³ë·É Àα¸, ´Ä¾î°¡´Â »çȸÀÇ °æÁ¦¼ºÀå¿¡ ÇÊ¿äÇÑ °Íµé | |
Àü ¼¼°è °÷°÷¿¡¼ Àα¸ ³ë·ÉÈ°¡ ºü¸£°Ô ÁøÇàµÇ°í ÀÖ´Ù. Åë³ä»ó Àα¸ ³ë·ÉÈ´Â °æÁ¦ ¼ºÀå¿¡ ºÎÁ¤Àû ¿µÇâÀ» ¹ÌÄ¡´Â °ÍÀ¸·Î ¾Ë·ÁÁ® ÀÖ´Ù. ±×·¯³ª ±×°ÍÀº °ú°ÅÀÇ Åë³äÀÏ»Ó, 4Â÷ »ê¾÷Çõ¸íÀÇ ½Ã´ë¿¡´Â ¸ÂÁö ¾ÊÀ» ¼öµµ ÀÖ´Ù. ÀΰøÁö´É, ÀÚµ¿È, °Ç°ÇÑ ³ë·É Àα¸, Æò±Õ ¼ö¸í ¿¬Àå µî Åë³ä¿¡ ¹ÝÇÏ´Â °á°ú¸¦ ³¾ ¼ö ÀÖ´Â ¿ä¼ÒµéÀÌ µîÀåÇÏ°í ÀÖ´Ù. |
Àü ¼¼°è °÷°÷¿¡¼ Àα¸ ³ë·ÉÈ°¡ ºü¸£°Ô ÁøÇàµÇ°í ÀÖ´Ù. Åë³ä»ó Àα¸ ³ë·ÉÈ´Â °æÁ¦ ¼ºÀå¿¡ ºÎÁ¤Àû ¿µÇâÀ» ¹ÌÄ¡´Â °ÍÀ¸·Î ¾Ë·ÁÁ® ÀÖ´Ù. ±×·¯³ª ±×°ÍÀº °ú°ÅÀÇ Åë³äÀÏ»Ó, 4Â÷ »ê¾÷Çõ¸íÀÇ ½Ã´ë¿¡´Â ¸ÂÁö ¾ÊÀ» ¼öµµ ÀÖ´Ù. ÀΰøÁö´É, ÀÚµ¿È, °Ç°ÇÑ ³ë·É Àα¸, Æò±Õ ¼ö¸í ¿¬Àå µî Åë³ä¿¡ ¹ÝÇÏ´Â °á°ú¸¦ ³¾ ¼ö ÀÖ´Â ¿ä¼ÒµéÀÌ µîÀåÇÏ°í ÀÖ´Ù.
¼¼°è´Â ºü¸£°Ô ³ëÈ ÁßÀÌ´Ù. ÇöÀç ¹Ì±¹¿¡¼¸¸ 65¼¼ ÀÌ»ó ³ë·É Àα¸°¡ ÀüüÀÇ 16%¸¦ Â÷ÁöÇÏ°í ÀÖ°í, 2035³âÀÌ µÇ¸é 21%¿¡ À̸¦ Àü¸ÁÀÌ´Ù. 2023³â¿¡´Â ¾Æ¸¶µµ ³ë·É Àα¸°¡ 18¼¼ ¹Ì¸¸ÀÇ ÀþÀº Àα¸º¸´Ù ´õ Å« ºñÁßÀ» Â÷ÁöÇÒ °ÍÀÌ´Ù. ÀÌ°ÍÀº ¹Ì±¹¸¸ÀÇ À̾߱Ⱑ ¾Æ´Ï´Ù.
Àα¸´ë±¹ Áß±¹Àº ¾î¶³±î? Áß±¹¿¡¼´Â ÀþÀº ¼¼´ë°¡ Á¡Â÷ÀûÀ¸·Î °¨¼ÒÇÏ°í ÀÖ°í, 1979³â¿¡ ÇÑ ÀÚ³à Á¤Ã¥ÀÌ µµÀԵDZâ Àü¿¡ ÅÂ¾î³ ¾öû³ ¼ýÀÚÀÇ »ç¶÷µéÀÌ ³ë·É Àα¸ ºñÀ²À» ´Ã¸®´Âµ¥ ¾ÕÀå ¼°í ÀÖ´Ù. ±× ¹ÛÀÇ ´Ù¸¥ ³ª¶óµéÀº? ´õ ºü¸£°Ô ³ëȵǰí ÀÖ´Ù. Àα¸ÀÇ 4ºÐÀÇ 1 ÀÌ»óÀÌ 65¼¼ ÀÌ»óÀÎ ÀϺ»Àº ´Ü¿¬ ÀÌ ºÐ¾ßÀÇ ¼±µÎ ÁÖÀÚÀÌ´Ù. µ¶ÀÏ, ÀÌÅ»¸®¾Æ, Çɶõµå ¹× ±âŸ À¯·´ ¿¬ÇÕÀÇ ¸¹Àº ±¹°¡µéµµ ÀϺ»ÀÌ °È´Â ±æ°ú Å©°Ô ´Ù¸£Áö ¾Ê´Ù. 2050³âÀÌ µÇ¸é À¯·´°ú ºÏ¹Ì Àα¸ÀÇ 4ºÐÀÇ 1ÀÌ 65¼¼ ÀÌ»óÀÌ µÉ °ÍÀÌ´Ù.
ÀÌ·¯ÇÑ Ãß¼¼´Â °ÅÀÇ ¸ðµç ±¹°¡¿¡¼ ¿©¼ºµéÀÌ ´õ ÀûÀº Àڳฦ °¡ÁüÀ¸·Î¼ ¹ß»ýÇÏ´Â °ÍÀÌ´Ù. Áï, ³·Àº Ãâ»ê·üÀÌ Àα¸ ³ë·ÉÈÀÇ °¡Àå Å« ¿äÀÎÀÌ´Ù. ±×¸®°í ¶Ç ´Ù¸¥ ¿äÀεµ ÀÖ´Ù. ¹Ù·Î Àå¼ö´Ù. ¿À´Ã³¯ »ç¶÷µéÀº °ú°ÅÀÇ »ç¶÷µéº¸´Ù ÈξÀ ´õ ¿À·¡ »ì°í ÀÖ´Ù. ¹°·Ð ÀÌ·¯ÇÑ Àå¼ö¿¡´Â ÇÑ°è°¡ ÀÖ´Ù. ¾Æ¹«¸® ÀÇ·á ±â¼ú ¹× °ø°ø º¸°Ç, »îÀÇ ÁúÀÌ °³¼±µÇ¾îµµ »ç¶÷ÀÌ 200³âÀ» »ì ¼ö´Â ¾ø´Ù. Áï, ±â´ë ¼ö¸íÀÌ ´Ã¾îµµ ±×°ÍÀº ¾î´À ÇÑ°è¼±¿¡¼ ±×Ä¥ °ÍÀ̱⠶§¹®¿¡ ÃÖ±Ù ¼ö ³â µ¿¾È ÀϺΠ¼±Áø±¹¿¡¼´Â ÀÌÁ¦ ±â´ë ¼ö¸í Áõ°¡°¡ Á¡Â÷ µÐȵǰí ÀÖ´Ù. ±×·¯³ª Àü ¼¼°èÀûÀ¸·Î º¸¸é, ±â´ë ¼ö¸íÀº ´çºÐ°£ °è¼Ó ´Ã¾î³¯ Àü¸ÁÀÌ´Ù. ¿À´Ã³¯ ÀϺ»¿¡¼ ÅÂ¾î³ ¿©ÀÚ ¾Æ±â´Â Æò±ÕÀûÀ¸·Î ¸î »ì±îÁö »ýÁ¸ÇÒ±î? ¾Æ¸¶µµ °ÅÀÇ 100³âÀ» »ì °ÍÀ¸·Î ¿¹»óµÈ´Ù.
ÀÌ·Î ÀÎÇØ Àü¹ÝÀûÀ¸·Î Àα¸ °í·ÉÈ°¡ °ÅÀÇ ´ëºÎºÐÀÇ »çȸ¿¡¼ ÁøÇàµÇ°í ÀÖ´Ù. °á°úÀûÀ¸·Î º¸¸é ¿ì¸®´Â ºÎ¸ð ¼¼´ëº¸´Ù ´õ ¿À·£ »îÀ» »ì °ÍÀÌ°í, ±× ´ö¿¡ ÀÏ»ý µ¿¾È ´õ ¸¹Àº ÀÏÀ» ÇÏ°Ô µÉ °ÍÀÌ´Ù. 1960³â¿¡ 65¼¼ÀÎ »ç¶÷À̶ó¸é, ¾Æ¸¶ ´ç½Ã¿¡´Â 79¼¼±îÁö »ì °ÍÀ¸·Î ¿¹»óÇÒ ¼ö ÀÖ¾ú´Ù. ¿äÁòÀº ¾î¶³±î? °ÅÀÇ 85¼¼±îÁö »ì °ÍÀ¸·Î ¿¹»óÇÏ°í ÀÖ´Ù. ÀÌ¹Ì 75¼¼¶ó¸é 87¼¼±îÁö »ì ¼ö ÀÖÀ» °ÍÀÌ´Ù.
ÀÌ°ÍÀº ¿ì¸®ÀÇ °æÁ¦, »çȸÀû ¹®ÈÀû °¡Ä¡, ½ÉÁö¾î ¿ì¸®°¡ »îÀ» ÀνÄÇÏ°í °èȹÇÏ´Â ¹æ½Ä°ú ÇüŸ¦ ¿ÏÀüÈ÷ ¹Ù²Ù´Â °Å´ëÇÑ º¯È¸¦ Àß º¸¿©ÁØ´Ù.
±×·¸´Ù¸é ÀÌ·¯ÇÑ ³ë·É Àα¸´Â °æÁ¦¿¡ ¾î¶² ¿µÇâÀ» ¹ÌÄ¡°Ô µÉ±î? ÀϹÝÀûÀÎ Åë³äÀº ¿©ÀüÈ÷ ³ë·É Àα¸°¡ °æÁ¦ ¼ºÀå¿¡ µ¶¼ÒÀûÀ̶ó´Â °ÍÀÌ´Ù. ù ¹ø° Áú¹®Àº ¡®±×µéÀ» Á¦¿ÜÇÏ¸é ´©°¡ ÀÏÀ» ÇÒ °ÍÀΰ¡?¡¯ÀÌ´Ù. ´õ ³ª»Û ¶Ç ÇϳªÀÇ Áú¹®Àº ¡®³ë·É Àα¸ÀÇ ÀÇ·á ¹× º¹Áö ÇÁ·Î±×·¥ ºñ¿ëÀ» ´©°¡ ³¾ °ÍÀΰ¡?¡¯ÀÌ´Ù. ÀÌ·¯ÇÑ Áú¹®µéÀº ³ë·É Àα¸¿¡ ´ëÇÑ ºÎÁ¤ÀûÀÎ Àǹ̸¦ ´ã°í ÀÖ´Ù.
ÀÌ·¯ÇÑ ³Á¦´Â ÀüÇüÀûÀ¸·Î ÇϳªÀÇ °£´ÜÇÑ ÇÔ¼ö·Î ¿ä¾àµÈ´Ù. ¹Ù·Î ºÎ¾ç·ü(the dependency ratio)ÀÌ´Ù. ºÎ¾çÀ²Àº ³ëµ¿ Àα¸¸¦ ³Ê¹« ³ªÀÌ°¡ ¸¹°Å³ª ¾î·Á¼ ȤÀº ¸öÀÌ ¾ÆÆļ ÀÏÀ» ÇÒ ¼ö ¾ø´Â »ç¶÷µéÀÇ ¼ö·Î ´Ü¼øÈ÷ ³ª´« °ªÀÌ´Ù. ±×¸®°í ¸¹Àº Àü¹®°¡µé°ú Á¤Ä¡ÀεéÀº ÀÌ·¯ÇÑ Ãß¼¼°¡ °¡Á®¿Ã ¹«¼¿î ¿¹ÃøÀ» »ç¶÷µé¿¡°Ô º¸¿©ÁÖ°í ½Í¾î ÇÑ´Ù. Àα¸Åë°èÇÐÀû À§±â°¡ ¿ì¸®¿¡°Ô ´Ù°¡¿À°í ÀÖÀ½À» °Á¶ÇÏ´Â °ÍÀÌ´Ù.
Àα¸ Àýº®À̳ª ÀÌ·Î ÀÎÇÑ ½ÃÇÑÆøź°ú °°Àº »óȲ¿¡ ´ëÇÑ °æ°í´Â ¸Å¿ì ºÒ±æÇÏ°Ô µé¸°´Ù. ÇÏÁö¸¸ Áø½ÇÀº °æÁ¦ÇÐÀÚµéÀÌ ³ë·É Àα¸°¡ ¼¼»ó¿¡ ¾î¶»°Ô ¿µÇâÀ» ¹ÌÄ¥ °ÍÀÎÁö¿¡ ´ëÇØ ¸¹Àº °ÍÀ» ¾ËÁö ¸øÇÑ´Ù´Â Á¡ÀÌ´Ù. ±×¸®°í Àα¸Åë°èÀÇ ¿µÇâÀº Àü ¼¼°è ¿©·¯ Áö¿ª¿¡¼ ÀÛ¿ëÇÏ´Â ¹®ÈÀû, °æÁ¦Àû, ±â¼úÀû ¿äÀο¡ µû¶ó Å©°Ô ´Þ¶óÁú °ÍÀÌ´Ù.
1980³âºÎÅÍ 2010³â±îÁöÀÇ µ¥ÀÌÅ͸¦ ¹ÙÅÁÀ¸·Î ÇϹöµå´ë °æÁ¦ÇÐÀÚ ´ÏÄÝ ¸¶¿¡½ºÅ¸½º(Nicole Maestas)´Â 60¼¼ ÀÌ»óÀÇ Àα¸°¡ 10% Áõ°¡Çϸé 1Àδç GDP ¼ºÀå·üÀÌ 5.5% °¨¼ÒµÇ´Â °ÍÀ¸·Î °è»óÇß´Ù. Áï, ÀÌ·¯ÇÑ °ú°Å°¡ ¹Ì·¡ÀÇ ÁöÇ¥¶ó¸é ¹Ì±¹ÀÇ ³ë·É Àα¸´Â À̹ø 10³â ³» 1.2%, ±× ´ÙÀ½ 10³â ³» 0.6%±îÁö °æÁ¦¸¦ µÐȽÃų ¼ö ÀÖ´Ù. ÀÌ·¯ÇÑ ÀÌÀ¯ Áß ÀϺδ ÀÏÇÏ´Â »ç¶÷ÀÌ Àû±â ¶§¹®ÀÏ ¼ö ÀÖÁö¸¸, ±× Áß 3ºÐÀÇ 2ÀÇ ³ëµ¿·ÂÀÌ Æò±ÕÀûÀ¸·Î ´ú »ý»êÀûÀÌ µÇ±â ¶§¹®ÀÌ´Ù.
±×·¯³ª ¸¶¿¡½ºÅ¸½º ±³¼ö´Â ÀÌ·¯ÇÑ ¿¹»óÀÌ ¿ª»çÀû Ãß¼¼¸¦ ±â¹ÝÀ¸·Î ÇÒ»Ó, ½ÇÁ¦·Î´Â ¿¹ÃøÀÌ ¾Æ´Ò ¼ö ÀÖ´Ù°í °æ°íÇÑ´Ù. ±×³àÀÇ °¡¼³Àº 1980³â¿¡¼ 2010³â »çÀÌ »ý»ê¼ºÀÌ ¶³¾îÁø °ÍÀº °¡Àå ¼÷·ÃµÇ°í °æÇèÀÌ ¸¹Àº »ç¶÷µéÀÌ ´ë·®À¸·Î Á÷ÀåÀ» ¶°³µ±â ¶§¹®ÀÓÀ» ±â¹ÝÀ¸·Î ÇÑ´Ù. ¶ÇÇÑ ±×µéÀº ´õ ¼º°øÀûÀÌ°í °Ç°Çϸç, ÀºÅð¿¡ ÇÊ¿äÇÑ °æÁ¦·ÂÀ» °®Ãß°í ÀÖ¾ú´Ù. ±×³à°¡ ¸Â´Ù¸é, ÀÌ°ÍÀº ±×µéÀÌ ³ë·ÉÀÌ µÇ¸é¼ ´ú »ý»êÀûÀ¸·Î º¯Çؼ°¡ ¾Æ´Ï¶ó °¡Àå »ý»êÀûÀÎ ±×µéÀÌ ÀÏÀ» ±×¸¸µ×±â ¶§¹®ÀÌ´Ù.
ÀÌ´Â »ý»ê¼ºÀÇ ´ë´ëÀûÀÎ Ç϶ôÀÌ ºÒ°¡ÇÇÇÑ °ÍÀÌ ¾Æ´ÔÀ» ÀǹÌÇÑ´Ù. »ç½Ç, »õ·Î¿î ±â¼ú°ú ºñÁî´Ï½º Á¤Ã¥Àº °¡Àå Àç´ÉÀÖ´Â »ç¶÷µéÀÌ ´õ ¿À·¡ ÀÏÇÏ°Ô ¸¸µé ¼ö ÀÖ´Ù. ƯÈ÷ ´õ °Ç°ÇÏ°í ÀÚ½ÅÀÌ ¼öÇàÇÏ´Â ¾÷¹«·Î ÀÚ½ÅÀ» Á¤ÀÇÇÏ´Â »óÀ§ 4ºÐÀ§ º£À̺ñ ºÕ ¼¼´ëÀÇ °æ¿ì ƯÈ÷ ±×·¸´Ù. ¹°·Ð – ºÒÇàÇÏ°Ôµµ - ÀϺΠº£À̺ñ ºÕ ¼¼´ë´Â ÀúÃàÀÌ ÁÙ°í ÀºÅð °èȹÀ» ¼¼¿ï ¼ö ¾ø¾î¼ ´õ ¿À·¡ ÀÏÀ» Çϱ⵵ ÇÑ´Ù.
MIT °æÁ¦ÇÐÀÚ ´ë·± ¾Ö½º¸ð±Û·ç(Daron Acemoglu)¿¡ µû¸£¸é, ¡°³ë·É »çȸ·Î ÀÎÇØ °æÁ¦°¡ ´õ ¾Çȵȴٴ Áõ°Å´Â °ÅÀÇ ¾ø´Ù.¡± 1990³âºÎÅÍ 2015³â±îÁöÀÇ GDP µ¥ÀÌÅ͸¦ »ìÆ캻 ¾Ö½º¸ð±Û·ç´Â Àα¸ ³ë·ÉÈ¿Í °æÁ¦ ¼ºÀå µÐÈ »çÀÌ¿¡´Â ¾Æ¹« »ó°ü°ü°è°¡ ¾øÀ½À» ¹ß°ßÇß´Ù. »ç½Ç Çѱ¹, ÀϺ», µ¶ÀÏ°ú °°Àº ±¹°¡µéÀº ±Þ°ÝÇÑ ³ë·ÉÈ¿¡µµ ºÒ±¸ÇÏ°í ½ÇÁ¦·Î ²Ï °æÁ¦¸¦ Àß ¿î¿µÇسª°¡°í ÀÖ´Ù.
¾Æ¸¶µµ °¡Àå Å« ÀÌÀ¯´Â ÀÚµ¿ÈÀÏ °ÍÀÌ´Ù. ³ëµ¿·ÂÀÌ ³ë·ÉÈµÈ ±¹°¡´Â ±×¿¡ ´ëÇÑ ´ëó·Î »ê¾÷¿ë ·Îº¿ äÅÃÀÌ º¸´Ù ´õ »¡¶ú´Ù. ¾Ö½º¸ð±Û·ç´Â ¡®³ë·ÉÈ·Î ÀÎÇÑ ºÎÁ¤ÀûÀÎ ¿µÇâ¿¡ ´ëÇÑ Áõ°Å°¡ ÀüÇô ¾øÀ½¡¯À» ¹ß°ßÇß°í, ÀÚµ¿È¿Í °ü·ÃµÈ »ý»ê¼º Áõ´ë°¡ ¡®³ë·ÉÈ¿¡ µû¸¥ Æĸê°ú ħü¡¯¸¦ ´Ü¼øÈ÷ ¿ÏȽÃÅ°´Â °Í¿¡ ±×Ä¡´Â ¾Ê´Â ±× ÀÌ»óÀ̶ó°í ¸»ÇÑ´Ù.
±×·¯³ª ÀÌ°ÍÀÌ ¡®±¹°¡°¡ ¹®Á¦¿¡ Á÷¸éÇÏÁö ¾Ê´Â´Ù¡¯´Â Àǹ̴ ¾Æ´Ï´Ù. ¾Ö½º¸ð±Û·ç´Â ÀÌ·¸°Ô °Á¶ÇÑ´Ù.
¡°¿ì¸®´Â »çȸ°¡ ³ë·É鵃 ¶§ ¹«½¼ ÀÏÀÌ ¹ú¾îÁú Áö ¾Ë ¼ö ÀÖÀ» ¸¸Å ÃæºÐÈ÷ ÁغñµÇ¾î ÀÖÁö ¾ÊÀ¸¸ç, ±×°ÍÀ» Ž»öÇÏ´Â ¹æ¹ýµµ ¸ð¸¨´Ï´Ù.¡±
°¡Àå Àß ¾Ë·ÁÁöÁö ¾ÊÀº °Í Áß Çϳª´Â ÀÏ¹Ý ¼ö¸í, °Ç° ¼ö¸í, °æÁ¦Àû À£ºù °£ÀÇ °ü°èÀÌ´Ù. Áö³ 100³â µ¿¾ÈÀÇ ±â´ë ¼ö¸í Áõ°¡´Â ¿ì¸®ÀÇ À§´ëÇÑ ±â¼úÀû ¼º°ú Áß Çϳª¿´´Ù. 20¼¼±â ÃÊ, Àΰ£ÀÇ Æò±Õ ¼ö¸íÀº 50¼¼ ÀüÈÄ¿´´Ù. ÀÌ°ÍÀÌ 1960³â±îÁö 70¼¼, 2010³â¿¡´Â °ÅÀÇ 80¼¼°¡ µÇ¾ú´Ù. ºü¸¥ Áøº¸ÀÇ ´ëºÎºÐÀº ¾î¸°À̵éÀ» ´õ °Ç°ÇÏ°Ô À¯ÁöÇ߱⠶§¹®ÀÌ´Ù. 1900³â¿¡´Â °ÅÀÇ 4¸í Áß 1¸íÀÌ 10¼¼ ÀÌÀü¿¡ »ç¸ÁÇß°í ÀÌ´Â Àüü Æò±Õ ¼ö¸í¿¡ Å« ¿µÇâÀ» ¹ÌÃÆ´Ù. ÀÌÈÄ Å« Áøº¸´Â ½ÉÇ÷°ü Áúȯ¿¡ ´ëÇÑ Ä¡·á¿´´Ù. ÀÌ·Î ÀÎÇØ ´ëºÎºÐÀÇ »ç¶÷µéÀÌ 70´ë±îÁö »ýÁ¸ÇÒ ¼ö ÀÖ°Ô µÇ¾ú´Ù.
±×·¯³ª ÀÌ ³î¶ó¿î ÁøÇàÀÌ °è¼ÓµÉ °ÍÀ̶ó´Â º¸ÀåÀº ¾ø´Ù. Æò±Õ ±â´ë ¼ö¸íÀº ÆòÁØȵǰí ÀÖÀ¸¸ç, ÀüüÀûÀ¸·Î 80¼¼°¡ Á¶±Ý ³Ñ´Â ¼öÁØ¿¡ µµ´ÞÇÏ°í ÀÖ´Â °ÍÀ¸·Î º¸ÀδÙ. Àϸ®³ëÀÌ´ë °øÁߺ¸°Ç´ëÇпø S. Á¦ÀÌ ¿Ã¼¢½ºÅ°(S. Jay Olshansky) ±³¼ö´Â ÀÌ·¯ÇÑ µÐȸ¦ ¼ö³â°£ ¿¹ÃøÇØ ¿Ô´Ù. ±×´Â ¿ì¸®°¡ Æò±Õ ¼ö¸íÀÇ »óÇѼ±¿¡ ±ÙÁ¢Çß´Ù°í ¸»ÇÑ´Ù.
¡°±â´ë ¼ö¸íÀ» ¾Æ¸¶µµ 80¼¼¿¡¼ 85¼¼±îÁö ¿Ã¸± ¼ö ÀÖÀ» °ÍÀÔ´Ï´Ù. ÀϺ»Àº ÀÌ¹Ì ±×°Í¿¡ °¡±î¿öÁö°í ÀÖ½À´Ï´Ù.¡±
Áö±Ý±îÁö ¿ì¸®°¡ ÇÒ ¼ö ¾ø¾ú´ø °ÍÀº ³ëÈ °úÁ¤ ÀÚü¸¦ ´ÊÃß´Â µ¥ °³ÀÔÇÏ´Â °ÍÀ̾ú´Ù. ±×·¯³ª ³ëÈ »ý¹°ÇÐÀ» ÀÌÇØÇÏ°íÀÚ ÇÏ´Â ¼ö½Ê ³â µ¿¾ÈÀÇ È¹±âÀûÀÎ ¹ßÀü °á°ú, ³ëÈ ¹æÁö ¾à¹°ÀÇ Ã¹ ¹ø° ¹°°áÀÌ Àΰ£¿¡°Ô Å×½ºÆ®µÇ´Â ÁßÀÌ´Ù. ¿Ã¼¢½ºÅ° ±³¼ö´Â ÀÌ·¸°Ô ¸»ÇÑ´Ù.
¡°±×·¯ÇÑ ¾àµéÀº ¿ì¸®°¡ ¿µ¿øÈ÷ »ì ¼ö ÀÖµµ·Ï ÇÏ´Â °ÍÀÌ ¾Æ´Ï¸ç, ¾Æ¸¶µµ ÈξÀ ´õ ¿À·§µ¿¾È »ì ¼ö ÀÖµµ·Ï ÇÏ´Â °Íµµ ¾Æ´Ò °Ì´Ï´Ù. ´Ù¸¸ ³ë·É¿¡µµ ºÒ±¸ÇÏ°í ´õ ¿À·¡ ´õ °Ç°ÇÏ°Ô »ì ¼ö ÀÖµµ·Ï µµ¿ï °ÍÀÔ´Ï´Ù.¡±
ÁÖ¿ä ³ëÈ ¹æÁö ¹°ÁúÀº ¾Æ¸¶µµ ´ÙÀ½°ú °°À» °ÍÀÌ´Ù.
- ¸é¿ª ±â´É¿¡ ¿µÇâÀ» ¹ÌÄ¡´Â ¶óÆĸ¶À̽Űú °°Àº ÈÇÕ¹°
- ½Ã¸£ÅõÀÎ(sirtuins)À¸·Î ºÒ¸®´Â ´Ü¹éÁú È°¼ºÈ ºÐÀÚ
- ¿À·£ ´ç´¢º´ Ä¡·áÁ¦ ¸ÞÆ®Æ÷¸£¹Î(metformin)
- ¼Õ»óµÇ°í ³ëÈµÈ ¼¼Æ÷¸¦ Á¤ÈÇÏ´Â ¼¼³î¸®Æ½(senolytic) ¾à¹°
ÇöÀç ÀÌ·¯ÇÑ ÈÇÐ ºÐÀÚ¿¡ ´ëÇÑ Èñ¸ÁÀº À̵éÀÌ ³ëÈ °ü·Ã Áúº´¿¡ µµ¿òÀÌ µÉ ¼ö ÀÖ´Ù´Â °ÍÀÌ´Ù.
±â¾ïÇØ¾ß ÇÒ Áß¿äÇÑ Á¡Àº »ç¶÷µéÀÌ ÀÏÀ» ¼öÇàÇÏ´Â µ¥ ÇÊ¿äÇÑ ¸ðµç ±â¼ú°ú µµ±¸¸¦ °¡Áö°í ÀÖ´Â ÇÑ ¿¬·É¿¡ °ü°è¾øÀÌ »ý»ê¼ºÀ» ³ô°Ô À¯ÁöÇÒ ¼ö ÀÖ´Ù´Â °ÍÀÌ´Ù. ÀÌ ¶§, °Ç°ÇÏ°í È°µ¿ÀûÀÎ °ÍÀÌ ±× Ãâ¹ßÁ¡À̸ç, ÃÖ÷´Ü ÀÇ·á ¼ºñ½º´Â ¹Ù·Î ±× ÁöÁ¡°ú Á÷Á¢ ¿¬°èµÈ´Ù. ¶Ç ´Ù¸¥ ¿ä¼Ò´Â Áö¼ÓÀûÀ¸·Î ¾÷µ¥ÀÌÆ®ÇÏ°í ÇнÀÇÏ´Â °ÍÀÌ´Ù. ÀÌ´Â 30¼¼ÀÎ »ç¶÷¸¸Å 65¼¼ÀÎ »ç¶÷¿¡°Ôµµ Áß¿äÇÏ´Ù. ¿À´Ã³¯ Àΰ£ÀÇ Áö½ÄÀº 7³â¸¶´Ù - ȤÀº ±× ÀÌÇÏÀÇ ½Ã°£¸¶´Ù – 2¹è¾¿ Áõ°¡ÇÏ°í ÀÖ´Ù. µû¶ó¼ ´ëÇÐÀ» Á¹¾÷ÇÑ ÈÄ 8³âÀÌ µÈ »ç¶÷À̳ª, 40³âÀÌ µÈ »ç¶÷À̳ª ¸ðµÎ ±¸½ÄÀÌ±ä ¸¶Âù°¡ÁöÀÌ´Ù. µÑ ¸ðµÎ ÃÖ°íÀÇ ÀÚ¸®¸¦ À¯ÁöÇÏ·Á¸é Áö¼ÓÀûÀÎ ±³À°À» ÇÊ¿ä·Î ÇÑ´Ù.
ÆäÀ̽ººÏ CEO ¸¶Å© ÁÖÄ¿¹ö±×ÀÇ À¯¸íÇÑ ¹ß¾ð ¡®ÀþÀº »ç¶÷µéÀÌ ´õ ½º¸¶Æ®ÇÏ´Ù¡¯´Â ¸»ÀÌ ³ª¿Â Áö, ÀÌ¹Ì 12³âÀÌ Áö³µ´Ù. 65¼¼ ¾ï¸¸ÀåÀÚ º¥Ã³ ijÇÇÅ»¸®½ºÀÎ ºñ³ëµå ÄÚ½½¶ó(Vinod Khosla)´Â ¡°35¼¼ ¹Ì¸¸ÀÇ »ç¶÷µéÀÌ º¯È¸¦ ÀÏÀ¸Åµ´Ï´Ù. 45¼¼ ÀÌ»óÀÇ »ç¶÷µéÀº »õ·Î¿î ¾ÆÀ̵ð¾î¶ó´Â Ãø¸é¿¡¼ º¸¸é ±âº»ÀûÀ¸·Î »ç¸ÁÇÑ »óÅÂÀÔ´Ï´Ù¡±¶ó°í ¸»Çß°í ÀÌÁ¦ ¾à 10³âÀÌ Áö³µ´Ù.
±×·¯³ª Çмú ¿¬±¸¿¡ µû¸£¸é, ÁÖÄ¿¹ö±×¿Í ÄÚ½½¶ó´Â Ʋ·È´Ù. 270¸¸ ¸íÀÇ Ã¢¾÷ÀÚµéÀ» ´ë»óÀ¸·Î ÇÏ´Â ¾ö°ÝÇÑ ¿¬±¸¸¦ ÅëÇØ, MIT °æÁ¦ÇÐÀÚµé°ú ¹Ì Àα¸Á¶»ç±¹, ³ë½º¿þ½ºÅÏ ´ëÇÐÀº ÃÖ°íÀÇ ±â¾÷°¡µéÀÇ ¿¬·ÉÀÌ Áß³âÀ̶ó°í °á·Ð ³»·È´Ù. °¡Àå ºü¸£°Ô ¼ºÀåÇÏ´Â ½ºÅ¸Æ®¾÷Àº Æò±Õ ¿¬·É 45¼¼ÀÇ Ã¢¾÷Àڵ鿡 ÀÇÇØ ¸¸µé¾îÁ³´Ù. 2018³â ¿¬±¸¿¡¼ ÀÌµé ¿¬±¸ÀÚµéÀº 50¼¼ÀÇ ±â¾÷°¡°¡ ¸Å¿ì ¼º°øÀûÀΠȸ»ç¸¦ ¸¸µé °¡´É¼ºÀÌ 30¼¼º¸´Ù °ÅÀÇ µÎ ¹è³ª ´õ ³ô´Ù´Â °ÍÀ» ¹ß°ßÇß´Ù. ±×¸®°í ÄÚ½½¶óÀÇ ¹ß¾ð°ú ´Þ¸®, ¾÷°è °æÇèÀÌ ¼º°øÀ» ¿¹ÃøÇÏ´Â µ¥ ÀÖ¾î ¸Å¿ì Áß¿äÇÑ ±àÁ¤ÀûÀÎ ¿ä¼ÒÀÎ °ÍÀ¸·Î ³ªÅ¸³µ´Ù.
³ë°ñÀûÀÎ ¿¬·É Æí°ßÀº ¶ÇÇÑ ½Ç¸®Äܹ븮°¡ °úÇÐÀûÀÎ Àü¹® Áö½Ä°ú ±â¼úÀ» ÇÊ¿ä·Î ÇÏ´Â »ý¹° ÀÇÇÐ, ûÁ¤ ¿¡³ÊÁö, ȤÀº ±âŸ À¯°ü ºÐ¾ß¿¡¼ ½ºÅ¸Æ®¾÷À» ¸¸µå´Â µ¥ ¿Ö ±×·¸°Ô ÇüÆí¾ø¾ú´ÂÁö¸¦ Àß ¼³¸íÇØÁØ´Ù. À̺¸´Ù ¾Õ¼± ¼±Çà ¿¬±¸¿¡¼, ³ë½º¿þ½ºÅÏ´ë °æÁ¦ÇÐÀÚ º¥ÀڹΠÁ¸½º(Benjamin Jones)´Â ¹°¸® °úÇаú ÀÇÇÐ ºÐ¾ß¿¡¼ °¡Àå À§´ëÇÑ °úÇÐÀû ¾÷ÀûÀº Á¶¼÷ÇÑ Ã»³âÀÌ ¾Æ´Ï¶ó Á߳⠼¼´ë¿¡¼ ³ª¿Ô´Ù´Â Áõ°Å¸¦ Á¦½ÃÇß´Ù.
ÀÌ À̾߱âÀÇ °á·ÐÀº ¹«¾ùÀϱî?
¼¼°è´Â ºü¸£°Ô ³ëȵǰí ÀÌ°ÍÀÌ Á¤È®È÷ ¹«¾ùÀ» ÀǹÌÇÏ´ÂÁö´Â ¸íÈ®ÇÏÁö ¾Ê´Ù. °á·ÐÀº Àα¸Åë°è»Ó¸¸ ¾Æ´Ï¶ó °æÁ¦Àû ÀÚ¿ø, ±â¼ú, Àΰ£ Çൿ¿¡ ´Þ·Á ÀÖ´Ù. ¿Ã¹Ù¸£°Ô¸¸ °ü¸®µÇ¸é ¹Ì±¹°ú ±âŸ ¸¹Àº ±¹°¡µéÀº ÀÌ »õ·Î¿î Çö½Ç¿¡ ÀûÀÀÇÒ ¼ö ÀÖÀ» °ÍÀÌ´Ù. ±×·¯³ª À߸øµÈ °ü¸®´Â ¾îµÎ¿î ¹Ì·¡·Î À̾îÁú ¼ö ÀÖ´Ù. ¿ì¸®°¡ È®½ÇÈ÷ ¾Æ´Â °ÍÀº »ç¶÷µéÀÌ ´õ ÀÌ»ó Àþ¾îÁöÁö´Â ¾Ê´Â´Ù´Â Á¡ÀÌ´Ù. ±×¸®°í ¿ì¸®´Â ÀÌ ¹®Á¦¸¦ Àû±ØÀûÀ¸·Î ÇØ°áÇϱâ À§ÇØ ´õ ÀÌ»ó ±â´Ù¸± ½Ã°£ÀÌ ¾ø´Ù.
ÀÌ·¯ÇÑ Àü ¼¼°è °¢ ±¹°¡ Àα¸ÀÌ ³ë·ÉÈ Ãß¼¼¸¦ °í·ÁÇÏ¿© ¿ì¸®´Â ´ÙÀ½°ú °°Àº ¿¹ÃøÀ» ³»·Á º»´Ù.
ù°, ¹Ì±¹¿¡¼ ¹Î°£ ¹× °ø°ø ºÎ¹®Àº °á±¹ 2022³â¿¡ À̸£·¯ ¿¬·É Æí°ßÀÌ ÀÖ¾úÀ½À» ÀÎÁöÇÏ°Ô µÉ °ÍÀÌ´Ù.
ÀÎÀç°¡ Á¡Á¡ ´õ ºÎÁ·ÇØÁö¸é¼, º¥Ã³ ijÇÇÅ»¸®½ºÆ®¿Í °í¿ëÁÖµéÀº ±×µéÀÇ ¿¬·É Æí°ß¿¡ ´ëÇØ Àç°íÇÏ°Ô µÉ °ÍÀÌ´Ù. ¹Ì±¹ Á¦Á¶¾÷ÀÇ ¸®¼î¾î¸µ, À̹ΠÁ¦ÇÑ, ÀÚµ¿È º¸±Þ°ú °°Àº ¶Ç ´Ù¸¥ Æ®·»µåµéÀÌ ¸ðµÎ ³ë·É ³ëµ¿ÀÚµéÀÇ Ã¤¿ë°ú À¯Áö¿¡ À¯¸®ÇÑ ¿ä¼Ò·Î ÀÛ¿ëÇÒ °ÍÀÌ´Ù. ¸¶Âù°¡Áö·Î, ¡®Æí°ß¡¯À» ÀνÄÇÏ°Ô µÈ º£À̺ñºÕ ¼¼´ë¿Í X¼¼´ëÀÇ Á¤Ä¡Àû ¾Ð·Â ¶ÇÇÑ °¡ÁßµÉ °ÍÀÌ´Ù. ÀÌ°ÍÀº ƯÈ÷ º£À̺ñºÕ ¼¼´ë¿Í ¹Ð·¹´Ï¾ó ¼¼´ë »çÀÌ¿¡ Ç×»ó ³¢¾îÀÖ¾ú´ø Àç´ÉÀÖ´Â X¼¼´ë¿¡°Ô »õ·Î¿î ±âȸ¸¦ âÃâÇØÁÙ °ÍÀÌ´Ù.
µÑ°, °í¿ëÁÖ, ±³À°ÀÚ, ±×¸®°í °¢ °³ÀεéÀº Áö¼ÓÀûÀÎ ÇнÀ ¹× ±â¼ú ½ÀµæÀ» ¸ñÇ¥·Î ÇÏ´Â ½Ç¹«¸¦ Á¡Â÷ÀûÀ¸·Î ´õ äÅÃÇÏ°Ô µÉ °ÍÀÌ´Ù.
2020³â´ëÀÇ ºñÁî´Ï½º´Â »õ·Î¿î ±â¼ú°ú ºñÁî´Ï½º ¸ðµ¨À̶ó´Â ¹°°á·Î Ư¡ Áö¿öÁú °ÍÀÌ´Ù. ¸ðµç ³ëµ¿ÀÚµéÀº ÀÚ½ÅÀÌ ±¸½ÄÀÌ µÇ´Â °ÍÀ» ÇÇÇϱâ À§ÇØ Áö¼ÓÀûÀ¸·Î Àç±³À°À» ¹Þ¾Æ¾ß ÇÒ °ÍÀÌ´Ù. ´ÙÇàÈ÷ ¿À´Ã³¯ ¿ø°Ý ÇнÀÀº ½º¸¶Æ®ÆùÀ» °¡Áø »ç¶÷À̶ó¸é ¾ðÁ¦ ¾îµð¼³ª ±³À°°ú Áö¿øÀ» ¹ÞÀ» ¼ö ÀÖ´Â ¼öÁØÀ¸·Î ¹ßÀüÇÏ°í ÀÖ´Ù. ÀΰøÁö´É°ú ¶óÀÌºê ±â¼ú Áö¿øÀ¸·Î °ÈµÇ´Â Áõ°Çö½ÇÀº ¼ö¸¹Àº ¾÷¹«¿¡ ´ëÇÑ ÇнÀ °î¼±[learnign curve, Àΰ£ÀÌ Ã³À½ ¾î¶² ÀÛ¾÷À» ¼öÇàÇÒ ¶§´Â ÀÛ¾÷¿¡ Àͼ÷ÇÏÁö ¾Ê¾Æ¼ ¸¹Àº ½Ã°£ÀÌ ÇÊ¿äÇÏÁö¸¸ ÀÛ¾÷À» ¹Ýº¹ÇÒ¼ö·Ï ¼÷´ÞÀÌ µÇ¾î ÀÛ¾÷½Ã°£ÀÌ ÁÙ¾îµå´Âµ¥, ÀÌ °°Àº Çö»óÀ» ÇнÀÈ¿°ú(learning effect)¶ó°í Çϸç ÀÌ È¿°ú¸¦ ¼öÇÐÀû ¸ðµ¨·Î Ç¥ÇöÇÑ °ÍÀÌ ÇнÀ°î¼± ¶Ç´Â ¿¬½À°î¼±ÀÌ´Ù]À» ±ØÀûÀ¸·Î ´ÜÃà½Ãų °ÍÀÌ´Ù.
¼Â°, ³ëÈ ¹æÁö ÈÇÐ ºÐÀÚ¿¡ ´ëÇÑ ¸ðµç ¿¬±¸´Â 2025³â±îÁö ¿©·¯ Ä¡·á¿¡ ´ëÇÑ Àΰ£ ½ÇÇèÀ¸·Î À̾îÁ® ¸¶Ä§³» ¼º°ú¸¦ °ÅµÑ °ÍÀÌ´Ù.
Èĺ¸ ¹°ÁúÀÇ ¾î¶² °ÍÀÌµç ¼º°øÇÑ´Ù¸é, ÀÚ¿¬ÀûÀÎ ³ëÈ °úÁ¤¿¡ °³ÀÔÇÏ¿© ƯÁ¤ Áúº´¿¡ ´ëÇÑ °ø°ÝÀÌ °¡´ÉÇÏ´Ù´Â ¾ÆÀ̵ð¾î°¡ °ËÁõµÉ °ÍÀÌ´Ù. Áï, ³ëÈ ÀÚü¸¦ Ä¡·áÇÔÀ¸·Î½á Áúº´ÀÇ ¿øÀÎÀ» ´ÊÃâ ¼ö ÀÖÀ» °ÍÀÌ´Ù. °úÇÐÀÚµéÀº ³ëÈ ¹æÁö ¾à¹°ÀÌ °á±¹ ³ë·É °èÃþÀÌ ¾àÇØÁö°í Àå¾Ö¸¦ ÀÏÀ¸Å°°í Áúº´¿¡ Ãë¾àÇØÁö´Â °ÍÀ» Áö¿¬½Ãų °ÍÀ¸·Î »ý°¢ÇÑ´Ù. ÇöÀç ¸Å¿ì À¯¸ÁÇÑ ÈÇÕ¹° Áß ÀϺδ ÀÌ¹Ì È¿¸ð, ¹ú·¹, ¼³Ä¡·ùÀÇ ¼ö¸íÀ» ±ØÀûÀ¸·Î ¿¬Àå½ÃÄ×Áö¸¸ ¿©ÀüÈ÷ Àΰ£¿¡¼´Â Á¤ÇüÈµÈ °á°ú¸¦ ¾òÁö ¸øÇß´Ù. MITÀÇ ³ëÈ ¹æÁö ¿¬±¸ÀÚ ·¹¿À³ª¸£µµ ±Í¶õÅ×(Leonard Guarente) ¹Ú»ç´Â ¡®°¡Àå Áß¿äÇÑ °ÍÀº °Ç°ÇÑ ¼ö¸íÀ» ¿¬ÀåÇÏ´Â °Í¡¯À̶ó°í ¸»ÇÑ´Ù. ³ëÈ ¹æÁö Çõ½ÅÀº ºÐ¸íÈ÷ ´Ù°¡¿À°í ÀÖ´Â ¹Ì·¡ÀÌ´Ù. ÀÌ´Â ÅëÇÑ °Ç°ÇÏ°í È°µ¿ÀûÀÎ ³ë·É Àα¸´Â »ý»êÀÚ¿Í ¼ÒºñÀڷμ °æÁ¦¿¡ µµ¿òÀÌ µÉ °ÍÀÌ´Ù.
³Ý°, ¾çÈ£ÇÑ °Ç° »óÅÂ, ¼÷·ÃµÈ ´ëü ÀηÂÀÇ ºÎÁ·, ¡®Á¶¿ëÇÑ ÀºÅ𡯿¡ ´ëÇÑ °ÅºÎ°¨Àº º£À̺ñ ºÕ ¼¼´ë°¡ ÀÌÀü ¼¼´ëº¸´Ù ÈξÀ ´õ ¿À·¡ ±Ù¹«ÇÒ °ÍÀ» ÀǹÌÇÑ´Ù.
2020³â ¹Ì±¹ ´ë¼± È帵éÀº °¡Àå ³ªÀÌ°¡ ¸¹Àº ÀÌÀü ´ëÅë·ÉµéÀÌ ÅðÀÓÀ» ÇßÀ» ¶§º¸´Ù ÇöÀç ´õ °í·ÉÀÌ´Ù. ÀÌ°ÍÀº À̵é°ú °°ÀÌ Àç´ÉÀ» °®Ãß°í ÀÖ°í ÇÏ´Â ÀÏ¿¡ ¸¸Á·ÇÏ´Â º£À̺ñ ºÕ ¼¼´ëµé »ó´ç¼ö°¡ ÃÖ¼ÒÇÑ 2030³â±îÁö ÀÏÀÚ¸®¿¡¼ ¶°³ªÁö ¾ÊÀ» °ÍÀÓÀ» ´ëº¯ÇÏ°í ÀÖ´Ù.
´Ù¼¸Â°, OECD Àüü¿¡¼ Àΰø Áö´ÉÀ» ÅëÇÑ ÀÚµ¿È Çõ¸íÀº ³ë·É Àα¸ÀÇ ÀºÅð¿¡µµ ºÒ±¸ÇÏ°í °æÁ¦ ¼ºÀå¿¡ È°·ÂÀ» ºÒ¾î ³ÖÀ» °ÍÀÌ´Ù.
Áõ±â ¿£Áø, Àü±â, ºÐ¾÷ Á¶¸³ ¶óÀΰú ¸¶Âù°¡Áö·Î, Àΰø Áö´ÉÀº ¼ö¸¹Àº ÀÀ¿ë ºÐ¾ß¸¦ ÈξÀ ´õ È¿À²ÀûÀÌ°í È¿°úÀûÀ¸·Î ¸¸µå´Â µ¥ Àû¿ë½Ãų ¼ö ÀÖ´Â ¹ü¿ë ±â¼úÀÌ´Ù. °ú°Å¿¡ ´Ù¾çÇÑ Çõ½Å ±â¼úµéÀÌ µµÀԵǾúÀ» ¶§¸¶´Ù, »çȸ´Â Áú¹®À» ´øÁ³´Ù. ¡®±â°è·Î ÀÛ¾÷ÀÌ ¼öÇàµÉ ¶§, ¸ðµç À׿© ÀηÂÀ¸·Î ¹«½¼ ÀÏÀ» ÇØ¾ß Çϴ°¡?¡¯ À̹ø¿¡ »çȸ´Â ¶Ç ´Ù¸¥ Áú¹®À» ´øÁö°í ÀÖ´Ù. ¡®ÀÏÇÏ´Â »ç¶÷ÀÌ ÃæºÐÇÏÁö ¾ÊÀ¸¸é ¾î¶»°Ô ¼ºÀåÇÒ ¼ö ÀÖÀ»±î?¡¯ ¾ÆÀÌ·¯´ÏÇÏ°Ôµµ ´ë´äÀº µ¿ÀÏÇÏ´Ù. ±×°ÍÀº Çõ½Å¿¡ ÀÖ´Ù. Àΰø Áö´ÉÀº ³ó¾÷À̳ª Á¦Á¶¾÷º¸´Ù ¼ºñ½º¿¡¼ ´õ À¯¿ëÇÑ ÃÖÃÊÀÇ ¹ü¿ë º¯Çü ±â¼úÀÌ´Ù. 2017³âºÎÅÍ 2035³â±îÁö ÁøÇàµÇ´Â ÀΰøÁö´ÉÀÌ ÁÖµµÇÏ´Â »ý»ê¼º Çõ¸íÀº ÀÌÁ¦ ¸· ±ÞµîÇϱ⠽ÃÀÛÇß´Ù. À̶§°¡ ¹Ù·Î º£À̺ñ ºÕ ¼¼´ë¿Í X¼¼´ë°¡ °¡Àå »ý»êÀûÀÌ µÇ°í, ¹Ð·¹´Ï¾ó ¼¼´ë´Â ¸¶Ä§³» ºûÀ» ¹ßÇÒ ±âȸ¸¦ ¾ò°Ô µÇ´Â ¼ø°£ÀÌ´Ù. ³ëÀÎ °£È£¿¡¼ ÀüÀÚ »ó°Å·¡, ÀÚÀ² ÁÖÇà Â÷·® ¹× 5G ½º¸¶Æ® Ȩ¿¡ À̸£±â±îÁö ÃÖ°í ±âȸ´Â ¿©ÀüÈ÷ ¿ì¸® ¾ÕÀ¸·Î ´Þ·Á¿À°í ÀÖ´Ù.
* *
References List :
1. Mar 15, 2019. The Trends Editors. Boomers are the Economy¡¯s ¡°Energizer Bunnies.¡±
https://audiotech.com/trends-magazine/boomers-are-the-economys-energizer-bunnies/
2. MIT Technology Review. August 21, 2019. David Rotman. Why you shouldn¡¯t fear the gray tsunami.
https://www.technologyreview.com/2019/08/21/133311/why-you-shouldnt-fear-the-gray-tsunami/#:~:text=The%20harm%20won't%20just,fear%20of%20our%20own%20selves
3. Jun 24, 2017. The Trends Editors. Making the Most of America¡¯s Aging Scientific Workforce.
https://audiotech.com/trends-magazine/making-americas-aging-scientific-workforce/
4. Mar 16, 2015. The Trends Editors. Economic Growth in an Aging World.
https://audiotech.com/trends-magazine/economic-growth-in-an-aging-world/
5. Dec 15, 2016. The Trends Editors. The Longevity Dividend.
https://audiotech.com/trends-magazine/the-longevity-dividend/
6. Sep 2, 2011. The Trends Editors. Global Demographics Favor U.S. Business.
https://audiotech.com/trends-magazine/global-demographics-favor-business/
The world is rapidly aging. Americans 65 and older are now 16% of the population and will make up 21% by 2035. At that point, they will outnumber those under 18. In China, the large numbers of people born before the one-child policy was introduced in 1979 are swelling the ranks of older people, even as younger age groups shrink. Other countries are even older. Japan, where more than a quarter of its population is 65 or older leads the way. But Germany, Italy, Finland, and much of the rest of the European Union aren¡¯t far behind. And, a quarter of the people in Europe and North America will be 65 or older by 2050.
This trend is being driven by low fertility rates as women in almost all countries are having fewer babies. The other factor is longer lives. While life expectancy has slowed its increase in some advanced countries in recent years, it continues its upward trend worldwide. A female baby born today in Japan is expected on average to live nearly 100 years.
Not only is the overall population aging; you will probably spend much more of your life being old then your parents did. In 1960, if you were 65, you could expect to live to around 79. These days, you¡¯re expected to live to nearly 85. If you¡¯re already 75, you should expect to live until 87.
This represents a huge shift that is changing our economy, our social and cultural values, and even the way we perceive and plan our lives. And while most managers and the general public have only recently begun to recognize the implications of this ¡°gray tsunami,¡± the Trends editors began telling our clients how to exploit the ¡°age wave,¡± 35 years ago.
Today, the conventional wisdom is that an aging population is toxic for economic growth. To begin with, who will do all the work? Even worse, how will we pay for all those old people¡¯s medical and welfare programs?
This conundrum is typically boiled down to one simple metric: the dependency ratio. The dependency ratio is simply the working-age population divided by the number of people who are too old, too young, or too ill to have a job. And many experts and politicians like to show scary projections of how trends in this ratio indicate that a demographic crisis is coming to get us.
The warnings of a demographic cliff or time-bomb sound ominous. But the truth is that economists don¡¯t know much about how an aging population will affect the world. And the impact of demography will depend to a large extent on the cultural, economic, and technological factors at play in various parts of the world.
On the basis of data from 1980 to 2010, Nicole Maestas, an economist at Harvard calculated, that a 10% increase in the population age 60 and older decreased growth in GDP per capita by 5.5%. That means that if the past is indicative of the future, the aging U.S. population could slow economic growth by 1.2 percentage points this decade and 0.6 percentage points in the next. Some of this would be because fewer people are working, but two-thirds of it would be because the workforce is less productive on average.
However, Maestas cautions that this projection is based on historical trends and may not be predictive. Her hypothesis is that productivity fell from 1980 to 2010 because the most skilled and experienced people left in larger numbers since, they were more successful, wealthier, and could afford to retire. If she¡¯s right, then it¡¯s not that workers become less productive as they age, but that the most productive ones stop working.
This means that a big drop in productivity isn¡¯t inevitable. In fact, new technologies and business policies can keep the most talented people working longer. And that¡¯s particularly true for top-quartile Baby Boomers who are healthier and tend to define themselves by the work they do. Less happily, some boomers also face shrinking savings and disappearing retirement plans that force them to stay active longer.
According to Daron Acemoglu, an MIT economist, ¡°there is very little evidence that aging societies are worse economically.¡± Looking at GDP data from 1990 to 2015, Acemoglu found no correlation between aging demographics and slowed economic growth. In fact, countries like South Korea, Japan, and Germany, are actually doing quite well despite rapidly aging populations.
Perhaps the biggest reason is automation. Countries with aging workforces have been quicker to adopt industrial robots to compensate. The automation-related boost to productivity is not simply ¡°softening the doom and gloom around aging,¡± Acemoglu says. He found a ¡°total lack of any evidence of negative effects from aging.¡±
However, that doesn¡¯t mean that countries won¡¯t encounter problems. As Acemoglu stresses, ¡°We¡¯re not sufficiently prepared to know what happens when the society ages, and we don¡¯t know how to navigate it,¡± optimally.
One of the biggest unknowns is the relationship between lifespan, health-span, and economic well-being. The increase in life expectancy over the last hundred years has been one of our great technological achievements. At the start of the 20th century, the average life expectancy was around 50; by 1960 it was 70, and by 2010 it was up to nearly 80. Most of the early progress was due to keeping children healthier—in 1900 nearly one in four died before age 10. Later progress came in the treatment of things like cardiovascular disease, allowing most people to live into their 70s.
But there is no assurance that this remarkable run will continue. Average life expectancy is leveling off and appears to be hitting a ceiling at just a little over 80. S. Jay Olshansky, at the University of Illinois at Chicago's school of public health, has been predicting this slowdown for years. He says we¡¯re near our upper limit for average life spans. ¡°Possibly we can get it up from 80 to 85,¡± he says, noting that ¡°Japan is closing in on it already.¡±
Until now, what we haven¡¯t been able to do is intervene to slow the aging process itself. However, the first wave of promising anti-aging drugs—the result of several decades of breakthroughs in understanding the biology of aging—is being tested in humans. Olshansky says, those drugs won¡¯t let us live forever and they probably won¡¯t even let us live much longer. But they could help us stay healthier longer in old age.
The key anti-aging molecules have been previously profiled in Trends. They include:
- rapamycin-like compounds that affect immune function,
- molecules that activate proteins called sirtuins,
- metformin, a longtime diabetes drug, and
- ¡°senolytic¡± drugs that clean up damaged and aging cells
For now, the hope for these molecules is that they can help with age-related ailments.
The important thing to remember is that people can remain highly productive regardless of their age as long as they have all of the skills and the tools needed to do the job. Being healthy and active is the starting point and that¡¯s where state-of-the-art health care comes in. The other factor is constantly updating and learning, which is as important for someone 65 as for someone who is 30. The reality is that, with human knowledge doubling every seven years or less, someone who has been out of college for eight years is almost as obsolete as someone whose been out for 40 years. Both need constant education to keep at the top of their game.
It¡¯s been 12 years since Facebook CEO Mark Zuckerberg famously asserted that ¡°young people are just smarter,¡± and almost a decade since 65-year-old billionaire venture capitalist Vinod Khosla told an audience, ¡°People under 35 are the people who make change happen,¡± adding, ¡°People over 45 basically die in terms of new ideas.¡±
However, academic research indicates that Zuckerberg and Khosla are wrong. In a rigorous study that looked at 2.7 million company founders, economists at MIT, the US Census Bureau, and Northwestern University concluded the best entrepreneurs are middle-aged. The fastest-growing startups were created by founders with an average age of 45. In a 2018 study, they found that a 50-year-old entrepreneur was nearly twice as likely to build a highly successful company as a 30-year-old. And contrary to Khosla¡¯s tweet, it turns out that industry experience was a significant positive in predicting success.
Blatant age bias might also explain why Silicon Valley has done such a terrible job of creating startups in biomedicine, clean energy, or other areas requiring scientific expertise and knowledge. In earlier research, one of the authors of last year¡¯s paper, Benjamin Jones, an economist at Northwestern, presented evidence that most great scientific achievements in the physical sciences and medicine come in middle age, not from the precocious young.
What¡¯s the bottom line?
The world is rapidly aging. It¡¯s unclear exactly what this means. The results will depend not only on demography but on economic resources, technology, and human behavior. Managed correctly, the United States and many other countries will be able to adapt to this new reality. But mismanagement could lead to a bleak future. The only thing we know for sure is that people aren¡¯t getting any younger. And we can¡¯t wait any longer to proactively address this challenge.
Given this trend, we offer the following forecasts for your consideration.
First, in the United States, the private and public sectors will finally come to grips with their age bias, as soon as 2022.
The increasing shortage of talent will force venture capitalists and employers to reconsider their age bias. Other trends such as the reshoring of American manufacturing, restrictions on immigration, and the prevalence of automation will all favor recruitment and retention of older workers. Similarly, with both Boomer and Xers perceiving ¡°bias,¡± political pressure will mount. This will create new opportunities especially for talented Gen-Xers who have always been squeezed between Boomers and Millennials.
Second, employers, educators, and individuals will increasingly adopt practices aimed at continuous learning and skills acquisition.
Businesses in the 2020s will be characterized by a wave of new technologies and business models. Every worker will need to be continuously retrained to avoid obsolescence. Fortunately, distance learning is finally advancing to the point where anyone with a smartphone can get training and assistance anywhere, anytime. Augmented reality, enhance by AI and live tech support, will dramatically reduce the learning curve for many jobs.
Third, all of the research on anti-aging molecules will finally pay-off with human trials of multiple treatments by 2025.
Once any of the candidates succeeds it will validate the idea that it¡¯s possible to attack certain illnesses by intervening in natural aging processes; in other words, by treating aging itself we can slow the contributing causes of disease. Scientists envision anti-aging drugs eventually delaying older people becoming frail and disabled and vulnerable to one illness after another. As we¡¯ve documented in Trends, some of these promising compounds have already dramatically extended the life span of yeast, worms, and rodents, but we still haven¡¯t achieved documented results in humans. As Leonard Guarente, an anti-aging pioneer at MIT says, ¡°The most important thing is extending the healthy life span.¡± At Trends, we¡¯re confident that anti-aging breakthroughs are coming, but the commercialization timeframe is unclear. Healthy, active oldsters will be a boon to the economy both as producers and consumers.
Fourth, good health, a shortage of skilled replacements, and an unwillingness to ¡°retire quietly¡± means that Baby Boomers will remain in the workforce far longer than previous generations.
Both of the 2020 presidential candidates are already older than the previous oldest President was when he left office. This simply illustrates the fact that a large share of talented and satisfied Boomers won¡¯t be leaving the workforce until at least 2030. And,
Fifth, across the OECD, an automation revolution enabled by artificial intelligence will reenergize economic growth, in spite of retirements.
Like steam engines, electricity, and the assembly line, artificial intelligence is a general-purpose technology that can be applied to make myriad applications dramatically more efficient and effective. When these other technologies were introduced, the society asked, ¡°What do we do with all the excess people, now that their jobs can be done by machines?¡± This time, society is asking ¡°How can we grow if we don¡¯t have enough people working?¡± Ironically, the answer is the same: innovate. Artificial intelligence is the first general-purpose transformative technology that¡¯s more useful in services than in agriculture or manufacturing. The AI-driven productivity revolution, running from 2017 to 2035, is just now beginning to surge. And that¡¯s exactly when the Boomers and Xers will be at their most productive and millennials will finally get their chance to shine. From eldercare to e-commerce, to autonomous vehicles and 5G smart homes, the best is yet to come.
**
References
1. Mar 15, 2019. The Trends Editors. Boomers are the Economy¡¯s ¡°Energizer Bunnies.¡±
https://audiotech.com/trends-magazine/boomers-are-the-economys-energizer-bunnies/
2. MIT Technology Review. August 21, 2019. David Rotman. Why you shouldn¡¯t fear the gray tsunami.
https://www.technologyreview.com/2019/08/21/133311/why-you-shouldnt-fear-the-gray-tsunami/#:~:text=The%20harm%20won't%20just,fear%20of%20our%20own%20selves
3. Jun 24, 2017. The Trends Editors. Making the Most of America¡¯s Aging Scientific Workforce.
https://audiotech.com/trends-magazine/making-americas-aging-scientific-workforce/
4. Mar 16, 2015. The Trends Editors. Economic Growth in an Aging World.
https://audiotech.com/trends-magazine/economic-growth-in-an-aging-world/
5. Dec 15, 2016. The Trends Editors. The Longevity Dividend.
https://audiotech.com/trends-magazine/the-longevity-dividend/
6. Sep 2, 2011. The Trends Editors. Global Demographics Favor U.S. Business.
https://audiotech.com/trends-magazine/global-demographics-favor-business/