Productivity growth and job reallocation in the Vietnamese manufacturing sector

eel industry, leading to substantial productivity growth for the industry as a whole. Lan and Minh (2018) applied static and dynamic Olley–Pakes decomposition method to examine the impact of technology spillover, reallocation of resources and competition to the productivity of Vietnamese manufacturing enterprises in the period 2000–2015. They show that the competitive effect in the reallocation process plays an important role in the productivity growth of manufacturing sectors. However, they do not directly correspond to a measure of job reallocation and productivity growth of the economy in transition. 173 Productivity growth and job reallocation 1.2 Job creation, destruction and job reallocation Job creation and job destruction are topics that have been written by many authors such as Bilsen and Konings (1998), Davis and Haltiwanger (1992) and Bojnec et al. (1998). For example, Bojnec et al. (1998) use a unique firm-level data based on traditional and newly established private firms to investigate gross job flows and labor demand in a transition period in Slovenia. They find that job destruction dominates job creation in the early years of transition, but later in the transition job destruction diminishes. They also find that newly established private firms are the most dynamic ones in terms of job creation. They estimate a reduced labor demand equation controlling for ownership and competitive pressure and find that the estimated employment elasticity with sales is rather low, 12 percent. Loecker and Konings (2006) follow Olley and Pakes (OP) and Foster et al.’s (2001) decomposition method and find the importance of entry and exit in job reallocation. They show that firm entry and exit are important in the creative destruction process and TFP is increased mainly due to existing firms’ increasing efficiency and through the net entry of firms. They also point out that state firms should become more efficient when jobs are destroyed, while private firms are characterized by the reallocation of employment to the more productive firms. This study uses Wooldridge’s method to estimate TFP and follows the decomposition method of Loecker and Konings (2006) to examine job reallocation across industries; ownership as well as firm size in the Vietnamese manufacturing industry. We also compare the results of dynamic decomposition from TFP estimated by different methods. The next section provides the methodology for estimating firms’ total factor productivity (TFP) as well as the procedure of static and dynamic decomposition of aggregate TFP. In Section 3 we describe the data set and summarize the main results. The last section provides the conclusion. 2. Methodology 2.1 The methods of estimating TFP We start by assuming a Cobb–Douglas production function as follows: Yit ¼ AitKbkit L bl it ; where Yit denotes real value-added in firm i in period t, Lit the labor input, Kit the real capital input and Ait the Hicksian neutral efficiency level of firm i in period t. Production function after taking natural logs is as follows: yit ¼ b0þbkkitþbl l itþeit ; where yit denotes log real value-added in firm i at time t, l the log of labor, k the log of real capital and: ln Aitð Þ ¼ b0þeit ; where β0 measures the mean efficiency level across firms and over time, εit is the time and producer specific deviation, which can be decomposed into two components: an observable component (or at least predictable) (vit) and unobservable component, a white noise error term uqit
 : yit ¼ b0þbkkitþbl l itþvitþuqit ; (1) where ωit¼ β0+vit represents firm-level productivity. In empirical studies, after estimating (1), researchers solve for ωit. The estimated productivity can then be calculated as follows: o^it ¼ v^itþ b^0 ¼ yitb^kkitb^l l it : (2) 174 JED 21,2 Productivity in levels can be obtained as exponential of o^it ; i.e., jit ¼ exp o^itð Þ. It has been shown that estimation (1) uses OLS which leads to biased productivity estimates, caused by the endogeneity of input choices and selection bias. Olley and Pakes (1996) developed a consistent semi-parametric estimator. This estimator addresses the simultaneity problem by using a firm’s investment decision to proxy for unobserved productivity shocks (it¼ω(iit, kit)). In OP algorithms, it requires that investment is strictly increasing in productivity, this allows to express unobservable productivity as a function of observable variablesωit¼ h(iit,kit)¼ i−1(iit, kit). Because only observations with positive investment can be used when estimating (1), this can lead to a significant loss in efficiency. So Levinsohn and Petrin (2003) (LP) use intermediate inputs rather than investment as a proxy (mit¼ω(mit, kit)). LP’s estimation algorithm differs from the algorithm introduced by OP in two important respects. First, they use intermediate inputs to proxy for unobserved productivity, rather than investment. The second difference between the approach using OP and LP is in the correction for selection bias. Both OP and LP assume that there is at least one input that can be adjusted at no cost and will react to the new information immediately. However, as Ackerberg et al. (2006) (ACF) and Bond and Soderbom (2005) stated, for the labor coefficient to be identified in the first stage of the estimation algorithm, it requires that there exists some variation in the data, independent of investment (or intermediate inputs for LP). The main difference between ACF’s approach and OP and LP is that, in ACF’s approach, they inverse “conditional” rather than “unconditional” input demand functions to control for unobserved productivity. This leads to results in a first stage that do not identify the coefficients on labor input. Instead, all coefficients are estimated in the second stage. Thus, all three semi-parametric-algorithms of OP, LP and ACF use the two-step estimation procedure to obtain consistent estimates of input elasticity. Wooldridge (2009) (WRDG) proposes to address the OP/LP problems by replacing the two-step estimation procedure with a generalized method of moments (GMM). We briefly discuss Wooldridge’s (2009) algorithm starting from the Cobb–Douglas production function (1). In particular, he shows how to write the moment restrictions in terms of two equations: these have the same dependent variable (yit) but are characterized by a different set of instruments. This approach has useful features with respect to previously proposed estimation routines: (1) it overcomes the potential identification issue highlighted byACF in the first stage; and (2) robust standard errors are easily obtained, accounting for both serial correlation and/or heteroskedasticity. The first step by OP/LP, the estimation of the parameters is addressed under the assumption that: E eitjl it ; kit ;mit ; l it1; kit1;mit1; . . .; l i1; ki1;mi1ð Þ ¼ 0: (3) Without imposing any functional form on the control function ωit¼ h(kit, mit). The second assumption exploits the Markovian nature of productivity. Following OP/LP, productivity according to the first-order Markov process is as follows: E witjl it ; kit ;mit ; l it1; kit1;mit1; . . .; l i1; ki1;mi1ð Þ ¼ E witjwit1ð Þ ¼ f h kit1;mit1ð Þð Þ: (4) Assumptions (3) and (4) directly lead to the formulation of the following two equations: yit ¼ b0þbl l itþbkkitþh kit ;mitð Þþvit ; (5) yit ¼ b0þbl l itþbkkitþ f h kit1;mit1ð Þ½ þuit : (6) 175 Productivity growth and job reallocation In the estimation the approach is to deal with the unknown functional form using nth-order polynomials in kit and mit, where the limiting case with kit and mit (i.e. n¼ 1) entering linearly should always be allowed. The orthogonality condition on the error term (uit) is given by: E uitjkit ; l it1; kit1;mit1; . . .; l i1; ki1;mi1ð Þ ¼ 0: (7) To estimate βl and βk, Wooldridge (2009) assumed that h(kit,mit) contains all polynomials of order 3 or less in the form: h kit ;mitð Þ ¼ l0þc kit ;mitð Þl: (8) Wooldridge (2009) also assume that f(.) can be approximated by a polynomial in h: f hð Þ ¼ r0þr1hþ    þrnhn: (9) Equations (8) and (9) can be substituted into Equations (5) and (6) to give the following equations: yit ¼ aþbl l itþbkkitþcitlþeit t ¼ 1; 2; . . .::Tð Þ; (10) and: yit ¼ yþbl l itþbkkitþr1 cit1lð Þþ    þrn cit1lð Þnþuit t ¼ 1; 2; . . .::Tð Þ; (11) where α and θ are the new constant parameters obtained through the aggregation of all constant terms, cit¼ c(kit, mit). The GMM estimators are applied to estimate Equations (10) and (11). Once βl and βk are obtained, the firm’s TFP can be computed by using Equation (2). 2.2 Static and dynamic decomposition 2.2.1 Static analysis: Olley–Pakes decomposition. Following OP, the aggregate TFP of industry j at time t (Φjt) is calculated as a share-weighted average of firm productivity φijt: Fjt ¼ X i Sijtjijt ; (12) where sijt stands for a firm-specific weight of firm i in industry j at time t. Given our interest in the process of job reallocation induced by productivity growth in manufacturing, we compute an aggregate productivity index using job share, rather than output-based market share: Sijt ¼ LijtP iLijt ; and SijtX0 and sum to 1: To assess how the evolution of aggregate TFP depends on firm-level improvement in TFP vs reallocation of employment between firms, this work follows the approach proposed by Bartelsman et al. (2013) that is based on Olley and Pakes’ (1996) decomposition method. This decomposition method splits the aggregate productivity index (Φt) into an unweighted mean and a sample covariance term. Formally the index Φt is decomposed as: Ft ¼ jtþ X i sitstð Þ jitjt
 ¼ jtþcovOPt ; (13) where jt represent unweighted mean productivity:j ¼ 1=Nt
P i jit . 176 JED 21,2 The second term on the right-hand side of (13) covOPt
 is known as the Olley–Pakes covariance. The covariance changes over time as resources are reallocated across the existing firms. The same decomposition of the aggregate TFP in Equation (13) could be broken down by some different categories ν (by ownership or by size or by industry): Ft uð Þ ¼ jt uð Þþ X i sit uð Þst uð Þð Þ jit uð Þjt uð Þ
 ¼ jt uð ÞþcovOPt uð Þ: (14) These decompositions will help us to understand whether the average productivity of light industry (L) and heavy industry (H) (or small and medium-sized (SM) firms with large-scale firms (LA), or private owned (PR) and state-owned (ST) firms) evolved differently. From where the within-industry decomposition can be defined as: Ft ¼ X uASET st uð Þ jt uð ÞþcovOPt uð Þ
 : (15) where SET denotes sets of H, L or SM, LA or PR, ST. This decomposition reflects both the actual component change, the un-weighted average and the covariance term, as well as the job share of the particular type. 2.2.2 Decomposition between categories following Collard-Wexler and Loecker (2015). In order to measure the importance of reallocation of resources among the categories mentioned above, we apply the same type of decomposition, but now the unit of observation is a category (heavy or light industry, small and medium or large firms, private or state-owned firms). This allows us to isolate the between– type reallocation component in aggregate productivity: Ft ¼ Ftþ X uASET st uð Þ12   Ft uð ÞFt
 ¼ FtþcovBt ; (16) where covBt is the “Between covariance” measuring the level of resource reallocation to categories, contributed to the aggregate productivity for the entire industry. 2.2.3 Dynamic decomposition. The above decomposition shows only a static version that may not reflect the dynamic impact of firms’ entry or exit on aggregate productivity. The dynamic decomposition method considers four distinct sets of producers in a given time window t−1 and t, where t is a one year window. More specifically, dynamic productivity decomposition examines the origin of productivity changes by three types of firms: survivors (S), entrants (E) and exiters (X). An entering firm in the industry is defined as a firm whose market share has increased from 0 and an exiting firm is defined as a firm with a market share of zero in each time window. Aggregate productivity growth can be decomposed as follows: DFt ¼ X iAS sitjitsit1jit
þX iAS sitjit X iAS sitjit1 ¼ X iAS sitjitsit1jit1
þX iAS sitjit1sit1jit1
þX iAS sitjit1sit1jit1
 177 Productivity growth and job reallocation þ X iAS sit1jitsit1jit
þX iAE sitjit X iAX sit1jit1 ¼ X Plant Improvement iA S sit1Djit 0 B@ 1 CAþ X Reallocation iA S jit1Dsit 0 B@ 1 CA þ X Covariance iA E Dsit1Djit 0 B@ 1 CAþ X Entry iA E sitjit X Exit iA X sit1jit1 0 B@ 1 CA: (17) In (17), the first term is the sum of changes in productivity with the share of labor at time t−1, which is referred to as a pure within-firm productivity increase or simply firm improvement; the second term is the sum of change in labor share multiplied by productivity at time t−1 and is referred to as the reallocation component (reallocation); the third term is the sum of the product changes in labor share and productivity and is referred to an interaction term or the covariance component and the last term is a net entry component (net entry). We further split up every component in the decomposition represented in Equation (17) according to ownership (private and state owned), industry (heavy and light industries) and size (small and medium firms and large firms). 3. Empirical research results 3.1 Data and basic patterns of gross job flows 3.1.1 Data. The data used in this study are obtained from the set of GSO annual survey data for firms from 2000 to 2016. We exclude observations that have not positive or lost value such as property, revenue and labor. Information is available on 535,165 firms from 2000 to 2016. Variables in value are expressed in units of Vietnam dong and deflated. In this study, value added (VA) is used to estimate TFP at the firm level. Data on VA are not available and are measured based on the income approach. Information on income compensation, fixed asset depreciation and profitability is available in the enterprise survey. Appendix 2 shows descriptive statistics on average, maximum and minimum values of capital, labor, value added, revenue and profit of the manufacturing industry. 3.1.2 Basic patterns of gross flows. We measure gross job flows as defined by Davis and Haltiwanger (1992). Job creation rate (pos) is the sum of all job gains at expanding and new establishments within a sector that is divided by the sector size. In the same way, we also define job destruction (neg) as the sum of all job losses at shrinking and dying establishments within a sector that is divided by the sector size. We determine that the sum of the two gives a measure for gross job reallocation (gross) and the difference between them generating net employment growth (net). If we take the difference between the ratio of gross job allocation and the absolute value of net employment growth, we will get a measure of excess job reallocation (excess). Such a measure tells us how much volatile job is taking place after having accounted for the job reallocation that is needed to meet certain aggregate job growth rates. This measure can be considered a better measure of the real churning that is happening in the labor market. 178 JED 21,2 We define two other measures that represent the role of entry and exit in the sum of all job gains and the sum of all job losses. The first measure is the average of the entry over the sum of all job gains (Entry/Pos) and the second measure is the average of the exit over the sum of all job losses (Exit/Neg) for the year, respectively. The share of total entry and exit in job reallocation can be defined as (Entry+Exit)/Gross. In the post-Doimoi period, the policies of equitization and enterprise law took effect, the economy had many changes especially many old jobs have been no longer appropriate and destructed, and many new jobs were created. Table II shows that on average the job creation rate (pos) and job destruction rate (neg) during 2000–2016 in the Vietnamese manufacturing sector are 20.42 and 12.06 percent, respectively. Thus, the job creation rate dominates the job destruction rate. This conclusion contradicts the conclusion of Loecker and Konings (2006). However, our conclusion is reasonable because Vietnam’s manufacturing sector accounts for a very small proportion and labor of the industry sector only accounts for about 10 percent of the population before the Doimoi period. From Doimoi period, many policies have been implemented, leading many old jobs canceled while the number of new jobs created by many private firms established and growing rapidly. Table I indicates the evolution of gross job flow over time and the annual average of the gross job reallocation is 32.48 percent during 2000–2016. Excess job reallocation fluctuates around the average value of 23.52 percent, indicating the ability to create and destroy high jobs simultaneously. The last three rows in Table I show that on average 41.77 percent of all job creation is accounted for by firms’ entry and 34.13 percent of all job destruction is accounted for by firms’ exit. The combined contribution of entry and exit of firms in the Vietnamese manufacturing sector during the period in job reallocation is 38.94 percent. In Table II, we slice the data into different subsets (by ownership, by industry and by size) that are most affected by Vietnamese government policies during the Doimoi period to highlight the heterogeneity of firms in terms of gross job flows. Specifically, in each subset, we divide it into two opposing sets: private and state-owned firms (second column), heavy and light industries (third column), SMEs and large firms (fourth column). We find that job creation is concentrated in private firms with job creation rates in state-owned firms averaging only 7.78 percent, much lower than the private firms 22.47 percent. However, the rate of job destruction of state-owned firms and private firms is almost the same (12.48 vs 11.77 percent). So in this period, the state-owned firms have been Contents 2000–2001 2007–2008 2008–2009 2014–2015 2015–2016 Average 2000–2016 Number of entry firms 3,539 11,717 12,811 20,861 18,757 10,987 Number of exits firms 2,013 5,539 8,462 11,285 13,633 7,539 Job creation (Pos) 11.97 19.65 18.38 16.95 18.68 20.42 Job destruction (Neg) 16.72 13.26 14.72 11.17 15.00 12.06 Net employment growth rate (Net) −4.75 6.40 3.66 5.79 3.69 8.36 Gross job reallocation (Gross) 28.70 32.91 33.11 28.12 33.68 32.48 Excess job reallocation (Excess) 23.95 26.51 29.44 22.34 29.99 23.52 Entry 1.68 10.33 8.51 5.76 8.08 8.53 Exit 11.12 2.74 3.41 3.38 6.89 4.12 Share of entry in job creation 41.77 Share of exit in job destruction 34.13 Share of entry and exit in job reallocation 38.94 Source: Calculated from GSO data Table I. Aggregate job flows (%) 179 Productivity growth and job reallocation downsizing substantially because of the renovation and equitization. This shows that private-owned firms are net job creators, while state-owned firms are net job destroyers. Thus, the Doimoi policy has motivated the private sector to develop more dynamically than the state economic sector. The last three lines of the upper and lower parts of Table II show that the contribution to the job destruction explained by the firm exit is 32.91 percent in the private sector while only 19.14 percent in the state-owned firms. This reflects the fact that the government still subsidizes state-owned firms (such as Thai Nguyen iron and steel plant with heavy losses has been invested). This also reflects the impact of equitization policy as well as the business laws enacted in the Doimoi period, and the market forces in the private sector outperform the state sector. So if new firms are more efficient they could push out old and inefficient firms; we expect the important role of entry and exit in the private sector where the restructuring process seems to be taking place and replacing unproductive state-owned firms with more productive private firms. The estimated indicators of job creation, job destruction, the rate of job reallocation as well as employment growth are presented in Table II. We can draw some conclusions: (1) The average job creation rates in heavy and light industries[1] are almost the same (23.51 and 20.38 percent); meanwhile, these rates are much different for large firms and small and medium firms (16.37 and 26.57 percent). (2) The average job destruction rates in heavy and light industries are almost the same (13.49 and 10.88 percent); meanwhile, these rates are much different for large firms and small and medium firms (9.12 and 16.38 percent). That respects the fact that the state still subsidy for large firms even though they are inefficient and on the other hand many private firms grow rapidly (Hoa Phat Steel, for example). By ownership By industry By size Contents State-owned firms Heavy manufacturing firms Large firms Pos 7.78 23.51 16.37 Neg 12.48 13.49 9.12 Net −4.70 10.02 7.25 Gross 20.26 37.00 25.49 Excess 14.22 26.71 17.66 Entry 0.00 11.32 6.56 Exit 2.39 5.07 2.49 Share of entry in job creation 0.00 48.15 40.04 Share of exit in job destruction 19.14 37.56 27.26 Share of entry and exit in job reallocation 11.79 44.29 35.47 Contents Private-owned firms Light manufacturing firms Small and medium firms Pos 22.47 20.38 26.57 Neg 11.77 10.88 16.38 Net 10.71 9.50 10.19 Gross 34.24 31.25 42.95 Excess 23.28 21.67 32.06 Entry 9.51 8.12 11.51 Exit 3.87 3.24 6.60 Share of entry in job creation 42.34 39.85 43.32 Share of exit in job destruction 32.91 29.76 40.31 Share of entry and exit in job reallocation 39.10 36.34 42.17 Source: Calculated from GSO data Table II. Aggregate job flows by ownership, by industry and by category (%) 180 JED 21,2 (3) The average gross job flows in heavy and light industries are 37.0 and 31.25 percent. These ratios, as shown in Table II, show an inverse relationship between gross job flows and firm size, which is a common pattern for market economies. (4) In the heavy and light industrial firms as well as the large firms and small and medium firms, the contribution to job creation by firm entry is almost more than 40 percent. This can be explained by the fact that although the state-owned economic sector has narrowed, the private sector has grown rapidly in both heavy industry and light industry. And not only many small firms are formed but also private firms are growing rapidly. 3.2 Results of decompositions We rely on the estimated productivity by the WRDG’s method to show the importance of reallocation, both cross and within categories (private vs state-owned firms, heavy vs light industry, large vs small and medium firms) in productivity growth that makes us to investigate the importance of entry and exit in productivity growth. 3.2.1 Static decomposition of productivity growth. Table III provides the results of aggregate TFP decomposition into components to consider their role in aggregate TFP changes from 2000 to 2016. Table I shows that the gross job reallocation is 26.27 percent during the period. Thus, the reallocation of employment has contributed an important part in the productivity growth of manufacturing. This indicates that the impact of the Vietnamese government’s post-renovation policies has helped the market mechanism work well and through which reallocation of resources has helped firms improve productivity. The results of the within decomposition at Table III show that the aggregate TFP of private and the state-owned firms, heavy and light industry, large and small and medium firms has changed to 3.093, −0.796, 0.315, 1.030, 0.936 and 1.361, respectively. In which, reallocation resources of the above-mentioned industries contributed 30.13, 11.18, 49.21, 37.77 and 21.28 percent, 29.02 percent among aggregate TFP growth, respectively. Thus, the reallocation of labor forces is toward the most productive firms. This reflects the fact that it Job allocation Job allocation Job allocation Aggregate TFP 2.323 2.323 2.323 2.323 Olley_Parkes Decomposition 2.323 2.323 2.323 2.323 Unweighted average 1.293 1.293 1.293 1.293 Covariance 1.031 1.031 1.031 1.031 Between decomposition 2.323 2.323 2.323 2.323 Unweighted average 2.354 2.354 2.743 2.299 Between Covariance −0.030 2.271 −0.420 0.025 Within decomposition 2.323 2.323 2.323 2.323 Light industry Heavy industry State owned Private owned Small and medium Large firms Aggregate TFP 1.030 0.315 −0.769 3.093 1.361 0.963 Unweighted average 0.640 0.161 −0.684 2.160 0.966 0.758 Within Covariance 0.389 0.155 −0.086 0.932 0.395 0.205 Notes: The aggregate TFP of manufacturing firms increased by 2.323 percent during the study period. Olley–Pakes static decomposition shows that this increase is due to the contribution of the firm becoming more productive (56 percent) and the job reallocation from unproductive firms to more productive firms (44 percent) Source: Estimated from GSO data Table III. Static decompositions of productivity growth change 2000–2016 (%) 181 Productivity growth and job reallocation is happening in the manufacturing sector of Vietnam after innovation (the share of total entry and exit in gross of heavy manufacturing firms is the highest (49.9 percent)). 3.2.2 Dynamic decomposition of productivity growth. Table IV presents the results of dynamic decomposition. Its columns show the results of TFP decomposition into components. Its rows report the results in one-year window decomposition for the entire sample. Aggregate productivity rose by 2.323 percent over the entire period while the internal firm improvement component accounted for 4.633 percent, lower than the results for the US steel industry by Collard-Wexler and Loecker (2015). The change in TFP is less than firm improvement because of the contribution of the reallocation component and the covariance component is negative. Improvements within the enterprise are positive across all time windows except for the first two windows. This result demonstrates that most of the productivity growth is explained by the internal improvement component. Thus, the restructuring of firms in the Vietnamese manufacturing industry, reflected in the aggregate job creation and job destruction process, seems to have resulted in substantial within-firm productivity growth and entry components. Meanwhile, the reallocation components which are negative in most time windows (except for the years 2000–2001, 2008–2009 and 2011–2012) indicate that the within-firm productivity growth is the main component for TFP growth. This result is consistent with the findings of Loecker and Konings (2006) for Slovenian manufacturing. In addition, the negative components between firms for most time windows suggest that growth in productivity has been associated with a process in which more productive firms are downsizing faster than less productive firms. The estimated results of productivity growth, productivity improvement and reallocation components for the years 2008–2009, 2011–2012 suggest that positive productivity growth of manufacturing is due to firm-improvement and the reallocation of resources from unproductive firms to more productive firms. The covariance term tells us how the level of productivity change is correlated with changes in employment. This component is negative in all time windows, which shows that firms that increase in terms of productivity become smaller in size. This conclusion holds Period Total change Firm improvement Reallocation Covariance Net entry Entry Exit 2000–2001 −0.051 −0.043 −0.147 −0.170 0.308 0.640 0.332 2001–2002 −0.413 −0.168 0.030 −0.274 0.000 0.334 0.334 2002–2003 0.031 0.191 −0.107 −0.132 0.079 0.298 0.220 2003–2004 0.017 0.091 −0.134 −0.081 0.141 0.346 0.205 2004–2005 −0.074 0.005 −0.021 −0.069 0.012 0.232 0.220 2005–2006 0.223 0.275 −0.051 −0.094 0.093 0.339 0.246 2006–2007 0.343 0.398 −0.190 −0.138 0.273 0.452 0.180 2007–2008 0.073 0.403 −0.113 −0.325 0.108 0.366 0.258 2008–2009 0.589 0.641 0.089 −0.297 0.156 0.476 0.319 2009–2010 −0.092 0.144 −0.064 −0.143 −0.029 0.319 0.348 2010–2011 0.090 0.088 −0.031 −0.167 0.200 0.542 0.343 2011–2012 0.420 0.498 0.090 −0.157 −0.011 0.304 0.315 2012–2013 0.062 0.052 −0.054 −0.081 0.145 0.510 0.365 2013–2014 0.133 0.720 −0.114 −0.545 0.073 0.478 0.404 2014–2015 0.664 0.765 −0.099 −0.222 0.220 0.514 0.295 2015–2016 0.308 0.572 −0.252 −0.196 0.185 0.563 0.379 Total 2.323 4.633 −1.170 −3.092 1.952 6.714 4.762 Source: Estimated from GSO data Table IV. Dynamic decomposition of productivity growth (%) 182 JED 21,2 true for all transitional economies, especially the post-socialist economy, as is the case with Loecker and Konings (2006). More importantly, however, the net firm entry component explains 1.952 percent of the observed aggregate productivity growth over the sample period, while the industry aggregate productivity growth is 2.323 percent in the same period during the period. The decomposition results show that the contribution to aggregate TFP growth of the firm improvement component clearly dominates the net entry component as it is over two times the magnitude on average (4.633 vs 1.952 percent). However, for a number of years such as 2001–2005, 2010–2011 and 2012–2013, the contribution of net entry to aggregate productivity growth dominates the contribution of firm improvement. The creative destruction process taking place in Vietnam’s manufacturing sector is not due to the reallocation of employment between existing firms and the covariance component, but the entry of more productive firms replacing inefficient firms and improving productivity within the firms. This suggests that encouraging firms’ entry is important to improve aggregate productivity. Table V presents a summary of the results of this decomposition broken down by categories (more details in Appendix 3). Total change in TFP of all subsets shows positive productivity growth except state-owned firms (−0.769 percent). Thus, the contribution of private-owned and state-owned firms to its productivity growth is 133 and −33 percent, respectively. Similarly, the contribution of small and medium enterprises and large enterprises is 58.56 and 41.44 percent, respectively. We can note that the growth of aggregate TFP reported in Table V is mainly due to firm improvement component and net entry component, except the case of state-owned firms (since the net entry of state-owned firms is negative). In other words, in general, firms have become more efficient in accordance with the above analysis. So after Doimoi, the restructuring of firms, reflected in the process of aggregate job creation and job destruction, seems to have resulted in an improvement in the efficiency within firms, making firms more productive. For example, the contributions of firm improvement component for the light industry, heavy industry and other industries are 2.755, 0.653 and 1.224 percent, respectively. On the other hand, the reallocation component of almost all categories (except for small and medium firms) in Column 4 is negative. These two results give the conclusion that growth in productivity has been associated with a process in which more productive firms are downsizing faster than less productive firms in most categories. This conclusion is consistent with the conclusion when we decompose TFP for the entire manufacturing industry above. The negative covariance component for all categories in Column 5 suggests that firms increasing productivity in all categories become smaller in size. Period Total change Firm improvement Reallocation Covariance Net entry Entry Exit State–owned firms −0.769 0.397 −0.740 −0.357 −0.069 0.866 0.936 Private firms 3.093 4.236 −0.430 −2.734 2.021 5.848 3.826 Light industry 1.030 2.755 −0.809 −1.738 0.822 3.247 2.425 Heavy industry 0.315 0.653 −0.398 −0.471 0.531 1.813 1.282 Other industry 0.979 1.224 0.038 −0.882 0.599 1.654 1.056 Small and medium firms 1.361 1.748 0.338 −1.640 0.914 3.587 2.673 Large firms 0.963 2.884 −1.507 −1.452 1.038 3.127 2.089 Source: Estimated from GSO data Table V. Dynamic decomposition of productivity growth by category (%) 183 Productivity growth and job reallocation Comparing the net entry and the firm improvement component, we find that the contribution of the net entry components of heavy manufacturing firms to their productivity growth is higher than the contribution of the firm improvement component. This shows that the creative destruction process in large firms is not caused by reallocation of employment among existing firms, but rather by the entry of more productive firms replacing unproductive firms. A typical example is that Hoa Phat Steel firm is a private firm newly established after Doimoi, but now it is dominating and becoming the largest steel job share in Vietnam. This suggests that encouraging firm entry or exit in the Vietnamese manufacturing industry is important for aggregate productivity growth. Therefore, policies fitting into the competitive market, by removing the barriers to entry and exit of firms, are necessary to help improve aggregate productivity. 3.2.3 Sensitive analysis. To consider whether TFPs that are estimated from four different methods (WRDG, LO, OP and ACF) have different conclusions when using the same Olley– Pakes dynamic decomposition, we calculated the Spearman rank correlation to see the correlation between the total changes in aggregate TFP estimated from four methods (WRDG, LP, OP and ACF). The results are reported in Tables VI and VII. The first part of Table VI presents the correlation between the total change in the aggregate TFP, and the second part of this table presents the correlation between the firm improvement components. It is clear that the changes in aggregate TFP estimated from different methods and the firm improvement components are very highly correlated (always higher than 0.80). Correlations between the total changes in TFP estimated fromWRDG and from LP, OP are also typically higher than 0.95. Part 1 of Table VII presents the correlation between the reallocation components and Part 2 is showing the correlation between the net entry components. The results in Part 1 of Table VII give the same conclusion as drawn from Table VI, but the results in Part 2 of Table VII are slightly different. Total change Firm improvement components WRDG LP OP ACF WRDG LP OP ACF WRDG 1.000 1.000 LP 0.9824* 1.000 0.9794* 1.000 OP 0.9618* 0.9912* 1.000 0.9794* 1.000* 1.000 ACF 0.8912* 0.8824* 0.8647* 1.000 0.8294* 0.8059* 0.8059* 1.000 Notes: PW matrix (obs¼ 16). *Statistical significance at 5 percent Source: Estimated from GSO data Table VI. Spearman rank correlation of total change components (or of firm improvement components) Reallocation components Entry components WRDG LP OP ACF WRDG LP OP ACF WRDG 1.000 1.000 LP 0.9941* 1.000 0.7971* 1.000 OP 0.9794* 0.9853* 1.000 0.9412* 0.8676* 1.000 ACF 0.8974* 0.8959* 0.8886* 1.000 0.7290* 0.5714* 0.7099* 1.000 Notes: PW matrix (obs¼ 16). *Statistical significance at 5 percent Source: Estimated from GSO data Table VII. Spearman rank correlation of reallocation components (or of entry components) 184 JED 21,2 4. Conclusion The purpose of this paper is to consider whether the industrialization process in Vietnam’s manufacturing industry after Doimoi is one of creative destruction. In order to check the robustness of the decomposition results, we applied Olley–Pakes dynamic decomposition for the aggregate TFP, estimated from WRDG, OP, LP and ACF. The results show that the composition of the aggregate TFPs is correlated very high (over 80 percent) except net entry components. Estimated results show that: first, the growth of aggregate productivity using the WRDG method is found 2.323 percent. Second, the Olley–Pakes dynamic decomposition shows that the contribution of private and the contribution of small and medium firms and large firms to the TFP growth are positive and account for 133, 58.56 and 41.44 percent, respectively. Third, job reallocation among the firms shows that the within-firm productivity and net entry components are the main reason for TFP growth rather than reallocation. Fourth, thus, the process of creative destruction take place in Vietnam’s manufacturing sector is not due to the reallocation of employment between existing firms and the covariance component, but the entry of more productive firms replacing inefficient firms and improve productivity within the firms. This suggests that encouraging firms’ entry is important to improve aggregate productivity. There are some policy implications suggested from the research results: the establishment of a policy that fits into the competitive market, by removing the barriers to entry and exit of firms, is necessary to help improve aggregate productivity. Besides, the government should also encourage and support firms improving productivity, especially for small and medium firms to improve aggregate productivity. Note 1. According to German et al. (2011), we divide industrial into broad categories of sector: light industry, heavy industry and other. A light industry consists of food products and beverages, tobacco products, textiles, wearing apparel, dressing and dyeing of fur, tanning and leather products, furniture, etc. Heavy manufacturing comprises coke, refined petroleum and nuclear fuel, chemicals and chemical products, rubber and plastic products, other non-metallic mineral products, basic metals, fabricated metal products. References Ackerberg, D.A., Caves, K. and Frazer, G. 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(2015), “Dynamic Olley-Pakes productivity decomposition with entry and exit”, Journal of Economics, Vol. 46 No. 2, pp. 362-375. Olley and Pakes (1996), “The dynamics of productivity in the telecommunications equipment industry”, The Econometric Society, Vol. 64 No. 6, pp. 1263-1297. Wooldridge, J.M. (2009), “On estimating firm-level production functions using proxy variables to control for unobservables”, Economics Letters, Vol. 104 No. 3, pp. 112-114. Appendix 1 Prior to the Doimoi, heavy industry was considered as a foundation and a priority for development. In this period, the majority of foreign investment and aid were concentrated on building large-scale plants (the investment capital for heavy industry increased more than 12 times while investment for light industry increased about 7 times) while the resources are limited, the economy is difficult, thought rushed heavy industry does not serve timely agriculture and light industry should lead to crisis, lose weight serious structure. After the renovation, the policy of industrialization has changed significantly since the development of heavy industry has gradually shifted to the direction of heavy industry development with the degree of power and agriculture and the consumer goods industry. In the period of 1987–1996, the economy gradually improved, quality and gradually escaped from the crisis. In the period of 1997–2006, agriculture and processing industry were considered as the breakthroughs and priorities of Vietnam. Therefore, from 2007 to now, Vietnam’s industrial policy has shifted to encourage the development of high-tech, complementary software technology to gradually develop the economy in depth. Before renovation After renovation Period Period 1960–1986 Period 1987–1996 Period 1997–2006 Period 2007–now Industrialization policy To prioritize the development of heavy industry on the basis of developing agriculture and light industry Agriculture is the leading front, developing heavy industry with level, power. Take agriculture, consumer goods industry and export as the focus To accelerate the industrialization and modernization of agriculture, to make breakthroughs in combination with the processing industry To encourage the development of high technologies, processing industries, software technology and subsidiary technologies. Prioritize Heavy industry Light industry Light industry High technology, processing industry Note: Vietnam’s industrialization policy has changed considerably before and after the renovation Table AI. Vietnam industrialization policy before and after Doimoi 186 JED 21,2 Appendix 2 The relative size of light and heavy industries dropped steadily over the period. By 2016, light and heavy industry accounted for 32.0 and 34.9 percent of the total capital employed in the manufacturing sector, respectively. The light industry remains the most labor-intensive sector, employing 61.6 percent of the total labor vs 17.2 percent for heavy industry are reported in Table AIV. Variables Manufacturing industry 2000–2016 Light manufacturing 2000–2016 Heavy manufacturing (2000–2016) Capital (K) Mean 12,583.62 11,577.38 13,208.75 SD 137,997.30 77,804.89 130,393 Skewness 115.83 30.56572 88.51467 Labor (L) Mean 114 177 63 SD 664 935 190 Skewness 48 36 13 Value added (VA) Mean 2,750.14 3,059.499 2,169.043 SD 45,819.22 25,120.77 16,484.12 Skewness 200.24 51.65395 39.67112 Revenue Mean 17,537.06 17,325.57 14,835.99 SD 356,862.20 122,137.3 176,914.2 Skewness 217.70 42.96253 135.3425 Profit Mean 902.73 760.2117 719.2143 SD 34,459.93 14,936.26 9,864.679 Skewness 227.07 74.9064 60.13158 Observations 535,165 214,894 178,873 Source: Calculated from GSO data Table AII. Description statistics 187 Productivity growth and job reallocation Appendix 3 Light industry Total change Firm improvement Reallocation Covariance Net entry Entry Exit 2000–2001 −0.185 −0.175 −0.448 −0.342 0.779 1.589 0.810 2001–2002 −0.990 −0.355 −0.100 −0.499 −0.037 0.815 0.852 2002–2003 0.108 0.530 −0.346 −0.254 0.179 0.711 0.532 2003–2004 0.065 0.253 −0.392 −0.138 0.342 0.834 0.491 2004–2005 −0.249 −0.049 −0.067 −0.102 −0.031 0.531 0.562 2005–2006 0.672 0.772 −0.152 −0.174 0.227 0.856 0.629 2006–2007 0.963 1.099 −0.594 −0.261 0.719 1.145 0.426 2007–2008 0.090 0.851 −0.333 −0.658 0.230 0.851 0.621 2008–2009 1.764 1.929 0.145 −0.643 0.333 1.173 0.840 2009–2010 −0.298 0.347 −0.190 −0.310 −0.145 0.745 0.891 2010–2011 0.369 0.210 −0.054 −0.269 0.482 1.304 0.822 2011–2012 1.121 1.229 0.247 −0.286 −0.068 0.688 0.756 2012–2013 0.747 0.496 −0.137 −0.004 0.392 1.245 0.854 2013–2014 0.408 1.945 −0.402 −1.502 0.366 1.344 0.977 2014–2015 2.183 2.184 −0.210 −0.360 0.569 1.312 0.742 2015–2016 0.978 1.761 −0.871 −0.408 0.496 1.438 0.942 Total 7.743 13.027 −3.905 −6.211 4.833 16.580 11.747 Light industry 3.176 7.342 −2.730 −3.326 1.890 7.954 6.063 Heavy industry 0.829 1.746 −1.296 −0.869 1.248 4.369 3.121 Other industry 3.738 3.939 0.120 −2.016 1.695 4.258 2.563 State-owned firms −2.005 1.160 −2.261 −0.659 −0.245 2.389 2.634 Private firms 9.748 11.867 −1.644 −5.552 5.078 14.191 9.114 Small and medium firms 3.582 4.103 0.530 −2.970 1.919 7.693 5.774 Large firms 4.162 8.923 −4.435 −3.241 2.914 8.888 5.973 Note: TFP is estimated by OP method Table AIII. Dynamic decompositions of productivity growth change 2000–2016 (percent) 188 JED 21,2 Light industry Total change Firm improvement Reallocation Covariance Net entry Entry 2000–2001 −0.122 −0.111 −0.309 −0.276 0.574 1.177 2001–2002 −0.744 −0.277 −0.024 −0.422 −0.021 0.606 2002–2003 0.070 0.377 −0.235 −0.208 0.136 0.534 2003–2004 0.042 0.180 −0.274 −0.120 0.255 0.623 2004–2005 −0.169 −0.021 −0.045 −0.095 −0.008 0.404 2005–2006 0.466 0.547 −0.105 −0.145 0.169 0.630 2006–2007 0.686 0.787 −0.407 −0.215 0.522 0.842 2007–2008 0.090 0.678 −0.232 −0.535 0.179 0.644 2008–2009 1.229 1.342 0.132 −0.506 0.261 0.870 2009–2010 −0.203 0.262 −0.133 −0.243 −0.089 0.564 2010–2011 0.224 0.154 −0.046 −0.242 0.357 0.973 2011–2012 0.807 0.912 0.176 −0.241 −0.040 0.525 2012–2013 0.385 0.263 −0.103 −0.059 0.283 0.927 2013–2014 0.286 1.436 −0.264 −1.106 0.220 0.950 2014–2015 1.471 1.540 −0.165 −0.321 0.417 0.964 2015–2016 0.663 1.208 −0.576 −0.324 0.354 1.052 Total 5.179 9.278 −2.611 −5.057 3.569 12.286 Light industry 2.179 5.310 −1.814 −2.747 1.430 5.902 Heavy industry 0.606 1.270 −0.882 −0.726 0.944 3.276 Other industry 2.395 2.697 0.086 −1.584 1.196 3.108 State-owned firms −1.412 0.247 −1.417 −0.086 −0.155 1.487 Private firms 6.555 8.943 −1.141 −4.971 3.725 10.798 Small and medium firms 2.605 3.109 0.481 −2.490 1.504 5.995 Large firms 2.575 6.168 −3.091 −2.568 2.065 6.291 Note: TFP is estimated by LP method Table AIV. Dynamic decompositions of productivity growth change 2000 no 2016 (percent) 189 Productivity growth and job reallocation Corresponding author Phung Mai Lan can be contacted at: lanpm@tlu.edu.vn Period Total change Firm improvement Reallocation Covariance Net entry Entry Exit 2000–2001 0.001 0.012 −0.091 −0.033 0.113 0.277 0.164 2001–2002 −0.117 −0.049 −0.037 −0.046 0.016 0.175 0.159 2002–2003 0.038 0.071 −0.065 −0.027 0.058 0.178 0.119 2003–2004 0.020 0.037 −0.076 −0.018 0.077 0.198 0.121 2004–2005 −0.020 0.004 −0.024 −0.016 0.016 0.145 0.128 2005–2006 0.073 0.094 −0.047 −0.021 0.046 0.181 0.135 2006–2007 0.088 0.115 −0.124 −0.028 0.125 0.231 0.107 2007–2008 −0.009 0.130 −0.085 −0.036 0.075 0.209 0.134 2008–2009 0.139 0.069 −0.031 −0.024 0.052 0.184 0.132 2009–2010 0.010 0.065 −0.036 −0.024 0.065 0.200 0.135 2010–2011 0.041 0.046 −0.037 −0.030 0.062 0.232 0.170 2011–2012 0.067 0.073 −0.002 −0.014 0.010 0.153 0.144 2012–2013 0.021 0.033 −0.037 −0.019 0.043 0.206 0.163 2013–2014 0.065 0.090 −0.047 −0.025 0.047 0.210 0.163 2014–2015 0.107 0.126 −0.077 −0.023 0.081 0.197 0.116 2015–2016 0.035 0.074 −0.106 −0.014 0.081 0.223 0.141 Total 0.558 0.953 −0.945 −0.398 0.947 3.179 2.233 Light industry 0.245 0.652 −0.633 −0.260 0.486 1.692 1.206 Heavy industry 0.050 0.133 −0.238 −0.063 0.218 0.777 0.560 Other industry 0.263 0.168 −0.074 −0.075 0.243 0.710 0.467 State-owned firms −0.435 0.058 −0.408 −0.023 −0.062 0.422 0.485 Private firms 0.993 0.895 −0.537 −0.374 1.009 2.757 1.748 Small and medium firms 0.445 0.389 −0.120 −0.233 0.409 1.631 1.222 Large firms 0.112 0.565 −0.825 −0.165 0.538 1.548 1.010 Note: TFP is estimated by ACF method Table AV. Dynamic decompositions of productivity growth change 2000–2016 (percent) For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com 190 JED 21,2