Do this problem set without page and word limit. Data excel attached.

2. The Miracle of Microfinance? As we will examine during the Microfinance section, rig- orous evidence on the impacts of microcredit programs was scarce in the 1990s and early 2000s, even though the sector was booming. Starting around 2010, several randomized trials of microcredit were undertaken to fill this evidence gap. This question walks you through some of the analysis in "The Miracle of Microfinance? Evidence from a Randomized Eval- uation" by Banerjee, Duflo, Glennerster, and Kinnan (2015, Hereafter BDGK). The paper is available at https://pubs.aeaweb.org/doi/pdfplus/10.1257/app.20130533. Read the first 11 pages (through the experimental design section). You can skim the rest; we will go over the results in more detail in class.

The study took place in 104 neighborhoods in the Indian city of Hyderabad. The researchers randomly selected half for the intervention. In those neighborhoods, non-governmental mi- crofinance organization (MFI) Spandana began operations. The remaining neighborhoods served as a control group with no intervention.

(a) BDGK's data are based on a set of surveys of households in the study area. The dataset miraclemfi.xlsx on eLC consists of a few key variables from these surveys. I've also posted a text file miraclemfi_labels.txt which provides a brief description of each vari- able.

Lets first "get our hands dirty" by summarizing a few variables.

i. Several features of the context are important for interpreting this study. First, mi- crofinance could be used either to grow a new business or start a new one. What fraction of households have an existing business?

ii. Other MFI's were operating in the area during the evaluation. What fraction of households were borrowing from other MFI's at endline 1?

iii. What fraction of households were in Spandana treatment neighborhoods?

(b) The basic question of the study is whether microfinance access influenced outcomes of interest.

The authors have a number of interesting survey outcomes; I've included a few of them in the dataset if you're interested. Here we're going to focus on women's empower- ment, as measured in the first endline survey. As is common in the literature, the au- thors define women's empowerment as a woman's role in certain household decisions. A number of survey questions are all aggregated into an index, women_emp_index_1. (See Footnote 27 and the associated discussion on page 49.) the _1 at the end of the variable name signifies that it was measured in the first of the two endline surveys. Suppose there was no experiment, and you examined the relationship between microfi- nance access and empowerment using survey data. Specifically, you compare average empowerment in areas with access to an MFI to empowerment in areas without access to an MFI.

Why might this not have a causal interpretation?

(c) Now let's use the experimental variation. Spandana began microfinance activities in a random set of villages in the data. In the remaining villages, they did not conduct any microfinance activities. Now suppose you compare average empowerment in areas with access to Spandana to empowerment in areas without access to Spandana.

Why does this have a causal interpretation, when the analysis in part (b) above does not?

(d) Now let's look at the data. It's a good idea to first see whether Spandana increased access to credit in treatment villages. Compare average MFI borrowing (any_mfi_1) between treatment and control villages. Use a procedure that produces a standard error. For example, you could regress MFI borrowing on treatment:

any_mfi_1i = a+btreatmenti +?i How much did Spandana's entry change MFI borrowing?

(e) Now that we've confirmed that the treatment increased borrowing, we can look at the effect on empowerment. Compare average empowerment in treatment and control vil-lages, again using a method that produces a standard error. The regression you can run is:

women_emp_index_1i = a+btreatmenti +?i

Is the result statistically significant? Because it is an index, the magnitude is difficult to interpret, but 0.05-0.1 standard deviations would be economically significant, given that empowerment is not the primary outcome in this case.

Note that we are attempting to replicate Column 7 of Table 7 in the BDGK paper. We are going to get slightly different results because of a weighting procedure used by BGDK and because of a standard-error correction. Excel's regression tool doesn't have enough options to get this exactly right.

(f) (Harder-won'tbegraded)Intheliterature,thereisoftenacorrelationbetweenwomen's empowerment and microfinance borrowing, but using randomized trials the causal ef- fect of borrowing on empowerment is small. What does this tell you about the type of women who choose to borrow from MFI's?

hhid areaid treatment any_old_biz area_pop_base hhsize head_age spouse_literate ownland spandana 1 othermfi_1 anymfi_1 home_durable_index_1 women_emp_index_1 credit_index_1 income_index_1 labor_index_1 consumption_index_1 social_index_1 357 1 1 1 272 5 40 1 1 0 0 0 2 0 0 0 0 0 0 1 1 1 272 6 32 1 0 3 0 0 0 0 0 0 1 1 0 272 4 43 0 1 0 0 0 4 0 0 0 -1 0 0 29 108 109 110 116 117 125 127 129 157 183 187 2232220 2 0 1 1 2 2 2 5 2 2 2 2 2 10 1 1 1 272 6 64 0 0 0 0 3 3 0 0 0 13 1 1 1 0 272 6 45 1 1 0 0 0 3 1 0 0 0 1 0 35 1 1 272 4 32 1 0 0 4 0 0 0 0 0 0 37 1 1 1 272 9 53 0 1 0 0 0 4 0 0 0 0 5 0 72 1 1 0 272 4 28 0 0 0 0 3 0 0 0 0 0 0 76 1 1 0 272 5 34 0 1 0 0 0 3 0 0 0 0 1 0 96 1 1 272 5 29 2 0 1 546 5 29 1 0 3 0 0 0 0 0 0 50 0 1 0 1 1 3 0 1 0 1 0 0 2 0 0 546 5 47 113 0 0 1 1 3 0 1 1 0 0 0 2 0 0 546 4 40 0 1 0 1 1 2 -1 0 0 0 0 0 2 0 546 6 37 0 1 0 0 0 1 -1 0 2 0 0 546 5 50 0 1 0 0 0 3 0 0 0 0 0 0 2 0 1 546 6 64 0 0 1 1 3 0 1 0 0 0 0 2 0 0 546 5 53 0 1 0 0 0 2 0 0 0 0 0 0 2 0 546 3 45 1 0 1 0 0 0 0 -1 0 2 0 1 546 6 31 0 1 0 0 0 0 1 0 0 0 -1 0 2 0 0 546 6 34 0 0 0 0 2 0 0 0 0 0 0 2 0 0 546 7 46 0 1 0 0 0 3 0 0 0 0 0 0 189 3 1 0 257 5 45 1 0 3 0 1 0 1 0 0 196 3 1 1 257 5 31 0 1 0 0 0 3 0 0 0 0 0 0 197 3 1 0 257 6 43 0 0 0 0 4 1 0 0 0 0 0 199 3 1 0 257 3 28 0 1 0 0 0 1 -1 0 0 0 0 0 203 3 1 1 257 5 34 1 0 4 0 0 2 0 0 0 207 3 1 0 257 4 34 1 1 0 0 0 3 1 0 1 0 1 0 209 3 1 0 257 5 35 0 0 0 0 2 0 0 0 0 -1 0 220 3 1 1 257 4 22 1 1 0 0 0 2 1 0 0 0 0 0 224 3 1 0 257 5 47 0 2 1 0 0 0 0 0 229 3 1 0 257 9 35 1 0 0 0 4 0 -1 1 1 1 0 248 4 1 0 173 6 40 0 1 0 0 0 3 0 0 0 0 0 0