Expanding the household coverage of global simulation models:

Transcription

1 . Expanding the household coverage of global simulation models: An application to Ghana Marijke Kuiper Lindsay Shutes FOODSECURE Technical paper no. 3 December 2014 INTERDISCIPLINARY RESEARCH PROJECT TO EXPLORE THE FUTURE OF GLOBAL FOOD AND NUTRITION SECURITY

2 Abstract Computable general equilibrium (CGE) models, with their depiction of the whole economy, are well placed to capture the impact of economic shocks and policies on household food and nutrition security. However, many of the global models available include only a single representative household which conceals the impact on the most vulnerable households. This paper presents an alternative method for creating the data for extending the coverage of global CGE models to include different household types, based on the theory and structure of the MyGTAP model (Walmsley & Minor, 2013). The method allows the user to make country-specific choices about the way that national household data are integrated with the existing GTAP database. We make use of the household data processing work already embedded in national Social Accounting Matrices to disaggregate the single household in the GTAP database commonly used in global CGE models. We believe this method is quicker than processing the household survey data from scratch and retains the household detail available in the national Social Accounting Matrices. We illustrate the method with an application for Ghana in which multiple household types are added to the MAGNET CGE model using a household module containing the MyGTAP model code. The addition of multiple household types adds a range of food and nutrition security indicators which can be used in combination with all other MAGNET modules including those covering biofuels and nutrition, to identify impacts varying by household type and inform policy interventions. The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7/ under Grant Agreement n FOODSECURE and the Dutch Government s Knowledge Base programme under KB LEI-1 and KB This paper is work in progress as part of Task 7.3 and comments are welcome. The authors are grateful to Peter Minor and Terrie Walmsley for generously sharing the MyGTAP data processing and modelling code and for valuable comments on the paper. The authors also thank Martine Rutten and Thom Achterbosch for useful comments on an earlier draft. Lindsay Shutes is grateful to the Centre for Agroecology, Water and Resilience (CAWR)at Coventry University, UK, for hosting her as a visiting scholar. The authors alone are responsible for any omissions or deficiencies. Neither the FOODSECURE project nor any of its partner organizations, or any organization of the European Union or European Commission is accountable for the content of papers in this series. 1

3 Contents 1. Introduction Methods for adding household detail to CGE models Introducing the SAM structure... 9 Original GTAP SAM... 9 National SAM Modified GTAP SAM with multiple households Adding household detail to global CGE models A SAM-based data procedure for adding household detail Separating the government & household accounts from the regional household Adjustment 1: Separating private household and government savings Adjustment 2: Separating private household and government expenditure Adjustment 3: Distributing factor and tax income to the private households and government Disaggregating the single private household into multiple household types Adjustment 4: Distributing factor income to multiple households Adjustment 5: Adding inter-household transfers Adjustment 6: Adding government transfers to multiple households Adjustment 7: Specifying the demand for domestic and imported commodities by household type Adjustment 8: Separating multiple household savings Adjustment 9: Adding household specific and government direct taxes Adjustment 10: Adding sales taxes by households Adjustment 11: Defining capital stock holdings and depreciation by household type and government Data sources for the SAM-based method Conclusions and further work References Appendix A: MAGNET Modules Appendix B: GTAP database accounts (Version 9) Commodities/Sectors (57 in total) Regions (140 in total)

4 1. Introduction Much of the concern over the high food prices in and was driven by the knowledge that poorer households spend a larger proportion of their income on food purchases, particularly on staple goods (De Janvry & Sadoulet, 2009; Zezza et al., 2008). Increases in the price of staples and other food products disproportionately affect the poor as they have less flexibility to reduce the consumption of other goods to maintain food consumption levels. Moreover, these poor households are likely to be food insecure 1 ; consuming near, or even below, the required daily intake for a healthy and productive life. The impact of high prices on food intake is, however, only one side of the story. Many of the world s poor are engaged in agriculture and may benefit from rising agricultural prices (World Bank, 2013). The overall effect of policies and shocks on food security is therefore determined by the interaction of prices and income 2. Net consuming households with little or no link to the agricultural sector are likely to experience increasing food insecurity in the face of rising prices yet net producing households may see a rise in income that outstrips the food price rise and improves their food security status (Zezza et al., 2008). Computable general equilibrium (CGE) models, with their depiction of the whole economy, are well placed to capture the impact of both prices and income on household food security, as well as analysing other economy-wide effects that could affect households, like terms of trade effects or the climate policies restricting access to land or promoting certain crops. However, many of the models available for measuring the impact of policies and shocks at the global level include only a single average household, representative of all households in a region. While this may be sufficient for many types of analysis, food and nutrition security (FNS) is an individual and household level phenomenon, in which household impacts depend upon differences in the level and source of income, and the level and pattern of consumption 3. A more detailed approach that accounts for these household characteristics is therefore needed. Indeed, an economic analysis that does not account for differences in income and consumption patterns will understate the FNS impacts on the most vulnerable households. Furthermore, a key objective in evaluating FNS impacts is to identify how governments can intervene to ameliorate negative impacts and support those who are most negatively affected. Identifying the pathways through which an economic shock or policy affects FNS is a core strength of economic simulation modelling and provides a sound basis for making policy recommendations. For these reasons, the FoodSecure modelling framework for assessing food and nutrition security, Toolbox , is being developed to include global CGE models with multiple types of households, where households are classified by rural/urban, income and/or geographical characteristics. Identifying households by these key characteristics allows the 1 Where food security is defined as when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life (FAO, 1998). 2 Various frameworks have been developed to analyse changes in food and nutrition security as summarised in Pieters et al. (2013). 3 We focus here on prices and income as key determinants of food and nutrition security. Other determinants such as intra-household food distribution and access to credit and insurance markets are important areas for future work. 4 Toolbox 2050 is a suite of models being developed to analyse long-term projections for food and nutrition security and the impact of policies on FNS. The toolbox includes two CGE models, MAGNET (LEI Wageningen UR) and MIRAGE (IFPRI), a partial equilibrium model, GLOBIOM (IIASA) and the IMAGE biophysical model (Netherlands Environment Agency, PBL). 3

5 different impacts of shocks on groups of households to be evaluated and enables evaluation of targeted policy interventions such as safety nets. The MAGNET CGE model is extended to include multiple households by incorporating the theory and model code of the MyGTAP model (Walmsley & Minor, 2013) in a new MAGNET household module. The Modular Applied GeNeral Equilibrium Tool (MAGNET) is a recursive dynamic, multi-sector, multi-region economic model (Woltjer et al., 2013; Woltjer, 2013). The model is developed around a core of the GTAP standard model and augmented with modular extensions that cover alternative production structures, land markets, changing income elasticities over time to account for Engel s Law, segmented agricultural and non-agricultural factor markets, the Common Agricultural Policy, biofuel directives, alternative investment specification, bilateral tariff rate quotas, quantities, nutrition (Rutten et al., 2013a), and now multiple household types. The introduction of multiple household types unpacks the impact of policies and shocks on the average household. As such, the extension can be used not only for examining impacts on vulnerable households but also the development of consumer groups within countries including the consumer elite. The MyGTAP model (Walmsley & Minor, 2013) is an extension of the standard GTAP CGE model that splits the regional household into three main agents - government, private households and investment - and links income to factor payments and taxes in each. Furthermore, the MyGTAP model includes multiple private household types for a single region of choice. This yields a global model, not unlike many single country models, which can be used for multiple household analysis including distributional issues. In all regions the regional household, which collects income, saves and allocates income to the private household and government, in the standard GTAP model, is removed and replaced with explicit income flows to households and government. Households receive income from the supply of factor services, remittances, foreign income and transfers and save a share of their income. The government receives income from tax revenues and aid payments and (dis)saves a share of its income which forms the internal balance. Separating the household and government accounts in this way introduces a direct link between changes in sources of income and change in household and government income; enriching country level analysis. In addition, MyGTAP allows for a greater number of factor types which strengthens the link between economic activity and household income. Where the MyGTAP model currently only allows for one region to have multiple private households (although all regions have the regional household removed, with a single private household) we extend the framework to allow for multiple private households in more than one region. By combining MyGTAP with MAGNET we gain the advantages of the modular design of the latter, including allowing us to combine household level detail derived from MyGTAP with any of the other modules included in MAGNET (see Appendix A), thus tailoring the complexity of the model to the question at hand. As MyGTAP is included as a separate module in MAGNET, it can be combined with all or some of the other modules as necessary, to give a household dimension to the analysis of topics such as biofuels, climate change and trade policies. By coding the data adjustments in a modular fashion, it also offers only part of the adjustments to users who might not be interested in having more detail at the household side but do want to have more detail in terms of production factors, which is part of the MyGTAP data processing procedure. The MyGTAP module in MAGNET yields a set of household level FNS indicators which are available in combination with all other modules. The extended model can therefore can be used to examine the FNS impacts of all types of policies including CAP reform, trade liberalisation and 4

6 biofuel policies. For example, the food security impacts of EU biofuel policy can be evaluated by running the model with the biofuel directive and household modules. The FNS indicators included are: domestic food production; producer food prices; food trade; household income decomposable into income by factor and transfers by type; the household tax burden; consumer food prices; the size and composition of the household food basket; the total and per capita nutrient intake per household; and household savings. The results of the model can also be used to construct a number of commonly used FNS indicators including self-sufficiency, the value of food production, protein supply, supply of protein of animal origin, GDP per capita, domestic food price index, share of food expenditure of the poor, (cereal) import dependency ratio and the value of food imports over total merchandise exports (FAO, 2014). The inclusion of multiple household types can also allow a range of poverty indicators to be included for each household, namely, the standard Foster-Greer-Thorbecke measures of headcount poverty, poverty gap ratio and the squared coefficient of variation of income among the poor (Foster et al., 1984). These poverty indicators can be computed using the actual distribution of income within each household type taken from the household survey. The data for the MyGTAP model can be generated using the accompanying MyGTAP Data Tool. The data tool uses user-specified weights to allocate the components of private household income and expenditure (including savings) across multiple household types. The weights can be taken from a household survey or from a Social Accounting Matrix. The weights approach includes a set of adjustment procedures to make the specified weights consistent with the GTAP data and the user is given the opportunity to iterate towards an acceptable set of weights. This paper presents an alternative method for creating the household data needed for including multiple households in global CGE models, such as those included in the Toolbox The method is based solely upon Social Accounting Matrices rather than user-provided weights and is referred to hereafter as the SAM-approach. The output is a set of data consistent with the MAGNET MyGTAP module, ready to be incorporated into the model. The paper introduces the SAM-based method through the example of including multiple household groups for Ghana in the GTAP database and in the MAGNET model using the MyGTAP theory and model code 5. The paper is structured as follows: a brief review of other available methods is presented in section 2. The structure of the social accounting matrix underlying the GTAP database is introduced and compared with a national SAM in section 3. This comparison yields a set of adjustments needed to generate a modified GTAP SAM with multiple households. The contribution of these adjustments to measuring FNS impacts is discussed in section 4 with the full SAM-based procedure for introducing multiple households into a global CGE model detailed in section 5. A list of potential data sources for applying the method for different countries is included in section 6 and conclusions and areas for further work are presented in section 7. Both the normalised weights approach (MyGTAP) and the SAM approach (MAGNET) offer a speedier, and potentially more accessible, alternative to methods that directly use household survey data. Using SAM data makes use of the household data processing work already embedded in national social accounting matrices (SAMs) by using the household accounts of 5 The MAGNET data procedure also disaggregates the five standard GTAP factors into more factors, however this not the focus of this paper. 5

7 national SAMs to disaggregate the single household in the GTAP database; thereby capitalising on the work already done by others to process the survey data into consistent household accounts. This method is quicker than processing the household survey data from scratch and retains the household detail available in the national SAMs. The procedure lends itself to collaborative work in the way envisaged by Hertel et al. (2011), where different modelling teams apply the method for separate countries and share their output, thereby constructing a multiple household GTAP database with broad country coverage. 2. Methods for adding household detail to CGE models There are several ways to include household level impacts in global economic analyses ranging from distributive analysis, in which the changes observed in the CGE model are distributed across different types of households using given or actual distributions of income (De Janvry et al., 1991; Decaluwé et al., 1999; Cororaton et al., 2005); micro-simulation, in which macroeconomic effects are transmitted to detailed household models (Hertel et al., 2011; Bussolo et al., 2010; Bibi & Chatti, 2006; Cogneau & Robilliard, 2000) and embedded households, in which the number of households is either increased to include multiple household groups (Minor & Walmsley, 2013; Bouët et al., 2012) or the full set of households included in the household survey (Chitiga & Mabugu, 2006; Annabi et al., 2005). Cororaton & Cockburn (2005) discuss the merits of each approach. The method presented in this paper pertains to embedding multiple representative household groups in a global CGE model. Extending the number of representative households using the MyGTAP model, is chosen over embedding the entire household survey as correctly specified representative household groups will accurately reflect the dynamics of the individual households within them, whilst respecting the computational constraints that exist for running global models with high levels of detail for multiple countries. The challenge in embedding multiple household types in a global CGE model lies both in capturing the starting point of each household (as shown in the model database) and the behaviour of each household in response to shocks and policy changes (as captured in the MyGTAP model code). This paper addresses the first challenge of how to include multiple representative household types in the database of a global CGE model. Bouët et al. (2013) identify two desirable characteristics for such a data processing method: first, the data process should yield a collection of national level datasets that provide us the opportunity to implement our model for a large set of countries and second, the process should grant enough flexibility to change the country coverage and the level of household disaggregation (Bouët et al, 2013 p.28). Two approaches have been used in the literature to include multiple household types in the database of global CGE models: the weights approach adopted in MyGTAP (Minor & Walmsley, 2013) and the household survey approach adopted in MIRAGE-HH (Bouët et al., 2012). The MyGTAP data procedure currently enables multiple household types and other developing country features such as remittances and aid to be added for a single region of the GTAP database. The procedure is based on the SplitCom program (Horridge, 2008) and involves 10 steps (Minor & Walmsley, p.9, 2013). Crucially, the MyGTAP data procedure requires the user to define the weights for the splitting of household consumption and factor income to households, although default weights are provided as a starting point. These weights could be taken from household survey data or national SAM accounts. 6

8 In MIRAGE-HH, the multiple household types are constructed using a bottom-up approach from the household survey data. The advantages of this approach include the use of the most recent household survey data, no limitation on the number of household types that can be included and updating of the GTAP values where the household survey data are deemed to be more accurate. The disadvantages are that the process is very time-consuming and is limited to countries for which household survey data are available. Using this approach, the current MIRAGE-HH (HouseHolds) model, includes 109 household types for Uruguay, 142 for Pakistan and 96 for Tanzania, with the household groups for Brazil and Vietnam under construction (Bouët et al., 2013). These are indeed rich data sets but costly to construct in terms of processing time. In the SAM approach presented in this paper, the household accounts of the national SAM (constructed from the household data) are used to construct the household income and expenditure flows in the global database. A comparison of the three data processing methods is shown in Figure 1. All three methods ultimately use household survey data to enumerate the income and expenditure flows of different household types in the global database. In the case of the weights approach, the household data (or national SAM) are used to derive weights that are applied to the single representative household to generate the data for the individual household groups. In the household survey approach, the data are processed directly into the household accounts in the global database. The SAM approach presents an intermediate method, using household accounts in national SAMs that have been constructed from household survey data. Figure 1 Comparison of household data processing procedures Weights approach Household survey data User-provided weights Global database with multiple household types SAM approach Household survey data National SAM with multiple household types Global database with multiple household types Household survey approach Household survey data Global database with multiple household types The MyGTAP Data Program takes the GTAP macro data as given, thus there are no adjustments in for example GDP when introducing multiple households. The MyGTAP data module in MAGNET follows the same approach, using the GTAP data as control totals. The motivation being that the MyGTAP module aims at disaggregating the GTAP private household; any other changes to GTAP data are coded in separate MAGNET data modules making them available to other uses of the model as well. This implies that, in both approaches, the income and expenditure of the government and private household must add up to the values for the regional household flows in the GTAP database, and the income and expenditure of multiple private households add up to those of the single private household. In contrast, the household survey approach used in the construction of the MIRAGE-HH database, allows for changing some aspects of the GTAP SAMs, 7

9 in particular on some aspects of the database that are largely reprocessed during the building of the GTAP database (e.g. VA share in the agricultural sectors), (Bouët et al., 2013). It should be noted that the weights approach used in the MyGTAP Data Tool has been fully reconciled with the SAM approach (see Appendix 1 of Minor & Walmsley, 2013). The approach outlined in this paper differs from the MyGTAP approach in a number of ways. First, in the SAM approach, the user must begin with a national SAM; a user must reconcile data in a SAM before entering them into the system. Second, the data processing steps are explicitly coded in order to produce the weights used to split out multiple private households. This allows for the introduction of country specific adjustments, for example, households do not explicitly receive income from national resources in the Ghana national SAM so the GTAP payment is spread over households according to their share in total income. The same situation, however, could be resolved differently in another region if necessary, for example, by associating national resource payments with land ownership by household. Third, like MyGTAP, the regional household is removed for all regions and multiple households can be introduced. Unlike MyGTAP, however, our approach has additional flexibility which allows some regions to take the standard GTAP regional household specification, others to take the separate government and private household specification and yet others to include multiple private households, according to the modeller s specification. Finally, the government may own factors and receive factor income. The second difference noted above warrants further mention as it reflects a difference in underlying philosophy between the MyGTAP and MAGNET approaches. MyGTAP makes use of an entropy procedure to bring the user supplied weights in line with the GTAP data. While the user can interact with the procedure by viewing the output at each stage of the iteration, we prefer to avoid automatic balancing procedures and require the user to code step by step data adjustments for each country in a SAM framework. There are two key points at which the income and expenditure accounts need to be balanced: the first when the regional household is split into a single private household and the government account and the second when the single private household is split into multiple households. The first balancing process ensures that government expenditure (including savings) is equal to government income, either by introducing government transfers to the household to increase government expenditure or increasing government income by introducing income from factors. The process is outlined in more detail in Adjustment 3 and necessarily brings the private household account into balance at the same time, as the two accounts sum to the regional household account. The second balancing adjustment brings the expenditure of the detailed households in line with their income from factors and transfers by adjusting the consumption of the household groups and associated taxes. The procedure is explained more fully in Adjustment 10. Arguably this approach is more transparent than automatic balancing procedures, however, it may be less accessible for users with little programming experience (although there are examples to follow) and may introduce path dependency as the data are added sequentially, compared to the MyGTAP data procedure where all weights are introduced at once and the program iterates towards a solution. Measuring the SAM-based approach against the desirable characteristics identified by Bouët et al. (2013), leads to the conclusion that the approach will yield a collection of national level datasets that provide us the opportunity to implement our model for a large set of countries and does... grant enough flexibility to change the country coverage. However, the level of household disaggregation is limited by the number of household groups in the national SAM, 8

10 which in some cases is quite small. Here the trade-off between the reduction in data processing time and data coverage is clearly evident and it may be best to consider the SAM approach as sufficient for most analyses, particularly those that are time-sensitive and requiring analyses for a broad range of countries, with the household survey approach being more appropriate for indepth country studies in which the income and consumption patterns of households are thought to differ significantly from the household groups in the national SAM. Finally, it should be noted that all three approaches by definition result in a global SAM, consisting of national SAMs with households. It is therefore possible to include results from the other approaches, where available, to get the broadest coverage possible. 3. Introducing the SAM structure Since every economic model has its corresponding accounting framework, and since every such framework can be set out as a SAM, it follows that every economic model has a corresponding SAM. Pyatt (p.330, 1988) Although the GTAP database is not traditionally held in a SAM format, the underlying accounting framework ensures that it can be rearranged into such a form (see McDonald & Thierfelder, 2004). The GTAP database in SAM form is a set of individual country SAMs linked by trade and capital flows; where each country SAM depicts the flow of income and expenditure between accounts in a particular year. Holding the GTAP data in SAM form allows the required consistency between the income to, and expenditure by, each account to be easily checked and provides a useful framework for visualising and implementing adjustments to the data. Original GTAP SAM The structure of the standard GTAP SAM is shown in Figure 2. All SAMs are a form of doubleentry bookkeeping and as such, each entry reflects both an expenditure by the column account and an income to the row account. For Version 9 of the GTAP database, these flows are included for 57 imported commodities, 57 domestic commodities, 57 activities, 5 factors (natural resources, land, capital, skilled labour, unskilled labour) and 1 private household in 129 regions (see the Appendix B for a full list of commodities and regions). For example, the household demand for imports captures the expenditure by private households (the column account) on all 57 types of imports. This forms an income to the imported commodities account (the row account). In this way, the SAM depicts the circular flow of income in an economy in a given year. As with the circular flow, the flow of income in one direction is matched by the flow of goods, services and factors in the other direction. Following the previous example, the expenditure of households on imported commodities is matched by a flow of those commodities from the imported commodity account to households in exchange for payment. Readers familiar with national SAMs may be surprised to see both a Regional Household and a Private Household account. The Regional Household receives both factor and tax income and allocates this income to private household consumption, government consumption and savings (Hertel, 1997). A consequence of this structure (put in place in part to handle data limitations resulting from the global coverage of the database) is the absence of a direct link between factor incomes and the private household, and tax revenues and the government. Moreover, there are no separate flows for private household and government savings. This is clearly a limiting factor 6 In version 9 of the GTAP database. 9

11 if we wish to use a GTAP style model for domestic policy analysis. We therefore create a separate private household and government account as part of the data adjustment procedure to link household income to factor income and government income to tax income (see Bouët et al. (2013), for a similar approach in the MIRAGE model). 10

12 Imported commodities Domestic commodities Figure 2 GTAP Social Accounting Matrix for a representative country (adapted from McDonald & Thierfelder, 2004) Imported commodities Domestic commodities Activities Factors Regional household Imported intermediate inputs Domestic intermediate inputs Private household Household demand for imports Household domestic demand Taxes Government Capital Margins Rest of world Government Investment demand for demand for imports imports Government domestic demand Investment domestic demand Exports of transport services Activities Supply matrix Output demand Factors Regional household Private household Payments to factors Taxes Import duties Export duties Sales, factor, production taxes Government Distributed factor incomes Factor income taxes Private household income Government income Tax revenue Exports of goods and services Total Import demand Domestic demand Factor income Regional household income Private household income Sales taxes Sales taxes Sales taxes Tax revenue Capital Depreciation Savings Trade balance Margins Transport Imports of margins on transport imports margins Rest of world Total Imports of goods and services Supply of imports Domestic supply Domestic output Factor payments Regional household expenditure Private household expenditure Tax payments Government expenditure Investment Margin expenditure Trade balance Income from rest of world Government income Savings Margin income Payments to rest of world 11

13 Figure 3 Social Accounting Matrix for Ghana 2005 with household detail (adapted from Breisinger et al., 2007) Commodities Activities Factors Private household Taxes Government Capital Rest of world Total Commodities Transaction costs Intermediate inputs Accra Urban coast Urban forest Urban south Urban north Rural coast Rural forest Rural south Rural north Government demand Investment demand, changes in stocks Exports of goods and services Activities Supply matrix Output demand Factors Payments to factors Household demand Total demand Factor income Accra Urban coast Urban forest Urban south Urban north Rural coast Rural forest Rural south Rural north Taxes Private household Government Capital Sales taxes, import tariffs Production taxes Factor income to government Tax revenue Government savings Rest of world Imports Government overseas transfers Total Total supply Domestic output Distributed factor income Factor payments Inter-household transfers Direct household taxes Household savings Private household expenditure Tax payments Transfers to households Government expenditure Investment Transfers to households from overseas Transfers to government from overseas Trade balance Income from rest of world Private household income Tax revenue Government income Savings Payments to rest of world 12

14 National SAM Aside from the regional household account, the structure of the GTAP SAM will be familiar for readers acquainted with national SAMs or single country CGE modelling. Indeed, it is precisely the similarity between the structure of the GTAP and national SAMs that enables the household data in the national SAM to be relatively easily incorporated into the GTAP SAM; thereby expanding the single household account in the global database into multiple household accounts for one or more regions. The structure of a Ghana SAM (Breisinger et al., 2007) is given as an example of a national SAM in Figure 3. The Ghana national SAM includes more detail than the GTAP SAM of the same region, including 70 commodities, 142 activities (with regional production for 27 agricultural activities), 13 factors of production and 9 households. The nine representative household groups are location based: Accra, Urban Coast, Urban Forest, Urban South, Urban North, Rural Coast, Rural Forest, Rural South and Rural North. This extra information on the household accounts is of particular interest as we can use it to expand the single private household in the Ghana region of the GTAP database into nine private household types. This will increase the ability of the global model to capture the distributive effects of shocks and policy changes that originate both in Ghana and elsewhere in the world. It should be noted that Ghana is used here as an example but the method presented is relevant for any country for which a national SAM with household detail is available (see section 6 for a list of readily available national SAMs with household detail for other regions). Modified GTAP SAM with multiple households The addition of multiple household types to the GTAP database results in a modified GTAP SAM, as shown in Figure 4. A comparison of the original GTAP SAM (Figure 2) with the GTAP SAM with multiple household types (Figure 4) highlights 11 differences in the income (rows) and expenditure flows (columns): 1. Separate private household and government savings 2. Separate private household and government expenditure 3. Explicit income flows to the single private household and government 4. Explicit factor income flows to multiple households 5. Inter-household transfers 6. Government transfers to multiple households 7. Demand for domestic and imported commodities by household type 8. Household savings by household type 9. Household specific direct taxes 10. Sales taxes by household type 11. Capital stock holdings and depreciation by household type and government 4. Adding household detail to global CGE models Each of the differences between the national and GTAP SAMs reflects an adjustment needed to add multiple households to the GTAP database to capture FNS impacts more fully. In this section, we consider each of these adjustments in terms of their contribution to modelling food and nutrition security in global CGE models. This section concludes with a summary of the FNS indicators produced by the extended MAGNET model. 13

15 Imported commodities Domestic commodities Figure 4 GTAP Social Accounting Matrix for Ghana with nine household types (where the numbers refer to the steps of the adjustment procedure) Imported commodities Domestic commodities Activities Factors Private household Taxes Government Capital Margins Rest of world Imported Government Investment intermediate Household demand for ❷❼ demand for demand for inputs imports imports imports Domestic intermediate inputs Government domestic demand Investment domestic demand Exports of transport services Exports of goods and services Activities Supply matrix Output demand Factors Payments to factors Factor income Private household Taxes Accra Urban coast Urban forest Urban south Urban north Rural forest Rural south Rural north Rural Accra Government Import duties Export duties Sales, factor, production taxes Factor income taxes Factor income Tax revenue Capital Depreciation Government Household savings ⓫ ❶❽ savings ❶ Margins Transport margins on imports Rest of world Total Imports of goods and services Supply of imports Domestic supply Distributed factor income Domestic output Factor payments Household domestic demand Inter-household transfers Sales taxes Private household expenditure ❷❼ Tax payments Government transfers to households ❸ ❹ ❺ ❻ ❾ ❸ ❿ ❸ ❷ ❷ Total Import demand Domestic demand Sales taxes Sales taxes Tax revenue Government expenditure Investment Trade balance Imports of transport margins Margin expenditure Trade balance Income from rest of world Private household income Governme nt income Savings Margin income Payments to rest of world 14

16 The first two adjustments pertain to the separation of expenditure by the single private household and government. Recall that in the standard GTAP database, saving is the provision of the regional household, which also receives all income. Separating savings and expenditure by the single private household and government allows for the private household to defer consumption through saving at a different rate from the government; something which is not possible under the standard GTAP specification. The third adjustment divides the income received by the regional household into separate income streams to the single private household and government. The income flow to the private household is then disaggregated to the multiple households in adjustment 4. These adjustments are important because they explicitly link tax revenues and factor ownership to government spending and the internal balance and the income from factor ownership to private household consumption and savings. Furthermore, the use of a factor ownership matrix allows for multiple sources of factor income to better capture the link between economic activities and household (and government) income. Although factor ownership matrices have been common place in single-country CGE models for some time (Lofgren et al. 2002; McDonald, 2009), their inclusion in global CGE models is novel. Both MyGTAP (Walmsley and Minor, 2013) and MIRAGE-HH (Bouët et al., 2013) capture household income from multiple sources. This represents a significant improvement over previous applications that mapped factor income one-to-one with household types, for example, all income from agricultural labour accrues to the rural household. Given that the overall effect of economic policies and shocks on food security is a combination of changes in incomes and prices, it is imperative that the income sources of households are clearly depicted in this way; particularly as the composition of income is likely to differ for different types of households. Consider a poorer household which derives the majority of its income from the supply of unskilled agricultural labour. An expansion in the agricultural sector, such as may arise following an increase in the demand for biofuels would, ceteris paribus, increase their income and FNS status, compared with households who supply unskilled labour to the manufacturing sector, even in the face of rising food prices. These adjustments present an extension to the GTAP database which pools factor income to private households with tax revenues under the income to the regional household. Inter-household transfers can be an important part of households resilience to shocks that negatively affect food and nutrition security; acting as a buffer to dampen negative impacts that arise from falling factor income. Adjustment 5 adds inter-household transfers to the database for regions with multiple households. The presence of inter-household transfers can make poorer households dependent upon the success of production activities in which the factors supplied by the households making the transfers are employed, thus creating indirect links between sectors in a country and/or regions. For example, rural households may receive income transfers from related households in the urban area, thereby creating a link between the success of the urban economy and the income of the rural household. Government transfers to households are not explicit in the standard GTAP database because they are subsumed in the regional household, yet these transfers are likely to be distributed unevenly across different household types. Government transfers can be an important intervention tool for supporting vulnerable households in the face of policies and shocks with uneven distributional effects and this necessitates their inclusion in a global CGE model through adjustment 6. Moreover, government transfers can act as a buffer to the negative impact of food price rises on FNS to the extent that keep pace with food prices inflation. 15

17 The interaction of income and price effects to determine household FNS necessitates improvements not only on the income side, but also on the consumption side. The different levels and patterns of consumption among food insecure households, such as spending a high proportion of their income on food and a higher share on staples, make them more vulnerable to price and income changes than the average household. We therefore include the detailed consumption patterns of different types of household in adjustment 7, to capture these effects and their implications for FNS. In addition to including households consumption patterns, it is important to identify whether products are sourced domestically or imported. The price of domestic food products are subject to changes in the domestic economy, whereas imported goods are affected by international conditions including exchange rate movements and the economic conditions in the producing countries. Explicitly including domestic and imported commodities is also important because domestic food security can be supported by imports of food products to the extent that imported goods are accessible to households. Conversely, shocks on world markets may increase import prices and negatively affect domestic FNS (Rutten et al., 2013b). This distinction is also of interest because the intervention mechanisms to affect domestic and imported food prices are different; with sales taxes operating on products from both sources, and import duties and the export taxes of the producing country affecting the price of imports. Adjustment 7 also accounts for different patterns of consumption by source for each household type. The savings rates of different types of households are introduced in adjustment 8. Identifying the different savings behaviour of different households is an important part of capturing their vulnerability and resilience as households with more savings will be better placed to cope in the face of falling income or rising prices. Adjustments 9 and 10 introduce household specific income and sales taxes. It is desirable to have household specific taxes in the CGE model not only to understand the tax burden of different types of households, but also as potential policy intervention tools. Sales taxes are levied at different rates on domestically produced and imported products in the GTAP model. In addition to this, we allow for the possibility that sales taxes could differ by household type in the modified database. Even without different rates, the tax incidence of different types of household will differ due to the pattern of consumption. Indeed, many countries have low or zero sales tax rates on basic necessities, such as food and children s clothing to support the poor who spend proportionally more of their income on such items. Finally, we track the ownership of capital by household type by explicitly identifying the capital stock held by each household and the government, along with the associated depreciation. We would expect to see that richer households would own more capital; with poorer households relying on income from labour. Applying these adjustments results in a global modelling database that includes the income and consumption patterns of different household types for key countries. Combining this database with behavioural relationships for each household type, using the MyGTAP code included in MAGNET, results in a wide range of FNS indicators for each household type. The range of indicators for each dimension of food and nutrition security in the MAGNET CGE model is shown in Figure 5, following the FoodSecure conceptual framework (Pieters et al., 2013). The indicators in bold are given a household dimension through the MAGNET household module. 16

18 Figure 5 Food and nutrition security indicators in MAGNET Food and Nutrition Security Status Stability Food availability Food access Food utilisation Vulnerability & Resilience Domestic food production (qo) Food producer prices (ps) Trade in food (pcif, pfob,qxs) Household income (yph) decomposable by source Factor income (pf, qf) Consumer food prices (pph) Government transfers (realtrng) Household food basket (qph) decomposable by food type and source (imported and domestic) Household nutrient consumption: total (NQTH) and per capita (NQPCH) and by source Household savings Aid payments Inter-household transfers (realtrnh) Remittances Household tax incidence When combined with the method presented in Rutten et al. (2013a). 17

19 In terms of availability, the model gives changes in domestic food production and producer prices by product and region, and trade by product and exporting and importing region. The indicators along the access dimension are enriched through the addition of household income which is decomposable by source, inter-household, government and overseas transfers, the tax burden of households and consumer prices by product in each region. Food utilisation is captured by the food basket which is available for each household type and decomposable by type of product such as staples, vegetables and fruits, meat and dairy and whether the food is sourced domestically or from abroad. In combination with the MAGNET nutrition module (Rutten et al., 2013a), the household module will also enrich the FNS indicators of food utilisation by providing the total and per capita macronutrients (calories, fats, protein and carbohydrate) consumed by people in each household from raw and processed food and via food consumed outside the home. This allows dietary diversity indicators to be calculated for each household type. 5. A SAM-based data procedure for adding household detail This section details the 11 data adjustments needed to include multiple household types in the database of global CGE models discussed in more general terms in sections 3 and 4. Applying these adjustments produces a set of headers consistent with the MyGTAP household code and suitable for all GTAP-based models as shown in Figure 6. Note that in practice, the household data are entered in satellite accounts, using the SAM-based approach. This is due to the modular nature of the MAGNET CGE model that allows the user to switch easily between the single household and multiple household versions of the model at the time of compilation. The implied adjustments to the GTAP SAM can be cross-referenced using the numbers in Figure 4. The 11 adjustments are grouped into two main steps: the separation of the GTAP regional household into the government account 7 and the private household (adjustments 1-3), and the separation of the private household into multiple household groups (adjustments 4-11). This allows for three possible household model options: the standard GTAP specification, a separate government account with a single private household, and a separate government account with multiple private households. In the MAGNET modular system, this flexibility allows the modeller to specify how much household detail is included for each region. As we take the GTAP values as the control totals 8, the sum of the government and the (multiple) private household(s) will always equal the GTAP values for the regional household. The original GTAP coefficients to which the household and government coefficients are related are shown in Figure 6. Separating the government & household accounts from the regional household The first step in adding multiple households to the GTAP database is to split the government and private household from the regional household. This is a simple split of the GTAP data which only uses additional data on the household saving rate to allocate regional savings. The split is applied to all regions of the GTAP database which allows the model to be run with separate private household and government accounts for all regions as an alternative to the standard regional household specification. 7 Which differs from the standard definition of government in the GTAP data because in our approach it receives income directly from factor income and tax revenue and includes explicit transfers and savings. 8 If the national SAM totals are considered an improvement over the GTAP values these will be included in MAGNET in a separate data module placed before the household data module. This modular approach makes such changes available to other model uses not related to adding more household detail. 18