How High are Prices in China, and Why?

Robert Feenstra, University of California, Davis

Alexis Antoniades, Georgetown University

John Romalis, University of Sydney

Mingzhi Xu, University of California, Davis

• For presentation at International Comparison of Income, Prices and Production, May 22‐23, 2015, CERGE‐EI, Prague

Goals of project:

1. Collect scraped data from phone app in China

2. Investigate regional prices in China (and in the United States)

3. Compare with Nielsen’s data for China, and use sales data

4. Compute cost of living in China and in the United States

1. Collection of Chinese price data

• Cell Phone APP : Wochacha‐ help consumers compare retail prices offered in particular city

‐ barcode‐based query

• Credibility‐ developed byWochacha Info Tech Co. Ltd in January 2010

‐ received capital injection from American Sequoia Capital, Ivy Capital etc.‐ by Feb 2014, number of users has exceeded 210 million across all the provinces in China

1. Collection of Chinese price data:

• Wochacha Data Source(1) Special Data collectors scan price data in retailing store and update price regularly

(2) Partnership with big retailing store (Supermarkets)

(3) Web scraping for online price (not retail stores)

(4) Retailors, producers and consumers can report price inaccuracies,  and Wochacha will correct them once verified

1. Collection of Chinese price data:

1. Collection of Chinese price data:

• APP scraping via programming (Sept – Nov 2014, U.S.)

‐ the first to use APP scraping to obtain research data

‐ 65 cities (only 63 cities for analysis use)

‐ 150 product modules (assign each product to a module in Walmart classification by keywords matching)

‐ rich supermarket variation: Walmart, Carrefour, Tesco, RT‐Mart, Lotus, Lianhua etc.

City Covered in Retail Price Data of China

China Scraped Price (2014), Toothpaste 62 citiesDependent Variables : ,

Toothpaste 62 CitiesUnbalance Balance

(1) (2) (3) (4)

0.00491** -0.00274 -0.0152*** -0.0142**(0.00205) (0.00348) (0.00534) (0.00692)

-0.0205*** -0.00872*** -0.0142*** -0.00677(0.00137) (0.00263) (0.00350) (0.00529)

Dummy of Capital City 0.0105*** 0.00942*** 0.0393*** 0.0258***(0.00151) (0.00183) (0.00318) (0.00388)

Constant 0.629*** 0.552*** 0.913*** 0.872***(0.00890) (0.0170) (0.0219) (0.0332)

Observations 42,245 42,245 3,420 3,420R-squared 0.009 0.003 0.064 0.034Number of group 1,853 26,978 57 1,596Province-Product FE YES YESProduct FE YES YESStandard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1GDP per capita is in 10,000 USD; GDP is in 1e8 USD; price is in USD

• , ∑ , , where , , is prices scraped from APP

• Regression results strengthen the difference of price regularity between U.S. and China • The price on average is lower in larger cities of China (while higher in larger cities of U.S.)

China Scraped Price (2014), Many products 63 citiesDependent Variables : ,

Mixed Product 63 CitiesUnbalance Balance

(1) (2) (3) (4)

0.000590 -0.0122*** -0.00103 -0.00697***(0.000932) (0.00135) (0.00215) (0.00259)

-0.0204*** -0.00730*** -0.0216*** -0.00829***(0.000661) (0.00103) (0.00183) (0.00194)

Dummy of Capital City 0.0177*** 0.00462*** 0.0198*** 0.00429***(0.000761) (0.000735) (0.00165) (0.00138)

Constant 1.073*** 0.995*** 1.170*** 1.094***(0.00414) (0.00649) (0.0111) (0.0120)

Observations 220,257 220,257 17,010 17,010R-squared 0.016 0.008 0.065 0.017Number of group 5,562 117,670 270 8,100Province-Product FE YES YESProduct FE YES YESStandard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1GDP per capita is in 10,000 USD; GDP is in 1e8 USD; price is in USD

• , ∑ , , where , , is prices scraped from APP

• Regression results strengthen the difference of price regularity between U.S. and China • The price on average is lower in larger cities of China (while higher in larger cities of U.S.)

China Scraped Price (2014), Toothpaste 22 citiesDependent Variables : ,

Toothpaste 22 CitiesUnbalance Balance

(1) (2) (3) (4)

0.00588* 0.131*** 0.00835** 0.105***(0.00326) (0.0121) (0.00346) (0.0122)

-0.0228*** -0.0532*** -0.0128*** -0.0347***(0.00195) (0.00825) (0.00194) (0.00835)

Dummy of Capital City 0.00512** 0.0105*** 0.0274*** 0.0216***(0.00230) (0.00295) (0.00254) (0.00330)

Constant 0.629*** 0.818*** 0.714*** 0.857***(0.0135) (0.0560) (0.0128) (0.0565)

Observations 21,926 21,926 6,952 6,952R-squared 0.007 0.039 0.027 0.080Number of group 1,784 18,544 316 5,372Province-Product FE YES YESProduct FE YES YESStandard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1GDP per capita is in 10,000 USD; GDP is in 1e8 USD; price is in USD

• , ∑ , , where , , is prices scraped from APP

• Regression results strengthen the difference of price regularity between U.S. and China • The price on average is lower in larger cities of China (while higher in larger cities of U.S.)

China Nielson Toothpaste Price (2011)Dependent Variables :

,

Unbalanced Sample Balanced Sample

(1) (2) (3) (4)

( Filter: 50% ) FE FE FE FE

-0.0163*** 0.0414*** -0.0204*** 0.0471***(0.00369) (0.00979) (0.00408) (0.0125)

0.00246 -0.0148** -0.00130 -0.0120(0.00189) (0.00620) (0.00215) (0.00743)

Dummy of Capital City 0.000546 -0.00647** 0.0136*** -0.00232(0.00235) (0.00255) (0.00292) (0.00325)

Constant 0.324*** 0.441*** 0.499*** 0.574***(0.0128) (0.0419) (0.0149) (0.0502)

Observations 11,492 11,492 3,916 3,916R-squared 0.003 0.017 0.023 0.026Number of group 1,064 9,574 178 3,026Province-Product FE YES YESProduct FE YES YESStandard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1GDP per capita is in 10,000 USD; GDP is in 1e8 USD; price is in USD

is tolerance relative to scraped price

• Onlyobserveweeklysales,weeklysoldweight,andunitsizeofcommodities

• , ∑ ∑ , , , ∗ , , , where , , ,, , ,

∑ ∑ , , ,

• Regression shows that price is lower in larger city (GDP) in China, but negative relationship is weak, which may be resulted from limited set of commodities, cities etc.

Robustness – China Nielson (2011)Dependent Variables :

,

Unfiltered Filtered ( 50%)

Sales Weighted Mean

Geometric Mean

Sales Weighted Mean

Geometric Mean

0.0321** 0.0322*** 0.0414*** 0.0385***(0.0135) (0.0120) (0.00979) (0.00836)

-0.0112 -0.00671 -0.0148** -0.0102*(0.00890) (0.00794) (0.00620) (0.00536)

Dummy of Capital City -0.00384 -0.00575* -0.00647** -0.00619***(0.00376) (0.00336) (0.00255) (0.00216)

Constant 0.383*** 0.372*** 0.441*** 0.427***(0.0597) (0.0534) (0.0419) (0.0364)

Observations 12,266 12,266 11,492 11,492R-squared 0.005 0.009 0.017 0.022Number of group 10,235 10,235 9,574 9,574Province-Product FE YES YES YES YES

Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1GDP per capita is in 10,000 USD; GDP is in 1e8 USD; price is in USD

is tolerance relative to scraped price

• , ∏ ̅ , ,/ where ̅ , , ∑ , , , ∗ , , , and , , ,

, , ,∑ , , ,

• Regression still implies that price is lower in larger city (GDP) in China

U.S. Nielson Toothpaste Price (2011)

Dependent Variables : ,

US Toothpaste

Unbalance Balance(1) (2) (3) (4)

0.0151*** 0.0121*** 0.00899*** 0.0101***(0.00283) (0.00189) (0.00316) (0.00202)

0.00224*** 0.00279*** 0.00420*** 0.00212***(0.000650) (0.000428) (0.000845) (0.000403)

Constant 1.043*** 1.045*** 1.066*** 1.075***(0.00293) (0.00178) (0.00307) (0.00184)

Observations 207,006 207,006 31,668 31,668R-squared 0.001 0.002 0.010 0.009Number of group 1,330 33,880 84 4,032State-Product FE YES YESProduct FE YES YESStandard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1GDP per capita is in 10,000 USD; GDP is in 1e8 USD; price is in USD

• , ∑ ̅ , , where ̅ , , ∑ , , ∗ , , , and , ,, , ,

∑ , , ,

• This way of calculation make it comparable with the scraped price data that do not have sales information • Regression shows that price is higher in larger city (GDP) in U.S.

Average Unit Cost 

• ,∑ ∑ ∑ , , , ∗ , , ,∈∑ ∑ ∑ , , , ∗∈

where ∈ , and is unit weight of product (oz)

• Average unit cost for common product is higher than non-common in China while lower in U.S.• Both types of commodities increase with per capita GDP in U.S. and China• Mixed patterns for U.S. vs China, common vs non-common when looking at market size (GDP)

Cost of Living (Sato‐Vartia, Feenstra, 1994)• (Sato‐Vartia, 1976) : constant commodity set

,, , , , ,

∈where ≡

/ ∑

∈ and ≡ /∑ ∈

• (Feenstra, 1994): variable commodity set ( ∈ ∩ ∅)

,,

, , , ,/

where  ∑  ∗  ∈∑  ∗  ∈  

1∑  ∗  ∈  , ∉∑  ∗  ∈  

,    1,

• New varieties / disappearing varieties will decrease/increase the price index

Cost of Living (Unweighted)• Absolute unweighted Geometric Price index for all products or for common product set

, ∏ ∈where  ⋂ ∈ and  is all the feasible product set of city  .

• Price index for city  with product space 

e , , ∗ /

Where  is constructed as:∑ ∗∈∑ ∗∈

1∑ ∗∈ , ∉

∑ ∗∈

Toothpaste brands included in Geo‐mean

1. All toothpaste brands

2. Toothpaste of three big international brands (Crest / Colgate /Sensodyne)

3. Toothpaste of three big international brands that are national‐common

Later:4. National‐common toothpaste which are sold in each city of dataset

Cost of Living Comparison (Relative to U.S.), 2011

(%) ($/oz) e , ($/oz)Country US China US China US China

Measure 1 100 100 0.567 0.298 0.567 0.298Ratio =1.00 Ratio =0.526 Ratio =0.526

Measure 2 80.2 40.5 0.5461 0.271 0.507 0.200Ratio =0.505 Ratio =0.496 Ratio =0.394

Measure 3 39.0 34.9 0.601 0.320 0.439 0.225Ratio =0.895 Ratio =0.532 Ratio =0.513

Note: Ratio is relative to US

Common Expenditure Shares (Measure 4), 2011

Common Product Unit Cost (Measure 4), 2011

Variety Adjusted Cost of Living (Measure 4), 2011

Cost of Living Comparison (Relative to U.S.), 2012

(%) ($/oz) e , ($/oz)Country US China US China US China

Measure 1 100 100 0.616 0.322 0.616 0.322Ratio =1.00 Ratio =0.523 Ratio =0.523

Measure 2 80.1 36.9 0.612 0.306 0.568 0.218Ratio =0.461 Ratio =0.500 Ratio =0.384

Measure 3 39.9 32.4 0.667 0.352 0.491 0.241Ratio =0.812 Ratio =0.528 Ratio =0.491

Note: Ratio is relative to US

Common Expenditure Shares (Measure 4), 2012

Common Product Unit Cost (Measure 4), 2012

Variety Adjusted Cost of Living (Measure 4), 2012

Goals of project:

1. Collect scraped data from phone app in China

2. Investigate regional prices in China (and in the United States)

3. Compare with Nielsen’s data for China, and use sales data

4. Compute cost of living in China and in the United States