Global Production Research

The Global Production Scoreboard Method & data

Purpose

The Global Production Scoreboard is designed to support the benchmarking of emerging and frontier economies (EFE) as manufacturing locations.

Approach

The Scoreboard comprises a range of potential and performance-related indicators being selected to provide deep insights into EFE supply capacity and competitiveness.

EFE are defined as countries with a per-capita income not exceeding USD 20.000 in 2017. Manufacturing industries are defined in terms of their output, covering the product categories of Sections 5 8 (minus Division 68) of the Standard International Trade Classification (Rev. 3).

Indicators

Labour cost []

This indicator shows total compensation cost per hour (in USD) for skilled industrial workers in major urban areas.

Skilled industrial workers refer to employees with vocational training and ca 10 years of experience with large companies in metal-working industries. They are assumed to be about 35 years old, married and have two children.

Total compensation cost per hour is calculated as the gross annual cost divided by the annual number of working hours. Gross annual cost includes (a) vacation pay, (b) supplementary benefits (bonuses, family assistance etc), and (c) mandatory taxes and social security contributions by the employer.

Our choice of indicator values is based on careful assessments of multiple data sources: (a) a labour cost study by UBS, using data collected through local interviews during March April 2018; (b) selected labour cost studies, relying mainly on official national statistics; and (c) our own cost estimates, obtained from a regression analysis of the UBS dataset on macroeconomic drivers of worker compensation in the manufacturing sector.

For the majority of countries, for which a reasonable degree of agreement is found to exist across the datasets (a) (c), indicator values are based on the UBS labour cost data. For a handful of remaining countries, indicator values are based on the cost estimates obtained from our regression model.

Skill base [+]

R&D capacity [+]

Infrastructure [+]

Government [+]

Local demand potential [+]

Foreign market access [+]

Export growth [+]

Export diversity [+]

High and medium-tech exports [+]

 

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