Land, Factor Markets, and Inequality in Rural China: Historical Evidence

Drawing on a unique household-level data set from northeast China in the 1930s, this paper explores the connections between the distribution of land, factor markets, and income distribution. We test whether patterns of income inequality were consistent with the predictions of a market-clearing, neoclassical model linking land and labor endowments, through factor markets to household income. While the model is consistent with some features in the data, we reject the hypothesis that factor markets worked perfectly and find support for the historian's intuition regarding the disproportionate impact of land inequality in the countryside. Nevertheless, where markets were more active, especially land rental markets, excess returns to land were diminished and inequality was lowest. This suggests that factor market development played a positive role in reducing inequality in rural China.     

Are Uniform Solutions Focal? – The Case of International Environmental Agreements

The application of uniform solutions has several drawbacks, notably their lack of cost efficiency and their inability to guarantee individual rationality. A proper specification of uniform solutions, however, reveals that uniform solutions that satisfy individual rationality always exist. When all countries hold private information about their own reduction costs, there only exists one solution that always satisfies individually rationality without use of side payments and the requirement of dominant strategy implementation: The solution that selects the smallest individually preferred uniform reduction which also give a theoretical explanation of the lowest common denominator effect.     

On Returning Inflation to Target

To gauge the breadth of current inflation and prospects for inflation returning to target, we consider disaggregated measures of CPI infl ation to evaluate trends and then consider diff erent scenarios for the realisation for wages and prices.

     

Tipped Wage Effects on Earnings and Employment in Full‐Service Restaurants

We exploit more than 20 years of changes in state‐level tipped wage policy and estimate earnings and employment effects of the tipped wage using county‐level panel data on full‐service restaurants (FSR). We extend earlier work by Dube, Lester, and Reich ( 2010 ) and compare outcomes between contiguous counties that straddle a state border. We find a 10‐percent increase in the tipped wage increases earnings in FSRs about 0.4 percent. Employment elasticities are sensitive to the inclusion of controls for unobserved spatial heterogeneity. In our preferred models, we find small, insignificant effects of the tipped wage on FSR employment.     

An Empirical Comparison of "Frisch" and "Time Series" Demand price Elasticities for individual Commodities

In a classical article in 1959, Ragnar Frisch [8] developed a procedure, which, under the assumption of want independence¹ and given commodity budget shares, income elasticities, and one own-price elasticity, allows one to calculate a complete matrix of own and cross price elasticities. Between broad commodity groups such an assumption (want independence) has becme increasingly accepted and in fact under the label of separability has formed the basis for a family of demand models that are increasingly used to estimate demand elasticities for broad commodity groups (the linear expenditure system, the Rotterdam model, etc.). At the individual commodity level however, the assumption of want independence seems less viable, e.g., the utility one derives from pork is in general not considered independent from one's consumption of beef. However, it has become increasingly common (and apparently acceptable) to find the Frisch methodology utilized to develop demand price elasticity estimates for individual agricultural commodities [4, 7, 17].     

Pairs trading with a mean-reverting jump–diffusion model on high-frequency data

This paper develops a pairs trading framework based on a mean-reverting jump–diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established statistical arbitrage strategy enables us to perform intraday and overnight trading. Essentially, we conduct a three-step calibration procedure to the spreads of all pair combinations in a formation period. Top pairs are selected based on their spreads’ mean-reversion speed and jump behaviour. Afterwards, we trade the top pairs in an out-of-sample trading period with individualized entry and exit thresholds. In the back-testing study, the strategy produces statistically and economically significant returns of 60.61% p.a. and an annualized Sharpe ratio of 5.30, after transaction costs. We benchmark our pairs trading strategy against variants based on traditional distance and time-series approaches and find its performance to be superior relating to risk–return characteristics. The mean-reversion speed is a main driver of successful and fast termination of the pairs trading strategy.     

Modeling time-varying higher-order conditional moments: A survey

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time-varying second-order conditional moment has become increasingly popular in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher-order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time-varying higher-order conditional moments in the last two decades. This paper outlines recent developments in modeling time-varying higher-order conditional moments in the economics and finance literature. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time-varying higher-order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher-order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.