diff --git a/pandas/core/arrays/arrow/dtype.py b/pandas/core/arrays/arrow/dtype.py
new file mode 100644
index 0000000000000..c0ecb0856f27f
--- /dev/null
+++ b/pandas/core/arrays/arrow/dtype.py
@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+import numpy as np
+import pyarrow as pa
+
+from pandas._typing import DtypeObj
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.base import StorageExtensionDtype
+
+from pandas.core.arrays.arrow import ArrowExtensionArray
+
+
+class ArrowDtype(StorageExtensionDtype):
+    """
+    Base class for dtypes for BaseArrowArray subclasses.
+    Modeled after BaseMaskedDtype
+    """
+
+    name: str
+    base = None
+    type: pa.DataType
+
+    na_value = pa.NA
+
+    def __init__(self, storage="pyarrow") -> None:
+        super().__init__(storage)
+
+    @cache_readonly
+    def numpy_dtype(self) -> np.dtype:
+        """Return an instance of the related numpy dtype"""
+        return self.type.to_pandas_dtype()
+
+    @cache_readonly
+    def kind(self) -> str:
+        return self.numpy_dtype.kind
+
+    @cache_readonly
+    def itemsize(self) -> int:
+        """Return the number of bytes in this dtype"""
+        return self.numpy_dtype.itemsize
+
+    @classmethod
+    def construct_array_type(cls):
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return ArrowExtensionArray
+
+    @classmethod
+    def construct_from_string(cls, string: str):
+        """
+        Construct this type from a string.
+
+        Parameters
+        ----------
+        string : str
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        if string == f"{cls.name}[pyarrow]":
+            return cls(storage="pyarrow")
+        raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'")
+
+    @classmethod
+    def from_numpy_dtype(cls, dtype: np.dtype) -> ArrowDtype:
+        """
+        Construct the ArrowDtype corresponding to the given numpy dtype.
+        """
+        # TODO: This may be incomplete
+        pa_dtype = pa.from_numpy_dtype(dtype)
+        if pa_dtype is cls.type:
+            return cls()
+        raise NotImplementedError(dtype)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # We unwrap any masked dtypes, find the common dtype we would use
+        #  for that, then re-mask the result.
+        from pandas.core.dtypes.cast import find_common_type
+
+        new_dtype = find_common_type(
+            [
+                dtype.numpy_dtype if isinstance(dtype, ArrowDtype) else dtype
+                for dtype in dtypes
+            ]
+        )
+        if not isinstance(new_dtype, np.dtype):
+            # If we ever support e.g. Masked[DatetimeArray] then this will change
+            return None
+        try:
+            return type(self).from_numpy_dtype(new_dtype)
+        except (KeyError, NotImplementedError):
+            return None
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray):
+        """
+        Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
+        """
+        array_class = self.construct_array_type()
+        return array_class(array)