This study investigates the impact of Sitio Electrification Program (SEP) on the socio-economic status of selected households in Matalom, Leyte. The SEP was implemented in 2011 with the aim of boosting the socio-economic development of many powerless villages all over the Philippines. The impact of SEP was quantified using pooled Ordinary Least Squares (OLS) regression applying the difference-in-difference estimation technique. The variables of interest include income, study hours and household assets. Results show that the SEP has no significant influence with income. However, positive relationships are found with the asset index of the households and study hours of the children. These imply that availability of electricity translates to accumulation of more household assets and increases the number hours of children spent studying at night. Although the effect on income is not significant, estimation results show robust increase in household assets. Estimation from the pooled OLS, on average, the study hours have increased by around 14 minutes per day and asset index by more than 2 per household. This research also finds out that the “willingness to connect” among households remains an issue in spite of the subsidies on initial household electrification expenses provided by the program. This issue can limit the positive effects that can be brought by the program. One potential approach to address this issue is through affordable amortization packages given to selected beneficiaries.

Information and studies about carbon emissions are available on national level but it is very limited on regional level. Hence, we focus on Eastern Visayas and aim to investigate the influence of rising income and other socio-demographic characteristics on household’s carbon emission level. The analyses reveal that income has a significant nonlinear relationship with carbon emission depicting a turning point. However, the turning point is very high implying that household carbon emissions in Eastern Visayas will increase given the current income range. In addition, other household characteristics such as age, educational attainment, civil status, floor area, access to electricity and urbanity significantly influence household carbon emissions. Basing from the results, income and other socio-demographic characteristics are significant determinants of household emissions. The strong and significant association of income and emissions indicates that we will be further expecting increase in emissions as households become richer. Implementation of policies regarding mitigating climate change should start at the household level since results show that households has a strong influence on the increasing carbon emission.

This study investigates the determinants of productivity in selected upland and lowland rice farms in Matalom, Leyte using quantile regression approach. Data on rice production are obtained from 40 upland and 40 lowland rice farming households which are randomly selected across all 30 barangays in Matalom, Leyte. Quantile regression analysis is used to provide complete characterization of the determinants of productivity at the higher and lower tails of the distribution. Results show that the factors affecting rice production differ across distribution. In the lower quantile, labor cost and fertilizer cost are the main determinants of rice production while in the median quantile, labor cost, fertilizer cost and farm area positively affect rice production. Moreover, in the upper quantile there are more determinants positively affecting rice production. These include labor cost, fertilizer cost, farm area, household size, male household head, and technical assistance provided to farmers. In addition, results of quantile regression are compared with ordinary least squares (OLS) estimation. The comparative analysis shows that there are some factors which do not have significant coefficient in the OLS estimation but are found to have significant effects with quantile regression. This shows that the coefficients estimated through OLS only provide a partial view of the determinants of rice productivity. By performing quantile regression, we are able to identify significant determinants of productivity which cannot be detected using the mean based regression approach.

This study investigates the influence of relevant macroeconomic variables on unemployment in the Philippines. Specifically, it aims to: find a coefficient that would characterize the relationship of the between unemployment and GDP using a standard cointegration approach; investigate relevant macroeconomic factors that may affect unemployment; and draw economic and policy implications that will guide policymakers in crafting public policies in improving employment generation. It used quarterly time series data from the first quarter of 1989 to fourth quarter of 2004. Cointegration test was used to determine the long-run relationship while an error correction was employed to determine the short-run behavior of the data. Results of the study reveals that in the long-run a 1% increase in GDP is associated with a reduction in unemployment by 0.7%. While in the short-run, GDP has a larger effect on cutting unemployment; an increase in GDP by 1% results to a decrease in unemployment by 0.95%. Thus there is evidence to indicate that Okun’s law is relevant in the Philippine economy. The sectoral analysis shows that the economy’s industrial and agriculture sectors are found to have a negative effect on the general unemployment level. However, the service sector on the other has a positive effect, implying that as its output grows, unemployment tends to rise as well. This is probably due to job and skills mismatch in the service sector which leads to structural unemployment. It is recommended that the industry sector be prioritized especially the manufacturing and construction subsectors, in the development planning process.

This study investigates the effectiveness of public spending on agriculture and education in analyzing its impact on the quality of life in the Philippines using the methods of seemingly unrelated regression. We used the available macroeconomic data with life expectancy as proxy variable to measure quality of life. Results show that public expenditure on education showed contrasting result on its effect on total factor productivity and literacy rate. Public expenditure on education showed negative impact on total factor productivity while literacy rate improves as public expenditure on education increases. This suggests that expenditure on education has more profound effect in improving quality of life in the Philippines as manifested by the positive and highly significant estimate. Results imply that with proper education, the farmers could easily adopt to new agricultural technologies, which can contribute to enhancing productivity thereby, potentially leading to the overall improvement of quality of life in the Philippines.