diff --git a/warn/scrapers/mi.py b/warn/scrapers/mi.py
index 6dc4e901..8078e2a8 100644
--- a/warn/scrapers/mi.py
+++ b/warn/scrapers/mi.py
@@ -1,12 +1,13 @@
 import re
 from pathlib import Path
 
+import pdfplumber
 from bs4 import BeautifulSoup
 
 from .. import utils
 from ..cache import Cache
 
-__authors__ = ["anikasikka"]
+__authors__ = ["anikasikka, Ash1R"]
 __tags__ = ["html", "pdf"]
 __source__ = {
     "name": "Michigan Department of Technology, Management and Budget",
@@ -24,17 +25,14 @@ def scrape(
     Keyword arguments:
     data_dir -- the Path were the result will be saved (default WARN_DATA_DIR)
     cache_dir -- the Path where results can be cached (default WARN_CACHE_DIR)
-
     Returns: the Path where the file is written
     """
     # Grabs the main page with the current year's data
     current_page = utils.get_url("https://milmi.org/warn/")
     current_html = current_page.text
-
     # Grabs the WARN archive years html page with previous data
     archive_web_page = utils.get_url("https://milmi.org/warn/archive")
     archive_web_html = archive_web_page.text
-
     # Write the raw current year's file to the cache
     cache = Cache(cache_dir)
     cache.write("mi/current.html", current_html)
@@ -80,6 +78,23 @@ def scrape(
             pdf_file = cache.download(cache_key, pdf_url)
         pdf_list.append(pdf_file)
 
+    # parse pdf data
+    pdf_data = []
+
+    # Parse the pdfs from 2007-2015
+    for file in range(len(pdf_list) - 9, len(pdf_list))[::-1]:
+        with pdfplumber.open(pdf_list[file]) as pdf:
+            for i in pdf.pages:
+                pdf_data += process_pdf_2007_2015(i.extract_text())
+
+    # Parse pdfs from 2000-2006, which use a different type of spacing
+    for file in range(len(pdf_list) - 9)[::-1]:
+        with pdfplumber.open(pdf_list[file]) as pdf:
+            for i in pdf.pages:
+                pdf_data += process_pdf_2000_2006(i.extract_text())
+
+    cleaned_data += pdf_data
+
     # Set the path to the final CSV
     # We should always use the lower-case state postal code, like nj.csv
     output_csv = data_dir / "mi.csv"
@@ -91,6 +106,126 @@ def scrape(
     return output_csv
 
 
+def process_pdf_2000_2006(txt):
+    """Process the 2000-2006 pdfs."""
+    # split at newline, remove space placeholders, parts that aren't layoff data
+    txt = txt.split("\n")
+    txt = [i.replace("\xa0", " ").replace("\xad", " ") for i in txt]
+    txt = txt[5:-5]
+
+    # contains all the data for the page
+    ans = []
+
+    for row in txt:
+
+        # used later to check if data is damaged
+        broken = False
+        # the final processed row goes here
+        final = []
+
+        # the furthest the company name column ends is at index 35
+        compname = 35
+
+        try:
+            # However, sometimes it ends before that,
+            # so we start at index 35 and go back until we hit a space
+            while row[compname] != " ":
+                # if not space is found, the row is smushed together
+                if compname == 0:
+                    broken = True
+                    break
+                compname -= 1
+            if broken:
+                txt.remove(row)
+                continue
+        except Exception:
+            # if this fails, the data is damaged (it fails on about 5 rows)
+            txt.remove(row)
+            continue
+
+        # add company name to final row
+        final.append(row[: compname + 1])
+
+        # Usually, the city name ends 21 characters after the company name
+        # this isn't always the case, so we do what we did with company column
+        cityname = compname + 21
+        try:
+            while row[cityname] != " ":
+                broken = False
+                if cityname == 0:
+                    broken = True
+                cityname -= 1
+            if broken:
+                txt.remove(row)
+                continue
+        except Exception:
+            # if it fails, the data is damaged (it only fails on two)
+            txt.remove(row)
+            continue
+
+        # add city name to final row
+        final.append(row[compname + 1 : cityname])
+
+        # temp contains the test of the string
+        # which has date, event type, and number affected in that order
+        temp = row[cityname + 1 :]
+
+        # split to get date, event type, and number affected seperately
+        temp = temp.split()
+
+        # they use numbers instead of these words in some of the pdf
+
+        # add date, event type, and number affected to final row
+        for j in temp:
+            final.append(j)
+
+        # eliminate extra space
+        final = [row.strip() for row in final]
+
+        # see function
+
+        # add row to page
+        ans.append(final)
+    return ans
+
+
+def process_pdf_2007_2015(txt):
+    """Process 2007-2015 pdfs."""
+    # split at newline
+    txt = txt.split("\n")
+    ans = []
+    for i in txt:
+        final = []
+        # split at spaces, which are used as column divders
+        raw = i.split(" ")
+        # remove space placeholders
+        final = [j.replace("\xa0", " ").replace("\xad", " ") for j in raw]
+
+        # removing edge cases such as the "Notes:" section and blank lines
+        if (
+            len(final) == 5
+            and (final[0] not in ["Company", "Company Name"])
+            and final[1] != "Received"
+            and final != [" ", " ", " ", " ", " "]
+        ):
+            ans.append(final)
+        # accounting for the 2008-2009 minor format changes
+        if len(final) == 6:
+            if (final[2][-4:] == "2008" or final[2][-4:] == "2009") and (
+                final[0] != "January 1 through December 31:"
+            ):
+                final.pop(-1)
+                ans.append(final)
+
+    return ans
+
+
+# there are around 15 lines with unique spacing issues
+# there doesn't seem to be a pattern to them,
+# so I just fixed them manually
+# there may be a more elegant solution
+
+
 def _parse_html_table(soup):
     black_list = [
         "TOTAL:",