The Peninsula Foundation

Category: Labour & Employment

  • Forecasting Unemployment Rate during the Pandemic

    Forecasting Unemployment Rate during the Pandemic

    Forecasting
    Forecasting, in simpler terms, is a process of predicting future values of a variable based on past data and other variables that are related to the variable being forecasted. For example, values of future demand for tickets for a particular airline company depend on past sales and the price of its tickets.
    Time-series data is used for forecasting purposes. According to Wikipedia ‘A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus, it is a sequence of discrete-time data.’ An example of time series data for monthly airline passengers is given below:

    Figure 1


    More technically, it is modelled through a stochastic process, Y(t). In a time series data, we are interested in estimating values for Y(t+h) using the information available at time t.  
    Unemployment rate
    Unemployment is the proportion of people in the labour force who are willing and able to work but are unable to find work. It is an indicator of the health of the economy because it provides a timely measure of the state of labour market and hence, overall economic activities. In wake of the impact of Covid-19 on economic activities throughout the world, unemployment rate analysis and forecasts have become paramount in assessing economic conditions.
    In India, unemployment rates have been on the higher end in recent times. According to data released by Statistics Ministry, unemployment rate for FY18 was 6.1%, the highest in 45 years. It is no co-incidence that GDP rates have also been declining successively for the past few years. The shock that Covid-19 has given to the economy has only worsened our situation. The unemployment rate rose to 27.1% as a whopping 121.5 million were forced out of work.

    Figure 2


    Source: CMIE
    Methodology
    The data used to forecast unemployment rates was sourced from CMIE website, which surveys over 43,000 households to generate monthly estimates since January 2016. The data has 56 monthly observations ranging from January 2016 to August 2020, data before 2016 was not available.
    Four popular econometric forecasting models (ARIMA, Naïve, Exponential Smoothing, Holt’s winter method) were used and the best performing model was chosen to forecast unemployment till December 2020.
    The forecasting models were programmed in R. The relevant codes are available upon request with the author. The Dicky-Fuller test and the Chow test for structural breaks were conducted using STATA, results of which are presented further in the article.
    Before beginning the analysis, I believe that the limitations of the analysis should be mentioned:

    • The sample size of 56 observations is not sufficient for a thorough analysis, ideally the sample size should have been 2-3 times larger than the available data. Smaller sample sizes lead to skewed forecasting results which are prone to errors.
    • The unemployment data from CMIE is an estimate and is a secondary source. In India, primary data is only collected once in 3-4 years, thus the forecasting results are only as good as the source of the data.
    • This is a univariate analysis, an Okun’s law based analysis of Unemployment rate as a function of GDP (output) and past trends would have been more suitable. However, since GDP data is only available quarterly and there are only 56 monthly observations available, it would have rendered the analysis insignificant with only 19 quarterly observations.
    • Forecasting being based on past trends, is prone to errors. The negative shock provided by Covid-19 to the economies worldwide has made it all the more difficult to forecast. A Bloomberg study analysed over 3,200 forecasts by IMF since 1999 and found that over 93% of the forecasts underestimated or overestimated the results with a mean error of 2 percentage points.

    Checking the stationarity of data
    In order to model build a model, we need to make sure that the series is stationary. For intuitively checking the stationarity, I plotted the data over time as indicated in Figure 2 above. I also plotted the correlograms (autocorrelations versus time lags) as shown in Figure 8 and 9 in appendix. The plot of data over time indicate varying mean, variance and covariance. The ACF and PACF plot show that autocorrelations function are persistent indefinitely.
    We perform the Augmented Dickey Fuller test at 2 lags. Result of the ADF test is shown in Table 1 below. The test statistic is insignificant at 5 per cent and the p-value is 0.1709, which is more than the accepted benchmark of 0.05. We fail to reject the null hypothesis of non-stationarity. We conclude that our series is non-stationary.

    Dicky-Fuller test on raw data

    Table 1

    		 —– Interpolated Dickey-Fuller —–
    Test statistic 1% critical value 5% critical value 10%critical value
    Z(t) -2.303 -3.576 -2.928 -2.599

     

    MacKinnon approximate p-value for Z(t) = 0.1709

    Converting the non-stationary series into stationary

    In order to transform the non-stationary series into stationary, we use differencing method (computing difference between consecutive observations).
    We plot the data over time, ACF and PACF again as shown in Figure 5 below and figure 10 and 11 in appendix, respectively. From the figures, we can intuitively say that the transformed series is stationary. Further, we used Augmented Dickey-Fuller tests to ascertain the stationary of our series. Table 2 shows the result of the ADF test. The test statistic is significant at 1,5 and 10 per cent levels and the p-value is less than 0.05. We reject the null hypothesis of non-stationarity of our series. The tests confirm that the series is stationary.

    Dicky-Fuller test on first difference data

    Table 2

    —– Interpolated Dickey-Fuller —–
    Test statistic 1% critical value 5% critical value 10%critical value
    Z(t) -5.035 -3.576 -2.928 -2.599

    MacKinnon approximate p-value for Z(t) = 0.0000

    Figure 3

    Naïve model
    Naïve models are the simplest of forecasting models and provide a benchmark against which other more sophisticated models can be compared. Thus, a Naïve model serves as an ideal model to start any comparative analysis with. In a naive model, the forecasted values are simply the values of the last observation. It is given by
    y^t+h|t=yt.
    Forecast results from Naïve method are presented below in figure 4 and table1.

    Figure 4

    Table 1

    Point forecast Lo 80 High 80 Low 95 High 95
    Sept 8.35 4.861900 11.83810 3.0154109 13.68459
    Oct 8.35 3.417081 13.28292 0.8057517 15.89425
    Nov 8.35 2.308433 14.39157 -0.8897794 17.58978
    Dec 8.35 1.373799 15.32620 -2.3191783 19.01918

    Box-Jenkins Approach

    1. Identification of ARIMA (p, d, q) model

    The data was split into training and testing dataset in 80:20 ratio. The training data was used for estimating the model, while the model was tested on the remaining 20 percent data. This is done in order to forecast the future values of the time series data.
    p, d and q in (p, d, q) stand for number of lags, difference and moving average respectively.
    The model best fitting the data was (0,1,3) as its Akaike Information Criterion (AIC) was the lowest amongst all the possible combinations of the order of the ARIMA model.
    The residuals from Arima model were found to be normally distributed, with a mean of 0.09 and zero correlation. This causes a bias in the estimates. To solve the problem of bias, we will add 0.09 to all forecasts. The ACF and line graph of residuals is attached in the appendix.
    After identification and estimation, several diagnostic tests were conducted to check if there were any uncaptured information in the model. Results of the diagnostics tests have been omitted from the article in interest of length.

    1. Forecasting

    The model that has been constructed was used to forecast unemployment rates for the next four months. The results are presented below in figure 5 and table 2.

    Figure 5

    Table 2

    Point forecast Lo 80 High 80 Low 95 High 95
    Sept 9.04 5.978858 11.93987 4.401073 13.51765
    Oct 9.77 5.183039 14.1951 2.797671 16.58054
    Nov 10.3 5.364191 15.06267 2.797157 17.62971
    Dec 10.3 5.280182 15.14668   2.668678   17.75819

    Exponential Smoothing method
    It is one of the most popular classic forecasting models. It gives more weight to recent values and works best for short term forecasts when there is no trend or seasonality in dataset. The model is given by:
    Ŷ(t+h|t) = ⍺y(t) + ⍺(1-⍺)y(t-1) + ⍺(1-⍺)²y(t-2) + …
    with 0<⍺<1
    As observed in the model, recent time periods have more weightage in the model and the weightage keeps decreasing exponentially as we go further back in time.
    The ⍺  is the smoothing factor here whose value was chosen to be 0.9 since it had the lowest RMSE among all other values.
    The forecast results are presented below:

    Figure 6


    Table 3

    Point forecast Lo 80 High 80 Low 95 High 95
    Sept 8.30 4.739288 11.87260 2.8512134 13.76068
    Oct 8.30 3.507498 13.10439 0.9673541 15.64454
    Nov 8.30 2.532806 14.07908 -0.5233096 17.13520
    Dec 8.30 1.700403 14.91149 -1.7963595   18.40825

     
    Holt Winters’ method
    The simple exponential function cannot be used effectively for data with trends. Holt-Winters’ exponential smoothing method is a better suited model for data with trends. This model contains a forecast equation and two smoothing equations. The linear model is given by:
    yt+h = lt + hbt
    lt  = αyt + (1-α)lt-1
    bt = β(lt-lt-1)+ (1-β)bt-1
    where, lt is the level (smoothed value).
    h is the number of steps ahead.
    bt is the weighted average of the trend.
    Just like the simple exponential smoothing method, lt shows that it is a weighted average of yt
    The α  is the smoothing factor here whose value was chosen to be 0.99 and  the β  value 0.0025 since they had the lowest RMSE among all other values.
    The forecast results are presented below:

    Figure 7

    Table 4

    Point forecast Lo 80 High 80 Low 95 High 95
    Sept 8.34 4.749288 11.9326 2.84121 13.84
    Oct 8.33 3.24 13.4243 0.54541 16.11977
    Nov 8.32 2.0800 14.5678 -1.2253 17.87316
    Dec 8.31 1.0963 15.53419 -2.725103   19.35565

     
    Evaluation
    To compare the models the two parameters chosen are:

    • Root mean square error (RMSE)
    • Mean absolute error (MAE)

    MAE is a measure of mean error in a set of observations/predictions. RMSE is the square root of the mean of squared differences between prediction and actual observation. RMSE is more useful when large errors are not desirable and MAE is useful otherwise.
    RMSE and MAE statistics for all the models are presented below:

    Naive ARIMA Exp Smoothing Holt Winters’
    RMSE 2.72 2.24 2.73 2.7
    MAE 1.05 1.034 1.06 1.05

    From the table it is clear that ARIMA/Box Jenkins method has both the lowest RMSE and MAE among the models under consideration while Exponential smoothing method has the highest MAE and RMSE among all.
    Therefore, the unemployment rate forecasts as per the Box Jenkins method for the next four months are:

    Sept 9.04
    Oct 9.77
    Nov 10.3
    Dec 10.3

    The way ahead?

    • The unemployment rate is expected to rise in the coming months. This is a bad sign for an economy that is already suffering.
    • With GDP forecasts getting lower and lower for the current financial year, the govt needs to act quick to mitigate the potential damage.
    • It is impossible to correctly ascertain the total impact of covid-19 on the economy and the range of the impact, but it is safe to say that we will be seeing the effects for a long time to come in some form or other.
    • We might see more and more people slip into poverty, depression, increased domestic violence and with potentially long term impact on human development parameters like child mal-nutrition, enrolment rates etc among other things.

    Some possible solutions

    1. Expansionary monetary policy: It is a common tool of dealing with high unemployment rate in the short term. Under expansionary monetary policy, the central bank reduces the rate of interest on which it lends money to the banks, subsequently the banks lower their rates which leads to a higher amount of loans being taken by business owners. This extra capital helps businesses to hire more workers and expand production, which in turn reduces unemployment rate.
    2. Expansionary fiscal policy: Under expansionary fiscal policy the government increases its spending, particularly in the infra-structure sector. It spends more money to build dams, roads, bridges, highways etc. This increased spending leads to an increase in employment as these projects require labour.
    3. Expand the scope of NREGS to urban areas permanently and a higher minimum wage for all : NREGS has proved to be really effective in alleviating poverty, improving quality of life and decreasing unemployment rate in rural areas. Given the unprecedented circumstances, the govt can consider expanding its scope to urban areas, so that it could provide employment to the millions of unemployed workers there. This increase in expenditure could also help the govt revive consumer demand, which is essential if we want to help the GDP get back on track.
    4. A stimulus package aimed at putting money into the hands of the poor :

    The govt should also consider providing at least a one-time transfer of funds to people just like the US govt did. Such a transfer of putting money directly into the hands of the poor is the most effective way of reviving consumer demand in the economy and many economists around the world have been calling for such a plan to be implemented. There is no better way of increasing consumer expenditure other than putting money into the hands of cash-starved people.

    Appendix:

    Figure 8

    Figure 9

     

    Figure 10

     

    Figure 11

     

    Figure 12

     

    Figure 13

     

  • COVID-19 Challenges for India: Tackling MSME Sector and Unemployment

    COVID-19 Challenges for India: Tackling MSME Sector and Unemployment

    The COVID-19 pandemic has shaken global markets as countries struggle to battle national and global health crisis. Indian government has announced an economic stimulus of  Rupees 20 lakh crore (Rs 20 trillion corresponding to $ 267 billion), roughly 10% of GDP for FY 21, in which six measures were framed for the Micro Small Medium Scale Enterprises (MSME). Government has allocated 3 lakh crore for collateral-free loans, additional debt and equity infusion with slew of other measures to protect the bruised MSME sector. The rise in the number of casualties and infected cases  all over the world present a grim picture. This is expected to result in a global recession that could lead to a loss of over $ 3 trillion to the global GDP. India, in an effort to contain the spread, has extended the lockdown at the cost of freezing almost 60 percent of its economy. Third extension of lockdown on May 3rd in order to flatten the curve will further contract the demand for next few quarters. IMF has revised India’s growth downwards to 1.9 percent for the year 2020 and 7.4 percent for the year 2021. Although the growth projection is not negative as in the cases of Eurozone and the US, India will need to overcome significant structural challenges to bring the economy back into a high growth trajectory. The cost of battling COVID-19 is not limited to the dip in growth but also includes the bleak prospects of a sizable percentage of the population being pushed below the poverty line.

    Apart from the virus, India faces two key challenges. Firstly, almost 80 percent of its labour force is part of the informal sector, which is expected to take major hit as a result of  the lock-down. Secondly, as India’s working age population will continue to expand  till 2055─ the cost of missing this demographic dividend will directly impact the future growth trajectory. Japan, China, South Korea and Singapore have capitalized on their demographic dividends and experienced double digit growths. The current disruption in the global economy will have a significant impact on India’s growth for the next few years. Therefore, diagnosing the systemic problems in the economy is crucial to developing a viable strategic economic policy. The Periodic Labour Force Survey (PLFS) notes that only nine percent of Indian workers are employed with organizations having more than 20 workers. Rest of the labour force are employed with small enterprises which have been forced to lay-off most of their employees due to the extended lockdown.

     Business Supply versus People Demand

    Contributing 30-35 percent of the GDP— Micro, Medium and Small scale industries face a higher risk of shutting down their production due to cash flow constraints. All India Manufacturers association reported that 43 percent of the MSMES will cease to operate with the lockdown extension. Around 99 percent of the MSMEs are dominated by Micro enterprises in which labour intensive production units are already under stress with restricted labour movements. Finance minister’s attempt at redefining MSME by including businesses with higher investment and turnover does not address the main problem of majority of unregistered micro enterprises shutting down due to less or nil operating capital.

    A total of 114 million people are employed in MSMEs and the shortage in working capital as a consequence of the lockdown would drive most businesses out of the market. Furthermore, an extended demand shock would curb the production and supply, as a result of which small industries with limited capital will most likely shut down. Additionally, 86 percent of the enterprises are unregistered and 71 percent of labourers have no written job contracts. Since most of the enterprises function in highly unorganised sectors, they would have been forced to lay off employees.  Thus relevant policies will need to be recalibrated in order to address the problem of unemployment– currently estimated to be 27.11 percent. The share of MSME exports is valued at $147.7 billion– showing an impressive jump from the previous value at $75 billion. The small number of exporting businesseswill be clamped down due to insufficient liquidity especially with weak global demand.  Hence, the policy must focus on balancing to keep the interest rates low in the long run and enhance discretionary spending to boost investors’ confidence. One of the six measures announced by the government is to protect the local MSMEs from unfair foreign competition. Pursuing a protectionist policy in the business sector before the recovery of domestic demand would imply higher risk of the economy being caught in a low demand cycle. Additionally, the recent exemption of labour laws threatens the workers’ income─ reducing the revival rate of consumer demand. According to a latest reading of the consumer demand risk map, casual labourers in both rural and urban areas are at highest risk of salvaging potential expenditure.

    Need to Reorganize MSME and Boost Employment

    Although strong relief packages are demanded, India has limited fiscal space. The slew of measures announced by the central bank to ease the liquidity will cushion the MSME sector during the lockdown period. However, incentivizing small scale businesses to operate amidst weak demand would need recapitalizing finance based on the firm’s productivity. A structural makeover of the business sector will call for measures beyond just monetary policy. While current economic stimulus aims at protecting the business sector, challenges remain in adopting a medium term policy given the unorganized structure. The OECD countries have broadly undertaken measures to reduce the impact on their Small and Medium Enterprises (SMEs) by providing wage subsidies, loan guarantees, direct lending and modified structural policies. The Reserve Bank of India (RBI) has similarly offered a much-needed loan moratorium, cuts in the Cash Reserve Ratio (banks minimum reserve requirement to be held with RBI) and working capital financing. Although the second round of relief package has focused on small industries, the expectation of a burgeoning fiscal deficit to 5.07 percent from revised estimate of 3.8 percent means that financial  stimulus is somewhat of a double edged sword.

    Even prior to the pandemic, unemployment was at a 45 year’s high at 8.5 percent and consumption was on downtrend. The economic response for India must factor in the welfare loss while assessing the economic consequence. In five out of the first ten years of entering its demographic dividend phase, Japan was experiencing double digit growth.  If India is not to lose out on growth momentum during the current stage of its youth bulge, it would require effective and radical policy measures to counter the problem. Economic relief packages during the crisis must be followed with strategies to provide economic security to the working age population across the country.

    To keep up with the growth of the working age population, estimates suggest that India must create 10 million jobs annually. Ease of doing business becomes a crucial factor in creating employment opportunities. Indian policymakers are tasked to identify the methods to sustain the operations of MSME sector post lockdown. The large workforce resulting from India’s youth bulge cannot be undermined by this crisis. Policy prescription to create rapid employment and facilitate business operations is the priority. For India, it is important to endeavour to balance the immediate financial response with continuous public and human capital investment. Biting the fiscal bullet is inevitable in a crisis situation but assessing the cost of growth foregone is crucial to strategize policies for future. The real challenge lies in the transition of role from being protective to promotional through structural operations by factoring in the consumption demand. Temporary infusion of money in businesses and renovation of MSME sector is much needed to realize the ‘Make in India’ dream.

    Image Credit: Adobe Stock

  • Need to Redefine MGNREGS: Response for a post pandemic Economy

    Need to Redefine MGNREGS: Response for a post pandemic Economy

    The Union budget 2020 was heavily criticized for allocating only INR 60,000 crore on the UPA flagship program, Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGA). Discontent continues even after the relief package mentioned INR 200 per person will be paid for the next three months. With 7.6 crore workers registered under MGNREGA program – around one trillion (INR) would be required to fulfill the promise. The pandemic has disrupted almost every physical activity, thereby disrupting the physical labour economy. The unfolding crisis across the country and  the poor health infrastructure especially in rural areas poses a major challenge to combat the spread of the virus .  According to the National Health Report, India’s government hospitals average a low figure of one bed per 1844 patients.  The magnitude of the health crisis becomes apparent with the inadequacy in health infrastructure in rural India. The ongoing COVID-19 crisis is reshaping the entire global economy and is expected to be a stress test for government institutions. Even after the crisis, policy making and social programs will remain the key areas in which continuous revision must happen – to build a resilient economy in the long run. As the pandemic influenced financial crisis looms large, it is opportune to discuss public employment programs in bridging infrastructure gaps and financial losses. 

     Demand driven workfare programs intend to provide 100 days of employment for rural households. This scheme was launched with an objective to alleviate poverty and create public assets.   Recognising the vagaries of the agriculture sector to provide stable employment, the program sought to guarantee minimum income for subsistence level labourers and also internalized short term shocks in the rural economy. In principle, the ‘right to work’ element offered a legitimate progress in public-policy discourse by empowering women and marginalized communities to work. The laudable results of the employment program have more or less achieved its social objectives by increasing individual asset creation and enhancing savings rate. Almost 50 percent of the population dependent on agriculture fall back to government employment schemes in times of labour market failure. Low productivity, inadequate modern technology, high dependence on rainfall and bottlenecks to reach the market are primary sources of such failures. Execution of public employment in India is  plagued by rampant corruption and efforts to effectively implement the scheme faces hurdles and results in marginal progress. In the wake of economic slump with falling consumption in rural India and high unemployment rates, infusing cash in the hands of people is always the priority. However, marginal increase in budget allocation for public work programs has invited criticism from the economists – expecting the rural economy to struggle with slow recovery. With acute shortage of skilled labourers and an education system failing to impart quality skill education, a public employment program can be more dynamic in resolving the socio-economic and food security problems. The primary objective is to offset short term economic disturbance and smoothen consumption expenditure, but the development of the program in responding to the needs of the community is also important.  Successful implementation of an employment program must factor-in convergence with other departments, quality of assets created and skill levels imparted under the program. .The three-week lockdown due to covid-19, further extended by two weeks, has exposed the inadequacy of public health infrastructure, more so in rural areas and for informal labour groups, to address their health and the resulting financial hardship. Converging the needs of villages to enhance better response during a crisis with the employment program would result in bringing accountability and creating assets.

    India has experimented with a plethora of universal public programs such as Public Distribution System and Integrated Child Development Scheme (ICDS). In a similar vein, MGNREGS has been an important public work programme with the aim of reducing poverty and enhancing income levels. At this juncture, revising and reviewing MGNREGA scheme with the objective to reduce leakage in the system is a priority. A clear balance between the twin objective of providing employment and creating infrastructure has been missing in the literature. The gap between theoretical policy and reality has raised  concern and the need to review the current approach . The obvious gap in infrastructure requirements identified during the time of crisis must converge with public programs. Such carefully designed schemes with tangible objectives will provide economic security in the short run and improve rural infrastructure in the long run. 

    Work completion rate can be used as a proxy for productivity because individual labour productivity is hard to ascertain with heterogeneous work projects. Although the official MGNREGA website suggests an average of 90 percent of work completion, open government data shows a decline in work completion rate from 43.8 percent in 2008-09 to 28.4  percent in 2015-16. Financial support through employment should account for both quality of assets created and the process of such creation. This would internally check and balance the operation of the scheme and intuitively bring in accountability. At present, the scheme contains the above mentioned elements but has not been used to evaluate the execution of the program. Convergence between departments to create assets and the work completion rate might explain the effectiveness of a program in physical terms. 

    An efficient model should enhance the skill levels of rural youth and is more than necessary to counter the loss of jobs already happening due to coronavirus lockdown. Unskilled and semi-skilled labourers will face lay-offs as industries with the recent norm on social distancing adjust to capital intensive businesses. The percentage of rural population in the age group of 15-59 receiving vocational training has reduced from 1.6 percent in 2011 to 1.5 percent in 2015-16. Unemployment rate among rural youth (15 to 29) has increased from 5 percent in 2011 to 17.4 percent in 2017-18. Although the highest unemployment rate is observed among rural females, the employability of rural youth reduces as education increases. The paradox of educated unemployment is not complex to decode, but a significant skill gap is the fundamental problem from the labour supply side. The Expanded Public Works Program (EPWP) introduced in South Africa to address the skill gap among the youth has succeeded in reducing poverty and unemployment rates. The program has been designed to create labour intensive projects not limited to infrastructure but extends to social, cultural and economic activities. The percentage of young workers under this scheme witnessed a rise from 7.73 % in 2017-18 to 10.06 % in 2019-20 in reference to the low levels of employment. This would mean the nature of the guarantee program has shifted from giving opportunities for seasonal unemployed to educated unemployed. The change is indicative of the deeper crisis faced in the rural economy and calls for a sustainable plan to use public programs as a tool to also impart skill training for the rural youth. State’s increasing dependence on work programmes to create employment needs to be revised based on community requirements. While enhancing rural employment is the immediate concern, the process of achieving it suffers from various executive problems such as corruption among government staff and individual’s lack of willingness to work. Amidst the lockdown situation due to COVID-19, unemployment will increase sharply. A well-devised strategy to address economic losses on priority and emphasis on health infrastructure through public employment must resonate in policy-making after the impact of the coronavirus crisis subsides. 

     

  • Rural Development and Gender Equality: A reality check in Tamilnadu

    Rural Development and Gender Equality: A reality check in Tamilnadu

    Category : Agriculture/Rural Development/Gender Equality

    Title : Rural development and gender equality: A reality check in Tamilnadu

    Author : Manjari Balu 06.01.2020

    Tamilnadu continues to be one of the fastest growing states in India, despite some major declines due to political instability, rampant corruption, and populist measures at the cost of development. Despite significant progress in literacy, women’s education, and some aspects of social security, there are still major shortfalls with respect to rural employment, skill development, and gender wage inequality. Tamilnadu has to develop a policy framework to achieve employability through quality secondary education for women, shifting focus from only enrolment of girls in primary education. Manjari Balu analyses this issue in Tamilnadu.


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  • PDC 2: Rising Unemployment and Economic Woes: Is India Missing its Demographic Dividend?

    PDC 2: Rising Unemployment and Economic Woes: Is India Missing its Demographic Dividend?

    Peninsula Discussion Club – Past Event

    PDC 2: Rising Unemployment and Economic Woes: Is India missing Demographic Dividend?

    Date: 02 November 2019

    Speaker: Professor Jothi Sivagnanam PhD, HOD, Dept of Economics, University of Madras

    India has stepped into its demographic dividend and the bulging youth population can possibly be a gift or curse for development. Recent concern over rising unemployment in India has been discussed from various dimensions. ‘Jobless Growth’ in India is evoking debates among economists and policymakers on how to capitalize on the human capital resource. The second discussion of TPF was an attempt to comprehend the looming crisis and identify factors that cause unemployment. Speaker of session, Professor Jothi Sivagnanam highlighted that demonetisation and the poor implementation of GST as the two major blunders that have disrupted the economy to a great extent. This is now causing high rates of unemployment. Poor quality in higher education and reluctance of state to correct the skill mismatch was discussed in detail. India’s growth as a global economic power, and its ability to dominate global markets can only be achieved if it focuses on development of high quality skills in its huge young population. The state has to prioritize developing skills at international standards in order to compete with established players. Participants pointed out the problems of archaic and rigid labour laws that stymie productivity and efficiency. It was pointed out that export oriented policies are vital to generate employment and high skills. In the realm of the fourth industrial revolution, the debate has to move past growth versus development due to the interdependence various sectors of the economy. The professor and one of the participants brought out the importance of balancing industrialization with education and social engineering. For example, Gujarat portrays a pro-business growth model, however, failed to succeed in its welfare policies, and hence, has serious inequalities and social problems. On the other hand Kerala, being socialistic in nature focused on development and failed to create a conducive zone for business development. With states having different characteristics and history, problem of unemployment cannot be treated as a universal problem. There is a substantial increase in the educated unemployment and vulnerability in informal sector. States need to address this by designing better quality education to meet the industry standards and regulate the labour laws. Given this backdrop, other specific issues were discussed during the meet.

    We welcome comments and further discussions on this blog page. Comments will be moderated in order to ensure discussions remain professional and ethical.

    PDC Past Event : 02 Nov 2019