12 th National Convention on Statistics (NCS) EDSA Shangri-La Hotel, Mandaluyong City October 1-2, 2013

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1 12 th National Convention on Statistics (NCS) EDSA Shangri-La Hotel, Mandaluyong City October 1-2, 2013 SUCs ASSET INVETORY AND MANAGEMENT TOWARDS FISCAL SUFFICIENCY by Nivea Louwah L. Dumaquita For additional information, please contact: Author s name : Nivea Louwah L. Dumaquita Designation : AIM Project Coordinator Affiliation : Philippine Association of State Universities and Colleges Address : Unit 105 National Tobacco Administration Bldg. Scout Reyes St., cor. Panay Avenue, Q.C. Tel. no. : pasucoffice@gmail.com

2 SUCS ASSET INVENTORY AND MANAGEMENT: TOWARDS FISCAL SUFFICIENCY by AIM Project Management Team Philippine Association of State Universities and Colleges (PASUC) ABSTRACT The national goal for the production function of the State Universities and Colleges SUCs is ultimate self-sufficiency for the SUCs with little or no subsidies from the national government. Amidst the challenge of budget constraints, SUCs most likely hold resources that are either still hidden or unrecognized, just waiting to be tapped. The main purpose of this paper is to present a profile of the Philippine SUCs in terms of their physical assets, research and development programs and human resources, to provide (1) a clustering of SUCs based on their labor-capital technology capabilities and (2) inputs for the development of appropriate business models for the individual SUCs given the regulatory policy framework of the national government. All 110 SUCs in the country were requested to participate in this study. These SUCs are categorized based on income sufficiency, type of institution, asset, labor and technology. Four (4) indices are formulated to construct the clustering frameworks. Intervals were set for these indices, such that the major characteristics of SUCs are classified as sufficient or not sufficient for income; large, medium, or small for asset; high, medium, or low for labor; and high or low for technology. Overall, the SUC Type Asset Profile Labor Technology Resource (TALT) Framework creates a better picture of SUC classifications by combining several aspects altogether. Generally, most SUCs have high business potentials with only very few being categorized under medium and low business potentials. By implementing business models aligned with the characteristics of the SUC, the TALT Framework shall transform into a positively skewed distribution with more SUCs occupying the low business potential category after having harnessed its resources and being transformed into financially sufficient institutions. Keywords: State Universities and Colleges (SUCs), asset inventory, asset management Page 1 of 15

3 SUCS ASSET INVENTORY AND MANAGEMENT: TOWARDS FISCAL SUFFICIENCY AIM Project Management Team Philippine Association of State Universities and Colleges (PASUC) INTRODUCTION The neoclassical economic theory supports three (3) bases for analyzing an economic activity: labor, capital and technology. Labor refers to the volume and quality of manpower in the organization, capital refers to the economic assets while technology pertains to the state of research, development, and technology advancement of the organization. Analyses under this classical economic formulation are undertaken on the assumption of a free economic market where the platform of regulations encourages innovations and market competition. Thus, although the state universities and colleges (SUCs) operate under government controls, the same three (3) economic bases apply in the analysis of their income-generating potentials. The national goal for the production function of the SUCs is ultimate self-sufficiency for the SUCs with little or no subsidies from the national government. This goal has been articulated in a series of papers by the Commission on Higher Education (CHED) (Padua (2000), Puno (2004), CHED-ADB-TA 2001 on corporati ation as part of the rationalization program for the public for public higher education in the Philippines. Statistics reveal that in the latter part of 1990s the SUC self-sufficiency index (SSI = income/subsidy x 100%) posted a dismal 1% steadily growing to 33% in At this rate of 2.13% annual growth rate in SSI, the SUCs will have been self-sufficient in 32 years a timeframe, undoubtedly, unacceptable to higher education stakeholders. This partly explains the government s more aggressive strategy to encourage the SUCs to use their economic assets to maximize their income-generating capabilities. Objective of the Study The main purpose of the paper is to present a profile of the Philippine SUCs in terms of their physical assets, research and development programs and human resources, specifically: 1. Provide a clustering of SUCs based on their labor-capital technology capabilities; 2. Provide inputs for the development of appropriate business models for the individual SUCs given the regulatory policy framework of the national government. Limitations Although 108 (98%) SUCs submitted the accomplished Asset Inventory Self-Survey Instrument (AISSI), some sections of the instrument were not filled-up by the respondents. Another limitation of this study is that the estimates of the land values were based on a published regional Land Valuation Assessment of the Bureau of Internal Revenue as of 2000 and may have some serious undervaluing implications. METHOD Clustering Frameworks All 110 State Universities and Colleges (SUCs) in the country were requested to participate in this study. These SUCs are categorized based on income sufficiency, type of institution, asset, labor and technology. The categorization of SUCs on income sufficiency is based on a self-sufficiency index (SSI), which takes into account the internally generated income of the SUC and its overnment Appropriation s Act AA subsidy. In terms of type, SUCs are classified as an agricultural (agri), a science and technology (S&T), or a non-science and technology (non-s&t) institution. This classification is based Page 2 of 15

4 on PASUC records. SUCs are also differentiated with respect to their surveyed assets. For convenience and uniformity, the asset profile index (API) is formulated to describe assets as large, medium, or small. Labor, on the other hand, is classified as high, medium and low using the human resource index (HRI), which takes into consideration the number of faculty members and corresponding research programs. Lastly, technology resource classification is described as either high or low based on the technology resource index (TRI). Combining all components of the clustering frameworks based on type, asset, labor and technology results to 54 classifications (Fig.1). Figure 1. SUC Type Asset Labor Technology (TALT) Framework Framework Indices Four (4) indices are formulated to construct the clustering frameworks. By setting intervals for these indices, the major characteristics of SUCs are classified as sufficient or not sufficient for income; large, medium, or small for asset; high, medium, or low for labor; and high or low for technology. SSI (self-sufficiency index), as described earlier, determines whether an SUC is sufficient or not sufficient by comparing its income and subsidy. The equation for this index is Internally Generated Income SSI = 100%. GAA Subsidy If SSI is greater than or equal to 80%, then the SUC is considered to have sufficient income; if SSI is less than 80%, then its income is not sufficient. The next index is API (asset profile index), which is described by the following equation: Asset Value API = 100% Asset Value Internally Generated Income Asset value and its components are further described by the following set of equations: Asset value Land value Equipment value Equipment value Equipment units Acquisition cost Land value Area Cost per area The SUC has a high business potential (HBP) if API is greater than 70%; if API is Page 3 of 15

5 between 40% and 70% inclusive, then it is classified as having medium business potential (MBP); and if API is less than 40%, then it has low business potential (LBP). From the equation, API can be interpreted as the portion of asset value within the context of the reported income generated internally. High API values would therefore imply that the SUC has relatively greater assets still unexploited. From a business viewpoint, however, the medium-api SUCs are likely the most favorable for business development programs since the potential for exploitable assets is already evident. Low-API SUCs, on the other hand, are those which are able to generate proportionally higher income in comparison with their assets. To describe the type of labor a particular SUC has, the HRI (human resource index) is formulated. This index is calculated in a two-fold fashion: first step involves the separate calculations of HRI 1 and HRI 2, followed by a combination of both expressed simply as HRI. Expressions for HRI and its components are given in the following set of equations: Number of faculty members with MS and PhD HRI 1 100% Total number of faculty members Number of research programs HRI 2 Number of faculty members with MS and PhD If HRI 1 is greater than or equal to 65%, then the SUC is said to have high quality of labor; otherwise, it is classified as low. The second component of HRI expressed as HRI 2 is formulated so as to mend an apparent shortcoming of HRI 1, that is, the failure to take into account the possible misalignment of faculty members. This misalignment issue is addressed by considering the number of research programs undertaken by the SUC. This resolution is based on the rationale that faculty members with advanced degrees are more likely to produce research outputs if their assignments align with their fields of specializations. If HRI 2 for an SUC is greater than or equal to 1, then it is said to have high quality of labor; otherwise, it is considered low. To combine HRI 1 and HRI 2, the following two-way table is used for reference: Table 1. Two-way table for HRI Referring to Table 1, an overall high rating for HRI is achieved if both HRI 1 and HRI 2 are also high. SUCs with relatively high number of faculty members with advanced degrees that perform research in line with their specializations are likely to score a high rating. On the other hand, SUCs with high number of faculty members with advanced degrees but are misaligned may achieve a high rating for HRI 1 but low in HRI 2 resulting to a medium rating for overall HRI. The last index used for the framework is referred to as TRI (technology resource index), which encapsulates the training programs (IIA), consultancy programs (IIB), research programs (IIC), business and entrepreneurial initiatives (IID) and program accreditation levels (IIE). Its equation is given by 3A B 5C 3D 3E TRI, where 15 A, B, C, D, and E are calculated from the data entries in IIA, IIB, IIC, IID, and IIE of the questionnaire, respectively. Page 4 of 15

6 Profiling Based on the Indices and SUC Type Out of 110 SUCs, a total of 108 completed AISSI forms were returned through , fax, or courier. In addition to the information gathered through the survey, other relevant records are incorporated into the calculation of certain indices. These records include: GAA subsidies for SUCs in 2011 extracted from Department of Budget and Management (DBM) website Zonal land valuation from Bureau of Internal Revenue (BIR) SUC program categories based from PASUC records In interpreting TRI, an SUC able to generate a value greater than or equal to 1.5 is said to have high technological resources; otherwise, it is low. The information derived from PASUC regarding classification identifies the SUCs as any one of the following program categories: Agriculture and Forestry Science and Technology General Comprehensive Teacher Education Pursuant to the objectives of the AIM project, the third and fourth program categories are lumped into one and are collectively known as non-science-and-technology (non-s&t) programs. Agriculture and forestry program category, on the other hand, is simply referred to as agriculture (agri). Finally, the science and technology program category is simply coded as S&T. Page 5 of 15

7 RESULTS AND DISCUSSIONS Self-Sufficiency Index (SSI) On a national scale, the results of the SSI calculations suggest that only 22.73% of the SUCs are self-sufficient as shown in Table 2. When grouped by major-island areas, this overall proportion varies slightly at approximately 2%. The SUCs in Mindanao have the highest SSI at 25%. Table 2. SSI of SUCs grouped by major-island areas potentials can benefit the most from business development programs so that internally generated income is raised relative to their total asset value. Luzon area has the highest percentage of SUCs with high APIs at 83.67% owing to their vast lands and other major assets. As a limitation to this index, 14 SUCs are unclassified due to the lack of information provided in the AISSI. Table 3. API of SUCs grouped by major-island areas Human Resource Index (HRI) SUC Types The distribution of SUC types revealed that 42.73% of the SUCs in the Philippines are classified under the non-s&t program category. It is important to note that for the purpose of this AIM project, the non-s&t program category comprises both general comprehensive and teacher education program categories which could account for this high percentage.luzon area has the highest percentage of non-s&t universities at 53.57%. Conversely, Mindanao area has the highest percentage of agricultural and S&T colleges or universities at 35.71% and 39.29%, respectively. On the aspect of faculty profile, the index values summarized in Table 4 shows that the Luzon area has the highest percentage of SUCs with high HRIs at 9.62%, followed by the Visayas area at 9.52% and the Mindanao area at 7.41%. For SUCs with medium HRIs, Luzon still has the highest percentage at 44.23%, then Mindanao at 37.04% and Visayas at 33.33%. On a national scale, 51% of the SUCs have low HRIs led by Visayas with 57.14%. Luzon and Mindanao areas are at 46.15% and 55.56%, respectively. Of the 110 SUCs, 10 are unclassified under this index for human resource. Table 4. HRI of SUCs grouped by major-island areas Asset Profile Index (API) Majority of the SUCs (73.96%) have high APIs, hence high business potentials, based from the established criteria as shown Table 3. Only eight (8) SUCs are considered to have low business potentials and only seventeen (17) have medium business potentials. The seventy-one (71) SUCs with high business Page 6 of 15

8 Technology Resource Index (TRI) Although the Visayas area lags behind Luzon and Mindanao in terms of API and HRI, results of the TRI calculations summarized in Table 5 suggest that 50.00% of the SUCs in this area are considered to have high TRI. Basing from the parameters of this index, SUCs in the Visayas area have the highest potential for development towards self-sufficiency. SUCs in the Mindanao area, on the other hand, do not fare well in this scale with only 34.62% considered as having high TRIs and 65.38% considered as having low TRIs. The area of Luzon has the highest number of SUCs with high TRI at 20. Nonetheless, this number is just 37.04% of the SUCs surveyed in the area; the remaining 62.96% or 34 SUCs have low TRIs. Table 5. TRI of SUCs grouped by major-island areas Profiling Based on Clustering Framework A fragmented picture of the landscape of the Philippine SUCs can be derived from the indices previously presented. Nonetheless, a more meaningful framework can be formed if multiple indicators are used simultaneously to cluster SUCs. Areas with the highest need can be identified and may be afforded with the most appropriate programs for development. These frameworks become the bases for the clustering of SUCs as well as the nature of the business model to be implemented. For convenience in classifying SUCs based on the four clustering frameworks, a numerical scheme is used to denote each classification. Classifications for SSI as sufficient or not sufficient are denoted by 1 and 2, respectively. For SUC type, agriculture is denoted by 1, S&T by 2, and non-s&t by 3. Large, medium, and small asset profiles classified under API are denoted by 1, 2, and 3, respectively. Using this numerical scheme, for instance, an SUC classified as ITA-111 is a sufficient agricultural institution with large asset profile under the ITA framework. Other indices as well follow a similar scheme. Income Sufficiency SUC Type Asset Profile (ITA) Framework In the group of sufficient SUCs shown in Figure 2, only SDSSU is classified as having low business potential. The latter belongs to the agricultural type and may have exploited majority of its agricultural resources resulting to the 113 classification. On the other hand, three (3) agricultural universities in Mindanao (MOSCAT, USM, and CarSU) have high business potentials. Their vast agricultural lands are, therefore, viable to business development programs. In the S&T and non-s&t types, SUCs in Luzon dominate the cluster around the HBP classification. BulSU, BatSU, PhilSCA, and RTU are the S&T SUCs with high business potentials alongside the non-s&t SUCs, namely: BengSU, CavSU, LSPU, and SLSU. In the Visayas region, BISU, CTU and WVSU are S&T and non-s&t SUCs, respectively, with high business potentials. Similarly in Mindanao, MUST and SKSU also have high business potentials belonging to the S&T and non-s&t types, respectively. Figure 2. SUCs with sufficient SSI clustered within the ITA Framework It is important to note the position of BPSU, SorSC and NONESCOST are the S&T SUCs with medium business potentials while BukSU in Page 7 of 15

9 the non-s&t group is the sole SUC classified as having MBP. These SUCs with medium business potentials are ideal for short-term business development programs since the initiatives that they have established to reach sufficiency can just be reinforced with more funding or deeper expertise. BPSU, SorSC and NONESCOST as S&T SUCs, for instance, can exploit more of their resources by enriching their scientific and technological programs. Figure 3 displays the group of nonsufficient SUCs categorized within the ITA framework. As expected, the list of HBP SUCs in the nonsufficient group is significantly longer than that of the sufficient group. This implication follows from the fact that nonsufficient SUCs are classified as such because of the resources left to be exploited in order to attain the desired sufficiency. Non-S&T SUCs with HBP classified under ITA-231 are most numerous as shown in the figure. In terms of length, this classification is followed by ITA-211 then by ITA-221, both of which are also under the HBP classification. Viewing from the other end, SUCs under the ITA-223 and ITA-233 classifications may have been in unfavorable positions since the LBP categorization suggests highly exploited resources in these institutions. Although resources are well exploited, these SUCs still remain in the nonsufficient category and may only have meager assets left for conversion into additional income. Thus, augmentation of their assets is more possibly the key to sufficiency in this case instead of business development programs. SUCs under the ITA-212, ITA-222, and ITA-232 classifications are ideal for short-term developmental goals since these SUCs are already classified as having medium potential and are likely on their way to sufficiency. Depending on the strengths of these SUCs, their thrusts can be reinforced through timely and appropriate support from concerned agencies. Looking at Figures 2 and 3, most SUCs in Luzon still belong to the HBP classification and therefore have higher potential for greater sufficiency. In fact, eighteen (18) SUCs from Luzon belonging in the non-s&t program category are classified under the ITA-231 classification. These SUCs have high business potentials and can benefit the most from long-term business development initiatives in order to reach favorable SSI levels. In the case of the seven (7) SUCs belong to the ITA-211 classification, as agricultural SUCs, these institutions may be promoted from HBP to MBP or LBP if profitable programs to exploit the land assets are implemented. Figure 3. SUCs with nonsufficient SSI clustered within the ITA Framework In the Visayas Area, CTU, WVSU and NONESCOST are the only three SUCs that has sufficient SSIs. Although sufficient, CTU and WVSU, which respectively belong to the S&T and non-s&t programs have high business potentials while NONESCOST, a S&T SUC, has medium business potential. None of the agricultural SUCs in Visayas has sufficient SSI. On the other hand, MUST is the only S&T SUC in the Mindanao Area classified as having high business potential. Thus, this institution still has greater room for sufficiency amidst its sufficient state and can benefit from business programs that would take advantage of its scientific and technological resources. Sharing a similar stature, SKSU is a non-s&t SUC with HBP classification. The vast land assets of MOSCAT, USM, and CarSU qualified them in the ITA-111 classification. Although already sufficient, these Page 8 of 15

10 SUCs still have high potentials for business development programs. On the right-hand portion of the figure, BukSU is on a particularly interesting position as the sole sufficient SUC with medium business potential. Thus, enrichment programs supportive of its current initiatives may be implemented so as to realize higher income. Income Sufficiency SUC Type Technology Resource (ITT) Framework classification undergo restructuring of their scientific and technological programs by enriching and transforming these into entrepreneurial initiatives. In the agriculture program category the number of SUCs in the ITT-211 classification equals that of the SUCs under ITT-212. These SUCs, especially those under ITT-212, can benefit from technological advancement programs that could materialize into income-generating arms of the SUC. TRI takes into account the business and entrepreneurial initiatives as well as the training, research, and consultancy programs undertaken by an SUC. Figure 4 shows that although the SUCs have income sufficiency, there are more SUCS with low TRI than High TRI. This pattern is seen to be consistent for all types of SUCs. It is also interesting to note that 12 of the 25 SUCs belong to the S&T program category with eight (8) coming from Luzon. Most of the S&T SUCs, therefore, are able to realize their income potentials by anchoring on their scientific and technological inclinations. Figure 4. SUCs with sufficient SSI clustered within the ITT Framework In the nonsufficient group as shown in Figure 5, most of the SUCs clustered under the non-s&t program category. ITT-231 and ITT-232 sum up to a total of thirty-four (34) SUCs with 14 and 20 SUCs, respectively. Comparing ITT-221 and ITT-222, a sharp difference can be observed. From this vantage point, it may be proposed that SUCs under the ITT-222 Figure 5. SUCs with nonsufficient SSI clustered within the ITT Framework As seen in Figure 5, all agricultural SUCs in Luzon are not sufficient. On the other hand, the number of nonsufficient SUCs in both the S&T and non-s&t program categories surpasses the number of sufficient SUCs in their respective program categories with nonsufficient non-s&t SUCs in Luzon, largely outnumbering the sufficient SUCs. With this circumstance, ITT-231 Page 9 of 15

11 and ITT-232 SUCs could capitalize on their consultancy programs to climb up the sufficiency ladder. ITT-131 and ITT-132 SUCs would be excellent benchmarking venues. In the Visayas area, a large clustering observed along the ITT-211 classification would seem to suggest that highly technological but agricultural SUCs are not able to realize sufficient income. Even highly technological non-s&t SUCs (ITT-231) are all nonsufficient. A good point can somehow be obtained from ITT-121 occupied by CTU alone, which could suggest that alleviating technology resource is a viable pathway towards self-sufficiency. In the Mindanao area, high-tri SUCs under the agriculture and S&T program categories prosper by capitalizing on their technological resources. Nonsufficient SUCs belonging to a similar economic and political landscape can, therefore, adapt similar technologies already being implemented by these model SUCs. Figure 6. SUCs with sufficient SSI clustered within the ITL Framework Income Sufficiency SUC Type Labor (ITL) Framework Human resource profile of sufficient SUCs under the non-s&t and agriculture program categories are distributed across the high, medium, and low HRIs as shown in Figure 6. ITL-123 classification, on the other hand, includes the most number of SUCs comprising BPSU, BulSU, TSU, PhilSCA, RTU, NONESCOST, BISU and CTU. It is surprising to see, however, that none of the S&T SUCs qualify for the ITL-121 classification. Even in nonsufficient S&T SUCs shown in Figure 7 not a single SUC qualifies for the high-hri classification. It can be noted that low-hri S&T SUCs are the most numerous accounting for eighteen (18) institutions. Human resource profile in non-s&t SUCs may also be pointed out as a reason for insufficiency on account of the fact that a large portion of these SUCs belong to the nonsufficient category, a sharp contrast to the eight (8) non-s&t SUCs that are sufficient. Figure 7. SUCs with nonsufficient SSI clustered within the ITL Framework Looking at Figure 7, a greater proportion of SUCs occupy the nonsufficient and compared to SUCs in the sufficient categories for all types. For Luzon area, therefore, investing on more research programs for nonsufficient SUCs could help improve their financial standings. Although SUCs under ITL-232 and 233 qualify for average HRI, they remain insufficient probably due to misalignment of faculty members with advanced degrees. In these SUCs, the ratio of faculty members with MS and Page 10 of 15

12 PhD may be high but the number of research outputs could be really low dragging down their ratings to medium. The effect of this scenario could possibly ripple into low income. In the agriculture program category, none of the SUCs qualifies for sufficiency although IfSU qualifies for high-itl rating. For this category, therefore, the nature of business models to be implemented is preferably outside the ITL framework. Displaying a similar behavior as the nonsufficient SUCs in Luzon, SUCs in the Visayas area have more number of nonsufficient SUCs than sufficient SUCs. Likewise, the ITL-232 classification in Visayas includes the most number of SUCs compared to other classifications. Unlike the SUCs in Luzon, however, S&T SUCs in Visayas are more likely insufficient; only two (2) S&T SUCs are sufficient out of six (6). PIT, in particular, is an ITL-222 SUC but fails to qualify for the sufficient category. ITL-211 of Visayas once again is similar to that of ITL-211 of Luzon. VSU shares a similar situation as IfSU, that is, although both SUCs have high ITL ratings, they remain insufficient in income. SUCs in Mindanao, meanwhile, have prosperous agricultural SUCs with USM and SDSSU earning high-itl classification and MOSCAT and CarSU earning medium-itl classification. If ITL-212 SUCs improve on their human resource profile, then there is a greater chance that these SUCs become sufficient. S&T SUCs in Mindanao, however, have mostly low-hri ratings. MUST, a sufficient SUC with medium-hri rating is a good indication that ITL-223 SUCs can improve on their human resources and become sufficient. SUC Type Asset Profile Labor Technology Resource (TALT) Framework Considering SUC type, asset profile, labor, and technology resources altogether, a more comprehensive framework is generated with a total of fifty-four (54) classifications as shown in Figure 8. Not having to consider the income sufficiency of every SUC, the TALT Framework provides a financially nondiscriminatory clustering and puts every SUC into level grounds of comparison. Throughout Figures 8 to 10, SUCs cluster around the HBP classification regardless of type. In Figure 8, IfSU, a TALT-1112 SUC, can move from HBP to MBP or LBP classification if efforts are aligned alongside the improvement of its technological resource since it has low TRI rating. All agricultural SUCs in the Visayas belong to the high API category, thus, high business potentials. VSU which combines the characteristics of high TRI and HRI is particularly the most receptive for business development programs with regards to the indices. USM, classified under TALT-1111 shares a similar case as VSU. A greater room for potential business developments is therefore realizable by harnessing the high human and technological resources of the institution. In the case of SDSSU under TALT-1311, the high HRI and TRI ratings combined with its low business potential classification could suggest proper management of its assets based on the TALT Framework. In Figure 9, TALT-2132 classification includes the most number of SUCs. SUCs in this category have the greatest potential for improvement in almost every aspect of the framework. In the S&T program category, EVSU and NONESCOST are the only SUCs occupying the MBP category. WVSCT, BISU and CTU all belong to the HBP category with low HRI. Improvements on the human resource aspect can certainly transform the business potentials of these SUCs. For S&T SUCs in Mindanao, DOSCST and SSCT are classified having high TRIs in the LBP category. A better portion of these SUCs assets are therefore harnessed for additional income through their high technology profile. Furthermore, JRMSU and SSC, belonging to the TALT-2232 have efficiently managed their human and technological resources; although the HRI and TRI values are low, both SUCs still managed to belong to the medium-api category. The TALT framework for non-s&t SUCs is shown in Figure 10. TALT-3132 SUCs can generate higher incomes if labor force and technological resource are improved. By improving on their technological resources, just as the case with CSPC under TALT-3231, a greater portion of the assets will be turned into profitable income-generating projects. Surprisingly, BU and CatSC are both high in HRI and TRI ratings but remain in the TALT-3111 classification. As non-s&t SUCs, BU and CatSC Page 11 of 15

13 can exploit both labor and technological resources so as to realize their business potentials. Three (3) non-s&t SUCs in Visayas belong to the MBP category for their APIs. CNU and LNU both have medium HRI ratings suggesting average human resource profiles. If LNU can improve on its technological resource, then its potential for business can be realized as demonstrated by CNU. Comparatively, ESSU can also follow the improvement pathway of CHMSC, which belongs to the low-hri category but is categorized under the medium business potential due to better harnessed resources. For ESSU, improvements on related to technology can potentially level up its profitability. Figure 8. Agricultural SUCs clustered within the TALT Framework Figure 9. S&T SUCs clustered within the TALT Framework Page 12 of 15

14 Figure10. Non-S&T SUCs clustered within the TALT Framework Overall, the TALT Framework creates a better picture of SUC classifications by combining several aspects altogether. Generally, most SUCs occupy the HBP category with only very few being categorized under medium and low business potentials. By implementing business models aligned with the characteristics of the SUC, the TALT Framework shall transform into a positively skewed distribution with more SUCs occupying LBP category after having harnessed its resources and being transformed into financially sufficient institutions. Page 13 of 15

15 Appendix A LIST OF SUCs WHO PARTICIPATED IN THE STUDY NATIONAL CAPITAL REGION Eulogio "Amang" Rodriguez Institute of Science and Technology EARIST Nueva Ecija University of Science and Technology Pampanga Agricultural College NEUST PAC Marikina Polytechnic College MPC Philippine Merchant Marine Academy PMMA Philippine Normal University PNU Ramon Magsaysay Technological University RMTU Philippine State College of Aeronautics PhilSCA Tarlac College of Agriculture TCA Polytechnic University of the Philippines PUP Tarlac State University TSU Rizal Technological University RTU REGION IV-A Technological University of the Philippines TUP Batangas State University BatSU REGION 1 Cavite State University CavSU Don Mariano Marcos Memorial State University DMMMSU Laguna State Polytechnic University Southern Luzon State University LSPU SLSU Ilocos Sur Polytechnic State College ISPSC University of Rizal System URS Mariano Marcos State University MMSU REGION IV-B North Luzon Philippines State College NLPSC Marinduque State College MSC Pangasinan State University University Of Northern Philippines PangSU UNP Mindoro State College of Agriculture and Technology MinSCAT CORDILLERA ADMINISTRATIVE REGION Occidental Mindoro State College OMSC Abra State Institute of Science and Technology ASIST Palawan State University Romblon State University PalSU RoSU Apayao State College ASC Western Philippines University WPU Benguet State University BengSU REGION V Ifugao State University IfSU Bicol University BU Kalinga Apayao State College KASC Camarines Norte State College CNSC Mountain Province State Polytechnic College MPSPC Camarines Sur Polytechnic Colleges CSPC REGION II Central Bicol State University of Agriculture CBSUA Batanes State College BatSC Catanduanes State College CatSC Cagayan State University CagSU Dr. Emilio B. Espinosa, Sr. Memorial State DEBESMS Isabela State University ISU College of Agriculture and Technology CAT Nueva Vizcaya State University NVSU Partido State University ParSU Quirino State College QSC Sorsogon State College SorSC REGION III REGION VI Aurora State College of Technology ASCOT Aklan State University ASU Bataan Peninsula State University BPSU Capiz State University CapSU Bulacan Agricultural State College BASC Carlos Hilado Memorial State College CHMSC Bulacan State University BulSU Guimaras State College GSC Central Luzon State University CLSU Iloilo State College of Fisheries ISCOF Don Honorio Ventura Technological State University DHVTSU Negros State College of Agriculture Northern Iloilo Polytechnic State College NSCA NIPSC Page 14 of 15

16 Northern Negros State College of Science NONESCO REGION XI and Technology ST Davao Del Norte State College DNSC University of Antique Western Visayas College of Science and Technology West Visayas State University UA WVCST WVSU Davao Oriental State College of Science and Technology Southern Philippines Agri-Business, Marine And Aquatic School Of Technology DOSCST SPAMAST REGION VII University Of Southeastern Philippines USEP Cebu Normal University CNU REGION XII Cebu Technological University Negros Oriental State University CTU NORSU Cotabato Foundation College of Science and Technology CFCST REGION VIII Cotabato City State Polytechnic College CCSPC Bohol Island State University BISU Sultan Kudarat State University SKSU Siquijor State College SiqSC University Of Southern Mindanao USM Eastern Samar State University ESSU CARAGA Eastern Visayas State University Leyte Normal University EVSU LNU Agusan Del Sur State College of Agriculture and Technology ASSCAT Naval State University NSU Caraga State University CarSU Northwest Samar State University NSSU Surigao Del Sur State University SDSSU Palompon Institute of Technology PIT Surigao State College of Technology SSCT Samar State University SSU AUTONOMOUS REGION IN MUSLIM Southern Leyte State University SLSU MINDANAO University of Eastern Philippines UEP Adiong Memorial Polytechnic State College AMPSC Visayas State University VSU Sulu State College SSC REGION IX Tawi-Tawi Regional Agricultural College TRAC Basilan State College BaSC J.H. Cerilles State College JHCSC Jose Rizal Memorial State University JRMSU Western Mindanao State University WMSU Zamboanga City State Polytechnic College ZCSPC Zamboanga State College of Marine Sciences and Technology ZSCMST REGION X Bukidnon State University BukSU Camiguin Polytechnic State College CPSC Central Mindanao University CMU Mindanao University of Science and Technology MUST Misamis Oriental State College of Agriculture and Technology MOSCAT Northwestern Mindanao State College of NWMSCS Science and Technology T Page 15 of 15