Empirical Project 9 Solutions

These are not model answers. They are provided to help students, including those doing the project outside a formal class, to check their progress while working through the questions using the Excel, R, or Google Sheets walk-throughs. There are also brief notes for the more interpretive questions. Students taking courses using Doing Economics should follow the guidance of their instructors.

Part 9.1 Households that did not get a loan

Region Proportion in the region living in small towns Proportion of regional households living in large towns Proportion of regional households living in rural areas Proportion of all households living in the region
Addis Ababa 0 1.00 0 0.06
Afar 0.15 0.10 0.76 0.03
Amhara 0.11 0.22 0.67 0.20
Benshagul Gumuz 0.10 0.00 0.90 0.02
Diredwa 0.00 0.47 0.53 0.04
Gambelia 0.08 0.12 0.80 0.02
Harari 0.00 0.27 0.73 0.03
Oromia 0.11 0.28 0.61 0.20
SNNP 0.09 0.18 0.73 0.23
Somalie 0.09 0.16 0.76 0.06
Tigray 0.07 0.37 0.56 0.12
Grand total 0.09 0.28 0.63 1.00

Solution figure 9.1 Proportion of sample living in towns vs rural areas, by ‘region’. (Note that numbers may not add up to 1 due to rounding.)

  Mean SD Min Max
Household size 4.58 2.40 1.00 16.00
Gender 0.30 0.46 0.00 1.00
Age 44.18 15.61 3.00 99.00
Young children 1.89 1.71 0.00 10.00
Working-age adults 2.58 1.52 0.00 10.00
Max education 7.53 7.28 0.00 30.00
Number of assets 14.90 17.23 0.00 203.00

Solution figure 9.2 Summary table for household demographics (all households).

Not rejected Rejected Blank or NA Sum
Applied for loan 1,363 201 1 1,565
Did not apply for loan 3,632 24 2 3,658
Blank or NA 37 2 0 39
Grand total 5,032 227 3 5,262

Solution figure 9.3 Loan applications and approvals.

Reason Proportion
Do Not Like To Be In Debt 0.19
Have Adequate Farm 0.19
Fear Not Be Able To Pay 0.17
Believe Would Be Refused 0.12
No Farm or Business 0.10
Do Not Know Any Lender 0.07
Too Expensive 0.05
Inadequate Collateral 0.05
Other (Specify) 0.04
Too Much Trouble 0.03

Solution figure 9.4 Most important reason for not applying for a loan.

Reason Proportion
Fear Not Be Able To Pay 0.28
Do Not Like To Be In Debt 0.24
Inadequate Collateral 0.09
Believe Would Be Refused 0.09
Too Expensive 0.07
Do Not Know Any Lender 0.06
Too Much Trouble 0.06
Have Adequate Farm 0.05
No Farm or Business 0.04
Other (Specify) 0.02

Solution figure 9.5 Second most important reason for not applying for a loan.

  1. Solution figure 9.6 compares the loan purposes of ‘successful’ and ‘denied’ borrowers. Compared with all households, a greater proportion of households that got loans indicated the purpose for the loan as consumption, and a smaller proportion of households that got loans indicated the purpose as for investment.

    Note: The entry ‘10’ is likely due to incorrect recording of responses, since ‘10’ is not a valid category in the original survey.

Purpose Successful Denied
10 0.00 0.02
Business start-up capital 0.15 0.26
Expanding business 0.08 0.14
Other (specify) 0.03 0.26
Purchase agricultural inputs for food crop 0.30 0.21
Purchase house/lease land 0.02 0.03
Purchase inputs for other crops 0.01 0.06
Purchase non-farm inputs 0.12 0.03
For consumption and personal expenses 0.20 0.00

Solution figure 9.6 Loan purpose for successful and denied (credit-excluded) borrowers.

Note

Although most categories are the same, the two tables are not exactly comparable because ‘Consumption and personal expenses’ was a separate category from ‘Other’ for successful borrowers, but was considered as one of the responses in ‘Other (specify)’ for credit-excluded households. Looking at the variable loan_purpose_other in the ‘All households’ tab, we can see that reasons related to consumption and personal expenses (such as medical, school, transport expenses) apply to around 20 or so households.

Household characteristic Successful Denied
Number of observations 1,363 201
Age of household head 43.40 41.20
Highest education in household 7.26 8.00
Number of assets 15.90 14.50
Household size 4.87 4.82
Number of young children 2.09 2.22
Number of working-age adults 2.75 2.76

Solution figure 9.7 Comparison of household characteristics (successful and denied borrowers).

  Rural   Small town (urban)   Large town (urban)  
  Successful Denied Successful Denied Successful Denied
Number of observations 903 128 128 17 332 56
Age of household head 45.10 43.40 42.10 37.50 39.20 37.30
Highest education in household 5.00 4.55 9.59 13.12 12.51 14.3
Number of assets 13.60 12.10 15.70 16.70 22.10 19.20
Household size 5.35 5.47 4.12 4.06 3.84 3.57
Number of young children 2.49 2.84 1.56 1.41 1.20 1.05
Number of working-age adults 2.91 2.88 2.54 2.33 2.42 2.36

Solution figure 9.8 Comparison of household characteristics, conditioning on the ‘rural’ variable.

Successful borrowers Denied borrowers Difference in means
Household characteristic Mean SD N Mean SD N Difference in means CI lower CI upper
Age of household head 43.40 14.30 1,361 41.20 12.90 201 2.15 0.21 4.09
Highest education in household 7.26 6.74 1,361 8.00 7.90 201 −0.73 −1.89 0.42
Number of assets 15.90 19.00 1,361 14.50 16.80 201 1.42 −1.13 3.97
Household size 4.87 2.35 1,361 4.82 2.35 201 0.05 −0.30 0.40
Number of young children 2.09 1.68 1,361 2.22 1.80 201 −0.14 −0.40 0.13
Number of working-age adults 2.75 1.49 1,361 2.76 1.42 201 −0.003 −0.22 0.21

Solution figure 9.9 Calculating 95% confidence interval for difference in means between ‘successful’ and ‘denied’ borrowers.

Solution figure 9.10 Difference in means between ‘successful’ and ‘denied’ borrowers by household characteristics, with 95% confidence intervals.

Household characteristic Successful borrowers Denied borrowers Discouraged borrowers Constrained borrowers
Number of observations 1,363 201 588 3,012
Age 43.37 41.21 43.28 44.84
Highest education in household 7.26 8.00 6.50 7.14
Number of assets 15.88 14.46 10.16 13.54
Household size 4.87 4.82 4.65 4.44
Number of young children 2.09 2.22 2.03 1.81
Number of working-age adults 2.75 2.76 2.49 2.49

Solution figure 9.11 Comparison of household characteristics by borrower type.

  1. One example of selection bias is the study of the determinants of prosperity. To do this, we can compare developing with developed countries. However, only developing countries with adequate resources and willingness collect and submit their data. The set of developing countries for which we have data is thus not representative of the population of developing countries.

Part 9.2 Households that got a loan

  Mean SD Min Max
Loan amount (principal) 26,896 783,587 1 30,000,000
Total amount 29,223 827,144 20 31,260,000

Solution figure 9.12 Summary measures of loan amount (principal) and total amount.

Solution figure 9.13 Loan amounts and interest rates.

Loan amount N Mean SD Min Max 1st quartile 2nd quartile 3rd quartile
Long term 211 172,718 2,072,736 20 30,000,000 1,000 3,700 8,000
Short term 717 3,017 8,398 40 150,000 480 1,500 3,500
Interest rate N Mean SD Min Max 1st quartile 2nd quartile 3rd quartile
Long term 211 0.19 0.27 0.00 2.24 0.00 0.14 0.25
Short term 717 0.11 0.17 0.00 1.12 0.00 0.05 0.17

Solution figure 9.14 Comparison of distribution of long-term and short-term loans.

Household characteristics Interest rate
Household size 0.11
Gender –0.02
Age 0.03
Young children 0.10
Working-age adults 0.05
Max education −0.08
Number of assets –0.05

Solution figure 9.15 Correlations between interest rate and household characteristics.

Proportion of 'borrowed_from'
Source of finance Large town (urban) Rural Small town (urban) Total
Bank (commercial) 0.02 0.00 0.00 0.01
Employer 0.04 0.00 0.01 0.01
Grocery/Local Merchant 0.08 0.05 0.10 0.06
Microfinance Institution 0.19 0.28 0.27 0.26
Money Lender (Katapila) 0.00 0.05 0.02 0.04
Neighbour 0.11 0.12 0.07 0.11
NGO 0.01 0.05 0.05 0.04
Other (specify) 0.06 0.12 0.04 0.10
Relative 0.49 0.32 0.43 0.37
Religious Institution 0.00 0.02 0.00 0.02

Solution figure 9.16 Source of loan, by variable ‘rural’.

Proportion of borrowed_from_other
Source of finance Large town (urban) Rural Small town (urban) Total
Bank 0.00 0.01 0.00 0.01
Cooperatives 0.42 0.43 0.40 0.43
Equib 0.05 0.01 0.00 0.01
From government 0.05 0.30 0.00 0.26
From individuals 0.05 0.01 0.00 0.01
From private business 0.00 0.02 0.00 0.02
From relatives 0.05 0.00 0.20 0.01
From women association 0.00 0.01 0.00 0.01
From Youth Association 0.00 0.01 0.00 0.01
HAB project 0.00 0.02 0.00 0.01
Iddir 0.16 0.15 0.00 0.14
Micro and small enterprise 0.11 0.00 0.00 0.01
Micro finance 0.00 0.04 0.40 0.05
Mobile 0.05 0.00 0.00 0.01
NGO 0.05 0.00 0.00 0.01

Solution figure 9.17 Source of loan, by variable ‘rural’ (‘Other’ category only).

Source of finance Large town (urban) Rural Small town (urban)
Bank (commercial) 1,814 619 0
Employer 602 290 0
Grocery/Local Merchant 166 259 176
Microfinance Institution 712 411 510
Money Lender (Katapila) 365 332 365
Neighbour 125 187 296
NGO 373 395 236
Other (specify) 274 372 806
Relative 237 217 393
Religious Institution 1,461 343 0
(blank) 1,096 289 151

Solution figure 9.18 Duration of loan (rounded to nearest day).

Source of finance Large town (urban) Rural Small town (urban)
Bank (commercial) 5,755,833 3,575 0
Employer 5,915 1,350 1,000
Grocery/Local Merchant 1,370 1,429 1,599
Microfinance Institution 14,525 3,852 7,543
Money Lender (Katapila) 350 1,360 5,150
Neighbour 602 837 686
NGO 5,532 1,4475 2,300
Other (specify) 3,912 1,842 3,829
Relative 7,872 1,576 6,802
Religious Institution 50,000 910 0

Solution figure 9.19 Loan amount (rounded to nearest whole number).

Source of finance Large town (urban) Rural Small town (urban)
Bank (commercial) 0.14 0.26 0.00
Employer 0.04 0.06 0.00
Grocery/Local Merchant 0.00 0.09 0.00
Microfinance Institution 0.16 0.18 0.15
Money Lender (Katapila) 1.00 0.43 0.09
Neighbour 0.00 0.12 0.00
NGO 0.06 0.12 0.05
Other (specify) 0.13 0.16 0.22
Relative 0.01 0.08 0.00
Religious Institution 0.00 0.20 0.00

Solution figure 9.20 Interest rate (rounded to two decimal places).

Source of finance Large town (urban) Rural Small town (urban)
Bank (commercial) 0.00 0.50 0.00
Employer 0.31 0.00 0.00
Grocery/Local Merchant 0.39 0.19 0.50
Microfinance Institution 0.41 0.13 0.31
Money Lender (Katapila) 0.00 0.27 0.50
Neighbour 0.35 0.25 0.43
NGO 0.25 0.31 0.20
Other (specify) 0.39 0.19 0.25
Relative 0.40 0.21 0.29
Religious Institution 1.00 0.24 0.00
(blank) 1.00 0.43 1.00

Solution figure 9.21 Proportion of households with a female head, according to source of finance.

  1. One hypothesis is that lack of knowledge of loan availability affects access by households to lending. The government could fund education programmes aimed at improving the poor’s understanding of financial services. The government could identify two poor regions with similar characteristics that are distant from each other. One region would serve as the control group while the other as the treatment group. The government could then randomly select and educate individuals from the treatment region. The causal effects of the policy could be assessed by comparing outcomes in the two regions after the treatment. During the study period, the government should avoid implementing other policies in the regions, especially those that can affect the regions differently. The two regions should be chosen such that they would evolve in similar ways without the policy, and that treatments on one region cannot indirectly affect outcomes in the other region.