The graph references a [paper by Emmanuel Saez](http://eml.berkeley.edu/~saez/saez-UStopincomes-2012.pdf) that itself references a [dataset](http://elsa.berkeley.edu/~saez/TabFig2012prel.xls).

The dataset includes two breakdowns, one including capital gains and one without. The following data is extracted from the capital gains-included set. It also omits the 1913–17 years since those don’t include 90–100 breakdowns.

    FIRST_YEAR = 1917

    #   0-100   90-100
    data = """
        17,167  69,540
        16,226  65,077
        16,094  64,884
        14,354  56,000
        12,536  54,134
        14,318  62,601
        15,937  66,077
        15,862  70,440
        16,469  76,341
        16,460  75,237
        16,735  78,098
        17,616  86,829
        18,165  84,848
        15,680  68,782
        14,016  62,433
        11,704  54,270
        11,554  52,686
        12,614  57,753
        13,797  61,388
        15,385  71,686
        15,752  69,674
        14,555  64,151
        15,463  70,384
        16,171  73,245
        18,906  79,273
        21,860  78,977
        25,439  85,702
        25,142  81,742
        25,083  86,343
        25,533  93,704
        24,252  83,300
        24,624  86,218
        24,148  83,917
        26,392  93,858
        27,114  92,777
        27,811  92,366
        28,904  93,381
        28,864  97,087
        31,120  105,613
        32,590  109,054
        32,468  107,109
        31,651  106,227
        33,894  115,251
        34,031  113,920
        34,851  119,383
        35,652  120,151
        36,629  123,750
        38,589  132,837
        40,473  140,768
        42,072  141,666
        43,592  150,152
        45,505  158,572
        45,460  154,244
        44,783  146,114
        45,096  150,336
        47,294  158,842
        48,037  160,122
        46,122  153,628
        43,664  145,980
        44,957  150,217
        45,445  152,620
        46,312  155,079
        46,828  160,210
        45,429  157,334
        44,922  155,178
        44,130  155,922
        44,370  161,427
        45,921  168,695
        47,191  177,254
        50,568  205,454
        48,179  184,266
        50,612  205,632
        50,178  201,136
        49,250  196,879
        47,606  188,259
        48,140  196,519
        47,713  194,119
        48,451  197,593
        50,071  210,870
        52,048  226,328
        55,059  245,806
        58,409  265,126
        61,175  284,274
        62,730  298,637
        58,381  261,682
        55,374  242,648
        55,054  245,140
        57,870  268,511
        60,410  291,983
        61,998  305,775
        64,287  319,762
        57,920  279,338
        53,082  246,840
        54,190  260,346
        53,781  258,838
        56,271  283,734
    """

Clean and parse the data:

    years = data.trim().split('\n').map (year_str) ->
        year_str.trim().split(/\s+/g).map (percentile) ->
            parseInt(percentile.replace(/,/g, ''))

Now the data looks like `[ [total, top_10], …]`

    calc = (start, end) ->
        delta1 = years[start - 1917][1] - years[end - 1917][1]
        delta2 = years[start - 1917][0] - years[end - 1917][0]
        return delta1 / delta2 * 0.1

    PERIODS = [
        [1953,1949]
        [1957,1954]
        [1960,1958]
        [1969,1961]
        [1973,1970]
        [1979,1975]
        [1990,1982]
        [2000,1991]
        [2007,2001]
        [2012,2009]
    ]

    @matchChart = (i) ->
        period = PERIODS[i]
        return calc(period[0], period[1])

    @growthShare = (year) ->
        unless (PERIODS.some (p) -> p[1] <= year <= p[0])
            return 0
        return calc(year, year - 1)

    @contractionShare = (year) ->
        if (PERIODS.some (p) -> p[1] <= year <= p[0])
            return 0
        return calc(year, year - 1)

# The original chart

![Matching chart]{bar=matchChart: 0..9}

# Share of growth per year

![Per year]{bar=growthShare: 1949..2012}

# Share of contraction per year

![Per year]{bar=contractionShare: 1949..2012}