Technological innovations like broadcast television and the internet challenge local newspapers' business model of bundling their local content with third-party content, such as wire national news. We examine how the entry of television affected newspapers and news diets in the United States. We construct a dataset of newspapers' economic performance and content choices from 1944 to 1964 and exploit quasi-random variation in the rollout of television to show its negative impact in the readership and advertising markets. Newspapers responded by reducing content, particularly local news. We tie this change to increased party vote share congruence between congressional and presidential elections.
I study the long-term effects of landing a first job at a large firm versus a small one using Spanish administrative data. Size could be a relevant employer attribute for inexperienced workers since large firms are associated with greater productivity, wages, and training. The key empirical challenge is selection into first jobs based on unobserved worker characteristics. I develop an instrumental variable approach that, keeping business cycle conditions fixed, leverages variation in the composition of labor demand that labor market entrants face. Initially matching with a larger firm persistently improves long-term outcomes, even through subsequent jobs. Mechanisms suggest better skill development at large firms.
We quantify the effects of the political development cycle – the fluctuations between the left (Maoist) and the right (pragmatist) development policies – on growth and structural transformation of China in 1953-1978. The left policies prioritized structural transformation towards non-agricultural production and consumption at the cost of agricultural development. The right policies prioritized agricultural consumption through slower structural transformation. The imperfect implementation of these policies led to large welfare costs of the political development cycle in a distorted economy undergoing a structural change.
This paper considers estimation of short-run dynamics in time series that contain a nonstationary component. We assume that appropriate preliminary methods can be applied to the observed time series to separate short-run elements from long-run slowly evolving secular components, and focus on estimation of the short-run dynamics based on the filtered data. We use a flexible copula-generated Markov model to capture the nonlinear temporal dependence in the short-run component and study estimation of the copula model. Using the rescaled empirical distribution of the filtered data as an estimator of the marginal distribution, Chen et al. (2022) proposed a simple, yet flexible, two-step estimation procedure for the copula model. The two-step estimator works well when the tail dependence is small. However, simulations reveal that the two-step estimator may be biased in finite samples in the presence of tail dependence. To improve the performance of short-term dynamic analysis in the presence of tail dependence, we propose in this paper a pseudo sieve maximum likelihood (PSML) procedure to jointly estimate the residual copula parameter and the invariant density of the filtered residuals. We establish the root-consistency and asymptotic distribution of the PSML estimator of any smooth functional of the residual copula parameter and invariant residual density. We further show that the PSML estimator of the residual copula parameter is asymptotically normal, with the limiting distribution independent of the filtration. Simulations reveal that in the presence of strong tail dependence, compared to the two-step estimates of Chen et al. (2022), the proposed PSML estimates have smaller biases and smaller mean squared errors even in small samples. Applications to nonstationary macro-finance and climate time series are presented.
The present study examines the assumptions, modeling structure, and results of DICE-2023, the revised Dynamic Integrated Model of Climate and the Economy (DICE), updated to 2023. The revision contains major changes in the treatment of risk, the carbon and climate modules, the treatment of nonindustrial greenhouse gases, discount rates, as well as updates on all the major components. Noteworthy changes are a significant reduction in the target for the optimal (cost-beneficial) temperature path, a lower cost of reaching the 2 °C target, an analysis of the impact of the Paris Accord, and a major increase in the estimated social cost of carbon.
Reclassification risk is a major concern in health insurance where contracts are typically 1 year in length but health shocks often persist for much longer. While most health systems with private insurers pair short-run contracts with substantial pricing regulations to reduce reclassification risk, long-term contracts with one-sided insurer commitment have significant potential to reduce reclassification risk without the negative side effects of price regulation, such as adverse selection. We theoretically characterize optimal long-term insurance contracts with one-sided commitment, extending the literature in directions necessary for studying health insurance markets. We leverage this characterization to provide a simple algorithm for computing optimal contracts from primitives. We estimate key market fundamentals using data on all under-65 privately insured consumers in Utah. We find that dynamic contracts are very effective at reducing reclassification risk for consumers who arrive at the market in good health, but they are ineffective for consumers who come to the market in bad health, demonstrating that there is a role for the government insurance of pre-market health risks. Individuals with steeply rising income profiles find front-loading costly, and thus relatively prefer ACA-type exchanges. Switching costs enhance, while myopia moderately compromises, the performance of dynamic contracts.
Considerable evidence in past research shows size distortion in standard tests for zero autocorrelation or zero 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 uncorrelated 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 innovations and regression residuals allowing for heteroscedastic uncorrelated and non-stationary data settings. 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 develop and test algorithms to detect Edgeworth cycles, which are asymmetric price movements that have caused antitrust concerns in many countries. We formalize four existing methods and propose six new methods based on spectral analysis and machine learning. We evaluate their accuracy in station-level gasoline-price data from Western Australia, New South Wales, and Germany. Most methods achieve high accuracy with data from Western Australia and New South Wales, but only a few can detect the nuanced cycles in Germany. Results suggest that whether researchers find a positive or negative statistical relationship between cycles and markups, and hence their implications for competition policy, crucially depends on the choice of methods. We conclude with a set of practical recommendations.
We document that sales of individual products decline steadily throughout most of the product life cycle. Products quickly become obsolete as they face competition from newer products sold by competing firms and the same firm. We build a dynamic model that highlights an innovation-obsolescence cycle, where firms need to introduce new products to grow; otherwise, their portfolios become obsolete as rivals introduce their own new products. By introducing new products, however, firms accelerate the decline of their own existing products, further depressing their sales. This mechanism has sizable implications for quantifying economic growth and the impact of innovation policies.