Dynamic Inputs and Resource (Mis)Allocation John Asker ...
Transcript of Dynamic Inputs and Resource (Mis)Allocation John Asker ...
Dynamic Inputs and Resource (Mis)Allocation
John Asker, Allan Collard Wexler and Jan De Loecker NYU, Princeton & NBER
September 23, 2013
CMU
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Research Question:
‘To what extent do dynamic production inputs (e.g. capital) and adjustment shocks shape dispersion of static MRPK (“capital misallocation”) at the industry/country level?’
• Approach: Build a model, and evaluate the reduced form and structurally estimated predictions on a bunch of different data sets
- ‘Macro-style’ IO
• Why is this interesting? 1. Get a sense of what can drive cross-industry and country MRPK
(and productivity) differences 2. Particularly second moments (Macro/Development literatures)
- e.g. Restuccia & Rogerson (2008), Hsieh & Klenow (2009), Midrigan and Xu (2013)
3. Persistent challenge in IO: Relate cross sectional patterns to time series behavior
4. Alternate framework for judging policy responses
Research question
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Think of an industry in which: • firms face some adjustment cost when changing capital stock, and; • face an AR(1) process on firms specific productivity.
Punchline:
Model
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Think of an industry in which: • firms face some adjustment cost when changing capital stock, and; • face an AR(1) process on firms specific productivity.
Punchline:
Industry Data
(US Census)
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
39% drop
• Think of an industry in which: • firms face some adjustment cost when changing capital stock, and; • face an AR(1) process on firms specific productivity.
Punchline:
Industry Data
(US Census)
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Think of an industry in which: • firms face some adjustment cost when changing capital stock, and; • face an AR(1) process on firms specific productivity.
Punchline:
Country Data
(WBES)
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Indonesia
Morocco
47% drop
• Think of an industry in which: • firms face some adjustment cost when changing capital stock, and; • face an AR(1) process on firms specific productivity.
Punchline:
Country Data
(WBES)
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Firm with: • Cobb-Douglas technology
• Constant elasticity demand (e = - 4)
• Sales Generating Function:
(Beta’s sum to 0.75)
• Which gives the MRPK in a static world as:
Model:
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Firm with: • Adjustment costs introduce dynamics in capital choice
• financial cost • one period time to build
• very standard AR(1) on log TFP introduces transitions in states
+ a one period time to build
Model:
Dynamics
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Analysis done via computation. • Parameter values either standard, or from ranges seen in data sets • Theorem establishes that everything is robust to a firm specific constant in the productivity process
Model:
Comparative Statics
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Lots of data sets • Each have their issues, which is why we use lots of them • For presentation, I’ll focus on US and WBES
Data:
Data and Measurement
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Measurement of productivity:
• Recall that
• Where beta’s sum to 0.75
• (log) TFPR is
• to get coeffs use
• and use the adding up constraint to get capital coeff
Data:
Data and Measurement
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Measurement of productivity:
• Recall that
• Where beta’s sum to 0.75
• (log) TFPR is
• to get coeffs use
• and use the adding up constraint to get capital coeff
• (and if you hate this, for Slovenia we use OLS estimates from Jan’s earlier work)
Data:
Data and Measurement
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Basic reduced form is that MPRK dispersion should be positively correlated with productivity volatility
• Hence:
Std(MRPK)
=
Constant +
Std( Δtfpr ) +
Year Dummies
Industry Level Analysis:
Reduced Form
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Industry Level Analysis:
Reduced Form
(same table, just bigger)
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Industry Level Analysis:
Reduced Form
US only
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Industry Level Analysis:
Reduced Form
Robustness
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Industry Level Analysis:
Reduced Form
Robustness
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Industry Level Analysis:
Reduced Form
Robustness
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Why capital?
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Industry Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Objective: to see how much of the differences in dispersion the model is capable of capturing
• What we need: • AR(1) on productivity process • Adjustment Costs • Sales generating function coeffs • discount rates, and depreciation rates which we lift from existing literature
• To get the AR(1) we estimate is from the productivity data, for each industry • Volatility is the sigma parameter
Industry Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Objective: to see how much of the differences in dispersion the model is capable of capturing
• What we need: • AR(1) on productivity process • Adjustment Costs • Sales generating function coeffs • discount rates, and depreciation rates which we lift from existing literature
• To get the adjustment costs we fit the model to the following moments (using GMM approach)
• proportion of firms with less than 5% y-o-y change in capital • proportion of firms with more than 20% y-o-y change in capital • Std(y-o-y change in log capital)
Industry Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Industry Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
US adj costs X US avg prod. coeffs X Only 1 period TTB 2x US adj costs
Industry Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
US adj costs X US avg prod. coeffs X Only 1 period TTB 2x US adj costs
Industry Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
US adj costs X X US avg prod. coeffs X Only 1 period TTB 2x US adj costs
Industry Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
US adj costs X X US avg prod. coeffs X Only 1 period TTB 2x US adj costs X
Industry Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
US adj costs X X US avg prod. coeffs X Only 1 period TTB X 2x US adj costs X
Country Level Analysis:
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Objective: to see how much of the differences in dispersion the model is capable of capturing at a cross-country level
• Basically the same analysis, but need a data set with a lot of countries
• Use the World Bank Enterprise Survey. • Good coverage of countries (33 we can use, all LDCs) • Small sample sizes in some countries
• we throw out anything with less than 25 obs, largest has just over 700 obs (Brazil)
• Time series created by asking managers “what was it like last year?”
• First, assess data quality (and model) by making sure it can replicate the reduced form analysis
• Then, do structural analysis
• Then look to see if AR(1) is related to anything observable
Country Level Analysis:
Reduced form
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Country Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Estimating the adjustment costs on all countries would be computationally expensive, and previous evidence from industry analysis suggests little or no return
• Hence we use the US adjustment costs.
Country Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Estimating the adjustment costs on all countries would be computationally expensive, and previous evidence from industry analysis suggests little or no return
• Hence we use the US adjustment costs.
• S^2 = .80 for WBES, S^2 = 0.9 for Tier 1
Country Level Analysis:
Structural
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Country Level Analysis:
What might generate productivity shocks?
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
Conclusion
1. Introduction 2. Model 3. Reduced Form 4. Structural 5. Cross-Country 6. Conclusion
Dynamic Inputs
• Dynamic nature of inputs capable of rationalizing the dispersion in MRPK
• How to interpret the dispersion differences?
• Welfare irrelevant? • if shock process is exogenous then firms acting much as the social planner would like them to
• Forget about distortions in capital allocations? • still room for static frictions to mess things up • IO guy is never going to claim that static frictions are not worthy of a policy intervention • less clear whether active reallocation is the answer
• Think carefully about policies that affect the shock process? • likely some room here for further thinking • evidence that firms respond to the predictability of their environment • to the extent that government can influence this, then it seems worth thinking about • note that the development literature (and others) have made progress here in micro studies