1 Growth, Inequality and Innovation : A CGE analysis of India * Vijay P. Ojha 1 Institute of Management Technology, Ghaziabad, India Basanta K. Pradhan Institute of Economic Growth, Delhi, India Joydeep Ghosh Institute of Economic Growth, Delhi, India Abstract This paper probes into the growth and distributional consequences of four basic policy options emanating from the three sources of economic growth, namely, physical capital, human capital and technological progress, with the help of a computable general equilibrium model of India. The simulation results show that, the efficacy of physical capital accumulation in augmenting growth and abating income inequality is greater than that of human capital accumulation. In the long term, however, the latter overtakes the former in promoting growth, but inequality worsens. When the two policies are commingled, growth improves but it continues to be inequality- augmenting. Finally, with concomitant Hicks-neutral technological progress, not only is growth enhanced further, but it turns out to be significantly inequality-mitigating. The emerging policy lesson is that any integrated policy of boosting investments in physical as well as human capital must be closely bound up with technological progress for growth to be inclusive. Key words: Economic Growth; Inequality; Technological Progess; CGE model, India ; Asia JEL Classification: C68, D58, I24, I28, J24, O33 * This paper is an outcome of a research project undertaken at the National Council of Applied Economic Research (NCAER), under the auspices of South Asia Network of Economic Research Institutes (SANEI). 1 Corresponding author: Vijay P Ojha, Institute of Management Technology, Ghaziabad, Rajnagar, Ghaziabad – 201001, India. Email: [email protected]Tel. +91 9871256407, +91 120 3004364
Transcript
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Growth, Inequality and Innovation : A CGE analysis of India*
Vijay P. Ojha1
Institute of Management Technology, Ghaziabad, India
Basanta K. Pradhan Institute of Economic Growth, Delhi, India
Joydeep Ghosh
Institute of Economic Growth, Delhi, India
Abstract
This paper probes into the growth and distributional consequences of four basic policy options
emanating from the three sources of economic growth, namely, physical capital, human capital
and technological progress, with the help of a computable general equilibrium model of India.
The simulation results show that, the efficacy of physical capital accumulation in augmenting
growth and abating income inequality is greater than that of human capital accumulation. In the
long term, however, the latter overtakes the former in promoting growth, but inequality worsens.
When the two policies are commingled, growth improves but it continues to be inequality-
augmenting. Finally, with concomitant Hicks-neutral technological progress, not only is growth
enhanced further, but it turns out to be significantly inequality-mitigating. The emerging policy
lesson is that any integrated policy of boosting investments in physical as well as human capital
must be closely bound up with technological progress for growth to be inclusive.
Key words: Economic Growth; Inequality; Technological Progess; CGE model, India ; Asia
JEL Classification: C68, D58, I24, I28, J24, O33
* This paper is an outcome of a research project undertaken at the National Council of Applied Economic Research (NCAER), under the auspices of South Asia Network of Economic Research Institutes (SANEI).
1 Corresponding author: Vijay P Ojha, Institute of Management Technology, Ghaziabad, Rajnagar,
Traditionally, a major preoccupation of policymakers in developing economies has been
aiding and facilitating rapid physical capital accumulation with a view to spur economic growth.
More recently, especially since the experience of miraculous growth in the East Asian Economies,
investment in human capital, apart from that in physical capital, has come to be employed as an
equal means by which economic growth can be fostered. Economists, though, have identified
human capital as an important contributor to economic growth, as early as with Schultz (1961).
Subsequently, in standard growth decomposition (Mankiw, Romer and Weil, 1992; Barro & Lee,
1993; and Azariadis and Drazen, 1990), investments in physical capital and human capital
together do not account for the entire quantum of growth, but leave a residual which is explained
by technological progress or, what has come to be known in the neoclassical growth literature as,
growth in total factor productivity (TFP). The third major determinant of economic growth over
and above physical and human capital accumulation is, thus, improvement in technology or TFP.
However, it may be noted that these three sources of growth are also, in the ultimate analysis,
three determinants of income distribution, even though that is not adequately emphasized in the
growth literature.
Our study, hence, is an empirical investigation into the efficacies of four basic policy
options stemming from the abovementioned three sources of economic growth from the point of
view of growth as well as equity, with the help of a computable general equilibrium (CGE) model
of India, so as to inform and advise policymakers in designing an appropriate policy package for
inclusive growth.
The evidence furnished by empirical models on the link between physical and human
capital accumulation, one one hand, and economic growth and the associated change in inequality
or poverty, on the other, varies considerably across countries. For example, Grimm (2005) has
analyzed the impact of an expansion in education in Cote d’Ivoire on the growth and distribution
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of income using a dynamic microsimulation model. The author finds that a policy which
universalizes primary education is capable of achieving only modest gains in income growth and
poverty reduction. The latter are inadequate for eradicating poverty, and, he therefore suggests
that expansion of education be accompanied by complementary policies, such as, enhancement of
physical capital investment and technological progress, while creating demand for skilled labor,
in order to increase the returns to education. Peng (2005) also uses a CGE model to examine the
efficacy of enhanced public education spending on education as an antidote to the adverse effect
of population ageing on economic growth in China. Not only does he find that augmenting human
capital accumulation per se (i.e., without any increase in TFP) leads to gains in gross domestic
product (GDP) which more than compensate for the losses in it likely to be caused by population
ageing, but also shows that if there is growth in TFP along with the accelerated human capital
accumulation, the GDP growth gains would be much larger.
In India too, the challenge at this juncture lies in taking utmost advantage of the emerging
demographic dividend (James, 2008), by boosting investments in physical and human capital
and, enhancing TFP growth through economic policy reforms (Bosworth et al, 2007; Rodrik and
Subramanian, 2004), to achieve faster growth and simultaneous reduction in inequality (poverty).
Ironically, since the launch of structural reforms in 1991, while there has been considerable
acceleration in economic growth, serious concerns have been expressed about the increasing
income inequality (Kijima, 2006; Dutta, 2005). Hence, realizing inclusive growth is increasingly
turning out to be a major challenge for policymakers in India. For understanding better the
intricacies of this policy challenge, a CGE model unraveling simultaneously the growth and
distributional consequences of increasing public expenditure on physical and human capital
accumulation - with and without concomitant technological progress – would be ideally suitable.
Significant previous studies in the context, are two different papers by Jung and
Thorbecke (2003) and Pieters (2010), even though neither of them focus on the impact of
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technological progress on growth and inequality. Jung and Thorbecke undertake a CGE analysis
of Tanzania and Zambia to show that higher public education expenditure provides higher
economic growth and higher incomes for the poor, but, it has a limited effect on poverty
alleviation, because of the mismatch between skilled labor supply and demand. Pieters’ study
pertains specifically to growth and inequality in India. It is a Social Accounting Matrix (SAM)-
based exploration of how the skill bias inherent in sectoral composition of the growth process in
India is responsible for increasing inequality. He, therefore, recommends that the growth pattern
in India be tweaked in favour of unskilled-labor-intensive sectors. In a sense, these generic
findings are shared by our study. However, our distinctive contribution lies in going beyond these
broad inferences, to probe deeper into the scope of policymaking in the face of disequalising
growth to eventually come up with novel and interesting policy implications for technological
innovation.
Table 1 : Sources of Growth in Output per Worker in India (in average annual percentage of change)
1980-1990 1990-2000 2000-2006
Output per worker 3.5 4.1 4.5
Contribution of Physical Capital 1.1 1.8 2.0
Contribution of Education (Human Capital) 0.3 0.4 0.4
Contribution of Land -0.1 0.0 -0.1
Contribution of TFP 2.2 1.8 2.1 Source : Bosworth and Maertens (2010)
The contributions to growth in the Indian economy from physical capital, human capital
and TFP estimated through a growth accounting exercise conducted by Bosworth and Maertens
(2010) are shown in table 1. Contribution of physical capital and human capital towards gains in
labour productivity is significant. However, TFP improvements are also contributing
susbtantially to growth. While, ex-post accounting for the contribution of TFP in India’s
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economic growth has been worked out by many analysts, such as Bosworth and Maertens
(2010), to our knowledge, there has not been a similar assessment by economists on the role that
ex-ante TFP could play in engendering inclusive growth. Some researchers, like Klenow and
Rodriguez-Clare (1997) and Easterly and Levine (2001), have perceived TFP as the driving force
behind growth. But, perdictably, in their perspective, the focus remains on TFP as an exogenous
source of growth only, not on its distributional implications.
Even in our study, TFP growth occurs exogenously, but, it has far-reaching implications,
as it is conceptually understood to be a Hicks-neutral shift in the production possibilty frontier
caused by technical innovation triggering, in a market economy, a series of connected responses
by economic agents that change the commodity and factor demands, their relative prices and the
consequent factor and personal incomes, the all-around impact of which is then assessed through
a multisectoral CGE model.
Policymakers in India mostly talk about physical and human capital accumulation as the
relevant policy instruments for sustaining and accelerating growth, seemingly implying that
technological progress is a necessary offshoot of these two policies. Such an inference, however,
would be grossly erroneous, in our opinion. In reality, physical and human capital accumulation
provide only the necessary condition but not a sufficient condition for technological innovations,
and weaving the latter into the former requires strategic and focussed actions which may or may
not be undertaken. Fortunately, of late, as we shall argue later, technological innovation has been
explicitly included in the Indian policy agenda as a potential contributor to both growth and
inclusion.
With this background, the precise objective of this paper is to study the impact of a tax-
financed increase in public expenditure on physical and human capital formation on GDP growth
and income distrbution using a quasi-dynamic CGE model. Importantly, the paper also explores
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the influence exercised on the growth and distributional outcomes of the enhanced investment in
physical and human capital by the associated technological progress, if any.
To this end, we first generate a baseline or business-as-usual (BAU) scenario to profile
and project the Indian economy over the period 2004-2030, and then simulate four alternative
policy scenarios with increased investments in physical and human capital financed through the
levying of additional income tax. TFP growth rates are exogenously given in the BAU scenario
and these are maintained in the first three policy scenarios, but they are raised in the fourth policy
scenario to evaluate the (supplementary or complementary) effects of technological innovation on
growth and inequality.
The rest of the paper is organized as follows. Section 2 presents a description of the model
structure. Section 3 describes the main features of the baseline scenario. In section 4 we report the
results of the four policy scenarios in comparison with the BAU scenario. Section 5 concludes
and suggests policy implications of the results.
2. Model structure
Our model is a multisectoral, neo-classical type price driven CGE model. Moreover,
because it is recursively dynamic, it has two parts : the static part (the within-period model) and
the intertemporal dynamics part (the interim-period sub-model). In formulating the static part of
the model, we follow an eclectic approach keeping in mind the institutional features peculiar to
the Indian economy. In particular, we draw upon a standard CGE model (Robinson et al, 1999)
and two India-specific CGE models by Mitra (1994) and Ojha and Pradhan (2006).
Intertemporally, the model adjusts through changes in the stocks of physical and human capital.
Physical capital is increased by investment, which is exogenously given. Human capital is
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augmented by the new supply of educated labour, which in turn is a function of public education
expenditure, like in the Jung and Thorbecke (2003) model. (The equations of the model, which
has been solved using the GAMS software with its PATH solver, are set out in Ojha and Pradhan
(2006).
The model has nine production sectors where each sector produces output by employing
factors of production which include intermediates, capital, and composite labor, which in turn, is
a nested constant elasticity of substitution (CES) aggregation of non-educated (unskilled),
secondary-educated (semi-skilled) and higher-educated (skilled) labor. At the beginning of a
period, the economy is endowed with a certain level of physical capital and human capital, in the
form of stocks of different types of labor. In any given period, the aggregate capital stock, as also
the labor stock of each of the three different skill types is fixed, but is inter-sectorally mobile.
Producers act as profit maximizers in perfectly competitive markets, i.e., they take factor and
output prices (inclusive of any taxes) as given and generate demands for factors so as to
minimize unit costs of output. The factors of production include intermediates and the primary
inputs – capital, and different types of labor. For households, the initial factor endowments are
fixed. They, therefore, supply factors inelastically. Their commodity-specific demands are
expressed, for given income and market prices, through the Stone-Geary linear expenditure
system (LES). Also households save and pay taxes to the government. Furthermore, households
are classified into five rural and four urban categories. The government is not assumed to be an
optimizing agent. Instead, government consumption, transfers and tax rates are exogenous policy
instruments. The rest of the world supplies goods to the economy which are imperfect substitutes
for domestic output, makes transfer payments and demands exports. The standard small-country
assumption is made, which implies that, India is a price-taker in import markets and can import
as much as it wants. However, because the imported goods are differentiated from the
domestically produced goods, the two varieties are aggregated using a CES function, based on
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the Armington assumption. As a result, the imports of a given good depend on the relation
between the prices of the imported and the domestically produced varieties of that good. For
exports, a downward sloping world demand curve is assumed. Furthermore, a constant elasticity
of transformation (CET) function is used to define the output of a given sector as a revenue-
maximising aggregate of goods for the domestic market and goods for the foreign markets. This
implies that the response of the domestic supply of goods in favour or against exports depends
upon the price of those goods in the foreign markets vis-à-vis their prices in the domestic
markets, given the elasticity of transformation between goods for the two types of markets. The
model is Walrasian in character. Markets for commodities and factors of production (capital and
three skill types of labor) clear through adjustment in prices. However, thanks to the Walras' law,
the model determines only relative prices. The consumer price index is chosen as the numeraire
and is, therefore, normalized to unity. The model determines endogenously the foreign savings in
the external closure. Finally, because the aggregate investment is exogenously fixed, the model
follows an investment-driven macro closure, in which the aggregate savings (i.e., the sum of
household, government, corporate and foreign savings) adjusts to satisfy the saving-investment
balance.
Having outlined the overall structure of the within-period model, we move now to
detailing some within-period model specifics followed by the description of the interim-period
sub-model, which intertemporally adjusts the relevant exogenous variables, before the model is
run for the subsequent period.
2.1 Production Structure
Our model categorizes the following nine production sectors: agriculture, fossil fuels,
manufacturing, electricity, construction, transport, health, education, and other services. Each sector
employs, apart from intermediate inputs, 4 primary inputs: capital and three types of labor inputs
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– unskilled or non-educated labor, semi-skilled or secondary educated labor and skilled or higher
educated labor – which combine through a nested CES aggregation scheme to form what is called
composite labor as shown below.
Figure 1: Nested production structure
Domestic Sectoral Gross Output
nest IV
Intermediate Input Bundle Value Added (VA)
nest III
Composite Labour (CL) Capital (K)
nest II
Skilled Labour Composite (SLC) Non-educated Labour (LL1)
(i.e., Unskilled Labour)
nest I
Secondary-educated Labour (LL2) Higher-educated Labour (LL3)
(i.e, .Semi-skilled Labour ) (i.e, .Skilled Labour )
Note that vertical lines in the above nesting diagram represent Leontief or fixed-
coefficients combinations, while the slanting lines represent CES combinations of the inputs
involved. In other words, while there are different degrees of substitutability possible within nests
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I, II and III, there is zero substitutability within nest IV. (The elasticities of substitutions for the
nests I, II and III are given in the Appendix A). The difference between the above production
nesting structure and the one followed in Jung and Thorbecke (2003) is of significance. In the
latter, non-educated and primary-educated labor combine in a Cobb-Douglas type nest with
elasticity of substitution equal to one, to form unskilled-labor composite, which, in turn, coalesce
with a higher-educated or skilled labor to yield composite labor. In the former, within nest I,
semi-skilled and skilled labor are combined to produce skilled labor composite, which, in nest II,
is aggregated with unskilled labor to produce composite labor. With a relatively lower elasticity
of substitution within nest II (as compared to nest I), this kind of nested production structure is a
reasonable approximation of the substitution possibilities of labor of different skill levels in the
Indian economy, where the duality between organized or formal (skilled and semi-skilled) labor
and unorganized or informal (unskilled) labor continues to persist and manifest in the wide wage
gap between them, despite more than half-a-century of industrialization.
2.2 Factor markets
Labor is intersectorally mobile. Wages are flexible and adjust to equilibrate the demand
and supply which is fixed within a period for each of the three types of labor – non-educated
labor, secondary-educated labor and higher educated labor. Full employment is assumed for the
three types of labor. Aggregate capital stock too is fixed within a period, but is mobile across
sectors so that there is a single market clearing return for capital which equates the sum of
sectoral demands for capital to its given aggregate supply.
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2.3 Factor Income, Household Income and other Institutions
Factor incomes (factor prices times the respective factor demands) are readily generated in
a CGE model, but factor income distribution may or may not serve well as a proxy for personal or
household income distribution. Most CGE models concerned with income distribution, therefore,
map factor incomes onto household incomes. The degree of detail in the classification of
households into groups or classes is study specific and is typically governed by the kind of data
that is available in the matter for the country in question. For example, based on Wobst’s (2001)
Tanzanian 1992 SAM and Hausner’s (1999) Zambian 1995 SAM, Jung and Thorbecke (2003)
incorporate four socioeconomic groups each in the CGE models of Tanzania and Zambia
respectively. However, in our CGE model, there are nine socioeconomic groups based on the
SAM by Ojha et al (2009). These nine groups are: rural non-agricultural self-employed, rural
agricultural labor, other rural labor, rural agricultural self-employed, other rural households, urban
self-employed labor, urban salaried labor, urban casual labor, and other rural households. These
household groups are the same as those in the SAM-based paper of Pieters (2009) on growth and
inequality in India. This is because, both the SAMs – of Ojha et al (2009) and of Pieters (2009) –
have a common origin in the SAM by Pradhan, Saluja and Singh (2006).
Households derive their income by selling the factors they own, labor (of three types) and
capital. The factor endowment shares across the nine household groups are given in table 2.
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Table 2: Factor endowment shares across household groups (percent)
Household group
Non-educated
labor
Secondary-educated
labor
Higher-educated
labor
Capital
Rural non-agricultural self-employed 20.34 13.98 2.65 0.07 Rural agricultural labor 19.54 4.33 0.63 0.00 Other rural labor 31.02 11.59 0.32 0.01 Rural agricultural self employed 14.69 21.68 9.45 0.28 Other rural households 1.37 0.50 0.00 0.08 Urban self-employed labor 2.59 8.86 8.79 0.12 Urban salaried labor 6.64 33.30 75.73 0.02 Urban casual labor 3.25 5.02 0.90 0.01 Other urban households 0.55 0.75 1.54 0.04 ALL 100.00 100.00 100.00 62.00Source: Pradhan and Roy (2003); capital shares from SAM (Ojha et al, 2009)
It may be noted that most of the secondary and higher educated belong to the urban
salaried and urban self-employed groups. Almost 85 percent of higher-educated and 42 percent of
secondary-educated workers come from these two groups. However, secondary-educated workers
are more evenly spread over the urban and rural groups. Urban groups have 48.5 percent of the
secondary-educated workers and rural groups have 52.5 percent of the secondary-educated
workers.
Further, it is noteworthy that, the capital shares of the nine household classes sum to only
62 percent, implying that the remaining 38 percent of the economy’s capital stock belongs to
other institutions, which include private corporate sector, public sector, government and rest of
the world. In other words, not all of the income derived from capital is exhausted by allocation to
the nine households. The part that remains after distribution to the households accrues to these
four institutions.
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2.4 Interim-period sub-model
In the interim-period sub-model, the physical and human capital stocks are updated.
Capital stock is exogenously given at the beginning of a particular period. However, our model is
recursively dynamic, which means that it is run for many periods as a sequence of equilibria.
Between two periods there will be additions to capital stock because of the investment undertaken
in the previous period. More precisely, capital stock for any year t+1 is arrived at by adding the
investment, net of depreciation, in year t to the capital stock at the beginning of the year t.
Between two periods there will also be additions to human capital stocks which is
modeled like in Jung and Thorbecke (2003). People opt for acquiring higher educational levels
motivated by the availability of educational facilities largely determined by the level and
effectiveness of public education expenditure, and the possibility of earning higher wage incomes,
which would amount to higher lifetime incomes notwithstanding the sacrifice of wage income
involved during the period of pursuit of education. More specifically, the output flow of labor of a
particular education level, is a function of the public education expenditure for that level of
education, and the workers’ wage earned at that educational level in comparison with the wage
earned by workers at the next lower level of education2 .
The flows of labor of different educational levels are interlinked with each other in a
manner depicted in Figure 2. From the total increase in labor force (MS1), which is obtained from
the population growth, using a fixed labor participation rate, some join the non-educated labor
pool (ML1), while others proceed to acquire secondary level education (MS2). From the latter
group, some directly enter the labor market as secondary-educated labor (ML2), while others
progress to receive higher education (MS3). Thus, higher-educated workers are produced and
2 For a detailed derivation of the function of the output flow of educated labor, see pages 704-708 of Jung
and Thorbecke (2003).
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supplied (ML3). (The equations interlinking the flows of labor of different educational levels are
given in Appendix A.)
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3. The baseline scenario
Our CGE model has been calibrated to the benchmark equilibrium data set of the Indian
economy for the year 20043, obtained basically from the SAM by Ojha et al (2009). The former
was aggregated and modified for the purpose of this study. The main modification pertains to the
disaggregation of labour into three categories: non educated, secondary educated and higher
educated.
Given the benchmark data set for all the variables and the elasticity parameters (see
Appendix A), the shift and share parameters are calibrated in such a manner that if we solve the
model using the base-year data inputs, the result will be the input data itself (Shoven and
Whalley, 1992).
Finally, using a time series of the exogenous variables of the model, we generate a
sequence of equilibria for the period 2004 to 2030. From the sequence of equilibria, the growth
paths of selected (macro) variables of the economy are outlined to describe the base-line scenario,
spanning the 27-year time interval, 2004 to 2030, which can be seen as consisting of two parts:
the eight-year historical period, 2004 to 2011, and the nineteen-year prospective period, 2012-
2030. The former is the part of the BAU profile to which historical validation applies, and the
latter is the part which is subjected to counterfactual policy shocks (dealt with in the next section).
As shown in table 4, real GDP in the baseline scenario grows at an annual average growth
rate of 8.25 percent throughout the 27-year period, 2004-2030. In a trifurcated periodization,
GDP grows at the rate of (i) 8.40 percent per annum in sub-period 1 (P1), which is the historical
period 2004-2011, (ii) 7.31 percent per annum in sub-period 2 (P2), which is the prospective
period, 2012-2020, and (iii) 8.89 percent in sub-period 3 (P3), which is the prospective period,
2020-2030. The swings in GDP growth are explained mainly by the growth profile of the three
3 The year 2004, is actually the financial year 2003-04 in the Indian economic calendar, which runs from 1st April 2003 to 31st March 2004. Henceforth, we refer to a financial year in the Indian economic system by using only the second element in the hyphenated numeral used to designate that year, i.e.,2004 will refer to what is actually 2003-04, 2005 would actually mean 2004-05, and so on.
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skill types of labor, as physical capital and TFP grow at more or less constant rate throughout the
27-year period (table 4). The decrease in the usage of unskilled labor tends to bring down the
GDP, while the increases in the inputs of semi-skilled and skilled labor with their respective
higher shares in the output produced are likely to raise GDP. Initially, the former effect is stronger
than the latter effect, but eventually it gets outweighed by the latter effect. In other words, human
capital accumulation impacts growth favorably, but only in the long-term.
It is noteworthy that, the sectoral composition of growth (as shown in table A.1) wherein
skill-intensive manufacturing and services gain ascendancy at the cost of unskilled-labor-
intensive sectors thus relegating the latter to relative unimportance is supportive of the by now
legendary idiosyncratic pattern of development in India (Kochhar et al, 2006; Bosworth et al,
2007). Such a skewed structure of economic growth has obvious implications for wage and
income inequalities, to which we now turn.
The abovementioned skill-intensive bias in the sectoral composition of the growth process
of the Indian economy has significant disequalising distributional consequences. All the three
indicators of inequality: (i) factor income shares (in GDP at factor cost) of the four primary
factors of production, (ii) wage inequality ratios of four paired combinations of the four factors,
and (iii) standard deviation of personal incomes (SDPI) across the nine household groups, show a
near consistent rise in inequality over the three sub-periods (table 6).
In sum, the high average growth rate of real GDP of 8.25 percent over the 26-year period
in the baseline scenario, is accompanied by an exacerbation of both wage and personal income
inequalities. This is because, the three key factors : (i) physical capital accumulation, (ii) human
capital formation, and (iii) TFP improvement to which the growth is attributable, do not have an
identical impact on distributional outcomes. The first and the third factors are inequality-
mitigating, even though the former is only weakly so, and the second, is inequality-augmenting.
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Interestingly, it is the latter effect which has been dominant over the 27-year period, and, hence,
the secular increase in inequality of wages and incomes observed during this period (table 6).
Our CGE result on growth being accompanied by increasing wage and income inequalities
is consistent with the conclusions on inequitable growth in India of various other studies using
different variants of the econometric decompostion methodolgy (Cain et al, 2009; Kijima, 2006,
Dutta, 2005), and of yet another study using SAM-based multiplier analysis (Pieters. 2009).
Interestingly, all these studies concur in identifying the cause of inequality-augmenting growth in
India, in increases in skill premium or returns to education along with relatively more rapid
growth in skill-intensive (or education intensive) sectors vis-à-vis unskilled-labor intensive
sectors. Indeed, in all these studies, on the one hand, human capital accumulation is recognized as
an important source of economic growth; on the other hand, expansion in education is found to be
a major cause for growing inequality. However, these studies, given their innate methodological
limitations, are unable to discern the underlying mechanisms through which human capital
accumulation may end up feeding a disequalizing growth process. Our CGE approach overcomes
this limitation to delineate the mechanism by which investment in education can skew the pattern
of growth in a manner that leads to a worsening of the income distribution. Further, this additional
insight emerging from our CGE model is useful in deciding upon policy interventions which may
be helpful in generating inclusive growth.
4. Policy scenarios
With a view to formulating policy guidelines for accelerating economic growth and
simultaneously reducing inequality, we develop four policy scenarios for the period, 2012-2030,
in which there is : (1) an increase in physical capital investment expenditure, (2) an increase in
public education expenditure, (3) an increase in physical capital investment expenditure as well as
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in public education expenditure, and (4) an increase in physical capital investment expenditure as
well as in public education expenditure, along with a growth in TFP. Additional income tax is the
source of finance in all the four policy scenarios, which are summarized in table 3. It may be
noted that for comparability the magnitude of the increases in expenditure in both the cases - of
physical capital and of human capital – is the same.
Table 6 : Inequality Indicators : Factor income shares, wage ratios, and SDPI’s
for BAU and policy scenarios
Year 2012
BAU Sco.1 Sco. 2 Sco.3 Sco. 4
YL1 0.30 0.30 0.30 0.30 0.30
YL2 0.16 0.16 0.16 0.16 0.16
YL3 0.21 0.21 0.22 0.22 0.22
YK 0.32 0.32 0.32 0.32 0.32
W3/W1 4.16 4.16 4.43 4.41 4.42
W2/W1 2.31 2.31 2.34 2.33 2.34
W3/W2 1.80 1.80 1.90 1.89 1.89
W3/WK 4.82 4.81 5.10 5.09 5.07
SDPI 3603.53 3601.12 3664.50 3661.93 3694.24
Year 2020 BAU Sco.1 Sco. 2 Sco.3 Sco. 4
YL1 0.31 0.31 0.30 0.30 0.31
YL2 0.19 0.19 0.18 0.18 0.18
YL3 0.26 0.26 0.27 0.27 0.26
YK 0.25 0.25 0.24 0.24 0.24
W3/W1 4.84 4.81 5.12 5.10 4.79
W2/W1 2.58 2.58 2.55 2.55 2.47
W3/W2 1.87 1.87 2.01 2.00 1.94
W3/WK 7.28 7.45 7.67 7.85 7.42
SDPI 6925.68 6931.40 7230.24 7233.82 7441.86
Year 2030
BAU Sco.1 Sco. 2 Sco.3 Sco. 4
YL1 0.20 0.20 0.18 0.18 0.26
YL2 0.22 0.22 0.21 0.21 0.20
YL3 0.41 0.40 0.45 0.44 0.36
YK 0.18 0.18 0.17 0.17 0.18
W3/W1 10.54 10.40 12.29 12.13 6.93
W2/W1 4.38 4.34 4.53 4.49 3.05
W3/W2 2.41 2.40 2.71 2.70 2.28
W3/WK 19.72 20.06 21.73 22.10 17.30
SDPI 22090.95 22035.21 25467.25 25400.31 21600.71Note : YL1: Factor income share for unskilled labor, YL2 : Factor income share for semi-skilled labor, YL3: Factor income share for skilled labor, YK: Factor income share for capital, W1 : Wage rate for unskilled labor, W2 : Wage rate semi-skilled labor, W3 : Wage rate for skilled labor, WK : Wage rate for capital, SDPI : Standard deviation of personal incomes
21
4.2 Policy scenario 2
The likely outcome of an increase in public education expenditure is very much
corroborated by the numerical results of this policy scenario. For the 18-year period, 2012-2030,
GDP in this scenario is on an average higher by 2.40 percent as compared to the BAU scenario.
For the sub-periods, P2 and P3, the GDP in this scenario is on an average 0.11 percent and 3.99
percent higher respectively in comparison to the BAU. For the sub-period P2, which may be
regarded as medium term, policy scenario 1 yields larger GDP gains than policy scenario 2.
However, scenario 2 has a huge edge over scenario 1 in generating GDP increments in the long-
term as shown in the sub-period P3 (table 5).
The factor income shares of unskilled labor in 2030 decline significantly from 0.20 in
BAU to 0.18 in scenario 2; for semi-skilled labor and capital also the income shares decline
marginally in this scenario in 2030. However, for skilled labor, factor income share in 2030 rises
significantly from 0.41 in BAU to 0.45 in policy scenario 2. All the four wage inequality ratios
increase right through the 18-year period, 2012-2030. SDPI also rises throughout the 18-year
period. In 2030, it is 25467.25 in this scenario as compared to 22090.95 in BAU.
To accommodate the rise in the income tax rate, saving-investment balance readjusts in a
manner similar to that in the previous simulation – i.e., government saving rises and household
saving declines.
4.3 Policy scenario 3
In policy scenario 3 policy scenarios 1 and 2 are realistically integrated by increasing both
physical capital investment expenditure and public education spending by 50 percent of the
baseline education spending level, and raising the income tax rate to provide the additional
finance, for all the years in the period, 2012-2030.
22
For the 18-year period, 2012-2030, GDP in this scenario is on an average higher by 2.53
percent as compared to the BAU scenario (table 5). For the sub-periods, P2 and P3, the GDP in
this scenario is on an average 0.83 percent and 4.06 percent higher respectively in comparison to
the BAU (table 5). The GDP gains in this integrated scenario seem to be a simple summation of
the GDP gains in the two previous scenarios. It is obvious that, there is an insignificant
complementarity effect arising from the joint implementation of the physical and human capital
augmentation policies.
And, because human capital enhancement dominates the outcome, GDP growth improves
but it is disequalising. All the three inequality indicators furnish evidence in favor of worsening
inequality, in much the same way as in policy scenario 2. However, the resulting inequality in this
scenario is somewhat less acute when compared to the former scenario (table 6). The possible
reason for this is the mildly equalizing impact of the simultaneous expansion of physical capital.
4.4 Policy scenario 4
In policy scenario 4 we increase both physical capital investment expenditure and public
education spending by 50 percent of the baseline education spending level, with the additional
finance being provided by a hike in the income tax rate, and, concomitantly increase TFP growth
by one percent per annum uniformly across all the nine sectors for all the years in the period,
2012-2020. Before we proceed to analyze the numerical results of this policy scenario, it is
important to spell out the import of the design of this simulation.
As argued in the first section above, we have treated the third key driver of economic
growth, namely, TFP, as exogenous. However, the ‘exogeneity’ of TFP is being maintained only
for analytical tractability. Intuitively speaking, the dependence of TFP improvement on
investment in physical and human capital, especially the latter, is hard to exaggerate. At the same
time, it is notable that the link between investment in physical capital and in education (and
23
research)4, and, growth in TFP, is not automatic or inevitable, but, depends crucially on astuteness
in policymaking. In other words, while perceptive and proactive public policy will orient physical
and human capital investment greatly towards rapid TFP growth, an inappropriate or complacent
one will fail to capture their potential fallout for TFP improvement. The previous simulation, i.e.,
policy scenario 3, was representative of the latter sort of policy. Policy scenario 4, on the other
hand, would serve well as a proxy for the former kind of policy. (In a sense, it is our surrogate for
endogenous growth policy simulation..
For the 18-year period, 2012-2030, GDP in this scenario is on an average higher by 6.19
percent as compared to the BAU scenario, For the sub-periods, P2 and P3, the GDP in this
scenario is on an average 4.59 percent and 7.63 percent higher respectively in comparison to the
BAU (table 5).
All the three inequality indicators clearly show a substantial decline in inequality relative
to the BAU scenario. Factor income share of unskilled labor increases from 0.20 in BAU scenario
to 0.26 in this scenario in 2030. On the other hand, factor income shares of semi-skilled labor and
skilled labor decline significantly. In 2030, all the wage inequality indicators show a significant
decline in comparison to those in BAU. Personal income inequality indicator, SDPI, in 2030 too
declines markedly with respect to that in BAU from 22090.95 to 21600.71 (table 6).
Evidently, the GDP gains in this scenario are far in excess of those in the previous
scenario. Further, when augmentation in physical and human capital investment is associated with
apposite technological progress, not only does it lead to faster economic growth, but also
considerably reduced inequality, as compared to the BAU scenario, unlike the previous scenario
which shows worsening inequality over the baseline scenario.
4 Note that ‘education’, which is the eighth sector in our 9-sector scheme, is a truncated reference to what in our SAM is actually ‘education and research’.
24
5. Conclusions and policy implications
We conclude by underlining the main policy lessons from the four policy scenarios
developed above. The policy lessons that emerge from our policy scenarios are in two parts.
In the first part, the lessons learnt are about the relative contributions of public policies
augmenting expenditure towards investment in physical capital (policy scenario 1) vis-a-vis
human capital (policy scenario 2). In the medium term, there are larger gains in GDP from an
expansionary physical capital investment policy, as compared to the expansionary educational
investment policy, although, in the long term, the latter hugely overtakes the former in generating
GDP growth. Moreover, the growth ensuing from the expansionary physical capital investment
policy is weakly equalizing, while the growth resulting from the expansionary educational
investment policy per se is substantially disequalizing, as it biases the structure of production
towards skill-intensive sectors, thus benefiting the skilled and semi-skilled workers, who
constitute the minority of the workforce, at the cost of the unskilled majority. Our CGE result that
expansionary physical capital investment policy is a reliable medium-term policy option, whereas
expansionary human capital investment policy is an effective long-term policy option, is not only
intuitively appealing, but also an important part of the conventional wisdom on economic growth.
As far as the differential distributional outcomes of the two policies are concerned, in the
available literature there are independent studies showing, one one hand, that physical capital
investment driven economic growth is inequality-mitigating (Calderon and Serven, 2004), and, on
the other hand, that human capital investment driven growth is inequality-augmenting (Kijima,
2006 and Cain et al , 2009). However, what is unique, to the best of our knowledge, about our
study is that, it churns out the divergent impacts of the two kinds of capital investment on income
distribution simultaneously through the channels of a market-based system mirrored in our CGE
model.
25
That said, it must be stressed that, to undertake a comparison of the importance of the two
forms of capital as contributors of inclusive growth is not to treat them as competing drivers of
economic growth. On the contrary, real world situations would inevitably be one of integration of
the two kinds of capital, rather than of a binary choice between the two types.
Hence, in the second part of the policy lessons, the implications of combining the
integrated policy package comprising of an enhancement in physical capital investment
expenditure as well as public education spending, with a (uniform) total factor productivity
improvement are explored. In this part, we compare policy scenario 3, in which the increase in
physical and human capital investment is not inducing any TFP improvement because policy is
not suitably designed to yield a technological dividend, with policy scenario 4, in which policy is
appropriately tweaked in a manner that the technological spillovers are harnessed thereby
associating the augmented physical and human capital investment with a TFP amelioration as
well. The difference in the outcomes of policy scenarios 3 and 4 are large and significant.
Policy scenario 3 turns out expectedly to be a bolder version of the policy scenario 2 (or,
equally, of the policy scenario 1). GDP gains in this integrated scenario – policy scenario 3 - are
approximately equal to sum of those attained in scenario 2 and scenario 1. Evidently, there is no
significant complementarity occurring between investments in physical and human capital. A
plausible reason for the absence of a complementarity effect is that the magnitude of the increase
effected for human capital in scenario 2 is a large one and completely dominates the relatively
small boost given to physical capital in scenario 15. Income distribution in this scenario also
improves marginally as compared to scenario 2, but, it remains seriously adverse relative to the
baseline scenario. That is to say, policy scenario 3 infact accentuates the inequitable growth of the
baseline scenario. It follows that, if the augmentation in physical and human capital investment is
5 Even, Jung and Thorbecke (2003) emphasize the importance of the level of physical capital investment being sufficiently high for there to be a strong complementarity between the former and human capital investment.
26
not associated with technological improvement, there may be significant growth achievements,
but the income inequality will most likely worsen, because the disequalizing impact of the
expansionary educational investment policy far outweighs the equalizing impact of the
expansionary physical capital investment policy. Other non-CGE based empirical studies
analyzing the post-liberalization inequitable growth process of the Indian economy have also
found human capital accumulation to be a major explanatory factor for the growing inequality in
it (Pieters, 2009; Cain et al, 2009 ; Kijima, 2006; Kochhar et al , 2006) , but, none of their authors
have suggested, like we do, a solution in the form of an innovation-driven inclusive growth.
The inequitable growth of policy scenario 3 may be converted into equitable growth if the
increased investment in physical and human capital (i.e., education and research) induces
technological progress as well. Indeed, orienting physical and human capital investment towards
simultaneous technological improvement is the key policy challenge for attaining inclusive
growth. This is clearly borne out by our policy scenario 4. In this scenario, the enhancement to
economic growth is much greater than that in case of policy scenario 3. Additionally, since the
induced technological progress applies even to sectors which are unskilled labor intensive, the
demand for unskilled labor is stimulated, which, in turn, causes wages of unskilled labor to rise in
relative terms. Factor income share of unskilled labor rises, and those of semi-skilled and skilled
labor decline. Wage inequality and the consequent personal income inequality also decline. In
short, there is both a substantial enhancement of economic growth and a significant abatement of
income inequality in policy scenario 4.
Our findings thus suggest that, the objective of inclusive economic growth is more likely
achievable if the integrated policy of augmenting investment in physical and human capital is
closely bound up with technological progress. Interestingly, this is now accepted wisdom among
policymakers in developing countries. Indeed, the microeconomic policy initiatives for fostering
inclusive growth through technological innovations has already translated into an impressive
27
action agenda in India (Dutz, 2007; Government of India, 2007; Vijayaraghvan & Dutz, 2012).
Even the cross-country empiricial evidence that is now available is strongly supportive of the
effectiveness of innovation-led inclusive growth. For example, Dutz et al (2011) have found
powerful econometric evidence for innovation – as proxied by the level of TFP - leading to
increases in output and employment growth, as well as in the relative contributions of the
unskilled labor force to the latter. Additionally, there are other similar studies (Lopez-Pueyo &
Mancebon, 2010; Salinas-Jimenez et al, 2006), which identify innovation as a key source of
productivity growth across various countries.
Finally, there are caveats or limitations of our paper which must be borne in mind. These
are two : (1) throughout our paper we have worked with uniform TFP growth in all sectors, and
(2) the technological innovations taking place over time are necessarily of the Hick-neutral type,
rather than the type which is biased towards capital and/or skilled labor as against unskilled labor,
such as the ones discussed in some other related works (Ahmed, forthcoming; Winchester &
Greenaway, 2007; Card & DiNardo, 2002). For the first limitation, it may be noted that, in reality,
TFP growth across sectors will necessarily be unequal, and policy too will target for preferentially
higher TFP growth rates for sectors that are labor intensive sector, especially, unskilled labor
intensive, rather than equal TFP growth for all sectors. But, the outcome in terms of inclusiveness
from this kind of selectivity in TFP promotion would in all likelihood be superior to that obtained
in our uniform TFP growth setting. In other words, the uniform TFP growth in all sectors is a
minimalist position (benchmark), which real-life policy endeavors ought to surpass. That is not to
deny, that there remains much scope for future research on how to induce TFP growth selectively
across sectors to achieve the best possible result for growth as well as equity. For the second
limitation, our rationalization is similar, namely, the reported analysis of the impact of
technological improvements of the Hicks-neutral kind will serve well as a starting point for
fruitful future research on the linkage between technology choice and income distribution.
28
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