92 5 (2012) 92 102 * a b c d 要 旨 Web 2 1) 2) 2012 1 30 2012 6 11 キーワード: JEL Classification Numbers: H55, D89 1. はじめに 23 15,020 29 2017 16,900 1 * a e-mail: masato.shikata@gmail.com b e-mail: Komamura@econ.keio.ac.jp c e-mail: inagaki@ier.hit-u.ac.jp d e-mail: k-tetsu@nii.ac.jp 1 2004 16 6 11 2 16 2005 50 3.4 20 1.7 1.7 2 40% 3 2. 先 行 研 究 および 分 析 課 題 1 2 3 1 (2001) (2005) (2007) 3 2009 21 62.1%
93 (2001) (2003) (2006) 2001 2001 2003 2006 4 (2008) Web (2005) 4 (2007) (2005) 55 Web 1 24% 6 6 6 7 29% 3 1 1 Web 3. 調 査 およびデータの 説 明 3.1. サンプリングフレームおよび 調 査 方 法 2009 2 Web 5 Web 1 (2005) 1 5 RISS (http://www.kansai-u.ac.jp/riss/ shareduse/database.html) 2009 2 10 2009 2 29 1 20 59 9050 30
94 5 (2005) 4 1 1 4525 6 5325 7 1 1 20 24 25 29 30 34 35 39 40 44 45 49 50 54 55 59 12 12 11 10 9 10 13 19 96 21 23 20 18 17 19 27 37 182 74 81 78 74 63 59 77 114 620 34 40 36 34 29 25 29 42 269 34 38 37 34 29 28 36 54 290 47 48 46 43 37 35 44 70 370 21 23 22 19 17 18 26 39 185 35 36 32 28 25 29 41 55 281 9 12 11 10 9 10 15 28 104 14 19 17 15 15 17 26 44 167 53 70 69 64 56 53 77 147 589 24 34 32 29 25 22 28 52 246 26 34 34 31 27 27 38 73 290 36 46 45 42 36 34 47 96 382 16 21 21 18 16 18 26 50 186 25 32 29 25 24 26 39 68 268 481 569 540 494 434 430 589 988 4525
95 3.2. 調 査 の 特 徴 2 20 59 0 20 59 6 6 20 59 6 3 1 1 1. 2. 2 1. 2. 3 1. 2. 7 1 (3) 1 (3) (1) (2) (3) 1 RISS 3.3. データの 特 徴 22% 19 36.1% 8 1 1 20 24 42% 25 39 25% 40 49 20% 50 59 15% 20 24 8 Q1: 1 Q2: Q1 Q3: 1
96 5 2 RISS 3 RISS 24% 7.66 6.6 1 6.7 2 6 7 6 6 7 29% 3 1 10 15 30% 8 15% 10 2011 15,020 2017 16,900 3 9 1 1 5 5 2 10% 9 10 1.5% 4. 年 金 保 険 料 納 未 納 の 分 析 1 0 2 6 6 10 A. B. 10 (2007)
97 11 12 2 (df/dx) 0 1 2 8 2 66,000 8 11 (1998) 1 2 3 4 5 1 2 3 4 5 α 0.875 12 1 1 2 3 4 5 5 5% 7% 2 100 500 100 15% 13 1 4% 5. 国 民 年 金 保 険 料 支 払 い 可 能 額 についての 分 析 2 13 Total 36.5% 26.4% 26.7% 23.0% 19.7% 17.5% 31.9% 23.9%
98 5 2 df/dx (4) df/dx 0.014 (0.005)** 2 0.001 (0.000)*** 0.042 (0.027) 0.044 (0.007)*** 0.050 (0.025)* 0.071 (0.035)* 0.050 (0.015)** 0.072 (0.022)** 0.022 (0.008)** 0.020 (0.010) 0.061 (0.006)*** 0.050 (0.009)*** 0.014 (0.008) 0.023 (0.011)* 2 0.001 (0.001)* 0.003 (0.001)** (1) 100 0.133 (0.015)*** 0.150 (0.022)*** 500 1,000 0.041 (0.016)* 0.023 (0.024) 1,000 0.061 (0.016)*** 0.059 (0.022)** (2) 0.031 (0.015)* 0.005 (0.021) 0.036 (0.016)* 0.016 (0.021) 0.023 (0.030) 0.034 (0.042) 0.002 (0.001)** 0.002 (0.001)* 0.034 (0.012)** 0.033 (0.017) 0.029 (0.015) 0.000 (0.021) 0.034 (0.015)* 0.080 (0.019)*** (3) 0.095 (0.032)** 0.098 (0.047)* 0.036 (0.015)* 0.026 (0.022) 0.065 (0.015)*** 0.045 (0.022)* 0.080 (0.012)*** 0.091 (0.017)*** 0.076 (0.025)** 0.089 (0.031)** N 5325 2683 Log likelihood 2342.059 1170.348 Pseudo R 2 0.1412 0.1443 (1) 100 500 (2) (3) (4) 0 1 ***p 0.001 **p 0.01 *p 0.05 p 0.10 6 6 2 3
99 3 OLS 0.018 (0.007)** 0.015 (0.006)* 0.000 (0.005) 0.018 (0.007)** 0.032 (0.073) 0.173 (0.072)* 0.107 (0.051)* 0.031 (0.072) 0.054 (0.031) 0.165 (0.076)* 0.033 (0.009)*** 0.141 (0.100) 0.001 (0.001) 0.001 (0.001) 0.000 (0.001) 0.000 (0.001) 0.048 (0.057) 0.093 (0.056) 0.023 (0.040) 0.024 (0.040) 0.038 (0.031) 0.019 (0.032) 0.028 (0.022) 0.027 (0.022) 0.131 (0.025)*** 0.156 (0.025)*** 0.144 (0.018)*** 0.144 (0.018)*** 0.019 (0.032) 0.036 (0.032) 0.029 (0.023) 0.028 (0.023) 2 0.001 (0.003) 0.002 (0.003) 0.001 (0.002) 0.001 (0.002) (1) 100 0.116 (0.057)* 0.132 (0.057)* 0.127 (0.041)** 0.126 (0.041)** 500 1,000 0.031 (0.069) 0.032 (0.069) 0.027 (0.048) 0.026 (0.048) 1,000 0.016 (0.067) 0.142 (0.068)* 0.079 (0.047) 0.077 (0.047) (2) 0.028 (0.060) 0.087 (0.060) 0.062 (0.042) 0.060 (0.042) 0.129 (0.060)* 0.022 (0.061) 0.076 (0.043) 0.078 (0.043) 0.066 (0.107) 0.076 (0.115) 0.005 (0.078) 0.003 (0.078) 0.020 (0.050) 0.030 (0.050) 0.008 (0.035) 0.008 (0.035) 0.015 (0.017) 0.020 (0.017) 0.017 (0.012) 0.018 (0.012) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.002 (0.060) 0.032 (0.059) 0.027 (0.042) 0.023 (0.042) 0.028 (0.061) 0.002 (0.059) 0.005 (0.042) 0.007 (0.042) (3) 0.013 (0.124) 0.047 (0.116) 0.041 (0.084) 0.036 (0.084) 0.077 (0.071) 0.142 (0.070)* 0.109 (0.050)* 0.111 (0.050)* 0.125 (0.072) 0.106 (0.076) 0.118 (0.052)* 0.119 (0.052)* 0.084 (0.055) 0.093 (0.055) 0.088 (0.039)* 0.090 (0.039)* 0.028 (0.136) 0.276 (0.147) 0.129 (0.100) 0.130 (0.100) 0.958 (0.373)* 1.300 (0.372)*** 1.152 (0.263)*** 1.054 (0.265)*** N 2683 2642 5325 5325 R 2 0.028 0.036 0.031 0.034 (1) 100 500 (2) (3) ***p 0.001 **p 0.01 *p 0.05 p 0.10
100 5 (Tversky and Kahneman 1974) 14 6. 結 論 と 政 策 インプリケーション 1 66,000 8 14 6.6 6.6 6.6 0.03086.6.1934 0.1% 3 1 3 1 (2005)
101 引 用 文 献 2001 43 134 154 2003 39(3) 268 280 2005 2007 35 2005 17 2007 87 100 118 2007 2001 42 44-60 2006 41(4) 385 395 2005. Vol. 54, No. 4, 421 436 2005 Tversky, A. and D. Kahneman, 1974. Judgment under uncertainty: Heuristics and biases. Science 185, 1124 1131. 2005 16 77 106 2008 44(2) 234 251 1998 2006 57(4) 344 356
102 5 A Web-based Survey on the National Pension Premium Payment: Notice of the Full Amount of Benefits Masato Shikata a, Kohei Komamura b, Seiichi Inagaki c and Tetsuro Kobayashi d Abstract This study is designed to analyze the national pension premiums for contributors subscribing to the national pension program. It has been found that the non-payment rate is high in people who do not know much about the full pensions they may be entitled to receive. In a Web-based questionnaire survey, the respondents were divided into two groups. One group received a notice as to their pension benefits, and the other group did not. The purpose of this experiment is to investigate the difference in payments between the two groups, and the highest possible amount of payments allowable for national pension premiums were also examined in both groups. The results indicate that (1) the notice of pension benefits decreased the potential limit of pension payment for those who expected higher pension benefits, and (2) the notice had a positive effect on raising the potential limit of pension premiums. (Received: January 30, 2012, Accepted: June 11, 2012) Keywords: The national pension, non-payment, an experimental test JEL Classification Numbers: H55, D89 a The Research Institute for Socionetwork Strategies, Kansai University e-mail: masato.shikata@gmail.com b Faculty of Economics, Keio University e-mail: Komamura@econ.keio.ac.jp c Institute of Economic Research, Hitotsubashi University e-mail: inagaki@ier.hit-u.ac.jp d Information and Society Research Division, National Institute of Informatics e-mail: k-tetsu@nii.ac.jp