Equal Access to Justice for All People? An Investigation of the Factors Driving the Distribution of Civil Legal Aid Grants

Joanna Schroeder, William & Mary

1. Purpose

This study is concerned with investigating the political, need-based, and capacity factors that drive the allocation of civil legal aid grants to organizations.

3. Literature Review: Need-based, capacity, and political explanations

3.1 Demand-side explanations

For the issue of civil legal aid, several factors, including not only community need for civil legal aid, but organizational and environmental capacity, comprise the demand-side.

Need-based factors

Poverty level is one of the greatest indicators of need for civil legal aid. According to the Justice Gap survey report, 71 percent of low-income households experienced at least one civil legal problem in 2016, and, because of a lack of resources, half of low-income Americans who seek legal aid for their issues will receive limited or no help. While poverty overall can be considered a barrier to justice, as it directly impacts an individual’s ability to afford legal counsel or qualify for aid, there are other practical barriers to justice associated with being in poverty. Other factors such as population density, education level, primary language, race and ethnicity, and access to high speed internet can all affect civil legal aid need.

Capacity factors

Recipient capacity describes the applied organizational and environmental factors that affect the allocation of civil legal aid. Capacity is critical to understanding the distribution of aid because donors, whether private or public, are ultimately concerned with their grants being used effectively and producing results. Organizational capacity describes the factors within an organization that influence the receipt of grants, such as revenue, number of employees, or number of volunteers. The other main piece of defining recipient capacity is the environmental capacity of organizations to receive grants, or factors surrounding an institution that can influence its ability to receive grants. Environmental capacity includes the affluence of the community surrounding a legal aid clinic and competition from nearby clinics.

3.2 Political explanations

In political science, theories of distributive politics describe how not only how government resources are allocated, but why they are allocated that way. Often, political explanations for resource allocation are juxtaposed against demand-side explanations, showing that political factors often drive resource allocation to inefficiency. While the most common distributive politics explanations focus on political factors in legislative bodies, as the appropriating committees, some theories describe the power of the executive branch in driving decisions of resource allocation. In the case of civil legal aid allocation, it is important to look at the amount of funding received by organizations located in a Congressional district represented by a member of LSC’s appropriating committee (Commerce, Justice, and Sciences) to see if there are political biases involved with public aid allocation. Fig 2. Factors Driving the Allocation of Civil Legal Aid

4. Data and Methods

The data for this study was collected from the Foundation Directory Online, a database containing observations of grantmaking for a variety of sectors including health, the environment, and legal aid. The results of this search were webscraped using Selenium Webdriver through Python, creating a datatable of information at the grant level of recipients, grantmakers, and grant information. Among the information attained was organization Employer Identification Number (EIN), a unique identifier used by the IRS for tax administration. The obtained EINs were fed into ProPublica’s nonprofit API so that organization information, including annual revenue, could be coded into the dataset. Additionally, addresses for each business were folded into the dataset for precise location information. The addresses were geocoded using the TAMU geocoder. Political and demographic information for each recipient organization was folded into the dataset using their locations.

The three dependent variables analyzed were: 1. Total Amount ($) of Grants. 2. LSC (Dummy). 3. Amount ($) of Grants from LSC.

4.1 Methods

This analysis used penalized, OLS, and geographically weighted regression to model the effect of the explanatory factors on the three outcomes of interest. The complementary methods approach of this study allows for a nuanced analysis of the data, where the strengths and weaknesses of several methods are balanced out to understand a more holistic picture of civil legal aid distribution.

4.2 Hypotheses

  1. Capacity will be the most influential factor in civil legal aid allocation for the amount ($) of grants for the entire network.
  2. Capacity will be the most influential factors in the receipt of aid from LSC.
  3. Community need-based factors will be the most influential factors in civil legal aid allocation for the amount ($) of grants from LSC.
  4. Political factors will not be influential for any of the outcomes of interest.

5. Analysis

5.1 DV 1: Total Amount ($) of Grants

The analysis of DV 1 showed that the model was not very well specified. Overall, organization revenue was the most significant factor the model. The GWR analysis showed that there is some geographic nuance with the need-based variables, showing patterns of both positive and negative relationships across the country.

5.2 DV 2: LSC (dummy)

The model for analysis of DV 2 was better specified than the previous model. Overall, capacity factors continued to be the most influential, with the most significant relationships being between organization revenue and competition within 10 miles and the outcome of interest. The GWR analysis showed mixed support for both need-based and environmental capacity influences on allocation of public civil legal aid.

Fig 3. GWR Analysis of DV3 (LSC [dummy]): Need-based factors. Click through the need-based factors on the right to see the geographic distribution of coefficients. Purple locations indicate a negative relationship and orange indicates a positive relationship with receiving grants from LSC.

5.3 DV 3: Amount ($) of Grants from LSC

The final model was the most well specified. Overall, these results support the importance of organizational capacity, while introducing the influence of political explanations into the amount of public aid received by organizations. Both the penalized and OLS regressions showed influence of organizational and political factors on the outcome of interest. The need-based factors, while significant, still yielded mixed results of negative and positive relationships.

Fig 4. Elastic Net Analysis of for DV 3. Elastic net is a type of regression that penalizes uninfluential factors in a model. At the optimal value of Lambda (dark black line), the factors with the largest absolute value coefficients are the most influential in the model. Hover over lines and click through variables to investigate the influence of each factor.

7. Discussion

Throughout all the models, the strongest support was provided for the theory of the importance of organizational capacity, with organization revenue consistently being influential and significant, as well as competition. Environmental capacity, though, consistently yielded conflicting results, at times supporting the hypothesis of a poor resource environment influencing greater public aid and at times indicating that public aid did match environmental capacity and resource demand for civil legal aid. The great variation in the influence of environmental capacity and need-based factors was demonstrated in the GWR analyses, where spatial context impacted the effects of the variables. Political factors were relatively important in influencing the distribution public civil legal aid, supported most substantially by the amount ($) of public aid received.

An interesting contradictory relationships that was discovered in the analysis was in the final analysis of the amount ($) of aid received from LSC. The relationship between population below 125 percent of the poverty line and amount ($) of aid was significant and positive, while the relationship between population below 100 percent of the poverty line and amount ($) of aid was negative and significant. This relationship could be important in explaining the contradictions between environmental capacity and need-based grantmaking observed in this analysis. Public aid, while better matching resources to demand than private aid, has an extremely narrow operational definition of civil legal aid need. Indeed, LSC, though acknowledging other contributors to civil legal aid need in research of the Justice Gap, includes only population below 125 percent of the poverty line in their grant formula calculation.

8. Conclusion

Overall, not only does this study contribute substantive findings to the literature, including the importance of organizational factors in the study of distributive politics, but it provides methodological innovation and paths for future research. The importance of organizational capacity in receipt of both private and public aid was underscored by this analysis. While need-based factors were more influential in receipt of public aid, comparison with environmental capacity revealed the drawback of a narrow operational definition of civil legal need. This study provides a basis for further investigation into the political biases of public civil legal aid, which were supported most prevalently in the analysis of the amount ($) of public aid received. More research into civil legal aid is necessary to better understand how the overall network of grantmakers and recipients should work to uphold the Constitutional principle of equal access to justice for all.