Merge pull request PSLmodels#1071 from jdebacker/prepop

Merging

Author: rickecon

CHANGELOG.md

@@ -6,11 +6,17 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 
+## [0.14.12] - 2025-11-07 12:00:00
+
+### Bug Fix
+
+- Use data for pre-time path population distribution (rather than inferring it) ((PR #1071)[https://github.com/PSLmodels/OG-Core/pull/1071])
+
 ## [0.14.11] - 2025-11-07 12:00:00
 
 ### Added
 
-- Adds Ethiopia demographic data mapping ((PR #1054)[https://github.com/PSLmodels/OG-Core/pull/1063])
+- Adds Ethiopia demographic data mapping ((PR #1063)[https://github.com/PSLmodels/OG-Core/pull/1063])
 -
 ## [0.14.10] - 2025-09-11 12:00:00
 
@@ -459,6 +465,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Any earlier versions of OG-USA can be found in the [`OG-Core`](https://github.com/PSLmodels/OG-Core) repository [release history](https://github.com/PSLmodels/OG-Core/releases) from [v.0.6.4](https://github.com/PSLmodels/OG-Core/releases/tag/v0.6.4) (Jul. 20, 2021) or earlier.
 
 
+[0.14.12]: https://github.com/PSLmodels/OG-Core/compare/v0.14.11...v0.14.12
 [0.14.11]: https://github.com/PSLmodels/OG-Core/compare/v0.14.10...v0.14.11
 [0.14.10]: https://github.com/PSLmodels/OG-Core/compare/v0.14.9...v0.14.10
 [0.14.9]: https://github.com/PSLmodels/OG-Core/compare/v0.14.8...v0.14.9

ogcore/SS.py

@@ -307,7 +307,7 @@ def inner_loop(outer_loop_vars, p, client):
             futures.append(f)
 
         try:
-            results = client.gather(futures, timeout=300)
+            results = client.gather(futures)
         except Exception as e:
             # Cancel remaining futures and fall back to serial computation
             print(

ogcore/__init__.py

@@ -20,4 +20,4 @@
 from ogcore.txfunc import *
 from ogcore.utils import *
 
-__version__ = "0.14.11"
+__version__ = "0.14.12"

ogcore/demographics.py

@@ -990,61 +990,94 @@ def get_pop_objs(
     # assert np.allclose(pop_counter_2D, pop_dist)
     assert np.allclose(pop_counter_2D, pop_2D)
 
-    """"
-    CHANGE - in OG-Core, we are implicitLy assuming pre-TP rates of mortality,
-    fertility, and immigration are the same as the period 0 rates.
+    # """"
+    # CHANGE - in OG-Core, we are implicitly assuming pre-TP rates of mortality,
+    # fertility, and immigration are the same as the period 0 rates.
+
+    # So let's just infer the pre-pop_dist from those.
+    # """
+    # pop1 = pop_2D[0, :]
+    # fert0 = fert_rates[0, :]
+    # mort0 = mort_rates[0, :]
+    # infmort0 = infmort_rates[0]
+    # imm0 = imm_rates_orig[0, :]
+    # pre_pop_guess = pop1.copy()
+
+    # # I can't solve this analytically, so set up a system of equation
+    # # to solve
+    # def pre_pop_solve(pre_pop_guess, pop1, fert0, mort0, infmort0, imm0):
+    #     pre_pop = pre_pop_guess
+    #     errors = np.zeros(E + S)
+    #     errors[0] = pop1[0] - (
+    #         (1 - infmort0) * (fert0 * pre_pop).sum() + imm0[0] * pre_pop[0]
+    #     )
+    #     errors[1:] = pop1[1:] - (
+    #         pre_pop[:-1] * (1 - mort0[:-1]) + pre_pop[1:] * imm0[1:]
+    #     )
+    #     # print("Max error = ", np.abs(errors).max())
+    #     return errors
+
+    # opt_res = opt.root(
+    #     pre_pop_solve,
+    #     pre_pop_guess,
+    #     args=(pop1, fert0, mort0, infmort0, imm0),
+    #     method="lm",
+    # )
+    # pre_pop = opt_res.x
+    # print(
+    #     "Success? ",
+    #     opt_res.success,
+    #     ", Max diff = ",
+    #     np.abs(opt_res.fun).max(),
+    # )
+    # pre_pop_EpS = pop_rebin(pre_pop, E + S)
+
+    # # Check result
+    # initial_pop_counter = np.zeros(E + S)
+    # newborns = (fert_rates[0, :] * pre_pop[:]).sum()
+    # initial_pop_counter[0] = (
+    #     1 - infmort_rates[0]
+    # ) * newborns + imm_rates_orig[0, 0] * pre_pop[0]
+    # initial_pop_counter[1:] = (
+    #     pre_pop[:-1] * (1 - mort_rates[0, :-1])
+    #     + pre_pop[1:] * imm_rates_orig[0, 1:]
+    # )
+    # # Test that using pre pop get to pop in period 1
+    # print("Max diff = ", np.abs(pop_2D[0, :] - initial_pop_counter).max())
+    # # assert np.allclose(initial_pop_counter, pop_2D[0, :])
 
-    So let's just infer the pre-pop_dist from those.
     """
-    pop1 = pop_2D[0, :]
-    fert0 = fert_rates[0, :]
-    mort0 = mort_rates[0, :]
-    infmort0 = infmort_rates[0]
-    imm0 = imm_rates_orig[0, :]
-    pre_pop_guess = pop1.copy()
-
-    # I can't solve this analytically, so set up a system of equation
-    # to solve
-    def pre_pop_solve(pre_pop_guess, pop1, fert0, mort0, infmort0, imm0):
-        pre_pop = pre_pop_guess
-        errors = np.zeros(E + S)
-        errors[0] = pop1[0] - (
-            (1 - infmort0) * (fert0 * pre_pop).sum() + imm0[0] * pre_pop[0]
-        )
-        errors[1:] = pop1[1:] - (
-            pre_pop[:-1] * (1 - mort0[:-1]) + pre_pop[1:] * imm0[1:]
-        )
-        # print("Max error = ", np.abs(errors).max())
-        return errors
-
-    opt_res = opt.root(
-        pre_pop_solve,
-        pre_pop_guess,
-        args=(pop1, fert0, mort0, infmort0, imm0),
-        method="lm",
-    )
-    pre_pop = opt_res.x
-    print(
-        "Success? ",
-        opt_res.success,
-        ", Max diff = ",
-        np.abs(opt_res.fun).max(),
-    )
-    pre_pop_EpS = pop_rebin(pre_pop, E + S)
+    NEW CODE - use the actual UN historical data instead of solving backwards
+    """
+    # Get percentage distribution for S periods for pre-TP period
+    # Use the actual UN historical data instead of solving backwards
+    # pre_pop_EpS = pop_rebin(pre_pop, E + S) -- this assignment is
+    # above on line 924, but keep for clarity
 
     # Check result
+    # Verify that the UN pre-period data is reasonably consistent
+    # with the period 0 population using the demographic transition equations
     initial_pop_counter = np.zeros(E + S)
-    newborns = (fert_rates[0, :] * pre_pop[:]).sum()
+    newborns = (fert_rates[0, :] * pre_pop_EpS[:]).sum()
     initial_pop_counter[0] = (
         1 - infmort_rates[0]
-    ) * newborns + imm_rates_orig[0, 0] * pre_pop[0]
+    ) * newborns + imm_rates_orig[0, 0] * pre_pop_EpS[0]
     initial_pop_counter[1:] = (
-        pre_pop[:-1] * (1 - mort_rates[0, :-1])
-        + pre_pop[1:] * imm_rates_orig[0, 1:]
+        pre_pop_EpS[:-1] * (1 - mort_rates[0, :-1])
+        + pre_pop_EpS[1:] * imm_rates_orig[0, 1:]
     )
-    # Test that using pre pop get to pop in period 1
-    print("Max diff = ", np.abs(pop_2D[0, :] - initial_pop_counter).max())
-    # assert np.allclose(initial_pop_counter, pop_2D[0, :])
+
+    max_diff = np.abs(pop_2D[0, :] - initial_pop_counter).max()
+    print("Pre-period population verification: Max diff = ", max_diff)
+
+    if max_diff > 100_000:
+        print(
+            "WARNING: Large difference between UN pre-period population "
+            + "and period 0 population ({:.2f}). ".format(max_diff)
+            + "This may indicate inconsistencies in the data or "
+            + "immigration rate calculations, but using UN historical "
+            + "data as it is more reliable than backward-solved estimates."
+        )
 
     # Create the transition matrix for the population distribution
     # from T0 going forward (i.e., past when we have data on forecasts)

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="ogcore",
-    version="0.14.11",
+    version="0.14.12",
     author="Jason DeBacker and Richard W. Evans",
     license="CC0 1.0 Universal (CC0 1.0) Public Domain Dedication",
     description="A general equilibrium overlapping generations model for fiscal policy analysis",