Productivity growth, catching-up and technology innovation in microfinance institutions in India: evidence using a bootstrap Malmquist Index approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Asif Khan ◽  
Rachita Gulati
Keyword(s):  
Productivity Growth ◽  
Productivity Index ◽  
Productivity Change ◽  
Microfinance Institutions ◽  
Truncated Regression ◽  
Content Type ◽  
Tfp Growth ◽  
Catching Up ◽  
Double Bootstrap ◽  
Size Groups

PurposeThis paper aims to examine the total factor productivity (TFP) change and its components: efficiency change and technical change in microfinance institutions (MFIs) in India operating from 2005 to 2018. The study also scrutinizes the variations in productivity levels across the distinct organizational form and size groups of MFIs. In addition to this, the authors identify the contextual factors that determine TFP growth, catching-up and technology innovation in MFIs.Design/methodology/approachThe study employs a smooth homogeneous bootstrap estimation procedure of Simar and Wilson (1999) for obtaining reliable estimates of Malmquist indices –productivity and its components – in a data envelopment analysis (DEA) framework for individual MFIs. In order to identify the determinants of productivity change and its components, the study follows Simar and Wilson's (2007) guidelines and applies a bootstrap truncated regression model. The double bootstrap procedure performs well, both in terms of allowing correct estimation of bias and deriving statistically consistent productivity estimates in the first and root mean square errors in the second stage of the analysis.FindingsThe empirical results reveal that the MFIs have shown average productivity growth of 6.70% during the entire study period. The observed productivity gains are primarily contributed by a larger efficiency increase at the rate of 4.80%, while technical progress occurs at 2.3%. Nonbanking financial companies (NBFC)-MFIs outperformed non-NBFC-MFIs. Small MFIs show the highest TFP growth in terms of size groups, followed by the large MFIs and medium MFIs. The bootstrap truncated regression results suggest that the credit portfolio, size and age of MFIs matter in achieving higher productivity levels.Practical implicationsThe practical implication drawn from the study is that the Indian MFI industry might adopt the latest technology and innovations in the products, risk assessment and credit delivery to improve their productivity levels. The industry must focus on enhancing the managerial skill of its employees to achieve a high productivity level.Originality/valueThis study is perhaps the initial attempt to explain the productivity behavior of MFIs in India by deploying a statistically robust double bootstrap procedure in the DEA-based Malmquist Productivity Index (MPI) framework. The authors estimate the bias-adjusted productivity index and its decompositions, which represent more reliable and statistically consistent estimates. For contextual factors responsible for driving productivity change, the study deploys a bootstrap truncated regression approach.

Measuring total factor productivity change of microfinance institutions in India using Malmquist productivity index

2018 ◽  
Vol 12 (1) ◽  
pp. 105-130 ◽  
Author(s):  
Dilip Ambarkhane ◽  
Ardhendu Shekhar Singh ◽  
Bhama Venkataramani
Keyword(s):  
Total Factor Productivity ◽  
Financial Institutions ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Productivity Change ◽  
Microfinance Institutions ◽  
Factor Productivity ◽  
Content Type ◽  
The Poor

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.

Economic slowdown, NPA crisis and productivity behavior of Indian banks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anju Goswami ◽  
Rachita Gulati
Keyword(s):  
Production Process ◽  
Global Financial Crisis ◽  
Banking Industry ◽  
Economic Stress ◽  
Productivity Index ◽  
Undesirable Output ◽  
Content Type ◽  
Tfp Growth ◽  
Robust Estimates ◽  
Indian Banks

PurposeThis paper aims to investigate the productivity behavior of Indian banks in the presence of non-performing assets (NPAs) over the period 1999 to 2017. The study examines whether Indian banks withstand the shocks of the global financial crisis (GFC) of 2007–2009 and sustain their total factor productivity (TFP) levels in the post-crisis economic turbulent period or not.Design/methodology/approachThe robust estimates of TFP and its components: efficiency change and technical change are obtained using the state-of-the-art and innovative sequential Malmquist-Luenberger productivity index (SMLPI) approach. The key advantages of this approach are that it explicitly allows the joint production of undesirable output (NPAs in our case) along with desirable inputs and outputs in the production process and precludes the possibility of spurious technical regress.FindingsThe empirical results of the study reveal that the Indian banking system has experienced a (−1) percent TFP regress, contributed solely by efficiency loss during the period under investigation. The GFC has slowed down the growth trajectory of TFP growth in the Indian banking industry. Among ownership groups, the effect of the GFC was pronounced on the public sector banks.Practical implicationsThe practical implication drawn from the study is that the Indian banks have not been able to successfully transmit the use of installed technology in a way to generate early warning signals and mitigate the risk of defaults so as to maximize their productivity gains in the banking industry.Originality/valueThis study is perhaps the first one to understand the productivity dynamics of the Indian banks in response to both endogenous (i.e. NPA crisis) and exogenous (i.e. global financial and economic stress) crises. Moreover, the authors obtain the robust estimates of TFP growth of Indian banks by explicitly accounting for NPAs as an undesirable output and equity as a quasi-fixed input in the bank production process.

Productivity growth of Indonesian rice production: sources and efforts to improve performance

2018 ◽  
Vol 67 (9) ◽  
pp. 1792-1815 ◽  
Author(s):  
Joko Mariyono
Keyword(s):  
Technological Change ◽  
Technical Efficiency ◽  
Production Technology ◽  
Productivity Growth ◽  
Scale Effects ◽  
Allocative Efficiency ◽  
Rice Production ◽  
Content Type ◽  
Tfp Growth ◽  
Efficiency Effects

PurposeThe purpose of this paper is to investigate the productivity of rice production by decomposing the growth of total factor productivity (TFP) into four components: technological change, scale effects, technical and allocative efficiencies.Design/methodology/approachThis study employed an econometric approach to decompose TFP growth into four components: technological change, technical efficiency, allocative efficiency and scale effect. Unbalanced panel data used in this study were surveyed in 1994, 2004 and 2014 from 360 rice farming operations. The model used the stochastic frontier transcendental logarithm production technology to estimate the technology parameters.FindingsThe results indicate that the primary sources of TFP growth were technological change and allocative efficiency effects. The contribution of technical efficiency was low because it grew sluggishly.Research limitations/implicationsThis study has several shortcomings, such as very lowR2and the insignificant elasticity of labour presented in the findings. Another limitation is the limited time period panel covering long interval, which resulted in unbalanced data.Practical implicationsThe government should improve productivity growth by allocating more areas for rice production, which enhances the scale and efficiency effects and adjusting the use of capital and material inputs. Extension services should be strengthened to provide farmers with training on improved agronomic technologies. This action will enhance technical efficiency performance and lead to technological progress.Social implicationsAs Indonesian population is still growing at a significant rate and the fact that rice is the primary staple food for Indonesian people, the productivity of rice production should increase continually to ensure social security at a national level.Originality/valueThe productivity growth is decomposed into four components using the transcendental logarithm production technology based on farm-level data. The measure has not been conducted previously in Indonesia, even in rice-producing countries.

scholarly journals Productivity Change of Microfinance Institutions in Bosnia and Herzegovina

2019 ◽  
Vol 14 (2) ◽  
pp. 23-33
Author(s):  
Velid Efendić ◽  
Nejra Hadžiahmetović
Keyword(s):  
Total Factor Productivity ◽  
Bosnia And Herzegovina ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Microfinance Institutions ◽  
Factor Productivity ◽  
Strategic Objectives ◽  
Average Decrease ◽  
Recent Financial Crisis

Abstract The main aim of this paper is to investigate the productivity changes of microfinance institutions (MFIs) in Bosnia and Herzegovina (BiH) during and after the recent financial crisis. The study covers the period starting from 2008 until 2015. Using the Malmquist Productivity Index (MPI) over the sample of 10 MFIs and a balanced panel dataset of 80 observations, this study explores technical and technological change as well as total factor productivity (TFP) change. The empirical findings indicate a decline in TFP in most of the analyzed periods with an average decrease of 2.5%. The study reveals an average technological decline in the industry of 1.7%, while technical efficiency change is recorded at the level of -0.8%. Overall, crisis efficiency recovery occurred during the period between 2009 and 2013. However, due to technological inefficiencies, average total factor productivity change remains negative. Hence, policy makers need to enhance the technological progress in order to meet their strategic objectives in BiH MFIs.

scholarly journals Total Factor Productivity Growth of Indian General Insurance Companies in the Recent Period: A Bootstrapped Approach

2019 ◽  
Vol 11 (1-2) ◽  
pp. 59-80
Author(s):  
Ram Pratap Sinha
Keyword(s):  
Private Sector ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
Malmquist Index ◽  
American Economic Review ◽  
Productivity Index ◽  
Insurance Companies ◽  
Productivity Change ◽  
Productivity Analysis ◽  
Factor Productivity

This study estimates Malmquist index of total factor productivity change of 14 major general insurers in India over the period 2009–10 to 2016–17 over 7 annual windows. The study decomposes total factor productivity index into its constituent components, using several approaches including Färe et al. (1989, Productivity Developments in Swedish Hospitals: A Malmquist Output Index Approach. Carbondale: Department of Economics, Southern Illinois University; 1992, Journal of Productivity Analysis 3(1): 85–101), Färe et al. (1994, American Economic Review 84(1): 66–83), Ray and Desli (1997, American Economic Review 87(5): 1033–39) and Wheelock and Wilson (1999, Journal of Money, Credit and Banking 31(2): 212–23). Furthermore, the study uses bootstrap data envelopment analysis (DEA) method to obtain bias-corrected point and interval estimates of Malmquist index and its components. Finally, the study makes a comparison of productivity performance between public and private sector insurers. The results indicate a modest growth in total factor productivity during the period contributed mainly by efficiency changes. The private sector insurers performed better than the public sector in terms of productivity growth. The variations in productivity performance indicate that insurer scale of activity can affect their performance. JEL Classification: G-23, C-61, D-21

Robust malmquist productivity measurement: evidence from Spanish hotel industry during the Great Recession

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nickolaos G. Tzeremes
Keyword(s):  
Great Recession ◽  
Hotel Industry ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Productivity Measurement ◽  
Content Type ◽  
Crisis Period ◽  
The Great Recession ◽  
Robust Version

PurposeThe purpose of this paper is to provide a robust version of the Malmquist Productivity Index (MPI) in order to evaluate hotels' productivity levels during the Great Recession.Design/methodology/approachBased on the order-m frontiers, we apply a robust version of an MPI. We decompose the productivity into three robust components. We use a sample of hotels operating in the Balearic Islands and Canary Islands, and we decompose and evaluate their productivity levels during the period 2004–2013. Moreover, we evaluate hotels' productivity performance during the pre-crisis period, the US subprime crisis period, the global financial crisis (GFC), the sovereign debt crisis period and the after-crisis period.FindingsOur findings show that productivity did not deteriorate due to the adverse effects of economic crisis. This is mainly driven by increased technical change and the ability to operate at optimal scales. The long-term investment in innovation policies which are related to services and processes, appear to be the dominating feature behind hotels' productivity levels, which helped the hotel industry to recover quickly from the Great Recession.Originality/valueThe vast majority of empirical studies evaluating the productivity change in the hotel industry apply MPIs which are based on data envelopment analysis (DEA). However, the productivity measurement which is based on the nonparametric framework is sensitive to sample characteristics. In order to avoid such shortcomings, we apply a robust version of the MPI.

Dynamic and static analysis of agricultural productivity in China

2018 ◽  
Vol 10 (2) ◽  
pp. 293-312 ◽  
Author(s):  
Panpan Diao ◽  
Zhonggen Zhang ◽  
Zhenyong Jin
Keyword(s):  
Agricultural Production ◽  
Agricultural Productivity ◽  
Policy Implications ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Western Region ◽  
Content Type ◽  
Tfp Growth ◽  
The Western Region

Purpose The purpose of this paper is to analyze agricultural total factor productivity (TFP) and input redundancies in different regions of China, and to bring out the policy implications for improving efficiency in agricultural production as well as environment protection. Design/methodology/approach Based on the provincial panel data during 1995-2014, the agricultural productivity of China and its regional disparity are analyzed. First, the agricultural TFP and its decomposition are dynamically evaluated by means of data envelopment analysis-Malmquist productivity index. Second, the agricultural radial production efficiency in year 2014 and the input redundancy changes from 1995 to 2014 are measured based on the BCC-slacks-based measure model. Findings The results showed that the overall agricultural TFP of China grew 4.3 percent annually during 1995-2014, mainly as a result of technical progress. However, the declines of technical efficiency and scale efficiency slowed down the agricultural TFP growth. The TFP growth in the Western region and Central region far exceeded the Eastern region in last few years. In 2014, most effective decision-making units were in the Western region. The input redundancies in the agricultural production increased substantially after 2006, especially for the pesticide use amount, reservoir capacity and agricultural machinery power. Originality/value Combining the dynamic and static analyses, the paper fulfilled the study of China’s agricultural productivity and the input redundancies in recent years, and also presented the regional disparities.

Does innovation matter for total factor productivity growth in India? Evidence from ARDL bound testing approach

2018 ◽  
Vol 13 (5) ◽  
pp. 1311-1329 ◽  
Author(s):  
Seenaiah Kale ◽  
Badri Narayan Rath
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Growth Accounting ◽  
Factor Productivity ◽  
Content Type ◽  
Tfp Growth ◽  
Innovation Activities ◽  
Long Run ◽  
Short Run ◽  
Testing Approach

Purpose The purpose of this paper is to examine whether innovation plays a significant role in the total factor productivity (TFP) growth in India at an aggregate level. Design/methodology/approach This study first estimates the TFP growth using a growth accounting framework. In the second stage, the authors examine the long-run and short-run impact of innovation on TFP growth using the ARDL bound testing approach. Findings The results indicate a cointegrating relationship between innovation and TFP growth. Further, coefficients of long-run elasticity show that the increase in overall innovation activities improves the TFP growth. Other factors such as human capital, financial development and FDI do not affect the TFP growth in the long run; however, these variables significantly affect the productivity growth in the short run. Practical implications Findings of the study suggest that the innovation-friendly policies such as the strengthening of intellectual property rights, R&D subsidies and innovation rebates may spur the productivity growth, and hence, good growth and prosperity as well. Originality/value Having devoted a large volume of literature to address the sources of economic growth, the present study focuses on the determinants of TFP growth in India which may fall in similar category but differ in several angles: First, the authors construct a TFP index using a growth accounting framework. Second, the authors construct an innovation index using principal component analysis which is new to the literature and also an innovation index. Third, given the scanty innovation activities in low developed countries like India and its widening role in the contemporary literature, special emphasis will be given to this aspect. Finally, the effect of the examined relationship on TFP growth in the long run and short run provides several implications for policy purpose to the developing nations like India.

Haze pollution, new-type urbanization and regional total factor productivity growth: based on a panel dataset involving all 31 provinces within the territory of China

Kybernetes ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruihan Zhang ◽  
Bing Sun ◽  
Mingyao Liu ◽  
Jian Hou
Keyword(s):  
Total Factor Productivity ◽  
Critical Mass ◽  
Estimation Method ◽  
Productivity Index ◽  
Factor Productivity ◽  
Content Type ◽  
Tfp Growth ◽  
Haze Pollution ◽  
New Type ◽  
Spatiotemporal Heterogeneity

Purpose This paper aims to analyze the spatiotemporal heterogeneity of regional total factor productivity (TFP) growth and explores how haze pollution and different levels of new-type urbanization affect China’s economic growth. Design/methodology/approach This paper constructs an index for evaluating the TFP growth of China’s 31 provinces by integrating slack-based measures and the Global Malmquist (GM) productivity index. Meanwhile, the panel threshold estimation method is used to examine the complex relationships among haze pollution, new-type urbanization and TFP growth. Findings The results reflect conspicuous spatiotemporal heterogeneity in TFP growth in China. Interestingly, the influence of haze pollution on TFP growth is limited by the “critical mass” of new-type urbanization in China. When new-type urbanization does not cross the first threshold, haze pollution has a negative but non-significant effect on TFP growth. When new-type urbanization crosses the first threshold but not the second, haze pollution has a significant positive impact on TFP growth. When new-type urbanization crosses the second threshold, haze pollution significantly and positively affects TFP growth with the strongest positive effect. Originality/value This study innovates by combining haze pollution and TFP growth and proposing an integrated framework from the perspective of new-type urbanization, providing insight into how different degrees of new-type urbanization impact the mechanism between haze pollution and TFP growth. Using panel data in China and emphasizing green development, a sustainable economy and new-type urbanization, this study contributes to the current studies on haze pollution and economic development based on developed countries.

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