Methodology note for India-US defence trade article

General/Data Sources

  1. Foreign Military Sales (FMS) data taken from the US Defense Security Cooperation Agency’s (DSCA) “Foreign Military Sales, Foreign Military Construction Sales and Military Assistance Facts” reports. The latest publicly-available edition is that of 2017. For 2003-2009 data, I have used the 2009 report. For 2010-2017 data, I have used the 2017 report. Note that across individual yearly reports, certain data entries may have minor discrepancies; I have resolved this issue by using two reports (2009 and 2017) dividing the 15-year time period (2003-2017) into two non-overlapping bins.
  2. Direct Commercial Sales (DCS) data taken for the US State Department’s Section 655 Reports to the US Congress. I have used 16 individual year reports to collate the 2003-2018 data set. Note that DCS shipment data for 2009 is missing. I have “filled” this missing entry by replacing it with the median of the 2008-2018 data. The SIPRI database of Section 655 Reports do not have country data for 2011 which was obtained from the Federation of American Scientists.
  3. Indian military modernisation data is from successive Union Budget Revised Expenditure numbers. For definitions used, see an earlier methodology note.

Figures

  1. Foreign Military Sales Agreements, 2003 – 2017: tabulation of FMS Agreements between 2003 and 2017 with data from 2009 and 2017 reports (see no. 1 above).
  2. Comparison of Foreign Military Sales, 1950-1999 and 2000-2005: Historical data for 1950-1999 taken from the 2009 DSCA report. (In 2000 and 2001, there were no FMS agreements between India and the US on account of sanctions from the 1998 nuclear tests).
  3. Direct Commercial Sales Authorized, Including Agreements (million USD): do note that this includes agreements signed in individual years (reported separately in the Section 655 Reports as Defence Services Authorized till 2013).
  4. Comparison of Agreements & Authorizations with Military Modernisation Budgets, 2006-2014:
    1. ‘Commercial and Government Agreements & Authorizations Total’ is the sum of FMS and DCS for each year between 2006 and 2014.
    2. ‘Foreign Component of Indian Military Modernisation Budgets Total’ was estimated by recording the modernization budgets for each year, and taking a fraction of that using the import content of the modernisation budget. (See points 1 and 2 in page 5 of an earlier methodology note for data sources.) Conversion to USD using each year’s reference exchange (USD/INR) rate, with data from the RBI.
    3. ‘Indian Military Modernisation Budgets Total’ was estimated by summing over the modernisation budgets of each year. (For definition of modernisation budget, see point 1 in page 7 of this note.)
  5. Deliveries and Shipment, 2003-2017: data from State Department Section 655 and DSCA reports. For 2009, the missing DCS shipment data was inferred from other observations (see no. 2 in the previous section).

India’s foreign and security policies 2014-19: A personal bibliography

Foreign policy and grand strategy

1. Getting India’s world right, Swarajya, March 2017.

A manifesto for a conservative foreign policy for India, based on classical realism.

2. Beyond India’s quest for a neoliberal order, The Washington Quarterly 40, no. 2 (2017): 145-161 [pdflink].

A mid-term assessment of Modi’s grand strategy. The article argues that it has by-and-large followed the ‘neoliberal/broad-power’ template that India has adhered to since 1991.

3. The BJP and Indian grand strategy [with Rahul Sagar] in Milan Vaishnav (ed.) “The BJP in Power: Indian Democracy and Religious Nationalism” (Washington, DC: Carnegie Endowment for International Peace, 2019) [report].

Deep-dive into Hindu-nationalist strategic doctrine. The article argues that while Modi has enthusiastically engaged with key partners diplomatically, hard-power capabilities gap given the impression of privileging optics over substance.

Military doctrines

4. India is not changing its policy of no first use of nuclear weapons, War on the Rocks, March 29, 2017.

In light of the 2017 debate on India’s nuclear No-First-Use posture. To be read in context of the promise to “[s]tudy in detail India’s nuclear doctrine, and revise and update it […]” in the 2014 BJP Manifesto.

5. India’s joint doctrine: A lost opportunity [with Shashank Joshi], Observer Research Foundation Occasional Paper No. 139, January 2018 [pdflink].

Examines the 2017 Joint Doctrine of the Indian Armed Forces.

Force structure, budgets, and management

6. The sobering arithmetic of a two-front war, Observer Research Foundation Special Report No. 67, July 2018.

Looks at India’s military balance with China and Pakistan over ten years, in light of the 2017-18 discussions on a two-front war.

7. Death by a thousand cuts, Firstpost (Print), March 23-29, 2019. Also available online here. (Methodology note for the article.)

Examination of twenty years of Indian defence budgets, spanning NDA-I, UPA-I and UPA-II, and NDA-II.

8. Narendra Modi’s defence policy: Ideation, management, and capabilities in Harsh V. Pant (ed.) “India’s Evolving National Security Agenda: Modi and Beyond” (New Delhi: Konark Publishers, 2019) [book].

Examines Modi’s defence policy.

On algorithmic bias

The latest entrant to the increasingly shrill debate on artificial intelligence is Alexandria Ocasio-Cortez, first-time Congresswoman from New York and darling of American left-liberals. At a recent event, Ocasio-Cortez sounded a warning bell about AI noting “[a]lgorithms are still made by human beings, and those algorithms are still pegged to basic human assumptions.” She went on to say: “They’re just automated assumptions. And if you don’t fix the bias, then you are just automating the bias.” Given to hyperbole (and enticed by prospects of a handsome royalty check), one American humanities professor even published a book titled “Algorithms of Oppression.” Closer home, two Indian experts — in a pioneering 2017 article — noted: “As coders and consumers of technology are largely male, they are crafting algorithms that absorb existing gender and racial prejudices.”

While well-meaning and presumably driven by social concerns, these statements — taken at their face value — are inaccurate. And the fallacy in all of them has to do with an incorrect characterization of what algorithms are and what they do. To see this, let us recap some basic notions from computing. Continue reading “On algorithmic bias”