Social accounting matrix for Ghana 2015.
Date: 2021-05-26
Institutions: Joint Research Centre
Subjects: Ghana, consumption, database, economic model, economic sector, household income, macroeconomics, national income, production, regional accounting, research report

Social Accounting Matrix for Ghana 2015 

Ferreira, V., Almazán-Gómez, M., Nechifor, V., Ferrari, E. 

2021 

 EUR 30720 EN

 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide 
evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European 
Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this 
publication. For information on the methodology and quality underlying the data used in this publication for which the source is neither Eurostat nor other 
Commission services, users should contact the referenced source. The designations employed and the presentation of material on the maps do not imply 
the expression of any opinion whatsoever on the part of the European Union concerning the legal status of any country, territory, city or area or of its 
authorities, or concerning the delimitation of its frontiers or boundaries. 

Contact information 
Name: Emanuele Ferrari 
Address: Calle Inca Garcilaso, Sevilla 
Email: Emanuele.ferrari@ec.europa.eu 
Tel.: +34 954488461 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC125199 

EUR 30720 EN 

PDF 

ISBN 978-92-76-38077-1 

ISSN 1831-9424 

doi:10.2760/432014 

Luxembourg: Publications Office of the European Union, 2021 

© European Union, 2021 

The reuse policy of the European Commission is implemented by the Commission Decision 2011/833/EU of 12 December 2011 on the reuse of Commission 
documents (OJ  L  330,  14.12.2011,  p. 39). Except otherwise noted, the reuse  of  this  document  is  authorised  under the Creative Commons  Attribution  4.0 
International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any 
changes are indicated. For any use or reproduction of photos or other material that is not owned by the EU, permission must be sought directly from the 
copyright holders. 

All content © European Union, 2021. 

How to cite: Ferreira, V., Almazán-Gómez, M.Á., Nechifor, V. and Ferrari, E., Social Accounting Matrix for Ghana 2015, EUR 30720 EN, Publications Office of the 
European Union, Luxembourg, 2021, ISBN 978-92-76-38077-1, doi:10.2760/432014, JRC125199. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Acknowledgements .......................................................................................................................................................................................................................................... 1 

Abstract ....................................................................................................................................................................................................................................................................... 2 

1 

Introduction..................................................................................................................................................................................................................................................... 3 

2  Social Accounting Matrices. Concept and general issues. .................................................................................................................................. 4 

2.1  Structure of a SAM ..................................................................................................................................................................................................................... 5 

2.2  Economic agents and accounts ..................................................................................................................................................................................... 7 

2.2.1 

Activities and Commodities ............................................................................................................................................................................... 7 

2.2.2 

Factors ................................................................................................................................................................................................................................... 7 

2.2.3 

Households ........................................................................................................................................................................................................................ 7 

2.2.4 

Enterprises ......................................................................................................................................................................................................................... 8 

2.2.5 

Government and Public Sector ....................................................................................................................................................................... 8 

2.2.6 

Saving-Investment ..................................................................................................................................................................................................... 8 

2.2.7 

Rest of the World ........................................................................................................................................................................................................ 8 

3  Ghana SAM 2015 ...................................................................................................................................................................................................................................... 9 

3.1  Home Production for Home Consumption (HPHC) approach ............................................................................................................. 9 

3.2  Structure and estimation of the 2015 Ghana SAM .................................................................................................................................... 9 

3.2.1 

Accounts and data sources ............................................................................................................................................................................ 10 

3.2.2 

SAM final adjustment and balancing ................................................................................................................................................... 13 

4  Ghana 2015 analysis with SAM data ................................................................................................................................................................................. 15 

4.1  Economy structure analysis ........................................................................................................................................................................................... 15 

4.2  Linear multiplier analysis ................................................................................................................................................................................................. 19 

4.2.1  Multipliers ....................................................................................................................................................................................................................... 19 

5  Conclusions .................................................................................................................................................................................................................................................. 22 

References ............................................................................................................................................................................................................................................................. 23 

List of abbreviations and definitions ........................................................................................................................................................................................... 24 

List of figures ..................................................................................................................................................................................................................................................... 25 

List of tables ........................................................................................................................................................................................................................................................ 26 

Annexes .................................................................................................................................................................................................................................................................... 27 

Annex 1. Accounts of the Ghana Social Accounting Matrix 2015 ........................................................................................................... 27 

Annex 2. On-line resources to download the Ghana Social Accounting Matrix 2015 .......................................................... 30 

Annex 3. The linear SAM model and multipliers ....................................................................................................................................................... 38 

 
 
 
Acknowledgements 

We are grateful to Karl Pauw from the International Food Policy Research Institute (IFPRI) for his updates on 
the Ghana SAM produced by the Ghana Statistical Services (GSS), Institute of Statistical, Social and Economic 
Research (ISSER) and IFPRI.  

A special thanks to our colleagues Arnaldo Caivano and Javier Castro to improve the diffusion of our work with 
the online application  

Authors 

V. Ferreira  

M. A. Almazán-Gómez 

Centro de Predicción Económica (CEPREDE). 

V. Nechifor 

E. Ferrari 

JRC - Seville 

JRC - Seville 

1 

 
 
 
 
 
 
 
Abstract 

A  Social  Accounting  Matrix  (SAM)  is  a  comprehensive  and  economy-wide  database  recording  data  on  all 
transactions between production activities, factors of production, institutions, and the rest of the world within a 
specific economy during a certain period. It has two principal objectives. First, it represents a complete snapshot 
of the economy showing the economic structure and the circular flow of income and expenditure in the country 
or region under analysis. Second, in order to analyse how the economy works and to predict the effects of policy 
interventions, it  is  used as  a database in  multisectoral  linear models  by  calculating  multipliers, and for  the 
calibration and exploitation of Computable General Equilibrium (CGE) models. This report presents Ghana's SAM 
for 2015, with the main purpose of providing a suitable database for implementing and evaluating the country's 
own  developmental,  social  and  economic  policies  and  initiatives.  To  this  end,  the  structure  of  the  SAM  is 
presented in detail, explaining the meaning of each account and indicating some estimations and modifications 
made. Considering the characteristics of the Ghanaian economy, this SAM shows a special structure to reflect 
the Home Production for Home Consumption (HPHC) issue and a high disaggregation of the agricultural and 
food sector. Furthermore, considering the SAM as a database, a descriptive analysis of the Ghanaian economy 
and the linear multipliers analysis are presented. Annex 2 explains how to download the matrix available online. 

2 

 
 
1 

Introduction 

The European Commission is committed to cooperate with developing countries to provide solutions to problems 
related to inequality, nutrition and food security. This is carried out by evaluating related policies, facilitating 
the access of researchers in these countries to analytical tools that allow them to carry out such assessments 
on their own. In this sense, the Joint Research Centre (JRC), the European Commission's in-house science service, 
is committed to provide support for: i) improvement of information systems on agriculture, nutrition and food 
security, ii) policy and economic analysis to support policy decision-making process and iii) scientific advice on 
selected topics concerning sustainable agriculture and food and nutrition security.  

In particular, the Economic of Agriculture Unit of the JRC Directorate D, Sustainable Resources, is responsible to 
elaborate the methodology and tools to provide economy-wide analysis related to sustainability of policies in 
the sectors of agri-food, sustainable resources and fight against food and nutrition insecurity. The analyses and 
tools proposed should support the EU institutions, and the partner countries for the elaboration and assessment 
of  policies  and  demand-driven  technical  and  scientific  advice.  Among  possible  scientific  tools,  economic 
simulation models represent interrelationships between selected economic variables and provide a simplified 
representation of economic reality to be used to quantify impacts of policy changes (i.e., ex-ante policy analysis). 

The  construction  of  databases  to  provide  evidence-base  policy  support  to  partner  countries  is  part  of  the 
objectives of the Pan-Africa Network for  Economic Analysis of Policies (PANAP). PANAP is a network among 
academic/research and institutional partners collaborating with the JRC in developing research on agricultural 
economics  and  policy  issues  with  a  focus  on  Africa.  PANAP  is  part  of  the  Action  Agenda  of  the  Political 
Declaration of the 3rd AU-EU Agriculture Ministerial Conference held in Rome on 21 June 2019 (EC Decision 
C(2019) 4277). 

At  the  launch  and  first  meeting  of  PANAP  (6-8  November  2019,  Addis  Ababa,  Ethiopia)  key  stakeholders, 
researchers,  data  analysists,  policy  makers,  from  national  and  multilateral  institutions  engaged  in  fruitful 
discussion on the role of science in supporting policy decision making in Africa with focus on the farming and 
food sectors (Morokong & Ferrari, 2020). The general objective of PANAP is to strengthen the liaison between 
researchers/scientists and policy makers in Africa, including relevant multi-lateral African institutions, and to 
stimulate their cooperation on selected topics linked to policy priorities in Africa. PANAP will also contribute in 
understanding and resolving scientific issues in the fields of agriculture and food security to support efficient 
policies. It will also facilitate achieving sustainability of the agri-food sectors to enhance food and  nutrition 
security in alignment with the Malabo Declaration Commitment 3 on ending hunger in Africa by 2025 and SDG 
1 and 2.  

Ghana is one of the countries analysed in this context, and the main pillar in the macroeconomic analysis is the 
use of a tailored version of a single-country Computable General Equilibrium (CGE) model to analyse some of 
the agricultural and rural policy priorities to reduce inequalities, considering the specificities of the economy in 
the country. In the case of Ghana, there are remarkable differences in employment and welfare between the 
southern and northern regions. Furthermore, there is particular interest in the agricultural, fishing, and forestry 
sectors.    Therefore,  the  promotion  of  policies  focus  on  the  development  of  the  norther  region  requires  the 
elaboration  of  specific  database  that  provide  a  description  of  the  regional  economies  with  the  interlinkage 
between them and the disaggregation of the main sectors.  

The calibration of CGE model requires a complex database system, called Social Accounting Matrix (SAM). The 
estimation of a new SAM for Ghana is an important achievement itself, because it provides detailed information 
about the economic structure of the country and serves, also, as main database for linear multisectoral models. 
The latest Ghana SAM for 2015 has been estimated jointly by the Ghana Statistical Services (GSS), the Institute 
of Statistical, Social and Economic Research (ISSER) and the International Food Policy Research Institute (IFPRI). 
This  report  presents  the  modifications  and  extensions  to  the  SAM  to  obtain  a  database  for  the  analysis  of 
different policies at stake. 

The rest of the report is structured as follows: the concept, general issues and the accounts description of Social 
Accounting Matrices are presented in Section 2, while section 3 describes the structure and estimation of the 
Ghana SAM 2015. That section begins with a kind of introduction of the Home Production for Home Consumption 
issue,  a  key  aspect  of  this  new  SAM.  Later,  description  of  structure  and  estimations  of  the  Ghana  SAM  is 
presented,  detailing  the  accounts  and  data  basis  used  and  the  final  adjustments,  SAM  balancing  and 
disaggregation procedures. The Section 4 shows a description of the Ghana Economy structure using SAM data 
and a simple multiplier analysis is presented to illustrate the usefulness of the SAM linear models in policy 
impact assessment. Finally, section 5 includes some conclusions and Annex shows some additional tables and 
presents the downloadable application.  

3 

 
2  Social Accounting Matrices. Concept and general issues. 

A  Social  Accounting  Matrix  (SAM)  is  a  comprehensive  and  economy-wide  database  that  enables  the 
representation of economic and social information on all the transactions carried out among the agents of a 
specific economy over a period of time, generally one year. A SAM includes the relationships between economic 
agents in terms of production, trade, income generation, consumption, saving, and investment. As such, a SAM 
is a very useful tools to understand the structure of an economy and as a standard database for economic 
modelling. In this sense, SAM can be used as a database for multisectoral linear models allowing the calculation 
of multipliers or it can also be used as a database for the application of more complex Computable General 
Equilibrium (CGE) models (Mainar-Causapé et al., 2018a). 

The origins of SAMs are found in the pioneering works of Stone (1947) among others, and subsequent advances 
in their use as  a model for  economic analysis  (Defourny  &  Thorbecke,  1984; Pyatt & Round, 1985).  A SAM 
extends the information provided by the Input-Output table by incorporating all economic transactions, using a 
more disaggregated accounting structure. This allows closing the circular flow of income and expenditure among 
production, consumption, employment, taxes and savings, and trade. For this reason, the elaboration of a SAM 
requires the use of many statistical sources, mainly the statistical systems of National Accounts, data on trade, 
as well as socio-economic data related to household income, employment and expenditure that can be obtained 
from  household  budget  surveys  and  labour  force  surveys.  With  this  information,  households  can  be 
disaggregated using socio-economic characteristics (e.g. income, education, rural-urban division, etc.) to analyse 
income distribution. The need for further information depends on the disaggregation required for each account 
depending on the type of analysis to be carried out.   

The underlying basis of a SAM is the concept of a circular flow of income represented (1) in Figure 1.  

Figure 1. The circular flow (simple version) 

Source: Mainar-Causapé et al., 2018b. 

The  circular  flow  of  income  of  a  SAM  reflects  the  interlinkages  among  production,  trade,  demand,  income 
generation and its redistribution between institutional sectors (Pyatt & Round, 1985), providing the opportunity 
to analyse the distribution of wealth and income. (2) 

On the one hand, a SAM shows a descriptive detail of the economy under analysis. On the other hand, it is a 
suitable  database  for  economic  modelling  that  considers  the  analysis  of  socio-economic  issues  such  as 
employment, poverty, income growth and distribution, trade, etc, allowing to evaluate the impact of different 
policies.  

(1) Represents the basics of the circular flow. The circular flow is more complicated, existing multiple transactions between institutions 

(savings, direct taxes, transfers, etc.) other flows as taxes on commodities or activities (Mainar-Causapé et al., 2018b). 

(2) Some problems of I-O frameworks are still present in the SAM framework (i.e. the use of coefficients and fixed prices for inputs and 

finished products and no substitution elasticities) (Mainar-Causapé et al., 2018a). 

4 

 
 
 
The SAM is a database represented by a square matrix, which shows all the transactions that take place between 
agents in an economy, determining how income is generated and how it is expended. For this purpose, each 
account is represented by a row and a column, which shows the origin of the income of the different accounts 
(by  the  row)  and  the  expenses  or  payments  made  (by  column),  both  in  monetary  values.  Thus,  each  cell 
(i, j) shows the transaction between account i and j, in which account i receives income from j.  

2.1  Structure of a SAM 

Typically, a Social Accounting Matrix has six basic groups of accounts: 

●  Activities or Commodities (or both, separated) 

●  Factors of production 

●  Private Institutions (Households and Corporations/Enterprises) 

●  Public Institution (Government) 

● 

(Combined) Capital accounts 

●  Accounts for the Rest of the World. 

Figure 2 shows the basic structure of a standard SAM (3); however the structure of the matrix will be determined 
by the accounts included in these six basic groups and the disaggregation of these accounts. The structure of a 
SAM, the  disaggregation of its accounts  and the  geographical scope  it represents (national, regional, multi-
regional, etc.) may be flexible according to the analysis to be carried out. 

(3) The general characteristic of this structure, as well as specific issues of its definition and composition can be found in Miller & Blair 

(2009), Eurostat (2008) or Mainar-Causapé et al. (2018b). 

5 

 
 
Figure 2. A Social Accounting Matrix (SAM) standard structure 

Commodities 

Margins 

Activities 

Factors 

Households 

Enterprises / 
Corporations 

Government 

Savings-
Investment 

Rest of the 
World 

Total 

Commodities  

Transaction 
costs (trade / 
transport) 

Intermediate 
(inputs) 
consumption 

Household 
consumption 

Government 
expenditure 

Investment and 
stock changes 

Exports 

Demand 

Margins  

Transaction costs 
(trade / 
transport) 

Activities  

Domestic 
production 

Factors  

Households  

Enterprises / 
Corporations  

Remuneration of 
factors / Factor 
income 

Factor income 
distribution to 
households 

(Inter 
Households 
transfers) 

Distribution of 
enterprise income 
to households 

Government 
transfers to 
households 

Factor income 
distribution to 
enterprises 

Government 
transfers to 
enterprises 

Government  

Net taxes on 
products 

Net taxes on 
production 

Factor income to 
Government / 
Factor taxes 

Direct Household 
taxes / Transfers 
to Government 

Direct Enterprise 
taxes / Transfers 
to Government 

Savings-Investment  

Rest of the World  

Imports 

(Depreciation) 

Household 
savings 

Enterprise savings 

Government 
savings 

(Capital 
accounts 
transfers) 

Factor income 
distribution to 
RoW 

Household 
transfers to RoW 

Enterprise income 
to Row 

Government 
transfers to RoW 

Margins 

Gross output / 
Production 
(activity 
income) 

Factor income 
from RoW 

Factor income 

Transfers to 
Households 
from RoW 

Transfers to 
Enterprises 
from RoW 

Transfers to 
Government 
from RoW 

Capital 
transfers from 
RoW (Balance 
of Payments) 

Household 
income 

Enterprise 
income 

Government 
income 

Savings 

Payments to 
RoW 

Total 

Supply 

Margins 

Costs of 
production 
activities 

Expenditure on 
factors 

Household 
expenditure 

Enterprise 
expenditure 

Government 
expenditure 

Investment 

Incomes from 
RoW 

Source: Aragie et al. (2017), Kiringai et al. (2007), Mainar-Causapé et al. (2018b) and Round (2003).

6 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.2  Economic agents and accounts 

2.2.1  Activities and Commodities  

The  standard  SAM  structure  (Figure  2)  distinguishes  between  Activities  (that  carry  out  the  production  of 
commodities) and Commodities (market goods and non-factor services). SAM flows are valued at production 
costs in the accounts of Activities and at market prices in the Commodities accounts (including indirect taxes 
on  raw  materials  and  transactions  cost  or  margins).  The  domestic  production  is  the  sum  of  the  values  of 
Activities  (at  production  prices).  To  obtain  the  supply  of  Commodities  (at  purchaser’s  prices),  net  taxes  on 
products,  margins  and  imports  must  be  added.  The  supply  of  Commodities  is  sold  domestically  (activities, 
households, government) or exported. 

By analysing the cells by columns, Activity accounts represent the intermediate consumption of commodities 
as inputs and the use of factors of production (such as labour, capital, land, etc.). The sum of the remuneration 
of factors plus taxes less subsidies on production is the value added by Activities. By rows, Activities cells show 
the value of commodities produced by each activity (in basic prices). 

In the Commodity columns, payments are made to Activities for the domestic production, the rest of the world 
(imports) and to the  government (payment of taxes - including  VAT (4)- or  receipt of subsidies on products, 
domestic and imported) (Mainar-Causapé et al., 2018a).  The column total of each Commodity is the supply 
value  at  domestic  purchaser  prices.  Next,  it  is  possible  to  read  across  the  Commodities  rows  to  determine 
commodities demand. Each Commodity row is composed of intermediate consumption (by Activities) and the 
final demand consumption by institutional sectors (Household and Government), investment and exports. 

In a SAM, trade flows (moving goods between producers, markets, and national borders, either for domestic, 
import or export) are associated with the transaction cost (trade and transport) which are part of the costs of 
supplying commodities. Therefore, for each product the SAM records costs associated with cost of marketing 
and transportation (margins) (Mainar-Causapé et al., 2018b).  

2.2.2 

Factors 

The factors of production relate to the primary factors used in the production process. Traditionally, they include 
capital and labour, as well as other types of factors, such as land or other natural resources. The production 
factors can be disaggregated according to the objective of the analysis. For example, labour account can be 
disaggregated  by  occupation  groups  or  educational  attainment  and  capital  factor  can  be  disaggregated  in 
accordance with their use i.e. agricultural/non-agricultural capital. 

The production factors collects, by row, the income received (wages, rent, etc.) by the activities and the rest of 
the world. These incomes are distributed to the owners of the factors of production (Household, Enterprises and 
Government) and the Rest of the World (shown by columns).  

2.2.3  Households 

The classification and disaggregation of the Household accounts in a SAM traditionally adopts the concept of 
Representative Household Group (RHG) which means that different household groups are represented by an 
aggregated account, assuming that all individual household in an RHG are, on average, affected in the same 
manner  by  a  policy  shock.  This  type  of  information  is  crucial  for  the  analysis  of  socio-economic  problems 
(unemployment, income distribution etc.) 

The  rows  for  Households  represent  the  incomes  received,  which  includes  income  from  factors  supplied  (as 
owners of labour, capital, land, or natural resources), transfers from Enterprises (distributed profits and direct 
transfers), from the Government (direct transfers), from the Rest of the World (usually for labour services and 
remittances) and from other households (Mainar-Causapé et al., 2018b). 

Household  expenditure  is  represented  by  the  household  column,  showing  the  use  of  household  income  for 
consumption  of  commodities,  payment  of  direct  taxes  to  the  government,  transfers  to  other  households 
(domestic  and  foreign);  the  difference  between  income  and  expenditure  represents  households’  savings  (or 
financing need if negative).  

(4)   Valued Added Tax (VAT) is tax on (final demand) commodities and NOT on tax on value added (Mainar-Causapé et al., 2018a). 

7 

 
2.2.4  Enterprises 

Enterprises receive incomes related to the asset ownership (capital, land, or natural resources) and income by 
transfers  form  other  institutions.  Those  revenues  are  then  transferred  to  other  institutions,  for  example 
households in the form of dividends, and are also used to pay direct taxes (corporation tax) or converted into 
savings. Enterprises represent the institutional part of the productive sector, so generally they do not consume 
inputs such as activities.  

2.2.5  Government and Public Sector 

The Government participates as a “productive activity” through the public sector and the marketed goods and 
services  resulting  from  their  activities  are  recorded  in  the  respective  Activity  and  Commodities  accounts 
(Mainar-Causapé et al., 2018a). Generally, the Public Administration institutional sector is represented by the 
Government account for the sector itself, while tax collection is captured through other accounts specific for 
each tax category (VAT, income, tariffs, etc.).  

By row, Government income comes from taxes, transfers received (domestic and foreign) and the remuneration 
of  factors  of  production  (including  any  government-owned  assets).  By  column,  Government  expenditure  is 
broken down into the consumption demand of goods and services, transfers to other institutions in form of 
subsidies  or  benefits  (household  and  enterprises)  or  to  other  countries  (e.g.  debt  services  payments). 
Government  savings  (government  surplus)  or  government  deficit  (if  negative),  which  enters  the  saving-
investment cell, is calculated as the difference between income and expenditure.  

2.2.6 

 Saving-Investment 

The row of this account records the savings generated by all domestic institutions (households, enterprises, and 
government) as well as the balance of foreign trade on capital account with the rest of the world. The column 
shows capital investment, the Gross Fixed Capital Formation (GFCF) and changes in stocks.  

2.2.7  Rest of the World  

The foreign sector account shows the economic interaction within the country (or region) analysed and the rest 
of the world. The foreign sector represents two trade accounts (imports and exports) and can be disaggregated 
depending on the specific interest of the analysis (e.g., ECOWAS, African Union and Rest of the world). 

The income received by the rest of the world account include imports of goods and services (commodities), 
transfers to abroad from institutions (households, businesses and government), and the remuneration of factors 
of production abroad. The Rest of the World expenditures are divided into the purchase of goods and services 
(exports),  the  payments  to  domestic  factors  of  production  used  abroad  and  transfers  recorded  from  other 
economies (factor payments, foreign loans and aid, remittances, etc.). The balance, which enters the saving-
investment cell, reflects the surplus or deficit with the Rest of the World and consequently the capital inflow (in 
case of negative trade  balance) or  outflow (positive trade  balance) between the Rest  of the  World and  the 
country at stake. 

8 

 
3  Ghana SAM 2015 

3.1  Home Production for Home Consumption (HPHC) approach 

In developing countries, the dual role of households as producers and consumers is often a typical characteristic 
of economies with a large share of subsistence agriculture; therefore, there is a need to define and estimate 
the home production for home consumption (HPHC) within a SAM. 

The classic Representative Household Group (RHG) includes the household behaviour as consumer of goods and 
services and as providers of factors of production (and receptor-contributors of transfers). The Home Production 
for Home Consumption (HPHC) concept is introduced in the SAM by assuming that households act as units of 
production of commodities consuming part or all of their output. Thus, it is necessary to expand the structure 
of the SAM including extra sets of column and rows as sub-columns and sub-rows of the commodity and activity 
accounts.  

In  the  case  of  the  Commodities  accounts,  this  requires  additional  rows  and  columns  to  separate  the 
commodities produced by these households for own consumption (HPHC as input or final product) and other 
marketed commodities (produced both by conventional productive activities and by households). The rows of 
these commodity accounts show the HPHCs use as intermediate inputs in the productive activities of households 
and their consumption in final demand of household (RHG). The column summarizes the contribution of the 
activities of households to each of these goods. HPHC commodities can only be produced by the RHGs that 
consume those commodities, so each RHG must be a household and an activity. The columns of the household’s 
activities show how they use inputs (HPHC and marketed), and the rows show the destination of their production 
as inputs (own-consumption goods or marketed commodities). All the tax and trade and transport costs incurred 
should be allocated solely to the marketed component of the commodity (Mainar-Causapé et al., 2018a). 

The inclusion of HPHC in a SAM requires more data from surveys and censuses and some additional estimations. 
Data  for  HPHC  requires  segmenting  consumption  demand  between  marketed  and  HPHC  commodities  and 
identifying the costs structures used in production. Typically, this will require reconciling data from different 
sources, especially household income and expenditure, labour force and agricultural (production) surveys.  

3.2  Structure and estimation of the 2015 Ghana SAM  

For this study, a new SAM for Ghana (base year 2015) was estimated based on the standard structure outlined 
above with some peculiarities. The new SAM includes specific accounts for the treatment of HPHC and includes 
a  high  regionalisation  based  on  social  characteristics  differentiated  across  administrative  regions.  
Consequently, this new framework would allow addressing specific issues related for example to production 
and productive factors for each region, in order to reduce inequality within and between regions. 

The Ghana SAM is based on the standard structure that distinguishes between activities and commodities. The 
structure and a short version of the SAM is summarised in Table 1.  

The regional breakdown in the SAM for Ghana 2015 correspond to 10 regions (reflective of the sub-national 
boundaries prior to the 2018 referendum). Each regional household is disaggregated into rural and urban and 
further separated into per capita expenditure quintiles. Finally, 100 different Representative Household Group 
(RHG) are detailed in the SAM – for each region, households are separated into the rural and urban groups and 
across income quintiles. Furthermore, each region has a specific agricultural activity producing 14 “subsistence 
commodities”  destined  for  home  consumption,  and  20  marketed  commodities.  The  remaining  35  marketed 
commodities are produced specific activities aggregated at a national level. The Ghana SAM further contains 
74 accounts of production factors. There are 3 types of labour, 1 for land factor and 4 types of capital. Labour 
and land factors are disaggregated across the 10 regions, with labour factors are also separated between rural 
and urban.  

In summary, the 2015 Ghana SAM contains 299 accounts; 45 activities (10 household agricultural activities 
and  35  activities),  69  commodities  (14  home  commodities  and  55  marketed  commodities),  74  factors  of 
production  (60  labour  accounts,  10  land  factors  and  4  capital  factors),  100  households  (disaggregated  by 
rural/urban,  region  and  quantiles)  and  4  taxes  accounts.  Finally,  there  is  a  single  account  for  margins,  one 
corresponding to enterprises, one for the government, one for the rest of the world, one for investing and saving 
and other for change in stocks. In Annex 1, Table A1 shows all accounts considered in the Ghana SAM 2015. 
In Annex 2 can be found the on-line resources to download the complete 2015 Ghana SAM.  

9 

This base SAM database for Ghana was also disaggregated for a specific analysis on the cocoa sector where 
cocoa output is detailed across the 10 administrative regions separate from the other agricultural production. 
This version of the Ghana SAM is available upon request. 

3.2.1  Accounts and data sources 

3.2.1.1  Activities and Commodities 

For the initial values of rows and columns for commodities and activities the Ghana SAM 2015 developed by 
the GSS, ISSER and IFPRI was considered (GSS, 2017).  

The HPHC approach mentioned before is introduced in the SAM by assuming that each household also acts as 
a  producing  unit.  Therefore,  estimates  are  required  for  the  additional  commodity  and  activity  accounts. 
Considering this approach, household can be divided between the classic Representative Household Group (RHG) 
as consumer of good and services, providers of factors of production (and receptor-contributors of transfers) 
and the household presented as units of production of commodities. The latter accounts represent the economic 
behaviour of households as producers of food commodities (agricultural and livestock products) and have been 
broken down regionally. In view of this approach, it is also necessary to separate accounts for commodities 
produced  by  these  households  for  own  consumption  and  other  marketed  commodities  (produced  both  by 
households and by conventional productive activities).  

The  SAM  for  Ghana  is  characterized  by  a  high  disaggregation  of  agricultural  and  food  activities  and 
commodities.  There  is  one  agricultural  households’  activity  for  each  region  that  produce  14  “subsistence 
commodities”  and  20  marketed  commodities  and  there  are  35  Activities  that  produce  35  marketed 
commodities.  

In summary, the 2015 Ghana SAM includes 45 activities (10 of them are households as producers), producing 
14 HPHC commodities and 55 marketed commodities. Table 2 summarised the breakdown of commodities and 
activities, and they are also show in detail in Appendix I Table A1 (Accounts of the Ghana Social Accounting 
Matrix 2015). 

3.2.1.2  Households 

In the case of the Ghana SAM 2015, the breakdown distinguishes between rural and urban households using 
the country official definition of these areas and disaggregated each one by regions. Moreover, households are 
further disaggregated into per capita expenditure quintiles using information from the 2016/17 GLSS7 (Ghana 
Living Standards Survey Round 7) (GSS, 2019). Quintiles are defined at the national level, meaning that the 
combined population of each quintile (rural and urban quintiles) is a fifth of the national population (GSS, 2017). 

As a result, the Ghana SAM 2015 contains 100 representative household groups (RHG). Such classification will 
allow for a good analysis of redistributive aspects and the specific impact of different policies. The description 
is summarized in Table 3. 

3.2.1.3  Factors of production 

The disaggregation of production factors is very important depending on the objective of the analysis. In the 
Ghana SAM 2015, factors are separated into three broad categories: labour, land and capital (see table 2). The 
SAM developed in this report classified the labour factors into three types of labour: skilled, semi- skilled and 
unskilled labour. Each labour factor is also regionalized given the 10 regions and further separated into rural 
and urban. This information is disaggregated using the data derived from the 2016/17 GLSS7 (GSS, 2019) and 
the Labour Force Survey 2015 (GSS, 2016). Also, four types of capital are considered: crops, livestock, mining 
and other, and one account for land factor (agricultural crops) for each region. 

3.2.1.4  Other accounts 

For the Public sector, the Ghana SAM disaggregated into the government account for the sector itself and other 
accounts representing different types of taxes and subsidies: direct, sales, import and exports, show on Annex 
1, Table A1. 

Direct taxes on enterprise and households (dirtax), includes corporate and other direct taxes paied by enterprises 
to the government and direct income or personal taxes paid by households (taxes on income, profits and capital 
gains that are “payable by individuals”) taken from the Government Financial Statistics (GFS). Lastly, net taxes 

10 

on products includes all indirect taxes imposed on goods and services: sales (saltax), export (exptax) and import 
tariffs (imptax) (GSS, 2017).  

Transaction  cost  includes  trade  and  transport  cost  from  moving  goods  between  producers,  markets  and/or 
national borders, either for domestic, import or export trade.  

Saving and investment account includes by row the domestic private saving by enterprises and households and 
the fiscal surplus (or deficit) for the government. By columns, includes the combination of gross fixed capital 
formation (investment on commodities as machinery, vehicles, and equipment) and change in stocks (dstk).  

Table 1. Ghana SAM 2015 with aggregated values. Millions of Ghanaian Cedis. 

Source: Own elaboration 

11 

 
 
 
Table 2. Activities and commodities disaggregated in Ghana SAM 2015 

Activities 

Forestry 
Crude Oil 
Other mining 
Meat, fish and dairy 
Fruit and vegetable processing 
Fats and oils 
Grain milling 
Sugar refining 
Other foods 
Beverages 
Tobacco (processed) 
Textiles 
Clothing 
Leather and footwear 
Wood and paper 
Petroleum 
Chemicals 
Non-metal minerals 
Metals and metal  
Machinery and equipment 
Other manufacturing 
Electricity, gas and steam 
Water supply and sewage 
Construction 
Wholesale and retail trade 
Transportation and storage 
Accommodation and food services 
Information and communication 
Finance and insurance 
Real estate activities 
Business services 
Public administration 
Education 
Health and social work 
Other services 

HPHC 
commodities 

Marketed commodities 

Representative 
Households Groups 
as activities  

Western 
Central 
Greater Accra 
Volta 
Eastern 
Ashanti 
Brong Ahafo 
Northern 
Upper East 
Upper West 
Upper West 

Maize 

Maize 
Sorghum and millet  Sorghum and millet 
Rice 
Pulses 
Groundnuts 
Other oilseeds 
Cassava 
Other roots 
Vegetables 
Fruits and nuts 
Cattle 
Poultry 
Other livestock 
Fishing 

Rice 
Pulses 
Groundnuts 
Other oilseeds 
Cassava 
Other roots 
Vegetables 
Sugar cane 
Tobacco 
Cotton and fibers 
Fruits and nuts 
Cocoa 
Coffee and tea 
Other crops 
Cattle 
Poultry 
Other livestock 
Forestry 
Fishing 
Crude oil 
Other mining 
Meat, fish and dairy 
Fruit and vegetable processing 
Fats and oils 
Grain milling 
Sugar refining 
Other foods 
Beverages 
Tobacco processing 
Textiles 
Clothing 
Leather and footwear 
Wood and paper 
Petroleum 
Chemicals 
Non-metal minerals 
Metals and metal products 
Machinery and equipment 
Other manufacturing 
Electricity, gas and steam 
Water supply and sewage 
Construction 
Wholesale and retail trade 
Transportation and storage 
Accommodation and food 
services 
Information and 
communication 
Finance and insurance 
Real estate activities 
Business services 
Public administration 
Education 
Health and social work 
Other services 

Source: Own elaboration based on GSS (2017).  

12 

 
 
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
Table 3. Description of factors and households in Ghana SAM 2015 

Notes 

Description 

Factors 
Unskilled labour 
Semi-skilled labour 

0-6 years of formal schooling 
Primary and secondary (7-12+ years of formal schooling and/or incomplete tertiary 
education) 
Complete tertiary education (e.g., degree, certificate, diploma). 

Skilled labour 
Land-agricultural crops  Harvested crop land 
Capital – crops 
Capital - livestock 
Capital - mining 
Capital - other 

For agricultural crops (e.g., tractors, irrigation infrastructures) 
For livestock (e.g., live animals, paddocks, beehives). 
For mining (e.g., mineral resources, mining equipment) 
Capital - other 

Households 
Rural -quintiles 1 to 5 

Rural households with or without crop and /or livestock incomes separated into 
national per capita expenditure quintiles.  

Urban -quintiles 1 to 5  Urban households with or without crop and /or livestock incomes separated into 

national per capita expenditure quintiles.  

Source: Own elaboration based on GSS (2017). 

3.2.2  SAM final adjustment and balancing 

For  the  construction  of  the  SAM  presented  in  this  report,  the  2015  Ghana  SAM,  developed  as  part  of  a 
collaboration between the Ghana Statistical Services (GSS), the Institute for Statistical, Social and Economic 
Research  (ISSER)  and  the  International  Food  Policy  Research  Institute  (IFPRI),  was  used.  Furthermore,  two 
sources  were  used  to  carry  out  the  aforementioned  disaggregation:  the  2016/2017  Ghana  Living  Standard 
Survey 7 (GSS, 2019) and the 2015 Labour Force Survey (GSS, 2016). 

The 7th Ghana Living Standards Survey (GLSS) it is a household-based survey focusing on key socio-economic 
characteristics and well-being of households in Ghana. The data was collected between 22nd October 2016 and 
17th October 2017 and provides information for understanding and monitoring living conditions in Ghana (GSS, 
2019).  The  survey  has  a  sample  size  of  15,000  households  with  a  response  rate  of  93.3%,  and  the  data 
collected  includes  detailed  information  on  demographic  characteristics,  education,  health,  employment, 
migration and tourism, housing, household agriculture, expenditure and income, financial services, among others 
(GSS, 2019). The Household  Questionnaire consisted of four modules, namely A, B, C and D. Data from the 
modules  (A,  B  and  C)  was  used  to  estimate  the  SAM  in  this  report.  The  Module  A  includes  7  sections  with 
information  regarding:  demographic  characteristics  of  respondents;  education  and  skills  training;  health; 
employment; migration, tourism and ICT and housing. The Module B covers household agriculture; household 
expenditure; income transfers; migration and remittances; credit, assets, savings and use of financial services. 
Lastly,  the  Module  C  comprises  Section  10  that  covers  non-farm  household  enterprises  focusing  on: 
employment, wage earnings, quantity and cost of materials purchased, value of production and sale of products, 
revenue, expenditure, assets of enterprises and consumption, payments and savings (GSS, 2019).  

The 2015 Labour Force Survey collects basic statistics on the labour force situation in Ghana, including for 
example indicators on employment and unemployment. It was performed from November 2015 to December 
2015 in each region of Ghana and covered 6,030 households (GSS, 2016).  

Using the microdata provided by the aforementioned surveys, it is possible to obtain the necessary information 
for the disaggregation of Household account in the sub-accounts of the structure of this SAM. 

Regarding the regions, it is necessary to distinguish between the southern and northern regions of Ghana as 
there  are  disparities  in  employment  and  welfare.  As  the  government  is  interested  in  promoting  policies  to 
improve equality among the regions, the 2015 national SAM for Ghana is disaggregated into 10 regions that 
are distinguished into northern and southern regions. The regional breakdown in the 2015 Ghana SAM includes 
in the northern region the Northern, Upper East and Upper West. The southern region is defined by the Brong-
Ahafo, Volta, Ashanti, Western, Central, Eastern and Greater Accra regions (GSS, 2017) (table 4). The regional 
disaggregation has been applied to both households, as productive units (activities), and as institutional units, 
and for labour and land factors. 

13 

 
 
Table 4. Regional description in Ghana SAM 2015 

Northern 

Southern 

Upper West 
Upper East 
Northern 

Central 
Western 
Greater Accra 
Volta 
Eastern 
Ashanti 
Brong Ahafo 
Source: Own elaboration based on GSS (2017). 

The  main  problem  in  estimating  a  disaggregated  SAM  is  to  find  a  way  to  incorporated  and  reconcile  the 
information from many sources. In order to address this issue and obtain an estimated and balanced matrix, 
the RAS and Cross Entropy  methods were used. The RAS procedure, also knows as a “biproportional” matrix 
balancing technique, is used to reconcile the data in a matrix with certain previously defined row and column 
values.  This  methodology  is  based  on  a  process  of  iterations  up  to  finding  the  balance  between  rows  and 
columns (Bacharach, 1970). The cross-entropy approach represents an extension of the standard RAS method 
and provides a flexible and powerful method for estimating a social accounting matrix (SAM) when dealing with 
scattered and inconsistent data (Robinson, et al., 1998). These methods are useful when there is not enough 
information but macroeconomic targets or targets for specific accounts and cells or sub-matrices are available 
(McDougall, 1999; Robinson et al., 2001).  

14 

 
 
 
 
 
 
4  Ghana 2015 analysis with SAM data 

4.1  Economy structure analysis 

A SAM facilitates the description of the economic reality of a country. The data analysis of the Ghana SAM for 
the year 2015 provided a snapshot of the characteristics of the Ghana economy. Starting with the supply of 
commodities (351,765 Millions of Ghanaian Cedis), 1.3 % belongs to the HPHC commodities.  

The macroeconomic analysis shows that domestic absorption represents 113.3% of the GDP and the foreign 
sector dependency shows that exports represent 39.6% and imports 53% of the GDP (figure 3). Regarding the 
composition of domestic absorption, private household consumption represents 59.3%, followed by investment 
representing 25.8% and government expenditure 14.9% (Figure 4). 

Figure 3. Domestic absorption, imports, and exports as % of the GDP. Ghana 2015. 

113.3%

39.6%

53.0%

Exports

Imports

Domestic absortion

Source: Own elaboration with Ghana SAM 2015. 

Figure 4. Domestic absorption composition. Ghana 2015.  

25.8%

14.9%

59.3%

Private consumption

Government consumption

Investment

Source: Own elaboration with Ghana SAM 2015. 

By analysing the pattern of household consumption, the primary sector and food industry stand out with 40.9% 
of expenditure  and the  second  most important one are  services  with 35.4%  of  the household consumption 
(Figure 5). Within agriculture, vegetables, fruits and nuts, other roots, and cassava and within the food industry 
sector grain milling, meat, fish and dairy, and beverages are the most demanded products by households. Of 
the expenditure on services, 28.3% is spent on accommodation and food services, 20.8% on other services and 
13.6% on information and communication. Regarding the expenditure in the manufacturing sector, it represents 
14.1% destined mainly for textiles (3.9%) and machinery and equipment (3.4%). 

15 

 
 
 
 
Figure 5. Household consumption pattern. Ghana 2015.  

Services; 35.4%

Primary sector 
and food 
industry; 40.9%

Manufactured ; 
14.1%

Construction; 
0.4%

Utilities; 7.7%

Petroleum and 
mining; 1.5%

Source: Own elaboration with Ghana SAM 2015. 

In the case of exports, the petroleum and mining activities represent the 48.1% (40.4% other mining) and the 
primary and food sector 14% (of which 8% refers to cocoa exports). For imports, the manufacturing sectors 
stand out with 56.4%, due to imports of machinery and equipment (27.3%) (figure 6). 

Exports

Services; 
31.1%

Figure 6. Exports and imports composition. Ghana 2015.  

Primary 
sector and 
food …

Imports

Services; 
30.3%

Primary sector and 
food industry; 10.5%

Petroleum 
and 
mining; 
2.7%

Manufactured ; 
6.8%

Petroleum 
and mining; 
48.1%

Manufactured ; 
56.4%

Source: Own elaboration with Ghana SAM 2015. 

Focusing on productive factors, labour, land, and capital represent 35.5 %, 6.9 % and 57.9 % of the value added, 
respectively (Table 5). Analysing the distribution of value added by activities (Table 5 and Figure 7), services 
stand out representing 49.6%, followed by primary sector with 19.9%. In the case of services, labour factor is 
particularly important with 64.9% (is  highly concentrated  the use  of Semi-skilled labour) and  46.1% of the 
value added is related to other capital factors. The land factor is exclusive to the primary sector and, to a lesser 
extent, labour factor in primary sector represents 20% (with 11.6% unskilled labour). In the case of capital, in 
addition to services, the construction sector stands out (25.8%). 

16 

 
  
 
 
 
Table 5. Distribution of factors by aggregate activities. Ghana 2015.  

Labour 

Capital 

Unskilled 
11.6% 
0.4% 

Semi-skilled 
8.3% 
0.9% 

Skilled 
0.1% 
0.0% 

Crops 
4.0% 
- 

Livestock  Mining 

1.9% 
- 

- 
- 

Other 
4.3% 
1.9% 

0.6% 

0.3% 
0.02% 
0.5% 
8.0% 

3.4% 

0.4% 

0.01% 
0.2% 
0.9% 
14.8% 

2.7% 
0.1% 
4.5% 
42.0% 

35.5% 

- 

- 
- 
- 
- 

Source: Own elaboration with Ghana SAM 2015. 

- 

- 
- 
- 
- 

8.96% 

2.1% 

- 
- 
- 
- 

2.8% 
2.1% 
25.8% 
46.1% 

57.9% 

6.9% 

Land 

100% 
- 

- 

- 
- 
- 
- 

Primary sector  
Food industry 
Petroleum and 
mining 
Manufactured  
Utilities 
Construction 
Services 
% of the Value 
added 

Figure 7. Distribution of factors and Value Added by aggregate activities. Ghana 2015.  

17.0%

25.8%

6.0%

49.6%

46.1%

64.9%

Services

Construction

Utilities

1.3%

2.1%

0.3%

Manufactured

2.7%
2.8%
3.1%

Petroleum and mining

7.9%

11.1%

4.4%

Food industry

1.5%
1.9%
1.3%

Primary sector

10.3%

19.9%

20.0%

Value Added

Capital

Labour

Source: Own elaboration with Ghana SAM 2015. 

Continuing with the analysis of the factors of production, the weight of households as activities in the generation 
of value added is analysed. Households as activities (focus on agricultural activities) represent 17.9% of the 
value added, which includes all the land factor, 19.1% of labour factors and 7.6% of capital factors (figure 8).   

17 

  
  
 
 
 
Figure 8. Share of agricultural activities in production factors. Ghana 2015.  

100.0%

19.1%

7.6%

17.9%

Labour factors

Land factors

Capital factor

Value added

Source: Own elaboration with Ghana SAM 2015. 

Finally, referring to factors of productions and households, the distribution of households' income by factors of 
production and by transfers from enterprises, the government, and the rest of the world, is represented in the 
Table 6. Almost 35.5% of households’ incomes come from compensation to labour and 48.8% correspond to 
transfers of enterprises. 

Table 6. Distribution of households’ income. Ghana 2015. 

Factors income 

Transfers 

Labour 

Land 

Capital 

Enterprise  Government 

Rest of 
the World 

Ghana 

Rural 

Urban 

Western  

Central 

Greater Accra 

Volta 

Eastern 

Ashanti 

h
t
u
o
S

Brong Ahafo 

  Northern 
h
t
r
o
N

Upper East 

Upper West 

35.5% 

42.6% 

32.8% 
37.1% 

29.2% 

32.5% 

37.6% 

32.5% 

34.8% 

41.4% 

53.0% 

63.1% 

37.9% 

6.9% 

17.5% 

2.8% 
6.2% 

6.4% 

0.8% 

9.5% 

15.1% 

3.9% 

16.7% 

17.3% 

20.0% 

9.9% 

3.4% 

48.8% 

1.3% 

4.1% 

8.4% 

1.5% 
5.5% 

3.0% 

0.3% 

10.0% 

6.8% 

2.3% 

3.5% 

4.8% 

4.4% 

19.4% 

26.3% 

57.5% 
43.3% 

53.2% 

63.4% 

33.3% 

39.9% 

55.1% 

32.0% 

18.7% 

6.0% 

11.4% 

1.1% 

1.4% 
1.8% 

1.9% 

0.8% 

2.2% 

1.4% 

0.9% 

1.5% 

1.5% 

1.3% 

4.9% 

4.1% 

4.1% 
6.0% 

6.3% 

2.2% 

7.4% 

4.3% 

2.9% 

4.8% 

4.7% 

5.1% 

16.5% 

Source: Own elaboration with Ghana SAM 2015. 

Labour is equally distributed among the urban and rural environments; however, business transfers are more 
concentrated for the urban area. According to the information provided in the SAM, 83% of the income received 
by  enterprises  corresponds  to  the  capital  factor  (mining  5%  and  83%  other)  and  the  remaining  12%  to 
government transfers. Those revenues are mainly transferred to households (representing 81%).  

Focusing  the  analysis  on  each  region,  business  transfers  are  concentrated  in  the  southern  regions  and 
government and rest of the world transfers are mainly distributed in the Upper West region (which belongs to 
the northern region). The labour factor distribution also stands out for the northern regions, with more than 
50%  for  Northern  and  Upper  East.  Both  capital  and  land  are  key  factors  in  income  in  the  rural  zones, 
representing 8.4% and 17.5% respectively. 

18 

 
 
 
 
 
 
4.2  Linear multiplier analysis 

The linear SAM models are a simple way to analyse the information provided by a SAM regarding the structure 
of an economy. They show the separate effects being generated in the economic activity of different agents 
due to the relationships of the circular flow of income. In this section the output, value added, and employment 
multipliers are calculated. A brief explanation of the calculation of the three multipliers is included in Annex 3.  

4.2.1  Multipliers  

The analysis of the multiplier effect shows those commodities with capacity to generate output, employment 
and  value  added  above  average,  and  therefore,  identifies  which  ones  are  suitable  to  be  promoted  through 
policies. Although the exact results obtained for each multiplier must be taken with some caution, multiplier 
analysis is a useful tool for ex-ante policy evaluation (Mainar-Causapé et al., 2018a).  

The values of the multipliers calculated for the Ghana economy 2015 are presented in Tables 7 and 8. The cells 
are shaded to compare those below or above the average. Each vale of the output multiplier shows the increase 
in gross output in sectors, due to one-unit exogenous injection into the final demand of a commodity (i.e. an 
exogenous increase in exports demand). As for example, the output multiplier of Maize indicates that a one-
unit increase in exogenous demand in Maize leads to 2.63 of output increase in the economy. Likewise, the 
value added multiplier indicate the new value added created by the additional production in responses to an 
exogenous shock in demand. Finally, the values of the employment multipliers measure the increment in the 
number of jobs generated by the exogenous increase in demand. Taking the multipliers of Table 7 into account, 
the primary sector group showed a higher backward income generation capacity than the economy average. 
Within agriculture, the calculation of the output multiplier highlights the importance of several products, with 
major  values for sorghum and  millet (2.68),  sugar cane  (2.70), tobacco (2.69), cotton and fibers (2.75) and 
cattle (2.70). On the other hand, other crops and rice have a weaker impact on the economy, both in production, 
value added and employment. Livestock commodities shows values clearly greater than the average for both 
output and value added multipliers, but less relevant in employment (although higher than average).  

The  employment  multiplier has value  clearly  above  average for most primary  sector  products,  with  highest 
impact for sugar cane, groundnuts, maize and oilseeds. Analysing this sector, the distribution of employment 
generated are concentrated with over 50% in “unskilled workers” (see Table 3). Only for cocoa, the number of 
additional jobs per million of additional output has a similar distribution towards the group of workers classified 
as “semi-skilled” (47%) and “unskilled” (51%). Considering agricultural products, the jobs generated as detailed 
by the employment multiplier are concentrated in the southern regions of Ghana. However, for sorghum and 
millet, pulses, tobacco and cotton and fibers the impact of the jobs generated in the northern region is slightly 
higher.  

The analysis shows that many of the commodities classified within food industry sector, such as meat, fish and 
dairy, fruit and vegetable processing and sugar refining have very low multiplier values both in production and 
value added. Within this group, only fats and oils and grain milling have an output and value added multipliers 
above  average.  For  the  food  industry  sector,  the  employment  multiplier  has  very  low  values  and  the  jobs 
generated by an exogenous impact of demand are distributed more than 50% among “semi-skilled” workers. 
Within this group, only  the employment multiplier of fats  and  oils and  grain milling  stand out, with slightly 
above average values and whose jobs generated impact predominates for “unskilled workers” as in the primary 
sector. 

19 

 
 
Table 7. Linear multipliers of primary sector and food industry commodities for Ghana 2015. 

Description 

Multipliers 

Output  Value added 

Maize 
Sorghum and millet 
Rice 
Pulses 
Groundnuts 
Other oilseeds 
Cassava 
Other roots 
Vegetables 
Sugar cane 
Tobacco 
Cotton and fibers 
Fruits and nuts 
Cocoa 
Coffee and tea 
Other crops 
Cattle 
Poultry 
Other livestock 
Forestry 
Fishing 
Meat, fish and dairy 
Fruit and vegetable 
processing 
Fats and oils 
Grain milling 
Sugar refining 
Other foods 
Beverages 
Tobacco processing 

Primary 
sector 

Food 
industry 

2.63 
2.68 
1.55 
2.64 
2.63 
2.57 
2.61 
2.64 
2.59 
2.70 
2.69 
2.75 
2.54 
2.37 
2.57 
1.20 
2.70 
2.50 
2.63 
2.59 
2.60 
0.82 
0.89 

2.60 
2.49 
0.64 
1.57 
1.56 
1.56 

1.70 
1.65 
0.95 
1.64 
1.70 
1.68 
1.62 
1.66 
1.61 
1.75 
1.68 
1.61 
1.55 
1.46 
1.50 
0.74 
1.64 
1.49 
1.64 
1.39 
1.63 
0.40 
0.44 

1.32 
1.14 
0.31 
0.76 
0.75 
0.69 

Employment 
209.36 
187.58 
109.73 
189.43 
205.53 
208.60 
185.07 
196.63 
189.69 
244.40 
189.44 
195.49 
170.91 
161.83 
160.72 
83.69 
186.07 
167.99 
189.07 
82.62 
183.35 
35.71 
46.12 

139.63 
133.56 
33.00 
72.22 
74.84 
59.59 

Source: Own elaboration with Ghana SAM 2015. Shaded cells: value greater than the average  

Focusing on table 8, the results indicate that for the manufactured and utilities sectors, none stand out in terms 
of job creation. Even though some products have an above average output multiplier, they do not automatically 
generate  high  value  added  multiplier,  accordingly  (for  example  wood  and  paper,  electricity  and  water). 
Something similar is for the petroleum and mining sector, with only other mining having an above-average 
output multiplier. The Construction is slightly above the average in output and value added, but significantly 
below the average for employment.  

The service sectors show above-average values in terms of output and value added for trade, information and 
communication, finance and insurance, public administration, health and education, but significantly lower in 
terms of employment. Only accommodation and food services and education have employment linkages above 
the global average.   

20 

 
 
 
 
Table 8. Linear multipliers of manufactures, utilities, services and other commodities for Ghana 2015.  

Petroleum 
and mining 

Manufactured  

Utilities 

Construction 

Services 

Description 

Crude oil 
Other mining 
Petroleum 
Textiles 
Clothing 
Leather and footwear 
Wood and paper 
Chemicals 
Non-metal minerals 
Metals and metal  
Machinery and equipment 
Other manufacturing 
Electricity, gas and steam 
Water supply and sewage 
Construction 
Wholesale and retail trade 
Transportation and storage 
Accommodation and food 
services 
Information and communication 
Finance and insurance 
Real estate activities 
Business services 
Public administration 
Education 
Health and social work 
Other services 

Output 
1.86 
2.28 
2.07 
1.49 
1.60 
0.78 
2.37 
0.81 
0.91 
0.65 
0.23 
0.77 
2.61 
2.28 
2.21 
2.73 
2.15 
2.16 

2.72 
2.56 
2.37 
1.40 
2.49 
2.80 
2.56 
2.20 

Multipliers 

Value added 
1.03 
0.68 
0.87 
0.71 
0.77 
0.38 
1.00 
0.37 
0.43 
0.30 
0.11 
0.37 
0.89 
0.77 
1.34 
1.28 
1.19 
1.24 

1.22 
1.41 
1.09 
0.56 
1.20 
1.47 
1.18 
1.43 

Employment 
83.25 
48.41 
61.33 
75.55 
64.03 
32.14 
71.03 
28.60 
54.30 
22.43 
9.45 
35.90 
62.39 
47.90 
67.09 
116.20 
90.94 
140.10 

75.00 
86.77 
52.56 
40.13 
90.67 
155.62 
120.96 
121.55 

Source: Own elaboration with Ghana SAM 2015. Shaded cells: value greater than the average  

21 

 
 
5  Conclusions 

This report presents the construction of Ghana's SAM for 2015, with the main purpose of providing a suitable 
database for implementing and evaluating the country's own developmental social and economic policies and 
initiatives. 

In summary, the new Ghana SAM 2015 presented in this report contains 299 accounts: 45 activities (10 for 
household  as  producers),  producing  55  marketed  commodities  and  14  HPHC.  It  considers  3  type  of  labour 
(skilled, unskilled and semi-skilled) in 10 regions and disaggregated in rural and urban (60 labour accounts), 1 
type of land for each region (10 land factor accounts) and 4 type of capitals: crops, livestock, mining and other 
(4 capital factors accounts). Regarding households, each of the 10 regions is divided in urban and rural and also 
disaggregated  in  quantiles  (100  households’  accounts).  Finally,  there  is  a  single  account  for  margins,  one 
corresponding to enterprises, other for the government, the rest of the world, one for investing and saving and 
other for change in stocks. Disaggregation between the northern and southern regions of the country is very 
important to promote policies focused on equality and development in the northern regions. In Annex 1, Table 
A1 shows all accounts considered in the Ghana SAM 2015. The Ghana 2015 SAM can be freely downloaded 
from DataM portal, as explained in the Annex 2. 

22 

References 

Aragie, E.; Dudu, H.; Ferrari, E.; Mainar-Causapé, A.; McDonald, S & Thierfielder, K, STAGE_DEV. A variant of the 
STAGE model to analyse developing countries, EUR 28627 EN, JRC Technical Reports, Publications Office of the 
European Union, Luxembourg, 2017, doi:10.2760/90737. 

Bacharach, M. Biproportional matrices and input-output change (Vol. 16), CUP Archive, 1970. 

Defourny, J., & Thorbecke, E. Structural Path Analysis and Multiplier Decomposition within a Social Accounting 
Matrix Framework. The Economic Journal, Vol. 94, No 373, 1984, pp. 111–136. https://doi.org/10.2307/2232220 

EUROSTAT. Eurostat Manual of Supply Use and Input-Output Tables. Methodologies and working papers, 
Publications Office of the European Union, Luxembourg, 2008, https://ec.europa.eu/eurostat/web/products‐
manuals‐and‐guidelines/‐/KS‐RA‐07‐013 

GSS (Ghana Statistical Service). Ghana Living Standards Survey (GLSS) 7, 2019.  

GSS (Ghana Statistical Service). Report on the 2015 Social Accounting Matrix (SAM) for Ghana; Accra: Institute 
of Statistical, Social and Economic Research (ISSER), University of Ghana; International Food Policy Research 
Institute (IFPRI), 2017. 

GSS (Ghana Statistical Service). Labour Force Survey 2015, 2016. 

Mainar-Causapé, A.J.; Boulanger, P.; Dudu, H.; Ferrari, E & McDonlad, S. Social Accounting Matrix of Kenya 2014, 
EUR  29056 EN, JRC  Technical  Reports, Publications  Office of the European  Union,  Luxembourg,  2018a,  doi: 
10.2760/852198.  

Mainar-Causapé, A.J.; Ferrari, E & McDonlad, S. Social accounting matrices: basic aspects and main steps for 
estimation,  EUR  2929  EN,  JRC  Technical  Reports,  Publications  Office  of  the  European  Union,  Luxembourg, 
2018b, doi: 10.2760/010600.  

McDougall, R.A.  Entropy Theory and RAS  are  Friends,  GTAP, Working Paper, 300, Department of  Agricultural 
Economics, Purdue University, 1991. 

Miller, R & Blair, P. Input-Output Analysis: Foundations and Extensions (2nd edition), Cambridge University Press, 
Cambridge, 2009. 

Morokong, T & Ferrari, E. The launch of the Pan-African Network for Economic Analysis of Policies (PANAP) in 
Addis Ababa, Ethiopia. AgriProbe, Vol. 17, No 1, 2020, pp. 40-41. https://hdl.handle.net/10520/EJC-1cdb9a8ec7  

Pulido, A & Fontela, E. Análisis Input-Output: modelos, datos y aplicaciones. Pirámides, Madrid, 1993.  

Pyatt, G & Round, J. Social Accounting Matrices: a Basis for Planning, The World Bank, Washington, 1985. 

Reinert, K. A & Roland-Holst, D.W. Social Accounting Matrices, edited by J. F. Francois y K. A. Reinert, Applied 
Methods for Trade Policy Analysis: A Handbook, Cambridge University Press, Cambridge, 1997. 

Round, J. Constructing SAMs for Development Policy Analysis: Lessons Learned and Challenges Ahead, Economic 
Systems Research, Vol. 15, No 2, 2003, pp. 161-183.  

Robinson, S., Cattaneo, A., & El-Said, M. Estimating a Social Accounting Matrix Using Cross Entropy Methods. 
Trade and Macroeconomics Discussion Paper No. 33. International Food Policy Research Institute, 1998. 

Robinson, S.; Cattaneo, A & El-Said, M. Updating and Estimating a Social Accounting Matrix Using Cross Entropy 
Methods. Economic System Research, Vol. 13, No 1, 2001, pp. 47-64.  

Stone, R. Measurement of national income and the construction of social accounts, Naciones Unidas, Ginebra, 
1947. 

23 

 
 
 
List of abbreviations and definitions 

A 

AH 

C 

CH 

CEM 

CGE 

Activities accounts 

Representative households’ groups as activities 

Commodities accounts 

HPHC Commodities 

Cross Entropy Method 

Computable General Equilibrium 

DataM 

JRC data portal of agro-economic modelling 

DIRTAX  Direct taxes 

DSTK 

Change in stocks 

ENTERP  Enterprise 

EXPTAX  Export taxes 

FCAP 

FLAB 

Capital factors 

Labour factors 

FLAND 

Land factors  

GOVERT  Government 

GSS 

Ghana Statistical Services 

GLSS 

Ghana Living Standard Survey  

HPHC 

Home Production for Home Consumption  

HH 

Households accounts 

IFPRI 

International Food Policy Research Institute 

ISSER 

Institute of Statistical, Social and Economic Research 

I-S 

Investment- Saving 

IMPTAX 

Import taxes 

PANAP 

Pan-Africa Network for Economic Analysis of Policies 

RoW 

RHG 

SAM 

Rest of the World accounts 

Representative Household Group  

Social Accounting Matrix 

SALTAX  Sales taxes 

TRCOST  Margins 

24 

 
List of figures 

Figure 1. The circular flow (simple version) ......................................................................... 4 

Figure 2. A Social Accounting Matrix (SAM) standard structure .................................................... 6 

Figure 3. Domestic absorption, imports, and exports as % of the GDP. Ghana 2015. ..........................15 

Figure 4. Domestic absorption composition. Ghana 2015. ........................................................15 

Figure 5. Household consumption pattern. Ghana 2015. ..........................................................16 

Figure 6. Exports and imports composition. Ghana 2015. .........................................................16 

Figure 7. Distribution of factors and Value Added by aggregate activities. Ghana 2015. ......................17 

Figure 8. Share of agricultural activities in production factors. Ghana 2015. ...................................18 

Figure A1. QR code – DataM URL: https://datam.jrc.ec.europa.eu .................................................30 

Figure A2. QR Code – direct bulk data download: 
https://datam.jrc.ec.europa.eu/datam/perm/dataset/9e5aa734-2dac-4e08-bf14-
d518151e4f53/download/dataset.zip ...............................................................................30 

Figure A4. QR Code – direct link to the data warehouse page of the dataset: 
https://datam.jrc.ec.europa.eu/datam/perm/dataset/9e5aa734-2dac-4e08-bf14-d518151e4f53 .............31 

Figure A5. Data warehouse page of the Ghana SAM 2015 .......................................................32 

Figure A6. Visualising the SAM on the screen ......................................................................32 

Figure A7. Exporting only part of the SAM in flat format .........................................................33 

Figure A8. QR Code – direct link to the interactive dashboard: 
https://datam.jrc.ec.europa.eu/datam/mashup/SAM_GH_2015 .....................................................33 

Figure A9. Navigating within the sheets ............................................................................33 

Figure A10. A generic dashboard ...................................................................................34 

Figure A11. Making an interactive selection .......................................................................34 

Figure A12. Result of an interactive selection .....................................................................35 

Figure A13. Instructions to change selections .....................................................................35 

Figure A14. How to download the SAM in traditional matrix format .............................................36 

Figure A15. Traditional matrix outcome ............................................................................36 

Figure A16. Downloading a SAM with codes .......................................................................37 

25 

 
List of tables 

Table 1. Ghana SAM 2015 with aggregated values. Millions of Ghanaian Cedis. ...............................11 

Table 2. Activities and commodities disaggregated in Ghana SAM 2015 ........................................12 

Table 3. Description of factors and households in Ghana SAM 2015 ............................................13 

Table 4. Regional description in Ghana SAM 2015.................................................................14 

Table 5. Distribution of factors by aggregate activities. Ghana 2015. ...........................................17 

Table 6. Distribution of households’ income. Ghana 2015. .......................................................18 

Table 7. Linear multipliers of primary sector and food industry commodities for Ghana 2015. ...............20 

Table 8. Linear multipliers of manufactures, utilities, services and other commodities for Ghana 2015. .....21 

Table A.1. Accounts of the Ghana Social Accounting Matrix 2015 ...............................................27 

26 

 
Annexes 

Annex 1. Accounts of the Ghana Social Accounting Matrix 2015 

Code 

ah_f_Wes 
ah_f_Cen 
ah_f_Acc 
ah_f_Vol 
ah_f_Eas 
ah_f_Ash 
ah_f_BrA 
ah_f_Nor 
ah_f_UpE 
ah_f_UpW 
a_fore 
a_coil 
a_omin 
a_meat 
a_fveg 
a_foil 
a_gmll 
a_sref 
a_food 
a_beve 
a_ptob 
a_text 
a_clth 
a_leat 
a_wood 
a_petr 
a_chem 
a_nmet 
a_metl 
a_mach 
a_oman 
a_elec 
a_watr 
a_cons 
a_trad 
a_tran 
a_hotl 
a_comm 
a_fsrv 
a_real 
a_bsrv 
a_padm 
a_educ 
a_heal 
a_osrv 
ch_maiz 
ch_sorg 

ch_rice 

Table A.1. Accounts of the Ghana Social Accounting Matrix 2015 

Description 

Code 

Description 

Western (HH as producers) 
Central (HH as producers) 
Greater Accra (HH as producers) 
Volta (HH as producers) 
Eastern (HH as producers) 
Ashanti (HH as producers) 
Brong Ahafo (HH as producers) 
Northern (HH as producers) 
Upper East (HH as producers) 
Upper West (HH as producers) 
Forestry 
Crude Oil 
Other mining 
Meat, fish and dairy 
Fruit and vegetable processing 
Fats and oils 
Grain milling 
Sugar refining 
Other foods 
Beverages 
Tobacco (processed) 
Textiles 
Clothing 
Leather and footwear 
Wood and paper 
Petroleum 
Chemicals 
Non-metal minerals 
Metals and metal  
Machinery and equipment 
Other manufacturing 
Electricity, gas and steam 
Water supply and sewage 
Construction 
Wholesale and retail trade 
Transportation and storage 
Accommodation and food services 
Information and communication 
Finance and insurance 
Real estate activities 
Business services 
Public administration 
Education 
Health and social work 
Other services 
Maize (HPHC commodities) 
Sorghum and millet (HPHC 
commodities) 
Rice (HPHC commodities) 

Urban- Skilled labour Upper East 

Rural- Semi-skilled labour Upper West 
Rural- Skilled labour Upper West 

Urban- Semi-skilled labour Ashanti 
Urban- Skilled labour Ashanti 
Rural- Unskilled labour Brong Ahafo 
Rural- Semi-skilled labour Brong Ahafo 
Rural- Skilled labour Brong Ahafo 
Urban- Unskilled labour Brong Ahafo 
Urban- Semi-skilled labour Brong Ahafo 
Urban- Skilled labour Brong Ahafo 
Rural- Unskilled labour Northern 
Rural- Semi-skilled labour Northern 
Rural- Skilled labour Northern 
Urban- Unskilled labour Northern 
Urban- Semi-skilled labour Northern 
Urban- Skilled labour Northern 
Rural- Unskilled labour Upper East 
Rural- Semi-skilled labour Upper East 
Rural- Skilled labour Upper East 

flab_urb_SSK_Ash 
flab_urb_SKL_Ash 
flab_rur_USK_BrA 
flab_rur_SSK_BrA 
flab_rur_SKL_BrA 
flab_urb_USK_BrA 
flab_urb_SSK_BrA 
flab_urb_SKL_BrA 
flab_rur_USK_Nor 
flab_rur_SSK_Nor 
flab_rur_SKL_Nor 
flab_urb_USK_Nor 
flab_urb_SSK_Nor 
flab_urb_SKL_Nor 
flab_rur_USK_UpE 
flab_rur_SSK_UpE 
flab_rur_SKL_UpE 
flab_urb_USK_UpE  Urban- Unskilled labour Upper East 
flab_urb_SSK_UpE  Urban- Semi-skilled labour Upper East 
flab_urb_SKL_UpE 
flab_rur_USK_UpW  Rural- Unskilled labour Upper West 
flab_rur_SSK_UpW 
flab_rur_SKL_UpW 
flab_urb_USK_UpW  Urban- Unskilled labour Upper West 
flab_urb_SSK_UpW  Urban- Semi-skilled labour Upper West 
flab_urb_SKL_UpW  Urban- Skilled labour Upper West 
Land - agricultural crops Western 
fland_Wes 
Land - agricultural crops Central 
fland_Cen 
Land - agricultural crops Greater Accra 
fland_Acc 
Land - agricultural crops Volta 
fland_Vol 
Land - agricultural crops Eastern 
fland_Eas 
Land - agricultural crops Ashanti 
fland_Ash 
Land - agricultural crops Brong Ahafo 
fland_BrA 
Land - agricultural crops Northern 
fland_Nor 
Land - agricultural crops Upper East 
fland_UpE 
Land - agricultural crops Upper West 
fland_UpW 
Capital - crops 
fcap_c 
Capital - livestock 
fcap_l 
Capital - mining 
fcap_m 
Capital - other 
fcap_n 
Enterprises 
enterp 
Rural farm - quintile 1 Western 
hh_rur_Wes_Q1 
Rural farm - quintile 2 Western 
hh_rur_Wes_Q2 
Rural farm - quintile 3 Western 
hh_rur_Wes_Q3 
Rural farm - quintile 4 Western 
hh_rur_Wes_Q4 
Rural farm - quintile 5 Western 
hh_rur_Wes_Q5 
Rural farm - quintile 1 Central 
hh_rur_Cen_Q1 

hh_rur_Cen_Q2 

Rural farm - quintile 2 Central 

27 

ch_puls 
ch_gnut 
ch_oils 
ch_cass 
ch_root 
ch_vege 
ch_frui 
ch_catt 
ch_poul 
ch_oliv 
ch_fish 
c_maiz 
c_sorg 
c_rice 
c_puls 
c_gnut 
c_oils 
c_cass 
c_root 
c_vege 
c_sugr 
c_toba 
c_cott 
c_frui 
c_coco 
c_coff 
c_ocrp 
c_catt 
c_poul 
c_oliv 
c_fore 
c_fish 
c_coil 
c_omin 
c_meat 
c_fveg 
c_foil 
c_gmll 
c_sref 
c_food 
c_beve 
c_ptob 
c_text 
c_clth 
c_leat 
c_wood 
c_petr 
c_chem 
c_nmet 
c_metl 
c_mach 
c_oman 
c_elec 
c_watr 
c_cons 
c_trad 

Pulses (HPHC commodities) 
Groundnuts (HPHC commodities) 
Other oilseeds (HPHC commodities) 
Cassava (HPHC commodities) 
Other roots (HPHC commodities) 
Vegetables (HPHC commodities) 
Fruits and nuts (HPHC commodities) 
Cattle (HPHC commodities) 
Poultry (HPHC commodities) 
Other livestock (HPHC commodities) 
Fishing (HPHC commodities) 
Maize 
Sorghum and millet 
Rice 
Pulses 
Groundnuts 
Other oilseeds 
Cassava 
Other roots 
Vegetables 
Sugar cane 
Tobacco 
Cotton and fibers 
Fruits and nuts 
Cocoa 
Coffee and tea 
Other crops 
Cattle 
Poultry 
Other livestock 
Forestry 
Fishing 
Crude oil 
Other mining 
Meat, fish and dairy 
Fruit and vegetable processing 
Fats and oils 
Grain milling 
Sugar refining 
Other foods 
Beverages 
Tobacco processing 
Textiles 
Clothing 
Leather and footwear 
Wood and paper 
Petroleum 
Chemicals 
Non-metal minerals 
Metals and metal products 
Machinery and equipment 
Other manufacturing 
Electricity, gas and steam 
Water supply and sewage 
Construction 
Wholesale and retail trade 

hh_rur_Cen_Q3 
hh_rur_Cen_Q4 
hh_rur_Cen_Q5 
hh_rur_Acc_Q1 
hh_rur_Acc_Q2 
hh_rur_Acc_Q3 
hh_rur_Acc_Q4 
hh_rur_Acc_Q5 
hh_rur_Vol_Q1 
hh_rur_Vol_Q2 
hh_rur_Vol_Q3 
hh_rur_Vol_Q4 
hh_rur_Vol_Q5 
hh_rur_Eas_Q1 
hh_rur_Eas_Q2 
hh_rur_Eas_Q3 
hh_rur_Eas_Q4 
hh_rur_Eas_Q5 
hh_rur_Ash_Q1 
hh_rur_Ash_Q2 
hh_rur_Ash_Q3 
hh_rur_Ash_Q4 
hh_rur_Ash_Q5 
hh_rur_BrA_Q1 
hh_rur_BrA_Q2 
hh_rur_BrA_Q3 
hh_rur_BrA_Q4 
hh_rur_BrA_Q5 
hh_rur_Nor_Q1 
hh_rur_Nor_Q2 
hh_rur_Nor_Q3 
hh_rur_Nor_Q4 
hh_rur_Nor_Q5 
hh_rur_UpE_Q1 
hh_rur_UpE_Q2 
hh_rur_UpE_Q3 
hh_rur_UpE_Q4 
hh_rur_UpE_Q5 
hh_rur_UpW_Q1 
hh_rur_UpW_Q2 
hh_rur_UpW_Q3 
hh_rur_UpW_Q4 
hh_rur_UpW_Q5 
hh_urb_Wes_Q1 
hh_urb_Wes_Q2 
hh_urb_Wes_Q3 
hh_urb_Wes_Q4 
hh_urb_Wes_Q5 
hh_urb_Cen_Q1 
hh_urb_Cen_Q2 
hh_urb_Cen_Q3 
hh_urb_Cen_Q4 
hh_urb_Cen_Q5 
hh_urb_Acc_Q1 
hh_urb_Acc_Q2 
hh_urb_Acc_Q3 

28 

Rural farm - quintile 3 Central 
Rural farm - quintile 4 Central 
Rural farm - quintile 5 Central 
Rural farm - quintile 1 Greater Accra 
Rural farm - quintile 2 Greater Accra 
Rural farm - quintile 3 Greater Accra 
Rural farm - quintile 4 Greater Accra 
Rural farm - quintile 5 Greater Accra 
Rural farm - quintile 1 Volta 
Rural farm - quintile 2 Volta 
Rural farm - quintile 3 Volta 
Rural farm - quintile 4 Volta 
Rural farm - quintile 5 Volta 
Rural farm - quintile 1 Eastern 
Rural farm - quintile 2 Eastern 
Rural farm - quintile 3 Eastern 
Rural farm - quintile 4 Eastern 
Rural farm - quintile 5 Eastern 
Rural farm - quintile 1 Ashanti 
Rural farm - quintile 2 Ashanti 
Rural farm - quintile 3 Ashanti 
Rural farm - quintile 4 Ashanti 
Rural farm - quintile 5 Ashanti 
Rural farm - quintile 1 Brong Ahafo 
Rural farm - quintile 2 Brong Ahafo 
Rural farm - quintile 3 Brong Ahafo 
Rural farm - quintile 4 Brong Ahafo 
Rural farm - quintile 5 Brong Ahafo 
Rural farm - quintile 1 Northern 
Rural farm - quintile 2 Northern 
Rural farm - quintile 3 Northern 
Rural farm - quintile 4 Northern 
Rural farm - quintile 5 Northern 
Rural farm - quintile 1 Upper East 
Rural farm - quintile 2 Upper East 
Rural farm - quintile 3 Upper East 
Rural farm - quintile 4 Upper East 
Rural farm - quintile 5 Upper East 
Rural farm - quintile 1 Upper West 
Rural farm - quintile 2 Upper West 
Rural farm - quintile 3 Upper West 
Rural farm - quintile 4 Upper West 
Rural farm - quintile 5 Upper West 
Urban - quintile 1 Western 
Urban - quintile 2 Western 
Urban - quintile 3 Western 
Urban - quintile 4 Western 
Urban - quintile 5 Western 
Urban - quintile 1 Central 
Urban - quintile 2 Central 
Urban - quintile 3 Central 
Urban - quintile 4 Central 
Urban - quintile 5 Central 
Urban - quintile 1 Greater Accra 
Urban - quintile 2 Greater Accra 
Urban - quintile 3 Greater Accra 

Transportation and storage 
Accommodation and food services 
Information and communication 
Finance and insurance 
Real estate activities 
Business services 
Public administration 
Education 
Health and social work 
Other services 
Trade and transport margin 
Trade and transport margin 

c_tran 
c_hotl 
c_comm 
c_fsrv 
c_real 
c_bsrv 
c_padm 
c_educ 
c_heal 
c_osrv 
trcost 
trcostexp 
flab_rur_USK_Wes  Rural- Unskilled labour Western 
flab_rur_SSK_Wes 
flab_rur_SKL_Wes 
flab_urb_USK_Wes  Urban- Unskilled labour Western 
flab_urb_SSK_Wes  Urban- Semi-skilled labour Western 
flab_urb_SKL_Wes  Urban- Skilled labour Western 
Rural- Unskilled labour Central 
flab_rur_USK_Cen 
Rural- Semi-skilled labour Central 
flab_rur_SSK_Cen 
flab_rur_SKL_Cen 
Rural- Skilled labour Central 
flab_urb_USK_Cen  Urban- Unskilled labour Central 
flab_urb_SSK_Cen  Urban- Semi-skilled labour Central 
flab_urb_SKL_Cen  Urban- Skilled labour Central 
flab_rur_USK_Acc 
flab_rur_SSK_Acc 

Rural- Semi-skilled labour Western 
Rural- Skilled labour Western 

hh_urb_Acc_Q4 
hh_urb_Acc_Q5 
hh_urb_Vol_Q1 
hh_urb_Vol_Q2 
hh_urb_Vol_Q3 
hh_urb_Vol_Q4 
hh_urb_Vol_Q5 
hh_urb_Eas_Q1 
hh_urb_Eas_Q2 
hh_urb_Eas_Q3 
hh_urb_Eas_Q4 
hh_urb_Eas_Q5 
hh_urb_Ash_Q1 
hh_urb_Ash_Q2 
hh_urb_Ash_Q3 
hh_urb_Ash_Q4 
hh_urb_Ash_Q5 
hh_urb_BrA_Q1 
hh_urb_BrA_Q2 
hh_urb_BrA_Q3 
hh_urb_BrA_Q4 
hh_urb_BrA_Q5 
hh_urb_Nor_Q1 
hh_urb_Nor_Q2 
hh_urb_Nor_Q3 
hh_urb_Nor_Q4 

Rural- Unskilled labour Greater Accra 
Rural- Semi-skilled labour Greater 
Accra 
flab_rur_SKL_Acc 
hh_urb_Nor_Q5 
Rural- Skilled labour Greater Accra 
flab_urb_USK_Acc  Urban- Unskilled labour Greater Accra  hh_urb_UpE_Q1 
hh_urb_UpE_Q2 
Urban- Semi-skilled labour Greater 
flab_urb_SSK_Acc 
Accra 
Urban- Skilled labour Greater Accra 
Rural- Unskilled labour Volta 
Rural- Semi-skilled labour Volta 
Rural- Skilled labour Volta 
Urban- Unskilled labour Volta 
Urban- Semi-skilled labour Volta 
Urban- Skilled labour Volta 
Rural- Unskilled labour Eastern 
Rural- Semi-skilled labour Eastern 
Rural- Skilled labour Eastern 

flab_urb_SKL_Acc 
flab_rur_USK_Vol 
flab_rur_SSK_Vol 
flab_rur_SKL_Vol 
flab_urb_USK_Vol 
flab_urb_SSK_Vol 
flab_urb_SKL_Vol 
flab_rur_USK_Eas 
flab_rur_SSK_Eas 
flab_rur_SKL_Eas 
flab_urb_USK_Eas  Urban- Unskilled labour Eastern 
flab_urb_SSK_Eas  Urban- Semi-skilled labour Eastern 
flab_urb_SKL_Eas 
flab_rur_USK_Ash 
flab_rur_SSK_Ash 
flab_rur_SKL_Ash 
flab_urb_USK_Ash  Urban- Unskilled labour Ashanti 

hh_urb_UpE_Q3 
hh_urb_UpE_Q4 
hh_urb_UpE_Q5 
hh_urb_UpW_Q1 
hh_urb_UpW_Q2 
hh_urb_UpW_Q3 
hh_urb_UpW_Q4 
hh_urb_UpW_Q5 
govert 
dirtax 
exptax 
imptax 
saltax 
i-s 
dstk 
row 

Urban- Skilled labour Eastern 
Rural- Unskilled labour Ashanti 
Rural- Semi-skilled labour Ashanti 
Rural- Skilled labour Ashanti 

Urban - quintile 4 Greater Accra 
Urban - quintile 5 Greater Accra 
Urban - quintile 1 Volta 
Urban - quintile 2 Volta 
Urban - quintile 3 Volta 
Urban - quintile 4 Volta 
Urban - quintile 5 Volta 
Urban - quintile 1 Eastern 
Urban - quintile 2 Eastern 
Urban - quintile 3 Eastern 
Urban - quintile 4 Eastern 
Urban - quintile 5 Eastern 
Urban - quintile 1 Ashanti 
Urban - quintile 2 Ashanti 
Urban - quintile 3 Ashanti 
Urban - quintile 4 Ashanti 
Urban - quintile 5 Ashanti 
Urban - quintile 1 Brong Ahafo 
Urban - quintile 2 Brong Ahafo 
Urban - quintile 3 Brong Ahafo 
Urban - quintile 4 Brong Ahafo 
Urban - quintile 5 Brong Ahafo 
Urban - quintile 1 Northern 
Urban - quintile 2 Northern 
Urban - quintile 3 Northern 
Urban - quintile 4 Northern 

Urban - quintile 5 Northern 
Urban - quintile 1 Upper East 
Urban - quintile 2 Upper East 

Urban - quintile 3 Upper East 
Urban - quintile 4 Upper East 
Urban - quintile 5 Upper East 
Urban - quintile 1 Upper West 
Urban - quintile 2 Upper West 
Urban - quintile 3 Upper West 
Urban - quintile 4 Upper West 
Urban - quintile 5 Upper West 
Government 
Taxes - direct 
Taxes - sales 
Taxes - import 
Taxes - export 
Savings-investment 
Change in stocks 
Rest of world 

Source: Own elaboration. 

29 

 
 
 
 
 
Annex 2. On-line resources to download the Ghana Social Accounting Matrix 2015 

The 2015 Social Accounting Matrix of Ghana is available on the public website “EC Data-Modelling platform of 
agro-economic research DataM". Links can be also accessed with the QR codes listed in this annex.  

Figure A1. QR code – DataM URL: https://datam.jrc.ec.europa.eu  

Source: JRC, 2021. 

Bulk download 

Using DataM, users can make a bulk download of SAM in a ZIP file (Dataset_JRC_-_Social_accounting_matrix_-
_Ghana_-_2015.zip)  containing  a  homonymous  CSV  file.  The  hyperlink  for  the  direct  bulk  download  is  in 
Figure A2.  

Figure A2. QR Code – direct bulk data download: 
https://datam.jrc.ec.europa.eu/datam/perm/dataset/9e5aa734-2dac-4e08-bf14-
d518151e4f53/download/dataset.zip   

Source: JRC, 2021. 

In the bulk download, the SAM is presented in a standard flat format as CSV file with header row. 
Conceptually, it contains a column for the spending agent, a column for the receiving agent and a column for 
the value in Millions of Ghanaian Cedi. See figure A3. 

30 

 
 
 
 
Figure A3. Bulk download of the matrix in flat table format 

Source: DataM, provided by the European Commission – Joint Research Centre. Dataset: JRC - BioSAMs for the EU Member States - 2015, 
accessed on 30/04/2021. 

In fact, the file contains also columns for the codes internally used in GAMS for the agents and the Year (always 
2015). The extra columns help for using the data in modelling tools, and for characterizing this file among other 
SAM's that are published by JRC. 

Interactive download 

—  DataM includes also a function for interactive download, which allows filtering the only part of interest of 

the datasets and to preview results on the screen. Find the direct link for the SAM in the figure A4. 

Figure A4. QR Code – direct link to the data warehouse page of the dataset: 
https://datam.jrc.ec.europa.eu/datam/perm/dataset/9e5aa734-2dac-4e08-bf14-d518151e4f53  

—  The link gives access to the screen in the figure A5. 

Source: JRC, 2021. 

31 

 
 
 
 
 
 
 
 
 
Figure A5. Data warehouse page of the Ghana SAM 2015 

Source: DataM, JRC, 2021. 

—  After specifying some filtering and pushing on the "Next" button, the data is visualised on the screen, see 

figure A6. 

Figure A6. Visualising the SAM on the screen 

Source: DataM, JRC, 2021. 

32 

 
 
 
—  The "Download results as CSV" option would produce a file similar to the one obtained in the bulk download, 

but only with the records reflecting the selection operated. See figure A7. 

Figure A7. Exporting only part of the SAM in flat format 

Source: DataM, provided by the European Commission – Joint Research Centre. Dataset: JRC – SAM Ghana - 2015, accessed on 
11/05/2021. 

Interactive dashboard 

—  Finally, users may explore and analyse the data through an interactive dashboard placed in the “PANAP 

network” section of the website (Figure A8).  

Figure A8. QR Code – direct link to the interactive dashboard: 
https://datam.jrc.ec.europa.eu/datam/mashup/SAM_GH_2015  

Source: JRC, 2021. 

—  The interactive dashboard allows users to undertake their own analysis of the dataset. 

—  It consists of a number of sheets that allow analysing data from different perspectives. 

Figure A9. Navigating within the sheets 

Source: DataM, JRC, 2021. 

Each sheet consists of a screen with a number of different visualisations (tables, charts and maps) and some 
filtering boxes. 

The key strength of the tool is that all these visualisations are interactive and interrelated. This allows users to 
study the data by means of simple mouse gestures. 

The DataM visualisation framework is quite intuitive; some basic guidelines to facilitate its use will follow. All 
DataM dashboards are similar to the example shown in Figure A10. 

33 

 
 
 
Figure A10. A generic dashboard 

Source: DataM, provided by the European Commission – Joint Research Centre. Dashboard: SAM Ghana - 2015, accessed on 11/05/2021. 

—  By clicking on any visualisation, for example by clicking on "Maize (home consumed commodities)" in the 
left  table  by  spending  agent,  all  the  visualisations  are  recalculated  using  data  concerning  only  the 
expenditures from maize activity. 

Figure A11. Making an interactive selection 

Source: DataM, provided by the European Commission – Joint Research Centre. Dashboard: SAM Ghana - 2015, accessed on 11/05/2021. 

—  For example, in Figure A12 the right table now shows all the agents that receives from Maize and the below 

diagram illustrates these flows. 

34 

 
 
 
Figure A12. Result of an interactive selection 

Source: DataM, provided by the European Commission – Joint Research Centre. Dashboard: SAM Ghana - 2015, accessed on 11/05/2021. 

The currently active selections are always shown in the dark-grey bar at the top. Selections can be cancelled or 
changed as explained in figure A13. 

Figure A13. Instructions to change selections 

Source: DataM, provided by the European Commission – Joint Research Centre. Dashboard: SAM Ghana - 2015, accessed on 11/05/2021. 

35 

 
 
 
 
 
 
 
 
 
Downloading the traditional matrix 

—  From the "Full matrix" sheet of the interactive dashboard, users can visualize and make the download in 

"xlsx" format of the SAM in traditional sparse-matrix aspect.  

Figure A14. How to download the SAM in traditional matrix format 

Source: DataM, provided by the European Commission – Joint Research Centre. Dashboard: SAM Ghana - 2015, accessed on 11/05/2021. 

The download icon at the top-right corner of the chart (see figure A14), which is visible when passed over with 
the mouse; allow the data for the chart to be downloaded. The "+" and "-" icons allow to expand the matrix in 
case there are nested dimension (for this pivot table, these buttons do not have any effect), whereas the last 
icon on the right is to show the chart in full-screen mode.  

See the aspect of the downloaded file in figure A15. 

Figure A15. Traditional matrix outcome 

Source: DataM, provided by the European Commission – Joint Research Centre. Dashboard: SAM Ghana - 2015, accessed on 11/05/2021. 

36 

 
 
—  JRC, especially in the modelling tools, uses codes for identifying the agents. By toggling the "Show codes" 
switch, the column and row headings of the SAM are changed from agent names to agent codes. See figure 
A16. 

Figure A16. Downloading a SAM with codes 

Source: DataM, provided by the European Commission – Joint Research Centre. Dashboard: SAM Ghana - 2015, accessed on 11/05/2021. 

Open data portals 

—  In the period subsequent to the publication of this report, following pages will5 be gradually activated on 

relevant open data portals, enabling to easily find this dataset on the web: 

1.  On  the  JRC  Data  Catalogue:  https://data.jrc.ec.europa.eu/dataset/d7423648-90b8-403d-8518-

ff2152f59820  

2.  On the EU Open Data Portal: https://data.europa.eu/euodp/data/dataset/d7423648-90b8-403d-

8518-ff2152f59820  

List of abbreviations and definitions 

CSV 

GAMS 

DataM 

Comma separated value 

General Algebraic Modelling System 

JRC data-modelling platform of agro-economics research 

QR code 

Quick response code 

SAM 

XLSX 

Social Accounting Matrix 

Microsoft Excel file format 

5 The time necessary for the activations of these services is beyond the control of the authors. 

37 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 3. The linear SAM model and multipliers 

SAM are used as databases for linear multiplier models, enabling the linkages between sectors to be analysed 
which is useful for studying the impact of different policies. 

The starting point is the Leontief's equilibrium equation (6), applied to the case of a SAM obtaining the SAM 
Leontief inverse (Pyatt & Round, 1985). The standard representation is as follows: 𝑀 = (𝐼 − A)(cid:2879)(cid:2869), where the 
matrix  𝐴  is  the  coefficient  matrix  (calculating  dividing  each  element  of  the  SAM  by  the  total  of  their 
corresponding column),  and each element 𝑚(cid:3036)(cid:3037) in 𝑀 shows the output requirements of account 𝑖 to increase 
the final demand of account 𝑗 by one unit (or the input requirements of account 𝑖 to produce one unit by account 
𝑗) (Mainar-Causapé et al., 2018a). This matrix is used as a tool to evaluate the capacity of each economic sector 
to generate output and employment in the rest of the economy through the analysis of multipliers.   

Output multiplier 

The sum of the multiplier values of the commodities column of 𝑴(cid:3028), shows the output multiplier. This multiplier 
indicates the increase in output of the economy generated as a result of a unitary exogenous shock in exogenous 
values  for  the  corresponding  commodity.  A  high  value  of  this  multiplier  indicates  an  account  with  a  large 
backward income expansion influence on the rest of the economy, given that its input requirements to cope 
with the increase when it receives an exogenous shock are transferred to its upstream input suppliers (Pulido 
& Fontela, 1993).   

Employment multiplier 

The employment multiplier provides the number of jobs that would be generated by an exogenous shock in 
final  demand.  First,  it  is  necessary  to  have  the  employment  vector  𝒆,  that  contains  the  ratios  between  the 
number of jobs and the output of each activity (per million of output value). The employment matrix  𝑬 is a 
diagonal matrix whose elements are the vector 𝑒 and is multiplied by the part of the multiplier matrix (𝑴(cid:3028)) that 
incorporates the rows corresponding to the productive accounts and the columns corresponding to commodities. 
The  expression  of  the  employment  multiplier  is  given  as:  𝑴𝒆 = 𝑬 × 𝑴𝒂.  Each  element  in  𝑴𝒆,  𝒎𝒆𝒊𝒋  is  the 
increment in the number of jobs in sector 𝑖  when there is a unit exogenous injection into the endogenous final 
demand account of 𝑗. The sum of the columns in the matrix shows the global effect on employment produced 
by the exogenous increase in demand. In summary, the multipliers reveal the number of additional jobs per 
million of additional output from each activity (Mainar-Causapé et al., 2018a). Notably, the results do not take 
social variables such as the quality of employment into account. Besides, due to the assumptions of the model 
mentioned above, the results should not be interpreted as an exact forecast of job creation due to exogenous 
shocks. Nonetheless, they are useful as an indicator of the economy commodities with a greater potential to 
generate jobs.  

Value added multiplier 

The Value added multiplier relates the new value added created in each sector in response to the exogenous 
shock (Miller & Blair, 2009). Similar to the calculation of the employment multiplier, a value added vector can 
be used to calculate the Value added multiplier. The vector 𝒗 contains the ratios between the value added and 
the output of each activity. The sum of the remuneration of factors plus taxes les subsidies on production is 
the value added by Activities. 

(6) Assuming Leontief technologies (i.e. with fixed prices and no substitution elasticities), the result should be taken with caution.  

38 

 
                                                      
 
GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the address of the centre nearest you at: 
https://europa.eu/european-union/contact_en 

On the phone or by email 

Europe Direct is a service that answers your questions about the European Union. You can contact this service: 

-by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

-at the following standard number: +32 22999696, or 

-by electronic mail via: https://europa.eu/european-union/contact_en 

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa website at: https://europa.eu/european-
union/index_en 

EU publications 
You can download or order free and priced EU publications from EU Bookshop at: https://publications.europa.eu/en/publications. Multiple copies of free 
publications may be obtained by contacting Europe Direct or your local information centre (see https://europa.eu/european-union/contact_en). 

 
 
 
 
 
K
K
J
J
-
-
N
N
A
A
-
-
3
3
0
0
7
7
2
2
0
0
-
-
E
E
N
N
-
-
N
N

doi: 10.2760/432014
ISBN 978-92-76-38077-1

doi:10.2760/432014
ISBN 978-92-76-38077-1