Dr. Robert Jantzen Professor of Economics Iona College Dr. Donn Pescatrice



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Wal-Mart and the U.S. Economy


Dr. Robert Jantzen


Professor of Economics

Iona College



Dr. Donn Pescatrice


Professor of Economics

Iona College


Dr. Andrew Braunstein


Professor of Business Economics

Hagan School of Business


Iona College

Corresponding Author:


Dr. Donn Pescatrice

Iona College

Department of Economics

715 North Avenue

New Rochelle, NY 10801

(914)-637-2729

(dpescatrice@iona.edu)

March, 2008


Wal-Mart and the U.S. Economy
Abstract
The Wal-Mart company, the world’s largest retailer and second-largest corporation, is a dominant U.S. business. This study investigates whether there are significant long-run relationships between the business of Wal-Mart and the overall U.S. economy as measured by an array of traditional macro-level variables. Cointegration analysis reveals that Wal-Mart sales generally move counter to overall economic conditions, dampened in more prosperous economic periods and buoyed in more sluggish economic environments. Consequently, trends in Wal-Mart sales may serve as a rather non-traditional contrarian economic bellwether.
Keywords Wal-Mart . macroeconomy . cointegration . bellwether
JEL Classifications E32 . L81 . M21

WAL-MART AND THE U.S. ECONOMY



Introduction

The Wal-Mart corporation has come under media scrutiny for a myriad of reasons in recent years. It continues to be the focus of countless news stories, case studies, and organizational analyses—some lauding its business acumen, others decrying its negative social, environmental, and economic impacts. The company’s expansion plans are increasingly subject to referendums as local governments question whether a Wal-Mart presence produces net economic and employment gains. In recent years, residents have turned back Wal-Mart penetration plans in the states of California, New York, Illinois, and Vermont, among others.

Why has the Wal-Mart corporation been the subject of such intense media scrutiny while often being cast as the villain by many labor, environmental, and local civic groups? The answer is rather obvious – it is too big to ignore.

Consider the following salient economic statistics reflecting Wal-Mart operations. Wal-Mart was the world’s largest company from 1992 to 2005, only recently dethroned when a spike in oil prices catapulted Exxon Mobil to the top. Wal-Mart is by far the largest retailer in the world, and operates about 3,900 stores in the U.S. and another 1,600 internationally. It is the number one retailer in the U.S. with 2006 annual sales of $270 billion, generating 2.0% of the U.S. gross domestic product [Wal-Mart website]. Wal-Mart’s total sales are larger than the combined sales of the next five big retailers--Home Depot, Kroger, Target, Costco, and Sears/Kmart [Basker, 2007].

Wal-Mart is a ubiquitous retailer with 92% of Americans reporting that they live close to a Wal-Mart, and 42% claiming to shop there every week [Pew Research Center, 2005]. The company serves approximately 19 million customers a day and 9 cents out of every U.S. retail dollar is spent in one of its stores. The company sells 30% of all U.S. staple goods (personal care, pet food, cleaning items, etc.), is the largest U.S. grocer (21% of total grocery sales), the biggest toy seller (19% of all toy sales), and the third-largest pharmacy (16%). The major consumer products companies Kellogg, Kraft, and General Mills each derive about 15% of their total sales from Wal-Mart stores. Wal-Mart is the largest employer in the U.S. – 1.3 million employees locally out of a total of about 1.8 million employees worldwide. It is the largest employer in 21 states, hiring 3.3% of all workers in its home state of Arkansas, and is also the largest employer in Mexico [Bianco & Zellner, 2003; Useem, 2003].

Because the Wal-Mart corporation is now a dominant U.S. firm, its operations may signal trends in aggregate economic conditions in general. That dominant U.S. businesses could have significant effects on the overall economy has long been established. However that economic history has not examined the link between such businesses and specific macro economy measures, a void this study attempts to fill by examining Wal-Mart.


A Brief History of Dominant U.S. Firms
Since the late 19th century, dominant firms have emerged in the extractive, manufacturing and retail sectors. One of the first such firms was Standard Oil which increased its refining market share from 4% in 1870 to 90% in 1890 [Boudreaux & Folsom, 1999]. Following Standard Oil, other U.S. firms that have dominated their markets and contributed substantially to GNP include U.S. Steel (1917 – 2.8% of GNP), the Great Atlantic and Pacific Tea Company (1932 – 1.5%), General Motors (1955 – 3.0%), Sears Roebuck (1983 – 1.0%), IBM (1990 – 1.2%), and now Wal-Mart (2006 – 2.0%) [Useem, 2003; Wal-Mart website].

At the turn of the century, improvements in transportation and communication made mass market production possible, spurring technological advances in products and production processes. Prior to the development of railroads, steamships and the telegraph, transportation and communication were slow, costly and limited. Central to the explanation of why American industries became oligopolized by large businesses are the studies of Chandler [1959, 1962, 1977, 1990]. In his view, large firms came to dominate industries because they were able to realize economies of scale and scope by commercializing the new technologies in vertically integrated organizations, internalizing the purchasing, manufacturing, marketing and distribution functions. Such economies, however, arose not only from the standard neoclassical efficiencies generated by large scale production, but also as a result of reduced transactions costs and improved coordination of multi-faceted operations. Equally important was the creation of new hierarchal corporate structures, where functionally decentralized divisions report to central offices that coordinate the various activities. These innovative administrative structures enabled firms to increase manufacturing throughput and inventory turnover, thereby increasing the utilization of firm overhead. In addition, as the newly specialized managers gained experience, their learned abilities led to additional operating improvements. Once a firm in an industry gained a cost advantage by vertically integrating, others had to quickly follow leading to oligopolies in American manufacturing, transportation and retailing.

For example, Standard Oil, a holding company that owned many differing companies, was able to reduce refining costs by 80% from 1870 to 1890 (and prices by 70%) by vertically integrating exploration, production, distribution and marketing functions [Boudreaux & Folsom, 1999]. Standard Oil also concentrated production in a few large refineries and invested heavily in research and development. The latter investments enabled the firm to extract more output per barrel of crude than competitors and to achieve scope economies through the development and marketing of many by-products like paint, lubricants, paraffin, etc. Standard Oil also employed a committee system where well-defined committees governed particular operations, while an overseeing executive staff coordinated the disparate activities [Hidy & Hidy, 1955]. These specialized committees developed considerable expertise in their respective areas, while central management standardized and integrated the operations of the differing companies constituting Standard Oil.

The first dominant retailer, A&P, was able to become the largest chain store in the world by 1930, larger than its three biggest rivals combined, by also vertically integrating and reforming its administrative structure [Tedlow, 1990]. With a business strategy strikingly similar to that of Wal-Mart today, A&P expanded rapidly at the expense of small independent stores by pursuing a low-price, low-frills policy. A&P achieved cost efficiencies not only by pressing for supplier discounts, but also by backward integrating its operations, serving as its own wholesaler and manufacturer. A&P, like Standard Oil, adopted a hierarchal administrative structure, decentralizing into regional and manufacturing divisions that reported to headquarters [Walsh, 1986]. As management gained in-house experience from supervising multiple stores, A&P was able to improve store layout and design, purchasing procedures and inventory control, all of which translated into lower prices and greater turnover.

A final example of the “Chandlerian” firm can be found in General Motors (GM), which by 1958 had grown into the largest builder of cars, buses and locomotives in the U.S. and the world, and was also the major supplier of parts to its car making rivals [Cray, 1980]. Additionally, GM was a major producer of a multitude of products including diesel and aircraft engines, trucks, refrigerators, air conditioners, water heaters and electric ranges. GM’s ascendancy began in the 1920s and came at the expense of Ford which controlled 59% of the market at that time [Kuhn, 1986]. Like A&P, GM implemented a multidivisional decentralized structure that improved management capabilities, allowing GM to take advantage of its large industrial scale. GM, unlike Ford, responded to the 1920s demand for product variety by marketing differing car models with a range of options, while also providing financing. In order to maximize production runs while satisfying demand for a diverse set of products, GM started planning production runs by carefully estimating each model’s demand. When inventory problems arose, GM was able to switch production from model to model by using standardized components and interchangeable manufacturing machinery for the various vehicles.

While the dominance of large corporations can be explained by the existence of scale economies, economic theory suggests that the consequences of competitive oligopolistic markets for the macro economy could be either positive or negative. On the positive side, Chandler and Hikino [1997] have argued that large industrial companies are responsible for the accelerating economic growth of the 20th century. Higher growth arose not only from the lower costs of greater scale, but because large firms invested more in physical and human capital, as well as research and development, thereby spurring the creation and commercialization of new technologies. In contrast, Weiss’ review [1989] of a variety of industry studies demonstrated that as firm size increases and the number of competitors decreases, prices increase. Similarly, Jensen [1993] has focused on the inefficiencies arising from the divergence of interests between salaried managers and corporate owners. Such agency problem inefficiencies became obvious during the 1970s and 1980s when large U.S. corporations failed to downsize when faced with excess capacity in maturing product markets. Because its compensation and prestige were tied to company sales, management refused to divest money losing operations and continued to invest in low-margin ventures.


PREVIOUS Wal-Mart Studies
Early research on Wal-Mart consists primarily of case studies analyzing the company’s efficiency, cost control and inventory management efforts [Foley & Mahmood, 1996; Ghemawat & Friedman, 1999; and Hays, 2003]. Later, Wal-Mart became the focus of a large number of studies investigating the company’s impact on a spectrum of local economic factors including its effects on retail businesses and sales, employment, wage and price pressure, poverty levels, local tax revenues, and health care expenditures, among others.

In particular, the Wal-Mart impact on local retailers and small “mom-and-pop” businesses has been widely analyzed. The results are generally mixed or inconclusive, and are highly dependent on the methodologies and data sets employed in the analyses. Stone [1995, 1997] investigated the impact of a Wal-Mart presence in towns of Iowa and found that local retailers of smaller towns are more adversely impacted by a Wal-Mart presence than those of larger towns. Further, retailers directly competing with Wal-Mart’s product line generally experience greater sales losses than businesses that offer non-competing products or services. Ozment and Martin [1990] have concurred, but they also showed that Wal-Mart’s entry into a market expands opportunities for non-competing businesses. Basker [2005a, 2007], analyzing many local markets across the nation, also demonstrated that the number of retail establishments decreases after a Wal-Mart market entrance, but only minimally. In contrast, Barnes, et al [1996], focusing on markets of the Northeast, concluded that neither sales growth nor the number of establishments are diminished by a Wal-Mart presence. Furthermore, aggressive pricing tactics are shown to be the most effective means of competing with Wal-Mart [McGhee & Rubach, 1996], and larger businesses tend to respond more aggressively to Wal-Mart competition than smaller retail firms [Khanna & Tice, 2000].

The impact of Wal-Mart on local retailers might also influence area employment. Critics of Wal-Mart have long argued that a Wal-Mart presence damages local employment and stifles wage gains. The results, however, of studies of employment and wage effects conflict. Hicks and Wilburn [2001] have found modest employment gains in West Virginia towns arising from a Wal-Mart presence. In a later study [Hicks, 2007], labor force participation is shown to be enhanced in Wal-Mart markets. Based on a broader study, Basker [2005a, 2007] has demonstrated that a Wal-Mart presence initially generates moderate new job creation that dissipates over time. She also noted that there were no employment repercussions in the retail sectors where Wal-Mart does not compete. In contrast, Neumark, Zhang and Ciccarella [2005] concluded that a Wal-Mart presence ultimately costs jobs.

To date, there is little evidence that a Wal-Mart entrance reduces prevailing wages, but real wages may benefit from a Wal-Mart presence. Basker [2005b] estimated that for a basket of consumer staples, local prices typically fall 1.5% to 3.5% in the short-run, and could possibly fall by four times that much in the long-run. Bianco and Zellner [2003] noted that the Wal-Mart business model has the capacity to diminish inflationary pressure through increased efficiency and productivity. Opponents of Wal-Mart contend that the price pressure exerted by the company is a major contributor to the demise of the local “mom-and-pop” businesses. However, Boyd [1997] has attributed much of the plight of local small retailers to the outlawing of retail price maintenance (strict implementation of manufacturers’ suggested retail prices) in the late 1970s.

Only a few studies have explored the impact of Wal-Mart on both consumers and businesses in the aggregate. Basker [2007] reported that Wal-Mart was responsible for about half the productivity growth as measured by sales per worker in the general business merchandise sector from 1982-2002. Wal-Mart also has a vast array of suppliers (about 65,000) and annually imports over $18 billion worth of products from China – about 15% of the U.S. total Chinese import of consumer goods [Basker & Van, 2007].

SCOPE AND PURPOSE
Since Wal-Mart is a ubiquitous retailer, the objective of this study is to assess whether the local area effects of Wal-Mart on employment, prices, and sales have resulted in measurable effects in the aggregate macro economy. Unlike earlier studies of dominant firms that have examined the impacts of large producers on their industries, the analysis will focus on whether there are identifiable linkages between a single dominant business, namely Wal-Mart, and macro-level variables reflecting various dimensions of the overall U.S. economy.

This study also investigates whether Wal-Mart sales can serve as a credible bellwether of various aspects of U.S. economic activity. Both the Federal Reserve and private economists already assess Wal-Mart’s prospects to help gauge the future direction of the overall U.S. economy [Mathews, 2003]. There is also evidence that Wal-Mart’s sales trend is viewed as a barometer of general economic conditions, catalyzing movements in stock markets [Reuters, 2003].


Data and Methodology

The analysis contrasts monthly U.S. Wal-Mart gross sales, obtained from the Wal-Mart website, with U.S. macro measures of income, consumer spending, employment, consumer credit, inflation and confidence, all in log form (Table 1). With the exception of the initial jobless claims and consumer credit variables, data for all of the macro variables were obtained from the Federal Reserve Bank of St. Louis website. Jobless claims were drawn from the U.S. Department of Labor website, while consumer credit information came from the Federal Reserve Board data site. Because Wal-Mart did not publish monthly U.S. sales prior to 2000, the primary study period was limited to the six-year period from January 2000 through December 2005. In order to extend the sample period, a supplementary analysis employing a longer data series that combined Wal-Mart world sales (from January 1995 to December 1999) with post-2000 U.S. only values was conducted to buttress the primary findings.

In order to assess whether Wal-Mart sales are tied to the U.S. macro-level variables, a three-step process was employed. First, because time-series variables are frequently nonstationary, augmented Dickey-Fuller (ADF) tests were used to identify whether Wal-Mart sales and the macro-level variables have unit roots, i.e., are integrated as I(1) variables. Analyses combining stationary and nonstationary variables, or variables integrated of differing orders, were not conducted because of the potential for spurious regression problems.

The second step utilized the Lambda Max tests proposed by Johansen [1988] to test whether Wal-Mart sales, which have a unit root, are cointegrated with the other I(1) macro-level variables. Although two variables might exhibit random walks, these tests assessed whether a stationary linear combination of the variables could still be formed, implying that a long-run equilibrium relationship exists between the two variables. For the cointegrated pairs, Johansen’s method was used to estimate the cointegrating vector characterizing the long-run relationship between the two variables. For the 1995-2005 period, a structural break was also incorporated to compensate for the discontinuity in the Wal-Mart data series that arose from combining world sales data (pre-2000) with U.S. only values (post-2000).

Because cointegrating vectors can only identify the long-run correlation between two variables, but not the causality, augmented Granger causality tests were conducted as the final step. For each Wal-Mart/macro variable pair identified as cointegrated, a Vector Error Correction Model (VECM) was estimated. The estimates generated by the VECMs were then used to determine whether Wal-Mart sales Granger-cause changes in the macroeconomic variables or vice versa.



Results

Table 2 displays the results of the augmented Dickey-Fuller (ADF) tests for unit roots for each variable, with the Schwartz Information Criteria (SIC) determining the optimal number of lags. If the ADF test cannot reject the null hypothesis (Ho) that the levels of a variable have a unit root, but rejects that their first differences have a unit root, then the variable is integrated of order 1, i.e., I(1). For both time periods, the ADF tests demonstrated that Wal-Mart sales and all of the macro variables have unit roots, i.e., they are integrated of order 1 following random walks about trends. (insert Tables 1 and Table 2 here).

The results of the Lambda Max (MAX) test for cointegration between the Wal-Mart sales variables and the macro-level economic measures are presented in Table 3, with the SIC again used to identify the optimal number of lags. Two variables are considered cointegrated if the MAX test rejects the null hypothesis (Ho) that no cointegrating vector exists (i.e., r = 0). The MAX results demonstrated that Wal-Mart sales are cointegrated with all of the macro variables, indicating that Wal-Mart sales growth has stable long-run relationships with multiple dimensions of the macro economy. (insert Table 3 here).

Table 4 contains the Johansen cointegrating vectors, normalized with respect to Wal-Mart sales, for each macro variable. The vectors show that Wal-Mart sales are contracyclical, with sales growth deteriorating as nearly all of the variables associated with economic growth accelerate. In particular, the pace of Wal-Mart sales is inversely related to both measures of income growth, i.e., personal income and disposable personal income. Wal-Mart sales also move counter to overall retail sales and nondurables. Correspondingly, Wal-Mart sales also grow more slowly when employment accelerates, but quicken when initial jobless claims increase. Poorer Wal-Mart performance is also associated with accelerating levels of inflation, outstanding consumer credit, and purchasing managers’ confidence. The conflicting findings observed for the differing time periods, however, between Wal-Mart’s growth and consumer sentiment suggest that the normal relationship between sentiment and the state of economy changed in the post-2000 period. Although the negative relationship observed for the overall 1995-2005 period is consistent with Wal-Mart sales moving counter to the general state of the economy, the positive relationship observed only for the post-2000 period probably reflects circumstances special to that period. Specifically, even though employment exhibited renewed growth early in 2002 as the recession ended, consumer sentiment continued to deteriorate. Consumer sentiment, which began to erode at the start of the contraction, fell further in the months leading up to the 2003 U.S. led invasion of Iraq, and dropped again after Hurricane Katrina in 2005. Consequently, the post-2000 positive relationship between Wal-Mart sales and sentiment is probably only the product of special events that depressed sentiment as the economy improved. (insert Table 4 here).

Since cointegration tests only establish the existence of a long-run relationship between two variables, but not the direction of causality, augmented Granger causality tests were conducted. Specifically, for each Wal-Mart/macro variable pair, vector error correction models (VECMs) of the following type were estimated using the cointegrating vectors obtained earlier, i.e.,

∆Wal-Martt = α0 + ∑ α1i ∆Wal-Martt-1 + ∑ α2i ∆Macrot-1 +

α (Wal-Martt-1 - 0 -  1Macrot-1) + 1t (1)

∆Macrot = 0 + ∑  1i ∆Wal-Martt-1 + ∑  2i ∆Macrot-1 +

(Macrot-1 -  0 -  1Wal-Martt-1) + 2t (2)

where Wal-Mart is the log of Wal-Mart U.S. sales, Macro is the macro variable being examined, and (Wal-Martt-1 - 0 -  1Macrot-1) and (Macrot-1 -  0-  1Wal-Martt-1) are the cointegrating vectors normalized to each left side variable, respectively. The cointegrating vectors for the first equation are reported in Table 4 while those in the second, while not reported, were generated by normalizing the cointegrating vectors to each macro variable.

If two variables are cointegrated, at least one must Granger-cause the other in a long-run equilibrium relationship. Long-run causality can be assessed by using t-tests to examine whether either  or are equal to zero. If  is not equal to zero, Wal-Mart sales depend on the behavior of the macro variable in the long-run. If  is not equal to zero, the macro measure depends on the performance of Wal-Mart. Table 5 contains the t-statistics assessing whether Wal-Mart, the macro variables, or both are the causal factor(s) in the long-run relationships identified earlier by the cointegrating vectors of Table 4. (insert Table 5 here).

The causality tests demonstrate that Wal-Mart sales, not unexpectedly, respond counter to changes in nearly all of the macro variables. Results for both time periods show that accelerating growth in nondurable or retail sales, consumer credit, gross and disposable income, inflation and employment all dampen Wal-Mart’s long-run sales growth. The causality tests also show, but only for the post-2000 period, that when business conditions improve and purchasing managers become increasingly optimistic, Wal-Mart sales suffer. Lastly, the post-2000 period result indicating that heightened consumer confidence leads to better Wal-Mart performance probably only reflects shocks noted previously for that period. In the aggregate, these results suggest that when employment and income conditions improve, consumers shop less at Wal-Mart and increase purchases from more upscale retailers, including those requiring financing.

The causality tests also demonstrate that Wal-Mart sales not only respond to changing macro economy conditions, but also generate changes in the labor market and business confidence measures. Specifically, the results for both time periods show that employment and business confidence are bidirectional causal with Wal-Mart growth, i.e., they not only influence Wal-Mart sales, but are also influenced by Wal-Mart’s performance. The longer period result also suggests that Wal-Mart growth has a deleterious impact on disposable income. The observed negative relationships between Wal-Mart sales and employment, disposable income and business confidence suggest that improving Wal-Mart sales reduce employment growth and depress purchasing managers’ views of the economy. Evidently, Wal-Mart sales growth comes at the expense of competitors and their employees.

Finally, the causality tests also show that jobless claims respond to changes in Wal-Mart’s performance, but they do not influence Wal-Mart sales. Accelerating Wal-Mart sales lead to greater jobless claims, reinforcing the earlier noted negative influence of Wal-Mart on employment. Wal-Mart’s negative impact on employment may reflect the company’s more efficient use of labor per sales dollar than the smaller retailers it displaces, or a greater propensity to sell imported goods, or both. Stronger Wal-Mart growth might also reflect a shift in consumer spending patterns away from durable goods, ultimately increasing jobless claims from displaced workers in these sectors.

The above findings demonstrate that Wal-Mart’s share of total sales is contracyclical, and that more prosperous economic environments are a bane to the business of Wal-Mart. When the economy is in a growth cycle characterized by strong demand, growing employment, rising incomes, accelerating prices and strong durable goods purchases requiring financing, Wal-Mart sales languish as other retailers prosper. In contrast, the company’s habitual low price policy attracts more customers during difficult economic periods, providing a boost to Wal-Mart sales as the economy sours. Evidently, Wal-Mart is a purveyor of inferior goods, garnering a greater share of consumer dollars when the economy is sluggish and a smaller share when the economy is vibrant.

Because Wal-Mart sales are cointegrated with macroeconomic performance, changes in Wal-Mart’s business might serve as a rather non-traditional contrarian economic bellwether. Specifically, a dampening of Wal-Mart’s sales growth might signal a more vibrant U.S. economy and vice versa. Wal-Mart monthly sales, reported promptly, may also provide more timely information about the future direction of the economy than some widely analyzed, yet more tardy, macroeconomic series.


Concluding Remarks
The U.S. economy has a long history of dominant businesses dating back to the Standard Oil Company of the early 1890s, followed by companies like A&P, General Motors, and now the Wal-Mart company. Past research on Wal-Mart’s influence on the economy has focused on its business model, efficiency efforts, impacts on competitors, and local wage and employment repercussions. Since the earlier studies have identified local economic impacts, and given its pervasive retail operations, an investigation of Wal-Mart’s macro economic effects seemed warranted. Consequently, this analysis focused on assessing the linkages between the business of Wal-Mart and the aggregate U.S. economy. A secondary objective was to determine if trends in Wal-Mart sales might provide insights into the vibrancy of the U.S. economy.

Cointegration techniques and causality tests were employed to identify the long-run and the causal relationships between Wal-Mart sales and an array of macro measures of production, income, credit, employment, prices, and confidence. The results demonstrated that Wal-Mart’s business moves counter to general economic conditions, quickening in slow growth periods and stagnating when the economy prospers. Not surprisingly, Wal-Mart sales began to accelerate in the 2nd quarter of 2007 as the economy deteriorated under the weight of subprime mortgages and high oil prices. Wal-Mart’s continued strong growth (+6.2%) in the 3rd and 4th quarters might also be signaling that the economy’s slide is likely to continue.

Causality tests showed that Wal-Mart’s business not only responds to the condition of the overall economy, but also influences the aggregate economy. Specifically, growth in Wal-Mart comes at the expense of competing U.S. firms and dampens overall employment levels.

The long-run relationships identified between Wal-Mart sales and movements in an array of U.S. macroeconomic measures suggest that further analysis is warranted in other economies characterized by dominant businesses. Such studies could also determine whether the long-run relationships depend on the sector of the dominant firm and the economic structure in which it operates.



References
Barnes, N.G., Connell, A., Hermenegildo, L. & Mattson, L. 1996. Regional Differences in the Economic Impact of Wal-Mart. Business Horizons, 39(4): 21-26.

Basker, E. 2005a. Job Creation or Destruction? Labor Market Effects of Wal-Mart Expansion. Review of Economics and Statistics, 87(1): 174-183.

________. 2005b. Selling a Cheaper Mousetrap: Wal-Mart’s Effect on Retail Prices. Journal of Urban Economics, 58(2): 203-229.

________. 2007. The Causes and Consequences of Wal-Mart’s Growth. Journal of Economic Perspectives, 21(3): 177-198.

Basker, E. & Van, P.H. 2007. Wal-Mart as Catalyst to U.S.-China Trade. Available at http://papers.ssrn.com/abstract=987583.

Bianco, A. & Zellner, W. 2003. Is Wal-Mart Too Powerful? Business Week (October 6): 100-110.

Boudreaux, D. J. & Folsom, B. W. 1999. Microsoft and Standard Oil: Radical Lessons for Antitrust Reform. Antitrust Bulletin, 44(3): 555-576.

Boyd, D. 1997. From ‘Mom-and-Pop’ to Wal-Mart: The Impact of the Consumer Goods Pricing Act of 1975 on the Retail Sector in the United States. Journal of Economic Issues, 31(1): 223-232.

Chandler, A. D. Jr. 1959. The Beginnings of “Big Business” in American Industry. Business History Review, 33(1): 1-31.

________. 1962. Strategy and Structure: Chapters in the History of the Industrial Enterprise, Cambridge, MA: MIT Press.

________. 1977. The Visible Hand: The Managerial Revolution in American Business, Cambridge, MA: Belknap Press.

________. 1990. Scale and Scope: The Dynamics of Industrial Capitalism, Cambridge, MA: The Belknap Press of the Harvard University.

Chandler, A. D., Jr., & Hikino, T. 1997. The Large Industrial Enterprise and the Dynamics of Modern Economic Growth, in Chandler, A. D., Jr., Amatori, F. & Hikino, T., Big Business and the Wealth of Nations, NY: Cambridge University Press, 24-57.

Cray, E. 1980. Chrome Colossus: General Motors and Its Times, New York: McGraw-Hill Book Company, 371-2.

Federal Reserve Bank of St. Louis website: http://research.stlouisfed.org/Fred2.

Federal Reserve Board website: http://www.federal reserve.gov/releases/g19/hist.

Foley, S. & Mahmood, T. 1996. Wal-Mart Stores, Inc., mimeo, Harvard Business School case study no. 9-794-024.

Ghemawat, P. & Friedman, G. 1999. Wal-Mart in 1999, mimeo, Harvard Business School case study no. 9-799-118.

Hays, C. 2003. The Wal-Mart Way Becomes Topic A in Business Schools. New York Times (July 27): C.10.

Hicks, M. J. & Wilburn, K. 2001. The Regional Impact of Wal-Mart Entrance: A Panel Study of the Retail Trade Sector in West Virginia. Review of Regional Studies, 31(3): 305-313.

Hicks, M. J. 2007. Wal-Mart’s Impact on Local Revenue and Expenditure Instruments in Ohio, 1988-2003. Atlantic Economic Journal, 35(1): 77-95.

Hidy, R.W. & Hidy, M.E. 1955. Pioneering in Big Business: 1882-1911. New York: Harper Brothers, 55-68, 323-332.

Jensen, M. C. 1993. The Modern Industrial Revolution, Exit, and the Failure of Internal Control Systems. Journal of Finance, 48(3): 831-880.

Johansen, S. 1988. Statistical Analysis of Cointegrating Vectors. Journal of Economic Dynamics and Control, 12(2-3): 231-254.

Khanna, N. & Tice, S. 2000. Strategic Response of Incumbents to New Entry: The Effect of Ownership Structure, Capital Structure, and Focus. Review of Financial Studies, 13(3): 749-774.

Kuhn, A. J. 1986. GM Passes Ford, 1918-1938; Designing the General Motors Performance- Control System, University Park, PA: Pennsylvania State University Press.

Mathews, S. 2003. Wal-Mart ‘Good Gauge’ of the U.S. Economy. Honolulu Advertiser (October 2) at http://the.honoluluadvertiser.com/article/2003/Oct/02/bz/bz12a.html.

McGee, S. & Rubach, M. 1996. Responding to Increased Environmental Hostility: A Study of the Competitive Behavior of Small Retailers. Journal of Applied Business Research, 13(1): 83-94.

Neumark, D., Zhang, J. & Ciccarella, S. 2005. The Effects of Wal-Mart on Local Labor Markets, National Bureau of Economic Research Working Paper 11782.

Ozment, J. & Martin, G. 1990. Changes in the Competitive Environment of Rural Retail Trade Areas. Journal of Business Research, 21(3): 277-287.

Pew Research Center for the People and the Press. 2005. Wal-Mart: A Good Place to Shop but Some Critics Too (December 15) at http://people-press.org/reports/display.php3?ReportID=265.

Reuters. 2003. Shares Slip on Wal-Mart Forecast. New York Times (November 14): C.6.

Stone, K. E. 1995. Impact of Wal-Mart Stores on Iowa Communities: 1983-93. Economic Development Review, 13(2): 60-69.

_________. 1997. Impact of the Wal-Mart Phenomenon on Rural Communities. In Increasing Understanding of Public Problems and Policies, Chicago, IL: Farm Foundation, 189-200.

Tedlow, R.S. 1990. New and Improved: The Story of Mass Marketing in America, New York: Basic Books, 182-258.

U.S. Department of Labor website: http://ows.doleta.gov/unemploy/wkclaims/report.asp

Useem, J. 2003. One Nation under Wal-Mart. Fortune (March 3): 64-78.

Wal-Mart Company website: http://walmartfacts.com/featuredtopics/?id=7

Walsh, W. I. 1986. The Rise & Decline of the Great Atlantic & Pacific Tea Company, Secaucus, N.J.: Lyle Stuart, 26-45.

Weiss, L. W. (ed.). 1989. Concentration and Price, Cambridge, MA: MIT Press.

Table 1


Variable List


Name Symbol Measure




Total Wal-Mart Sales Wal-Mart $ Billions

Non-Durable Personal Consumption NonDurables $ Billions

Retail Sales (excluding Food Services/Restaurants) RetailSales $ Millions

Personal Income Income $ Billions

Disposable Personal Income DispIncome $ Billions

Civilians Employment 16+ Employed Thousands

Initial Jobless Claims (weekly average per month) Claims Thousands

Consumer Price Index (urban/sa) CPI

Outstanding Consumer Credit Credit $ Millions

University of Michigan Consumer Sentiment Index Sentiment

Purchasing Managers’ Index Managers

Table 2


Augmented Dickey-Fuller Test Results



2000-2005 Data:

1995-2005 Data:

Variable

Log

Log

Conclusion

Log

Log

Conclusion

Wal-Mart

-1.650

-3.342*

I(1)

-1.203

-8.877*

I(1)

NonDurables

-1.410

-7.095*

I(1)

1.273

-15.016*

I(1)

RetailSales

-1.856

-5.814*

I(1)

-.310


-11.522*

I(1)

Income

-1.313

-4.095*

I(1)

-1.091

-13.821*

I(1)

DispIncome

-2.754

-7.495*

I(1)

-.275

-13.816*

I(1)

Employed

-.887

-3.740*

I(1)

-.927

-12.137*

I(1)

Claims

-2.995

-8.240*

I(1)

-1.924


-6.334*

I(1)

CPI

-1.870

-6.512*

I(1)

.670

-9.605*

I(1)

Credit

-2.686

-4.647*

I(1)

-2.479

-3.535*

I(1)

Sentiment

-2.876

-6.168*

I(1)

-2.732 a

-8.341*

I(1)

Managers

-2.603

-4.096*

I(1)


-2.460

-5.808*

I(1)

Notes: a indicates that the ADF t-value was significant at the 10% level and * at the 5% level. For the 2000-2005 period, the Wal-Mart variable includes only U.S. domestic sales while the 1995-2005 period combines World sales (from 1995-1999) with U.S. only sales (from 2000-2005).

Table 3


Lambda Max Test Results




2000-2005 Data

1995-2005 Data

Variable

Ho

Ha

Test Result

Ho

Ha

Test Result

NonDurables

r = 0

r = 1

16.6*

r = 0

r = 1

27.9*


RetailSales

r = 0

r = 1

14.0a

r = 0

r = 1

27.2*

Income

r = 0

r = 1

12.2 a

r = 0

r = 1

42.2*

DispIncome

r = 0

r = 1

17.9*

r = 0

r = 1

38.0*

Employed

r = 0

r =1

43.2*

r = 0

r = 1

26.5*


Claims

r = 0

r = 1

34.4*

r = 0

r = 1

35.7*

CPI

r = 0

r = 1

24.3*

r = 0

r = 1

27.0*

Credit

r = 0

r = 1

80.2*

r = 0

r = 1

37.8*

Sentiment

r = 0

r = 1

27.4*

r = 0

r = 1

36.4*

Managers


r = 0

r = 1

12.3 a

r = 0

r = 1

36.0*
Notes: a indicates Ho can be rejected at the 10% level and * at the 5% level.

Table 4


Johansen Method Cointegrating Vectors


2000-2005 Data:

1995-2005 Data:

lnWal-Mart = 8.842 – 1.515 lnNonDurables

lnWal-Mart = 12.654 – 2.016 lnNonDurables

lnWal-Mart = 17.207 – 1.599 lnRetailSales

lnWal-Mart = 23.370 – 2.094 lnRetailSales

lnWal-Mart = 16.016 – 2.063 lnIncome

lnWal-Mart = 16.016 – 2.209 lnIncome

lnWal-Mart = 12.902 – 1.747 lnDispIncome

lnWal-Mart = 16.301 – 2.130 lnDispIncome

lnWal-Mart = 55.425 – 4.920 lnEmployed


lnWal-Mart = 102.794 - 8.925 lnEmployed

lnWal-Mart = -12.462 + 1.645 lnClaims

lnWal-Mart = -9.500 + 1.184 lnClaims

lnWal-Mart = 15.651 – 3.544 lnCPI

lnWal-Mart = 20.938 - 4.567 lnCPI

lnWal-Mart = 19.105 – 1.515 lnCredit

lnWal-Mart = 25.399 - 1.959 lnCredit

lnWal-Mart = -9.535 + 1.487 lnSentiment

lnWal-Mart = 72.350 - 16.423 lnSentiment

lnWal-Mart = 1.505 – 1.090 lnManagers

lnWal-Mart = 6.354 - 2.259 lnManagers

Note: the 1995 – 2005 cointegrating vectors included a structural break to compensate for the measurement change that occurred in 1/2000 for the Wal-Mart data series.

Table 5


Granger Causality Tests Using the VECM Model





2000-2005 Data

1995-2005 Data


Relationship tested:

Long Run t-statistic

Long Run t-statistic

NonDurables → Wal-Mart

-4.133*

-5.326*

Wal-Mart → NonDurables

.721

.687

RetailSales → Wal-Mart

-3.744*

-6.307*

Wal-Mart → Retail Sales

.597

.875

Personal Income → Wal-Mart

-3.784*

-6.671*

Wal-Mart → Personal Income

-.768

1.523

Disposable Income. → Wal-Mart

-4.094*

-4.827*

Wal-Mart → Disposable Income


.668

1.998*

Employed → Wal-Mart

-2.513*

-4.827*

Wal-Mart → Employed

2.384*

2.777*

Jobless Claims→ Wal-Mart

-.408

-.794

Wal-Mart → Jobless Claims

-3.636*

-3.276*

CPI → Wal-Mart

-5.148*

-5.387*

Wal-Mart → CPI

1.230

1.099

Consumer Credit → Wal-Mart

-11.895*

-6.819*

Wal-Mart → Consumer Credit

.0114


2.725*

Consumer Sentiment→ Wal-Mart

-5.192*

.0816

Wal-Mart → Consumer Sentiment

-1.818a

2.298*

Purchasing Mgrs’ Index → Wal-Mart

-2.567*

1.105

Wal-Mart → Purchasing Mgrs’ Index

2.205*

3.416*

Notes: a indicates significant at the 10% level and * at the 5% level.



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