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: 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.
Fair, transparent, and efficient government procurement benefits the public. In this information age, where insight generation from big data has proven to be powerful, using the tools of Data Analytics can help ensure integrity and transparency in public procurement.
From years 2018 to 2020, Layertech has worked under the support and grant of Hivos’ Open Contracting programme on improving government procurement data use and disclosure. Layertech worked with the Local Government Unit (LGU) Legazpi city, academic institutions such as Bicol University and Southern Luzon Technological College, individual business owners and business organizations such as the Albay Chamber of Commerce and Industry, and Civil Society Organizations (CSO) such as Gayon Bicol LGBTQI organization and their regional network.
This compilation was produced to share experiences and methodologies of Layertech and partners, in mining government procurement data to answer advocacy-related questions of CSOs; to generate business insights; to help suppliers and procuring entities identify bottlenecks and inefficiencies in various procurement stages; and, to uncover trends and red flags in the procurement process.
Target readers of this handbook are assumed to have basic working competency in mathematics, statistics, and basic data manipulation. Working knowledge in coding such as in R and Python, would allow the reader to better appreciate the use-cases and replicate them.
Please NOTE that this handbook focuses on the data sources, filtering, and modelling methods used. Links to the papers (published AND manuscripts) are provided at the end of every use-case, and it is encouraged that the reader visit the links to view the full papers and studies. Some of the papers featured in this handbook may be updated, supplemented, and improved in the future.
You are free to use the contents of this document in any way permitted by the copyright and related legislation that applies to your use. No permission is required from the rights-holders for non-commercial use. Please credit and/or link to www.OCDEX.tech and Layertech Labs.
Attribution and USE of Handbook
Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.
DOWNLOAD THE FIRST VOLUME of the Procurement Analytics Handbook for FREE HERE:
(This article and the manuscript were submitted by the research team and may be updated in the future)
Education has always been a “primary commodity” in the Philippines. With the implementation of the Universal Access to Quality Tertiary Education Act (Republic Act 10931), State Universities and Colleges (SUC) in the country are now mandated to provide free education to all tertiary students enrolled in their programs. Because of this, about 3.2 million college students were reported to enroll in the year 2018 alone [1] and this number is expected to double due to the implementation of the said law.
The Effect of COVID-19 Pandemic to Tertiary Education
The current COVID-19 has crippled the delivery of tertiary education in the country. Nationwide suspension of classes from all levels was hence declared as a mitigation measure to control and contain the spread of COVID-19 across all regions in the country. As a result, a temporary academic freeze has been widely observed from March 2020 until July 2020. This has been the case since various SUCs in the country are still addressing the unresolved dilemma of balancing safety and delivery of quality education to all SUC stakeholders upon resumption of classes in August 2020.
The COVID-19 Challenge to Resumption of Classes
With the sense of urgency to resume all classes despite the COVID-19 situation in the country, it has been widely suggested that face-to-face classes be shifted to online modes of learning this August 2020 [2]. This proposed transition, however, has been met with a huge resistance by various faculty, administrative staff, and college students. In the banner “#NoStudentLeftBehind”, online classes have been dubbed as “anti-poor” and “education solely for the privileged” [3].
As recent facts stated, only 17% of college students have been reported to have the capacity to connect to the internet wherein only 5% of such students have stable internet connectivity at home [3]. With these cited facts, it is expected that enrollment to SUCs this academic year would plunge to at least 70% [4]. Shifting then to purely online modes of learning this August 2020, hence, may not be feasible given the present situation of college students in the country.
Readiness of College Students for Online Learning – The Case of Bicol Region
Through the research grant given by Layertech Software Labs, Inc. and Hivos – People Unlimited, a group of faculty researchers from Bicol University College of Engineering (BUCENG) conducted a feasibility study solely focusing on this present dilemma. The capacity of SUCs and their students, hence, to undertake this proposed transition to online classes this August 2020 were thus reported to be comprehensively studied. In a pilot study conducted in the nine (9) SUCs in Region V, 160 (about 60%) of the 242 college students who willingly participated in a survey conducted from June-July 2020 were reported to have a monthly household income below PHP25,000. Even prior to the pandemic, about 60% of these students largely rely on cellular phones to accomplish academic tasks given to them and this represented a recurring monthly expense of PHP1,000 on cellular data alone. It was also reported that this observed expense is expected to at least double when online classes are implemented this August 2020. With these facts, it is without a doubt that these less privileged college students will have the most disadvantage if a purely online mode of learning is implemented upon the resumption of classes this August 2020.
Assessment of SUC EATing patterns – The Case of Bicol Region
With these described evidences, enabling college students to undertake the proposed online transition would then largely rely on the capacity of SUCs to deliver quality education through the said modes of learning this August 2020.
With the goal of assessing the nine (9) SUCs in the Bicol Region within the context of Education Access in Tenders (EAT), the same group of researchers from BUCENG looked closely into the information technology (IT) related procurement activities of such SUCs from the period 2016-2020. Upon a comprehensive evaluation of the collected contracts, these researchers reported two categories classifying the capacity of SUCs in Bicol Region to successfully implement the proposed online transition, namely “fully capable” and “partially capable”. “Fully capable” SUCs were reported to have largely invested on computer servers, internet coverage and bandwidth, library and learning managements systems, subscription to online databases, and acquisition of software for research and instructional use. These capabilities, as reported by these researchers, corresponded to an equivalent investment amounting to PHP241.19 million.
In contrast, while “partially capable” SUCs were reported to be to financially secure such investments, no relevant awarded IT tenders from 2016-2020 necessary to ensure effective implementation of the proposed online transition were found for such SUCs.
Collectively, seven (7) of the nine (9) SUCs in Bicol Region were seen to be “fully capable” of implementing online classes this August 2020. Though these reported findings implied that the Bicol Region as a whole is seen to effectively implement the proposed online transition, the EATing patterns of the remaining two (2) SUCs which cater to the majority of at least 3000 enrolled college students in the region indicated that much still needs to be done to ensure all college students, regardless of privilege, be given a fair and equal access to quality tertiary education. The researchers then strongly recommended that SUCs classified as “partially capable” benchmark on the IT related procurement strategies that of “fully capable” SUCs.
(This article is submitted by the research team and may be updated in the future.)
The COVID-19 pandemic has largely affected local government units (LGU) in the Philippines, guven the continuous rise of confirmed COVID-19 cases within their respective areas of jurisdiction. In mid-June 2020 alone for example, a spike in the COVID-19 cases was observed across all regions in the country.
As of July 22, 2020, a total of 72,269 confirmed COVID-19 cases has been reported, of which 46,803 cases are classified as active [1]. It is therefore imperative for the LGUs to immediately contain this spike in confirmed COVID-19 cases to prevent further spread of infection in their areas.
Possible Cause of Recent Spike in COVID-19 Cases
With the sudden implementation of a nationwide lockdown across all provinces in the Philippines, about 84,000 individuals remained stranded in Metro Manila for the duration of the imposed mitigation measures in the country [2]. In this case, a humanitarian effort to send these locally stranded individuals (LSI) to their respective home provinces was launched.
As the COVID-19 data published daily by the Department of Health indicated [1], the recent spike in confirmed COVID-19 cases mid-June 2020 could be linked to the return of LSIs to their respective home provinces.
Cross-examination of recently published COVID-19 data on regions (i.e. Region V) reporting a significant number of arriving LSIs further supplemented this observation [3]. As a result, there have been reported instances of mismanagement on the containment protocols of possible COVID-19 positive cases due to the onset of this described occurrence [4]. Hence, there is then a need to assess the capacity of LGUs to properly manage the arrivals of possible COVID-19 positive LSIs while ensuring that the arriving individuals are not discriminated and properly received according to the required medical protocols .
Capacity of LGUs to ACT on Arriving LSIs
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. Using COVID-19 related procurement data and officially press released COVID-19 data for the case of Region V, a system classifying the capacity of LGUs in the said region to accommodate, contain, and treat (ACT) arriving LSIs was formulated.
Under the developed system, three general LGU classifications were then derived namely, “low”, “moderate”, and “high”.
LGUs classified under the “high” category were reported to have at least three (3) hospitals and at least 10,000 PPEs readily available for ACTing on the arrival of possible COVID-19 positive LSIs. This then implied according to the researchers that LGUs categorized as “high” are the most equipped on ACTing on arriving LSIs. On the other hand, “moderate” LGUs were reported to have 1-2 hospitals readily available and a significantly low number of active COVID-19 cases within their respective areas of jurisdiction while “low” LGUs were also reported to have 1-2 hospitals within their vicinity but their active COVID-19 cases were relatively higher than that of “moderate” and “high” LGUs. In this perspective as cited by the researchers, LGUs (i.e. Oas, Albay) classified into “moderate” or “low” categories are highly advised to exercise due scrutiny and adhere to stringent health protocols while ensuring LSIs are not discriminated upon their arrival.
With the validation studies done on the case of Region V, this developed classification system is indeed seen to be helpful in providing data-driven insights that will specifically aid decision-makers on creating policies for proper management on the arrival of such individuals.
This report is made in support to the international open data movement, and to generate insights to help optimize the procurement process of procurement entities. For this report, State University Data is used, although the method is applicable to all procuring entities in general.
Please note that Layertech is not giving any conclusive statements about the agencies and organizations mentioned in this report. We highly encourage that additional research and validation be conducted when using the information stated in this report.
This report is a use-case created under the Open Contracting Grant of Hivos. The views and insights in this document do not necessarily reflect those of Hivos.
Kindly take time to read the notes, recommendations, scope and delimitation mentioned in this report.
This is document is under CC3 License. This is produced by Layertech. Please do not use the information without proper credit and/or Link to our website. For more information, comments and suggestions, please contact learning@layertechlab.com or visit www.OCDEX.tech
The Annex C of the 2016 Implementing Rules and Regulations of the Government Procurement Reform Act (GPRA) Defines the Minimum, Maximum, and Recommended Operational Timeline for each step of the procurement process.
Below are visual timelines for the procurement of Goods and Services, Infrastructure Projects, and Consulting Services.
Prescribed Procurement Timeline for Goods and Services Prescribed Procurement Timeline for Consulting Services Prescribed Procurement Timeline for Infrastructure Projects
For more details, please refer to the 2016 Implementing Rules and Regulations of the GPRA LAW found HERE.
Layertech Labs led the Procurement Data Analytics and Business Intelligence Workshop for Business Owners (and Bankers) in Albay.
Businesses were introduced WHERE to get official Philippine procurement data, HOW to process the data with both technical and non-technical tools, HOW to spot malicious/exaggerated graphs and data, as well as a short introduction how to use procurement business intelligence to inform business decisions and bidding strategies.
Businesses in Albay, some are owners, and C-executives themselves, attended the hands-on workshop at The Oriental Hotel Legazpi, Albay, Philippines.
At the end of the workshop, the participants shared their outputs and insights on Philippine Procurement, and how they can use Business Intelligence from PhilGEPS data to influence their Bidding Strategies.
The workshop was made possible thanks to Hivos, under the Open Contracting Programme, and the Albay Chamber of Commerce and Industry.
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.
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