We study financial efficiency in the nonprofit sector and document that organizations that rely mainly on commercial revenues are more efficient in managing their overhead and administrative expenses compared with nonprofits that rely mainly on donations. We also record a positive relationship between the extent of a nonprofit's reliance on donations and its efficiency in generating them. Our findings suggest economies of scale in the nonprofit sector and also a positive (negative) relationship between receiving government grants (membership income) and overall efficiency. We discuss what our findings imply for social enterprises and traditional nonprofits.

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This paper examines the determinants of remittance behavior relying on a dataset of migrants living in the United Kingdom, Germany and the United States who remit to Ghana and Nigeria. Controlling for endogeneity of several independent variables, the model measures the effects on the amount sent of the following variables: Home Country (Ghana or Nigeria), Host Country (United States, United Kingdom or Germany), Remittance Fees, Relationship to the Receiver, Purpose of Remittance, Financial Obligations in the Host Country, and Demographics. The results indicate that remittances are primarily influenced by the purpose of the remittance and from where the remittance is sent. It appears that different generations chose to migrate to different countries, creating differences in income, education and age. These differences along with financial obligations in the host country contribute to higher remittance levels. In regards to the development affects, the results of this paper hint that altruistic reasons for remitting may be associated with higher levels of remittances, while more self‐interested reasons for remitting are associated with lower levels of remittances. These results indicate that it may be difficult to design policies to encourage the use of remittances for development purposes, as remittances are primarily used for basic needs.

Purpose -The purpose of this paper is to empirically demonstrate that drivers of venture capital (VC) investments are different across three broadly defined sectors: high-technology manufacturing, medium-technology manufacturing and services, and low-technology services. Moreover, such differences also exist across industries within each of these sectors. Design/methodology/approach -The basic hypothesis is that "not only different stages of VC investments have different drivers, but VC investments in different sectors of the economy are also driven by different drivers." The paper tests this hypothesis using a Poterba (1989) type supply and demand framework in the multivariate time-series regression analysis. Findings -This paper empirically demonstrates that drivers of VC investments are different across three broadly defined sectors: high-technology manufacturing, medium-technology manufacturing and services, and low-technology services. Moreover, such differences also exist by stages of investment and across industries within each of these sectors. In particular, the paper finds that the importance of the number of VC-led initial public offering (IPO) transactions as the main driver of VC investment decreases with the level of technology involved in the sector. IPO transactions are particularly important in software, networking and equipment, and business products and services industries. In contrast to earlier literature, however, the paper do not find a more pronounced effect of IPOs for seed and late stages of VC investments. Similarly, the positive impact Sarbanes-Oxley Act of 2002 -which mainly impacts public companies -also intensifies with a decrease in the level of technology involved in the sector, and the paper do not find a negative impact. The Act is important particularly for VC investments in medium-and low-tech sectors and in early or expansion stages. Originality/value -In analyzing the determinants of VC in a supply and demand framework as in Poterba (1989), the paper differentiates between different sectors (17 industries) and stages of VC (four stages: seed, early, expansion, late). Such level of differentiation is novel and allows more refined and better targeted public policy measures.