Journal articles: 'Productivity- India'

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Tripathi, Amarnath, and A. R. Prasad. "Agricultural Productivity Growth in India." Journal of Global Economy 4, no. 4 (December 31, 2008): 322–28. http://dx.doi.org/10.1956/jge.v4i4.113.

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The case of Indian agricultural performance was impressive. The food production and increases in productivity are essential for meeting the growing demands for food in the future. There is widespread opinion that this growing demand can be met by increased use of inputs or increases in agricultural productivity. Productivity growth of agriculture in India over the past four decades was the result of a combination of factors such as new incentives to farmers offered by the government who considered them as autonomous economic agents, and physical factors such as land, labour, capital (in the form of machines, working animals, irrigation system, and so on), and intermediate inputs such as fertilizer. Indian agricultural growth has been less dependent on the conventional inputs of capital. Capital was computed as the sum of the value of agricultural machinery, farm equipment and tools, transport equipment in farm business, land improvements, investments in private and public irrigation, and farm houses in Indian agriculture. As the growth of agriculture increases the importance of conventional inputs of capital becomes lesser in comparison to modern inputs of capital. Since mid 1960s, a package of modern inputs of capital such as high yield variety seeds, chemical fertilizers, tractor etc. has been continuously used with increasing trend in Indian agriculture. This was main cause of the remarkable growth in output of agriculture during 1970s and 1980s decades. This paper is aimed at analyzing the impact of some production variables (input) on agricultural productivity growth (output) in Indian agriculture from 1969-70 to 2005-06. The question here is whether or not these different variables have an impact on agricultural production.

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Walsh, Barry. "Judicial Productivity in India." International Journal for Court Administration 1, no. 1 (January 15, 2008): 23. http://dx.doi.org/10.18352/ijca.123.

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Bhattacharya, Poulomi, and Badri Narayan Rath. "Innovation and Firm-level Labour Productivity: A Comparison of Chinese and Indian Manufacturing Based on Enterprise Surveys." Science, Technology and Society 25, no. 3 (May 22, 2020): 465–81. http://dx.doi.org/10.1177/0971721820912902.

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This article examines the impact of innovation on labour productivity by using latest World Bank Enterprise Surveys data and compares the results between Chinese and Indian manufacturing sector. The article uses cross-section data based on two surveys that were conducted by the World Bank in 2012 and 2014 for China and India, respectively. By employing simple ordinary least squares (OLS) regression technique, we find that innovation affects the labour productivity positively for Chinese as well as Indian manufacturing firms, but its impact on firm productivity is relatively weak in case of India as compared to China. Second, other factors such as average wage of the workers, education of production workers and training do significantly boost the labour productivity of Chinese manufacturing firms as well as for Indian firms. Third, our results based on firm size also indicate that the impact of innovation activities on labour productivity is higher in case of large firms as compared to medium firms. However, innovation does not affect the labour productivity of small manufacturing firms for both China and India. In terms of policy, it is important for both Chinese and Indian manufacturing firms to keep pursuing innovation activities, in order to spur productivity, which would strengthen firms’ growth.

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N, Ganeshmurthy A., Rupa T. R, and Shivananda T. N. "Enhancing Mango Productivity through Sustainable Resource Management." Journal of Horticultural Sciences 13, no. 1 (June 30, 2018): 1–31. http://dx.doi.org/10.24154/jhs.2018.v13i01.002.

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Mango the “King of fruits” is the most important fruit crop in India and the area under mango is the largest among fruit crops (2,516 thousand ha) with a production of around 19.69 million tonnes. India ranks first in mango production in the world contributing 41% of the total world production of mango. Among Indian states, with a total output of 4.3 million MT, Uttar Pradesh stands first as mango producing state. (Fig.1).This is followed by Telangana (2.73 million MT), Karnataka (1.75 million MT) and Bihar (1.36 million MT). Particularly in India all stages of mango fruit are used starting from immature to over ripe stages. Mango has specific problem of alternate bearing leading many times to low yields or no yield.

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N, Ganeshmurthy A., Rupa T. R, and Shivananda T. N. "Enhancing Mango Productivity through Sustainable Resource Management." Journal of Horticultural Sciences 13, no. 1 (June 30, 2018): 1–31. http://dx.doi.org/10.24154/jhs.v13i1.18.

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Mango the “King of fruits” is the most important fruit crop in India and the area under mango is the largest among fruit crops (2,516 thousand ha) with a production of around 19.69 million tonnes. India ranks first in mango production in the world contributing 41% of the total world production of mango. Among Indian states, with a total output of 4.3 million MT, Uttar Pradesh stands first as mango producing state. (Fig.1).This is followed by Telangana (2.73 million MT), Karnataka (1.75 million MT) and Bihar (1.36 million MT). Particularly in India all stages of mango fruit are used starting from immature to over ripe stages. Mango has specific problem of alternate bearing leading many times to low yields or no yield.

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Ambarkhane, Dilip, Ardhendu Shekhar Singh, and Bhama Venkataramani. "Measuring total factor productivity change of microfinance institutions in India using Malmquist productivity index." Indian Growth and Development Review 12, no. 1 (April 8, 2018): 105–30. http://dx.doi.org/10.1108/igdr-12-2017-0105.

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PurposeMicrofinance institutions (MFIs) provide small loans and other financial services to the poor. These institutions are established for helping the poor to raise income levels and to reduce poverty. Recently, MFIs are required to reduce their dependence on grants and subsidies. Consequently, they face conflicting objectives of improving reach and profitability. These can be achieved by improving productivity. This paper aims to investigate productivity change in 21 major MFIs in India which are rated by Credit Rating and Information Services of India Limited in 2014.Design/methodology/approachThis paper attempts to examine total factor productivity change in 21 major Indian MFIs during the period from 2014 to 2016 using Malmquist productivity index. The inputs and outputs are selected considering objectives of outreach and financial sustainability. The authors have categorized MFIs in three categories, namely, large, medium and small, depending on asset size.FindingsIt is revealed that large MFIs are able to catch up with industry best practices by improving their systems and processes, but they need to improve scale efficiency. The Reserve Bank of India has recently initiated a policy of granting banking licenses to those financial institutions which have good outreach and are financially strong. It can be used for shortlisting MFIs before granting permission to operate as banks. The method can also be used for benchmarking them for productivity. It can also be replicated in other countries.Originality/valueIn India, MFIs are playing important role in economic development by providing microcredit to the poor. However, very few studies have been undertaken regarding productivity of MFIs in India. The present study intends to fill this gap. It will facilitate benchmarking of MFIs as competitive and sustainable financial institutions catering to the requirements of small borrowers.

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Bagchi, Prantik, and Santosh Kumar Sahu. "Energy Intensity, Productivity and Pollution Loads: Empirical Evidence from Manufacturing Sector of India." Studies in Microeconomics 8, no. 2 (July 16, 2020): 194–211. http://dx.doi.org/10.1177/2321022220930968.

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We explain the relationship between energy intensity and productivity for the organized manufacturing sector of India. Using data from the secondary sources, we explain the relationships at aggregate, state and industry levels. The novelty of this paper lies in bringing in pollution loads in explaining inter-industry variations in energy intensity. Results of this study indicate that the organized manufacturing sector of India has gained energy efficiency and productivity. We found heterogeneity among Indian states in productivity growth and energy intensity. The results indicate that small states performed well whereas large states fall in the productivity paradox. The productivity dilemma hypothesis is validated at industry level analysis however, results are inconsistent to validate the decoupling growth hypothesis. Pollution loads as classified by Government of India, plays a vital role in explaining energy intensity variations across industries, which calls for better policies aiming at pollutive industries specifically to achieve sustainable growth for the manufacturing sector of the Indian economy.

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BISHNOI, OP. "Solar radiation and productivity in India- I: Potential productivity." MAUSAM 37, no. 4 (April 7, 2022): 501–6. http://dx.doi.org/10.54302/mausam.v37i4.2577.

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The efficiency with which the plants store solar energy was determined and expressed as the product of seven factors describing the dependence of dry matter production on latitude and season, cloudiness and aerosol contents of the atmosphere, on the spectral composition of the radiation, on the quantum. need of the photochemical process, on leaf area index and leaf arrangement, on the concentration of carbon dioxide In the canopy and diffusion resistance of leaves, and on the fraction c f the assimilates used in respiration. The potential and net dry matter productivity of different crops grown between 8 and.30° latitudes in India shows that the efficiencies of radiation interception, diffusion and respiration .provide the environment constraints. These efficiencies are also influenced by the canopy architecture of crops in terms of the distribution of foliage densities and inclination index of foliage densities.

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MONTEIRO, Jeronimo Guilherme Remigio. "Measuring Productivity and Efficiency of Seaports in India using DEA technique." Central European Review of Economics and Management 2, no. 3 (September 27, 2018): 153. http://dx.doi.org/10.29015/cerem.529.

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Aim: In this paper we are looking at the seaports (in India called ‘major ports’) from the context of its trade and India’s strategic importance in trade world after the initiation of economic reforms in 1991. It empirically estimates the levels of productivity and efficiency of seaports in India. This paper applies DEA technique to assess productivity and efficiency of seaports in India. Design/Research methods: DEA technique is extensively used in the literature of economics to provide measures of firms’ technical efficiency. These measures rank the firms by looking at their apparent performances over a period of time. DEA is a frontier model which is non-parametric since no functional specification or form is required to be mentioned. Conclusions/Findings: The DEA results as discussed and reported in the paper have shown how Indian ports are performing over the years. This investigation alone is not sufficient to develop a benchmark in the port system of India. Rather it will do well to have a closer look at the Indian ports from the physical and financial performance point of view. This study made use of data envelopment analysis (DEA) to generate what we call an efficiency benchmarks and assessment of the Indian ports sector. With this modest attempt to investigate the port sector of India several issues are in the open one can further analyze and come to desired conclusions. Originality/value of the paper: The main role of a port is to transfer goods between two transport modes. As far as Indian ports are concerned, there are few studies with regard to productivity and efficiency of the port sector. Since, there is an attempt in recent years to overhaul the infrastructure sectors of the Indian economy and especially seaports. There is a need to look at issues in port sector as well. Productivity and efficiency concerns should be the main aspect of the benchmarking of the performance of today’s Indian ports. Limitations of the research: Second stage DEA, distance function approach, Bayesian techniques, Carlo Monte techniques, can be alternatively used.

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Banker, Khyati K., Danny Liew, Zanfina Ademi, Alice J. Owen, Afsana Afroz, Dianna J. Magliano, and Ella Zomer. "The Impact of Diabetes on Productivity in India." Diabetes Care 44, no. 12 (October 21, 2021): 2714–22. http://dx.doi.org/10.2337/dc21-0922.

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OBJECTIVE Diabetes increases the risk of premature mortality and considerably impacts on work productivity. We sought to examine the impact of diabetes in India, in terms of excess premature mortality, years of life lost (YLL), productivity-adjusted life years (PALYs) lost, and its associated economic impact. RESEARCH DESIGN AND METHODS A life table model was constructed to examine the productivity of the Indian working-age population currently aged 20–59 years with diabetes, followed until death or retirement age (60 years). The same cohort was resimulated, hypothetically assuming that they did not have diabetes. The total difference between the two cohorts, in terms of excess deaths, YLL and PALYs lost reflected the impact of diabetes. Data regarding the prevalence of diabetes, mortality, labor force dropouts, and productivity loss attributable to diabetes were derived from published sources. RESULTS In 2017, an estimated 54.4 million (7.6%) people of working-age in India had diabetes. With simulated follow-up until death or retirement age, diabetes was predicted to cause 8.5 million excess deaths (62.7% of all deaths), 42.7 million YLL (7.4% of total estimated years of life lived), and 89.0 million PALYs lost (23.3% of total estimated PALYs), equating to an estimated Indian rupee 176.6 trillion (U.S. dollars 2.6 trillion; purchasing power parity 9.8 trillion) in lost gross domestic product. CONCLUSIONS Our study demonstrates the impact of diabetes on productivity loss and highlights the importance of health strategies aimed at the prevention of diabetes.

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Kar, Saibal, and Meghna Dutta. "Outsourcing and Productivity During Economic Crisis: Evidence from Indian Manufacturing Firms." Arthaniti: Journal of Economic Theory and Practice 17, no. 2 (September 10, 2018): 168–82. http://dx.doi.org/10.1177/0976747918792638.

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Outsourcing as a form of production reorganisation has important implications for factor productivity. Empirical verification of this relationship for India, however, is unavailable in the recent times. To fill this gap, we measure the relationship between outsourcing and multifactor productivity for Indian firms between 2010 and 2014. We use fixed-effect panel data regression and GMM estimates to establish that outsourcing raises productivity significantly at the source. The outsourcing–productivity link has important policy implications for developing countries, such as India. Relative inflexibility of labour market institutions and slow-moving legal procedures may otherwise restrict the restructuring of firms under duress. Outsourcing-related productivity improvements might have helped to overcome such disadvantages even during the global crisis of 2008–2009. JEL: D24, L6

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12

Das, Deb Kusum, Suresh Chand Aggarwal, Abdul Azeez Erumban, and Pilu Chandra Das. "What is new about India’s economic growth? An industry level productivity perspective." Indian Growth and Development Review 13, no. 1 (June 6, 2019): 11–36. http://dx.doi.org/10.1108/igdr-05-2018-0054.

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Purpose The dynamics of economic growth in India continues to engage economists and still remains much debated. The trends and patterns of growth observed in India have seen acceleration in growth in Indian economy in the period following macroeconomic reforms and policy changes in investment and trade regimes. However, when and how did India transform itself from Hindu rate of growth to the present growth regime continues to be debated. Design/methodology/approach Using INDIA KLEMS data set, this study provides a distinctive perspective on India’s economic growth. A unique data set comprising 27 sectors of Indian economy at a disaggregate industry level for a period of 30 years, beginning 1980s, attempts to understand the dynamics of India’s growth from the contribution of industries that comprise the Indian economy. Findings This productivity data set offers a new way of analyzing the dynamics of growth including the sources of growth. The growth empirics allow evaluation of the relative significance of total factor productivity growth vis-a-vis input accumulation in accounting for output growth. In addition, the authors were able to document the industry contributions to aggregate growth. In this way, they were able to analyze the importance of the constituent industries within the different sectors of the economy − agriculture, manufacturing, construction and market, as well as non-market services in accounting for the observed growth in India. In conclusion, the industry perspective offers a new and analytical way of discerning new aspects of India’s march to higher growth regimes in post-1990s era. Originality/value A unique data set comprising 27 sectors of Indian economy at a disaggregate industry level for a period of 30 years, beginning 1980s, attempts to understand the dynamics of India’s growth from the contribution of industries that comprise the Indian economy.

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Singh, Veer Virendra. "Agricultural Productivity in India since 1951 : Plan-wise Analysis." Contemporary Social Sciences 27, no. 2 (April 1, 2018): 181–87. http://dx.doi.org/10.29070/27/57480.

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Ramakrishna and Chaya Degaonkar. "RICE EXPORT FROM INDIA: TRENDS, PROBLEMS AND PROSPECTS." International Journal of Research -GRANTHAALAYAH 4, no. 7 (July 31, 2016): 122–36. http://dx.doi.org/10.29121/granthaalayah.v4.i7.2016.2604.

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In India, agriculture is the main occupation. About 58 per cent of the population depends upon agriculture. It is the backbone of the Indian economy. In agriculture, rice is one of the most important food crops of India. It feeds more than 50 per cent of the world’s population. Rice is continues to play vital role in the national exports. India is one of the important countries in the world in export of rice. Indian rice exports are reached first place in the world markets. This study has analysed the trends and variability of rice export, assess the prospects of rice export and the various problems/constraints of rice export from India to various countries in the world. The study based on secondary data. The time series data on export of rice from India for the period 2001-02 to 2013-14.This paper indicates that there is good market for some Indian rice varieties, especially basmati rice in the world market. Pakistani basmati, as a sole competitor of India, does not have the unique aroma and taste as the Indian varieties. During 2012-13 the foreign earning from basmati rice has increased considerably. Despite considerable area being under rice cultivation in the country, the share of India in world rice export is very low which can be attributed to the fact that the average productivity of rice in the country is low and even in some rice producing states, the productivity is 40 to 50 per cent lower than the national average, in spite of having good potential capacity. The reasons of low productivity of rice in the country may be due to use of less quantity of chemical fertilizers, pesticides, more area under traditional varieties and more dependence on rain. If we want to increase our exports share in the world rice market, the production growth rate should be adequate surplus in the country. This requires careful analysis of low productivity of rice in the country. Besides, more important, the critical analysis is necessary to enquire into the constraints involved in rice export from India.

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Sen, Samudra. "Indian Judiciary Imprisoned: An Integrated AHP–TOPSIS Approach to Judicial Productivity." Global Business Review 21, no. 2 (May 1, 2018): 586–603. http://dx.doi.org/10.1177/0972150918765319.

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Many countries around the world have a large number of cases pending in their courts for long years. Research has been conducted on the productivity of courts and judicial systems around the world. Different methodologies have been employed in evaluating the productivity of judicial systems. Indian courts are also in a sorry state due to huge backlog of cases pending in courts. However, for India, though there are many papers on the state of judicial system suggesting ways to clear this backlog, there is hardly any research on courts’ productivity. This article addresses this research gap using an integrated analytic hierarchy process (AHP) and technique of order of preference by similarity to ideal solution (TOPSIS) approach in evaluating court productivity in India. Data pertaining to the Supreme Court and the High Courts in India have been evaluated to rank the courts in order of their productivity. Performance of the courts has also been evaluated on the basis of a few established judicial indices. It is revealed that judges’ productivity is directly proportional to the caseload per judge but the same may not be true for court productivity. Further, poor judicial administration also contributes to piling of cases as backlogs.

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Vijayakumar, A., and M. Krishnaveni. "Total Factor Productivity in India: Evidence from Indian Paints Industries." Management and Labour Studies 30, no. 2 (May 2005): 150–61. http://dx.doi.org/10.1177/0258042x0503000203.

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Goldar, Bishwanath, and Yashobanta Parida. "Intangible Capital and Firm Productivity." South Asia Economic Journal 18, no. 2 (September 2017): 246–75. http://dx.doi.org/10.1177/1391561417732198.

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An estimate of intangible capital stock is made for a sample of about 3,200 Indian corporate firms for 2012–2013, based on investments made by the firms in various intangible assets during the previous 10 years. For manufacturing and services firms of the sample, three alternate specifications of a production function are estimated in which intangible capital is taken as an input. This analysis clearly reveals that intangible capital has a significant positive impact on productivity of manufacturing and services firms in India. The rate of return to intangible capital is found to be much higher than that to tangible capital.

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RANA, RAJESH K., and MD EJAZ ANWER. "Potato production scenario and analysis of its total factor productivity in India." Indian Journal of Agricultural Sciences 88, no. 9 (September 25, 2018): 1354–61. http://dx.doi.org/10.56093/ijas.v88i9.83339.

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India is second largest producer of potatoes in the world after China. India showed tremendous growth in potato production during last one and half decade, however, this growth is led more by the area expansion than the yield enhancement. For further analysis on nature of productivity growth in Indian potato sector the computation of Total Factor Productivity (TFP) was done with the help of Malmquist Productivity Index (MPI). Year 2005 being the inflection point in the growth in Indian agriculture was used as period break year for this study and two periods, viz. pre-period (1997 to 2004) and post period (2005 to 2013) were considered for all analysis and descriptions. Bihar, West Bengal and Uttar Pradesh states constitute about 74% of Indian potato production hence, these states were assumed to represent Indian potato scenario. Except mild decline in potato productivity growth in Uttar Pradesh, area, production and productivity growth of potato showed acceleration in post-period compared to the pre-period in all the states. TFP improved in all the three states in post period however, in West Bengal the growth was negative (-2.3) even in the post period. Except Bihar where efficiency change was positive (1%) in pre-period, and further improved in post-period (2.1%), the efficiency change stagnated in all other cases. The TFP improvement in all the cases was either solely or mainly led by the technical change.

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Fujita, Natsuki. "LIBERALIZATION POLICIES AND PRODUCTIVITY IN INDIA." Developing Economies 32, no. 4 (December 1994): 509–24. http://dx.doi.org/10.1111/j.1746-1049.1994.tb01053.x.

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Bathla, Seema, and Alwin D’souza. "Inter-sectoral Productivity Differentials in India." South Asia Economic Journal 16, no. 1 (March 2015): 82–102. http://dx.doi.org/10.1177/1391561415575129.

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De, Prabal K., and Priya Nagaraj. "Productivity and firm size in India." Small Business Economics 42, no. 4 (October 5, 2013): 891–907. http://dx.doi.org/10.1007/s11187-013-9504-x.

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Mahajan, N., V. Singh, N. Kaur, and A. Gupta. "Integrating Digital Mission and Sustainable Development Goal-9 for Building Technologies: A study of Navratna Companies of India." IOP Conference Series: Earth and Environmental Science 1084, no. 1 (October 1, 2022): 012010. http://dx.doi.org/10.1088/1755-1315/1084/1/012010.

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Abstract The study focuses on 10 Navratna companies based in India that come under the central public sector enterprises. The study focuses on the Digital Mission by the Government of India, its impact on the industries operating in India and how technologies have affected the overall productivity of Indian enterprise with their approach to achieve SDG-9. The study finds that the Navratna companies have been successfully working towards achieving the goals of SDG-9 for building resilient infrastructure, sustainable industrialisation and fostering innovation but still they need to use the technologies to their maximum potential in terms of improving their overall productivity and delivery. The data for the study has been collected through secondary resources like websites, annual reports, research materials and company websites. The implication of the study is that other companies operating in India and abroad can learn about implementing the sustainable development goal in their day-to-day practices to increase productivity and improve infrastructure, thereby fostering innovation and growth. This will help India reach its goal of becoming a major manufacturing hub and a digitally advanced nation.

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Singh, Gopal, Kuntal Mukherjee, Alok Kumar Singh, and Amar Nath Jha. "Fuzzy Decision Support System to Enhance Productivity in Indian Coal Mining Industry." Journal of Cases on Information Technology 19, no. 1 (January 2017): 50–59. http://dx.doi.org/10.4018/jcit.2017010105.

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Coal powers the Indian economy. Over 70% of the entire power generated in the country is coal based. Coal India produces over 84% of the entire country's coal. As coal mining is a capital intensive industry, it is important to maximize productivity. This paper explores how adoption of “Kayakalp” Model of e- governance in CCL, a subsidiary of Coal India, resulted in complete transformation of the company in terms of production and productivity. The fuzzy based decision support system has investigated this fact.

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Das, Ramesh Chandra. "Sustainability Through Total Factor Productivity Growth in Agriculture Incorporating Institutional Factors." International Journal of Social Ecology and Sustainable Development 14, no. 1 (March 10, 2023): 1–16. http://dx.doi.org/10.4018/ijsesd.319717.

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With the increasing population pressure and food insecurity problem in India, and the problem of unsustainable social and economic systems, it was inevitable to shift away the focus of agricultural activities from the traditional system to the modern one. Having followed a series of agricultural policies in India, the role of irrigation and institutional credits cannot be denied upon the growth of output in the sector. There are some studies on the total factor productivity growth (TFPG) in Indian agriculture, but a few studies cover the role of irrigation and bank credit upon agriculture output. The present study computes TFPG out of the institutional roles through public irrigation facilities and commercial bank credit besides traditional inputs in Indian agricultural sector for the post globalized era of 1991-2019. Using the growth accounting approach, the study finds that the institutional factors such as public irrigation facilities and bank finances have contributed significantly to the growth of the agricultural output in India as the values of TFPG have increased over time.

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KUMAR, RAKESH, P. S. SLATHIA, RAJINDER PESHIN, S. K. GUPTA, S. K. GUPTA, and M. S. NAIN. "Performance analysis of rapeseed-mustard crop under different agro-climatic conditions of Jammu Division of J & K state." Indian Journal of Agricultural Sciences 88, no. 3 (March 16, 2023): 463–68. http://dx.doi.org/10.56093/ijas.v88i3.78676.

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Rapeseed-mustard is the second most important edible oilseed crop in India after groundnut. It has to play a significant role in making India self-reliant in oilseed sector. At present contribution of India to the world acreage and production is 19.29% and 11.12% respectively. Productivity of rapeseed mustard in J & K is 6.98 q/ha which is 37.72% of global productivity and 67.85% of national productivity. Keeping in view low productivity of rapeseed mustard crop in J & K state, a study was undertaken with randomly selected 200 rapeseed mustard growers 100 each from subtropical and temperate zone of Jammu division covering Jammu, Samba, Reasi and Doda districts to ascertain the performance of rapeseed mustard crop under farmer field conditions. Multiple linear regression model was applied to work out the factors affecting productivity of rapeseed mustard crop. The results revealed that proper irrigation, spray of insecticide, proper sowing method, thinning, use of basal dose of urea at sowing time and proper weed management significantly affected the productivity of Indian mustard (Brassica juncea) with R2 value 0.534. The drivers of productivity of gobhi sarson (Brassica napus) were proper time of sowing and irrigation with R2 value 0.253 while use of phosphorus and irrigation at proper time were found to be significantly affecting the productivity of toria (Brassica rapa) with R2 value 0.757. The factors affecting the productivity of hybrid gobhi sarson were proper weed control and knowledge about different production recommendations with R2 value 0.080.

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Tiwari, Ranjit, Harishankar Vidyarthi, and Anand Kumar. "Nexus between Intellectual Capital and Bank Productivity in India." Journal of Risk and Financial Management 16, no. 1 (January 16, 2023): 54. http://dx.doi.org/10.3390/jrfm16010054.

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This paper empirically investigates the influence of intellectual capital on changes in total factor productivity of 36 BSE-listed banks in India from 2005 to 2019. This study employs a two-stage analysis that begins by investigating changes in total factor productivity using the Malmquist Productivity Index estimated through Data Envelopment Analysis, and then computes intellectual capital and its sub-components within the Value Added Intellectual Coefficients model framework. Then, using the System Generalised Method of Moments, we investigate the impact of intellectual capital on changes in total factor productivity. According to our findings, productivity growth is primarily driven by efficiency changes rather than technological changes. Furthermore, regression results show that the intellectual capital index and its two sub-components, human capital and capital employed, have a strong positive impact on bank productivity. This research could help bank senior executives measure their productivity and intellectual capital, identify relevant intellectual capital elements that contribute to productivity and develop future policies to encourage and improve their intellectual potential. Furthermore, this is one of the few studies in the Indian context that examines the nexus between intellectual capital and productivity using the Malmquist Productivity Index.

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Amit Barman and Saikat Mazumder, Supriti Manna, Dabasmita Saha, Subham Roy Chowdhury. "Onion Productivity and Price Change Aspects in India: An Overview." International Journal for Modern Trends in Science and Technology 6, no. 10 (November 24, 2020): 118–22. http://dx.doi.org/10.46501/ijmtst061021.

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AlliumCepathe scientific name of an onion. After potato Onion is the second most essential vegetable in our India. And also onions are very famous in all around the world. In India, the productivity of onion is about 13 lakh tons every month, and in the world, the onion productivity ratio is about 19.40 million. There are about 100 types of onion throughout the world and basically, India produces 9 types of major onion. Onion is very rich in vitamin C (12%)content and vitamin B-6( 5%)content and also rich in minerals like iron(1%) and calcium(2%). In the last 10 years, Maharashtra( 4905.0 thousand tons) is the biggest onion producing state in India. But in 2019 onion prices overlap at Rs 10 kg from Rs 100 kg as demand loss will take place. A huge amount of wastage of onion takes place every year due to bad Monsoon, transport system and the amount of wastage is nearly 25% of the total production. In 10 years a remarkable increase in the production of onion in India and also an increase in the area about 768,000 ha to 1064,000 ha. Onion production has been several benefits like it reduces our cholesterol level, fights against inflammation, decreases triglycerides and it contains a high amount of antioxidants. The impact of onion production on the Indian economy, an increase of onion productivity, price drop, Government policies related to onion production also have been focused on in this review.

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Sato, Takahiro, and Aradhna Aggarwal. "Productivity Dynamics and Rural Industrialization in India." Journal of Interdisciplinary Economics 32, no. 1 (December 3, 2019): 23–46. http://dx.doi.org/10.1177/0260107919875585.

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Since the late 1990s, industrialization in India has been driven by the rural organized manufacturing sector. This paper examines the effects of firms’ dynamics on rural industrialization in India, using plant-level panel data, to investigate the characteristics of rural industrialization in India in recent years. In particular, the paper focuses on productivity differences among continuing, entering, and exiting firms. The results show that both labour and total factor productivity of the organized manufacturing sector in rural areas increased during 2000–2006 and the aggregate productivity growth is supported by the productivity growth of the continuing firms, the entry of productive firms, and the exit of less-productive firms. The paper can conclude that firms’ productivity dynamics contributed to the current rural industrialization in India. JEL: O14, O47, O53

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KALE, SEENAIAH, and BADRI NARAYAN RATH. "DOES INNOVATION ENHANCE PRODUCTIVITY IN CASE OF SELECTED INDIAN MANUFACTURING FIRMS?" Singapore Economic Review 64, no. 05 (November 7, 2018): 1225–50. http://dx.doi.org/10.1142/s0217590818500340.

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This study examines the effects of innovation on productivity of Indian Manufacturing firms. Despite the voluminous literature on this area, the demanding line, i.e., various types of innovation effects on productivity growth, received little attention particularly in the Indian context; hence, our study fills the gap by employing firm-level data from Hyderabad and Bengaluru cities of India from 2011 to 2013. The estimated results confirm the significant impact of innovation on productivity upsurge in Indian manufacturing firms. Further, we investigate the spatial aspects of innovation considering the two cities separately. However, such city-based analysis does not produce any different findings.

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Goswami, Asmita, and K. Narayanan. "Technological Efforts, Firm Ownership and Productivity: A Study of Information Technology Service Firms in India." South Asia Economic Journal 23, no. 1 (March 2022): 86–109. http://dx.doi.org/10.1177/13915614211070793.

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The article presents differences in firm-level total factor productivity in the information technology (IT) service firms in India over the period 2000–2016. It is an attempt to study whether technological efforts contribute to productivity differences in the firms in conjunction with several other firm specific characteristics. Controlling for endogeneity in inputs, the estimation of productivity through semi-parametric techniques indicates considerable heterogeneity in productivity across firm types. Technological efforts of firms have a significant impact on the productivity of IT firms. Both embodied technology imports and in-house R&D contribute to higher productivity. While the Indian firms are observed to be more productive compared to their foreign counterparts, the technological efforts also differ due to ownership factors and that contributes to changes in productivity. JEL: D24, O14, O39

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31

Prakash, Anand, Sanjay Kumar Jha, Kapil Deo Prasad, and Abhishek Kumar Singh. "Productivity, quality and business performance: an empirical study." International Journal of Productivity and Performance Management 66, no. 1 (January 9, 2017): 78–91. http://dx.doi.org/10.1108/ijppm-03-2015-0041.

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Purpose The purpose of this paper is to empirically investigate linkage among productivity, quality, and business performance in home-based brassware units in India. Design/methodology/approach This study involved action research of home-based brassware units applying procedures for three-stage least-squares (3SLS) regression analysis, with data obtained through questionnaire survey based on convenience sampling. Findings This study has supported the established belief that quality leads to productivity, and subsequently productivity leads to better business performance for home-based brassware units in India. The consistent and logical result of this study using 3SLS regression analysis has provided empirical understanding of the appropriate linkage among productivity, quality, and business performance. Research limitations/implications This study has limitations of findings, as it studied the home-based brassware units in the Indian context only. Practical implications This study implies that marketable home-based brassware products are to be produced by taking into account boundaries of production within the framework of goals and value created by motivation and dependability for monitoring the business performance. Identifying an appropriate linkage among productivity, quality, and business performance may project a holistic evaluation of the policy development related to home-based brassware units. Originality/value This is an original study to test empirical linkages among productivity, quality, and business performance using 3SLS regression analysis particularly for home-based brassware units in India.

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Gupta, S. P. "GLOBALISATION-RESTRUCTURING AND INTERNATIONAL COMPETITIVENESS." Paradigm 1, no. 1 (July 1997): 7–12. http://dx.doi.org/10.1177/0971890719970102.

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The paper discusses the challenges of transfonnation in India. The first restructuring required in India is building a world scale domestic market by opening its economy to new products and services. The second task is to build world class competitors by enabling the economy to upgrade itself. For this India should be able to develop, overtime, MNCs of Indian origin. India has to transform itself from a controlled planned economy to a market economy. The country must also allow mergers and acquisitions since none of the Indian firms have world scale operations. For competitiveness, there is an imperative need for improving total factor productivity in India.

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Mangla, Sanjay. "Trade liberalization and firm-level productivity: A panel data analysis of the Indian iron-steel industry." Ekonomski anali 63, no. 219 (2018): 7–32. http://dx.doi.org/10.2298/eka1819007m.

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The iron-steel industry in India contributes about 3% of gross domestic product and provides employment for more than half a million people. However, although steel production in India has increased at a trend growth rate of 7.83% during the post-reform period between 1991-1992 and 2012-2013, this does not necessarily indicate efficient utilization of production factors, as it can also result from a higher level of inputs. Therefore, it is important to record productivity growth and identify its determinants. This study estimates total factor productivity (from firm-level data) in the Indian iron-steel industry and examines the impact of trade liberalization (measured as decline in input tariffs, output tariffs, and effective protection rate) on productivity during the abovementioned period.

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Banga, Rashmi, and Dinesh Kumar. "India‘s Exports of Software Services." Science, Technology and Society 16, no. 3 (November 2011): 285–307. http://dx.doi.org/10.1177/097172181101600303.

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Services sector in India showed a remarkable resilience to global economic crisis and grew at a rate of 9.3 per cent explaining around 88 per cent of the growth rate in real GDP of India in 2008–09. Exportsof software services helped considerably in providing the necessary resilience. The main objective of the paper is to examine the role of external demand and productivity growth in exports of software services. Global income elasticity of demand for export of software services is estimated for the period 1970–2008. Sources of total factor productivity growth in Indian IT services firms are identified using Data Envelopment Analysis for the period 1994–95 to 2007–08. It is found that high income demand elasticity for India–s software services has contributed substantially to its growth. But more importantly, there has been a steep rise in productivity growth in the post-2000 period which is attributable mainly to technological innovation.

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P, SHIVASWAMY G., AVINASH KISHORE, KUHU JOSHI, ANUJA A. R, and K. N. SINGH. "Impact evaluation of seed replacement on pulse productivity in India." Indian Journal of Agricultural Sciences 92, no. 1 (January 31, 2022): 90–94. http://dx.doi.org/10.56093/ijas.v92i1.120846.

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Pulses are traditionally an essential part of the Indian diet and primary protein source for the poorer and the vegetarian population in the country. Pulse productivity has been stagnant in India due to the widespread use of low-quality farm-saved seeds and low seed replacement rates. The present study was carried out during 2019â€“20 to assess the drivers of seed replacement and its ex-post impact on yields of chickpea and pigeonpea in India. The study is based on the data on 1764 chickpea and 944 pigeonpea farmers from the nationally representative Situation Assessment Survey of Agricultural Households conducted during 2013. A probit model was used to study the drivers of seed replacement, and coarsened exact matching technique used to assess the impact on yields causally. We found that access to irrigation and institutional credit can increase seed replacement and result in increased chickpea productivity. Chickpea farmers in districts under the National Food Security Mission on pulses (NFSM-pulses) are more likely to be replacing seeds. In pigeonpea, access to irrigation alone is the key driver. Further, using coarsened exact matching estimation, we found that seed replacement is indeed beneficial for chickpea farmers and would lead to increased chickpea productivity in India.

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Misra, Biswa Swarup. "Determinants of total factor productivity in Indian states." Indian Growth and Development Review 13, no. 1 (October 9, 2019): 259–82. http://dx.doi.org/10.1108/igdr-01-2019-0008.

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Purpose This paper aims to compute total factor productivity (TFP) growth for India as well as for its 19 major states and to explore the determinants of TFP at the state level by considering the spillover effects. Design/methodology/approach TFP growth has been obtained using growth accounting equation. Further, the TFP growth estimates were used to derive TFP levels using the translog index procedure. Given the policy focus on building infrastructure and expanding financial access, we have estimated the impact of irrigation, electricity, road, health, education and financial depth on TFP using the Spatial Durbin Model to account for spillover effects. Findings Computing TFP growth for two sub periods, namely, 2001-2008 and 2009-2015, the study finds a deterioration in TFP growth for India as well as for 10 of the 19 states under study in the post global financial crisis period. The author find that TFP is positively impacted by irrigation, health and road infrastructure. While financial depth and education were statistically insignificant, installed capacity of electricity had a negative impact on state level TFP. Research limitations/implications 'The author provides rationale for the empirical findings considering the country context. The findings of this study act as pointers for shaping higher growth on a sustained basis in India. The study helps to assess the productivity growth in the new states, namely, Jharkhand, Chhattisgarh and Uttarakhand, that were carved out in 2000 vis a vis their parent states. This assessment is useful especially for the states of Jharkhand and Chhattisgarh which were created to address economic backwardness in certain pockets of the parent states. Originality/value First, it provides TFPG estimates for India as well as 19 major states during the 2000-2015 period. Second, this study helps to understand how TFPG for India as well as each of the 19 states have behaved in the post global financial crisis period. Third, the study helps to assess the productivity growth in the three newly created states in 2000 vis a vis their parent states. Fourth, this is the first attempt which considers the spatial interdependence among the states to estimate the impact of financial and infrastructural development on productivity in the Indian states.

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Debnath, Avijit, Niranjan Roy, Priyanka Dasgupta, and Nazira Mazumder. "On productivity differential of export composition: evidence from India." Journal of Chinese Economic and Foreign Trade Studies 7, no. 1 (January 28, 2014): 38–50. http://dx.doi.org/10.1108/jcefts-01-2013-0002.

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Purpose – This paper aims to analyse the relationship between exports and non-export gross domestic product (GDP) in the context of Indian economy during 1988-2012. It considers export both at aggregate and disaggregated levels to examine whether export-led growth (ELG) hypothesis is sensitive to types of goods India exports. Design/methodology/approach – The OLS-based autoregressive distributed lag (ARDL) model has been employed to analyse the potential long-run equilibrium relationship. Further, the error correction model within the ARDL framework is applied to examine the short-run and long-run causal relationship between non-export GDP, export and other variables. The study is based on secondary data. Findings – The study indicates that at aggregate level, exports do not have any significant impact on output of non-export sector, and therefore, it is maintained that ELG hypothesis is not valid at aggregate level in India; when the authors disaggregate exports into merchandise and services exports, the latter has been found to have positive spillover effects on non-export sector of the economy. However, the association between merchandise export and non-export GDP is found to be statistically insignificant. When the authors further disaggregated merchandise exports, the authors observed that primary-product export has a negative association with non-export GDP, but export of manufacturing products found to have a significant positive impact on non-export GDP. Finally, export of petroleum product shows a negative long-run association with non-export GDP, but the association is statistically insignificant. Originality/value – It is not the case that India can simply increase its exports per se and be sure of witnessing economic growth, but instead it is the composition and the concentration of these exports that matters.

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Rosegrant, Mark W., and Robert E. Evenson. "Agricultural Productivity Growth in Pakistan and India: A Comparative Analysis (Distinguishedl Lecture)." Pakistan Development Review 32, no. 4I (December 1, 1993): 433–51. http://dx.doi.org/10.30541/v32i4ipp.433-451.

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Estimates of partial factor productivity growth for rice and wheat in India and Pakistan have shown relatively rapid growth in yields per hectare since adoption of modem rice and wheat varieties began in the mid-to-Iate 1960s [Byerlee (1990); Rosegrant (1991)]. Yields per hectare for rice and wheat grew slowly prior to the green revolution, then increased dramatically (Table 1). In Pakistan, yield growth from 1965 to 1975 was particularly rapid, but declined sharply after that. Indian yields grew more slowly than in Pakistan in the early green revolution period, but higher yield growth was sustained in India after 1975.

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Jana, Sajal, and Maniklal Adhikary. "Manufacturing Productivity during Economic Reforms in India." Arthshastra : Indian Journal of Economics & Research 3, no. 5 (October 1, 2014): 20. http://dx.doi.org/10.17010/aijer/2014/v3i5/55992.

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Ray, Pradip K., and S. Sahu. "Productivity Management in India: A Delphi Study." International Journal of Operations & Production Management 10, no. 5 (May 1990): 25–51. http://dx.doi.org/10.1108/01443579010005245.

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Chakrabarti, Prabhas Kumar, and Juin Choudhary. "Wage-Productivity Relationship in Coal India Limited." Journal of Social Sciences 8, no. 1 (January 2004): 29–43. http://dx.doi.org/10.1080/09718923.2004.11892400.

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Datt, Gaurav, and Martin Ravallion. "Farm productivity and rural poverty in India." Journal of Development Studies 34, no. 4 (April 1998): 62–85. http://dx.doi.org/10.1080/00220389808422529.

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Narayanan, Sudha. "The productivity of agricultural credit in India." Agricultural Economics 47, no. 4 (May 24, 2016): 399–409. http://dx.doi.org/10.1111/agec.12239.

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Sundara, B. "Agrotechnologies to Enhance Sugarcane Productivity in India." Sugar Tech 13, no. 4 (November 9, 2011): 281–98. http://dx.doi.org/10.1007/s12355-011-0109-x.

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Managi, Shunsuke, and Pradyot Ranjan Jena. "Environmental productivity and Kuznets curve in India." Ecological Economics 65, no. 2 (April 2008): 432–40. http://dx.doi.org/10.1016/j.ecolecon.2007.07.011.

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Chandrasekaran, M., and Bhavani Sridharan. "Productivity Trends in Cotton Industry in India." Indian Economic Journal 41, no. 2 (December 1993): 61–70. http://dx.doi.org/10.1177/0019466219930206.

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Nath, Bhola, and Debasis Bhattacharya. "Historical pattern of rice productivity in India." Environment Conservation Journal 24, no. 1 (January 15, 2023): 225–31. http://dx.doi.org/10.36953/ecj.12292333.

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Forecast of productivity (yield) has an importance over production and area separately because it depends on both. Trend of the same reveals the necessity of the resources to be managed, for increasing yield in future. The forecast values of the series are obtained using autoregressive integrated moving average (ARIMA) model and the trend is determined by the means of Mann Kendall’s trend test. In the present work we have found that the productivity of rice for overall country shows an increasing trend. Mann Kendal’s trend analysis reported that the productivity has a steadily increasing trend which was also evident from the Sen’s slope coefficient (Q). ARIMA (1,1,1) model with constant was found to be appropriate model for forecasting the productivity of rice. The forecast values were obtained for the subsequent four years starting from 2018 to 2021. Forecast error was also calculated and it was found to be less than 2 per cent i.e., 1.36 per cent.

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Bhareja, Tushar, and Neha Anand. "A pragmatic study of India: productivity analysis." International Journal of Public Sector Performance Management 11, no. 4 (2023): 520. http://dx.doi.org/10.1504/ijpspm.2023.10056501.

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Anand, Neha, and Tushar Bhareja. "A pragmatic study of India: productivity analysis." International Journal of Public Sector Performance Management 11, no. 4 (2023): 520–32. http://dx.doi.org/10.1504/ijpspm.2023.131350.

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Singh, Awadhesh Pratap, and Chandan Sharma. "Does India do IT? The nexus of IT, skills, organizational factors and productivity." Journal of Economic Studies 47, no. 3 (March 5, 2020): 597–626. http://dx.doi.org/10.1108/jes-03-2019-0100.

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PurposeThe goal of this study is to investigate the nexus among TFP (total factor productivity), IT (information technology) capital accumulation, skills and key plant variables of 34 Indian industries for the period of 2009–2015.Design/methodology/approachAnnual Survey of Industries (ASI) data series are extracted and formulated using Microsoft SQL server. The authors employ Wooldridge (2009) technique to estimate productivity. To investigate the linkages among productivity, IT, skills and key plant variables, the authors estimate specifications using system generalized method of moments (sys-GMM). Advanced estimation techniques such as Heckman two-step process, probit equations, inverse Mills ratio and panel cointegration are applied to overcome problems of nonstationarity, omitted variables, endogeneity and reverse causality.FindingsThe results indicate that the level of IT capital influences the TFP of Indian industries, so does the level of skilled workers. The outcome suggests that intermediate capital goods, location and ownership type enable the strength of IT capital and that in turn boosts productivity. The authors fail to find any impact of regional factors and contractual labor on IT capital and productivity. While medium-level gender diversity is statistically significant to influence productivity, however, no complementarities exist between gender diversity and IT capital accumulation. The results also indicate that IT demand of Indian industries is sensitive to availability of skilled workforce, fuel and electricity and access to short-term funding.Originality/valueTo the authors' knowledge, this is the first study to investigate the nexus among TFP, IT capital accumulation, skills and organizational factors using ASI unit level data. Besides this, the paper offers two more novelties. First, it uses Wooldridge (2009) technique to estimate productivity, which is used by a handful of studies in the context of India. Second, the study identifies factors that impact productivity growth, IT demand and its adoption in Indian industries and thus contributes to growth and development literature.

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Journal articles on the topic 'Productivity- India'

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