U.S. productivity in agriculture and R&D

This paper examines the impact of R&D on multifactor productivity in the U.S. agricultural sector over the 1910–1990 period. We use the Bennet–Bowley indicator to measure agricultural productivity based on a multiple output-multiple input technology. We demonstrate the relationship between the price dependent Bennet–Bowley indicator and the Luenberger productivity indicator which is constructed from directional distance functions without requiring price information. These performance measures are dual to the profit function which arguably makes them especially useful in the agricultural setting. We employ time-series techniques to investigate the effect of R&D on the pattern of productivity growth. We find that we cannot reject the presence of a cointegrating relationship between the two series and that productivity growth in the U.S. agriculture responds positively to R&D expenditure with a lag of between four and ten periods.

A primal-dual approach to nonparametric productivity analysis: The case of U.S. agriculture

Nonparametric methods for measuring productivity indexes based on bounds for the underlying production technology are presented. Following Banker and Maindiratta, the lower bound is obtained from a primal approach while the upper bound corresponds to a dual approach to nonparametric production analysis. These nonparametric bounds are then used to estimate input-based and output-based distance functions. These radial measures provide the basis for measuring productivity indexes. Application to times series data on U.S. agriculture indicates a large gap between the primal lower bound and the dual upper bound. This generates striking differences between the primal and dual nonparametric productivity indexes.Respectively, professor and associate professor of Agricultural Economics, University of Wisconsin-Madison. Seniority of authorship is equally shared. We would like to thank Rolf Färe and an anonymous reviewer for useful comments on an earlier draft of the paper. This research was supported in part by a Hatch grant from the College of Agriculture and Life Sciences, University of Wisconsin-Madison.     

Learning-by-doing and the sources of productivity growth: A dynamic model with application to U.S. agriculture

The significance of learning to productivity growth is formulated within a dynamic adjustment-cost framework. Explicitly treating the acquisition of knowledge as a firm-specific capital good entering the production function along with other conventional inputs, the dynamic optimization model integrates the learning-by-doing hypothesis with technical change, scale, and disequilibrium input use effects in the aggregate productivity analysis. The theoretical framework is applied to examining the dynamic components accounting for the growth of U.S. production agriculture over the 1950–82 period. The results imply a less important role for technical change and assign a substantial role to the previously unmeasured contribution of learning-by-doing to the growth of aggregate agriculture industry.The editor for this paper was Melvyn Fuss.     

Panel data analysis of U.S. coal productivity

We analyze labor productivity in coal mining in the United States using indices of productivity change associated with the concepts of panel data modeling. This approach is valuable when there is extensive heterogeneity in production units, as with coal mines. We find substantial returns to scale for coal mining in all geographical regions, and find that smooth technical progress is exhibited by estimates of the fixed effects for coal mining. We carry out a variety of diagnostic analyses of our basic model and primary modeling assumptions, using recently proposed methods for addressing ‘errors-in-variables’ and ‘weak instrument bias’ problems in linear and nonlinear models.     

A profit function approach to measuring productivity growth: The case of U.S. manufacturing

This article proposes a method of estimating productivity growth using an estimated profit function. The approach has the advantage of incorporating endogenous changes in profit-maximizing output levels that would result from productivity changes. As with the cost function, it can be easily adapted to accomodate the presence of quasi-fixed factors. The article first develops the methodology and shows the equivalence between the proposed measure and other measures of productivity based on cost or production functions. An empirical application to the measurement of productivity changes in the U.S. manufacturing industry is presented next. The profit-function measure is compared to a nonparametric measure based on the same data and to the results of other studies of U.S. manufacturing.The refereeing process of this paper was handled through J. deMelo.     

The dynamics of efficiency and productivity growth in U.S. electric utilities

This study recognizes explicitly the efficiency gain or loss as a source in explaining the growth. A theoretically consistent method to estimate the decomposition of dynamic total factor productivity growth (TFP) in the presence of inefficiency is developed which is constructed from an extension of the dynamic TFP growth, adjusted for deviations from the long-run equilibrium within an adjustment-cost framework. The empirical case study is to U.S. electric utilities, which provides a measure to evaluate how different electric utilities participate in the deregulation of electricity generation. TFP grew by 2.26% per annum with growth attributed to the combined scale effects of 0.34%, the combined efficiency effects of 0.69%, and the technical change effect of 1.22%. The dynamic TFP grew by 1.66% per annum for electric utilities located within states with the deregulation plan and 3.30% per annum for those located outside. Electric utilities located within states with the deregulation plan increased the outputs by improving technical and input allocative efficiencies more than those located outside of states with deregulation plans.

Public inputs and dynamic producer behavior: endogenous growth in U.S. agriculture

This paper is an attempt to understand the impact of public R&D and public infrastructure on the performance of the U.S. agricultural sector during the last part of the twentieth century. A neoclassical Solow growth model is not sufficient for this understanding given the sustained growth performance of the sector. We base our analysis on a well-known endogenous growth model, the ‘AK model’ where non-convexities are introduced through non-rival inputs. Based on these models and within the dynamic models that rationalize private and public decision making, we have identified three testable hypotheses regarding the aggregate agricultural production technology. They are: (1) increasing returns to scale over all inputs; (2) positive effect of additional units of public inputs on the long-run demand for private capital; and (3) negative impact of public inputs on cost. They are tested using two estimation procedures on two data sets for U.S. agriculture. One, covering the period 1948–1994, developed by USDA, the other, covering the period 1926–1990, from Thirtle et al. Maximum likelihood estimates do not conform to the regularity and behavioral properties of the economic model rendering them unusable for testing these hypotheses. Bayesian estimates, although not totally satisfactory, do not reject the hypotheses after prior imposition of some of the regularity conditions. This supports the notion of an important role for public inputs on the rapid and sustained growth of the sector. We calculate that, on average, one additional dollar spent on public R&D stock reduces private cost by $6.5, implying a return on these public expenses of 190%.

Information technology and U.S. productivity growth: evidence from a prototype industry production account

The rapid productivity growth in the US during the Information Age, prior to the dot-com bust in 2000, and the large contribution of the IT producing sector, is well known. Less known are the sources of the surprisingly rapid TFP growth during the slow growth period after 2000. We construct an account of US economic growth by aggregating over detailed industries using a new data set based on the NAICS classification. We find that, post 2000, TFP originating from the IT-Producing sector decelerated relative to the IT boom, but still accounted for 40% of aggregate productivity growth. This deceleration was counterbalanced by the contribution from IT-Using sectors, which buoyed aggregate TFP growth to almost the same rate as the 1995–2000 period. For aggregate GDP, the contributions to the growth rate of 2.8% during 2000–2007 were: capital input (1.7% points), labor input (0.4) and TFP (0.7).     

A comparison of multifactor productivity calculations of the U.S. agricultural sector

Since the late 1970s, there have been fourteen studies that have estimated multifactor productivity (MFP) growth rates for the U.S. agricultural sector. The estimates of average annual MFP growth rates have ranged from 1.15 to 1.94 percent per year for studies using the gross productivity approach. The purpose of this paper is to identify the reasons for these different estimates. We consider theoretical and empirical factors and do not find any single reason that satisfactory explains the variation. The alternative estimates appear to be most sensitive to the coverage of years.     

Predictability of the U.S. Dollar Index using a U.S. export and import price index-based relative PPP model

We use U.S. export and import price indexes to construct a relative purchasing power parity-based model of the nominal U.S. Dollar Index. The model is successful in predicting the future direction of change in the U.S. Dollar Index over a six-month period up to 68% of the time. Finally, the model, in combination with a simple linear, recursive technique, is able to statistically significantly outperform the random walk in predicting the value of the U.S. Dollar Index at terms of less than four months for the period from 1996 to 2005. The paper provides important implications for investors who are interested in the direction of change in the Dollar’s value, forecasting the level of the U.S. Dollar Index, as well as the extent of over- and undervaluation of the U.S. Dollar, in general.     

Efficiency, productivity, and scale economies in the U.S. property-liability insurance industry

The paper examines efficiency, productivity and scale economies in the U.S. property-liability insurance industry. Productivity change is analyzed using Malmquist indices, and efficiency is estimated using data envelopment analysis. The results indicate that the majority of firms below median size in the industry are operating with increasing returns to scale, and the majority of firms above median size are operating with decreasing returns to scale. However, a significant number of firms in each size decile have achieved constant returns to scale. Over the sample period, the industry experienced significant gains in total factor productivity, and there is an upward trend in scale and allocative efficiency. More diversified firms and insurance groups were more likely to achieve efficiency and productivity gains. Higher technology investment is positively related to efficiency and productivity improvements.     

Schooling and productivity growth: evidence from a dual growth accounting application to U.S. states

This paper exploits the dual growth accounting framework to determine the effect of schooling on total-factor productivity (TFP) growth for U.S. states over 1980–2010. The paper contributes to the literature by constructing TFP growth measures, in the process, both real wage growth and real user cost growth measures at the state level. The growth accounting exercise reveals that states display considerable heterogeneity in TFP growth with both real wage growth and real user cost growth equally contributing to it. While our econometric estimates reveal significant TFP growth effects associated with college education, they are considerably lower in magnitude when compared to the earlier studies. Productivity growth effects associated with high school education remain insignificant. Our empirical findings are further validated by the use of instrumental variable estimation to address the endogeneity of the schooling variables and are also robust to the inclusion of a rich set of control variables, alternative TFP growth measures as well as to the tests to draw robust inferences in the presence of weak instruments.     

Forecasting U.S. population totals with the Box-Jenkins approach

"The use of the Box-Jenkins approach for forecasting the population of the United States up to the year 2080 is discussed. It is shown that the Box-Jenkins approach is equivalent to a simple trend model when making long-range predictions for the United States. An investigation of forecasting accuracy indicates that the Box-Jenkins method produces population forecasts that are at least as reliable as those done with more traditional demographic methods."     

Externalities, efficiency, regulation, and productivity growth in the U.S. electric utility industry

This paper examines the decomposition of total factor productivity growth for firms subject to regulation, given the production of a bad output. The production of good and bad outputs provides benefits and costs to society. Corporate socially responsible firms recognize the cost to society of producing the bad output. The paper separates the production technology and regulation effects from both the scale and technical change components. The paper also examines the measurement and decomposition of productivity growth when not accounting for production of the bad output. Using a 1992–2000 panel of 34 U.S. investor-owned electric utilities, results indicate that improvements in the scale, efficiency change, and technical change components contributed to positive growth. Not accounting for production of the bad output led to, on average, an overestimation of both the rate of productivity growth, and the contributions of scale economies and technical change to changes in productivity growth.

Productivity and efficiency at bank holding companies in the U.S.: a time-varying heterogeneity approach

This paper investigates the productivity and efficiency of large bank holding companies (BHCs) in the United States over the period 2004–2013, by estimating a translog stochastic distance frontier (SDF) model with time-varying heterogeneity. The main feature of this model is that a multi-factor structure is used to disentangle time-varying unobserved heterogeneity from inefficiency. Our empirical results strongly suggest that unobserved heterogeneity is not only present in the U.S. banking industry, but also varies over time. Our results from the translog SDF model with time-varying heterogeneity show that the majority of large BHCs in the U.S. exhibit increasing returns to scale, a small percentage exhibit constant returns to scale, and an even smaller percentage exhibit decreasing returns to scale. Our results also show that on average the BHCs have experienced small positive or even negative technical change and productivity growth.     

The fundraising efficiency in U.S. non-profit art organizations: an application of a Bayesian estimation approach using the stochastic frontier production model

This article examines how efficient art organizations are in raising funds from private giving. We measure fundraising efficiency using a Bayesian estimation approach using the stochastic frontier production model. We show that fundraising efficiencies are generally quite low for art organizations in the U.S. when private giving is only considered as a fundraising output; however, when the effect of fundraising on ticket sales is considered, fundraising efficiencies improve substantially. We also show that government grants have a negative impact on fundraising efficiency and therefore partially crowd out private giving.     

Does pay affect productivity or react to it?: Examination of U.S. manufacturing

There may be a bi-directional relationship between wages and labor productivity. According to conventional theory, employers reward improvements in productivity by raising pay. It also has been argued that wage increases can provide an incentive to improve productivity. This study applies a technique by Geweke to identify the feedback between pay and productivity in U.S. manufacturing. For the 1949–1998 period, measures of directional feedback indicate that both “pay as reward” and “pay as incentive” behaviors have occurred, but the results vary across manufacturing subsectors.     

Project analysis using a linear approach (P.A.U.L.A.)

We define a general model (called PAULA) for the valuation, optimal management and selection among mutually exclusive safe projects. By exploiting the formal and financial features of the associated linear problems (primal and dual), we put forward two proposals to define an optimal internal financial law (IFL). They may be used to reduce the multiplicity of the IFLs and to avoid economically arbitrary outcomes.Research projectsCariplo andMurst.     

An entropy approach to size and variance heterogeneity in U.S. commercial banks

In this paper, we investigate the effect of bank size differences on cost efficiency heterogeneity using a heteroskedastic stochastic frontier model. This model is implemented by using an information theoretic maximum entropy approach. We explicitly model both bank size and variance heterogeneity simultaneously. We find that non-performing loans, federal insurance premium, legal expenses and director fees drive bank inefficiency as the bank becomes larger. Moral hazard, bank management and a ??too big to fail?? doctrine are likely explanations for the results from this study.