Benhabib / Bisin / Jackson Handbook of Social Economics

1. Auflage 2010
ISBN: 978-0-444-53715-7
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 568 Seiten

Reihe: Handbooks in Economics

How do economists understand and measure normal social phenomena? Identifying economic strains in activities such as learning, group formation, discrimination, and peer dynamics requires sophisticated data and tools as well as a grasp of prior scholarship. In this volume leading economists provide an authoritative summary of social choice economics, from norms and conventions to the exchange of discrete resources. Including both theoretical and empirical perspectives, their work provides the basis for models that can offer new insights in applied economic analyses. - Reviews the recent approaches that enable economists to separate influences of culture from those caused by economic and institutional environments - Explores the recent willingness among economists to consider new arguments in the utility function - Presumes that these investigations can eventually be translated into policies

Autoren/Hrsg.

Benhabib, Jess

Bisin, Alberto

Jackson, Matthew O.

Inhaltsverzeichnis

1;Front Cover;1
2;Handbook of Social Economics;4
3;Copyright;5
4;Contents-Volume 1A;6
5;Contents-Volume 1B;12
6;Contributors;16
7;Part Three: Peer and NeighborhoodEffects;18
7.1;Chapter 18: Identification of Social Interactions;20
7.1.1;1. Introduction;22
7.1.2;2. Decision Making in Group Contexts;24
7.1.3;3. Linear Models of Social Interaction;30
7.1.4;4. Social Networks and Spatial Models of Social Interactions;53
7.1.5;5. Discrete Choice Models of Social Interactions;71
7.1.6;6. Experimental Approaches;89
7.1.7;7. Suggestions for Future Directions;101
7.1.8;8. Conclusions;109
7.1.9;A1. Derivation and Analysis of Equilibria in the Linear in Means Model;111
7.1.10;A2. Proof of Theorems 3, 4, 5 and 7 on Social Networks;114
7.1.11;A3. Equilibrium Properties of Discrete Choice Models with Social Interactions;121
7.1.12;References;125
7.2;Chapter 19: Econometric Methods for the Analysis of Assignment Problems in the Presence of Complementarity an;132
7.2.1;1. Introduction;134
7.2.2;2. An overview of empirical matching models;136
7.2.3;3. Identification and estimation of one-to-one matching models when match output is observed;141
7.2.4;4. Identification and estimation of one-to-one matching models when match output is unobserved: equilibrium approaches;174
7.2.5;5. Segregation in the presence of social spillovers;196
7.2.6;6. Treatment response with spillovers;211
7.2.7;7. Areas for further research;213
7.2.8;References;214
7.3;Chapter 20: Peer Effects in Education: A Survey of the Theory and Evidence;220
7.3.1;1. Introduction;221
7.3.2;2. Theory;222
7.3.3;3. Econometric Issues;260
7.3.4;4. Empirical Evidence: Peer Effects;277
7.3.5;5. Concluding Remarks;323
7.3.6;References;327
7.3.7;Further Reading;330
7.4;Chapter 21: The Importance of Segregation, Discrimination, Peer Dynamics, and Identity in Explaining Trends i;332
7.4.1;1. Trends in the Racial Achievement Gap;333
7.4.2;2. Segregation;334
7.4.3;3. Information-Based Models of Discrimination;340
7.4.4;4. Peer Dynamics;344
7.4.5;5. Identity;353
7.4.6;6. Conclusion;356
7.4.7;References;356
7.5;Chapter 22: Labor Markets and Referrals;360
7.5.1;1. Introduction;361
7.5.2;2. The Theoretical Literature;362
7.5.3;3. Direct Evidence on Usage of Informal Search Methods;365
7.5.4;4. Labor Market Referrals and Neighborhood Effects;373
7.5.5;5. Randomized and Natural Experiments;379
7.5.6;6. Directions for Future Research;383
7.5.7;References;384
7.6;Chapter 23: Labor and Credit Networks in Developing Economies;390
7.6.1;1. Introduction;391
7.6.2;2. Identification and Estimation of Network Effects;393
7.6.3;3. Networks, Growth, and Efficiency;408
7.6.4;4. Conclusion;420
7.6.5;References;420
7.7;Chapter 24: Risk Sharing Between Households;422
7.7.1;1. Introduction;423
7.7.2;2. Efficient risk sharing;424
7.7.3;3. Forms of risk sharing;426
7.7.4;4. The motives for risk sharing;428
7.7.5;5. Risk sharing groups and networks;437
7.7.6;6. Conclusion;442
7.7.7;References;442
7.8;Chapter 25: Neighborhood Effects And Housing;448
7.8.1;1. Introduction;450
7.8.2;2. Spatial Models of Location with Social Interactions;454
7.8.3;3. Endogenous Neighborhood and Contextual Effects in Housing Markets;471
7.8.4;4. Neighborhood Effects and the Geometry of the Canonical Urban Model;485
7.8.5;5. Hierarchical Models of Location with Social Interactions;494
7.8.6;6. Conclusion;503
7.8.7;References;504
8;Index-Volume 1A;508
9;Index-Volume 1B;548

Leseproben

vii Social networks with unknown network structure 42
All the results in this section so far have taken the social network matrix A as known. This severely restricts the domain of applicability of existing identification results on social networks. We finish this section by considering how identification may proceed when this matrix is unknown. In order to do this, we believe it is necessary to consider the full implications of the interpretation of linear social interactions models as simultaneous equations systems. While this interpretation is given in studies like Bramoullé et al., the full implications of this equivalence have not been explored. This is evident if one observes that the matrix form of the general social networks model may be written as I-JA )?=( cI+dA )x+e   (54) where for expositional purposes, the constant term is ignored. From this vantage point, it is evident that social networks models are special cases of the general linear simultaneous equations system of the form ?=?x+e.   (55) Systems of this type, of course, are the focus of the classical identification in econometrics, epitomized in Fisher (1966) and comprehensively summarized in Hsiao (1983). One can go further and observe that the assumption that the same network weights apply to both contextual and endogenous social interactions is not well motivated by theory, and regard equation (55) as the general specification of a linear social networks model where the normalization Gii = 1 for all i is imposed. From this vantage point it is evident that the distinction between J and A is of interest only when A is known a priori, as is the case both for the linear in means model and the more general social networks framework. Following the classical literature, one can then think of the presence or absence of identification in terms of whether particular sets of restrictions on (55) produce identification. All previous results in this section are examples of this perspective but rely on the very strong assumption of a particular way of imposing these restrictions, i.e., G = I - JA and B = cI + dA for known A. Note that the results we have described do not employ information on the variance covariance matrix of the reduced form error structure, which is one source of identifying information and the basis for Graham’s (2008) results. The simultaneous equations perspective makes clear that the existing results on identification in linear social networks models can be extended to much richer frameworks. We consider two classes of models in which we interpret all agents i = 1,…, nV as arrayed on a circle. We do this so that agents 1 and nV are immediate neighbors of one another, thereby allowing us to work with symmetric interaction structures. First, assume that each agent only reacts to the average behaviors and characteristics of his two nearest neighbors, but is unaffected by anyone else. This is a linear variation of the model studied in Blume (1993). In terms of the matrices G and B, one way to model this is to assume that, preserving our earlier normalization, Gii = 1 and Gii-1 Gii+1 = ?1 for all i, Gij = 0 otherwise; Bii = b0, Bii - 1 = Bii+1 = b1 for all i, and Bij = 0 otherwise, where here (and for the remainder of this discussion), all indices are mod nV. The model is identified under theorem 5 since the nearest neighbor model may be interpreted via the original social networks model via restrictions on A. For our purposes, what is of interest is that identification will still hold if one relaxes the symmetry assumptions so that Gii-1 = ?i-1, Gii+1 = ?i1, Bii-1 = bi-1 and Bii+1 = bi1. If these coefficients are nonzero, then the matrices G and B fulfill the classical rank conditions for identification, cf. Hsiao (1983, theorem 3.3.1) and one does not need to invoke theorem 4 at all. Notice that it is not necessary for the interactions parameters to be the same across agents in different positions in the network. Relative to Bramoullé et al., what this example indicates is that prior knowledge of A can take the form of the classical exclusion restrictions of simultaneous equations theory. From the vantage point of the classical theory, there is no need to impose equal coefficients across interactions as those authors do. Imposition of assumptions such as equal coefficients may be needed to account for aspects of the data, e.g., an absence of repeated observations of individuals. But if so, then the specification of the available data moments should be explicitly integrated into the identification analysis, something which has yet to be done. Further, data sets such as Add Health, which produce answers to binary questions concerning friends, are best interpreted as providing 0 values for a general A matrix, but nothing more in terms of substantive information. This example may be extended as follows. Suppose that one is not sure whether or not the social network structure involves connections between agents that are displaced by 2 on the circle, i.e., one wishes to relax the assumption that interactions between agents who are not nearest neighbors are 0. In other words, we modify the example so that for all i, Gii = 1, Gii-1 = Gii-1, Gii-2 = ?i-2, Gii+2 = ?i2, Gij = 0 otherwise, Bii= bi0, Bii-1 = bi-1, Bii+1 = bi1, Bii-2 = bi-2, Bii+2 = bi2, and Bij = 0 otherwise. If the nearest neighbor coefficients are nonzero, then by Hsiao’s theorem 3.3.1 the coefficients in this model are also identified regardless of the values of the coefficients that link non-nearest neighbors. This is an example in which aspects of the network structure are testable, so that relative to Bramoullé et al. one does need to exactly know A in advance in order to estimate social structure. The intuition is straightforward, the presence of overlapping network structures between nearest neighbors renders the system overidentified: so that the presence of some other forms of social network structure can be evaluated relative to it. This form of argument seems important as it suggests ways of uncovering social network structure when individual data are available, and again has yet to be explored. Of course, not all social network structures are identified for the same reason that without restrictions, the general linear simultaneous equations model is unidentified. What our argument here suggests is that there is much to do in terms of uncovering classes of identified social networks models that are more general than those that have so far been studied. For a second example, we consider a variation of the model studied by Bramoullé et al., which involves geometric weighting of all individuals according to their distance; as before we drop the constant term for expositional purposes. Specifically, we consider a social networks model i =c x i +d ? j?i a ij( ? ) x j +J ? j?i a ij ( ? ) ? j + e i . The idea is that the weights assigned to the behaviors of others are functions of an underlying parameter ?. In vector form, the model is =cx+dA( ? )x+JA( ? )?+e,   (56) where ( ? )=( 0 ? ?2 … ?0?? ? 2… ? k ? k ? k-1…… ? k ? k ? k-1? ? 2…?? ? 20 )   (57) Following Bramoullé et al., x is a scalar characteristic. The parameter space for this model is P = {(c, d, J, ?) ? R2 × R+ × [0, 1)}. The reduced form for this model is = ( I-JA(?) ) -1 ( cI+dA(? ) )x+ ( I-JA(?) ) -1 e. Denote by F : P ? nv2 the map ( c,d,J,? )= ( I-JA(?) ) -1 ( cI+dA(? ) ).   (58) The function F characterizes the mapping of structural model parameters (c, d, J, ?) to reduced form parameters. We will establish what Fisher (1959) calls complete identifia- bility of the structural...