nep-eff New Economics Papers
on Efficiency and Productivity
Issue of 2020‒01‒20
eight papers chosen by

  1. Energy Efficiency Indicators: Estimation Methods By Pillai N., Vijayamohanan; AM, Narayanan
  2. What makes a productive Russian firm? A comparative analysis using firm-level data By Lenka Wildnerova; Hansjörg Blöchliger
  3. Measurement of Water Productivity in Seasonal Floodplain Beel Area By Hossain, Istiaque; Alam, Md. Mahmudul; Siwar, Chamhuri; Bin Mokhtar, Mazlin
  4. Does Remote Work Improve or Impair Firm Labour Productivity? Longitudinal Evidence from Portugal By Natália P. Monteiro; Odd Rune Straume; Marieta Valente
  5. Estimating Production Functions with Fixed Effects By Abito, Jose Miguel
  6. Technology, Intangible Assets and the Decline of the Labor Share By Mary O'Mahony; Michela Vecchi; Francesco Venturini
  7. Trade Liberalization, Input Intermediaries and Firm Productivity: Evidence from China By Fabrice Defever; Michele Imbruno; Richard Kneller
  8. Growth Accounting and Regressions:new approach and results By Tiago Sequeira; Hugo Mourão

  1. By: Pillai N., Vijayamohanan; AM, Narayanan
    Abstract: Traditionally, there are two basically reciprocal energy efficiency indicators: one, in terms of energy intensity, that is, energy use per unit of activity output, and the other, in terms of energy productivity, that is, activity output per unit of energy use. A number of approaches characterize the efforts to measure these indicators. The present paper attempts at a a comprehensive documentation of some of the analytical methods of such measurement. We start with a comprehensive list of the estimation methods of energy productivity indicators. Note that the methods fall under three heads: traditional single factor productivity analysis, decomposition analysis and multi-factor productivity analysis. The paper takes up each of these in detail, starting with the traditional indicators identified by Patterson to monitor changes in energy efficiency in terms of thermodynamic, physical-thermodynamic, economic-thermodynamic and economic indicators. When we analyze the indicator in terms of energy intensity changes, the corresponding index falls under two major decomposition methods, namely, structural decomposition analysis and index decomposition analysis. The structural decomposition analysis is discussed in terms of its two approaches, viz., input-output method and neo-classical production function method; and the index decomposition analysis in terms of Laspeyres’ and Divisia indices. In the multi-factor productivity approach, we consider the parametric and non-parametric methods, viz., stochastic frontier model and data envelopment analysis respectively.
    Keywords: Energy efficiency, measures, index decomposition, Divisia index, stocastic frontier, data envelopment
    JEL: C1 C13 Q40 Q49
    Date: 2019–11
  2. By: Lenka Wildnerova; Hansjörg Blöchliger
    Abstract: Productivity in Russia has fallen steadily over the past 15 years. This paper explores micro-level data to understand the contribution of individual firms to aggregate productivity. Overall, firm-level data corroborate the decline in aggregate productivity and a widening productivity gap against several European countries. They also show that the gap between “the best” and “the rest” has widened in Russia, similar to other countries. Russian markets are quite concentrated, i.e. dominated by few large firms. Larger firms tend to be more productive, but firms at the productivity frontier have become smaller and younger over time, suggesting that more support for young and innovative firms could help raise productivity. Foreign ownership is associated with higher productivity, and there is evidence that foreign firms generate positive productivity spillovers for domestic firms. Service firms belong to the most productive, yet the service sector remains underdeveloped. Mining is also very productive but less than in other countries. Differences in productivity across regions are large, even controlling for many other determinants, suggesting a lack of capital and labour mobility and knowledge transfer across regional borders.
    Keywords: entry and exit of firms, firm-level productivity, foreign ownership, industrial organisation, privatisation, productivity gap, regional productivity differences, Russian economy
    JEL: D24 L16 O43
    Date: 2019–12–23
  3. By: Hossain, Istiaque; Alam, Md. Mahmudul (Universiti Utara Malaysia); Siwar, Chamhuri; Bin Mokhtar, Mazlin
    Abstract: Water scarcity is becoming a central issue in agricultural activities around the world. As agriculture is one of the major consumers of freshwater, more yield or output using same or less amount of water has become the global interest. Therefore water productivity (WP) is also considered as an indicator of agricultural productivity. Several research works have been conducted on WP values of different yields, tools and technologies to improve WP. Most of the studies on water productivity considered the crop productivity using limited water resource and searched for better technologies for improving crop water productivity. Researchers also concentrated on improving irrigation efficiency or water use efficiency at field level for irrigation through water management and for yielding more crops. But there is a research gap in assessing values of water productivity in aquatic ecosystems especially floodplain areas. A floodplain area remains dry and flooded in two different seasons. Thus, a combined valuation of aquatic resources and rice production in two different seasons are very important to measure the efficient usages of the lands. So, this study deals with how to measure annual aggregate water productivity for dry season and flood season in a floodplain beel area.
    Date: 2019–02–28
  4. By: Natália P. Monteiro; Odd Rune Straume; Marieta Valente
    Abstract: Whether or not the use of remote work increases firm labour productivity is theoretically ambiguous. We use a rich and representative sample of Portuguese firms, and within-firm variation in the policy on remote work, over the period 2011-2016, to empirically assess the causal productivity effect of remote work. Our findings from estimations of models with firm-fixed effects suggest that the average productivity effect of allowing remote work is significantly negative, though relatively small in magnitude. However, we also find a substantial degree of heterogeneity across different categories of firms. In particular, we find evidence of opposite effects of remote work for firms that do not undertake R&D activities and for firms that do, where remote work has a significantly negative (positive) effect on labour productivity for the former (latter) type of firms. Negative effects of remote work are also more likely for small firms that do not export and employ a workforce with a below-average skill level.
    Keywords: remote work, firm labour productivity, panel data
    JEL: D24 L23 M54
    Date: 2019
  5. By: Abito, Jose Miguel
    Abstract: I propose an estimation procedure that can accommodate fixed effects in the widely used proxy variable approach to estimating production functions. The procedure allows unobserved productivity to have a permanent component in addition to a (nonlinear) Markov shock. The procedure does not rely on differencing out the fixed effect and thus is not restricted to within-firm variation for identification. Finally, the procedure is easy to implement as it only entails adding a two stage least squares step using internal instruments.
    Keywords: Production function, Estimation, Fixed Effects, Unobserved productivity, Proxy variables, Errors-in-Variables, Instrumental variables
    JEL: C0 C01 L0 L00 O4
    Date: 2019–12–24
  6. By: Mary O'Mahony; Michela Vecchi; Francesco Venturini
    Abstract: We investigate the decline of the labor share in a world characterized by rapid technological changes and increasing heterogeneity of capital assets. Our theoretical model allows for these assets to affect the labor share in different directions depending on the capital-labor substitution/complementary relationship and the workers’ skill level. We test the predictions of our model using a large cross-country, cross-industry data set, considering different forms of tangible and intangible capital inputs. Our results show that, over the 1970-2007 period, the decline of the labor share has been mainly driven by technical change and Information and Communication Technology (ICT) assets, mitigated by increasing investments in R&D based knowledge assets. Extending to other forms of intangible capital from 1995 onwards, we find that intangible investments related to innovation increase the labor share while those related to the organisation of firms contribute to its decline, particularly for the low and intermediate skilled workers. Our results are robust to an array of econometric issues, namely heterogeneity, cross sectional dependence, and endogeneity.
    Keywords: labor shares, technological change, ICT capital, intangible capital
    JEL: C23 E24 E25 O33
    Date: 2019–10
  7. By: Fabrice Defever; Michele Imbruno; Richard Kneller
    Abstract: We investigate theoretically and empirically the role of wholesalers in mediating the productivity effects of trade liberalization. Intermediaries provide indirect access to foreign produced inputs. The productivity effects of input tariff cuts on firms that do not directly import therefore depends on the extent that wholesalers are a feature of input supply within an industry. Using firm level data from China, we document that wholesalers play no such role for direct importers. However, other firms experience productivity gains from reducing input tariffs if trade intermediation of foreign inputs within their sector is high. They suffer efficiency losses otherwise.
    Keywords: firm heterogeneity, trade liberalization, intermediate inputs, productivity, intermediaries, China
    JEL: F12 F13
    Date: 2019
  8. By: Tiago Sequeira; Hugo Mourão
    Abstract: We seek for determinants of the sources of growth. Using a growth accounting method that accounts for time variations in factor shares, we run growth regressions for a panel of 101 countries between 1950 and 201. Our methodology takes into account the specific features of the data (namely outliers, heterogeneity, and cross panel correlations) and overcomes most criticisms previously raised on growth regressions. The most important evidence reveals that government current expenditure decreases the factor shhares and has no effect on total factor productivity (TFP). Trade affects the TFP and the Biased Technical Chance (BTC) components, decreasing the factor shares. Moreover, human capital decreases TFP and increases the BTC contribution to growth. This unveils the channels through wchich determinants of growth act in influencing economic growth.
    Keywords: Economic Growth;Growth Accounting;Growth Regressions;Time-varying shares;Government Expenditure; Robust estimation;Bootstrap
    JEL: O47 O50
    Date: 2020–01

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