Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=raee20

Download by: [Research & Evidence Division] Date: 24 October 2016, At: 00:15

The Journal of Agricultural Education and ExtensionCompetence for Rural Innovation and Transformation

ISSN: 1389-224X (Print) 1750-8622 (Online) Journal homepage: http://www.tandfonline.com/loi/raee20

The perceived impact of agricultural advice inEthiopia

Alexander Hamilton & John Hudson

To cite this article: Alexander Hamilton & John Hudson (2016): The perceived impact ofagricultural advice in Ethiopia, The Journal of Agricultural Education and Extension, DOI:10.1080/1389224X.2016.1245151

To link to this article: http://dx.doi.org/10.1080/1389224X.2016.1245151

Published online: 23 Oct 2016.

Submit your article to this journal

View related articles

View Crossmark data

The perceived impact of agricultural advice in EthiopiaAlexander Hamiltona and John Hudsonb

aDepartment for International Development, Sudan, BFPO 5312, Ruislip, UK; bDepartment of Economics,University of Bath, Bath, UK

ABSTRACTPurpose: We examine the impact of advice given by extensionagents to Ethiopian farmers, as perceived by the farmersthemselves. Design/methodology/approach: Using survey data from2014, we analyze the perceived impact of advice on farmers’incomes and crop yields. We use a bootstrapped instrumentalvariable (IV) estimator and the conditional mixed processestimator. Theoretical implications: The impact of advice willdepend upon its relevance and whether and how efficiently it isimplemented by the farmer. This in part depends upon thefarmer’s ability and on the impact of fully implemented advice onoutput, which will vary from farm to farm. Findings: There is apositive perceived impact of most advice on both crop yields andincome. However, some advice works better in drought-affectedareas and other in non-drought-affected areas. Fertilizers havemore impact on crop yields than income, possibly reflecting costfactors. There is evidence that the farmers’ ability to implementthe advice increases with their level of education and that adviceis being tailored to the needs of the individual. Practicalimplications: Advice has a positive impact on both crop yields andincome. However, not all advice is equally effective andeffectiveness varies according to farmer and farm characteristics.There is little evidence of credit advice having a positive impact.Originality/value: The paper is one of only a few to analyzefarmers’ perceptions of advice impact and, as far as we are aware,is the first to analyze how advice effectiveness varies according tofarmer and farm characteristics.

ARTICLE HISTORYReceived 24 August 2015Accepted 3 October 2016

KEYWORDSExtension agents; farmers’income; crop yields; fertilizeradvice; land managementadvice; credit advice

Introduction

A core part of the Ethiopian government’s investment in agriculture is the public agricul-tural extension system, with a substantial increase in the number of people graduating asextension or development agents (DAs) (Davis et al. 2010). The Government has alsoestablished farmer training centers, with two hectares of demonstration fields (Lefort2012), in every local administrative area (there are 18,000 nationwide) with three exten-sion agents at every training center. This effort could lead to Ethiopia having theworld’s highest ratio of extension agents to farmers1 and is reflected in the proportionof fields that use extension services rising from just over 5% in 2008 to 12% in 2011(Khan et al. 2014).

© 2016 Wageningen University

CONTACT John Hudson j.r.hudson@bath.ac.uk Department of Economics, University of Bath, Bath, BA2 7AY, UK

JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION, 2016http://dx.doi.org/10.1080/1389224X.2016.1245151

There is evidence of an impact on agricultural efficiency. Dercon et al. (2009) showedthat one visit from a DA raised production growth by 7% and reduced poverty by 10%.The posting of agricultural extension agents in local communities has improved theirattentiveness to farmers’ needs and constraints, and enhanced the working relationshipbetween them (Cohen and Lemma 2011). However, others have tended to be more scep-tical about the impact of extension agents (Lefort 2012; Pender, Place, and Ehui 2006).Davis et al. (2010) also note that agricultural productivity remains low, inputs arescarce and expensive, and market and credit access are extremely limited and accordingto Buchy and Basaznew (2005), women-focused extension is also limited.

In general, Ethiopia has tended to be viewed unfavorably in terms of agricultural pro-ductivity (Dercon and Christiaensen 2011; Spielman et al. 2010). However in recent years,certainly since 2008, there has been a substantial, even dramatic, improvement in cerealyields. This has been accompanied by a 12.5% expansion in land under cereal productionbetween 2003 and 2012; hence it is not the case that more marginal land has disappearedfrom the picture, raising the average productivity of what is left. Apart from the extensivechanges to the extension system, other changes impacting on Ethiopian agricultureinclude substantial transport improvements which between 2000 and 2013 tripled thelength of all-weather surface roads, and a rapid increase in the urban population ofsome 3.7 million (Bachewe et al. 2015). Together these have increased the market for com-mercial crops and partly as a consequence output–input price ratios have increased sub-stantially. There has also been increased access to credit, which saw the number of activeborrowers rise from about half a million in 2003 to approximately 3.5 million in 2014.

In this paper, we will be focusing on the potential role of the extension system in explain-ing this recent success. Specifically, we analyze perceptions of the difference that the advicemade to the recipients of that advice with respect to (i) crop yields and (ii) income. Self-perceptions data have the advantage that the focus is on the difference made by the exten-sion service advice, whereas actual output and income can change for numerous reasonsunconnected with that advice. To include all the factors that might affect yields andincome is difficult and such data are also likely to be subject to measurement errors. Weassume that the farmers in evaluating the impact of the advice take account of all theseother factors. In addition, because the dependent variable relates to the impact of advice,we can examine how that impact varies with variables such as the farmer’s age, educationand water resources. Other studies have tended to focus on the impact of variables suchas education on productivity, but not on the effectiveness of advice.

The main research question is whether extension advice benefits farmers both in termsof their crop yields and income. We expect this to be the case, but the literature is ambig-uous on the issue. A secondary question is what types of advice work best and in whatcontext. There is relatively little literature on this. However, there is a literature whichsuggests that different types of technology, for example, fertilizers, work better in someconditions rather than others (Dercon and Christiaensen 2011; Kassie et al. 2010). Thisis of relevance, but advice on implementing a technology is different to the implemen-tation of that technology. In addition part of this research relates to more than adecade ago and, as already noted, in this time the extension system has substantiallychanged, technology has changed and Ethiopia has changed. Hence, there is a need fornew research in this area.

2 A. HAMILTON AND J. HUDSON

We will be analyzing the impact of advice on (i) agricultural practices, (ii) land manage-ment, (iii) fertilizers, (iv) marketing, (v) access to credit facilities and (vi) animal husban-dry practices. We find that extension agent advice does lead to a perceived positive impacton both crop yields and income. However, the effectiveness of the advice depends uponthe level of education of the farmer. It also differs between drought-affected and non-drought-affected lands. The paper proceeds as follows. In the next section we discussthe relevant literature. We then discuss, from a theoretical perspective, how advicemight impact on farmers and other methodological issues, and we also present thedata. The penultimate section presents the results and finally we conclude the paper.

Background

The literature on the extension program has been somewhat ambivalent in terms of itsimpact. Extension services generally have positive impacts on nutrition and povertyreduction (Dercon et al. 2009). However, their success has been said to be constrainedby weaknesses elsewhere in the system. Hence EEA/EEPRI (2006) argue that distributionchannels and institutions are flawed, the formal seed system has weaknesses, and there is alack of markets, both for inputs and outputs. Agents transfer knowledge to farmers, withrelatively little knowledge flow in the reverse direction (Buchy and Basaznew 2005), whichcan lead to the knowledge not being tailored to the farmer’s needs. The literature arguesthat the extension system has focused on the distribution of standard packages to farmers,including seeds and commercial fertilizer, credit, soil and water conservation, livestockand training. Efforts to promote other aspects of sustainable land management have con-centrated on soil erosion without consideration of the underlying socioeconomic reasonsfor low soil productivity (Kassie et al. 2010). As a consequence, advice has been givenwhich has been unprofitable, risky or irrelevant given the farmer’s resource constraints(Pender, Place, and Ehui 2006). Lefort (2012) cites research which concludes the extensionprogram was not much use to farmers. However, Spielman et al. (2010) also note that aseries of reforms have been made to redress these weaknesses.

Berhanu and Poulton (2014) argue that the extension system is used to promote gov-ernment control, with extension workers facilitating scrutiny and control of activities.Extension workers engage in non-extension activities, such as administration, creditrepayment and tax collection (Kelemework and Kassa 2006). Apart from diverting theirattention from extension services, this can also strain relationships with local farmers.In addition, it is claimed that in their allocation of seeds, fertilizers and credit, extensionworkers prioritize farmers loyal to the governing coalition. It is also important to stressthat extension agents are not always transmitting knowledge and advice which canimmediately add to the farmer’s productivity. They may also be concerned with societalimpact. For example, in some cases the advice relates to environmental factors (Abegazand Wims 2015) for which the farmer may perceive little personal benefit, althoughthis is not to say that such benefit is absent.

More recent evidence is a little more positive, although still emphasizing that practicescould be better. Elias et al. (2013) found that participation in the extension programincreased productivity by about 20%. Other factors which influenced productivityincluded age, male head of household and plot characteristics. Despite this, crop yieldswere below the targets set by the extension program. Khan et al. (2014) conclude that

THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 3

woreda-level spending on agricultural extension workers is associated with higher yieldsfor major crops and increases the probability that farmers will improve their farmingtechniques.

There is relatively little evidence on the relative impact of different forms of advice, butsome related work has been done on the different constraints facing, and the differenttechnologies used by, farmers. One of the reasons Dercon and Christiaensen (2011)gave for the poor performance of Ethiopian agriculture was lack of fertilizer use. Lackof knowledge and skills in adopting modern inputs was only a very minor constraint in1999, which would suggest only a limited role for extension agent advice in this respect.Nor was lack of credit deemed a major factor. Fertilized plots were characterized bygreater yields than non-fertilized plots, although not in periods of extreme droughtsand floods. Thus, Dercon and Christiaensen argued that fertilizer use is a high return,but high risk technology. Kassie et al. (2010) find evidence of a strong impact of land man-agement practices on agricultural productivity in the low agricultural potential areas. Inthe high agricultural potential region, however, fertilizers have a very significant and posi-tive impact on crop productivity, whereas land management practices have no significantimpact. Fertilizers may be less profitable in such areas due to a lack of soil moisture.Hence, their analysis raises the important point that the impact of different forms ofadvice and increasing knowledge may not be the same in all areas, but vary accordingto local conditions.

Methods

We will be analyzing the impact of advice on both crop yields and inccome. For themoment, we focus on the impact on crop yields (Y), although the analysis for incomefollows a similar path. We assume Y to be a function of resources, which are in turn a func-tion of knowledge:

Yit = Aig(Sit , Lit,Kit , Fit , Wit), (1)

where i denotes the ith farmer and t the time period. Ai denotes overall efficiency withwhich the different factors of production are used, that is, it is total factor productivity(TFP) defined at the level of the individual farmer. In our analysis, we assume that thisis the vehicle by which extension agent advice impacts on output. g(.) can be regardedas the basic output of the farm independent of the characteristics and expertise of thefarmer. The production function is composed of land (S), labor (L), capital (K), fertilizer(F) and water (W). The impact of extension agent advice to farmer i (Ei) on output is thengiven by

∂Yi

∂Ai

∂Ai

∂EiEi =

∑6

j=1

∂Yi

∂Ai

∂Ai

∂EijEij = g(Sit , Lit,Kit , Fit , Wit)

∑6

j=1

∂A∂Ej

Eij. (2)

This is the combined impact of the six specific types of advice in our analysis.2 Eij is a con-tinuous measure of the advice given to individual i on advice type j, although we only havea discrete measure of this, which takes a value of one if advice was given. It has a lowerbound of zero, which is operative when advice of this type is not given. The marginalimpact of any one piece of advice is composed of ∂Ai/∂Eij, the impact of this advice on

4 A. HAMILTON AND J. HUDSON

TFP, and secondly, the impact of TFP on output, which is from (1) just g(.). ∂Ai/∂Eij willdepend upon the characteristics of the farm (T). It may also depend upon the character-istics of the farmer, with more knowledgeable farmers more able to implement the adviceefficiently, although at the same time more knowledgeable farmers may be less likely toseek advice. In this case, we link knowledge to education (Ed) and age, the latterthrough learning by doing. Thus,

∂Ai

∂Eij= fj(Edi, Agei, Ti, Wi). (3)

The dependent variable is coded 1 if the extension agent advice was perceived as makingno difference, 2 if it made some difference and 3 if it made a lot of difference. The responselies in the kth category if:

ak−1 , g(.)∑6

j=1

fj(.)Eij , ak; k = 1, . . . , m. (4)

In our analysism = 3. Note that α0 = −∞ and αm = +∞, and hence we estimate just a1 anda2. Define Zi,k = 1 if g(.)

∑6j=1 fj(.)Eij is in the kth category, and otherwise Zi,k = 0. Lin-

earizing g(.)∑6

j=1 fj(.)) we can estimate both the coefficients and the dividing points (αk)between the different categories by ordered probit. The independent variables will includefarm characteristics, individual characteristics and dummy variables operative if a particu-lar type of advice was given. This suggests a number of hypotheses which we will betesting:

H1: Extension agent advice impacts positively on both crop yields and income.

H2: The degree of the impact for different types of advice will depend upon the characteristicsof the farmer and the characteristics of their farm.

H3: The degree of impact of this advice will be greater for more educated individuals and alsofor older individuals, although we can expect these individuals to be less likely to receiveadvice.

H1 reflects our first research question, on whether extension advice benefits farmers interms of both income and crop yields. The other two hypotheses relate to our secondresearch question as to what types of advice work best and under which conditions.Our theoretical analysis has helped inform both of our research questions. The advicewill make a difference if it is relevant advice and if the farmer, given their own andtheir farm’s characteristics, will implement it efficiently. The analysis also emphasizesthat different types of advice will have different impacts, which are linked to differencesin ∂Ai/∂Eij.

Data

The data were obtained from the Woreda and City Benchmarking Survey (WCBS) col-lected in 2014 using a multi-stage stratified sampling approach based on the remotenessand food security levels of households. Within each region the sub-sample size was deter-mined by population (based on census data). Data were collected on 326 kebele in 48

THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 5

woreda covering the whole of the country.3 In total 7429 individuals were interviewed.This survey is focused on rural areas. The variables we use are defined in Table 1. Infor-mation on the characteristics of the plot relates to whether the individual grew crops andhad animals. Most people sampled (60.8%) raised both animals and grew crops, a substan-tial proportion just grew crops (31.3%) and an even smaller proportion just had animals(7.9%).

The proportions receiving one, two, three, four, five and six types of advice as listed inthe appendix were 27%, 16%, 17%, 14%, 5% and 13%, respectively. Other advice is feasible,which is why 8% received none of the types of advice specified. Table 2 shows thesummary data as it varies across individual characteristics. It is noticeable that theyoung tend to be more likely recipients of advice, possibly reflecting that they will havelearnt less ‘by doing’. It is also noticeable how the highly educated are more likely tohave received advice on marketing and credit. The final two columns relate to theimpact this advice, in general, has had on crop yields and the individual’s income.4 Theresponses ranged from 1 (none) to 3 (a lot). Thus, we assume that output and incomecannot fall as a result of the advice received. The responses to both questions werefairly enthusiastic, although very slightly more so for crop yields than income. Thebiggest gainers in both respects tend to be the better educated. An important questionrelates to the role of the advice apparently not included for example, mechanization;crop protection measures etc.? To an extent they may be subsumed within one of theother categories, particularly agricultural practices, which as we note from Table 2 wasthe most frequently cited form of advice. Any advice which still falls outside these cat-egories would be picked up by the constant term in the regressions.

Regression results

In Table 3, we present the results relating to the impact on yields.5 Column 1 shows thatadvice received on animal husbandry, marketing and land management were all signifi-cant at the 1% level of significance and fertilizers at the 5% level. Advice on agricultural

Table 1. Data definitions.Socioeconomic, demographic variablesAge Age in yearsEducation Coded from 1 (no schooling) to 24 (degree) and 25 above degreeMale Coded 1 ifa maleFamily size Number of people currently living in the individual’s householdPlot characteristicsGrows crops Coded 1 if the individual grows crops, otherwise 0Rears animals Coded 1 if the individual rears animals, otherwise 0Drought Coded 1 if the individual suffers from regular periods of drought in the sense of a shortage of

drinking water, otherwise 0Received advice on (coded 1 for yes and 0 no)Agricultural practices; land management; fertilizer; marketing; credit facilities; animal husbandryImpactCrops improve/Incomeimproves

The difference the above advice has made to the crop yield/incomeranging from 1 (none) to 3 (a lot)

Kebele based variables (average of responses of others in the individual’s locality). Regional dummy variables relate to Tigray,Amhara, Oromiya, SNNP, Binshangul Gumuz, Afar, Somali and Gambela. Because of the small numbers of people in oursample who are in Gambela we join this region together with SNNP, the two being adjacent in the south west part of thecountry. We also join the second smallest region, Afar, with Tigray, who are adjacent on the northern edge of Ethiopia

6 A. HAMILTON AND J. HUDSON

practices was not significant. However being in receipt of advice on credit was significantlynegative. This does not imply that output was lowered as a result of the advice. Rather, itimplies that being in receipt of advice on credit increases the probability of extension agentadvice having no impact. However, this does alert us to a potential problem of endogene-ity. To the extent that the farmer is the one seeking this advice, rather than being profferedit by the extension agent or some other person, then it could signal that the individual is infinancial problems. At the very least it reflects an interest by the farmer in gaining access tocredit. The negative sign in the regression may be picking this up. Another aspect of endo-geneity is that it is possible that the individual selected for the advice is in some way moreable to use it.

Because of this possibility, in the second regression we instrument the advice variablesin a two-stage process. Firstly, we regress the advice variables on all the exogenous vari-ables present in column 1 plus a series of variables relating to the extent others in the indi-vidual’s kebele received different forms of advice. That is, taking credit as an example, foreach individual we calculate the average number of people in their kebele, excluding them-selves, who received advice on credit. These effectively act as instruments. The secondstage of the estimation takes the predicted values from these regressions and re-estimatesthe relationships shown in column 1. The results are shown in column 3.2. The mainchange is the insignificance of advice on marketing and also on credit. A Hausman testindicated that the two sets of coefficients were significantly different and hence theneed for an instrumental variable (IV) approach. This two-stage estimation technique issimilar to that employed by Adams, Almeida, and Ferreira (2009). The literature tendsto bootstrap the standard errors (Clarke and Windmeijer 2012) and this has been done.On each of the 100 iterations of the bootstrap, both stages of the two-stage estimation tech-nique were estimated. The t statistics reported are based on these standard errors.

In addition to the above, we also estimated the equations using IVs in the context of aconditional mixed process (cmp) model (Roodman 2011), that is estimating a systemwhere the different equations can have different kinds of dependent variables. Weassume joint normality of the error terms of the different equations. It is a full system tech-nique, which takes account of potential correlation between the different error terms in theequations. There are potentially eight equations in the system, one for each of the advicevariables and two more for the impact variables, that is, the impact on crops and income.However, this makes considerable demands on the data and we had to simplify theequations in order to obtain estimates. Firstly, we estimated each of the policy impact

Table 2. Summary data relating to individual characteristics.Animal

husbandry Credit Marketing FertilizerLand

managementAgriculturalpractices

Cropsimprove

Incomeimproves

All 0.62 0.291 0.329 0.679 0.524 0.817 2.36 2.3Young <30 0.631 0.321 0.329 0.731 0.561 0.847 2.37 2.29Older ≥30 0.612 0.264 0.326 0.634 0.488 0.787 2.35 2.29Male 0.621 0.278 0.317 0.665 0.525 0.815 2.36 2.3Highlyeducated

0.585 0.342 0.447 0.707 0.523 0.845 2.51 2.41

No education 0.622 0.292 0.313 0.678 0.501 0.804 2.322.28

Notes: The final two columns relate to the average response which varied from 1 (none) to 3 (a lot) to the difference thesupport has made. All other columns relate to the proportion receiving advice in the different headings.

THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 7

Table 3. Regression results: impact on crop yields.Sample Probit: Full 3.1 IV 2 stage Full 3.2 Probit: Full 3.3 Drought 3.4 No drought 3.5 CMP: Full 3.6 Drought 3.7 No drought 3.8

Extension agent adviceAnimal husbandry 0.255**

(5.35)0.4696*(2.41)

0.4695*(2.41)

0.9789**(4.07)

0.1662(0.52)

0.6085**(5.27)

0.6273**(4.44)

0.648**(3.22)

Credit −0.2344**(4.77)

0.0069(0.03)

−0.0284(0.12)

−0.2431(0.95)

0.9285*(2.11)

−0.3592*(2.42)

−0.1748(1.03)

−0.2966*(2.18)

Marketing 0.4172**(8.76)

0.3156(1.72)

0.4122*(2.38)

−0.0737(0.35)

0.9992**(2.91)

0.5683**(4.45)

−0.1576(0.93)

1.452**(13.77)

Fertilizers 0.0884*(1.98)

0.8863**(3.68)

1.006**(4.24)

0.8349*(2.46)

0.7405*(2.01)

0.8513**(7.69)

0.9966**(8.28)

0.7314**(4.29)

LM – land management 0.2874**(6.47)

0.6833**(3.09)

AG – agricultural practices 0.0756(1.30)

0.1408(0.61)

LM + AG 0.8374**(2.69)

0.3536(0.73)

2.378**(4.74)

1.134**(5.82)

0.3289(0.74)

1.222**(4.67)

Individual characteristicsLog age 0.2891**

(4.52)0.1094(1.62)

0.102(1.51)

−0.0723(0.83)

0.1991(1.79)

0.0518(0.78)

−0.0664(0.75)

0.1341(1.33)

Education 0.0264**(5.26)

0.0244**(4.05)

0.0249**(4.16)

0.0192*(2.34)

0.0236**(2.91)

0.0276**(5.61)

0.0262**(3.86)

0.0122(1.67)

Male −0.0538(1.52)

Log family size −0.0785(1.78)

Farm characteristicsCrops 1.316**

(12.81)0.6006**(3.43)

0.4652**(3.04)

0.058(0.26)

0.3388(1.39)

−0.2539(1.60)

−0.3369(1.18)

0.279(1.14)

Animals −0.0251(0.51)

−0.3715**(2.91)

−0.3685**(2.83)

−0.3783*(2.35)

−0.4832*(2.50)

−0.1909*(2.16)

−0.2073(1.94)

−0.1815(1.22)

Constant −1.562**(5.20)

−1.232**(2.97)

−2.250**(4.99)

Estimated cutoff pointsCutoff point 1 a1 0.2798

(1.06)−0.7927**(2.66)

−0.7646*(2.56)

−0.9719*(2.26)

−0.3325(0.69)

Cutoff point 2 a2 2.573**(9.65)

1.477**(4.91)

1.503**(5.00)

1.570**(3.65)

1.801**(3.71)

Observations 5192 5185 5185 2928 2257 5192 2932 2260Log likelihood −3733 −3768 −3770 −2010 −1534 −12516 −7489 −4612X2 1228 1089 1089 380.9 850.8 355571 159143 6384

Notes: Equation 3.1 estimated by ordered probit, 3.2–3.5 by a two stage instrumental variable ordered probit with bootstrapped standard errors, 3.6–3.8 by a conditional mixed processor estimatorwith a binary dependent variable. (.) denotes t statistics and **/* denotes significance at the 1% and 5% levels. Regional dummy variables included in all regressions.

8A.H

AMILTO

NANDJ.H

UDSO

N

variables separately. Secondly, we used binomial probit for the impact equations ratherthan ordered probit, with the variable differentiated between a lot of impact (coded 1)and some or no impact. Finally, we combined two of the advice variables together, thatis land management and agricultural practices. These potentially relate to all farmersand also to farming per se rather than other aspects of the business and have a reasonablyhigh correlation. The cmp estimator is complex, but the interpretation of the coefficientsand t statistics is as with techniques such as OLS.

The results are shown in column 3.6 and are similar to previously. The main differenceis the significance, at the 1% level, of the advice variable relating to both agricultural prac-tices and land management and also marketing advice. In column 3.3, we replicate theseusing the two stage approach used in 3.2. Comparing 3.3 and 3.6, the only significantdifference relates to the negative coefficient on the credit advice variable. Using both tech-niques provides a robustness check on the findings and the cmp results largely confirmthose of the two stage IV approach.6 Taking the equations as a whole, family size isnever significant, nor gender and were dropped after the first equation.7 More educatedpeople and older people tend to have benefited more from the advice than others, althoughthe latter only in 3.1. However, in the cmp regression the variable relating to crop growersis no longer positively significant.

The literature has suggested that the impact of advice may vary according to the con-ditions facing the individual, for example soil moisture. We do not have in the database ameasure of this nor rainfall in the kebele, but we do have a variable which asked the indi-vidual whether they were usually subject to water shortages for drinking at some time inthe year. Slightly over 51% responded that they were subject to such shortages. We nowsplit the sample into those who were and were not subject to such water shortages.Columns 3.4 and 3.5 relate to those who were and were not subject to water shortages.The positive impact of marketing advice is limited to the latter as is that of land managementand agricultural practices. However, the negative impact of credit advice is evident only forthose in non-drought areas. Finally, we note that animal husbandry advice is only significantfor those in areas of drought and advice on fertilizers is significant in both areas. Theseequations were estimated using the two stage approach. Columns 3.7 and 3.8 show theresults of using the cmp estimator. The main difference is the positive significance ofanimal husbandry advice in both areas. In Table 4, we look at the results pertaining toincome. The results are very similar. This was to be expected, but it was always possiblethat because of expenses incurred in implementing the advice, a positive impact on yieldwould not translate into a positive impact on income. The main difference to Table 3 isthe significantly negative coefficient on credit advice in both areas, implying that receiversof such advice are significantly less likely to have perceived a beneficial impact on income.In addition, those with either crops or animals were also less likely to perceive benefits.

Because the regression is limited to those who received advice, the possibility exists forsample selection bias. When we tested for this in the two-stage IV regressions the evidencewas mixed. The inverse Mills ratio is insignificant in the income regression, but significantin the crop yield regression, although not in drought-affected areas. The significance of thecoefficients reported in Table 3 for the full sample for crop yields did not change and nordid those in the non-drought regression. The main difference in both these regressions wasan increase in the size of the coefficient on animal husbandry advice. The sample selectionequation included a kebele based variable reflecting the number in the kebele, other than

THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 9

Table 4. Regression results: impact on income.Sample Probit: Full 4.1 IV 2 stage Full 4.2 Probit: Full 4.3 Drought 4.4 No drought 4.5 CMP:Full 4.6 Drought 4.7 No drought 4.8

Extension agent adviceAnimal husbandry 0.326**

(7.20)0.8695**(5.52)

0.773**(4.82)

1.380**(6.33)

0.4807(1.62)

0.6428**(5.19)

0.8796**(6.24)

0.5656**(2.70)

Credit −0.2863**(6.27)

−0.0429(0.19)

−0.0815(0.35)

−0.5072(1.76)

0.4657(1.16)

−0.612**(4.10)

−0.6696**(3.36)

−0.4433**(2.85)

Marketing 0.4026**(8.83)

0.2882(1.63)

0.4343*(2.47)

−0.0009(0.00)

1.153**(3.73)

0.6562**(4.91)

−0.103(0.53)

1.466**(12.21)

Fertilizers 0.1489**(3.30)

0.149(0.59)

0.583*(2.40)

0.5567(1.65)

0.3363(0.82)

0.7453**(5.43)

0.901**(6.00)

0.7655**(3.49)

Land management 0.2292**(5.12)

0.6268**(2.61)

Agricultural practices −0.0486(0.85)

0.2364(1.08)

LM + AG 0.739*(2.23)

−0.0341(0.07)

2.039**(3.55)

1.332**(5.64)

0.0223(0.04)

1.164**(2.96)

Individual characteristicsLog age 0.1423*

(2.24)0.0612(0.92)

0.0787(1.17)

−0.1311(1.33)

0.1717(1.62)

−0.0833(1.25)

−0.1737(1.86)

−0.078(0.76)

Education 0.0177**(3.78)

0.017**(3.33)

0.0179**(3.47)

0.0147*(2.00)

0.0109(1.40)

0.0153**(3.14)

0.0173*(2.55)

−0.0046(0.63)

Male −0.0065(0.19)

Log family size 0.0081(0.19)

Farm characteristicsCrops 0.2137*

(2.22)−0.2432(1.63)

−0.2617*(1.96)

−0.0601(0.25)

−0.8143**(3.88)

−0.7315**(4.45)

−0.3371(0.98)

−0.3603(1.33)

Animals −0.1338**(2.81)

−0.5482**(5.07)

−0.6039**(5.42)

−0.4797**(3.21)

−0.8772**(4.73)

−0.3407**(3.67)

−0.1669(1.49)

−0.5536**(3.74)

Constant −0.939**(3.14)

−1.829**(3.39)

−0.6396(1.36)

Estimated cutoff pointsCutoff point 1a1 −2.423**

(4.16)−2.500**(8.96)

−1.043**(3.64)

−0.8468(1.87)

−1.210**(2.84)

Cutoff point 2a2 −0.3297(0.57)

−0.4381(1.57)

1.003**(3.51)

1.586**(3.52)

0.6052(1.40)

Observations 5195 5188 5188 2928 2260 5190 2930 2260Log likelihood −4096 −4154 −4177 −2095 −1843 −12556 −7433 −4688X2 697.6 590.5 566.6 301.6 535.1 111195 3517 64356

Notes: Equation 4.1 estimated by ordered probit, 4.2–4.5 by a two-stage instrumental variable ordered probit with bootstrapped standard errors, 4.6–4.8 by a conditional mixed processor estimatorwith a binary dependent variable. (.) denotes t statistics and **/* denotes significance at the 1% and 5% levels. Regional dummy variables included in all regressions.

10A.H

AMILTO

NANDJ.H

UDSO

N

the respondent, receiving advice. Apart from this, older and more educated people wereless likely to receive advice. Men were more likely to receive advice, as were those inlarge families and those with animals or crops. This is potentially consistent with thosewho report a bias in extension advice towards men (Buchy and Basaznew 2005). A variabledifferentiating drought from non-drought areas was insignificant.

In Table 5, we show the probabilities of the respondent finding that the advice made ‘alot of difference’ under different scenarios. These are obtained from the two stage instru-mental ordered probit regressions. They are based on a 45-year-old individual with crops,but no animals. The regional variables are averaged according to sample population. Thefirst element in the first column shows the probability for someone with the above charac-teristics who receives none of the specific types of advice listed and has no schooling. Thesecond column relates to someone with an education corresponding to ‘grade 8’, and thisraises the probability to 0.0627. In the second row, we have the probabilities for someoneof the above characteristics who received advice on animal husbandry. These are consider-able higher, reflecting the effective nature of this advice. However, the most effective formsof advice are on fertilizers and the combined advice on land management and agriculturalpractices. The next two columns relate solely to farmers in drought areas. The most impor-tant forms of advice are on animal husbandry and fertilizers, with land management andagricultural practices being third. This is in sharp contrast to the non-drought areas, wherethe most effective advice is on agricultural planning and land management. The results forincome are also shown. The main difference is the increased impact of marketing advice.Some of these impacts look quite small, although it should be borne in mind that they dorelate to the probability of having made ‘a lot of difference’ and also that multiple forms ofadvice are often given which substantially increases this probability.

Conclusions and policy implications

With respect to the main research questions, we find that extension agent advice impactspositively on both crop yields and income as reflected by the farmers’ own perceptions.

Table 5. Probabilities of advice have a ‘lot of difference’ under different scenarios.Full sample Drought areas Non-drought areas

Education None Grade 8 None Grade 8 None Grade 8

Crop yieldNone 0.040 0.063 0.059 0.082 0.0036 0.0067Animal husbandry 0.099 0.144 0.280 0.341 0.0059 0.0105Marketing 0.089 0.131 0.051 0.072 0.0458 0.0702Credit 0.037 0.059 0.036 0.051 0.0394 0.0612Fertilizer 0.226 0.299 0.233 0.289 0.0259 0.0415Planning and land man. 0.179 0.243 0.113 0.150 0.3790 0.4620IncomeNone 0.059 0.080 0.087 0.110 0.0146 0.0186Animal husbandry 0.214 0.264 0.508 0.561 0.0445 0.0545Marketing 0.129 0.166 0.088 0.110 0.1520 0.1760Credit 0.050 0.069 0.031 0.041 0.0431 0.0529Fertilizer 0.163 0.206 0.211 0.251 0.0325 0.0403Planning and land man. 0.204 0.253 0.082 0.104 0.4430 0.4820

Notes: These probabilities are based on regressions 3.3–3.5 and 4.3–4.5. They relate to a 45-year-old individual with crops,but no animals in a typical region. They show the probability of a single type of advice making ‘a lot of difference’ to cropyield and income.

THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 11

This confirms the first hypothesis and confirms the more favorable findings of Elias et al.(2013) and Khan et al. (2014), rather than, frequently older, research such as Pender, Place,and Ehui (2006) and Lefort (2012). We also find such advice to have a varying impactaccording to both the farmer’s characteristics, such as education, and the characteristicsof the farm. The pattern of significance suggests that agricultural advice yields its bestresults when targeted at those with the ability to use it, that is, the better educated.However, it is also suggestive perhaps that the advice given to less well educated peopleneeds to be different, made simpler or given in more detail. This helps answer thesecond of our research questions and partially supports the third hypothesis, althoughwe find only limited evidence of impact being related to age.

There are also significant differences in the impact of advice between different areas.This again relates to the second research question and provides confirmation of oursecond hypothesis. In drought-affected areas, advice on animal husbandry and fertilizersis most effective. In non-drought areas, advice on marketing and land management andagricultural practices is best. The impact of marketing on crop yields is plausibly an indir-ect one whereby farmers respond to increased prices and a greater ability to sell output byincreased effort. Our findings are consistent with our theoretical analysis which suggestedthat advice had the greatest potential in areas and conditions most conducive to applyingthe advice. This differential impact of advice in different areas is consistent with the resultsof Kassie et al. (2010). However, they find that advice on land management practicesworks best in low agricultural potential areas which is slightly at odds with our findings.In addition, they found advice on fertilizers to have a very significant and positive impacton crop productivity in high potential areas, whereas we found this advice worked well inboth types of areas. Why the differences with Kassie et al.? Firstly, there is the time period.Their data relate to 1998 and 2001. Ours is more recent. Technology moves on and whatmight have been the case over a decade ago may no longer be the case today. Secondly,their analysis relates to usage of fertilizer, minimum tillage, etc. Our data relate to exten-sion agent advice on these technologies. The advice may feasibly be to use less fertilizer, orto use it in a different manner. Thirdly, some technologies may take several years beforethey have an impact. Hence Schmidt and Tadesse (2014) conclude that that sustainableland and water management infrastructure in the Ethiopian highlands has a positiveimpact on the value of production only seven years after construction. This might betoo long a time frame for our results to be picking up. Finally, there may be a differencebetween their split of high and low productivity areas and ours of drought and non-drought areas.

In addition, more educated individuals and older people were significantly less likely toreceive advice. The kind of advice received also differed between drought and non-droughtareas and also varied with the size of the family, education and gender, suggesting thatadvice is targeted rather than given randomly. Thus, this does not support the observationmade by Kassie et al. (2010) that the advice showed little variation across differentenvironments nor responded to household-specific factors. This again confirms part ofthe third hypothesis. Thus in terms of the three hypotheses we set out to test, all threehave been largely substantiated.

Expanding further on our second research question, some advice has been found tobe more effective than others. In virtually, all the regressions, advice on credit appearsto have the least positive impact on both yields and income, especially the latter. This

12 A. HAMILTON AND J. HUDSON

may be linked to credit being heavily under the control of the government and possiblybeing used for political purposes (Berhanu and Poulton 2014). Advice on credit is alsoalmost always given in tandem with other advice and the combined effect, as can beseen from the coefficients in Tables 3 and 4, is generally positive, at least for yields.Nonetheless, it does raise questions about the recent emphasis on credit (Bacheweet al. 2015).

We did not have enough data to investigate whether different types of advice workbetter in tandem. But a variable equal to the number of types of advice given, althoughnegative, gave only weak evidence for declining returns with respect to the amount ofadvice given. Hence, advice appears best if given on several dimensions with the impactbeing largely cumulative. This is consistent with Teklewold et al.’s (2013) conclusionthat different types of sustainable land practices work best when adopted in combinationrather than isolation.

There is an important policy issue in that extension system advice can be unsuccessfuleither because the problem lies with the ‘extension message’ or in the way the message istransmitted. Too often the latter tends to get the blame for lack of impact while theproblem may be with the ‘advice’ or message. In a sense that is the more seriousproblem in indicating that the advice is flawed to begin with, whereas retraining can alle-viate problems with the messenger. Our analysis may be the start of a process of determin-ing where the problem lies, if indeed there is a problem. In this context, our resultstentatively show that with much of the advice being effective in at least some contextsthe messenger is at least partially exonerated. This also links in to the current debate onthe relative roles of R&D and technology transfer (Anandajayasekeram 2011). Ourresults point to the importance of the latter, in the specific context of extension agents.But often new knowledge comes from R&D and the roles of research centers remain ofpotential importance and here perhaps the linkages between agricultural researchcenters and extension agents could be improved (Abegaz and Wims 2015; Teshome, deGraaff, and Kassie 2015). Similarly, the use of regression analysis to quantify the impactof extension agent advice has provided results which may be potentially useful to improv-ing and informing extension agents’ advice in part by internalizing the results of this typeof analysis within the formal education system.

Notes

1. http://www.farmingfirst.org/wordpress/wp-content/uploads/2012/06/Global-Forum-for-Rural-Advisory-Services_Fact-Sheet-on-Extension-Services.pdf.

2. There are types of advice given in addition to the six specified, but for the purpose of thetheory we focus on the impact of these six types of advice.

3. The kebele is the lowest administrative tier in Ethiopia’s federal structure, below the woreda.4. If the question had been on what had happened to yields and income, then just focusing on

those who received advice would be problematic. But, we cannot ask a similar question ofthose who did not receive advice as the question we are analysing pertains to the impactthe advice had on yields and income. Obviously, this question cannot be asked of thosewho did not receive advice.

5. The constant term has been absorbed into the cut-off points for the ordered probitregression.

6. The coefficients cannot be readily compared as the one relates to ordered probit and the otherto probit.

THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 13

7. They were however significant at times in the equations relating to policy advice, rather thanimpact and have been retained in those. For example, land management advice tended toincrease with family size, which may reflect the size of the holding, and credit advice declineswith family size. There was weaker evidence of credit and marketing advice declining formen.

Acknowledgements

We acknowledge the valuable comments of referees and the editor.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Department for International Development (DFID).

Notes on contributors

Dr Alexander Hamilton is a political economist, and evaluation specialist with significantacademic and field experience in fragile states. He has numerous publications and researchexperience in the fields of corruption, impact evaluations, public financial management,economic policy, and econometrics. He also has field experience from work conductedin Ethiopia, Senegal, Sudan, and Yemen. He has an MPA in Public and EconomicPolicy form the London School of Economics and a DPhil (PhD) from the Universityof Oxford.Professor John Hudson studied at the Universities of London and Warwick. He is a pro-fessor of economics at the University of Bath. He has published more than 100 journalpapers in leading journals in all areas of economics and the wider social sciences, but inparticular development economics.

References

Abegaz, D. M., and P. Wims. 2015. “Extension Agents’ Awareness of Climate Change in Ethiopia.”The Journal of Agricultural Education and Extension 21 (5): 479–495.

Adams, R., H. Almeida, and D. Ferreira. 2009. “Understanding the Relationship Between Founder –CEOs and Firm Performance.” Journal of Empirical Finance 16 (1): 136–150.

Anandajayasekeram, P. 2011. “The Role of Agricultural R&D within the Agricultural InnovationSystems Framework.” In Report Prepared for the ASTI/IFPRI-FARA Conference.

Bachewe, F. N., G. Berhane, B. Minten, and A. S. Taffesse. 2015. “Agricultural Growth in Ethiopia(2004–2014): Evidence and Drivers.” ESSP Working Paper 81. Addis Ababa: International FoodPolicy Research Institute/Ethiopia Strategy Support Program (ESSP).

Berhanu, K., and C. Poulton. 2014. “The Political Economy of Agricultural Extension in Ethiopia:Economic Growth and Political Control.” Development Policy Review 32 (S2): s197–s213.

Buchy, M., and F. Basaznew. 2005. “Gender-blind Organizations Deliver Gender-biased Services:The Case of Awasa Bureau of Agriculture in Southern Ethiopia.” Gender, Technology andDevelopment 9 (2): 235–251.

14 A. HAMILTON AND J. HUDSON

Clarke, P. S., and F. Windmeijer. 2012. “Instrumental Variable Estimators for Binary Outcomes”.Journal of the American Statistical Association 107 (500): 1638–1652.

Cohen, M. J., and M. Lemma. 2011. Agricultural Extension Services and Gender Equality. Discussionpaper. Washington, DC: International Food Policy Research Institute.

Davis, K., B. Swanson, D. Amudavi, D. A. Mekonnen, A. Flohrs, J. Riese, and E. Zerfu. 2010. In-depth Assessment of the Public Agricultural Extension System of Ethiopia andRecommendations for Improvement. Discussion paper. Washington, DC: International FoodPolicy Research Institute.

Dercon, S., and L. Christiaensen. 2011. “Consumption Risk, Technology Adoption and PovertyTraps: Evidence from Ethiopia.” Journal of Development Economics 96 (2): 159–173.

Dercon, S., D. O. Gilligan, J. Hoddinott, and T. Woldehanna. 2009. “The Impact of AgriculturalExtension and Roads on Poverty and Consumption Growth in Fifteen Ethiopian Villages.”American Journal of Agricultural Economics 91 (4): 1007–1021.

EEA/EEPRI. 2006. Evaluation of the Ethiopian Agricultural Extension with Particular Emphasis onthe Participatory Demonstration and Training Extension System (PADETES). Addis Ababa:Ethiopian Economic Association/Ethiopian Economic Policy Research Institute.

Elias, A., M. Nohmi, K. Yasunobu, and A. Ishida. 2013. “Effect of Agricultural Extension Programon Smallholders’ Farm Productivity: Evidence from Three Peasant Associations in the Highlandsof Ethiopia.” Journal of Agricultural Science 5 (8): 163–181.

Kassie, M., P. Zikhali, J. Pender, and G. Köhlin. 2010. “The Economics of Sustainable LandManagement Practices in the Ethiopian Highlands.” Journal of Agricultural Economics 61 (3):605–627.

Kelemework, F., and H. Kassa. 2006. Assessment of the Current Extension System of Ethiopia: ACloser Look at Planning and Implementation. Addis Ababa: Ethiopian Economic Association/Ethiopian Economic Policy Research Institute.

Khan, Q. M., J. P. Faguet, C. Gaukler, and W. Mekasha. 2014. Improving Basic Services for theBottom Forty Percent: Lessons from Ethiopia. Washington, DC: The World Bank.

Lefort, R. 2012. “Free Market Economy, ‘Developmental State’ and Party-state Hegemony inEthiopia: The Case of the ‘Model Farmers.’” Journal of Modern African Studies 50 (4): 681–706.

Pender, J., F. Place, and S. Ehui, eds. 2006. Strategies for Sustainable Land Management in the EastAfrica Highlands. Washington, DC: International Food Policy Research Institute.

Roodman, D. 2011. “Estimating Fully Observed Recursive Mixed-process Models with CMP.” StataJournal 11 (2): 159–206.

Schmidt, E., and F. Tadesse. 2014. “Sustainable Agriculture in the Blue Nile Basin: Land andWatershed Management Practices in Ethiopia.” Environment and Development Economics 19(5): 648–667.

Spielman, D. J., D. Byerlee, D. Alemu, and D. Kelemework. 2010. “Policies to Promote CerealIntensification in Ethiopia: The Search for Appropriate Public and Private Roles.” Food Policy35 (3): 185–194.

Teklewold, H., M. Kassie, B. Shiferaw, and G. Köhlin 2013. “Cropping System Diversification,Conservation Tillage and Modern Seed Adoption in Ethiopia: Impacts on Household Income,Agrochemical Use and Demand for Labor.” Ecological Economics 93: 85–93.

Teshome, A., J. de Graaff, and M. Kassie 2015. “Household-level Determinants of Soil and WaterConservation Adoption Phases: Evidence from North-Western Ethiopian Highlands.”Environmental Management 57 (3): 620–636.

THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 15