The Sixth Five Year Plan, as outlined in Bangladesh's Poverty Reduction Strategy Paper, targets strategic growth and employment. The medium-term macroeconomic framework plan entails the involvement of both the private and public sectors. Human resources development strategy programs reaching out to the poor and the vulnerable population, as well as environment, climate change, and disaster risk management, have been included in the plan. Managing regional disparities for shared growth and strategy for raising farm productivity and agricultural growth have been outlined. Diversifying exports and developing a dynamic manufacturing sector are all inclusive in the proposed plan.

Abstract

The Sixth Five Year Plan, as outlined in Bangladesh's Poverty Reduction Strategy Paper, targets strategic growth and employment. The medium-term macroeconomic framework plan entails the involvement of both the private and public sectors. Human resources development strategy programs reaching out to the poor and the vulnerable population, as well as environment, climate change, and disaster risk management, have been included in the plan. Managing regional disparities for shared growth and strategy for raising farm productivity and agricultural growth have been outlined. Diversifying exports and developing a dynamic manufacturing sector are all inclusive in the proposed plan.

Results from the Dynamic CGE Model of Bangladesh

Introduction

The macro-economic framework for the SFYP has been used to generate consistent macro economic projections for the plan period. A dynamic computable general equilibrium (DCGE) model, based on an updated input-output table and a social accounting matrix (SAM) for Bangladesh for FY07, has been used to derive the sectoral implications of the macro projections considered in the SFYP. The key outcomes of the macroeconomic framework are linked to the DCGE model. Furthermore, an Employment Satellite Matrix (ESM), constructed for FY07 has been linked to the sectoral output growth derived from the DCGE model to generate the sectoral employment impacts.

The reason for employing a dynamic CGE model is due to fact that a dynamic CGE model is capable of capturing the growth effects of policy reforms. The inability of the static CGE model to account for growth effects make them inadequate for long-run analysis of the economic policies. They exclude accumulation effects and do not allow the study of transition path of an economy where short-run policy impacts are likely to be different from those of the long-run. To overcome this limitation we use a sequential dynamic CGE model. This kind of dynamics will not be the result of inter-temporal optimisation by economic agents. Instead, these agents have myopic behaviour. It is a series of static CGE models that are linked between periods by updating procedures for exogenous and endogenous variables. Capital stock is updated endogenously with a capital accumulation equation, whereas population (and total labour supply) is updated exogenously between periods. Also other variables such as public expenditure, transfers, technological change or debt accumulation are updated over time. The sequential dynamic CGE model has two major modules: static module and dynamic module. Descriptions of the static and dynamic modules of the model are presented in the subsequent sections.

Static Module of the DCGE Model

Production bloc

In each sector there is a representative firm, which earns capital income, pays dividends to households and pays direct income taxes to the government. We adopt a nested structure for production. Sectoral output is a Leontief function of value added and total intermediate consumption. Value added is in turn represented by a CES function of capital and composite labour. The latter is also represented by a CES function of two labour categories: skilled labour and unskilled labour. Both labour categories are assumed to be fully mobile in the model. In different production activities we assume that a representative firm remunerates factors of production and pays dividends to households.

The equations of the production bloc are provided below. The description of the variables and parameters is provided in the Annex.

(1)XSj=Min[CIjioj,VAjνj]
(2)VAj=AjKL[iKLLDiρikl+(1iKL)KDiρikl]1/ρiKL
(3)LDi=AjLL[iLLQLiρiLL+(1iLL)NQLiρiLL]1/ρiLL
(4)CIj=iojXSj
(5)DIi,j=aiji,jCIj
(6)LDi=(αiKL1αiKL)σiKL(riwi)σiKLKDi
(7)NQLi=(αiLL1αiLL)σiLL(wqwnq)αiLLQLi

Income and demand bloc

Households earn their income from production factors: skilled and unskilled labour, agricultural and non-agricultural capital. They also receive dividends, intra-household transfers, government transfers and remittances and pay direct income tax to the government. Household savings are a fixed proportion of total disposal income. Household demand is represented by a linear expenditure system (LES) derived from the maximisation of a Stone-Geary utility function. The model includes nine household categories according to characteristics of the household head, as identified in the HES household survey. Five of these categories correspond to rural households and four are of urban households. Minimal consumption levels are calibrated by using guess-estimates of the income elasticity and the Frisch parameters.

The government receives direct tax revenue from households and firms and indirect tax revenue on domestic and imported goods. Its expenditure is allocated between the consumption of goods and services (including public wages) and transfers. The model accounts for indirect or direct tax compensation in the case of a tariff cut. The equations are provided below:

(8)YHh=λhWQwqΣjQLj+λhWQNΣjNQLj+λhRΣnagrnagKDnag+λhLΣagragKDag+PindexTGh+PindexTHh,hj+PindexTWHh+DIVh
(9)YDHh=YHhDTHh
(10)SHh=νψhYDHh
(11)YF=λRFΣiriKDi+λLF.rl.LAND
(12)SF=YFΣhDIVhe.DIVROWDTF
(13)YG=ΣiTIi+ΣiTIEi+ΣiDTHh+DTF
(14)SG=YGGPINDEXΣhTGh
(15)TIi=txi(PiXSiPEiEXi)+txi(1+tmi).e.PWMiMi
(16)TIMi=tmie.PWMiMi
(17)TIEi=teiPEiEXi
(18)DTHh=tyhhYHh
(19)DTH=tyf.YF
(20)CTHh=YDHhSHh
(21)PCICi,h=PCiCi,hmin+γi,h(CTHhΣjPCjCj,hmin)
(22)G=CGserPCser
(23)INVi=μiITPCi
(24)DITi=ΣjDIj

International Trade

We assume that foreign and domestic goods are imperfect substitutes. This geographical differentiation is introduced by the standard Armington assumption with a constant elasticity of substitution function (CES) between imports and domestic goods. On the supply side, producers make an optimal distribution of their production between exports and local sales according to a constant elasticity of transformation (CET) function. Furthermore, we assume a finitely elastic export demand function that expresses the limited power of the local producers on the world market. In order to increase their exports, local producers may decrease their free on board (FOB) prices. The equations are provided below:

(25)XSi=BiE[βiEEXikiE+(1+βiE)DikiE]1kiB
(26)EXi=[(PEiPLi)(1βiEβiE)]τiEDi
(27)EXDi=EXDio.(PWEiPEFOBi)elasti
(28)Qi=AiM[αiMMiρiM+(1αiM)DiρiM]1ρiM
(29)Mi=[(PDiPMi)(αiM1αiM)]σiMDi
(30)CAB=ΣiPWMiMi+λROWΣiriKDi/e+DIVROWΣiPEFOBiEXi

Price blocs

The prices equations are provided below. The nominal exchange rate is the numéraire in each period.

(31)(25)PVj=PjXSjΣiPCiDIi,jVAj
(32)ri=PViVAiwiLDiKDi
(33)wi=wq.QLiwnq.NQLiLDi
(34)PDi=(1+txi)PLi
(35)PMi=(1+txi).(1+tmi).e.PWMi
(36)PEi=e.PEFOBi1+tei
(37)PCiQi=PDiDi+PMiMi
(38)PiXSi=PLiDi+PEiEXi
(39)Pinv=Πi(PCiμi)μi
(40)Pindex=ΣiδiPVi

Equilibrium Condition

General equilibrium is defined by the equality (in each period) between supply and demand of goods and factors and the investment-saving identity. The equations are provided below:

(41)Qi=DITi+ΣhCi,h+INVi+Dstki
(42)EXi=EXDi
(43)LSQ=ΣjQLj
(44)LSNQ=ΣjNQLj
(45)IT+ΣiPCiDstki=ΣhSHh+SF+SG+e.CAB

Dynamic Module of the DCGE Model

In every period capital stock is updated with a capital accumulation equation. We assume that the stocks are measured at the beginning of the period and that their flows are measured at the end of the period. We use an investment demand function to determine how new investments will be distributed between the different sectors. This can also be done through a capital distribution function15. Investment here is not by origin (product) but rather by sector of destination. The investment demand function used here is similar to those proposed by Bourguignon et al. (1989), and Jung and Thorbecke (2003). The capital accumulation rate (ratio of investment to capital stock) is increasing with respect to the ratio of the rate of return to capital and its user cost. The latter is equal to the dual price of investment times the sum of the depreciation rate and the exogenous real interest rate. The elasticity of the accumulation rate with respect to the ratio of return to capital and its user cost is assumed to be equal to two. By introducing investment by destination, we respect the equality condition with total investment by origin in the SAM (Social Accounting Matrix). Besides this, investment by destination is used to calibrate the sectoral capital stock in base run.

Total labour supply is an endogenous variable, although it is assumed to simply increase at the exogenous population growth rate. Note that the minimal level of consumption in the LES function also increases (as do other nominal variables, like transfers) at the same rate. The exogenous dynamic updating of the model includes nominal variables (that are indexed), government savings and the current account balance. The equilibrium between total savings and total investment is reached by means of an adjustment variable introduced in the investment demand function. Moreover, the government budget equilibrium is met by a neutral tax adjustment.

The model is formulated as a static model that is solved sequentially over a certain period time horizon.16 The model is homogenous in prices and calibrated in a way to generate “steady state” paths. In the baseline all the variables are increasing, in level, at the same rate and the prices remain constant. The homogeneity test (for example, a shock on the numéraire – the nominal exchange rate – with the “steady state” characteristics) generates the same shock on prices, and unchanged real values, along the counterfactual path. This method is used to facilitate welfare and poverty analysis since all prices remain constant along the business as usual (BaU) path.

It is, however, important to note that, in contrast to the static CGE models, which make counterfactual analysis with respect to the base run (generally the initial SAM), a dynamic CGE model allows the economy to grow even in the absence of a shock. This scenario of the economy (without a shock) is termed as the business-as-usual (BAU) scenario. The counterfactual analysis of any simulation under the dynamic CGE model is, therefore, done with respect to this growth path. One of the salient features of the dynamic model is that it takes into account not only efficiency effects, as also present in the static models, but also accumulation effects. The sectoral accumulation effects are linked to the ratio between the rate of return to the capital stock and the cost of investment goods. The equations of the dynamic bloc are provided below.

(46)KDi,t+1=(1δ)KDi,t+Indi,t
(47)LSQt+1=(1+ng).LSQt
(48)LSNQq=(1+ng).NQLt
(49)Ci,h,t+1min=(1+ng)Ci,h,tmin
(50)Indi,tKDi,t=AiIK(Ri,tUi,t)2
(51)Ui,t=Pinvt(ir+δi)
(52)ITt=pinvt.ΣiIndi,t
(53)SGt+1=(1+ng)SGt
(54)CABt+1=(1+ng)CABt
(55)TGt+1=(1+ng)TGi
(56)CGt+1=(1+ng)CGt
(57)Dstkt+1=(1+ng)Dstkt
(58)DIVt+1=(1+ng)DIVt
(59)DIVROWt+1=(1+ng)DIVROWt
(60)TWHt+1=(1+ng)TWHt
(61)THh,hj,t+1=(1+ng)THh,hj,t
(62)EXDt+1o=(1+ng)EXDto

The Bangladesh Social Accounting Matrix for 2006-07

For the purpose this exercise, a SAM for 2006/07 for Bangladesh has been constructed. SAM 2006/07 composed of 189 accounts. The distributions of 189 accounts are: (i) 86 activities; (ii) 86 commodities; (iii) 4 factors of production; (iv) 11 current institutions; and (v) 2 capital institutions. Data on various components of the demand side have been collected from Bangladesh Bureau of Statistics (BBS). In particular, data on public consumption by 86 commodities, gross fixed capital formation by 86 commodities, and private consumption by 86 commodities have been obtained from BBS. The vector of private consumption data is further distributed among the eight representative household groups using the unit record data of Household Income and Expenditure Survey (HIES) of 2005. Data on exports of goods and services are collected from the Export Promotion Bureau and Bangladesh Bank (i.e. the central bank of Bangladesh). Supply side composed of value added and imports of goods and services. We used disaggregated BBS data to derive the value added vector for the 86 activities. Data on imports of goods and services are collected from the Bangladesh Bank and the National Board of Revenue. Information on direct and indirect taxes and subsidies has been collected from the National Board of Revenue and the Finance division, Ministry of Finance. Input-output flow matrix for 2006/07 has been derived by using newly conducted surveys for few selected activities and updating the previous technology vectors using secondary information. More specifically, out of the 86 activities, technology vectors of five important activities such as paddy, livestock, poultry, pharmaceuticals and information technology (ICT) have been derived using the field survey data. The technology vectors of the remaining activities are updated using secondary information.

For the DCGE model, we use an aggregated version of the SAM of Bangladesh that includes 15 sectors, four factors of production: skilled and unskilled labour, agricultural and non-agricultural capital. An important feature of the SAM is the decomposition of the households into seven groups. Households are classified in terms of location - urban and rural. In case of rural households, occupation and ownership of agricultural capital by the household is the main criterion to differentiate household groups. Initially making a preliminary distinction between agricultural and non-agricultural occupation groups, the agricultural group is then classified into four classes according to ownership of agricultural capital. Thereby there are five groups: Landless (No cultivable land); marginal farmers (up to 0.49 acre of land); small farmers (0.5 to 2.49 acres of land); large farmers (2.50 acres of land and above); Non-Agricultural. Urban households are classified into two categories according to the educational level of the household head. These are: Low Education (below class X) and High Education (above class X). Table 1 provides the features of the 2006-07 SAM of Bangladesh.

Table 1:

Features of 2006-07 SAM of Bangladesh

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Results of the DCGE Model and the Projections

Sectoral Growth Rates

The growth rates for 14 sectors of the DCGE model for the period between 2009-10 and 20014-15 are reported in Table 2. It appears that the cereal crop sector will experience a growth rate of around 4.7 percent during the plan period, whereas the commercial crops sector will experience higher growth rate than that of crop agriculture suggesting a shift in the production pattern in the agricultural sector. By 2014-15, the growth rate of the overall agricultural sector will be 4.3 percent. The overall industrial sector will experience a rise in growth rate by almost 100 percent at the end of plan period from its current growth rate. Growth in the textile and clothing sector, machinery sector and other industries will be the major drivers of the industrial growth rate. The construction and services sectors will also experience steady rise in growth rates.

Table 2:

Sectoral Growth Rates

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Source: DCGE Model
Sectoral Shares in GDP

Differential growth rates for different sectors, as reported in Table 2, lead to varying sectoral shares in GDP over the plan period. The agricultural sector, as a whole, will experience decline in the share in GDP from 18.6 percent in 2009-10 to 15.5 percent in 2014-15. All the agricultural sub-sectors will experience decline in their respective shares. The major fall will be in the case of cereal crops.

In the case of industrial sector, the overall share will rise from 19.1 percent in 2009-10 to 22.3 percent in 2014-15. All industrial sub-sectors, except chemical-fertiliser, will experience rise in their shares in GDP. The most notable rise will be in the cases of textile and clothing and machinery sectors.

The share of construction sector in GDP will rise by 0.3 percentage points by the end of the plan period from its current share. The share of services sector will fall by 0.4 percentage points by the end of the plan period.

Sectoral Employment

As mentioned before, the sectoral output changes from the DCGE model are linked to the employment satellite matrix to derive the sectoral employment effects of the sectoral growth. Table 4 presents the projections of the total number of labour force to be employed by different sectors during the plan period. The number of labour force employed in the agricultural sector will rise from 25628150 in 2009-10 to 31159781 in 2014-15. All agricultural sub-sectors will experience rise in employment. However, the most significant rise in employment will be observed in the industrial sector where employment will rise from 6792516 2009-10 to 11073693 in 2014-15. Sub-sectors like textile and clothing and other industries will generate major incremental employment in the industrial sector. Finally, the constriction and services sectors will also generate substantial additional employment during the plan period. The growth rate in employment will rise from its current level of 5.04 percent to 7.19 percent by the end of the plan period. The annual average additional employment will be 4124001.

Table 3:

Sectoral Shares in GDP

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Source: DCGE Model
Table 4:

Sectoral Employment (Number of labour force employed)

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Source: DCGE Model and Employment Satellite Matrix

Table 5 indicates that during the plan period the agricultural sector as a whole will encounter fall in the share of employment from its current 44.5 percent to 39.8 percent at the end of plan period. Three major agricultural sub-sectors, i.e. cereal crops, commercial crops and livestockpoultry-fishing will experience fall in the shares by notable margins.

Table 5:

Sectoral Employment Share

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Source: DCGE Model and Employment Satellite Matrix

In the case of industry, the sector as a whole will experience a rise in the share of total employment from 11.8 percent in 2009-10 to 14.2 percent in 2014-15. The notable rises in the shares of employment are observed in sub-sectors like textile and clothing and other industries. The construction sector will go through some marginal rise in the share whereas the services sector will experience rise in the share by 2 percentage points during the plan period.

Annex Description of the Variables and Parameters of the Dynamic CGE Model

Endogenous variables

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Exogenous variables

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Parameters

Production functions
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CES function between capital and labor

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CES function between skilled and unskilled labor

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CES function between imports and domestic production

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CET function between domestic production and exports

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LES consumption function

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Tax rates

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Other parameters

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Reference

  • Abbink, G. A., M. C. Braber, and S. I. Cohen. 1995. “A SAM-CGE Demonstration Model for Indonesia: A Static and Dynamic Specifications and Experiments.” International Economic Journal 9 (3): 1533.

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  • Bourguignon, F., W. H. Branson, and J. de Melo. 1989. “Macroeconomic Adjustment and Income Distribution: A Macro-Micro Simulation Model.” OECD Technical Paper 1. Paris.

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  • Jung, H. S., and E. Thorbecke. 2003. “The Impact of Public Education Expenditure on Human Capital, Growth, and Poverty in Tanzania and Zambia: A General Equilibrium Approach.” Journal of Policy Modeling 25: 70125.

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15

Abbink et al (1995) use a sequential dynamic CGE model for Indonesia where total investment is distributed as a function of base year sectoral shares in total capital remuneration and sectoral profit rates.

16

The model is formulated as a system of non linear equations solved simultaneously as a constrained non-linear system (CNS) with GAMS/Conopt3 solver.

Bangladesh: Poverty Reduction Strategy Paper
Author: International Monetary Fund. Asia and Pacific Dept