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GOVERNMENT STRATEGIC INTERACTIONS

GOVERNMENT STRATEGIC INTERACTIONS. Course GY460 Techniques of Spatial Economics analysis Nicola Francesco Dotti, Philip David Wales Seminar - 21 st October, 2008. Summary.

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GOVERNMENT STRATEGIC INTERACTIONS

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  1. GOVERNMENT STRATEGIC INTERACTIONS Course GY460 TechniquesofSpatialEconomicsanalysis Nicola Francesco Dotti, Philip David Wales Seminar - 21st October, 2008

  2. Summary • PresentationofBrueckner, J. K. (1998): "Testing for Strategic Interaction among Local Governments: The Case of Growth Controls," Journal of Urban Economics, n. 44 (3), pag. 438-467 • PresentationofFiglio, D.N., V.W. Kolpin, W.E. Reid (1999): “Do States Play Welfare Games?”, Journal of Urban Economics, n. 46 (3) pag. 437-454 • Discussion

  3. 1. Brueckner (1998) Interdependence in growth-control measures between local governments behavior. Hypothesis : Growth- control is affected by: • City characteristics • Competing cities behaviors

  4. [1/2] Model assumptions • Cities in a common urban system*, • Consumers are mobile, • People prefers little cities. r = y – tx – c (1.1) Rent (r) could rise if • supply restriction by local governments, • providing amenities. (NB renters utilities is constant) If the local government doesn’t provide any growth control limit, the city will grow until urban rent = rural rent = 0 * The dataset includes the cities in California, in 1988, which have almost 25,000 inhabitants.

  5. [1/3] The empirical model 1.2 To test the presence of interaction, the authors use this model (eq. 10, pag. 450), where Zi = sum of the measures for control–growth adopted by the ith city Wz = is the weighted sum of the measures of behavior of other cities S = characteristics of the city i. W (distance) is defined in 4 different ways • wij = 1* • wij = 1 / dij • wij = Pj • wij = Pj / dij * (3 different ways to calculate if distance decay < 50, < 100 or < 150 miles)

  6. [1/4] Results • The spatial interdependence variable (Wz) is always significant, using each definition of W. This suggests spatial interdependence. However: • The size of the effect varies depending on the definition of W, and • The mechanics of the interdependence are not defined (cross section data).

  7. Figlio, Kolpin and Reid (1999) • This paper is concerned with how unemployment benefit levels are set across the United States. • Although there are federally mandated minimum requirements, the level of benefits paid to the unemployed is a state matter, and so may be different in California and Texas, in Washington and Louisiana. • This research aims to uncover whether States set their benefits strategically – whether there is evidence of ‘welfare competition’ among states

  8. [2/2] Welfare Competition • Why would states set benefit levels strategically? • Economically, States may act together because: • There may be a ‘first mover advantage’ in raising benefit levels: allowing one state to attract flows of labour. States co-ordinate to prevent large labour flows • There may also be a first mover advantage in cutting benefit levels: allowing one state to attract tax-paying workers, cutting taxes, etc. • Politically, States may not want to separate from the ‘herd’ for fear of political repercussions.

  9. [3/2] The Empirical Model • The model Figlio et al present is a first-difference, fixed effects model incorporating spatial patterns in the dependent variable and IV estimation to control for potential endogeneity in the explanatory variable. Formally, they estimate: • Where

  10. [4/2] Results • The results they present suggest that there is evidence of spatial interaction in the setting of benefit levels. • In their ‘preferred’ specification the model predicts that if one state increases their benefit levels by $1, neighbouring states will increase their benefits by roughly $0.90.

  11. Questions

  12. 1) What are the research objectives in [1] and [2]. What are the similarities in the basic idea and what are the differences? [1] The research objective is to discover if there is strategic behavior between local government in growth-control behavior. [2] The research objective is to look for evidence of strategic behavior among State governments in the way they set benefit levels. Principle similarity: Both are looking for evidence of strategic spatial interaction among local government agents. Principle difference: The empirical approach adopted: [2] uses a panel data fixed effects model to examine first differences.

  13. 2) What data do the two studies use to test their hypotheses (geographical scope, geographical units, time dimension?). What key advantage does the time dimension in [2] provide when it comes to estimation? [1]Two kinds of data are used • A survey by Glickfeld and Levine [20] about the types of growth control measures in each jurisdiction in California in 1998; • The structural characteristics of the analyzed cities. The dataset is a cross-section and city-based. [2] The data used in [2] are the ADFC benefit levels and the value of food stamps in the 48 contiguous states of the USA between 1983 and 1994. • Demographic and labour market characteristics • Political changes and migration flows The key advantage of [2] is that the panel allows the empirical model to be specified in terms of first differences, and allows a full compliment of spatial and temporal dummies to control for shocks which affect all states in all time periods.

  14. 3) Both studies estimate spatial regression models. What kind of spatial model is it: spatial lag, spatial error? Both articles use a spatial lag model In [1] author studies a spatial lag in the interaction among cities, using 4 different definition of distance. In [2] the spatial weight matrix W is applied to the dependent variable and included as an explanatory variable

  15. 4) How do [1] and [2] compare in terms of the way spatial patterns are incorporated in their regression models. What are the similarities? What are the differences?4.1) What problems might the weighting scheme in [2] introduce in the regression model (p.440) In paper [1], the incorporate spatial variables are defined as follow (see pag. 456) • wij = 1 • wij = 1 / dij • wij = Pj • wij = Pj / dij The measure of “neighborhood” is defined using migration flows. “we use total population migration rather than low-income migration to mitigate the potential endogeneity of the weighting scheme” (Figlio et al., pag. 440)

  16. 5) The central problem with estimation of these spatial models is the “endogeneity” of the spatially weighted variable (i.e. it is correlated with the error term). What main methods do the two studies use to try to overcome this? [1] The spatial interaction is estimated using 4 different definitions of wij in order to overcome a shortcoming in the theory which does not define a priori what kind of spatial interactions exist. [2] As in [1], the spatial interaction is brought into the model using a spatial weights matrix, but an IV procedure is used to estimate the change in neighbour benefit levels as well to reduce the endogeneity problem further

  17. 6) What instruments does [2] use. Looking at the OLS results in Table 1, Column 2 can you spot any puzzling results regarding the validity of these instruments? • Figlio et al use an IV procedure to estimate changes in the level of neighbour benefits. The instruments used are: • Change in States’ female unemployment rate • Change in States’ ratio of female: male employees • Change in weekly wages • However, none of these variables appear to be significantly correlated with changes in benefit levels [See next slide].

  18. 7) How does [1] interpret the coefficient Φ in eq (10) i.e. the coefficient on Wx in Table 4 and 5? Is this in line with the theory? (10) pag 450 • In eq. (10) the coefficient Φ “represents the slope of the reaction function” (pag. 450).

  19. 8) How does [2] interpret the coefficients on neighbour states benefits, in particular the difference between the OLS and IV coefficient? • The paper skates over the OLS coefficients, which aren’t very good. Just one of the variables in this specification is significant and of the expected sign. [2] attributes this to the mis-specification of the equation which can be corrected using the IV method. • The IV estimation improves the results. Figlio et al estimate the model for all the contiguous US states, and then separately for each region. They find that at the top level, a $1 increase in neighbour benefits increases states benefits by $0.90.

  20. 9) What are the results in [2], Table 3 intended to illustrate? • The results in Table 3 show the results from a separate estimation, in which neighbour benefits are interacted with a dummy to say whether they have been reduced. • This part of the paper is looking to see whether states respond asymmetrically to changes in neighbour benefits: Do states respond more readily to falling neighbourhood benefits than rising neighbourhood benefits?

  21. t=1.52 t=2.25

  22. 10) Comment on how plausible you find the results in these two papers. [1] PRO • The link between local government behavior and the share of income of landowners and citizens y - c (9), • The theoretical framework includes different spatial/dimensional weights of the interaction among local governments CONS • Static model: behavior could be affected by past trends. • The competitive game could be conceived as a multiple game with iterative interactions and multiple choices, different through the time (e.g. Differences in flexibility between big and little city).  [2] PRO • The link between local government behavior and the share of income of landowners and citizens y - c (9), • The theoretical framework includes different spatial/dimensional weights of the interaction among local governments CONS • Static model: behavior could be affected by past trends. • The competitive game could be conceived as a multiple game with iterative interactions and multiple choices, different through the time (e.g. Differences in flexibility between big and little city).

  23. 10) Comment on how plausible you find the results in these two papers. • The content and econometric approach of this paper are designed to answer the research questions posed by the authors, but employed a bit too naively. • The IV procedure used is not fully reported: no indication is given of the success of the IVs in estimating the dependent variable. • The results may also be a result of infrequent changes to the ADFC and food stamp values. As this is a model of first differences, many states employing a ‘zero’ change would tend to strengthen the appearance of co-ordination. • Although well laid out, the econometric approach is slightly off-course. A temporal model, with changes in one state feeding into changes in another would be more rigorous and convincing. • No inclusion of broader measures of benefits: some states may use a different instrument to deliver changes to benefit recipients.

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