Abstract
ABSTRACT
Using a unique sample of new Ph.D. economists in 1987 and 1997, we examine how job seekers and their employers alter their search strategies in strong versus weak markets. The 1987 academic market was strong while the 1997 market was much weaker. A multimarket theory of optimal search suggests that job seekers will respond to a weakening market by lowering their reservation utility. This in turn affects their search strategies at the extensive margin (which markets to enter) and the intensive margin (how many applications to submit per market). Meanwhile, employers respond to the weakening market by raising their hiring standards. The combination of strategies on the supply and demand sides suggest that high quality applicants will obtain an increased share of academic interviews in weak markets while applicants from weaker schools will increasingly secure interviews outside of the academic market. Empirical results show that in the bust market, graduates of elite schools shifted their search strategies to include weaker academic institutions, while graduates of lower ranked schools shifted their applications away from academia and toward the business sector. In bust conditions, academic institutions increasingly concentrate their interviews on elite school graduates, women and U.S. residents.
J44, J60
INTRODUCTION
The market for Ph.D. economists has attracted a great deal of attention in the literature. This is not surprising given the personal stake that many economists hold regarding the performance of this market. Yet, another motivation for studying this particular labor market rests on its highly organized structure, characterized by three distinct segments: advertisement of jobs during the few months prior to the Allied Social Science Association (ASSA) meetings, interviewing candidates at the ASSA meetings in late December or early January, and on-site visits and offers during the few months after the ASSA meetings.
Most studies of this market concentrate on explaining variation in earnings or type of employment, with variables such as gender, age, race, nationality, quality of Ph.D. institution, and number of publications significantly affecting employment or earnings success (see, e.g., Barbezat (1992) , Broder (1993) , Formby et al. (1993) , Singell and Stone (1993) , McMillan and Singell (1994) , Kahn (1995) , Siegfried and Stock (1999) and List (2000) ). Although employment outcomes of new Ph.D. economists have been thoroughly addressed, little attention has been given to how new Ph.D. economists set search strategies. Moreover, as studies tend to examine outcomes in one hiring period, evidence of intertemporal change in search strategies and outcomes is lacking.
To fill the current gaps in the literature, we address the intertemporal relationship between applicant characteristics, search strategies, and outcomes of new Ph.D. economists. This is accomplished through the use of a survey instrument that documents behavior and outcomes of participants in the Ph.D. labor market in two years−1987 and 1997−that we loosely deem as "boom" and "bust" markets, respectively. 1 We organize the remainder of the paper as follows. Section II discusses the nature of those two markets. Section III presents a model that illustrates how agents on the supply and demand side will alter search strategies in response to perceived strength of the market. In Section IV, we discuss the survey instrument that was used to gather information on Ph.D. candidate behavior in the 1987 and 1997 markets. As shown in Section V, the survey results indicate that search intensity is naturally tied to the severity of excess labor supply. In Section VI, a model of search outcome is estimated across the two market periods. The results show that the effect of various applicant characteristics on numbers of interviews, visits, and job offers is related to the strength of the market. Concluding remarks are provided in Section VII.
II. THE 1987 AND 1997 MARKETS FOR Ph.D. ECONOMISTS
We concentrate on two labor market periods, 1986-87 and 1996-97 . Serendipitously, the last years coincided exactly with Siegfried and Stock's (1999) survey paper on labor market outcomes for Ph.D. economists. They found that graduates from higher ranked schools were more likely to have a full time job, were paid better, and were more likely to agree that their job was commensurate with their education. Graduates from weaker programs were less successful financially, but virtually all were employed. Nevertheless, Siegfried and Stock refer to a substantial number of graduates from all ranks who expressed disappointment in their market outcomes, with typical assessments about the market ranging from the most positive ("I got lucky") to less so ("It sucks!").
The negative reaction of new Ph.D.s to the 1997 market is understandable in light of data from Job Openings for Economists-see Table 1 . While 1997 was unusually bleak, there were substantial differences across sectors. In particular, the academic and government sectors were weak, while the business sector was the strongest in years. Using the number of advertised jobs per new Ph.D. as a measure of the arrival rate of job offers, arrival fell 18 percent over the ten-2 year period reported in Table 1 . The decline was most pronounced in academia (-28 percent) and government (-32 percent), while arrival rates rose in business. Statistics on the job market for economists reported by Siegfried (2002) indicate that no market since 1996-97 was worse in terms of the number of new jobs per new Ph.D. recipient including that of the [2001] [2002] recession year. Consequently, the 1996-97 market seems well characterized as a "bust" market, while the 1986-87 market can be characterized as a "boom".
As a discipline, economics is oriented toward optimizing behavior, and so new Ph.D.s should adapt their search strategies to available market information. The economics labor market is structured to make information on market strength easy to obtain. The market is centered on the annual Allied Social Science Associations (ASSA) meetings. 2 Each fall, prospective Ph.D. recipients submit applications to academic institutions, government and international agencies, and private employers. Information on the demand-side of the market is virtually costless to obtain, as most jobs are advertised in the American Economic Association's publication, Job Openings for Economists (JOE) . Since the number of jobs by field and type of employer are known early in the market cycle, job seekers have unusually good information on which to base expected returns from search. Nonetheless, their search strategies must be set by early December, as the great majority of interviews are conducted at the annual meetings. The opportunities for sequential search are therefore quite limited.
Submission of applications is not costless. In addition to mailing costs, for example, there are costs associated with the time required to research employer attributes and tailor materials to match different employers. Consequently, it may not be optimal to apply to every job, or even to apply to every job in one's field. Instead, the applicant must decide how many applications to submit and what type of employers to target, based on information available 3 about the strength of labor demand. On the other side of the market, employers must decide how many people to interview and what type of applicants to pursue. These strategies are formalized in the next section.
III. SEARCH INTENSITY IN MULTIPLE MARKETS
Using a framework that integrates features of Fallick (1992) and Stern (1989) , we show how a weakening labor market affects search strategies on the extensive margin (i.e., how many markets to sample) and on the intensive margin (i.e., how many applications to submit per market). The model's main features are summarized here, and the details of the derivations are included in the Appendix.
Job Search by Applicants
Suppose there are J employer submarkets within the market for new Ph.D.s. 3 Submarket j opens in the fall when employers each decide to advertise one job vacancy with an attached precommited compensation level, w. Using the announcements as information on market strength, job seekers opt to search in submarket j. Each of the seekers perceives a distribution of compensation packages (w) in each submarket, defined by the cumulative distribution function, F j (w). Compensation packages include salary, benefits, and teaching and research support. 4 Seekers also perceive the probability δ j ( , ) that an application will yield a job offer. The arrival rate of offers, δ j , is treated as a parameter by individual job seekers when they are deciding on applications. It will be larger when seekers perceive that there are more job vacancies, , and when they perceive that there are fewer seekers, . Later in the search process when matching occurs, employers and job seekers may adjust their strategies by entering Letting a rejection be viewed as a zero compensation offer, the compensation outcome from one application in submarket j is distributed according to
Seekers will submit A j applications in submarket j and then select the best compensation package, w . The probability that A j applications submitted to sector j will generate an offer of
The cumulative distribution function for receiving a best offer of w across J submarkets is designated as ) w ( Γ with an associated density function, ) w ( γ . Their functional forms are given in the Appendix.
Applications cost c j apiece . Application costs may differ across markets in that they include the costs of acquiring information on the employer and a hedonic component that reflects the applicant's taste or distaste for the market as well as the pecuniary costs of submitting application materials. For example, application costs at schools with graduate programs may be lower because applicants already have good information on the attributes of those schools and may have a preference for applying to academic institutions.
5
Define ξ as the reservation compensation level which is the lowest compensation package the applicant would accept. As shown in the Appendix, the optimum strategy is to set the reservation compensation level at
which is the multimarket equivalent of the form derived by Stern (1989) . Equation (2) implies that seekers will set a constant reservation compensation level across all submarkets.

Extensive Search
Extensive search involves deciding in which of the J employer submarkets to search. The conditions dictating which markets a seeker will target come from the first order conditions for the optimal number of applications per submarket. Differentiating (2) with respect to A j and setting 0 dA dξ j = for all j, these J first order conditions for applications in the jth submarket are of the form
The left-hand side of (3) is the discounted expected marginal return from an additional application in submarket j and the right-hand side is the marginal cost of an application in submarket j. If condition (3) is violated for all positive values of A j , then the optimum strategy is to set A j = 0, so that the applicant will decide to eliminate submarket j from his search set. The n j seekers in submarket j are those for whom condition (3) is satisfied.
There are two interesting cases under which condition (3) is violated. One is when an individual's reservation compensation level is high relative to the distribution of compensation offers in submarket j. The other interesting case is when δ j = 0 because the individual perceives he cannot get an offer in submarket j. As we argue below, the likelihood of these cases will differ by the quality of the seeker and by whether the labor market is in boom or bust.
Intensive Search
Search intensity is the number of applications per submarket. The optimal search intensity is determined by the level of A j that makes condition (3) hold with equality. Our primary concern is to analyze how search intensity changes in response to changes in the strength of sectoral labor demand. For our purposes, it is convenient to describe a bust as a 6 decrease in the arrival rate of offers in a given sector j.
6 7 A decline in δ j will have an ambiguous effect on sector j applications, but will unambiguously increase applications in the other sectors . That implies that the decrease in offer arrival rates in the academic and government sectors between 1987 and 1997 should increase search intensity outside of government and academia, but the effect on academic or government sector applications is uncertain.
Search and Seeker Quality in Boom and Bust Markets
Applicants who signal high ability to the market, presumably those from the most prestigious graduate programs, will have the highest arrival rate of offers and hence, the highest relative ξ in both boom and bust markets. As ξ increases, the probability that condition (3) is violated rises, and so high perceived ability applicants will have the highest probability of excluding a submarket. This incentive for elite seekers to exclude a submarket falls in bust conditions as ξ declines.
To illustrate the rationale, suppose that elite graduates search in "prestigious" submarket 1 but not in submarket j during boom conditions. Further, suppose that a bust occurs in submarket 1 such that no vacancies are announced, meaning that = δ 1 =0. Reservation compensation ξ falls, and so the probability that constraint (3) is satisfied for submarket j increases, as does the probability that elite graduates enter submarket j. Generalizing, we would expect that job seekers from top programs would search more extensively in bust than in boom markets.
v
In all market conditions, graduates of lower perceived quality will have lower expected arrival rates and thus lower reservation compensation levels than will those from elite institutions. As a consequence, the inequality condition in (3) is less likely to be violated in any 7 given submarket, thus graduates from lower ranked programs will be less likely to exclude markets. The exception is that as the arrival rate δ 1 of offers in prestige submarket 1 approaches 0, G 1 (w) approaches 1, the left-hand-side of (3) approaches zero, and search in submarket 1 becomes fruitless. This case is most likely to occur in bust markets as rises relative to .
Consequently, lower perceived quality graduates have a higher probability of dropping out of prestige submarkets such as 1 during a bust. 
Search by Employers
Because the number of applications to submit is a choice of job seekers alone, it was appropriate to model the number of applications submitted with a model of one-sided search.
However, search outcomes such as the number of interviews, site visits, or job offers will reflect choices of both job seekers and employers. To evaluate search outcomes, we need to develop a model of two-sided search. Before tackling the equilibrium problem, however, we need to describe how employer strategies will differ in boom and bust markets.
We assume that each employer faces a distribution of applicants in each of K supply submarkets. 8 For simplicity, we assume that each employer is looking to fill one job, so search strategies are modeled in the context of a specific job and not the firm's overall hiring strategy for all personnel. 9 The incumbent's marginal revenue product, net of the precommited compensation level, is (q k -w), which is determined by a draw from the distribution Q(q k -w).
Each interview in submarket k results in an accepted offer with probability θ k ( , ). We assume initially that the employer takes this probability as parametric in setting its interview strategy. θ k will be higher in years when fewer employers are perceived to be competing for applicants (low ) and when they perceive a higher number of seekers, . An interview will
generate zero net revenue with probability . ) θ (1 k − Hence, the distribution of returns to interviews in the kth submarket is
Upon receipt of applications in each of the k submarkets, the employer must decide how many to interview and from which submarkets. The best interviewee across all markets will generate , q the highest marginal product. The evaluation cost is h k per applicant which can differ across markets. The probability that I k interviews will generate a top offer of 
Equation (5) shows that the firm's optimal strategy is to set a common reservation net of wage productivity level across all supply submarkets that it decides to hire from. Workers with net productivity below that level will not become employed. The remaining K first-order conditions set the number of interviews to set up for each market:
If condition (6) is violated for all positive values of I k , the employer will avoid interviewing in that market. Therefore, the number of employers opting to recruit in supply submarket k, will be those for whom condition (6) is satisfied for at least one interview.
We can illustrate the partial equilibrium response of an employer to a bust market as follows. Suppose as before that the bust is submarket specific so that no vacancies are announced in prestigious employer submarket 1 ( = δ 1 = 0). By (3), seekers will unambiguously increase their intensive and extensive search in all other markets, implying that in any given submarket k, θ k will increase. By (5), employers in k will respond by raising the minimum net productivity standard that all seekers would have to meet. This means that either wages have to fall, expected productivity has to rise, or some combination of the two. If employers cannot adjust the wage level from its precommited level, then the rising net productivity standard also means a rising absolute productivity standard, and so weaker applicants will find it more difficult to get interviews in the bust market. 
Equilibrium
In order to make the characterization of equilibrium tractable, we need to assume that all of the applicants to a submarket of employers are ex ante identical. To do this, we assume that in each year, the process of sorting applicants across submarkets results in a pool of applicants within a submarket who have an identical probability of success. Furthermore, all employers within the submarket are ex ante identical in expected productivity.
11 In other words, after the initial announcement of vacancies and the initial receipt of applications, ex ante substandard applicants in terms of expected productivity are dropped by the market, as are ex ante substandard employers. The remaining v k employers and n k seekers meet the ex ante quality standard. This process is driven by equations (3) and (5), where notional v k is substituted by true v k and notional n k is substituted by true n k . Substandard applicants find that the actual realization of δ k is 0, and so condition (3) is violated, prompting their withdrawl. Substandard employers find that the realization of θ k is 0, and so condition (5) The assumption that sorting results in ex ante identical employers and seekers oversimplifies the empirical context, in that the various Ph.D. submarkets undoubtedly have considerable heterogeneity across employers and job seekers that will affect observed outcomes.
Nevertheless, there are some useful insights that can be extracted from the simplification.
A job match will occur if both the employer and the job seeker are satisfied that their union will generate sufficient surplus to meet each of their respective reservation requirements.
A representative firm in submarket k requires enough productivity to pay for ε k + w k , while the seeker requires enough to generate ξ k . The minimum acceptable match would generate exactly
The matching function that determines the probability of finding a successful match in the k th submarket is
The function m(·) determines the number of interviews, which increases in the number of qualified applicants, n k , and in the arrival rate of job offers, .
Because all seekers in a submarket are ex ante identical, they will have the same probability of obtaining an interview or of obtaining a match, but some will land multiple offers while others come up empty. With probability u k , a seeker will fail to land a match. When an interview occurs, a draw is made on the revenue distribution. The only acceptable matches will be those for which which occurs with probability The equilibrating process works as follows: Suppose initially that so that no firm would be willing to pay the reservation wage. Employers will withdraw vacancies from the market. As they do, falls, workers will lower their reservation wages and employers will raise their reservation revenue level. In the limit, vacancies will continue to be removed until . When and , both employers and workers will expect to earn rents from the match. In that case, additional seekers have an incentive to enter and more employers will have an incentive to offer vacancies.
Equilibrium Sorting in Boom and Bust Markets
Our concern is limited to the question of how employers and job seekers respond to a decrease in labor demand. A complete analysis of the properties of the model is beyond the scope of this paper, although it would be possible using simulation methods. We begin as before, supposing that the bust occurs in the relatively prestigious submarket 1 so that v 1 =0.
Both high and low ability job seekers will lower their reservation wages across all submarkets including the less prestigious submarket k. The resulting rise in θ k will cause employers in k to 12 raise their reservation net productivity level ε k . The minimum acceptable match productivity level may not change however, as the increase in ε k is accompanied by the decline in ξ k , leading to an ambiguous change in .
The downward sorting of high quality seekers into submarket k creates difficulties for the lower quality seekers normally in k. Because the sorting leads to ex ante homogeneous job seekers in k, the increase in expected quality will cause ex ante lesser quality applicants to drop out of k as for them, δ k drops to zero in the bust.
The bust will induce an increase in search intensity in k, and so n k rises. The matching function increases in n k but decreases in and so the impact of the bust on the probability of matches (interviews, site visits and job offers) is uncertain. Consequently, the probability that a seeker in k will search unsuccessfully in k may rise or fall in the bust. , /n v k k Similarly, using (8), we cannot predict if wages in k will rise or fall. There are two reasons. First, the average quality of applicants in k rises, so the match rent may rise, even if the minimum acceptable match productivity rises. Consequently, there may be more surplus to distribute between the employer and the worker. Second, while the reservation wage for all seekers falls, the seekers in submarket k during the bust are not the same seekers in submarket k in the boom period. It is possible that the reservation wages for high quality seekers in the bust are higher than the reservation wages for low quality seekers in k during the boom. In fact, average starting salaries in the Ph.D. economics market do not seem to fall in bust periods, presumably due to some combination of sorting on quality and rising match capital in the bust market.
What then helps the lower quality applicants in bust markets who have been displaced from submarket k? There is a residual employer submarket R that does not bother seeking Ph.D. 13 economists in boom conditions because condition (3) is violated for all job seekers at all application levels. With n R = 0 and θ R = 0, condition (6) will be violated at any positive level of interviews, and so employers in submarket R would not pursue applicants from submarket R in the boom. In the bust market, the decrease in ξ R for seekers in R increases the likelihood that condition (3) is satisfied, raising the probability that n R > 0, that θ R > 0, and that condition (6) is satisfied for employers in submarket R. Consequently, some employers interview in the Ph.D. market only in bust conditions.
At the risk of caricaturing the market, the prestige employer submarket in our empirical application might include highly-ranked Ph.D. granting institutions. An example of less prestigious submarket k would be liberal arts colleges with heavy teaching loads or lower-tier Ph.D. granting institutions. An example of submarket R could be state and local governments or small private employers that normally do not seek to employ Ph.D. economists. Our theory suggests that when there is a bust in the prestige Ph.D. granting market, higher quality applicants will shift into less prestigious markets such as teaching colleges or lower tier Ph.D. granting institutions, potentially displacing lower quality applicants. Meanwhile, employers who do not normally hire large numbers of Ph.D. economists such as private employers or state agencies may enter the market to take advantage of the opportunity to hire Ph.D. economists from lower ranked schools. The main predictions of the theory and our empirical findings are summarized in Table 2 .
IV. DATA
As discussed previously, respondents attended the ASSA meetings that year, which also happened to be in New Orleans.
16
Our surveys solicited responses on applicant characteristics, search strategies, and success at the ASSA meetings. Similar to recent studies (e.g., Barbazet (1992), Broder (1993) , Formby et al. (1993) , Singell and Stone (1993) , Kahn (1995) , and Siegfried and Stock (1999) Table 3 provides empirical definitions of the key variables.
Applicant characteristics across the two market years are reported at the top of and Stock (1999) , with the exception that respondents to the surveys are slightly more likely to be female and U.S. residents than the population of Ph.D. recipients as a whole.
V. SEARCH STRATEGIES FOR APPLICANTS
Our data on the number of applications submitted by submarket reflect supply-side search strategies only, given seeker perceptions of the strength of labor demand. Our theoretical model suggests that as labor market demand weakens, reservation wages should fall and the incentives to exclude a given market should decrease. Because those from premier institutions (Tier 1) would have the greatest incentive to exclude sectors when demand is strong, we should find the greatest increase in extensive search among elite graduates during a bust market. The results in Table 4 . In all cases, the probability of excluding a market fell as the overall labor market weakened from 1987 to 1997. Furthermore, over the same time frame, the comparative static estimates indicate that business sector applications increased by 4.8, which is consistent with the predicted search intensity response.
Theory does not generate an unambiguous prediction of how weakening demand in academia and government affects search intensity in those sectors. Yet, if we take the numbers literally, graduates of top-tier schools appear to search more intensively in the academic sector as the academic market weakens, most noticeably by applying to undergraduate colleges and full-17 time teaching jobs that they would have avoided in the boom. Graduates of third tier institutions reduced their academic applications and shifted in dramatic fashion toward business applications.
Interestingly, the average search intensity by sector for top-tier graduates in the 1997 bust market is similar to the search intensity of third-tier graduates in the 1987 boom market. Also, the exclusion strategies of first-tier graduates in 1997 are similar to those of third-tier graduates in 1987. In short, graduates of stronger programs search in weak markets much as graduates of weaker programs search in strong markets. This suggests that as the market weakens, stronger applicants shift their sights toward less prestigious outlets that were previously left to weaker applicants, displacing third tier graduates from their normal markets. The third tier graduates then seek submarkets that open because of the bust conditions in the prestige markets.
VI. SEARCH OUTCOMES
Search outcomes including the number of interviews, site visits and job offers are the result of the matching process between employers and seekers. The theory does not generate sharp predictions on the probability of success in the bust, but it does predict that employers would be raising the minimum quality level, potentially limiting opportunities for lower quality graduates in the more prestigious submarkets. At the same time, some employers that avoid the Ph.D. market in a boom enter in a bust market, providing a new source of employment for lower quality seekers. We focus on three sequential indicators of successful search in the primary market centered on the ASSA meetings: 1) initial interviews at the meetings; 2) site visits; and 3) job offers.
Summary statistics on search outcome for the three submarkets are presented in Table 5 .
Applicants in 1997 had less success on average, as the average number of interviews, visits, and offers were significantly lower in 1997 versus 1987 for graduates of all tiers. The exception is 18 the business submarket, where success probabilities were roughly comparable in 1997 and 1987.
Our conjecture about employer and applicant search strategies does appear consistent with the data. The ratio of Tier 1 to Tier 3 academic interviews rises from 1.5 to 1 in 1986-87 to 2.4 to 1 in 1996-97 indicating that Tier 3 applicants are being atypically sorted out of the weakening academic market.
Furthermore, with respect to the academic submarket, graduates of the top two tiers averaged more than one academic offer in 1987, yet only 0.7 offers in 1997. Indeed, top-tier graduates received academic offers in the bust market at nearly identical rates to third-tier graduates in boom markets, consistent with the earlier finding that top-tier candidates in 1997 used search strategies that were similar to Tier 3 job seekers in 1987.
All tiers had significantly less success in the government submarket in 1997.
Government success rates for top-tier graduates dropped so much that they were indistinguishable from those of third-tier graduates.
The business sector took up the slack in the market. The top tier graduates experienced no decline in average offers in the business sector, and lower tier graduates had rising average numbers of site visits and offers in 1997. Table 5 estimates do not control for important factors such as gender, age, and applicant quality that may affect search outcomes between the two years. As the market goes from boom to bust, employers will raise their hiring standards. This subsection examines how the matching process, measured by the number of interviews, site visits and offers, changes from boom to bust markets.
Outcomes Conditional on Job Seeker Attributes
Referring once more to equation (7), matches can be measured by their number, m, or by the probability of success, 1-[P( )] m . We opted to concentrate on the number of matches, m, as it provides additional heterogeneity in outcomes to explain. It also avoids complications associated with possible variation in the threshold output level, across employers that we cannot control as our data set does not identify the attributes of the employers. We require an econometric specification that can accommodate the count nature of our match success indicators. Let m ij be the number of matches for job seeker i in submarket j, and X i be the seeker's demographic and productive characteristics. A convenient option is the negative where lnλ i = β X i , m ij and X i are as defined above, β is a vector of estimable parameters, and where exp(u) has a gamma distribution with mean 1 and variance α. If α = 0, (9) reduces to the Poisson regression. In our applications, the restriction that α = 0 was rejected in all specifications, so we report only results from the negative binomial specification. Table 6 presents the estimation results for interviews in the academic, business, and government submarkets. We could not reject the hypothesis that the coefficients were equal across tiers, and so we report only the regressions that pool across tiers. However, the null hypotheses that the coefficients were equal across submarkets or equal across boom and bust periods were easily rejected. Thus, we estimate the model for each submarket and time period separately.
′
The first two columns of Table 6 ).
Men have a significant disadvantage in getting academic interviews compared to observationally identical women. The male disadvantage increases significantly in the bust market. The coefficients imply that male interview arrival rates were 18 percent less than otherwise comparable female seekers in the boom. In the bust, the disadvantage increases to a 41 percent lower arrival rate of interviews. Older applicants obtain fewer interviews, but the impact is small numerically. The disadvantage lessened in the bust market, but the numerical difference is insignificant. Residents had a small and statistically insignificant advantage in the bust market, a reversal from the boom.
Our theory suggested that academic employers will raise hiring standards and concentrate on elite school graduates in a bust market. This appears to be the case, with sharp increases in the arrival rate of academic interviews for Tier 1 and Tier 2 applicants relative to those from less prestigious programs. However, the advantage of having a publication or scholarship disappeared in the bust market, as the respective estimated coefficients are not significantly different from zero.
21
In general, seeker attributes associated with a decline in relative academic interview matches were associated with stronger arrival rates of business interviews. Men were more than twice as likely to land business interviews in the bust market after having a modestly lower interview arrival rate in the boom. In the bust market, Tier 3 graduates who were losing 21 relatively in the academic market had an increased probability of landing interview matches in business.
The government sector barely participated in the 1997 market in terms of offers. The number of interviews held was just over half the number of business interviews, almost an exact reversal of the relative position of the two submarkets in the 1987 boom. In both boom and bust markets, resident status is critical for an interview match in the government sector, reflecting hiring restrictions on foreign nationals. The other attributes show evidence of sorting. The Tier 3 disadvantage in interview matches in the bust academic market is also reversed in the government market. Likewise, men who lost relative position in the academic market faced a higher arrival rate of interview matches in the government sector.
Across the three subgroups, the story from Table 6 appears to be that when the labor market weakens for doctorates in economics, the graduates of elite institutions and women gain market share of interviews in the academic market. Graduates of lesser institutions and male seekers get crowded into other markets, gaining market share of interviews there.
Visits and Offers
We replicate the specification for our two other measures of matches, site visits and offers, in Table 7 . For two main reasons, we confine our discussion to the academic market.
First, because the academic market closes by the end of the academic year, our results for that market are relatively complete. The other markets continue through the summer, and we do not observe decisions that late into the year. Second, in part due to that problem, our site visit and offer measures in the government sectors are dominated by a preponderance of zeroes, and so the formal test of the hypothesis of equal coefficients across boom and bust markets could not be rejected in those markets because of a preponderance of imprecisely estimated coefficients. 22 However, the sign patterns for visits and offers in those submarkets matched their respective signs in the interview regressions reported in Table 6 . Consequently, similar patterns of matching across boom and bust markets appear to hold across all three search outcomes.
22
Because the regressions for site visits and offers tell similar stories, we will discuss both pairs of estimates simultaneously. In both boom and bust markets, men experience significantly greater difficulty obtaining visits and offers compared to otherwise comparable women, and the percentage decline in arrival rates is greatest for offers (-62%) than site visits (-51%) or interviews (-41%). However, men make up the gap in the business submarket. Residents, who had a small match advantage in terms of interviews in the bust market, have a significantly improved match probability in academic site visits and offers. Nonresidents make up the gap only partially in the business market in that the resident advantage in that market shrinks in size and becomes insignificant.
The top-tier graduates continue to enjoy an advantage in academic site visits and offers in the bust market, but the coefficients fall in magnitude and precision. This is consistent with the results in Table 4 where elite graduates retain their advantage in academic market outcomes in the bust market, but they face the greatest proportional decrease in visits and offers compared to their boom market levels. The peculiar disadvantage for publications found for interviews in the bust academic market follows through to site visits and offers, although the precision of the estimate is suspect.
Compared to the results for interviews in Table 6 , the results in Table 7 reveal less crowding out of the academic market on the basis of quality. The difference reflects the fact that elite graduates in boom markets average multiple site visits and offers, whereas third tier graduates average less than a single academic offer in the boom. In the bust, third-tier applicants 23 do still less well, but the largest decline in matches during the bust is for the first tier graduates who, on average, have gone from multiple offers to less than one.
Those graduates who were not matched in the academic market generally find employment in other submarkets, even in the bust market of 1997 (Siegfried and Stock, 1999 ).
As discussed above, they found that elite graduates were more likely to find employment in the academic market and to be satisfied with the job they found. Their findings are consistent with our result that tier 3 seekers get crowded out of favored submarkets in the bust and end up searching in submarkets they may not have expected to enter.
VII. CONCLUDING REMARKS
This paper shows the importance of job market conditions on the search strategies and outcomes for first-time job seekers in the economics Ph.D. labor market, both in theory and in the data. In bust markets, graduates at all quality tier levels are less likely to exclude markets.
However, the changes are most noticeable for elite graduates who search more intensively at weaker academic institutions. Employers raise hiring standards in bust markets, which means that weaker schools concentrate more intensively on graduates from higher-ranked schools. This serves to crowd graduates from lower-tier schools into less favored sectors such as business.
Left unexplored is whether graduates who are crowded out of their favored markets during the bust ultimately reenter the market to seek a more favorable match later in their careers. If so, we should find more job switching for bust year graduates than for boom year graduates. On the other hand, those placed in the academic market during bust years should be atypically productive as they had to meet a higher hiring standard in order to be employed. This would be particularly true for men and for nonresidents hired in bust years who seemingly faced greater difficulty being matched in the bust market relative to otherwise comparable women and 24 residents. If this reflects a tendency to set higher threshold hiring levels for men and nonresidents, then we would expect that men and nonresident academicians hired in bust years to be even more productive on average.
23 These questions could be explored with longitudinal data on the careers of boom and bust graduates.
Notes
1 The "1987 market" coincides with the 1986-87 academic year and the "1997 market"
coincides with the 1996-97 academic year.
). 0 ε k 13 Note that because we take random draws from the distribution of q k , there will be heterogeneity in wage outcomes, even if expected wages are equal across seekers ex ante.
14 Pissarides (2000) model assumes that employers can continue hiring until expected returns from an additional search is driven to zero ( = That condition is unlikely to hold in our application because employers generally cannot add additional Ph.D. vacancies within a year. In multiple year settings, it would make sense to impose the zero expected profit condition. 15 Five schools refused to distribute the survey, with no apparent relationship between school rank and willingness to cooperate. 16 We replicated our results deleting those 11 job seekers who did not attend the ASSA meetings in 1987. Results were not sensitive to their inclusion or deletion, so we used the larger sample.
17 For the 1987 survey, interview, visit, and offer counts were obtained from the initial survey; whereas for the 1997 survey, interview, visit, and offer counts were obtained via a follow-up e-mail survey.
18 Several individuals graduated from programs ranked below the top 100 (Tier 4). While their search strategies also differed between 1987 and 1997, due to the small sample size in 1987, we exclude this group from Table 3 . We estimated all of our regressions with and without this group. Since the results were largely insensitive to the inclusion of Tier 4, favoring degrees of freedom we included these individuals in all regressions. Descriptive statistics for Tier 4 individuals are available upon request.
19 To make the subsectors large enough to evaluate, we include Banking or finance, Business or industry, and Consulting or research under our aggregated Business group. There was little change in the importance of these subcomponents between the two years.
28
Consulting/research institutes made up 67% of the market in 1987 and 60% in 1997. 20 Although there was little change between 1987 and 1997 in the percentage of respondents that applied for academic jobs (i.e., 95% applied for academic jobs in 1987, while 96% applied in 1997), as well as government jobs (i.e., 56% in 1987 and 47% in 1997) , the percentage of respondents applying to business jobs increased from 39% to 62%. 21 One could rationalize the drop in importance of self-reported publications as an indication of the proliferation of low quality journals, but we have no information on the quality of the publications. The sharp increase in the proportion reporting publications in the bust market suggests that the definition of "publication" differed across the two markets.
22 Note that our interview information in the business and government sectors will be less subject to truncation than are our site visit and offer measures, and so our interview regressions will be subject to less measurement error. 23 This also demonstrates why studies of discrimination that look only at those in the academic market can yield invalid inferences. Suppose, for example, that women face discrimination in the business sector. This will cause women to lower their reservation wages across all markets relative to men. It will also cause women to search more intensively in sectors other than business. Other things equal, more women will be matched in the academic market than men, but with their lower reservation wages, women are likely to end up being paid less on average than men. An earnings function estimated in the academic market will find that women are paid less than men, but the source of the discrimination is in the business sector and not in academia.
29 where ξ is the reservation compensation level. The first term in brackets is the expected value of the best offer received while the second term is the probability that no offer of at least ξ is received across the J submarkets, multiplied by V*, the expected value of searching again next year. The second term is the aggregated search costs across J markets. Setting ξ = V = V * generates the optimum formula for the reservation wage (2) in the text.
39
Firm Search Strategies
The distribution of net marginal products in submarket k is given by equation (4) 

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