IVOD_ID |
162231 |
IVOD_URL |
https://ivod.ly.gov.tw/Play/Clip/1M/162231 |
日期 |
2025-06-04 |
會議資料.會議代碼 |
委員會-11-3-26-15 |
會議資料.會議代碼:str |
第11屆第3會期社會福利及衛生環境委員會第15次全體委員會議 |
會議資料.屆 |
11 |
會議資料.會期 |
3 |
會議資料.會次 |
15 |
會議資料.種類 |
委員會 |
會議資料.委員會代碼[0] |
26 |
會議資料.委員會代碼:str[0] |
社會福利及衛生環境委員會 |
會議資料.標題 |
第11屆第3會期社會福利及衛生環境委員會第15次全體委員會議 |
影片種類 |
Clip |
開始時間 |
2025-06-04T11:53:07+08:00 |
結束時間 |
2025-06-04T12:13:02+08:00 |
影片長度 |
00:19:55 |
支援功能[0] |
ai-transcript |
video_url |
https://ivod-lyvod.cdn.hinet.net/vod_1/_definst_/mp4:1MClips/dfdeed74d30b98286ee55292c7545cdf69516374867620cee4d45e0249ca5227e3a4a211d9ab1f3d5ea18f28b6918d91.mp4/playlist.m3u8 |
委員名稱 |
林淑芬 |
委員發言時間 |
11:53:07 - 12:13:02 |
會議時間 |
2025-06-04T09:00:00+08:00 |
會議名稱 |
立法院第11屆第3會期社會福利及衛生環境委員會第15次全體委員會議(事由:邀請勞動部部長就「勞工退休金制度改革,含勞保、勞退執行現況」進行專題報告,並備質詢。) |
transcript.pyannote[0].speaker |
SPEAKER_01 |
transcript.pyannote[0].start |
0.03096875 |
transcript.pyannote[0].end |
0.04784375 |
transcript.pyannote[1].speaker |
SPEAKER_01 |
transcript.pyannote[1].start |
1.17846875 |
transcript.pyannote[1].end |
3.03471875 |
transcript.pyannote[2].speaker |
SPEAKER_01 |
transcript.pyannote[2].start |
7.32096875 |
transcript.pyannote[2].end |
10.71284375 |
transcript.pyannote[3].speaker |
SPEAKER_02 |
transcript.pyannote[3].start |
15.75846875 |
transcript.pyannote[3].end |
16.34909375 |
transcript.pyannote[4].speaker |
SPEAKER_01 |
transcript.pyannote[4].start |
16.56846875 |
transcript.pyannote[4].end |
21.36096875 |
transcript.pyannote[5].speaker |
SPEAKER_01 |
transcript.pyannote[5].start |
21.56346875 |
transcript.pyannote[5].end |
26.27159375 |
transcript.pyannote[6].speaker |
SPEAKER_01 |
transcript.pyannote[6].start |
26.87909375 |
transcript.pyannote[6].end |
55.24596875 |
transcript.pyannote[7].speaker |
SPEAKER_01 |
transcript.pyannote[7].start |
55.83659375 |
transcript.pyannote[7].end |
56.30909375 |
transcript.pyannote[8].speaker |
SPEAKER_02 |
transcript.pyannote[8].start |
57.59159375 |
transcript.pyannote[8].end |
58.43534375 |
transcript.pyannote[9].speaker |
SPEAKER_01 |
transcript.pyannote[9].start |
58.72221875 |
transcript.pyannote[9].end |
59.36346875 |
transcript.pyannote[10].speaker |
SPEAKER_01 |
transcript.pyannote[10].start |
59.59971875 |
transcript.pyannote[10].end |
59.80221875 |
transcript.pyannote[11].speaker |
SPEAKER_02 |
transcript.pyannote[11].start |
59.80221875 |
transcript.pyannote[11].end |
60.61221875 |
transcript.pyannote[12].speaker |
SPEAKER_01 |
transcript.pyannote[12].start |
59.93721875 |
transcript.pyannote[12].end |
61.21971875 |
transcript.pyannote[13].speaker |
SPEAKER_01 |
transcript.pyannote[13].start |
61.79346875 |
transcript.pyannote[13].end |
63.22784375 |
transcript.pyannote[14].speaker |
SPEAKER_01 |
transcript.pyannote[14].start |
63.48096875 |
transcript.pyannote[14].end |
68.30721875 |
transcript.pyannote[15].speaker |
SPEAKER_01 |
transcript.pyannote[15].start |
71.63159375 |
transcript.pyannote[15].end |
75.69846875 |
transcript.pyannote[16].speaker |
SPEAKER_01 |
transcript.pyannote[16].start |
76.40721875 |
transcript.pyannote[16].end |
79.17471875 |
transcript.pyannote[17].speaker |
SPEAKER_01 |
transcript.pyannote[17].start |
79.63034375 |
transcript.pyannote[17].end |
81.26721875 |
transcript.pyannote[18].speaker |
SPEAKER_01 |
transcript.pyannote[18].start |
82.95471875 |
transcript.pyannote[18].end |
94.68284375 |
transcript.pyannote[19].speaker |
SPEAKER_02 |
transcript.pyannote[19].start |
88.79346875 |
transcript.pyannote[19].end |
89.58659375 |
transcript.pyannote[20].speaker |
SPEAKER_02 |
transcript.pyannote[20].start |
95.25659375 |
transcript.pyannote[20].end |
95.59409375 |
transcript.pyannote[21].speaker |
SPEAKER_01 |
transcript.pyannote[21].start |
96.99471875 |
transcript.pyannote[21].end |
101.12909375 |
transcript.pyannote[22].speaker |
SPEAKER_01 |
transcript.pyannote[22].start |
101.46659375 |
transcript.pyannote[22].end |
107.72721875 |
transcript.pyannote[23].speaker |
SPEAKER_01 |
transcript.pyannote[23].start |
108.48659375 |
transcript.pyannote[23].end |
114.40971875 |
transcript.pyannote[24].speaker |
SPEAKER_01 |
transcript.pyannote[24].start |
114.83159375 |
transcript.pyannote[24].end |
158.60534375 |
transcript.pyannote[25].speaker |
SPEAKER_01 |
transcript.pyannote[25].start |
159.95534375 |
transcript.pyannote[25].end |
160.66409375 |
transcript.pyannote[26].speaker |
SPEAKER_01 |
transcript.pyannote[26].start |
160.91721875 |
transcript.pyannote[26].end |
166.03034375 |
transcript.pyannote[27].speaker |
SPEAKER_01 |
transcript.pyannote[27].start |
166.87409375 |
transcript.pyannote[27].end |
167.92034375 |
transcript.pyannote[28].speaker |
SPEAKER_01 |
transcript.pyannote[28].start |
169.10159375 |
transcript.pyannote[28].end |
170.72159375 |
transcript.pyannote[29].speaker |
SPEAKER_01 |
transcript.pyannote[29].start |
171.95346875 |
transcript.pyannote[29].end |
173.06721875 |
transcript.pyannote[30].speaker |
SPEAKER_01 |
transcript.pyannote[30].start |
173.62409375 |
transcript.pyannote[30].end |
176.79659375 |
transcript.pyannote[31].speaker |
SPEAKER_01 |
transcript.pyannote[31].start |
178.31534375 |
transcript.pyannote[31].end |
179.07471875 |
transcript.pyannote[32].speaker |
SPEAKER_01 |
transcript.pyannote[32].start |
180.17159375 |
transcript.pyannote[32].end |
180.62721875 |
transcript.pyannote[33].speaker |
SPEAKER_01 |
transcript.pyannote[33].start |
182.36534375 |
transcript.pyannote[33].end |
187.41096875 |
transcript.pyannote[34].speaker |
SPEAKER_01 |
transcript.pyannote[34].start |
187.52909375 |
transcript.pyannote[34].end |
189.70596875 |
transcript.pyannote[35].speaker |
SPEAKER_01 |
transcript.pyannote[35].start |
190.51596875 |
transcript.pyannote[35].end |
190.90409375 |
transcript.pyannote[36].speaker |
SPEAKER_01 |
transcript.pyannote[36].start |
191.08971875 |
transcript.pyannote[36].end |
199.91534375 |
transcript.pyannote[37].speaker |
SPEAKER_01 |
transcript.pyannote[37].start |
200.52284375 |
transcript.pyannote[37].end |
203.15534375 |
transcript.pyannote[38].speaker |
SPEAKER_01 |
transcript.pyannote[38].start |
203.93159375 |
transcript.pyannote[38].end |
205.29846875 |
transcript.pyannote[39].speaker |
SPEAKER_01 |
transcript.pyannote[39].start |
205.63596875 |
transcript.pyannote[39].end |
207.52596875 |
transcript.pyannote[40].speaker |
SPEAKER_01 |
transcript.pyannote[40].start |
208.03221875 |
transcript.pyannote[40].end |
214.00596875 |
transcript.pyannote[41].speaker |
SPEAKER_01 |
transcript.pyannote[41].start |
214.20846875 |
transcript.pyannote[41].end |
223.82721875 |
transcript.pyannote[42].speaker |
SPEAKER_01 |
transcript.pyannote[42].start |
224.09721875 |
transcript.pyannote[42].end |
228.31596875 |
transcript.pyannote[43].speaker |
SPEAKER_01 |
transcript.pyannote[43].start |
229.05846875 |
transcript.pyannote[43].end |
230.39159375 |
transcript.pyannote[44].speaker |
SPEAKER_01 |
transcript.pyannote[44].start |
230.86409375 |
transcript.pyannote[44].end |
232.77096875 |
transcript.pyannote[45].speaker |
SPEAKER_01 |
transcript.pyannote[45].start |
233.42909375 |
transcript.pyannote[45].end |
235.20096875 |
transcript.pyannote[46].speaker |
SPEAKER_01 |
transcript.pyannote[46].start |
236.14596875 |
transcript.pyannote[46].end |
237.36096875 |
transcript.pyannote[47].speaker |
SPEAKER_01 |
transcript.pyannote[47].start |
238.08659375 |
transcript.pyannote[47].end |
238.76159375 |
transcript.pyannote[48].speaker |
SPEAKER_02 |
transcript.pyannote[48].start |
238.12034375 |
transcript.pyannote[48].end |
238.77846875 |
transcript.pyannote[49].speaker |
SPEAKER_02 |
transcript.pyannote[49].start |
239.16659375 |
transcript.pyannote[49].end |
239.79096875 |
transcript.pyannote[50].speaker |
SPEAKER_01 |
transcript.pyannote[50].start |
240.11159375 |
transcript.pyannote[50].end |
241.34346875 |
transcript.pyannote[51].speaker |
SPEAKER_02 |
transcript.pyannote[51].start |
241.02284375 |
transcript.pyannote[51].end |
241.66409375 |
transcript.pyannote[52].speaker |
SPEAKER_01 |
transcript.pyannote[52].start |
241.91721875 |
transcript.pyannote[52].end |
243.87471875 |
transcript.pyannote[53].speaker |
SPEAKER_02 |
transcript.pyannote[53].start |
243.87471875 |
transcript.pyannote[53].end |
243.97596875 |
transcript.pyannote[54].speaker |
SPEAKER_01 |
transcript.pyannote[54].start |
243.97596875 |
transcript.pyannote[54].end |
244.12784375 |
transcript.pyannote[55].speaker |
SPEAKER_01 |
transcript.pyannote[55].start |
244.97159375 |
transcript.pyannote[55].end |
246.00096875 |
transcript.pyannote[56].speaker |
SPEAKER_01 |
transcript.pyannote[56].start |
246.57471875 |
transcript.pyannote[56].end |
250.06784375 |
transcript.pyannote[57].speaker |
SPEAKER_01 |
transcript.pyannote[57].start |
250.27034375 |
transcript.pyannote[57].end |
259.26471875 |
transcript.pyannote[58].speaker |
SPEAKER_01 |
transcript.pyannote[58].start |
259.46721875 |
transcript.pyannote[58].end |
262.70721875 |
transcript.pyannote[59].speaker |
SPEAKER_01 |
transcript.pyannote[59].start |
264.05721875 |
transcript.pyannote[59].end |
270.99284375 |
transcript.pyannote[60].speaker |
SPEAKER_01 |
transcript.pyannote[60].start |
271.48221875 |
transcript.pyannote[60].end |
274.48596875 |
transcript.pyannote[61].speaker |
SPEAKER_01 |
transcript.pyannote[61].start |
275.36346875 |
transcript.pyannote[61].end |
277.55721875 |
transcript.pyannote[62].speaker |
SPEAKER_01 |
transcript.pyannote[62].start |
278.06346875 |
transcript.pyannote[62].end |
278.95784375 |
transcript.pyannote[63].speaker |
SPEAKER_01 |
transcript.pyannote[63].start |
279.98721875 |
transcript.pyannote[63].end |
291.93471875 |
transcript.pyannote[64].speaker |
SPEAKER_01 |
transcript.pyannote[64].start |
292.74471875 |
transcript.pyannote[64].end |
300.62534375 |
transcript.pyannote[65].speaker |
SPEAKER_01 |
transcript.pyannote[65].start |
301.95846875 |
transcript.pyannote[65].end |
303.07221875 |
transcript.pyannote[66].speaker |
SPEAKER_01 |
transcript.pyannote[66].start |
303.27471875 |
transcript.pyannote[66].end |
311.30721875 |
transcript.pyannote[67].speaker |
SPEAKER_01 |
transcript.pyannote[67].start |
311.34096875 |
transcript.pyannote[67].end |
313.26471875 |
transcript.pyannote[68].speaker |
SPEAKER_01 |
transcript.pyannote[68].start |
313.68659375 |
transcript.pyannote[68].end |
318.12471875 |
transcript.pyannote[69].speaker |
SPEAKER_01 |
transcript.pyannote[69].start |
319.12034375 |
transcript.pyannote[69].end |
322.27596875 |
transcript.pyannote[70].speaker |
SPEAKER_01 |
transcript.pyannote[70].start |
322.57971875 |
transcript.pyannote[70].end |
325.54971875 |
transcript.pyannote[71].speaker |
SPEAKER_01 |
transcript.pyannote[71].start |
325.98846875 |
transcript.pyannote[71].end |
336.87284375 |
transcript.pyannote[72].speaker |
SPEAKER_01 |
transcript.pyannote[72].start |
337.24409375 |
transcript.pyannote[72].end |
355.16534375 |
transcript.pyannote[73].speaker |
SPEAKER_01 |
transcript.pyannote[73].start |
356.46471875 |
transcript.pyannote[73].end |
357.15659375 |
transcript.pyannote[74].speaker |
SPEAKER_01 |
transcript.pyannote[74].start |
357.44346875 |
transcript.pyannote[74].end |
359.72159375 |
transcript.pyannote[75].speaker |
SPEAKER_01 |
transcript.pyannote[75].start |
360.05909375 |
transcript.pyannote[75].end |
363.80534375 |
transcript.pyannote[76].speaker |
SPEAKER_01 |
transcript.pyannote[76].start |
364.48034375 |
transcript.pyannote[76].end |
366.74159375 |
transcript.pyannote[77].speaker |
SPEAKER_01 |
transcript.pyannote[77].start |
368.69909375 |
transcript.pyannote[77].end |
382.18221875 |
transcript.pyannote[78].speaker |
SPEAKER_01 |
transcript.pyannote[78].start |
382.90784375 |
transcript.pyannote[78].end |
386.40096875 |
transcript.pyannote[79].speaker |
SPEAKER_01 |
transcript.pyannote[79].start |
386.68784375 |
transcript.pyannote[79].end |
387.91971875 |
transcript.pyannote[80].speaker |
SPEAKER_01 |
transcript.pyannote[80].start |
388.57784375 |
transcript.pyannote[80].end |
389.15159375 |
transcript.pyannote[81].speaker |
SPEAKER_01 |
transcript.pyannote[81].start |
390.36659375 |
transcript.pyannote[81].end |
400.66034375 |
transcript.pyannote[82].speaker |
SPEAKER_01 |
transcript.pyannote[82].start |
401.95971875 |
transcript.pyannote[82].end |
412.47284375 |
transcript.pyannote[83].speaker |
SPEAKER_01 |
transcript.pyannote[83].start |
413.29971875 |
transcript.pyannote[83].end |
429.53346875 |
transcript.pyannote[84].speaker |
SPEAKER_01 |
transcript.pyannote[84].start |
429.85409375 |
transcript.pyannote[84].end |
438.93284375 |
transcript.pyannote[85].speaker |
SPEAKER_01 |
transcript.pyannote[85].start |
439.43909375 |
transcript.pyannote[85].end |
441.63284375 |
transcript.pyannote[86].speaker |
SPEAKER_01 |
transcript.pyannote[86].start |
442.51034375 |
transcript.pyannote[86].end |
444.36659375 |
transcript.pyannote[87].speaker |
SPEAKER_01 |
transcript.pyannote[87].start |
445.26096875 |
transcript.pyannote[87].end |
448.93971875 |
transcript.pyannote[88].speaker |
SPEAKER_01 |
transcript.pyannote[88].start |
449.29409375 |
transcript.pyannote[88].end |
452.83784375 |
transcript.pyannote[89].speaker |
SPEAKER_01 |
transcript.pyannote[89].start |
453.19221875 |
transcript.pyannote[89].end |
463.21596875 |
transcript.pyannote[90].speaker |
SPEAKER_01 |
transcript.pyannote[90].start |
463.50284375 |
transcript.pyannote[90].end |
465.25784375 |
transcript.pyannote[91].speaker |
SPEAKER_01 |
transcript.pyannote[91].start |
465.79784375 |
transcript.pyannote[91].end |
470.25284375 |
transcript.pyannote[92].speaker |
SPEAKER_01 |
transcript.pyannote[92].start |
471.68721875 |
transcript.pyannote[92].end |
481.13721875 |
transcript.pyannote[93].speaker |
SPEAKER_01 |
transcript.pyannote[93].start |
481.69409375 |
transcript.pyannote[93].end |
488.03909375 |
transcript.pyannote[94].speaker |
SPEAKER_01 |
transcript.pyannote[94].start |
488.76471875 |
transcript.pyannote[94].end |
500.81346875 |
transcript.pyannote[95].speaker |
SPEAKER_01 |
transcript.pyannote[95].start |
501.06659375 |
transcript.pyannote[95].end |
512.23784375 |
transcript.pyannote[96].speaker |
SPEAKER_01 |
transcript.pyannote[96].start |
513.33471875 |
transcript.pyannote[96].end |
516.22034375 |
transcript.pyannote[97].speaker |
SPEAKER_01 |
transcript.pyannote[97].start |
516.96284375 |
transcript.pyannote[97].end |
522.14346875 |
transcript.pyannote[98].speaker |
SPEAKER_01 |
transcript.pyannote[98].start |
522.63284375 |
transcript.pyannote[98].end |
523.39221875 |
transcript.pyannote[99].speaker |
SPEAKER_01 |
transcript.pyannote[99].start |
523.76346875 |
transcript.pyannote[99].end |
527.29034375 |
transcript.pyannote[100].speaker |
SPEAKER_01 |
transcript.pyannote[100].start |
527.35784375 |
transcript.pyannote[100].end |
542.69721875 |
transcript.pyannote[101].speaker |
SPEAKER_00 |
transcript.pyannote[101].start |
547.40534375 |
transcript.pyannote[101].end |
547.45596875 |
transcript.pyannote[102].speaker |
SPEAKER_01 |
transcript.pyannote[102].start |
547.45596875 |
transcript.pyannote[102].end |
547.48971875 |
transcript.pyannote[103].speaker |
SPEAKER_00 |
transcript.pyannote[103].start |
547.48971875 |
transcript.pyannote[103].end |
548.16471875 |
transcript.pyannote[104].speaker |
SPEAKER_00 |
transcript.pyannote[104].start |
548.78909375 |
transcript.pyannote[104].end |
556.97346875 |
transcript.pyannote[105].speaker |
SPEAKER_01 |
transcript.pyannote[105].start |
555.30284375 |
transcript.pyannote[105].end |
574.59096875 |
transcript.pyannote[106].speaker |
SPEAKER_00 |
transcript.pyannote[106].start |
563.16659375 |
transcript.pyannote[106].end |
563.33534375 |
transcript.pyannote[107].speaker |
SPEAKER_00 |
transcript.pyannote[107].start |
575.11409375 |
transcript.pyannote[107].end |
578.06721875 |
transcript.pyannote[108].speaker |
SPEAKER_00 |
transcript.pyannote[108].start |
578.35409375 |
transcript.pyannote[108].end |
583.82159375 |
transcript.pyannote[109].speaker |
SPEAKER_01 |
transcript.pyannote[109].start |
584.39534375 |
transcript.pyannote[109].end |
587.65221875 |
transcript.pyannote[110].speaker |
SPEAKER_00 |
transcript.pyannote[110].start |
584.44596875 |
transcript.pyannote[110].end |
585.54284375 |
transcript.pyannote[111].speaker |
SPEAKER_01 |
transcript.pyannote[111].start |
588.44534375 |
transcript.pyannote[111].end |
589.49159375 |
transcript.pyannote[112].speaker |
SPEAKER_02 |
transcript.pyannote[112].start |
592.24221875 |
transcript.pyannote[112].end |
594.45284375 |
transcript.pyannote[113].speaker |
SPEAKER_01 |
transcript.pyannote[113].start |
594.45284375 |
transcript.pyannote[113].end |
601.03409375 |
transcript.pyannote[114].speaker |
SPEAKER_01 |
transcript.pyannote[114].start |
601.23659375 |
transcript.pyannote[114].end |
601.74284375 |
transcript.pyannote[115].speaker |
SPEAKER_01 |
transcript.pyannote[115].start |
602.43471875 |
transcript.pyannote[115].end |
605.72534375 |
transcript.pyannote[116].speaker |
SPEAKER_01 |
transcript.pyannote[116].start |
606.48471875 |
transcript.pyannote[116].end |
608.23971875 |
transcript.pyannote[117].speaker |
SPEAKER_01 |
transcript.pyannote[117].start |
609.38721875 |
transcript.pyannote[117].end |
618.24659375 |
transcript.pyannote[118].speaker |
SPEAKER_01 |
transcript.pyannote[118].start |
618.78659375 |
transcript.pyannote[118].end |
621.65534375 |
transcript.pyannote[119].speaker |
SPEAKER_01 |
transcript.pyannote[119].start |
622.00971875 |
transcript.pyannote[119].end |
625.30034375 |
transcript.pyannote[120].speaker |
SPEAKER_01 |
transcript.pyannote[120].start |
625.90784375 |
transcript.pyannote[120].end |
635.91471875 |
transcript.pyannote[121].speaker |
SPEAKER_01 |
transcript.pyannote[121].start |
637.41659375 |
transcript.pyannote[121].end |
639.44159375 |
transcript.pyannote[122].speaker |
SPEAKER_01 |
transcript.pyannote[122].start |
639.76221875 |
transcript.pyannote[122].end |
652.08096875 |
transcript.pyannote[123].speaker |
SPEAKER_01 |
transcript.pyannote[123].start |
653.41409375 |
transcript.pyannote[123].end |
655.72596875 |
transcript.pyannote[124].speaker |
SPEAKER_01 |
transcript.pyannote[124].start |
656.21534375 |
transcript.pyannote[124].end |
666.18846875 |
transcript.pyannote[125].speaker |
SPEAKER_01 |
transcript.pyannote[125].start |
666.40784375 |
transcript.pyannote[125].end |
667.11659375 |
transcript.pyannote[126].speaker |
SPEAKER_01 |
transcript.pyannote[126].start |
668.21346875 |
transcript.pyannote[126].end |
669.90096875 |
transcript.pyannote[127].speaker |
SPEAKER_01 |
transcript.pyannote[127].start |
672.65159375 |
transcript.pyannote[127].end |
681.94971875 |
transcript.pyannote[128].speaker |
SPEAKER_01 |
transcript.pyannote[128].start |
682.33784375 |
transcript.pyannote[128].end |
689.10471875 |
transcript.pyannote[129].speaker |
SPEAKER_01 |
transcript.pyannote[129].start |
689.25659375 |
transcript.pyannote[129].end |
698.11596875 |
transcript.pyannote[130].speaker |
SPEAKER_01 |
transcript.pyannote[130].start |
698.89221875 |
transcript.pyannote[130].end |
704.57909375 |
transcript.pyannote[131].speaker |
SPEAKER_01 |
transcript.pyannote[131].start |
705.72659375 |
transcript.pyannote[131].end |
709.08471875 |
transcript.pyannote[132].speaker |
SPEAKER_01 |
transcript.pyannote[132].start |
709.64159375 |
transcript.pyannote[132].end |
712.00409375 |
transcript.pyannote[133].speaker |
SPEAKER_01 |
transcript.pyannote[133].start |
712.66221875 |
transcript.pyannote[133].end |
714.19784375 |
transcript.pyannote[134].speaker |
SPEAKER_01 |
transcript.pyannote[134].start |
715.02471875 |
transcript.pyannote[134].end |
720.72846875 |
transcript.pyannote[135].speaker |
SPEAKER_01 |
transcript.pyannote[135].start |
721.36971875 |
transcript.pyannote[135].end |
727.37721875 |
transcript.pyannote[136].speaker |
SPEAKER_01 |
transcript.pyannote[136].start |
728.10284375 |
transcript.pyannote[136].end |
734.58284375 |
transcript.pyannote[137].speaker |
SPEAKER_01 |
transcript.pyannote[137].start |
735.03846875 |
transcript.pyannote[137].end |
735.76409375 |
transcript.pyannote[138].speaker |
SPEAKER_01 |
transcript.pyannote[138].start |
736.47284375 |
transcript.pyannote[138].end |
740.99534375 |
transcript.pyannote[139].speaker |
SPEAKER_01 |
transcript.pyannote[139].start |
741.43409375 |
transcript.pyannote[139].end |
743.74596875 |
transcript.pyannote[140].speaker |
SPEAKER_01 |
transcript.pyannote[140].start |
743.84721875 |
transcript.pyannote[140].end |
746.74971875 |
transcript.pyannote[141].speaker |
SPEAKER_01 |
transcript.pyannote[141].start |
747.18846875 |
transcript.pyannote[141].end |
756.73971875 |
transcript.pyannote[142].speaker |
SPEAKER_01 |
transcript.pyannote[142].start |
757.12784375 |
transcript.pyannote[142].end |
763.27034375 |
transcript.pyannote[143].speaker |
SPEAKER_01 |
transcript.pyannote[143].start |
764.41784375 |
transcript.pyannote[143].end |
774.81284375 |
transcript.pyannote[144].speaker |
SPEAKER_01 |
transcript.pyannote[144].start |
775.11659375 |
transcript.pyannote[144].end |
778.03596875 |
transcript.pyannote[145].speaker |
SPEAKER_01 |
transcript.pyannote[145].start |
778.37346875 |
transcript.pyannote[145].end |
781.15784375 |
transcript.pyannote[146].speaker |
SPEAKER_01 |
transcript.pyannote[146].start |
782.00159375 |
transcript.pyannote[146].end |
796.39596875 |
transcript.pyannote[147].speaker |
SPEAKER_01 |
transcript.pyannote[147].start |
797.35784375 |
transcript.pyannote[147].end |
798.16784375 |
transcript.pyannote[148].speaker |
SPEAKER_01 |
transcript.pyannote[148].start |
798.89346875 |
transcript.pyannote[148].end |
800.27721875 |
transcript.pyannote[149].speaker |
SPEAKER_01 |
transcript.pyannote[149].start |
800.68221875 |
transcript.pyannote[149].end |
808.63034375 |
transcript.pyannote[150].speaker |
SPEAKER_02 |
transcript.pyannote[150].start |
810.46971875 |
transcript.pyannote[150].end |
812.56221875 |
transcript.pyannote[151].speaker |
SPEAKER_02 |
transcript.pyannote[151].start |
813.08534375 |
transcript.pyannote[151].end |
825.94409375 |
transcript.pyannote[152].speaker |
SPEAKER_01 |
transcript.pyannote[152].start |
825.62346875 |
transcript.pyannote[152].end |
829.18409375 |
transcript.pyannote[153].speaker |
SPEAKER_01 |
transcript.pyannote[153].start |
829.65659375 |
transcript.pyannote[153].end |
832.76159375 |
transcript.pyannote[154].speaker |
SPEAKER_01 |
transcript.pyannote[154].start |
833.31846875 |
transcript.pyannote[154].end |
835.79909375 |
transcript.pyannote[155].speaker |
SPEAKER_01 |
transcript.pyannote[155].start |
837.28409375 |
transcript.pyannote[155].end |
838.83659375 |
transcript.pyannote[156].speaker |
SPEAKER_02 |
transcript.pyannote[156].start |
838.39784375 |
transcript.pyannote[156].end |
839.52846875 |
transcript.pyannote[157].speaker |
SPEAKER_01 |
transcript.pyannote[157].start |
839.22471875 |
transcript.pyannote[157].end |
840.59159375 |
transcript.pyannote[158].speaker |
SPEAKER_01 |
transcript.pyannote[158].start |
840.84471875 |
transcript.pyannote[158].end |
842.32971875 |
transcript.pyannote[159].speaker |
SPEAKER_01 |
transcript.pyannote[159].start |
843.17346875 |
transcript.pyannote[159].end |
847.49346875 |
transcript.pyannote[160].speaker |
SPEAKER_02 |
transcript.pyannote[160].start |
843.22409375 |
transcript.pyannote[160].end |
843.39284375 |
transcript.pyannote[161].speaker |
SPEAKER_01 |
transcript.pyannote[161].start |
848.23596875 |
transcript.pyannote[161].end |
851.18909375 |
transcript.pyannote[162].speaker |
SPEAKER_01 |
transcript.pyannote[162].start |
853.01159375 |
transcript.pyannote[162].end |
853.82159375 |
transcript.pyannote[163].speaker |
SPEAKER_01 |
transcript.pyannote[163].start |
854.88471875 |
transcript.pyannote[163].end |
859.52534375 |
transcript.pyannote[164].speaker |
SPEAKER_01 |
transcript.pyannote[164].start |
860.60534375 |
transcript.pyannote[164].end |
867.27096875 |
transcript.pyannote[165].speaker |
SPEAKER_01 |
transcript.pyannote[165].start |
867.52409375 |
transcript.pyannote[165].end |
871.37159375 |
transcript.pyannote[166].speaker |
SPEAKER_01 |
transcript.pyannote[166].start |
871.84409375 |
transcript.pyannote[166].end |
874.27409375 |
transcript.pyannote[167].speaker |
SPEAKER_01 |
transcript.pyannote[167].start |
874.71284375 |
transcript.pyannote[167].end |
877.05846875 |
transcript.pyannote[168].speaker |
SPEAKER_01 |
transcript.pyannote[168].start |
877.39596875 |
transcript.pyannote[168].end |
879.96096875 |
transcript.pyannote[169].speaker |
SPEAKER_01 |
transcript.pyannote[169].start |
881.17596875 |
transcript.pyannote[169].end |
881.85096875 |
transcript.pyannote[170].speaker |
SPEAKER_01 |
transcript.pyannote[170].start |
882.44159375 |
transcript.pyannote[170].end |
885.85034375 |
transcript.pyannote[171].speaker |
SPEAKER_01 |
transcript.pyannote[171].start |
886.44096875 |
transcript.pyannote[171].end |
889.47846875 |
transcript.pyannote[172].speaker |
SPEAKER_01 |
transcript.pyannote[172].start |
889.49534375 |
transcript.pyannote[172].end |
892.80284375 |
transcript.pyannote[173].speaker |
SPEAKER_01 |
transcript.pyannote[173].start |
893.00534375 |
transcript.pyannote[173].end |
899.65409375 |
transcript.pyannote[174].speaker |
SPEAKER_01 |
transcript.pyannote[174].start |
900.10971875 |
transcript.pyannote[174].end |
901.47659375 |
transcript.pyannote[175].speaker |
SPEAKER_01 |
transcript.pyannote[175].start |
902.06721875 |
transcript.pyannote[175].end |
903.68721875 |
transcript.pyannote[176].speaker |
SPEAKER_01 |
transcript.pyannote[176].start |
904.00784375 |
transcript.pyannote[176].end |
906.37034375 |
transcript.pyannote[177].speaker |
SPEAKER_01 |
transcript.pyannote[177].start |
907.73721875 |
transcript.pyannote[177].end |
909.00284375 |
transcript.pyannote[178].speaker |
SPEAKER_01 |
transcript.pyannote[178].start |
909.84659375 |
transcript.pyannote[178].end |
911.38221875 |
transcript.pyannote[179].speaker |
SPEAKER_01 |
transcript.pyannote[179].start |
911.75346875 |
transcript.pyannote[179].end |
913.15409375 |
transcript.pyannote[180].speaker |
SPEAKER_01 |
transcript.pyannote[180].start |
913.86284375 |
transcript.pyannote[180].end |
915.46596875 |
transcript.pyannote[181].speaker |
SPEAKER_01 |
transcript.pyannote[181].start |
916.19159375 |
transcript.pyannote[181].end |
917.06909375 |
transcript.pyannote[182].speaker |
SPEAKER_01 |
transcript.pyannote[182].start |
918.45284375 |
transcript.pyannote[182].end |
919.07721875 |
transcript.pyannote[183].speaker |
SPEAKER_01 |
transcript.pyannote[183].start |
919.76909375 |
transcript.pyannote[183].end |
954.34596875 |
transcript.pyannote[184].speaker |
SPEAKER_01 |
transcript.pyannote[184].start |
954.98721875 |
transcript.pyannote[184].end |
966.54659375 |
transcript.pyannote[185].speaker |
SPEAKER_01 |
transcript.pyannote[185].start |
966.90096875 |
transcript.pyannote[185].end |
970.05659375 |
transcript.pyannote[186].speaker |
SPEAKER_01 |
transcript.pyannote[186].start |
970.52909375 |
transcript.pyannote[186].end |
976.87409375 |
transcript.pyannote[187].speaker |
SPEAKER_01 |
transcript.pyannote[187].start |
978.84846875 |
transcript.pyannote[187].end |
979.55721875 |
transcript.pyannote[188].speaker |
SPEAKER_02 |
transcript.pyannote[188].start |
980.21534375 |
transcript.pyannote[188].end |
981.14346875 |
transcript.pyannote[189].speaker |
SPEAKER_01 |
transcript.pyannote[189].start |
981.97034375 |
transcript.pyannote[189].end |
982.15596875 |
transcript.pyannote[190].speaker |
SPEAKER_02 |
transcript.pyannote[190].start |
983.06721875 |
transcript.pyannote[190].end |
983.79284375 |
transcript.pyannote[191].speaker |
SPEAKER_01 |
transcript.pyannote[191].start |
984.21471875 |
transcript.pyannote[191].end |
986.81346875 |
transcript.pyannote[192].speaker |
SPEAKER_01 |
transcript.pyannote[192].start |
986.86409375 |
transcript.pyannote[192].end |
986.93159375 |
transcript.pyannote[193].speaker |
SPEAKER_01 |
transcript.pyannote[193].start |
987.60659375 |
transcript.pyannote[193].end |
992.80409375 |
transcript.pyannote[194].speaker |
SPEAKER_02 |
transcript.pyannote[194].start |
989.36159375 |
transcript.pyannote[194].end |
989.73284375 |
transcript.pyannote[195].speaker |
SPEAKER_01 |
transcript.pyannote[195].start |
993.27659375 |
transcript.pyannote[195].end |
994.81221875 |
transcript.pyannote[196].speaker |
SPEAKER_01 |
transcript.pyannote[196].start |
995.28471875 |
transcript.pyannote[196].end |
1001.03909375 |
transcript.pyannote[197].speaker |
SPEAKER_01 |
transcript.pyannote[197].start |
1001.46096875 |
transcript.pyannote[197].end |
1004.59971875 |
transcript.pyannote[198].speaker |
SPEAKER_01 |
transcript.pyannote[198].start |
1005.03846875 |
transcript.pyannote[198].end |
1008.37971875 |
transcript.pyannote[199].speaker |
SPEAKER_01 |
transcript.pyannote[199].start |
1010.45534375 |
transcript.pyannote[199].end |
1013.81346875 |
transcript.pyannote[200].speaker |
SPEAKER_01 |
transcript.pyannote[200].start |
1014.03284375 |
transcript.pyannote[200].end |
1015.45034375 |
transcript.pyannote[201].speaker |
SPEAKER_01 |
transcript.pyannote[201].start |
1016.04096875 |
transcript.pyannote[201].end |
1027.56659375 |
transcript.pyannote[202].speaker |
SPEAKER_01 |
transcript.pyannote[202].start |
1028.42721875 |
transcript.pyannote[202].end |
1030.68846875 |
transcript.pyannote[203].speaker |
SPEAKER_01 |
transcript.pyannote[203].start |
1031.53221875 |
transcript.pyannote[203].end |
1032.98346875 |
transcript.pyannote[204].speaker |
SPEAKER_01 |
transcript.pyannote[204].start |
1033.52346875 |
transcript.pyannote[204].end |
1034.82284375 |
transcript.pyannote[205].speaker |
SPEAKER_02 |
transcript.pyannote[205].start |
1035.48096875 |
transcript.pyannote[205].end |
1038.33284375 |
transcript.pyannote[206].speaker |
SPEAKER_01 |
transcript.pyannote[206].start |
1039.27784375 |
transcript.pyannote[206].end |
1039.88534375 |
transcript.pyannote[207].speaker |
SPEAKER_01 |
transcript.pyannote[207].start |
1040.59409375 |
transcript.pyannote[207].end |
1041.55596875 |
transcript.pyannote[208].speaker |
SPEAKER_01 |
transcript.pyannote[208].start |
1042.72034375 |
transcript.pyannote[208].end |
1058.90346875 |
transcript.pyannote[209].speaker |
SPEAKER_01 |
transcript.pyannote[209].start |
1059.30846875 |
transcript.pyannote[209].end |
1063.08846875 |
transcript.pyannote[210].speaker |
SPEAKER_01 |
transcript.pyannote[210].start |
1063.45971875 |
transcript.pyannote[210].end |
1066.68284375 |
transcript.pyannote[211].speaker |
SPEAKER_01 |
transcript.pyannote[211].start |
1067.56034375 |
transcript.pyannote[211].end |
1080.19971875 |
transcript.pyannote[212].speaker |
SPEAKER_01 |
transcript.pyannote[212].start |
1080.68909375 |
transcript.pyannote[212].end |
1097.59784375 |
transcript.pyannote[213].speaker |
SPEAKER_01 |
transcript.pyannote[213].start |
1098.15471875 |
transcript.pyannote[213].end |
1120.19346875 |
transcript.pyannote[214].speaker |
SPEAKER_01 |
transcript.pyannote[214].start |
1122.06659375 |
transcript.pyannote[214].end |
1125.67784375 |
transcript.pyannote[215].speaker |
SPEAKER_01 |
transcript.pyannote[215].start |
1125.99846875 |
transcript.pyannote[215].end |
1126.08284375 |
transcript.pyannote[216].speaker |
SPEAKER_01 |
transcript.pyannote[216].start |
1126.33596875 |
transcript.pyannote[216].end |
1127.93909375 |
transcript.pyannote[217].speaker |
SPEAKER_01 |
transcript.pyannote[217].start |
1128.73221875 |
transcript.pyannote[217].end |
1136.37659375 |
transcript.pyannote[218].speaker |
SPEAKER_01 |
transcript.pyannote[218].start |
1136.74784375 |
transcript.pyannote[218].end |
1141.05096875 |
transcript.pyannote[219].speaker |
SPEAKER_01 |
transcript.pyannote[219].start |
1141.96221875 |
transcript.pyannote[219].end |
1143.68346875 |
transcript.pyannote[220].speaker |
SPEAKER_01 |
transcript.pyannote[220].start |
1144.20659375 |
transcript.pyannote[220].end |
1147.29471875 |
transcript.pyannote[221].speaker |
SPEAKER_01 |
transcript.pyannote[221].start |
1148.22284375 |
transcript.pyannote[221].end |
1150.11284375 |
transcript.pyannote[222].speaker |
SPEAKER_01 |
transcript.pyannote[222].start |
1150.53471875 |
transcript.pyannote[222].end |
1153.03221875 |
transcript.pyannote[223].speaker |
SPEAKER_02 |
transcript.pyannote[223].start |
1154.60159375 |
transcript.pyannote[223].end |
1156.76159375 |
transcript.pyannote[224].speaker |
SPEAKER_02 |
transcript.pyannote[224].start |
1158.01034375 |
transcript.pyannote[224].end |
1158.43221875 |
transcript.pyannote[225].speaker |
SPEAKER_02 |
transcript.pyannote[225].start |
1159.76534375 |
transcript.pyannote[225].end |
1160.25471875 |
transcript.pyannote[226].speaker |
SPEAKER_00 |
transcript.pyannote[226].start |
1160.25471875 |
transcript.pyannote[226].end |
1160.32221875 |
transcript.pyannote[227].speaker |
SPEAKER_01 |
transcript.pyannote[227].start |
1161.31784375 |
transcript.pyannote[227].end |
1190.59596875 |
transcript.pyannote[228].speaker |
SPEAKER_02 |
transcript.pyannote[228].start |
1193.07659375 |
transcript.pyannote[228].end |
1193.54909375 |
transcript.pyannote[229].speaker |
SPEAKER_02 |
transcript.pyannote[229].start |
1193.97096875 |
transcript.pyannote[229].end |
1194.96659375 |
transcript.whisperx[0].start |
7.346 |
transcript.whisperx[0].end |
10.254 |
transcript.whisperx[0].text |
好 謝謝主席 是不是還是請我們洪部長 |
transcript.whisperx[1].start |
16.605 |
transcript.whisperx[1].end |
37.092 |
transcript.whisperx[1].text |
部長大家都很關心這個勞保的這個基金的問題喔至少我個人也很擔心但我想到說大家覺得說領的越來越多繳費的越來越少可是那我就認真的看了一下新一個世代投入職場 |
transcript.whisperx[2].start |
38.112 |
transcript.whisperx[2].end |
54.524 |
transcript.whisperx[2].text |
然後現在有在繳保費的我都很好奇那一年他的出生的人口是多少那我就假設是25歲好了因為大學畢業嘛22歲23歲那今年2025年我就看了一下2020年你知道那一年出生的小孩出生的小孩有幾個嗎大概 |
transcript.whisperx[3].start |
57.907 |
transcript.whisperx[3].end |
68.192 |
transcript.whisperx[3].text |
十幾萬我告訴你你會嚇一跳我看了也嚇一跳2020年出生的小孩有30萬5千3百12個你知道去年剩下13萬4千8百56個我這個是統計這絕對不是後面寫的人口統計 |
transcript.whisperx[4].start |
83.117 |
transcript.whisperx[4].end |
88.839 |
transcript.whisperx[4].text |
2000年是三十萬五千三百一十二個那個時候就在講說人口少子化危機2000年大家講說現在這繳的少三十萬人還叫繳的少嗎 |
transcript.whisperx[5].start |
108.507 |
transcript.whisperx[5].end |
128.328 |
transcript.whisperx[5].text |
但如果再找一個數字相較於1982年那一年出生的小孩有40萬個我就找40萬的40萬掉到30萬 2000年其實在2000年已經就掉到30萬了2000年是因為農年再加上2000年 |
transcript.whisperx[6].start |
129.349 |
transcript.whisperx[6].end |
158.129 |
transcript.whisperx[6].text |
那為什麼講這個是從40萬人掉到30萬人花了18年的時間少掉了25%四分之一的人口數那個時候我們就說這個勞保的財務危機了勞保的財務危機可是你知道從2000年然後掉到再少四分之一就財務危機少三分之一是幾年多快的速度嗎 |
transcript.whisperx[7].start |
160.174 |
transcript.whisperx[7].end |
164.215 |
transcript.whisperx[7].text |
5年2005年出生數剩下20萬5854人2005年短短5年的時間又減10萬他不是10萬是3分之1再來對 跟 跟花多少的時間對30萬變成要變15萬這樣 兇兇落來 |
transcript.whisperx[8].start |
191.659 |
transcript.whisperx[8].end |
207.16 |
transcript.whisperx[8].text |
這個速度也是非常的快如果不要講一個特殊的年是2010年的話其實就是2021年20年他從30萬掉到剩15萬二分之一所以2000年為界 |
transcript.whisperx[9].start |
208.061 |
transcript.whisperx[9].end |
235.16 |
transcript.whisperx[9].text |
他到2020年掉了二分之一如果少了四分之一就是財務危機那那麼快的速度下墜到三分之一就少了三分之一然後再更快的速度二十年時間少了二分之一那我現在講這個勞保的財務危機大家都知道開源節流 開源怎麼開源啊費率 法定費率工具用完了 剩下幾%現在費率多少% |
transcript.whisperx[10].start |
238.16 |
transcript.whisperx[10].end |
243.543 |
transcript.whisperx[10].text |
法定費率最高到多少法定費率沒有修法的話 緊繃啊天花板到了 沒有開圓的工具了那我們再打開投保薪資的天花板投保薪資天花板是要開到多高啊 |
transcript.whisperx[11].start |
264.09 |
transcript.whisperx[11].end |
273.944 |
transcript.whisperx[11].text |
你少掉了二分之一的人耶如果再跟四十萬的人口來講那個不是二分之一四十萬變成十五萬耶這裡打多開也開不了 |
transcript.whisperx[12].start |
280.031 |
transcript.whisperx[12].end |
290.883 |
transcript.whisperx[12].text |
大家都知道 這個不是結構不結構 入不敷出 已經沒有任何的手段可以再繼續開源了好 去年2024年 這個政府撥補1300億 投資收益的創新高1595億的投資收益 |
transcript.whisperx[13].start |
302.111 |
transcript.whisperx[13].end |
307.053 |
transcript.whisperx[13].text |
也是一樣入不敷出前年2023年政府撥補550億投資收益1100億那是死好呢那是死壞 你投資收益很壞的要安住 |
transcript.whisperx[14].start |
319.201 |
transcript.whisperx[14].end |
336.449 |
transcript.whisperx[14].text |
所以在這種狀況裡面 我們看你們的精算報告這次的精算報告也出來了去年的勞保收支還短差665億這個已經是連續8年這個逆差 大家都知道我剛才剩下人口數給你聽 你會怕死啊 |
transcript.whisperx[15].start |
337.669 |
transcript.whisperx[15].end |
351.957 |
transcript.whisperx[15].text |
潛藏的負債不要講啊 這個從變成14兆就不要講收不抵資也不用再講了 惡化也不用再講政府如何加大力道 我想喔啊 一樣啦 已經撥不到1300億啦 你是要怎麼加大力道啦還可以多大啦然後 這個到底是怎麼 怎麼辦喔你把年紀想起來 頭皮會發麻捏頭皮真的會發麻欸 |
transcript.whisperx[16].start |
369.015 |
transcript.whisperx[16].end |
381.89 |
transcript.whisperx[16].text |
這個人口就是牽動這個退休這個勞保基金最重要的變數這個人口結構這麼大的變化大家都知道人口危機是國安危機可是就是在這裡那勞保的普通事故啊因濟保費在這個 |
transcript.whisperx[17].start |
390.407 |
transcript.whisperx[17].end |
395.91 |
transcript.whisperx[17].text |
投保人數就是現在還是40萬的人在繳費的現在繳費的應該都是一年生40萬的人在繳費的吧今年是40萬人那個世代的人在繳費的啦今年是30萬跟20萬的還沒到職場啦 |
transcript.whisperx[18].start |
413.325 |
transcript.whisperx[18].end |
420.129 |
transcript.whisperx[18].text |
那是更恐怖的啦一年才有40萬的離開的人現在在投保可是光是這樣子2021年投保人數從1074萬到去年已經下降到1048萬少了26萬了那勞保普通事故因紀的保險費是4881億但實際保險的給付已經高達5546.3億元 |
transcript.whisperx[19].start |
442.563 |
transcript.whisperx[19].end |
469.966 |
transcript.whisperx[19].text |
所以這樣的撥補是撥補我們是很輕盈的但是就不是辦法那這種這麼大的人口結構的問題之下這個我的助理叫我跟你們大家講雖然我也不曉得我認不認同但他們都選擇不婚不生快樂一生但是這樣下去他們覺得不婚不生的人也要貧窮一生了啊 |
transcript.whisperx[20].start |
471.721 |
transcript.whisperx[20].end |
474.164 |
transcript.whisperx[20].text |
然後呢 我要再問你一個很嚴肅的課題喔勞保分得這麼大 勞保可以賺嗎你說政府付不付 最終責任沒有重要啊全部所有的人口的一半 一千萬全部都是在這個社會保險裡面 |
transcript.whisperx[21].start |
488.797 |
transcript.whisperx[21].end |
510.933 |
transcript.whisperx[21].text |
都不會負你的責任,政府不會負你的責任,你能夠一個盡權,所有人都不會負你的責任所以這不是重點,要不要負最終責任,現在是政府要怎麼負最終責任啊再來另外一個問題是,你知道有多少的受僱者或是多少的人,他的老年經濟的安全,老年經濟安全 |
transcript.whisperx[22].start |
513.393 |
transcript.whisperx[22].end |
542.495 |
transcript.whisperx[22].text |
真的都沒搞 吃得老的時候都仰賴勞保的月退再充當他的這個勉強度餘生的棺材本啦我說這個很務實的 又很現實的喔不要說勞退喔 勞退沒多少錢啦都仰賴勞保主要的經濟 老年經濟來源仰賴勞保的比例有多高 你告訴我 |
transcript.whisperx[23].start |
549.292 |
transcript.whisperx[23].end |
561.902 |
transcript.whisperx[23].text |
各位報告喔 我們對勞工的老年經濟保障還是希望他們跟世界...不要講你的希望 我現在要問你現實面 真正的現實面台灣有多少人 他退休之後所有的收入所有經濟的來源 他生活都靠而已去報這個月退多少人 你跟我說啦 |
transcript.whisperx[24].start |
575.168 |
transcript.whisperx[24].end |
589.296 |
transcript.whisperx[24].text |
不過勞保目前的家保人口是一千多萬嘛那但是在勞退那邊也有七百多萬啦所以是 我在問什麼 你在回答什麼我問東你答西 王店員你不知道喔 |
transcript.whisperx[25].start |
592.433 |
transcript.whisperx[25].end |
618.039 |
transcript.whisperx[25].text |
數字上面我可能可以我們要再去確認我告訴你大概全台灣沒有人可以講得出一個數字因為沒有人敢面對沒有人敢調查也大家都烏著眼睛都不要去想不要去想但是我跟你說喔就如我剛才一開始就講的現在的年輕人不要問我們上一代的現在要捏錢的啦多少人仰賴這個勞保月退再當生活費的啦 |
transcript.whisperx[26].start |
618.817 |
transcript.whisperx[26].end |
619.918 |
transcript.whisperx[26].text |
因為他沒有能力去為自己的老年經濟做準備養小孩繳房貸他在養上一代 |
transcript.whisperx[27].start |
637.675 |
transcript.whisperx[27].end |
651.922 |
transcript.whisperx[27].text |
別說他問我們自己啦大家各位在座的各位有幾個人你繳了房貸養了小孩然後給父母生活背後你還有能力為你自己的老年經濟安全有餘裕做準備的這個小孩我說很輕鬆的喔 |
transcript.whisperx[28].start |
656.294 |
transcript.whisperx[28].end |
667.052 |
transcript.whisperx[28].text |
我坐著一眼望去 多少人都仰賴勞保的月退當生活開支的唯一財源這個問題很嚴重 |
transcript.whisperx[29].start |
672.714 |
transcript.whisperx[29].end |
697.746 |
transcript.whisperx[29].text |
我也不知道要怎麼問 所以不能破產的但是我現在要跟你講 你是政府 你再怎麼逃 再怎麼躲 躲不了撥補是好事 但撥補也解決不了漲保費 漲保費也沒辦法 手段工具都用清了沒有可以再上漲的空間了 |
transcript.whisperx[30].start |
698.956 |
transcript.whisperx[30].end |
708.827 |
transcript.whisperx[30].text |
打開天花板,天花板進去繳,收入也有限現在未來是怎樣?我再說一個問題,你不要去思考過不?不對,AI的時代來了AI的時代來了 |
transcript.whisperx[31].start |
715.129 |
transcript.whisperx[31].end |
734.522 |
transcript.whisperx[31].text |
三年五年整個社會整個世界都就長得不一樣AI的速度之快啊讓這個社會可能三年就多一輪啊多一輪啊所以那個對勞動市場最大的衝擊是什麼大家都覺得失業啊被替代啊然後來了 |
transcript.whisperx[32].start |
736.519 |
transcript.whisperx[32].end |
762.362 |
transcript.whisperx[32].text |
你就要勞保基金的改革 說那我們不能開源 我們節流叫啊 大家啊 你要比較少的就已經很少了 要給他少 要不然就看我而已全世界都在把退休的年齡往後延可是我想AI時代來這是一個很殘忍 很殘酷的打擊來了因為可能會很多人力被替代掉而會有大量的過剩的勞動力 |
transcript.whisperx[33].start |
764.491 |
transcript.whisperx[33].end |
780.766 |
transcript.whisperx[33].text |
所以如果大家全世界來講說要領年金的年齡往後延那並不是基於並不是基於勞動力需求而已並不是基於勞動力需求的短缺恐怕真正的殘酷是基於 |
transcript.whisperx[34].start |
782.422 |
transcript.whisperx[34].end |
792.089 |
transcript.whisperx[34].text |
老年幾戶的這個基金不夠所以這叫大家都不能退休不能退休問題是沒頭腦啊AI時代來了勞動力大量的勞動力過剩人力被取代這樣要怎麼做你們有沒有想過AI時代對整個就業市場勞動力市場的衝擊除了失業以外還會有什麼影響保證你們有想過嗎 |
transcript.whisperx[35].start |
810.892 |
transcript.whisperx[35].end |
835.318 |
transcript.whisperx[35].text |
確實這個AI技術的滲透率越來越高其實現在其實在很多面向看起來都會對於整體的勞動環境會造成影響像是我想維恩其實也一直很關注這個問題不是啦我在講跟我們這個勞保基金有關的啦對於整個勞動力市場就業人口除了失業還有可能會產生什麼影響 |
transcript.whisperx[36].start |
837.375 |
transcript.whisperx[36].end |
847.243 |
transcript.whisperx[36].text |
失業保費繳的人就少了然後人類可能會更長壽更長壽以後活得越久領得越多活得越久領的人越多這個是這個社會保險會瓦解不是會瓦解是將近崩潰 |
transcript.whisperx[37].start |
860.629 |
transcript.whisperx[37].end |
876.443 |
transcript.whisperx[37].text |
不只是勞保還有健保 所幸喔 勞保不是唯一的那我再 不要忘了 我們現在移工我們的移工 我們現在83萬的移工都在繳保費 但是第14年以後不准他們領退休欸 |
transcript.whisperx[38].start |
881.223 |
transcript.whisperx[38].end |
888.948 |
transcript.whisperx[38].text |
嘿 所以我們的政策都卡在第14年欸我們是用這種殘忍的手段欸可是這種手段會不會有一天在國際勞工的組織裡面這種公約裡面要求台灣不能再這樣子下去或是人家提告到國際法庭去提告覺得我們這種管制手段都叫人搬移 |
transcript.whisperx[39].start |
907.773 |
transcript.whisperx[39].end |
911.98 |
transcript.whisperx[39].text |
這樣而已要安坐你做政府的有太重嗎這問題很嚴重的所以在這種狀況裡面 |
transcript.whisperx[40].start |
920.117 |
transcript.whisperx[40].end |
943.871 |
transcript.whisperx[40].text |
這個我如果沒有必要又沒有感覺你如果必要我們在講這個社會保險這個問題你知道2023年OECD和G20的國家年金統計調查了42個國家除了冰島墨西哥美國和G20的印尼四國以外這個四國不要談了其餘各國和我國的勞保性質相近的就是確定給複製的 |
transcript.whisperx[41].start |
946.312 |
transcript.whisperx[41].end |
966.184 |
transcript.whisperx[41].text |
這些國家的提存基金的比例都是100%以上印尼沒有啦 印尼是97%啦各國當中最低的是冰島 雖然也曾經破產過但他們年金制度的提存基金比例也有28啦但是不要講這個冰島 這比較特殊 這個 |
transcript.whisperx[42].start |
970.586 |
transcript.whisperx[42].end |
978.898 |
transcript.whisperx[42].text |
但是在我國2021年的精算報告勞保的已提存的基金比例是幾%我國的6.19%人家都100% |
transcript.whisperx[43].start |
987.657 |
transcript.whisperx[43].end |
1008.178 |
transcript.whisperx[43].text |
好沒關係因為可能制度上有一點差異不一樣但是但是這個這個也是我們可以走的方向可是就人口結構惡化成這個樣子連確定確定幾副字能不能夠適應在未來的改革能不能這樣子都還是一個問號啊 |
transcript.whisperx[44].start |
1010.652 |
transcript.whisperx[44].end |
1019.159 |
transcript.whisperx[44].text |
確定第一波是已經負債這麼多了確定幾副你們有沒有評估過從13萬40萬變成30萬就已經這麼大的財務危機啊30萬變成13萬確定確定幾副字真的也提得出來嗎可以100%嗎你們有評估過嗎這是現在當然現在是這是制度上面的設計 |
transcript.whisperx[45].start |
1039.315 |
transcript.whisperx[45].end |
1056.99 |
transcript.whisperx[45].text |
不是啦政府的功能和角色就國安的危機當然就是未雨綢繆要先試算20年後的危機那我們才能知道怎麼因應而現在我告訴你不管開源或節流所有的手段工具都用竊了沒了沒了然後我就想政府你要想大家都覺得在這裡修法要修什麼啊 |
transcript.whisperx[46].start |
1067.611 |
transcript.whisperx[46].end |
1096.43 |
transcript.whisperx[46].text |
再降低給付再延長這個退休這一些都不是工具了我們過去想像的工具都已經不是工具了更何況AI時代來了所以部長這個是很嚴肅的而且是我相信對勞動部而言你們真的還必須要重新嚴肅的去思考的事過去以後我們確定提撥制變成確定給付制就好了 |
transcript.whisperx[47].start |
1098.231 |
transcript.whisperx[47].end |
1108.817 |
transcript.whisperx[47].text |
我們確定給父子的愛 歡樂人口突然間 人的越來越多五十萬的世代人要再活一年四十萬人的那個世代人要再活一年四十萬人的那個世代人要再活 要再生人的越久 活的越多現在叫的是一年四十三萬的時侯 要安寧 |
transcript.whisperx[48].start |
1122.25 |
transcript.whisperx[48].end |
1140.807 |
transcript.whisperx[48].text |
人家去叫你 我就確定幾副 付得出來嗎所以這個知識體大啦 我只能在這裡與眾欣賞的在跟大家講一個最殘酷現實 我今天要講一個重點不管開源或節流 所有的工具都用完了 |
transcript.whisperx[49].start |
1142.251 |
transcript.whisperx[49].end |
1152.22 |
transcript.whisperx[49].text |
幾乎都沒辦法我們可以想像的勞保的改革手段的工具沒了這是很嚴重的事情好 謝謝 |
transcript.whisperx[50].start |
1161.565 |
transcript.whisperx[50].end |
1165.947 |
transcript.whisperx[50].text |
因為你們不知道說多少勞工仰賴勞保的老年給付剛好你們自己有一個勞工生活及就業狀況調查113年度調查勞工退休後的生活費的來源目前沒有規劃的人28%有規劃的72%但有規劃的裡面仰賴勞保老年給付和勞工退休金的高達四成四所以44加28等於72%都算了 |
transcript.whisperx[51].start |
1193.08 |
transcript.whisperx[51].end |
1194.923 |
transcript.whisperx[51].text |
謝謝!謝謝! |