Whose productivity leads the world in each industry?

Comment

Trade liberalization is necessary but not sufficient to get the full return to trade reforms in the absence of liberalization of non-tradable goods through competition policy. Even if resources to augment factor growth are readily available, that too could fizzle out because of diminishing returns. Sri Lanka has made only limited progress on trade liberalization in the post-conflict era and at present has no competition policy to speak of. Sustained growth has to be based on total factor productivity (TFP) growth. Otherwise, Sri Lanka will have to keep raising savings to increase capital, assuming that labor growth sees a steady increase. However, as living standard increases, population growth declines, and labor supply does not grow fast. In fact, Sri Lanka is now past the demographic dividend phase and already in a situation where there is a steady decline in population. In this context, low average TFP growth means that the country has to keep on increasing saving and borrowing from abroad to raise GDP growth, based on factor augmentation. This leads to a difficult macroeconomic situation as excessive foreign borrowing is needed to augment domestic savings that Sri Lanka cannot afford to service in view of the fact that access to capital is more costlier than before. The study finds TFP growth in Sri Lanka to be low. It finds that trade and competition policies that would have raised TFP growth were not sustained after their initial introductions.

The objective of the present study is three-fold: to employ an aggregated data to investigate the total factor productivity (TFP) growth of the Malaysian rice sector; to investigate the sources of the TFP growth; and, to examine and extend the Glass and McKillop procedure for computing TFP growth. To this end, we establish several procedures which make it possible for us to: (i) link the TFP analysis with the theory of production; (ii) disentangle the sources of TFP growth into scale and technological change effects; and (iii) apply, compute, examine and extend the Glass and McKillop procedure for computing TFP growth.The finding of the study is as follows. (i) Using the standard procedure forcomputing TFP growth it was found that the average TFP growth for Malaysian rice farming was 1.37%, of which the scale effect contributed 0.29% and the remaining 1.08% was due to the technological change effect. (ii) Using the extended Glass and McKillop procedure, however, it was found that the average TFP growth, the technological change effect and the scale effect were 3.48%, 3.19% and 0.29%, respectively. (iii) Comparing these two results, derived from two different procedures, we concluded that the difference in magnitude of the TFP growth was due to the two distinct procedures for computing the technological change effect per se.

India’s decelerating wheat- and rice-yield growth rates have led to questions of whether India’s agricultural sector will be able to meet future food demands. To explore this issue, ERS researchers measure sector-level agricultural total factor productivity (TFP) growth and evaluate how public policies affected TFP from 1980 to 2008. During this period, substantial regional differences in TFP growth emerged: the Indian West and South achieved faster TFP growth than the rest of the country, largely due to rapid growth in horticulture and animal products. Of the policies hypothesized to stimulate TFP, India’s public agricultural research and higher education programs had the greatest effect on TFP growth, followed by public investments in irrigation infrastructure. These effects propelled TFP in Northern and Western India more than in the rest of the country. Groundwater irrigation from wells accelerated TFP more than surface-water irrigation from canals. Other drivers of TFP growth included research investments of international institutions and an emerging private sector. Public investment in rural education has had mixed effects, depending on education levels. These findings support an optimistic view that Indian agriculture will be able to meet the broadening spectrum of future food demands. Critical to that optimism, though, is continued innovation from public and private research systems, especially in seed development, and from irrigation and high-value-commodity production technologies.

Imperfections in the credit market can hamper the flow of factors from less productive to more productive firms and result in a lower aggregate total factor productivity (TFP). Depth of such misallocation will depend on per capita income, the level of imperfections in the credit market, and the distribution of entrepreneurial productivity. Under some parameter configurations, we find that per capita income and TFP may affect each other so that an economic boom may cause higher resource misallocation, lower TFP, and economic recession. At the same time, an economic recession may have a “cleansing effect” on TFP leading to a lower resource misallocation, higher TFP, and economic boom. In other words, economic success may breed the failure and the failure can become a precondition for success so that the boom-bust cycles in resource misallocation, TFP, and per capita income may become endogenous.

I. Introduction Total factor productivity (TFP) growth is an important measure of potential output growth given the nature of the diminishing returns to input use in the long run. Thus, Malaysia in her drive to enjoy sustainable growth to raise its living standards is set on focusing on TFP growth as stated in Malaysia's Second Industrial Master Plan 1996-2005. In fact, the manufacturing sector which has increased its contribution to gross domestic product (GDP) output from 19.3 per cent in 1979 to 34.2 per cent in 1996 has been identified as a key growth engine in this transformation process. Hence, it is imperative and timely for an analysis on the productivity growth performance of this sector to be undertaken. This study adds to the existing empirical literature in three ways. First, previous studies on Malaysian manufacturing have only considered the nonfrontier measure using the divisia translog index approach. To date, using the nonfrontier approach, Tham (1996, 1997) and the Productivity Report 1999 provide evidence of declining TFP growth for the Malaysian manufacturing sector in the 1990s (see Table 3). (1) How would this result compare with the use of the frontier approach? Will the frontier models also provide low TFP growth measures? This is one of the issues addressed in this article. As for the earlier studies, the nonparametric technique adopted computes TFP growth as a residual since it measures anything and everything of output growth that is not accounted by input growth. More importantly, the translog index TFP growth measure ignores the concept of technical inefficiency (by unrealistically assuming that all industries are technically efficient) and inaccurately interprets technical change as TFP growth. Thus in this study, frontier measures are used to overcome these major drawbacks. In the productivity literature, TFP growth is shown to be composed of both technical change (frontier shift) and technical efficiency (catching up effect). While the frontier effect indicates how far the efficient frontier itself has shifted over time due to the use of better technology and equipment, the catching up effect reflects how far the industry has moved towards the efficient frontier due to the better use of technology and equipment. The second difference in this study is that empirical robustness is ensured by the use of both the parametric and nonparametric frontier approaches to calculate TFP growth. Under the parametric approach, a stochastic production frontier model incorporating non-parallel shifts is estimated. With the nonparametric approach, the data envelope analysis (DEA) technique is used. Using a panel data set of twenty-eight manufacturing industries (see Appendix 1 for a list) from 1981 to 1996, a measure of TFP growth is first obtained and then decomposed to technical change and change in technical efficiency for both models. The results are then compared to previous studies with a focus on the Malaysian manufacturing sector as TFP growth studies on the aggregate economy may have broad implications that are not necessarily reflective of the TFP growth performance of specific sectors in the economy. The third contribution of this article is that the comparative performance of the results from alternative methodologies would add to similar work by Bjurek and Hjalmarsson (1990), Coelli and Perelman (1999), and Kumbhakar, Heshmati, and Hjalmarsson (1999) which provide mixed evidence of similarities in the results from the use of various models. Often, the choice of the method is said to depend on a range of factors. For instance, if the researcher simply wants to know if output growth is TFP or input-driven growth, then either approach would suffice. However, to answer questions on maximum productive or best practice output levels, the stochastic frontier can be used to understand the industries' catching up behaviour with respect to its own maximum potential, while DEA allows for the study of the performance of each industry relative to efficient industries in the sample. …

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. …

In this paper we try to measure and to explain total factor productivity (TFP)growth in Tunisia over the period 1983-1996. We do not measure TFP growth by the conventional Solow residual. Instead we define TFP as the shift of the economy's production frontier, which we obtain year by year by a linear programming method, a sort of aggregate DEA analysis. We then decompose this aggregate TFP growth into a Solow residual, a terms of trade effect, and a shift in demand composition. We also proceed to a decomposition of TFP growth into individual factor productivity growth rates: those of labor, decomposed into five types, of capital and of the allowable trade deficit. We find that potential TFP has grown by 0.4 percent per year over the whole period. But, it is especially after 1991 that TFP has grown. Before that, it tended to display negative growth rates. Labor turns out to be the most important contributor to total factor productivity growth. Only in the last period did capital play an important role. The Solow residual was the main driver of TFP growth. Changes in the terms of trade and demand composition were detrimental to TFP growth.

This paper compares the total factor productivity (TFP) growth performance of the Chinese mainland and the Four Asian Tigers during their high-growth period and examines the effect of growth strategies pursued by these economies on TFP growth using a state-space model. Our research results show that TFP growth is quite limited in these economies, which is mainly attributed to their growth strategy. No significant productivity gains arise from the rapid growth of investments, trade openness, and an undervalued currency in these economies. The TFP growth is even found negatively related to trade openness for South Korea, the exchange rate undervaluation for Chinese Taiwan and Singapore, and the falling relative price of capital for the Chinese mainland, Singapore and South Korea. Government interventions encourage long-term TFP growth for the Chinese mainland and Taiwan, but hinder it in other economies. Higher inflation reduces TFP growth in Chinese Taiwan and Singapore.

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.

Continuous cotton ( Gossypium hirsutum L.) production was examined using data from Alabama's long‐term Old Rotation experiment (c. 1896). Index values were used to examine trends in productivity and sustainability for 95 yr. Treatments studied were those receiving (i) no N fertilizers and no winter legumes for 95 yr, (ii) only winter legumes as a source of N, and (iii) chemical fertilizer N. Three sets of index numbers were calculated from all inputs and outputs involved in the production systems: (i) total factor productivity (TFP), which accounts for all direct production inputs, but which does not consider production externalities; (ii) productivity relative to a base plot;and (iii) total social factor productivity (TSFP), which accounts for all direct production inputs as well as externalities of soil erosion and pesticide use. Viewed from the 95‐yr perspective of the Old Rotation experiment, all three treatments fulfill at least one criterion required for a system to be considered sustainable. Output per unit of input is higher in 1991 than in 1896, even when externalities are valued. None of the systems showed a linear trend in output or TFP over the life of the experiment;productivity cycles are present in all three systems, despite a positive overall trend. An average annual rate of TSFP growth of 1.8%/yr was attained. Accounting for erosion and pesticide externalities reduced the annual productivity growth rate by 0.2%/yr. The system that has neither an organic nor a chemical source of added N was less productive and less sustainable than the two other systems, with a 0.3%/yr TSFP growth rate. The plots using organic and chemical sources of N had similar productivity impacts. Valuing soil erosion and pesticide externalities had only a modest effect on measured productivity. The most dramatic single event to affect the productivity of cotton farming was the introduction of the mechanical cotton picker. The impact of this technology was powerful enough to offset the effect of many other changes in the system. Research Question Is cotton production in the southeastern USA sustainable? How do we measure sustainability of a crop that has been produced for almost 200 yr in the same region but has a reputation for depleting the soil of nutrients, extensive soil erosion, and high pesticide use? The objective of this study was to use input and output indexes and a calculation of total factor productivity (TFP) to determine if cotton production using different management strategies is sustainable over nearly a century of continuous production. Literature Summary Most researchers agree that a sustainable system should maintain or enhance agricultural production, reduce the level of production risk for the farmer, protect natural resources, be economically viable, and be socially acceptable. Measuring all of these attributes of a production system is very difficult. However, using the extensive data available from historical, long‐term experiments should provide insight as to sustainability of certain production systems. Alabama's Old Rotation (c. 1896) is the oldest continuous cotton experiment in the world. Input and output (yield) records and estimates allow calculation of TFP indexes over the 95‐yr history of continuous cotton production. Different cotton production systems can be compared. Study Description Three continuous cotton systems from the Old Rotation were chosen for comparison: (i) No N and no winter legumes since 1896 (No N), (ii) winter legumes (crimson clover and/or vetch) as the only source of N since 1896 (winter legumes), and (iii) no winter cover crop and 120 lb N/acre as ammonium nitrate since 1956 (N fertilizer). Where input records were not recorded (e.g., labor, costs, machinery, etc.), they were estimated from USDA, Alabama Agricultural Experiment Station, and Alabama Cooperative Extension Service publications. Soil erosion estimates for the three cropping systems on a Pacolet fine sandy loam, were made using Erosion Productivity Index Calculator modeling. Input, output, TFP, and total social factor productivity (TSFP) indexes for 95 yr were calculated. Total social factor productivity includes estimated values for the negative offsite effects of soil erosion and pesticide use. Applied Questions Is continuous cotton production sustainable? Viewed from the 95‐yr perspective of the Old Rotation, the no N, winter legume, and N‐fertilized continuous cotton plots all fulfill at least one criterion required for a system to be sustainable. Output per unit of input is higher in 1991 than in 1896, even when externalities (erosion and pesticides) are valued. The average growth rates on the No N plot are 0.5%/yr for TFP and 0.3%/yr for TSFP. On the winter legume plot, TFP and TSFP grew at a rate of 2.0%/yr and 1.8%/yr, respectively. The plots using organic and chemical sources of N had similar productivity records. None of the systems shows a linear trend in TFP over the history of the experiment. Productivity cycles are present in all three systems, despite the positive overall trend. An important focus of future research will be to explain whether these cycles are related to weather, technology, or changes in the resource base. As one would expect, the system that has neither an organic or a chemical source of added N is less productive than the two other systems. This system compares even more poorly when externality costs are assigned. Organic and chemical sources of N have similar productivity impacts. How have externalities such as soil erosion and the negative impact of pesticide use on the environment affected TFP? Soil erosion and pesticide externalities have had only a modest effect on measured productivity. The no N plot indexes are not changed at all; TFP on the legume and N‐fertilized plots decreased by 4 and 6%, respectively. The main conclusions of the previous question are therefore unaffected. How have technological advancements affected long‐term productivity/sustainability of continuous cotton production? The most dramatic single event to affect productivity was the introduction of the mechanical cotton picker around 1960. The impact of this technology is powerful enough to offset the effect of many other changes in the system. This advancement allowed cotton production to move from a labor‐intensive environment with increasing labor costs per pound of yield to an environment where harvesting costs were not seriously affected by increasing yields. Because technological advancements cannot be predicted into the future, predicting the long‐term sustainability of a system becomes very difficult.

The present paper discusses total factor productivity (TFP) in China, including its past success, the current slowdown, and the potential for future growth. It begins by documenting the development of TFP growth over the past three and a half decades, its driving forces and its contribution to the economic growth of the country. It then analyses the reasons for the current slowdown of TFP and economic growth, addresses the institutional imperfections that hinder growth, and explains the government policies and strategies aimed at fostering TFP. Next, it explores the potential for TFP growth from the perspective of institutional reform, investment in research and development and human capital. The paper concludes that although the resources of the past successful TFP have decreased or diminished, further institutional reform, increasing investment in research and development and human capital, and strategies promoting indigenous innovation will become new engines for future TFP growth in China. As the country’s TFP is still at a low level compared with advanced economies, there is large scope for China to maintain relatively high TFP growth, although uncertainty and risk are associated with this process.

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.

Economic theory has identified a number of channels through which openness to international financial flows could raise productivity growth. However, while there is a vast empirical literature analyzing the impact of financial openness on output growth, far less attention has been paid to its effects on productivity growth. We provide a comprehensive analysis of the relationship between financial openness and total factor productivity (TFP) growth using an extensive dataset that includes various measures of productivity and financial openness for a large sample of countries. We find that de jure capital account openness has a robust positive effect on TFP growth. The effect of de facto financial integration on TFP growth is less clear, but this masks an important and novel result. We find strong evidence that FDI and portfolio equity liabilities boost TFP growth while external debt is actually negatively correlated with TFP growth. The negative relationship between external debt liabilities and TFP growth is attenuated in economies with higher levels of financial development and better institutions.

This study uses industrial panel data for Japanese manufacturing to estimate the sources of productivity growth by simultaneously considering embodied technical progress, spillover effects, and openness, after controlling for returns to scale, imperfect competition, and capacity utilization. Estimation results show the existence of considerable embodied technical progress and interindustry externalities of capital investments positively affecting productivity growth. Furthermore, embodied technical progress causes research and development (R&D) capital to affect productivity growth insignificantly, suggesting that the impact of R&D is realized only after being embodied into other capitals. From sector‐wise estimations, we notice differences in factors affecting productivity growth between the durable and nondurable manufacturing sectors. (JELD24, O30)