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Thursday, November 26, 2020

What ails public procurement: an analysis of tender modifications in the pre-award process

by Shubho Roy and Anjali Sharma.

One source of low state capacity in India lies in the ability of the state to contract with private persons. The contracting process starts at procurement and runs till the final payment or dispute resolution. This process suffers from delays and disputes, to the point where managers in government feel uncertain about whether a given contract will work correctly, and private firms feel that doing business with the government is problematic. While difficulties in the last step (payment delays (link, link) and/or contract disputes (link, link) tend to loom large in the discussion, these are the final manifestations of weaknesses of the overall process of government contracting.

Research on government contracting is required in order to diagnose the sources of difficulty and design solutions. As an example, Lewis-Faupel et. al. (2016), analyse a database of 35,600 contracts awarded by the government under the Pradhan Mantri Gramin Sadak Yojana (PMGSY) to understand whether electronic procurement is associated with better procurement outcomes. They find some improvement in quality but not much impact on costs and delays. While state capacity in government contracting is an important problem, in the Indian context, there is a limited empirical literature on the subject. Due to difficulties with data, most studies rely either on small data sets (Goyal (2019)) or on the case study approach (Nag (2015)).

In this article we use a new dataset about the public procurement process in India to measure one aspect of the public procurement tender process: modifications made by the procuring entity to tender documents. We analyse the frequency and nature of these modifications and the possible impact these modifications might have on entities that participate in government tenders. We find that a large proportion of tenders that are published see modification and that government procurers make frequent modifications, especially in high value tenders. We offer some speculation on the causes and consequences of tender modifications.

Difficulties of measurement of the public procurement process

The Indian state buys through various entities and at various levels. Each government department or entity is responsible for following the General Financial Rules (GFR) and purchasing the goods and services it needs to meet its operating requirements. Most government departments or entities do not have a centralised purchasing office. Purchasing is, often, distributed geographically and by value. Small value purchases (relative to the entity's budget) are usually carried out by lower rung offices, while larger value purchases are carried by regional or even the national headquarters. Similarly, purchases of goods or services may be carried out at the location where it is required. For example, a contract to maintain an agency's local office may be advertised only in the city where the local office is situated. The fact that purchasing takes place at many locations makes it harder to assemble datasets about it.

The first step in the process is tendering for bids. Advertisements for government procurement called Notice Inviting Tenders (called NITs in this article) are typically published in local newspapers where the government entity is interested in procuring goods or services. If the proposed procurement is above a certain threshold, it is advertised in newspapers with a national circulation. Any attempt to get data from NITs would call for scanning hundreds of newspapers, every day.

A new opportunity for measurement

In 2011, a website called the Central Public Procurement Portal (CPPP) was established. From 2012 onwards, all central government organisations, including Central Public Sector Enterprises (CPSEs) and autonomous and statutory bodies under the Central government, are required to publish their tenders on the CPPP. Since then, in addition to publishing of tenders and awards information online a large part of the tendering process has also been automated using the portal. The mandate has evolved from e-publish to e-procure.

This website allows us to observe tenders from the date they are published till they are awarded. That is, from the time that the general public/interested bidders are informed that the government is interested in purchasing some goods or service or works, to the time that the government selects a supplier and awards the contract.

The problem of tender modification

In this article, we focus on one measure of state capability in the procurement process: modifications that are made to the NIT during the procurement process. When the government issues the NIT, interested vendors respond by submitting a bid document to the government. The NIT contains details about: (1) the item being procured (including details such as procurement category, technical specifications, bill of quantity, estimated value of procurement, delivery specifications and period of work), (2) qualifications for eligible bidders (financial and technical eligibility conditions), and (3) critical dates for the tender process (publishing date, submission date, opening date). Any modification that is made to the NIT after it is first published is a source of substantial cost on all entities interested in bidding for the government procurement. For instance, if the government modifies the qualification requirements for the bidder after publishing the NIT, those who originally qualified for the NIT may not qualify after the change. The effort taken to develop a bid is then wasted. Once tender modifications are endemic, at every stage in the process, bidders build in expectations about future fluctuations through tender modifications, and this reshapes their decisions to try to sell to the government and the price at which engagement with the government could be profitable.

The government often requires potential bidders to submit 'earnest money deposits' or EMD with their bids, which is a financial guarantee. Potential bidders need to take steps to tie up the funds required for this EMD. This typically involves an explicit cost, such as getting a line of credit from a bank, or an opportunity cost, such as keeping this money aside and not using it for any other purpose. If the period of the tender process is then extended by the government procurer, the period for which this cost has to be borne by the potential bidder also gets extended. In large value tenders, this cost can be substantial. This cost has to borne by all potential bidders. For instance, if six firms bid for a civil works tender where the EMD requirement is Rs.1 million, the aggregate cost to the economy is the cost of keeping Rs.6 million of capital aside till one supplier is selected. The selected supplier can internalise this cost in its bid. But for the five that are not selected, this cost does not have the offsetting benefit. It will influence the supplier's overall business, not just its current or future government engagement.

Similarly, modifications in the original technical specification of the goods and services in the NIT, may require interested bidders to substantially change their bid documents or even drop out of the process after incurring the costs of putting together the bid documents. If the date of the delivery of goods or services is modified, the ability of the supplier to manage its supply chain is hampered. Even when a firm feels confident that it will win a contract, unpredictable delays in tendering hamper efficiency in the production and supply process.

We find three mechanisms through which the NIT can be modified: (1) cancellation, (2) re-tendering, and (3) corrigenda. An outright cancellation is where the government procurer retracts its decision to purchase some goods or service. A re-tender is where a published NIT is withdrawn and replaced with a new NIT. This fresh NIT usually has substantially different provisions and requirements for the bidders. Corrigenda are issued when the government procurer wishes to make changes to the NIT that it deems as not so substantial as to warrant a re-tender, and instead amends certain terms of the NIT. Each of these three mechanisms of modifying NITs introduce uncertainty and costs for all potential bidders in the public procurement process.

These three mechanisms also make the tender process uncertain and costly for the government procurer. Procurers have to spend time and resources to prepare NIT documents, and frequent document changes add to this cost. For instance, if a tender is published and then cancelled, the effort and the resources go to waste. Similarly, frequent corrigenda also require time and resources to manage and monitor the change management process. If modifications to the NIT create disputes and complaints about probity from potential bidders, additional costs of investigation and enforcement alongside possible litigation arise. If frequent modifications to the NIT create an outcome of too few or no bidders, there can even be an extreme outcome of a failed procurement.

Given the uncertainty and costs that modifications to NIT can impose on both the government procurers and the bidders, we think of these three mechanisms for making changes as sources of errors in the tender lifecycle. Our dataset makes possible the measurement of the `modification rate' which is a metric of the friction in the procurement process.

A few entities account for most of the procurement

In November, 2020 there were more than 26,000 government procuring organisations registered on the CPPP. An average of 0.225 million tenders were published on the portal in FY 19 and FY 20 each. These tenders had an estimated value of Rs.8 trillion in each of these two years. We find that 11 procuring organisations within the Union government accounted for around 50% of the NITs by count and more than 80% of the NITs by value in both FY 18-19 and FY 19-10. Table 1 gives the details of the NITs published by these entities.

Table 1: The largest organisations by NIT count and value across FY 19 and FY 20

Organisation Share in count (%) Share in value (%) Average tender size (Rs. million)

Airports Authority of India 2.0 1.8 32
Border Roads Organisation 2.1 0.7 12
Defence Research and Development Organisation 3.3 0.2 3
Delhi Metro Rail Corporation 0.2 1.2 188
E-in-C Branch of Military Engineer Services 24.8 2.8 4
Engineers India Ltd 0.5 31.4 2,163
Food Corporation of India 2.1 14.6 241
IHQ of MoD (Army)-(OSCC) 14.9 1.3 3
Ministry of Road Transport and Highways 1.1 8.5 263
National Highway and Infrastructure Development Corporation 0.1 2.0 893
National Highway Authority of India 0.9 19.6 795

Total 51.9 84.2 57

The modification rate is high

For each of these entities, we study all three mechanisms for making modifications to NITs: cancellation, re-tendering and corrigenda. For cancellation and re-tendering, we examine the data from the CPPP analytics dashboard over the two year period, i.e. FY 2018-19 and FY 2019-20.

For corrigenda, we find that there is no aggregate data available and it has to be hand collected at the level of each NIT. We focus on collecting corrigenda data for NITs published by the organisations in Table 1. The tenders for which we collect data are those that are currently not active. A tender that is not active is one which has either been awarded or cancelled. We focus on NITs published in the period of January to March, 2020. We pick this period for our analysis as it is before to the start of the Covid lockdown and allows us to study the corrigenda pattern in normal times. In this period, we record which NITs have no corrigenda and which NITs have at least one or more corrigenda. This analyse a set of 11,714 tenders across the 11 procuring organisations in Table 1 for the presence or absence of corrigenda.

Table 2 gives a summary of the modification rate for the three mechanisms. It shows that across the 11 procuring organisations, potential bidders face substantial uncertainty due to tender modifications. Since the corrigenda data is from a period different from the tender cancellation and re-tendering data, these three modification rates cannot be added up to arrive at the aggregate modification rate. However, it is clear that a large proportion of NITs see changes, and that issuing corrigenda is the most frequent mechanism through which NITs are changed. Corrigenda are issued for nearly 20% of the NITs that get published in our analysis period.

We may expect that organisations that do more tendering would develop greater organisational capability to do this well. We might expect that difficulties of contracting in the past would have triggered off feedback loops into organisation design that address these difficulties. However, across these 11 entities, we see the reverse relationship. This raises concerns about the extent to which difficulties faced by these organisation have generated feedback loops into modified organisation design and reduced tendering inefficiencies.

Table 2: Sources of modifications in NITs (in %)

Organisation Re-tender Cancellation Corrigendum
(FY 19 & FY 20) (FY 19 & FY 20) (Jan-Mar 2020)

Airports Authority of India 4.4 13.0 26.6
Border Roads Organisation 2.8 8.0 7.5
Defence Research and Development Organisation 4.4 7.0 18.3
Delhi Metro Rail Corporation 0.1 25.3 56.3
E-in-C Branch of Military Engineer Services 16.6 5.7 17.2
Engineers India Ltd 5.7 4.3 43.0
Food Corporation of India 1.4 7.3 35.6
IHQ of MoD (Army)-(OSCC) 6.0 10.4 10.5
Ministry of Road Transport and Highways 7.8 15.3 43.1
National Highway and Infrastructure Development Corporation 1.7 28.2 78.7
National Highway Authority of India 0.5 16.0 19.5

Total 10.5 8.1 19.5

Corrigenda are frequent

We turn to the nature and the frequency of the corrigenda being issued. For this, we identify a sample of active tenders with corrigenda for the entities listed in Table 1. Our analysis in Table 2 relied on tenders that were not active. However, on the CPPP website, corrigenda details are only available for tenders that are currently active. An active tender is one for which the tender lifecycle is yet to be completed. In this analysis, we identify 104 tenders. These tenders had 536 corrigenda issued against them when the sample was collected. Since, these are active tenders, more corrigenda may have been added to them subsequently.

While a cancellation terminates the procurement process and the re-tender starts it afresh, a corrigendum keeps the process running. The government procurer may make incremental changes to terms of the NIT with a corrigendum. For example, the government may add details to the technical specifications on the item being procured. Or it may change the date by which bids have to be submitted. Or it might demand additional documents from bidders as proof of eligibility. While the changes introduced by a single corrigendum may be small, over time, with many corrigenda, these changes may become substantial. So while corrigenda do not terminate the process, they introduce significant uncertainty in the procurement process.

Our analysis of 536 corrigenda across 104 tenders shows that changes by corrigenda are common. Table 3 presents some of the features of these corrigenda. We observe:

  • On an aggregate basis, around five or more corrigenda are issued for every tender. This number varies across organisations, with entities like NHAI issuing as many as 30 corrigenda per tender.

  • Around 60% of these corrigenda are issued to change the bidding dates. The remaining 40% are to make changes such as alterations to the technical specifications, the bill of quantity or for issuing more details or clarifications than were given in the original NIT document.

  • There is a systematic under-estimation by procuring organisations of the time it will take to get bids. Across the 11 procuring organisations, the original estimate of the time it will take to receive bids was 16 days. However, through frequent issuance of corrigenda this period was extended to 81 days. And since these are active tenders, the bid period may increase even further.

  • Some entities, like NHAI and NHIDC not just have more tenders with corrigenda but also more corrigenda per tender.

Table 3: Frequency and nature of corrigenda

Avg. tender size (Rs. million) Avg. corri-genda /tender (No.) O/w Date change (%) Original bid period (Avg. in Days) Revised bid period (Avg. in Days)

Airports Authority of India 504.2 2.5 60 17 33
Border Roads Organisation 264.5 4 63 22 64
Defence Research and Development Organisation 9.7 1.6 63 18 26
Delhi Metro Rail Corporation 2,811.9 1.3 77 7 14
E-in-C Branch of Military Engineer Services 1.9 1.2 8 7 7
Engineers India Ltd NA 3.3 33 16 31
Food Corporation of India 1.6 2 - 16 19
IHQ of MoD (Army)-(OSCC) 2.0 3.8 89 16 49
Ministry of Road Transport and Highways 1.9 1 100 7 7
National Highway and Infrastructure Development Corporation 1,561.9 4.4 20 10 24
National Highway Authority of India 7,620.3 29.7 67 44 584

Total 1,848.2 5.2 60 16 81

An illustration of changes through corrigenda for a complex project

Table 3 presents a subset of a larger set of parameters on which the original tender is modified through the corrigenda. We illustrate the extent to which original tenders can experience change using one example of a tender for a complex procurement: the NHAI Contract to upgrade a stretch of road at Panipat from two lanes to four lanes. The estimated value of the tender is Rs. 2.1 billion and the estimated period of work post award is 2 years. Potential bidders are required to submit an EMD of Rs. 21 million, 1% of the tender value.

Table 4 is a brief summary of the timeline of the NIT. As of the date of data extraction, the NIT has been amended 31 times, and has been active for 693 days as against the original bid period of 44 days.

Table 4: An example of frequent changes to a tender

Date NIT change event Time elapsed (in days)

10th Dec. 2018 Original NIT published on CPPP website. Original bid submission timeline: 23rd January, 2019. Bids to be opened on on 24th January, 2019. -
11th Dec. 2018 NHAI publishes first corrigendum. This adds a new set of information about the procurement. Additional documents added to the NIT by NHAI. 1
21st Jan. 2019 NHAI issues second corrigendum two days before the final date of submitting bids. This corrigendum extends the submission timeline from 23rd January to 12th February, 2019. 42
11th Feb. 2019 One day prior to the submission deadline, bid submission date extended by 7 more days to 19th February, 2019. 63
13th Feb. 2019 NHAI publishes three corrigenda on a single date. They are titled "Revised Tender Documents", "Financial Proposal", and "Letter". After nearly two months of publication of original NIT, NHAI makes substantial changes to the planned procurement. Interested bidders would have to go back to the drawing board and redo their bids. On 13th February, the deadline to submit the bids was 19th February, so the bidders would have just six days to absorb all the changes, make fresh documents and submit them. 65
14th Feb. 2019 The NHAI publishes another corrigendum. This one contained replies to queries raised by the bidders. The clarifications may have required the bidders to redo their bids or make substantial changes. 66
18th Feb. 2019 One day before the bid submission deadline, NHAI changes the deadline for the third time. Now, the final date for submitting bids would be the 25th of February, 2019. 70
22nd Feb. 2019 to 19th Oct. 2020
The NHAI goes on to publish 25 more corrigenda, each extending the deadline for submitting bids by about two weeks. The bid submission timeline is now 17th November, 2020. 679
2nd Nov. 2020
We extract corrigendum data for this tender. The tender is still active so more corrigenda can be added. 693


In this article, we have used a novel dataset and discovered some new facts about the public procurement process.

While there is significant decentralisation in the procurement process across government entities, there is a possibility of improving public procurement by initiating reforms at a few entities that make up the bulk of procurement activity.

A large proportion of the tenders published are modified by the government procurer, either through a corrigendum or through re-tendering and tender cancellation. These tender modifications are taking place at a scale that increases uncertainty in the procurement process. There is a need for organisational reform within government organisations, so that better analysis is done before the first document is unveiled, so that the need for changes thereafter is minimum.

How the state contracts with private persons is one important element of the overall problem of state capacity. There is a need to build knowledge, and a literature, on this subject. This article constitutes one small element of that overall research program.


Sean Lewis-Faupel, Yusuf Neggers, Benjamin A. Olken and Rohini Pande (2016), Can Electronic Procurement Improve Infrastructure Provision? Evidence from Public Works in India and Indonesia, American Economic Journal: Economic Policy, Vol. 8, No. 3.

Bodhibrata Nag (2015), Combating Corruption in Indian Public Procurement - Some Exploratory Case Studies, The Journal of Institute of Public Enterprise, Vol. 38, No. 1&2.

Yugank Goyal (2019), How Governments Promote Monopolies: Public Procurement in India, American Journal of Economics and Sociology, Vol. 78, No. 5.

Anjali Sharma is a researcher with the Finance Research Group. Shubho Roy is a doctoral candidate at the University of Chicago. The authors would like to thank Susan Thomas for discussions and useful inputs and Charmi Mehta and Sejal Gajjar for assistance with collecting the data.

Monday, November 23, 2020

The problems of public procurement and payment delays: A review of the recent literature

by Sourish Das and Rabia Khatun.


'Public procurement'- the purchase of goods and services by the state from private enterprise -- tends to be a large part of economic activity in any country. The World Bank estimated that globally, public procurement in 2018 amounted to USD 11 trillion or 12 percent of global GDP(Bosio and Djankov, 2020). In India, these estimates are higher at 30 percent (Khan, 2017) and recent budget announcements suggest that these estimates are likely to increase.

Such magnitudes have a large multiplier effect on economic activity and economic growth. But the multiplier effect is dampened by the 'marginal cost of public funds' or MCPF which is the cost incurred by a rupee of public spending (Kelkar and Shah, 2019). In an ideal world, public procurement works well, and goods/services that are available in the private market for Rs.1 are purchased for Rs.1 by the government. In the real world, public procurement processes introduce an additional friction, an inefficiency, where the government pays Rs.A when purchasing something worth Rs.1. Every deficiency of public procurement procedures drives up the A.

There is a friction in taxation (the MCPF which Kelkar and Shah (2019) refer to as a cost of Rs.3 upon the economy when the government obtains Rs.1 as taxes). Similarly, there is a friction in contracting-out (the government pays A when obtaining services worth 1). These two come together in shaping the overall effectiveness of government action. A government that wishes to purchase (or contract-out)goods/services worth Rs.1 ends up with a true total cost for society of 3A. On the taxation side, this motivates research on understanding and reducing the MCPF. On the expenditure side, this motivates research on understanding and improving public procurement so as to obtain a reduced value for A.

The conventional processes of government do not produce information about these two elements of inefficiency. Researchers have to create mechanisms through which these estimates can be obtained. For example, there is a widely held perception that delays of payments are a persistent problem in public procurement. Such delays in payment translate into higher costs of doing business by the private enterprises that render services or deliver products to government or public sector enterprises, and raises the MCPF of public procurement. As has been happening elsewhere, the perception of the higher cost of doing business with the public sector is increasingly occupying the public discourse in India as a critical element of what is driving stress in the financial health of the corporate sector. At present, we have informal estimates about the difficulties faced in public procurement in India. As an example, Sahu (2020) recently estimated the size of the delayed payments from the Union government as totalling Rs.9.5 lakh crore, an estimate that was culled from public sources. The data presented included pending dues to road projects at NHAI, from power generating companies and power grid, in the sugar and fuel ecosystem, food distribution at FCI and to the micro, small and medium enterprises. But beyond such broad, aggregate estimates, there is little that is understood about the mechanics that drive this quantum of delay. What needs to be set right to solve the problem is not well understood.

In the present literature, two key features emerge. One is the issue of late payments by the state. This has become increasingly recognised as a major problem after the Financial crisis of 2008 and after the European debt crisis of 2009. Perhaps as a consequence, almost all of the studies are based on data from countries of the EU. A second central concern appears to be the effect of such late payment by governments on the financial health of firms, particularly Small and Medium Enterprises or SMEs. SMEs have been in the policy headlights over the last decade as a critical base of employment growth. Any factor influencing their financial health has also been highlighted as an important area of reform. SMEs are particularly affected by any adverse impact of payment delays.

In this article, we survey the literature on delays in payments by government and their consequences. We find it useful to classify this literature into two lines of thought about delayed payments in public procurement: (1) these hurt the profit of the private sector and increases the probability of bankruptcy, particularly for smaller businesses; and together (2) such delays have a significant negative impact on economic growth. Additionally, this literature shows pathways for setting up measurement systems that can then be used to regularly monitor the impact of public procurement processes on economic agents and the economy. Four papers appear to be the basis of understanding, which are Connell (2014), Checherita et al. (2016), Obeng (2016), and Conti et al. (2020).

Much of the work uses two components to measure late payments: payment delays and the duration of payment delays. Payment delay is calculated over agreed contractual period and it is the ratio of absolute delay (in days) to the agreed contractual period. Payment duration refers to agreed contractual period plus the absolute delay in days over agreed contractual period and is the sum of agreed contractual period plus payment delay. The data for payment delay and payment duration is obtained from Intrum Justitia, a private credit management firm which conducts an annual written survey among several thousand firms in 29 European countries. The survey results are published as the annual European Payment Index Report. Among other statistics, the survey reports the average annual payment duration and the average annual contractual payment period, both of which are further disaggregated into consumer, business-to-business, and public sector debtors terms.

The impact of delayed payments in public procurement on the health of firms

Connell (2014) attempts to estimate the economic effects of late payments that firms face in some European countries (Greece, Italy, Portugal, Spain) regarding delays in payments in Business to Business (B2B) and Government to Business (G2B) transactions with two questions:

  1. How can the cost to firms associated with government late payments be approximated? This cost is estimated as the short-term financial cost of firms associated with late payments. In order to calculate this, they use the volume of claims against the public administration, the average annual interest rate for loans to non-financial corporations and the average government payment delays expressed as a fraction of a year.
  2. Do liquidity constraints associated with payment delays put the firms out of business? A panel regression is run between payment delays and the firm's exit rate. This was done for B2B and G2B transactions separately. The exit rate is defined as the ratio of death firms to the total number of active firms. The regression controls for size of the firms involved, country fixed effects to control for national time-invariant characteristics, and business cycles variables to control for changes in financial conditions.

The paper finds that payment delay is statistically significant and negative across all the countries studied, with higher payment delays being seen with higher exit rates. The estimated financial cost as a percentage of GDP in 2012 ranges from 0.19 percent in Greece to 0.005 percent in Finland. A one point reduction in the payment delay ratio would reduce exit rates by about 2.8 or 3.4 percentage points in a B2B transactions. As expected, these effects are exacerbated with business cycle effects. The results also show that bigger firms, with a larger number of employees, are more likely to survive the deleterious effects of payment delays. In G2B transactions, a one point reduction in the delay ratio leads to a decrease in exit rates of about 1.7 to 2 percentage points. The effect is lower than payment delays in B2B transactions which is suggested as being due to the different representations of SMEs in these different types of transactions. The overall findings of this study suggest that payment delays in commercial transactions by the public administration and private entities have detrimental effects on the health of a firm, and exacerbate the burden of already financially constrained firms which ultimately push them out of business.

Delayed payments in public procurement and its impact on the economy

Checherita et al. (2016) analyze the impact of government payment delays on private firms and on economic growth. They argue that increased delays in public payments can affect private sector liquidity and profits and hence ultimately economic growth. This study defined payment delays by including various measures of the accounts payable data from government accounts (as defined in ESA 1995 code AF.7) along with the other measures of payment duration defined earlier. In addition to the short-term impact of payment delays from government on real GDP growth, the study also analyses profit growth measured by economy wide gross operating surplus, and bankruptcy measured by the probability of default (using Moody's measure of distance to default) over the period spanning 1993 to 2012.

Using a panel regression analysis, they find a negative relation between delayed payments and growth. The results show that a one standard deviation change in delayed payments reduces the growth rate by 0.8-1.5 percent, and a one percent increase in arrears reduces growth by 0.6-0.9 percent. The paper finds a statistically significant impact of delayed payments on the growth rate of operating surplus of firms. A one standard deviation increase in delayed payments reduces profit growth by 1.5-3.4 percent. Finally, their results suggest that delayed payments reduce the distance to default. In similar work, Fiordelisi et, al. (2012) show that economic growth in Italy would have been an additional 0.38 per cent if the government paid its trade loans within 30 days.

Obeng (2016) investigates the impact of payment delays caused by a liquidity crisis in the European Union, using changes in the pattern of late payments among EU companies between 2005 and 2014. The paper finds the following features about payments delays during the financial crisis: payment delays increased across the board; delays had a higher negative impact on SMEs, low profitability firms, and low liquidity firms; significant variation in how delays increased depending upon the sector that the firm operated in. The paper analyses the variability of firm late payments under different macroeconomic conditions using data for 54,277 EU firms over the period 2005 to 2014 from the AMADEUS database, a commercial European firm database. A fixed effects regression model to estimate the impact of selected macroeconomic shocks on payment delays finds that the financial crisis has a significant negative impact on payment delays of accounts receivable, even after controlling for firm characteristics such as profitability, liquidity, size, sector, country, credit collections, and credit period.

This literature establish that impact of delayed payments by the government on firms and economy is negative and significant. The next strand of the literature asks what can be done to reduce the economic cost of delayed payments, and to improve the MCPF of public procurement.

Conti et al. (2020) analyze the regulatory framework of the EU (called the Directive on Late Payments or DLP) concerning delayed payments by government. This paper focuses on G2B commercial relationships, starting by investigating the impact of the DLP on firm survival, employment and investment. They use sector level data for a sample of 23 EU countries (and Norway) from 2008-2015, using 38 two-digit sectors from the Structural Business Statistics(SBS) database (an Eurostat firm database which provides information on European firms). The authors construct the exit rate of firms for a given sector in a country as the ratio between the number of enterprises that cease activity and the stock of active enterprises in a given year and for a given country-sector unit. A difference-in-differences analysis finds that after the introduction of the Directive, the exit rate of firms decreased in sectors that sell a larger fraction of their output to the government. They also find that there is an increase in employment in those sectors more connected with the government, and conclude that more discipline in government payment terms can have considerable positive effects on economic activity.


The results of the above studies present the first empirical estimates of the quantum of the negative impact on the economy when the government delays payments for procurement transactions.Some indicative estimates of the economic impact include:

  1. One standard deviation worsening in delayed payments reduce firm profit growth by 1.5-3.4 percent.
  2. One point reduction in delayed payments reduce firm exit rates by 1.7-2.0 percent.
  3. One standard deviation worsening in delay of payments reduce economic growth rate by 0.8-1.5 percent.
  4. Paying trade loans in 30 days imply an additional 0.33 percent economic growth.

Even with the caveat that these are values estimated for countries and firms operating in the countries in the EU, where contract performance and enforcement tend to be some of the best in the world, these are useful benchmarks to frame the impact of problems of public procurement for us in India. Such an exercise is particularly pertinent for the current times, where the COVID-19 pandemic has resulted in a severe reduction in GDP growth and there is a large scale loss of jobs. One estimate puts the reduction in the Indian economy at 23.9 per cent in the April to June quarter of 2020 (Choudhury, 2020).

India has followed the global response to such a systemic shock, with the state becoming the saviour of last resort and rolling out economic interventions in the form of income support schemes and various public expenditure programs. However, the present situation of the Indian fiscal conditions place constraints on the credibility and sustainability of new spending. What the above literature suggests, in addition to these recent interventions, is that India would do well to find ways and means to clear her dues to direct and indirect suppliers, particularly given that a large fraction of Indian enterprises are micro, small and medium enterprises. Sahu (2020) reports that INR 5 lakh crore out of the reported INR 9.5 lakh crore of dues from the government was due to MSMEs. If reducing the delays in payments can reduce the distress related bankruptcy of such firms by even one percent, it can have a material impact on the health of these firms and continued availability of avenues for employment. More importantly, such an action will improve the confidence of small traders and vendors across the country in participating in G2B transactions. If payments can be made on time, it will reduce the MCPF and strengthen the channels through which the state can deliver a positive impact on economic growth at the time when it is most required, and to those who need the support the most.

One path suggested in international literature is to put in place a regulatory framework on public procurement. However, there is no clear evidence that indicates that this can be successful in reversing payment delays. For example, Banerjee et al. (2020) show that e-governance reforms of the MNREGA system does deliver a positive impact on reduced leakage in social benefit programs but fails to reduce payment delays. Further, Roy and Uday (2020) analyse the link between the presence of a legal framework and the corruption and they find no correlation between the two.


What the existing studies show is the importance of establishing systems through which the impact of the public procurement processes can be understood. Unlike in the various EU countries where these studies have been carried out, there are no systematic empirical studies that have been done in India to quantify the economic cost of delayed payments on firms and the economy. A first step towards solving the problem of delayed payments and the overall processes of public procurement would be to facilitate opportunities to gather information of the impact of these process on the operational health of firms. Such information needs to developed for India and made largely available to the research community to get a sound empirical understanding of the process of public procurement and how to improve the cost of doing business with the Indian State.


Abhijit Banerjee, Esther Duflo, Clement Imbert, Santhosh Mathew and Rohini Pande (2020), 'E-governance, accountability and leakage in public programs: Experimental evidence from financial management reform in India', American Economic Journal: Applied Economics, 12(4).

Cristina Checherita-Westphal, Alexander Klemm, and Paul Viefers (2016), 'Governments payment discipline: The macroeconomic impact of public payment delays and arrears'. Journal of Macroeconomics, 47: 147-165.

Erica Bosio and Simeon Djankov (2020), 'How large is public procurement?', World Bank Blogs, 5 February.

Franco Fiordelisi, Davide Mare, Nemanja Radic, Ornella Ricci, Philip Molyneux, and Thomas Weyman Jones (2012). 'Government late payment: the effect on the Italian economy', Doctoral Dissertation, School of Economics and Business, Loughborough University, UK.

Gaurav Choudhury (2020), 'India's GDP contracts 23.9 per cent in Q1FY21 as lockdowns, restrictions bludgeon economy', 1 September.

Isaac Kwame Essien Obeng (2017), 'Delaying payments after the financial crisis: evidence from EU companies', Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(2): 447-463.

Maurizio Conti, Leandro Elia, Antonella Rita Ferrara and Massimiliano Ferraresi (2020), 'Government late payments and firms survival: evidence from the EU', Technical report, Societia Italiana di economica pubblica, Working paper No. 753.

M. H. Khan (2017), 'Public procurement issues with government of India', Lal Bahadur Shastri National Academy of Administration (LBSNAA).

Prashant Sahu (2020), 'Forget stimulus, clear your dues: Rs 7 lakh crore unpaid dues to industry by central govt depts and PSUs', in Financial Express, 8 September.

Shubho Roy and Diya Uday (2020), 'Does India need a procurement law?', The LEAP Journal blog, 19 August.

Vijay Kelkar and Ajay Shah (2019), 'In service of the Republic: the art and science of public policy', Penguin Allen Lane.

William Connell (2014), 'Economic impact of late payments', Technical report, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.

Rabia Khatun is an independent researcher and Sourish Das is associate professor at the Chennai Mathematics Institute. The authors would like to thank Susan Thomas for comments and suggestions on the article.

Wednesday, November 18, 2020

The problem of minimum public shareholding in public sector enterprises

by Sudipto Banerjee, Sarang Moharir, Renuka Sane.

In 2009-10, the government of India increased the minimum public shareholding (MPS) threshold for listed companies from 10% to 25%. The government's rationale for the MPS is that a minimum public float of shares addresses secondary market imperfections like concentration of shares and price manipulation. The Securities and Exchange Board of India (SEBI) has specified several methods that listed firms can use to expedite their MPS compliance. One of the methods is the offer for sale of shares through the stock exchange (OFS-SE). This was introduced in 2012 to facilitate compliance in a broad-based and transparent manner. Prior to the OFS-SE, the government divested shares through OFS by issuing a prospectus. This was a cumbersome and time-consuming process. Since 2012, the government has used the OFS-SE method to undertake disinvestment of CPSEs to meet the MPS threshold.

In 2010, when the Securities Contract (Regulations) Rules were amended [Rule 19A(1)] to increase the MPS threshold from 10% to 25%, listed Central Public Sector Enterprises (CPSEs) were exempted. The government withdrew the exemption in 2014, and set a deadline of August 2017 for compliance with the MPS. This was extended by a year to 2018 and again by two years to 2020. Recently, listed CPSEs got another extension of one year till August 2021. Despite the extensions, 37 CPSEs out of the total 77 listed CPSEs had not met the MPS requirement as on December 31, 2019.

As we approach the August 2021 deadline, we ask if disinvestments through the OFS-SE route have achieved the 25% MPS target. This question is relevant for all disinvestments. We, however, focus on the one's done through OFS-SE as this route was designed to meet the MPS threshold. This study is useful for two reasons. First, it gives us a sense of how much more disinvestment the government has to undertake to meet the MPS. Second, the government's record on meeting the MPS threshold for CPSEs sends a strong signal of its own commitment to the MPS.


We sourced transaction data from BSEPSU. We only consider CPSEs where at least 5% stake was divested through the OFS-SE route between 2012 and 2019. This gives us a sample of 22 CPSEs (with 31 transactions) out of the total 77 CPSEs.

Since OFS-SE is a secondary market transaction, details like name of the purchaser, the number of shares purchased and the final sales price are not available in the public domain. Therefore, we studied each annual report issued in the year of the OFS-SE transaction to document the change in the shareholding pattern of the top ten shareholders. Further, we used these changes to identify the possible purchaser of shares. For example, 5% stake of Power Finance Corporation (PFC) was divested in 2015; LIC's shareholding in PFC increased from 4.81% to 9.08% in the same year. We assume that LIC purchased a stake in the OFS-SE transaction of PFC in 2015.

Results: other CPSEs as shareholders

Table 1 shows the shareholding of CPSEs that had undergone OFS-SE as of March 2019. Public shareholding contains CPSE shareholding (column 4) i.e., shares held by other CPSEs in these companies. Since CPSEs are themselves government owned, it is useful to evaluate public shareholding after removing their holdings. As an example, National Fertilizers Ltd. has a public shareholding of 25.29% and meets the MPS requirement of 25%. The following CPSEs are listed under the public shareholding category of National Fertilizers Ltd., i.e. LIC (11.31%), NIA (1.76%), GIC (1.48%), Canara Bank (0.69%), OIC (0.29%). The total share of these firms (15.53%) is deducted from the public share of National Fertilizers (25.29%). Public shareholding of National Fertilizers at 9.76% does not meet the MPS threshold. When the share of CPSEs is excluded from the public shareholding category, 13 out of the 22 CPSEs failed to meet the MPS requirement as of March 2019.

Table 1: Shareholding of CPSEs that have undergone OFS-SE (March 2019)
Company Promoters’ share-holding Public share-holding Shareholding of CPSEs (included within public shareholding) Whether MPS requirement is met when share of CPSE is not considered?
BHARAT ELECTRONICS LTD. 55.93% 44.07% LIC (3.61%) Yes
CONTAINER CORP. OF INDIA LTD. 54.80% 45.20% LIC (3.08%) Yes
ENGINEERS INDIA LTD. 52.00% 48.00% LIC (4%) Yes
HINDUSTAN COPPER LTD. 76.05% 23.95% LIC (12.14%) No
INDIAN OIL CORP. LTD. 51.50% 48.50% ONGC (14.20%), LIC (6.51%), OIL (5.16%), IOC SHARES TRUST (2.48%) No
INDIA TOURISM DEVELOPMENT CORP. LTD. 87.03% 12.97% LIC (3.22%), NIC (0.13%) No
MMTC LTD. 89.93% 10.07% LIC (3.39%), UIC (0.24%), GIC (0.18%), NIA (0.11%) No
MOIL LTD. 65.69% 34.31% LIC NEW ENDOWMENT PLUS BALANCED FUND (7.12%), UIC (1.05%), NIA (0.35%), OIC (0.46%) Yes
NATIONAL ALUMINIUM CO. LTD. 52.00% 48.00% LIC (8.2%), NIC (0.61%) Yes
NATIONAL FERTILIZERS LTD. 74.71% 25.29% LIC (11.31%), NIA (1.76%), GIC (1.48%), CANARA BANK (0.69%), OIC (0.29%) No
NHPC LTD. 73.33% 26.67% LIC (7.31%), PFCL (2.43%), REC (1.75%) No
NLC INDIA LTD. 80.85% 19.15% LIC (3.34%), UTI (0.83%), NIA (0.47%) No
NMDC LTD. 72.28% 27.72% LIC (12.9%), LIC NEW ENDOWMENT PLUS BALANCED FUND (2.03%), SBI (0.38%), NIA (0.34%) No
NTPC LTD. 54.50% 45.50% LIC JEEVAN PLUS NON UNIT FUND (11.51%) Yes
OIL INDIA LTD. 59.57% 40.43% LIFE INSURANCE CORPORATION OF INDIA P & GS FUND (12.19%), IOCL (4.71%), HPCL (2.47%), BPCL (2.47%) No
OIL & NATURAL GAS CORP. LTD. 62.98% 37.02% Yes
REC LTD. 52.63% 47.37% LIC (2.30%), CPSE ETF (3.57%) Yes
STATE TRADING CORP.OF INDIA LTD. 90.00% 10.00% LIC (0.91%), NIA (0.89%), OIC (0.07%) No

Source: Company Annual reports

Results: LIC as shareholder

Table 2 indicates an increase in shareholding of LIC (whose 100% shares are held by the government) in the CPSEs post OFS-SE transactions. As an example, Hindustan Copper went through disinvestment in FY16 and FY17. This lead to a decrease in government shareholding from 89.95% in 2016 to 76.05% in 2017, at the end of the two transactions. Shares of LIC increased from 5.27% at the beginning of FY16 to 12.14% in FY18. Similarly, National Fertilizers was disinvested in FY17, where the government's share decreased from 92.5% to 80%. Shares held by LIC in the company had increased from 4.16% to 11.32% in FY18.

Table 2: OFS-SE transactions and purchases by LIC
Name of entity Year Stake divested LIC's share before disinvestment LIC's share post disinvestment
NMDC FY12 10% 5% 5.54%
NTPC FY13 9.5% 5.91% 7.66%
NALCO FY13 6.09% 3.25% 6.02%
SAIL FY14 5% 6.61% 10.11%
COAL INDIA LTD. FY15 10% 2.10% 7.24%
DREDGING CORP. OF INDIA LTD. FY15 5% 2.99% 5.86%
POWER FINANCE CORP. LTD. FY15 5% 4.81% 9.08%
NHPC FY16 11.36% 3.11% 8.83%
HINDUSTAN COPPER LTD. FY16 7% 5.27% 10.70%
NBCC FY16 15% 0% 8.11%
HINDUSTAN COPPER LTD. FY17 6.83% 11.14% 14.25%
MOIL FY17 10% 3.84% 7.11%
COAL INDIA LTD. FY18 5.19% 8.97% 10.94%

Source: Annual reports

In the sample of 31 transactions concerning the 22 CPSEs selected for our study, the Life Insurance Corporation (LIC) increased its holding in 16 transactions. For six transactions, the top ten shareholders' names were not disclosed in the annual reports. LIC's equity did not change in the remaining nine transactions.


Out of the total 77 NSE-listed CPSEs, 37 CPSEs had not met the MPS threshold as on December 31, 2019. The government will have to do a lot more to achieve full compliance with the MPS by August 2021. Out of the 22 CPSEs that went through disinvestment by the OFS-SE route, 13 CPSEs do not meet the MPS once we exclude the share of CPSEs. LIC purchased equity in more than 50% of CPSEs in our sample.

One of objectives of disinvestment is to promote public ownership of CPSEs. This also provides an opportunity to citizens to participate in the wealth of CPSEs. The MPS also seeks to widen ownership in listed companies. Under the Securities Contracts (Regulation) Rules and SEBI (Issue of Capital and Listing Disclosure Requirements) Regulations, shareholding of CPSEs and LIC may be considered as public, but their inclusion does not align with the goals of either disinvestment or the MPS. This question also assumes relevance given CAG's recent observation (Para 1.3.2) that disinvestment from one public sector firm to another 'did not change' stake of the government in the disinvested CPSEs. Disinvestment which truly widens CPSE ownership to individuals and institutions outside of the government should be an important goal for policy.

The authors are researchers at the National Institute of Public Finance and Policy. The authors would like to thank Karthik Suresh and Srishti Sharma for useful discussions.

Monday, November 16, 2020

Get by with a little help from my friends (and shopkeepers): Household borrowing in response to Covid 19

by Renuka Sane and Ajay Shah

The lockdown in the early days of the Covid 19 pandemic in India impacted on on economic activity. Between April and August 2020, 18.9 million salaried people lost their jobs, difficulties were faced by migrant labourers, and small and medium businesses. Deshpande (2020) shows that overall employment dropped sharply post-lockdown, with larger drops for women than men. Household incomes were adversely affected. As an example, survey work by Lee, Sahai, Baylis and Greenstone (2020) shows that two months into the lockdown poor and non-migrant workers in Delhi saw a drop of 57% in their incomes, with 9 out of 10 workers reporting that their weekly income had fallen to zero. Bertrand, Krishnan and Schofield (2020) measure the fraction of households who say they are able to survive on their own for a week, and in April that value was 34% in the overall population and 50% or more for below-median household income.

How would households cope with such a shock? Economic theory suggests that households desire consumption smoothing. One mechanism for consumption smoothing is borrowing. For example, there was an increase in household borrowing after demonetisation (Karmarkar and Narayan, 2020; Wadhwa, 2019; Chakraborty and Sane, 2019). This connects to the working of the financial system. While India has made a lot of progress in ownership of a bank account, and increased electronic payments, access to formal credit remains low.

What do we expect about household borrowings?

Borrowing during the Covid crisis is shaped by three factors:

  1. Income transfers: The government of India announced a stimulus package worth Rs.1.7 trillion after the lockdown. This included food security measures as well as direct cash transfers to poor households. This may have helped households deal with the immediate crisis.

  2. Low demand: As people were at home owing to the lockdown, demand may have been affected. It is also possible that households saw this job loss as permanent, and hence cut back on expenditures in a way they would not have had they seen this as a temporary disruption. This also fits with the view that precautionary savings increase after a deep crisis (Rajadhyaksha, 2020). However, this may be true for households in the higher income distributions, but is unlikely to be the case for those below median income.

  3. Supply constraints: India's financial system has faced difficulties since 2012. This has manifested itself as business failure at ILFS and other financial firms, large and small. Credit growth was decelerating prior to the lockdown. The difficulties for the financial sector increased when the Reserve Bank of India announced a moratorium on all loan repayments for three months from March to May 2020, and then extended it for another three months. These moratoriums made it more difficult for financial firms to assess the credit quality of borrowers. Overall bank credit growth was 5.8% in September 2020 compared to 8.1% in September 2019. From 2018 onwards, when certain borrowers faced supply constraints, they would have had to deleverage (repaying old loans while not getting new ones) or default.

The grand question of the field consists of understanding the economic condition of households in India in 2020, in examining how consumption was held up through new kinds of labour supply and through borrowing, and in obtaining insights into these three distinct economic forces that are in play. In this article, we discover some new facts that contribute towards this overall research agenda.


We source data from the Consumer Pyramids Household Survey (CPHS) for the months of May, June, July and August from the years 2016 - 2020. The borrowing data comes from the Aspirational India table within CPHS. Using this we ask three questions:

  1. Did households have debt outstanding at the time of the survey? This helps us understand the total number of borrowers in the economy.
  2. What are the sources from whom households have outstanding borrowings? This tells us whether households borrow from the formal or the informal sector.
  3. What is the purpose for which households have outstanding borrowings? This tells us if households are borrowing for consumption expenditure, for consumer durables, or for running their businesses.

CPHS does not provide information on the value of debt outstanding. We are, therefore, not able to analyse the impact on borrowing on an intensive margin. Our analysis is restricted to understanding the proportion of households borrowing from various sources, for various reasons, i.e. on the extensive margin. Household weights for each wave are provided by CPHS -- these are used to get population estimates.

Results: The number of borrowers

Table 1 presents the number and percentage of households having debt outstanding in the months of May - August in each of the five years. The number of borrower households had been consistently increasing till 2019. In May - August 2016, 12% of the population had debt outstanding. This increased to 50% by 2019. The number, however, fell in 2020 to 45% of the population. The fall has been greater in urban regions than rural.

Table 1: Number and share of borrowers in the population

in million in million in million
May - Aug 2016 34.8
May - Aug 2017 81.9
May - Aug 2018 136.0
May - Aug 2019 154.7
May - Aug 2020 141.6

Disentangling explanations: Sources of borrowing

Given that a large proportion of households did not have enough to live on for more than a couple of weeks, we would have expected a huge increase in the number of borrower households. In order to investigate the sources of this drop, we begin by analysing the role of the financial system in the household borrowing story by studying the sources of borrowing.

Table 2 presents the percentage of borrower households borrowing from each source. We find that the biggest drop in borrowing is from banks: in 2019, 26% of borrower households had borrowed from banks - this has dropped to 20% in 2020. The proportion of households borrowing from money lenders has also dropped - from 7% in 2019 to 4% in 2020. The drop in households borrowing from banks and money lenders was higher in urban regions than rural regions. There has been a lot of discussion in India about the increased risk aversion of banks. A fall in the number of borrower households may be a result of this phenomenon.

Table 2: Sources of borrowing
SOURCE May - Aug 2019
May - Aug 2019
May - Aug 2020
May - Aug 2020
Banks 26.6% 25.6% 21.9% 15.3%
Money Lenders 7.1% 7.1% 4.6% 3.4%
Employer 0.5% 1.4% 0.5% 1.0%
Relatives/Friends 14.5% 13.3% 21.1% 27.2%
Shops 52.0% 50.7% 57.6% 49.8%

There has been a concurrent rise in the number of households who have borrowed from friends and family from 14% in 2019 to 21% in rural regions, and from 13% to 27% in urban regions. The sharp increase in the borrowing from friends and family suggests that some smoothing of consumption expenditure is likely to have occurred using informal social networks that play an important role in the economic lives of those in developing countries (Munshi, 2014).

Household borrowing from shops increased in rural India - from 52% to 58%, and fell slightly in urban India. It is interesting to recall that Chakraborty and Sane (2019) had found that between the years 2016 and 2018 (i.e. after demonetisation), the biggest rise in borrowing was from shops, especially by those in the lower income deciles. This seems to be true in the current situation as well, especially in rural regions.

In difficult times, it was not banks, money lenders, and employers that mattered. It was friends and family, and the neighbourhood shops. It appears that non-financial firms and cash flow management by the retail supply chain have been more important than financial firms. The connections from the formal financial system to these shops could then be unusually influential.

Disentangling explanations: Purpose of borrowing

Examining the purpose for which households borrow can tell us something about the demand for credit. Table 3 presents the top five reasons for borrowing in 2020, and compares it with 2019.

Table 3: Purpose of borrowing
SOURCE May - Aug 2019
May - Aug 2019
May - Aug 2020
May - Aug 2020
Consumption 62.3% 59.8% 70.1% 65.7%
Business 12.9% 9.1% 19.2% 8.2%
Debt Repayment 7.3% 8.8% 9.1% 11.9%
Housing 7.3% 9.7% 2.4% 4.7%
Durables 5.1% 8.6% 1.6% 4.3%
Investments 6.4% 1.9% 0.4% 0.5%

In May-August 2019, 62% of rural and 60% of urban borrower households had borrowed for reasons of consumption expenditure. In May-August 2020, this had risen to 70% and 66% of rural and urban borrower households respectively. This is consistent with the importance of consumption smoothing, and of many households not having enough resources to survive for more than a few weeks. The increase for reasons of consumption expenditure is higher in urban regions. Urban India was more likely to be affected because of both the Covid infections and the intensity of the lockdown than rural India. Income transfers from the government are also likely to have targeted rural households than urban households.

There has been an increase in borrowing for business and debt-repayment reasons in this period as well. The numbers for rolling over debt went from about 9% to 12% of borrower households in urban regions, and from 7% to 9% of borrower households in rural regions. This suggests that households who would otherwise have serviced debt through business or personal income took recourse to borrowing when those cashflows subsided. A personal insolvency law that is able to provide some relief to debtors and allow for restructuring of the larger loans can help alleviate some of this stress.

The fall in the number of borrower households seems to be driven by the fall in the borrowings for housing, durables purchase and investments. It is also likely that large purchases such as housing and durables are made through bank loans. The fall in the borrowings from banks may be a result of a fall in these large durable purchases.


We study the response of households on borrowings during the 2020 lockdown. We do not have data on the value of debt outstanding. We expected that there would be an increase in the number of households that borrow owing to the disruptions to economic activity. However, it is remarkable, that despite the large shock, overall, there has been a reduction in the number of households that borrow. This fall is driven by fewer households borrowing from banks, and fewer households borrowing for housing, and consumer durables purchases. Households continue to borrow for consumption expenditure, business and debt repayment. The most utilised sources of borrowing are friends and family and shops.

This work suggests many interesting possibilities for downstream research. For example, one can study the differences in borrowing patterns between households with different income and wealth profiles, as well as the correlation between sources and purpose of borrowing. It will also be possible to evaluate whether income transfers from the government led to a fall in the number of borrower households. Similarly, one can ask whether different health outcomes play a role in their borrowing outcomes.

The number of households choosing to borrow has been different from what happened after demonetisation. There may be several reasons for this - the magnitude of the disruption, the length of time for which it lasted, the possibility of more permanent impacts on labour markets among others. This leaves us with interesting research possibilities to understand household behaviour and their interaction with financial markets.


Ashwini Deshpande (2020), The Covid-19 Pandemic and Lockdown: First Effects on Gender Gaps in Employment and Domestic Work in India, Working Paper 30, Ashoka University.

Azim Premji University (2019), "State of Working India 2019", Technical Report, Centre for Sustainable Employment.

Kaivan Munshi (2014), "Community Networks and the Process of Development", Journal of Economic Perspectives, 28(4), pp: 49-76.

Kenneth Lee, Harshil Sahai, Patrick Baylis, and Michael Greenstone (2020), "Job Loss and Behavioral Change: The Unprecedented Effects of the India Lockdown in Delhi", Working Paper, EPIC India.

Marianne Bertrand, Kaushik Krishnan, and Heather Schofield (2020), "How are Indian households coping under the COVID-19 lockdown? 8 key findings", Rustandy Centre for Social Sector Innovation, Chicago Booth.

Niranjan Rajadhyaksha (2020), "The covid shock could alter people's financial priorities", Livemint, 5 May 2020.

Sagar Wadhwa (2019), "Impact of demonetization on household consumption in India, Working paper.

Subhamoy Chakraborty and Renuka Sane (2019), "Household debt over time", The Leap Blog, 24 May 2019.

Sudipto Karmarkar and Abhinav Narayanan (2020), "Do households care about cash? Exploring the heterogeneous effects of India's demonetization", Journal of Asian Economics, 69.


Sane is a researcher at the National Institute of Public Finance and Policy, Shah is an independent scholar. We thank four anonymous referees, Kaushik Krishnan, Radhika Pandey and Anjali Sharma for useful comments.

Wednesday, November 11, 2020


Position for researchers in public finance and public procurement

The Finance Research Group, Mumbai is an inter-disciplinary group of researchers working in the fields of financial markets, household and firm finance, bankruptcy law, land markets and public finance management and public procurement. In these fields, the group engages in academic and policy oriented research and advocacy.

An indicative list of the project outputs generated by the Finance Research Group is below:

The Finance Research Group is looking for two researchers to work on a project to understand the impact of public finance management and public procurement issues on the private sector:

  1. A senior researcher, and
  2. A research associate

Requirements for position of Senior Researcher

As a senior researcher, you will be expected to take a lead on delivering on the project objectives. You will be part of the core group of this project, building a pipeline of research ideas, and executing them. This will mean pursuing independent research as well as supervising and advising team members in their research. The requirements for the role of a senior researcher are:

  • You must have 5 plus years of work experience and very strong written and spoken English.
  • A background in management/public economics/public policy will be preferred.
  • One of the key deliverables of this project is a survey. Having experience with conducting field surveys and managing survey agencies will be desirable.

Requirements for position of Research Associate

As a research associate, you will work on project deliverables under the supervision of a senior researcher. The requirements for the role of research associate are:

  • A background in economics/science/management/data science/ computation/public economics/public policy will be preferred.
  • Prior work experience of 1 -- 2 years will be desirable.
  • A quantitative/computational orientation will be desirable.

General requirements

The Finance Research Group functions on free and open source software systems like Linux, Latex, R and others. If appointed, you will be required to learn and use these software systems. You must be willing to adapt to technology, work long hours and deliver quality products within defined timelines.

You must be comfortable in working in an inter disciplinary research environment with people from varying backgrounds such as economics, law, public policy and data science. You must be curious and passionate about research and must be willing to work on independent outputs as well as in teams.

The remuneration offered will be commensurate with your skill and experience and will be comparable with what is found in other research institutions.

Contact details

Interested candidates must email their resume with the subject line: Application for "Senior Researcher/ Research Associate" at the Finance Research Group, to Ms. Jyoti Manke at by 30th November, 2020.