Li, Y., Ning, Y., Liu, R., Wu, Y., Hui Wang, W.: Fairness of classification using users’ social relationships in online peer-to-peer lending. In: Proceedings of the 2014 International Conference on Social Computing, pp. Lee, E.L., et al.: Fairness-aware loan recommendation for microfinance services. Kang, J.D., Schafer, J.L., et al.: Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data. Ishwaran, H., Rao, J.S., et al.: Spike and slab variable selection: frequentist and Bayesian strategies. Hill, J.L.: Bayesian nonparametric modeling for causal inference. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. IEEE (2013)Ĭhoo, J., Lee, C., Lee, D., Zha, H., Park, H.: Understanding and promoting micro-finance activities in. In: 2013 IEEE 13th International Conference on Data Mining, pp. 38(3), 773–794 (2014)Ĭalders, T., Karim, A., Kamiran, F., Ali, W., Zhang, X.: Controlling attribute effect in linear regression. 7(1), 22–53 (2015)īurtch, G., Ghose, A., Wattal, S.: Cultural differences and geography as determinants of online prosocial lending. Technical report (2016)īanerjee, A., Duflo, E., Glennerster, R., Kinnan, C.: The miracle of microfinance? Evidence from a randomized evaluation. 107(5), 278–81 (2017)Īthey, S., Imbens, G.W., Wager, S., et al.: Efficient inference of average treatment effects in high dimensions via approximate residual balancing. 90(2), 347–368 (2008)Īthey, S., Imbens, G., Pham, T., Wager, S.: Estimating average treatment effects: supplementary analyses and remaining challenges. KeywordsĪlfaro, L., Kalemli-Ozcan, S., Volosovych, V.: Why doesn’t capital flow from rich to poor countries? An empirical investigation. We then extend these models to incorporate fairness constraints based on our empirical analysis. We formally investigate and quantify the hidden biases prevalent in different loan sectors using recent tools from causal inference and regression models that rely on Bayesian variable selection methods. We find that the influence from economic factors and loan attributes can have substantially different roles to play for different sectors in achieving faster funding. In this paper, we investigate lender perceptions of economic factors of the borrower countries in relation to their preferences towards loans associated with different sectors. Traditional research studies have investigated various factors behind lender preferences purely from the perspective of loan attributes and only until recently have some cross-country cultural preferences been investigated. Kiva, in particular, allows lenders to fund projects in different sectors through group or individual funding. It has now been possible to overcome some of the obstacles associated with such supply of funds with the advent of online platforms like Kiva, Prosper, LendingClub. Traditionally, even for small supply of funds, banks would act as the conduit between the funds and the borrowers. Can someone provide insight as to why I am getting this discrepancy? As of now it seems the points measure (18 km) is the correct one and not the 21 km one.Over the last couple of decades in the lending industry, financial disintermediation has occurred on a global scale. At some places, the points are overlapping (as shown in image below), but I would expect this to results in higher estimation of distance when measuring as distance between points compared to the length of the line, and not a lower one as is currently. I cannot figure out why this discrepancy is occurring. using geodist command in stata) or by manually measuring the distance between the points (using the ruler tool in ArcMap), this distance comes out to be ~18 km. However, when I do the same calculation using other software (e.g. The csv had 277 points the calculated length of the line feature after step 4 was ~21 km. My workflow was as follows:Ģ) Convert latlongs to points and create a feature fileģ) Use points to line tool in the Data Management toolbox to draw a line from the points (the points are in sequence and have unique IDs)Ĥ) Measure the length of the line feature by calculating geometry. I have a csv of a GPS trace with lat-longs that I want to convert into a line so I can measure the distance.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |