Cross sectoral linkages to explain structural transformation in Nepal

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Cross Sectoral Linkages to explain Structural Transformation in Nepal Muhammad Aamir Khan Assistant Professor PII: DOI: Reference:

S0954-349X(19)30195-X https://doi.org/10.1016/j.strueco.2019.11.005 STRECO 878

To appear in:

Structural Change and Economic Dynamics

Received date: Revised date: Accepted date:

24 May 2019 29 August 2019 10 November 2019

Please cite this article as: Muhammad Aamir Khan Assistant Professor , Cross Sectoral Linkages to explain Structural Transformation in Nepal, Structural Change and Economic Dynamics (2019), doi: https://doi.org/10.1016/j.strueco.2019.11.005

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HIGHLITES: 

Many developing countries considers export-oriented Structural Transformation as panacea for economic development.



This research aims to quantifies the economy-wide impacts of Export and productivity oriented Structural transformation in Nepal.



Using Input Output framework, Higher Backward linkages are in Hotels, Food, Wood, Textile and Wearing Apparel while Construction, Agriculture, Hunting, forestry and fishing, are the sectors with Higher Forward linkages in Nepal.



Sectoral and Labor Productivity in favor of promising sectors (Manufacturing and Services) have significant positive effects (in terms of growth, welfare, household income) on Nepal economy.

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Cross Sectoral Linkages to explain Structural Transformation in Nepal Muhammad Aamir Khan1 ABSTRACT: Many developing countries considers export-oriented Structural Transformation as panacea for economic development. Existing literature on economic growth and structural change also relies on trade data for policy implication on country’s competitiveness and long-term growth prospects. With this backdrop, this research aims to identify cross sectoral linkages using Input-Output (IO) Data and quantifies the economy-wide impacts of Trade and productivity oriented Structural transformation in Nepal on macro as well as at household level using a Global Commutable General Equilibrium (CGE) Model. The model is calibrated with latest Nepal Social Accounting Matrix 2007-08. Using Input Output framework, sectors with Higher Backward linkages are Hotels, Food, Wood, Textile and Wearing Apparel. This means inputs of these sectors come predominantly from other sectors of the economy. Whereas Construction, Agriculture, Hunting, forestry and fishing, are the sectors with Higher Forward linkages. This implies the fact that the outputs of these sectors are used as inputs in other sectors directly or indirectly on a large scale. The simulation results show that Sectoral and Labor Productivity in favor of promising sectors (Manufacturing and Services) have significant positive effects (in terms of growth, welfare, household income) on Nepal economy.

Keywords: Structural Transformation; Cross Sectoral Linkages; Input Output Models; Nepal 1- INTRODUCTION: The evidence that Structural Transformation can be a catalyst to economic development is compelling (Felipe, 2012). Prior to explaining the correlation between structural transformation and economic development, one must be familiar with the underlying driving features of this phenomenon. In brief, it encompasses the reallocation/shifting of the economic activity across three extensive sectors i.e. Agriculture, Manufacturing and Services with an attempt to endeavor an economic well-being (Herrendorf and Rogerson, 2014). Nonetheless, countries witness these spectacular sectorial changes in decades, as economic and social structures are usually reluctant to major shifts. Therefore, the economic structural transformation can be termed as continuous and long-lived process of transferring the productive resources from inefficient agriculture sector to highly sophisticated manufacturing sector along the lines of economic development.

1

Assistant Professor, Department of Economics, COMSATS University Islamabad, Pakistan. Email ID:

[email protected].

2

Policy analysts have been striving hard to give any final direction to the association between economic development and structural changes bearing in mind the dilemmas of developing economies. Every year, policies are implemented, investment projects are designed, and collaborative efforts are executed in order to get closer to development level of rich countries. Many economically developed countries have successfully experienced the structural transformation by pipelining the excess resources from labor intensive sector to capital intensive sector, as, such reallocation will allow the labor to learn more capital oriented techniques and upgrade their skills. By contrast, more than 50% population of Sub-Saharan African and Asian countries are finding their livelihood in agriculture sector which is practicing the orthodox ways even in the time of modernization. Sequentially, structural heterogeneity and lack of linkages among traditional and productive sector and isolation of the later form the rest of economic activities in the country, further hinders the process of economic expansion and sustainability. These countries are usually characterized by dual economy which reflects a high need for the absorption of labor force in modern manufacturing sector shaped by sophisticated and innovative production practices. Kuznets (1966) enumerated the structural transformation among the core driving vehicles of economic growth as it tends to upgrade the level of productivity and endorses a balanced sector-wise employment. Simultaneously, modern sector provides the room for the development of traditional sector by buying the inputs form it, imparting the employment opportunities, building infrastructure, generating new ideas and transferring remittances. Similarly, Lewis (1954) opined the fact that transfer of factors of production from non-capitalist (low wage and less productive) to capitalist (high wage, high productivity) sector will trigger the pace of economic efficiency. As, release of surplus labor from traditional sector will tend to push up the minimal subsistence wages leading to enrichment of the overall productivity in the country. On the other hand, just a mere sectorial redistribution of resources will not yield the requisite outcomes especially for the export-oriented economies if the industrial output is not internationally competitive. Moreover, Caselli (2005) and Restuccia (2010) tried to give the answer of why less developed countries remained so poor. They argued that low level of agriculture productivity and allocation of major portion of labor force to agriculture have not enabled them to walk out of poverty trap. Gollin (2002, 2007) figured out how low agriculture productivity account for huge income differences across the World. So, the footprints of structural transformation lead to right direction through a change in the demand pattern, development of a new products, adoption of new technologies and shift in the pattern of international competition. Agriculture sector outperformed other sectors in poverty reduction. Agriculture growth induced by small farmers remained more effective in reducing poverty as compared to the growth made by large farmers (Dorosh & Thurlow, 2018). Similarly, different sub-sectors within the service sector have not performed homogenously in terms of poverty reduction; caused by shifting the surplus labour from agriculture sector to non-agriculture sector. Trade, transportation and mining sectors performed at 3

parity, agro-processing exceeded while financial and government services under performed the agriculture sector in poverty reduction (Christiaensen & Martin, 2018). Caliendo et al. (2017) gave the concept of disaggregated productivity across the different sectors. Geographical factors such as region-specific land and other structures that are impossible to move mainly accounted for productivity differentials. However, structural transformation could be seen with novel approaches from the perspective of both intra and inter sector productivity differentials. For instance, total working hours are usually split into household production/services and market production/services. With increase in the GDP, employment in the household services as well in the market production declined and lifted up in market services (Bridgman, 2018). Nevertheless, different sub-sectors within the service sector do not act complementary as they did not enjoy the same level of productivity growth. Utilities and financial intermediaries experienced the uplift while the personal service sector remain deprived of the productivity growth in spite of the structural transitions (Aroca & Garrido, 2018). Productivity gaps are typically larger in case of manufacturing sector. Moreover, less developed countries are more exposed to such productivity difference within a manufacturing sector, consequentially, reallocation of resources within and across the sectors will increase the opportunities of economic development (Bah, 2011). Economically developed nations experienced the non-identical notion of structural changes on account of the dissimilar composition of final demand induced by varying input-output linkages (Sposi, 2015). The new growth models hold on to the fact that with the globalization of technology, structural transformations will allow low-income countries to catch up the development with the greater pace than the developed countries as they are able to use the ideas and technology already invented by the later countries. Accordingly, by means of technological cooperation and flow of ideas, China and Asian emerging economies (Hong Kong, Taiwan and Singapore) managed to emerge as new economic powers in international sphere. The gentle shift from the localism to internationalism led by the ever-increasing trade among the countries aided the successful structural transformation. Low agriculture productivity can be cured by abating the protectionist attitudes and sequentially letting the countries to made food imports. Instances upon the favorable role of food trade have come from the Great Britain in 19th Century and from South Korea during the last 50 years. However, the free trade policies rather than vain attempt of restrictions may have better prompted the process of structural shifts in South Korea (Teignier, 2018). Betts et al. (2017) probed into the outcomes of Korean trade policies, featuring the tariff and subsidies liberalization, for the possible structural changes. Tariff liberalization promoted trade accompanying the agriculture imports and broadening the industrialization. On the contrary, Subsidy liberalization dropped the trade with decreased exports and narrowed down the industrial output. In this way, both type of trade reforms neutralized each other’s effect leaving the structural pattern of economy 4

unchanged. Moreover, trade induced structural change has likely possibility to inflate the skill premium in the labour market by allocating the excess labour of less productive manufacturing sector to the service sector. Drag to the trade costs and changing patterns of comparative advantage induce the skilled based structural change which results in the gradually declining share of manufacturing in global output (Cravino & Sotelo, 2019). Despite of the greater openness in goods market and greater gain from the goods consumption, structural shift of expenditures away from agriculture to services sector dampened the trade growth carried out by declining openness in the last decades (Lewis, 2018). On the contrary, Kehoe (2018) considered the two countries dynamic general equilibrium model featuring US and rest of the world and illustrated that declining share of goods sector is attributed to the productivity differentials across the sectors in lieu of the US trade deficit. Trade deficit could have improved with the repayment of the debts, yet the goods industry employment share would still keep on falling. There are also evidences against the role of structural transformation in the productivity growth slumped in US economy in post-World War-II years. Duernecker et al. (2017) envisaged a limited unbalanced growth slowdown; carried out by reallocation of the economic sources to less productive industrial sector, in the near future, due to the gradually increasing share of the relatively productive service sector.

Less

productivity in the developed countries during the past and currently in developing countries is due to the high labor turnover and absenteeism, lack of institutionalized discipline to channelize the labor market forces and paucity of adequate financial support for the sector in the phase of structural changes in the economy (Oqubay, 2018). Structural changes performed in both directions, in the pursuance of green economy and managing the ecological scarcities. In china, structural changes in manufacturing sector left the positive impact on energy and carbon dioxide emission adjusted total factor productivity and negative on energy adjusted total factor productivity (Li & Lin, 2017). Urbanization and industrialization contributed the most to the CO2 emissions. Interprovincial trade linkages caused the most environmental deterioration rather than the external demand for the Chinese exports. Further, manufacturing, transportation and real estate activities occupied the greater share in total CO2 emissions respectively. As far as households are concerned, income-oriented activities caused greater emissions in contrast with the expenditureoriented activities. Moreover, CO2 emissions prevailed at the same level despite the shift to the services led economic structure in China (Liao, 2017). Extensive structural changes cannot be observed under its own steam as there are number of factors cited for such transformations. Instantly, Dekle and Vandenbroucke (1969) empirically studied the episodes of structural transformation in China. They found that reduction in the relative size of government is mainly responsible for transfer of labor to non-agriculture sector. As, less government interventions induced the tax cut and shrink the cost of labor mobility. Bettsa, Giri and Verma (2017) 5

and Sposi (2012) considered the case of South Korea and identified that international trade and trade reforms primarily served as a source of structural changes. Changes in relative international labor productivity, per capita income and tariffs and subsidies are the three important transmission channels through which international trade exert its influence on sectorial allocation. Matsuyama (2009) also identified the role of international trade in shifting of labor to manufacturing sector by taking an example of both mature and growing economies such as Germany, Japan, Taiwan and Hong Kong. Ngai and Pissarides (2007) illustrated the structural changes in multi-sector growth model and discovered that labor share across different sectors changes over the time due to difference in total factor productivity. Rendall (2013) and Akbulut (2011) highlighted the dynamics of increasing female labor force participation rate. They appraised that involvement of females in market activities notably in services sector provided the ground for the change in economy’s structural composition. Ngai and Petrongolo (2017) also concluded the same by arguing that female participation in labor force will attempt to accelerate the overall productivity by shrinking the gender wage gap. Moreover, wage inequality also has an implication for the shift of economic activity. Skilled labor is usually acquired by services sector and costly to hire. So, both the rising wage differentials and rising payoffs in services are pushing the economic activity towards this sector (Buera and Kaboski, 2012a). Nuess (2017) discussed the changes in aggregate real income, changes in relative sectoral prices, inputoutput linkages (channelized by firm’s supply of final goods in turn demand for intermediaries) and evolving international trade on global scenario by means of comparative advantage as the possible driving forces. However, the role of global trade in structural transformation is overlooked by most of the researches on theoretical ground in spite of its promising role the in real world. Swiecki (2017) studied sector biased technological growth, non-homothetic preferences, international trade and relative factor cost as the potential sources behind the reallocation of economic activity across the sectors. Sector biased technological growth came up as the most prominent factor, however, nonhomothetic preferences are responsible for labor outflow from agriculture sector in poor countries. Moreover, international trade and relative factor cost are significant for labour reallocation only at the level of individual country not on the global scenario. Further, transitory process is speedy in the imports competing industries rather the exports-oriented industries. Slightest of the transitions are explained by the increasing trade, much of the labour outflows to service sector are attributed to the new entrants and returnees from unemployment (Dauht et al. 2017). Non-economic factors also account for the structural transitions in modern times. In the same manner, human capital plays a vital part in the reallocation process of factors across different sectors. Heterogeneous labor characteristics directs the flow of labor towards non-agriculture sector, as returns in service sector i.e. education and health care are intimately associated with the year of schooling, However, the relation is little weak in case of retail trade (Caselli and Coleman, 2001) and (Herrendorf and Schoellman, 2015). Martins (2019) argued that sector specific policies are difficult to 6

study as they vary from country to country but from the perspective of general policy matters, human and physical capital accumulation, investment in education and improvement in economic structure tend to be crucial. With this backdrop on Structural Transformation and economic development, this research investigates the cross sectoral linkages and structural transformation in Nepal. The rest of the study is organized as follows. Section 2 present an overview of Structural Transformation specifically in case of Nepal. This is followed by a section on methodological framework, which comprises of two parts. First section discusses the methods to calculate Backward and forward linkages in various industries in Nepal using Input Output Framework. Second part discussed Data mapping and Global CGE model used to study the economy wide impact of Structural Transformation on Nepal. Section 3 presents the Results and discussion, Sensitivity analysis is presented in Section 4, followed by concluding remarks in Section 5.

2 Structural Transformation in Nepal The above discussion inferred that structural transition is the lifeblood for the growth of the country as well as for the living standard of its residents. Nevertheless, the pattern underlying such changes are detrimental for the nature of the development that country will experience to the larger extent. South Asian countries are perfect example for this as region undergo through the huge transition in the last three decades. In the past where most of the GDP was added by agriculture, contrast dramatically with present where service sector accounts for approximately 60 percent of GDP in most South Asian countries. However, these countries especially Nepal is still unable to catch up with developed nations as they directly made a jump from agriculture to services without going through major expansion of manufacturing sector. So, structural transformation which results in inefficient equilibrium will not produce desired outcomes (Sen, 2016). Nepal has been experiencing gradual shift of resources away from Agriculture to services sector. As a matter of fact, more than 60% of value added was provided by primary sector in 1960’s, whereas, at present it generates only 28%. On the other side, currently, 58% of value added in GDP comes from services as compared to 16% in 1960’s (Basnet et.al, 2014). However, trends are little abrupt in manufacturing sector. Until 1990s share of industries in GDP kept on increasing, afterwards, it experiences a downfall. Moreover, Labor market underwent limited shift between agriculture and services as major portion of labor force is still earning livelihood in the former. As far as labor productivity is concerned, it remains stagnant in agriculture, whereas, recording fall in industries and rise in services over the period Export share as percentage of GDP was 23.2 in 2000’s while now it’s below 10 percent, while the import share as percentage of GDP is keep rising and its 42 percent compared to 8 percent in 1970 (Table 1). The Gross Domestic Product (hence forth GDP) raised overtime as during civil conflict (1996-2006) the average GDP was 3.8% which 7

raised to 4.4% on average after the end of civil war (2007-2013) (Sapkota, Nakarmi & Khatri, 2014). In FY 2015-16 it experienced low growth due to trade commotions and earth quakes, however the country in FY2017 recovered its growth to 7.5% by trade normalization and post-earth quakes reforms. The GDP per capita income of the Nepal stood around 200 US dollar for a decade until 2002 (see Table 1). The reason for slow growth in the GDP per capita is the low productivity of major economic sectors. However, huge inflows of remittances have increased the per capita income by more than 100 percent in 2010. Therefore, worker remittances have played an important role in improving the livelihood of the country’s population.

Table 1: Nepal Macro economic variables over time Nepal GDP Growth GDP Per Capita (US $) Exports (% GDP) Imports (% GDP) Gross Capital Formation (% GDP)

1970 1975 1980 1985 2.57 1.4 -2.32 6.1 72.17 118.2 130.5 156.6 4.9 8.9 11.5 11.5 8.3 13.3 18.7 20

1990 4.64 193.5 10.5 21.6

1995 2000 3.4 6.2 205.6 231.4 24.9 23.2 34.5 32.4

2005 3.4 317 14.5 29.4

2010 4.8 592.2 9.5 36.4

5.96

18.1

25.1

26.4

38.2

14.4

18.2

22.5

24.3

2015 2017 3.3 7.5 747.1 835.1 11.6 9.7 41.4 42.1 39

Source: World Bank and World Economic Outlook Data set.

More recently, Growth mainly has been propelled by primary Sector (agriculture) and Tertiary Sector (services) as shown in Figure 1. Wholesale and retail trade also observed different intensity of growth and has significant impact on GDP over the time period. while other sectors i.e. Manufacturing, Construction, Real estate and renting and other industries have fragile role in the GDP. Whereas the projected growth for FY2018 is expected to be 5.9% with less agriculture and more services sector contribution.

Figure 1: Structural Transformation in Nepal

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42.5

Source: Acharya (2018); Nepal Central Bureau of Statistics

Due to the low productivity in major economic sectors, Nepal has faced slow structural economic transformation of labor movement from low productive activities to high productive activities. The movement could be within the sector (for example from subsistent farming to high technology agricultural activities) or between sectors (such as from agriculture to industrial sector). The transformation is entirely based on the economic returns such as wages and other benefits. However, declining foreign investment, low industrial growth, and small investment in productive assets have held back the process of economic transformation. Low wages and small returns on domestic economic activities caused huge flight of laborer to other countries. Therefore, workers remittances hold a significant portion of the country’s GDP. Almost 30 percent of the government revenue comes from the foreign workers. Nepal has failed to achieve rapid transformation due to the slow industrialization and regressed manufacturing growth. There has been structural transformation between economic sectors, but the overall productivity remained constant. Since 1960, the share of agriculture in country’s GDP reduced from 68 percent to only 31 percent while that of services sector increased from 16 percent to 47 percent. Industrial sector holds the smallest share of value-added in GDP. Figure 2 shows the structural economic shift between three major sectors.

Fig 2: Value-Added by Sectors

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Value-Added by Economic Sectors Percentage share

100% 80% 60% 40% 20% 0% 1960S

1970S

1980S

1990S

2000S

2010S

Year Agriculture

Industry

Services

Source: WDI Data

The employment of laborer in different sectors of the economy has changed over time. The share of agriculture sector in total employment of the country reduced from 81 percent in 1990 to 66 percent in 2010. However, it still holds the highest rank in providing livelihood to the two third of the population. Nevertheless, the labor productivity remained stagnated. Services sector provide employment to only 20 percent while industrial sector could only absorb 13 percent. Majority of the labor left the country to find employment abroad. Therefore, there has not been any major transformation of laborer between sectors. Low level of investment, old infrastructure, and outdated technology are some of the reasons for low labor productivity.

This paper develops an innovative approach to the analysis of structural transformation and cross sectoral linkages in Nepal. The approach first augments an Input Output framework to identify Forward and backward linkages and then adapted a Global Computable General Equilibrium (CGE) model calibrated with multiple Households and factor types using a latest and comprehensive Social Accounting Matrix of Nepal. The model is used to simulate the effects of Export and productivity oriented structural transformation on macro, sector, and welfare indicators.

3. Methodological Framework 3.1 Input Output Model This research first uses an IO framework to identify backward and forward linkages in Nepal over the time. A unique feature of an Input–Output (IO) table is that it provides the mechanism for detailing 10

the Direct and Indirect linkages between Production and Trade (Blackman,Foronda & Mariasingham, 2017) . In the framework of an IO model, those effects are captured in backward and forward linkages. Backward linkages indicate demand-driven interconnection with upstream sectors whereas forward linkages have relationship of supply provision through downstream sectors. Moreover, direct linkages are those, which capture impacts in first round of production/ circulation or consumption; while total linkages capture both- direct and indirect impacts. Industries with higher linkages are important for policy making. Therefore, this research also investigates the relationship between sectors in the Nepal economy using Input Output table over the time. These IO Tables are of year 2010 to 2017 and are provided by the Asian Development Bank (2018, revised in 2019). These tables are of dimension 35*35 sectors and the detailed sectoral list is shown in Table 2.

Table 2: Sectoral List of Nepal IO Tables 2010-17 Sector 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Sector Description

Sector

Agriculture, hunting, forestry, and fishing

19

Mining and quarrying

20

Food, beverages, and tobacco

21

Textiles and textile products Leather, leather products, and footwear Wood and products of wood and cork Pulp, paper, paper products, printing, and publishing Coke, refined petroleum, and nuclear fuel

22 23 24

Chemicals and chemical products Rubber and plastics Other nonmetallic minerals Basic metals and fabricated metal Machinery, nec

27 28 29 30

Electrical and optical equipment Transport equipment Manufacturing, nec; recycling Electricity, gas, and water supply Construction

32 33 34 35

25 26

31

Sector Description Sale, maintenance, and repair of motor vehicles and motorcycles; retail sale of fuel Wholesale trade and commission trade, except of motor vehicles and motorcycles Retail trade, except of motor vehicles and motorcycles; repair of household goods Hotels and restaurants Inland transport Water transport Air transport Other supporting and auxiliary transport activities; activities of travel agencies Post and telecommunications Financial intermediation Real estate activities Renting of M&Eq and other business activities Public administration and defense; compulsory social security Education Health and social work Other community, social, and personal services Private households with employed persons

Source: Asian Development Bank (2018, revised in 2019). To find cross sectoral linkages between the industries, following formulae is applied to the Nepal IO Tables of year 2010 to 2017.

i) Direct & Total Backward linkages ∑

(Chenery & Watanabe, 1958) ----------11

(1)

∑

(Rasmussen, 1957) -----------

(2)

ii) Direct & Total Forward linkages ∑

(Chenery & Watanabe, 1958) -----------

(3)

∑

(Rasmussen, 1957) -----------

(4)

Reference- Beyers, 1976; Jones, 1976; Chenery & Watanabe, 1958. iii) Sectoral Output Share (SOS) -----------

(5)

Where: : Backward linkages : Forward Linkages depicts ‘Direct’ linkages depicts ‘Total’ linkages aij: Technical coefficient i.e. elements of A matrix i.e. technology matrix (or direct input coefficient/ input-output coefficient) matrix. : elements of Leontief inverse or the total requirements matrix. bij : elements of allocation (direct-output coefficient) matrix. These coefficients represent the distribution of sector i’s outputs across sectors j that purchase inter-industry inputs from i gij: elements of Ghosh Inverse (output coefficient matrix). These coefficients can be interpreted as measuring ‘the total value of production that comes about in sector j per unit of primary input in sector i. xi: sectoral output totals i.e. elements of X matrix i.e. Transaction matrix : Total Output ratio shows the comparative importance and contribution of sectors in the economy. It depicts how the sector is involved in generating output (and employment) in the economy. 3.2 Global Computable General Equilibrium (CGE) Model This research also provides a quantitative assessment of the likely economy-wide impacts of Structural transformation via productivity improvements and Trade policies in Nepal on macro as well as at household level using a Global Computable General Equilibrium (CGE) model. Three models i.e. econometric models, CGE models and Input Output models all are used to compute the effects of 12

different policies and each of them have their own advantages and disadvantages. Econometric models are simple and are convenient to observe effect of a policy on an industry. Though such models do not rely microeconomics theories of neoclassical so they may not emphasis producer and consumer behavior, profit maximization and utility. Computable General equilibrium model2 is an economic model which is employed for analyzing the government policy changes, change in technology, productivity and environment etc. using real economic data. It is multi-sectoral model that explains the explicit information about the behavior of economic agent. It treats households as utility maximizing agents and firms as cost minimizing and profit maximizing agents of the economy. The model assumes that agents’ decisions about the production and consumption are based on prices which are determined by the equilibrium conditions of demand and supply. The model allows for obtaining the numerical values of the coefficients that are estimated using the base data. This research adapted multiregional, multisector, computable general equilibrium model, with perfect competition and constant returns to scale. This updated CGE model named as MyGTAP model, developed by Walmsley and Minor (2013a), which is an extended version of the GTAP model (Hertel and Tsigas 1997).3 The GTAP model is based on a common global database, the GTAP database (Aguiar, Narayanan, and McDougall (2016)).

This updated global CGE model include several new characteristics that are helpful in examining the behavior of multiple households (Walmsley and Minor, 2013a). First, it allows more flexibility in the treatment of government savings and spending by removing the regional household of the standard GTAP model and replacing it with a separate government and private households. Second, the model allows for additional factors of production and multiple private households; and third, the model also includes transfers between government and households and among household groups, as well as foreign remittances and capital income. These additions allow for the assessment of policy impacts on different household groups. While many of these additional features are standard in the MyGTAP framework, the inclusion of multiple households and additional factors requires additional data to be supplied from a social accounting matrix (SAM) or household survey. These data are then incorporated into the augmented MyGTAP framework using a facility developed by (Walmsley and Minor, 2013a). Table 3 illustrate the difference between standard GTAP model and MyGTAP model.

Table 3: Difference b/w Global GTAP(CGE) and MyGTAP CGE model Global GTAP (CGE) model 2

MyGTAP Model

Also known as Applied general equilibrium model (AGE)

3 The model is solved using the software GEMPACK (Harrison and Pearson, 1996).

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Single Regional Household

Less detailed income-expenditure system; Income Sources are taxes and factor incomes. Further distributes in three components: Government expenditure, private household expenditure and saving-investment expenditure. Income of households and income from factors of production cannot be linked thus limiting deep analysis. Constant difference of elasticity (CDE) function in household consumption, thus limits analysis of subsistence consumption in poor economies. No transfer b/s household and Government Standard 5 factors of production (Land, skilled labor, unskilled labor, capital and natural resource)

Multiple and differentiated household’s types. Governemnt income and expenditure to tax revenue.

Link

More detailed accounting system increasing understanding of the relationships. Factor incomes, Remittances, taxes and Aid are transferred by household and government transfers to multiple households and government. The income is spent/save by different Households expenditure and savings, and Government expenditure and savings. The household and factor income are linked by connecting each household income with factor of production shares of that household. CDE function + Availability of Linear expenditure function (LES) for multiple household private consumption. Transfers between household and government and also foreign Aid and remittances. Option of Flexible factors categories (e.g. Urban and Labor, multiple types of land and capital)

Source: www.mygtap.org

In this section we outline the additional data included in the MyGTAP model to disaggregate households and factor types in case of Nepal.

3.2.1 Incorporating Multiple Household and Factors This research used two different types of datasets: the latest released GTAP Database 9a (Aguiar et.al, 2016) and latest available comprehensive Nepal SAM 2007-08. The GTAP database 9a represents the world economy for three reference years, 2004, 2007 and 2011. We used the latest base year, 2011. The database is composed of 140 regions, 119 countries and 21 aggregated regions and 57 sectors for every region. Keeping in view the structure of Nepal Economy and to facilitate computation, the number of regions has been aggregated to 10 regions (Annex 1) and the number of commodities/sectors to 9 (Annex II). The latest available Nepal Social Accounting Matrix (SAM) 2007-084 comprises of 57 sectors/commodities with 7 household types based on occupation and location (i.e. Rural and urban). Out of total 57 activates/commodities, 14 are agricultural activities, 4 are mining activates, 24 manufacturing activates, 3 utilities and 12 types of Services activities. The gross value added has been divided into three factors of production, i.e. labor, capital, and land. The main labor has been divided into three types, i.e. unskilled, semi-skilled, and skilled. The disaggregation of activities, commodities, factors and institutions in SAM 2007 is given in Table 4.

4 Those interested in complete description and major adjustments that were made to reconcile data sources for Nepal SAM, please follow the link: https://econpapers.repec.org/paper/pramprapa/37903.htm

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The latest available GTAP 9a database (Aguiar et.al 2016) is modified by breaking down the regional household into multiple households using the MyGTAP data tool, documented in Minor and Walmsley (2013). According to the MyGTAP model, the government receives income from taxes, foreign aid in, less foreign aid out (in the case of Nepal, zero) and other transfers from government to private households. The private households on the other hand collect income from factor endowments, net foreign labor remittances, net foreign capital rents, transfers from the government, and transfers from other households. The households’ incomes are spent on consumption of goods and services and savings.

Table 4: Disaggregation and Description of Nepal SAM Accounts

Sets Activities Agriculture (14)

Mining (4) Manufacturing (24)

Utility (3) Services (12)

Description of Elements Paddy, Wheat, Other Grain, Vegetables & Fruits, Oilseed, Sugar-cane, Jute and Other Plant Fibers, Other Crops, Cattle, Other Animal Product, Raw Milk, Wool, Forestry, and Fishing Coal, Oil, Gas and Other Mining Meat, Meat Product, Vegetable Oil, Dairy Product, Other Grain Mill, Sugar, Other Food Product, Beverage-Tobacco, Textile, Wearing Apparel, Leather Product, Lumber, Paper & Paper Product, Petroleum, Chemical and Rubber, Mineral Product, Iron & Steel, NonFerrous Metal, Fabricated Metal, Motor-Vehicle, Other Transport Equipment, Electronic Equipment, Other Mech. & Equipment, Other Manufacturing. Electricity, Gas and Water Construct, Trade, Other Transport, Water Transport, Air Transport, Communication, Financial Intermediaries, Insurance, Other Business Services, Recreation and Other Services, Government Services and Dwelling

Commodities Agriculture (14)

Mining (4) Manufacturing (24)

Utility (3) Services (12)

Paddy, Wheat, Other Grain, Vegetables & Fruits, Oilseed, Sugar-cane, Jute and Other Plant Fibers, Other Crops, Cattle, Other Animal Product, Raw Milk, Wool, Forestry, and Fishing Coal, Oil, Gas and Other Mining Meat, Meat Product, Vegetable Oil, Dairy Product, Other Grain Mill, Sugar, Other Food Product, Beverage-Tobacco, Textile, Wearing Apparel, Leather Product, Lumber, Paper & Paper Product, Petroleum, Chemical and Rubber, Mineral Product, Iron & Steel, NonFerrous Metal, Fabricated Metal, Motor-Vehicle, Other Transport Equipment, Electronic Equipment, Other Mech. & Equipment, Other Manufacturing. Electricity, Gas and Water Construct, Trade, Other Transport, Water Transport, Air Transport, Communication, Financial Intermediaries, Insurance, Other Business Services, Recreation and Other Services, Government Services and Dwelling

Factors of Production (3) Labour (1) Labour Capital (2) Capital and Land Current Institutions (10) Households (7) Rural: landless, Agricultural marginal farmer, Agricultural small farmer, Agricultural large

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Others (2) Capital Institution (1)

farmer. Urban: Households with low educated heads, Households with medium educated heads and households with high educated heads Govt and Rest of the World

Source: Nepal SAM 2007-08 (Rehan and khondker, 2011)

Households’ categorization is crucial because conclusions concerning welfare of households may depend on how the population is subdivided. The population may be divided based on sources or size of income. The Nepal SAM 2007-08 provides detailed information on 07 types of household classified by rural and urban categories. The SAM 2007 has 07 household’s types as provided in table 5.

Table 5: Nepal Household Categories in SAM 2007-08

1 2 3 4 5 6 7

Household Types

Income (Million Rupees)

Rural Land Less Rural Land Small Rural Land Medium Rural Land Large Urban Lower Educated Urban Medium Educated Urban Higher Educated

153221.19 97779.96 260833.94 111864.22 43002.07 43221.82 65553.22

Source: Nepal SAM 2007-08,

On the income side, information on the 03 factors of production from the Nepal SAM 2007 is mapped with the standard 8 GTAP production factors. The mapping of single labor type in Nepal SAM to 5 labor types of GTAP Data base also required defining not only the ownership of these 5 labor types by households, but also their use in the production of each of the 57 GTAP commodities. So, I first take the shares of GTAP and then mapped those with SAM and disaggregates the labor to 5. Table 6 shows the description of GTAP labor types used in this research.

16

Table 6: Five Individual GTAP Labor Types and used in this Study

Source: Walmsley and Carrico (2016)

Figure 3: Overview of the Nepal data in the GTAP Database and model after the modifications

Source: Author mapping based on MyGTAP approach

17

3.3 Policy Experiment / Simulation: This research investigates the potential economy wide impact of structural transformation on both aggregate as well as at household level in Nepal. Nepal like many other developing countries need a sustained focus on increasing productivity and improving skills in export-oriented sectors like Manufacturing, Textile and Clothing and Services for more inclusive Structural Transformation. Five different simulation were undertaken to assess the prospects for structural change in Nepal. 1) Manufacturing Productivity:

Increase in Manufacturing output by 10 Percent in Nepal

coupled with increase in the labor productivity in manufacturing sector by 5 percent. 2) Services Productivity: Increase in Services output by 10 Percent in Nepal. coupled with increase in the labor productivity in Services sector by 5 percent. 3) Textile and Clothing Productivity: Increase in Textile output by 10 Percent in Nepal. coupled with increase in the labor productivity in Textile and wearing apparel sector by 5 percent. 4) Trade War: 5 percent increase in import tariffs across the board (All countries/regions and in all tradable commodities) 5) Trade Liberalization: 5 percent decrease in import tariffs across the board (All countries/regions and in all tradable commodities) In the next section we examine the impact of these simulations on Nepal economy.

4. Results and Discussion 4.1 Sectors with Higher Backward and Forward Linkages in Nepal The rationale behind identifying the sectors with highest linkages as key sectors is that an increase in investment or productivity in these sectors would spread much more over the rest of the sectors, than those having low linkage values. In Nepal, Hotels, Restaurants and Wood, products of wood, Textile and clothing, and Food, Beverages and tobacco are the sectors which have higher backward linkages from other sectors. This means inputs of these sectors come predominantly from other sectors of the economy. Whereas ‘Construction’, In land Transport, ‘Agriculture, hunting, forestry and fishing and Non-Metallic minerals’ are the sectors with Higher Forward linkages. This implies the fact that the outputs of these sectors are used as inputs in other sectors directly or indirectly on a large scale. Figure 4-5 shows sectors with Higher Backward and Forward Linkages value (Direct, Indirect and Total), while Figure 5-6 illustrates the linkage values of all 35 sectors in Nepal economy over the years. For complete list of Backward and Forward linkages in all sectors over the period 2010-17, Please refer to Annex III and IV. Figure 4: Sectors with Higher Direct and Total Backward Linkages

18

Source: Based on author’s calculation using Nepal IO Tables from 2010-2017

Figure 5: Sectors with Higher Direct and Total Forward Linkages

Source: Based on author’s calculation using Nepal IO Tables from 2010-2017

Figure 6: Direct and Total Backward Linkages in All Sectors

Source: Based on author’s calculation using Nepal IO Tables from 2010-2017

Figure 7: Direct and Total Forward Linkages in All Sectors

Source: Based on author’s calculation using Nepal IO Tables from 2010-2017

19

We can find that over the years, the ranks of industries keep on varying. This result would further enable us to comment on the changing output shares of different sectors in the economy. (Refer Table 7 for the Sectoral output shares). These sectoral output shares depict the changing structure of the economy over the years. As mentioned earlier, output shares and linkages are correlated with each other to some extent.

Table 7: Sectoral Output shares in Total Output over the years Sector

2010

2011

2012

2013

2014

2015

2016

2017

1

0.1553

0.1685

0.1602

0.1513

0.1454

0.1420

0.1424

0.1335

2

0.0030

0.0029

0.0030

0.0031

0.0031

0.0032

0.0029

0.0034

3

0.0443

4

0.0074

0.0418

0.0433

0.0435

0.0439

0.0432

0.0410

0.0453

0.0065

0.0070

0.0074

0.0075

0.0076

0.0072

0.0071

5

0.0014

0.0012

0.0014

0.0014

0.0015

0.0015

0.0014

0.0014

6

0.0027

0.0026

0.0027

0.0026

0.0026

0.0025

0.0024

0.0028

7

0.0028

0.0027

0.0028

0.0031

0.0032

0.0031

0.0030

0.0030

8

0.0166

0.0198

0.0177

0.0208

0.0224

0.0222

0.0211

0.0227

9

0.0139

0.0143

0.0134

0.0148

0.0156

0.0159

0.0151

0.0158

10

0.0060

0.0055

0.0056

0.0059

0.0060

0.0060

0.0056

0.0061

11

0.0154

0.0150

0.0147

0.0149

0.0151

0.0149

0.0140

0.0161

12

0.0281

0.0274

0.0270

0.0283

0.0292

0.0278

0.0257

0.0280

13

0.0046

0.0041

0.0045

0.0051

0.0054

0.0060

0.0059

0.0056

14

0.0115

0.0105

0.0112

0.0126

0.0135

0.0148

0.0143

0.0145

15

0.0015

0.0013

0.0017

0.0019

0.0018

0.0020

0.0018

0.0019

16

0.0075

0.0068

0.0073

0.0075

0.0076

0.0076

0.0074

0.0076

17

0.0087

0.0089

0.0081

0.0083

0.0086

0.0082

0.0082

0.0114

18

0.0482

0.0473

0.0488

0.0469

0.0459

0.0472

0.0451

0.0542

19

0.0051

0.0047

0.0050

0.0049

0.0049

0.0048

0.0047

0.0043

20

0.0078

0.0072

0.0079

0.0082

0.0080

0.0080

0.0077

0.0073

21

0.0495

0.0459

0.0485

0.0475

0.0475

0.0472

0.0457

0.0416

22

0.0126

0.0115

0.0136

0.0133

0.0132

0.0142

0.0146

0.0202

23

0.0379

0.0348

0.0363

0.0359

0.0360

0.0357

0.0361

0.0347

24

0.0009

0.0007

0.0009

0.0011

0.0007

0.0007

0.0005

0.0007

25

0.0082

0.0076

0.0080

0.0078

0.0076

0.0075

0.0075

0.0073

26

0.0074

0.0063

0.0064

0.0067

0.0070

0.0071

0.0072

0.0081

27

0.0141

0.0137

0.0133

0.0131

0.0131

0.0128

0.0130

0.0158

28

0.0187

0.0185 0.0278

0.0175

0.0191

0.0246

0.0292

0.0192 0.0290

0.0182

29

0.0194 0.0252

0.0273

0.0295

0.0313

0.0290

30

0.0156

0.0148

0.0154

0.0148

0.0146

0.0149

0.0158

0.0146

31

0.0123

0.0087

0.0123

0.0113

0.0107

0.0116

0.0123

0.0116

32

0.0302

0.0240

0.0299

0.0286

0.0280

0.0294

0.0304

0.0265

33

0.0078

0.0064

0.0078

0.0074

0.0071

0.0080

0.0086

0.0093

34

0.0180

0.0175

0.0179

0.0168

0.0161

0.0156

0.0167

0.0240

20

35

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Source: Based on author’s calculation using Nepal IO Tables from 2010-2017

4.2 Impact on Macro Economic Variable (Constant 2011 prices)

Table 8 illustrates the impact of all simulation designed in this study on real GDP (changes in output measured at base prices), terms of trade, welfare (Equivalent variation) and on government income. Results show that 10 percent increase in manufacturing output will have a positive impact of 1.25 percent in Real GDP which is equivalent to 117.5 Million US Dollars. While in case of Services productivity there is a positive impact of 2.05 percent from baseline which in dollar term is equivalent to 387 million US $. While Trade liberalization scenario has a modest but positive impact on Nepal Real GDP. Tariff Reduction usually lowers the price of imported commodities, thereby reducing the cost of intermediate goods for domestic producers. This coupled with increased export demand, induces an increase in the country’s production. Trade Liberalization also has two counterweighing effects on output levels. Firstly, the decreased costs of intermediate inputs produce beneficial forward linkages to domestic production and hence will lead to industrialization. Secondly, due to further strong import competition will have an adverse effect on the profitability of import-competing firms. In case of Trade war simulation i.e. Import tariff increase by 5 % across the board has a negative impact on Nepal GDP by 7.1 Million US Dollars.

Table 8: Nepal key Macroeconomic variables (% Changes, Constant 2011 Prices) Nepal

Real GDP Welfare Terms of Trade (tot) Government Income

Q0 Manu % changes (Monetary Change) 1.25(117.43) 391 2.07 4.96

Qo Serv % changes (Monetary Change) 2.05(387) 625 1.46 3.81

Qo Text % changes (Monetary Change) 0.41(78.08) 150 0.76 3.17

Trade War % changes (Monetary Change) -0.04(-7.10) -5.33 0.08 0.85

Trade Lib % changes (Monetary Change) 0.04(6.89) 687 -0.09 -0.87

Source: Author simulations

To analyze the welfare effect, the mostly widely used measurement is the Equivalent Variation (EV) which can be decomposed into various compositions that include ‘allocative efficiency’, ‘terms of trade’ and ‘change in capital stock’. Allocative efficiency’ implies an optimal domestic production, i.e. when the production represents the consumer choices. In other words, when the marginal costs of production are equal to the marginal utility of that output, this is called allocative efficiency. Improvements in the terms of trade (TOT) also leads to increase in overall welfare as it leads to avail 21

higher prices of exports as compared to what is paid for imports. Welfare Results show that highest overall welfare has been seen in case of Trade liberalization scenario. Trade liberalization may also lead to increase in capital stock that in turn enhances the domestic productive capacity and so overall welfare. In case of Services the welfare increase is 625 Million US dollars while in case of manufacturing it is 391 Million US $. As discussed in methodology, this research replaced the regional household in Standard GTAP model with a separate government and private households. So, we also focus on the importance of structural transformation and trade policy on government revenue. Result show an increase in government income in all scenarios except Trade liberalization. Government income will increase much more in case of increase in manufacturing and services productivity. Though Import tax revenues are also a major source of government revenue for developing countries. Losses to these revenues by Nepali government due to tariff reforms in case of trade liberalization simulation will likely have to be replaced by taxes elsewhere in the economy.

4.3 Changes in Trade Productivity improvements enhance the balance of trade and promote trade leading to more exports and imports. Table 9 illustrates the impact of all research simulations on Nepal Sectoral Exports. Simulation results show that an increase in manufacturing output coupled with labor productivity will have a positive impact on exports of light manufacturing5 by 7.9 percent and Heavy manufacturing6 by 3.13 percent. Simulation 2 shows an increase in Service export by 6.57 percent while Simulation 3 shows increase in Textile and clothing sector by 3.16 percent. In case of Trade Liberalization simulation, the reduction in tariffs and subsequently lower prices of imports purchased for intermediate use will also cause a decline in domestic prices by reducing production costs, thereby causing exports to rise. The impact on sectoral production therefore depends on the extent to which the increase in exports outweighs or is outweighed by the fall in domestic sales due to substitution towards imported commodities. Multi-lateral Trade liberalization is generally good for Nepal as exports of almost all sectors will increase with notable increase in light manufacturing, textile and wearing apparel and processed food. Trade war simulation with increase in import tariff across the border will still have a positive impact on Processed food. Table 10 presents the impact on sectoral imports of all the simulation designed in this research. The results show increase in imports of almost all sectors. Simulation 4 (Trade war) show reduction in Nepal import of all sectors due to increase in the import tariffs. Increased Tariffs are supposed to give a competitive advantage to Nepali domestic

5 Includes leather, wood, paper products, Fabricated Metal Products, motor vehicles and parts, other transport equipment’s and other manufacturing. 6 Includes Chemical rubber and plastic, Non ferrous metals, electronic items Other Machinery & Equipment etc.

22

producers of same product. Domestic producer’s prices would be lower; hence domestic demand will increase but it may also trigger inflation when tariffs increase the prices of imports.

Table 9: Changes in Nepal Export

Agriculture Extraction ProcFood TextWapp LightMnfc HeavyMnfc Util_Cons TransComm Services

BAU7 (Million US $)

Q0 Manu

Qo Serv

Qo Text

Trade War

Trade Lib

105.1 12.859 61.961 343.707 86.14 255.44 36.057 147.911 366.395

-9.36 -7.06 -3.33 0.11 7.9 3.13 -17.73 -7.13 -8.84

-7.9 -4.78 -2.71 0.2 -0.45 -0.77 -10.1 -5.42 6.57

-3.06 -1.62 -1.02 3.16 -0.12 -0.21 -5.74 -2.56 -2.9

1.3 0.01 2.88 0.31 -0.21 -0.47 -0.09 -0.14 -0.01

1.13 0.25 1.41 0.64 1.66 0.21 -0.26 0.04 -0.17

Source: Author simulations

Table 10: Changes in Nepal Import

Agriculture Extraction ProcFood TextWapp LightMnfc HeavyMnfc Util_Cons TransComm Services

BAU (Million US $) 421.369 108.114 254.22 985.225 690.78 3038.443 65.755 493.739 294.187

Q0 Manu

Qo Serv

Qo Text

Trade War

Trade Lib

4.95 1.93 2.32 0.69 -0.87 0.26 7.31 4.35 4.52

4.13 0.8 1.89 0.38 0.5 0.47 3.36 3.25 -2.15

1.65 0.21 0.71 -0.14 0.19 0.15 1.84 1.56 1.47

-1.13 -0.25 -1.39 -0.64 -1.64 -0.21 0.25 -0.06 0.15

1.13 0.25 1.41 0.64 1.66 0.21 -0.26 0.04 -0.17

Source: Author simulations

4.4 Effects on Real Returns to factors Table 11 illustrates the impact on Real Factor wages. Results show an increase in the real factor wages of almost all labor types in first 2 simulations except agricultural and low skilled labors, with major return is accrued to Capital. Thus, better demand for labor, which mainly sprouts from increase in production/output results in better wages for labor workers involved in production of these goods.

7 Business as usual.

23

The real factor wages will decline in case of increase in import tariffs and there will be a modest but positive impact on Nepal Factors of production if tariffs are removed across the border.

Table 11: Percent Changes in Real Factor Wages in Nepal Factor description Land Technical and professional Clerks Service shop Officers_managers_professional Agriculture other low skilled Capital

Q0 Manu -0.87 0.74 0.61 0.49 0.39 -0.11 1.86

Qo Serv -0.21 0.11 0.06 0.47 0.88 -0.09 1.36

Qo Text -0.01 0.48 0.46 0.49 0.53 0.46 1.15

Trade War -0.13 -0.24 -0.30 -0.20 -0.22 -0.15 -0.22

Trade Lib 0.135 0.243 0.302 0.197 0.216 0.147 0.222

Source: Author simulation

.

4.5 Changes in Household Income A unique feature of the model used in this study is the capability to disaggregate the regional household into both private and government entities. This disaggregated analysis stands in contrast to a typical "national welfare analysis" often cited in CGE analysis in that we do not suppose that all stakeholders will be impacted equally - the assumption is that any structural change or productivity improvement have distributional impacts and that the impacts on poor households should be given special consideration when making economic policy (Minor and Mureverwi, 2013). The changes in relative wages lead to changes in the household incomes. Household incomes are primarily composed of factor income, such that the changes in the wages shape the changes in household incomes. The results in table 12 show the impact on real incomes of all 7 household types under all scenarios. The results indicate that income of all the households increase in first 2 simulation except the rural land less and rural small. In case of 3rd Simulation there is increase in all factors which in turn is the effects of the reallocation of production that favors cotton lint/yarn, textile, and wearing apparel sectors.

Table 12: Percent Changes in Nepal household Income Rural Land Less Rural Land Small Rural Land Medium Rural Land Large Urban Lower Educated Urban Medium Educated Urban Higher Educated

Q0 Manu -0.29 -0.09 0.87 0.91 0.65 0.79 0.3

Qo Serv -0.17 0.07 0.13 0.35 0.85 0.94 1.01

24

Qo Text 0.45 0.81 0.75 0.72 0.84 0.88 0.92

Trade War -0.04 -0.05 -0.03 -0.05 -0.08 -0.11 -0.14

Trade Lib 0.03 0.04 0.03 0.04 0.07 0.09 0.12

Source: Author simulation

5. Sensitivity analysis This research also conducts a sensitivity analysis to test some of the important modeling assumptions to see its impact on overall results. The results presented in this paper are based on assumption of full employment.

However, with a high unemployment rate in Nepal, we test the assumption of

unemployment of unskilled Agri labor and review changes in the assumption regarding the trade balance. Finally, we check the results of systematic sensitivity analysis by level of shocks.

5.1 Unemployment As discussed in methodology about model assumptions and reported above, the results presented in this research assumes full employment, i.e., that labor inputs cannot easily be increased in response to increased demand. The unemployment rate in Nepal, while improving, was reported to be around 3 percent in 2018. The unemployment rate as reported call into question the validity of the full employment assumption. Since unemployment is assumed in this scenario estimates of employment changes can be estimated. We found that with unemployed Agricultural labor there is an increase in the real wages of all other types of labor with notable increase in skilled form of labor e.g Technical and professionals.

5.2 Fixing the trade balance In our core scenario presented earlier we assume that the trade balance is fully flexible and ultimately a function of domestic savings and investment (and any changes in foreign income flows). This means that the trade balance is driven by our model assumptions that savings is a constant share of income and investment (including foreign investment) is driven by rates of return. In developing countries like Nepal, foreign investment usually has a fixed percentage of GDP and hence the trade balance should be fixed as a share of the country’s GDP or income. We test this scenario of a fixed trade balance on the results and found no material change in the results.

5.3 Level of Shocks Sensitivity analysis is usually based on the size of shocks and can be undertaken in several ways. In systematic sensitivity analysis (SSA) number of simulations are carried out with a sampling distribution of the shocks employed in the model. The goal is to identify any critical points in which the shocks values may result in significantly different results.

25

We first examine the impact of doubling and halving the shocks. We find that doubling the shocks, more than doubles the gains to real GDP. The results of our SSA on the key shock variables showed similar results.

6. CONCLUSION AND POLICY RECOMMENDATION: Policy makers have been struggling to establish a conclusive direction to the association between economic development and structural changes bearing in mind the dilemmas of developing economies. Many economically developed countries have successfully experienced the structural transformation by shifting resources from less productive sectors to more productive sectors of the economy. By contrast, developing countries are still heavily dependent upon agriculture sector which is practicing the orthodox ways even in the time of modernization. Against this backdrop, this research aims to identify backward and forward linkages along with quantitative assessment of the likely economy-wide impacts of Structural transformation via productivity improvements in Nepal on macro as well as at household level by using the global commutable general equilibrium model. The model is calibrated using latest Social Accounting Matrix (SAM) of Nepal. The rationale behind identifying the sectors with highest linkages as key sectors is that an increase in investment or productivity in these sectors would spread much more over the rest of the sectors, than those having low linkage values. In Nepal, Hotels & Restaurants, Food, Beverage & Tobacco and Wood, products of wood and Cork are the sectors which have higher backward linkages from other sectors. This means inputs of these sectors come predominantly from other sectors of the economy. Whereas ‘Construction’ and ‘Agriculture, hunting, forestry and fishing’ are the sectors with Higher Forward linkages. This implies the fact that the outputs of these sectors are used as inputs in other sectors directly or indirectly on a large scale. This research also analyzes the impact of alternative policies aimed at promoting the process of structural transformation in Nepal, using a Global Commutable General Equilibrium Model calibrated with latest Nepal Social Accounting Matrix. Results using General equilibrium models show that 10 percent increase in manufacturing output coupled with increase labor productivity have a positive impact of 391 Million US dollars in Nepal. Trade liberalization scenarios shows that Multi-lateral Trade liberalization is generally good for Nepal as exports of almost all sectors will increase with notable increase in light manufacturing, Textile and clothing. Real factor wages of almost all labor types increases in first 2 simulations except agricultural and low skilled labors, with major return is accrued to Capital. Thus, better demand for labor, which mainly sprouts from increase in production/output results in better wages for labor workers involved in production of these goods. The real factor wages will decline in case of Trade War (Increase in import tariffs) scenario and there will 26

be a modest but positive impact on Nepal Factors of production if tariffs are removed across the border. This research also conducts a sensitivity analysis to test some of the important modeling assumptions to see its impact on overall results. The goal is to identify any critical points in which the shocks values may result in significantly different results. We found that with unemployed Agricultural labor there is an increase in the real wages of all other types of labor with notable increase in skilled form of labor e.g Technical and professionals. While considerable progress has been made in terms of database development, additional research is needed to use a global supply chain model to study the relationship between economic diversification and Nepal insertion into global value chains. Moreover, one can also examines the structural transformation of the economy as it pertains to labor migration from one sector to another. Annex I: Regional Aggregation used in this research

Source: Author’s own aggregation using GTAP 9a Data Base

Annex II: Sectoral Aggregation used in this study Code Agriculture Extraction Proc Food TextWapp Lightmnfc HeavyMnfc Util_Cons TransComm Services

Comprising GTAP sectors (code) Pdr,wht,gro,osd,c_b,pfb,ocr,pcr,v_f, Ctl, oap, rmk, wol, cmt, omt Frs,fsh,coa,oil,gas,omn Vol mil sgr ofd b_t Tex wap Lea lump pp fmp mvh otn omf P_c crp nmm i_s nfm ele ome Ely gdt wtr cns Trd otp wtp atp cmn Ofi ise obs ros osg dwe

Source: Author’s own aggregation using GTAP 9a Data Base

Annex III: Backward Linkages in all Sectors over the Period 27

Sector

2010

2011

2012

2013

2014

2015

2016

2017

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

0.2401 0.2046 0.5023 0.3571 0.3338 0.6008 0.2216 0.0170 0.1706 0.3877 0.4514 0.3463 0.0231 0.0519 0.0391 0.4427 0.4004 0.5210 0.2476 0.1814 0.2457 0.5653 0.4240 0.0000 0.3335 0.3020 0.4475 0.1933 0.3300 0.4415 0.4283 0.3782 0.3964 0.2847 0.0000

0.2198 0.1487 0.5107 0.3791 0.3718 0.5854 0.2165 0.0137 0.1572 0.4063 0.4417 0.3455 0.0233 0.0544 0.0440 0.4639 0.4226 0.4771 0.1595 0.1170 0.1582 0.6693 0.3746 0.0000 0.2550 0.2997 0.4131 0.2684 0.2597 0.3889 0.2328 0.2628 0.2904 0.2783 0.0000

0.2486 0.2080 0.5194 0.3751 0.3516 0.6059 0.2210 0.0161 0.1785 0.4178 0.4776 0.3681 0.0237 0.0534 0.0367 0.4558 0.4122 0.5200 0.2473 0.1748 0.2451 0.5675 0.4369 0.0000 0.3355 0.3428 0.4631 0.2567 0.3259 0.4411 0.4305 0.3890 0.4063 0.2862 0.0000

0.2115 0.1603 0.4812 0.3304 0.3149 0.5772 0.1885 0.0128 0.1497 0.3745 0.4365 0.3290 0.0186 0.0442 0.0305 0.4159 0.3916 0.4833 0.1811 0.1196 0.1795 0.6102 0.3860 0.0000 0.2775 0.2951 0.4161 0.2420 0.2742 0.3979 0.3185 0.3080 0.3322 0.2616 0.0000

0.2097 0.1482 0.4601 0.3105 0.2937 0.5576 0.1723 0.0114 0.1351 0.3490 0.4145 0.3021 0.0166 0.0392 0.0297 0.3889 0.3918 0.4652 0.1584 0.1081 0.1568 0.6651 0.3554 0.0000 0.2509 0.2673 0.3906 0.2578 0.2576 0.3807 0.2326 0.2623 0.2880 0.2710 0.0000

0.2094 0.1467 0.4592 0.3010 0.2799 0.5599 0.1709 0.0112 0.1299 0.3449 0.4116 0.3101 0.0146 0.0350 0.0264 0.3791 0.3854 0.4671 0.1582 0.1076 0.1566 0.6677 0.3531 0.0000 0.2498 0.2590 0.3825 0.2553 0.2586 0.3809 0.2335 0.2626 0.2902 0.2695 0.0000

0.2081 0.1504 0.4551 0.2978 0.2827 0.5531 0.1656 0.0111 0.1272 0.3427 0.4106 0.3130 0.0136 0.0338 0.0261 0.3637 0.4039 0.4540 0.1575 0.1071 0.1560 0.6819 0.3616 0.0000 0.2704 0.2627 0.3899 0.2398 0.2353 0.3627 0.2895 0.2519 0.3477 0.2739 0.0000

0.2712 0.1993 0.5479 0.3637 0.3532 0.6252 0.2027 0.0132 0.1525 0.3996 0.4565 0.3582 0.0183 0.0404 0.0311 0.4487 0.5576 0.5630 0.1846 0.1195 0.1823 0.6879 0.4288 0.0000 0.3496 0.4099 0.5738 0.2738 0.2520 0.3726 0.1984 0.1734 0.4179 0.3360 0.0000

Annex IV: Forward Linkages in all Sectors over the Period

Sector 1 2 3 4 5 6 7 8

2010

2011

2012

1.5747 0.0840 0.6258 0.2956 0.0318 0.2008 0.1043 0.0389

1.8656 0.0891 0.7565 0.3534 0.0367 0.2206 0.1148 0.0416

1.7386 0.1017 0.6558 0.3216 0.0322 0.1980 0.1051 0.0401

2013

2014

2015

2016

2017

1.4922

1.5014

1.4120

1.4163

1.6616

0.0850 0.6416 0.3104 0.0306 0.1936 0.0972 0.0371

0.0729 0.6322 0.3194 0.0308 0.1958 0.0941 0.0355

0.0742 0.5968 0.3133 0.0290 0.1926 0.0924 0.0352

0.0802 0.5597 0.3301 0.0268 0.1893 0.0872 0.0359

0.0959 0.7322 0.3356 0.0358 0.2013 0.1059 0.0373

28

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

0.1355 0.2441 0.7018 0.5288 0.0094 0.0366 0.0059 0.4366 0.2641 2.1831 0.0723 0.0683 0.5670 0.2929 1.7153 0.0000 0.2147 0.2018 0.3551 0.2563 0.2957 0.5164 0.2286 0.4048 0.1902 0.3043 0.0000

0.1419 0.2511 0.7143 0.5465 0.0095 0.0373 0.0069 0.3830 0.3304 2.0412 0.0417 0.0515 0.3885 0.3791 1.3854 0.0000 0.1739 0.2006 0.4029 0.4214 0.2406 0.5258 0.1126 0.2772 0.1236 0.3157 0.0000

0.1397 0.2600 0.7237 0.5592 0.0097 0.0365 0.0062 0.3801 0.2706 2.2714 0.0835 0.0660 0.5733 0.3295 1.6719 0.0000 0.2194 0.1862 0.3745 0.3674 0.2947 0.5213 0.2197 0.4184 0.1983 0.3365 0.0000

0.1287 0.2337 0.6709 0.5075 0.0086 0.0324 0.0056 0.3586 0.2650 1.9805 0.0603 0.0504 0.4161 0.3652 1.1762 0.0000 0.1650 0.1657 0.3377 0.3334 0.2597 0.4656 0.1524 0.3397 0.1534 0.2884 0.0000

0.1245 0.2174 0.6453 0.4815 0.0084 0.0308 0.0054 0.3530 0.2797 1.8514 0.0437 0.0476 0.3633 0.4023 1.0419 0.0000 0.1504 0.1658 0.3360 0.3521 0.2514 0.4446 0.1157 0.3062 0.1284 0.2709 0.0000

0.1198 0.2218 0.6384 0.4984 0.0083 0.0304 0.0054 0.3558 0.2571 1.9561 0.0437 0.0468 0.3646 0.4110 1.0275 0.0000 0.1478 0.1666 0.3132 0.3346 0.2891 0.4376 0.1236 0.3043 0.1456 0.2604 0.0000

0.1140 0.2245 0.6216 0.5174 0.0081 0.0302 0.0054 0.3747 0.2921 1.9395 0.0418 0.0453 0.3447 0.4160 1.2318 0.0000 0.1640 0.1791 0.3176 0.3492 0.2897 0.4395 0.1691 0.2860 0.1888 0.2798 0.0000

0.1362 0.2644 0.6955 0.5782 0.0095 0.0352 0.0060 0.3963 0.4649 2.5734 0.0410 0.0464 0.3506 0.5329 1.1457 0.0000 0.1895 0.2549 0.5023 0.4687 0.2491 0.4030 0.1008 0.1721 0.2291 0.4453 0.0000

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