USE OF E-COMMERCE DATAInternational comparisons and a micro-perspective

Michael Polder, OECD-STI/EAS

Business Statistics User Event: How E-commerce is changing the shape of business, October 8 2015, BIS conference centre, London, UK

• A sketch of messages from recent OECD publications

• From statistics to economic analysis:– Using ICT survey micro-data to investigate the relation

between • E-commerce and competition

• E-commerce, innovation, and productivity

– Cross-country analysis with micro-data

Overview

A new STI Scoreboard coming up…

2015

To be released October 20

• Relative to broadband and website, e-commerce adoption is still fairly low

• … and there is substantial variation between countries

Sketches from recent OECD publications

• Magnitude of cross-border e-commerce is limited

• … pointing at serious obstacles to ‘go international’

Sketches from recent OECD publications

• Magnitude of cross-border e-commerce is limited

• … similarly for online purchases by individuals

• … pointing at a lack of confidence and trust

Sketches from recent OECD publications

• Online purchases by individuals are up…

• But still substantial country variation

• … and a gap between young and old

Sketches from recent OECD publications

• A new development: ‘m-commerce’ and ‘m-payment’

• Increasing importance and policy interest

• … But measurement?

Sketches from recent OECD publications

Relatively few firms are engaging in e-commerce

… especially cross-border e-commerce

Likewise, few individuals buy online from abroad

However, overall, online purchasing is increasing

… although a significant age gap remains

Overall there is still a substantial amount of cross-country variation in the adoption of e-commerce by both firms and households

Sketches from recent OECD publications

• ICT usage by enterprises• Relation of e-commerce with

– Competition– Innovation– Productivity

• Need firm-level analysis– It is (too) often forgotten that the micro-data

underlying national statistics is a valuable product!

Use of e-commerce data for economic analysis

• Background– Consequences of online trading:

• Lower search cost• Lower transaction cost• Markets become more transparant

– E.g. Brynjolfsson and Smith (2000, Management Science): “Frictionless markets”

• Increases (price) competition• Motivates firms to operate cost efficiently

E-commerce and competition

• Data (Netherlands only)– ICT usage and e-commerce enterprise survey– Structural Business Statistics– Innovation Survey– Period: 2002-2010

E-commerce and competition

E-commerce and competition

E-commerce and competition

E-commerce and competition

Markups lower in manufacturing relative to services, which indicates stronger competition in manufacturing

Source: CBS (2015), ICT and Economic Growth.

E-commerce and competition

Increases in e-sales and e-commerce in general have significantly lowered markups, in all sectors

Source: CBS (2015), ICT and Economic Growth.

• Background– Innovation is a driver of economic growth– Crépon-Duguet-Mairesse (CDM):

• Firms invest in R&D (innovation input)

• … this leads to knowledge generation (product innovation)

• … which ultimately results in increased business performance (productivity)

– Variations:• Griffith et al. (2004): product and process innovation

• Brynjolfsson et al: ICT investments and complementary organizational changes

E-commerce and competition

E-commerce, innovation and productivity

E-commerce, innovation and productivity

• Data (Netherlands only)– ICT usage and e-commerce enterprise survey– Structural Business Statistics– Innovation Survey– Period: 2004-2010

E-commerce and competition

E-commerce, innovation and productivity

ManufacturingPRODUCT

PROCESS ORGANIZATIONAL E-COMMERCE

coef coef coef coefproduct innovation -- 0,447*** 0,100* 0,112***process innovation 0,447*** -- 0,425*** -0,032*organizational innovation 0,100** 0,425*** -- 0,036e-commerce innovation 0,112*** -0,032* 0,036 --broadband intensity 0,160 -0,105 0,424*** -0,008R&D performer 1,315*** 0,428*** -- --

ServicesPRODUCT

PROCESS ORGANIZATIONAL E-COMMERCE

coef coef coef coefproduct innovation -- 0,626*** 0,239*** 0,106***process innovation 0,626*** -- 0,459*** 0,055***organizational innovation 0,239*** 0,459*** -- 0,062***e-commerce innovation 0,106*** 0,055*** 0,062*** --broadband intensity 0,427*** -0,054 0,257*** 0,074*R&D performer 1,134*** 0,640*** -- --

E-commerce, innovation and productivity

Manufacturing product process

organizational

e-commerce n.s. COMP COMP

Services product processorganizational

e-commerce SUBS COMP COMP

• In terms of productivity gains: e-commerce and process innovation complement each other

– That is: combination of both leads greater increase in productivity than the sum of their effects in isolation

• Same for e-commerce and organizational innovation

• Combination of product innovation with e-commerce does not have any excess productivity gains in manufacturing, and leads to lower gains in services (substitutes)

• Background:– Comparability of micro-level studies– What is the impact of institutional setting,

regulation, and policy?– Lack of available cross-country micro-data

Cross-country analysis using micro-data

Cross-country analysis using micro-data

Source: Bartelsman, Hagsten and Polder (2015), forthcoming

• ESSNet projects on data linking and ICT impact (ICT Impact, ESSLimit, ESSLait)

• 14 participating NSOs• ‘Micro-moments database’

– Indicators based on ‘micro-aggregated’ data– … from linked EC, IS, PS, trade databases– … going beyond usual breakdowns of aggregate

data

• Moreover: harmonized ‘distributed’ micro-datasets at various NSOs allow for remote execution type of empirical analysis

Cross-country analysis using micro-data

Cross-country analysis using micro-data

Source: Bartelsman, Hagsten and Polder (2015), forthcoming

• Examples:– Dispersion of productivity growth and ICT

• Productivity growth by country × time × industry• … and ICT vs non-ICT intensive firms

– Resource allocation:• Employment growth by by country × time × industry • … and by quartile of the firm-level productivity

growth distribution

Cross-country analysis using micro-data

• Examples:– Dispersion of productivity growth and ICT

• Productivity growth by country × time × industry• … and ICT vs non-ICT intensive firms

– Resource allocation:• Employment growth by by country × time × industry • … and by quartile of the firm-level productivity

growth distribution

Cross-country analysis using micro-data

Cross-country analysis using micro-data

International comparability is in general hampered by differences in•surveys between EU and non-EU and/or non-OECD countries•frequency of the survey•voluntary or compulsory nature of the survey•coverage of subsamples, especially in small countries•reference and recall periods•treatment of outliers and multinationals•ranges recorded in surveys•sectoral coverage•…

Selected data issues

Micro-data analysis benefits from

•Increased coverage of firms over time

•Coordination of sampling design between surveys (e.g. innovation and ICT)

•Stability of definitions and concepts over time

•Harmonization of definition and concepts across surveys

•Increased international harmonization allows the analysis of micro-data in a cross-country setting

Data issues

Any Questions??

Thank you for listening