Total Factor Productivity of the Japanese Rice Industry

2 previous empirical studies. It performs an empirical examination of the model with regional panel data of the manufacturing sector in Japan. A city level panel data constructed mainly from the Census of Manufacturers for the 1996−2006 is used for empirical analysis. The revenue function including parameters for the transportation costs of each industry is estimated. The results support the existence of positive transportation costs, and show the estimated transportation costs for the manufacturing sector are higher than those for the primary sector and lower than those for the service sector. Chapter 3 Plant Productivity Dynamics and Private and Public R&D Spillovers: Technological, Geographic and Relational Proximity Chapter 3 investigates the knowledge spillovers and examines the effects of R&D spillovers on total factor productivity (TFP) with a large panel of Japanese manufacturing plants matched with R&D survey data (1987–2007). This chapter simultaneously examines the role of public (university and research institutions) and private (firm) R&D spillovers, and the different effects due to technological, geographic, and relational (buyer-supplier) proximity. Estimating dynamic long difference models and allowing for a gradual convergence in TFP and geographic decay in spillover effects, the results show that technologically proximate private R&D stocks positively affect TFP growth, which decay with distance and become negligible at around 500 kilometers. In addition to knowledge spillovers from technologically proximate R&D stocks, ‘relational’ spillovers from buyer and supplier R&D stocks exert positive effects on TFP growth that are similar in magnitude. The elasticity of TFP is highest for public R&D (corrected for industrial relevance), in particular for plants operated by R&D-conducting firms. This chapter does not find evidence of geographic decay in the impact of public and relational spillovers. Over time, declining R&D spillovers appear to be responsible for a substantial part of the decline in the rate of TFP growth. The exit of proximate plants operated by R&D-intensive firms plays a notable role in this process and is an important phenomenon in major industrial agglomerations such as Tokyo, Osaka, and Kanagawa. Chapter 4 Effects of Regional Human Capital on Business Entry: a Comparison of Independent Startups and New Subsidiaries in Different Industries Chapter 4 and Chapter 5 examine the effects of labor pooling. Chapter 4 aims to investigate

This paper consists of two parts. In the first part we carry out a traditional growth accounting exercise for the private business sectors of the Swedish economy. A search for structural breaks during the sample period, using Chow tests with a dynamic specification of Total Factor Productivity (TFP) growth rates, and Granger causality tests are carried out for the nine sectors of the Swedish economy. We combine the growth rates of value added and hours worked and calculate labor productivity for the period 1960–1999. In order to facilitate comparisons we present Swedish and international results. To a large extent we are able to replicate the Swedish results. The slow down in TFP growth rates in the 1970s can be identified with the first and the second oil shocks in 1973 and 1979. The other structural breaks occurred in the early 1990s and could possibly be identified with the tax reform of the century in 1991 and the severest of recessions that took place in the Swedish economy. The Granger causality tests indicate that growth rates in investment Granger causes growth rates in TFP for the agriculture and the financial institutions, real estate and other business, while TFP growth rates in mining and quarrying, and manufacturing granger causes growth rates in investment.In the second part of the paper, we Hodrick–Prescott filter the data, and calculate cross correlations of detrended output, hours, investment and TFP at different leads and lags. The results indicate that investment leads TFP for agriculture, hunting, forestry and fishing, electricity gas and water, and for education, health and social work and community social and personal services. Investment lags TFP for the mining and quarrying, manufacturing industry, and for financial institutions and insurance companies, real estate renting and business service companies. Hours worked lead the TFP cycle for mining and quarring, manufacturing and wholesale/retail trade. The decomposition of TFP into trend and cyclical component dates the business cycle. Standard deviations on the cyclical components of value added, hours worked, TFP, and gross investment reveals that the most volatile variables are gross investment, followed by TFP, GDP and hours worked.The contribution of this part of the paper lies in the disaggregated data set containing annual information for the period 1963–1999, and in the application of several analytical tools to the growth accounting exercise results. In addition such an extensive growth accounting exercise has not been carried out for the private business sectors of the Swedish economy.

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.

This paper applies the \(\beta \)-convergence regression model in order to assess convergence of total factor productivity among Vietnamese provinces for manufacturing industries. Specifically, we express this model in the form of a Spatial Durbin Model (SDM), which allows us to take into account the presence of omitted variables that can be spatially correlated and correlated with the initial level of productivity. We calculate the annual total factor productivity (TFP) of 63 Vietnamese provinces and 6 manufacturing industries, using the results of the structural estimation of a value-added production function from firm data over the period from 2000 to 2012. The regression of growth rates of TFP over this period on the initial levels of productivity using SDM shows that there is convergence in most industries, i.e. the gap between lower-productivity and higher-productivity provinces decreases. These results also show the importance of modeling the indirect effect of the initial level of productivity of a province on its TFP growth rate, through its effect on neighboring provinces. The inclusion of these indirect effects is made possible by SDM and increases the speed of convergence for most considered manufacturing industries, except for metal and machinery, and transportation and telecommunication.

This chapter discusses the different possible roles played by the Japanese government to enhance the total factor productivity (TFP) growth rate during the “growth miracle period” (1955–1973) as well as the “two lost decades” (1990–2009). The growth accounting exercise conducted in this chapter using the Japanese data shows that TFP was not only the driving force for the rapid economic growth of the growth miracle period but also a decline in the TFP growth rate was responsible for the sluggish economies, particularly in the 1990s. High net technology imports were demonstrated to have likely played an important role in the TFP’s rapid rise during the growth miracle period. However, possible causes behind the TFP’s slower growth rate during the “two lost decades” remains actively debated in literature. Caballero et al. (Am Econ Rev 98(5):1943–1977, 2008) offers the possible explanation of zombie firms, i.e., unproductive firms that should exit the market but survive because of support from banks or the government. If “zombie lending” is a major cause of slower TFP growth rate, opening the markets and letting firms compete through deregulation would be one promising policy the Japanese government could enact in order to boost the TFP growth rate.

(ProQuest: ... denotes formulae omitted.)1. INTRODUCTIONThe work of Solow (1957) and Abramovitz (1956) and more recent analyses (Casseli, 2005; Hall and Jones, 1999) demonstrate that total factor productivity (TFP) is the key driver of long-run income growth. Klenow and Rodriguez-Clare (1997) estimate that roughly 90 percent of the differences in of income per capita can be explained by differences in total factor productivity. It is also well documented that advanced economies (OECD) lead technological change and innovation while developing economies lag behind in the technological frontier and tend to adopt (with a lag) technologies developed in technology-leading countries (Besley and Case, 1993; Archibugiand and Pietrobelli, 2003). In addition, technologies created in leading countries may not be appropriate to be used in technology-backward economies (Basu and Weil, 1998; Acemoglu and Zilibotti, 2001). Thus, there are significant differences in levels and of productivity between advanced and developing economies.Studies examining cross-country TFP differences find strong evidence against global TFP convergence (Klenow and Rodriguez-Clare, 1997; Hall and Jones, 1999; Di Liberto et al. 2011). Di Liberto, Pigliaru and Chelucci (2011) show that most countries underperform respect to the U.S. in terms of TFP growth (p.168) as well as that the TFP gap across countries is persistent.1 While there is strong empirical evidence against global TFP convergence, there is evidence in favor of club convergence. Miller and Upadhyay (2002) group countries by income quartiles and find that there is absolute TFP convergence for countries in the lowest and highest income quartiles, but no convergence for countries in intermediate income quartiles. Kumar and Chen (2012) find that health and education have a significant positive effect on TFP and conditional TFP convergence. Papalia and Silvia's (2013) results also support club convergence. Madsen (2007, 2008) show that knowledge transmitted internationally through trade and patents has contributed significantly for TFP convergence among OECD countries. Di Liberto and Usai (2013) show that a polarization is taking place across European regions, with only a few regions emerging as TFP leaders while most regions are lagging behind, causing the TFP gap between these two clusters to widen.Loko and Diouf (2009) provide a comprehensive discussion of the factors that determine TFP and might explain the patterns (convergence, or lack thereof) discussed above. For the sake of simplicity, this study groups the factors affecting TFP into three categories. The first group consists of macroeconomic factors that either hinder or boost productivity growth. Economic instability (e.g inflation), a large government, and taxation distortions supposedly create market inefficiencies and, thus, negatively affect productivity (Barro, 1991; Loko and Diouf, 2009). On the other hand, overall openness to international trade and capital mobility are expected to boost productivity growth. International trade spurs competition - which leads to innovation - as well as serves as a channel for technology diffusion among nations. Thus, economies that are more open to trade are expected to have higher productivity (Dollar and Kraay, 2004; Wacziarg and Welch, 2008; Barro and Sala-i-Martin, 1995). The same rationale applies to capital flows. Openness to capital flows (Foreign Direct Investment) is associated with technology diffusion and knowledge transfers, which in turn boosts productivity (Borensztein et al. 1998). The composition of output (i.e, if intensive in services, agriculture, or manufacturing) has also been identified as a driver of productivity growth. In particular, nonagricultural economies have experienced faster productivity (Poirson, 2000; Jaumotte and Spatafora, 2007).The second group of factors includes variables that measure the quality of labor (human capital). …

To explore the competitiveness of the Finnish dairy chain, we analysed its productivity performance relative to that of other Baltic countries: Sweden, Denmark, Germany, Poland, and the three Baltic states. We used partial productivity indicators and indices of total factor productivity (TFP) to investigate productivity growth and productivity levels in both dairy farming and dairy manufacturing, using data from the Farm Accountancy Data Network as well as national industrial statistics. At farm level, there are enormous differences in the level of labour productivity across the eight countries: a dairy farmer in Denmark produces 13 times more milk than one in Latvia or Lithuania. Labour productivity in Finland is also significantly lower than in the other old EU countries – not only Denmark, the clear leader, but also Germany and Sweden. Further, there is evidence that Estonia is catching up with Finland in terms of labour productivity. A decomposition analysis then shows that the cross-country differences in labour productivity on farms are driven primarily by differences in labour requirements per cow, while differences in milk yields account for a much smaller share of the difference. Thus, the key to high labour productivity in dairy is the farm structure and the adoption of mechanical innovations, while differences in adoption of biological innovations (e.g., genetic improvement, feeds) are relatively less important. In a second step, a growth accounting exercise indicates that growth in farm-level production in the four older EU members has occurred through different channels, but that TFP growth rates have been roughly comparable from 1995 to 2010. Thus, the competitive position of Finnish dairy farms relative to those in Sweden, Germany and Denmark has not changed greatly over the last two decades. More positively, we find that in recent years (i.e., since 2004), TFP on Finnish farms has grown much faster than on German and Swedish farms. Altogether, Finnish farms appear in the process of raising their productivity to the level achieved by German and Swedish farms, while Danish farms are probably out of reach. Extending the comparison to include the new EU members reveals that dairy farms in those countries are lagging behind Finnish ones in terms of productivity and are not catching up. Although Estonian farms, which are on average relatively large, have recorded impressive increases in yields and labour productivity, this has been achieved more by substitutions of other production factors for labour than real efficiency gains. The processing level of the Finnish dairy supply chain appears more competitive when benchmarked against the processing sectors of the old EU members, although TFP growth has been slow in absolute terms. However, the productivity of dairy manufacturing in Poland and Lithuania is increasing rapidly and converging towards the levels observed in the older EU countries. Overall, the evolution documented in the paper is consistent with the view that transferring technologies and organisational forms from the productivity leaders to the productivity “laggards” is easier in the manufacturing sector than in primary production, due to the typical difference in the size of firms as well as the more pronounced reliance of the primary sector on country-specific agro-ecological conditions.

The added value method is used to screen the input and output indicators of the real estate industry. The DEA-Malquist method was used to measure the total factor productivity (TFP) of the real estate industry in China’s 30 provinces from 2006 to 2017. Further, the convergence test of the real estate industry TFP in eastern, central and western China was conducted. It was found that the average growth rate of real estate industry TFP in China’s 30 provinces during the period was 3.3%. Technical efficiency was the main driving force for TFP growth, and the contribution of technological progress was relatively small. In the improvement of technical efficiency, pure technical efficiency is obviously improved, and the improvement of scale efficiency is limited. The absolute β convergence test was carried out. From the national perspective, the TFP gap in the real estate industry in each province has a significant narrowing trend; In terms of regions, the TFP gap in the real estate industry in the eastern provinces has a narrowing trend, but it is not significant; The growth of TFP in the real estate industry in the central region has a significant catch-up characteristic, and the TFP gap between provinces has gradually narrowed; The TFP gap in the real estate industry in the western provinces has significantly expanded.

• By 2050, global agricultural demand is projected to grow by 70-100 percent due to population growth, energy demands, and higher incomes in developing countries. Meeting this demand from existing agricultural resources will require raising global agricultural total factor productivity (TFP)1 by a similar level. Maintaining the U.S. contribution to global food supply would also require a similar rise in U.S. agricultural TFP. • TFP growth in U.S. agriculture is predicated on long-term investments in public agricultural research and development (R&D). Productivity growth also springs from agricultural extension, farmer education, rural infrastructure, private agricultural R&D, and technology transfers, but the force of these factors is compounded by public agricultural research. • The rate of TFP growth (and therefore output growth) of U.S. agriculture has averaged about 1.5 percent annually over the past 50 years. Stagnant (inflation-adjusted) funding for public agricultural research since the 1980s may be causing agricultural TFP growth to slow down, although statistical analyses of productivity growth trends are inconclusive. • ERS simulations indicate that if U.S. public agricultural R&D spending remains constant (in nominal terms) until 2050, the annual rate of agricultural TFP growth will fall to under 0.75 percent and U.S. agricultural output will increase by only 40 percent by 2050. Under this scenario, raising output beyond this level would require bringing more land, labor, capital, materials, and other resources into production. • Additional public agricultural R&D spending would raise U.S. agricultural productivity and output growth. Raising R&D spending by 3.73 percent annually (offsetting the historical rate of inflation in research costs) would increase U.S. agricultural output by 73 percent by 2050. Raising R&D spending by 4.73 percent per year (1-percent annual growth in inflation-adjusted spending) would increase output by 83 percent by 2050.

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This article applies the Window Malmquist Index (WMI) approach to measure changes in agricultural Total Factor Productivity (TFP) for the United States and a sample of nine European countries for the period 1973 to 1993. The dataset used in this article is obtained from Ball et al. (2001). The WMI is constructed by combining Data Envelopment Analysis, window analysis with the Malmquist index approach. Furthermore, the ‘Kruskal and Wallis rank test’ is used for testing frontier shifts among observed periods. The article also explores the question of convergence in TFP across the countries under consideration, by testing for β- and σ-convergence, as well as for stochastic or long-run convergence. The results show wide variation in the rate of TFP growth across countries with an average trend growth rate of 1.62%. The results indicate the presence of β-convergence but the absence of σ-convergence for the full period under consideration but the presence of both β- and σ-convergence for the sub-period 1983 to 1993. Finally, a wide spectrum of panel unit root test results support the presence of long-run convergence among the sample countries.

The difference and convergence of total factor productivity of inter-provincial water resources in China based on three- stage DEA-Malmquist index model

We examine whether affiliation in a multi-hospital system contributes to higher rates of total factor productivity (TFP) growth, technological progress and cost efficiency. With a 1996 to 1999 panel of 248 US hospitals (some are private nonprofit (church-related and other nonprofit) and the remaining are public (government, nonfederal)), empirical results indicate that urban system member hospitals experienced higher rates of both TFP growth and technical progress than the rates of TFP growth and technical progress experienced by urban nonsystem hospitals. Rural system member hospitals experienced smaller rates of both TFP decline and technical regress than the rates of TFP decline and technical regress experienced by rural nonsystem hospitals.

This article examines total factor productivity (TFP) convergence across the regions of Kazakhstan over the period of 1997–2013. Using a growth accounting methodology we found that the average level of TFP fell by almost 40% over the period under consideration. Several panel unit root tests confirm that the whole set of Kazakh regions and the group of non-oil regions converged in terms of TFP, while the group of oil-rich regions diverged. This result explains sigma-divergence of the GRP per capita across the regions of Kazakhstan by divergence in capital intensity.

The study dealt with the whole scenario of modern variety potato production in Bangladesh covering the period from 1980-81 to 2005-06. The study estimated the extent of shift of production function or the supply curve of modern variety of potato in Bangladesh. The total factor productivity index was estimated using the Tornqvist-Theil index formulation. The growth rate of area, production and yield were found increasing steadily from the year 1980-81. A substantial change has been started from the year 1998-99. The trend of inputs used was found increasing. Almost all the partial as well as the input, output and total factor productivity indices were also found increasing. It may be concluded that for sustaining the present growth of modern variety potato production, the development of new varieties of potato and extensive extension works are needed. Key words: Growth rate, partial factor productivity, total factor productivity, modern variety potato. DOI: 10.3329/jard.v7i1.4423 J Agric Rural Dev 7(1&2), 65-71, June 2009