their relative success in estimating output multipliers. In doing so, we use each formula to regionalize the survey-based national inputoutput table for 1995 (Statistics Finland, 2000). We then estimate the type I output multiplier for each sector in each region. Finally, we compare these LQ-based estimates with the survey-based estimates for each region (ibid.). Our focus on output multipliers is motivated by their importance in regional analysis, along with the fact that many earlier studies have attempted to derive satisfactory estimates of such multipliers. This multiplier analysis will be complemented later in the paper by a detailed consideration of regional input coefficients. We now need to consider possible ways of assessing the accuracy of the estimated sectoral multipliers in a given region. The following measures will be examined here: = (100/n) / mj (16) μ1 Σj = (100/n) (17) μ2 Σj = 100 (18) μ2* = 100 qj / mj (19) μ3 Σj = 100 (20) μ4 = (1/n) || / mj (21) μ5 Σj sd = [(1/n) {( || / mj ) (22) Σj μ5}2]0.5 where is the estimated type I output multiplier for sector (column sum of the LQ-based j Leontief inverse matrix) in a given region, mj is the corresponding survey-based multiplier, qj is the proportion of regional output produced in sector and n = 37 is the number of sectors. j is a statistic that has been used in many earlier studies, thereby facilitating μ1 comparisons. Another useful characteristic of is its ability to measure bias. This is the μ1 main reason why is our preferred measure. Even so, it be argued that the inclusion μ1 of the unitary components (direct effects) in the formula tends to exaggerate the apparent precision of the estimated multipliers, as only the indirect effects need to be estimated.
their relative success in estimating output multipliers. In doing so, we use each formula to regionalize the survey-based national inputoutput table for 1995 (Statistics Finland, 2000). We then estimate the type I output multiplier for each sector in each region. Finally, we compare these LQ-based estimates with the survey-based estimates for each region (ibid.). Our focus on output multipliers is motivated by their importance in regional analysis, along with the fact that many earlier studies have attempted to derive satisfactory estimates of such multipliers. This multiplier analysis will be complemented later in the paper by a detailed consideration of regional input coefficients. We now need to consider possible ways of assessing the accuracy of the estimated sectoral multipliers in a given region. The following measures will be examined here: μ1 = (100/n) Σj / mj (16) μ2 = (100/n) Σj (17) μ2* = 100 (18) μ3 = 100 Σj qj / mj (19) μ4 = 100 (20) μ5 = (1/n) Σj || / mj (21) sd = [(1/n) Σj {( || / mj ) μ5}2]0.5 (22) where is the estimated type I output multiplier for sector j (column sum of the LQ-based Leontief inverse matrix) in a given region, mj is the corresponding survey-based multiplier, qj is the proportion of regional output produced in sector j and n = 37 is the number of sectors. μ1 is a statistic that has been used in many earlier studies, thereby facilitating comparisons. Another useful characteristic of μ1 is its ability to measure bias. This is the main reason why μ1 is our preferred measure. Even so, it be argued that the inclusion of the unitary components (direct effects) in the formula tends to exaggerate the apparent precision of the estimated multipliers, as only the indirect effects need to be estimated.
RegStudies_V6_wpaper.docx