Russian Journal of Agricultural and Socio-Economic Sciences, 2013, №2 (14) Февраль


ФЕДЕРАЛЬНАЯ СЛУЖБА ПО НАДЗОРУ В СФЕРЕ СВЯЗИ, ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ И МАССОВЫХ КОММУНИКАЦИЙ (РОСКОМНАДЗОР)

   РОССИЙСКИЙ ЖУРНАЛ СЕЛЬСКОХОЗЯЙСТВЕННЫХ И СОЦИАЛЬНО                   ЭКОНОМИЧЕСКИХ НАУК

RUSSIAN-ENGLISH JOURNAL


    Russian Journal of Agricultural and Socio-Economic Sciences


№2(14), February 2013


ISSN 2226-1184, http://www.rjoas.com



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СОДЕРЖАНИЕ

       Российский журнал сельскохозяйственных и социальноэкономических наук

                              выпуск февраль

А.Р. Аник, З. Бауер
       IMPACT OF CORRUPTION ON FARM PRODUCTION AND PROFIT
Д.Н. Нмаду, М.А. Оджо, Ф.Д. Ибрахим PROSPECTS OF SUGAR PRODUCTION AND IMPORTS: MEETING THE SUGAR DEMAND
OF NIGERIA BY YEAR 2020
Ф.О. Акоджа
ALLOCATIVE EFFICIENCY OF FEEDS AMONG POULTRY FARMERS IN DELTA STATE, NIGERIA
        К.Ф. Нана Яв, С. Асуминг-Бремпонг, Ф.Н. Мабе
ANALYSIS OF COCOA-BASED AGRICULTURAL KNOWLEDGE AND INFORMATION SYSTEMS IN THE EASTERN REGION OF GHANA
Г.О. Оногву, К.Д. Арене
TRADE, REVENUE AND WELFARE EFFECTS UNDER AN ECONOMIC PARTNERSHIP AGREEMENT BETWEEN BURKINA FASO AND THE EUROPEAN UNION
       Г.Б. Адесиджи, С.Т. Баба, И.С. Тьябо EFFECTS OF CLIMATE CHANGE ON
POULTRY PRODUCTION IN ONDO STATE, NIGERIA
          Б.О. Огунбамере, С.Б. Мустафа, Ю.Л. Идриса
CAPACITY BUILDING FOR CLIMATE CHANGE ADAPTATION: MODULES FOR
AGRICULTURAL EXTENSION CURRICULUM DEVELOPMENT
С. Мунонго, С.К. Шаллоне
ESTIMATING THE ROLE OF AGRICULTURAL TECHNOLOGIES IN IMPROVING RURAL HOUSEHOLD WELFARE: A CASE OF MASVINGO
Я. Тагарирофа, Б. Цазовачии
EXPLORING THE POLITICS OF LOCAL PARTICIPATION IN RURAL DEVELOPMENT PROJECTS: SMALL DAMS REHABILITATION PROJECT IN ZIMBABWE
Г. Симбараше
     CLIMATE CHANGE, VARIABILITY AND SUSTAINABLE AGRICULTURE IN ZIMBABWE’S RURAL COMMUNITIES

2(14) 2013

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CONTENT

Russian Journal
of Agricultural and Socio-Economic
Sciences

issue
February

A.R. Anik, S. Bauer
IMPACT OF CORRUPTION ON FARM PRODUCTION AND PROFIT
J.N. Nmadu, M.A. Ojo, F.D. Ibrahim PROSPECTS OF SUGAR PRODUCTION AND IMPORTS: MEETING THE SUGAR DEMAND OF NIGERIA BY YEAR 2020
F.O. Achoja
ALLOCATIVE EFFICIENCY OF FEEDS AMONG POULTRY FARMERS IN DELTA STATE, NIGERIA
C.F. Nana Yaw, S. Asuming-Brempong, F.N. Mabe
ANALYSIS OF COCOA-BASED
AGRICULTURAL KNOWLEDGE AND INFORMATION SYSTEMS IN THE EASTERN REGION OF GHANA
G.O. Onogwu, C.J. Arene
TRADE, REVENUE AND WELFARE EFFECTS UNDER AN ECONOMIC PARTNERSHIP
AGREEMENT BETWEEN BURKINA FASO AND THE EUROPEAN UNION
G.B. Adesiji, S.T. Baba, I.S. Tyabo EFFECTS OF CLIMATE CHANGE ON
POULTRY PRODUCTION IN ONDO STATE, NIGERIA
B.O. Ogunbameru, S.B. Mustapha,
Y.L. Idrisa
CAPACITY BUILDING FOR CLIMATE CHANGE
ADAPTATION: MODULES FOR AGRICULTURAL EXTENSION CURRICULUM DEVELOPMENT
S. Munongo, C.K. Shallone
ESTIMATING THE ROLE OF AGRICULTURAL
TECHNOLOGIES IN IMPROVING
RURAL HOUSEHOLD WELFARE:
A CASE OF MASVINGO
J. Tagarirofa, B. Chazovachii
EXPLORING THE POLITICS OF LOCAL PARTICIPATION IN RURAL DEVELOPMENT PROJECTS: SMALL DAMS REHABILITATION PROJECT IN ZIMBABWE
G. Simbarashe
CLIMATE CHANGE, VARIABILITY
AND SUSTAINABLE AGRICULTURE
IN ZIMBABWE’S RURAL COMMUNITIES

Food and Agriculture Organization of the United Nations

The AGRIS - International Information System for the Agricultural Sciences and Technology (http://agris.fao.org) initiative was set up by the FAO - Food and Agriculture Organization of the United Nations (http://www.fao.org) in the 70s and created a worldwide cooperation for sharing access to agricultural science and technology information. Based on available technologies, AGRIS was initially collecting bibliographic references for a central database. However, since the advent of the Internet in the late 90s AGRIS has become the brand name for a network of centres, which are promoting the exchange of agricultural science and technology information through the use of common standards and methodologies.
The AGRIS open archives and bibliographical databases cover the many aspects of agriculture, including forestry, animal husbandry, aquatic sciences and fisheries, and human nutrition, extension literature from over 100 participating countries. Material includes unique grey literature such as unpublished scientific and technical reports, theses, conference papers, government publications, and more.

AGRIS today is part of the CIARD (Coherence in Information for Agricultural Research for Development) initiative, in which the CGIAR (http://www.cgiar.org), Global Forum on Agricultural Research (http://www.egfar.org and FAO collaborate to create a community for efficient knowledge sharing in agricultural research and development.

CIARD RING - Routemap to Information Nodes and Gateways (http://ring.ciard.net) is a global registry of web-based services that give access to any kind of information pertaining to agricultural research for development (ARD). It is the principal tool created through the CIARD initiative to allow information providers to register their services in various categories and so facilitate the discovery of sources of agriculture-related information across the world. The RING aims to provide an infrastructure to improve the accessibility of the outputs of agricultural research and of information relevant to ARD management.

AIMS - Agricultural Information Management Standards (http://aims.fao.org) is a web portal managed by the FAO. It disseminates standards and good practices in information management for the support of the right to food, sustainable agriculture and rural development. AIMS underpins CIARD the international initiative which seeks to improve information access and coherence in and between organizations.

AIMS supports the implementation of structured and linked information and knowledge by fostering a community of practice centered on the themes of interoperability, reusability and cooperation. It shares vocabularies, methodologies, tools and services in order to promote coherence in agricultural information.


DOAJ - Directory of Open Access Journals (http://www.doaj.org) is a website maintained by Lund University which lists open access journals. The project defines open access journals as scientific and scholarly journals that meet high quality standards by exercising peer review or editorial quality control and use a funding model that does not charge readers or their institutions for access. As of January 2013, the database contained 8532 journals. The aim of DOAJ is to increase the visibility and ease of use of open access scientific and scholarly journals thereby promoting their increased usage and impact.


EPPO - European and Mediterranean Plant Protection Organization (http://www.eppo.int) is an intergovernmental organization responsible for European cooperation in plant health. Founded in 1951 by 15 European countries, EPPO now has 50 members, covering almost all countries of the European and Mediterranean region. Its objectives are to protect plants, to develop international strategies against the introduction and spread of dangerous pests and to promote safe and effective control methods.

As a Regional Plant Protection Organization, EPPO also participates in global discussions on plant health organized by F.A.O. and the International Plant Protection Convention Secretariat. Finally, EPPO has produced a large number of standards and publications on plant pests, phytosanitary regulations, and plant protection products.

Russian Journal of Agricultural and Socio-Economic Sciences, 2(14)

IMPACT OF CORRUPTION ON FARM PRODUCTION AND PROFIT

Asif Reza Anik, Assistant Professor
Department of Agricultural Economics
Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh
E-mail: anikbdl 979@gmail.com

Siegfried Bauer, Professor
Institute of Farm and Agribusiness Management Justus Liebig University Giessen, Germany E-mail: Siegfried.Bauer@agrar.uni-giessen.de

ABSTRACT
By analyzing experiences of 210 rice farmers belonging to six villages of six different districts in Bangladesh this article estimates the impact of corruption on farm production and profit. Unlike existing literature corruption here is defined in broader term. Both explicit and implicit cost items are included while calculating the cost of corruption. Through estimating the marginal physical product of fertilizer, a relative impact of corruption on farm production is estimated. Then by hypothesizing different scenarios with different levels of corruption, changes in a farmer’s benefit cost ratio is estimated. It has been observed that with reducing cost of corruption farmers observe higher benefit cost ratio and vice versa. Cost of corruption is found to be relatively higher in restricted input market situation and relatively lower when the market is more competitive. Thus our results are suggestive for competitive market policy to reduce corruption which will ultimately result in more farm production and profit.

KEY WORDS
Cost of corruption; Bangladeshi farm households; Marginal physical product; Benefit cost ratio; Scenario analysis.

     Corruption as a research topic has gained much interests of the academicians and researchers. Since the last decade of the previous century, there has been an increasing trend in corruption literature. The economic literature about the impact of corruption can be classified into two broad categories. A group of researchers found inverse relationship between corruption and different development indicators and argued that corruption to be harmful for development.
     Inverse correlation between corruption level and income across countries are well documented in literature (Treisman, 2000; Paldam, 2002; Chetwynd et al., 2003; Gundlach and Paldam, 2009). In general, countries with higher economic growth experience a low level of corruption and vice versa. Corruption increases costs and hence reduces incentives for investors. The entrepreneurs encounter higher costs while obtaining licenses and permits. It also creates uncertainty. Ultimately an investor’s profit margin is at a lower level, and investment is discouraged. Mauro (1997) showed that high levels of corruption are associated with lower levels of investment as a share of GDP and with lower GDP growth per capita. Empirical evidence regarding negative impact of corruption on investment and capital accumulation are also well documented in the works of Lambsdorff (2003) and World Bank (2000).
     Tanzi and Davodi (1997) tested four hypotheses to explain relationship between corruption and growth. Their results establish that higher levels of corruption are associated with: (1) increasing public sector investment, but productivity decreases, (2) reduced government revenues and fewer resources available for productive expenditures, (3) lower expenditures on operations and maintenance, and (4) reduced quality of public infrastructure i.e. road conditions, power and water losses, telecom faults, and the proportion of railway trains in use.
     Del-Monte and Papagni (2001) observed bureaucratic corruption to reduce economic growth and the efficiency of public expenditures in Italy. Murphy et al. (1991) found that in a

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corrupt environment, officials may find it profitable to be involved in rent-seeking activities rather than being involved in productive activities.
      The literature also highlights the relationship between corruption and income inequality. Corruption is generally found at lower level in countries where income is more evenly distributed (Krueger, 1974; Rose-Ackerman, 1978; Gupta et al., 1998).
      Contrary to this group of scholars, another group has mentioned some positive impacts of corruption. The most popular and common justification supporting such a positive role of corruption rests on the ‘grease the wheels’ hypothesis. The works of Leff (1964), Leys (1965), and Huntington (1968) are the pioneers for this hypothesis. According to them, corruption may be beneficial in a second-best world because of the distortions caused by ill-functioning institutions. To overcome inefficiency in the bureaucracy, investment is needed to ‘speed’ or ‘grease’ things.
      According to Leff (1964), a bribe is an incentive for better performance for the officials and hence can promote economic growth by speeding up bureaucratic processes. He also observed, in circumstances where government spending is not efficient, corruption may improve the quality of investment. Through bribes, firms can evade taxes. Such evasion will reduce public revenue, but overall efficiency of the economy can be improved when compared to the government, firms or private entrepreneurs can invest efficiently.
      Bardhan (1997) observed that corruption may help in overcoming bureaucratic rigidities when there is competition between bribers. Lui (1985) showed that a system built on bribery will lead to an efficient process for allocating licenses and government contracts, since the most efficient firms will be able to afford the highest bribes. Beck and Maher (1986) and Lien (1986) argued for corruption to play efficiency enhancing role. According to them, corruption may help officials make right choices, especially when the officials do not have enough information or are not competent in some decisions. As the most efficient company can pay the highest amount of bribe, selection of companies based on bribes can replicate the outcome of a competitive auction.
      Through bribes, private agents can minimize the consequences of some unfavorable policies (Bailey, 1966). Bribe may simply work as a hedge against bad public policies (Leff, 1964). This is particularly true if due to some reasons, government policies or institutions are biased against entrepreneurship. By paying bribe, companies can evade or limit the adverse effects of these unfavorable policies.
      Though the available literature offer some important insights about impact of corruption, they also have some limitations. The dominant trend here is to use different indices to measure corruption in a country. Unavailability of data regarding corruption experiences at micro or individual levels versus the easy availability of aggregate level data is perhaps the major factor for this trend (Mocan, 2008). In some literature bribery and corruption are used as synonyms, or they do not distinguish between these two, i.e. RoseAckerman, (1999); Wei, (1999). A common tendency in the literature is to begin with corruption in the title and then to focus only on bribery, i.e. Swamy et al. (2001); Torgler and Valev (2006); Torgler and Dong (2008); Shaw (2009) etc. Johnston (2000) observed that different corruption indices were biased to measuring bribery. Furthermore, in the existing literature, the implicit cost of bribery (i.e. the cost of negotiating for bribery, time wastage, etc) is not considered. For bribes, the literature incorporates only the amount of bribe.
      Svensson (2003) identified three common features of the available corruption literature. These are (1) cross-country analyses; (2) analyses based on perception indices; and (3) foreign experts’ assessments on overall corruption in a country (i.e. among different data sources used for constructing Transparency International’s Corruption Perception Index and World Bank’s Governance Indicators database some are foreign experts survey). Corruption is identified as an outcome of countries’ policy-institutional environment in the literature. He also mentioned some that due to the aggregated nature of the data, cross country analyses can hardly tell anything about variations within country. Moreover, serious questions can be raised about acceptability of these studies due to perception biases.
      One of the most challenging parts in corruption research is to conceptualize and indentify corruption. Johnston (2000) pointed out several difficulties in measuring corruption.

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Russian Journal of Agricultural and Socio-Economic Sciences, 2(14)

Due to the secretive nature of corruption, it is often difficult to find direct witnesses of it. Especially when there is a win-win situation, neither the bribe payer not the receiver is willing to report the corruption. Lack of a common definition of corruption makes measurement more troublesome. This happens as corruption depends on the social, cultural, and legal context, and all these can vary over time and place. The complex relationship between corruption, scandal, and crime adds more difficulties in distinguishing these while measuring corruption.
      Keeping in mind the learning from the available literature and severity of corruption in Bangladesh, we design this study to know the impact of corruption on farm production and profit. While doing the contribution of this article is mainly twofold. Firstly: this article is not only confined with bribery, rather implicit cost of corruption is also included. Secondly: to our best knowledge this is the first article in the thin body of micro-level corruption literature where impact of corruption on farm production and profit is estimated.

METHODOLOGY

      Sampling design and collection of data.The primary data needed for the study were collected through a farm household survey. Multi-stage sampling technique was employed to identify the sample households. In the first the above median rice producing districts in 200809 were selected purposively. Then these 32 districts were ranked based upon the proportion of households in each district who had experienced corruption. Ranking here was done using Transparency International Bangladesh’s (TIB) database ‘National Household Survey 2007 on Corruption.’ The top and bottom three districts from this ranking (i.e. the districts where the incidence of corruption was highest and lowest) were selected. The six districts selected were: Lalmonirhat, Nilphamary, Comilla, Sirajgong, Naogaon and Narsingdi. From each district, the sub-district that produced the most rice in 2008-09, was selected. Then from each of these sub-districts, one agricultural block and then one village were selected, again using the same criteria used for selecting sub-districts. Thus there were six districts: Enayetpur (Naogaon), South Sordubi (Lalmonirhat), Mosjidpara (Nilphamary), Mukimpur (Sirajgong), Rajapara (Comilla), and Char Belabo (Narsingdi). In the final stage, 35 rice-growing farm households from each village were randomly selected from the lists of farmers made available by the local agricultural extension office. These lists classify farmers into different groups based on their land holding status. Our sampling frame used four categories of farmers: marginal, small, medium, and large. Landless farmers and those not growing rice were excluded. To avoid over or under-representation from a specific category of farmers within a village, we calculated the proportion of each category of farmers within each village. This figure was then used to determine the number of different categories of farmers interviewed in each village. Thus the survey interviewed 210 farmers from six villages in six different districts of the country.
      For collecting necessary quantitative data a semi-structured interview schedule was designed to collect detailed information about rice farming in two seasons during the period 2008-09. The seasons are namely: Aman and Boro. Aman is grown during the rainy season whereas Boro rice is cultivated during the winter (dry season). In Aman season farmers cultivate mostly traditional varieties using natural rainfall, whereas during Boro farmers grow high yielding varieties using irrigation water. Requirement of different inputs and hence the associated costs are much higher in Boro season compared to that of Aman season. There are another rice growing season namely Aus. As compared to other season production here is much less and very few farmers do rice farming, we concentrate on Aman and Boro in our analysis. Furthermore one Focus Group Discussion (FGD) in each village was conducted with the farmers to know nature of corruption that experience.
      Measuring the cost of corruption in the agricultural sector. In our work we use the definition of Tanzi (1995) which Begovic (2005) described as the ‘most promising’ from an analytical view point. Tanzi (1995) used the concept of ‘arm’s-length principle’ while defining corruption. The arm’s-length principle ensures that all individuals are treated equally and allows no space for showing favoritism to anyone. This principle ignores the existence of any

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kind of relationship except an official relationship. By using this principle, Tanzi (1995) defined corruption as “the intentional noncompliance with the arm’s-length principle aimed at deriving some advantage for oneself or for related individuals from this behavior.” Three basic elements of corruption can be identified from this definition. The first condition describes corruption as a violation of the arm’s-length principle. The second and third criteria describe that such violations are intentional and for personal benefit. A unique feature of this definition is that its scope is not confined to only the public or private sector.
      Corruption may burden the farmers with costs. Some of these are explicit in nature. Higher prices compared to government fixed prices in the input market and bribes while collecting irrigation subsidies are examples of explicit costs of corruption. Similarly, the monetary value of inputs that have been ‘stolen’ by extension agents is another example.
      Along with these explicit costs, corruption can also impose some implicit costs. For example, when a farmer has to make several visits to negotiate a bribe amount for collecting irrigation subsidies, along with bribery he experiences excess transportation costs and wastes time. If a farmer encounters a high price in the input market due to corruption, he may not purchase the entire quantity needed and may wait for reduced corruption so that he can purchase at lower price. This ultimately results in several visits, and hence transportation and labor costs increase. In order to create an impression among the farmers that there is actually a supply shortage in the input market, the dealers often refuse to sell farmers their total required quantity, even if the farmer agrees to pay a higher price. Eventually farmers’ time is wasted, and they have to bear excess transportation costs. Cost of time wasted has implications on farmers’ liquidity constraints. If a farmer had to spend more time collecting inputs, he may have to hire more labor to work in the fields. Even if the farmers do not need to hire labor during that time, they might lose some income if they have opportunity costs for their own labor.
      By incorporating different explicit and implicit cost items, which were possible to collect reliably, the total cost of corruption (CoC) is calculated. The cost of corruption that farmers experience from the input market can be divided into several components. These are excess payments and/or bribes in input market, the cost of time wasted, and excess transportation costs. Along with these, the monetary value of a lower quantity of inputs received by the farmers from the extension office while organizing demonstration plots is also included as a component of the cost of corruption.
      Hence, the cost of corruption (CoC) = excess payment in the input market + bribes while collecting irrigation subsidies + time wasted while collecting inputs expressed in monetary value + excess transportation costs + the monetary value of a lower quantity of inputs received from the extension office.
      These cost components are defined as follows:
      Excess payment in the input market (BDT): The difference between the governmentally fixed retail price and the actual price paid by the farmers. Only inputs subsidized by the government are considered here.
      Bribes while collecting irrigation subsidies (BDT): Amount of bribes paid by irrigation pump owners while collecting irrigation subsidies.
      Monetary value of time wasted (BDT): A farmer may have his time wasted due to repeated visits to dealers and while waiting in queue. A farmer’s time wasted is incorporated into the CoC only when the following two conditions were satisfied:
    -   Farmer’s were asked, and it was made sure, that the repeated visit was not due to a lack of capital or willingness to purchase.
    -   Secondary data about fertilizer supply in the local market were checked in order to determine whether there was any shortage of supply. Only when there was no real supply shortage a farmer’s time wasted was considered.
      Time wasted is measured in man-days and then multiplied by the wage rate in order to generate the monetary value of wasted time. Here we have used farm-specific wage rates that are available at the time of collecting that specific input.
      Excess transportation costs (BDT): A farmer may have to bear excess transportation costs for repeated visits and separate transportation of small quantities instead of one large

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Russian Journal of Agricultural and Socio-Economic Sciences, 2(14)

transport load. We have incorporated excess transportation costs as a part of the total cost of corruption only when there was no supply shortage and when farmers had enough capital and were willing to purchase input. Here excess transportation costs include the cost of the vehicle that is obtained from the respondents during interview.
      Cost of corruption in government extension services (BDT): Some of the demonstration plot organizers reported not receiving the proper quantity of inputs from the extension office for their demonstration plots. The monetary value of these inputs that the extension office was supposed to but did not supply constituted the final part of the cost of corruption.
      Measuring the impact of corruption on farm production and profit. Corruption adds additional cost to farmers, and hence the shadow price of inputs (i.e. marginal costs of input purchase) increases. Higher prices or costs restrict farmers from using more inputs. Ultimately production is at lower level. Here we try to estimate relative changes in production for an input in corrupt and non-corrupt world. This will enable us to know the effect of one additional unit of input on production.
      A farm is said to be at its optimal input use level when the input’s marginal benefit to the farm equals the marginal cost to the farm of purchasing the input. At this level, the marginal value product (MVP) of an input should equal the marginal cost (MC) of that input. But in the real world, there might be deviations from such assumption. There are uncertainties regarding production and price. Sudden increase in input price may not allow farmers to obtain their optimal level of input bundles. Natural disaster or pest attacks may destroy the production.
      By assuming that our sample farmers are at optimal level of using fertilizer, we can say:

MVPf =MCf (1)


here, MVPf and MCf are marginal value product and marginal cost of fertilizer; MCf is the price of using the last unit of fertilizer. MVPf can be obtained by multiplying the output price (Py) with the marginal physical product (MPPf ) of the paddy.

MVPf = MPPf x Py     (2)

      MPPf is the additional output that can be produced by employing one more unit of fertilizer while holding all other inputs constant. Hence,

d y   , ₓ
MPPf = ду (3)


      By replacing MVPf we can rewrite Equation 1 as follows:


MPPf x Py =Pf

dУ Pf
MPPf = — =       (4)
f df Py


        Thus the ratio of fertilizer price (Pf ) to paddy price (Py) gives the marginal physical product for the fertilizer. Here we have used weighted average price for both paddy and fertilizer. Weighted average price of fertilizer (Pf ) is calculated by the following formula:


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Russian Journal of Agricultural and Socio-Economic Sciences, 2(14)

P = Qu X Pu + QTSP X PTSP + QMP X PMP + QDAP X PDAP    (5)
                    f              Qu + QTSP + QMP + QDAP


here, Qᵤ , QTSP , QMP and QDAP indicate quantity of urea, TSP, MP, and DAP purchased; and Pᵤ , PTSP , PMP and PDAP indicate the price of urea, TSP, MP, and DAP. To obtain weighted average prices in the corrupt world, we used actual fertilizer prices paid by the farmers. In the corruption-free world, we used government fixed price.
      By the following formula weighted average price of a paddy (Py ) is calculated:


Py =

Qi x Pi ⁺ Q2 x P, ⁺...⁺ Qn x Pn Qi ⁺ Q 2 ⁺ ... ⁺ Qn


(6)

here, Qᵢ, Q₂, , Qₙ indicates quantities of paddy sold in different installments, and Pᵢ,P₂ .,..., Pₙ are the associated prices with different installments.
      Impact of corruption on farm return is calculated through undiscounted benefit cost ratio (BCR). BCR is calculated as the ratio of a farm’s gross income to total costs. The value of straw was included along with value of paddy while calculating gross income. BCR is calculated based on both variable cost and total cost. The list of variable inputs include: rented in land, seed, fertilizer, organic manure, hired labor, pesticides, irrigation, hired equipments and marketing. The components of fixed cost are own land, own labor, own equipments and interest on operating capital.

RESULTS AND DISCUSSION

      Sources and forms of corruption in the agricultural sector. Corruption in the agricultural input market. As paying subsidies directly to the farmers would surely result in higher administrative and monitoring costs, the government in Bangladesh pays subsidy through importers, since the domestic production of mineral fertilizer is not enough the meet the demand. In 2008-09, domestic production of urea, gypsum and triple super phosphate (TSP) covered around 50%, 10% and 40% of the total demand, respectively. For diammonium phosphate (DAP) and muriate of potash (MOP), the country had to completely rely on imports (Kafiluddin and Islam, 2008). Unfortunately only a few farmers get benefit from the subsidy program, and the benefit is mostly enjoyed by the middlemen (MoA, 2006). In Bangladesh, four types of fertilizers, urea, DAP, TSP and MOP, are subsidized. By deciding on maximum selling prices at the local retail level, the government estimates the amount of subsidies that an importer has to receive. However in most of the cases, dealers sell fertilizer for more than the governmentally fixed price. Lack of monitoring and supervision on the part of responsible government authorities allows dealers to continue their corrupt practices. There might be confusion when considering such practices to be corruption, because one may prefer to identify them as result of market failure. However, all the elements considered essential by Tanzi (1995) when identifying an act or behavior as corrupt - violation of the arm’s-length principle, intentional in nature, and for personal benefit - are present in the practices of dealers. We therefore consider these to be corruption.
      A common practice of the dealers is not to sell inputs by giving the excuse of a supply shortage, even though there is enough in stock (MoA, 2006; TIB, 2010). Such an artificial crisis is necessary for the dealers in order to create unrest and panic in the market so that they can force the famers to purchase at higher prices. Due to this, farmers have to wait in long queues and make repeated visits to the dealers. At the local or village level, only government-appointed dealers can operate in the subsidized fertilizer market. The market is not open for everyone. To become a dealer or importer one has to get prior approval from

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