Penetration and Employment Generation under Platform Economy

ACADEMIA Letters Penetration and Employment Generation under Platform Economy Kunal Anand, Independent Researcher Abstract With the economy and job sector rapidly moving towards more short-term and contractual new economy jobs. Cab aggregators, Hyperlocal delivery, and E-commerce have emerged as the biggest job creators in this category. These “gig” jobs account for about 3.5% of the total tertiary sector jobs today, which is expected to rise to around 8% of the total by the year 2030 when their number will reach around 1 crore. By that year they are expected to account for roughly 50% of the additional jobs created in the tertiary sector each year. Further, these jobs are also important because they are: 1. Low Skilled and hence will be able to absorb largely unskilled Indian workforce 2. Distributed in their nature, hence giving opportunity in smaller cities and towns As per the study, major factors impacting the penetration of these services are Urbanization, Disposable income( GDP-expenditure), and Internet infrastructure(speed and penetration). Unclear and arbitrary regulations regarding these sectors, particularly at the state level, are constricting the expansion of these services and preventing them from reaching their full potential in terms of job creation. However, there are hints that these services might be reaching a saturation level in the Tier I cities and amongst the top income earners, and to expand to the next 100 million consumers, these services will have to adapt themselves to their needs which include, inter alia, more costeffective services, a more varied product range and offering services in vernacular language. Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 1 Expansion of these services has not gone down well with entrenched lobbies that operated in this sector including Auto-Taxi unions, Vyapari Sanghs, etc. who have been opposing these services tooth and nail. These cannot be ignored in the Indian context as these unions enjoy significant public and political support. It would bode well for new entrants to reach out to these new players to reach out to these incumbents to minimize the resistance to their expansion. Following study has tried to identify the factors determining the growth and expansion of new economy services and their corresponding impact on employment generation by conducting statistical experiments. Regression methods have been applied to New Economy services data across countries over a period of time to find correlations between various independent factors over growth and expansion of new Economy Services Important sectors considered under New Economy Jobs are: 1. Ride-Hailing Services eg, Ola, Uber, Cabify 2. E-Commerce Services eg, Amazon, Flipkart 3. Hyperlocal Services eg, Swiggy, DoorDash, Instacart, UberEats 4. Online travel, tourism, and hotel booking eg, Makemytrip, Oyo 5. Online entertainment eg, Netflix, Prime Video, and others Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 2 Penetration and Employment Generation under New Economy Services Introduction India is witnessing a high rate of economic growth and has been the fastest-growing economy in the world for quite some time. In the last three decades, the tertiary sector has become the steerer of India’s developmental story. It is expected to play a much more significant role in the future growth of India. To accelerate the growth of the tertiary sector, its sub-sectors, and the Indian economy accordingly, it is necessary to understand and project the potential of this sector and its sub-sectors in improving the economy and creating jobs. Though every geography and its socio-economic, as well as political spectrum, is different from the other. One can still find a correlation between them in terms of their demography and develop models to predict their maximum economic potential. The goal of this study is to derive a predictive model for identifying the penetration of New Economy Services and their potential to create jobs at different levels. Every geography of India (city/state/national) has been characterized by a standard set of socio-economic and demographic parameters like urban population, per capita GDP, consumer expenditure, internet penetration, etc. The study tries to identify the correlation between a particular sector’s job growth with those defined parameters of specific geography. Such a correlation will help in predicting the present penetration rate, job growth, existing job market scenario, potential saturation point for specific jobs, and the scope of more employment generation in such a sector. Modeling Approach To establish the correlation between various parameters, a regression model was used based on the growth and changes in input parameters. It was used to predict the penetration level of new economy services in different geographies of India. The objective of the model is to showcase the job creation potential of these new economic services to help the Government, policymakers, businesses, and others to make informed decisions. For the model, socio-economic databases were created for different geographies. These databases were based on certain independent variables such as Urban population, GDP/capita, average consumer expenditure, etc., and dependent variables such as market size, user base, and the penetration rate of NEJ services. Multiple iterations were conducted to identify the best fitting relation between the growth and the penetration of New Economy services with the socio-economic parameters of a geAcademia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 3 ographic entity. With the assumption that the growth of the userbase leads to an increase in the number of jobs proportionally, the number of workforces required to serve the respective userbase has been projected. Hypothesis 1. Economic sectors, especially the services sector creates or has the potential to create a number of jobs in any geography which is a function of demographic, social, and economic parameters of that particular geography 2. It is possible to predict the potential of job creation by a job sector if one has the data points of all identified variables concerning that specific geography. Projection of Penetration Rate Equation Penetration rate of any service in a particular geography = function (Population, Disposable income, CDGP, education level, ease of doing business, etc.) Yi = f(X1, X2, X3, X4, …) Yi=β0+ ∑βi*Xi+ ฀ Yi= Penetration rate of Respective sector β0= Constant Xi= Independent variable of particular geography for any particular year βi= Coefficient of that particular variable ฀= Error value Projection of Jobs After projecting the penetration rate of a particular service in geography, following method (unitary method) have been used to identify the number of jobs created by these services in any particular geography: Job Count (Geography) = Job Count (Reference)* User_base (Geography)/ User_base(Reference) Note: User Base= Penetration_Rate* Population Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 4 Regression Results and Insights Regression methods have been applied to the penetration rate of these sectors using the past data of various countries and states over a period of time. Different socio-economic parameters were taken as an independent variables for these services. After multiple iterations, the following factors came out to be impacting the penetration rate the most. Belowtable lists the important parameters impacting the penetration rate of these three sectors and corresponding coefficients for each parameter. Table 9: Regression Co-efficients Insights Following insights can be taken out from the regression table given above: 1. Important socio-economic parameters impacting the growth and expansion of Ridehailing and Hyperlocal services are the same and follow a similar pattern 2. Parameters affecting the penetration rate of New Economy services can be grouped into three parts. First is the demographic parameters, which include the urban population. Second is economic parameters, which include GDP/Capita, Average Consumer Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 5 Expenditure, and Average expenditure on clothing and footwear. Third is the internet infrastructure parameters, which include internet penetration and average internet speed. 3. Ride-hailing services and hyperlocal services have higher coefficient factors with urban populations than e-Commerce services. It implies that ride-hailing and hyperlocal services penetration are more impacted by urbanization compared to e-Commerce services. Hyperlocal and ride-hailing services are largely urban-centric services, but eCommerce services have their presence in small towns and rural areas too. Overall, all these services require a certain amount of demand from the market and are positively correlated with the level of urbanization 4. While most of the major economies of the world have crossed a 50% urbanization level, India is still at a 34% urbanization level. Suggesting an excellent potential for the growth of these services 5. There is a strong correlation between average expenditure on clothing and footwear with an increase in penetration of e-Commerce services, which implies that clothing and footwear form one of the most important commodities purchased from e-Commerce sites. 6. Internet speed is not an important factor for the growth of e-Commerce services, whereas it becomes a very important factor for the growth and penetration of the other two. Ridehailing and hyperlocal services are real-time services, and faster internet speed is very important for the usage of these services. Such is not the case with e-Commerce services. 7. Coefficient for internet penetration is higher for e-Commerce services compared to ridehailing and hyperlocal services. 8. India lags far behind with respect to internet infrastructure when compared with other major economies. Though India is catching up quickly, it is expected to have a penetration level of 60% by 2023 9. Also, India is far behind other major economies in average internet speed (3 Mbps in 2019, Statista). Every 1 Mbps increase in average internet speed is likely to result in a .004% increase in penetration of ride-hailing services Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 6 10. Average consumer expenditure has a negative correlation with the penetration of New Economy Services. A negative correlation with consumer expenditure implies that higher consumer expenditure reduces the spending capacity of users. Assumptions and Risks associated with a Regression approach 1. Multiple factors like political parameters, bureaucratic problems, cultural aspects, etc. have not been taken into account because of the problem of identifying them quantitatively. These factors do impact the growth and expansion of economic services. 2. Model does not take into account any disruption in technology like the arrival of dronebased delivery for e-Commerce, self-driving car in ride-hailing services, etc 3. Model assumes that taken socio-economic parameters are independent of each other. However, such is not the case. The average expenditure is correlated with GDP/capita, and urbanization will have a correlation with the increase in internet infrastructure. But since these parameters will have very minimal correlation with each other and therefore they can be ignored 4. Data gathering, source, projections, and assumptions about the database of identified parameters are discussed in the appendix section 5. By the accounts of industry stakeholders, the 4-wheeler cab ride-hailing market is reaching saturation. However, they are experiencing growth in 2-wheeler and 3-wheeler ridehailing in tier 2 and smaller towns. It has been assumed that the growth of these services will follow the same path as the growth of 4-wheeler cabs in tier 1 cities and growth in job creation will remain the same Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 7 Appendix DATA GATHERING AND PROJECTION Urban Population (in millions): Census data from 2001 and 2011 have been used to project the Urban Population in 2019. Problem Projecting city/state/nation wise population in 2019 using data of 2001 and 2011 Simple extrapolation of population growth from 2001 and 2011 predict population in 2019 Population growth rate of city/state/nation across India followed the same pattern as in between the years 2001 to 2011 2011 District wise Urban Population: SECC Census, 20112001 District wise Urban Population: Census data, 2001 Solution Assumptions Database Used Internet Penetration (Percentage): Problem Level of internet penetration/ Smartphone usage in a particular city/state/nation Using State Wise Urban Internet Penetration as a proxy to the internet penetration in respective city State-wide average Urban Internet Penetration is equal to Internet penetration in cities of those states Internet in India: India Internet Data, 2019 Solution Assumptions Database Used Average expenditure on Clothing and Footwear (PPP in USD) Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 8 Problem Calculating average Consumer Spending on Clothing and Footwear per capita for different city/state/nation Consumer Pyramid Survey, 2014 by CMIE. Consumer expenditure per capita data was taken from Consumer Pyramid Survey, 2014 by CMIE which was then extrapolated to 2019 by taking CAGR of 5% annually CAGR have remained the same and constant across all cities in India Consumer Pyramid Survey Data: Consumer Pyramids Survey, 2014 [India] Solution Assumptions Database Used GDP Per capita (PPP in USD): Problem GDP of identified city/state/nation in 2019 City/State/Nation wise GDP data was taken from IIMA database of District wise annual GDP (Current in INR). Data was then converted into per capita GDP (PPP in USD) by taking appropriate factors No Assumptions GDP share of different cities using past data: IIMA Data Solution Assumptions Database Used Consumer Spending Per Capita (PPP in USD) Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 9 Problem Calculating average Consumer Spending per capita for different city/state/nation Data Source: Consumer Pyramid Survey, 2014 by CMIE. Consumer expenditure per capita data was taken from Consumer Pyramid Survey, 2014 by CMIE which was then extrapolated to 2019 by taking CAGR of 5% annually CAGR has remained the same and constant across all cities in India Consumer Pyramid Survey Data: Consumer Pyramids Survey, 2014 [India] Solution Assumptions Database Used PROJECTION OF NEW ECONOMY JOBS UNDER RIDE-HAILING COMPANIES User Definition: Anyone who has made two bookings per week is considered a user. After projecting the penetration rate of Ride-Hailing services in any geography, we used the following method to identify the number of drivers required in that particular geography to serve the userbase. Count of drivers is calculated using the formula below: <pre>Drivers Count(Geography)= Drivers Count(India, 2016)* User_base(Respective Geographhy)/ User_base(India, 2016)</pre> Note: Userbase= Penetration_Rate* Population Reference: Data Source: Number of OlaCabs and Uber drivers in India, as of July 2016 (in 1,000s), (Mint Research) Statista, 2016: <pre> Year 2016 Total Drivers 900000 Penetration 0.06534 User base(million) 28.714 </pre> Data is used to define the “Number of Cab Drivers/ Userbase” where Userbase is“Penetration rate* Population”. Considering this factor remained the same across city and years, we extrapolated the number of Cab Drivers across different geographies for different years. PROJECTION OF NEW ECONOMY JOBS UNDER E-COMMERCE COMPANIES User Definition: Anyone who made at least one purchase online in the last 12 months is considered as a user. After projecting the penetration rate of e-Commerce services in each of the city, we used the following method to identify the number of Jobs created by these services in any particular city: <pre>Job Count(Geography)= Job Count(India_Year)*User_base(Geography)/ Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 10 User_base(India_Year)</pre> Note: User Base= Penetration_Rate*Population Overall job scenario for the e-Commerce sector was taken from the KPMG Report, Impact of e-Commerce on employment in India. The paper predicted the number of jobs in the e-Commerce sector by 2021. Reference: Data Source: Number of Jobs under different verticals of the E-Commerce sector in India, as of July 2021, KPMG Impact of e-Commerce on Employment in India: <pre> Year Total Jobs Total Logistic Jobs 2021 14,50,500 8,00,000 Total Warehousing Jobs 2,50,000 Total Technology Jobs 3,00,000 Other Jobs User base(million) 1,00,000 805.57 </pre> Above data is used to define the “User Base for such services” where Userbase is“Penetration rate* Population”. We use this data to project the number of jobs created by e-commerce services. Our assumption is that “User Base/Workforce” is a constant factor and remains the same. PROJECTION OF NEW ECONOMY JOBS UNDER HYPERLOCAL COMPANIES User Definition: Anyone who has made four orders per week is considered as a user of hyperlocal services. After projecting the penetration rate of hyperlocal services in any geography, we used the following method to identify the number of delivery partners (DPs) required in that particular geography to serve the userbase. Count of DPs is calculated using the formula below: <pre>DPs Count(Geography)= DPs Count(India, 2019)* User_base(Respective Geograhhy)/ User_base(India, 2019)</pre> Note: Userbase= Penetration_Rate* Population Reference: Data Source: Number of Delivery Partners in India, as of Sept 2019 (in 1,000s) (Your Story) Sept 2019: <pre> Year 2019 Total DPs 528571 Penetration 0.0879 User base(million) 38.63 </pre> Data is used to define the “Number of DPs/ Userbase” where Userbase is“Penetration rate* Population”. Considering this factor remained the same across cities and years, we extrapolated the number of DPs across different geographies for different years Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 11 References 1. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?locations=IN 2. https://ourworldindata.org/urbanization 3. https://timesofindia.indiatimes.com/business/india-business/india-to-have-859-million-smartphonesusers-in-2022-assocham-pwc/articleshow/69252335.cms 4. India’s Employment Crisis: Rising Education Levels and Falling Non-agricultural Job Growth 5. https://www.icpsr.umich.edu/icpsrweb/DSDR/studies/36782/datadocumentation 6. https://secc.gov.in/statePopulationCountUrban 7. http://www.censusindia.gov.in/DigitalLibrary/MFTableSeries.aspx 8. https://imrbint.com/images/common/ICUBE%E2%84%A2_2019_Highlights.pdf 9. https://images.livemint.com/r/LiveMint/Period2/2016/07/08/Photos/Processed/g_covertable_web.jpg 10. https://assets.kpmg/content/dam/kpmg/in/pdf/2016/12/impact-of-ecommerce-on-employmentin-india.pdf Academia Letters, November 2021 ©2021 by the author — Open Access — Distributed under CC BY 4.0 Corresponding Author: Kunal Anand, [email protected] Citation: Anand, K. (2021). Penetration and Employment Generation under Platform Economy. Academia Letters, Article 4159. https://doi.org/10.20935/AL4159. 12