diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 929471acb3105..aca1320980720 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -166,3 +166,10 @@ Bug Fixes
   not lexically sorted or unique (:issue:`7724`)
 - BUG CSV: fix problem with trailing whitespace in skipped rows, (:issue:`8679`), (:issue:`8661`)
 - Regression in ``Timestamp`` does not parse 'Z' zone designator for UTC (:issue:`8771`)
+
+
+
+
+
+- Bug in `StataWriter` the produces writes strings with 244 characters irrespective of actual size (:issue:`8969`)
+
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 45d3274088c75..cd37efd8e0991 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -1409,7 +1409,7 @@ def _maybe_convert_to_int_keys(convert_dates, varlist):
     return new_dict
 
 
-def _dtype_to_stata_type(dtype):
+def _dtype_to_stata_type(dtype, column):
     """
     Converts dtype types to stata types. Returns the byte of the given ordinal.
     See TYPE_MAP and comments for an explanation. This is also explained in
@@ -1425,13 +1425,14 @@ def _dtype_to_stata_type(dtype):
     If there are dates to convert, then dtype will already have the correct
     type inserted.
     """
-    #TODO: expand to handle datetime to integer conversion
+    # TODO: expand to handle datetime to integer conversion
     if dtype.type == np.string_:
         return chr(dtype.itemsize)
     elif dtype.type == np.object_:  # try to coerce it to the biggest string
                                     # not memory efficient, what else could we
                                     # do?
-        return chr(244)
+        itemsize = max_len_string_array(column.values)
+        return chr(max(itemsize, 1))
     elif dtype == np.float64:
         return chr(255)
     elif dtype == np.float32:
@@ -1461,6 +1462,7 @@ def _dtype_to_default_stata_fmt(dtype, column):
     int16   -> "%8.0g"
     int8    -> "%8.0g"
     """
+    # TODO: Refactor to combine type with format
     # TODO: expand this to handle a default datetime format?
     if dtype.type == np.object_:
         inferred_dtype = infer_dtype(column.dropna())
@@ -1470,8 +1472,7 @@ def _dtype_to_default_stata_fmt(dtype, column):
         itemsize = max_len_string_array(column.values)
         if itemsize > 244:
             raise ValueError(excessive_string_length_error % column.name)
-
-        return "%" + str(itemsize) + "s"
+        return "%" + str(max(itemsize, 1)) + "s"
     elif dtype == np.float64:
         return "%10.0g"
     elif dtype == np.float32:
@@ -1718,10 +1719,11 @@ def _prepare_pandas(self, data):
                     self._convert_dates[key]
                 )
                 dtypes[key] = np.dtype(new_type)
-        self.typlist = [_dtype_to_stata_type(dt) for dt in dtypes]
+        self.typlist = []
         self.fmtlist = []
         for col, dtype in dtypes.iteritems():
             self.fmtlist.append(_dtype_to_default_stata_fmt(dtype, data[col]))
+            self.typlist.append(_dtype_to_stata_type(dtype, data[col]))
 
         # set the given format for the datetime cols
         if self._convert_dates is not None:
diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py
index a99bcf741792f..6a3c16655745e 100644
--- a/pandas/io/tests/test_stata.py
+++ b/pandas/io/tests/test_stata.py
@@ -593,10 +593,12 @@ def test_minimal_size_col(self):
         with tm.ensure_clean() as path:
             original.to_stata(path, write_index=False)
             sr = StataReader(path)
+            typlist = sr.typlist
             variables = sr.varlist
             formats = sr.fmtlist
-            for variable, fmt in zip(variables, formats):
+            for variable, fmt, typ in zip(variables, formats, typlist):
                 self.assertTrue(int(variable[1:]) == int(fmt[1:-1]))
+                self.assertTrue(int(variable[1:]) == typ)
 
     def test_excessively_long_string(self):
         str_lens = (1, 244, 500)
@@ -850,7 +852,6 @@ def test_categorical_order(self):
         # Check identity of codes
         for col in expected:
             if is_categorical_dtype(expected[col]):
-                print(col)
                 tm.assert_series_equal(expected[col].cat.codes,
                                        parsed_115[col].cat.codes)
                 tm.assert_index_equal(expected[col].cat.categories,
diff --git a/pandas/lib.pyx b/pandas/lib.pyx
index 82408cd460fcd..2a5b93d111acc 100644
--- a/pandas/lib.pyx
+++ b/pandas/lib.pyx
@@ -898,17 +898,17 @@ def clean_index_list(list obj):
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def max_len_string_array(ndarray[object, ndim=1] arr):
+def max_len_string_array(ndarray arr):
     """ return the maximum size of elements in a 1-dim string array """
     cdef:
         int i, m, l
-        length = arr.shape[0]
+        int length = arr.shape[0]
         object v
 
     m = 0
     for i from 0 <= i < length:
         v = arr[i]
-        if PyString_Check(v) or PyBytes_Check(v):
+        if PyString_Check(v) or PyBytes_Check(v) or PyUnicode_Check(v):
             l = len(v)
 
             if l > m: