972 resultados para household investment decisions
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
A-1 - Monthly Public Assistance Statistical Report Family Investment Program
Resumo:
In this paper we study the commuting and moving decisions of workers in Catalonia (Spain) and its evolution in the 1986-1996 period. Using a microdata sample from the 1991 Spanish Population Census, we estimate a simultaneous, discrete choice model of commuting and moves, thus indirectly addressing the home and job location decisions. The econometrical framework is a simultaneous, binary probit model with a commute equation and a move equation
Resumo:
The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.