Some evidence on the relationship between performance-related pay and the shape of the experience-earnings profile.

1. Introduction and Background

In this paper we explore the shape of the experience-earnings profile for individuals employed under different types of contracts: fixed-wage, performance-related pay Performance-related pay is money paid to someone relating to how well he or she works at the workplace. Car salesmen, production line workers etc. may be paid in this way or through commission. (PRP PrP A prion protein. See Prion. ), and self-employment

The perspective and/or examples in this article do not represent a world-wide view. Please [ edit] this page to improve its geographical balance. contracts. We follow Lazear (1979, 1981) and Lazear and Moore Moore, city (1990 pop. 40,761), Cleveland co., central Okla., a suburb of Oklahoma City; inc. 1887. Its manufactures include lightning- and surge-protection equipment, packaging for foods, and auto parts. (1984) in hypothesizing that the slope of the experience-earnings profile reflects agency costs Agency Costs

The costs resulting from an agent performing services for a principal.

Notes:
Agency costs are generally the commissions earned by agents.
See also: Agency Problem, Agent, Principal

Agency costs , and the reduction thereof, with increased agency costs inducing steeper steep¹
adj. steep·er, steep·est
1. Having a sharp inclination; precipitous.

2. At a rapid or precipitous rate: a steep rise in salaries.

3.
a. profiles. If monitoring costs are high, firms may implement compensation schemes designed to encourage employees to self-select behavior in the firms' interests. One such scheme defers a substantial amount of compensation until the later years of tenure tenure, in education
tenure, in education, a guarantee of the permanence of a college or university teacher's position, awarded upon successful completion of a probationary period, usually seven years. . The resulting experience-earnings profile provides a penalty for shirking Shirking

The tendency to do less work when the return is smaller. Owners may have more incentive to shirk if they issue equity as opposed to debt, because they retain less ownership interest in the company and therefore may receive a smaller return. (Lazear and Moore 1984).

We, therefore, presume pre·sume
v. pre·sumed, pre·sum·ing, pre·sumes

v.tr.
1. To take for granted as being true in the absence of proof to the contrary: We presumed she was innocent. that the profiles of workers employed under fixed-wage contracts are steeper than those of self-employees for whom the issue of agency does not arise because of the duality Duality (physics)

The state of having two natures, which is often applied in physics. The classic example is wave-particle duality. The elementary constituents of nature—electrons, quarks, photons, gravitons, and so on—behave in some respects of principal and owner (Lazear and Moore 1984). The interesting case is that of workers employed under PRP. If such contracts represent a hybrid hybrid (hī`brĭd), term applied by plant and animal breeders to the offspring of a cross between two different subspecies or species, and by geneticists to the offspring of parents differing in any genetic characteristic (see genetics). between fixed-wage and self-employment, then one would expect their experience-earnings profiles to lie somewhere between these two extreme cases. Thus, PRP and a steep experience-earnings profile may be regarded as substitute mechanisms for inducing employee effort.

The nature of the experience-earnings profile has important implications for labor market labor market A place where labor is exchanged for wages; an LM is defined by geography, education and technical expertise, occupation, licensure or certification requirements, and job experience behavior. If agency considerations prevail, then issues arise concerning the credibility Believability. The major legal application of the term credibility relates to the testimony of a witness or party during a trial. Testimony must be both competent and credible if it is to be accepted by the trier of fact as proof of an issue being litigated. of long-term Long-term

Three or more years. In the context of accounting, more than 1 year.

long-term

1. Of or relating to a gain or loss in the value of a security that has been held over a specific length of time. Compare short-term. employment contracts--firms may have an incentive to replace expensive "older" workers with cheaper "younger" recruits. The shape of the experience-earnings profile may also influence decisions to quit To exit the current program. jobs. Experienced "generally" trained workers may face more options in the labor market than their "firm-specifically" trained counterparts. But both types of worker may have more labor market options than those "older" workers whose earnings reflect agency considerations.

We generalize generalize /gen·er·al·ize/ (-iz)
1. to spread throughout the body, as when local disease becomes systemic.

2. to form a general principle; to reason inductively. Lazear and Moore (1984) by allowing for the intermediate category of PRP, which enables us to further explore the agency explanation behind the positive slope of the experience-earnings profile. Our presumption A conclusion made as to the existence or nonexistence of a fact that must be drawn from other evidence that is admitted and proven to be true. A Rule of Law.

If certain facts are established, a judge or jury must assume another fact that the law recognizes as a logical is that all three employment contracts can be nested in the form

[W.sub.j] = (1 - [[lambda].sub.j]) [bar.w] + [[lambda].sub.j]f(e;[theta Theta

A measure of the rate of decline in the value of an option due to the passage of time. Theta can also be referred to as the time decay on the value of an option. If everything is held constant, then the option will lose value as time moves closer to the maturity of the option. ]), (1)

where j = fw, prp, se denotes "fixed-wage," "PRP," and "self-employment," respectively, [w.sub.j] denotes total remuneration REMUNERATION. Reward; recompense; salary. Dig. 17, 1, 7. , [bar.w] the component of total remuneration that is "fixed" (i.e., independent of worker performance), and f(e; [theta]) some function mapping the relationship between worker performance (i.e., effort), e, "uncertainty," [theta], and pay. (1) [[lambda].sub.j] represents the degree of equity held by a worker in his/her enterprise, that is, the proportion of total remuneration that is dependent on performance. We assume that for fixed-wage employment, [[lambda].sub.fw] = 0; for PRP contracts, [[lambda].sub.prp] [member of] (0, 1); and for self-employment, [[lambda].sub.se] = 1. Our hypothesis An assumption or theory.

During a criminal trial, a hypothesis is a theory set forth by either the prosecution or the defense for the purpose of explaining the facts in evidence. is that the shape of the experience-earnings profile depends critically on [[lambda].sub.j]. To be sure, we predict that agency costs decline monotonically with [[lambda].sub.j] such that the slope of the PRP earnings profile falls between the zero-equity, fixed-wage, and 100% equity, self-employed self-em·ployed
adj.
Earning one's livelihood directly from one's own trade or business rather than as an employee of another.

self profiles. (2)

2. Data and Methodology

Our empirical em·pir·i·cal
adj.
1. Relying on or derived from observation or experiment.

2. Verifiable or provable by means of observation or experiment.

3. analysis draws on three British data sets: the British Social Attitudes Surveys The British Social Attitudes survey is the leading social research survey in Britain and is produced by the National Centre for Social Research

Each year around 3,300 randomly selected adults are asked to give their views on an extensive range of topics. 1985, 1987, 1993, and 1996, the British Household Panel Surveys The British Household Panel Survey (BHPS), carried out at the Institute for Social and Economic Research of the University of Essex, is an instrument for social and economic research. A sample of British households was drawn and first interviewed in 1991. 1991-1999, and the British Family Expenditure Surveys 1997/98, 1998/99, and 1999/00.

The British Social Attitudes Surveys (BSAS BSAS Buenos Aires
BSAS British Society of Animal Science
BSAS Boston Security Analysts Society
BSAS Bachelor of Science in Applied Science
BSAS Bachelor of Science in Applied Sociology
BSAS Brazilian Society for the Advancement of Science ) are an annual series of cross-section cross section also cross-sec·tion
n.
1.
a. A section formed by a plane cutting through an object, usually at right angles to an axis.

b. A piece so cut or a graphic representation of such a piece.

2. surveys initiated by Social and Community Planning Research in 1983. The sample comprises individuals, aged 18 and over, living in private households whose addresses were on the electoral registrar See domain name registrar. ; 114 out of 650 Parliamentary constituencies in Great Britain Great Britain, officially United Kingdom of Great Britain and Northern Ireland, constitutional monarchy (2005 est. pop. 60,441,000), 94,226 sq mi (244,044 sq km), on the British Isles, off W Europe. The country is often referred to simply as Britain. were selected. A polling district was randomly selected from each constituency A constituency is any cohesive corporate unit or body bound by shared structures, goals or loyalty. It can be used to describe a business's customer base and shareholders, or a charity's donors or those it serves. with addresses being randomly chosen from each district.

The surveys conducted in 1985, 1987, 1993, and 1996 contained questions relating to relating to relate prep → concernant

relating to relate prep → bezüglich +gen, mit Bezug auf +acc  the presence of PRP: whether in the year of interview the respondent In Equity practice, the party who answers a bill or other proceeding in equity. The party against whom an appeal or motion, an application for a court order, is instituted and who is required to answer in order to protect his or her interests. had received some component of their total remuneration in the form of: (i) a productivity-linked bonus scheme; (ii) an annual bonus (at the employing organization's discretion); (iii) a share ownership or share option scheme; or (iv) a profit-sharing profit-sharing
Noun

a system in which a portion of the net profit of a business is shared among its employees

profit-sharing n → participación f de empleados en los beneficios  scheme. Individuals who reported that they had participated in any of the four schemes were labeled as PRP employees. (3) Selecting out all male respondents In the context of marketing research, a representative sample drawn from a larger population of people from whom information is collected and used to develop or confirm marketing strategy. in fixed-wage, PRP, or self-employment with complete records rendered 1467, 783, and 491 individuals, respectively. (4)

Our second data set is based on the British Household Panel Survey (BHPS BHPS British Household Panel Study
BHPS Balestier Hill Primary School (Singapore)
BHPS Bronzeville Historical Preservation Society ). This is a random sample survey conducted by the Institute for Social and Economic Research of each adult member of a nationally representative sample of more than 5000 private households. For wave 1, the interviews took place in 1991. The same individuals are reinterviewed in successive waves.

We explore data from the 1991-1999 surveys. In the first wave (1991), all individuals were asked whether their pay includes bonuses or profit sharing profit sharing, arrangement by which employees receive, in addition to their wages, a share of the net profits of a business. The purpose is to give them an incentive to increase their output through enhanced morale, less wasteful use of materials, better care of , thereby enabling us to identify PRP employees. For the period 1992-1995, this question was asked only to individuals who changed their jobs. We, therefore, assume that individuals who did not change job remain in their 1991 employment type. We specify an unbalanced panel of data wherein where·in
adv.
In what way; how: Wherein have we sinned?

conj.
1. In which location; where: the country wherein those people live.

2. the minimum number of times an individual is in the sample is one and the maximum is nine. Our sample comprises 4594 fixed-wage employees, 2806 PRP employees, and 1153 self-employees.

Our third data set is drawn from the Family Expenditure Survey (FES) for Great Britain. This is a nationally representative survey that has been conducted annually since 1957. Approximately ap·prox·i·mate
adj.
1. Almost exact or correct: the approximate time of the accident.

2. 10,000 households are selected each year to take part in the FES, and the average response rate is approximately 70%.

We use data from the 1997-1998, 1998-1999, and 1999-2000 surveys. (5) Our subsample sub·sam·ple
n.
A sample drawn from a larger sample.

tr.v. sub·sam·pled, sub·sam·pling, sub·sam·ples
To take a subsample from (a larger sample). comprises working males aged between 18 and 65 who are either self-employed or employed under a fixed-wage contract or a contract characterized char·ac·ter·ize
tr.v. character·ized, character·iz·ing, character·iz·es
1. To describe the qualities or peculiarities of: characterized the warden as ruthless.

2. by PRP. Those individuals classified as being employed under a PRP contract were those in receipt of a productivity-linked bonus, profit-related pay, dividends from employee share ownership, an incentive bonus, or a performance-related bonus. Our sample consists of 8405 male respondents comprising 5965 fixed-wage employees, 1201 PRP employees, and 1239 self-employees.

It is apparent that there is some tension between our theoretical and empirical definitions of PRP. The former defines PRP as any contract in which current pay is related to current performance broadly defined. Some jobs reward performance with a promotion and/or and/or
conj.
Used to indicate that either or both of the items connected by it are involved.

Usage Note: And/or is widely used in legal and business writing. a salary increment To add a number to another number. Incrementing a counter means adding 1 to its current value. rather than with an explicit bonus. Moreover, some bonus schemes may also be used to identify "fast-track fast track
n. Informal
The quickest and most direct route to achievement of a goal, as in competing for professional advancement: "Making complaints against the public is hardly the fast track to elective office" " employees, and for these, a revelatory component may be in operation as the employer learns more about a worker's skills and ability in the early years following an initial hire. (6) The BHPS, but not the BSAS or FES, asks specific questions relating to promotions and salary increments in each year, and we, therefore, augment aug·ment
v. aug·ment·ed, aug·ment·ing, aug·ments

v.tr.
1. To make (something already developed or well under way) greater, as in size, extent, or quantity: our BHPS empirical analysis by including dummy variables This article is not about "dummy variables" as that term is usually understood in mathematics. See free variables and bound variables.

In regression analysis, a dummy variable to control for these factors. (7)

The empirical definitions of PRP in the three data sets are quite explicit. The BSAS and FES adopt similarly broad definitions that include productivity-linked and discretionary bonuses, share ownership/options, and profit sharing. The BHPS focuses on just bonuses (type not defined) and profit sharing. (8) Which definition is more appropriate is largely a matter of interpretation, but our results are broadly consistent across the three data sets.

Sample statistics of the key variables for each of the data sets are set out in Table 1. (9) A common finding in all three data sets is the relatively low earnings of self-employed respondents. The problems of accurately measuring pay from self-employment are well documented (see Eardley and Corden 1996; Hamilton Hamilton, city, Bermuda
Hamilton, city (1990 est. pop. 3,100), capital of Bermuda, on Bermuda Island. It is a port at the head of Great Sound, a huge lagoon and deepwater harbor protected by coral reefs. 2000). Because our focus is on the slope of the experience-earnings profile, measurement error is not too problematic if the earnings of the self-employed are consistently underreported.

Our analysis is based on a Mincerian earnings equation of the form:

1n [w.sub.ijt] = [X.sub.ijt][B.sub.j] + [[alpha].sub.j][E.sub.ijt] + [[beta].sub.j][E.sup.2.sub.ijt] + [[epsilon].sub.ijt], (2)

where [w.sub.ijt] represents the hourly earnings of an individual i employed in employment type j at time t, with j = s, prp, se representing the three employment categories. (10) [X.sub.ijt] represents a vector of personal and workplace characteristics including education, occupational status, and industrial affiliation affiliation (

fil´ēā´sh , and [E.sub.ijt] denotes labor force experience at time t, proxied by the respondent's age at time t less his/her age when he/she completed full-time full-time
adj.
Employed for or involving a standard number of hours of working time: a full-time administrative assistant.

full education. (11) It is apparent that a first-best estimation estimation

In mathematics, use of a function or formula to derive a solution or make a prediction. Unlike approximation, it has precise connotations. In statistics, for example, it connotes the careful selection and testing of a function called an estimator. of Equation 2 would utilize a panel data source. We adopt such an approach with the BHPS sample, which follows the same individuals across 1991 to 1999. In the case of the BSAS and FES analysis, we are obliged o·blige
v. o·bliged, o·blig·ing, o·blig·es

v.tr.
1. To constrain by physical, legal, social, or moral means.

2. to use pooled cross-section data. This is not too problematic: If we assume that the vector of personal/ workplace characteristics is stable across time then [X.sub.ijt] = [X.sub.ij], [for all] t. (12)

Estimation is further complicated on account of potential sample selection bias. Our earnings data derive de·rive
v.
1. To obtain or receive from a source.

2. To produce or obtain a chemical compound from another substance by chemical reaction. from observing observing,
v 1. to look or notice through visual inspection.
2. to quietly look at the client's inhalation and exhalation patterns to discern the breath wave and perceive areas that need therapeutic intervention. a particular employment contract (i.e., fixed-wage, PRP, or self-employment), and there may be variables that affect both the probability probability, in mathematics, assignment of a number as a measure of the "chance" that a given event will occur. There are certain important restrictions on such a probability measure. of observing such a contract and the return to any factors in the earnings equation. To take account of such considerations, we control for sample selection bias. Probit In probability theory and statistics, the probit function is the inverse cumulative distribution function (CDF), or quantile function associated with the standard normal distribution. analysis with three discrete A component or device that is separate and distinct and treated as a singular unit. outcomes is used to model the determination of Z, prob([Z.sub.i] = j), that is, the probability of being in one of the three possible types of employment, which is then used to calculate the standard inverse mills ratio The inverse Mills' ratio is a concept in statistics. It is the ratio of the probability density function over the cumulative distribution function of a distribution. term, [[delta].sub.ijt]=[phi]([H.sub.ijt])/[PHI]([H.sub.ijt]), where [H.sub.ijt] = [[PHI].sup.-1]([P.sub.ijt]), [P.sub.ijt] denotes the predicted probability of individual i at time t being employed under contract type j, [phi](.) represents the probability density function Probability density function

The function that describes the change of certain realizations for a continuous random variable. of the standard normal distribution, and [PHI](.) represents the cumulative density function Cumulative density function is a self-contradictory phrase resulting from confusion between:

probability density function, and
cumulative distribution function.

The two words cumulative and density contradict each other. of the standard normal distribution.

By incorporating [[delta].sub.ijt] into the wage regression regression, in psychology: see defense mechanism.

regression

In statistics, a process for determining a line or curve that best represents the general trend of a data set. , we control for the possibility that particular types of individuals may be employed under specific types of contract.

To explore the robustness of our findings, we experimented with three alternative measures of labor market experience. First, for all three data sets, we estimated age-earnings profiles. Second, for the BSAS only (such information is not available in the BHPS or FES), we estimated employer tenure-earnings profiles for fixed-wage and PRP employees, making use of the question asked in 1993 and 1996: How long have you been continuously employed by your present employer? And finally, for the BHPS only, we estimated job tenure-earnings profiles, making use of the following question: What was the date you started working in your present position? This question is asked to all employed respondents, whereas all self-employed respondents were asked, On what date did you start doing your present job? By that I mean the beginning of your current spell spell, word, formula, or incantation believed to have magical powers. The spell can be used for evil or good ends; if evil, it is a technique of sorcery. Many authorities believe that the spell was the precursor of prayer. of doing the work you are doing now on a self-employed basis? Thus, the responses to both questions are used to calculate current job tenure for employees and self-employees.

3. Results

We estimate a standard quadratic quadratic, mathematical expression of the second degree in one or more unknowns (see polynomial). The general quadratic in one unknown has the form ax²+bx+c, where a, b, and c are constants and x is the variable. Mincerian wage equation as depicted de·pict
tr.v. de·pict·ed, de·pict·ing, de·picts
1. To represent in a picture or sculpture.

2. To represent in words; describe. See Synonyms at represent. by Equation 2. In the case of the cross-section data, we employ standard ordinary least squares (OLS OLS Ordinary Least Squares
OLS Online Library System
OLS Ottawa Linux Symposium
OLS Operation Lifeline Sudan
OLS Operational Linescan System
OLS Online Service
OLS Organizational Leadership and Supervision
OLS On Line Support
OLS Online System ) techniques, whereas in the case of the BHPS we have employed a random effects Random effects can refer to:

Random effects estimator
Random effect model

estimator. (13) We have experimented with a number of combinations of explanatory ex·plan·a·to·ry
adj.
Serving or intended to explain: an explanatory paragraph.

ex·plan variables as well as changes to our set of overidentifying instruments in the selection equation, and we have found the regressions to be generally well specified spec·i·fy
tr.v. spec·i·fied, spec·i·fy·ing, spec·i·fies
1. To state explicitly or in detail: specified the amount needed.

2. To include in a specification.

3. and highly robust. (14) The selectivity selectivity /se·lec·tiv·i·ty/ (se-lek-tiv´i-te) in pharmacology, the degree to which a dose of a drug produces the desired effect in relation to adverse effects.

selectivity

1. terms suggest that ignoring selection issues would have led to significant bias in the estimated coefficients. In general, our findings accord with the previous literature, suggesting a generally positive relationship between earnings and education as well as a concave Concave

Property that a curve is below a straight line connecting two end points. If the curve falls above the straight line, it is called convex. relationship between earnings and experience. For brevity Brevity
Adonis’ garden

of short life. [Br. Lit.: I Henry IV]

bubbles

symbolic of transitoriness of life. [Art: Hall, 54]

cherry fair

cherry orchards where fruit was briefly sold; symbolic of transience. , in Tables 2 to 4, only one specification, which includes both educational certificates and years of education, is presented. (15) Tables 2, 3, and 4 present the results relating to the BSAS, the BHPS, and the FES, respectively.

It is apparent that the least robust regressions

This article or section is written like a personal reflection or and may require .
Please [ improve this article] by rewriting this article or section in an . are those for the self-employed respondents. This is not surprising. Such workers will not be motivated mo·ti·vate
tr.v. mo·ti·vat·ed, mo·ti·vat·ing, mo·ti·vates
To provide with an incentive; move to action; impel.

mo by Mincerian-type arguments to the same extent as their employed counterparts and, under an (extreme) argument whereby the earnings profile is a reflection of agency issues only, would not face the necessity of rewarding themselves with an upward sloping slope
v. sloped, slop·ing, slopes

v.intr.
1. To diverge from the vertical or horizontal; incline: a roof that slopes. See Synonyms at slant.

2. profile to ensure efficient effort over their life cycle (Lazear and Moore 1984).

It is apparent from Tables 2 to 4 that the estimated coefficients on the various labor force experience proxies (i.e., Age, Years in Labor Force, Job Tenure, and Employer Tenure) support our theoretical assumption that the slope of the earnings profile for PRP workers lies between those of their fixed-wage and self-employed counterparts. In terms of the BSAS and FES (Tables 2 and 4), the three profiles are significantly different from one another at the 1% level of significance irrespective of irrespective of
prep.
Without consideration of; regardless of.

irrespective of
preposition despite the experience proxy See proxy server.

(networking) proxy - A process that accepts requests for some service and passes them on to the real server. A proxy may run on dedicated hardware or may be purely software. used. In terms of the BHPS (Table 3), the three profiles are significantly different from one another at the 1% level of significance when experience is proxied by age or job tenure, whereas the profiles associated with PRP and self-employment (but not fixed wage and PRP) are significantly different from each other at the 1% level when experience is proxied by years in the labor force. Figures 1 to 5 present the estimated experience-earnings profiles for each set of results, with--for reasons of brevity--the exception of the age--earnings profiles. (16)

Our BHPS results also highlight the positive relationship between the dummy variable indicating whether the respondent had been promoted in the current year (Promotion) and the earnings of fixed-wage and PRP workers, and between the incremental Additional or increased growth, bulk, quantity, number, or value; enlarged.

Incremental cost is additional or increased cost of an item or service apart from its actual cost. pay scale dummy variable (Annual Increment) and the earnings of PRP workers. We experimented with interacting Annual Increment and Promotion with the various experience proxies. These results confirm the validity of our findings regarding the experience-earnings profiles. (17) Moreover, in the case of the Promotion variable, they also cast additional light on the roles played by human capital and agency. Interacting the various experience proxies with Promotion suggested that the ordering of the three profiles was not affected by whether respondents had enjoyed a promotion in the current year. Indeed, we found that the difference in the three slopes, for example, the agency effect was actually more pronounced for those periods that fell in between promotions. Such findings support the argument that human capital may be more relevant to obtaining promotions, whereas agency effects appear to operate primarily between promotions. (18)

Lambda as a Continuous Variable

The FES and the BHPS (1997-1999) record the total amount of remuneration received in the form of the bonus. In Equation 1, total remuneration is split into a fixed and a variable component, the division depending on the size of [lambda]. For the FES and the BHPS, we can, therefore, proxy [lambda] by calculating the ratio of bonus to total pay:

[??] = Bonus Pay/Total Pay = [lambda]f(e; [theta])/(1 - [lambda][bar.w] + [lambda]f(e; [theta])

Sample statistics for both the FES and BHPS samples relating to [lambda] are set out in Table 5.

Our assumption is that a higher value of [lambda] raises the expected cost of early-term shirking on the part of the worker and thereby leads to a flattening

Ellipticity redirects here. For the mathematical topic of ellipticity, see elliptic operator.

The flattening, ellipticity, or oblateness of an oblate spheroid is the "squashing" of the spheroid's pole, down towards its equator. of the experience-earnings profile. To investigate this, we ran three earnings regressions pooling the self-employed, the PRP employees, and the fixed-wage employees together for each of the two data sets (see Tables 6 and 7). In the case of the FES, we conduct OLS analysis, whereas in the case of the BLIPS, we adopt a random effects approach given the panel element to the data. Once again, an unbalanced panel is analyzed an·a·lyze
tr.v. an·a·lyzed, an·a·lyz·ing, an·a·lyz·es
1. To examine methodically by separating into parts and studying their interrelations.

2. Chemistry To make a chemical analysis of.

3. in which the minimum (maximum) number of times an individual is in the sample is one (three).

Specification 1 illustrates that higher values of [lambda] are associated with significantly lower earnings--this effect is probably reflecting the presence of self-employed workers within our sample. Specifications 2 and 3 include an interaction between experience and [lambda]. Our results suggest that higher levels of [??] are indeed associated with a flattening of the experience-earnings profile. In the case of the FES, our findings suggest that as the estimated coefficient coefficient /co·ef·fi·cient/ (ko?ah-fish´int)
1. an expression of the change or effect produced by variation in certain factors, or of the ratio between two different quantities.

2. of [??] moves from zero to unity, the annual rate of return (in terms of log hourly earnings) to an additional year of labor market experience falls from just under 5% to less than 1%. To summarize sum·ma·rize
intr. & tr.v. sum·ma·rized, sum·ma·riz·ing, sum·ma·riz·es
To make a summary or make a summary of.

sum , these findings provide further support for the hypothesis that the slope of the experience-earnings profile is influenced by both the presence and extent of PRP.

Training

Our results support the hypothesis that experience-earnings profiles reflect agency considerations, with PRP profiles generally lying somewhere between fixed-wage and self-employed profiles. Furthermore, we would argue that our findings are relatively robust given that we have used three different data sets (including both cross-section and panel data) as well as exploring our theoretical assumptions via two different approaches and specifying alternative measures of experience. However, it is apparent that an alternative explanation can be offered. It may be the case that PRP workers undertake relatively less training than their fixed-wage counterparts, implying relatively high (low) starting (future) earnings and thus flatter profiles. It is difficult to rationalize ra·tion·al·ize
v.
1. To make rational.

2. To devise self-satisfying but false or inconsistent reasons for one's behavior, especially as an unconscious defense mechanism through which irrational acts or feelings are made to appear such an argument; it might seem that workers remunerated re·mu·ner·ate
tr.v. re·mu·ner·at·ed, re·mu·ner·at·ing, re·mu·ner·ates
1. To pay (a person) a suitable equivalent in return for goods provided, services rendered, or losses incurred; recompense.

2. under PRP are, if anything, more likely to respond to investments in training than their fixed-wage counterparts. We investigated the relative likelihood of fixed-wage and PRP respondents having undertaken some form of training at their place of work. Two of our three data sets (BSAS and BHPS) contained information regarding whether respondents had received training.

The BSAS survey for 1987 asked respondents whether, in the two years preceding the survey interview, they had been (i) asked to do anything just for practice in order to learn the work; (ii) given any special talks or lectures about the work; (iii) placed with more experienced people to see how the work should be done; (iv) sent around to different parts of the organization to see how the work is done; (v) asked to read things to help learn about the work; (vi) taught or trained by anyone while actually doing the work; (vii) sent on any courses to introduce new methods of working. We created two variables: a dummy variable (Train) that takes the value of one if the respondent answered "yes" to any of these questions and an index (Trains) that equals the number of these questions to which the respondent had answered "yes" to the various types of training. Thus, Train represents a binary Meaning two. The principle behind digital computers. All input to the computer is converted into binary numbers made up of the two digits 0 and 1 (bits). For example, when you press the "A" key on your keyboard, the keyboard circuit generates and transfers the number 01000001 to the dummy variable indicating whether or not an individual has received any training, and Trains represents an index, which ranges from 0 to 7, indicating the number of types of training undertaken. We are, therefore, able to focus on both the incidence (Train) and the intensity of training (Trains).

Participants in the BHPS were asked whether in the preceding year they had participated in any off-the-job off-the-job adj off-the-job training → formación f fuera del trabajo

off-the-job adj off-the-job training → formation professionnelle extérieure  education or training. Relevant summary statistics for the training variables for both data sets are set out in Table 8. Because our focus is on the incidence of training at relatively low levels of experience, we present summary statistics for four levels of experience. In the case of the BSAS, it appears that PRP employees do receive less training than their fixed-wage counterparts. This finding may be taken as support for a training effect. The situation is reversed, however, at the lowest category of experience, which arguably ar·gu·a·ble
adj.
1. Open to argument: an arguable question, still unresolved.

2. That can be argued plausibly; defensible in argument: three arguable points of law. is the focus of our attention. In the case of the BHPS, the support for the training explanation is less clear-cut, with PRP employees, in general, being characterized by a higher incidence of training.

We explore the incidence and intensity of training across PRP and fixed-wage employees by conducting probit analysis (dependent variable Train) and ordered probit In statistics, ordered probit is a flavor of the popular probit analysis, used for ordinal dependent variables. Similarly, the popular logit method also has a counterpart ordered logit. analysis (dependent variable Trains). Our findings are set out in Table 9. In the case of the BSAS, the PRP dummy variable has no effect on the probability that the respondent had so engaged in training. The probability of training was, however, significantly negatively related to the respondent's experience in the labor market, perhaps reflecting the fact that most training is undertaken by relatively younger workers. To ascertain whether there was any interaction between such experience and the PRP dummy Sham; make-believe; pretended; imitation. Person who serves in place of another, or who serves until the proper person is named or available to take his place (e.g., dummy corporate directors; dummy owners of real estate). , we ran a second specification of the dichotomous di·chot·o·mous
adj.
1. Divided or dividing into two parts or classifications.

2. Characterized by dichotomy.

di·chot and ordered probit models, this time including a series of experience-PRP interaction terms such as Experience less than 5 years*PRP; Experience more than 5 years but less than 10 years*PRP; Experience more than 10 years but less than 20 years*PRP; and Experience more than 20 years*PRP. The coefficients on these terms were insignificant in the dichotomous probit, but those on the last three were significantly negative in the ordered probit. Thus, it does appear that more experienced workers receive relatively less training than their non-PRP counterparts, ceteris paribus Ceteris Paribus

Latin phrase that translates approximately to "holding other things constant" and is usually rendered in English as "all other things being equal". In economics and finance, the term is used as a shorthand for indicating the effect of one economic variable on . It should be noted, however, that we are primarily interested in the incidence of training at the lower level of experience.

We repeated this analysis for the BHPS. In the case of the panel data set, the results from employing a random effects probit estimator suggest that PRP employees are more likely to receive training, which provides evidence contrary to the training explanation. Our results are, therefore, somewhat mixed, and the BSAS findings allude to allude to
verb refer to, suggest, mention, speak of, imply, intimate, hint at, remark on, insinuate, touch upon see see, elude the possibility that differences in the experience-earnings profile may not be driven solely by agency consideration. But these findings are not reflected in the BHPS.

4. Final Comments

This paper has focused on the relationship between experience--earnings profiles and the degree of worker equity within an enterprise. We further explore Lazear and Moore's (1984) thesis This article or section has multiple issues:
* It may require general cleanup to meet Wikipedia's quality standards.

Please help [ improve the article] or discuss these issues on the talk page.
This article is about the thesis in academia. that the nature of the profile reflects agency considerations by focusing not only on those workers with zero or 100% equity (i.e., fixed-wage and self-employed workers, respectively) but also on those with a fractional fractional

size expressed as a relative part of a unit.

fractional catabolic rate
the percentage of an available pool of body component, e.g. protein, iron, which is replaced, transferred or lost per unit of time. level of equity, for example, workers remunerated under some form of PRP. Our presumption is that PRP employees face an intermediate level of agency costs and as such require an intermediate profile. Our empirical analysis of three British data sets offers support for this view.

Our results might be interpreted Translated from source code into machine code one line at a time. See interpreted language and interpreter.

interpreted - interpreter as support for the argument that the shape of the experience-earnings profile reflects agency considerations. As such, they highlight important issues pertaining per·tain
intr.v. per·tained, per·tain·ing, per·tains
1. To have reference; relate: evidence that pertains to the accident.

2. to the credibility of long-term employment contracts because employers may be tempted to replace experienced workers with less costly, but equally productive, novices. But the latter will not remain "young" forever, and whether they will be inclined to work for a firm that is unable to guarantee them employment in their dotage dot·age
n.
The loss of previously intact mental powers; senility. Also called anility. is an open question.

References

Blinder, Alan A`lan´

n. 1. A wolfhound. S. (ed.). 1990. Paying for productivity: A look at the evidence. Washington Washington, town, England
Washington, town (1991 pop. 48,856), Sunderland metropolitan district, NE England. Washington was designated one of the new towns in 1964 to alleviate overpopulation in the Tyneside-Wearside area. , DC: The Brookings Institution Brookings Institution, at Washington, D.C.; chartered 1927 as a consolidation of the Institute for Government Research (est. 1916), the Institute of Economics (est. 1922), and the Robert S. Brookings Graduate School of Economics and Government (est. 1924). .

Booth, Alison Alison

betrays old husband amusingly with her lodger, Nicholas. [Br. Lit.: Canterbury Tales, “Miller’s Tale”]

See : Adultery L., and Jeff Frank. 1999. Earnings, productivity and performance-related may. Journal of Labor Economics The Journal of Labor Economics, published by the University of Chicago Press presents international research examining issues affecting the economy as well as social and private behavior. 17:447-63.

Eardley, Tony, and Anne Anne, British princess
Anne (Anne Elizabeth Alice Louise), 1950–, British princess, only daughter of Queen Elizabeth II and Prince Philip, duke of Edinburgh. She was educated at Benenden School. Corden. 1996. Self-employed earnings and income distribution: Problems of measurement. Social Policy Report No. 5, Social Policy Research Unit, University of York This article is about the British university. For the Canadian university, see York University.
The University of York is a campus university in York, England. .

Hamilton, Barton BARTON, old English law. The demesne land of a manor; a farm distinct from the mansion. H. 2000. Does entrepreneurship en·tre·pre·neur
n.
A person who organizes, operates, and assumes the risk for a business venture.

[French, from Old French, from entreprendre, to undertake; see enterprise. pay? An empirical analysis of the returns to self-employment. Journal of Political Economy 108:604-31.

Jovanovic, Boyan Boyan may refer to:

Boyan (bard), a bard active at the court of Yaroslav the Wise.
Boyany, the Bukovinian city named Boyan in Yiddish.
Bojan, a common Slavic given name.
Boyan, a Hasidic dynasty

. 1979. Job matching and the theory of turnover. Journal of Political Economy 87:1972-90.

Lazear, Edward Edward

killed his father at his mother’s instigation. [Br. Balladry: Edward in Benét, 302]

See : Patricide P. 1979. Why is there mandatory retirement A mandatory retirement age is the age at which persons who hold certain jobs or offices are required by statute to step down, or retire.

Typically, mandatory retirement ages are justified by the argument that certain occupations are either too dangerous (military personnel) ? Journal of Political Economy 87:1261-84.

Lazear, Edward P. 1981. Agency, earnings profiles, productivity and hours restrictions. American American, river, 30 mi (48 km) long, rising in N central Calif. in the Sierra Nevada and flowing SW into the Sacramento River at Sacramento. The discovery of gold at Sutter's Mill (see Sutter, John Augustus) along the river in 1848 led to the California gold rush of Economic Review 71:606-20.

Lazear, Edward P., and Robert Robert, Henry Martyn 1837-1923.

American army engineer and parliamentary authority. He designed the defenses for Washington, D.C., during the Civil War and later wrote Robert's Rules of Order (1876).

Noun 1. L. Moore. 1984. Incentives, productivity and labor contracts. Quarterly Journal of Economics The Quarterly Journal of Economics, or QJE, is an economics journal published by the Massachusetts Institute of Technology and edited at Harvard University's Department of Economics. Its current editors are Robert J. Barro, Edward L. Glaeser and Lawrence F. Katz. 99:275-95.

Murphy, Kevin KEVIN Keepers of the Eternal Vigilance of the Islamic Nation (fictional, from White Teeth by Zadie Smith) M., and Finis Welch Welch , William Henry 1850-1934.

American pathologist and bacteriologist who discovered the bacteria that causes gas gangrene. . 1990. Empirical age-earnings profiles. Journal of Labor Economics 8:202-29.

Sarah Brown Sarah Brown can be:

Sarah Joy Brown - Sarah Brown (actress) - an American actress best known for portraying Carly Corinthos on the daytime soap opera General Hospital.
Sarah Brown (nee Macaulay) - wife of British Prime Minister Gordon Brown.

, Department of Economics, University of Sheffield The University of Sheffield is a research university, located in Sheffield in South Yorkshire, England. Reputation
Sheffield was the Sunday Times University of the Year in 2001 and has consistently appeared as their top 20 institutions. , Sheffield S Sheffield, city, England
Sheffield, city (1991 pop. 470,685), N England, at the confluence of the Don River and four tributaries. Sheffield was one of the leading industrial cities of England. It has been a center of cutlery manufacture since the 14th cent. 1 4DT, England England, the largest and most populous portion of the United Kingdom of Great Britain and Northern Ireland (1991 pop. 46,382,050), 50,334 sq mi (130,365 sq km). It is bounded by Wales and the Irish Sea on the west and Scotland on the north. ; E-mail sarah.brown@sheffield.ac.uk; corresponding author. and John G. Sessions, Department of Economics and Intemational Development, University of Bath, Bath BA2 7AY, England; E-mail j.g.sessions@bath.ac.uk.

We are particularly grateful to two anonymous Nameless. See anonymous post and anonymous Web surfing. referees for excellent comments. We are grateful to the Data Archive (1) A file that contains one or more compressed files. Most archive formats are also capable of storing folders in order to reconstruct the file/folder relationship when decompressed. See archive formats. at the University of Essex The University of Essex is a British plate glass university. It received its Royal Charter in 1965. The university's main campus is located at Wivenhoe Park on the outskirts of Colchester (the oldest recorded town in Britain) in the English county of Essex, less than a mile from for supplying the British Social Attitudes Surveys 1985, 1987, 1993, and 1996, the British Household Panel Surveys 1991-1999, and the Family Expenditure Surveys 1997/98, 1998199, and 1999/00. Our analysis has benefited from discussions with Saul Saul, first king of the ancient Hebrews. He was a Benjamite and anointed king by Samuel. Saul's territory was probably limited to the hill country of Judah and the region to the north, and his proximity to the Philistines brought him into constant conflict with them. Estrin estrin /es·trin/ (es´trin) estrogen.

es·trin
n.
See estrogen.

estrin (es´trin),
n , Gianni De Fraja Gianni De Fraja is the William Tyler Professor of Economics in the Department of Economics at the University of Leicester and Research Fellow (CEPR) [1]. Between 1999 and 2005, he was Managing Editor of Bulletin of Economic Research [2]. , and Stephen Stephen, 1097?–1154, king of England (1135–54). The son of Stephen, count of Blois and Chartres, and Adela, daughter of William I of England, he was brought up by his uncle, Henry I of England, who presented him with estates in England and France and Pudney. Helpful comments were also received from seminar participants at the Universities of Brunei Brunei (br

nī`) or Brunei Darussalam (där'əsəläm`), officially State of Brunei Darussalam, sultanate (2005 est. pop. , Essex, Kent, Lancaster Lancaster, city, England
Lancaster (lăng`kəstər), city (1991 pop. 43,902) and district, county seat of Lancashire, NW England, on the Lune River. , Leicester Leicester (lĕs`tər), city (1991 pop. 324,394) and district, Leicestershire, central England. The city is connected by canals with the Trent River and London, and it is also a railway center. , and Sheffield. The normal disclaimer (networking) disclaimer - Statement ritually appended to many Usenet postings (sometimes automatically, by the posting software) reiterating the fact (which should be obvious, but is easily forgotten) that the article reflects its author's opinions and not necessarily those of the applies.

Received June June: see month. 2004; accepted June 2005.

(1) Uncertainty may be related, for example, to conditions in the output market.

(2) Equation 1 defines PRP as any contract in which current pay is related to worker performance broadly defined. The practical operation of such schemes is certainly more nuanced; see, for example, Blinder (1990), Booth and Frank (1999).

(3) Clearly, the four schemes are diverse in nature and, as such, create different incentives. Ideally, we would classify clas·si·fy
tr.v. clas·si·fied, clas·si·fy·ing, clas·si·fies
1. To arrange or organize according to class or category.

2. To designate (a document, for example) as confidential, secret, or top secret. individuals according to according to
prep.
1. As stated or indicated by; on the authority of: according to historians.

2. In keeping with: according to instructions.

3. the type of PRP scheme. Such an approach would be somewhat problematic, however, because of the low number of observations in each scheme.

(4) In order to abstract from issues related to labor market participation, we focus on male employees only.

(5) Before this period, the dataset See data set. had a different structure, and some of the variables required for our analysis are not available.

(6) We are grateful to an anonymous referee A judicial officer who presides over civil hearings but usually does not have the authority or power to render judgment.

Referees are usually appointed by a judge in the district in which the judge presides. for highlighting this point.

(7) The specific questions are: Promotion--If you have been promoted or changed grades, please give me the date of that change; Annual Increment--Some people can normally expect their pay to rise every year by moving to the next point on the scale as well as receiving negotiated pay rises. Are you paid on this type of incremental scale? Zero-one dummy variables were created from both of these questions.

(8) There is also some distinction between the BSAS/FES and BHPS definitions of self-employment. In the BSAS and FES, individuals are categorized cat·e·go·rize
tr.v. cat·e·go·rized, cat·e·go·riz·ing, cat·e·go·riz·es
To put into a category or categories; classify.

cat according to the following question: In your main job, are you an employee or a self-employee? In the BHPS, individuals are asked to specify their current labor force status, with options including "paid employment," "selfemployment," "unemployed," "retired," "on maternity leave maternity leave n → baja por maternidad

maternity leave maternity n → congé m de maternité

maternity leave maternity n ," "in family care," "full-time student Full-Time Student

A status that is important for determining dependency exemptions. An individual enrolled in a post-secondary institution may be eligible for certain tax breaks.

Notes:
The full-time status is based on what the individual's school considers full time. ," "long-term sick/ disabled," "on a government training scheme," and "other."

(9) A small number of individuals with more than one job, individuals employed by the armed forces, and agricultural workers were excluded from the analysis of each data set.

(10) The definition of hourly earnings differs across our three data sets according to the survey questions asked of respondents. For the BHPS, hourly earnings are defined as labor income in the previous month divided by the number of hours normally worked per month. For the BSAS, they are defined as the respondent's gross annual earnings divided by the number of hours the respondent works per week multiplied mul·ti·ply¹
v. mul·ti·plied, mul·ti·ply·ing, mul·ti·plies

v.tr.
1. To increase the amount, number, or degree of.

2. Mathematics To perform multiplication on. by 52. For the FES, hourly employed earnings are defined as the normal gross weekly wage divided by usual weekly hours, whereas hourly self-employed earnings are defined as normal gross income from self-employment divided by usual weekly hours worked.

(11) Following Murphy and Welch (1990), we also experimented with cubic and quartic quar·tic
adj. Mathematics
Of or relating to the fourth degree.

[Latin qurtus, fourth; see quart + -ic. experience terms; the results are available on request.

(12) However, where a good match has been made between the employer and the employee, earnings will be relatively high, and tenure will be relatively long (Jovanovic 1979). Cross-sectional data Cross-sectional data in statistics and econometrics is a type of one-dimensional data set. Cross-sectional data refers to data collected by observing many subjects (such as individuals, firms or countries/regions) at the same point of time, or without regard to differences in time. , which contain no information about the quality of a job match, may bias estimates of the returns to job tenure.

(13) The fixed-effects estimation results are available from the authors by request.

(14) The overidentifying instruments for the underlying sample selection model included region of residence and a variety of demographic See demographics. controls (ethnicity ethnicity Vox populi Racial status–ie, African American, Asian, Caucasian, Hispanic , marital status marital status,
n the legal standing of a person in regard to his or her marriage state. , number of children, previous spells of unemployment, and private education). The sample selection models, which were estimated using probit analysis with three discrete outcomes, are generally well specified. For reasons of brevity, the sample selection results are not presented here but are available from the authors on request. The results reported in Tables 2 to 4 have all been corrected for sample selection bias (uncorrected results are also available on request).

(15) As pointed out by an anonymous referee, the shape of the earnings profiles may be influenced by trade union membership, unionization of the workplace, and employment in the public sector. Our results are robust to the inclusion of such controls.

(16) Because we are primarily interested in the relative slopes of the earnings profiles, the intercept intercept

in mathematical terms the points at which a curve cuts the two axes of a graph. terms in Figures 1 to 5 have been set to zero.

(17) The results are available on request.

(18) We are grateful to an anonymous referee for highlighting such issues.

Table 1. Summary Statistics: Key Variables

                                   Fixed-Wage
                                   (N = 1467)

                                         Standard
Variable                        Mean     Deviation

British Social Attitudes Surveys 1985, 1987, 1993, 1996
  Log hourly earnings            1.716       0.619
  Years in labor force (YILF)   21.722      12.694
  Age                           38.644      11.902
  Employer tenure (a)            9.227      11.505
  Years of education            11.941       2.191
  Degree                         0.192       0.394
  Further education              0.179       0.384
  A level                        0.141       0.348
  GCSE grades A to C             0.197       0.398
  GCSE grades below C            0.065       0.247
  Other qualification            0.013       0.113

                                   Fixed-Wage
                                  (N = 14,284)

                                         Standard
Variable                        Mean     Deviation

British Household Panel Survey 1991-1999
  Log hourly earnings            1.926       0.517
  Years in labor force (YILF)   24.820      12.435
  Years of education            13.224       4.018
  Job tenure                     4.944       6.451
  Age                           38.088      11.568
  Annual increment               0.294       0.455
  Promotion                      0.053       0.224
  Degree                         0.177       0.381
  Further education              0.241       0.428
  A level                        0.144       0.351
  GCSE grades A to C             0.195       0.396
  GCSE grades below C            0.195       0.396
  Other qualification            0.035       0.185

                                   Fixed-Wage
                                   (N = 5965)

                                         Standard
Variable                        Mean     Deviation

Family Expenditure Survey 1997/98, 1998/99, and 1999/00
  Log hourly earnings            2.120       0.564
  Years in labor force (YILF)   22.283      12.413
  Age                           39.644      11.618
  Years of education            12.361       2.777
  Degree                         0.224       0.417
  Further education/A level      0.192       0.394
  GCSE                           0.365       0.482
  Less than GCSE                 0.218       0.413

                                  PRP (N = 783)

                                         Standard
Variable                        Mean     Deviation

British Social Attitudes Surveys 1985, 1987, 1993, 1996
  Log hourly earnings            1.771       0.610
  Years in labor force (YILF)   22.253      12.325
  Age                           38.789      11.643
  Employer tenure (a)           10.757      10.713
  Years of education            11.540       1.885
  Degree                         0.102       0.303
  Further education              0.171       0.377
  A level                        0.160       0.367
  GCSE grades A to C             0.223       0.417
  GCSE grades below C            0.089       0.286
  Other qualification            0.010       0.101

                                  PRP (N = 6212)

                                         Standard
Variable                        Mean     Deviation

British Household Panel Survey 1991-1999
  Log hourly earnings            2.029       0.515
  Years in labor force (YILF)   23.588      11.754
  Years of education            13.214       3.485
  Job tenure                     4.468       6.106
  Age                           36.830      11.005
  Annual increment               0.418       0.493
  Promotion                      0.113       0.316
  Degree                         0.159       0.366
  Further education              0.278       0.448
  A level                        0.160       0.366
  GCSE grades A to C             0.206       0.404
  GCSE grades below C            0.054       0.225
  Other qualification            0.032       0.176

                                  PRP (N = 1201)

                                         Standard
Variable                        Mean     Deviation

Family Expenditure Survey 1997/98, 1998/99, and 1999/00
  Log hourly earnings            2.492       0.574
  Years in labor force (YILF)   21.767      11.247
  Age                           39.493      10.376
  Years of education            12.726       2.735
  Degree                         0.277       0.448
  Further education/A level      0.216       0.412
  GCSE                           0.357       0.479
  Less than GCSE                 0.149       0.356

                                 Self-Employed
                                    (N = 491)

                                         Standard
Variable                        Mean     Deviation

British Social Attitudes Surveys 1985, 1987, 1993, 1996
  Log hourly earnings            1.613       0.802
  Years in labor force (YILF)   25.193      12.398
  Age                           41.433      12.258
  Employer tenure (a)               --        --
  Years of education            11.566       1.968
  Degree                         0.112       0.316
  Further education              0.147       0.354
  A level                        0.175       0.380
  GCSE grades A to C             0.220       0.415
  GCSE grades below C            0.104       0.305
  Other qualification            0.016       0.127

                                 Self-Employed
                                  (N = 3716)

                                         Standard
Variable                        Mean     Deviation

British Household Panel Survey 1991-1999
  Log hourly earnings            1.855       0.842
  Years in labor force (YILF)   29.939      11.872
  Years of education            12.925       3.933
  Job tenure                     8.409       8.544
  Age                           42.903      11.078
  Annual increment                --          --
  Promotion                       --          --
  Degree                         0.120       0.325
  Further education              0.249       0.432
  A level                        0.126       0.332
  GCSE grades A to C             0.222       0.416
  GCSE grades below C            0.044       0.205
  Other qualification            0.047       0.212

                                 Self-Employed
                                  (N = 1239)

                                         Standard
Variable                        Mean     Deviation

Family Expenditure Survey 1997/98, 1998/99, and 1999/00
  Log hourly earnings            1.601       1.085
  Years in labor force (YILF)   27.195      11.104
  Age                           44.320      10.535
  Years of education            12.125       2.810
  Degree                         0.194       0.395
  Further education/A level      0.157       0.364
  GCSE                           0.362       0.481
  Less than GCSE                 0.287       0.453

(a) Figures relate to 1993 and 1996 only because this information
was provided only in these years.

Table 2. British Social Attitudes Survey 1985, 1987, 1993, 1996:
Dependent Variable, Log Hourly Earnings

                                 Fixed-Wage

                                  Spec. A

                          Coeff          T Stat

Age                       0.0675         9.93 *
[Age.sup.2]              -0.0007        -8.47 *
YILF                        --            --
[YILF.sup.2]                --            --
Employer tenure             --            --
Employer [tenure.sup.2]     --            --
Years of education        0.0317         3.47 *
Selectivity term          0.0088         0.13
Constant                 -1.2737        -6.40 *

Year dummies                         Yes
Highest ed. cert.                    Yes
Occupation dummies                   Yes
Industry dummies                     Yes
Turning point (years)               48.2
[R.sup.2]                           0.5797
F statistic                [87.80.sub.(24, 1442)]
Number of obs.                      1467

                               Fixed-Wage

                                 Spec. B

                          Coeff          T Stat

Age                         --             --
[Age.sup.2]                 --             --
YILF                       0.0483         13.38 *
[YILF.sup.2]              -0.0008         10.57 *
Employer tenure             --             --
Employer [tenure.sup.2]     --             --
Years of education         0.0439          4.64 *
Selectivity term          -0.2068         -2.97 *
Constant                  -0.3485         -2.08

Year dummies                         Yes
Highest ed. cert.                    Yes
Occupation dummies                   Yes
Industry dummies                     Yes
Turning point (years)               30.2
[R.sup.2]                           0.5675
F statistic                 [87.50.sub.(24, 1442)]
Number of obs.                      1467

                               Fixed-Wage

                                Spec. C

                          Coeff          T Stat

Age                         --             --
[Age.sup.2]                 --             --
YILF                        --             --
[YILF.sup.2]                --             --
Employer tenure            0.0234         8.32 *
Employer [tenure.sup.2]   -0.0003        -4.85 *
Years of education         0.0104         0.90
Selectivity term           0.0740         0.93
Constant                   1.0783         5.64 *

Year dummies                         Yes
Highest ed. cert.                    Yes
Occupation dummies                   Yes
Industry dummies                     Yes
Turning point (years)               39.0
[R.sup.2]                           0.4541
F statistic                 [30.76.sub.(22, 801)]
Number of obs.                      1467

                                   PRP

                                 Spec. A

                          Coeff          T Stat

Age                        0.0551         5.97*
[Age.sup.2]               -0.0006        -4.86*
YILF                        --             --
[YILF.sup.2]                --             --
Employer tenure             --             --
Employer [tenure.sup.2]     --             --
Years of education         0.0301         2.54
Selectivity term          -0.4043        -3.97 *
Constant                  -0.2468        -0.74

Year dummies                         Yes
Highest ed. cert.                    Yes
Occupation dummies                   Yes
Industry dummies                     Yes
Turning point (years)               45.9
[R.sup.2]                           0.6499
F statistic                 [64.80.sub.(24, 758)]
Number of obs.                       783

                                   PRP

                                 Spec. B

                          Coeff          T Stat

Age                         --             --
[Age.sup.2]                 --             --
YILF                       0.0360         7.36*
[YILF.sup.2]              -0.0005        -5.25*
Employer tenure             --             --
Employer [tenure.sup.2]     --             --
Years of education         0.0421         3.38 *
Selectivity term          -0.3600        -4.50 *
Constant                   0.3199         1.28

Year dummies                         Yes
Highest ed. cert.                    Yes
Occupation dummies                   Yes
Industry dummies                     Yes
Turning point (years)               36.0
[R.sup.2]                           0.651
F statistic                [71.66.sub.(24, 758)]
Number of obs.                       783

                                   PRP

                                Spec. C

                          Coeff          T Stat

Age                         --             --
[Age.sup.2]                 --             --
YILF                        --             --
[YILF.sup.2]                --             --
Employer tenure            0.0159         3.73 *
Employer [tenure.sup.2]   -0.0002        -3.67 *
Years of education         0.0315         1.66
Selectivity term          -0.2590        -2.48
Constant                   1.5331         3.93 *

Year dummies                         Yes
Highest ed. cert.                    Yes
Occupation dummies                   Yes
Industry dummies                     Yes
Turning point (years)               39.8
[R.sup.2]                           0.434
F statistic                 [17.22.sub.(22, 335)]
Number of obs.                       783

                                   Self-
                                 Employed

                                 Spec. A

                          Coeff          T Stat

Age                        0.0171         0.80
[Age.sup.2]               -0.0001        -0.51
YILF                        --             --
[YILF.sup.2]                --             --
Employer tenure             --             --
Employer [tenure.sup.2]     --             --
Years of education         0.0647         1.52
Selectivity term           0.8313         10.90 *
Constant                   0.4793         0.54

Year dummies                         Yes
Highest ed. cert.                    Yes
Occupation dummies                   Yes
Industry dummies                     Yes
Turning point (years)               85.5
[R.sup.2]                           0.6882
F statistic                   [120.16.sub.(24, 466)]
Number of obs.                       491

                                  Self-
                                Employed

                                 Spec. B

                          Coeff          T Stat

Age                         --             --
[Age.sup.2]                 --             --
YILF                       0.0175         1.73
[YILF.sup.2]              -0.0002        -1.08
Employer tenure             --             --
Employer [tenure.sup.2]     --             --
Years of education         0.0241         0.73
Selectivity term           0.2151         2.12
Constant                   0.7479         1.07

Year dummies                         Yes
Highest ed. cert.                    Yes
Occupation dummies                   Yes
Industry dummies                     Yes
Turning point (years)                74
[R.sup.2]                           0.303
F statistic                 [16.99.sub.(24, 466)]
Number of obs.                       491

Highest education certificates: Degree, Further education, A level,
GCSE grades A-C, GCSE grades below C, Other qualification.

Occupation dummies: Professional, Other nonmanual, Skilled manual,
Semiskilled, Unskilled manual.

Industry dummies: Energy, Metal extraction, Metal goods, Other
manufacturing, Construction, Distribution, Transport and
communications, Banking, Other services.

Robust standard errors are reported.

Tests of equality of the estimated coefficients of age and age
squared across the PRP (self-employed) and fixed-wage (PRP) wage
equations led to a test statistic of 7.23 (8.67).

Tests of equality of the estimated coefficients of YILF and
YILF squared across the PRP (self-employed) and fixed-wage (PRP)
wage equations led to a test statistic of 7.10 (8.46).

Tests of equality of the estimated coefficients of Employer tenure
and Employer tenure squared across the PRP and fixed-wage wage
equations led to a test statistic of 6.25.

* Statistically significant at the 1 % level for a two-tailed test.

Table 3. British Household Panel Survey: 1991-1999:
Dependent Variable, Log Hourly Earnings

                                    Fixed-Wage

                           Spec. A              Spec. B

                      Coeff     T Stat     Coeff     T Stat

Age                   0.0895    35.29 *     --         --
[Age.sup.2]          -0.0010   -30.83 *     --         --
YILF                   --         --       0.0578    37.00 *
[YILF.sup.2]           --         --      -0.0008   -30.27 *
Job tenure             --         --        --         --
Job [tenure.sup.2]     --         --        --         --
Years of
  education          -0.0007    -0.44      0.0155     9.13 *
Selectivity term     -0.1025   -10.58 *   -0.1059   -10.92 *
Promotion             0.0284     2.61 *    0.0292     2.68 *
Annual increment      0.0076     1.19      0.0078     1.22
Constant             -0.2252    -4.19 *    0.6561    18.33 *

Year dummies               Yes                  Yes
Highest ed. cert.          Yes                  Yes
Occupation
  dummies                  Yes                  Yes
Industry dummies           Yes                  Yes
Turning point (yr)         44.8                 36.3
[R.sub.2] within          0.0780               0.0749
[R.sub.2] between         0.4186               0.4196
[R.sub.2] overall         0.3926               0.3918
Wald chi-square        [4145.sub.30]        [4124.sub.30]
Number of obs.            14,284               14,284
Nos. of groups            4594                 4594

                         Fixed-Wage              PRP

                           Spec. C              Spec. A

                      Coeff     T Stat     Coeff     T Stat

Age                    --         --       0.0821    21.99 *
[Age.sup.2]            --         --      -0.0009   -18.90 *
YILF                   --         --        --         --
[YILF.sup.2]           --         --        --         --
Job tenure            0.0192    15.00 *     --         --
Job [tenure.sup.2]   -0.0005    -9.73 *     --         --
Years of
  education           0.0016     0.93      0.0001     0.04
Selectivity term     -0.0676    -6.79 *    0.0457     2.85 *
Promotion             0.0661     5.68 *    0.4917     4.17 *
Annual increment      0.0011     0.17      0.0191     2.24
Constant              1.5417    53.44 *   -0.0901    -1.05

Year dummies               Yes                  Yes
Highest ed. cert.          Yes                  Yes
Occupation
  dummies                  Yes                  Yes
Industry dummies           Yes                  Yes
Turning point (yr)         19.2                 45.6
[R.sub.2] within          0.0356               0.1153
[R.sub.2] between         0.348                0.4284
[R.sub.2] overall         0.3269               0.4206
Wald chi-square        [2507.sub.30]        [2538.sub.30]
Number of obs.           14,284                6212
Nos. of groups           4594                  2806

                             PRP                  PRP

                           Spec. B              Spec. C

                      Coeff     T Stat     Coeff     T Stat

Age                    --         --        --         --
[Age.sup.2]            --         --        --         --
YILF                  0.0542    23.72 *     --         --
[YILF.sup.2]         -0.0008   -19.02 *     --         --
Job tenure             --         --       0.0151    7.18 *
Job [tenure.sup.2]     --         --      -0.0003   -3.88 *
Years of
  education           0.0147     6.17 *    0.0017    0.72
Selectivity term      0.0476     2.96 *   -0.0099   -0.59
Promotion             0.0500     4.24 *    0.0777    5.97 *
Annual increment      0.0195     2.29      0.0030    0.34
Constant              0.7021    11.69 *    1.6222   33.70 *

Year dummies               Yes                  Yes
Highest ed. cert.          Yes                  Yes
Occupation
  dummies                  Yes                  Yes
Industry dummies           Yes                  Yes
Turning point (yr)         33.9                 25.2
[R.sub.2] within          0.1152               0.057
[R.sub.2] between         0.4304               0.3618
[R.sub.2] overall         0.4195               0.3536
Wald chi-square        [2553.sub.30]        [1668.sub.30]
Number of obs.            6212                 6212
Nos. of groups            2806                 2806

                        Self-Employed        Self-Employed

                           Spec. A              Spec. B

                      Coeff     T Stat     Coeff     T Stat

Age                   0.0607    2.26        --         --
[Age.sup.2]          -0.0007   -2.33        --         --
YILF                   --         --       0.0336    1.96
[YILF.sup.2]           --         --      -0.0006   -2.11
Job tenure             --         --        --         --
Job [tenure.sup.2]     --         --        --         --
Years of
  education          -0.0086   -0.58      -0.0064   -0.41
Selectivity term      0.0019    0.03       0.0046    0.06
Promotion              --         --        --         --
Annual increment       --         --        --         --
Constant             -0.1138   -0.18       0.6329    1.45

Year dummies               Yes                  Yes
Highest ed. cert.          Yes                  Yes
Occupation
  dummies                  Yes                  Yes
Industry dummies           Yes                  Yes
Turning point (yr)         43.4                 28
[R.sub.2] within          0.0099               0.0097
[R.sub.2] between         0.0353               0.0345
[R.sub.2] overall         0.0381               0.0389
Wald chi-square        [68.45.sub.28]       [67.49.sub.28]
Number of obs.            3716                 3716
Nos. of groups            1153                 1153

                        Self-Employed

                           Spec. C

                      Coeff     T Stat

Age                    --         --
[Age.sup.2]            --         --
YILF                   --         --
[YILF.sup.2]           --         --
Job tenure            0.0022    0.22
Job [tenure.sup.2]    0.0002    0.80
Years of
  education          -0.0059   -0.40
Selectivity term      0.0362    0.51
Promotion              --         --
Annual increment       --         --
Constant              0.9139    3.08 *

Year dummies               Yes
Highest ed. cert.          Yes
Occupation
  dummies                  Yes
Industry dummies           Yes
Turning point (yr)         5.5
[R.sub.2] within          0.0101
[R.sub.2] between         0.0337
[R.sub.2] overall         0.0383
Wald chi-square        [67.82.sub.28]
Number of obs.            3716
Nos. of groups            1153

Highest education certificates: Degree, Further education,
A level, GCSE grades A to C, GCSE grades below C, Other qualification.

Occupation dummies: Managerial, Professional, Intermediate nonmanual,
Sales, Clerical, Personal services, Skilled manual, Semiskilled manual,
Unskilled manual.

Industry dummies: Energy, Extraction, Engineering, Manufacturing,
Construction, Distribution, Transport, Storage and communication,
Finance, Other nonmanufacturing.

Robust standard errors are reported.

Tests of equality of the estimated coefficients of age and age
squared across the PRP (self-employed) and fixed-wage (PRP) wage
equations led to a test statistic of 18.37 (126.76).

Tests of equality of the estimated coefficients of YILF and YILF
squared across the PRP (self-employed) and fixed-wage (PRP) wage
equations led to a test statistic of 1.82 (18.08).

Tests of equality of the estimated coefficients of job tenure
and job tenure squared across the PRP (self-employed) and
fixed wage (PRP) wage equations led to a test statistic of
12.30 (40.98).

* Statistically significant at the 1% level for a two-tailed test.

Table 4. Family Expenditure Survey 1997/98, 1998/99, and 1999/00:
Dependent Variable, Log Hourly Earnings

                                         Fixed-Wage

                               Spec. A                Spec. B

                         Coeff      T Stat      Coeff      T Stat

Age                      0.0677     15.59 *       --         --
[Age.sup.2]             -0.0007    -14.28 *       --         --
YILF                       --         --        0.0422     15.44 *
[YILF.sup.2]               --         --       -0.0007    -14.36 *
Years of education       0.0160      2.10       0.0311      4.33 *
Selectivity term         0.1679      2.99 *     0.1956      3.04 *
Constant                 0.7400      6.54 *     1.5724     17.09 *

Year dummies                    Yes                    Yes
Highest ed. cert.               Yes                    Yes
Occupation dummies              Yes                    Yes
Industry dummies                Yes                    Yes
Turning point (years)           47.9                   30.1
[R.sup.2]                      0.3306                 0.3304
F statistic                 [141.13.sub.           [143.52.sub.
                            (25, 5939)]            (25, 5939)]
Number of obs.                 5965                   5965

                                              PRP

                               Spec. A                Spec. B

                         Coeff      T Stat      Coeff      T Stat

Age                      0.0553     5.27 *        --         --
[Age.sup.2]             -0.0005    -3.77 *        --         --
YILF                       --         --        0.0380     7.21 *
[YILF.sup.2]               --         --       -0.0005    -4.34 *
Years of education       0.0412     3.15 *      0.0610     4.66 *
Selectivity term        -0.4378    -4.15 *     -0.4896    -4.47 *
Constant                 1.3877     4.02 *      2.0280     8.20 *

Year dummies                    Yes                    Yes
Highest ed. cert.               Yes                    Yes
Occupation dummies              Yes                    Yes
Industry dummies                Yes                    Yes
Turning point (years)           55.3                   38.0
[R.sup.2]                      0.4356                 0.4430
F statistic                 [33.48.sub.            [35.88.sub.
                            (25, 1201)]            (25, 1201)]
Number of obs.          1201                   1201

                                      Self-Employed

                               Spec. A                Spec. B

                         Coeff      T Stat      Coeff      T Stat

Age                     -0.0019    -0.06          --         --
[Age.sup.2]             -0.0001    -0.40          --         --
YILF                       --         --       -0.024     -1.31
[YILF.sup.2]               --         --        0.0001     0.47
Years of education      -0.0162    -0.68       -0.0388    -1.53
Selectivity term        -0.403     -1.57       -0.6098    -2.44
Constant                 3.4795     2.61 *      4.4272     4.30 *

Year dummies                    Yes                    Yes
Highest ed. cert.               Yes                    Yes
Occupation dummies              Yes                    Yes
Industry dummies                Yes                    Yes
Turning point (years)           9.5                    120.0
[R.sup.2]                      0.3306                 0.1522
F statistic                 [141.13.sub.           [10.26.sub.
                            (24, 1214)]            (24, 1214)]
Number of obs.                 1239                   1239

Highest education certificates: Degree, Further education, A level,
GCSE grades A-C, GCSE grades below C, Other qualification.

Occupation dummies: Professional, Other nonmanual, Skilled manual,
Semiskilled, Unskilled manual.

Industry dummies: Energy, Metal extraction, Metal goods, Other
manufacturing, Construction, Distribution, Transport and
communications, Banking, Other services.

Robust standard errors are reported.

Tests of equality of the estimated coefficients of age and age
squared across the PRP (self-employed) and fixed-wage (PRP) wage
equations led to a test statistic of 56.02 (163.98).

Tests of equality of the estimated coefficients of YILF and YILF
squared across the PRP (self-employed) and fixed-wage (PRP) wage
equations led to a test statistic of 68.71 (345.83).

* Statistically significant at the 1% level for a two-tailed test.

Table 5. Lambda ([??]) Summary Statistics

                     Family Expenditure Survey 1997/98,
                             1998/99, 1999/00

[??]                 All Workers      PRP Workers

Mean                    0.152         0.054
Standard deviation      0.349         0.069
Minimum                 0.000         9.28 x [10.sup.-6]
Maximum                 1.000         0.476
Observations         8405          1201

                     British Household Panel
                        Survey 1997-1999

[??]                 All Workers   PRP Workers

Mean                    0.234         0.302
Standard deviation      0.364         0.223
Minimum                 0.000         0.001
Maximum                 1.000         0.981
Observations         9187          2840

Table 6. Continuous Lambda, FES 1997/98, 1998/99, 1999/00:
Dependent Variable, Log Hourly Earnings

                             Spec. 1              Spec. 2

                         Coeff     T Stat     Coeff     T Stat

Age                      0.0755    18.08 *    0.0753    19.06 *
[Age.sup.2]             -0.0008   -15.75 *   -0.0008   -16.71 *
YILF                      --         --        --         --
[YILF.sup.2]              --         --        --         --
Lambda                  -0.5354   -16.81 *     --         --
Lambda * age              --         --      -0.0193   -5.69 *
Lambda * [age.sup.2]      --         --       0.0002    2.21
Lambda * YILF             --         --        --         --
Lambda * [YILF.sup.2]     --         --        --         --
Years of education       0.0109     1.56      0.0112    1.60
Constant                 0.8587     7.21 *    0.8444    7.23 *

Year dummies                 Yes                  Yes
Highest ed. cert.            Yes                  Yes
Occupation dummies           Yes                  Yes
Industry dummies             Yes                  Yes
[R.sup.2]                   0.3071               0.3088
F statistic               [154.92.sub.         [152.53.sub.
                          (25, 8379)]          (26, 8378)]
Nos. of obs.                 8405                 8405

                             Spec. 3              Spec. 4

                         Coeff     T Stat     Coeff     T Stat

Age                     0.0783     21.06 *     --         --
[Age.sup.2]             -0.0008   -18.45 *     --         --
YILF                      --         --       0.0457    20.48 *
[YILF.sup.2]              --         --      -0.0008   -17.03 *
Lambda                  0.6085     1.37      -0.5566   -17.51 *
Lambda * age            -0.0478   -2.22        --         --
Lambda * [age.sup.2]    0.0005     1.88        --         --
Lambda * YILF             --         --        --         --
Lambda * [YILF.sup.2]     --         --        --         --
Years of education      0.0111     1.58       0.0257     3.78 *
Constant                0.7882     6.94 *     1.826     19.36 *

Year dummies                 Yes                  Yes
Highest ed. cert.            Yes                  Yes
Occupation dummies           Yes                  Yes
Industry dummies             Yes                  Yes
[R.sup.2]                   0.3088               0.3077
F statistic               [152.53.sub.         [[156.21.sub.
                          (26, 8378)]          (25, 8379)]
Nos. of obs.                 8405                 8405

                             Spec. 5              Spec. 6

                         Coeff     T Stat     Coeff     T Stat

Age                       --         --        --         --
[Age.sup.2]               --         --        --         --
YILF                     0.0488    22.77 *    0.0489    23.19 *
[YILF.sup.2]            -0.0008   -19.29 *   -0.0008   -19.57 *
Lambda                    --         --       0.0259     0.19
Lambda * age              --         --        --         --
Lambda * [age.sup.2]      --         --        --         --
Lambda * YILF           -0.0413   -9.91 *    -0.0432   -3.72 *
Lambda * [YILF.sup.2]    0.0007    5.59 *     0.0007    3.15 *
Years of education       0.0256    3.76 *     0.0256    3.76 *
Constant                 1.7869   19.04 *     1.7850   19.03 *

Year dummies                 Yes                  Yes
Highest ed. cert.            Yes                  Yes
Occupation dummies           Yes                  Yes
Industry dummies             Yes                  Yes
[R.sup.2]                   0.3113               0.3113
F statistic               [156.61.sub.         [155.47.sub.
                          (26, 8378)]          (27, 8377)]
Nos. of obs.                 8405                  8405

Highest education certificates, occupation dummies, and industry
dummies are as in Table 4.

Standard errors adjusted according to the White-Huber approach
because of the presence of heteroskedasticity.

Lambda = PRP/total pay.

* Statistically significant at the 1% level for a two-tailed test.

Table 7. Continuous Lambda, BHPS 1997-1999:
Dependent Variable, Log Hourly Earnings

                                      Spec. 1

                             Coeff           T Stat

Age                          0.0872          10.81 *
Age (2)                     -0.0010         -10.04 *
YILF                           --              --
YILF (2)                       --              --
Job tenure                     --              --
Job tenure (2)                 --              --
Lambda                      -0.2620         -7.51 *
Lambda*age                     --              --
Lambda*age (2)                 --              --
Lambda*YILF                    --              --
Lambda*YILF (2)                --              --
Lambda*job tenure              --              --
Lambda*job tenure (2)          --              --
Years of education          -0.0071          -1.74
Constant                    -0.1690          -1.01
Year dummies
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                      0.0020
[R.sub.2] between                     0.1647
[R.sub.2] overall                     0.1473
Wald chi-squared                   [808.sub.26]
Nos. of observations                   9187
Nos. of groups                         4464

                                      Spec. 2

                             Coeff           T Stat

Age                          0.0791          9.58 *
Age (2)                     -0.0009         -8.17 *
YILF                           --              --
YILF (2)                       --              --
Job tenure                     --              --
Job tenure (2)                 --              --
Lambda                         --              --
Lambda*age                   0.0063          1.74
Lambda*age (2)              -0.0003         -3.83 *
Lambda*YILF                    --              --
Lambda*YILF (2)                --              --
Lambda*job tenure              --              --
Lambda*job tenure (2)          --              --
Years of education          -0.0069          -1.69
Constant                    -0.1071          -0.63
Year dummies                           Yes
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                      0.0034
[R.sub.2] between                     0.1677
[R.sub.2] overall                     0.1498
Wald chi-squared                   [848.sub.27]
Nos. of observations                   9187
Nos. of groups                         4464

                                      Spec. 3

                             Coeff           T Stat

Age                          0.0774          8.56 *
Age (2)                     -0.0008         -7.34 *
YILF                           --              --
YILF (2)                       --              --
Job tenure                     --              --
Job tenure (2)                 --              --
Lambda                      -0.1945          -0.46
Lambda*age                   0.0159           0.75
Lambda*age (2)              -0.0004          -1.59
Lambda*YILF                    --              --
Lambda*YILF (2)                --              --
Lambda*job tenure              --              --
Lambda*job tenure (2)          --              --
Years of education          -0.0069          -1.69
Constant                    -0.0748          -0.41
Year dummies                           Yes
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                     0.0034
[R.sub.2] between                    0.1676
[R.sub.2] overall                    0.1496
Wald chi-squared                  [847.sub.28]
Nos. of observations                  9187
Nos. of groups                        4464

                                      Spec. 4

                             Coeff           T Stat

Age                            --              --
Age (2)                        --              --
YILF                         0.0510         10.55 *
YILF (2)                    -0.0008         -9.39 *
Job tenure                     --              --
Job tenure (2)                 --              --
Lambda                      -0.2637         -7.55 *
Lambda*age                     --              --
Lambda*age (2)                 --              --
Lambda*YILF                    --              --
Lambda*YILF (2)                --              --
Lambda*job tenure              --              --
Lambda*job tenure (2)          --              --
Years of education           0.0021           0.48
Constant                     0.8103          7.82 *
Year dummies                           Yes
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                      0.0021
[R.sub.2] between                     0.1620
[R.sub.2] overall                     0.1453
Wald chi-squared                   [792.sub.26]
Nos. of observations                   9187
Nos. of groups                         4464

                                      Spec. 5

                             Coeff           T Stat

Age                            --              --
Age (2)                        --              --
YILF                         0.0483          9.63 *
YILF (2)                    -0.0007         -7.51 *
Job tenure                     --              --
Job tenure (2)                 --              --
Lambda                         --              --
Lambda*age                     --              --
Lambda*age (2)                 --              --
Lambda*YILF                 -0.0023          -0.56
Lambda*YILF (2)             -0.0002          -2.28
Lambda*job tenure              --              --
Lambda*job tenure (2)          --              --
Years of education           0.0023           0.54
Constant                     0.7823          7.56 *
Year dummies                           Yes
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                      0.0037
[R.sub.2] between                     0.1657
[R.sub.2] overall                     0.1486
Wald chi-squared                   [839.sub.27]
Nos. of observations                   9187
Nos. of groups                         4464

                                      Spec. 6

                             Coeff           T Stat

Age                            --              --
Age (2)                        --              --
YILF                         0.0525          9.74 *
YILF (2)                    -0.0008         -7.79 *
Job tenure                     --              --
Job tenure (2)                 --              --
Lambda                       0.3631           2.13
Lambda*age                     --              --
Lambda*age (2)                 --              --
Lambda*YILF                 -0.0280          -2.20
Lambda*YILF (2)              0.0002           0.73
Lambda*job tenure              --              --
Lambda*job tenure (2)          --              --
Years of education           0.0025           0.58
Constant                     0.7265          6.81 *
Year dummies                           Yes
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                      0.004
[R.sub.2] between                     0.1659
[R.sub.2] overall                     0.1492
Wald chi-squared                   [843.sub.28]
Nos. of observations                   9187
Nos. of groups                         4464

                                      Spec. 7

                             Coeff           T Star

Age                            --              --
Age (2)                        --              --
YILF                           --              --
YILF (2)                       --              --
Job tenure                   0.0201          4.90 *
Job tenure (2)              -0.0005         -3.35 *
Lambda                      -0.2412         -6.87 *
Lambda*age                     --              --
Lambda*age (2)                 --              --
Lambda*YILF                    --              --
Lambda*YILF (2)                --              --
Lambda*job tenure              --              --
Lambda*job tenure (2)          --              --
Years of education          -0.0050          -1.20
Constant                     1.4589         18.66 *
Year dummies                           Yes
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                      0.0025
[R.sub.2] between                     0.1485
[R.sub.2] overall                     0.1315
Wald chi-squared                   [689.sub.26]
Nos. of observations                   9187
Nos. of groups                         4464

                                      Spec. 8

                             Coeff           T Stat

Age                            --              --
Age (2)                        --              --
YILF                           --              --
YILF (2)                       --              --
Job tenure                   0.0286          5.85*
Job tenure (2)              -0.0006         -3.33 *
Lambda                         --              --
Lambda*age                     --              --
Lambda*age (2)                 --              --
Lambda*YILF                    --              --
Lambda*YILF (2)                --              --
Lambda*job tenure           -0.0376         -4.85 *
Lambda*job tenure (2)        0.0007           2.41
Years of education          -0.0051          -1.22
Constant                     1.4065         18.04 *
Year dummies                           Yes
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                      0.0019
[R.sub.2] between                     0.1475
[R.sub.2] overall                     0.1313
Wald chi-squared                   [683.sub.27]
Nos. of observations                   9187
Nos. of groups                         4464

                                      Spec. 9

                             Coeff           T Stat

Age                            --              --
Age (2)                        --              --
YILF                           --              --
YILF (2)                       --              --
Job tenure                   0.0242          4.75 *
Job tenure (2)              -0.0005         -2.64 *
Lambda                      -0.1600         -3.41 *
Lambda*age                     --              --
Lambda*age (2)                 --              --
Lambda*YILF                    --              --
Lambda*YILF (2)                --              --
Lambda*job tenure           -0.0177          -1.82
Lambda*job tenure (2)        0.0002           0.69
Years of education          -0.0050          -1.21
Constant                     1.4409         18.36 *
Year dummies                           Yes
Highest ed. cert.                      Yes
Occupation dummies                     Yes
Industry dummies                       Yes
[R.sub.2] within                      0.0019
[R.sub.2] between                     0.1520
[R.sub.2] overall                     0.1336
Wald chi-squared                   [698.sub.28]
Nos. of observations                   9187
Nos. of groups                         4464

Highest education certificates,
occupation dummies, and industry
dummies are as in Table 3.

Standard errors adjusted according
to the White-Huber approach because
of the presence of heteroskedasticity.

Lambda = PRP/total pay.

* Statistically significant at the
1% level for a two-tailed test.

Table 8. Training Frequencies

                                 PRP Employees

                                             Standard
                         Obs.      Mean      Deviation

BSAS 1987
  Train
    Exp. < 5 yr             28      0.9286      0.2623
    5 < Exp. < 10 yr        31      0.7742      0.4250
    10 < Exp. < 20 yr       61      0.7541      0.4342
    Exp. > 20 yr           153      0.5882      0.4938
    All employees          273      0.6813      0.4668
  Trains
    Exp. < 5 yr             28      3.8929      2.0788
    5 < Exp. < 10 yr        31      2.6129      2.0278
    10 < Exp. < 20 yr       61      2.4590      2.1952
    Exp. > 20 yr           153      1.7190      1.9717
    All employees          273      2.2088      2.1395
BHPS 1991-1999
  Train                   6212      0.1718      0.3772
    Exp. < 5 yr             68      0.3088      0.4654
    5 < Exp. < 10 yr       765      0.2065      0.4051
    10 < Exp. < 20 yr     2015      0.1856      0.3889
    Exp. > 20 yr          3364      0.1528      0.3598
    All employees         6212      0.1718      0.3772

                              Fixed-Wage Employees

                                             Standard
                        Obs.     Mean        Deviation

BSAS 1987
  Train
    Exp. < 5 yr             50      0.9000      0.3030
    5 < Exp. < 10 yr        52      0.7885      0.4124
    10 < Exp. < 20 yr      101      0.6931      0.4635
    Exp. > 20 yr           203      0.6847      0.4658
    All employees          406      0.7266      0.4463
  Trains
    Exp. < 5 yr             50      3.7000      2.0923
    5 < Exp. < 10 yr        52      2.6923      2.2798
    10 < Exp. < 20 yr      101      2.4158      2.2058
    Exp. > 20 yr           203      1.9803      1.9447
    All employees          406      2.3916      2.1400
BHPS 1991-1999
  Train                 14,284      0.1246      0.3303
    Exp. < 5 yr            198      0.2475      0.4326
    5 < Exp. < 10 yr      1792      0.1735      0.3788
    10 < Exp. < 20 yr     4000      0.1465      0.3537
    Exp. > 20 yr          8294      0.1006      0.3008
    All employees       14,284      0.1246      0.3303

                                 All Employees

                                             Standard
                        Obs.     Mean        Deviation

BSAS 1987
  Train
    Exp. < 5 yr             78      0.9103      0.2877
    5 < Exp. < 10 yr        83      0.7831      0.4146
    10 < Exp. < 20 yr      162      0.7160      0.4523
    Exp. > 20 yr           356      0.6433      0.4797
    All employees          679      0.7084      0.4548
  Trains
    Exp. < 5 yr             78      3.7692      2.0758
    5 < Exp. < 10 yr        83      2.6627      2.1768
    10 < Exp. < 20 yr      162      2.4321      2.1951
    Exp. > 20 yr           356      1.8680      1.9579
    All employees          679      2.3181      2.1401
BHPS 1991-1999
  Train                 20,496      0.1389      0.3458
    Exp. < 5 yr            266      0.2632      0.4412
    5 < Exp. < 10 yr      2557      0.1834      0.3871
    10 < Exp. < 20 yr     6015      0.1596      0.3663
    Exp. > 20 yr        11,658      0.1156      0.3198
    All employees       20,496      0.1389      0.3458

Table 9. PRP and Training Incidence (Summary of Results)

                                           BSAS 1987

                              Probit (1)          Ordered Probit (1)

                            Dep Var = Train         Dep Var = Trains

                           Coeff      T Stat       Cceff      T Stat

Experience                -0.0335     -5.95 *     -0.0324     -7.47 *
PRP dummy                 -0.0700      -0.59      -0.0071     -0.08
(Exp. < 5) * PRP            --          --          --          --
(5 < Exp. < 10) * PRP       --          --          --          --
(10 < Exp. < 20) * PRP      --          --          --          --
(Exp. > 20) * PRP           --          --          --          --
Log likelihood                 -339.3235               -1219.0619
Nos. of observations              679                     679

                                            BSAS 1987

                            Probit (2)              Ordered Probit (2)

                            Dep Var = Train         Dep Var = Trains

                           Coeff      T Stat       Coeff      T Stat

Experience                -0.0311      -4.72 *    -0.0292      -5.81 *
PRP dummy                  0.2736       0.64       0.6316       2.04
(Exp. < 5) * PRP           0.0130       0.10      -0.0878      -0.99
(5 < Exp. < 10) * PRP     -0.0525      -0.87      -0.0899      -2.07
(10 < Exp. < 20) * PRP    -0.0211      -0.76      -0.0440      -2.15
(Exp. > 20) * PRP         -0.0116      -0.88      -0.0211      -2.12
Log likelihood                  -338.37818              -1215.8068
Nos. of observations               679                     679

                                        BHPS 1991-1999

                              Probit (1)              Probit (2)

                            Dep Var = Train         Dep Var = Train

                           Coeff      T Stat       Coeff      T Stat

Experience                -0.183      10.49 *     -0.020      10.15 *
PRP dummy                  0.106      3.30 *       0.015       0.12
(Exp. < 5) * PRP            --          --         0.099       2.27
(5 < Exp. < 10) * PRP       --          --        -0.002       0.14
(10 < Exp. < 20) * PRP      --          --         0.003       0.40
(Exp. > 20) * PRP           --          --         0.005       1.15
Log likelihood                -6738.750                -6733.579
Nos. of observations            20,496                   20,496

Controls were also included for occupation,
region, industry, education, firm size,
marital status, and ethnicity.

* Statistically significant at the 1% level
for a two-tailed test.

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Author:	Sessions, John G.
Publication:	Southern Economic Journal
Date:	Jan 1, 2006
Words:	9995
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