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CREATION JOB OR DESTRUCTION? LABOR MARKET EFFECTS OF WAL-MART EXPANSION Emek Basker* Abstract This paper estimates the effect of Wal-Mart expansion on retail employment at the county level. Using an instrumental variables approach to correct for both measurement error in entry dates and endogeneity of the timing of entry, I nd that Wal-Mart entry increases retail employment by 100 jobs in the year of entry. Half of this gain disappears over the next ve years as other retail establishments exit and contract, leaving a long-run statistically signi cant net gain of 50 jobs. Wholesale employment declines by approximately 20 jobs due to Wal-Mart s vertical integration. No spillover effect is detected in retail sectors in which Wal-Mart does not compete directly, suggesting Wal-Mart does not create agglomeration economies in retail trade at the county level. I. Introduction al-Mart Corporation employs nearly one million workers in the United States more than any other private company and over 300,000 additional workers worldwide. It is rumored to have plans to hire as many as 800,000 additional workers in the next ve years. USA Today quotes a retail-industry consultant as saying that Wal-Mart created more jobs in the 1990s than any other company (Hopkins, 2003). Has Wal-Mart created more jobs than it destroyed? Given the level of public interest in Wal-Mart and other big box retailers, there has been surprisingly little independent research on their impact on local labor markets.1 Research into this question is hampered by paucity of data on Wal-Mart and the other large retail chains and by concerns about endogeneity of the entry decision. Firms respond to local conditions when they expand or relocate establishments more so in the nontradable retail sector than in tradable sectors (like manufacturing) so it is difReceived for publication December 5, 2002. Revision accepted for publication February 19, 2004. * University of Missouri. I thank Daron Acemoglu, Olivier Blanchard, and Sendhil Mullainathan for their support and guidance. I have also bene ted from the comments of Josh Angrist, Saku Aura, David Autor, Glenn Ellison, Bengte Evenson, Amy Finkelstein, Guido Kuersteiner, Jeffrey Miron, Peter Mueser, Whitney Newey, Marko Tervio, Ken Troske, and Howard Wall, two anony mous referees, and seminar participants at Arizona State University, the Board of Governors of the Federal Reserve System, Case Western Reserve University, the Federal Reserve Banks of Atlanta and St. Louis, Hebrew University, MIT, the University of Chicago GSB, the University of Missouri, Tel Aviv University, the U.S. Census Bureau, Washington University in St. Louis, and Wellesley College. I thank Amy Mok, Steven Sadoway, and David Von Stroh for their help with typing, and Dorothy Carpenter, Kathy Cosgrove, Rich Lindrooth, Erich Muehlegger, Mike Noel, Jon Zinman, and especially Maurice Drew, Catherine Friedman, and Steven Sadoway for helping me gather data for this project. This research was completed while visiting the Federal Reserve Bank of St. Louis, which I thank for its hospitality. The views expressed are not necessarily those of the Federal Reserve Bank of St. Louis or the Federal Reserve System. 1 Exceptions include Stone s (1997 and elsewhere) Iowa case studies and other local studies modeled on his approach. These studies do not employ any exogenous variation to predict. Wal-Mart entry, so their results are dif cult to interpret. Findings range from signi cant job loss to mild job creation. W cult to disentangle the direct effect of expansion from the indirect effects of the conditions that lead to it.2 This paper attempts to quantify the impact of Wal-Mart entry on county-level retail employment by exploiting exogenous variation in the timing of store entry. I use a unique data set containing the locations and opening dates of all U.S. Wal-Mart stores to estimate the effect of Wal-Mart entry on retail employment in the county, as well as on employment in other industries and in surrounding counties. To address endogeneity concerns, my instrumental variables (IV) speci cation exploits the variable lag between storeplanning dates and store-opening dates. Store numbers, assigned by Wal-Mart during the planning process, are used to proxy for planning dates. Because my data cover a long time period (1977 1998) and approximately 1,750 counties, I am able to examine the dynamics of county-level retail employment over a 10-year period surrounding Wal-Mart entry, separately estimating short- and long-run effects. One way Wal-Mart entry could affect labor markets is by increasing average ef ciency in the retail sector, so fewer workers are needed per sale. Foster, Haltiwanger, and Krizan (2002) nd that nearly all productivity growth in the retail sector in the last decade can be accounted for by reallocation of workers due to net entry of establishments; they do not name individual companies in their analysis, but Wal-Mart expansion likely represents an important force in this reallocation. Diffusion of Wal-Mart s ef cient practices perhaps due to learning/imitation by competitors may lead other retailers to decrease employment more than proportionately to the decrease in their market share.3 Another possible effect could arise from externalities Wal-Mart creates for other retailers in the county. If WalMart increases customer traf c in the store s vicinity like an anchor store in a traditional mall (see Pashigian and Gould, 1998) the number and size of other retailers could increase, leading to an increase in retail employment. At the county level, this effect is likely to be small because of the nontraded nature of retail services, which operates against concentration in the industry (Holmes and Stevens, 2004). To test this hypothesis against the alternative that Wal-Mart merely captures some of the business of existing retailers, I estimate the effect of Wal-Mart entry on the number of retail establishments in different size categories and its effect on 2 Bertrand and Kramarz (2002) cleverly avoid this problem, using variation in permits given to large retailers due to exogenous variation in the composition of zoning boards, to analyze the effect of entry on French labor markets. They nd that regulation limiting entry of large retailers has slowed employment growth in the French retail industry. 3 Wal-Mart s lower prices diffused throughout the local market may partially offset this effect by increasing demand for retail services (see Basker, 2004). The Review of Economics and Statistics, February 2005, 87(1): 174 183 2005 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology JOB CREATION OR DESTRUCTION? LABOR MARKET EFFECTS OF WAL-MART EXPANSION FIGURE 1. LOCATION OF 175 WAL-MART STORES, VARIOUS YEARS retail employment in establishments not directly competing with Wal-Mart. For the second exercise, I use employment in two sectors: restaurants, and automobile dealerships and service stations.4 If Wal-Mart merely substitutes for other retailers, the number of small and medium-size retail establishments should decline, and Wal-Mart will not have any impact on retailers in sectors where it does not compete directly. If Wal-Mart entry creates positive externalities for other retailers, the number of small and medium-size retail establishments could increase, as could employment in restaurants and automobile dealerships. I also test for a negative indirect effect of Wal-Mart entry on retail employment in neighboring counties to see whether Wal-Mart entry imposes negative externalities on nearby communities. To see how much of Wal-Mart s measured effect on retail employment is due to a change in the classi cation of workers in the county from wholesale to retail, I also estimate the effect of Wal-Mart entry on wholesale employment. Finally, a falsi cation test estimates Wal-Mart s effect on manufacturing employment. I nd that immediately after entry, retail employment in the county increases by approximately 100 jobs; this gure declines by half over the next 5 years as some small and medium size retail establishments close. Wholesale employment declines by approximately 20 jobs over ve years. Restaurant employment increases slightly; there is no change in employment in manufacturing or in automobile dealerships and service stations. No effect can be detected on retail employment in neighboring counties, due to very large con dence bands. The remainder of the paper is organized as follows. Section II provides background information on Wal-Mart. Section III describes the data. My empirical strategy is explained in section IV, and results are presented and discussed in section V. Section VI concludes. 4 These sectors are the only two retail two-digit. SIC sectors in which it can be argued Wal-Mart is not a direct competitor. Some Wal-Mart stores do include fast-food restaurants and/or gas stations, but the prevalence of these departments during the sample period is very small. II. Wal-Mart Background The rst Wal-Mart store opened in Benton County, Arkansas, in 1962. By the time the company went public in 1969 it had 18 stores throughout Arkansas, Missouri, and Oklahoma. The company slowly expanded its geographical reach, building new stores and accompanying distribution centers further and further away from its original location, and continued, at the same time, to build new stores in areas already served. Figure 1 shows maps of the 48 contiguous states with approximate locations of Wal-Mart stores over time to illustrate this point. By 1998 Wal-Mart had approximately 2,400 stores in all 50 states and approximately 800,000 employees in the United States. The company grows as measured by the number of employees and the number of stores it operates by the week. The largest retailer in both the United States and the world, Wal-Mart currently operates in 10 countries. Wal-Mart operated discount stores as well as Supercenters, which include grocery departments and constitute approximately one-third of all current Wal-Mart stores. The typical Wal-Mart store spans 100,000 150,000 square feet and employs 150 350 people, many of them in part-time jobs. (Supercenters employ 400 500 workers each.) By 1998, one-quarter of the 1614 counties entered by Wal-Mart had more than one store. Wal-Mart is extremely ef cient even compared with other big-box retailers. Lehman Brothers analysts have noted Wal-Mart s leading logistics and information competencies (Feiner et al., 2001). The Financial Times has called Wal-Mart an operation whose ef ciency is the envy of the world s storekeepers (Edgecliffe-Johnson, 1999). Wal-Mart s competitive edge is driven by a combination of conventional cost-cutting and sensitivity to demand conditions and by superior logistics and distribution systems. The chain s most-cited advantages over small retailers are economies of scale and access to capital markets, whereas against other large retail chains the most commonly cited 176 THE REVIEW OF ECONOMICS AND STATISTICS FIGURE 2. COUNTIES INCLUDED IN ANALYSIS (SHADED REGIONS) factor is superior logistics, distribution, and inventory control.5 Wal-Mart s cost savings extend to its employment practices; it has been accused of requiring employees to work off the clock and using illegal-immigrant labor (through contractors) (see, e.g., Greenhouse, 2002; Buckley and Daniel, 2003). Such practices, if true, could reduce WalMart s measured employment without reducing its actual labor inputs. Wal-Mart s low wages are also said to contribute to its measured productivity. Wal-Mart wage data are not publicly available, but several sources estimate the current typical hourly wage of a Wal-Mart associate to be $7 $8/ hour (Hopkins, 2003). These wages are on a par with wages paid by other large discount chains (like K-Mart and Target), but are typically below union rates.6 III. A. Wal-Mart Stores Data I use data on the locations and opening dates of 2,382 Wal-Mart stores in the United States, collected primarily from Wal-Mart annual reports (Wal-Mart Stores, Inc., 1971 2003), Wal-Mart editions of Rand McNally Road Atlases, and annual editions of the Directory of Discount Department Stores (Chain Store Guide, 1979 1982, 1987 1993). The available data include store location (by town) and store number. The following data sources provide one measure of opening dates: Vance and Scott (1994) list store entries to 1969, the year the company became publicly traded. Annual reports between 1970 and 1978 include lists of current stores; after 1978 annual reports list only the number of stores per state. The annual Directory of Discount Department Stores provides store lists between 1979 and 1993. For recent years I use a special edition of the popular Rand McNally road 5 Details on Wal-Mart s operations can be found in Harvard Business School s three case studies about Wal-Mart (Ghemawat, 1989; Foley and Mahmood, 1996; Ghemawat and Friedman, 1999). 6 In markets where Wal-Mart competes directly with unionized retailers, it is said to match the union wage (Saporito, 2003). atlas which contains a list of Wal-Mart store locations, and includes each store s company-assigned number. The variable WMopenjt gives the number of new stores to open in county j in year t based on these directories and store lists. I also construct an alternate (counterfactual) set of WalMart entry identi ers using a combination of companyassigned store numbers (from the Rand McNally atlases) and the net change in the number of stores each year (from company annual reports). Wal-Mart assigns store numbers roughly in sequential order, with store 1 opening rst, followed by store 2, and so on. Following this practice, I assign entry dates to stores sequentially, based on their store numbers. This assignment method provides a very good approximation to the likely order in which the stores were planned. Aggregating these store-level entry dates to the county-year level, I construct WMplanjt, which gives the number of stores that would have opened in county j in year t had the stores opened in the order in which they were planned. More details on variable construction are in section 1 of the Appendix. B. Employment Data The unit of observation is a county-year. Of the 3,111 counties in the contiguous 48 states, I limit the data set to the 1,749 counties with total employment in 1964 above 1,500, positive employment growth between 1964 and 1977, and no Wal-Mart entry prior to 1977. The counties included in the analysis are shown in gure 2. Annual county-level employment data by two-digit SIC (three-digit NAICS) for 1977 1999 come from the Census Bureau s County Business Patterns (CBP) serial (U.S. Bureau of the Census, 1964 1999). The panel contains 40,227 observations (1,749 counties over 23 years).7 Unfortunately, CBP does not contain wage data. 7 The relevant SIC (NAICS) codes are as follows. Retail: SIC 52 59 except 55 and 58, NAICS 44 except 441 and 447; wholesale: SIC 50 51, NAICS 42; restaurants: SIC 58, NAICS 721; automobile: SIC 55, NAICS 441 and 447; manufacturing: SIC 20 39, NAICS 31. JOB CREATION OR DESTRUCTION? LABOR MARKET EFFECTS OF WAL-MART EXPANSION TABLE 1. SUMMARY STATISTICS Mean Population Retail employment Automotive retail employment Restaurant employment Wholesale employment Manufacturing employment Number of small retail establishments* Number of medium retail establishments* Number of large retail establishments* 120,500 5,000 1,000 3,000 3,000 7,500 360 35 8 Median 42,000 1,300 350 800 550 2,300 130 11 1 177 *Small establishments: 1 19 employees; medium: 20 99; large: 100 . store on the level of retail employment.9 Plots of the coef cients (L) show the evolution of employment over a 10-year period, starting 5 years before and ending 5 years after Wal-Mart entry into a county. The coef cient 12, intended to capture the permanent effect of Wal-Mart entry on employment 6 or more years after entry, is omitted from the graphs because it is identi ed using relatively few observations. The OLS estimates are valid if Wal-Mart entry is correctly measured and exogenous to employment changes. Unfortunately, WMopenjt is measured with error, and may be endogenous to retail employment outcomes. An IV speci cation is used to correct these problems. B. Measurement Error Table 1 lists some summary statistics for labor-market data. More details are in Appendix A.2. IV. A. OLS Regressions Methodology Because the data do not appear to contain unit roots, the analysis is done using employment levels. (See section 3 of the Appendix for details on unit root tests. Results using rst differences are extremely similar to the ones reported here.) For county j in year t I estimate retailjt popjt tk k t urbanjk yeart j j countyj (1) WMopenjt L popjt ujt , where retailjt is retail employment; popjt is population size; yeart is a year dummy; urbanjk {urban, suburban, rural} is a set of three urbanization dummies allowing for different year xed effects for urban, suburban, and rural counties;8 countyj is a county dummy; WMopenjt is the number of Wal-Mart store openings in the county; and (L) is a lag polynomial with ve leads, a contemporaneous value, and six lags (the sixth lag represents the cumulative period 6 or more years after year t; the reference period is 6 or more years before year t): L 1 Measurement error in the Wal-Mart entry variable WMopenjt takes a particular form: though the entered counties are correctly identi ed, the timing of entry may be incorrectly measured due to errors in the directories. (An extreme example of such errors is the lack of updating of the Directory of Discount Department Stores between 1990 and 1993, but other errors exist as well.) The counter factual variable WMplanjt is also measured with error, by construction: it represents the number of stores that would have opened had stores always opened in the order in which they were planned. An IV approach, in which leads and lags of WMplanjt are used to instrument for leads and lags of WMopenjt, can be used to correct for this measurement error if the measurement errors in the two variables are classical and uncorrelated. That the measurement error across the two variables is uncorrelated seems plausible.10 But because WMopenjt and WMplanjt are both discrete, their measurement error is not classical: the actual number of Wal-Mart stores in city j in year t differs from the measured number by an integer whose expected mean is different from 0. This induces a slight bias in the IV results reported here.11 C. Endogeneity Another dif culty in assessing the impact of Wal-Mart entry on the level and composition of county employment is 9 The use of per capita terms on both the left- and right-hand sides of equation (1) could cause a spurious correlation between the variables that would bias the estimated coef cients. In practice, the year-to-year variation in county population is small enough that it is not driving the results; the results are robust to normalization by a constant such as the 1990 county population. See also footnote 18. 10 This assumption would be violated if some stores, for example in metropolitan areas, experienced shorter planning phases and were also more likely to appear in the directories sooner, due to better directory coverage. This does not appear to be the case. 11 Because store closings are exceedingly rare, when the directories report zero new Wal-Marts in town, the expected number of openings is some (small) positive number. Similarly, when the reported number of new Wal-Marts is 1, the expected number is less than 1. Kane, Rouse, and Staiger (1999) suggest a GMM estimator to address this problem. Unfortunately, due to the size of the panel and the hundreds of covariates, their solution is not computationally feasible in this setting. F5 8 2 F4 9 3 F3 10 4 F2 11 5 F 12 6 7 L L2 L3 L4 L5 L, 6 where L is the lag operator and F is the lead operator. The error term ujt is clustered at the county level to allow for arbitrary autocorrelation. Both employment and the Wal-Mart variable are divided by the current county population, so the coef cients (L) can be interpreted as the effect of one additional Wal-Mart 8 A county is de ned as urban if it was inside an MSA (metropolitan statistical area) in 1960; suburban if it was 25 miles from the nearest MSA in 1960; and rural otherwise. 178 THE REVIEW OF ECONOMICS AND STATISTICS the possible endogeneity of Wal-Mart s entry decision with respect to retail employment. This potential endogeneity has two dimensions: Wal-Mart may select the counties it enters nonrandomly, and it may choose the timing of entry nonrandomly. If Wal-Mart selects counties whose growth rates exceed those of nonentered counties, a spurious positive effect will be registered by the estimated coef cients (L). To address this concern, I limit the analysis to counties that constitute a good control group for entered counties: counties with a 1964 population above 1,500 and a positive average growth rate of total employment between 1964 and 1977. I also remove counties entered by Wal-Mart before 1977, to eliminate concerns about the endogeneity of employment growth. Wal-Mart entered 75% of the remaining 1,749 counties between 1977 and 1998, compared with only 13% of the excluded counties.12 To address endogeneity of the timing of entry (conditional on the counties selected for entry), I rely again on store planning dates described above. This identi cation strategy assumes that Wal-Mart plans its store entries well in advance of entry and cannot accurately forecast exact market conditions at the time for which entry is planned. Because the company may ne-tune entry dates based on current market conditions, the actual entry date may be endogenous, but can be instrumented for using the date for which entry was planned. For the purposes of exposition, suppose Wal-Mart has a once-and-for-all effect on retail employment (so we can estimate a simple differencesin-differences model). We would like to estimate the coef cient from the equation WM retailjt 0 retail jt j and year t is independent of the error term in equation (2); and planned Wal-Mart stores affect retail employment per capita only insofar as they are correlated with the actual construction of Wal-Mart stores. That is, (ujt WMplanjt ) 0. , As this discussion suggests, the IV estimator will be biased if plans to build a Wal-Mart store spur the building of a strip mall or the closing of an existing store in anticipation of Wal-Mart s entry, even if Wal-Mart does not actually open a store in that county the year it planned The to.13 estimator will also be biased if Wal-Mart s planners can foresee employment uctuations at the time of the store s planning, or if planning dates anticipate that a growth spurt will occur over the next few years and the timing of entry is then adjusted to coincide (on average) with such a spurt. Because the regression equation is exactly identi ed (12 leads and lags of WMplanjt instrument for 12 leads and lags of WMopenjt), these identifying assumptions cannot be tested directly. I employ an indirect test instead, using the lead coef cients 1 5: if Wal-Mart times entry to take advantage of retail growth spurts, then (under most conditions) we should see some increase in retail employment in 1 5. This increase would be apparent in OLS estimates, but will be absent in IV estimates if the IV strategy corrects for this endogeneity.14 I also estimate the effect of Wal-Mart entry on manufacturing employment, using it as a falsi cation test: if WalMart planned entry to coincide with general employment increases, manufacturing employment would increase with Wal-Mart entry. As the results below show, this is not the case. I conclude that the IV strategy appears to correct for endogeneity as well as measurement error. V. A. Retail Employment Results where retailWM is retail employment county j in year t in the jt presence of a Wal-Mart store, and retail0 is retail employjt ment in the absence of the Wal-Mart store. Because we cannot observe both retailWM and retail0 for a given countyjt jt year pair, OLS estimates implicitly assume that 0 retail jt popjt t t yeart j j countyj ujt , (2) To begin, I present OLS results using WMopenjt entry dates in gure 3.15 Retail employment is shown to increase by approximately 40 jobs in the year of entry, up to half of which are eliminated within ve years. In addition, 20 jobs 13 Anecdotal evidence suggests that small retailers tend to continue operating as long as they can, even when this is not pro t-maximizing (Peled, 2001). 14 The test is imperfect. To see this, suppose Wal-Mart forecasters can predict which counties will experience growth spurts in retail employment over the next few years, and plan to open stores in those counties. Under this scenario, Wal-Mart s planned entry dates would coincide imperfectly with growth spurts, but actual opening dates could be adjusted (for example, by delaying construction) to fall precisely during these spurts. If employment growth of the sort Wal-Mart uses to ne-tune its entry arrives in isolated spurts (i.e., Wal-Mart does not enter counties experiencing sustained growth in retail employment that lasts for several years), there will be no pre-entry growth in the IV estimates even if Wal-Mart entry is not causally associated with any increase in employment. 15 Throughout the paper, the 95% con dence intervals shown use asymptotic standard errors clustered at the county level. The reference period is 6 or more years prior to Wal-Mart entry. (ujt WMopenjt ) 0. That is, the presence of a Wal-Mart store in county j in year t is uncorrelated with the error term in the retail employment equation: controlling for some basic county characteristics (in this case, simply county xed effects), Wal-Mart entry is exogenous. This assumption is a very strong one, and unlikely to be true. The IV strategy described above corrects for this endogeneity concern under two identifying assumptions: the number of planned Wal-Mart stores (per capita) for county 12 Indistinguishable results are obtained if the sample is limited instead to entered counties. JOB CREATION OR DESTRUCTION? LABOR MARKET EFFECTS OF WAL-MART EXPANSION FIGURE 3. EVOLUTION OF 179 RETAIL EMPLOYMENT (OLS) FIGURE 4. EVOLUTION OF RETAIL EMPLOYMENT (IV) are estimated to have been created in the year before Wal-Mart entry. Though this increase is small in absolute magnitude, it is statistically signi cant and disconcertingly large relative to the estimated postentry effect.16 The IV results are shown in gure 4. The effect of entry is estimated much more cleanly at approximately 100 jobs. In the years immediately following entry, there is a loss of 40 60 jobs. The net effect at the 5-year horizon, however, is positive and signi cant (p-value 0.0003). Recall that the typical Wal-Mart store employs 150 350 workers. These results suggest that employment increases by less than the full amount of Wal-Mart s hiring, even before allowing other rms time to fully adjust to WalMart s entry. Part of this discrepancy can be explained by buyouts of existing chain stores by Wal-Mart Corporation, and prompt exit and cutbacks by other retailers. Another (albeit unlikely) possibility is that Wal-Mart replaces existing part-time jobs with full-time jobs. CBP employment gures do not control for hours worked, so full-time and part-time employees are weighted equally. Very little is known about employment conditions at Wal-Mart, including the prevalence of part-time work. A reasonable prior is that Wal-Mart employees work fewer hours than other retail workers [using French data, Bertrand and Kramarz (2002) nd that entry of large retailers is increases part-time employment relative to all retail employment]. Wal-Mart claims that 70% of its employees work 28 hours a week or more (Wal-Mart, 2001a). This gure is within the norm for workers in the discount retail industry (Peled, 2001), and also in keeping with the rest of the retail industry: the 30th percentile of hours worked by retail employees, computed from the March Current Population Survey (CPS) for 1978 1999, is 28 hours across employer size, state, and year. As noted in section IV C, if the timing of entry were endogenous, we would expect to see an increase in the county s retail employment prior to entry. No such effect is 16 evident in the leading coef cients, although, as footnote 14 makes clear, this is not conclusive evidence in support of the identifying assumption. B. Retail Establishments To capture the effect of Wal-Mart on the number of retail establishments, I estimate IV regressions replacing the leftside variable retailjt /popjt by estabjt /popjt, where estabjt is the number of retail establishments in county j at year t in each of three size categories. To con rm that Wal-Mart s creation can be detected in the data, I estimate the regressions using the number of large retail establishments (with 100 or more employees). IV results are shown in gure 5; the estimated coef cients mirror those on retail employment shown in gure 4. The increase in the number of large retail establishments, approximately 0.7, suggests that Wal-Mart s entry often coincides with exit or contraction of other large retailers. In some cases, Wal-Mart acquired a large number of stores from a competitor; in other cases, incumbent establishments FIGURE 5. EVOLUTION OF NUMBER OF LARGE RETAIL ESTABLISHMENTS (IV) Reduced-form estimates using WMplanjt are extremely similar. 180 THE REVIEW OF ECONOMICS AND STATISTICS FIGURE 6. EVOLUTION OF NUMBER OF SMALL RETAIL ESTABLISHMENTS (IV) FIGURE 7. EVOLUTION OF NUMBER OF MEDIUM RETAIL ESTABLISHMENTS (IV) may have chosen to exit preemptively.17 There is a small decline in the number of large establishments in subsequent years. Figure 6 shows the effect of Wal-Mart on the number of small establishments (with fewer than 20 employees). It shows a decline of four retail establishments within 5 years of Wal-Mart entry, three of them within 2 years of entry. The number of medium-size establishments (with 20 99 employees), shown in Figure 7, decreases by approximately 0.7 in the second year following entry, then remains at. C. Other Sectors Wal-Mart competes with establishments in a wide array of sectors, some more directly than others. Wal-Mart Supercenters compete directly with grocery stores, whereas discount stores do not; all Wal-Mart stores compete with apparel stores, hardware stores, bookstores, music stores, and so on. Moreover, because Wal-Mart is vertically integrated, it competes against wholesalers as well as retailers. In this section, I look for an effect of Wal-Mart on wholesale employment, which could be due to Wal-Mart s indirect competition with wholesalers. I also look for an effect of Wal-Mart on two retail segments restaurants and automobile dealerships and service stations where Wal-Mart does not compete. Finally, as a falsi cation exercise, I estimate Wal-Mart s effect on manufacturing employment. The estimated effect of Wal-Mart entry on county-level wholesale employment is shown in gure 8. The observed decline of approximately 20 wholesale jobs following WalMart entry is marginally signi cant (p-value 0.0682). I use employment in restaurants and in automobile sales and service to test for an agglomeration effect of Wal-Mart entry. If these sectors expand following Wal-Mart entry, one 17 Examples of establishment acquisition include Wal-Mart s 1977 purchase of 16 Mohr Value Discount Department Stores in Missouri and Illinois, and its 1981 purchase of 106 stores in nine states from Kuhn sBig K Stores Corp. interpretation could be that Wal-Mart creates positive externalities for other retailers in the area. I nd no evidence that Wal-Mart entry affects either of these two sectors. The evolution of employment by restaurants is shown in gure 9. Although restaurant employment per capita grows throughout the period surrounding Wal-Mart entry, there is no discontinuity in that trend associated with the entry. Figure 10 shows employment in automobile sales and service. Here there is no trend whatsoever: Wal-Mart entry is not associated with any changes in employment in this sector. These results suggest that agglomeration economies generated by Wal-Mart (if any) must be at a level of aggregation substantially smaller than the county. Manufacturing employment is shown in gure 11. The con dence intervals are very large and show some large uctuations over the 10 year period shown, but no substantial increase or break in manufacturing employment can be seen before or at the time of Wal-Mart entry. It appears unlikely that Wal-Mart plans its new stores based on prior knowledge about future growth in the manufacturing sector. Because the typical Wal-Mart store has 150 350 employees less than 2% of total employment in the average FIGURE 8. EVOLUTION OF WHOLESALE EMPLOYMENT (IV) JOB CREATION OR DESTRUCTION? LABOR MARKET EFFECTS OF WAL-MART EXPANSION FIGURE 9. EVOLUTION OF 181 RESTAURANT EMPLOYMENT (IV) FIGURE 10. EVOLUTION OF AUTOMOBILE DEALERSHIP STATION EMPLOYMENT (IV) AND SERVICE county at the time of the Wal-Mart entry it is unlikely to have a signi cant effect on total county employment, and indeed, the estimated effect on total employment (not shown) is statistically 0.18 D. Neighboring Counties If Wal-Mart s effect on the retail industry in the entered county is due to agglomeration economies, entry could produce negative effects on neighboring counties employment (in both the retail and wholesale sectors). Unfortunately, estimating Wal-Mart s effect on neighboring counties with any precision is impossible, as the con dence intervals around the point estimates are very large. I de ne counties as neighbors if the distance between their geographic centers is within some xed range (e.g., 5, 10, or 25 miles). To estimate the effect of Wal-Mart entry in county j on retail employment in the surrounding area, I use the same strategy as with own-county effects, but replace retail employment per capita in the entered county with retail employment per capita in neighboring counties. I include controls for the number of Wal-Mart stores in neighboring counties to avoid estimating a spurious relationship between Wal-Mart entry in county j and employment in neighboring counties (in as much as Wal-Mart entry dates are correlated in neighboring counties). In the estimated results, not shown, con dence intervals are too large to reject any effect of Wal-Mart, positive or negative, on employment in the distributive trades in neighboring counties. VI. Conclusion with its market power, could cause a decline in the number of retail jobs in the community. In this paper, I quantify the effect of Wal-Mart entry on retail employment and on the number of retail establishments. Using an IV approach to correct for endogeneity of Wal-Mart entry and for measurement error in the data, I conclude that Wal-Mart entry has a small positive effect on retail employment at the county level while reducing the number of small retail establishments in the county. I also nd a small negative effect of Wal-Mart entry on wholesale employment. I nd no effect on two retail sectors in which Wal-Mart does not compete directly: restaurants and automobile sales and service. The latter fact suggests that Wal-Mart does not create large agglomeration externalities at the county level. Wal-Mart s effect on neighboring counties cannot be precisely estimated, so I cannot determine whether Wal-Mart entry reduces retail employment in neighboring counties. The experiment is a clean one, because I am able to identify the date of entry precisely, using an IV speci cation. The effect I estimate is a exible reduced-form effect, allowing both Wal-Mart and other rms in the county of entry to adjust to the shock over a period of several years. FIGURE 11. EVOLUTION OF MANUFACTURING EMPLOYMENT (IV) Wal-Mart entry has raised concerns in many communities about the changes it may cause in the size and structure of the retail industry. Wal-Mart s reputed ef ciency, combined 18 I have also tested for an effect of Wal-Mart entry on county population, using annual Census Bureau estimates of county population for the years 1977 1999. I nd no effect of Wal-Mart entry on population. 182 THE REVIEW OF ECONOMICS AND STATISTICS Pashigian, B. Peter, and Eric D. Gould, Internalizing Externalities: The Pricing of Space in Shopping Malls, Journal of Law and Economics 41:1 (1998), 114 142. Peled, Micha, Store Wars: When Wal-Mart Comes to Town, Independent Television Service lm (2001). Saporito, Bill, Can Wal-Mart Get Any Bigger? Yes, a Lot Bigger. . . Here s How, Time (January 13, 2003). Stone, Kenneth E., Impact of the Wal-Mart Phenomenon on Rural Communities. (pp. 189 200), in Increasing Understanding of Public Problems and Policies (Chicago: Farm Foundation, 1997). U.S. Bureau of the Census County Business Patterns (Washington, DC: U.S. Government Printing Of ce, (1964 1999). Vance, Sandra S., and Roy V. Scott, Wal-Mart: A History of Sam Walton s Retail Phenomenon (New York: Twayne Publishers, 1994). Wal-Mart Stores, Inc. (1971 2001). Annual Report. Wal-Mart Stores, Inc. (2001a). Wal-Mart Associate Handbook. Finally, because I use a large panel of approximately 1,750 counties over 23 years, and because Wal-Mart entry is a large shock relative to the size of the local retail market in most counties median county retail employment in 1990 was only 1,500, and the average Wal-Mart store had approximately 300 employees the effect can be estimated with fair precision. Of course, these effects represent the average impact of Wal-Mart and may not be representative of any individual county s experience. The small magnitude of the estimated net effect of WalMart on retail employment is striking in light of the level of public discussion on this topic. Other effects Wal-Mart entry for example, on prices, tax revenue, or the environment have not been ruled out by this analysis19; nor has the possibility that the small net county-level effects described here mask much larger reallocations at the subcounty level. Publicly available data cannot address that concern, so it remains an open question. REFERENCES Basker, Emek, Selling a Cheaper Mousetrap: Entry and Competition in the Retail Sector, University of Missouri working paper no. 04-01 (2004). Bertrand, Marianne, and Francis Kramarz, Does Entry Regulation Hinder Job Creation? Evidence from the French Retail Industry, Quarterly Journal of Economics 117 (2002), 1369 1414. Buckley, Neil, and Caroline Daniel, Wal-Mart vs. the Workers, Financial Times (November 20, 2003). Chain Store Guide, Directory of Discount Department Stores (New York: Business Guides, Inc., (1979 1982, 1987 1993). Chain Store Guide, Directory of Discount Stores (New York: Business Guides, Inc., (1983 1986). Edgecliffe-Johnson, Andrew, A Friendly Store from Arkansas, Financial Times (June 19, 1999). Feiner, Jeffrey M., Anthony H. O Andraia, Jeff Black, Christopher E. Jones, and Randal J. Konik, Wal-Mart Encyclopedia X: Building a Global Brand (New York: Lehman Brothers, 2001). Foley, Sharon, and Takia Mahmood, Wal*Mart Stores, Inc. Harvard Business School case study no. 9-794-024 (1996). Foster, Lucia, John Haltiwanger, and C. J. Krizan, The Link between Aggregate and Micro Productivity Growth: Evidence from Retail Trade, NBER working paper no. 9120 (2002). Ghemawat, Pankaj, Wal-Mart Stores Discount Operations, Harvard Business School case study 9-387-018 (1989). Ghemawat, Pankaj, and Gregg Friedman, Wal-Mart in 1999, Harvard Business School case study N9-799-118 (1999). Greenhouse, Steven, Suits Say Wal-Mart Forces Workers to Toil Off the Clock, New York Times (June 25, 2002). Holmes, Thomas J., and John J. Stevens, Geographic Concentration and Establishment Size: Analysis in an Alternative Economic Geography Model, Journal of Economic Geography 4:3, (2004), 227 250. Hopkins, Jim, Wal-Mart s In uence Grows, USA Today (January 29, 2003). Kane, Thomas J., Cecilia Elena Rouse, and Douglas Staiger, Estimating Returns to Schooling When Schooling is Misreported, Princeton Industrial Relations Section working paper no. 419 (1999). Levin, Andrew, Chien-Fu Lin, and Chia-Shang James Chu, Unit Root Tests in Panel Data: Asymptotic and Finite-sample Properties, Journal of Econometrics 108:1 (2002), 1 24. Maddala, G. S., and Shaowen Wu, A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test, Oxford Bulletin of Economics and Statistics 61 (1999), 631 652. 19 APPENDIX Data and Empirical Issues 1. Wal-Mart Data Table A1 shows the sources from which store opening dates, used in the construction of the variable WMopenjt, were drawn. Chain Store Guides Directories of Discount Department Stores from 1990 1993 are available, but are largely uninformative; the directories appear not to have been updated in those years. For stores that do not appear in the 1989 directory, but do appear in the 1995 Rand McNally road atlas (i.e., existed in 1994), opening dates are assigned according to the following algorithm. From the annual reports, I obtain the net increase in the number of Wal-Mart stores in each state each year. Because very few store closures occur, I use the net increase to proxy for the number of new stores to open each year in each state. For example, in Arizona, ve new stores opened in 1990, seven in 1991, and one each in 1992 and 1993. Using the list of stores that existed in 1994 but not in 1989, I assign entry dates randomly, in proportion to their probability of opening in each year. Each store that opened in Arizona during this period 5 is assigned the opening date 1990 with probability 14; 1991 with proba1 bility 1; and 1992 and 1993 with probability 14 each. In all, 680 stores 2 opening dates are assigned in this way: 203 in 1990, 145 in 1991, 181 in 1992, and 151 in 1993.20 The accuracy of this method depends critically on Wal-Mart not reassigning numbers in the event of store closure. Only 40 stores closed over the entire period 1964 1999, so the latter condition appears to be satis ed; this also implies that reassignment of store numbers cannot be common. 2. Employment Data In counties with a small number of employers, data on the total number of employees in a sector may be omitted from County Business Patterns to avoid disclosure of the number of employees in individual rms; a range (1 19, 20 99, etc.) is then given instead of an exact number of employees. In those instances, I assume that the actual number of workers is a weighted mean of the lower and upper bounds on the given employ2 1 ment range (with weight 3 on the lower bound and 3 on the upper bound). For example, a rm with 1 19 employees is assigned a value of 7.21 3. Unit Roots To test whether county-level employment per capita contains unit roots, I run a Dickey-Fuller (DF) test on each county series separately, 20 Entry dates assigned in this way are measured with error, but they are unbiased. 21 I chose to weight the lower and upper bounds of each interval by 21 11 (3, 3), respectively, rather than (2, 2), because counties small enough to elicit concerns about disclosure of information on individual rms in aggregate data seem likely to have a disproportionate number of small employers. The results are robust to this speci cation. See Basker (2004) for an analysis of Wal-Mart s effect on prices. JOB CREATION OR DESTRUCTION? LABOR MARKET EFFECTS OF WAL-MART EXPANSION TABLE A1. DIRECTORY SOURCES Years 1962 1969 1970 1978 1979 1982 1983 1986 1987 1989 1990 1993 1994 1997 *Chain Store Guide (1983 1986). FOR 183 WAL-MART OPENING DATES Source Employment Retail Wholesale Restaurant Automotive Manufacturing TABLE A2. UNIT ROOT TESTS Dickey-Fuller % Rejected 9.43 13.72 13.72 12.12 6.12 Maddala-Wu p-Value 0.0000 0.0000 0.0000 0.0000 0.0000 Levin-Lin p-Value 0.0000 0.0000 0.0000 0.0000 0.0000 Vance and Scott (1994) Wal-Mart Annual Reports Directory of Discount Department Stores Directory of Discount Stores* Directory of Discount Department Stores See text Rand McNally road atlas after removing year xed effects interacted with 1960 urbanization status (urban, suburban, rural). By construction, a 5% rejection rate is to be expected at the 95% con dence level if the series have unit roots. Because DF tests sometimes fail to reject unit roots even when none are present, I also use the more powerful Maddala-Wu (1999) and Levin-Lin, (Levin, Lin, & Chu, 1993) panel unit root tests. The validity of panel unit root tests depends on the series being independent realizations of a single common process. Table A2 reports the test results. The rst column shows the fraction of counties for which county-by-county Dickey-Fuller tests rejected the presence of unit roots at 95% signi cance. The rejection rates of 6% 14% for these series are higher than the expected 5% under the null hypothesis of unit roots. The second and third columns report p-values from MaddalaWu and Levin-Lin tests, respectively. A common unit root process is rejected by both tests for each series.

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