VOLUME 2 of the OCDex Public Data Analytics Series
Data science and analytics has demonstrated its power in informing decision-making and problem-solving. Data can reveal trends and insights that would have otherwise been obscured. It can give decision-makers key information needed to craft effective and optimal solutions to organizational problems. It can help predict potential bottlenecks and challenges, so that organizations may come prepared when it happens. Data science and analytics is a sought out skill in the digital age.
The Covid-19 pandemic and its resulting limitations on mobility has forced many transactions and communications to migrate from the physical space to the digital space. This sudden global digitalization resulted in an increase in data produced and a subsequent increase in the potential game-changing insights that these data may be hiding.
While many in the private sector have been seen leveraging the power of data for business insights and maximization of revenue, the public sector is yet to catch up in terms of digitalization and data utilization, especially in developing countries. The power of data would especially help communities and local governments in coming up with efficient, effective, and inclusive policies and solutions to problems.
The aim of the 2022 OCDex project run is to bring data scientists and analysts together, and demonstrate how analysis of government data can be used to help solve problems in local communities. The project aims to demonstrate how it can help inform local policymaking and project planning, and how citizens and researchers can participate and help their respective local government units in overcoming community challenges hand-in-hand. This handbook hopes to convince local governments and authorities to invest in good data housekeeping and integrate data science and analytics into their decision-making.
This handbook features how academics and data enthusiasts used public data to help inform solutions to various community problems such as healthcare, inclusivity, and accessibility for persons with disabilities, fairness, and transparency in public procurement, and ensuring enough supply of utilities. Lastly, this handbook presents a replicable model of cooperation between local governments and their local researchers and data enthusiasts toward the effective use of data science and analytics for community building.
Author: Mia Amor C. Tinam-isan (Mindanao State University – Iligan Institute of Technology)
Data analytics has been the talk of the town. It finds its way across agencies (government or private), businesses, or various institutions in discovering valuable information about the existing and overwhelming amount of data. Data in these times exponentially increase and extracting valuable information from these data is essential. Information from the analysis can be used for decision making, coming up with a good marketing strategy, or even in establishing policies and guidelines from a broad test base. However, local government units per se in various parts of the Philippines, have not yet maximized the impact of data analytics. Varying factors might contribute to this such as unorganized data and non-computerized processes.
As part of the academic community, we can implement programs, webinars, and workshops that will empower our LGU to exploit their available data. It is imperative to partner with NGAs, and LGUs, and develop a working strategy for local government digitization. We can propose a simple initiative and start from the automation of LGU processes to organize and ensure the quality and the integrity of data for analysis. We can also venture into partnering with private agencies such as OCDex which has experienced in partnering with LGUs and had already developed numerous government data analytics applications. As of the moment, the College is continuously having a dialog with different offices of LGU-Iligan in crafting MOA for the digitization of the city government.
This article is the author’s reflection on the insight gained from the recently concluded OCDex 2022 Public Data Analytics Fellowship Trainings.
For more information about the article, please reach out to the author: miaamor.catindig@g.msuiit.edu.ph or Layertech labs support at learning@layertechlab.com
Author: Sittie NB Pasandalanb (Mindanao State University – Iligan Institute of Technology)
Despite the reality of reduced budgets of the education sector especially of state universities and colleges (SUCs), institutions of learning have a good share of the annual National Expenditure Plan of the government. Generally, appropriations of SUCs can be categorized into three: Personnel Services (PS), Capital Outlay (CO), and Maintenance and Other Operating Expenses (MOOE). Of these three categories, the MOOE should be of great interest as this gives insight to how a higher education institution (HEI) of the government utilizes government funds, ergo tax payers’ money.
MOOE is funds to be used for necessities (such as electricity and water) and for activities. HEIs are institutions expected to promote conservation of energy, it is worthy to look into the electricity and water bills of HEIs as these would speak of how HEIs are walking the talk. As employees of HEIs are taxpayers themselves, spending for activities should be examined to determine judiciousness in utilizing government funds, ergo taxpayers’ money.
There is the disconnect between spending from one’s own pocket to spending from another’s pocket. Most likely, one finds it easier to spend from another’s pocket than one’s own. This begs the question of whether employees in HEIs (taxpayers in HEIs) can connect with the funds allocated to HEIs as taxes they have paid to the government, ergo their money.
In the case of MSU-IIT, the MOOE for 2021 amounted to Php 297,321,732.24 (from Php 222,402,237.68 in 2020). The question on how the amount was utilized and if the expenses are necessities or mere expenditures to utilize funds allocated by the government begs to be answered.
This article is a reflection on the OCDex 2022 Fellowship Programme for Researchers
For more information about the article, please reach out to the author: sittie.pasandalan@g.msuiit.edu.ph or Layertech labs support at learning@layertechlab.com
Authors: Barajas, J.R., Aspra, N., Gealone, P.J., Lucero, A., Padua, O., Ramos, M.
Motivated by ensuring transparency, fairness, and efficiency in public procurement at the time of the COVID-19 pandemic, a team of engineers and faculty from Bicol Region (Region 5) in the Philippines collected, digitized, and analyzed public procurement data to inform the COVID-19 response of select Local Governments in the country.
Highlights of the Report:
• On average, only 2 out of 10 LGU contracts have been awarded in 2020.
• For every Php1 spent, approximately Php1 remains unspent in the procurement of goods and services made by LGUs.
• A total of Php481 billion were distributed across all LGUs in the country for 2020 but only 10% of this budget was allocated for the procurement of drugs and medicines. 40% of this budget went to construction projects.
• Excluding LGU contracts not posted in the PhilGEPS website, only Php10 billion (2.16% of the total LGU budget) was allocated for COVID- 19 related contracts.
• An equivalent amount of Php720 million was potentially lost from 786 LGU contracts flagged as irregular.
• Audit findings for LGUs were primarily centered on directing accountable officers to comply to documentary requirements mandated by existing circulars, memorandums, and Philippine laws.
• A logistic regression model with an accuracy of 91.29% was developed to identify contracts that are potentially irregular.
Short Summary of the Report:
From examination of 296,220 local government unit contracts, this project was able to develop a logistic regression model capable of predicting potentially irregular LGU contracts posted on the PhilGEPS website for the fiscal year 2020 at an accuracy of 91.29%. Validation of the model using metrics derived from the confusion matrix revealed that the developed model had a recall score of 1.0 and a precision score of 0.029. While the precision of the model may be low, the high recall score is deemed more important in this use-case since it would be more costly for an LGU to miss out on irregular contracts. Overall, the developed prediction model is seen to be highly beneficial as a decision support tool for LGUs since this could potentially narrow down the number of awarded LGU contracts to be legally reviewed resulting in a faster turnover of review cycles conducted within a given fiscal year.
The team’s collected datasets are available for download in the OCDex open data portal, attribution to the authors and contributors is required for use.
Are you interested in this report? Do reach out to us at learning@layertechlab.com so that we can directly connect you with the authors, as well as the documentations they submitted.
(This article and the manuscript were submitted by the research team and may be updated in the future)
As of July 22, 2020, a total of 72,269 total COVID-19 cases has been reported, of which 46,803 are still classified as active cases [1]. With this continual rise of COVID-19 cases, it is estimated that this would cost about PHP2.2 trillion economic losses and is equivalent to at least a 2% contraction in the nominal gross domestic product (GDP) of the country [2]. As a consequence of this economic loss, about 26% of businesses operating in the country have already closed [3] resulting to about 100,000 Filipinos losing their jobs in the 1st half of 2020 [4] Indeed, there is an immediate need to control the spread of COVID-19 in the country to further mitigate the impact of the said disease to the Filipino people.
Fastracking the Public Procurement Process
The COVID-19 crisis in the country is a race against time. As seen from the success stories of our neighboring ASEAN countries, fast, efficient, and integral procurement played a crucial role in securing a “COVID-free” nationwide status. In response to this sense of urgency brought about the by the COVID-19 pandemic, the Government Procurement Policy Board (GPPB) recently issued a series of resolutions to shift current publicly held procurement related to COVID-19 into negotiated procurement (emergency cases) and relax existing guidelines on the submission of vital bid documents [5]-[8]. For instance, expired business or mayor permits and unnotarized bid documents submitted by eligible bidders could already be accepted under these new GPPB issuances.
Challenge on Procurement Integrity and Transparency
While it is necessary to fastrack public procurement in response to the COVID-19 pandemic, it is equally important to ensure integral and transparent implemented procurement processes to protect the interest of the Filipino people. Considered as one of the most vulnerable to corruption especially in this time of an emerging health crisis, relaxation of the imposed regulations is seen to compromise the overall integrity of the present public bidding processes. The acceptance of unnotarized bid documents, for instance, could promulgate misrepresentation on the capacity of an eligible bidder to faithfully undertake any contractual obligations if such barriers are removed.
REACT Risk Indexing System
Through a research grant given by Layertech Software Labs, Inc. and Hivos – People Unlimited, a group of faculty researchers from Bicol University College of Engineering looked closely into this present dilemma. Considering that shortening the time needed in awarding government contracts is of utmost priority, a rapid evaluator and assessor of contractor traits (REACT) risk indexing system was proposed and investigated as an intervening and supplemental tool to aid in upholding the integrity of the presently changed public procurement process on construction and infrastructure tenders related to the COVID-19 pandemic.
Using historical data periodically published by GPPB, assessment of an eligibility of a contractor based on its previous performance was further simplified by these researchers. Through REACT, three general classification of contractors were reported namely, “low risk”, “moderate risk”, and “high risk”. Contractors that were classified as “low risk” in the proposed risk indexing system were found to be the most eligible as these contractors were reported to have at most an average negative slippage of 7.5%. In accordance to the findings reported by these researchers, it was further suggested that necessary precautions and scrutiny be exercised to those contractors that would be classified as “moderate risk” and “high risk” contractors since these contractors were found to have an average negative slippage of at least 24.9% which is well beyond the acceptable negative slippage of 15%.
Indeed, as presented in the case study of the proposed REACT risk indexing system to the publicly available historical data of contractors in Region V, the applicability of the proposed system to preserve integrity of the presently relaxed public procurement process has been validated.
With the validation of the efficacy of the proof of concept as reported by the researchers, the proposed REACT risk indexing system is seen to be a plausible data-driven solution in light of the challenges in the public procurement process amidst the COVID-19 pandemic.
“TEAM ANATA”, a mix of advocates and Computer Science and IT Students, presents and discusses how they used data to identify key issues in HIV/AIDS in the Philippines, its urgency, and comes up with data-backed recommendations for government budgeting for HIV-related kits, trainings, and anti-retro-viral drugs.
This is Team Anata’s presentation at the Datathon2020: Evidence-Based Lobby and Advocacy competition and workshop, held at Bicol University College of Science, February 22-23, 2020.
Follow this page for more updates, as this study gets further substantiated. The contents of this page may be modified, updated, and further substantiated.
PhilGEPS releases Philippine procurement datasets in excel format. These are downloadable in their official PHILGEPS OPEN DATA PORTAL.
Below is a mapping Layertech and Partners in Legazpi made to convert PH-based procurement datasets to OCDS format. This is being used by partner researchers, developers, and students to create localized procurement monitoring tools.
Please consider making a kind donation to help us maintain and improve our work.
Cookie Consent
This website uses cookies or similar technologies, to enhance your browsing experience and provide personalized recommendations. By continuing to use our website, you agree to our Privacy Policy.
Cookies
This website uses cookies or similar technologies, to enhance your browsing experience and provide personalized recommendations. If you think that's ok, just click "Accept all". You can also choose what kind of cookies you want by clicking "Settings".
Read our Privacy Policy
Loading…
Reload document 
Open in new tab 
