Based on an endogenous growth model, we show that intermediate goods markets imperfections can curb incentives to improve productivity downstream. We confirm such prediction by estimating a model of multifactor productivity growth in which the effects of upstream competition vary with distance to frontier on a panel of 15 OECD countries and 20 sectors over 1985-2007. Competitive pressures are proxied with sectoral product market regulation data. We find evidence that anticompetitive upstream regulations have curbed MFP growth over the past 15 years, more strongly so for observations that are close to the productivity frontier.

Based on an endogenous growth model, we show that intermediate goods markets imperfections can curb incentives to improve productivity downstream. We confirm such prediction by estimating a model of multifactor productivity growth in which the effects of upstream competition vary with distance to frontier on a panel of 15 OECD countries and 20 sectors over 1985-2007. Competitive pressures are proxied with sectoral product market regulation data. We find evidence that anticompetitive upstream regulations have curbed MFP growth over the past 15 years, more strongly so for observations that are close to the productivity frontier. Keywords: Productivity, Growth, Regulations, Competition, Catch-up. JEL classification: O43, L5, O57, L16, C23 RésuméEn s'appuyant sur un modèle de croissance endogène, nous montrons dans cette étude que les imperfections de marché dans les secteurs amont abaissent les incitations à améliorer la productivité en aval. Cette conjecture est confirmée empiriquement par l'estimation d'un modèle qui différencie les effets potentiels, sur la productivité globale des facteurs (PGF), d'une concurrence insuffisante dans les secteurs amont selon la distance à la frontière technologique sectorielle. Ces estimations sont réalisées sur un panel de 15 pays de l'OCDE et 20 secteurs d'activité sur la période 1985-2007. La concurrence en amont est mesurée par des indicateurs sectoriels de régulation sur les marchés des biens. Les résultats montrent que, sur les 15 dernières années, les régulations anticompétitives dans les secteurs amont ont affaibli les gains de PGF, tout particulièrement pour les observations proches de la frontière technologique.

This paper investigates the effects of the education level, product market rigidities and employment protection legislation on growth. It exploits macro-panel data for OECD countries. For countries close to the technological frontier, education and rigidities are significantly related to TFP growth. The contribution of the interaction between product market regulation and labour market rigidity seems particularly substantial.Keywords: Productivity; Growth; Regulations; Market Rigidities; Education JEL codes: O47; J24; J68; L40; 057 RésumésCette étude propose une analyse empirique des effets sur la croissance du niveau d'éducation et des rigidités sur les marchés des biens et du travail. Elle mobilise pour cela des données macroéconomiques sur un panel de pays de l'OCDE. Pour les pays proches de la frontière technologique, l'éducation et les rigidités de marchés sont corrélées positivement avec la croissance de la PGF. La contribution de l'interaction entre les rigidités sur le marché des biens et les rigidités sur le marché du travail apparaît importante.

THIS SUPPLEMENT contains proofs and additional results that complement the paper "Altruism in Networks." The first section studies the transfer cost minimization problem that underlies the potential maximization problem of the main paper, and uses it to derive additional properties of equilibrium transfers, and provide different proofs of some results. The generic uniqueness of equilibrium transfers is a consequence of this analysis. We also use this section to explain the connections between our altruistic transfer game and two classical transportation problems: the minimum-cost flow problem, and the MongeKantorovich optimal transportation problem. In the second section, we show convergence of best-response dynamics in the transfer game. In the third section, we look at conditions for the presence or absence of transfer intermediaries, and provide a proof of Theorem 2 of the paper. Finally, in the fourth section, we consider comparative statics with respect to initial income profiles and altruism. We prove the genericity result used in the proofs of the paper, and provide some additional comparative statics results. APPENDIX A: THE COST MINIMIZATION APPROACHIn this section, we analyze in detail the cost minimization problem and use it to draw connection with classical linear programming problems, and to prove generic uniqueness of equilibrium transfers. We also exhibit some additional properties of optimal transfer networks such as cyclical monotonicity. The presentation of the first results borrows from Galichon (2011). For an overview of the use of optimal transport methods in economics, see Galichon (2016).Recall that the maximization of the potential is related to the cost minimization problemwhere A = {(i j) : α ij > 0} is the set of arcs of the altruistic network α, and S(y) = {T ∈ S : ∀i y i = y 0 i − j t ij + j t ji } is a closed convex polytope since it is defined by a finite number of weak inequalities. Note that S(y) is unbounded if the altruism network α admits a directed cycle, since one can then indefinitely increase the transfers of any T ∈ S(y) along the cycle while still reaching y from y 0 . This problem is a classical linear programming problem known as the Minimum Cost Flow problem. Indeed, if each c ij is interpreted as the marginal transportation cost between i and j, this problem consists of minimizing transportation cost over the network of agents with the constraint of reaching distribution y from distribution y 0 . In network flow problems, a transfer profile T is called a flow, and we will sometimes use this terminology. A first useful result from the network flow literature (see Galichon (2011)) says that any flow can be decomposed into paths and cycles. Before we do that, we partition the set of agents into three sets: the set of net givers I G = {i : y i < y 0 i } (or sources), the set of net receivers I R = {i : y i > y 0 i } (or sinks), and the remaining agents. We let P ij be the set of paths between i and j in the altruism network, P = (i j)∈I G ×I R P ij , be the set of paths ...