We review research that measures time preferences-i.e., preferences over intertemporal tradeoffs. We distinguish between studies using financial flows, which we call "money earlier or later" (MEL) decisions and studies that use time-dated consumption/effort. Under different structural models, we show how to translate what MEL experiments directly measure (required rates of return for financial flows) into a discount function over utils. We summarize empirical regularities found in MEL studies and the predictive power of those studies. We explain why MEL choices are driven in part by some factors that are distinct from underlying time preferences.

Most simulated micro-founded macro models use solely consumer-demand aggregates in order to estimate preference parameters of a representative consumer, for use in policy evaluation. Focusing on dynamic models with time-separable preferences, we show that aggregation holds if, and only if, momentary utility functions fall in the Identical-Shape Harmonic Absolute-Risk Aversion (ISHARA) utility class, identifying which parameters of ISHARA utility functions are allowed to vary over time. Given this theoretical result, it should be easy to empirically reject the aggregation properties that the macroeconomic representative-consumer identification approach requires: it suffices to show that permanent incomes guaranteeing the same living standard across households of different size violate an affine relationship. In order to test the validity of this affine equation, we develop a vignette survey that produces appropriate data without demand-estimation restrictions imposed by models. Surprisingly, in six countries, this equation is not rejected, lending support to using consumer-demand aggregates.

Purpose The purpose of this paper is to contribute to the literature on microcredit impacts by quantifying the gender disaggregated effects of long-term borrowing on capital accumulation in order to address the existing gap. Separate models are estimated for male-headed and female-headed households to determine if the effects of microcredit differ between these gender types. Design/methodology/approach The paper adopts the method proposed by Deaton (1990) in which he specifies a model without borrowing restrictions whereby the household maximizes an inter-temporal utility function. To account for self-selection and endogeneity of micro credit, the fixed effects instrumental variable approach is used. Data are disaggregated by gender and analyzed separately. Findings The paper finds that micro credit indeed increases productive assets and human capital but has no significant effect on non-productive assets. One striking result is that after disaggregating the data by gender, the authors find no effect of micro credit on women-headed households. Practical implications The paper provides an empirical evidence for the need to address gender issues in finance and lending. Furthermore, targeted lending particularly to women makes a great difference in the fight against poverty. Originality/value This paper fills the gap on gender and micro credit impacts on capital accumulation in a developing country context.

A method that uses fuzzy c-means (FCM) is proposed for credit scoring based on unsupervised learning of a set training data. Data vectors are composed of significant applicant attributes and corresponding expert decisions. Two new statistical cost functions Jm and Jσ are introduced to evaluate the candidate models by k -fold cross validation based on the mean and the standard deviation of the decision attributes. A linguistic approach based on the fuzzy-valued Choquet integral is suggested to rank the consumer loan applicants. The lower and upper imprecise probabilities are used as a capacity measure in Choquet integral to determine the utility ranking of the consumer loan applicants. This thesis proposes an algorithm to calculate the applicant’s non-expected utility by using imprecise probabilities of accepted cases over the Fuzzy C-Means clusters for fuzzy Choquet integral. The method is applied on consumer loan evaluations for a financial institution to verify expert decisions in parallel to extracting linguistic rules of decision making. In the suggested approach linguistic fuzzy valued Choquet integral is used as measure of fuzzy utility. The results indicate that the proposed method is successful in ranking the consumer loan applications with only six fails in total of 135 applications. Keywords: Fuzzy c-means, Fuzzy clustering, Sugeno integral, Fuzzy valued Choquet integral, imprecise probability …………………………………………………………………………………………………………………………………………………………………………………………………