Money or Ideas? A Field Experiment on Constraints to Entrepreneurship in Rural Pakistan

Xavier Giné, DECFP Xavier Giné, DECFP Ghazala Mansuri, PRMPRGhazala Mansuri, PRMPR

Money or Ideas? A Field Experiment on Constraints to Entrepreneurship in Rural Pakistan

Xavier Giné, DECFP Xavier Giné, DECFP Ghazala Mansuri, PRMPRGhazala Mansuri, PRMPR

• Entrepreneurship plays a central role in the process of economic growth and development (Knight, 1921; Schumpeter, 1942)– In Solow’s (1957) seminal paper, only a modest fraction of the

increase in output per worker was driven by increases in capital, the rest was attributable to technical change, requiring entrepreneurial talent.

• Some countries have grown dramatically while others have remained stagnant, but it is hard to believe that poor countries lack entrepreneurial talent.

• So what are the main barriers to entrepreneurship in a poor country?

MotivationMotivationMotivationMotivation

• Access to Finance– Large empirical and theoretical literature (Blanchflower and

Oswald, 1984; Holtz-Eakin, Joulfaian and Rosen, 1994a and 1994b and more recently Paulson, Townsend and Karaivanov, 2006; de Mel, McKenzie and Woodruff, 2008 and Banerjee et al. 2010)

– Mohamed Yunus sides with this view:“Giving the poor access to credit allows them to immediately put into practice

the skills they already know”

(Yunus, Banker to the Poor 1999)

• Access to business skills or managerial capital– Builds on the occupational choice models of Lucas (1978) with

the assumption that managerial capital can be taught (Bloom and Van Reenen, 2010; Bruhn, Karlan and Schoar, 2010 and Schoar, 2010).

MotivationMotivationMotivationMotivation

• In partnership with Pakistan Poverty Alleviation Fund (PPAF), we conduct a randomized field experiment with one of its largest MFI partner organizations.

• We interviewed 4,160 members from 4 different geographical regions organized in groups.

• 2x2 design offering:– Business Training

• Groups divided into two equal sized bins: BT and no BT• Members of BT groups were offered a 8 day course (36 hours).

– Loan Lottery• Eligible members were allowed to submit loan requests of up to Rs 100K,

(current limit around Rs 15K) for a 7 month period• If request is approved, then the borrower enters a lottery: If winner, borrower

gets loan approved. If loser, borrower gets regular loan size based on their loan cycle

• So clients fall in one of four categories.

What do we do?What do we do?What do we do?What do we do?

• What are the key constraints to entrepreneurship?– Is it lack of skills or lack of capital?

• To MFI clients:– How beneficial is business training (BT)? Does it lead to the

creation and management of more profitable enterprises? Does it lead to lower business failure?

– Does it improve client retention?

– Does current loan size inhibit enterprise growth and profitability? Ie, are clients constrained?

• To MF Institutions:– Does it improve repayment? Client retention? Is it cost-effective?

Key QuestionsKey QuestionsKey QuestionsKey Questions

GM KV DFS

Target Audience MF clients, not all entrepreneurs

MF clients, all entrepreneurs

MF clients, all entrepreneurs

Program Design Local firm based on ILO

Local firm and FFH

FFH and researchers

Instructors MF staff MF staff ?

Total Number of Hours

46 11 15-18

Frequency Daily, 9am-4pm

Once a week, 30 min

Once a week, 3 hours

Duration 6+2 days 22 weeks 6 weeks

BT TrainingBT TrainingBT TrainingBT Training

BT: Group ExerciseBT: Group ExerciseBT: Group ExerciseBT: Group Exercise

BT: Village AssessmentBT: Village AssessmentBT: Village AssessmentBT: Village Assessment

TimelineTimelineTimelineTimeline

Nov 06 Jan 07

Baseline Survey

Orientation for BT

Feb-May 07

BT Rollout Loan Lottery

June 08Nov 07 Dec 08

Follow-up Survey

• 3 sources of data:– MIS (administrative) data from NRSP, including client retention,

loan disbursement and repayment.– Baseline Survey (before BT was offered) and Follow-up Survey.

• FU surveys include a business visit of all businesses operated by clients. In addition, surveys include a variety of questions on the socio-economic characteristics of clients.

DataDataDataData

Data: Baseline Member CharacteristicsData: Baseline Member CharacteristicsData: Baseline Member CharacteristicsData: Baseline Member CharacteristicsMale Female p-value

Individual Characteristics

Age 37.83 37.3 0.17

Education 5.24 2.51 0.00

Entrepreneurship

Ever in Business (1=Yes) 0.62 0.6 0.39

Digit Span Recall 3.81 2.6 0.00

Index of Business Know ledge 0.24 -0.44 0.04

Index of Optimism -0.07 -0.37 0.00

Index of Trust -0.02 -0.06 0.76

Risk Aversion 0.01 0.31 0.00

Income and Assets

Log of Expenditures 8.28 8.26 0.43

Land (acres) 5.52 2.22 0.00

Credit Constraints (1=Yes) 0.09 0.12 0.00

Empowerment

Number of decisions taken (out of 8) 3.31 1.76 0.00

Client Characteristics

Months as MFI client 27.4 22.89 0.00

Distance to meeting place 7.4 8.51 0.00

Data: Baseline Business CharacteristicsData: Baseline Business CharacteristicsData: Baseline Business CharacteristicsData: Baseline Business Characteristics

Male Female p-value

Type of Business

Manufacture (Embroidery, Handicrafts, Tailor) 0.06 0.59 0.00

Retail (shopkeeping) 0.43 0.16 0.00

Services (Transport, electrician, construction, etc.) 0.1 0.02 0.11

Business characteristics

Age of Business 15.2 11.6 0.00

Location is fixed (1=Yes) 0.9 0.97 0.00

Location inside home (1=Yes) 0.42 0.88 0.00

All sales in village (1=Yes) 0.44 0.63 0.00

Inputs from places within 10 km. (1=Yes) 0.57 0.76 0.00

Operates all months (1=Yes) 0.81 0.79 0.17

Business is registered (1=Yes) 0.07 0.03 0.00

Full time paid workers (1=Yes) 0.02 0.02 0.65

Log sales 8.81 7.67 0.00

Management

Manager/sole worker (1=Yes) 0.94 0.84 0.00

Book keeping (1=Yes) 0.21 0.1 0.00

No previous occupation (1=Yes) 0.32 0.72 0.00

• The response rate in the follow-up survey for the remaining regions is 85%, including individuals that are no longer clients.– There are no differences in attrition rate between clients offered

BT or winning the Loan Lottery

Data: Response RateData: Response RateData: Response RateData: Response Rate

• Business Training was implemented as planned. – 82% of clients offered BT correctly recall the offer at FU.– Attendance rate is 98% (transport, lunch and per diem allowance

was provided).

• Loan Lottery implementation was not perfect.– Only 35% of clients recall the offer at FU.– Most lottery applicants that lost believe to have been rejected by

NRSP. – Only 30% of eligible clients ended up applying over the 7-month

period.

Implementation of Experimental DesignImplementation of Experimental DesignImplementation of Experimental DesignImplementation of Experimental Design

• We report Intent-to-Treat (ITT) effects– Disregard possible heterogeneity in exposure to treatment

• First difference (only FU data) or double-difference (BL and FU when available)

Yij1 = α + β1 BTj + β2 LLij+ β3 BTj and LLij + γXij + δ Yij0 + εij,

– Fixed effects at the branch level, clustering SEs at the level of the borrower group, the unit of randomization

• Follow Kling, Liebman, and Katz (2007) and construct standardized measures for families of outcomes.– Convert each variable k to a z-score, the summary measure will

be

Zij = Σk zijk/k, where zijk = (Yijk - μk) / σk

Estimation MethodsEstimation MethodsEstimation MethodsEstimation Methods

Business related outcomesBusiness related outcomes

Business Knowledge

New Business CO member involved

New Business CO member not involved

Failed Business (1=Yes)

Aggregate Business Practices

Aggregate Business Operations

Aggregate Sales and Profits

(1) (2) (3) (4) (5) (6) (7)Business Training (1=Yes) 0.058* -0.006 -0.001 -0.034 0.131** 0.043 -0.021

(0.031) (0.008) (0.012) (0.028) (0.062) (0.027) (0.054)Lottery Winner (1=Yes) -0.014 -0.012 -0.007 -0.002 0.099 0.081** 0.013

(0.037) (0.013) (0.019) (0.036) (0.082) (0.035) (0.071)BT and LW x Female 0.075* -0.004 0.01 -0.014 0.166** 0.047 -0.08

(0.038) (0.013) (0.018) (0.037) (0.079) (0.035) (0.066)

Business related outcomesBusiness related outcomes

Business Knowledge

New Business CO member involved

New Business CO member not involved

Failed Business (1=Yes)

Aggregate Business Practices

Aggregate Business Operations

Aggregate Sales and Profits

(1) (2) (3) (4) (5) (6) (7)Business Training (1=Yes) 0.058 -0.011 -0.012 -0.061* 0.122 0.067* 0.023

(0.043) (0.013) (0.016) (0.037) (0.085) (0.036) (0.070)BT x Female 0 0.013 0.023 0.06 0.018 -0.061 -0.111

(0.062) (0.017) (0.025) (0.055) (0.118) (0.052) (0.102)Lottery Winner (1=Yes) 0.014 -0.019 -0.015 -0.004 0.061 0.095** -0.008

(0.045) (0.018) (0.024) (0.045) (0.105) (0.046) (0.092)LW x Female -0.066 0.016 0.017 0 0.097 -0.034 0.065

(0.073) (0.023) (0.035) (0.068) (0.150) (0.067) (0.138)BT and LW 0.066 -0.016 -0.017 -0.047 0.246** 0.084* -0.07

(0.051) (0.016) (0.020) (0.046) (0.102) (0.044) (0.085)BT and LW x Female 0.021 0.028 0.062* 0.077 -0.225 -0.098 -0.022

(0.073) (0.024) (0.036) (0.069) (0.138) (0.069) (0.128)

• Among controls, business failure is correlated with:– Age (-), Digit span recall (-), wealth (land) (-)

Bounds for Male BusinessesBounds for Male Businesses

Lower Bound

Unadjusted Treatment

EffectUpper Bound

(1) (2) (3)Aggregate Business Practices -0.271*** 0.066 0.369***

(0.103) (0.110) (0.112)Aggregate Business Operations -0.05 0.079 0.233***

(0.055) (0.057) (0.044)Aggregate Sales and profits -0.234** 0.023 0.278***

(0.095) (0.103) (0.096)

Individual and Household OutcomesIndividual and Household Outcomes

Income and Assets

Access to Credit

Decision-Making

CO Cohesion

Outlook on Life

(1) (2) (3) (4) (5)Business Training (1=Yes) 0.094*** 0.045 0.123 0.102*** 0.05

(0.028) (0.031) (0.123) (0.039) (0.033)BT x Female -0.056 -0.061 -0.083 -0.031 0.067

(0.041) (0.046) (0.162) (0.053) (0.047)Lottery Winner (1=Yes) 0.046 0.04 0.045 0.032 0.058

(0.036) (0.046) (0.163) (0.037) (0.039)LW x Female -0.02 -0.104* -0.146 0.012 0.032

(0.050) (0.062) (0.205) (0.061) (0.054)BT and LW 0.144*** 0.007 -0.067 0.145** 0.115***

(0.038) (0.043) (0.161) (0.060) (0.040)BT and LW x Female -0.167*** -0.039 0.096 -0.139** 0

(0.051) (0.060) (0.207) (0.068) (0.059)

• Are gender differences masking differences in other characteristics?– Inclusion of a range of controls does not affect results

• Training received by women is of low quality– The same staff trained male and female members

• If labor markets are missing for women, the quality of the marginal entrepreneur will be lower for females. Women may see business as fall-back option (Lucas, 1978; Emran et al, 2007).

• Women face mobility and time constraints that prevent them from capitalizing on BT– Training designed for non-literate audience– Time Allocation Analysis

What explains gender differences? What explains gender differences?

Time AllocationTime AllocationTime AllocationTime Allocation

Male Female Male FemaleTime spent in businesses 3.73 1.97 0.00 5.40 2.93 0.00

Time spent in production of goods and services (of main business)

1.27 1.24 0.82 1.29 0.94 0.00

Time spent in bookeeping/ management (of main business)

1.03 0.32 0.00 1.47 0.47 0.00

Time spent in personal activities 7.47 8.21 0.00 7.07 7.71 0.00

Time spent in hh/family activit ies 2.29 6.64 0.00 2.16 6.43 0.00

Time spent in paid worktime 2.20 0.62 0.00 1.41 0.51 0.00

Time spent agricultural work 2.29 0.56 0.00 1.95 0.41 0.00

N. Obs. 1814 1680 1149 989

Business OwnersAll CO MembersMeans P-value

of t-testMeans P-value

of t-test

• Evidence that BT led to improved business and household outcomes, especially among men.

• The impact of Loan Lottery is weaker, perhaps because clients were not credit constrained.

• Treatments seem also successful from institutional perspective:– BT increases demand for larger loans without an effect on

repayment

• Caveat: Children schooling is adversely affected– BT raised income but also the opportunity cost of children, so

net affect is ambiguous.

ConclusionsConclusionsConclusionsConclusions