Competition, Uncertainty, and Productivity Dispersion

Transcription

1 Competition, Uncertainty, and Productivity Dispersion Kaoru Hosono (Gakushuin University) Miho Takizawa (Toyo University) Kenta Yamanouchi (Keio University) Compara>ve Analysis of Enterprise Data Sogang University September,

2 Composition 1. Introduc>on 2. Literature Review 3. Model and Simula>on 4. Data and Methodology 5. Es>ma>on Results 6. Conclusion 2

3 1. Introduction Produc>vity Shock Produc>vity Dispersion This paper inves>gates the effects of produc>vity shocks and uncertainty on the produc>vity dispersion across producers. Using a large dataset of manufacturing plants in Japan, we find that the nega>ve impact of uncertainty is stronger when the product market is compe>>ve. If the vola>lity of the produc>vity shocks decreases by half, the aggregate produc>vity increases by 0.7% on average for all industries, and by 2.1% for compe>>ve industries. 3

4 Productivity Shocks and MRPK Dispersion Under favorable produc>vity shock, plants adjust their inputs to the op>mal amount. If adjustment is insufficient by adjustment costs (e.g. >meto-build), marginal revenue product of capital (MRPK) is deviated from rental rate (E to A, not B). MRPK A Rental Rate E B Productivity shock K 4

5 Changes of Productivity and MRPK If the amount of capital is adjusted immediately a^er the produc>vity shock, MRPK must be constant and equal to rental cost. The posi>ve correla>on between the changes in produc>vity and MRPK implies insufficient adjustment. 5

6 Implication If the social planner can see the shocks and reallocate capital from low MRPK plants to high MRPK plants immediately, then aggregate output would be increased. - Measure of misalloca>on in Hsieh and Klenow (2009) Given the amounts of capital for the plants are dynamically op>mized, the distor>ons in rental prices may not be closely related to the produc>vity dispersion within industry. - The produc>vity dispersion is not a good proxy for resource misalloca>on if the produc>vity shocks explain those produc>vity dispersions. 6

7 This Paper This paper inves>gate the adverse effects of uncertainty on produc>vity dispersion across plants, considering the degree of product market compe>>on. We conduct numerical simula>on for investment model and reduced form es>ma>on, using a large panel dataset of manufacturing plants in Japan. The results show that the adverse effects of uncertainty are stronger for industries with tougher compe>>on. Using the es>ma>on results, we conduct counterfactual analysis to explore the changes of the aggregate produc>vity when the vola>lity of the produc>vity shocks decreases by half 0.7% for all industries 2.1% for compe>>ve industries 7

8 Related Literature 1. Competition and uncertainty-investment relationship Uncertainty and investment A. Theory Convexity of MRPK with respect to produc>vity: posi>ve Real op>ons theory: nega>ve B. Empirical studies: nega>ve Bloom, Bond, and Van Reenen (2007), Bloom (2014) Compe>>on and uncertainty-investment rela>onship A. Theory Caballero (1991) B. Empirical studies Guiso and Parigi (1999), Bulan (2005) Ghosal and Loungani (1996, 2000) 8

9 Baseline framework Hsieh and Klenow (2009) Related Literature 2. Resource Misallocation Compared with the U.S., the alloca>ve efficiency is very low in China and India. Dispersion of TFPR within industry is used to measure the alloca>ve efficiency. Uncertainty Higher >me-series vola>lity contributes to larger cross-sec>onal dispersion of MRPK. Asker, Collard-Wexler, and De Loecker (2014) 9

10 Model Asker, Collard-Wexler, and De Loecker (2014) Aggregate output: Demand: Produc>on: Sales: Q i = B i ε 1 P i ε # Q = $ % Q i = A i K i α K L i α L M i α M, α K +α L +α M =1 S i = Ω i K i β K L i β L M i β M, Ω i = A i B i ( B i Q i ) 1 1 & ε 1 ε di ' (, P ε Q =1 ε ( ) 1 1 ε, βx = % 1 1 $ ε # & (α X ' Profit: # π (Ω it, K it ) = (β K +ε 1 ) β L % $ p L & ( ' β L # (β K +ε 1 ) β % M $ p M & ( ' β M (β K +ε 1 ) 1/(β K +ε 1) β Ωit K K /(β K +ε 1 ) it Dynamic process Capital: TFPR: K it = δk it 1 + I it 1 ω it = ρω it 1 +σκ it, κ it ~ N(0,1) 10

11 Value func>on: Capital adjustment cost: Aggregate TFP: Produc>vity dispersion: Model V(Ω it, K it ) = maxπ (Ω it, K it ) C(I it, K it,ω it )+ β V(Ω it+1,δk it+1 + I it )ϕ(ω it+1 Ω it )d Ω it+1 I it Ω it+1 C(I it, K it,ω it ) = I it +1 Iit >0 ( * )* { } C K " F+ π (Ω it, K it )+ C Q+ I K K it it $ # A = Q K α K Lα L M α M ( ) Dispersion = SD ln MRPK i K it % ' & 2 + -,- +1 I it <0 ( * )* { } C K " F π (Ω it, K it )+ C Q K K it $ # I it K it % ' & 2 + -,- Uncertainty: ln MRPK i = ln(β K )+ s i k i Compe>>on: Volatility = σ ε = 2, 4, 6 11

12 Simple case Case 1: No capital adjustment costs A A = * εσκ exp it ε ( ε 1) ( 1 α K ) di exp( εσκ it )di Larger vola>lity induces rela>vely lower aggregate TFP. Case 2: Add Log-normal distribu>on εσ ε ε 1 ln A ε 2 σ 2 = A * ε ε 1 SD( ln MRPK i ) = ( )( 1 α K ) ( )( 1 α K ) ( )( 1 α K ) = α K ε ε 1 ( )( 1 α K ) ε ε 1 1 ε 1, A * - ( ) Var ln MRPK i The dispersion of MRPK increases with the vola>lity. The effect of the vola>lity on the rela>ve TFP is stronger when the compe>>on is tougher. 12

13 Procedure Simulation 1. Simulate the model for 10,000 firms over 550 periods (discard ini>al 50). 2. Pool the simulated data (10,000*500 obs) and calculate sta>s>cs. SD(MRPK) for 10,000 firms at any par>cular period is close to the counterpart from the pooled data. 13

14 Simulation result 1 Large uncertainty increases the dispersion in MRPK. When the compe>>on is tough, the effects of uncertainty are large. The amount of op>mal produc>on strongly depends on produc>vity in the case of tougher compe>>on, so the vola>lity of produc>vity is largely reflected into large dispersion of MRPK. Symmetric costs SD(lnMRPK) volatility ε = 2 ε = 4 ε = 6 14

15 Data Source: Census of Manufacturing published by METI Coverage: All manufacturing plants with more than 30 employees located in Japan Period: Industry: 4 digit (491 industries) 15

16 Measurement (plant-level variables) Log-linearized produc>on func>on: s it = β Ks k it + β Ls l it + β Ms m it +η i + year t + ω it +ζ it ω it = ρ ω it 1 +ξ it ω it = η i + year t + ω it +ε it : TFPR System GMM es>ma>on: Blundell and Bond (1998, 2000) s it = π 1 k it + π 2 k it 1 + π 3 l it + π 4 l it 1 + π 5 m it + π 6 m it 1 + π 7 s it 1 +η i * + year t * +ω it ( ) = 0 s 3 E x it s Δω it E Δx it 2 ( η * i +ω it ) ( ) = 0 (Log of) MRPK: " MRPK it = ln S it $ # K it % ' = ln(β Ks )+ s it k it & 16

17 Measurement (industry-level variables) Produc>vity Dispersion: Compe>>on: Constant return to scale is imposed. Our main results are robust when we use the method of De Loecker and Warzynski (2012). Uncertainty: Dispersion st = SD st (MRPK it ) Markup s = ε s ε s 1 = 1 β Ks + β Ls + β Ms Volatility st = SD st (ω it ω it 1 ) Posi>vely correlated with Economic Policy Uncertainty Index in Arbatli et al. (2017) 17

18 Estimation method 1. Greater uncertainty reduces investment and results in the MRPK dispersion. ( ) st = βvolatility st + FE s + u st SD MRPK 2. The impact of uncertainty is weaker in the market where compe>>on is severer. SD( MRPK ) st = γ 1 Volatility st +γ 2 Volatility st * Markup s + FE s + u st 3. The probability of investment and the investment rate is related to uncertainty and compe>>on. 1 {I /K>0.05}it = λ 1 ln MRPK it + λ 2 Volatility st + λ 3 ln MRPK it *Volatility st + FE i + FE t + u it I it / K it = λ 1 ln MRPK it + λ 2 Volatility st + λ 3 ln MRPK it *Volatility st + FE i + FE t + u it 18

19 Summary statistics 19

20 MRPK Dispersion and Investment A. SD(MRPK) B. Frac>on of Investment Status C. Change in Within Term of SD(MRPK) D. Change in Between Term of SD(MRPK) 20

21 Volatility and MRPK Dispersion The measures of vola>lity and MRPK dispersion are posi>vely correlated. 21

22 Volatility and MRPK Dispersion The effects of vola>lity on MRPK dispersion are stronger in small markup industries. 22

23 Estimation results 1 SD(MRPK) is posi>vely correlated with vola>lity measure. The effects of vola>lity are strong in the industries with small markup. 23

24 Estimation results 2 Large MRPK plants are more likely to conduct posi>ve investment. When uncertainty is large, the investment is less likely to be made even if MRPK is large. The nega>ve effects of uncertainty are not observed in the industries with tougher compe>>on. Nega>ve investment are not affected by uncertainty. 24

25 Estimation results 3 Large MRPK plants tend to conduct more investment. Under large uncertainty, the investment rate is less affected by MRPK. The nega>ve effects of uncertainty are observed in the industries with tough compe>>on. 25

26 Counterfactual Analysis Using the es>mates, we conduct a counterfactual analysis to explore the effects of uncertainty on aggregate produc>vity. 1. Predict the investment rates at plant level when vola>lity decreases by half. 2. Generate hypothe>cal dataset. 3. Aggregate the hypothe>cal data to calculate TFP gain in Hsieh and Klenow (2009). SD(MRPK) reduces by around 2.4%. Aggregate TFP increases by 0.7% on average for all industries. The increase is larger for compe>>ve industries, 2.1%. 26

27 Summary This paper inves>gates the effects of produc>vity shocks and uncertainty on the produc>vity dispersion across producers. Using a large dataset of manufacturing plants in Japan, we find that the nega>ve impact of uncertainty is stronger when the product market is compe>>ve. If the vola>lity of the produc>vity shocks decreases by half, the aggregate produc>vity increases by 0.7% on average for all industries, and by 2.1% for compe>>ve industries. 27