量化历史班版aer02transport costs

1、Trade and Growth in HistoryCarol H. ShiueUniversity of Colorado, NBER, CEPRPrepared for the PKU 2015 Summer School in Quantitative EconomicsTrade and Growth in HistoryMany studies showing increasing economic trade and integration in relation to developmentBoth domestically as well as internationally

2、In relationship to Commodity trade, Capital market integrationAnd also labor market integration,Trade and Growth in HistoryTrade not only has clear welfare gains, but it is also only relatively recentPreviously, two main barriers that prevented trade:Political barriers were highTransport costs were

3、highTrade and Growth in History“Revolutionary developments in transport have been an essential feature of the rapid growth of the western world of the past two centuries. Reduction in the cost of carriage has enabled specialization and division of labor on a national and international basis to repla

4、ce the relatively self-sufficient economies that predominated in the western world two centuries ago.” (North, 1958).Freight rate indices(w/r) in Free Trade Old World(w/r) in Protected Old World( w/r ) in the New WorldInequality over the last 2000 yearsTrade and HistoryIs access to commodity markets

5、 through more trade a precondition for structural change and overall economic development and growth historically?Is it the necessary and sufficient condition for growth? “Transport Costs and the Geography of Arbitrage in Eighteenth Century China” Carol H. ShiueUniversity of Colorado, NBER, CEPRPrep

6、ared for the Peking University Summer School in Quantitative HistoryJuly 2015Motivation and Implications Conventional belief: historically, reductions in cost of transport contributed to subsequent growth in the West Implications also important for trade and development policyTrade and growth: “engi

7、ne” (Nurske, 1961) vs. “handmaiden” (Kraske, 1970)Motivation and ImplicationsThe trade question also is at the basis of why there might have been re-occurrences of famine and food crisesHistorical famines were fairly common (Landes 1989; Appleby 1979).“people could be starving to death in one provin

8、cethere would be no way to get the food across the few score miles that separated them that did not consume all the food that could be carried” (Evans 1984)Motivation and ImplicationsConsider China:Could argue: High transport costs in many provinces.Failure to attain substantial economic growth. As

9、a test of market development-What can price correlations reveal? Regional correlations only reflect common weather (supply) shocksno trade in this caseSelf-sufficient economies, relatively segmented markets 2. Correlations systematically related to the costs of trade (distance) between the markets a

10、nd arbitrageSystematic patterns of integrated markets and diversified local shocks is evidence of trade1718Contributions to the literatureExtent of the Market in ChinaMarketing regions or “macro-regions” (Skinner 1977)40% of value of all traded commodities was in grain (Wu 1985)10-20% of grain consu

11、med in Yangzi Delta (18th c) was imported (Pomeranz 2000, 2001)Large volumes do not mean that markets were integratedintegrated markets can occur even under zero tradeNeed more comprehensive studies on urban and rural Price studies (e.g. Marks 1991, Perdue 1992, Li 2000) Treated each province separa

12、telyNo means to test for common supply shocks driving the correlations“Transport Costs and the Geography of Arbitrage”, (AER 2002)This paper uses price data to assess the extent of trade Core 10 provinces and 54 years: larger sample than had existed, covers all prefecturesTakes into account supply s

13、hocksthe main driver of spurious correlation that would make correlations per se problematicResults of this paper suggests China may be more integrated than previously thought, counters conventional wisdom that pre-industrial economies were regionally self-sufficient until very recentlyIn addition,

14、intertemporal effects are important substitutes for trade, further reducing the sole importance of trade in inducing growth Historical ContextMarkets in Qing ChinaGeographically, provinces are as large as European nationsLarge cost differences in waterway transport vs. land transportA 9-10 fold cost

15、 difference between water and land over long distancesNo political barrier to trade (as in Europe)Outline of remainder of PresentationA Model of Trade and StorageDataEmpirical ApproachResults A Benchmark Model of Trade and StorageWhether it is economically rational to store depends on next periods p

16、rices, which varies across regions according to regional accessibility of markets; i.e. there is a substitution effect between obtaining commodities via trade versus storage (Wright and Williams, 1991)Assumptions:Weather shocks exogenous and i.i.d.Price takers, rational expectations on ing yields an

17、d priceA Benchmark ModelStorage is an intertemporal arbitrage decision:k is the constant per unit cost of storagePt and St is the price and storage at time tr is the rate of interest A Benchmark ModelTransport costs, z, drive a wedge between prices in the two economies, a & b:Predictions of the Mode

18、lLet z =transport costs and z* = price difference between marketsKey Predictions:If z z*, there is no trade. There is only storage. Storage smooths harvest-to-harvest (or year-to-year) price fluctuations within each marketEach region stores independentlyPrices in each region are not related to zPric

19、es co-move with common supply shocks (weather)If z z* or z = z*, regions store as well as trade. Trade smooths intra-year price fluctuations among different marketsTrade and storage mechanisms are to some extent substitutesPrice co-movements between markets are associated with transport costsHigher

20、price correlations where z is lowerDataA new sample of sub-provincial data created from published and unpublished archival sources; 10 provinces (121 prefectural markets) for 54 years, 1742-1795. Prices Bi-annual data on mid-grade rice.Archival source: liangjia dan price reports of the Qing, collect

21、ed myself from microfilms housed in the No. 1 Archives of Beijing. Weather Degree of annual “wetness and dryness”, including droughts and floods.Analysis is based on qualitative weather variable from 1 to 3. Source: State Meteorological Administration (1981). DistanceDistance between prefectural mar

22、kets using spherical formula. Latitude and longitude of historical location of capital cities. Source: Playfair (1965).282930Empirical ApproachGroup 1): River region: prefectures where the capital is no more than 250 km from a major river (34 prefectures)Group 2): Coastal region: prefectures along t

23、he coast. Includes 9 prefectures along the coast situated at the mouth of the Yangzi River and also in Group 1 (28 prefectures)Group 3): Semi-Inland regions: prefectures next to Group 1 and 2, with capitals located outside a zone of approximately 50-75 kilometers from those boundaries (34 prefecture

24、s)Group 4) Inland regions: prefectures for which the prefectural capital lies approximately 150 kilometers or more from the nearest boundary of Group 1 and 2 (34 prefectures)Empirical ApproachInterregional Market Integration and Common Weather Shocks: prices to reveal the underlying pattern of trade

25、Use an ex-ante classification of markets based on distance from a major waterwayobjective albeit arbitrary Remove influence of weather on price correlationRegress Price correlationi j on a constant, Distancei j + Weatheri j + error termIs there a systematic pattern consistent with the presence of tr

26、ade? Bilateral price correlation in relation to distance and weatherDistance is negative, stronger for inland areas, less so for river areasWeather taken out of price correlation. Panel II takes out time trend, same resulta 1 km increase in distance in inland areas has a two or three times as large

27、a price reduction in PC as river areasLower panels allow for a spline within each group to see if there is a critical distance beyond which there is “no trade”Intertemporal market integrationIs there a difference in the price response to local weather as we move across groups? Is it the case that re

28、gions that did not have access to waterways (semi-inland and inland)i.e. more limited means of using trade to spread shockswere therefore also more vulnerable to local harvest shocks from weather?This addresses the claim that people in rural areas were more vulnerable to supply shocks. Intertemporal

29、 market integrationDefine:Pt is the mean price in each year (month 8)Qj, t is the current year t weather in prefecture j, takes on values 1 (normal), 2 (fair), 3 (bad)Qj, t-1 is last years weatherQ*j, t is a fixed effect for worst years of weather = 3R is a fixed effect for the river and coastal pre

30、fecturesYear is annual time trend Intertemporal market integrationCol (1): All prefectures: shows this year & last years weather matter, with smaller impact from the worst weather. Worst weather (= 0.023) signif at 10% but not 5%. Response to last years weather in landlocked areas o (=0.122). Col (2

31、): Waterway prefectures (river and coastal) versus non-waterway (semi and inland) results show the worst shocks from last years weather do not matter in non-waterway regions , but they DO matter significantly in waterway prefectures signif; however, last years supply fluctuations dissipate more rapi

32、dly in waterway prefectures. Compare o to 1 (=0.029). Intertemporal market integrationIn general, trading regions trade with each other, and is less affected by the weather shocks, but for particularly bad shocks, the more landlocked areas are actually on the whole BETTER at ing the severe shocks.Th

33、is points to the important presence of intertemporal trade, ie. storage of grain. Col (3) and (4): Shows results are robust to the inclusion of a time trendCarryover from year-to-yearPrice patterns Do regions differ in the degree of year-to-year price variation?First, rank order all prefectures by c

34、oefficient of variation (c.v.), which is the (std dev)/mean Reveals variability relative to the meanRanking further classed into 4 bins of 30 prefectures each: from 1 (highest c.v.) to 4 (lowest c.v.)high c.v. means that year to year variations are highFor each bin, count how many prefectures belong

35、 in each geographic groupRiver and Coastal regionsInland regionsSemi-inland regionsCarryover from year-to-yearPrice patterns Do regions differ in the degree of year-to-year price variation?Yes, highest year-to-year coefficient of variation in the river and coastal areas (intuitively: not storing muc

36、h, so each year is a new drawtrading regions smooth shortages among each other, but a really bad shock can hit everyone)Intermediate variation in the semi-inland regionLowest year-to-year coefficient of variation in the inland regions(due to greater storage, relatively low variation)Interpretation:

37、Consistent with inland prefectures doing most of their smoothing through storage, producing stable year-to-year price patterns.Smoothing within harvest yearDo regions differ with respect to within-year price variation?Defined approximately as period right before harvest to right after: Pt,8/Pt,2 (th

38、ink of as slope of prices within the year)Again, rank in 4 bins from highest to lowest Large price ratios within the year because storage does not allow much within year smoothing compared to spot trade Highest within year variation in the inland areas areasIntermediate variation in the semi-inland

39、regionLowest within year variation in the river and coastal areasRiver and Coastal regionsSemi-inland regionsInland regionsStorage patterns across prefecturesDoes the data on actual storage tend to support this picture of low storage in trading regions and high storage in more landlocked areas? Data pulled from local