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University of Groningen
Productivity Differentials in the U.S. and EU Distributive Trade Sector:
Statistical Myth Or Reality?
Marcel TimmerRobert InklaarBart van Ark
University of Groningenand The Conference Board
OECD Workshop on Services15 & 16 November 2004
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BackgroundGrowth of value added per hour worked in trade, U.S. and EU
1980-1995 1995-2002 1980-1995 1995-2002 1980-1995 1995-2002U.S. 2.0 4.3 4.0 8.1 2.5 7.1EU-15 1.9 1.2 2.7 1.6 2.0 1.5
France 1.9 0.5 4.7 1.2 3.4 1.5Germany 1.4 -1.0 2.7 1.6 2.2 1.3Netherlands 2.4 1.3 2.1 3.5 2.5 1.1U.K. 3.8 4.2 3.8 2.5 2.4 4.0
Wholesale trade Retail tradeMotor vehicle trade
Distributive trade sector accounts for large part of post-1995 productivity boom in U.S. (Triplett & Bosworth, 2004)Big part of EU-US productivity differential is traced to this sector (van Ark, Inklaar and McGuckin, 2002)
Combination of ICT and organisational innovation has transformed sector into “information industry” (McGuckin, Spiegelman and van Ark, 2004)
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But is the EU-US productivity growth differential real or is it a
measurement issue?
Impact of “inside the box” quality adjustments for ICT on U.S. trade output measureExperimental estimates of double deflated real margin instead of real sales indexSources of productivity growth in trade: increase in service output or saving in intermediate inputs?
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Main conclusions
There is a slight upward bias in U.S. trade output measures due to ICT but much more so in wholesale than in retail servicesDouble deflated estimates of real output margin do not change aggregate retail measure in U.S., but cause shifts across retail industriesDecline in sales price is main cause of productivity gain in trade, but saving in intermediate inputs add to U.S. advantageMeasurement issues suggest upward bias but U.S. advantage remains intact
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Changing business models in distributive trade should focus attention
on determinants of gross margin ...
Labour
Intermediate inputs
Purchases of goods
sold
Sales of goods
Gross margin
Value added Capital
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… in particular from perspective of real or volume growth
Variables Observed Imputed Values Imputed QuantitiesVariables
Sales Price (pS) pS
Sales Quantity (qS) qS = S - p S
Sales Value (S) S
Goods Purchases Value (C) C
Margin Quantity (qM) qM= qS
Margin Value (M) M = S - C
Intermediate Inputs Price (pII) pII
Intermediate Inputs Quantity (qII) qII = II - pII
Intermediate Inputs Value (II) II
Value Added Value (VA) VA = M - IIValue Added Quantity (qVA) QVA = w [qM - (1-w) qII]
Volume of trade services does not only depend on number of transactions (number of boxes) but also on quality of service provided, which is key for success of failure of industryThe quality of products (“inside the box”) must be distinguished from the quality of the retail service
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EXERCISE 1: DOES ICT PRODUCT QUALITY AFFECT EU-US DIFFERENTIAL?
Not the share of ICT consumption but the price change of ICT products makes the
difference between US and Europe
Price change1995 2003 1995-2003
France 3.5 3.9 -5.8Germany 3.4 2.4 -3.5Netherlands 4.9 4.8 -4.8UK 5.2 5.6 -8.1US 4.2 4.1 -13.0
Share in goods
Share of audio-visual, photographic and information processing equipment in household consumption expenditure, and associated CPI
change
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Excluding core industries that trade ICT products reduces U.S. retail sales volume marginally, but it is more substantial for
wholesale tradeContributions of Industries to Real Sales Growth in the U.S., 1987-2002
1995-2002
Industry name 1987-1995 1995-2002over 1987-
1995
total retail 3.1 4.6 1.5total retail excluding 4431 and 4541 2.7 3.8 1.2difference 0.4 0.8 0.4
total wholesale 4.1 4.2 0.1total wholesale excluding 4234 and 4236 2.9 2.7 -0.2difference 1.2 1.5 0.3
Real sales
4234 = Commercial equipment; 4236 = Electrical and electronic goods; 4431 = Electronics and appliance stores; 4541 = Electronic shopping and mail order houses
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Exercise 1 on impact of ICT provides upper bound estimate of bias
Not the whole of ICT-related trade industries should be considered, but only products which use hedonic price indicesEuropean countries also partly use hedonic price indices or alternative quality adjusted method (e.g. high frequency matched model deflators for ICT)… but it may not be confined to ICT: on the whole, U.S. proceeds faster in applying quality adjusted price indices throughout
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EXERCISE 2: DOES USE OF SALES INDEX GIVE BIAS TO U.S. ADVANTAGE?
Three methods:use of real sales index (common practice, but different degree of detail across countries)use of margin prices (experimental stage: BLS)use of double deflation of sales and purchased of goods sold
Double deflation cancels “inside the box” price change out --> better proxy of real margin in line with services providedProblems:
Data requirements high => purchase pricesSensitive to measurement error => margin is residual
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Exercise 2a: aggregate double deflation for trade sector as a whole (retail + wholesale)
Calculation of change in purchase price:
( ) Di
IIi
ICi pwpwp &&& −+= 1
Calculation of change in implict margin price:
[ ]( )CMSM
M pvpv
p &&& −−= 11
Simplified flow of goods for retail goods
Wholesale and
retail trade
Final consumption
Imports
Domestic production
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Implicit margin prices - based on double deflation - decline everywhere but with
differences across countriesSales, purchases and margin prices for household consumption goods
Sales Purchases of which: Marginprices prices Domestic Imports prices
France 1.9 0.7 0.9 -2.0 4.5Germany 1.7 1.3 1.3 -0.1 2.9Netherlands 1.0 0.4 0.5 -0.1 2.1UK 3.3 4.1 3.2 0.9 1.4US 2.2 2.2 1.7 0.5 2.0
Sales Purchases of which: Marginprices prices Domestic Imports prices
France 1.3 0.5 0.7 -0.2 2.9Germany 0.7 0.9 0.8 0.1 0.0Netherlands 1.3 1.0 1.0 0.0 1.7UK 0.4 0.3 0.7 -0.4 0.7US 0.6 0.9 1.1 -0.2 0.1
Note: Germany: 1991-1995 instead of 1987-1995
1987-1995
1995-2002
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With double deflation US (and UK !) trade sectors retain growth advantage
over continental European countries & it upwardly biases Europe rather than U.S.
National Accounts
Double deflated
National Accounts
Double deflated
France 2.3 -0.1 2.3 -0.4Germany n.a. 1.1 n.a. 2.4Netherlands 2.9 2.7 2.6 2.1UK n.a. n.a. n.a. 6.9US 3.3 3.4 5.3 5.3Germany 1987-1995 refers to 1991-1995
1987-1995 1995-2002
Growth of retail trade gross output, National Accounts versus double deflated prices, 1995-2001
Exercise 2b: double deflation by retail category puts much higher demand on data
Imports
Domestic production
Consumption
Retail: Department
stores
$22bln$13bln $9bln
Wholesale
10%
24%
46%
20%
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EXERCISE 3: IS U.S. OUTPUT GROWTH IN TRADE DRIVEN BY MORE OUTPUT OR
LESS INPUTS?Variables Observed Imputed Values Imputed Quantities
Variables
Sales Price (pS) pS
Sales Quantity (qS) qS = S - p S
Sales Value (S) S
Goods Purchases Value (C) C
Margin Quantity (qM) qM= qS
Margin Value (M) M = S - C
Intermediate Inputs Price (pII) pII
Intermediate Inputs Quantity (qII) qII = II - pII
Intermediate Inputs Value (II) II
Value Added Value (VA) VA = M - IIValue Added Quantity (qVA) QVA = w [qM - (1-w) qII]
A. Retail (annul average change, %)
1 Quantity of output x O/VA ratio 4.5 7.4 3.02 Difference between quantity of sales and output 0.3 0.3 0.03 Quantity of sales x O/VA ratio 4.8 7.7 2.94 Value of sales x O/VA ratio 8.3 7.7 -0.65 Price of sales x O/VA ratio 3.6 0.0 -3.6
6 Quantity of intermediate inputs x II/VA ratio 1.7 0.7 -0.97 Value of intermediate inputs x II/VA ratio 3.1 1.8 -1.38 Price of intermediate inputs x II/VA ratio 1.4 1.0 -0.4
9 (=1- 6) Quantity of value added 2.8 6.7 3.9
B. Wholesale
1 Quantity of output x O/VA ratio 6.3 5.8 -0.52 Difference between quantity of sales and output -0.2 0.1 0.33 Quantity of sales x O/VA ratio 6.0 5.8 -0.24 Value of sales x O/VA ratio 8.2 3.9 -4.35 Price of sales x O/VA ratio 2.1 -1.9 -4.1
6 Quantity of intermediate inputs x II/VA ratio 2.3 -0.6 -2.97 Value of intermediate inputs x II/VA ratio 3.4 -0.3 -3.78 Price of intermediate inputs x II/VA ratio 1.1 0.2 -0.8
9 (=1- 6) Quantity of value added 3.9 6.6 2.7
1987-1995 1995-20021995-2002
over 1987-95
1987-1995 1995-20021995-2002
over 1987-95
In U.S. retailing about a quarter of acceleration is due to saving in intermediate inputs
A. Retail (annul average change, %)
1 Quantity of output x O/VA ratio 4.5 7.4 3.02 Difference between quantity of sales and output 0.3 0.3 0.03 Quantity of sales x O/VA ratio 4.8 7.7 2.94 Value of sales x O/VA ratio 8.3 7.7 -0.65 Price of sales x O/VA ratio 3.6 0.0 -3.6
6 Quantity of intermediate inputs x II/VA ratio 1.7 0.7 -0.97 Value of intermediate inputs x II/VA ratio 3.1 1.8 -1.38 Price of intermediate inputs x II/VA ratio 1.4 1.0 -0.4
9 (=1- 6) Quantity of value added 2.8 6.7 3.9
B. Wholesale
1 Quantity of output x O/VA ratio 6.3 5.8 -0.52 Difference between quantity of sales and output -0.2 0.1 0.33 Quantity of sales x O/VA ratio 6.0 5.8 -0.24 Value of sales x O/VA ratio 8.2 3.9 -4.35 Price of sales x O/VA ratio 2.1 -1.9 -4.1
6 Quantity of intermediate inputs x II/VA ratio 2.3 -0.6 -2.97 Value of intermediate inputs x II/VA ratio 3.4 -0.3 -3.78 Price of intermediate inputs x II/VA ratio 1.1 0.2 -0.8
9 (=1- 6) Quantity of value added 3.9 6.6 2.7
1987-1995 1995-20021995-2002
over 1987-95
1987-1995 1995-20021995-2002
over 1987-95
In wholesale entire productivity gain in U.S. is due to saving of intermediate inputs
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Are intermediate input savings real?
Saving on intermediate inputs might be sign of greater dominance of “lean retailing”, competitive pressures and new entries… but we need comparative evidence for European countries to assess the differencesIncome approach in U.S. might overstate value added growth and therefore understate growth in intermediate inputs
cross-check with census-based approach suggests such problem in wholesale but not in retail
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Main conclusions
There is a slight upward bias in U.S. trade output measures due to ICT but much more so in wholesale than in retail servicesDouble deflated estimates of real output margin do not change aggregate retail measure in U.S., but cause shifts across retail industriesDecline in sales price is main cause of productivity gain in trade, but saving in intermediate inputs add to U.S. advantageMeasurement issues suggest upward bias but U.S. advantage remains intact
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