1 / 17

Urban and Regional Economics

Urban and Regional Economics. Week 3. Tim Bartik. “Business Location Decisions in the U.S.: Estimates of the Effects of Unionization, Taxes, and Other Characteristics of States” Journal of Busines and Econmic Statistics , Vol. 3, No. 1, Jan. 1985, pp. 14-22.

bdale
Download Presentation

Urban and Regional Economics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Urban and Regional Economics Week 3

  2. Tim Bartik • “Business Location Decisions in the U.S.: Estimates of the Effects of Unionization, Taxes, and Other Characteristics of States” Journal of Busines and Econmic Statistics, Vol. 3, No. 1, Jan. 1985, pp. 14-22. • This is a more technical article, and hence I will present this one.

  3. According to Bartik, what explains locational choices of manufacturing firms for new branch plants? Firms are profit maximizers, thus expected profitability determines locational choice.

  4. Expected Profits depend on: • Labor market conditions. • Other input prices • e.g., energy, land, etc. • Agglomeration economies • Fiscal conditions • e.g., taxes, local subsidies, public services, etc. • First review simple logit, and then more complex conditional logit.

  5. Suppose we examine a choice to locate in Wisconsin. locwisc=1 if yes, and locwisc=0 otherwise Assume locwisc=f(taxrate) Only 1’s & 0’s revealed. Need to keep predictions in 0-1 range Pr(locate in WI) Simple Logit Model 1 0 1/taxrate

  6. Logistic model uses following functional form • ln[P/(1-P)]=B’X • where P=prob. of Wisconsin (ie., locwisc=1), X is set of independent variables, B is vector of coefficients including constant. • This transformation keeps prediction in 0-1 range. • Conditional logit is more complex.

  7. Conditional Logit Model • Here we consider more than one alternative. • For example, firms choosing between states have 49 states that are alternatives to actual choice. • Look at the application for this paper.

  8. Empirical Approach: Conditional Logit • Again, dependent variable is not continuous. • i.e., either you choose a location, or you don’t. • We now look at multiple alternatives • Probability of locating firm i at location k. • pr(locatei,k)=f(expected profitsi,k) • i,k=B’X + ei,k • where X=vector of locational factors, B is a vector of parameters, and e is a disturbance term. • Need to compare location k with all other j locations. • Thus, pr(locatei,k)=exp(B’X)/jexp(B’X)

  9. Some Econometric Issues • One problem is an assumption that is made regarding the error term: • “Independence of Irrelevant Alternatives” • Implies no relationship between alternatives not chosen. • Not realistic here: • If profits for one southern state are higher than a northern state, it is reasonable to assume a neighboring southern state also is more profitable than the northern state

  10. Can use Nested Logit • Think of this as a hierarchical decision process. • You first choose the region you are moving to (e.g., the south) and you then choose the specific state. • Bartik notes that a nested logit can be estimated if one uses a set of regional dummy variables in the conditional logit equation.

  11. Second Econometric Issue • We don’t have data on true alternatives (i.e., the sites). Rather we have data at state level. • Uses state-wide averages to distinguish one state from another. • Suppose land area is proxy for number of alternative sites. • Important question: • Are all sites within the state equally probable?

  12. Dartboard Theory • If correlation of unobserved within-state characteristics between alternative sites in the state is zero, then larger states have more alternatives. • So-called dartboard theory. • If you have twice the land area, you have twice the probability of being chosen. • If correlation is one, then larger states have no more alternatives. • Thus: Significant land area Dartboard!

  13. Data • Used D&B data for 1972 and 1978. • Looked at all manufacturing (SIC 20-39) • Determined plant openings, closings, acquisitions and divestitures. • Cross-checked for accuracy by calling firms. • Look at Table 1 for variable definitions • land area, unionization rates, work stoppages, tax rates, road miles, existing manuf. acivity, pop density, wage rate, education, construction costs, energy prices • Also included regional dummies

  14. Findings: Tables 2-4 • Dartboard Theory Confirmed • 10% increase in land area increases probability of that state being chosen by 10% • It must be the case that unobserved characteristics within states are not correlated. • Large effect of unionization. • 10% increase in %-unionization in state reduces number of branch plants by 30-45% • State tax rates have expected sign. • Corp. is significant, property not quite. • Elasticity is small, but corp. tax more important than corp. property tax.

  15. Other Findings • Infrastructure has slight positive influence. • i.e., road miles positive but elasticity approx. (0.4). • Existing manufacturing increases new starts. • Elasticity (0.8-0.9) • High wages reduce new starts. • Elasticity (0.9) • Remaining variables insignificant. • Added work stoppage variable not significant. • Slightly reduced unionization magnitude. • Unionization still neg. and significant and 2x work stop.

  16. Comments?

  17. Look at determinants of county growth Leichenko paper Presented by:

More Related