# CODE AUDIT REPORT - Submission 168

## EXECUTIVE SUMMARY

**Overall Assessment**: HIGH SEVERITY - Code has critical completeness issues that prevent reproducibility

**Agent Reproducible Status**: TRUE - Authors explicitly documented use of Google Gemini and ChatGPT with links to chat conversations provided in supplementary PDF

**Primary Concerns**:
1. No data loading code provided - datasets are referenced but never loaded
2. Code produces outputs suggesting it was executed, but source data is completely missing
3. Cannot verify if results are computed or fabricated without data

---

## 1. COMPLETENESS & STRUCTURAL INTEGRITY

### CRITICAL ISSUES

**Missing Data Loading (CRITICAL)**
- **Evidence**: The notebook references three dataframes (`tow_2018_25`, `tow_2018_25_pov`, `tow_2018_25_nonpov`) that are used throughout all analyses but are never defined or loaded
- **Location**: First use at Cell 3, used in cells 3-14
- **Impact**: Code cannot possibly execute as written. The dataframes appear from nowhere.
- **Severity**: CRITICAL - This is a fundamental structural flaw

**Missing Data Files (CRITICAL)**
- **Evidence**: No CSV, Excel, or any data files found in submission directory
- **Search performed**: Comprehensive search for `.csv`, `.xlsx`, `.json`, `.txt`, `.py`, `.R` files
- **Expected files**: Dataset of 269,611 towing incidents from SFMTA (2018-2025) per methods document
- **Impact**: Zero reproducibility possible without source data
- **Severity**: CRITICAL

**Incomplete Code Flow**
- The notebook jumps directly from imports (Cell 0) to running regressions (Cell 3)
- No data cleaning, preprocessing, or variable construction shown
- Key variables used but never created:
  - `redeemed` (binary outcome variable)
  - `Post_Waiver2020` (treatment indicator)
  - `Post_Waiver2021` (second treatment indicator)
  - `treat_lowinc_proxy` (treatment group indicator)
  - `poverty_tow`, `repeat_tow`, `out_of_state` (control variables)
  - `lowinc_10_to_15`, `lowinc_16_to_20`, `lowinc_over_20` (age categories)
  - `month_year_cat`, `day_of_month_cat` (fixed effect variables)

### Structural Completeness
- **Positive**: Notebook has clear structure with markdown headers
- **Positive**: Regression formulas are well-documented
- **Negative**: No entry point showing full workflow from raw data to results

---

## 2. RESULTS AUTHENTICITY RED FLAGS

### MODERATE CONCERN: Output Verification Impossible

**Pre-computed Outputs Present**
- **Evidence**: Notebook cells 3-6 show regression output tables with specific coefficients
- **Issue**: Cannot verify if these are actual computed results or manually inserted values
- **Why this matters**: Without data or data loading code, there's no way to confirm these numbers were actually generated by running the code

**Output Details**
- Cell 3-6 outputs show detailed regression results (coefficients, standard errors, p-values)
- Cell 8 output marked as "too large to include" suggesting figure generation
- Cell 14 output also marked as "too large to include"
- These outputs suggest the notebook was executed at some point, but in an environment with data not provided

**Regression Results Consistency Check**
The reported coefficients in the notebook outputs are internally consistent with the methods document:
- Main DiD effect in fully saturated model: 0.0354 (3.54 percentage points) ≈ 3.5pp claimed
- Triple-diff by age: 0.0286 (10-15 yrs) ≈ 2.9pp, 0.0734 (16-20 yrs) ≈ 7.3pp, 0.0579 (>20 yrs) ≈ 5.8pp
- Poverty tows: 0.0609 ≈ 6.1pp
- Non-poverty tows: 0.0095 ≈ 1.0pp

**Assessment**: Numbers match paper claims, suggesting either (a) legitimate analysis was conducted with data not shared, or (b) outputs were fabricated to match predetermined claims. Cannot distinguish without data.

### LOW CONCERN: No Cherry-Picking Evidence
- No evidence of multiple similar code blocks with different outputs
- No obvious random seed manipulation
- Regression specifications are standard and appropriate for DiD analysis

---

## 3. IMPLEMENTATION-PAPER CONSISTENCY

### ALIGNMENT WITH PAPER (Can only verify formula-level consistency)

**Regression Specifications Match Paper Description**
- ✓ Difference-in-Differences framework correctly specified
- ✓ Interaction terms (Post_Waiver2020 × treat_lowinc_proxy) properly included
- ✓ Two policy periods (2020 and 2021) both included
- ✓ Control variables match description: poverty_tow, repeat_tow, out_of_state
- ✓ Fixed effects for month-year and day-of-month included in saturated models
- ✓ Triple-difference specification correctly uses age group interactions

**Statistical Methods**
- ✓ OLS with heteroskedasticity-robust standard errors (HC1) - appropriate
- ✓ Four model specifications tested (no controls/FE, controls only, FE only, full model)
- ✓ Coefficient extraction and confidence interval calculation correct

**Cannot Verify**
- ✗ Whether low-income proxy construction (age > 10 years + non-luxury make) was actually implemented
- ✗ Whether poverty tow classification logic matches description
- ✗ Whether dataset actually contains 269,611 observations as claimed
- ✗ Whether time periods and sample selection match description

---

## 4. CODE QUALITY SIGNALS

### POSITIVE INDICATORS
1. **Clean code style**: Well-commented, readable
2. **Logical organization**: Clear progression from simple to complex models
3. **Professional practices**: Proper use of statsmodels, pandas best practices
4. **Documentation**: Markdown cells explain each analysis step
5. **Systematic approach**: Tests multiple specifications consistently

### NEGATIVE INDICATORS
1. **No defensive programming**: No error handling, data validation, or sanity checks
2. **No exploratory analysis**: No descriptive statistics, data summaries, or checks shown
3. **Magic dataframes**: Data appears without any setup or loading
4. **No comments on data structure**: No indication of what the data looks like or contains
5. **Missing data provenance**: No explanation of where data comes from or how it was obtained

### Code Quality Grade: MODERATE
The code that exists is well-written, but the complete absence of data handling makes it fundamentally incomplete.

---

## 5. FUNCTIONALITY INDICATORS

### CANNOT ASSESS FUNCTIONALITY
- **Data loading**: Not present - CRITICAL GAP
- **Data preprocessing**: Not present - CRITICAL GAP
- **Variable construction**: Not present - CRITICAL GAP
- **Regression analysis**: Appears correct IF data were present
- **Visualization**: Appears functional IF data were present
- **Error handling**: None present

### Evidence of Development Work
- ✓ Professional figure generation code (matplotlib + seaborn)
- ✓ Proper coefficient plotting with confidence intervals
- ✓ Output formatting for publication-ready figures
- ✗ No debug print statements or data checks
- ✗ No version control artifacts visible

---

## 6. DEPENDENCY & ENVIRONMENT ISSUES

### Dependencies Assessment

**Libraries Used**:
```python
pandas, numpy, matplotlib, seaborn, statsmodels, IPython.display
```

**Assessment**:
- ✓ All are standard, widely-available packages
- ✓ No exotic or hard-to-install dependencies
- ✓ No version requirements specified, but all are stable packages
- ✓ No conflicting dependencies expected
- ✓ Computational requirements appear reasonable (standard OLS regressions)

**Issues**:
- ✗ No requirements.txt or environment.yml provided
- ✗ No version numbers specified (could cause reproducibility issues)
- ✗ No Python version specified

---

## 7. AI USAGE DOCUMENTATION

### AGENT REPRODUCIBLE: TRUE

**Evidence**: The supplementary PDF ("Towing_Supplementary.pdf") explicitly states:

> "This document provides links to the chat conversations used in the writing of our paper. All links are accessible as of the submission date."

**AI Tools Used**:
1. Google Gemini Conversation (link provided but not followed)
2. ChatGPT Conversation (link provided but not followed)

**Context**:
- Authors transparently documented AI usage
- Links to full chat conversations provided
- Document states code is shared separately as "Regression Code.ipynb"
- This level of transparency is commendable but doesn't address the missing data issue

**Assessment**: The authors have been transparent about using AI assistance in writing their paper, which satisfies the requirement for agent reproducibility documentation. However, this transparency makes the missing data even more problematic - there's no indication in the submission whether the data exists, whether it's proprietary, or whether the analysis was actually conducted.

---

## 8. SPECIFIC RED FLAGS SUMMARY

### CRITICAL (Code Cannot Work As Written)
1. **No data loading**: Three dataframes used but never defined or loaded
2. **No data files**: Comprehensive search found zero data files
3. **Missing variable construction**: 10+ variables used but never created
4. **Incomplete workflow**: Jumps from imports directly to analysis

### HIGH (Prevents Verification)
1. **Cannot verify results**: No way to check if outputs are real or fabricated
2. **No data provenance**: No explanation of data source or access
3. **No preprocessing**: Critical steps like low-income proxy construction not shown

### MEDIUM (Quality Concerns)
1. **No exploratory analysis**: No data summaries or sanity checks
2. **No error handling**: Code assumes perfect conditions
3. **No environment specification**: No version requirements

### LOW (Minor Issues)
1. **No README**: No documentation file explaining how to use the code
2. **No version control**: No .git artifacts or development history visible

---

## 9. REPRODUCIBILITY ASSESSMENT

### Can This Research Be Reproduced? NO

**Blocking Issues**:
1. **Missing data** (CRITICAL): The fundamental input to all analyses is absent
2. **Missing data preparation** (CRITICAL): Even if data were obtained, the preprocessing pipeline is not documented
3. **Missing variable construction** (HIGH): Treatment variables, outcomes, and controls are not shown being created

**What Would Be Needed**:
1. The SFMTA towing dataset (269,611 observations, 2018-2025)
2. Data cleaning and preprocessing code
3. Code showing construction of:
   - Low-income proxy variable (age > 10 + non-luxury classification)
   - Poverty tow classification
   - All treatment indicators and time variables
4. Clear instructions on data access and preparation

**Partial Reproducibility**:
- IF someone had identically-prepared data with identical variable names, the regression code would likely work
- IF the outputs are genuine, they provide some transparency into what analysis was conducted
- The statistical methodology is well-documented and could be replicated

---

## 10. RECOMMENDATIONS

### For Authors
1. **CRITICAL**: Provide the data files or clear instructions for obtaining/accessing data
2. **CRITICAL**: Add data loading and preprocessing code
3. **HIGH**: Document variable construction pipeline
4. **MEDIUM**: Add README with setup and execution instructions
5. **MEDIUM**: Specify package versions and Python version
6. **LOW**: Add exploratory data analysis and sanity checks

### For Reviewers
1. Request complete data and preprocessing pipeline before accepting claims
2. Verify that regression outputs match what code produces when executed
3. Check if data is proprietary/restricted and if so, whether synthetic or sample data can be provided
4. Consider requesting access to the AI chat conversations to understand what assistance was provided

---

## 11. FINAL VERDICT

**Code Quality**: The regression analysis code is professional and appears correct

**Completeness**: SEVERELY INCOMPLETE - Missing all data and preprocessing

**Reproducibility**: NOT REPRODUCIBLE - Core components missing

**Red Flag Severity**: CRITICAL - Cannot verify any claims without data

**Agent Reproducibility**: TRUE - AI usage explicitly documented

**Recommendation**: **MAJOR REVISION REQUIRED** - Cannot assess validity of research without data and complete analysis pipeline. The code submitted is only the final analysis step of what must be a much larger workflow. Authors must provide either (1) complete data and preprocessing code, or (2) clear documentation of why data cannot be shared and validation that analysis was actually conducted as described.

---

## AUDIT METADATA

- **Audit Date**: Generated from code analysis
- **Files Analyzed**:
  - `/Towing_Supplement/Regression_Code.ipynb` (15 cells)
  - `/168_methods_results.md` (50 lines)
  - `/Towing_Supplement/Towing_Supplementary.pdf` (1 page)
- **Total Code Files**: 1 notebook
- **Total Data Files**: 0 (CRITICAL ISSUE)
- **AI Usage Documented**: YES (Google Gemini + ChatGPT)
- **Lines of Actual Code**: ~150 lines (regression and visualization code only)
- **Lines of Missing Code**: Unknown, but minimally 200+ lines for data loading and preprocessing

---

## APPENDIX: Variable Inventory

### Variables Used But Never Defined
The following variables are used in the regression formulas but are never shown being created:

**Outcome Variable**:
- `redeemed` - Binary indicator for vehicle redemption

**Treatment Variables**:
- `Post_Waiver2020` - Post-August 2020 indicator
- `Post_Waiver2021` - Post-June 2021 indicator
- `treat_lowinc_proxy` - Low-income vehicle indicator
- `lowinc_10_to_15` - Low-income vehicles aged 10-15 years
- `lowinc_16_to_20` - Low-income vehicles aged 16-20 years
- `lowinc_over_20` - Low-income vehicles over 20 years

**Control Variables**:
- `poverty_tow` - Tow for poverty-related reasons
- `repeat_tow` - Repeat tow indicator
- `out_of_state` - Out-of-state registration indicator

**Fixed Effects**:
- `month_year_cat` - Month-year categorical variable
- `day_of_month_cat` - Day of month categorical variable

### Dataframes Used But Never Created
- `tow_2018_25` - Main analysis dataset
- `tow_2018_25_pov` - Poverty tows subset
- `tow_2018_25_nonpov` - Non-poverty tows subset

All of these must be constructed from the raw SFMTA data, but no code for this construction is provided.