Keywords: Adjustment costs, learning, young firms
We propose an explanation for the rapid post-entry growth of surviving firms found in recent studies. At the core of our theory is the interaction between adjustment costs and learning by entering firms about their efficiency. We show that linear adjustment costs, i.e., proportional costs, create incentives for firms to enter smaller and for successful firms to grow faster after entry. Initial uncertainty about profitability makes entering firms prudent since they want to avoid incurring superfluous costs on jobs that prove to be excessive ex post. Because higher adjustment costs imply less pruning of inefficient firms and faster growth of surviving firms, the contribution of survivors to growth in a cohort's average size increases. For the cohort of 1988 entrants in the Portuguese economy, we conclude that survivors' growth is the main factor behind growth in the cohort's average size. However, initial selection is higher and the survivors' contribution to growth is smaller in services than in manufacturing. An estimation of the model shows that the proportional adjustment cost is the key parameter to account for the high empirical survivors' contribution. In addition, firms in manufacturing learn relatively less initially about their efficiency and are subject to larger adjustment costs than firms in services.
JEL Classification: E24, L11, L16
In recent years there has been renewed interest in explaining patterns of firm dynamics, with new longitudinal datasets confirming heterogeneities between firms of different size and age. In particular, small and young (surviving) firms tend to grow faster and have higher failure rates than large and old firms, and both job creation due to the scaling-up of firm size and job destruction due to firm exit decrease with age.2 Moreover, entering firms tend to be small, but survivors grow rapidly after entry and are the main factor behind the shift to the right of a cohort's size distribution.3 These patterns differ markedly across sectors and countries, suggesting that both technological differences and country specific factors matter.4
This paper proposes an explanation for the leading role of survivors' growth in post-entry firm dynamics based on the interaction between adjustment costs and a learning-about-efficiency mechanism. Following a literature that uses adjustment costs to account for some dynamic properties of firms' labor demand, such as Campbell and Fisher (2000) , we show that proportional costs can impact the lifetime dynamics of firms' labor demand in a way consistent with the data. To implement our theory, we use a standard model of firm dynamics with passive learning. In order to check the empirical fit of our model, we also assume that inefficient firms are pruned from the market, although the predictions of our theory hold even in the absence of a selection mechanism (e.g. when exit is not allowed).
Our contribution is twofold. First, we contribute to the empirical literature by introducing a decomposition of the change in a cohort's average size into a survivor component and a non-survivor component, and by using this decomposition as the centerpiece in a structural estimation of adjustment costs. Given the emphasis on survivors' growth, our measure allows a quick assessment of how well a particular theory matches the data in that respect. We apply our decomposition to the 1988 cohort of entrants in the Portuguese economy, using the Quadros de Pessoal dataset. Similarly to Cabral and Mata (2003) , we find that growth of survivors is the main force behind the change in the cohort's average firm size. However, we also find that growth of survivors is especially intense in the initial years after entry and that there are significant cross-sector differences in terms of our decomposition. In particular, initial exit rates are smaller and the survivors' contribution to changes in size is higher in manufacturing than in services.
Second, we contribute to the theoretical literature by introducing linear adjustment costs into a model of Bayesian learning about efficiency. Our assumption of linear or proportional costs is justified by the finding of high inaction rates in employment adjustment, in varying degrees across sectors. Our model builds on Jovanovic (1982) by adding proportional costs that apply not only to regular labor adjustment, but also to job creation at entry and job destruction at exit. We show that proportional adjustment costs create incentives for firms to start smaller and, if successful, grow faster after entry. We prove this analytically in a simplified model in which there is no exit of firms. This result shows that proportional costs can generate firm growth without selection. When firms are allowed to exit, selection intensifies the effects of adjustment costs on firm growth, while costs to adjustment reduce exit rates. Therefore, adjustment costs increase the contribution of surviving firms to growth in the cohort's average size.
All that is needed for firm growth under linear adjustment costs is the existence of a learning environment that generates a stochastic process for perceived efficiency with both persistence and decreasing uncertainty in age.5 The intuition for why firms grow faster and display smaller exit rates under proportional adjustment costs is that initial uncertainty about true profitability makes entering firms prudent; that is, they enter small and "wait and see" since they want to avoid incurring superfluous entering/hiring costs and firing/shutdown costs on jobs thatprove to be excessive ex post. This implies that surviving firms will grow faster, even though adjustment costs imply that there are fewer firms exiting the market and therefore less pruning of inefficient firms.
The assumption that entering firms face a Bayesian learning problem concerning their efficiency is standard in selection theories and has been advanced as an explanation for the high rates of exit, job creation, and job destruction among young firms. The initial literature on adjustment costs used a (strictly) convex specification in an attempt to explain the sluggishness in input responses to aggregate shocks. However, the assumption that costs of adjustment are linear is now standard in dynamic labor demand models, following a number of studies since the late 1980s that have documented the importance of inaction in employment adjustment at the micro level.6 Since strictly convex costs imply smooth adjustments over time, whereas linear costs imply immediate adjustment when it occurs, allowing for strictly convex costs, instead of linear costs, in the context where they also apply at entry and exit, would bias our analysis and eventually make our argument stronger. In the case of hiring/entering costs, entering firms would prefer to start smaller and adjust gradually to their optimal size, even if their perceived productivity remained unchanged or learning was absent. For firing/exiting costs, firms experiencing large declines in perceived productivity would adjust downwards in several steps, a scenario that would make firms start smaller to attenuate its effects. Therefore, by avoiding a bias towards firm growth, our decision to assume linear costs is conservative and permits a simplification of the methods employed to measure the effects of adjustment costs.7
To assess our model quantitatively, we calibrate and estimate a version of the model with finite learning horizon and positive dispersion in entry size. We conclude that linear costs are the key element to account for the high empirical contribution of survivors to changes in a cohort's average size. A calibration/estimation for the manufacturing and services cohorts also suggests that firms in manufacturing learn relatively less initially about their efficiency and are subject to substantially larger adjustment costs than firms in services.
This paper is related to the literature on both adjustment costs and firm dynamics. Within the literature on adjustment costs, the paper is associated with theories that use linear adjustment costs to explain certain aspects of the dynamic behavior of labor demand and job flows. Well-known examples are Bentolila and Bertola (1990) , Hopenhayn and Rogerson (1993) , and Campbell and Fisher (2000) . Bentolila and Bertola (1990) and Hopenhayn and Rogerson (1993) analyze the effects of proportional firing (and hiring) costs on the dynamics of hiring and firing decisions, and on average labor demand. Both papers conclude that high firing costs make hiring and firing adjustments more sluggish, but they disagree on the implications of that for long-run employment. Campbell and Fisher (2000) use proportional costs of job creation and job destruction to explain the higher aggregate volatility of job destruction found in the U.S. manufacturing sector. These costs imply that in reaction to aggregate wage shocks employment changes at contracting firms are larger than employment changes at expanding firms. What is new in our paper is the assumption that adjustment costs apply equally to the entry/exit decisions and the hiring/firing decisions.8
Within the literature on firm dynamics the paper is connected with theories that attempt to explain the stylized facts on the lifecycle dynamics outlined above. The two main explanations for these facts are theories based on selection of firms and theories based on financing constraints.9 Selection theories stress the tendency for firms that accumulate bad realizations of productivity to exit the market and for firms that accumulate good realizations to survive and expand. This implies a composition bias towards larger and more efficient firms as smaller, inefficient, and slow-growing firms gradually exit the industry. Representative papers of selection theories are Jovanovic (1982) , Hopenhayn (1992) , Ericson and Pakes (1995) , and Luttmer (2007) . In all cases productivity realizations are exogenous, except in Ericson and Pakes (1995) where they are to some extent endogenous.
Meanwhile, theories employing financing constraints argue that some imperfection in financial markets causes young firms to have limited access to credit, forcing them to enter at a suboptimally small scale. As firms get older and survive, they establish creditworthiness and build up internal resources that enable them to expand to their optimal size. Important contributions to this literature are those of Cooley and Quadrini (2001) , Albuquerque and Hopenhayn (2004) , and Clementi and Hopenhayn (2006) . In Cooley and Quadrini (2001) a transaction cost on equity and a default cost on debt imply that equity and debt are not perfect substitutes, resulting in a positive dependence of firm size on the amount of equity. In Albuquerque and Hopenhayn (2004) and Clementi and Hopenhayn (2006) lenders introduce credit constraints because of limited liability of borrowers and enforcement of debt contracts, in the first case, and because of asymmetric information on the use of funds or the return on investment, in the second case.
Cabral and Mata (2003) analyze whether these two theories are consistent with the evolution of a cohort's size distribution in the Portuguese manufacturing sector. They find that, as the cohort ages, the firm size distribution shifts to the right largely due to growth of surviving firms rather than exit of small firms. In addition, they find that in the first year after entry younger business owners are associated with smaller firms but that is no longer the case once the cohort gets to age seven. Assuming that age is a proxy for the entrepreneur's initial wealth, the authors conclude that the age-size effect supports the idea of financially constrained firms starting at a suboptimal size and present a model with financing constraints capturing this effect.
More recently, Angelini and Generale (2008) use survey and balance sheet information for Italian manufacturing firms to analyze the impact of financing constraints on the evolution of the firm size distribution. They find that financially constrained firms tend to be small and young, although this does not have a significant effect on the overall firm size distribution. Moreover, they find that financing constraints decrease firm growth, with this effect being entirely due to small firms. In particular, being young and financially constrained does not have any additional effect. Based on these results and the fact that young firms grow faster than old firms, the authors conclude that financing constraints are not the main factor behind the evolution of the firm size distribution. In line with their argument, this paper interprets the facts presented in Cabral and Mata (2003) and other cross-sector evidence as the result of the interaction between adjustment costs and learning about efficiency.10
To our knowledge, this work is the first to suggest adjustment costs as an explanation for differences in firm dynamics by age. The paper by Cabral (1995) is nearest to this paper. In his model, firms must pay a proportional sunk cost to increase their production capacity. He argues that, in a model with Bayesian learning, a proportional capacity cost would make small entering firms grow faster than large entering firms. The reason is that small entrants are those whose initial profitability signals were not good, so their exit probabilities are higher, and therefore they choose to invest more gradually. Unlike our model, Cabral's model depends on the existence of selection. Also, by analyzing a size-growth relationship, his model is not able to explain why some large entering firms also grow substantially.
The paper is organized as follows. In section 2, we present evidence of firm dynamics for a cohort of entering firms. In section 3, we build the general model, obtain optimality conditions, and provide heuristic arguments explaining the effects of adjustment costs. In section 4, using a simplified version of the model we analytically prove the effect of linear adjustment costs on survivors' growth. In section 5, we calibrate and estimate a finite learning horizon version of the model and quantify the contribution of adjustment costs to firm dynamics. Section 6 concludes. All proofs are left for an appendix.
There is a well established literature on the identification and explanation of differences in behavior between young and old firms. In this section, we analyze firm dynamics in a cohort of entering firms. We use Quadros de Pessoal, a database containing information on all Portuguese firms with paid employees. This dataset originates from a mandatory annual survey run by the Ministry of Employment, which collects information about the firm, its establishments, and its workers. All economic sectors except public administration are included. The panel we have access to covers the period 1985-2000. Information refers to March of each year from 1985 through 1993, and to October of each year since the reformulation of the survey in 1994. On average the dataset contains 250,000 firms, 300,000 establishments, and 2,500,000 workers in each year.
The literature on firm dynamics typically finds that young firms grow faster than old firms. Using kernel density estimates of the firm size distribution in a cohort of entrants, Cabral and Mata (2003) argue graphically that the cohort's evolution is mostly due to growth of survivors rather than exit of small firms. Their analysis points to the need for a measure of the contribution of survivors versus non-survivors to the growth in a given cohort's average size. To accomplish this, we propose a decomposition of the cohort's cumulative growth that will later allow an assessment of the empirical relevance of adjustment costs. We consider the following decomposition:
In general, the growth in a cohort's average size can originate from growth of survivors or from smaller initial size of non-survivors. Any theory of firm dynamics should consider both these sources of growth. Our measure allows a check on whether a particular theory can explain the key source of growth in a cohort's average size. The survivor component compares the current average size of period survivors with their initial average size, so that it measures how much survivors have grown. The non-survivor component compares the average initial size of period non-survivors with the average initial size of period survivors, so that it measures how relatively small non-survivors were initially.
We can obtain a similar decomposition for employment-weighted moments. The weighted decomposition contains information about the entire distribution of employment, not just its cross-sectional mean, and is affected both by within- and between-firm growth. Therefore, the weighted decomposition would be more relevant for assessing a richer model that considers the reallocation of employment shares between firms within the cohort. In the results that follow we focus on the unweighted decomposition because it analyzes within-firm growth, which in our model is the most relevant statistic to assess the effect of adjustment costs on the incentives for firms to grow.12
We can also produce a decomposition based on the cohort's annual growth instead of the cohort's cumulative growth. However, the annual version of the above decomposition is more sensitive to two aspects that would complicate the analysis in the paper. First, the annual survivor component is significantly affected by the business cycle, especially after the first few years of life. To control for this, we would need to somehow remove the cyclical part of the survivor component. Second, as the age of the cohort increases, the annual survivor component becomes increasingly sensitive to downsizing and exit by some survivors that become technologically outdated and consequently relatively less efficient. To fully consider this aspect of the data would force us to introduce additional parameters into the model that we present in section 3. Therefore, we believe that by employing a decomposition based on the cohort's cumulative growth we avoid having to adjust the analysis for these two aspects, and instead focus on how intense is survivor's growth while learning-about-efficiency effects are significant.
In table 6, we present the evolution of exit rates and the share of firm growth due to the survivor component in the 1988 cohort of entering firms for the overall economy.13 In 1988 there were entering firms. The exit rate is very high initially but tends to decrease as firms get older.14 However, ten years after entry only of the initial entrants remain active. There is significant growth in the cohort's average size, especially in the first few years, which is mostly due to the growth of survivors rather than to the exit of small inefficient firms: survivors' growth contributes around to the growth in the cohort's average size.15
Table 6 presents similar evidence on cohort dynamics for the manufacturing and services sectors.16 We include the employment shares of each sector in the 1988 cohort of entering firms, which are close to shares in the overall economy. Although manufacturing has a much higher employment share than services, the number of entering firms in services surpasses that of manufacturing ( and , respectively). Both sectors display a cumulative exit rate around by 1999. However, initial differences in exit rates are more significant, with manufacturing displaying the smallest values, and services displaying the highest values. In terms of initial size, manufacturing has the largest entrants, and services the smallest. Although manufacturing has the largest entrants, it exhibits more growth in average employment and a larger contribution of survivors to that growth than services.
We perform two robustness checks on the previous findings. First, we redo our calculations using establishments rather than firms as the unit of analysis. For the 1988 establishment cohort, we obtain similar results, although exit rates and the survivor component are higher than in the case of firms. Second, we examine an alternative cohort to make sure our results are not driven by business cycle conditions. The Portuguese economy experienced an expansion between 1986 and 1991, a period of slow growth with a recession between 1992 and 1994, and another weaker expansion between 1995 and 2000. The growth rates of real GDP were in 1989, in 1992, and in 1995, so that the 1991 cohort did not face as favorable a macroeconomic environment as the 1988 cohort. However, the results for the 1991 cohort are, in all dimensions, very similar to those presented above. The results for the 1994 cohort are also very similar, but with slightly smaller values for the survivor component in the first few years after entry.17
In table 6, we provide evidence on the properties of labor adjustment in the 1988 cohort of entering firms. Namely, we present three characteristics of the distribution of adjusted growth rates, conditional on survival, in 1989 and 1993: the fraction of firms that do not adjust employment, , and the fractions of firms that increase/reduce their size by less than , /.18 The table shows that the incidence of inaction is very high, increases with age, and is higher in services than in manufacturing. This may reflect technology-induced differences in adjustment costs, or job indivisibilities affecting to a larger extent the services sector for having a higher share of small firms. The table also shows that the large majority of firms have adjustment rates within the interval. A high rate of inaction and small adjustment is usually considered consistent with the presence of linear or proportional adjustment costs. In addition, comparing the columns and it appears that the 1989 growth distributions are more left-skewed than the 1993 distributions, suggesting that survivors tend to grow more initially, especially in manufacturing. The evidence on inaction justifies our assumption of linear adjustment costs in the model that we present next.
In this section, we introduce linear adjustment costs into a model of Bayesian learning about efficiency. We derive conditions for optimal employment over time and present heuristic arguments about the effects of adjustment costs on the path of employment. Our model is based on Jovanovic (1982) , adding adjustment costs and using a different specification for the idiosyncratic shock.
We assume an industry with competitive output and input markets. Current profits of a representative firm are defined by
Concerning technology we make the following assumption.
(a) is , , , , , and . (b) Letting denote the firm's age and 0 the period in which the firm enters, the stochastic process of is defined by
Part (a) ensures a well defined interior optimum. In some of the analyses below, we will assume that is a power function. Meanwhile, part (b) establishes that in each period productivity is stochastic with a constant mean over the firm's lifetime. The productivity component, , is made of two parts: , which is observed before entry, and , which is never directly observed by the firm. Intuitively, can be thought of as indexing ex ante efficiency, measuring initial technology choice, while indexes ex post productivity, measuring how well a firm performs within its technology choice.
The introduction of is essential to obtain a non-degenerate distribution of firms' entry size, allowing an analysis of the contribution of survivors to growth in the cohort's average size. In contrast, the absence of in Jovanovic's (1982) model generates a degenerate distribution of firms' entry size. Under this scenario, for any period after entry, survivors and non-survivors have the same average initial size implying a value of to our measure of the survivors' component. By assuming , we avoid this aspect of Jovanovic's model.
Before entry the firm knows the parameters governing the stochastic process of , i.e., , , and , and learns its ex ante productivity, , after paying a research cost, . After entry, the firm will learn about its specific ex post productivity, , over time as it observes the realizations of productivity, . In particular, the firm forecasts period- productivity based on the ex ante efficiency parameter and on the past realizations of productivity, . Similarly to Zellner (1971) , a firm with age has the following Bayesian posterior distribution for at the beginning of period :
In lemma 2 of appendix A we show that, for purposes of predicting , the information set can be summarized by , where is the period- forecast of the productivity coefficient based on the information available at the beginning of period . That is, , where is the expectation conditional on the period- information set.
We now lay out the timing assumptions.
In the absence of adjustment costs, while deciding whether to stay one more period or to exit, the firm compares the expected profit in case it stays, , with the opportunity cost of doing so, , the value it would recover by selling the (exogenous) capital initially acquired, i.e.,
At entry, we have , and in equilibrium expected profits must compensate for the cost of acquiring capital, i.e., . Since markets are competitive and there is no friction in the entry and exit processes, in equilibrium the research cost equals expected gains at the research phase, i.e., . If more firms will initiate research and later enter the industry, causing a decrease in output price until equality is restored. A strictly positive fixed research cost, , is essential to avoid the extreme situation where trial research is so high that only the highest productivity firms enter and survive. Because there is no reliable capital stock variable in Quadros de Pessoal, we do not make the capital decision endogenous to the model. Instead, we assume that firms are homogeneous along the capital dimension and face the same opportunity cost of remaining in activity, .
Up to this point, the only differences between our model and Jovanovic (1982) are that in the latter model the efficiency parameter implicitly affects the cost function and the cohort's entry size distribution is degenerate. Therefore, without adjustment costs there would be no intertemporal linkages in our model aside from the exit decision. As in Jovanovic, because is strictly increasing in , the exit decision is characterized by an age-dependent exit threshold. For values of above or equal to that threshold, the firm would stay and choose employment to maximize current period profits. For values of below that threshold, the firm would leave the industry, since its expected profitability is below the opportunity cost. The increasing confidence the firm puts in as it grows older implies that the exit threshold is increasing with age. This is the driving force underlying Jovanovic's result that the size distribution and the survival probability increase with age.
We now introduce linear adjustment costs into the model. The adjustment cost for continuing firms, , is defined as
With adjustment costs, the problem now becomes,
In general, we could allow for asymmetry among the cost parameters in , , and . However, asymmetries between the cost of regular firing and the cost of firing at exit or between the cost of regular hiring and the cost of hiring at entry lead to biases in entry and exit decisions. For example, if the per unit regular hiring cost is higher than the per unit entry hiring cost, then firms will hire more workers at entry in order to save on expected future higher hiring costs. Similarly, if the per unit regular firing cost is smaller than the per unit exit firing cost, then firms facing the prospect of exit will fire workers before exiting the industry, saving on expected future higher exit firing costs. To avoid these biases, throughout the paper we assume symmetry between the parameters in , , and . A more interesting distinction is between firing and hiring costs. We will see below that the conclusion of the paper is immune to asymmetries between the costs of adding and subtracting workers.
In solving the firm's problem, we consider a two-step optimization procedure where the firm first chooses optimal employment in each of three possible scenarios, and then selects the scenario with the highest pay-off. More precisely,
In contrast, the non-differentiability of the objective function generates an inaction region in the employment policy, within which optimal employment does not vary with changes in productivity.
Equations (6), (7) and (8) are marginal conditions, similar to the smooth pasting conditions in the (S, s) model literature, and they state that if the firm adjusts then the marginal adjustment cost must equal the expected present discounted value of the marginal revenue product for all future periods in which the firm is still in the industry, minus the increase in the exit cost when the firm decides to exit. This is the discrete-time analog of the continuous-time result present in Nickell (1986) and Bentolila and Bertola (1990) , adjusted for the fact that now we also have an exit decision. Because the firm will not change employment if the marginal cost of adjustment exceeds its marginal benefit for the first unit of adjustment, proportional costs imply inaction in the employment decision of the firm.
Although the results in proposition 6 do not allow a formal proof of the effects on firm growth of adjustment costs in this general model, the following corollary of proposition 6 enables us to make qualitative heuristic statements about those effects.
As we have seen above, in the absence of adjustment costs, optimal employment is determined solely to maximize current period profits, so that . Therefore, firms' growth is essentially a by-product of a selection mechanism: those firms that are inefficient, and therefore small, exit, while those firms that are efficient survive and grow. There is an additional source of positive growth when the frictionless employment decision rule, , is convex in . Because of Jensen's inequality and because is a Martingale, surviving firms will grow over time: . However, will not be convex in for general .21
In arguing heuristically about the impact of the proportional cost on firm growth we use the property that is weakly increasing in , and that is locally weakly increasing in . Because it is not immediately obvious why firing and hiring costs should give similar incentives for firm growth, we analyze separately these two costs.22 We present in figure 1 the case where there is a hiring cost, , and no firing cost, . This figure assumes a given . For that specific value of , and are the frontiers between non-adjustment and upward and downward adjustment, respectively. Therefore, if there will be no adjustment and the marginal benefit of an additional unit of labor (represented by the dashed line) is contained in the interval . To simplify the argument, we consider a firm whose sequence of productivity draws is such that in every period it has a perceived productivity equal to the unconditional mean of , even though the firm's uncertainty over next period decreases with age.
Case 1: Hiring Cost: ,
Because the firm starts at the hiring margin, we must have at entry, and , for all subsequent periods, , with the two extremes of the interval representing firing and hiring of workers, respectively. Consider first a situation where exit is not allowed. Under this assumption, (9) would become
The Bayesian learning mechanism implies both persistence and a reduction in variance with age in the Markov process associated with . The effect of persistence, that is, the fact that , was analyzed in the previous paragraph. The reduced uncertainty in the posterior estimate of productivity will be reflected in a smaller inaction region as firms accumulate information on realized productivity; that is, decreases with . This causes an increase in for those firms already at the hiring margin, which must be balanced by an increase in for the right hand side of (9) to remain equal to . As firms become more certain about their true productivity they are more willing to adjust to their long run optimal size. Because most firms are at the hiring margin, this will cause a further increase in average size.
Consider now the possibility of exit. In this case, the uncertainty reduction as the firm ages implies a decrease in the exit probability, and a further increase in the future-periods component of in (9). Consequently, needs to increase further in order to offset that.24 On the other hand, the smaller exit probability implies less pruning of inefficient slow-growing firms as a cohort ages, which tends to make growth in average firm size smaller. Therefore, we will have less cohort growth due to non-survivors and more cohort growth due to survivors, so that survivors' contribution to average firm growth in the cohort should increase when exit is allowed.
Case 2: Firing Cost: ,
In this case, we have , , . Assume first that exit is not allowed. The intuition is the same as in case 1. In comparison with , when firms start smaller and subsequently hire more frequently than they fire. As firms age, the reduction in variance of causes an increase in , which must be compensated by an increase in for firms at the hiring margin. When exit is possible, those effects become more intense, since the exit probability will decrease as firms age.
From the heuristic intuition we have just given it becomes clear that proportional hiring and proportional firing costs reinforce each other in creating incentives for firms to grow. In the end, our assessment of the relevance of linear adjustment costs for firm growth will depend on how well a pure selection model can fit the empirical evidence, and on how much adjustment costs improve the fit. Before we move into a quantitative assessment, we present analytical results for a simple version of the general model.
In this section, we analyze a model where firms' efficiency is revealed after the first period of life and where firms' lifetime horizon is know with certainty at entry. We assume that firms live for periods, where is any integer greater than , and that no exit is allowed prior to age . These two simplifications allow us to determine the effect of linear adjustment costs on firm growth.
The introduction of adjustment costs implies an additional expected operating cost for entering firms. Therefore, the equilibrium price must increase to generate higher expected future profits that compensate for the costs incurred while adjusting to optimal size. As a consequence, pre-entry pruning of inefficient firms should increase while post-entry pruning should decrease. This is optimal from a social point of view, since with higher adjustment costs there should be less experimentation in order to save in unrecoverable costs. Therefore, the assumption that exit is exogenous is not critical for the results in this section. Since adjustment costs attenuate post-entry pruning, even if exit was endogenous to the model, the relative contribution of survivors to growth in the cohort's average size would increase through this channel. By eliminating any exit prior to we focus only on the incentives for survivors to grow.
To formulate the problem, we use the fact that once the firm learns its true efficiency in period 2, it will adjust once and for all to its long run employment level.25 Then, if upon exit at age firms recover the initial investment net of exit costs, in period 2 we have
We examine the impact of adjustment costs on the log growth rate of employment, rather than the standard growth rate, in order to attenuate the effect of Jensen's inequality on firm growth.26 In this simple model, the inaction region of optimal employment can be expressed as an interval: . Therefore, the average log growth rate between period 1 and period 2, conditional on , is defined as
Optimal employment in period 2 is determined by
We then have the following result concerning the effects of changes in and on the cohort's average log growth rate of employment.
Consider first the hiring cost. In the proof, we show that an increase in decreases both and . The impact of on the growth rate depends on two opposing effects. First, while in the case of the cost of hiring can be equally spread out over periods with certainty, in the case of it will be spread out over either periods or one period, depending on whether the firm learns in period that it has overhired. Therefore, ex ante a proportionately greater part of is attached to period in the case of than in the case of , affecting more than . This explains the positive effect on growth of for . Second, the hiring cost on can possibly be spread out over periods, while the hiring cost on can only be spread out over periods. This affects more than , and explains why the effect of on growth is not necessarily positive for finite . However, as increases the first effect dominates so that decreases more than and growth increases.29
With respect to there is always a positive effect on growth, independently of the lifetime horizon. This occurs because an increase in decreases and increases . This positive effect always dominates the uncertain effect due to the fact that also decreases with .
When there are both hiring and firing costs and these costs are identical ( ), then an increase in has a positive effect on , for sufficiently high , where the required is lower than in item (a) of proposition 8.
In the previous two sections, we developed heuristic and some formal arguments about the effect of adjustment costs on the incentives for firms to start smaller and grow faster after entry. In this section, we assess the contribution of adjustment costs to explain some of the basic facts on firm dynamics found in section 2, both for the overall economy and for the manufacturing and services sectors. To accomplish this, we perform a calibration/estimation of the model using computational methods.
To simulate the infinite learning horizon model we follow the suggestion of Ljungqvist and Sargent (2004) and consider an approximation where firms live forever, but learn their ex post true productivity component, , with certainty at some age .30
We assume that is a power function, i.e., , . Under this assumption, when adjustment is costless, optimal employment conditional on survival is a convex function of , so that Jensen's inequality implies growth of employment even if there is no selection. As in the previous section, in order to avoid any growth due to Jensen's inequality, we take logs of all variables and analyze the effects of adjustment costs on the log-growth rate.
Concerning the productivity distribution, we assume that is lognormally distributed, i.e., .31 This assumption is made for computational simplicity, and it seems reasonable on empirical grounds (see Aw et al., 2004) . In addition, this assumption is not critical as the results in section 4 suggest that the distribution of productivity mostly affects the intensity of the effect of adjustment costs on firm growth, but not the sign. In fact, proposition 8 is derived independently of the particular distribution of . With the log-normal distribution assumption, the transition law for the s is as follows.
Since we assume that the firm enters the industry already knowing its ex ante productivity component (see assumption 1), we will get a non-degenerate distribution of initial size. This occurs because , and has positive variance in the cohort's initial distribution. The next proposition analyzes the properties of the optimization problem after is revealed to the firm in period .
|, , .|
This result allows a considerable simplification of the computational algorithm, since it implies a finite horizon dynamic programming problem. In appendix B, we present some details concerning the computational algorithm used to simulate and estimate the model. In the following subsections we calibrate and estimate the model and do a sensitivity analysis.
We calibrate and estimate our model to match statistics from the 1988 cohort of entering firms, both for the overall economy and the manufacturing and service sectors. We first calibrate parameters related to inputs directly from the data. We then use the simulated method of moments to estimate the parameters associated with the learning process and the adjustment cost. These estimates are obtained so that the model generated moments match the evolution of firm size, of exit rates, and of the survivor component observed in the data. As discussed in appendix B, we find the set of parameter values that minimize the method of moments objective function by using a simulated annealing method. This optimization method is robust to local minima, to discontinuities, and to the discretization implemented in order to simulate the model.32 A central element to our estimation strategy is a decomposition of the change in the cohort's average size into a survivor component and a non-survivor component. This decomposition forces the model to match not only the growth in the cohort's average size but also the contribution of surviving and non-surviving firms to that growth. Similarly to section 2, with , our decomposition is defined as
Prior to estimation, we calibrate some parameters. The parameters and are calibrated with data from INE (1997) containing the Inquérito Annual às Empresas from 1990 to 1995. These data are considered reliable and cover all firms in the Portuguese economy, with sampling among firms with less than 20 workers. We measure as the 1990-1995 average of the cost share of labor in value added, and as the 1990-1995 average cost per worker. We can also obtain these values at the sectoral level. We deflate all nominal variables using the GDP sectoral price indices available in the updated version of Séries Longas para a Economia Portuguesa in Banco de Portugal (1997) . The real interest rate is calibrated as the 1990-1995 average of the implicit real interest rate on public debt transactions on the secondary market of the Lisbon Stock Exchange. The data was also taken from Banco de Portugal (1997) . We deflate the nominal interest rates using the December-to-December consumer price index from INE (1990-5) . The discount rate is then obtained as , where is the average real interest rate.
The remaining parameters, , , , , , and are estimated using a simulated method of moments estimator, which attempts to make the model match closely the evidence on cohort dynamics presented in section 2. In particular, the estimates are selected to minimize a weighted sum of the distance between the following moments in the model and the data: (a) the time-series of the mean of log-employment conditional on survival, ; (b) the time-series of the cumulative change in the standard deviation of log-employment conditional on survival, ;33 (c) the time-series of the cumulative exit rate, ; (d) and the time-series of the survivor component, as defined in (12). In estimating the above parameters, the output price is normalized to , and the initial research cost, , is obtained by the equilibrium condition .34
The decision to estimate , instead of calibrating it, deserves some discussion. First, the main purpose of this parameter is to induce endogenous exit as it represents the firm's opportunity cost of remaining in activity. In Hopenhayn (1992) , the same result is accomplished using a fixed per period operating cost. Since the per period operating cost can be seen as the periodic payment in an annuity with a present discounted value of , the two mechanisms are equivalent. Second, because Quadros de Pessoal misses any reliable capital stock variable, we consider the capital decision to be exogenous. A rough estimate of the magnitude of is the present discounted value of an annuity with annual payments equal to the 1990-1995 average of value added minus labor costs, using the same deflators as for .35 Because the sample is biased towards surviving firms, this measure overestimates the value of , and we cannot use it as a reference to calibrate . Consequently, we estimate jointly with the remaining parameters in the model.
We present in table 6 the calibrated and estimated parameters for the three cohorts, both for the model with (AC) and without (NAC) adjustment costs, and in figures 2 and 3 we plot the data and simulated moments in the estimated AC and NAC models. We start by making some general remarks on our estimates. First, more information is revealed ex post and there is significant noise in the learning process ( , ). Second, consistently with our expectations, all estimates of are below the rough estimates presented in footnote 34. Third, the inferred values for are close to of . Finally, the standard errors of the estimated parameters are relatively small, suggesting that parameters are estimated with good precision.36
For the overall economy cohort, the AC model implies an estimate for the proportional cost of about of the annual wage. By comparing the estimated NAC and AC models, in terms of the objective function and the simulated moments in figure 2, we conclude that the proportional cost clearly improves the overall fit of the model, with a particularly notable improvement in the fit of the survivor component. Although the NAC model can generate moments on firm size and exit rates that are close to equivalent empirical moments, it cannot satisfactorily match the empirical survivors' contribution. That is, the NAC model cannot explain the main source of growth in the cohort's average size, since survivors contribute much more to growth in the data than in the NAC model. This shortcoming is especially intense in the initial years after entry, when the distance between the survivor component in the simulated NAC model and in the data is largest, suggesting that in the absence of adjustment costs learning has a larger initial impact on the exit of small inefficient firms than on growth of survivors.
In discussing the results for the manufacturing and services sectors we consider the estimates for the AC model. Manufacturing firms learn relatively less initially about their efficiency than services firms ( is smaller in manufacturing). Moreover, in order to account for the higher survivor component, adjustment costs in manufacturing are larger than in services (proportional costs amount to and of the annual wage, respectively). Because of larger adjustment costs and lesser relative knowledge about efficiency at entry, manufacturing firms have higher incentives to start relatively small and to gradually adjust to optimal size as they survive and their uncertainty is resolved. In addition, the smaller relative knowledge at entry in manufacturing explains the lower relative initial research cost ( is lower in manufacturing) and the higher entry rate ( equals in manufacturing and in services).
As can be seen from the last row of table 6, while the introduction of adjustment costs improves markedly the fit of the model for the overall economy and manufacturing cohorts, it improves only marginally the fit for the services cohort.37 Therefore, the form of adjustment costs considered in the paper seems more relevant for the average firm in the manufacturing sector and the overall economy than for the average firm in the services sector. More generally, although the introduction of adjustment costs clearly improves the overall fit of model, especially in what concerns the survivor component, the model cannot explain entirely the path of the survivor component in the data. In fact, in all three cohorts the initial growth of survivors seems larger than what the AC model can explain. This might be a consequence of the discretization scheme adopted for in the simulation.38 More importantly, this might reflect other explanations for firm growth that cannot be captured by adjustment costs in our model, such as some mechanisms through which financing constraints operate.
In this subsection we explain some aspects of the calibration/estimation exercise and provide a detailed sensitivity analysis to all parameters in the model. First, we do not attempt to match the level of the cross-sectional variance of log-employment, but only its change over time. This is because to fit the dispersion in employment, the model would require substantially larger values for both and . This would allow the model to match and but would also imply an excessive rate of growth in . However, this shortcoming is not a serious problem. It implies that only a fraction of the observed cohort's employment dispersion can be attributed to a Bayesian learning process about efficiency. The remaining part could be attributed to heterogeneity in the initial choice of technology.
For instance, consider a model where capital is endogenous and suppose that a firm chooses its initial stock of capital, , based on the realization of a random variable indexing technology choice. Assume further that, after selecting , the firm keeps its capital stock unchanged for the remainder of its life. If the production function has constant returns to scale, if the total opportunity cost is proportional to , i.e., , then we can easily prove that , where is the value function conditional on the chosen . Therefore, in this alternative framework, dispersion in would govern the initial dispersion in employment and only the subsequent evolution in employment dispersion would depend on the Bayesian learning process.39 This is the reason why we attempt to match only the evolution of , but not its level. In the estimated models presented in table 6 less than of the observed dispersion in the cohort's log-employment can be attributed to the learning process, with the percentage smaller in the manufacturing sector and higher in the services sector.40
Second, the value of affects the long-run contribution of survivors, since a relatively smaller initial dispersion would make the average size of exiting firms closer to the average size of surviving firms in the entry period, and in this case most growth would be due to survivors. In the aforementioned extended model with an initial choice over , if we had we would have a non-degenerate initial distribution of size, entirely due to the heterogeneity in , but the survivors' component would be in each period. This would occur because the distribution of initial size among exiting firms would be equal to the distribution of initial size among surviving firms. This also explains why even with heterogeneity over , we would still need to assume in order to match the empirical facts on the importance of the survivor component.
While we could increase the long-run contribution of survivors by tinkering with the ratio , without adjustment costs the model cannot match satisfactorily the observed flatness in the path of the survivor component. For any choice of and , it will always be the case that the survivor component will exhibit a substantially increasing path in the absence of adjustment costs. Note also that the ratio affects both the exit rate and the evolution of the firm size dispersion. If this ratio becomes too small, post-entry exit rates become excessively high and the size dispersion increases too fast. This is the reason why in the NAC model we cannot find a value for this ratio that attains the long-run contribution of survivors found in the data, and simultaneously matches the behavior of the cumulative exit rate and the evolution of the size dispersion. Therefore, the value we select for this ratio is disciplined by the exit rates and the evolution of firm size dispersion in the cohort.
Third, to show that proportional adjustment costs are crucial to fit the evidence on the contribution of survivors to growth in the cohort's average size, in figure 4 we perform a sensitivity analysis with respect to each parameter in the model. We take as benchmark the estimated NAC model for the overall economy cohort in table 6. We vary each parameter around its benchmark value and plot the implied cumulative exit rate and survivor component at four different ages of the cohort (ages , , , and ). We present both the exit rate and the survivor component to show that the essential shortcoming of the NAC model is the inability to increase the survivor component without an unreasonable increase in exit rates.
From figure 4, we see that the model with costless adjustment cannot match satisfactorily the contribution of survivors to growth, even if we allow parameters (except for the proportional cost) to vary one by one from their benchmark values. In fact, no other parameter besides the proportional cost (in the lower-right plot) can increase the survivor component without changing much the exit rate. In addition, adjustment costs imply more than just a mere level effect on the survivor component, as the increase in the survivor component at age is larger than the increase at age , shrinking the distance between the survivor component at different ages of the cohort. In summary, the main effect of these costs is to put more emphasis on individual firm growth in the initial years of life, when exit of inefficient firms is very intense.41
To emphasize the role of the proportional adjustment cost in replicating the evidence on the contribution of survivors, in figure 5 we present the impact of changes in on the survivor component, using as benchmark the estimates for the AC model in the overall economy cohort. We conclude that allowing for even a small value of has a substantial impact on the survivors' contribution, with a larger effect in the initial years after entry.
In this paper, we show that a model with linear adjustment costs and learning about efficiency generates incentives for firms to enter smaller and, if successful, expand faster after entry. For a cohort of entrant firms in the Portuguese economy, we present evidence showing that growth in the cohorts' average size is driven largely by growth of survivors rather than by pruning of small inefficient firms, with rapid growth of survivors in the initial post-entry years and significant cross-sector differences in the contribution of survivors. A calibration and estimation of the model reveals that the proportional cost is the key parameter to explain the high contribution of survivors to growth in the cohorts' average size. Furthermore, due to a higher contribution of survivors, adjustment costs need to be substantially higher in manufacturing than in services.
The empirical success of our model in better approximating the growth of survivors as the main source for growth suggests that adjustment costs do play a significant role in post-entry firm size adjustments. Our results suggest that selection theories are more relevant to explain firm exit than growth of survivors. Our specification of adjustment costs assumes that they are proportional to the adjustment size and apply equally at entry, exit, and during regular job creation and destruction. These costs could capture aspects such as costs to the organization, layout, and optimization of the production process, and hiring and firing costs. Although we have not collected evidence documenting the nature of these costs, we would expect them to be larger in sectors employing more complex technologies, such as in manufacturing industries. Potentially, part of the adjustment costs we estimate could also reflect the impact of financial frictions, although we would expect these to be concentrated in entry costs associated with the acquisition of capital, and not so much in regular job creation and destruction costs.
More generally, financing constraints theories should also play a role in explaining growth of survivors, besides what can possibly be captured by adjustment costs in our model, although there is not much evidence that financing constraints can explain cross-sector differences. Notwithstanding this, Angelini and Generale's (2008) conclusion that financing constraints are not the main determinant behind the evolution of the firm size distribution suggests that any government intervention to eliminate financing constraints might not change the lifetime dynamics of firm size we find in this paper. In addition, this paper suggests that, in sectors where adjustment costs are high and learning is important, government policies aimed at curbing financing constraints might not produce the intended results, as firms under those circumstances have incentives to start smaller and, if successful, expand faster, even if financing constraints are eliminated.
We prove the proposition in several steps.
If it is optimal for the firm to adjust upwards, then we must solveand if it is optimal for the firm to adjust downwards, we must solve Now, the derivative can be rewritten as
For each of the above derivatives we have,
|, , , ,|
For the unconditional distribution, just note that is a sum of normal random variables, and that
We now prove that the firm does not exit at time after remaining in the industry at time , . Because the firm stays at time , then . Now assume that in period the firm exits, so that
The weighting matrix is estimated as the sample covariance matrix of the moments in (v), adjusted for the simulation size
We use a simulated annealing method to search for the set of parameter values that minimizes the method of moments objective function,47
The standard errors of the estimated parameters are obtained as follows
where the matrix of derivatives is computed numerically.
|Sector||EmpSh 88||CumEx 89||CumEx 92||CumEx 99||AvEmp 88||CGrEmp 89||CGrEmp 92||CGrEmp 99||SurComp 88-89|
|Parameter||All NAC||All AC||Manu NAC||Manu AC||Serv NAC||Serv AC|