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Federal Reserve Bank of Chicago Location of Headquarter Growth During the 90s Thomas H. Klier WP 2002-19 Location of Headquarter growth during the 90s Thomas H. Klier Federal Reserve Bank of Chicago December, 2002 tklier@frbchi.org Abstract This paper examines the location of headquarter growth of large public companies during the 1990s. Headquarters continue to be attracted by large metropolitan areas. Yet, among that group they continue to disperse into the medium-sized centers. The model results suggest that headquarter growth is elastic with respect to population growth. In addition, average January temperature emerges as a predictor of headquarter growth. Furthermore, the paper identifies 6 different categories of gross flows underlying the net change of headquarters observed during the 90s. There is strong variation among the 50 largest metro areas in terms of the composition of these gross flows. On average, entry and exit represent over 2/3 of all gross flow activity. Including information on the composition of gross flows noticeably improves the formal model. JEL codes: R 12, R 30, L 20 Key words: Headquarter location, amenities, gross flows ________________________________________ The author would like to thank Tyler Diacon for excellent research assistance and Bill Testa for helpful comments. Motivation The growth and locational patterns of large corporate headquarters have been a subject of research dating back to the latter half of the twentieth century (see Lichtenberg, 1960, Evans, 1973, and Quante, 1976, for a synopsis of earlier work). Ross (1987) compares corporate headquarter location between 1955 and 1977. Studies using more recent data to track the distribution of headquarters over time tend to rely on Fortune 500 data. Horst and Koropeckyi (2000) and Holloway and Wheeler (1991) base their timeseries analysis on data for Fortune 500 companies. Holloway and Wheeler (1991) conduct their empirical analysis for the 1980s using annual data for that decade. Horst and Koropeckyi (2000) utilize the same data from 1975 through 1999 (in five-year intervals). A set of different papers analyzes larger data sets but only utilizes their crosssectional information. Shilton and Stanley (1999) draw on data for all publicly traded companies, regardless of company size, and Davis (2000) draws on data from the Census Survey of Auxiliary Establishments. Klier and Testa (2002) combine these two aspects of the literature and present information on a panel of all large publicly traded companies they tracked for the 1990s. A common finding in all these papers is the high degree of concentration among headquarters. For example, Shilton and Stanley (1999) report that 40 percent of their sample is located in only 20 U.S. counties. They explain this stylized fact by the comparative advantage of cities to support headquarters operations. In fact, Horst and Koropeckyi (2000) report a strengthening of that effect during the 1990s as evidenced by a substantial drop of Fortune 500 headquarters located in non-metropolitan counties. In addition, the advantage of certain cities in hosting headquarters operations seems to depend little on the historic and perhaps serendipitous presence of individual companies. For example, despite Boston’s ongoing strength as a domicile of Fortune 500 companies headquarters, only two of the 15 present in 1999 had been there since 1975 (Horst and Koropeckyi, 2000). At the same time, headquarters concentrations continues to be shifting toward metro areas that do not rank at the top of the size distribution. In 1955, the first year the Fortune 500 list was compiled, the New York metro area was home to 31 percent of all 2 company headquarters on the list, the vast majority of which were located right in the city (28 percent of all Fortune 500 headquarters). While the metro area share of national headquarters remained stable until the early 1970s, the city began to lose headquarters to its surrounding areas in the mid-1960s. For the last 30 years, the share of headquarters domiciled in the New York metro area has been steadily declining. By 1999, it had fallen to 10 percent of Fortune 500 companies (see Quante, 1976, and Horst and Koropeckyi, 2000). Ross (1987) finds the biggest gains not among the largest cities but among other large cities that often experience rapid population growth during the same time period. Holloway and Wheeler (1991) find that “in many ways the changes experienced during the 1980s in location of major corporate headquarters and the assets they control were not qualitatively different from those experienced earlier. New York continued its decline for a third decade and…the chief beneficiaries were other large centers that had large enough infrastructures to be attractive as corporate headquarters locations.” (p.72) In their analysis of gross flows of headquarters they find that mergers and acquisitions, as opposed to direct relocations, are a direct mechanism leading to the deconcentration of headquarters. Klier and Testa (2002) analyze a more broadly defined set of observations and find the long-term trend of deconcentration of headquarters to have continued during the 90s. This paper expands on Klier and Testa (2002) in the following way: it investigates more closely what metro area level characteristics can explain the redistribution of headquarters experienced during the 90s. It also adds information on the gross flow of headquarters, allowing for a much richer discussion of the dynamics of headquarter location during the 90s. Data Information on the location and characteristics of companies comes from Compustat data on publicly traded companies for the year 1990 and 2000. The data represent a panel of all public companies whose shares are traded in the U.S., with the exception of American Depositary Receipts (ADRs), closed-end mutual fund index 3 shares, and pre-Financial Accounting Standards Boards (FASB) companies.1 Active companies are either publicly traded companies or are required to file with the Securities and Exchange Commission. The database identifies the headquarter location, the company-wide employment, and the company’s assets. This paper focuses on the location of large company headquarters, where large is defined as total worldwide employment of at least 2,500. Headquarter locations are aggregated by metropolitan areas. Specifically, the paper uses the most extensive definition of metropolitan areas, the so-called consolidated metropolitan statistical area (CMSA).2 Thus, the results are not affected by relocations of headquarters from a central city to a suburban location within the same metropolitan area. The underlying assumption is that a metropolitan area’s different locales share common attributes relevant to the siting of a headquarter. Some important attributes include hub airports, access to business service firms, and a common skilled labor pool. Applying the 2,500 employee cutoff, results in 1,397 metropolitan area based records in 1990 and 1,805 in 2000. The actual data work is performed on a slightly smaller set. After excluding publicly traded subsidiaries as well as banks, there are 1,243 records of large companies in 1990 and 1,700 records in 2000, about 20% of the database.3 In essence, the data is considerably larger than the Fortune 500, yet it includes essentially all the year 2000 Fortune 500 companies. Changing distribution of headquarters among the largest 50 MSAs 1 Compustat created “pre-FASB” company records upon introduction of FASB rule 94 regarding the accounting of financial service subsidiaries to show consistency between current and historical data. 2 For example, the Chicago CMSA encompasses the primary metropolitan statistical areas (PMSAs) of Chicago, IL, Gary, IN, Kankakee, IL, and Kenosha, WI. 3 Eliminating publicly traded subsidiaries of publicly traded holding companies avoids double counting. For example, both UAL Corp. and United Airlines, its subsidiary, are included in the database. They are both are headquartered at the same address and report the same employment. Depository institutions, that is SIC group 60, were excluded as the banking sector was impacted systematically different from the rest of the economy by the loosening of bank-specific regulations during the 90s. Large financial institutions gravitated towards larger metropolitan areas during the 90s. This is the result of profound regulatory changes which encouraged firm consolidation and market expansion. At the same time the number of all publicly traded banks, regardless of size, went up by more than 2.5, from 196 to 514, during the 90s, despite the consolidation. 4 During the 90s the number of large publicly traded companies in the US grew by 37 %. At the same time, the concentration of these companies’ headquarters among the most populous of metropolitan areas didn’t change at all (see table 1). Yet, the distribution of headquarters within the 50 largest metro areas changed much more noticeably. This is shown by means of a Lorenz curve (see figure 1). A Lorenz curve graphs cumulative frequency distributions. It shows the degree to which a distribution is concentrated by the distance between the actual distribution and the 45 degree line, which represents an egalitarian distribution. Figure 1 graphs the cumulative distribution of headquarters on one axis versus the cumulative distribution of metropolitan areas on the other axis. In that distribution, each metro area is treated as an equally weighted entity. The shape of the plotted line reveals the degree of concentration in the distribution of headquarters. For example, if each of the largest 50 metropolitan areas contained the same number of corporate headquarters, the graph line would be identical to the 45 degree line. In contrast, to the extent that some metropolitan areas host disproportionate numbers of headquarters, the graph curve will be bowed out toward the “southeast,” away from the 45 degree line. Figure 1 shows these curves for both 1990 and 2000 to illustrate changes in the concentration of headquarters within the largest 50 metropolitan areas. We can see that for the entire range the distribution became less concentrated during the last decade. In the year 2000 about 60% of large company headquarters reside in the 10 largest of the 50 largest MSAs. Figures 2 and 3 report the distributions of employment and assets at the large public companies. The distribution of employment at the companies in the data changed to a larger extent than the distribution of headquarters. At the end of the decade it is almost coincident with the headquarter distribution. Assets of large public companies behave quite differently. First, their distribution is noticeably more unequal. Second, it remains essentially unchanged during the 90s, with 80% of all assets attributed to the 10 largest MSAs.4 Table 1 provides some more detail on the changing distribution of assets. 4 Halloway and Wheeler (1991) report New York’s share of Fortune 500 company’s assets at 39% in 1980 and 37% in 1987, over 5.5 times that of the runner up. The Compustat data this paper is based on show New York’s assets in the year 2000 to be 6 times the size of the runner up MSA, representing 37% of all MSA headquartered assets. 5 We can see that despite the loss of headquarters, New York’s share of assets remained unchanged during the 90s. Table 2 breaks out the net flow of headquarters experienced during the 90s by MSA. Column 7 lists each MSAs share of the stock of headquarters in 1990. Column 8 shows the MSAs share of the sum of net flows during the 90s. 18 of the 50 MSAs listed experienced a share of net change that is greater than their share of the stock of headquarters at the beginning of the decade (percentages listed in bold). Only 2 of these, Washington D.C. and San Francisco, are in the 5 most populous MSAs. Mid-sized metropolitan areas were the gainers not only because of headquarters choices, but also because they also grew faster in population size. They emerged as sizable markets so that their companies and headquarters grew along with them. Nonetheless, the growing prominence of mid-sized metropolitan areas does not account for the entire shift of headquarters toward these places. Figure 4 illustrates the distribution for headquarters across all industries, as well as for population for the largest 50 metro areas in 1990 (Figure 4a) and 2000 (Figure 4b). We can see that headquarters are more concentrated among metro areas than population. This is true for both 1990 and 2000. However, during the 1990s the relative difference between the distribution of headquarters and population narrowed. This is demonstrated in figure 4c, which plots the vertical distance between both distributions at both points in time. While the contour of that distance has not changed much, it narrowed across the entire range of the distribution during the decade. In addition, from panels a and b of Figure 4 we can tell that that movement was driven in large part by a redistribution of headquarters as opposed to a redistribution of population. Model The remainder of the paper tries to explain the growth of headquarters across metro areas by means of multiple regression analysis. The dependent variable in the model is the percent change in the number of headquarters in a metropolitan area. In order to minimize the effect of a small base at the start of the decade, the data include only the 50 largest metropolitan areas. The descriptive data presented earlier suggest a 6 number of influences on the change in the concentration of headquarters during the last decade. The high degree of concentration of headquarters among a relatively small number of metro areas suggest the existence of scale effects in hosting headquarter operations. This effect is measured by the level of population. While the coefficient for this variable should reflect the scale effect, since the model is estimated only for the largest metro areas it should also pick up the redistribution from the largest to mediumsized metro areas. Hence, the expected sign is ambiguous. Also included is a variable measuring the percent change in population during the decade. This variable is expected to capture the shifting of markets away from the traditional centers of commerce and population and show a positive sign. One might also see such a response to growing population because the universe of large companies is increasingly composed of service rather than manufacturing companies. In addition, service companies tend to be more regional than national or international in market scope. Two variables control for the sectoral composition of the metropolitan areas. The first of these two is the share of manufacturing earnings in all nonfarm earnings (1989 data) in each metropolitan area. It is expected to be negatively related to the growth in headquarters as the Northeast and Midwest have been losing their dominance in manufacturing production to other regions. However, as documented by Rees (1978) and others, headquarters tend to remain behind, or follow regional demand shifts only with long lags. Second, a comparable share for employment in the FIRE sector proxies for the degree to which a metro area specializes in the provision of business services. The following suggests a positive relationship to headquarter growth. Much of the activity in FIRE industries is of the type purchased and outsourced by headquarters. Purportedly owing to the forces of globalization, headquarters are increasingly seeking to locate where such services are accessible. The model also controls for the regional composition of headquarters growth by means of a binary variable that measures if the MSA is loacted in the South, as defined by the Census region. Two variables try to capture metro area level amenities. From the FAA’s T100 data one can obtain the number of foreign destinations served by non-stop flights originating at an MSA’s airports. The variable included in the model measures the 7 percent change in destinations during the 90s. A larger choice of international destinations is expected to make a MSA more attractive as a headquarter location. A second variable, the average daily temperature in January, is trying to measure a region’s amenities in broader terms. Headquarter operations may want to locate where people want to live. Finally, the model also includes a variable measuring the education of the MSAs workforce (percent of workforce with bachelor degree). One of the frequently mentioned metro area attributes valued by headquarter operations is the presence of a skilled labor pool.5 The regression results point to the effect of the change in population in influencing headquarters growth at the metro area level (see table 4). Headquarter growth is elastic with respect to population growth: An increase in the growth of population by 1 percent is associated with a bigger increase in the growth of headquarters. The variable measuring average daily January temperature turns out to be very powerful. It is consistently highly significant. Its coefficient suggest that an increase in the average daily January temperature by one degree is accompanied by a 0.03 percent increase in the growth rate of headquarters of large public companies. Relative to model two, adding a measure of the growth in international air connections as well as the education of the metro area level workforce does not add explanatory power. In fact, the average temperature variable by itself can explain over 20% percent of the variation in the dependent variable. Identifying gross flows This part of the paper adds information on the gross flows of headquarters by MSAs. The underlying idea is that the gross flows resulting in the observed net changes can provide rich information to explain the overall observed net change in headquarters (see Holloway and Wheeler, 1991). The fact that the Compustat uses unique I.D. 5 The data on temperature can be found at: http://ggweather.com/ccd/meantemp.htm, the data on international destinations can be found at: http://ostpxweb.ost.dot.gov/aviation/international-series/ 8 numbers that do not get recycled after a company drops out allows to identify the gross flows without knowing individual companies’ histories. Specifically, one can identify companies that were present in 1990 but no longer in the database in 2000 – i.e.exiters --, and, if the change occurred in the opposite direction, entrants.6 Furthermore, as the units of observation are individual MSAs, companies can relocate, and will be counted as inor –outmovers. Finally, because this paper focuses only on large public companies, one has to allow for companies changing size during the decade. That is, a company that was large in 1990 can fall below the 2,500 employment in 2000 (it falls into the “shrink” category if it stays in the same place, if it also relocated, it is counted as an outmove). Correspondingly, if a company grows in size but stays in the same metro area, it is classified as “grow”. If it relocates during the decade, it is counted as an inmove. Table 5 lists the 6 categories of gross flows thus obtained.7 It also demonstrates the accuracy with which the gross flows add up to the previously obtained net flow. 18 of 1486 records could not be accounted for this way, as for these the employment field was blank in either 1990 or 2000. The following analysis is based only on the positively identified gross flows. Table 6 turns the gross flows reported in the previous table into shares of the total gross flow activity. That number is obtained by adding the flows across the 6 categories identified above in each metro area. Table 6 presents these shares in addition to the headquarter count in 1990, the net change of headquarters as well as the sum of gross flow activity. Several points can be made about that table. First, the level of gross flows is on average 3.5 times larger than the level of net change. In fact, for the largest metro areas, such as New York and Los Angeles, it is larger by approximately an order of magnitude. Across all 50 metro areas, new entrants and exits represent by far the largest share of gross flows. Together they account for 72% of gross flow activity. The growth of existing companies represents 13% of overall gross 6 These categories, while uniquely defined, contain several possible separate cases. E.g. a record can be treated as “exit” if the company was bought by another company (the I.D. number of the buying entity survives), or if it went out of business. Similarly, an “entry” can represent an existing private company going public by way of a IPO, or an existing public company spinning off one of its divisions as a separate entity. As these cases can have different policy implications, there might be interest in tracking distinctions like these. Research currently underway will allow me to distinguish these cases. 7 They are Exit, Move in, Move out, Shrink, Grow, and New. For a given MSA, the stocks at beginning and end of the decade relate to the gross flows in the following way: stock of HQs in 1990 + Move in + Grow + New – Exit – Move out – Shrink = stock of HQs in 2000. 9 flows, with the remaining categories (shrink in size as well as in- and out moves) jointly accounting for only 15% of overall activity. Secondly, there are noticeable differences across the 50 metro areas in terms of the composition of gross flows. For example, both Portland, Oregon, and Salt Lake City, rank highly in terms of share of gross activity represented by companies exiting the database as well as existing companies falling below the 2,500 employment threshold during the 90s. Conversely, Nashville, Tennessee, experienced the second highest share of new companies during the 90s. Metro areas that have a level of gross flow activity of 10 and have been experiencing high shares of headquarters moving in are San Diego, Orlando, Greensboro, and West Palm Beach. Incidentally, three of these four metro areas are in the group of 5 with the highest mean January temperatures of the MSAs included in this study. Figures 5 through 10 represent scatter plots of each of the 6 gross flows vs net increase of headquarters. In Table 4 one can see the effect of including some of the gross flow shares in the regression model (see columns 8-16). The two variables included are the share of inmovers and the share of exiters (for data see Table 6). The direction of the estimated effect is as expected: A larger share of inmovers is associated with an increase in headquarter growth, whereas a larger share of exiters is associated with a decrease in headquarter growth. Unambiguously the inclusion of these variables raises the explanatory power of the regression equation explaining the growth rate of headquarters. At the same time the main effects found in columns 1-7 continue to hold: The elasticity of headquarter growth with respect to population growth; the positive effect of higher average January temperature on headquarter growth (note, however, that the inclusion of the gross flow variables cuts the size of that temperature effect in half); and the positive effect of an increase in the share of FIRE employment on regional headquarter growth. Conclusion This paper investigates the location of headquarters growth of large public companies during the 90s. It addresses this question with data that include all publicly traded companies. Two trends, established in previous literature, are confirmed. 10 Headquarters disproportionately locate in large metropolitan areas. Within that group, headquarters continue to disperse toward medium-sized, fast-growing metropolitan areas. In addition, this paper presents information on the gross flows of headquarters underlying the observed net changes. On average, entry and exit of companies to or from a metro area tend to represent over 2/3 of all gross flow activity for the 50 largest MSAs. Formal modelling establishes the importance of population growth and amenities, as well as the composition of the gross flows in explaining the location of headquarter growth. Future research will disaggregate the largest two gross flow categories further in an effort to explain them directly. 11 References Compustat database, 1990, 2000 J.C. Davis. 2000. Headquarters, localization economies and differentiated service inputs, Brown University mimeo Alan W, Evans. 1973. The Location of the headquarters of industrial companies. Urban Studies, 10, 387-395 Steven R., Holloway, James O. Wheeler. 1991. Corporate Headquarters Relocation and Changes in Metropolitan Corporate Dominance, 1980-1987. Economic Geography (67:1), 54-74 Toni Horst, Sophia Koropeckyi, 2000. “Headquarters effect,” Regional Financial Review, February, pp. 16-29 Thomas Klier, William Testa, 2002. “Location trends of large company headquarters during the 90s,” Economic Perspectives, Federal Reserve Bank of Chicago, pps.12-26. Robert M. Lichtenberg. 1960. One-tenth of a nation. Cambridge, Mass. Harvard University Press Yukako Ono. 2001. Outsourcing Business Services and The Role of Central Administrative Offices. Mimeo. Federal Reserve Bank of Chicago Quante, Wolfgang, 1976, The Exodus of Corporate Headquarters from New York City, New York: Praeger. John Rees. 1978. Manufacturing Headquarters in a post-industrial Urban Context. Economic Geography Vol. 54 No 4, pp. 337-354. Christopher Ross. 1987. Organizational Dimensions of Metropolitan Dominance: Prominence in the Network of Corporate Control. American Sociological Review Vol. 52, pp.258-267. L. Shilton, C. Stanley. 1999. Spatial patterns of headquarters, Journal of Real Estate Research Vol. 17, 341-364 12 Table 1 Distribution of population, headquarters, and assets across metro areas Top 5 MSAs Top 5 x NY Rank 6 to 22 Rank 23 to 50 Top 50 Remainder All POPULATION 1990 2000 28% 27 18 18 28 29 15 16 71 72 28 28 100 100 HEADQUARTERS 1990 2000 36 33 19 20 36 37 15 17 87 87 13 13 100 13 100 ASSETS 1990 2000 49 53 16 16 35 27 10 12 94 93 6 7 100 100 Table 2: Net change in headquarters for 50 largest metro areas MSA New York--Northern New Jersey--Long Island, NY--NJ--CT-PA CMSA 2000 Population HQs90 HQs2000 Net change Growth rate share of net change share of base 21,199,865 208 227 19 9% 19% 5% 16,373,645 71 84 13 18% 7% 3% Chicago--Gary--Kenosha, IL--IN--WI CMSA 9,157,540 88 102 14 16% 8% 3% Washington--Baltimore, DC--MD--VA--WV CMSA 7,608,070 36 63 27 75% 3% 7% San Francisco--Oakland--San Jose, CA CMSA Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA 7,039,362 46 84 38 83% 4% 9% 6,188,463 51 67 16 31% 5% 4% Boston--Worcester--Lawrence, MA--NH--ME--CT CMSA 5,819,100 51 63 12 24% 5% 3% Detroit--Ann Arbor--Flint, MI CMSA 5,456,428 25 30 5 20% 2% 1% Dallas--Fort Worth, TX CMSA 5,221,801 57 68 11 19% 5% 3% Houston--Galveston--Brazoria, TX CMSA 4,669,571 38 69 31 82% 4% 8% Atlanta, GA MSA 4,112,198 26 51 25 96% 2% 6% Miami--Fort Lauderdale, FL CMSA 3,876,380 14 30 16 114% 1% 4% Seattle--Tacoma--Bremerton, WA CMSA 3,554,760 18 17 -1 -6% 2% 0% Phoenix--Mesa, AZ MSA 3,251,876 10 23 13 130% 1% 3% Minneapolis--St. Paul, MN--WI MSA 2,968,806 36 48 12 33% 3% 3% Cleveland--Akron, OH CMSA 2,945,831 34 32 -2 -6% 3% 0% Los Angeles--Riverside--Orange County, CA CMSA San Diego, CA MSA 2,813,833 9 18 9 100% 1% 2% St. Louis, MO--IL MSA 2,603,607 23 39 16 70% 2% 4% Denver--Boulder--Greeley, CO CMSA 2,581,506 13 27 14 108% 1% 3% Tampa--St. Petersburg--Clearwater, FL MSA 2,395,997 8 19 11 138% 1% 3% Pittsburgh, PA MSA 2,358,695 17 19 2 12% 2% 0% Portland--Salem, OR--WA CMSA 2,265,223 13 13 0 0% 1% 0% Cincinnati--Hamilton, OH--KY--IN CMSA 1,979,202 15 21 6 40% 1% 1% Sacramento--Yolo, CA CMSA 1,796,857 1 2 1 100% 0% 0% Kansas City, MO--KS MSA 1,776,062 16 17 1 6% 1% 0% Milwaukee--Racine, WI CMSA 1,689,572 17 24 7 41% 2% 2% 2% 1644561 2 9 7 350% 0% Indianapolis, IN MSA 1,607,486 7 11 4 57% 1% 1% San Antonio, TX MSA 1,592,383 5 7 2 40% 0% 0% Norfolk--Virginia Beach--Newport News, VA--NC MSA 1,569,541 3 5 2 67% 0% 0% Las Vegas, NV--AZ MSA 1,563,282 7 13 6 86% 1% 1% Columbus, OH MSA 1,540,157 12 20 8 67% 1% 2% Charlotte--Gastonia--Rock Hill, NC--SC MSA 1,499,293 9 12 3 33% 1% 1% New Orleans, LA MSA 1,337,726 6 6 0 0% 1% 0% Salt Lake City--Ogden, UT MSA 1,333,914 6 4 -2 -33% 1% 0% Greensboro--Winston-Salem--High Point, NC MSA 1,251,509 6 14 8 133% 1% 2% Austin--San Marcos, TX MSA 1,249,763 1 2 1 100% 0% 0% Nashville, TN MSA 1,231,311 8 25 17 213% 1% 4% Orlando, FL MSA Providence--Fall River--Warwick, RI--MA MSA 1,188,613 3 6 3 100% 0% 1% Raleigh--Durham--Chapel Hill, NC MSA 1,187,941 1 3 2 200% 0% 0% Hartford, CT MSA 1,183,110 13 12 -1 -8% 1% 0% Buffalo--Niagara Falls, NY MSA 1,170,111 5 5 0 0% 0% 0% 1135614 5 7 2 40% 0% 0% West Palm Beach--Boca Raton, FL MSA 1,131,184 2 13 11 550% 0% 3% Jacksonville, FL MSA 1,100,491 4 6 2 50% 0% 0% Rochester, NY MSA 1,098,201 6 6 0 0% 1% 0% Grand Rapids--Muskegon--Holland, MI MSA 1,088,514 3 8 5 167% 0% 1% Oklahoma City, OK MSA 1,083,346 4 6 2 50% 0% 0% Louisville, KY--IN MSA 1,025,598 6 9 3 50% 1% 1% 996,512 12 20 8 67% 1% 2% 409 38% 100% 100% Memphis, TN--AR--MS MSA Richmond--Petersburg, VA MSA TOTAL 162,514,411 1077 1486 Table 3: Summary statistics Variable headquarter growth population in 1990 population growth south manufacturing earnings share 1989 FIRE earnings share 1989 international air destinations growth average daily temperature in Jan percent bachelor degree exiter's share of gross flow inmover's share of gross flow Mean 0.75 2.84 0.18 0.42 0.19 0.07 1.02 37.34 22.95 0.25 0.11 Std. Dev. 0.96 3.44 0.15 0.50 0.08 0.03 1.57 13.19 4.74 0.13 0.16 Min Max -0.33 0.85 -0.02 0.00 0.03 0.00 -1.00 11.80 13.80 0.00 0.00 5.50 19.55 0.83 1.00 0.39 0.15 8.00 67.20 38.50 0.50 1.00 Table 4: regression results Models Variable 1 2 3 4 5 6 7 8 9 10 11 12 12 14 15 16 intercept -0.62 0.67 -0.04 0.04 2.31 0.95 1.21 1.89 8.5 4.99 0.61 0.28 -1.79 0.79 -0.05 0.04 1.68 0.93 3.17 1.96 6.76 4.77 0.32 0.29 0.03 0.01 -1.74 0.79 -0.06 0.04 1.59 0.93 2.9 1.94 6.24 4.76 -1.73 0.86 -0.06 0.95 1.58 0.95 2.77 2.12 6.54 5.1 -0.59 0.36 -2.61 1.08 -0.05 0.05 1.43 0.94 3.38 2 5.71 4.91 -1.31 0.64 -0.01 0.04 2.55 0.85 2.62 1.75 7.88 4.48 0.75 0.25 0.55 0.61 -0.03 0.03 1.65 0.79 0.95 1.56 8.83 4.1 0.36 0.23 -0.07 0.66 -0.01 0.03 1.92 0.78 1.81 1.56 8.41 3.97 0.49 0.24 -1.95 0.75 -0.04 0.04 1.87 0.88 3.33 1.84 5.9 4.5 -0.38 0.71 -0.04 0.03 1.17 0.77 2.34 1.6 7.24 3.92 -0.63 -0.73 -0.03 0.03 1.35 0.77 2.62 1.6 6.97 3.88 0.04 0.01 0.04 0.01 0.01 0.08 0.04 0.01 -2.37 1.04 -0.06 0.04 1.57 0.94 3.48 1.99 5.89 4.89 0.27 0.29 0.03 0.01 0.03 0.01 0.03 0.01 0.03 0.01 -2.07 0.74 -0.02 0.04 2.06 0.87 3.86 1.81 6.7 4.39 0.52 0.27 0.02 0.01 -0.43 0.72 -0.04 0.03 1.22 0.78 2.48 1.63 7.47 3.96 0.15 0.24 0.02 0.01 -0.79 0.74 -0.02 0.03 1.5 0.78 2.96 1.62 7.32 3.88 0.28 0.25 0.02 0.01 -3.63 0.77 0.58 0.52 0.89 0.64 -3.25 0.81 0.6 0.53 -3.56 0.79 0.58 0.51 1.12 0.67 -3 0.84 0.61 0.53 population change in population Manuf. Share FIRE share South Avg. Jan temp. change internat. Destinations % foreign born % bachelor degree 0.02 0.03 0.05 0.01 -1.85 2.9 0.02 0.03 share of inmovers 2.4 0.7 share of exiters R squared Adj. R squared 0.29 0.21 0.38 0.29 0.36 0.29 0.36 0.27 standard errors listed below coefficient estimates statistically significant coefficients in bold 0.24 0.22 0.39 0.29 0.38 0.28 0.44 0.37 -3.82 0.81 0.53 0.47 1.38 0.68 -3.09 0.87 0.57 0.5 1.75 0.7 0.44 0.37 2.08 0.7 0.49 0.4 Table 5: Gross flows of headquarters for 50 largest MSAs MSA New York--Northern New Jersey--Long Island, NY--NJ-CT--PA CMSA Los Angeles--Riverside--Orange County, CA CMSA Chicago--Gary--Kenosha, IL--IN--WI CMSA Washington--Baltimore, DC--MD--VA--WV CMSA San Francisco--Oakland--San Jose, CA CMSA Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA Boston--Worcester--Lawrence, MA--NH--ME--CT CMSA Detroit--Ann Arbor--Flint, MI CMSA Dallas--Fort Worth, TX CMSA Houston--Galveston--Brazoria, TX CMSA Atlanta, GA MSA Miami--Fort Lauderdale, FL CMSA Seattle--Tacoma--Bremerton, WA CMSA Phoenix--Mesa, AZ MSA Minneapolis--St. Paul, MN--WI MSA Cleveland--Akron, OH CMSA San Diego, CA MSA St. Louis, MO--IL MSA Denver--Boulder--Greeley, CO CMSA Tampa--St. Petersburg--Clearwater, FL MSA Pittsburgh, PA MSA Portland--Salem, OR--WA CMSA Cincinnati--Hamilton, OH--KY--IN CMSA Sacramento--Yolo, CA CMSA Kansas City, MO--KS MSA Milwaukee--Racine, WI CMSA Orlando, FL MSA Indianapolis, IN MSA San Antonio, TX MSA Norfolk--Virginia Beach--Newport News, VA--NC MSA Las Vegas, NV--AZ MSA Columbus, OH MSA Charlotte--Gastonia--Rock Hill, NC--SC MSA New Orleans, LA MSA Salt Lake City--Ogden, UT MSA Greensboro--Winston-Salem--High Point, NC MSA Austin--San Marcos, TX MSA Nashville, TN MSA Providence--Fall River--Warwick, RI--MA MSA Raleigh--Durham--Chapel Hill, NC MSA Hartford, CT MSA Buffalo--Niagara Falls, NY MSA Memphis, TN--AR--MS MSA West Palm Beach--Boca Raton, FL MSA Jacksonville, FL MSA Rochester, NY MSA Grand Rapids--Muskegon--Holland, MI MSA Oklahoma City, OK MSA Louisville, KY--IN MSA Richmond--Petersburg, VA MSA TOTAL HQs90 EXIT SURVIVE Move in Move out SHRINK GROW NEW zero empl zero empl discrepancy HQs2000 1990 2000 208 71 88 36 46 87 30 32 14 18 90 28 51 17 24 13 3 6 6 2 18 10 1 0 3 14 3 4 5 1 25 9 10 10 27 95 44 35 31 31 51 51 25 57 38 26 14 18 10 36 34 9 23 13 8 17 13 15 1 16 17 2 7 5 3 7 12 9 6 6 6 1 8 3 1 13 5 5 2 4 6 3 4 6 12 17 25 11 26 15 7 4 6 5 13 11 4 5 8 5 8 5 5 0 4 4 2 3 3 1 3 3 2 3 5 2 1 2 0 0 5 3 3 0 1 2 1 0 2 2 27 22 13 27 20 16 6 10 5 23 18 3 13 5 2 7 7 10 1 11 12 0 3 2 2 2 9 7 3 0 4 0 6 3 1 7 2 1 2 2 2 1 4 2 9 3 2 1 2 4 2 3 0 1 0 1 6 1 3 0 2 1 2 1 1 2 3 2 1 0 2 2 1 1 0 4 0 1 1 0 1 1 2 5 0 0 1 0 2 0 5 2 1 1 1 1 3 1 0 0 2 2 3 0 0 2 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 2 2 0 2 2 2 1 1 0 0 2 0 2 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 11 10 3 7 12 4 5 1 4 10 3 1 4 0 3 1 0 2 0 2 2 1 1 2 0 1 2 1 0 1 3 1 2 1 0 1 0 1 2 1 3 1 2 1 3 26 29 13 31 33 29 16 6 13 15 9 8 20 19 13 9 5 8 0 3 8 5 5 2 3 8 7 3 2 3 3 1 16 1 2 3 2 3 3 3 1 5 0 4 8 418 542 92 60 53 195 642 1077 Exit: 1990 ID not found in 2000 Survive: same ID at both points in time; breaks up into movers and nonmovers (survive) Shrink: large in 1990, not in 2000; distinguish outmovers Grow: not large in 1990, large in 2000; breaks into inmovers and nonmovers (grow) New: 2000 ID not found in 1990, includes spinoffs etc. zero employment 1990: either "survive" or "shrink" zero employment 2000: either "survive" or "grow" 5 1 1 1 1 1 1 2 1 2 2 1 0 0 -1 0 227 84 102 63 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 67 63 30 68 69 51 30 17 23 48 32 18 39 27 19 19 13 21 2 17 24 9 11 7 5 13 20 12 6 4 14 2 25 6 3 12 5 7 13 6 6 8 6 9 20 1486 Table 6: Shares of gross flow by MSA MSA New York--Northern New Jersey--Long Island, NY--NJ--CT-PA CMSA Los Angeles--Riverside--Orange County, CA CMSA Chicago--Gary--Kenosha, IL--IN--WI CMSA Washington--Baltimore, DC--MD--VA--WV CMSA San Francisco--Oakland--San Jose, CA CMSA Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA Boston--Worcester--Lawrence, MA--NH--ME--CT CMSA Detroit--Ann Arbor--Flint, MI CMSA Dallas--Fort Worth, TX CMSA Houston--Galveston--Brazoria, TX CMSA Atlanta, GA MSA Miami--Fort Lauderdale, FL CMSA Seattle--Tacoma--Bremerton, WA CMSA Phoenix--Mesa, AZ MSA Minneapolis--St. Paul, MN--WI MSA Cleveland--Akron, OH CMSA San Diego, CA MSA St. Louis, MO--IL MSA Denver--Boulder--Greeley, CO CMSA Tampa--St. Petersburg--Clearwater, FL MSA Pittsburgh, PA MSA Portland--Salem, OR--WA CMSA Cincinnati--Hamilton, OH--KY--IN CMSA Sacramento--Yolo, CA CMSA Kansas City, MO--KS MSA Milwaukee--Racine, WI CMSA Orlando, FL MSA Indianapolis, IN MSA San Antonio, TX MSA Norfolk--Virginia Beach--Newport News, VA--NC MSA Las Vegas, NV--AZ MSA Columbus, OH MSA Charlotte--Gastonia--Rock Hill, NC--SC MSA New Orleans, LA MSA Salt Lake City--Ogden, UT MSA Greensboro--Winston-Salem--High Point, NC MSA Austin--San Marcos, TX MSA Nashville, TN MSA Providence--Fall River--Warwick, RI--MA MSA Raleigh--Durham--Chapel Hill, NC MSA Hartford, CT MSA Buffalo--Niagara Falls, NY MSA Memphis, TN--AR--MS MSA West Palm Beach--Boca Raton, FL MSA Jacksonville, FL MSA Rochester, NY MSA Grand Rapids--Muskegon--Holland, MI MSA Oklahoma City, OK MSA Louisville, KY--IN MSA Richmond--Petersburg, VA MSA TOTAL HQs90 Net change sum of exit share move in move out shrink gross flows share share share grow new share share 208 71 88 36 46 19 13 14 27 38 252 99 88 66 82 0.35 0.30 0.36 0.21 0.22 0.05 0.03 0.07 0.09 0.02 0.07 0.10 0.01 0.00 0.04 0.06 0.03 0.05 0.08 0.01 0.10 0.09 0.11 0.15 0.33 0.38 0.44 0.40 0.47 0.38 51 51 25 57 38 26 14 18 10 36 34 9 23 13 8 17 13 15 1 16 17 2 7 5 3 7 12 9 6 6 6 1 8 3 1 13 5 5 2 4 6 3 4 6 12 16 12 5 11 31 25 16 -1 13 12 -2 9 16 14 11 2 0 6 1 1 7 7 4 2 2 6 8 3 0 -2 8 1 17 3 2 -1 0 2 11 2 0 5 2 3 8 64 70 29 69 67 45 32 15 23 38 28 21 35 30 22 22 12 17 1 11 17 11 12 8 4 16 14 7 6 10 12 3 21 3 2 11 6 10 10 6 8 9 2 11 14 0.27 0.36 0.38 0.38 0.22 0.16 0.13 0.40 0.22 0.34 0.39 0.19 0.14 0.27 0.23 0.36 0.42 0.29 0.00 0.36 0.24 0.18 0.25 0.38 0.25 0.19 0.21 0.29 0.50 0.50 0.17 0.33 0.10 0.00 0.00 0.45 0.50 0.30 0.00 0.17 0.25 0.11 0.00 0.18 0.14 0.05 0.03 0.03 0.03 0.06 0.04 0.09 0.00 0.04 0.00 0.04 0.29 0.03 0.10 0.00 0.09 0.08 0.12 1.00 0.09 0.12 0.27 0.17 0.13 0.00 0.13 0.14 0.14 0.17 0.00 0.33 0.00 0.05 0.33 0.00 0.09 0.17 0.20 0.50 0.00 0.00 0.11 0.00 0.18 0.00 0.08 0.03 0.03 0.01 0.01 0.02 0.09 0.07 0.00 0.00 0.07 0.10 0.09 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.10 0.00 0.00 0.13 0.11 0.00 0.09 0.00 0.03 0.03 0.00 0.03 0.03 0.04 0.03 0.07 0.00 0.00 0.07 0.00 0.06 0.00 0.05 0.00 0.08 0.00 0.00 0.09 0.06 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.13 0.00 0.00 0.09 0.07 0.17 0.14 0.10 0.10 0.18 0.09 0.16 0.07 0.17 0.26 0.11 0.05 0.11 0.00 0.14 0.05 0.00 0.12 0.00 0.18 0.12 0.09 0.08 0.25 0.00 0.06 0.14 0.14 0.00 0.10 0.25 0.33 0.10 0.33 0.00 0.09 0.00 0.10 0.20 0.17 0.38 0.11 1.00 0.09 0.21 0.41 0.41 0.45 0.45 0.49 0.64 0.50 0.40 0.57 0.39 0.32 0.38 0.57 0.63 0.59 0.41 0.42 0.47 0.00 0.27 0.47 0.45 0.42 0.25 0.75 0.50 0.50 0.43 0.33 0.30 0.25 0.33 0.76 0.33 1.00 0.27 0.33 0.30 0.30 0.50 0.13 0.56 0.00 0.36 0.57 1077 409 1471 0.28 0.07 0.04 0.04 0.13 0.44 Figure 1: Distribution of Large Company HQs 100 90 Cum. Freq. of Large Company HQs 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Cum. Freq. of MSAs 1990 2000 70 80 90 100 Figure 2: Distribution of Large Company Employment 100 90 80 Cum. Freq. of Employment 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Cum. Freq. of MSAs 1990 2000 70 80 90 100 Figure 3: Distribution of Large Company Assets 100 90 Cum. Freq. Of Asset Holdings 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Cum. Freq. of MSAs 1990 2000 70 80 90 100 Figure 4a: Distribution of headquarters and population, 1990 100 90 Cumulative frequency of HQs 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Cumulative frequency of MSA's Headquarters Population 70 80 90 100 Figure 4b: Distribution of headquarters and population, 2000 100 90 Cumulative frequency of HQs 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Cumulative frequency of MSA's Headquarters Population 70 80 90 100 Figure 4c: Vertical distance between the two distributions 8.5 6.5 4.5 2.5 0.5 0 10 20 30 40 50 60 -1.5 2000 1990 70 80 90 100 Figure 5: Exit 6 44 5 4 Percentage Change In HQs 27 3 40 2 38 47 12 39 24 1 11 18 50 48 1420 36 19 17 31 4 510 32 45 49 26 30 28 6 46 0 0.00 0.10 0.20 37 332343 2 0.30 29 15 7 3 98 21 1 25 1613 22 0.40 -1 Share Of Gross Flows Accounted For By Exit 41 42 34 35 0.50 0.60 Figure 6: Move Out 6 44 5 4 Percentage Change In HQs 27 3 38 40 2 47 20 36 14 19 39 37 24 4 1 50 32 30 48 45 26 29 23 15 33 25 42 34 22 0 35 0.00 10 11 12 17 31 5 28 7 3 9 6 8 13 0.02 0.04 0.06 1 16 18 49 21 41 0.08 -1 Share of Gross Flows Accounted For By Move Out 43 2 0.10 46 0.12 0.14 Figure 7: Move In 6 44 5 4 Pct Change In HQs 27 3 40 38 2 47 20 37 14 12 19 11 5 10 4 31 1 30 50 18 32 2849 48 45 26 43 23 29 15 33 7928 6 3 21 25 42 34 46 16 1 22 13 41 0 35 0.00 0.20 36 17 39 24 0.40 0.60 0.80 -1 Share of Gross Flows Accounted For By Move In 1.00 1.20 Figure 8: Shrink 6 44 5 4 Percentage Change In HQs 27 3 38 40 2 47 36 14 19 24 17 39 37 1 30 32 20 12 5 28 48 29 23 43 33 15 8 21 34 42 41 0 0.00 11 10 18 50 4 3 0.04 1 13 16 0.06 45 49 26 7926 0.02 31 22 0.08 25 46 35 0.10 0.12 -1 Share of Gross Flows Accounted For By Shrink 0.14 0.16 0.18 Figure 9: Grow 6 44 5 4 Percentage Change In HQs 27 3 40 38 2 47 19 24 17 11 31 20 14 12 36 10 18 324 50 28 49 26 45 4323 33 6 2915 7 8 9 2 3 21 1 25 42 34 22 13 4116 0 35 0.00 0.20 1 30 37 39 5 48 46 0.40 0.60 0.80 -1 Share of Gross Flows Accounted For By Grow 1.00 1.20 Figure 10: New 6 44 5 4 Percentage Change In HQs 27 3 38 2 40 47 36 24 39 37 1 48 29 0 0.00 0.20 34 1642 35 17 5 28 156 33 7 289 1 321 22 13 49 43 25 41 46 12 0.40 31 4 10 32 45 26 23 14 20 19 11 18 50 30 0.60 0.80 -1 Share Of Gross Flows Accounted For By New 1.00 1.20 Working Paper Series A series of research studies on regional economic issues relating to the Seventh Federal Reserve District, and on financial and economic topics. Extracting Market Expectations from Option Prices: Case Studies in Japanese Option Markets Hisashi Nakamura and Shigenori Shiratsuka WP-99-1 Measurement Errors in Japanese Consumer Price Index Shigenori Shiratsuka WP-99-2 Taylor Rules in a Limited Participation Model Lawrence J. Christiano and Christopher J. Gust WP-99-3 Maximum Likelihood in the Frequency Domain: A Time to Build Example Lawrence J.Christiano and Robert J. Vigfusson WP-99-4 Unskilled Workers in an Economy with Skill-Biased Technology Shouyong Shi WP-99-5 Product Mix and Earnings Volatility at Commercial Banks: Evidence from a Degree of Leverage Model Robert DeYoung and Karin P. Roland WP-99-6 School Choice Through Relocation: Evidence from the Washington D.C. Area Lisa Barrow WP-99-7 Banking Market Structure, Financial Dependence and Growth: International Evidence from Industry Data Nicola Cetorelli and Michele Gambera WP-99-8 Asset Price Fluctuation and Price Indices Shigenori Shiratsuka WP-99-9 Labor Market Policies in an Equilibrium Search Model Fernando Alvarez and Marcelo Veracierto WP-99-10 Hedging and Financial Fragility in Fixed Exchange Rate Regimes Craig Burnside, Martin Eichenbaum and Sergio Rebelo WP-99-11 Banking and Currency Crises and Systemic Risk: A Taxonomy and Review George G. Kaufman WP-99-12 Wealth Inequality, Intergenerational Links and Estate Taxation Mariacristina De Nardi WP-99-13 Habit Persistence, Asset Returns and the Business Cycle Michele Boldrin, Lawrence J. Christiano, and Jonas D.M Fisher WP-99-14 Does Commodity Money Eliminate the Indeterminacy of Equilibria? Ruilin Zhou WP-99-15 A Theory of Merchant Credit Card Acceptance Sujit Chakravorti and Ted To WP-99-16 1 Working Paper Series (continued) Who’s Minding the Store? Motivating and Monitoring Hired Managers at Small, Closely Held Firms: The Case of Commercial Banks Robert DeYoung, Kenneth Spong and Richard J. Sullivan WP-99-17 Assessing the Effects of Fiscal Shocks Craig Burnside, Martin Eichenbaum and Jonas D.M. Fisher WP-99-18 Fiscal Shocks in an Efficiency Wage Model Craig Burnside, Martin Eichenbaum and Jonas D.M. Fisher WP-99-19 Thoughts on Financial Derivatives, Systematic Risk, and Central Banking: A Review of Some Recent Developments William C. Hunter and David Marshall WP-99-20 Testing the Stability of Implied Probability Density Functions Robert R. Bliss and Nikolaos Panigirtzoglou WP-99-21 Is There Evidence of the New Economy in the Data? Michael A. Kouparitsas WP-99-22 A Note on the Benefits of Homeownership Daniel Aaronson WP-99-23 The Earned Income Credit and Durable Goods Purchases Lisa Barrow and Leslie McGranahan WP-99-24 Globalization of Financial Institutions: Evidence from Cross-Border Banking Performance Allen N. Berger, Robert DeYoung, Hesna Genay and Gregory F. Udell WP-99-25 Intrinsic Bubbles: The Case of Stock Prices A Comment Lucy F. Ackert and William C. Hunter WP-99-26 Deregulation and Efficiency: The Case of Private Korean Banks Jonathan Hao, William C. Hunter and Won Keun Yang WP-99-27 Measures of Program Performance and the Training Choices of Displaced Workers Louis Jacobson, Robert LaLonde and Daniel Sullivan WP-99-28 The Value of Relationships Between Small Firms and Their Lenders Paula R. Worthington WP-99-29 Worker Insecurity and Aggregate Wage Growth Daniel Aaronson and Daniel G. Sullivan WP-99-30 Does The Japanese Stock Market Price Bank Risk? Evidence from Financial Firm Failures Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman WP-99-31 Bank Competition and Regulatory Reform: The Case of the Italian Banking Industry Paolo Angelini and Nicola Cetorelli WP-99-32 2 Working Paper Series (continued) Dynamic Monetary Equilibrium in a Random-Matching Economy Edward J. Green and Ruilin Zhou WP-00-1 The Effects of Health, Wealth, and Wages on Labor Supply and Retirement Behavior Eric French WP-00-2 Market Discipline in the Governance of U.S. Bank Holding Companies: Monitoring vs. Influencing Robert R. Bliss and Mark J. Flannery WP-00-3 Using Market Valuation to Assess the Importance and Efficiency of Public School Spending Lisa Barrow and Cecilia Elena Rouse Employment Flows, Capital Mobility, and Policy Analysis Marcelo Veracierto Does the Community Reinvestment Act Influence Lending? An Analysis of Changes in Bank Low-Income Mortgage Activity Drew Dahl, Douglas D. Evanoff and Michael F. Spivey WP-00-4 WP-00-5 WP-00-6 Subordinated Debt and Bank Capital Reform Douglas D. Evanoff and Larry D. Wall WP-00-7 The Labor Supply Response To (Mismeasured But) Predictable Wage Changes Eric French WP-00-8 For How Long Are Newly Chartered Banks Financially Fragile? Robert DeYoung WP-00-9 Bank Capital Regulation With and Without State-Contingent Penalties David A. Marshall and Edward S. Prescott WP-00-10 Why Is Productivity Procyclical? Why Do We Care? Susanto Basu and John Fernald WP-00-11 Oligopoly Banking and Capital Accumulation Nicola Cetorelli and Pietro F. Peretto WP-00-12 Puzzles in the Chinese Stock Market John Fernald and John H. Rogers WP-00-13 The Effects of Geographic Expansion on Bank Efficiency Allen N. Berger and Robert DeYoung WP-00-14 Idiosyncratic Risk and Aggregate Employment Dynamics Jeffrey R. Campbell and Jonas D.M. Fisher WP-00-15 Post-Resolution Treatment of Depositors at Failed Banks: Implications for the Severity of Banking Crises, Systemic Risk, and Too-Big-To-Fail George G. Kaufman and Steven A. Seelig WP-00-16 3 Working Paper Series (continued) The Double Play: Simultaneous Speculative Attacks on Currency and Equity Markets Sujit Chakravorti and Subir Lall WP-00-17 Capital Requirements and Competition in the Banking Industry Peter J.G. Vlaar WP-00-18 Financial-Intermediation Regime and Efficiency in a Boyd-Prescott Economy Yeong-Yuh Chiang and Edward J. Green WP-00-19 How Do Retail Prices React to Minimum Wage Increases? James M. MacDonald and Daniel Aaronson WP-00-20 Financial Signal Processing: A Self Calibrating Model Robert J. Elliott, William C. Hunter and Barbara M. Jamieson WP-00-21 An Empirical Examination of the Price-Dividend Relation with Dividend Management Lucy F. Ackert and William C. Hunter WP-00-22 Savings of Young Parents Annamaria Lusardi, Ricardo Cossa, and Erin L. Krupka WP-00-23 The Pitfalls in Inferring Risk from Financial Market Data Robert R. Bliss WP-00-24 What Can Account for Fluctuations in the Terms of Trade? Marianne Baxter and Michael A. Kouparitsas WP-00-25 Data Revisions and the Identification of Monetary Policy Shocks Dean Croushore and Charles L. Evans WP-00-26 Recent Evidence on the Relationship Between Unemployment and Wage Growth Daniel Aaronson and Daniel Sullivan WP-00-27 Supplier Relationships and Small Business Use of Trade Credit Daniel Aaronson, Raphael Bostic, Paul Huck and Robert Townsend WP-00-28 What are the Short-Run Effects of Increasing Labor Market Flexibility? Marcelo Veracierto WP-00-29 Equilibrium Lending Mechanism and Aggregate Activity Cheng Wang and Ruilin Zhou WP-00-30 Impact of Independent Directors and the Regulatory Environment on Bank Merger Prices: Evidence from Takeover Activity in the 1990s Elijah Brewer III, William E. Jackson III, and Julapa A. Jagtiani WP-00-31 Does Bank Concentration Lead to Concentration in Industrial Sectors? Nicola Cetorelli WP-01-01 On the Fiscal Implications of Twin Crises Craig Burnside, Martin Eichenbaum and Sergio Rebelo WP-01-02 4 Working Paper Series (continued) Sub-Debt Yield Spreads as Bank Risk Measures Douglas D. Evanoff and Larry D. Wall WP-01-03 Productivity Growth in the 1990s: Technology, Utilization, or Adjustment? Susanto Basu, John G. Fernald and Matthew D. Shapiro WP-01-04 Do Regulators Search for the Quiet Life? The Relationship Between Regulators and The Regulated in Banking Richard J. Rosen Learning-by-Doing, Scale Efficiencies, and Financial Performance at Internet-Only Banks Robert DeYoung The Role of Real Wages, Productivity, and Fiscal Policy in Germany’s Great Depression 1928-37 Jonas D. M. Fisher and Andreas Hornstein WP-01-05 WP-01-06 WP-01-07 Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy Lawrence J. Christiano, Martin Eichenbaum and Charles L. Evans WP-01-08 Outsourcing Business Service and the Scope of Local Markets Yukako Ono WP-01-09 The Effect of Market Size Structure on Competition: The Case of Small Business Lending Allen N. Berger, Richard J. Rosen and Gregory F. Udell WP-01-10 Deregulation, the Internet, and the Competitive Viability of Large Banks and Community Banks WP-01-11 Robert DeYoung and William C. Hunter Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards Christopher R. Knittel and Victor Stango WP-01-12 Gaps and Triangles Bernardino Adão, Isabel Correia and Pedro Teles WP-01-13 A Real Explanation for Heterogeneous Investment Dynamics Jonas D.M. Fisher WP-01-14 Recovering Risk Aversion from Options Robert R. Bliss and Nikolaos Panigirtzoglou WP-01-15 Economic Determinants of the Nominal Treasury Yield Curve Charles L. Evans and David Marshall WP-01-16 Price Level Uniformity in a Random Matching Model with Perfectly Patient Traders Edward J. Green and Ruilin Zhou WP-01-17 Earnings Mobility in the US: A New Look at Intergenerational Inequality Bhashkar Mazumder WP-01-18 The Effects of Health Insurance and Self-Insurance on Retirement Behavior Eric French and John Bailey Jones WP-01-19 5 Working Paper Series (continued) The Effect of Part-Time Work on Wages: Evidence from the Social Security Rules Daniel Aaronson and Eric French WP-01-20 Antidumping Policy Under Imperfect Competition Meredith A. Crowley WP-01-21 Is the United States an Optimum Currency Area? An Empirical Analysis of Regional Business Cycles Michael A. Kouparitsas WP-01-22 A Note on the Estimation of Linear Regression Models with Heteroskedastic Measurement Errors Daniel G. Sullivan WP-01-23 The Mis-Measurement of Permanent Earnings: New Evidence from Social Security Earnings Data Bhashkar Mazumder WP-01-24 Pricing IPOs of Mutual Thrift Conversions: The Joint Effect of Regulation and Market Discipline Elijah Brewer III, Douglas D. Evanoff and Jacky So WP-01-25 Opportunity Cost and Prudentiality: An Analysis of Collateral Decisions in Bilateral and Multilateral Settings Herbert L. Baer, Virginia G. France and James T. Moser WP-01-26 Outsourcing Business Services and the Role of Central Administrative Offices Yukako Ono WP-02-01 Strategic Responses to Regulatory Threat in the Credit Card Market* Victor Stango WP-02-02 The Optimal Mix of Taxes on Money, Consumption and Income Fiorella De Fiore and Pedro Teles WP-02-03 Expectation Traps and Monetary Policy Stefania Albanesi, V. V. Chari and Lawrence J. Christiano WP-02-04 Monetary Policy in a Financial Crisis Lawrence J. Christiano, Christopher Gust and Jorge Roldos WP-02-05 Regulatory Incentives and Consolidation: The Case of Commercial Bank Mergers and the Community Reinvestment Act Raphael Bostic, Hamid Mehran, Anna Paulson and Marc Saidenberg WP-02-06 Technological Progress and the Geographic Expansion of the Banking Industry Allen N. Berger and Robert DeYoung WP-02-07 Choosing the Right Parents: Changes in the Intergenerational Transmission of Inequality Between 1980 and the Early 1990s David I. Levine and Bhashkar Mazumder WP-02-08 6 Working Paper Series (continued) The Immediacy Implications of Exchange Organization James T. Moser WP-02-09 Maternal Employment and Overweight Children Patricia M. Anderson, Kristin F. Butcher and Phillip B. Levine WP-02-10 The Costs and Benefits of Moral Suasion: Evidence from the Rescue of Long-Term Capital Management Craig Furfine WP-02-11 On the Cyclical Behavior of Employment, Unemployment and Labor Force Participation Marcelo Veracierto WP-02-12 Do Safeguard Tariffs and Antidumping Duties Open or Close Technology Gaps? Meredith A. Crowley WP-02-13 Technology Shocks Matter Jonas D. M. Fisher WP-02-14 Money as a Mechanism in a Bewley Economy Edward J. Green and Ruilin Zhou WP-02-15 Optimal Fiscal and Monetary Policy: Equivalence Results Isabel Correia, Juan Pablo Nicolini and Pedro Teles WP-02-16 Real Exchange Rate Fluctuations and the Dynamics of Retail Trade Industries on the U.S.-Canada Border Jeffrey R. Campbell and Beverly Lapham WP-02-17 Bank Procyclicality, Credit Crunches, and Asymmetric Monetary Policy Effects: A Unifying Model Robert R. Bliss and George G. Kaufman WP-02-18 Location of Headquarter Growth During the 90s Thomas H. Klier WP-02-19 7
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