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Consumer Arbitrage Across a Porous Border. (2012). Tappata, Mariano ; Head, Keith ; Chandra, Ambarish.
In: CEPR Discussion Papers.
RePEc:cpr:ceprdp:8730.

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  24. The estimated crossing fraction is given by dividing ˆ nc by our estimate of cars at risk, Nc = Popct × CPCc × 30. Census division populations, Popct, are available annually from Cansim Table 051-0034, provided by Statistics Canada. Car registration data used for generating CPCc come from Statistics Canada publication 53-219-XIB (“Road Motor Vehicle Registrations 1998”).
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  25. The secondary method of calculating distances (employed in columns (5) and (6) of Table 7) takes into account the fact that crossers from a given census division do not always use the same port. At the CD level, we know shares of crossers from each CD that cross at 102 different ports. We use the average shares of the top 5 ports over the 1990 to 2010 period to construct weighted average distance and time from the CD’s geographic centroid. This measure generates several outliers in large CDs that have centroids that are far from the border but populations that are concentrated close to the border. A.3 Prices, exchange rates, and incomes Exchange rates obtained from Pacific Exchange Rate Service (fx.sauder.ubc.ca).
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  26. The US Consumer Price Index is the US city average for all items and all urban consumers, not seasonally adjusted (Series ID CUUR0000SA0 from bls.gov/cpi# data). Canadian prices are from CANSIM Table 3260020, 2009 basket, all items. We choose July 1993 as the base period because in that month the nominal exchange rate was equal to the annual purchasing power parity rate provided by the OECD and thus the RER was approximately 1. Prices for regular unleaded gasoline at self service filling stations are obtained from CANSIM Table 3260009 for a major urban centre for each of the border provinces. We obtained median household income from the CHASS Canadian Census Analyser for the years 1991, 1996, 2001, and 2006. We linearly interpolated and extrapolated around July of each census year to obtain the monthly data from 1990 to 2010. B Additional Figures
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  27. Thomadsen, R. (2005). The effect of ownership structure on prices in geographically differentiated industries. RAND Journal of Economics 36(4), 908–929.

Cocites

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  2. Cross-Border Shopping and Employment Patterns in the Southwestern United States. (2019). Fullerton, Thomas ; Walke, Adam G.
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  3. Hump-shaped cross-price effects and the extensive margin in cross-border shopping. (2019). Ulsaker, Simen ; Steen, Frode ; Friberg, Richard.
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  4. A case study on Germany’s aviation tax using the synthetic control approach. (2019). Borbely, Daniel.
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  5. Heterogeneity in the tax pass-through to spirit retail prices: Evidence from Belgium. (2019). Serse, Valerio ; Hindriks, Jean.
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  6. Hump-shaped cross-price effects and the extensive margin in cross-border shopping. (2019). Steen, Frode.
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  7. Tax Avoidance through E-Commerce and Cross-Border Shopping. (2019). Harbolt, Benjamin.
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  8. A case study on Germanys aviation tax using the synthetic control approach. (2018). Borebly, Daniel.
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  9. Hump-shaped cross-price effects and the extensive margin in cross-border shopping. (2018). Ulsaker, Simen ; Steen, Frode ; Friberg, Richard.
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  10. Exchange rates, cross-border travel, and retailers: Theory and empirics. (2018). Baggs, Jen ; Lapham, Beverly ; Fung, Loretta.
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  11. Fuel tourism in Dutch border regions: Are only salient price differentials relevant?. (2018). Jonker, Nicole ; Jansen, David-Jan.
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  12. Price elasticity of gasoline smuggling: A semi-structural estimation approach. (2018). Ghoddusi, Hamed ; Rahmati, Mohammad H ; Rafizadeh, Nima.
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