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Cutting logistics costs and complying with exacting European product standards will go a long way in boosting shipments

India is the EU’s ninth largest trading partner with 2.4 per cent of the bloc’s overall trade. Bilateral trade (in both goods and services) touched €115 billion in 2017.

EU exports to India have grown from €24.2 billion in 2006 to €45.7 billion in 2018. India’s exports to the EU have also risen steadily from €22.6 billion in 2006 to €45.82 billion in 2018 with the largest sectors being engineering goods pharmaceuticals gems and jewellery other manufactured goods and chemicals. Trade in services has also trebled between 2005 and 2016 reaching €28.9 billion. India is among the few nations that run a surplus in services trade with the EU.

India has been negotiating a broad-based trade and investment agreement with the EU since 2007. After several rounds of talks the negotiations for a comprehensive Free Trade Agreement (FTA) were suspended in 2013 due to a gap in the level of ambition between the EU and India.

Policy-makers here fear that an FTA will not help India to make inroads in EU’s market and the gains will minimal However the failure to sign an FTA has not reduced the bonhomie between the two partners. In the recently concluded 15th Summit of India and EU the broad consensus that emerged is to strengthen the EU-India Strategic Partnership.

Despite the positive vibes between the two sides the moot question is India’s exports in the competitive EU market are not doing well even in products where we have competitive advantage relative to peers. Take agricultural commodities apart from processed rice the share of India in EU’s import is invariably less than 3 per cent. Even in processed rice the share of ASEAN countries is more than double that of India even though India’s production is way above that of ASEAN.

EU imports more marine products from ASEAN than India despite its longer coastline. A tiny country like Bangladesh exports more labour intensive products such as apparels and leather products than India. Even in pharmaceutical sector where India is a strong players its presence in the EU market is not as that of ASEAN countries or China. Even though India has a modern petrochemical sector EU imports more by-products of same (chemicals rubber plastic products) from China and ASEAN than India. Surely there is a deeper problem why India is not able to penetrate EU’s market.

The key factors

At the outset import tariff is no more a barrier for exports for any country. So a lower share of India’s export in the EU market may arise due to the following factors:

High production cost in India leading to higher import cost in EU market compared to other countries

High logistics costs and poor connectivity that make Indian exports uncompetitive;

Inefficiency in trade facilitation measures leading to high cost of export or consignments being rejected which has spill-over effects;

India’s exports being subjected to higher para-tariff in comparison to other countries;

India’s exports not meeting the stringent European standard. In the past quite often Indian products have been rejected/banned due to failure to comply with EU standards and this legacy is affecting India’s exports.

Reducing production cost takes time as it is influenced by multiple factors including the cost of capital. With regard to Points 2-5 government intervention may play a major role: Every rupee saved in logistics/trade facilitation measures matters a lot in keeping the production cost low and thereby increasing the competitiveness of the economy. Moreover in the case of perishable items each hour wasted in transportation increases the risk of consignment being rejected.

Take the case of export of floriculture/fruits from the Pune region via Mumbai airport. As a recent NCAER study on ‘Logistics Costs’ indicates the journey from Pune to Mumbai a distance about 150 km normally takes about 7 hours which is significantly more than the flight time to Europe. Also an exporter may have to send consignment by air from Pune to Mumbai at high cost to meet the export deadline if the cargo is expected to reach the Mumbai metropolitan region when movement of truck is prohibited in the city. Direct connectivity from Pune to Europe could tilt the balance in favour of exports to EU.

In the past Indian products have been rejected/banned due to failure to comply with EU standards. In this respect the Chinese export strategy could offer lessons. China produces similar goods for exports with price differentials concomitant with different standards. Thus for the EU market they produce goods complying with European standards at higher price than what they produce for African/Indian market. This way they protect their brand value and manage costs too.

Indian producers do not pay as much attention to complying with specific market standards as managing with jugaad. This mind-set needs to change if India plans to penetrate the EU market in a big way.

The writer Sanjib Pohit is Professor at NCAER. Views expressed are personal

A Retrospective on H1: 2020-21.

In the September Quarterly Review of the Economy NCAER had forecasted that GDP would contract in H1: 2020-21 although the rate of contraction would moderate in Q2: 2020-21 compared to Q1: 2020-21.   In such times cost-cutting is a common response of firms.  These typically take two forms — (i) layoffs: firms reduce their workforce and; (ii) pay-cuts: firms implement pay cuts. While the former has a dual impact of aggravating unemployment & reducing consumption the latter reduces consumption. Of course both of them will have a wider multiplier impact on the macro-economy.  Understanding the nuanced variation in the responses across firm-size and type of workers can thus have key policy implications.

We use the June and September 2020 Rounds of NCAER Business Expectations Survey (N-BES) to analyse firms’ responses to SARS-CoV-2 in terms of their employment and wages decisions about their permanent and temporary workers during H1: 2020-21. Wherever relevant we use data from previous rounds too.  The N-BES has been conducted on a quarterly basis since 1991 and includes 500-600 firms across six Indian cities. Temporary workers are typically low-skilled with contract duration of less than one year and limited social benefits if any. Permanent workers are engaged in specialised core tasks of the firm.

Layoffs

The share of firms which responded that they had reduced temporary workers over the last three months went from 7.5% in March 2020 to 44.7% in June 2020 to 30.9% in September 2020.  The corresponding numbers for permanent workers were 2.6% 30.2% and 23.2There are important distinctions between responses of small (Annual Turnover ≤ Rs 100 crores) and large firms (Annual Turnover > Rs 100 crores).

In June while 30% of small firms said they had laid off some permanent workers in the last three months only 24% of large firms had done so (Figure 1). Similarly 41% of smaller firms responded that they had laid off temporary workers in the duration between April and June compared to 35% of larger firms. Unsurprisingly firms were more likely to terminate the employment of temporary workers compared to permanent workers owing to the skill premium of the permanent workers.

By September however the likelihood of lay-offs across firms had declined substantially. Further the distinction across the two types of firms and workers had nearly vanished. Between July and September nearly 20% of small and large firms were likely to terminate employment of permanent workers. The corresponding figure for temporary workers were 18% for small firms and 24% for large firms.

As the economy has recovered the propensity of firms to use lay-offs as a cost-adjustment measure lessened.

Pay-cuts

Nearly 49% of firms said they were levying pay-cuts for their permanent workers in June 2020 compared to March 2020 levels. By September 2020 this ratio fell to 35%. For temporary workers 59% of firms in June 2020 and 57% in September 2020 responded they had reduced wages.

In the early aftermath of the crisis small and large firms were similar in the likelihood of levying pay-cuts on their permanent workers. Nearly half of the small firms imposed pay-cuts on their permanent workers compared to March levels whereas the proportion for large firms was 44%. Just as for layoffs firms were more likely to reduce wages for temporary workers—61% of small firms and 51% of large firms said they have reduced wages for their temporary workers.

However during the subsequent recovery small and large firms become distinct in their usage of pay-cuts for permanent workers. While 42% smaller firms continued with pay-cuts for their permanent workers compared to the levels in March only 20% of large firms did so. Likelihood of pay-cuts by firms for temporary workers remained nearly unchanged between the two rounds.

Thus compared to small firms large firms have been faster in restoring wages of their regular staff and temporary workers remain subject to lower wages—a clear sign of dual speed recovery among businesses.

Implications for Policymaking

First labour markets in terms of retrenchment of workers eased in September 2020 and compared to June 2020. Further sentiments about hiring workers improved during the same period. The lower likelihood of employment termination for all types of workers eases pressure on the calls for urban employment guarantee schemes.

Second persisting pay-cuts for temporary workers may have second order effect on lower purchasing power of households. Given that temporary workers make up nearly 80% of the urban hired workers  (PLFS) lower consumption by such a large proportion may translate into a slow recovery. In this context wage support programmes may appear tempting but intervening in reallocation of resources for short-term gains may not be advisable. Instead government may focus on pushing a targeted fiscal stimulus for sectors which employ low-skilled temporary workers.  A complementary policy would be to develop a voluntary Social Registry system which can protect people from similar future vulnerabilities.

The above analysis also has micro-economic implications for the Indian labour market. Smaller firms are more likely to impose a pay-cut and temporary workers are more likely to receive one. Given that permanent workers are more educated this will increase the skill premium and inter-firm wage inequality in the Indian labor market. In the long term reskilling & up skilling programs may shield workers in case of similar shocks.

Views are personal

Media News: Surjit S Bhalla & Abhinav Motheram 

The more academic American Association for Public Opinion Research’s (AAPOR) postmortem report on the 2016 election polls recognised that state polls ‘clearly underestimated Trump’s support in the Upper Midwest’.

India 2014. Britain 2016. The US 2016. India 2019. And now the US 2020. What do the above five national elections have in common? The fact that seasoned professional and previously accurate pollsters got significant elements of the elections wrong is a pointer. Identification of the cause(s) is important.

Not for the mere desire for more accurate polls but also because opinion polls today both reflect and form public opinion. The latter enhanced by social media to a much greater degree than ever before. Opinion polls are the bread and butter for the cottage industry of poll aggregators (e.g. Fivethirtyeight). Their models seek to ‘inform’ the public with polling odds; getting it right should be an important part of business. Is there a pattern to so many election forecasts going wrong in rece ..

Was 2020 close to warrant soul-searching? Don’t look at the aggregate vote or the electoral college. Both are indicative but no cigar. Look at the closeness of the races statewise. Look at the percentage difference in the vote share — 2016 was very similar to 2000 e.g. less than 2% separated the two parties in six seats seven seats if the bar is 2.5%. The 2020 results are likely to parallel 2016. The last time the same closeness was observed in two consecutive elections was 1976 and 1980. T ..

The more academic American Association for Public Opinion Research’s (AAPOR) postmortem report on the 2016 election polls recognised that state polls ‘clearly underestimated Trump’s support in the Upper Midwest’. The polls did the same in 2020. Not all pollsters are wrong to the same degree. Ann Selzer who leads the Des Moines Register Poll in Iowa a respected authority uses a random digit dialling (RDD) method of polling — i.e. randomly selected respondents rather than derived from traditi ..

Several of the reasons are statistical and therefore easily correctable. More important errors have to do with a changing world order. A new world where there is an ongoing Battle of the Elites. The old elite the Establishment; the new elite the Upstarts. As usual cricket provides a useful parallel — Gentleman vs Players. This ongoing social struggle is an important cause of polling errors. Remember Mitt Romney’s 47% and Hillary’s ‘deplorables’? Trump received a higher fractionof African A ..

There may be an analogy here with ‘perspectives’ on lockdowns. The old elite welcomed them because they could work from the comfort of their homes; the rest detested them because it meant a significant loss in incomes. Even viruses are political. To account for this changing social-political demographic we need to correct the polling data for the realistic possibility of lying. In this lying there is an anger against the system. A correction was tried and tested on elections in India the late  ..

The fractions who are lying need not be very large to cause major errors. Assume one candidate has a 10-point lead — A is at 55 and B is at 45. If only 5 percentage points (PPT) of Camp A lie a 10-point lead is turned into a dead heat. The consistently good polling company Trafalgar Group seems to have a method that works and one which in principle is not that far away from our 1989 method. To mitigate the social desirability bias they ask the respondents not only how they themselves will vot ..

(Bhalla is executive director International Monetary Fund (IMF) representing India Sri Lanka Bangladesh and Bhutan and Motheram is research analyst at National Council of Applied Economic Research-National Data Innovation Centre (NCAER-NDIC). Views are personal)

Dr. Tarujyoti Buragohain NCAER surmises that high prices of horticulture products due to lack of necessary infrastructure and quality are the major constraint for India in the export market. Moreover diversifying into organic and value added horticulture products may help improve export prospects.

The Lead Bank Scheme of the RBI involves a ‘lead bank’ in each district of India that coordinates financial services in the district and a ‘convenor bank’ in each state that monitors these activities of the lead banks in the state. Analysing quarterly district-level data on credit disbursement for 2004-2019 this article finds that in districts for which lead bank and convenor bank belong to the same corporate entity credit disbursement is 21% higher.

The Reserve Bank of India’s (RBI) ‘Lead Bank Scheme’ is among the oldest financial inclusion programmes in the country. Initiated in 1969 the scheme assigns the role of lead bank to a public-sector bank in each district based on the overall capabilities of the bank in the district. The lead bank coordinates with local financial-market agents to identify and address bottlenecks in the provision of financial services and to conduct public outreach programmes such as financial literacy camps in the district. Further another public-sector bank is assigned the role of convenor bank for the state which involves monitoring the performance of lead banks of the state. The chairman of the convenor bank executes this function through quarterly meetings with lead banks. These meetings are regularly attended by senior bureaucrats and politicians of the state and representatives of local press and NGOs (non-governmental organisations). If it is found that the lead bank has failed to meet targets penalties are imposed which may include allotting some funds to (low-yield) Rural Infrastructure Development Fund.

Given this organisational structure of the scheme some districts have their lead and convenor banks belonging to the same corporate entity. What effect does this alignment have on the financial-market outcomes in the district? Theories of organisational economics suggest that lower costs of intra-firm monitoring and punishment lead to higher effort by employees (Williamson 1981 Holmstrom 1999). In this context the lead bank of an ‘aligned’ district should be exerting more effort as they are reviewed by their immediate supervisors. If so one can expect higher credit disbursement in these districts.

For most public-sector banks in India recruitment is conducted only for junior-level employees whereas the senior positions such as for lead bank personnel are filled through promotions based on merit-cum-tenure. Further attrition and termination of employment remains low in these organisations. In such an environment managers subject to review by their immediate superiors are more likely to exert higher effort in the assigned tasks.

If the personnel of the lead bank experience added organisational pressure from their own supervisors if the convenor bank belongs to the same corporate entity then one can expect credit disbursement through branches of lead banks to be higher in ‘aligned’ districts compared to branches of other banks in that district and branches of the same bank in non-aligned districts. However data on branch-district level credit disbursement are not publicly available (see Acharya et al. 2019). Therefore I test the hypothesis using RBI data on quarterly district-level credit disbursement from 2003-04:Q4 (fourth quarter of the financial year) to 2018-19:Q4 (Gupta 2020). Nearly 44% of the districts are aligned. All else being equal aligned districts should have higher credit disbursement as monitoring by one’s own chairman should imply higher effort by lead bank personnel in aligned districts.

District ‘alignment’ and credit disbursement

My econometric analysis shows that aligned districts have 21% higher credit disbursement on average as compared to non-aligned districts over the study period.

However these two categories of districts could differ in terms of other characteristics – besides alignment – that influence credit disbursement. For example the (unobservable) demand for credit may be higher in aligned districts. To address this issue I analyze how credit disbursement changes in 38 districts which experience a change in alignment. For these cases the change in credit disbursement and alignment can be observed for a particular district which overcomes a potential bias in the  results induced by differences in unobservable district-specific characteristics. I find that for this group of districts credit disbursement increases (decreases) by 28% when a district becomes aligned (non-aligned).

Although lead bank of a district does not change change in alignment may occur on account of reasons such as change in convenorship of the state (for example from Union Bank of India to State Bank of India (SBI) in Manipur in 2005 and Allahabad Bank to Bank of India in Jharkhand in 2013) formation of a new state (for example Telangana1 in 2014) or merger of a bank and its associates (for example SBI in 2017). Could the change in alignment of a district be driven by credit disbursement instead of alignment affecting credit as posited here? In Figure 1 I plot the trend of (log of) credit in the quarters leading up to the change in alignment for districts that exhibit a change in alignment (‘treated’) and neighbouring districts that do not (‘control’) experience any change in alignment. The pre-trends appear to be parallel indicating the absence of any unusual change in credit disbursement that could influence the choice of lead and convenor banks.

Impact of negative income shock on long-term savings: Aligned vs. non-aligned districts

Next I observe the impact of a negative income shock on long-term savings. Specifically if aligned districts do have lower bottlenecks to access credit then the ability to borrow easily should ameliorate the impact of an exogenous (external) economic downturn on long-term savings (Eswaran and Kotwal 1990).

I use scanty monsoon (20% below normal) in a district as proxy for a negative income shock and quarterly term deposits2 as a measure of long-term savings for the period 2012-20163 After a scanty monsoon (third quarter of a financial year) aligned districts should have access to credit which should prevent their long-term savings from getting depleted. Consistent with this hypothesis term deposits in non-aligned districts are found to fall by 25% in the quarter after a scanty monsoon but there is no such reduction in term deposits for aligned districts.

Could monsoon rainfall be correlated with unobservable district-level characteristics? If aligned districts are more susceptible to scanty monsoon rainfall then households may be better prepared for consumption smoothening which may bias the above result. A further statistical test of difference in district-wise departure of monsoon rainfall from normal levels rules out this concern.

Conclusion and policy implications

In India’s Lead Bank scheme each district is assigned a public-sector bank as lead bank to improve district-level financial inclusion and each state is assigned a public-sector bank to monitor the lead banks. My study finds that when district- and state-level agents of this scheme belong to the same corporate entity credit disbursement is 21% higher. The results are explained by lower cost of monitoring of the state-level agent which induces higher effort by district-level managers.

Whether greater credit disbursement is good or bad from a social welfare perspective is a separate matter. This requires assessing ‘adverse selection’4 or ‘moral hazard’5 in allocating credit at the district level. To do so one would need data on district-level non-performing assets (NPAs) which to my knowledge is currently unavailable in the public domain.

This limitation notwithstanding the results shed light on how organisational design may influence organisational performance. Complex organisational set-ups operate in the various welfare schemes in India such as distribution of subsidised fertilisers LPG (liquefied petroleum gas) distribution Pradhan Mantri Fasal Bima Yojana (crop insurance scheme) UDAN (regional aviation connectivity scheme) and so on. Implications from theories of organisational economics may help understand and address distortions and ultimately improve public service delivery.

Notes:

1. The state of Andhra Pradesh was bifurcated to form two states – Telangana and the residual region of Andhra Pradesh – in 2014.
2. District-wise monsoon rainfall was available only for 2012-2016.
3. Term deposits are long-term saving instruments which offer high risk-free returns.
4. Adverse selection occurs when buyers and sellers in a market have unequal information and this can distort the usual market process and lead to market failure.
5. Moral hazard is a situation in which one party gets involved in a risky event knowing that it is protected against the risk and the other party will incur the cost. It arises when both the parties have incomplete information about each other.