<commit_before>---
title: Appendix
layout: gift
---


## Final Observations and Review

The appendix to this report is a consolidated list of projects extracted from the case studies. In addition to this are  other projects, which there was not space to include in the main body of the report, that are however useful examples of tools, approaches and contacts for those interested in fiscal transparency. The list includes the name of the project and the organisation, the geographical focus - where it was implemented as well as the local and national scope, the stage in the fiscal process and the type of technology, link to the tool and a short background. You can access the list at: http://bit.ly/TTAPF-projects.

We would like to encourage users to help keep this list up to date - if you would like to add a project, please fill in the online form: http://bit.ly/TTAPF-projects-form

The following page contain some thoughts on additional tools and methods, which can be used to further the aims of fiscal transparency:
* Reputations at risk: Conflict of interest tracking
* Collecting Feedback Using Telephones - The Punjab Model
* Mobile Money: Fighting Corruption


## Reputations at Risk: Conflict of Interest Tracking


SNA 
Social network analysis (SNA) is a research method developed in many different fields independently (mathematics, biology and social sciences) which has only recently made its entrance into political contexts recently. The basic idea - modelling political environments as networks of actors and their relations - has many applications: lobbying and campaign contributions (http://influenceexplorer.com/), company ownerships (http://theyrule.net), staffing or even drugs trade and informal economies (http://www.reportingproject.net/proxy/en/). Information structured in such a form also lends itself to a wide variety of methods of analysis and visualisation. Starting with network visualisations and profile pages similar to Facebook profiles analytics can also include centrality and degree metrics, such as the ones used by Google to rank web sites. The goals of such offerings in a transparent context can include the tracking of organisational associations, shared board members between companies and, ultimately, the discovery of conflicts of interests (cf. http://littlesis.org). For more examples of organisations who use this approach, please see http://bit.ly/TTAPF-projects. 

Memopol
La Quadrature du Net’s Political Memory (Memopol, http://memopol.lqdn.fr/) aims to track the position of members of the European parliament with regards to specific issues that the advocacy group is working on and allowing contributions to be crowd-sourced. This virtual “roll call” is used as a campaigning tool and contains related public statements, positions, procedural involvement and personal contact information for the MEPs. Simplified versions of this tool are increasingly used in digital rights campaigns across Europe, a recent example is the decision on Passenger Name Record privacy in the Parliament (http://pnr.digitalegesellschaft.de/) 

Campaign speeches
[Put in text box] “My Name is XXXX, I am a member of the Kenyan parliament for the constituency of XXXX in the 2007-2012 election cycle. During my time in parliament, I have positioned myself against taxes for MPs.3
Of the Development Funds allocated to my constituency, I have spent 12mn KSH in 2010 and 8mn KSH in 2009. Since 2007, I’ve funded 201 projects, of which 72 (9mn KSH) related to Education, 56 (7.2mn KSH) related to Health and 20 (4.2mn KSH) to Infrastructure.
The largest projects I have funded include… “

Turning data into textual narratives to enable wider audiences in its analysis is a recent idea, first implemented for UK schools (http://schooloscope.com/). The mechanism could be used to make data-driven, automatically generated campaign speeches for MPs and other political actors, especially where they have a direct responsibility for the decisions detailed in the data - e.g. constituency development funds in Kenya where individual politicians are able to choose how to allocate large pots of money in their constituency and thus can be held personally accountable.

## Collecting Feedback Using Telephones - The Punjab Model

Background
In Jhang in Southern Punjab, a man called <NAME>, the District Coordination Officer - lead civil servant - developed a simple model to combat corruption in interactions between the citizen and the state. The project was called the Jhang model. It started off by Zubair asking everyone in his office who had transactions with the public to take their phone details. These might be the details of people who were renewing a driving license, paying tax, registering a property etc.  He would then call four or five people each day and ask them how their interactions with his staff were.

Since this point the Jhang model has been scaled up to a province wide programme that is now called the Punjab Model. This approach has several advantages over other uses of communications technology:
* Monitoring can be stepped up or scaled back depending on time resources or reports of corruption. Civil servants are aware that they can be checked up on at any time and hence, should be less likely to take bribes.
* Contact is on an individual basis - contrast with example of mass texting of everyone in a catchment area under an antenna in the Participatory Budgeting in the DRC example.
Voice-based. Suitable for interaction with audience with low rates of literacy.

For more information see: http://www.punjabmodel.gov.pk/ as well as this article by <NAME>[1].

[1] http://bit.ly/PEPL_Punjab 

## Mobile Money: Fighting Corruption

Mobile money systems such as M-Pesa have revolutionised money flows in some developing and conflict-ridden countries. The sister of the M-Pesa system in Afghanistan (M-Paisa) trialled as a mechanism of paying policemen. Up until that point money had been siphoned off at other points in the system that many of the policemen actually believed they had received a payrise when the scheme was implemented[1]. Mobile money is discrete, instant (eliminating the need to take time off work to travel to the nearest bank to pick up or drop off money) and traceable. It can be sent directly to the intended recipient. 

Mobile money is not everywhere, yet, and it is not without its problems[2] but its popularity is growing. Organisations such as Frontline SMS are developing organisational interfaces, so that small and medium sized enterprises can also start making use of the digital payments and expand their business. In some places it is also possible to pay bills using Mobile Money. This digital format for sending and receiving money could be a useful tool to governments and to the auditing and accountability community, who would be able to more effectively trace records of income from taxation and verify that intended recipients of government funds had received them. 

[1] http://www.technologyreview.com/business/39828/
[2] http://bit.ly/IMTFI-MPaisa <commit_msg>trimmed the appendix page<commit_after>---
title: Appendix
layout: gift
---


## Final Observations and Review

The appendix to this report is a consolidated list of projects extracted from the case studies. In addition to this are  other projects, which there was not space to include in the main body of the report, that are however useful examples of tools, approaches and contacts for those interested in fiscal transparency. The list includes the name of the project and the organisation, the geographical focus - where it was implemented as well as the local and national scope, the stage in the fiscal process and the type of technology, link to the tool and a short background. You can access the list at: http://bit.ly/TTAPF-projects.

We would like to encourage users to help keep this list up to date - if you would like to add a project, please fill in the online form: http://bit.ly/TTAPF-projects-form
{'fraction_non_alphanumeric': 0.03904392441496684, 'fraction_numerical': 0.006131898385683895, 'mean_word_length': 4.5966386554621845, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 3, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2211084', 'n_tokens_mistral': 1860, 'n_tokens_neox': 1734, 'n_words': 1209}