Gabriela Ramos - Official Welcoming

1. THE PRODUCTIVITY -
INCLUSIVENESS NEXUS
GABRIELA RAMOS,
Special Councellor to the OECD Secretary-
General, Chief of staff and Sherpa
2016 Conference of the Global Forum on Productivity
Structural Reforms for Productivity Growth

2. • Productivity growth has declined since the 1990s
• Annualised growth of labour productivity (output per hour worked)
The big picture
Note: OECD, euro area, G20 and non-OECD are aggregated using GDP-PPP weights. OECD includes all OECD countries, and euro area
includes all euro area countries, in both cases except Estonia. G20 includes all G20 countries except South Africa. Non-OECD is
Argentina, Brazil, China, Colombia, India, Indonesia, Latvia, Lithuania, Russia and Saudi Arabia. Data for several countries begin between
1991 and 1995, not in 1990. Labour productivity for non-OECD countries is measured per worker, not per hour worked.
Source: OECD estimations using OECD National Accounts database; OECD Productivity database; International Labour Organisation
database.
0
1
2
3
4
5
6
OECD USA Euro area Japan G20 Non-OECD
1990-2000 2000-07 2007-14

3. .
0.0
0.1
0.2
0.3
0.4
0.5
2001 2002 2003 2004 2005 2006 2007 2008 2009
Frontier firms
(3.5% per annum) All firms
(1.7% per annum)
Non-frontier firms
(0.5% per annum)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
2001 2002 2003 2004 2005 2006 2007 2008 2009
Frontier firms
(5.0% per annum)
All firms
(0.3% per annum)
Non-frontier firms
(-0.1% per annum)
Manufacturing Sector Services Sector
Notes: “Frontier firms” corresponds to the average labour productivity of the 100 globally most productive firms in each 2-digit sector in ORBIS. “Non-frontier firms” is the average of all other firms. “All firms” is
the sector total from the OECD STAN database. The average annual growth rate in labour productivity over the period 2001-2009 for each grouping of firms is shown in parentheses. The broad patterns
depicted in this figure are robust to: i) using different measures of productivity (e.g. MFP); ii) following a fixed group of frontier firms over time; and iii) excluding firms that are part of a multi-national group (i.e.
headquarters or subsidiaries) where profit-shifting activity may be relevant.
Source: Andrews, D., C. Criscuolo and P. Gal (2015), "Forntier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries" ECO/CPE/WP1(2015)6/ANN2
Labour productivity; index 2001=100 Labour productivity; index 2001=100
Frontier firms are racing ahead

4. The gap between frontier and lagging regions
was widening even before the crisis
Averages of highest top 10% (frontier), lowest 75% and lowest 10% (lagging)
regional GDP per worker, TL2 regions

5. Income inequality has increased in most
OECD countries
Gini coefficients of income inequality, mid-1980s and 2013, or latest date available
0.15
0.25
0.35
0.45
0.55
0.65
Mid-1980s (early 1990s for EEs) 2013 or latest
Increase Little change Decrease
"Little change" in inequality refers to changes of less than 1.5 percentage points
Data for Indonesia, Argentina, Peru, Brazil, South Africa, India and China (grey bars) come from external sources are not strictly comparable with the OECD Income Distribution Database data (blue bars). The
Gini coefficients are based on equivalised incomes for OECD countries, Colombia, Latvia and the Russian Federation and per capita incomes for other partner countries except India and Indonesia for which per
capita consumption was used. Mid-1980s data for Argentina and Mexico refer to 1986 and 1984 respectively. Mid1990s data for Mexico, Peru and Indonesia refer to 1994, 1997 and 1996 respectively. Mid
2000s data for Mexico, Chile and Russian Federation refer to 2004, 2006 and 2008.
Source: OECD Income Distribution Database (IDD) www.oecd.org/social/income-distribution-database.htm.

6. The top 10% own around 50% of net
wealth on average across the OECD
-10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
USA AUT NLD DEU PRT LUX CAN NOR FRA GBR FIN AUS ITA BEL ESP GRC SVK
TOP 10% TOP 5% TOP 1% Bottom 60%
OECD-17 average (top 10% )
Wealth shares of top percentiles of the net wealth distribution 2010 or last
available year

7. The Productivity-Inclusiveness Nexus shows
possible linkages and points to win-win policies

8. Inequality lowers the skills of poor
Average numeracy score by parent educational background (PEB) and
inequality

9. Productivity dispersion across firms has likely
contributed to widening of the wage distribution
- 200
- 100
0
100
200
300
400
500
600
700
800
900
2005 2006 2007 2008 2009 2010 2011 2012
Change in real wages in different parts of the productivity distribution of firms in Chile

10. What does the Nexus Mean for Policy?