A Tunisian social accounting matrix for impact analysis of macroeconomic policies and shocks

doi:10.21203/rs.3.rs-2117673/v1

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A Tunisian social accounting matrix for impact analysis of macroeconomic policies and shocks

https://doi.org/10.21203/rs.3.rs-2117673/v1

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The instability caused by the events of 2011, combined with the global economic crisis, caused problems on all levels for Tunisia, hence the need for a comprehensive data framework that consists of a social accounting matrix to assist the government in policy formulation. We used cross-entropy to balance a matrix with data collected mainly from the integrated economic accounts table, the supply-use table, and other sources. We have constructed a matrix with definitive data at the macro-economic and micro-economic levels to offer a coherent and complete representation of the economic and social system while reflecting the interdependencies of institutional groups. This matrix is used as a database in a recursive dynamic computable general equilibrium model to assess the impact on economic growth and reducing unemployment of a policy of increasing public spending on infrastructure investment. The Tunisian matrix can allow decision-makers to orient their choice, and supervise social policies and their link with economic development.

JEL: (E16), (E24), (E6), (H5)

social accounting matrix

national government expenditures

macroeconomic policy

unemployment

Before the fall of the regime in 2011, Tunisia had emerged as one of the fastest-growing economies in MENA since the 1990s. After this period, the indicators of social and economic well-being deteriorated markedly, and Tunisia is encountering difficulties in implementing a coordinated strategy.

The construction of a social accounting matrix (SAM) allows us to represent the Tunisian economy considering the context, the phenomena to be understood, and the availability of data. The need to develop or make this matrix is explained by the fact that it is an accounting framework that is micro and macro-consistent, coherent, and oriented towards the analysis of economic and social development policies.

In the present work, we present the Tunisian economy and the SAM. Then we give the generic structure of a SAM. In the second part, we describe the standard SAM and the development of a macro-SAM for Tunisia. In the third part, we detail the disaggregation of the macro matrix, and we give an overview of the structure of the Tunisian economy in 2010, taking into account the constructed matrix. We finish the document with an application of this matrix in a computable general equilibrium model to analyse the impact of a policy of increased spending on public infrastructure investments on economic growth and unemployment reduction.

Tunisia represents the complex relationship between economic policies, institutions, and political change. Faced with the problems of monitoring and evaluating the economic policies implemented, Tunisia needs to answer the question of which methods and which tools to use to represent the economy and apprehend the effects of the measures of the policies implemented?

2.1. Context of Tunisia

Youth unemployment, corruption, human rights violations, extreme poverty, and regional disparities, among others, are among the factors that triggered the January 14, 2011 movement later known as the « Arab Spring ».

The Tunisian economy, characterized by a strong competitiveness, had masked inequitable growth and the high unemployment rate for young graduates. In 2008, the unemployment rate of under 30 reached nearly 30%, double the overall unemployment rate. The 25–29-year unemployment rate increased from 12.6% in 1984 to 25.2% in 2008.

Young academic graduates have been particularly affected by the deterioration of job creation in the Tunisian economy. The unemployment rate for higher education graduates increased from 3.8% in 1994 to 21.6% in 2008 and 30.6% in 2010 (Haouas et al. 2012). The creation of jobs in the private sector remains concentrated in low-skilled jobs, while private investments are relatively low and closely controlled by the government.

Regional disparities in Tunisia are structural and institutional and enforced for decades. Overall, the coastal regions tend to be wealthier than the interior regions. These disparities are much more dominant in the governorate of Tunis because of the political dominance of networks directed at the political regime.

Factories and employment opportunities have long been concentrated along the Tunisian coast, while the interior regions are isolated from these economic activities not only by distance, but above all, by a lack of infrastructure, transport, and information networks. The population and economic activities are concentrated in the governorate of Tunis and the governorate of Sfax, with the coastal regions accounting for 75% of non-agricultural employment. These regional disparities have caused a large gap between average consumption and poverty in the coastal and inland regions.

In 2010, while the poverty rate (national average) was around 15.5%, regional disparities in poverty levels are significant. Similarly, there is a great disparity in employment opportunities between regions. For example, since 2004, the average unemployment rate has exceeded 22.6% in Jendouba, Kef, Kasserine, and Gafsa. In addition, almost 65% of public investments are allocated to coastal areas. As a result, health care needs in the mid-west region are largely unmet, and youth illiteracy remains very high.

Following the "Revolution", investments fell sharply in almost all sectors, except for the electronics sector. Foreign Direct Investment (FDI) decreased by 29.2% in 2011 compared to 2010. As a result, 182 foreign companies closed (64 Italian companies, 61 French companies, and 10 German companies), resulting in the loss of 10,930 jobs. The decline in FDI has been particularly severe in tourism sector where losses were estimated at 83.3%, while manufacturing and energy recorded losses of 42.4% and 19% respectively.

Tunisia needs a growth performance that should not only be essential to provide job opportunities to the population, mainly the poor, but also to enable the government to mobilize resources to finance social expenditure to link the growth strategy to pro-poor policies. Therefore, it needs a SAM that is a complete data, covering the whole economy, and generally used to represent the Tunisian economy.

2.2. Context of the SAM

2.2.1. History of construction of SAM

Therefore, in the economic literature, several methods for assessing the economic effects of development operations have been advocated by various organizations, such as the World Bank, the Organization for Economic Co-Operation and Development (OECD), and The Food and Agriculture Organization of the United Nations (FAO).

It was not until the late 1960s that the first SAM was built by Stone (1985) for the United Kingdom and SAMs for other developed countries. In the 1970s, World Bank economists proposed using social accounting matrices for planning (Pyatt and Round 1985). These same matrices have been used since then to calibrate computable general equilibrium models, they include in the same table the flows from the integrated economic accounts table (IEAT) and the supply and use table (SUT), and they give a social dimension by integrating, in addition to data on production, information on the distribution of income and expenditure between different categories of households.

SAM was born from the observation or the questioning of the central framework of the system of national accounts (SNA) of 1968 that did not consider some socio-economic realities. This system was inadequate because of the employment, inequality, and standard of living problems.

2.2.2. Definition, objectives, and usefulness of SAM

The SAM is a table with a double-entry that presents the accounts of the Nation. It gives an overall quantified vision of its structure and its economic circuits. A social accounting matrix develops the interrelations between the supply and use table (SUT) and the accounts of institutional sectors; a characteristic orientation of a SAM highlights the role of individuals in the economy and can be reflected, in particular, by additional breakdowns of the household sector and a detailed representation of the labour market, distinguishing, for example, the various categories of employed persons ((Bank Brussels and York (1993)).

The SAM is part of the big family of economic tables and constitutes a generalization of the input-output (IO) table of Leontief. On the one hand, the SAM offers a coherent presentation of the transactions which take place in a given economy, whether it is a country, a region, or even a set of countries or regions, and, on the other hand, it provides economic policy-makers with the accounting basis for an analytical framework capable of facilitating their choice (Decaluwé et al. 2001).

The power of a SAM lies in the possibility of choosing the type of disaggregation that best suits the study of a given subject. Therefore, it proceeds from a multisectoral linear specification, photographs all of the flows between agents and sectors for a given year, proposes a framework and consistent data (corresponding to a reference year) for the overall models of the economy, comprising detailed nomenclatures of agents, such as branches of activity, categories of persons employed and institutional sub-sectors, comprising various socio-economic groups of households.

The great interest of the SAM is to allow great flexibility in the type of disaggregation of the categories of agents to which one will attribute specific economic behaviours. The variable geometry of SAM gives them greater flexibility than that provided by SUT. Thus, everyone depending on their modelling objectives will be able to choose the division that suits the analysis they propose to perform.

The SAM can integrate the specificities of households and characteristic variables, also macroeconomic (prices, taxation, trade, ...) or structural variables (goods and services market, labour market, money market). The consequences of macroeconomic decisions on populations can be direct (income transfers, improved well-being, price variation, fiscal policy) or indirect (degradation of ecosystems, land policy).

As a result, the SAM allows highlighting the interactions between the different actors of an economy. The computable general equilibrium (CGE) models applied to SAM have the power to combine the characteristics of the two approaches: the global analysis framework and the microeconomic approach.

Building a SAM leads to three positive results. First, a SAM displays information to illustrate the structure of an economy in an enlightening way. Second, exposing the inconsistencies between data from different sources. Third, a SAM provides all or at least a large portion of the data needed for different types of other models, including multiplier models and CGE models (Round 2003).

2.2.3. Structure of a standard SAM

The main interest of a SAM is to represent the relations between employment, the distribution of income, and the structure of production. For this, the SAM is a detailed description of the production accounts, two accounts of the factors of production, accounts of the institutional sectors, and accounts of goods and services. This representation can give a detailed classification of production factors and the accounts of the institutional sectors, making it possible to go beyond a simple description of the formation of disposable income from the primary distribution and the policy of redistribution of the government.

The particularity of the SAM lies in its flexibility allowing great suppleness in the disaggregation of activities, institutional units, and productive factors. The SAM is not limited to a schematization of the sphere of an economy; it can incorporate the financial sector of an economy or the so-called non-economic production activities by the SNA 93. The inclusion of these activities does not necessarily require a modification of the initial structure of the SAM when satellite accounts are used for this purpose (recommendation of SNA 93).

Although SAM can take various configurations, its presentation in the form of the IO table is a common characteristic. It traces the accounting flows in an economy at a given period, generally a year. These flows are in the form of on-the-line revenue and column expenditure. The accounting consistency of a SAM is guarantee by the equality between the total revenue (row) and the total expenditure (column) for each account.

In general, a standard SAM presented in the form of five aggregated accounts: a current account of productive factors (labour and capital); a current account of institutional units (households, firms, government, and rest of the world); an account of productive activities; a product account (goods and services); and finally, an investment account.

Unfortunately, in the case of Tunisia, there are only two SAMs constructed for the International Food Policy Research Institute (IFPRI) published in 2016 and 2020(Thabet (2016), ben Salhine et al. (2020)), the first one focused on agriculture and trade, the second one regionalized and both of them with estimated data. Our SAM is the first one with definitive data.

3.1 Accounts of TUNISIA macro-SAM of 2010

The Macro Matrix constructed for the year 2010 includes ten accounts which are the factors of production capital (K) and labour (L), the account of economic activities (J), the account of products (I), the accounts of institutional agents which are firms (F), households (H), the government (GVT) and the rest of the world (ROW), the investment savings account (INV) and finally the export (X) accounts.

The productive factors account represented by labour and capital, each of which can divided into several sub-accounts depending on the study objectives and data availability. Factors of production receive the income from the sale of their services to production activities in the form of wages and capital rent and then distributed to institutional units in the form of labour and capital income.

The account of institutional units includes the households, firms, the government, and the rest of the world, each of which can break down into several sub-accounts.

The household sub-account receives factor income (L and K), and transfers income from F, GVT, and ROW. It pays taxes and social security contributions, acquires consumer goods and services, transfers to other institutional units, residual income invested/disinvested by the H via the K account.

The firm's sub-account receives a share of the profits generated by productive activities and transfers income from other institutional units. This income is transferred as interest, dividends, rent, tax, and compulsory social security contributions to owners of financial assets, shareholders, and the GVT, the residue of the F sub-account charged to the K account.

The government sub-account retains a share of the income generated by agents and economic transactions as compulsory levies (taxes on income and wealth, taxes on production and products, and taxes on imports and exports) and transfers income from other institutions. GVT account income allocated to purchases of public administration services, transfers, and grants to H, F, and ROW; excess or deficit income transferred to the K account.

The rest of the world sub-account makes payments when Tunisia exports and receives transfers from abroad and as a foreign savings (the current account deficit can be negative).

The productive activities account generates revenue from the sale of products. The expenses of these activities include the purchase of raw materials and intermediate products, the payment of factor rental service (L and K) and the payment of taxes on production net of subsidy.

The product account buys goods and services from local and foreign producers (imports), to sell them to H, GVT, and J, in the form of final consumption, productive inputs and investment.

The investment account combines gross fixed capital formation and inventory changes. It collects savings from resident and non-resident institutional units for investment.

Table 1 presents the structure of the macro-SAM in the form of a matrix, indicating the cells in which the various payments appear.

The choice of the base year is not arbitrary. It was motivated, first of all, by the fact that the year 2010 is considered the last normal year before the revolution and consequently, no random or accidental event was noted during this period, then, this year was stable and exceptional from the point of availability view of data sources.

The data used in the construction process is based on two main publications from the national institute of statistics (NIS): the integrated economic accounts table (IEAT) (2010) and the supply and use table (SUT) (2010). Other important sources of information include the publication of a household survey (2011)^{^[1]}, the annual report of the central bank 2010 and the "Economic Budget" (2010).

3.2 Balancing the SAM

The construction of SAM requires, as mentioned above, the use of the most recent economic data available in a coherent framework. However, these data generally come from a wide variety of sources (national accounts, surveys, ...). Several balancing techniques are used to balance the SAM (Round 2003; Lemelin 2008). The most commonly used methods are RAS and minimization of the deterministic approach to minimizing cross-entropy as used in the work of Robinson (1991) and Robinson et al. (2001).

The choice of cross-entropy is not arbitrary as well as the choice of the year 2010, this method is today widely used for economists, it has theoretical foundations, notably information theory. This method is flexible because, unlike the RAS^{^[2]} method, it can use any available information, on a total of a row or a column or even on a cell of the matrix only. Finally, in a more coherent way, it takes into account simultaneously the whole matrix to modify this matrix and not only a part of it, a simultaneous modification and not a modification in a determined order as in the RAS method.

Table 1

Basic macro-SAM structure for Tunisia 2010

	L	K	H	F	GVT	ROW	J	I	INV	TOTAL
L							added value of labor			labor income
K							added value of capital			capital income
H	labor income	household capital income		Firms transfers to household	Government transfers to household	Rest of the world transfers to household				household income
F		business capital income	Household transfers to firms		Government transfers to firms	Rest of the world transfers to firms				firms income
GVT			Household transfers to government	Firms transfers to government		Rest of the world transfers to government		Taxes on products		government income
ROW			Household transfers to rest of the world	Firms transfers to rest of the world	Government transfers to rest of the world			import		rest of the world income
J								production		production
I			household consumption		government consumption		intermediate inputs		investment	product demand
X						Export				Export
INV			Household savings	Firms savings	Government savings	Rest of the world savings				saving
TOTAL	distribution of labor income	capital income distribution	household spending	Business spending	government spending	exchange earnings	Production cost	Product offer	investment

Source: Pyatt and Round (1985) and author’s modifications

The cross entropy-method

We constructed a matrix using the SUT, IEAT, national accounts, state budgets and balance of payments. This SAM was then balanced using cross-entropy. As part of our work, we used a method inspired by the work developed by Robinson et al. (2001) and, Lemelin et al. (2013). Our matrix is the solution to the following problem using GAMS[3]:

Table 2 present constructed matrix at the aggregated level, we proceed to the disaggregation of the different accounts of the matrix. Generally, the detail of the accounts of the matrix depends on the objective of the study and the specifications of the model as for the institutional detail, activities, products, taxes, and factors of production.

Table 2

Macro SAM for Tunisia 2010 (in millions of Tunisian dinars)

	L	K	H	F	GVT	ROW	J	I	X	INV	TOT
L							25400,33				25400,33
K							32972,41				32972,41
H	25400,33	19976,05		3904,59	4356,94	5239,19					58877,12
F		11377,70	1138,52		1101,88	284,02					13902,13
GVT		1618,65	8832,81	1951.84		466,54	4304 ,21	6483,48			23657.53
ROW			92,04	3135,13	827,27			32693,01			36747,45
J								98333,40	27444,73		125778,13
I			44148,19		12852,68		63101,18			17407, 83	137509,89
X						27444,73					27444,73
INV			4665,55	4910,56	4518,76	3312,94					17407, 83
TOT	25400,33	32972,41	58877,12	13902,13	23657,53	36747,45	125778,13	137509,89	27444,73	17407, 83

Source: Author’s own calculations based on collected data.

[1] Made by the International Office work and Centre for Research and Social Studies

[2] RAS is a classic procedure for adjusting a contingency table to fixed margins

[3] The General Algebraic Modeling System (GAMS) is a high-level modeling system for mathematical optimization. GAMS is designed for modeling and solving linear, nonlinear, and mixed-integer optimization problems.

The objective of the Macro Matrix disaggregation is to highlight the sectors with the consequent interrelations in the detail of taxes, exchanges, and employment. The disaggregated matrix has 76 accounts with a fine disaggregation for government, activities, and service products. All these accounts are shown in Table 3.

Table 3

The full dimension of the Tunisian Micro National SAM
Industries (J) -Agriculture and fishing (AF) -Agrifoods industries (AI) -Tobacco industry (TI) -Textile, clothing and leather (TCL) -Various industries (VI) -Oil refining (OR) -Chemical industries (CI) -Building materials, ceramic and glass (BMCG) -Mechanical industries and Electric (MEI) -Oil and natural gas extraction (ONGE) -Mines (M) - Electricity and Gas (EG) -water (W) -Building and civil engineering (BCE) -Maintenance and repair (MR) -Hotel and restaurant services (HRSR) -Transport (TR) -Post and telecommunications (PT) -Financial services (FS) -Other Merchant Services (OMS) -Public administration (PA) Production factors (L, K) -workers with no education (LN) -workers with primary education (LP) - with secondary education (LS) - workers with higher education (LHS) -Capital	Commodities (I) -Agriculture and fishing (AF) -Agrifoods industries (AI) -Tobacco industry (TI) -Textile, clothing and leather (TCL) -Various industries (VI) -Oil refining (OR) -Chemical industries (CI) -Building materials, ceramic and glass (BMCG) -Mechanical industries and Electric (MEI) -Oil and natural gas extraction (ONGE) -Mines (M) - Electricity and Gas (EG) -water (W) -Building and civil engineering (BCE) -Maintenance and repair (MR) -Hotel and restaurant services (HRSR) -Transport (TR) -Post and telecommunications (PT) -Financial services (FS) -Other Merchant Services (OMS) -Public administration (PA) Other (AG) - direct taxes (AG.TD) - indirect taxes (AG. TI) - taxes on imports (AG.TM) - wage-bill taxes LN (AG. LN) - wage-bill taxes LP (AG. LP) - wage-bill taxes LS (AG.LS) - wage-bill taxes LSH (AG.LHS) - taxes on capital (AG.CAP)	Exports -Agriculture and fishing (AF) -Agrifoods industries (AI) -Tobacco industry (TI) -Textile, clothing and leather (TCL) -Various industries (VI) -Oil refining (OR) -Chemical industries (CI) -Building materials, ceramic and glass (BMCG) -Mechanical industries and Electric (MEI) -Oil and natural gas extraction (ONGE) -Mines (M) Transport (TR) -Post and telecommunications (PT) -Financial services (FS) -Other Merchant Services (OMS) Accumulation (OTH) -Saving and Investment (INV) -Inventory changes (VSTK) Institutions (AG) - Households (H) - Firms (F) - Government (GVT)

4.1. Disaggregation of Macro Matrix Accounts

4.1.1. Production factors

Labour: The disaggregated SAM counts four categories of work broken down by level of education: workers with no education (LN), workers with primary education (LP), workers with secondary education (LS), and workers with higher education (LHS).

Wages by the level of education are established based on the report which, reflects the difference in monthly wages between the categories of work by the level of education. This report made by the International Office work and centre for Research and Social Studies (2012) is based on a survey on the structure of wages in Tunisia.

According to this survey, the average monthly remuneration of employees with higher education and those with secondary education is 2.5 times and 1.15 times that of employees with no education, respectively. Based on the average remuneration, the wage gap which separates the different work categories from the no education category is calculated according to the report established by the survey.

4.1.2. Activities and Products account

The disaggregation of activities and products is carried out based on the SUT of the year 2010. Having the consistency of the accounting data with the data on employment in occupied staff required retaining detail of the activities less important than that provided for by the basic SUT.

Taxes

According to the 1993 accounting rules, the SUT records separately the non-deductible or payable value-added tax (VAT) which measures the difference between the VAT payable and the deductible VAT, import duties, other taxes, and subsidies on products. Except for import taxes, this accounting approach does not distinguish the origin of the yield from product taxes according to whether it is local or imported.

Our SAM contains eight tax accounts (direct taxes (AG.TD), taxes on imports (AG.TM), indirect taxes (AG. TI), four wage-bill taxes (AG. LN), (AG. LP), (AG.LS), (AG.LHS), and finally taxes on capital (AG.CAP)).

Exports

We have taken all the exported products present in the table of exports in the account of the nation used in this work which are among fifteen.

Given the various adjustments made, the product of this work is a square matrix with 76x76 accounts. This matrix makes it possible to account for certain characteristics of the Tunisian economy and both a conceptual framework and a data system on which analyses of economic and social policy issues and the evaluation of the economic and social consequences of exogenous changes can be based, and which will serve as a basis of data for the computable general equilibrium (CGE) model.

4.2. The Tunisian economy according to the 2010 social accounting matrix

We present the main characteristics of the Tunisian economy using the results of this SAM. The focus will be on Production, factors of production, and institutional units (households and firms).

The breakdown of GDP at factor costs between capital and labour shows in Fig. 1 that almost 56.49% of the income generated in the economy which amounted in 2010 to 58372.74 mn dinars is distributed to capital including the mixed income of individual entrepreneurs and 43.51% is distributed to salaried work.

In Fig. 2 it is notable that the structure of the cost of production at the level of the economy as a whole, which corresponds to the production value evaluated at 125778.13 MD, is linked for 50.16% to expenditure on intermediate consumption (of local and imported origin), for 20.19% of labour compensation, for 26.21% for capital income and 3.42% for taxes net of subsidy. The added value created in the economy represents 49.83% of the value of production.

Analysis of the cost structure by activity in Fig. 3 shows divergence in the relative weights of the cost components. According to our SAM, the sectoral analysis of GDP shows that the branches of activity that predominate the Tunisian economy are terms of production agri-foods industries and agriculture and fishing (14.05%), mechanical industries, and electric (13.56%) followed by public administration (10.39) and, other merchant services (9.03%).

For agriculture and fishing and other merchant services, the cost structure is dominated by capital income with 58.47% and 63.76%, the respective agriculture and fishing weights of intermediate consumption and payroll are 24.85% and 14.25%, and 2.43% for taxes. Unlike agri-foods industries, its cost structure is dominated by intermediate consumption. It represents 78.52%, 11.43% for capital, 8.59% for labor, and 1.46% for taxes. There is a strong link between the agriculture and fishing sector and the agri-foods industries sector. Agriculture and fishing provide most of the intermediate consumption for agri-foods industries (60%) (Table4).

The cost structure of textile, clothing, leather, chemical industries, mechanical industries, electric, and building and civil engineering activities are dominated by intermediate consumption expenditure, which translates into the low added value generated by these activities. Thus, for these activities, the cost of the primary factors of production capital and labour represent respectively:

- Textile, clothing and leather: 8.47% and 18.19%.

- Chemical industries: 10.07% and 9.24%

- Mechanical industries and electric: 9.18% and 17.67%

- Building and civil engineering: 22.45% and 10.18%

Table 4

Intermediate consumption structure (in millions of Tunisian dinars)
	J	J	J	J	J	J	J	J	J	J	J	J	J	J	J	J	J	J	J	J	J
	AF	AI	TI	TCL	VI	OR	CI	BMCG	MEI	ONGE	M	EG	W	BCE	MR	HRSR	TR	PT	FS	OMS	PA
IAF	360,86	5031,95	48,93	109,83	132,15		74,37	0,32	0,43					2,85		237,23				16,31	59,05
IAI	857,76	1546,49	5,17	49,98			119,52	0,21	0,71		0,21			4,25		1281,37				37,57	85,17
ITI																28,92
ITCL	0,48	12,14		5173,53	68,93		3,99	4,85	7,65					9,73	18,41	17,47	6,07	9,53	1,81	137,32	227,85
IVI	3,77	302,59	59,41	285,98	1319,82		111,76	29,87	389,58	17,46	19,12	1,28	11,53	614,47	4,88	4,05	15,12	15,91	35,50	161,65	119,67
IOR	182,82	191,04	33,62	142,08	82,32		180,20	112,54	169,65	169,10	161,51	51,32	24,88	351,47	15,74	65,18	477,66	81,14	4,89	127,59	172,26
ICI	265,34	246,79	4,75	378,93	729,09		4589,93	27,97	477,42	31,06	27,52		7,59	82,93	4,76	11,51	1,40	0,57	4,02	184,81	68,69
IBMCG		32,05			8,40		16,91	440,73	80,86	2,16	4,59		0,10	1544,31		13,42	0,09			0,75	2,48
IMEI	26,30	355,99	24,19	439,98	254,43		162,90	118,99	9049,35	12,84	17,21	367,98	0,93	1891,67	325,24	93,63	146,80	113,35	11,11	576,57	134,74
IONGE	0,85	59,43		15,06	24,03	341,21	147,12	11,11	41,89	143,54		804,17		11,72		114,16	0,15		20,86	15,36	7,66
IM		14,00		11,31	0,12		960,82	126,95	11,42	2,22	26,82			205,99		5,90
IEG	2,61	194,60	7,81	152,64	48,12		75,27	95,87	282,51	108,65	56,09	11,66	24,65	11,64	5,57	78,96	14,80	35,87	6,99	168,53	105,20
IW	1,85	9,58	0,10	11,00	7,68		25,14	1,00	4,46	1,11	0,56	0,80		6,84	0,11	4,34	0,38	0,10	0,09	24,93	4,01
IBCE	0,61									73,74				135,26		2,48			2,84	25,12	5,14
IMR		20,10		10,31	1,78		0,88	0,32	0,86		3,55			62,48	0,44	3,86	27,31	19,57	11,57	105,80	7,90
IHRSR				13,72			0,13					26,53				0,74	22,25		38,99	4,40	64,76
ITR	1,99	46,67	0,79	189,05	28,25		96,08	323,25	256,63	359,68	209,45	8,38	0,30	217,72	0,87	115,89	2602,49	32,28	33,21	226,17	228,45
IPT	4,84	47,41	3,84	292,63	28,29		39,84	23,70	305,07	103,82	9,47	9,43	0,65	80,64	2,71	27,07	18,88	79,23	120,66	406,84	253,67
IFS	6,88	111,71	2,37	116,31	52,56		115,10	22,72	242,02	57,29	24,50	7,20	5,41	110,88	0,58	12,57	73,65	2,43	98,22	427,37	88,25
IOMS	18,33	174,59	6,37	269,43	141,73	5,09	131,45	30,38	643,14	134,17	62,13	20,15	2,59	530,86	19,28	33,53	91,01	224,34	138,08	683,12	389,62
IPA
Source: Author’s own calculations based on collected data

For Transport sector, 40.39% of the cost of production is linked to the costs of capital, 16.63% constitutes the remuneration of labour, and 40.15% comes from intermediate consumption. The net subsidy tax linked to production represents 2.83% of direct production costs.

Table 5shows that overall, the capital factor receives more than 56% of added value and the rest for work in 2010. However, the sectoral analysis shows that there are branches of activity that are strongly dominated by the capital factor and the other by the work factor.

Table 5

Shares of labour and capital in the added value
	LSAV (MNTND)	CSAV (MNTND)	LSAV (%)	CSAV (%)
AF	995,07	4082,93	19,60%	80,40%
AI	919,01	1222,06	42,92%	57,08%
TI	41,18	20,20	67,09%	32,91%
TCL	1991,30	956,83	67,54%	32,46%
VI	321,79	886,72	26,63%	73,37%
OR	33,55	90,16	27,12%	72,88%
CI	800,36	872,36	47,85%	52,15%
BMCG	457,19	493,50	48,09%	51,91%
MEI	3013,80	1565,52	65,81%	34,19%
ONGE	2625,03	1593,99	62,22%	37,78%
M	136,06	391,98	25,77%	74,23%
EG	264,34	335,20	44,09%	55,91%
W	130,75	56,30	69,90%	30,10%
BCE	911,50	2009,18	31,21%	68,79%
MR	54,75	197,80	21,68%	78,32%
HRSR	1106,64	2198,17	33,49%	66,51%
TR	1449,41	3519,35	29,17%	70,83%
PT	938,06	1960,69	32,36%	67,64%
FS	847,56	1238,10	40,64%	59,36%
OMS	668,94	7238,09	8,46%	91,54%
PA	7694,04	2043,27	79,02%	20,98%
TOTAL	25400,33	32972,41	43,51%	56,49%
Source: Author’s own calculations based on collected data.

Notes: 1. “LSAV” and “CSAV” refer to labor share of the added value and capital share of the added value, respectively.

2. “MNTND” refer to millions of Tunisian dinars

The sectors which have a strong contribution of the capital factor in added value are other merchant services (91.54%), agriculture and fishing (80.40%), maintenance and repair (78.32%), mines (74.23%), various industries (73.37%), oil refining (72.88%) and transport (70.83%). On the other hand, the sectors that add the most to the added value by the labour factor are public administration (79.02%), water (69.90%), textile, clothing and leather (67.54%), tobacco industry (67.09%), mechanical industries and electric (65.81%) and oil and natural gas extraction (62.22 %).

According to the NSI, in terms of the qualification of work used, for the economy as a whole, 16.2% of the population employed in 2010 has the higher level. In table 6, it is notable that the share of the employed with primary education and less constitutes almost half (48.8%) of the total employed population. The rest (37.2%) are those with secondary education.

Table 6

Labour structure by activity and by level of education

	LN (mnTND)	LP (mnTND)	LS (mnTND)	LHS (mnTND)	total L (mnTND)	LN %	LP%	LS%	LHS	L %
AF	284,70	458,73	212,28	39,36	995,07	1,12%	1,81%	0,84%	0,15%	3,92%
AI	42,93	294,88	362,46	218,75	919,01	0,17%	1,16%	1,43%	0,86%	3,62%
TI	3,20	10,25	18,13	9,60	41,18	0,01%	0,04%	0,07%	0,04%	0,16%
TCL	80,15	719,18	964,86	227,11	1991,30	0,32%	2,83%	3,80%	0,89%	7,84%
VI	7,75	75,43	143,55	95,05	321,79	0,03%	0,30%	0,57%	0,37%	1,27%
OR	0,52	2,86	8,91	21,25	33,55	0,00%	0,01%	0,04%	0,08%	0,13%
CI	21,24	143,73	294,71	340,67	800,36	0,08%	0,57%	1,16%	1,34%	3,15%
BMCG	20,64	160,75	187,94	87,86	457,19	0,08%	0,63%	0,74%	0,35%	1,80%
MEI	37,81	569,53	1420,10	986,36	3013,80	0,15%	2,24%	5,59%	3,88%	11,87%
ONGE	24,12	266,96	558,61	1775,35	2625,03	0,09%	1,05%	2,20%	6,99%	10,33%
M	6,70	39,66	50,72	38,97	136,06	0,03%	0,16%	0,20%	0,15%	0,54%
EG	2,53	15,04	91,66	155,11	264,34	0,01%	0,06%	0,36%	0,61%	1,04%
W	5,18	24,76	50,42	50,39	130,75	0,02%	0,10%	0,20%	0,20%	0,51%
BCE	116,75	449,07	288,20	57,47	911,50	0,46%	1,77%	1,13%	0,23%	3,59%
MR	0,72	21,35	26,92	5,75	54,75	0,00%	0,08%	0,11%	0,02%	0,22%
HRSR	28,13	319,18	564,60	194,73	1106,64	0,11%	1,26%	2,22%	0,77%	4,36%
TR	27,84	285,08	602,93	533,56	1449,41	0,11%	1,12%	2,37%	2,10%	5,71%
PT	7,25	51,86	298,14	580,80	938,06	0,03%	0,20%	1,17%	2,29%	3,69%
FS	6,93	16,95	144,36	679,33	847,56	0,03%	0,07%	0,57%	2,67%	3,34%
OMS	25,85	120,49	208,86	313,75	668,94	0,10%	0,47%	0,82%	1,24%	2,63%
PA	188,39	544,84	2077,73	4883,08	7694,04	0,74%	2,15%	8,18%	19,22%	30,29%
TOTAL	939,36	4590,58	8576,08	11294,31	25400,33	3,70%	18,07%	33,76%	44,47%	100%

Source: Author’s own calculations based on collected data.

Notes: we report the individuals labor income by level of education, and in millions of Tunisian dinars and in percentage.

This table summarizes the results of individuals labour income by level of education. The main remarks we can make are:

- The labour income of individuals without a level of education reaches its highest level in the agriculture and fishing sector with 1.12% and the lowest level is recorded for the oil refining sector with 0.0021%. This category contributes to the total income of work by only 3.70%.

- The labour income of individuals with primary education presents 18.07% of total labour income; the largest contribution is attributed to the textile, clothing and leather and public administration sectors.

- The share of income from work of individuals with secondary education is greater than the two previous categories combined with 33.76%.

-Individuals with the higher level obtain 44.47% of labour income despite the high level of unemployment for the higher category.

The distribution of labour income by sector shows that only three sectors present more than half of the total income which are public administration, mechanical industries and electric and oil and natural gas extraction.

We adopt the economic policy and poverty (pep-1-t) model of Decaluwé et al. (2012). It is a recursive dynamic CGE which means that each period is solved as static equilibrium. This model is used to evaluate the impact on economic growth and unemployment reduction of a policy of increased spending on public infrastructure investments.

5.1 Methodology

CGE models detect interdependencies between different commodity markets, factor markets, and the public and private sectors of the economy. It analyzes the impact of a targeted policy shift in one sector of the economy on the rest of the economy. CGE model is an essential analytic tool, as policies can often have indirect effects that are difficult to quantify.

Investment in public infrastructure affects the economy both on the demand side and on the supply side. On the demand side, investments in public infrastructure increase public spending (investments combined with operating and maintenance costs). The result is an increase in overall demand that stimulates an increase in production.

In terms of supply, investments in public infrastructure reduce production costs and increase efficiency and productivity. There are three effects of infrastructure investment on the supply side of the economy. Those that are a factor of production, those that complement other factors of production, and those which stimulate the accumulation of factors(Fedderke and Garlick 2008).

We developed a recursive CGE with investment in public infrastructure as a positive externality through total factor productivity in the production function, adding an equation for unemployment.

To evaluate the impact of Tunisian public infrastructure investments, we have conducted four simulations in which only the public sector is included. To account for the continued increase in these investments, we introduced shocks five years in a row, the initial shock being in 2010.

In the first simulation (SIM 1), we assume that no funding mechanism placed and that capital is reallocated from the private sector to the public sector. Public investment in infrastructure increases by 10% in the first five years, funded by a government budget deficit. In the second simulation (SIM 2), the same principle is adopted as in the first simulation by adding 5% to the increase. In the third simulation (SIM 3), increased investment in public infrastructure is financed by increasing corporate taxes. It increases the corporate tax rate by 1.15 percent each year for the first five years. In the fourth simulation (SIM 4), we adopt the same principle as the third simulation by increasing corporate tax by 2%.

Business as usual (BAU) is the reference year (2010). For the BAU, the population grew by 1.11%, the population growth rate in Tunisia in 2010. The supply of labor increases at the same rate as the demographic index. This also applies to the current account balance, the minimum consumption of products in the demand equations of the linear expenditure system, general government current expenditure, public investment by category and by sector, as well as changes in Stocks assumed to grow at the same rate of the population growth rate of 1.116%. This assumption for the above variables to increase at the same rate as labor supply is made so that the model simulates a steady growth path.

5.2 Results

The increase in expenditure investments in public infrastructure produce a positive effect on GDP, which has undergone an increase comparing to the BAU in the four simulations (table 7). The most favorable change for Tunisia occurred in the fourth simulation.

Table 7 shows that the Tunisian government revenue increased relative to the BAU for the four simulations during the years of the shocks. The most favorable results for the first-year result from the second simulation.

Table 7

Variation of macroeconomic variables compared to the BAU

		2011(t+1)	2012(t+2)	2015(t+5)	2020(t+10)	2030(t+20)
GDP	SIMULATION n°1	0,3781	0,7256	1,8059	0,2410	0,2025
	SIMULATION n°2	0,5654	1,1008	2,7516	0,3641	0,3072
	SIMULATION n°3	0,5023	0,5792	0,7916	0,1946	0,1686
	SIMULATION n°4	0,8642	1,3098	2,6770	0,5397	0,4722
government revenue	SIMULATION n°1	0,8645	1,6195	3,7692	-0,0768	-0,0510
	SIMULATION n°2	1,2980	2,4777	5,8456	-0,1177	-0,0782
	SIMULATION n°3	0,2502	0,2547	0,2444	-0,0908	-0,0744
	SIMULATION n°4	0,4335	0,6026	0,9815	-0,2549	-0,2098
household income	SIMULATION n°1	0,8051	1,5057	3,4863	-0,1157	-0,0817
	SIMULATION n°2	1,2092	2,3052	5,4149	-0,1768	-0,1249
	SIMULATION n°3	0,8292	0,8930	1,0261	-0,0585	-0,0502
	SIMULATION n°4	1,4439	2,1049	3,9227	-0,1626	-0,1403
firm income	SIMULATION n°1	0,8290	1,5524	3,6089	-0,0879	-0,0578
	SIMULATION n°2	1,2447	2,3755	5,6000	-0,1346	-0,0887
	SIMULATION n°3	1,0705	1,1620	1,3651	-0,0209	-0,0176
	SIMULATION n°4	1,8623	2,7276	5,1459	-0,0576	-0,0491
CPI	SIMULATION n°1	0,3066	0,5546	1,1581	-0,2745	-0,2149
	SIMULATION n°2	0,4608	0,8507	1,8076	-0,4156	-0,3263
	SIMULATION n°3	0,3412	0,3383	0,2936	-0,1879	-0,1600
	SIMULATION n°4	0,5967	0,8237	1,3188	-0,5194	-0,4461
household consumption budget	SIMULATION n°1	0,4970	0,9459	2,3015	0,1592	0,1335
	SIMULATION n°2	0,7450	1,4423	3,5433	0,2398	0,2021
	SIMULATION n°3	0,8378	0,9421	1,2129	0,1628	0,1359
	SIMULATION n°4	1,4526	2,1770	4,3388	0,4527	0,3815

Source: Author’s own calculations based on collected data.

For household income, we find a similar result to the government's income in terms of average and in the years of the shock. The second simulation has the highest deviation from the BAU, followed by the fourth simulation (Table 7).

A similar result to household income is observed during the analysis of firm income for the first three simulations. The only difference is that the deviation from the BAU becomes more significant for the fourth simulation, which presents the best result.

The Consumer Price Index (CPI) simulations results indicate that increased investment in public infrastructure has an overall positive impact on the economy. The CPI decreases relative to the BAU scenario in the short and long term. It increases during the years of the shocks only.

As expected, the household consumption budget results are similar to the household income in the short and long term. The simulation that dominates is the fourth simulation followed by the second simulation. Table 7 shows that the household consumption budget in Tunisia increases in the four simulations and at all periods following the increase in income and the decrease in the CPI.

Unemployment rate

Table 8 clearly shows that investments in public infrastructure reduce the unemployment rate, whether through budget deficits or taxation. In both forms of financing, the higher the amount, the lower the unemployment rate.

Workers with no education (LN): Financing the increase in public infrastructure investment through deficit gives better results for LN-type workers during the first five years, while financing through taxes gives better results to the unemployment rate for this category in the short and long term. The second simulation produce the best variation in the year of the shock, and the fourth simulation gives the best variation in the short and long run.

Workers with primary education (LP): we observe a similar result to LN. Financing by tax gives the best variations in the short and long term, but these variations are better than LN. We also find that each increase in public infrastructure spending decreases the unemployment rate every year.

Table 8

Variation in the unemployment rate compared to the BAU scenario

		2011(t+1)	2012(t+2)	2015(t+5)	2020(t+10)	2030(t+20)
LN	SIMULATION 1	-1,6261	-3,0244	-6,8855	0,0491	0,0325
	SIMULATION 2	-2,4271	-4,5736	-10,377	0,0833	0,0548
	SIMULATION 3	-1,9218	-2,1054	-2,5306	-0,0527	-0,0247
	SIMULATION 4	-3,3093	-4,8286	-8,9499	-0,1277	-0,0567
LP	SIMULATION 1	-1,3646	-2,5478	-5,8657	-0,0314	-0,0331
	SIMULATION 2	-2,0382	-3,8580	-8,8687	-0,0405	-0,0456
	SIMULATION 3	-1,6783	-1,8501	-2,2625	-0,1114	-0,0806
	SIMULATION 4	-2,8922	-4,2428	-7,9805	-0,2974	-0,2168
LS	SIMULATION 1	-1,1001	-2,0809	-4,9693	-0,3343	-0,2699
	SIMULATION 2	-1,6438	-3,1525	-7,5258	-0,5035	-0,4081
	SIMULATION 3	-0,8359	-0,9571	-1,2851	-0,2553	-0,2094
	SIMULATION 4	-1,4429	-2,1814	-4,4109	-0,7060	-0,5844
LHS	SIMULATION 1	-1,1456	-2,1763	-5,2627	-0,4965	-0,4209
	SIMULATION 2	-1,7113	-3,2953	-7,9616	-0,7484	-0,6381
	SIMULATION 3	-0,1895	-0,2604	-0,4844	-0,3101	-0,2735
	SIMULATION 4	-0,3264	-0,5666	-1,4790	-0,8536	-0,7648

Source: Author’s own calculations based on collected data.

Workers with secondary education (LS): The results show that deficit financing is the best option for job creation in the years of the shock. The higher the amount invested, the biggest the change in unemployment. The fourth simulation is always the best in both the short and long term.

Workers with higher education (LHS): In the first five years, funding public infrastructure investments by budget deficit is much more beneficial in reducing unemployment. Similar to other job categories, the second simulation shows the best variation over the first five years. The fourth simulation shows the best change in the short and long run.

Despite the efforts made in this article to present the economy of Tunisia well, a more detailed breakdown is limited at the data level. We collected data for the social accounting matrix from various sources.

In this document, we first gave the general presentation of the social accounting matrix, then we presented all the stages of the construction of a macro MCS from different sources of data on the Tunisian economy, to using cross entropy methodology.

We disaggregated this matrix to highlight the sectors with the interrelations. Thus, we were able to describe the Tunisian economy from 2010 SAM. Given the various adjustments made, the product of this work is a square matrix with 76x76 accounts. This matrix makes it possible to account for certain characteristics of the Tunisian economy and both a conceptual framework and a data system on which analyses of economic and social policy issues and the evaluation of the economic and social consequences of exogenous changes and which served as a basis of data for the computable general equilibrium model.

We used the results of our SAM to present the main characteristics of the Tunisian economy. We focused on the Contribution of factor costs in the added value structure, structure of production cost, Cost of production by sector, Intermediate consumption structure, Shares of labour and capital in the added value, and Labour structure by activity and by the level of education.

This SAM allows decision-makers to orient their choice, to supervise many policy analyses to face these challenges. It represents the reference state of the Tunisian economy for the year 2010. This SAM is used in a computable general equilibrium model to assess the impact on economic growth and reducing unemployment of a policy of increasing public spending on infrastructure investment.

We can conclude that the increase in investment in public infrastructure is generally beneficial for the Tunisian economy for both types of financing, and the higher the amount invested, the results are better. GDP increases while the price level decreases relative to the baseline scenario path for the entire period (20 years) and all scenarios. Business, household, and government revenues increase in the short and long term, regardless of the financing mechanism. The unemployment rate decreases compared to the reference scenario trajectory in all the simulations.

Availability of data and materials

The datasets generated during and/or analysed during the current study are available in the Central Bank of Tunisia and National Institute of Statistics, [https://www.bct.gov.tn/bct/siteprod/index.jsp, http://www.ins.tn/ ]

Competing interests

The authors declare that they have no competing interests

Funding

The authors did not receive support from any organization for the submitted work.

The authors have no relevant financial or non-financial interests to disclose.

Authors' contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by (Jabeur SALHI) and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Jabeur SALHI: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation, Visualization, Investigation, Software, Writing- Reviewing and Editing.

Saloua BENAMMOU: Supervision, Validation.

Acknowledgements

Not applicable

Bank Brussels W, York N (1993) System of National Accounts
ben Salhine R, ben Younes A, el Kadhi Z, et al (2020) 2015 Regionalized Social Accounting Matrix for Tunisia A Nexus Project SAM
Decaluwé B, Lemelin A, Robichaud V, Maisonnave H (2012) pep-1-t the PEP standard single country, recursive dynamic CGE model
Decaluwé B, Martens A, Savard L (2001) La politique économique du développement et les modèles d’équilibre général calculable: une introduction à l’application de l’analyse mésoéconomique aux
Fedderke J, Garlick R (2008) Infrastructure Development and Economic Growth in South Africa: A Review of the Accumulated Evidence
Haouas I;, Sayre E;, Yagoubi M (2012) Youth Unemployment in Tunisia: Characteristics and Policy Responses". Topics in Middle Eastern and North African Economies, electronic journal. 14:
International Office work and centre for Research and Social Studies (2012) ENQUETE SUR LA STRUCTURE DES SALAIRES, Tunisie 2011 Rapport final
Lemelin A (2008) Modèles économiques régionaux : un survol de la littérature – Cahier technique et méthodologique
Lemelin A, Cockburn J (2013) Ismaël Fofana 2 1 BALANCING A SOCIAL ACCOUNTING MATRIX: BALANCING A SOCIAL ACCOUNTING MATRIX: THEORY AND APPLICATION 1
Pyatt G, Round J (1985) Social accounting matrices: A basis for planning
Robinson S (1991) Macroeconomics, financial variables, and computable general equilibrium models. World Development 19:1509–1525. https://doi.org/10.1016/0305-750X(91)90003-Z
Robinson S, Cattaneo A, El-Said M (2001) Updating and estimating a social accounting matrix using cross entropy methods. Economic Systems Research 13:47–64. https://doi.org/10.1080/09535310120026247
Round J (2003) Chapter 14-Social Accounting Matrices and SAM-based Multiplier Analysis Chapter 14 Social Accounting Matrices and SAM-based Multiplier Analysis
Stone R (1985) The disaggregation of the household sector in the national accounts. Social accounting matrices : a basis for planning ; a World Bank symposium

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A Tunisian social accounting matrix for impact analysis of macroeconomic policies and shocks

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Abstract

Figures

1. Introduction

2. General Presentation Of Tunisia And The Sam

2.1. Context of Tunisia

2.2. Context of the SAM

2.2.1. History of construction of SAM

2.2.2. Definition, objectives, and usefulness of SAM

2.2.3. Structure of a standard SAM

3. Construction Of The Sam For The Tunisian Economy

3.1 Accounts of TUNISIA macro-SAM of 2010

3.2 Balancing the SAM

4. Disaggregation Of Macro Matrix Accounts: The 2010 Disaggregated Matrix

4.1. Disaggregation of Macro Matrix Accounts

4.1.1. Production factors

4.1.2. Activities and Products account

4.2. The Tunisian economy according to the 2010 social accounting matrix

5. A Computable General Equilibrium Model Using Our Sam

5.1 Methodology

5.2 Results

6. Conclusion

Declarations

References

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