CEMFI

About: CEMFI is a based out in . It is known for research contribution in the topics: Unemployment & Estimator. The organization has 71 authors who have published 499 publications receiving 46553 citations. The organization is also known as: Center for Monetary and Financial Studies.

Entrenchment and Severance Pay in Optimal Governance Structures

Abstract: This paper explores how motivating an incumbent CEO to undertake actions that improve the effectiveness of his management interacts with the firm's policy on CEO replacement. Such policy depends on the presence and the size of severance pay in the CEO's compensation package and on the CEO's influence on the board of directors regarding his own replacement (i.e., entrenchment). We explain when and why the combination of some degree of entrenchment and a sizeable severance package is desirable. The analysis offers predictions about the correlation between entrenchment, severance pay, and incentive compensation. THERE ARE SUBSTANTIAL CROSS-SECTIONAL DIFFERENCES in the control exercised by corporate boards of directors. In some cases, the board can and does fire the CEO at will. In others, directors are effectively the puppets of the CEO and exert independent power only in extreme situations. The standard view in the literature is that, ideally, shareholders should have full control of the board of directors and that any form of CEO entrenchment is necessarily undesirable. This position, however, ignores some important interactions between managerial incentive problems and shareholder activism which we explore here. Indeed a CEO's influence on the board frequently reflects not so much a discretionary choice of shareholders but the endogenous accumulation of power in the hands of the incumbent CEO (Hermalin and Weisbach, 1998). Yet we challenge the view that full shareholder control of the board of directors is necessarily the most desirable governance structure. We identify a potential conflict between inducing a CEO to improve the effectiveness of his management and allowing the shareholders to benefit from every valuable managerial replacement. We find that the solution to the CEO's incentive problem may rest on the allocation of power in the board of directors as well as on more traditional instruments such as severance pay and incentive compensation. The key insight of our analysis is that, in

Transitions to and From Self‐employment in Spain: An Empirical Analysis

Abstract: This paper investigates the influence of individual characteristics and the business cycle on the probability of entry into self-employment and on self-employment duration. We estimate multinomial logit and discrete competing risks models using data from a longitudinal sample of Spanish men for the period 1985-1991. The results indicate that unemployment rises the probability of entering self-employment, but also increases the hazard of leaving self-employment, specially into unemployment. Moreover, receiving unemployment benefits significantly reduces the probability of entering self-employment. Liquidity constraints are important in determining entrepreneurial selection, but only for those who become self-employed with employees.

Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models

Abstract: This paper proposes a procedure for the estimation of discrete Markov decision models and studies its statistical and computational properties. Our Nested Pseudo-Likelihood method (NPL) is similar to Rust's Nested Fixed Point algorithm (NFXP), but the order of the two nested algorithms is swapped. First, we prove that NPL produces the Maximum Likelihood Estimator under the same conditions as NFXP. Our procedure requires fewer policy iterations at the expense of more likelihood-climbing iterations. We focus on a class of infinite-horizon, partial likelihood problems for which NPL results in large computational gains. Second, based on this algorithm we define a class of consistent and asymptotically equivalent Sequential Policy Iteration (PI) estimators, which encompasses both Hotz-Miller's CCP estimator and the partial Maximum Likekihood estimator. This presents the researcher with a ''menu'' of sequential estimators reflecting a trade-off between finite-sample precision and computational cost. Using actual and simulated data we compare the relative performance of these estimators. In all our experiments the benefits in terms of precision of using a 2-stage PI estimator instead of 1-stage (i.e., Hotz-Miller) are very significant. More interestingly, the benefits of MLE relative to 2-stage PI are small.