Рассадовская_резюме_ENG (1138202), страница 2
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In the existing literature there are results concerning the relationshipbetween social capital and facts of corruption, the direction of communication invarious studies is different. The objective of this work is to identify the relationshipof social capital exactly with corruption expectations and to determine the directionof this relationship.2. The level of corruption expectations depends on the structure of socialnetworks of potential bribe givers. The literature has results demonstrating thatcorruption may be the result of a habit or a social norm, therefore it is important todetermine the significance of the environment in shaping corruption expectations.The next two chapters are devoted to testing these relationships on microand macro data using the methodology suggested in this research.In the second chapter the hypothesis on the relationship between corruptionperceptions and social capital is tested.
To perform that new specifications ofregression models are suggested.On the macro level nonparametric estimations on the data by country showthe relation between corruption perceptions and key economic indicators. Theanalysis on the data of Corruption Perception Index and shadow economy givessignificant difference between transaction and developed economies (according tothe World Bank classification). It is also mentioned that the relation betweenshadow economy and corruption can be both complementary and substitutional.7We also notice that the growth of formal institutions leads to decrease ofcorruption expectations (see Table 1).Table 1Estimation with institutional indicators. Explained variable — Corruption Perception Index.
Reduced table: onlyinstitutional variables. RE — random effects, FE — fixed effects, «+» — significantly positive, «–» — significantlynegative, «no» — insignificant. Significance at 10%.AllTransaction economiesOthersREFEREFEREFE++++++Control for corruption+++nono+Government efficiency++++++Policy efficiencynonono+++Role of lawnonononono–no–nono+1595159512761276319319Political stability+Freedom of speechObs.Source: author's estimations based on World Bank data and data of Buehn, Schneider (2012).Including trust variables in the model (WVS data, see Table 2) shows thatthere is a significant relationship between social capital and corruption perceptions.Therefore, the results on the macro level confirm the approach that considerscorruption not only as an economic but also as an institutional phenomenon.Table 2Estimation with trust indicators.
Explained variable — Corruption Perception Index. Reduced table: only trustvariables and shadow economy. *** - significance at 1%.StandardIn-groupOut-grouptrusttrusttrustHonestyNon-Generalized trustspecifictrustShadow ec. (% of GDP)TrustObs.-0,559***-0,723***-0,672***-0,749***-0,632***-0,589***(0,0968)(0,152)(0,151)(0,141)(0,140)(0,151)44,66***98,18***87,71***53,63***80,28***119,3***(9,315)(30,17)(20,23)(18,47)(16,04)(23,11)660353344358358344Source: author's estimations based on World Bank data, WVS data and data of Buehn, Schneider (2012).Estimations on individual data (RLMS-HSE1, 2006) involve social capital intrust variables and lack of fear to lose job. These characteristics reflect cognitivesocial capital.
Model estimations (see Table 3) show insignificance of income and“Russia Longitudinal Monitoring survey, RLMS-HSE”, conducted by the National Research University HigherSchool of Economics and ZAO “Demoscope” together with Carolina Population Center, University of NorthCarolina at Chapel Hill and the Institute of Sociology RAS18significance of interpersonal trust, whereas at macro level the welfare wassignificant.Table 3Estimations for informal paymentsReduced table: only key variables. Explained variable is informal payment for the services listed in the head of thetable.
«+» — significantly positive, «–» — significantly negative, «no» — insignificant. Significance at 15%.RegistrationHousingRegistrationRoadof civilPoliceCourtsVariablemanagement of propertypolicestatusnonono–no–––+nono–nonono–nono–noHas subordinatesno–no+nonoGovernmental jobno–nono+noReal incomeDistrust tomanagementLack of fear to losejobFemalenononono+++no–noSingleSource: author's estimations based on RLMS-HSE data.The existence of relationship between formal and informal institutions wastested in this work for the same year (2006) on the data on European SocialSurvey. The distributions on interpersonal trust in Russia, Ukraine and Europeancountries (including Estonia, that has also socialist past) were compared.
ForRussia and Ukraine there is a local maximum in the lower trust area, which is nottypical for other countries in the survey.The results of the second chapter allow to talk about the importance ofinstitutional environment for corruption perceptions.In the third chapter the hypothesis on the change of corruption perceptionsdepended on the structure of social links is tested. A method of empiricalestimation of corruption perceptions diffusion is suggested, that considers socialnetworks characteristics in absence of dynamic data.
The structure of socialnetworks allows to estimate social capital of the explored group quantitatively.We suggest estimating the level of social capital and its relationship withcorruption expectations on the structure of social network, where the members ofthe group are the nodes of the network and social links are the edges. Following9Adali (2010), we consider the frequency, the destination and the aim of theinteractions. As for each network the density of answers is different, we suggestthe algorithm of choosing the number of questions to build the network.
The aim ofthe algorithm is to get the network of one connected component.The suggested method is the following. Let θi∈ [0, 1] be an individualcharacteristic of agent i, reflecting his or her (subjective) level of corruption~expectations, A is the contingency matrix with elements aij , A is the same matrixnormalized for each node by the number of outcoming links, has elementsa~ij aij nk 1 aik~, AT is contingency matrix normalized for each node by the numberof incoming links.
Then reciprocal influence of agents is suggested to be estimatedby the following system of equations:̃ + , = ̃ + (1 − )(3)where θ is the vector of characteristics with the size 1× n, n is the number of nodesin the network, α is the scalar weight of outcoming links, (1 – α) is the scalarweight of incoming links, therefore measures the influence of linked nodes on thecharacteristics of the agent, is the vector of individual deviation from the average.The value α defines the general measure of influence for the network, and εdefines the deviation of the network from the steady state, when individualcomponents become insignificantly small.Therefore, the formal task of α identification can be reduced to minimizationof ε norm, and the estimations (if the network is close to steady state) show howimportant are outcoming links compared to incoming.
The practical implicationcan be found in defining the expected state of network and the importance ofopinion of the society.Empirical analysis using the method is performed on the data of a surveyconducted within this research in 2014-2015 in four Russian universities: inNizhny Novgorod, St. Petersburg, Vladivostok and Voronezh. Each survey is10conducted for a relatively localized social group: students of the same year or studygroup, 6 groups in all, 370 respondents aged 20-22 years old.The first part of the questionnaire consists of questions identifying thestructure of the social network (“With whom to do go to gym?”, “Who helps youin studying?”). The second part is devoted to measuring corruption expectations,including general attitude to corruption, the evaluation of the rate of sanctions,readiness to pay bribes.Three groups of proxy variables were formed on the answers:1.individual loyalty to corruption;2.the estimation of the level of corruption in the society;3.the level of justifying corruptive actions.The estimations are made for each proxy separately.
The values of happen todiffer for the groups under evaluation - probably due to their location far from thesteady state or due to the difference in starting characteristics. The methodsuggested can be used to further testing similar diffusion of the characteristics ingroups, moreover, the implication is not limited to corruption expectations, butalso can be found in other studies, where peer effects are considered (Poldin etal. (2016), Ahern et al. (2014), Gaviria (2001)).
Further development of thismethod is worth focusing on the exploration of group characteristics that havesignificant influence on the value of parameter: for example, network density, thenumber of incoming and outcoming links.Consequently, we have achieved the following results.1. The suggested specifications of econometric models revealed on theempirical data by country a negative relationship between the level of corruptionperceptions and welfare, a positive relationship between the level of corruptionperceptions and the weakness of formal institutions.
New empirical results havebeen obtained, supporting the hypothesis that the corruption perceptions can befound not only in complementary, but also in substitutional relations with the sizeof the shadow economy. This conclusion suggests that the nature of corruption11may differ: it can be a result of excessive either insufficient control of the market.Significant differences have been revealed between corruption perceptions indeveloped and post-socialist countries, including those related to the differentquality of formal and informal institutions and the peculiarities of the historicaldevelopment of countries, for example, the socialist past. The great significance ofinformal institutions (trust) in comparison with the formal ones has been revealed.2.
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