Economies of Density in E-Commerce: A Study of ’s Fulfillment Center Network ∗

Jean-Fran¸coisHoude Peter Newberry Department of Economics Department of Economics UW-Madison & NBER The State University

Katja Seim The Wharton School University of Pennsylvania & NBER

November 2018

Abstract We examine the cost-saving benefits associated with the expansion of Amazon’s distribution network from 1999 to 2018. We demonstrate that, in expanding their network, Amazon can save on shipping costs by shortening their routes and vertically integrating into sorting services previously provided by third party shippers. This comes with the cost of lower revenues due to liabilities and higher operating costs of locating in more expenisve areas. Using detailed data on online transactions, we estimate a model of demand for retail goods and show that consumers’ is sensitive to sales taxes. We then use the demand model along with detailed information about Amazon’s network of facilities in order to estimate the costs associate with expanding the network. Using a moment inequalities approach, we infer the cost savings that render the observed network roll-out optimal. We find that Amazon’s average shipping costs have decreased by roughly 20% from 1999 to 2018, with 40% of that reduction coming vertical integration. Additionally, we examine how sale-tax laws which gave e-commerce firms a price advantage over their online competitors hampered Amazon’s ability to reach the efficient scale.

Keywords: e-commerce, sales tax, Amazon, distribution, logistics, shipping JEL Codes: H71, L11, L81

∗All correspondence may be addressed to the authors via e-mail at [email protected], [email protected], or [email protected]. A previous version of this paper was circulated under the title “Sales Tax, E-Commerce, and Amazon’s Fulfillment Center Network.” We thank Josalyn Geiman, Mallick Hossain, Olleskii Khvastunov, Nitin Krishnan, and Xinrong Zhu for research assistance, and the Dean’s Research Fund for financial support. We have benefited from conversations with participants at various conferences and seminars, as well as discussions with John Asker, Alan Collard-Wexler, Paul Grieco, Jin-Hyuk Kim, Brad Larsen, Robin Lee, Francesca Molinari, Charles Murry, Amedeo Piolatto, Imke Reimers, Fanyin Zheng, and others. 1 Introduction

The Internet might have made it possible for the smallest of businesses to sell their wares, but the price of delivering cardboard boxes highlights just how powerful an advan- tage size can be in the Internet economy.1

Online retail has grown substantially over the last decade and makes up 9.6% of all retail sales as of 2018.2 Amazon.com, henceforth Amazon, is a key contributor of this growth, with US net sales increasing from $5.7 billion in 2006 to $80 billion in 2016.3 Amazon’s rise has been accompanied by a substantial increase in concentration in online retail, as Figure 1 illustrates. This trend counters initial expectations that the internet would facilitate highly competitive markets due to lower consumer search costs, lower fixed costs as a seller’s physical location is less material to the consumer, and a reliance on an already existing network of distribution and shipping companies whose services would put online retailers into competition with the offerings of brick-and-mortar stores. In this paper, we explore the possible sources for the increase in concentration with a focus on the competitive advantage that comes with the cost savings of a large-scale distribution network. We investigate the economies of scale in distribution using Amazon as a case study. As Amazon’s revenue has grown, so has its network of distribution centers, so called fulfillment centers, which we denote as FCs going forward. The number of FCs has grown from 5 centrally located centers in 1999 to 128 spread out across the US by 2018.4 A larger distribution network has implications for firm profitability on both the demand and supply sides. The expansion of the network has the potential to affect consumer willingness-to-pay through two opposing demand-side forces. First, if consumers value the convenience of receiving packages faster and expansion leads to shorter delivery times, then such convenience effects constitute a quality attribute of Amazon’s service that may in part be responsible for the firm’s increasing market share. Second, under a 1992 U.S. Supreme Court decision (Quill Corp. vs. 504 U.S. 298), a mail-order retailer, such as an online seller, is required to collect sales tax on the customer’s behalf if the retailer has a physical presence or “nexus”, including a warehouse, in the customer’s state of residence. Fulfillment and sortation center additions, if they entail expansion into a new state, may thus increase sales tax inclusive prices on Amazon. As earlier literature (Einav et al., 2014; Baugh et al., 2014) shows, demand is sensitive to sales taxes as they raise the effective price of an online transaction. To limit the revenue implications of exposing customers to new sales tax liabilities, a supply- side response would thus be not to establish a presence in a high-tax state, especially if it were

1See “FedEx’s Price Rise Is a Blessing in Disguise for Amazon,” Times 5/9/2014, http://nyti.ms/ 1fUabBh, accessed on 1/17/2017. 2See www.census.gov/retail/mrts/www/data/pdf/ec_current.pdf, accessed on 11/11/2018. 3See annual reports at http://phx.corporate-ir.net/phoenix.zhtml?c=97664&p=irol-reportsannual. 4These figures do not include FCs or sortation centers. See Section ?? for details on distribution center types.

1 populous. In addition, a new fulfillment center adds fixed costs of operating the facility, relative to a smaller network. It also limits possible economies of scale in the cost of processing orders that large, centralized facilities generate. As the distribution network expands into more urban areas, the company furthermore faces higher factor costs, in the form of higher wages and rental rates of the FC properties. Adding a distribution center also makes the existing distribution network more dispersed, how- ever, shortening the distance to a subset of customers. This can translate into shipping cost savings in one of two ways. First, Amazon’s distribution facilities may be closer, on average, to its shippers’ local sorting facilities, reducing the nodes through which packages travel in the shipment process. This reduces the shippers’ cost, providing Amazon with bargaining power in negotiations with shippers over the per-package rates they charge. Second, by being closer to its customers, Amazon has been able to add to its logistics process certain aspects of the outbound delivery of packages for which it traditionally relied on third-party parcel courier companies. Since 2014, Amazon has built approximately 40 sortation centers in large urban markets where the company aggregates packages from one or several nearby fulfillment centers into zip code areas and delivers them either to the local post office or an alternative third-party delivery company for last-leg delivery. This vertical integration of package fulfilment and sortation has reduced the company’s reliance on parcel courier companies, and generates cost savings due to efficiencies in supply chain pricing and faster delivery times. The resulting economies of density significantly affect Amazon’s overall profitability as shipping comprises a large share of Amazon’s operating costs.5,6 On net, then, the implicit tax-related costs of building a warehouse in a new state, together with fixed cost and order processing cost differences, offset the benefits of expansion due to proximity to the consumer. We empirically quantify this trade-off between higher tax-inclusive prices and fixed and order processing costs, on the one hand, and additional shipping cost savings on the other, while control- ling for a potential increase in platform quality through, for example, system-wide improvements in shipping convenience and for the observed expansion of Amazon’s product assortment over the sample period. To accomplish this, we combine data on the purchase behavior at Amazon and other online competitors by a large, representative consumer sample with detailed information on the location and characteristics of Amazon’s fulfillment centers over time. We take a revealed preference approach similar to that of Holmes (2011) that uses, as one source of identification, the fact that expansion of the FC network into a new state results in lost revenue due to new sales tax liabilities. Therefore, observing such an expansion in the data must mean that it also results in a

5Amazon’s financial statements report net shipping costs between 3-5% of net sales for the period 2006 to 2015. These costs totaled over $5 billion in 2015. See annual reports at http://phx.corporate-ir.net/phoenix.zhtml? c=97664&p=irol-reportsannual. 6Expansion also affects the distance of “inbound” shipments from the goods suppliers to Amazon’s FCs. As we discuss below, suppliers are shipping goods to brick and mortar retail outlets all around the country; as a result, we assume that these costs do not change materially as Amazon expands its FC network.

2 reduction of shipping costs sufficient to outweigh the tax-driven revenue declines. The first step in this approach is to estimate a model of demand in order to pin down consumer tax sensitivity and response to shipping times. We use data on both online and offline purchasing of retail goods that we construct by combining the comScore Web Behavior database, Forrester Research surveys, and household spending data from the Consumer Expenditure Survey. In the model, a representative consumer in a county chooses her yearly expenditures on product varieties sold through four different modes of shopping: (1) Amazon.com, (2) a taxed online competitor (e.g., walmart.com), (3) a non-taxed online competitor (e.g., overstock.com), and (4) a taxed offline competitor (e.g., Wal-Mart). The amount of sales tax charged to the consumer varies across both time and county due to FC entry and local sales tax laws, implying that variation in spending across these dimensions identifies the consumers’ sensitivity to sales tax. Similar to identification in a difference-in-difference approach, we use fixed effects to control for time varying mode preferences and county-specific determinants of spending. The results of the demand estimation suggest that consumers are sensitive to taxes and that their tax elasticity is around −1.4, similar to the estimates from Einav et al. (2014) at −1.8 and Baugh et al. (2014) at −1.5. The magnitude of this estimate implies that moving from a non-taxed regime to a taxed regime at the average sales tax rate of 6.5%, all else equal, results in reduction of expenditures at Amazon of 9.3%. We find little evidence that the entrance of a FC leads to increased sales for Amazon due to increased convenience at a local level. The lack of effect of shipping time on demand is likely due to Amazon’s local shipping options and prices not changing drastically over the period of our sample. We find significant increases in platform quality, however, which comprises nationwide improvements in shipping speeds that the densification of the distribution network facilitates. Mean preferences for the other two online shopping modes are relatively constant over the sample period, suggesting that price reductions and/or improvements in platform quality have made Amazon more attractive. We find furthermore that Amazon’s increased platform quality went hand-in-hand with an expansion of the variety of goods that the platform sells, which many cite as an explanation for Amazon’s success.7 Taken together, these estimates imply that overall, there are negative revenue effects of opening a fulfillment center in a new state, which must be offset by a decrease in costs. In the second step, we quantify the shipping cost savings from the larger FC network. We specify a profit function with two variable cost contributions governed by the distribution network. First, we model order-processing costs at a distribution center as a function of the number of orders flowing through the FC and local wage rates for FC employees. We allow for economies of scale in order processing costs, providing an incentive for the company to invest in larger FCs. We allocate the observed distribution of demand across locations to FCs based on the distribution of capacity across FCs and the distance between the customer location and the FC network. Second,

7See http://www.nytimes.com/2009/09/20/business/20amazon.html, accessed on 1/12/2017.

3 we specify the variable cost of shipping the processed orders to consumers to be proportional to the shipping distance from the FC to the consumer’s county. We explicitly account for the firm’s decision to vertically integrate into sortation and allow shipping cost to fall discretely when an order travels from an FC associated with a sortation center to a consumer in the sortation center’s catchment area. We assume that the observed sequence of fulfillment center placements and size investments is optimal so that the discounted profit stream under the observed network roll-out is at least as large as under any alternative sequencing of fulfillment center openings and size choices. The network configuration affects revenue through the sales tax channel, fixed costs through the wages and rents paid in the counties where Amazon FCs are located, and variable costs through the total orders processed at each FC and the total shipping distance across customers. We compute perturbations to the observed network roll-out similar to those of Holmes (2011): to construct alternative network roll-outs, we rely on swaps of fulfillment center opening dates that have offsetting effects on profitability through revenue and shipping cost effects. For example, moving up the opening date of a FC in a low-populated, low-tax state and in turn delaying the opening of a FC in a more populous, high-tax state implies both higher revenue streams due to lower exposure to sales tax early on, but longer shipping routes and thus higher cost. Relying on revealed preference, a profit comparison of the actual and such counterfactual roll- outs allows us to impose bounds on the firm’s shipping cost per order such that the observed roll-out dominates its perturbations. We estimate these bounds on the effect of shipping distance on variable cost using a moment inequalities estimator based on the methods of Pakes et al. (2015) and Andrews and Soares (2010). In doing so, we rely on similar instruments to Holmes’ (2011), adjusted for the nature of our problem where any measurement error in revenue affects both the difference in pre-shipping profit and in revenue-scaled shipping distance and thus total shipping cost under the actual and perturbed FC roll-outs. We use the bounds estimates to compare cost under the enlarged and original distribution networks, estimating the cost saving of network expansion without having to solving the dynamic problem that Amazon faces. Our estimates imply that Amazon’s average cost of shipping an order decreased from $4.80 to $4.00 over the sample period. Highlighting the potential supply chain efficiencies associated with vertical integration into sortation, we find that 40% of the decrease in estimated order shipping costs is due to in-house sortation. These estimates are robust to alternative instrument choices, various proxies for shipping cost, and the inclusion of preferences for same-day shipping. This paper is related to several strands of literature. An increasing body of work focuses on the estimation of demand in online retail markets, with the studies focusing on the effects of sales tax and the gains from variety being the most relevant to our study. Einav et al. (2014) estimate the demand response to sales tax using eBay data, exploiting the fact that a buyer has the option to buy from an out-of-state seller who does not charge sales tax. They are also able to estimate the consumer sensitivity to distance from observing the locations of both buyers and sellers. Baugh

4 et al. (2014) uses a differences-in-differences approach to estimate the effect of the ‘Amazon tax’, or changes in various states’ laws between 2013 and 2015 that forced Amazon to collect sales tax. In estimating the tax sensitivity, neither set of authors is able to fully consider substitution to other taxed or non-taxed online outlets. We thus contribute to this literature by expanding our analysis of the tax sensitivity and the estimation of demand for retail goods beyond a single online firm to a large number of online and offline retailers. Our focus on product variety as a dimension of Amazon’s quality is motivated by work examining the gains from variety in e-commerce, including Brynjolfsson et al. (2003) and Quan and Williams (2016). Brynjolfsson et al. (2003) uses data from online bookstores to demonstrate that consumer welfare gains from increased product variety far outweigh the also sizable gains due to lower prices and increased competition. Findings in Quan and Williams (2016) suggest that the extent of such gains varies significantly across local markets, depending on the availability of competing, localized, assortment in brick-and-mortar stores. This highlights the importance of controlling for localized variation in the attractiveness of the offline – and other online – shopping modes in estimating demand, as we do in assessing the contribution of quality differences across shopping channels to demand, which we show to largely reflect variation in product variety that provides large online retailers such as Amazon with a clear advantage over their smaller competitors. To our knowledge, we are the first to investigate the possible convenience effects associated with a broader distribution network that reduces delivery times, a quality attribute that consumers may value independently of variety differences across shopping modes. Our analysis is also related to recent operations research and economics literature on manage- ment of distribution networks for online firms (see Agatz et al. (2008) for an overview) and on the relationship between a brick-and-mortar retailer’s store location choices and its distribution network. Zheng (2014) relates the proximity of a rival’s fulfillment center to the chain’s expected future entry. Building on Barwick (2008) who estimates the scale economies associated with op- erating multiple stores in close geographic proximity, without decomposing the sources of such scale economies, Holmes (2011) directly estimates the savings in distribution costs associated with clustering stores near a fulfillment center. These studies take the configuration of the distribution network as given and use variation in the distances from a fulfillment center to potential store locations to identify the parameters of interest. Instead, we study the development of the network of FCs as a strategic choice for the firm. Little work to date has studied such classic industrial organization questions as the role of cost differences in affecting firms’ competitive positions in the context of distribution in online markets. The remainder of the paper is organized as follows. The next section describes Amazon’s FC network and summarizes relevant aspects of sales tax laws. Section 3 specifies Amazon’s optimiza- tion problem and the components of their profit function, while Section 4 presents the demand side analysis and results. Section 5 provides estimates of variable processing costs and distribution cost

5 savings. Section 6 concludes.

2 Background

In this section, we document the patterns of household spending on Amazon and the expansion of Amazon’s distribution network. We describe how the placement and size of distribution centers and the timing of their openings respond to Amazon’s changing logistics strategy and the company’s desire early on to limit its customers’ exposure to sales tax liabilities.

2.1 Spending

The primary data source for online spending is the comScore Web Behavior Database. ComScore tracks the online purchasing and browsing activity of a random sample of internet users. The users give comScore explicit permission to monitor their browsing and purchasing activity over the course of a calendar year. Each user is associated with a machine (computer) and any activity on that machine, including activity from other users, is recorded. Therefore, the activity on each machine can be assumed to represent the activity from a household. The first panel of Table 2 presents details on the coverage of the sample from 2006-2016. The sample begins with nearly 40,000 households in 2006, shrinks over the years to under 13,000 in 2014 and increases again to over 25,000 in 2016. Nevertheless, every state in the continental US, the District of Columbia, and an average of 70% of US counties are represented in the data each year. For each household, we observe every order from an online domain over the course of the year, where an order consists of all the items bought in a single ‘basket’. For each order, we observe the name of the domain where the order occurred, the time of the order, the product category and price for each individual item in the order, and a ‘basket total’, or the total cost to the consumer for the order, including shipping and taxes. In addition, we observe the zip-code, county and state for the household, along with categorical variables representing demographic household characteristics such as age and race of the head of household and household income. It is important to note that we only observe the domain name of the purchase, meaning we do not observe Amazon expenditures apart from purchases made from third party sellers on . In various parts in the paper, we discuss how we deal with this issue. Using these data, we construct the yearly expenditures on Amazon for each household. One weakness of the comScore data is that we only observe transactions for households for that made at least one transaction online. To account for this, we use data from Forrester Research to calculate the likelihood that a household with a given combination of household characteristics purchases at least one item online. By merging this likelihood with the comScore data, we construct the expected expenditures for a given set of household characteristics.

6 Another weakness of data is that we do not observe expenditures on the household’s non- monitored devices, for example, tablets and cell phones. To account for this, we inflate each households expenditures on Amazon by a yearly factor, where the yearly factor is determined by matching the comScore spending data to the average household spending calculated from Amazon’s financial statements. For other domains, we compute similar factors, bu match data on total online spending from the US department of commerce. Additionally, when calculating the expenditures for the other online modes, we exclude spending on categories that Amazon does not carry, for example, dating websites (e.g., match.com), travel websites (e.g., orbtiz.com) and food delivery sites (e.g., dominos.com) from the comScore data. The second panel of Table 2 displays the weighted average household spending on Amazon and other domains, where the weights are population weights based on household characteristics. See the data appendix for details on how we compute the weights. Additionally, we also display the average household spending on retail across online and offline shopping modes, which is calculated using data from the Consumer Expenditure Survey (CEX). The table shows that spending on online retail has increased substantially over this period, with Amazon being a key contributor to this increase. The final row of the table indicates that Amazon’s share of online retail has increased from 0.17 to over 0.50 by 2016.

2.2 Distribution

We obtain information on Amazon’s distribution network from the supply-chain consulting com- pany MWPVL, International (http://www.mwpvl.com/). MWPVL provides information on the location, size, employment, type, and opening and – where applicable – closing date of each distri- bution facility. We rely on the facility type designation to identify two primary types of distribution centers. First, we group all distribution centers that fill orders and ship non-grocery items directly to consumers into a single category of fulfillment center. This includes non-sortable centers, han- dling large items that the firm cannot ship in combination with any other products, and small and large sortable centers, handling items of varying size that the firm is able to combine into a single package when shipping. We thus abstract from specialized distribution centers, including ‘PrimeNow Hubs’ that handle same day deliveries, Amazon Fresh grocery delivery centers, return centers, and distribution centers for select high-value items such as jewelry. Second, we identify ‘sortation centers’ that the company started building intensively in 2014. At such facilities, the company groups already filled shipments by destination zip code prior to the last-leg delivery to the consumer by third-party shippers. Amazon has expanded its fulfillment center network from 5 facilities in 1999 to 128 by 2018. These facilities are sizable and have grown in square footage over time, with the average 1999 facility having a size of close to six hundred thousand square feet, compared to a size of more than 800 thousand square feet in 2018. Table 1 and Figures 2 and 3 demonstrate the fulfillment

7 center expansion. Table 1 displays the number of FCs, the number of states with a FC, and the network density in miles, measuring population-weighted average great-circle distance between the closest FC to a county. As an indication of densification, the average distance to the consumer has fallen from 307 miles in 1999 to only 71 miles in 2018. Most of this remarkable drop in the average distance to serve a consumer reflects the expansion of the distribution network into the most densely populated states along the two coasts in the mid-2010s. Figures 2 and 3 illustrate these patterns. The maps display county-level FC locations, shading counties by numbers of households, for 2000, 2006, 2012, and 2018. Panel (b) of Figure 3 displays, in addition to FC locations, the location of recently opened sortation centers. As Table 1 illustrates, Amazon built a network of 41 regional sortation centers between 2014, primarily, and 2018. At these facilities, the company groups packages filled at one more nearby fulfillment centers by three-digit zip code area within a pre-defined region around the sortation center. It then delivers the resulting pallets to shippers for last mile delivery – either the local post office associated with the three-digit zip code area or a local courier that picks up the pallet from one of Amazon’s delivery stations’, of which the company operates 75 by 2018. By vertically integrating sortation into its distribution process, Amazon thus controls a larger share of the outbound delivery process itself, as opposed to relying on parcel courier companies such as UPS or FedEx. The resulting logistics model increasingly resembles that of large brick-and-mortar retail chains such as Walmart, which control their entire outbound transportation network from a network of regional distribution centers and company-owned transportation infrastructure. The logistics company MWPVL points to three primary advantages of Amazon’s integration of sortation into its distribution process:8 (1) a reduced dependence on parcel courier companies in meeting the highly seasonal retail demand, which, in the case of Amazon, results in the hiring of an additional 70,000 distribution center associates during the holiday season at the end of the year. Delivery delays during such demand spikes are not only costly to online retailers, but common.9 (2) efficiency gains from eliminated double marginalization by parcel courier companies for sortation, Table 1 and Figure 3 illustrate that sortation centers primarily serve large urban areas. Industry estimates suggest that the coverage region of a sortation center ranges from 50 to 100 miles; in the statistics we display in Table 1, we assume that a sortation center serves a given county if the county centroid is within 100 miles from the sortation center location. We furthermore assume that the sortation center aggregates packages from all fulfillment centers within 100 miles of the sortation center location. Based on these measures of access, the rapid buildout of sortation centers has resulted in 82% of all households being served by a sortation center by 2018. The expansion of the network of FCs and SCs allowed Amazon to implement Prime Free Same-

8See the article at http://www.mwpvl.com/html/amazon_building_new_sortation_network.html, accessed on 11/10/2018, for detail. 9The Wall Street Journal, for example, cites estimates that only 89% of UPS deliveries during the 2017 holiday period were delivered on time. See https://www.wsj.com/articles/ups-overwhelmed-by-online-orders-warns- of-some-delivery-delays-1512496479.

8 Day and/or Next-Day shipping. In 2014, Amazon began to offer this expedited shipping to Prime members in certain markets at no cost, and they expanded the availability of this service over time. In order to examine the rollout of these services, we collected data from Amazon’s online tool that displays whether or not a given zip-code qualifies in November of 2017.10 We then examined Amazon’s press releases and articles from local papers in order to determine the date at which large metropolitan areas become eligible. We don’t observe the roll-out dates for individual zip-codes within a metropolitan area, so we assume that all eligible zip-codes within a metropolitan area became eligible at the same time. According to these data 43% (31%) of households in the US were eligible for Next (Same) Day delivery by the end of 2015, and these numbers expanded to 52% (36%) in 2016. Some additional aspects of the distribution network expansion are noteworthy. In the early years of expansion until 2010, Amazon placed FCs in relatively low population states that were close to highly populated areas. For example, Amazon opened two FCs in , both of which were on the border close to that state’s major cities. The company also placed FCs in states with relatively low sales tax. For example, the company opened a FC in , in addition to the one it already operated in , both of whom are close to major East Coast cities and have zero sales tax. These patterns suggest that the company actively chose locations that mitigate consumer ex- posure to sales taxes, either in terms of level of sales tax or size of the customer base exposed to sales tax. Earlier literature on the effect of sales tax on online purchasing highlights the value such limited exposure may have for Amazon’s revenue: work going back to Goolsbee (2000a) and Goolsbee, 2000b has found that online shoppers are sensitive to sales taxes, both in the extensive margin of shopping online at all (see, e.g., Alm and Melnik (2005) and Ballard and Lee (2007), and Scanlan (2007) who finds that this sensitivity is heterogeneous across the level of tax rates), and typically using data from a single product category, in the intensive margin of how much to spend (Ellison and Ellison, 2009; Smith and Brynjolfsson, 2001; Anderson et al., 2010; Goolsbee et al., 2010).11 Throughout the period of our sample, online retailers collect and remit sales tax on behalf of the consumer provided the retailer has a physical presence in the consumer’s state. A 1992 U.S. Supreme Court decision concluded that the U.S. Constitution’s “prohibits a State from imposing the duty of collection and payment upon a seller whose only connection with customers in the State is by common carrier or by mail.” The court ruled that a mail-order retailer, including an e-commerce firm, does not have to collect sales tax from consumers unless it has a physical presence, or a ‘nexus’, in that consumer’s state of residence.

10The online tool was accessed from https://www.amazon.com/Prime-FREE-Same-Day-Delivery/b?ie=UTF8& node=8729023011 11There is also evidence on the response of consumers to sales taxes in offline markets, analyzing the substitution of shopping expenditures across state and country borders (Agarwal, Chomsisengphet, Ho, and Qian, 2013a; Asplund, Friberg, and Wilander, 2007) and over time due to tax holidays (Agarwal, Marwell, and McGranahan, 2013b). Chetty, Looney, and Kroft (2009) estimate how the saliency of tax rates affects consumer demand.

9 In such instances, it is instead the duty of consumers to file a ‘use-tax’ return every year, which calculates tax liability on purchases from out-of-state vendors. Few individuals comply with this rule.12 For most online firms, a physical presence takes the form of an office headquarter or a distri- bution center, implying that these firms likely do not have to collect sales taxes in many states. As the popularity of e-commerce has grown, policy makers have suggested that the differential tax collection responsibilities may be giving online firms, and in particular Amazon as the dominant online retailer, an unfair advantage over their brick-and-mortar competitors. Given the low rates of out-of-state purchase reporting for use tax compliance by consumers, states are also likely losing out on millions of dollars of tax revenue (see Bruce et al., 2009). As early as 2008, states began passing legislation that expanded the definition of ‘nexus’ to apply to locations of the firm’s ‘affiliates’, typically websites that allow retailers, such as Amazon, to advertise on their site. For example, if an blogger had a link to Amazon on her site, then Amazon would have to charge sales tax to all Illinois residents. Not surprisingly, Amazon and other big retailers responded to such laws by shutting down their affiliate programs in the relevant states.13 Amazon acknowledged in its 2008 annual report, page 16, that “A successful assertion by one or more states or foreign countries that we should collect sales or other taxes on the sale of merchandise or services could ... decrease our ability to compete with traditional retailers, and otherwise harm our business.” It is important to note that while the opening of a fulfillment center in a new state consists of the establishment of a nexus, Amazon frequently negotiates with states over the exact timing of when its sales tax collection obligation takes effect. For example, Amazon first built a FC in Pennsylvania in 2006, but did not begin to charge sales tax until 2011. Such a delay is often due to legal battles with the state government as to what constitutes a nexus. On the other hand, sometimes Amazon charges sales tax even when the company does not have a FC in that state. This can be due to changes in state laws (e.g., New York) or because of legal agreements with the state to begin charging sales tax ahead of the opening of a FC (e.g., ). The latter explain the, at times, significant discrepancy between the number of states where Amazon’s customers pay sales taxes and the number of states where Amazon has a FC. In a highly publicized move, the company voluntarily began collecting and remitting sales tax on all of its sales, regardless of customer location, in April 2017. To investigate the role of sales tax variation across locations, we obtain annual information on

12In the 45 states that have a use tax, only about 1.6 percent of taxpayers paid it in 2013. See NPR, “Most People Are Supposed To Pay This Tax. Almost Nobody Actually Pays It.” April 2013, http://www.npr.org/sections/ money/2013/04/16/177384487/most-people-are-supposed-to-pay-this-tax, accessed on 1/12/2017. This com- pares to an estimated 60.3 percent of the US population who made online purchases in 2013 according to eMarketer 2016, albeit not necessarily all from out-of-state retailers. 13See e.g., http://techcrunch.com/2011/06/10/amazon-shuts-down-associates-affiliate-program-in- connecticut-over-online-sales-tax/, and http://www.kansascity.com/news/local/article325412/Amazon- shuts-down--associates-program-over-sales-tax-dispute.html accessed on 1/12/2017.

10 county-level sales tax rates from Tax Data Systems, now part of Thomas Reuters, and verify for each state the year in which Amazon started remitting sales tax on behalf of the consumer. See Section for detail. The sales tax data illustrate Amazon’s ability to control consumer sales tax exposure through its choice of distribution center locations: sales tax rates are positively correlated with population (across states, correlation of between 0.35 and 0.4 across 2006 to 2018). Entry into a small state in close proximity to a large state, such as the Nevada and Delaware examples above, has limited tax implications for only a small population, while allowing the firm to serve both states’ populations more efficiently. When Amazon did expand its distribution network to highly populated states, it initially focused on states with a relatively low sales tax rates (e.g., Pennsylvania with an average tax rate of 6.1% in 2013, compared to 6.9% across the top 20 US states in terms of population and a maximum in those states of 9.5% (TN)). As Amazon grew in scale, however, the network of FCs expanded beyond low-tax low-population states, presumably to be closer to population hubs despite sales tax implications and higher fixed costs of warehousing in densely populated areas. For example, by 2014, we see entry into highly populated states, such as California and , and high tax states, such as (9.5% tax rate in 2013). By 2018, Amazon added FCs in , Illinois, North Carolina, and . Table 1 summarizes the implied growth in the share of households on whose behalf Amazon remits sales tax over the sample period, illustrating the clear break in the share of affected households post 2013. The table also highlights that by 2017, when Amazon announced it would collect sales tax on behalf of all households, the expansion of its distribution network had resulted in agreements with individual states that already exposed 98% of US households to sales tax; as such the change in corporate policy did not affect a large share of new customers. We conclude this section by providing systematic evidence on the role of negotiations between Amazon and state governments over the onset of the company’s sales tax remittance liability.14 Figure 4 summarizes the time-series relationship between the onset of sales tax collection and the initial entry into a state – through the construction of a distribution facility – as a weighted scatter plot where the weight is the number of entries into new states in a given year. We define the tax collection lag as the difference in years between initial onset of tax collection and distribution facility entry. The figure illustrates that between 2002 and 2012, Amazon negotiates tax “delays” for most new distribution centers. Between 2012 and 2016, in contrast, Amazon’s new state entries are associated with negative lags, or tax “leads” where the company agreed to begin collecting sales tax on purchases from such states even before it completed the construction of its distribution

14In addition to negotiating with states over the effectiveness of its tax collection obligation, Amazon at times receives other forms of government financial assistance when opening a FC in a new locality. The online government subsidy data base Goodjobfirst.org lists 39 different economic development programs that Amazon benefitted from during 2006 and 2013. The vast majority consist of ongoing tax credits and training cost reimbursements, which affect the firm’s variable profit. Six consist of grants or reduced interest loans. Since we do not observe Amazon’s ultimate take-up of such measures, we incorporate their effect on Amazon’s choice of when to enter a location via a measurement error component to variable profit.

11 center. We formalize the graphical evidence in a regression where we regress the tax collection lag on a flexible function of the FC entry year. See Table 3, which suggests that the lag between the onset of tax collection and entry shrinks by 0.4 years every year, but falls by an additional 2.3 years after 2012. We believe that the time trend in the lag reflects the increasing importance for Amazon to be close to its customers due to increases in volume, which state governments and Amazon take into account when they are negotiating. We further highlight the bargaining power Amazon may have in negotiations with state governments by including a proxy for the value of the outside option of entering into a different state in place of the state under consideration. The proxy we employ is the minimum tax rate among states that share the census region of the state under consideration but that do not yet have an Amazon distribution center, at the time of Amazon’s entry into that state. It thus captures the availability of a nearby attractive location with low tax rates. The evidence in Table 3 suggests that Amazon is indeed able to negotiate for higher tax lags when the minimum tax rate in nearby available states is low: a one percentage point increase in the minimum tax rate increases the tax lag by about half a year. In our analysis below, we assume that the regression model in Table 3 summarizes Amazon’s perception of the relationship between FC entry and sales tax collection when making its network location decisions. Overall, the pattern of expansion and location choices thus imply that there exists a trade-off between being close to customers and charging them sales tax. Our main goal is to investigate this trade-off empirically and we describe the formal model of Amazon’s network roll-out next.

3 Model

In this section we specify a model of an online retailer (Amazon) who sells products via a web-based platform, and then ships each ordered product through its the distribution network. We start by providing a broad overview of Amazon’s logistic problem, and introduce the relevant notation. We then describe the dynamic optimization problem, as well as the relevant components of the profit function.

3.1 Logistic of Online Transactions

We focus on two key types of facilities: (i) fulfillment centers (FCs), and (ii) sortation centers (SCs). FCs are used both to store goods, and process orders (i.e. combine multiple items into boxes), while SCs are processing facilities whose role is to arrange packages by ZIP code. Figure 5 illustrates the flow of orders across the network. We assume that orders originating from county i are processed by the closest FC that has the order in stock, where the likelihood that an order is in stock at FC c is a function of the FC’s capacity (measured in square footage). Therefore, the total number of orders from consumers in county i that are processed by FC c are

12 a function of the distance between c and i and the capacity of c. Once processed, the shipment process begins, which typically involves multiple stops and can involve different modes of transportation (e.g., planes versus trucks). If consumer i is located outside of the coverage zone of a SC, the order is transferred immediately to a third-party carrier who is responsible for the entire shipment process, including sorting and delivering the order to its final destination. Third party delivery companies like Fedex and UPS rely on their extensive hub-spoke network of sorting facilities and delivery stations in order to increase the efficiency of this process. Otherwise, if consumer i is located in the coverage zone of a SC, and the order is processed by a connected FC (see below), the shipment process goes through Amazon’s integrated sorting network until the final delivery from the SC to the consumer (i.e. the ‘last mile’). This process involves continuously shipping orders (via trucks) between FCs and affiliated SCs, and sorting packages into groups of common destination ZIP codes. Local delivery companies are then responsible of handling the last mile of delivery, a step that is most often performed by USPS, although Amazon is increasingly using independent drivers (i.e., Amazon Flex).

3.2 Network Expansion Problem

We are interested in modeling the investment decisions leading to the expansion of Amazon’s distribution network. This involves choosing the location and capacity of new facilities; both decisions are assumed to be irreversible investments. We use the following notation to describe the distribution network structure. The location of a facility (FC or SC), denoted by l, is assumed to be chosen from a finite set of possible locations: l = 1,...,L. Each location can accommodate up to N FCs, and one SC. Let klj,t denote the capacity of facility j = 1,...,N in location l, in year t. This capacity is either zero if the facility is inactive, or positive if a prior investment was made. The size of each SC is assumed to be constant, so we summarize the network of SCs by Ylt, which is an indicator equal to 1 if location l has an active SC in period t. Therefore, the distribution network in period t is given by the distribution of FC capacity and SC activity status across locations: St = {kl1,t, . . . , klN,t,Ylt}l=1,...,L = {Klt,Ylt}l=1,...,L. The previous network definition implies that there exists a finite number of potential facilities, indexed by c = 1,...,C. The location and type (FC or SC) of a given facility c are fixed and exogenously given. The endogenous decision by Amazon determines the timing of entry and the capacity level of that facility, denoted by t(c) and kct respectively. We use Ct to denote the set of fc facilities active in period t: Ct = {c|c ∈ {1,...,C}, t(c) ≤ t, Type(c) = FC}. The set of active sc SCs is defined analogously (Ct ), and Ct denotes the union of both sets. Let at denotes the vector of locations and capacity levels (for FCs) for facilities that become active in year t. We are interested in understanding the tradeoffs associated with the location and size of each new active facility, conditional on the number of facilities of both types built every year.

13 As in Holmes (2011), we characterize the expansion of the network as the outcome of a constrained dynamic optimization problem with perfect foresight:

∞ 0 X t Π a = max β πt (St) s.t. St = F (at,St−1) (1) at∈At∀t t=0

fc sc where β is Amazon’s discount factor, At is the constraint that nt and nt facilities are build in period t, and F (at,St−1) defines the network structure in t conditional on last-period’s network and period t’s investment.

The flow of profits associated with network St is given by:

S L πt (St) = µRt(St) − VCt (Qt,St) − VCt (Qt,St) − Ft(St) (2) where Qt is the geographic distribution of orders across consumer locations, µRt(St) is the net S revenue generated in period t, µ is the gross profit margin on retail transactions, VCt (Qt,St) L is the variable shipping cost, VCt (Qt,St) is the variable processing cost, and Ft(St) is the fixed processing cost. The revenue function and the distribution of orders across consumer locations are derived from a CES model of demand for retail goods. The variable shipping and processing costs depend on the distribution of orders across the networks, and the unit cost of shipping and processing orders. The fixed-cost is function of the rental price of capital across locations. We describe how these components relate to the network configuration in the following subsections.

Demand

We follow Einav et al. (2014) and specify a representative agent CES utility model. The representa- tive household from county i receives utility by purchasing retail goods via four different shopping “modes”. There are three online modes: mode 1 is Amazon, mode 2 is the collection of online firms that have a national offline presence, and mode 3 is the collection of online firms that don’t have an offline presence. The offline mode, mode 0, is the collection of local brick and mortar retailers. The shopping modes are differentiate in terms of whether or not they have to collect sales tax from the consumer. Amazon’s tax status in year t depends on the consumer’s location, while mode 2 and 0 charge sales tax to all consumers and mode 3 does not charge sales tax to any consumers. The shopping modes are also differentiated in terms of their posted prices, the variety of products they sell, and their platform quality.

14 The utility of the representative household from county i in year t is given by:

  σ 3 Z 1−σ X 1/σ σ−1 Uit(qi0t, ..qi3t) =  vijt(ω) qijt(ω) σ dFjt(ω) j=0

where qijt(ω) is the quantity of product variety ω purchased from shopping mode j in year t, vijt(ω) is the marginal utility for variety ω sold by mode j, and Fjt(ω) is the distribution of product varieties offered by mode j in year t. The offline mode is assumed to have a single variety. We specify the marginal utility of variety ω as a separable function of preferences for variety and ω:

vijt(ω) = αijt · ω and assume that product varieties are price competitively according to the following hedonic func- tion:

ln(˜pjt(ω)) = ln(ρ) + ln(ω) wherep ˜jt(ω) is the posted price of purchasing one unit of variety ω, including any shipping charges and excluding sales tax. We restrict Fjt(ω), and thus posted prices, such that they do not vary across households. While some online firms appear to price discriminate based on a household’s location, there is limited evidence that this is widespread.15 Further, Amazon attempted to implement price discrimination in 2000, and quickly abolished it after a backlash from customers.16 The final price charged to the household for a product bought on mode j is made up of the posted price and the sales tax charged to a household in county i:

taxable taxable pijt(ω) =p ˜jt(ω)(1 + 1ijt τit) = ρω(1 + 1ijt τit)

taxable The sales tax rate is given by τit and 1ijt is a dummy variable indicating whether or not mode j is required to collect sales tax from consumer i in year t.

Given prices, the distribution of variety, and her budget for retail goods, Bit, the household in county i solves:

max Uit(qi0t, ..qi3t) qi0t,..qi3t 3 X Z s.t. pijt(ω)qijt(ω)dFjt(ω) ≤ Bit j=0

15See e.g., http://www.wsj.com/articles/SB10001424127887323777204578189391813881534, accessed on 1/17/2017. 16See http://www.bizjournals.com/seattle/stories/2000/09/25/daily21.html and http://news.cnet.com/ 2100-1017-240700.html, accessed on 1/17/2017.

15 resulting in the optimal expenditure: Z 1−σ σ−1 eijt = vijt(ω)pijt(ω) Pit BitdFjt(ω) Z taxable 1−σ σ−1 = αijtω(ρω(1 + 1ijt τit)) Pit BitdFjt(ω) Z 1−σ taxable 1−σ σ−1 2−σ = αijtρ (1 + 1ijt τit)) Pit Bit ω dFjt(ω)

1 P3 R 1−σ  1−σ This function includes the Dixit-Stiglitz price index, Pit = j=0 vijt(ω)pijt(ω) dFjt(ω) . The network, St, impacts the convenience of shopping on Amazon through shipping speeds and impacts the tax status of transactions made on Amazon. Therefore, we explicitly denote the preference term and the tax dummy variable for Amazon as a function of the network configuration in year t:

αi1t ≡ αi1t(St) taxable taxable 1i1t ≡ 1it (St) meaning household i’s expenditures on Amazon are: Z 1−σ taxable 1−σ σ−1 2−σ ei1t(St) = αi1t(St)ρ (1 + 1i1t (St)τit)) Pit Bit ω dF1t(ω)

This allows us to derive Amazon’s total revenue function in year t:

X Rt(St) = Mitei1t(St) i where Mit is the number of households in county i. From the model, we can also determine the number of orders from county i: Z −σ taxable −σ σ−1 1−σ qit = Mitqi1t(St) = αi1t(St)ρ (1 + 1i1t (St)τit)) Pit Bit ω dF1t(ω)

We drop the j subscript and the St in the first equivalence for ease of exposition in the sections that follow. Finally, we denote the distribution of orders for the set of I counties as:

Qt = {qit∀i ∈ I}

Order Flow

The flow of orders across the network is determined by two factors: the availability of orders across FCs and the locations of the FCs relative to the consumers.

16 We model the availability of an order in location l as a binomial random variable independently ¯ ¯ P distributed across l with probability φt(klt), where klt = j klj,t is the total FC capacity in that location. Conditional on being available in location l, orders are processed by facilities in proportion of their size. This implies that the probability that an order can be processed by facility c is given by: ¯ ¯  kct Pr(Available|kct, klt) = φt klt × ¯ klt ¯ We parameterize the availability probability use as an exponential function that is increasing in klt ¯ ¯ at a rate determined by the availability parameter, ψt : φt(klt) = 1 − exp(−ψtklt). Next, we assume that orders are processed by the closest FC with available inventory. Since the availability of orders is independently distributed across locations, the unconditional Origin- Destination (O-D) probability matrix, in which each element is the probability that an order from consumer i is shipped from FC c, takes the following form:   u Y ¯  ¯ kct Ωci(St) =  1 − φt kl0t  φt(klt) ¯ (3) 0 klt l |dilt>dil0t

The distance between county i and the location of facility c is dilt. The above matrix can lead to unfulfilled orders (i.e. sum of columns less than one), and so we use the conditional fulfillment probability O-D matrix to predict the flow of orders processed by FC c: u X Ωci(St) qct = Ωci(St)qit, where Ωci(St) = P u , (4) 0 fc Ω 0 (St) i c ∈Ct c i The volume of orders processed by a SC is determined by the volume of local orders processed by FCs affiliated with the SC. This is determined by two functions: (i) the downstream catchment area of an SC facility (Dic), and (ii) the set of upstream FCs connected to a given SC facility (Uc,c0 ). The catchment area is an indicator variable equal to one if facility c is the closest SC to county i, and the great-circle distance between c and i is less than threshold d¯sc. The set of FCs connected with a given SC c is similarly defined as all FCs located within d¯fc miles of facility c. Note that based on this definition, an FC can be linked with more than one SC, but counties are associated with a single SC. This leads to following order flow function for SC c:

X X  qct = Uc,c0 · Di,c Ωc0i(St)qit (5) i 0 fc c ∈Ct

17 Shipping Cost

The unit cost of shipping an order from FC c to county i is function of the distance between the two locations, and a base cost which is affected by the degree of vertical integration:

VI gci(St) = θo + θv1ic (St) + θddci (6)

VI nP  o where 1 (St) = 1 0 sc Uc0,c · Di,c0 > 0 is an indicator variable equal to one if orders be- ic c ∈Ct tween i and FC c are shipped through one of Amazon’s sorting facilities. The “distance cost” comes from the fact that longer shipping distances are associated with a heavier reliance on third party shippers and more expensive shipping methods. The parameter θd therefore measures the cost savings associated with a denser network. The parameter θv measures the variable cost savings arising from sorting packages via integrated facilities, instead of outsourcing this step to third-party shippers. Importantly, there exists a complementarity between the location of FCs and SCs. By increas- ing the density of the FC network (holding fixed SCs), Amazon can increase the flow of orders processed by SC facilities. This leads to a reduction in shipping cost through a reduction in dis- tance to consumers, and a decrease in the fraction of orders processed by third-party shippers (i.e. ↓ outsourcing). Aggregating across all origins and destinations, we can write the total shipping cost function as follows: S X X VCt (Qt,St) = qitΩci(St)gci(St) (7) c∈Cfc i

Variable Processing Costs

Amazon hires employees to process orders at their FCs and SCs. The number of employees needed at each facility in each year depends on the quantity of orders and the size of the facility. Since facility size is fixed in the short-run, labor is the main variable input. We use a Cobb-Douglas production function to derive the implied labor demand function of each facilities:

γ fc −1/γ qct = afcLckct → L (qct, kct) = Afckfc qct sc qct = ascLc → L (qct, kct) = Ascqct where Afc, Asc and α are technology parameters that we estimate using data on facility-level employment data. The labor processing cost is obtained by aggregating labor demand across locations multiplied

18 by the average annual wage of retail employees:

L X −1/γ X VCt (Qt,St) = wctAfckfc qct + wctAfcqct fc c∈Csc c∈Ct t

Fixed Processing Cost

We specify the fixed cost of operating facility c as:  kct × (rct + κPopDensct) If Type(c) = FC Fct = sc k × (rct + κPopDensct) If Type(c) = SC which depends on the capacity of the facility, the local rental rate for a square foot of capacity, rct, and the PopDensct is the population density of the county in which facility c is located. Recall that we assume that all SCs have the same square footage, which we now denote ksc. The parameter κ captures two important factors affecting the relative profitability different locations. First, our rental cost variable is measured with error, and likely underestimates the magnitude of the cost difference between renting large storage spaces in urban versus rural locations. In addition, locating facilities close to urban cores involve potentially large congestion cost that can disrupt the supply chain, and in particular increase the cost of shipping goods between facilities; either for re-stocking reasons, or to ship orders between FCs and SCs. Peripheral locations closer to regional airports or other logistic facilities reduce these frictions, which lead to lower fixed-costs. Importantly, the fixed-cost function abstracts away from any common fixed or sunk cost asso- ciated with the opening of new facilities. This is because we focus solely on the decision of where to locate each facility, and not how many to open each year. As a result, common fixed and sunk costs are not identified from our reveled-preference inequalities.

4 Demand

In the following section, we discuss the estimation of the demand model, which allows us to derive the revenue function, R(St), and the distribution of orders, Qt, over the period 1999-2018. Before doing so, we first use the transaction level data in the comScore sample to provide preliminary evidence of the trade-off between convenience (via shipping speeds) and sales tax. Specifically, we run the following linear-probability model:

taxable Amazonitr = αLog(1 + 1it τit) + λConvenienceit + Xitrβ + itr where the dependent variable is a dummy variable indicating whether or not consumer i bought from Amazon in transaction r. Included as a regressor is the tax rate charged on Amazon, which

19 is determined by a dummy variable indicating whether or not transactions are taxed in consumer i’s county in year t and the sales tax rate, τit. The vector Xitr includes product category, year, and county fixed effects, dummy variables representing the categorical demographic variables, and two measures of local offline competition: the number of big and small offline retailers (greater and less than 50 employees) in consumer i’s county The results of four specifications are presented in Table 5, where the estimates of β are sup- pressed. Each specification differs in the measure of convenience we include. The effect of taxes is consistent across all specifications and indicates that consumers are sensitive to taxes. The coeffi- cient implies that going from a 0% tax rate to the average tax rate of 6.5% results in about a 0.01 decrease in the probability a consumer purchases from Amazon.17 On the other hand, the measures of convenience are not significant in any of the specifications, which suggests that the convenience effects of expansion are not local. Instead, expansion has increased convenience nationally via faster shipping times. The fact that Amazon offers Same Day and Next Day delivery in some markets and not others is likely because Amazon competes harder against the outside option (offline option) in these markets compared to rural areas. That is, the demand response to a reduction of shipping time from 2 days to 1 day in in an urban market is similar to a reduction from 3 days to 2 days in a remote area. Therefore, the national convenience effect, along with other aspects of Amazon’s quality, is captured by the year fixed effect (estimates not displayed), which increases every year from 2006 to 2016.

4.1 Structural Model

In section 3, we derived the optimal expenditures on mode j for a representative consumer from county i in year t: Z 1−σ taxable 1−σ σ−1 2−σ eijt = αijtρ (1 + 1ijt τit)) Pit Bit ω dFjt(ω)

Recall that mode 1 is Amazon, mode 2 is taxed online competitors, mode 3 is non-taxed online competitors and mode 0 is the offline option. We assume that mode 0 has only one variety meaning that spending at offline outlets given by:

1−σ σ−1 ei0t = αi0t(pi0t(1 + τit)) Pit Bit 17The tax variable is approximately the tax rate, so a 1 percentage point increase in the tax rate, results in a 0.0016 decrease in the probability of purchasing from Amazon.

20 Dividing the expenditures of mode j by expenditures on the outside option and taking logs results in the following relative share equation: Z taxable 2−σ e˜ijt = ln(αijt) − ln(αi0t) − (1 − σ) ln(pi0t) + (1 − σ) (ln(1 + 1ijt τit) − ln(1 + τit)) + ln( ω dFjt(ω))(8) | {z } τ˜ijt | ω{z } ξjt(σ) where the last term represents the effect of product variety in year t on the demand for mode j. We parameterize tastes for mode j as:

u ln(αijt) = ξjt + λjZit + γjCit + ijt

u where ξjt is a mode-year fixed effect that represents the overall quality of the mode, including the national convenience, the vector Zit contains demographics of the representative household, and

Cit contains the two measures of local offline competition. The preference for the offline shopping mode equals:

ln(αi0t) − (1 − σ) ln(pi0t) = ∆ξi0t + i0t

The term ∆ξi0t represents time-varying preferences for online shopping, and we assume that it consists of a county-level, time-invariant, preference for online shopping, ξ¯i, and a time trend that captures changes in preferences for online shopping at the level of the county’s Census Division, 18 ∆ξrt, or ∆ξi0t = ξ¯i + ∆ξrt. Additionally, we do not observe prices for the offline option,p ˜i0t, so we assume that ∆ξi0t, along with λjZit and γjCit, are able to account for county-level and/or time-series variation at brick and mortar retailers. Finally, the demand shocks ijt and i0t are assumed to be independent across i, t, and j. Under these assumptions, equation 8 can be re-written as:

u ω ¯ e˜ijt = ξjt + ξjt(σ) +λjZit + γjCit + ξi − ∆ξrt + (1 − σ)˜τijt + ijt − i0t (9) | {z } ξjt where we combine the mode-year quality and the variety term into one mode/year effect, ξjt.

Equation 8 is our primary estimating equation, which features the preference parameters θd = {σ, ξ, λ, γ}. See Appendix for a full step-by-step derivation of this equation. The model also allows us to derive the relationship between the distribution of product variety or equivalently, posted prices, and the distribution of transacted prices. The within-mode market- share of variety ω in year t is:

v (ω)p (ω)−σP σ−1B ω1−σ s (ω) = ijt ijt it it = ijt R −σ σ−1 R 1−σ vijt(ω)pijt(ω) Pit BitdFjt(ω) ω dFjt(ω) 18There are nine Census Divisions: Pacific, Mountain, West North Central, West South Central, East North Central, East South Central, Middle Atlantic, South Atlantic, and New England.

21 This share does not vary across county, so we replace sijt = sjt and derive the mean and the variance of the transacted prices for mode j in year t:

Z Z ω1−σ meanjt = p˜jt(ω)sjt(ω)dFjt(ω) = ρω R 1−σ dFjt(ω) (10) ω dFjt(ω) and: Z varjt = (˜pjt(ω) − meanjt)Fjt(ω) (11) Z ω1−σ = (ρω − meanjt)R 1−σ Fjt(ω) ω Fjt(ω)

We parameterize the distribution of ω as a log-normal distribution where the mean mjt and standard deviation sjt evolve according to a linear time trend:

mjt = mj0 + mj1t and:

sjt = sj0 + sj1t

These twelve parameters and ρ are the ‘variety parameters’, which we denote θv.

4.2 Data

D We estimate the demand parameters θ = {θd, θv} in two steps. In the first step, we estimate θd by matching the relative spending shares. Then given the estimate of σ, we estimate the variety u parameters by matching the price distribution, which allows us to separate the preference term ξjt ω from the variety effect ξjt(σ). In order to derive the relative shares, we construct the mode level expenditures for the rep- resentative agent in each county in each year. We first calculate the weighted average household level expenditures in county i using population weights for each set of observed household char- acteristics. There a number of counties where this calculation is based on a limited number of observations in the comScore data, which could lead to inaccurate spending measures and zero shares. We deal with this issue by running a weighted mode level regression of the representative household’s expenditures on a large number of county level characteristics and a state level average of spending. We then use these estimates to predict the distribution of expenditures, which serve as our measures of eijt. See Appendix for a complete description of the above derivation. There, we also provide evidence that our qualitative results are confirmed by the raw data and are not sensitive to the way we construct the expenditure variable. Finally, in order to calculate ei0t, we construct total retail spending of the representative household in each county using data from the

22 Consumer Expenditure Survey. In order to derive the transacted price distributions, we smooth the distribution of prices by running a regression of the log of the transacted prices on mode dummy variables and mode level time trends. We then use the estimates of the regression to predict the mean and standard deviation of prices for each mode over time, which are displayed in Table 6. The average transacted prices Amazon have been stable over our sample and are lower than the other two modes. The average prices for mode 3 have also been stable, whereas the prices on mode 2 have decreased. The variation in prices on Amazon is also lower than the other two modes over the sample period, but it has been increasing at a much faster rate.19 The price moments provide preliminary evidence that the product assortment on Amazon has increased relative to the other modes, something that is in line with anecdotal evidence.20 This expansion could be from selling in more categories and/or also selling a larger variety of products within a category. In Appendix, we provide preliminary evidence that the second explanation is an important determinant of the patterns in the price distribution, which is in line with the hedonic pricing model we presented above. The final piece of data included in the model are the demographics of the representative con- sumer from each county, Cit. We derive these observables by calculating the weighted average characteristics in each county. One issue to address is the fact that Amazon Marketplace transactions may or may not be taxed. According to most state laws, sellers are required to collect sales tax from consumers who reside in a state in which the sellers’ goods are housed. For example, if a seller located in California has goods that are stored in a FC in Pennsylvania, that seller must charge sales tax to all consumers in Pennsylvania. If the seller does not have any goods housed in Ohio, then the seller does not have to collect sales tax from consumers who live in Ohio. To the extent that sellers use the Fulfilled by Amazon program (FBA), then Amazon is in charge of where the sellers products are stored, meaning high volume sellers are likely to be taxed similar to Amazon. But sellers who are lower volume and/or do not use FBA likely do not have to charge sales tax to many consumers. We account for this by allowing for a percentage of Amazons sales to be untaxed. This involves adjusting the tax rate charged on Amazon by the share of taxed transactions. We do not observe the share of untaxed transactions directly but we do have information on the percentage of transactions that are completed through an Amazon Marketplace seller for each year. This information comes from Statista. We assume that a consumer can always find an untaxed seller and adjust the tax rate by the marketplace share.

19In Appendix, we present the estimates of the smoothing regression and the raw price moments, which confirm these patterns. 20ARTICLE

23 4.3 Estimation

To estimate the preference parameters θd, we run a fixed effects linear regression of the relative share on Zit, Cit,τ ˜ijt, and mode-year, region-year, and county fixed effects. We assume that the demand shocks are conditionally independent of the regressors, meaning that exogenous variation inτ ˜ijt, or the difference between the effective tax rate on mode j and mode 0 identifies σ. Differences in the effective tax rates comes from both within-mode variation over time and within county variation across mode. The former implies that the identification is much like a standard diff-in-diff where the treated group includes households who are subject an effective tax change because of either Amazon’s physical presence or a change in the tax rate, and the control group includes households who are not subject to a change. Given the estimate of σ, we estimate the variety parameters via GMM. Specifically, we match the mean and variance of the transacted price distribution for each mode in each year of the data. The moments are given by:

m11(θv; σ) = meanjt − MEANPjt and:

m12(θv; σ) = varjt − VARPjt where MEANPjt and VARPjt are 33 by 1 vectors of the observed mean and variance of the trans- acted prices for each mode and year. We stack these moments to form m(θv) and estimate param- eters by minimizing the objective function:

0 min G(θv) = m(θv) W m(θv) θv where W is an efficient weighting matrix. The identification of these parameters simply comes from the variation in the transacted prices, conditional on the estimate of σ in the first stage.

4.3.1 Results

The estimated parameters and standard errors are presented in Table 7, which the exception of the region-effects and the state effects, which are excluded due to the number of parameters. The estimated elasticity of substitution is approximately 1.4 and is precisely estimated. The estimates of λj suggest that higher income households, younger households, and non-white households spend more via the online channels. For Amazon, the effect of age and Asian population is the strongest among the three modes, while the income effects is behind mode 2. The estimates of γj indicate that the number of large offline competitors per square mile has a negative impact on the expenditures on Amazon and mode 2, suggesting that Amazon and domains with an offline presence face competition from offline competitors. However these effects are not precisely estimated. The hedonic parameter, which represents the price of an additional unit of quality, is estimated

24 to be $10.50. The parameters that determine the mean and standard deviation of the the log- normal variety distribution are also presented. We use these parameters to construct the mean and standard deviation of ω, which we discuss in the following subsection.

4.3.2 The Growth of Amazon, 1999-2018

We document the growth of Amazon over the past 20 years. To do this, we use the estimates of the demand model along with additional data in order to project county spending both backwards from 1999-2005 and forwards from 2017-2018 and predict revenues for counties which we don’t observe within the 2006-2016 sample.

We collect data on the variables in Cit and Zit for the years and counties outside of our sample 21 from Census. In addition, we assume that the values of ∆ξrt for the years outside our sample are equal to the average of the estimated values across the in-sample years. There was no systematic dynamic pattern in these effects, so this is a reasonable assumption. For the unobserved counties 22 within our sample period, we use the estimated value of ∆ξrt associated with county’s region.

For all out of sample observations, we assume that the state effects are equal to the estimates of ξ¯s. Because of the linear form of variety parameters, we are able to project the distribution of ω and thus, prices, for all of the years from 1999-2018. Figure 6 shows that the coefficient of variation is increasing for all modes, suggesting that all online modes are expanding the variety of goods they are selling. However, Amazon has been expanding the variety of goods faster than their online competitors, surpassing Mode 3 in 2015 and approaching Mode 2 in 2018. This is especially impactful impactful considering the other online modes consist of many different domains. Finally, we project the mode-year fixed effects for 1999-2005 and 2017-2018 by matching the observed data from Amazon’s financial statements and from the US Department of Commerce. Specifically, using the estimates of all other parameters, we can predict total revenue for each mode for any value of ξjt. Therefore, for each year outside of the sample, we find the mode effects that minimize the sum of the squared differences between the predicted revenue and the observed revenue across the three modes.23 Using the estimated and the projected parameters, we predict household level spending on each mode in the contiguous US for the years 1999-2018 under the observed network configuration. We then multiply by the number of households in order to construct total spending each county. Figure 7 presents the dynamics of Amazon’s US market share over this time period and also shows the market share dynamics under two alternative scenarios. The first is if the product variety was fixed to it’s 1999 levels and the second is if the product variety and the preferences for Amazon,

αi1t, are fixed to their 1999 levels. Recall that αi1t includes both the quality of the platform

21EXPLAIN. 22EVIDENCE 23We again use 2006 year as an anchor of 2006 spending growth rates allow us to predict the correct revenue for modes 2 and 3.

25 and the national convenience. The figure indicates that approximately 25% of Amazon’s revenue growth can be attributed to the expansion of product variety, while the remaining portion is due to improvements in quality and convenience. We further examine Amazon’s growth by looking at the geographic distribution of changes in markets shares across the United States in Figures 8 and 9. The top panel of Figure 8 displays the percentage change in county level market shares from 1999 to 2018, while the second panel shows the absolute change in market shares. We see substantial growth in some of the larger markets across the country, but what is striking is the growth in the rural parts in the middle of country. This is in contrast with the rural areas of the southeastern portion of the Unites States, which tend to be lower-income compared to the middle of the country. This suggests that Amazon has been able to compete with the brick-and-mortar firms (i.e., Wal-Mart) in relatively higher income rural areas, while at the same time growing the urban markets. Further, these figures suggest that Amazon’s growth in demand is not necessarily tied to nearby FCs. In fact, many of the highest growth areas are far from an FC, suggesting the importance of the entire network in shaping demand. However, because the majority of Amazon’s demand and thus, shipments, is coming from the urban areas, it is important for them to have their FCs close to populated areas. This can be seen in Figure 9, which displays the share of the total revenue growth that can be attributed to each county. Not surprisingly, the county share of the revenue growth is largest in the most populated areas, near where the FCs and SCs are located. The estimates and the projections of the demand model allow us derive the function mapping the network configuration in year t to spending on Amazon for each county/year, Rit(St). The network impacts revenue and orders through the effective tax rate charged on Amazon, which changes the prices on Amazon relative to the other modes. Figure 10 shows the amount of total revenue that is lost due to taxes from 1999-2000. That is we assume that Amazon’s sales tax rate is 0 for the entire sample period and calculate their total revenue. The figure shows the difference between the revenue under the observed effective tax rates and the revenue under no sales tax. Amazon begins to charge sales tax in some states starting in 2006, losing a small amount of revenue. Starting in 2012, however, we see a significant increase in the number of states that are taxed and the losses associated with these changes. The losses cross $1 billion dollars in 2015 and are $1.7 billion in 2018, suggesting that changes in Amazon’s sales-tax liability have a significant impact on their revenue. Finally, we use the county level revenue function in order to derive the total revenue function for year t, Rt(St). We also use the county level revenues and the mean transacted price in order to determine the total quantity of orders for county i:

Rt(St) qit = Mit meanjt which make up the distribution of orders for the set of N counties in year t, Qt.

26 5 Supply

We now turn to estimating the supply-side of the model, which has four primary elements: the order flow model, the FC and SC labor demand functions, the shipping costs, and the fixed costs. We first estimate the order flow model and the labor demand functions using FC and SC employment data, then we use these results and the demand model as inputs to estimate the shipping costs and fixed costs via a revealed preference approach.

5.1 Order Flow and Labor Demand

We discuss the identification and estimation the parameters of the model related to the flow of L orders within the network and labor demand: θ = {ψ, γ, Afc,Asc}. The technology parameters are assumed to be constant over time, and we exploit variation over time and across locations in demand, employment and capacity to identify the parameters. The parameter determining the availability probability (ψ) is allowed to vary over time to account for changes in the variety of products over time on Amazon.com. Since we observe limited time-series variation in employment across facilities, we assume that the evolution of this parameter is inversely proportional to the variety in year t relative to the 2018 level and estimate a single parameter ψ:

CV2018(ω) ψt = ψ × CVt(ω) where CVt(ω) is the estimated coefficient of variation of ω in year t.

5.1.1 Data

We assembled two employment data-sets. First, using Amazon’s financial statements, we construct an estimate of the total number of employees dedicated to distribution for each year between 1999 and 2017. Amazon reports on his hiring website that roughly 125,000 workers worked in one of its distribution-related facility in 2017, but only report company-wide employment figures for the other years. We use this 2017 number to calculate the ratio of distribution of employees to distribution-related cost share from the annual statement (54.5%), and assume that this ratio is constant throughout the sample. Then, using variation over time in the reported distribution cost share, we infer the share of employees working in distribution every year. This share is fairly stable over time, and varies between 20% and 28%. Figure 11a plots the evolution over time in the number of employees working in distribution. Our second employment data-set is a cross-section of employment records at the facility level for the more recent year. We obtain an estimate of the number of employees working at 102 fulfillment centers, and 25 sortation centers (out 133 and 55 facilities respectively). Those employment figures

27 are either realized employment numbers obtained from surveys, or projections reported in the media at the time of opening. Figures 11b and 11b plot the distribution of employment for the two facility types. The median size of an FC is 1000 employees, while the median SC has 300 employees. We also construct an estimate of the total number of employees working in the fulfillment network over time using Amazon’s annual financial statements and other online sources.

5.1.2 Estimation

Since we have incomplete and noisy employment data, we use a method-of-moment approach to estimate the parameters. In particular, we summarize the establishment-level information using the following reduced-form (or auxiliary) regression pooling across FCs and SCs:

log Empi = b0 + b11(FCi) · log ki + b21(FCi) · log Pop Densityi  fc  fc  +b3 log 1 + Ni + b41(SCi) · 1 Ni > 0 + b51(SCi) + ei (12)

fc where 1(·) is an indicator function, and Ni is the number of FCs located in same cluster as facility i. The population density is measured by averaging counties located within 50 miles of a facility. Within the model, this regression is estimated using the latest year of each facility with available employment data (i.e. 2018 for most). To summarize the time-series information, we force the model to match exactly the reported 2017 employment number, and include as extra moment the average annual growth rate in log-total employment (23%, s.e. = 0.02). After combining these two sets of moments (6+2), the parameters are estimated by GMM using the estimated variance-covariance matrix of the empirical moments as a weighting matrix.24 Although the model is technically “over-identified”, we chose the moments with the following heuristic identification in mind. The relationship between size of the local market and labor demand is determined primarily by the slope of the availability probability function (ψ). We have two proxies for local market size in the auxiliary regression: population density in neighboring counties (for FC) and number of adjacent FCs. When ψ is large, orders are more likely to fulfilled by the closest FC which increase the volume of orders (and therefore employees) in more populous locations, as well as the volume of connected SCs.

Similarly, the intercepts of the reduced-form regression (b0 and b5), as well as the total number of employees, identify the number of employees required to process an order on average (Afc and

Asc respectively). Finally, the technology parameter, γ, determine the relationship between labor and capital in the data.

24Since we are combining moment from two sources, we use a block-diagonal matrix. We set the weight associated with the moment corresponding to the 2017 employment level to an arbitrarily high level since this moment is not per-se estimated.

28 5.1.3 Results

The estimates of θL are reported in Table 8. The estimate of γ is significantly different from one, implying an important concavity in the production function with respect to capital. Since the production function is assumed to be proportional to labor, the model still implies that each facility exhibit increasing return to scale: 1 + γ > 1. The availability slope parameter is close to one, implying that most orders are fulfilled by the closest clusters: in 2018 over 70% of orders come from the closest FC. With these estimates, we calculate the average labor costs per order for each year in our sample. In 1999, the average cost of labor is 60 cents, but it increases to 95 cents by 2018. The increase of labor costs comes from three different sources. First, there have been general increases in wage rates over time. Second, Amazon is opening facilities in locations with higher wage rates. And third, by opening SCs, Amazon now must staff the FCs and the SCs in order to ship an order, whereas outsourced shipments only required employees at the FC. Therefore, they are paying more to labor and less to shipping as they vertically integrate..

5.2 Cost of Shipping and Fixed Costs

The remaining elements of the cost function to estimate are the costs of shipping and the fixed costs. Recall that cost of shipping an order is:

VI gci(St) = θo − θv1ci (St) + θxdci and the fixed costs are:  kct × (rct + κPopDensct) If Type(c) = FC Fct = sc k × (rct + κPopDensct) If Type(c) = SC

S We denote the parameters of these functions θ = {θo, θx, θv, κ}. Because solving for the dynamic model of the optimal network roll-out in order to estimate θS is computationally challenging, we take a revealed preference approach similar to that of Holmes (2011). That is, we find the values of the parameters that render the observed network optimal compared to suboptimal perturbations of thenetwork, partially identifying the parameters via a moment inequality estimator.

5.2.1 Inequalities

The discounted stream of profits under network rollout a is:

∞ S X t S Π(a; θ ) = β π(at; θ ) t=0

29 Amazon chooses the optimal sequence of FC and SC openings, a0, meaning that profit under any alternative network configuration is suboptimal. We focus on alternative roll-outs that swap the opening dates of two facilities. Therefore, swapping the opening date of facility c with facility c0 must result in lower profit:

0 Π(a0; θS) − Π(ac,c ; θS) ≥ 0

The advantage of swapping the opening dates of facilities is that the post-sample payoff is the same under the observed and the perturbed network configuration, so these elements will cancel out. Therefore, we do not have to account for possible profit changes beyond 2018. Note that we swap only the opening date, meaning the size of facility c remains the same, regardless of its opening date. The linearity of the profit function allows us to denote the inequalities as functions of the differences in the elements of the per-period profit function and θS:

c,c0 c,c0 c,c0 c,c0 c,c0 y − (θoo + θxx − θvv + κd ) ≥ 0 (13) where the differences are:

0  0 0 0  yc,c = Rev(a0) − Rent(a0) − Wage(a0) − Rev(ac,c ) − Rent(ac,c ) − Wage(ac,c )

0 0 oc,c = Orders(a0) − Orders(ac,c ) 0 0 xc,c = Distance(a0) − Distance(ac,c ) 0 0 vc,c = VIorders(a0) − VIorders(ac,c ) 0 0 dc,c = Density(a0) − Density(ac,c )

The differences are made up of:

2018 0 X t−1999 Rev(a ) = β µRt(St) t=1999 which is the discounted stream of revenue net cost of goods sold, where Rt(St) is the revenue function derived in Section 4, µ is Amazon’s gross margin, and β is the discount rate. We observe the gross margin in Amazon’s financial statements, which is approximately 0.23 for the years in our data. We set the discount rate to 0.95. The discounted sum of orders across the entire sample is:

2018 0 X t−1999 X Orders(a ) = β qct t=1999 fc c∈Ct

30 while the orders there are processed through an SC are:

2018 0 X t−1999 X VIorders(a ) = β qct sc t=1999 c∈Ct

The total discounted wage bill is:

2018 0 X t−1999 X fc X sc Wage(a ) = β wctL (qct, kct) + wctL (qct, kct) t=1999 fc c∈Csc c∈Ct t

The total shipping distance is calculated from the order flow and demand functions:

2018 X t−1999 X X Distance(a) = β Ωci(St)qit t=1999 fc i c∈Ct

Finally, the fixed cost elements are:

2018 X t−1999 X X Rent(a) = β rctkct + rctkct t=1999 fc c∈Csc c∈Ct t 2018 X t−1999 X X Density(a) = β PopDensctkct + PopDensctkct t=1999 fc c∈Csc c∈Ct t

We assume that each of the observed differences are measured with error, such that:

0 0 0 D˜c,c = Dc,c + c,c ∀ D ∈ {y, o, x, v, d}

These errors could come from a variety different sources, including: (i) measurement error in or omitted contributions to cost elements, (ii) estimation error in the firm’s demand function, or (iii) a mis-representation of the role of the political environment on Amazon’s profit and decision making. We assume that these errors are independent observed pre-determined differences between rollout a0 and ac,c0 , such as differences in the distance to populated areas, average tax rates, average wages, and average rents. Assuming a set of R non-negative instruments zc,c0 that are functions of these pre-determined differences, we construct the following moment inequalities:

h c,c0  c,c0 c,c0 c,c0 c,c0 ˜c,c0 i h c,c0 c,c0 i E zr y˜ − (θoo˜ + θxx˜ − θvv˜ + κd ) + E zr  ≥ 0 | {z } =0 where c,c0 represents the sum of the errors across all the elements in Equation 13. This leads to the following set of R sample moment inequalities as in Pakes et al. (2015), where

31 each element is given by:

1 X 0  0  0 0 0 0  zc,c y˜c,c − θ o˜c,c + θ x˜c,c − θ v˜c,c + κd˜c,c =m ˜ (θS) ≥ 0 (14) M r o x v r c,c0

The number of perturbations used in estimation is M.

5.2.2 Calculating Profit Elements

To construct the inequalities, we calculate each of the differences in equation 13. We determine the household spending for each county under the observed network in each year, Rt(St), and the 0 counterfactual network, Rt(St), using the revenue function derived in Section 4 The spending depends on St through the effective tax rate. The swapping of the opening date of facility c and c0 results in a different effective tax rate for the households living in the state where c (c0) is located only if c (c0) is the first entry into that state. For example, swapping the opening date of the first FC that opened in California (late) with the first FC opened in (early) means that sales taxes will be charged to earlier to the consumers in California and later to the consumers of Kentucky, compared to the observed roll-out. However, as discussed in Section 2, if c opens in year t it does not necessarily mean that the effect tax rate changes in year t, as Amazon uses its bargaining position to negotiate delays in the implementation date. Therefore, we allow the lag between entry and change in the effective tax rate to depend on Amazon’s bargaining position. We use the regression results in Section 2 as a reduced form bargaining model and predict the lag between the entry date and the implementation date for a given roll-out. Using the predicted lag, rounded to the nearest whole number, we calculate the effective tax rates for each county in each year of our sample and then use the revenue function to predict total Rev(a) for any roll-out a. Because the bargaining model may predict implementation dates that are different from the observed dates, we also use this procedure to calculate total revenue under the observed network roll-out. Finally, because we did not find evidence of a local convenience effect, there will be no opposing demand side effect of expansion. That is, the network a only impacts demand through changes in the effective tax rate. Once we calculate the revenue for each county and year, we calculate the number of orders:

0 0 Rit(St) qit = p¯t wherep ¯t is a the average transacted price that is calculated from the model. Using the distribution of orders and the estimated order flow matrix, we calculate the total number of orders that flow

32 through each FC and SC under network St.

0 X ∗ 0 0 qct = Ωci(St)qit i 0 X X  ∗ 0 0 qct = Uc,c0 · Di,c Ωc0i(St)qit i 0 fc c ∈Ct which allow use to calculate the total number of orders shipped and the total number of vertically integrated orders. Then, using the order flow and the distances between counties, we can calculate the the total shipping distance under St:

˜ X X 0 dt = Ωci(St)dciqit i fc c∈Ct

To calculate the total labor costs, we determine the amount of labor is needed to satisfy the flow of orders at each FC and SC using the estimated labor demand functions and then multiply the labor demand at each facility by the local wage rates where that facility is located:

0 X fc X sc Wt = wctL (qct, kct) + + wc0tL (qc0t)+ fc c0∈Csc c∈Ct t

Total rental costs are calculated in a similar fashion, except they are not a function of the order flow. Given the locations of facilities and sizes in year t, we multiply the size of the FCs and SCs by the local rental rates where the facility is located:

0 X X Rt = rctkct + rc0tkc0t fc c0∈Csc c∈Ct t

Similarly, the total population density in year t is calculated by summing the population density across the locations of SCs and FCs operating that year.

X X Dt = PopDensctkct + PopDensctkc0t fc c0∈Csc c∈Ct t

In addition to the profit components, we also calculate a number of measures of the swaps that are informative of the profitability of that swap. For example, we calculate the difference in the discounted sum of the population weighted average tax rate between the observed network and the swap, and the differences in the average wage rates and average rental rates. Additionally, we calculate the difference in total distance shipped, but instead of using the the demand in order to calculate the order flow, we use population measures, providing an exogenous shifter of total shipping distance. With the exogenous order flow, we also construct an exogenous shifter of the

33 total number of vertically integrated orders.

5.2.3 Identification

We partially identify the four parameters through the trade-off Amazon faces when they make their network decisions. For example, entering a high-tax and highly populated area early relative to a low-population low tax area results in lower revenue due to the tax effect but also shorter shipping distances. Observing this choice by Amazon implies that the cost saving from the shorter shipping distance must have offset the lost revenue. Therefore, this type of observation is informative of the lowest the cost savings must be in order to offset the revenue loss, or a lower bound of θx. The opposite observation is informative of the upper bound: if Amazon entered a low-tax, low- population early, it must be that the increase in shipping cost did not outweigh the benefits on the demand side. Similar trade-offs exist between wages, rents and shipping distance as well, meaning, for example, observations that increase wages but decrease shipping distance are informative of the lower bound of θx. Trade-offs are also informative of the bounds for other parameters. If Amazon enters a high tax area early, they will lower their demand, but they will also lower the total cost of shipping. These observations informs us about the lowest level of θo such that the lower cost of shipping outweighs the lost demand. If Amazon puts an SC in a high rent/high wage area early but increases the number of vertically integrated orders, it is informative of the lowest level of θv such that the savings from vertical integration outweigh the increase in rents and wages. Finally, observing Amazon enter a high population density area early, implies that the savings in the shipping distance and/or vertical integration must off-set the costs of population density, providing information on the lower bound of κ. The opposite observations in each of theses scenarios are informative of the upper bound of these parameters. To examine some of these trade-offs, we present Figure 12. The figures display scatter-plots where each point represents a perturbation. In the top-left, we plot the change in total shipping distance on the y-axis and the change in the weighted average tax rate on the x-axis. The figure indicates that the perturbations where the tax rates are lower than the under the observed net- work had longer shipping routes, demonstrating that Amazon generally faces a trade off between taxes (revenue) and shipping distance. Similarly, the top right figure shows that perturbations where Amazon pays higher wages have shorter shipping distances. The trade-off between vertically integrated orders and rents as well as between density and shipping distance are also displayed. This suggests that the instruments we choose should be informative of this trade-off. Therefore, we propose to use dummy variables indicating whether or not the perturbation is informative of one of the trade-offs that Amazon faces. For example, one instrument could be a dummy variable indicating whether or not the perturbation increases revenue and increases shipping distance, which is informative of the upper bound of θx. Similarly, an instrument could be a dummy variable

34 indicating whether or not the perturbation increases vertically integrated orders and increased the wages paid. However, an issue with these instruments is that changes in revenue, the number of vertically integrated orders and total wages are not exogenous. For example, if the source of the error is in the revenue function, then all three of these measures depend on that error and thus, so do the instruments. Therefore, we use the exogenous shifters of these measures discussed above. Specifically, there are three sets of instruments. The first are dummy variables indicating the specific trade offs, (1) revenue-distance (θx), (2) rent-vertically integrated orders (θv), (3) the revenue-total orders(θo), and (4) the density-distance trade-offs (κ), but we replace the endogenous parts of the trade-off with the exogenous shifters we discuss in the previous section. For this set, there one set of instruments per parameter. The second set of instruments includes the first set, but then adds more sets of dummy vari- ables. Specifically, we now add an indicators for the wage-distance, rent-distance, wage-vertically integrated orders, and density-vertically integrated orders trade-offs, resulting in eight instruments. The third set of instruments breaks the dummy variables down into the levels of the changes we see in the perturbations. For example, we include dummy variables for each a large, medium or small increase in distance and with increase in revenue. Another complication in estimating the parameters is selecting what perturbations to use. It might be the case that there are unobservables that are correlated with our profit components that we would like to cancel out. We therefore, select swaps of ‘similar’ facilities. We only use swaps that feature facilities the same type (FC or SC) and facilities that are of similar size. For the size restriction, we don’t swap any FCs (SCs) that are in the top 25% of size with FCs (SCs) in the bottom 25%.

5.2.4 Estimation

The goal is to find the maximum and minimum values of θS such that all moments are satisfied and to provide inference on the values of these bounds. Due to the moments being informative about bounds on θS, rather than a point estimate, inference under moment inequalities requires methods that differ from the methods traditionally used in a GMM setting. Therefore, we follow Andrews and Soares (2010) (AS hereafter) in constructing confidence sets in which the true value of θS falls 95% of the time and report these sets as our estimates. Intuitively, the AS method involves estimating a test statistic for a given value of θS and rejecting the value of θ if the test statistic is less than the 95th percentile of the test statistic distribution computed from a number of “bootstrapped” samples of perturbations. The confidence set is the set of θSs that are not rejected. S For a candidate value of the shipping cost parameter θ0 , we calculate the Y sample moments 0 ˆ S m˜ (θ ) and the Y × Y sample variance covariance matrix of the empirical moments Σ(θ0 ). We ˆ S estimate Σ(θ0 ) using the method in Holmes (2011) to account for both error in the demand model and correlation across perturbations. See appendix B for details. We then calculate the test

35 statistic:

S  1 S  ˆ S −1  1 S  T (θ0 ) = min M 2 m˜ (θ0 ) − t Σ(θ0 ) M 2 m˜ (θ0 ) − t (15) t≥0

1 S where t is a Y by 1 vector. Here, the term (M 2 m˜ (θ0 )−t) is equal to 0 when the moment is satisfied 1 S and M 2 m˜ (θ0 ) when it is not. Therefore, the test statistic equivalent to a standard GMM objective function after replacing any positive moment with 0. In order to calculate the critical value for the test statistic, we draw R bootstrap samples of perturbations. We sample C facilities from the population with replacement, where C is the observed number of facilities, to form a set which we denote Cr for a given bootstrap sample r. We then construct a set of perturbations ar from swapping the opening dates of the facilities contained in Cr. Note that this could result in swaps appearing multiple times and/or swaps eliminated from the bootstrap sample. For each bootstrap sample r, we form:

S ∗∗ S  ˆ ∗∗ S −1 ∗∗ S  Tr(θ0 ) = min m˜ r (θ0 ) − t Σr (θ0 ) m˜ r (θ0 ) − t (16) t≥0

∗∗ ˆ ∗∗ wherem ˜ r and Σr are calculated using the bootstrap sample of perturbations and the procedure in appendix B. The sample of R bootstrapped values of Tr allow us to find a critical value for the original estimation sample’s value of T as the (1 − α)th sample quantile. Denote this critical value S S S S S asc ˆ(θ0 , 1 − α). If T (θ0 ) > cˆ(θ0 , 1 − α), we reject the hypothesis that θ = θ0 and eliminate θ0 as a candidate value. As AS suggest, we use a grid search in order to determine the confidence set. We set R = 2000 and α = 0.95.

5.2.5 Results

We present the estimated bounds on the parameters are presented in Table 9. Each of the first three specifications are associated with the sets of instruments described above. For these specifications, we do a grid search and find the minimum and maximum values of the parameters, conditional on all the moments are satisfied. Not surprisingly, as we add more instruments, the intervals get smaller. In the last column, we use the 3rd set of instruments and utilize the AS method described above. Therefore, these intervals are the 95% confidence sets for these parameters. The base cost of shipping an order is between $2.90 and $5.52, which is inline with reported charges that Amazon pays for shipping.25 The distance parameter indicates that the shipping charges per order are between 7 and 14 cents per additional 100 miles the package is shipped. This represents cost reductions on the part of Amazon or the third party shippers that come from

25CITE

36 expansion of the FC network. Shipping an order through an SC saves Amazon between 35 and 63 per order, meaning that vertical integration cuts shipping cost by more than 10%. Finally, it is relatively expensive to run a facility in an densely populated area as a 500 sq ft facility costs between $300 thousand and $1 million per unit of density (100 hh/mile). We use the mid-points of these intervals in order to demonstrate how Amazon’s costs have changed over time. We note that the mid-point is one of the accepted points of θS in the AS method. We present three plots in Figure 13. The top two plots show the share of the total for the different elements of the cost function. The total fixed costs share (rent+density) are relatively constant over time, but within those costs, the density costs are becoming relatively more important. This implies that expansion is leading to more and more unobserved costs that are associated with being in more urban areas. The labor cost share and the shipping cost share are largely inversely correlated. This is due to the fact that the expanding the network lowers shipping costs, but it also results in Amazon paying higher wages in more urban areas and employing more people in order to vertically integrate. That is, to send a vertically integrated package, Amazon needs employees at both the FC and SC, whereas outsourced delivery just requires employees at the FC. This relationship is especially striking starting in 2013, when Amazon begins to roll-out the SC network. During this time period, the shipping cost share falls from 83% to around 75%. The bottom plot of Figure 13 displays the yearly average shipping cost per order and the average shipping cost if Amazon outsourced their orders to third party shippers. That is, we calculate the total cost of shipping as if 0 of the orders are vertically integrated and then divided this number orders. We see that average costs decline from about $4.80 to just over $4.50 as Amazon expands their FC network between 2000 and 2010. As Amazon builds out the SC network starting in 2011, the average shipping costs drop even more dramatically, from about $4.50 in 2010 to just over $4.00 in 2018. If Amazon outsourced all of their shipments, their average shipping cost would have been around $4.35. This suggests that approximately 40% of the reduction of average shipping cost comes from vertical integration, representing a significant effect of the expansion SC network. Savings from expansion of the FC network are also significant, as the drop in average costs ignoring vertical integration are 8% (40 cents). Further, this is a lower bound of the benefits of FC expansion due to the complementarities between FC and SC expansion.

6 Conclusion

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39 Tables and Figures

Table 1: Expansion of Fulfillment Center Network

FCs SCs States Network Share of Share of Year Count Ave Size Count Ave Size w/ Facility Density HH Served HH Taxed (100k) (100k) (miles) by SC 1999 5 5.91 0 - 5 307 0 0 2000 5 5.91 0 - 5 307 0 0 2001 6 6.00 0 - 5 301 0 0 2002 6 6.00 0 - 5 301 0 0 2003 7 5.75 0 - 6 297 0 0 2004 7 5.75 0 - 6 297 0 0 2005 9 5.56 0 - 7 269 0 0.03 2006 12 5.40 0 - 7 268 0 0.03 2007 12 5.40 0 - 9 268 0 0.03 2008 15 6.20 0 - 11 253 0 0.11 2009 16 6.41 0 - 11 253 0 0.11 2010 17 7.10 0 - 11 257 0 0.11 2011 25 7.38 1 3.18 11 240 0.02 0.11 2012 31 7.59 1 3.18 12 226 0.02 0.19 2013 42 8.23 3 3.15 15 195 0.13 0.39 2014 57 8.12 12 3.52 19 139 0.42 0.65 2015 67 8.02 22 3.35 27 123 0.66 0.77 2016 74 8.06 28 3.20 30 120 0.74 0.84 2017 98 8.09 32 3.14 32 102 0.77 0.98 2018 128 8.26 41 3.31 34 71 0.82 0.98 Notes:

40 Tables and Figures

Table 2: Household Expenditures

comScore Sample Average HH Spending Amazon Households Counties Amazon All Online All Retail Online Year (000) (%) ($/year) ($/year) ($/year) Share 2006 38.3 84 43 254 6,399 0.170 2007 39.5 85 66 318 6,649 0.207 2008 20.8 74 83 331 6,644 0.249 2009 16.7 67 94 353 6,174 0.265 2010 15.4 66 137 418 6,113 0.327 2011 17.4 68 194 509 6,484 0.382 2012 18.3 67 301 721 6,439 0.418 2013 16.5 62 394 901 6,061 0.438 2014 12.0 54 502 1,087 6,300 0.462 2015 16.3 64 587 1,209 6,120 0.486 2016 24.8 72 686 1,337 6,615 0.513 Notes: Households refers to the number of households in the comScore data, together with the percentage of counties they represent. Expenditures are the unweighted average spending by the representative household in each county.

Table 3: Tax Lag Regression

(1) VARIABLES Tax year - Entry year

Minimum tax in unoccupied states from the same region -41.80** (17.05) State entry year -0.418*** (0.107) 1(Entry year¿2012) -2.295* (1.251) Constant 847.1*** (214.9)

Observations 23 R-squared 0.861 Mean dep. variable 2.217 SD dep. variable 4.112 Mean tax variable 0.0406 SD tax variable 0.0222 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes:.

41 Table 4: Taxed and Non-Taxed Competitors

Sales Rank Taxed (Share) Non-Taxed (Share)

1 walmart.com ebay.com 2 jcpenney.com qvc.com 3 bestbuy.com dell.com 4 macys.com yahoo.net 5 apple.com hsn.com 6 homedepot.com overstock.com 7 victoriassecret.com fingerhut.com 8 target.com amway.com 9 staples.com newegg.com 10 gap.com orientaltrading.com

Total 102 292 Notes: Table displays top 10 domains in terms of 2013 expenditures in the comScore data that we define as taxed and non-taxed.

Table 5: Propensity to Purchase From Amazon

(1) (2) (3) (4) Variable name

Amazon Tax -0.161** -0.164** -0.162** -0.163** (0.077) (0.083) (0.079) (0.079) Log Distance -0.000 (0.003) Next Day Available 0.000 -0.005 (0.006) (0.008) Same Day Available 0.008 (0.008)

Obs 2,695,475 2,695,475 2,695,475 2,695,475 R-Sq 0.322 0.322 0.322 0.322

Notes: .

42 Table 6: Online Prices

Amazon Taxable Non-Taxable Mean StDev Mean StDev Mean StDev

2006 29.33 40.94 37.93 56.31 38.57 59.91 2007 29.45 41.82 37.61 56.55 38.57 60.20 2008 29.58 42.71 37.29 56.78 38.56 60.49 2009 29.71 43.62 36.97 57.02 38.55 60.78 2010 29.84 44.56 36.66 57.26 38.55 61.07 2011 29.97 45.51 36.35 57.50 38.54 61.37 2012 30.10 46.48 36.04 57.74 38.53 61.66 2013 30.23 47.47 35.74 57.98 38.53 61.96 2014 30.36 48.49 35.43 58.22 38.52 62.26 2015 30.50 49.52 35.13 58.46 38.51 62.56 2016 30.63 50.58 34.84 58.70 38.51 62.86 Notes: Households refers to the number of households in the comScore data, together with the percentage of counties they represent. Expenditures are the unweighted average spending by the representative household in each county.

Table 7: Demand and Variety Estimates

Parameter All Modes Est SE Elasticity of Substitution - σ 1.332 0.084 Hedonic Parameter - ρ 9.839 0.351 Parameter Amazon Mode 2 Mode 3 Est SE Est SE Est SE Variety Distribution:

Mean Constant-mj0 0.011 0.050 0.225 0.050 - -

Mean Trend-mj1 -0.023 0.003 -0.029 0.003 -0.008 0.003

StDev Constant- sj0 1.351 0.013 1.436 0.013 1.531 0.013

StDev Trend sj1 0.015 0.001 0.011 0.001 0.004 0.001 Consumer Preferences: age Age - λj -0.01 0.001 -0.005 0.001 -0.007 0.001 income Log(Income) - λj 0.029 0.009 -0.011 0.009 -0.08 0.009 asian Asian - λj 1.745 0.079 1.49 0.079 1.351 0.079 black Black - λj 0.43 0.012 0.529 0.012 0.54 0.012 Offline Competition: large retailers Large Retailers per Capita - γj 0.021 0.035 -0.005 0.035 0.018 0.035 small retailers Small Retailers per Capita - γj 0.002 0.001 0.003 0.001 0.002 0.001 Notes:

43 Table 8: GMM Estimation Results: Order Flow Model

Results Goodness of Fit Parameters Est. SE Moments Data SE Pred. Regression: Log Empl. Availability – ψ 0.97 0.50 Intercept 5.87 0.45 5.36 Capital – γ 0.44 0.18 Log density (50 m.) 0.17 0.06 0.23

Afc 0.35 0.01 1(FC) x Log capacity (/1M) 0.63 0.20 0.70

Asc 0.06 0.01 1(SC) x Nb. FC in cluster (log) 0.10 0.16 0.45 1(SC) -1.60 0.19 -1.86 J-Test – χ2 10.12 Growth rate: Log total empl. 0.24 0.01 0.23 P-value 0.04 2017 Empl. (log) 4.83 0.01 4.83

Table 9: Shipping and Fixed Cost Estimates

Parameter (1) (2) (3) AS

Base Cost ($1): θo [1.18, 18.05] [2.50, 4.99] [4.24, 4.76] [2.90, 5.52]

Dist. Cost ($1/100m): θx [-3.21, 0.19] [0.05, 0.12] [0.10, 0.11] [0.07, 0.14]

VI savings ($1): θv [0.15, 1.03] [0.17, 0.59] [0.47, 0.53] [0.35, 0.63] Density ($/100 hh): κ [-16.35, 1.20] [0.45, 0.95] [0.80, 0.91] [0.63, 1.04] Notes:

44 Figure 1: Concentration in Online Retail, 2006-2013 .2 .15 .1 Herfindahl-Hirschman Index Herfindahl-Hirschman .05 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year

Notes: Authors’ calculations using comScore Web Behavior database restricted to categories in which Amazon has a presence. “Expenditure HHI” and “Transaction HHI” denote Herfindahl-Hirshman indices based on market shares using the raw expenditures and number of transactions, respectively, at each retailer in each year.

45 Figure 2: Amazon’s Fulfillment Center Expansion (a)

(a) Amazon FC (red) network, 2000

(b) Amazon FC (red) network, 2006

Notes: Maps exclude Amazon Fresh, Returns, and Sortation Centers. Shading represents number of households per state. We display the state-level population-weighted average 2013 sales tax rate in each map

46 Figure 3: Amazon’s Fulfillment Center Expansion (b)

(a) Amazon FC (red) network, 2012

(b) Amazon FC (red) and SC (gold) network, 2018

Notes: Maps exclude Amazon Fresh, Returns, and Sortation Centers. Shading represents number of households per state. We display the state-level population-weighted average 2013 sales tax rate in each map

47 1 Consumer in county ! places order 1 Consumer in county ! places order

c c 2 Order assigned to FC based on 2 Order assigned to FC based on • Distance between ! and # • Distance between ! and # • Size of FC c (square footage) • Size of FC c (square footage)

Case 1: Outsourced sortation Case 2: Integrated sortation (pre-2011; post 2011: some FCs c; some counties ! ) (post 2011: some FCs #; some counties ! )

3 • Package transferred to integrated 3 • 3rd party shipper delivers package to sortation center and sorted if within sortation facility local to county ! via ~150m of county ! plane or truck • Transfer to last-mile shipper • Fixed rate per package

4 • Sorting of packages into zip-code groups 4 • Local delivery to consumer • Transfer to last-mile shipper

5 • Local delivery to consumer

(a) Third Party Sortation (b) Integrated Sortation

Figure 5: Logistic of Online Transactions

Figure 4: Tax Date Lags 15 10 Lag (Years)Lag 5 0

2000 2005 2010 2015 Year of FC Entry

Notes:

48 Figure 6: Coefficient of Variation 4 ω 3.5 3 2.5 Coefficient of VariationCoefficient of 2 2000 2005 2010 2015 2020 year

Amazon Mode 2 Mode 3

Notes: .

Figure 7: Estimated Amazon Share of Online Retail, 1999-2018 .8 .6 .4 Online Market Share Online .2 2000 2005 2010 2015 2020 year

Observed 1999 Variety 1999 Variety and Quality

Notes: .

49 Figure 8: Distribution of Amazon’s Growth (a)

(a) % Change in Market Share, 1999-2018, and FC (red) and SC (gold) network, 2018

(b) Absolute Change in Market Share, 1999-2018, and FC (red) and SC (gold) network, 2018

Notes:

50 Figure 9: Distribution of Amazon’s Growth (b)

(a) County Share of Revenue Growth, 1999-2018, and FC (red) and SC (gold) network, 2018

Notes:

51 Figure 10: Lost Revenue Due to Taxes, 1999-2018 1 .8 .6 .4 Total ($ B) Lost Revenue .2 0 2000 2005 2010 2015 2020 year

Notes: .

Figure 11: Distribution of employment by facility types and year .4 .3 125 100 .3 .2 75 .2 Fraction Fraction 50 .1 .1 25 Total distribution employees (x1000) 0 0 0 2000 2004 2008 2012 2016 0 200 400 600 800 0 1000 2000 3000 4000 Year Establishment employees Establishment employees

(a) Total Employment (b) Sortation Center (c) Fulfillment Center

52 Figure 12: Trade-Offs of Expansion

(a) Tax-Distance Trade-Off (b) Wage-Distance Trade-Off 800 1000 600 500 400 200 0 Change in Shipping Distance Shipping in Change Distance Shipping in Change 0 -200 -500 -.02 -.01 0 .01 -6000 -4000 -2000 0 2000 Change in Average Tax Rate Change in Average Wage (c) Rent-VI Orders Trade-Off (d) Density-Distance Trade-Off 800 200 600 100 400 200 0 Change in VIin Orders Change Change in Shipping Distance Shipping in Change 0 -200 -100 -.1 -.05 0 .05 .1 .15 -600 -400 -200 0 200 Change in Average Rent Change in Density

Notes:

53 Figure 13: Cost Saving from Expansion .2 .84 .82 .15 .8 .1 % % of Total Cost .78 % % of Total Cost .05 .76 0 2000 2005 2010 2015 2020

year .74 2000 2005 2010 2015 2020 Labor Land Density year

(a) Labor and Fixed-Cost Share (b) Shipping Cost Share 4.8 4.6 4.4 Average Cost ($) 4.2 4 2000 2005 2010 2015 2020 year

Overall Outsourced

(c) Average Shipping Cost

Notes:

54 A Appendix: Data

Calculating Expenditures

In what follows, we provide a description of the procedure to calculate the expenditures for the representative household in county i and year t on the three different modes of shopping. First, we annualize each household’s online expenditures by grossing up the observed weekly expenditures by 52 weeks, where the observed weekly expenditures are the total expenditures divided by the total number of weeks we observe any browsing behavior by the household. We do this because the browsing data suggests that many households are only in the sample for a short time period.

Denote the expenditures for household h in year t as eht. We present the annual average of household online expenditures in the first column of Table A.1. The second column displays the average number of transactions calculated in a similar man- ner to expenditures, while the third column shows the percentage of households with zero online expenditures. Interestingly, we see decreasing average expenditures and transactions over time. Column 3 indicates that this is due to an increasing percentage of comScore households with zero online purchases. This not in line with outside evidence about the take-up of online purchasing; eMarketer estimates the share of such internet users who do not make online purchases is 32% in 2009. While comScore’s sampling procedure could be causing this pattern, a more plausible explana- tion is that comScore does not record a household’s transactions because the household deactivated the behavior monitor or because the household uses a second computer (e.g., at work) or a mobile device for all of their online shopping. Because we do not know which households truly do not pur- chase online and have zero expenditures, we choose to exclude all households with zero transactions from the sample. In order to account for the extensive margin of becoming an online buyer, we sup- plement the comScore data with survey data from Forrester Research, Inc. (www.forrester.com). The survey data comes from Forrester’s “North American Technographics Online Benchmark Survey”, which was conducted from 2006-2007 by mail and 2010-2014 online. It surveyed between 30,000 and 60,000 households in the United States and Canada. The exact content of the survey questions varies by year, but generally the most pertinent question for us is the one which asks the user “Have you bought anything online in the past three months?”. The survey also asks for information about the age, income, race, and zip code of the respondent. Documentation for the survey is available from the authors upon request. Patterns in the Forrester responses are in line with anecdotal evidence about the use of the take up of e-commerce (see the last column of Table A.1). We first use the Forrester data to estimate the extent of online shopping for a given demographic group by running the following linear probability regression:

P r(Dht = 1) = β0 + β1Zh + β2Ih + γt + ht

55 where the dependent variable, Dht, indicates whether the household made any online purchases in the past three months. The explanatory variables Z denote a set of dummy variables indicating the characteristics of respondent household h (i.e., race, age and census region), I is a continuous measure of their income, and γt are yearly dummy variables. We then use the estimates of the model to predict the probability that a household in the comScore data made any online purchase in year t,p ˆht, based on their demographics. For the years 2008 and 2009, we use linear interpolations for a given demographic group based on the predictions in 2007 and 2010. Using these predicted purchase probabilities, we calculate the Forrester adjusted expected online expenditures from data for comScore households with positive expenditures,e ˜ht =p ˆhteht. We report raw averages of online expenditures for the Forrester adjusted data, along with transactions that are adjusted in the same way as expenditures, in Table A.1. Household expenditures increase from $250 per year in 2006 to $374 in 2013.

Table A.1: Household Online Purchasing

Raw comScore Data Forrester Expenditures Transactions Offline Adjusted Adjusted Offline Year ($) Only (%) Expenditures ($) Transactions Only (%)

2006 248 2.5 49.8 250 2.5 55.5 2007 260 2.6 51.2 248 2.4 60.8 2008 201 2.1 59.3 274 2.8 - 2009 148 1.5 66.7 282 2.9 - 2010 129 1.4 67.9 288 3.1 32.1 2011 134 1.4 69.3 342 3.5 23.0 2012 155 1.8 63.6 327 3.8 23.9 2013 164 2.2 60.1 374 5.0 15.5 Notes: Expenditures and Transactions are the average amount of per-household spending and number of online purchase transactions. Offline Only denotes the share of households with no online expenditures in the comScore data. Forrester Offline Only denotes the share of respondents who answered no to the question whether they had shopped online in the previous three months in the Forrester Technographics Survey.

Next, we aggregate across households to derive the expenditure of a representative household in county i in year t on online retail. We start by calculating the average online expenditures for demographic group z in county i and year t, in aggregate and for mode j:

z 1 X e¯it = e˜ht and Nit(z) h∈Hit(z)

where Hit(z) is the set of comScore households in demographic group z in county i and Nit(z) is the size of this set.

56 Each demographic group is defined by age of the head of the household, the household income level, and the race of the head of the household. We find the comScore sample of households to be generally representative of the Unites States population according the 2010 Census, with three exceptions: (1) the head of the household is younger, (2) a higher percentage of the households are white, and (3) the household income is higher. De los Santos et al. (2012) similarly compare the sample of comScore users in 2002 and 2004 to the Computer Use Supplement of the Current Population Survey and find that the sample generally compares well with the population of online shoppers. We account for the possibility that comScore may be over- or undersampling certain demographic groups using sampling weights. We classify each comScore household by income and race and age of the head of household, and calculate a household-specific sampling weight based on the relative prevalence of each demographic category in the comScore data and the 2010 Census at the county level. z Using the resulting weights for demographic group z, denoted wi , and the average expenditures from the comScore data, we create the total online expenditures for a representative consumer in a given county: X z z eit = wi e¯it z and the modes’ representative shares and expenditures:

X z z sijt = wi sijt and eitj = sijteit. z

z Here, sijt are demographic group level shares for each shopping mode in each county calculated from the raw ComScore data: z z e¯ijt sijt = z e¯it z e¯ijt represents the annual average expenditures on mode j for county i across all households in z z demographic group z ande ¯it is the sum ofe ¯ijt across all three modes.

Shipping Costs

In order to identify the lowest cost FC for a given county, we estimate a shipment cost function that predicts the average per pound cost of shipping a package between a given origin and destination county using data from the 2012 Commodity Flow Survey (CFS). Assuming that this cost is proportional to the shipping cost for Amazon allows us to predict which FC is the lowest cost FC for each county. The CFS consists of individual commodity shipments that are described by type, origin and destination, value, weight, mode of transportation, distance shipped, and ton-miles of commodities shipped. We select shipments from the CFS based on the modes of transportation that Amazon uses (e.g., air) and the commodity type Amazon sells (e.g., electronic merchandise).

57 We first calculate the costs per pound for each of these shipments, which are approximated by multiplying the unit cost (per-pound per-mile) by the distance shipped. We do not observe the unit cost, so for the shipment modes besides ‘stepwise-pricing parcel’ we assume the unit costs are equal to the 2012 ‘Average Freight Revenue Per Ton-mile’, which we obtain from the Department of Transportation.26,27 For parcel service shipments, we calculate the postage costs of a subsample of 131 shipments in the CFS using the Postage Price Calculator on USPS website, and then run a regression of the postage price on an interaction of the shipping weight and shipping distance. We then use the estimated coefficient as our unit cost per ton-mile and predict total parcel service costs per pound for the remainder of the data. Next, we specify and estimate the following shipment total cost function:

Ci = β0 + β1di + Oi + Di + i (A.1) where Ci is the cost per pound of shipment i, di is the great-circle distance between the origin and the destination, and Oi and Di are origin and destination county level fixed effects. Importantly, while the shipping costs were calculated using the ‘distance shipped’, di is measured as the great- circle distance, allowing us to predict a measure of costs in our data. In practice, we estimate a number of different specifications which include interactions and/or non-linear functions of distance and choose the model with the best fit. Using the estimates of βˆ, we are able to predict the average shipping cost per pound for each FC (f) and county (i) pair in our data, which we denote as Cif . The lowest cost FC is then the FC which solves:

∗ fi = argmin Cif f

B Appendix: Estimation

Calculating µ

Amazon reports the total amount of revenue in the “Media” and “Electronics and Other General Merchandise” categories in North America, which is roughly equivalent to the revenue we predict from our model. Revenue from Canada and Mexico is not included in the comScore data. Therefore, these sales are accounted for through the ‘multiplier’ that we use to match the predictions of the model in the financial reports. See Section ?? for a discussion of these multipliers. They also report the “Cost of Goods Sold” for all of their sales. We compute the cost of goods sold for North America by multiplying the total cost by the ratio of sales from North America to total sales. This

26https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_ statistics/html/table_03_21.html 27Due to missing data for the ‘Barge’ and ‘Truck’ modes, we predict 2012 unit costs using the estimates of an AR-1 regression.

58 provides us with a value for the “gross margin”. However, as Amazon states, the reported cost of goods sold includes both outbound shipping costs and inbound shipping costs (through wholesale prices). Recall that we assume that inbound shipping costs from suppliers to Amazon do not vary with the network of FCs. As we are estimating the outbound shipping costs, we exclude them from the gross margin by adding in the reported “Net Shipping Cost as a Percentage of Revenue” to the gross margin. This value grows over time, but we suspect this is due to an increase in Amazon’s non-shipping related activities (i.e. and digital goods). We therefore set µ = 0.23.

Estimation of Variance-Covariance Matrix

In our problem, we cannot simply calculate Σ(ˆ θ0) using equation 3.2 in AS because (1) there is first stage error in our revenue equation and (2) there exists correlation across a because the same FCs are used in many perturbations. We take the approach described in Section 8.1 of Holmes (2011) in order to account for these two issues. Specifically, we draw a subsample of FCs of size b << J, where J is the total number of FCs. We keep the perturbations in which both the swapped FCs are in the subsample, thereby forming the ‘deviation subsample’ indexed by s. We then take a draw of the tax parameterσ ˆs from a J ˆ σ ˆ σ normal distribution centered atσ ˆ with variance b Σ , where Σ is the estimated variance from the first demand model. Note that in order to account for first stage error, we only draw the tax parameter and assume that all other features of the demand model approximately cancel out when calculating the perturbation. This assumption is made for simplification because for each draw of the parameters, we must compute all the perturbations. There are over 90 parameters of the demand model, meaning we would have to draw a large number of vectors of parameters in order to have variation across the full joint distribution. Instead, we take 100 draws of the tax parameters and calculate the set of perturbations for 100 times. While the non-linearity of the demand model imply that the other components of demand won’t exactly cancel out, we believe that it is a good approximation and that the results will not be significantly affected. j,k j,k We calculatey ˜s andx ˜s with the deviation subsample and the new tax parameter σs to form:

J J 1 X X m˜ (θ0) = zj,k[˜yj,k − θ0x˜j,k] l,s M l,s s s j=1 k=J+1

0 resulting in the l × 1 vector →; deml,s(θ )). The variance-covariance matrix is then estimated from the S different deviation subsamples:

b Σ(ˆ θ0) = varcov(m ˜ (θ0)) J wherem ˜ (θ0) is the S × l matrix of moments.

59 J ˆ 0 In practice, we set b = 3 and S = 200 and use the diagonal matrix equivalent of Σ(θ ) as our weight matrix in Equation (15). The reason for the latter is that we ran into a number of instances where the full matrix was not invertible.

Bootstrap Moments and Variance Covariance Matrix

We follow the procedure is described in Section 4.2 of Andrews and Soares (2010). From the ∗ 0 ˆ ∗ 0 bootstrap sample of R perturbations, we calculate {m˜ r(θ ), Σr(θ )} where:

1 1 ∗ 0 2 ˆ ∗ 0 − 0 0 m˜ r(θ ) = Mr (D (θ )) 2 (m ˜ r(θ ) − m˜ (θ )) and: ˆ ∗ 0 ˆ ∗ 0 − 1 ˆ ∗ 0 ˆ ∗ 0 − 1 Σr(θ ) = (Dr (θ )) 2 Σr(θ )(Dr (θ )) 2

0 The elements ofm ˜ r(θ ) are given by:

Jr J ∗ 0 1 X X j,k j,k 0 j,k m˜ l,r(θ ) = zl,r [˜yr − θ x˜r ] Mr j=1 k=Jr+1 and: ˆ ∗ 0 ˆ ∗ 0 Dr (θ ) = Diag(Σr(θ )) ˆ ∗ 0 Diag(X) represents a vector containing the diagonal elements of X. Here, Σr(θ ) is computed using the same subsampling method described above, but replacing the the ‘population’ of FCs from which ∗ 0 ˆ ∗ 0 we draw the deviation subsample with Jr. We then eliminate the elements of {m˜ r(θ ), Σr(θ )} where: 0 1 m˜ l(θ ) 1 2 2 M 0 > κ = (ln(M)) νˆl(θ ) 0 0 0 wherem ˜ l(θ ) is the lth element ofm ˜ (θ ) andν ˆl(θ ) is the corresponding standard deviation taken from Σ(ˆ θ0). This results in a weakly smaller set of moments and their variance-covariance matrix:

∗∗ 0 ˆ ∗∗ 0 {m˜ r (θ ), Σr (θ )}

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