Market integration and competition Jan De Loecker Princeton University and KU Leuven NBER and CEPR World Bank Poverty Summer University July 21, 2017
Talking points Short overview on nexus competition-trade-firm performance (my view of course) Focus on competition (or lack thereof, e.g. abuse market power, such as cartels, price fixing, etc.) Pass-through of tariffs, integration, etc. Sources of incomplete pass-through: upstream/downstream vs demand Identify frictions in LDCs, using actual data on candidate drivers of frictions.
Introduction and background Large literature on firm performance and competition broadly defined spanning IO, macro, trade and development. Interest in impact of change operating environment both in a domestic and international setting. E.g., 1.9 mln google scholar papers on productivity and trade. Requires estimating production functions to recover measures of productivity or at least this is the practice. Today I will be concerned with a more careful treatment of what this productivity measure picks up; and what we can learn from it.
A general framework De Loecker and Goldberg (2014) provides general framework nesting empirical work; allowing for variation in prices (output/input), and markups in addition to standard productivity. The main insight: distinct literatures on pass-through, productivity and market power, are connected through the relationship of sales to expenditures: p it = γ it c it. Focus on trade liberalization shocks as a shock to competition and cost. Clean setting to exploit plausible exogenous variation to residual demand and cost curves. The underlying premise is to not commit to any particular demand system and a model of competition.
A framework for measuring performance Consider firm performance (π it ) as the residual of: s it = e itβ + π it (1) The substantive question of interest: competition and firm performance: through cost and market access. Exploiting exogenous shocks to producers, explains focus on recent drastic changes in removal trade barriers (China, India, Latin-America, CEECs, etc.). Fundamental issue: do we recover productive efficiency? Competition/cost shocks will affect prices both output/input. Bottom line: the underlying pass-through parameter γ it is a fundamental parameter.
Impact of globalization When countries open up to foreign markets: 1. output prices (change residual demand curve i.e. competition), 2. input prices (change marginal cost curve), 3. scale effects (depends on share SR/LR cost curve), 4. productivity effects: 4.1 Active: investment, technology, R&D, etc. (costly $ and time), 4.2 Passive: so-called X-inefficiency. 5. extensive margin (product entry/exit) Framework to separately identify requires all components; otherwise we obtain incorrect view on impact of say increased competition (i.e. import competition). Until recently, predominant framework only considers 4.2.
The basic problem Basic identities (in logs) s q + p (2) e h x h + z h (3) Homogenous good producing perfectly competitve industry with CRS Cobb-Douglas production (q = x α + ω), with perfectly competitive input markets: Deflating appropriately leads to π = ω. p t + q it = (x it + z t ) β + π it (4) In fact β L = wl PQ and β K = 1 β L and ω can be computed and analyzed the so-called factor share approach (advantage robust to measurement error in output and input use median)
Relaxing market structure and homogeneity leads to: π = π(ω, p, z) More structure needed to identify impact of a shock. The input price z: actual input price variation (e.g. geography) and input quality variation (product differentiation).
Cases 1. Case 1: no price variation 2. Case 2: price variation: 2.1 Output price variation 2.2 Input price variation 2.3 Output and input price variation
Case 1: Strong assumptions Consider s it = e it β + π it assume neither output nor input price variation across firms. OLS is biased due to well known simultaneity and selection biases focus of the literature (OP/LP/ACF/GRN). Treatment of the unobserved productivity shock is not independent of the unobserved output and input price problem consistent framework is crucial. In practice, both price and productivity variation will most likely play an important role, and as a consequence ignoring (unobserved) prices in the estimation of production functions will lead to biased results; and vice versa.
Case 2: price variation Structural error π it contains the output price (p it ) and the vector of input prices (z it ). Sales and expenditure data does not deliver an estimate of productivity, nor will it deliver the vector of production function coefficients (α vs β). To see bias rewrite standard deflated PF: sit = eit α + ω it + pit zit α (5) = (x it + zit ) α + ω it + pit zit α, where denotes deflated values, i.e. z it = z it z t.
Case 2.a.: Output price variation (z it = 0) Standard case in IO, which assume that input prices are equalized across firms (cond geography), but product differentiation leads to price variation. Output price variation p it is correlated with e it (which is equal to x it in this scenario) Ceteris paribus, E(xp) < 0: downward bias of α and RTS output price bias. Recent work on bilateral oligopoly would make input prices result of bargaining of some sort, further highlighting connection. (See e.g. Crawford and Yurugoklu and follow-up work).
Case 2.b: Input price variation (p it = 0) Irrelevant in practice, but highlights observing prices is not solution. Lead to a strong negative bias in the estimated coefficients: Firm facing higher input prices will have higher input expenditures that will not lead to higher physical output input price bias. Close to no attention in the literature so far, except DLGKP. Direct evidence for failure: q it = e itβ + ɛ it, (6) yields non-sensical estimates like negative labor coefficient: role of quality/differentiation.
Recap Conclusion: The message so far is that with sales and expenditure data alone, one cannot, in general, recover the underlying components of firm performance or identify productivity. Solution: As always in empirical work, there are two ways out of such a situation: 1. either one collects more data, 2. or one makes additional assumptions that will allow identification.
More data are always preferable, but... Output prices: available but often unit values and associated problems Output prices recorded at the product level, calls for product-level PF but input expenditures are only recorded at the firm level. Multi-product production functions for is not possible unless one adopts one of three approaches: 1. eliminate MP firms [large share activity] 2. aggregate product prices to the firm level [requires specific demand system] 3. devise a input allocation mechanism [requires demand system/market structure].
And now what? More data do not eliminate the need for assumptions and structure see e.g, FHS (2008). Focus on homogeneous product industries leaves us with the bulk of economic activity unaccounted for. The set of industries characterized by substantial product differentiation comprises a large share of economic activity: differentiated products require differentiated inputs, so that we would expect input prices to vary across firms, even when these firms are located in the same region and even when input markets are perfectly competitive.
Conclusion so far While the use of additional data on prices can improve on certain aspects of estimation and identification, the need to introduce assumptions remains. The question is not whether or not one needs assumptions, but which set of assumptions is less restrictive.
Pass-through, market power and productivity Three seemingly unrelated literatures measure the very same relationship between prices and cost of production. To see this, let us start out with the often specified production function and we rewrite it to reflect the practice of using deflated variables: q = x β x x + ω (7) q + p = x β x x + ω + p β x w x x }{{} tfp (8) where we denote the various inputs by x and the corresponding input prices by w x such that x = x + w x.
Three cases Let only change in industry be output tariff (residual demand elasticity) or input tariff (cost), and technically CRS. and remember tfp = ω + p x β xw x. p = γ 0 + γ w x + ɛ p (9) tfp = α 0 + α τ + ɛ ω (10) Literature considers w and τ to be exchange rate, tax, service fees, tariffs and interested in pass-through.
Stylized classification Output markets global local Input markets global local Globalization process effects industries/regions differently along these dimensions. Output-input market interaction raises old issues such as Trade and Competition policy: substitutes or complements.
Three stylized case to highlight bias in E(tfp) τ = α 1. Local industry with imported inputs: ˆα = E( ω) τ 2. Global industry with local inputs: ˆα = E( ω) τ 3. Global industry with global inputs: ˆα = E( ω) τ + E( p) w 1. + E( p) τ + E( p) τ Large share of economic activity in global markets, with continuous borders. In contrast to tradition in IO (BR), and fully segmented markets in open macro.
Towards a solution: Production Approach De Loecker and Warzynski (2012) show how to obtain markups (marginal cost) uniquely relying on cost minimization wrt a variable input of production h. This leads to markup (µ = p/c) µ = θ(h) PQ E(h) (11) Key obs: no assumption on conduct, demand or market structure Intuition: PQ wl cq wl. Marginal cost recovered point-wise through price data.
Multi-product & Differentiation: DLGKP 2016 Challenge: output elasticities with output/input price heterogeneity and unobserved productivity shocks, with multi-product production. Main idea: using price and quantity data at product level, estimate physical output elasticities using double control function: 1. Control for productivity through inverting input demand equation, ω = ω(l, m, k, z). 2. Control for input prices through inverting general demand equation: ν = ν(p, ms, a). Apply single-product technology to multi-product and recover input allocation (economies of scope). Delivers θ and input allocations for product specific expenditures.
Illustration Consider simple vertical model of demand: price sufficient statistic of quality: q it = e itα + ν(p it ) + ω it (12) For markups µ and marginal costs mc apply De Loecker-Warzynski: µ = α h S E h (13) mc = p µ (14) DLGKP go on an do this for multi-product firms and recover input allocation allowing for economies of scope and extensive margin adjustments.
Recent applications Import competition and trade reforms: India and Belgium, De Loecker, Goldberg, Khandelwal and Pavcnik (2016, ECMA), DLGKP hereafter. De Loecker, Fuss and Van Biesebroeck (2015, in progress), DLFVB hereafter.
Average Tariffs Trade reforms in India India s Trade Liberalization 1.2 1 0.8 0.6 0.4 We exclude data after 1997 0.6 0.5 0.4 0.3 0.2 Std. Dev Tariffs 0.2 0.1 0 0 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 Average Tariffs Std Deviation Tariffs Source: Topalova (2007) How were firms affected? DLGKP develop a framework to recover markups, marginal costs and productivity for each product/firm/year observation and then relate this to both: output and input tariffs.
A stylized picture of market power and trade reforms Panel A: Residual demand channel Panel B: Cost channel Price& Price& P0& P0& μ0& μ1& P1& mc0& μ0& μ1& P1& mc0& Q1& Q0& MR1& D1& MR0& D0& Quan+ty& Q0& Q1& MR0& mc1& D0& Quan+ty& Instruments: output and input tariffs for demand and cost. Main Result: At estimated parameters, input tariff effects dwarf output tariffs effect, and lead to large changes in marginal costs that are almost fully turned in markup increases.
Change in prices Density 0.5 1 1.5 2 2.5 Distribution of Prices -.5 0.5 Log Prices 1989 1997 Sample only includes firm-product pairs present in 1989 and 1997. Observations are de-meaned by their time average, and outliers above and below the 3rd and 97th percentiles are trimmed.
Change in markup and mc distribution Distribution of Marginal Costs Density 0.5 1-2 -1 0 1 2 Log Marginal Costs 1989 1997 Sample only includes firm-product pairs present in 1989 and 1997. Observations are de-meaned by their time average, and outliers above and below the 3rd and 97th percentiles are trimmed. Density 0.5 1 1.5 Distribution of Markups -2-1 0 1 2 Log Markups 1989 1997 Sample only includes firm-product pairs present in 1989 and 1997. Observations are de-meaned by their time average, and outliers above and below the 3rd and 97th percentiles are trimmed.
Distributional implications Owners of capital (short run, discuss) Consumers Example: clothing and textiles in EU market.
Belgian firms and Chinese import competition Popular notion that Chinese import competition hurts firms. Similar framework is used by DLFVB to study impact of increased imports from China on Belgian manufacturing firms. 0.08 0.07 0.06 0.05 0.04 Average StDev 0.03 0.02 0.01 0 1995 1997 1999 2001 2003 2005 2007 2009
Firm performance and import competition We evaluate impact of CIC on measures of firm performance (y), allowing for decomposition of response. ln y ist = α i + α s + α t + α p z s t + ɛ ist (15) TFPR TFPQ Price Markup MC MC CIC -0.888-0.166-0.722*** 0.208** -0.930*** -0.684** TFPQ -0.957*** Own wage 0.305*** Observations 52,192 52,192 52,192 52,192 52,192 52,192 R-squared 0.992 0.966 0.967 0.728 0.967 0.998 Points to input channel and reduced input prices of equipment and intermediates i.e., mc = f (w, ω, Q). Evidence for market-based effects: upstream input prices adjust, leading to cost reductions downstream w/o imports.
Pulling results Impact of increased global competition for firms is nuanced i.e., same shocks impacts prices through direct competitive effect cost of production through input markets Total impact depends on demand/market structure or pass-through Heterogeneity in output and input prices prevent detecting fundamental mechanism behind growth - both at the micro and macro level i.e. through reallocation (otherwise aggregate PPI works fine). Π t = i s it π it (16) = π t + cov (s it, (ω it + p it z it )) (17)
A call for IO in development/trade/productivity Market power is pervasive in development economies, perhaps even more due to weak institutions and lack of enforcing competition policy. (recent report World Bank) Traditional focus on households needs to be complemented with careful analysis of producers and presence of market power, to better understand drivers of growth. Issues like pass-through, market power need much more central role in understanding lack of growth, etc. Nothing new: Levinsohn (1993) study of market power.
Role for IO economists? IO and work in LDCs: challenges 1. Data, data, data,... IO and work in LDCs: comp. advantages 1. Empirical frameworks and methods to tackle market power in complicated setting 2. Reduced form and counterfactual simulations in toolkit 3. Pay great deal of attention to institutional details
Market power and misallocation Recent macro-development literature keeps talking about so-called frictions but ignores the obvious one. Example of Indian trade reforms by DLGKP is telling; or Trash pick-up cartel in Dakar, and many more. Data collection efforts should keep it in mind and focus of research. Data suggests cartels/barriers in key industries: food, infrastructure, transportation and for poor consumers. Recent study by Atkin and Donaldson (2016) using local price data for sample of African countries, find markups increase with distance to origin, suggesting market power in supply chain.
A start... P i MP i (1 + η 1 i ) = w i (18) Implications towards policies at best dubious. Conclusions drawn from highly stylized models with homogeneous parameters of production, demand and absence of dynamics in inputs. Asker, Collard-Wexler and De Loecker (2015) point on volatility and adjustment cost. Focus on market power: real variation in residual demand elasticity across firms and implications for potential growth due to detecting market power and figuring out where it is going wrong and under which conditions. Talk about oil market.
If interested, here are papers underlying my talk De Loecker and Goldberg, 2014, Annual Review of Economics De Loecker and Warzynski, 2012, American Economic Review De Loecker, Goldberg, Khandelwal and Pavcnik, 2016, Econometrica De Loecker, Fuss and Van Biesebroeck, 2015, in progress