The Indian economy was in a state of deceleration well before Covid-19 made its impact in early 2020. This can be inferred from the declining trends of four important macroeconomic variables that indicate the health of the economy in the last quarter of 2019.
According to the data released by the Ministry of Statistics and Programme Implementation (MoSPI), quarterly GDP growth rate, industrial output in eight core sectors, gross tax revenue, and demand for electricity had plummeted by the end of 2019 significantly from its previous trends to be noted as remarkable. Therefore, as far as the Indian economy was concerned, the Covid-19 pandemic and its long containment exacerbated an already dire situation.
The International Labour Organisation (ILO) and the Asian Development Bank (ADB), in their report no tackling the youth employment crisis in Asia and the Pacific’ released in 2020, estimate India’s youth unemployment at 32.5 percent with the loss of 6.1 million full time jobs mainly in agriculture, construction, and retail sectors as a result of the Covid-19 pandemic.
Although the lifting of the lockdown and the beginning of economic activity is important, revival of the health of the economy would require concerted policy action across many sectors with a long-term vision that anticipates economic opportunities and risks.
Impact of automation
The fourth industrial revolution is the broader context in which the problem is situated and policy solutions sought. There are three main waves that fourth industrial revolution brings for lower middle-income countries such as India. The first is automation and its associated job polarization that impacts both wage levels and the structure of employment.In particular, automation in India has resulted in the change in labour composition as well as decline in labour productivity and labour share in income in medium-high technology manufacturing. Job polarisation is one of the impacts by which middle-skill jobs that require routine cognitive and manual applications are automated while high and low-skill occupations are preserved.
As job polarization co-exists with the excessive supply of secondary and tertiary educated labour force in India, educated middle-skill workers from middle-skill jobs have been pushed into relatively low-skill manufacturing and service occupations.
Technology-related automation also makes traditional manufacturing vulnerable to shocks. In India, the transition of agricultural labourers often from rural and peri-urban areas to low-skill manufacturing sectors such as construction and textiles in urban areas signals distress in traditional manufacturing sector to provide employment to these groups.
Therefore, the Indian unemployment problem in manufacturing and service sector from the skill-set perspective reveals that low-skill and middle-skill workers remain precarious and underemployed.
The shift of manufacturing to relatively a small number of countries have also led to the concentration of manufacturing activities globally. The sluggish growth of manufacturing in middle-income countries has been partly as a result of declining demand due to low growth rates in high-income countries.
The rise of automation has also led to reshoring of parts of production back to high-income countries, depriving the middle-income countries of productivity and employment. Therefore, middle-income countries have increasingly examined development strategies through other means, that include turning to service sector, encouraging entrepreneurship in small and medium sectors and bundling services with manufacturing.
Demand management has been identified as an important factor in conceiving industrial policy. In this context, the advent of big data analytics opens up new market segments and introduces domestic market expansion as a strategy of economic development for middle-income countries poised with suitable human resources such as India.
The arrival of big data and the progressive digitalization of technology through internet-of-things (IoT), artificial intelligence (AI), and machine learning (ML) has resulted in two types of demand-led impacts in middle-income countries.
First, big data opens up new market segments in various sectors by creating heterogenous demand for differential varieties of existing product and services. Second, big data also opens up a new market for data analytics that permits the information technology industry to upgrade technology capability and diversify its product portfolio.
In the Indian data analytics industry, the presence of multi-product firms, an expanding domestic market, and presence of mature technology encourages demand-led product differentiation and competitive market.
Another promising trajectory due to improved design capabilities is the upgrading of low-cost innovation projects. Frugal innovation is a type of design innovation approach in which low and middle-income economies provide a market to develop appropriate, adaptable, affordable, and accessible services and products. The focus on core functionality, performance optimization, and cost minimization differentiates frugality from a traditional mindset of innovation.
Compared to traditional innovation, frugal innovations have low technical intensity (relative volume of research and development expenditure) as well as technological complexity (number of internal components), but an inclusive impact on low-income or cost-conscious communities.
An increment in usability, quality, or price-differentiation of an original frugal innovation results in second-degree frugal innovation called reverse innovation. Reverse innovations are disruptive as new entrants into established markets. With an additional investment in technology and managerial competency, frugal innovations could be introduced among cost-conscious customers even in high-income economies.
The first step is digital and research skilling of the tertiary educated workforce through expansive public and private investment in training. This approach involves large-scale investment by the public sector and the private firms. India has so far demonstrated a poor record in the investment on skill training provided by the private sector compared to competitors such as Vietnam or the Philippines.
The second step is to establish institutional linkages across universities, public and private research centers to encourage marginal innovation by developing new products, processes, and business models. This approach involves re-imagining the role of the state as innovation facilitator creating institutional channels that connect formal and informal sector as well as domestic and international players.
The third step is to focus on data governance issues such as localization as part of industrial and innovation policy. The inclusion of data in industrial policy involves serious and sustained conversation between various stakeholders to ensure equity and parity in participation and distribution of resources. In the era of digital platforms and algorithmic management, data governance has to be trodden with transparency and due consultation to make industrial policy work for small entrepreneurs, workers, and consumers as much as the big capital holders.
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*Teaches at Kumaraguru College of Liberal Arts and Science, Bharathiar University, Coimbatore, Tamil Nadu; is advisor at Cambridge Development Initiative, United Kingdom. This article is based on Prof Kuriakose’s intervention at a webinar jointly organized by Impact and Policy Research Institute (IMRPI) and Counterview on Industrial Policy for Innovation and Employment Creation: Challenges and Way Forward towards Make in India & #AtmaNirbharBharat
Therefore, the Indian unemployment problem in manufacturing and service sector from the skill-set perspective reveals that low-skill and middle-skill workers remain precarious and underemployed.
Impact of big data
The second wave of technology is that of big data and the opening of new middle-market segments of consumer driven service sectors such as banking, finance, insurance, retail, healthcare and data analytics. Export-led industrialization as a strategy of economic development for middle-income countries is increasingly being questioned because of the decreasing levels of value added and employment growth in the manufacturing sector. |
| Youth job losses and unemployment rate, estimates, 2020 |
The rise of automation has also led to reshoring of parts of production back to high-income countries, depriving the middle-income countries of productivity and employment. Therefore, middle-income countries have increasingly examined development strategies through other means, that include turning to service sector, encouraging entrepreneurship in small and medium sectors and bundling services with manufacturing.
Demand management has been identified as an important factor in conceiving industrial policy. In this context, the advent of big data analytics opens up new market segments and introduces domestic market expansion as a strategy of economic development for middle-income countries poised with suitable human resources such as India.
The arrival of big data and the progressive digitalization of technology through internet-of-things (IoT), artificial intelligence (AI), and machine learning (ML) has resulted in two types of demand-led impacts in middle-income countries.
First, big data opens up new market segments in various sectors by creating heterogenous demand for differential varieties of existing product and services. Second, big data also opens up a new market for data analytics that permits the information technology industry to upgrade technology capability and diversify its product portfolio.
In the Indian data analytics industry, the presence of multi-product firms, an expanding domestic market, and presence of mature technology encourages demand-led product differentiation and competitive market.
Innovation capabilities
The third and the final wave is machine learning and internet-of-things capabilities. The beginning of this wave is already altering innovation spaces, research and development in medium to high-technology manufacturing in India, making them more competitive and export-oriented.Another promising trajectory due to improved design capabilities is the upgrading of low-cost innovation projects. Frugal innovation is a type of design innovation approach in which low and middle-income economies provide a market to develop appropriate, adaptable, affordable, and accessible services and products. The focus on core functionality, performance optimization, and cost minimization differentiates frugality from a traditional mindset of innovation.
Compared to traditional innovation, frugal innovations have low technical intensity (relative volume of research and development expenditure) as well as technological complexity (number of internal components), but an inclusive impact on low-income or cost-conscious communities.
An increment in usability, quality, or price-differentiation of an original frugal innovation results in second-degree frugal innovation called reverse innovation. Reverse innovations are disruptive as new entrants into established markets. With an additional investment in technology and managerial competency, frugal innovations could be introduced among cost-conscious customers even in high-income economies.
New industrial policy agenda
It is clear from the detailed understanding of the context that India needs to invest in three broad areas if the objective is to use the fourth industrial revolution to encourage human-centered economic growth.The first step is digital and research skilling of the tertiary educated workforce through expansive public and private investment in training. This approach involves large-scale investment by the public sector and the private firms. India has so far demonstrated a poor record in the investment on skill training provided by the private sector compared to competitors such as Vietnam or the Philippines.
The second step is to establish institutional linkages across universities, public and private research centers to encourage marginal innovation by developing new products, processes, and business models. This approach involves re-imagining the role of the state as innovation facilitator creating institutional channels that connect formal and informal sector as well as domestic and international players.
The third step is to focus on data governance issues such as localization as part of industrial and innovation policy. The inclusion of data in industrial policy involves serious and sustained conversation between various stakeholders to ensure equity and parity in participation and distribution of resources. In the era of digital platforms and algorithmic management, data governance has to be trodden with transparency and due consultation to make industrial policy work for small entrepreneurs, workers, and consumers as much as the big capital holders.
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*Teaches at Kumaraguru College of Liberal Arts and Science, Bharathiar University, Coimbatore, Tamil Nadu; is advisor at Cambridge Development Initiative, United Kingdom. This article is based on Prof Kuriakose’s intervention at a webinar jointly organized by Impact and Policy Research Institute (IMRPI) and Counterview on Industrial Policy for Innovation and Employment Creation: Challenges and Way Forward towards Make in India & #AtmaNirbharBharat
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