12.06.2012 - giacomo de giorgi

Introduction Baseline Intervention Data Empirics Results ITE′s Results

BUSINESS LITERACY AND DEVELOPMENT:EVIDENCE FROM A RANDOMIZED CONTROLLED

TRIAL IN RURAL MEXICO

Gabriela Calderon 1 Jesse Cunha2

Giacomo De Giorgi3

1Stanford 2NPS and UC-Santa Cruz3Stanford and Columbia, BREAD, CEPR and NBER

December, 2012


Motivation

I Self employment is prevalent in the developing world

I In 2002, the % of self-employed non-agricultural workers is above 60% insub-Saharan Africa, the Middle East, North Africa, Latin America, and Asia

I Small businesses are often badly managed and rarely have any formalaccounting [DeMel et al., 2008 and 2009; Karlan and Valdivia, 2011;Bloom et al., 2012]. In particular low returns to capital for females[DeMel et al., 2008]

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 [Morduch and Karlan, 2009], also mis-allocationof capital [Banerjee and Duflo, 2012]


Introduction

I In early 2008 we designed a business literacy intervention inpartnership with a newly founded Mexican NGO: CREA

I Previous designs had the provision of business training attached tomicrofinance operations [Field et al., 2010; Karlan and Valdivia, 2011;Drexler et al., 2011]. This makes it harder to identify the parameter ofinterest [DeMel et al., 2012]. Bloom et al. (2012) is an exception onmanagerial capital in large Indian firms. See MacKenzie and Woodruff(2012) for a survey.

I We use a two-stages randomization which helps us identifying ITE ′s[Miguel and Kremer, 2004; Angelucci and De Giorgi, 2009] and dealwith GE effects

I Provision of business classes in late 2009 for 6 weeks (48 hours in total)

I Baseline data summer 2009, first follow-up late summer/fall of 2010,Second follow-up in the spring of 2012


Preview of the Results

I We find large, positive and significant effects on:

I Profits, Revenues, and # of clients. Importantly on formal accounting

I These results are confirmed both in the short and medium run, i.e. 6and 30 months after the intervention

I Also some evidence of ITE ′s. Important for policy and the evaluationand the intervention.

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

I Better accounting, lower costs, higher mark-up and to some extentlower prices seem part of the picture

I Death of the (far) left-tail (for the “treated”)


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). Some effects on revenues and business knowledge

I Field et al. (2010): cultural (norms) barrier to high returns. Sample frommicrofinance.

I Bloom et al. (2012): large textile firms (300+) in India, management consulting atthe plant level. Improvement in productivity, decentralization of decisions, use ofcomputers for data recording. Informational barriers seem the issue.

I Drexler et al. (2010): micro-entrepreneurs in the Dominican Republic (linked tomicrofinance). 2 treatment arms: (i) rule-of-thumb (separate business frompersonal accounts) produced positive effects on accounting practices. (ii) Basicaccounting, no effects. Both have no effects on profitability and investmentbehavior.

I Public good providers McKenzie and Woodruff (2012) have a detailed survey ofthe papers out there


Accounting0

.2.4

.6.8

Fra

ctio

n

Personal NotesFormal Accounting

NoneOther

accounting

Accounting Practices at Baseline


Sectors0

1020

3040

Per

cent

Ready-to-eat foodGeneral grocery

Prepared foodHandicrafts

ClothingOther resale

sector


Size0

2040

60P

erce

nt

02

46

810

1214

size


CREA (www.crea.org.mx)

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, two 4-hour classes per week. about 25women per class, 2 teachers per class (professors, graduate andundergraduates students).

I Strong emphasis on practical examples related to own business.

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 circulated

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.


Comparison with Other Studies

29

Table 3: Key Characteristics of Training Delivery

Training content Course Length Participant Actual Attendance

Study Training Provider new or established? (hours) Cost (USD) Cost (USD) Rate

Berge et al. (2012) Training professionals New 15.75 0 $70 83%

Bruhn and Zia (2012) Training organization New 6 0 $245 39%

Calderon et al. (2012) Professors & Students New 48 0 n.r. 65%

De Mel et al. (2012) Training organization Established (ILO) 49-63 0 $126-140 70-71%

Drexler et al. (2012)

"Standard" Local instructors New 18 0 or $6 $21 50%

"Rule-of-thumb" Local instructors New 15 0 or $6 $21 48%

Field et al. (2010) Microfinance credit officers New (a) 2 days 0 $3 71%

Giné and Mansuri (2011) Microfinance credit officers New (b) 46 0 n.r. 50%

Glaub et al. (2012) Professor New 3 days 0 $60 84%

Karlan and Valdivia (2011) Microfinance credit officers Established (FFH) 8.5-22 (c) 0 n.r. 76-88%

Klinger and Schündeln (2011) Training professionals Established (Empretec) 7 days 0 n.r. n.r.

Mano et al. (2012) Local instructors New (d) 37.5 0 $740 87%

Premand et al. (2012) Govt. office staff New 20 days + 0 n.r. 59-67%

Sonobe et al. (2011)

Tanzania Training professionals New (d) 20 days 0 >$400 92%

Ethiopia Training professionals New (d) 15 days 0 75%

Vietnam - Steel Training professionals New (d) 15 days 0 39%

Vietnam - Knitwear Training professionals New (d) 15 days 0 59%

Valdivia (2012) Training professionals New 108 (e) 0 $337 (f) 51%

Notes:

(a) Shortened version of existing program + new content on aspirations added.

(b) Adapted from ILO's Know About Business modules

(c) Training sessions were each 30 minutes to 1 hours, and up to 22 sessions occurred, but only half had done 17 sessions over 24 months.

(d) Based in part on ILO content + Japanese Kaizen content.

(e) Although only 42% of those attending completed at least 20/36 sessions, and only 28% attended 30 sessions or more.

(f) The basic training cost $337, while the technical assistance plus basic training cost $674.

FFH denotes Freedom from Hunger; ILO denotes the International Labor Organization.


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


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.


Class Exercise

Section 10 Exercise

Suppose the first week you sell 1 tableclothThe second week you sell 2 tableclothsThe third week you sell 2 tableclothsand the fourth week you sell 5 tablecloths

b) What is your income for this month?

a) How many tablecloths do you have left over at the end of the month?

d) If your profits were to be zero for this month, what price should you have set for your tablecloths?

c) How much are your profits at the end of the month? That is, how much money do you earn this month?

Part 2: Each week, you spend 5 pesos for cloth and 5 pesos in salaries in order to make tablecloths. Each month has 4 weeks.

Now we are going to do an exercise, but I want to let you know that the numbers are invented, as is the example. If you have any questions, please ask me.

Part 1: Imagine that you produce 5 tablecloths every week and that each tablecloth costs 10 pesos.

If they do no answer of don't want to answer, STOP, and leave the other parts blank.


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), and a “cleaner”policy evaluation since treated and controls operate in the samemarketplace.

I Sample of villages includes:- within 2 hours 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

To see all the details that are visible on the screen, use the "Print" link next to the map.

©2010 Google - Map data ©2010 Google, INEGI -

RSS View in Google Earth Print Send Link Where in the World Game

The placemark has been moved. Undo

Los Ramírez, Río Grande, México - Google Maps http://maps.google.com/

1 of 2 11/8/2010 4:29 PM


Businesses map within a village


Entrepreneurs (1)


Entrepreneurs (2)


Entrepreneurs (3)


Down scaling

- We originally selected 25 villages (10 treatment). However, forbudgetary reasons and given that CREA was unable to implement theclasses in all treatment 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


Timing

I Baseline survey: July-August 2009. Entrepreneurs invited to classes:early October 2009.

I Classroom sessions: late October to December 2009.

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

I Second Follow-up survey: late March to May 2012.

- 65% take-up rate for the classes (107 over 164 invitees). We focus onITT ′s

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


I Attendance was recorded and homeworks were required.

I Outcomes of Interest: Profits, revenues, the # of clients, accountingpractices, costs, mark-up, employment

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

I Then wild bootstrap (Cameron et al., 2008) and RandomizationInference (due to the small number of clusters (17)).

I Also attrition...bounding exercises and rematch.


Intensity of treatment

05

1015

hom

ewor

ks

0.0

5.1

.15

Den

sity

1 13attendance

Density homeworks

®

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


Balance Table

Treatment Control

(1)=(2)

p‐value

10th

percentile

90th

percentile N

(1) (2) (3) (4) (5) (6)

Business Characteristics

Daily profits 132.24 154.92 0.42 10.00 300.00 760(16.06) (22.61)

Weekly profits 364.39 400.79 0.36 25.00 1000.00 731(26.28) (33.71)

Knows daily profits 0.87 0.91 0.48 1.00 1.00 848(0.06) (0.03)

Knows weekly profits 0.83 0.89 0.34 0.00 1.00 843(0.06) (0.03)

Daily revenue 456.16 405.96 0.90 1.00 0.00 874(55.18) (35.89)

Weekly revenue 1,311.55 1,291.49 0.81 0.00 84.00 628(110.95) (95.53)

Number of daily clients 16.99 17.61 0.22 6.00 240.00 875(2.04) (1.33)

Total number of workers, including owner 1.58 1.65 0.22 1.00 3.00 865(0.05) (0.04)

Keeps formal business accounts 0.01 0.04 0.09 0.00 0.00 873(0.01) (0.01)

Weekly hours worked by the owner 39.43 39.19 0.95 6.00 84.00 866(3.19) (1.65)

Age of business (months) 81.24 91.47 0.37 4.00 240.00 874(10.10) (7.75)

Replacement value of business capital 8,062.61 9,238.82 0.30 0.00 12200.00 875(1,009.51) (1,023.20)

Personal Demographics

Reservation wage, monthly 2,986.29 2,974.28 0.94 1000.00 5000.00 696(92.06) (140.90)

Maximum loan available if needed 8,703.94 9,016.38 0.89 500.00 20000.00 689(1,079.86) (1,951.88)

Monthly interest rate on a potential loan 5.48 6.43 0.17 0.08 10.00 506(0.62) (0.32)

Score on math exercise (percent correct) 0.39 0.47 0.10 0.00 0.75 864(0.04) (0.03)

Years of education 5.96 6.07 0.72 2.00 9.00 846(0.32) (0.13)

Age 46.04 45.67 0.60 28.00 65.00 869(0.48) (0.53)

Roof is made of temporary material 0.33 0.32 0.92 0.00 1.00 844(0.09) (0.05)

Entire sample

Notes:

(1) Sample includes all women interviewed in the baseline survey.

(2) Robust (s.e.) clustered at the village level.

(3) All monetary variable are measured in Mexican Pesos (~13 pesos / 1 U.S. dollar).

(4) Reservation wage is the minimum stated monthly wage a women would accept in order to quit her business.


Eligible: Attendees vs. Non-attendees

Attended

Did not

attend

(1)=(2)

p‐value

10th

percentile

90th

percentile N

(1) (2) (3) (4) (5) (6)


Daily profits 110.83 177.91 0.34 10.00 300.00 141(28.90) (43.62)

Weekly profits 286.11 536.22 0.13 25.00 1000.00 131(44.80) (107.25)



Daily revenue 337.85 690.53 0.18 1.00 0.00 164(75.24) (243.80)

Weekly revenue 1,018.70 1,897.25 0.74 0.00 84.00 130(173.77) (462.05)













Age 46.98 44.25 0.32 28.00 65.00 163(0.91) (1.80)


Among those assigned to treatment

Notes:






Attrition

Attrited Present

(1)=(2)

p‐value

10th

percentile

90th

percentile N

(1) (2) (3) (4) (5) (6)


Daily profits 125.38 155.65 0.23 10.00 300.00 760(15.47) (21.78)

Weekly profits 441.46 384.72 0.40 25.00 1000.00 731(66.29) (28.59)



Daily revenue 359.27 426.92 0.32 1.00 0.00 874(29.59) (35.86)

Weekly revenue 1,532.55 1,246.13 0.81 0.00 84.00 628(265.15) (71.28)













Age 45.41 45.81 0.75 28.00 65.00 869(1.29) (0.41)


Attrition in 2nd Follow‐up

Notes:






Quitting

Quit No Quit

(1)=(2)

p‐value

10th

percentile

90th

percentile N

(1) (2) (3) (4) (5) (6)


Daily profits 125.48 185.82 0.14 10.00 300.00 636(12.64) (39.23)

Weekly profits 335.44 433.36 0.09 25.00 1000.00 608(34.57) (43.78)



Daily reveune 390.83 462.28 0.07 1.00 0.00 727(49.98) (54.37)

Weekly revenue 1,073.08 1,418.66 0.94 0.00 84.00 518(86.81) (137.43)













Age 44.36 47.22 0.00 28.00 65.00 723(0.66) (0.53)


Quit Business in 2nd Follow‐up

Notes:






Summary

I A sobering picture emerges: i. micro firms (1.6 size), ii. low profits (30USD perweek), iii. low schooling (6 years), iv. basically no formal accounting, v. unable todo simple math and “optimal” pricing calculations

I Invitees and controls are not statistically different, but...

I Here attrition is defined as people who dropped from the sample and we couldn’tre-interview (∼ 15% at first follow-up). Smaller for the treated (13%). Virtually allof the attrited women either moved out of the village or were not available on theday of the interview. A further 10% attrition by the 2nd follow-up

I Amongst the non-attriters, 21% of the sample quit their business by the firstfollow-up, and 50% had quit by the second follow-up.

I Quit rates are indistinguishable across treatment groups in both the firstfollow-up (p-value=0.70) as well as at the second follow-up (p-value=0.47).

I Not surprisingly, quitters have lower profits and revenues at baseline thannon-quitters, are about three years younger, and are poorer in terms of the oneindicator of wealth that we observe (the roof of their house is made out of atemporary material).


Empirics

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

yit = α+ βTi +3∑

t=2

δtWAVEt +3∑

t=2

γt(Ti ∗ WAVEt) + εit (1)

yit = α+ βTi + δPOST t + γ(Ti ∗ POSTt) + εit (2)

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

I With spill-over within village, γ is biased (we can estimate this), we thenuse between village variation (Hp. no spillover or GE to control villages).

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 (3)

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.

I Focus on between village variation, controlling for baseline X ′s(precision)


Results

Table : Main results between village

ITT TTE ITT TTE ITT TTE ITT TTE ITT TTE ITT TTE

Outcome =(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Treatment -0.075 -0.040 -0.102 -0.145 -0.005 0.018 0.005 0.008 -0.016 -0.009 -0.010 -0.001(0.115) (0.206) (0.117) (0.174) (0.138) (0.221) (0.095) (0.173) (0.118) (0.181) (0.085) (0.139)

Post 0.057 0.082 0.255 0.288 0.026 0.061 0.062 0.085 -0.062 -0.056 -0.051 -0.027(0.075) (0.071) (0.092)** (0.087)*** (0.065) (0.070) (0.115) (0.120) (0.053) (0.055) (0.030) (0.031)

Treatment*Post 0.390 0.423 0.295 0.337 0.172 0.211 0.304 0.436 0.161 0.193 0.166 0.202(0.097)*** (0.143)*** (0.113)** (0.158)** (0.127) (0.180) (0.127)** (0.199)** (0.144) (0.208) (0.063)** (0.091)**

p-values wild boot (0.001) (0.019) (0.195) (0.029) (0.279) (0.019)lower bound 0.131 0.118 0.148 0.171 0.089 0.096 0.144 0.172 0.062 0.093 0.091 0.111

Observations 1,411 1,411 1,324 1,324 1,515 1,515 1,469 1,469 1,355 1,355 1,795 1,795

ln(Daily Clients) ln(Standardized)

Notes: ***p<0.01, ** p<0.05, * p<0.1. (1) Robust (s.e.) clustered at the village level. P-values of wild bootstrap to account for small number of village clusters (Cameron, Gelbach and Miller, 2008). Lower bounds estimated using a matching procedure described in the text. In all regressions we control for baseline: size, sector, business age, replacement value, lack of business skills, risk aversion, age, education, # of rooms, exercise score, and dummy indicators for missing controls.

ln(Daily Profits) ln(Weekly Profits) ln(Daily revenues) ln(Weekly revenues)


Results (without X ′s)

Table : Main results between village


Outcome =(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Treatment -0.039 -0.058 -0.131 -0.191 -0.034 -0.051 -0.082 -0.123 -0.070 -0.106 -0.034 -0.051(0.143) (0.213) (0.143) (0.206) (0.166) (0.249) (0.145) (0.220) (0.154) (0.236) (0.112) (0.171)

Post 0.056 0.056 0.294 0.294 0.090 0.090 0.105 0.105 -0.057 -0.057 -0.026 -0.026(0.076) (0.076) (0.082)***(0.082)*** (0.074) (0.074) (0.119) (0.119) (0.073) (0.073) (0.033) (0.033)

Treatment*Post 0.297 0.397 0.219 0.307 0.118 0.169 0.273 0.387 0.172 0.240 0.140 0.195(0.104)** (0.153)** (0.125)* (0.176) (0.131) (0.194) (0.144)* (0.212)* (0.170) (0.247) (0.072)* (0.112)


Observations 1,114 1,114 1,051 1,051 1,164 1,164 1,163 1,163 1,063 1,063 1,389 1,389

ln(Daily Clients) ln(Standardized)

Notes: ***p<0.01, ** p<0.05, * p<0.1. (1) Robust (s.e.) clustered at the village level.

ln(Daily Profits) ln(Weekly Profits) ln(Daily revenues)ln(Weekly revenues)


A look at the mechanisms, accounting

ITT TTE ITT TTE ITT TTE ITT TTE ITT TTE ITT TTE ITT TTE

Outcome =(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

Treatment -0.024 -0.034 -0.083 -0.101 -0.066 0.001 -0.047 -0.079 0.045 0.054 0.078 0.088 -0.037 -0.063(0.016) (0.023) (0.138) (0.217) (0.157) (0.256) (0.063) (0.098) (0.090) (0.162) (0.100) (0.163) (0.092) (0.140)

Post 0.014 0.016 -0.119 -0.070 -0.488 -0.440 0.143 0.133 0.076 0.087 0.105 0.097 -0.119 -0.112(0.009) (0.009)* (0.141) (0.154) (0.153)*** (0.167)** (0.067)** (0.066)* (0.045) (0.044)* (0.110) (0.113) (0.042)** (0.042)**

Treatment*Post 0.046 0.064 0.373 0.426 0.647 0.867 0.138 0.195 -0.030 -0.045 -0.329 -0.456 0.162 0.224(0.022)** (0.030)** (0.269) (0.361) (0.279)** (0.392)** (0.098) (0.140) (0.099) (0.128) (0.132)** (0.173)** (0.076)** (0.109)*

p-values wild boot (0.049) (0.186) (0.034) (0.180) (0.769) (0.024) (0.024)lower bound 0.049 0.074 0.031 0.047 0.344 0.529 0.112 0.172 -0.155 -0.237 0.109 0.148 0.131 0.187

Observations 1,432 1,432 755 755 958 958 879 879 1,196 1,196 926 926 1,354 1,354

ln(# goods)


1{Account}ln(Monthly Profits

good-by-good)ln(Monthly Revenues

good-by-good)ln(Mark-up good-by-

good) ln(Mean Prices) ln(Mean Costs)


A look at the mechanisms, employment

ITT TTE ITT TTE ITT TTE ITT TTE ITT TTE

Outcome =(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Treatment -0.018 0.054 -0.008 -0.015 0.013 0.048 -0.023 -0.038 0.185 0.533(0.023) (0.088) (0.038) (0.058) (0.027) (0.041) (0.022) (0.038) (2.954) (4.654)

Post 0.025 0.071 0.056 0.045 0.132 0.155 0.196 0.202 -1.070 -1.047(0.053) (0.059) (0.033) (0.038) (0.059)** (0.068)**(0.053)***(0.054)*** (1.828) (1.894)

Treatment*Post -0.001 -0.040 0.016 0.034 -0.201 -0.281 0.188 0.255 3.481 4.293(0.127) (0.172) (0.061) (0.089) (0.085)** (0.118)** (0.097)* (0.134)* (3.663) (5.446)

p-values wild boot (0.996) (0.800) (0.031) (0.070) (0.356)lower bound 0.003 -0.045 -0.027 -0.079 -0.075 -0.140 0.223 0.332 -0.347 -0.645

Observations 1,432 1,432 755 755 958 958 879 879 1,068 1,196


ln(Size) ln(Paid Employment) ln(Co-owners)ln(Unpaid

employment) ln(Hours per week)


Summary

I Results point towards positive effects on profits (35%), revenues (25%)and clients (20%)

I Importantly accounting practices are changing, lowering costs, moregoods variety, changes in the ownership structure, more hours worked(insignificant)

I Quitting behavior is mixed, but then so it should (treatment versusselection), however the bottom 2% of treated disappears while not thesame for the controls (in terms of baseline profits)

I Business death rates are very high in this context (50%) so that themarket is operating towards some constrained optimum


Results: within and between variation


Outcome =(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Treatment -0.079 -0.117 -0.128 -0.186 -0.050 -0.074 -0.109 -0.165 -0.072 -0.110 -0.047 -0.072(0.128) (0.194) (0.111) (0.161) (0.149) (0.223) (0.119) (0.183) (0.134) (0.206) (0.093) (0.143)

Post 0.066 0.066 0.319 0.319 0.074 0.074 0.133 0.133 -0.072 -0.072 -0.021 -0.021(0.066) (0.066) (0.068)*** (0.068)*** (0.061) (0.061) (0.096) (0.096) (0.064) (0.064) (0.032) (0.032)

Treatment*Post 0.286 0.390 0.193 0.273 0.134 0.192 0.245 0.352 0.187 0.260 0.135 0.191(0.089)*** (0.132)*** (0.099)* (0.137)* (0.137) (0.203) (0.124)* (0.183)* (0.150) (0.219) (0.066)* (0.102)*


Observations 1,411 1,411 1,324 1,324 1,515 1,515 1,469 1,469 1,355 1,355 1,795 1,795


ln(Standardized)ln(Daily Profits) ln(Weekly Profits) ln(Daily revenues) ln(Weekly revenues) ln(Daily Clients)


Summary

I It seems that the distribution of profits is shifting to the right for inviteesin the post period

I Overall quitting and attrition behavior is mixed, but then so it should(treatment versus selection), however the bottom 2% of treateddisappears while not the same for the controls (in terms of baselineprofits)

I Business death rates are very high in this context (50%) so that themarket is operating towards some constrained optimum


Results: ITT Distribution0

.1.2

.3.4

kde

nsi

ty ln

_p

rofit

_ld

0 2 4 6 8 10x

Controls Treated

Baseline

0.1

.2.3

.4kd

en

sity

ln_

pro

fit_

ld

0 2 4 6 8x

Controls Treated

Post


Results: Natural Mortality

0.1

.2.3

.4kd

en

sity

ln_

pro

fit_

ld

0 2 4 6 8 10x

Baseline Post

Controls


Results: (Un)natural Mortality

0.1

.2.3

.4kd

en

sity

ln_

pro

fit_

ld

0 2 4 6 8x

Baseline Post

Treated


Conclusions

I We find large, positive and significant effects: profits, revenues and#clients

I These are both short (6 months) and medium run results (30 months)

I The big question is which ones are the mechanisms?

I Better accounting, lower costs, more sales and clients, more goods (alsohigher registration rate)

I Death of the left tail (both selection and treatment)

I Ownership and employment structure

I Credit doesn’t seem is the big part of the story

I Can it all be measurement? It seems that many outcomes, measureddifferently, are pointing in one direction.


Comparison with Other Studies

35

Table 9: Impacts on Business Profits and Sales

Study Gender % increase 95% CI % increase 95% CI

Berge et al. (2012)

Male 5.4% (-20%, +38%) 31.0% (-4%, +79%)

Female -3.0% (-23%, +22%) 4.4% (-23%, +22%)

Bruhn and Zia (2012) Mixed -15% (-62%, + 32%) n.r. n.r.

Calderon et al. (2012) Female 24.4% (-1%, 56%) 20.0% (-2%, +47%)

De Mel et al. (2012)

Current Enterprises Female -5.4% (-44%, +33%) -14.1% (-68%, +40%)

Potential Enterprises Female 43% (+6%, +80%) 40.9% (-6%, +87%)

Drexler et al. (2012)

"Standard" Mostly Female n.r. n.r. -6.7% (-24.5%, +11.2%)

"Rule-of-thumb" Mostly Female n.r. n.r. 6.5% (-11.4%, +24.4%)

Giné and Mansuri (2011) Mixed -11.4% (-33%, +17%) -2.3% (-15%, +13%)

Male -4.3% (-34%, +38%) 4.8% (-14%, +27%)

Female n.r. (a) n.r. n.r. (a) n.r.

Glaub et al. (2012) Mixed n.r. n.r. 57.4% (c) n.r.

Karlan and Valdivia (2011) Mostly Female 17% (b) (-25%, +59%) 1.9% (-9.8%, +15.1%)

Mano et al. (2012) Male 54% (-47%, +82%) 22.7% (-31%, +76%)

Valdivia (2012)

General training Female n.r. n.r. 9% (-8%, +29%)

Training + technical assistance Female n.r. n.r. 20.4% (+6%, 37%)

Notes:

95% CI denotes 95 percent confidence interval. Impacts significant at the 10% level or more reported in bold.

n.r. denotes not reported.

(a) They look at an aggregate sales and profitability measure and find no significant impact for either gender.

(b) Impact on profit from main product.

(c) Calculated as difference-in-difference calculation. Study reports difference in log sales is significant at the 1% level.

Profit increases are scaled as a percentage of the control group mean to enable comparability.

When multiple rounds are used, longest-term impacts available are reported.

Profits Revenues


Results (no X ′s)


Outcome =

(1) (2) (3) (4) (5) (6) (7) (8) (7) (8) (7) (8) (7) (8)

Treatment ‐0.079 ‐0.117 ‐0.128 ‐0.186 ‐0.050 ‐0.074 ‐0.109 ‐0.165 ‐0.072 ‐0.110 ‐0.047 ‐0.072 ‐0.032* ‐0.035

(0.128) (0.194) (0.111) (0.162) (0.149) (0.223) (0.119) (0.183) (0.134) (0.206) (0.093) (0.143) (0.017) (0.020)

1{Wave=2} ‐0.055 ‐0.055 0.214** 0.214** 0.038 0.038 0.181** 0.181** ‐0.065 ‐0.065 ‐0.013 ‐0.013 0.002 0.001

(0.072) (0.072) (0.094) (0.094) (0.068) (0.068) (0.083) (0.083) (0.080) (0.080) (0.033) (0.033) (0.013) (0.011)

1{Wave=3} 0.169* 0.169* 0.394*** 0.394*** 0.126 0.126 0.244*** 0.244*** ‐0.066 ‐0.066 ‐0.042 ‐0.042 0.083*** 0.087***

(0.089) (0.089) (0.082) (0.082) (0.082) (0.082) (0.078) (0.078) (0.074) (0.074) (0.051) (0.051) (0.019) (0.019)

Treatment*1{Wave=2} 0.340*** 0.462*** 0.217 0.304* 0.065 0.096 0.215 0.313 0.251 0.337 0.130* 0.185 0.052 0.033

(0.108) (0.156) (0.130) (0.173) (0.129) (0.193) (0.145) (0.218) (0.148) (0.210) (0.072) (0.110) (0.034) (0.021)

Treatment*1{Wave=3} 0.279* 0.383* 0.203 0.288 0.191 0.262 0.140 0.206 0.159 0.226 0.143 0.200 0.073 0.059

(0.152) (0.210) (0.157) (0.220) (0.171) (0.245) (0.177) (0.244) (0.208) (0.294) (0.102) (0.146) (0.066) (0.083)

Observations 1,407 1,407 1,320 1,320 1,536 1,536 1,471 1,471 1,358 1,358 1,795 1,795 1,844 1,844Treatment*1{Wave=2}=

Treatment*1{Wave=3} 0.752 0.772 0.936 0.946 0.396 0.411 0.763 0.752 0.619 0.635 0.908 0.921 0.790 0.784

1[Accounting]Standardized Outcomes

Notes: ***p<0.01, ** p<0.05, * p<0.1. (1) Robust (s.e.) clustered at the village level.

ln(Daily Clients)ln(Daily Profits) ln(Weekly Profits) ln(Daily revenues) ln(Weekly revenues)


Results Pooled (no X ′s)


Outcome =

(1) (2) (3) (4) (5) (6) (7) (8) (7) (8) (7) (8) (7) (8)

Treatment ‐0.079 ‐0.117 ‐0.128 ‐0.186 ‐0.050 ‐0.074 ‐0.109 ‐0.165 ‐0.072 ‐0.110 ‐0.047 (0.143) ‐0.035* ‐0.035

(0.128) (0.194) (0.111) (0.161) (0.149) (0.223) (0.119) (0.183) (0.134) (0.206) (0.093) ‐0.072 (0.019) (0.020)

Post 0.029 0.029 0.279*** 0.279*** 0.082 0.082 0.207*** 0.207*** ‐0.066 ‐0.066 ‐0.024 ‐0.024 0.032*** 0.034***

(0.069) (0.069) (0.078) (0.078) (0.061) (0.061) (0.061) (0.061) (0.065) (0.065) (0.032) (0.032) (0.010) (0.010)

Treatment*Post 0.320*** 0.437*** 0.218* 0.306* 0.116 0.166 0.183* 0.266 0.193 0.268 0.136* 0.192* 0.062 0.040

(0.091) (0.132) (0.115) (0.158) (0.131) (0.196) (0.103) (0.157) (0.160) (0.231) (0.066) (0.102) (0.040) (0.026)

P‐value: Random Inference [0.028] [.001] [.16] [.05] [.369] [.2] [.165] [.035] [.168] [.044] [.067] [.013] [.073] [.105]

Observations 1,407 1,407 1,320 1,320 1,536 1,536 1,471 1,471 1,358 1,358 1,795 1,795 1,844 1,844

1[Accounting]

Standardized

Outcomesln(Daily Clients)ln(Daily Profits) ln(Weekly Profits) ln(Daily revenues) ln(Weekly revenues)

Notes: ***p<0.01, ** p<0.05, * p<0.1. (1) Robust (s.e.) clustered at the village level. Permutation test two‐sided p‐values within [brackets] for the treatment effect, based on 5000 replications.


Results (with X ′s)

90%

BETA=.136

01

23

45

kden

sity

BE

TA

15

-.4 -.2 0 .2 .4x

Randomization Inference (5000 reps)ITT on STD Outcomes

90%BETA=.192

01

23

45

kden

sity

BE

TA

16

-.4 -.2 0 .2 .4x

Randomization Inference (5000 reps)TT on STD Outcomes

12.06.2012 - giacomo de giorgi

Documents

ite s miguel

business literacy intervention

simple business accounting

separate business

evidence of ite

provision of business

business knowledge field

formal accounting demel