Russian Journal of Agricultural and Socio-Economic Sciences ...

ФЕДЕРАЛЬНАЯ СЛУЖБА ПО НАДЗОРУ В СФЕРЕ СВЯЗИ, ИНФОРМАЦИОННЫХТЕХНОЛОГИЙ И МАССОВЫХ КОММУНИКАЦИЙ (РОСКОМНАДЗОР)РОССИЙСКИЙ ЖУРНАЛ СЕЛЬСКОХОЗЯЙСТВЕННЫХ И СОЦИАЛЬНО-ЭКОНОМИЧЕСКИХ НАУКRUSSIAN-ENGLISH JOURNALRussian Journalof Agricultural and Socio-EconomicSciences№8(8), August 2012ISSN 2226-1184, http://www.rjoas.com

СОДЕРЖАНИЕРоссийский журналсельскохозяйственных и социальноэкономическихнаукCONTENTRussian Journalof Agricultural and Socio-EconomicSciencesвыпуск 8(8) issueавгуст 2012 AugustМ.О. ИзотовОсобенности экономического подхода приисследовании феномена коррупцииС. НайэмThe rationale behind weakly tied networkingof the bangladeshi diaspora in MalaysiaС. Мустафа, П. Бзугу,И. Али, А. АбдуллахиDeterminants of adaptation to deforestationamong farmers in Madagali local governmentarea of Adamawa state, NigeriaГ.Д. АкквахA threshold cointegration analysis of asymmetricadjustments in the Ghanaian maize marketsЛ.А. ШиловаСоциально-экономическое развитиеЦентрального федерального округаРоссийской Федерации с учетомметодологических аспектов инновационногоуправления отходами3 M. IzotovFeatures of an economic approach at researchof corruption phenomenon6 S. NayeemThe rationale behind weakly tied networkingof the bangladeshi diaspora in Malaysia15 S.B. Mustapha, P.M. Bzugu,I.M. Ali, A. AbdullahiDeterminants of adaptation to deforestationamong farmers in Madagali local governmentarea of Adamawa state, Nigeria21 H. de-Graft AcquahA threshold cointegration analysis of asymmetricadjustments in the ghanaian maize markets26 L. ShilovaSocio-economic development of the Centralfederal district of Russian Federation withmethodological aspects of innovation wastemanagement

M.O. IZOTOV, Orel State Agrarian UniversityABSTRACTFEATURES OF AN ECONOMIC APPROACH AT RESEARCHOF CORRUPTION PHENOMENON M.O. Izotov, Researcher.. , !"#$%Orel State Agrarian University, Orel City, Russia"&!%$ '!#"!( '"" $("!$(, '. ")&, !!$*Phone: +7 (920) 812-90-64, E-mail: max198522@mail.ruReceived August 10, 2012In article features of an economic approach are considered when developing the anti-corruptionmeasures directed on restriction of possibilities of any discretion and excessive intervention of civilservants in economic activity, including through differentiation of functions and specification of competences.The special urgency of researches of a problem of corruption as special social phenomenonis noted.+ -, - , . ! - .KEY WORDSCorruption; Social and economic researches; Phenomenon; Model; Economic approach.,- " ; # - ; $ ; % ; & . .!&(#(( "(/* !"(/(/ "!!$ -!%/ 01(!( !& %" ( %&2 ."-0&(/ $!!&(#$* %"".3$$ %% !0'!3$&2' *&($*. ( $($(/ $/(!*3(& "*# !3$&24 $ '/$"4 %,%5#* $ %"4 !$ ! !1(!(%&# "$$( ."0&(/$%$ %"".3$$ $0"20 ! ( . 6%/$(!% %(, "/%4 ($-!$3$&2 7%& 70-( ''. XX .,!8"/$"&!* ! .#4# $!!&(#$$8(/( %"".3$$. '&! (/, %"".-3$* $(!* $!4#* $ .!&%$, *-&*(!* "3$&2 ($ #5( "/&2 ) "-$#!29 .(#($*, ."&( -45#($( .$/&2' !.!0 "(&$3$$$("(! !&$*4 '"$(!$ "(!"-!. (!2, «"(7($( #2 (*2) *% .$-"(!* 5( %&2%&*3$9 " $ '#, $ &90( #"'( 6%/$(!%( "(7($(»[1]. ."$/(", . -%%("/, #' $!(&( #' .#4#, %"".3$* .-"(#(&*(!* %% «"$#!2 6%/$(!%-' .(#($* !&$*4 "$!%, 0!&&(-' !("7($(/ ."(!.&($* $ /5/%$(/ ('» [2]..$!(!* %"".3$( .(#($(."$ ./1$ /#(&$ «."$3$.& – '(», #(-&2 .$!9 "04 ''("! [3]. /#(&2 4"%("$( $/7($*"(4 !$%: ."$3$.&, '( $ %&$(-. "$3$.&/ *&*(!* &$3 $&$ "'$-3$*, !*1$( .("(# !0 3(&$ $ 0&#91$("(!"!/$ #&* $4 "(&$3$$. (' "&$ /'!.2 %% -&$0 '!#"!( "'$&$ (#/!, '!#"! $&$ 01(! 3

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Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012THE RATIONALE BEHIND WEAKLY TIED NETWORKING OF THE BANGLADESHIDIASPORA IN MALAYSIAABSTRACTSultana Nayeem, Associate ProfessorDepartment of Development Studies, University of Dhaka, Dhaka-1000, BangladeshPhone: +880-1924752886, E-mail: sultana_nayeem@yahoo.caReceived August 7, 2012This paper is an exploration of the survival strategies of the Bangladeshi Diaspora in Malaysia. Tocope with the realities, Bangladeshi migrants develop different forms of survival strategies. As a resultintra and inter-ethnic strong and weak ties are formed in the receiving country. Empirical analysisdepicts that respondents with weak ties have higher income mobility than those with strong ties. It alsodemonstrates that the Bangladeshi migrants of the study areas do not restrict themselves only to theirclose social networks; rather they develop distant networks for higher social mobility. Or in otherwords, though the ideal socio-cultural model emphasizes community cohesion (something that can beconceptualized as an example of a tightly structured social system), the actual behavior of the Bangladeshimigrants indicates a loosely or disintegrated social system. Migrant’s embeddedness in the ongoingsocial relations and power structures regulates the nature and strength of these ties.KEY WORDSMixed embeddedness; Bangla bazaar; Bangladeshi diaspora; Strong ties; Weak ties.Migration and settlement of Bangladeshis inMalaysia is a common fact during this period ofglobalization. The question is, therefore, notwhether Bangladeshis migrate, instead, how theysurvive in a foreign country and develop differentstrategies to improve their fortunes. Bearing thisquestion in mind an intensive ethno-survey wasconducted both in Bangladesh (country of origin)and Malaysia (country of residence) 1 . These migrantsare found as the heterogeneous groups ofpeople, embedded in the diverse realities and liabilitiesof the origin and host societies 2 .Therefore, in this study, the readers maycome across their non-homogeneous interests and1 Sultana, N. 2008. The Bangladeshi Diaspora in Malaysia. OrganizationalStructure, Survival Strategies and Networks, ZEF DevelopmentStudies. LIT: Berlin2 Research for this study was carried out through the financial assistanceof DAAD (German Academic Exchange Service). Field researchin Malaysia and Bangladesh was conducted among the immigrantsand their families from June 2005 till August 2006. Fieldresearch data are derived from an interview-survey among 150current Bangladeshi migrants in Malaysia and intensive ethnographicfieldwork in Bangladesh and Malaysia. Along with 165 semistructuredinterviews (with returned and current migrants), somegroup discussions were arranged with returned migrants, their families,friends and neighbourhoods as well as with a N.G.O (establishedby well-off repatriated migrants). Sources of secondary datawere literature reviews, newspaper articles, published and unpublishedreports and conference papers.concomitant coping strategies. There (in the receivingsociety) networking is an asset. Our analysishas moreover evidenced that depending ontheir demands firstly of adaptation (to cope andsurvive in a foreign country) and secondly ofupward mobility both strong and weak ties havebeen developed along the lines of horizontal andvertical networking. These networking are formulated(by the migrants) on the basis of the diverseeveryday realities they face through their“mixed embeddedness” 3 . Regarding this, both thesocio-economic, political and institutional structuresof the origin and receiving countries as wellas migrants’ socio-economic statuses, transnationalcontacts and duties are found as the regulators.Thus, along with bonds of strong and weakties different types of alliances and cleavages aredeveloped by the well-off and poor migrants inthe horizontal level. Consequently, instead ofhomogeneity, heterogeneity and diversity are thecommon criteria for the Bangladeshi Diaspora inMalaysia.However, apart from these primordial typesof networking, a kind of inter-ethnic strong (in-3 Robert Kloosterman, Joanne van der Leun and Jan Rath. 1999.Mixed Embeddedness: (In) formal Economic Activities and ImmigrantBusinesses in the Netherlands, p. 2.6

SULTANA NAYEEM, University of Dhakater-ethnic friendship, marital and other kinds ofintimate relationships) and weak ties (commercialnetworking like partnership in business etc.) werealso found as the outcome of the migrants’ embeddednessin the receiving society, with whichafter all they had to cope with.The question is which of these characteristicsare prominent, how do they contribute to theorganizational structure of the community and inwhat context does that happen? Therefore, in thenext sections at first we will define how strongand weak ties are operationalized. Later on, Granovetter’s(1973, 1983, and 1985) conception of“strong” and “weak ties” and the concomitantstrength of weak ties (posited by him) will beanalyzed testing against the data of this study 4 .This type of comparison is important on theground that the formulation of different forms ofnetworking, like friendship (for adaptation) ordistant relationships (for survival in the host society),were defined by the migrants as a mustthat may bear a resemblance to his (Granovetter)point of view. This study will continue discussionhighlighting the following aspects: methodology;operational definitions of strong and weak ties;revisiting Granovetter’s conception on strong andweak ties.Methodology. A brief overview of thesources of data and the methods that were appliedto collect data is important to support the argumentsof this study. Field research in Malaysiaand Bangladesh was conducted among the immigrantsand their families from June 2005 till August2006. Field research data are derived froman interview-survey among 150 current Bangladeshimigrants in Peninsular Malaysia and intensiveethnographic fieldwork (with returned andcurrent migrants) in Bangladesh and PeninsularMalaysia.Applying snowball sampling (somethingusually applied to find hidden population) Bangladeshirespondents were selected. In fact, neitherthe immigration department of the host countrynor the emigration authorities of the homecountry had any concrete information about Bangladeshimigrants. Likewise, the central statisticsbureau, the local police as well as the Bangladesh4 Mark S. Granovetter. 1973. The Strength of Weak Ties. AJS volume78, Number 6 and Mark S. Granovetter. 1983. The Strength ofWeak Ties: A Network Theory Revisited. Sociological Theory,volume 1. See also Mark Granovetter. 1985. The University ofChicago. Economic Action and Social Structure: The Problem ofEmbeddedness. AJS Volume 91 Number 3, p. 481-510.High Commission in Malaysia failed to provideany data about the “hotspots” of Bangladeshi migrantsor their statistical figure in Malaysia. Onthe contrary, each of them spoke of the migrants’essentially mobile character. They also emphasizedthat Bangladeshi migrants used to stay andwork there as undocumented workers and even,some of them could be found as husbands of Malaysians.Consequently, some open-ended questionswere added in the survey questionnaires thatprovided information about the nature of theirintegration, embeddedness in local society andcoping strategies.Besides, utilizing the networks of the respondentsselected for interview-survey, intervieweeswere selected for more intensive interviews.These in-depth interviews were conducted to seewhether or not the best solution was taken byBangladeshi migrants to deal with their multidimensionalembeddedness. Data, collected inthat way, revealed that social networks, developedalong the lines of horizontal and verticalnetworking, supported the migrants bearing theirsocio-cultural and psychological responsibilitiesin the transnational hubs. The said networks,moreover, decreased their costs of migration.Operational definitions of strong andweak ties. Trust is a common factor in close socialnetworks. Unless an extreme situation(struggle for a scarce resource, e.g. an opportunityof migration to a foreign country) arrives, respectivefellow feelings, duties and moral obligationsremain intact within these strong ties thatmay echo the examples of the Chinese Gunaximodel as portrayed in Hammond’s work 5 . Referringto the work of Gao and Ting Toomey (1998)he has argued that the unconditional sharing ofinformation (even secret) within the insider networks,is the main function of the Gunaxi relationship.In these relationships members are consideredhighly trustworthy and they are obliged tomaintain that honor. According to him, within theinsider networks relationships are perceived asfamily or like a family that cannot be altered exceptunder extreme conditions. However, it isnow known to us from the previous section’s discussionthat with the exception of friends (thatmeans fictive ties) most of the actors of strongties always remain intimate since they possesscommon goals as each other’s consanguine andaffine.5 Scott et al. (2004)7

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012For weak ties, the opposite situation is supposedto be normal, since this relationship is developedto mitigate different kinds of supply anddemand. Since their (migrants’) close networksfail to fulfill their necessities (because of limitedcapabilities), they develop weak ties. For example,managing a way of migration or finding alternativesources of income etc. are fulfilled bydeveloping weak ties. The actors of weak ties arenot closely related to each other and hence do notmeet frequently. In the same way, these peopleare not morally bound to assist each other, unlesstheir service is purchased by the clients. Thoughthe relationship is also developed instrumentalizingnational brotherhood, but it mainly worksdepending on commercial exchange, instead ofrelying only on moral obligations (that can beseen in strong ties).Due to mutual demand for upward mobilitywell-off brokers support their clients (after extractingservice charges from them) as parts oftheir manpower business; while on the otherhand, in order to find ways of migration or higherincome mobility, poor migrants develop relationshipswith them. Better-off businessmen cumbrokers depend on poor migrants to make a profitvia manpower and “hundy” business. They (thepoor migrants) are also the buyers of Bangladeshigoods and food stuffs from these entrepreneurs’enterprises, which operate in the study areas.Hence the actors of weak ties know that these tiesare secondary (derivative/unoriginal) in natureand are essentially a tool for upward mobility.Accordingly it delineates that within the weakties - all the actors cannot belong to the same socio-economicposition and are not the possessorsof equal capabilities (as well).As a matter of fact, if they all held equalpower, information and capabilities to commandany act, then none of them would waste moneyand time on these then essentially useless theirweak ties. Rather, as a rational human being, theywould find their own way depending solely ontheir strong ties. As an opposite form of relationshipthe strong ties on the other hand, are informaland primary (fundamental/original) in nature.They become parts of their close social networksnaturally, owing to their embeddedness in kinship(blood related and marital kin) and friendshipcircles. So, these are the strong and weak tiesituations in the study areas of Peninsular Malaysia.Nevertheless, to make clear how strong andweak ties are operationalized in this research,discussion on the following variables are incorporatedto extract ideas about the roles and modesof networking as a potential survival strategy.The variables and values are:Frequency - the following questions wereasked to find out how often actors of a networkmeet or get chances for interpersonal interaction:With whom are you living here? With whomare you working here? Do you have any fixedplace or area for community get together? Whereis this meeting place? When do you usually meet?How do you pass your leisure time? Normallywhere do you meet your inter-ethnic friends?Concerning these queries the following tableis constructed:Ethnic (61.3%)HousemateAlone (6.7%)Ethnicand inter-ethnic(32%)Co-workersEthnicAnd inter-ethnic(94.6%)Not fixed(5.3%)Source: Survey data in Peninsular Malaysia.Table 1 – Frequency of NetworksFixedplace forcommunityget togetherYes(97.3%)No(2.7%)WithinNeighbor-hood1-Enjoyingmovies athome (9.3%)2-Gossiping inlocal restaurant(12%)3-Visiting localfriends (10%)Passingleisure timeOutside1-Visiting Kotaraya(23.3%)2-Going to pub 12.7%)3-Attending religious,political meetings andcommunity get together(32.6%)Get togetherwith inter-ethnicfriends1-Housemates(18%)2-Workmates(48.7%)3-Mosjid andrestaurant(15.3%)4-Neighbour(18%)8

SULTANA NAYEEM, University of DhakaTable 1 denotes that apart from maintainingnetworks with their housemates, workmates andneighbors, Bangladeshis also attend different politicaland religious meetings and cultural celebrations.There are certain places for this kind ofcelebration which the migrants (both poor andbetter-off) try to visit. It is found that the betteroffmigrants become the conveners of these culturaland politico-religious programs, where poormigrants participate in their free times. They visitKotaraya (‘Bangla Bazaar’) to buy Bangladeshigoods from ethnic enterprises as well as to meetpowerful labor brokers.The data also indicates that the migrants’common living and working environment providesthem the opportunity for regular contactwith their ethnic and inter-ethnic friends. Thecommon living and working niche as well as everydayinteractions enable them to develop friendshipswith each other. In fact, along with thecommon experiences of immigration and everydaycontacts, they (the Bangladeshis) face moreor less the same realities as neighbors and workmates.Besides, they are found to spend their leisurehours together in their common surroundings(apart from visiting distant places and weaktie based networks). However, while the locals donot need to face an alien way of life (since theyare all insiders there), owing to regular correspondenceeither in local restaurants or at theirhome and work places both of these groups stillget the chance to construct networks. Bangladeshismeet the locals of Muslim belief in localmosque and “suraus”. Because of the same religiousbackground (Islam) most of the Bangladeshimigrants and their (Malay, Indian Muslim andIndonesians etc.) friends enjoy and practice moreor less similar religious festivals and rituals.Strength and Intensity - To pull out informationon the emotional intensity, reciprocal exchangeand respective obligations to each othersin the network some questions were asked, suchas:Whom do you consider your friend? How doyou define your friend? Why do you maintainnetworks? How do you manage jobs and workpermits? How did you get your dwelling place?Who introduced you to your employer? Where doyou go to acquire a better job and other facilities?Do you pay them? Why do you assistpoor/undocumented workers? How did you migrate?How do you send remittance to homecountry? How did you learn Bahasa? How doyou manage to cope here? Why are you livingtogether with others? How did/will you manageto stay longer? Why do you maintain homelandcontacts? Why did you prefer inter-ethnic marriage?How did you meet your inter-ethnicspouse? How did you manage your business visa?How did you get this property? How did youmanage risks?As a matter of fact, friendship ties werefound primarily within people of the same socioeconomicbackground. For example, un-skilledand semi-skilled workers identified other workersas their friends who often also were their housemates,neighbors and workmates. In the sameway, no professional and businessman was foundwho considered a semi-skilled and un-skilledworker as his or her friend. It is not likely that allof the neighbors or co-workers converted eachother’s friend. Nor was national brotherhood consideredas the only factor for friendship. Rather,migrants emphasized those as their friends whomthey could trust in need. According to RanjanMallik (he used to work in a furniture factory ofKajang),“My co-workers, likewise Bangladeshiworkers in Kajangjaya, are my friends. We arenot so well-off. We have to send money to ourfamilies. I also have some friends in my hometown. They take care of my family. Sometimes Isend gifts for them or try to make phone calls.But I don’t have any girlfriend. I also don’t likethe rich and educated Bangladeshis. They behavelike we are not their countrymen. I know theywould not assist me in danger. I don’t know anyfemale workers. It is better to avoid them, becausethey might create extra burdens and ourfamilies would not be happy about that.”It can be ascertained (from the above comment)that professionals and businessmen generallyare not friends with poor income groups. Atthe same time, it also assists us to know whatthey (the poor migrants) expect from theirfriends. Provision of trust, certainty, regular correspondenceand assistance are the qualitiesamong others through which their friendship isdefined. On the basis of these moral and reciprocalobligations (for each other) they maintainfriendship even with their home-based residues.Transnational networking helps them by allowingthem to maintain contact with their home basedfriends. They (the migrants) are also assured thattheir friends reciprocate their obligations to themigrants by taking care of their families. These9

SULTANA NAYEEM, University of Dhakaunless he increases his stock of informationthrough forming weak ties.The hypothesis of the following statisticalanalysis is migrants with weak ties have higherincome mobility than others with strong ties.Some proxies are made to define weak and strongties, e.g. respondents were asked how they passedtheir leisure time? Their answers are coded as:Weak tie: visiting ‘Bangla Bazaar’ of Kotaraya,going to pubs and shopping centres, attendingreligious and political discussion and communityget togethers.Strong tie: watching movies using satellite(with the housemates), visiting local friends(neighbours) and gossiping in a restaurant (local).We have regressed their monthly wage withboth quantitative regressors: job alteration, lengthof stay in years in Malaysia and qualitative ordummy regressor: weak or strong tie. ANCOVA(Analysis of Covariance) model is as follows:Yi= β1 + β2D2i+ β3X2i+ β4X3i+ ui,where, Y = monthly wage of the ith migrant iniRM, X 2= job alteration of the ith migrant,iX 3=ilength of staying in years of the ith migrant, D 2=i1, if the migrant has weak tie; 0, otherwise (ifstrong tie). With ui= error term, β = constant,1β2, β3,β = coefficients of dummy, job alteration4and length of staying respectively. The followingregression results are obtained:∧Y i = −7 .295 + 363.779D2i+ 93.556X2i+ 83. 228X3ise = (250.001) (173.518) (74.742) (19.806)t = (-.029) (2.096) (1.252) (4.202)p-value= (.977) (0.038)* (.213) (0.000)*R 2 = .171, n= 150Where, se means standard error of the estimatedcoefficient, t is t-statistic, p-value is theprobability value, * - indicating p-value is significantat 5% level of significance, R 2 is multiplecoefficient of determination, i.e. the 17% variationin wages are explained by the regressors andn is the sample size.From the above results, it can be concludedthat the dummy variable and length of stay havestatistically significant relationship with thewage. Duration of migrant life in the host countryhas the strong positive effect on wage. Keepingall other variables constant, the average monthlywages of migrants with weak ties are higher byabout RM 363.78 than those with strong ties. Ourdata in this study is cross-sectional one whereheteroscedasticity may involve frequently. So, weassume that u is normally distributed with meani22zero and variance σ , i.e. u ~ii N(0,σi) . We testby graphical method and White’s general heteroscedasticitytest (White, 1980) with the nullhypothesis: H 0 : there is no heteroscedasticity inthe error variance and found H 0 may be rejected.4000300020001000-1000-2000020 40 60 80 100 120 140Residual Actual FittedFigure 1 – Plotting residual to check heteroscedasticyFrom the above figure, it is depicted that theresidual term of the fitted regression showingheteroscedasticity for different values of the regressors.The variances of the error (according tothe black line of zero value) are fluctuating fordifferent values at different band width.Table 2 – White Heteroskedasticity Test ResultsF-statistic 9.932964 Probability 0.000000Obs*R-squared 38.46709 Probability 0.0000008000600040002000So, this study performed the White heteroscedasticity-consistentvariances and standard errortest for the remedy of heteroscedasticity andto get robust standard errors.Table 3 – White Heteroskedasticity-Consistent StandardErrors & CovarianceVariable Coefficient Std. Error t-Statistic Prob.CONST -7.295058 279.9356 -0.026060 0.9792Dummy 363.7786 130.4603 2.788424 0.0060Alteration of job 93.55614 82.85140 1.129204 0.2607Length of staying 83.22766 33.05351 2.517968 0.0129011

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012From the above results, it is depicted thatstandard errors are changed from the earlier estimatesbut the dummy and duration of abode inMalaysia remain significant at 5% level of significance.So, there is significantly and robustlyhigher average wage of migrants with weak tiesthan those with strong ties.Our regression result shows that job alterationis insignificant and is very much related tothe other realities of the study areas (of PeninsularMalaysia). It was noticed that migrantschanged their jobs or were forced to quit it notonly for higher earning, but also for other causes.Such as: (1) dismissal from job by the employer,(2) to find a permanent job (though sometimesthe new one is not highly paid), (3) being arrested(as undocumented workers) they lost their job,(4) for security, (5) to prolong their duration ofstay they change jobs (to bypass law that imposesa restriction on the un-skilled and semi-skilledmigrant workers saying that they are not allowedto work for more than ten years), (6) due to sickness(caused by a hard working job), (7) someworkers identified some jobs as “risky” (immigrationpolice might come and want to checktheir ‘jalan card’) and not preferable (e.g. workingunder sun or in open space, mostly, in theconstruction site) and changed jobs etc.However, the above result regarding thecontributions of weak ties on the higher incomemobility of migrants can also be shown throughthe following line graph:Figure 2 – Contribution of Strong and Weak Ties for Higher Income MobilityThe line graph depicts that migrants withweak ties have higher income mobility than thosewith strong ties. Though we do not see a smoothline graph, the average monthly income rate nonethelessshows that weak tied groups on averagemanage to stay longer and earn RM 6000, whereasfor strong tied groups the highest incomerate is around RM 2000 in their 9th year. The linegraph does not indicate that strong tied networkshelp them to earn this amount. Rather, it isknown to us that documented migrant workers’wages are increased by the factory authority accordingto their level of work experience. Theyallow them to work for a maximum of 10 yearsand provide them increments on the basis of theirwork experiences. As a result, the highest amountof income (of the strong tie groups) shows thatthey managed to earn this amount in their ninthyear according to the employment act of thecountry. After that, concerning strong ties we can12

SULTANA NAYEEM, University of Dhakasee a decreasing rate of earning until their 11 yearof stay. In their 12th and 13 years they slightlyincreased their slim earnings, but in the end werenot successful at this. They even failed to reachtheir previous rate of income (RM 2000), thoughthey had stayed in Malaysia for more than 10years. Or in other words, their length of stay didnot assist the strong tied groups to earn more.Though they had stayed more than 10 years, theyhad restricted themselves to their own (ethnical)domain. Limited capabilities and information(which in any case they already possessed) failedto show them paths for higher income mobility.On the contrary, regarding weak ties thoughwe notice a lower income rate at the very beginning,it finally helps the migrants to reach theirpeak. Migrants who migrated depending only onweak ties (commercial labour brokers) could notadapt to the foreign environment in the beginning.As they lacked the strong ties, necessary tocope (with foreign customs, language, ways ofworking etc.) with their immigrant life, theyfailed to earn more. But after staying a while theygradually achieved local knowledge on “what todo, where to go” and also managed to developnew networks, necessary for upward mobility.These new ties not only provided them with theopportunities for higher income mobility, butalso incorporated risks. Therefore, the decreasingrates of income of the followers of weak ties canbe explained highlighting risks. It is found that inthe host society migrants encounter differenttypes of risks ranging from intra-ethnic tension tointer-ethnic conflict. For example, while some ofthe poor migrants identify their weak ties or patronsas exploiters, they also express their fears ofTamil Indians. However, to overcome thesedrawbacks they need to find another weak tie (awell-connected person both with the local powerstructure and other well-off countrymen). Theytry to find a better one who likewise possessesthe necessary capabilities, links (networks) andwho is also knowledgeable in combating risks.To find a “risk free” & better strategy they formnew weak ties so they can stay longer in thestudy areas and also manage opportunities forhigher income mobility. Though they considerweak ties as commercial and exploitative, theystill do not prefer to depend only on their trustworthystrong ties. Their strong ties representtheir own workmates, housemates as well as relatives(who possess more or less same level ofinformation and networks that they themselvesalready have). Having failed to find a suitable jobat home, they migrate to Malaysia to make theirfortunes (within a short time frame). They alsowant to earn more for a “better future”. Theyknow, if they fail to earn enough money, theywill be unable to maintain their expenditures bothat home and abroad. As a result, even thoughthey consider their weak ties exploitative theydepend on them unless better ones are found.Through criticizing them they feel psychologicalrelief. They also express their expectations towardswell-off countrymen. For example, “He isrich, but very polite! Not like others.” This singlesentence denotes how through discursive practicesmigrants are expressing their experiences andexpectations towards well-off people. Accordingto Moerman (1988),“In every moment of talk, people are experiencingand producing their cultures, their roles,their personalities.”However, though weak ties are identified asexploitative, the line graph nevertheless showsthat respondents with weak ties have higher incomemobility than those with strong ties. At thesame time, it also demonstrates that the Bangladeshimigrants of the study areas do not restrictthemselves only to their close social networks;rather they develop distant networks for highersocial mobility. Thus the migrants of a certainarea remain connected with other migrants fromdifferent parts of the host society. They thus getopportunities to construct interpersonal networksthrough weak ties, because most of the less privilegedmigrants visit either ‘Noakhali’ group or‘Barishal’ group for their higher social mobility.Through their transnational business activities,the better-off merchants assist poor migrants sothat they can maintain transnational networkswith their kith and kin.Conclusion. Through this study, we havecome to notice a dilemma concerning migrants’perceptions on weak ties and their actual behavior.On the one hand, most of the poor migrantsperceive their intra-ethnic weak ties as exploitativeand they depend on them for higher incomemobility and for long term settlement in the receivingcountry. In fact, their strong tied networksare noticed as poor and less connectedwith the macro level authorities. Hence they lackthe necessary power and information, requiredfor upward mobility. Therefore, they need to dependon weakly tied networks in order to overcomeeconomic insolvency and achieve higher13

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012social status and prestige both in the origin andreceiving countries. The well-off businessmencum manpower agents, who make up the weakties of these poor migrants, also try to continuethe relationship because of their business interests.Along with kinship based strong ties, it alsoincorporates weak ties, where profit maximization,conflicting interests etc. are major concerns.Or in other words, though the ideal socio-culturalmodel emphasizes community cohesion (somethingthat can be conceptualized as an example ofa tightly structured social system), the actual behaviorof the migrants indicates a loosely or disintegratedsocial system. On the contrary, thewell-off professionals and professionals cummerchants mainly depend on trust based strongties, since these networks possess enough informationand contacts required for upward mobility.Micro level individual’s life is thus connectedwith the macro level authorities, while the migrant’sembeddedness in the ongoing social relationsand power structures regulates the natureand strength of these ties.REFERENCESGao, G. and Ting-Toomey, S. 1998. CommunicatingEffectively with the Chinese.Thousand Oaks, CA: Sage.Granovetter, Mark S. 1973. The Strength ofWeak Ties. The American Journal of Sociology,Volume 78, No. 6, 1361-1380.Granovetter, Mark S. 1983. The Strength ofWeak Ties: A Network Theory Revisited.Sociological Theory, Volume 1, 201-233.Granovetter, Mark S. 1985. Economic Actionand Social Structure: The Problem of Embeddedness.The American Journal of Sociology,Volume 91, No. 3, 481-510.Kloosterman, Robert, Joanne van der Leun andJan Rath. 1999. Mixed Embeddedness:(In) formal Economic Activities and ImmigrantBusinesses in the Netherlands. InternationalJournal of Urban and RegionalResearch, Volume 23, No. 2, 253-267.Michael Moerman. 1988. University of PennsylvaniaPress. Talking Culture. Ethnographyand Conversation Analysis, p. prefacexi.Scott C. Hammond & Lowell M. Glenn. E:2004. The Ancient Practice of ChineseSocial Networking: Gunaxi and SocialNetwork Theory, Vol. 6 Nos. 1, p. 24-31.Sultana, Nayeem. 2010. The Solutions of the‘Trans-migrants’ Dilemma. The BangladeshiDiaspora in Malaysia. Dhaka UniversityJournal of Development Studies,1(1): 181-192. Dhaka University Journalof Development Studies, 1(1): 181-192.Sultana, Nayeem. 2010. Re-visiting theStrength of Weak Ties: Bangladeshi Migrationto Malaysia. Journal of DiasporaStudies, 3(2): 115-142.White, H. 1980. A Heteroscedasticity ConsistentCovariance Matrix Estimator and aDirect Test of Heteroscedasticity. Econometrica,Volume.48, 817-818.14

S.B. Mustapha, P.M. Bzugu, I.M. Ali, A. Abdullahi, University of MaiduguriDETERMINANTS OF ADAPTATION TO DEFORESTATION AMONG FARMERSIN MADAGALI LOCAL GOVERNMENT AREA OF ADAMAWA STATE, NIGERIAABSTRACTS.B. Mustapha, P.M. Bzugu, I.M. Ali, A. Abdullahi, ResearchersUniversity of Maiduguri, NigeriaPhone: +2347060573884, E-mail: shettimabulama@yahoo.comReceived August 9, 2012The study examined the determinants of adaptation to deforestation among farmers in Madagali LocalGovernment Area of Adamawa state, Nigeria. Structured interview schedule were used to obtain informationfrom 200 respondents selected through simple random sampling techniques. The data collectedwere analyzed using descriptive (frequencies and percentages) and inferential (chi – squaretest) statistics. The result indicated that majority (84%) of the respondents were male with 21-40 yearsof age representing 58.50%. The study also showed that most (45%) of the respondents had attainedpost primary education with majority (80%) having 6 and above years of farming experience. The respondentsperceived fuel wood extraction as the major (42%) cause of deforestation in the study area.The result showed that the most (30.50%) frequently employed adaptation strategy against deforestationwas reducing quantity of fire wood consumption. The study further showed that the factors whichsignificantly influenced adaptation to deforestation among the respondents were age, farming experienceand educational status with X 2 =9.216, 8.697 and 11.238 at P

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012consciousness about environment and its conservation[5]. Adaptation in this regard, is the evolutionaryprocess whereby population becomes bettersuited to its habitats. In other words, adaptationis learning to cope with the impacts of climatechange (deforestation) as reported by [6].Tropical deforestation plays a central role inmany of the most acute environmental threats,including global climate change, habitat degradationand unprecedented species extinction. Scientificand public concerns about these and otherpotentially massive ecological disruptions haveincited a frowning number of studies that aims toquantify adaptation to deforestation processamong farmers. Therefore, this study sought toassess adaptation to deforestation among farmersin Madagali Local Government Area of Adamawastate.Statement of the problem. In Nigeria, thegreatest problem facing human existence hasbeen deforestation and environmental degradation.As a result of deforestation, trees have undergonesubstantial deterioration, particularly inthe northern part of Nigeria. The vegetation ofAdamawa State is a characteristic of habitat thathas been altered due to human interference withits previous natural climate formulation. The cultivation,grazing activity, cutting of trees for firewood and other purposes have a demonstratableeffect on the livelihoods of the farming communities.Some attempts have been made to study theeffects of demonstration in Nigeria [9, 10]. Howeverthere has not been any empirical study intothe adaptation of deforestation among farmersimplied in the study area. This study was therefore,conducted to provide empirical informationon adaptation to deforestation among farmers inMadagali Local Government Area of AdamawaState. The study was carried out to address thefollowing research questions:i. what were the socio economic characteristicof the respondents?ii. what were the perceived causes of deforestationamong the respondents?iii. what were the adaptation strategies employedagainst deforestation among the respondents?iv. what was the relationship between socioeconomiccharacteristics of the respondents andtheir adaptation to deforestation in the studyarea? andv. what were the constraints faced by respondentsin adaptation to deforestation in thestudy area?Objective of the study. The main objectiveof the study was to assess the adaptation strategiesemployed against deforestation by farmers inMadagali Local Government Area of AdamawaState. The specific objectives of the study wereto:i. examine the socio-economic characteristicof the respondents.ii. identify the perceived causes of deforestationby the respondents,iii. determine the adaptation strategies employedagainst deforestation by the respondents,iv. examine the effect of socio-economicfactors on adaptation to deforestation by the respondents,andv. investigate the constraints faced by respondentsin adaptation to deforestation in thestudy area.METHODOLOGYThe study area. The study was carried outin Madagli Local Government Area (LGA) ofAdamawa State. Madagali LGA is located betweenlatitudes 10 0 and 11 0 and longitudes 12 0and 15 0 of the Greenwich meridian and coversapproximately an area of 903 km 2 [2]. The areahas an estimated population of 134,827 [8]. Thevegetation is made up of grasses and somestunted trees in some parts of the area. The rainfalllasts for about 4-5months in a year with anaverage rainfall of 700-1000 mm per annum [2].The dry season begins in November and terminatesin early June of the following year. Farmingis the principal economy of the people in thearea. The climate and the rich alluvial soil of thearea favours the cultivation of food crops such assorghum, millet, maize, rice and cassava. It alsofavours the production of local cash crops such ascowpea, groundnuts, sesame and sugar cane on alarge scale basis. Livestock production is alsovery important in the study area and is one of thelargest concentrations of cattle in Adamawa state.Fishing is a common practice among those livingaround riverbank areas.16

S.B. Mustapha, P.M. Bzugu, I.M. Ali, A. Abdullahi, University of MaiduguriSources of data. Primary data was mainlyused for the study. These were generated fromfarming households head through the use ofstructured and protested interview schedules. Theinterviews were conducted by enumerators fromBorno State Agricultural Development Programme(BOSADP) who were trained for thepurpose. Secondly, information from Areas ExtensionOfficers (AEOS) of BOSADP and AreaForestry Officers (AFOS) of the ministry of environmentwere used to complement the primarydata. Other sources of secondary information includes,textbooks, journals and other write up thatwere relevant to the study.Sampling procedure and techniques ofdata analysis. Simple random sampling techniqueswere employed to select the respondentsof the study. To ensure effective coverage of thestudy area, respondents were randomly selectedfrom each of the districts that contituuted thestudy area (Duhu, Gulak, Kirchinga, Madagaliand Sukur). Five villages were chosen randomlyfrom each district because of the fair distributionof villages among the districts, making a total of25 villages selected from the districts. Eight respondentswere selected at random from each ofthe selected 25 villages making a total of 200respondents as the sample size of the study. Thedata were analyzed using descriptive (frequencydistribution and percentages) and inferential (chisquaretest) statistics. The frequency distributionand percentages were used to achieve specificobjectives (i), (ii), (iii) and (v). While the chisquaretest was used to achieve objective (iv).The chi-square statistics was used as expressedbelow:X 2 = [(0i-Ei) 2 (Ei) -1 ],where X 2 = chi-square statistic; Oi = observe valueof variable; Ei = expected value of variable; = summation sign. The variable used as determinantsin the study were: Gender (GD) = 1 if male,0 otherwise; Age (AG) = 1 if < 40 years, 0 otherwise;Marital status (MS) = 1 if married, 0 otherwise;Farming experience (FE) = 1 > if 6years,0 otherwise; Educational status (ES) = 1 if >WAEC/SSCE/TC 0 otherwise; Household size(HS) = 1 if up to >6 in number, 0 otherwise;Access to agricultural credit (AC) = 1 if acquiredcredit, 0 otherwise; Access to extension services(AE) = 1 if had contact with extension agents, 0otherwise; Access to mass media (am) = 1 if hadaccess to mass media, 0 otherwise.RESULTS AND DISCUSSIONSocio-economic characteristics of respondents.The distribution of respondents by socioeconomiccharacteristics is presented in Table 1.The socio-economic variables studied includegender, age, marital status, farming experience,educational status, household size and extensioncontact of the respondents. Table 1 shows thatmajority (84%) of the respondents were male,while female constitutes only 16% in the studyarea. This implies that gender is a significant factorin agriculture because of its vital role in determiningfarming activities in the study area.This could also influence the adaptive capacity todeforestation. In addition, majority (85%) of therespondents were married and were in their activeand economically productive age (21-40 years)representing 58.50%, therefore labour demandingstrategies could also be employed.On the level of education, the result showsin Table 1 that most (45%) of the respondentshad attained post primary education. This impliesthat the respondents could apprehend the techniquesof adaptation strategies against deforestation.The result also shows that majority (80%) ofthe respondents had 6 years and above farmingexperience. The implication could be that theyemploy adaptation strategies against deforestationout of their experience. Table 1 indicatedthat majority (81%) of the respondents hadhousehold size of 6 in number and above, whileonly 19% of the respondents had less than 6 innumber. This implies that adaptation strategiesthat demands more hands/labour could be met bythe respondents. The study, also indicates thatmost (44%) of the respondents had no contactwith extension agents over the year. However,38% attested that they had 1-3 contacts with extensionagents per annum. The study implies thatthere was low level of extension services whichaffects the awareness and use of adaptation strategiesagainst deforestation in the study area.17

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012GenderAge (years)Table 1 − Distribution of respondents by socio-economic characteristics (N=200)Socio-economic variable Frequency (No.) Percentage (%)Male 168 84.00Female 32 16.0020 and below 42 21.0021-30 55 27.5031-40 62 31.0041 and above 41 20.50Marital StatusMarried 171 85.50Single 29 14.50Farming experience(years)Less than 6 40 20.006-10 57 28.0011-15 63 31.5016 and above 40 20.00Educational statusIlliterate 19 09.50Quranic education 23 11.50Primary education 68 34.00WAEC/SSCE/TC 70 35.00Tertiary education 20 10.00Household size (number)Less than 6 38 19.006-10 72 36.0011-15 65 32.5016 and above 25 12.00Extension contact (per year)No contact 88 44.001-3 76 38.004-6 30 15.007 and above 06 03.00Source: Field survey, 2010.Perceived causes of deforestation by respondents.Table 2 shows the perception of respondentson the causes of deforestation in thearea. The study reveals that most (42%) of therespondents perceived fuel wood extraction asthe major cause of deforestation. This was closelyfollowed by farming activities (27.50%) andbush burning (12.50%). While, the least perceivedcause of deforestation was road/buildingconstruction with only 4% of the respondentsattesting to it. The study shows that the rootSource: Field survey, 2010.cause of deforestation was among the rural communitiesas perceived by the respondents. Theimplication could be that adaptation strategiesagainst deforestation that could be targeted towardsfarming communities might effectivelyyield the desired result with the support of extensionservices. This was because, the deforestationwas perceived to cause by human activities ratherthan natural as only 6% of the respondentsclaimed that deforestation is caused by drought.Table 2 − Respondents perception on causes of deforestation (N=200)Causes Frequency (No.) Percentage (%)Farming activities 55 27.50Fuelwood extraction 85 42.50Bush burning 25 12.50Road/building construction 8 04.00Overgrazing 15 07.50Drought 12 06.0018

S.B. Mustapha, P.M. Bzugu, I.M. Ali, A. Abdullahi, University of MaiduguriAdaptation Strategies employed by respondents.Table 3 indicated the adaptation strategiesemployed against deforestation by respondents.The result shows that the most (30.50%)frequently employed adaptation strategy againstdeforestation was reducing quantity of firewoodconsumption among the respondents. This wasfollowed by the practice of zero tillage (23%) inthe study area. While, the least employed adaptationstrategy was the use of fuel efficient woodstoves (3.50%), which was closely followed bythe use of alternative energy (kerosene),represented by 5% of the respondents. The studyimplies that the respondents might be ready touse alternative sources of energy if given the opportunity(availability and affordability), thiscould be attributed to the impact of deforestationin the study area.Table 3 − Distribution of respondents by adaptation strategies employed against deforestation (N=200)Causes Frequency (No.) Percentage (%)Use of fuel efficient wood stoves 07 03.50Protection of economic trees 27 13.50Reducing quantity of firewood consumption 61 30.50Practice of zero tillage 46 23.00Use of alternative energy (kerosene) 10 05.00Participation in farm forestry 21 10.00Use of cow dung for cooking 16 08.00Use of corn straw for cooking 12 06.00Source: Field survey, 2010Determinants of adaptation to deforestationamong respondents. The chi-square resultsof the determinants of adaptation to deforestationamong respondents were presented in Table 4.The variables studied include gender, age, maritalstatus, farming experience, educational status,household size, access to agricultural credit andaccess to extension services. The result showedthe significant variables which include age, farmingexperience and educational status; X 2 =9.216,8.697 and 11.238 at P < 0.05 respectively. Theage of the farmer affected the farmer’s knowledgeand the awareness of the activities in thesurrounding environment among other farmers.This indicates that the age influences the farmerscapacity to adapt deforestation. The finding supportsthat of Krishna [7], that age significantlyinfluence the adaptive capacity of farmers againstdeforestation. Farming experience affected thefarmer’s knowledge and awareness of deforestationand its impact in the farming communities.This shows that farming experience influencesthe farmer’s adaptive capacity to deforestation.Also, an educated farmer could readily accessinformation on deforestation and how it could beadapted. Formal education could therefore be acritical factor influencing the effectiveness offarmer’s adaptive capacity to deforestation; asreported by Oladusu et al [9].Table 4 − Chi-square results of the determinants of adaptation to deforestation in the study areaCauses Degree of freedom Calculated x2 P-value RemarkGender 1 1.286 3.841 SNAge 3 9.216 7.815 SSFarming experience 3 8.697 7.815 SSEducational status 4 11.238 9.488 SSAccess to agricultural credit 4 7.923 9.488 SNAccess to extension service 3 5.862 7.815 SNSource: Field survey, 2010x2 = calculated chi-squareSS = Statistically significant at at P

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012credit services. The implication could be that therespondent’s level of adaptation to deforestationcould be affected negatively.Constraints of adaptation to deforestationby respondents. The constraints of adaptation todeforestation was presented in Table 5.Table 5 − Distribution of respondents by constraints adaptation to deforestation (N=200)Causes Frequency (No.) Percentage (%)Lack of awareness 37 18.50Lack of sufficient labour 25 12.50Lack of accessible alternative energy 48 24.00Lack of capital 50 25.00Poor extension services 40 20.00Source: Field survey, 2010.The result reveals that lack of capital wasasserted by most (25%) of the respondents asconstraint to adaptation against deforestation.This could not be unexpected, due to the fact thatmost of the farming households in the study areaare peasant farmers with low income earnings.The second most important constraint was lack ofaccessible alternative energy, as asserted by 24%of the respondents. This could be due to partly to, as a result of the high prices associated withmost of the alternative sources of energy e.g kerosene,and therefore respondents could not affordto use. This implies that the respondents levelof adaptation against deforestation could beaffected negatively which does not augur well forthe environment and the society as well. Otherconstraints faced by the respondents in adaptationto deforestation were poor extension services,lack of awareness and lack of sufficient labourwith 20%, 18.50% and 12.50% of the respondentsclaiming respectively.REFERENCESAjayi, O.C (2007). User acceptability of sustainablesoil facility technologies. Lessonsfrom farmers knowledge, attitudes andpractices in southern Africa. Journal ofSustainable Agriculture, vol. 30 (3): 21-40ASMLS (1999). Adamawa State Ministry ofLand and Survey (ASMLS), Annual report,Adamawa State, Nigeria.Ayuba, H.K. (2008). Towards combating desertificationand deforestation in Yobe state,Nigeria. Yobe Journal of Environment andDevelopment, vol. 1, No. 1pp 1-5.FAO (2005). State of the World’s Forests.Food and Agricultures Organization(FAO) of the United Nations, Rome, Italy.FAO (2007). Deforestation: Tropical Forests inDecline. Food and Agriculture Organization(FAO) of the United Nations, Rome,Italy.Gwary, D.M. (2010). Climate Change Adaptationand Migration Options for ImprovingFood Security in Nigeria. Lead paper presentingat the 6th National Cconference inOrganic Agriculture held at the Universityof Maiduguri, Nigeria 22nd – 24th November.Krishna, K. (2004). Effects of deforestation ontree diversity and livelihoods of localcommunity: A case study from Nepal.M.sc. Dissertation, Department of plantEcology, Lund university, Sweden.NPC (2006). National Population Commission(NPC); Provisional census figure; Abuja,Nigeria.Oladosu, I.O., Ogunwale, A.B. and Ayanwuyi, E.(2002). Farmers perception of effects ofdeforestation on agricultural productionand economic activities in selected ruralcommunities in Orire Local GovernmentArea of Oyo State, Nigeria. InternationalJournal of Business and Common MarketStudies vol. 1 (1): 209-216.Oseoneoba, G.J. (1992). Fuelwood exploitationfrom natural ecosystem in Nigeria: Socioeconomicand Sociological implication.Journal of Rural Development, vol. 1 (1):141-155.20

H. DE-GRAFT ACQUAH, University of Cape CoastA THRESHOLD COINTEGRATION ANALYSIS OF ASYMMETRIC ADJUSTMENTSIN THE GHANAIAN MAIZE MARKETSABSTRACTHenry de-Graft Acquah, Senior LecturerUniversity of Cape Coast, Cape Coast, GhanaPhone: +00233245543956, E-mail: henrydegraftacquah@yahoo.comReceived August 14, 2012This paper analyzes the long-run equilibrium relationship between retail and wholesale Ghanaianmaize prices with cointegration test assuming asymmetric adjustment. Using the Enders-Siklos asymmetriccointegration tests, it is found that the retail and wholesale prices are cointegrated with thresholdadjustment. Furthermore, the adjustment process is asymmetric when the retail and wholesaleprices adjust to achieve the long-term equilibrium. Finally, there is faster convergence for negativedeviations from long-term equilibrium than for positive deviations. These results imply that price increasestend to persist whereas decreases tend to revert quickly towards equilibrium.KEY WORDSThreshold cointegration; Asymmetric adjustment; Price transmission; Maize; Equilibrium relationship;Negative deviations.Cointegration technique has been extensivelyemployed to investigate relationship amongprice variables. The two widely used cointegrationmethods are Johansen and Engle-Grangertwo-step approaches. However these methodsassume symmetric relationship between variables.These methods do not test for the possibilitythat the long run relationship may beasymmetric in nature. The technique of Endersand Siklos (2001) is well suited to the task of uncoveringlong-run relationships between timeseries when deviations from the long-run areasymmetric in nature. The technique generalizesthe standard Dickey–Fuller test by allowing forthe possibility of asymmetric movements in timeseriesdata. This makes it possible to test forcointegration without maintaining the hypothesisof asymmetric adjustment to a long-term equilibrium.This study is aimed at empirically testingEnders and Siklos’s hypothesis of asymmetricadjustment to a long-run equilibrium between theGhanaian retail and wholesale maize prices. First,this study test for the order of integration of theprice series. Second the study analyzes asymmetricadjustment using threshold cointegration methodology.Asymmetric price transmission. Empiricalstudies analyzing whether prices rise faster thanthey fall, have categorised the price dynamicsinto symmetric and asymmetric processes. Meyerand von Cramon-Taubadel (2004) notes thatthose processes for which the transmission differsaccording to whether the prices are increasing ordecreasing (i.e. asymmetric price transmission)are of keen interest. By definition, asymmetry isan unreciprocal relationship between rises andfalls in prices.Price transmission has extensively been studiedin agricultural commodity markets. However,a major limitation of some earlier studies(Mohanty, Peterson and Kruse, 1995; Boyd andBrorsen, 1998) is that they fail to take into accountthe possibility of the presence of an equilibriumrelationship between any price series beingexamined (von CramonTaubadel, 1998). The firstattempt to draw on cointegration technique intesting for asymmetry in vertical price transmissionis von Cramon-Taubadel and Fahlbusch(1994) and later elaborated by von Cramon-Taubadel (1998). Numerous price transmissionstudies (Capps and Sherwell, 2007)) implementsvon Cramon-Taubadel and Loy testing procedurefor asymmetric price transmission or some variantsof their proposed Error Correction Modeling(ECM) approach.Following the introduction of the thresholdtechnique, it is possible to consider an intuitivelyappealing type of ECM in which deviation from21

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012the long-run equilibrium between two prices willlead to a price response if they exceed a specificthreshold level. Balke and Fombe (1997) pointout that the presence of fixed costs of adjustmentmay prevent economic agents from adjustingcontinuously. Only when deviation from equilibriumexceeds a critical threshold do the benefitsof adjustment exceed the costs and cause economicagents to act to move the system back towardsthe equilibrium. Due to the above reasons,the threshold models of dynamic economic equilibriumhave gained increased attraction in theanalysis of price transmission asymmetries. Subsequently,several studies (Esso, 2010; Awokuseet al, 2009; Meyer, 2004;Cook, 2003;Cook,2003; Cook, 2003; Cook et al, 2002; Hansen,2002; Cook, 2000; and Balke and Fomby, 1997)measuring asymmetric price transmission haveused the threshold modeling approach.METHODOLOGYStationarity test. Kwiatkowski et al. (1992)proposed a LM-test for testing trend and/or levelstationarity (henceforth: KPSS-test). They considerthe following model: eq. (1) eq. (2)where is a random walk and the error process isassumed to be i.i.d (0, ). The initial value r 0 isfixed and corresponds to the level. If =0, thenthis model is in terms of a constant only as deterministicregressors. Under the null hypothesis, is stationary and therefore is either trendstationary or in the case of =0, level stationary.First, regress on a constant or on a constantand a trend depending on whether onewants to test level or trend stationary; second,calculate the partial sums of the residuals fromthis regression as: eq. (3)The test statistic is then defined as: !" #$ % eq. (4),with & ' being an estimate of the error variancefrom step one. The authors suggest the utilizationof a Bartlett window w(s, l) =1-s/ (l+1) as anoptimal weighing function to estimate the longrun variance& ' ; that is:& ' ( )*+ , - - ,.// eq. (5)The upper tail critical values of the level andtrend stationary version are given in Kwiatkowskiet al. (1992).Econometric Model. The Engle- Grangertwo- stage approach focuses on the time seriesproperty of the residuals from the long run equilibriumrelationship (Engle and Granger, 1987).Consider the retail prices and 0 thewholesale prices both of which are integrated ofthe order one.Let the co integration relationship be: 1 2 0 eq. (6),where measures the deviation from the equilibriumrelationship between 0 and . Consistentestimates of the equilibrium error can be obtainedusing ordinary least squares method. Forthe two variables to be cointegrated, should bestationary.In order words, rejecting the null hypothesisof no co integration, that is 3=0 against acceptingthe alternative hypothesis of cointegration, that is-2

H. DE-GRAFT ACQUAH, University of Cape CoastWhere 7 is the Heavside indicator, ; thenumber of lags, 3 3 and the coefficients and :the threshold value. The lag ; is specified to accountfor serially correlated residuals and it canbe calculated using Bayesian Information Criteria(BIC) or Akaike Information Criteria (AIC).The threshold value :can be specified as zero.Alternatively, Chan (1993) proposes a searchmethod for obtaining a consistent estimate of thethreshold value. A super consistent estimate ofthe threshold value can be attained with severalsteps. First, the process involves sorting in ascendingorder the threshold variable, i.e. forthe threshold model. Second, the possible thresholdvalues are determined. If the threshold valueis to be meaningful, the threshold variablemust actually cross the threshold value. Thus, thethreshold value : should lie between the maximumand minimum values of the threshold variable.In practice, the highest and lowest 15% ofthe values are excluded from the search to ensurean adequate number of observations on each side.The middle 70% values of the sorted thresholdvariable are used as potential threshold values.Third, the threshold model is estimated with eachpotential threshold value. The sum of squarederrors for each trial can be calculated and the relationshipbetween the sum of squared errors andthe threshold value can be examined. Finally, thethreshold value that minimizes the sum ofsquared errors is deemed to be the consistent estimateof the threshold. Against this background,two competing models are considered namely theThreshold model with : = 0 (i.e. TAR) and theconsistent threshold model with : estimated.Given the alternative models, model selectionprocedures such as the Akaike Information Criterion(AIC) and Bayesian Information Criterion(BIC) provides a basis for choosing between theThreshold Model (TAR) and Consistent ThresholdModel (CTAR). A model with the lowestAIC and BIC should be preferred.Insights into the asymmetric adjustments inthe context of a long run cointegration relationcan be obtained with two tests. First, an F-test isemployed to examine the null hypothesis of nocointegration (H 0 : 1 = 2 = 0) against the alternativeof cointegration with either TAR or ConsistentTAR threshold adjustment. The test statisticis represented by F. This test does not follow astandard distribution and the critical values inEnders and Siklos (2001) should be used. Thesecond one is a standard F-test to evaluate thenull hypothesis of symmetric adjustment in thelong-term equilibrium(H 0 : 1 = 2 ). Rejection ofthe null hypothesis indicates the existence of anasymmetric adjustment process.Data. This study employs weekly retail andwholesale prices for maize from January 1994 toDecember 2003 from Kumasi in the Ashanti Regionof Ghana. The weekly data for all prices areGhana cedi per 100kg and given the high level ofinflation in the period covered, prices are deflatedusing consumer price index (CPI) deflator. Thedata was obtained from the Ministry of Food andAgriculture in Ghana.RESULTS AND DISCUSSIONUnit root test. To determine the datageneratingproperties of the individual data, theKwiatkowski, Phillips, Schmidt and Shin (KPSS)test for stationarity (Kwiatkowski, et al., 1992)was performed. The results of the KPSS test inTable 1 show that the retail and wholesale pricesare non stationary and integrated of the order one.n/nWholesale(Levels)Wholesale(FirstDifference)Retail(levels)Retail (FirstDifference)Table 1 – KPSS unit root testTeststatisticsSource: Author’s calculationCritical values10 % 5 % 2.5% 1%0.2755 0.119 0.146 0.176 0.2160.017 0.119 0.146 0.176 0.2160.3839 0.119 0.146 0.176 0.2160.0151 0.119 0.146 0.176 0.216Threshold cointegration analysis. Thenonlinear cointegration analysis is conducted usingthe Threshold Autoregression models. TheTAR and Consistent TAR models are estimatedand the results are reported in Table 2. In selectingan appropriate lag to address possible serialcorrection in the residual series, a maximum lagof 12 is specified and tried at the beginning. Diagnosticanalyses on the residuals through AICand BIC all reveal that a lag of 2 is sufficient. Inestimating the threshold values for consistentTAR, the method by Chan (1993) is followed.The lowest sum of squared errors for the consistentTAR model is 1191.69 at the threshold valueof -2.554. Alternatively, the threshold value for23

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012TAR is set at 0. While the competing thresholdcointegration models have similar results (table2), the consistent TAR model has the lowest AICstatistic of 1908 and BIC statistic of 1930, andtherefore, is deemed to be the best. Focusing onthe results from the consistent TAR model, thisstudy finds that the F-test for the null hypothesisof no cointegration has a statistic of 18.371 and itis highly significant at the 1% level. Thus, theretail and wholesale prices of maize in Ghana arecointegrated with threshold adjustment.Table 2 – Estimates of the speed of adjustments parameters of the Threshold Modeln/n Threshold Autoregressive Model (TAR) Consistent Threshold Autoregregressive Model (CTAR)3 -0.12057(-4.517)* -0.11202(-4.5671)*3 -0.14732(-3.948)* -0.19787(-4.327)*3 = 3 17.081(0.000)** 18.371(0.000)**3 = 3 < 0.354(0.550)** 2.779(0.096)** 0 -2.554SSE 1197.313 1191.693AIC 1911 1908BIC 1932 1930Notes: * Values in the parentheses are t values. ** Values in the parentheses are estimated probability values; outside parenthesesare the F statistic values. Source: Author’s calculation.Furthermore, the F statistic for the null hypothesisof symmetric price transmission has avalue of 2.779 and it is also significant at the10% level. Therefore, the adjustment process isasymmetric when the retail and wholesale pricesof Ghanaian maize adjust to achieve the longtermequilibrium.The point estimate for the price adjustmentis -0.11202 for positive shocks and -0.19787 fornegative shocks. The point estimate of 3 (-0.11202) for the retail and wholesale prices indicatesthat approximately 11.2 % of a positivedeviation from the long-run equilibrium relationis eliminated within a week. Alternatively, thepoint estimate of 3 (-0.19787) indicates that19.8 % of a negative deviation from the long-runequilibrium relation is eliminated within a week.In effect, the adjustment is almost 1.7 times fasterfor negative deviations from equilibrium than forpositive deviations. Therefore, there is substantiallyfaster convergence for negative (belowthreshold) deviations from long-term equilibriumthan positive (above threshold) deviations.Model estimation results suggest that theConsistent TAR model detects asymmetry whilstTAR model fails to support this evidence. Theseresults imply that differences in inferences arepossible depending on weather the threshold parameteris estimated from the data or imposed bythe researcher.CONCLUSIONThis study estimated the price transmissionin the Ghanaian maize market using retail andwholesale prices. Specifically, the study testedfor the order of integration of the price series andanalyzed asymmetric adjustment using thresholdcointegration methodology. The threshold cointegrationtechnique makes it possible to test forcointegration without maintaining the hypothesisof a symmetric adjustment to a long-term equilibrium.The results of the KPSS test show that theretail and wholesale prices are non stationary andintegrated of the order one. The retail and wholesaleprices of maize in Ghana are cointegratedwith threshold adjustment. The Enders and Silkos(2001) procedure provides support for the alternativehypothesis of asymmetric adjustment. Thefindings of this study indicate that there is a fasterconvergence for negative deviations from longtermequilibrium than positive deviations. Theseresults suggest that price increases tend to persistwhereas decreases tend to revert quickly towardsequilibrium.Furthermore, alternative threshold modelingapproaches leads to differences in conclusion. Itis recommended that CTAR be used togetherwith the TAR and the cause of the positiveasymmetry identified be investigated.24

H. DE-GRAFT ACQUAH, University of Cape CoastREFERENCESAwokuse, T.O. and Wang, X. (2009), ThresholdEffects and Asymmetric Price Adjustmentsin U.S. Dairy Markets. CanadianJournal of Agricultural Economics.Balke, N.S and Fomby, T.B. (1997). ThresholdCointegration. International EconomicReview, 38, 627-645.Boyd, M.S. and Brorsen, B.W. (1998). PriceAsymmetry in the US Pork MarketingChannel, North Central Journal of AgriculturalEconomics, 10, pp.103-109.Capps, O. and Sherwell, P. (2007). AlternativeApproaches in Detecting Asymmetry inFarm-Retail Prices Transmission of FluidMilk. Journal of Agribusiness, 23 (3),313-331.Chan, K.S. (1993). Consistency and limitingdistribution of the least squares estimatorof a threshold autoregressive model. TheAnnals of Statistic, 21, 520-533.Cook, S. (2000). Frequency domain and timeseries properties of asymmetric error correctionmodels, Applied Economic, 32,pp. 297-307.Cook, S. and Holly, S. (2002) Threshold specificationfor asymmetric error correctionmodels, Applied Economics Letters, 9,pp. 711–13.Cook, S. (2003). A sensitivity analysis of thresholddetermination for asymmetric errorcorrection models, Applied EconomicLetters, 10, pp. 611-616.Cook, S. and Manning N. (2003) The power ofasymmetric unit root tests under threshold- consistent estimation. Applied EconomicLetters, 35, pp. 1543-1550.Cook, S. (2003). The properties of asymmetricunit root tests in the presence of misspecifiedasymmetry, Economics BulletinVol. 3, No. 10 pp. 1-10.Enders, W. and Siklos, P.L. (2001). Cointegrationand threshold adjustment.Journal ofBusiness and Economic Statistics, 19,166-167.Engle, R.F. and Granger, C.W.J. (1987). Cointegrationand error correction: Representation,estimation and testing. Econometrica,55, 251-276.Esso L.J. (2010) Threshold cointegration andcausality relationship between energy useand growth in seven African countries,Energy Economics, 32(6): 1383-1391Hansen, B.E. and Seo, B. (2002). Testing forTwo-Regime Threshold Cointegration inVector Error Correction Models. Journalof Econometrics, 110, pp. 293-318.Kwaitkowski, D., Phillips, P.C.B., Schmidt P,Shin Y. (1992). Testing the null hypothesisof stationarity against the alternative ofa unit root. Journal of Econometrics, 54,159-178.Mohanty, S., Peterson, E.W.F. and Kruse, N.C.(1995). Price Asymmetry in the InternationalWheat Market. Canadian.Journal ofAgricultural Economics, 43, 355-366.Meyer, J. and von Cramon-Taubadel, S.(2004), Asymmetric Price Transmission:A Survey. Journal of Agricultural Economics,55: 581–611.Meyer, J. (2004) Measuring market integrationin the presence of transaction costs—athreshold vector error correction approach.Journal of Agricultural Economics,31:327-334V. Cramon-Taubadel, S. (1998). Estimatingasymmetric Price Transmission with theError Correction Representation: An Applicationto the German Pork Market”,European Review of Agricultural Economics,25, pp. 1-18.V. Cramon-Taubadel, S. and Fahlbusch, S.(1994). Identifying asymmetric priceTransmission with error correction models.Poster Session EAAE European Seminarin Reading.25

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012SOCIO-ECONOMIC DEVELOPMENT OF THE CENTRAL FEDERAL DISTRICTOF RUSSIAN FEDERATION WITH METHODOLOGICAL ASPECTS OF INNOVATIONWASTE MANAGEMENTСОЦИАЛЬНО-ЭКОНОМИЧЕСКОЕ РАЗВИТИЕ ЦЕНТРАЛЬНОГО ФЕДЕРАЛЬНОГООКРУГА РОССИЙСКОЙ ФЕДЕРАЦИИ С УЧЕТОМ МЕТОДОЛОГИЧЕСКИХ АСПЕКТОВИННОВАЦИОННОГО УПРАВЛЕНИЯ ОТХОДАМИL.A. Shilova, Post-graduate StudentЛ.А. Шилова, аспирантNational Research University – Moscow State University of Civil Engineering, Moscow, RussiaНациональный исследовательский университет – МГСУ, Москва, РоссияPhone: +7 (495) 287-49-19, E-mail: shilova_lyubov@mail.ruABSTRACTReceived June 29, 2012The author proposed a method to improve the mechanism for assessing the ecological safety of territorieswith regional geo-environmental tensions, social and environmental performance. The necessityto assess the re-use of material and natural resources and to provide business entities right to changethe fees for environmental pollution by industrial wastes, if they are re-used in different industries.АННОТАЦИЯАвтором предложена методика совершенствования механизма оценки экологической безопасноститерриторий с учетом региональной геоэкологической напряженности, социальных иэкологических показателей. Отмечена необходимость оценки степени вторичного использованияматериальных и природных ресурсов и предоставления субъектам предпринимательскойдеятельности права изменения размеров платежей за загрязнение окружающей среды промышленнымиотходами в случаях их повторного использования в различных отраслях народногохозяйства.KEY WORDSSustainable development; Production waste; Recycling; Evaluation methodology; Methodology.КЛЮЧЕВЫЕ СЛОВАУстойчивое развитие; Отходы производства; Вторичное использование; Методика оценки;Методология.Центральный федеральный округ состоитиз 18 субъектов РФ: (Белгородская, Брянская,Владимирская, Воронежская, Ивановская, Калужская,Костромская, Курская, Липецкая,Московская, Орловская, Рязанская, Смоленская,Тамбовская, Тверская, Тульская и Ярославскаяобласти, а также г. Москва) и занимаетплощадь в 650,2 тыс. кв. км, что составляет3,8% территории Российской Федерации.В наименьшей степени округ обладаетпростыми ресурсами, так например, на одногожителя приходится лишь 0,8 га сельхозугодий(порядка 1,5 га – среднероссийское значение),приблизительно 3000 куб. м пресной воды вгод и всего 0,6 га леса (что в 10 раз меньше,чем по стране.) На территории округа разведаноболее 10,5 тыс. месторождений 38 видовполезных ископаемых.Доля запасов промышленных категорийжелезных руд составляет 59 % от общероссийских,мела – 64%, гипса – 57%, доломитов– 45%, тугоплавких глин – 41%, формовочныхматериалов – 31%, цементного сырья – 27%,огнеупорных глин – 18%, стекольного сырья –26%. Имеются запасы песка, гравия, щебня,камня.Роль Центрального федерального округанаиболее велика в производстве электроэнергии(22,1% общероссийских объемов), прокатачерных металлов (19,2%), выплавке стали(17%), производстве цельномолочной продукции(35%), хлебобулочных изделий26

L.A. SHILOVA, National Research University – Moscow State University of Civil Engineering(29,1%), водки и ликероводочных изделий(37,9%) различных видов тканей (30%), отдельныхвидов продукции химической и нефтехимическойпромышленности. Лидирующиеотрасли пищевой промышленности − сахарная,мукомольно-крупяная, маслобойная,мясная, спиртовая, кондитерская, плодоовощнаяи табачно-махорочная.Производительность труда в целом поЦентральному федеральному округу в 1,4 разавыше, чем в России, в 2,2 раза выше среднемировой,и на 20% ниже средней по ЕвропейскомуСоюзу. Такое соотношение достигаетсяочень высокими показателями г. Москвы (в2,9 раза выше среднероссийского значения).Однако, наряду с этим нарастает проблемаобращения с отходами производства и потребленияи ухудшением экологической обстановкив регионе.Рисунок 1 − Структура отходов производства и потребленияЖилищно-коммунальные службы наибольшейпроблемой считают твердые бытовыеотходы (ТБО), т.к. образуются они повсеместнои представляют собой смесь разнообразногофракционного состава, что затрудняетих сбор, обезвреживание и утилизацию.Например, только на территории г. Москвыобразуется несколько миллионов тоннотходов, в том числе 3 млн. тонн твердых бытовыхотходов и практически все они подвергаютсязахоронению на территории Московскойобласти. Утвержденная норма накоплениябытовых отходов на 1 человека в год составляет1 куб. м или 250 кг, на рисунке представленаморфологическая структура указанногообъема отходов.1,80% 1% 0,60% 0,50% 0,70%4,40% 3,20%4,90%7%38,20%9,10%28,60%Бумага, картонПищевые отходыДерево, листьяТекстильКожа,резинаПолимерные материалыКостиЧерный металлЦветной металлСтеклоКерамика, камниОтсевРисунок 2 − Структура образования отходов в Москве, утвержденная нормами накоплениябытовых отходов на 1 человека в год [1]Согласно официальным данным − среднийежегодный темп прироста отходов производстваи потребления составляет 14%. Ежегодныйприрост объема ТБО в регионах ЦФОпо оценкам «Гринпис России» и Росстата составляетоколо 10%. На рис. 3. представленакарта распределения количества мусора(кг/год) на душу населения областных городовЦФО по данным за 2008 г.27

Russian Journal of Agricultural and Socio-Economic Sciences, No. 8 (8) / 2012Рисунок 3 − Количество мусора (кг/год) на душу населения областных городов ЦФОпо данным за 2008 г. Источник: Росстат.Экономические механизмы охраны окружающейсреды получили свою реализациюв Российской Федерации в виде платы за выбросы,сбросы загрязняющих веществ в окружающуюсреду и размещение отходов. Размерыплаты за негативное воздействие и порядокее определения регламентировалисьпостановлением Совета Министров РСФСР от9 января 1991 г. № 13 «Об утверждении на1991 год нормативов платы за выбросы загрязняющихвеществ в природную среду ипорядка их применения».Позднее природоохранными органамибыл подготовлен документ, который утверждалсяпостановлением Правительства РоссийскойФедерации от 28 августа 1992 г.№632 , где также предусматривался экономическиймеханизм управления отходами и внесениеопределенной платы за их размещение вэкологические фонды федерального, региональногои местного уровней.Дифференциация платы должна происходитьна уровне каждого субъекта Федерациипутем введения в расчеты соответствующихкоэффициентов (ранее они назывались коэффициентамиэкологической ситуации и экологическойзначимости). Практика природопользованияпоследних двадцати лет показала,что подобный методологический подходсодержит ряд недоработок и может быть модифицированв соответствии с требованиямиконцепции «устойчивого развития».По мнению автора в данном случае целесообразноиспользовать нижеприведеннуюформулу для оценки степени экологическойбезопасности оцениваемой территории области,введя туда новые дифференцированныекоэффициенты региональной геоэкологическойнапряженности, в которые были бы заложенысоциальные и экологические показатели:ПЦ = ∑ Бэбi=1плМkРГэнКИ, гдеБ пл − базовый норматив платы за загрязнениеокружающей среды, руб.т/год;М − реальная масса загрязнителей, попадающихна территорию размещения отходов; т/год;K И − коэффициент индексации платы, ежегодноутверждаемый Минприроды России посогласованию с Минфином России и МинэкономразвитияРоссии; П − количество видовзагрязняющих отходов; K РГЭн − коэффициентрегиональной геоэкологической напряженности,рассчитываемый по формуле:28

L.A. SHILOVA, National Research University – Moscow State University of Civil EngineeringkРГЭнрегρ .= ⋅ k , гдеρЦФОρ − число жителей на 1 кв. км по региону;ρ регk− число жителей на 1 кв. км рассматриваемогорегиона; Эф − коэффициент, учитывающийэкологические факторы (состояниепочвы) по территориям экономических ре-Эфгионов РФ согласно Постановлению ПравительстваРФ от 12.06.2003 №344.На рис. 4 представлены данные по коэффициентурегиональной геоэкологическойнапряженности для регионов ЦФО, за исключениемгорода Москвы, для которого коэффициентбудет завышен из-за большого притокатрудовых мигрантов (численность населенияг. Москвы − 11552 тыс. человек).Московская областьБелгородская область1,914,2Липецкая областьТульская областьВоронежская область1,511,651,64Ивановская областьВладимирская областьКурская область1,341,341,27Брянская областьЯрославская областьКалужская областьТамбовская областьОрловская областьРязанская область0,990,950,920,860,860,79Смоленская областьТверская область0,430,53Костромская область0,30 0,5 1 1,5 2 2,5 3 3,5 4 4,5Коэфф. региональной геоэкологической напряженностиРисунок 4 − Расчет коэффициента региональной геоэкологической напряженностидля регионов Центрального федерального округа Российской ФедерацииКроме того, необходимо учитывать степеньвторичного использования материальныхи природных ресурсов (или процент использованияотходов) на территории области.Субъектам предпринимательской деятельностинеобходимо предоставить право коррекцииразмеров платежей за загрязнение окружающейсреды промышленными отходами вслучаях их повторного использования в любыхотраслях: капитальном строительстве(шлаки), металлургии (металлолом), в производствебумаги (макулатура) и т.д. Подобныйинновационный методологический подходимеет пионерный характер и находится настадии диссертационного исследования.БИБЛИОГРАФИЯБурейко Е.Н. «Экология городов. Отходыпроизводства и потребления» [Электронныйресурс]. - Режим доступа:http://www.portal-slovo.ru (дата обращения02.08.2012).Воробьев А.Е. Основы природопользования:экологические, экономические иправовые аспекты. Учебное пособие /А.Е.Воробьев и др. /Под ред. проф.В.В.Дьяченко. – Ростов-на-Дону: Феникс,2006. 544 с.Кураев С.Н., Мамин Р.Г. Экологическая конверсияи устойчивое развитие РоссийскойФедерации. М.: ТИССО, 2003. 88с.Эффективность государственного управления.Пер. с англ. Под ред. С.А. Батчиковаи др. М.: Консалбанкир, 1998. 842с.29

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