Nexus of Total Factor Productivity, IT & Skills: A Literature Review.
| Author | Singh, Awadhesh Pratap |
| Position | Information technology |
Introduction
Mr. Modi, the Prime Minister of India quoted jokingly describing the power of Information Technology, "status is now not whether you are awake or asleep, it is whether you are online or offline". Taking the analogy from Mr. Modi, Information Technology in the context of India has truly evolved. It is an important pillar that established India on global map. Over the years, India has made its own unique place in the global arena of IT and ITeS sectors due to its large pool of skilled resources and state of the art IT infrastructure. It is needless to say that IT has tremendous potential to transform organizations, economies and regions. This is why by acknowledging this fact and the way India transformed itself with the help of IT service sector, several developing countries have started investing into IT infrastructure, skill building, IT adoption and framing IT friendly policies to foster their own turf positively. Indian IT sector played an unusual role to shape the country's economy. According to NASSCOM, the sector aggregated revenues of US$160 billion in the year of 2017. The export revenue share stands at 65% and domestic revenue is at 30%. The advent of startups culture in India is a major driving force in IT expenditure which is expected to boost the domestic revenue of IT services to 13% year on year. While the expenditure of IT has been growing steadfast, it is important to examine the role of IT in the domestic industrial productivity.
In 2012, Government of India unveiled a new IT policy to focus on application of technology-enabled approaches to promote growth in education, health, skill development, financial inclusion, employment generation and governance. The policy outlined two key goals: first-materialize the full potential of ICT by making it accessible within the reach of whole country and second, leveraging the capability and human capital to make India as the global hub for IT and ITES Services by 2020. Therefore, the goal of the IT policy is to deploy ICT in all segments of economy and provide IT solutions to the world. The policy targets to achieve these objectives by enabling the collaboration between central and state administrations and adopting several key programs like Make in India, Startup India, Standup India and Ease of doing business (National Information Technology Policy, 2012).
Given that India has already established itself as a leading global player in IT exports and in the light of new IT policy, it is important to carefully examine the role of IT and skills to promote productivity. To achieve this objective, we undertake a literature review due to two reasons: first, literature review is relatively easier to undertake, and second, findings are relatively simple to interpret. Although, there is a major drawback that it may not yield the right outcome if sample (number of papers) is not large enough. To overcome this shortcoming, this paper considered two approaches. First, key papers are taken into consideration. Second, it took a large enough sample (35 papers) to avoid selection biases. The study is divided into four sections. First section classifies the literature into various categories such as types of productivity estimation technique employed, types of data used and based on the major findings observed. Second section carries out a deep dive of the papers chosen and draw findings. Third section brings in Indian context and fourth section concludes the review.
Classifying the Studies
Table 1 exhibits some of the important productivity studies in the context of linkages among IT expenditure, total productivity and skills after 2000. The table summarizes the studies using 8 key factors, namely, research with year of publication, period of data considered, countries considered, scope in terms of data i.e. industry, country or plant, sector if applicable, econometric techniques employed, variables and their relationship explained, results in terms of relationship among IT, productivity and skills that exists and major findings.
In terms productivity estimation techniques that are employed to estimate TFP, the studies can be grouped into 3 major categories. First, quantitative studies that employ the traditional estimation techniques such as Growth Accounting Model (GAM), Coubb-Douglas Production Model (CDPM), Dynamic General Equilibrium Model (DGEM) or Stochastic Frontier (SF). Second, quantitative studies that employ the advanced productivity estimation techniques such as Levinsohn and Petrin (LP in short). Third, qualitative researches that employed surveys or literature reviews.
Fair amount of research has been carried out under the first category that takes traditional productivity estimation techniques into account. Authors such as Schreyer (2000), Gordon (2000), Pohjola (2001), Kraemer et al. (2001), Lee et al. (2003), Qiang (2004), Gretton et al. (2004), Maliranta et al. (2004), Hempell, et al. (2004), Doms et al. (2004), Spyros (2004), Black et al. (2004), Jorgenson (2005), Indjikianet et al. (2005), Hempell (2005), Brynjolfsson et al. (2006), Joseph et al. (2007), Han et al. (2011), Chang et al. (2012), Bloom et al. (2012), Dedrick et al. (2013), Shahiduzzaman et al. (2015), Wamboye et al. (2016), Liao et al. (2016), Chung (2018) and Edquist et al. (2017) supported this. Handful of studies employed the advanced estimation techniques to unveil the linkages of IT, skills and productivity. A few key ones are: Liu at el. (2011) and Sharma et al. (2013) wherein both the authors employed LP technique. A few studies employed qualitative techniques such as survey and literature reviews and are categorized under third group. The key ones include: Kenny (2002), Baldwin et al. (2002), Holt et al. (2009), Chandra (2009) and Tisdell (2017).
In terms of data employed, the studies can be classified into three broad categories. First, those with focus on plant level or unit level data. Second, studies which employed industry level data. Third, those which were carried out with country level or regional level data.
Many studies were carried out with plant level data. A few key ones are included in Table 1, e.g. Baldwin et al. (2002), Chandra et al. (2002), Gretton et al. (2004), Maliranta et al. (2004), Hempell et al. (2004), Doms et al. (2004), Spyros (2004), Black et al. (2004), Hempell (2005), Brynjolfsson et al. (2006), Chandra (2009), Chang et al. (2012), Bloom et al. (2012) and Sharma et al. (2013).
Similar to plant level data, good amount of research was done by taking industry level data to unveil the linkages among IT, skills and productivity. Key ones that are included in this literature review are: Schreyer (2000), Gordon (2000), Lee et al. (2003), Jorgenson (2005), Joseph et al. (2007), Han et al. (2011), Dedrick et al. (2013), Shahiduzzaman et al. (2015), Wamboye et al. (2016), Liao et al. (2016), Tisdell (2017), Chung (2018) and Edquist et al. (2017). A handful of studies that were focused on country or regional level data are also included; Pohjola (2001), Kraemer et al. (2001) and Qiang et al. (2004).
By looking at the outcome, the studies can be broadly divided into 3 categories: first, those who accepted the relationship among skills, IT and productivity. Second, those who either rejected this relationship or were inconclusive. Third, those who accepted the relationship with conditions. The first category is represented by a large number of authors: Schreyer (2000), Gordon (2000), Kenny (2002), Baldwin et al. (2002), Chandra et al. (2002), Qiang et al. (2004), Gretton et al. (2004), Maliranta et al. (2004), Hempell et al. (2004), Doms et al. (2004), Arvanitis (2003), Black et al. (2004), Jorgenson (2005), Hempell (2005), Brynjolfsson et al. (2006), Joseph et al. (2007), Hold et al. (2011), Chang et al. (2012), Sharma et al. (2013), Shahiduzzaman et al. (2015), Wamboye et al. (2016), Chung (2018), Edquist et al. (2017).
There are a handful of studies that fall into second category with either inconclusive evidences or refute the claim that IT and skills promote productivity. Kraemer et al. (2001), Lee et al. (2003), Holt et al. (2009), Chandra (2009) and Dedrick et al. (2013) are a few of those that come in the second category. There are a handful studies that accepted the nexus of IT, skills and productivity with a condition such as: Pohjola (2001), Indjikian et al. (2005), Bloom et al. (2012), Liao et al. (2016) and Tisdell (2017).
IT, Productivity & Skills: A Deep Dive
Based on the widely used estimation technique of productivity literature-growth accounting (GA) model, Schreyer (2000) acknowledged the importance of IT capital goods on economic growth for G7 countries. He analyzed that there is little evidence that they are inherently...
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