Construction of a Social Accounting Matrix for Chad

Transcription

1 Construction of a Social Accounting Matrix for Chad David S. Garber ERSA working paper 426 April 2014 Economic Research Southern Africa (ERSA) is a research programme funded by the National Treasury of South Africa. The views expressed are those of the author(s) and do not necessarily represent those of the funder, ERSA or the author s affiliated institution(s). ERSA shall not be liable to any person for inaccurate information or opinions contained herein.

2 Construction of a Social Accounting Matrix for Chad DavidS.Garber April 4, 2014 Abstract The use of Applied General Equilibrium(AGE) models to contribute to discussions on economic growth and poverty alleviation in less developed countries has become widespread, particularly as data collection capabilities have improved over the past several years. Nevertheless the existence of a Social Accounting Matrix(SAM), the central source of data for any AGE model, remains lacking for many developing countries, often rendering the AGE exercise impractical in such cases. This paper details the construction of a SAM for a particularly datachallengedcontext,thatofchad. TheSAMwasbuiltforuseinanAGE model that looks at the interactions between Chad s oil revenue spending decisions and subsequent impacts on economic growth and household welfare. The resulting SAM is the only publically available example for Chad to account for economic flows at the dawn of the country s oil economy. The paper offers the AGE researcher interested in data-poor countries an example of a strategy to weave together sparsely available information intoasamofsufficientcredibilityasinputtoamodel. 1 Introduction TheimplementationofanAppliedGeneralEquilibrium(AGE) 1 modelrequires the knowledge or estimation of a large number of parameters representing various shares and elasticities that characterize the economy being analyzed. The Social Accounting Matrix(SAM) organizes the data, facilitating the calculation of such parameters, and is thus an essential foundational resource of economics research employing AGE models. The poor quality and frequent unavailability of data for highly impoverished countries such as Chad, however, pose a major constraint to the construction of asamthatcanbeusedasacredibleinputtoanagemodel. Eventhemost Economic ResearchSouthernAfrica (ERSA) 1 AGE models are often called and are equivalent to Computable General Equilibrium (CGE) models, the former name preferred by the Australian school of general equilibrium modelling. 1

3 aggregated SAMs require data, for example on intermediate demand values, that are at best estimates made by institutions external to the country. For this reason there are relatively few SAMs representing the world s least developed economies. This paper details the construction of a Social Accounting Matrix for Chad, subsequently used in an AGE model by Garber(2009, 2014 forthcoming) that looks at the probable socio-economic outcomes of differing revenue spending regimes following the emergence of the nation s oil sector. The work relies primarily on data published by Chad s national statistics office, the Institut National de la Statistique, les Etudes Economiques et Démographiques(INSEED). This data is supplemented by information published by the International MonetaryFund(2007)aswellasthatfromahandfulofotherinternationalbodies (the African Development Bank (ADB), the Bank of Central African States (BEAC), the World Trade Organization, etc.) Household data is drawn from the household survey, ECOSIT, unique in Africa for its focus on the informal sector. Foranyremainingdatathatappearsunavailable,IrelyonaSAMforNiger, constructed by Dorosh and Nssah(1991). While Niger is not an exact economic replicaofitsneighbortotheeast,itsresemblancetochadisstrongerthanthat of any other nation. The existence of a SAM for Niger (an endeavor that, at thetime,musthaverequiredagreatamountofdataminingandcourage)isa fortunate resource for approximating unknown values for Chad. This paper begins with a presentation of the Social Accounting Matrix its goalanditscomponents. ThisisfollowedbyadetaileddiscussionontheconstructionofaSAMforChad. InitiallyahighlyaggregatedversionoftheSAM is constructed. Then paper sets out to disaggregate the aggregated accounts of the matrix into component accounts when useful and feasible. The poor quality and unavailability of some data inevitably results in a SAM that is initially unbalanced(i.e., inflows not equal to outflows). There are several proposed methods to balance a SAM. For the aggregate SAM I correct the relatively small inconsistencies by inspection rather than by any systematic numerical procedure. The disaggregated SAM, containing a much larger number ofdataitems,isfirstcorrected,totheextentthatispossible,byinspection. The remaining residuals(most less than 1% imbalance) are corrected using the Cross- Entropy method introduced in the context of SAM construction by Robinson et al(2000). 2 What is a Social Accounting Matrix? As mentioned elsewhere in the literature, a SAM has often been mistakenly referredtoasamodel. 2 Itisnotamodel. Ratheritisaconvenientmethodof organizingtheeconomicflowsbetweenagentsofaneconomyinagivenyear 3. 2 See Round(2003) 3 ItisimportanttorecognizethatanygivenSAMrepresentsinter-agenteconomicflowsfor the year of its source data. Thus, without further updating, the most accurate interpretation 2

4 The representation compiles data from a wide variety of sources and organizes them in a convenient table. The matrix can then be used for a wide variety of modeling purposes including the derivation of many of the parameters in an AGE model. The columns of the matrix represent payments, or flows, from economic agents to other agents that are shown in the rows. Likewise the rows of the matrix show incoming revenues to agents from those shown in the columns. ThestructureissimilartotheInputOutput(IO)table;howevertheSAMgoes wellbeyondtheiotableinattemptingtoshowflowsbetweenallagentsinan economyratherthanonlythoseinvolvingthesupplyanduseofoutput. 4 The SAM attempts to show the entirety of flows in an economy. In its fairly aggregated form a SAM represents the flows between production activities, commodity markets, primary factors, the current account of institutions, the capital account of institutions, and a Rest of World(ROW) account. Each of these may be further disaggregated according to modeling needs and constraints. The level of disaggregation will depend on the modeling goal of the researcher aswellasondataavailability. ForexampleanAGEmodelthataimstoshow the impacts of an economic change on a number of defined household groups implies the need for a SAM that shows flows towards and away from each of these household groups. Poor data availability necessitates a combination of data estimation and sector aggregation. A key feature of the SAM is that column totals equal row totals. This is required by the definition of an economic equilibrium that the SAM is representing. Supply of commodities equals the demand for commodities; total government expenditure must equal total government revenue(allowing for savings and borrowing); receipts from the rest of the world must equal payments totherestoftheworld,etc. LookingattheupperlefthandcornerofTable1,onecanidentifythecomponents of a typical Inter-industry Flow matrix. In fact the construction of such a table is a requirement and only a small component of the work involved in writing a SAM. An activities agent(i.e., producers) is shown to purchase market commodities as intermediate inputs to production. The commodities agent, representing goods available for purchase from the domestic markets, in turn purchases output from the activities agent. Separating output into activities and commodities is standard in SAM construction. Oneoftheadvantagesofdoingsoistoallowfortherepresentationof multiple commodities produced by one activity and multiple activities supplying the same commodity. For example one may want to show separate informal and formal manufacturing activities that supply the same commodity. Marketing margins are represented as the intersection of the commodity ofitsuseinapolicysimulationimpliesanintroductionofthenewpolicyinthesamreference year. 4 A subtle distinction may be made between Supply-Use tables and Input-Output tables. The former, often in terms of purchaser prices and shown throughout multiple tables, provides the basis for an input-output table, which consolidates all information in basic prices into one symmetric table similar to a SAM. 3

5 account row and column. Such marketing margins are services purchased in the delivery of each commodity from source to market. Often the construction of SAMs ignores these marketing costs due to lack of information. In addition marketing is not frequently included in a typical Input Flow matrix. The lack of information on marketing margins is a weakness of many SAMs. Moving in the southeast direction of Table 1, one encounters the factor agents. Inthemacro-versionoftheSAM,Ishowadisaggregationintolabor and capital. AGE models may call for further disaggregation into labor types (educated and uneducated, for example). Factors are the recipients of the value addedfromeachactivityagentaswellasfromtherestoftheworld. Factorpayments are then distributed among institutions in the economy(factor owners) andtherestoftheworld. Continuing in the southeast direction of Table 1, one finds the current accounts of institutions. The most basic disaggregation of these accounts would be one that separates government current accounts from all others including households and enterprises. Many AGE models attempt to show the distributional impacts of economic phenomena. For such a case one needs a SAM that disaggregates households into different household types. Obviously the greater the disaggregation, the more challenging is the data availability requirement. The successful disaggregation of household types requires that a comprehensive household survey has been conducted and its data be available. For the least developed economies this requirement poses a serious challenge in accurately disaggregating the household accounts. For cases in which household data is unavailable, one is obliged to resort to estimations based on data from similar contexts. Households receive factor payments. Usually all wage payments are assumed to accrue to households. Households also receive transfers from other households, the government, and from the rest of the world (remittances). Households spend resources on goods and services(commodities), transfers to other households, income(direct) taxes, and savings(the capital account). Household receipts must equal household outlays(including savings). The government is represented as both an activity and as an institution. The activityproducesanoutputthatisoftenvaluedonthebasisoffactoruseand intermediate demand. Then the government institution consumes most if not all of this government commodity. The government receives its resources from tax payments, incoming transfers, and borrowing. Tax sources include those from production and sales(indirect taxes), income taxes from households and enterprises(direct taxes), and trade taxes(tariffs and duties). The government also may receive current account transfers from the rest of the world. The government then spends its resources on the government(public services) output, transfers to households and enterprises, and savings. The savings from institutions flows into the capital(and financial) accounts. Fixed capital formation includes the demand by institutions for the commodities that comprise investment(investment goods). It also includes the changes in capital stock. (Financial) capital may also flow between capital accounts. Capital account receipts include the savings by institutions along with capital 4

6 transfers from the rest of the world. Finally the ROW account compiles information that is typically found in a Balance of Payments (BOP) table. The ROW receives flows from import sales, interest payments on loans, and transfers to the ROW. ROW outlays include payments for exported goods, capital and current transfers to the subject economy, and payments for exported factors. The SAM requires payments to the ROW to be equal to receipts from the ROW. Inflows and outflows of the ROW are often balanced by a net lending or borrowing term shownas either a positive flow from the ROW to domestic capital accounts(borrowing) or one from domestic capital accounts to the rest of the world(lending). 3 Construction of an Aggregate SAM for Chad The availability of data required for constructing a SAM for Chad is sparse. This perhaps explains the reason for which no published SAM for Chad exists atthetimeofwriting. 5 Iattempttopiecetogetherdatafromanumberofkey sourcesihavebeenabletolocate,includingthatwhichwasacquiredduringa period of fieldwork in the country between September 2005 and March It is assumed here that the best data available is that which is collected by national(or local) entities rather than by international organizations, with the beliefthatthose whoaremostfamiliarwiththesourceof thedataare in a position to provide the most reliable data. It also recognizes, however, that there may exist particular capacities for data collection and/or estimation at the international level that do not exist at the local level. The effort to locate needed data begins with the few existing national sources, namely that published by Chad s National Statistics body, INSEED. When local source information is unavailable, it turns to the information available from international organizations. For cases in which data seems unavailable from any source,theeffortreliesonasamforniger(doroshandnssah,1991),usingits share structure as a basis for value estimations for Chad. Theyear2000ischosenasthebasisfortheChadSAMpresentedinTable 2. 6 Thisistheyearbeforesignificantconstructionofoilfieldsbeganandthus showsaveryusefulsnapshotofchad spre-oileconomy. 7 Inadditionthedata availability for 2000 is more complete(and consistent across sources) than for later years. In general a top-down approach is used to construct the aggregate SAM. 5 A SAM for Chad, unavailable at the time of writing, was used in Levy (2007). An effort currently under way, sponsored by the Partnership for Economic Policy (PEP), intends to build an AGE model for Chad as well as an underlying SAM: 6 The work originally began with a SAM for 2002 (whose work is available upon request). The 2002 SAM reflects the volatility, a particular disequilibrium, introduced by oil field development rather than the business-as-usual economy of Chad that is important to document for subsequent analysis of the oil sector. 7 Constructionoftheoilfieldsdidbeginasearlyas2000;howeverdatashowsthatthebulk of oil development activities did not begin until

7 First,anattemptismadetobasethevaluesintheSAMontheavailabledata from Comptes et Agrégats de la Nation, , (National Accounts and Aggregate Values) published by INSEED and from an IMF 2007 report on Chad 8. Therowandcolumntotalsareinevitablyunequalduetopoorinformation. Balancing the SAM is accomplished by analyzing each row-column pair and correcting the element that seems the least compromised when doing so. The resulting SAM values are substantially similar to those found in the National Accounts. Table 3 compares values used in the aggregate SAM of Table 2withthosefromCANandIMF2007. The only possibly significant weakness in the aggregate SAM concerns the oil sector. The available data shows only values for investment spending from the oil sector,butnotitssource. Itisassumedherethatthisisfullyfundedbycapital transfers from abroad. At this time no determination is made on the components oftheoilsectorcurrentaccountalthoughthisisaneededimprovementtothe SAM. Finally it should be noted that the value for marketing margins is estimated laterinthechapterandisprimarilyusedasatooltobalancethedisaggregated SAM. Including marketing margins has no effect on the balancing of the aggregate SAM. For reasons discussed later it is not unreasonable to assume that such margins exist and are in addition to the intermediate demand flows shown (often incompletely or inaccurately) by most data sources. 4 Disaggregating the Chad SAM 4.1 Production The aggregate values on production come from the Comptes et Agrégats de la Nation, , published by Chad s National Statistics Agency, the Institut de la Statistique, des Etudes Economiques et Démographiques(INSEED). Production is valued at 1,577 billion FCFA($US 2.2 billion@ 710 FCFA/$US), slightly adjusted to billion for reasons explained in Table 3. Intermediate demandisvaluedat623billionfcfa.thisimpliesafigureforvalueadded(or alternatively,factorreturnsandtaxes)of954.1billionfcfa 9. Activities are disaggregated into sectors that are of particular interest to the modeling work found in Garber(2009, 2014 forthcoming). The disaggregation specifies output values, intermediate demand, and factor returns, for each of the sectors. TheROWaccountthatisshowninTable6showsimportandexport flows for each of the sectors. The breakdown of production sectors approximately follows that found in the document. The sectors appearing in CAN are: 8 Ihereon use theabbreviation,can,to referto thisdocument 9 Due to the small size of indirect taxes on producers, the term, value added, is used interchangeably with factor returns. This is frequently done in national accounts documentation. 6

8 1. Food Crops 2. Industrial Crops 3. Livestock 4. Forestry, Fishing, and Mining 5. Oil Extraction(irrelevant for 2000) 6. Non-Oil, Non-Cotton Manufacturing 7. Cotton Fiber Manufacturing 8. Oil Field Development 9. Handicrafts( Artisanat ) 10. Water and Electricity 11. Construction and Public Works 12. Commerce 13. Transportation 14. Public Administration 15. Other Services I reorganize these into the following groupings for the Chad SAM: 1. Non-Cotton Agriculture 2. Cotton Agriculture 3. Livestock 4. Forestry, Fishing,(Non-Oil) Mining 5. Non-Cotton, Non-Oil Formal Manufacturing 6. Cotton Fiber Manufacturing 7. Oil Field Development 8. Informal Manufacturing 9. Construction and Public Works 10. Services 11. Public Administration The seed cotton production sector is made distinct from the original agricultural sector so as to reflect the traditional importance of the cotton sector inchadtoboththecultivatorandtheprocessor. Allothercropproductionis aggregated into the non-cotton agriculture sector. This ignores the emerging importance of Gum Arabic production; however, the work to obtain the data needed to separate out a cash crops sector other than cotton was judged greater than the potential benefits to this exercise. Furthermore all non-government services are grouped into one services sector. While value added by sector is a figure available in CAN, the intermediate demand block is nowhere available for the country. Thus the effort relies on an admittedly approximate estimate by imputing intermediate demand shares from Dorosh and Nssah s 1987 SAM for Niger. Reasonable assumptions that make this technique credible include: The Nigerien economy, particularly in terms of production, is structurally similar to the Chadian economy. 7

9 Intermediate demand flows of similar sectors are fairly constant across Niger and Chad. The structure of economic flows has not significantly changed across time (1987 to 2000) Table4comparestheeconomiesofNigerandChadinbothyears2000and The two economies are similar in key areas. Manufacturing sector value addedisverysmall(forboth,lessthan20%oftotalvalueaddedin2000). The valueofexportsasapercentageofgdpisalsoquitelow,whilethecorresponding figure for imports is significantly and similarly higher. The two economies rely heavily on one or two commodities for export income: Uranium and livestock (73% of the 2000 total) in the Niger case, Cotton and Livestock in the caseofchad(76.5%ofthe2000totalexportvalue). The primary and important difference between the two economies is that Niger s economy in 1987(as well as today) relies heavily on high-valued Uranium exports. OntheonehandthisposesastarkcontrasttotheeconomyofChadin 2000hoseexporttotalsatthetimeshowaheavyrelianceoncottonandlivestock. On the other hand, with the emergence of oil exports, Chad s economy may reasonably be expected to even further resemble that of Niger. This may prove useful in further comparative analyses between the two nations. To correct for this structural difference between the two economies, the value of components representing Niger s Uranium extraction sector is subtracted out of the overall value. Converting intermediate demand values for Niger to those for Chad involves the following steps: 1. Reorganize sectors in the Niger study to conform to those used in the Chad SAM. 2. Compute value added and intermediate demand as a percentage of total outputforeachsectorinthenigeriotable. 3. UsingValueAddedfiguresforChadfromCAN,applythecomputedpercentages of Step 2 to calculate output of each sector in Chad as well as intermediate demand by each sector. 4. Make minor changes to estimates in step 3 to conform with figures in aggregate Chad SAM as well as to conform with Chad-specific economic structure. 5. Compute the structure of each sector s intermediate demand in the Niger IO Table. 8

10 6. Apply the percentages in Step 5 to the intermediate demand totals for Chad to produce a structural breakdown of each sector s intermediate demand. 7. Adjust for any obvious errors and structural differences between the two economies. Table 5 shows all producer outlays. The activities purchase intermediate inputs, that when subtracted from output values, leaves a return for labor, land, and capital. These factor returns(not yet disaggregated into factor type) are shown in the row labeled, factors. The estimation process described above allows the completion of the intermediate demand block (the intersection of the activities columns with the commodities rows). The table shows that intermediate demand in the primary sectorisquitelowwhileitishighandexceedsvalueaddedforthemanufacturing sectors. As the cotton crops, cotton fiber, and oil development sectors are naturally not part of the Niger IO tables, it was necessary to turn to other sources to estimate the corresponding intermediate demand values. For the seed cotton production sector I assume the same intermediate demand structure as for all other crops. The intermediate demand structure of the cotton processing sector follows from IMF It appears that the sector s principal intermediate input costs, other than purchases of raw cotton, are on purchases of services transportation, financial goods, marketing, etc. The cost of services is computed as a residual after subtracting the published raw cotton cost(imf 2007) from the total intermediate demand cost. The figures for cotton fiber revenue and valueaddedinthesectoragreewiththosethatappearinbothcanandimf Oil development activities were occurring in 2000 but did not accelerate until 2001 and Value added from oil activities in 2000 is published in CAN. Output of the oil development sector, as seen in Table 6, is calculated as a residual to balance with the aggregate output figure (1,578.6 FCFA) of the aggregate SAM. The intermediate demand for this one sector is then broken down, by assumption, into services demand(50%), formal manufactures demand (25%), and construction demand(25%). 4.2 Absorption Table6showstheoutputblockoftheaggregateSAM flowsfromthecommodities accounts to the activities accounts(the domestic producers) and imports. The commodities column shows the availability of goods to both intermediate andfinalusers. AlsoincludedinthetableareoutlaysbytheROWaccountto the commodities accounts(exports). Output figures were computed on the basis of the value added-to-output ratios from the Niger SAM, applying them to the value added figures found 9

11 incan.therearetwoexceptions. Thefiguresforthe cottonfibersectorare based on those in IMF Oil development output, as already mentioned above,iscalculatedasaresidualsothattotaloutputmatchesthatfoundinthe aggregate SAM( billion FCFA). Export values by sector are found in CAN. However the aggregate export value differs throughout the document (from 167 to 197 billion FCFA). The highervalueisusedsinceitapproachesthevaluecalculatedonthebasisofthe itemized breakdown of export values found in the same document(190 billion FCFA).Toagreewiththe197billionFCFAfigure,theremaining7billionFCFA is allocated to the livestock sector since exports from the sector are among those most likely to be under-reported in official statistics. Import values are among the most difficult to find, and they are perhaps among the less reliable. The value of services imports is reported in CAN. The value of oil sector development-related imports of goods is also reported in CAN. The oil development sector is assumed to import exclusively formal sector manufactured goods. WhatremainsinthetotalimportvaluefigurereportedinCANisassumed to be divided between agricultural goods and manufactured goods such that agricultural imports comprise roughly 5% of total agricultural output sold on the domestic market. Food imports are primarily comprised of processed food (a manufactured good) rather than of primary sector goods. Note that the government collects indirect taxes from both the activities agent and the commodities agent. Although not included in the presented tables,itisassumedthattheaggregatevalueforsuchtaxesissplitamongsectors in proportion to the size of the sector s output value. 4.3 Factor Payments The value added figure of billion FCFA, shown in both the aggregate SAMandinTable5,isdistributedtoamixofprimaryfactors,disaggregated according to the modelling needs of the researcher as well as to the availability constraints of data. With few exceptions statistics on factor use by sector are virtually unavailable for Chad. Thus construction of the factor payments block againreliesonthe1987samforniger,valuesforchadbeingimputedthrough share values from Niger. ThemixofprimaryfactorsfoundintheNigerSAMisrestructuredtoreflect the Chadian economy and the needs of subsequent modeling work. The four land types found in the Niger SAM are aggregated into one. Its division of formal capital and informal capital is retained. Its division between quality labor and non-quality labor is also retained, renamed privileged labor and non-privileged labor. 10 Theprocessofestimatingfactorpaymentsisdescribed in the following steps: 10 Indeeditseemsthatthe educated laboroftenidentifiedinresearchasalaborcategory and assumed employed in skilled jobs is, in reality, a privileged labor class that is able to access such jobs. While attainment of education is perhaps (but probably not) necessary for employment, it is certainly not a sufficient condition. 10

12 1. calculate shares of total factor payments by sector attributed to each type offactorinthenigersam. 2. applythesesharestothetotalvalueaddedfiguresforeachsectorinthe Chad SAM to produce a factor payment structure for each sector. 3. correct for differences between the two SAMs in production structure(oil, cotton,etc.). It is assumed that seed cotton production shows the same factor payment shares as overall agricultural production. It is assumed that cotton fiber production has the same factor shares as formal manufactures. It is assumed that thefishery,forest,andminessectorissimilartotheforestrysectorintheniger SAM. The information in the Niger SAM does not allow for proper identification of values for any non-uranium mining that would be applicable to the small-scale mining(comprised mainly of Natron, or native sodium carbonate bakingsoda)thatoccursinchad. For the oil development sector it is assumed that 75% of returns are to formal capital. The remaining 25% is divided evenly between privileged and non-privileged labor. In the last quarter of 2001, with a Chadian workforce of 2,933people,Essopaid$US2.8billioninsalaries. Bythelastquarterof2000, Essohadhiredonlyjustfewerthan200Chadians ontheprojectsource; thus we can safely assume that labor payments (the 25% figure) were much lower thanthis. TheresultingfactorpaymentstructurethatIuseisshowninTable 7. The calculated values show that of the total value added, 52.2% is comprised of payments to non-privileged labor, followed by payments to informal capital (20.6%), to formal capital (10.3%), to privileged labor (8.5%), and to land (8.3%). Subsequently, factor payments are distributed to the current accounts of institutions and the ROW. All formal capital is assumed distributed between the aggregate enterprise, the government and the ROW. Using formal capital return sharesfromthenigersam,72.43billionfcfaofthe97.88billionfcfashown intable7isallocatedtotheenterprise. Theremaining25.45billionFCFAis split between the government and the ROW. The task of distributing other returns appropriately among the different households groups will be treated in the next section. 4.4 Household Consumption Information on household consumption is sparse beyond the aggregate level found in CAN. Disaggregation of household consumption involves both defining household types and then identifying the structure of final expenditure for each of these types There are two sources of information that inform the estimations. The household survey, ECOSIT(l Enquête sur la Consommation des Ménages et le Secteur informel au Tchad), was conducted in (and again in 2002). It covers 11

13 nearly half of the Chadian population. The study provides a snapshot of the spending structure for various household types; however the commodity structuredefinedinthesurveyisquitedifferentthanthatdefinedinthesam.the effort is thus supplemented somewhat by information in the 1987 Niger SAM which provides expenditure shares on the commodities that more closely correspondtothosedefinedinthesamofthiswork. In eliminating three of the minor rural types defined in ECOSIT and rescaling the population shares, two rural household types are defined: agricultural andpublicservant. Theymakeup74%and21%oftheruralpopulationrespectively. Urban households are divided into four types: informal, public servant, private wage earner, and capitalist-rentier. This differs from the categorization used by ECOSIT which includes an urban agricultural household. While urban agriculture does indeed occur in Chad, it is a relatively minor activity. Additionally, some of the sites considered by ECOSIT to be cities, and where urban agriculture occurs most frequently, perhaps are better categorized as rural. ECOSIT includes an other household types category. This category is ignored due to its ambiguity. Finally, a capitalist-rentier class, one that ECOSIT seems to neglect, is included. Thisgroupisexpectedtobesmall;howevertheimpactofitseconomic activity is expected to be significant. It is assumed that 10% of informal households as defined in ECOSIT are of this capitalist-rentier type. This seems plausible as the value of average revenues of informal households shown in ECOSIT is otherwise implausibly higher than the other types. This indicates that some of these informal households may be managing larger, high-revenue-generating businesses that contrast with the typical image of small-scale informal business. Table 8 shows the resulting population structure of Chad. Chad s population is primarily rural. It is also notable that the public sector heads of household represent 1.6 million people of Chad s total population of 6.7 million. Asdiscussedabove,itisassumedthatasegmentoftheurbaninformalhousehold population identified in ECOSIT is actually a capitalist-rentier class. Thus it was necessary to estimate revenue and expenditure figures for this capitalist rentier class and subtract them from those estimated for the urban informal class as a whole. To do this ECOSIT data was used to estimate the income distribution(per decile) for the economy as a whole. This distribution was appliedtotherevenue figuresfor the urbaninformal classasawhole, assuming that the wealthiest decile represents this capitalist-rentier class. The result is that the urban capitalist-rentier group owns 39.1% of the factors of the urban informal class as a whole. It is also assumed, for the sake of simplicity, that 39.1% of ECOSIT s urban informal group s expenditure represents that of the capitalist-rentier class. The steps taken to estimate household final expenditure are the following: Using ECOSIT data calculate total final expenditure by each household type. 12

14 Using the Niger SAM expenditure shares by household types similar to those in the Chad SAM, calculate expenditure on each commodity identifiedinchadsam. ECOSIT data also shows the size of outgoing household transfers. It is assumed that outgoing household transfers accrue exclusively to urban wage households and rural agricultural households. On the one hand this is a matter of convenience. However it also appears in reality that inter-household transfers flowinthedirectionfromrichtopoorandfromurbantorural. Direct taxes (income tax) are calculated based on each household type s share in total factor income. Informal households are assumed to pay no taxes (following what is often one of the defining characteristics of the informal sector). Table 9 shows the SAM disaggregation of household and enterprise outlays into six household types and the one aggregate enterprise. Enterprise outlays are calculated as a residual to conform to the balancing requirement of the aggregate SAM. The value in Table 9 for total final consumptionby households andenterprises(884 billion FCFA) corresponds to that found under non-public consumption in CAN. The value for total payments to government of 34.8 corresponds to the sum of the figures for direct, property and non-tax revenue to government found in CAN. The figure for total inter-household transfers is calculated based on the ECOSIT household survey. Private sector savings is discussed and calculated in a later section. 4.5 Private Sector Income Table 7 shows the allocation of value added to the various primary factors. The intersection between the factors columns of the SAM and the institutions rows represents the factor ownership structure among these institutions. The factor ownership(orincome)blockofthesamisshownintable10. The steps taken to estimate the structure of factor ownership is similar to that for the estimation of private sector final expenditure. These are: Adjust ECOSIT data on household total revenue to reflect the household structure(6 household types) of the Chad SAM. Using Value Added figures from Table 7, distribute factor income from factors to households and enterprises based on factor ownership shares from the Niger SAM. One challenge of producing this factor income distribution block of the SAM is to reconcile the aggregate SAM figure for value added( billion FCFA) with that from the ECOSIT data. The ECOSIT report does not provide income sourcesintermsofthesamefactorsrepresentedinthesam.thus,whileusing thetotalhouseholdrevenuefiguresgiveninecosit,onemustrelyinparton intuition to attribute this total revenue to the different factors identified in the SAM.TheNigerSAMisusedtoinformthisprocess. 13

15 Privileged labor payments are assumed to be allocated to both rural and urbanpublicsectorhouseholdsaswellastosomeoftheurbanwagehouseholds. Nearly half of non-privileged labor payments accrue to rural agricultural households, the remainder received by the other households. Land is owned by rural households and enterprises. Informal capital payments are assumed to accrue forthemostparttoenterprises,whichisdonesimplytobalancetheblockwith aggregate totals already derived in the aggregate SAM as well as in other parts ofthesam. 4.6 Government Current Outlays Government is structured in the SAM such that its current consumption is comprised exclusively of the output of the government activity agent. Following national accounts data from CAN that agree with those from the IMF, government current consumption is equal to 77.6 billion FCFA in Total government outlays are billion FCFA. These values are shown in Table 11. Transfers from government include social security payments, interest paid to enterprises (banks among them), and to the ROW account. As shown in Table 11, household transfers are distributed among the household types in proportion to their revenue shares. Informal households are assumed to not receive transfers from the government, following the idea that such households lie outside the formal system that allocates such transfers. Table 11 shows the structure of outgoing government transfers. National accounts data provides a value of domestically-financed government investment. This component of investment(in the capital accounts column of Table 13) is partially financed through government savings. Such government savings amounts to 10.7 billion FCFA or around 10.4% of all public investment. 4.7 Balance of Payments: Incoming Flows TheROWcolumnoftheSAMshowsresourceinflowstoChadfromtheROW. Themixofexports(valuedat197billionFCFA)isalreadyshowninTable6. The ROW account is also made up of incoming current and capital transfers. The former, for example, may include remittances from abroad. The latter may include foreign-financed investment. For the sake of simplicity, current transfers to households are assumed distributed according to each type s population share. The ROW account pays factors for factor use overseas. Borrowing from abroadisreflectedastheintersectionoftherowcolumnandthecapitalaccounts rows. These values were already determined in the formation of the aggregatesamassummarizedintables2and Capital Account Outflows The intersection between capital account columns and commodity rows, as shown in Table 13, represents investment expenditure by each of the institutions. The aggregate values for investment are derived from national accounts 14

16 data. Government investment totals billion FCFA, a value found both in CANandIMF2007. Thelatterdocumentshowsavaluefortotalprivateinvestmentof103billionFCFA,46billionFCFAofwhichisrelatedtotheoilsector. Changes in inventories are valued at 15 billion FCFA. Thus economy-wide fixed capital formation(plus change in stocks) is valued at 221 billion FCFA. To determine the structural mix of investment demand, this effort turns to the Global Trade Analysis Project (GTAP) database that contains SAMs for several nations including eight from Africa. The average shares of investment demand in each of the commodities identified by the GTAP SAMs is calculated over the eight African countries. The commodity mix of GTAP is then restructuredtoconformtothatofthechadsam.theresultingsharesaretheapplied to the investment aggregates for the three capital institutions here to produce a structure of investment demand shown in the Chad SAM. The available (albeit incomplete) information on fixed capital formation found in CAN is also incorporated into the SAM. The CAN document accounts for164billionofthe221billionfcfaofinvestmentfoundinthechadsam. Some of the values in CAN may be attributed to a particular institution (for example, oil services ). An attempt is made to allocate investment spending as shown in CAN, then supplementing it with share values from the GTAP database. The largest components of investment demand are found to be on formal manufactured and construction goods. The 39.6% and 43.4% shares shown in Table 13 compare similarly to the 35.2% and 52.1% African-average shares calculated in GTAP. The livestock sector in the Chad SAM accounts for 5.6% of total investment demand while in GTAP it accounts for almost nothing. 4.9 Savings In general savings is calculated as a residual to balance total outlays and total revenues for each institution. For households this implies a negative savings of billion FCFA in the aggregate, ranging from billion FCFA for the rural agricultural household to 3.63 billion FCFA for the urban public sector household. This translates into an aggregate household dis-savings rate of approximately 1% of total household revenues. As mentioned above the government savings total of 10.7 billion FCFA corresponds to the value of budget-financed investment expenditure listed in both CAN and IMF Enterprise savings of billion FCFA (7.8% of total enterprise revenues) is used as a residual to match with the aggregate SAM value for combined household and enterprise savings(12.15 billion FCFA). The resulting disaggregated SAM is shown in its entirety in Table 14. Upon inspection one may notice that the only significant imbalance (shown in the final row of values) lies within the commodities accounts. At this point the discussion turns to balancing the Chad SAM. The inclusion of marketing margins is considered. This will nearly balance the SAM. The small imbalances that remain are removed through the cross-entropy numerical method. 15

17 5 Balancing the Disaggregated SAM The disaggregated SAM whose construction was outlined in the preceding discussion is in balance, with the noticeable exception of the disaggregated commodities rows and columns. This section details the technique used to balance the SAM. The first step involves accounting for marketing margins that are often ignored in the construction of national accounts and input-output tables. The second step uses the Cross-Entropy method(robinson, 2000) to correct for the remaining minor discrepancies. TheunbalancedSAMshowsalargesurplusofsupplyinservicesandalarge deficit in the supply of formal manufactured goods. It is reasonable to assume that these discrepancies can be explained by the existence of marketing margins that have not yet been identified or have been included but underestimated in thei-otableblockofthesam.marketingmarginsareveryhighinchad. The landlocked country s markets are linked by an impressively poor road system, and traveling can often be dangerous due to possible raids by bandits, said to be comprised of the unemployed ex-military. Given the high proportion of formal manufactures in import totals as well as the need to physically move agricultural goods to markets throughout the country, most of the marketing costs are allocated to the agricultural and formal manufactures sectors. Other sectors that I assume to exhibit marketing margins are the livestock sector as well as fish, forestry, and mines. After correcting for marketing margins, the SAM is within 5% balance; and in most cases column totals match row totals within 1 2 %. This intermediate stepsamisshownintable15. At this point I turn to the numerical method of cross-entropy(a technique whose application to SAMs was made well-known by Robinson et al. (2000)) to balance the remaining inconsistencies in a manner that minimizes deviations from the original SAM. The Cross-Entropy method is derived from information theory and brought into economics by Theil(1967). In the context of SAM balancing, it amounts to choosing new values for a balanced SAM that minimizes the Kulback-Leibler cross-entropy distance between the original SAM and the fully balanced one. The distance is defined as follows: Equation 1: Kulback-Leibler Cross-Entropy Distance Ω= a ij ln a ij a ij i j a ij =balancedsamcolumncoefficients a ij =unbalancedsamcolumncoefficients Ω=distance To balance the unbalanced SAM one chooses new column coefficient values that both balance the SAM and minimize the above-defined distance from the original values. One may also constrain the solution such that particular values of the original SAM are fixed. While further discussion of the measure is beyond 16

18 the scope of this work, the principal first order condition for the problem is shown in equation 2. Equation 2: First-Order Condition for Distance Minimization Problem a ij =a ije (1+λ i+λ j ) λ i,j =lagrangianmultipliersforrow/columnsumtotalconstraints Based on code generously provided by Fofana et al. (2005), a modified code (available upon request) is written in GAMS that applies the cross-entropy technique to the unbalanced SAM of Table 15. During the balancing procedure the cell values of the original aggregated SAM are maintained. The final balanced SAMisfoundinTable16. 6 Conclusion OneoftheprimarychallengesfacedbytheAGEmodelerwhosefocusisonissues ofeconomicdevelopmentandpovertyistolocateacoherentsetofinputdata in the form of a Social Accounting Matrix. Indeed some countries, including Chad,areseldomthefocusofseriousAGEresearchduetothelackofaSAM andthedifficultyofobtainingthedatatobuildone. TheproductshownhereistheonlypublicallyavailableSAMforChadand represents the dawn its oil era. It would be especially useful for research focusing onthesector simpactonawidearrayofvariablesinthenation seconomy(see Garber 2009, 2014 forthcoming). The mainintent of this paper has beento provide the reader an example, perhaps a template, of techniques to weave together sparse information into a SAM that closely approximates the real economic flows of a data-poor country. The work demonstrates that serious economic analysis is possible in even the seemingly most data sparse contexts. References [1] Dorosh, P. A., and B. E. Nssah. A Social Accounting Matrix for Niger: Methodology and Results. Working Paper No. 18. Ithaca, N.Y., U.S.A.: Cornell Food and Nutrition Policy Program [2] Garber, David S. Oil, Development, and Corruption: An Applied General Equilibrium Model to Explore Welfare and Production Implications of Oil Revenue Spending in Chad. (Forthcoming working paper 2014). [3]. Oil, Investment, and Economic Development: A Detailed Look at the Advent of Chad s Oil Sector. PhD dissertation, University of Wisconsin Madison. Ann Arbor: ProQuest/UMI, (Publication No. AAT ). 17

19 [4] L Institut national de la statistique, des études économiques et démographiques(inseed), 2005, Comptes et agrégats de la nation, (N djamena, Chad). [5] L Institut national de la statistique, des études économiques et démographiques(inseed), 1998, L Enquête sur la consommation et le secteur informel du Tchad( ): rapport final. [6] International Monetary Fund, 2007, Chad: Selected Issues and Statistical Appendix, (Country Report No. 07/28). [7] Levy, Stéphanie. Public Investment to Reverse Dutch Disease: The Case of Chad. Journal of African Economies 16.3(2007): [8] Robinson, Sherman, Andrea Cattaneo, and Moataz El-Said."Updating and Estimating a Social Accounting Matrix using Cross Entropy Methods." Economic Systems Research 13.1(2001): [9] Round, Jeffrey. Social Accounting Matrices and SAM-based Multiplier Analysis. Techniques and Tools for Evaluating the Poverty Impact of Economic Policies. Ed. F. Bourguigno and L.A. Pereira da Silva. World Bank and Oxford University Press, September [10] Theil, Henri. Economics and Information Theory. Rand McNally, Chicago,

20 Agent Activities Commodities Factors Current Accounts Capital - Financial Accounts ROW TOTAL Possible Disaggregation Capital Labor Enterprises Households Government Enterprises Households Government Activities Commodities Factors Capital Labor Intermediate Demand Value Added to Capital Value Added to Labor Domestic Production Marketing Margins Table 1: Social Accounting Matrix in Aggregate Form Final Household Consumption Final Government Consumption Fixed Capital Formation and Change in Stocks Exports Sales of Domestic Output Aggregate Demand Capital Total Returns from Payment to ROW Capital Labor Payments from ROW Total Payment to Labor Current Accounts Capital - Financial Accounts Enterprises Households Government Enterprises Households Indirect Taxes Indirect Taxes Capital Returns Distributions Labor Returns Distributions Interhousehold Transfers Direct Taxes Direct Taxes Enterprises Savings Household Savings Govt Transfers to Enterprises Govt Transfers to Households Capital -- Financial Transfers Remittances Public Current Transfers Capital Transfers Aggregate Enterprise Income Aggregate Household Income Aggregate Government Receipts Enterprise Capital Receipts Household Capital Receipts Rest of World TOTAL Government Total Production Costs Imports Absorption Capital Returns to ROW Total Capital Returns Labor Payments to ROW Total Wages Current Enterprise Spending Current Household Spending Govt Savings Current Government Spending Capital - Financial Transfers to ROW (current external balance) Total Investment Public Sector Capital Transfers Foreign Exchange Receipts Public Capital Receipts Foreign Exchange Payments 19

21 Table 2: Aggregate SAM for Chad Activities Commodities Factors HHs and Enterprises Oil Govt Capital Accounts ROW Total HHs and Enterprises Oil Govt Activities Activities Commodities Commodities Factors Factors HHs and Enterprises HHs and Enterprises Oil 0 Oil Govt Govt HHs and Enterprises HHs and Enterprises Oil Oil Govt Govt ROW ROW Total All values in billion FCFA ($US 1 = 710) Table 3: Comparison of SAM Values to Those from Other Sources Item SAM Other Sources Notes Intermediate Demand 623 agrees w CAN Value Added agrees w CAN Taxes on Activities 1.5 agrees w IMF payroll taxes Output CAN adjusted for balance Sales Tax 38 agrees w CAN net taxes on products Imports 386 between used middle value to balance Factor Payments to HH and enterprises na residual of total factor payments Factor Payments to Government 8.3 na Residual calculation Factor Payments to ROW 12.3 agrees w IMF Household/Enterprise Current Consumption 884 agrees w CAN all non-public final consumption Inter-household Transfers 66.4 na estimated using ECOSIT survey HH/Enterprise Income Tax 38.2 agrees w IMF Inter-household Transfers na used as a residual to balance Government Current Consumption 77.6 agrees w CAN current spending minus transfers Government Transfers to HH/Enterprises 13.4 agrees w CAN transfers plus domestic interest Government Savings 10.7 Agrees w CAN/IMF Domestic financed investment expenditure Government Payments to ROW 8.9 agrees w CAN interest to foreign banks Household Enterprise Investment Demand 60.5 agrees w CAN non-oil pvt investment plus ½ value stock changes Oil Investment Demand 46 agrees w CAN Government Investment Demand agrees w CAN plus ½ value stock changes Exports 197 agrees w CAN ROW current transfers to HH/Enterprises 4.6 agrees w CAN ROW current transfers to government 24.6 agrees w CAN ROW capital transfers to hh/enterprises IMF adjusted for balance ROW capital transfers to oil IMF adjusted for balance ROW capital transfers to government CAN adjusted for balance 20