Social networks are important for finding jobs, but which ties are most useful? Granovetter has suggested that “weak ties” are more valuable than “strong ties,” since strong ties have redundant information, while weak ties have new information. Using 6 million Facebook users’ data, we find evidence for the opposite. We proxy for job help by identifying people who eventually work with a pre-existing friend. Using objective tie strength measures and our job help proxy, we find that most people are helped through one of their numerous weak ties but a single stronger tie is significantly more valuable at the margin.

We extend search theory to multiple indivisible units and perfectly divisible assets, solving them respectively with induction and recursion. Buyer demands and prices are random, and the seller can partially exercise orders. With divisible assets, the Bellman value function is increasing and strictly concave, and the optimal reservation price falls in the position, reflecting increasing holding costs (opportunity cost of delaying optionality for inframarginal units). The marginal value exists, and is strictly convex with a falling purchase cap density. Our model is amenable to price-quantity bargaining; e.g., greater buyer bargaining power is tantamount to greater search frictions.

We implement a regression discontinuity design using the continuous raw Advanced Placement (AP) exam scores, which are mapped into the observed 1–5 integer scores, for over 4.5 million students. Earning higher AP integer scores positively affects college completion and subsequent exam-taking. Specifically, attaining credit-granting integer scores increases the probability that a student will receive a bachelor’s degree within 4 years by 1–2 percentage points per exam. We also find that receiving a score of 3 over a 2 on junior year AP exams causes students to take between 0.06 and 0.14 more AP exams senior year.

Does access to 4-year colleges affect degree completion for students who would otherwise attend 2-year colleges? Admission to Georgia’s 4-year public sector requires minimum SAT scores. Regression discontinuity estimates show that access to this sector increases 4-year college enrollment and college quality, largely by diverting students from 2-year colleges. Access substantially increases bachelor’s degree completion rates for these relatively low-skilled students. SAT-retaking behavior suggests students value access to 4-year public colleges, though perhaps less than they should. Our results imply that absolute college quality matters more than match quality, and they suggest potential unintended consequences of free community college proposals.

We develop a continuum player timing game that subsumes standard wars of attrition and pre‐emption games, and introduces a new rushes phenomenon. Payoffs are continuous and single‐peaked functions of the stopping time and stopping quantile. We show that if payoffs are hump‐shaped in the quantile, then a sudden “rush” of players stops in any Nash or subgame perfect equilibrium. Fear relaxes the first mover advantage in pre‐emption games, asking that the least quantile beat the average; greed relaxes the last mover advantage in wars of attrition, asking just that the last quantile payoff exceed the average. With greed, play is inefficiently late: an accelerating war of attrition starting at optimal time, followed by a rush. With fear, play is inefficiently early: a slowing pre‐emption game, ending at the optimal time, preceded by a rush. The theory predicts the length, duration, and intensity of stopping, and the size and timing of rushes, and offers insights for many common timing games.

John Formby, Terry Seaks, and W. Smith (hereafter FSS) argue that the P-Gini coefficient is affected by the arbitrary choice of the age to a degree which brings the validity of [the] age-related measure into question (FSS, 1989, p. 2). More pointedly, a sufficiently narrow age partition, the P-curve can always be driven to the L-curve. Convergence [of the P-Gini] to a nonzero estimate does not occur... (p. 4). These conclusions I believe result from a misapplication of Gastwirth's theorem on disaggregation, and a failure to observe statistical rules relating to sample size and sampling error. I will show that when these rules are observed, the value of the P-Gini does not converge to zero but properly reflects the relative importance of the nonlife-cycle factors affecting the distribution. When calculating the traditional L-Gini, the more disaggregation the better; the number and accuracy of the sample points are the only consideration since all are thrown into one conceptual box and compared in terms of income size. But if we try to identify the factors which account for income inequality in terms of age versus nonage related factors, we are setting up two conceptual boxes (the age-Gini and the P-Gini) and we are no longer simply dealing with a Gastwirth-type problem. Statistical considerations come into play; for example, we must have a sufficient number of sample points in each conceptual box in order to give a reliable estimate of the importance of each factor. The key-allocating device which I employ is the age-Gini, derived from the average age-income profile. The age-Gini shows the amount of inequality that would exist if all nonage-related sources of inequality were eliminated. When calculating this coefficient, the means of the age-groups are used in order to wash out all random and nonagerelated influences, but this separating device works well only if the means are based on large samples. Otherwise, sampling errors create spurious variation and impart an upward bias to the value of the age-Gini. FSS (p. 4) drive the age-Gini value up to the L-Gini by increasing the number of agegroups until they equal the number in the sample. Since the means of the age-groups are now based on samples of one, they become as erratic as the individual incomes, and impart the maximum upward bias to the age-Gini. It is true that the age-income profile (and the age-Gini) are conceptually refined by using smaller age intervals, but unless sample size is large compared to the number of age intervals, the gains from conceptual purification will be more than offset by the greater sampling errors of the age means. This kind of limitation is shared by many other statistical measures which do not thereby lose their validity or usefulness. Under what conditions will the true or limiting value of the P-Gini emerge? FSS in their footnote 3 state that there is no limiting value other than zero. Let us test this claim. Assume we have a scatter diagram of income (Y) and age (X), and wish to show average income in relation to age. We start with a finite number of age-groups and plot their mean incomes on the diagram. By continuously reducing the age interval and increasing sample size, we end up with a curve passing through the true means of infinitely small age intervals: this defines the average age-income profile. Since for each person we have data on income and age, we can with this curve (or an approximation of it) calculate the age-Gini and L-Gini without grouping for age or income. The age-income curve allows us to determine the mean income (u) at any given age and for all persons. *Department of Economics, Portland State University, P.O. Box 751, Portland, OR 97207.