Tor Eriksson

Sabrina Teyssier
email: teyssier@gate.cnrs.fr

Marie-Claire Villeval Mars

Marie-Claire Villeval
email: villeval@gate.cnrs.fr

E P Lazear

A Ichino

K Lang

L Vilhuber

R Mohr

K P Chen

Effort Self-Selection and the Efficiency of Tournaments

Keywords: M52, J33, J31, C81, C91 Tournament, Performance Pay, Incentives, Sorting, Selection, Experiment tournois, rémunération à la performance, incitations, sélection, tri, expérience

. However, since the efficiency of performance-related pay is attributable both to its incentive effect and to its selection effect among employees (Lazear, 2000), it is important to investigate the ex ante sorting effect of tournaments. This paper reports results from an experiment analyzing whether allowing subjects to self-select into different payment schemes helps in reducing the variability of performance in tournaments. We show that when the subjects choose to enter a tournament, the average effort is higher and the between-subject variance is substantially lower than when the same payment scheme is imposed. Mainly based on the degree of risk aversion, sorting is efficiency-enhancing since it increases the homogeneity of the contestants. We suggest that the flexibility of the labor market is an important condition for a higher efficiency of relative performance pay.

Résumé

. Toutefois, dans la mesure où l'efficacité de la rémunération à la performance peut être attribuée à la fois à un effet d'incitation et à un effet de sélection parmi les salariés (Lazear 2000), il est important d'analyser l'effet de sélection ex ante des tournois. Cet article relate une expérience destinée à tester si la possibilité donnée aux sujets de choisir entre différents modes de rémunération permet de réduire la variance de la performance en tournois. Nous montrons que lorsque les sujets choisissent d'entrer en compétition, l'effort moyen est plus élevé et la variance interindividuelle est substantiellement réduite par rapport à un traitement où le même mode de rémunération est imposé. L'auto-sélection, fondée en grande partie sur le degré d'aversion au risque, améliore l'efficience car elle accroît l'homogénéité des compétiteurs. Ceci suggère que la flexibilité du marché du travail est une condition importante pour l'accroissement de l'efficience de la rémunération à la performance.

Introduction

The use of promotion tournaments is fairly widespread especially in the higher ranks of firms and organizations. The incentive property of tournaments has been studied extensively in the theoretical literature [START_REF] Lazear | Rank-Order Tournaments as Optimum Labor Contracts[END_REF][START_REF] Green | A Comparison of Tournaments and Contracts[END_REF][START_REF] Nalebuff | Prizes and Incentives: Towards a General Theory of Compensation and Competition[END_REF][START_REF] O'keeffe | Economic Contests: Comparative Reward Schemes[END_REF] for a survey see [START_REF] Mclaughlin | Aspects of Tournament Models: A Survey[END_REF]. The empirical studies, based on survey or experimental data, are fewer and many survey analyses use sports rather than business data [START_REF] Prendergast | The Provision of Incentives in Firms[END_REF]). These studies have confirmed that the tournament efficiency depends on the spread between the winner's and the loser's prizes, the number of prizes at stake, the size of the tournament, and the degree of uncertainty faced by the employees. 1 However, both theoretical models and empirical studies also point to some factors that limit the incentive effect of tournaments, such as collusion among employees or even sabotage (see [START_REF] Lazear | Pay Equality and Industrial Politics[END_REF] for a theoretical analysis and Harbring and Irlenbusch 2004 for some experimental evidence). More generally, most laboratory experiments have provided evidence of tournaments being associated with a high variance in effort (see in particular [START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF][START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF][START_REF] Harbring | An Experimental Study on Tournament Design[END_REF][START_REF] Harbring | An Experimental Study on Tournament Design[END_REF][START_REF] Van Dijk | Incentives Systems in a Real Effort Experiment[END_REF]. This variance of effort, which is found to be larger in tournaments than in an equivalent piece-rate scheme, reduces the overall efficiency of tournaments.

The principal aim of this paper is to show that previous experimental evidence regarding the variability of effort in tournaments is misleading because the experiments have not accounted for sorting, that is, that agents typically choose to participate in a tournament. The large 1 Studies based on survey data include [START_REF] Bognanno | Corporate Tournaments[END_REF], Ehrenberg and Bognanno (1990b), [START_REF] Eriksson | Executive Compensation and Tournament Theory: Empirical Tests on Danish Data[END_REF], [START_REF] Knoeber | Testing the Theory of Tournaments: An Empirical Analysis of Broiler Production[END_REF], [START_REF] Main | Top Executive Pay: Tournament or Teamwork?[END_REF]. Studies based on experimental evidence include [START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF], [START_REF] Harbring | An Experimental Study on Tournament Design[END_REF], [START_REF] Nalbantian | Productivity under Group Incentives: An Experimental Study[END_REF], [START_REF] Orrison | Multiperson Tournaments: An Experimental Examination[END_REF], [START_REF] Schotter | Asymmetric Tournaments, Equal Opportunity Laws, and Affirmative Action: Some Experimental Results[END_REF]. variability observed in earlier studies is usually explained by the game nature of the tournament, which requires the agents to elaborate a strategy that is more cognitively demanding than the maximizing behavior required by a piece-rate system [START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF]. Indeed, in addition to the stochastic technology of production, the agents have to cope with strategic uncertainty. 2 The use of high-variance strategies may also be related to both the difficulty of the task and the ability of the individuals [START_REF] Vandegrift | Task Difficulty, Incentive Effects, and the Selection of High-Variance Strategies: An Experimental Examination of Tournament Behavior[END_REF].

The hypothesis that we test in this paper is that the variability of effort may be reduced -and thus the efficiency of tournament increased -by allowing people to choose their payment scheme, i.e. allowing them to enter or not the competition. More precisely, we suggest that the observed high variance of effort may be due to the fact that in previous experiments very risk averse or under-confident subjects are imposed such a competitive payment scheme. For example, facing these two sources of uncertainty, some of these subjects "drop-out", i.e. choose the minimum effort, securing the loser's prize without bearing any cost of effort, whereas others choose the maximum effort, securing the winner's prize but with an inefficiently high cost of effort. Had the subjects been given the choice, like in flexible labor markets where people can choose to enter or shy away from competitive occupations, very risk averse subjects would probably not have entered the competition and the overall variance of effort would be lower.

By testing whether the performance variability is reduced by the ex ante sorting effect of tournaments, our paper contributes to a very recent literature about the importance of both incentive and sorting effects in the determination of payment schemes' efficiency, initiated by Lazear (2000). 3 This literature shows that sorting influences economic behavior. Earlier, the sorting function of tournaments has mainly been documented with respect to their ability to select ex post the best performers. However, their ex ante sorting effect is considerably less studied and none of the previous empirical studies have been concerned with the impact of ex ante sorting on the variability of performance. 4 To study the ex ante sorting effect of tournaments and its impact on the variability of effort, we have designed a laboratory experiment based on the comparison between a Benchmark Treatment and a Choice Treatment, and involving 120 student-subjects. In the Benchmark Treatment, half of the subjects are paid according to a piece-rate payment scheme and the other half enters pair-wise tournaments. This treatment consists of a one-stage game in which the subjects choose their level of effort knowing their payment scheme and the uncertainty of the environment. We find, in line with earlier experiments, that in this treatment, the variance of effort is substantially higher in the tournament than in the piece-rate payment scheme. In the Choice Treatment, we add a preliminary stage in which the subjects choose between a piece-rate scheme and a tournament. Those who choose the tournament are paired together. In the second stage, each subject decides on his level of effort. In both treatments, the individual outcome depends on both the effort level and an i.i.d. random shock. The difference between 3 In the Safelite study (Lazear, 2000), half of the productivity gain associated with the introduction of variable pay is attributed to its ability to sort the most skilled employees. Experimental tests include [START_REF] Cadsby | The Effects of Compensation Schemes on Self-Selection and Productivity: An Experimental Investigation[END_REF], and [START_REF] Eriksson | Other-Regarding Preferences and Performance Pay: An Experiment on Incentives and Sorting[END_REF], on sorting and incentives; [START_REF] Lazear | Sorting in Experiments with Applications to Social Preferences[END_REF] on sorting and social preferences; [START_REF] Bohnet | Compensating the Cooperators: Is Sorting in the Prisoner's Dilemma Game Possible?[END_REF] on sorting and cooperation. 4 In the theoretical literature, [START_REF] Fullerton | Auctioning Entry into Tournaments[END_REF] propose an auction design in order to limit the entry into tournaments to selected highly qualified contestants. [START_REF] Hvide | Risk Taking in Selection Contests[END_REF] show, however, that the selection efficiency of tournaments is not necessarily increased by improving the quality of the contestant pool. In the empirical literature, Ehrenberg and Bognanno (1990a) show that higher winners' prizes attract better players and [START_REF] Knoeber | Testing the Theory of Tournaments: An Empirical Analysis of Broiler Production[END_REF] propose setting minimum standards to get rid of the poor performing competitors. [START_REF] Niederle | Do Women Shy Away from Competition?[END_REF] and Datta [START_REF] Gupta | Male and Female Competitive Behavior -Experimental Evidence[END_REF] identify the importance of gender in the ex ante sorting effect of tournaments. [START_REF] Fershtman | Quitting[END_REF] introduce a possibility of quitting in a dynamic tournament with asymmetric players. [START_REF] Bonin | Cross-Sectional Earnings Risk and Occupational Sorting: The Role of Risk Attitude[END_REF] use survey data and show that risk averse individuals are more likely to be sorted into occupations with low earnings risk, and [START_REF] Dohmen | Sorting, Incentives and Performance[END_REF] observe that risk averse subjects prefer fixed payments over piece-rate or tournament schemes.

the two payment schemes emanates from the strategic uncertainty associated with the tournament setting.

By comparing the subjects' behavior in the two treatments, we can identify the impact of sorting on the average and the variance of effort. We also seek to identify determinants of selfselection. The equilibrium effort level is higher in the tournament than under the piece-rate scheme but the expected utility of both compensation schemes is the same. Hence, risk-neutral subjects should be indifferent between the two schemes. For their part, risk averse subjects can adopt a less risky scheme by choosing the piece-rate scheme. We measure the subjects' risk aversion by using the lottery procedure proposed by [START_REF] Holt | Risk Aversion and Incentive Effects[END_REF].

Our experiment delivers three main findings. First, the key novel finding is that the employees' choice of pay schemes contributes to a considerable reduction in the variance of effort among contestants in the tournament. Second, the average effort is higher when the subjects can select their payment scheme, which suggests that the sorting effect reinforces the incentive effect of both tournaments and variable pay schemes. Third, the subjects self-select mainly according to their degree of risk aversion. A cluster analysis identifies a category of under-confident and hesitant subjects who tend to shy away from competition. The resulting greater homogeneity of contestants improves the overall efficiency of tournaments.

The remainder of the paper is organized as follows. Section 2 presents the theoretical framework and the experimental design. Section 3 gives the experimental procedures. Section 4 describes and analyzes the experimental evidence. Section 5 discusses the results and concludes.

Theory and Experimental Design

The model

Consider an economy with identical, risk-neutral agents. Agent i has the following utility function, separable in payment and in effort:

() () () ii i i Ue up c e =- (1)
with u(p i ) concave and c(e i ) convex.

The production technology is stochastic and output is increasing in the agent's effort:

() ii yf e i ε =+ (2)
with f(e i ) = e i for the sake of simplicity and ε i is an i.i.d. random shock distributed over the interval [-z, +z]. Only individual outcomes are observable; individual effort is not observable, neither by the principal nor the other agents. The cost function is increasing and convex ()

2 i i e ce s = (3)
with s > 0, c(0) = 0, c'(e i ) > 0 and c"(e i ) > 0.5

In the labor market, some firms pay the agents a piece-rate compensation scheme and other firms use tournaments. If there is a perfect mobility in the labor market at no cost, in the first stage the agents choose their firm (i.e. their payment scheme) and, in the second stage, they decide on their level of effort. Let us first solve the equilibrium effort levels under each mode of payment.

In the piece-rate system, the agent's payment depends only on his own outcome. The payment consists of a fixed wage, denoted by a, corresponding to an input-based payment and a linear piece-rate, denoted by b, corresponding to an output-based payment. Under this compensation scheme, the agent's utility function becomes: 2 () .

PR i ii i e Ue a b y s =+ - (4)
The first order condition is:

() '0 PR i i i U bce e δ δ =- =
Thus, the equilibrium effort of each agent under the piece-rate payment scheme depends positively on the incentive, b, as well as the cost scaling factor, s:

* . 2
PR bs e =

(5)

In the firms practicing tournaments, the agents play a non-cooperative game with incomplete information like in [START_REF] Lazear | Rank-Order Tournaments as Optimum Labor Contracts[END_REF]. In pair-wise tournaments, two prizes are distributed: W is the winner's prize allocated to the agent whose outcome is the highest and L is the loser's prize, allocated to the other agent, with W > L. The magnitude of the difference between the two outcomes does not affect the determination of the winner of the tournament.

The agent's utility is:

( ) () () , ii T ii j ii

Wc e i f y y Ue e

Lc e i f y y

->   =  -<   ( ) j j (6)
The agents being symmetric, the probability to win the tournament, pr(e i ,e j ), reduces to the probability that the difference in individual random terms exceeds the difference between individual effort levels: The maximization program yields the following first order condition:

( ) i j j i , ij pree p
( )( ) ( ) ,, 2 0 T ii j i j i
ii EU e e pr e e e WL ee

δ δ δδ =-- s = (8)
We obtain a pure symmetric Nash equilibrium, where effort increases with the prize spread and decreases with both the cost of effort and the size of the shock distribution:

( )

** . 4 TT ij WL s ee z - == (9)
Having determined the equilibrium effort level under each payment scheme, we now turn to the first stage problem. The agent chooses his firm by comparing his expected utility under each payment scheme. He is thus indifferent between the two schemes when:

( )

The experimental design

The instructions have been kept as close as possible to [START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF] (see Appendix).

Two treatments. In the Benchmark Treatment, a single decision is made: the choice of the level of effort, knowing the cost function, the distribution of the random term and the compensation rule. An important difference from the set-up in [START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF] is that in a session, half of the subjects are exogenously and randomly attributed a piece-rate payment scheme and the other half a tournament scheme. The proportion is unknown to the subjects but they aware of the coexistence of two modes of payment. In contrast, [START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF] organized separate sessions in which players were paid either a piece-rate or according to a tournament. We wish to keep the social environment comparable with that of the Choice Treatment in which both schemes coexist in the same session in unknown proportions. The game is repeated 20 times.

The Choice Treatment is similar to the Benchmark except that in the first stage of each period, the subjects choose to be paid according to either a piece-rate scheme or a tournament scheme.

Those who have opted for the tournament are pooled together and paired. In case of an uneven number of contestants, one subject is randomly chosen and paid according to a piece-rate scheme; he is informed of this before deciding on his level of effort. There is no mobility cost, i.e. the subjects are free to move to the other payment scheme in each new period at no cost.

The design of this game enables between-subject but not within-subject comparisons since each treatment is played by different subjects. The latter would have required submitting all the subjects to the exogenous piece-rate scheme, then to an exogenous tournament, then to the choice treatment. It would then have been necessary to alternate between the various treatments to control for potential order effects within the Benchmark and between the Benchmark and the Choice Treatments. Our design is simpler and allows the subjects to play more repetitions of a same treatment. 6

Matching protocol. Unlike most experiments on tournaments, here we adopt a stranger matching protocol. This is motivated by the constraint of the Choice treatment: if we had used a partner matching protocol, a subject who is willing to choose the tournament but who is 6 We took care of using similar pools of subjects in both treatments. The average age of the subjects is 20,9 years and the average safety index is 5.3 in both subgroups; Kolmogorov-Smirnov tests indicate that the distribution of age and safety index are not different between treatments. The proportion of men is 45% in the Benchmark and 47% in the Choice Treatment. A Wilcoxon test accepts the equality between treatments.

paired with a person who always chooses the piece-rate, would be prevented from competing throughout the game. A drawback of our matching protocol is, however, that we reinforce the complexity of the tournament game due to conjectural variations, making it harder to make inferences about the opponent's behavior. If errors in inferences were the source of the greater variability of effort in tournaments, this should entail a greater variability of effort in our design than in standard games. However, this should not affect the comparison between treatments.7

Parameters. Effort can take any integer value in the set:

{ } 0,1,...,100 i e ∈ .
In the cost function, s = 150, so that . The random shocks vary in the interval [-40,+40]. In the tournament, the winner's prize has been set at W = 96 and the loser's prize at L = 45. In the piece-rate scheme, the fixed wage, a, amounts to 45 and the piece-rate, b, is equal to .52, meaning that each unit of outcome gives .52 to the agent. These values ensure that the certain payment is the same under both schemes. Without such a fixed wage equal to the loser's prize, it could be rational for a risk averse agent to choose the tournament and a minimum effort in order not to bear the consequences of a negative random shock on wages under a purely linear piece-rate scheme. Therefore, with our design, only the strategic uncertainty makes a difference between the two schemes.

() 2 /150

ii ce e = Given these values, and assuming the agents to be risk neutral and rational, those who are paid according to a piece-rate scheme should provide the effort e i PR* = 39, according to equation (5); those who enter the tournament should provide the effort e i T* = 48, according to the pure strategy Nash equilibrium in equation ( 9). The players should be indifferent between the two payment schemes since the expected utility of both is the same (EU i PR = EU i T = 55), but if choosing the tournament they have to work harder.

Elicitation of risk aversion. The above predictions hold for risk-neutral subjects. One would expect that risk averse subjects i) reduce their effort level under each mode of payment and ii)

are more likely to stay out of the tournament to avoid the strategic uncertainty linked to the competition [START_REF] Lazear | Rank-Order Tournaments as Optimum Labor Contracts[END_REF]. To elicit the risk aversion of our subjects, we used the lottery procedure proposed by [START_REF] Holt | Risk Aversion and Incentive Effects[END_REF].

At the end of the sessions (in order not to influence the game), the subjects filled out a questionnaire with 10 decisions (see the instructions in Appendix). Each decision consists of a choice between two paired lotteries, "option A" and "option B". The payoffs for options A are either €2 or €1.6, whereas the riskier options B pay either €3.85 or €0.1. In the first decision, the probability of the high payoff for both options is 1/10. In the second decision the probability increases to 2/10. Similarly, the chances of receiving the high payoff for each decision increases as the number of the decision increases. When the probability of the high payoff is high enough, subjects should cross over from option A to option B. Risk neutrality corresponds to a shift at the fifth decision, while risk loving subjects are expected to move earlier and risk averse subjects as from the sixth decision. The subjects made 10 decisions but only one was selected at random for payment.

Experimental Procedures

The experiments have been conducted at the GATE laboratory, Lyon, France. The experiment was computerized, using the Regate software [START_REF] Zeiliger | A Presentation of Regate, Internet Based Software for Experimental Economics[END_REF]. We recruited 120 undergraduate students from three business or engineering schools, trying to guarantee a fair gender distribution in each session (46% of male participants in total). Six sessions with 20 subjects in each have been organized; 3 for the Benchmark Treatment and 3 for the Choice Treatment.

Thanks to the 20 repetitions of the game, we collected a total of 2400 observations. Upon arrival, each subject was randomly assigned a computer. Instructions were distributed and read aloud. Attached to the instructions was a sheet displaying the decision costs associated with each possible effort from 0 to 100. Questions were answered in private. The participants had had to answer a series of questions about the computation of payoffs under each payment scheme. The experiment started once all the participants answered correctly.

No communication was allowed.

In the Benchmark Treatment, at the beginning of the session and for its whole duration 10 subjects were attributed the piece-rate scheme and 10 the tournament scheme. In the Choice Treatment, in each period they had to tick either the "mode X" (piece-rate) box or the "mode Y" (tournament) box to choose their payment scheme for the current period. In both treatments, they selected their effort ("decision number") by means of a scrollbar. This being done, they had to click a button to generate their "personal random number" that was added to their effort choice to constitute their individual outcome ('result"). Under the tournament scheme, the computer program compared the outcomes of the two contestants in each pair and determined who was to receive the winner's prize ("the fixed payment M") and who to get the loser's prize ("the fixed payment L"). In case of a tie, a fair random draw determined the allocation of prizes among the pair members. At the end of the period, each subject received a feedback on his payoff and in case of a tournament, on the difference between his outcome and his competitor's outcome. In each new period, the pairs involved in a tournament were randomly reconstituted.

After the completion of the 20 periods, the risk aversion post-experimental questionnaire was distributed and read aloud. Subjects noted on a sheet of paper the option they chose for each of the 10 lottery decisions. After all participants had made their decisions, each subject had to throw a ten-sided die twice: once to select the decision to be considered and a second time to determine her payoff for the option chosen, A or B.

All the transactions, except the lottery, were conducted in points, with conversion into Euros at a rate of 80 points = €1. Payment consisted of the sum of payoffs during each period plus the lottery payment and a €3 show-up fee. On average, the subjects earned €17.4. The sessions lasted approximately one hour, excluding the lottery draw and payment that was made in private in a separate room for confidentiality.

Experimental Results

Mean and Variance of Effort

Table 1 displays summary statistics about the mean and the distribution of effort by payment scheme and by treatment.

First, we check whether we observe in our Benchmark treatment, like in the previous experiments, both a higher mean and a greater variance of effort under the tournament than under the piece-rate pay scheme. In this treatment, the average effort is 46.5 under the piecerate scheme and 53.3 in the tournaments. Both numbers are significantly above the equilibrium effort levels (39 and 48, respectively; t-test, p=0.000). As predicted, the agents exert more effort in a competitive setting (Mann-Whitney U test, p=0.000). As regards the variance of effort, our results corroborate those of previous experiments. Averaging over all the periods, the total variance is 369 under the piece-rate scheme and 652 in tournaments.

Thus the variability of effort is clearly higher under the competitive scheme.

Fig.1. Evolution of the effort decisions by treatment and by mode of payment over time

Next, we turn to consider the influence of the possibility given to the subjects to choose their payment scheme. Table 1 and Figure 1 reveal a substantial increase of the average effort from 53.3 in the Benchmark Treatment to 61.6 in the Choice Treatment. Interestingly, average effort also increases from 46.5 in the Benchmark to 50.4 for the agents who choose to be paid a piece-rate. As a consequence, the differences relative to the equilibrium effort values are even larger when agents self-select. As for the tournaments, we note that while the subjects on average play the equilibrium effort in the last four periods in the Benchmark Treatment, this behavior cannot be observed in the Choice Treatment although there is a slight decline in effort over time. The choice of the payment scheme is associated with a slower convergence to equilibrium. The variability of effort across the game may be explained by both a time-varying behavior (learning dimension) and time-invariant inter-individual characteristics (heterogeneity dimension). To gauge the relative importance of these two dimensions, in Table 2 we decompose the variance into its within and between components. The between-subject variance of effort in tournaments accounts for two thirds of the total variance in the Benchmark Treatment. It is four times lower and it accounts for less than 40%

of the total variance in the Choice Treatment. 8 Consequently, when people self-select, the population of voluntary contestants is more homogeneous in terms of exerted effort and the variability of effort is mainly due to an intra-individual component. The within-subject variance of effort in tournaments shows that in the Choice Treatment, the variability of effort is lower than in the Benchmark: the subjects learn less or they are less hesitant. Similar differences in the between-subject and within-subject variances are observed for the piece-rate scheme: both are lower when subjects can self-select.

The descriptive statistics shown above refer to averages. Next, we account for individual characteristics. In Table 3, regressions (1) and ( 2) give the results of OLS regressions of the effort decisions in each treatment, with robust standard errors and accounting for clustering of the individuals; regressions (3) and ( 4) use panel data analysis with fixed effects. Explanatory variables include a time trend to capture learning, the mode of payment to capture the impact of competition, the random shock in the previous period and a series of individual characteristics such as gender, age, experience of experiments, and the degree of risk aversion.

The risk aversion variable (coded from 1 to 10) corresponds to the number of the decision where the subject crosses over from the safer to the riskier option in the lottery test: the higher this number, the more risk averse the subject.

The main differences between the two treatments are related to the influence of the mode of payment and of risk aversion. In the Benchmark Treatment, the subjects do not exert a significantly higher effort in tournaments than under a piece-rate scheme. In contrast, competition stimulates performance when the subjects self-select, possibly because they consider that being volunteers, their opponents are also willing to compete hard. Risk aversion has a significant negative impact on effort when the payment schemes are imposed on the subjects: considering the uncertainty of the environment, risk-averse subjects reduce their cost.

This variable is not significant in the Choice Treatment, suggesting that risk aversion plays a role in the sorting process, but not once the choice has been made. The reverse is true for gender. In both treatments effort declines over time but this tendency is less pronounced when the subjects are allowed to choose their scheme. Although the periods are independent, the subjects in both treatments adjust their effort downwards (upwards) when they have got a positive (negative) random shock in the previous period. The third and fourth columns of Table 3 display the estimations accounting for the longitudinal character of the data and including individual fixed effects. We may note that the coefficients to the lagged random shock are reduced but continue to differ from zero, whereas the other coefficients do not change much. In particular, it is worth remarking that the estimate to the tournament dummy in the choice treatment is larger, not smaller, when fixed effects are entered. This is interesting because the fixed effects, in addition to the individual characteristics included in columns 1 and 2, are also picking up the impact of time-invariant unobservables.

Behavior in the Choice Treatment clearly differs from behavior in the Benchmark Treatment.

It is therefore important to understand what determines sorting.

Sorting

In the Choice Treatment, the competitive scheme is chosen in 50% of the cases. Its relative frequency declines slightly from 52.7% in the first ten periods to 47.3% in the subsequent ten periods. This corresponds to the theoretical prediction since the expected utility in the tournament and the piece-rate scheme is the same. Does it mean that subjects choose at random or can we identify subjects' characteristics that predict their behavior?

A first candidate for a determinant of sorting is risk aversion. Table 4 compares the distribution of our subjects in terms of risk attitude to the results in [START_REF] Holt | Risk Aversion and Incentive Effects[END_REF]. Note: The number of safe choices corresponds to the number of the decisions with the "safe" option A, and thus corresponds to the "risk aversion" variable in our econometric analysis.

We observe higher proportions of risk lovers and more than slightly risk averse subjects than in Holt and Laury's pool of subjects, but the differences are small. A Kolmogorov-Smirnov exact test does not reject the hypothesis of equality of distribution functions between our Benchmark and Choice Treatments.

Fig.3. Proportion of safe lottery choices and frequency of the tournament choices

Clearly, the subjects who choose the tournament less frequently are more risk averse than the other categories. 9 All risk-averse subjects considered together (who made at least 5 safe lottery choices) choose the tournament in 45% of the periods, whereas the corresponding proportions are 60% for the risk neutral subjects and 56% for the risk lovers. A Poisson count model of the total number of tournaments chosen by the subject throughout the session has been estimated, including individual characteristics. It shows that only risk aversion exerts a significant influence and its marginal effect is important: crossing over from the safer to the riskier option one decision later in the lottery choices reduces by 77.8% the number of tournament choices.

We have also conducted an econometric analysis of the choice of the tournament scheme, the results of which are reported in This analysis confirms that the degree of risk aversion is an important determinant of the choice of the competitive scheme, whereas demographic characteristics exert no effect.

The tournament choice is also affected by previous outcomes. The regression shows that it declines over time and that bad luck in the previous period increases the probability to enter the competition. This may reflect the subjects' attempts to get the winner's prize to compensate for small earnings in the previous period. Lastly, descriptive statistics indicate that if 70% of those who won a tournament in the previous period choose to remain in the competitive scheme, this percentage decreases to 55% among those who lost the previous competition.

Overall, these results suggest that risk aversion may be an important determinant of occupational choices. They are consistent with the survey analysis by [START_REF] Bonin | Cross-Sectional Earnings Risk and Occupational Sorting: The Role of Risk Attitude[END_REF] carried out on German data, which shows that risk averse employees tend to concentrate in jobs with low earnings risks.

Heterogeneity of Behavior in Tournaments

To investigate the behavioral origins of the reduction of effort variability when individuals self-select into tournaments, we adopt a cluster analysis that helps in identifying different types of behavior. In order to partition the sample, we retain three variables that summarize each individual's decisions: her frequency of tournament choices, her mean effort in the tournament and its standard deviation. In the Benchmark treatment, we only consider the last two variables. We apply the hierarchical Wald method based on the minimization of the intragroup variance to identify the clusters that sum up the strategies. Clusters have been grouped so that each one includes at least 10% of the subjects.

In both treatments, four main categories of tournament players are identified displaying similar characteristics; therefore we use the same denomination of clusters. The so-called "underconfident competitors" are subjects who exert an excessively high level of effort (more than 50% above the equilibrium), with relatively low standard deviation. The "motivated competitors" are subjects who exert a level of effort still higher than the equilibrium but closer to it. The "hesitant competitors" group consists of subjects who alternate levels of effort below and above the equilibrium and are characterized by the highest standard deviation of effort. Lastly, "economizing competitors" are subjects who follow a stable strategy based on the choice of a level of effort below the equilibrium.

Table 6 summarizes the statistics that characterize these behaviors in each treatment. The first column indicates the proportion of each cluster in the population. The second column represents the relative frequency with which the tournament has been played during the session. The following columns give the mean individual effort and within-individual standard deviation of effort in the tournament. The last column gives the between-subject standard deviation within each cluster. In the Choice Treatment, the analysis identifies two main categories of subjects according to their frequency of tournament choice. Frequent competitors, who compete in at least half of the periods, are characterized by a lower within-subject variance of effort than the occasional competitors, who choose the tournament in about one third of the periods.

When they can select their payment scheme, the individuals who enter more frequently into the tournament are both the motivated and the economizing competitors. The group of motivated competitors is very homogenous as indicated by the low between-subject deviation.

The relative importance of this group (40% of all the subjects involved in this treatment) contributes to explain the lower variance of effort in tournaments when individuals can selfselect. In contrast, the group of economizing competitors shows the lowest within-subject and the highest between-subject variance of effort. It includes subjects who choose a minimum cost but can expect to win the tournament by chance. It also includes subjects who exert a level of effort slightly below equilibrium, possibly due to overconfidence or perception biases with respect to uncertainty, such as misconceptions of chance [START_REF] Kahneman | Judgment under Uncertainty: Heuristics and Biases[END_REF] or illusion of control over external events originated in being given a choice [START_REF] Langer | The Illusion of Control[END_REF]).11 Whatever the explanation, their low-cost choices enable them to earn more on average than the motivated competitors (45.8 and 42.7, respectively).

The hesitant and the under-confident subjects are occasional competitors. The high withinsubject variance of effort of the first group suggests that, facing the strategic uncertainty attached to the tournament, these subjects make errors both above and below the equilibrium.

Entering the tournament less often reinforces the difficulties of learning the equilibrium.

Consequently, they earn less on average in the tournament than the frequent competitors (40.8 points). The group of under-confident competitors is also not able to compute the equilibrium but always exerts a very high level of average effort in tournaments (73.2, i.e. 52.5% above the equilibrium). As a consequence, even if they win relatively often the competition, the cost of effort is too high and thus they earn considerably less on average than under the piece-rate scheme (36 and 50.6 points, respectively).

The comparison between treatments indicates that the reduction of the effort variability in tournaments when agents can self-select is due to the fact that the most extreme categories in terms of average effort and the most unstable agents tend to stay out of the competition. The experiment points to a potential limitation of sorting. The motivated competitors provide an over-supply of effort and their net earnings are not very high. These subjects do not enter into a rat race (effort does not increase over time), but nevertheless, sorting reinforces a tendency to exert excess effort from some employees.

Discussion and Conclusion

In a one-shot game environment, our results confirm that both the average level and the variance of effort are higher under a tournament than under a piece-rate payment scheme. This higher variability of effort has long been considered an important disadvantage since the employers have to bear uncertainty as to how the agents behave in relative performance compensation schemes. However, by analyzing an experimental setting which accounts for a key feature of markets, that the agents can choose their payment scheme, our results paint a fundamentally different picture. A major finding of this paper is that when the subjects enter the tournament voluntarily, the average effort is higher and the variance of effort is substantially lower compared to situations in which the same payment scheme is imposed.

In our experiment, average effort in the freely chosen tournament is 32.5% higher than in the exogenously imposed piece-rate scheme. This differential can be decomposed into an incentive and a sorting effect. The difference between effort levels in the imposed piece-rate and in the imposed tournament is an estimate of the incentive effect of tournaments: here, this is of the magnitude of a 14.6% increase in effort. The difference of 17.9% between the total increase in effort and the estimated incentive effect can be attributed to the sorting effect of tournaments. The sorting effect makes up a little more than half of the total increase in effort;

this is comparable to Lazear's (2000) corresponding estimates in connection with the switch from a fixed pay to a variable pay scheme. This confirms the importance of taking sorting into account when evaluating the efficiency of compensation schemes.

Another important and new result is that sorting significantly decreases the variance of effort in tournaments. When agents freely enter the tournament, the between-subject variance is four times smaller than when this scheme is imposed and it is even lower than the variance of effort under the piece-rate scheme. It is worth noting that we obtain this result in spite of the increased complexity of the task to be performed as compared with previous studies.

Consequently, our experiment does not lead to the same recommendations as [START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF], who suggest that to attract contestants, an employer should offer them a higher expected utility than under a piece-rate scheme. Our conclusion is rather that labor market flexibility, in particular the absence of restrictions on mobility between firms, is a key condition for a higher efficiency of relative performance pay.

Our results indicate that the efficiency-enhancing effect of sorting derives from the resulting greater homogeneity of contestants. Since tournaments involve higher uncertainty than the piece-rate scheme, risk averse subjects choose them to a lesser extent. Under-confident subjects also prefer the piece-rate scheme since they exert too much effort in the tournament, entailing an excessive cost of effort. Hesitant subjects, alternating between above and below equilibrium effort levels, are not attracted by the tournament either. On the other hand, individuals who are motivated to work hard do not hesitate to choose the tournament in which equilibrium effort is higher. Among frequent contestants, the motivated competitors represent the biggest and the most stable category. Thus, the homogeneity of the contestants is higher when the tournament is chosen and this contributes to the lower variance of effort. More homogeneity does not, however, give rise to collusion. Beyond this, our results suggest that introducing more competitive payment schemes in some occupations would sort employees and that the attitude toward risk may be an important driver of mobility between firms or sectors.

Having demonstrated that sorting has profound implications for the level and variance of effort in tournaments, we think further work should focus on how sorting is affected both by the prize structure and by differences in individuals' skills and social preferences: if people care about the negative externalities imposed on others by their individual effort, they may try not only to reduce their level of effort in relative pay scheme [START_REF] Bandiera | Social Preferences and the Response to Incentives: Evidence from Personnel Data[END_REF], but also to stay out of a competition. Our work suggests more generally the importance of reconsidering the influence of sorting in many economic decisions.

Appendix. Instructions of the Choice Treatment

You are about to participate in an experiment on decision-making organized for the GATE research institute and the Aarhus School of Business in Denmark. During this session, you can earn money. The amount of your earnings depends on your decisions and on the decisions of the participants you will have interacted with. During the session, your earnings will be calculated in points, with 80 points = 1Euro

During the session, losses are possible. However, they can be avoided with certainty by your decisions. In addition, if a loss would occur in a period, the gains realized during the other periods should compensate this loss.

At the end of the session, all the profits you have made in each period will be added up and converted into Euros.

In addition, you will receive a show-up fee of 3 Euros. You will have also an opportunity to earn additional money by participating in a decision task at the end of the session. Your earnings will be paid to you in cash in a separate room in order to preserve confidentiality.

The session consists of 20 independent periods.

___________

Description of each period

Each period consists of two stages.

In stage 1, you choose between two modes of payment, mode X and mode Y.

In stage 2, you carry out a task.

Your profit during each period depends on the mode of payment you have chosen and on your result from the task.

Description of the task

o A table is attached to these instructions: numbers, from 0 to 100, are given in column A. In the second stage of each period, your task consists of selecting one of these numbers. This number will be called your "decision number". Associated with each number is a cost, called "decision cost". These decision costs are listed in column B. Note that the higher the decision number chosen, the greater is the associated cost. You make your choice by means of a scrollbar on your computer screen and you confirm this choice by clicking the "OK" button.

o Then, you have to click a button on your screen that will generate a random number. This number is called your "personal random draw number". This number can take any value between -40 and + 40. Each number between -40 and + 40 is as likely to be drawn and there is one independent random draw between -40 and + 40 for each subject in the lab.

Your "result" for the task is the sum of your decision number and your personal random draw number.

Your result = your decision number + your personal random draw number

Choice of the mode of payment and calculation of your payoff

There are two different modes of payment, mode X and mode Y. In the first stage of each period, you choose to be paid according to mode X or to mode Y. If you like, you can change the mode of payment at each new period.

Description of mode of payment X

If you choose the mode of payment X, your result is multiplied by 0.52. You also receive a fixed amount of 45 points. Next, the decision cost associated to the choice of your decision number is subtracted. Note, the amount subtracted (your decision cost) is only a function of your decision number; that is, your personal random draw number does not affect the amount subtracted.

29

Your payoff thus depends on your decision number and your personal random draw number. Your net payoff under mode X is thus given by the following formula:

Your net payoff of the period under mode X = 45 + (your result * 0.52) -your decision cost At the end of the period, you are informed about your result and about your net payoff for the current period.

Example of net payoff calculation under mode of payment X

For example, say that you choose a decision number of 55 and you draw a personal random number of 10. Your net payoff calculation will look like:

45 + [(55 + 10) * 0.52] -20.17 = 58.63

Description of mode of payment Y

If you choose the mode of payment Y, another subject in the room, who has also chosen the mode of payment Y, is paired with you at random for the current period. This subject is called your "pair member". The identity of your pair member will never be revealed to you.

Your pair member has an identical sheet as yours. Like you and simultaneously, he has to select a decision number and he will draw his personal random number. As for you, the "result" of your pair member is computed by adding his decision number and his personal random draw number.

Then, the computer program will compare your result and the result of your pair member.

-If your result is greater than your pair member's result, you receive the fixed payment M, equal to 96 points.

-If your result is lower than your pair member's result, you receive the fixed payment L, equal to 45 points.

-In case of equal results, a fair random move decides on which subject receives M and who receives L.

Whether you receive M or L as your fixed payment depends only on whether your result is greater or not than your pair member's. It does not depend on how much bigger it is.

To determine your net payoff, the decision cost associated with the choice of your decision number is subtracted. Note, the amount subtracted is only a function of your decision number; that is, your personal random draw number does not affect the amount subtracted.

Therefore, your net payoff depends on your decision number, your personal random draw number, and your pair member's decision number and his personal random draw number.

Your net payoff under mode Y is given by the following formula:

Your net payoff of the period under mode of payment Y = Fixed payment (M or L) -your decision cost At the end of the period, you are informed about your result; you are told by how much your total is greater or less than that of your pair member and you are informed about your net payoff for the current period.

Example of net payoff calculation under mode of payment Y

For example, say that pair member A chooses a decision number of 25 and draws a personal random number of 20, while pair member B selects a decision number of 55 and draws a personal random number of -5.

A's result is: 25 + 20= 45 B's result is: 55 -5= 50

B's result is larger than A's result. Thus, B receives M (=96) and A receives L (=45).

A's net payoff is: 45 -4.17 = 40.83 B's net payoff is: 96 -20.17 = 75.83 To sum up, in each period you make two decisions:

-In stage 1, you choose between mode of payment X and mode of payment Y. Note that if an uneven number of participants has chosen mode Y, one of these participants will be randomly chosen and paid according to mode X. To be paid according to mode Y, pairs must be formed. This participant will be informed of this before moving to stage 2.

-In stage 2, you select your decision number and you draw a personal random number. Your net payoffs for the current period are then computed.

At the end of a period, a new period starts automatically. Each period is independent. The random draws are independent from one period to the next. In each period, under mode of payment Y, pairs are composed at random among the participants who have chosen this mode of payment.

- ----------------If you have any question regarding these instructions, please raise your hand. Your questions will be answered in private. Throughout the entire session, talking is not allowed. Any violation of this rule will result in being excluded from the session and not receiving payment.

Thank you for your participation. We thank you for filling out this form that enables you to earn additional money. The attached sheet of paper shows ten decisions. Each decision is a paired choice between "Option A" and "Option B". You will make ten choices and record these in the column on the right, but only one of them will be used in the end to determine your additional earnings. Let us explain how these choices will affect your earnings.

Decision Costs

Here is a ten-sided die that will be used to determine this payoff. The faces are numbered from 1 to 10 (the "0" face of the die will serve as 10). After you have made all of your choices, and when you come to the other office to receive your payment, you will throw this die twice:

-once to select one of the ten decisions to be used, -and a second time to determine what your payoff is for the option you chose, A or B, for the particular decision selected.

Even though we ask you to make ten decisions, only one of these will end up affecting your earnings. However, you will not know in advance which decision will be used. Obviously, each decision has an equal chance of being used in the end.

• Look at Decision 1. Option A pays 2 € if the throw of the dice is 1, and it pays 1.6 € if the throw is 2-10. Option B yields 3.85 € if the throw of the dice is 1 and it pays 0.1 € if the throw is 2-10.

• Look at Decision 2. Option A pays 2 € if the throw of the dice is 1 or 2, and it pays 1.6 € if the throw is 3-10. Option B yields 3.85 € if the throw of the dice is 1 or 2 and it pays 0.1 € if the throw is 3-10.

The other decisions are similar, except that as you move down the table, the chances of a higher payoff for each option increase. In fact, for Decision 10 in the bottom row, the dice will not be needed since each option pays the highest payoff for sure, so your choice here is between 2 € and 3.85 €.

To summarize, -you will make ten choices. For each decision row, you will have to choose between Option A and Option B. You may choose A for some decision rows and B for other rows. You may change your decisions and make them in any order.

-When you come to the other room to receive your earnings from the experiment, you will throw the tensided die to select which of the ten decisions will be used.

-Then, you will throw the die again to determine your money earnings for the Option you chose for that Decision.

Earnings (in Euros) for this choice will be added to your previous earnings, and you will be paid all earnings in cash.

If you have any question, please raise your hand. Your questions will be answered in private. Please do not talk with anyone.

---------------Please indicate for each of the following 10 decisions if you choose Option A or Option B.

Your decision Decision 1

Option A: 1/10 of 2 € and 9/10 of 1.6 € Option B: 1/10 of 3.85 € and 9/10 of 0.

Fig. 2 .

 2 Fig.2. Dispersion of effort in the tournaments by treatment and category of periods

Figure 3

 3 Figure 3 relates the frequency of our subjects' tournament choices to their proportion of safe

  10 of 2 € and 5/10 of 1.6 € Option B: 5/10 of 3.85 € and 5/10 of 0.1 € Option A O Option B O Decision 6 Option A: 6/10 of 2 € and 4/10 of 1.6 € Option B: 6/10 of 3.85 € and 4/10 of 0.1

Table 1 .

 1 Summary statistics on average level and variance of effort

			Average effort			Mean variance of effort
	Periods	All	1	1-10 11-20	All	1	1-10	11-20
	Piece-rate
	Benchmark Tr.	46.5	55.7	48.9	44.0	368.9	388.1	380.3	354.8
	Choice Tr.	50.5	47.6	51.3	49.7	227.9	192.4	221.9	231.7
	Tournament
	Benchmark Tr.	53.3	60.0	55.6	50.9	652.3	663.8	674.4	633.5
	Choice Tr.	61.6	65.8	63.4	59.8	258.2	319.4	238.4	259.7

Table 1 and

 1 Figure2also show a dramatic change in the variability of effort when agents selfselect. Comparing the Benchmark with the Choice Treatment, we find that the variance under the piece-rate diminishes from 368.9 to 227.9 (-38.2%) and the variance in the tournament decreases from 652.3 to 258.2 (-60.4%). Not only is the variability of effort lower when

agents self-select, but now the tournament cannot be considered as more unstable than the piece-rate. Levene's robust test statistic rejects the hypothesis of equality of variance between the tournament and the piece-rate in the Benchmark Treatment (z=48.93, p<0.000) but accepts it in the Choice Treatment (z=.135).

Table 2 .

 2 Decomposition of the variance of effort

	Variance	Between-subject	Within-subject	Total
	Benchmark Treatment
	Piece-rate	193.7 (52.5)	175.2 (47.5)	368.9 (100)
	Tournament	434.6 (66.6)	217.7 (33.4)	652.3 (100)
	Choice Treatment
	Piece-rate	120.0 (52.7)	107.9 (47.3)	227.9 (100)
	Tournament	101.8 (39.4)	156.4 (60.6)	258.2 (100)

Note: Percentages of the total variance in parentheses.

Table 3 .

 3 Determinants of the effort decision

		Regressions with robust	Panel data analysis
		standard errors	with fixed effects
	Treatments	Benchmark (1)	Choice (2)	Benchmark (3)	Choice (4)
	Periods	-.4888***	-.3049***	-.4822***	-.2958***
		(.1342)	(.0953)	(.0728)	(.0689)
	Tournament	5.1846	10.3899***	-	11.8411***
		(4.7936)	(1.4860)		(.8753)
	Lagged random shock	-.0743***	-.0399**	-.0393**	-.0294*
		(.0252)	(.0166)	(.0171)	(.0162)
	Risk aversion	-2.4814**	-0.1011	-	-
		(1.1169)	(.8539)
	Male subject	-5.4184	-6.5433***	-	-
		(4.8132)	(2.2455)
	Age	-3.1068	.3910	-	-
		(1.8634)	(0.4398)
	Experience	4.0601	-.3474	-	-
		(8.0158)	(2.9161)
	Nb obs.	1140	1140	1140	1140
	Prob>F	0.000	0.000	0.000	0.000
	R 2	0.1088	0.1630	0.0167	0.1198
	Note: Standard errors in parentheses; *** significant at 1% level, ** at 5% level, * at the 10% level.

Table 4 .

 4 Distribution of risk attitudes

	Our experiment

Table 5 .

 5 Regression (1) estimates a panel probit model with random effects, and regression (2) a panel logit model with fixed effects. 10

Table 5 .

 5 Determinants of the tournament choice

		Panel Probit model,	Panel Logit model
		with random effects (1)	with fixed effects (2)
	Periods	-.0173**	-.0295**
		(.0074)	(.0125)
	Lagged random shock	-.0030*	-.0051*
		(.0017)	(.0029)
	Risk aversion	-.1461**
		(.0745)
	Male subject	-.0227
		(.2176)
	Age	-.0349
		(.0355)
	Experience	-.0923
		(.2564)
	Constant	1.7981**
		(.8511)
	Nb observations	1140	1102
	Wald χ 2 / LR χ 2 Prob>χ 2	14.61 0.023	8.60 0.014
	Log Likelihood	-691.00	-517.07
	Note: standard errors in parentheses; ** significant at 5% level, * at 10% level.

Table 6 .

 6 Behavior in tournaments

		Share in the	Relative	Mean effort Within-	Between-
		population	frequency		subjects SD	subjects SD
	Benchmark Treatment
	Underconfident Competitors	30.0	100	74.5	6.3	4.2
	Motivated Competitors	30.0	100	59.9	10.8	5.4
	Hesitant Competitors	30.0	100	40.6	20.5	7.1
	Economizing Competitors	10.0	100	7.6	9.9	7.1
	Choice Treatment
	Frequent competitors
	Motivated Competitors	40.0	57.9	61.9	9.4	3.7
	Economizing Competitors	18.3	50.9	44.6	7.6	15.6
	Occasional competitors
	Hesitant Competitors	10.0	35.8	53.1	32.6	6.5
	Underconfident Competitors	31.7	34.4	73.2	10.7	6.3

Table Column

 Column

	A	Column B	Column A	Column B	Column A	Column B
	Decision Nb	Cost of Decision	Decision Nb	Cost of Decision	Decision Nb	Cost of Decision
	0	0.00	35	8.17	70	32.67
	1	0.01	36	8.64	71	33.61
	2	0.03	37	9.13	72	34.56
	3	0.06	38	9.63	73	35.53
	4	0 .11	39	10.14	74	36.51
	5	0.17	40	10.67	75	37.50
	6	0.24	41	11.21	76	38.51
	7	0.33	42	11.76	77	39.53
	8	0.43	43	12.33	78	40.56
	9	0.54	44	12.91	79	41.61
	10	0.67	45	13.50	80	42.67
	11	0.81	46	14.11	81	43.74
	12	0.96	47	14.73	82	44.83
	13	1.13	48	15.36	83	45.93
	14	1.31	49	16.01	84	47.04
	15	1.50	50	16.67	85	48.17
	16	1.71	51	17.34	86	49.31
	17	1.93	52	18.03	87	50.46
	18	2.16	53	18.73	88	51.63
	19	2.41	54	19.44	89	52.81
	20	2.67	55	20.17	90	54.00
	21	2.94	56	20.91	91	55.21
	22	3.23	57	21.66	92	56.43
	23	3.53	58	22.43	93	57.66
	24	3.84	59	23.21	94	58.91
	25	4.17	60	24.00	95	60.17
	26	4.51	61	24.81	96	61.44
	27	4.86	62	25.63	97	62.73
	28	5.23	63	26.46	98	64.03
	29	5.61	64	27.31	99	65.34
	30	6.00	65	28.17	100	66,67
	31	6.41	66	29.04
	32	6.83	67	29.93
	33	7.26	68	30.83
	34	7.71	69	31.74

Post experimental questionnaire [Instructions for the test of risk aversion directly taken from Holt and Laury, 2002 ]

The variance of effort is diminished when the subjects play against an automaton choosing the same common knowledge effort level repeatedly, but not when more information is given about the past effort of a human competitor. The observed variance can thus be related to the strategic nature of the game.

In this design, s is assumed to be similar for all agents, for the sake of simplicity.

[START_REF] Bull | Tournaments and Piece-Rates: An Experimental Study[END_REF] reject the errors in inference explanation of the variance: in fixed pairs, giving the subjects a feedback on the effort chosen by their opponent does not reduce the variance of effort.

If we remove the observations with a level of effort of 0 or 100(73 and 41, respectively), the between-subject variance in the tournament still represents 64% (270.6/419.9) of the total variance in the Benchmark and 42% (75.1/181.0) in the Choice Treatment. Thus the structure of the variance remains the same as when all the contestants are considered.

We checked that although made at the end of the sessions, the lottery decisions do not result from the behavior in the main game instead of explaining it. In fact, there is no correlation between the number of safe choices in the lotteries and the individual's net payoff.

We also tested a selection model with random effects, including the treatment variable in the non-selection equation, fitted by simulated maximum likelihood. The selection equation included risk aversion, demographic variables and lagged random shock. The results are consistent with Table3 and 4, except that gender looses its significance. However, the robustness of this selection model is not big because of the small number of variables, especially time-varying ones.

In the first period of the game, after the subjects have chosen their level of effort, we asked them: « How big do you estimate your chances are that you will draw a random number that increases your payoff ? ». 14% reported a probability lower than .49 and 13% a probability exceeding .50. 61% of the optimistic subjects opted for the tournament, whereas the corresponding percentages are 47% for the pessimistic and 48% for the well-calibrated subjects. According to a probit regression (not shown) including only the first period data and individual observable characteristics, optimism marginally but significantly (at the 10% level) increases tournament entry. If all periods are considered, miscalibration is no longer significant since subjective beliefs are revised throughout the game.


Eunice Ndunge Mutuku

Dr Felix Kioli

Ngunzoanddr Benoit

Psychosocial Challenges Facing Athletes (Medium and Long distance Runners) in Eldoret, Kenya

All over the world, sports have been known to bring social cohesionat the individual, family and socio-economic development to a country. Over the years, in Kenya, athletics have promoted development, aesthetic value and recognition to the country and individuals as well. Despite the positive impact athletics can have on development to a country and individuals, studies have shown that athletes do not receive much psychosocial support to reach their full potentials and participate effectively in the general development of the country. This study examined the psychosocial challenges facing athletes in Eldoret, Kenya. The study specifically investigated individual-related psychosocial challenges; family and community-related psychosocial challenges facing athletes; and the coping strategies devised by athletes to deal with psychosocial problems in the study area.The study was informed by Social Breakdown Theory (SBT) theory. The study used descriptive research design. The study targeted medium and long distance runners in Eldoret and limited itself to 99 professional athletes from 8 locations that are major sources of athletes in Eldoret through simple random sampling. The study used snowball sampling in selecting key informants (elite athletes) from each selected location and government officials from the sports ministry. Both quantitative and qualitative methods of data collection were used. The study employed both qualitative and quantitative data analysis. In qualitative analysis, the data was presented by use of quotes and narrative descriptions. Quantitative analysis involved derivation of statistical descriptions and interpretation of data by use of descriptive statistics that purely relied on numerical values. The study found that athletes (medium and long-distance runners), have not been given much psychosocial support. Instead, they are exposed to much vulnerability aggravated by lack of social acceptance, overdependence by close kin, isolation, lack of mentorship and peer support and are thus unable to participate effectively in the sport. The study recommends that there is a need to designing appropriate interventions that mitigate psychosocial challenges among athletes to promote athletes' wellness and personal development.

Introduction

The 20 th Century saw a massive growth in the extent to which sport impacted on the lives of many of the world's populations. In part, this has been due to the progressive internationalization, professionalization and commercialization of various sports including football, short and long distance running, rugby and cricket, which developed in England in the latter half of the 19 th Century and their evolutionary offshoots in North America including baseball, American football and basketball.2 Physical activities have long been the integral part of the human life and used as a major tool to achieve physical and mental perfection. 3 Despite socio-economic deprivation among athletes in the country, Kenya has produced world and international running champions repeatedly. For the past 30 years, Kenyan runners have been well recognized for their success in distance running. 4 These athletes have become role models for the younger generations, who take up running in the hope of a better future for themselves. Favorable environmental conditions, motivational and socio-economic factors are all possible reasons for the unsurpassed achievements of Kenyan distance runners. 5 Notwithstanding the positive impact athletics has on the lives of athletes and Kenya's development, studies have shown that athletes, particularly medium and long distance runners, do not receive much social and even spiritual support to reach their full potentials. This is partly confirmed by Wahome who asserts that in Kenya, despite the athletes bringing international glory and injecting into the national economy substantial foreign exchange, there seem to be no clearly defined sports policies such as social protection or finance policies to support the development of athletics in the country. 6 Hence, athletes are exposed to too much vulnerability aggravated by over-dependence on unforthcoming sponsors, close kin, isolation, lack of mentorship and peer support. As a result, athletes are faced with many economic, social and psychological challenges ranging from the individual level to family and community. According to this study, psychosocial challenges refer to factors that affect the psychological, economic or socio-cultural spheres of athletes.

The inclination of this study is towards the realm of aspects that inflame psychosocial challenges, which include socio-economic status, education level, family situation, lifestyles, social relations and interaction with the family and other community members. According to the Social Breakdown Theory (SBT) by Kuypers and Bengtson 7 , these aspects can be as a result of labeling, one's financial status, health and education and as a result, social and psychological challenges experienced along the way, affect them negatively. Others become deviants of the values, norms and morals of the society, which in return have adverse societal implications as well as on their careers and development. Over their life course, athletes are likely to face a variety of life transitions that require the acquisition of new norms, behaviors, and roles in order to live as per the expectations of society. However, their struggle to adjust to societal norms while, accommodating new ways of life, comes with it psychosocial challenges.

This study recognizes that prolonged psychosocial challenges may cause long-term negative effects in the life of an athlete. It is within this background that this study examined the psychosocial challenges facing athletes, with particular interest in Eldoret, Kenya. The study was guided by the following research questions; what are the individual-related psychosocial challenges facing athletes in Eldoret? What are family and community-related psychosocial challenges facing athletes in Eldoret? And which coping strategies have been devised by athletes to deal with psychosocial challenges in the study area? Methodology 4 Ibid. 5 Kong P. W. and de Heer, H., Anthropometric, gait and strength characteristics of Kenyan Distance Runners,Journal of Sports Science and Medicine, 7, 2008, pp.499-504. 6 Wahome M., Kenya Rowing and Canoe Association (KRACA), Daily Nation, August 24, 2005. 7 Kuypers J. A. and V. L. Bengtson, Social Breakdown and Competence: A Model of Normal Aging, Human Development, 16, 1973, pp.181-201. The study used descriptive research design and targeted medium and long distance runners in Eldoret. The study limited itself to 99 professional athletes from 8 locations that are major sources of athletes in Eldoret, by stratification of athletes into locations and then applying simple random sampling. According to this study a professional athlete is a person who has participated in national and international competition for more than two years. The study used snowball sampling in selecting 4 key informants (elite athletes) from each selected location and 4 government officials from the sports ministry. Both quantitative (through use of questionnaire) and qualitative (Observation and observation guide) methods of data collection were used. The study employed both qualitative and quantitative analysis. 1 above shows that majority (71, 76.7%) of the respondents were male, whereas 28 (23.3%) of the respondents were females. This reflects the fact that more men were involved in middle and long distance running than their female counterparts. The researchers argue that the idea of sports has always had a masculine viewpoint. It has been seen as unladylike for female to participate in certain sports, let alone those that are primarily male dominated. From interview discussions, it was observed that the Kalenjin culture associates male with athletics such as medium and long distance running. The researchers observed that this situation is also experienced in other parts of the country. ) of the respondents who were actively involved in middle and long distance running were between 26-33 years, whereas the least 12(10.0%) number of respondents involved in the profession were between 42-49 years. It was established that the age between 26-33 years is when most respondents had gained their competence. Therefore, they are participating in various competitions to maximize their full potentials.

Findings and Discussions

Demographic Characteristics of Athletes Characteristic

Individual-related Challenges

The research sought to establish the average monetary gains per annum of the athletes from their participation in athletics in order to determine the extent of influence of athletics on the life of the youth. The findings showed that the majority of the respondents had received some form of monetary rewards as a result of athletics. Fifteen percent of the respondents reported to have received an average of over 1.5 million shillings, while 12% respondents reported to have received between Kshs. 500,000 and 1 million, and the rest reported to have received between Kshs. 100,000 ($1,000) to Kshs. 500,000 ($5,000).However, athletes are faced with many individual-related challenges that eventually detach them from the family and society as well.

Therefore, this study sought to understand the individual related challenges facing athletes in the study area. It was revealed that athletes (medium and long distance runners) in the study area face a myriad of psychological challenges. For instance, the majority (68%) of the athletes, especially those who are first timers in the competition, faced personal challenges such as lack of a sponsorship.

Many of the individual athletes in the study said that they have always been cash strapped with constant appeals made through the media such as daily newspapers, televisions and radio for well-wishers to salvage them. The worst hit are normally self-sponsored medium and long distance runners who are denied the opportunity to participate in various national and international championships because of lack of sponsorship. This situation makes them miss opportunities for their career and personal development. For example, a self-sponsored youth in Chepkoilel camp could not sustain himself in one of the championships in the country league despite being highly rated by the Athletics Kenya (AK) body.

The authors observed that ironically in Kenya, despite the athletes bringing international glory and injecting into the national economy substantial foreign exchange, there seems to be no clearly defined sports financing governmental policy, especially for distance runners. Hence, on many occasions, Kenyan runners have failed to meet their financial requirements for effective growth and development. At the same time, most athletes come from poverty ridden regions, therefore, they have not been able to pay for themselves to compete in local events and occasionally, upon struggling to train well for their fitness in competitions, they fail to participate in various championships within and outside the country. To realize this objective sound financing strategies must be employed.

On the same note, the research sought to investigate the availability of privately-owned athletic facilities that promote athletics. The findings indicated that 90% of the respondents were aware of privately owned athletic facilities that promote athletics in the study area whereas, 10% of the respondents were not aware of any privately owned facilities. The majority (70%) of the respondents indicated that the privately owned facilities were expensive, while 30% of the respondents reported that the privately owned athletic facilities were affordable. The result of the findings shows that while indeed privately owned athletic facilities that promote athletics do exist, they are however, mostly expensive and thus, unaffordable for the majority of the athletes.

The respondents were asked to mention some of their sources of finance used to help them participate in various competitions. The study found that sponsor companies such as Safaricom, and Kenya Commercial Bank provided the biggest support, particularly for the renowned athletes. About 30% of the respondents acknowledged company sponsors as their main financiers. Further, 10% of the respondents indicated that a runners' own contributions, and wellwishers such as Safaricom, and Kenya Commercial Bank (KCB), were instrumental in funding their activities, especially at the local level. For those athletes who could not afford to raise any cash for sponsorship, they have never participated in competitive events. The researchers observe that the country, apart from the salaries given by the parastatal clubs to their athletes, doesn't have proper strategies to support talents in the country. At the same time, sports governance and coordination in Kenya suffers from multiple problems including weak and inadequate existing sports policies, poor financial management and leadership and failure to invest equally in all sports.

Through key informant interviews, the study revealed that coaches also play a major role in athlete's achievement. For instance, they determine the athletes' sponsorship for instance, looking for sponsors within and outside the country; however, this depended on an athlete's performance. The coach is also under pressure to win. Those athletes who do not compete well may end up losing sponsorship. The study also found that structurally embedded discrimination, social stereotyping that subjects female athletes to conflict between social norms for femininity such as submissiveness, grace, beauty and attributes needed for athletic success such as strength, aggressiveness and achievement, greatly affected their performance.

Family-related Psychosocial Challenges

The researchers were interested in understanding family-related psychosocial challenges facing athletes in the study area. The majority (84%) of the respondents reported that medium and long distance runners, like any other athlete, require financial, non-financial and welfare issues as incentives for participation in athletics. However, the study found that the runners are faced by many family demands. It was evident from the study that their families depended on them financially, so much that they could not invest in themselves and their families for future. In contrast, those families that had a generation of athletes did not experience many challenges as they could cost share the financial needs in terms of supporting other family members.

Newcomers to athletic competitions were also given incentives by older athletes. The incentives were reasons why they participated in athletic competitions. In poverty-stricken families, there was a lot of pressure to financially support all members in the household including wife, children, siblings, parents, grandparents and relatives. This finding explains why some distance runners are poor and end up retiring without making any economic investments. During the study it was found that some athletes engaging in extra-marital relationships, because of their sudden wealth, which led to unplanned haphazard spending, break up of their marriages, transmission of sexually transmitted infections, and high cases of alcoholism. These challenges negatively affected them socially, psychologically and economically, hence, causing imbalance between their careers and personal needs.

Community-Related Psychosocial Challenges

The researchers asked respondents about their feelings about the sporting activities. The majority (70%) of the respondents who participated in professional athletic competitions and succeeded gained social acceptance in their families and communities. This motivated them to increase their participation in athletic activities in order to bring honor and respect in their community. The recognition associated with achievement among the athletes led to the development of an athletic identity and the influence of others as well as the athlete himself/herself affected them psychologically and cognitively.

It was evident through interviews that athletes' good performance in competitions was greatly influenced by parents, siblings, coaches and friends through moral and spiritual support, as well recognition in the community.However, the pressures and demands to win in competitions brought with it many dangers. The study found that it not only brought about reinforcement but also became negative because athlete's failures become news events that people discussed publically. Athletes were faced with a myriad of psychosocial challenges and found relationships with others quite traumatic. For instance, given that they faced pressure to perform better, many of them strained themselves during training to get fit for competition, which rendered them susceptible to injuries and psychological disorders and hence poor performances in athletic competitions.

Conclusion and Recommendations

Kenya is recognized worldwide for its prowess in athletics and for producing world-class athletes. Medium and long distance running is highly dominated by athletes in their twenties and early thirties and, as such, constitutes an increasingly important part of social and economic development of the country. The study examined the psychosocial challenges facing athletes in Eldoret town. The study found that in Kenya, athletes (medium and long-distance runners), have not been given much psychosocial support. Instead, they are exposed to many psychosocial challenges aggravated by inadequate social acceptance, overdependence by close kin, isolation, lack of mentorship and peer support. As a result, they are faced with many social and psychological challenges ranging from individual, family and community level. These have negatively affected their participation and performance in the sport. The study recommends that there is a need to design appropriate interventions that mitigate psychosocial challenges among athletes and thus promote their wellness and personal development.

Table 1 :

 1 Sex of the respondents

	Sex

  Traditionally, sports are associated with masculinity, and therefore, considered inappropriate for women to participate in sports. This observation concurs with Knight & Giuliano 8 who asserted that participation in sports that require what are considered typically 'male characteristics' such as speed and strength can present an 'image problem' for many female athletes. The study was also interested on understanding the age of respondents. The result is shown in Table2 below:

	Total	99	100.0
	Analysis in table 2 above indicates that majority 38 (40.0%
	Table 2: Age of the respondents
	Age Category	Frequency	Percent (%)
	18-25	25	26.0
	26-33	38	40.0
	34-41	24	24.0
	42-49	12	10.0

MA, Department of Sociology and Anthropology, Maseno University, Kenya.

Noakes T. D., Physiological models to understand exercise fatigue and the adaptations that predict or enhance athletic performance, Scand J Med SciSports, 10, 2000, pp.123-145.

Manners, J., Kenya's running tribe; The SportsHistorian, 17(2), 1997, pp.14-27.

Knight J. L. & Giuliano T.A., Blood, sweat, and jeers: The Impact of the Media's Heterosexist Portrayals on Perceptions of Male and female athletes, Journal of SportBehavior, 26, 2003, pp.272-285.


Dr Bezabeh Wolde

Dr Benoit Gaudin

Grass-Root Training: a Challenge for Ethiopian Athletics

This paper analyses the problem of grass-roots training in Ethiopian athletics, presenting the different institutions involved in athletics and their respective reasons for the lack of interest and involvement in grass-root training activities. The overall picture provided by this description reflects the poor conditions of Ethiopian athletics and the prevalence of a predatory system of selection in lieu of a comprehensive institutional organization for the promotion of sport. The data collected for this research proceed from interviews with officials of athletics federations and sponsors over several years of professional activity of the first author and on the basis of fieldwork observations by the second author.

The limits of Ethiopian athletics

In the global division of athletic labor, long distance races are currently monopolized by East-African runners, mainly Kenyan and Ethiopian. Regional pioneers since the 1960 Olympic Games when Abebe Bikila won the first gold medal for Ethiopia and sub Saharan African, Ethiopian athletics gained momentum in the 1990s with the arrival of its first female athletes of international level and, since the 2000s, with a succession of world records carried by a generation of outstanding runners. Nowadays, sport and more specifically athletics is directly or indirectly interwoven with the life of most Ethiopians and athletics has become part of their identity and pride. 2 Yet it now appears that Ethiopian athletics has reached its limits and that it would be difficult for it to progress or grow without undertaking major structural reforms. 3 Its areas of expertise are indeed confined to long-and middle-distance and training structures are relatively limited when compared to those of its direct competitor, Kenya, whose runners are much more numerous today on the international market and present in a much broader range of disciplines, for instance steeple chase and throwing events.

In Ethiopia, the focus placed -by all involved institutions -on the top level is now been disputed. Grass roots athletics has not yet received the due attention from officials, despite years and decades of international sporting success. It can be argued that Ethiopian athletics does not really need grass-root training structures in order to win medals in international competitions and to challenge neighboring Kenya in the road-race circuit, but the lack of any formally structured grass-root training is a serious handicap for the local sports authorities in their ambition to diversify the areas of sports excellence beyond solely distance running disciplines.

The issue of providing training for beginner and intermediate athletes is particularly relevant for federations at the national and regional level, and more specifically for the most important of these regional federations, the Addis Ababa Athletics Federation, which concentrates within its constituency the highest number of the country's clubs: forty-two.

The national and regional federations have long neglected the work of training, solely focusing on selecting the best athletes during the few annual competitions they organise for this purpose. This 'predatory' system neither fits their official duties (the promotion of sport), nor serves their ambitions to increase the potential number of international medals. As long as nobody takes the time to properly train throwers, jumpers and sprinters, the number of Olympic medals likely to be won by any Ethiopian delegation may never exceed the record of eight, all won in long distance, at the Sydney Olympics.

The political authorities in charge of sport have recently affirmed their will to change this situation and to move from a predator management mode (the so-called 'flag pole system') to a pyramidal training system, attending to all levels of athletic training from grass-root to elite. The idea that the grass-root level is the key for development and diversification of the Ethiopian athletics seems to make its way, but it is still struggling to impose itself among all stakeholders of the athletic world, for a series of reasons that we are exposing here.

Ministry of Youth, Sport & Culture (formerly "Federal Sport Commission")

During the last eight editions (2008-2015) of the general assembly of both the national and Addis Ababa regional federations, the remarks of all officials of the Sports Commission were about the lack of attention from the side of the federations on the development of grass root athletics, both in quality and quantity. As authorities are seeking a sustainable supply of athletes, the 'pyramid system' previously adopted by the eastern European countries is said to be the solution that should be promoted, even if it is at the same time noted that this method is expensive in terms of finance.

As Ethiopia is a poor country, sport is not really a priority in the budget of the federal government. Thus, even if athletics is the only medal providing sport, the Ethiopian Athletics Federation is hardly funded by the public sector and depends heavily on private sponsors. As little as it can be, this public support is even poised to be further reduced, as the Federal Sport Commission, through its Sport Policy, stated that sport should be gradually 'released from government subsidy '. 4 The National and Regional Federations

In the mission statement of each federation, it is clearly and explicitly stated that they should play an active role in grass-root athletics. Yet none of the athletics federations are taking this seriously. So at all levels they fail to fulfill their mission as they abide to the 'rule of market' and cannot go against the choice of their funding sponsors. These sponsors being private companies, their funds are being earmarked for very specific purposes: organizing local competitions or participating in foreign events. As a consequence, all other sectors of activity not oriented towards 'producing medals' are being neglected or deprived of funding. This is the case of grassroot projects or even of training in throwing and jumping disciplines.

The sponsors

The two main federations of the country, Ethiopian and Addis Ababa, are funded by two major sponsors: Adidas and Pepsi, which are only interested in major competitions. The agreement between these sponsors and the federations restrict the allocation of funds to the organization of domestic competitions and participation of Ethiopian athletes to international competitions. Therefore the request for development of grass-root programs has no place inside these agreements. The market-driven sponsors are interested in competitions to advertise their products so that they can yield more profit. They do not see any particular interest in supporting the formation and training of beginner athletes.

The runners" agents (locally known as "managers")

Every year, in January or February, a cross-country competition is held in Addis Ababa, a couple of weeks before a similar competition happens in Nairobi, Kenya. These events serve as scouting opportunities for the international runner's agents, who usually live in Europe or North America and come to East-Africa once a year to select and 'sign' the best contestants of these local competitions. Conveniently, the Ethiopian and the Kenyan national athletics federations take the opportunity of the agents' presence to hold a meeting with them and to discuss all current issues, renew their contracts and settle their accounts.

During the meeting held in 2015, the agents' attitude towards working on grass roots athletics was very gloomy. From their point of view, managing an athlete at that level is impossible, as runners are likely to switch from one agent to another. Any early investment is seen as too risky for them as it may not yield any return at all. They also argue that the rules and regulations of the management of runners by both national and international associations (IAAF) do not support them as the contracts are signed for one year and the infringers, shifting from one agent to another, are not sued nor punished. The agents' interest seems to be in hunting the elite athletes once per year from national or international competitions only. Some of them, among the most well established such as Global Sport Communication, have established training groups, which are locally called 'projects', but not at the grass-root level.

The First Division Clubs

The 'first division' of Ethiopian athletics is composed by the six best clubs of the moment. In 2015 they were: Defense (also known as 'Mekelakeya' or '20th Mechanised'), Federal Police ('Omedla'), EEPCO (Ethiopian Electric & Power Corporation, aka 'Electric'), Ethiopian Commercial Bank (or 'Negd Bank'), Federal Prisons ('Maremia') and Government House Agency ('Betoch'). In Addis Ababa, all other registered clubs are called 'second division'. The first division is an open and competitive group: only the clubs with the highest number of points can integrate the first division. Points are gained through regional and national competition, both individually and by team. Inside the First Division group, Mekelakeya stands one head above the others clubs, both by the number of points collected each year and by its long-lasting presence over the last decades inside this elite group.

Because they are funded by powerful parastatals, the first division clubs can afford to grant some facilities to their athletes: a good standard of coaching, transportation to the various places of training by minibus, food and accommodation, shoes and sport clothes, contacts and contracts with private sponsors, official recognition and social prestige, and even some pocket money or the possibility to get a military career. Evil tongues balance this list of advantages by adding that athletes get brainwashed. None of the first division clubs have, nor ever have had, a training center for beginners.They simply do not want to invest their time, resources and money in athletes who might leave them at the end of their training time, fleeing to other clubs or to another sport such as football.

The other clubs

While the 'First Division' label is widely known and used, the 'Second Division' label is less popular and only used in Addis Ababa. Outside the capital city, no other town can claim to have enough athletics clubs to establish any divisions among them.

The common point between all these non-First-Division clubs is that they provide their best athletes to the 'First Division' clubs. These second division clubs appear and disappear at any time of the season for unknown reasons. During the 2015 season, the list of second-division clubs that we could document included the following names: Adama club, Addis Ababa University Athletic Club, Africa athletics club, Addis Hiwot Athletics Club, Assela Athletics Sport Club, Bokoji Athletics academy center, Cosmo Enginering Athletics Club, Geta Zeru, Haisha, Karamara Athletics Club, Lucy Club, Mandela Sport Club, Mashambani Athletics Club, Negash SC, Nekemte Athletics Club, Oni Niskanen, Oromiya Athletics Club, Oromiya Road Constrution, Saint George, Saint Yared, Sidama Bunna Athletics Club, Tirunesh Dibaba Training Center, Walia andYaya Athletics club.

The "projects"

There are several differences between what are referred to in Ethiopia as 'Clubs' and 'Projects'. First, an age difference: clubs are for senior athletes, whereas Projects are usually for under 17. Second, a administrative difference: Clubs are nongovernmental, whereas Projects are governmental or run by the ministry in charge of Sport or by one of the athletics federations, be they regional or national. Thirdly, there is a legal difference between clubs and projects: the former are granted autonomy and are ruled by status, whereas the later are run as part of their administration of origin. Finally, the selection of athletes by regional federations for domestic competitions can only be made from projects, and not from clubs, otherwise the regional Addis Ababa Athletics Federation would be too advantaged.

For all these reasons, 'projects' can be seen as the obvious level for the organization of grassroot training. Yet it is not the case, mainly because of the cruel lack of funding which makes any attempt of organize something impossible. The interview with two senior national coaches revealed that they witnessed the appearance and the disappearance of many projects without achieving their objectives. In a rule of thumb, projects are deprived of any coaching staff, transportation vehicles, sport facilities and even support for athletes (food, accommodation, shoes, clothes, etc.). And from the personal observation of the first researcher of this paper on some project sites, the role of the local community in supporting these 'projects' seems close to none.

Potential paths to unlock the situation and other recommendations.

As the core issue of grass-roots training regards its funding, possible solutions include either a changeof policy from the public sector towards its involvement and support in federations, clubs and 'projects' or the diversification of the sources of funding, ranging from local, communitybased level private companies to international sport institutions or enterprises. Other recommendations include: 1.

The local community should be involved in the development process, directly or indirectly, starting from the woreda level (the lowest administrative level in Ethiopia).

2.

From the experiences of other countries, where 'projects' usually belong to higher clubs, eg-football, we recommend that athletics 'projects' belong to clubs at the national or federal or city Administrations levels.

3.

Project sites should be equipped at least with indoor activities to help the recovery process.

4.

The rights and responsibilities of athletes, clubs and agents need to have a legal basis.

5.

The government should request that federations invest in other disciplines, bringing coaches from outside the country and giving scholarships for local coaches to go and study abroad.

6.

Athletes should be given a basic education, for instance in family-life, health, the international commercial circuit and career management.

7.

Positive entrepreneurs supporting their own clubs should receive better official recognition and be allowed higher participation in the sector.

Sources

-

  Ethiopianand Addis Ababa Athletics federations general assembly documents (2008-15) -Ethiopian sport policy (April1998) -Speeches of sport commissions officials (2008-15) Interviews Melaku Deresselong term National coach interview (August 2014) Tolossa Kotulong term National coach interview (August 2014)

Associate Professor, Dept of Sport Science, Addis Ababa University.

Bezabeh W. & Gaudin B., The Institutional Organization of Ethiopian Athletics, Les Annales d'Ethiopie -Review of Centre Français des Etudes Ethiopiennes, vol 23, 2008 dated 2007, pp.471-494.

Hylton K. & Bramham P. (eds), Sport development policy process and practice, Routledge: London and NY, 2008; Watt D., Sport Management and Administration, Routledge: London and NY, 1998.

http://www.mysc.gov.et/sport.html