AGRICULTURALECONOMICS

Agricultural Economics 35 (2006) 79–89

Is agriculture the engine of growth?

Richard Tiffin∗, Xavier IrzDepartment of Agricultural and Food Economics, University of Reading, Earley Gate, P.O. Box 237, Reading, RG6 6AR, Reading, UK

Received 3 February 2004; received in revised form 12 January 2005; accepted 3 June 2005

Abstract

We seek to address formally the question raised by Gardner (2003) in his Elmhirst lecture as to the direction of causality between agriculturalvalue added per worker and Gross Domestic Product (GDP) per capita. Using the Granger causality test in the panel data analyzed by Gardner for 85countries, we find overwhelming evidence that supports the conclusion that agricultural value added is the causal variable in developing countries,while the direction of causality in developed countries is unclear. We also examine further the use of the Granger causality test in integrated dataand provide evidence that the performance of the test can be increased in small samples through the use of the bootstrap.

JEL classification: C22, C23, O41

Keywords: Agriculture and growth; Granger causality; Unit roots in panel data; Bootstrap

1. Introduction

In his Elmhirst lecture, Gardner (2003) considers the sourcesand constraints on agricultural growth. As part of the paper heinvestigates the relationship between the growth in agriculturalvalue added per worker and GDP per capita for 52 develop-ing countries. He provides evidence of a positive relationshipbetween these growth rates and poses the question: “What isthe direction of causality?” There is a substantial literature thatprovides conflicting evidence in answering the question. Forexample some suggest that the export of surplus resources fromagriculture leads to agriculture driving economic growth. Oth-ers suggest that increases in the nonagricultural wage lead torelocation and increases in agricultural productivity therebyimplying that causality runs from general economic growth toagriculture. Gardner (2003) provides limited information con-cerning the methods used to answer this question, however, it isconcluded that an “investigation of lags . . . does not show agri-culture as leading.” In this article our aim is to further investigatethe question posed by Gardner using the data he analyzed. Ouranalysis employs the Granger causality test and as such com-plements recent work by Gemmell et al. (2000) by applying asimilar approach to a panel of countries.

∗Corresponding author. Tel.: +44 118 378 89651; fax: +44 118 975 64671.E-mail address: j.r.tiffin@reading.ac.uk (R. Tiffin).

Section 2 reviews the literature on the relationship betweenagricultural and economic growth. The testing procedures weemploy are discussed in general in Section 3 while the specificdetails of the procedure we employ are given in Section 4.Section 5 presents the results and Section 6 concludes.

2. Insights from the literature

Most early development strategies, advocated by Rosenstein-Rodan, Nurkse, and Hirshman among others, emphasized in-dustrial development as the main source of economic growthand were biased against the agricultural sector (Schiff andValdez, 1998). However, the poor growth performance of manynewly independent countries, particularly in the 1970s, chal-lenged this view, and the need for agricultural growth becamepart of the conventional wisdom with the Berg report (WorldBank, 1982a) and the 1982 World Development Report (WorldBank, 1982b). This new orthodoxy considers that agriculturalgrowth is causally prior to industrial development, based onconceptual arguments and some empirical evidence that wenow review.

Following the classical analyses of Kuznets (1964), Johnstonand Mellor (1961), and Schultz (1964), several contributions ofagriculture to overall economic growth and development areusually acknowledged. There is, of course, the direct contribu-tion that an increase in agricultural value added makes to GrossDomestic Product (GDP), which, once expressed in growth rate,

c© 2006 International Association of Agricultural Economists

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is proportional to the sector’s share of the economy; however,this accounting relationship does not imply causality as thetwo variables (agriculture value added and GDP) then evolvesimultaneously.

The generation of a surplus, i.e., resources that can be ex-ported from agriculture to the rest of the economy to supportthe process of development, seems much more important to ex-plain a potential causality from agricultural to general economicgrowth. First, it is necessary for the agricultural sector to pro-duce food for the workers engaged in other areas of economicactivity and, even if this argument weakens in the context ofan open economy, it remains valid as many agricultural goodsretain a degree of nontradability. It follows that agriculturalgrowth can form a pre-condition for the release of labor fromagriculture to the rest of the economy. In addition to this directtransfer of resources away from agriculture, output growth inagriculture is also likely to result in a decrease in the price offood, which is a wage good, and hence induces economic growththrough two mechanisms. A relatively low price of food allowsindustrialists to pay low wages, which boosts the profitabilityand competitiveness of the industrial sector, and might resultin increased savings and investments (as in Lewis’s celebrateddual-economy model, [Lewis, 1954]), as well as the creation ofnonagricultural jobs. Furthermore, a decline in the price of foodeffectively increases the real income of net-purchasers of foodand the resulting disposable income can help stimulate demandfor nonagricultural products.

In a similar vein, agriculture can supply the capital necessaryto finance industrial development or the provision of publicgoods by the state. This remains important because, in spite ofthe recent liberalization of capital markets, it is well establishedthat for a large majority of countries, investment relies primar-ily on domestic savings (Ventura, 1997). Although this transferof resources can rely on voluntary savings by the agriculturalpopulation, given the right set of incentives (Griffin, 1979),many governments have historically accelerated the process bytaxing agriculture directly or indirectly. Hence, early industri-alization in Japan in the last decades of the nineteenth centurywas largely financed by a land tax, representing over 80% offiscal revenues at the time (Ghatak and Ingersent, 1984), and inmany developing countries, agriculture still makes a substan-tial net contribution to government revenue (Schiff and Valdez,1992). Similarly, scarcity of foreign exchange in low-incomecountries may restrict the purchase of capital goods and otherimports essential to investment. Here again, growth in output oftradable farm commodities can make a contribution by eithersubstituting food imports or increasing exports. Finally, agricul-ture is a source of raw materials for several industrial subsectorsthat can therefore potentially benefit from agricultural growth.This argument appears most important for countries at earlystages of development, because the textile, food processing,and other agriculturally based industries require little technol-ogy and physical capital but are relatively labor intensive, andhence “fit” the resource endowment of these countries particu-larly well.

In addition to the above direct effects, increased productionin agriculture has an impact on other sectors through a seriesof linkages. These include production links, both “upstream”from the farm in demand for inputs and services for agricultureand “downstream” from the farm in the demand for processing,storage, and transport of produce. There are also consumptionlinks as farmers and farm laborers spend their increased incomeson goods and services that are provided by the nonagriculturalsector. While conceptually simple, these growth linkages aredifficult to measure but most empirical studies have estimatedlarge multipliers, explained primarily by the strength of theconsumption linkages (Thirtle et al., 2003). Furthermore, theseideas have been developed to support the design of so-calledagriculture-led industrialization strategies that stress the impor-tance of agriculture in creating a market for industrial products(Adelman, 1995).

From the previous discussion, the potential for agriculture tocause general economic growth seems compelling, particularlyin the context of developing countries. It is necessary, however,to recognize that several arguments presented in the literaturesuggest that the causality might run in the opposite direction,i.e., from nonagricultural to agricultural growth. The main the-sis relates to the work initiated by Gardner and Mundlak, whoemphasize adjustments in the labor market as the engine ofgrowth in agricultural value added per worker and agriculturalincome. Simply stated, an increase in the nonagricultural wagerate results in a reallocation of labor from agriculture to non-agriculture, which in turn increases labor productivity andvalue-added per worker in the agricultural sector. Accordingto this view, there exists a fundamental surplus of labor inagriculture and raising living standards in that sector can onlybe achieved by growth in the nonfarm economy to which farmworkers have access (Gardner, 2000). Empirical support for thisidea originates both from developed and developing countries.Hence, Gardner (2000) establishes from a historical analysis ofU.S. agricultural development that income growth in the non-farm sector is more fundamentally important in increasing farmincome than any specifically agricultural variable. Estudillo andOtsuka (1999) find that growth in the nonfarm economy is thekey driver of growth in agricultural wage rates in the Philip-pines. Mundlak et al. (2004), in a comparison of agriculturaldevelopment in Indonesia, the Philippines, and Thailand, con-clude in favor of the presence of a clear oversupply of laborin agriculture that is only reinforced by recent technologicalchange in the sector. Finally, Butzer and Larson (2002) con-clude from a study of inter-sectoral migrations in Venezuelathat “as labor migrates from agriculture to nonagriculture, laborproductivity in agriculture increases, reducing the inter-sectoraldifference.” Another reason why agriculture might benefit fromnonfarm growth is that agricultural growth depends largely onthe provision of “modern” inputs and technology from the in-dustrial sector (Hwa, 1988). As a consequence, growth gen-erating technological change in the manufacturing sector canspill-over to agriculture and hence cause growth in that sector(Gemmell et al., 2000).

R. Tiffin, X. Irz / Agricultural Economics 35 (2006) 79–89 81

We are therefore left with two opposing views of the role ofagriculture in the process of economic growth. Traditionally,agricultural economists consider that growth in the farm sectorprovides the food, raw materials, labor, capital, and foreignexchange necessary to finance subsequent growth in the restof the economy, while simultaneously generating an additionaldemand for industrial goods and services. Lately, argumentshave been presented that support the reverse causality, withgrowth in per capita GDP generating an increase in value-addedper worker in agriculture via migration of farm workers to theother sectors of the economy.

Recently, the question has been addressed by developingdynamic general equilibrium (DGE) models where differentsectors, including agriculture, interact in the process of growth.The appeal of this approach arises from its internal consis-tency in piecing together the ideas outlined above, but this oftencomes at the cost of making a number of simplifying assump-tions. Some important themes emerge from this line of work,in particular with respect to the importance that agriculturalproductivity plays in triggering the process of industrializationand development or in influencing the long-term rate of eco-nomic growth. Hence, Murphy et al. (1989) demonstrate thatan increase in agricultural productivity is necessary for the ini-tiation and continuation of the growth process. The result isexplained primarily by the assumption of increasing returns toscale in the industrial sector, due to high fixed costs, whichimplies that the size of the market for industrial goods deter-mines whether the sector can operate profitably. In this context,the demand generated by agriculture for industrial products iscrucial in making the industrial sector, which ultimately drivesthe growth process, viable. Along similar lines, a new strandof the growth literature concerns itself with the escape from a“Malthusian trap,” i.e., a regime of growth where any increasein productivity is negated by population growth, hence prevent-ing any real increase in living standards. For instance, Kogeland Prskawetz (2001) establish in a model with endogenous fer-tility and technological progress, that an exogenous increase inthe rate of agricultural productivity growth is all that is neededto explain the stylized facts of the UK industrial revolution. In asimilar vein, Gollin et al. (2002) calibrate a two-sector growthmodel to demonstrate that a one time increase in agriculturalproductivity can have dramatic consequences for the speed of acountry’s development, and therefore identify agricultural pro-ductivity as a key determinant of underdevelopment. Irz andRoe (2000) reiterate the finding that a minimum rate of produc-tivity growth in agriculture is necessary to counter populationgrowth and avoid the Malthusian trap and, more importantly,that the demographic and technological characteristics of sev-eral sub-Saharan countries are broadly consistent with such apoverty trap. It follows that an agricultural revolution is nec-essary in that part of the world to trigger sustained economicgrowth. Escape from the Malthusian trap is also analyzed byHansen and Prescott (2002), Lucas (1998), and Gallor and Weill(1999). Altogether, these studies tend to confirm the consensusamong development economists that agricultural productivity

is essential, at early stages of development, either to overcomethe Malthusian forces of diminishing returns to a growing laborforce operating on a fixed resource base, or to provide an outletfor industrial products.

DGE models have also been used to explain how the es-sential nature of food consumption can explain the growth dy-namics of low-income countries. Steger (2000) establishes thatthe transitional dynamics of the neoclassical growth model aredramatically modified when subsistence consumption is intro-duced, with early stages of development characterized by lowsavings, little investment, and, as a result, slow growth. It fol-lows that the ability of the agricultural sector to produce beyondthe subsistence needs of its population has dynamic effects asit increases the aggregate saving rate. Steger (2000) formalizesthis idea in a two-sector model of exogenous growth where theimportance of Engel’s law is recognized. Finally, Steger (2000)takes a slightly different avenue to reach the identical conclu-sion that the structural change of an economy (i.e., the change inits sectoral composition induced in part by productivity growthin agriculture) is crucial in raising its saving rate.

A last group of DGE studies emphasizes externalities asthe key to understanding the contribution of agriculture to thegrowth process. An original model by Wichman (1995) inves-tigates the dynamic implications of an empirically plausiblepositive relationship between nutrition and labor productivity.In this framework, agricultural growth increases food consump-tion, improves nutrition, and, ultimately, raises labor productiv-ity in all sectors. When the externality is dynamic, meaning thatbetter nutrition not only raises labor productivity instantly butalso improves learning, the importance of agricultural produc-tivity growth in the development process is amplified. Pineres(1999) extends the human capital models of endogenous growthto a multisector setting to investigate the ability of the farm sec-tor to generate long-term growth. The empirical application toColumbia concludes that growth in the nontraditional agricul-tural export sector is superior to manufacturing growth in rais-ing the long-term rate of economic growth. Finally, Matsuyama(1992) demonstrates in a two-sector endogenous growth modelhow the benefits from a one-time productivity gain in agricul-ture depends on the trade regime of the country. In a closedeconomy framework, an increase in agricultural productivityleads to a reallocation of labor away from agriculture becauseof Engel’s law; since the postulated engine of growth is learning-by-doing in manufacturing, this reallocation induces a rise in therate of economic growth. On the contrary, in an open-economyframework, increased agricultural productivity reinforces thecountry’s comparative advantage in agriculture, induces laborto reallocate away from manufacturing where the learning ef-fect takes place and therefore reduces the long-term rate ofgrowth.

Altogether, the DGE literature reinforces the idea that, forlow-income countries, agricultural growth plays a central rolein launching the development process and stresses the impor-tance of productivity growth in the sector. We also note thatwe are not aware of any DGE study establishing that general

82 R. Tiffin, X. Irz / Agricultural Economics 35 (2006) 79–89

economic growth causes agricultural growth. Furthermore,though insightful, this literature sheds little light on the direc-tion of the causality between agricultural and general economicgrowth in countries with relatively high living standards.

Finally, the inter-relationship of sectors in the process ofdevelopment has also been analyzed econometrically for cross-sections of countries or for particular countries in a time-seriesframework. Taking the former approach, Stern regresses the (av-erage) rate of economic growth on the rate of agricultural growthfor a sample of developing countries to find a significant andpositive relationship for the period 1965–1980 but not for theperiod 1980–1990 (Stern, 1996). Echevarria (1997) establishesthat in a sample of 62 countries, there is evidence of a positiverelationship between average rate of growth and agriculture’sshare of GDP over the 1970–1987 period. Though suggestiveof interactions among sectors in the process of growth, thesestudies ignore the developments in time-series econometrics ofthe last two decades.

Several country studies take a more rigorous stance in tack-ling the same question. Most relevant to our endeavor isGemmell et al. (2000) who investigate the importance of inter-sectoral linkages for agricultural growth in Malaysia. They firstextend the growth model of Feder (1982) to derive an estimableequation that relates agricultural value-added to only manufac-turing value-added in the two-sector version of the model, orto manufacturing value-added and services value-added in thethree-sector version. This model is important in that it justifiesthe estimable equation detailed in the next section. The problemof endogeneity of the variables is circumvented by the adoptionof a VAR approach to the estimation of the model, which alsoallows for an analysis of Granger causality among the model’svariables. The empirical results demonstrate that expansion ofmanufacturing output causes negative agricultural growth inthe short run, as sectors compete for a fixed endowment ofresources, but positive agricultural growth in the long run, sug-gesting that manufacturing growth spills-over to the farm sector.By contrast, expansion of the agricultural sector does not affectthe other sectors of the economy. The findings are interpretedas indicating that manufacturing growth stimulates demand foragricultural commodities and provides the agricultural sectorwith new inputs and technology, but contradicts the conven-tional wisdom of the agricultural economics literature. On thecontrary, Kanwar (2000) takes a similar econometric approachto demonstrate that in India, while the agricultural sector affectsthe process of income generation in the manufacturing sector,the reverse is not true. Here, the author justifies the findingby relying on the standard arguments that agricultural growthrelaxes the wage goods, raw material, and foreign exchangeconstraints and provides a potentially large market for man-ufactured products. Although not analyzing causality, Martinand Warr (1992) and Martin and Warr (1993) establish in anerror-correction framework that an essential source of growth inagricultural income in Indonesia and Thailand is economy-widecapital accumulation, which pulls out surplus labor from ruralareas. Finally, Koo and Jinding (1992) estimate a simple partial

adjustment model showing that in China, agricultural growthcauses growth of the industrial sector, but the industrial sectordoes not contribute to the growth of the agricultural sector. Thisreflects the “squeeze” that the Chinese state exerted on agricul-ture from 1950 to 1980 by imposing artificially low agriculturalprices among other policies. Altogether, these studies fail toidentify a unique causal relationship between agricultural andeconomic growth; we also note that, to the best of our knowl-edge, the literature does not present any rigorous cross-countryanalysis of this relationship. This is a gap that we aim to fill.

3. Tests for integration, cointegration, and noncausality

When a time series has a unit root in its autoregressive repre-sentation it is subject to a stochastic trend and is consequentlynonstationary. Such a series is said to be integrated. When thestochastic trend is common to multiple time series a linearcombination of the series exists, which is not integrated andthe series are said to be cointegrated. Procedures for testing forintegration1 and for cointegration2 have been widely applied. Itis recognized, however, that these procedures often have limitedpower to reject a false null hypothesis. A growing literature nowexists presenting the case for the use of panel data to increasethe power of tests for integration and cointegration.

Maddalla and Wu (1999) present a comparison of the Levinet al. (2002) and Im et al. (1997) procedures for testing for unitroots in panel data. They also introduce an alternative based ona procedure for testing a joint hypothesis using the combinedsignificance levels from the tests of the constituent hypotheses.Maddalla and Wu (1999) note that the Im et al. (1997) testcan be viewed in such a light where the joint hypothesis to betested is that all the series in the panel are subject to a unit root.The procedure requires the significance levels (πi) from tests ofthe null hypothesis that there is a unit root in each of the seriesin the panel. It is noted that, because the significance level (π i )of a test is a uniform random variable on the interval 0 − 1,−2 ln πi has a χ2 distribution with 2 degrees of freedom distri-bution and hence

λ = −2N∑

i=1

ln πi, (1)

has a χ2 distribution with 2N degrees of freedom where N isthe number of cross-sectional units in the panel.

The procedures proposed by Pedroni (1999) for testingfor cointegration in a panel are based on the cointegratingregression

yit = αi + βixit + uit i = 1, . . . , N, (2)

where {yit | t = 1, . . . , Ti} and {xit | t = 1, . . . , Ti} are integratedtime series, Ti is the number of observations in the time seriesof the ith cross-sectional unit, α it represents the deterministic

1 For example, the augmented Dickey–Fuller and the Philips Zα and Zt tests.2 For example the Engle and Granger and Johansen procedures.

R. Tiffin, X. Irz / Agricultural Economics 35 (2006) 79–89 83

component and may include a constant, trend, seasonal dummyvariables, etc., β i is a parameter, and uit is the disturbance term.Note that β i and α i are permitted to vary between time series.Two of the tests developed by Pedroni (1999) are essentiallyanalogues of the Levin et al. (2002) and Im et al. (1997) testsapplied to test for a unit root in the uit series. Other tests are ina similar vein but are based on the variance ratio and Phillipsand Perron tests instead of the Dickey–Fuller test.

The Granger causality test is based on the following VARmodel:

zt = µ +p∑

i=1

Aizt−i + ut , (3)

= ψxt + ut , (4)

where zt = (vt , yt)′, µt = (µ1, µ2)′, ut = (vvt , uyt)′, Ai is the2 × 2 dimensional matrix of parameters ai.jk(j , k = 1, 2), ψ =(µ, A1, . . . , Ap) and xt = (1, z′

t−1, . . . , z′t−p)′. The restrictions

necessary for agricultural value added (v t) to be noncausal ofGDP (yt) are that ai.21 = 0, i = 1, . . . , p.3 These can be ex-pressed in the conventional way

r′vec(ψ ′) = 0, (5)

with

r = s1 ⊗ s, (6)

and

s1 =(

0

1

), s =

(0

Ip ⊗ s3

), s3 =

(1

0

), (7)

where 0 is a row vector of zeros conformable with the remainderof s. Because of the presence of stochastic regressors in a VAR,tests of the hypotheses are asymptotic, with the Wald statistic:

λ = (r′vec(ψ′))

[r′((XX′)−1 ⊗ �u

)r]−1

(r′vec(ψ ′)), (8)

being most common, where X = (x1, . . . , xT ), ψ and �u areestimates of the parameter vector and covariance matrix of u,respectively (see Lutkepol, 1993, pp. 64–68).

The VAR model in (3) can also be rewritten in VECM form

�zt = xt� + �zt−p + ut , (9)

where xt = (1,�zt−1, . . . ,�zt−p+1),� = (µ,�1, . . . ,�p−1)′,µ = (µ1, µ2)′�i , and � are the 2 × 2 matrices of parame-ters γ i·jk and πjk(j , k = 1, 2). When the series are cointegrated,� is of reduced rank and can be decomposed as � = αβ ′ wereα and β are (2 × 1) dimensional matrices. β is known as thecointegrating vector.

3 The reverse can be tested using the restrictions ai.12 = 0, i = 1, . . . , p.Where both sets of restrictions are rejected, causality runs in both directionsand feedback is said to exist.

The distribution of the test statistic in (8) in the presenceof integration and cointegration is considered by Sims et al.(1990), Toda and Phillips (1993), and Lutkepohl and Reimers(1992). In general, the distribution of the test statistic dependson the properties of the system. Toda and Yamamoto (1995)show, however, that when the true model is a VAR(p) and thefollowing VAR(p + d) is estimated:

zt = ψxt + xt + ut , (10)

where d is the maximum order of integration in the variables ofthe model and xt = (z′

t−p−d , . . . , z′t−p−1−k)′, the distribution of

the Wald statistic based only on the ψ coefficients as definedin (8) has an asymptotic χ2(p) distribution regardless of theproperties of the cointegrating vectors.

From the perspective of applied research, it is perhaps moreimportant to consider the small sample properties of the teststatistic. In this vein, Zapata and Rambaldi (1997) conduct aMonte Carlo exercise comparing the small sample performanceof a standard Wald test, the Wald test modified in accordancewith the suggestion of Toda and Yamamoto (1995), and the like-lihood ratio proposed by Mosconi and Gianini (1992). Broadlyspeaking, their results show that all three tests have an empiricalsize which is close to the nominal size in sample sizes in excessof a hundred. The power of the three tests is good at all samplesizes except for the case of the modified Wald statistic whichis shown to have low power in samples with 25 observations.Given the size of the sample used here, this is significant.

Recent interest in the use of the bootstrap as a method of im-proving the small sample performance of tests in a cointegratingframework stems from the work of Li and Maddala (1997). Inparticular, they consider ways in which dependencies betweenthe observed variables in the model may be preserved in thebootstrap sample. These may arise in the type of model underconsideration here for two reasons: (1) due to contemporane-ous correlation between the elements of the panel and (2) due toserial correlation within the elements arising, for example dueto misspecification of the number of lags in the Dickey–Fullerequation. Li and Maddala (1997) also argue that the residualsused in generating the bootstrap sample should be generatedusing the parameter values assumed under the null hypothesis.In devising the methods we use here we follow Li and Maddala(1997) first in using the stationary bootstrap as our method ofresampling, and second, in using a sampling scheme in whichthe residuals generated under the null hypothesis are resampledand used to produce pseudo data using the estimates obtainedwith the null hypothesis imposed. We use the bootstrap to pro-duce critical values in each of the three major tests applied here:the Dickey–Fuller test; the Johansen procedure, and the test fornoncausality.

4. Empirical procedure

Each of the series is tested for the presence of a unit rootby estimating an ADF equation both with and without the

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deterministic trend. The number of lags in the ADF equationis chosen by testing for serial correlation using the Breusch–Godfrey statistic (Breusch, 1978; Godfrey, 1978). It is recog-nized that this procedure is imperfect and that the ADF test issensitive to the number of lags chosen. We therefore use thebootstrap procedure that has been outlined to account for thefact that errors in the test equation may not be white noise.The procedure adopted in testing for a unit root in the agricul-tural value added series of the ith country as follows.4 First thetest statistic is obtained as the t-statistic on δ in the followingequation:

�vit = µi + δivit−1 + γit + uit . (11)

The following equation is then estimated:

�vit = µi + uit , (12)

where µ is a constant. A bootstrap sample {u∗it | t = 1, . . . , T } is

obtained using the stationary bootstrap by drawing u∗i1 randomly

from {uit }, the set of estimated residuals from (12). u∗i2 is then

obtained according to the following:

Pr[u∗

i2 = uij

] = 1 − ϕ, (13)

Pr[u∗

i2 = uik

] = ϕ, (14)

where j = i + 1 if i < T, j = 1 otherwise and k �= j . {µt} isused to produce a pseudo sample {�v it} using Eq. (12) with theestimated value of µ. Equation (11) is then estimated and thetest statistic for the pseudo sample is saved. Critical values areobtained by repeating the procedure and computing the centilesof the saved test statistic.

Having eliminated the series for which the unit root can berejected by applying the ADF test to the individual series, weuse the Maddalla and Wu (1999) procedure that is outlinedin Section 3 in order to exploit the increased power of thistest to confirm the conclusion that the remaining series aresubject to unit roots. The test statistic is estimated by estimatingEq. (11) with each series in the panel. The significance levelsfor use in computing the statistic in Eq. (1) are obtained bycomparing the t-statistic on δ with a sample of 500,000 drawsfrom the distribution of this statistic under the null hypothesis.5

Maddalla and Wu (1999) note that the Fisher test assumes thatthe component test statistics are independent and that this maybe unrealistic in a panel data setting. We therefore follow theirsuggestion in employing the stationary bootstrap to generatecritical values. The procedure used is essentially the same asthat employed in generating critical values for the ADF test

4 We outline the procedure for one of the variables and the alternative hy-potheses of a deterministic trend. The equation used for testing GDP is obviouswhile the stationary alternative is tested by omitting the trend in Eq. (11).

5 This sample is produced by repeatedly estimating Eq. (11) using simulateddata generated by a first-order autregressive model that is subject to a unit root.

above. A bootstrap sample {u∗t }, where u∗

t = {u∗1t , . . . , u∗

Nt}, isproduced by resampling the residuals from Eq. (12) in N-tuplessuch that each observation in the sample has observations fromeach cross-sectional unit in the same time period. Equation (12)is then used to produce pseudo data under the null hypothesisand the test statistic in (1) is computed. Critical values areproduced by repeating the procedure.

We test for cointegration using Pedroni’s (1999) procedure7. Thus we estimate the following:

yit = αi + βivit + γit + uit i = 1, . . . , N, (15)

for each series in the panel. The residuals from this regressionare tested for unit roots by estimating the following:

�uit = δi uit−1 + vit , (16)

and the test statistic is computed as the arithmetic mean of thet-statistics on δ i across the cross-sectional units. Critical valuesare obtained by bootstrapping under the null hypothesis thatthe series are subject to unit roots but not cointegrated. Pseudodata on v it and yit are thus generated in exactly the same way asfor use in generating critical values for the panel unit root test.Critical values are obtained as the centiles of the test statisticobtained through repeated estimation of Eqs. (15) and (16) usingthe pseudo data.

The final stage is to test for causality. In the light of the MonteCarlo results in Zapata and Rambaldi (1997) we choose to ap-ply the modified Wald test suggested by Toda and Yamamoto(1995) by estimating Eq. (10). Our explanation is for the casein which we test causality from agricultural value added toGDP. We obtain bootstrap critical values for this test as fol-lows. First the VECM is estimated under the joint restrictionsrequired for Granger noncausality and cointegration. This initself entails two steps, the first follows Johansen and Juselius(1990, pp. 199–200) in estimating α and β under the restric-tions that the rank of � is one and α = (α1, 0)′. The secondstep is to estimate � following Mosconi and Gianini (1992, p.407).6 The estimated residuals from the restricted VECM areresampled and used with the estimated parameters to producepseudo data which satisfies the null hypothesis. Critical valuesfor the test are obtained, by resampling repeatedly and testingthe null hypothesis using the modified Wald statistic. We usea Monte Carlo experiment to assess the use of the bootstrap inconducting this test. This is described in Appendix B.

6 Zapata and Rambaldi (1997) note there are errors in the formula given byMosconi and Gianini (1992). We note that Zapata and Rambaldi (1997) do notcorrect all of the errors and apply the following formula here:

� = � − �s1(s′1�s1)−1s′

1�V(V′(xx′)−1V)V′(xx′)−1, (17)

where x = (x′1, . . . , x′

T ),

V =(

0Ip−1 ⊗ s

), (18)

s = (1, 0)′, and � and � are as defined in Mosconi and Gianini (1992).

R. Tiffin, X. Irz / Agricultural Economics 35 (2006) 79–89 85

Table 1Rejections of the null hypothesis of a unit root using ADF test

Country Series Statistic 5% bootstrap C.V.

Austria GDP −3.777 −3.061Belgium GDP −3.531 −2.733Denmark GDP −2.550 −2.112France GDP −5.671 −3.707Germany GDP −2.119 −1.729Greece GDP −4.328 −3.571Italy GDP −4.846 −2.751Japan GDP −7.591 −3.825Mauritania GDP −3.842 −3.390Mexico GDP −2.392 −2.353Norway GDP −2.594 −1.979Papua New Guinea GDP −2.872 −2.581Portugal GDP −2.707 −2.478South Africa GDP −3.061 −2.955Spain GDP −5.079 −3.398Sweden GDP −2.902 −2.805Thailand Ag. val. added −1.753 −1.463Togo GDP −3.176 −2.928Venezuala Ag. val. added −2.815 −2.276

Following Gardner, we use data on agricultural value-addedper worker and GDP per capita in constant 1995 US$ fromWorld Development Indicators.7 We exclude countries forwhich either series has no observations prior to 1972, whichgives 85 countries. The remaining series are of varying lengths(see Appendix A).

5. Results

The results of the ADF tests for unit roots in each of theindividual series for which the null hypothesis of a unit root isrejected are given in Table 1. The full set of results are givenin Appendix C. The ADF test was also carried out omittingthe term in the deterministic trend in Eq. (11). This leads to areduction in the number of rejections. Of the series which rejectwith the trended model, only Denmark, Spain, and Sweden re-ject when the trend is omitted. Of the series that fail to reject inthe trended model Brazil (Agricultural Value Added) and China(GDP) reject with the trend omitted. Visual analysis suggeststhat the trended alternative is the most appropriate hypothesisand it is well known that the test lacks power against this al-ternative when the trend is omitted from the test equation. Wetherefore conclude that there is evidence to support the exclu-sion of the countries in Table 1 from further analysis on thegrounds that they are not integrated. To confirm the conclusionthat the remaining series are subject to a unit root we applythe Maddalla and Wu (1999) panel procedure. The results arepresented in Table 2 and in all cases, the null hypothesis that theremaining series are subject to a unit root is not rejected regard-less of the alternative hypothesis that is used. The next step is totest for cointegration using the Pedroni (1999) procedure. The

7 These data are available online at http://www.worldbank.org/data/.

Table 2Results of the Maddalla and Wu panel unit root tests

Series Alternative hypothesis Statistic 95% bootstrap C.V.

GDP Trend stationarity 245.779 350.444Ag. val. added Trend stationarity 106.701 207.301GDP Stationarity 81.941 197.623Ag. val. added Stationarity 106.701 125.992

Table 3Countries exhibiting bi-directional causality

Country Stat GDP � AVA C.V. 95% Stat AVA � GDP C.V. 95%

Burundi 37.061 21.253 31.478 26.085Fiji 15.511 7.543 50.959 8.294Finland 8.295 6.409 7.517 4.101Malawi 12.393 9.588 70.347 7.641Senegal 62.331 32.132 227.172 16.604

Table 4Countries exhibiting causality from GDP to agricultural value added

Country Stat GDP � AVA C.V. 95% Stat AVA � GDP C.V. 95%

Benin 20.431 9.011 1.937 6.986Honduras 6.689 4.531 0.283 4.346Korea, Rep. 15.616 6.802 1.571 5.506Peru 8.674 4.526 0.030 4.449

test statistic obtained is −2.586 compared with a bootstrap crit-ical value at the 95% level of −1.723 and we therefore concludethat there is pairwise cointegration between GDP per capita andagricultural value added in all of the countries remaining in thepanel.

Having established that the series cointegrate we proceed totest for Granger causality. The results are given in Tables 3–5.In each of these Tables, columns 2 and 3 give the test statisticand bootstrapped 5% critical value for the hypothesis that GDPis noncausal of agricultural value added and columns 4 and 5give the statistics and critical values for the noncausality in theopposite direction. Table 4 shows the countries for which onlythe hypothesis that GDP does not cause agricultural value addedcan be rejected, Table 3 shows the countries for which bothhypotheses can be rejected, and Table 5 shows the countries forwhich only the hypothesis that agricultural value added does notcause GDP can be rejected.8 The striking feature of these resultsis that for the vast majority of cases, the evidence points to thefact that agricultural value added is causal of GDP. Althoughthis conclusion applies mainly to developing countries, we alsonote the unexpected presence of five developed countries inTable 5, a point on which we expand in the conclusion.

8 Neither hypothesis can be rejected for the countries that do not appear inTables 3, 4, or 5.

86 R. Tiffin, X. Irz / Agricultural Economics 35 (2006) 79–89

Table 5Countries exhibiting causality from agricultural value added to GDP

Country Stat 95% Stat 95%GDP � AVA C.V. AVA � GDP C.V.

Australia 52.089 71.902 53.603 7.917Barbados 1.475 11.630 33.520 5.227Botswana 1.567 6.008 17.193 3.165Burkina Faso 7.307 25.943 36.017 13.017Canada 0.190 7.243 13.608 6.134Chad 7.438 13.613 98.161 20.286Dominican Republic 11.983 28.156 1,175.511 7.474El Salvador 0.959 57.144 389.257 23.668The Gambia 0.962 15.657 31.734 13.000Ghana 7.015 22.670 210.053 28.523Guatemala 0.284 6.130 4.511 4.166India 5.972 6.420 30.355 6.175Kenya 14.516 33.420 48.496 37.032Lesotho 0.438 22.031 52.067 7.752Morocco 91.389 96.257 186.939 8.432Nepal 12.263 8.208 12.458 7.368The Netherlands 4.970 17.851 11.744 6.539Niger 4.049 22.022 86.901 29.063Paraguay 0.876 5.467 9.207 3.900Sierra Leone 62.331 5.486 14.895 5.865Singapore 1.839 12.799 18.743 5.497Sri Lanka 2.079 8.103 38.628 5.401Swaziland 0.218 19.932 27.003 10.870Syria 4.277 19.396 162.010 11.939Trinidad and Tobago 8.387 234.871 1,747.736 10.052Tunisia 1.079 13.261 339.002 5.762UK 13.808 122.774 16,949.25 53.091U.S. 1.923 10.833 226.527 7.552Uruguay 0.254 10.645 111.797 5.877Zimbabwe 0.997 10.107 35.888 6.625

6. Conclusion

This article extends the analysis presented by Gardner in his2003 Elmhirst lecture on the relationship between agriculturaland general economic growth by rigorously applying moderntime-series econometric methods. After summarizing the ex-tensive literature on the role of agriculture in the process ofgrowth and development, the article has reviewed issues asso-ciated with the application of Granger causality tests to inte-grated data, before applying the tests to establish the direction ofcausality between GDP and agricultural value added in a panelof developed and developing countries. The estimated VARmodel, though extremely simple, represents a reduced form ofthe structural model of Feder (1982) as was first suggested byGemmell et al. (2000). Our contribution is twofold: first, theempirical application represents the first rigorous attempt9 totest for causality between agricultural and general economicgrowth in a cross-country setting; and second, at an economet-ric level, we extend the analysis of Zapata and Rambaldi (1997)

9 By rigorous attempt we simply refer to methods that, in the words of Gardner,address explicitely “the econometric problems of sorting out causal effects fromtrending time series” Gardner (2003).

to consider the effect of bootstrapping the Granger causality testand show that it improves the size of the test in short time seriesin comparison with the use of asymptotic critical values.

Applying the test to data from “World Development Indica-tors,” we find that in the vast majority of cases causality runs inone direction from agricultural value added per worker to GDPper capita, or, in other words, that agriculture is the engineof growth. This view is consistent with the popular paradigmamong agricultural economists that agricultural productivitygrowth is necessary to “get the economy moving” because itreleases a surplus of food, labor, raw materials, capital, andforeign exchange, while simultaneously generating demand forindustrial goods and services. On the other hand, it seems tocontradict the contention expressed recently by Gardner thatgrowth in agricultural income is driven primarily by changesoutside the sector, and most importantly growth of labor de-mand in the nonfarm economy. This is a comforting result foragricultural economists because it validates the standard policyrecommendations aimed at stimulating growth and develop-ment through expansion of agriculture. Hence, investments inagricultural research and the provision of extension servicesas well as appropriate price incentives to farmers appear to beeffective and necessary ways of stimulating the other sectors ofthe economy.

However, while this set of results would have been expectedby many in the profession as far as developing countries areconcerned, a surprising finding is the evidence in support ofagriculture driving growth in highly developed economies aswell. Clearly, the relative economic importance of agriculturein these countries is not high, and in this context it is difficultto explain why the sector might retain its leading role in thegrowth process. A first possible explanation is that the dataspan three decades over which the structural transformation ofdeveloped economies proceeded. For instance, the agriculturalworkforce in Canada decreased from 8% of the total in 1970to barely more than 2% in 2000 (FAOSTAT), hence releasingan important amount of productive labor from agriculture thatcontributed to economic growth. The same argument applies toAustralia and the Netherlands but does not appear very convinc-ing for the two countries in Table 5 that are at more advancedlevels of their structural transformation (UK, U.S.). A secondjustification relies on the observation that, for a majority (12)of developed countries in our data set, the series are simply notintegrated (Table 1), which suggests that there is no long-termrelationship between agricultural value added per worker andGDP per capita. Once that is recognized, the situation of Aus-tralia, Canada, the Netherlands, UK, and United States shouldbe regarded as the exception rather than the rule. We also notethat, with the exception of the UK, these five countries have aclear comparative advantage in agriculture and are major ex-porters on world agricultural markets. Our results can thereforebe re-interpreted as suggesting that, with the possible exceptionof countries with highly competitive agricultures, the farm sec-tor does not drive the growth process in developed countries.

R. Tiffin, X. Irz / Agricultural Economics 35 (2006) 79–89 87

Acknowledgments

The authors are grateful to the two anonymous referees fortheir helpful comments. All remaining errors are the responsi-bility of the authors.

Appendix A: Dates of first observations

Table A.1

Country Ag. val. added GDP

Algeria 1961 1960Argentina 1965 1960Australia 1971 1960Austria 1971 1960Bangladesh 1961 1960Barbados 1966 1960Belgium 1970 1960Belize 1970 1960Benin 1970 1960Botswana 1961 1960Brazil 1965 1960Burkino Faso 1970 1960Burundi 1965 1960Cameroon 1965 1960Canada 1971 1965Central African Republic 1965 1960Chad 1967 1960Chile 1961 1968China 1961 1960Colombia 1965 1960Democratic Republic of Congo 1968 1960Republic of Congo 1961 1960Costa Rica 1961 1960Cote d’Ivoire 1961 1960Denmark 1966 1960Dominican Republic 1965 1960Egypt 1965 1960El Salvador 1961 1960Fiji 1965 1960Finland 1961 1960France 1971 1960The Gambia 1966 1966Germany 1971 1971Ghana 1965 1960Greece 1961 1960Guatemala 1965 1960Guinea-Bissau 1970 1970Guyana 1961 1960Honduras 1961 1960India 1961 1960Indonesia 1961 1960Italy 1970 1960Jamaica 1961 1960Japan 1961 1960Kenya 1964 1960Korea 1961 1960Lesotho 1970 1960Madagascar 1970 1960Malawi 1967 1960Malaysia 1970 1960Mali 1967 1967Mauritania 1961 1960

Continued

Appendix A Continued.

Country Ag. val. added GDP

Mexico 1965 1960Morocco 1965 1960Nepal 1965 1960The Netherlands 1971 1960Nicaragua 1965 1960Niger 1961 1960Nigeria 1961 1960Norway 1971 1960Pakistan 1961 1960Papua New Guinea 1961 1960Paraguay 1962 1960Peru 1961 1960Philippines 1961 1960Portugal 1971 1960Rwanda 1965 1960Senegal 1961 1960Sierra Leone 1964 1960Singapore 1961 1960South Africa 1961 1960Spain 1971 1960Sri Lanka 1961 1960Swaziland 1971 1970Sweden 1970 1960Syria 1965 1960Thailand 1961 1960Togo 1965 1960Trinidad and Tobago 1966 1960Tunisia 1965 1961Turkey 1968 1968United Kingdom 1971 1960United States 1971 1960Uruguay 1961 1960Venezuela 1961 1960Zambia 1965 1960Zimbabwe 1969 1960

Appendix B: Monte Carlo experiment

In order to assess the extent to which the bootstrap improvesthe performance of the noncausality test we repeat Zapata andRambaldi’s (1997) Monte Carlo experiment with their DGP(3).We carry out 5,000 Monte Carlo trials and for each trial wegenerate critical values using a bootstrap sample of 1,000 ob-servations. The probability ϕ used in the bootstrap (see Eqs.(13) and (14)) is 0.1. Table B.1 presents the results of the trialalong with the results reported by Zapata and Rambaldi (1997)for comparison. Results are reported with the order of the esti-mated VAR ( p, see Eq. (3)) equal to the true order, p = 2 withcases where it is over-fitted ( p = 3) and under-fitted ( p = 1).In all cases, the nominal size of the tests is 0.05. The resultsshow that the use of the bootstrap gives an improvement in sizeover the use of asymptotic critical values at the expense of thepower of the test. The power of the bootstrap test is reasonablewith a sample size of 40 provided the correct number of lags areused in the estimation. The consequences of underestimatingthe number of lags are more severe than overestimation. Un-derestimation results in a severe reduction in the power of the

88 R. Tiffin, X. Irz / Agricultural Economics 35 (2006) 79–89

Table B.1Comparison of size (S) and power (P) of the modified Wald test with asymptotic(A) and bootstrap (B) critical values for Zapata and Rambaldi’s model 3,nominal size 0.05

Repetitions 25 30 40 50 75 100 200 400

p = 1 S A 0.146 – – 0.087 – 0.062 0.054 0.048B 0.030 0.029 0.027 0.026 0.032 0.028 0.027 0.030

P A 0.477 – – 0.835 – 0.988 1.000 1.000B 0.030 0.025 0.022 0.024 0.023 0.031 0.149 0.616

p = 2 S A 0.229 – – 0.108 – 0.070 0.052 0.051B 0.030 0.032 0.037 0.043 0.043 0.054 0.050 0.050

P A 0.645 – – 0.937 – 0.998 1.000 1.000B 0.141 0.258 0.481 0.693 0.937 0.987 1.000 0.999

p = 3 S A 0.344 – – 0.140 – 0.078 0.064 0.043B 0.028 0.039 0.046 0.046 0.048 0.047 0.047 0.050

P A 0.727 – – 0.930 – 0.999 1.000 1.000B 0.082 0.114 0.155 0.179 0.284 0.416 0.895 1.000

test and underrejection of the null hypothesis. Overestimationhas only a small impact on the size of the test relative to thecorrectly parameterized model, and there is also some reductionin power.

Appendix C: Results of ADF tests

Table C.1

Country Ag. val. added GDP

Stat. C.V. Stat. C.V.

Algeria −2.754 −4.682 −1.817 −4.104Argentina −2.279 −3.499 −1.711 −2.971Australia −4.900 −5.660 −2.187 −3.372Austria −1.823 −3.949 −1.048 −3.465Bangladesh −1.432 −4.065 −0.783 −3.596Barbados −2.958 −3.805 −2.227 −2.895Belgium −0.957 −3.548 −1.785 −3.313Belize −2.487 −5.109 −1.782 −2.696Benin −3.112 −4.227 −1.788 −3.195Botswana −0.953 −2.512 0.165 −2.762Brazil −6.018 −5.620 −0.645 −3.337Burkina Faso −3.620 −6.327 −3.663 −5.204Burundi −1.960 −3.966 −0.680 −4.010Cameroon −1.709 −3.626 −0.965 −3.066Canada −3.031 −3.099 −2.174 −3.034Central African Republic 0.051 −2.950 −1.968 −3.662Chad −3.413 −6.233 −1.970 −3.726Chile −2.648 −3.882 −1.109 −2.991China −2.489 −3.830 −4.526 −3.093Colombia −1.521 −3.488 0.404 −2.629Democratic Republic of Congo −2.240 −3.782 −0.939 −3.246Republic of Congo −3.039 −4.228 −0.650 −2.651Costa Rica −1.569 −3.152 −1.375 −3.047Cote d’Ivoire −2.296 −4.321 −2.032 −2.769Denmark −3.857 −4.768 −3.375 −3.288Dominican Republic −3.003 −5.411 −1.717 −3.262Egypt. −2.731 −3.464 −1.165 −2.822El Salvador −2.727 −4.497 −1.343 −3.697

Continued

Appendix C Continued.

Country Ag. val. added GDP

Stat. C.V. Stat. C.V.

Fiji −4.702 −5.859 −1.880 −3.967Finland −3.071 −3.531 −1.682 −2.876France −3.260 −4.799 −2.502 −2.865The Gambia −1.526 −3.243 −1.721 −3.497Germany −3.084 −5.634 −1.753 −3.277Ghana −2.041 −3.232 −0.590 −3.297Greece −2.946 −6.063 −2.757 −2.984Guatemala −1.753 −3.022 −1.469 −3.194Guinea-Bissau −2.238 −4.178 −3.254 −4.489Guyana −2.662 −5.193 −1.351 −3.018Honduras −2.456 −3.502 −1.529 −3.148India −4.217 −5.601 1.915 −2.229Indonesia −1.016 −3.387 −0.171 −2.676Italy −2.455 −3.687 −4.846 −2.751Jamaica −1.689 −2.887 −1.982 −2.412Japan −2.175 −4.134 −7.591 −3.825Kenya −1.724 −3.933 −1.842 −2.920Korea, Republic −2.086 −4.411 −0.192 −1.768Lesotho −3.768 −4.527 −1.255 −1.917Madagascar −2.269 −4.247 0.084 −2.177Malawi −2.656 −4.621 −2.115 −2.874Malaysia −1.657 −4.186 −0.755 −1.758Mali −3.316 −3.631 −1.647 −2.471Mauritania −2.320 −4.325 −3.842 −3.390Mexico −2.986 −4.854 −2.392 −2.353Morocco −5.270 −6.475 −0.940 −1.851Nepal −2.319 −4.132 0.753 −2.504The Netherlands −3.179 −3.735 −2.172 −2.446Nicaragua −1.224 −3.294 −0.108 −2.464Niger −1.939 −4.720 −0.556 −2.460Nigeria −1.589 −3.089 −1.865 −2.915Norway −2.301 −4.160 −2.594 −1.979Pakistan −2.079 −3.996 −2.040 −2.098Papua New Guinea −2.756 −3.750 −2.872 −2.581Paraguay −1.114 −3.298 −2.178 −3.442Peru −1.413 −3.738 −2.295 −2.432Philippines −2.383 −3.973 −1.888 −2.297Portugal −2.706 −3.714 −2.707 −2.478Rwanda −3.201 −5.051 −2.107 −3.241Senegal −5.282 −5.723 −2.386 −3.353Sierra Leone −0.684 −2.962 0.551 −2.653Singapore −1.458 −4.065 −2.253 −2.879South Africa −3.720 −5.367 −3.061 −2.955Spain −3.338 −4.508 −5.079 −3.398Sri Lanka −2.560 −4.330 1.034 −2.253Swaziland −3.430 −4.282 −1.820 −2.321Sweden −2.493 −4.093 −2.902 −2.805Syrian Arab Republic −3.791 −5.383 −1.705 −2.571Thailand −3.301 −6.140 −0.651 −2.102Togo −2.983 −4.261 −2.858 −3.280Trinidad and Tobago −1.891 −5.678 −1.610 −3.364Tunisia −3.087 −4.015 −1.763 −3.332Turkey −3.447 −6.096 −2.755 −3.988United Kingdom −1.301 −3.721 −2.433 −3.499United States −2.100 −4.179 −2.751 −3.471Uruguay −4.598 −4.743 −1.779 −2.684Venezuela, RB −1.976 −3.034 −2.187 −3.506Zambia −5.112 −6.544 −2.391 −3.732Zimbabwe −3.420 −5.268 −1.005 −3.054

R. Tiffin, X. Irz / Agricultural Economics 35 (2006) 79–89 89

References

Adelman, I., 1995. Beyond export-led growth, Institutions and DevelopmentStrategies: Selected Essays of Irma Adelman. Edwar Elgar, Cheltenham, pp.290–302.

Breusch, T., 1978. Testing for autocorrelation in linear dynamic models. Aust.Econ. Papers 17, 334–355.

Butzer, R., Larson, D. F., 2002. Intersectoral migration in Venezuela. Econ.Development and Cultural Change, pp. 227–248.

Echevarria, C., 1997. Changes in the sectoral composition associated witheconomic growth. Int. Econ. Rev. 38(2), 431–452.

Estudillo, J., Otsuka, K., 1999. Green Revolution, human capital, and off-farmemployment: changing sources of income among farm households in centralluzon, 1966–1994. Economic Development and Cultural Change 47, 497–523.

Feder, G., 1982. On exports and economic growth, journal of developmenteconomics. J. Dev. Econ. 12, 50–73.

Gallor, O., Weill, D., 1999. From malthusian stagnation to modern growth. Am.Econ. Rev. 89(2), 150–154.

Gardner, B., 2000. Economic growth and low income agriculture. Am. J. Agric.Econ. 82, 1059–1074.

Gardner, B., 2003. Causes of Rural Economic Development. Document Trans-fer Technologies. Durban, South Africa.

Gemmell, N., Lloyd, T., Mathew, M., 2000. Agricultural growth and inter-sectoral linkages in a developing economy. J. Agric. Econ. 51(3), 353–370.

Ghatak, S., Ingersent, K., 1984. Agriculture and Economic Development.Wheatsheaf Harvester Press, Brighton, UK.

Godfrey, L., 1978. Testing against general autoregressive and moving averagemodels when the regressors include lagged dependent variables. Economet-rica 46, 1293–1302.

Gollin, D., Parente, S., Rogerson, R., 2002. The role of agriculture in develop-ment. Am. Econ. Rev. 92(2), 160–164.

Griffin, K., 1979. The Political Economy of Agrarian Change. MacMillan,London.

Hansen, G., Prescott, E., 2002. From Malthus to Solow. Am. Econ. Rev. 92(4),1205–1217.

Hwa, E., 1988. The contribution of agriculture to economic growth: someempirical evidence, world development. World Dev. 16(11), 1329–1339.

Im, K. S., Pesaran, M. H., Shin, Y., 1997. Testing for Unit Roots in Heteroge-neous Panels. University of Cambridge, Cambridge.

Irz, X., Roe, T., 2000. Can the world feed itself? Some insights from growththeory. Agrekon 39(4), 513–528.

Johansen, S., Juselius, K., 1990. Maximum likelihood inference on cointegra-tion with applications to the demand for money. Oxford Bull. Econ. Stat.52, 169–210.

Johnston, B., Mellor, J., 1961. The role of agriculture in economic development.Am. Econ. Rev. 51, 566–593.

Kanwar, S., 2000. Does the dog wag the tail or the tail the dog? Cointegrationof Indian agriculture with nonagriculture. J. Policy Modelling 22(5), 533–556.

Kogel, T., Prskawetz, A., 2001. Agricultural productivity growth and escapefrom the Malthusian trap. J. Econ. Growth 6, 337–357.

Koo, W., Jinding, L., 1992. An intersectoral perspective on the relationshipbetween the agricultural and industrial sectors in Chinese economic devel-opment. In: Bellamy, M., Greenshields, B. (Eds.), Issues in AgriculturalDevelopment—Sustainability and Cooperation. Dartmouth, Aldershot.

Kuznets, S., 1964. Economic growth and the contribution of agriculture. In:Eicher, C., Witt, L. (Eds.), Agriculture in Economic Development. McGraw-Hill, New York.

Levin, A., Lin, C., Chu, C., 2002. Unit root tests in panel data: Asymptotic andfinite sample properties. J. Econometrics 108, 1–24.

Lewis, W., 1954. Economic development with un-limited supplies of labour.The Manchester School 22, 139–191.

Li, H., Maddala, G., 1997. Bootstrapping cointegrating regressions. J. Econo-metrics 80, 297–318.

Lucas, R., 1998. The Industrial Revolution: Past and Future. University ofChicago, Chicago.

Lutkepohl, H., Reimers, H., 1992. Granger-causality in cointegrated VAR pro-cesses. Econ. Lett. 40, 263–268.

Lutkepol, H., 1993. Introduction to Modern Time Series Analysis. SpringerVerlag, Berlin.

Maddalla, G., Wu, S., 1999. A comparative study of unit root tests with paneldata and a new simple test. Oxford Bull. Econ. Stat. (Special Issue), 631–652.

Martin, M., Warr, P., 1993. Explaining the relative decline of agriculture: asupply-side analysis for Indonesia. The World Bank Research Observer7(3), 381–401.

Martin, W., Warr, P., 1992. The declining economic importance of agriculture: asupply-side analysis for Thailand. Working Paper in Trade and Development92/1. Australian University, Research School of Pacific Studies, Canberra.

Matsuyama, K., 1992. Agricultural productivity, comparative advantage andeconomic growth. J. Econ. Theory 58(2), 317.

Mosconi, R., Gianini, C., 1992. Non-causality in cointegrated systems: rep-resentation estimation and testing. Oxford Bull. Econ. Stat. 54, 399–417.

Mundlak, Y., Larson, D., Butzer, R., 2004. Agriculture dynamics in Thailand,Indonesia and the Philippines. Aust. J. Agric. Resour. Econ. 48(1), 95–126.

Murphy, K., Schleifer, A., Vishny, R., 1989. Income distribution, market sizeand industrialization. Q. J. Econ. 104(3), 537–564.

Pedroni, P., 1999. Critical values for cointegration tests in hetergeneous panelswith multiple regressors. Oxford Bull. Econ. Stat. (Special Issue), 653–670.

Pineres, A. D., 1999. Externalities in the agricultural export sector and economicgrowth: a developing country perspective. Agric. Econ. 21, 257–267.

Schiff, M., Valdez, A., 1992. The Plundering of Agriculture in DevelopingCountries. World Bank.

Schiff, M., Valdez, A., 1998. Agriculture and the macroeconomy. In: Gardner,B., Rausser, G. (Eds.), Handbook of Agricultural Economics. ElsevierScience, Amsterdam.

Schultz, T., 1964. Transforming Traditional Agriculture. Yale University Press,New Haven.

Sims, C., Stock, J., Watson, M., 1990. Inference in linear time series modelswith some unit roots. Econometrica 58, 113–144.

Steger, T., 2000. Economic growth with subsistence consumption. J. Dev. Econ.62, 343–361.

Stern, N., 1996. Growth theories, old and new, and the role of agriculture ineconomic development. FAO Economic and Social Development Paper 136,FAO.

Thirtle, C., Piesse, J., Lin, L., 2003. Th impact of research led productivitygrowth on poverty in Africa, Asia and Latin America. World Dev. 31(12),1959–1975.

Toda, H., Phillips, P., 1993. Vector autoregressions and causality. Econometrica61, 1367–1393.

Toda, H., Yamamoto, T., 1995. Statistical inference in vector autoregressionswith possibly integrated processes. J. Econometrics 66, 225–250.

Ventura, J., 1997. Growth and interdependence. Q. J. Econ. 112(1), 57–84.Wichman, T., 1995. Food consumption and growth in a two sector economy.

Working Paper, Technical University Berlin.World Bank, 1982a. Accelerated Development in Sub-Saharan Africa: An

Agenda for Action. World Bank.World Bank, 1982b. World Development Report 1982. World Bank.Zapata, H., Rambaldi, A., 1997. Monte carlo evidence on cointegration and

causation. Oxford Bull. Econ. Stat. 59, 285–298.