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Trial Title
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Building Managerial Capital for Women Microentrepreneurs
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Digital Technology Adoption
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Abstract
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Before
Poor managerial capital has often been cited as a reason for low productivity in microenterprises. Managerial capital is the ability to make good business decisions (strategic and operational). This experiment will focus on building a specific aspect of managerial capital – the ability to understand one’s own business. I do this using a digital app, MeraBills, that helps microentrepreneurs maintain detailed records and view simple analytics of their business.
A majority of microenterprises in developing countries do not maintain business records. A major barrier to maintaining business records, is the low perceived returns to record-keeping. For example, a long list of financial data in a diary can be difficult to interpret and learn from. In this study, we increase the perceived returns to record-keeping by enabling entrepreneurs to use their business data while making business decisions. Working with 1200 low-income women microentrepreneurs, I ask two questions. Firstly, can microentrepreneurs use their own business data to make better management decisions? Second, does this translate to improved firm outcomes? Combining app data with survey data, this study will unpack the effects of this digital app on business decision making and firm outcomes.
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After
Adoption of technology can enable growth and increase productivity in firms in low and middle-income countries. However, adoption remains low for seemingly profitable technologies across varied settings. In this project I offer one specific explanation: that high learning costs at onset leads firms to make sub-optimal adoption choices. To study adoption choice, I offer firms access to a digital technology that helps them manage cash flow and maintain records of business transactions. In a field experiment in India, I randomize firms to treatment arms where learning costs are subsidized to varying degrees. In a setting where learning costs are salient and adoption choice is observable, I examine whether a temporary intervention that can subsidize learning costs can move firms from a low adoption equilibrium to a high adoption one. To guide the key predictions of the paper, I set up a model that describes technology adoption choices made by a firm that optimizes on an infinite horizon in discrete time. I then apply the model to shed light on the mechanisms through which temporarily subsidizing learning costs can enable to a firm to move to a high-adoption equilibrium.
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Trial Start Date
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September 02, 2021
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June 20, 2022
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Trial End Date
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December 31, 2022
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June 01, 2023
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Last Published
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Before
September 02, 2021 11:08 AM
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June 17, 2022 06:57 PM
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Intervention (Public)
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Before
This intervention will provide access to a digital app that helps microentrepreneurs process the information in their business data. The theory of change is that enabling entrepreneurs to learn from their business data can be a means of building managerial capital and in turn, improving firm outcomes. In this study, I ask two questions. One, can microentrepreneurs use their own business data to make better management decisions? Two, does this translate to improved firm outcomes? I answer these using a field experiment with 1200 women led microenterprises in Karnataka, India.
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After
In this project, I investigate the role of learning costs on digital technology adoption by women microentrepreneurs in the state of Karnataka in India. Over the last decade, digitalisation has slowly transformed how informal micro-firms operate in rural areas. This transformation involves a sharp rise in new and emerging digital technologies, however, the adoption still remains low. To help rationalize the low adoption of digital technology, I argue that high learning costs faced by a firm limits adoption of digital technologies. This study examines whether a temporary intervention that can subsidize learning costs can move firms from a low adoption equilibrium to a high adoption one. To guide the key predictions of the paper, I set up model that describes technology adoption choices made by a firm that optimizes on an infinite horizon in discrete time. I complement the model with a field experiment where I randomize firms to treatment arms where learning costs are subsidized to varying degrees, and study how this drives adoption choice. I also present findings on the firm level effects of sustained adoption. Further, to understand the comparative advantage of various firms from digital technology adoption, I study the heterogeneity in treatment effects by size of firm, level of human capital and behavioral parameters.
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Intervention Start Date
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September 06, 2021
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June 20, 2022
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Intervention End Date
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March 31, 2022
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June 01, 2023
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Primary Outcomes (End Points)
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Before
There are two groups of primary outcomes. First, Business Performance. This includes variables such as business revenue, expenditure and profits. The second group is Business Practices. This includes variables that measure record-keeping, marketing practices, managing customers and managing trade credit.
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The two primary outcome variables are - (i) a binary measure of adoption and (ii) the extent of adoption. Data for the primary outcomes is collected using the MeraBills dashboard which collects real time data on when and how the app is used.
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Primary Outcomes (Explanation)
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For the binary adoption dummy, real time data from the app is aggregated to the daily level. To study the extent of adoption, the real time data is aggregated to calculate the number of transactions entered weekly on the digital platform. Using this data, I create weekly measures that are then pooled for the study period.
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Experimental Design (Public)
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Before
To understand the effect of a digital tool on business decision-making, I design a field experiment in India. I will randomly vary access to three treatment arms which differ in the degree to which they help entrepreneurs process business information. The control group gets access to a paper template to maintain records. This group is not provided any support to process information from their business data. The second treatment arm receives access to the MeraBills app for a period of three months. The app helps process and understand business data. A third treatment arm receives access to the MeraBills app for three months as well as three visits (a month apart) from a field officer who works with them to interpret the data/ statistics on the app. Combining app data with survey data, I will unpack the effects of this digital app on business decision making and firm outcomes.
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After
The sample is comprised of 320 women-led microenterprises that are located across the Kolar, Tumkur, Chikkabalapura, and Ramanagar districts on Karnataka. The design randomizes participants into three groups: one control and two treatment groups corresponding to the three experimental treatments described below. All three groups are offered access to the app. To study whether learning costs drive adoption decisions, I vary the extent to which subsidize learning costs across the three arms.
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Planned Number of Clusters
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1200 women microentrepreneurs
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320 women microentrepreneurs
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Planned Number of Observations
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1200 women microentrepreneurs
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320 women microentrepreneurs
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Sample size (or number of clusters) by treatment arms
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Before
~400 microentrepreneurs in the Paper/ Control group ; ~400 microentrepreneurs in the App / T1 group; ~400 microentrepreneurs in the App
+ Business advice / T2 group.
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~100 microentrepreneurs in the Control group ; ~110 microentrepreneurs in the T1 group; ~110 microentrepreneurs in the T2 group.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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For power calculations, I assume 2\% of the control group will use the app, 15\% of T1 will use the app and 30\% of T2 will use the app. I estimate the sample size for two-sample comparison of proportions (for T2 vs. T1 and T1 vs. C), where the proportion in each group are given by the proportion that will use the app. This shows that a sample of 288 participants is enough to detect the above change in proportions with a power of 85\%. In this project, I set the sample size 320 (slightly greater than 288). I also run power calculations by simulating the experiment data and obtain similar estimates. \cite{mckenzie2012beyond} provides useful insights on optimal design. He shows that using multiple rounds of data collection and averaging outcomes over these rounds, leads to substantial increases in power, especially when outcomes are poorly correlated over time (which is often the case for business outcomes). Using the app, I collect weekly data on outcome variables and pool the data over the rounds.
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Secondary Outcomes (End Points)
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The secondary outcomes of interest are under two broad categories: business practices and firm outcomes. Under business practices, I look at outcomes such as, managing trade credit, managing inventories, managing customers etc. For firm outcomes, I look at profits, revenues, costs, probability of shutting down, probability of starting new business, number of employees and time use. The study has other auxiliary outcomes of interest such as Women’s Economic Empowerment and time use.
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