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ANALYSIS OFENVIRONMENTAL

EFFICIENCYVARIATION

STIJNREINHARD, C. A. KNOXLOVELL, ANDGEERTTHIJSSEN

In this article, we develop and implement a methodology for analyzing the sources of variation in

environmental efficiency across producers. We formulate a two-stage model. In the first stage, we use

stochasticfrontier analysis to estimate both technical and environmental efficiency. In the second stage,

we again use stochastic frontier analysis to regress estimated environmental efficiency scores against a

variety of technology, physical environment, and management variables. In this stage we estimate the

impact of each explanatory variable on environmental efficiency, and we derive conditional estimates

of environmental efficiency from the one-sided error component. We illustrate our methodology with

an empirical application to a panel of Dutch dairy farms. We find evidence of relatively low levels of

environmental efficiency, and we find that environmental efficiencycan be improved through a number

of policy options, including the provision of farmers with more insight into the nutrient balance of their

farms.

Key words: agriculture, environmental efficiency, nitrogen surplus.

The empirical analysis of any type of efficiencyshould have two components: (a) the esti-mation of its variation across producers, and(b) the identification of its determinants. Theformer provides an indication of the severityof the inefficiency problem, and the latter pro-

vides evidence on the sources of the problem.The former has been widely studied, in agri-culture and elsewhere, although the latter hasbeen studied less frequently.1 Both have tend-ed to examine the nature and sources oftechnical efficiency rather than environmentalefficiency, and the distinction is important.

In this article, we develop a methodology foran empirical analysis of the sources of vari-

Stijn Reinhard is a seniorresearcher at theAgricultural EconomicsResearch Institute(LEI),TheHague. Knox Lovell is a professor intheDepartment of Economics at theUniversity of Georgia. At thetime of this research, Geert Thijssen was associate professor in theDepartment of Economics and Management at the WageningenUniversity.

This research was carried out in cooperation with the MansholtGraduate School, Wageningen. The authors are grateful to SpiroStefanou and two AJAE reviewers for their suggestions and de-tailed comments.

1 Farm management studies have investigated determinants offarm success, typically measured in financial terms, since theearly 20th century; Fox, Bergen and Dickson review these studies.Second-stage regression analyses investigating the determinantsof farm productive efficiency apparently began with Sitorus andTimmer, who used Data Envelopment Analysis in the first stage,

and Pitt and Lee, who used a fixed effects regression in the firststage. We are not aware of any previous studies that have useda second-stage regression to explain variation in environmentalefficiency.

ation in environmental efficiency. We studyDutch dairy farming, where environmentaldegradation has been severe, and where theabatement of nitrogen emissions is a policy ob-

jective of the Dutch government. Recently, thefocus of Dutch policy analysis is on the effect

of various policies on environmental efficiencyin Dutch agriculture.

Previous articles provided insight into howenvironmentally efficient Dutch dairy farmingis (Reinhard, Lovell and Thijssen; ReinhardandThijssen).There we found evidence of sub-stantial variation in environmental efficiencybeneath best practice, which itself may be ab-solutely inefficient. However, the formulationof policy designed to improve the environmen-tal performance of dairy farming requires thatthe impact of various characteristics on envi-ronmental efficiency be identified. Therefore,the objectives of this article are to identifythe sources of variation in environmental effi-ciency across farms, to quantify their impacts,and to reassess farm environmental efficiencyin light of these characteristics. This agendaraises two questions: (a) what variables are as-sociated with variation in environmental effi-ciency?; and (b) what methodology is most ap-propriate to incorporate these variables into amodel of environmental efficiency?

We proceed in two stages. In the first stage,we formulate and estimate a composed errorstochastic production frontier model, in which

Amer. J. Agr. Econ.84(4) (November 2002): 10541065Copyright 2002 American Agricultural Economics Association


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Reinhard, Lovell, and Thijssen Analysis of Environmental Efficiency Variation 1055

conventional and environmentally detrimen-tal inputs are combined to produce marketableoutput. In this framework estimates of techni-cal efficiency are extracted from the one-sidederror component, while estimates of environ-mental efficiency are derived from estimates

of parameters in the model, including bothtechnology parameters and parameters de-scribing the distribution of the one-sided errorcomponent.

In the second stage, we formulate and es-timate a composed error stochastic environ-mental efficiency frontier model, in which theenvironmental efficiencies estimated in thefirst stage are regressed against a set of ex-planatory variables. The explanatory variablesare identified in a comparison between a com-prehensive dairy farming model2 and the em-

pirical model we use to estimate (uncondi-tional) environmental efficiency.

Two types of information emerge from thesecond stage. One is sample-wide evidence onthe directions and magnitudes of the impactsof the explanatory variables on estimated envi-ronmental efficiency. This evidence is derivedfrom the estimated coefficients of the deter-ministic part of the environmental efficiencyfrontier. The other is producer-specific evi-dence on the ability of individual producers

to keep up with best practice environmentalefficiency standards, conditional on their cir-cumstances as characterized by their explana-tory variables. This evidence is extracted fromthe one-sided error component of the environ-mental efficiency frontier model.

Both types of information generated in thesecond stage are useful for policy purposes.Thefirst identifies factors exerting significantimpacts on the environmental efficiencyof dairy farming. To the extent that thesefactors are subject to government influence,they can be manipulated to improve overallenvironmental performance. The secondidentifies factors that can be used in thedesign of incentive-based regulation, since itidentifies environmentally (in)efficient dairyfarms whose performance has been achieveddespite (or because of) these factors.

Our article is structured as follows. In thenext section we present our two-stage stochas-tic frontier methodology, followed by a com-prehensive model of dairy farming to identify

potential determinants of variation in environ-

2 Section Determinants of Environmental Efficiency and Datacontains a description of this model, more details can be found inReinhard.

mental efficiency, and a description of our dataset. The impacts of the explanatory variablesand the farm-level conditional environmentalefficiency scores are then presented. Thefinalsection concludes with a summary and discus-sion of ourfindings.

Modeling, Estimating, and ExplainingEnvironmental Efficiency

We define environmental efficiencyEE as theratioofminimumfeasibletoobserveduseofanenvironmentally detrimental input, given tech-nology and the observed levels of output andconventional inputs. Thus

EE = min{: F(X, N) Y} 1(1)

where F() is theproduction frontier, X RN+isa vector of conventional inputs, N R+ is anenvironmentally detrimental input,3 and YR+is output.

Since output production typically takesplace after input decisions are made, technicalefficiency TE isdefined as the ratio of observedto maximum feasible output, given technologyand observed usage of all inputs. Thus

TE = [max{ :Y F(X,N)}]1 1.(2)

Agricultural output is typically treated as astochastic variable because of weather condi-tions, diseases, and other exogenous randomforces. We, therefore, convert the determinis-tic relationshipY F(X,N) to the stochasticproduction frontier

Yi t= F(Xi t,Ni t;) exp{Vi t Ui}(3)i= 1, . . . ,I, t= 1, . . . ,T

where F(Xi t,Ni t;) is the deterministic ker-nel of the stochastic production frontier

[F(Xi t,Ni t;) exp{Vi t}], is a technologyparameter vector to be estimated, Vi tiid N(0,2v ) captures random events be-yond the control of farmers, and Ui i.i.d.N+(,2u ) captures time-invariant technicalinefficiency in production.

The stochasticversion ofTEiis derived from(3) as

TEi= Yi t/[F(Xi t,Ni t;) exp{Vi t}](4)= exp{Ui } 1, i= 1, . . . ,I.

3 The environmentally detrimental input is labeled Nbecause inourempirical work this variable is surplusnitrogenemanating fromuse of fertilizer and manure; see Reinhard, Lovell and Thijssen formore information on the environmental aspects of Dutch dairyfarming.


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1056 November 2002 Amer. J. Agr. Econ.

The Battese and Coelli (1988) estimator forTEi is

TEi= E[exp{Ui }/(Vi t Ui )](5)

= 1 ( i/)

1

(

i/

) exp

i+

1

22

i= 1, . . . ,Iwhere() is the standard normal distributionfunction, = uv/(2u+ 2v )1/2, and i=[(Vi t Ui )2u+ 2v ]/(2u+ 2v ). The tech-nology and error component parameters(,2v ,

2u ,) are estimated using maximum

likelihood techniques. Derived estimates of

andi are inserted into the second and thirdline of (5) to generate estimates ofTEi .4

To derive a stochastic version ofEE we needto specify a functional form for F(Xi t,Ni t;).Specifying (3) in translog form gives

ln Yi t= 0 +

j

jln Xi t j+ nlnNi t(6)

+ 12

j

k

jk lnXi t jln Xi tk

+ j j nlnXi t jln Ni t+ 1

2nn(lnNi t)

2 + Vi t Ui .

wherejk = kj. The logarithm of the outputof an environmentally efficient producer is ob-tained by replacing Ni t with Ni t and settingUi= 0. Setting (6) and the output of the envi-ronmentally efficient producer equal and solv-ing for lnEEi t= ln Ni t lnNi t= ln yieldsthe environmental efficiency estimator

lnEEi t(7)

=n +

j

j nlnXi t j+nnlnNi t

n +

j

j nlnXi t j+nnlnNi t2

2nnUi

0.5

nn.4 Itis also possibleto estimate (3)usingconventional fixedeffects

or random effects panel data techniques, in which case estimatesofTEi would be obtained from normalized farm effects.

Environmental efficiency is calculated usingthe positive root in (7).

For the second-stage analysis of variation intechnical efficiency, two methods have beendeveloped in literature; for an overview seeKumbhakar and Lovell (chap. 7). The stan-

dard approach is to regress estimated tech-nical efficiencies against a set of explanatoryvariables. OLS is frequently used (e.g., Hallamand Machado), although a limited dependentvariable technique such as tobit is preferred(e.g., Weersink, Turvey and Godah). However,regardless of the estimation procedure em-ployed, the two-stage approach suffers froma fundamental inconsistency. It is assumed inthefirst stage that technical efficiencies are in-dependently identically distributed (iid), butthis assumption is contradicted in the second-

stage regression in which estimated technicalefficiencies are assumed to have a functionalrelationship with the explanatory variables.

Battese and Coelli (1995) originally raisedthe objection to the use of estimates ofTEi asdependent variables in a second-stage regres-sion. However, their argument does not ap-ply to the similar use of estimates ofEEi t. Al-though technical efficiency is estimated froman error component, environmental efficiencyis calculated from parameter estimates de-

scribing the structure of production technol-ogy and the one-sided error component. Thisis an important distinction. While the iid as-sumption on the Ui is inconsistent with the useof estimates ofTEi as dependent variables ina second-stage regression, no such assumptionis made concerning EEi t. Thus it is permissibleto use estimates ofEEi tas dependent variablesin a second-stage regression.

We depart from OLS or tobit convention byusing maximum likelihood techniques to esti-mate a stochastic frontier regression model inthe second stage. Our strategy is to estimatea stochastic environmental efficiency frontier,andto obtainrevised estimatesof environmen-tal efficiency that are conditioned on variationin the explanatory variables. A second-stagefrontier approach offers both economic andstatistical advantages over a second stage OLSor tobit approach. The economic intuition be-hind the frontier approach is that it providesa characterization of the relationship betweenbest practice environmental efficiency and the

explanatory variables, and it partitions devi-ations from best practice to statistical noiseand conditional environmental inefficiency re-maining even after variationin theexplanatoryvariables has been accounted for. Neither OLS


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nor tobit can provide this information. From astatistical perspective, our estimates ofEEi tdonot approach, much less cluster at, their limit-ing value of unity, and so tobit offers no advan-tage over OLS.5 In addition, OLS parameterestimates are biased and inconsistent if the dis-

turbance term is skewed with nonzero mean.6

Finally, the estimation of a stochastic frontierenables us to test the symmetry and zero meanhypotheses justifying OLS. The presence ofskewness can indicate that environmental in-efficiency remains even after accounting forthe impacts of the explanatory variables. Thusestimation of a stochastic frontier generatesrevised estimates of environmental efficiencyconditioned on thevariationin theexplanatoryvariables. Neither OLS nor tobit can providethis information.

The stochastic environmental efficiencyfrontier regression model can be expressed ingeneral form as

EEi t= G(Zi t; ) exp

Vi t Ui

(8)

i= 1, . . . ,I, t= 1, . . . , Twhere G(Zi t; ) is the deterministic kernel ofthe stochastic environmental efficiency fron-tier [G(Zi t; ) exp{Vi t}],Zi t is a vector ofexplanatory variables expected to influence

environmental efficiency, is a vector of pa-rameters to be estimated,Vi t i.i.d.N(0,2v)and Ui i.i.d. N+(,2u). In this formula-tion variation in estimated environmental effi-ciency is apportioned to three sources: (a) theimpacts of the explanatory variables capturedby G(Zi t; ); (b) the impact of measurementerror and other sources of statistical noise re-flected inVi t; and (c) an unexplained shortfallof environmental efficiency beneath best prac-tice observed in the sample reflected in Ui .Conditional environmental efficiency CEEi tisdefined and estimated exactly as in (4) and (5),and so

CEEi= EEi t/

G(Zi t; ) exp

Vi t

(9)

= expUi 1, i= 1, . . . ,Iwhich is estimated exactly asTEi is estimatedin (5), with (Vi t,U

i ) replacing (Vi t,Ui ). Al-

though estimates ofEEi tobtained in thefirststage of the analysis donot take variation in ex-

5 A histogram of the temporal means of EEit is provided infigure 2, and the resulting similarity of OLS and tobit estimatesis verified in table 2.

6 A comparison of MLEand OLSparameter estimates in table 2illustrates the bias in the OLS parameter estimates.

planatory variables into consideration, the im-pacts of these variables are incorporated intoestimates of CEEt. Since for any farm theirnet impact can be either positive or negative,CEEi > = < EEi t.7

Determinants of EnvironmentalEfficiency and Data

The main idea behind our approach to theidentification of explanatory variables is thatif we can use all the relevant relations in dairyfarming to compute the (first-stage) efficiencyscores, we will not find any inefficiency of ei-ther type. Thus variation in efficiency scores isassumed to be caused by omitted variables and

measurement errors in thefirst-stage analysis.The estimated environmental efficiencyfrontier is compared with a comprehensivemodel of dairy farming to identify the omit-ted variables and measurement errors that areexpected to affect environmental efficiency.Figure 1 provides a scheme of the aforemen-tioned model of dairy farming summarizingall elements of dairy models from differentsciences and different levels of aggregation.The model is described in detail in Reinhard.Dairy farming consists ofroughage produc-

tion andlivestock production. The inputscan be divided into variable inputs (in-cluding nutrients),laborand capital.Themarketable output is mainly an aggregate ofmilkand beef.The physical environmentconsists of the exogenous physical factors re-lated to land location (for instance weather,soil quality). Theinstitutional environmentinfluences almost all inputs, outputs, and theproduction process; for example, regulationson the utilization of land, production quotas

for milk, levies on excess manure. Technol-ogy is disaggregated into embodied and dis-embodied technological change. The environ-mental pressure captures the nutrient flowsfrom dairy farming into thenatural environ-ment. For example, nitrogen surplus consists,among other things, of evaporation of ammo-nia and leaching of nitrates into groundwater(see Reinhard, Lovell and Thijssen).

We assume that calculated environmen-tal inefficiencies are due in part to omitted

7 EEit in (7) varies across farms and through time, since it is afunction ofXitandNit. In principleCEEi in (9) also varies acrossfarms and through time. However, the hypothesis that CEEi istime-varying is rejected by the data, perhaps due to the temporallyshort panel.


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Figure 1. A schematic of a comprehensive model of dairy farming

variables and measurement error. Thus thefactors depicted infigure 1 that are not mod-

eled appropriately in the first stage must beincorporated in the second stage. In the firststage we specify a stochastic production fron-tier with a single output (an index of dairy farmoutput), three conventional inputs (labor andindexes of capital and variable inputs), a sin-gle environmentally detrimental input (nitro-gen surplus), and a time trend. Disembodiedtechnological change is captured by the timetrend, and embodied technological change iscaptured by interactions of the time trend withthe conventional inputs. Environmental effi-ciency is calculated, conditional on stochasticdisturbances, as in (7). Thisfirst-stage analysisis described in detail in Reinhard, Lovell andThijssen.

Two elements of the production processare not taken into account in the first stage

at all; namely the physical environment andthe institutional environment (see figure 1).These elements are outside the control of thefarmer.

Another problem is the measurement ofthe variables used in the first stage. Variableswhose productive capacities are partly incor-porated will cause apparent inefficiencies. Theproductive capacity of family labor in the firststage is expressed in hours. Labor quality isnot accounted for in this variable, and produc-tive capacity depends on quality as well. La-bor quality includes the farmers ability andlearning by doing. Variable inputs and out-puts are aggregated from several components.Information about quality is relatively well


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preserved in this aggregation (Reinhard,Lovell and Thijssen). However, information islost about the nitrogen content of the inputsand the outputs. The quantity of nitrogen ininputs and in outputs defines the nitrogen sur-plus. To correct for this loss of information, we

use variables reflecting nitrogen content of in-put and output.By using the aggregate capital stock vari-

able in the production frontier we implicitlyassume that the capital service flow of itscomponents is identical. Capital service flowscannot be measured directly. Therefore, weuse additional information on capital services.The physical environment captures differencesin physical environment (e.g., solar radiation,soil, and infrastructure). The institutional en-vironment is determined by government regu-

latory agencies and is outside the control ofthe farmer. In the research period the im-pact of Dutch environmental policy differsacross farms, because it depends on the phos-phate surplus. In figure 1 factors that are notmodeled in the first stage are marked onlywith II, while factors that are not completelymodeled in the first stage are marked withI and II.

Technological development is assumed tobe captured in the first stage. However, the

technology employed can differ across indi-

Table 1. The Explanatory Variables Used

Variable Mean Min Max SD

Labor qualityAgricultural education (4 categories)

Age of manager (years) 47.5 21 78 11.4Years manager 20.2 1 54 11.1Years FADN 2.7 1 9 1.9Labor share of manager (%) 85.2 12.5 100 17.0

Off-farm income (NLG) 28,091 0 365,203 24,579Nitrogen in inputsFeed per cow (NLG/cow) 1110.8 122.6 6139.4 627.3N fertilizer per hectare (kg/hectare) 256.1 0 569.9 84.7

Nitrogen in outputDairy (%) 78.2 66.7 99.9 5.6

Capital specificationNumber of cows 75.7 11 270 41.4Sales and growth (NLG/cow) 743 641 2,850 258Milk yield (kg/cow) 5315 2326 7673 868

Physical environmentSoil types (7-soil-type dummies)

Region (5 regional dummies)Institutional environment

Year dummies (three-year dummies)

Note: The proportion of observations described by the dummy variables appears in table 2.

vidual farms. In a stochastic production fron-tier setting, the economic behavior of farmersis implicitly assumed to be either output orprofit maximization, conditional on the inputs.The pursuit of alternative objectives or farm-ing styles can also be a reason that farmers are

not on the frontier (Van der Ploeg, Rentingand Roex). Farming styles are not readily ob-servable (Dijk et al.), and so they cannot beused as explanatory variables. We might missminor relations as well, and also some mea-surement error is still present in the variablesspecified in thefirst stage. Also the proxies weuse in the second stage do not capture thesefactors completely. For instance region and soiltype are used to describe the physical envi-ronment, but these two variables cannot cap-ture all variation in solar radiation, precipita-

tion, etc. Therefore, we also have to deal withan omitted variables problem in the secondstage. The variation of these omitted charac-teristics in the second stage, U, is capturedby the inefficiency component of the second-stage stochastic frontier model. The second-stage regression model is expressed in log-linear form, the variables appear in table 1,and alternative estimation procedures areused.

In table 1 we present the available explana-

tory variables in the Dutch Farm Accountancy


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Data Network (FADN).8 We use off-farm in-come as a proxy for the drive and motiva-tion of the farmer. The abilities and capaci-ties of the farmer are proxied by education,which is distinguished in four categories. Theshare of the managers labor in total family

labor is used to proxy the quality of familylabor (we assume that the manager is morehighly qualified than the other family mem-bers). The background and experience is prox-ied by the age of the manager, the number ofyears of experience as a farm manager, andthe number of years of participation in FADN.FADN participants receive an extensive bal-ance sheet and nutrient account, and they havereduced their nutrient surplus more than farm-ers without a nutrient account (Poppe et al.,p.78).

Nitrogen in inputs is proxied by the quan-tity offeed bought per cowand the quantityof nitrogen fertilizer per hectare. The quantityof purchased feed per cow also incorporatesinformation about the presence of intensivelivestock. The share of dairy farming in totalproduction is an indicator of the specializa-tion of the production process and is an indica-tor of nitrogen in output. Differences betweenintensive and extensive farming are includedin the variable feed bought per cow. Vari-

ation between specialized and mixed farm-ing is captured by nitrogen in outputs. Capi-tal specification is obtained by the size of theherd andsales and growth per cow; the milkyield is an indicator of the quality of the herd.We do not use farm size as a separate vari-able because it is strongly correlated with herdsize.

The physical environment is proxied by soiltype and regional dummies. The regional dum-mies reflect differences in solar radiation, wa-ter availability, infrastructure, etc. Changesin the institutional environment are proxiedby year dummies. We assume that regula-tion affects all farmers identically. The yeardummies also incorporate annual differencesin weather conditions. All variables, Zi t, ex-cept the dummy variables are normalizedby their sample means. The normalized vari-ables are independent of units of measure-ment, and the mean impact of each variable iszero.

8 A brief description of theDutchFADN, andsummary statisticsforthe first-stage variables (Yit,Xit,Nit ),canbefoundinReinhard,Lovell and Thijssen.

An allocation rule whether to put the Zj i tin thefirst stage or second-stage frontier doesnot exist (to our knowledge). In thefirst stageproduction frontier inputs obeying the charac-teristics of inputs (substitution, nondecreasingrelation with output) were used. In the second

stage, Zj i tspecifying characteristics of the in-puts (and output) are used that are expectedto influence environmental efficiency. No Zj i tis an input, according to the conventional def-inition.

An Empirical Investigation into theDeterminants of Environmental Efficiency

In this section we implement the second stageof our two-stage model. In this stage, we quan-tify the relationships between the explana-tory variablesand theenvironmental efficiencyscores EEi twe obtained in the first stage. Sinceit is the environmental efficiencies we wishto explain, we summarize the distribution oftheir temporal means in figure 2. With an over-all sample mean of 0.441 and standard devia-tion of 0.249, there is considerable variation inenvironmental efficiency to be explained in asecond-stage regression.

We used the software package FRONTIER(Coelli) to generate maximum likelihoodestimates of the stochastic environmental effi-ciency frontier specified in (8). We began withthe entire set of explanatory variables listed intable 1. The contribution of each variable wasevaluated by computing the likelihood-ratiotest statistics. Variables not contributingsignificantly (at the 90% level) were deleted.The dummy variables for soil and educationwere aggregated into a smaller number of cat-egories whenever the number of observationsin a category was small and the categories hada comparable impact. We used one dummyvariable for education (1= agricultural educa-tion or higher education; 0 = otherwise), twodummy variables for soil type (Soil 1= 1 if soiltype is sea sediment clay; 0 = otherwise; Soil4=1 if soil type is sand; 0=otherwise), andone dummy variable for region (Region 3 = 1if location is in eastern, middle and southernlivestock regions; 0=otherwise). Number ofyears of experience as farm manager, off-farm

income, growth and sales per cow and the1992dummyvariable,werealsonotsignificant,and were deleted. The remaining explanatoryvariables in the model and their parameter


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Figure 2. Histogram of environmental efficiency scores (613 farms)

estimates appear in the MLE column oftable 2.9

We tested the appropriateness of the fron-tier specification by computing the skewness ofthe OLS residuals. The

b1statistic (Schmidt

and Lin) is0.76, indicating that the OLSresiduals do indeed exhibit the expected neg-

ative skewness, and that a stochastic frontiermodel is appropriate. We tested the robust-ness of the model by supplying starting valuesdiffering from the OLS estimates, and foundthe parameter estimates to be robust to alter-native starting values. The half-normal restric-tion on the truncated normal model was re-

jected, with a likelihood-ratio test statistic of69.6 for the null hypothesis that = 0. Theestimated value of [= 2U/(2V + 2U)] in-dicates that environmental inefficiency exists,with a likelihood-ratio test statistic of 1,872 forthe null hypothesis that = 0. The relativelylarge estimatedvalue ofindicates that almosttheentireerrorinthesecondstageisduetoun-explained (by the explanatory variables) envi-ronmental inefficiency. The role of statisticalnoise in explaining the original environmentalefficiency scores is very small. The hypothesisthat the farm-specific conditional environmen-

9 Also a model was estimated in which two additional explana-tory variables, feed per cow and nitrogen fertilizer per hectare,were deleted. Although each is statistically significant in the MLEcolumn, and a likelihood-ratio test rejects the hypothesis that theycan be deleted from the model, each is arguably not exogenous.Fortunately, estimates and significance levels of the remaining pa-rameters arerobustto thedeletion of these twovariables. The rankcorrelation coefficient for the two sets of results is 0.981.

tal efficiencies vary through time is rejected.Thus we have 1,545 observations on EEi t butonly 613 observations on CEEi .

As a robustness check, the final two columnsoftable2reportOLSandtobitestimatesoftheparameters describing the environmental effi-ciency frontier. As expected, OLS and tobit

parameter estimates are very similar, and bothare very different from the MLE parameterestimates. These patterns provide compellingevidence in favor of the use of a stochastic fron-tier in the second stage.

The MLE parameter estimates presented intable 2 convey two types of information: (i)the impacts of the explanatory variables onenvironmental efficiency; and (ii) estimates ofconditional environmental efficiency, obtainedfrom (9) as the ratio of observed to maximumfeasible environmental efficiency.

The parameter estimates provide estimatesof partial elasticities of EEi t with respect toeach explanatory variable. For most variablesplausible expectations can be formed of thesigns of the partial elasticities, but we haveno expectations of their magnitudes. Agricul-tural education or higher education (in con-trast to no education or general education)is positively (at 90%) related to environmen-tal efficiency. Experience, as measured by theage of the farm manager, has a negative ef-

fect on EEi t. This is in line with Weersink,Turvey and Godah (p. 453), who argue thatinexperienced farmers tend to be more knowl-edgeable about recent (environment-friendly)technological advances than are their older


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Table 2. Parameter Estimates for the Stochastic Environmental Efficiency Frontier

MLEOLS Tobit

Parameter Standard Parameter ParameterVariablesa estimate error estimate estimate

Constant 0.294 0.062

0.577

0.571

Labor qualityAgricultural education (0.89) 0.097 0.052 0.056 0.061Age of the manager 0.099 0.057 0.468 0.464Years in FADN 0.059 0.021 0.019 0.017Manager share in family labor 0.109 0.045 0.070 0.070

Nitrogen in inputsFeed per cow 0.209 0.035 0.476 0.472N fertilizer per ha 0.162 0.035 0.171 0.171

Nitrogen in outputsPercentage dairy 0.560 0.188 1.585 1.583

Capital specification

Number of cows 0.206 0.034 0.572 0.573Milk yield 0.706 0.114 1.948 1.943Physical environment

Soil 1 (0.13) 0.200 0.053 0.475 0.477

Soil 4 (0.49) 0.151 0.047 0.160 0.156Region 3 (0.37) 0.171 0.049 0.211 0.220

Institutional environmentDummy 1993 (0.26) 0.079 0.021 0.057 0.058Dummy 1994 (0.25) 0.101 0.024 0.122 0.121

4.203 0.457 0.983 0.0022 4.494 0.439

aThe parenthetical value behind the dummy variables indicates the percentage of the total observations that are described by each dummy

variable.bThe parameter estimate differs significantly from zero at the 95% level.

counterparts. Participation in the FADN hasa positive and significant effect onEEi t, prob-ably due to the knowledge farmers gain fromextensive balance sheets and nutrient accountsprovided by LEI. The ratio of the quantity oflabor by the manager(s) to total family laborhas a positive and significant effect on EEi t.

The herd size has a negative and significant ef-fect on EEi t. Since more cows produce moremanure, this estimate seems logical. Remark-ably, the herd size is positively (significantly)correlated to the technical efficiency measure.Although we selected highly specialized dairyfarms from the FADN, these farms can stillhave other activities such as fattening hogs orveal calves. Farms with a relatively high shareof dairy activities are significantly more envi-ronmentally efficient, because they are not in-volved in activities producing a large nitrogensurplus. Milk yield is strongly positively relatedto environmental efficiency. Farms purchasingconsiderable feed per cow (these farms arealso likely to have hogs or poultry) are signif-

icantly less environmentally efficient. Not sur-prisingly, a large nitrogen fertilizer applicationper hectare is significantly and negatively re-lated to environmental efficiency. The effect onenvironmental efficiency of having type 1 soil(rich in clay from sea sediments) is significantlypositive, since this is a fertile soil type. Con-

versely, type 4soil (which is sandy) has a sig-nificantly negative effect, since sand is the leastfertile soil type in the Netherlands. Locationin region 3 (a combination of eastern, middle,and southern regions specialized in livestockproduction) has a significantly positive impacton environmental efficiency. A possible expla-nation is that the concentration of livestockfarms in these regions results in a relatively lowfeed price. Finally, environmental efficiencyexhibited significant improvement in 1993 rel-ative to 1991 and 1992, and again in 1994 rela-tive to 1993 (in accordance with the results ofReinhard, Lovell and Thijssen).

Explanatory variables with a large partialelasticity (see table 2), and which can be


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Table 3. Comparison of Environmental Efficiency Scores EEi andConditional Environmental Efficiency Scores CEEi(613 observations)

Mean Minimum 25th (%) Median 75th (%) Maximum

EEi 0.434 0.00+ 0.239 0.429 0.621 0.95

CEEi 0.572 0.00+ 0.374 0.626 0.793 0.95

influenced relatively easily by the government,are the main targets for designing policy. Themilk yield shows the largest elasticity. A moreproductive breed of cows can be expected toincrease environmental efficiency. This leadsalso to a reduction of the herd, conditionalon the milk production, which will raise en-vironmental efficiency as well. The govern-ment can improve the milk yield by encour-

aging genetic research. Reduction of feed percow and nitrogen fertilizer per hectare will in-crease environmental efficiency as well. Whileour methodology does not provide guidanceon how to implement these reduced applica-tions of variable inputs, technical extensionservices can focus on suitable strategies toreduce the amount of feed per cow and ni-trogen fertilizer per hectare. The mineral ac-counts that have been mandatory since 1998for farms with more than 2.5 milk cows perhectare (or equivalent livestock) will stimulatefarms to reduce their nitrogen consumption aswell, and provide insight into their nutrient ac-counts. Young, well-educated farmers are themore environmentally efficient farmers, and soeducation of older farmers to acquaint themwith new environment-friendly technologiesis likely to increase environmental efficiency.Farmers participating for a longer period inthe FADN learn from the information theyreceive in the FADN balance sheets and nu-trients accounts. When they are provided with

extensive balance sheets and nutrient accountsof their farms, farm managers can be expectedto learn how to improve their environmentalperformance.

The unexplained shortfall of environmen-tal efficiency beneath best practice observedin the sample is reflected in U. Conditionalenvironmental efficiency scores CEEi , ad-

justed for the explanatory variables charac-terizing each farm, are computed from Uiusing (9). They provide estimates of environ-mental efficiency, conditional on the explana-tory variables. Whereas in the first stage a farmmay be penalized for its unfavorable circum-stances, these factors are accounted for in thesecond stage. Farm managers may rightly ob-

ject to their EEi tscores, which do not incorpo-rate exogenous influences, but they have lessreason to object to their CEEi scores, whichdo incorporate exogenous influences.

First-stage temporal means of EEi t scores(noted asEEi ) and second-stageCEEi scoresare summarized in table 3. The sample meanof theCEEi is 0.572 with a minimum value of0.00+ and a maximum value of 0.95. In 94%

of the observations CEEi >EEi . The rankcorrelation of the first-stage EEi scores andthe second-stage CEEi scores is 0.926. Thedispersion of the EEi scores and the CEEiscores is of similar magnitude, with samplestandard deviations of 0.235 and 0.252, respec-tively. The distribution of the conditional envi-ronmental efficiency scores appears in figure 3.The distribution differs in large part from thedistribution of the first-stage environmentalefficiency scores because no distribution isimposed on the EEi t scores while the CEEiscores are assumed to follow a truncated nor-mal distribution.

The adjustment to EEi t scores by the ex-planatory variables leads to generally higherCEEi scores. The unexplained part ofEEi tre-flected byCEEi is due to a number of factors.We had to use proxies to model the factorsomitted in the first stage or not accurately mea-sured. We could not incorporate all relevantinformation in the second stage (e.g., farmingstyles). We modeled only the most important

factors; for instance solar radiation, temper-ature, and water are not modeled explicitly.The first-stage environmental efficiency scoresmay not accurately reflect environmental effi-ciency (Reinhard, Lovell, and Thijssen, p. 56,footnote 11).

The magnitude of theCEEi gives an indica-tion of the problem confronting environmentalpolicy. We do not have explanatory variablesavailable to clarify this portion of environ-mental efficiency. Therefore, we cannot read-ily provide instruments for policy makers toreduceCEEi . Complementary methods (e.g.,visiting farms) may provide more insight intothe factors determining CEEi . For instance,farmers can be interviewed to obtain more


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Figure 3. Histogram of conditional environmental efficiency scores (613 farms)

information about their objectives and moti-vation, and other elements of dairy farmingthat cannot be captured in a balance sheet.

Summary and Conclusions

We have developed an analytical frameworkwithin which to estimate the impact of var-ious explanatory variables on environmentalefficiency as a second-stage stochastic environ-mental efficiency frontier. The environmen-tal efficiency scores were computed in afirst-stage analysis. Our second stage differs fromother approaches found in the literature, be-cause we apply a stochastic frontier in the sec-ond stage. The second-stage parameter esti-mates reflect impacts of explanatory variablesthat can guide policy to increase environmen-tal efficiency. This methodology also generatesestimates of conditional environmental effi-ciency that identify farms with relatively highand relatively low environmental efficiency,conditional on their explanatory variables. Weshowed that the second stage can be imple-mented empirically, by estimating conditionalenvironmental efficiency scores for each farmin a panel of 613 Dutch dairy farms during the199194 period.

The mean conditional environmental effi-

ciency is higher than the first-stage environ-mental efficiency because we explain a por-tion of the environmental efficiency with theexplanatory variables (among others, indica-tors of labor quality and the physical environ-

ment). We found that insight into the nutrientbalance, and the milk yield each affects the en-vironmental efficiency score positively.

[Received January 2000; final revision receivedSeptember 2001.]

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