Zugl.: Tübingen, Univ., Diss., 2006

Zugl.: München, Universiẗat der Bundeswehr, Diss., 2008. / Includes bibliographical references (p. 289-308).

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My dissertation within macroeconomics puts special emphasis on uncovering the effects of market power within product and labor markets. I conduct these studies using novel empirical techniques and detailed granular data sets at the firm- and household-levels.In the first chapter, coauthored with Shruti Mishra, we consider how firms’ price-setting decisions are affected by the properties of their markup. We start by designing a general oligopoly framework that accounts for firm heterogeneity, firm granularity, and the effects of market share distribution. We use this structural model to decompose the effect of price on the quantity demanded into a direct price effect and an indirect effect coming from the impact of the market-level aggregates, such as market-level price. This decomposition allows us to take care of all the degrees of heterogeneity in a flexible manner. Under plausible assumptions, the most crucial of which we test in the data, all the information about the distribution of shares within the market will be accounted for by the variation of the market aggregates. Under these conditions, we can estimate the structural parameters that do not depend on the distribution of shares within the market. We use the model to inform our empirical strategy and apply it to the ACNielsen Retail Scanner Data. We test the assumptions put forward by the theory, estimate structural parameters and then use the decomposition formulas to calculate the elasticity of the firm’s demand and other parameters important for the markup variation. We find that elasticity depends sharply on the firm’s market share and decreases significantly as market shares increase. There is a positive dependence of demand elasticities on relative prices (superelasticity), in line with Marshall’s second law of demand. Additionally, elasticity depends on the levels of competitiveness within the market. Even if a firm’s market share stays the same, its elasticity decreases if the market becomes less competitive. Lastly, we apply our estimates to calculate the optimal pass-through of marginal costs into prices and strategic complementarity. We find that an individual firm’s pass-through is contained between zero and one, but depends sharply on the firm’s market share. We find that strategic complementarity between two firms depends on both of their shares and is not symmetric so the degree of strategic complementarity between a small and a large firm, between two small firms, between two large firms, or between large and small firms would all be different. We then assess the non-linear effects of the marginal cost shock on the price and find that pass-through depends positively on the size of the marginal cost shock. This means that the total effect of marginal cost shock on prices is non-linear and that firm prices are more responsive to marginal cost increases than to marginal cost decreases. For market leaders, the pass-through of a large negative marginal cost shock would be close to zero, while the pass-through of a large positive marginal cost shock would approach that of small firms. In the second chapter, coauthored with Rui Mano, Yannick Timmer, and Anke Weber, we study the effect of the firm granularity in the labor market on their hiring decisions. We argue that prevalence of firms controlling large vacancy shares plays an important role in the transmission of monetary policy to labor demand and wage growth and can partially explain the flattening of the wage Philips curve after the GFC. Accommodative monetary policy raises the marginal product of labor, incentivizing all firms to hire more. However, since the wage elasticity of labor demand is lower for high vacancy share firms, they can hire more workers without raising wages disproportionately. We study this effect in the Burning Glass Technology vacancy microdata and, consistently with this mechanism, show that accommodative monetary policy increases labor demand more for high vacancy share firms and that this comes without a disproportionate response in wages. In aggregate, this implies that due to the presence of firms controlling large vacancy shares, accommodative monetary policy can lead to a decline in the unemployment rate that is decoupled from an increase in wage growth. Quantitatively, a firm at the 50th percentile of vacancy share distribution increases its labor demand by ≈ 7% in response to a 10 basis point surprise monetary loosening while a firm at the 95th percentile of the vacancy share distribution increases labor demand by ≈ 9%. Moreover, the effect of monetary policy shocks on firms with high vacancy share is much more persistent, with effects economically large and statistically significant at least for eight quarters. At the same time, there is no comparable differential response of wages, so even though firms with high vacancy shares hire more, they don’t have to increase their wages by more. In this case, more hiring does not result in a comparable increase in wage inflation. This channel can partly explain the flattening of the wage Phillips curve and the “wage-less” recovery after the Global Financial Crisis.In the third and last chapter, coauthored with Shruti Mishra, we study the impact of wealth heterogeneity on labor supply decisions. In the standard model, the positive wealth effect should decrease the willingness to supply labor. In the macroeconomic setting, this means that the direction and the magnitude of the wealth effect will determine whether people search for jobs more actively after a monetary intervention. For example, if unemployed consumers are indebted, they experience a negative wealth effect after a monetary contraction, search for jobs more actively and increase their probability of finding a job, therefore, reducing the total unemployment response. The sign and magnitude of the overall effect of monetary policy on unemployment will therefore depend on whether unemployed consumers are indebted and the magnitude of their debt. To study this mechanism, we develop a theoretical framework with heterogeneous consumers and employment search efforts and then decompose the effect of the monetary policy shock on aggregate unemployment. We test the prediction of the model in both micro and aggregate data. To test the prediction of the model in the aggregate, we estimate the coefficient of the interaction term between the debt-to-income ratio and Romer and Romer monetary policy shock. For the microdata, we use a similar regression with unemployment and mortgage variables for individual consumers from the PSID panel dataset. Consistently with the proposed mechanism, we find that the intuitive negative effect on employment of the monetary contraction is virtually non-existent or even reversed for indebted consumers. The three chapters together paint a complex picture of the impact of market power on macroeconomic variables. First, product market power impacts price-setting decisions of the firms and affects the dynamic of prices and inflation, effectively leading less concentrated economies to behave as if they have more flexible prices. Second, firms that control large share of vacancies in their labor market conduct hiring differently from their smaller counterparts leading to more quantity expansion. Lastly, labor markets exhibit complex supply dynamics as well, with labor supply potentially intensifying during recessions, which might lead the bargaining power of firms to become countercyclical. All these effects hold first-order significance for macroeconomic dynamics and influence our ability to project the future or asses the effects of monetary policy.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.