The plant life-cycle of the average wage of employees in US manufacturing

Dinlersoz, Emin; Hyatt, Henry R.; Nguyen, Sang V. Article The plant life-cycle of the average wage of employees in US manufacturing IZA Journal of Labor Economics Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Dinlersoz, Emin; Hyatt, Henry R.; Nguyen, Sang V. (2013) : The plant life-cycle of the average wage of employees in US manufacturing, IZA Journal of Labor Economics, ISSN 2193-8997, Springer, Heidelberg, Vol. 2, pp. 1-23, https://doi.org/10.1186/2193-8997-2-7 This Version is available at: https://hdl.handle.net/10419/92274 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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IZA Journal of Labor Economics ORIGINAL ARTICLE Open Access The plant life-cycle of the average wage of employees in U.S. manufacturing Emin Dinlersoz*, Henry R Hyatt†and Sang V Nguyen† *Correspondence: [email protected] †Equal contributors 1Center for Economic Studies, U.S. Census Bureau, 4600 Silver Hill Road, Suitland, MD 20746, USA Abstract This paper explores the evolution of the average wage of employees over the life-cycle of a manufacturing plant. The average wage starts out low for a new plant and increases along with labor productivity as the plant ages. As a plant approaches exit, its average wage falls, but more slowly than it rises in the case of growing plants. Moreover, the average wage does not fall as fast as productivity does. A dynamic model of labor quality and quantity choice by plants is estimated to assess the costs of altering labor quality and quantity over the plant life-cycle. JEL codes: J31, J21, J24, L60, L23, L11, D24 Keywords: The average wage of employees; The evolution of the average wage; Plant productivity; Plant life-cycle; Employment dynamics; Adjustment costs; Manufacturing 1 Introduction How does the average wage (the total wage bill per employee) a manufacturing plant pays to its employees change over the plant’s life-cycle?1Are there substantial differences in the time-paths of the average wage between growing versus failing plants? How are the changes in the average wage related to changes in productivity over the plant’s life-cycle? The answers to these questions matter for the theories of wage contracts, human capital accumulation on the job, adjustment costs in factors of production such as labor quantity and quality, the productivity-wage relationship, and the evolution of organizational complexity and employee hierarchy. This paper provides comprehensive evidence on the evolution of the average wage along the life-cycle of a manufacturing plant using data from the U.S. Census Bureau’s Census of Manufactures over the period 1963-1997. Some facts are presented on how the average wage changes as a plant enters an industry, grows, and ages, and as it approaches exit. The patterns exhibited by the average wage are tied to the evolution of labor productivity. A dynamic model of plant-level labor quality and quantity adjustment is also built and estimated to explore the asymmetric patterns observed in the evolution of the average wage for growing versus declining plants. Empirical evidence on the connection between a plant’s age and its average wage is scant. Brown and Medoff (2003) find that older firms tend to pay higher wages on average. Their analysis is based on a relatively small number of workers, and highly established, older firms. Kölling et al. (2013) largely confirm Brown and Medoff’s (2003) findings using a larger dataset that links establishments to workers in Germany, while Heyman (2007) © Dinlersoz et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2013, 2:7 http://www.izajole.com/content/2/1/7 2013 Dinlersoz et al. IZA Journal of Labor Economics Page 2 of 23 finds some evidence in favor of a positive relationship using Swedish data. These studies do not track firms or plants over time, and do not consider the joint dynamics of the average wage and productivity along the firm life-cycle. In addition, while age measures a firm’s distance from entry, it does not contain information on its distance to exit, and thus cannot fully account for where a firm stands in its life-cycle. The samples used in these previous studies also do not adequately represent the firm age distribution, especially its left tail. This paper uses data for the entire set of manufacturing plants available in the U.S. Census Bureau’s Census of Manufactures (CM) over a span of three decades. It characterizes the evolution of the average wage and labor productivity as plants move away from their entry point, and as they approach exit. The findings indicate that new plants start out with lower average wage and productivity compared with the established ones, but surviving plants achieve higher productivity and pay higher average wage as they age. Failing plants, on the other hand, experience a decline in both the average wage and productivity. For such plants, the average wage does not fall as fast as it rises in the case of surviving new plants. Furthermore, the average wage neither rises nor falls as fast as labor productivity does. Compared with the mature plants, failing plants are burdened by an increasingly larger wage bill as a fraction of their revenues, whereas new entrants and young firms incur a smaller wage bill relative to their revenues. The asymmetric evolutions of labor productivity and the average wage over the life-cycle of a plant are the focus of the paper. The model in this paper connects a plant’s productivity to its average wage. A plant’s life-cycle dynamics are driven by two random processes: one that drives productivity, and the other the wage rate per unit of labor quality the plant faces. A plant chooses the quality andquantityofitslaborforceinthepresenceofthesetworandomelements.Themotivation behind labor quality, in addition to quantity, stems from the prior empirical evidence which suggests that worker quality accounts for a considerable portion of the wage differential across plants of different ages.2A further motivation is the evidence on a strong positive relationship between a firm’s labor quality and productivity.3Worker quality can have alternative interpretations, such as human capital, a worker’s skill, effort, and hours, or the degree of essentiality of a worker in the production process. A higher quality labor force comes at a higher average wage. This relationship is supported by the earlier work of Gort et al. (1990) and Gort et al. (1993), which suggest that variations in the average wage across plants are mainly due to the differences in human capital rather than the differences in the prices of a given labor type. The average wage in the model thus has an exogenous component that the plant takes as given, and an endogenous component that depends on the average labor quality, which is chosen by the plant. As a plant’s productivity increases, its employment or the average quality of its labor force can also increase depending on the relative costs of these two inputs. Similarly, a decline in productivity can put a downward pressure on labor quality or employment. However, there are many frictions in the process of labor quantity and quality adjustment, giving rise to adjustment costs. These costs can dampen the movements in the average wage in either direction. Adjustment costs in labor quantity and quality represent the effects of a variety of considerations, including the frictions in reorganizing production (e.g. plant expansion and contraction) and the frictions in the labor market (e.g. search costs), the effects of institutions (e.g. labor unions), and regulations.4Exogenous shocks to the wage rate per unit quality of labor can force a plant to alter its average wage at a cost. For instance, 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 3 of 23 some workers can exercise their outside options if their wages are not adjusted when the demand for their services rises in the economy. Plants experiencing fast productivity growth may need to reorganize their workforce. Such reorganization may involve creating new jobs and hierarchies, as well as training, and moving labor across ranks and tasks. Similarly, as a plant experiences persistent episodes of low productivity, it may shed some of its employees who are non-essential for the main activity of the plant. It may also reduce worker compensation in real terms. Some workers may also quit in anticipation of the plant’s exit. The plant may then have to reorganize its remaining labor force to maintain production. All of such events imply costly adjustments to the average wage. To assess the magnitude of the adjustment costs associated with the average wage, the model’s parameters are estimated. The estimates reveal that labor quality, inferred indirectly from the data on employment, wage bill, and revenue, is an important input of production. Furthermore, there are statistically and economically significant asymmetric adjustment costs. When only the continuing plants are considered, the mean annual cost of average-wage adjustment in any direction constitutes up to 1.6% of a plant’s revenue at the median of the adjustment cost distribution. For plants adjusting their average wage downwards, the annual adjustment cost claims, on average, up to 3.6% of revenue. For plants adjusting their average wage upwards, the annual adjustment cost makes up as much as 0.8% of revenue on average. These shares are much higher when only the exiting plants are considered. A version of the model with both the average-wage and employment adjustment costs is also estimated. A plant may not be able to adjust its average wage holding employment constant. Similarly, changes in employment can induce changes in theaveragewageduetochangesinthecompositionofaplant’slaborforce.Allowingfor both channels of adjustment addresses potential biases due to using only one. This paper contributes in a number of dimensions to the literature on the plant-life cycle of wages. First, it reveals a set of facts pertaining to the evolution of the average wage over a plant’s life-cycle. Many prior studies do not analyze how the average wage changes along the entire plant life-cycle. Second, it explores the role of various frictions, represented by adjustment costs, in understanding how the average wage changes over a plant’s life. Third, it considers the dynamic interaction between labor quality and quantity at the plant level. This interaction has implications for the time-paths of the average wage and employment, and for the adjustment costs associated with both. Finally, the data used here is much richer and more complete than those in most of the previous studies.5 The rest of the paper is organized as follows. Section 2 describes the data and presents the empirical findings. Section 3 introduces the model. The estimation methodology is presented in Section 4. The estimation results are discussed in Section 5. Section 6 concludes. 2 The evolution of the average wage 2.1 Data The primary data source is the U.S. Census Bureau’s Longitudinal Research Database (LRD). The LRD describes several aspects of manufacturing plants’ production, including the total value of shipments and value added, as well as employment and total wage bill. The LRD also contains information on the classification and identification of plants, such 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 4 of 23 as plants’ ownership, location, and industry, as well as various status codes that identify birth, death, and ownership changes. These identifying codes are used in developing the longitudinal plant linkages.6 The analysis focuses on a subset of the LRD that includes eight waves of the Census of Manufactures (CM): 1963, and 1967-1997 quinquennially. The focus on these years are driven by the fact that plants are classified consistently into SIC industry codes, for which there was a substantial revision and transition into NAICS industry codes in 2002. The number of plants in the CM range from 305,691 in 1967 to 400,036 in 1997. Using permanent plant numbers, plants were linked from these CM’s to form an unbalanced panel for the period 1963-1997. Plant entry, exit, and continuation were identified. A plant is observed at most eight times, when it appears in all waves of the CM. The variables for the analysis are constructed as follows. A plant’s revenue is its value of shipments deflated to 1987 dollars using 4-digit SIC level industry price deflators from the NBER-CES Manufacturing Industry Database based on the 1987 SIC code definitions.7The deflated value added is also calculated as an alternative measure of revenue, which is used in the model’s estimation. Employment is a plant’s total number of workers engaged in production and non-production activities. The main wage variable, the total wage bill, is deflated to 1987 dollars using CPI from Bureau of Labor Statistics. Using a plant’s deflated revenue, its deflated wage bill, and its employment, three ratios were constructed for each plant-CM wave observation: (a) the average wage – the ratio of the total wage bill to employment, (b) labor productivity – the ratio of revenue to employment, and (c) the ratio of the total wage bill to revenue, equivalent to the ratio of (a) to (b). The empirical analysis describes the life-cycle evolutions of these three ratios. 2.2 Main findings The life-cycle effects on key plant-level variables are estimated using alternative specifications of the following OLS regression Yit =α+ τ βτ XXτ it + τ βτ EEτ it +β ZZit +ιit +εit,(1) where iindexes plants, tindexes census years, Yit is either the average wage, labor productivity, or the wage-bill-to-revenue ratio, Xτ it is an indicator of whether a plant is τ ∈{0, 5, 10, 15, 20}years to its exit point (the last census it is observed), Eτ it is an indicator of whether a plant is τ∈{0, 5, 10, 15, 20}years away from its entry point (the first census it is observed), and Zit is a vector of plant-level controls.8A full set of industry-year fixed effects, ιit, were also added to control for time and industry specific effects, such as the effects of industry life-cycles and aggregate shocks.9The omitted category, referred to as the ‘mature plants’, contains the plants that are more than twenty years away from their entry or exit, and the plants whose entry or exit do not fall into the sample period. The error term εit is assumed to be clustered by plant. Of particular interest are the coefficients βτ Xand βτ Eof the life-cycle indicators Xτ it and Eτ it, which track the evolution of the dependent variable Yit as a plant moves away from entry into maturity and as it approaches exit. The coefficients βτ Xand βτ Equantify the magnitudes of these life-cycle effects. βτ Xmeasures how much being τyears away from exit matters for Yit, controlling for the time from entry as identified by the indicators Eτ it, in addition to other controls. Similarly, βτ Emeasures how much being τyears from 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 5 of 23 entry matters for Yit, controlling for the time to exit identified by Xτ it, in addition to other controls. Specification I in Table 1 generates Figure 1. Consider, first, the evolution of the average wage in Figure 1a. New plants begin with an average wage that is roughly $2,700 lower than that of the mature plants. It takes at least twenty years for this gap to drop below $300. On the other hand, the average wage starts to fall as a plant approaches exit. The plants that are twenty years away from exit have about $200 lower average wage than the mature plants. This gap grows to $2,000 by the time of exit. The average wage tends to rise faster for surviving entrants than they fall for plants approaching exit. This is indicated by the different slopes of the average wage profiles for the two parts of the life-cycle on either side of the y-axis. Next, turn to the evolution of labor productivity in Figure 1b. Compared with the mature plants, new plants start off with approximately $5,700 productivity disadvantage that diminishes over time. Failing plants exhibit a much larger productivity disadvantage. Exiting plants have about $10,000 lower productivity. Even as early as ten years prior to exit, the plants that eventually exit have around $4,000 lower productivity. Labor productivity tends to fall faster for plants nearing exit than it rises for aging entrants. Table 1 OLS estimates for the evolution of average wage, labor productivity, and wages to revenue ratio Dependent variable: Average wage Revenue/Employment Wage bill/Revenue ($1,000) ($1,000) (%) Independent variables I II I II I II Exit -2.031*** -1.846*** -9.852*** -8.962*** 0.675*** 0.598*** [0.018] [0.018] [0.160] [0.157] [0.038] [0.038] 5 years to exit -1.010*** -0.897*** -5.220*** -4.939*** 0.634*** 0.635*** [0.019] [0.019] [0.174] [0.171] [0.042] [0.041] 10 years to exit -0.695*** -0.613*** -3.920*** -3.914*** 0.461*** 0.497*** [0.024] [0.024] [0.219] [0.215] [0.053] [0.052] 15 years to exit -0.447*** -0.412*** -2.566*** -3.010*** 0.273*** 0.369*** [0.031] [0.030] [0.275] [0.269] [0.066] [0.065] 20 years to exit -0.159*** -0.179*** -1.240*** -2.281*** 0.031 0.211** [0.038] [0.038] [0.347] [0.339] [0.083] [0.082] Entry -2.714*** -2.356*** -5.668*** -1.998*** -1.733*** -2.236*** [0.017] [0.017] [0.156] [0.155] [0.037] [0.038] 5 years from entry -1.782*** -1.514*** -2.228*** 0.282* -1.247*** -1.580*** [0.019] [0.019] [0.170] [0.168] [0.041] [0.041] 10 years from entry -1.180*** -0.964*** -1.141*** 0.571** -0.653*** -0.865*** [0.023] [0.023] [0.207] [0.204] [0.050] [0.049] 15 years from entry -0.628*** -0.469*** -0.151 0.759*** -0.219*** -0.312*** [0.026] [0.026] [0.239] [0.234] [0.057] [0.057] 20 years from entry -0.229*** -0.120** 1.537*** 1.634*** -0.181** -0.152 [0.044] [0.044] [0.397] [0.389] [0.095] [0.094] Industry x year fixed effects YYYYYY Other controls NYNYNY N1,219,769 1,219,769 1,219,769 1,219,769 1,219,769 1,219,769 R20.29 0.30 0.38 0.41 0.43 0.44 Notes: Standard errors in brackets. (*),(**),(***) indicate significance at 10%,5%,1%, respectively. Other controls include a cubic spline in plant size (measured by total employment) and an indicator of multi-plant firm. The omitted category is the plants that are more than twenty years away from their entry or exit, and the plants whose entry or exit points do not fall into the sample period. 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 6 of 23 −3 −2.5 −2 −1.5 −1 −.5 0 Excess average wage in $1,000 Entry 5 yrs from entry 10 yrs from entry 15 yrs from entry 20 yrs from entry/to exit 15 yrs to exit 10 yrs to exit 5 yrs to exit Exit Life−Cycle stage (Years from first/last census) (a) −10 −8 −6 −4 −2 0 2 Excess labor productivity in $1,000 Entry 5 yrs from entry 10 yrs from entry 15 yrs from entry 20 yrs from entry/to exit 15 yrs to exit 10 yrs to exit 5 yrs to exit Exit Life−Cycle stage (Years from first/last census) (b) −.02 −.015 −.01 −.005 0 .005 .01 Excess average wage to labor productivity ratio Entry 5 yrs from entry 10 yrs from entry 15 yrs from entry 20 yrs from entry/to exit 15 yrs to exit 10 yrs to exit 5 yrs to exit Exit Life−Cycle stage (Years from first/last census) (c) Figure 1 Plant life-cycle evolution of : (a) average wage, (b) labor productivity, (c) average wage to labor productivity ratio. Notes: The horizontal line at zero represents the normalized value for the omitted category. Dashed lines are +/−2 std. errors. 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 7 of 23 The findings in Figures 1a and 1b together suggest the evolution of the ratio of a plant’s wage bill to its revenue, equivalent to the fraction of a worker’s productivity provided to the worker in the form of wages. The estimated life-cycle path for this ratio is shown in Figure 1c. New plants spend a lower fraction of their revenue on wage bill compared with the mature plants, although this gap largely disappears over the next fifteen years for surviving plants. Plants approaching exit, however, exhibit a small but statistically significant increase in this ratio. Failing plants on average spend a higher fraction of their revenue on their wage bill compared with the mature plants. The findings continue to hold when other controls, Zit, are added. Specification II includes a cubic polynomial in plant size (employment), which is highly positively associated with wages (see, e.g., Brown and Medoff (1989)), in addition to an indicator of whether the plant is part of a multi-unit firm, and location (state) fixed effects. While the magnitudes of the life-cycle effects for the case of the average wage are now somewhat smaller in absolute value, their signs and significance resemble those in specification I. The most important difference is in the case of labor productivity for young plants. Compared with specification I, young plants now exhibit a much faster productivity growth, and seem to wipe out their productivity disadvantage vis á vis the mature plants by their fifth year after entry. As a result, the wage-bill-to-revenue ratio for entering plants is now lower, and stays lower longer as they age. The overall theme of Table 1 can be summarized as follows. The average wage is low for entering plants, and gradually approaches that of the mature plants. As plants get closer to exit, the average wage falls, but not as fast as it increases for surviving and aging entrants. Labor productivity is also much lower for entering plants, but it rises as plants age. Plants nearing exit have a relative productivity disadvantage, visible even twenty years prior to exit. Griliches and Regev (1995) find a similar pattern in an early study of Israeli firms and dub this effect “the shadow of death”: the firms that are due to exit in the future are less productive in the present.10 The average wage rises slower than labor productivity does for young plants as they survive and age. It also falls slower than labor productivity does for failing plants. However, the relative rate of growth in the average wage versus labor productivity in young plants differs from the corresponding relative rates of decline in failing plants. This asymmetry manifests itself in the evolution of the wage-bill-to- revenue ratio. For young plants, the wage bill constitutes a smaller fraction of revenue compared with the mature plants, whereas plants approaching exit are burdened with a wage bill that claims a larger fraction of their revenue compared with the mature plants. The evolutions depicted in Figure 1 can be interpreted as the typical or average evolutions that would take place if a plant goes through all stages of the life-cycle. One concern is a potential composition bias in these evolutions due to differences in plant life-span. For instance, short-lived plants may be a special group. They may be born with a much lower productivity and may live for a period of at most 2 or 3 censuses, and experience a monotonic decline in average wages and productivity over that period until exit. As a result, the latter part of the life-cycle trends in Figure 1 may be driven mainly by such plants. As a further robustness check for the patterns in Figure 1, and as an alternative to the panel analysis which pools all plants, the evolution of the average wage is plotted in Figure 2 over the life-span of plants that are born within the sample period and live for a given number of censuses; a minimum of 2, and a maximum of 6. This longitudinal analysis ensures that the same set of plants are observed over time for any given life-span. 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 8 of 23 4.0 3.5 3.0 2.5 2.0 1.5 1.0 6 censuses 5 censuses 4 censuses 3 censuses 2 censuses Lifespan: Excess average wage in $1000 Figure 2 Plant life-cycle evolution of the average wage by plant life span. Notes: Excess average wage is measured with respect to the omitted category. The estimated life-cycle effects in the case of the average wage plotted in Figure 2 are obtained using a similar regression specification to (1) with only a set of dummies that indicate the number of years from entry interacted by a life-span dummy.11 The full set of results for this exercise are in Table 2. The inverted-U shaped pattern in Figure 1a also emerges for each life-span in Figure 2. Even relatively short-lived plants appear to experience an increase in the average wage after entry, and a decline before exit. Note also in Table 2 that the average wage declines slower than productivity does as plants approach exit, similar to the pattern found in Table 1. The results thus appear to be robust to life-span considerations. Table 2 OLS estimates for the evolution of average wage, labor productivity, and wages to revenue ratio Wage bill/Revenue Dependent variable: Average wage ($1,000) Revenue/Employment ($1,000) (%) Independent variables I II I II I II Lifespan = 2 Entry -3.490*** -3.042*** -9.638*** -5.740*** -1.159*** -1.661*** [0.035] [0.035] [0.315] [0.310] [0.076] [0.075] Exit -3.775*** -3.356*** -12.241*** -8.871*** -0.692 *** -1.111*** [0.035] [0.035] [0.315] [0.310] [0.076] [0.075] Lifespan = 3 Entry -3.134*** -2.713*** -8.227*** -4.762*** -1.125*** -1.561** [0.047] [0.047] [0.425] [0.417] [0.102] [0.101] 5 years from entry -2.958*** -2.570*** -8.118*** -5.077*** -0.651*** -1.025*** [0.047] [0.047] [0.423] [0.415] [0.101] [0.101] Exit -3.274*** -2.920*** -11.227*** -9.112*** -0.131 -0.362*** [0.048] [0.048] [0.436] [0.427] [0.104] [0.104] 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 15 of 23 Table 3 GMM estimates for the model’s parameters using CM sample Revenue measure: Total value of shipments (deflated) Adjustment costs Adjustment cost (conventional growth rate) (alternative growth rate) Wage Wage and employment Wage Wage and employment adjustment adjustment adjustment adjustment Continuing All Continuing All Continuing All Continuing All plants plants plants plants plants plants plants plants Parameter (I) (II) (III) (IV) (I) (II) (III) (IV) α0.12*** 0.11*** 0.12*** 0.09*** 0.12*** 0.13*** 0.12*** 0.14*** [0.0005] [0.0006] [0.002] [0.0006] [0.0004] [0.0004] [0.0005] [0.001] γ0.19*** 0.18*** 0.25*** 0.24*** 0.27*** 0.42*** 0.18*** 0.13*** [0.0004] [0.0005] [0.002] [0.0007] [0.0009] [0.002] [0.002] [0.002] λU2.89*** 2.62*** 10.73*** -0.98*** 5.94*** 14.18*** 1.25*** 2.77*** [0.05] [0.04] [0.41] [0.01] [0.11] [0.15] [0.03] [0.08] λD35.61*** 63.10*** 20.81*** 56.48*** 9.55*** 6.30*** 10.10*** 10.46*** [0.25] [0.42] [0.74] [0.33] [0.07] [0.06] [0.12] [0.33] vU- - 8.62*** 0.56*** - - 1.97*** 13.43*** [0.52] [0.03] [0.42] [0.72] vD- - 23.17*** 42.91*** - - 8.90*** 88.21*** [4.82] [1.03] [0.12] [1.94] Revenue measure: Value added (deflated) α0.36*** 0.39*** 0.25*** 0.33*** 0.32*** 0.37*** 0.27** 0.39*** [0.0005] [0.0008] [0.003] [0.0007] [0.0002] [0.001] [0.001] [0.003] γ0.47*** 0.44*** 0.37*** 0.39*** 0.35*** 0.27*** 0.22*** 0.42*** [0.002] [0.001] [0.004] [0.002] [0.0002] [0.001] [0.002] [0.002] λU1.39*** 1.00*** 3.73*** 1.38*** 3.44*** 3.25*** 1.04*** -0.54*** [0.04] [0.02] [0.24] [0.04] [0.08] [0.06] [0.03] [0.05] λD15.63*** 13.95*** 29.32*** 19.25*** 10.31*** 8.34*** 6.82*** 1.37*** [0.17] [0.20] [0.47] [0.21] [0.06] [0.06] [0.08] [0.22] vU- - 3.46*** 1.13*** - - 2.03*** 17.74*** [0.28] [0.04] [0.32] [0.79] vD- - 41.42*** 47.60*** - - 6.69*** 58.32*** [2.70] [0.72] [0.09] [2.20] N804,245 986,977 804,245 986,977 804,245 986,977 804,245 986,977 Notes: Standard errors in brackets. (*),(**),(***) indicate significance at 10%,5%,1%, respectively. In all specifications in Table 3, the estimates for the production function parameters αand γhave the expected signs, and fall in the interval (0, 1). The average estimated value of α+γwhen revenue is measured by the value of shipments is about 0.37. When value added is used as the revenue measure in the bottom panel, the estimates of αand γ are much higher, and still highly significant. The estimates of α+γaverage 0.72 across specifications. The estimated magnitudes of γare comparable to, and sometimes exceed, those of α. The importance of labor quality relative to quantity in production accords with Griliches and Regev’s (1995) and Bahk and Gort’s (1993) findings that labor quality plays an important role in explaining productivity differences across plants. In most of the specifications in Table 3, the estimates of the adjustment cost parameters have the expected signs and they are highly significant.21 The estimates reveal asymmetry in adjustment costs. Except for one case (specification II with alternative growth rate), downward adjustment cost parameters are higher than upward adjustment cost 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 16 of 23 parameters, both for the average-wage and employment adjustment. The equality of the upward and downward adjustment cost parameters are rejected at high levels of significance across most specifications. Furthermore, the downward adjustment cost parameters are generally larger in absolute value when all plants are considered (specifications II and IV), compared with the case of continuing plants only (specifications I and III). This difference suggests that failing plants face higher downward adjustment costs. The adjustment cost parameter estimates are generally smaller under alternative growth rates. 5.2 The magnitude of the adjustment costs Because the adjustments in the average wage and employment apply to different bases in various adjustment cost specifications, it is not appropriate to simply compare the estimated magnitudes of λUand νU,orλDand νD, to gauge the relative importance of different adjustment costs. The approach here is to assess the cost of adjustment for the average wage versus employment by comparing the shares of the wage and employment adjustment costs in a plant’s initial revenue over a 5-year window between two censuses. The average annual adjustment costs are calculated over a 5-year period between two census years tand t+5, whereas the current revenue is calculated for census year t. Table 4 presents the quartiles of the revenue shares of adjustment costs based on the parameter estimates in Table 3. Rather than picking a specification arbitrarily, for each adjustment cost parameter its maximum estimate across all specifications for the case of total value of shipments in Table 3 is used to obtain the estimates in Table 4. This approach provides an upper bound on the revenue share of adjustment costs. Panel (a) considers estimates from the models using continuing plants only, corresponding to specifications I and III in Table 3. The estimated revenue shares of adjustment costs are calculated at the median of the corresponding adjustment cost distribution. For the model with the average-wage adjustment only, the average annual adjustment cost is 0.8% of revenue when both directions of adjustment are considered together, and around 0.4% and 2% when upward and downward adjustments are considered, respectively. For the model with both the average-wage and employment adjustment, the average-wage adjustment cost is about 1.0% of revenue, and the upward average-wage adjustment cost constitutes about 0.6% of revenue, while the downward average-wage adjustment cost makes up about 1.6% of revenue. The average annual employment adjustment cost has a significant share, nearly 4%, of revenue when downward employment adjustments are considered. For upward employment adjustments, this share is about 3%. A similar pattern emerges in panel (b) of Table 4, which focuses on the estimated parameters using all plants, corresponding to specifications II and IV in Table 3. For the model with the average-wage adjustment only, upward and downward adjustment costs make up, respectively, about 0.8% and 3.5% of revenue. For the model with both adjustment margins, these shares are about 0.1% and 3.2%, respectively. For employment adjustment, upward adjustment cost is about 5.9% of revenue, whereas the downward adjustment cost constitutes about 8.2% of revenue. Adjustment costs constitute a much larger fraction of revenue when only the exiting plants are considered, as panel (c) of Table 4 indicates. For almost all model specifications and for both upward and downward adjustment, the shares in panel (c) are much higher compared with panels (a) and (b). These magnitudes suggest that downward adjustment costs can potentially be a substantial burden for plants, especially for those nearing exit. 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 17 of 23 Table 4 Adjustment costs as a percentage of revenue (a) Estimates using continuing plants only Model with wage Model with wage and adjustment only employment adjustment Adjustment Cost Upward Downward Upward Downward Percentile All Upward Downward All (Wages) (Wages) (Employment) (Employment) Wage Adjustment 25 0.1% 0.1% 0.4% 0.2% 0.1% 0.3% 50 0.8% 0.4% 2.0% 1.0% 0.6% 1.6% 75 3.7% 1.5% 7.6% 4.2% 2.7% 6.2% Employment Adjustment 25 0.6% 0.6% 0.5% 50 3.2% 2.9% 3.8% 75 14.3% 12.6% 16.9% (b) Estimates using all plants: Continuing plants only Model with wage Model with wage and adjustment only employment adjustment Adjustment Cost Upward Downward Upward Downward Percentile All Upward Downward All (Wages) (Wages) (Employment) (Employment) Wage Adjustment 25 0.3% 0.2% 0.7% 0.0% 0.0% 0.6% 50 1.6% 0.8% 3.5% 0.4% 0.1% 3.2% 75 7.4% 3.5% 13.4% 3.4% 3.9% 2.9% Employment Adjustment 25 1.2% 1.2% 1.1% 50 6.7% 5.9% 8.2% 75 29.9% 26.0% 35.9% (c) Estimates using all plants: Exiting plants only Model with wage Model with wage and adjustment only employment adjustment Adjustment Cost Upward Downward Upward Downward Percentile All Upward Downward All (Wages) (Wages) (Employment) (Employment) Wage Adjustment 25 0.5% 0.2% 1.3% 0.1% 0.0% 1.1% 50 3.0% 1.2% 6.7% 0.9% 0.1% 6.0% 75 14.2% 5.3% 25.7% 7.9% 0.5% 23.0% Employment Adjustment 25 2.4% 1.8% 3.4% 50 14.8% 10.0% 22.4% 75 70.6% 48.5% 94.7% Howmuchdoadjustmentcostsintheaveragewagechangeoverthelife-cycleofaplant? The evolution of adjustment costs along the plant life-cycle is depicted in Figure 3 for continuing plants. These estimates are based on the largest adjustment cost parameter estimates from the model with wage adjustment cost only using all plants, corresponding to specifications labelled II in the left panel of Table 3. These specifications also generate the left side of panel (b) in Table 4. For the plants that are five years from their first census, the average-wage adjustment cost makes up about 2.3% of revenue annually at the median adjustment cost. This share increases slightly as new plants age and then declines to just 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 18 of 23 1 1.2 1.4 1.6 1.8 2 2.2 2.4 Percent of revenue 5 yrs from entry 10 yrs from entry 15 yrs from entry 20 yrs from entry/to exit 15 yrs to exit 10 yrs to exit 5 yrs to exit Life−Cycle stage (Years from first/last census) Figure 3 Plant life-cycle evolution of wage adjustment cost as a share of revenue. Estimates based on the model with wage adjustment (continuing plants only). below 1.6% by the time a plant is 20 years away from entry. Somewhat reversal of this pattern is observed for the plants approaching exit. The average-wage adjustment cost grows to about 2.2% of a plant’s revenue five years before its exit, doubling from about 1.1% when a plant stands twenty years from exit. 5.3 Estimation using annual data The estimates for annual adjustment costs were so far obtained using data in CM, which has quinquennial frequency. A longer time period between two consecutive observations of a plant could lead to lower adjustment cost estimates, as plants may have more flexibility to adjust over a longer horizon. If adjustment costs are highly convex, plants would tend to spread adjustments over time. The estimation is repeated using annual frequency data from the US Census Bureau’s Annual Survey of Manufactures (ASM) for the period 1972-2009. As in the case of the CM, plants are linked over time. The unbalanced panel constructed from the ASM has a number of important differences from the panel from the CM. First, ASM pertains mainly to the relatively large plants.22 Furthermore, a plant may enter to or drop from the ASM panel simply because its size changes to a level that is above or below the threshold of inclusion with certainty in the ASM. Second, the sampling frame for the ASM changes every five years, implying that some plants may enter, whereas others may be dropped from the sample over time. Therefore, actual plant entry/exit do not necessarily coincide with entry to/exit from the sample. The results based on the ASM thus may differ from the ones based on the CM for reasons in addition to the differences in the frequency of observation.23 The results from the GMM estimation based on the ASM panel are in Table 5. Sample weights in the ASM are used to obtain the population estimates. The results appear to be qualitatively similar across Table 3 and Table 5. Most importantly, downward adjustment costs are still larger than upward adjustment costs, and upward adjustment costs 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 19 of 23 Table 5 GMM estimates for the model’s parameters using ASM sample Revenue measure: Total value of shipments (deflated) Adjustment costs Adjustment cost (conventional growth rate) (alternative growth rate) Wage Wage and employment Wage Wage and employment adjustment adjustment adjustment adjustment Continuing All Continuing All Continuing All Continuing All plants plants plants plants plants plants plants plants Parameter (I) (II) (III) (IV) (I) (II) (III) (IV) α0.11*** 0.11*** 0.10*** 0.07*** 0.14*** 0.14*** 0.09*** 0.09*** [0.00006] [0.00006] [0.002] [0.0001] [0.00007] [0.00006] [0.0004] [0.0001] γ0.17*** 0.17*** 0.22*** 0.20*** 0.21*** 0.31*** 0.18*** 0.12*** [0.00007] [0.00012] [0.00008] [0.0001] [0.0003] [0.0003] [0.0003] [0.0003] λU3.72*** 1.72*** 3.44*** 0.83*** 1.45*** 1.32*** 1.56*** 1.31*** [0.006] [0.004] [0.004] [0.004] [0.007] [0.008] [0.004] [0.008] λD1.31*** 2.33*** 2.76*** 1.46*** 18.82*** 2.89*** 19.73*** 2.89*** [0.008] [0.002] [0.009] [0.001] [0.052] [0.007] [0.049] [0.007] vU- - 0.64*** 2.77*** - - 1.53*** 1.77*** [0.21] [0.14] [0.538] [0.221] vD- - 96.39*** 32.65*** - - 44.39*** 43.49*** [40.5] [6.0] [15.6] [1.94] Revenue measure: Value added (deflated) α0.26*** 0.26*** 0.10*** 0.17*** 0.31*** 0.32*** 0.32*** 0.33*** [0.0002] [0.0002] [0.0005] [0.0002] [0.0001] [0.0001] [0.0001] [0.0003] γ0.20*** 0.19*** 0.19*** 0.19*** 0.16*** 0.17*** 0.13*** 0.17*** [0.0001] [0.0001] [0.0001] [0.0001] [0.0003] [0.0003] [0.0004] [0.0003] λU0.77*** 1.14*** 2.33*** 0.33*** 1.73*** 1.17*** 1.44*** 1.21*** [0.005] [0.003] [0.005] [0.001] [0.005] [0.006] [0.006] [0.004] λD1.93*** 1.24*** 2.88*** 0.24*** 12.16*** 1.81*** 14.39*** 1.82*** [0.008] [0.001] [0.008] [0.001] [0.04] [0.006] [0.041] [0.006] vU- - 0.62*** 0.33*** - - 1.52*** 1.48*** [0.221] [0.113] [0.741] [0.255] vD- - 99.48*** 32.84*** - - 86.32*** 37.10*** [22.0] [6.2] [14.2] [7.2] N3,926,908 3,927,407 3,926,908 3,927,407 3,926,908 3,927,407 3,926,908 3,927,407 Notes: Standard errors in brackets. (*),(**),(***) indicate significance at 10%,5%,1%, respectively. Number of observations reflect sampling weights used. are generally small for both the average wage and employment. However, there are a few important differences. First, in Table 5 the estimates of the production function parameters αand γare generally smaller. This discrepancy could be driven by the fact that the plants in the sample used for the results in Table 5 are much larger, and may have different production technologies compared with the smaller ones in the sample used for the results in Table 3. Second, the estimates of the downward employment adjustment cost parameters are much larger compared with those in Table 3, whereas the estimates of the downward average-wage adjustment cost parameters have comparable magnitudes. 5.4 A comparison with the prior findings In general, the empirical regularities documented here agree with the earlier findings based on data from other countries and periods of time. Brixy et al. (2007) use the 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 20 of 23 matched employee-employer data in Germany for the period 1997-2001, and find that the average wage is lower in new firms but rises as firms get older, similar to the findings here. Audretsch et al. (2001) also analyze the connection between wages, productivity and firm age in Netherlands, and obtain similar findings. The “shadow-of-death” effect, i.e. lower productivity observed in failing firms even many years before they exit, is also noted in a recent study by Carreira and Teixeira (2011) using data on Portuguese firms. Fackler et al. (2013) also find a similar effect in the cases of East and West Germany. In the case of French manufacturing firms over the period 1990-2002, Bellone et al. (2006) document that mature firms that have lower and declining productivity compared with other mature firms tend to exit with higher likelihood. They also observe that the growth of new entrants is gradual, and it takes time for them to catch-up with the mature firms. Combined with the findings for the U.S. documented here, these prior studies suggest that the life-cycle evolution of productivity and wages tend to be similar across countries. How do the estimates of adjustments costs compare with some of the prior findings in the literature? There does not exist a sizeable prior literature on the estimation of the adjustment costs for the average wage, or individual wages. However, several studies have provided estimates of labor adjustment costs. These studies span a variety of model structures, samples, and aggregations, making the comparison inherently difficult. Nevertheless, most studies find positive labor adjustment costs, based on different specifications. Adjustment costs are positive and significant in studies using a quadratic or higher-order specification with or without interactions among different adjustment margins.24 The estimates of labor adjustment costs here are within the range of the estimates found in these previous studies, though there is strong evidence of asymmetric adjustment costs here. The estimates indicate relatively large downward employment adjustment costs compared with the upward adjustment costs. This asymmetry in labor adjustment was also found in earlier work by Jaramillo et al. (1993) using large firms in Italy for the period 1958-1988, and by Nilsen et al. (2007) in the case of Norwegian manufacturing plants for the period 1986-1995. Kramarz and Michaud (2010) also present evidence of asymmetry in hiring versus terminating costs in the case of French establishments. Furthermore, as shown in Table 4, the average-wage adjustment costs are smaller when the average-wage adjustment costs are estimated together with the employment adjustment costs, suggesting that not controlling for labor quantity adjustments may introduce bias in the adjustment cost estimates for labor quality, and vice versa. 6Conclusion This paper investigated the dynamics of the average wage paid to employees along the lifecycle of a manufacturing plant. Some stylized facts emerge. New plants start with a lower average wage compared with the mature plants. A surviving plant’s average wage increases and catches up with those of the mature plants. The average wage of a plant approaching exit falls, but not as steeply as it rises in the case of surviving new plants. The total wage bill constitutes a lower fraction of a young and growing plant’s revenue, because its average wage does not increase as fast as it labor productivity does. For plants approaching exit, the average wage does not fall as fast as labor productivity does, implying that a higher fraction of revenue must be dedicated to wage bill. 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 21 of 23 A model of plant-level dynamics was introduced to explore the potential role of frictions in the form of adjustment costs in the average wage. In the model, changes in the average wage result from both changes in average labor quality and changes in the wage rate per unit of quality. The estimated parameters of the model reveal evidence of asymmetric adjustment costs for the average wage. The estimated upper bounds on the revenue share of the average-wage adjustment costs indicate economic significance of such costs at the plant level, especially those associated with downward adjustment. The estimates remain economically significant when the average-wage adjustment cost is considered jointly with the employment adjustment cost. The relatively high cost of adjusting the wage bill downward when a plant’s productivity falls persistently may increase the likelihood of exit and speed up the demise of failing plants. Further research can quantify the importance of these costs in exit. Such effects can potentially be larger in the case of plants in Europe, which tend to face stricter labor regulations than in the U.S. The movements in the average wage at the plant level can also be decomposed further. A challenge is to separately measure the cost of adjustments in the wage rate per unit of labor quality, and the cost of adjustments due to changes in average labor quality only. Finally, the co-evolution of the average wage and labor quality also suggest a theory of plant life-cycle that emphasizes organizational complexity and hierarchy. For instance, new plants may start with a simple worker hierarchy that progressively becomes more complex with the addition of workers of higher quality and wages. Plant decline triggers a sluggish dismantling of this hierarchy and slow reduction in labor costs due to potentially high costs of changing the employee composition and wages. Endnotes 1Throughout this paper, the term “average wage” refers to a plant’s total wage bill per employee, not the average hourly wage rate of employees. The average hourly wages are not available in the data used here. 2See, e.g., Brown and Medoff (2003) and Kölling et al. (2013), Griliches and Regev (1995), and Bahk and Gort (1993). The analysis here does not attempt to disentangle the effect of worker versus plant characteristics on wages. 3See, e.g., Bahk and Gort (1993) and Griliches and Regev (1995). 4Unions in manufacturing, while much weaker in recent times, were effective for much of the sample period used in this paper, which goes back to 1963. Union presence in a plant could thus have played a role in the volatility of plant-level average wage in the earlier part of the sample period. Unfortunately, there does not exist a comprehensive data on the union status of plants in the U.S. 5Most prior work focus on employment and capital adjustment, and either use relatively small samples or non-representative samples from the U.S. firm population. Other studies use aggregate data, which may hide patterns at the micro level. Examples of prior work include, among others, Alonso-Borrego (1998), Shapiro (1986), Pfann and Palm (1993), Hall (2004), and Merz and Yashiv (2007). 6See also http://www.census.gov/ces/dataproducts/economicdata.html. 7See http://www.nber.org/data/nbprod2005.html. 8The total employment of a plant was restricted to the range 5 to 10, 000 employees. In addition, the top and bottom percentiles of the three dependent variables were trimmed to reduce the influence of any outliers. 9The regression specifications are similar to those used in Foster et al. (2008). 10 Such effects were also found in studies using data from other countries. See, e.g., Carreira and Teixeira (2011), Fackler at al. (2013), and Bellone et al. (2006). 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 22 of 23 11 The time-to-exit indicators are excluded in these regressions, as including them together with the time-from-entry indicators would lead to collinearity with the life-span indicator. 12 Worker quality, q, can have alternative interpretations, such as a worker’s skill level, human capital, effort (including hours worked), or the degree of essentiality of a worker in production. 13 This wage rate is allowed to be plant-specific, reflecting, for instance, the plant’s local labor market conditions. 14 When there are no adjustment costs, the elasticity of output with respect to labor quantity and quality must be the same (α=γ) for interior maximizers to exist for the plant’s optimization problem. The presence of adjustment costs that differ across factors breaks this constraint, and allows interior maximizers when α= γ. 15 These two different adjustments are not separately identified in the data used here. 16 Suppose that the productivity of new entrants have c.d.f.H0(θt). Subsequent productivity draws come from Hτ(θt|θt−1), which is strictly decreasing in θt−1,and H1(θt+1|θt)<H0(θt)for all θt. There can also be age effects, e.g. Hτ(θt|θt−1)is strictly decreasing in τfor τ<τand strictly increasing in τfor τ>τ, for some τ≥1. An entrant thus starts with a productivity that is lower, in a first-order-stochastic sense, than that of an age-1 incumbent. A surviving plant’s next period productivity is higher in a first-order-stochastic sense, the higher its current period productivity. Such a process can generate the gradual decline in average productivity of failing plants prior to exit, and the gradual increase in average productivity of surviving plants following entry. See Hopenhayn (1992) [Section 5] for a general characterization of stochastic processes that can generate the observed evolutions of productivity. 17 Such monotonicity holds under certain restrictions on the parameters of the model. 18 The results were not significantly different when twice-lagged versions of the variables were also used as instruments. 19 The first order conditions for this case are available upon request. 20 In all specifications in Table 4, the top and bottom 1% of the plant level distributions of average wage, wage bill-to-revenue ratio, and labor productivity were trimmed to reduce the influence of some major outliers. The total employment of a plant was also restricted to the range 5 to 10, 000 employees. 21 In two cases, the estimated upward adjustment cost parameters are negative, with small absolute values. These cases also emerge in some prior work with quadratic adjustment costs (e.g. Hall (2004)) and may result from a combination of remaining outliers in the data and measurement error. An implicit constraint on the adjustment cost parameters is non-negativity. As a robustness check, the model was re-estimated subject to these constraints. If the model’s specification is not largely at odds with the data, moving from the unconstrained to the constrained estimation should not result in drastic sign and significance changes in other parameters. The constrained estimates were zero for the negative parameter estimates in Table 3, and the other parameter values were very similar to those in Table 3. 22 The Annual Survey of Manufactures (ASM) is a sample survey of approximately 50,000 establishments. A new sample is selected at 5-year intervals beginning the second survey year subsequent to CM. Large plants are sampled with certainty, and smaller plants are sampled according to a stratified random sampling with probabilities that vary with plant size. See http://www.census.gov/manufacturing/asm/how_the_ data_are_collected/index.html. 23 A minimum plant size requirement of 50 employees is imposed to ensure that the estimation is not unduly influenced by smaller plants. 24 See, e.g., Shapiro (1986), Pfann and Palm (1993), Alonso-Borrego (1998), and Merz and Yashiv (2007)), with the exception of Hall (2004), who finds quadratic costs that are not significantly different from zero using sector level data. 2013, 2:7 http://www.izajole.com/content/2/1/7 Dinlersoz et al. IZA Journal of Labor Economics Page 23 of 23 Competing interests The IZA Journal of Labor Economics is committed to the IZA Guiding Principles of Research Integrity. The authors declare that they have observed these principles. Acknowledgements Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. We thank the editor, Pierre Cahuc, and two anonymous referees for useful suggestions. We are also grateful to Baris Kaymak, Francesco Lotti, Kristin McCue, Devesh Raval, Lars Vilhuber, and various seminar and conference participants for comments. Responsible Editor: Pierre Cahuc Received: 9 August 2013 Accepted: 23 October 2013 Published: References Alonso-Borrego C (1998) Demand for labour inputs and adjustment Costs: Evidence from Spanish manufacturing firms. Labour Econ 5(4): 475–497 Audretsch DB, van Leeuwen G, Menkveld B, Thurik R (2001) Market dynamics in the Netherlands: Competition policy and the role of small firms. Int J Ind Organ 19(5): 795–821 Bahk B, Gort M (1993) Decomposing learning by doing in new plants. 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In: Laffont JJ, Sims C (eds) Advances in Econometrics: Proceedings of the 6th World Congress of the Econometric Society. Cambridge University Press, Cambridge Pfann G, Palm FC (1993) Asymmetric adjustment costs in non-linear labour demand models for the Netherlands and U.K. manufacturing Sectors. Rev Econ Stud 60(2): 397–412 Shapiro MD (1986) The dynamic demand for labor and capital. Q J of Econ 101(3): 513–542 Stokey NL, Lucas RE (1989) Recursive methods in economic dynamics. Harvard University Press, Cambridge Cite this article as: Dinlersoz et al.:The plant life-cycle of the average wage of employees in U.S. manufacturing. IZA Journal of Labor Economics 2013, 2:7 http://www.izajole.com/content/2/1/7 22 Nov 2013 10.1186/2193-8997-2-7 2013, 2:7