Upload
jason-richard
View
217
Download
0
Embed Size (px)
Citation preview
1
Limiting Currency Volatility toStimulate Goods Market Integration: A Price Based Approach
byDavid Parsley and Shang-Jin Wei
Vanderbilt University Brookings
Institution
2
Introduction
Does exchange rate stabilization affect goods market integration?
We distinguish two types of stabilization Instrumental - reducing exchange rate
volatility through intervention in the fx market
Institutional - reducing volatility through an explicit currency board or common currency
3
Introduction
Two strands of empirical research into goods market integration: studies examining actual flows of goods price-based studies (e.g., PPP, LOP)
We adopt a price-based approach a unique multi-country data set on
prices of very disaggregated products (e.g., light bulbs & onions)
4
Studies of observed trade flows
McCallum (95), Wei (96), Heliwell (98) conclusion: observed volume of trade
across national boundaries are much less than within countries
Rose (2000), Frankel and Rose (2000), Rose and Engel (2000), Rose and van Wincoop (2001) conclusion: common currencies increase
bilateral trade by as much as 300%
5
Limitations of observed trade flows
Two countries may have similar endowments and autarkic prices low trade
two countries may trade extensively with a 3rd country but little w/each other
Wei (1996) argues welfare implications from observed trade flows need auxiliary assumptions
6
Intuition
If two countries produce similar (highly substitutable) output, increased trade may raise welfare only marginally
Alternatively, if two countries have distinct comparative advantages, a slight rise in trade may substantially raise welfare
7
Economist Intelligence UnitPrice Data
Local currency price comparisons for > 160 goods and services from up to 122 cities
We select 95 goods and 83 cities
8
1. Apples (1 kg) (supermarket) 49. Onions (1 kg) (supermarket)2. Aspirin (100 tablets) (supermarket) 50. Orange juice (1 l) (supermarket)3. Bacon (1 kg) (supermarket) 51. Oranges (1 kg) (supermarket)4. Bananas (1 kg) (supermarket) 52. Peaches, canned (500 g) (supermarket)5. Batteries (two, size D/LR20) (supermarket) 53. Peanut or corn oil (1 l) (supermarket)6. Beef: filet mignon (1 kg) (supermarket) 54. Peas, canned (250 g) (supermarket)7. Beef: ground or minced (1 kg) (supermarket) 55. Pork: chops (1 kg) (supermarket)8. Beef: roast (1 kg) (supermarket) 56. Pork: loin (1 kg) (supermarket)9. Beef: steak, entrecote (1 kg) (supermarket) 57. Potatoes (2 kg) (supermarket)10. Beef: stewing, shoulder (1 kg) (supermarket) 58. Razor blades (five pieces) (supermarket)11. Beer, local brand (1 l) (supermarket) 59. Scotch whisky, six years old (700 ml) (supermarket)12. Beer, top quality (330 ml) (supermarket) 60. Sliced pineapples, canned (500 g) (supermarket)13. Butter, 500 g (supermarket) 61. Soap (100 g) (supermarket)14. Carrots (1 kg) (supermarket) 62. Spaghetti (1 kg) (supermarket)15. Cheese, imported (500 g) (supermarket) 63. Sugar, white (1 kg) (supermarket)16. Chicken: fresh (1 kg) (supermarket) 64. Tea bags (25 bags) (supermarket)17. Chicken: frozen (1 kg) (supermarket) 65. Toilet tissue (two rolls) (supermarket)18. Cigarettes, local brand (pack of 20) (supermarket) 66. Tomatoes (1 kg) (supermarket)19. Cigarettes, Marlboro (pack of 20) (supermarket) 67. Tomatoes, canned (250 g) (supermarket)20. Coca-Cola (1 l) (supermarket) 68. Tonic water (200 ml) (supermarket)
Sample of Economist Price Data
9
Abu Dhabi, UAE Colombo, Sri Lanka London, United Kingdom San Francisco, United StatesAmman, Jordan Copenhagen, Denmark Los Angeles, United States San Jose, Costa RicaAmsterdam, Netherlands Dakar, Senegal Luxembourg, Luxembourg Santiago, ChileAsuncion, Paraguay Detroit, United States Madrid, Spain Sao Paulo, BrazilAthens, Greece Douala, Cameroon Manila, Philippines Seattle, United StatesAtlanta, United States Dublin, Ireland Mexico City, Mexico Seoul, South KoreaAuckland, New Zealand Guatemala City, Guatemala Miami, United States Singapore, SingaporeBahrain, Bahrain Helsinki, Finland Montevideo, Uruguay Stockholm, SwedenBangkok, Thailand Hong Kong, Hong Kong Moscow, Russia Sydney, AustraliaBeijing, China,P.R. Honolulu, United States Mumbai, India Taipei, TaiwanBerlin, Germany Houston, United States Nairobi, Kenya Tehran, IranBogota, Colombia Istanbul, Turkey New York, United States Tel Aviv, IsraelBoston, United States Jakarta, Indonesia Oslo, Norway Tokyo, JapanBrussels, Belgium Johannesburg, South Africa Panama City, Panama Toronto, CanadaBudapest, Hungary Karachi, Pakistan Paris, France Tunis, TunisiaBuenos Aires, Argentina Kuala Lumpur, Malaysia Pittsburgh, United States Vienna, AustriaCairo, Egypt Kuwait, Kuwait Port Moresby, Papua New Guinea Warsaw, PolandCaracas, Venezuela Lagos, Nigeria Prague, Czech Republic Washington DC, United StatesCasablanca, Morocco Libreville, Gabon Quito, Ecuador Zurich, SwitzerlandChicago, United States Lima, Peru Riyadh, Saudi Arabia
Cities Included
10
Let be the U.S. dollar price of good k in city i at time t. For a given city pair (i,j) and a given good k at a time t, we define the common currency percentage price difference as:
tkjPtkiPtkijQ ,,ln,,ln,,
Our Approach
11
Our Approach
We study all bilateral price comparisons the data allow.
There are 3403 city pairs (=(83x82)/2) – each with 11 (annual) time periods.
Thus, for each of the 95 prices the vector of price deviations will contain 37,433 (3403x11) observations without missing values.
12
Dispersion in Price Differences
We focus on the cross sectional dispersion (across goods) of common currency price differentials for each city-pair and time period
Any particular realization of the common currency price differential, Q(ij,k,t) can be either positive or negative without triggering arbitrage as | Q(ij,k,t) | < the cost of arbitrage
13
Dispersion and Market Integration
The existence of arbitrage costs implies that must fall within a range
Any reduction to barriers to trade (i.e., movements toward market integration) should reduce the no-arbitrage range. Therefore the strategy we adopt is to study a measure of the dispersion of Q(ij,k,t) through time
14
Table 1: Percentage Price Deviations in Absolute Value (averaged over all years)
Asuncion-TaipeiLight Bulbs 65.4Onions 115.0
Paris-Vienna (1990-1998, pre-euro)Light Bulbs 13.4Onions 45.3
Paris-ViennaLight Bulbs 11.4Onions 40.1
Chicago-HoustonLight Bulbs 8.9Onions 42.7
15
Table 2: Dispersion and its DeterminantsAverages across city pairs and time
Observations Dispersion Distance Exch Rate Vol TariffsAll City Pairs 36531 0.638 8215 0.067 22.3
Hard Peg City Pairs 454 0.576 8602 0.001 9.8
U.S. Only City Pairs 975 0.378 2681 0.0 0.0
CFA Only City Pairs 110 0.629 3139 0.027 41.9
Euro City Pairs 110 0.419 1273 0.0 0.0
16
Intercity Price Dispersion
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.30
0.35
0.40
0.45
0.50
0.55
0.60Chicago-Houston
Chicago-Paris
Paris-Vienna
17
Regression analysis
We estimate the following baseline equation:
tijij
ijijij
dummiestimeandcityTariff
EuroUSCFAHPeg
xrvoldistdisttijqV
,8
7654
32
11 )()ln()ln(),(
18
Table 3: Benchmark Regression Results
Equation 1 Equation 2 Equation 3Log Distance 0.1267 0.1320 0.1216
(0.0213) (0.0230) (0.0229)
Log Distance Squared -0.0060 -0.0063 -0.0057(0.0014) (0.0015) (0.0015)
Nominal Exchange 0.0393 0.0362 0.0542Rate Variability (0.0114) (0.0116) (0.0100)
Hard Peg -0.0438 -0.0325 -0.0248(0.0065) (0.0070) (0.0067)
CFA -0.0149 -0.0102 -0.0090(0.0148) (0.0156) (0.0152)
U.S. -0.1104 -0.1015 -0.0955(0.0044) (0.0048) (0.0047)
Euro -0.0342 -0.0247 -0.0165(0.0056) (0.0059) (0.0055)
Weighted Avg. Tariff 0.0044 0.0041 0.0043(0.0001) (0.0001) (0.0001)
Absolute Wage 0.0021 0.0302Difference (0.0014) (0.0038)
Absolute Wage -0.0025Difference Squared (0.0003)
Adjusted R2 .23 .22 .23Number of Observations 27406 21863 21863
Robust standard errors are in parenthesis. All equations include city and time fixed effects.
19
Discussion
Dispersion increases with distanceExchange rate variability increases
dispersion reducing it to zero from the sample
average, reduces dispersion by .26% (=.067*.039*100)
Participating in a Hard Peg reduces dispersion by 4.4% - an order of
magnitude bigger
20
Discussion (continued)
Being a member of the CFA has no effect
Being a member of the euro ~ to hard peg
Being in a political union (US) has the largest institutional effect
21
Tariff Equivalents
Effect of the euro: ~ 4 percentage point reduction in tariffs on the same order of magnitude as the elimination of tariffs under the common market program
Effect of reducing xr volatility to zero for any random pair of countries is only 0.3%
Effect of political & economic Union (U.S.) ~13 percentage point reduction in tariffs
22
Summary
Institutional exchange rate stabilization has a much larger effect than instrumental stabilization
reducing xr vol < hard peg < full economic & political integration
The effect is non-trivial. On the order of the common market effect
A non-credible peg (CFA) has no effect
23
Robustness & Extensions
Additional explanatory variablesre-definitions of explanatory
variablesdifferent measures of dependent
variablealternative econometric
specifications
24
Conclusions
Institutional exchange rate stabilization matters for goods market integration.
The economic benefits of currency unions (Hard pegs) are an order of magnitude larger than simply reducing exchange rate vol to zero
Our results suggest that further economic and political integration can have an additional substantial impact on goods market integration