Market Forces and Price Dispersion: Evidence from China s Passenger Car. Markets

Transcription

1 Marke Forces and Prce Dsperson: Evdence from Chna s Passenger Car Markes Lnhu Yu 1 and Pnlang Luo 2 1 The Unversy of Hong Kong and 2 Fudan Unversy 1. Inroducon A large body of leraure has addressed prce dsperson or volaon of he Law of One Prce (LOP) n varous markes. Prevous fndngs have shown ha prce dsperson are more common n nernaonal markes han n nranaonal markes (Frankel and Rose, 1996; Engel and Rogers, 1996; Rodrk, 2000; Taylor and Taylor, 2004), and he LOP holds beer n developed counres han n developng counres (Parsley and We, 1996; Engel and Rogers, 2001; Roger, 2001; Cecche, Mark, and Sonora, 2002; Ceglowsk, 2003; Goldberg and Verboven, 2005). Chna s economc mracle n he pas 30 years has aroused world-wde neress on Chnese-syle marke economy ncludng he marke negraon of Chna. In a semnal paper, Young (2000) fnds subsanal prce dsperson of seleced consumer goods, agrculural producs and ndusral maerals n Chna durng 1980s-1990s. Ths fndng combned wh some oher evdences promp her o conclude ha here exss serous marke fragmenaon n Chna, whch may endanger susanable economc developmen of Chna. Ponce (2002) a he same me fnds decreasng rend of ner-provnce rade flows n Chna, whch furher srenghens Young (2000) s fndngs. More recen work have shown evdences of mprovng 1

2 negraon of Chna s markes n laer 1990s and early 2000s (Fan and We, 2007), bu he realsc suaon accordng o survey papers by L e al (2004a, 2004b) s sll worryng, especally n some ndusres where frms have consderable monopolsc powers, e.g. auo ndusres.(wang, 2003; Qu, 2005; Zhao and Anand, 2009). Many scholars have also nvesgaed he causes of marke fragmenaon n Chna. Ba e al (2004) fnd ha afer Chna s fscal decenralzaon reform n 1994, local governmens end o proec sae-owned enerprses (SOEs) for ax purpose, whch caused marke fragmenaon and slow regonal specalzaon across Chna s provnces. Ln e al (2005) argue ha Chnese cenral governmen emphaszes oo much on cach-up of provnces n economc growh, whch resuls n large amouns of redundan consrucons and serous marke fragmenaon n Chna. Chen e al (2007) argue ha openness nequaly of Chna s regons caused by me and magnude of openness also accouns for he marke dsnegraon n Chna. A common feaure of he above sudes s hey all focus on he desrucve forces of governmen o he marke negraon of Chna. Acually, excep for governmen nervenon, some sponaneous marke forces can also lead o serous marke fragmenaon. For example, Verboven (1996) nvesgaes he causes of prce dsperson n European car markes and fnds ha demand elascy, mpor quoa and colluson of car frms accouned for a consderable par of car prce dfferences across European counres. Goldberg and Verboven (2001), by esmang margnal coss and demand funcons of cars n European counres n he 1990s, fnd ha demand elascy combned margnal coss well explan he car prce gaps across European counres. In addon, O Connell and We (2002) nvesgae he prce dscrepances of seleced commodes n U.S markes. Ther sudy ndcaes ha echnology gaps and preference dfferences of cusomers across ces are among he marke frcons ha lead o prce dspary. These sream of works reveal he effec of marke forces on prce dsperson n commody markes, and provde us neresng angles o deec possble reasons of volaon of LOP. However, o our bes knowledge, few sudes have aemped o look 2

3 no he prce dsperson n Chna from such angles. Ths research, focusng on Chna s locally-produced passenger car markes, ams o denfy he possble reasons for subsanal car prce dsperson across Chna s regons deeced by us. Dfferen from mos exsng works ha arbue marke fragmenaon of Chna o governmen nervenon, hs sudy reles on demand and supply analycal framework, and aemps o nerpre car prce dsperson as he resuls of sponaneous marke forces,.e., ncome effecs from demand sde and compeon effecs from supply sde. We also emprcally es hs heorecal explanaon. We choose Chna s locally-produced passenger car markes for nvesgaon for he followng reasons. Frs, passenger car ndusry as one of he key ndusres o Chna s economy has long been accused of s subsanal marke fragmenaon and prce dsperson across regons (Harw, 2001; Thun, 2006). Second, passenger car as a commody has he advanages of unform n qualy, hus s much easer o denfy her prces for dfferen models, whch s a major reason why car markes are frequen nvesgaed n leraure on marke negraon. By collecng he sellng prces of 20 popular locally-produced passenger car models n Chna s 36 bg ces durng , we fnd ha car prces as a whole were experencng rsng dvergence across Chna s bg ces durng ha perod. We furher conrol for he coss n car prces usng hedonc prcng model and hus oban he markup level of passenger cars n each cy. The resuls show ha car prce dsperson s sll ousandng durng he perod examned, whch reflecs he poor marke negraon of passenger cars n Chna. Based on he assumpon ha Chna s passenger car markes are olgopolsc, we llusrae how cross-regon demand and supply changes affec car prces, and evenually lead o prce dsperson. We hen buld a smple olgopolsc prcng model, based on whch we nerpre prce dfferenals across ces as he resuls of ncome effec from demand sde and compeon effec from supply sde. As he las sep of our research, we emprcally es our heorecal predcon usng a comprehensve daase conanng boh car prces and car sales n Chna s 36 bg ces. 3

4 Ths research conrbue o he exsng leraure n wo ways. Frs, by provdng new evdences from Chna s passenger car markes, we aemp o shed lgh on he ongong debae abou he qualy of marke negraon n Chna n recen years. Second, we also come up wh wo explanaons for car prce dsperson n Chna from he perspecve of marke forces, whch has been long been gnored by exsng sudes on marke negraon n Chna. The res of he paper are organzed as follows. In secon 2, we brefly nroduce he background of Chna s passenger car markes. In secon 3, we descrbe he daases and car prce dsperson across Chna s ces. In secon 4, we llusrae mechansm of prce dsperson drven by demand and supply changes n an olgopolsc marke and come up wh our predcon based on a smply olgopolsc prcng model. In secon 5, we emprcally es our predcons from he heorecal model. Secon 6 are concludng remarks and polcy mplcaon. 2. Chna s Passenger Car Marke Chna s passenger car ndusry sared n 1950s bu experenced very slow growh unl 1980s when Chnese governmen me he problem of ever-ncreasng needs for offcal cars. Volkswagen, recognzng he huge poenal of hs marke, grabbed he opporuny of esablshng jon-venure wh Chna s domesc car producer (Shangha Auomove co., Ld) n early 1980s. Ths JV occuped more han half of Chna s passenger car markes for over 10 years unl oher auomoble ans surged no Chna n early 2000s. We presen n Fgure 1 he oal oupu of passenger cars n Chna durng I s shown ha he frs marke boom appeared n he early 1990s when he annual car oupu growh rae exceeded 100% n 1992 and Sarng from 2000, hs marke agan experenced srong growh. E.g. he oal oupu ncreased from less han 10 mllon n 2002 o 40 mllon n 2006, whch s almos quadrupled whn 4 years. Fgure 2 shows he marke shares of car companes n Chna n I s shown ha VW sll ranks he frs alhough has los mos of s prevous markes n Chna (from marke share of 80% n peak year o 17% n 2006). New enrans 4

5 ncludng GM, Honda, Hyunda, Toyoa, Nssan, ec., accoun for he vas majory of marke shares los by VW. Ineresngly, mos passenger companes n Chna are JVs beween local companes and world auo ans. Local car companes only accoun for less han 26% of he oal car oupu n Chna n Local companes lke Chery, BYD and Geely are growng rapdly n las decade, and have played mporan roles n hs marke. The rapd growh of passenger car markes n Chna mrrors he perssen and srong economc growh of Chna n he pas 30 years, whch can be seen n Fgure 3 showng per capa dsposable ncome of urban household n Chna from 1978 o Producon (Mllon) % 140% 120% 100% 80% 60% 40% 20% 0% Annual growh rae Toal auo producon Auo producon growh Passenger car producon Passenger car producon growh Fgure 1. Producon and Growh Rae of Chna s Passenger Car Indusry (Source: Chna Auomove Indusry Yearbook 2007) Chna s passenger car ndusry has long been srcly regulaed by he governmen before In he regulaory era, enry lcenses, producon, and even he sellng prces were conrolled by he governmen. Mos prevous regulaon erms were llumnaed gradually afer Chna s jonng WTO n For example, Snce 2001 he governmen no longer dcae prces of cars, and reurn prcng 5

6 power o auo frms. Ths resuls n ferce prce wars n car markes snce hen. 1 Fgure 4 shows he prce ndces of locally-produced passenger cars n Chna n based on sellng prces of 20 car models n hs research. I should be noed ha he overall car prce s declnng sharply, almos droppng 17% by 2006 comparng wh Noe ha even unl recenly prce cu looks sll he mos effecve and popular way for sellng cars n Chna. Msubsh, 1% Peugeo, 5% Fa, 1% BMW, 1% Volvo, 0% Mazda, 3% VW, 17% Suzuk, 4% Nssan, 5% Toyoa, 6% Honda, 8% Ka, 3% GM, 11% Hyunda, 7% D.C., 0% Ford, 3% Domesc, 26% Chery, 7% Geely, 5% FAW Xal, 5% BYD auo, 2% Brllance, 1% Ohers, 6% Fgure 2. Marke Share of Local-produced Passenger Car Brands n Chna (2006 sales) (Source: Chna's auo marke almanac, 2007) There are wo mporan characerscs of Chna s passenger car markes. Frs, mos auo companes n Chna srcly prohb her dsrbuors from sellng cars across regons. Dsrbuors volang hs rule wll be deprved he qualfcaon of dsrbuon. Ths measure acually faclaes auo companes o charge dfferen prces n dfferen regons (Thun, 2006). Second, auo companes mosly rely on local dsrbuors o provde afer sales servces for her cusomers, whch resuls n 1 Anoher reason for prce wars s capacy surplus wh new enrans ener and expanson of exsng frms. 6

7 dscrmnaon of local dsrbuors agans users buyng her cars non-locally. E.g., hey may be charged hgher prces for reparng cars or changng pars (Qu, 2005). Due o hgh coss and possble roubles facng, people prefer buyng cars from local sellers. As a resul, passenger car markes are acually fragmened n dfferen regons of Chna. We herefore base our analyss on hs mporan assumpon. RMB 12,000 10,000 8,000 6,000 4,000 2, Average Dsposalable Income of Urban Household Fgure 3. Annual Dsposable Income of Urban Household n Chna ( ) (Source: Chna Sascal Yearbook, 2006) Car Prce Indces Based on 20 models :Q1 2004:Q2 2004:Q3 2004:Q4 2005:Q1 2005:Q2 2005:Q3 2005:Q4 2006:Q1 2006:Q2 2006:Q3 2006:Q4 Fgure 4. Prce Indces of Chna s Locally-produced Passenger Cars ( ) 7

8 3. Descrpon of Prce Dsperson a. Daa The prce daa used n hs research s from Chna Prce Informaon Nework (CPIN) a subsdary of Naonal Developmen and Reform Commsson (NDRC) of Chna. I conans monhly pre-ax sellng prces of 20 locally-produced passenger cars n Chna s 36 bg ces from 2004 January o 2006 December. 2 Car models provded n he daase represen he mos popular cars from hgh-end o low-end, ncludng execuve cars, large famly cars, small famly cars, super-mn cars, and cy cars. We show he dealed nformaon of car models and ces n Appendx I and Appendx II, respecvely. Due o mssng nformaon, hs prce daase s an unbalanced panel havng observaons. We also oban from Chna Auo Crculaon Assocaon (CACA) a comprehensve daase conanng sales nformaon of all passenger cars sold n Chna durng Table 1 presens he summary sascs of prces and prce dfferenals of car models. The average prces of 20 car models range from 39,300 o 504,100 RMB (abou 4,775-61,251 USD). The average prce dfferenals of hese models across ces range from 2,500 o 30,800 RMB (abou 304-3,743 USD). In Fgure 5, we show he coeffcen of varaon (CV) of car prces cross ces. I s seen ha CVs of car prces are rsng over me, whch ndcaes ha car prce dfferenals are geng larger and larger across regons. We furher shows he CVs of prces for dfferen groups of cars classfed by prce ranges n Fgure 6. I s seen ha cars prced beween 60, ,000 RMB and below 60,000 RMB, ypcally economy cars, have he hghes CVs of prces. In conras, he cars prced beween 200, ,000 RMB have he lowes CV of prces. To exclude he possble mpac 2 These 36 ces are 4 muncpales (Bejng, Shangha, Tanjn and Chongqng), 27 provncal capal ces and 5 naonal-level bg ces (Dalan, Qngdao, Nngbo, Xamen and Shenzhen). 3 I repors provnce-level sales nformaon of each model a monhly nervals. Alhough sales are mosly repored a car model level, we are sll unable o oban accurae sales nformaon of several car models because her sales nformaon s provded a more aggregaed level. E.g. a brand level. We fnally have exac sales nformaon for 14 models. 8

9 of oulyng observaons, we also presen n Fgure 7 he nerquanle range (IQR) of prces for each group of cars. Conssen wh above fndngs, he IQRs are sll ncreasng for all car groups, whch ndcaes ha our fndngs are no drven by oulers. Table 1. Summary Sascs of Car Prces ( ) Sellng prce Prce dfferenal No. of Obs. Mean S.D. No. of Obs. Mean S. D. Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Noe: un of prce s 1,000RMB (121.5USD) 9

10 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model Model 11 Model 12 Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Model 19 Model :Q1 2004:Q2 2004:Q3 2004:Q4 2005:Q1 2005:Q2 2005:Q3 2005:Q4 2006:Q1 2006:Q2 2006:Q3 2006:Q4 Fgure 5. CV of Car Prces for 20 Models ( ) 10

11 9 <60,000RMB 60, ,000RMB 120, ,000RMB 180, ,000RMB >300,000RMB :Q1 2004:Q2 2004:Q3 2004:Q4 2005:Q1 2005:Q2 2005:Q3 2005:Q4 2006:Q1 2006:Q2 2006:Q3 2006:Q4 Fgure 6. CV of Car Prces by Caegory ( ) 1.25 <60,000RMB 60, ,000RMB 120, ,000RMB 200, ,000RMB >300,000RMB :Q1 2004:Q2 2004:Q3 2004:Q4 2005:Q1 2005:Q2 2005:Q3 2005:Q4 2006:Q1 2006:Q2 2006:Q3 2006:Q4 Fgure 7. Inerquanle Range of Car Prces by Caegory ( ) 11

12 b. A formal es of Prce Dsperson The car prces dsperson presened above gve us an mpresson ha Chna s passenger car markes were experencng marke dsnegraon durng Bu o reach an concluson, we need a more rgorous es. We follow Goldberg and Verboven (2001, 2005) o formally es he prce dsperson usng a hedonc prcng model. The am of hs approach s o oban markup level of cars n each regon by elmnang cos componens from car prces. Usually cos componens of a car nclude manufacurng coss ha can be well capured by s physcal characerscs (e.g. dsplacemen, horse power, weghs, lengh, ec.), ranspor coss from assembly plans o desnaon markes measured by dsance, and sales coss (e.g. adversemen expenses, promoon expendure, ec.) whch vares wh car models and correspondng frms and can be conrolled for by full se of model dummes and frm dummes. Therefore, we employ he followng hedonc prcng regresson model: p = γ X + µ d + θ + θ + θ + θ + θ + ε, (1) a a a a, f a, a where, p, s log sellng prce of he car model n cy a a me, X s a vecor of physcal characerscs of model, a d s dsance beween assembly plan of model and cy a, θ f and a θ are frm dummes and model dummes, θ s full ses of cy-me dummes capurng markup levels of car prces n each cy a me. We also allow for he possble economc cycle, sales season and regon specfc facors ha affec he car prces by conrollng for me dummes θ and cy dummes θ n he regresson equaon. The regresson resuls of equaon (1) are repored n Table 2. We frs regress log prces on all covaraes excep cy-me dummes and repor he resuls n column 1. I s shown ha dsance has sgnfcanly posve mpac on sellng prces of cars. Besdes, he prces of cars can be well 12

13 explaned by her varous physcal characerscs. Noe ha R-square of regresson n column 1 s 0.88, whch ndcaes ha all covaraes n he regresson accoun for abou 88% of oal car prces. As a second sep, we furher pu cy-me dummes no he regresson and show he resuls n column 2. In addon o conssen esmaes n column 1 and column 2, more mporanly, R-square of column 2 ncreases o 0.98, whch mples ha markups capured by cy-me dummes accoun for 10% of car prces. So, we can ulze hs nformaon o deec varaon of regonal markup level of cars. Table 2. Hedonc Prcng Regresson Dependen: Log prce Hedonc(1) Hedonc(2) Log dsance *** *** ( ) ( ) Dsplacemen (L) *** *** ( ) ( ) A/T ** ** ( ) ( ) Acceleraon (SECs/100km) *** *** ( ) ( ) Fuel consumpon (L/100km) *** *** ( ) ( ) Wegh (Ton) *** *** ( ) ( ) Frm dummes Yes Yes Model dummes Yes Yes Cy dummes Yes Yes Tme dummes Yes Yes Cy-me dummes (markups) No Yes R-squared Observaons Noes: Robus sandard errors are repored n he parenhess. ***, **, and * ndcae sascal sgnfcance a he 1%, 5%, 10% level, respecvely. 13

14 bejn anjn shjazhuang ayuan huhehaoe shenyang dalan changchun harbn shangha nanjng hangzhou nngbo hefe fuzhou xamen jnan qngdao nanchang zhengzhou wuhan changsha guangzhou shenzhen nannng hakou chongqng chengdu kunmng guyang lasa xan lanzhou xnng ynchuan wulumuq 15% 10% 5% 0% -5% -10% -15% -20% -25% Fgure 8. Markup Level of Passenger Car Prces n Chna Ces ( ) 14

15 We se Bejng as benchmark n each perod and plo relave value of all esmaed cy-me dummes n Fgure 8. Ths fgure ndcaes ha markup levels of cars across regons were sll wdenng durng he perod examned. More exacly, he markup dfferenals across regons are up o less han 10% (lowes: Zhengzhou; hghes: Shenzhen) n January of 2004, 4 bu has evolved o around 20% (lowes: Xan; hghes: Bejng) n hree years by 2006 December. Snce we use he markups of Bejng as benchmark for comparson, he declnng rend of he mos lnes ndcaes ha markups of cars n Bejng move gradually from moderae level a he begnnng o he hghes among all 36 ces by he end of By conras, Chongqng, Jnan and Tayuan have been among he ces wh lowes markup level of car prces snce The nformaon delvered by Fgure 8 furher verfy he exsence of prce dsperson n Chna s passenger car markes. 4. Theorecal model a. Demand and Supply Analycal Framework Once he exsence of prce dsperson n Chna s passenger car markes have been verfed, we go furher o nvesgae wha drves ncreasng car prce dfferenals across ces n Chna. Prevous sudes have carefully examned car prce dsperson n European counres. For example, Goldberg and Verboven (2001) decompose car prces no coss and markup, and fnd ha markup levels of cars vary subsanally across European counres, whch may accoun for he consderable car prce dfferenals across European counres around Luz (2004) also looks no he prce dfferenals n European car markes and fnds ha exchange raes, ranspor coss, and marke sze are major reasons for car prce dsperson n Europe. Smlar o Europe counres, Chna s passenger car markes, alhough exemp from he problems lke exchange rae, mpor quoa, rade barrers, ec., are acually segmened due o he reasons prevously menoned. In hs crcumsances, marke forces 4 Car prces n Lhasa, he capal cy of Bbe, are consdered as oulers because Lhasa s very far away from mos oher ces n Chna, makng s ranspor coss prohbvely hgh and he demand here s also dsnc from oher places. 15

16 should play mporan roles n drvng car prce dsperson n Chna as shown n exsng sudes of European car markes. Therefore, we aemp o dscover he reasons for car prce dsperson n Chna under demand and supply framework. Followng IO leraure, equlbrum prces are deermned by demand of buyers and supply of producers. In Fgure 8, we llusrae he evoluon of car prces facng demand and supply changes, whch evenually forms prce dsperson across regonal markes. P S P S1 S1* G d G d * D2* D2 D1* D1 Q G s G s * S2 S2* D Q Fgure 9. Impacs of Demand and Supply Changes on Prce Dsperson For a car producer n an olgopolsc marke, he demand curve hey face n a regon s manly affeced by he ncome level of he regon and he preference of cusomers n he regon. And he supply curve hey face s manly affeced by hs npu coss and opmal oupu sraeges (also hs compeors oupu sraeges). In he lef panel of Fgure 9, we show he mpac of cross-regon demand changes on car prce. Assume D1 and D2 are demand curves of a car model n cy 1 and cy 2, respecvely, and S s supply curve of he car model. Prce gap beween cy 1 and cy 2 s hus G d. Gven supply do no change, demand curves of cy 1 and cy 2 move o D1* and D2*, respecvely, whch enlarges prce gap o G * d. Smlarly, n he rgh panel of Fgure 9, we show he mpac of cross-regon supply change on car prce. Gven demand do no change, f supply curve of wo ces change from S1 and S2 o S1* and S2*, respecvely, he prce gap beween hem enlarges from G s o G * s accordngly. In sum, ncrease of demand gap and ncrease supply gap beween wo ces boh lead o ncrease of prce gap, bu her effecs offse when hey move n he same drecon, so he evenual prce gap beween hem depends on her ne effecs. 16

17 b. An Olgopolsc Prcng Model Lke European car markes, Chna s passenger car markes can also be characerzed as olgopolsc markes ha are fragmened n dfferen regons. Assumng ha all car producers sell n naon-wde markes, and hey prce dfferenly n each regon for each car model o maxmze her profs. Le us assume he demand funcon of car model as p = e rq q, (2) a a a a η where a p denoes sellng prce of car model n cy a, and a e, a q and a q are ncome level of cy a, supply level of car model n cy a, and supply level of model s compeors n cy a, respecvely. Ths demand funcon make sense because sellng prce ncreases wh ncome level and decreases wh supply level (boh own and compeors ). Therefore, he profs of car model s N N N a a a a a a a a, (3) π = p q q c q F where a s ranspor coss of car model from s assembly plan o cy a, c s margnal coss of model, F s fxed coss of producon, and N s he oal number of ces. The frs order condon of prof funcon s π q a a a a a = e 2 rq q ( + c ) = 0. (4) η Accordngly, we oban he opmal oupu and prce of model n cy a as ( q ) ( e q c ) 2r a * 1 a a a = η ( p ) = ( e + + c q ). (5) 2 a * 1 a a a η And he prce dfferenal beween cy a and cy b s a, b * a * b * 1 a b 1 a b 1 a b ( p ) = ( p ) ( p ) = ( e e ) + ( ) η( q q ). (6) Therefore, frs-dfferenced erm of a, b ( )* p s 17

18 a, b * 1 a, b a, b η a, b a, b 1 a, b η a, b ( p ) = ( e e 1 ) ( q, q, 1 ) = e q,. (7) As seen n equaon (7), he margnal coss c and he ranspor coss a are cancelled ou, hus he prce dfferenals of car model across ces s deermned by ncome dfferenals and s compeors supply dfferenals across ces. Posve sgn of mples he posve drvng force of ncome dfferenals on a, e b car prce dfferenals from demand sde (.e. ncome effec), whle he negave sgn of mples a, b q, negave drvng force of compeon on car prce dfferenals from supply sde (.e. compeon effec). We wll emprcally es for hese wo knds of forces n he nex secon. 5. Emprcal Model and Esmaon Sraegy Based on equaon (7), we arrve a he followng dynamc panel regresson model for esmaon: 5 DPD = α + γ DID + β DSD + ε, (8), 0, g, g,, g where he dependen varable DPD, denoes frs-dfferenced prce dfferenals of model beween ces whn cy-pars n me, DID, denoes frs-dfferenced urban household dsposable ncome dfferenals beween ces whn cy-pars, and DSD, g, s frs-dfferenced supply dfferenals of all car models oher han n car group g 5, and ε, s error erms. We dfferenae compeon effec of dfferen car groups because cars n one group don compee drecly wh cars n oher groups due o her dfferen marke posonng. E.g. supply change of execuve cars lke Auo A6 suppose o have lle mpac on sellng prces of cy cars lke Suzuk seres. Followng he ypcal classfcaon of passenger cars n Chna, we classfy all 20 car models no fve groups: Group 1: Execuve cars/full-sze cars (prce >300,000 RMB) Group 2: Large famly cars/md-sze cars (prced180, ,000 RMB) 5 We calculae whn-group compeve supply by deducng he car model s supply from he oal supply of s group, and use oal supply as compeve supply of oher groups. 18

19 Group 3: Small famly cars/compac cars (prced 120, ,000 RMB) Group 4: Supermn cars /Subcompac cars (prced 60, ,000 RMB) Group 5: Cy cars (prce <60,000 RMB) As car sales nformaon s unavalable a cy-level, we hus dsaggregae provncal-level sales o cy-levels by GDP share of ces n he provnce. 6 We oban urban household dsposable ncome n each quarer from chna sascal yearbook, and aggregae monhly prce and sales nformaon a quarerly level accordngly. As we has menoned before, we only have accurae sales nformaon of 14 car models. As o he economercal ssues of emprcal par, alhough frs-dfferenced varables are pu no our regresson, here are sll some concerns abou hs approach. For one, alhough all unobservable me-nvaran facors are elmnaed n frs-dfferenced regresson, ndependen varables may sll be correlaed wh unabsorbed me-varyng varables hdden n error erms, whch causes bas of esmaon. For anoher, DSD, g, s endogenous varable because of s poenal reverse causaly wh dependen varable DPD, g,. Leavng hese endogeney ssues unsolved wll make our esmaon resuls based. To oban conssen esmaes n our regresson model, we use wo-sep sysem GMM esmaor (Arellano and Bover, 1995; Blundell and Bond, 1998), whch uses lagged frs-dfferences as well as levels as nsrumens for esmaon of equaon (8). Ths esmaor has sgnfcan mers over alernave esmaors, whch can help us easly deal wh endogeney concerns n esmaon. Alhough sysem GMM approach s more advanageous han mos oher esmaon approaches, we also need o be careful ha easly produces nvald esmaes. So correc applcaon of hs mehod srcly reply on some mporan es sascs for auocorrelaon, overdenfcaon and exogeney of nsrumens (Roodman, 2006). Logarhm of varables are used n he regresson, and we expec posve esmae of γ (ncome effec of model ) and negave esmae of n prevous heorecal analyss. β, g (compeon effec of all car models oher han ) as predced 6 We also red he mehod of usng populaon share o dsaggregae he provnce-level daa o cy-level, and he resuls are very smlar. 19

20 6. Emprcal Resuls We repor n Table 3 he wo-sep sysem GMM esmaon resuls for he 14 car models. 7 Insead of poolng all car models ogeher for esmaon, we esmae equaon (8) one by one for each car model, and all he resuls are shown n column Our reason for dong so s ha dfferen car model belongs o dfferen groups whch may have dfferen demand funcon from oher groups, so he esmaon resuls obaned from pooled sample regresson may be mxed or even msleadng. Separaely esmang he regresson model help us clearly dsenangle dfferenal ncome effecs and compeon effecs for dfferen cars. As DSD s an endogenous varable, we use s lag 2 and deeper lags of all ndependen varables as nsrumens for he frs-dfferenced equaons. And we also use lagged frs-dfferences as nsrumens for level equaons. We rely on Hansen J sasc o es he valdy of nsrumens, and rely on p-value of Arellano-Bond m1 and m2 es o ensure he absence of auocorrelaon of order 2 n he resduals. The correspondng ess resuls are repored a he boom of Table 3. As we can see from, he Hansen J sasc of all esmaon equaons do no rejec he null hypohess of no msspecfcaon, and all esmaons pass Arellano-Bond m1 and m2 ess,.e. rejec zero frs-order correlaon n m1 and accep zero second-order correlaon n m2, suggesng he valdy of momen condons we adoped n sysem GMM esmaon. The frs row of Table 3 shows he esmaed coeffcens for he lagged dependen varables, whch are all sgnfcan and posve for all car models, whch ndcae he roue dependence of prce dfferenals over me. Ths resul s acually conssen wh he emprcal evdence of rsng car prce dfferenals across Chna s regons durng The second row of Table 3 repors he esmaed coeffcens for Log DID. We fnd all esmaes are sgnfcan and posve, whch mples ha ncome dfferenals across regons brng ou car prce dfferenals across regons. Ths makes perfec sense nuvely and s conssen wh exsng economc heores. The magnude of esmaes (.e. prce elascy of ncome) range from o 0.259, whch means ha 1% ncome dfferenal change wll resul n 4%-26% s changes of car prce dfferenals n he same drecon. Noe ha car models wh smaller prce elascy are hose subcompac cars or cy cars ha have cheaper prces, whle car models wh hghes prce elascy are hose large famly cars or md-sze cars 7 The frs row of each column shows he number of car models. Due o daa unavalably, esmaon resuls of model 12, 16, 17, 18, 19, and 20 are mssng. For he name and dealed descrpon of each car model, please refer o Appendx I. 20

21 whch are relavely expensve and specally arge hgh-end users who are nsensve o prces. Row 2-5 shows he esmaed coeffcens of Log DSD, whch reflec he compeon effec on car prces (.e. prce elascy of subsue supply). Snce dfferen car models belong o dfferen groups, we underlne he correspondng esmaes obaned from her own groups. I s found ha all esmaes underlned are negave and sgnfcan, whch suggess ha for a car model ncrease of supply from s compeors n a cy wll resul n decreased prce dfferenals beween hs cy and oher ces. Ths s n lne wh our expecaon ha ncreasng compeon (refleced as compeors ncrease her supply) n a car marke wll subsequenly decrease he car prces n he marke. The magnude of esmaed coeffcens range from o , ndcang ha 1% ncrease of supply dfferenals of he model s subsue models beween wo ces wll resul n 4.3%-13.8% decreases of prce dfferenals beween hese wo ces. Dfferen from ncome effec, compeon effec show ha hgh-prce cars are more sensve o compeon han cheaper ones,.e., her prces end o drop more subsanally once her compeors ncrease supply. I s also neresng o look a he compeon effec from oher groups (esmaed coeffcens no underlned). We fnd n mos cases ha supply dfferenals of a car s neghborng groups have some negave effec, bu he negave effec dsappears or even become posve when he groups are far away from he car model. Our explanaon for hs neresng fndng s ha wo ces wh subsanal dfferenals n supply of a ceran car model may have dsnc characerscs (e.g. ncome level, preference, ec.), whch n urn fuel he prce dfferenals. Overall, he emprcal fndngs obaned from wo-sage sysem GMM esmaon are n lne wh our expecaon and well suppor our predcons from heorecal analyss. 7. Conclusons and Polcy Implcaon In hs research, usng prces of cars n Chna s bg ces, we frs verfy he car prce dsperson n Chna durng Moreover, we also nvesgae he causes of prce dsperson from he perspecve of marke forces, whch has been gnored by exsng sudes on marke dsnegraon n Chna. Our heorecal predcon suggess ha ncome dfferenals and supply dfferenals across Chna s ces may drve car prce dfferenals n Chna. And hs predcon s srongly suppored by he emprcal evdences. 21

22 Table 3. Two-sep Sysem GMM (Dependen varable: Log dfferenced prce dfferenals beween ces; Lag 2 for FD equaons, and lag 1 for level equaons) No. of Car Model L.DPD ** *** *** *** *** *** ** *** *** *** *** *** *** ** (0. 104) (0. 129) (0. 113) (0.096) (0. 094) (0. 106) (0. 110) (0. 102) (0. 109) (0. 093) (0. 094) (0. 101) (0. 105) (0. 108) Log DID ** *** *** *** *** *** ** *** *** *** *** *** *** ** (0. 034) (0. 079) (0. 015) (0. 029) (0. 031) (0. 063) (0. 048) (0. 026) (0. 068) (0. 033) (0. 012) (0. 037) (0. 052) (0. 076) Log DSD _G *** ** *** *** *** * *** *** *** *** *** (0.017) (0. 132) ( ) (0. 018) (0. 023) (0. 149) (0. 072) (0. 017) (0. 083) (0. 018) (0. 009) (0. 016) (0. 081) (0. 025) Log DSD _G ** ** ** ** * *** * ** (0. 034) (0. 053) ( ) (0. 027) (0. 025) (0. 051) (0. 082) (0. 026) (0. 063) (0. 021) ( ) (0. 022) (0. 078) (0. 027) Log DSD _G * * * ** * *** ** ** ** *** (0.027) (0. 029) (0. 014) (0. 022) (0. 023) (0. 032) (0. 027) (0. 033) (0. 054) (0. 038) (0. 013) (0. 035) (0. 039) (0. 050) Log DSD _G ** ** * *** *** *** ** *** *** ** *** *** *** (0. 027) (0. 014) (0. 013) ( ) (0. 025) (0. 016) (0. 014) (0. 026) (0. 031) (0. 031) (0.017) ( ) (0. 016) (0. 028) Log DSD _G *** *** *** ** *** *** *** ** *** ** *** ** *** (0. 016) (0. 035) (0.014) (0. 018) ( ) (0. 045) (0. 033) (0. 029) (0. 049) (0. 035) (0. 014) (0. 062) (0. 046) (0. 038) p-value of Hansen J p-value of m1 es p-value of m2 es NO of Obs Noes: All varables n he regresson are logged. ***, **, and * denoe sascal sgnfcance of he 1%, 5% and 10%, respecvely. Robus sandard errors are n he parenhess. G1-G5 are fve groups of car models classfed by prces from hgh o low. Esmaes underlned are effecs from he correspondng models own groups. Esmaon resuls for car model 12, 16, 17, 18, 19, 20 are unavalable due o mssng sales nformaon. 22

23 There are wo major fndngs of hs research. Frs, we fnd rsng prce dsperson n Chna s passenger car markes durng he examnaon perods, whch ndcaes he poor negraon level of Chna s passenger car markes. Second, we fnd ha ncome effec from demand sde and compeon effec from supply sde sgnfcanly accoun for he prce dsperson n Chna s passenger car markes, whch sheds lgh on he exsng explanaons of marke dsnegraon n Chna n recen years. The fndngs of hs research have some meanngful mplcaons for Chna s exsng economc polces. Frsly, rsng dsperson n Chna s passenger marke has proven o be parly drven by ncome nequaly across Chna s regons, whch s mosly nduced by developmen mbalance n Chna. Leavng hs ssue unsolved, Chna s akng he rsks of headng o serous marke dsnegraon whch wll defnely endanger susanable growh of Chna s economy. Secondly, s essenal ha mplc rade barrers creaed by bg frms wh monopolsc power, whch are prevalng n some ndusres lke passenger ndusry, should be resrced n order o faclae beer marke negraon n Chna. 7. References Arelleno, M. and O. Bover (1995), Anoher Look a Insrumenal Varable Esmaon of Error Componen Models, Journal of Economercs, 68, pp Ba, Chong-En, Yngjuan Du, Zhgang Tao, and Sarah Tong (2004), Local Proeconsm and Regonal Concenraon: Evdence for Chnese Indusres, Journal of Inernaonal Economcs, 63, pp Blundell, R. S. and S. Bond (1998), Inal Condons and Momen Resrcons n Dynamc Panel Daa Models, Journal of Economercs, 87, pp Cecche, S. G., N. C. Mark, and R. J. Sonora (2002), Prce Index Convergence among Uned Saes Ces, Inernaonal Economc Revew, 43, pp Ceglowsk, Jane (2003), The Law of One Prce: Inranaonal Evdence for Canada, Canadan Journal of Economcs, 36, pp Chen, M., Q. Gu, M. Lu, and Z. Chen (2007), How Do Chna Keep Scale Economy n Economc Growh An Emprcal Research on Economc Openness and Marke Fragmenaon n Chna s Domesc Commody Marke, Chna Economc Quarerly, 10, pp (n Chnese) Engel, Charles and John H. Rogers (1996), How Wde s he Border, Amercan Economc Revew, 86, pp Engel, Charles and John H. Rogers (2001), Volang he Law of One Prce: Should We Make a Federal Case Ou of I? Journal of Money, Cred, and Bankng, 33, pp.1 15 Fan, C. S. and X. We (2006), The Law of One Prce: Evdence from he Transonal Economy of Chna, Revew of Economcs and Sascs, 88, pp Frankel, J. A. and Andrew K. R. (1996), A Panel Projec on Purchasng Power Pary: Mean Reverson Whn and Beween Counres, Journal of Inernaonal Economcs, 40, pp

24 Goldberg, P. and Verboven, F. (2001), The Evoluon of Prce Dsperson n he European Car Marke, Revew of Economc Sudes, 68, pp Goldberg, P. and Verboven F. (2005), Marke Inegraon and Convergence o he Law of One Prce: Evdence from he European Car Marke, Journal of Inernaonal Economcs, 65, pp Harw, Erc (2001), The Impac of WTO Membershp on he Auomoble Indusry n Chna, The Chna Quarerly, 167, pp L, S., Y. Hou, Y. Lu, and B. Chen (2004a), A Survey Sudy on Chna s Local Proeconsm Evdence from Enerprses Sample, Economc Research References, 6, pp (n Chnese) L, S., Y. Hou, and B. Chen (2004b), A Survey Sudy on Chna s Local Proeconsm Prelmnary Resul from Non-Enerprse Samples, Economc Research References, 18, pp (n Chnese) Ln, Y. and P. Lu (2005), Local Proeconsm and Marke Dsnegraon: Invesgaon From Developmen Sraegy, workng paper, Economc Developmen Forum. No. FC Luz, M. (2004), Prcng n Segmened Markes, Arbrage Barrers and he Law of One Prce: Evdence from he European Car Marke, Revew of Inernaonal Economcs, 12, pp O'Connell, Paul G. J. and Shang-Jn We (2002), The Bgger They Are, he Harder They Fall: Real Prce Dfferences across U.S. Ces, Journal of Inernaonal Economcs, 56, pp Parsley, Davd and Shang-jn We (1996), Convergence o he Law of One Prce whou Trade Barrers or Currency Flucuaons, Quarerly Journal of Economcs, 111, pp Ponce, S. (2002), Is Chna s Marke Approachng o Dsnegraon? A Comparson on Chna s Inranaonal and Inernaonal Marke Inegraon, Essays on World Economy, No.1, pp (n Chnese) Rodrk, D. (2000), How Far Wll Inernaonal Economc Inegraon Go? Journal of Economc Perspecves, 14, pp Roodman, D. (2006), How o Do xabond: An Inroducon o Dfference and Sysem GMM n Saa, Cener for Global Developmen Workng Paper No Washngon, DC: Cener for Global Developmen. Qu, Larry D. (2005), Chna s Auomove Indusry, MBA case, HKU. Taylor, Alan M. and Taylor, Mark P. (2004), The Purchasng Power Pary Debae, Journal of Economc Perspecves, 18, pp Taylor, Alan M. (2001), Poenal Pfalls for he Purchasng-Power-Pary Puzzle? Samplng and Specfcaon Bases n Mean-Reverson Tess of he Law of One Prce, Economerca, 69, pp Thun, Erc (2006), Changng Lanes n Chna: Foregn Drec Invesmen, Local Governmens, and Auo Secor Developmen, Cambrdge Unversy Press, New York Verboven, F. (1996), Inernaonal Prce Dscrmnaon n he European Car Marke, Rand Journal of Economcs, 27, pp Wang, Hua (2003), Polcy Reforms and Foregn Drec Invesmen: The Case of he Chnese Auomoble Indusry, Journal of Economcs and Busness, 6, pp Young, Alwyn (2000), The Razor s Edge: Dsorons and Incremenal Reform n he People s Republc of Chna, Quarerly Journal of Economcs, 115, pp Zhao, J. Z. and Jadeep Anand (2009), A mullevel Perspecve on Knowledge Transfer: Evdence from he Chnese Auomove Indusry, Sraegc Managemen Journal, 30, pp

25 Appendx Appendx I. Descrpon of he 20 Car Models Model No Model Name Plan Locaon Group Dsp AT/MT Accel. 1 Chevrole_SaL1.6L_m_sd Jnan, Shandong G4 1.6 M Buck_Regal2.5GL Shangha G2 2.5 A Suzuk_Alo_klwzSC7081 Chongqng G5 0.8 M Croen_ElyseeSX16L_m Wuhan, Hube G4 1.6 M Fukang1.4L_m Wuhan, Hube G4 1.4 M Honda_Accord2.4L_a_sd Guangzhou, Guangdong G2 2.4 A Sanana3000_18L_m_sd Shangha G3 1.8 M Sanana_GL1.8L_m_sd Shangha G4 1.8 M Passa_ly2.0L_m Shangha G2 2 M Polo1.4L_a_sd Shangha G4 1.4 A Xal_Junya7101a_sd Tanjn G5 1 M AudA6_2.8L_sd Changchun, Jnln G1 2.8 A Jea_CIF16L_m_sd Changchun, Jnln G4 1.6 M Bora_1.8L_m Changchun, Jnln G3 1.8 M Chery_Qyun16GL_m_sd Wuhu, Anhu G4 1.6 M BJ_Jeep2024 Bejng G3 2.2 M Changan_SC6350 Chongqng G5 1 M Changcheng_SafuCC6470BY Shjazhuang G4 2.2 M Changfeng_CFA6470 Changsha, Hunan G2 2.4 M Hafe_MnyHF6370 Harbn, Helongjang G5 1.1 M Noes: Dsp=Dsplacemen; Accel.=Accelerae(Seconds, 0-100km/hrs); Fuel Cons=Fuel consumpon (L/100km); Horse PW=Horse power; Cyln.=Cylnder; Spd=Speed Fuel Cons Wegh (Ton) Horse PW No. of Cyln Lengh (Meer) Max Spd Prce (04-06) 25

26 Appendx II. Geographc Dsrbuon of 36 ces n Chna 26