In Sports Economics, production functions are often estimated by relating the winning
percentage of teams (Y)to inputs indicating performance in certain aspects of the game.
However, this omits the quality of management.Assume that you could measure the
quality of pitching and hitting by a single index L, and that managerial ability is
represented by M, which is assumed to be constant over time.The production function
would then be specified as follows: $$Y _ { i t } = \beta _ { 0 } + \beta _ { 1 } L _ { i t } + \beta _ { 2 } M _ { i } + u _ { i t }$$ where i is an index for the baseball team, and t indexes time and all variables are in logs.
(a) Assume that managerial ability is unobservable but is positively related, in a linear way, to $L$. Explain why the OLS estimator $\widehat { \beta } _ { 1 }$ is inconsistent in the case of a single crosssection, i.e., if you attempt to estimate the above regression for a single year. Do you expect this coefficient to over- or under-estimate $\beta _ { 1 }$ ?

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The Answer of In Sports Economics, production functions are often estimated by relating the winning
percentage of teams (Y)to inputs indicating performance in certain aspects of the game.
However, this omits the quality of management.Assume that you could measure the
quality of pitching and hitting by a single index L, and that managerial ability is
represented by M, which is assumed to be constant over time.The production function
would then be specified as follows: $$Y _ { i t } = \beta _ { 0 } + \beta _ { 1 } L _ { i t } + \beta _ { 2 } M _ { i } + u _ { i t }$$ where i is an index for the baseball team, and t indexes time and all variables are in logs.
(a) Assume that managerial ability is unobservable but is positively related, in a linear way, to $L$. Explain why the OLS estimator $\widehat { \beta } _ { 1 }$ is inconsistent in the case of a single crosssection, i.e., if you attempt to estimate the above regression for a single year. Do you expect this coefficient to over- or under-estimate $\beta _ { 1 }$ ?

In Sports Economics, Production Functions Are Often Estimated by Relating Yit=β0+β1Lit+β2Mi+uitY _ { i t } = \beta _ { 0 } + \beta _ { 1 } L _ { i t } + \beta _ { 2 } M _ { i } + u _ { i t }Yit​=β0​+β1​Lit​+β2​Mi​+uit​

In Sports Economics, Production Functions Are Often Estimated by Relating $Y _ { i t } = \beta _ { 0 } + \beta _ { 1 } L _ { i t } + \beta _ { 2 } M _ { i } + u _ { i t }$