Sevilla May 2011, WIOD 2 nd Consortium Meeting 1 WP 6
Methodological research related to the database WP 6.4 Multiplier
bias from Supply and Use Tables Jos M. Rueda-Cantuche (JRC-IPTS)
Erik Dietzenbacher (RUG, NL) Esteban Fernndez (University of
Oviedo, ES) Antonio F. de Amores (Pablo de Olavide University, ES)
Slide 2 Sevilla May 2011, WIOD 2 nd Consortium Meeting 2 Rationale
Slide 3 Sevilla May 2011, WIOD 2 nd Consortium Meeting 3 Following
Dietzenbacher (2006): Leontief inverse, L, plays a relevant role in
inter- industry economics L captures direct + indirect effects of
an exogenous shock on industry/commodity output L can also
describes the inter-industry core of a CGE model Slide 4 Sevilla
May 2011, WIOD 2 nd Consortium Meeting 4 As far as, typically, L =
(I A) -1, it is subject to the many sources of measurements errors
that are very well known for IOT, A and SUT. Hence, it seems
plausible assuming A stochastic, which leads to L is biased, with
input coefficients: totally independent (Simonovits, 1975)
biproportionally stochastic (Lahiri, 1983) moment-associated (Flam
and Thorlund-Petersen, 1985). Slide 5 Sevilla May 2011, WIOD 2 nd
Consortium Meeting 5 More recently, stochastics was alternatively
imposed on the intermediate transactions of an input-output table
rather than on its technical coefficients (e.g. Dietzenbacher,
2006). The findings of these experiments turned out that the bias
tends to be rather small and needs a large sample size to get
significant relevance. Complementary to Roland-Holst (1989). Slide
6 Sevilla May 2011, WIOD 2 nd Consortium Meeting 6 This work
however shifts the attention to supply and use tables, which really
constitute the basic units of the elements of an input-output table
and therefore, of the technical coefficients. Six kind of
multipliers discussed in the form of multiplier matrices Supply-use
based Monte Carlo experiment Slide 7 Sevilla May 2011, WIOD 2 nd
Consortium Meeting 7 Multiplier matrices Slide 8 Sevilla May 2011,
WIOD 2 nd Consortium Meeting 8 B = input coefficient per unit of
industry output C = share of industry output stemming from
producing one commodity D = commodity output proportions (market
shares) Domestic supply and use table Slide 9 Sevilla May 2011,
WIOD 2 nd Consortium Meeting 9 M1 = Industry technology assumption
for product by product tables M6 = Product technology assumption
for product by product tables M2 = Fixed product sales structure
for industry by industry tables M5 = Fixed industry sales structure
for industry by industry tables M3 = D T M1 (industry by product)
M4 = C -1 M6 (industry by product) M7 = D -T M2 (product by
industry) M8 = C M5 (product by industry) Slide 10 Sevilla May
2011, WIOD 2 nd Consortium Meeting 10 Monte Carlo experiment Slide
11 Sevilla May 2011, WIOD 2 nd Consortium Meeting 11 Empirical
application for Spain (2006): Monte Carlo experiment 1.Data
sources: SUT (basic prices), 59 ind. x 59 prod. (Eurostat and INE).
2.Two approaches: Supply-side Use-side Slide 12 Sevilla May 2011,
WIOD 2 nd Consortium Meeting 12 Randomization of VRandomization of
u, f, h, w Reconciliation h, f, p RAS-based Solution SUPPLY-SIDE
APPROACH Slide 13 Sevilla May 2011, WIOD 2 nd Consortium Meeting 13
Randomization of VRandomization of u, f, h, w RAS-based Solution
USE-SIDE APPROACH Slide 14 Sevilla May 2011, WIOD 2 nd Consortium
Meeting 14 Slide 15 Sevilla May 2011, WIOD 2 nd Consortium Meeting
15 ITM C X C FPS I X I ITM I X C PTM I X C FIS I X I PTM C X C
Slide 16 Sevilla May 2011, WIOD 2 nd Consortium Meeting 16
CONCLUSIONS: 1.The absolute average values of the bias in all cases
are not very significant and may probably be considered negligible
as in Roland-Holst (1989) and Dietzenbacher (2006). 2.This is just
an almost definite answer that deserves further research for a
bigger number of iterations (say, 10,000); more countries and/or
years and different levels of variability in the randomized supply
and use values. Slide 17 Sevilla May 2011, WIOD 2 nd Consortium
Meeting 17 Thank you! http://ipts.jrc.ec.europa.eu