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An Empirical Analysis of Sugarcane Production in Fiji, 1970-2000
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200508666
Ecol1omic Analysis & Poli .... y
Vol J4 No.1. March ::!()04
53
AN EMPIRICAL ANALYSIS OF SUGARCANE PRODUCTION IN FIJI, 1970-2000
Paresh Kumar Narayan Department O/ACCOlflltiflg, Finance lind Economics GrijJilh BIIS;'less School Gr~!Tilh University
Fiji is a small island country ill the South P.tcific. Sugar is its oldest industry. being in the vanguard of cl.:onolllic growth and development for over a century. Today
the oldest industry, not only in Fiji but also in the South Pacific Island counlries, is shrivelling and is poised on the precipice of collapse. The literature to dale has
singled out expiring land leases and the in
I.
INTRODUCTION
Fiji is a small island country in the South Pacific. Sugar is its oldest industry, being in the vanguard ofecollomic growth and development forover a century. Today the oldest industry, not only in Fiji but also in the South Pacific Island countries, is shrivelling and is poised on the precipice of collapse. The literature to date has singled out expiring land leases and the inability of the government to resolve this issue as the key to the current calamity (see, for instance, Reddy and Yanagida 1998; Prasad and Tisdell 1996; La12000; Lal et al. 200 I; Reddy 200 [; Kurer200 l). Despite the plethora of studies on Fiji's sugar industry, there has been no empirical analysis to elucidate the factors that determine sugarcane production in Fiji. The present study aims to remedy this deficiency. The goal of this paper is to delineate both the short-run and long-run relationships between sugarcane production and capital. labour and prices. The paper is organised as follows. In the next section we provide an overview of Fiji's sugar industry. In Section 3 a description of the econometric model and data set is provided. [n section 4 the model is described and tested. while the results
Copyright of Full Text rests with the original copyright owner and, except as permitted under the Copyright Act 1968, copying this copyright material is prohibited without the permission of the owner or its exclusive licensee or agent or by way of a licence from Copyright Agency Limited. For information about such licences contact Copyright Agency Limited on (02) 93947600 (ph) or (02) 93947601 (fax)
54
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Vnl.J4 No.1. Marth 2004
are discussed in section 5. The final section draws some conclusions and indicates pol icy recommendations. 2.
AN OVERVIEW OF SUGAR INDUSTRY IN FIJI
There is a general consensus in the literature on the importance of the sugar industry in leading Fiji's drive for economic success. The industry's exports in 1999 were valued at F$263.2 million - equivalent to around 13 percent of gross domestic product. which in turn comprised 22 percent of total exports. There are four sugar mills in the country, employing around 4,500 workers. In addition. there are 14.300-15.000 cane cutters and abou12.000 lorry operators. In all. about 25 percent of the ecollomically active populution derive their income from the industry, The labour force in the industry is made up ofbolh family (household) and hired labour. In 1975. there were only 16.995 sugarcane farmers. while in 1998 their Ilumbers had increased to 22.146 - u 30 percent increase - with an average farm holding of about 4.6 hectares. Area harvested increased from 44,000 hectares in 1975. reaching its peak in 1993 (75.000 hectares) before falling sharply to settle at 45.000 hectares in 2000. In the corresponding period the average sugarcane production per hectare increased from 49.1 tannes/hectare to 59.2 lonnes/hectare in 1993. and settled at 32 tonnes/hectare in 2000 (Fiji Bureau of Statistics 2(02). The performance of the industry is credited to the preferential agreements that Fiji has received since 1975. Fiji's sugar industry has benefited immensely under the Sugar Protocol of the Lome Convention. Since 1975. a substantial amount of the sugar quota. which currently stands at 197,000 tonnes, has been offered. 'Fiji currently has a quota equivalent to 0.9 percent (14.000 tonnes) of the total United States sugar import quota, ;111d bilateral agreement with Malaysia allows for the export of 90.000 tonnes of sligar per year' (Lal et (II. 200 I. p. 3). The sligar price offered to Fiji under the Lome L:onvention is 2-3 times above the world market price of sligar (Narayan 1999). Around 85 to 90 percent of sugar production is exported, while the rest meets local consumption demand. The United Kingdom and the European Union are Fiji's main sugar market - collectively accounting for around 45% of total sugar exports. followed by Malaysia which buys 24% of Fiji's sugar wilile Japan purchases around 20%. The rest of the sugar is sold to Singapore, South Korea, Canada and the United States.
3. MODEL SPECIFICATION AND DATA In this section, a Cobb-Douglas specification to sugar production in Fiji is adopted. Two proxies forcapital are used: area harvested and fertiliser lIsage. Together with the customary labour force variable, price paid to sugarcane farmers is also incorporated. The long-run model of sugar cane production takes the following form:
InQ, = an + a, In AH, + alln LAH, + a.dnf"ER, + a, InPricq + where an represents a constant;
£,
(I)
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In QI represents the log of sugarcane production (000 tonnes) at time t; In AH, represents the area harvested (000 hectares) at time I: In LABt represents the log of labotll' force at lime t;
In
"~ER(
represents the log of fertiliser usage at time f:
In Prh'e, represents the per tonne price (F$) paid to farmers: and £1
represent'; the error term.
Area harvested. labour force. fertili;
4.
METHODOLOGY: THE ARDL APPROACH TO COINTEGRATION
The concept of cointegration was pioneered by Granger (1981) and extended by Engle and Granger( 1987).lt is based on the premise that. as is common knowledge now ,many economic times series are non-stationary. In spite ofthis.an appropriate linear combination between trending variables could remove the cOlllmon trend component. The resulting linear combinations of the time series variables will thus be stationary, implying that the relevant time series variables are cointegrated. Evidence of cointegratioll rules out the possibility of the estimated relationship being 'spurious'. We apply cointegration tests to ascertain the existence of long-run relationships amongst the variables. In this regard. and given the small sample size of our data series (31 observations), we adopt the autoregressive distributed lag (ARDL) approach to cointegratiol1 pioneered by Pesaran et al. (200 I). There are llumerous advantages of this methodology. First. the need for pre-testing the order of integration. an absolute necessity in other cointegration methodologies. is not requ ired. Put differently. the ex istence of a long-run relationship is independent of whether the explanatory variables are 1(0) or I( I). Second, valid asymptotic inferences on short- and long-run parameters can be deduced under least squares estimates of the ARDL model, that is. ordinary least squares estimates are super consistent, particularly in small samples such as the present one. Essentially, estimating the long-run relationship is a two step procedure. In the first step. the existence of a long-run relationship as predicted by theory between the variables under consideration is examined. In the event that the existence of a Data from Statistics.
1970~1974
arc unpublished and were obt:.lincrJ from thc Fiji Bureau of
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Economic Arwlysis & Polil'y
long-run relatiollship is supported. then the next stage involves estimating the longrun and short-run parameters (see Pesaran (1997) and Pesaran el at. (20U I )). The ARDL approach begins by estimating the following error correction
model: (2)
to investigate the existclH.,:e of a long-run relationship, Pesaran el (II. (200 I) proposed the bounds test based on the Wald or F-statistic. If the F-test is used, it translates into esti mating the significance of a joint zero restriction on the a values
of the error corrcl:tioll model. The distribution of the F-test is non-standard, and the critical values are provided by Pesaral1 and Pesaran (1997) and Pesaran el a/. (200 I). Two sets of critical values exbt: one set is calculated assuming that all variables included in the ARDL model are /(1) and the other is estimated on the assumption that variables are /(0), If the computed F-statisti<.: falls outside the inclusive band. a <.:onclusive inference can be made without having a priori knowledge of tile orderofintegration of the variables. Forexample, if the F-statistic is higher than the upper critical bound, then the null hypothesis of 110 cointegration is rejei.:ted. If this is the <':<-lse, then in the second stage the orders of the lags of the ARDL model are selei.:ted by Akaike or Schwarz information criteria, and the model is the estimated by ordinary least squares.
5. EMI'IRICAL FINDINGS In the first step of estimating equation I, we estimate the following long-run relationship:
~lnQ, ""o"'J + !h,<.!~lnQ,_, + !b,v~lnAHI_i + !d;utilnLABf-I ,.1
P
+
,.1
i.I
P
~ civ 6.ln FER'_i + ~ .t:{} ~ln Price'_i + 'J'IV InQ'_1 + 'h v In AH 1_1 (3)
+ II'\{! In LA8'_1 + If! ~(! In FER I _ l + IJI ~{} In Pr ice l _1
Given the limited number of observations (31) and the use of annual data we take fJ = 2, that is. the number of lags is two. We apply the F-lest. denoted by F ( QIAH. LAB.FER'?ri"e), for ascertaining the existence ofiong-rlln relationships. Q The null hypothesis of the '11011 existence of a long-run relutiollship' can be written as follows: H() :IIIIIJ ,,"II'~IJ ""I/'.lIJ "" I/'~{} "'I/J~IJ ... 0; the alternative hypothesis that there exists a long-run relationship is: HI: lJ111' ~ lJ1~{! ~ lJ1J{! ~ lJ1~(J ~ 4J s(J ~ O. The calcula(ed F-statistic ,FQ("') =5 .8431, is higher than (he upper bound critical values of 4.049 at the 5% level and 5.122 at the I % level. Therefore we reject the null of 'no long-run relationship'. Having found a long-run relationship. we estimate e4uation I using the following ARDL (p,l/.r.s,t) model:
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'I
57
,
In(!, ~",,+ ~/!,ln(!,_, + ~¢,lnA",_, + ~y,lnLABH +
~Il,
,.f>
InFER,_, +
~{i1,
,.11
(4)
InPric£"_i +,11,
In estimating equation 4. again a maximum of two lags is used. that is. i = 2 and the model is selected using the Schwartz Bayesian criterioll. From this. th~ static long-rull model for sugarcanc production is derived ..:! The results arc presentcd in
table I. TABLE I LONG·RUN RESULTS OF THE DETERMINANTS OF SUGARCANE PRODUCTION IN FIJI, 1970·2000 Variable
C(l11stunt InLAH, InLAB t InFERI
InPricc,
Parameter estimate -3.3524 0.9721'" 0.6861" 0.1227'" O.OMW'·
Pnlbabilily OJ206 (l.ooon (l.IOI2 o.(lI05 (l.()OO~
Note: .•. .signilkance at the I % level .• signitkance :II the 5% level
All variables appeared with the expected signs. Areil harvested has the largest effect on sugarcane production. The result implies that if area harvested increases by 1% sugarcane production will increase by 0.97%. This finding has direct implications for the currcntland problems in Fiji. Since 1997 sugarcane land leases have been expiring at a rapid rate. The bulk of the land. whose leases have expired. remains unused and has reverted to bushland. This points to a situation of declining usage of land for sugarcane production. Put di fferently the area harvested has fallen
- from 73,000 hectares in 1997 to 45,000 hectares in 2000. In the corresponding period sugarcane production fell from 3,280 thousand (onnes to 1.900 thousand (onnes (Fiji Bureau of Statistics 200 I). The empirical evidence supports this. It is
Ihus clear that if Fiji's land problem is not solved immediately ,sugarcane pm(\uction will fall and if Fiji is not able to meet its export quota requirements. which is the most likely outcome. then it will lose its lucrative overseas markets. This will have serious repercllssions. not only on farmers. but also on the economy as a whole given the importance of the sugar industry in Fiji's economic development. The labour force estimate indicates that a I % growth in labour force increases sugarcane production by around 0.68%. Fertiliser usage also appeared with a positive sign - a I % increase ill fertiliser usage increases sugarcane production by around 0.1 %. For details regarding the estimatioll of long-run and short-rull results sec Pcsaran and Pesaran (1997) and Pesaran ct al .. 200 I.
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Lastly, prices r~cei vell by sugarcane farmers also exert a positive influence 011 sugan.:anc prouuctioll- a I % increase in price paid to farmers increases production by around 0.06%. Next we look al the short-run results. Again, as in the long run, all results are consistent with theoretical expectations and are statistically significant at the 1% level except of the coefficient on labour lagged one-period which is significant at the 5% level (tahle 2).
TAIlLE 2 ECM RESULTS OF THE DETERMINANTS OF SUGARCANE PRODUCTION IN FIJI Variable
Coefficients
Constant 6.lnQ,,/
-0.3X61"· 0.0654 t.30X6··· -0.1069 1.5614*** 1.3424**
6.lnAH 6InAH,_1
III nLAB( 6.1 nLAlJ,_1 t1lnFER,
O.4SX4*n
6,lnFER,./
0.2701 ~N
6.1 nPRin:'/
0.4151*** -0.8094***
Ee,., Notes:
I-statistics
4.9270 0.4229 7.4469 0.4429 2.6250 2.50X6 4.X74X 3.0020 4.8272 5.311
Probability 0.0001 0.6774 O.lXIOO 0.6631 O.Ot72 0.0219 0.0001 0.0077 0.))(101 0.0000
... signilkam;c at the 1ek level
.. sig.nifi"::llll:C at the 5% lcve
The results indicate the importance of area harvested in determining sugarcane production - a I % increase in area harvested increases sugarcane production by 1.3%. This clearly reneets the negative effects of expiring land leases and the subsequent non-use of land on sugarcane production inlhe short-run. On the other hand. a I% increase in labour force increases sugarcane output by around 1.6% in the current period; the one-period lagged effect is 1.4%. Similarly, a I % increase in the usage of fertiliser in the current period increases output by 0.4%; the one period lagged effect is 0.3%. Lastly, a I % increase in price paid to sugarcane farmers increases sugarcane production by some 0.4%. The error correction term Ee,.I • which measures the speed of adjustment to restore equilibrium in the dynamic model. appeared with a negative sign and was statistically significant at the I% level ensuring that the series is non-explosive and that long-run equilibrium call be attained. The coefficient of -0.8, for instance, implies that a deviation from long-run sugarcane production this period is corrected by ahout 80 percent in the next - an indication that once shocked convergence to equilibrium is rapid. Apart from the high signi ficance levels of variables and the ex istence of a longrun relationship, our model is statistically well behaved. We applied a number of diagnostic It:sts to the error correction model (table 3). There is no evidence of
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59
autocorrelation in the disturbance of the error term. The ARCH tests suggest that the errors are homoskedastic and independent of the regressors. The model passes the Jarque-Bera normality test suggesting that the errors are normally distrihuted. The RESET test indicates that the model is correctly specified. while the F-forecast test indicates the predictive power/accuracy of the model. In additioll, the adjusted R-squared of the model is high implying an excellent fit of the model - 90% of the variations in sugarcane production is explained by the regressors.
TABLE3 GOODNESS OF FIT DIAGNOSTIC TESTS FOR THE ECM REPORTEDINTABLE2 Goodness of Fit and Dingnoslies
R' R' a
xL"
Statistics 0.9374 0.9027 0.0785
(2)
3.2037
X,~"n" (2)
0.8342
X,IRCf/
OJI389
,
XfVIliI'. (20)
Xk,.,,,,
21.2584
(2)
3.2433
Ff"nnHI (6.19)
1.0638
Where ( l is the standard emlrof the regrcs.sion: X!,I,,',,( 2) is the Brcusch-Godfrcy LM test ftlr :luttJ(orrclation: X!Nw,,,(11 is the Jarque-Bera normality tcst: X!/tu·f:/(2l is the Ramsey test for omitted v:lriablcslt"il1l:tionaJ
form: X!IH,,,,.(2()) is the White tc.st for heterosccdmaicity: FI;",....'..1 (6.19) is the Chow prellidive f"illlre test (whcn calculating this tc.st. 1995 was chosen as the starting point for forecasting). CritiClrl values for X!t21= 5.99
Lastly. the stability of the regression coefficients is evaluated lIsing the cUlllulative SUIll (CUS UM) and the cUlllul"ti ve SUIll of squares (CUS UMSQ) of the recursive residual test for structural stability (Brown et (fl. 1975). The regression equation appears stable given that neither the CUSUM nOl' the CUSUMSQ test statistics exceed the bounds of the 5% level of significance (Figure I).
6.
CONCLUSIONS AND POLICY IMPLICATIONS
In this paper error correction and cointegration techniques. based on the recently developed autoregressive distributed lag modelling approach. were applied to construct a sugarcane production model for Fiji covering the period 1970-2000. In so doing this paper has made a significant contribution in explaining the detenn inants of sugarcane production in Fiji. Econometric findings have demonstrated that area harvested has a strong and statisticall y significant impact on sugarcane production. The study therefore supports recent calls recommending that the Fiji government find immediate solutions to the land crisis (see for instance Reddy 1998; Prasad and Tisdell 1996; Lal 2000; Lal et al. 200 I; Reddy 200 I: Kurer 200 I).
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Ecollumic Analysis & Poli.:y
FIGURE I CUSUM TEST AND CUSUM OF SQUARES TEST
'5,-
---, -~-~-
----
-.-
--~-
5
-5 _ _ .0
CUSUM
----- 5%. Significance
I
L6 - , - - - - - - - - - - - - - - - - - - - - - ,
1.2 0.8 0.4
- _._._ . _ ._-------------?~
0.0
CUSUM of Squares
_u
5%
Significance
I
Since 1997, when the first lot of leases began expiring, sugarcane production has fallen dramatically - the average sugarcane production per hectare fell from around 49 lonnes/hectare (0 32 tannes per hectare while, in the corresponding period, area harvested declined from 72,000 heclares to 45,000 heclares. Expiring land leases have been accompanied of non-lise of land for sugarcane plantation by landowners. At this rate. in the event of no solution to the land problem. it is likely that Fiji will not be able to meet its sugar quota in future. This will have deleterious social and economic repercussions on the economy given its historic reliance on the sugar industry. Kurer (200 I, p.103) propounded the following on Fiji's sugar industry: Tht: repossession of land will not destroy the sugar industry in Fiji, but there is little: doubt that sugarcane output will fall as a consequence both in the short and the long tenn. In the short term output will fall because of the ullI..:ertainty surrounding lease renewals and the inexperience and
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Vol.J4 Nu.1 . Man:h 2004
61
undercapitalisation of incoming Fijian farmers. Negative long-term effects will arise from ... an inefficient system of land management. ... Thus a large-scale transferoflanu to Fijian management will generate a majordrop in output even in the long-run .... The empirical findings 011 the effect of area harvested on sugarcane production espollse Kurer's view. They, however. put acaveat on hisclaim that the repossession of land will not destroy the sugar industry in Fiji. In the event that Fiji is not able to meet the contracted sugar quota, the importers (United Kingdom and European Union) will obviously look for alternative suppliers to meet their demand. This implies a loss of a significant export market wilh high export earnings and employment creator. Even considering a scenario where landowners start harvesting land for sugarcane: by the time the new farmers gain farming and management experience. it will be difficult for the industry to sell its sugar in the open market. given stiff competition and high domestic cost of production (Davies 1997). Given these scenarios. the collapse of the industry is imminenl. In the lightofthis. it is imperative that the government c1iverts resources toward a speedy solution to the country's land problem. The importance of the sugar industry to Fiji's economy should not be underestimated. It is not clear how slU.:cessful recent public forums and seminars, on finding a solution to the land problem, have been. While it is important to bring together industry experts to such a forum. progress will not be made if the government does not consider the policy options resulting from such forums. The message is clear: industry stakeholders. experts and the government need to work together if a speedy solution is to be found.
REFERENCES Barbour, P. (1997). 'The ALTA issue & its impact on investment in Fijj's sugar industry'. Journal of Pac(jic Agriculture. vol.4. pp.109-20. Brown, R.L., Durbin. 1., & Evans, J .M. (1975), 'Techniques for testing the constancy of regression relations over time (with discussion)', Journal ofthe Royal Statistical Societ." B. vo1.37, pp.149-192. Davies. J. (1997). An Analysis oIthe Ejjiciellcy of Fiji's Sf/gar Calle Rail System. Sugar Commission of Fiji. Suva. Fiji Bureau of Statistics (200 I). Current Economic Statistics. Fiji Bureau of Statistics. Suva. Kurer. O. (200 I l, 'Land tenure and sugar production in Fiji: Property rights and economic performance'. Pacific Ecollolllic Blllletill, vol.16, pp.94-1 05. Lal, P. Applegate. L. & Reddy. M. (200 I l, ALTA or NLTA: WI",(s ill the 1I01lle.' Land Tenure Dilemma (lnd the Fiji Sligar Illdll.\'Iry. Working Paper No.46. Land Tenure Centre. University of Wisconsin, Madison.
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Lal, P, (2000),' Land, lome and the Fiji sugar industry', in B.v, Lal (ed,), Fiji Beji",e lite Storm. Canberra: Asia Pacific Press. Lal, B,V, (2000), Fi;i 8~/()re Ihe Siorl/l, Canberra: Asia Pacific Press, Mahadevan, R, (200 I), ,Assessing the output and producti vity growth of Malaysia's manufa<.:turing sector' , Jot/mal (~l Asiafl Economics. vol.12, pp .587-597.
Narayan. P.K. (1999), 'The causes and cOl1sequencesof sugarcane burning in Fiji', Del'e!opllleIJlBullelill, vol.49, pp,103-107, Pesaran H,M, & Shin Y, (199S),Autoregressivedistributed lag modelling approach to cointegration analysis, DAE Working Paper Serie!; No.95/4, Department of Economics. University of Cambridge. Pesaran H,M, Shin Y, Smith R, (1996), Testing the existence of a long-run relationship, DAE Workillg Pilper Series No,9622, Department of Applied
Economics, University of Cambridge. Pesaran, H,M, & Pesaran, B, (1997), Micro];1 4.0, Oxford University Press, England. Pesaran, M,H, Shin, Y. & Smith, RJ, (2001), 'Bounds testing approaches to the anal ysis of level relationships'. Jot/mal of Applied Eco/lometrics. vo1.16, pp.289-326, Pillips, C.P.B. & Perron, P. (1988), 'Testing for a unit root in time series regression', Biomelrica, vol.7S, pp.33S-46. Prasad, S, & Akram-Lodhi, A,H. (1998), 'Fiji and the sugar protocol: A case for trade based development co-operation' , Development Policy Reviell', vo1.16, pp.39-60. Prasad, P,C. & Tisdell, C. (1996), 'Getting property rights 'right': Land tenure in Fiji', Pacijic Eco/lomic Bulletin, vol.ll, pp.31-48. Reddy, M. & Yanagida, 1. F. (1998), 'Fiji's sugar industry at the crossroads', Pacijic Ecol/o/llic Blfllelill, vol. 13, pp,72-88,
Reddy, M. (2001), ulf1d Resource in Fiji: Issues, Options alUl Aiterllati\'es?, Centre for DevelopmentStudies,School of Social and Economic Development, The University of the South Pacific, Suva,
200508666
Ecol1omic Analysis & Poli .... y
Vol J4 No.1. March ::!()04
53
AN EMPIRICAL ANALYSIS OF SUGARCANE PRODUCTION IN FIJI, 1970-2000
Paresh Kumar Narayan Department O/ACCOlflltiflg, Finance lind Economics GrijJilh BIIS;'less School Gr~!Tilh University
Fiji is a small island country ill the South P.tcific. Sugar is its oldest industry. being in the vanguard of cl.:onolllic growth and development for over a century. Today
the oldest industry, not only in Fiji but also in the South Pacific Island counlries, is shrivelling and is poised on the precipice of collapse. The literature to dale has
singled out expiring land leases and the in
I.
INTRODUCTION
Fiji is a small island country in the South Pacific. Sugar is its oldest industry, being in the vanguard ofecollomic growth and development forover a century. Today the oldest industry, not only in Fiji but also in the South Pacific Island countries, is shrivelling and is poised on the precipice of collapse. The literature to date has singled out expiring land leases and the inability of the government to resolve this issue as the key to the current calamity (see, for instance, Reddy and Yanagida 1998; Prasad and Tisdell 1996; La12000; Lal et al. 200 I; Reddy 200 [; Kurer200 l). Despite the plethora of studies on Fiji's sugar industry, there has been no empirical analysis to elucidate the factors that determine sugarcane production in Fiji. The present study aims to remedy this deficiency. The goal of this paper is to delineate both the short-run and long-run relationships between sugarcane production and capital. labour and prices. The paper is organised as follows. In the next section we provide an overview of Fiji's sugar industry. In Section 3 a description of the econometric model and data set is provided. [n section 4 the model is described and tested. while the results
Copyright of Full Text rests with the original copyright owner and, except as permitted under the Copyright Act 1968, copying this copyright material is prohibited without the permission of the owner or its exclusive licensee or agent or by way of a licence from Copyright Agency Limited. For information about such licences contact Copyright Agency Limited on (02) 93947600 (ph) or (02) 93947601 (fax)
54
ECOlltllllit: An'llysis & Poli.:y
Vnl.J4 No.1. Marth 2004
are discussed in section 5. The final section draws some conclusions and indicates pol icy recommendations. 2.
AN OVERVIEW OF SUGAR INDUSTRY IN FIJI
There is a general consensus in the literature on the importance of the sugar industry in leading Fiji's drive for economic success. The industry's exports in 1999 were valued at F$263.2 million - equivalent to around 13 percent of gross domestic product. which in turn comprised 22 percent of total exports. There are four sugar mills in the country, employing around 4,500 workers. In addition. there are 14.300-15.000 cane cutters and abou12.000 lorry operators. In all. about 25 percent of the ecollomically active populution derive their income from the industry, The labour force in the industry is made up ofbolh family (household) and hired labour. In 1975. there were only 16.995 sugarcane farmers. while in 1998 their Ilumbers had increased to 22.146 - u 30 percent increase - with an average farm holding of about 4.6 hectares. Area harvested increased from 44,000 hectares in 1975. reaching its peak in 1993 (75.000 hectares) before falling sharply to settle at 45.000 hectares in 2000. In the corresponding period the average sugarcane production per hectare increased from 49.1 tannes/hectare to 59.2 lonnes/hectare in 1993. and settled at 32 tonnes/hectare in 2000 (Fiji Bureau of Statistics 2(02). The performance of the industry is credited to the preferential agreements that Fiji has received since 1975. Fiji's sugar industry has benefited immensely under the Sugar Protocol of the Lome Convention. Since 1975. a substantial amount of the sugar quota. which currently stands at 197,000 tonnes, has been offered. 'Fiji currently has a quota equivalent to 0.9 percent (14.000 tonnes) of the total United States sugar import quota, ;111d bilateral agreement with Malaysia allows for the export of 90.000 tonnes of sligar per year' (Lal et (II. 200 I. p. 3). The sligar price offered to Fiji under the Lome L:onvention is 2-3 times above the world market price of sligar (Narayan 1999). Around 85 to 90 percent of sugar production is exported, while the rest meets local consumption demand. The United Kingdom and the European Union are Fiji's main sugar market - collectively accounting for around 45% of total sugar exports. followed by Malaysia which buys 24% of Fiji's sugar wilile Japan purchases around 20%. The rest of the sugar is sold to Singapore, South Korea, Canada and the United States.
3. MODEL SPECIFICATION AND DATA In this section, a Cobb-Douglas specification to sugar production in Fiji is adopted. Two proxies forcapital are used: area harvested and fertiliser lIsage. Together with the customary labour force variable, price paid to sugarcane farmers is also incorporated. The long-run model of sugar cane production takes the following form:
InQ, = an + a, In AH, + alln LAH, + a.dnf"ER, + a, InPricq + where an represents a constant;
£,
(I)
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In QI represents the log of sugarcane production (000 tonnes) at time t; In AH, represents the area harvested (000 hectares) at time I: In LABt represents the log of labotll' force at lime t;
In
"~ER(
represents the log of fertiliser usage at time f:
In Prh'e, represents the per tonne price (F$) paid to farmers: and £1
represent'; the error term.
Area harvested. labour force. fertili;
4.
METHODOLOGY: THE ARDL APPROACH TO COINTEGRATION
The concept of cointegration was pioneered by Granger (1981) and extended by Engle and Granger( 1987).lt is based on the premise that. as is common knowledge now ,many economic times series are non-stationary. In spite ofthis.an appropriate linear combination between trending variables could remove the cOlllmon trend component. The resulting linear combinations of the time series variables will thus be stationary, implying that the relevant time series variables are cointegrated. Evidence of cointegratioll rules out the possibility of the estimated relationship being 'spurious'. We apply cointegration tests to ascertain the existence of long-run relationships amongst the variables. In this regard. and given the small sample size of our data series (31 observations), we adopt the autoregressive distributed lag (ARDL) approach to cointegratiol1 pioneered by Pesaran et al. (200 I). There are llumerous advantages of this methodology. First. the need for pre-testing the order of integration. an absolute necessity in other cointegration methodologies. is not requ ired. Put differently. the ex istence of a long-run relationship is independent of whether the explanatory variables are 1(0) or I( I). Second, valid asymptotic inferences on short- and long-run parameters can be deduced under least squares estimates of the ARDL model, that is. ordinary least squares estimates are super consistent, particularly in small samples such as the present one. Essentially, estimating the long-run relationship is a two step procedure. In the first step. the existence of a long-run relationship as predicted by theory between the variables under consideration is examined. In the event that the existence of a Data from Statistics.
1970~1974
arc unpublished and were obt:.lincrJ from thc Fiji Bureau of
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Economic Arwlysis & Polil'y
long-run relatiollship is supported. then the next stage involves estimating the longrun and short-run parameters (see Pesaran (1997) and Pesaran el at. (20U I )). The ARDL approach begins by estimating the following error correction
model: (2)
to investigate the existclH.,:e of a long-run relationship, Pesaran el (II. (200 I) proposed the bounds test based on the Wald or F-statistic. If the F-test is used, it translates into esti mating the significance of a joint zero restriction on the a values
of the error corrcl:tioll model. The distribution of the F-test is non-standard, and the critical values are provided by Pesaral1 and Pesaran (1997) and Pesaran el a/. (200 I). Two sets of critical values exbt: one set is calculated assuming that all variables included in the ARDL model are /(1) and the other is estimated on the assumption that variables are /(0), If the computed F-statisti<.: falls outside the inclusive band. a <.:onclusive inference can be made without having a priori knowledge of tile orderofintegration of the variables. Forexample, if the F-statistic is higher than the upper critical bound, then the null hypothesis of 110 cointegration is rejei.:ted. If this is the <':<-lse, then in the second stage the orders of the lags of the ARDL model are selei.:ted by Akaike or Schwarz information criteria, and the model is the estimated by ordinary least squares.
5. EMI'IRICAL FINDINGS In the first step of estimating equation I, we estimate the following long-run relationship:
~lnQ, ""o"'J + !h,<.!~lnQ,_, + !b,v~lnAHI_i + !d;utilnLABf-I ,.1
P
+
,.1
i.I
P
~ civ 6.ln FER'_i + ~ .t:{} ~ln Price'_i + 'J'IV InQ'_1 + 'h v In AH 1_1 (3)
+ II'\{! In LA8'_1 + If! ~(! In FER I _ l + IJI ~{} In Pr ice l _1
Given the limited number of observations (31) and the use of annual data we take fJ = 2, that is. the number of lags is two. We apply the F-lest. denoted by F ( QIAH. LAB.FER'?ri"e), for ascertaining the existence ofiong-rlln relationships. Q The null hypothesis of the '11011 existence of a long-run relutiollship' can be written as follows: H() :IIIIIJ ,,"II'~IJ ""I/'.lIJ "" I/'~{} "'I/J~IJ ... 0; the alternative hypothesis that there exists a long-run relationship is: HI: lJ111' ~ lJ1~{! ~ lJ1J{! ~ lJ1~(J ~ 4J s(J ~ O. The calcula(ed F-statistic ,FQ("') =5 .8431, is higher than (he upper bound critical values of 4.049 at the 5% level and 5.122 at the I % level. Therefore we reject the null of 'no long-run relationship'. Having found a long-run relationship. we estimate e4uation I using the following ARDL (p,l/.r.s,t) model:
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57
,
In(!, ~",,+ ~/!,ln(!,_, + ~¢,lnA",_, + ~y,lnLABH +
~Il,
,.f>
InFER,_, +
~{i1,
,.11
(4)
InPric£"_i +,11,
In estimating equation 4. again a maximum of two lags is used. that is. i = 2 and the model is selected using the Schwartz Bayesian criterioll. From this. th~ static long-rull model for sugarcanc production is derived ..:! The results arc presentcd in
table I. TABLE I LONG·RUN RESULTS OF THE DETERMINANTS OF SUGARCANE PRODUCTION IN FIJI, 1970·2000 Variable
C(l11stunt InLAH, InLAB t InFERI
InPricc,
Parameter estimate -3.3524 0.9721'" 0.6861" 0.1227'" O.OMW'·
Pnlbabilily OJ206 (l.ooon (l.IOI2 o.(lI05 (l.()OO~
Note: .•. .signilkance at the I % level .• signitkance :II the 5% level
All variables appeared with the expected signs. Areil harvested has the largest effect on sugarcane production. The result implies that if area harvested increases by 1% sugarcane production will increase by 0.97%. This finding has direct implications for the currcntland problems in Fiji. Since 1997 sugarcane land leases have been expiring at a rapid rate. The bulk of the land. whose leases have expired. remains unused and has reverted to bushland. This points to a situation of declining usage of land for sugarcane production. Put di fferently the area harvested has fallen
- from 73,000 hectares in 1997 to 45,000 hectares in 2000. In the corresponding period sugarcane production fell from 3,280 thousand (onnes to 1.900 thousand (onnes (Fiji Bureau of Statistics 200 I). The empirical evidence supports this. It is
Ihus clear that if Fiji's land problem is not solved immediately ,sugarcane pm(\uction will fall and if Fiji is not able to meet its export quota requirements. which is the most likely outcome. then it will lose its lucrative overseas markets. This will have serious repercllssions. not only on farmers. but also on the economy as a whole given the importance of the sugar industry in Fiji's economic development. The labour force estimate indicates that a I % growth in labour force increases sugarcane production by around 0.68%. Fertiliser usage also appeared with a positive sign - a I % increase ill fertiliser usage increases sugarcane production by around 0.1 %. For details regarding the estimatioll of long-run and short-rull results sec Pcsaran and Pesaran (1997) and Pesaran ct al .. 200 I.
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Lastly, prices r~cei vell by sugarcane farmers also exert a positive influence 011 sugan.:anc prouuctioll- a I % increase in price paid to farmers increases production by around 0.06%. Next we look al the short-run results. Again, as in the long run, all results are consistent with theoretical expectations and are statistically significant at the 1% level except of the coefficient on labour lagged one-period which is significant at the 5% level (tahle 2).
TAIlLE 2 ECM RESULTS OF THE DETERMINANTS OF SUGARCANE PRODUCTION IN FIJI Variable
Coefficients
Constant 6.lnQ,,/
-0.3X61"· 0.0654 t.30X6··· -0.1069 1.5614*** 1.3424**
6.lnAH 6InAH,_1
III nLAB( 6.1 nLAlJ,_1 t1lnFER,
O.4SX4*n
6,lnFER,./
0.2701 ~N
6.1 nPRin:'/
0.4151*** -0.8094***
Ee,., Notes:
I-statistics
4.9270 0.4229 7.4469 0.4429 2.6250 2.50X6 4.X74X 3.0020 4.8272 5.311
Probability 0.0001 0.6774 O.lXIOO 0.6631 O.Ot72 0.0219 0.0001 0.0077 0.))(101 0.0000
... signilkam;c at the 1ek level
.. sig.nifi"::llll:C at the 5% lcve
The results indicate the importance of area harvested in determining sugarcane production - a I % increase in area harvested increases sugarcane production by 1.3%. This clearly reneets the negative effects of expiring land leases and the subsequent non-use of land on sugarcane production inlhe short-run. On the other hand. a I% increase in labour force increases sugarcane output by around 1.6% in the current period; the one-period lagged effect is 1.4%. Similarly, a I % increase in the usage of fertiliser in the current period increases output by 0.4%; the one period lagged effect is 0.3%. Lastly, a I % increase in price paid to sugarcane farmers increases sugarcane production by some 0.4%. The error correction term Ee,.I • which measures the speed of adjustment to restore equilibrium in the dynamic model. appeared with a negative sign and was statistically significant at the I% level ensuring that the series is non-explosive and that long-run equilibrium call be attained. The coefficient of -0.8, for instance, implies that a deviation from long-run sugarcane production this period is corrected by ahout 80 percent in the next - an indication that once shocked convergence to equilibrium is rapid. Apart from the high signi ficance levels of variables and the ex istence of a longrun relationship, our model is statistically well behaved. We applied a number of diagnostic It:sts to the error correction model (table 3). There is no evidence of
VoU4 Nu.1. Man.:h 20114
59
autocorrelation in the disturbance of the error term. The ARCH tests suggest that the errors are homoskedastic and independent of the regressors. The model passes the Jarque-Bera normality test suggesting that the errors are normally distrihuted. The RESET test indicates that the model is correctly specified. while the F-forecast test indicates the predictive power/accuracy of the model. In additioll, the adjusted R-squared of the model is high implying an excellent fit of the model - 90% of the variations in sugarcane production is explained by the regressors.
TABLE3 GOODNESS OF FIT DIAGNOSTIC TESTS FOR THE ECM REPORTEDINTABLE2 Goodness of Fit and Dingnoslies
R' R' a
xL"
Statistics 0.9374 0.9027 0.0785
(2)
3.2037
X,~"n" (2)
0.8342
X,IRCf/
OJI389
,
XfVIliI'. (20)
Xk,.,,,,
21.2584
(2)
3.2433
Ff"nnHI (6.19)
1.0638
Where ( l is the standard emlrof the regrcs.sion: X!,I,,',,( 2) is the Brcusch-Godfrcy LM test ftlr :luttJ(orrclation: X!Nw,,,(11 is the Jarque-Bera normality tcst: X!/tu·f:/(2l is the Ramsey test for omitted v:lriablcslt"il1l:tionaJ
form: X!IH,,,,.(2()) is the White tc.st for heterosccdmaicity: FI;",....'..1 (6.19) is the Chow prellidive f"illlre test (whcn calculating this tc.st. 1995 was chosen as the starting point for forecasting). CritiClrl values for X!t21= 5.99
Lastly. the stability of the regression coefficients is evaluated lIsing the cUlllulative SUIll (CUS UM) and the cUlllul"ti ve SUIll of squares (CUS UMSQ) of the recursive residual test for structural stability (Brown et (fl. 1975). The regression equation appears stable given that neither the CUSUM nOl' the CUSUMSQ test statistics exceed the bounds of the 5% level of significance (Figure I).
6.
CONCLUSIONS AND POLICY IMPLICATIONS
In this paper error correction and cointegration techniques. based on the recently developed autoregressive distributed lag modelling approach. were applied to construct a sugarcane production model for Fiji covering the period 1970-2000. In so doing this paper has made a significant contribution in explaining the detenn inants of sugarcane production in Fiji. Econometric findings have demonstrated that area harvested has a strong and statisticall y significant impact on sugarcane production. The study therefore supports recent calls recommending that the Fiji government find immediate solutions to the land crisis (see for instance Reddy 1998; Prasad and Tisdell 1996; Lal 2000; Lal et al. 200 I; Reddy 200 I: Kurer 200 I).
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FIGURE I CUSUM TEST AND CUSUM OF SQUARES TEST
'5,-
---, -~-~-
----
-.-
--~-
5
-5 _ _ .0
CUSUM
----- 5%. Significance
I
L6 - , - - - - - - - - - - - - - - - - - - - - - ,
1.2 0.8 0.4
- _._._ . _ ._-------------?~
0.0
CUSUM of Squares
_u
5%
Significance
I
Since 1997, when the first lot of leases began expiring, sugarcane production has fallen dramatically - the average sugarcane production per hectare fell from around 49 lonnes/hectare (0 32 tannes per hectare while, in the corresponding period, area harvested declined from 72,000 heclares to 45,000 heclares. Expiring land leases have been accompanied of non-lise of land for sugarcane plantation by landowners. At this rate. in the event of no solution to the land problem. it is likely that Fiji will not be able to meet its sugar quota in future. This will have deleterious social and economic repercussions on the economy given its historic reliance on the sugar industry. Kurer (200 I, p.103) propounded the following on Fiji's sugar industry: Tht: repossession of land will not destroy the sugar industry in Fiji, but there is little: doubt that sugarcane output will fall as a consequence both in the short and the long tenn. In the short term output will fall because of the ullI..:ertainty surrounding lease renewals and the inexperience and
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Vol.J4 Nu.1 . Man:h 2004
61
undercapitalisation of incoming Fijian farmers. Negative long-term effects will arise from ... an inefficient system of land management. ... Thus a large-scale transferoflanu to Fijian management will generate a majordrop in output even in the long-run .... The empirical findings 011 the effect of area harvested on sugarcane production espollse Kurer's view. They, however. put acaveat on hisclaim that the repossession of land will not destroy the sugar industry in Fiji. In the event that Fiji is not able to meet the contracted sugar quota, the importers (United Kingdom and European Union) will obviously look for alternative suppliers to meet their demand. This implies a loss of a significant export market wilh high export earnings and employment creator. Even considering a scenario where landowners start harvesting land for sugarcane: by the time the new farmers gain farming and management experience. it will be difficult for the industry to sell its sugar in the open market. given stiff competition and high domestic cost of production (Davies 1997). Given these scenarios. the collapse of the industry is imminenl. In the lightofthis. it is imperative that the government c1iverts resources toward a speedy solution to the country's land problem. The importance of the sugar industry to Fiji's economy should not be underestimated. It is not clear how slU.:cessful recent public forums and seminars, on finding a solution to the land problem, have been. While it is important to bring together industry experts to such a forum. progress will not be made if the government does not consider the policy options resulting from such forums. The message is clear: industry stakeholders. experts and the government need to work together if a speedy solution is to be found.
REFERENCES Barbour, P. (1997). 'The ALTA issue & its impact on investment in Fijj's sugar industry'. Journal of Pac(jic Agriculture. vol.4. pp.109-20. Brown, R.L., Durbin. 1., & Evans, J .M. (1975), 'Techniques for testing the constancy of regression relations over time (with discussion)', Journal ofthe Royal Statistical Societ." B. vo1.37, pp.149-192. Davies. J. (1997). An Analysis oIthe Ejjiciellcy of Fiji's Sf/gar Calle Rail System. Sugar Commission of Fiji. Suva. Fiji Bureau of Statistics (200 I). Current Economic Statistics. Fiji Bureau of Statistics. Suva. Kurer. O. (200 I l, 'Land tenure and sugar production in Fiji: Property rights and economic performance'. Pacific Ecollolllic Blllletill, vol.16, pp.94-1 05. Lal, P. Applegate. L. & Reddy. M. (200 I l, ALTA or NLTA: WI",(s ill the 1I01lle.' Land Tenure Dilemma (lnd the Fiji Sligar Illdll.\'Iry. Working Paper No.46. Land Tenure Centre. University of Wisconsin, Madison.
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Lal, P, (2000),' Land, lome and the Fiji sugar industry', in B.v, Lal (ed,), Fiji Beji",e lite Storm. Canberra: Asia Pacific Press. Lal, B,V, (2000), Fi;i 8~/()re Ihe Siorl/l, Canberra: Asia Pacific Press, Mahadevan, R, (200 I), ,Assessing the output and producti vity growth of Malaysia's manufa<.:turing sector' , Jot/mal (~l Asiafl Economics. vol.12, pp .587-597.
Narayan. P.K. (1999), 'The causes and cOl1sequencesof sugarcane burning in Fiji', Del'e!opllleIJlBullelill, vol.49, pp,103-107, Pesaran H,M, & Shin Y, (199S),Autoregressivedistributed lag modelling approach to cointegration analysis, DAE Working Paper Serie!; No.95/4, Department of Economics. University of Cambridge. Pesaran H,M, Shin Y, Smith R, (1996), Testing the existence of a long-run relationship, DAE Workillg Pilper Series No,9622, Department of Applied
Economics, University of Cambridge. Pesaran, H,M, & Pesaran, B, (1997), Micro];1 4.0, Oxford University Press, England. Pesaran, M,H, Shin, Y. & Smith, RJ, (2001), 'Bounds testing approaches to the anal ysis of level relationships'. Jot/mal of Applied Eco/lometrics. vo1.16, pp.289-326, Pillips, C.P.B. & Perron, P. (1988), 'Testing for a unit root in time series regression', Biomelrica, vol.7S, pp.33S-46. Prasad, S, & Akram-Lodhi, A,H. (1998), 'Fiji and the sugar protocol: A case for trade based development co-operation' , Development Policy Reviell', vo1.16, pp.39-60. Prasad, P,C. & Tisdell, C. (1996), 'Getting property rights 'right': Land tenure in Fiji', Pacijic Eco/lomic Bulletin, vol.ll, pp.31-48. Reddy, M. & Yanagida, 1. F. (1998), 'Fiji's sugar industry at the crossroads', Pacijic Ecol/o/llic Blfllelill, vol. 13, pp,72-88,
Reddy, M. (2001), ulf1d Resource in Fiji: Issues, Options alUl Aiterllati\'es?, Centre for DevelopmentStudies,School of Social and Economic Development, The University of the South Pacific, Suva,