Employment Sector
Employment Working Paper No. 88
2011
Dynamic Social Accounting Matrix
(DySAM ): Concept, Methodology and
Simulation Outcomes
The case of Indonesia and Mozambique
Jorge Alarcón, Christoph Ernst, Bazlul Khondker, PD Sharma
Employment
Intensive
Investment
Programme
Copyright © International Labour Organization 2011
First published 2011
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ILO Cataloguing in Publication Data
Alarcón, Jorge; Ernst, Christoph; Khondker, Bazlul; Sharma, P. D.
Dynamic social accounting matrix (DySAM) : concept, methodology and simulation outcomes : the case of Indonesia and Mozambique /
Jorge Alarcón, Christoph Ernst, Bazlul Khondker, PD Sharma ; International Labour Office, Employment Sector, Employment Intensive
Investment Programme. - Geneva: ILO, 2011
1 v. (Employment working paper, No.88 )
ISBN: 9789221250418;9789221250425 (web pdf)
International Labour Office; Employment Sector
promotion of employment / employment / data base / data collecting / methodology / Indonesia / Mozambique
13.01.3>
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Preface
The primary goal of the ILO is to contribute, with member States, to achieve full and
productive employment and decent work for all, including women and young people, a goal
embedded in the ILO Declaration 2008 on Social Justice for a Fair Globalization, 1 and
which has now been widely adopted by the international community.
In order to support member States and the social partners to reach the goal, the ILO
pursues a Decent Work Agenda which comprises four interrelated areas: Respect for
fundamental worker‘s rights and international labour standards, employment promotion,
social protection and social dialogue. Explanations of this integrated approach and related
challenges are contained in a number of key documents: in those explaining and elaborating
the concept of decent work2, in the Employment Policy Convention, 1964 (No. 122), and in
the Global Employment Agenda.
The Global Employment Agenda was developed by the ILO through tripartite
consensus of its Governing Body‘s Employment and Social Policy Committee. Since its
adoption in 2003 it has been further articulated and made more operational and today it
constitutes the basic framework through which the ILO pursues the objective of placing
employment at the centre of economic and social policies.3
The Employment Sector is fully engaged in the implementation of the Global
Employment Agenda, and is doing so through a large range of technical support and
capacity building activities, advisory services and policy research. As part of its research
and publications programme, the Employment Sector promotes knowledge-generation
around key policy issues and topics conforming to the core elements of the Global
Employment Agenda and the Decent Work Agenda. The Sector‘s publications consist of
books, monographs, working papers, employment reports and policy briefs.4
The Employment Working Papers series is designed to disseminate the main findings
of research initiatives undertaken by the various departments and programmes of the
Sector. The working papers are intended to encourage exchange of ideas and to stimulate
debate. The views expressed are the responsibility of the author(s) and do not necessarily
represent those of the ILO.
José Manuel Salazar-Xirinachs
Executive Director
Employment Sector
1
See http://www.ilo.org/public/english/bureau/dgo/download/dg_announce_en.pdf
2
See the successive Reports of the Director-General to the International Labour Conference: Decent
work (1999); Reducing the decent work deficit: A global challenge (2001); Working out of poverty
(2003).
3
See http://www.ilo.org/gea. And in particular: Implementing the Global Employment Agenda:
Employment strategies in support of decent work, “Vision” document, ILO, 2006.
4
See http://www.ilo.org/employment.
iii
Foreword
The Employment Intensive Investment Branch (EMP/INVEST) of the ILO has a long
tradition in the development and use of employment impact assessment methodologies.
They have been developed with the purpose to compare the cost-effectiveness and
employment dimension of different technologies applied in the implementation of
infrastructure investment. Another objective of these methodologies has been to evaluate
the effectiveness of already implemented infrastructure investment with regard to
employment and general economic variables. The Dynamic Social Accounting Matrix
described in this paper is a logical development of these assessment tools by
EMP/INVEST. For many decades, ILO has been using Input-Output Tables around the
world. Static Social Accounting Matrices have been introduced more recently, which
expands Input-Output Tables by introducing social transfers of enterprises, households and
the Government. Indonesia and Mozambique have been the first two countries where
EMP/INVEST assisted the Governments in the construction of a DySAM. The particularity
of a DySAM is a) the inclusion of a satellite account with disaggregated employment data
by activity, b) the inclusion of technology choices (labour-based, capital based), and c) the
possibility to up-date data for years, where input-output tables are not available (relaxing
some assumptions).
This working paper should help technicians and modelers gain a basic understanding
of the functioning of the DySAM and its potential for applications in concrete situations,
not only for infrastructure investment but also in respect of various other government
spending and public policies. Its strength is its multi-sectoral approach and it has already
been applied in the analysis of trade policies and fiscal stimulus packages, including
infrastructure investment and other measures applied to the whole economy, e.g. tax cuts or
sectors and subsidies. Satellite accounts facilitate the inclusion of real data, not just on
employment but also on the environment by generating an analysis of environmental
shocks and policies. Another strength is that it allows a better understanding of the impact
of policies and programmes on specific target groups. In this context, it has been used to
look specifically at female and young workers, and also on rural and urban workers. The
DySAM allows not only the evaluation of the effectiveness of past programmes or the
simulation and comparison of the possible outcome of future policies or policy mix but also
the evaluation of external shocks such as an economic crisis, a natural disaster or a trade
opening.
Azita Berar Awad
Director
Employment Policy Department
Terje Tessem
Chief, Employment Intensive
Investment Programme
iv
Contents
Page
Preface ...................................................................................................................................................... iii
Foreword .................................................................................................................................................. iv
Abbreviations ......................................................................................................................................... viii
Glossary.................................................................................................................................................... ix
Introduction ............................................................................................................................................... 1
Preamble: In search of tools to promote employment centred development .................................. 1
Social Accounting Matrix ............................................................................................................... 2
Guide to the Report ......................................................................................................................... 4
1 Dynamic Sam Methodology ................................................................................................................... 5
1.1 Overview ................................................................................................................................... 5
1.2 Database and data handling....................................................................................................... 6
1.2.1 Consistency verification ............................................................................................... 6
1.2.2 Derivation of consistent macro data series ................................................................. 6
1.2.3 Source data compilation and building consistency...................................................... 7
2 Dynamic SAM and satellite modules ................................................................................................... 10
2.1 Derivation of Dynamic Macro SAMs ..................................................................................... 10
2.2 SAM Transformation Methodology ........................................................................................ 11
2.3 Derivation of Dynamic Sectoral SAM .................................................................................... 11
2.3.1 DySAM Algorithm ...................................................................................................... 12
2.3.2 DySAM Algorithm Comparison for Indonesia and Mozambique .............................. 13
3 Derivation of dynamic SAM model for impact analysis ...................................................................... 18
4 Simulation Design and Impact Analysis .............................................................................................. 20
4.1 Introduction ............................................................................................................................. 20
4.2 Total backward linkages ......................................................................................................... 20
4.2.1 Dynamic backward linkages ...................................................................................... 21
4.2.2 Total backward linkage comparison .......................................................................... 21
4.3 Dynamic SAMs multipliers and linkages (total) ..................................................................... 23
4.4 Summary observations of the policy indicators – the case of Indonesia................................. 25
5. Satellite accounts and transformation and employment ...................................................................... 27
5.1 Introduction ............................................................................................................................. 27
5.2 The Employment Satellite Module ......................................................................................... 28
5.2.1 General ...................................................................................................................... 28
5.2.2 Employment Methodology and Modelling ................................................................. 29
v
5.3 Employment summary results: the case of Indonesia ............................................................. 30
6 Simulation the Case of Indonesia: Fiscal Stimulus Package Infrastructure ......................................... 33
6.1 Simulation Scenario: the case of Indonesia............................................................................. 33
REFERENCES ........................................................................................................................................ 39
APPENDIX ............................................................................................................................................. 41
1 Backward linkages: Evolution over time for a selected number of endogenous accounts......... 41
Glossary of Symbols: .......................................................................................................... 41
2 Dynamic Macro SAMs [s3]: The Case of Mozambique ...................................................................... 49
Table
Page
Table 1: Data development steps ............................................................................................................... 8
Table 2: DySAM Algorithm Comparisons – Step 1................................................................................ 13
Table 3: DySAM Algorithm Comparisons – Step 2................................................................................ 14
Table 4: DySAM Algorithm Comparisons – Step 3................................................................................ 14
Table 5: Matrices for accounts of the Indonesian DySAM (2000 – 2008) ............................................. 15
Table 6: DySAM Algorithm Comparisons – Step 4................................................................................ 15
Table 7: Endogenous and exogenous accounts of a SAM....................................................................... 18
Table 8: Indonesia: Comparison of selected Commodity and Activity total backward linkages ............ 23
Table 9: Summary of the impact of injections by account type (The information in this table is very
similar to the information in the last para of section 4.) ......................................................... 25
Table 10: Indonesia DySAM 2008 Correlation Matrix ........................................................................... 26
Table 11: Indonesia DySAM 2008 Average partial backward linkages ................................................. 27
Table 12: DySAM summary labour multipliers by accounts for 2008 (Unit Persons) ........................... 31
Table 13: DySAM Total Labour Multipliers by Construction Type for 2008 (persons) ........................ 32
Table 14: DySAM partial activity multipliers by construction type for 2008 (billion IDR) ................... 32
Table 15: Stimulus Package by Items and Delivery Levels .................................................................... 33
Table 16: Economy-wide Impacts of FSPC Injection of 10,665.0 billion rupiahs in 2009 (billion rupiahs)
................................................................................................................................................ 34
Table 17: Net Cost of the Construction Fiscal Stimulus Package in 2009 (billions of rupiahs) ............. 34
Table 18: Total Impact on Job creation 2009: Economy Wide, Construction by Type and Crops ......... 35
vi
Table 19: Intra Account Impact on Job creation 2009: Economy Wide, Construction by Type and Crops
................................................................................................................................................ 36
Table 20: Share of new employment by location and gender, 2008 (percentage) .................................. 36
Table 21: Macro SAMs for selected years .............................................................................................. 49
Figures
Page
Figure 1: Principal circular ‗closed‘ economic flow ................................................................................. 3
Figure 2: Derivation procedure of a dynamic macro SAM ..................................................................... 10
Figure 3: Dynamic SAM Flow Chart: Indonesia .................................................................................... 13
Figure 4: The dynamic SAM for Indonesia (2000-2008) ........................................................................ 16
Figure 5: The dynamic SAM for Mozambique (2000-2008) .................................................................. 17
Figure 6: Indonesia: Over time trend of total backward linkages for selected economic Activities ....... 24
Figure 7: Relations between the SAM and satellite accounts; Extended SAM (ESAM) ........................ 28
Figure 8: Commodity Account and Activity Account ............................................................................ 41
Figure 9: Total Backward Linkage of Factor Accounts (1-14) ............................................................... 44
Figure 10: Total Backward Linkage of Household Accounts (1-14) ...................................................... 46
Figure 11: Total Backward Linkage of Household Accounts (15-28) .................................................... 47
Figure 12: Total Backward Linkage of Household Accounts (29-35) .................................................... 48
Figure 13: Behaviours of Activity and Commodity Accounts of the Dynamic SAMs ........................... 51
Figure 14: Behaviours of Household and Government Accounts of the Dynamic SAMs ...................... 52
vii
Abbreviations
APS
CM or Co
CMEA
DySAM
EPI
ESAM
FD
FDOI or iE
FoF
FP or Fp
FSPC
GOI
HH or iH
ILO
iG, iTx and iSu
ISI
MCM
MDGs
ME
MTCs
PA or A
PRSP
RoWor wCu
SAM
SNA
TM
UN
UNSD
Average Propensity to Spend (An)
Commodities
Coordinating Ministry for Foreign Affairs (Indonesia)
Dynamic Social Accounting Matrix
Export Promotion Industrialization Strategy
Extended Social Accounting Matrix
Final Demand
Final Demand Other Institutions
Flow of Funds
Factors of production
Fiscal Stimulus Package Construction
Government of Indonesia
Households
International Labour Office
Institution Government, Taxes and Subsidies
Import Substitution Industrialization Strategy
Matrix of Imported Commodities by Demand Type
Millennium Development Goals
Manpower equivalence
Meticals (currency in Mozambique)
Production Activities
Poverty Reduction Strategy Paper
Rest of World Current Account
Social Accounting Matrix
System of National Accounts (UNSD 1993 Recommendations)
Trade Margins
United Nations
United Nations Statistical Division
viii
Glossary
Backward linkage = an exogenous injection into the system increases the income of the
corresponding account and then cascades on the incomes of all other endogenous
accounts, the column-wise sum of all these effects constitutes its total backward
linkage. The column-wise sum within each account is the total partial linkage.
Forward linkage = effect read row-wise represents the amounts of expenditures per
account that are made ‗available‘ for the expansion in other accounts; can be
interpreted as market potential availability.
Data module = specially developed data set, where time series and SAM data are made
consistent; forms the basis of the DySAM.
Endogenous account = a set of economic variables that are determined within a model.
The set is therefore not subject to direct manipulation by the modeller since that would
override the model. In SAM models, production and incomes are almost always
endogenous.
Endogenous variable = economic variable that is part of the endogenous account. It is
therefore not subject to direct manipulation by the modeller since that would override
the model. In trade models, the quantity of trade itself is almost always endogenous.
Exogenous account = a set of economic variables that are taken as given within an
economic model. The set is, therefore, subject to direct manipulation by the modeller.
Exogenous variable = variable that is part of an exogenous account. It is, therefore, subject
to direct manipulation by the modeller.
Intra-account effects = intra-account effects measure impacts within the account where
the injection enters. In DySAM modelling defined as the sum of the injection (I) and
transfer (T) effects (M1). Only exists for accounts with intra-account transactions.
Induced effects = induced effects measure impact outside the account where the injection
enters, e.g. household expenditures, from the income earned in a directly or indirectly
affected activity. In DySAM modelling defined as the sum of open loop (O) and close
(C) loop effects (OC). It exists for all accounts.
Injections = autonomous effects on exogenous accounts/variables; for example, increase in
investment expenditures, government purchases, and/or exports.
Leakages = are exogenous expenditures accounts/variables in the model; for example,
saving, taxes, remittances and imports. In SAM modelling these transactions are
defined as a B Matrix.
Macro control totals = consistent macro values derived from the process crossing over
time series with the SAM. The overtime values are used to anchor the building process
of the DySAM by applying stepwise iterative and RAS methods.
Manpower equivalence = correcting the number of persons employed with a factor that
uses a norm reflecting ―actual‖ working days.
Placeholder value = proxy values used when ―true‖ data is absent, scarce or inaccurate; the
proxy values can be replaced once the ―true‖ values are available.
Note: Glossary is self-defined or taken and adjusted from: Deardorff‘s Glossary of
International Economics (http://www-personal.umich.edu/~alandear/glossary/ , and
http://www.amosweb.com/cgi- in/awb_nav.pl?s=wpd&c=dsp&k=injectionsix
leakages+model , extracted on28 July 2010) and other sources (e.g. from Indonesian
Data and DySAM reports).
x
Introduction
Preamble: In search of tools to promote employment
centred development
Employment generation is an accepted and effective strategy for reducing
poverty and progressing development in many developing nations. The strategy in
developing countries is based on the recognition that a wage income is a primary
source of income for poor household groups. Therefore, creating additional
employment opportunities and/or raising the wage income of the existing employed
population are central themes in most poverty reduction strategies.
A typical Poverty Reduction Strategy Paper (PRSP; or similar goal-based
policy agenda) will often promote investment projects that are geared to achieving
an agreed level of poverty reduction by increasing (or enhancing) ‗returns‘ to
labour. Since investment is a proximate determinant of employment generation, a
natural question in the mind of development planners pertains to the efficiency of
such investments to total employment generation (direct, indirect and induced over
short- medium- and long-term time horizons). Infrastructure investment is a major
element of analysis and requires special attention, as: i) it represents an important
share of public spending; ii) it represents the ―flexible part of public spending,
which can be more easily adjusted in good or bad times; iii) it has a multiplier effect
on the private sector and private companies mostly implementing that type of
investment.
Various types of analytical tools may be adopted to assess the impact of investment on
employment. However, since investment is a component of the national aggregate demand,5
a ‗Keynesian‘ type of demand driven (multiplier) approach may prove to be the most
suitable choice for understanding such questions. The Social Accounting Matrix (SAM) is
an accounting platform that offers such an approach.
A workshop hosted by the Employment Intensive Investment Branch (Emp/INVEST)
of the ILO on employment impact assessment methodologies in March 2008 clearly
demonstrated the common interest of different branches, programmes and external partners
for the development and use of appropriate tools and methodologies to assess the
employment impacts of public policies and investments, particularly those related to
infrastructure. There was a strong consensus that a social accounting matrix (SAM), which
is based on input-output methodology, would be the most suitable tool and should,
therefore, be developed further.
It allows a better understanding of the impact and transmission channels of external
shocks, e.g. a financial crisis or a trade opening, or macro and sectoral policies through the
various sectors towards the target groups at the micro level, meaning different types of
workers or households. It can also be used for the analysis of public infrastructure
5
The relationship can be expressed as: F = C + I + G + (E-M).
1
investment, public spending in general, and also other sectoral policies and trade policies,
etc. The SAM not only permits the evaluation of the effectiveness of past programmes and
the simulation and comparison of the possible outcome of future policies or policy mix but
also allows the evaluation of external shocks.
Social Accounting Matrix
A SAM can be considered to be an extension of input-output tables, which have been
used extensively by the ILO over recent decades to measure,6 among other things, the
direct and indirect employment effects of public investment through a multiplier analysis.
The major deficit encountered with input-output tables is that they do not include detailed
data about the distributional side of economic processes. That is, they do not contain data
on the expenditure pattern of the economic actors (government, enterprises, and
households). A SAM brings together, in a coherent way, data on income creation and
production as national accounts and input-output tables do, and also includes information
7
on incomes received by different institutions and related spending.
As a result, the ILO started using a static SAM to analyze the impact of trade on
employment, as in the case of Costa Rica, India and South Africa (see Kucera, Roncolato,
2011, Ernst, Sánchez-Aconchea, 2008). An employment satellite account was introduced
with real employment data that was disaggregated by sector, which allowed a detailed
analysis of the employment impact of trade strategies and policies.8 As SAM methodology
covers a single and non-current period of data, there was a need to develop a dynamic SAM
(DySAM). More concretely, a DySAM has to be able to deal with the four main problems
of a static SAM, including:




A SAM model is static with fixed coefficients;
data in the SAM refers to one single period (normally a year);
the year of the SAM is normally not current; and
A SAM lacks behavior.
Comparisons between the ‗traditional‘ static SAM modelling and DySAM modelling
can be summarized as follows:
1. Dynamic SAM‘ (DySAM) describes an instrument based on an existing ‗static‘ Social
Accounting Matrix (SAM) for the economy of a country and the available data from
national accounts, BoP, budget and financial statistics.
2. The static SAM gives a snapshot of the economy, while a DySAM shows the consistent
evolution of the economic structure over time, for periods covering the years before and
after the static SAM.
6
SAM as a planning policy instrument was proposed by G. Paytt and E.Thorbeke in 1976, as part of
the ILO World Employment Programme.
7
A SAM, therefore, displays the following elements: 1. Inputs, 2. Outputs, 3. Factor incomes created
in domestic production, 4. Distribution of these factor incomes, 5. Redistribution of these factor
incomes over these institutions, 6. Expenditure of the institutions on consumption, investment, 7.
Savings made by them. For more information, see van Heemst, Ch. 1, in Alarcon (1991) et al.
8
See Ernst, Sánchez-Aconchea on Costa Rica (2008) and Kucera, Roncolato on India and South
Africa (2010).
2
3. DySAM thus helps to identify cross section and time series data problems and enhances
data gathering processes.
4. Several sequential SAMs over time imply dynamics.
5. Over time shifts reflect technology choices.
6. A DySAM lessens the need to calculate expenditure income elasticities, in order to
introduce behaviour, i.e. SAM fixed-price model (see Pyatt and Round, 1979).
7. There will always be one DySAM period that matches surveys (e.g. labour, household
expenditure, population, etc), which eliminates the need to introduce time-bound
assumptions.
8. An employment satellite account for one or several years with disaggregated labour
market data can be added and coupled with the DySAM, and matched with the exact
year of the particular survey.
9. Allows the use of place holders when information is scarce, missing or not fully
reliable, this can done via satellites, for instance, to dynamize the sectoral
disaggregation of the construction sector.
10. The use of place holder values eliminates the need to hold up programming before
‗final‘ data are provided.
11. The DySAM can be updated when new data become available or when a more current
SAM and/or System of National Accounts (SNA) time series data comes on-stream.
The Dynamic SAM can be used to support and strengthen the process of developing
coherent national strategies by, inter alia, analysing the effects of investment planning on
the economy. It can be used to specifically explore the relationship between intensive
employment strategies, job creation, and poverty reduction.
Figure 1: Principal circular ‘closed’ economic flow
Source: Adapted from Fig. 1 (pp. 12) in Alarcon (2007), see also DySAM Reports (2010)
The Dynamic SAM may be used for: (i) Counterfactual simulation analysis (e.g.
magnitude of exogenous injections) at any year within the period for which it is computed.
This helps to validate valuable experiences such as analysis of completed public
policies/programmes; and (ii) Short-run policy simulations from the terminal year and after.
Using the DySAM approach may be viewed as a ‗full-information‘ data model, which
3
mitigates exclusive use of a dated static SAM or a SNA, the latter of which typically has
low resolution to capture the circular flow operating in the economy (c.f. Flow Chart 1).
It can clearly be seen that a time-consistent and reliable database9 is paramount.
Although, consistency is a shared characteristic of all serious modelling efforts, it does
require added importance when deriving dynamic SAM multiplier sequences. In addition, it
is clear that the base year SAM structure, the number of accounts, the types of
classifications and the account openings will limit or enrich the quality of analysis that may
be envisaged.
While modelling with a dynamic SAM, similar to static SAM modelling, satellite
accounts can be used to introduce a wider range of analysis. Satellites can be of the
‗expanding‘ or ‗extending‘ types. The former refers to the use of information to ‗blow out‘
existing entries in the SAM. For instance, the original SAM household and labour factor
classifications can be increased or altered. Similarly, the construction sector can be
separated into various types of activities or commodities (i.e. infrastructure, roads,
irrigation, etc.). The latter refers to the extension of certain accounts with directly linked
physical information. Such information types can be as varied as employment,
environmental aspects, types of housing, demographic information and morbidity satellite
tables, to name a few.
The DySAM multiplier analysis, using the SAM framework, helps us to gain a better
understanding of the dynamic-interdependent linkages between the different sectors of the
economy and the institutional agents at work within the society, namely households,
enterprises and the government.
Guide to the Report
The purpose of the paper is to provide an overview and a general understanding of the
DySAM and its potential for use. The paper starts by explaining various data issues, and
then describes the methodology in general and the new elements that DySAM introduces,
particularly its dynamic nature and the employment satellite account. Indonesia and
Mozambique are the first two cases for which a DySAM methodology has been developed
that take ILO‘s specific needs into account. These two countries serve as illustrations. The
last chapter focuses on simulation and impact analysis. This is followed by a conclusion on
the major findings.
9
Firstly, the degree of effectiveness of the DySAM depends on the quality, quantity and consistency
of the data used for it. This is not exclusive to DySAM, it is a shared condition, since, any serious
policy decisions should be based on, even though limited, empirical analysis. Secondly, it is
unacceptable and self-defeating to shy away from dealing with data problems (see point one)
because data do not improve themselves. The best approach is to start working with the existing data
to expose the kind of problems they have, since data refer to different periods, and times series need
to be crossed over with survey data; this would be the best way to improve existing data quality and
consistency. Hence, examining/testing the SAM and crossing it over with other data (SNA, LFS) can
provide good insights and thus make a significant contribution to finding objective ways to improve
it; e.g. the SAM helps to create a consistency between survey data and financial flows and even
physical data (employment). And thirdly, since most developing countries already have SAMs, there
is a basis to upon which to build.
4
1 Dynamic Sam Methodology
1.1 Overview
The term ‗Dynamic SAM‘ (DySAM) describes an instrument based on an existing
‗static‘ Social Accounting Matrix (SAM) for any economy and the available up-to-date
time series of national accounts (SNA). The methodology of building a Dynamic SAM
entails the following elements:
Re-verification of the existing static SAM: The starting point for deriving a dynamic
SAM is the availability of a balanced static SAM. In line with convention, all desirable
properties (including balances of the SAM accounts) of the static SAM are assessed. The
base static SAM is referred to as [s0]. If required, the static SAM is thoroughly adjusted to
conform to the desirable properties for subsequent dynamic transformation.
Constructing a time series of macro control totals: This is done for each block of
accounts of the static macro SAM (e.g. Commodity-Activity,10 Factors-Institution,
Institution-Institution, etc.) using the available SNA time series for the economy and using
the static macro SAM shares to interpolate for those accounts not available in the SNA.
This is entitled the ‗Dynamic Macro SAM‘ and labeled as [d0]. This resembles the concept
of a National Accounting Matrix or aggregated macro SAM.
Constructing the dynamic sectoral SAM (DySAM): The DySAM algorithm uses the
structures derived from the original base static SAM [s0] (intermediate use, factorial and
institutional income distributions, etc) and constrains them to the control totals derived in
[d0]. Since the controls totals are different from year to year, the algorithm proceeds to
generate interior structures for each block, which are compatible and consistent throughout
the economic system as typified by the SAM. This year-by-year iterating, consistency
seeking, circular process can be characterized as a step-by-step loop process for
generating/updating the SAM and making structural adjustments. The process:
1. Provides the necessary information for all subsequent years up until the last year for
which the consistent data are available in the database [d0]. The DySAM algorithm also
performs/imposes ‗reality checks,‘ which requires that the input data sets (historical
SNA data and the SAM) and the estimated DySAM follow the recommended
accounting practices (the 1993 UNSD SNA recommended conventions).11
2. Computes the sequence of multipliers (forward/backward/decompositions): to gain
insight into the evolution of the dynamic and interdependent processes that generates
the observed economic time series.
10
The commodity activity dichotomy does not appear in the SNA and is not common in I-O, it was first introduced in the I-O
framework by Alarcon (see Alarcon et.al. 1984 and Chapter 4 in Alarcon et.al. 1991). It was formalized for the SAM framework
by Pyatt, he states “activities have to be understood as a process, while a commodity is a good or service” and is in-bedded in
industry technology vs. commodity argument. See Pyatt Sec. 2 (1994).
11
Among the most important aspects is the non-negativity of the values for input use, final consumption or exports.
5
1.2 Database and data handling
This subsection summarises the process and methods for deriving consistent time
series of SNA control totals and features of the static SAM. Illustrations will refer to either
the Indonesia 2005 SAM or the Mozambique 2001 SAM and will be indicated as
appropriate.
1.2.1 Consistency verification
Although, consistency is a shared characteristic of all serious modelling efforts, it
acquires added importance when deriving the dynamic SAM multipliers sequences. For
example, all the data made available from the Governments of Indonesia (GOI) and
Mozambique (GOM) for DySAM implementation has been checked against the consistency
framework requirements. Two iterative rounds of data refinement have been performed.
Each successive round of iteration refocused the investigation and allowed new data
anomalies to be identified. Reconciliation iterations are very fruitful exercises, and the
reconciliation process serves two important ‗goals'.
1.2.2 Derivation of consistent macro data series
The dynamic SNA macro SAMs for a time series (t=1...n) are derived using the
information provided mainly by the SNA of a country. More specifically, the following
accounts are required to generate the macro data sets for any economy:






The real side (supply, production and demand).
Government budget.
Money and credit.
Balance of payments.
Population, and
Sectoral data: real and nominal GDP and employment.
The numerical specifications of accounting frameworks (SNA, I-O, SAM) are needed,
in order to accurately represent the economy of a given country and this depends on the
availability of consistent and balanced data sets. Experience demonstrates that even when
extensive data are available, there are barriers due to inconsistencies and failure to find a
balance across different components of the data. It is thus essential to assess the consistency
features of a country‘s data before embarking on constructing a DySAM. Derivation of
consistent macro data sets should be conducted in accordance with the following three
steps:
1. data collection activities of all relevant data, SNA, I-O and SAM;
2. completeness and consistency assessment of the available data sets; and
3. derive consistent data sets using the DySAM data module
Subsequently, the relevant macroeconomic data needs to be compiled in a Macro
Social Accounting Matrix framework (macro-SAM) and Flow-of-Funds framework (FoF),
in order to assess the intra and inter-accounts consistency of all the official data sets.
6
Box 1: The advantages of using a macro-SAM /FoF framework for handling data
1. It assesses data consistency using a single-entry system (maximising the efficiency of a ‘SAM accounting’
approach).
2. It examines overall data consistency by linking the real side information of current institutions (macro-SAM) to
the financial flows of the institutions (FoF).
3. It measures resource gaps of current institutions and subsequent gap financing by resources drawn from
institutions within the purview of an integrated framework.
4. It is scalable. The resolution of the consistent data structures, embedded in the SAM/FoF frameworks, may be
increased to be commensurate with specific country data sets. This creates a reliable data baseline for policy
modelling efforts.
Macroeconomic data sets are generally of two types, namely flows and stocks. All
values of the variables on the real side (i.e. production (activity/commodity); institutions,
government budget; and balance of payment accounts are flow variables. All monetary
12
variables are reported as stock variables.
1.2.3 Source data compilation and building
consistency
The activities of data review and consistency building are crucial for the successful
numeric calibration of the DySAM. All the data made available and provided by
governments for the DySAM construction need to be checked against the consistency
framework requirements (see below). As part of the process, two iterative rounds of data
refinement are usually performed. Each successive round of iteration refocuses the
investigation and allows new data anomalies to be identified. Reconciliation iterations are
necessary and are very fruitful exercises, and the reconciliation process serves two
important goals, namely:
1. Upon completion, the government will have an improved and solid base of reliable and
consistent country data upon which to build quantification systems. These data will be
balanced across all macroeconomic accounts and, importantly, will retain the economic
character of the original country‘s ‗source data‘.
2. The reconciliation exercise helps the government to identify specific targets for future
data strengthening activities.
Each new cycle of data changes requires a re-working of the ‗data module,‘ including
reality checks (e.g. non-negativity restrictions and compliance with SNA and other
accounting recommended practices) and balance checks across all accounts. The data are
processed series-by-series to locate any new data anomalies or reconciliation needs.
Compilation and building of DySAM data set for a DySAM model proceeds according
to the following iterative steps outlined in the table below. Each of the iterations requires an
12
a. Barring a few exceptions, almost all the flow variables should, in principle, depict either a positive value or a zero value
(i.e. +, 0). Stock Change, although a flow variable, is an exception that may depict any one of these values (i.e. ─, +, 0).
b. Almost all the monetary stock variables should, in principle, depict a positive value (i.e. +). Flow values derived from the
monetary stock values may, however, depict any one of these values (i.e. ─, +, 0).
c. Any deviation from the above two conditions needs careful attention during the compilation process of data sets.
7
intensive review of the specific changes and checks that have to be imposed on the entire
data set.
Table 1: Data development steps
Step
Description
Data collection
A data collection template is designed, which contains six accounts. The six templates include the
following information:
The real side (supply, production and demand)
Government Budget
Money and Credit
Balance of Payments
Population
Sectoral Data-Real and Nominal GDP and Employment
The government/national institutions provide ‘official’ data sets covering a requested period and
data gaps are identified.
Compilation and
building – First
iteration
The DySAM team compiles the macro data sets and includes complement placeholder values (i.e.
proxies for missing and obvious erroneous values, such as deficit financing information), which can
be drawn from various sources such as the International Monetary Fund, World Bank, UNSD.
The intra block (e.g. budget and BOP) and inter-block inconsistencies are corrected in the DySAM
data module in a way that the differences between the original values of the variables and the
revised (adjusted) values are kept to a minimum.
Major characteristics of the first complied data set are assessed and reported to the government
counterparts for their consideration and feedback.
Compilation and
building: Iteration 2
A second iteration is conducted when the team incorporates new information and revises
placeholder values (i.e. after discussing with the data producing agencies) into the data module
through direct contacts with data providers.
The second round of iteration generates a significantly improved data set that is used for
constructing the DySAM.
It should be noted that good communication between the DySAM team and the
national institutions can help reduce the overall number of data issues in the given country.
This process of the elimination and refinement of the original ‗source data‘ is quite
common and is necessary, in order to acquire reliable and consistent country data that can
be used to build important and useful quantification systems.
Most of the time, SAMs require some reworking, such as the re-ordering of accounts,
adjusting valuation by allocating trade margins (TM) and grouping the institutional
accounts and converting valuation into producer‘s prices. Re-ordering refers to organizing
the accounts to follow the circular economic flow (see figure 1). This is mainly done for
analytical reasons, as is easier to follow the cascading flow of income throughout the
economy. The consolidated Macro SAM, which is re-ordered, fully balanced and valued at
purchaser‘s prices, is used to benchmark the DySAM. The example below further illustrates
the process.
8
Box 2: Re-ordering, adjustment and conversion of SAM 2001: The case of Mozambique
The first task was to re-order the Mozambican SAM accounts in a way that follows the circular economic flow. Then the
separation into endogenous and exogenous for the modelling process is made.
The Mozambique SAM was valued at purchaser prices and, contrary to convention, the TM were placed in three rows in
the intersection of the commodity-activity mapping and without keeping the zero balance (i.e. double counting).
Therefore, the three TM rows were collapsed into a single row and transferred to the trade row entry in the commoditycommodity mapping. Furthermore, the row sum was placed with a negative, in order to maintain the zero row wise
balance, in the trade-trade diagonal entry. These meant that the commodity entries were reduced to 27 and the double
counting was thus eliminated.
Elements of the institutional account, which were previously dispersed, were grouped together in a single account.
Following the most conventional presentations of SAMs, the capital account was placed after the domestic current
accounts and before the consolidated rest of the world account. Again following convention, the entry that accounts for
the closure of the economic systems—the ‘Rest of the World’ (foreign) savings—was kept at the intersection of the
Savings-Investment account row and Rest of the World column.
The next step was to convert the SAM to producers’ prices. When all commodities carry the same trade margin, the TM
collapsed row entry is used to derive the TM matrices for intermediate, final household consumption and enterprises to
match the 27 SAM commodity input entries and the 167 using activities and final demand. Since all the remaining
elements of the final demand were presented in single column vectors, e.g. government, gross capital formation and
exports, the application of the mentioned assumption presented fewer problems.
The fact that some SAM breakdowns are not homogeneous is problematic for DySAM modelling. For example, the
Mozambique SAM presented some activities broken into a combination of sub-classifications, namely urban, rural, north,
south and Maputo; this resulted in some activities presenting seven sub-classifications while others presented only four. If
the SAM was used at such full disaggregation, for intermediate and household demand, the TM row would have to be
blown out into 27 by 167 and 27 by 35 entries, correspondingly. Instead, classifications were streamlined, with the main
purpose being to make it simpler and easier to understand. With that in mind, and for the purposes of the DySAM,
economic activities were collapsed into three main regions (Rural - North, Centre and South; Urban - North, Centre and
South; and Maputo). A larger number of sub-classifications did not necessarily add any value or clarity to the analysis
and the data behind it became very shaky.
The final SAM of Mozambique has six main accounts. As a result of the conversion to producers’ prices, the macro
values and totals for commodity accounts, production activities and MCM cannot coincide with the original entries valued
at purchaser’s prices. Furthermore, the original separate TM entries were deleted because they became redundant. For
reasons that are not clear discrepancies between the row totals (incomings) and the expenditure totals (outgoings) were
found and these had to be addressed before the SAM could be made consistent.
The adjustments and conversions made throughout the source data compilation and
consistency process reflect recommendations found in SNA conventions and the
requirements of the DySAM. Additionally, the SAM had to undergo a series of reality
checks. These reality checks are associated with a stricter and more specific observance of
the SNA and other recommendations and conventions. Furthermore, SAM modelling,
maths and programming restrictions related to the DySAM‘s dynamic algorithm need to be
taken into consideration as well.
The SAM modelling reality check is meant to indicate whether empty intra-account
intersections in the base SAM are the result of design, default or definition. Default
concerns those entries that could be booked differently, for instance negative net taxes,
indicating that subsidies greater than taxes appear as zero in the subsidy cells; they are zero
by design. In contraposition, there are accounts which do not have transactions, but in a
cascading direction the zero block intersection has to be empty; they are zero by definition.
The importance of such distinctions lies in the fact that no simulations are possible via
those intersections that are zero by definition.
There are other entries to which reality checks must be applied. These reality checks
are made to confirm adherence to more specific SNA conventions. In the case of fixed
capital formation, reality checks need to make sure that only those that are indicated in the
UNSD SNA 1993 recommendations (agriculture (sheds, silos, drainage, etc. when built by
famers themselves), the planting of fruit trees, livestock, machinery, equipment and
9
transport equipment production and construction) should show positive entries. Exceptions
to this convention are cases in which the government defence sector builds its own
complexes and barracks or when education and health sectors build their own physical
facilities. The forestry account can also be counted as part of fixed capital formation when
re-forestation programmes are operating. As a result of balancing efforts, the account can
show entries in other commodities and/or activities, and some of the entries may be
negative. Finally, there are accounts or single entries that are placed simply for accounting
reasons: among the former, there may be full import matrices by demand type, primary,
secondary and disposable income modules; among the latter, negative entries in main
diagonal cells. Since, they do not add analytical value, they are collapsed and/or deleted.
2 Dynamic SAM and satellite modules
2.1 Derivation of Dynamic Macro SAMs
The DySAM algorithm requires a time series (t = 1... n) of macro SAMs that are
consistent with SNA macro-meso control totals. It also requires using the structure of the
latest available static SAM (s0). This dynamic macro SAM is referred to as [s3 (t)] and it
contains all the macro controls that are necessary to build the DySAM. However, as all the
macro controls required for module [s3 (t)] are typically not available in the SNA dataset,
the construction of [s3 (t)] is undertaken in two steps, namely:


First - build the consistent macro data set based on the available SNA information. This is
referred to as the SNA macro SAM [d0 (t)].
Second - merge [d0 (t)] with the static SAM for the base period [s0] to generate [s3 (t)].
The derivation procedure of the dynamic macro SAM is diagrammed below.
Figure 2: Derivation procedure of a dynamic macro SAM
Base Static SAM (Given)
s0ij
Where ij denote
dimension
It has X number of nonzero entries
SNA SAM (Derived)
d0ij(t)
Non-zero entries Y <
X
Static SAM Structure
share s0ij
10
Dynamic SAM (Derived)
s3ij(t)
Where ij denote dimension
It contains X number of
non-zero entries
Box 3: The relationship between SNA and SAM: Case of Mozambique
T
hIn the case of Mozambique, the non-zero elements of static 2001 SAM [s0] number 37 (after adjustment),
i while the elements of SNA macro SAMs [d0 (t)] for the period 2000-2008 period number just 15. The one-toone correspondence between the dynamic SNA-SAMs and the static 2001 SAM is established for these 15
scommon elements only. The estimates of the remaining 22 elements—which along with the already defined
15 elements would constitute the entire set of non-zero elements of the dynamic macro SAMs—have been
mderived using the structural information of the static 2001 SAM and the controls of the dynamic SNA-SAMs
e[d0(t)].
t For instance, value additions are defined as capital, labour and land value added in the static 2001 SAM of
hMozambique. However, this breakdown of value added is not reported in the SNA. Value added is reported
oas a consolidated figure. Thus, the shares of these three types of value added in total value added as
Tobserved in 2001 are used to derive the three types of value added for each year of the reference period, i.e.
h2000-2008.
i
s method has been used for all the other elements where no direct one-to-one
correspondence could be found between SNA-SAM elements and elements of the dynamic
SAM. For instance, savings of institutions, which are derived as the residual between total
receipts and total payments, have been used to close the accounts. Finally, the identity
between savings (i.e. derived from the savings of four institutions) and investment has been
enforced, in order to ensure the overall balance of the dynamic SAMs for each year of the
reference period. The estimated dynamic macro SAMs for Mozambique for selected years
are reported in the Appendix I.
2.2 SAM Transformation Methodology
Once the dynamic macro SAM has been derived and adjustments to the static SAM
have been completed, the adjusted static SAM is transformed into a dynamic SAM by
linking it to a dynamic macro-meso control framework, the ‗DySAM Data Module‘. The
DySAM Data Module is specially designed to generate the macro-meso controls for the
static SAM.
As the control flows are incorporated into the static SAM it becomes dynamic and
moves forward in time (2001-2008). This ensures that the DySAM has the following
attributes:




Establishment of the dynamic flows across each account over the time period.
Establishment of consistency for each year of the time period.
Separation of DySAM accounts into ‗endogenous‘ and ‗exogenous‘ categories.
Generation of dynamic SAM multipliers decompositions to estimate direct/intra-account
transmission effects within the same account (e.g. injection in commodities and impact
on commodities) and indirect/induced effects among accounts (e.g. higher wages
stimulating higher consumption and thus tax collection). Quantification of these
dynamic transmission chains (intra-account and induced) allows SAM-based dynamic
models to be constructed.
2.3 Derivation of Dynamic Sectoral SAM
In the context of the issues above, once the static SAM is thoroughly adjusted and the
dynamic macro SAM has been constructed, all necessary inputs are ready for building the
dynamic sectoral SAM with the same resolution as the baseline static SAM.
11
2.3.1 DySAM Algorithm
The algorithm is designed to generate full SAMs for each year. The process entails
13
four main steps, namely: 1) build the data inputs for the DySAM as described above; 2)
14
raise the static SAM with the corresponding dynamic macro controls, which generates a
15
sequence of SAMs that are balanced at the macro level but are unbalanced at the interior
16
sectoral account levels nesting within the corresponding macros; 3) the balancing of
accounts at the sectoral level, which starts with the commodity-activity blocks by
initializing an iterative sequence of demand-side adjustments with supply anchors, a key
assumption being that supply is more robustly estimated than demand; and lastly, 4) matrix
rebalancing, which ensures the balance of components sub-matrices using the RAS
technique17, thereby reducing sectoral imbalances, over-time, to the infinitesimal.
To illustrate the DySAM process the algorithm flow chart referring to Indonesia is
used; this shows the steps of DySAM construction.
13
The two principal data inputs are (1) the static baseline SAM and (2) the dynamic Macro SAM (s3
(t)) both of which have identical non-null transaction blocks.
14
The macro controls are of three types: 1) the sum of a matrix, such as input use; 2) the sum of a
vector, such as fixed capital formation; or 3) a scalar value such as foreign savings.
15
This imbalance is because the observed structural dynamics of the economy displayed in the
macro control time series diverges from that inherent in the baseline static SAM. However, this is
the ―best/least discrepant initial estimate‖ of the DySAM based on current data. To also be a
―feasible estimate‖ all sectoral accounts must also balance. This task is accomplished in steps (3) and
(4), which are sequential and convergent iterative steps in estimating the (balanced) DySAM.
16
It follows directly that the sum of sectoral imbalance in the initial DySAM estimate will be zero by
design since they are balanced (that is zero) at the macro level. This property of the magnitude of
sectoral imbalances is also preserved in the sequence of all balancing iterations. It is crucial to ensure
that the sequential iterative steps are convergent and find closure for all sectoral accounts of the time
series of SAMs which comprise the estimated DySAM.
17
This step is invoked for accounts whose components are matrices, such as input use or household
final demand.
12
Figure 3: Dynamic SAM Flow Chart: Indonesia
Flow Chart: Dynamic SAM Algorithm
1
2
Build Input Datasets
Dynamic Macro
SAM (2000-2008)
Baseline Static
SAM 2005
x1: 11x11
s3: 84x84
Initial DySAM
(Sector Imbalance)
Raise the Static SAM
using Dynamic Macro
Controls
3
Initalise Demand
Side Iterating
Adjustment with
Supply Anchor
(Co+A)
4
Reduce imbalance to
infinitessimal using
the RAS
x2: 84x84
j0 (51x84)
Iterations (16)
Imbalance Range
(1% to 28%)
j16 [51x84]
Imbalance Range
(< 0.02% )
Balanced DySAM
(x4)(84x84)
RAS Matrices
(Co A), (iH FL),
(Co iH)
Error Range
(<1.0E(-14) %)
2.3.2 DySAM Algorithm Comparison for Indonesia and
Mozambique
The algorithm to build a DySAM has been used successfully for Indonesia and
Mozambique. This section compares the four steps of the DySAM building process for
these two countries18.
Step 1: Build Input Datasets. The sectoral static SAM for Indonesia refers to the
year 2005 and its dimensions are 84x84 whereas for Mozambique it refers to year 2001 and
its dimensions are 183x183. The dimensions of the consistent dynamic macro SAMs for
both countries are 11x11 and refer to the period 2000 to 2008.
Salient features of building the DySAM for Indonesia and Mozambique follow.
Table 2: DySAM Algorithm Comparisons – Step 1
Country
Base Year Static
SAM
SAM Dimension
Time Series of Macro
SAMs
Macro SAM
Dimensions
Mozambique
Indonesia
2001
2005
183 x 183
84 x 84
2000-2008
2000-2008
11 x 11
11 x 11
18
Recently, a DySAM for Venezuela has also been completed.
13
Step 2: Raise the Static SAM using Dynamic Macro Controls. This step generates
the initial DySAM sequence for 2000 to 2008, which balance at the macro level but are
unbalanced at the sectoral level. Essentially, in this step all prior information that is to be
preserved in the DySAM is loaded. For instance, most countries, including Indonesia, have
more disaggregated information on the supply side, such as value added, taxes and imports.
This supply-side information may be incorporated19 in the initial DySAM by using, as
raising factors, vector controls that sum up to the corresponding macro controls.
On the demand-side, the accounts implicated span the commodity and activity space,
and for Indonesia20 have the dimensions 51x84. Correspondingly, on the supply-side in the
matrix layout these accounts have the dimensions 84x51. The relative discrepancy between
demand and supply at the sectoral level in step 2 ranges over 1 per cent to 28 per cent.
Table 3: DySAM Algorithm Comparisons – Step 2
Country
Initial DySAM
Demand Side
Dimension
Supply Side
Dimension
Demand/Supply
Imbalance
Mozambique
Indonesia
2000-2008
2000-2008
104 x 183
51 x 84
183 x 104
84 x 51
-26% to 29%
1% to 28%
Step 3: Balance commodity-activity accounts. This is done by initializing an
iterative sequence of demand-side adjustments with supply anchors. The demand vectors
implicated in step 3 iterations are the intermediate demand vector (Co A) and the final
demand block of column vectors (Co iH), (Co iG) (Co Cc) and (Co wC). These are the
component demand vectors that are balanced with respect to the supply vectors (Total Row
Commodity) and (Total Row Activity).
For Indonesia, 16 iterations reduced the initial relative discrepancy between demand
and supply from a maximum of 28 per cent to less than 0.02 per cent - a 1,400-fold
reduction.
Table 4: DySAM Algorithm Comparisons – Step 3
Country
Initial DySAM
Mozambique
Indonesia
2000-2008
2000-2008
Demand/Supply
Imbalance
-26% to 29%
1% to 28%
Number of Iterations
Imbalance at final iterate
32
16
<|4.8 e-03|%
<0.02%
19
Preserving additional supply-side information on value-added, imports and taxes overtime in the
DySAM requires that adjustments of sectoral imbalances in the commodity-activity accounts take
place on the demand-side. This is done in step 3 of the algorithm.
20
Please see
Figure 4: The dynamic SAM for Indonesia (2000-2008)
14
Step 4: Ensure the balance of components sub-matrices using the RAS21 technique and
reduce sectoral level imbalances to infinitesimal. The matrices entering the accounts of the
Indonesian DySAM are given in the table below.
Table 5: Matrices for accounts of the Indonesian DySAM (2000 – 2008)
Account
Commodity
Activity
Factor incomes
Household labour income
Intra household transfers
Matrix description
Commodity - activity intermediate inputs table
Household final consumption matrix
Activity - commodity domestic supply table
Factor incomes the factor labour matrix
Household income from labour
Intra household transfers
Code
Co A
Co iH
A Co
FL A
iH FL
iH iH
Matrix size
24 x 27
24 x 10
27 x 24
16 x 27
10 x 16
10 x 10
RAS Iterations
40
80
80
Source: Part I: Data Report: Indonesia (091202): Indonesia Dynamic SAM Data Report; Derivation of Consistent Data Set.
Three of these six matrices namely, the supply table (A Co), the labour value added
(FL A) matrix, and the inter-household transfer matrix (iH iH) are not implicated in the
RAS iterative adjustments. The matrices implicated in RAS balancing are (Co A) the
intermediate input table, (Co iH) the household final consumption matrix and (iH FL) the
matrix of household income from labour requiring, respectively, 40, 80 and 80 iterations to
achieve convergence in all accounts over the period 2000-2008.
Table 6: DySAM Algorithm Comparisons – Step 4
Country
Rebalanced
Matrices(RAS)
Dimension
Number of RAS
Iterations
Convergence at final
iterate
Mozambique
( Co A)
( Co iH)
27 x 77
27 x 35
40
40
<|5.0 e-05|%
Indonesia
( Co A)
( Co iH)
( iH FL)
24 x 27
24 x 10
10 x 16
40
80
80
<|1.0 e-14|%
The verification that balance is achieved for all accounts for each year (2000-2008) is
reported in the mentioned reports; see Appendix under 3.
In general, there is a connection between the accuracy of baseline country data, the
number of iterative sequences and the number of blocks requiring RAS balancing. The
21
RAS refers to a bi-proportional iterative method used to estimate non negative matrices on the
basis of limited information, the acronym reflect the right-hand-side of the original equation or (ri
aijsj).r and s are row and column multipliers respectively.It was originally proposed by Bacharach
(1970) as part of the Cambridge Growth Project lead by R. A. Stone during the 60s. Extensive and
exhaustive reviews of this technique can be found in Lahr and Mesnard (2004) and Kratena and
Zacharias (2004). Since then, there have been innumerable ‗improvements‘, those interested may
please refer to Oosterhaven (2005) or Fofana et al. (2002) with the generalized RAS (GRAS) for
updating I-O. In order to update SAMs, other explicit methods have been developed by, among
others, Robinson and El-Said (2000) and Robinson et. al. (2001).
15
character of these connections will differ across countries, as well as within countries when
estimation procedures are revised.
Figure 4: The dynamic SAM for Indonesia (2000-2008)
#
Dimension
#
ACCOUNT
84 Label
Commodity
Activity
84
1 24 Co
2 27
A
17
14
Factor (F)
8
1
9
1
10
1
11
1
12
1
1
24
2
27
3
16
4
1
5
10
6
1
Institutions (i)
7
1
Co
A
FL
FK
iH
iCr
iG
iTx
iSu
Cc
wCu
TC
0
(Co A)
0
0
(Co iH)
0
(Co iG)
0
0
(Co Cc))
(Co wCu)
(Co TC)
(A Co)
0
0
0
0
0
0
0
(A iSu)
0
0
(A TC)
Factor Labor
3 16 FL
0
(FL A)
0
0
0
0
0
0
0
0
(FL wCu)
(FL TC)
Factor Capital
4
1
FK
0
(Fk A)
0
0
0
0
0
0
0
0
(Fk wCu)
(Fk TC)
Household
5 10
iH
0
0
(iH FL)
(iH Fk)
(iH iH)
(iH iCr)
(iH iG)
0
0
0
(iH wCu)
(iH TC)
Corporate
6
1
iCr
0
0
0
(iCr Fk)
(iCr iH)
(iCr iCr)
(iCr iG)
0
0
0
(iCr wCu)
(iCr TC)
Government
7
1
iG
0
0
0
0
(iG iH)
(iG iCr)
(iG iG)
(iG iTx)
0
0
(iG wCu)
(iG TC)
Tax
8
1
iTx
(iTx Co)
0
0
0
0
0
0
0
0
0
(iTx wCu)
(iTx TC)
Subsidy
9
1
iSu
0
0
0
0
0
0
(iSu iG)
0
0
0
0
(iSu TC)
Capital A/C
10 1
Cc
0
0
0
0
(cC iH)
(cC iCr)
(cC iG)
0
0
0
(cC wCu)
(cC TC)
World
Cosolidated
Current A/C
11 1 wCu
0
(wCu A)
(wCu FL)
(wCu Fk)
(wCu iH)
(wCu iCr)
(wCu iG)
0
(wCu iSu)
(wCu Cc)
0
(wCu TC)
(TR Co)
(TR A)
(TR FL)
(TR Fk)
(TR iH)
(TR iCr)
(TR iG)
(TR iTx)
(TR iSu)
(TR Cc)
(TR wCu)
Total Row/Col
Balance
12 1
TR
1
Bal
(TC-TR) Co (TC-TR) A
(TC-TR) FL (TC-TR) Fk
(TC-TR) iH (TC-TR) iCr (TC-TR) iG (TC-TR) iTx (TC-TR) iSu (TC-TR) Cc (TC-TR) wCu
16
Figure 5: The dynamic SAM for Mozambique (2000-2008)
Set: x1 (r1, c1)
Static SAM for Mozambique 2001 (Producer Prices/ National)
#
ACCOUNT
43
Institutions (i)
2
3
4
5
6
7
8
9
10
11
27
77
23
3
7
3
35
1
4
2
1
1
Label
Co
A
FL
Fk
Fn
iC
iH
iG
iT
Cc
wC
TC
Co
0
(Co A)
0
0
0
0
(Co iH)
(Co iG)
0
(Co Cc)
(Co wC)
(A iH)
0
0
0
0
<x1( CoA) Co
<x1( CoiH) Co
r 1Ac1>
<x1( CoCc) Co
r 1iHc1>
r 1ACcc1>
<x1
<x1
Commodity
Activity
1
2
27
77
12
1
183
#
Dimension
33
Factor (F)
<x1( ACo) Ar 1
<x1( AiH) Ar 1iH
Coc1>
c1>
A
(A Co)
0
0
0
0
0
( CoiG)
Cor 1iG
c1>
( Co
wC) Co
r 1AwC
c1>
<x1
(Co TC)
( Co
TC) Co
r 1>
<x1( A
(A TC)
TC) A
r 1>
<x1( FLA) FL
r 1Ac1>
<x1
Factor Labor
3
23
FL
0
(FL A)
0
0
0
0
0
0
0
0
(FL wC)
(Fk A)
0
0
0
0
0
0
0
0
(Fn A)
0
0
0
0
0
(Fn iG)
0
0
0
(iC iG)
(iH iC)
0
(iH iG)
0
0
( FL
<x1
(FL TC)
TC) FL
0
(Fk TC)
TC) Fk
0
0
(Fn TC)
TC) Fn
0
0
0
(iC TC)
0
0
(iH wC)
(iG iT)
0
0
wC) FL
r 1wC
c1>
( FL
r 1>
<x1( FkA) Fk
r 1Ac1>
<x1
Factor Capital
4
3
Fk
0
( Fk
r 1>
<x1( FnA) Fn
r 1Ac1>
<x1
Factor Land
5
7
Fn
0
( FniG)
Fnr 1iG
c1>
<x1
( Fn
r 1>
<x1( iCFk) iCr 1
Fkc1>
Corporate
6
3
iC
0
0
0
(iC Fk)
<x1( iHFL) iHr 1
FLc1>
Household
7
35
iH
Government
8
1
iG
0
0
0
<x1( iHFn) iHr 1
<x1( iC
iG) iCr 1
iGc1>
<x1( iC
TC) iC
r 1>
<x1( iHiC) iHr 1iC
Fnc1>
c1>
0
(iH FL)
0
(iH Fn)
0
0
0
0
(iG iC)
<x1( iH
iG) iHr 1
iGc1>
<x1( iH
wC) iH
r 1wC
c1>
<x1( iH
(iH TC)
TC) iH
r 1>
(iG TC)
<x1( Tr iG) iG
<x1( iGiC) iGr 1iCc1>
Tax
9
4
iT
<x1( iGiT) iGr 1iTc1>
<x1( iTCo) iTr 1
<x1( iTA) iTr 1A
<x1( iTiC) iTr 1iC
<x1( iTiH) iTr 1iH
Coc1>
c1>
c1>
c1>
(iT Co)
(iT A)
0
0
0
(iT iC)
(iT iH)
<x1( cCiC) cC
<x1( cCiH) cC
r 1iCc1>
r 1iHc1>
0
c1>
0
0
0
<x1( iT
(iT TC)
TC) iT
r 1>
<x1( cCcC) cC
r 1cCc1>
<x1
<x1
Capital A/ C
10
2
Cc
0
0
0
0
0
(cC iC)
(cC iH)
(cC iG)
World Current
A/ C
11
1
wC
(wC Co)
0
0
0
0
(wC iC)
0
0
Total Row/ Col
12
( cCiG)
cCr 1iG
c1>
0
(cC cC)
(cC wC)
0
(wC Cc)
0
( cC
wC) cC
r 1wC
c1>
<x1
(cC TC)
( cC
TC) cC
r 1>
(wC TC)
<x1( wCTC)
<x1( wCCo) wCr 1Coc1>
1
<x1( wCiC) wCr 1iCc1>
(TR A)
(TR FL)
(TR Fk)
(TR Fn)
(TR iC)
TR
(TR Co)
<x1( Tr A) Ac1>
<x1( Tr FL) FLc1>
<x1( Tr Fk) Fkc1>
<x1( Tr Fn) Fnc1>
<x1( Tr iC) iCc1>
<x1( Tr iH) iHc1>
Bal
(TC-TR) Co
(TC-TR) A
(TC-TR) FL
(TC-TR) Fk
(TC-TR) Fn
(TC-TR) iC
(TC-TR) iH
(TR iH)
<x1( TC- TR) Coc1>
<x1( TC- TR) Ar 1>
<x1( TC- TR) FLr 1>
<x1( TC- TR) Fkr 1>
<x1( TC- TR) Fnr 1>
<x1( TC- TR) iCr 1>
<x1( TC- TR) iHr 1>
(TR iG)
(TR iT)
(TR Cc)
c1>
(TC-TR) iG
<x1
<x1( Tr iT) iTc1>
<x1( Tr cC) cCc1>
(TC-TR) iT
(TC-TR) Cc
<x1( TC- TR) iTr 1>
<x1( TC- TR) cCr 1>
( TC- TR) iG
Layout & Dimensions
r 1>
(TR wC)
<x1( Tr wC)
<x1( Tr iG) iG
<x1( Tr Co) Coc1>
Balance
wCr 1>
<x1( wCcC) wCr 1cCc1>
wCc1>
(TC-TR) wC
<x1
( TC- TR) wC
r 1>
02/ 08
17
3 Derivation of dynamic SAM model for impact
analysis
The move from a SAM data framework to a SAM model or multiplier framework
requires a specification of the SAM accounts as ‗exogenous‘ and ‗endogenous‘. Generally,
accounts intended to be used as policy instruments are made exogenous and accounts a
priori specified as objectives or targets must be made endogenous.
For any given injection into the exogenous accounts (i.e. instruments) of the SAM,
influence is transmitted through the interdependent SAM system among the endogenous
accounts. The interwoven nature of the system implies that the incomes of factors,
institutions and production are all derived from exogenous injections into the economy via
a multiplier process. The multiplier process is developed on the assumption that when an
endogenous income account receives an exogenous expenditure injection, it spends it in the
same proportions as shown in the matrix of average propensities to spend (APS). The
elements of the APS matrix are calculated by dividing each cell by its corresponding
column sum total and are referred as direct effects.
Table 7: Endogenous and exogenous accounts of a SAM
Exogenous
Endogenous
Rest of World
Capital
account
Subsidy
Indirect tax
Government
Corporate
Exogenous
Households
Factor capital
Factor labour
Production
activities
Social Accounting Matrix
Commodities
Endogenous
Commodities
Production
activities
Factor labour
Factor capital
Households
Corporate
Government
Indirect tax
Subsidy
Capital account
Rest of World
The multiplier analysis using the SAM framework helps to understand the linkages
between the different sectors and the institutional agents at work within the economy.
The economy-wide impacts of the exogenous shocks are examined by changing the
growth targets of exogenous vectors. More specifically, the differential ‗growth‘ targets can
be set under different ‗simulations‘ for various sectors, in order to estimate their effects on
output, value-added or GDP, consumption expenditure or basic needs and household
income.
18
If an exogenous shock is injected into the SAM system (e.g. rise in expenditure of
infrastructure programmes injected through the capital account), the first effect will be to
increase the income of the corresponding account (i.e. commodity or activity). The increase
will trigger effects, in a cascading manner, on all other endogenous accounts: factors,
households and basic-needs. Furthermore, those effects will spill over onto the exogenous
leaks block.
Therefore, the DySAM Multiplier Framework or DySAM Model provides a major
insight into the following issues:
1
Examination of the expenditure and technological structure of the sectors oriented
towards the production of basic intermediate and final goods and services (An).
2
Examination of the expenditure structures of factors of production, institutions and
the demand for goods and services of domestic and foreign origin (An).
3
Assessment of the impacts on the endogenous SAM accounts caused by exogenous
shocks in a clear and differentiated manner via the multipliers matrix Ma.22
4
Identification of key sectors, commodities, factors of production and institutional
accounts in the economy and quantifies the main linkages (total and partial backward
and forward).
5
Display of the dynamics of the production structure, factorial and institutional
income formation via the dynamic multipliers DyMa (yearly sequence of Ma
matrices).
6
Assessment of the effects of incomes of households and their impact on production
via their corresponding demand.
7
Evaluation of the effects of the government budget and its impact on production via
corresponding demand/supply variations.
8
Assessment of the effects on deficit/surplus of the government budget and of the
balance of payments resulting from government budget policy outlays and on the
external sector (BxMa).
9
Analysis of the direct and indirect impacts via An and (Ma-An), respectively.
10 Assessment of the intra-transfer (M1) as well as induced effects (O+C).23
11 Assessment of employment impacts by activities and across labour types and regions
using the Employment Satellite Module.
22
The Ma = (I - An)-1 which is the matrix of cumulative production multipliers/technology
coefficients or the Generalized Leontief inverse, provides a numerical assessment of the direct and
indirect effects arising out exogenous injections on the output of each activity or commodity.
23
M1 = (I - A0)-1 which are the intra-group or intra transfer effects. O = (M2 - I). M1 = M2 M1 –
M1 or open-loop multiplier measures the net extra-group effects or net cross-effects arising out of
an initial injection when it has completed a tour outside the original accounts without returning to it.
For instance, if the initial injection takes place into households it measures the effects into factor
incomes via demand expenditures WANTS, commodities and activities. C = (M3 - I). M2 . M1 = M3
M2 M1 – M2M1 or closed-loop multiplier, which measures the net contribution of circular effects or
net inter-group effects that arise after the original injection has completed a tour through all groups
of accounts and returned to the one where it originally started. It measures the effect of an injection
on household income after going through expenditures, commodities, activities and factor incomes.
For details on decomposition, please refer to Pyatt & Round (1979) and Defourny & Thorbecke
(1985).
19
12 Design of simulations in alternative scenarios and analysis on the simulations.
13 Basis for the development of computable general equilibrium models.
4 Simulation Design and Impact Analysis
4.1 Introduction
In this section the major characteristics of the dynamic multipliers are discussed. This
is required to understand the transmission mechanism at work in the country and is a prerequisite to designing, analysing and interpreting simulation scenarios. This analysis can be
used to formulate evidence-based policy stances and to prioritize specific exogenous stimuli
that support the policy stance.
The characteristics of an economy (Mozambique or Indonesia) are embedded in the
country SAM structure. These characteristics and the relative strength of various
transmission chains are revealed in an analysis of the ‗backward‘ and ‗forward‘ linkages
computed in the SAM multiplier module. The DySAM also provides insight into the
evolution of these characteristics.
The total backward linkages are the column sums of the multiplier matrix Ma.
Although grossly overestimated (see Sec. 200), they do provide valuable information about
the degree of integration within an account and with the other economic accounts of the
DySAM. This indicator, when employed with insight, can be used to determine which
activities contribute most to growth as a result of an exogenous increase in final demand.
Total forward linkages are the row sums of the multiplier matrix Ma. These indicators,
taking into account their caveats, can help to understand the importance of a commodity for
the rest of the economy in terms of intermediate demand and can be used as an indicator of
market availability. For example, a commodity that exhibits high forward linkages is said to
be important in the process of expansion or high growth. Its identification is, therefore,
pivotal in the economic management of potential bottlenecks.
The analysis presented will focus first on total backward linkage temporal
comparisons and account-wise partial backward linkages for the end year. This analysis
will be framed in the context of two stylised policy stances: one that would prioritise
growth and another that would prioritise income distribution.
4.2 Total backward linkages
In this section the estimated total backward linkages of the endogenous accounts are
reviewed. Two points need to be taken into consideration. Firstly, total backward linkages
are only the first of a series of indicators providing pointers for policy formulation. While
interpreting total backward linkages, it should be remembered that the aggregation of the
four account impacts into a total magnitude involves double, in this case in two instances,
counting.24 Secondly, the static backward linkages differ from the corresponding dynamic
24
This ―double counting‖ inherent in total backward linkages is not present in the account-wise
partial backward linkages. In the cases of Indonesia and Mozambique, the Commodity-Activity and
Factor Incomes-Institutional Incomes mean twice double counting.
20
backward linkage, by a scale factor reflecting the discrepancy between the SNA macro
estimates and the corresponding baseline SAM estimates.25
4.2.1 Dynamic backward linkages
The dynamic profiles of the backward linkages for Indonesia and Mozambique share
some salient features, namely the values of backward linkages of the endogenous accounts
reveal, on the whole, a decreasing trend from 2000 to 2004 or 2005 and a slightly
increasing behaviour in 2007 and 2008. From 2004/2005 onwards, some of these changing
behaviours may reflect, among other things, the growing/declining importance of the
imports, savings and government revenue and expenditure, which are treated as exogenous
accounts of the dynamic SAM model. It is known that the higher the values of exogenous
(or leak) accounts, the lower the degree of endogeneity and hence backward linkages.
The extent of changes in the value of backward linkages is significantly different for
each element of the endogenous account for each year of the reference period. This reflects
changes over time and strength of integration. The time evolution is not smooth, attesting to
the non-manipulative methodology used. The varying results are also a consequence of
supply constraints and the interplay of the domestic and foreign economic factors and
actors, as well as their response to changes in the general economic environment, including
changes in price relatives.
This observation further illustrates that the dynamic SAM captures the behaviour of
actors, entrepreneurs and households, among others. It shows that changes from year to
year to reflect changes in average propensities to spend (matrix An). This characteristic of
the DySAM lessens the need to introduce expenditure elasticities as a way to parameterise
the move from a ‗behaviour less‘ (static) accounting multiplier SAM model to the fixedprice SAM model. This is because an equivalence exists between income elasticities
adjustments and changes over time captured in the DySAM.
4.2.2 Total backward linkage comparison
Dynamic multiplier estimates (Ma) are available for the period 2000-2008 and the
corresponding static multipliers estimates refer to the 2005 ‗baseline‘ SAM for Indonesia.
To analyse changes over time in the Indonesian economy, it is necessary to start with a
snapshot comparison of the total backward linkages for 200826 with the static backward
linkages for 2005. These are presented in the bar graphs (see Figure 7-11, ILO, 2010b).
These charts display the total backward linkages for the component elements of each block
of endogenous accounts, namely commodity (Co), activity (A), factor (Fp), household (iH)
and company (iC). The entries in each of these bar graphs displays the magnitude of the
total multiplier for each element of the respective block and they are sorted in ascending
order27 on the values for 2008.
25
Please see the corresponding countries‘ data reports. Clearly, if the SAM for 2005 or 2001 had
been used, correspondingly for Indonesia and Mozambique, to benchmark their SNA estimations
this scale discrepancy would not have been present and the two estimates would have coincided.
26
These estimates are computed in the dynamic multiplier module. Please also see Appendix, 2.
27
This presentation mode permits convenient visual detection of shifts in ordering between 2001 and
2008 pointing to differences in the overtime evolution of components elements. This may, in turn,
21
For example, for Indonesia (see Figure 6: Indonesia Total Backward Linkage of
Commodity Account Figure ), a feature of all the 2005 linkages is that they are higher than
the 2008 estimates in all account blocks. This is a result of the noted discrepancy between
the SNA macro estimates and the corresponding baseline SAM estimates, resulting in a
pervasive upward bias in the (static) 2005 linkage estimates. The magnitude of the bias is
not similar in all estimates and is, on the whole, lower for Mozambique. It is reported that
for both countries, the linkages are higher for the commodity and activity blocks and lower
for the factor and institution blocks.
Figure 6: Indonesia Total Backward Linkage of Commodity Account
Static 2005
Dynamic 2008
The sizes of commodity
backward linkages for 2005 static
SAM (red bar) were all lower
than those for generated by the
DySAM for 2008 (blue bar). The
lowest 2008 is 3.8 and for 2005
4.09, at the other end the highest
for 2008 is 7.36 and for 2005
7.65. For both years the values of
the backward linkages of the top
six
activities
(i.e.
crops,
Restaurant, Other Agriculture
Mining-Quarry, Government etc,
Livestock and Trade) are above 6
both under the static and dynamic
simulations. Given their high
potential
these
commodities
should be targeted if growth
enhancement is the strategy of the
government.
Compare Total Commodity Backward Linkage
Ma(Tr Co): Ascending Order
c CoalMetalPetrol c5
c ChemFertClayCement c5
c PaperPrintTranspMetal c5
c RealEstate BusinessSrv c5
c AirWaterTrp Communicatn c5
c OthIndivHHSrv c5
c ForestHunt c5
c ElecGasWater c5
c BankInsuranceSrv c5
c WeaveTextileGarmentLeather c5
c HotelAffairs c5
c Construction c5
c Fishery c5
c FoodDrinkTobacco c5
Lowest
5
commodity
backward
linkages
ranged
between 3.8 and close to 4.71. In
decreasing order these are: Airwater transport, Weave Textile,
Coal-metal petroleum, Electricity
Gas Water Chemical-fertilizer and
Paper-printing and metal.
c Storage OthTrpSrv c5
c Wood c5
c TradeSrv c5
c LandTrpSrv c5
c MiningQuarry c5
c Livestock c5
c GovDefEduHlthFilm OthSocSrv c5
c OthAg c5
c Restaurant c5
c Crops c5
4
6
8
10
12
The backward linkages of
the remaining 13 commodities
which ranged between 4.6 and
6.37 can be categorised as the
middle group.
Largest decreases in backward linkage values in the year 2008 compared to 2005, in
decreasing order, are found for Food industry (16%), Hotels Affairs (10%) Restaurant (9%)
and Mining and Quarry (8%), most are found among the top commodities. On the other
hand, smallest decreases in backward linkage values in the year 2008 compared to 2005 are
trigger another round of investigation (not attempted in this report) into the determinants of these
differences.
22
found for Other Agri. and Crops (4%), Fishery, Government etc and Bank (3%), some are
top other not. The first set of activities thus appears to be relatively more dynamic than the
second set.
Additionally, for Indonesia the SNA and SAM discrepancies are illustrated in the table
below, where the total backward linkages ratios, reflecting the comparison of the
Indonesian static 2005 SAM with the Indonesian dynamic 2008 SAM, are presented. The
linkages for 2005 are consistently higher; this is reflected by the fact that the ratios are all
over unity; in some cases the upward bias is 7 per cent (Restaurants), while in other cases it
is as low as 2 per cent (Crops).
Indonesia
c Crops
c OthAg
c Trade Services
c Restaurant
a Crops
a Other Agriculture
a Livestock
a Road
Labour Intensive
a Road Capital
Intensive
a Irrigation
Table 8: Indonesia: Comparison of selected Commodity and Activity total backward linkages
2005 Static
10.14
9.74
9.37
10.19
9.24
8.85
8.93
8.72
7.16
7.66
2008 DySAM
9.82
9.40
8.86
9.51
9.06
8.66
8.55
8.42
6.88
7.33
Ratio
1.03
1.04
1.06
1.07
1.02
1.02
1.04
1.04
1.04
1.04
4.3 Dynamic SAMs multipliers and linkages (total)
The figure presented below can clearly show that although most accounts appear to
follow a pattern over time, rising from below the values of the static SAM (see blue line)
from 2000 to 2004, falling thereafter, and rising but remaining below the 2005 levels.
Whereas, in the case of Mozambique there is not a clearly distinguishable pattern (see
Mozambique DySAM Report, April 2010, sec. 4.1).
23
Figure 6: Indonesia: Over time trend of total backward linkages for selected economic Activities
s_ST_Ma
s5_IRsCL_0324_Dy
"s5 Ma (Tr A) A c5"[a Storage OthTrpSrv c5] : Sum All Backward Linkage (Tr A) (1x27)
10
8
6
"s5 Ma (Tr A) A c5"[a BankInsuranceSrv c5] : Sum All Backward Linkage (Tr A) (1x27)
10
8
6
"s5 Ma (Tr A) A c5"[a RealEstate BusinessSrv c5] : Sum All Backward Linkage (Tr A) (1x27)
8
7
6
"s5 Ma (Tr A) A c5"[a GovDefEduHlthFilm OthSocSrv c5] : Sum All Backward Linkage (Tr A) (1x27)
20
13
6
"s5 Ma (Tr A) A c5"[a OthIndivHHSrv c5] : Sum All Backward Linkage (Tr A) (1x27)
10
8
6
2000
2003
2006
2009
Time (Year)
The main reasons for the varying results have been indicated above but it is useful to
remember that changes over time can be equated with income elasticities shifts as a way to
move from the ‗behaviour less‘ accounting multiplier SAM model to the fixed-price SAM
model.
The importance of backward linkages for the ranking according to importance, i.e. the
potential contribution to the expansion of the economy, is presented by the block diagonal
backward linkages as shown in Appendix 1.28
Dynamic multiplier estimates (Ma) are available for the period 2000-2008 and the
corresponding static multipliers estimates refer to the 2005 ‗baseline‘ SAM for Indonesia.
To analyse changes over time in the Indonesian economy, a snapshot comparison of the
total backward linkages of 2008 with the static backward linkages for 2005 were presented
in the report indicated above.
The major observations are:
A feature of all the 2005 linkages is that they are higher than the 2008 estimates in
all account blocks. This is a result of the noted discrepancy between the SNA macro
estimates and the corresponding baseline SAM.29
28
It is pertinent to recall the two caveats relevant to the total backward linkage comparisons. Firstly,
total backward linkages are only the first in a series of indicators providing pointers for policy
formulation. While interpreting total backward linkages it
24
Playing a role is the growing importance of imports, savings and government revenue
and expenditure, which are treated as exogenous accounts of the dynamic SAM model.
All account blocks, with the exception of household (iH) and company (iC), evidence
structural shifts between 2005 and 2008. This suggests that household income groups seem
to be more evenly inserted in the income streams, when compared to factor labour income,
which have a bias towards urban areas.
This analysis can be used to guide the formulation of evidence-based policy and can
help determine the growth model that best suits the particular economy. In general, high
backward linkages, especially partial backward linkages, can be used to design policy
packages with the highest linkages in one account or, if policy priorities so indicate, a mix
of partial linkages of different sizes within each main account block. It can also help design
a policy mix by combining desired backward linkages across selected accounts, e.g. growth
combined with incomes policies. The most common basis for designing policy packages is
outlined in Table 4.2, below.
4.4 Summary observations of the policy indicators – the
case of Indonesia
A full and detailed presentation of the Indonesian impact analysis in each of the four
endogenous blocks was presented in the corresponding report. In this document, only
highlights of the most relevant findings are presented. The total impact and partial impacts
are reported. Furthermore, the impact analysis presents a decomposition by type - intraaccount transfer impacts (M1) and induced impacts (O+C). As these impacts are a sequence
of a unitary injection into each of the endogenous accounts they may be compared. The
policy indicators based on this analysis of impact are collated below.
Table 9: Summary of the impact of injections by account type (The information in this table is very similar
to the information in the last para of section 4.)
Account
Description
Commodity
and activity
accounts
A policy package that can potentially generate the highest possible growth in commodities should be
considered, and commodity groups that have the highest partial backward linkages should be targeted.
Differences in the degree of endogeneity focus attention on the role of imports (and other leaks).
Factor and institutional income formation stand to benefit little in a growth strategy, e.g., when injections
take place via the commodity account.
Factor account
Labour types benefit almost equally from induced effects, notwithstanding the fact that urban based
labour categories occupy the top four ranks.
A factor incomes policy favoring a specific category of workers is non-distorting. It will not bias
commodity growth, activity growth or institutional income formation.
Household
account
The induced impacts are more potent than the direct impact component.
A direct income policy is distribution-neutral, as the induced transmission to other households is as large
as the intra account transmission. Therefore, income distribution will largely remain unchanged.
29
The magnitude of the bias is not similar in all estimates. They are higher for the commodity and
activity blocks and lower for the factor and institution blocks. This is also reflective of the interplay
of the economic factors and the constraint imposed by economic events, domestic and external
25
For the year 2008 (see Table below), in the case of Indonesia, when an injection is
made into the commodity or activity account, the correlations between these two accounts
(Co, A; A, Co) are close to unity. This implies that injections in either the commodity or
activity account will greatly benefit each other. This is because there is a unique
relationship between these accounts, in that they are both are domestic in nature.
However, correlations of commodity and activity account with the rest of the accounts
are low. For instance, the correlations with factor incomes (Fp) and institutional incomes
(iE) are low (below 0.6). This implies that injection in commodities or activities will not
benefit factor incomes or institutional incomes greatly. This means that growth policies are
not compatible with incomes polices.
Alternatively, factor incomes and institutional incomes have correlations with
commodities and activities that are close to unity. The implication is that injections into
factor or institutional accounts will benefit one another and also significantly impact the
growth of production accounts. This means that income policies (income distribution and
poverty alleviation strategies) are complementary with growth policies.
Table 10: Indonesia DySAM 2008 Correlation Matrix
Indonesia DySAM 2008 Correlation Matrix
Co
A
Fp
iE
Co
1.0
0.999
1.000
0.998
A
0.993
1.0
1.000
0.998
Fp
0.595
0.527
1.0
0.999
iE
0.563
0.506
0.999
1.0
To further illustrate the injection effects, see Table 11, an injection of IDR 1 billion
(approximately USD 100,000) into the commodity account (Co) generates an average
increase of IDR 2.74 billion in itself, meaning in the commodity account (Co), and IDR
2.60 billion in the activity account (A). The incomes of institutions (iE) will increase by
IDR 1.50 billion but factor incomes (Fp) will grow only by IDR 1.40 billion.
An injection of IDR 1 billion into the activity account (A) generates IDR 2.74 billion
in itself and only IDR 1.83 billion in the commodity account (Co). Furthermore, the growth
impact on account factor incomes (Fp) will be IDR 1.45 billion and institutional incomes
(iE) IDR 1.55 billion (see Table 8 below).
The indication is that policies that tend to stimulate commodities (Co) (via exports,
household or government demand) are to be preferred to those stimulating activity accounts
(A). The reason is that the multipliers are weaker since there are leaks into imports when
the activity account expands.
An injection of IDR 1 billion into the factors of production (Fp) account will generate
IDR 2 billion within itself, namely factors of production account, and IDR 2.11 billion in
the institutional income account (iE). Moreover, the impact on growth will be IDR 2.06
billion for the commodity account (Co) and IDR 1.94 billion for the activity account (A).
An injection of IDR 1 billion into the institutional incomes account (iE) will generate
IDR 2.06 within itself and IDR 0.96 billion in the factors of production account (Fp).
Furthermore, the impact on growth will be IDR 1.95 billion in the commodity account (Co)
and IDR 1.85 billion in the activity account (A).
26
Table 11: Indonesia DySAM 2008 Average partial backward linkages
Indonesia DySAM 2008 Average Partial
Backward Linkages
Main
Accounts
Co
A
Fp
iE
Co
2.74
2.60
1.40
1.50
A
1.83
2.74
1.45
1.55
Fp
2.06
1.94
2.00
2.11
iE
1.95
1.85
0.96
2.06
The broad indication is that income policies oriented towards increasing factor
incomes will be more balanced and may render the highest income gains. A second choice
may be the expansion of commodities promoting the highest economic growth. These
comparisons deepen the understanding of the impact that is formed through the circular
transmission.
5. Satellite accounts and transformation and
employment
5.1 Introduction
In principle there are two types of satellites, the expansion and the extension type. The
shift from an ‗expanded data‘ SAM structure to an ‗expanded‘ SAM Multiplier Model, to
derive the ‗extended‘ Employment Multiplier Module is analogous to the general SAM
Multiplier Module.
One of the main aims of the DySAMs created for Indonesia and Mozambique was to
assess the impact of infrastructural investments in general and labour-intensive versus
capital-intensive road construction in particular.
Therefore, the information required to build the employment satellite must be
compatible with the entries and accounts as presented in the SAM and must be separated by
the location and must disaggregate the construction sector. In most cases it is important to
separate construction by type. For Indonesia, the four types are road labour intensive, road
capital intensive, irrigation and construction rest. For Mozambique, the types are rural and
urban roads, rural and urban infrastructure, irrigation, highways and buildings, houses and
construction rest. The activities were correspondingly allocated to three regions (rural,
urban and Maputo), hence there are a total of nine sub-sectors for the case of Mozambique.
A reference table to expand the construction sector with corresponding input
structures could be derived after some research and data probing. Subsequently, the capital
formation column (labelled as capital balance in the SAM) and the government
consumption column were separated by the same types of construction. Household
consumption did not require expansion because the corresponding entries were zero, in
conformity with SNA 1993.
The calculations with the increased construction resolution show the impacts of
injection of government infrastructural (types of road, irrigation, etc) investments, and thus
assesses the largest total potential contribution to growth arising out of each construction
type by region.
27
5.2 The Employment Satellite Module30
5.2.1 General
One of the main purposes for building a DySAM in Indonesia and Mozambique was
to evaluate the employment impact of policy shocks. The methodology for such an
assessment is further elaborated here.
A SAM is money-metric. However, labour by economic activity can be used to build a
‗bridge‘ between the SAM and information on employment. It is possible to extend a SAM
in many ways and a SAM can be connected with demographic (labour and, households),
housing, education, health and capital stock information. Inclusion of such information can
extend the analysis to include capital and labour output ratios, as well as per head and per
household requirements in terms of food nutrition intake (calories, vitamins, and proteins),
households housing, education and health. Information on emissions or pollutants into the
environment, whether they are related to production, consumption by household and
unwanted can also be included.
The figure below is a graphical representation showing that the SAM is at the core and
that other satellite matrices can be coupled to the SAM and thus that their impact can be
measured and accounted for.
Figure 7: Relations between the SAM and satellite accounts; Extended SAM (ESAM)
Source: Adapted from Fig 4. Alarcon (Revision 2007).
30
This section is based on Sec. 4.2; for Construction-Employment Analysis on Sec. 4.3, see Part II,
ILO, 2010b.
28
Such an extension can be accomplished by coupling satellite modules with the
money metric SAM.31 Some well-known satellite modules are the following:
1. Social module (well-being, education, health and housing)
2. Demographic module (population, labour and households)
3. Labour and/or Employment Equivalent by Activity
4. Green Jobs and Environmental module (green employment, natural resources and
emissions)
5. Institutional uses of financial resources (flow of funds)
At the outset, it should be noted that the satellite account for each of the above
modules should be built after the entries in the SAM have been agreed. The relations with
the money metric SAM should be made explicit by extending the SAM itself with the
appropriate systems in rows and columns.
The modelling can be achieved by using formulae that are analogous to those used in
input-output/SAM modelling. For an employment satellite account, the technical concepts
of average labour-output and capital-output relations need to be introduced. Both ratios
reflect the inverse level of labour and investment average productivity and can, therefore,
help to illustrate the level of employment and investment demand that can be expected
when injections are applied into the system.
The analysis that can be undertaken is similar to the SAM multiplier analysis. Labour
multipliers will show how an external injection will generate labour places in all economic
activities. Introducing employment, investments and households as a vector(s) of ratios, in
a manner similar to the matrix of leakages, and pre-multiplying the matrix of multipliers
(Ma) by the ratios, the performed calculations and results will be analogous to the
multipliers of the matrix of leaks. However, as these variables differ in nature and have
different dimensions, the interpretation of impacts is in physical terms, e.g. the ratios are
not based on propensities to spend but on the labour and investment ratios. Henceforth, in
the case of injections, the interpretation of the corresponding multipliers will show the
levels of employment and volume of investments that are compatible with the expansion or
contraction of the economy.
5.2.2 Employment Methodology and Modelling
The modelling of employment can be achieved by using formulae that are analogous
to those used in input-output/SAM modelling. The technical concepts of average labouroutput and capital-output relations are introduced. From economic theory and input-output
modelling perspectives, we know that both ratios show, by implication, the level of labour
and investment productivity; therefore, the analytical validity of this treatment is not
symbolic and can help to illustrate the level of employment and investment demand that
can be expected when injections are applied into the system. In the present case, the labour
figures per economic activity have been used to this effect. The interpretation and ensuing
analysis presented is similar to the SAM multiplier. Capital stock figures per economic
activity usually suffer from lack of information. Labour multipliers will show how an
external injection will generate labour places in all economic activities.
31
One such example can be found in Alarcon et al (2000).
29
Concretely, introducing employment levels as a vector(s) below the matrix of leakages
(L), all performed calculations and results will be similar to the matrix of leaks with caveats
regarding the nature and dimensions.
The formal methodological explanation about how the satellites can be understood by
re-interpreting the so-called leak multipliers or exogenous SAM multiplier, which can be
derived simply by pre multiplying the Ma by the B matrix.
Defining L (the employment satellite variables) as:
L (t) = λ Y(t)
Furthermore, the SAM model solution is:
Y = Ma X
Replacing Y with Ma X in the labour equation:
L (t) = λ Ma X(t)
Where:
L is a matrix/vector of employment
Y is a vector of incomes of endogenous variables
X is a vector of expenditures of exogenous variables
A is the matrix of average expenditure propensities for endogenous accounts
λ is the matrix/vector of labour-output ratios
t is time
Ma = (I – A) –1 is a matrix of aggregate accounting multipliers (generalized Leontief
inverse)
λ Ma = B (I – A) –1 is a vector/matrix of aggregate labour multipliers.
5.3 Employment summary results: the case of
Indonesia
One of the main aims of the DySAM is to assess the employment impact of
infrastructure investments in general and labour intensive versus capital intensive
road construction in particular.32 In this section, we present a brief analysis of the link to
employment generation as a stimulus originating in construction and how it propagates
through the transmission chains. In the concluding section, we focus more specifically on
the construction-employment connection. The following panels in the table provide a
summary the employment impacts for all endogenous accounts by type of impacted
account.
32
For details, please refer to Part II: Indonesia DySAM Report (ILO; 2010a), Section 4.
30
Table 12: DySAM summary labour multipliers by accounts for 2008 (Unit Persons)
Labour Multipliers Activities (Lm A)
2008
Top 5 Average
Bottom 5
Average
Total Average
Labour Multipliers Commodities (Lm Co)
Total
81
Intra-account
58
Induced
24
2008
Top 5 Average
Total
78
Intra-account
55
Induced
23
18
41
5
23
13
18
Bottom 5 Average
Total Average
18
40
5
22
13
18
Labour Multipliers Factor of Production (Lm Fp)
2008
Top 5 Average
Bottom 5 Average
Total Average
Top 5 Average
(exc. Capital)
Total
34
25
30
Intraaccount
0
0
0
Induced
34
25
30
28
0
28
Labour Multiplier Institutions (Lm iE)
2008
Top 5 Average
Bottom 5 Average
Total Average
Top 5 Average (exc.
Enterprises)
Total
35
22
29
Intraaccount
0
0
0
Induced
35
22
29
27
0
27
The four panels of the above table (Table 12) summarize the labour multipliers—total
and decomposition in intra-account and induced—for all four endogenous accounts. It is
clear that the two highest labour multipliers belong to the activity and commodity accounts.
A unit injection (1 billion rupiahs) in the activity account generates, on average, 41 labour
places (one labour place is one employee/worker) and 40 if the injection is via the
commodity account. This reflects the unique relationship between commodities and
activities.
Although activities are the agencies hiring labour, the above results pinpoint
where the stimulus for this hiring originates. The circular process equilibrates and
the employment attributable to intra-account transfer (M1) and induced (OC)33
impacts can be determined.
In Table 12 it can be seen that in the activity and commodity account, the intra transfer
impact (M1) is more than twice the induced impact (OC) for the top five accounts in these
sets. For the bottom 5 accounts the induced impacts are larger, approximately 55 per cent of
the total impact The table also shows that the impacts derived from institution and factor
accounts are lower than those of the activity and commodity accounts, and are also entirely
induced impacts.
The table below shows the employment multipliers and their decomposition for the
four construction activities for 2008. For convenient reference the 2005 Static SAM
estimates are also reported.
33
In SAM modelling, the multipliers Ma can be decomposed into M1 or the effect within (intra group) the account in which the
injection takes place (in this case within the production accounts Co and A), O the effect when the injection moves to the other
accounts (in this case Fp and iE) and C when the effects comes back to the account where the injection took place. For our
purpose, O+C is defined as induced (extra group) effect. More details on decomposition are found in Footnote 14.
31
Table 13: DySAM Total Labour Multipliers by Construction Type for 2008 (persons)
Construction: Road
Labour Intensive
30.9
Construction: Road
Capital Intensive
8.6
18.4
DySAM Total
Static SAM 2005
DySAM intraaccount
DySAM induced
Construction: Irrigation
Construction: Rest
8.6
13.7
14.9
15.3
16.7
49.3
23.5
23.9
30.4
50.7
24.8
25.4
31.8
Table 13 shows that labour intensive road construction has the highest labour
multiplier, mainly as result of it having the highest integration with the rest of the
production and distribution, as reflected by the intra-transfer effect. This is because it uses
only domestically produced inputs and the leakage is zero. The other three-construction
activities show that the induced effect is dominant, indicating that the main propagation
arises via extra group accounts impacts. The static SAM 2005 multipliers show the same
pattern and are only slightly higher than their corresponding labour multipliers per
construction activity. This is because of the scale shift between the SNA and dynamic
macro SAM estimates for 2005.
The results in Table 13 are in line with the partial multiplier estimates for construction
activity given in Table 12
Table 14: DySAM partial activity multipliers by construction type for 2008 (billion IDR)
Construction: Road
Labour Intensive
Construction: Road
Capital Intensive
Construction:
Irrigations
Construction: Rest
DySAM intra-account
1.94
1.64
1.82
1.62
DySAM induced
1.21
0.98
1.01
1.09
Total
Intra-account share
of total
3.15
2.62
2.83
2.71
61.5%
62.5%
64.3%
59.7%
Labour-intensive road construction has the highest activity multiplier (see Table12on
accounts, intra-account and induced effect), while Table 13 confirms that this construction
type also has the highest labour multipliers. Clearly, for policy purposes, if the main
objective is to generate employment regardless of skill levels, promoting labour intensive
road construction will generate twice the number of jobs compared to capital-intensive road
construction and irrigation.
32
6 Simulation the Case of Indonesia: Fiscal
Stimulus Package Infrastructure
Indonesia‘s response to the crisis was designed to maintain purchasing power by
offering price subsidies on education, palm oil conversion, as well as on generic medicine
and wage income transfer. A second strategy was to cushion companies operations and
raise their competitiveness. The major means of achieving this were the reduction of
electricity tariffs for the industry, including a decrease of solar pricing, tax allowance,
expansion of the financing for the SMEs and export simplification procedures and
guarantees. A major contribution of the package was for infrastructure, e.g. 12.2 trillion
(see Budget in Table 15), the amount was earmarked to incentivise the economy via
construction-related production (FSPC).34 These investments include the rehabilitation of
roads, airports, seaports, railways, housing, traditional markets, rice warehouses and
strengthening training institutions.
Table 15: Stimulus Package by Items and Delivery Levels
Budget
IDR trillion
Fiscal Stimulus in trillion IDR
Tax cut for companies, workers and individuals
43.0
Tax subsidies and import duties exemption
13.3
Infrastructure expenditure
Diesel and electricity subsidies +PNPM
12.2
4.7
Total
73.3
Realisation
IDR trillion
43
Realization Per
cent
100 %
21.4 %
10,815
88.7 %
86.8 %
82.7 %
Source: CMEA: Total package 73.2 trillion IDR (1.4% of GDP and IDR 9,100 = USD 1)
6.1 Simulation Scenario: the case of Indonesia
To lessen the impact of the 2008 global economic crisis, in the fiscal year 2009 the
government provided a fiscal stimulus amounting to IDR 12.2 trillion. The total realization
rate in 2009 reached a reasonably high level of 88.7 per cent. The missing part is mostly
due to inefficiencies in the infrastructure component and the lack of demand for subsidies
from businesses. Therefore, the realized amounts, e.g. IDR 10,815 billion infrastructure, is
simulated here as injection via capital formation ‗cC capital‘ account ( see table 16).The
main purpose of the scenario is to calculate the different economic and labour growth
impacts using the Indonesia DySAM model the impact of the FSPC policy on the economy,
including:





Commodity Account
Activity Account
Labour Factor Account
Institutional Account
Job Creation
34
Capital formation of infrastructure expenditure increases by an average of 18.47 per cent annually.
The stimulus was added on top of it.
33
Of the 10.815 IDR trillion to construction, the GOI allocated 10.665 trillion rupiahs
directly to infrastructure works and 150 billion rupiahs to build public school and public
health facilities, e.g. facilities directly undertaken by the government in 2008; see Table 15.
Considering that the volume of capital formation in construction was, on average over the
2000-2008 period, about IDR 416,549.23 billion, the executed/injection amount
represented 3 per cent.
Table 16: Economy-wide Impacts of FSPC Injection of 10,665.0 billion rupiahs in 2009 (billion rupiahs)
Impacted Accounts
A: Forecast 2009 +
injection
B: Forecast 2009
Base
Injection Effect (A-B)
Growth Effect
Commodity Output
10,117,070.7
10,086,525.3
30,545.4
0.303%
Activity Output
Factor Income Value Added or
GDP (factor cost)
9,717,032.2
9,687,837.2
29,195.0
0.301%
4,904,091.1
4,890,629.3
13,461.8
0.275%
Institution Income
5,663,943.4
5,649,536.3
14,407.1
0.255%
Government Income
860,800.03
858,511.45
2,288.58
0.27%
Source: DySAM Output and own calculations.
The impact of the FSPC programme on each of the main four accounts can be found in
Table 16, where in column (A) the forecast for 2009 plus the impact of the injection is
presented, while in column (B) the forecast for 2009 is shown without the injection. The
difference between the two is the net injection impact, these values by main account are
presented in column (A-B), and the FSPC impacts vary from 14,407 billion IDR for
institutional incomes to 30,545 billion IDR for commodity output. The total effect of the
FSPC is close to 2.3 trillion rupiahs for the government budget.
In terms of growth, the impact on production (commodity and activity output)
translates into growth rates slightly over 0.3 per cent. In contrast, income generation growth
reaches 0.27 per cent for factor income (GDP at factor cost in table 16) and 0.255 per cent
for institution income, (see table 16 last column). Put into perspective, the 10.8 trillion
IDRs amounts to 0.15 of GDP and generates 0.275 per cent GDP growth.
Since the government receives income back via taxes, calculated at about 2.3 trillion,
the net cost to the government amounts to 8.6 trillion rupiahs (see Table 17).
Table 17: Net Cost of the Construction Fiscal Stimulus Package in 2009 (billions of rupiahs)
Injection Fiscal Stimulus
Package
10,815.00
Effect on Government Income
2,288.58
Net Cost Fiscal Stimulus Package
8,526.42
In terms of the impact on employment, the almost 11 trillion rupiahs FSPC via
construction generate 287,000 thousand jobs (see Table 18). Looking at construction by
type, 9 per cent of these jobs are generated in road labour intensive building and only 1.7
per cent in irrigation.
It is interesting to see that the job creation impact on the largest economic activity,
namely Crops, stands to create close to 90 thousand labour places or 28.5 per cent of all the
additional employment, which is much higher than its share in overall employment
(19.6%).
34
Table 18: Total Impact on Job creation 2009: Economy Wide, Construction by Type and Crops
JOB CREATION by
activities
Total Economy Wide
Employment Increase
(Growth)
287,060 (0.26%)
Share
Road LabourIntens.
25,722 (9%)
9.0%
Road CapitalIntens.
8,539 (9%)
3.0%
Irrigation
4,851 (9%)
1.7%
Construction Rest
11,125 (9%)
3.9%
Crops
81,951 (0.22)
28.5%
100%
ME Factor(*)
ME Persons (*)
ME Share
1.02
292,801
100.0%
1.16
29,791
10.2%
1.16
9,890
3.4%
1.16
5,619
1.9%
1.16
0.80
12,884
4.4%
22.3%
65,204
(*) Source: Total Manpower, Manpower Equivalence (ME) and Average Work Hours Per Week, by Business Classification: BPS SAM Indonesia,
2005.
The results need to be corrected for over- or underemployment, using the provided
data, which have been derived from manpower equivalence (ME) factors of the 2005 SAM.
For the entire labour force, the factor is 1.02 (same table, fourth column), thus the actual
employment level is close to 293,000 labour equivalent places. For Construction and Crops,
the ME factor is 1.16, one of the highest among all reported economic activities. The
employment equivalent shares are now considerably higher for construction activities but
lower for crops activities by as much as 6.2 percentage points, an activity with high
underemployment. Labour-intensive road construction now creates 29,800 full-time jobs
instead of 25,700 jobs without correction for ME.35
For policy purposes, it is also important to understand how the total effect is formed.
decomposing the employment effects into induced (O+C, meaning open and closed
loop)and intra-account effect M1 (which is similar to a production coefficient). The IntraAccount Effect arises out of intra-account transactions only and shows how much an
activity is integrated with the production structure. Table 19 shows that the Intra Account
Effect, the effect that dominates for all construction activities, which is not surprising given
that construction is the objective of the injection thus showing 99.5 per cent of the total
impact. However, the share contribution varies to the extent that now Labour-Intensive
Road Construction contributes 22.5 per cent of the total Intra-Account Effect (it was only
9%; see Table 13). In addition, the other construction activity shares have gone up
considerably.
For Crops (see Table 19), this effect is only 2.8 per cent, since most of the effect arises
as induced via the ‗workings of the economy‘ or via the cascading effect throughout the
economy (through other accounts such as factor income, institutions, etc.). At the economywide level, there is also a dominance of the induced effect (60.4% compared with 39.6% of
the Intra-Account Effect).
35
Other national studies came up with other figures, which can be explained by differences in
assumptions when applying their models, mainly on: 1. Economic growth forecasts, 2. definition of a
job, 3. definition of multipliers, 4. application of economic versus engineering methods of
calculating multipliers, 5. time span.
35
Table 19: Intra Account Impact on Job creation 2009: Economy Wide, Construction by Type and Crops
JOB CREATION by
Activities
Employment
Increase
Share
ME Persons (*)
Share Total
Total Economy Wide
113,803
100.0%
116,079
39.6%
Road Labour Intensive
25,602
22.5%
29,652
99.5%
Road Capital Intensive
8,499
7.5%
9,859
99.5%
Irrigation
Construction Rest
Crops
4,829
4.2%
5,602
99.5%
11,073
9.7%
12,845
99.5%
2,314
2.0%
1,841
2.8%
To conclude, Table 20 shows that most of the employment will take place in rural
areas (59%) and will be male employment (62.3% urban + rural male). Looking at
construction, the figures show a lower share of urban workers (48.4%) as compared with
the total economy and a very strong domination of male workers, with over 97.6 per cent.
Table 20: Share of new employment by location and gender, 2008 (percentage)
Urban
Male
Urban
Female
Rural
Male
Rural
Female
Total
Urban
Total
Rural
Total
2008
Economy wide
25.4%
15.6%
36.9%
22.1%
41.0%
59.0%
100.0%
Construction
46.9%
1.6%
50.8%
0.8%
48.4%
51.6%
100.0%
7. Conclusions and Recommendations
To sum up, the DySAM Model with expanded construction activity accounts and an
extended employment satellite module has operational utility for the following purposes:

In addition to using an accounting framework it also provides a simple economic
modeling: Based on an accounting framework, some static SAM Model assumptions
are relaxed: technology/behaviour is not fixed: price relatives change; dynamics
generating the interdependent evolution of the economy evolution of backward and
forward linkages; it exhibits behaviour and lessens the need to calculate
expenditure/income elasticities.

It is a guiding tool for sectoral policy making: An assessment of the employment
generating potential across the activity set or sectors, as well as any activity subset or
sub-sectors such as construction.

It facilitates targeting: An assessment of the employment impacts of activities across
labour types by gender, location and age cohorts or income or skill levels using
simple variants of the extended employment satellite module.

It allows the evaluation of the effectiveness of past programmes and policies and the
simulation of future ones: A quantitative assessment of how injections into
exogenous accounts transmit through the interdependent endogenous accounts to the
impact on economic growth and employment generation.
36

It helps in choosing between different policy options or policy mixes: Inform the
design of policy packages consistent with alternative policy priorities (employment
first, growth first, etc.) via comparative scenario analysis.

It includes technology choice, as a change in the technological composition of
activities will lead to different employment outcomes in the future.

Various effects can be decomposed: A decomposition of the total impacts into intraaccount (including direct and indirect) and induced impacts, backward and forward
linkages.
Data requirement may be a serious issue. The major challenge is to incorporate and
reconcile information from various social and economic sources, including data from
previous years. Nevertheless, most developing countries already have a SAM that can be
updated. Moreover, good policy advice should be evidence-based. Macro data also need to
be underpinned by micro data competence at the sectoral level.
From the data side, one reason to use the DySAM model is that it harmonizes and
reconciles various datasets, it helps identify and resolve data inconsistencies and errors. The
platform is user-friendly with regard to simulations and also with regard to up-dating by
technical specialists.
Simulations on the Indonesian fiscal stimulus package on construction (FSPC) have
shown not only the high employment impact on measures economy-wide (almost 300,000
jobs) and in particular in the construction sector, especially in labour-intensive road
construction (almost 30,000 jobs) but also, the positive reflow of funds via tax income (IDR
2.3 trillion out of IDR 10.815 trillion). Furthermore, the FSPC package benefits more rural
than urban workers, these figures are more mitigated looking only at the construction
sector.
There are various areas for extension and expansion of the DySAM in the future.
Special groups of workers (women, the youth) and households can be analyzed by the use
of satellite accounts (extension); these are people who often benefit from targeted support
programmes. Environmental issues can be discussed through the expansion of the SAM
(opening for green sub-sectors) or expansion via an environmental satellite account (e.g.
showing real carbon emissions by activity, or water use, etc.). It can be used to analyze the
external sector (capital, FDI, flows, exports, imports and special tariff arrangements), the
national financial sector (e.g. microfinance and employment), poverty reduction strategies
(e.g. MDG, PRSPs) looking at the right policy mix between social protection and
employment, industrial policies or the general budgeting process.
To bring things to a conclusion, we want to indicate that the DySAM with an
employment satellite account has the potential to be an effective tool for undertaking
impact analysis on exogenous injections such as public investment on employment (e.g. the
fiscal stimulus package in Indonesia) and its employment generating potential. This can be
done by activity and across labour types by gender, location and other types using simple
variants of the extended employment satellite module. It combines various levels of
analysis, including macro, meso, and micro levels. It also includes technology choices and
allows the decomposition of the total impacts into intra-account and induced impacts. It
thus provides useful consistent numerical information for decision-makers in the design of
policy packages, as well as the basis for comparative scenario analysis that can consider the
best way to achieve policy priorities (employment first, growth first, labour or capitalintensive methods, Co2 emissions by economic sector, etc.).
37
38
REFERENCES
Alarcon, J., J. van Heemst, S. Keuning, W. De Ruijter and R. Vos, (1991) The Social
Accounting Framework for Development, Chapter 1. Avebury: Gower House.
Alarcon, J., van Heemst, J. and N. de Jong (2000) “The social Accounting matrix Extended with
Social and Environmental Indicators: An Application to Bolivia”; Economic Systems
Research, Journal of The International Input-Output Association, Vol. 12, No. 4,
December, 2000. Pp. 47-496).
Alarcon, J.V., E. Delabastida and R. Vos, (1984) ‗La Matriz de Insumo-Producto Adaptada
para la Planificación de las necesidades básicas, Ecuador 1975 y 1980. Quito ISSPREALC,‘ Working Paper No. Q/8421. Revised version in J. V. Alarcon et al. (1991):
The Social Accounting Framework for Development, Chapter 4. Avebury: Gower House.
Alarcon, J. V., (Revision 2007) Social Accounting Matrix-based Modelling; Extensions to
Wellbeing and Environment and Computable General Equilibrium Models; Applications
using the SAMs for Ecuador 1975 and Bolivia 1989.MASD Post-graduate course
teaching Materials.Institute of Social Studies.
Bacharach, M. (1970) Biproportional Matrices & Input-Output Change, Cambridge University
Press, 200 Euston Road, London NW1 2DB. Doctoral Dissertation as part of the R. A.
Stone Growth Cambridge Project).
Bartelmus, P., Stahmer, C. and van Tongeren, J. (1991) “Integrated Environmental and
Economic Accounting; Framework for SNA Satellite Systems”. The Review of Income
and Wealth, Series 37 No. 2, pp. 111-147.
Defourny, J. and E. Thorbecke, (1984) ―Structural Path Analysis and Multiplier
Decomposition within a Social Accounting Matrix‖, Economic Journal, No 94, March
1984, pp.111-136.
Ernst, C, and Sánchez-Aconchea, D. (2008) Offshoring and employment in the developing
world: The case of Costa Rica. Employment Working Paper, No. 4. Geneva: ILO.
Fofana, I, Lemelin, A., and Cockburn, J. (2002) Balancing a Social Accounting Matrix.
Laval: Centre de Recherche en Économie et Finances Appliquées (CREFA) Université
Laval.
GHK (2010) Estimating green jobs in Bangladesh. A report prepared by GHK for the ILO.
Geneva, ILO.
ILO (October 2008) Decent Work for Sustainable Development – The Challenge of Climate
Change”. Geneva.
ILO (2009a) Global Challenges for sustainable Development Strategies for Green Jobs, G8
Labour and Employment Ministers Conference Niigata, Japan.
ILO (2009b) Mozambique DySAM Report. Geneva: ILO.
ILO (2010a) Indonesia DySAM Report. Geneva: ILO.
ILO (20120b) Indonesia DySAM Report: Labour Satellite revised with Expanded Construction
Economic Activity. Geneva: ILO.
39
Kratena, K. and Zacharias, G. (2004) Input-Output coefficient change using bi-proportional
econometric adjustment functions, Economic Systems Research, Vol. 16, No. 2, June,
pp. 191 - 203. Special Issue: Biproportional Techniques in Input-Output Analysis.
Kucera, D, and Roncolato, L. (2011) Trade liberalization, employment and inequality in India
and South Africa, No.1, International Labour Review, ILO.
Lahr, M. L. and Mensnard, L. (2004) Biproportional Techniques in Input-Output Analysis:
table
updating and structural analysis change using, Economic Systems Research, Vol. 16, No. 2,
June, pp. 115-134. Special Issue: Bi-proportional Techniques in Input-Output Analysis.
Oosterhaven, J. (2005) GRAS versus Minimizing Absolute and Square Differences a
Comment, Economic System Research, Journal of the International Input-Output
Association, Vol. 17, No. 3 (September) 2005.
Pyatt, G, E. Thorbecke, (1976), Planning techniques for a Better Future, ILO, World
Employment Programme. A WEP Study, ILO Geneva.
Pyatt, G and ,Jeffrey Round, (1977) 'Social Accounting Matrices for Development Planning‘,
The Review of Income and Wealth, Series 23, No. 4, 1977 (339-63).
Pyatt, G , Jeffrey Round, (1979) 'Accounting and Fixed Price Multipliers in a Social Accounting
Matrix Framework', Economic Journal, Vol. 89, No. 356, pp. 850-873.
Pyatt, G, Jeffrey Round, (1979). ―Multiplicative Decomposition, Poverty and Income
Distribution in a SAM Framework”, the Vietnamese Case. Unpublished.
Pyatt, G., (1994), ―Modelling Commodity Balances: A Derivation of the Stone Model‖, The
Richard Stone Memorial Lecture, Part I, Economic Systems Research, Vo. 6, No. 1, 1994.
Pyatt, G, J.I. Round (eds), (1985), ―Social Accounting Matrices: A Basis for Planning‖.
Washington DC: World Bank.
Round, J., (2003), ―Social Accounting Matrices and SAM-based Multiplier Analysis‖, in:
Bourgignon, F. and L. Pereira da Silva (eds), The Impact of Economic Policies on
Poverty and Income Distribution: Evaluation Techniques and Tools, The World Bank,
Washington D.C. pp. 269-287.
Robinson, S. and El-Said, M. (2000) ‗GAMS Code for Estimating A Social Accounting Matrix
(SAM) Using Cross Entropy (CE) Methods’. TMD Discussion paper No. 64.
Washington, DC, International Food Policy Institute (IFPRI).
Robinson, S., Cattaneo. A. and El-Said. M. (2001) ‗Updating and Estimating a Social
Accounting matrix Using Cross Entropy Methods’. Economic Systems Research 13 (1):
47-64
Van Heemst, J.J.P.: ―Introduction‖ in: The Social Accounting Framework for Development,
in Alarcon et al., 1991. The Social Accounting Framework for Development, Avebury:
Gower House, England.
UNSD (1993) ―Integrated Environmental and Economic Accounting, Studies in Methods”, Ser.
F. Nr 61, NY.
40
APPENDIX
1 Backward linkages: Evolution over time for a
selected number of endogenous accounts
Glossary of Symbols:

s_ST_Ma: Static SAM 2005 matrix of multipliers

s_Dy_Ma: Dynamic SAM 2000-2008 matrices of multipliers
Note: for the interpretation of the codes and symbols please refer to Figure 4, see
Total Row/Col, where: (Tr Co) total of the Commodity (Co) and Tr A Total of the Activity
(A).
Figure 8: Commodity Account and Activity Account
Backward Linkage of Commodity Accounts (1-14)
s_ST_Ma
s_Dy_Ma
s_ST_Ma
s_Dy_Ma
"s5 Ma (Tr Co) Co c5"[c Maize c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
"s5 Ma (Tr Co) Co c5"[c Cashew c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
15
10
"s5 Ma (Tr Co) Co c5"[c Rice c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
10
"s5 Ma (Tr Co) Co c5"[c Cotton c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
15
10
"s5 Ma (Tr Co) Co c5"[c Wheat c5] : Sum All Backward Linkage (Tr Co) (1x24)
1
0.9
15
0.8
"s5 Ma (Tr Co) Co c5"[c OthGrain c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
10
"s5 Ma (Tr Co) Co c5"[c OthCrops c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
10
"s5 Ma (Tr Co) Co c5"[c Cassava c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
10
"s5 Ma (Tr Co) Co c5"[c Livestock c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
10
"s5 Ma (Tr Co) Co c5"[c Bean c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
10
"s5 Ma (Tr Co) Co c5"[c Forestry c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
10
"s5 Ma (Tr Co) Co c5"[c OthBasicFood c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
10
2000
10
"s5 Ma (Tr Co) Co c5"[c OthExportCrop c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
10
"s5 Ma (Tr Co) Co c5"[c Fish c5] : Sum All Backward Linkage (Tr Co) (1x24)
20
15
2002
2004
Time (Year)
2006
2008
41
10
2000
2002
2004
Time (Year)
2006
2008
Activity Account
Total Backward Linkage of Activity Accounts (1-16)
s_ST_Ma
s_Dy_Ma
s_ST_Ma
s_Dy_Ma
"s5 Ma (Tr A) A c5"[aR Maize c5]: Sum All Backward Linkage (Tr A) (1x27)
20
"s5 Ma (Tr A) A c5"[aU Cassava c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aU Maize c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aM Cassava c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aM Maize c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aR Bean c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aR Rice c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aU Bean c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aU Rice c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aM Bean c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aR OthGrain c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aR OthBasicFood c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aU OthGrain c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aU OthBasicFood c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aR Cassava c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aM OthBasicFood c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
10
2000
15
2002
2004
Time (Year)
2006
10
2000
2008
42
2002
2004
Time (Year)
2006
2008
Total Backward Linkage of Activity Accounts (17-32)
s_ST_Ma
s_Dy_Ma
s_ST_Ma
s_Dy_Ma
"s5 Ma (Tr A) A c5"[aU OthCrops c5]: Sum All Backward Linkage (Tr A) (1x27)
20
"s5 Ma (Tr A) A c5"[aR Cashew c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aM OthCrops c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aU Cashew c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aR Livestock c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aR Cotton c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aU Livestock c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aU Cotton c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aR Forestry c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aR OthExportCrop c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aU Forestry c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aU OthExportCrop c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
"s5 Ma (Tr A) A c5"[aM Forestry c5]: Sum All Backward Linkage (Tr A) (1x27)
20
10
"s5 Ma (Tr A) A c5"[aM OthExportCrop c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
14
10
"s5 Ma (Tr A) A c5"[aR OthCrops c5]: Sum All Backward Linkage (Tr A) (1x27)
20
8
"s5 Ma (Tr A) A c5"[aR Fish c5]: Sum All Backward Linkage (Tr A) (1x27)
20
15
15
10
2000
2002
2004
Time (Year)
2006
10
2000
2008
43
2002
2004
Time (Year)
2006
2008
Factor Account
Note: Sum total backward linkages. The interpretation of the codes and symbols can
be found in Figure 4; see Total Row/Col, where: (Tr Fp) and (Tr iH) are Total Row of the
Factors (Fp) and institutions (iE).
Figure 9: Total Backward Linkage of Factor Accounts (1-14)
s_ST_Ma
s_Dy_Ma
s_ST_Ma
s_Dy_Ma
"s5 Ma (Tr Fp) Fp c5"[fL Skill Inf M c5]: Sum All Backward Linkage (Tr Fp) (1x17) "s5 Ma (Tr Fp) Fp c5"[fn RuC c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
20
17
17.5
14
15
11
12.5
8
10
"s5 Ma (Tr Fp) Fp c5"[fL SemiSk Inf M c5]: Sum All Backward Linkage (Tr Fp) (1x17) "s5 Ma (Tr Fp) Fp c5"[fn RuS c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
20
17
17.5
14
15
11
12.5
8
10
"s5 Ma (Tr Fp) Fp c5"[fK Rural c5]: Sum All Backward Linkage (Tr Fp) (1x17)
"s5 Ma (Tr Fp) Fp c5"[fn UrN c5] : Sum All Backward Linkage (Tr Fp) (1x17)
20
20
17
17.5
14
15
11
12.5
8
10
"s5 Ma (Tr Fp) Fp c5"[fK Urban c5]: Sum All Backward Linkage (Tr Fp) (1x17)
"s5 Ma (Tr Fp) Fp c5"[fn UrC c5] : Sum All Backward Linkage (Tr Fp) (1x17)
20
20
16.5
17
13
14
9.5
11
6
8
"s5 Ma (Tr Fp) Fp c5"[fK Project c5]: Sum All Backward Linkage (Tr Fp) (1x17)
"s5 Ma (Tr Fp) Fp c5"[fn UrS c5] : Sum All Backward Linkage (Tr Fp) (1x17)
2
20
1.75
17
1.5
14
1.25
11
1
8
"s5 Ma (Tr Fp) Fp c5"[fn RuN c5]: Sum All Backward Linkage (Tr Fp) (1x17)
"s5 Ma (Tr Fp) Fp c5"[fn UrM c5] : Sum All Backward Linkage (Tr Fp) (1x17)
20
20
17.5
17
15
14
12.5
11
10
8
2000
2002
2004
2006
2008
2000
2002
2004
2006
2008
Time (Year)
Time (Year)
44
Backward Linkage of Factor
s_ST_Ma
s_Dy_Ma
s_ST_Ma
s_Dy_Ma
"s5 Ma (Tr Fp) Fp c5"[fL HiSkl For c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
"s5 Ma (Tr Fp) Fp c5"[fL Skill For C c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
14
14
8
8
"s5 Ma (Tr Fp) Fp c5"[fL Skill For N c5]: Sum All Backward Linkage (Tr Fp) (1x17) "s5 Ma (Tr Fp) Fp c5"[fL SemiSk For C c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
20
14
14
8
8
"s5 Ma (Tr Fp) Fp c5"[fL SemiSk For N c5]: Sum All Backward Linkage (Tr Fp) (1x17)"s5 Ma (Tr Fp) Fp c5"[fL UnSkl For C c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
20
15
14
10
8
"s5 Ma (Tr Fp) Fp c5"[fL UnSkl For N c5]: Sum All Backward Linkage (Tr Fp) (1x17) "s5 Ma (Tr Fp) Fp c5"[fL Skill Inf C c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
20
14
15
8
10
"s5 Ma (Tr Fp) Fp c5"[fL Skill Inf N c5]: Sum All Backward Linkage (Tr Fp) (1x17) "s5 Ma (Tr Fp) Fp c5"[fL SemiSk Inf C c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
20
14
15
8
10
"s5 Ma (Tr Fp) Fp c5"[fL SemiSk Inf N c5]: Sum All Backward Linkage (Tr Fp) (1x17) "s5 Ma (Tr Fp) Fp c5"[fL UnSkl Inf C c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
20
15
15
10
10
"s5 Ma (Tr Fp) Fp c5"[fL UnSkl Inf N c5]: Sum All Backward Linkage (Tr Fp) (1x17) "s5 Ma (Tr Fp) Fp c5"[fL Skill For S c5]: Sum All Backward Linkage (Tr Fp) (1x17)
20
20
14
15
10
2000
2002
2004
Time (Year)
2006
2008
8
2000
45
2002
2004
Time (Year)
2006
2008
Institution Accounts
Figure 10: Total Backward Linkage of Household Accounts (1-14)
s_ST_Ma
s_Dy_Ma
s_ST_Ma
s_Dy_Ma
"s5 Ma (Tr iE) iE c5"[ih RuN q1 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
"s5 Ma (Tr iE) iE c5"[ih RuC q5 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
14
10
"s5 Ma (Tr iE) iE c5"[ih RuN q2 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
8
"s5 Ma (Tr iE) iE c5"[ih UrC q1 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
14
10
"s5 Ma (Tr iE) iE c5"[ih RuN q3 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
8
"s5 Ma (Tr iE) iE c5"[ih UrC q2 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
14
10
"s5 Ma (Tr iE) iE c5"[ih RuN q4 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
8
"s5 Ma (Tr iE) iE c5"[ih UrC q3 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
14
10
"s5 Ma (Tr iE) iE c5"[ih RuN q5 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
8
"s5 Ma (Tr iE) iE c5"[ih UrC q4 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
14
14
8
"s5 Ma (Tr iE) iE c5"[ih UrN q1 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
8
"s5 Ma (Tr iE) iE c5"[ih UrC q5 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
13
10
"s5 Ma (Tr iE) iE c5"[ih UrN q2 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
6
"s5 Ma (Tr iE) iE c5"[ih RuS q1 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
10
2000
15
2002
2004
Time (Year)
2006
10
2000
2008
46
2002
2004
Time (Year)
2006
2008
Institution Accounts
Figure 11: Total Backward Linkage of Household Accounts (15-28)
s_ST_Ma
s_Dy_Ma
s_ST_Ma
s_Dy_Ma
"s5 Ma (Tr iE) iE c5"[ih UrN q3 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
"s5 Ma (Tr iE) iE c5"[ih RuC q5 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
14
10
"s5 Ma (Tr iE) iE c5"[ih UrN q4 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
14
14
8
"s5 Ma (Tr iE) iE c5"[ih UrN q5 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
14
8
"s5 Ma (Tr iE) iE c5"[ih UrC q2 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
14
8
"s5 Ma (Tr iE) iE c5"[ih RuC q1 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
8
"s5 Ma (Tr iE) iE c5"[ih UrC q3 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
14
10
"s5 Ma (Tr iE) iE c5"[ih RuC q2 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
8
"s5 Ma (Tr iE) iE c5"[ih UrC q4 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
14
10
"s5 Ma (Tr iE) iE c5"[ih RuC q3 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
8
"s5 Ma (Tr iE) iE c5"[ih UrC q5 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
13
10
"s5 Ma (Tr iE) iE c5"[ih RuC q4 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
10
2000
8
"s5 Ma (Tr iE) iE c5"[ih UrC q1 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
6
"s5 Ma (Tr iE) iE c5"[ih RuS q1 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
15
2002
2004
Time (Year)
2006
2008
10
2000
47
2002
2004
Time (Year)
2006
2008
Institution Accounts
Figure 12: Total Backward Linkage of Household Accounts (29-35)
s_ST_Ma
s_Dy_Ma
s_ST_Ma
s_Dy_Ma
"s5 Ma (Tr iE) iE c5"[ih RuS q2 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
"s5 Ma (Tr iE) iE c5"[ih UrN q3 c5] : Sum All Backward Linkage (Tr iE) (1x11)
20
15
15
10
"s5 Ma (Tr iE) iE c5"[ih RuS q3 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
10
"s5 Ma (Tr iE) iE c5"[ih UrN q4 c5] : Sum All Backward Linkage (Tr iE) (1x11)
20
14
15
10
"s5 Ma (Tr iE) iE c5"[ih RuS q4 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
8
"s5 Ma (Tr iE) iE c5"[ih UrN q5 c5] : Sum All Backward Linkage (Tr iE) (1x11)
20
14
15
10
"s5 Ma (Tr iE) iE c5"[ih RuS q5 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
8
"s5 Ma (Tr iE) iE c5"[ih RuC q1 c5] : Sum All Backward Linkage (Tr iE) (1x11)
20
15
14
8
"s5 Ma (Tr iE) iE c5"[ih UrS q1 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
10
"s5 Ma (Tr iE) iE c5"[ih RuC q2 c5] : Sum All Backward Linkage (Tr iE) (1x11)
20
15
14
8
"s5 Ma (Tr iE) iE c5"[ih UrS q2 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
10
"s5 Ma (Tr iE) iE c5"[ih RuC q3 c5] : Sum All Backward Linkage (Tr iE) (1x11)
20
15
14
8
"s5 Ma (Tr iE) iE c5"[ih UrS q3 c5]: Sum All Backward Linkage (Tr iE) (1x11)
20
10
"s5 Ma (Tr iE) iE c5"[ih RuC q4 c5] : Sum All Backward Linkage (Tr iE) (1x11)
20
15
13
6
2000
2002
2004
Time (Year)
2006
10
2000
2008
48
2002
2004
Time (Year)
2006
2008
2 Dynamic Macro SAMs [s3]: The Case of
Mozambique
Even though, dynamic Macro SAMs have been derived for the years 2000 to 2008, the
structures of some selected SAMs are presented here for illustration. In particular, SAMs
derived for 2000, and from 2005 to 2008, are presented below.
Table 21: Macro SAMs for selected years
Year 2000
Com
Act
Commodity
Activity
FacLab
FacCap
FacLand
Fac
Lab
cC Capital
cC StkChg
w Current
AC
Total
Supply
Year 2005
Commodity
ig
YTa
x
ig
CoT
ax
ig Atax
ig
Mdut
y
7438.32
cC
Cap
19487.34
cC
StkChg
825.78
w
Cur
AC
5542.7
2
15655.26
0.14
15655.
26
42304
.55
10332.
62
362.59
15724.88
160.20
227.44
985.30
3558.22
-77.23
2992.88
622.71
985.30
3558.22
-77.23
-77.23
2992.88
2992.88
1771.1
0
5334.46
13322.
11
-114.55
825.78
15929.2
3
108003.25
Act
42304
.55
FacL
ab
15655.
26
Fac
Cap
1121.0
3
FacL
and
15724.
88
Ent
19093.24
54145.8
4
HH
92346.
61
22798.
00
222908.
25
20313.12
825.78
3164.0
1
111497.26
7553.73
ig
Govt
985.30
ig
YTa
x
3558.22
ig
CoT
ax
-77.23
ig Atax
2992.88
ig
Mdut
y
19440.7
7
20313.12
825.78
19093.
24
cC
Cap
cC
StkChg
w
Cur
AC
27207.75
1152.93
40248.
30
Total
Demand
291893.61
245706.25
96258.38
7.85
35622.27
96266.23
35622.27
2550.49
0.38
35622.
27
96266.2
3
2550.87
198.89
2550.87
23511.
03
1784.9
1
35821.16
1837.0
3
457.62
4401.4
0
215.17
9945.84
-222.15
7329.9
3
124622.78
21670.18
2616.4
9
4401.40
9945.84
9945.84
-222.15
-222.15
7329.93
7329.93
3110.5
9
6861.6
7
16815.
88
1572.52
1152.93
51709.6
0
Com
54145.83
7553.73
3558.22
291893.
62
42304.55
1121.03
69.62
1121.0
3
94.56
Com
120462.43
15655.26
1120.89
120462.
43
Total
Demand
108003.25
0.97
cC Capital
Year 2007
ig
Govt
42303.5
HH
cC StkChg
w Current
AC
Total
Supply
HH
38167.5
2
10021.1
5
Ent
ig Govt
ig
IncomeTax
ig CoTaxNet
ig ATaxNet
ig Mduty
Ent
49000.76
Commodity
Activity
FacLab
FacCap
FacLand
Fac
Lan
d
97982.1
0
Ent
HH
ig Govt
ig
IncomeTax
ig CoTaxNet
ig ATaxNet
ig Mduty
Fac
Cap
1152.93
7199.4
6
245706.25
Act
151277.8
4
96266.2
3
FacLa
b
35622.
27
FacC
ap
2550.87
FacL
and
35821.
16
Ent
28360.68
58909.06
124622
.77
21670.1
8
HH
ig
Govt
126381
.74
24384.
71
49
4401.4
0
9945.84
ig
YTax
ig
CoTa
x
-222.15
7329.9
3
ig Atax
ig
Mdu
ty
58909.
06
28360.67
1152.93
cC Cap
cC
StkC
hg
w Cur
AC
34279.41
1452.5
9
61269.22
Total
Demand
399045.51
Activity
30239
1.56
30927.
18
FacLab
FacCap
FacLand
10.06
48331.78
0.47
48331
.78
130612.
02
HH
ig Mduty
cC Capital
cC StkChg
w Current
AC
Total
Supply
Year 2008
2354.75
9224.0
0
14717.
00
-353.30
10532.
80
9224.00
-353.30
10532.80
2999.
39
6342.4
5
8851.9
3
17538.22
1452.59
71404.
13
Com
333318.7
5
Act
130612.
02
FacLa
b
48331
.78
Fac
Cap
3460.
94
FacL
and
48687
.84
Ent
81172.25
169146
.40
HH
151912
.13
35992.
80
35192
0.91
35731.99
1452.59
9768.
12
176051.6
3
34412.
44
ig
Govt
9224.0
0
14717.
00
ig
YTax
ig
CoTa
x
-353.30
10532.
80
ig Atax
ig
Mdu
ty
27592.
37
35732.00
1452.5
9
81172.25
cC Cap
cC
StkC
hg
w Cur
AC
Total
Demand
42509.65
1801.3
5
67395.89
467263.01
387913.71
151989.7
8
10.40
56246.75
152000.18
56246.75
4027.16
5624
6.75
152000.
19
4027.7
0
3712
3.38
339.7
4
4721.
32
0.54
4027.70
408.07
56654.82
938.94
2433.16
11678.
70
16576.
00
-401.63
10935.
30
196523.36
39128.12
6957.3
8
11678.70
16576.
00
16576.00
-401.63
-401.63
10935.
30
10935.30
3102.
60
1661.0
6
10188.
20
29359.15
1801.35
87830.
81
46726
3.02
169146.40
34412.44
5495.0
3
10532.
80
HH
cC Capital
cC StkChg
w Current
AC
Total
Supply
819.27
14717.00
Ent
ig Mduty
48687.84
-353.30
FacLab
FacCap
FacLand
ig Govt
ig
IncomeTax
ig
CoTaxNet
ig ATaxNet
31899
.42
291.9
3
14717.
00
Commodity
Activity
3460.94
356.06
3460.
94
3728.
97
39904
5.49
130612.03
48331.78
3460.47
Ent
ig Govt
ig
IncomeTax
ig
CoTaxNet
ig ATaxNet
333318.74
130601.9
6
1801.35
1136
7.78
387913.7
0
152000.
19
5624
6.75
4027.7
0
5665
4.82
44311.00
99198.59
196523
.36
39128.
12
50
11678.
70
16576.
00
-401.63
10935.
30
44311.00
1801.3
5
99198.59
3 Behaviours of Selective Accounts of the
Dynamic SAM: The Case of Mozambique
Figure 13: Behaviours of Activity and Commodity Accounts of the Dynamic SAMs
s3_Mz_0217
s3_Mz_0217
"s3 (Tr A)" : Macro control. Total (A) payments
400,000
320,000
480,000
240,000
360,000
160,000
240,000
80,000
0
120,000
"s3 (Co A)" : Macro control. (Co) receipts from (A). (Input use)
0
200,000
"s3 (A Co)" : Macro control. (A) receipts from (Co). (Domestic Supply)
160,000
400,000
120,000
320,000
80,000
40,000
240,000
0
160,000
"s3 (Fk A)" : Macro control. (Fk) receipts from (A).
80,000
60,000
48,000
0
36,000
"s3 (iT Co)" : Macro control. (iT) receipts from (Co).
24,000
20,000
12,000
16,000
0
12,000
"s3 (FL A)" : Macro control. (FL) receipts from (A).
200,000
8,000
160,000
4,000
120,000
0
80,000
"s3 (iT Du)" : Macro control. (iT) receipts from (wC). (Duty)
40,000
20,000
0
16,000
"s3 (Fn A)" : Macro control. (Fn) receipts from (A).
6,000
12,000
4,800
8,000
3,600
4,000
2,400
1,200
0
"s3 (wC Co)" : Macro control. (wC) receipts from (Co). (Import)
0
"s3 (iT A )" : Macro control. (iT) receipts from (A).
100,000
0
80,000
-120
60,000
-240
40,000
-360
-480
20,000
-600
0
2000
2002
2004
2000
2002
2004
2006
2008
Time (Year)
Time (Year)
"s3 (Tr Co)" : Total (Co) payments. Supply
600,000
51
2006
2008
Figure 14: To illustrate their dynamic evolution reflecting the policy stances of the
government on revenue, expenditure, transfer programmes and savings instruments please
see Error! Reference source not found.XXXX for the SAM Layout and labelling
conventions used in the figures.
For instance, a sharp rise in revenue mobilization from the indirect sources (iT Co)
and direct sources (iT iH) is recorded from 2004 onward, perhaps capturing either reforms
in tax administration, increases in the tax rate or a combination of these two instruments.
The rise suggests that the country had embarked on a new and improved tax regime from
2004 onward.
Figure 14: Behaviours of Household and Government Accounts of the Dynamic SAMs
s3_Mz_0217
s3_Mz_0217
"s3 (Tr iH)" : Macro control. Total (iH) payments
200,000
160,000
120,000
80,000
40,000
0
"s3 (A iH)" : Macro control. (A) receipts from (iH). (Home Production)
40,000
32,000
24,000
16,000
8,000
0
"s3 (Cc iH)" : Macro control. (Cc) receipts from (iH)
20,000
16,000
12,000
8,000
4,000
0
"s3 (Co iH)" : Macro control. (Co) receipts from (iH).
200,000
160,000
120,000
80,000
40,000
0
"s3 (iT iH)" : Macro control. (iT) receipts from (iH)
8,000
6,400
4,800
3,200
1,600
0
2000
2002
2004
Time (Year)
2006
2008
"s3 (Tr iG)" : Macro control. Total (iG) payments
40,000
32,000
24,000
16,000
8,000
0
"s3 (Cc iG)" : Macro control. (cC) receipts from (iG).
20,000
15,200
10,400
5,600
800
-4,000
"s3 (Co iG)" : Macro control. (Co) receipts from (iG). Cg
40,000
32,000
24,000
16,000
8,000
0
"s3 (Fn iG)" : Macro control. (Fn) receipts from (iG).
0.6
0.48
0.36
0.24
0.12
0
"s3 (iC iG)" : Macro control. (iC) receipts from (iG)
600
480
360
240
120
0
"s3 (iH iG)" : Macro control. (iH) receipts from (iG)
1,000
800
600
400
200
0
2000
2002
2004
Time (Year)
2006
2008
The savings behaviours (Cc iH) of households do not display systematic patterns. Such
behaviours are expected as they are derived residually incorporating the total receipts and
payments of their respective accounts. Similar behaviours have also been observed for the
elements of other accounts of the dynamic SAMs (not reported here). The dynamic behaviours
of the macro SAMs will also influence behaviours of the dynamic sectoral SAMs, multipliers
and linkages.
52
Employment Working Papers
2008
1
Challenging the myths about learning and training in small and medium-sized
enterprises: Implications for public policy;
ISBN 978-92-2-120555-5 (print); 978-92-2-120556-2 (web pdf)
David Ashton, Johnny Sung, Arwen Raddon, Trevor Riordan
2
Integrating mass media in small enterprise development: Current knowledge and good
practices;
ISBN 978-92-2-121142-6 (print); 978-92-2-121143-3 (web pdf)
Gavin Anderson. Edited by Karl-Oskar Olming, Nicolas MacFarquhar
3
Recognizing ability: The skills and productivity of persons with disabilities.
A literature review;
ISBN 978-92-2-121271-3 (print); 978-92-2-121272-0 (web pdf)
Tony Powers
4
Offshoring and employment in the developing world: The case of Costa Rica;
ISBN 978-92-2-121259-1 (print); 978-92-2-121260-7 (web pdf)
Christoph Ernst, Diego Sanchez-Ancochea
5
Skills and productivity in the informal economy;
ISBN 978-92-2-121273-7 (print); 978-92-2-121274-4 (web pdf)
Robert Palmer
6
Challenges and approaches to connect skills development to productivity and
employment growth: India;
unpublished
C. S. Venkata Ratnam, Arvind Chaturvedi
7
Improving skills and productivity of disadvantaged youth;
ISBN 978-92-2-121277-5 (print); 978-92-2-121278-2 (web pdf)
David H. Freedman
8
Skills development for industrial clusters: A preliminary review;
ISBN 978-92-2-121279-9 (print); 978-92-2-121280-5 (web pdf)
Marco Marchese, Akiko Sakamoto
9
The impact of globalization and macroeconomic change on employment in Mauritius:
What next in the post-MFA era?;
ISBN 978-92-2-120235-6 (print); 978-92-2-120236-3 (web pdf)
Naoko Otobe
10
School-to-work transition: Evidence from Nepal;
ISBN 978-92-2-121354-3 (print); 978-92-2-121355-0 (web pdf)
New Era
53
11
A perspective from the MNE Declaration to the present: Mistakes, surprises and newly
important policy implications;
ISBN 978-92-2-120606-4 (print); 978-92-2-120607-1 (web pdf)
Theodore H. Moran
12
Gobiernos locales, turismo comunitario y sus redes:
Memoria: V Encuentro consultivo regional (REDTURS);
ISBN 978-92-2-321430-2 (print); 978-92-2-321431-9 (web pdf)
13
Assessing vulnerable employment: The role of status and sector indicators in Pakistan,
Namibia and Brazil;
ISBN 978-92-2-121283-6 (print); 978-92-2-121284-3 (web pdf)
Theo Sparreboom, Michael P.F. de Gier
14
School-to-work transitions in Mongolia;
ISBN 978-92-2-121524-0 (print); 978-92-2-121525-7 (web pdf)
Francesco Pastore
15
Are there optimal global configurations of labour market flexibility and security?
Tackling the ―flexicurity‖ oxymoron;
ISBN 978-92-2-121536-3 (print); 978-92-2-121537-0 (web pdf)
Miriam Abu Sharkh
16
The impact of macroeconomic change on employment in the retail sector in India:
Policy implications for growth, sectoral change and employment;
ISBN 978-92-2-120736-8 (print); 978-92-2-120727-6 (web pdf)
Jayati Ghosh, Amitayu Sengupta, Anamitra Roychoudhury
17
From corporate-centred security to flexicurity in Japan;
ISBN 978-92-2-121776-3 (print); 978-92-2-121777-0 (web pdf)
Kazutoshi Chatani
18
A view on international labour standards, labour law and MSEs;
ISBN 978-92-2-121753-4 (print);978-92-2-121754-1(web pdf)
Julio Faundez
19
Economic growth, employment and poverty in the Middle East and North Africa;
ISBN 978-92-2-121782-4 (print); 978-92-2-121783-1 (web pdf)
Mahmood Messkoub
20
Global agri-food chains: Employment and social issues in fresh fruit and vegetables;
ISBN 978-92-2-121941-5(print); 978-92-2-121942-2 (web pdf)
Sarah Best, Ivanka Mamic
54
21
Trade agreements and employment: Chile 1996-2003;
ISBN 978-92-121962-0 (print); 978-92-121963-7 (web pdf)
22
The employment effects of North-South trade and technological change;
ISBN 978-92-2-121964-4 (print); 978-92-2-121965-1 (web pdf)
Nomaan Majid
23
Voluntary social initiatives in fresh fruit and vegetable value chains;
ISBN 978-92-2-122007-7 (print); 978-92-2-122008-4 (web pdf)
Sarah Best, Ivanka Mamic
24
Crecimiento económico y empleo de jóvenes en Chile: Análisis sectorial y
proyecciones;
ISBN 978-92-2-321599-6 (print); 978-92-2-321600-9 (web pdf)
Mario D. Velásquez Pinto
25
The impact of codes and standards on investment flows to developing countries;
ISBN 978-92-2-122114-2 (print); 978-92-2-122115-9 (web pdf)
Dirk Willem te Velde
26
The promotion of respect for workers‘ rights in the banking sector:
Current practice and future prospects;
ISBN 978-92-2-122116-6 (print); 978-2-122117-3 (web pdf)
Emily Sims
2009
27
Labour market information and analysis for skills development;
ISBN 978-92-2-122151-7 (print); 978-92-2-122152-4 (web pdf)
Theo Sparreboom, Marcus Powell
28
Global reach - Local relationships: Corporate social responsibility, worker‘s rights and
local development;
ISBN 978-92-2-122222-4 (print); 978-92-2-122212-5 (web pdf)
Anne Posthuma, Emily Sims
29
Investing in the workforce: Social investors and international labour standards;
ISBN 978-92-2-122288-0 (print); 978-92-2-122289-7 (web pdf)
Elizabeth Umlas
30
Rising food prices and their implications for employment, decent work and
poverty reduction;
ISBN 978-92-2-122331-3 (print); 978-92-2-122332-0 (web pdf)
Rizwanul Islam, Graeme Buckley
55
31
Economic implications of labour and labour-related laws on MSEs: A quick review of
the Latin American experience;
ISBN 978-92-2-122368-9 (print); 978-92-2-122369-6 (web pdf)
Juan Chacaltana
32
Understanding informal apprenticeship – Findings from empirical research in
Tanzania;
ISBN 978-92-2-122351-1 (print); 978-92-2-122352-8 (web pdf)
Irmgard Nübler, Christine Hofmann, Clemens Greiner
33
Partnerships for youth employment. A review of selected community-based initiatives;
ISBN 978-92-2-122468-6 (print); 978-92-2-122469-3 (web pdf)
Peter Kenyon
34
The effects of fiscal stimulus packages on employment;
ISBN 978-92-2-122489-1 (print); 978-92-2-122490-7 (web pdf)
Veena Jha
35
Labour market policies in times of crisis;
ISBN 978-92-2-122510-2 (print); 978-92-2-122511-9 (web pdf)
Sandrine Cazes, Sher Verick, Caroline Heuer
36
The global economic crisis and developing countries: Transmission channels, fiscal
and policy space and the design of national responses;
ISBN 978-92-2-122544-7 (print); 978-92-2-122545-4 (web pdf)
Iyanatul Islam
37
Rethinking monetary and financial policy:
Practical suggestions for monitoring financial stability while generating employment
and poverty reduction;
ISBN 978-92-2-122514-0 (print); 978-92-2-122515-7 (web pdf)
Gerald Epstein
38
Promoting employment-intensive growth in Bangladesh: Policy analysis of the
manufacturing and service sectors;
ISBN 978-92-2-122540-9 (print); 978-92-2-122541-6 (web pdf)
Nazneen Ahmed, Mohammad Yunus, Harunur Rashid Bhuyan
39
The well-being of labour in contemporary Indian economy: What‘s active labour
market policy got to do with it?;
ISBN 978-92-2-122622-2 (print); 978-92-2-122623-9 (web pdf)
Praveen Jha
40
The global recession and developing countries;
ISBN 978-92-2-122847-9 (print); 978-92-2-122848-6 (web pdf)
Nomaan Majid
56
41
Offshoring and employment in the developing world: Business process outsourcing in
the Philippines;
ISBN 978-92-2-122845-5 (print); 978-92-2-122846-2 (web pdf)
Miriam Bird, Christoph Ernst
42
A survey of the Great Depression as recorded in the International Labour Review,
1931-1939;
ISBN 978-92-2-122843-1 (print); 978-92-2-122844-8 (web pdf)
Rod Mamudi
43
The price of exclusion: The economic consequences of excluding people with
disabilities from the world or work;
ISBN 978-92-2-122921-6 (print); 978-92-2-122922-3 (web pdf)
Sebastian Buckup
44
Researching NQFs: Some conceptual issues;
ISBN 978-92-2-123066-3 (print), 978-92-2-123067-0 (web pdf)
Stephanie Allais, David Raffe, Michael Young
45
Learning from the first qualifications frameworks;
ISBN 978-92-2-123068-7 (print), 978-92-2-123069-4 (web pdf)
Stephanie Allais, David Raffe, Rob Strathdee, Leesa Wheelahan, Michael Young
46
International framework agreements and global social dialogue:
Lessons from the Daimler case;
ISBN 978-92-2-122353-5 (print); 978-92-2-122354-2 (web pdf)
Dimitris Stevis
2010
47
International framework agreements and global social dialogue:
Parameters and prospects;
ISBN 978-92-2-123298-8 (print); 978-92-2-122299-5 (web pdf)
Dimitris Stevis
48
Unravelling the impact of the global financial crisis on the South African labour
market;
ISBN 978-92-2-123296-4 (print); 978-92-2-123297-1 (web pdf)
Sher Verick
49
Guiding structural change: The role of government in development;
ISBN 978-92-2-123340-4 (print); 978-92-2-123341-1 (web pdf)
Matthew Carson
50
Les politiques du marché du travail et de l'emploi au Burkina Faso;
ISBN 978-92-2-223394-6 (print); 978-92-2-223395-3 (web pdf)
Lassané Ouedraogo, Adama Zerbo
57
51
Characterizing the school-to-work transitions of young men and women:
Evidence from the ILO school-to-work transition surveys;
ISBN 978-92-2-122990-2 (print); 978-92-2-122991-9 (web pdf)
Makiko Matsumoto, Sara Elder
52
Exploring the linkages between investment and employment in Moldova:
A time-series analysis
ISBN 978-92-2-122990-2 (print); 978-92-2-122991-9 (web pdf)
Stefania Villa
53
The crisis of orthodox macroeconomic policy: The case for a renewed commitment to
full employment;
ISBN 978-92-2-123512-5 (print); 978-92-2-123513-2 (web pdf)
Muhammed Muqtada
54
Trade contraction in the global crisis: Employment and inequality effects in India and
South Africa;
ISBN 978-92-2124037-2 (print); 978-92-2124038-9 (web pdf)
David Kucera, Leanne Roncolato, Erik von Uexkull
55
The impact of crisis-related changes in trade flows on employment: Incomes, regional
and sectoral development in Brazil;
Forthcoming
Scott McDonald, Marion Janse, Erik von Uexkull
56
Envejecimiento y Empleo en América Latina y el Caribe;
ISBN 978-92-2-323631-1 (print); 978-92-2-323632-8 (web pdf)
Jorge A. Paz
57
Demographic ageing and employment in China;
ISBN 978-92-2-123580-4 (print); 978-92-2-123581-1 (web pdf)
Du Yang, Wrang Meiyan
58
Employment, poverty and economic development in Madagascar: A macroeconomic
framework;
ISBN 978-92-2-123398-5 (print); 978-92-2-123399-2 (web pdf)
Gerald Epstein, James Heintz, Léonce Ndikumana, Grace Chang
59
The Korean labour market: Some historical macroeconomic perspectives;
ISBN 978-92-2-123675-7 (print); 978-92-2-123676-4 (web pdf)
Anne Zooyob
60
Les Accords de Partenariat Economique et le travail décent:
Quels enjeux pour l‘Afrique de l‘ouest et l‘Afrique centrale?;
ISBN 978-92-2-223727-2 (print); 978-92-2-223728-9 (web pdf)
Eléonore d’Achon; Nicolas Gérard
58
61
The great recession of 2008-2009: Causes, consequences and policy responses;
ISBN 978-92-2-123729-7 (print); 978-92-2-123730-3 (web pdf)
Iyanatul Islam, Sher Verick
62
Rwanda forging ahead: The challenge of getting everybody on board;
ISBN 978-92-2-123771-6 (print); 978-92-2-123772-3 (web pdf)
Per Ronnås (ILO), Karl Backéus (Sida); Elina Scheja (Sida)
63
Growth, economic policies and employment linkages in Mediterranean countries:
The cases of Egypt, Israel, Morocco and Turkey;
ISBN 978-92-2-123779-2 (print); 978-92-2-123780-8 (web pdf)
Gouda Abdel-Khalek
64
Labour market policies and institutions with a focus on inclusion, equal opportunities
and the informal economy;
ISBN 978-92-2-123787-7 (print); 978-92-2-123788-4 (web pdf)
Mariangels Fortuny, Jalal Al Husseini
65
Les institutions du marché du travail face aux défis du développement:
Le cas du Mali;
ISBN 978-92-2- 223833-0 (print); 978-92-2-223834-7 (web pdf)
Modibo Traore, Youssouf Sissoko
66
Les institutions du marché du travail face aux défis du développement:
Le cas du Bénin;
ISBN 978-92-2-223913-9 (print); 978-92-2-223914-6 (web pdf)
Albert Honlonkou, Dominique Odjo Ogoudele
67
What role for labour market policies and institutions in development?Enhancing
security in developing countries and emerging economies;
ISBN 978-92-2-124033-4 (print); 978-92-2-124034-1 (web pdf)
Sandrine Cazes, Sher Verick
68
The role of openness and labour market institutions for employment dynamics during
economic crises;
Forthcoming
Elisa Gameroni, Erik von Uexkull, Sebastian Weber
69
Towards the right to work:
Innovations in Public Employment programmes (IPEP);
ISBN 978-92-2-124236-9 (print); 978-92-2-1244237-6 (web pdf)
Maikel Lieuw-Kie-Song, Kate Philip, Mito Tsukamoto, Marc van Imschoot
70
The impact of the economic and financial crisis on youth employment: Measures for
labour market recovery in the European Union, Canada and the United States;
ISBN 978-92-2-124378-6 (print); 978-92-2-124379-3 (web pdf)
Niall O’Higgins
59
71
El impacto de la crisis económica y financiera sobre el empleo juvenil en América
Latina: Medidas des mercado laboral para promover la recuperación del empleo
juvenil;
ISBN 978-92-2-324384-5 (print); 978-92-2-324385-2 (web pdf)
Federio Tong
72
On the income dimension of employment in developing countries;
ISBN: 978-92-2-124429-5 (print);978-92-2-124430-1 (web pdf)
Nomaan Majid
73
Employment diagnostic analysis: Malawi;
ISBN 978-92-2-123101-0 (print); 978-92-2-124102-7 (web pdf)
Per Ronnas
74
Global economic crisis, gender and employment:
The impact and policy response;
ISBN 978-92-2-14169-0 (print); 978-92-2-124170-6 (web pdf)
Naoko Otobe
2011
75
Mainstreaming environmental issues in sustainable enterprises: An exploration of
issues, experiences and options;
ISBN 978-92-2-124557-5 (print); 978-92-2-124558-2 (web pdf)
Maria Sabrina De Gobbi
76
The dynamics of employment, the labour market and the economy in Nepal
ISBN 978-92-2-123605-3 (print); 978-92-2-124606-0 (web pdf)
Shagun Khare , Anja Slany
77
Industrial policies and capabilities for catching-up:
Frameworks and paradigms
Irmgard Nuebler
78
Economic growth, employment and poverty reduction:
A comparative analysis of Chile and Mexico
ISBN 978-92-2-124783-8 (print); 978-92-2-124784-5 (web pdf)
Alicia Puyana
79
Macroeconomy for decent work in Latin America and the Caribbean
ISBN 978-92-2-024821-8 (print); 978-92-2-024822-5 (web pdf)
Ricardo Ffrench-Davis
A complete list of previous working papers can be found on:
htt:// www.ilo.org/employment
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Employment Sector
For more information visit our site:
http://www.ilo.org/employment
International Labour Office
Employment Sector
4, route des Morillons
CH-1211 Geneva 22
Email: [email protected]
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