Abstract: We analyze a large stratified random sample of firms that provide us with measures of performance and each firm’s top manager’s perception of the severity of business environment constraints faced by his/her firm. Unlike most existing studies that rely on external and aggregated...

We analyze a large stratified random sample of firms that provide us with measures of performance and each firm’s top manager’s perception of the severity of business environment constraints faced by his/her firm. Unlike most existing studies that rely on external and aggregated proxy...

We analyze a large stratified random sample of firms that provide us with measures of performance and each firm’s top manager’s perception of the severity of business environment constraints faced by his/her firm. Unlike most existing studies that rely on external and aggregated proxy...

Peru's national policy on productivity and competitiveness relies on the Global Competitiveness Index (GCI) by the World Economic Forum. We analyze the subjective component of GCI and show that, in the labor market area, this index has been largely constructed with opinion data coming from a...

This paper assesses the merits of using surveys of business perceptions of growth constraints as a guide to growth-enhancing fiscal policy reforms. Using endogenous growth models in which the government levies an income tax to provide public inputs to the production of private firms, the paper...

How large are the economies of scale of living together? And how do partners share their resources? The first question is usually answered by equivalence scales. Traditional estimation and application of equivalence scales assumes equal sharing of income within the household. This paper uses...

In this paper we propose an alternative approach to the estimation of ordered response models. We show that the Probit-method may be replaced by a simple OLS-approach, called P(robit)OLS, without any loss of efficiency. This method can be generalized to the analysis of panel data. For...