A third study on the IRA by Uchimura and Suzuki (2009) clarified the interrelationships between the four IRA issues cited by Manasan (2007), using the fiscal capacity of the LGUs. Uchimura and Suzuki (2009) clarified the interrelationships between the four IRA issues cited by Manasan (2007) using the fiscal capacity of the LGUs. This present study provides an assessment of the importance of population in the IRA formula.
Moreover, any evaluation of IRA (the second question of this study) should be based on clear criteria, which in this study is shared growth (hence the first question of this study). In the next discussions, major criticisms of the IRA tend to contradict their criteria. This study serves as a prelude to a more in-depth analysis of the IRA towards a macro evaluation of decentralization and shared growth.
Both critiques of the IRA formula essentially reject the hypothesis that the IRA formula could contribute to shared growth. Questioning the validity of Critique 1 would imply the validity of the opposite claim that the IRA contributes to cross-county growth sharing.
No significant correlation between the levels of average income and the Gini coefficient. The absence of correlation
Since the IRA is seen as an incentive mechanism that can help or hinder decentralization, the exploratory analysis will indicate the correlations between the IRA and the various measures of shared growth. Based on this indication, the author could tentatively consider any province, such as Laguna, on whether IRA could be associated with shared growth. Benguet was chosen as the second province based on the observed correlation pattern of provinces (Appendix 3).
Using aggregated and annual data, this study examined the correlation of provincial performance measures for median income, poverty incidence and income distribution; and found patterns (1, 2, and 3) in support of an inclusive type of growth versus shared growth. Looking at the correlation of IRA and performance metrics (average income and poverty incidence), a positive correlation was found between IRA and average income (Model #4), but no significant correlation between IRA and poverty incidence (Model #5).
A significantly negative correlation between the levels of average income and poverty incidence. This correlation was
There was a weak positive to no correlation between levels of poverty incidence and the Gini coefficient. Poverty incidence
There was no significant correlation between the IRA per capita and poverty incidence. The coefficient of correlation between
To place the above provincial correlations in the national context, the correlation between national performance measures was also investigated: the Gini coefficient (Gini); poverty incidence (PI); and real GDP per capita (RGDPPC). However, due to limited data, correlations of IRA with performance measures were not investigated. There was no significant correlation between real GDP per capita and the Gini coefficient, while a significant negative correlation resulted between real income per capita and the incidence of poverty.
In general, patterns 1 and 2 suggested that growth of per capita income was more of the inclusive type rather than the shared type. This type of growth is more associated with a reduction in poverty incidence, but with no significant change in income distribution. The correlations for national performance measures appeared to support Patterns 1 and 2, although only on an actual basis.
Theoretically, one way this could happen is when the incomes of the top income percentiles grow faster than those of the lowest income percentiles. Correlation matrix analysis for performance indicators revealed no significant correlation between poverty incidence and Gini coefficient indicators. The following section discusses the results of the above correlation in relation to two criticisms of the IRA formula after presenting the definition of the current IRA formula.
The IRA is 40 percent of the gross internal revenue of the national government on the third preceding fiscal year. 50 percent of the total amount allocated to their LGU type shall be allocated to each of their respective components in proportion to their population;. 25 percent of the total amount allocated to their LGU type shall be allocated to each of their respective components in proportion to their land area; and.
60 percent of the total amount allocated to barangay shall be allocated to each barangay in proportion to its population; and 2.
Given the negative tone of Critique 1, it can be concluded that causality 1 is being assumed, as it is consistent with the "rich-are-getting-richer" tone of the critique. In fact, Causality 2 is compatible with a performance-based allocation of IRAs, where higher per capita income is rewarded with higher per capita IRAs. Based on an inspection of the IRA formula, there are at least two possibilities where the positive correlation between per capita income and per capita IRA can occur.
Population will be negatively correlated with income per capita, assuming a production function with population and land as inputs to production. An increase in population is associated with a decrease in both IRA per capita via the IRA formula, and income per capita via the production function. The arrowhead between population and income per capita indicates that population causes income per capita, under the assumption that population is an input to production.
This follows from the accounting identity underlying the relationship between IRA per capita and population. Population will be positively correlated with higher income through the dynamics of an inter-province migration. Using per capita income as a proxy for wages, a province with a higher per capita income induces immigration.
An increase in population is associated with an increase in IRA, via the IRA formula; and income, via the two-sector migration hypothesis. The arrowhead between population and income indicates that income drives population growth, as implied by an inter-county migration driven by higher incomes in the destination province. The implicit causality between population and income in Possibility 2 is very similar to Causality 2, where higher income per capita causes a higher IRA per capita.
The significantly negative sign of the beta estimate indicated unconditional beta convergence, with poorer provinces growing faster than richer provinces, indicating convergence in average annual family income across provinces as poorer provinces approached richer ones.
Again referring to the IRA formula, a plausible explanation was readily found for the positive correlation between IRA and BIR collection. An increase in population has been accompanied by an increase in IRA allocation, based on the IRA formula. This conclusion is very remarkable considering that the IRA formula was not really intended to be performance-based.
Rather, the IRA formula was primarily intended to support the decentralization of government functions. This stems from the IRA formula itself, which is not directly related to the development efforts of the LGUs. Benguet, on the other hand, looks more towards adjusting the land area and population components of the IRA formula.
Benguet also pointed to the issue of including the disputed areas of the province in the calculation of the IRA. Benguet Province lamented that the projects funded by the IRA lacked flexibility to truly meet the development needs of the LGU. One is the causal relationship between income per capita and IRA, which is crucial for correct assessment of the IRA formula.
Nearly three decades have passed since the creation of the Local Government Code, with the IRA as one of its main supporting components. The critique of the IRA formula addressed in this study contributes to such a re-evaluation, but suffers from the application of inconsistent criteria. One recommendation that emerges from this study is to be clear about the criteria when evaluating the IRA.
Another recommendation is to use shared growth as criteria for judging the IRA and the larger program of decentralization.
Based on its qualitative definition, the IRA per province for a certain year “ t ” would be expressed by the following formula
Even with clear criteria applied, a re-evaluation of the IRA should also shed light on the causal relationships between the relevant variables, such as that between the IRA and the median income of counties. The author is indebted to the University of the Philippines Los Baños for the Basic Research Grant for conducting the research for this article. De Los Reyes of UPLB's College of Economics and Management; Alberto Javier Iniguez-Montiel of the Graduate School of Economics, University of Tokyo for assisting the author in accessing the database of Kurita and Kurosaki (2011); and the author's research fellowship mentor, Professor Josefina T.
This is evidenced by the first part of option 1, which states that per capita IRA would be negatively related to population through the IRA formula.
Assuming a standard Cobb-Douglas production function, which includes land and population as inputs to production, Output can be
Proof of Possibility 2
Information Related to Case Studies
Higher average annual household income (as well as average annual household expenses) is associated with a higher per capita IRA allocation and a lower incidence of poverty. The difference between the two provinces is that Benguet has consistently higher per capita income but consistently lower IRA allocation. Laguna municipalities, on the other hand, were stable for only two of the four years.
Based on this selection, key informant interviews were generally conducted in both provinces on the following questions: Given the opportunity, what revisions would you like to see in the IRA formula. Can you provide us with the details of your SGLG Scorecard for the last four years.
In the last four years of SGLG, how many times did the municipality of your province receive the most SGLG. What is more important to you, reducing poverty or reducing the Gini coefficient. What is more important to you, growth by reducing poverty or growth by reducing the Gini coefficient.
Exploratory analysis of the correlation between local income and internal revenue allocation: Focus on municipalities that converted to cities from 1994 to 2009. The relationship between IRA and local government expenditures: Evidence from a cross-section of Philippine cities. A Study on the Population Factor in the Internal Revenue Allocation's Allocation and Utilization for Local Philippine Population Management.
HRM Reform in Decentralized Local Government: Empirical Perspectives on Recruitment and Selection in the Philippines and Thailand. A Study on the Improvement of the Internal Revenue Allocation (IRA) System in the Republic of the Philippines. Growth Dynamics, Poverty and Inequality: A Panel Analysis of Regional Data from Thailand and the Philippines.
