Liberalization Policy, Agricultural trade, and Agricultural Growth:

An empirical analysis of the Libyan experience

Youssef Abdulhamid Mustafa Alkhurmani * Hartini Binti Mohammad b

a Faculty of Economics, University of Az-Zawiya, Az-Zawiya, Libyab Faculty of Economics and Muamalat, Universiti Sains Islam Malaysia,

ABSTRACT

This study investigates the impact of agricultural exports and imports on agricultural growth under the

application of trade liberalization policies after the suspension of international sanctions in 1999. This paper

had not only adopted the ARDL bounds testing approach to test for cointegration, but had also conducted a

further investigation on the direction of causality by incorporating dummy liberalization as an exogenous

variable in the VECM framework. The results had confirmed the existence of a long-run relationship among the

series. The respective agricultural exports and imports were projected to have a modest positive and a negative

impact on agricultural production. Agricultural labor and area under cultivation were also proven to be the

most significant determinants. The results had supported a unidirectional relationship running from agri-

production to agricultural exports and from agricultural imports to agri-production.

Keywords: agricultural exports, agricultural imports, liberalization, Libya

1. Introduction

In the past few months, the term “liberalization of trade” has been receiving growing attention among

researchers and academicians, particularly in the aftermath of Brexit, the implementation of America First

policy and the so-called upcoming trade war between global economic powers, where some of these countries

had announced imposing tariffs on each other. Trade liberalization has been playing a pivotal role on

improving economic performance by stimulating GDP growth as well as influencing structural exchange in the

industrial, agriculture and services sectors through the reduction and elimination of both tariff and non-tariff

barriers that may obstruct international trade (Pupongsak, 2010). This has sparked a debate from the arguments

derived from the economic theory, which suggests that the alleviation or reduction in the restrictions on

* Corresponding author.: Youssef Abdulhamid Mustafa Alkhurmani Tel.: +60182373435 Malaysia, E-mail address: khrmaney@gmail.com.

1

international trade would initially lead to lower prices and greater competition. According to the World Bank

(WB), the aim of introducing trade liberalization is to correct the on-going balance of payment deficits of

developing countries and to promote trade by accelerating economic growth, expanding agricultural production

and subsequently lead to a better economic performance. Trade liberalization has been identified in the

literature as a factor of economic growth, where both classical and neoclassical trade theories have perceived

international trade to be incentive of economic growth as it boosts efficient allocation of resources in areas of

comparative advantage as well as allowing economies to better capture the potential benefits of increasing

returns to scale and economies of specialization. The impacts of foreign trade on economic growth have been

studied by many economists from various points of views and one of them was how exports and imports can be

used as indices of trade openness. While some studies had identified a positive association between trade

openness and economic growth, there were others, however, who had found no association, or even a negative

association between the two. Among some of the evidences that had displayed mixed effects of trade openness

on economic growth were those conducted by Edwards (1992), Edwards (1998), Ben-David (1993), Baldwin

(2004), Noguer and Siscart (2005). Although Yanikkaya (2003) had found significant threshold effects in the

relationship between trade and growth, Uğurlu (2010) and Abbas (2014) on the other hand, had discovered

trade openness to deteriorate economic growth.

Due to the dominance of petroleum and natural gas sector upon Libya’s economy, numerous studies have

been focusing their studies on hydrocarbon output and intentionally or unintentionally ignoring the non-oil

sector such as a commerce in agricultural production which is the fundamental component in the developed and

developing countries. From the research perspective, while there had been numerous research papers dedicated

to studying the impact of agricultural exports and imports on economic growth such as Ekanayake (1999), Karp

and Perloff (2002); Ardeni and Freebairn (2002); Lopez (2002): Sanjuán-López and Dawson (2010); Moseykin

and Levchenko (2014). Forgha and Aquilas (2015); Alam and Myovella (2016); Kalaitzi and Cleeve (2017);

Matandare (2017); Bakari and Mabrouki (2018), studies on the impact of agricultural exports and imports on

agricultural growth are unquestionably scarce in literature. As such, by utilizing the most widely used

econometric method, namely the autoregressive distributed lag (ARDL), this paper had made a major

contribution to the literature by estimating the relationship between indices of agricultural trade openness and

agricultural growth. The empirical results of the causal relationship will be the first literature attempt that

2

regards the policies of trade liberalization as an exogenous variable in the Vector Error Correction Model

(VECM).

The purpose of this study is to analyze the impact of agricultural exports and imports on agricultural growth

under the trade liberalization policies that had started immediately after the suspension of international

sanctions in 1999 by using the Cobb–Douglas production function. The analysis and investigation on the

direction of causality between indices of agricultural trade openness and dependent variables in both short-run

and long-run were conducted by incorporating the trade liberalization policies as an exogenous variable in the

VECM Granger Causality test. Prior to conducting the statistical analysis, the data were subjected to primary

examinations as a way of determining their characteristics. Hence, this study had utilized basic descriptive

statistics and the augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to check the order of

integration of the variables. The outline of this paper had been organized as such, where Section Two describes

the trade liberalization policies and the agricultural trade in Libya during the last three decades, which is then

followed by a brief literature overview on the relationship between trade liberalization and growth rate in

Section Three. While Section Four is a review of the stationary tests of time series data and the results derived

from the ARDL and Johansen (1991) cointegration tests, VECM analysis and the Granger Causality tests under

GIRF framework, the last section of the paper had focused on the conclusion and policy implications.

2. Liberalization policy and agricultural trade in Libya

The last three decades had seen the economy of Libya being restricted by the economic sanctions imposed

by the United States (USA) and United Kingdom (UK) during the 1980s and by the United Nations (UN)

during 1990s that resulted in the distortions of its foreign trade. It was not until the end of 1999 that the

Security Council had adopted the resolution of suspending the sanctions through political compromise between

the Libyan government and the western nations. Since the suspension of the economic sanctions, the

government had been gradually applying trade liberalization policies as a way of reducing and abolishing some

state control in order to build an economy that is more market-oriented by restructuring the economic

mechanism from central administrative planning to mostly market-driven pricing and exposing the economy to

the world through trade (oil exports and imports) and foreign investment (Masoud, 2009). Furthermore, the

World Bank (2010) had indicated that as a result of these programmes, Libya has the most liberal trading

regime in both the Middle East and North Africa (MENA) regions and within the upper-middle-income

3

countries. However, the foreign trade in Libya had still encountered certain challenges, where a service fee of 4

% is imposed on all imported goods and the government still maintains a monopoly over some of the imports.

Since the policies of trade liberalization in Libya began at the end of the 1990s and early 2000s to meet the

World Trade Organization (WTO) requirements1, the contribution of net trade to GDP had jumped from 5 % in

the 1980s to around 28 % of GDP in the 2000s (see Figure 1). The changes in the share of net trade had most

likely reflected the application of trade liberalization policies in the achievement of economic objectives.

Figure 1: GDP components during the 1980s, 1990s, and 2000s

1980s 1990s 2000s0.0

20.040.060.080.0

100.0

Final consumption expenditure Gross capital formationnet trade

prec

enta

ge

Using UNCTAD (2017).

Conversely, the joint report from the World Food Programme (WFP) and the Food and Agriculture

Organization (FAO) in 2011 had not only revealed a decline in the growth of agricultural production over the

last few years from 9.8 % in 2006 to 2.4 % in 2010, but the contributions from both the manufacturing and

agriculture sectors to GDP had also tumbled since the lifting of the international sanctions in 2003. The

declining agriculture output in the country’s GDP (table 1) was also accompanied by the diminishing

employment in the sector. From the 1960’s up to 2000, the aggregate sector (agriculture, fishing, and forestry)

had been the leading employer sector with approximately 239,000 employees making about 17 % of the

country’s total labor force. Although Libya’s overall trade balance had been positive because of its large

exports of oil, the agricultural trade balance, however, had been demonstrating very unfavourable trade

balances as a result of increasing domestic demand for foods. For this reason, Libya had to import most of its

food commodities to satisfy its domestic demand since they represent a substantial fraction of the country’s

food needs, while the value of agri-food product exports had only accounted for less than 0.6 % of all its total

1 World Bank (WB) in its report on Libya’s economy 2006 acknowledged that, in 2005, the Libyan government has eliminated duties on commodities and goods of approximately 3,500 products, and roughly 80 remaining goods and services were subject to duties between 5 and 50 percent.

4

exports from 1998 to 2001 (Abidar & Laytimi, 2005). Based on the data provided, the exports of agricultural

goods (Appendix, table A) had been insignificant if compared to the agricultural imports that made up of more

than 93 % of its agricultural trade (Appendix, table A). The trade imbalance of agricultural output had been

increasing over time largely because of the low increase in productivity, where 98.5 % of the trade was shown

to be from imports, while the rest were from the export trade. Although the ratios of agricultural imports and

agricultural exports to total agricultural trade had shown approximately the same values for each year of the

respective period, there is, however, a huge disparity shown between agriculture imports and agriculture

exports.

1981-85 1986-90 1991-95 96-2000 2001-05 06-2010 11-2013

GDP 31,379 26,133 32,579 33,297 39,884 72,570 69,193

Agri-GDP 906 1,735 2,470 2,678 1,271 1,701 730

Contribution% 2.9 6.6 7.6 8.0 3.2 2.3 1.05Table 1: GDP and agri-GDP, the value in million USD for selected periods.Using: UNCTAD (2017).

3. A Brief Literature Review

While there had been no evidence shown in the past literature on the effects of trade on productivity growth,

the recent years had seen the emergence of theoretical evidence (Melitz, 2003; Melitz and Ottaviano, 2008)

showing a positive relationship between trade liberalization and industry productivity growth. However, in a

study conducted by Topalova and Khandelwal, (2011), they stated that not all theoretical models of trade could

predict the aggregate increase of productivity. Young (1991), for instance, had argued that opening up to trade

may force the country into particular sectors that are not conducive to economic growth. The conventional issue

that the trade would lead to productivity gains has found support from earlier studies such as Edwards (1998),

who had used different indices of trade policy2 to investigate if the evidence had supported the view. From his

analysis, he had found more open countries to experience faster productivity growth as the regressions were

robust to the use of openness indicator, estimation technique, time period and functional form. However,

Edwards had only concentrated on the six measures of trade policy-inducted distortions, among which are the

Sachs and Warner index and black market premium that measures import distortions. In examining the extent to

which less developed countries that hardly invest in research and development but benefit from the

2 The following indicators used Sachs and Warner Openness Index, World Development Report Orientation, Leaner’s Openness Index, Average Black Market, Average Import Tariff on Manufacturing, Average Coverage of Non-Tariff Barriers, Heritage Foundation Index of Distortions in International Trade, and Collected Trade Taxes Ratio and Wolf’s Index of Import Distortions.

5

technologies performed in industrial countries, Coe, Helpman, and Hoffmaister (1997) had found that by

trading with an industrial country that has large ‘stocks of knowledge’ from its cumulative R&D activities, a

developing country can boost its productivity by importing a larger variety of intermediate products and capital

equipment embodying foreign knowledge. A positive and significant association between the increase of import

competition and productivity growth had also been found in seven Latin American countries during 1970–99.

This indicates that the increased import competition of services and commodities had forced surviving firms in

these countries to be more competitive (Paus, Reinhardt, and Robinson, 2003). In another study that was

conducted on trade orientation and the economic growth of 97 countries in 1979-89, Sachs and Warner (1995)

had found the countries, where their economies were considered open by using a dummy variable technique,

grew by an average of 2.44 percentage points more rapidly than closed countries. Other researchers (Rodriguez

and Rodrik 2001), however, had argued that this conclusion would not have an impact on the tariffs and non-

tariff barriers on a trade that distinguish the two kinds of trade regimes, but a combination of the state

monopoly of exports and the black market premium exchange rate. While examining the impact of trade

liberalization on 22 developing economies that have applied trade liberalization policies since the mid-1970s,

Santos‐Paulino & Thirlwall (2004) had found liberalization policies not only spurred export increase, but had

also raised the total import surge even more, leading to a deterioration of both trade balance and balance of

payments. In the World Bank study that focused on growth before and after trade liberalization, Salinas and

Aksoy (2006) had included samples of developing countries that have gone through the transition from

socialism to the market economy as well as countries that had conflicts and other non-economic upheavals. The

study had also discovered trade liberalization to be the reason for a significant increase in GDP per capita

growth, where the average growth increase had varied between 1.2 and 2.6 percent.

According to Rodriguez & Rodrik (2001), most of the existing cross-country growth studies had not taken

endogeneity into consideration. This indicates that the studies could not identify the direction of causal

relationship between trade and growth rate since patterns of international trade can also be influenced by

country size irrespective of its trade policy adopted. To address the endogeneity problem, Frankel and Romer

(1999) had constructed measures of the geographic component of countries’ trade and used those measures to

obtain instrumental variables estimates of the effect of trade on income. From their results, they had found

some countries having more trades as a result of being near to well-populated countries, while some had less

trades due to their distance from most of the other countries. However, their results had provided no evidence

6

that the ordinary least-squares estimates had overstated the effects of trade. Furthermore, they also suggested

that trade has a quantitatively large and robust, though only moderately statistically significant, positive effect

on income. Rodriguez and Rodrik (2001), however, had argued that the trade estimate was just simply

capturing non-trade effects since the geography-based instrument that had been used in these studies was likely

to be correlated with other geographic variables that affect income through non-trade channels. Bassanini &

Scarpetta (2002) had also addressed the problem of endogeneity by demonstrating that small countries with a

low GDP were more exposed to international trade irrespective of the competitive advantage or policy of

foreign trade, while the competitive pressure in large economies to a large extent had stemmed from domestic

competitors.

Based on the effect of trade liberalization on agriculture production in MENA (Middle East and North

Africa) studied by Minot, Chemingui and Thomas (2007) on the 13 MENA countries, they had discovered the

benefits of trade liberalization to a given country to be largely dependent on the degree of liberalization carried

out by the country. Most of the gains that were obtained from agricultural trade liberalization were found to be

associated with the domestic reforms implemented by the governments rather than the changes of trade policies

that were carried out in other countries. Furthermore, the gains from multilateral trade liberalization were found

to be greater than the benefits from bilateral trade agreements with Europe, the United States or the region’s

own trade agreements. Another case in point is the study conducted by Hassine and Ayed (2007), where they

had examined the association between trade openness and productivity growth in the Mediterranean countries’

agricultural sector, in particular on the effect of exports quality on productivity at the aggregated farming

sector. The impact of quality on productivity was evaluated through the estimation of a dynamic production

function by the GMM estimator over five groups of agricultural products on a panel of nine South

Mediterranean Countries and five European Union Countries for the period 1990 to 2005, where they had found

trade openness to enhance agricultural productivity growth. The results seemed to imply that product quality

had contributed to agricultural productivity.

4. Data Source and Measurement

Due to the limitation of data, this study had used the annual data from 1980 to 2013 since the use of

economic time series data is seen to be the best way of dealing with temporal effects. This study had also

7

mainly focused on crops and animal production rather than the entire agricultural sector3, where the major

source of data used (except fertilizer consumption) were gathered from The Food and Agriculture Organization

Corporate Statistical Database (FAOSTAT 2017). The dependent variable ( y t ¿ is agricultural production4 (

APt), a proxy for agricultural growth ( AGt ) that is measured in values expressed in million dollars (constant

2004-2006), while the set of explanatory variables (x¿¿ had comprised agricultural labor ( ALt ) measured as

the ratio of labor number to the agricultural area per 1000 hectare, agricultural capital ( AK t) measured as the

number of machinery tractors used in the agriculture sector, fertilizer consumption (FCt ) measured as

kilograms of mineral fertilizers used per hectare of arable land and the data obtained from African

Development Bank Group (ADBG 2017) as well as the area under cultivation ( AUC t) calculated as a

percentage to land area. This paper had also utilized two indicators of agricultural trade openness, which are the

agricultural export and imports ( AX t, AM t ) measured as a percentage of agri-production ( AX t/ APt ,

AM t / APt ¿. This research had treated the variable intensity of agricultural exports and imports in the

agricultural growth equation to be naturally more exposed to foreign agricultural trade since Libya had relied

heavily on its agricultural trade to satisfy its local demand with a relatively small Agri-GDP. All of the

variables were performed using natural log transformations as a way of reducing the occurrence of

heteroskedasticity in the time series data (Gujarati, 2004). Based on economic theory and a majority of the

previous studies, the coefficients signs of the a priori expectations in explanatory variables (x¿¿ were found to

be positive (β¿ > 0).

5. Theoretical framework

Trade liberalization has been identified in the empirical growth literature as a factor of growth rate, while

both neoclassical and new growth theories had perceived trade liberalization to be an incentive of economic

growth that boosts efficient allocation of resources through comparative advantage and specialization.

Significant growth rates had been frequently linked with countries that implement policies of liberalization in

3 FAO had defined the agricultural sector as the sum-total of crop production, livestock, forestry and fishing.

4 Agricultural production refers to the actual harvested production from the field, where production includes the quantities of the commodity sold in the market and the quantities consumed or used by the producers

8

their economies and growing openness to the global exchange of commodities and services as well as to new

ideas and technologies. In order to formally examine the impact of agricultural trade openness indices on

agricultural growth, this study had used a standard Cobb–Douglas production function to gauge the

technological relationship between physical capital and labor and the amount of output that can be produced by

these two inputs. The production function of a given period is therefore shown below:

APt ¿ At .( K tβ 1, Lt

β 2) 0 ¿ β ¿1 (1)

Where ( APt ¿ is a function of labor input correlated with the human body (Lt) and physical capital stock (

K t ), the coefficient At represents the level of technology, while β1 and β2 refer to the partial elasticities of

respective variables5. This study had disaggregated capital input into the two major components of physical

capital stock in agriculture sector, which are the number of agricultural machinery tractors used and fertilizer

consumption as a way of identifying their individual impact on agricultural production. Therefore, this

production function is denoted as follows:

APt=A t .¿¿) (2)

Where ( APt ¿ is the agricultural production in time t, while (K t ¿ , ¿) and ( ALt ¿ refer to the

corresponding tractors, fertilizer consumption and the labor used in the agricultural sector for the period t. We

have extended the Cobb–Douglas production function by assuming that technology can be determined by area

under cultivation ( AUC t) and indicators of agricultural trade openness ( AX t, AM t ). While a larger size of

cultivation area would mean more harvest from farm production, the indicators of agricultural trade openness

are seen as contributors to agricultural growth. There were several endogenous growth models that had

categorized a number of channels in which trade liberalization can have a positive effect on productivity

growth. To begin with, apart from the increasing competition of goods market that forces domestic producers to

improve efficiency (Schmidt, 1997; Aghion et al, 2005; Melitz, 2003), the imported intermediate goods would

also enable domestic producers to adopt better productivity and technological methods in their production

processes (Grossman & Helpman, 1991). The increased access to newer and larger varieties of intermediate

inputs had also led to gains in levels of productivity (Romer, 1990; Feenstra, 2004), while (Baldwin & Gu,

2004) had found the participation of domestic firms in the export market to generate productivity gains through

5 The explanatory variables considered in this study had been based on the variables most commonly used in the theoretical and empirical literatures.

9

learning by doing effects. The incorporation of agricultural exports and imports into the Cobb–Douglas

production function was found to be consistent with the studies conducted by Faridi, (2012) Gilbert et al.,

(2013) and Bakari & Mabrouki (2017). Hence, the previous equation had been altered to become:

At= α . AUC tβ AX t

γ AM tδ (3)

where α is time-invariant constant,( AUC t) is the area under cultivation and ( AX¿¿ t)¿ and

( AM¿¿ t )¿ are indices of agricultural trade openness. The following equation is therefore obtained by

substituting Eq. (3) from Eq. (2):

APt=¿ ƒ (α . K tβ1 , FC t

β 2, ALtβ3 , AUC t

β4 , AX tβ5 , AM t

β 6¿ (4)

In following the methods used by Sachs and Warner (1995) and Wacziarg and Welch (2003), the study had

included a trade liberalization index in the final model as a dummy variable (LIBt ) to capture the structural

change of agricultural growth as a result of trade liberalization policies6. Thus, the dummy variable takes the

values zero for pro-liberalization (1980-1998) and one for past-liberalization (1999-2013). By using the

logarithm method, Eq. (4) can be modeled as follows:

Ln APt = α 0+ β1ln A K t+β2ln FCt +β3ln ALt+ β4ln AUC t+ β5ln AX t

+β6ln AM t+β7 LIB t +μt (5)

where the ln APt is the natural log of agricultural growth, ln AK t is the natural log of tractors used, FCt is

the natural log of fertilizer consumption, ln ALt is the natural log of labor in the agriculture sector, ln AUC t

is the natural log of area under cultivation, ln lag AX t is the natural log of agricultural export, ln AM t is the

natural log of the agricultural imports, LIBt is liberalization dummy and μt is random variable which is

assumed to be ε t ∼N ¿)

6 The elasticities measure the responsiveness to a change of levels in the explanatory variables utilized in the production process (Bao Hong, 2008).

The sum of these elasticities provides information on the returns to scale. The three measures involved are constant, increasing and decreasing

returns to scale. Constant returns to scale occur when the proportional change in the inputs and outputs is equivalent, which is represented as β1 +

β2 = 1. On the other hand, the increasing and decreasing returns to scale occur when the proportional change in input is more and less than the

proportional change in output, which are represented as β1 + β2 > 1 and β1 + β2< 1 respectively.

10

As a way of empirically analyzing the long-run relationships between the various variables, this study had

utilised the autoregressive distributed lag co-integration (ARDL) procedure that was developed by Pesaran et

al., (2001). This procedure has several statistical advantages over alternative procedures such as the Engle &

Granger (1987) two-step residual-based procedure for testing the null of no co-integration and the system-based

reduced rank regression approach pioneered by Johansen (1988, 1995) and Johansen & Juselius (1990)

(Narayan & Smyth, 2005). First and foremost, it can be utilized irrespective of whether regressors are of I(0) or

I(1) or mutually integrated and had been shown from the Monte Carlo examination to be superior and provided

consistent results for a small sample (Pesaran and Shin, 1999). This method not only enables researchers to

derive a dynamic unrestricted error correction model (UECM) through a simple linear transformation, it has

also made it possible for different variables of models to have different optimal numbers of lags, which is not

permitted in Johansen’s models (Pahlavani et al., 2005)7. According to Pesaran et al. (2001) Pesaran (2010)

the augmented ARDL ( p , q1 , q2 , ….. qk¿can represented by the following equation:

Ω ( L , p ) y t=α 0∑i=1

k

β i ( L ,q i ) x¿+δ ' w t+εt (6)

Where Ω ( L , p )=1−Ω1 L−Ω2 L2−……….−ΩP LP and

β i ( L , q i)=β i 0+ βi 1 L+ βi 2 L2+¿ ……+β iqi Lqi , i = 1, 2… k

Where, y t represents the dependent variable, x¿ are the explanatory variables, L represents a lag operator,

and W t is the S×1 vector of deterministic variables. The ECM representation of the ARDL

( p̂ , q̂1 , q̂2 ,… .., q̂k) model can be obtained by rewriting (6) in terms of the lagged levels and first

differences of y t , X1 t ,X2 t ,X kt and w t .first note that;

y t= ∆ y t+ y t−1, y t−s = y t−1−∑j−1

s−1

∆ y t − j, s = 1,2,….p (7)

and similarly

w t=∆ w t+w t−1

7 This study had incorporated liberalization dummy (LIB) as a critical factor that contributes to sustained agricultural growth through the increasing

performance of agricultural exports or imports.

11

x¿=∆ x¿+x i ,t−1 , x i ,t−s = x i ,t−1−∑j−1

s−1

∆ y t− j , s = 1,2,….q i (8)

Substituting the relation in Eq (7) and Eq (8) into Eq (6) and after some rearrangements, we have;

∆ y t=∆α 0−¿Ω(1, p̂)ECM t−1+∑i=1

k

β i x¿−∑j−1

p̂−1

Ω¿ y t− j−∑j−1

K

∑i=1

q̂i−1

β¿ij ∆ xi , t−1+δ ˊ ∆ w t+ ε t

(9)

Where ECM t−1 denotes to the correction term determines as follows;

ECM t= y t−α̂−∑i=1

k

β i x¿−δ ˊ ∆ w t (10)

Therefore, based on Eq (5), it is possible to rewrite the ARDL bounds testing approach with dummy variables

as follows:

∆ InAPt =α 0+∑i=1

ρ

α1 i ∆ InAPt−i+∑i=1

q1

α2 i ∆ InK t−i+∑i=1

q2

α3 i ∆ InFCt−i+

∑i=1

q4

α4 i∆ InALt−i+∑i=1

q 4

α5 i ∆ InAUC t−i+∑i=1

q5

α6 i ∆ InAX t−i+ ∑i=1

q6

α7 i ∆ InAM t−i+

α 8 i InAPt−1 + α 9 i InK t−1

+ α 10i InFC t−1 + α 11i InALt−1 +α12 i InAUC t−1 + α 13i InAX t−1 +

α 14 i InAM t−1

+ α 15i LIBt−1+μt (11)

Where ∆ is a first difference operator,¿ q6¿are the optimal lag length. The conditional ARDL long-run model

is estimated as follows:

InAPt= α 0+¿ ∑i=1

ρ

α1 InAP t−1 +∑i=1

q1

α2 InK t−1 + ∑i=1

q2

α3 InFCt−1 +∑i=1

q3

α4 InALt−1

12

+ ∑i=1

q4

α5 InAUC t−1+ ∑i=1

q5

α6 InAX t−1∑i=1

q6

α 7 InAM t−1+ α 8 LIB t+μt

(12)

Where:

α i=α̂i(1,+ q̂i)Ω(1 ,+ p̂)

=α̂i 0+α̂ i 1……+ α̂i q̂

1−Ω̂1−Ω̂2−… Ω̂ p̂

, i = 1,2,….k

(13 )

The short-run dynamics can be derived by constructing an ECM from Eq. (9):

∆ InAPt =α 0+∑i=1

ρ

α1 i ∆ InAPt−i +∑i=1

q1

α2 i ∆ InK t−i+∑i=1

q2

α3 i ∆ InFCt −i+

∑i=1

q3

α4 i∆ InALt−i+ ∑i=1

q4

α5 i ∆ InAUC t−i+ ∑i=1

q5

α6 i ∆ InAX t −i +∑i=1

q6

α7 i ∆ InAM t−i+

ECM t−1 +α 8 LIB t−1+υ1 (14)

Once, cointegration is found between the series, the next step is to test the direction of causality between the

variables8. The VECM model containing exogenous variables was earlier utilized by Ramey (1993) for

examining the effect of seasonality and monetary policy disturbance on the money market. In recent years,

studies such as (Dutta and Ahmed, 2004) and (Siddiqi and Chani 2014) have been employed the dummy

variable of trade liberalization as an exogenous variable. The VECM Granger causality based on the VAR

model with a dummy variable as follows:

∆ X t=α0 + Π X t + ∑i=1

k −1

Γ i ∆ X t + ∑j=1

n

ϑ N D j ,t + ε t (15)

Where X t= (1, ∆ X t−1,……..,∆ X t− p+1),Γ= = -(I-θ1-……θi-), (i= 1, ….,p-1) and Π are the 2 × 2

matrices of parameters D j ,t is a dummy variable j at time t, (k=1,2), n represents the dummy variables such

8 While the ARDL approach had been regarded as the most efficient cointegration vector, it, however has two limitations, where i) it fails to provide

robust results in the presence of I(2) series (Pesaran, Shin 2001) and ii) it is founded on the assumption for the presence of a long-run relationship among the variables (Dergiades, Tsoulfidis 2008)

13

as seasonal variation and structural break, ϑ N are n ×k vectors. In this study, only trade liberalization policies

have been considered, therefore n=1. Based on Eq (15).

The VECM Granger causality of variables can also be rewritten as matrices of parameters form included

dummy variable as follows:

(1−L )[ln APt

ln K t

ln FC t

ln ALt

ln AUC t

ln AX t

ln AM t

] = [a1

a2

a3

a4

a5

a6

a7

] + ∑i=1

ρ

(1−L ) [b11 i b12i b13 i b14 i b15i b16 i b17 i

b21 i b22i b23 i b24 i b25i b26 i b27 i

b31 i b32i b33 i b34 i b35i b36 i b37 i

b41 i b42i b43 i b44 i b45i b46 i b47 i

b51 i b52i b53 i b54 i b55i b56 i b57 i

b61 i b62i b63 i b64 i b65i b66 i b67 i

b71 i b72i b73 i b74 i b75i b76 i b77 i

] × [

lnAPt−1

lnK t−1

lnFC t−1

lnALt−1

lnAUC t−1

lnAX t−1

lnAM t−1

] + [

αβγδηθϑ] ECM t−1 + [

μ1 t

μ2 t

μ3 t

μ4 t

μ5 t

μ6 t

μ7 t

] + DLIBt

Where (1−L ) is the difference operator and ECM t−1is lagged residual term generated from long run

relationship and ε 1t , ε2 t , ε3 t , ε4 t , ε 5t , ε6 tand ε 7 tare error terms assumed to be iid ε t ∼N ¿), E (ε t )

= 0 and variance covariance matrix is constant over time, whereasDLIBt is a dummy variable of trade

14

liberalization which is included in the VECM as an exogeneity variable. The tests of examination are whether

or not the null hypothesis of no- Granger causality between the series is significant at a certain probability

against the alternative hypothesis stating the existence of a causal relationship. The rejection of the hypotheses

are constructed on the test of chi-squared χ2 of the Wald criterion. Notably, the rejection of the null hypothesis

suggests that there is confirmation of Granger causality.

6. Empirical results and discussions

6.1. Descriptive Statistic of agricultural trade

Since the freezing of international sanctions on Libyan economy in 1999, the government had been gradually

applying measures to develop the country's economy through policies that advocate economic openness, where

the basic objective of these reforms was to remove or reduce all barriers and restrictions to trade and payments

in the country. For this reason, we have provided descriptive statistics for the pre- and post-liberalization trade

characteristics by dividing the entire period of study into two groups, where the first group had consisted of 19

observations (1980-1998) and the second group with 15 observations (1999-2013). The purpose of the small

number of observations (Table 2) was to have preliminary information on the pattern of agricultural trade

openness in the pre-and post-liberalization eras. However, this method had not incorporated any controlled

variables and therefore, trade liberalization alone cannot be regarded as accountable for the growth of

agricultural exports and imports changes and the agri-trade balance.

From the results shown in Table 2, the total agricultural trade had increased slightly from 148.06 % as the

percentage of agricultural production during the pre-liberalization to 164.6 % during the post-liberalization. The

table had also shown two different trends between the two periods, where the agricultural exports had

demonstrated a declining trend, while the imports had shown an upward trend. Therefore, the trade

liberalization policies had appeared to have a positive impact on agricultural imports and a negative impact on

agricultural exports.

15

Table 2 Descriptive Statistics- Indicators of agricultural trade Openness (percentage of agri-production)Pro –liberalization 1980-1998 Past – liberalization 1999-2013

Agri-total trade

Agri- export

Agri- import

s

Agri-trade balance

Agri-total trade

Agri- export

Agri- imports

Agri-trade balance

Mean 148.06 2.1849 145.88 -143.69 164.65 1.2871 163.36 -162.07

Median 137.95 0.3095 135.57 -135.43 149.86 0.9499 148.91 -147.96

Maximum 239.54 7.6378 239.54 -92.88 374.61 5.4171 373.21 -65.34

Minimum 99.52 0.0000 96.20 -239.53 69.62 0.4063 67.48 -371.82

Std. Dev. 34.22 2.5348 34.74 35.43 86.64 1.2514 86.88 87.14

Skewness 1.08 0.7078 1.13 -1.17 1.14 2.5945 1.13 -1.12

Kurtosis 3.97 2.1996 4.12 4.21 3.59 9.1588 3.56 3.53

Observation

s19 19 19 19 15 15 15 15

Source: Food and Agriculture Organization Corporate Statistical Database (FAOSTAT.2017)

6.2 Graphical Data Plots

To provide a graphical view of the variables used, a further analysis on the figures had led to the inference

that time series data can be non-stationary that are characterized by an upward trend (lnAPt , and lnAK t ) or

downward trend (lnALt), while the rest of the regressors tend to exhibit both upward and downward trends.

Although (lnAPt ¿,(lnALt) and (lnAK t ¿ had not shown any structural breaks, (lnAuct ¿ and (

lnAX t ¿, however had demonstrated a minor upward break in the middle and end of 1980s respectively,

while (lnFC t ¿ had depicted a slight downward break in 2011. Furthermore, the variables of (lnAM t ¿,

and (lnAX t ¿ appeared to have distinct linear upward and downward patterns after the end of the 1990s,

which had corresponded to the application of trade liberalization. In this analysis, the structural break in the

data is captured by the trade liberalization dummy (DLIBt ¿ . Conversely, when all of the regressors are

plotted in first differences (not reported), they will most likely display stationarity, where the variables are

expected to reveal time independent and mean-reverting tendencies inherently observed in stationary time

series (Appendix, Figure A).

6.3 Unit root test for stationarity

16

The initial step had been to test the integration order between the variables to ensure that there is no variable

integrated at I(2) prior to applying the ARDL. As such, Enders (1995) had suggested the Augmented Dickey-

Fuller (ADF) (1981) and the Phillips–Perron (PP) (1988) tests to be the appropriate methods for testing unit

roots. If the two tests reinforce each other, then we can have confidence in the results. The unit root tests were

performed at level and at first difference for both with the intercept and with the intercept and trend term, where

the optimum lag of the model was selected by using the Schwartz Information Criterion (SIC) as suggested by

Pesaran and Shin, (1997). As shown in Tables (3), the results had clearly indicated the first difference

integration (integrated of order one I(1)) of all the time-series variables (except agricultural exports), which

implied that the unit root test had rejected the null hypothesis for the variables in first difference. These results

had therefore provided justification for using the bounds approach (ARDL) in checking for cointegration.

Table 3: Unit Root of Time-Series Variables with only intercept with intercept and trend

Variables

ADF unit root test PP unit root test ADF unit root test PP unit root test

T-statistics P–values T-statistics P–values T-statistics P–values T-statistics P–values

InAPt-1.503 0.519 -1.659 0.441 -1.844 0.660 -1.978 0.5910

∆ InAPt-4.963 0.000* -4.979 0.000* -5.092 0.001* -5.493 0.000*

InAK t-2.310 0.174 -3.352 0.020** -1.661 0.745 -1.688 0.734

∆ InAK t-4.605 0.000* -4.619 0.000* -4.758 0.003* -6.039 0.000*

InFC t-2.158 0.224 -2.506 0.123 -2.810 0.203 -3.015 0.143

∆ InFC t-4.968 0.000* -6.097 0.000* -5.304 0.000* -6.423 0.000*

InALt0.168 0.966 0.115 0.962 -2.068 0.542 -1.363 0.853

∆ InALt-4.170 0.002* -4.033 0.003* -4.107 0.014** -3.963 0.020**

InAUC t-2.532 0.117 -2.580 0.107 -3.059 0.132 -3.428 0.064

∆ InAUCt-4.481 0.001* -4.411 0.001* -5.104 0.001* -4.830 0.002*

InAX t-3.151 0.032** -3.216 0.027** -3.085 0.126 -3.142 0.113

∆ InAX t-7.886 0.000* -7.886 0.000* -7.682 0.000* -7.682 0.000*

InAM t-0.226 0.924 -1.471 0.535 0.157 0.996 -0.788 0.956

∆ InAM t-6.101 0.000* -5.820 0.000* -7.323 0.000* -12.613 0.000*

* and ** indicate statistical significance at 1%, 5%, and levels, respectively

17

6.4 ARDL Cointegration Test Results

As confirmed in the previous section, since all the analyzed time-series variables had been first difference

integration using the ADF and PP unit root tests, therefore, the ARDL bounds test can be used to check on the

long-run relationship. However, the application of the ARDL bounds testing is very sensitive to the lag length

selection that had been included in the model (Shahbaz, 2012). For this reason, we had used the concept

proposed by Bahmani-Oskooee and Gelan (2006) by choosing a maximum of two lags on each first differenced

variable and used the Schwarz’s Bayesian Information Criterion (SBC) to select the optimum lags. While

Lütkepohl (2006) had stated that the dynamic link between the variables can be captured by lag length

selection, Narayan (2005) had suggested that the SIC would be the best for lag selection in ARDL bounds

testing with small number of observations. The initial step of the ARDL model that uses a lag specification as

determined by the lag length criteria was performed by the Ordinary Least Squares (OLS) method as a way of

testing the presence of cointegration between the series (Pesaran, et al 2001). The null hypothesis was stated as

none cointegration existence between the variables (i.e., β1 = β2 = β3 = β4 = β5 = 0) against the

alternative hypothesis of cointegration existence between the series (i.e., β1 ≠ β2 ≠ β3 ≠ β4 ≠ β5 ≠ 0). The

study had used the critical bounds that were generated by Narayan (2004) in the co-integration test as opposed

to those of Pesaran et al. (2001) since the critical bounds that were generated by Pesaran et al. (2001) are not

only suited to be used in large sample sizes, but would also provide a biased decision on the co-integration

results from the downward critical bounds (Narayan and Narayan, 2005 Narayan and Narayan, 2004). In the

bounds testing approach, the null hypothesis will be rejected if the calculated F-statistic is found to have a

greater value than the upper bound of the critical values that was stated by Narayan (2004) and will be accepted

if it had proven otherwise. However, if the calculated Ϝ-statistic value had fallen between the upper and lower

critical values as suggested by Narayan (2004), then the test will be deemed as inconclusive, which would then

require a prior statistical information on the integration order before a decision can be made.

Table 4: ARDL bounds Testing Cointegration Approach AnalysisEstimated models F-statistics Critical values (T=34¿¿

Ϝ AP( APt/ AK t,FCt , ALt, AUC t, AM t , AX t)

6.655* Significant level Lower bounds I(0) Upper bounds I(1)

Ϝ AK( AK t/ APt,FCt ,

ALt , AUC t, AM t , AX t)

1.327 1% level 3.875 5.846

ϜFC(FCt / AK t, APt, ALt 3.634 5% level 2.798 4.258

18

, AUC t, AM t , AX t)

Ϝ AL( ALt / AK t,FCt , APt

, AUC t, AM t , AX t)

1.770 10% level 2.316 3.621

Ϝ AUC( AUC t/ AK t,FCt ,

ALt , APt, AMt , AX t)

2.185

Ϝ AM( AM t / AK t,FCt ,

ALt , AUC t, APt, AX t)

0.937

Ϝ AX( AX t/ AK t,FCt ,

ALt , AUC t, AM t , APt)

1.497

Note: * represent significance at 1%, #: Critical values bonds are collected from Narayan. (2004) and the number of regressors is 7.

The regression analysis had involved both a constant and trend coefficients for the levels as well as for the

first differences of the variables since the dynamic nature of the variables and the time-series do not revolve

around zero. As shown in Table (4), the agri-production model with the computed F-statistic value of Ϝ AP(

APt/ AK t,FCt , ALt , AUC t, AM t , AX t) = 6.655 had exceeded the upper bound critical value of

5.846 at 1 % level of significance. This indicates the existence of a meaningful long-run relationship between

the series when the regressions are normalized on APtvariables. The finding suggests that these series move

together in the long-run and will not move ‘too far away’ from each other independently. The existence of

cointegration had been in line with the findings derived from the studies conducted by Rahimi, & Shahabadi,

(2011), Shahbaz (2012), Umer, (2014), where they had also employed the ARDL method.

The ARDL approach of testing cointegration was complemented with Johansen and Juselius’s multivariate

maximum likelihood approach as a way of providing robust evidence on the results and to diffuse any

uncertainties on the possible existence of multiple cointegration vectors. The Johansen method had involved

two tests statistics to determine the number of cointegrating relations (rank of Π=r), where the first test is a

trace statistic that tests the null hypothesis of no cointegrating vector (r = 0) against a general alternative of one

or more cointegrating vectors (r > 0), while the second test is the test of maximal eigenvalue (ʎ-max) that tests

the null hypothesis of r cointegrating vector(s) against the specific alternative of r + 1 cointegrating vector(s).

As depicted in Table (5), both tests had rejected the null hypothesis of no cointegrated vectors at 5% significant

level since their test statistics had been greater than their corresponding critical values, thus confirming the

existence of the long-run relationship. Since the analysis had shown the presence of at least one long-run

19

cointegrating equation, this has therefore provided justification for the use of VAR Granger Causality in

gauging the short-run relationship.

Table 5. Johansen tests of cointegration.Cointegrating

rank (r)(H 0)

Trace statistics Critical Value (5%)

Cointegrating

rank (r)(H 0)

ʎ-max statistics Critical Value (5%)

r = 0 170.8166** 125.6154 r = 0 57.71343** 46.23142

r = ≤ 1 113.1032** 95.75366 r = ≤ 1 46.44340** 40.07757

r = ≤ 2 66.65982 69.81889 r = ≤ 2 23.51096 33.87687

r = ≤ 3 43.14886 47.85613 r = ≤ 3 22.17035 24.58434

r = ≤ 4 20.97852 29.79707 r = ≤ 4 13.24772 21.13162

r = ≤ 5 7.730794 15.49471 r = ≤ 5 7.546433 14.26460

r = ≤ 6 0.184360 3.841466 r = ≤ 6 0.184360 3.841466

Notes: ** Denotes rejection of the null-hypothesis and critical values at 5% level of significance, respectively. r represents the number of co-integrating vectors for the co-integration test with constant and trend. We stop at the first r where we fail to reject the null hypothesis. The critical values (5%) for the tests are taken from MacKinnon-Haug-Michelis. The optimum length auto-selected using SIC

The empirical results of the long-run relationship as presented in Table (6) had revealed a positive and

significant association between the agricultural export and agri-production at 5 % level of significance. The

results obtained had conformed to a priori anticipations, where theoretically, an increase of export is expected

to be followed by an increase in domestic production. However, a decrease in the agricultural export was

observed following the application of trade liberalization policies (see Table 2). The argument posed here was

that the contribution had not been enough to justify the performance of the agricultural export as the main

engine of agri-production growth, which could be due to the fact that export infrastructures and the sector had

suffered from its lack of human resources. The impact of agricultural imports on agri-production growth, on the

other hand, was shown to be negative and statistically significant, which implied that an increase of 1 % in

agricultural imports would lead to a 9.8 % decline in agri-production growth ceteris paribus. The coefficient

( AM t) was expected to be positive since the penetration of imported goods would increase the competition

of goods market and force domestic producers to improve efficiency as a way of retaining market shares while

forcing the least efficient ones to exit (Schmidt, 1997; Aghion et al, 2005; Melitz, 2003). Although this process

can spur growth production for domestic producers, the empirical results, however, had shown otherwise, in

which an increase in the agricultural imports had occurred following the application of liberalization policies

(see Table 2). This could be due to the vast majority of agricultural imports being consumer products instead of

intermediate goods that would enable domestic producers to adopt better productivity and increase production.

As for the dummy variable that represented the policies of trade liberalization, this had shown a significant

negative impact on agri-production growth. The findings from the previous studies had indicated that when 20

protection is reduced at a moderate rate, the increase in productivity is highest, but when protection is reduced

at an extremely fast rate or when it is not reduced at all, the increase in productivity is low (Urata & Yokota

1994). Table (6) had also shown the agricultural physical capital is proxied by the farm tools of fertilizer

consumption with insignificant positive coefficient and the number of agricultural machinery tractors at 5%

significance level with a negative sign coefficient. These two coefficients had displayed unexpected signs since

they are projected to play a crucial role in agricultural development. However, the result of fertilizer

consumption had been consistent with the findings obtained by (Kawagoe et al., 1985). Apart from the

economic sanctions during the 1980s and 1990s that had led to a decline in Libyan oil revenue and affected

investments in other sectors, the incomplete measurement of physical capital in terms of quality and quantity

had also been one of the reasons for biased productivity estimates (Wiebe et al., 2001). As shown in Table (6),

labor ( ALt ) and area under cultivation ( AUC t), were also found to be the main determinants of agri-

production growth, where they had displayed a positive impact with 1 % of significance level.

Table 6: ARDL results of Long-run impact

Notes: *, ** and *** indicate significance at the 1%, 5% and 10% significance level, respectively

The error correction

coefficient (ECM), which determines the speed of adjustment, had a statistically significant coefficient with a

negative sign (Table 7). This implies that the model is stable, where approximately two-thirds of the

disequilibrium error from the previous period had been modified in that one period. Interestingly, the short-run

dynamics results had provided empirical evidence that the coefficients of labor ( ALt ) and area under

cultivation ( AUC t) to be the most significant determinants of agri-agriculture growth not only in the long-

run, but also in the short-run as well. As part of the agricultural physical capital, the number of agricultural

machinery tractors had a positive short-run coefficient of 0.14, but was proven to be not statistically significant. 21

regressors Coefficient Standard error T-Statistic P-valueConstant -13.858** 5.100 - 2.716 0.014

@trend 0.071* 0.013 5.499 0.000

InAK t- 0.507*** 0.258 - 1.961 0.065

InFC t0.0152 0.018 0.816 0.425

InALt0.917* 0.298 3.068 0.006

InAUC t5.555** 1.962 2.830 0.011

InAX t0.029** 0.015 1.835 0.082

InAM t- 0.098** 0.035 - 2.769 0.012

DLIBt- 0.173* 0.057 -3.021 0.002

However, its one period lagged coefficient had been 0.5, which suggests that a 1 % increase in tractors used

will raise the agricultural production growth by 5.0 % with one lag. The results had also shown the short-run

impact of agricultural exports on the agriculture production to be small and insignificant just like in the long-

run scenario, which supported the fact that agricultural export is a poor enhancer of agricultural production.

Finally, the coefficients of agricultural imports ( AM t ) and dummy liberalization (LIBt ) were found to be

significant at 1 percent, which implied that their impacts would be the same in both short- and long-run periods.

Table 7: ARDL results of the error correction term

Notes: *, ** and *** indicate significance at the 1%, 5% and 10% significance level, respectively. Figures in [ ] are p-values, Lagrange multiplier test for residual serial correlation, Ramsey’s RESET test using the square for functional form, a test of skewness and kurtosis for normality and the regression

of squared residuals on squared fitted values

The stability tests of the long and short-run parameters were confirmed by CUSUM and CUSUMSQ. As

depicted in (Figure 3), the CUSUM and CUSUMSQ statistics had fallen inside the critical bounds at 5 %

significance level. Therefore, the estimated coefficients were found to be stable with no tendencies toward

systematic or stochastic variations. The models had also passed all of the diagnostic tests (table 9) at 5 % level

of significance with high explanatory powers (R2=77.5 percent). Hence, the ARDL models that were adopted

22

regressors Coefficient Standard error T-statistic P-valueConstant -10.137** 4.594 - 2.206 0.039

@trend 0.052* 0.013 3.926 0.001

∆ InAK t0.140 0.227 0.615 0.545

∆ lnAk (1)t0.515** 0.200 2.569 0.018

∆ InFC t0.011 0.013 0.833 0.414

∆ InALt0.670** 0.256 2.612 0.016

∆ InAUC t9.303* 2.108 4.411 0.000

∆ InAX t0.021 0.013 1.608 0.123

∆ InAM t- 0.072* 0.023 - 3.104 0.005

DLIBt- 0.127* 0.034 - 3.641 0.002

ECM t−1-0.731* 0.157 - 4.653 0.000

Diagnostic tests

Test statistics LM Version F Version Serial Correlation CHSQ( 1)= .5500[.458] F(1, 18)= .3148[.582] Functional Form CHSQ( 1)= .0368[.848] F(1, 18)= .0207[.887] Normality CHSQ( 2)= .02807[.869] Not applicable Heteroscedasticity CHSQ( 1)= .9051[.341] F(1, 30)= .8732[.358]

R2=0.7752

AdjustedR2 =0.6333

F-statistic =6.555* DW = 2.2132

in this study were found to be robust, where it had precisely estimated and represented the short and long-run

relationships between the variables (Appendix, Figure B)..

6.5 VECM Granger Causality

Based on the test results of the long-run relationships between lnAPt ,lnALt lnAK t , lnFC t,

ln AUC t, lnAX t and ln AM t in the previous section, the Granger's Causality test (1969) was further

used to examine the relationship between the variables. The Granger causality test was applied using VECM,

where this approach had allowed us to distinguish between short-run and long-run Granger causality. The

VECM Granger causality had used a dummy liberalization as an exogenous variable and the Wald test was

conducted to see if the explanatory variables had been significant. Since our data were from a relatively small

sample size, the selection of the appropriate lag length for VECM was selected based on the Schwarz

information criteria (SIC). Also, since the data had not exhibited any trends in logarithmic values (not reported)

at levels, we had therefore selected a no trend intercept in VECM. In this study, the null hypothesis will not be

accepted if the probability value of χ2 is less than 10 percent. While the short-run causality is determined with

a test on the joint significance of the lagged explanatory variables through the use of an F-test or Wald test, the

long-run causality, on the other hand, is examined by the statistical significance of coefficient of the lagged

error-correction term¿¿).

The results in Table (8) had revealed the null hypothesis of ∆ lnAX t of not being a Granger cause of

∆ lnAP t and was not rejected at the level of significance, while the ∆ lnAP t that does not Granger causes

∆ lnAX t had been rejected at 5 %level of significance. This indicates that there is a unidirectional causality

running from agri-production growth to agricultural exports in the short-run, where the causality runs had

validated the Growth-Led Export hypothesis in the short run. On the other hand, the null hypothesis of

∆ lnAM t for not being a Granger cause of ∆ lnAP t had been rejected at 1 % level of significance but had

been insignificant otherwise. Therefore, a unidirectional causality had been found between agricultural imports

and agri-production growth in the short run. The results had therefore supported the Import-Led Growth

hypothesis in the short run, where it had shown robust evidence that agricultural imports could dramatically

boost the performance of agri-production that is consistent with the neo-growth theory. This could be attributed

to the fact that Libya has limited resources in the agriculture sector and consequently had to import agricultural 23

raw materials for many of its agri-productions. Moreover, the results had shown the rejection of the null

hypothesis for non-causality from ∆ lnALt , ∆ lnFC t , ∆ lnAK t, ∆ lnAUC t, ∆ lnAX t and

∆ lnAM t to ∆ lnAP t at 1 % significance level as well as depicted the long-run unidirectional relationship

flowing from explanatory variables to dependent variable development. For this reason, the joint long and short

run causality outcomes had validated the causal analysis for the long- and short-run periods.

Short-Run F-statistic [Ρ-values]Long-Run[t-statistic] Joint (Short-Run and Long-Run) F-statistic [Ρ-values]

DependentVariable ∆ lnAP t∆ lnALt∆ lnFC∆ lnAK t∆ lnAUC t∆ lnAX t∆ lnAM tECM t−1

ECM t−1

∆ lnAP t

ECM t−1

∆ lnALt

ECM t−1

∆ lnFCECM t−1

∆ lnAK t

ECM t−1

∆ lnAUC t

ECM t−1

∆ lnAX t

ECM t−1

∆ lnAM t

∆ lnAP t-

11.15*[0.00]

6.81**[0.03]

0.869[0.64

7]

15.4*[0.000]

4.096[0.12

9]

25.3*[0.000] -0.542*

[-4.141]-

20.8*[0.000]

20.22*[0.000]

23.42*[0.000]

19.06*[0.000]

19.96*[0.000]

29.35*[0.000]

∆ lnALt2.530

[0.282]- 0.024

[0.987]1.260[0.53

2]

3.093[0.213]

0.325[0.84

9]

2.343[0.309]

0.020[0.147]

3.92[0.269]

- 0.039[0.998]

1.648[0.648]

3.112[0.374]

0.480[0.923]

3.612[0.306]

∆ lnFC t4.73**

*[0.09]

0.580[0.748]

- 2.131[0.34

4]

1.794[0.407]

0.583[0.74

7]

1.484[0.476]

0.809[0.310]

5.891[0.117]

0.590[0.898]

- 2.627[0.452]

2.207[0.530]

1.550[0.670]

2.468[0.481]

∆ lnAK t0.474

[0.788]1.835

[0.399]0.224

[0.893]- 0.230

[0.891]1.639[0.44

0]

0.383[0.825]

- 0.120[-0.568]

2.146[0.542]

3.317[0.345]

0.583[0.900]

- 1.485[0.685]

1.910[0.591]

0.783[0.675]

∆ lnAUC t6.17**[0.045]

5.63***

[0.059]

0.419[0.810]

2.293[0.31

7]

- 0.422[0.80

9]

6.26**[0.043]

-0.039*[-1.875]

6.297[0.098]

6.896***

[0.075]

4.084[0.252]

4.016[0.259]

- 5.044[0.168]

6.3***[0.097]

∆ lnAX t6.91**[0.031]

13.2*[0.001]

7.48**[0.023]

10.0*[0.00

6]

8.44**[0.014]

- 2.069[0.355]

-0.114[-0.051]

7.2***[0.065]

13.299*

[0.004]

7.6***[0.054]

17.35*[0.000]

10.9**[0.012]

- 2.684[0.442]

∆ lnAM t0.008

[0.996]6.23**[0.044]

1.194[0.550]

3.448[0.17

8]

0.306[0.858]

2.920[0.23

2]

- -1.723[-1.549]

4.340[0.227]

8.094**

[0.044]

3.707[0.294]

3.621[0.305]

4.490[0.179]

3.105[0.375]

-

Diagnostic Tests Statistic Prob

Jarque-Bera TestBreusch-Godfrey LM TestHeteroskedasticity Test: ARCHR-Squared 0.83 Adjusted R-Squared 0.63

0.12(2)0.334(2)3.00(2)

0.930.440.06

Table 10: results of VECM Granger Causality including liberalization dummy as an Exogenous VariableNote: *, *** and *** are statistical significance at the 1%, 5%, and 10% levels, respectively and the numbers in brackets indicate the lag length

The other interesting results shown from Table (8) had been the unidirectional causality running from

agricultural labor (∆ lnALt ) to agri-production ( ∆ lnAP t), which imply that the government should

promote more participation in the labor force through greater investments on training programmes. The results

had also revealed the bidirectional causality between fertilizer consumptions (∆ lnFC t) and agri-production

24

(∆ lnAP t), which suggests that fertilizers being a major component of capital input, is the key component for

attaining high and fast agricultural returns in the short- and long run periods. Finally, the bidirectional causality

shown between the area under cultivation (∆ lnAUC t) and agri-production (∆ lnAP t) suggests that land

should be exploited as much as possible to not only promote agricultural exports volume of the country but also

to GDP growth. The results from the Diagnostic tests in (Table 10) had shown that serial correlation, normality,

and Heteroskedasticity tests were accepted for the examination of statistical reliability and validity. At a certain

lag length, the VECM Granger causality had consisted of residuals that are normally distributed, homoscedastic

and free from LM autocorrelation.

6.6 Generalized Impulse Responses Function (GIRF)

Since the VECM approach test had not been able to provide the relative strength of the causal relationship

beyond the selected time period, the researchers had to utilize the so-called impulse response functions to

analyse the causal links between the economic variables within the VECM framework. Hence, by focusing on

the variables of interest, namely the agricultural exports, agricultural imports and agri-production growth, this

study had implemented the Generalized Impulse Responses Function (GIRFs) method developed by Koop et

al., (1996) and Pesaran and Shin (1998) to examine the dynamic interaction between variables in the system.

According to Lutkepohl and Reimers (1992), the impulse response functions (IRF) can also be employed to

summarize the relationships of variables in a cointegrated system. The GIRFs are the invariant to reorder of the

variables in the VAR, which are used to provide a further understanding on how shocks to agricultural imports

and exports affect agricultural growth rate and vice versa under the dummy of trade liberalization policies and

as an exogenous variable at VECM level. A simulation was run in the Generalized Impulse Responses Function

(GIRFs) to reflect the 15 periods of a typical business cycle and to ensure sufficient time for tracing the effects

of the shocks to variables in the system. As shown in Figure (2), the agri-production to agricultural export was

found to be at an initial constant rate during the first period before accelerating to the equilibrium line for the

rest of the period. A similar pattern was also seen in the response of agricultural export to a positive one-time

shock to agri-production. As expected from the bi-directional causality, the Export-Led Growth and the

Growth-Led Export hypotheses for the long-run were supported in the case of Libyan agricultural production

under the trade liberalization policies. The third and fourth panels of the figure, on the other hand, had indicated

the response of agricultural imports to a positive one-time shock to agri-production and vice versa. The third 25

graph, which had shown no significant effect, had revealed a negative influence of agricultural imports on agri-

production over time that is consistent with the result of the negative impact of agricultural imports on agri-

production in the long-run obtained from the ARDL method. As for the last panel in Figure (2), the agricultural

imports had experienced a positive response of up to three years, then slowed down to five years before

demonstrating a positive response for the rest of the periods. The relatively larger and positive response of the

agricultural imports had implied that under the trade liberalization policies, the Growth-Led Import hypothesis

proposition had been supported in the long-run.

Figure 2. Responses of agricultural variables to one-standard-deviation shock including liberalization dummy as an Exogenous Variable at VECM level

Note. The horizontal axis is time (years), and the vertical axis is the magnitude of the response to the impulse (%).

7. Conclusion and policy implications

This study had provided an empirical impact of agricultural exports and imports on agricultural growth

under the trade liberalization policies. The preliminary analysis conducted on the time-series variables was

found to be initially non-stationary in levels and stationary at first difference integration. By using the

autoregressive distributed lag cointegration (ARDL) procedure, the researcher had found the existence of a

long-run relationship between the series. The ARDL method had yielded more economically plausible results,

which supported the fact that the agricultural exports and imports had a modest positive and a negative

correlation to agricultural growth respectively, thus implying that the agricultural production is driven by other

explanatory variables rather than the indicators of agricultural trade openness. Moreover, agricultural labor (

ALt ) and area under cultivation ( AUC t) were found to be the most significant determinants of agriculture

26

-.04

-.02

.00

.02

.04

2 4 6 8 10 12 14

Response of LAP to LAX

-.8

-.4

.0

.4

.8

2 4 6 8 10 12 14

Response of LAX to LAP

-.04

-.02

.00

.02

.04

2 4 6 8 10 12 14

Response of LAP to LAM

-.2

-.1

.0

.1

.2

2 4 6 8 10 12 14

Response of LAM to LAP

production in Libya not only in the long-run but also in the short-run, hence providing a robust evidence that

the agricultural sector in Libya can be classified as labor-intensive rather than capital-intensive farming. This

paper had also utilised the Vector Error Correction Model (VECM) Granger Causality and Generalized Impulse

Responses Function (GIRF) frameworks. While the VECM had revealed a unidirectional causality running

from agri-production growth to agricultural exports and a unidirectional causality between agricultural imports

and agri-production growth in the short run, the GIRF framework, on the other hand, had indicated that under

the policies of trade liberalization, the hypothesis of the Import -Led Growth proposition had not been

supported in the long-run. The results from the GIRF framework had shown agricultural exports playing a

pivotal role in boosting agricultural output in Libya, particularly beyond the selected period. As such, the

government should formulate strategies to revive and rebuild the agricultural sector through affirmative policies

of liberalization. This can be attained by providing complementary policies such as the establishment of

infrastructural facilities and modern farming tools. The Granger Causality of VECM and GIRF frameworks had

also indicated rising agricultural exports to aid in the increase of agri-production, which is caused by the

increase of agricultural imports. Therefore, a surge in the openness of the economy through the application of

trade liberalization policies would not only lead to increased investments in the agricultural sector, but will also

eliminate inefficiencies in this sector and boost GDP growth through the influx of technology.

27

References

Abbas, S. (2014). Trade liberalization and its economic impact on developing and least developed countries. Journal of International Trade Law and Policy, 13(3), 215-221.

Abidar, A., & Laytimi, A. (2005). National Agriculture Policy-Libya. Medfrol Project, National School of Agriculture, Meknès, Morocco.

Aghion, P., Bloom, N., Blundell, R., Griffith, R., & Howitt, P. (2005). Competition and innovation: An inverted-U relationship. The Quarterly Journal of Economics, 120(2), 701-728.

Alam F and Myovella G (2016). Causality between Agricultural Exports and GDP and its Implications for Tanzanian Economy. Journal of Economics, Finance and Accounting. Volume 3, Issue:1, 1-18.

Ardeni P G and Freebairn J (2002). The Macroeconomics of Agriculture. In: Handbook of Agricultural Economics, edition 1, volume 2, chapter 28, pages 1455-1485 Elsevier.

Bahmani-Oskooee, M., & Gelan, A. (2006). Black market exchange rate and the productivity bias hypothesis. Economics Letters, 91(2), 243-249.

Bakari, S., & Mabrouki, M. (2017). The Effect of Agricultural Exports on Economic Growth in South-Eastern Europe: An Empirical Investigation Using Panel Data.

Bakari, S., & Mabrouki, M. (2018). The Impact of Agricultural Trade on Economic Growth in North Africa: Econometric Analysis by Static Gravity Model.

Baldwin, J. R., & Gu, W. (2004). Trade liberalization: Export-market participation, productivity growth, and innovation. Oxford Review of Economic Policy, 20(3), 372-392.

Bao Hong, T. (2008). Cobb-Douglas Production Function. Unpublished Paper. Bassanini, A., & Scarpetta, S. (2002). The driving forces of economic growth. OECD Economic studies,

2001(2), 9-56Ben-David, Dan (1993), "Equalizing Exchange: Trade Liberalization and Income Convergence, “Quarterly

Journal of Economics, 108, 653-79.Coe, D. T., Helpman, E and . Hoffmaister,A. W(1997) “North–South R&D Spillovers,” Economic Journal

107 : pp134–49.Dergiades, T., & Tsoulfidis, L. (2008). Estimating residential demand for electricity in the United States, 1965–

2006. Energy Economics, 30(5), 2722-2730.Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root.

Econometrica: Journal of the Econometric Society, 1057-1072. Dutta*, D., & Ahmed, N. (2004). An aggregate import demand function for India: a cointegration analysis.

Applied Economics Letters, 11(10), 607-613.Edwards, S. (1992). Trade orientation, distortions and growth in developing countries. Journal of development

Economics, 39(1), 31-57. Edwards, S. (1993). Openness, trade liberalization, and growth in developing countries. Journal of economic

Literature, 31(3), 1358-1393. Edwards, S. (1998). Openness, productivity and growth: what do we really know? The Economic Journal,

108(447), 383-398. Enders, W. 1995. Applied econometric time series. New York: Wiley.Ekanayake, E.M. (1999). Exports and Economic Growth in Asian Developing Countries: Cointegration and

Error Correction Models. Journal of Economic Development, 24(2), 43-56.FAO, (2011). FAO Achievement in Libya. Food and Agriculture Organization (4-35).Faridi, M. Z. (2012). Contribution of Agricultural Exports to Economic Growth in Pakistan. Pakistan Journal of

Commerce & Social Sciences, 6(1).Feenstra, R., & Looi Kee, H. (2004). On the measurement of product variety in trade. American Economic

Review, 94(2), 145-149. Forgha N.G and Aquilas N.A (2015) "The Impact of Timber Exports on Economic Growth in Cameroon: An

Econometric Investigation", Asian Journal of Economic Modelling, Vol. 3, No. 3, pp. 46-60.Frankel, J. A., & Romer, D. H. (1999). Does trade cause growth? American Economic Review, 89(3), 379-399.

28

Gilbert, N. A., Linyong, S. G., & Divine, G. M. (2013). Impact of agricultural export on economic growth in Cameroon: Case of banana, coffee and cocoa. International Journal of Business and Management Review, 1(1), 44-71.

Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438.

Grossman, G., & Helpman, E. (1991). Innovation and growth in the global economyMIT press. Cambridge, MA.

Gujarati, D. N. (2004). Basic econometrics: Tata McGraw-Hill Education.Hassine-Belghith, N. B., & Ayed-Mouelhi, R. B. (2007). Productivity and product quality: Evidence from

agricultural products in South Mediterranean Countries. New Medit, 6(1), 12. IMF International Monetary Fund report. (2005). Socialist People's Libyan Arab Jamahiriya 2004 Article IV

Consultation. Staff Report; Staff Statement; and Public Information Notice on the Executive Board Discussion: Internat. Monetary Fund.

Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of economic dynamics and control, 12(2-3), 231-254.

Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: Journal of the Econometric Society, 1551-1580.

Johansen, S., & Juselius, K. (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.

Johansen, S., & Juselius, K. (1994). Identification of the long-run and the short-run structure an application to the ISLM model. Journal of econometrics, 63(1), 7-36.

Kalaitzi A.S and Cleeve E (2017). Export-led growth in the UAE: multivariate causality between primary exports, manufactured exports and economic growth. Eurasian Bus Rev. DOI 10.1007/s40821-017-0089-1

Karp L S and Perloff J M (2002). A Synthesis of Agricultural Trade Economics. In: Handbook of Agricultural Economics, edition 1, volume 2, chapter 37, pages 1945-1998 Elsevier.

Kawagoe, T., Hayami, Y., & Ruttan, V. W. (1985). The intercountry agricultural production function and productivity differences among countries. Journal of development Economics, 19(1-2), 113-132.

Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of econometrics, 74(1), 119-147.

Lopez R (2002). The Economics of Agriculture in Developing Countries: The Role of the Environment. In: Handbook of Agricultural Economics, edition 1, volume 2, chapter 22, pages 1213-1247 Elsevier.

Lütkepohl, H., & Reimers, H.-E. (1992). Impulse response analysis of cointegrated systems. Journal of economic dynamics and control, 16(1), 53-78.

Lütkepohl H (2006). ¨ New Introduction to Multiple Time Series Analysis. Springer-Verlag, New York.Matandare, M.A. (2017). "Agriculture Exports and Economic Growth in Zimbabwe." International Journal of

Social Science and Economic Research, vol. 2, no. 12, 2017, pp. 5503-5513. December.Masoud, N. M. (2009). Libya’s economic reform programme and the case for a stock market. University of

Huddersfield. Melitz, M. J. (2003). The impact of trade on intra‐industry reallocations and aggregate industry productivity.

Econometrica, 71(6), 1695-1725. Melitz, M. J., & Ottaviano, G. I. (2008). Market size, trade, and productivity. The Review of Economic Studies,

75(1), 295-316. Minot, N., Chemingui, M., & Thomas, M. (2007). Impact of trade liberalization on agriculture in the Near East

and North Africa. Moseykin, Y. N., & Levchenko, I. V. (2014). The Impact of Agricultural Import Tariff on Economic Growth:

Evidence From Mercosur Countries. Chinese Business Review, 13(3).Narayan, P.K., Narayan, S., 2004. The J-Curve: evidence from Fiji. International Review of Applied

Economics 18 (3), 369–380.Narayan, P., & Smyth, R. (2005). Trade liberalization and economic growth in Fiji. An empirical assessment

using the ARDL approach. Narayan, P. K., & Narayan, S. (2005). Estimating income and price elasticities of imports for Fiji in a

cointegration framework. Economic Modelling, 22(3), 423-438. Noguer, M., Siscart, M., 2005. Trade raises income: a precise and robust result. J. Int. Econ.65 (2), 447–460.Pahlavani, M., Wilson, E., & Worthington, A. C. (2005). Trade-GDP nexus in Iran: An application of the

autoregressive distributed lag (ARDL) model. Paus, E., Reinhardt, N., & Robinson, M. (2003). Trade liberalization and productivity growth in Latin

American manufacturing, 1970-98. The Journal of Policy Reform, 6(1), 1-15. 29

Pesaran, B., & Pesaran, M. H. (2010). Time series econometrics using Microfit 5.0: A user's manual: Oxford University Press, Inc.

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326.

Pesaran, M. H., & Shin, Y. (1997), January). An autoregressive distributed lag modelling approach to cointegration analysis. England: University of Cambridge. Pesaran, M.

Pesaran, M. H., Y. Shin, (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Economics Letters 58, 17–29.

Pesaran, M.H., Shin, Y., (1999). An autoregressive distributed-led modeling approach to cointegration analysis. In: Strom, Steinar (Ed.), Econometrics and Economic Theory in the 20th Century. The Ragnar Frisch Centennial Symposium. Cambridge University Press, Cambridge.

Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.

Pupongsak, S. (2010). The effect of trade liberalization on taxation and government revenue. University of Birmingham.

Rahimi. and Shahabadi, A. (2011), Trade Liberalization and Economic Growth in Iranian Economy, Bu-Ali Sina University, Hamedan, Iran.

Ramey, V. (1993, December). How important is the credit channel in the transmission of monetary policy?. In Carnegie-Rochester Conference Series on Public Policy (Vol. 39, pp. 1-45). North-Holland

Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society. Series B (Methodological), 350-371.

Rodriguez, F., & Rodrik, D. (2000). Trade policy and economic growth: a skeptic's guide to the cross-national evidence. NBER macroeconomics annual, 15, 261-325.

Romer, P. M. (1990). Endogenous technological change. Journal of political Economy, 98(5, Part 2), S71-S102. Sachs, J. D., & Warner, A. M. (1995). Economic convergence and economic policies: National Bureau of

Economic Research.Sanjuán-López, A.I. and Dawson, P.J. (2010). Agricultural Exports and Economic Growth in Developing

Countries: A panel Co-Integration Approach. Journal of Agricultural Economics, 61(3), 565-583.Salinas, G., & Aksoy, A. (2006). Growth before and after trade liberalization (Vol. 4062): World Bank

Publications.Santos‐Paulino, A., & Thirlwall, A. P. (2004). The impact of trade liberalisation on exports, imports and the

balance of payments of developing countries. The Economic Journal, 114(493).Schmidt, K. M. (1997). Managerial incentives and product market competition. The Review of Economic

Studies, 64(2), 191-213. Shahbaz, M. (2012). Does trade openness affect long run growth? Cointegration, causality and forecast error

variance decomposition tests for Pakistan. Economic Modelling, 29(6), 2325-2339.Siddiqi, M. W., Ali, A., & Chani, M. I. (2014). Import Demand, Economic Development and Trade

Liberalization in Pakistan: An Empirical Analysis. Bulletin of Business and Economics (BBE), 3(2), 131-141.

Topalova, P., & Khandelwal, A. (2011). Trade liberalization and firm productivity: The case of India. Review of economics and statistics, 93(3), 995-1009.

Uğurlu, E. (2010). growth and openness relationship in the eu15: panel data analysis. Ekonomika, 89. Umer, F. (2014). Impact of trade openness on economic growth of Pakistan: An ARDL approach. Journal of

Business & Economic Policy, 1(1), 2014. Urata, S., & Yokota, K. (1994). Trade liberalization and productivity growth in Thailand. The Developing

Economies, 32(4), 444-459. Wacziarg, R., & Welch, K. H. (2003). Trade liberalization and growth: New evidence. The World Bank

Economic Review, 22(2), 187-231. WFP, FAO. (2011). Food Security in Libya – An Overview Regional Bureau for the Middle East,Regional

near East Bureau, TCES Emergency Unit. Wiebe, K. D., Soule, M. J., & Schimmelpfennig, D. E. (2001). Agricultural productivity for sustainable food

security in sub-Saharan Africa. FAO Economic and Social Development Paper, 55-74. World Bank. (2006). Socialist People’s Libyan Arab Jamahiriya Country Economic Report. Document of the

World Bank.World Bank. (2010). “Libya Trade Brief.” World Trade Indicators 2009/10: Country Trade Briefs. Washington,

DC. World Bank. Available at http://www.worldbank.org/wti.Yanikkaya, H. (2003). Trade openness and economic growth: a cross-country empirical investigation. Journal

of development Economics, 72(1), 57-89.30

Young, A. (1991). Learning by doing and the dynamic effects of international trade. The Quarterly Journal of Economics, 106(2), 369-405.

Appendices: Table A: Major commodities of agri-exports, value (000) US Dollar (selected periods)

Source:

FAOSTAT (2017) over 1981-2013

Table B: Major commodities of agri-imports, value (000) USD

1981-85 86-90 91-95 96-2000 01-05 06-2010 2011-13Wheat 243,986 34,580 81,153 319,744 335,351 1,368,350 473,643

Flour wheat 408,863 460,820 709,844 873,369 1,193,595 688,154 81,507Barley 225,541 406,949 401,023 166,320 112,425 224,326 99,291

Maize 121,417 237,381 189,164 222,513 224,931 520,463 156,086

Sugar refined 249,292 275,419 322,612 306,448 264,453 436,989 150,405

Food wastes 550,024 02,761 466,420 244,666 107,621 78,012 14,766

Milk 245,635 122,834 127,824 192,300 248,785 345,364 84,163

Cattle 598,403 287,670 314,584 152,617 6,580 59,399 68,563

Sheep 858,949 238,931 102,706 65,375 6,912 18,941 110,060

Source: FAOSTAT (2017) over 1981-2013

Figure A: Graphic Plots of regressors in Levels

31

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

1980 1985 1990 1995 2000 2005 2010

LAL

2.150

2.155

2.160

2.165

2.170

2.175

2.180

2.185

2.190

1980 1985 1990 1995 2000 2005 2010

LAUC

4.8

4.9

5.0

5.1

5.2

5.3

5.4

5.5

1980 1985 1990 1995 2000 2005 2010

LAK

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1980 1985 1990 1995 2000 2005 2010

LFC

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1980 1985 1990 1995 2000 2005 2010

LAX

4.0

4.4

4.8

5.2

5.6

6.0

1980 1985 1990 1995 2000 2005 2010

LAM

6.4

6.5

6.6

6.7

6.8

6.9

7.0

7.1

1980 1985 1990 1995 2000 2005 2010

LAP

1981-85 86-90 91-95 96-2000 01-05 06-2010 2011-13

Skins sheep - - 34,113 45,080 4,399 2,760 1,917

Oil maize - - 12,500 43,300 2,904 - 11

Groundnuts - 6,078 23,809 34,400 8,535 7 21

onions dry - 1,788 20,296 20,447 18 113 167.3

Hides cattle - 16,424 13,308 6,570 1,345 471 993.3

wool - 11,510 5,370 3,741 5,297 1,767 1,225

Figure B CUSUM and CUSUMSQ Stability CUSUM 5% Significance CUSUM of Squares 5% Significance

Plot of Cumulative Sum of RecursiveResiduals

The straight lines represent critical bounds at 5% significance level

-5

-10

-15

0

5

10

15

1983 1988 1993 1998 2003 2008 20132013

Plot of Cumulative Sum of Squaresof Recursive Residuals

The straight lines represent critical bounds at 5% significance level

-0.5

0.0

0.5

1.0

1.5

1983 1988 1993 1998 2003 2008 20132013