Types of Assistance Received and Deadweight Effects: A User’s Guide

Introduction

When analyzing the impact of various assistance programs for the self-employed, understanding the types of assistance received and the concept of deadweight is crucial. This article delves into the nuances of beneficiary experiences, the provable effectiveness of these programs, and the associated economic implications.

Understanding Beneficiaries of Assistance

A significant portion of respondents—between 10% to 20%—benefit from the various forms of assistance analyzed. For example, the rate for hiring new workers stands at approximately 9.7%, while those who recapture unemployment benefits to start a new business hover just above this at 20.5%. However, the standout in this spectrum is the discounted self-employment contribution, which benefits a notable 37.7% of applicants. This particular measure directly impacts self-employed individuals’ finances, making them acutely aware of the benefits as they experience immediate financial relief.

Charting the Data

As depicted in Figure 2, the percentage of beneficiaries and non-beneficiaries by assistance type illustrates the variances in uptake. The discounted self-employment contribution’s significance stems from its immediate and clear economic benefits, as it directly benefits the pockets of the self-employed.

The Concept of Deadweight

Deadweight describes the phenomenon where assistance or subsidies provide benefits even to those who would have pursued action regardless of the support. To gauge this effect, respondents were asked whether they would have started their business without the received aid. About 40% of respondents indicated they would have launched their business independently, highlighting a critical insight into the efficiency of these assistance measures.

The Deadweight Effect in Action

As shown in Figure 3, the deadweight percentage exceeds 40% for nearly all assistance schemes, with non-repayable grants marking the highest at 60.3%. The prolonged processing time for such grants often makes it impractical for self-employed individuals to rely solely on them to launch their businesses, illustrating a gap between intention and reality.

Job Creation Trends Among Self-Employed Individuals

In analyzing those who have received types of assistance, a striking 67.4% of respondents reported having no employees. Among those who do, the mean number of employees stands at 3.11, a notable figure that varies widely within the sample. To eliminate biases that might distort the results, a Propensity Score Matching (PSM) method was implemented to fairly compare beneficiaries of assistance with non-recipients.

Methodology Overview

The matching process removes biases introduced by sociodemographic and labor characteristics, allowing for a clearer comparison of outcomes. The most effective method employed was kernel smoothing alongside common support, leading to a significant decrease in bias between treated and control groups.

Outcome Evaluation: Mean Number of Employees

The Average Treatment Effect on the Treated (ATT) was calculated to determine if assistance recipients had a different average number of employees compared to non-recipients. Curiously, the ATT revealed a non-significant difference, casting doubt on any widespread positive impact of assistance programs on job creation. However, a closer look shows that specific types of assistance may still yield variations in hiring practices.

Individual Assistance Impact

By examining individual policies, evidence suggests that free courses, hiring subsidies, and low-interest loans are associated with higher employee averages compared to non-recipients. Conversely, business planning advice and non-repayable grants were correlated with fewer employees, highlighting a potential misalignment between assistance goals and outcomes.

Earnings of Self-Employed Individuals

The second major indicator of assistance effectiveness looks at the earnings of self-employed workers, which average €1,183 per month with a substantial standard deviation of €529. The PSM methodology was again deployed to evaluate differences in earnings before and after assistance.

Analyzing Earnings Disparities

Following the matching process, it was found that the group without assistance earned, on average, €30.20 more than those who received aid, a difference that remained consistent post-matching. This finding suggests that existing programs might not enhance earnings as initially intended.

Individual Assistance Impact on Earnings

Similar to employment rates, not all assistance impacted earnings positively. Free courses and hiring subsidies were linked to higher earnings, with the latter showing a substantial positive distinction of €153. In contrast, business plan assistance and discounted self-employment contributions led recipients to earn less than their non-beneficiary counterparts.

Logistic Regression Model

To gain further insight, a Logistic regression model was crafted, focusing on the likelihood of increased earnings versus previous income. Interestingly, while individual assistance types did not significantly affect the odds, a combination of free courses and non-repayable grants indicated potential for higher earnings.

Final Thoughts

Understanding the efficacy of assistance programs for the self-employed reveals complex market dynamics. While some programs show promise in boosting employment and earnings, significant sections of the data highlight limitations and unintended consequences, like high deadweight rates. Exploring these relationships helps delineate the effective and ineffective facets of assistance in supporting self-employment, ultimately providing insights for policymakers aiming to enhance economic outcomes.