Firms, Nonprofits, and Cooperatives: A Theory of Organizational Choice

Abstract This paper formalizes the difference between firms, nonprofits, and cooperatives and identifies optimal organizational choice. In a model of quality provision, we find a clear ranking of quality produced: Firms provide lowest and nonprofits highest levels of quality. Efficiency, however, depends on the competitive environment, the decision making process and technology. Cooperatives are optimal when decision making costs are low. Else, cooperatives are increasingly dominated by either nonprofits or firms (depending on the incremental costs of quality production). Finally, changes in the competitive environment affect organizational choice: Increased competition induces a shift towards firm organization and away from nonprofits.Theory of the Firm;Cooperatives;Nonprofits;Organizational Choice;organizational change

The FDI-Growth Nexus in Latin America: The Role of Source Countries and Local Conditions

Foreign Direct Investment (FDI) has surged in Latin America (LA) since the mid 1990s. European and North American FDI is of capital importance. We investigate the FDI-growth nexus in LA allowing for different source countries, regional hetero- geneity, interaction terms with FDI, and more than 20 growth determinants. We use Bayesian Model Averaging to address model uncertainty and to select the best mod- els and most robust parameters. The principal finding is that a positive FDI-growth nexus in LA requires a functioning legal framework and macroeconomic stability. We also find that European FDI is only indirectly correlated with productivity growth, whereas North American FDI is more robust and thus directly correlated with pro- ductivity growth.FDI-growth nexus;model uncertainty;Bayesian Model Averaging;Latin America

Prioritizing Policies for Pro-Poor Growth: Applying Bayesian Model Averaging to Vietnam

Pro-Poor Growth (PPG) is the vision of combining high growth rates with poverty reduction.Due to the myriad of possible determinants of growth and poverty a unique theoretical model for guiding empirical work on PPG is absent, though.Bayesian Model Averaging is a statistically robust framework for this purpose.It addresses the existent parameter and model uncertainty by not choosing a single model but averaging over all possible ones.Using data for the 61 Vietnamese provinces we are able to ascertain a prioritization of all used determinants of poverty, growth and of PPG of our large set of explanatory variables.poverty determinants;growth determinants;pro-poor growth;model uncertainty;Vietnam

The FDI-Growth Nexus in Latin America:The Role of Source Countries and Local Conditions

Foreign Direct Investment (FDI) has surged in Latin America (LA) since the mid 1990s. European and North American FDI is of capital importance. We investigate the FDI-growth nexus in LA allowing for different source countries, regional hetero- geneity, interaction terms with FDI, and more than 20 growth determinants. We use Bayesian Model Averaging to address model uncertainty and to select the best mod- els and most robust parameters. The principal finding is that a positive FDI-growth nexus in LA requires a functioning legal framework and macroeconomic stability. We also find that European FDI is only indirectly correlated with productivity growth, whereas North American FDI is more robust and thus directly correlated with pro- ductivity growth

A Comparison of Two Averaging Techniques with an Application to Growth Empirics

Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical growth model, many new (endogenous) growth models have been proposed. This causes a lack of robustness of the parameter estimates and makes the determination of the key determinants of growth hazardous. The current paper deals with the fundamental issue of parameter estimation under model uncertainty, and compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with weighted-average least squares (WALS), a method that has not previously been applied in this context.

Intraspecific diversity of the rhizodeposition of Lupinus angustifolius L. regarding the phosphorus mobilization in the soil

The cropping of lupines (Lupinus spp.) for protein production is rising worldwide. The growth of lupines is often limited by P deficiency, caused by low P bioavailability in soils. The rhizodeposition is a leading control of the P mobilization in the soil, i.e. especially by the release of phosphatases and organic acids. In the present study 20 genotypes of L. angustifolius (19 accessions from different geographic origins and the cultivar Boruta) were tested on their molecular-chemical composition of the rhizodeposition in P-deficiency by pyrolysis-field ionisation mass spectrometry (Py-FIMS) and on the phosphatase and ß-glucosidase activities in the rhizosphere soil. The intraspecific diversity of the composition of the rhizodeposits was especially large for the relative abundance of carbohydrates and in this way in a specific impact on the microbial activity in the rhizosphere by selective promotion under some genotypes by easily available C sources for the microbial rhizosphere community. This was confirmed by a large variation in the thermal stability of the rhizodeposits of different genotypes, a varying pH level in identical cultivation conditions and in varying activities of alkaline and acid phosphomonoesterases and ß-glucosidase in the rhizosphere. Furthermore, the data revealed a strong variation in the release of alkaloids into the rhizosphere during the growth with a further impact on the microbial activity. In conclusion, the use of the quality of the rhizodeposition as an indicator of the potential for P mobilization in P-deficient soils highlighted a broad intraspecific diversity within L. angustifolius. This is a promising basis for a selection of highly P efficient genotypes within this species for further breeding strategies of productive cultivars

Multivariate Lagrange inversion formula and the cycle lemma

International audienceWe give a multitype extension of the cycle lemma of (Dvoretzky and Motzkin 1947). This allows us to obtain a combinatorial proof of the multivariate Lagrange inversion formula that generalizes the celebrated proof of (Raney 1963) in the univariate case, and its extension in (Chottin 1981) to the two variable case. Until now, only the alternative approach of (Joyal 1981) and (Labelle 1981) via labelled arborescences and endofunctions had been successfully extended to the multivariate case in (Gessel 1983), (Goulden and Kulkarni 1996), (Bousquet et al. 2003), and the extension of the cycle lemma to more than 2 variables was elusive. The cycle lemma has found a lot of applications in combinatorics, so we expect our multivariate extension to be quite fruitful: as a first application we mention economical linear time exact random sampling for multispecies trees

Small RNA Profile in Moso Bamboo Root and Leaf Obtained by High Definition Adapters

Moso bamboo (Phyllostachy heterocycla cv. pubescens L.) is an economically important fast-growing tree. In order to gain better understanding of gene expression regulation in this important species we used next generation sequencing to profile small RNAs in leaf and roots of young seedlings. Since standard kits to produce cDNA of small RNAs are biased for certain small RNAs, we used High Definition adapters that reduce ligation bias. We identified and experimentally validated five new microRNAs and a few other small non-coding RNAs that were not microRNAs. The biological implication of microRNA expression levels and targets of microRNAs are discussed