diff --git a/ogcore/pensions.py b/ogcore/pensions.py
index 17a4da179..6cb0f055a 100644
--- a/ogcore/pensions.py
+++ b/ogcore/pensions.py
@@ -424,8 +424,6 @@ def deriv_NDC(r, w, e, Y, per_rmn, p):
         d_theta_empty = np.zeros(per_rmn)
         delta_ret_amount = delta_ret(r, Y, p)
         g_ndc_amount = g_ndc(r, Y, p)
-        print("g_ndc = ", g_ndc_amount)
-        print("delta_ret = ", delta_ret_amount)
         d_theta = deriv_NDC_loop(
             w,
             e,
@@ -579,9 +577,7 @@ def deriv_PS_loop(w, e, S, S_ret, per_rmn, d_theta, vpoint, factor):
 
 
 @numba.jit
-def deriv_NDC_loop(
-    w, e, per_rmn, S, S_ret, tau_p, g_ndc_value, delta_ret_value, d_theta
-):
+def deriv_NDC_loop(w, e, per_rmn, S, S_ret, tau_p, g_ndc_value, delta_ret_value, d_theta):
     for s in range((S - per_rmn), S_ret):
         d_theta[s - (S - per_rmn)] = (
             tau_p
diff --git a/tests/test_pensions.py b/tests/test_pensions.py
index 0d7d678db..130b3e274 100644
--- a/tests/test_pensions.py
+++ b/tests/test_pensions.py
@@ -374,3 +374,168 @@ def test_deriv_NDC(args, d_NDC_expected):
     d_NDC = pensions.deriv_NDC(r, w, e, Y, per_rmn, p)
 
     assert np.allclose(d_NDC, d_NDC_expected)
+
+
+
+
+
+################pension benefit: DB############
+demographics_ndc = deepcopy(demographics)
+households_pb = deepcopy(households)
+pensions_db = deepcopy(pensions_class)
+pensions_db.pension_system = 'DB'
+households_pb.S = 7
+households_pb.S_ret = 4
+w_db = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
+e_db = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
+n_db = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
+pensions_db.last_career_yrs = 3
+pensions_db.yr_contr = S_ret
+pensions_db.rep_rate_py = 0.2
+Y = None
+j_ind = 1
+firms.g_y = 0.03
+factor = 2
+omegas = None
+lambdas = 1
+pension_expected_db = [0, 0, 0, 0, 0.337864778, 0.327879365, 0.318189065]
+classes_pb = (demographics_ndc, households_pb, firms, pensions_db)
+args_pb = (w_db, e_db, n_db, r, Y, lambdas, j_ind, factor)
+
+################pension benefit: NDC############
+households_ndc = deepcopy(households)
+pensions_ndc = deepcopy(pensions_class)
+pensions_ndc.pension_system = 'NDC'
+pensions_ndc.ndc_growth_rate = 'LR GDP'
+pensions_ndc.dir_growth_rate = 'r'
+households_ndc.S = 4
+households_ndc.S_ret = 2
+w_ndc = np.array([1.2, 1.1, 1.21, 1])
+e_ndc = np.array([1.1, 1.11, 0.9, 0.87])
+n_ndc = np.array([0.4, 0.45, 0.4, 0.3])
+Y = None
+j_ind = 1
+firms.g_y = 0.03
+demographics_ndc.g_n_SS = 0.0
+r = 0.03
+factor = 2
+pensions_ndc.tau_p = 0.3
+pensions_ndc.k_ret = 0.4615
+demographics_ndc.mort_rates_SS = np.array([0.01, 0.05, 0.3, 0.4, 1])
+omegas = None
+lambdas = 1
+pension_expected_ndc = [0, 0, 0.279756794, 0.271488732]
+classes_ndc = (demographics_ndc, households_ndc, firms, pensions_ndc)
+args_ndc = (w_ndc, e_ndc, n_ndc, r, Y, lambdas, j_ind, factor)
+
+################pension benefit: PS############
+demographics_ppb = deepcopy(demographics)
+households_ppb = deepcopy(households)
+pensions_ppb = deepcopy(pensions_class)
+pensions_ppb.pension_system = 'PS'
+households_ppb.S = 7
+households_ppb.S_ret = 4
+w_ppb = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
+e_ppb = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
+n_ppb = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
+omegas = (1/households_ppb.S) * np.ones(households_ppb.S)
+demographics_ppb.omega_SS = omegas
+pensions_ppb.vpoint = 0.4
+factor = 2
+Y = None
+lambdas = 1
+j_ind = 1
+firms.g_y = 0.03
+pension_expected_ppb = [0, 0, 0, 0, 0.004164689, 0.004041603, 0.003922156]
+classes_ppb = (demographics_ppb, households_ppb, firms, pensions_ppb)
+args_ppb = (w_db, e_db, n_db, r, Y, lambdas, j_ind, factor)
+
+test_data = [(classes_pb, args_pb, pension_expected_db),
+             (classes_ndc, args_ndc, pension_expected_ndc),
+             (classes_ppb, args_ppb, pension_expected_ppb)]
+
+@pytest.mark.parametrize('classes,args,pension_expected', test_data,
+                         ids=['DB', 'NDC', 'PS'])
+def test_pension_benefit(classes, args, pension_expected):
+    '''
+    Test of pensions.get_pension_benefit
+    '''
+
+    demographics_ndc, households, firms, pensions = classes
+    w, e, n, r, Y, lambdas, j_ind, factor = args
+
+    pension = pensions.pension_benefit(
+        demographics_ndc, households, firms, w, e, n, r,
+        Y, lambdas, j_ind, factor)
+    assert (np.allclose(pension, pension_expected))
+############SS or complete lifetimes############
+households_pl1 = deepcopy(households)
+pensions_pl1 = deepcopy(pensions_class)
+households_pl1.S = 3
+households_pl1.S_ret = 2
+per_rmn = households_pl1.S
+w = np.array([1.2, 1.1, 1.21])
+e = np.array([1.1, 1.11, 0.9])
+firms.g_y = 0.03
+demographics.g_n_SS = 0.0
+pensions_pl1.ndc_growth_rate = 'LR GDP'
+pensions_pl1.dir_growth_rate = 'r'
+r = 0.02
+pensions_pl1.tau_p = 0.3
+pensions_pl1.k_ret = 0.4615
+pensions_pl1.delta_ret = 1.857010214
+pensions_pl1.g_ndc = firms.g_y + demographics.g_n_SS
+demographics.mort_rates_SS = np.array([0.1,	0.2, 0.4, 1.0])
+deriv_NDC_loop_expected1 = np.array([0.757437326, 0.680222841, 0.0])
+d_theta_empty = np.zeros_like(w)
+
+#args3 = (pensions_pl1, households_pl1, w, e, per_rmn)
+args3 = (w, e, per_rmn, households_pl1.S, households_pl1.S_ret, firms.g_y,
+         pensions_pl1.tau_p, pensions_pl1.g_ndc, pensions_pl1.delta_ret,
+         d_theta_empty)
+
+################Incomplete lifetimes#################
+households_pl2 = deepcopy(households)
+pensions_pl2 = deepcopy(pensions_class)
+households_pl2.S = 4
+households_pl2.S_ret = 2
+per_rmn = 3
+#w = np.array([1.2, 1.1, 1.21, 1.25])
+#e = np.array([1.1, 1.11, 0.9, 1.0])
+w = np.array([1.1, 1.21, 1.25])
+e = np.array([1.11, 0.9, 1.0])
+firms.g_y = 0.04
+demographics.g_n_SS = 0.0
+pensions_pl2.ndc_growth_rate = 'LR GDP'
+pensions_pl2.dir_growth_rate = 'r'
+#r = np.array([0.05, 0.03, 0.04, 0.03])
+r = np.array([0.03, 0.04, 0.03])
+pensions_pl2.tau_p = 0.3
+pensions_pl2.k_ret = 0.4615
+pensions_pl2.delta_ret = 1.083288196
+pensions_pl2.g_ndc = firms.g_y + demographics.g_n_SS
+#demographics.mort_rates_SS = np.array([0.1, 0.2, 0.4, 1.0])
+demographics.mort_rates_SS = np.array([0.2, 0.4, 1.0])
+deriv_NDC_loop_expected2 = np.array([0.396808466, 0.0, 0.0])
+#d_theta_empty = np.zeros_like(w)
+d_theta_empty = np.zeros(per_rmn)
+args4 = (w, e, per_rmn, households_pl2.S, households_pl2.S_ret, firms.g_y,
+         pensions_pl2.tau_p, pensions_pl2.g_ndc, pensions_pl2.delta_ret,
+         d_theta_empty)
+
+test_data = [(args3, deriv_NDC_loop_expected1),
+             (args4, deriv_NDC_loop_expected2)]
+
+@pytest.mark.parametrize('args,deriv_NDC_loop_expected', test_data,
+                         ids=['SS', 'incomplete'])
+def test_deriv_NDC_loop(args, deriv_NDC_loop_expected):
+    """
+    Test of the pensions.deriv_NDC_loop() function.
+    """
+    (w, e, per_rmn, households.S, households.S_ret, firms.g_y, pensions_pl2.tau_p,
+     pensions_pl2.g_ndc, pensions_pl2.delta_ret, d_theta_empty) = args
+
+    deriv_NDC_loop = pensions.deriv_NDC_loop(
+        w, e, per_rmn, households.S, households.S_ret, firms.g_y,
+        pensions_pl2.tau_p, pensions_pl2.g_ndc, pensions_pl2.delta_ret,
+        d_theta_empty)
\ No newline at end of file