratio of commercial bank assets to commercial bank assets plus central bank assets was used. Khan and Qayyum (2007) use total bank deposit liabilities; clearing house amount; private credit and the stock market capitalization, each as a ratio of GDP to develop Financial development index for Pakistan. Hye and Dolgopolova (2011a), Hye (2011b), and Hye and Islam (2013) computed financial development index for India, China and Bangladesh. These studies used four proxy indicators of financial sector development in the case of China and India,9 but in the case of Bangladesh, they used five proxy for financial development i.e. Liquid liabilities (M3) as % of GDP, Domestic credit provided by banks (% of GDP); Domestic credit to the private sector (% of GDP); Money plus quasi money divided by money; and Market capitalization of listed companies (% of GDP). Our contribution here is to modify the method of construction of the FLI. We compute financial liberalization by using Liquid liabilities (M3) (% of GDP); foreign direct investment (% of GDP); portfolio investment flows (% of GDP); market capitalization of listed companies (% of GDP), and number of domestic companies listed. The benefit of these indicators is the data are easily accessible, and The hypothesis here is that a lower value represents the highest degree of policy intervention in the financial sector. Each measure of financial sector captures a different aspect. According to the literature, Liquid liabilities (M3) as a % of GDP is used to measure the Financial Deeping or the efficiency of the bank sector. A steady increase showing a development in the financial system. The foreign direct investment (% of GDP), and portfolio investment flows (% of GDP) are expected to increase due to external account liberalization10 The market capitalization of listed companies 4 Computed a FLI in case of 13 developing countries by using interest rates deregulation, reduction of entry

barriers, reduction of reserve requirements, reduction of credit controls, privatization of state Banks and and strengthening of prudential regulation. The countries under study were Argentina, Brazil, Chile, India, Indonesia, Malaysia, Mexico, Pakistan, Peru, Philippines, Rep. Korea, Taiwan, Thailand. 5 Developed financial liberalization in case of in case of India by using six indicator of financial liberalization. These indicators are interest rate liberalization, reduction in reserve requirements, pro-competition measures, increased prudential regulation, stock market development and international financial liberalization. 6 Developed financial indicator in case of Nepal by using eight component of financial liberalization. These indicators are interest rate liberalization, removal of entry barriers, reduction in reserve requirements, easing credit controls, introduction of Prudential Regulations, stock market reform, privatization of state-owned banks and external account liberalization. 7 Constructed financial liberalization in case of Botswana by using five indictors of financial reforms like: interest rate liberalization, exchange rate liberalization, reduction in reserve requirement, authorization of new banks and privatization of banks and securities markets. 8 Hye and Wizarat (2011c) computed financial liberalization in case of Pakistan by using eleven indicators : Islamization, interest rate deregulation, credit controls, stock market reforms, Prudential Regulations, privatization of financial institutions, removal of entry barriers, non performing loans, external account liberalization, debt management reforms and open market operations. 9 These four indicators of financial development are as follows: market capitalization of listed companies, liquid liabilities and domestic credit to the private sector as a percentage of GDP, and M2/M1. 10 Diwan (2001), Prasad et al. (2004), Cerra and Saxena (2005) explained that the foreign International capital flows have led to higher volatility. This volatility has led to financial and economic crises in low income

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Q. M. A. Hye et al. Table 1 Principal components analysis of financial liberalization index Eigenvalues: (Sum = 5, Average = 1) Number

Value

Difference

Proportion

Cumulative value

Cumulative proportion

1.000

3.127

2.349

0.625

3.127

0.625

2.000

0.778

0.193

0.156

3.904

0.781

3.000

0.585

0.212

0.117

4.489

0.898

4.000

0.373

0.235

0.075

4.862

0.972

5.000

0.138

–

0.028

5.000

1.000

PC 2

PC 3

PC 4

PC 5

Eigenvectors (loadings) Variable

PC 1

SM

0.531

0.055

−0.158

−0.166

−0.814

FDI

0.488

−0.225

−0.067

−0.704

0.459

LDC

0.454

−0.039

−0.610

0.577

0.295

PE

0.341

0.846

0.353

0.061

0.198

M

0.397

−0.479

0.688

0.374

0.017

Ordinary correlations SM MC

1.000

FDI

0.798

FDI

LDC

PE

M

1.000

LDC

0.740

0.591

1.000

PE

0.543

0.354

0.353

1.000

M

0.550

0.565

0.414

0.260

1.000

MC Market capitalization of listed companies (% of GDP); FDI Foreign direct investment, net inflows (% of GDP); LDC Listed domestic companies, total; PE Portfolio equity, net inflows (BoP, current US$); M Liquid liabilities (M3) as % of GDP

(% of GDP), and number of domestic companies listed are measured the size of stock markets in the economy. By using these indicators, we are able to capture in a better way, the reality of Pakistan. FLI can represent the complete feature of financial and capital markets in Pakistan. The weight of each series is computed by using the principal component method (PCM). Table 1 reports the results from the PCM. The first PC explains about 62.5 %, the second PC explains 15.6 %, the third and fourth PC another 11.7 and 7.5 % respectively and the last principal component accounts for 2.8 % of the standardized variance. Thus we select the first PC to calculate financial liberalization index. The first PC is a linear combination of the five standard measures of financial development with weights given by the first eigenvector. The individual contributions of each series MC, FDI, LDC, PE and M to the standardized variance of the first principal component are found to be 53.1, 48.8, 45.4, 34.1 and 39.7 % respectively. Footnote 10 continued countries (see Singh 2003; Easterly 2001). These crises are negatively related to growth, and other growth indicators like investment and saving (Diwan 1999).

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Does economic liberalization promote economic growth in Pakistan? 120 100 80 60 40 20 0 1975

1980

1985

1990

1995

2000

2005

2010

Fig. 1 Financial liberalization index

This study uses these weights to construct a summary measure of FLI, as shown in Fig. 1. This index describes the structural changes of financial sector liberalization in Pakistan. The Fig. 1 shows that from 1988 the FLI increases vertically, and then show some increasing trend, which time represents the higher policy liberalization. The FLI decline from 1997. 3.2.2 Construction of trade openness index The empirical literature indicates that most of studies have used the following three proxies to test the impact trade openness on EG: export divided by GDP, import divided by GDP, and export plus import divided by GDP. The advantage of these three proxy indicators are that the data is easily available. It is assumed that a lower value of these trade indicators are representing the higher degree of policy intervention in trade sector. The vital weights are calculated by using the principal component analysis (PCA). The eigenvalues show that the first principal component accounts for about 65 % cumulative proportion of variation (See Table 2). The second component explains another 35 % and last principal component demonstrates 0.00 % standardized variation. It is the first principal component that shows greater variation as compared to other combination of variables. The first eigenvector values as a weight is used in our study to construct a composite measure of trade openness and denoted as TLI. The separate contribution of TD; M and X in standardized variance of the first principal component, i.e. 71.6, 54 and 44.2 % respectively. The Fig. 2 shows the graph of composite trade liberalization index (TLI) in Pakistan which represents the variation in the trade sector. 3.2.3 Construction of economic liberalization index The aim of this study is to compute an ELI in case of Pakistan by using the financial and trade sector liberalization indicators. The eigenvalues indicate that the first principal component determines about 47.1 % cumulative proportion of variation (See Table 3). The second explains another 23.7 % and, 12.3, 9.1, 0.5, 1.9, 1 % and last principal component demonstrates 0.00 % standardized variation respectively. It is confirmed that the first principal component show greater variation as compared to other combination of variables. So, this study uses the first eigenvector values as a weight to compute a composite measure of economic liberalization index and denoted as ELI. The separately contribution of MC; LDC;

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Q. M. A. Hye et al. Table 2 Principal components analysis for trade openness index Number

Value

Difference

Proportion

Cumulative value

Cumulative proportion

1.000

1.949

0.898

0.650

1.949

0.650

2.000

1.051

1.051

0.350

3.000

1.000

3.000

0.000

–

0.000

3.000

1.000

PC 1

PC 2

PC 3

Eigenvectors (loadings) Variable TD

0.716

0.010

−0.698

M

0.540

−0.641

0.546

X

0.442

0.768

0.464

Ordinary correlations TD

M

X

TD

1.000

M

0.748

1.000

X

0.625

−0.052

1.000

TD Merchandise trade (% of GDP), M Imports of goods and services (% of GDP), X Exports of goods and services (% of GDP) 140 130 120 110 100 90 80 70 1975

1980

1985

1990

1995

2000

2005

2010

Fig. 2 Trade liberalization index

FDI; EX; PE; M and TD11 in standardized variance of the first principal component, i.e. 47.4, 44.1, 41, 40.1, 30.8, 29.6 and 26.5 % respectively. The Fig. 3 shows the graph of ELI, which represents the changes in economic policies. 3.3 Estimation methodology This study employs different cointegration methods—JJ cointegration, Fully Modified Least Squares, error correction model, and rolling regression method—in order to test the association between variables. Before applying cointegration technique, it is crucial to investigate the 11 The last component (M) shows negative correlation with other variables, so we use only seven indicators

weight in the ELI calculation process.

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Does economic liberalization promote economic growth in Pakistan? Table 3 Principal components analysis of economic liberalization index Number

Value

Difference Proportion Cumulative value

Cumulative proportion

1.000

3.771

1.877

0.471

3.771

0.471

2.000

1.893

0.907

0.237

5.664

0.708

3.000

0.987

0.261

0.123

6.651

0.831

4.000

0.725

0.328

0.091

7.376

0.922

5.000

0.398

0.249

0.050

7.774

0.972

6.000

0.149

0.072

0.019

7.923

0.990

7.000

0.077

0.077

0.010

8.000

1.000

8.000

0.000

–

0.000

8.000

1.000

PC 1

PC 2

PC 3

PC 4

PC 5

Eigenvectors (loadings) Variable

PC 6

PC 7

PC 8

MC

0.474 −0.091

0.123

0.016

−0.289 −0.723

0.382

LDC

0.441 −0.141

−0.361

−0.151

−0.193

0.586

0.503

0.000

FDI

0.410 −0.029

0.451

−0.285

−0.434

0.223 −0.556

0.000

0.269 −0.182 −0.424

0.464

0.000

X

0.401

0.122

−0.566

−0.057

PE

0.308

0.056

0.152

0.915

0.036

0.187 −0.075

0.000

M

0.296 −0.366

0.412

−0.179

0.751

0.059

0.000

XM IM

0.100

0.265

0.606

−0.095

−0.116

0.224 −0.032 −0.041 −0.698

−0.002

0.672

0.360

−0.101

0.057

0.114

0.308

0.546

Ordinary correlations SM

LDC

FDI

X

PE

M3

XM

M

MC

1.000

LDC

0.740

FDI

0.798

0.591

EX

0.602

0.788

0.338

1.000

PE

0.543

0.353

0.354

0.358

1.000

M

0.550

0.414

0.565

0.216

0.260

TD

0.333

0.303

0.320

0.625

0.284

−0.082 1.000

−0.086 −0.283

0.122

−0.052

0.059

−0.288 0.748

IM

1.000 1.000

1.000 1.000

120 100 80 60 40 20 0 1975

1980

1985

1990

1995

2000

2005

2010

Fig. 3 Economic liberalization index

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Q. M. A. Hye et al.

level of integration. To do so, this study uses the ADF test to examine the order of integration before the Johansen (1995) methods are used. The Johansen cointegration test is based on two statistics (λtrace and λmax ). First ‘Trace test’ cointegration rank r is that λtrace = −T

n 

ln(1 − λˆ j )

j = r +1

Second, λmax maximum number of cointegrating vectors against r + 1 is presented in the following way    λmax (r, r + 1) = −T ln 1 − λˆ j Johansen (1995) also recognized λtrace and λmax critical values. Next this study applies Fully Modified Least Squares and error correction model in order to show the long run and short run coefficient.

4 Empirical findings The empirical literature shows that economic series present trending behavior or nonstationarity in the mean. A significant econometric task is to determine the correct form of the trend in the data. For this purpose, Augmented Dickey–Fuller test of unit root is employed in this analysis. In Table 4, the results indicate that all variables are integrated of order one. After determining the order of integration, this study uses the JJ co-integration method for the examination

Table 4 Results of ADF test Variables

Level

1st Difference

Constant

Constant, linear trend

Constant

Constant, linear trend

None

−1.833

−2.095

0.125

−2.436

2.329

−3.876***

−4.084**

−2.983***

2.441

−5.569***

−5.681***

Ln(F L I )

−0.967

−4.421***

−1.562

0.541

−4.014***

−3.977**

Ln(T L I )

−3.879***

−1.972

−1.708

−0.123

−3.145*

−3.392*

−3.229*

Ln(L I )

−1.003

−1.441

0.693

−3.946***

−3.921**

−3.802***

Ln(S M)

−1.048

−1.661

0.111

−4.186***

−4.144**

−4.065***

Ln(F D I )

−2.039

−2.832

−1.418

−3.346**

−3.276*

−2.327*

Ln(P E)

−1.557

−1.377

−0.829

−12.817***

−12.658***

−12.982***

Ln(M)

−1.531

−3.179

0.262

−4.934***

−5.094***

−5.003***

Ln(L DC)

−1.108

−1.639

0.093

−6.171***

−6.111***

−2.686***

Ln(E X )

−1.866

−1.969

−0.226

−5.548***

−5.508***

−5.661***

Ln(I M)

−2.458

−2.786

−0.141

−4.263***

−4.391***

−4.344***

Ln(T D)

−2.181

−1.928

−0.247

−4.074***

−4.235***

−4.186***

Ln(K ) Ln(Lskill)

None

∗∗∗;∗∗;∗ represents the 1; 5; and 10 % level of significance

123

Does economic liberalization promote economic growth in Pakistan? Table 5 JJ cointegration analysis Model

Trace statistic r =0

r ≤1

r ≤2

r ≤3

r ≤4

Model-1: (Y, Lskill , K, FLI)

61.686*

31.362

12.771

3.541

Model-2: (Y, Lskill , K, TLI)

78.698***

36.793**

19.362

7.029

Model-3: (Y, Lskill , K, FLI, TLI)

92.269**

53.899

28.761

16.958

Model-4: (Y, Lskill , K, ELI) Model-5: (Y, Lskill , K, TLI, MC)

62.164* 111.556***

– – 7.030

30.998

12.286

3.426

64.803**

31.014

13.187

5.846

–

Model-6: (Y, Lskill , K, TLI, FDI)

115.765***

58.992**

26.018

12.595

1.657

Model-7: (Y, Lskill , K, TLI, PE)

115.052***

62.074*

31.752

14.971

4.321

Model-8: (Y, Lskill , K, TLI, M)

103.856***

67.566**

40.694*

18.997

8.814

Model-9: (Y, Lskill , K, TLI, LDC)

89.171*

52.516

27.644

15.933

6.076

Model-10: (Y, Lskill , K, FLI, EX)

90.672*

58.698

31.302

17.792

8.764

Model-11: (Y, Lskill , K, FLI, IM)

97.603***

54.835**

21.309

9.399

3.409

Model-12: (Y, Lskill , K, FLI, TD)

97.004***

54.597**

24.009

11.531

3.628

∗∗∗;∗∗;∗ respectively represents 1; 5; and 10 % level of significance

of the long run association between the variables. The trace statistic of JJ co-integration method is used to conclude the long run relationship. Since the JJ Cointegration approach is very sensitive to the lag order employed, this study uses the SBC method to determine the optimal lag order earlier to estimate cointegration tests. In Table 5, the results of trace test indicate the same conclusion that there is cointegration relationship exist in 1–12 models. But the cointegration vector is different, in Model-1, 3, 4, 9 and 10 there is one cointegrating vector. The two cointegrating vectors are found in Model-2, 5, 6, 7, 11 and 12, only in model-8 the three cointegrating vector are found. The long run coefficients are calculated by using Fully Modified Least Squares method. Table 6 shows the results of long run coefficients. The results from Model-1 to 12 show that human capital [Ln(L Skill )] and physical capital positively determine the EG. The FLI and ELI are statistically insignificant, and trade liberalization index negatively related with growth in the long run. Our results are, in general, against the theoretical statement of Mckinnon (1973), Shaw (1973), Lucas (1988) and Romer (1986), and earlier empirical findings in case of Pakistan, Khan and Qayyum (2007) who found that trade openness (export plus import divided by GDP) and financial development to have a positive impact on EG. Similarly, Chaudhry et al. (2010) found one percent increase in trade openness (exports plus imports divided by GDP) increases EG by 3.06 %. We also estimate the impact of individual proxies of economic liberalization on EG. The results indicate that all five financial liberalization indicators : Ln(S M), Ln(F D I ), Ln(P E), Ln(M) and Ln(L DC) are statistically insignificant in the long run. The results of trade liberalization indicators show that Ln(I M) and Ln(T D) have negatively impacted the EG. Table 7 represents the short-run coefficients. The physical capital, skill labor force, FLI, trade liberalization, and EL have positive and statistically significant coefficients in the short run. All proxy indicators of financial liberalization and trade liberalization indicate a positive impact on EG. The error correction term shows the speed of adjustment from short run

123

123

–

Ln(M)

–

–

Ln(P E)

–

–

Ln(F D I )

Ln(T D)

–

Ln(E L I )

Ln(S M)

Ln(I M)

–

Ln(T L I )

–

–

Ln(F L I )

–

0.001

Ln(Lskill)

Ln(E X )

0.621***

Ln(K )

Ln(L DC)

1.537

0.922***

Intercept

(1)

–

–

–

–

–

–

–

–

–

−0.441***

–

0.439***

1.007***

2.241*

(2)

Table 6 Long run coefficients

–

–

–

–

–

–

–

–

–

−0.473***

0.006

0.436***

1.030***

1.885

(3)

–

–

–

–

–

–

–

–

0.004

–

–

0.611***

0.919***

1.621

(4)

–

–

–

–

–

–

–

0.013

–

−0.441***

–

0.439***

1.007***

2.241*

(5)

–

–

–

–

–

–

−0.008

–

−0.507***

–

0.473***

1.051***

1.478

(6)

–

–

–

–

–

–

–

–

–

–

–

0.002

–

−0.201

–

–

–

−0.001

–

–

−0.461***

–

0.428***

1.039***

1.654

(9)

–

–

−0.609***

–

0.436***

1.094***

1.853

(8)

–

–

−0.344**

–

0.566***

0.984***

1.933*

(7)

–

–

−0.161

–

–

–

–

–

–

0.024

0.576***

0.919***

2.083

(10)

– −0.472***

−0.326*** –

–

–

–

–

–

–

–

0.006

1.419***

1.043***

1.026

(12)

–

–

–

–

–

–

–

−0.025

0.492***

1.047***

0.121

(11)

Q. M. A. Hye et al.

0.012**

–

–

–

–

–

Ln(Lskill)

Ln(F L I )

Ln(T L I )

Ln(E L I )

Ln(S M)

Ln(F D I )

Ln(F D I (−1))

–

–0.308***

0.554

R-squared

–

Ln(I M(−1))

EC M(−1)

–

Ln(I M)

Ln(T D)

–

–

Ln(L DC)

Ln(E X )

–

0.154***

Ln(K (−1))

–

−0.136***

Ln(K )

Ln(P E)

0.173***

Ln(Y (−1))

Ln(M)

0.009

0.321**

Intercept

(1)

–

–

–

–

–

–

–

–

–

–

(2)

0.570

0.562

–

−0.304***

–

–

–

–

–

–

–

–

–

–

0.016**

–

–

0.153**

−0.135**

0.177**

0.305**

0.009

(4)

−0.305***

–

–

–

–

–

–

–

–

–

0.043

0.012**

0.149***

−0.132***

0.160***

0.239*

0.010

(3)

Table 7 Short run coefficients (error correction model)

0.649

−0.402***

–

–

–

–

–

0.018**

–

0.032

–

0.143***

−0.165***

0.208***

0.312**

0.012*

(5)

0.596

−0.324***

–

–

–

0.009***

−0.00001

–

0.060*

–

0.174***

−0.132***

0.106**

0.389**

0.008

(6)

0.536

−0.374***

–

–

−0.001*

–

–

–

–

0.040

–

0.146***

−0.142***

0.168***

0.307**

0.011

(7)

0.522

−0.261***

–

0.037*

–

–

–

–

–

0.027

–

0.156***

−0.123**

0.151***

0.317*

0.009

(8)

0.541

−0.206***

0.004∗

–

–

–

–

–

–

0.060*

–

0.127***

−0.099**

0.156***

–

0.008

(9)

0.643

−0.375***

–

–

–

0.073***

–

–

0.008

0.131***

−0.141**

0.198***

0.299**

0.012*

(10)

0.602

−0.339***

–

0.044**

−0.012

–

–

–

0.011*

0.178***

−0.175***

0.191***

0.438***

0.008

(11)

0.551

−0.216**

0.067**

–

–

–

–

–

0.011*

0.125**

−0.099**

0.149***

–

0.008

(12)

Does economic liberalization promote economic growth in Pakistan?

123

Q. M. A. Hye et al. 3

2

1

0

-1

-2 1983

1990

1997

2004

2011

Window size 13 Fig. 4 Coefficient of Ln(K) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4)

disequilibrium to long run equilibrium. In this analysis the error correction term shows the 21.6–40.2 % adjustment per year. 4.1 Estimation results of rolling window regression method The rolling window regression is used to check the stability of coefficients of the variables in the selected data period. The available cointegration econometric techinques assume that coefficients of the estimate model remain constant throughout the sample. In the reality, economy cannot continue in similar manner, and, economic indicators thus oscillated. Consequently, the estimated coefficients of economic indicators cannot remain the same throughout sample. By using rolling window regression method, we can estimate the coefficient of each observation of the sample size by fixing the size of rolling window. If the economic indicators change overtime, this method is able to capture the variability. Figures 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and 16 show the results of the estimated coefficients using the rolling window regression method. The estimated coeffients shows that the impact of EL on EG is not stable throughout the sample.

5 Conclusion In this paper, we have constructed a FLI, trade openness index, and ELI for Pakistan for the period of 1971–2011. The augmented Dickey–Fuller unit root tests has been employed to determine the order of integration. The JJ cointegration, Fully Modified Least Squares and error correction model were used to estimate the long run and short run relationship. The stability of the cofficients was checked by using the rolling window regression method. The long-run results indicate that TLI is negatively associated to EG while the available empirical studies in case of Pakistan show positive relationship between EG and trade openness (export plus import divided by GDP).

123

Does economic liberalization promote economic growth in Pakistan? 3

2

1

0

-1 1983

1990

1997

2004

2011

Window size 13 Fig. 5 Coefficient of Ln ( Lskill) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4) 2

1

0

-1

-2 1983

1990

1997

2004

2011

Window size 13 Fig. 6 Coefficient of Ln(FLI) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4) 1.0 0.5 0.0 -0.5 -1.0 -1.5 1983

1990

1997

2004

2011

Window size 13 Fig. 7 Coefficient of Ln(TLI) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4)

123

Q. M. A. Hye et al. 2 1 0 -1 -2 -3 1983

1990

1997

2004

2011

Window size 13 Fig. 8 Coefficient of Ln(ELI) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4) 0.1

0.0

-0.1

-0.2

-0.3 1983

1990

1997

2004

2011

Window size 13 Fig. 9 Coefficient of Ln(SM) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4) 0.5 0.4 0.3 0.2 0.1 0.0 -0.1 -0.2 -0.3 -0.4 1983

1990

1997

2004

2011

Window size 13 Fig. 10 Coefficient of Ln(FDI) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4)

123

Does economic liberalization promote economic growth in Pakistan?

0.02 0.00 -0.02 -0.04 -0.06 -0.08 1983

1990

1997

2004

2011

Window size 13 Fig. 11 Coefficient of Ln(PE) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4) 2.0

1.0

0.0

-1.0

-2.0 1983

1990

1997

2004

2011

Window size 13 Fig. 12 Coefficient of Ln(M) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4) 2 1 0 -1 -2 -3 1983

1990

1997

2004

2011

Window size 13 Fig. 13 Coefficient of Ln(LDC) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4)

123

Q. M. A. Hye et al. 1.0

0.0

-1.0

-2.0 1983

1990

1997

2004

2011

Window size 13 Fig. 14 Coefficient of Ln(EX) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4) 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 1983

1990

1997

2004

2011

Window size 13 Fig. 15 Coefficient of Ln(IM) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4) 1.0 0.5 0.0 -0.5 -1.0 -1.5 1983

1990

1997

2004

2011

Window size 13 Fig. 16 Coefficient of Ln(TD) and its two*S.E. bands based on rolling OLS (Dependent variable: Ln(Y), Total no. of regressors: 4)

123

Does economic liberalization promote economic growth in Pakistan?

In contrast, our empirical study shows that TLI (as well as individual proxies) is negative related to EG in the long run, and a 1 % increase in trade openness reduces real GDP growth by a range of 0.441–0.473 % in the case of Pakistan. Thus, this study rejects the earlier finding for this country by Khan and Qayyum (2007) who concluded a positive link between trade openness and EG, and a 1 % increase in trade openness expanded EG by 0.371 %. The empirical findings of this study also do not support the theoretical explanation of Romer (1986), and as well as the earlier empirical findings of cross country case by Romer (1986), Edwards (1989, 1992), Villanueva (1994), Wacziarg (2001) and Halit (2003). Chaudhry et al. (2010) found a 1 % increase in trade openness enhances EG by 3.06 %. The FLI is statistically insignificant which results are equal to the theoretical justifications of Robinson (1952), Lewis (1955) and Lucas (1988). Additionally the results have failed to lend support of theoretical studies of Schumpeter (1911), Goldsmith (1969), Hicks (1969), Mckinnon (1973) and Shaw (1973). In the short run, FLI, TLI and ELI are positively related to EG. The individual proxies estimated model also support these results. The policy implication that emerges from this study is that overall, our findings support the Structuralists and Post-Keynesian school of thought that full EL impedes EG. This study suggests the following policy implications: • Human capital in Pakistan is positively related with economic growth. This indicates that human capital is playing important role in the growth process. Presently Pakistan is spending 2.1 % of GDP on education (GOP 2011), which is much below than other regional countries like India, Bangladesh and Nepal. An increase of the expenditures on education sector is vital in order to sustain EG by enhancing human capital. • Financial reforms need to be revised. A system of sectoral credit allocation should be introduced. Such a system will increase the investment level in real the sector of the economy. Thus productive investments will expedite the process of EG in the long run.

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