Presentation on theme: "WP10 Linkages with Micro Data Overview, remaining work, and some new results Brussels, March 16-17, 2007 Eric Bartelsman The EUKLEMS project is funded."— Presentation transcript:
WP10 Linkages with Micro Data Overview, remaining work, and some new results Brussels, March 16-17, 2007 Eric Bartelsman The EUKLEMS project is funded by the European Commission, Research DG, 6th Framework Programme, Priority 8, "Policy Support and Anticipating Scientific and Technological Needs".
SC LMD EUKLEMS Longitudinal Micro Data National Accounts Industry Data Single country Macro and Sectoral Timeseries Surveys, Business Registers Multiple countries N.A. Micro-data linkages DMD EUKLEMS+ SC LMD+ Replicated LMD+
WP10 Objectives Integrate (existing) micro measures of within industry firm-level distributions with the EU-KLEMS data. Conduct empirical work using the augmented EU KLEMS data to explore recent theoretical models linking growth to firm dynamics and heterogeneity. Facilitate the analysis of productivity in work package 9 on technology and innovation by providing firm-level data Investigate potential to use firm level data in future updates of EU KLEMS Productivity Database.
EUKLEMS+: Integrating micro indicators Concordance are done Firm-level indicators are being documented/organized. Looking for pilot-projects: EUKLEMS+ Transition economies and Turkey (Bartelsman&Scarpetta) WP9 ??? Labor market flexibility
Links with WP 9 WP10 access to micro data may provide indicators for WP9 We may consider using expertise built up in: OECD - micro innovation project Eurostat/ONS ICT-firm performance project. This project uses linked micro datasets (PS, Ecommerce, R&D) and infrastructure at NSOs. Also EUKLEMS industry hierarchy is used.
Micro-data for updating EUKLEMS Many indicators, especially those outside of SNA framework, may be collected directly from linked micro datasets using distributed micro data analysis. Labor quality Investment by industry/asset Harmonized approach can coordinate technical details, eg sample reweighting. An inventory of available sources and metadata needs to be made Volunteer: someone with overview of WP2/3
Creative Destruction and Productivity in the Turkish Manufacturing Industry Eric Bartelsman Vrije Universiteit Amsterdam, Tinbergen Institute, IZA (joint with Stefano Scarpetta, OECD)
Small manufacturing firms, 1990s (firms with 10 to 20 employees as a percentage of total) FirmsEmployment Turkey25.383.59 Argentina45.9910.70 Brazil46.319.23 Chile27.504.96 Colombia28.704.74 Mexico39.975.90 Estonia36.077.21 Hungary34.884.71 Latvia39.737.66 Romania34.571.80 Slovenia22.471.84
Cross-country shift-share analysis of firm size Sectoral composition Average size of firms Interaction between sectoral comp. and sizeTotal Turkey0.010.080.030.12 Argentina0.07-0.35-0.05-0.34 Brazil-0.02-0.190.00-0.20 Chile0.00-0.03-0.05-0.08 Colombia0.03-0.070.02-0.03 Mexico0.06-0.030.020.05 Estonia-0.10-0.180.08-0.20 Hungary0.000.31-0.160.16 Latvia-0.10-0.07-0.02-0.20 Romania0.561.710.002.27 Slovenia0.031.08-0.220.89
Gross and net firm turnover: Transition Economies
Eric Bartelsman, John Haltiwanger and Stefano Scarpetta* *Vrije Universiteit Amsterdam, Tinbergen Institute and IZA; University of Maryland, NBER, and IZA; OECD and IZA. January 2007 Cross Country Differences in Productivity: The Role of Allocative Efficiency
Overview Healthy, market economies exhibit the following features at the firm level: Large dispersion of productivity across firms within narrowly defined sectors. Must be due to some form of friction Market power Economies of scope/decreasing returns Adjustment frictions and idiosyncratic shocks: Search and matching frictions Labor and capital adjustment costs Entry and exit costs Strong positive covariance between market share and productivity Static allocative efficiency Market selection is productivity enhancing Market selection yields exit of less productive firms and establishments High pace of input (e.g. jobs) and output reallocation Uncertainty, experimentation, learning and selection as well as time varying idiosyncratic shocks
Distortions and Allocative Efficiency? Working conjecture: Emerging and transition economies have market structure and institutions that distort allocative efficiency Distortions: Imperfect competition Subsidies (explicit) or quotas/rationing of allocation to insiders/incumbents/favored businesses Credit constraints to young and small businesses Bribes and corruption (unevenly applied) Doing Business inefficiencies: Costly to start up a business Costly to adjust employment Poor or inefficient infrastructure (telephones, roads, electricity) Key point: Distortions have idiosyncratic component: cross-sectional allocation is distorted Entry barriers act in this way naturally Credit constraints for young and small businesses Arbitrary and capricious application of regulations
Aggregate productivity and allocation Olley and Pakes (1996) static decomposition: where: N: # of firms in a sector; The first term is the unweighted average of firm-level productivity The second term (OP cross term) reflects allocation of resources: do firms with higher productivity have greater market share. Requires representative cross sectional samples but does not require accurate longitudinal linkages Cannot quantify directly importance of entry and exit By construction, cross term takes out country effects in productivity levels, so abstracts from some aspects of measurement error
Allocative efficiency (OP cross term): The gap between weighted and un-weighted labor productivity, 1990s
A Model of Mis-Allocation (Based on Rogerson and Restuccia (2003) (and similar to Hsieh and Klenow (2006)) Consumers supply labor inelastically and maximize utility: Firms maximize profits: Ex ante firms do not know productivity or distortion but know distribution. Pay entry fee, learn their draws, decide whether to produce. Productivity and distortion draws are firm-specific and time invariant Very low productivity/high distortion firms dont produce Decreasing returns yields dispersion in productivity/distortions Overhead labor yields correlation between TFP and LP
Entry/Selection Ex Ante Joint Distribution Exogenous probability of exiting in each period given by λ
Aggregate Relationships and Steady State Equilibrium μ(A,τ,) is ex post distribution E is aggregate entry
Note: All reported statistics are at steady state equilibrium reflecting selection. γ = 0.9, λ =.10, this is consistent with evidence of exit rates in the United States and other OECD countries (Bartelsman et al. 2004) R=.03 and δ=.12, roughly consistent with long run real interest rates and depreciation rates in OECD countries. f=.01,log(c e )=12.43 Ex ante A distribution: mean(log(A))=10.57, std(log(A))=0.35 Preliminary Calibration and Numerical Analysis of Model CaseMean log(LP) Std log(LP) OP cross term log(LP) Mean log(A) Std log(A) OP cross term log(A) Avg(K/L)Diff log(cons) Fraction survive No distortion12.050.060.0510.720.270.431141840.000.77 Random output distortion 12.150.25-0.1510.740.300.38180224-0.350.44 Random capital distortion 12.140.060.0510.740.260.42453316-0.630.72 Correlated output distortion 11.720.35-0.1010.490.320.16108946-0.580.64
Relationship Between Productivity and Employment: No Distortions
Relationship Between Labor Productivity and Employment (Random distortion case)
Observations Distortions affect various margins Entry: amount and characteristics Allocation among survivors OP cross term reflects allocation for given survivors Uncorrelated scale distortion Slight increase in mean TFP/LP Lower survival Lower OP cross term for both TFP and LP Affects entry and selection Lower consumption (C) through lower productivity and excess churn
Observations (2) Uncorrelated factor-mix distortion Slight increase in Mean TFP and LP again Lower survival Not much impact on OP cross term for either TFP and LP K/L too high Lower C primarily through distorted high capital investment Correlated scale distortion Mean TFP/LP much lower OP cross term very low Adverse impact on survival So: wrong firms survive, allocation poor amongst survivors and too much churn (everything wrong!) Much lower C from all of these factors
Open questions Can we account for differences across countries in productivity via the distribution of distortions? If so, what are these distortions? Static vs. Dynamic distortions? Is wedge between marginal product and factor prices because of adjustment costs (dynamic) or a more permanent distortion (e.g., favored businesses) Can we quantify the reduction in distortions in transition economies that account for rising allocative efficiency? Implications for growth: transition dynamics vs. steady state growth? Missing pieces (many): E.g., Differentiated products version of model More frictions? (Experimentation/learning/adjustment costs)
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