Category Archives: Economic Activity

How the 2019’s Top 25 Employee Favorite Firms in India operated in 2020, accounting for the pandemic shockwave?

 

SRITNE INSIGHTS ON THE JOB MARKET (1)

LinkedIn released an article in April 2019, citing the top 25 firms with a firm size of at least 500 employees where the Indian skilled professionals aspired to work. Linkedin used the below-mentioned parameters to construct the list:

  1. Count of Job Posting views (Interest)
  2. Count of professionals viewing career’s page of the company (Engagement)
  3. Count of job-seekers connecting with the company (Job Demand)
  4. Company’s Retention Rate of Employees (Retention Rate)

The list of companies in the order as ranked by LinkedIn is as follows:

Flipkart (Walmart), Amazon, Oyo rooms, One97 Communications (Paytm), Uber, Swiggy, TCS, Zomato, Google, Reliance Industries, EY, Adobe, BCG, Yes Bank, IBM, Daimler Mercedes), Freshworks, Accenture, OLA Cabs, ICICI Bank, PWC, KPMG, L&T (and it’s subsidiary L&T InfoTech), Oracle, Qualcomm, and Deloitte.

Due to the lack of data points on Freshworks, we exclude it from our study and replace it with Deloitte to account for all the Big four accounting firms in our analysis. (Note: Deloitte was one of the “top 25 firms where India wants to work” list produced by Linkedin in 2018).

Things changed as of Mid-March 2020. The world started to witness the spread of the deadly coronavirus, an unanticipated event, the pandemic situation was in place. The country (India) witnessed a 2-month lockdown in the second quarter of 2020. The people of the country and the economy was disturbed. Many firms were not resilient to this shockwave. They suffered huge losses, lost their business, laid off their workforce. It was an unpleasant chain of events that had an economic and mental impact on the workforce. On the other hand, some firms were resilient to this shockwave by having the following characteristics:

  1. heavily tech-oriented nature of work & projects that can be handled remotely,
  2. a resilient business model and new initiatives that innovated solutions to help the populace during the pandemic and churned profits,
  3. finances to sustain losses and support their workforce.

We studied how this list of top 25 firms where the Indian workforce sought employment anticipating a successful career responded to the COVID-19 Crisis. We cite the Naukri Stepup Employee Survey data and the AmbitionBox Employee Reviews data (from Info Edge) to support our study.


THE SURVEY DATA:

We utilize the Survey conducted by Naukri during the pandemic as a part of their Stepup Insights Initiative. To uncover the insights from this survey data, we must first understand how the Survey was conducted and how Naukri aggregated employee responses.

Visit the link (https://www.naukri.com/mnjuser/survey-covid) to understand the Survey outline and experience the framework used to collect the data. The framework asks the survey responders to talk about their firms’ response to COVID. Except for the Textual Review, all other responses collected are Boolean in Nature. After collecting the Boolean responses, for a company, they take the collective sum of the Boolean Responses and measure what fraction of total responses voted true for an option.

Figure-1 shows the number of responses collected by the firm name from our chosen top-25 list. The data is unbalanced as the survey sample was not restricted. Large Firms with a more prolonged presence in India had more reviews than smaller firms and firms with a comparatively shorter presence. The data is cross-sectional, and the data points were collected during and post-lockdown in India.

Figure-1: Survey Responses count by Company Name 

Note: The Survey Data is a sample set of opinions voiced by the employees on the benefits offered, hiring activities, and salary issues concerning their firm. The data points collected are largely anonymous, and the analysis results must be acknowledged objectively.


INSIGHTS FROM THE NAUKRI SURVEY DATA

We have three insights to offer from this Survey on the chosen top 25 firms:

  1. Benefits provided to employees during the pandemic.
  2. Hiring Activities during the pandemic
  3. Salary related Insights during the pandemic

ANALYZING THE BENEFITS OFFERED FROM THE SURVEY DATA

Figure-2: Benefits-Survey Visualization

The Survey collected responses on seven different types of benefits offered by employers to their employees during the pandemic. Out of these seven benefits, we can see that none of the employers have been reported to provide the benefit of essential commodities. This is acceptable because only the firms that employed minimum wage workers offered this particular benefit, and the minimum wage employees primarily do not actively participate in voluntary online surveys.

  1. The benefit of Work From Home:
Figure-2a: The Benefit of Remote Work

The Survey Data shows that, on average, 6 to 7 employees out of 10 at these 25 firms benefited from remote work allowance. Employees at Consulting and Technology firms (KPMG, EY, Delloite, PWC, TCS, Adobe, Oracle) had the highest remote work allowance. Employees at the Booming Startup Firms (OYO, Ola, Swiggy, Uber, Zomato) and Reliance (the largest Indian Conglomerate by Market Share) reported below-average remote work allowance.

2. The benefit of Work Place Sanitization:

Figure-2b: The Benefit of Work Place Sanitization

During the lockdown, only the essential workers were allowed to travel to their workplace by taking all necessary precautions; they benefited from Work Place Sanitization. Post-Lockdown, some employers urged the employees to go onsite for jobs that could not be taken care of remotely. Mercedes, ICICI Bank, and Yes Bank are the top 3 firms, with at least three on ten employees having workplace sanitization benefits. The lesser approval rating for this benefit implies that majorly Work From Home was in place.

3. The benefit of Job Security:

Figure-2c: The Benefit of Job Security

On average, only 1 in 10 employees reported the benefit of Job Security. TCS had the highest approval rating, with 47% of its Employees denoting assurance of no layoffs. KPMG, PWC, Amazon, Google, and EY are the other firms with at least 3 in 10 employees reporting Job Security benefits. Startups (Uber, Swiggy, OYO, Ola, Paytm), consulting firms (Accenture, BCG, Delloite), Manufacturing firms (Qualcomm, Mercedes), and software firms (Oracle, Adobe) offered no Job Security to their employees.

4. The benefit of Salary on time:

Figure-2d: The Benefit of Salary Credited on Time

On average, 5 out of 10 employees at these firms benefited from salary credited on time with no delays. OYO ranks last in this list, indicating that many of this firm’s employees faced hiccups receiving salaries on time.

5. The benefit of Employee Care Programs and Online Counseling:

Figure-2e: The benefit of Employe Care Programs and Online Counseling (Sorted by Employe Care Programs)
Figure-2f: The benefit of Employe Care Programs and Online Counseling (Sorted by Online Counseling)

On average, 2 in 10 employees reported the benefit of employee care programs, and 1 in 10 employees reported the benefit of Online Counseling. Qualcomm and Adobe took care of their employees by offering this benefit during the pandemic. Largely Tech Skilled labor and Employees at MNCs of international origin had the benefit of online counseling.


ANALYZING HIRING TRENDS RESPONSES FROM THE SURVEY DATA

Figure-3: Hiring Trends Visualization

On Average, 3 in 10 employees reported Hiring Actively, 4 in 10 reported Hiring Freeze, 2 in 10 reported Layoffs in their departments at their firms. At Amazon India, 8 in 10 employees reported hiring activities at their firm in their departments. This was evident in many articles by reputed news outlets talking about amazon going on a hiring spree during the pandemic. Additionally, the combined value of Hiring Freeze and layoffs was lowest at Amazon. Flipkart Qualcomm and Google were the other firms with at least 5 in 10 employees reporting hiring activities. Employees at Swiggy, Uber, Ola, OYO, and Zomato (the most flourishing and well-known startups in India) reported the highest layoffs and lowest Hiring activity.

According to the Survey, there were very few employment offers canceled at these firms during the pandemic. On average, only 2 in 100 employees reported that their company canceled offer letters sent out to new hires.

Figure-4: Hiring Currently vs Hiring Layoff Plot

Figure-4 indicates that startups suffered the most in maintaining their workforce during the pandemic. Another interesting hiring pattern we get to see here is at Accenture, India. It fired thousands of employees during the pandemic and posted vacancies for 2x times the no. of employees it fired (Reference: While Accenture fires thousands, it is also hiring thousands (consultancy.in)). When we take a closer look at what happened, we see that they fired labor in low-performing ranks and hired highly skilled, educated, and qualified labor in high-demand areas showing that the company witnessed a shift in the demand for new skills that supported ‘capacity building with speed’ during the pandemic.


ANALYZING SALARY RESPONSES FROM THE SURVEY DATA

Figure-5: Salary Trends Visualization

On average, 5 in 10 employees who didn’t lose their Job reported that the lockdown had no impact on their Salary, 2 in 10 reported that their appraisal cycle was delayed, 1 in 10 reported that the salary variables had an impact.

According to the Survey, Employees of OYO, Zomato, Uber, and Ola, suffered the impact of salary cuts and the salary not being credited the most. 62% of OYO Employees and 45.5 % of Zomato Employees who took the survey reported salary cuts.


ADDITIONAL INSIGHTS FROM AMBITION BOX REVIEWS DATA

We utilized 12099 reviews posted during the year 2020 for the chosen top 25 firms, to understand the variation in employee sentiment and ratings. Figure6 shows the count of review responses collected by company name.

Figure-6: Review Counts by Company Name

DECLINE IN EMPLOYEE RATINGS DURING LOCKDOWN

Figure-7: Time Series Rating Distribution

DECLINE IN POSITIVE SENTIMENT EXPRESSED IN THE TEXTUAL REVIEWS DURING THE LOCKDOWN

Figure-7: Time Series Sentiment Score Distribution

WORD CLOUD OF REVIEWS

The collective Average of ratings and sentiments expressed by the employees across all the chosen 25 companies shows that the ratings and positive sentiment dropped sharply during the lockdown, and it continued to decrease until the end of the lockdown. Furthermore, there was a sharp spike during the month of June-the Unlock Phase. The declining trend recovers gradually to Jan-2020 levels by the end of the year 2020.

CONCLUSION:

Quick adoption of Work from home is evident across all the top 25 employee favorite firms. Employees at Startups reported less hiring and more layoffs as compared to employees at large and well-established firms. On Average, Just 1 in 10 employees at these firms who took the survey reported the benefit of job security indicating that this advantage is influenced by the demand for the products and services of the firms, policies of the government, ability of the management to introduce innovation and adoption to the new normal, and the strength of the employees to deliver output with speed.

Stay tuned for our next blog on “The sustainability analysis of Occupations Suitable for Machine Learning and Remote Work during the COVID-19 Pandemic”

Visit the link below to access the Tableau dashboard.

https://public.tableau.com/views/SRITNEBlogonJobMarkets-Survey/StoryDashboard?:language=en&:display_count=y&publish=yes&:origin=viz_share_link

Author: Sachin Kumar S

Research Associate at SRITNE ISB

Digify

Digify! – #Digital Transformation for MSME and Platform Play for #Start-ups

There is a digital platform opportunity in MSME for every vertical. The Shopify kind. Shopify helped MSMEs build an online presence. But why stop there?

Why not ‘digify’ the whole value chain? Pick up a vertical and offer end to end (raw material to sales) digital platform to MSMEs in each vertical:

  • Strategic direction: Forecasting trend opportunities through big data analytics,
  • Digital mapping and integration of processes of supply and demand,
  • Collating all above info to source Just-In-Time Supply,
  • Smart Factory

Benefits:

  • Reduced inventory
  • Significant cut in lead time to hit the market
  • Faster decision-making to capture market share
  • Improved bottom line

Apparel Business | Alibaba’s Xunxi

Alibaba Group Holding Ltd. has now set its sight on a new target: the country’s outdated factories. Alibaba’s path to smart manufacturing starts with garments, a market worth 2.2 trillion yuan ($328 billion) in China last year based on Euromonitor International’s estimates. Alibaba has said that one in four clothes purchases in the country was shipped via its e-commerce platforms, granting it access to an ocean of data that it is now deploying to assist domestic garment makers in design and production planning.

It is also centralizing the material procurement process to help reduce costs. Artificial intelligence, robotic arms, as well as many other in-house technologies have also been put into use at the Xunxi factory prototype. It usually takes months for apparel companies to bring a new design from runway to stores, but Alibaba claims it can cut order lead times by 75% with its solutions. This would address the growing demand for instant gratification among China’s Gen-Z consumers. For instance, with the help of AI, designers can review simulated rendering effects on so-called digital fabrics on their computer screens, rather than going through a time-consuming process to dye the fabric. Needless to say, all the robotics and IoT will be in place.

Auto-repair Business | Tencent

Problem statement:

The company provides 3-year warranty, and the company dealers/service centres take care of the car during that time. The independent garages handle most jobs thereafter.

Solution:

Demand Aggregation:

  • Urban Clap of Auto-repair
  • Mobile-based pick up and drop
  • Quality guaranteed by the app
  • Consumers do not need to interface with small mechanic shops

Supply Aggregation:

  • Get Uber driver-type supply aggregation of the small mechanics shops
  • After critical mass is built the app starts backward integrating and digitizes those processes for the mechanic shop. Plus, open own garages/franchise
  • Develop Swiggy-like digital backend for your suppliers

Target: Young car owners.

Besides, China’s big tech companies are circling the auto-repair business in a bid to capture the boom in vehicle maintenance as the country’s vast car fleet begins to age.

Tencent and Alibaba are trying to make the unorganized repair shops with the choice of joining one of their new smartphone-based networks.

Alibaba’s strategy is to link its popular Tmall e-commerce platform with a new physical chain of Tmall-branded garages to create a maintenance service that starts on a smartphone and ends in a bricks-and-mortar store. Tencent has two affiliates that are both building new garages and joining with existing ones to provide physical endpoints for online auto-part sales.

Opportunities for/in India:

Indian start-ups, MSMEs have an opportunity to make billion-dollar businesses with such platforms in industries like bulk drugs, chemicals, auto repair, textile, etc. An MSME in this industry can create one for itself and then make a business out of it. India has some unique challenges such as contract workers in these industries. Such platforms can provide a win-win model for workers and employers by eliminating the intermediaries.

Guest Author: Sameer Sankhe (Head, Digital Transformation – Tata Projects)

Using High Frequency Electricity Data to look at Economic Activity Impact of Coronavirus Lockdown

Following a national address by PM Narendra Modi on 24th March 2020, a nationwide lockdown to contain the spread of the coronavirus was initiated. This lockdown involved halting of all economic activities barring those that were need to maintain essential supplies. Due to earlier confusion as to the details, even essential supplies took a hit. People were confined to their residences, and movement were restricted. All major service organizations shifted to a work from home mode of carrying out business.

Following the first round of lockdown, between 24th March to 14th April, a second continuation was declared starting 15th April to last till 3rd May with conditional relaxations in some regions where spread had till then been contained. The last round of lockdowns with further easing were continued from 3rd May till 17th May first and then later from 17th to 31st May by the National Disaster Management Authority. Following the lapsing of lockdown on 31st May, an Unlock 1.0 phase was declared starting 1st June with phase wise opening of all major industrial and retail activity.

Our data of daily consumption of electricity comes from the National Load Despatch Centre of the Power System Operation Corporation Limited which optimum scheduling and despatch of electricity through the national grid. They release daily, weekly and monthly reports of power consumed, energy supplied being reported in million units of energy supplied to each state grid. Using this data, we can then track the change in consumption of electricity nationwide which becomes a proxy for economic activity following from Cicala (2020) and Benedikt and Radulescu (2020). The fall in consumption of electricity can be a predictor of total output contraction in the period and the recovery in consumption can be a harbinger of the expected speed of recovery.

We remove seasonal components from the data that can distort our results, these seasonal aspects come from whether a time period is the harvesting period or are summer months which can impact electricity consumption. [i]

Overview of Power Consumption in India

India has been seeing a steady increase in Electricity consumed with a trend year on year growth rate of 3.5% with significant heterogeneity amongst state wise consumption growth rates. Bihar leads the electricity consumption growth rate with growth rate around 13% with North East states following close with rates ranging from 7% to about 10%. Amongst larger states, Telangana, Madhya Pradesh and Uttar Pradesh show higher rates of growth.

Figure 1 represents the per capita[ii] mean annual power consumption.

Figure 2 presents the overall trendline for India till 2019.

Impact of Coronavirus Lockdown

To estimate the impact of the lockdown we use data from financial year 2014 onwards to estimate the expected consumption during the lockdown period, accounting for the seasonality and trend. Using those trends and seasonality, we find that beginning the lockdown period there was a significant deviation between the actual and the predicted consumption of electricity. The actual consumption of electricity nationwide was consistently lower than the predicted one across all phases of the lockdown. This fall from the predicted value was the most for the 1st period of lockdown with an average fall of 12.25%. This shortfall in electricity consumption decreased across the phases with the 2nd period of lockdown witnessing only an 8% decrease while the further phases had no statistically significant difference from similar periods in previous years.

Table 1

Phase of Lockdown Change in Consumption compared to same period in previous years
Phase 1 (25th March – 14th April) -12.25%
Phase 2 (15th April – 3rd May) -8.07%
Phase 3 (4th May- 17th May) No statistically significant difference
Phase 4 (17th May – 31st May) No statistically significant difference
Unlock 1.0 (1st June onwards) No statistically significant difference

Further, as we can see from Figure 3 since the beginning of lockdown 3.0, we have seen an increase in actual consumption of electricity which is consistent with the increasing economic activity as restrictions were eased across rounds of lockdown.

Figure 3 The Actual and Predicted Consumption across the various phases of Lockdown

We also note an increase in consumption of electricity across the phases with phase 2 seeing a 5.4% increase in consumption. Table 2 provides the average increase in electricity consumption over the previous phase of lockdown. We see that each round sees an increase over the previous round till we come to Unlock 1.0 which saw no significant difference as compared to phase 4 (at 5% level).

Table 2

Phase of Lockdown Change in Consumption compared to the previous period of lockdown
Phase 2 (15th April – 3rd May) 5.38%
Phase 3 (4th May- 17th May) 8.60%
Phase 4 (17th May – 31st May) 10.55%
Unlock 1.0 (1st June onwards) No statistically significant difference

We thus see the data showing up the kind of trends we expect to see. The national level statistics do hide considerable heterogeneity in state level consumption of electricity with certain states exhibiting above average decreases while some states surprisingly see an increase in consumption. For more on this, stay tuned for the 2nd part in this series.

References

  • Cicala, Steve. Early Economic Impacts of COVID-19 in Europe: A View from the Grid. Tech. rep. Online, last accessed: May 6, 2020. University of Chicago, 2020.
  • Janzen, Benedikt, and Doina Radulescu. “Electricity Use as a Real Time Indicator of the Economic Burden of the COVID-19-Related Lockdown: Evidence from Switzerland.” (2020).

[i] Our analysis excludes Andaman and Nicobar Islands and Lakshadweep islands as they are not connected to the National Grid

[ii] Per 2011 population census

About the Author:

Ashutosh Dwivedi is a Research Associate at SRITNE.

Follow us on twitter: #ISBSRITNE

Follow us on LinkedIn: https://www.linkedin.com/showcase/sritne

Positive Spillover Effects of Uber on NYC’s Yellow Taxis

The entry of for-hire vehicles (FHV) through on-demand taxi service companies such as Uber and Lyft have disrupted the traditional taxi service industry. The unique business model of these companies allows cab drivers to ply on the road without the need for the coveted million-dollar taxi medallions. Thus, the heavily regulated taxi industry saw a rise in the number of taxis on the road. This increase in the supply of taxis resulted in demand for rides shifting from the incumbent taxis, such as the Yellow taxis of New York, to the new entrants in the market, Uber, Lyft, and the like. Since the Yellow taxis depend on their daily trips for their earnings, a fall in the number of trips will consequently result in a fall in their earnings. Thus, leading to the general belief that the entry of the new entrants resulted in a decline in earnings of the incumbent taxis. In this study, we show that the presence of these new on-demand taxi service companies does disrupt the traditional taxi market by taking away the demand from incumbent taxi drivers. However, the incumbent taxi drivers can learn on the job and hence can increase their overall total earnings despite the fall in demand.

This study uses trip level data of Yellow taxis and FHVs (comprising of Uber, Lyft, and the like) in New York City for 2015-2019. We use this trip level data to compute Yellow taxi driver’s earnings, trip count, and the distance traveled. We calculate the trip count of FHVs, and we use this to measure the intensity of FHV’s presence in a given taxi zone and time. Using OLS, we compute the effect of FHV’s presence on Yellow taxi driver’s earnings, distance traveled, and trip count.

The number of Yellow taxis plying on the roads of New York City at a time is fixed by the number of medallions, which is, in turn, regulated by the Taxi and Limousine Commission. Most of the Yellow taxi drivers lease a taxi daily. This encourages drivers to set a daily wage target, and upon reaching that target, they go off the streets. This form of labor supply discourages taxi drivers from maximizing their earnings and utility by working more on high paying days and less on low paying days. The entry of additional taxis in the form of Uber, Lyft, and the like increased the number of cabs, thereby changing the dynamics of the market by increasing the supply of taxis. Hence, we expect the demand would now be spread across the increasing supply base. This translates to reduced demand for an individual Yellow taxi, and one would expect that they will be unable to meet their daily earnings targets or be forced to reduce their daily earnings targets. Eventually, resulting in a fall in their earnings per trip.

Our results support the initial hypothesis of reduced earnings. An increase in FHV trips by 24 rides result in a fall of Yellow taxi’s average hourly fare per trip by 8 dollars, and a reduction in Yellow taxi’s average distance traveled per trip by 20%. These results are for the entire city of New York. However, these results are not the same when we consider parts of the city at a time. That is, the effect of FHV’s presence differs by taxi zones. 1 In taxi zones, where traditionally (before Uber’s entry), you have high Yellow taxi activity (demand), we notice a fall in demand for Yellow taxi with an increase in FHV’s activity (FHV trip count). That is, with an increase in FHV trips by 24 rides, the Yellow taxi’s average hourly fare per trip falls by 10 dollars. While the average distance traveled per trip falls by 13%. These results hint towards a reduction in the supply of Yellow taxis with an increase in the supply of FHVs. That is a substitution between the supply of Yellow taxis and FHVs.

When we consider taxi zones where traditionally you have had low Yellow taxi activity, the supply of Yellow taxi rises with the same increase in FHV’s activity. An increase in FHV trips by 24 rides will result in a reduction of Yellow taxi’s average hourly fare per trip by 49 dollars. However, in these taxi zones, the average distance traveled per trip increases by over 220%. Here, the results point towards an increase in the supply of Yellow taxis with an increase in the supply of FHVs. That is, the supply of Yellow taxis and FHV are complementary to each other.

Manhattan and Brooklyn are two boroughs where we, traditionally, notice high Yellow taxi activity. This can be seen through the heatmap below. Thus, we re-run our results by comparing Manhattan and Brooklyn against the other three boroughs: Bronx, Queens, and Staten Island. These results confirm our previous findings; a substitution between the supply of Yellow taxis and FHVs in Manhattan and Brooklyn and a complementary effect between the supply of Yellow taxis and FHVs in the other three boroughs.

So far, we have looked at the average hourly earnings per trip and distance traveled per trip for an individual taxi driver. However, another crucial element of a taxi driver’s total earnings is the number of trips it makes in a shift. When we account for the number of trips made, we are able to calculate the overall earnings and total distance traveled by all taxi drivers during a given shift. These aggregate results indicate that the total hourly earnings and total distance traveled increases by 1348 dollars and 116 miles, respectively, with a concurrent rise in FHV trips by 24 rides. We are thereby indicating that overall, the presence of Uber, Lyft, and the like increases the demand for Yellow taxis.

Figure 1 Heatmap depicting Yellow taxi trips by taxi zone before Uber’s entry. A darker shade indicates a higher number of trip

A heightened FHV presence leads to a fall in earnings and distance traveled for an individual taxi driver. However, this individual-level effect of FHV is heterogeneous as Yellow taxi drivers are learning on the job and moving towards areas where they face less competition by the new entrants and increased demand. Further, upon accounting for the number of trips made by Yellow taxi drivers, we find that the total earnings and distance traveled of all taxi drivers increases with an increase in FHV trips.

We suggest that this phenomenon can be explained by a behavioral shift that the presence of Uber, Lyft, and the like have brought about. We postulate that the people are now habituated to taking taxi rides for commutes and hence, increasing the total demand for taxi rides. The increase in demand must be significant enough for it to spill over onto Yellow taxis despite the rise in taxi supply. Testing of this theory is beyond the scope of this study, and we leave it open for future studies to explore.

Previous studies have documented a reduction in the total earnings of the incumbent taxi driver; therefore, this study is contrary to these previous studies. Though promising, we have only used New York city taxi trip data to arrive at the results. Hence, they are not indicative of a similar phenomenon in other cities/countries. Due to data limitations, we could not calculate the distance or the fare for FHVs, nor could we estimate our results at an individual driver level. With an increase in the availability of high-resolution data covering more geographic regions, we will be able to answer these questions for more cities and with better precision.

 

About the Author:

Rohin Nandakumar is a Research Associate at SRITNE.

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