This project aims to perform data anlysis on an integrated dataset of company information, extractives concessions data and a set of politically exposed persons (PEPs). The goal is to find conflicts of interest and linkages between PEPs and mining/drilling rights.

The data is partially extracted from public sources, from semi-public data sources and from manually conducted research by CIP.

The flexicadastre_scrape.py and flexicadastre_parse.py scripts are importers for FlexiCadastre, a GIS solution developed by SpatialDimension and used to store extractives licensing info for a variety of countries.

Hermes is an effort to extract structured data from bulletins released as Part III of the official gazette of Mozambique. The data holds information on companies registered in the country since the late seventies, as well as associated personalities. It is unclear if a) the data is complete and verified, and b) this dataset if considered an official release by the Government of Mozambique.

This dataset is manually curated by researchers at CIP. It lists key personel in top positions of government and FRELIMO. The tabular layout is inspired by the Popolo data standard.

The scraper for the public section of the Mozambican government gazette will download all published PDF files that form Part III. Documents will also be uploaded to sourceAFRICA automatically, if the necessary credentials are provided.

All ETL scripts in this package are geared towards generating tables in a SQL database. Many of them will only work when used against PostgreSQL 9.3 or later (i.e. with support for the LEVENSHTEIN() function). Your database should be created with the UTF-8 encoding, e.g. with the command:

$ createdb -E utf-8 mozambique

Before running any of the scripts, make sure to set the environment variables explained in env.tmpl.sh, specifically DATABASE_URI which must be a valid connection string of the form postgresql://user:password@host/database_name. If you wish to upload updated versions of the government gazette to sourceAFRICA, you also need to specify your credentials as DOCCLOUD_USER and DOCCLOUD_PASS.

Finally, you must ensure that Python's virtualenv package is installed and available on the system path.

All ETL commands in this package are specified through the Makefile which can be studied for the available targets. To install all Python dependencies, for example, run the following command:

$ make install

If you want to execute a specific set of scrapers and data loaders, you can call up specific targets:

$ make flexi
$ make hermes
$ make pep
$ make boletin

This should leave you with a freshly stocked SQL database for your analytical pleasure.

The following is a guided tour / documentation of the data. Queries are shown as raw SQL, but can be largely ignored in favour of the linked result sets by those not familiar with the language.

We'll start exploring the data from the concessions. The table mz_flexicadastre combines all layers from the source data, which we can summarize like this:

SELECT layer_name, COUNT(*) FROM mz_flexicadastre
    GROUP BY layer_name
    ORDER BY COUNT(*) DESC;

result

Next, we can have a look at the most interesting field in this table, parties - the set of all company and person names which hold mineral rights. Let's see who is top of the list.

SELECT f_normtxt(parties), COUNT(*) FROM mz_flexicadastre
    WHERE parties IS NOT NULL
    GROUP BY f_normtxt(parties)
    ORDER BY COUNT(*) DESC;

result (NOTE: f_normtxt is a custom SQL function, defined in src/setup.sql)

We can run the same sort of query on the scraped data from the company register. We would expect that there is only a single entry for each entity name, but that is not true:

SELECT f_normtxt(nome_da_entidade), COUNT(*) FROM hermes_company
    WHERE nome_da_entidade IS NOT NULL
    GROUP BY f_normtxt(nome_da_entidade)
    ORDER BY COUNT(*) DESC;

result

It seems that a single entity name in the company register will often occur multiple times. An explanation could be that Hermes' data entry failed to reconcile multiple notices regarding a single company in the gazette. Concerned about the data quality of the Hermes Pandora dataset.

Next, let's try and join between both datasets, i.e. see how many of the concession holder entries match company records. To do this, we'll generate normalized versions of the company names on both the company registry and the concessions data.

SELECT fx.parties_norm, COUNT(fx.id)
    FROM hermes_company AS co, mz_flexicadastre AS fx
    WHERE
        co.nome_da_entidade_norm IS NOT NULL
        AND fx.parties_norm IS NOT NULL
        AND co.nome_da_entidade_norm = fx.parties_norm
    GROUP BY fx.parties_norm
    ORDER BY COUNT(fx.id) DESC;

result

This is much better than assumed, the expectation was to find barely any overlap. Out of 2430 distinct company names in the concessions data, 160 are an immediate match. We can also loosen the join criterion using PostgreSQL's LEVENSHTEIN function which returns the edit distance between two strings:

SELECT fx.parties_norm, COUNT(fx.id)
    FROM hermes_company AS co, mz_flexicadastre AS fx
    WHERE
        co.nome_da_entidade_norm IS NOT NULL
        AND fx.parties_norm IS NOT NULL
        AND LEVENSHTEIN(co.nome_da_entidade_norm, fx.parties_norm) < 3
    GROUP BY fx.parties_norm
    ORDER BY COUNT(fx.id) DESC;

Unfortunately, this query takes pretty much forever. An alternative approach might be to generate a lookup table with LEVENSHTEIN distances.

To link politically exposed persons (PEP) data with the Hermes company registry, we need to first explore the hermes_relation table, which contains information on all relations indicated in the Hermes database. It shows us that the relations table has several types of data:

SELECT DISTINCT rel_label, rel_key FROM hermes_relation;

Next, we can try and make a connection between the PEP table and the 'associated people'.

SELECT hr.target_name_norm, COUNT(*)
    FROM hermes_relation AS hr, pep AS pe
    WHERE
        hr.target_name_norm IS NOT NULL
        AND LENGTH(hr.target_name_norm) > 2
        AND hr.rel_key = 'socios_pessoas'
        AND pe.full_name_norm IS NOT NULL
        AND hr.target_name_norm = pe.full_name_norm
    GROUP BY hr.target_name_norm
    ORDER BY COUNT(*) DESC;

results

This is the first result in our data expedition which has some journalistic value. In total, it shows 40 matching names. It seems like Central Committee member Marina Pachinuapa and Governor Paulo Auade are good businesspeople, with a dozen companies in their name, each.

A very important caveat is that this is based purely on name matching, so it would be necessary to validate (e.g. via the Boletin) that these people actually are the same.

Again, we can make that search fuzzy based on LEVENSHTEIN distance, which expands the result set from 40 to a total of 111 potential matches.

SELECT hr.target_name_norm, COUNT(*)
    FROM hermes_relation AS hr, pep AS pe
    WHERE
        hr.target_name_norm IS NOT NULL
        AND LENGTH(hr.target_name_norm) > 2
        AND hr.rel_key = 'socios_pessoas'
        AND pe.full_name_norm IS NOT NULL
        AND LEVENSHTEIN(hr.target_name_norm, pe.full_name_norm) < 3
    GROUP BY hr.target_name_norm
    ORDER BY COUNT(*) DESC;

results

Based on this linkage, we can generate a full report of possible matches for further inquiry. We'll use datafreeze to export a CSV file for this query:

SELECT hc.id_do_registo AS company_id,
		hc.nome_da_entidade AS company_name,
       hr.target_name AS company_person_name,
       pe.given_name AS pep_given_name,
       pe.family_name AS pep_family_name,
       pe.menbership_role AS pep_menbership_role,
       pe.organization_name AS pep_organization_name
    FROM hermes_company AS hc, hermes_relation AS hr, pep AS pe
    WHERE hc.id_do_registo = hr.id_do_registo
       AND hr.target_name_norm IS NOT NULL
       AND LENGTH(hr.target_name_norm) > 2
       AND hr.rel_key = 'socios_pessoas'
       AND pe.full_name_norm IS NOT NULL
       AND LEVENSHTEIN(hr.target_name_norm, pe.full_name_norm) < 3;

The full CSV output is available at reports/pep_companies.csv.

Given our preliminary experiments in joining concessions to companies, and companies to PEPs, we can how look for any direct associations. I'll eat my bicycle if we get any matches.

      SELECT fx.layer_name AS conc_layer_name,
             fx.name AS conc_name,
             fx.parties AS conc_parties,
             hc.id_do_registo AS company_id,
             hc.nome_da_entidade AS company_name,
             hr.target_name AS company_person_name,
             pe.given_name AS pep_given_name,
             pe.family_name AS pep_family_name,
             pe.menbership_role AS pep_menbership_role,
             pe.organization_name AS pep_organization_name
          FROM hermes_company AS hc, hermes_relation AS hr,
               pep AS pe, mz_flexicadastre AS fx
          WHERE hc.id_do_registo = hr.id_do_registo
             AND fx.parties_norm = hc.nome_da_entidade_norm
             AND hr.target_name_norm IS NOT NULL
             AND LENGTH(hr.target_name_norm) > 2
             AND hr.rel_key = 'socios_pessoas'
             AND pe.full_name_norm IS NOT NULL
             AND LEVENSHTEIN(hr.target_name_norm, pe.full_name_norm) < 3;

There's a few candidates. The full CSV output is available at reports/pep_concessions.csv.

To expand the set of matches to be less precise, we're re-writing the normalization functions to account for common variations in the way that company and people names are written:

CREATE OR REPLACE FUNCTION f_mz_company(t varchar) RETURNS varchar AS $$
  BEGIN
    RETURN TRIM(
      regexp_replace(
        regexp_replace(
          regexp_replace(
            regexp_replace(
              regexp_replace(
                f_normtxt(t),
                '\([0-9,\.]*%?\)', '', 'g'
              ),
              '\( ?(moz|mozambique|moc).?\)', '', 'g'
            ),
            '[, ]+(lda|ltd)\.?', ' limitada', 'g'
          ),
          '\W+', ' ', 'g'
        ),
        '\s+', ' ', 'g'
      )
    );
  END;
$$ LANGUAGE plpgsql;

While this is monstrous, all it really does is: remove all percentages in brackets (e.g. (100.0%) on concessions), remove references to Mozambique in brackets, replace mentions of lda with limitada, replace all non-text characters with whitespace, and finally, collapse all consecutive whitespace.

This increases the number of potential concession matches from 5 to 33, the number of PEP-held companies from 111 to 250.

Taking a step back, the company registry dataset is probably the most interesting one, because it provides a rich source of different linkages. Let's start by having a look at the companies with the most people associated with them:

SELECT hc.id_do_registo,
        LEFT(MAX(hc.nome_da_entidade), 80) AS company_name,
        MIN(data_da_escritura),
        COUNT(DISTINCT hr.target_name_norm)
    FROM hermes_company AS hc, hermes_relation AS hr
    WHERE hc.nome_da_entidade IS NOT NULL
        AND hc.id_do_registo = hr.id_do_registo
        AND hr.rel_key = 'socios_pessoas'
    GROUP BY hc.id_do_registo, hc.nome_da_entidade_norm
    ORDER BY COUNT(DISTINCT hr.target_name_norm) DESC;

results

A lot of the top-ranking companies seem to be trade associations and unions, which makes sense. Obviously, the reverse question is much more interesting: who are the people associated with the largest number of companies?

SELECT MAX(hr.target_name) AS name,
        COUNT(DISTINCT hc.nome_da_entidade_norm) AS companies
    FROM hermes_company AS hc, hermes_relation AS hr
    WHERE LENGTH(hr.target_name_norm) > 1
        AND hc.id_do_registo = hr.id_do_registo
        AND hr.rel_key = 'socios_pessoas'
    GROUP BY hr.target_name_norm
    ORDER BY COUNT(DISTINCT hc.nome_da_entidade_norm) DESC;

results

The top guy, José Manuel Caldeira, appears to be a corporate lawyer and probably acts as a secretary for the 106 companies he is tied to, but most of the other names on this list don't show up much on Google. I'm relatively sure that in the hands of an experienced Mozambican journalist, this list would yield some interesting leads.

Given that we now have improved normalization of company and person names, it also makes sense to return to the mining concessions. First, let's look at the companies with the most concessions there again:

SELECT parties_norm, COUNT(*) FROM mz_flexicadastre
    WHERE parties_norm IS NOT NULL
    GROUP BY parties_norm
    ORDER BY COUNT(*) DESC;

results

Interestingly, not to many big international names show up here, although the top ten do include two companies that appear to be from China - not a surprise, we're talking about African resources.

We can also link all the way across to the associated persons to make a simplified table of the big shots in Mozambican mining:

SELECT MAX(hr.target_name) AS name,
        COUNT(DISTINCT hc.nome_da_entidade_norm) AS companies,
        COUNT(DISTINCT fx.id) AS concessions
    FROM hermes_company AS hc,
        hermes_relation AS hr,
        mz_flexicadastre AS fx
    WHERE fx.parties_norm = hc.nome_da_entidade_norm
        AND LENGTH(hr.target_name_norm) > 1
        AND hc.id_do_registo = hr.id_do_registo
        AND hr.rel_key = 'socios_pessoas'
    GROUP BY hr.target_name_norm
    ORDER BY COUNT(DISTINCT fx.id) DESC;

results

The result from this query is very informative in two ways: it tells us that we have a lot of work left with regards to data de-duplication, but it also shows that just looking at companies connected to concessions is not enough: there's clearly a lot of connectivity between different concession holders, they share large parts of their boards.

The US SEC requires companies to file a so-called Exhibit 21 as part of their annual reports. Exhibits 21 list subsidiaries of a particular country across the world, and this data is made available in a scraped form as part of the CorpWatch API.

It can be downloaded and imported using the corpwatch make target, then we can do queries for companies in Hermes which are also listed as subsidiaries to US-listed companies:

SELECT co.nome_da_entidade AS hermes_name,
        cc.cw_id AS id,
        cc.company_name AS filed_name,
        cp.company_name AS parent_company
    FROM
        hermes_company AS co, corpwatch_companies AS cc,
        corpwatch_company_relations AS cr, corpwatch_companies AS cp
    WHERE
        LENGTH(co.nome_da_entidade_norm) > 2
        AND co.nome_da_entidade_norm = cc.company_name_norm
        AND cr.target_cw_id = cc.cw_id
        AND cr.source_cw_id = cp.cw_id;

results

There aren't many results, so that avenue of research doesn't seem very promising (it's worth checking back in on it once we've done a significant amount of data cleaning).

Next, we wanted to improve the linkage between company names in Hermes and Flexicadastre. To this end, two approaches were adopted in parallel:

• Uploading all Hermes company names to an instance of nomenklatura, which provides an OpenRefine reconciliation API. This allows a user to manually reconcile all candidates in the FlexiCadastre dataset against their best match in Hermes using the OpenRefine reconciliation interface.

• Manually generating two tables of mappings within the database, with each company/person name occuring in the source data, and a canonical/normalized version. This table can be exported to CSV, cleaned in an external application, and then be re-imported into the application.

The command for the latter approach is make dedupe - this will load existing CSV alias files, generate new mappings from source data, and dump the output.

Using such mapping tables, joins between the tables can be performed like this:

SELECT COUNT(DISTINCT coa.name_norm)
    FROM
        hermes_company AS co, mz_flexicadastre AS fx,
        dedupe_company AS coa, dedupe_company AS fxa
    WHERE
        coa.name_plain = co.nome_da_entidade
        AND fxa.name_plain = fx.parties
        AND fxa.name_norm = coa.name_norm;

We can also use this extended mapping of alias names to refine our previous query regarding the linkages between PEPs and concession ownership using this data, like this:

SELECT DISTINCT fx.layer_name AS conc_layer_name,
        fx.name AS conc_name,
        fx.parties AS conc_parties,
        hc.id_do_registo AS company_id,
        hc.nome_da_entidade AS company_name,
        hr.target_name AS company_person_name,
        pe.given_name AS pep_given_name,
        pe.family_name AS pep_family_name,
        pe.menbership_role AS pep_menbership_role,
        pe.organization_name AS pep_organization_name
    FROM
        hermes_company AS hc,
        hermes_relation AS hr,
        pep AS pe,
        mz_flexicadastre AS fx,
        dedupe_company AS hca,
        dedupe_company AS fxa
    WHERE hc.id_do_registo = hr.id_do_registo
        AND hca.name_plain = hc.nome_da_entidade
        AND fxa.name_plain = fx.parties
        AND fxa.name_norm = hca.name_norm
        AND hr.rel_key = 'socios_pessoas'
        AND pe.full_name_norm IS NOT NULL
        AND LENGTH(pe.full_name_norm) > 2
        AND LEVENSHTEIN(hr.target_name_norm, pe.full_name_norm) < 3;

This more than doubles the amount of recognized linkages, as visible in reports/pep_concessions_dedupe_links.csv.

• MIREM - Mozambique, Ministry of Mineral Resources (MIREM)
• See also: Google Translate PT -> EN

The project is lead by Don Hubert with Resources for Development Consulting in collaboration with CIP. Technical support is provided by ANCIR in collaboration with ICFJ.