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This chapter is a radical critique of the neoclassical growth theory, justifying ways out of mainstream economics. It has three parts. The first one analyzes growth theories from the Classical representation to the endogenous growth models. The second part demonstrates that the “new growth theory” is not a break with Solow's formalization. To prove it, we build an original Solowian endogenous growth model. Then, this neoclassical macrodynamic framework is technically, deeply critized in a third part. We show that both exogenous and endogenous neoclassical models prove to be incapable to explain growth in the long period. We concentrate on the ambiguities surrounding the hypothesis of single agent, as well as on the role of the state, in particular when it is considered as a “planner” by the neoclassicals. Endogenous growth models do not correspond to macrodynamization of the Walrasian general equilibrium, nor have solid microeconomic bases. We advocate in favor of rehabilitating state's intervention in social areas and of reactivating Marxist theoretical reflections regarding social planning and class analysis in the current time of structural crisis of the capitalist world system.

This chapter proposes M-estimators of a fractional response model with an endogenous count variable under the presence of time-constant unobserved heterogeneity. To address the endogeneity of the right-hand-side count variable, I use instrumental variables and a two-step procedure estimation approach. Two methods of estimation are employed: quasi-maximum likelihood (QML) and nonlinear least squares (NLS). Using these methods, I estimate the average partial effects, which are shown to be comparable across linear and nonlinear models. Monte Carlo simulations verify that the QML and NLS estimators perform better than other standard estimators. For illustration, these estimators are used in a model of female labor supply with an endogenous number of children. The results show that the marginal reduction in women's working hours per week is less as women have one additional kid. In addition, the effect of the number of children on the fraction of hours that a woman spends working per week is statistically significant and more significant than the estimates in all other linear and nonlinear models considered in the chapter.

We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

Marcet, and Nicolini (2003) and Milani (2014) demonstrate within the adaptive learning framework that a forecast error-based endogenous gain mechanism that switches between constant gain and decreasing gain may be more effective than the former alone in explaining time-varying parameters. In this paper, we propose an alternative endogenous gain scheme, henceforth referred to as CEG, that is based on recent coefficient estimates by the economic agents. We then show within a controlled simulation environment that CEG outperforms both constant gain learning as well as the aforementioned switching gain algorithm in terms of mean squared forecast errors (MSFE). In addition, we demonstrate within the context of a New Keynesian model that forecasts generated under CEG perform better in certain dimensions, particularly for inflation data, compared to constant gain learning. Combined with the fact that the proposed gain scheme ports easily to existing likelihood based inferential techniques used in constant gain learning, it is readily applicable to richer, more dynamic economic models.

Wealthy rural areas, or rural areas in wealthy regions, have a specificity that should be taken into consideration both in empirical and theoretical research. In most of the cases, rural development in these areas depends not only on the capacity of rural communities to mobilise endogenous resources, but also to be able to link endogenous resources with outside networks. In Italy this approach has widely been put into practice through strategies centred on the link between local food and its place of production. To explore the implications of this link, the paper will explore the implication of an adoption of the concept of ‘terroir’. Terroir can be seen as a mix of a set of localised invariants in the space related to natural, cultural, and social spheres. It is highly specific of a place, as it is produced and reproduced through localised processes. The peculiarity of the ‘terroir’ is that it is embodied into the product, which means that it is the source of local products’ identity and specificity. Local products are then a component of a broader socio-technical system, and product and terroir co-evolve. What are the mechanisms that make local products keys to rural development in a neo-endogenous perspective? In a neo-endogenous perspective, valorisation of local products is mainly related to its capacity to be recognised and evaluated by outside observers as different (and possibly better) from others. This capacity is embodied into what Bourdieu calls symbolic capital. Symbolic capital becomes a thread linking ‘terroir’ and the product to external observers, and convey to them meanings like notoriety, reputation, and trust. In order to be able to create, maintain, and increase symbolic capital, rural communities activate communication practices within and outside themselves. This may generate conflicts as well as strengthen identities and alliances. Three case studies will show the network building processes related to the creation of symbolic capital and its mobilisation into food production and marketing. The Cutigliano case shows how a small community borrows symbolic capital from the outside to enhance its capacity to sell a local cheese outside the area. The Colonnata case shows the risk that neo-endogenous strategies generate interlocal conflicts, hampering its competitiveness as a whole. The Chianti case shows an internal conflict over the use of the symbolic capital with both positive effects on the public debate and potential negative effects on the cohesion of the area. All the three cases make possible a reflection on governance, and especially on the role of the state (or the regional administration) in the governance of these processes.

Principal component (PC) techniques are commonly used to improve the small sample properties of the linear instrumental variables (IV) estimator. Carrasco (2012) argue that PC type methods provide a natural ranking of instruments with which to reduce the size of the instrument set. This chapter shows how reducing the size of the instrument based on PC methods can lead to poor small sample properties of IV estimators. A new approach to ordering instruments termed ‘normalized principal components’ (NPCs) is introduced to overcome this problem. A simulation study shows the favourable small samples properties of IV estimators using NPC, methods to reduce the size of the instrument relative to PC. Using NPC we provide evidence that the IV setup in Angrist and Krueger (1992) may not suffer the weak instrument problem.