P rocedia - Social and Behavioral Sciences  58  ( 2012 )  1158 – 1165
1877-0428 © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the 8th International Strategic Management Conference doi: 1
0.1016/j.sbspro.2012.09.1097
1
Nizamettin Bayyurt. Tel.: +90-212-866-3300; fax: +90-212-866-3342.
E-mail address : bayyurt@  8th  International Strategic Management Conference
The Impacts of Governance and Education on Agricultural
Efficiency: An International Analysis
Nizamettin Bayyurt a
b  a
Fatih University, Istanbul, 34500, Turkey  b Fatih University,Institute of Social Sciences, Istanbul, 34500, Turkey
Abstract
The main aim of this study is to explain the interaction between governance, education and agricultural efficiency
and to expose the impacts of governance and education on agricultural efficiency by a global context. Agricultural
efficiency was measured as the ratio of agricultural outputs to agricultural inputs by Data Envelopment Analysis
ricultural land (km2), fertilizer (tons), the number of tractors, and labor. The output is
produced add value in agricultural area as USD currency. In this study, we combined DEA and a regression analysis
in a worldwide context. For this purpose, in the first stage, we used DEA model (output-oriented, constant return to
scale model) to analyze the agricultural efficiency of countries. And in the second stage, we used Panel Data
Regression Analysis to find the effects of Worldwide Governance Indicators (WGI), education index, and country
type
.
2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of The 8th
International Strategic Management Conference
Key words: Agricultural Efficiency; Governance; Data Envelopment Analysis; Panel Data Regression.
1. Introduction
Agricultural productivity is one of the most important problems of the world. High food prices,
climate change, civil wars, and the global financial crisis bring very serious problems such as food safety,
hunger and malnutrition in the world. Due to its importance the United Nations
2015 is "fight against hunger and poverty".
Available online at www.sciencedirect
© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the 8th International Strategic Management Conference
1159 N izamettin Bayyurt and Senem Yılmaz /  P rocedia - Social and Behavioral Sciences 58 ( 2012 )1158 – 1165 There are lots of studies in literature concerning agricultural productivity. And also in recent years it
has begun to reali
Governance has become a hot topic on the critical role it plays in determining social welfare. In 2003,
the former Secretary General of the United Nations, Kofi Annan, reflects a growing consensus when he
states that good governance is perhaps the single most important factor in eradicating poverty and promoting development. Not surprisingly, governance as a term has progressed from obscurity to widespread usage, particularly in the last decade. Governance is about the more strategic aspects of steering: the larger decisions about direction and roles. That is, governance is not only about where to go,
but also about who should be involved in deciding, and in what capacity [Graham et al. (2003)].
For measures of the quality of governance, the World
(WGI, such as Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption) have been produced (World
Bank, 2011). The
six WGI are recognized by many researchers as the most effective tools for assessing the status of governance in different countries.
The main aim of this study is to explain the interaction between governance and agricultural productivity and to expose the impacts of governance on agricultural productivity by an international
context using 64 countries over the period 2002-2008. For 64 countries, data are gathered from the World
Bank database.
2.Literature Review
There are some researches that have been done on Agricultural Productivity Analysis in literature in
some regions such as India [Dayal, E., 1984], Spanish Region [Millan and Aldaz, 1998], European Union
and Eastern Region [Serrao, A., 2003], MENA region [Jemma and Dhif, 2005], Nigeria [Fakayode et al. 2008], Vietnam [Minh and Long, 2008], etc.
Lio and Liu (2008) analyzed 118 countries, whether a relationship exist between agricultural productivity and governance indicators for the years 1996, 1998, 2000 and 2002 in their study. They
found that when independent variables included in the model separately, the rule of law, control of corruption and government effectiveness increase agricultural productivity. When all of the variables
were included in the model at the same time while rule of law significantly increases the agricultural efficiency, political stability and voice and accountability have emerged a significant decrease in agricultural efficiency. In that study it is concluded that countries of which citizens respect to regulatory
quality have higher efficiency in agriculture. Low agricultural efficiency has been seen in more democratic countries is one the other important finding.
Studies have been conducted on farmers' production differences of rich and poor countries. Why do farmers in poor countries cannot produce as much as farmers in rich countries? Schultz (1964) argues that
the farmers in poor countries are poor, but effective. They are able to allocate their useful resources in
rational ways, but do not reach high efficiency. The reason of this condition is explained as the inadequate
supply of modern agricultural technologies.
Olson (1996) argued that due to the absence specialization and adequate institutional framework,
many poor countries are only wasting money and resources. Individual rational behaviors can result with
social inefficiencies because of institutional defects.
Governance affects agricultural productivity through many channels. First, bad governance affects
efficiency of production by imposing unpredictable taxes (Camposs et al, 1999). Many countries with
weak regulations and protectionist policies put high indirect taxes in agriculture. Krueger et al. (1991), in
-1983, determined that the market-unfriendly macro-economic policies caused indirect taxes in agriculture by more than three times that of
1160N izamettin Bayyurt and Senem Yılmaz /  P rocedia - Social and Behavioral Sciences 58 ( 2012 )1158 – 1165 direct taxes. They also viewed that these policies have a deterrent effect in agricultural production. The governance infrastructure may affect agricultural performance in several ways. For instance, the government creates and maintains institutions that are crucial to the functioning of the market system. The protection of property rights and a judicial system administering justice and enforcing contracts strongly affect the incentives for production and investment. In addition, good governance supports a competitive and low-transaction-cost environment, which encourages agricultural innovation and stimulates the adoption of new technologies and forms of organization. The government acts as an important provider of rural infrastructure, public goods and services, and essential information for agriculture for farmers. The government also determines macroeconomic policies that affect both agricultural production and investment. In some countries, agricultural development has been seriously hindered by market-unfriendly policies that are characteristics of bad governance.
The majority of individuals will lead the efforts for the protection of property in a country where the rul
e of law is weak. Most of the resources of a society where corruption is widespread devote to unearned incomes rather than productive activities. Agricultural Organizations, agricultural projects, irrigation units are usually encountered the most corrupted units in countries. Corruption is an obstacle on agricultural development (World Bank, 2007).
However, in some cases, poor governance would cause high efficiency and good governance may result in low efficiency. The best known example for that is "Grease the Wheels" hypothesis. In countries with a slow and inefficient bureaucracy, corruption increases efficiency (Huntington, 1968). Political stability may not provide economic efficiency at all times. Because many reforms accelerating the economic efficiency, were made in times of crisis (Binswaeger and Deininger, 1997).
3.Methodology
3.1.Analytical Techniques
In this study firstly, agricultural productivity as the ratio of agricultural outputs to agricultural inputs is measured by Data Envelopment Analysis (DEA) which is an efficiency measurement technique.
2), fertility (tons), the number of tractors
output is produced add value in agricultural area as USD currency. gricultural efficiencies by using DEA (output-oriented, assuming constant returns to scale technology) [Charnes et. al., 1978]
stage.
3.2.Data Envelopment Analysis
Data Envelopment Analysis (DEA) is a linear programming based nonparametric method for measuring the relative efficiency of Decision Making Units (DMUs). DEA creates a frontier function by comparing the ratios of multiple inputs to multiple outputs of similar units taken from the measured observations (Charnes, Cooper, and Rhodes 1978). It was first proposed by Charnes et al. (1978) based on the work of Farrell (1957). Since it was first proposed with CCR model by Charnes et al (1978), some extensions of the model have been developed. Over the years this methodology has been applied across a variety of sectors. An important advantage of DEA is that it is independent of the units measuring inputs and outputs allowing great flexibility in specifying the outputs/inputs to be studied. This is very important in the context of this study as the input and output variables have different units of measurement.
Two models in DEA have been largely utilized in efficiency measurements (i) input-oriented and (ii) o
utput-oriented models. With input-oriented DEA, the linear programming model is configured to
1161
N izamettin Bayyurt and Senem Yılmaz  /  P rocedia - Social and Behavioral Sciences  58  ( 2012 )  1158 – 1165 determine how much the input use of a country could achieve the same output level. With this model, the possible reduction in the levels of the inputs conditional to fixed outputs is found. In contrast, by output-oriented DEA, the linear programme is configured to determine a countrygovernance
fixed inputs. In the context of this study, output based efficiency measures are suitable for the country level inputs in our data. It is important to use a DEA output based model to measure how much output can be produced from a given level of inputs. The envelopment surface will differ depending on the scale assumptions that describe the model. Two scale assumptions are generally employed: constant returns to scale (CRS), and variable returns to scale (VRS). The latter comprises both increasing and decreasing returns to scale. CRS reflects the fact that output will change by the same proportion as inputs are changed (e.g. doubling of all inputs will double output).  VRS reflects the fact that production technology may demonstrate increasing, constant and decreasing returns to scale. In this study we use CRS model.
An output oriented CCR DEA model in the literature, can be expressed below for m inputs, s outputs and n DMUs:
r
j i all for s s m i s x x n j s y y t s s s Max
r i j i
n j j ij
ik r n
j j rj k rk
m i s
i r i k k ,,0,,,...,1,0,...,1,0.)
(1111  The DMU k
k is 1. If it is less than 1, DMU k  is inefficient.  The efficiency frontier defined by the above CCR model reveals constant returns to scale (CRS) (Cook and Zhu, 2005). As an extension of CCR DEA model, Banker et al. (1984) referred as BCC model for variable returns to scale (VRS).
3.3. Data and Variables
Data on 64 countries over the time period of 2002 through 2008 are used in the empirical analysis. Our country selection process depends on data availability in World Bank. The variables used in the first stage for DEA analysis given below.
Output:
Value added: Produced add value in agricultural area as USD currency,
Inputs:
Agricultural land (land): It is estimated by the arable land used for farming, forestry, and
production activities. It is measured in km2.
Fertilizers: It refers to the sum of pure weight of nitrogen, phosphate, potash, and complex
fertilizers which were used for agriculture. It is measured in tons.
1162N izamettin Bayyurt and Senem Yılmaz /  P rocedia - Social and Behavioral Sciences 58 ( 2012 )1158 – 1165 Machinery (tractors): It is considered as capital input for the agricultural production activities such as plowing, irrigation, draining, harvesting, farm product processing, etc. It is measured
in one unit of tractor.
Labor (labor): Participants in the economically active population in agriculture, i.e. employment in agriculture as a percentage of total employment.
Since the 1990s, development researchers and practitioners have focused on good governance as both a means of achieving development and a development objective in itself. The World Bank has defined good governance as epitomized by predictable, open and enlightened policy making; a bureaucracy imbued with a professional ethos; an executive arm of government accountable for its actions; and a strong civil society participating in public affairs; and all behaving under the rule of law. In response to the growing demand for measures of the quality of governance, a number of aggregate governance indicators have been produced, such as the W ide Governance Indicators (W
GI).
The WGI rank countries with respect to six aspects of good governance: Voice and Accountability, Political Stability and Violence, Government Effectiveness, Rule of Law, Regulatory Quality, and Control of Corruption.
The Worldwide Governance Indicators are based on several hundred variables produced by 25 different sources, including both public and private (commercial) data providers. The WGI cover 213 countries and territories (Thomas, 2008).
The Worldwide Governance Indicators are defined as follows:
Voice and accountability: captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.
Political stability and absence of violence: measures the perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including domestic violence and terrorism.
Government effectiveness: captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.
Regulatory quality: captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.
Rule of law: captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.
Control of corruption: captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests (see /governance/wgi/pdf/).
primary, secondary and tertiary gross enrolment. In agricultural economics literature ed
on agricultural productivity have been discussed much. So we include education into our model together with the WGI.
,
we constructed the following linear regression model: For the panel regression analysis dependent variable is country agricultural efficiency and independent variables are six governance indicators, country education index and country type (developed or developing). Analysis has been run for developing and developed countries separately as well.

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