Reallocation, Productivity, and the Ecuadorian Economic Crisis

German Cubas∗ Anson T.Y. Ho† Kim P. Huynh‡ David T. Jacho-Ch´avez§

February 16, 2011

Abstract

Ecuador’s large economic crisis in the late 1990s serves as an important case study of resource reallocation. We conduct an empirical analysis using firm level data for 1998-2007 to investigate resource reallocation, firm turnover, and productivity patterns. We use the model by Restuccia and Rogerson (2008) to examine firm-level distortions and productivity during the crisis. Our results indicate that distortions increased as the crisis deepened and decreased after the Ecuadorian economy recovered. Additionally, we decompose productivity changes as suggested by Petrin and Levinsohn (2010) and find that there is a large negative reallocation effect during the crisis.

Keywords and phrases: Reallocation, Productivity, Distortions, Firm Dynamics. JEL codes: F31, D24, L11, O11.

∗Bank of Uruguay, Diagonal Fabini 777, Oﬁcina 501 Montevideo, Uruguay CP 11100. Email: german- [email protected]. †Department of Economics, University of Iowa, W210 John Pappajohn Bus Bldg, Iowa City, IA 52242, USA. Email: [email protected]. ‡Corresponding Author: Bank of Canada, 234 Wellington Ave., Ottawa ON, K1A 0G9, Canada. Email: [email protected] and Department of Economics, Indiana University, 105 Wylie Hall, 100 S Woodlawn, Bloomington, IN 47405, USA. E-mail: [email protected]. §Department of Economics, Indiana University, 105 Wylie Hall, 100 S Woodlawn, Bloomington, IN 47405, USA. E-mail: [email protected].

1 1. Introduction

Ecuador’s economic crisis serves as an important case study of economic reallocation. In the late 1990s, the Ecuadorian economy suffered a series of shocks including natural disasters, worsening balance of trade, and the spillover effects from other financial crises. These unfavourable events resulted in a domestic financial crisis that led to large industrial reallocation. In short, Ecuador was a developing small open economy that underwent structural reforms in the labour market. Also, Ecuador was the first country to officially dollarize in 2000. To understand the impact of this crisis, we undertake a study of firm dynamics in Ecuador during this period. In this paper, we document the stylized facts for firm reallocation and productivity patterns during the period 1998-2007. We use data from the Annual Survey of Manufacturing and Mining to look at two broad industries classified as Light and Heavy. The Heavy industry is classified as capital-intensive while the Light industry is considered labour-intensive. The stylized facts are: One, both industries underwent large amounts of resource reallocation in terms of job reallocation and firm turnover in terms of entry and exit. Two, firm employment size for the Light industry became right-skewed as there was a trend to bigness in the vein of Lucas (1978). The proportion of large firms (200+ employees) increased from 0.09 in 1998 to 0.14 in 2007. For the Heavy industry, the proportion of intermediate firms (50-99 and 100-199 employees) also increased from 0.24 in 1998 to 0.31 in 2007. Three, the entrant labour productivity increased for both industries. Understanding the role of reallocation is of utmost importance. Works by Davis and Halti- wanger (1990) and Caballero and Hammour (1994) highlight the role of allocative efficiency during recessions or the cleansing effect of recessions. Barlevy (2002) counters that search frictions may prevent firms from efficiently reallocating resources or the sullying effects of recessions. However, little is known about the role of reallocation during an economic crisis. Evidence from Japan indicates that the natural selection mechanism may not lead to efficient reallocation, see Nishimura, Nakajima, and Kiyota (2005). Whereas Hallward-Driemeier and Rijkers (2010) document the case for Indonesia and find that financial market imperfections could account for the attenuated relationship between productivity and survival. However, there maybe alternative explanations for these phenomena. We are agnostic on the catalyst for reallocation. Rather, we view reallocation from the lens of industry dynamic model suggested by Hopenhayn and Rogerson (1993). We use this model to quantify the output distortions before, during, and after the crisis period. Previous examples of this methodology such as Restuccia and Rogerson (2008) and Hsieh and Klenow (2009) were applied to a snapshot of the various economies. We extend this methodology by quantifying the dynamic evolution of the output distortion and total factor productivity distributions of firms in Ecuador. To understand the source of changes in labour productivity we compute TFP and distortions at the firm level.

2 We find that output distortions fell after 2000 when the economy started to recover and substantial economic reforms took place in Ecuador. Moreover, post-2000 the output distribution, especially in the Heavy industry, has a lower variance and mean which imply that frictions in the economy were lessened. Furthermore, the mean of the TFP distribution increased and firms became more productive. A decomposition of the TFP and labour productivity indicates that there were large changes in productivity within firms, reflecting the substantial output distortion during the economic crisis. Finally, we use a novel method of functional principal components to analyze the dynamic evolution of these distributions. This methodology provides an econometric test to evaluate whether the densities have actually changed or whether it was due to randomness. We find that the changes to firm size are mostly transitory, while changes to labour productivity, output distortions, and total factor productivity are permanent. The overall results indicate a selection effect, that is, firms that survived got larger and more productive. This paper is organized in the following fashion: Section 2 offers a background on the economic conditions during this period; Section 3 describes the data used, offers some descriptive statistics, and investigates the reallocation and productivity patterns; Section 4 analyzes the output distortions and links it to the results on aggregate productivity growth decompositions; Section 5 utilizes a statistical methodology to analyze the evolution of firm distributions through time; and, Section 6 concludes.

2. Background

This section aims to provide a stylized summary about the currency crisis, and the subsequent official dollarization in Ecuador from 1998 to 2000. For a detailed analysis of the Ecuadorian currency crisis, see Beckerman (2002), and Jácome (2004).1 The Ecuadorian currency crisis originated from a series of external shocks. While agriculture products and crude oil were the major exports of Ecuador, El Niño floods in the late 1997 and 1998 destroyed vast agricultural areas in the coastal region and reduced Ecuador’s agricultural productions. Oil prices in the world market also sank to its historically low - less than 10 USD per barrel - significantly reducing the total revenue of the debt-ridden Ecuadorian government, whose fiscal deficit was 6.2 percent and total debt/GDP ratio was 66.3 percent. Worse still, as the effects of the Asian financial crisis spilled over to Latin America, the foreign loanable funds available to the Ecuadorian government and private banks were further reduced. The outbreak of the crisis was triggered by the closure of a small bank in April 1998. It

1We thank former Ecuadorian president Jamil Mahuad for personally explaining the political and socio- economic situation of his presidency in the period 1998-2000 during his Indiana University campus visit last March 23rd - 28th, 2010.

3 deteriorated market sentiment and evolved into widespread bank runs. As the lender of last resort, the Central Bank of Ecuador (CBE, hereafter) provided emergency loans to illiquid banks, reaching about 30 percent of the money base by the end of September 1998. Bank deposits fled from Ecuadorian Sucre, the domestic currency, to US dollars and created pressure on the international reserves. In the last quarter of 1998, the CBE’s net international reserves shrank by 7.6 percent and the cumulative inflation reached 15 percent and real GDP grew only 0.1 percent. In early December 1998, as a measure to restore stability in the banking sector, the AGD law was passed to establish the Guarantee of Deposit Agency (Agencia de Garant´ıade depósitos - AGD) for providing deposits guarantee. The government also introduced a one percent financial transaction tax aimed at supporting the weak public finance. However, this tax proved critical to the massive withdrawals of deposit holders from the banking system in order to avoid the transaction tax. It aggravated the insolvency in banks and led to more bank failures, which in turn deepened the monetization of the banking crisis through the deposit guarantee. By early 1999, speculations on the depreciation of Sucre further intensified, see Figure 1. The CBE could no longer defend its crawling band exchange rate and moved to free floating in February 1999. Between January and February, Sucre had depreciated about 50 percent, causing substantial balance sheet effects on banks and further damaged their solvency. In March, the Ecuadorian government declared a bank holiday and a widespread freeze of bank deposits. Although these measures temporarily halted the fall of the Sucre exchange rate and stabilized inflation, the payment system was severely impaired and led to a seven percent drop of GDP in 1999. Nonetheless, the deposit withdrawal had not been eased when deposits were gradually ‘unfreezed’ from mid-1999. In face of the persistently increasing government debts and a reduced level of international reserves, the Ecuadorian government suspended payments on its external debts. As a result, domestic banks holding government securities had their assets further eroded, creating another blow to the already damaged banking system. As the crisis deepened, two more large banks failed and the deposit guarantee had brought the base money real growth rates to above 50 percent. With an increasing dollarization of bank liabilities and a plummeting demand of Sucres, the government officially dollarized the economy on January 11, 2000 at a fixed rate of 25,000 Sucres per US dollar. In the Ecuadorian currency crisis, Sucre depreciated 274 percent from 1999 to 2000. Out of the 40 banks that existed in 1998, 16 failed, resulting in a substantial decrease in labour productivity. The real output per worker dropped about nine percent during the crisis. The aftermath recovery was slow, it took Ecuador about a decade to recover to the pre-crisis levels of labour productivity.

4 3. Data

3.1. Data Description

Our study considers the Ecuadorian firm level annual data from 1998 to 2007. The data set is taken from the annual survey of manufacturing and mining (Encuesta Anual de Manufactura y Miner´ıa) prepared by the Ecuadorian National Institute of Statistics and Censuses (Instituto Nacional de Estadistica y Censos, hereafter INEC). The data set covers a cross section of firms with at least 10 employees in each year. Each firm is identified by a unique registration number. Output is defined as the value-added, which is the total value of production minus the total value of intermediate inputs (i.e. raw materials, parts and accessories, and packing). Production is measured at cost value, so that the variation in firms’ mark-ups does not affect our productivity calculation. Labour productivity is calculated as total output divided by the total number of workers, excluding shareholders and non-paid family workers. Capital is defined as the annual average net capital. Output and capital stock are deflated to 2002-US dollars by the sector specific producer price index2 and the general producer price index respectively. Since monetary values in the data set were reported in domestic currency (Ecuadorian sucres) before the official dollarization in year 2000, nominal values in year 1998 and 1999 are converted into US dollars by the official exchange rate at the time of dollarization (25,000 Ecuadorian sucres per US dollar). There was substantial depreciation of Ecuadorian sucre against the US dollar in 1998 and 1999. This exchange rate movement is taken into account when we use the producer price index to transform the pertinent variables into 2002-US dollars. We classified firms in the data set into the Light industry or the Heavy industry categories by their two-digit International Standard Industrial Classification (ISIC) code. All the panels (firms) that reported negative output or negative capital in one or more years in the survey were dropped. We also dropped firms with top and bottom one percent output-labour ratio and/or capital-labour ratio to get rid of outliers. The composition of the data is shown in Table 1. We also ensure the longitudinal consistency of the data by checking for false entry and exit. There are several possibilities that a firm entered into or exited from the survey. Firms included in the survey for the first time when they start up as new entrants, or by expanding their employment to at least 10 employees. For this reason, some firms entered and exited multiple times during the survey periods. For our entry and exit analysis, we only count the first entry and last exit as legitimate. A third way for firms to be included in the survey is to have switched their product line from other industries. However, firms’ entry and exit due to switching from other sector is minimal. 2We used the Serie de Índices y Variaciones del IPP, Total (Nacional y Exportación) as readily available at http://www.inec.gov.ec.

5 3.2. Descriptive Statistics

Table 2 provides some descriptive statistics of the Heavy and the Light industries for employment size and labour productivity. For both industries there is an upward trend in the mean employment size of firms, with a more pronounced trend for the Light industry. However, the median levels of employment size do not change much, indicating that most of the movement is due to the right-tail firms becoming larger. This result is confirmed by the increase in the 75th quantile. Huynh and Jacho-Chávez (2007) suggest to illustrate these stylized facts graphically with conditional density plots and a perspective plot (Hyndman, Bashtannyk, and Grunwald, 1996). Figure 2 confirms that the firm size distribution has become less positively skewed but the mean stays about the same. As a result the dispersion also increases as the standard deviation is increasing over the years. Table 3 provides further nonparametric evidence as the proportion of firms with 200+ employees increases, especially for light industries. Figure 2 shows an upward trend in the mean of labour productivity that is more pronounced for the Heavy industry. The upward trend occurs at all quantiles and the distribution seems to become more negatively skewed. Some of this movement is caused by an increase in dispersion in labour productivity in 1998 to 2002 and then gradually a decrease. The labour productivity distribution by 2007 is compact and has a higher mean. As an overall trend, both industries have firms that are not only larger but are more productive.

3.3. Turnover & Reallocation Patterns

Table 4 illustrates the survival patterns for the Heavy and the Light industries. The survival patterns are computed per cohort based on the observed entry year, only the pre-1998 cohort does not have an observed entry year so they are placed in the same bin. The Light industry has a slightly lower survival rate than the Heavy industry. On average about 30 percent of firms exit in five years. There are differences in the first year exit rate due to the timing of entry - ranging from 5 to 12 percent. Table 5 presents some further turnover and reallocation patterns. On the firm’s intensive margin, job creation is greater than job destruction with the exception of 1999 and 2003. The top left panel of figure 3 shows that job creation is higher for the Light industry for 1998-2004, and after that the Heavy industry has a slight edge. The job destruction is also usually higher for the Light industry. As a result, the level of job reallocation (combining both creation and destruction) is usually higher for the Light industry, especially during the years 1999-2004. The entry and exit rates are indicative of the firm’s extensive margin. The entry rates are lower than exit rates in 1999, the peak year of the crises. After that, the trend reverses with some exceptions. The top right panel of figure 3 shows that the entrant rate increases steadily until

6 about 2004 then there is some drop in 2005 and 2006. The exit rate is highest in 1999, but it does not vary as much as the entry rate. The bottom left panel of Figure 3 shows that the mean employment size of entrants in the Light industry is larger than that in the Heavy industry. The Light industry firm entrant size is for the most part larger than that of the exitors. The variation in entrant size is not as large as exitor size. The bottom right panel of Figure 3 illustrates the level of labour productivity for entrants and exitors are increasing over time. During the crisis, the entrant firm labour productivity is dramatically lower than the exitor productivity for both the Heavy and the Light industries. There is a recovery of labour productivity in the Heavy industry but the Light industry displays more variation. This variation is caused by the movements in the firm size, output measures, price deflators, and entry/exit rates.

4. Output Distortions & Productivity Analysis

To understand the resources allocation and the firm turnover documented in the above section, we need to investigate the source of heterogeneity in firm dynamics. We utilize the model in Hopenhayn and Rogerson (1993) in which firms make their decisions on entry/exit and scale of production based on their firm-specific total factor productivity (TFP). We relaxed the as- sumption in Hopenhayn and Rogerson (1993) that these firm specific TFPs are fixed once the firms entered the market, and allowed them to be time dependent. Changes in the firm specific productivities are attributed to changes in the firm level distortions àla Restuccia and Rogerson (2008). Related work in this line of research includes Guner, Ventura, and Xu (2008) and Hsieh and Klenow (2009).

4.1. Measuring Output Distortions & TFP

First, we estimate the firm-level production function. Then, we construct the firm-specific time- dependant TFP, and further study the evolution of the TFP distribution. The motivation of separating the firm-specific TFP and the distortion is that Ecuador was an economy under dramatic changes and we are interested in exploring how the frictions might have been reduced when the economy was dollarized and labour market reforms were carried out. To begin with, firms produce output (Y ) via a Cobb-Douglas production function in which j capital (K) and labour (L) are the inputs. Let (zit) denote TFP for firm i in industry j ∈ {heavy, light} at time t. The production function can be written as:

j αj γj Yit = zitKit Lit , (4.1) where αj and γj are the capital and labour shares of output respectively. We take logarithms

7 of (4.1) and the production function can be rewritten as

j log Yit = log zit + αj log Kit + γj log Lit. (4.2)

To construct the firm specific TFP conditional on time t, we first estimate the factor shares by using a fixed-effects panel data estimator as in Pavcnik (2002). Specifically, we estimate (4.2) by

log Yit = ci + αj log Kit + γj log Lit + ǫit where ci is the fixed effect and ǫit is the random component for firm i at time t. Results are reported in Table 6. For both the Light and the Heavy industries, the sum of the estimated factor shares is smaller than one, i.e. αˆj +ˆγj < 1, implying that firms exhibit decreasing return to scale, which is consistent with the specification of the model in Hopenhayn and Rogerson (1993). Note that the labour share of output in the Light industry is higher than that in the Heavy industry, because the Light industry is more labour intensive than the Heavy industry via our industry classification. j With the estimates ofα ˆj andγ ˆj, we are able to construct the firm-level TFP (zit) as

j log zit = log Yit − αˆj log Kit − γˆj log Lit. (4.3)

As in Restuccia and Rogerson (2008), we aim at estimating a generic family of idiosyncratic distortions to firm’s decisions, and so we assume that the distortions take a form of output tax or subsidy. Hsieh and Klenow (2009) have shown that this output tax can be viewed as distortions that affect both the capital and labour decision of a firm. We assume the constructed TFP of j j firm i at time t is a function of idiosyncratic output distortion (τi,t), a constant growth rate (g ), j and an “underlying” firm-specific TFP (zi ) that is realized when firm i enters the market, and is invariant across time. j j j j log zit = log zi + t log 1+ g + log 1 − τit (4.4) where t is a time dummy with t = 0 for observations in 1998. Production function estimates j are reported in Table 6. The idiosyncratic output distortion tax (τit) can then be computed as:

zj τ j =1 − it (4.5) it j j t zi (1 + g )

j j If τit is positive, then the firm faces an output tax; and if τit is negative, the firm is receiving an output subsidy. The model implies that the upper bound of output tax is one, because firms j will not make any profits and will exit the market if τit ≥ 1. Table 7 provides the estimates of the output distortions. The estimates of the distribution and the evolution of output distortions and TFP provide us some insights into the productivity

8 distribution of Ecuadorian firms. However, the picture is not complete since firm dynamics is ignored. Differences in firm size may stem from differences in their TFP. As a result, firms that are more productive produce a bigger share of total output. By simply looking at the TFP distribution, the productivity of the economy is not completely clear. Furthermore, the interaction between output distortions, firm dynamics in terms of entry/exit and production scale, and the subsequent changes in productivity are neglected.

4.2. Productivity Decompositions

A systemic method is required to quantify the competing eﬀects between technological progress and reallocation. There are many ways to accomplish this goal but we utilize Petrin and Levin- sohn’s (2010) proposed methodology to measure aggregate productivity growth (APG). The decomposition is novel since it directly confronts non-neoclassical issues such as plant-level heterogeneity, entry and exit of goods, ﬁxed and sunk costs, markups, returns to scale and adjustments costs. Interested readers are referred to the original paper for details. For brevity, we reproduce the discrete-time APG decomposition as:

¯ v v AP G ≡ Dit∆ log zit (4.6) i APG TE ¯ v v v ¯ v v + Dit(ǫik − s¯ikt)∆ log Xikt + Dit(ǫij − s¯ijt)∆ log Mijt i k i j

APG RE ¯ v v − Dit∆ log Fit, i APG Fixed ¯ v where Dit is the average of plant i’s value-added Domar weights from period t − 1 to t, ∆ is the v v first difference operator, log zit is the value-added residual, ǫik and ǫij are elasticities of output of each K + N inputs,s ¯ikt ands ¯ikt are the average over period t − 1 to t revenue shares for each input. log Xikt denotes the primary inputs in the production function, log Mijt are the v intermediate inputs and log Fit are the fixed and sunk costs for each plant. The first term is Technical Efficiency (TE) which entails how within firm productivity is changing. The second term is the Reallocation term (RE) which details how firm inputs are being reallocated. The final term is the combination of fixed and sunk costs incurred by the firm (Fixed). v The value-added residual (log zit) is calculated by subtracting the weighted-components of the production function from value-added measure or:

v v v v log zit = log V Ai − ǫik log Xikt − (ǫij − s¯ijt) log Mijt. (4.7) k j

9 In our empirical work we will utilize both OLS and Fixed-Effects panel data methods to estimate the parameters of the production function. Table 8 displays the APG decompositions for the Light and Heavy industries for the period 1999-2007. In both cases, APG and VA growth are roughly the same, which implies that changes due to the labour force either Unskilled and/or Skilled contributed only a minor amount to changes VA. Overall, the average change in APG and VA growth was higher for the Light relative to the Heavy industry. The variance of APG and VA was higher for the Heavy industry. In terms of timing, the Light industry was hit with a large negative APG and VA in 2000 while the Heavy industry it occurred earlier in 1999 and persisted until 2000. The Heavy industry also faced negative APG and VA growth in 2005 and 2007. The underlying cause of this discrepancy is borne in differences in the APG TE and APG RE terms. Focusing on the Fixed-Effects estimates, we see that in 1999 for the Light industry there is a large growth in VA which is mostly due to APG TE with a negative amount of APG RE (21.17 versus -5.20). While in 2000 the decline in VA growth is due to mostly a decrease in APG TE whereas there was a positive APG RE growth of (18.84 versus -6.31). However, the story is the exact opposite in the Heavy industry. VA decline is due mostly to the APG RE term especially a large amount in 1999 (-21.55) and small amount in 2000 (-3.69). Overall, the Light industry APG TE mean and variance is higher than that of Heavy industry. While for the APG RE the Heavy industry variance is almost three times as high as the Light industry.

4.3. APG and Output Distortions

We observe that APGRE for both the Light and Heavy industries are negative in 1999. It implies that resources reallocation decreased APG during the crisis. In the Restuccia and Rogerson (2008) framework, this happens when the output tax become more severe and further distort firms production decisions. When the output distortions increased or is taxed, the inefficiency will induce firms to adjust their production scale in order to maximize their profits. As a result, these firm will reduce its production scale, leading to a higher marginal output and will lead to resources reallocation that decreases the aggregate productivity. From the lens of Petrin and Levinsohn (2010), an increase in output tax will drive up the value of marginal product (VMP), because the wedge between output elasticity of input and revenue share of input will be bigger. The decrease in input usage will lead to the firm APGRE to be always negative. The intepretation of negative APGRE is consistent with the output distortion. However, there is a correction with respect to output distortion, a firm’s input usage will depends on the marginal product or the wedge between the output elasticity of input and the revenue share of input. On aggregate, the effect will depend on the Domar weight of firms facing output tax/subsidies and negative/positive wedge, as well as the firms facing output taxes. Our calculations show that the inefficiency created by the shift in the output tax distribution

10 dominates its correction eﬀect, resulting in a negative APGRE in 1999.

4.4. Reconciling Alternative Productivity Decompositions

The APG decomposition proposed by Petrin and Levinsohn (2010) sums up the changes at the plant-level to a standard aggregate productivity growth measure but alternative productivity decompositions such as Baily, Hulten, and Campbell (1992) (and subsequent variants) do not. To provide a comparison, we provide estimates of the BHC decomposition:

(s + s ) (log z + log z ) BHC = it i,t−1 ∆ log z + it i,t−1 ∆s , (4.8) 2 it 2 it i i

BHCTE BHCRE where BHCTE and BHCRE are the BHC technical eﬃciency and reallocation term. Table 9 compares the APG and BHC productivity decompositions. The Light industry term has a lower mean and variance relative to the APGRE while for the Heavy industry it is the opposite. Also, the signs and magnitudes are sometimes diﬀerent. For example, in 1999 Light industry BHCRE was positive whereas the APGRE was negative. Only in subsequent years 2000-2001 did the

BHCRE become negative. In the Heavy industry both productivity decompositions yielded a negative estimate but the absolute magnitude of the BHCRE was less than the APGRE. Looking at the raw entrant labour productivity numbers the

5. Evolution of Firm Distributions

This section uses a functional principal components analysis to characterize the cross-section and time-series variation in firm size, labour productivity, output distortions, and TFP have been computed. This method is nonparametric and does not rely parametric methods to characterize the distributions. For example, recent work by Huynh and Jacho-Chávez (2010) demonstrate the efficacy of these nonparametric methods using data from the UK on firm size distributions. This method allows one to decompose the changes into a set of factors (principal components) that change over time and to understand which part of the distribution is changing over time. Due to the novelty of this method in economics we briefly describe the methodology in the next section.

5.1. Functional Principal Component Analysis

We now brieﬂy describe Kneip and Utikal’s (2001) approach to analyze jointly the underlying 2007 population densities {ft}t=1998. In particular, to characterize diﬀerences and similarities of

11 2007 {ft}t=1998, we assume their expansions into the ﬁrst L principal components, g1, g2,..., gL, and represent each ft in terms of the model

ft = f + θtj gj, (5.1) j=1

2007 where f = t=1998 ft/10 is the common mean and L ≤ 10 corresponds to the number of nonzero eigenvalues of the empirical covariance operator. Model (5.1) implies that each ft can be obtained by adding to f a transformation of compromising common components g1, g2,

..., gL, with varying strengths encapsulated in the coefficients θtj. Since ft represent densities obtained for each time period t = 1998, . . . ,2007, then the time evolution of their respective coefficients θt1, θt2,..., θtL provides information about the evolution of the main differences and similarities between the underlying distributions.

The unknown g1, g2,..., gL and θt1, θt2,..., θtL, can be obtained from the 10 × 10 matrix M, whose elements are deﬁned by Mts = ft − f, fs − f, ∀t, s = 1998, . . . ,2007.

In particular, the unknown components gr, and parameters θtr relate to the 10 eigenvectors, ⊤ pr = (p1998;r,...,p2007;r) , r = 1,..., 10, with corresponding eigenvalues λ1 ≥ λ2 ≥ ..., ≥ λ10 of M through: 2007 2007 θ f θ = λ1/2p , and g = λ−1/2 p f = t=1998 tr t . (5.2) tr r t;r r r t;r t 2007 2 t=1998 t=1998 θt 2007 2007 2 Since the total variation of the family {ft}t=1998 is given by f = t=1998 ft − f = 10 10 r=1 λr, the relative importance of each component gr is given by λr/ r=1 λr. 5.2. Estimation and Inference

Firstly, given observations in each sample of ﬁrm size, labour productivity, output distortions, nt 2007 and TFP, generically {{Xit}i=1}t=1998, we estimate the 10 unknown densities using a Nadaraya- Watson kernel as suggested by Kneip and Utikal (2001), i.e.

nt 1 x − Xit f b = K , t,ht ntht i=1 ht 5/4 2007 where the bandwidth ht = bt , and {bt}t=1998 are found by Silverman’s rule-of-thumb which are readily computed in np (Hayﬁeld and Racine, 2008) for each sample nt. The elements Mts of M can then be consistently estimated by

[1] [2] [3] Mts = Mts − Mts + Mts , (5.3)

12 where

1 nt ns x−Xit x−Xjs b b K b K b dx ; if t = s [1] ntnshths i=1 j=1 ht hs Mts =  1 nt nt x−Xit x−Xjt  2b2 i=1 j= i K b K b dx ; if t = s, nt ht ht ht 1 2007 M[2] = (M[1] + M[1]), and ts 10 tl ls l=1998 2007 2007 [3] 1 [1] Mts = 2 Mlk . (10) l=1998 k=1998

After numerically calculating the eigenvalues λr, and their respective eigenvectors pr of M for r =1, 2,..., 10, we estimate θ , and g analogously to (5.2), i.e. tr r 2007 2007 θtrf b∗ 1/2 −1/2 t=1998 t,ht θtr = λr pt;r, and gr = λr pt;rf b∗ = , (5.4) t,ht 2007 2 t=1998 t=1998 θtr ∗ −1/5 where ht = bt × 10 . Kneip and Utikal (2001, Theorem 1, pp. 525) proved that for −1/2 any fixed r ∈ {1,..., 10}, λr − λr = Op( 10/n) and |θtr − θtr| = Op(n ), where n ≡ min(n1998,...,n2007), i.e. consistency was established but not asymptotic distribution was de- rived. 10 Figure 6 displays the plot of λr/ r=1 λr for each industry and relevant variables. These types of plots are known as Scree plots, but due to the time-series dimension we call them Dynamic Scree plots. The obvious observation is that the first eigenvalue dominates the scree plot both firm size and labour productivity. In the case of firm size, three eigenvalues accounts for almost all the variation in the data. For labour productivity in the heavy industry, there is some extra variation due to an increase in eigenvalues. 2007 2007 2007 We now proceed to analyze the dynamics of {θt1}t=1998, {θt2}t=1998, and {θt3}t=1998 through ˆ time. Figure 7 illustrates the evolution of the deviations of theθ relative to its initial conditions. Analyzing firm size, for the Light industry the first component decreases and stays negative while the second and third components negligible. For the Heavy industry, the first component deviation display large transitory swings, the second component is negative and permanent, while the third component has minor cycle component. These patterns are indicative of a structural change in the firm size distribution with some minor deviations. Overall, the Light industry firm size distribution saw an increase in larger firms. The ratio of firms with 200+ workers increased from 0.09 in 1998 to about 0.14 in 2007, while the size class 0-49 decreased from 0.64 to 0.58 in the same period. The 50-99 and 100-199 size classes did not change much. For the Heavy industry, the proportion of firms with 0-49 workers fell from 0.68 to 0.61. However, most of the changes occurred in 50-99 and 100-199 size classes when the proportions changed from 0.17 to 0.20 and 0.06 to 0.11, respectively. The changes are not monotonic and they may be the reason

13 for the transitory effects of the first and third component for the Heavy industry. The estimated dynamic strength components for labour productivity is clear, the first component for both the Heavy and Light industries shows a dramatic increase. The second and third components are transitory with the Heavy industry displaying most of the variation. The large increase in the first component coincides with the the increase in labour productivity distribution. The Light industry changes up until 2002 when the distribution appears to stabilize. The Heavy industry undergoes a change in the labour distribution throughout the time and even decreases a bit at the last year observed 2007. For the output distortion distribution, the Light industry displays the first estimated dynamic strength component decreasing while for the Heavy industry it is positive with a spike at the end. The second and third components for the Light industry are negligible while the second component for the Heavy industry is persistently negative. These results confirms that output distortions are decreasing for the most part. The dynamics of TFP is similar to labour productivity. The first component for both Light and Heavy are increasing while the second and third components are negligible. These dynamic strength components highlight that the labour productivity and TFP distributions are similar.

6. Conclusions

Using firm level data for Ecuador, we conduct an empirical analysis and document novel stylized facts about firm reallocation and productivity patterns during a large crisis in this developing country. We focus our analysis for two broad industries classified as Light and Heavy. We first find that both industries went through a process of large job reallocation and firm turnover in terms of entry and exit in the period analyzed. When the crisis deepened, net entry rate and net job creation are both negative. Second, we find that labor productivity of entrant firms was dramatically lower than that of the exiting firms. Our labor productivity decomposition shows that entrant firms start with lower labor productivity relative to the industrial average. Interestingly, firms with higher productivity grow in size across time. To understand the source of changes in labor productivity we compute TFP and distortions at the firm level. We find that output distortions falls after 2000 when the economy starts to recover and substantial economic reforms took place in Ecuador. Moreover, post-2000 the output distribution, especially in the Heavy industry, has a lower variance and mean which implies that frictions in the economy were lessened. Furthermore, the mean of the TFP distribution increased and firms became more productive. A decomposition of the TFP and labor productivity indicates that there were large changes in productivity within firms, reflecting the substantial output distortion during the economic crisis. Finally, net entry only accounts for a small fraction of the total weighted change in pro-

14 ductivity. Our functional principal component analysis suggests that there is a structural change in the ﬁrm size, labor productivity, output distortions, and TFP distributions. The timing of the documented structural change in the distribution of these variables coincides with the dollarization of the Ecuadorian economy which will be explored in future research.

Acknowledgments

We acknowledge the usage of the np package by Hayfield and Racine (2008) and the use of the Quarry High Performance Cluster at Indiana University where all the computations were performed. We thank Paul Carrillo, John Earle, Jamil Mahuad, Oswaldo Molina, Mitsukuni Nishida, Amil Petrin, B. Ravikumar, Bob Rijkers, Richard Rogerson, Kjetil Storesletten, Gus- tavo Ventura, Kirk White and participants of various workshops for comments. We thank Eric Bartelsman for sharing his productivity decomposition programs. The staff at the Ecuadorian National Statistics Office (INEC) provided great assistance with the data and answered many of our queries, in particular Galo Arias, Margarita Viera, Telmo Molina and Verónica Velázquez. Ho thanks the U.S. Census Bureau for financial support. Jacho-Chávez thanks the Center for Latin American and Caribbean Studies (CLACS) at Indiana University for financial support. The views expressed in this paper are those of the authors. No responsibility for them should be attributed to the Bank of Canada. All errors are our own.

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17 Table 1: Classiﬁcation of Industries based on two-digit International SIC

Proportion of ISIC Light Industries Observations Firms 15 Foodproductsandbeverages 0.51 0.52 16 Tobacco products 0.00 0.00 17 Textiles 0.14 0.14 18 Wearing apparel; dressing and dyeing of fur 0.15 0.15 19 Tanning and leather products 0.06 0.06 36 Furniture; manufacturing n.e.c. 0.14 0.14 18

ISIC Heavy Industries Observations Firms 23 Coke, reﬁned petroleum and nuclear fuel 0.01 0.01 24 Chemicals and chemical products 0.26 0.26 25 Rubber and plastics products 0.30 0.30 26 Other non-metallic mineral products 0.22 0.22 27 Basic metals 0.04 0.04 28 Fabricated metal products 0.16 0.18

Note: An observation refers to values reported by a unique ﬁrm in a particular year. Since our study period is from 1998 to 2007, there can be at most nine observations associated with a particular ﬁrm. Table 2: Descriptive Statistics

Light Industry

Employment Size Labour Productivity Year N Mean S.D. Q25 Q50 Q75 Mean S.D. Q25 Q50 Q75 1998 691 85.16 165.23 19 34 79 5.34 5.29 2.11 3.60 6.59 1999 626 88.64 197.94 18 34 75 4.98 4.93 1.82 3.21 6.12 2000 628 96.25 225.87 18 35 80 4.85 4.95 1.88 3.06 6.10 2001 626 106.68 302.70 19 34.5 82 5.69 5.10 2.32 3.93 7.13 2002 621 116.34 342.33 18 36 85 5.86 4.49 2.82 4.21 7.47 2003 621 108.67 284.68 17 35 88 6.51 5.09 3.06 4.84 8.29 2004 645 109.75 291.53 17 34 89 6.86 5.57 3.10 5.11 8.75 2005 632 120.23 324.76 17 34.5 90.5 6.88 5.63 3.10 4.98 8.76 2006 630 126.41 311.63 18 35 96 6.85 5.53 3.10 5.13 8.92 2007 648 126.40 326.55 18 36 100 7.32 6.56 3.09 5.23 8.97

Heavy Industry

Employment Size Labour Productivity Year N Mean S.D. Q25 Q50 Q75 Mean S.D. Q25 Q50 Q75 1998 352 64.01 93.94 18 32 62.5 6.27 5.68 2.54 4.23 8.29 1999 326 59.72 83.85 18 30 64 4.80 4.75 1.87 3.26 6.00 2000 322 64.75 87.71 18 33.5 72 5.38 5.50 1.88 3.46 6.60 2001 326 69.69 95.69 18 34.5 73 6.87 5.84 2.71 5.09 8.91 2002 329 68.20 98.92 18 36 71 7.87 6.29 3.29 5.85 10.59 2003 335 67.49 93.30 19 35 72 7.84 5.72 3.57 6.30 10.55 2004 347 67.21 95.58 18 33 72 7.94 6.25 3.76 6.11 9.73 2005 345 69.90 104.95 17 31 72 7.42 5.74 3.53 5.82 9.26 2006 350 76.00 114.67 19 34 77 7.67 6.36 3.56 5.89 9.40 2007 386 72.61 109.70 18 34 76 7.02 5.32 3.28 5.33 9.24

Note: The descriptive statistics are: the number of observations (n), average (Mean), standard deviation (SD), and the 25/50/75 percentile (Q25/Q50/Q75).

19 Table 3: Firm Size Descriptive Statistics

Light Industry Employees 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 0-49 0.64 0.63 0.60 0.61 0.61 0.60 0.60 0.61 0.59 0.58 50-99 0.17 0.18 0.19 0.18 0.18 0.18 0.18 0.16 0.16 0.16 100-199 0.11 0.11 0.10 0.11 0.10 0.10 0.11 0.11 0.11 0.11 200 or more 0.09 0.09 0.11 0.11 0.11 0.12 0.12 0.13 0.14 0.14

Heavy Industry Employees 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 0-49 0.68 0.69 0.66 0.62 0.64 0.65 0.65 0.63 0.61 0.61 50-99 0.17 0.17 0.16 0.20 0.18 0.17 0.18 0.19 0.20 0.20 100-199 0.06 0.07 0.10 0.10 0.10 0.11 0.09 0.09 0.10 0.11 200 or more 0.08 0.07 0.07 0.08 0.08 0.07 0.08 0.09 0.09 0.08

Note: The numbers represent the fraction of ﬁrms in the industry in a speciﬁc year.

20 Table 4: Survival Rates

Light Industry Cohort 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Pre- 1998 1.00 0.87 0.82 0.76 0.70 0.64 0.59 0.56 0.54 0.49 1999 . 1.00 0.93 0.86 0.79 0.73 0.68 0.65 0.62 0.57 2000 . . 1.00 0.91 0.84 0.77 0.71 0.68 0.65 0.60 2001 . . . 1.00 0.91 0.83 0.77 0.73 0.70 0.65 2002 . . . . 1.00 0.89 0.81 0.78 0.74 0.68 2003 . . . . . 1.00 0.90 0.85 0.81 0.75 2004 ...... 1.00 0.91 0.86 0.80 2005 ...... 1.00 0.92 0.86 2006 ...... 1.00 0.90 2007 ...... 1.00

Heavy Industry Cohort 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Pre- 1998 1.00 0.89 0.84 0.78 0.74 0.71 0.64 0.61 0.58 0.57 1999 . 1.00 0.92 0.87 0.81 0.78 0.70 0.66 0.64 0.62 2000 . . 1.00 0.92 0.85 0.81 0.73 0.69 0.66 0.65 2001 . . . 1.00 0.92 0.87 0.78 0.75 0.71 0.69 2002 . . . . 1.00 0.94 0.83 0.78 0.75 0.72 2003 . . . . . 1.00 0.88 0.84 0.79 0.77 2004 ...... 1.00 0.92 0.88 0.83 2005 ...... 1.00 0.92 0.87 2006 ...... 1.00 0.93 2007 ...... 1.00

Note: Survival rate refers to the proportion of ﬁrms that continued their operations from the previous year.

21 Table 5: Reallocation Patterns

Light Industry 1999 2000 2001 2002 2003 2004 2005 2006 2007 JC 0.148 0.192 0.200 0.166 0.140 0.148 0.142 0.139 0.146 JD 0.167 0.114 0.083 0.129 0.167 0.094 0.065 0.141 0.049 JR 0.315 0.306 0.283 0.295 0.307 0.242 0.207 0.280 0.195 E Rate 0.045 0.070 0.083 0.077 0.110 0.126 0.063 0.063 0.105 X Rate 0.133 0.065 0.078 0.079 0.103 0.087 0.092 0.078 0.094 E Size 32 29.5 21.5 22 25.5 25 18.5 27 23 X Size 23 21 24 21 15.5 19.5 17.5 16 22 E LP 2.60 2.33 2.77 4.18 4.17 4.36 3.13 6.07 5.26 X LP 3.21 2.63 2.40 4.19 3.15 3.76 4.51 4.37 4.41

Heavy Industry 1999 2000 2001 2002 2003 2004 2005 2006 2007 JC 0.074 0.143 0.160 0.159 0.117 0.141 0.118 0.151 0.148 JD 0.125 0.083 0.130 0.121 0.133 0.095 0.067 0.086 0.048 JR 0.199 0.226 0.290 0.280 0.250 0.236 0.184 0.236 0.197 E Rate 0.040 0.065 0.089 0.091 0.081 0.153 0.067 0.080 0.148 X Rate 0.107 0.068 0.074 0.079 0.060 0.112 0.075 0.080 0.062 E Size 14 28 24 24.5 37 22 19 30.5 22 X Size 16 18 15 30.5 15.5 30 29 18.5 21.5 E LP 1.49 2.63 3.82 5.22 7.04 5.67 4.90 5.54 4.63 X LP 3.43 1.78 2.20 3.33 5.19 6.37 6.91 5.06 3.83

Note: Job creation (JC) is defined as the sum of new jobs at expanding firms and new entrants. Job destruction (JD) is defined as the sum of all lost jobs at contracting firms and exitors. Job reallocation (JR) refers to the sum of job created and job destructed. JC, JD, and JR are expressed as fractions of employment in the current year. Entry (E) rate and exit (X) rates are expressed as fractions of existing firms in the current year. Size is measured in terms of employment while labour productivity (LP) is output divided by employment.

22 Table 6: Production Function Estimates Light Heavy

αs (capital) 0.1418 0.1688 (0.0087)∗∗∗ (0.0135)∗∗∗ γs (labour) 0.6408 0.5749 (0.0176)∗∗∗ (0.0270)∗∗∗ Constant 2.1418 2.3088 (0.0716)∗∗∗ (0.1082)∗∗∗

Firm-level σc 0.7591 0.7396

Random σǫ 0.3985 0.4049

ρ(ci,Xi) 0.7036 0.6556

Note: The number of firm and total observations are: 1108 and 6368 for Light industry while 601 and 3418 for Heavy Industry. Standard errors are in parentheses. *, **, and *** is the statistical significance at the 10%, 5% and 1% levels, respectively. Firm-level variance is σc while Random σǫ is the idiosyncratic random error term. ρ(ci,Xi) is the correlation between the fixed effect and the observables.

Table 7: Output Distortions

Heavy Industry Light Industry Year N Mean S.D. Q25 Q50 Q75 N Mean S.D. Q25 Q50 Q75 1998 352 -0.158 0.445 -0.345 -0.040 0.087 691 -0.127 0.469 -0.274 -0.024 0.116 1999 326 0.186 0.306 0.058 0.230 0.369 626 0.075 0.362 -0.079 0.123 0.293 2000 322 0.166 0.358 0.038 0.201 0.368 628 0.122 0.403 0.009 0.188 0.323 2001 326 0.055 0.313 -0.047 0.112 0.223 626 0.095 0.323 -0.031 0.139 0.276 2002 329 0.038 0.305 -0.056 0.087 0.209 621 0.136 0.245 0.012 0.160 0.280 2003 335 0.143 0.293 0.054 0.170 0.306 621 0.188 0.229 0.076 0.200 0.320 2004 347 0.198 0.300 0.121 0.242 0.349 645 0.255 0.205 0.149 0.271 0.373 2005 345 0.309 0.211 0.229 0.334 0.432 632 0.302 0.220 0.211 0.330 0.425 2006 350 0.352 0.220 0.302 0.384 0.476 630 0.368 0.204 0.295 0.397 0.486 2007 386 0.454 0.170 0.409 0.461 0.548 648 0.420 0.215 0.361 0.430 0.534

j j j zit j Note: The idiosyncratic output distortion tax (τ ) is computed as τ =1 − j j t . If τ > 0 it it zei (1+g ) it j then the ﬁrm faces an output tax while τit < 0, the ﬁrm is receiving an output subsidy.

23 Table 8: Aggregate productivity growth (APG) Decomposition

Light Industry

TE APGRE Year APG VA Unskilled Skilled OLS FE OLS FE 1999 15.96 15.79 -0.12 -0.05 25.14 21.17 -9.18 -5.20 2000 -12.53 -12.27 0.19 0.08 -19.75 -18.84 7.22 6.31 2001 10.41 10.96 0.45 0.10 -2.71 2.93 13.12 7.48 2002 10.06 10.36 0.21 0.10 4.20 6.26 5.86 3.79 2003 10.44 9.99 -0.40 -0.05 11.93 11.57 -1.49 -1.13 2004 6.17 6.35 0.09 0.09 0.28 1.48 5.90 4.69 2005 7.34 7.54 0.13 0.06 6.89 6.12 0.46 1.23 2006 9.03 9.16 0.04 0.10 3.56 4.90 5.47 4.13 2007 3.24 3.22 0.00 -0.02 1.04 3.17 2.20 0.07 Average 6.68 6.79 0.07 0.04 3.06 3.88 3.28 2.37 Std. Dev. 8.01 7.93 0.23 0.07 11.34 10.05 6.30 4.01

Heavy Industry

TE APGRE Year APG VA Unskilled Skilled OLS FE OLS FE 1999 -15.69 -16.41 -0.21 -0.51 9.26 5.86 -24.95 -21.55 2000 -4.44 -4.29 0.05 0.11 1.49 -0.76 -5.93 -3.69 2001 19.61 19.96 0.16 0.18 -7.99 1.14 27.60 18.48 2002 9.61 9.45 0.00 -0.16 3.73 5.66 5.88 3.95 2003 2.59 2.68 -0.03 0.12 2.75 2.60 -0.15 0.00 2004 6.82 6.99 0.07 0.10 -1.54 -0.96 8.37 7.78 2005 -7.35 -7.28 0.03 0.03 0.12 0.72 -7.47 -8.08 2006 13.10 13.37 0.17 0.10 2.24 6.54 10.86 6.56 2007 -4.97 -4.90 0.07 0.00 -5.70 -4.66 0.73 -0.31 Average 2.14 2.18 0.04 0.00 0.44 1.61 1.66 0.35 Std. Dev. 11.21 11.43 0.12 0.21 4.85 3.57 14.47 11.20

Note: VA is value-added while Unskilled and Skilled refers to the respective labour class contribution to APG. TE refers to Technical Eﬃciency and RE is the reallocation term. The TE and APGRE terms are estimated from the production function via two methods: Ordinary Least Squares (OLS) and Fixed-Eﬀects (FE) model.

24 Table 9: Comparison of Productivity Decompositions

Light Industry

Year APG BHC TE BHCRE APGRE 1999 15.96 34.39 21.17 13.22 -5.20 2000 -12.53 -22.41 -18.84 -3.57 6.31 2001 10.41 0.27 2.93 -2.66 7.48 2002 10.06 8.34 6.26 2.08 3.79 2003 10.44 16.70 11.57 5.13 -1.13 2004 6.17 0.59 1.48 -0.90 4.69 2005 7.34 1.20 6.12 -4.91 1.23 2006 9.03 10.84 4.90 5.94 4.13 2007 3.24 2.61 3.17 -0.56 0.07 Average 6.68 5.84 3.88 1.53 2.37 Std. Dev. 8.01 15.22 10.05 5.75 4.01

Heavy Industry

Year APG BHC TE BHCRE APGRE 1999 -15.69 -5.38 5.86 -11.24 -21.55 2000 -4.44 7.20 -0.76 7.96 -3.69 2001 19.61 5.36 1.14 4.22 18.48 2002 9.61 3.44 5.66 -2.22 3.95 2003 2.59 7.60 2.60 5.01 0.00 2004 6.82 -1.22 -0.96 -0.26 7.78 2005 -7.35 -2.10 0.72 -2.83 -8.08 2006 13.10 11.27 6.54 4.73 6.56 2007 -4.97 -4.98 -4.66 -0.32 -0.31 Average 2.14 2.36 1.61 0.56 0.35 Std. Dev. 11.21 5.99 3.57 5.76 11.20

Note: APG is the productivity decomposition suggested by Petrin and Levinsohn (2010) while BHC is taken from Baily, Hulten, and Campbell (1992). TE is the Technical Efficiency term while there are two different reallocation term from BHCRE and APGRE . All estimates are based on Fixed-Effects estimation of the production function.

25 Figure 1: Ecuadorian Sucre-US Dollar Exchange Rate and Oil Prices

Ecuadorian Sucre − US Dollar Nominal Exchange Rate 30

0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 1000s of Sucre / USD

Changes in the Real Effective Exchange (1997=100) 160 140 120 100 80 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year Quarterly Ecuador Oriente Oil Spot Price FOB 60

USD per Barrel 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Coefficient of Variation 0.2

0.1

0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year

Note: Data on Sucre-US dollar exchange rate comes from the World Development Index. Changes in the real effective exchange rate is relative to the real effective exchange rate in 1997. Data on the quarterly oil prices is obtained from the U.S. Energy Information Administration. We calculated the standard deviation of the oil price within the quarter of interest, and then calculated the coefficient of variation. Figure 2: Size and Labour Productivity Distributions

8 Light − Log(Employment Size)

4 2006

2004

Year 2 2002

8 2000 0 6 4 1998 2000 2002 2004 2006 2008 2 1998 Year

6 Light − Log(Labour Productivity)

2006

2004 0 Year

2002

−2 2000 3 2 1 1998 2000 2002 2004 2006 2008 0 1998 Year

8 Heavy − Log(Employment Size)

4 2006

2004

Year 2 2002

2000 6 0 5 4 3 1998 2000 2002 2004 2006 2008 2 1998 Year

6 Heavy − Log(Labour Productivity)

2006

2004 0 Year

2002

−2 2000 3 2 1 1998 2000 2002 2004 2006 2008 0 1998 Year

Note: Bandwidths chosen by Silverman’s rule-of-thumb with a second-order gaussian kernel.

b 2007 The estimated densities, {ft,ht }t=1998, are displayed in the ﬁrst column in a perspective plot (Hyndman, 1996). Second column displays “highest density regions,” i.e. the smallest region of the sample space containing 50% (darkest gray), 95% (medium gray), and 99% (lightest gray) probabilities (Hyndman, 1996). This graph also marks (by •) the empirical modes for each estimated density. Figure 3: Turnover and Reallocation Measures Light − Job Creation & Destruction Heavy − Job Creation & Destruction 0.20 0.20 Job Creation Job Destruction

0.15 0.15

0.10 0.10 Fraction of FirmsFraction of FirmsFraction

0.05 0.05

2000 2002 2004 2006 2000 2002 2004 2006

Year Year Light − Entry & Exit Rates Heavy − Entry & Exit Rates

Entry 0.14 Exit 0.14

0.12 0.12

0.10 0.10 Rates Rates 0.08 0.08

0.06 0.06

0.04 0.04

2000 2002 2004 2006 2000 2002 2004 2006

Year Year Light − Mean Entrant & Exitor Employment Size Heavy − Mean Entrant & Exitor Employment Size

Entrants Exitors 80 80

60 60

Employment Size Employment 40 Size Employment 40

20 20

2000 2002 2004 2006 2000 2002 2004 2006

Year Year Light − Mean Entrant & Exitor Labour Productivity Heavy − Mean Entrant & Exitor Labour Productivity

9 Entrants 9 Exitors 8 8

7 7

6 6

5 5 Labour Productivity Labour Productivity 4 4

3 3

2000 2002 2004 2006 2000 2002 2004 2006

Year Year Figure 4: Distributions for Light Industry

Output Tax 0.5

0.0

2006

2004 −0.5 Year

2002 −1.0 1 2000 0.5 0 −0.5 1998 2000 2002 2004 2006 2008 −1 1998 Year

Log(Rental Rate of Capital) 0.025

0.020

0.015

2006 0.010

2004 Year

2002 0.005

0.03 2000 0.025 0.02 0.000 0.015 0.01 1998 2000 2002 2004 2006 2008 0.005 1998 0 Year

Log(Average Firm Wage) 3

1 2006

2004

Year 0 2002

3 2000 −1 2 1 1998 2000 2002 2004 2006 2008 0 1998 Year

Log(Firm−level TFP) 2

0 2006

2004

Year −1 2002

−2 2 2000 1 0 −1 1998 2000 2002 2004 2006 2008 −2 1998 Year

Note: Bandwidths chosen by Silverman’s rule-of-thumb with a second-order gaussian kernel.

0.5

0.0

2006

2004 −0.5 Year

2002 −1.0 1 2000 0.5 0 −0.5 1998 2000 2002 2004 2006 2008 −1 1998 Year

0.035 Log(Rental Rate of Capital) 0.030

0.025

0.020

2006 0.015

2004

Year 0.010 2002 0.005

2000 0.03 0.000 0.02 0.01 1998 2000 2002 2004 2006 2008 1998 0 Year

Log(Average Firm Wage) 3

1 2006

2004

Year 0 2002

3 2000 −1 2 1 0 1998 2000 2002 2004 2006 2008 1998 −1 Year

Log(Firm−level TFP) 2

0 2006

2004 −1 Year

2002

−2 2 2000 1 0 −1 1998 2000 2002 2004 2006 2008 −2 1998 Year

Note: Bandwidths chosen by Silverman’s rule-of-thumb with a second-order gaussian kernel.

b 2007 The estimated densities, {ft,ht }t=1998, are displayed in the ﬁrst column in a perspective plot (Hyndman, 1996). Second column displays “highest density regions,” i.e. the smallest region of the sample space containing 50% (darkest gray), 95% (medium gray), and 99% (lightest gray) probabilities (Hyndman, 1996). This graph also marks (by •) the empirical modes for each estimated density. Figure 6: Dynamic Scree Plot Light Industries − Log(Employment Size) Heavy Industries − Log(Employment Size)

0.5 0.5

0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1

0.0 0.0

2 4 6 8 10 2 4 6 8 10

r r Light Industries − Labour Productivity Heavy Industries − Labour Productivity

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0

2 4 6 8 10 2 4 6 8 10

r r Light Industries − Output Tax Heavy Industries − Output Tax

0.6 0.6

0.5 0.5

0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1

0.0 0.0

2 4 6 8 10 2 4 6 8 10

r r Light Industries − Log(Firm−Level TFP) Heavy Industries − Log(Firm−Level TFP)

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0

2 4 6 8 10 2 4 6 8 10

r r T Note: Each plot displays λr/ r=1 λr. Figure 7: Estimated Dynamic Strength Components Light Industries − Log(Employment Size) Heavy Industries − Log(Employment Size)

0.05 0.05

0.00 0.00

−0.05 −0.05

−0.10 −0.10 1998 2000 2002 2004 2006 1998 2000 2002 2004 2006

year year Light Industries − Log(Labour Productivity) Heavy Industries − Log(Labour Productivity)

0.2 0.2

0.1 0.1

0.0 0.0

−0.1 −0.1

1998 2000 2002 2004 2006 1998 2000 2002 2004 2006

year year Light Industries − Output Tax Heavy Industries − Output Tax

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0

−0.2 −0.2

−0.4 −0.4

1998 2000 2002 2004 2006 1998 2000 2002 2004 2006

year year Light Industries − Log(Firm−Level TFP) Heavy Industries − Log(Firm−Level TFP)

0.20 0.20 ^ ^ ^ ^ θ t;1 − θ 1998;1 θ t;1 − θ 1998;1 0.15 ^ ^ 0.15 ^ ^ θ t;2 − θ 1998;2 θ t;2 − θ 1998;2 θ^ − θ^ θ^ − θ^ 0.10 t;3 1998;3 0.10 t;3 1998;3

0.05 0.05

0.00 0.00

−0.05 −0.05

1998 2000 2002 2004 2006 1998 2000 2002 2004 2006

year year

Note: Each ﬁgure plots θtr − θt0r for r =1, 2, 3.