Turning Crisis into a Sustainable Opportunity Regarding Demand for Training and New Skills in Labor Market: An Empirical Analysis of COVID-19 Pandemic and Skills Upgradation

Abstract

The COVID-19 pandemic has brought rampant changes in skill needed in the labor market. It has accentuated technological disruption leaving millions in dire need of reskilling and upskilling. In this paper, we empirically analyze the impact of the COVID-19 pandemic related lockdown on the thrust of skills upgradation among people. By analyzing the Google trends data of 13 countries, we test the effect of the lockdown implementations on the urge to upgrade the skills through online searches for skills enhancement. Using difference-in-difference estimation approach, we found a substantial hike in the frequency of search terms related to skills upgradation. Our results suggest that people are utilizing the excess time, made available due to lockdowns, by exploring avenues to enhance their skills to accumulate human capital. The online educational platforms have been proven vital. The findings of this study establish the causal link between use of online education platforms and human capital development.

1. Introduction

The rapid global spread of the Coronavirus Disease (COVID-19 pandemic) is the worst disaster to hit the world in recent times. At the time of writing of this article, the global COVID-19 cases have exceeded 109 million with over 2.4 million recorded deaths (See for details: https://covid19.who.int/) (accessed on 10 July 2022) On the economic front, the estimated financial losses to individuals and countries also paint a bleak picture for the economy. Baseline estimates (World Bank, 2021) [1] expected a 4.3% decrease in global GDP, which has led academics and experts (for instance Yamin, 2020 [2], Asghar et al., 2020 [3]) to call it the worst economic downturn since the great depression.

The economic impact and the loss of GDP shook the labor markets around the world. Of the 104 countries for which IMF released the unemployment data, 98 countries witnessed an increase in their unemployment rates (Figure 1). Moreover, the ensuing social distancing implied a new normal of virtual connectivity and reduced in-person delivery. The increased unemployment rate and the evolution in communication regime are tell-tale forbearers of a structural change in labor markets across the globe.

The rapid spread of COVID-19, and the emergence of social distancing as the only short-term solution to stem its spread in the absence of a vaccine, either changed the nature of many industries or decisively altered the work etiquette in place. Most, if not all, economic and social activities have either entirely shifted to online platforms or make partial useof it. Typical examples include the dramatic rise in the demand for online shopping, tele-health, virtual education facilities, etc. (Batool et al., 2020 [4]; Bhatia, 2021 [5]; Marek et al., 2021 [6]). Similarly, a work from home option is now commonplace in most firms, while physical meetings have been replaced by virtual gatherings. One may call this an abrupt change or an early onset of the virtual communications age fast-tracked by the COVID-19 pandemic. This structural change in labor market has created a massive mismatch in the skill sets of the workforce, leaving a large number of workers without adequate skills to compete in the post-COVID-19 era. The expanded skill gap can further exacerbate the inequality in wages, disproportionate probabilities of success in labor market, etc.

Learning new skills is fundamental for human capital development in the contemporary world (Sima et al., 2020 [7]). Much of the research on human capital theory also claims that skills are the most vital factor of a worker’s productivity earnings (Schultz, 1961 [8]; Mincer, 1974 [9]; Becker, 1993 [10]). Skills are developed either through formal training or practical experience. In the age of social distancing and lockdowns with high unemployment rates, avenues for practical experience shrunk. On the other hand, technological innovations have made formal training more accessible. The development of information and communication technology (ICT) is essential for human development (Pérez-Castro et al., 2021 [11]). COVID-19 related lockdown and quarantine measures provided a stuck-at-home population with an opportunity to upgrade their skills using technology. Individuals have the option of utilizing their free time and enhance their productivity and human capital through online learning and skill development. Thus, it can be stated that COVID-19 has created a skill gap while simultaneously providing people with an opportunity to acquire new skills.

Obtaining a formal education is one strategy to close the skills gap in the job market. Traditional educational programs (e.g., degree/diploma programs given by universities, vocational institutes, etc.) are more difficult to enter in the age of pandemic and subsequent lockdowns for many reasons, including restrictions on mobility, lesser disposable income, and so on. The importance of online learning platforms has grown as they offer students a way to make up for time spent locked out of the classroom (Villafuerte, 2020 [12]). Platforms such as Coursera, edX, etc. have been providing free courses, long before the COVID-19 pandemic, ranging from conventional education to specific courses designed for career and skill development. These e-learning platforms have become significantly more popular in recent times due to flexibility and lower costs. Moreover, e-learning offers high degree of manageability and at-times even better instructional aids (Pituch and Lee, 2006 [13]), especially when considering the skill enhancement education. However, Mendon et al. (2022) [14] argued that brand love is differentiated from satisfaction aspects.

In online learning, the users are in charge of their schedule, they can choose what to learn, when to learn, and how to learn. E-learning gives access of world-class learning materials to the wider population that do not have access to vocational training institutions otherwise (Muhamad, 2020 [15]). Participants save on hassle and wasted time related to traveling, preparation, etc. With high smart phone penetration and faster data services, the technology requirements have also dwindled since most courses on these platforms can be attended using smart phones. In some cases, the online educational platforms provide a highly personalized set up to cater to the individual needs of learners. They allow users to keep track of their progress and suggest courses to acquire new skills based on their background.

Even before COVID-19, there was a massive surge in the demand for online training. Large organizations are increasingly preferring online training for skills enhancement, since it allows them to train their massive workforce in a shorter time and a cost-effective manner). According to a study by Brandon Hall Group, employees spend 40–60% less time studying a particular material in e-learning than in a traditional classroom setting. Research Institute of America observes that e-learning boosts retention rates by 25–60% as compared to 8–10% with traditional methods. Thus, the global market value of edTech platforms is expected to reach US$350 billion by 2025 (Research and Markets, 2019 [16]). These numbers are expected to be much higher owing to the ongoing surge in online learning demand due to the COVID-19 pandemic.

A type of edTech, Massive Open Online Courses (MOOC), are the online courses that are accessible to all, mostly free of cost. MOOCs allow users to learn new skills in a flexible and affordable manner. Coursera and edX are leading MOOC providers. Figure 2 (Data Source: The report by classcentral.com available at https://www.classcentral.com/report/mooc-stats-2020, https://www.classcentral.com/report/edx-2020-review and https://www.classcentral.com/report/coursera-2020-year-review) (accessed on 15 June 2022) reports the number of learners of MOOC (registered at all online platforms), Coursera, and edX over the past five years. While the number of users was already following an upward trajectory, the COVID-19 pandemic time period has seen an abnormal jump in the number of users, signifying users’ attempts to build their skills.

This paper contributes to this discussion by analyzing the motivation and search for skill upgradation among citizens of different countries during the COVID-19 period, and the effectiveness of e-learning platforms in bridging this gap. We employ the data from Google Trends and analyze Google searches of 10 keywords related to e-learning and skill upgradation in countries where lockdowns were enforced during the month of March, 2020. Our results suggest that the COVID-19 pandemic has enhanced the drive for up-skilling in labor force by using the lockdown time to emerge stronger from this crisis. The results will help policymakers and educators to better utilize the online resources to combat the skills gap that occurred due to COVID-19.

The paper is organized as follows: Section 2 provides a review of the literature, data and methodology are explained in Section 3, results are discussed in Section 4, Section 5 concludes the discussion, and limitations of the study are presented in Section 6.

2. Literature Review

In an economy, crises are inevitable as described by Naeem et al. (2019) [17]. COVID-19 (Coronavirus) has a high human-to-human transmission rate. Therefore, under the guidance of the World Health Organization (WHO), many countries chose to enforce nation-wide lockdowns in 2020. The closure of educational institutions during lockdown turned COVID-19 into an educational crisis besides the economic crisis.

Kaffenberger (2021) [18] highlighted the learning losses of children due to COVID-19 and suggested the importance of mitigation policies in reducing that loss. According to him, a three-month closure of schools can lead to a full year of learning loss to the children, however, with proper remediations, this loss can be halved. Due to the closure of educational institutes, e-learning became the new normal. The World Bank (2020) [19] also suggested that virtual learning can be the solution to mitigate educational losses while keeping students and teachers safe. However, Al Lily et al. (2020) [20] pointed out the differences in traditional distance learning and the crisis distance learning, calling the latter as suddenly, unreadily, and forcefully implemented. They maintain that this rapid transition does not meet the standard of real distance learning. Arıker et al. (2021) [21] provided an interesting approach about creating social value through reskilling and upskilling the unemployed for after COVID-19 pandemic.

Analyzing the e-learning experiences in Malaysia, Azlan et al. (2020) [22] show that the students prefer face-to-face teaching over online teaching, while also appreciating the flexibility offered by e-learning. Ahmed et al. (2020) [23] also studied the future of learning in the post-COVID-19 era. They developed a model of The Polarity Approach for Continuity and Transformation (PACT)™ and conducted a virtual mapping session to determine the pros and cons of face-to-face and distance learning. Their results show that distance learning provided a good environment for most students, but it has financial challenges and accessibility issues. Pituch and Lee (2006) [13] highlighted the importance of system characteristics on the use of e-learning. The study analyzed the student′s intention to use different e-learning systems. Data was collected from 259 college students and structural equation modelling (SEM) was used for empirical analysis. The results showed that system characteristics have the strongest effect on the intention to use e-learning.

The spread of COVID-19 has also caused a severe shortage of service sector workers. The hardest hit are those who work in the tourist industry, but also include those in the travel industry and the entertainment industry (Abiad et al., 2020 [24]). However, certain sectors such as technology, health care, and online retail sectors require additional labor to combat the enhanced demand (Agrawal et al., 2020 [25]). This digital surge will cause long-run changes in the post-COVID-19 labor market. Old age workers who lack digital skills will become the most vulnerable as existing research shows that modern skills depreciate faster due to new technological advancements (Lovász, and Rigó, 2013 [26]; Ilmakunnas and Maliranta, 2016 [27]). Thus, the workers need to keep on accumulating human capital in form of new skills to keep up with the changes in technology (Kim and Park, 2020 [28]). This skill-building might help them obtaina higher paying job in the future. According to Agrawal et al. (2020) [25], reskilling workers is essential for coming back stronger from this crisis. They also stated that the COVID-19 pandemichas sped up the adaptation of digital training and skill development tools due to their cost-effectiveness. Thus, individuals should utilize online educational platforms to enhance their skills. Impey and Formanek (2021) [29] suggest that the people are mainly enrolling in online courses to enhance their professional skills. Webb et al. (2021) [30] analyzed the issues faced by higher education institutions during the COVID-19pandemic. The rapid surge in digitalization of online learning should focus on the practical and inclusive digital intervention.

Schultz (1961) [8] is among the pioneers of research work on human capital theory. He highlighted the importance of investment in human capital as the major cause of the difference between worker’s earnings, and postulates that expenditures on education and training by workers is a proxy for investment in human capital. He argues that people invest in themselves as a way to increase their welfare. Becker (1962) [31] examined relevant issues on investing in human capital and suggested that many workers increase their productivity by learning new skills and perfecting old ones while on the job.

Our study will fill the existing research gap in many ways. It extends the literature on the impact of COVID-19 on the skill gap. Currently, studies on this topic are focused on the theoretical aspects without enough empirical work due to unavailability of actual data. We overcome this issue by using Google trends data of searches related to upskilling, revealing labor force behavior and providing useful insights into the use of online learning and educational platforms during the COVID-19 pandemic.

3. Data and Methodology

3.1. Data and Variables

Since our focus is also on the deliberate investment of people in their human capital, we argue that people used the stay-at-home excess time, made available due to lockdown and quarantine measures, to engage in online learning and training as a way to build new skills and increase their future earnings.

To introduce the role of the COVID-19 pandemic in the model, we follow the Ben Porath (BP) [32] model of optimal investment in human capital. In this model, individuals allocate their limited time between work and investing in human capital (e.g., acquiring knowledge and skills). This model provides insights into the human capital decisions made by individuals. Due to time resource constraint, individuals face a trade-off between earnings and skill-building. COVID-19 has resulted in a technological disruption, increasing the rate of human capital obsolescence due to increased unemployment in many sectors as a large number of activities are now shifted online. Resultantly, the workers need to increase their accumulation of human capital through skill-building. Online educational platforms provide the service to this end, with reduced costs due to efficient and cost-effective modes of skill-building.

We use google search trends data to decipher the impact of COVID-19 on skill development in order to bridge the skill gap created by the disruption due to the pandemic. Google search trends data represents the searches made by users of google search engine in a given region. We postulate that the increase in searches related to the online educational platforms primarily represent the labor force participants. Users of Google search engine, not in labor force, have little reason to change their search behavior about online education since their need for such service has not been affected by the COVID-19 pandemic. Similarly, the users querying google for online education are doing so primarily to enhance their skills related to the labor market. If such searching was for non-labor market reasons (e.g., hobbies, etc.), the pattern for searches should not have changed dramatically due to the lockdowns. Thus, the uptick in searches related to online education and training primarily captures the interest in online education of those in labor force attempting to enhance their skills. This establishes the causal link between investment in human capital formation during lockdowns and the genuine fear of an emerging skill gap.

We use Google searches of the following seven ‘search terms’ and three online ‘educational platforms’.

Search terms: Online Training, Online course, Online learning, E-learning, Career development, Do it yourself, Massive Open Online Course (MOOC).

Educational platforms: Coursera, edX, Udemy.

Data on the above search terms are collected for 13 countries that enforced nation-wide lockdown during the month of March, 2020. The list of countries included in the study along with their respective lockdown start and end date is given below in the following Figure 3.

3.2. Google Trends

Google trends data is a form of big data having proven effective in predicting tourism decisions, financial market fluctuations, disease outbreaks, and even predicting COVID-19 hotspots (Carneiro and Mylonakis, 2009 [33]; Önder, 2017 [34], Kurian, et. al., 2020 [35]). Google trends datado not provide the number of actual searches; rather it provides an index for search intensity of a keyword over the requested time period in a geographical area. Index value of each day is calculated by the number of daily searches of that topic divided by the maximum number of daily searches of that topic over the requested time period in that geographical area. The index value ranges from 0 to 100, where 100 indicates the highest searches and zero indicates insufficient search volume on a specific day.

For estimation purpose, we employ daily data from 1st January to 30th June for 2019 and 2020. This time period is selected since the lockdowns were implemented during this period of 2020. We then compare the data of 2020 in this period to the previous year by employing difference-in-difference methodology. Data is used till 30 June 2020 since afterwards, the lockdown restrictions were lifted in many countries included in the model.

Google trends daily data availability is only for a period of less than nine months and due to this obstacle, we have to download daily data in two separate files, one file for 2019, i.e., 1 January 2019 to 30 June 2019, and the other for the same dates in 2020. However, downloading data into two separate files creates compatibility issues; the scaling factors used to calculate the 0–100 score are not the same in 2019 and 2020, implying that the daily data of 2019 and 2020 are not comparable directly as their denominator (the maximum number of searches during one day in the period) is not the same. For example, a value of 50 during the 2019 period may represent fewer searches than 35 in the 2020 period. To overcome this obstacle, we use weekly Google trends data (which is available for the full time period) to re-scale the daily data of 2019 and 2020 as suggested by Brodeur et al. (2020) [36].

3.3. Normalizing of Daily Data

Following the literature (Brodeur et al., 2020 [36]; Batool et al., 2020 [4]), re-scaling the daily data using weekly figures is conducted in the following steps:

Step 1: Download the daily data of each search term in all countries for 2019 and 2020, denoted by $D_{i,c,t}$ where $i$ = search term, $c$ = country, and $t$ = time which is either 2019 or 2020. Use this daily data to calculate the weekly averages: $Av{g}_{i,c,t}$.

Step 2: Download the weekly Google trend data of each search term and for each country over the same period of 2019 and 2020, denoted by $Weekl{y}_{i,c,t}$. Calculate weekly weights by computing the ratio of $Weekl{y}_{i,c,t}$ and weekly averages calculated in the first step.

$${weight}_{i,c,t} =\frac{Weekl{y}_{i,c,t}}{{Avg}_{i,c,t}}$$

Step 3: Multiply the daily data $(D_{i,c,t}$) with the corresponding weekly weight $weigh{t}_{i,c,t}$ to obtain the rescaled data: $RS{D}_{i,c,t}$.

$${RSD}_{i,c,t}=D_{i,c,t}\times {Weight}_{i,c,t}$$

Step 4. The rescaled data obtained in step 3 may not be within the range of 0 to 100 and as a last step, we normalize the rescaled data (RSD_i,c,t) dividing the by the its maximum value and multiply by 100. It brings the daily data within the range of 0 to 100, as per the original format of Google Trends data.

$$N_{i,c,t}=\frac{{RSD}_{i,c,t}}{\max \left({RSD}_{i,c,t}\right)}\times 100$$

3.4. Methodology

We use the difference-in-difference (DID) estimator for estimating the change in search frequency of different ‘search terms’. DID estimation is currently popular in analyzing the effect of COVID-19 on different variables (Chen et al., 2020 [37]; Lyu and Wehby, 2020 [38]). DID estimation is also helpful in controlling for the seasonal and country-specific shocks.

Following Brodeur et al. (2020) [36], we formed the following model:

$$N_{i,c,t}={\beta }_{1}LOC{K}_{i,c}{\hspace{0.17em}}^{*}YEA{R}_i+{\beta }_{2}LOC{K}_{i,c}+{\beta }_{3}COVI{D}_{i-1,c}+{\epsilon }_i$$

where, N_i,c,t is the daily Google searches for a search term. LOCK_i,c is a dummy variable whose value is zero on the dates when the lockdown was not in place and one when the lockdown was enforced. YEAR_i is also a dummy variable that takes the value of 1 in 2020 and 0 in 2019. COVID_i−1,c represents COVID-19 related deaths per million on the previous day of ‘search term’ in country c.

4. Results and Discussions

4.1. Cross-Country Comparison

Cross-country comparison of Google trends weekly searches is provided in Figure 4. We plot the raw weekly searches of e-learning (the comparison of only “E-learning” is reported here to conserve space. The comparison of other search terms depicts similar results and can be obtained by contacting the authors) for the period January to June 2019, and in the same period of 2020. We observe a number of thought provoking stylized facts from Figure 4a–c). As can be seen from Figure 4, the searches were comparatively much lower before COVID-19 pandemic and related lockdowns (January to June 2019). However, since the beginning of lockdowns in March 2020, the intensity of searches for e-learning increased in all the countries, even in the countries that were not searching much for the e-learning before the pandemic. The upward (upward trend may be even higher in some countries due to search in native languages instead of only English language) trend for e-learning searches during the lockdown period is more clear in case of UK, Italy, Germany, France, Spain, Pakistan, South Africa, Russia, India, and Bangladesh, as depicted in Figure 4a,b. In Mexico, Argentina, and Brazil, there are more fluctuations for e-learning searches. This can be due to preference for a search engine other than google, due to use of a different language for web-browsing, etc.

4.2. Difference-in-Difference Estimation

Table 1. Results of DID estimation for ‘Search Terms’.

Coefficient	Career Development	Do It Yourself	E-Learning	MOOC
LOCKi,c × YEARi	0.92 ** (0.36)	7.56 *** (2.38)	16.41 *** (2.86)	8.57 *** (1.90)
Coefficient	Online Learning	Online Course	Online Training
LOCKi,c × YEARi	11.53 *** (2.85)	12.53 *** (3.39)	5.78 * (2.81)

Robust Standard errors are reported in parenthesis. *** Significant at 1% level, ** Significant at 5%, * Significant at 10% level.

shows the results of difference-in-difference estimation for the seven ‘search terms’ related to skill development (for robustness check, we used different treatments, such as using the date of lockdown announcement or the date of first case reported instead of lockdown enforcement. We also used COVID-19 cases per million instead of COVID-19 death per million. The results in all these cases are similar, hence they are not reported and can be obtained from authors upon request).

The results show a significant increase (as all coefficients being positive) in the Google trend of all the search terms after the enforcement of lockdown. The biggest difference observed is in the searches of e-learning (β = 16.41, p < 0.01). It shows that people were using their time in lockdown to search for ‘e-learning’ to enhance their knowledge and skills. The lowest difference appears in the searches of ‘career development’ (β = 0.92, p < 0.05), however, the positive sign indicates a trend of more searches during the lockdown. The searches of ‘online training’ also reveal a positive trend (β = 5.78, p < 0.1). It shows that companies and individual workers were looking for online training resources. Google searches for ‘MOOC’, ‘do it yourself’, ‘online learning’, ‘online course’ are also showing a significant and positive difference in the volume of searches before and after lockdown. It is an indication of people’s investment in their human capital and also showing that people were utilizing the time of lockdown to increase their skills and knowledge, indicating a deliberate attempt to enhance skills in the age of COVID-19 and beyond. These results are consistent with current research that also reveals a many fold increase in the use of online learning and training (Zhu and Liu, 2020 [39]; Dhawan, 2020 [40]).

Table 2. Results of DID Estimation for Online Educational Platforms.

Coefficient	Coursera	edX	Udemy
LOCKi,c × YEARi	25.47 *** (3.86)	12.95 *** (2.65)	18.76 *** (3.54)

Robust Standard errors are reported in parenthesis. *** Significant at 1% level.

shows the results of differences-in-difference estimation for Google searches of three online educational platforms.

Coursera, edX, and Udemy are leading EdTech platforms. They enable their users to learn and obtain certificates from the top institutions in the world. These platforms have been enabling learners around the globe to develop new skills and expand their knowledge. The results of DID estimation shows that the Google searches of all three platforms increased exponentially during the lockdown. The largest difference is in the searches of Coursera (β = 25.47, p < 0.01) as it is the largest MOOC provider (Mamgain et al., 2014 [41]). The searches of edX and Udemy also show highly significant and positive during the lockdown. These results validate the surge in the number of users of Coursera and other platforms reported in Figure 1. These jumps in searches may also explain the significant increase in number of enrolled students and profits of these platforms during 2020 (For details check: https://www.classcentral.com/report/the-second-year-of-the-mooc/,https://www.classcentral.com/report/coursera-2020-year-review/(accessed on 25 June 2022) and https://www.classcentral.com/report/edx-2020-review/) (accessed on 27 June 2022).

4.3. Difference-in-Difference Estimation: Graphical Analysis

Figure 5 plots the difference in yearly Google searches before and after the lockdown enforcement. The red dots show the average daily searches in 2020 and the grey dots show the average daily searches in 2019. The date (lockdown dates are different as depicted in Figure 3 but for the graphical analysis, we assume 15 March 2020 as lockdown date for all countries) of lockdown enforcement is set equal to zero in 2020.

It can be seen that the daily searches were following a parallel and smooth trend in the absence of COVID-19. However, after the lockdown announcements, the daily searches witnessed a clear hike during 2020. The trend of all three online platforms also represents a significant increase in 2020’s search volume as compared to 2019’s search. The stay-at-home orders nearly doubled the search volume in the post-lockdown period of 2020. This positive trend appears stronger for the first 50 days after lockdown announcement. The search trend of e-learning, MOOC, and other search terms was almost identical in the pre-lockdown time of 2019 and 2020, however, the lockdown enforcement resulted in a substantial increase in the searches of all seven ‘search terms’.

5. Conclusions

The COVID-19 pandemic has wreaked havoc in all sectors of the economy. The precautionary measures like lockdown, work from home options, and closure of educational institutes have left millions of people with free time on their hands. COVID-19 forced everyone; from students to workers, from educational institutions to businesses, to look for online substitutes of traditional learning and training. Some companies, for instance Facebook, have already decided to continue with the remote work environment in the post-COVID-19 pandemic era (Sandler, 2020 [42]). It indicates that the future of learning and work will highly rely on online educational platforms. This has left a large segment of the workforce in dire need of skill up gradation for the post-COVID-19 pandemic era. Moreover, Naeem et al. (2021) [43] also argued that economies are dynamic while economic factors keep changing over time.

Based on the Google trends data for 13 countries in 2019 and 2020, our analysis shows that people are using their time to turn this crisis into an opportunity with the help of online resources. People were searching for online courses, training, and learning options. They are enhancing their skill through online career development tools and e-learning. These results are consistent with Human capital theory of Schultz (1961) [8] as it shows that people were carrying out an investment on themselves and they are using the lockdown time to enhance their skillset. These results provides insights into the opportunity that occurred in this crisis and it can be considered a ray of sunshine in the dim economic climate of the COVID-19 pandemic era.

The online platforms are playing an important role in bridging the skill gap created by COVID-19. Thus, labor force participants as well as employers are investing in human capital development in line with the emerging and changing needs created and exacerbated by this multi-faceted disruption. This situation has also shown the importance of e-learning due to cost-effectiveness and flexibility, especially due to its ability to serve varying interests and swiftly evolve as per the needs of the market. Thus, the findings of this study form a basis for recommending enhanced use of digital platforms and online learning tools to mitigate the losses occurred during the COVID-19 pandemic.

6. Policy Implications

Our findings are novel, establishing a positive causal social reliance on use of online learning platforms for skills acquisition and enhancements. Policy makers shall be cognizant of this emerging scenario, and adapt policy accordingly. The implications are far ranging, as the causality and the trend established signify the shift towards greater reliance on online resources, as using MOOCs and other platforms for learning reflect. Education policy can now devise cost effective ways for skills delivery, especially technical and vocational skills, using online platforms. However, this will require provision of necessary infrastructure (for instance broadband internet, devices, etc.) for a more equitable benefits distribution. The findings also have policy implications for the labor market policy, as skills acquisition through virtual platforms significantly increase the flexibility of the labor supplied. Thus, the future of the labor market appears different from the contemporary market, and the policy needs to adjust accordingly. Lastly, policy regarding provision and availability of infrastructure and regulations related to the internet shall be modified to underscore the significance of internet for wider macro-economy. Moreover, policy implications represent an essential factor in order to achieve sustainable development.

7. Limitations of the Study

This study has few limitations. Firstly, we used data of seven search terms related to online learning and skill development, future studies can explore this relation further by using more search terms. Secondly, there should be further study to investigate long term effect of this surge in online learning during the COVID-19 pandemic era. Lastly, it is highly recommended to study the trend of internet searches on search platforms other than Google.