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A seemingly trivial but quite important piece of information came to the fore on 27th November 2017. The Maharashtra Government fearing backlash shelved the bill of labour reform that otherwise would have eased the burden of 37 234 factories reeling under unviable losses. Politicisation of labour issues is the crucial factor for denigrating the status of manufacturing and its productivity in different states.

Indian labour market’s current state

There are close to 45 labour laws for only 7 percent (NSS 68th Round) of the organised sector workers in India. The labour laws in India makes a distinction between people who work in “organised” and people working in “unorganised” sectors. This received a major update in the Industrial Disputes Act of 1947 that mandate almost all aspects of the employer-employee interface. There are 15 major laws that are important in terms of settling industrial disputes compensation of wages and so on.  The figure below shows the number of labour laws in a state. Madhya Pradesh has the highest number of labour laws as per the above list while Assam has only 6.

Figure 1: Number of labour laws in operation among major states

Source: Author’s compilation from State Government web sources

Complexity and inconsistencies among labour laws are cited as a key deterrent to job creation in the formal sector. Of the country’s 473 million workers as of 2011-12 over 90 percent are informal workers without recourse to social security benefits such as pension medical care or other facilities. Many of these laws are outmoded and confusion prevails over several issues. Moreover if the size of a factory grows it increasingly becomes subject to constraining legislation that adversely impacted the level of productivity.

Mandatory registration for all workers

It is in this context that the Ministry of Labour and Employment introduced the draft Labour Code (March 2017) on Social Security and Welfare to regulate conditions for retirement health disability and other forms of security for all the workers including those in the unorganised sector. The proposed code subsumed 15 of the existing labour laws to promote universalisation of social security and extends to the entire workforce earning any income in cash or kind above the minimum wage. The regulation for registration of workers stipulates that if a worker is working in multiple entities then s/he must choose the employer through which s/he wants to register and all other employers would need to be informed. No entity including households can employ an unregistered worker and must register the worker within a specified time. Therefore compliance with the code would be an enormous challenge for employers to maintain records of the daily payouts to different workers over a period of time. 

 As per the code employers and entities employing more than the threshold number of workers must also register themselves. The definition of ‘entities’ includes households employing workers. However registering each household that employs domestic workers may turn out to be difficult while ensuring that they adhere to the norms of the code. 

Contribution to the social security fund

Moreover the code mandates that each worker and entity contribute towards the social security fund to be set up by the state governments. The maximum limit for contributions by employers is 17.5 percent of the wages. In the formal sector workers would need to pay 12.5 percent of the wage or monthly income while the same proportion equally applies to the workers from the unorganised sector. For the owner-cum-worker category the person needs to pay 17.5 percent in his capacity as an owner and 12.5 percent as a worker. Thus a master craftsman operating from home may have to set aside 30 percent of income towards social security which is a substantial proportion and difficult in terms of its implementation. The disposable income of workers earning slightly more than the minimum wage would drop significantly if the payment social security contribution is strictly adhered to. Extending this compulsion for informal sector workers with uncertain and irregular incomes might prove to be an intolerable burden for them. However the state government at its own discretion can waive such contributions for a maximum of five years for some of the identified workers. It also offers for welfare funds to be set up for specific classes of workers for which the central or state governments could make contributions. The code also empowers the state governments to pay contributions on behalf of specific workers or refund contributions made by employers. 

Policy interventions to ensure smooth implementation

The labour market institutions in India need to move to a flexible set-up for the employers in employment decisions to build competitiveness in an aggressive global arena. A strong social security system could offer protection to workers for temporary unemployment periods. The code does not appear to have this provision. A universal social security system for workers is a laudable initiative but the government would need to provide this through its own contributions for poorer workers. In this count the role of the MNREGA may need to be redesigned to provide benefits for all workers including unemployment medical insurance or retirement benefits.    

The universal coverage of obligatory registrations and contributions would need to be non-restrictive and synchronised for poorer workers so that the country can move towards higher job generation with adequate social security. On earlier occasions well-intentioned legislation (like the Land Acquisition Act) could not create a desirable impact due to their applicability on the ground. The draft social security code is going to be one of the most credible initiatives by the government but the real challenge lies in its execution and compliance. 

By Saurabh Bandyopadhyay 

Investment decisions of firms are centred on location choice investment magnitude and selection of industry. Of these information on location choice is the most asymmetric making it is imperative to inform potential investors regarding the characteristics and advantages/disadvantages of specific territorial choices within the country. In this article Anjali Tandon highlights the importance of a systematic evidence-based index that ranks Indian states based on investment potential. 

The investment decisions of business firms are centred on the choice of location magnitude of investment and selection of the industry. Decisions on the latter two – magnitude and sector of investment – are based relatively more on factors that are internal to the firm. In contrast information on location choice is asymmetric and relates largely to external factors beyond the firms’ control. While India is rated highly in terms of global investment potential due to fast economic growth huge domestic market and improving ease of doing business (EoDB) (World Bank 2018) it is also imperative to provide information to potential investors on the characteristics and advantages/disadvantages of specific territorial choices within the country.  

Business decisions related to location are strategically linked to predictions of expected gains on future investment. These are in turn rooted in the firms’ experiences pertaining to the performance of existing investments in land labour infrastructure economy governance and the business environment. The firm-level experiences vary widely across regions and states. Further the decision of the Centre to accord increasing recognition to the states as equal partners has borne fruit by fostering healthy competition among the states. Simultaneously the states have proactively embraced reforms to project themselves as potential destinations for investment for both foreign and domestic firms. For instance complete digitisation of land records in Karnataka gives an edge to reform of land leasing laws which in turn facilitate land use for industrial purposes. Similarly Maharashtra government’s compliance exemption on labour laws for retail and commercial establishments employing less than 10 persons is likely to have drawn small-sized new entrants. Likewise Maharashtra also extended additional tax incentives under the Package Scheme of Incentives to facilitate investment in the state. 

Therefore it is important to facilitate informed investment decisions through a single composite indicator based on a number of evidence-based parameters. Further comparable information on each of the individual sub-components of the aggregate indicator is needed to highlight the best practices followed at the state level. 

Competitive spirit is driving the Indian states to rush in for reforms to improve EoDB and expedite clearance of pending projects. A clear impact is seen at a broad national level from India’s moving up by 30 spots within a year in World Bank’s EoDB rankings. However the road ahead is long and much needs to be achieved particularly with regard to delays in starting a new business. The heterogeneity in growth patterns across states is noted from the difference in performance at sub-regional and state levels in terms of indicators such as infrastructure trade and transport costs etc. (Das et al. 2013). Different states have exhibited varying levels of dynamism with regard to policy reforms. There are many factors contributing to the variation. These are broadly related to factor markets of land labour and capital within a state. Initiatives at the state level are needed to reform the land market by revisiting their leasing (and land use) laws (Panagariya 2015). Similarly amending labour laws through specific exemptions in a state can give a certain degree of flexibility to businesses. State policies for investment promotion can be instrumental in driving more private capital which is particularly important for the low-income states. 

Also relevant is the presence of existing industries that have framed the current profile of factors within a state. The internal demand for investment within states is another crucial factor which in turn is determined by the policy framework in the states (Bandyopadhyay 2013).  

A survey of the literature shows an information gap with regard to advantages of spatial locations for investment. The Department of Industrial Policy and Promotion’s (DIPP) assessment of states’ regulatory experience in business is a move in this direction. However DIPP state rankings capture the state governments’ ability and success in implementing business reforms (DIPP 2016). This is likely to have a bureaucratic approach for measuring the streamlining of regulatory procedures. These aspects showcase the intent of the state government not necessarily the effect of the reforms. A ground assessment of the business perspective is much needed to assist the investor in locational choices. Such an exercise should cover a wide range of issues such as land labour infrastructure economy governance (including reform implementation as measured by the DIPP) and the business environment at the state level.  

NCAER State Investment Potential Index

There is a need to match the reform with the ground realities through an index with a much wider scope of coverage (National Council of Applied Economic Research (NCAER) 2017). The rigour of recent policy reforms in some states and also in the states that have maintained a foothold in drawing investments for quite some time emphasises the need to update the investor on performance of the states. The NCAER State Investment Potential Index (N-SIPI) provides a single composite score for the states based on their potential to attract investment. The index is constructed on six broad pillars that are classified under four broad categories as being factor-driven (land and labour) efficiency-driven (infrastructure) growth-driven (economic climate and political stability and governance) and perceptions-driven (responses to the survey1). While the first five pillars are based on secondary information the perceptions pillar is a survey-based indicator and captures the future optimism of an investing firm through its judgment of business and financial conditions over the coming year. The survey structure is designed to capture the business sentiment from experiences on the ground. 

Under each of the six pillars there are a number of sub-indicators. The secondary data-based pillars namely land labour infrastructure economy and governance are based on 4 9 10 11 and 9 sub-indicators respectively. The land pillar has fewer sub-indicators compared with other pillars due to challenges in obtaining comparable and reliable state-wise data on land. The perceptions pillar is based on survey responses of firms on questions related to land labour infrastructure economic conditions governance and political stability and business climate. All sub-indicators within a pillar are assigned equal weights. Further the weights of the components of a sub-indicator are equally distributed. Weighted arithmetic means (averages) of the sub-indicators are used to obtain scores for individual pillars. The composite index N-SIPI is obtained as the geometric mean2 of the six pillars. 

Based on N-SIPI it turns out that the states of Gujarat Delhi and Andhra Pradesh topped the chart among the 21 states included in the study in 2017 (Figure 1). The states of Bihar Uttar Pradesh and West Bengal are located the other end of the scale. At a pillar level the leaders include Madhya Pradesh under the land pillar Tamil Nadu under the labour pillar Delhi under the infrastructure pillar Gujarat under the economy pillar Haryana under the governance and political stability pillar and Gujarat under the perception survey pillar of the study. The regional divide often observed in other spheres is reflected in investment potential as well with the southern and western states performing far better than the eastern and central states. The regional disparity is undeniable and unless prudently addressed could lead to further widening of gaps between the states at the upper and lower ends of the index.

Figure 1. Ranks showing investment potential of states

In conclusion it needs to be emphasised that N-SIPI is one of the many indices being developed by different agencies to benchmark the states on the EoDB front. However ranking states in terms of their investment potential is more forward-looking than other extant indices. To the extent investment decisions are based on assessment of the future N-SIPI is a valuable addition to the newly-emerging literature on competitive cooperative federalism. 

Notes:

The sample comprises 1030 firms in 44 districts across 20 states and the Union Capital Territory of Delhi and was selected using stratified two-stage sampling method.

The geometric mean is defined as the nth root of the product of n numbers

By Anjali Tandon

This column describes NCAER’s new ‘nowcasting’ model which seeks to predict India’s GDP numbers at frequent intervals − typically on a monthly or quarterly basis − by exploiting the incremental information in published data on economic indicators.

In the words of Dr. Lawrence Klein¹ “Models go out of date fast. Data base gets revised new kinds of economic problems come to the fore; and sometimes behavior changes. India appears to be in need of a serious model building effort designed to function as an ongoing effort over many years turning out rolling forecasts every month or two on the most refined time period possible…“

Over the years NCAER (National Council of Applied Economic Research) has developed several models for forecasting and policy analysis. At present NCAER relies on an econometric model for forecasting quarterly GDP (gross domestic product) and overall prices. In addition NCAER has also developed what is essentially a structural model of the Indian economy with a medium-term perspective. A salient feature of the model is that it incorporates sources of productivity growth which enables assessing the impact of foreign capital boost in agricultural growth and infrastructure development on the economy. This model is essentially used to forecast GDP for the medium term and for policy analysis. Both these models have primarily used databases from the last decade. The Central Statistics Office (CSO) has replaced the earlier 2004-05 base year with 2011-12 and updated the National Account Statistics (NAS) methodology to align with more recent international guidelines. Subsequently the databases on the Indian economy have undergone substantial revisions in the wake of recent changes in the GDP methodology and measurement issues with regard to price indices. Currently both these models are fully operational. 

However the first release of quarterly GDP/GVA² (gross value added) growth is published approximately 7-8 weeks after the end of the reference quarter. Importantly CSO’s growth estimates avoid assessing the current state of the economy. In order to reduce the time lag between the actual growth during a reference quarter and the official release of the quarterly growth estimates we are developing a new ‘nowcasting’ model at NCAER. Nowcasting models try to capture the flow of data releases in real time throughout a week and month within a quarter. Accordingly we update the GDP/GVA growth estimates by using relevant data releases that are published within the same quarter. Our model exploits a large number of correlated data series and tries to extract potentially relevant signals about the state of the economy. The Dynamic Factor Model (DFM) can capture such relevant signals from large number of data releases and at the same time help avoid the so-called ‘curse of dimensionality’³  problem. It has received increasing attention in the recent macro-econometrics literature and can help identify a few dynamic factors that can represent co-movements in a large number of time series data⁴. The time series data can typically represent different measures of economic activities within an economy and include data on domestic production prices manufacturing turnover construction business surveys labour market statistics interest rate monetary aggregates stock market indices and miscellaneous indicators that are available at more frequent intervals (weekly/monthly). Econometric estimation is undertaken to identify dynamic factors for each of the sectors based on the realised high-frequency data that are available for the relevant sectors. 

Our new nowcasting model-building effort is aimed at successfully estimating such dynamic factors using data-shrinkage techniques and nowcasting Indian GVA numbers at frequent intervals − typically on a monthly/quarterly basis − by exploiting the incremental information in published data on economic indicators. In other words nowcasting involves an exercise of predicting the present the very near future and the very recent past − and that means it is more effective in shorter horizon forecasting. The model is expected to accurately estimate and forecast the rate of growth of the Indian economy.