BUSINESS LITERACY AND DEVELOPMENT: EVIDENCE FROM A RANDOMIZED TRIAL IN RURAL MEXICO. Gabriela Calderon 1 Jesse Cunha 2 Giacomo De Giorgi 3

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1 BUSINESS LITERACY AND DEVELOPMENT: EVIDENCE FROM A RANDOMIZED TRIAL IN RURAL MEXICO Gabriela Calderon 1 Jesse Cunha 2 Giacomo De Giorgi 3 1 Stanford University 2 NPS 3 Stanford University and NBER March, 2011 World Bank

2 Motivation Self employment is prevalent in the developing world In 2002, the % of self-employed non-agricultural workers in, above 60% on average in sub-saharan Africa, the Middle East, North Africa, Latin America, and Asia Small businesses are often badly managed and rarely have any formal accounting. In particular low returns for females As a consequence small businesses might be unprofitable and destined to fail Do those businesses lack managerial capital? Most of the discussion has focused on credit

3 Introduction In early 2008 we had the opportunity to partner with a newly founded NGO: CREA Previous designs had the provision of classes attached to microfinance operations. This makes it harder to identify the parameter of interest The two-stages randomization helps us identifying ITE which might be large and important Provision of business classes in late 2009 for 6 weeks Baseline data summer 2009, follow-up late summer/fall of 2010 Let me here reiterate that this is very much work in progress

4 Preview of the Results We find large, positive and significant effects of the business classes on: Revenues, # of clients and profits. Also on formal accounting This are short-run results, about 7-8 months after the intervention Also quite large and mostly positive ITE s. Important for the evaluation and the intervention. Results on revenues, costs and profits are confirmed if we compute them or simply use the reported measures

5 Related Literature De Mel, McKenzie and Woodruf (2008), provided capital (RCT design) to entrepreneurs. They find very low return on capital for females Karlan and Valdivia (2008), business training added to microfinance program in Peru. 30 to 60 minute class during the repayment meetings (supposed to last 22 weeks). Effects on revenues and business knowledge Field, Jayachandran and Pande (2010, AER P&P): cultural (norms) barrier to high returns. Sample from microfinance. Find support from norms limiting returns. Bloom, Eiffert, Mahajan, McKenzie and Roberts (2010): large textile firms (300+) in India, management consulting at the plant level. Improvement in productivity, decentralization of decisions, use of computers for data recording. Informational barriers seem the issue. Drexler, Fisher and Schoar (2010): micro-entrepreneurs in the Dominican Republic (linked to microfinance). 2 treatment arms: (i) rule-of-thumb (separate business from personal accounts) produced positive effects on accounting practices. (ii) Basic accounting, no effects. Both have no effects on profitability and investment behavior. Use of Accounting Practices at Baseline

6 Accounting Accounting Practices at Baseline Fraction Personal Notes Formal Accounting accounting None Other

7 Sectors Sectors Fraction clothing handicrafts general grocery, other resale prepared food ready-to-eat-food stationery & cosmetics sector

8 Size Business size Fraction size

9 CREA ( We are involved with the Business Skills Course (6 topics, 2 classes per topic): - Simple business accounting - How to set prices - Taxes and legal issues - Business organization and choice of products - Marketing - Sales

10 Classes Free (no tuition). 6 weeks, 4-hour classes per week. about 25 women per class, 2 teachers per class (Professors, Graduate Students, and Undergraduates). Emphasize practical examples. Assign and collect homeworks related to attendees businesses. Incentives: - completion certificate from CREA, the Institute for Women of Zacatecas (government agency) and the Autonomous University of Zacatecas - Raffle every week conditional on attendance and completion of homework - Future CREA courses based on regular attendance

11 Course Structure For each one of the six courses, a handout of 30 pages was given. Each handout contained: - The definition of a concept (revenues, total costs, unitary costs, profits). - Importance of understanding a concept. - Examples on how to compute it. - Exercises in class, so that the women compute it by themselves. - Homeworks, on material taught in class, applied to attendees businesses. Attendees have to hand in the homework on the second class of the specific section. Teachers give feedback on the homeworks.

12 Example Suppose that Belen has a store that sells beauty products. She sells makeup, hair products, and products for nails. Below is a list of articles that she sold today: Beauty Items Quantity Item Unit Price Subtotal 3 Nail polish $10 $30 1 Shampoo $30 $30 2 Eyeshadows $20 $40 Total $100 (EXPLANATION) As we can see in this bill of sale, Belen sold 3 nail polish for $10 pesos each (3 x $10), generating a revenue of $30 pesos, 1 anti-dandruff shampoo for $30 pesos (1 x $30) gererating a revenue of $30 pesos, and 2 eye shadows for $20 pesos each (2 x $20) generating a revenue of $40 pesos. In total, Belen had revenues of $100 pesos today. After each example attendees go through a similar problem by themselves

13 Exercise 2: Leticia sells in her grocery store pineapple candy that she produces herself. Leticia needs your help to calculate her revenues from September 17th. Below is a list of products that she sold. Please calculate the revenue for each item and then calculate her total revenues. ABARROTES LETY Ventas del dia Quantity Item Unit Price Subtotal 20 Dulces de pina $ Kilos de tomate $6 10 Kilos de cebolla $5 4 Kilos de naranja $10 Total

14 Sample exercise: Accounting How to determine revenues, costs and profits of their business: - Revenues: List all the products/services you sell and multiply by the price. Add up and calculate total revenues. - Costs: List all the costs that your business incurs. Determine which are fixed costs and variable costs. Calculate your unitary costs. - Profits: Calculate your profits based on the revenues and costs obtained. Class problem: Determine prices - Costs: Calculate the unitary costs. - Competitors: Identify competitors, and their prices. - Consumers: Identify who are their consumers, and how much they are willing to pay for their product. The exercise emphasized the importance of these 3 factors in order to determine prices and not incur in losses.

15 Experimental Intervention Two stage randomization: - village level (hold classes in village or not) - within treated villages (classes offered to some women and not to others). Randomization procedure: multivariate matching algorithm (at both village and within village level) Design allows for identification of spill-over effects ITE s within villages (we also collected socio-economic networks data). Sample of villages includes: - within 2 hour drive of Zacatecas City - have between 100 and 500 women entrepreneurs (identified by 2005 Mexican census). - In practice, this only excludes the smallest hamlets and the 3 largest cities. Zacatecas is high altitude, dry, and agricultural. Most villages are surrounded by farmland. Good road access to villages, but isolated none-the-less.

16 Experimental Intervention Within village: - Women entrepreneurs identified by knowledgeable locals found the diputado or commisiario (mayor-like position), and asked him to get at least 3 knowledgeable women to list all women entrepreneurs - Included in baseline if (i) worked for herself and (ii) sold a good, rather than a service. - The universe of women within chosen villages fitting this category were included in baseline. Baseline survey includes: - 17 villages - about 50 female entrepreneurs per village

17 Introduction Baseline Los Ramírez, Río Grande, México - Google Maps Intervention Empirics Results Experimental villages The placemark has been moved. Undo RSS View in Google Earth Print Send Link Where in the World Game 2010 Google - Map data 2010 Google, INEGI - To see all the details that are visible on the screen, use the "Print" link next to the map.

18 Businesses map within a village

19 Introduction Pics Baseline Intervention Empirics Results

20 Introduction Pics Baseline Intervention Empirics Results

21 Introduction Pics Baseline Intervention Empirics Results

22 Timing Baseline survey: July-August Eligibles invited to classes: early October Classroom sessions: late October to December Follow-up survey: late July to September Attrition: - We originally selected 26 villages (10 treatment). However, for budgetary reasons and given that CREA was unable to implement the classes in all treatment localities - We scaled down the initial selection to 17 villages (7 Treatment and 10 Control) before the intervention - We then invited 25 randomly selected women to attend the classes in those 7 villages - Of the 175 invited to attend, 62 did not show up for class despite our attempts - We asked other participants to nudge them. We also sent someone from the team and extended a second invitation - Overall compliance is about 65%, we look mainly at ITT. We also look at IV to recover the TT, and importantly at the ITE

23 Attendance was recorded and homeworks were required. Outcomes of Interest: we focus on Profits, Revenues, the # of Clients, accounting. We will explore costs, mark-up and so on Inference would be pretty straightforward with a large sample size and many villages. Unfortunately we have neither. We will show standard DID on ITT and TT, and IV. Then Randomization Inference (due to the small number of clusters (17))

24 Intensity of treatment Fraction homeworks attendance dots: handed-in homeworks About 50% of the women attended at least six classes and handed-in at least 6 homeworks

25 Empirics ITT is identified (with no spill-over effects), γ : y it = α + βt i + δpost it + γ(t i POST it ) + ɛ it (1) i is individual, and t time (pre and post intervention). With spill-over within village, γ is biased (downward in this case), we then use between village variation (Hp. no spillover or GE to control villages). TT is identified, γ, by the IV estimate of the effect of attendance where the IV is the invitation T. y it = α + βa i + δpost it + γ(a i POST it ) + ɛ it (2) by instrumental variables. A i equals 1 if the woman attended classes, and zero otherwise, and T i, T i POST it are used as instruments for A i, A i POST it respectively.

26 Balance Table Comparions across treatment groups Business Characteristics P-value, 10 th 90 th ptile Control Treatment Obs. equality ptile Daily profit (21.84) (15.84) Weekly profit (39.56) (45.49) 1[Knows daily profit] (0.03) (0.06) 1[Knows weekly profit] (0.03) (0.06) Daily revenues (24.66) (60.60) Weekly revenues 1, , (101.99) (137.04) Number of daily clients (1.27) (1.92) Total number of workers, including owner (0.04) (0.05) Weekly hours worked by the owner (1.60) (3.13) Age of business, in months (7.73) (10.15) 1[Registered with a government agency] (0.05) (0.05) Replacement value of business capital 8, , (992.20) (1,110.71) 1[Keeps formal business accounts] (0.01) (0.01) Personal Information and Demographics Reservation wage, monthly 2, , (140.51) (78.60) Maximum loan available, if needed 8, , (1,875.50) (1,054.39) Monthly interest rate on a potential loan (0.32) (0.63) Score on math exercise (% correct) (0.03) (0.04) Age (0.53) (0.51) Number of household members (0.08) (0.18) 1[Roof is made of temporary material] (0.05) (0.09) Number of rooms in the house (0.06) (0.11) Notes: Sample includes all women interviewed in the baseline survey. Robust (s.e.) clustered at the village level. All monetary variable are measured in Mexican Pesos (~13 pesos / 1 U.S. dollar). Reservation wage is the minimum stated monthly wage a women would accept in order to quit her business.

27 Eligible: Attendees vs. Non-attendees Business Characteristics Within treatment group P-value on tests of equality of means Not attended Attended Obs. Daily profit (40.52) (28.28) Weekly profit (97.69) (93.98) 1[Knows daily profit] (0.09) (0.04) 1[Knows weekly profit] (0.09) (0.04) Daily reveune (220.31) (69.98) Weekly revenue 1, , (436.57) (152.35) Number of daily clients (3.10) (1.92) Total number of workers, including owner (0.13) (0.06) Weekly hours worked by the owner (4.28) (3.98) Age of business, in months (19.51) (7.76) 1[Registered with a government agency] (0.09) (0.04) Replacement value of business capital 8, , (1,914.16) (1,339.85) 1[Keeps formal business accounts] (0.02) (0.01) Personal Information and Demographics Resrevation wage, monthly 2, , (255.61) (143.87) Maximum loan available, if needed 8, , (1,755.28) (1,572.24) Monthly interest rate on a potential loan (1.10) (0.63) Score on math exercise (% correct) (0.06) (0.05) Age (1.75) (0.94) Number of household members (0.21) (0.25) 1[Roof is made of temporary material] (0.07) (0.11) Number of rooms in the house (0.27) (0.13) Notes: Sample includes all non-attrited women that were assigned to the treatment group and who continue to run their business one year after the baseline survey. Robust (s.e.) clustered at the village level. All monetary variable are measured in Mexican Pesos (~13 pesos / 1 U.S. dollar). Reservation wage is the minimum stated monthly wage a women would accept in order to quit her business.

28 Results Outcome Intent to Treat effect Treatment on the Treated effect Observations (1) (2) Log(Daily profits) ,245 s.e. (0.269) (0.379) 90% c.i. [ ] [ ] Log(Weekly profits) ,197 s.e. (0.344) (0.460) 90% c.i. [ ] [ ] 80% c.i. { } { } Log(Weekly revenues) 0.422*** 0.610** 1,283 s.e. (0.125) (0.208) 90% c.i. [ ] [ ] Log(Number of daily clients) 0.276* 0.351* 1,079 s.e. (0.147) (0.179) 90% c.i. [ ] [ ] 80% c.i. { } { } 1[Keeps formal business accounts] ,528 s.e. (0.015) (0.021) 80% c.i. { } { } Notes: Intent to Treat effect is estimated using a differences-in-differences model. Treatment on the Treated effect is estimated using the offer of classes as an instrument for class attendance. Robust (s.e.) clustered at the village level. ***p<0.01, ** p<0.05, * p<0.1. Permutation based confidence intervals between [ ], { }.

29 Results: between village variation Outcome Intent to Treat effect Treatment on the Treated effect Observations (1) (2) Log(Daily profits) 0.531* 0.717* 1,165 s.e. (0.274) (0.403) 90% c.i. [ ] [ ] Log(Weekly profits) ,119 s.e. (0.322) (0.443) 80% c.i. { } { } Log(Weekly revenues) 0.517*** 0.761*** 1,208 s.e. (0.140) (0.234) 90% c.i. [ ] [ ] Log(Number of daily clients) ,040 s.e. (0.168) (0.233) 80% c.i. { } { } 1[Keeps formal business accounts] ,387 s.e. (0.016) (0.021) 80% c.i. { } { } Notes: Intent to Treat effect is estimated using a differences-in-differences model. Treatment on the Treated effect is estimated using the offer of classes as an instrument for class attendance. Robust (s.e.) clustered at the village level. ***p<0.01, ** p<0.05, * p<0.1. Permutation based confidence intervals between [ ], { }.

30 Conclusions We find large, positive and significant effects of the business classes on: Revenues, # of clients and profits. Also on formal accounting This are short-run results, about 7-8 months after the intervention Also quite large and mostly positive ITE s (negative for clients). Important for the evaluation and the intervention. Results on revenues, costs and profits are confirmed if we compute them or simply use the reported measures We are now investigating the mechanisms, including for the ITE s. Why such big improvements? What in particular is effective?

31 1[Quit business post-treatment] Results: ITE s Outcome Indirect Treatment Effect Observations Log(Daily profits) ,182 (0.204) Log(Weekly profits) 0.685* 1,137 (0.343) 1[Knows daily profits] ,302 (0.037) 1[Knows weekly profits] ,291 (0.057) Log(Daily revenues) ,176 (0.140) Log(Weekly revenues) 0.449** 1,218 (0.180) Log(Number of daily clients) ,046 (0.200) 1[Keeps formal business accounts] ,411 (0.022) Score on math exercise (% correct) ,374 (0.053)