D Nº 027-2010-CNM San Isidro, 14 de febrero de 2011

Aggregate consumer gains due to faster price convergence can be calculated from the equation (3.15). One of the interesting features of these equations is that the welfare gain is proportional to the choice of demand elasticity. Since this elasticity has been estimated in other papers, we calibrate a demand elasticity of −6 and simply note that the welfare gain using any other elasticity (η) equals the welfare gain in Table3.7multiplied by η/6. In all cases, we base our estimates of the impact of e-retail on the rate of convergence on

Table 3.6 column 5. All of the data has been converted into 2014 yen. The first columns shows the estimated welfare gains due to price convergence in 2014 and the second column gives the counterfactual welfare gain that would have occurred if price convergence for goods available online had remained at the pre-e-commerce rate (i.e., (Dt = 0)) as given

by equation (3.14). We see that price convergence across regions during this time period led to a welfare gain of 4,315 billion yen in 2014 (about 38 billion US dollars) for residents of our sampled cities. In the second column, we compute the counterfactual gain that would have occurred if the speed of convergence had remained at the pre-e-commerce rate. This gain is lower: 2,917 billion yen in 2014. Thus, the difference between these two columns—1,218 billion yen— (approximately 11 billion US dollars) constitutes the annual welfare gain for consumers in our sample of cities in 2014.

Table 3.7: Counterfactual Welfare Gain

Year WˆR

2014(Dt= 1) Wˆ2014R (Dt= 0) ∆ ˆW2014E Total Expenditure Expenditure on Goods

2014 4,135 2,917 1,218 42,262 26,991

Notes: Unit is in billions of yen. The first three columns show welfare gains due to price arbitrage in 2014 with and without e-commerce firms, and their difference.

This number is difficult to interpret because it is only computed for residents in our sampled cities. To obtain a sense of how much this matters for welfare, we deflate the number by the total amount of expenditures of our sampled households, which is reported in the fourth column of Table3.7. We obtain an estimate of the welfare gain which equals 2.9 percent of consumer expenditures.10

One feature of the Jensen (2007) approach is that it is possible that certain locations that had on average low prices might actually lose as a result of more uniform pricing. We

10_{To get some sense of how large this is, we can compare the gain toBrynjolfsson et al.(2003) estimate}

of the gains due to Amazon’s entry into U.S. book market. That paper estimated a gain of less than 1 billion dollars in 2000—only 0.015 percent of U.S. personal consumption expenditures in that year. In other words, our estimate is about eight times as large.

Figure 3.4: Counterfactual Welfare Gain Per Household vs. Log of Population

../Rakuten/Paper/figures/welfare_vs_population_1997_2016_v15.pdf

Data source: NSFIE, JSB, and authors’ calculation. Notes: This plot plots the city-level welfare gain in 2014 per household due to enhanced arbitrage against log population in 2014. The number of observations is 48.

explore this in Figure3.4, where we plot the per household welfare gain in each Japanese city against the population of the city. There are two striking features of the plot. First, there are substantial welfare gains for the largest Japanese cities. The six largest cities in Japan—Tokyo, Yokohama, Osaka, Nagoya, Sapporo, and Kobe—all experienced welfare gains as a result of e-commerce’s effect on price arbitrage. However, more than half of the cities in our sample experienced losses and most of these losses accrued in small cities.

While we do not have data on the share of college graduates or average household income by city for Japan, we do have it by prefecture, which lets us understand how these welfare gains are distributed across regions. For prefectures with two or more cities in them, we assume that the welfare gain is equal to the population weighted average of the welfare gain in each city. In Figures3.5 and3.6, we plot the welfare gains per household in each prefecture against the share of college-educated people or the average household income in that prefecture. The data make clear a very strong positive correlation between our estimated welfare gains and income per household and a less strong, but also positive correlation between gains per household and the share of the prefecture with college education. These results suggest that the e-commerce can create winners and loserthrough pricing effects because new technologies like e-commerce benefit high-income, highly educated consumers, but it may raise costsfor low-income, less educated households. These

Figure 3.5: Counterfactual Welfare Gain Per Household vs. Share of College Education

../Rakuten/Paper/figures/welfare_vs_education_1997_2016_v15.pdf

Data source: NSFIE, JSB, and authors’ calculation. Notes: This plot plots the city-level welfare gain in 2014 per household due to enhanced arbitrage against the share of college education. The number of observations is 47.

Figure 3.6: Counterfactual Welfare Gain Per Household vs. Household Income

../Rakuten/Paper/figures/welfare_vs_income_1997_2016_v15.pdf

Data source: NSFIE, JSB, and authors’ calculation. Notes: This plot plots the city-level welfare gain in 2014 per household due to enhanced arbitrage against the household income in 2014. The number of observations is 47.

differences are economically quite significant. Households in Tokyo had gains of ¥200,000 per household (around $1,800), but low-income, low-education prefectures like Miyazaki (located in the southern tip of the main archipelago) or Akita (located in the north of Japan’s main island) actually lost comparable amounts as a result of e-commerce.11