performance measurement: does education impact productivity?

Performance Measurement: DoesEducation Impact Productivity?

Josephine A. Larbi-Apau and Daniel Bruce Sarpong, PhD

Productivity, a critical performance indicator,measures value of output per given levels ofproduction inputs. It has been used to estimate

how much of an observed rate of change of an indus-try’s output can be explained by the rate of change inone level of input or combined inputs and has beenapplied consistently to determine industrial growth(Griliches, 1964, 1963; Philips, 1994). Growth in pro-ductivity, such as in agribusiness or the commercialpoultry industry, can be attributed to research anddevelopment, extension service, education, technolo-gical infrastructure (U.S. Department of Agriculture,Economic Research Service, 2007; Aning, 2006), andother specified inputs. With education, agribusinessmanagers can increase productivity through adoptingand employing cost-effective production inputs, pro-duction mixes, and practices, as well as using bettermanagement and marketing strategies. These man-agers could increase productivity through increasedoutput supply and farm revenue, lowered consumerprices for poultry products, and improved generalperformance of the farm business than managers withlower educational levels.

Agricultural production in Ghana is generally characterized by small-scale farming with low productivity. The commercial poultry industry ischaracterized by intensive management (confined, capital intensive, andemploying cost- and labor-saving devices) and is the mainstay for growth inthe industry. In contrast, traditional family poultry farms are characterizedby extensive management systems (backyard free range and scavenging).The commercial poultry industry depends not only on imported capitalinputs such as feed ingredients, additives, supplements, and veterinary

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& 2010 International Society for Performance Improvement

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/piq.20069

This study investigated the impactof managers’ educational levels on pro-ductivity in the commercial poultry in-dustry in Ghana. The level of educationof 33 production managers of the poul-try farms were factored into a Cobb-Douglas production function withother explanatory variables. The com-puted percentage change in productiv-ity due to higher education relative tosecondary education was 10%. The in-teraction terms of basic education, ex-perience, and extension visits werepositive and not statistically significant.Targeting management educationcould increase productivity in the com-mercial poultry industry. Educatedmanagers have a higher propensity toadopt technology and alternative pro-duction mix for effectiveness and effi-ciency. This study concludes thathigher educational level had a positiveimpact on productivity in the commer-cial poultry industry and should beharnessed for improved performancein the domestic and global market.

medicine, but also on exotic breeds of chicken (Somuah, 2000). In this article,industry is used to refer to a conglomerate of production mix includingpoultry birds, feed mills, and hatcheries.

Generally the average diet in Ghana is heavily dependent on starchystaples and cereals (Levin, 1998), with a minimal amount of animal protein.With an estimated 20 million people (according to the 2000 census) and aprojected 2.8% annual population increase, the per capita consumption oftotal traded chicken in Ghana, including imported chicken, is estimated at0.7 kilograms per person annually, a figure far below the average for Africa at3.1 kilograms and that of the world at 7.9 kilograms. Estimates for theconsumption of eggs follow the same trend, with Ghana at 0.74 kilograms perperson annually as compared to Africa’s 1.9 kilograms and the world’s 5.9kilograms (Darko, 2000). Ghana’s low per capita consumption trends partlyreflect weak production in the poultry subsector and consumer purchasingpower for foods containing high-valued nutrients (Levin, 1998). A marketsurvey conducted by Adom-Boakye, Ennin, and Poku (2000) indicated thatabout 80% of the total traded chicken is imported. Domestic productionconstitutes only 20% (Adom-Boakye et al., 2000; Somuah, 2000) and has notchanged substantially for almost 7 years. Aning (2006) reported a domesticproduction of 18%.

Low domestic production is attributed to the high cost of production;lack of marketing strategies, market research, and development; highmortality rates; inefficiencies in operational processes; and lack of manage-rial skills (Aning, 2006; Darko, 2000; Somuah, 2000). Particularly crucial tothis study is the management input and how it influences productivity andperformance in the commercial poultry industry. The U.S. Department ofAgriculture, Economic Research Service (2007) notes that ‘‘poor educationwill indirectly and ultimately reduce agricultural productivity.’’ If managershave higher levels of education, management and general performance couldbecome more effective and efficient, leading to increased production andproductivity (Weir & Knight, 2000; Appleton & Balihuta, 1996; Bigsten,1994; Philips 1994; Jamison & Lau, 1982) and increased competitiveness.Being competitive implies the ability to compete favorably on both domesticand international markets through increased production, affordable con-sumer prices, and market differentiation.

Overview of Empirical Framework

Agricultural Productivity Measurement

Productivity in agriculture can be measured as a single factor indicator,where output is related to one measure of input, or as a multifactorproductivity, where output is related to combinations of inputs. Theproduction function is used to describe the production processes thatgenerate production output. An increase or a decrease in productivity ischaracterized by a shift in the production function, which could result in achange in the relationship between output and input. The Cobb-Douglas

82 DOI: 10.1002/piq Performance Improvement Quarterly

function is one of the most commonly used functional forms for estimatingagricultural production frontier because it is easy to use and interpret (Lovell,1993; Christensen, Jorgenson & Lau, 1971). Although it restricts all Allenpartial elasticities of substitution to be constant and equal to 1 for any pair ofinputs for all points of input space, it also assumes strong separability in itsestimation function. For example, production output elasticity measures theresponsiveness of output to a change in levels of inputs consumed inproduction, holding all other things equal. The usual estimation procedureis assumed to work with the side conditions forprofit maximization in competitive products andfactor market. Under this assumption, side condi-tions for profit maximization imply a system ofsemilogarithmic equations with one equation foreach input. Each of these equations gives the share ofinput as a linear function of the logarithm. A naturallogarithm or simply logarithm is used in estimatingnonlinear functions.

Evidence of Effect of Education on Productivity

Investing in education in agribusiness translates into human capitaldevelopment and is comparable to investing in production inputs such ascapital. Increasing productivity implies a transformation from traditional tomodernagriculture,whichinvolvesboth technical change andthe presenceofinput, finance, and marketing systems to increase farm production anddelivery toconsumersat competitiveprice (Poulton,Kydd,& Dorward,2006).

Most empirical studies on agricultural productivity (output) haveused the value of output as the dependent variable (Weir & Knight, 2000;Weir, 1999; Appleton & Balihuta, 1996; Pinckney, 1996; Cotlear, 1990;Jamison & Lau, 1980). The total value of agricultural productivity has beenestimated as a function of labor inputs, land, other fixed inputs, purchasedvariable inputs such as seed and fertilizers, and human capital (Pinckney, 1996;Appleton & Balihuta, 1996). Human capital variables considered in thisestimation are levels of schooling or education, experience, extension visits(Pinckney, 1996; Jamison & Lau, 1982), and age (Pinckney, 1996). Rosenzweig(1995) and Feder, Murgai, and Quizon (2004) suggest a greater tendency ofeducated farmers to adopt agricultural innovations and modern inputs.

According to Weir (1999), several different measures of education maybe used, different categories of labor may be considered, and differentproduction functions may apply to different farming systems. The use ofthe entire pooled sample of farm households in rural Ethiopia constrains thecoefficients of each explanatory variable to be the same across differentfarming systems, which, according to Jamison and Lau (1982), may present amisspecification and result in a biased estimated coefficient. Thus, it may benecessary to estimate different production functions for different regions ordifferent farming systems (Appleton & Balihuta, 1996).

Education was entered into the aggregate production function as aninput, with its estimated coefficient as the only source of productivity

Investing in education inagribusiness translatesinto human capitaldevelopment and iscomparable to investing inproduction inputs such ascapital.

Volume 22, Number 4 / 2010 DOI: 10.1002/piq 83

(Philips, 1994; Lipton, 1985; Griliches, 1964). The marginal product ofeducation captures the worker effect of education, reflecting an increase inoutput while holding physical resources constant (Appleton & Balihuta1996; Cotlear, 1990). Rosenzweig (1995) and Feder et al. (2004) also suggestthe allocative effect of education and explain the possible changes inproduction that can occur when educated farmers adopt agriculturalinnovations and modern inputs to increase productivity. That is, an educatedor functional literate production manager may change allocative efficiencyby varying the combination of selected outputs and inputs to increaseproduction or affect productivity by choosing a more appropriate productmix. By means of dummy variables (coded variables), Pinckney (1996) usesthe education of the agricultural household decision makers in Kenya andTanzania to estimate the effect of education on agricultural productivity(output). Productivity was estimated by expressing output as a function of allinputs in production (output 5 f [capital, labor, other inputs]).

The significant effects of education on productivity are mixed and varied(Philips, 1994; Bigsten, 1994; Jamison & Lau, 1982). In a productivity study offarmers using traditional versus modern technologies in Peru, Cotlear (1990)finds that education has different effects on different farming systems.Bigsten’s (1994) results on livestock production indicate mixed and sig-nificant results. The combined effect of crop and livestock production onproductivity is positive and insignificant. Philips (1994) reviewed 12 studiesand 22 data sets and reported that an additional 4 years of schooling leads toan average increase of 10% in output—7.6% for traditional farming and 11.4%for modern farming systems. Philips’s survey was geographically diverse, andthe results were mixed. Under certain conditions, the effects of schoolingwere stronger in Asia than in Latin America, regardless of the degree ofmodernization. The implications for the assumed applicability of Asianfindings to Africa could be strong; however, too few studies using Africandata were included to draw meaningful conclusions.

Appleton and Balihuta (1996) and Cotlear (1990) suggest that educationhas both cognitive and noncognitive effects on labor productivity. Cognitiveoutputs of schooling include the transmission of specific information as wellas the formation of general skills and proficiency. Education also producesnoncognitive changes in attitude, beliefs, and habits. Increased literacy andnumeracy may help farmers acquire and understand information andcalculate appropriate input quantities in a modernizing or rapidly changingenvironment. Rosenzweig (1995) argues that education may either increaseprior access to external sources of information or enhance the ability toacquire information through experience with new technology.

The hypotheses validated in this study on the production function arethat (1) each of the value factor inputs— the human capital variables, andcapital, labor, and other purchased variable inputs that are integrated into theCobb-Douglas production function—will be positively associated with high-er productivity, all other things being equal, and (2) the impact of educationalvariables on productivity is differentially positive. The dependent variable isproductivity, and the independent variables are education, experience, labor,


capital, the farm manager’s influence as the owner of the farm, the farmmanager’s function as a full-time employer, and visits from extension serviceagents to provide assistance in agricultural improvement and development.

The Data and Empirical Model

Study Area

The data came from a cross-sectional survey of 33 commercial poultryfarms in the greater Accraregion(GAR) in Ghana. The choice of the study areawas based on figures for poultry census estimates and projections from 1991 to1999 (Livestock Planning and Information Unit, 1991, 1998, 1999, 2000). By2000, the GAR had the highest poultry population and was the major supplierof poultry products in the country. The study examined five districts withsimilar cultures: Ga, Dangbe East, Dangbe West, Tema Metropolitan assem-bly, and Accra Metropolitan Assembly. Most of the poultry enterprises werelocated within the city and suburban areas of Accra, Tema, and Amasaman.Due to inadequate large numbers of hatchery and feedmill units, as well as theintegration and production mix, and the possible separability problems withmeasuring individual units, aggregated or pooled data of birds, hatcheries, andfeed mills were used in the econometric and statistics model.

Study Participants

A cross-sectional methodology was used to engage managers of thecommercial poultry managers in the study area. A randomized sampling wasinitially used to draw participants from a list of 739 commercial poultry farmsin the GAR provided by the Accra Metropolitan Assembly. However, a pilotsurvey indicated that most of the farms on the list were either dysfunctionalor produce only during festive occasions such as Christmas when demand ishigh. Others existed in name only. The study participants were thereforedrawn from existing functional farms for the study: 120 poultry farms andfarm managers were initially served and surveyed.

Data Collection

A print-based structured and semistructured questionnaire was used togather data from 120 participants from the GAR; the return rate was 50%. Ofthe 60 respondents, 27 participants with obviously inaccurate and inadequateresponses were excluded; hence, data for 33 respondents were used for theanalyses. Data were collected on demography, production capacity, quantityof output and input with corresponding actual market value, educationallevels, number of visits from the government agricultural extension services,and years of experience in the poultry industry. These data sets wereconsidered sufficient for the analysis due to inadequate documentation andrecords. Data were subjected to econometric techniques and descriptive, andmultiple regression statistics in Microsoft Office Excel, Windows 2000. Otherdata and information were gathered from the Ministry of Food and Agricul-ture and its Livestock Planning and Information Unit, Accra Metropolitan


Assembly (Agricultural Section) and the Veterinary Service. Other sourcesinclude the Ghana Statistical Services; Customs, Excise and PreventiveService; Ghana Foods and Drugs Board; Greater Accra Poultry FarmersAssociation; and other relevant published and unpublished literature.

Although the data were collected between April and December 2000,they are still considered relevant since the commercial poultry industry hasremained economically static with minimal growth. For example, in review-ing the structure and importance of the commercial and village-basedpoultry in Ghana, Aning (2006) reports the GAR still had the largest marketshare and production capacity remained the same. Aning reports continuouscompetition with cheaper poultry imports, low biosecurity practices, andinefficiencies in production and marketing strategies. Hatcheries and feedmills continue to produce below capacity, and domestic production andimports of poultry products represent only 18% and 24% for 2001 and 2005,respectively (Aning, 2006).

Measurement of Production Inputs and Output

The production inputs measured were labor, capital, purchased variableinputs, and human capital variable estimates. The levels of the independentexplanatory variables were estimated as follows. The labor variable comprisesthe value of actual total labor hours of all reported labor on the commercialpoultry industry studied, whether hired, casual, skilled,or unskilled, includingmanagement. Household labor was negligible in the study. The capital inputmeasured the total volume of capital services, including repairs, maintenance,and depreciation of equipment such as incubators, electrical generators, feedmills, and farm structures. Other productive capital services were totalmachine hours; repairs and maintenance of feeders, drinkers, and otherheating devices such as lanterns and coal pots (used in lieu of electric poweroutages); egg crates and carts; and any others specified. Purchased variableinputs included actual payments for and costs of utilities such as water,electricity, telephone services, carriage, and transportation; day-old chicks(for meat and eggs); veterinary services and medicine; and hatchery eggs, feedconsumed by the birds, and feed ingredients for the feed mills.

The human variables considered in the study are self-reported levels ofmanagement education (no formal, basic, secondary, and tertiary, that is,postsecondary), age, and experience. Experience was estimated as the totalnumber of years spent working in the poultry industry. Also measured werethe influences of the owner as the farm/production manager, the influence ofthe manager working as a full-time employer, and the number of extensionvisits to the farms. Government extension services are provided to farmers inthe form of in-service training, innovative practices, farm visits, creditschemes, and general support for agricultural development. Output wasmeasured as the total quantity and value of the poultry farm products (tableand hatchery eggs, meat, day-old chicks from the hatcheries if the particularfarm has a commercial hatchery, and feed produced for sale). All inputs wereweighted on quantity and market price share and the total gross output inquantity and actual market value.


Empirical Test of Impact of Education on Productivity

Econometric techniques and statistical models were applied in theestimation of the production function to obtain direct productivity of thepoultry farm based on valued gross output and combined inputs. Applyingthe theory of production, we assume similar production activities acrossunits of the poultry industry. The Cobb-Douglass production function, acommonly used econometric model to represent the relationship of outputto inputs is generally given by Y 5 A L aKb (which can also be represented inlogarithmic form as log Y 5 log A 1 a log L 1 b log K, or InY 5 InA 1 a InL1 b InK), where Y is the total production, L is labor, K is capital, and A is totalfactor productivity. The a and b represent changes of labor and capital inresponse to a change in output, whose values are determined by other factors,such as applied technology. Natural logarithm or simple logarithm is used inestimating nonlinear functions for variance-stabilization transformation onoutput, where output is given by In(Q) or log Y.

The Cobb-Douglass production function provides adequate representa-tion and has been widely used for productivity estimates in developingcountries (Feder et al. 2004; Croppenstedt & Muller, 1998; Pinckney, 1996;Rosenzweig, 1995). This study’s model follows that of Pinckney (1996), whoby means of dummy variables (coded variables) used education of theagricultural household decision makers in Kenya and Tanzania to estimatethe effect of education on agricultural productivity. The total value of outputof the commercial poultry industry studied was assumed to be a function ofexpenditures on factor inputs such as capital, other purchased variableinputs (such as day-old chicks, feed consumed, the hatchery eggs), and thehuman capital variables, which include levels of education, and experience.The specification of the basic model is:

InQij ¼ a0 þ b1InKAPi þ b2InLABi þ b3InOTHi

þ b4InDOCi þ b5InFEEDi þ b6ED1i þ b7ED2i

þ b8ED3i þ b9ED4i þ b10XPEi þ b11XPE2

þ b12OWNi þ b13JOBi þ b14EVISi þ ei ð1Þ

where

Qi Total value of output of farm i in local currency

KAPi Total value of productive capital services such as repairs, maintenance,

and depreciation of fixed inputs of farm i in local currency

OTHi Total value of purchased variable inputs such as payments for electricity,

water, medication, carriage or transport charges, and hatchery eggs of

farm i in local currency

DOCi Total value of day-old chicks on the farm i in local currency

FEEDi Total value of feed consumed on the farm i in local currency

LABi Actual total value of labor hours, including all reported labor on the farm i

ED1i Education dummy variable 5 1 if no formal education; 0 otherwise


ED2i Education dummy variable 5 1 if primary, middle, or junior secondary

school; 0 otherwise

ED3i Education dummy variable 5 1 if secondary/vocational/technical; 0

otherwise

ED4i Education dummy variable 5 1 if polytechnic/college/university; 0

otherwise

XPEi Estimated as the number of years spent on the poultry farm i

XPE2 Experience squared

JOBi Dummy 5 1 if working in the poultry farm i as a full-time job; 0 otherwise

OWNi Dummy 5 1 if production manager is the owner of the farm i; 0 otherwise

EVISi Dummy 5 1 if contact with the extension service; 0 otherwise

bi Estimated coefficient

ei Error term

Ini Natural logarithm (log linear function of inputs)

Except for experience, all the human capital variables entered theequation as dummy variables and are in levels (qualitative variables) ratherthan in logarithms, implying that a unit change in these variables produces aconstant change in the logarithmic value of the industry’s production. Forexample, if the productivity equation is semilogarithmic and ED is measuredin different levels of education, that is,

In ðQÞ ¼ aþ b1InK þ b2EDþ e ð2Þ

then b2 5 percentage change in output for a change from one level ofeducation to the other, holding capital input (k) constant. If we let output Q1

5 InQi when the educational level of the manager is at a higher level andoutput Q0 5 InQ0 when the educational level of the manager is one levelbelow, then g, which is given by g 5 (eQ

1 –eQ0 )/eQ

1 or, equivalently, log (1 1 g)5 Q1–Q0 5 b2, represent the percentage change in output due to a higherlevel of education (Baltagi, 1998).

Consequently, a set of dummy variables (coded qualitative variables) isincluded in the estimate to account for the possibility that different levels ofeducation have different effects on productivity. That is, the bi estimates ofthe dummy variables of education are interpreted as expected differences inproductivity between different levels of education (EDi’s), all other thingsbeing equal. If 1 is assigned to the education variable under consideration, allothers are assigned the value 0 in dummy variable estimates. Dummyvariables are useful because they provide the means to use a single regressionequation to represent multiple groups (Trochim, 2006) and allow easyinterpretation of the b coefficients (McClave, Benson, & Sincich, 2005).

The hypothesis validated in this study is that each of the physical inputs ofproduction, including capital, labor, other purchased inputs, and the humancapital variables, that are integrated into the Cobb-Douglas productionfunction are expected to be positively associated with higher productivity,


all things being equal. For education variables in equation 2, we furtherhypothesize that there would be a corresponding change in output inresponse to a change in one level of the EDi’s (education) at the margin,while other inputs and characteristic variables are held constant. The changein productivity in response to a change in the education variables is expressedas b2 5 (qInQ)/qEdi40. Therefore, the specific a priori expectations on allthe estimated parameters of equation 1 are:

b1 � b10 > 0

b11o0

b12 � b14 > 0

For example, the estimated coefficient of capital a priori is expected to bepositive (b140), while the estimated coefficient of experience squared isnegative (b11o0). Similar hypotheses are interpreted for all other bi’s.

Model of Best Fit

The results in equation 1 indicate a high correlation between variables inthe estimation, where the educational variables were found not to bestatistically significant (see Appendix A). This high correlation among thevariables necessitated eliminating some variables and aggregating othervariables into an interactive term (combinations) to avoid the estimationproblems associated with high multicollinearity. For example, feed was highlycorrelated with total output, capital input, and other purchased variableinputs. Similarly, production manager as the owner of the farm (OWN) washighly correlated with the manager working as a full-time employer (JOB).Since the results of basic education (ED2) in equation 1 showed no significantdifference, we incorporated an interaction term of basic education (ED2),experience (XPE), and extension visits (EVIS) to determine the interactioneffect of the three variables on productivity. Other variable inputs (OTH), feed(FEED), and OWN were also dropped from the equation due to their highcorrelation with capital (see Appendix B). The equation was rerun to estimatethe impactofeducationlevelsof the managers onproductivityusinga modelofbest fit, which is represented as

InQij ¼ a0 þ b1InKAPi þ b2InLABi

þ b3ED4i þ b4ED3i þ b5ED2i þ b6XPEi

þ b7EVISi þ b8JOBi þ ei ð3Þ

where

Qi Total value of output of farm i in local currency

KAPi Total value of productive capital services such as repairs,

maintenance, and depreciation of fixed inputs of farm i in

local currency

LABi Actual total value of labor hours, including all reported labor on

farm i


ED3i Education dummy variable 5 1 if

secondary/vocational/technical; 0 otherwise

ED4i Education dummy variable 5 1 if

polytechnic/college/university; 0 otherwise

ED2�XPE� EVIS Dummy variable 5 1 if interaction of primary/middle

school/junior secondary school � experience � extension

visits (interactive term); 0 otherwise

JOBi Dummy 5 1 if working on the poultry farm i as a full-time job;

0 otherwise

EVISi Dummy 5 1 if contact with the extension service; 0 otherwise

bi Estimated coefficient

ei Error term

Ini Natural logarithm

To test whether different structures resulting from variations in thedifferent farms such as numbers and kind of birds and feed mills andhatcheries’ capacities had an effect on the model, the White heteroskedasticitytest was used to test the null hypothesis, that is, to determine whether the errorvariances were constant. The results are presented and discussed next.

Results and Discussion

Managers

Twenty-two of the respondents in the study were tertiary-level gradu-ates, eight had completed secondary education, and three were at a basiclevel. None of the managers was illiterate or had received no formaleducation. Ten of the respondents were females, and 23 were males. Theages of the managers ranged from 23 to 61 years, with a working experience of1 year to 30 years in the commercial poultry industry. Twenty-two of themanagers were working full time on commercial poultry farms, and 11 werepart-time managers. The study also ascertained formal knowledge in generalagriculture and agribusiness, and the results indicate 20 of the respondentshad formal education in agriculture. The remaining 13 had a diverseeducational background, including retired public and civil servants, com-prising medical persons, educators, and engineers working as part-timeoperators. While it might be expected that owners of large poultry farmswould employ more qualified specialized staff in agribusiness and manage-ment, most of these cohorts were managing on a part-time basis forsupplementary income. For relatively smaller farms, it did not matter if theyhad agricultural education.

Impact of Education on Productivity in the Commercial Poultry

Industry

The study assumes the effects of education to be neutral. That is,education does not affect the productivity of capital, variable inputs, or feed


consumed differentially. Table 1 summarizes the regression results of thererun restricted equation 2—the model of best fit. Basic education (ED2),experience (XPE), and extension visits (EVIS) were incorporated as aninteractive term (ED2 � XPE � EVIS). The coefficients of education atthe secondary level (ED3) and education at the tertiary level (ED4) and ofcapital (KAP), labor (LAB), and job (JOB) are reported and discussed. All theincluded variables have the a priori expected signs, and the human capitalvariables produced relatively positive coefficient results. The coefficient(elasticity) of capital is estimated at 0.400 and is statistically significant at 1%,indicating that a one percentage change in capital led to a 4% change inoutput, other factors being held constant. The impact of capital on produc-tivity is positive. The estimated coefficient of labor is statistically significantat the 1% level. The coefficient value is 0.8, which means labor has positiveimpact on productivity. It indicates that all factors being held constant, a 1%proportional increase in labor input used (actual number of hours worked)produced a proportional 8% increase in output.

The estimated coefficient of the interactive term, a combination of basiceducation, extension visits, and experience (ED2 � EVIS � XPE), waspositively related to productivity, which suggests a positive complementaryrelationship among these three variables. It also implies that managers withonly the basic level of education (primary, middle, junior secondary school)may require some years of experience in the poultry business and visits fromthe extension services to function better, as required. The coefficient

TABLE 1 REGRESSION RESULTS OF THE IMPACT OF EDUCATION ONPRODUCTIVITY IN THE COMMERCIAL POULTRY INDUSTRY: MODELOF BEST FIT (DEPENDENT VARIABLE: InQ)

VARIABLES COEFFICIENT T-STATISTICS PROBABILITY

INTERCEPT �2.130 (�0.737) 0.467

InKAPi 0.400�� (2.834) 0.008

InLABi 0.803�� (3.673) 0.001

ED4 2.408� (1.241) 0.063

ED3 1.533 (1.204) 0.239

ED2 � XPE � EVIS 0.451 (1.038) 0.301

JOBi 0.609 (1.059) 0.299

R2 5 0.841 Adjusted R2 5 0.804 F-statistics 5 22.904�� N 5 33

White heteroskedasticity test

F-statistics 5 0.963939; probability 5 0.520140

Observations � R2 5 14.13993; probability 5 0.439338

Source. Compiled from the survey data.Note. Estimated t-statistics are in parentheses. Estimate of the model is derived from the poultry farms’ data in the GAR.Significant levels for one-tail test: �� 1%, � 10%.


(elasticity) of secondary education (ED3) is 0.239, and the correspondingprobability level is 1.533. Secondary education is positively related toproductivity. Education at the tertiary level (ED4) has a positive estimatedcoefficient of 2.408 and is statistically significant at 10%. The computedpercentage change in productivity due to tertiary education (ED4) relative tothe lower educational level is recorded at 10.1% (e2.408–1), which implies thata change in educational level from the lower levels to the tertiary levelincreased productivity by 10 percentage points.

The driving force or causes behind increased productivity are notexplained by this model of estimation. The results seem to corroborate withother studies that suggest that raising the educational level of farm decision

makers increases agricultural productivity (Weir, 1999;Appleton & Balihuta, 1996; Jamison & Lau, 1982).Appleton and Balihuta (1996) describe the signs of thehuman capital variables as robust, with mixed values andsignificant. The positive impact of higher levels of

education on productivity may be ascribed to the presence of more highlyeducated people to manage the commercial poultry industry better. It mayalso reflect the application of relatively modernized and technology-orientedapproaches to managing the medium- and large-scale farms by thesecohorts.

The R2 value of 0.841 represents the percentage (84%) of variability inproductivity, which is explained by the explanatory variables. It indicatesstrong, positive relationships between the dependent and independentvariables and tells how well the model fits the data for predicting productivity.This R2 performance may be said to be good since production is influenced bya number of unobserved variables, such as access to credit, motivation, andother similar factors that could not be explained or considered in this studyspecifications. The computed F-value of 22.9 indicates that the explanatoryvariables significantly explained the changes in productivity in the commer-cial poultry industry and that the estimated regression equation can be usefulfor predicting the dependent variable of productivity. The White hetero-skedasticity test suggests there are no problems with the nonconstantvariance of the error terms; hence, there seems to be not much differencein the structure of the farms in terms of number and kinds of birds, feed millsand hatcheries, and capacities.

Conclusion

Tertiary education (postsecondary) of managers is positively related toproductivity and increased productivity by 10% in the poultry industrystudied, which implies that investment in higher education could have apositive impact on the commercial poultry industry and predict an increasein productivity by 10 points on the margin. This is particularly importantsince improved performance of the commercial poultry industry is driven byadvances in technology, effective organization of production, reduction of

The impact of capital onproductivity is positive.


waste and long-term liabilities, efficient use of resources, lower operationalcosts, and increased competitive advantage, which are dependent on edu-cated managers.

The combination of basic education, extension visits, and experience waspositively related to productivity, which suggests a positive complementaryrelationship of these three variables. We therefore recommend that com-mercial poultry managers with only a basic education and little or noexperience to be provided with in-service training, short courses, andfrequent visits by the agricultural extension services to make them moreproductive. In addition, experiences in family poultry farming can be drawnon to improve technical and economic efficiency for increased production,productivity, and performance. Managers with higher levels of educationand technical and management expertise will have to use technology that isavailable and transferable to compete favorably with cheap imports, improveproduction effectiveness and efficiency, and maximize sales through marketsurveys.

Ghana remains an agricultural country, and support for higher levels ofagricultural education could improve the performance of the commercialpoultry sector and agricultural development. Investment in specializedknowledge in agribusiness management could improve the performance ofcommercial poultry farm managers, leading to higher productivity in thepoultry industry.

This study sets the agenda for further research into managementfunctions and performance behavior that ultimately influence productionand productivity. Productivity improvement comes with better productionmanagement and the use of technological innovation, which are partiallydependent on the level of a manager’s education. Schooling and educationvariables, including experience, skills, and specialized knowledge couldbe studied to determine their allocative, technical, and worker effectsdifferentially in the commercial poultry industry. This study supports thetheory that ‘‘econometric techniques can be applied to estimate parametersof a production function and so obtain direct measures of productivitygrowth’’ (Organization for Economic Co-operation and Development,2001). Education may help farmers to acquire and understand informationand calculate appropriate input quantities in a modernizing or rapidlychanging environment. New knowledge and innovation lead to new inputsand technology, which produce increased effectiveness and efficiency if theyare adopted and used. In addition to serving as a reference for practice, thisstudy sets out a framework for further empirical studies in the overall growthand development of the poultry sector of the economy in Ghana and sub-Saharan Africa.

Limitations and Suggestions for Further Research

The study focused on chicken as the most popular species andlargest population of poultry. Further research could include other


species, such as ducks, turkeys, and ostriches, whose production is growingin most farms surveyed. This primary study analyzed only 33 commercialpoultry farms sited in the greater Accra region. The lack of formalcensus, listing, and inadequate information on the existing poultry com-mercial poultry producers since 1997 posed problems for data collection.The Cobb-Douglass production is criticized for its assumed constantrate of return and suitability for estimating the production function onnational levels. However, many studies on developing countries have usedthe model. We suggest using other conceptual and econometric modelsto ascertain the impact of education on productivity in this sector ofagriculture. There are no other known studies that this one could compareits computed parameters with. We recommend that further work be carriedout in the major commercial poultry producing areas in Ghana to establishthese findings.

Appendix A: Regression Results of the Impact ofEducational Levels on Productivity in the CommercialPoultry Industry: Equation 1 (Dependent Variable: InQ)

VARIABLES COEFFICIENT T-STATISTICS PROBABILITIES

INTERCEPT �2.176 (�0.865) 0.397

InKAPi 0.164 (1.267) 0.220

InLABi 0.298 (1.401) 0.177

InOTHi 0.449�� (4.052) 0.001

InFEEDi 0.361�� (2.186) 0.041

ED3 �0.160 (�0.178) 0.861

ED4 0.202 (0.217) 0.831

XPE �0.015 (�0.117) 0.908

XPE2�0.000 (�0.023) 0.982

OWN 0.205 (0.398) 0.695

JOB 0.045 (0.888) 0.931

EVIS �0.090 (�0.208) 0.838

R2 5 0.923; adjusted R2 5 0.880; F-statistics 5 21.719; N 5 32

White heteroskedasticity test:

F-Statistics 5 1.436139; probability 5 0.244457

Observations � R2 5 19.36118; probability 5 0.250379

Source. Computed from the survey data.Note. Estimated t-values are in parentheses. The model estimate is derived from data on thepoultry farms in the GAR. Significance levels for one-tailed test: ��1%, ��5%.


References

Adom-Boakye, K., Ennin, S. A., & Poku, F. (2000, May). A market study of chicken: Ghana and

Nigeria. Paper presented at a seminar on Improving the Competitiveness of the

Ghanaian Poultry Industry, Accra, Ghana.

Aning, K. G. (2006). The structure and importance of the commercial and village based

poultry in Ghana. In United Nations, Food and Agriculture Organization, final review

report: Poultry review-Ghana. Accra: Ghana.

Appleton, S., & Balihuta, A. (1996). Education and agricultural productivity: Evidence from

Uganda. Journal of International Development, 8, 415–444.

Baltagi, H. B. (1998). Econometrics. Berlin: Springer-Verlag.

Bigsten, A. (1994). Education and income determinations in Kenya. London: Gower.

Appendix B: Correlation Coefficient Between Estimated Variables

LTQ LKAP LLAB LFEED LOTH ED3 ED4 XPE XPE2 JOB OWN EVIS

LTQ 1 0.832�� 0.279 0.788�� 0.910�� 0.208 0.233 0.136 0.133 �0.480�� 0.548�� 0.184

(.000) (110) (0.000) (0.000) (0.210) (0.164) (0.414) (0.452) (0.002) (0.000) (0.269)

LKAP 0.832�� 1 0.431� 0.612�� 0.794�� 0.128 0.099 0.090 0.125 �0.444�� 0.499�� 0.226

(0.000) (0.012) (0.000) (0.000) (0.457) (0.572) (0.601) (0.466) (0.007) (0.002) (0.185)

LLAB 0.279 0.431� 1 0.135 0.340� �0.105 0.124 �0.103 �0.114 �0.143 0.143 �0.127

(0.110) (0.012) (447) (0.043) (0.543) (0.478) (0.550) (0.507) (0.406) (0.406) (0.460)

LFEED 0.788�� 0.612�� 0.135 1 0.629�� 0.127 0.267 0.022 �0.008 �0.487�� 0.397� 0.003

(0.000) (0.000) (0.447) (0.000) (0.453) (0.110) (0.895) (0.965) (0.002) (0.015) (0.988)

LOTH 0.910�� 0.794�� 0.340� 0.629�� 1 �0.211 0.222 0.219 0.224 �0.458�� 0.491�� 0.179

(0.000) (0.000) (0.043) (0.000) (0.197) (0.181) (0.181) (0.1700) (0.003) (0.002) (0.277)

ED3 �0.208 �0.128 �0.105 �0.127 �0.211 1 �0.785�� 0.239 �0.231 0.088 0.118 �0.182

(0.210) (0.457) (0.543) (0.453) (0.197) (0.000) (0.138) (0.152) (0.591) (0.469) (0.262)

ED4 0.233 0.099 0.124 0.267 0.222 0.785�� 1 0.269 0.167 0.011 �0.298 0.114

(0.164) (0.572) (0.478) (0.110) (0.181) (0.000) (0.098) (0.311) (0.947) (0.065) (0.488)

XPE 0.136 0.090 �0.103 0.022 0.219 �0.239 0.269 1 0.964�� 0.158 0.082 �0.012

(0.414) (0.601) (0.550) (0.895) (0.181) (0.138) (0.098) (.000) (0.332) (0.614) (0.941)

XPE2 0.133 0.125 �0.114 �0.008 0.224 �0.231 0.167 0.964�� 1 0.066 0.127 0.024

(0.425) (0.466) (0.507) (0.965) (0.170) (0.152) (0.311) (0.000) (0.674) (0.435) (0.882)

JOB 0.548�� 0.499�� 0.143 �0.397� 0.491�� 0.118 �0.298 0.082 0.127 1 �0.434�� 0.043

(0.000) (0.002) (0.406) (0.015) (0.002) (0.469) (0.065) (0.614) (0.435) (0.005) (0.793)

OWN �0.480�� 0.444�� 0.143 0.487�� 0.458�� 0.088 0.011 0.158 0.066 �0.434�� 1 �0.313�

(0.002) (0.007) (0.406) (0.002) (0.003) (0.591) (0.947) (0.332) (0.684) (0.005) (0.049)

EVIS �0.184 �0.226 �0.127 0.003 �0.179 �0.182 0.114 �0.012 0.024 �0.313� �0.043 1

(0.269) (0.185) (0.460) (0.988) (0.277) (0.262) (0.488) (0.941) (0.882) (0.049) (0.793)

Source. Computed from the survey data.Note. Significance levels for two-tailed test: ��.01 and �.05 levels.


Christensen, L. R., Jorgenson, D. W., & Lau, L. J. (1971). Conjugate duality and the

transcendental logarithmic production function. Econometrica, 39, 255–256.

Cobb, C. W., & Douglass, H. P. (1928). A theory of production. American Economic Review,

18(Suppl. 1), 139–165.

Cotlear, D. (1990). The effect of education on farm productivity. In K. Griffin & J. Knight

(Eds.), Human development and the international development strategy for the 1990s

(pp. 73–100). London: Macmillan.

Croppenstedt, A., & Muller, C. (1998). The impact of health and nutritional status of farms on

their productivity and efficiency: Evidence from Ethiopia (Mimeo). Oxford: UK Centre

for the Study of African Economics.

Darko, K. (2000, May). Improving the poultry industry in Ghana. Paper presented at a

seminar organized by the Ministry of Food and Agriculture, Accra, Ghana.

Feder, G., Murgai, R., & Quizon, J. B. (2004). Sending farmers back to school: The impact of

farmer field schools in Indonesia. Review of Agricultural Economics, 26(1), 45–62.

Griliches, Z. (1963). The sources of measured productivity growth, U.S. agriculture,

1940–60. Journal of Political Economy, 71, 331–346.

Griliches, Z. (1964). Research expenditures, education and the aggregate agricultural

production function. American Economic Review, 54, 961–967.

Jamison, D. T., & Lau, L. J. (1982). Farmer education and farm efficiency. Baltimore, MD: Johns

Hopkins University Press.

Levin, C. (1998). Ghana’s agricultural sector and prospects for the future (Occasional Paper

No. 15). Accra, Ghana: Institute of Economic Affairs.

Lipton, M. (1985). Education and farm efficiency: A comment. Economic Development and

Cultural Change, 34, 167–168.

Livestock Planning and Information Unit. (1991). Livestock and poultry production in Ghana

(Occasional report). Accra, Ghana: Ministry of Food and Agriculture.


(Occasional Report). Accra, Ghana: Ministry of Food and Agriculture.





Lovell, C.A.K. (1993). Production frontiers and production efficiency. In H. O. Fried, C.A.K.

Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques

and applications (pp. 3–67). New York: Oxford University Press.

McClave, J. T., Benson, G. P., & Sincich, T. (2005). Statistics for business and economics (9th

ed.). Upper Saddle River, NJ: Pearson.

Organization for Economic Co-operation and Development. (2001). Measuring productiv-

ity: Measurement of aggregate and industrial-level productivity growth. Organization

of Economic Co-operation and Development. Retrieved June 19, 2007, from http://

www.oecd.org/dataoecd/59/29/2352458.pdf

Philips, J. M. (1994). Farmer education and farm efficiency: A meta-analysis. Economic

Development and Cultural Change, 43, 149–165.

Pinckney, C. T. (1996). Does education increase agricultural productivity in Africa?

Education and agricultural productivity in Kenya. With K. Husbands & T. Konyango.

In N. Burton, & C. Hill (Eds.), The evaluation in public expenditure in Africa

(pp. 111–138). Economic Development Institute (EDI) Learning Resources series.

Washington, DC: World Bank.

Poulton, C., Kydd, J., & Dorward, A. (2006). Overcoming market constraints to pro-poor

agricultural growth in sub-Saharan Africa. Development Policy Review, 24(3),

243–277.

Rosenzweig, M. R. (1995). Why are there returns to schooling? American Economic Review

Papers and Proceedings, 85, 153–158.

Somuah, C. (2000, May). The poultry industry: Making it more competitive. Paper presented

at a seminar on the poultry industry, Center for Scientific and Industrial Research,

Accra.


Trochim, W. M. K. (2006). Research methods: Knowledge base. Retrieved August 9, 2008,

from http://www.socialresearchmethods.net/kb/

U.S. Department of Agriculture, Economic Research Service. (2007). Agricultural produc-

tivity in the United States. Retrieved December 12, 2007, from http://www.ers.usda.

gov/Data?AgProductivity/

Weir, S. (1999). The effects of education on farmer productivity in rural Ethiopia (Working

paper series 99–7). Oxford: Centre for the Study of African Economies, Department of

Economics, Oxford University.

Weir, S., & Knight, J. (2000). Adoption and diffusion of agricultural innovations in Ethiopia:

The role of education. Oxford: Centre for the Study of African Economies, Department

of Economics, Oxford University.

JOSEPHINE A. LARBI-APAU

Josephine A. Larbi-Apau is a doctoral candidate in instructional tech-nology and a Thomas C. Rumble Fellow at the College of Education, WayneState University. She is an assistant director of education, program manager,and agribusiness consultant and has over 10 years of teaching experience. Sheis a graduate of Cornell University and the University of Ghana, Legon, and aWinrock International Fellow. Mailing address: 384 College of Education,Wayne State University, Detroit, MI 48202. E-mail: [email protected],[email protected]

DANIEL BRUCE SARPONG

Daniel Bruce Sarpong, PhD, is an agriculturalist/economist, head ofdepartment, and senior lecturer at the University of Ghana, Legon. He hasover 10 years of research and teaching experience and more than 40 articlesin peer-reviewed journals and conference and workshop papers. He providesscientific analysis of the socioeconomics of agricultural households and theirimpacts on macroeconomic, agricultural development, environmental, andresource issues. He is the principal investigator of several studies andprojects, including for the World Bank Department for InternationalDevelopment, UK; Institute of Statistical, Social and Economic Research,University of Ghana; United Nations Development Program; and GlobalEnvironment Facility. Mailing address: Department of Agricultural Eco-nomics & Agribusiness, College of Agriculture and Consumer Sciences,University of Ghana, Legon-Accra, Ghana. E-mail: [email protected],[email protected]


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