CAPITAL MISALLOCATION AND AGGREGATE FACTOR PRODUCTIVITY

We propose a sectoral-shift theory of aggregate factor productivity for a class of economies with AK technologies, limited loan enforcement, a constant production possibilities frontier, and finitely many sectors producing the same good. Both the growth rate and total factor productivity in these economies respond to random and persistent endogenous fluctuations in the sectoral distribution of physical capital which, in turn, responds to persistent and reversible exogenous shifts in relative sector productivities. Surplus capital from less productive sectors is lent to more productive ones in the form of secured collateral loans, as in Endogenous debt limits slow down capital reallocation, preventing the equalization of riskadjusted equity yields across sectors. Economy-wide factor productivity and the aggregate growth rate are both negatively correlated with the dispersion of sectoral rates of return, sectoral TFP and sectoral growth rates. If sector productivities follow a symmetric two-state Markov process, many of our economies converge to a limit cycle alternating between mild expansions and abrupt contractions. We also find highly periodic and volatile limit cycles in economies with small amounts of collateral.

We propose a sectoral-shift theory of aggregate factor productivity for a class of economies with AK technologies, limited loan enforcement, and a constant production possibilities frontier. Both the growth rate and TFP respond to random and persistent endogenous fluctuations in the sectoral distribution of physical capital which, in turn, responds to reversible exogenous shifts in relative sector productivities. Surplus capital from less productive sectors is lent to more productive ones in the form of secured collateral loans, as in Endogenous debt limits slow down capital reallocation, preventing the equalization of risk-adjusted equity yields across sectors. Economy-wide factor productivity and the aggregate growth rate are both negatively correlated with the dispersion of sectoral rates of return, sectoral TFP and sectoral growth rates. We also find highly volatile limit cycles in economies with small amounts of collateral.

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.

Comment

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.

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.

China has been the world's largest automobile producer since 2009, but it still lags behind other countries in terms of productivity. Based on the National Bureau of Statistics of China (NBSC) firm-level data and the improved approach proposed by Ackerberg et al. (2015), this paper investigates the contribution of total factor productivity (TFP) growth to the Chinese automobile industry and evaluates the impact of firm entry and exit on TFP growth. The empirical results show that the TFP of the Chinese automobile industry grows at 10.7% per year. Joint venture and foreign-owned firms have a significantly higher TFP growth rate than others. Large-scale firms have a higher TFP growth rate than do small-scale firms, but the latter have caught up after 2004. Moreover, the entry of new firms and exit of old firms significantly improve the aggregate TFP growth rate.

Capital Misallocation and Aggregate Factor Productivity

• 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.

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.

The purpose of this paper is to provide empirical estimates of total factor productivity (TFP) growth rates in the electric power industry for the United States, Japan, and Korea. in this paper, the conventional Divisia index number approach and two cost function approaches are used to assess TFP growth rates during the 1972–1996 period. the TFP growth rates in the United States, where regulatory reform has been progressing gradually since 1978, did not show much difference from those in Japan, and were considerably lower than those achieved by Korea, where little reform has taken place. Decomposition analysis based on the total cost function shows that the major sources of TFP growth were the scale effect in Japan, the capacity utilization effect and the scale effect in Korea, and technical change in the United States. Since the mid-80s, the increases in TFP growth were due to improvements in the capacity utilization effect in all three countries.

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.

Earlier research largely ignored the effects of climate change on the growth of agricultural total factor productivity (TFP) in Africa. This study shows how climate inputs impact TFP growth in addition to other productivity growth indicators and metrics, as well as how they can impact overall input efficiency as productivity drivers. We use a panel of 42 African nations from 1999 to 2019 and a nonparametric data envelopment analysis-Malmquist technique. The non-parametric analysis revealed that the average growth rate of the non-climate-induced TFP estimates was 1.9%, while the average growth rate of the climate-induced TFP estimates was 2.4%. Accounting for temperature and precipitation separately, TFP grew by 2.3% on average. This growth rate (2.3%) is slightly less than the combined effect of temperature and precipitation (2.4%) but higher than the typical TFP growth rate (1.9%) that ignores climate variables, indicating that TFP growth in African agriculture risks being underestimated when climate inputs are ignored. We also find the distribution of the climate effects to vary across regions. In northern Africa, for example, the temperature-induced TFP growth rates were negative due to rising temperature in the region. Evidence from the decomposed TFP estimates indicates that climate variables also influence productivity determinants. However, technology improvement is fundamental to mitigating the effects of extreme weather inputs on TFP growth in Africa's agriculture. As a result, a few policy suggestions are provided to help policymakers deal with the effects of climate change on TFP growth in Africa's agriculture and ensure food security. The study advocated for a reevaluation of the climate–agriculture effect in order to fully comprehend the role of climate factors and their contributions to agricultural TFP growth in Africa.

This study aimed to measure Thailand’s total factor productivity and also investigate the impact of financial intermediation efficiency on it during 2001-2024. The financial intermediation efficiency in this study was measured by three indicators, including interest rate spread, business sector credit ratio and non-performing loans ratio. Additionally, the growth accounting equation was applied to calculated the total factor productivity growth rate, whereas Autoregressive Distributed Lag model was employed to examine the impact of financial intermediation efficiency on it. The findings revealed that Thailand’s total factor productivity growth was volatile over the study period, with several years of negative performance, and that its average growth rate was only 0.15% per year. The long-run results from Autoregressive Distributed Lag model indicate that financial intermediation efficiency significantly affects total factor productivity growth in the long run as business sector credit has a positive effect on total factor productivity growth, while non-performing loans exert a negative impact on it. However, the interest rate spread does not affect total factor productivity growth in the long run. In the short run, the results further confirmed that total factor productivity growth is significantly determined by financial intermediation efficiency. Specifically, the change in business sector credit has the positive effect on the change in total factor productivity growth whereas the change in interest rate spread has the negative impact on it. Nevertheless, the change in non-performing loan does not have any effect on the change in total factor productivity growth in the short run. The findings provide practical guidance for policymakers and financial regulators in improving credit allocation and supporting productivity-driven economic growth

This chapter studies productivity growth between 1941 and 1948 in nine sectors other than manufacturing. These sectors include construction, wholesale and retail trade, agriculture, railroad transportation, electric and gas utilities, telephone and telegraph, trucking and warehousing, and mining. Sectoral total factor productivity (TFP) growth rates along with their shares in the national economy are used to apportion national economy productivity growth among the different sectors. The chapter then compares TFP advance in the private domestic economy in the 1929–41 and 1941–48 time periods. Aggregate TFP growth was relatively higher in the 1929–41 period than in the 1941–48 period, a conclusion that appears to be robust to the choice of different deflators to calculate real output growth. In sectors outside manufacturing, a combination of strong aggregate demand, scarce labor, and (in most cases) meager capital growth seems to have been a more powerful stimulus to TFP advance over the course of wartime mobilization and demobilization than conditions faced within manufacturing.