Diss. Uppsala : Uppsala universitet, 2006.

Doctorat en sciences sociales, politiques et économiques / info:eu-repo/semantics/nonPublished

While the phenomenon of informality in labor markets is pervasive in many parts of the world, its interaction with the aggregate behavior of economies is not well understood. In this dissertation, I explore the connection between informality and the macroeconomy in two main ways. The first way is to augment a search-and-matching model of labor markets in the tradition of Mortensen and Pissarides (1994) with aggregate shocks and an informal sector. The second is to consider an Aiyagari (1994) setting in which the existence of an informal sector feeds back into the labor income risk and savings decisions of heterogeneous agents. The parameters of both models are chosen so as to match features of micro-data I obtain from Brazil. This dissertation is thus divided into three chapters: the first one presents the data and findings from the empirical exploration. The second chapter describes the model of informality over the business cycle and presents its results. The third chapter introduces the heterogeneous agents model with informality and the conclusions derived therefrom. The first chapter divides the empirical analysis into two components. Firstly, I analyze how informality is distributed over education, income and occupational groups, and how formal-informal income differentials behave over these categories. I find that informality decreases in average income, and that the formal-informal income differential is higher among low income workers. The second component pertains to the evolution of informality over time. I show that, in the time period covered by the data, the rate of informality has a strongly cyclical pattern, which is mostly explained by cyclical variation in formal job creation. In the second chapter, in co-authorship with Livio Maya, we show in a parsimonious model of business cycles and informal labor markets that the differential risk of formal and informal contracts plays a potentially important role in generating the patterns of job creation found in the data. The main finding is that generating substantial countercyclicality in the informality rate in our calibration requires the price of risk to be highly countercyclical. In the third chapter, also in co-authorship with Livio Maya, we show the transition path of a policy designed to fight informality in a heterogeneous agents setting. The main finding is that while eliminating the informal sector makes the economy more productive and reduces unemployment in the long run, the short term impact is influenced by general equilibrium effects. In particular, unemployment increases in the short run due to the impact of the policy on interest rates. Moreover, the effects of such policy are sensitive to the assumptions on the destination of the extra tax revenues derived from increased formalization in the transition path.

This dissertation revolves around robustly measuring and using the information sets of the centralbank and financial markets in order to measure exogenous monetary policy. Modern central banks aggressively use all the available information at their disposal to effectively set monetary policy. This problem of “foresight” renders traditional time series methods ineffective; the information edge of central banks is too large. In the first chapter, I discuss refinements to existing narrative methods, which attempt to the central bank’s own forecasts to capture the information set of the central bank, thus removing their information edge over the econometrician. In the second chapter, I explore how the information sets of financial agents differ central banks and show that there is little direct information transfer between central banks and financial markets around monetary policy actions. Finally, the third chapter details how to use the information sets of financial sector actors to estimate exogenous monetary policy actions that is robust to financial sector revisions about the economy which can be due to the monetary policy actions.

This dissertation studies the role of occupation-specific human capital in explaining the long-run decline in labor market dynamics observed in the United States for the past four decades. Chapter 1 presents empirical facts on labor market outcomes by required occupation-specific training. This is to provide evidence that (i) required length of occupation-specific training is a proxy for the specificity of human capital to perform the occupation and that (ii) increasing occupation specificity has led to the decline in labor market dynamics. First, I find from the Dictionary of Occupational Titles and O*NET that for the past four decades, within occupations, there has been an increase the amount of time needed to become trained in the occupation. I then find from the Survey of Income and Program Participation that the average wage loss experienced by occupation switchers after unemployment increases when their occupation held before unemployment has faced over time an increase in occupation-specific training. I take this as evidence that the observed increase in occupation-specific training over time has made human capital less transferable across occupations. I then proceed to use the Monthly Current Population Survey, combined with the required length of occupation-specific training by occupation from the Dictionary of Occupational Titles and O*NET, to do a shift-share decomposition of the decline in labor market outcomes. The decline in the aggregate job separation rate and the increase in unemployment duration is accounted for mostly by the increase in specific training within occupations. Motivated by my empirical analysis, in Chapter 2, I then build a search-and-matching model to learn how the increase in specificity within occupations explains the decline in the aggregate job separation rate. The main ingredients are endogenous job separations and occupation-specific human capital that workers acquire during employment and lose when they switch occupations. My model has two occupation specificity parameters: (i) the average duration of occupation-specific training and (ii) the output gap by which nontrained workers are less productive because they have not yet acquired the occupation-specific capital. To ask my model how much of a decline it predicts in the aggregate job separation rate when occupations become more specific, the occupation specificity parameters in the model are increased to match the increase in occupation specificity in the data. The increase in the average duration of occupation-specific training matches the required length of occupation-specific training from the Dictionary of Occupational Titles and O*NET. The increase in the output gap is informed by the estimated increase in the wage penalty faced by occupation switchers (relative to non-occupation switchers) when their previously held occupation requires more occupation-specific training, obtained from the Survey of Income and Program Participation. The model predicts 60% of the decline in the aggregate job separation rate. Chapter 3 relaxes the assumption that occupation switching is exogenous in Chapter 2, endogenizing occupation switching in addition to job separations. The model predicts a greater increase in the average unemployment duration in line with the data. In the model, the longer unemployment spells are due to the unemployed trained workers, whose human capital has become more specific to their previous occupation, choosing not to switch occupations. If they switch occupations, they could quickly end their unemployment spell. This would however come at the cost of larger wage cuts because their human capital has become less transferable to a different occupation. Occupation switchers would also have to earn these lower wages for a longer period of time until they become trained in their new occupation. Hence, despite a low probability of getting reemployed in the same occupation as before, previously trained workers increasingly choose not to switch occupations, which increases the average unemployment duration.

This dissertation studies information economics in strategic and decision settings. In Chapter 1, I introduce flexible endogenous monitoring into dynamic moral hazard. A principal can commit to not only an employment plan but also the monitoring technology to incentivize dynamic effort from an agent. Optimal monitoring follows a Poisson process that produces rare informative signals, and the optimal employment plan features decreasing turnover. To incentivize persistent effort, the Poisson monitoring takes the form of "bad news'' that leads to immediate termination. Monitoring is non-stationary: the bad news becomes more precise and less frequent. In Chapter 2, which is joint work with Qingmin Liu, we analyze a model of strategic exploration in which competing players independently explore a set of alternatives. The model features a multiple-player multiple-armed bandit problem and captures a strategic trade-off between preemption---covert exploration of alternatives that the opponent will explore in the future---and prioritization---exploration of the most promising alternatives. Our results explain how the strategic trade-off shapes equilibrium behaviors and outcomes, e.g., in technology races between superpowers and R&D competitions between firms. We show that players compete on the same set of alternatives, leading to duplicated exploration from start to finish, and they explore alternatives that are a priori less promising before more promising ones are exhausted. In Chapter 3, I study how a forward-looking decision maker experiments on unknown alternatives of spatially correlated utilities, modeled by a Brownian motion so that similar alternatives yield similar utilities. For example, a firm experiments on its size that yields unknown, spatially correlated profitability. Experimentation trades off the opportunity cost of exploitation for the indirect inference from the explored alternatives to unknown ones. The optimal strategy is to explore unknown alternatives and then exploit the best known alternative when the explored becomes sufficiently worse than the best. The decision maker explores more quickly as the explored alternative worsens. My model predicts the conditional Gibrat's law and linear relation between firm size and profitability.

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.

This thesis studies four independent resource allocation problems with different assumptions on information available to the central planner, and strategic considerations of the agents present in the system. We start off with an online, non-strategic agents setting in Chapter 1, where we study the dynamic pricing and learning problem under the Bass demand model. The main objective in the field of dynamic pricing and learning is to study how a seller can maximize revenue by adjusting price over time based on sequentially realized demand. Unlike most existing literature on dynamic pricing and learning, where the price only affects the demand in the current period, under the Bass model, price also influences the future evolution of demand. Finding arevenue-maximizing dynamic pricing policy in this model is non-trivial even in the full information case, where model parameters are known. We consider the more challenging incomplete information problem where dynamic pricing is applied in conjunction with learning the unknown model parameters, with the objective of optimizing the cumulative revenues over a given selling horizon of length 𝑻. Our main contribution is an algorithm that satisfies a high probability regret guarantee of order 𝑚²/³; where the market size 𝑚 is known a priori. Moreover, we show that no algorithm can incur smaller order of loss by deriving a matching lower bound. We then switch our attention to a single round, strategic agents setting in Chapter 2, where we study a multi-resource allocation problem with heterogeneous demands and Leontief utilities. Leontief utility function captures the idea that for certain resource allocation settings, the utility of marginal increase in one resource depends on the availabilities of other resources. We generalize the existing literature on this model formulation to incorporate more constraints faced in real applications, which in turn requires new algorithm design and analysis techniques. The main contribution of this chapter is an allocation algorithm that satisfies Pareto optimality, envy-freenss, strategy-proofness, and a notion of sharing incentive. In Chapter 3, we study a single round, non-strategic agent setting, where the central planner tries to allocate a pool of items to a set of agents who each has to receive a prespecified fraction of all items. Additionally, we want to ensure fairness by controlling the amount of envy that agents have with the final allocations. We make the observation that this resource allocation setting can be formulated as an Optimal Transport problem, and that the solution structure displays a surprisingly simple structure. Using this insight, we are able to design an allocation algorithm that achieves the optimal trade-off between efficiency and envy. Finally, in Chapter 4 we study an online, strategic agent setting, where similar to the previous chapter, the central planner needs to allocate a pool of items to a set of agents who each has to receive a prespecified fraction of all items. Unlike in the previous chapter, the central planner has no a priori information on the distribution of items. Instead, the central planner needs to implicitly learn these distributions from the observed values in order to pick a good allocation policy. Additionally, an added challenge here is that the agents are strategic with incentives to misreport their valuations in order to receive better allocations. This sets our work apart both from the online auction mechanism design settings which typically assume known valuation distributions and/or involve payments, and from the online learning settings that do not consider strategic agents. To that end, our main contribution is an online learning based allocation mechanism that is approximately Bayesian incentive compatible, and when all agents are truthful, guarantees a sublinear regret for individual agents' utility compared to that under the optimal offline allocation policy.

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