2014年3月15日 星期六

由科技與策略趨勢看:大數據及資料之關係將改變企業的未來 ( Big Data Will Transform The Future Of Business )[ビッグデータは、ビジネスの未来を変えていく] - Genome, Big Data With NORA And Data analysis formed knowledge will be another business in future )

Philip Evans is a senior partner and managing director in BCG’s Boston office. He founded BCG’s
media sector and has consulted for corporations worldwide in the financial services, consumer goods, media, and high-technology industries. He has also advised governments on military strategy, homeland security, and national economic policy.

Philip's long-term interest is the relation between information technology and business strategy. He is a coauthor of, among other publications, four Harvard Business Review articles, one of which, “Strategy and the New Economics of Information,” won HBR’s McKinsey Award. Blown to Bits (coauthored with Tom Wurster) was the best-selling book worldwide on technology and strategy in 2000 and has been translated into 13 languages. He is a frequent speaker on technology and strategy at industry, corporate, and academic conferences and has given keynote addresses at events convened by Bill Gates, Michael Milken, and the World Economic Forum. ( 菲利普的長遠專注於資訊技術和業務策略之間的關係。他是一個四篇哈佛商業評論出版文章中之合著作者,其中之一,“策略與資訊產業的新經濟學”榮獲哈佛商業評論的麥肯錫獎。被炸成碎片(合著與湯姆·沃斯特)是最暢銷的技術和戰略的全球本書於2000年,已被翻譯成13種語言。他常於企業和學術會議中對技術和戰略主題做發言,並給予比爾·蓋茨、邁克爾·米爾肯、及世界經濟論壇召開的事件上演講。)

I'm going to talk a little bit about strategy and its relationship with technology. We tend to think of business strategy as being a rather abstract body of essentially economic thought, perhaps rather timeless. I'm going to argue that, in fact, business strategy has always been premised on assumptions about technology, that those assumptions are changing, and, in fact, changing quite dramatically, and that therefore what that will drive us to is a different concept of what we mean by business strategy. ( 我要談一點關於策略及其與技術的關係。我們傾向於認為企業策略的作為基本經濟思想的一個相當抽象的主體,也許是相當歷久彌新。我會爭辯說,事實上,經營策略一直前提是對技術的假設,這些假設發生變化,而且,事實上,變化相當巨烈,因此,這巨烈變化將推動我們不同的概念也就是我們所說的經營策略。)

Let me start, if I may, with a little bit of history. The idea of strategy in business owes its origins to two intellectual giants: Bruce Henderson, the founder of BCG, and Michael Porter, professor at the Harvard Business School. Henderson's central idea was what you might call the Napoleonic idea of concentrating mass against weakness, of overwhelming the enemy. What Henderson recognized was that, in the business world, there are many phenomena which are characterized by what economists would call increasing returns -- scale, experience. The more you do of something, disproportionately the better you get. And therefore he found a logic for investing in such kinds of overwhelming mass in order to achieve competitive advantage. And that was the first introduction of essentially a military concept of strategy into the business world. ( Note:  Bruce Henderson's reference on  business strategy and Michael Porter Value chain )
( 讓我開始,如果可以的話,講一點點經營策略歷史。策略在企業的理念起源於兩個思想巨人:BCG的創始人 "布魯斯·亨德森"及哈佛商學院教授 "邁克爾·波特"。亨德森的中心思想是你可以稱之為大規模集中打擊及壓倒了弱勢的敵人之拿破崙戰略。亨德森公認為,在商業世界中,有許多現象其特點是經濟學家稱之報酬遞增 - 規模之經驗。你做的更多的努力,你得到更好的報酬。因此,他發現,投資於這類種壓倒性的量,來實現競爭優勢的邏輯。本質上,這是第一個引進軍事概念戰略進入商業世界。)

Porter agreed with that premise, but he qualified it. He pointed out, correctly, that that's all very well, but businesses actually have multiple steps to them. They have different components, and each of those components might be driven by a different kind of strategy. A company or a business might actually be advantaged in some activities but disadvantaged in others. He formed the concept of the value chain, essentially the sequence of steps with which a, shall we say, raw material, becomes a component, becomes assembled into a finished product, and then is distributed, for example, and he argued that advantage accrued to each of those components,
and that the advantage of the whole was in some sense the sum or the average of that of its parts. And this idea of the value chain was predicated on the recognition that what holds a business together is transaction costs, that in essence you need to coordinate, organizations are more efficient at coordination than markets, very often, and therefore the nature and role and boundaries of the cooperation are defined by transaction costs. It was on those two ideas, Henderson's idea of increasing returns to scale and experience, and Porter's idea of the value chain, encompassing heterogenous elements, that the whole edifice of business strategy was subsequently erected. ( 波特同意這個前提,但他加入一些條件來合格化。他正確地指出,那這一切都很好,但企業實際上有多個步驟。它們具有不同的組件,並且每個組件可以由不同的策略來驅動。一個公司或一個企業實際上是有些策略及組件是優勢,但有些是弱勢。他形成了價值鏈,本質上,我們可以說成幾步序列,一個原材料成為一個組件,再經組裝為成品,然後再經通路經銷,例如,他認為優勢是在每個組件,而整個的優勢是在某種意義上是所有組件總和,或說大部分平均組件的總和。而這個想法在價值鏈中認識到持有生意往來是交易成本,本質上,這在需要更有效的協調組織來超越市場,很多時候,合作中角色界線是定義為交易成本。這正是兩種觀念,亨德森的報酬遞增的規模和經驗及波特的價值鏈理念,涵蓋異質元素並豎立整個經營策略的大架構。)

Now what I'm going to argue is that those premises are, in fact, being invalidated. First of all, let's think about transaction costs. There are really two components to transaction costs. One is about processing information, and the other is about communication. These are the economics of processing and communicating as they have evolved over a long period of time. As we all know from so many contexts, they have been radically transformed since the days when Porter and Henderson first formulated their theories. In particular, since the mid-'90s, communications costs have actually been falling even faster than transaction costs, which is why communication, the Internet, has exploded in such a dramatic fashion. Now, those falling transaction costs have profound consequences, because if transaction costs are the glue that hold value chains together, and they are falling, there is less to economize on. There is less need for vertically integrated organization, and value chains at least can break up. They needn't necessarily, but they can. In particular, it then becomes possible for a competitor in one business to use their position in one step of the value chain in order to penetrate or attack or disintermediate the competitor in another. ( 當這些下降的交易成本產生深遠的影響,因為如果交易成本持有價值鏈是黏一起的,交易成本正在下降,就有較少的節約。這裡是垂直整合的組織需求下降,垂直整合的價值鏈至少可以分手。他們不一定需要,但是他們可以有。特別是,它那麼對於競爭對手就有可能在這一個行業中使用他們的位置在價值鏈中的一個步驟與組織,以在另一個滲透、攻擊另一個競爭對手或跳過它的通路。)

That is not just an abstract proposition. There are many very specific stories of how that actually happened. A poster child example was the encyclopedia business. The encyclopedia business in the days of leatherbound books was basically a distribution business. Most of the cost was the commission to the salesmen. The CD-ROM and then the Internet came along, new technologies made the distribution of knowledge many orders of magnitude cheaper, and the encyclopedia industry collapsed. It's now, of course, a very familiar story. This, in fact, more generally was the story of the first generation of the Internet economy. It was about falling transaction costs breaking up value chains and therefore allowing disintermediation, or what we call deconstruction. ( 在 leather bound 書的時代,百科全書業務基本上是一個分銷業務。大部分的費用是佣金的推銷員。CD-ROM後,網際網路出現了,新技術形成很多規模更便宜的訂單分佈之知識,百科全書行業崩潰。當然現在這是一個很熟悉的故事。其實,這更普遍的說這是第一代互聯網經濟的故事。是交易成本下降打破了價值鏈,因此允許跳過中介,或者我們稱之為通路之解構。)

One of the questions I was occasionally asked was, well, what's going to replace the encyclopedia The Wikipedia, of course, is an encyclopedia created by its users. And this, in fact, defines what you might call the second decade of the Internet economy, the decade in which the Internet as a noun became the Internet as a verb. It became a set of conversations, the era in which user-generated content and social networks became the dominant phenomenon. Now what that really meant in terms of the Porter-Henderson framework was the collapse of certain kinds of economies of scale. It turned out that tens of thousands of autonomous individuals writing an encyclopedia could do just as good a job, and certainly a much cheaper job, than professionals in a hierarchical organization. So basically what was happening was that one layer of this value chain was becoming fragmented, as individuals could take over where organizations were no longer needed. ( 其中一個我偶爾問的問題是,這是怎麼回事,誰取代百科全書大英百科全書讓不再有商業模式?前一段的答案變得明顯。我們知道它是什麼:它是維基百科。現在有什麼特別之處維基百科是不是它的分佈。有什麼特別的維基百科是它的生產方式。維基百科,當然是由其用戶創建的百科全書。而這一點,其實你可以稱之為互聯網經濟的第二個十年,這十年中,互聯網作為一個名詞成為了互聯網作為一個動詞。它成為一組對話,其中用戶生成內容和社交網絡成為佔主導地位的現象的時代。現在,究竟意味著波特 - 恆基框架中是某些種類的規模經濟的崩潰。原來,自主的幾萬個體寫作的百科全書,一樣可以做這工作,而且肯定比在一個專業層次組織人員便宜得多。所以基本上發生了什麼事是一層這個價值鏈變得支離破碎,因為那裡已不再需要組織的個人可以接管。)
Facebook business model in business model canvas
when Britannica no longer has a business model? And it was a while before the answer became manifest. Now, of course, we know what it is: it's the Wikipedia. Now what's special about the Wikipedia is not its distribution. What's special about the Wikipedia is the way it's produced.

But there's another question that obviously this graph poses, which is, okay, we've gone through two decades -- does anything distinguish the third? And what I'm going to argue is that indeed something does distinguish the third, and it maps exactly on to the kind of Porter-Henderson logic that we've been talking about. And that is, about data. If we go back to around 2000, a lot of people were talking about the information revolution, and it was indeed true that the world's stock of data was growing, indeed growing quite fast. but it was still at that point overwhelmingly analog. We go forward to 2007, not only had the world's stock of data exploded, but there'd been this massive substitution of digital for analog. And more important even than that, if you look more carefully at this graph, what you will observe is that about a half of that digital data is information that has an I.P. address. It's on a server or it's on a P.C. But having an I.P. address means that it can be connected to any other data that has an I.P. address. It means it becomes possible to put together half of the world's knowledge in order to see patterns, an entirely new thing. If we run the numbers forward to today, it probably looks something like this. We're not really sure. If we run the numbers forward to 2020, we of course have an exact number, courtesy of IDC. It's curious that the future is so much more predictable than the present. And what it implies is a hundredfold multiplication in the stock of information that is connected via an I.P. address. Now, if the number of connections that we can make is proportional to the number of pairs of data points, a hundredfold multiplication in the quantity of data is a ten-thousandfold multiplication in the number of patterns that we can see in that data, this just in the last 10 or 11 years. This, I would submit, is a sea change, a profound change in the economics of the world that we live in.

Now, what does that imply in terms of business? Well, I got a hint of this some years ago. Back in around 2003 or so, I was doing some consulting for the Pentagon, of all august institutions, on the subject of network-centric warfare, and in that context I met a gentleman called Jeff Jonas, a brilliant engineer who had made his fortune designing the security systems in Las Vegas. Jeff said to me, "Next time you're in Las Vegas, Philip, why don't you stop by and I'll take you on the tour. You can meet NORA. NORA will show you a good time." NORA was not his girlfriend. NORA is the Non-Obvious Relational Awareness system, a real-time fraud control system developed by Jeff, which supports all of the casinos in Las Vegas. We were in the security room of the Bellagio Hotel in Las Vegas, and on the monitor I saw this happen. A woman was playing blackjack against the dealer. There was nobody else at the table. She was winning too much. They know how likely that is, and this wasn't likely. So the first thing they do is they use facial recognition, see if she's staying at the hotel. She wasn't. Then they can kind of run the cameras backwards, tracing her movements back through the hotel to the parking garage, where they found her car. They could then run NORA to find who owned the car. The car was owned by Hertz Las Vegas. Within a second or so, NORA pulled down the Hertz Las Vegas application. Now they knew who the woman was. Where was she staying? Well, they pool the data across the hotels. It turned out she was staying in a hotel across the street. Had she gambled in that hotel? No. Very strange behavior, staying in one hotel, gambling in another. Then came the really interesting thing. NORA looked for a connection between the woman and the dealer, because a very high fraction of fraud in Las Vegas is committed when the staff are actually in illicit collaboration with customers. It turned out, what NORA did was to look through 6,000 databases, public and private, some owned by the Bellagio, some by other hotels, some police records, and so on. It turned out that 10 years earlier, this woman's brother had been the dealer's roommate. And it took NORA six seconds to work that fact out. It cost the woman and the dealer six years. This was NORA in action. It's what today of course we would call big data, long before the term had been formulated.

Now notice some very interesting things about this, most of all the fact that NORA runs as a cooperative across the entire of the strip. These casinos, which are otherwise competing aggressively with each other actually collaborate when it comes to the management of their security systems. They pool data into a common database that is run essentially as a co-op for this specific purpose. Why? Because the scale of NORA, what NORA is trying to do, blows past the scale that even a very large casino can possibly do for itself. The value chain is not big enough to accommodate the economies of scale that are inherent in this particular activity. And that principle, I would suggest, is actually a fundamental and pervasive one. In essence, what happens is that because of these colossal economies of scale in data, what used to be value chains that ran separately are compelled, in order to achieve those economies of scale, to create some kind of common utility, some common resource, a co-op, a pool, a vault of data within which those insights can be gathered.

Now, NORA is a relatively trivial example in the sense that if NORA failed, it wouldn't exactly be the end of civilization. But consider something vastly more important, where the logic in fact is exactly the same, the logic of healthcare. The first human genome, that of James Watson, was mapped as the culmination of the Human Genome Project in the year 2000, and it took about 200 million dollars and about 10 years of work to map just one person's genomic makeup. Since then, the costs of mapping the genome have come down. In fact, they've come down in recent years very dramatically indeed, to the point where the cost is now below 1,000 dollars, and it's confidently predicted that by the year 2015 it will be below 100 dollars -- a five or six order of magnitude drop in the cost of genomic mapping in just a 15-year period, an extraordinary phenomenon. Now, in the days when mapping a genome cost millions, or even tens of thousands, it was basically a research enterprise. Scientists would gather some representative people, and they would see patterns, and they would try and make generalizations about human nature and disease from the abstract patterns they find from these particular selected individuals. But when the genome can be mapped for 100 bucks, 99 dollars while you wait, then what happens is, it becomes retail. It becomes above all clinical. You go the doctor with a cold, and if he or she hasn't done it already, the first thing they do is map your genome, at which point what they're now doing is not starting from some abstract knowledge of genomic medicine and trying to work out how it applies to you, but they're starting from your particular genome.

Now think of the power of that. Think of where that takes us when we can combine genomic data with clinical data with data about drug interactions with the kind of ambient data that devices like our phone and medical sensors will increasingly be collecting. Think what happens when we collect all of that data and we can put it together and use precisely the NORA-type techniques in order to find patterns we wouldn't see before. This, I would suggest, perhaps it will take a while, but this will drive a revolution in medicine. Fabulous, lots of people talk about this.

But there's one thing that doesn't get much attention. How is that model of colossal sharing across all of those kinds of databases compatible with the business models of institutions and organizations and corporations that are involved in this business today? If your business is based on proprietary data, if your competitive advantage is defined by your data, how on Earth is that company or is that society in fact going to achieve the value that's implicit in the technology? They can't.

So essentially what's happening here, and genomics is merely one example of this, is that technology is driving the natural scaling of the activity beyond the institutional boundaries within which we have been used to thinking about it, and in particular beyond the institutional boundaries in terms of which business strategy as a discipline is formulated. The basic story here is that what used to be vertically integrated, oligopolistic competition among essentially similar kinds of competitors is evolving, by one means or another, from a vertical structure to a horizontal one. Why is that happening? It's happening because transaction costs are plummeting and because scale is polarizing. The plummeting of transaction costs weakens the glue that holds value chains together, and allows them to separate. The polarization of scale economies towards the very small -- small is beautiful -- allows for scalable communities to substitute for conventional corporate production. The scaling in the opposite direction, towards things like big data, drive the structure of business towards the creation of new kinds of institutions that can achieve that scale. But either way, the typically vertical structure gets driven to becoming more horizontal.

The logic isn't just about big data. If we were to look, for example, at the telecommunications industry, you can tell the same story about fiber optics. If we look at the pharmaceutical industry, or, for that matter, university research, you can say exactly the same story about so-called "big science." And in the opposite direction, if we look, say, at the energy sector, where all the talk is about how households will be efficient producers of green energy and efficient conservers of energy, that is, in fact, the reverse phenomenon. That is the fragmentation of scale because the very small can substitute for the traditional corporate scale.

Either way, what we are driven to is this horizontalization of the structure of industries, and that implies fundamental changes in how we think about strategy. It means, for example, that we need to think about strategy as the curation of these kinds of horizontal structure, where things like business definition and even industry definition are actually the outcomes of strategy, not something that the strategy presupposes. It means, for example, we need to work out how to accommodate collaboration and competition simultaneously. Think about the genome. Think about NORA We need to accommodate the very large and the very small simultaneously. And we need industry structures that will accommodate very, very different motivations, from the amateur motivations of people in communities to maybe the social motivations of infrastructure built by governments, or, for that matter, cooperative institutions built by companies that are otherwise competing, because that is the only way that they can get to scale.

These kinds of transformations render the traditional premises of business strategy obsolete. They drive us into a completely new world. They require us, whether we are in the public sector or the private sector, to think very fundamentally differently about the structure of business, and, at last, it makes strategy interesting again.

参考




2014年3月6日 星期四

Big Data 時代來臨!海量資料分析成為新產業! ( Big Data Trend came, Data Analysis become new profitable business and industry )

思科:2018年全球行動資料傳輸量將暴增至190 EB


「行動(Mobile)」一詞過去曾經只涵蓋手機、平板電腦,現在則已泛指各式各樣的穿戴裝置,例如智慧手錶、健康狀態追蹤器、谷歌眼鏡(Google Glass)等等。思科(Cisco System)認為,這些科技新品將帶動資料傳輸需求,預期到了2018年,全球行動資料傳輸量將暴增至190 exabytes (1 exabyte等於十億gigabytes)。

CNBC、Re/code報導,思科5日在年度預測報告中指出,2018年190 exabytes的行動資料傳輸量等於較2013年的傳輸量跳增11倍,也相當於42兆份影像、或是4兆片影片。這也是2000年固網、行動網路總傳輸量的190倍。

在行動資料用量快速增加的環境下,使用這些資料的行動裝置數量也將在2018年由2013年的70億台跳升至100億台。根據聯合國統計,2018年全球人口僅76億人,意味著屆時行動裝置的數量會是全球總人口的1.4倍。

行動資料傳輸量有如此爆炸性的成長,主要得益於穿戴科技的普及,思科把智慧手錶、Google Glass等穿戴裝置統一歸類至「機器對機器的連結」類別(大多屬於物聯網領域),這個分類在2013年僅佔全球行動資料傳輸量的1%,預料到了2018年會增加至6%。另外,思科也預估,2018年全球有在使用的穿戴裝置數量將由2013年的不到2,200萬台躍升至接近1.7億台。

Big Data淘金!資料分析商Tableau賺翻 股價飆13%
Tableau provide big data analysis and visual display

海量資料(Big Data)席捲而來,資料分析軟體也開始大受歡迎,Tableau Software Inc.在這波潮流的帶動下,本季營收有望創下佳績,激勵公司股價在5日飆漲近13%。

資料分析暨視覺化軟體商Tableau 4日於美國股市盤後公佈2013年第4季(10-12月)財報:營收較2012年同期跳增95%至8,150萬美元;授權金營收年增93%至5,800萬美元;本業每股稀釋盈餘達0.20美元。根據Thomson Reuters I/B/E/S調查,分析師原本預期該公司Q4營收將達6,710萬美元,本業每股盈虧則損益兩平。

Tableau指出,Q4期間該公司擴大了與亞馬遜雲端服務「Amazon Web Services (AWS)」的合作關係,支援在AWS平台執行的Tableau伺服器,並為亞馬遜雲端資料倉儲服務「Amazon Redshift」增添新的資料連接器。

此外,Q4期間還完成179筆價值超過10萬美元的銷售訂單,並新增超過1,800個客戶帳號。

Tableau執行長兼總裁Christian Chabot在新聞稿中表示,市場商機仍在不斷增加當中,該公司希望在2014年協助更多客戶透過資料洞悉未來營運的方針。

Thomson Reuters、barron`s.com報導,Tableau 在電話會議上預估本季(1-3月)營收將介於6,100-6,300萬美元之間。根據Thomson Reuters I/B/E/S調查,分析師原本預期該公司本季營收僅將達6,030萬美元,Tableau 5日聞訊大漲12.82%,收89.61美元,創2013年5月17日掛牌以來收盤新高;掛牌迄今漲幅已達76.6%。
海量資料需求夯,除了Tableau創下營運佳績外,磁碟陣列大廠Dot Hill Systems Corp.也在1月9日調高上季財測,這激勵該公司當日盤中股價狂漲逾25%。

Dot Hill Systems執行長Dana Kammersgard當時在聲明稿中表示,2013年期間該公司與許多垂直市場的重量級客戶敲定合作契約,這會成為2014年在海量資料、石油與天然氣、多媒體暨娛樂業中繼續成長的主要基石。

海量資料分析架構Hadoop每年成長55% 創辦人吃驚

儲存、處理、分析海量資料(Big Data)的開放技術架構「Hadoop」已成為產業最熱門的關鍵字,研究估計相關市場每年的成長率高達55%,速度之快連創辦人 Doug Cutting 都嚇了一跳。

英國商業科技新聞網站Computing 7日報導,根據市調機構Transparency Market Research的預估,全球Hadoop市場目前平均以每年近55%的速度快速成長,預計2018年市場規模將上看209億美元。對此,Cutting在接受專訪時表示,他當時完全不知道 Hadoop 會獲得如此大的迴響,就連甲骨文(Oracle)、IBM與微軟(Microsoft)也都還未發覺管理、儲存海量資料會有如此高的需求。

Cutting指出,開放原始碼(open source)是Hadoop能夠續存並強化的關鍵,人們不喜歡太過依Google Inc.)以及大學實驗室卻能藉此贏得優勢。
賴由某一業者控制的平台,但這種作法卻非上述傳統軟體業者所長,而雅虎(Yahoo!)、谷歌(

如今,Hadoop已成為全球企業儲存、處理與分析海量資料時的核心技術,且目前還在繼續演進當中。Cutting在2009年離開雅虎,接手Hadoop開發商Cloudera的架構長一職。目前,財星五百大企業的生產專案中,已有60%採用Cloudera的技術。Cutting表示,目前Cloudera大多數的業務成長都來自現有客戶,未來替換需求應該會高於新客戶所帶來的業務量。

IDC:2012-16年台灣巨量資料市場CAGR達40%
world wide big data revenue by service segments

IDC 2012-2016年亞太區(不含日本)巨量資料市場分析與預測報告(APEJ Big Data Technology and Services 2012-2016 Forecast and Analysis)研究顯示,亞太區(不含日本)的巨量資料市場成長力道強勁,估市場規模將從2012年的3億美元成長到2016年的17.6億美元,年複合成長率(CAGR)達47%。另台灣巨量資料市場規模則估可從2012年的1130萬美元成長到2016年的4610萬美元,年複合成長率也達到40%。

IDC亞太區巨量資料與分析研究總監Craig Stires指出,面對仍在持續不斷演進的巨量資料分析技術以及多元的市場需求,廠商們除持續不斷推出各種解決方案,亦積極於投資、訓練合作夥伴,以提供企業客戶更完整的諮詢顧問服務。而根據IDC的觀察,目前以金融、電信、政府、零售、製造與能源產業對於數據資料分析的需求最為強勁。

同時台灣市場對於巨量資料分析技術需求也在逐步提高。IDC台灣市場分析師蔡宜秀指出:「台灣的金融、製造、電信與零售等企業客戶正在嘗試、或者是計畫透過巨量資料分析技術,優化營運績效。在越來越多的企業客戶紛紛釋出對巨量資料分析技術的興趣後,將有更多的本土廠商、諮詢顧問投入該塊市場,進而逐步完善台灣的巨量資料生態系統。」

分析
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2014年3月1日 星期六

馬總統下的勞工只是繳稅工具? 台灣勞工是全世界公認被政府苛待,政府支出給勞工勞保歲出比重更是世界低之比重

美人權報告 指台勞工受苦、官員貪腐
每次增稅, 間接衝擊就是勞工薪資及勞工工作機會,
難道, 馬總統不知道嗎?

美國國務院今天公布2013年全球人權報告,台灣部分,從總統馬英九2012年連任以來進行討論,問題主要在於勞工、家庭傭工權益遭剝削以及官員腐敗等,並提及洪仲丘案、前行政院秘書長林益世涉貪案、9月政爭等案例。

 雖然馬總統認為勞動部已經很努力,不該因關廠工人的單一事件使外界有勞工很慘的「錯覺」,但美國國務院的報告仍指出,勞工遭到剝削,是台灣的人權問題的一大問題。

 報告稱,台灣因疲弱經濟表現和國外製造的遷移,導致勞工的實際薪資水準倒退,即使有最低薪資的保證,仍有許多勞工只能領到微薄薪水,並且,對部分領域而言,公司違反法律,讓勞工超時加班是常態。外籍勞工方面,尤其是家庭看護,至今沒有最低薪資和加班費的保障,且缺乏休假和休息。

 報告也訪問勞工公會和民間團體,他們指出,勞動部(報告稱勞委會)只有294名稽查員,監察率實在太低,導致勞動違法行為和工作條件不安全的情況普遍,政府無法對企業行承有效的威懾作用。

政府找錢 從三大稅負下手
勞保新制下根本還沒有多少人領勞工退休金, 勞保基金怎會錢少這麼
多?主因是勞『勞工保險條例第2條規定, 普通事故保險包含生育、
傷病 ... , 原來馬政府選舉開的勞工福利是從我們跟雇主籌的退休
勞保基金來騙我們的票.

健全財政中長程四年計畫出爐,政府未來將視國家財政改善程度,決定是否再增稅,增稅財源優先指向三大稅負,包括調整房產稅、開徵能源稅及特定政務附加捐,例如「募兵捐」。據指出,年底之前,將先確定有無需要開徵能源稅;明年著手研議調整房產稅。至於以募兵、12年國教等特定政務支出所需財源不足時,也將考量在營業稅或房屋、地價等財產稅上,加徵一定比率的附加捐。

財政部強調,中、長程健全財政方案有關稅制改革或增加財源的計畫,仍要視短期財政改革措施對改善國家財政的實質成果而定,目前只是策略方案,還沒有具體增稅的計畫。財政部長張盛和說,如果國家財政已經步上健康的結構,政府絕對沒有再增稅的必要。

財政部昨(27)日向行政院院會提出財政健全方案報告,此項為期四年、涵蓋短中長期健全財政策略的執行計畫,江宜樺責成各部會依方案內容落實辦理,並由副院長毛治國擔任專案小組召集人,監督落實成效。

張盛和周一已先公布健全方案屬於「短期」(一年內完成)的執行計畫,包括自104年度起控制舉債上限不超過前三年平均國內生產毛額38.6%,同時將調增綜所稅率一級至45%(富人回饋稅),恢復銀行、保險本業營業稅率至5%,並且調降股利所得可扣抵稅額比率至50%。估計僅增稅部分,國庫一年可淨增700億元。

短期計畫預定今年上半年完成所需修法工程,最快下半年、最慢明年即實施。財政部昨天也一併向行政院提出財政健全的中、長期計畫。根據財政部規劃,公平合理的稅制將持續性滾動檢討,年底前財政部將協同經濟部、環保署先研議有無開徵能源稅的必要;2016年之前,需就不動產課稅制度提出改革方案;2017 年之前,再視特定政務需要開徵附加捐,例如,財政部提出加徵「募兵捐」的建議。

台灣公務員平均退休福利比勞工退休福利好三倍以上,嚴重苛待勞工
台灣若要提供這麼高的工務員福利, 需開除 65%之公務員?

台灣勞工長期缺乏政府稅收資源分配之概念,因此被兩政黨夾持走所有利益,其實勞工是繳稅大眾 超過986萬勞保人數,含眷屬超過 1800萬人,是否愛台灣其實由 (勞保給付+勞委會勞工福利支出)/政府歲出合計 就看出來。

台灣政府會不會花太多錢,政府一年花超過1.93兆之浪費造成的債務,其中83%都非勞工花費而政府人事與退休經常性支出,人民沒錢養政府,真正問題就會造成台灣政府組織過大經常性支出過高,幾十萬中央及地方政府人員一年花掉近3兆而真正用在刺激民間經濟卻低於300億, 真正勞工勞保及就業福利才685億占中央歲出之3.08%; 相較於勞工繳的綜所稅總額近3000億占總繳稅比重24.5%,1000萬勞工不覺得奇怪嗎? 這是拼經濟拼財政健全嗎?
這政府仍是極權體制的政府歲出分配比重, 請勞工告到美國及世界人權組織, 讓台灣關系法增添勞工人權監督部份

美國每年撥給勞工之歲出超過 23%,台灣真正勞工福利才約中央歲出之3.08%,政府造成債務不應該由人民負責。美國、加拿大、英國、德國、日本都是繳稅十年以上,老人退休金及勞工社會福利就由政府稅收來支出,為什麼台灣變成由勞保基金來支出?政府補助勞保基金僅有10%是全世界民主國家最低的,政府如此苛待勞工,勞工竟被政府增稅拉近『貧富懸殊』騙了幾十年。勞工我們是這國家繳稅最多的族群,人數也已經1000萬人,為什麼這麼被政府苛待呢?

郭台銘:贊成富人稅 但應監督政府不要亂花錢!
勞工及公務員退休金來自稅收分配比率相差實在太大, 而且勞工
退休金給付只有 10%來自政府支出, 政府支出分配太不公平, 也
就是說終身勞工平均得到之政府退休金給付支出只有24.3萬, 而
勞工30年累計平均繳給政府綜所稅約超過98萬, 政府嚴重苛待勞工

對於財政部擬針對年收入千萬元以上者,將所得稅率拉高至45%,鴻海集團(2317-TW)董事長郭台銘表示贊成「富人稅」,不過政府花錢要監督,「不要讓政府亂花錢」。郭台銘並建議,應該成立「繳稅聯盟」,監督政府,讓錢都花在刀口上。

郭台銘遠在西班牙參加MWC大展,但當國內記者詢問相關「富人稅」議題時,郭台銘表示,他早就提出不要徵證所稅,改徵「富人稅」取代,因此他贊成富人多繳稅。郭台銘表示,「我不算富人,因為我的財產已經信託捐出去了,而且忙到沒有時間花錢,現在都投資在高科技產業」,「政府要我怎麼繳稅,我贊成」,有錢人應該多繳一點。

不過郭台銘特地強調政府花錢應該要監督,建議成立「繳稅聯盟」監督,讓政府不要亂花錢。

分析
  • 要請馬政府列出增稅支出給『募兵、12年國教』細目,以『2012國防部支出透視』可看出真正政府大幅經費在養退休軍人,我們根本不相信馬政府,人民期待『募兵制』是節省軍事經費及精兵化,不期待馬政府再增加經常性支出;
  • 要實施『募兵制』時應先修正退休俸制度將它降到 65%或公務員退休俸限制在月領43900 ( 因為勞工勞保最高保險薪資也被限定在43900,以致勞工勞保退休給付月領小於21900 ):主因是以士兵24.9萬人全為志願役之二等兵最高薪資計算,台灣每位軍官含士官之一年經常性支出是95萬,遠高於民間平均薪資支出比率,其實最嚴重問題是軍公教95%替代率之退休俸制度,它使得軍公教退休相關經常支出高達 4358億,約占政府經常支出 27%,它讓政府無法有政策消減浮爛支出,因為優退人員也是拿 90% ~ 95% 薪資之退休俸,既使人員因優退而減少,政府開銷改善幅度很低,除非更改 90% ~ 95% 薪資之退休俸制度將它降到 65%,政府開銷才能因優退而大幅改善經常性支出浮濫之問題。人民養不起馬政府,沒錢就不要做嘛,人民不在乎『募兵、12年國教』
  • 美國每年撥給勞工之歲出超過 23%,台灣真正勞工福利才約中央歲出之3.08% ( 是中央+地方歲出之2.28% ),政府補助勞保基金僅有10%是全世界民主國家最低的,政府應該將調高政府勞保補助比率來提升勞工福利,因為勞工繳的綜所稅總額遠遠高於政府給勞工相關補助之支出,勞工最高綜所稅調高為45%,與福利國家之55%很接近,且1000萬勞工總共繳綜所稅已經是台灣繳給中央總稅收約24.5%,福利國家支出給繳稅勞工之福利占政府支出超過30%遠比台灣高出6倍,政府根本苛待勞工。
  • 從勞保退休制度、政府歲出給勞保補助小於3%及政府挪用勞保基金來支出勞工社會福利看,台灣政府真的是苛待勞工,而且,違反全世界勞工人權
    • 為什麼勞工繳稅比重占稅收24.5%而政府歲出補助勞保小於3%?( 註:若在考慮中央政府經常性支出占歲出83%,意味勞工繳的稅都去養公務員,很奇怪的歲出分配制度)。
    • 若仔細估算勞保退休制度,為什麼勞工勞保繳費超過33年且繳稅也超過30年還需要65歲退休?( 註:將勞工勞保最高投保薪資限制在 43900,繳稅卻沒有上限,就很明顯迫害勞工,還要一定壓迫勞工超過33年嗎?而相較於軍教,平均退休年齡才51歲,勞工不覺的奇怪嗎?怎會要求勞工工作超過33年去養對經濟貢獻最低的公務員呢?)。
    • 若再估算整體勞工繳的綜所稅約3000億及勞工對經濟貢獻產生之稅收如營所稅、營業稅、貨物稅,勞工理應得到政府最好照顧且勞工社會福利也應由稅收來支出,才能讓整個國家更有經濟力,怎會如此虧待勞工呢?那國家經濟成長率會越來越低。
  • 成立「繳稅聯盟」監督,讓政府不要亂花錢,同時監督政府勞工繳的綜所稅總額約2900 ~ 3000億是否100% 用在勞工福利,讓政府歲出更公平分配;
  • 更何況,根據"十二年國教通過之法規"在免學費部分,根據三讀條文,高中職及五專前三年均採「符合一定條件者免納學費」,由教育部編列預算補助學生。依教育部規畫,高職及五專前三年全面免學費,高中則排除家戶年所得超過一四八萬元者;但未具我國國籍、重讀及入學方式未依規定的私校生不適用免學費制度。結果造成大部分中所得以上之勞工都無法享受免學費制度,這免學費制度根本大部份都是砍到中所得以上之勞工福利,中所得以上之勞工繳稅及健保的等級較高,卻將勞保最高投保薪資限制在 43900,根本迫害勞工
  • 台灣需要直接由人民監督政府,因為總統及立委根本只重視高官及公務員利益,才會讓勞工長期受政府苛待,不要再相信馬政府,它只想一直想把人民口袋的錢透過增稅及國營單位漲價變成政府經常性花費,以馬總統 633、黃金十年、『選後不會漲價』都形成騙局,這次「財政健全」政策很明顯又是騙錢拐錢,從薪資、身障扣除額 各調高2萬元,全體上班族才少繳約80億,多收的800億之90%又分配養黨國肥貓與退休軍公教。 就看出馬總統是最會拐納稅錢及騙人民的總統
  • 馬總統執政這幾年,每年都提出增稅議題,真正問題就是不敢面對退休俸制度錯誤及組織龐大無效率,政府增加稅收來解決軍公教人員逐年增加之費用, 也是不可行,因為稅收增加率根本比不上軍公教人員逐年增加費用之成長率,再度證明我們預估是對的,最後倒楣將是那些年齡低於39歲之公務員勞工須記住我們必須盡快拿回勞保基金管理及監督權,既然這些錢都是勞工的,根本不能給一個將要破產的政府來管理;( 註:勞保基金只要結合勞工繳的綜所稅來經營,勞工繳的綜所稅100% 用在勞工福利上,以勞工65歲退休年齡計算是永遠不可能倒,但軍公教的退休福利制度只要依政府逐年增加之經常性支出是必倒的,現在國債都是他們造成的卻不去修正錯誤制度,是不會有救的 )。
  • 最近,立法院社福衛環委員會昨天初審通過勞工保險條例,決議勞保生育給付將由一個月提高為給予二個月的平均月投保薪資,雙生以上者則依比例增給;這些勞保條例規定是挪用勞保基金之惡法, 生育、傷病、失能給付由政府歲出來支出給付應由稅收來支出. 否則就造成勞保基金大失血, 用你我退休之基金編出之勞工社會福利是騙勞工選票; 
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