How to Predict Public Trust in Government from Survey Features

Public trust in government is one of the most important indicators in political science, governance research, and public policy analysis. 




Governments with high public trust often experience stronger institutional stability, better policy compliance, and improved civic participation. 

Low trust, on the other hand, may signal corruption concerns, economic dissatisfaction, or institutional weakness.

Machine learning allows researchers and analysts to predict public trust using survey data collected from citizens. 

Instead of manually analysing thousands of responses, we can train classification models to identify patterns that explain why some citizens trust government institutions while others do not.

In this tutorial, you will learn how to build a practical machine learning workflow that predicts public trust in government from survey features using Python and scikit-learn.


What Does “Public Trust” Mean?

Survey datasets often contain questions like:

  • “How much do you trust the national government?”

  • “How much do you trust parliament?”

  • “How satisfied are you with democracy?”


These responses are usually categorical:

  • No trust

  • Low trust

  • Moderate trust

  • High trust


For machine learning, we typically convert this into a binary target:

  • 1 = Trust

  • 0 = No Trust


The Dataset

We will assume a survey dataset similar to:

  • Afrobarometer

  • World Values Survey

  • Gallup World Poll

  • National governance surveys


Typical survey features include:

  • Age

  • Education level

  • Employment status

  • Income perception

  • Political participation

  • Access to services

  • Satisfaction with the economy

  • Urban vs rural residence

  • Media consumption

In this case we use: Our World in Data


Step 1: Import Libraries

import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import (
    accuracy_score,
    classification_report,
    confusion_matrix,
    roc_auc_score
)

Step 2: Load the Survey Dataset

df = pd.read_csv("government_trust_survey.csv")

print(df.head())

Example columns:

Feature                                                    Description
ageRespondent age
educationHighest education level
employmentEmployment status
economic_conditionPersonal economic perception
corruption_perceptionPerceived corruption
media_accessFrequency of news access
regionGeographic region
trust_governmentTarget variable


Step 3: Create the Target Variable

Suppose survey responses are:

  • “Not at all”

  • “Just a little”

  • “Somewhat”

  • “A lot”

We can convert them into binary values.

df["trust_binary"] = df["trust_government"].apply(
    lambda x: 1 if x in ["Somewhat", "A lot"] else 0
)


Why Binary Classification?

Binary classification simplifies interpretation and allows models like Logistic Regression to estimate the probability that a respondent trusts government institutions.


The model outputs probabilities between 0 and 1.


Step 4: Select Features and Target

X = df.drop(columns=["trust_government", "trust_binary"])

y = df["trust_binary"]

Step 5: Separate Numerical and Categorical Features

Survey data usually mixes numeric and categorical variables.

numeric_features = [
    "age"
]

categorical_features = [
    "education",
    "employment",
    "economic_condition",
    "corruption_perception",
    "media_access",
    "region"
]


Step 6: Build Preprocessing Pipelines

We must handle:

  • Missing values

  • Categorical encoding

  • Feature scaling

numeric_transformer = Pipeline(steps=[
    ("imputer", SimpleImputer(strategy="median")),
    ("scaler", StandardScaler())
])

categorical_transformer = Pipeline(steps=[
    ("imputer", SimpleImputer(strategy="most_frequent")),
    ("onehot", OneHotEncoder(handle_unknown="ignore"))
])

preprocessor = ColumnTransformer(
    transformers=[
        ("num", numeric_transformer, numeric_features),
        ("cat", categorical_transformer, categorical_features)
    ]
)

Step 7: Split Train and Test Data

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.2,
    random_state=42
)


Step 8: Build the Machine Learning Pipeline

model = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("classifier", LogisticRegression(max_iter=500))
])


Step 9: Train the Model

model.fit(X_train, y_train)

Step 10: Make Predictions

predictions = model.predict(X_test)

probabilities = model.predict_proba(X_test)[:, 1]

Step 11: Evaluate Performance

print("Accuracy:")
print(accuracy_score(y_test, predictions))

print("\nROC-AUC:")
print(roc_auc_score(y_test, probabilities))

print("\nClassification Report:")
print(classification_report(y_test, predictions))

Understanding the Metrics

Accuracy

Accuracy measures overall correct predictions.


ROC-AUC

ROC-AUC measures how well the model separates trust from non-trust respondents.

  • 1.0 = perfect separation

  • 0.5 = random guessing

Precision and Recall

These metrics become important if one class dominates the dataset.

For example:

  • Most respondents may distrust government

  • Or most respondents may trust government

In such cases, accuracy alone becomes misleading.


Step 12: Examine Feature Importance

For Logistic Regression, coefficients show directional influence.

feature_names = model.named_steps[
    "preprocessor"
].get_feature_names_out()

coefficients = model.named_steps[
    "classifier"
].coef_[0]

importance_df = pd.DataFrame({
    "feature": feature_names,
    "coefficient": coefficients
})

print(
    importance_df.sort_values(
        by="coefficient",
        ascending=False
    ).head(10)
)


Positive coefficients increase trust probability.
Negative coefficients decrease trust probability.

You may discover patterns such as:

  • Better economic perceptions increase trust

  • Corruption perceptions reduce trust

  • Higher media exposure influences trust differently across regions


Important Real-World Considerations

Survey Bias

Survey responses may contain:

  • Nonresponse bias

  • Political fear bias

  • Regional sampling bias

  • Social desirability bias

Machine learning models cannot automatically fix poor survey design.


Correlation Is Not Causation

If economic satisfaction predicts trust, this does not prove economic satisfaction causes trust.

The model identifies predictive relationships, not causal relationships.


Ethical Concerns

Predicting political trust requires responsible use:

  • Protect respondent privacy

  • Avoid political manipulation

  • Ensure transparent methodology

  • Prevent discriminatory profiling


Why Logistic Regression Works Well Here

Logistic Regression performs strongly on survey datasets because:

  • Survey variables are often structured

  • Relationships are semi-linear

  • Outputs remain interpretable

  • Policymakers can understand coefficients

Complex models like Random Forests or XGBoost may improve accuracy slightly, but interpretability often matters more in governance research.


Extending the Project

You can improve the project by:

  • Testing Decision Trees

  • Using Random Forests

  • Applying cross-validation

  • Handling class imbalance

  • Using SHAP values for explainability

  • Predicting trust scores instead of binary labels



Predicting public trust in government with machine learning combines data science, political science, and public policy analysis. 

The goal is not merely prediction accuracy, but understanding the societal patterns associated with institutional confidence.

The strongest governance analytics projects balance:

  • Statistical rigor

  • Ethical responsibility

  • Transparent interpretation

  • Policy relevance

Machine learning becomes valuable when it helps researchers and policymakers move from anecdotal assumptions to evidence-based understanding of citizen sentiment.


Build a Job‑Ready Portfolio in 16 Python Projects — Proven, Practical, and Profitable for $288.


Unfortunately you will have to use simulated data to complete this tutorial. You can change the code using Colab AI Gemini. 




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