Knowledge that Transforms

Potential home buyers may initiate contact with a real estate agent by asking to see a particular advertised house. This paper asks whether an agent's response to such a request depends on the race of the buyer or on whether the house is located in an integrated neighborhood. Like previous research about the causes of housing discrimination, this paper uses data from fair housing audits, a matched-pair technique for comparing the treatment of equally qualified black and white home buyers. However, we shift the focus from differences in the treatment of paired buyers to agent decisions concerning an individual house. Using a sample of all houses seen during the 1989 national Housing Discrimination Study, we estimate a random-effect, multinomial logit model to explain a real estate agent's joint decisions concerning whether to show each house to a black auditor and to a white auditor. We find evidence that agents interpret an initial housing request as an indication of a customer's preferences, but also are more likely to withhold a house from all customers when it is in an integrated suburban neighborhood (redlining). Moreover, agents' marketing efforts increase with asking price for white, but not for black, customers; blacks are more likely than whites to see houses in suburban, integrated areas (steering); and the houses agents show are more likely to deviate from the initial request when the customer is black than when the customer is white. These three findings are consistent with the possibility that agents act upon the belief that some types of transactions are relatively unlikely for black customers (statistical discrimination).

Empirical research over the last decade has uncovered predictive relationships between the slope of the yield curve and subsequent real activity and inflation. Some of these relationships are highly significant, but their theoretical motivations suggest that they may not be stable over time. We use recent econometric techniques for break testing to examine whether the empirical relationships are in fact stable. We consider continuous models, which predict either economic growth or inflation, and binary models, which predict either recessions or inflationary pressure. In each case, we draw on evidence from Germany and the United States. Models that predict real activity are somewhat more stable than those that predict inflation, and binary models are more stable than continuous models. The model that predicts recessions is stable over our full sample period in both Germany and the United States.

This paper examines the construction of a price index based on an estimated-demand system. In principle the method examined can produce a price index that takes account of the introduction of new products and quality changes in existing products. However, I isolate two key assumptions that have to be made in order to interpret the demand estimates into welfare measures. Using estimates of a brand-level demand system for ready-to-eat cereal, I demonstrate the empirical importance of the assumptions. For the data I use, depending on the interpretation of the demand estimates, a price index can range between a 35% increase over the five years examined to a 2.4% decrease.

We derive a structural index for labor market density based on the Ellison-Glaeser index for industry concentration. The labor market density index serves as a proxy for the number of workers that are potentially available for jobs in a particular area. The index is based on observed home-work location patterns. It is particularly useful for testing theories where the scale of the market matters. We apply this index to a standard wage equation and find that it explains almost half of the cross-region wage variance.

Estimates of the NAIRU are usually derived either from a Phillips curve or from a wage curve. This paper investigates the correspondence between the operational NAIRU concepts and the steady state of a dynamic wage-price model. We derive the parameter restrictions that secure that correspondence. The full set of restrictions can be tested by econometric analysis of the wage-price system, and this method is demonstrated for Norwegian data. A set of necessary conditions can be tested from estimated wage curves alone. Existing international evidence from empirical wage equations are reinterpreted in light of these conditions.

This paper investigates depository institutions' decisions whether or not to impose surcharges (direct usage fees) on nondepositors who use their ATMs. In addition to documenting patterns of surcharging, we examine motives for surcharging, including both direct generation of fee revenue and the potential to attract deposit customers who wish to avoid incurring surcharges at an institution's ATMs. Consistent with expectations, we find that the probability of surcharging increases with both the institution's share of market ATMs and the time since surcharging was first allowed in the state, and decreases with increasing local ATM density. Further, we find evidence consistent with the use of surcharges to attract deposit customers who are new to the local banking market, but we find no evidence that larger banks use surcharges as a means to attract existing customers away from smaller local competitors.

This study investigates whether higher input use per stay in the hospital (treatment intensity) and longer length of stay improve outcomes of care. We allow for endogeneity of intensity and length of stay by estimating a quasi-maximum-likelihood discrete factor model, where the distribution of the unmeasured variable is modeled using a discrete distribution. Data on elderly persons come from several waves of the National Long-Term Care Survey merged with Medicare claims data for 1984–1995 and the National Death Index. We find that higher intensity improves patient survival and some dimensions of functional status among those who survive.

We use information from the television game show with the highest guaranteed average payoff in the United States, Hoosier Millionaire, to analyze risktaking in a high-stakes experiment. We characterize gambling decisions under alternative assumptions about contestant behavior and preferences, and derive testable restrictions on individual risk attitudes based on this characterization. We then use an extensive sample of gambling decisions to estimate distributions of risk-aversion parameters consistent with the theoretical restrictions and revealed preferences. We find that although most contestants display risk-averse preferences, the extent of the risk aversion implied by our estimates varies substantially with the stakes involved in the different decisions.

Contrary to the predictions of the standard life-cycle model, many low-lifetime-income households accumulate little wealth relative to their incomes compared to households with high lifetime income. I use data from the Panel Study of Income Dynamics and a correlated random-effects generalized-method-of-moments estimator to decompose the rich-poor gaps in wealth-to-permanent-income ratio into the portions attributable to differences in characteristics such as labor market earnings, income uncertainty, observed demographics, and the utilization of transfer programs which may have stringent income and liquid-asset tests, and those attributable to differences in the estimated coefficients on the respective characteristics. The results suggest that wealth-to-permanent-income ratios are increasing in permanent labor income and income uncertainty, but that transfer income, with or without asset tests, discourages liquid-asset accumulation. The decompositions indicate that most of the rich-poor wealth gap is attributable to differences in average characteristics and not coefficients. The leading factor driving the gap between the rich and poor in the ratio of liquid wealth to permanent income is asset-tested transfer income, whereas the leading factor driving the gap in the ratio of net worth to permanent income is labor-market earnings.

A youth homicide reduction initiative in Boston in the mid- 1990s poses particular difficulties for program evaluation because it did not have a control group and the exact implementation date is unknown. A standard methodology in program evaluation is to use time series variation to compare pre- and postprogram outcomes. Such an approach is not valid, however, when the timing of program implementation or effect is unknown. To evaluate the Boston initiative, we adapt from the time series literature an unknown-breakpoint test to test for a change in regime. Tests for parameter instability provide a flexible framework for testing a range of hypotheses commonly posed in program evaluation. These tests both pinpoint the timing of maximal break and provide a valid test of statistical significance. We evaluate the results of the estimation using the asymptotic results in the literature and with our own Monte Carlo analyses. We conclude there was a statistically significant discontinuity in youth homicide incidents (on the order of 60%) shortly after the intervention was unveiled.