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Relevant bibliographies by topics / Productivity- India

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Academic literature on the topic 'Productivity- India'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Productivity- India"

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Vencappa, Dev V. "Trade policy, productivity and wages in India." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430483.

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Khachatryan, Armen. "Market access and aggregate crop productivity in India." Weikersheim Margraf, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2867933&prov=M&dok_var=1&dok_ext=htm.

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Corlett, Janet E. "Leucaena/millet alley cropping in India : microclimate and productivity." Thesis, University of Nottingham, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328810.

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Marshall, Fiona M. "Resource partitioning and productivity of perennial pigeonpea/groundnut agroforestry systems in India." Thesis, University of Nottingham, 1995. http://eprints.nottingham.ac.uk/12206/.

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The productivity of two spatial arrangements of a perennial pigeonpea/groundnut agroforestry system was examined in relation to the capture and use of light and water and alterations in microclimatic conditions. Line planted (5.4 m alleys) and dispersed arrangements (1.8 x 1.2 m spacing) of pigeonpea were compared, using populations of 0.5 plants m2 for pigeonpea and 33 plants m2 (0.3 x 0.1 m spacing) for groundnut in both treatments. Sole pigeonpea and groundnut treatments were included for comparison. The experiment was conducted between July 1989 and March 1991 on a 0.6 ha plot of Alfisol at ICRISAT Center, Andhra Pradesh, India, using a randomised block design with four replications. The first groundnut harvest took place in October 1989, while pigeonpea was harvested for grain and fodder in January 1990, and was cut to a height of 0.5 m during the 1990 dry season and again in August 1990 after a second groundnut crop was sown. The second groundnut harvest took place in November 1990 and the final pigeonpea grain harvest was in January 1991. Light interception, soil and leaf temperatures and saturation deficit were continuously monitored in all treatments and at various distances from the pigeonpea in the line and dispersed treatments, whilst windspeed was monitored at a single location in each treatment. Regular destructive samples of groundnut were used to establish effects on growth and development and the results were considered in relation to the concurrent physical measurements to determine the environmental factors influencing productivity. In order to establish a water balance, rainfall records were maintained, runoff plots were installed and soil moisture content was measured regularly throughout the drying cycle. Transpiration by pigeonpea was monitored using a heat balance technique, while transpiration by groundnut and soil surface evaporation were estimated from micrometeorological data. As pigeonpea is initially slow growing, there was little reduction in groundnut yield in either the line or dispersed treatments in 1989 and there was a slight intercrop advantage in overall biomass production when expressed in the terms of the land equivalent ratios. In 1990, groundnut pod yield was reduced by 20 and 44 % in the line and dispersed treatments relative to the sole crop, despite substantial increases in the light conversion coefficient for the shaded groundnut. The lower pod yield resulted from the delayed onset of pod initiation and a slower rate of development, and was mainly due the effects of shading by the pigeonpea canopy, although mild water stress may have been a minor contributory factor. The small reductions in saturation deficit and soil and leaf temperatures experienced by the shaded groundnut had a negligible effect on growth and development. There was a considerable increase in overall biomass production in the line and dispersed treatments as compared with 1989 due to rapid pigeonpea growth, which reflected an increase in overall resource use rather than in the light conversion coefficient or water use ratios of the systems. The influence of spatial arrangement on the growth and productivity of pigeonpea became apparent after the 1990 dry season. Biomass production by pigeonpea in the dispersed treatment was approximately double that of the line planting between August 1990 and January 1991. This was entirely due to increased transpiration by the dispersed pigeonpea as a result of greater utilisation of stored soil moisture and reduced losses by surface evaporation and deep drainage. There was no difference in the water use ratio. To examine further the mechanisms responsible for the differences in productivity and water use by the line and dispersed pigeonpea, trench profile methodology was used to examine the root systems in December 1990. The root system of the dispersed pigeonpea was distributed over the entire 2.0 m depth x 2.7 m width exposed soil profile, whilst that of the line arrangement occupied no more than 50 % of the same area. The results of this work are discussed in relation to previous studies of resource use and productivity in intercropping and agroforestry systems, and possible applications and future developments are considered. Finally, the major physical and socioeconomic factors determining the potential of perennial pigeonpea/groundnut agroforestry systems for adoption by farmers in semi-arid India are discussed.

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Ravishankar, Geetha. "Bank mergers and their efficiency and productivity effects : the case of India." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/33740.

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This thesis examines the efficiency and productivity effects of mergers among Scheduled Commercial Banks in India over the post-economic reform period 1991–2005. It does so using the dual methodologies of Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) applied to two datasets, the first comprising retail commercial banks and the second encompassing the entire commercial banking system. Under the DEA methodology, the potential for pre-merger efficiency gains is assessed using the Bogetoft and Wang (2005) approach and a comparative analysis with the realised post-merger efficiency is undertaken. A method to judge the success or failure of a merger along efficiency criteria is also provided. Under the latter methodology, an insight into the impact of the merger on the efficiency of the merging banks, the determinants of inefficiency and that of the variance of inefficiency is provided using a range of SFA models.

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Mahambare, Vidya. "Economic reforms in India : impact on savings and productivity of the manufacturing sector." Thesis, Lancaster University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274202.

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Mishra, Abhishek. "ITES/BPO industry in India : a study of growth, market structure and productivity." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614280.

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Kaur, Japneet <1991&gt. "Impact of Climate Change on Agricultural Productivity and Food Security Resulting in Poverty in India." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/10586.

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“Tackling Climate Change is closely linked to poverty alleviation and economic development; I would call them different sides of the same coin.” - Paul Polman, CEO, Unilever Climate change has been considered to have catastrophic effects on planet Earth. It has become a major barrier to developing economies, like India where agriculture accounts for 55 per cent of its total working population (Registrar General 2013) and constitutes about 14.1 per cent of its GDP (GoI, 2013). Moreover, due to the alteration in climate, crop productivity is being affected adversely resulting in food and livelihood security issues. This study is based on state level data of 4 major seasonal Indian crops- Rice, Wheat, Cotton, Sugarcane which comprise of Food and Cash crops for the time span of 2004 to 2013. 7 agriculturally intensive states with varied climatic conditions have been taken into consideration for the study. States under tropical zones include West Bengal, Maharashtra, Uttar Pradesh, Gujarat, while the subtropical regions are Andhra Pradesh, Tamil Nadu and Karnataka. This thesis makes an attempt to analyze the impact of climate change on Indian Agriculture and food security. It also examines the implications of climate change on food security and evaluates the multiple benefits of mitigation and adaptation. Cobb Douglas production function will be incorporated in this model to simulate this impact of climate change on agricultural productivity. Majority of the crops taken into consideration are expected to be adversely affected by the future climatic conditions. Local adaptation practices have also been scrutinized, highlighting the role of institutional support, national adaptation strategies and resilience at different scales.

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Furuta, Manabu. "Three Essays on the Indian Manufacturing: Wage Inequality, Export and Informality." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225372.

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Tangen, Alyssa. "The Impacts of Expected Structural Changes in Demand for Agricultural Commodities in China and India on World Agriculture." Thesis, North Dakota State University, 2009. https://hdl.handle.net/10365/29866.

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The objective of this study is to evaluate the changes in import and export demand in China and India on the United States and global agriculture in 2018. A spatial equilibrium model is developed to optimize production and trade in China, India, and other major importing and exporting regions in the world. This research focuses on four primary crops: wheat, com, rice and soybeans. In the model China and India are divided into 31 and 14 producing and consuming regions, respectively. The model also includes five exporting countries and ten importing countries/regions. The results indicate that India will be able to stay largely self-sufficient in 2018 and China will increase its soybean and com imports to meet rising domestic demand. The research also gives perspectives on production and trade in the United States and other major exporting and importing countries.

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Books on the topic "Productivity- India"

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Tapiawala, Medha P. Banking reforms and productivity in India. New Delhi: New Century Publications, 2010.

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Economic Development Institute (Washington, D.C.), ed. Women, poverty, and productivity in India. Washington, D.C: World Bank, 1992.

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Productivity and growth in Indian manufacturing. Delhi: Oxford University Press, 1991.

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E, Evenson Robert. Agricultural research and productivity growth in India. Washington, D.C: International Food Policy Research Institute, 1998.

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Varma, Poornima. Rice Productivity and Food Security in India. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3692-7.

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Paltasingh, Kirtti Ranjan. Trade liberalization and industrial productivity in India. New Delhi: New Century Pubns, 2011.

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Singh, Vir. Animal feeding and production in India. Delhi: Daya Pub. House, 2011.

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Rajput, M. S. Agricultural productivity in India: A district-wise study of Rajasthan. Delhi: Discovery Pub. House, 1985.

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Ray, Subrata Kumar. Agricultural growth in India. New Delhi: Serials Publications, 2010.

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Bhatia, D. P. Capital and productivity in India: Concepts, problems, and estimations. New Delhi: Khama Publishers, 1992.

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Book chapters on the topic "Productivity- India"

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Bishnoi, T. R., and Sofia Devi. "Cost Efficiency and Productivity." In Banking Reforms in India, 121–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55663-5_5.

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Swarup, Biswa. "Determinants of productivity." In Development and Economic Growth in India, 103–15. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003278351-5.

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Santhi, R., S. Maragatham, R. Abishek, and R. Selvam. "Potassium in Soils of India." In Soil Constraints and Productivity, 531–52. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003093565-30.

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Priya, Ratan. "Land Degradation and Agricultural Productivity." In Land Degradation in India, 93–100. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68848-6_5.

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Karthikeyan, P. K., and D. Gokul. "Sugarcane Cultivation in Saline Soils of India." In Soil Constraints and Productivity, 481–90. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003093565-27.

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Durairaj, Jawahar, and T. Sherene Jenita Rajammal. "Soil Salinity and Sodicity in India – Adaptation and Mitigation Options." In Soil Constraints and Productivity, 511–29. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003093565-29.

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Das, Pilu Chandra, and Deb Kusum Das. "India’s Manufacturing Story: Productivity and Employment." In India Studies in Business and Economics, 13–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9397-7_2.

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Pal, Labanya. "Foreign direct investment and productivity spillovers." In Neoliberalism in the Emerging Economy of India, 173–92. 1 Edition. | New York : Routledge, 2021. | Series: Routledge studies in the modern world economy: Routledge, 2021. http://dx.doi.org/10.4324/9781003131762-12.

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Srinivasarao, Ch, C. Subha Lakshmi, S. Rakesh, M. Mohanty, G. Narayanaswamy, S. Kundu, G. Pratibha, J. V. N. S. Prasad, Rajbir Singh, and A. Arunachalam. "Soil Constraints and Management Options for Sustainable Rainfed Production Systems of India." In Soil Constraints and Productivity, 491–509. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003093565-28.

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Sasmal, Joydeb. "Technological Change and Productivity Growth in Agriculture." In India Studies in Business and Economics, 9–77. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0895-5_2.

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Conference papers on the topic "Productivity- India"

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Vidya Vinodini, M. D., L. R. Reshmi Nair, K. G. Aiswaria Lakshmi, V. R. Anitha, P. Reshma, R. Ajith Kumar, and T. K. Manojkumar. "Modeling of rice productivity in India." In 2014 International Conference on Data Science & Engineering (ICDSE). IEEE, 2014. http://dx.doi.org/10.1109/icdse.2014.6974631.

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Prasanth, Rv. "'Process overheads' aka the 'productivity killers'." In the 7th India Software Engineering Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2590748.2590771.

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Kamma, Damodaram, G. Geetha, and J. Padma Neela. "Countering Parkinson's law for improving productivity." In the 6th India Software Engineering Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2442754.2442768.

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Pillutla, Ravi Shankar, and Anuradha Alladi. "Methodology to enable individual skills to enhance industry productivity." In 2012 Annual IEEE India Conference (INDICON). IEEE, 2012. http://dx.doi.org/10.1109/indcon.2012.6420805.

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Borse, Vaishnavi, Wricha Mishra, and Gareth Loudon. "Sorry you're on mute: Towards determining the factors that impact the productivity and job motivation of remote workers and a conceptual framework." In India HCI 2021: India HCI 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3506469.3506475.

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Kamma, Damodaram, and Pankaj Jalote. "Effect of task processes on programmer productivity in model-based testing." In the 6th India Software Engineering Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2442754.2442758.

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Dey, Swapan Kumar. "Coal Bed Gas Productivity Prediction By Stoneley Wave Analysis." In SPE Oil and Gas India Conference and Exhibition. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/129020-ms.

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Tripathi, S. K., G. Pranuthi, S. Dubey, G. V. Kumar, and L. Mfwango. "Characterization of climate and crop productivity using DSSAT for SW Uttarakhand, India." In FOOD AND ENVIRONMENT 2013. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/fenv130111.

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Zhang, Fuxiang, Yongjie Huang, Xiangtong Yang, Kaibin Qiu, Xuefang Yuan, Fang Luo, Yang Zhang, and Zijun Zheng. "Natural Productivity Analysis and Well Stimulation Strategy Optimization for the Naturally Fractured Keshen Reservoir." In SPE Oil & Gas India Conference and Exhibition. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/178067-ms.

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Mahadevan, Amala, Jing He, and Gualtiero Spiro Jaeger. "Relating Biological Productivity to Temperature Fronts in the Northern Indian Ocean." In 2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS). IEEE, 2021. http://dx.doi.org/10.1109/ingarss51564.2021.9792124.

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Reports on the topic "Productivity- India"

1

Gupta, Kanupriya, and Abhijit Sen Gupta. Enhancing Productivity for Poverty Reduction in India. Asian Development Bank, April 2020. http://dx.doi.org/10.22617/brf200117-2.

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Lemos, Renata, Karthik Muralidharan, and Daniela Scur. Personnel Management and School Productivity: Evidence from India. Cambridge, MA: National Bureau of Economic Research, January 2021. http://dx.doi.org/10.3386/w28336.

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Lemos, Renata, Karthik Muralidharan, and Daniela Scur. Personnel Management and School Productivity: Evidence from India. Research on Improving Systems of Education (RISE), February 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/063.

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This paper uses new data to study school management and productivity in India. We report four main results. First, management quality in public schools is low, and ~2σ below high-income countries with comparable data. Second, private schools have higher management quality, driven by much stronger people management. Third, people management quality is correlated with both independent measures of teaching practice, as well as school productivity measured by student value added. Fourth, private school teacher pay is positively correlated with teacher effectiveness, and better managed private schools are more likely to retain more effective teachers. Neither pattern is seen in public schools.

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Dougherty, Sean, Verónica C. Frisancho Robles, and Kala Krishna. Employment Protection Legislation and Plant-Level Productivity in India. Cambridge, MA: National Bureau of Economic Research, December 2011. http://dx.doi.org/10.3386/w17693.

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Babu, Suresh Chandra, and S. J. Balaji. Agricultural productivity, inter-sectoral labor shift, and economic growth in India. Washington, DC: International Food Policy Research Institute, 2020. http://dx.doi.org/10.2499/p15738coll2.133787.

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Kishore, Avinash, and Vartika Singh. Seeds, water, and markets to increase wheat productivity in Bihar, India. Washington, DC: International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134451.

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Kumar, Anjani, Sunil Saroj, and Ashok K. Mishra. Crop insurance and crop productivity: Evidence from rice farmers in Eastern India. Washington, DC: International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134239.

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Mitra, Sabyasachi. Drivers and Benefits of Enhancing Participation in Global Value Chains: Lessons for India. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200430-2.

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Enhancing participation in global value chain (GVCs) can facilitate development outcomes that India strives to achieve, including generating productive employment opportunities, increasing labor productivity, and gaining a larger share of global exports. This paper draws from the Asian Development Bank’s Multiregional Input–Output Database and highlights the role of various drivers of GVCs participation across primary, manufacturing, and services sectors. It also evaluates the drivers and economic consequences of participating in different segments of GVCs, which can apply to India’s potential development outcomes. Results of the study indicate increasing GVC participation can positively impact the economy and contribute to raising per capita income, labor productivity, investment, and exports.

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Pillai, Aditya Valiathan, Chandni Singh, Sheetal Patil, Tamanna Dalal, Nihal Ranjit, and Prathijna Poonacha. How is India Adapting to Heatwaves? An Assessment of Heat Action Plans with Insights for Transformative Climate Action. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/hiah03.2023.

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Extreme heat poses an unprecedented challenge to health and productivity in India. Heatwaves (prolonged periods of extreme heat) have increased in frequency in recent decades due to climate change. Landmark heatwaves (1998, 2002, 2010, 2015, 2022) have each led to large death tolls (according to government estimates) and extensive economic damage by reducing labour productivity and affecting water availability, agriculture and energy systems. Governments across India at the state, district and municipal levels have responded by creating heat action plans (HAPs), which prescribe a variety of preparatory activities and post-heatwave response measures across government departments to decrease the impact of heatwaves. These documents are meant to be iterated upon and refined over time.

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Ryan, Nicholas. Energy Productivity and Energy Demand: Experimental Evidence from Indian Manufacturing Plants. Cambridge, MA: National Bureau of Economic Research, May 2018. http://dx.doi.org/10.3386/w24619.

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