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Archives of Business Research – Vol. 9, No. 11

Publication Date: November 25, 2021

DOI:10.14738/abr.911.11204. Tiwari, P., & Akhter, J. (2021). Orphan Imports and Lost Exports in India’s Trade: A Quantitative Assessment of the Significance and

Implications for Illicit Financial Flows. Archives of Business Research, 9(11). 92-107.

Services for Science and Education – United Kingdom

Orphan Imports and Lost Exports in India’s Trade: A Quantitative

Assessment of the Significance and Implications for Illicit

Financial Flows

Praveen Tiwari

PhD Scholar

Department of Business Administration

Aligarh Muslim University, Aligarh 202 002, India

Prof. Javaid Akhter

Professor

Department of Business Administration

Aligarh Muslim University, Aligarh 202 002, India

ABSTRACT

Orphan imports and lost exports refer to import and export transactions that have

been reported by only one of the two trading partners. They are excluded from

computations of trade mis-invoicing based on comparing partner country trade

statistics. We show that India’s trade with 19 trading partners over 2000-2018 not

only indicates substantial trade mis-invoicing but also significant orphan and lost

trade in the commodities displaying mis-invoicing. We also show that the amounts

involved show an uptrend and are more pronounced in imports, with the orphan

imports recorded by India being more than 15 times the orphan imports recorded

by partner countries. Therefore, any conclusion on illicit flows through mis- invoicing in these commodities will be incomplete without analysing the impact of

orphan and lost trade. We analyse some possible causes and discuss specific

commodity-level examples to demonstrate that orphan and lost trade could not

only lead to re-adjustment of computed amounts of trade mis-invoicing but, in the

worst scenario, indicate serious fraud, with important implications for illicit flows.

The paper’s finding that only a few commodities account for bulk of the amounts in

orphan and lost trade could facilitate better analysis and mitigation measures.

Key Words: Illicit Financial Flows, Trade Mis-invoicing, Round tripping of funds, Orphan

Imports, Lost Exports

INTRODUCTION

One of the popular methods of computing the Illicit Financial Flows is the Partner Country

Method (PCM), which is based on a comparison of the trade data reported by the two trading

sides and treating the differences as proxy for the illicit flows through trade. For example, if

country A reports import of $1000 from country B, while the corresponding export reported by

country B is $800, the difference of $200 could be on account of export under-invoicing by B

(value transferred to country A through export was more than the export proceeds reported to

the authorities in B-an illicit outflow) or import over-invoicing (the funds transferred out of

country A was more than the value of imports). While using the PCM, care is taken to discount

the import values, reported on CIF basis, by a suitable factor, to make it comparable with export

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Tiwari, P., & Akhter, J. (2021). Orphan Imports and Lost Exports in India’s Trade: A Quantitative Assessment of the Significance and Implications for

Illicit Financial Flows. Archives of Business Research, 9(11). 92-107.

URL: http://dx.doi.org/10.14738/abr.911.11204

values, which are reported on FOB basis. Based on the IMF Manual, the commonly used discount

factor is 10 percent although the IMF has revised it downwards to 6 percent recently.

The databases commonly used for the PCM include the IMF’s DOTS and UN’s Comtrade

databases, both based on the trade data reported by the member countries. The UN Comtrade

database contains details of commodities involved in trade transactions, down to their 6-digit

HS codes. As trade involves two parties, each trade transaction will be reported twice,

separately by the exporting country and the importing country. The export reported by the

reporting country will be reported as import by the partner country. In an ideal situation these

values should match once the CIF import values have been suitably deflated. However, in

reality, the values from both sides do not match and the difference is taken as an indication of

mis-invoicing and the associated illicit flows. In addition, in many cases the records have values

reported on only side i.e., either the reporter or the partner country. Thus, the transactions

reported in UN Comtrade database can be divided into two parts:

a) Trade values reported by the reporter that also have a corresponding value (not

necessarily the same) reported by the partner country. We will call them ‘Matched

transactions’.

b) Trade values that have been reported by only one party -either the reporter or the

partner country, without the corresponding matching value reported on the other side.

We will call them ‘Unmatched transactions’.

Since it is difficult to apply the Partner Country Method to the transaction of type (b), they are

usually left out in studies of Illicit Financial Flows. In this paper we examine such one-sided

unmatched transactions and their possible implications for the trade gap analysis and the

associated illicit financial flows in India’s trade with its 19 major trading partners during the

period 2000-2018.

SURVEY OF LITERATURE

The existing definitions of IFFs point to two essential elements -(i) cross border financial

transfers and (ii) relation with illegal activity (see Forstater, 2018 for a compilation of some

important definitions). According to the Global Financial Integrity (GFI) the IFFs take place

primarily through trade mis-invoicing (TM). The UN’s Economic Commission for Africa (2015)

refers to trade mis-pricing as the falsification of price, quality and quantity. Some of the

important studies that have used trade data to estimate the TM include McDonald (1985),

Fisman and Wei (2004), Biswas and Marjit (2005, 2007), Beja (2006), and Buehn and Eichler

(2011).

In the recent years, more emphasis has been given to analysis of data at the product level, rather

than aggregated trade data, which tends to under-estimate the trade mis-invoicing due to

canceling out of under-invoicing and over-invoicing during the process of aggregation. Carrere

and Grigoriou (2014) reported that deliberate misreporting contributes significantly to the

trade gaps and ran a probit on orphan imports using HS 6-digit level UN Comtrade data. An

UNCTAD study (December 2016) using product level data from UN Comtrade reported

substantial misinvoicing in imports and exports of Chile, Zambia, Nigeria, Cote d’Ivoire and

South Africa (see also Ndikumana, 2016). Slim and Carton (2018), using UN Comtrade data for

35 OECD countries over the period 2006-2016, concluded that trade mis-invoicing was a

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Archives of Business Research (ABR) Vol. 9, Issue 11, November-2021

Services for Science and Education – United Kingdom

significant factor. Other similar studies include Gara et al. (2018) for Italy’s external trade (2010

to 2013) and Cheung et al (2020) for Germany. GFI (2010, 2013, 2014, 2015, 2019) has

published a series of studies highlighting illicit financial flows from the developing and under- developed countries to the developed countries through trade mis-invoicing. Nitsch (2016)

provided a comprehensive critique of the GFI’s estimates of the IFFs from the developing

countries and suggested a systematic analysis of observed differences in matched partner trade

statistics at the product level.

Of the few available empirical studies on the IFF out of India, the report of the Global Financial

Integrity (Kar, 2010) estimated that India lost $213 billion due to IFF during 1948-2008, mostly

through trade mis-invoicing. Jha and Truong (2014) calculated the Capital Flight through TM

as $186.64 billion for the period 1988-2012. GFI (2019) used the Comtrade data and estimated

that India lost revenue of US$13 billion in 2016 due to trade misinvoicing. In this study, GFI

excluded the ‘unmatched’ trade values ascribed to orphan and lost trade. For example, out of

reported import of $356.7 billion in 2016, the ‘matched’ transactions amounted to $ 214.9

billion (60 percent) and out of reported export of $260.3 billion, the ‘matched’ transactions

amounted to $192.8 billion (74 percent). This means that a significant part of the reported data

is excluded from drawing conclusions on illicit flows through trade mis-invoicing.

SCOPE, DATA AND METHODOLOGY

The study covers India’s trade with 19 of its major trading partners: Australia, China, Hong

Kong, Indonesia, Japan, Malaysia, South Korea, Singapore, Thailand (Asia); Belgium, France,

Germany, Italy, Switzerland, UK (Europe); Saudi Arabia (Middle East), South Africa (Africa); and

Brazil, USA (Americas). These countries accounted for about 59 percent of India’s trade in

March 2020.

The study uses the trade data from the UN Comtrade (http://Comtrade.un.org/data/) for the

period 2000 to 2018. The 6-digit HS Code data for the 19 trade partners contains import and

export transactions in 5008 commodities. As Import connotes inflow of goods and outflow of

funds while Export connotes outflow of goods and inflow of funds, import under-invoicing (UI)

and export over-invoicing (OI) will connote illicit inflows and import over-invoicing and export

under-invoicing will connote illicit outflows. If India’s import (Mi) from country j is country j’s

Export (Ej) to India, and India’s export to country j (Ei) is country j’s import from India (Mj);

and the import (CIF) is deflated for insurance & freight to make it comparable with exports,

reported on FOB basis, the illicit flows can be depicted as follows:

(1)Illicit Inflow into India: (a) through Import under-invoicing i.e., negative values of

[(Mi/a)-Ej] and Export over-invoicing i.e., positive values of [Ei-(Mj/a)]; and

(2)Illicit outflows from India: (a) Import over-invoicing i.e., positive values of [(Mi/a)-Ej]

and Export under-invoicing i.e., negative values of [Ei-(Mj/ a)]

Where ‘a’ is a deflator for converting the CIF import values to an equivalent of FOB exports. The

data was analysed using Tableau and MS-Excel.

The unmatched transactions can be categorized as Lost Exports and Orphan Imports as follows:

1. Lost Exports: where the export has been reported from one side but the corresponding

import has not been reported from the other side. These can be of two types: