BUSINESS LITERACY AND DEVELOPMENT: EVIDENCE FROM …siteresources.worldbank.org/INTFR/Resources/Cunha.pdfBUSINESS LITERACY AND DEVELOPMENT: EVIDENCE FROM A RANDOMIZED TRIAL IN RURAL

Introduction Baseline Intervention Empirics Results

BUSINESS LITERACY AND DEVELOPMENT:EVIDENCE FROM A RANDOMIZED TRIAL IN RURAL

MEXICO

Gabriela Calderon 1 Jesse Cunha2

Giacomo De Giorgi3

1Stanford University 2NPS3Stanford University and NBER

March, 2011

World Bank


Motivation

I Self employment is prevalent in the developing world

I In 2002, the % of self-employed non-agricultural workers in, above 60%on average in sub-Saharan Africa, the Middle East, North Africa, LatinAmerica, and Asia

I Small businesses are often badly managed and rarely have any formalaccounting. In particular low returns for females

I As a consequence small businesses might be unprofitable and destinedto fail

I Do those businesses lack managerial capital? Most of the discussionhas focused on credit


Introduction

I In early 2008 we had the opportunity to partner with a newly foundedNGO: CREA

I Previous designs had the provision of classes attached to microfinanceoperations. This makes it harder to identify the parameter of interest

I The two-stages randomization helps us identifying ITE which might belarge and important

I Provision of business classes in late 2009 for 6 weeks

I Baseline data summer 2009, follow-up late summer/fall of 2010

I Let me here reiterate that this is very much work in progress


Preview of the Results

I We find large, positive and “significant” effects of the business classeson:

I Revenues, # of clients and profits. Also on formal accounting

I This are short-run results, about 7-8 months after the intervention

I Also quite large and mostly positive ITE ′s. Important for the evaluationand the intervention.

I Results on revenues, costs and profits are confirmed if we computethem or simply use the reported measures


Related Literature

I De Mel, McKenzie and Woodruf (2008), provided capital (RCT design) toentrepreneurs. They find very low return on capital for females

I Karlan and Valdivia (2008), business training added to microfinance program inPeru. 30 to 60 minute class during the repayment meetings (supposed to last 22weeks). Effects on revenues and business knowledge

I Field, Jayachandran and Pande (2010, AER P&P): cultural (norms) barrier tohigh returns. Sample from microfinance. Find support from norms limitingreturns.

I 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. Informationalbarriers seem the issue.

I Drexler, Fisher and Schoar (2010): micro-entrepreneurs in the DominicanRepublic (linked to microfinance). 2 treatment arms: (i) rule-of-thumb (separatebusiness from personal accounts) produced positive effects on accountingpractices. (ii) Basic accounting, no effects. Both have no effects on profitabilityand investment behavior. Use of Accounting Practices at Baseline


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Accounting Practices at Baseline


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clothinggeneral grocery, other resale

handicraftsprepared food

ready-to-eat-foodstationery & cosmetics

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CREA (www.creoencrea.org)

I We are involved with the Business Skills Course (6 topics, 2 classes pertopic):

- Simple business accounting

- How to set prices

- Taxes and legal issues

- Business organization and choice of products

- Marketing

- Sales


Classes

I Free (no tuition). 6 weeks, 4-hour classes per week. about 25 womenper class, 2 teachers per class (Professors, Graduate Students, andUndergraduates). Emphasize practical examples.

I Assign and collect homeworks related to attendees’ businesses.

I 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


Course Structure

I For each one of the six courses, a handout of 30 pages was given.

I 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.

I Attendees have to hand in the homework on the second class of thespecific section.

I Teachers give feedback on the homeworks.


Example

I 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 soldtoday:

Beauty ItemsQuantity Item Unit Price Subtotal3 Nail polish $10 $301 Shampoo $30 $302 Eyeshadows $20 $40

Total $100

I (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-dandruffshampoo for $30 pesos (1 x $30) gererating a revenue of $30 pesos, and 2 eyeshadows for $20 pesos each (2 x $20) generating a revenue of $40 pesos. Intotal, Belen had revenues of $100 pesos today.

I After each example attendees go through a similar problem by themselves


I Exercise 2: Leticia sells in her grocery store pineapple candy that sheproduces herself. Leticia needs your help to calculate her revenuesfrom September 17th. Below is a list of products that she sold. Pleasecalculate the revenue for each item and then calculate her totalrevenues.

ABARROTES LETYVentas del dia

Quantity Item Unit Price Subtotal20 Dulces de pina $3.505 Kilos de tomate $610 Kilos de cebolla $54 Kilos de naranja $10

Total


Sample exercise: Accounting

I How to determine revenues, costs and profits of theirbusiness:

- 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 arefixed costs and variable costs. Calculate your unitary costs.

- Profits: Calculate your profits based on the revenues and costs obtained.

I 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 arewilling to pay for their product.

I The exercise emphasized the importance of these 3factors in order to determine prices and not incur in losses.


Experimental Intervention

I Two stage randomization:- village level (hold classes in village or not)

- within treated villages (classes offered to some women and not to others).

I Randomization procedure: multivariate matching algorithm (at bothvillage and within village level)

I Design allows for identification of spill-over effects ITE ′s within villages(we also collected socio-economic networks data).

I Sample of villages includes:- within 2 hour drive of Zacatecas City

- have between 100 and 500 women entrepreneurs (identified by 2005Mexican census).

- In practice, this only excludes the smallest hamlets and the 3 largest cities.

I Zacatecas is high altitude, dry, and agricultural. Most villages aresurrounded by farmland. Good road access to villages, but isolatednone-the-less.


Experimental Intervention

I Within village:

- Women entrepreneurs identified by knowledgeable locals found the“diputado” or “commisiario” (mayor-like position), and asked him to get atleast 3 knowledgeable women to list all women entrepreneurs

- Included in baseline if (i) worked for herself and (ii) sold a good, ratherthan a service.

- The universe of women within chosen villages fitting this category wereincluded in baseline.

I Baseline survey includes:- 17 villages

- about 50 female entrepreneurs per village


Experimental villages

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Businesses map within a village


Pics


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Timing

I Baseline survey: July-August 2009. Eligibles invited to classes: earlyOctober 2009.

I Classroom sessions: late October to December 2009.

I Follow-up survey: late July to September 2010.

I Attrition:- We originally selected 26 villages (10 treatment). However, for budgetary

reasons and given that CREA was unable to implement the classes in alltreatment localities

- We scaled down the initial selection to 17 villages (7 Treatment and 10Control) before the intervention

- We then invited 25 randomly selected women to attend the classes inthose 7 villages

- Of the 175 invited to attend, 62 did not show up for class despite ourattempts

- We asked other participants to nudge them. We also sent someone fromthe team and extended a second invitation

- Overall compliance is about 65%, we look mainly at ITT . We also look atIV to recover the TT , and importantly at the ITE


I Attendance was recorded and homeworks were required.

I Outcomes of Interest: we focus on Profits, Revenues, the # of Clients,accounting. We will explore costs, mark-up and so on

I Inference would be pretty straightforward with a large sample size andmany villages. Unfortunately we have neither.

I We will show standard DID on ITT and TT , and IV.

I Then Randomization Inference (due to the small number of clusters(17))


Intensity of treatment

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1 3 5 7 9 11 13attendance

dots: handed-in homeworks

I About 50% of the women attended at least six classes and handed-in at least 6homeworks


Empirics

I ITT is identified (with no spill-over effects), γ :

yit = α+ βTi + δPOST it + γ(Ti ∗ POSTit) + εit (1)

i is individual, and t time (pre and post intervention).

I With spill-over within village, γ is biased (downward in this case), wethen use between village variation (Hp. no spillover or GE to controlvillages).

I TT is identified, γ, by the IV estimate of the effect of attendance wherethe IV is the invitation T.

yit = α+ βAi + δPOST it + γ(Ai ∗ POSTit) + εit (2)

by instrumental variables. Ai equals 1 if the woman attended classes,and zero otherwise, and Ti ,Ti ∗ POSTit are used as instruments forAi ,Ai ∗ POSTit respectively.


Balance Table

Control Treatment Obs.P-value, equality

10th ptile

90th ptile

Business CharacteristicsDaily profit 155.86 132.55 798 0.38 10 300

(21.84) (15.84)

Weekly profit 489.28 470.40 768 0.74 30 1200(39.56) (45.49)

1[Knows daily profit] 0.91 0.87 889 0.51 1 1(0.03) (0.06)

1[Knows weekly profit] 0.89 0.84 884 0.37 0 1(0.03) (0.06)

Daily revenues 394.58 460.77 881 0.36 40 950(24.66) (60.60)

Weekly revenues 1,345.82 1,387.36 850 0.81 120 3600(101.99) (137.04)

Number of daily clients 17.70 16.87 844 0.74 3 35(1.27) (1.92)

Total number of workers, including owner 1.64 1.60 906 0.52 1 3(0.04) (0.05)

Weekly hours worked by the owner 38.97 38.98 905 1 6 84(1.60) (3.13)

Age of business, in months 90.32 82.20 915 0.48 4 240(7.73) (10.15)

1[Registered with a government agency] 0.35 0.22 659 0.06 0 1(0.05) (0.05)

Replacement value of business capital 8,991.06 7,897.01 916 0.36 0 18900(992.20) (1,110.71)

1[Keeps formal business accounts] 0.03 0.01 916 0.08 0 0(0.01) (0.01)

Personal Information and DemographicsReservation wage, monthly 2,970.34 2,936.63 732 0.81 1000 5000

(140.51) (78.60)

Maximum loan available, if needed 8,737.11 8,503.03 725 0.91 500 20000(1,875.50) (1,054.39)

Monthly interest rate on a potential loan 6.41 5.42 527 0.16 0.08 10(0.32) (0.63)

Score on math exercise (% correct) 0.47 0.40 904 0.13 0 0.75(0.03) (0.04)

Age 45.68 46.10 909 0.56 28 65(0.53) (0.51)

Number of household members 4.31 4.37 911 0.76 2 7(0.08) (0.18)

1[Roof is made of temporary material] 0.31 0.31 885 0.99 0 1(0.05) (0.09)

Number of rooms in the house 2.91 2.91 882 0.99 2 4(0.06) (0.11)

Comparions across treatment groups

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.

Giacomo DeGiorgi

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Giacomo DeGiorgi

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Giacomo DeGiorgi

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Eligible: Attendees vs. Non-attendees

Not attended Attended Obs.Business Characteristics

Daily profit 176.12 110.31 148 0.32(40.52) (28.28)

Weekly profit 603.59 403.81 138 0.29(97.69) (93.98)

1[Knows daily profit] 0.82 0.90 171 0.20(0.09) (0.04)

1[Knows weekly profit] 0.80 0.86 170 0.26(0.09) (0.04)

Daily reveune 654.26 355.88 165 0.30(220.31) (69.98)

Weekly revenue 1,871.70 1,126.56 160 0.18(436.57) (152.35)

Number of daily clients 17.67 16.42 159 0.67(3.10) (1.92)

Total number of workers, including owner 1.53 1.64 166 0.46(0.13) (0.06)

Weekly hours worked by the owner 41.43 37.59 169 0.48(4.28) (3.98)

Age of business, in months 85.42 80.38 171 0.77(19.51) (7.76)

1[Registered with a government agency] 0.18 0.24 136 0.51(0.09) (0.04)

Replacement value of business capital 8,897.66 7,327.83 171 0.50(1,914.16) (1,339.85)

1[Keeps formal business accounts] 0.02 0.01 171 0.75(0.02) (0.01)

Personal Information and DemographicsResrevation wage, monthly 2,732.84 3,035.16 135 0.43

(255.61) (143.87)

Maximum loan available, if needed 8,637.78 8,436.40 136 0.94(1,755.28) (1,572.24)

Monthly interest rate on a potential loan 4.24 5.93 103 0.14(1.10) (0.63)

Score on math exercise (% correct) 0.40 0.39 171 0.89(0.06) (0.05)

Age 44.18 47.17 170 0.27(1.75) (0.94)

Number of household members 4.10 4.52 170 0.24(0.21) (0.25)

1[Roof is made of temporary material] 0.20 0.37 167 0.02(0.07) (0.11)

Number of rooms in the house 3.33 2.69 167 0.11(0.27) (0.13)

Within treatment group P-value on tests of equality of means

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.

Giacomo DeGiorgi

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Giacomo DeGiorgi

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Giacomo DeGiorgi

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Results

Intent to Treat effect

Treatment on the Treated effect Observations

(1) (2)Outcome

Log(Daily profits) 0.450 0.590 1,245s.e. (0.269) (0.379)

90% c.i. [-.429 .425] [-.612 .556]

Log(Weekly profits) 0.086 0.115 1,197s.e. (0.344) (0.460)

90% c.i. [-.509 .514] [-.696 .680]

80% c.i. {-.403 .403} {-.521 .536}

Log(Weekly revenues) 0.422*** 0.610** 1,283s.e. (0.125) (0.208)

90% c.i. [-.358 .369] [-.748 .556]

Log(Number of daily clients) 0.276* 0.351* 1,079s.e. (0.147) (0.179)

90% c.i. [-.329 .314] [-.542 .471]

80% c.i. {-.248 .250} {-.414 .373}

1[Keeps formal business accounts] 0.019 0.031 1,528s.e. (0.015) (0.021)

80% c.i. {-.022 .021} {-.026 .030}

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 [ ], { }.


Results: between village variation

Intent to Treat effect

Treatment on the Treated effect Observations

(1) (2)Outcome

Log(Daily profits) 0.531* 0.717* 1,165s.e. (0.274) (0.403)

90% c.i. [-.531 .463] [-.693 .615]

Log(Weekly profits) 0.173 0.252 1,119s.e. (0.322) (0.443)

80% c.i. {-.438 .424} {-.619 .574}

Log(Weekly revenues) 0.517*** 0.761*** 1,208s.e. (0.140) (0.234)

90% c.i. [-.393 .403] [-.693 .682]

Log(Number of daily clients) 0.242 0.330 1,040s.e. (0.168) (0.233)

80% c.i. {-.279 .285} {-.439 .457}

1[Keeps formal business accounts] 0.024 0.037 1,387s.e. (0.016) (0.021)

80% c.i. {-.033 .031} {-.044 .044}

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 [ ], { }.


Conclusions

I We find large, positive and “significant” effects of the business classeson:

I Revenues, # of clients and profits. Also on formal accounting

I This are short-run results, about 7-8 months after the intervention

I Also quite large and mostly positive ITE ′s (negative for clients).Important for the evaluation and the intervention.

I Results on revenues, costs and profits are confirmed if we computethem or simply use the reported measures

I We are now investigating the mechanisms, including for the ITE ′s. Whysuch big improvements? What in particular is effective?


Results: ITE ′s

Indirect Treatment

Effect ObservationsOutcome

Log(Daily profits) 0.201 1,182(0.204)

Log(Weekly profits) 0.685* 1,137(0.343)

1[Knows daily profits] -0.045 1,302(0.037)

1[Knows weekly profits] 0.018 1,291(0.057)

Log(Daily revenues) -0.182 1,176(0.140)

Log(Weekly revenues) 0.449** 1,218(0.180)

Log(Number of daily clients) -0.211 1,046(0.200)

1[Keeps formal business accounts] 0.031 1,411(0.022)

Score on math exercise (% correct) 0.001 1,374(0.053)

1[Quit business post-treatment] -0.023 610-0.037

Notes:Indirect treatment effect is estimated using a differences-in-differences model.Robust (s.e.) clustered at the village level. ***p<0.01, ** p<0.05, * p<0.1

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BUSINESS LITERACY AND DEVELOPMENT: EVIDENCE FROM …siteresources.worldbank.org/INTFR/Resources/Cunha.pdfBUSINESS LITERACY AND DEVELOPMENT: EVIDENCE FROM A RANDOMIZED TRIAL IN RURAL