Chasing the Dream: Industry‐Level Productivity Developments in Europe

This study investigates the effects of industrial robot adoption in the production process on unemployment rate, employment ratio in manufacturing, and total factor productivity (TFP) growth in 52 countries, and real wage growth in 31 and 20 OECD member countries for 2007–2019. The operating stock of robots per employee significantly impacts these variables; robot adoption lowers the unemployment rate and raises TFP and real wage growth. However, it reduces the employment ratio in manufacturing. In addition, the slight but significant positive contribution of robot adoption is observed only in the 90-percentile (top 10-percentile) of the real wage distribution. Interestingly, workers in the bottom and top tails (10- and 90-percentiles) of the wage distribution asymmetrically benefit from robotization. The industry ratio of value-added improves the labor market by reducing the unemployment rate and raising the employment ratio in manufacturing, TFP growth, and real wage growth. The information and communication technology (ICT) development also positively contributes to the employment ratio in Asia’s manufacturing, TFP growth, and real wage growth.

The study examines the resources of economic growth in the Czech Republic in the course of years from 1992 until 2004. Using the growth accounting method, it analyses the contribution of individual factors to economic growth. Special attention is given to total factor productivity, which, apart from labour, also includes a fixed capital stock at constant prices. Compared to the previous period, the acceleration of the growth of total factor productivity decisively contributed to the speeding up of economic growth in the years 1999-2004. Furthermore, the study examines growth resources in six national economy sectors and analyses the contribution of individual sectors to the growth of macroeconomic total factor productivity. The analysis has shown that namely industry, transport, communications, and other services were involved in the speeding up of the growth of macroeconomic total factor productivity. A comparison of the dynamics of total factor productivity of the CR and EU-15 at the macroeconomic level has shown that while in 1992-1998, the growth of total factor productivity was slower in the CR, after 1998, it was faster (in 1999-2004, the average annual growth rate in the CR was 2.2% and 0.6% in EU-15). In the years 1996-2004, for which revised data are available for the CR, the average annual growth rate of total factor productivity in the CR was 1.5%, compared to 0.7% in EU-15. The analysis indicated that since 1999, total factor productivity in the CR has been converging to the EU-15 level, accelerating in 2003 and 2004, thereby achieving 63% of the EU-15 level in 2004.

Total factor productivity (TFP) growth in Britain's railways in the last part of the nineteenth century and the first decade of the twentieth has been widely studied, not least because it can throw light on the question of the causes of overall slowdown in industrial growth. This article is concerned with the detailed mechanics of measuring TFP growth and with the use of results to compare growth across different companies. The article disaggregates TFP growth between different activities performed by railway companies (provision of locomotive power, operation of carriages and wagons, provision of permanent way, and working of traffic), and it also develops detailed measures of capital stock and capital costs using disaggregate data on assets employed in each activity. These improvements to existing methodology reduce, rather than increase, existing estimates of TFP growth. Consequently the results confirm the previously observed conclusion that productivity growth was slow. They show that while there were significant increases in goods train operating efficiency in the first decade of the twentieth century, the additional resources that were employed meant that these increases were slow to translate into overall TFP growth.

The objective of this article is to measure productivity growth in Sri Lanka’s food manufacturing industry during the 1978–2014 period and briefly review the policy measures adopted over time aiming to improve productivity in the industry. Total Factor Productivity (TFP) growth trends were measured adopting the Cobb–Douglas production function framework using the data extracted from the Annual Surveys of Industries conducted by the Department of Census and Statistics, Sri Lanka. TFP growth was computed for the entire industry as well as for its sub-divisions with emphasis on the scale of operation, type of industry, and policy regimes. Even though the average TFP growth in Sri Lanka’s food manufacturing industries was found to be negative (−0.61%) for the entire period (1978–2014), it is noteworthy that average TFP growth improved over time from −2 per cent during the 1978–1994 sub-period to 0.7 per cent over the 1994–2014 sub-period. Furthermore, large-scale food manufacturing industries achieved higher TFP growth compared to that of their small-scale counterparts. Distilling, rectifying spirits, manufacture of grain mill products and prepared animal feeds industries showed positive and higher TFP growth while cocoa, chocolate and confectionery industries demonstrated a lower TFP growth. Food safety management systems and investments in infrastructure through Public-Private Partnerships were some policy measures that led the productivity growth in Sri Lanka’s food manufacturing industries.

Using US input–output data for the period 1958–87, I find strong evidence that industry total factor productivity (TFP) growth is significantly related to the TFP performance of the supplying sectors, with an elasticity of almost 60%. R&D intensity is also found to be a significant determinant of industry TFP growth, with an estimated return of about 10–13% and the return to embodied R&D is estimated at 43%. Direct productivity spillovers, from the technological progress made by supplying sectors, appear to be more important than spillovers from the R&D performed by suppliers. They also play a key role in explaining changes in manufacturing TFP growth over time. Changes in the contribution made by direct productivity spillovers to TFP growth account for almost half of the slowdown in TFP growth in manufacturing from 1958–67 to 1967–77, and for 20% of the TFP growth recovery in this sector from 1967–77 to 1977–87. Changes in R&D intensity and embodied R&D are relatively unimportant in explaining movements in manufacturing TFP growth over these three periods.

Purpose – China and India have made significant strides in transforming their agricultural sectors to cut hunger and poverty for the masses through improved agricultural productivity. Given limited land and shift of labor to non-agricultural sector, increasing productivity will continue to be central in agricultural growth in the twenty-first century. The purpose of this paper is to provide comparative analysis of the agricultural total factor productivity (TFP) growth in the two countries. It complements existing literature by examining the evolution and drivers of TFP at disaggregated sub-national level. Richer data allows a deeper understanding of the nature and drivers of TFP growth in the two countries. Design/methodology/approach – This paper applies different analytical framework to address different research questions using data since 1980. China study estimates a parametric output-based distance function using a translog stochastic frontier function. Productivity growth index and its multiple components are calculated using parameters derived from the parametric approach to identify the characteristics of technology such as structural bias. India study first applies data envelopment analysis to estimate the aggregate productivity growth index, technical change (TC), and efficiency change. Next productivity indexes by for traditional crops are estimated using growth accounting framework at state level. Finally, a panel regression links TFP on its determinants. Findings – Several common themes emerge from this comparative study. Faced with similar challenges of limited resources and growing demand, improving productivity is the only way to meet long-term food security. Agriculture sector has performed impressively with annual TFP growth beyond 2 percent in China and between 1 and 2 percent in India since the 1980s. The TFP growth is mainly propelled by technological advance but efficiency had been stagnant or even deteriorated. This study provides a granular picture of within country heterogeneity: fast growth in the North and Northeast part of China, South and East of India. Research limitations/implications – The study suggests some possible policy interventions to improve agricultural productivity, including investment in agricultural R & D to create advanced production technology, effective extension programs and supportive policies to increase efficiency, and diversification from staple crops for sector-wide growth. The India study suggests certain policies may not be contributing much to productivity growth in the long run due to a negative impact on environment. Further studies are needed to expand the productivity analysis to take into consideration of the negative externalities to the society. Data enhancement to account for quality-adjusted inputs could improve the estimation of productivity growth. Originality/value – Each country study reveals certain prospects of the agricultural sector and production technology. China analysis statistically confirms the existence of technical inefficiency and technology progress, suggests the translog form is appropriate to capture the production technology and satisfies conditions stipulated in theoretical models. The results indicate TC does not influence the contribution of output or input to the production process. India study pinpoints the lagging productivity growth of traditional crops, which still derives growth from input expansion. Although different states benefited from different crops, sector-wide productivity gain is primarily the result of diversification to high-value crops and livestock products.

An earlier paper by Diewert (J Prod Anal 43(3):367–387, 2015) provided some new decompositions of economy wide labour productivity growth and Total Factor Productivity (TFP) growth into sectoral effects. The economy wide labour productivity growth rate turned out to depend on the sectoral labour productivity growth rates, real output price changes and changes in sectoral labour input shares. A puzzle is that empirically, the real output price change effects, when aggregated across industries, did not matter much. The economy wide TFP growth decomposition into sectoral explanatory factors turned out to depend on the sectoral TFP productivity growth rates, real output and input price changes and changes in sectoral aggregate input shares. The puzzle with this decomposition is that empirically all of these price change effects and input share effects did not matter much when they were aggregated over sectors; only the sectoral TFP growth rates contributed significantly to overall TFP growth. The present paper explains these puzzles.

Discovering the Sources of TFP Growth: Occupational Choice and Financial Deepening

This article studies the effects of remittances on economic growth and the sources of growth:capital accumulation and Total Factor Productivity (TFP) growth. This approach is different from previous studies, in that, it focuses on the transmission mechanism instead of the impact of remittances on economic growth. We find that remittances have conflicting effects on the two sources of growth: capital accumulation and productivity growth. Remittances have a significantly positive impact on capital accumulation while the impact on TFP growth is insignificant. These findings suggest that while remittances enhance investment and contribute to physical capital accumulation, the lack of efficiency enhancing effect or possible adverse impact on TFP growth would make the net effect on economic growth ambiguous.

Productivity growth can be decomposed into its sources, namely technical changes, technical efficiency changes, and business scale changes. The role of each of these sources is important to be studied because Indonesia experienced a decline in total factor productivity (TFP) growth in 1990-2015 which indicates a productivity issue. The results of previous studies by Margono Sharma (2006) and Ikhsan (2007) regarding the decomposition of productivity growth in the manufacturing industry sector in Indonesia using a stochastic frontier approach, there are still disagreement about the contribution of technical changes and technical efficiency changes to TFP growth. This is thought to be caused by differences in the specifications of the technical efficiency models used, namely Battese Coelli (1992) and Lee Schmidt (1993). The two models have different estimation methods, Battese Coelli (1992) use maximum likelihood estimate, while Lee Schmidt (1993) use non-linear least squares. Hence the scalar parameters that determine the pattern of technical efficiency over time (η in Battese Coelli (1992) and θ in Lee Schmidt (1993)) are different.This study uses data for the period of 2003-2014 in the manufacturing industry sector. The results of this study indicate that different technical efficiency model specifications produce a different relative contribution of TFP growth sources. Therefore, choosing a technical efficiency model to decompose TFP growth should be thorough if the results will be used in important policy making.

We examine the importance of total factor productivity (TFP) growth in middle-income countries based on cross-country panel data for the period 1975–2014. We find that TFP growth contributed significantly to a country’s upward transition from middle-income to high-income country group. The TFP growth model reveals that the catch-up effect, human capital, smaller population, weak currency, and research and development (R&D) growth are significant sources of TFP growth. We do not find a systematic difference in the TFP growth models for middle-income countries. In analyzing the role of factors influencing TFP growth at different income stages, strengthening innovative activities and building innovative capacities are important in overcoming the challenges that middle-income countries face when transitioning to the high-income group. Governments of upper-middle-income countries need to initiate reform to motivate innovation by optimizing national R&D systems, and redesigning the educational system to target promoting innovation.

This study first explores why the shares of factor inputs have not been measured correctly and concludes that the earlier findings are biased due to the miscalculation of factor shares which have produced low estimated total factor productivity (TFP) growth in the East Asian countries. Second, three approaches are proposed to empirically illustrate the impact of capital and labor shares on the estimates of TFP growth. It is suggested that TFP growth in the East Asian economies will be understated if net indirect taxes and imperfect competition profit are ignored. Finally, by taking the net indirect taxes and imperfect competition profits into account, the result of this paper indicates that Taiwan's economy has enjoyed an average annual TFP growth rate of 3.6% over the period 1980–1999.

In this study, we estimate total factor productivity (TFP) growth as well as multilateral TFP index for 25 contiguous China provinces over the 1985–2007 period. Agricultural output growth for each province was decomposed into TFP growth and input growth, where input growth was further disaggregated into contributions from growth of labor, capital, land, and intermediate goods. Over the study period, TFP growth contributed 2.7 percentage points to output growth annually, which was slightly higher than the input growth contribution of 2.4 percentage points per annum. On average, the annual rate of productivity growth peaked during 1996–2000, at 5.1%. It slowed in 2000–2005 to a rate of 3.2% per annum and declined in the most recent years (2005–2007) to −3.7%. Differences in productivity among regions persisted over the entire period. The tendency toward faster TFP growth in relatively well‐off coastal regions may imply a widening of regional inequality.

This thesis examines factors affecting aggregate total factor productivity (TFP) growth in developing countries. While the first two essays are devoted to exploring trade-embedded foreign R&D spillovers, the last two essays are more concerned about firm dynamics and misallocation of resources across firms. Although foreign R&D spillovers have been studied intensively since the emergence of the innovation-led growth models in the early 1990s, little research has been conducted on the impact of Free Trade Agreements (FTAs) on North-South diffusion processes. Also, few studies have incorporated domestic R&D and trade liberalisation to examine North-South R&D spillovers at industry level. The first two essays fill these gaps. The last two essays contribute to the literature by partly addressing the long-lasting puzzle of why productivity dispersion in developing countries is large and persistent, even within narrowly-defined industries. In particular, these essays explore the role of firm dynamics, including entry and exit, and reallocation of resources across firms in reducing TFP gap in the manufacturing sector of a transitional economy, namely Vietnam. Given the recent proliferation of FTAs, the first essay investigates the impact of FTAs on North-South R&D spillovers. Using panel data covering 56 developing countries and 15 OECD countries over 1980-2008, the results show that North-South R&D spillovers are sizeable. FTAs have, however, a negative impact on this diffusion process. This finding may be attributed to decreases in the variety of goods imported, and the switching of imports from more efficient non-member countries to less efficient member countries. The second essay employs the industry-level data in Vietnamese manufacturing over 2000-2009 to address the foreign technology diffusion from 16 OECD countries. The empirical results indicate that the domestic own industry R&D improves TFP growth in the sector, but in a much smaller scale than the foreign R&D counterparts. The foreign R&D spillovers themselves differ in diffusion channels, as Vietnamese manufacturing’s TFP seems to benefit more from the foreign R&D embedded in the inter-industrial relation through the Input-Output tables than that embedded in import from the same industries abroad. Despite experiencing several major trade liberalisations in the manufacturing sector over the study period, the sectoral TFP has seen little impact from these reforms. The third essay studies the impact of market deregulation on the aggregate TFP of Vietnamese manufacturing. Over 2000-2008, Vietnam experienced a big market deregulation which induces a massive entry of private firms amid privatisation of state-owned enterprises (SOEs). The results reveal that during this period firms’ entry and exit makes up around a half of aggregate TFP growth. Private firms account for most of this TFP improvement as they exhibit a “growth” effect over the state entrants and a “level” effect over the foreign counterparts. Despite huge entry of private firms, SOEs face lower hazard rates than the private counterparts, even accounting for productivity differentials. This may be attributed to SOEs’ receipt of favourable treatment from the government that prevents them from exposure to the same degree of market experimentation as experienced by the private firms. The last essay examines whether firm dynamics improves allocative efficiency in Vietnamese manufacturing over 2000-2008. Given that this is a period of unprecedented growth in domestic credit supply, the essay also investigates the impact of the credit policy on capital misallocation across firms in the sector. It finds that firms’ entry and exit contributes significantly to the allocative efficiency improvement in Vietnamese manufacturing, particularly in the second half of the study period. The across-firm TFP dispersion within 4-digit ISIC industries is large and persistent because SOEs disproportionately absorb a lion’s share of credit. In that context, providing more credit to the SOEs, relatively to their private counterparts, would yield an anti-capital distortion reducing effect rather than the desired capital distortion reducing effect of commercial and subsidised credit.

Since the early 1990s, discussions on total factor productivity (TFP) have become increasingly important for Singapore, and there has been substantial amounts of empirical work done in this area using both cross-country and inter-temporal analysis. This article reviews the conceptual and empirical aspects of some of the TFP growth studies on Singapore and provides suggestions for future research. Introduction Total factor productivity serves as an important measure of the productive performance of an economy for the following reasons. First, unlike the partial productive measure, it considers the contribution of more than one input to output. Second, it is important to the growth process in the long run, as there are constraints imposed by population growth, together with diminishing returns that set in as capital intensity is increased. One way to secure economic growth beyond these limits is to secure ongoing increases in TFP. This is a very relevant issue for Singapore, which the OECD has upgraded to the status of an advanced newly developing country. Although Singapore has enjoyed impressive levels of economic growth, in terms of sustainable long-term growth given by TFP, it was Young (1992) who first argued that Singapore was nowhere near its twin city, Hong Kong. This was then emphasized by Krugman (1994) who singled out Singapore as the only newly-industrialized economy (NIE) which experienced no TFP growth. But Lim (1986, p. 5) once commented that, if a country can raise its standard of living so spectacularly with a very low TFP growth, does it then matter whether TFP growth is low?' Peebles and Wilson (1996, p. 205) as a reply to Lim, argued that an important point is missed in this comment which only looks at the benefits resulting from high growth and ignores the cost of achieving such growth. This means that TFP analysis in Singapore is not to be taken lightly. It was noted that most developed countries at the same development stage as Singapore were having TFP growth rates between 2 per cent and 4 per cent while Singapore registered an insignificant 0.4 per cent between 1980 and 1992. The target is now set to reach at least 2 per cent in order to sustain a (labour) productivity growth of 4 per cent and a GDP growth of 7 per cent.2 The importance of TFP in Singapore is further reflected in the numerous studies undertaken to examine the issue. Review of TFP Studies Table 1 provides a summary of various studies' estimates of TFP growth for Singapore's aggregate economy, manufacturing (Manu) and services (Serv). Most of the studies above show that TFP growth in Singapore has been insignificant, particularly in comparison with the NIEs and other countries. After the pioneering work of Tsao (1982) on Singapore, various studies have reexamined this issue. While Wong and Tok (1994) and Rao and Lee (1995) showed that TFP growth increased in the latter half of the 1980s, the National Productivity Board (1994) and Sarel (1997) showed improvements in TFP growth for the early 1990s. However, the Department of Statistics (1997) and Renuka M. (1998) show otherwise and Leung's (1997) results are inconclusive for the early 1990s. It is obvious that TFP results can vary significantly from one another and this is due to different methodologies used and different time periods of study. The objective of this article is to critically analyse some of the often-cited cross-country and inter-temporal TFP studies in order to understand the problems that still exist in the analysis of TFP growth. In particular, the conceptual and empirical aspects of the studies are reviewed and suggestions for future research are put forth. Unfortunately, the space constraint and the disparate and complex nature of the various studies reviewed are not amenable to a more integrated approach to the article.3 For instance, a detailed comparison of various methodologies in TFP measure and their caveats as well as data problems in Singapore could not be discussed extensively but an attempt was made to incorporate these issues within the studies whenever possible. …