Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes

We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than individuals in nearby but not identical blocks. We find significant evidence of social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in sociodemographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.

Cooperation & Competition Between Navigation Systems in the Rat Brain: The Role of the Hippocampus and Striatum During a Dissociative Maze Task

While many tend to think of memory systems in the brain as a single process, in reality several experiments have supported multiple dissociations of different forms of learning, such as spatial learning and response learning. In both humans and rats, the hippocampus has long been shown to be specialized in the storage of spatial and contextual memory whereas the striatum is associated with motor responses and habitual behaviors. Previous studies have examined how damage to hippocampus or striatum has affected the acquisition of either a spatial or response navigation task. However even in a very familiar environment organisms must continuously switch between place and response strategies depending upon circumstances. The current research investigates how these two brain systems interact under normal conditions to produce navigational behavior. Rats were tested using a task developed by Jacobson and colleagues (2006) in which the two types of navigation could be controlled and studied simultaneously. Rats were trained to solve a plus maze using both a spatial and a response strategy. A cue (flashing light) was employed to indicate the correct strategy on a given trial. When no light was present, the animals were rewarded for making a 90º right turn (motor response). When the light was on, the animals were rewarded for going to a specific goal location (place strategy). After learning the task, animals had a sham surgery or dorsal striatum or hippocampus damaged. In order to investigate the individual role of each brain system and evaluate whether these brain regions compete or cooperate for control over strategy, we utilized a within-animal comparisons. The configuration of the maze allowed for the comparison of behavior in individual animals before and after specific brain areas were damaged. Animals with hippocampal lesions showed selective deficits on place trials after surgery and learned the reversal of the motor response more rapidly than striatal lesioned or sham rats. Unlike previous findings regarding maze learning, animals with striatal lesions showed deficits in both place and response trials and had difficulty learning the reversal of motor response. Therefore, the effects of lesions on the ability to switch back and forth between strategies were more complex than previously suggested. This work may reveal important new insight on the integration of hippocampal and striatal learning systems, and facilitate a better understanding of the brain dynamics underlying similar navigational processes in humans.

The Discontinuous Nature of Kriging Interpolation for Digital Terrain Modeling

Kriging is a widely employed method for interpolating and estimating elevations from digital elevation data. Its place of prominence is due to its elegant theoretical foundation and its convenient practical implementation. From an interpolation point of view, kriging is equivalent to a thin-plate spline and is one species among the many in the genus of weighted inverse distance methods, albeit with attractive properties. However, from a statistical point of view, kriging is a best linear unbiased estimator and, consequently, has a place of distinction among all spatial estimators because any other linear estimator that performs as well as kriging (in the least squares sense) must be equivalent to kriging, assuming that the parameters of the semivariogram are known. Therefore, kriging is often held to be the gold standard of digital terrain model elevation estimation. However, I prove that, when used with local support, kriging creates discontinuous digital terrain models, which is to say, surfaces with “rips” and “tears” throughout them. This result is general; it is true for ordinary kriging, kriging with a trend, and other forms. A U.S. Geological Survey (USGS) digital elevation model was analyzed to characterize the distribution of the discontinuities. I show that the magnitude of the discontinuity does not depend on surface gradient but is strongly dependent on the size of the kriging neighborhood.

Shadow Profit Maximization and a Generalized Measure of Inefficiency

Determining the profit maximizing input-output bundle of a firm requires data on prices. This paper shows how endogenously determined shadow prices can be used in place of actual prices to obtain the optimal input-output bundle where the firm.s shadow profit is maximized. This approach amounts to an application of the Weak Axiom of Profit Maximization (WAPM) formulated by Varian (1984) based on shadow prices rather than actual prices. At these prices the shadow profit of a firm is zero. Thus, the maximum profit that could have been attained at some other input-output bundle is a measure of the inefficiency of the firm. Because the benchmark input-output bundle is always an observed bundle from the data, it can be determined without having to solve any elaborate programming problem. An empirical application to U.S. airlines data illustrates the proposed methodology.