diff --git a/src/dcegm/pre_processing/state_space.py b/src/dcegm/pre_processing/state_space.py
index 2158d33d..a34192dd 100644
--- a/src/dcegm/pre_processing/state_space.py
+++ b/src/dcegm/pre_processing/state_space.py
@@ -56,7 +56,7 @@ def create_state_space_and_choice_objects(
         state_space_names=states_names_without_exog + exog_states_names,
     )
 
-    model_structure = {
+    model_structure_raw = {
         "state_space": state_space,
         "choice_range": jnp.asarray(options["state_space"]["choices"]),
         "state_space_dict": state_space_dict,
@@ -70,8 +70,8 @@ def create_state_space_and_choice_objects(
         "map_state_choice_to_parent_state": map_state_choice_to_parent_state,
         "map_state_choice_to_child_states": map_state_choice_to_child_states,
     }
-    # Return model structure with reduced dtypes
-    return jax.tree.map(create_array_with_smallest_int_dtype, model_structure)
+
+    return jax.tree.map(create_array_with_smallest_int_dtype, model_structure_raw)
 
 
 def test_state_space_objects(
@@ -136,8 +136,6 @@ def create_state_space(options):
     n_periods = state_space_options["n_periods"]
     n_choices = len(state_space_options["choices"])
 
-    need_signed_dtype = _contains_negative_value(state_space_options)
-
     (
         add_endog_state_func,
         endog_states_names,
@@ -273,10 +271,7 @@ def create_state_choice_space(
     state_space_names = states_names_without_exog + exog_state_names
     n_periods = state_space_options["n_periods"]
 
-    need_signed_dtype = _contains_negative_value(state_space_options)
-
     dtype_exog_state_space = get_smallest_int_type(n_exog_states)
-
     dtype_state_choice_space = get_smallest_int_type(n_states * n_choices)
     max_int_state_choice_space = np.iinfo(dtype_state_choice_space).max
 
@@ -487,18 +482,12 @@ def add_endog_states(id_endog_state):
 
 
 def create_indexer_for_space(space):
-    """Create indexer for space.
-
-    We need to think about which datatype we want to use and what is our invalid number.
-    Who doesn't like -99999999? Will anybody ever have 10 Billion state choices.
-
-    """
+    """Create indexer for space."""
 
     # Indexer has always unsigned data type with integers starting at zero
     data_type = get_smallest_int_type(space.shape[0])
     max_value = np.iinfo(data_type).max
 
-    # Account for negative entries
     max_var_values = np.max(space, axis=0) - np.min(space, axis=0)
 
     map_vars_to_index = np.full(
@@ -549,30 +538,16 @@ def create_array_with_smallest_int_dtype(arr):
             return arr.astype(get_smallest_int_type(arr.max()))
         else:
             return arr
-    return arr
+
+    else:
+        return arr
 
 
 def get_smallest_int_type(n_values):
     """Return the smallest integer type that can hold n_values."""
 
-    int_types = [np.uint8, np.uint16, np.uint32, np.uint64]
+    uint_types = [np.uint8, np.uint16, np.uint32, np.uint64]
 
-    for dtype in int_types:
+    for dtype in uint_types:
         if np.iinfo(dtype).max > n_values:
             return dtype
-
-
-def _contains_negative_value(d):
-    """Check recursively if (nested) dictionary contains any negative values."""
-
-    if isinstance(d, dict):
-        return any(_contains_negative_value(v) for v in d.values())
-    elif isinstance(d, list):
-        # Convert list to numpy array then check for negatives
-        return _contains_negative_value(np.array(d))
-    elif isinstance(d, np.ndarray):
-        return np.any(d < 0)
-    elif isinstance(d, (int, float)):
-        return d < 0
-
-    return False