Post-reform research on inter-sectoral linkages using IOTTs in India

Research on India’s post-liberalisation economy using IOTTs as an analytical tool has been considerable, with India’s production and demand structure at the aggregate level of agriculture, industry (manufacturing plus non-manufacturing industries) and service sectors analysed in studies by Reference SaikiaSaikia (2011) 1968–1969 to 2003–2004, Reference Sastry, Singh, Bhattacharya and UnnikrishnanSastry et al. (2003) 1968–1969 to 1993–1994 and Reference Das and PatnaikDas (2015) 1979–1980 to 1998–1999. A common finding of these studies is increased dependence on industrial inputs by agriculture over time but reduced dependence on agricultural inputs by industry, reflecting broad-based growth of the industrial sector. Services tended to be more strongly related to industry than agriculture over time. In contrast, Reference HansdaHansda (2001) and Reference BhowmikBhowmik (2003) focus specifically on the importance of the service sector in inter-sectoral production and demand. Reference BhowmikBhowmik (2003) analyses IOTTs for 1968–1969 to 1993–1994 to show that production intensity in services increased during the pre-reform period and that the metal products, machineries, trade and banking sectors had all been key recipients of service’s intensity. Reference HansdaHansda (2001), examining 1993–1994, argues the importance of services to the Indian economy is due to its intensive use in production within other sectors. To date there is no research that analyses services – manufacturing inter-linkages, over an extended period, post India’s liberalisation. Such analysis is attempted in this paper.

Analysis of input–output linkages

This section examines the inter-linkages between services and manufacturing, and linkages with the wider production system of India’s economy through applying different input–output methods. To start, we examine the structural changes in output and employment during the post-liberalisation era as a backdrop to changes in services – manufacturing inter-linkages. Figure 1 displays the change in output composition and Figure 2, the change in employment composition.

Figure 1. Change in value added shares across sectors (at 2004–2005 prices).

Source: Author’s calculations using KLEMS database (3^rd Edition), RBI.

Figure 2. Change in employment shares across sectors.

Source: Author’s construction using KLEMS database (3^rd edition), RBI).

Note. The horizontal axis shows the time period and the vertical axis shows the magnitude of percentage of variance due to the concerned sector.

These figures show a structural imbalance within the Indian economy in terms of sectoral output and employment composition coinciding with a pattern of general economic growth. In 1993–1994 – early liberalisation^{Footnote 3} – agriculture and allied activities, manufacturing and services contributed 28%, 15% and 39% respectively, to India’s gross value-added production. In 2013–2014 these shares were 14%, 15% and 55% respectively. The corresponding employment shares in 1993–1994 were 64%, 11% and 18%, respectively, changing to 45%, 11% and 28%, in 2014–15. While services’ and manufacturing’s value-added production had grown much faster than that of agriculture and allied activities, they were unable to absorb employment at commensurate rates. An overwhelmingly large share of India’s employment continued to be generated in agriculture and allied activities with nine of the 19-percentage point fall in employment share gained in the construction sector (increasing from 3% to 12%),^{Footnote 4} providing a cushion to workers engaged in low productivity employment.

Table 1 maps India’s production structure through usage intensity – the share of each sector’s contribution to total economic production. Table 1 shows the sectoral shares for total input cost incurred – sum value of all intermediate use transactions for all upstream sectors – to produce the total gross output of India’s economy over time. The sectoral shares are computed from the total intermediate use column of the IOTTs. The mathematical expression for total input cost and sector shares of total input cost have been provided in Supplemental Appendix A (Sage: Please hyperlink).

Table 1. Sectoral share of total input cost in the Indian economy (shares at current prices).

Source: Author’s calculations based on IOTTs. CSO for the years 1993–1994 to 2007–2008 and IOTT prepared by Reference Singh and SalujaSingh and Saluja (2016) for the year 2013–2014.

# Construction, Electricity, Gas and Water Supply.

The sector ‘Public administration and defence’ contains ‘0’ entries in all cases as it enters IOTTS only as a Final expenditure under the heading ‘Government Final Consumption Expenditure’. It is therefore not shown in the table.

Table 1 shows that services and manufacturing have made the greatest contributions to India’s total input costs, during the post-reforms period. Manufacturing’s contribution to the total input costs of production has not only been much larger than services but increased over the post-reforms period. In contrast, services’ share of input costs declined after 1998–1999. This finding is striking given that services’ value-added share had grown rapidly during the same period. Why has intensity usage for services’ inputs not increased then? The decline in services’ share of input costs does not seem to be due to a fall in the relative prices for services vis-a-vis manufacturing. Supplemental Appendix Table A1 and Figure A1 show that the ratio of implicit deflators for both manufacturing and services had been stable, around 1 during the entire postreform period. Technological change may have improved productivity in services’ contribution to inputs (increase in output produced per unit use of services) during this period, which is not being reflected in services’ intensity of use or share in cost. Recent studies like Reference Guerrieri and MelicianiGuerrieri and Meliciani (2005), Reference Driemeier and NayyarDriemeier and Nayyar (2018), and Reference Kucera and JiangKucera and Jiang (2019) have shown that services’ intensity of production, measured by input shares, has actually increased during the course of the last four decades, particularly in advanced economies. But between 1995 and 2011, in the eight emerging economies (EE) including India, the average share has been stagnant (Reference Kucera and JiangKucera and Jiang, 2019). Moreover, services’ intensity of production/input share was much lower in the EEs in contrast to G7 economies where input shares increased during the same interval. The non-increasing intensity of service use in India as depicted by Table 1 is consistent with these observations, confirming an absence of increased integration of services as an input in India’s production structure. While in 2009, India’s service share in manufacturing production was much lower than that of the G7 economies, although India was a relatively better performer within the EEs with respect to intensity of services use. In general, this suggests that the importance of services in the production structure of EEs tends to be lower than in advanced economies, but the decline in the intensity of service use in India, observed in Table 1, needs further explanation.

The decreased monetary cost share of services as an input in production costs supports explanations by Reference Eichengreen and GuptaEichengreen and Gupta (2011a) and Reference Ghose and RamaswamyGhose (2015) who argue that the rapid growth of services in India has been largely driven by final demand as opposed to intermediate demand. Table 2 below shows the distribution of demand components for manufacturing and services.

Table 2. Distribution of total demand for manufacturing and services (as percentage).

Source: As for Table 1.

The distribution of demand components of sectors is presented in the rows of an IOTT. Table 2 shows that intermediate and final demands were equally important to manufacturing during this period, but for services, the contribution of final demand to total demand was much greater than intermediate/inter-industry demand. Most recently, in 2013–14, the relative importance of final demand has only increased for services. Domestic consumption and exports have played a major role in driving India’s service sector growth, not only does domestic private consumption and exports compose 90% of final demand for services but there has also been a sharp rise in services’ share of total domestic private consumption and exports during the liberalisation era. Between 1993–1994 and 2013–2014 the service share of domestic private consumption increased from 39% to 51% and that of exports increased from 34% to 38%. (See Supplemental Appendix Table A2–A4).

Tables 3 and 4 depict the intensity with which both manufacturing and services use inputs from other sectors. These sectors have been provided for completeness while the focus of the analysis remains on manufacturing and services.

Table 3. Manufacturing’s intensity of usage: share of sectoral inputs to manufacturing (sector inputs as % of total manufacturing input cost).

Source: As for Table 1.

Table 4. Service intensity of usage of inputs from other sectors (sector inputs as % of total services input cost).

Source: As for Table 1.

Manufacturing and services account for much larger input cost shares in each other’s production compared to all other sectors. Services input cost share to manufacturing declined marginally by one percentage point from 1993–1994 to 2007–2008, with a major decline to 2013–2014 (See Table 3). The moderate declines in services input cost shares are consistent with the stagnant service shares in manufacturing production observed in the EEs during 1995 and 2011, discussed previously; while the sharp fall in services’ input share in 2013–2014 coincided with important changes in India’s manufacturing cost structure.

From 2007–2008 to 2013–2014 there were sharp jumps in the shares of mining and quarrying, agriculture, and non-manufacturing industry to manufacturing’s input cost. India faced multiple issues on the macroeconomic front during the post-2007–2008 global financial crisis period, such as high food and fuel inflation, and rapid exchange rate depreciation (Reference GoyalGoyal, 2013; Lok Sabha Secretariat, 2013; Reference MohantyMohanty, 2013). Between 2011 and 2013 the global price of crude oil was at historically high levels (Ministry of Petroleum and Natural Gas, 2015). India meets most of its crude oil demand through imports and crude is a major import item for the economy (Lok Sabha Secretariat, 2013). Exchange rate depreciation, high oil prices and elevated food inflation not only led to high inflation generally but raised raw material costs and squeezed the profits of Indian corporations (Reference MohantyMohanty, 2013). The structural shift in manufacturing’s input cost structure after 2007–2008 can be explained by the high costs of raw materials, fuel and energy during this period. These trends are also confirmed by the KLEMS (Capital (K), Labour (L), Energy (E), Material (M), Services (S)) database which shows that the energy and material shares in manufacturing’s input cost rose steadily between 2007 and 2008 and 2013–2014.^{Footnote 5}

The decline in service input share of manufacturing during the liberalisation era is counter intuitive. International evidence suggests that service input intensity to manufacturing increases over the course of economic development, as observed in the cross country analyses by Reference ParkPark (1987), Reference Park and ChanPark and Chan (1989), Reference Guerrieri and MelicianiGuerrieri and Meliciani (2005) and more recently Reference Driemeier and NayyarDriemeier and Nayyar (2018). According to Reference Driemeier and NayyarDriemeier and Nayyar (2018) the share of embodied services – the share of services in gross value of manufactures’ exports – has increased, globally, from 34% to 35% from 1995 to 2011, with this increase being more pronounced in the European region. Reference Guerrieri and MelicianiGuerrieri and Meliciani (2005), based on analysis of several developed countries, suggest that an economy’s ability to develop an export oriented competitive service sector is associated with services linkages with the manufacturing sector. In India on the other hand, while exports have become an important source of service sector growth, domestic manufacturing intensity of service use has declined.

Table 4 shows that service sector input cost share was persistently higher than manufacturing input cost share in service production. This finding also stands out in contrast with the typically observed service production structures. Reference ParkPark (1987), based on his research of East Asian and Pacific Basin economies, shows that service sector production is much more dependent on manufacturing than on services,^{Footnote 6} at relatively lower levels of economic development. The importance of services to service sector production is greater than manufacturing at higher levels of economic development, as depicted by the case of Japan and the United States in Reference ParkPark’s (1987) analysis. India seems to have graduated to this stage rather uncharacteristically for its level of development. Like manufacturing, the sharp rise in the share of non-manufacturing industry total input costs to services between 2007–2008 and 2013–2014 can also be attributed to high fuel inflation in the Indian economy. The KLEMS database confirms that during the same period the energy share of service input costs doubled from 18% to 36%.^{Footnote 7}

India’s manufacturing–services production linkages had weakened during a period of rapid service sector growth, especially in terms of manufacturing’s use of service inputs. Further, services had used service inputs more intensively compared to manufacturing inputs.

Manufacturing and services impact on output and employment: A comparison

As discussed earlier, the seminal work of Reference HirschmanHirschman (1958) provided the foundational understanding to view the economy as a system of inducement mechanisms where economic activity in one domain stimulates other economic activities. There are two important concepts here – backward and forward linkages. Backward linkages depict the demand stimulus a sector creates to induce production in other sectors by using inputs from these other sectors in the economy. Forward linkages capture the production stimulating/enabling impact a sector creates on the other sectors by being used as an input in other sectors. The role of forward linkages as an inducement mechanism is argued to be dependent on the existence of backward linkages. Therefore, backward linkages assume central importance here.^{Footnote 8} IOTTs allow us to compute backward linkages through Leontief inverse matrices, and forward linkages through Ghosh inverse matrices, both of which are derived from IOTTs.^{Footnote 9} Further, inter-sectoral backward linkage coefficients allow us to compute the employment created in an upstream sector due to use in downstream sector production (See Reference Miller, Lahr, Lahr and MillerMiller and Lahr, 2001). Supplemental Appendix B for both mathematical exposition and explanation of computation of Leontief and Ghosh matrices and method to compute indirect employment. Tables 5 –7, respectively, depict the backward linkages, forward linkages and indirect employment generation for manufacturing and services.

Table 5. Manufacturing and service sector backward linkages.

Source: As for Table 1.

M: Manufacturing; S: Services.

Table 6. Manufacturing and service sector forward linkages.

Source: As for Table 1.

M: Manufacturing; S: Services.

Table 7. Sectoral share of indirect employment in India (as a percentage of total indirect employment generated in the economy).

Source: Author’s calculation using India KLEMS database and IOTTs, various years. Author’s calculations using KLEMS database (3^rd Edition), Reserve Bank of India (RBI).

Each cell in Table 5 reflects the total direct and indirect demand generated for the sector placed in the row, in response to a unit of final demand generated by the sector named in the column, expressed as a fraction/multiple of one unit of final demand. For example, the first entry in row 6 shows that INR 1 of final demand for manufacturing generated INR 2.37 of output in the economy. These entries have been extracted from the Leontief inverse matrix calculated from India’s IOTTs. It can be clearly seen here that the backward linkages of both manufacturing and services increased during the post-reforms period but manufacturing persistently stimulated more demand than services. Although services grew rapidly, it remained weaker in stimulating production in other sectors. This finding is similar to that observed by Reference TregennaTregenna (2008) for South Africa in 2005.

Each cell in Table 6 reflects the coefficient total direct and indirect supply generated for the sector placed in the row, in response to a unit of value added generated in the sector depicted in the column, expressed as a fraction/multiple of its one unit of its value added. These entries have been extracted from the Ghosh inverse matrix calculated from the IOTTs. The coefficients of forward linkages for manufacturing are much higher than that of services, indicating that every unit of value-added production by manufacturing has been associated with larger economic value creation in the onward supply chain compared to every unit of value-added production by services.

These findings are consistent with manufacturing’s strong integration within an economy’s production structure compared to other sectors. In India’s case, these findings are important because they contrast the weaker integration of services with its growth dynamism and the deeper integration of manufacturing with its stagnant share in India’s output.

Next, we compare the indirect employment impact of production in manufacturing and service sectors depicted in Table 7 below.

Row 6 of Table 7 shows total indirect employment generated in the Indian economy, as a percentage of the total (direct and indirect) employment, has been rising over time. Indirect employment has been calculated using employment intensity of gross output for each sector and a Leontief inverse matrix. Detailed mathematical explanation of the calculations are provided in Supplemental Appendix B. Indirect employment increased from 24% in 1993–1994 to 36% in 2013–2014 suggesting increased inter-connectedness between sectors resulted in employment creation during the post-reforms period. Manufacturing activities accounted for 55–60% of total indirect employment in India’s economy, the largest across all sectors and almost 2.5 to 3 times higher than services. This occurred despite manufacturing’s employment share stagnating during the post-reforms period.

Comparing the sectors from the perspective of their development potential, it is clear that manufacturing activities demonstrate a high level of inter-connectedness in the production structure of the Indian economy that contrasts sharply with services. In structuralist terms, manufacturing performed as a key or leading sector in stimulating output and employment in other sectors of the economy through greater inter-connectedness in both upstream and downstream production processes. Manufacturing activities were associated with much higher output and employment spillovers to other sectors compared to services. In contrast, the sectoral complementarities remained low for services despite the phenomenal growth of ICT and related sub-sectors in the post-liberalisation period – questioning the capacity of services to be the engine of growth for India.

Analysis of input–output linkages presented important evidence comparing the inter-connectedness of manufacturing and services within the production structure of the economy. However, there are two methodological limitations. First, the impact of various sectors within the production system works only through the channel of transaction-based linkages. Since sectors also tend to impact each other within an economy due to non-transactional spillovers i.e. externalities of production activities that affect other sectors positively or negatively are not captured by the I-O technique. For example, in the Kaldorian sense (Reference KaldorKaldor; 1967) manufacturing exhibits industry level economies of scale which has positive impacts on individual firms in creating increasing returns. This kind of positive externality cannot be captured just by looking at the static transaction coefficients between sectors. Secondly, the impact multipliers computed in the I-O framework assume that transaction coefficients between sectors are fixed for every round of production which does not allow for the dynamic production process to impact the coefficients. Time series methods can be used to capture the existence and strength of dynamic relationships between sectors allowing both transactional and non–transactional impacts on each other. This analysis is carried out in the next section.

The methodology and analysis

The Augmented Dickey Fuller stationarity tests of manufacturing and services in their logarithmic form shows that they are both non-stationary in the level form but are stationary in the first difference (log difference) form that is both series are integrated of order 1 during the period under consideration (Supplemental Appendix Tables D1–D4). This allows us to perform the tests for co-integration between the two variables. We find that services and manufacturing are co-integrated by using the Johannsen’s Co-integration test (See Reference EndersEnders, 2010, pp. 401) results of which are presented in Supplemental Appendix Table D5. This shows the existence of a long-term relationship between the outputs of the two sectors. The presence of a co-integrating relationship between services and manufacturing allows us to follow a vector error correction model (VECM) that involves estimating a relationship between two time series variables which includes both short- and long-run relationships. In a VECM all the variables are endogenous.^{Footnote 11} The following equations represent the VECM for manufacturing and services value added time series in India based on the results obtained from the VECM:

$\begin{array}{c}\Delta Lse{r}_t=0.0639-0.828*\left(Lse{r}_{t-1}-1.3050*Lman{u}_{t-1}+2.8495\right)\\ +0.1433*\Delta Lse{r}_{t-1}+0.03781*\Delta Lman{u}_{t-1}+{\mu }_{st}\to \left(1\right)\end{array}$

$\begin{array}{c}\Delta Lman{u}_t=-0.0423+0.2369*\left(Lse{r}_{t-1}-1.3050*Lman{u}_{t-1}+2.8495\right)\\ +0.2057*\Delta Lse{r}_{t-1}+0.2039*\Delta Lman{u}_{t-1}+{\mu }_{mt}\to \left(2\right)\end{array}$

Equations (1) and (2) above depict the estimated VECM for the logarithm of services and manufacturing value added – $Lser$ and $Lmanu$ , respectively, capturing both long- and short-run dynamics. This system captures the impact of services and manufacturing value added on each other and with their own lagged values. The left-hand side (LHS) in both equations depict one period change in services and manufacturing value added in time t from t−1, respectively. The terms on the right-hand side (RHS) depict all the variables that impact the LHS in both equations, respectively. The terms on the RHS in both equations depict: the constant; the error correction term multiplied by the co-integrating equation shown in brackets; and the long run relationship between services and manufacturing value added, followed by the coefficients multiplied with the lag of first difference of services and manufacturing value added logarithms for the period t−1 and the error term, in that order. Supplemental Appendix Table D7 depicts the details of the estimated VECM.

Supplemental Appendix Table D8 shows that both the error correction terms in respective equations (1) and (2) are statistically significant. The error correction term for equation (1) is negative showing that the deviations of the entire system between the short-run and long-run relationships between services and manufacturing value added are adjusted back to the long-run relationship. The coefficients of $\Delta Lse{r}_{t-1}$ and $\Delta Lman{u}_{t-1}$ in equation (1) are not statistically significant implying that short-run changes in services and manufacturing in the immediately preceding period do not have a statistically significant impact on the present change in service value added. The coefficient of $\Delta Lman{u}_{t-1}$ in equation (2) is also statistically insignificant but the coefficient of $\Delta Lse{r}_{t-1}$ is statistically significant. The short-run changes in the service value added in the immediately preceding period impacted manufacturing value added positively while manufacturing did not impact itself in the short-run in a statistically significant way.

The R square value is low for the VECM but the Wald test (Supplemental Appendix Table D9) shows that the joint explanatory power of the model parameters is statistically significant and the variations in the trajectory of services and manufacturing value added are explained by the explanatory variables in the system. The model shows that services and manufacturing outputs have been in a stable long-term relationship and changes in service sector output impact manufacturing output in the short-run.

VECM also allows us to estimate the impulse response function (IRF) and variance decomposition (VD). IRF depicts the impact of a shock arising in variable i in time t on variable j in time t+p. The IRFs for services and manufacturing value added on each other and themselves are provided in Figure 3. VD decomposes the variance in variable i due to a shock that can be decomposed into the percentage contributions due to variable i and j, respectively. VD is used to compare the relative importance of the shocks emanating from different variables in the system, in addition to the trajectory of the shock in both contemporaneous and future time horizons. Figure 4 below shows the VD for services and manufacturing value added.

Figure 3. Impulse Responses of Services and Manufacturing to shocks from each other.

Source: Author’s construction using KLEMS database (3^rd edition), RBI).

Note. The horizontal axis shows the time period and the vertical axis shows the magnitude of impulse response in the concerned sector.

Figure 4. Variance Decomposition of shocks to services and manufacturing.

The upper panel in Figure 3 shows that over a 10 year period the impulse responses of service output to a shock in both services and manufacturing increases over time and then flattens but remains elevated. The service sector impacts itself much more than does manufacturing. The lower panel shows the impacts on manufacturing output. The impact of service sector shocks on manufacturing increases initially and then tapers but remains elevated towards the end of the period. The impact of manufacturing sector shocks on manufacturing gradually decays over time. Service sector shocks impact manufacturing more than manufacturing impacts itself.

The upper panel in Figure 4 shows that most of the shock related fluctuations in services are due to the service sector itself, although these marginally decline over time. At the same time manufacturing’s shock related contribution to service sector fluctuations increased over time but stayed marginal. The lower panel shows that initially, manufacturing sector fluctuations are almost equally contributed to by shocks from both services and manufacturing. Over time the contribution of services increases steeply and that of manufacturing declines. Both IRF and VD depict that it is the service sector that has a much greater impact on both services and manufacturing output than vice versa. Further the Granger causality results in Supplemental Appendix Table D12 confirm that the services impact manufacturing output and not vice-versa.

Interpreting the results

The results of the bivariate time series involving VECM, Granger Causality, IRF, and VD tests in the previous section, all point out that service sector output growth has been self-driven and manufacturing sector output growth has not been an important determinant in this process. These results strongly support the results from the input–output analysis which show that service sector inter-connectedness with manufacturing has not intensified during the post-reforms period and remains weak. It is generally postulated in several studies like Reference ParkPark (1987), Reference Park and ChanPark and Chan (1989), Reference Francois and ReinertFrancois and Reinert (1996), Reference Guerrieri and MelicianiGuerrieri and Meliciani (2005), Reference Driemeier and NayyarDriemeier and Nayyar (2018) and Reference Kucera and JiangKucera and Jiang (2019) that increased manufacturing use of services has been an important aspect of service sector growth globally. For India in contrast, both input–output analysis and time series analysis show that manufacturing has not been an important factor in determining the trajectory of service sector growth. Services have been driven more by factors outside the production system, that is, final demand, primarily through domestic private consumption and exports as shown in Section 2.

The time series analysis indicates that service growth has played a role in impacting manufacturing output growth, that is, the growth trajectory of manufacturing was responsive to the growth trajectory in services as far as the direction of statistically estimated causality is concerned. Together, the relatively low intermediate linkages between services and manufacturing and the disproportionate role of final demand in rapid service sector growth depicted by input-output analysis indicate that this responsiveness is an indication of a demand side impact.

There are reasons to believe that the demand side impulses of services on manufacturing is an outcome of certain aspects of the Indian growth process that are neither sustainable nor desirable in the long run. Reference Ghose and RamaswamyGhose (2015, Reference Ghose2016, Reference Ghose2019a, Reference Ghose2020) argues that rapid service sector growth concentrated in certain specific sub-sectors like ICT and related services, has benefited a small section of the working population at the upper end of the income distribution. This in turn has influenced the demand patterns in the economy including those for manufactured commodities such that manufacturing growth during this period has been dominated by less labour-intensive and more skill-intensive and import-intensive industries.^{Footnote 12}

Evidence shows that India’s top income deciles have experienced faster income growth than the bottom deciles during the post-liberalisation era (Reference Banerjee and MaheshwariBanerjee and Maheshwari, 2020). For example, 52% of the overall increase in India’s GDP between 1991 and 2011 accrued in the top 20% of the income distribution while the share for the bottom 20% was only 5%. Our own computations from the Consumer Expenditure Surveys of the National Sample Survey Office show that between 1993–1994 and 2011–2012 the ratio of monthly per capita expenditure (MPCE) of the top-most decile to bottom-most decile increased from 7 to 10 (See Supplemental Appendix Tables E1–E4 for categories of expenditure over different deciles). The concentration of income and consumption at the top ends of the respective distributions in India clearly depicts the narrow base of domestic demand. This is contrary to the massification of demand that is considered pivotal in sustaining manufacturing expansion due to the minimum scale requirements for efficient production (Reference ChaiChai, 2018). Broad-based domestic demand played a critical role enabling first tier East Asian industrialisers (Reference Storm and NaastepadStorm and Naastepad, 2005) and China (Reference GhoseGhose, 2019b) to build their manufacturing capacity in labour-intensive industries and helped them become major exporters in their initial phase of industrialisation.

The services driven demand stimulus on Indian manufacturing, based on a small section of the beneficiaries of ICT and related services boom, has possibly run its course. The significant slowdown in urban demand including those for high-end consumer goods were already evident from around 2015. Compounded by a crisis in the agricultural sector, the Indian economy was battling significant demand deficiency and consequent growth deceleration before Covid-19 derailed the growth process completely (Reference Dev and SenguptaDev and Sengupta, 2020).