Street Food Safety in Megalopolises

Last registered on August 25, 2022

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Pre-Trial

Trial Information

General Information

Title

Street Food Safety in Megalopolises

RCT ID

AEARCTR-0008797

Initial registration date

April 18, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

April 20, 2022, 4:41 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

August 25, 2022, 4:28 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

India

Region

West Bengal

Primary Investigator

Name

Denni Tommasi

Affiliation

University of Bologna

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Caitlin Brown

PI Affiliation

University of Manchester

Contact Investigator

Additional Trial Information

Status

In development

Start date

2022-04-18

End date

2022-10-31

Keywords

Behavior, Health, Labor

Additional Keywords

Informal sector, street food vending, micro-entrepreneurs, sanitation

JEL code(s)

I15, O12, O17, Q18

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Street vending, the activity of selling goods and services in the streets without having a permanent built-up structure, is a large and growing sector across the developing world: vendors make up between 2 and 24 per cent of total urban informal employment in African, Asian and Latin American cities (Wongtada, 2014; ILO, 2018). Among street vendors, food sellers represent the most visible group as they provide affordable and nutritional food to 2.5 billion consumers every day, especially to those of low- and middle- incomes (FAO, 2007). However, street-food vendors are also considered a threat for public health, as street food is one of the main determinants of foodborne diseases.

Currently, India is the leading country among emerging economies attempting to structurally transform this sector. The 2014 “National Act for Urban Street Vendors” aims to formalize the unorganized street vending sector through a number of regulatory measures, including also recommendations concerning health and hygienic standards for vendors selling food (NPUSV, 2009). However, implementation of this policy has been hampered by a variety of factors, such as the lack of compliance and enforcement of regulations and poor targeting of scarce public resources (Daniele, Mookerjee, and Tommasi, 2021). Progress is further complicated by the absence of microeconomic details about the activities conducted by the main actors in this market: street vendors and consumers.

In this project, we take a microeconomic approach and focus specifically on the public health aspects of the ongoing transformation of the street vending sector. In particular, we aim to understand in detail the factors that can influence the production and consumption of safer food in urban street food markets in Kolkata, India. We use this context as our “laboratory” to gain new insights that can be transferred to other cities, states and countries experiencing similar issues in this yet formalized sector.

In collaboration with international and national organizations, we plan to provide (subsidizing) for free health-related infrastructure to a random sample of street-food vendors that are too costly for them to buy and that are not provided by the local authorities. This is an in-kind transfer worth $400 per vendor (roughly, 3-month net income in our context). A second treatment arm will provide additional training on safe food preparation practices, in addition to the infrastructure. The experiment is to test whether this investment is sufficient to sustain a “better equilibrium” in terms of adoption and practices that we can observe and measure concerning the production of safer food.

In addition, we also plan to conduct a comprehensive series of discrete choice experiments on consumers to elicit their preferences and experiences with safe street food. Combining these data, we aim to answer the following set of questions: 1) How safe is street food and its inputs in this market and how stable are these statistics? 2) What is the correlation between food safety, production inputs, safe food handling practices and behaviours? 3) Are prices a signal of higher food safety in different markets? 4) Are poorer consumers more at risk from unsafe street food? 5) What are consumer preferences for safe street food? 6) How do they react to changes in prices and quality of the street food?

Despite the size and importance of the street vending sector, there is relatively little research on the relationship between food safety and its microeconomic determinants, such as production inputs and prices. Our project will fill this gap while providing a complementary analysis to any macroeconomic investigation of the issue. We attempt to bridge two important strands of research which occur in economics and microbiology: namely, work on food safety practices among informal vendors in developing countries and the health impacts of unsafe food.

We build upon the seminal work by Daniele et al (2021), which is the most comprehensive (microeconomic) study on the public health issues surrounding the informal street vending sector. We also expand on the existing microbiological literature that has documented harmful pathogens in street food in developing countries (Abrahale et al. (2019) provides a review) and the work that has documented the extent of unsafe food safety practices among street food vendors (Muinde and Kuria (2005); Chukuezi (2010); Muyanja et al. (2011); Samapundo et al. (2015); among many others).

External Link(s)

Registration Citation

Citation

Brown, Caitlin and Denni Tommasi. 2022. "Street Food Safety in Megalopolises." AEA RCT Registry. August 25. https://doi.org/10.1257/rct.8797-1.1

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Experimental Details

Interventions

Intervention(s)

We are providing (subsidizing) for free a set of infrastructure to vendors that are too costly for them to buy and that are not provided by the local authorities either. This is an in-kind transfer worth 400$ per vendor (roughly, 3-month net income in our context). In addition, we also plan to conduct a comprehensive series of discrete choice experiments on consumers to elicit their preferences and experiences with safe street food. The experiment is to test whether this subsidized investment is sufficient to sustain a “better equilibrium” in terms of adoption and practices (that we can observe and measure) concerning the production of safer food. The simple hypothesis underlying our intervention is that this subsidized investment could enable vendors to offer consumers a wider array of desirable and profitable products and services by removing the cost barrier. We intend to verify that this is appropriate in our context by investigating both that vendors have high perceived costs of providing hygienic services at baseline and that consumers have a positive willingness to pay for food that is perceived as more hygienic.

Intervention Start Date

2022-06-06

Intervention End Date

2022-08-19

Primary Outcomes

Primary Outcomes (end points)

Behavior of the street food vendors in terms of adopting safe and hygienic food handling methods. We are interested in whether or not they use the infrastructure provided; if so, what are the impacts on their business outcomes, if not, why and what can be done to improve the safety of street food given this. Some specific outcomes we are interested in are:

Is there a visible facility for the vendor to wash hands?
If yes, is there soap visible nearby?
How does the water tank to clean the dishes look at the moment?
Does the vendor use soap to clean the dishes?
Is there a towel/cloths for the vendor to wipe hands?
Is the vendor using an apron?
Is the vendor wearing a hair cover?
Does the stall have a garbage bin?
If yes, how does the garbage bin look like at this moment?
Are the ingredients, raw or half-cooked food separated from cooked food?
Is the cooked food covered?
Are the ingredients/raw food covered?
Are tongs/spatulas/other tools being used for cooking?
Is the food served with spoons, tongs or any other tools?
Does the vendor wash hands before cooking/handling food?
What does the counter where food is prepared look like?
Is the food being served on disposable plates?
If no, what is the food being served on?
Is there a garbage bin for consumers to dispose of their waste?
If yes, how does the garbage bin look like at this moment?
If there a facility for drinking water for customers?
If yes, how is drinking water distributed?
Is there a hand washing facility for customers?
If yes, how does this facility look at the moment?
If yes, is there soap available?

Primary Outcomes (explanation)

We will construct various summary measures of the key outcome variables in the form of weighted indexes.

Secondary Outcomes

Secondary Outcomes (end points)

Awareness of the street food vendors in terms of health hazards, as:

Are you familiar with the National Act for Urban Street Vendors 2014?
What do you think are possible contaminants of food; that is, things that can make food not safe to eat?
What do you use (or instruct employees to use) to have clean hands?
When do you think it is needed to wash hands?
Do you treat the water used for cooking or cleaning?
If yes, how do you treat the water used for cooking and cleaning?
Do you treat the drinking water provided to customers at your stall?
If yes, how do you treat the drinking water provided to customers at your stall?
How often do you replace the water in which dishes are washed?
How often do you empty your garbage bin?
How do you check if meat is cooked properly?
What do you do with leftover cooked food at the end of the day?
What do you do with leftover raw ingredients at the end of the day?

Over the past week, what days of the week was your kiosk closed?
Over the past week, on average, how many hours per day did you spend preparing the food to sell?
Over the past week, on average, how many hours per day did you sell street food?
Over the past week, on average, how many minutes per day did you spend cleaning at the end of the day?
Over the past week, on average, how many customers did you usually have each day?
Over the past week, on average, what were your daily sales?
Over the past week, on average, what were your daily total expenditures?
Over the past week, on average, what were your daily profits?
What do you expect your daily profits to be over the next 7 days?

3.2. Prices
Over the past week, did you display the personalized menu card that we gave you?
Over the past week, did you change any of the prices displayed on your menu card?
If yes, are the price changes publicly visible?
If yes, please note the new prices
If no, did you change the price of the items?

Secondary Outcomes (explanation)

Experimental Design

Our design is a stratified (blocked) random assignment with 2 levels. Hence, the sample is divided in clusters of street vendors which are then randomly assigned to different treatment groups, namely: treatment C, T1 and T2. Since the assignment is at the cluster level, all vendors in a particular cluster are assigned to the identical treatment group. The assignment of the treatments, coupled with regular monitoring of their food handling practices throughout the intervention, allows us to observe and identify changes in their behavior. The different treatment groups have different incentive schemes to reward the vendors for improving their practices, allowing us to identify the relative importance of factors that might be prompting their decisions. Finally, a measure of the spatial distance between different clusters allows us to estimate the spillover effects or externalities arising from the treatments.

Experimental Design Details

*** Main characteristics of our design

● Areas:
o There are 3 areas: 1) Sector V, 2) Dalhousie and 3) Hazra.
o Sector V is the IT hub of Kolkata, where consumers have higher income and vendors adopt safer practices.
o Both Dalhousie and Hazra are quite large and diverse areas in KMC.
o Dalhousie is the central business district of Kolkata. It means that, practically, Dalhousie is the only office area, so major consumers are employees. This area has a greater number of vendors and a variety of foods available. There are only food vendors here.
o Hazra (+ Rashbihari) has offices, hospitals, religious temple (India famous Kali temple – every year hundreds of pilgrims from different parts of India visit the temple), markets, cinema hall and residential area. So, the consumer types are employees, tourists, pilgrims, shoppers, residential people, people visiting hospital etc. and that also implies the consumer’s financial capacity has a huge difference. This area has a smaller number of vendors than Dalhousie and also less variety of food. Also, there are not only food vendors; there are many types of vendors like garments, shoes, jewellery, etc.

● Clusters:
o In each area, we divide vendors in natural urban clusters of 1, 2 to 3, 4 to 8 vendors each.
o A cluster is defined in the following way:
▪ If a vendor does not have anyone nearby in a ray of 30 meters forms a cluster on his own. This is because he is a “monopolist” of food in his surroundings.
▪ If a vendor has 1 or 2 neighbours nearby in a ray of 30 meters forms a cluster of 2 or 3 vendors. They are “oligopolists” of food in their surroundings.
▪ In streets where there are many vendors one after the other, we take the distance from the leftmost vendor to the rightmost vendor and consider a cluster the collection of those vendors that are at most 30 meters apart from each other. This means that in some cases we have clusters of 4 vendors or 5 vendors. In rare cases, we have clusters of 6 vendors (1 case) or 8 vendors (3 cases).
▪ In our sample, there are 88 clusters in total. 31 in Sector V, 29 in Dalhousie, and 28 in Hazra.

● Vendors are allocated to 3 treatment groups:
o C: receive a personal banner and menu card.
o T1: receive the same as C, plus the infrastructure.
o T2: receive the same as T1, plus capacity building.

● Group sizes:
o C: 100 vendors, distributed in roughly 35 clusters.
o T1: 90 vendors, distributed in roughly 35 clusters.
o T2: 90 vendors, distributed in roughly 35 clusters.

Hence, our sample is made of 280 vendors, in roughly 90 clusters, distributed in 3 areas (Sector V, Dalhousie, Hazra).

Randomization Method

Randomization done in office by a computer.

Randomization Unit

Randomization is done at the cluster level, i.e. small groups of vendors working close to each other in the same street.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

105 natural urban clusters of vendors on the street.

Sample size: planned number of observations

280 street vendors.

Sample size (or number of clusters) by treatment arms

100 street vendors in C, 90 street vendors in T1, 90 street vendors in T2.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Our design is a stratified (blocked) random assignment with treatment at level 2 and outcome variables at level 1. The calculations below show that our experiment is able to capture the following effects: For p_ind_total: 0.21 < effect size < 0.33 For p_ind_facilities: 0.21 < effect size < 0.37 For p_ind_handling: 0.21 < effect size < 0.34 For p_ind_costumer: 0.21 < effect size < 0.36 More in details, for power analysis, we can use the Stata command -pc_simulate- developed by Burlig, Fiona, Louis Preonas, and Matt Woerman (2020). "Panel Data and Experimental Design." Journal of Development Economics 144: 102548. The command provides power calculations for a setting like ours if you already have pilot data that you can use. We do have data from 2015-2016 on 4 indexes: 1) p_ind_facilities, 2) p_ind_handling, 3) p_ind_costumer and 4) p_ind_total. These data are for 1 wave before treatment and 5 waves after treatment. Since in this current experiment we have 12 waves after treatment, we can only do a power analysis assuming half of the sample size. This power analysis will be our upper bound. * cluster-level random assignment set seed 123 *** Index 1 pc_simulate p_ind_facilities, model(ANCOVA) mde(0.065 0.07 0.075) i(id) t(period) n(64) p(0.5) pre(1) post(5) alpha(0.05) /// vce(cluster) idcluster(block) sizecluster(3) bootstrap replace sum p_ind_facilities di 0.075/.2020061 * Effect size < 0.37127592 *** Index 2 pc_simulate p_ind_handling, model(ANCOVA) mde(0.065 0.07 0.075) i(id) t(period) n(64) p(0.5) pre(1) post(5) alpha(0.05) /// vce(cluster) idcluster(block) sizecluster(3) bootstrap replace sum p_ind_handling di 0.065/.1883959 * Effect size < 0.34501812 *** Index 3 pc_simulate p_ind_costumer, model(ANCOVA) mde(0.075 0.08 0.085) i(id) t(period) n(64) p(0.5) pre(1) post(5) alpha(0.05) /// vce(cluster) idcluster(block) sizecluster(3) bootstrap replace sum p_ind_costumer di 0.08/.223318 * Effect size < 0.35823355 *** Index 4 pc_simulate p_ind_total, model(ANCOVA) mde(0.035 0.04 0.045) i(id) t(period) n(64) p(0.5) pre(1) post(5) alpha(0.05) /// vce(cluster) idcluster(block) sizecluster(3) bootstrap replace sum p_ind_total di 0.045/.1363067 * Effect size < 0.33013784 To calculate a lower bound, we use the familiar -sampsi- command. This command does not require previous data, hence it can be used to perform power calculations assuming any number of repeated measures of the observations. The problem with -sampsi- is that it does not allow for cluster randomization. Hence, power calculation with -sampsi- will constitute a lower bound because, even though we can assume we have 12 waves, we need to assume that ICC = 0. More specifically, we need to make 3 simplifications: 1) Treatment is administered at level 1. ● In our experiment, the treatment is administered at level 2 for simplification and logistical reasons. ● However, vendors run their business individually, and the treatments are delivered at the individual level. Hence, given the context and the specific implementation, this is not a strong simplification. ● In practice this means that ICC is very small. 2) Stratification does not help explaining the outcome variables. • We use two variables on which we stratify on. • Based on the data from 2015, the variable “area” explains only 5% of the variation. • We don’t have data that helps understand how much cluster size explains. • So, in practice, this is a conservative simplifying assumption. 3) Spillover effects are negligible. • We don’t know whether ex ante spillover effects are negligible. • Based on the data from 2015, it is unlikely to observe spillover effects. Also because vendors are credit constrained. • In practice this means that, if there is an effect, this effect is only “direct”. The indirect effect = 0. • However, given how vendors are distributed on the street, and given the fact that we know exactly each vendor’s GPS location, we will be able to measure spillover effects. So, we will be able to relax this assumption. In our context, we have 90 vendors per treatment group. We observe each vendor 1 time before treatment, and 12 times after treatment. To calculate key parameters, I use the data from 2015. I type the following codes: use 1.data_vendors.dta,clear keep if food_meal==1 | food_heavy==1 tsset id period Then, for each index of behaviour, I calculate the correlation between follow-up measurements: reg p_ind_total L.p_ind_total i.area reg p_ind_costumer L.p_ind_costumer i.area reg p_ind_handling L.p_ind_handling i.area reg p_ind_facilities L.p_ind_facilities i.area Each of the following calculations guarantee a power > 0.80 * Power C vs T1 sampsi 0 0.21, sd(1) method(ancova) pre(1) post(12) r1(.3) n1(100) n2(90) sampsi 0 0.20, sd(1) method(ancova) pre(1) post(12) r1(.2) n1(100) n2(90) * Power C vs T1 + T2 sampsi 0 0.18, sd(1) method(ancova) pre(1) post(12) r1(.3) n1(100) n2(180) sampsi 0 0.17, sd(1) method(ancova) pre(1) post(12) r1(.2) n1(100) n2(180) * Power T1 vs T2 sampsi 0 0.22, sd(1) method(ancova) pre(1) post(12) r1(.3) n1(90) n2(90) sampsi 0 0.20, sd(1) method(ancova) pre(1) post(12) r1(.2) n1(90) n2(90) Hence, under the above simplifications, and using ANCOVA method, we should be able to estimate a MDES > 0.21.

Supporting Documents and Materials

IRB