Essays on factors affecting aggregate productivity growth

The role of services in India's post-reform economic growth

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.

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.

Recent literature has been debating the performance of high-technology zones (HTZs) in developing countries. This paper contributes to this debate by examining how national HTZs in China affect allocative efficiency of resources among Chinese cities as well as China’s total factor productivity (TFP) growth. This paper finds that resource misallocation among cities reduces China’s TFP growth by 4.5% of which capital misallocation accounts for 2.4% and labour misallocation accounts for 2.1%. Moreover, we find that the establishment of national HTZs in China increases aggregate TFP by improving the allocative efficiency of capital among cities: that is, redistributing more capital from lower to higher productivity cities. In contrast, we find little evidence of TFP gain driven by technical efficiency following the establishment of HTZs. This paper thus reveals a potential positive effect of establishing HTZs in developing countries: although establishing HTZs may not improve the technical efficiency of these countries, it may nevertheless mitigate these countries’ resource misallocation and enhance their aggregate TFP.

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.

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.

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

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. …

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.

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.

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.

Total factor productivity (TFP) determines long‐term economic growth and is a comprehensive industry‐level productivity measure. This paper proposes Jorgenson's method as an appropriate TFP measurement for the construction industry. The method is less restrictive than the conventional Chau's approach, as it does not impose the Hick Neutral Technical Change assumption. Jorgenson's method is then applied to estimate TFP growth in the construction industry of Singapore over 1984–1998. TFP growth is found down by 1.53% per annum over this period, indicating that the performance of TFP in the construction industry lags behind the rest of economy. TFP growth is also found to be fluctuating over time and tends to move in tandem with the construction business cycle. As a monitor of progress towards TFP achievement, factors influencing TFP growth in the construction industry of Singapore over 1984–1997 are identified. Seven factors are found to be significantly related to TFP growth. Among them, economies of scale, R&D by the industry, investment allowance granted and labour unions are leading contributors to TFP growth; while foreign worker, construction accidents and pre‐cast are major hampers. The general methodology presented in this study can be applied to other countries. Future studies are required to find appropriate indicators for factors unquantified.

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.

This study investigates the impact of trade openness and specialization on sectoral Total Factor Productivity (TFP) in Morocco from 1998 to 2018, shedding light on the intricate dynamics of the trade-productivity nexus. By adopting a sectoral-level approach, it addresses the limitations of macroeconomic analyses and provides a more granular understanding of trade’s effects, filling a critical research gap for Morocco. While theory suggests that trade openness could enhance long-term TFP, Morocco’s structural constraints result in mixed short-term effects, benefiting developed sectors while challenging others. The findings reveal a negative long-term impact on TFP, driven by structural inertia, weak sectoral linkages, reliance on high-tech imports rather than domestic innovation, and limited technological spillovers. However, in the short term, specialization, as measured by revealed comparative advantage, has a modest positive effect on TFP growth across most sectors, except for the food sector. The effects of inter- and intra-sectoral specialization are mixed: while it enhances TFP in sectors like Telecommunications, Agriculture, and Utilities, it negatively affects sectors such as Commerce, Logistics, and Other Manufacturing. Trade intensity boosts TFP in Other Services, Commerce, and Chemicals but hampers other sectors. While human capital enhances TFP in the short term, machinery and equipment investment drives sustained growth. These findings underscore the need for structural reforms in Morocco to strengthen human capital, optimize resource allocation, and channel investments into high-potential sectors. Moreover, targeted sectoral policies that enhance competitiveness and leverage intersectoral linkages are crucial for maximizing the gains from trade liberalization and fostering sustainable productivity growth in Morocco.

Estimating UK Capital Adjustment Costs