8 research outputs found
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κ³Όνλν λκ²½μ μ¬ννλΆ(μ§μμ 보μ 곡), 2022.2. μ΄μ±μ°.This study aims to propose the 'evaluation framework for mixed policies' by developing the policy attributes taxonomy and evaluation methodology. We explored some specific points of multiple intervention evaluation compared to the evaluation of a few policy involvements. Through empirical analysis, we tried to illustrate the points of budget decision-making considering the multiple policies situation. The empirical fields are the social services for the elderly and the SMEs support policies in South Korea.
The evaluation of the multiple policies has various additional aspects due to the mix, which we can not easily observe in evaluating individual policy cases.
First of all, the evaluation of mixed policies differs from evaluating individual policies in the aspects of baseline setting for a performance. Beyond the aspect of dealing with multiple treatments, the reference point of performance evaluation varies depending on the dimension of the mix. In a mixed situation of policies with different goals of an individual project level or across various sectors or fields, it is necessary to redefine the effect, set the shared goal, and then build an evaluation strategy.
In addition, in mixed policy situation, interactions between policies occur. Complementary relationships or synergies occur depending on whether the direction of this interaction coincides with the direction of each policy's effectiveness. It is an area that cannot be grasped only by the sum of the simple individual policy effects.
Finally, when measuring the performance of joint policies, the size of the total effect increment can be caused both by the increased input size or the mix itself. In order to grasp the performance of the mix itself, an evaluation perspective should base on the assumption of the same input. The effect per project or cost-effectiveness could be an example. Since the comparative alternative must secure mutual independence and exclusivity at this time, it should be a comparison between policy portfolios (=mix types), not between the policies.
According to the peculiarities of these mixed policy evaluations, this study established and exemplified the following three evaluation frameworks necessary for evaluating multiple interventions.
First, it is an analysis framework for the program's taxonomy criteria and project attributes for identifying mixed policies and analyzing policy portfolios. The policy portfolio is used to identify the policies' designs and roles to achieve joint performance, distinguish commonalities and differences between policies, structurize the relation of each policy and establish evaluation strategy based on dimensions of the policy mix.
The criteria for policy taxonomy were composed of five main items: 'policy goal', 'logical relation', 'policy target', 'policy means', and 'delivery system' based on the theoretical background of policy components and characteristics. In addition, 'policy goal' was distinguished into 'macro policy goals' and 'direct objectives of a project', and 'policy means' were subdivided into 'policy outputs' and 'delivery vehicles, which are methods for delivering them.
Following the seven criteria configured in this way, the suitability of the item composition was checked by referring to previous studies, the existing classification system, and international standards and overseas cases. Using the configured "inspection criteria of the appropriate taxonomy" from the literature review, we checked cases using the existing government's policy classification systems and proposed improvement plans.
Second, a methodology development study was conducted to analyze the synergy effect of mixed policies using the logical relation from the policy attribute taxonomy.
The logical evaluation framework of the composition of interaction effects (complementary relationships, synergies) through previous studies was established, and an interpretation method was established according to the definition and effect direction of positive and negative synergies. The analysis method of synergy effect was statistically calculated by utilizing the difference between the sum of individual policy effects of single policy beneficiaries and the integrated effect of multiple policy beneficiaries.
This process of synergy effect analysis was demonstrated in the empirical study of the effect of improving life satisfaction of the multiple elderly welfare service, and implications were derived. As a result of the analysis, benefiting from many policies did not always increase the synergy effect. The analysis, according to the logical relation, confirmed that the synergy effect varies depending on the type of policy mix. It was suggested that a design based on the demand preference of beneficiaries is needed when designing a policy portfolio in the future.
Finally, a mixed policy efficiency-based evaluation framework was established through cost-effectiveness analysis of multiple policies portfolio. In the case of individual policy cost-effectiveness analysis, the evaluation focuses on identifying comparative advantages between alternatives and choosing to make budget decisions. However, the cost-effectiveness analysis of mixed policy provides effectiveness information based on the same input by mixed policy type and compares results of each actual beneficiary mix type. In the situation of multiple policy benefits, it is desirable to compare the performance of each mixed type(=each policy portfolio) to meet the independence and mutual exclusivity, which are prerequisites for economic evaluation alternatives.
The subject of CEA's empirical analysis for mixed policies was companies that receive SMEs policies from multiple fields between a set of SME support projects. We analyzed cost-effectiveness from three perspectives: sales growth, value-added growth, and employment growth. In addition, to illustrate the comparison of effects on the same input premise, implications were derived using visualization data for each mixed policy type from five perspectives: β total effect, β‘ total effect per project, β’ synergy effect, β£ synergy effect per project, and β€ cost-effectiveness. As a result of the analysis, in the case of SME support policies, the mixed policy between program fields was not more efficient than the benefits of some single field.
The contribution of this study is to suggest general guidelines for mixed policy evaluation so that the situation of multiple policy implementation and mixed policy benefits can be managed, evaluated, and reflected in decision-making. This was intended to be visualized and exemplified through policy portfolio analysis using the establishment of a policy attributes taxonomy, synergy effect analysis due to policy interaction, and cost-effectiveness analysis between policy mixed types.νλ 곡곡 μ μ±
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This study aims to propose the 'evaluation framework for mixed policies' by developing the policy attributes taxonomy and evaluation methodology. We explored some specific points of multiple intervention evaluation compared to the evaluation of a few policy involvements. Through empirical analysis, we tried to illustrate the points of budget decision-making considering the multiple policies situation. The empirical fields are the social services for the elderly and the SMEs support policies in South Korea.
The evaluation of the multiple policies has various additional aspects due to the mix, which we can not easily observe in evaluating individual policy cases.
First of all, the evaluation of mixed policies differs from evaluating individual policies in the aspects of baseline setting for a performance. Beyond the aspect of dealing with multiple treatments, the reference point of performance evaluation varies depending on the dimension of the mix. In a mixed situation of policies with different goals of an individual project level or across various sectors or fields, it is necessary to redefine the effect, set the shared goal, and then build an evaluation strategy.
In addition, in mixed policy situation, interactions between policies occur. Complementary relationships or synergies occur depending on whether the direction of this interaction coincides with the direction of each policy's effectiveness. It is an area that cannot be grasped only by the sum of the simple individual policy effects.
Finally, when measuring the performance of joint policies, the size of the total effect increment can be caused both by the increased input size or the mix itself. In order to grasp the performance of the mix itself, an evaluation perspective should base on the assumption of the same input. The effect per project or cost-effectiveness could be an example. Since the comparative alternative must secure mutual independence and exclusivity at this time, it should be a comparison between policy portfolios (=mix types), not between the policies.
According to the peculiarities of these mixed policy evaluations, this study established and exemplified the following three evaluation frameworks necessary for evaluating multiple interventions.
First, it is an analysis framework for the program's taxonomy criteria and project attributes for identifying mixed policies and analyzing policy portfolios. The policy portfolio is used to identify the policies' designs and roles to achieve joint performance, distinguish commonalities and differences between policies, structurize the relation of each policy and establish evaluation strategy based on dimensions of the policy mix.
The criteria for policy taxonomy were composed of five main items: 'policy goal', 'logical relation', 'policy target', 'policy means', and 'delivery system' based on the theoretical background of policy components and characteristics. In addition, 'policy goal' was distinguished into 'macro policy goals' and 'direct objectives of a project', and 'policy means' were subdivided into 'policy outputs' and 'delivery vehicles, which are methods for delivering them.
Following the seven criteria configured in this way, the suitability of the item composition was checked by referring to previous studies, the existing classification system, and international standards and overseas cases. Using the configured "inspection criteria of the appropriate taxonomy" from the literature review, we checked cases using the existing government's policy classification systems and proposed improvement plans.
Second, a methodology development study was conducted to analyze the synergy effect of mixed policies using the logical relation from the policy attribute taxonomy.
The logical evaluation framework of the composition of interaction effects (complementary relationships, synergies) through previous studies was established, and an interpretation method was established according to the definition and effect direction of positive and negative synergies. The analysis method of synergy effect was statistically calculated by utilizing the difference between the sum of individual policy effects of single policy beneficiaries and the integrated effect of multiple policy beneficiaries.
This process of synergy effect analysis was demonstrated in the empirical study of the effect of improving life satisfaction of the multiple elderly welfare service, and implications were derived. As a result of the analysis, benefiting from many policies did not always increase the synergy effect. The analysis, according to the logical relation, confirmed that the synergy effect varies depending on the type of policy mix. It was suggested that a design based on the demand preference of beneficiaries is needed when designing a policy portfolio in the future.
Finally, a mixed policy efficiency-based evaluation framework was established through cost-effectiveness analysis of multiple policies portfolio. In the case of individual policy cost-effectiveness analysis, the evaluation focuses on identifying comparative advantages between alternatives and choosing to make budget decisions. However, the cost-effectiveness analysis of mixed policy provides effectiveness information based on the same input by mixed policy type and compares results of each actual beneficiary mix type. In the situation of multiple policy benefits, it is desirable to compare the performance of each mixed type(=each policy portfolio) to meet the independence and mutual exclusivity, which are prerequisites for economic evaluation alternatives.
The subject of CEA's empirical analysis for mixed policies was companies that receive SMEs policies from multiple fields between a set of SME support projects. We analyzed cost-effectiveness from three perspectives: sales growth, value-added growth, and employment growth. In addition, to illustrate the comparison of effects on the same input premise, implications were derived using visualization data for each mixed policy type from five perspectives: β total effect, β‘ total effect per project, β’ synergy effect, β£ synergy effect per project, and β€ cost-effectiveness. As a result of the analysis, in the case of SME support policies, the mixed policy between program fields was not more efficient than the benefits of some single field.
The contribution of this study is to suggest general guidelines for mixed policy evaluation so that the situation of multiple policy implementation and mixed policy benefits can be managed, evaluated, and reflected in decision-making. This was intended to be visualized and exemplified through policy portfolio analysis using the establishment of a policy attributes taxonomy, synergy effect analysis due to policy interaction, and cost-effectiveness analysis between policy mixed types.νλ 곡곡 μ μ±
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3. 볡ν©μ μ±
ν¨κ³Ό λΆμμ λ°©λ²λ‘ 41
μ 4μ₯ μ μ±
λ° μ¬μ
μμ± λΆλ₯μ²΄κ³ μ°κ΅¬ 47
μ 1μ μ μ±
λΆλ₯체κ³μ μ΄λ‘ μ κ·Όκ±° 49
1. μ μ±
λΆλ₯체κ³μ μ μ λ° μ€μμ± 49
2. μ μ±
λΆλ₯체κ³μ μ€κ±°κΈ°μ€ 57
3. λΆλ₯μ²΄κ³ κ΅¬μ± νλΉμ± κ²μ¦κΈ°μ€ 60
μ 2μ μ°κ΅¬λ°©λ²λ‘ 62
1. μ μ±
λ° μ¬μ
μμ± λΆλ₯μ²΄κ³ κ°λ° 62
2. λΆλ₯μ²΄κ³ κ΅¬μ± νλΉμ± κ²μ¦κΈ°μ€ μ립 64
3. μ¬λ‘λΆμ λ° κ·λ©μ λΆλ₯, μ€μ¦ 64
μ 3μ νν λΆλ₯μ²΄κ³ λ° μ νμ°κ΅¬, ν΄μΈμ¬λ‘ λΆμ 66
1. νν μ 보μμ€ν
μμ λΆλ₯μ²΄κ³ νν© 66
2. μ μ±
λ° μ¬μ
λΆλ₯μ²΄κ³ κ΄λ ¨ μ νμ°κ΅¬ 71
3. ν΄μΈ μ μ±
λ° μ¬μ
μμ±λΆλ₯ νν© 80
4. μ νμ°κ΅¬ λ° ν΄μΈμ¬λ‘ μμ¬μ μ’
ν© 85
μ 4μ μ μ±
λΆλ₯체κ³μ νλͺ© μ€μ 87
1. μ μ±
λͺ©νμ λ°λ₯Έ μ μ±
μ λΆλ₯ 88
2. μ§μ λ
Όλ¦¬(logical relation) 96
3. μ μ±
λμμ λ°λ₯Έ λΆλ₯ 97
4. μ μ±
μλ¨μ λ°λ₯Έ λΆλ₯ 98
5. μ λ¬μ²΄κ³(delivery system) 101
μ 5μ λΆλ₯μ²΄κ³ κ·λ©μ μ κ² λ° μ€μ¦λΆμ λμ μ¬μ
λΆλ₯ 105
μ 6μ μ κ²° 111
μ 5μ₯ 볡ν©μ μ±
μ μλμ§ ν¨κ³Ό λΆμ 115
μ 1μ μ΄λ‘ μ λ°°κ²½ λ° μ νμ°κ΅¬ κ³ μ°° 118
1. μ΄λ‘ μ λ°°κ²½ 118
2. 볡ν©μ μ±
λΆμ μ νμ°κ΅¬ 122
3. κ°λ³ λ
ΈμΈλ³΅μ§μλΉμ€ μ¬μ
μ€κ³ λ° μ νμ°κ΅¬μ ν¨κ³Ό λΆμ 124
μ 2μ μ°κ΅¬ μ€κ³ 127
1. μ°κ΅¬ μλ£μ κ°μ€ 127
2. μ°κ΅¬ λͺ¨ν 129
3. λ³μμ μ‘°μμ μ μμ κΈ°μ ν΅κ³ 133
μ 3μ λΆμ κ²°κ³Ό 137
1. μνμ μ±
μ λ°λ₯Έ ν¨κ³Ό: κ°λ³μ μ±
, 볡μμ μ±
133
2. μνμ μ±
μ‘°ν©μ λ°λ₯Έ ν¨κ³Ό: 볡ν©μ μ±
μλμ§ λΆμ 140
μ 4μ μκ²° λ° μμ¬μ 144
μ 6μ₯ 볡ν©μ μ±
μ λΉμ©-ν¨κ³Ό λΆμμ°κ΅¬ 153
μ 1μ μ λ‘ 153
μ 2μ μ΄λ‘ μ λ°°κ²½ λ° μ νμ°κ΅¬ λΆμ 154
1. μ΄λ‘ μ λ°°κ²½ 154
2. μ€μκΈ°μ
μ μ±
μ λ³΅ν© κ΄λ ¨ μ νμ°κ΅¬ 157
μ 3μ μ°κ΅¬λ°©λ²λ‘ 162
1. μ°κ΅¬λ°μ΄ν°μ κ°μ€ 162
2. λΆμλͺ¨ν 164
3. λ
Όλ¦¬μ μλμ§ν¨κ³Ό λΆμ λ°©λ² 165
μ 4μ λΆμ κ²°κ³Ό 167
1. ν¨κ³Ό κ΄μ λ³ ν΄μ 167
2. μ’
μλ³μλ³ ν΄μ 174
μ 5μ κ²°λ‘ λ° μμ¬μ 183
1. μ°κ΅¬λ΄μ© μμ½ 184
2. μ°κ΅¬μ νκ³ 184
3. μ°κ΅¬μ μμ λ° μμ¬μ 185
4. λΆμκ²°κ³Όμ νμ© 186
μ 7μ₯ κ²°λ‘ λ° ν₯ν μ μ±
κ³Όμ 191
μ 1μ κ²°κ³Όμ μμ½ λ° μμ 191
μ 2μ μ°κ΅¬κ²°κ³Όμ νμ© 194
μ 3μ μ°κ΅¬μ νκ³ 197
μ 4μ ν₯ν μ μ±
κ³Όμ 198
μ°Έκ³ λ¬Έν 200λ°