Note: This application uses the entirety of a country’s treaty practice as benchmark. Predicted output may thus not necessarily correspond to a country’s most recent treaty design.
Sampling the data from the trained neural network...
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Background
We employed a 2-layer LSTM torch-rnn with 512 nodes per layer, sequence length of 200 characters and a dropout factor of 0.5 to train it on 80% of the data on 1626 English-language delineated bilateral investment treaties (10% were used for validation and test sets) for each issue area corpus separately. Corpus texts were constructed by concatenating split article texts back to one large text file, preserving the article numbers and names in headers, which precede each article text within each treaty. We trained each model for 10 epochs (case with priors) or 50 epochs (case without priors). Validation set loss was typically lower than train set loss due to a high dropout factor specified, signalling little overfitting. Then we specified the starting sequence of “#Article” (signifies a new article delimiter in train data) and generated 150 strings of 100,000 characters from the trained model with a temperature of 0.5 (a factor between 0 and 1 by which the predicted character probabilities are divided to supply more innovative results) for each issue area.
“With thousands of treaties, many ongoing negotiations and multiple dispute-settlement mechanisms, today’s IIA regime has come close to a point where it is too big and complex to handle for governments and investors alike.”
Using text-as-data analysis, we reduce this complexity and allow policy-makers, arbitrators, and scholars to:
Assess the consistency of a country’s BIT network (cf. USA before and after 2004 Model BIT)
Trace the evolution of BITs over time (cf. early German treaty practice)
Compare similarities between individual treaties, revealing patterns of convergence and divergence (cf. countries adopting USA 2004 Model BIT)
Understand how the Trans-Pacific Partnership is different from earlier treaty practice
Guided tour
Explains the intuition and approach behind our methodology and presents the main capabilities of this website
Close to 3000 bilateral investment treaties (BITs) have been concluded since 1959
Most BITs provide investors with the right to sue their host country for a BIT violation before international arbitration
Over 550 of such investment claims have been filed to this date
Scholars and arbitrators have recognized that common principles underlie investment treaties. Amongst others, BITs typically provide for:
Compensation in case of expropriation
Fair and equitable treatment of investment
Full protection and security
Non-discrimination (national and most-favoured nation treatment)
At the same time, scholars and arbitrators have noted that treaties diverge in their individual wording. The lack of adequate empirical tools, however, has long made it difficult to quantify just how different or how similar these treaties are.
Mapping BITs now remedies this shortcoming allowing users to discover uniformity and diversity among BITs for themselves.
Treaty world
The large heat map compares 1628 English-language BITs concluded between 1959 and 2014. Every treaty occurs once on the horizontal axis and once on the vertical axis. We've developed a continuous metric to gauge similarity between treaties (see Methodology section for the details). This metric ranges from 0 (dark red, full similarity) to 1 (bright yellow, no similarity):
0
.25
.50
.75
1
. Each field of the heat map represents two BITs being compared.
Each treaty is compared with itself along the diagonal line. The two sides of the heat map above and below that line are symmetric. Black quadrangles are the borders of individual country treaty networks. Alternatively, they delimit the clusters of similar treaties.
Functionality
Zoom in: each field is labeled with the treaty dyad being compared
Search for a treaty: look for specific BIT to locate it along the diagonal line and explore the space around it
Find similar treaties: click on a treaty square to find BITs that are most similar to each treaty in the pair
Understand differences between treaties: click on a treaty square and go to the “Term usage” tab to see which words are differently used in between the treaties.
3 sorting options
For each bilateral treaty, we identified the wealthier treaty party based on its GDP per capita at the time of signature. Where a treaty involves an OECD member, or, alternatively, a BRIC country, that country is always named first as wealthier party by default.
Therefore, you can sort the treaties on the axes heat map in two ways: by wealthier party or by less wealthy counterpart. Within parties, the treaties are always sorted by date of signature. In addition to that, we've identified clusters of similar treaties. By choosing the third option you sort the treaties by clusters.
Country profiles
We show the world map and color countries depending on their engagement in BIT network. We've obtained the data on the universe of BITs signed from UNCTAD. The more treaties a country signed, the darker is the shade of blue. However, we have English-language treaty texts for 51% of the treaties ever signed. The potential undersampling is displayed with red color palette.
Finally, we rank the countries based on the coherence of their treaty networks. The lower the mean distance between treaties struck by a country, the darker is the shade of green on the map for this country.
Click on a country to view a heat map of its BIT network. Each field of the heat map represents two BITs being compared. You can reorder the heat map to match your research interest:
Chronological order: by default each country network is ordered by along ascending years
Pick your base line: click on a treaty along the vertical or horizontal axes to re-order the heat map in relation to that treaty
We also provide links to UNCTAD country profiles to give users background information on countries' economies, international trade and FDI.
Main findings
Our study reveals the following insights:
Rule-makers vs rule-takers: developed countries are the rule-makers in the BIT universe. Their treaty networks are considerably more consistent than the treaty networks of developing countries that are the rule-takers of the system.
Idiosyncratic treaty networks: the investment treaty universe is less homogenous than what one may have expected. Developed countries' treaty networks display strong idiosyncratic features.
Revealing innovation: our heat maps trace innovation in national BIT programs as countries switch from one treaty model to the next. In the heat map changes are made visible as multiple red quadrangles within the same country’s network.
Underlying research
Alschner, W. and D. Skougarevskiy (2015). Consistency and Legal Innovation in the BIT Universe. Stanford Public Law and Legal Theory Working Paper Series No. 2595288. http://ssrn.com/abstract=2595288
This research has benefitted from the funding and support of the following grants and projects:
SNF Doc Mobility Grant
SNF Project “Convergence versus Divergence? Text-as-data and Network Analysis of International Economic Law Treaties and Tribunals”
SNIS Project “Diffusion of International Law: A Textual Analysis of International Investment Agreements”
NCCR Trade Regulation
From text to data
This website breaks new ground by mapping 1628 English language investment treaties – 51% of the BIT universe. For our TPP special we augment the data with Investment Chapter texts from 51 Free Trade Agreement, and 7 Multilateral Investment Treaties.
Second, we manually edit these texts, remove side-letters and schedules of reservations and correct typos, optical character recognition errors, and other mistakes in underlying data sources. We also unify the treaty spelling, converting all British English words to their American English counterparts (e.g. “favour” to “favor”).
Third, we transform text into data by computing treaty differences. Every treaty is dissected into its constituent 5-character substrings (5-grams) preserving word order:
Suppose there are two treaties: one containing a phrase “shall not be permitted” and second containing a phrase “shall be permitted”. Their 5-character substrings will be:
“shall not be permitted”
“shall be permitted”
shall
shall
hall_
hall_
all_n
ll_no
l_not
_not_
not_b
ot_be
t_be_
all_b
ll_be
l_be_
_be_p
_be_p
be_pe
be_pe
e_per
e_per
_perm
_perm
permi
permi
ermit
ermit
rmitt
rmitt
mitte
mitte
itted
itted
We then compare these treaties by the extent to which they share 5-character substrings. The above two treaties have 21 unique 5-grams, of which 11 feature in both treaties, or 52%. Substracting this figure from 100% yields 48%, a measure of dissimilarity between two treaties. Formally, this is known as the Jaccard distance, which ranges between 0 and 1:
Identical BITs: all 5-character substrings will be in both BITs yielding a perfect similarity coefficient of 0
Completely dissimilar BITs: no 5-character substring is shared between two BITs; the similarity coefficient will be 1, which represents the maximum distance between two treaties
The resulting 1628×1628 distance matrix stores all bilateral similarities.
Fifth, we apply Affinity propagation clustering to the dissimilarity matrix in order to uncover structure in the BIT universe. We set the preference q (the a priori suitability of a point to serve as an exemplar) to the lowest quantile of the similarities (minimum similarity). That produces 94 clusters.
Sixth, we come up with a way to interactively display the dissimilarities, on a heat map. It compares BITs based on their similarities (dark red) and differences (bright yellow).
Seventh, for each treaty we locate its 20 closest neighbours in terms of Jaccard distance. We also uncover the cleavages between treaties in a pair by comparing word usage. To this end, we construct a document-term matrix from the treaties (stop words are removed, British spelling differences are mitigated with VarCon). With the aid of the document-term matrix we compute proportions of word use for each word in each treaty. Applying the two-proportion z-test, we find the words that are 10% significantly differently used in the selected treaty pair and display them in an interactive chart.
Eighth, we examine the subsets of treaties by country and compute internal coherence of signed treaties for each country. This enables us to rank countries based on their treaty network coherence. We exclude countries that struck less than 4 treaties from this ranking to ensure that the results are not driven by treaty network size.
Wolfgang Alschner
Wolfgang is a post-doctoral researcher at the World Trade Institute and at the Graduate Institute of International and Development Studies.
Before obtaining his Ph.D. at the Graduate Institute and JSM at Stanford Law School, Wolfgang worked for the Institute's International Law Department as Professor Joost Pauwelyn's research and teaching assistant. He was, amongst others, responsible for the courses ‘International Trade Law’, ‘International Investment Law’ and the ‘International Trade and Investment Law Clinic’. Wolfgang also acted repeatedly as academic advisor to the Graduate Institute WTO Summer Programme.
Dmitriy is a Ph.D. candidate at The Graduate Institute of International and Development Studies and a researcher at the Institute for the Rule of Law at the European University at St. Petersburg.
He works at the intersection of Law and Economics, applying economic analysis to various fields of law and seeking to answer legal questions with hard data. Dmitriy is active in sentencing research and economics of crime studies.
When using for non-commercial purposes, please quote as Alschner, Wolfgang and Dmitriy Skougarevskiy. (2015) Mapping BITs (http://mappinginvestmenttreaties.com).
Sampling the data from the trained neural network...
