The methodological approach is framed in the tradition of structural estimation in empirical industrial organisation in the economics profession. This approach uses discrete choice models for the estimation of demand and adds a simulated supply side to compute the industry equilibrium given by the observed data. Adding a simulated supply side to account for firms' pricing behaviour, the observed market equilibrium can be found. Moreover, by changing supply or demand conditions, the framework allows for the design of counterfactuals that simulate policy changes.
The model used here is a modified version of Duch-Brown et al. (2015), and was developed by researchers from the JRC/IPTS. The model is a partial equilibrium approach using detailed pricing data for both online and offline sales of specific products, allowing estimating with a high level of accuracy demand substitutability and market equilibrium.
From a market analysis perspective, there are three potential competitive constraints: demand substitution, supply substitutability, and potential competition. Demand substitution constitutes the most immediate and effective disciplinary force on suppliers, and in particular to their pricing decisions. Supply substitutability and potential competition are relevant in the medium to long terms, since they imply the need of adjustments through tangible or intangible assets, additional investments or strategic decisions, all of which would imply significant changes in the markets under
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consideration104. Hence, a precise estimation of demand substitutability is essential to the analysis of the effects of changes in the institutional setting of a given sector, and this is the basis for the approach taken here.
We consider the demand for four consumer electronic products: smartphones, tablets, desktops and laptops. Consumers can choose among a large variety of products that are differentiated in quality. Furthermore, consumers can either purchase these electronic products in a traditional brick-and-mortar shop (offline) or they can purchase the products through an online distribution channel. Finally, consumers can also decide not to buy an electronic product at all, in which case they can spend their money on other goods. To model the substitution patterns, a two-level nested logit model is used which allows for market segmentation according to two discrete dimensions: i) quality, which can be either high or low; and ii) the distribution channel, which is either offline or online (as in Duch-Brown et al., 2015). This model is useful since the nesting parameters enable one to assess to which extent consumers view products in the same distribution channel and/or quality category as closer substitutes. Assuming that consumers choose the product with the highest utility, one can obtain the choice probabilities for every product in every country, including the probability of purchasing the outside good (McFadden, 1978). At the aggregate level, these choice probabilities can be equated to the market shares, relative to a hypothesised potential market, defined here as representing 40% of country population.105 The demand model can be used to compute consumer surplus (McFadden, 1978 or Anderson et al. 1992), and to compute the own- and cross-price elasticities of demand (see Duch- Brown and Martens, 2016 for details). If the model conforms to the basic principles of consumer theory, the model translates preference correlations into aggregate substitution patterns. Products in the same subgroup will have higher cross-price elasticity than products in a different subgroup. Hence, one can therefore assess to which extent the online distribution channel substitutes for the traditional brick-and-mortar channel, or to what extent it provides a new source of differentiation that raises total sales for consumer electronics rather than displaces existing sales.
An oligopolistic supply side is added to the model to infer marginal costs and current economic profits; as well as to define the observed market equilibrium. The model assumes that firms maximize profits, and that they compete in prices in a differentiated products setting (Bertrand competition). As shown by Berry (1994) and Berry, Levinsohn and Pakes (1995), the profit maximising conditions can be used to compute the current marginal costs. Furthermore, this system can be used to perform policy counterfactuals, and in particular the effects of removing the GB restrictions to cross-border e- commerce. The model also calculates consumer welfare (consumer surplus) and producer welfare (profits) changes, by computing the welfare measures in the different counterfactuals and in the observed market equilibrium.
2.2 Policy options
The policy counterfactuals implemented assume that, from an observed equilibrium, lifting GB restrictions will allow consumers to buy online from the cheapest provider, thus partially arbitraging
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Alternatively, one can see this as the difference between (comparative) static and dynamic approaches.
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Alternative definitions of the market size give similar results. See Duch-Brown and Martens (2016) for further details.
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away price differentials. With the help of the model, the system of profit maximising conditions can be used to compute the counterfactual equilibrium prices if online products have become accessible across the border. It is possible, in turn, to compute the counterfactual sales, profits, and consumer surplus. The model includes both transport costs for every country pairs as well as VAT rates, which would also have an effect on price differences.
A further step in the model is to incorporate potential reactions from retailers from lifting GB restrictions. One way to do it is to assume a two-stage game in which the first stage is described by the consumers' reaction to an integrated EU online market. In this stage, we assume that consumers are able to buy online from the cheapest provider, independently of their respective locations. This triggers online cross-border purchases. Both offline and online retailers in those markets more affected by sales diversion will react, undercutting prices to adapt to the new competitive scenario, basically effective competition from cheaper prices available online abroad. At this stage, firms recognise that consumers can now buy from anywhere and adjust their prices accordingly. This will bring prices further down, both offline and online. The new equilibrium will also change consumer and producer surplus. Consumers will face even lower prices. However, it is a-priori uncertain how consumers will react when both offline and online domestic retailers cut prices. It may or may not be cheaper to purchase across the border. Due to the existence of transaction costs, a simple price reduction could make the domestically available products cheaper for domestic consumers. Domestic firms will sell more, but with a lower mark-up, so the net change in profits is an empirical question that cannot be settled a-priori.
This scenario describes a situation where there is "full lifting of all GB restrictions". We translate that scenario in costless price arbitrage and search for new varieties in online cross-border shopping, subject to parcel delivery costs plus a trade cost margin of 25% on top of the parcel delivery costs. A less ambitious policy scenario is also simulated that only partially lifts GB restrictions. The "shop-like- a-local" option implies delivery of the order in the country of the trader from where it can be picked up by the buyer or by a transport intermediary who delivers the good to the buyer in another country. We assume that these intermediary services add a fixed trade of 20€ per purchase, irrespective of the country of residence of the buyer and seller. There are already a number of online firms that provide this type of intermediary shipment services to help consumers get around GB restrictions. They have different fee structures, either flat rates (around 5-10€ per parcel) or a surcharge on shipping rates, or a combination of both. We combine all these options in a fixed rate of 20€ on top of parcel delivery costs. In summary, the less ambitious policy scenario results in model simulation with relatively higher trade costs than the "full" scenario that lifts all GB restrictions.
We do not simulate results for the "improving transparency" and "banning blocking of access" policy options because their quantitative impact on cross-border online trade is likely to be marginal. Under the transparency option, web shops should indicate clearly at the beginning of the ordering process whether any delivery restrictions apply and inform consumers upon request about the justifications for different treatment. We assume that this makes no difference in the number of cross-border purchases. The "Banning blocking of access" option would prohibit denial of access to a website. Currently, about 98% of all websites can be accessed and 49% deliver the product order (See Annex with results from the Mystery Shopping Survey). An increase to 100% access implies a 2% increase in the 49% that are already successful at the moment, or 50% successful orders in total. The difference compared to the current situation is only marginal and falls within the error margins of the estimation results.
85 2.3 Extension to all online sales
In order to extend the results from electronics products to all online goods sales, we use a simple linear extrapolation method that proceeds as follows:
First, we extend the results from the 4 electronics products to all electronics sales. To do that, we use the share of these products in the aggregated consumer electronics industry. On average, these four products represent about 60% of total consumer electronics retail sales, with differences across countries. We linearly extrapolate our estimated measures of welfare and sales by country to cover the entire electronics sector using the corresponding shares for each of the 10 countries for which we have detailed data, assuming that these 4 products are representative for the entire electronics products sector, and that the share would remain constant in the absence of GB. Second, we extend geographically from EU10 to EU28. The 10 countries in the sample represent 85% of the EU-28 consumer electronics industry. We have information of the share of each of the countries not included in the detailed database, so we can impute a value that corresponds to the observed share in 2014. For this purpose, using the share of electronics in total retail sales by country, and assuming proportionality, we linearly extrapolate to get the corresponding values for each one of the missing countries. We can then compute also the EU28 aggregate for consumer electronics. Third, we extend from electronics to all online goods sales. In a similar procedure, we use the share of electronics in total e-commerce by country, considering that this proportion does not change across countries and linearly extrapolate the results. It is again possible to compute the EU-28 aggregated figure.
Finally, we introduce corrections for two indicators that change with respect to our benchmark sector (consumer electronics): price differences between country markets and the extent of GB. First, we take the ratio of average cross-border price differences between the benchmark product (electronics, as observed in the detailed GfK dataset) and other product categories (as observed in the more aggregated Mystery Shopping Survey). We weight the figures obtained before by this measure in order to take into consideration that more price arbitrage will occur in sectors with higher online price differences between countries. Second, we take the ratio of the probability that GB occurs in our benchmark product (electronics) and in the target product (as measured by the Mystery Shopping Survey) to correct for the potential trade expansion in each product category after lifting GB restrictions. In so doing, the procedure considers also the maximum potential trade creation opportunities derived from online price differences and GB intensities. Detailed results in Duch- Brown and Martens (2016) based on the Mystery Shopping Survey indicate that cross-country price differences in clothing are 4% higher than in consumer electronics. Hence, there is more price variation in clothing than in consumer electronics. Moreover, attempts to buy clothing online cross- border are only 82% as likely to get geo-blocked as attempts in consumer electronics. In other words, clothing has more cross-border price variation but less GB, compared to consumer electronics.