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Showing posts with label ARTICLE. Show all posts
Showing posts with label ARTICLE. Show all posts

Thursday, 2 December 2021

Contract labour and firm growth in India

 

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Nick Tsivanidis

University of California at Berkeley

ntsivanidis@berkeley.edu

There is considerable evidence indicating that the Industrial Disputes Act (IDA), 1947 – which made it illegal for large companies to downsize – had a powerful disincentive effect for entrepreneurs in India. Using Annual Survey of Industries data, this article shows that constraints on large firms diminished since the early 2000s largely due to exploitation of a loophole pertaining to contract labour, rather than a de jure change in the labour laws.

In the late 1940s, newly independent India, fearful of the job losses if large British companies were to leave the country, passed a law that made it illegal for large companies to downsize. This law, which became known as the Industrial Disputes Act (IDA), 1947, was likely successful in ameliorating the immediate crisis, but it also likely distorted incentives for Indian entrepreneurs. Who would want to invest if one was stuck paying workers that are no longer needed if the investment turned out to be unsuccessful?

There is considerable evidence that the IDA did, in fact, have this powerful disincentive effect. First, the Indian manufacturing sector is characterised by a large number of informal firms, a small number of large firms, and a high marginal product of labour1 in large firms. Second, while US manufacturing firms typically grow by a factor of eight over three decades, the typical manufacturing firm in India does not grow at all over its life cycle2. These facts suggest that a law like the IDA discourages Indian entrepreneurs from growing, and consequently there are few large productive firms and too many small unproductive firms.

Relaxation of labour constraints among Indian firms

In a recent study (Bertrand et al. 2021), we show that constraints on large firms appear to have diminished since the early 2000s, despite the fact that there has been no change in the IDA. The reforms that started in 1991 largely dismantled reservations for small-scale industries, and the industrial licensing laws, but left the IDA untouched. Consider the evidence in Figures 1 and 2, drawn from the micro-data of India’s Annual Survey of Industries. Figure 1 plots the distribution of employment by firm size in Indian manufacturing. It shows that the employment share of large Indian firms increased between 2000 and 2015. Figure 2 shows that average value-added (VA) per worker is increasing in firm employment in 2000 and 2015, but this relationship is more attenuated in 2015 compared to 2000, particularly for firms with more than 100 workers. If the marginal product is proportional to the average product of labour, and profit-maximising firms equate the marginal product of labour to the cost of labour, then this suggests that the effective cost of labour has diminished for larger Indian firms compared to smaller firms.

Figure 1. Firm size distribution, 2000 versus 2015

Notes: (i) Figure shows employment-weighted distribution of firm employment. (ii) Right panel shows coefficients and 95% confidence intervals from non-parametric regressions of log VA/worker on log employment using Epanochnikov kernel with a bandwidth of 0.6. (iii) Employment here is defined as the number of non-managerial workers. (iv) Log VA/worker is residualised by industry and year fixed effects.

Figure 2. Value added per worker by size, 2000 versus 2015

Notes: (i) Figure shows coefficients and 95% confidence intervals3 from non-parametric regressions of log VA/worker on log employment using Epanochnikov kernel with a bandwidth of 0.6. (ii) Employment here is defined as the number of non-managerial workers. Log VA/worker is residualised by industry and year fixed effects4.

The decline in the bite of the IDA does not come from a de jure change in Indian labour laws, but rather due to a workaround from the rapid development of the labour contracting industry in India since the early 2000s. The IDA only applies to a firm's full-time employees; workers supplied through third-party intermediaries are not the firm's employees for the purposes of the IDA. Contract workers are employees of the staffing companies, and the staffing companies are required to abide by the IDA. This loophole provides customer firms with the flexibility to return the contract workers to the staffing company without being in violation of the IDA. Figure 3 shows the probability that contract workers account for more than 50% of total firm employment as a function of total firm employment. Among smaller firms, there has been no discernible increase in the share of firms where contract labour is at least 50% of the workforce. In contrast, there has been a dramatic increase among larger firms, particularly those with more than 100 workers. We trace this increase to a 2001 decision by the Supreme Court of India that made large firms less reticent to rely on a large pool of contract workers for ‘core’ activities5.

Figure 3. Contract labour use and firm size: 2000 versus 2015

Note: Plot shows point estimates and 95% confidence intervals from non-parametric regression of the probability a plant hires more than 50% of its non-managerial workers through contractors on (log) non-managerial employment.

In sum, the relaxation of labour constraints facing large Indian firms since the early 2000s came from exploiting a loophole rather than a de jure change in Indian labour laws. In this sense, this episode is another example of what many people in India call jugaad, which very roughly, means finding informal solutions to problems. But as with all informal solutions, it potentially raises other costs that may make further progress difficult. In particular, one may be concerned about further growth potential when most productive firms rely on contract labour for such a large share of their workforce. Future work should also consider the implications of this development for labour training, skill upgrading, and bargaining power.

A version of this article first appeared on VoxEU.

Notes:

  1. Marginal product of labour is the increase in a firm’s production when an additional unit of labour is added.
  2. See Hsieh and Olken (2014) on the firm-size distribution in India, and Hsieh and Klenow (2014) for evidence on low life-cycle growth in Indian manufacturing.
  3. A confidence interval is a way of expressing uncertainty about estimated effects. A 95% confidence interval, means that if you were to repeat the experiment over and over with new samples, 95% of the time the calculated confidence interval would contain the true effect.
  4. Fixed effects control for time-invariant unobserved individual characteristics.
  5. The 2001 Supreme Court decision made it explicit that firms that employed contract workers did not have to absorb these workers in the event of a downsizing. Prior to this decision, Indian labour law was not clear on this point.

Further Reading

  • Bertrand, M, C Hsieh and N Tsivanidis (2021), ‘Contract Labor and Firm Growth in India’, NBER Working Paper No. 29151.
  • Hsieh, Chang-tai and Peter Klenow (2014), “The Life-Cycle of Manufacturing Plants in India and Mexico”, Quarterly Journal of Economics, 129(3): 89-108.
  • Hsieh, Chang-tai and Benjamin Olken (2014), “The Missing “Missing Middle”, Journal of Economic Perspectives, 28(3): 1403-1448. 

 

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Wednesday, 1 December 2021

Foreign currency corporate borrowing: Risks and policy responses

 

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Siddharth Vij

University of Georgia

siddharth.vij@uga.edu

Non-financial corporations in emerging market economies increasingly rely on foreign currency debt, and are exposed to sudden stops in capital flows and currency depreciations. Analysing data on 1,786 Indian firms during 2004-2019, this article shows that favourable global funding conditions are a much more significant determinant of foreign currency borrowing than firm-level factors. Further, it suggests that RBI’s macroprudential policies have been effectively mitigating these risks.

Non-financial corporations in emerging market economies (EMEs) increasingly rely on foreign currency debt for financing. Since the Global Financial Crisis (GFC) of 2008, the amount of dollar-denominated debt of EME corporations has quadrupled1. Research has shown that interest rate differentials between EMEs and the US have contributed significantly to this phenomenon (Bruno and Shin 2017). In essence, EME corporations prefer to borrow in foreign currency when there is a ‘carry’, meaning foreign interest rates are low relative to domestic interest rates. This carry trade borrowing leaves the firms exposed to sudden stops in capital flows and associated currency depreciations (Bruno and Shin 2020). More broadly, the accumulation of external debt on private balance sheets can lead, or contribute, to currency depreciation spirals and thereby pose risks for EME growth and financial stability (Acharya et al. 2015, Du and Schreger 2017). These include risks to domestic growth from large corporate distress and spillover effects on the domestic financial system.

Therefore, attention has naturally turned to policy responses to these risks posed by the build-up of foreign currency debt. Theoretical contributions suggest that macroprudential regulation can mitigate these risks (Acharya and Krishnamurthy 2019, Erten et al. 2020). However, such regulation might have leakages or unintended consequences that undo its intended effects (Ahnert et al. 2020). Ultimately, uncovering the effects of such policies requires empirical analysis.

In new research (Acharya and Vij 2021), we do this by studying a specific instance of macroprudential regulation2 targetted at the foreign currency borrowing of non-financial corporations. We examine the case of India which, as is typical of EMEs, has seen a sustained increase in dollar debt issued by non-financial corporations. Figure 1 shows the total amount of foreign currency debt outstanding and the Indian rupee/US dollar foreign exchange rate for the period between 2004 and 2019. Although the rupee has steadily depreciated against the dollar, the outstanding stock of dollar debt has steadily increased.

Figure 1. Foreign currency corporate debt and Indian rupee/US dollar exchange rate

Note: The figure shows the evolution of the rupee/dollar exchange rate, and the stock of foreign currency debt outstanding for the period March 2004-March 2019.

Source: Exchange rate data are from Datastream and data on outstanding debt are from the Ministry of Finance.

In response to the rise in dollar debt, in 2015, India’s central bank, the Reserve Bank of India (RBI), lowered the maximum permitted interest rate at which Indian borrowers could borrow in foreign currency debt markets. This move was in the aftermath of the ‘taper tantrum’ episode of May-August 2013 in which India faced significant capital outflows and currency depreciation3. The aim of reducing these interest rate caps for foreign currency debt was to restrict access to only those firms that could borrow at relatively low interest rates – presumably higher quality and lower risk borrowers – as these borrowers would be less likely to face rollover problems in a sudden stop.

The dynamics of corporate foreign currency borrowing

To examine the dynamics of corporate foreign currency borrowing and the effects of the macroprudential regulation, we construct a detailed dataset on Indian firms that borrow abroad building on publicly available data from the RBI. We have data on every instance of foreign debt issuance (including amount, maturity, and debt type), matched to accounting and stock market data on the borrowing firms from Prowess. Our final sample includes 1,786 firms that, on average, borrow twice during our study period of 2004 to 2019. In our sample, 5% of the firms borrow more than 10 times over the period.

We first examine the factors that explain borrowing in external markets. Some hypothesised reasons for borrowing abroad are: (1) Exporters can naturally hedge their foreign currency borrowing through their revenues; (2) Firms investing in foreign assets (for example, oil and gas companies) want to finance those assets in the same currency (Caruana 2016); and (3) Firms borrow abroad at a cheaper interest rate and invest it locally at higher interest rates (Shin and Zhao 2013). The third reason is a corporate carry trade that is profitable if the firm can unwind the trade before the currency depreciates or if the central bank steps in to prevent depreciation.

Our analysis shows that the carry trade motive plays an important role in foreign currency borrowing particularly in the period of low US interest rates following the GFC. We define a Sharpe Ratio4-like proxy for the profitability of the carry trade5. Figure 2 shows that our carry trade measure is positively correlated with the aggregate foreign currency debt issuance in the period following the GFC. Our econometric results show that the same firm is more likely to borrow in foreign currency when the carry trade is more profitable in the post-GFC period. The carry trade does not explain borrowing in the period before the global crisis, pointing to the importance of US monetary policy easing in explaining global financial flows (Rey 2013).

Figure 2. Foreign currency debt issuance and the carry trade

Notes: (i) The figure plots the total number of foreign currency debt issues each quarter against CT, a proxy for the profitability of the dollar carry trade. CT is the difference in three-month interest rates between India and the US scaled by the implied volatility of three-month foreign exchange options. (ii) The sample period is from January 2004 to September 2019.

Stock market data show that the returns of Indian foreign currency borrowers become more sensitive to movements in the rupee/dollar exchange rate as they borrow more. This indicates that the borrowers are not fully hedging the foreign exchange risk that comes from the new debt. Firms that are more likely to borrow when the carry trade is more profitable – whom we call ‘carry trade borrowers’ – see the most increase in risk.

Periods of market stress

We use the taper tantrum episode as a natural experiment to analyse what happens to carry trade borrowers during periods of market stress. Our event studies around taper announcements indicate that carry trade borrowers experience significantly larger equity market declines due to the announcement (Figure 3).

Figure 3. Taper tantrum event study

Notes: (i) The figure shows the cumulative abnormal return (CAR) for stocks of foreign currency borrowers that borrow when the carry trade is more profitable relative to other foreign currency borrowers. (ii) The event date is 19 June 2013, a date on which Chairman of the Federal Reserve, Ben Bernanke, indicated that tapering of quantitative easing would commence later in 2013. (iii) A multivariate market model is used for estimating abnormal returns with the NIFTY return proxying for the market return while Indian rupee/US dollar return proxies for FX return. The estimation window is 180 calendar days and ends five trading days before the event date.

In response to the taper tantrum, many EMEs significantly altered their macroprudential policy for the external sector by tweaking the capital control frameworks (Bergant et al. 2020). India was one of them. We focus on the RBI’s reduction of the maximum interest rate at which firms could borrow abroad, in 2015. We find that this macroprudential policy action had significant effects on carry trade borrowing. Following the reduction in the interest rate cap, carry trade profitability no longer significantly explains foreign currency borrowing. We show that following the policy change, firms with higher interest expenses and those with a higher import share of raw materials were the ones most affected. This shows that the regulation worked as intended, by preventing the ex-ante riskiest borrowers from borrowing in foreign currency to take advantage of a carry trade.

We further confirm the efficacy of the macroprudential regulation by testing the equity market reaction of foreign currency borrowers during periods of market stress following the interest rate cap change. We conduct tests analogous to our taper tantrum analysis for the period of market stress at the beginning of the Covid-19 pandemic in March 2020. For EMEs, this period was characterised by unprecedented portfolio outflows and tightening of financing conditions (Corsetti and Marin 2020). In our event study analysis, we find that carry trade borrowers do not do any worse during this crisis compared to other borrowers. This suggests that under the new macroprudential regime, the risks arising from carry trade borrowing by Indian corporates has been substantially mitigated.

Macroprudential regulation of external sector

Overall, our results indicate that favourable global funding conditions are a much more significant determinant of foreign currency borrowing by EME corporations than individual firm-level factors. As we entered a new cycle of US monetary policy easing owing to the Covid-19 pandemic, foreign currency borrowing might have accelerated along with the attendant risks. As this cycle is turning with the firming up of US long-term interest rates, excessively risky foreign currency borrowing by corporates might hurt domestic growth and financial stability down the line as capital flows to EMEs retrench. Our analysis suggests that the proper targetting of capital controls in macroprudential regulation of the external sector can play an important role in reducing such vulnerability.

This article first appeared on VoxEU.


Notes:

  1. See Bank of International Settlements statistics (Table C3) available here.
  2. Macroprudential regulation is the approach to financial regulation that aims to mitigate systemic risk.
  3. This was triggered by the US Federal Reserve announcements indicating that the tapering of quantitative easing was imminent, leading to a surge of capital outflows and asset price declines in EMEs (Sahay et al. 2014).
  4. Sharpe ratio measures the performance of an investment against a risk-free asset after adjusting for its risk.
  5. This is the difference of short-term interest rates between India and the US, normalised by the implied volatility of the exchange rate backed out from foreign exchange options.

Further Reading

  • Acharya, VV and A Krishnamurthy (2019), ‘Capital Flow Management with Multiple Instruments’, in A Aguirre, M Brunnermeier and D Saravia (eds.), Monetary Policy and Financial Stability: Transmission Mechanisms and Policy Implications.
  • Acharya, VV, S Cecchetti, J De Gregorio, S Kalemli-Ozcan, P R Lane and U Panizza (2015),Corporate Debt in Emerging Economies: A Threat to Financial Stability?’, Report for the Committee on International Economics Policy and Reform (CIEPR), Brookings Institute and Centre for International Governance Innovation. Available here.
  • Acharya, VV and S Vij (2021), ‘Foreign Currency Borrowing of Corporations as Carry Trades: Evidence from India’, NBER Working Paper No. 28096.
  • Ahnert, Toni, Kristen Forbes, Christian Friedrich and Dennis Reinhardt (2020), “Macroprudential FX regulations: Shifting the snowbanks of FX vulnerability?", Journal of Financial Economics, 140(1): 145-174. Available here.
  • Bergant, K, F Grigoli, N-J Hansen and D Sandri (2020), ‘Macroprudential regulation can effectively dampen global financial shocks in emerging markets?’, VoxEU, 12 August.
  • Bruno, Valentina and Hyun Song Shin (2017), “Global Dollar Credit and Carry Trades: A Firm-Level Analysis", The Review of Financial Studies, 30(3): 703-749. Available here.
  • Bruno, Valentina and Hyun Song Shin (2020), “Currency Depreciation and Emerging Market Corporate Distress", Management Science 66(5): 1935-1961. Available here.
  • Caruana, J (2016), ‘Credit, commodities and currencies’, Lecture at the London School of Economics and Political Science. Available here.
  • Corsetti, G and E Marin (2020), ‘The dollar and international capital flows in the COVID-19 crisis’, VoxEU, 3 April.
  • Du, W and J Schreger (2017), ‘Sovereign Risk, Currency Risk, and Corporate Balance Sheets’, Working Paper.
  • Erten, B, A Korinek and JA Ocampo (2020), ‘Managing capital flows to emerging markets’, VoxEU, 11 August.
  • Bergant, K, F Grigoli, NJH Hansen and D Sandri (2020), ‘Dampening Global Financial Shocks in Emerging Markets: Can Macroprudential Regulation Help?’, in World Economic Outlook, p. 53-75, International Monetary Fund.
  • Rey, H (2013), ‘Dilemma Not Trilemma: The Global Financial Cycle and Monetary Policy Independence’, Global Dimensions of Unconventional Monetary Policy, Jackson Hole Symposium on Economic Policy. Available here.
  • Sahay, R, VB Arora, AV Arvanitis, H Faruqee, PM N'Diaye and TM Griffoli (2014), ‘Emerging Market Volatility; Lessons from The Taper Tantrum’, IMF Staff Discussion Notes 14/9.
  • Shin, HS and LY Zhao (2013), ‘Firms as Surrogate Intermediaries: Evidence from Emerging Economies’, Mimeo, Princeton University. Available here

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Understanding ‘consumer price index’ and consumption baskets

 

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Ananya Goyal

National Institute of Public Finance and Policy

ananya.goyal@nipfp.org.in

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Radhika Pandey

National Institute of Public Finance and Policy

radhika.pandey@nipfp.org.in

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Renuka Sane

National Institute of Public Finance and Policy

renuka.sane@nipfp.org.in

The Consumer Price Index (CPI) is used as an indicator in various policymaking contexts, and has gained greater significance since the adoption of the inflation targeting framework in India. In this post, Goyal et al. examine how the weights of various commodities from the 2011-12 Consumer Expenditure Survey and the 2019 Consumer Pyramid Household Survey, compare with the weights used in CPI. 

The consumer price index (CPI) is used as an indicator in various policymaking contexts, such as monetary policy, for determining dearness allowances of government employees, wages under welfare schemes, etc. In particular, the use of the CPI-Combined index has gained greater significance since the adoption of the inflation targeting framework in India. 

A key component of CPI is the consumption basket of households. Most inflation indices are designed like Laspeyres Price Index1, which requires a fixed consumption basket. The NSSO (National Sample Survey Office) Consumer Expenditure Survey (CES) is used to determine the household consumption basket and weights of the various commodities in the current CPI series. However, the last publicly available data on household consumption baskets is from 2011-12 NSSO CES. Another source of data on consumption baskets that has become available in recent years is the CMIE (Centre for Monitoring the Indian Economy) Consumer Pyramid Household Survey (CPHS), which is a longitudinal survey of about 150,000 households with data on consumption, income, household assets and liabilities and other demographic characteristics. Given the importance of CPI in macroeconomic policy, in a recent study (Goyal et al. 2021), we examine how the weights of various commodities from the 2011-12 CES and the CPHS2, compare with the weights used in CPI.

We draw on official sources and document the methodology of constructing the CPI, and highlight important details on the construction of the CPI – in particular the details on commodity classification, and reference and recall periods. We compare the weights of commodities in the CPI series with the share of each item in the total consumption expenditure in the 2011-12 CES as well as the 2019 CPHS3. 

Comparing CES and CPI 

To compare the weights of CES with CPI, it is important to ensure that CPI and CES are measuring consumption expenditure on the same basket of goods and services. Hence, we map the commodity groups and sub-groups in CPI (base year 2012) to 2011-12 CES at the item level. The components of these categories match closely and differ by name for only two categories4. There is one major difference – the ‘Housing' category in CPI includes the imputed rent of owner-occupied housing while this is not included in the ‘Rent, Taxes, Cess' category in the 2011-12 CES. 

Table 1 compares the CPI weights with CES expense shares. We observe differences in CPI-CES weights in nearly all categories in the urban sector, with a difference of up to 13 percentage points within a category. However, the CPI methodology documentation does not specify the reasons for deviating from expense shares in CES 2011-12. 

Table 1. Comparing CPI (base Year 2012) weights and 2011-12 CES expense shares

Panel a. CPI and CES shares, including housing

CPI category (CES)

CES weights

CPI weights

 

Rural

Urban

Rural

Urban

Food and beverages

0.529

0.426

0.54

0.363

Clothing and footwear

0.07

0.063

0.074

0.056

Pan, tobacco and intoxicants

0.032

0.016

0.0326

0.0136

Housing (rent, taxes, cess)

0.007

0.07

0

0.21

Miscellaneous

0.18

0.231

0.17

0.19

Fuel and light

0.079

0.067

0.079

0.055

Education

0.036

0.069

0.034

0.056

Health (Medical)

0.067

0.055

0.068

0.048

Panel b. CPI and CES shares, excluding housing

Food and beverages

0.529

0.456

0.54

0.463

Clothing and footwear

0.07

0.069

0.074

0.07

Pan, tobacco and intoxicants

0.032

0.017

0.0326

0.017

Housing

0.007

 

0

 

Miscellaneous

0.18

0.249

0.17

0.244

Fuel and light

0.079

0.072

0.079

0.071

Education

0.036

0.074

0.034

0.07

Health (Medical)

0.067

0.06

0.068

0.061

The ‘Food' category has a weight of 36.3% in urban CPI and 42.6% in urban CES – the highest weight of any category. A striking difference in weights is in the category ‘Housing' where the CPI assigns no weight for rural households while weight assigned for urban regions is 21%. These differ from CES expense shares found for ‘Housing’ – the share of ‘Housing'CES is only 7% in urban sector. As the inclusion of imputed rent is the only notable difference we find in CES and CPI methodology, we verify if this inclusion is the cause of difference, in two ways. First, if the weight of imputed rent is added to the category ‘Housing', the total expense share of ‘Housing' in CES is similar to its CPI weight. (RESOLVED)

Second, we verify if CPI methodology for ‘Housing' accounts for the difference in weights of other categories as well. We exclude ‘Housing' from both CPI and CES, by deducting it from the ‘Total expense' (denominator) in CES, and recalculating the expense shares for all categories. In CPI, ‘Housing' weight is redistributed to other categories for the purpose of this analysis. The result is that CPI and CES weights match (on approximating to two decimal places). This indicates that the weights of all categories are impacted by the share attributed to ‘Housing' in CPI, due to inclusion of imputed rent in the category. 

This inclusion of the imputed rent component of ‘Housing' in CPI presents other issues as well – imputed rent includes subsidised or concessional housing provided to employees, such as house rent allowance (HRA) to government employees. The high relative weight assigned to imputed rent in CPI creates volatility in CPI whenever dearness allowances or pay commissions are announced (Morris 2021). Hence, CPI commodity weights are not comparable to the household expense shares observed in the official household survey on which they are based, until we adjust for the weights of ‘Housing' group. Once we exclude ‘Housing’ from CES and CPI weights, we find that weights used in CPI broadly match expense shares observed in CES. 

Thus, we find that while CPI is based on 2011-12 CES, CPI weights are not entirely consistent with those of the CES. However, the difference in weights is primarily on account of the ‘Housing’ category. 

Comparing CPHS and CPI  

We compare the weights of different commodities in the CPI to expense shares implied by a new, independent, nationally representative household survey, the CMIE CPHS. CPHS provides us with monthly household consumption expenditure, among other income and household demographic characteristics, and is available since 2014. 

To compare the expense shares of CPHS with CPI weights, it is important to ensure that the CPI and CPHS are measuring consumption expenditures on a broadly similar basket of goods and services. We map CPI commodity classification to CPHS classification, by comparing the item-wise list of CPI with category and sub-category descriptions of CPHS (at the highest level of disaggregation)6. A detailed mapping for CPI-CPHS commodity classification is presented in Appendix of the paper

We then compare the weights assigned to each commodity by CPI, to the household expense shares estimated using the CPHS data. Table 2 compares 2019 CPHS expense shares and CPI (base year 2012) weights for major categories for urban and rural sectors in India7. 

Table 2. Comparing 2019 CPHS expense shares and CPI (base year 2012) weights

CPI category

CPHS weights

CPI weights

 

Rural

Urban

Rural

Urban

Expense shares, excluding housing

Food

0.51

0.461

0.54

0.459

Clothing

0.052

0.049

0.074

0.07

Intoxicants

0.043

0.034

0.032

0.017

Household goods and services

0.041

0.055

0.038

0.05

Personal care and effects

0.055

0.06

0.043

0.045

Transport and communication

0.129

0.163

0.076

0.122

Cooking fuel and electricity

0.09

0.087

0.079

0.071

Recreation

0.024

0.032

0.013

0.025

Education

0.032

0.034

0.034

0.07

Health

0.026

0.026

0.068

0.061

CPI weights and CPHS expense shares of several commodity groups match closely – ‘Food’, ‘Household goods and services’, and ‘Recreation’ in urban areas have less than a 1 percentage point difference. Similarly, ‘Intoxicants’, ‘Household goods and services’, ‘personal care and effects’, ‘Cooking fuel and electricity’, ‘recreation’, and ‘education’ have close to a 1 percentage point difference in rural CPI-CPHS weights. 

Other differences remain – ‘Transport and communication’, ‘Clothing and footwear’, and ‘Health’ have more than a 2 percentage point difference in both urban and rural CPI-CPHS weights, and ‘Education’ in urban CPI-CPHS weights. 

Since food constitutes roughly half of the CPI basket, we show the difference in the weighing pattern within the category of ‘Food’. We find that the difference in CPI-CPHS weights in the ‘Food' category is not distributed evenly across its sub-categories or even regions. The components of ‘Food' that contribute to differences in weights between CPI and CPHS are different for urban and rural sectors. In urban sector, this difference is driven primarily by difference in weights of ‘Sugar and confectionary’, ‘Vegetables’, ‘Meat, eggs, fish’, ‘Milk and products’, and ‘Prepared meals’. In the rural sector, the CPI-CPHS difference of nearly 3 percentage points in ‘Food’ is driven by ‘Cereals and pulses’, ‘Vegetables’, ‘Spices’, ‘Fruits’, and ‘Prepared meals’. 

Thus, we find that several categories have different weights in CPI and CPHS, particularly in the urban region. However, the magnitude of these differences reduce, once the category of ‘Housing’ is excluded from both the CPI and CPHS. There could be several reasons for the observed difference in CPI weights and 2019 CPHS expense shares – difference due to the difference in time period examined (2011-12 to 2019), CPI-CPHS commodity classification, or recall periods used8. Differences could also be attributed to changes in consumer baskets due to changes in socioeconomic covariates from 2011-12 to 20199. 

Conclusion

While CPI methodology reports that its weights are based on 2011-12 CES, CPI weights closely match those of 2011-12 CES only once the sub-group ‘Housing' is excluded from the total consumption expenses. In the case of CPHS, differences in some categories such as ‘Food’, ‘Household goods and services’, and ‘Recreation’ are less than 2 percentage points. Differences in the shares of commodities such as ‘Transport and communication’, ‘Health’, ‘Education’, and ‘Intoxicants’ are larger. In particular, CPI underestimates household expenses on one of the largest commodity groupings, ‘Transport and communication’, by 4-5 percentage points in both rural and urban regions. Excluding ‘Housing’ from both CPI and CPHS makes them more comparable to each other and to 2011-12 CES. 

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Notes:

  1. Laspeyres Price Index is defined by the OECD as, “A price index defined as a fixed weight, or fixed basket, index which uses the basket of goods and services of the base period”. 
  2. We use CPHS consumption expenses data of 2019 for this analysis, as the lockdown and post-lockdown consumption of 2020-21 is not expected to represent usual household consumption patterns.
  3. We use CPHS consumption expenses data of 2019 for this analysis, as the lockdown and post-lockdown consumption of 2020-21 is not expected to represent usual household consumption patterns.
  4. The sub-group ‘Health' in CPI is called ‘Medical' in CES, while ‘Housing' in CPI refers to ‘Rent, Taxes, Cess' in CES.
  5. Adding 18% weight of imputed rent to 7% rent, taxes, and cess and changing the base to 118, we recalculate the share of ‘Housing’ as (7+18)/118 = 21%.
  6. A detailed mapping for CPI-CPHS commodity classification is presented in the appendix of Goyal et al. (2021). This mapping is useful for further research using CPI in CPHS work, as it maps commodities at item level and yields comparable groups of commodities.
  7. Here, we exclude ‘Housing' from CPI weights and ‘Total Expense' from CPHS, given the issues with the ‘Housing’ category.
  8. Recall periods for CPI commodity groups range from 7-365 days, while recall periods for CPHS range from 30-120 days. For more details, see Goyal et al. (2021).
  9. For more details, see Goyal et al. (2021).

Further Reading

  • Goyal, A, R Pandey and R Sane (2021), ‘Consumption baskets of Indian households: Comparing estimates from the CPI, CES and CPHS’, NIPFP Working Paper No. 343. 
  • Morris, S (2020), ‘The Error in the CPI, Which Precludes It Use in Inflation Targeting’, GIM Working Paper Series WPS No.01/EC/May 2021.  Social media is bold.


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Assessing the credibility of sub-national budgets in India

The Covid-19 crisis has put immense pressure on the finances of Indian states. Fiscal consolidation in the post-pandemic period will depend on growth revival, increased fund flows, and efficient budget management processes. In this context, Jena and Singh assess the credibility of sub-national budgets during the period 2012-2019, in terms of planned and actual revenues and expenditures – both at the aggregate and individual state levels.

The Covid-19 pandemic has put the finances of states in India under immense pressure. The adverse impacts were particularly severe in FY (financial year) 2020-21 as the states faced loss of their own revenue, reduced transfers from the Centre, changes in spending priorities, emergence of vulnerabilities in service delivery, and loss of livelihoods (Sen 2020). After the pandemic, fiscal consolidation – a reduction in the fiscal deficit – in the states will depend on the revival of the growth process and increased fund flows. In this context, implementing the budgets as planned, to avoid shifting objectives, exceeding deficit targets, and compromising on critical service-delivery promises will be key to the budget management process.

Budget credibility is about the intents and outcomes of annual budgetary activities, and impact on programme management and achievement of results. Respecting the sanctity of budgetary provisions helps establish fiscal discipline and improve the decision-making process. An efficient budgeting system supports the accepted theory that decentralised planning and budgeting helps improve efficiency and accountability (Oates 1972, 2005). In a federal country like India, where the state governments bear major functional responsibilities, a credible budget is crucial to reduce uncertainty and risks in fiscal management (Rao 2009). 

Unbiased projection is the most important feature of a credible budget. Revenue forecasting errors due to weaknesses in technical capacity and lack of availability of complete information in a timely manner, are considered the foremost reasons for deviations from planned budgets (Simson and Welham 2014). While overestimation leads to an unsustainable allocation of resources, excessive conservatism in revenue forecasts results in surplus resources at hand that are then spent without going through the regular planning and accountability structure.    

Study methodology

In a recent study (Jena and Singh 2021), we assess budget credibility on both the revenue and expenditure sides of the budget, at the aggregate level (that is, aggregate of all states) and individual state levels. We compare the difference between the original approved budget and the year-end outturn as a percentage of budget estimates and score them on an ordinal scale of A to D following the PEFA (Public Expenditure and Financial Accountability) framework (PEFA, 2016). We assess two blocks of three years each: from 2012-13 to 2014-15 (pertaining to three years of the award of 13th Finance Commission (FC)), and from 2016-17 to 2018-19 (pertaining to three years of the award of 14th FC). While scoring the deviations, at least two of the three years are considered to remove any spikes or outlier years. To measure the variance between achieved and planned spending in various categories, an adjustment is made to remove the effects of changes in aggregate expenditure

Table 1. Scoring methodology 

Score

Dimension

Aggregate revenue outturn

A

Actual value lies between 97% and 106% of budgeted amount

B

Actual value lies between 94% and 112% of budgeted amount

C

Actual value lies between 92% and 116% of budgeted amount

D

Performance is less than C score

Revenue composition outturn

A

Variance in revenue composition less than 5%

B

Variance in revenue composition less than 10%

C

Variance in revenue composition less than 15%

D

Performance is less than a C score

Expenditure outturn

A

Actual value lies between 95% and 105% of budgeted amount

B

Actual value lies between 90% and 110% of budgeted amount

C

Actual value lies between 85% and 115% of budgeted amount

D

Performance is less than C score

Performance across revenue and expenditure

Our findings indicate that the performance of states in terms of remaining close to budget estimates for the total revenue receipt has been low in both the blocks of three years (Table 2). While states fared better in their own tax effort, central transfers, particularly the grants component, deviated considerably from projections. The tax devolution,1 which increased after the recommendations of the 14th FC, remained closer to the projections in the second period. The unpredictability of grants from the central government continued unabated, resulting in large deviations.

On the expenditure side, states performed relatively better with the deviation from budget projections remaining below 10% except for the FY2014-15. Revenue expenditure, which accounts for about 85% of total expenditure, performed better and influenced the behaviour of total expenditure. The performance of capital expenditure has been considerably volatile, and received a score of ‘D’ in both the blocks.

Table 2. Broad fiscal variables: Deviation as percentage of budget estimates


2012-13

2013-14

2014-15

Score

2016-17

2017-18

2018-19

Score

Total revenue receipts

-5.97

-10.22

-14.17

D

-11.3

-8.37

-8.26

C

Own tax revenue

1.99

-6.35

-7.54

B

-14.05

-1.96

-5.32

B

Own non-tax revenue

-2.56

4.22

-5.63

A

-16.4

-8.48

-3.28

C

Central transfers 

-15.75

-17.78

-22.16

D

-7.43

-14.87

-11.81

D

  Tax devolution

-4.64

-7.89

-15.06

C

4.40

-9.22

-1.51

A

  Grants

-28.48

-28.98

-28.19

D

-22.99

-23.11

-25.97

D

Total expenditure

-2.91

-8.74

-13.91

B

-7.39

-9.18

-7.93

B

Revenue expenditure

-1.01

-4.82

-10.39

A

-6.32

-8.25

-7.1

B

Capital expenditure

-18.05

-15.92

-16.8

D

-22.56

-16.02

-16.36

D

Source: Authors’ calculations based on PEFA methodology.

The assessment of budget credibility at the state level for broad categories of revenue receipts and expenditure, more or less reflects the results derived for all states (Table 3). Out of 18 states, 14 in the first block and 12 in the second block, show low scores of C and D in terms of aggregate revenue receipt, implying a large variation from the budget estimates. While performance for own revenue shows mixed results across the states, there has been a decline in the second assessment period.

Table 3. Budget credibility for individual states

Fiscal variables

2012-13 to 2014-15

Score

2016-17 to 2018-19

Score

Total revenue

MH

A

GA, KR, MP, MH

A

KR, RJ

B

GJ, OD

B

GJ, MP, OD, TN, UP

C

RJ, TN, UP, WB

C

AP, BH, CG, GA, HR, JH, KL, PN, WB

D

AP, BH, CG, HR, JH, KL, PN, TL

D

Own tax revenue

GJ, MH, KR

A

KR

A

AP, CG, MP, OD, RJ

B

MP, MH, OD, TN

B

BH, GA, JH, UP, WB

C

GA, GJ, RJ, 

C

HR, KL, PN, TN

D

AP, BH, CG, HR, JH, KL, PB, TL, UP, WB

D

Tax devolution


A

AP, GJ, KR, KL, MP, MH, RJ, TN, WB

A

HR, JH, MP, OD

B

CH, GA, HR, UP

B

BH, CG, GA, GJ, KR, KL, MH, PN, RJ, TN, UP, WB

C

JH, OD, PN

C

AP

D

BH, TL

D

Grants


A


A


B


B


C

MP, RJ 

C

AP, BH, CG, GA, GJ, HR, JH, KR, KL, MP, MH, OD, PN, RJ, TN, UP, WB

D

AP, BH, CH, GA, GJ, HR, JH, KR, KL, MH, OD, PN, TN, TL, UP, WB

D

Revenue expenditure

KL, MH, PN, RJ, TN, WB

A

AP, GJ, KR, KL, MP, MH, RJ, TN, WB

A

CG, GJ, HR, KR, OD, UP

B

CH, GA, HR, UP

B

AP, GA, MP

C

JH, OD, PN

C

BH, JH

D

BH, TL

D

Capital outlay

KR, UP

A

KR, KL, MP, OD, 

A

GJ

B

GJ, WB

B

BH, MP, RJ

C

JH

C

AP, CG, GA, HR, JH, KL, MH, OD, PN, TN, WB

D

AP, BH, CH, GA, HR, MH, PN, RJ, TN, TL, UP

D

Source: Authors’ calculations using budget documents and finance accounts (various years).

Note: List of abbreviations for states can be found here. In addition, OD stands for Odisha and TL stands for Telangana.

Our results show poor performance of the central transfer categories, particularly the grants component. In both the blocks, almost all the states get a score of D in the case of grants, and a large number of states score ‘C’ for tax devolution in the first block. The performance of tax devolution has improved in the second block after the recommendation of the 14th FC for an enhancement of the tax devolution to states.

Following broad classification of revenue and capital expenditure, we find that a large number of states managed to receive good scores for revenue expenditure, implying a low deviation from budget estimates. As the revenue expenditure includes committed spending heads,2 meeting them becomes a priority for the states. On the other hand, capital expenditure remained residual in the system in the budget implementation process with high deviation from budget estimates. 

Several factors influenced the performance of the states in achieving the budget plans. The growth performance of the national economy, changes in central transfers system following the 14th FC recommendations, and teething problems in implementation of GST (goods and services tax) affected the revenue realisation of states. The varying levels of deviation over the years in expenditure categories imply several implementation issues across the departments, in addition to shortfalls in central grants. Deviations of 10% or more pose serious challenges to executing government programmes3

Dealing with budget credibility issues

While an unbiased revenue projection mechanism becomes important to improve the sanctity of the budget, it cannot always be explained mechanically. Looking at the results at the aggregate and individual state levels, it becomes clear that central transfers affect the ability of states to achieve the projected revenue and improved levels of public service delivery through the implementation of the planned budget. Own-revenue efforts and expenditure management are two other crucial factors that affect budget credibility. A solid macro-fiscal projection at the state level is necessary, along with the performance of the national economy ensuring predictability in fund flows.   

Appropriately designing the institutions that govern the decisions over public finances would improve the environment of the budgeting system. Establishing a medium-term perspective, bringing performance orientation in the budgeting process, improving fiscal transparency, and providing for an independent review of the fiscal stance of the governments, are relevant reforms that would strengthen the budget management system and programme implementation (Brumby and Hemming 2013, Robinson 2007, Allen Schick 2014, Kopits and Craig  1998). Addressing local economic factors and the institutional environment becomes important in implementing the budget. In the context of improving budget credibility (Jena 2016), the spending departments need to develop internal capacity, actively manage changes in a transparent manner, and plan their activities keeping performance indicators in consideration.


Notes:

  1. Tax devolution refers to the distribution between the Centre and the states of the net proceeds of taxes.
  2. Spending on interest payment, pension, and salary and wages is usually considered as committed spending. Aggregate committed spending for all states accounts for about 48% of revenue expenditure.
  3. A short fall in central grants leads to a cut in state government spending resulting in the states facing challenges in executing government programmes.

Further Reading

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Current state of play in India’s services trade

 

Author Image

Anil Kumar Kanungo

Lal Bahadur Shastri Institute of Management

Anil@lbsim.ac.in

Since the 1990s liberalisation reforms, the services sector has been seen as a key driver of India’s economy. Against the backdrop of the WTO Ministerial Conference, Anil Kumar Kanungo examines the current state of India’s services trade – India’s participation in multilateral trade organisations and agreements, barriers to services trade, and opportunities and prospects for growth.

The contribution of India’s services sector1 to the Indian economy is well-recognised. With a significant share of over 50% in GDP (gross domestic product), and accounting for 36% of total exports (UNCTAD (United Nations Conference on Trade and Development), 2017), the sector has demonstrated robustness and potential over the past 15 years. Since the 1991 reforms and subsequent liberalisation, the sector has been seen as a key driver of India’s economy. Its role in integrating India with the world economy is also noteworthy, as the end of the 20th century witnessed a proliferation of BPO (business process outsourcing) firms and offshoring services activities, where India took the lead2. However, the share of services in employment in the economy, at about 25%, is quite low relative to its contribution to GDP. This is partly due to the advancement and innovation in ICT (information and communication technology) and use of artificial intelligence (AI) and machine learning, and also due to higher skill level and worker productivity involved in certain sectors such as telecommunications, banking, finance, etc. (Banga 2005). In contrast, about 53% of India’s labour force is currently engaged in agriculture, while its contribution to GDP is 15.3% for the year 2020.

Exports of services registered about US$ 20.86 billion in October 2021, slightly higher than the US$ 16.89 billion in October 2020. Net growth in services in October 2021 vis-à-vis October 2020 and October 2019 has been 6.06% and 13.69%, respectively. Table 1 below provides the latest data on trade in services of India. As the lockdowns have been lifted and economic activity is slowly resuming around the world, the rebounding of some services sectors is noticeable.

Table 1. India’s services sector during 2019-2021

Services

October 2021

(US$ billion)

October 2020

(US$ billion)

October 2019

(US$ billion)

Growth vis-à-vis October 2020 (%)

Growth vis-à-vis October 2019 (%)

Exports

20.86

16.89

17.53

23.52

19.01

Imports

12.71

9.21

10.36

38.09

22.68

Net services

8.14

7.68

7.16

6.06

13.69

The Covid-19 crisis has caused a huge setback to India’s service sector. During the first half of FY2020-21, the sector contracted by almost 16% on a year-on-year basis, led by a big slump in sub-sectors such as trade, hotels, transport, communication, and services related to broadcasting (Ministry of Finance, 2021). In the second quarter of FY2021-22, there have been some signs of recovery in some services sub-sectors. The estimated value of services export for October 2021 was US$ 20.86 billion – a positive growth of 23.5% vis-à-vis October 2020, and of about 19% vis-à-vis October 20193.

These activities can propel the economy to a higher growth trajectory as compared to the period since March 2020 when the pandemic first hit India. In the first quarter of FY2020-21, services related to trade, hotels, transport, communications, and services related to broadcasting witnessed a negative growth of 48.1%, and comparatively, during the same period in FY2021-22, these services experienced a positive growth of 24% because of relaxations in rules and regulations relating to unlocking of economic activities. This in turn indicates the rapid pace at which some areas of the services sector are bouncing back (Ministry Of Statistics and Programme Implementation, 2021).

Services trade

As the quantum of services traded across the world increased around the early 1980s, the Uruguay Round of multilateral trade negotiations, for the first time, brought services trade under the ambit of the multilateral trading system. The GATS (General Agreement on Trade in Services) classified services trade into four different modes, and current world trade transactions in services are done as per these modes4.

The GATS has two kinds of provisions. First, general obligations – some of these are applied to all service sectors (for example, transparency), and some only to scheduled, specific commitments (for example, Article XI: Payments of Transfers). Second, specific commitments – such commitments are negotiated specific to the commitments, which each signatory would like to undertake in the sector.

Both types of provisions have amplified the transparency and liberalisation requirements that countries need to fulfil in order to participate in services trade. In accordance with these requirements, trade policy changes brought about by India need to be published and made available on the WTO (World Trade Organization) website. The Council of Trade in Services is to be made aware of these changes, and informed in advance, or at least annually, of the introduction of any new laws, or changes to existing laws, regulations, and administrative guidelines. WTO member countries are also required to promptly answer queries, regarding specific information requests, put to them by other member countries.

As far as commitments are concerned, developing countries such as India engage in negotiations, and undertake market access agreement, with varying degrees of restrictions, to liberalise the sectors where they believe they have core competence or comparative advantage. The GATS agreement is progressive5 in nature and was adopted in the year 2000. It allows the member countries to follow a positive list6 approach to liberalisation, as India does, and allows countries to make commitments in suitable sectors and modes.

India in the WTO

India had a jump-start in the services sector, which has been reflected in many studies7. To sustain such growth and export of services in world trade, India has, from time to time, undertaken major initiatives such as liberalising sectors such as IT and ITes (information technology-enabled services), tourism and hospitality services, transport and logistic services, etc., and making specific commitments such as in financial consultancy services, advisory services etc. India’s participation in the WTO’s ministerial conferences – the most recent being the ongoing one at Geneva between 30 November and 4 December 2021 – provided a platform to vociferously articulate its advantage in various subsectors and indicated the need to promote its market commitments especially in Modes 1 and 48. The Doha Ministerial Conference in 2001 registered such aspirational concerns of developing countries, and India in particular, for the first time, and brought them to the forefront of the negotiations.

As a prominent player of the services trade in the world economy, India is keen to push for liberal commitments in Modes 1 and 4, and in sectors such as computer services where the country has an export interest. The motive of Indian government is quite clear – to steer the liberalisation process and the negotiation at WTO in a firm and effective manner. Considering the stalled negotiations in services at a multilateral level in the WTO, India has fostered regional linkages in services, investment, and trade facilitation. India has signed bilateral agreements covering services with the Association of Southeast Asian Nations (ASEAN), Malaysia, Japan, Republic of Korea, and Singapore. India is also negotiating bilateral agreements, which will encompass services, with countries such as Canada, New Zealand, and Australia, and regional blocs such as the EU. India is also a part of the Regional Comprehensive Economic Partnership (RCEP) negotiations9. These commitments are positioned in a better manner than the revised offers10 submitted during the Doha round of negotiations.

Recently, India’s trade in services has been slow due to factors such as the deadlock in services negotiations; developed countries’ agenda to push agricultural negotiations, and NAMA (Non-Agricultural Market Access), which are primarily industrial or manufacturing sectors; and issue of environment and climate change to an extent.

Considering that the long-drawn Doha round of WTO negotiations did not yield any results, with no immediate gain in sight, several countries floated the idea of a plurilateral agreement to reduce barriers to trade in services. Significant member countries agreed to form a workable alternative to GATS, which came to be known as the Trade in Services Agreement (TiSA). The objective of the TiSA is to strengthen rules and improve market access for trade in services. The intention behind this agreement is to negotiate between parties, to address discriminatory barriers to cross-border trade in services, and to provide a more predictable investment environment for service suppliers. India has not yet signed this agreement as it feels it will lose its capacity and strategic advantage in negotiations (Mukherjee and Kapoor 2017).

There are significant barriers to India’s services sector in terms of market access and exports. Such barriers are largely in the form of non-tariff barriers and regulations. Modes 1 and 4, where India has demonstrated its competitive advantage, are currently facing such barriers. Market access, national treatment and regulations are viewed as significant barriers in services. Stringent regulations such as work permit and visa requirements, or licensing conditions can act as regulatory barriers which are imposed by USA and EU countries. These issues should be negotiated in the upcoming ministerial conference.

Given the significant interest India has in services trade, a proposal of this nature would help gauge the costs that can be reduced to make our service providers even more competitive at the global level. However, no definite mechanism has been identified to measure the precise real costs to arrive at exact competitiveness and this still requires discussion. Certain international experts consider India to be a country that may delay or stall trade agreements, as all its sectors in services do not have a competitive advantage (UN, 2007). The issue under debate here is that the nature and scope of the TFS is not yet very clear to India.

Way forward

India’s potential in the services sector looks promising. As services trade in the current era of the digital economy requires application and advancement of technology, the Indian government is taking initiative in employing AI and machine learning (NITI Aayog, 2021), as the key support system to drive services trade11.

Given India’s long-established core competence in the IT and ITes sectors, the country should portray itself as a harbinger of trade liberalisation in services. The role of the WTO in such an effort is crucial. India expects the WTO to play an effective role in regaining confidence and trust among developed and developing countries. It is imperative for services sector reforms and liberalisation to continue, and like-minded member countries could also engage in fostering such a consensus among other member countries.

In the WTO, proposals are valid only if they have the consensus and support of the WTO members. In order to garner such support, there is a need to collect data and information, and conduct background research on services sector in India. Countries like the US, Canada, and Singapore have large databases for services trade, whereas India still lacks such facilities. India is still developing a framework and process for the collection of services trade statistics, and as of now, there is no bilateral services trade data available in the public domain. India also does not have a comprehensive database or business directory for its own service providers engaged in international trade.

Overall, the Indian government should work closely with and support their counterparts in different regions and countries, who have shared their interests in the services trade sector. It is also important that different ministries in the Indian government coordinate to implement domestic reforms effectively, such as the removal of market access barriers in key services sectors like financial, retail, banking, insurance, etc. Currently, different states have different slabs or rates of taxation as the GST (goods and services tax) is still in the process of complete uniformity and formalisation in the Indian economy. The rationalisation of taxation policy, especially in a federal structure like India, needs to be justified for the future growth of services trade.


Notes:

  1. India’s services sector covers a wide range of activities such as finance, insurance, hotels and restaurants, transport, storage and communication, real estate, business services, community, social and personal services, and services associated with construction.
  2. The destination for most US business service outsourcing is India. US firms now account for about 80% of India’s BPO market. India’s comparative advantage lies in its highly developed IT (information technology) sector, and reputation for low-cost but high-quality work (Greene 2006).
  3. The latest data for services sector released by the Reserve Bank of India (RBI) is for July 2021. The data for August 2021 is an estimation, which will be revised based on RBI’s subsequent release.
  4. Namely Mode 1 (cross-border supply), Mode 2 (consumption abroad), Mode 3 (commercial presence), and Mode 4 (movement of natural persons).
  5. The agreement is considered progressive because it provides countries with the opportunity to make market access commitments in sectors where they have the potential to benefit from free and fair world trade as well as make those services sectors globally more tradeable, liberal, and open.
  6. Under a positive list approach, countries undertake national treatment and market access commitments, specifying the type of access or treatment offered to services or service suppliers in scheduled sectors.
  7. See Eichengreen and Gupta (2010), Rupa Chanda (1999, 2002).
  8. Recently, Mode 1 has experienced a rise in potential due to the accessibility to advanced technology. With respect to Mode 4, most WTO member countries kept Mode 4 unbound in sectoral schedules and have referred to their horizontal schedules of commitments. Countries such as India have raised concerns that their mode of primary export interest, that is, Mode 4, has not been adequately liberalised in the Uruguay Round.
  9. This includes ASEAN and six countries: namely, Australia, China, India, Japan, Republic of Korea, and New Zealand.
  10. Services negotiations in WTO are designed in the format of request-offer approach, in which countries extend requests to their trading partners to liberalise sectors of their trade interest. In response to that, trading partners examine the requests within their limitations and domestic regulations and extend the offers. Negotiations are also based on reciprocity, which implies that countries seeking commitments may have to give commitments in areas of export interest to its trading partners.
  11. In her 2021-22 Budget speech, Finance Minister Nirmala Sitharaman introduced the Ministry of Corporate Affairs (MCA)’s revamped portal, which will use data analytics, AI, and machine learning to make regulatory filings easier for businesses.

Further Reading

  • Banga, R (2005), ‘Critical issues in India’s service-led growth’, ICRIER Working Paper No. 171.
  • Chanda, R (1999), ‘Movement of natural persons and trade in services: liberalising temporary movement of labour under the GATS’, ICRIER Working Paper 51. Available here.
  • Chanda, R (2002), Globalization of services: India's opportunities and constraints, Oxford University Press, US.
  • Dash, Ranjan Kumar and Purna Parida (2012), “Services trade and economic growth in India: an analysis in the post-reform period”, International Journal of Economics and Business Research, 4(3): 326-345.
  • Eichengreen, B and Poonam Gupta (2010), ‘The Services Sector as Road to India’s Economic Growth?’, ICRIER Working Paper 249.
  • Findlay, C and T Warren (eds.) (2013), Impediments to trade in services: Measurements and Policy Implications, Routledge.
  • Mattoo, Aditya, Randeep Rathindran and Arvind Subramanian (2006), “Measuring services trade liberalization and its impact on economic growth: An illustration”, Journal of Economic Integration, 21(1): 64-98.
  • Ministry Of Statistics and Programme Implementation (2021), ‘Estimates of Gross Domestic Product for the First Quarter (April-June) 2021-2022’, Report.
  • Mukherjee, A (2013), ‘The Services Sector in India’, D Park and M Noland (eds.), Developing the Service Sector as an Engine of Growth for Asia.
  • NITI Aayog (2021), ‘Responsible AI’, Report.
  • Nordas, Hildegunn K and Dorothée Rouzet (2017), “The impact of services trade restrictiveness on trade flows”, The World Economy, 40(6): 1155-1183.
  • Reserve Bank of India (2021), ‘Annual Report 2020-21’.
  • United Nations (2007), ‘Industrial Development for the 21st Century: Sustainable Development Perspectives’, Department of Economic and Social Affairs. 

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