# README for Replication Package

**Paper:** "It is not just about the price: Matching subsidies fail when the ask can be avoided"

**Authors:** Lata Gangadharan, Philip J. Grossman, Lingbo Huang, Erte Xiao

**Journal:** Economic Journal

## Overview

This replication package contains all materials necessary to reproduce the results in the paper. The paper presents two laboratory experiments investigating how the option to avoid charitable giving requests affects donor behavior under matching incentives ($1:$1 match). The experiments were conducted at the Monash Laboratory for Experimental Economics (MonLEE) in partnership with the Salvation Army's Melbourne 614 Project using Qualtrics for data collection.

The code in this package processes experimental data and generates all tables and figures in the manuscript. The analysis is conducted using Stata 16.1. Running the complete replication should take approximately 5-10 minutes.

**Contents:**
- `data/`: Raw and processed experimental data
- `code/`: Stata do-files for data cleaning and analysis
- `output/`: Generated tables and figures
- `documentation/`: Experimental instructions, design documents, and ethics approval

## Data Availability and Provenance Statements

This paper uses data collected by the authors through laboratory experiments. All data were generated as part of this research project.

- [x] This paper does not involve analysis of external data (data were generated by the authors via laboratory experiments).
- [x] All data are provided as part of this replication package.

### Statement about Rights

- [x] I certify that the author(s) of the manuscript have legitimate access to and permission to use the data used in this manuscript.
- [x] I certify that the author(s) of the manuscript have documented permission to redistribute/publish the data contained within this replication package.

### Summary of Availability

- [x] All data **are** publicly available.

#### Summary of Data Availability

| Data.Name | Data.Files | Location | Provided | Description |
| -- | -- | -- | -- | -- |
| Experiment 1 Raw Data | exp1_raw.dta, exp1_raw.csv | data/raw/ | Yes | Raw data |
| Experiment 2 Raw Data | exp2_raw.dta, exp2_raw.csv | data/raw/ | Yes | Raw data |
| Experiment 1 Processed | exp1_processed.dta | data/processed/ | Yes | Cleaned and coded data |
| Experiment 2 Processed | exp2_processed.dta | data/processed/ | Yes | Cleaned and coded data |

### Details on Data Sources

#### Experiment 1

**Datafile:** `data/raw/exp1_raw.dta`, `data/raw/exp1_raw.csv`

The data were collected through a laboratory experiment conducted at the Monash Laboratory for Experimental Economics (MonLEE). The experiment used a 2x2 between-subjects design manipulating (1) whether participants could avoid the giving request and (2) whether donations were matched ($1:$1). A total of 403 participants were recruited across 34 sessions and randomly assigned to one of four treatment conditions: NoAvoid-NoMatch, NoAvoid-Match, Avoid-NoMatch, and Avoid-Match. Donations were directed to clients of the Salvation Army's Melbourne 614 Project (unemployed and disadvantaged residents of Melbourne).

#### Experiment 2

**Datafile:** `data/raw/exp2_raw.dta`, `data/raw/exp2_raw.csv`

The data were collected through two incentivized surveys designed to elicit normative beliefs about the actions taken in Experiment 1. In the first survey, 100 participants were presented with instructions for the two Avoid treatments. In the second survey, 61 participants received instructions for the two No Avoid treatments. Participants rated the "desirability" of various behaviors and made incentivized guesses about the most popular ratings (second-order normative beliefs).

## Dataset List

| Data file | Source | Notes | Provided |
|-----------|--------|-------|----------|
| `data/raw/exp1_raw.dta` | Experiment 1 | Stata format | Yes |
| `data/raw/exp1_raw.csv` | Experiment 1 | CSV format | Yes |
| `data/raw/exp2_raw.dta` | Experiment 2 | Stata format | Yes |
| `data/raw/exp2_raw.csv` | Experiment 2 | CSV format | Yes |
| `data/processed/exp1_processed.dta` | Cleaned Exp 1 | Analysis-ready | Yes |
| `data/processed/exp2_processed.dta` | Cleaned Exp 2 | Analysis-ready | Yes |

**Data Dictionary:** Variable labels and descriptions are embedded in the Stata `.dta` files. Key variables include:
- `extensive`: Binary indicator of giving (1=gave, 0=did not give)
- `checkbook`: Continuous measure of out-of-pocket giving amount
- `idAvoid`: Treatment indicator (1=No Avoid, 2=Avoid)
- `match`: Match treatment indicator (0=No Match, 1=Match)
- `doAvoid`: Whether participant avoided the ask (Avoid condition only)
- `treatment`: Treatment string label

## Computational Requirements

### Software Requirements

- [x] The replication package contains a setup program to configure the working environment.

**Stata** (code was last run with Stata 16.1)
  - `estout` package (for regression tables)
  - The program `code/stata/0PathSetup.do` will configure all paths
  - Standard Stata commands are used throughout

### Controlled Randomness

- [x] No Pseudo random generator is used in the analysis described here.

The analysis uses deterministic statistical tests (Fisher's exact tests, Wilcoxon rank-sum tests, probit regressions). No random number generation is required for reproduction.

### Memory, Runtime, Storage Requirements

#### Summary time to reproduce

Approximate time needed to reproduce the analyses on a standard (2024) desktop machine:

- [x] <10 minutes

#### Summary of required storage space

Approximate storage space needed:

- [x] < 25 MBytes

#### Computational Details

The code was last run on a **MacBook Pro with macOS Sonoma**.

All analyses can be completed on a standard laptop with minimal memory requirements.

## Description of Programs/Code

The analysis is organized into the following Stata do-files:

### Setup and Master Files

- `code/stata/0PathSetup.do`: Configures global paths for the replication package. **Must edit line 6** to set the folder path to your local directory.
- `code/stata/1Master.do`: Master file that runs all data cleaning and analysis in sequence.

### Data Cleaning

- `code/stata/2clean_exp1.do`: Cleans and processes raw data from Experiment 1. Generates `data/processed/exp1_processed.dta`.
- `code/stata/2clean_exp2.do`: Cleans and processes raw data from Experiment 2. Generates `data/processed/exp2_processed.dta`.

### Analysis

- `code/stata/3analysis_exp1.do`: Generates all tables and figures for Experiment 1, including:
  - Table 2: Estimated proportion of each type
  - Table S2: Descriptive statistics
  - Table S3: Probit regression of frequency of giving
  - Table S4: Probit regression of avoiding the ask
  - Figure 1a: Frequency of giving
  - Figure 1b: Out-of-pocket giving
  - Figure S3: Intensive margin analysis
  - Figure S4: Distribution of giving

- `code/stata/3analysis_exp2.do`: Generates all tables and figures for Experiment 2, including:
  - Table 3: Normative beliefs
  - Table S5: Descriptive statistics

## Instructions to Replicators

Follow these steps to reproduce all results:

1. **Download and extract** the replication package to your local machine.

2. **Edit paths**: Open `code/stata/0PathSetup.do` and modify line 6 to set the folder path:
   ```stata
   global folder "/path/to/your/replication_package/paper"
   ```

3. **Run the setup file**: Execute in Stata:
   ```stata
   do "code/stata/0PathSetup.do"
   ```

4. **Run the master file**: Execute in Stata:
   ```stata
   do "code/stata/1Master.do"
   ```

   This will automatically:
   - Clean both experiment datasets
   - Generate all tables and figures
   - Save outputs to `output/tables/` and `output/figures/`

5. **Verify outputs**: Check that the following files were generated:
   - Tables: `table2.tex`, `table3.tex`, `tableS2.tex`, `tableS3.tex`, `tableS4.tex`, `tableS5.tex`
   - Figures: `figure1a.png`, `figure1b.png`, `figureS3.png`, `figureS4.png`

### Alternative: Run Individual Files

If you prefer to run files individually (in order):

1. Run `0PathSetup.do` first
2. Run `2clean_exp1.do` then `3analysis_exp1.do`
3. Run `2clean_exp2.do` then `3analysis_exp2.do`

## List of Tables and Programs

The provided code reproduces:

- [x] All numbers provided in text in the paper
- [x] All tables and figures that use data (see tables below)

### Main Text

| Figure/Table # | Program | Output file | Note |
|----------------|---------|-------------|------|
| Table 1 | - |  | Theoretical predictions |
| Table 2 | 3analysis_exp1.do | output/tables/table2.tex | Estimated proportion of each type |
| Table 3 | 3analysis_exp2.do | output/tables/table3.tex | Normative beliefs |
| Figure 1a | 3analysis_exp1.do | output/figures/figure1a.png | Frequency of giving |
| Figure 1b | 3analysis_exp1.do | output/figures/figure1b.png | Out-of-pocket giving |
| Figure 2 | - |  | Decision tree |

### Appendix (Online Appendix)

| Figure/Table # | Program | Output file | Note |
|----------------|---------|-------------|------|
| Figure S1 | - |  | Decision tree |
| Figure S2 | - |  | Procedures in Experiment 1 |
| Figure S3 | 3analysis_exp1.do | output/figures/figureS3.png | Intensive margin |
| Figure S4 | 3analysis_exp1.do | output/figures/figureS4.png | Distribution of giving |
| Figure S5 | - |  | Solicitation envelopes |
| Figure S6 | - |  | Solicitation emails |
| Table S1 | - |  | Theoretical predictions |
| Table S2 | 3analysis_exp1.do | output/tables/tableS2.tex | Descriptive statistics (Exp 1) |
| Table S3 | 3analysis_exp1.do | output/tables/tableS3.tex | Probit regression |
| Table S4 | 3analysis_exp1.do | output/tables/tableS4.tex | Probit regression of avoiding |
| Table S5 | 3analysis_exp2.do | output/tables/tableS5.tex | Descriptive statistics (Exp 2) |

## Documentation

The `documentation/` folder contains:

- `experimental instructions.pdf`: Complete experimental instructions and decision tasks shown to participants
- `experimental design.pdf`: Detailed description of the experimental design
- `ethics approval letter.pdf`: IRB/ethics committee approval documentation

## References

Experimental data were collected by the authors. For methodology and experimental procedures, please refer to the manuscript and documentation files included in this package.

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## Acknowledgements

This README follows the template guidelines from the Social Science Data Editors (Lars Vilhuber, Miklós Koren, Joan Llull, Marie Connolly, Florian Oswald, Peter Morrow).
