1 / 39

October 2006

RETURNS TO CAPITAL IN MICROENTERPRISES: EVIDENCE FROM A FIELD EXPERIMENT Chris Woodruff, UC San Diego (With David McKenzie and Suresh de Mel). October 2006. The project. We estimate returns to capital for a set of very small household enterprises.

ora-copeland
Download Presentation

October 2006

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. RETURNS TO CAPITAL IN MICROENTERPRISES: EVIDENCE FROM A FIELD EXPERIMENTChris Woodruff, UC San Diego(With David McKenzie and Suresh de Mel) October 2006

  2. The project • We estimate returns to capital for a set of very small household enterprises. • No paid employees, capital of less than $US 1000 (~lower 25% of distribution of self employed in Sri Lanka)

  3. Returns to capital in microenterprises --Why do we care? • Large portion of urban labor market is self-employed. About one-third in Sri Lanka • What is the potential for growth of these enterprises? • Even absent sustained growth, what is the potential for increasing incomes among these individuals?

  4. Why might returns be high or low? • Low returns: • Minimum scale of investment / non-convex production sets • High returns: • Capital constraints • Risk / uncertainty

  5. Evidence on returns to capital in enterprises Among others: • Banerjee and Duflo 2003 (74%) • Bigsten et al (30%) • Udry and Anogol 2006 (60%) • McKenzie and Woodruff 2006 (10%/month)

  6. What is wrong with existing evidence? • Some from cross section: Worry about conflating ability and capital investment • Some from loan programs: Measure only for the self-selected sample that applies for credit • McKenzie and Woodruff suggests that returns are high in the broad sample of firms, yet take up rates for loan programs are low

  7. The Experiment • Randomized experiment where we provide grants to enterprises to create exogenous variation in capital stock • Selected 618 firms in three districts in southern Sri Lanka (Kalutara, Galle, Matara) • Sample drawn from block-to-block census in selected GNs • Surveyed first in March 2005, then quarterly since (5 waves used in the paper – now up to 7 waves)

  8. The Experiment • Firms in three zones: • Suffered direct damage from tsunami • In coastal zone, but no damage • Farther inland • In this paper, we exclude firms directly affected by the tsunami

  9. The enterprises • All had less than 100,000 SLR ($US1000) in capital (not counting land and buildings) in baseline survey • Half in retail, the other half in manufacturing / services (clothing, lace, bamboo, food products)

  10. Sri Lanka: capital shock • After the first and third round of the survey, randomly selected firms were given capital shock • ~$100 or ~$200, in cash or equipment • 59% of firms received treatment • Larger treatment is: • About 75% of median capital stock • About 6 months of reported earnings • Use grants rather than loans because we want to measure the full spectrum of firms

  11. Capital Shock • Baseline survey asked what firms would purchase if they had: % Inventories 5,000 68% 10,000 59% 15,000 49% Most profitable 17% Median most profitable investment is 25,000; 2/3rds say less than 30,000; 20,000 is enough for 42% of the firms to make their most profitable investment

  12. Capital Shock • About 55% of the in-kind treatments were inventories • Of the cash treatments invested in the enterprise, about 2/3rds were spent on inventories

  13. Pre-treatment means

  14. Post-treatment means

  15. Estimate FE regression Also consider revenues, log profits

  16. Results Median 7000 3000

  17. Results

  18. Interpretation • 10,000Rs treatment increases profits by 560-712Rs. i.e. a 5.6-7.1% return • Log specification gives around 4% return.

  19. Non-linearities in returns?

  20. Cash vs Equipment Treatments • Cash was given without restrictions, told they could purchase anything they wanted, for themselves, household, business, or other • Asked owners what they had done with treatment • Approximately 58% of cash was invested in business, additional 12% saved, 6% used to repay loans.

  21. Cash vs. equipment treatments

  22. Higher profits or higher reported profits? • Possible concern is that this might just reflect a change in reporting of profits: 1) Perhaps treated firms trust us more and so are less likely to underreport • Address this by looking at reporting done after treatment for sales in periods before treatment • Asked March 2005 sales in Round 1, and then re-asked about these in Round 2, after some firms were treated. => No significant differences between groups in ratios ii) Second group of firms were treated in November, interviewed at start of October and in January. Compare ratio of October sales (asked Jan) to September sales (asked Oct) for treated vs untreated => again find no significant difference between groups.

  23. Higher profits or higher reported profits? 2) Perhaps then firms overreport profits after treatment, since they want to show us that giving them money is good? • If this was the case, we would expect them to overreport the share of the cash treatment invested in business • But on average say about 55-65% invested in business, and return we get from cash treatment is 2/3rd return from equipment treatment => Appears that treated firms are not overreporting

  24. Results so far: • Real returns 5-6% per month • No evidence inconsistent with linearity of returns • Only small decay over time • Returns are much higher than interest rates on micro loans (3-7% per year) • What “explains” this gap?

  25. Females vs Males • Many microfinance organizations concentrate on lending to women • Is there any evidence to support them having higher returns? • See that women invest lower share of the cash treatment in business on average (67% vs 88%, p=0.08).

  26. Males vs Females

  27. What do the firms say are their constraints?

  28. How do firms finance existing business? • Only 3.1% have bank account • 89% got no start-up funding from bank or microfinance • 71% relied entirely on own savings and family for start-up funds • 83-100% of firms making purchases of equipment between waves used only own savings and family to finance this  Internal capital market of household is major source of funds.

  29. Heterogeneity of returns • Model of capital constraints, risk and uncertainty. • Household has endowment of assets, earns money from market labor of other household members. • Can finance capital stock through borrowing, and through its internal capital market • With well-functioning credit and insurance markets, will choose capital stock such that marginal return to capital = market interest rate • With missing markets, marginal return to capital will exceed market rate

  30. Heterogeneity of returns • Predictions: • Returns lower when capital constraints less severe • More workers in household (baseline) • Lower entrepreneurial ability (measures: education; digit span test; self-efficacy; time solving maze) • Higher wealth (durable assets) • Returns higher when more risk and uncertainty • CRRA estimated with lottery exercise • Uncertainty from subjective distn of profits

  31. Heterogeneity of returns

  32. Heterogeneity of returns

  33. Conclusions • Shocks to capital were large and random, and hence uncorrelated with ability • Suggested returns 4-8% per month • No evidence of non-linearities in returns • Returns higher where capital constraints bind tighter • No evidence returns affected by risk, uncertainty

More Related