“While we follow most of the empirical literature in specifying a linear relation between inequality and growth…, it is worth noting that this need note be the case. Banerjee and Duflo (1999) present some simple models and empirical evidence that changes in inequality in either direction lower growth. Galor and Moav (2001) develop a theoretical model in which inequality raises growth at low levels of development (where returns to physical capital are high, and so a reallocation of wealth to richer households with higher saving propensities raises growth), but lowers growth at higher levels of development (where returns to human capital are high, but a reallocation of wealth to richer households makes it more difficult for poor households to invest in education.”
“Financial service providers would be well-served by any technique or tool that would allow them to predict in advance who the high performers might be. […] Creating that selection tool is a high priority for [Asim] Khwaja, of Harvard’s Kennedy School. Khwaja’s work focuses on the developing world’s small firms—enterprises that have outgrown microcredit, but that still lack the collateral and the size to easily secure financing from a mainstream bank. These businesses typically find it very difficult to grow past the micro level for lack of investment capital. […] High-potential microentrepreneurs need financing, and the banks that fund them need an inexpensive and reliable way to sift through a pool of candidates and pull out those with the highest potential for success.
The challenge is not insignificant. The banks and venture capital firms that typically provide business financing screen ideas for their business value and the entrepreneur’s ability to pay back by delving into credit or business histories or conducting an in-depth evaluation of the business idea. These options are not viable with microfirms, however, because of their small size and smaller predicted returns. For small firms, banks really need to know about the ideas, skills, and trustworthiness of the individual borrower.
Khwaja[’s] automated psychographic evaluation [test] for measuring an entrepreneur’s ability and honesty…is based on tools used by human resource departments in developed countries. These tests are prevalent in other contexts, they are difficult to game, and the results tend to correlate with entrepreneurial success. […] Khwaja’s pilot data show that the test meets or exceeds the predictive ability of credit scoring models used in developed countries, and it effectively predicts financial success for micro or small business entrepreneurs who do not have financial histories.
The test is also uncovering some nonintuitive indicators of business failure. For example, test takers who scored higher for intelligence actually achieve lower profits; honesty also correlates with lower than average profits—in both cases, these effects were stronger for women than men. The indicators of success seem more obvious. Individuals with strong drive do much better, and those with business skills do moderately better than the average.”
“Karlan and Zinman (2009a) uses a credit-scoring methodology to evaluate the impact of loans to microentrepreneurs in urban Philippines. The methodology used is similar to Karlan and Zinman (2008a, 2008b), earlier, however, there the focus was on loans made to employees. Here the effects are much more muted, and some findings cast doubt on the traditional microfinance narrative. Business owners’ profits increase, but not through investment in productive assets or working capital. Moreover, the treatment effects are stronger for groups that are not typically targeted by microlenders: male and higher income entrepreneurs. There is evidence that treated businesses actually shrink in size and scope, including the shedding of paid employees. The results suggest that borrowers used credit to reoptimize business investment in a way that produced smaller, lower cost, and more profitable businesses. The question remains as to how credit enabled this change: why did business owners need to borrow to reduce staff? One potential explanation is household risk management: treated individuals substitute out of formal insurance products, while also reporting a greater ability to borrow from friends or family in an emergency. It is possible that before credit entrepreneurs were retaining unproductive employees as a kind of informal mutual benefit scheme. Those employees, even if unprofitable, were an additional place to turn in times of need.”
“A lack of access to reliable savings accounts appears common to the poor everywhere, as documented in Stuart Rutherford’s (2000) fascinating book, The Poor and their Money. Rutherford describes many strategies the poor use to deal with this problem. For example, they form savings “clubs,” where each person makes sure that the others save. Self-Help Groups (SHGs), popular in parts of India and present in Indonesia as well, are saving clubs which also make loans to their members out of the accumulated savings (they are also sometimes linked to banks). In Africa, Rotating Savings and Credit Associations (ROSCAs) allow people to lend their savings to each other on a rotating basis. Others pay deposit collectors to collect their deposits and put them in a bank. Others deposit their savings with local money-lenders, with credit unions (which are essentially larger and much more formally organized self-help groups) or in an account at the local post office. Indeed, one reason why many of the poor respond so well to microcredit is not necessarily because it offers them credit, but because once you take a loan and buy something with it, you have a disciplined way to save-namely, by paying down the loan.”