The Macroeconomics of Bargain Hunting , by Greg Kaplan and Guido Menzio
Greg Kaplan is a Assistant Professor of Economics at Princeton University. His research interests are in applied macroeconomics. Guido Menzio is Associate Professor of Economics at the University of Pennsylvania. His focus is on macroeconomic applications of search theory. Kaplan’s RePEc/IDEAS profile and Menzio’s RePEc/IDEAS profile
Bargain hunting refers to the activities in which buyers can engage in order to acquire goods and services at lower prices. For example, buyers may acquire goods and services at lower prices by spending more time searching for cheap sellers or by waiting for temporary sales. The goal of our current research is to understand the macroeconomic implications of aggregate changes in the extent of bargain hunting. In particular, we want to understand how aggregate changes in the extent of bargain hunting affect the pricing strategy of sellers, the incentives of sellers to enter or expand their presence in the product market and, in turn, their demand for the inputs used in production and retailing. Given the availability of pricing data for consumption goods, our research focuses on the effect of bargain hunting in the retail market. Yet, the mechanisms highlighted in our research are likely to operate at any other stage of the production chain.
1. Different people, different prices
The first step in our analysis is to identify the types of buyers who pay low prices in the consumption goods market. Aguiar and Hurst (2007) documented that older people pay lower prices than younger people. For example, they showed that people aged 60 to 64 pay roughly 3% less for the same consumption goods than people aged 25 to 29. In Kaplan and Menzio (2013a), we use the Kielts Nielsen Consumer Panel (KNCP) to document that–among the working age population–the non-employed pay lower prices than the employed. Indeed, we found that the non-employed pay between 1 and 5% less than the employed for the same consumption goods. The smaller difference obtains when we define goods at the level of the barcode. The larger difference obtains when we define goods by their characteristics, rather than by their barcode.
2. Price dispersion: evidence and theory
The second step in our analysis is to understand why sellers charge different prices for identical goods, as this determines the cause of price differentials between different types of buyers, as well as the effect on sellers of changes in the distribution of buyers’ types. In Kaplan and Menzio (2013a), we use KNCP to measure the extent of price dispersion for identical goods within the same city and during the same quarter, and to decompose price dispersion into three different sources, each related to alternative theories of price dispersion. To illustrate the spirit of our decomposition, consider two bottles of ketchup that sold at different prices in the same market and during the same period of time. First, the two bottles may have sold at different prices because one was sold at an expensive store (i.e. a store where goods are on average expensive) and the other was sold at a cheap store. We refer to this source of price dispersion as the store component. Second, the two bottles of ketchup may have sold at different prices because, although they were sold at equally expensive stores, one was sold at a store where ketchup is expensive (relative to the average price of that store) and the other was sold at a store where ketchup is cheap (relative to the average price of that store). We refer to this source of price dispersion as the store-specific good component. Third, the two bottles of ketchup may have sold at different prices because, although they were sold at the very same store, one was sold at a high price and the other was sold at a low price perhaps because of a temporary sale. We refer to this source of price dispersion as the transaction component.
The decomposition of price dispersion allows us to assess the relative importance of four popular theories of price dispersion: (i) amenities, (ii) heterogeneous monopolists, (iii) intertemporal price discrimination and (iv) search frictions. According to the amenity theory of price dispersion, the product market is perfectly competitive and, yet, identical goods trade at different prices only because they are bundled with different amenities (e.g., location of the store, customer care provided at the store, etc…). For example, a bottle of ketchup will be expensive at a store with a parking lot reserved to its customers and will be cheap at a store without a reserved parking lot. Since amenities are generally specific to a store, rather than to a particular good or transaction, the amenity theory implies that, that for any good, most of the dispersion in prices should be accounted for by variation in the expensiveness of the stores at which the good is sold. That is, the store component should account for most of price dispersion.
According to the monopoly theory of price dispersion, identical goods are sold at different prices because they are traded by local monopolists who face different marginal costs or different demand elasticities (see, e.g., Golosov and Lucas 2007). For example, a monopolist who faces a relatively inelastic demand for ketchup will charge a higher price for the same bottle of ketchup than a monopolist who faces a relatively elastic demand. As long as the differences in marginal costs and demand elasticities between stores are correlated across goods, then the monopoly theory implies that most of the dispersion in prices for any particular good should be accounted for by variation in the expensiveness of the stores at which the good is sold. That is, the store component should again account for most of price dispersion.
According to the theory of price discrimination, identical goods are sold at different prices because local monopolists vary their price over time in order to discriminate between different types of buyers (see, e.g., Conlisk et al. 1984, Sobel 1984 or Albrecht et al. 2012). For example, consider a monopolist facing a constant inflow of low valuation buyers who have a high intertemporal elasticity of substitution for consumption and a flow of high valuation buyers who cannot substitute consumption intertemporally. The monopolist will find it optimal to follow a pricing cycle. In particular, the monopolist will keep the price relatively high for several periods. At this relatively high price, high valuation buyers will purchase the good, while low valuation buyers will wait. Eventually, the number of low valuation buyers will be sufficiently large to induce the monopolist to lower the price for one period and sell to all of them. According to the theory of intertemporal price discrimination, the variation in prices for the same good should be accounted for by variation in the price at which the good is sold at the same store on different days during the same quarter. That is, the transaction component should account for most of price dispersion.
The presence of search frictions in the product market can simultaneously explain why buyers do not arbitrage away price differences and why sellers choose to charge different prices (see, e.g., Burdett and Judd 1983). Consider a market populated by a large number of sellers and buyers. Due to search frictions, an individual buyer cannot purchase from any seller in the market, but only from a subset of sellers. In particular, some buyers are able to purchase from only one seller (uncontested buyers), while other buyers are able to purchase from multiple sellers (contested buyers). In this environment, if all sellers charged the same price, an individual seller could increase its profits by posting a slightly lower price and sell not only to the uncontested buyers it meets, but also to the contested ones. Hence, in equilibrium, identical sellers must randomize over the price of the good and price dispersion obtains. Depending on the pattern of randomization across goods and days, the search theory of price dispersion may generate variation that is accounted for by the store component (if sellers randomize in a way that is strongly correlated across goods), by the store-good component (if sellers randomize independently across goods) and by the transaction component (if sellers randomize independently across goods and days). What distinguishes the search theory of price dispersion from other theories is the fact that it can generate dispersion in the price of a good that is sold at stores that are on average equally expensive.
Empirically, we find that the store component accounts for only 10% of the variance of transaction prices for the same good in a given city and quarter. This finding suggests that the amenity and monopoly theories of price dispersion are unlikely to be quantitatively very important. In contrast, the store-good component accounts for 35 to 45% of the variance of prices, while the transaction component accounts for the remaining variance. These findings suggest that search frictions and intertemporal price discrimination are the most likely causes of price dispersion. Importantly, both the search and intertemporal price discrimination theories imply that the types of buyers who pay lower prices (e.g., the old and the unemployed) achieve such lower prices because they are more likely to be bargain hunters, that is, by devoting time and effort to visiting multiple stores, seeking out temporary sales or finding close substitutes for goods. Both theories imply that an increase in the fraction of bargain hunters will induce sellers to lower their prices without any concurrent change in the costs of producing and retailing goods.
3. Bargain hunting and shopping externalities
In Kaplan and Menzio (2013b), we combine a search-theoretic model of the product market with a search theoretic model of the labor market to understand the general equilibrium implications of aggregate changes in bargain hunting brought about by changes in the fractions of employed and unemployed buyers. In particular, we model the product market as in Burdett and Judd (1983). The equilibrium of this market determines the extent of price dispersion and, given the difference in search intensity between employed and unemployed buyers, the extent to which unemployed buyers pay lower prices. We model the labor market as in Mortensen and Pissarides (1994). The equilibrium of this market determines the fraction of workers who are unemployed and the difference in income between employed and unemployed workers.
Our main theoretical finding is that changes in the composition of buyers can have such a strong effect on sellers as to generate multiple equilibria. The finding is intuitive. When a firm expands its workforce, it creates external effects on other firms. On the one hand, the expanding firm increases the tightness of the labor market and hence makes it more costly for other firms to hire additional workers. We refer to this effect as the congestion externality of employment. On the other hand, the expanding firm tilts the composition of buyers towards types who have more income to spend and less time to search for low prices (i.e. employed buyers). This increases other firms’ demand and market power, and hence, increases their value from expanding their presence in the product market, which entails hiring additional workers. We refer to these effects as the shopping externalities of employment. If the differences in income and/or shopping time between employed and unemployed buyers are sufficiently large, the shopping externalities dominate the congestion externality, employment decisions of different firms become strategic complements and multiple rational expectations equilibria obtain. Different equilibria are associated with different expectations about future unemployment. Yet, in all equilibria, expectations are rational, in the sense that the realized path of unemployment coincides with the expected one.
Our main quantitative finding is that–when calibrated to the observed differences in shopping behavior between the employed and the unemployed–the economy features multiple rational expectations equilibria. In particular, we calibrate the model economy to match the empirical differences in shopping time (25%), prices paid (-2%) and expenditures (-15%) between unemployed and employed workers, as well as the rates at which workers transit between unemployment and employment. Given these calibration targets, the economy has three steady states: one with an unemployment rate of approximately 5%, one with an unemployment rate of approximately 9% and one with no economic activity. Moreover, for any initial unemployment rate, there are rational expectations equilibria leading to each one of the three steady states. Multiplicity obtains because the firms’ value from entering or scaling up their presence in the product market turns out to be fairly sensitive to the unemployment rate. Interestingly, this happens not so much because unemployed buyers spend less than employed buyers, but mainly because unemployed buyers search more than employed buyers. That is, the firms’ value from participating in the product market is quite sensitive to the unemployment rate because the unemployment rate has a rather strong effect on competitiveness of the product market.
The existence of multiple rational expectations equilibria suggests that economic fluctuations may be due not only by changes in fundamentals (i.e. technology, preferences or policy), but also to changes in the agents’ expectations about future unemployment. We formalize this idea by developing a version of the calibrated model in which the agents’ expectations about long-run unemployment follow a 2-state Markov switching process. In the optimistic state, agents expect to reach the steady state with the lowest unemployment rate (5%). In the pessimistic state, agents expect to reach the steady state with the intermediate unemployment rate (9%). Shocks to the agents’ expectations generate fluctuations in unemployment, vacancies and job-finding rates that are large compared to those generated by productivity shocks. Moreover, unlike productivity shocks, shocks to the agents’ expectations generate large, procyclical fluctuations in the value of firms and rather small, countercyclical fluctuations in real labor productivity. Interestingly, the response of the economy to a negative expectation shock looks a lot like the behavior of the US economy during the Great Recession and its aftermath. This finding suggests the possibility that the financial crisis may have acted as a coordination device in focusing the agents’ expectations about future unemployment towards the pessimistic steady state.
Our theory of multiple equilibria is theoretically novel. Unlike Benhabib and Farmer (1994), our theory does not require increasing returns to scale in production. Unlike Diamond (1982), our theory does not require increasing returns in matching. Unlike Heller (1986), our theory does not hinge on demand externalities. Instead, our theory of multiple equilibria builds on two simple mechanisms. The first mechanism links unemployment, search and competition: when unemployment is lower, buyers spend less time searching for low prices and, in doing so, they make the product market less competitive and drive prices up. The second mechanism links revenues, entry and labor demand: when revenues are higher because of either higher demand or higher prices, new firms want to enter the product market, established firms want to scale-up their presence in the product market and, since both activities require some labor, labor demand increases.
4. Direction for future research
The fact that buyers can affect the price they pay for goods and services by engaging in bargain hunting activities has profound implications for the behavior of the macroeconomy. Our current work shows that, because of the differences in the amount of time spent shopping by employed and unemployed buyers, the unemployment rate has a strong effect on the competitiveness of the product market, on the number and size of sellers and, in turn, on labor demand. Indeed, the effect of the unemployment rate is so strong as to create multiple equilibria and, hence, open the door for non-fundamental shocks. Our current work provides just one example of the effect of bargain hunting on the macroeconomy. For instance, it would be interesting to study the macroeconomic effects of changes in bargain hunting brought about by changes in the age distribution rather than by changes in unemployment. Similarly, it would be interesting to study the macroeconomic effect of aggregate changes in bargain hunting in the intermediate market rather than in the consumption goods market.
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