We consider a nonlinear pricing environment with private information. We provide profit guarantees (and associated mechanisms) that the seller can achieve across all possible distributions of willingness to pay of the buyers. With a constant elasticity cost function, constant markup pricing provides the optimal revenue guarantee across all possible distributions of willingness to pay and the lower bound is attained under a Pareto distribution. We characterize how profits and consumer surplus vary with the distribution of values and show that Pareto distributions are extremal. We also provide a revenue guarantee for general cost functions. We establish equivalent results for optimal procurement policies that support maximal surplus guarantees for the buyer given all possible cost distributions of the sellers.
The 1996 US welfare reform introduced time limits on welfare receipt. We use quasi-experimental evidence and a lifecycle model of marriage, divorce, program participation, labor supply and savings to understand the impact of time limits on behavior and well-being. Time limits cause women to defer claiming in anticipation of future needs, an effect that depends on the probabilities of marriage and divorce. Time limits cost women 0.5% of life-time consumption, net of revenue savings redistributed by reduced taxation, with some groups affected much more. Expectations over future marital status are important determinants of the value of the social safety net.
Considerable evidence in past research shows size distortion in standard tests for zero autocorrelation or cross-correlation when time series are not independent identically distributed random variables, pointing to the need for more robust procedures. Recent tests for serial correlation and cross-correlation in Dalla, Giraitis, and Phillips (2022) provide a more robust approach, allowing for heteroskedasticity and dependence in un-correlated data under restrictions that require a smooth, slowly-evolving deterministic heteroskedasticity process. The present work removes those restrictions and validates the robust testing methodology for a wider class of heteroskedastic time series models and innovations. The updated analysis given here enables more extensive use of the methodology in practical applications. Monte Carlo experiments confirm excellent finite sample performance of the robust test procedures even for extremely complex white noise processes. The empirical examples show that use of robust testing methods can materially reduce spurious evidence of correlations found by standard testing procedures.
We provide evidence of the role of community networks in emergence of Indian entrepreneurship in early stages of cotton and jute textile industries in the late 19th and early 20th century respectively, overcoming lack of market institutions and government support. From business registers, we construct a yearly panel dataset of entrepreneurs in these two industries. We find no evidence that entry was related to prior upstream trading experience or price shocks. Firm directors exhibited a high degree of clustering of entrepreneurs by community. Consistent with a model of network-based dynamics, the stock of incumbent entrepreneurs of different communities diverged non-linearly, controlling for year and community fixed effects.
We model the world economy as one system of endogenous input-output relationships subject to frictions and study how the world's input-output structure and world's GDP change due to changes in frictions. We derive a sufficient statistic to identify frictions from the observed world input-output matrix, which we fully match for the year 2011. We show how changes in internal frictions impact the whole structure of the world's economy and that they have a much larger effect on world's GDP than external frictions. We also use our approach to study the role of internal frictions during the Great Recession of 2007–2009.
We examine the evolutionary selection of attitudes toward aggregate risk in an age structured population. Aggregate shocks perturb the population's consumption possibilities. Consumption is converted to fertility via a technology that exhibits first increasing and then decreasing returns to scale, captured in the simplest case by a fertility threshold. We show that evolution will select preferences that exhibit arbitrarily high aversion to aggregate risks with even very small probabilities of sufficiently low outcomes. These findings complement the familiar result that evolution will select for greater aversion to aggregate than idiosyncratic risks by identifying circumstances under which the difference can be extreme.
This paper studies a linear panel data model with interactive fixed effects wherein regressors, factors and idiosyncratic error terms are all stationary but with potential long memory. The setup involves a new factor model formulation for which weakly dependent regressors, factors and innovations are embedded as a special case. Standard methods based on principal component decomposition and least squares estimation, as in Bai (2009), are found to suffer bias correction failure because the order of magnitude of the bias is determined in a complex manner by the memory parameters. To cope with this failure and to provide a simple implementable estimation procedure, frequency domain least squares estimation is proposed. The limit distribution of this frequency domain approach is established and a hybrid selection method is developed to determine the number of factors. Simulations show that the frequency domain estimator is robust to short memory and outperforms the time domain estimator when long range dependence is present. An empirical illustration of the approach is provided, examining the long-run relationship between stock return and realized volatility.
This paper studies the response of agricultural production to rural labor loss during the process of urbanization. Using household microdata from India and exogenous variation in migration induced by urban income shocks interacted with distance to cities, we document sharp declines in crop production among migrant-sending households residing near cities. Households with migration opportunities do not substitute agricultural labour with capital, nor do they adopt new agricultural machinery. Instead, they divest from agriculture altogether and cultivate less land. We use a two-sector general equilibrium model with crop and land markets to trace the ensuing spatial reorganization of agriculture. Other non-migrant village residents expand farming (land market channel) and farmers in more remote villages with fewer migration opportunities adopt yield-enhancing technologies and produce more crops (crop market channel). Counterfactual simulations show that over half of the aggregate food production losses driven by urbanization is mitigated by these spillovers. This leads to a spatial reorganization in which food production moves away from urban areas and towards remote areas with low emigration.
We study auction design for bidders equipped with non-expected utility preferences that exhibit constant risk aversion (CRA). The CRA class is large and includes loss-averse, disappointment-averse, mean-dispersion, and Yaari's dual preferences as well as coherent and convex risk measures. Any preference in this class displays first-order risk aversion, contrasting the standard expected utility case which displays second-order risk aversion. The optimal mechanism offers “ full-insurance” in the sense that each agent’s utility is independent of other agents’ reports. The seller excludes less types than under risk neutrality and awards the object randomly to intermediate types. Subjecting intermediate types to a risky allocation while compensating them when losing allows the seller to collect larger payments from higher types. Relatively high types are willing to pay more, and their allocation is efficient.
A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite sample performance of the proposed test and estimators. An empirical application to the S&P 500 index highlights the usefulness of the proposed procedures in practical work.