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The purpose of this paper is to isolate and evaluate the causal effect of the changes in eligibility criteria on labor force participation (LFP) and exit to retirement of the cohorts affected by the reform that canceled most of the early pensions in Poland in 2009. At the individual level the reform created a huge discontinuity in treatment of different generations.

The authors rely on Polish Labor Force Survey and employ regression discontinuity design to evaluate the change in participation subsequent to the eligibility reform among the treated cohorts.

The authors find a statistically significant, but economically small discontinuity at the timing of the reform. The placebo test shows no similar effects in earlier or later quarters. Yet, the pure treatment effects are insignificant in vast majority of the specifications.

There are some limitations of the data used in the research. It does not cover total population and some panel attrition can be expected. Authors also needed to cope with the lack of required details in survey questions. The main limitation of the method lies in the measurement of the immediate (short-term) effects while in many cases people require more time that 1–2 quarters for the decision after policy change.

The reduction of outflows to retirement was much less pronounced than could have been expected, largely due to already relatively lower propensity to retire early.

There are two main policy implications of the study. First, constraining the pension eligibility criteria for retirement are frequently opposed by social actors. It is often considered that early retirement is a privilege – awarded on a basis of occupation or even simply employment in an industry. In many countries – e.g. France, Italy, Germany – attempts to make the eligibility criteria more strict resulted in general strikes and Poland was no exception from this rule. If treatment effects of the large and radical eligibility reform are small in participation rates and pension take-up rates, then immediate fiscal effects are bound to be small as well, even if in the desirable direction. This may explain why – given the strong social resistance – in many countries eligibility reforms are delayed or narrowed in scope. Second, the economic rationale for strong social resistance to eligibility reforms builds on assuming either a relatively high valuation of leisure time after exiting the labor market or a relatively high subjective valuation of the unemployment risk after passing the early retirement age threshold. If leisure preference is overstated, reducing eligibility may be opposed as such, but eligibility alone is irrelevant for household decision making. Meanwhile, unemployment risk may be mitigated via alternative instruments, such as employment protection legislation, as is the case in Poland. Depending on a specific composition of the two factors in a given country, the effects of the eligibility reforms may be as high as in Switzerland or as low as in Poland.

First, the authors provide an analysis of discontinuities in transitions from activity to retirement, rather than focusing on the labor market status. The panel dimension of the data permits to observe directly the flows into retirement/inactivity, controlling for age and birth cohort. Second, the authors complement a pure discontinuity in cohort analysis with a fuzzy design, because in addition to age eligibility the authors also analyze the effects of changes in occupational eligibility. Third, the authors provide a benchmark for the estimates in the actual quarter of the reform by a series of placebo and conditional specifications. This allows to evaluate the (immediate) size and heterogeneity of the treatment effects. The authors find small effects of age eligibility reduction and effectively no effects of occupational eligibility. Hence, increased LFP of the elderly, observed even prior to the reform, seems to be driven by factors unrelated to early pension eligibility.

The purpose of this paper is to develop a Housing Eligibility Assessment Scoring Method (HEASM) for low-income Urgent Repair Programs (URPs).

In order to develop a practical HEASM that incorporates the prevailing eligibility assessment criteria for low-income URPs, a case study research approach was adopted. Emergent themes and patterns in predominant eligibility assessment criteria and methods are derived from program documents utilized by a successful State Urgent Repair Program (SURP) and its 42 Community Partners operating in the Southeastern region of the USA. Coupled with interviews and the expert analysis of SURP staff, the quantitative analysis of 11,414 repaired homes and literature reviews were used to categorize predominant eligible housing repairs and costs.

The five key eligibility assessment criteria categories that emerged from the data analysis are: location, owner-occupancy, family needs, housing repair, and estimated repair costs. The framework of the proposed HEASM is guided by these five categories.

URP decision makers are provided with a simple, practical, and objective eligibility assessment method that can be easily modified to accommodate the unique eligibility criteria and local program conditions. This method should improve the eligibility assessment, prioritization, and the eventual selection of qualifying applicants. Consequently, the capacity of URPs to provide funding to their targeted populations with the most critical needs would be enhanced. Insights could drive the impetus to modify existing URP.

This article estimates the causal effect of benefit levels on elderly enrollment in two public assistance programs by using the variation in eligibility and benefit levels introduced by old-age pension benefits. The findings are threefold. First, the low take-up among the elderly is not driven by changes in the composition of the eligible pool. Second, old-age pensions decrease the use of public assistance programs by decreasing the gain of participation – the potential benefits. Third, we find program-specific responses: a $100 increase in potential Supplemental Security Income (SSI) benefits leads to a 4–6 percentage point increase in the take-up probability, but we are unable to estimate consistent results for the Supplemental Nutrition Assistance Program (SNAP). Together with the fact that eligible individuals who begin receiving old-age pensions continue to participate in SSI more often than they maintain SNAP enrollment, the different program response could be due to preference for cash over in-kind transfers.

This paper aims to evaluate the social safety nets in Tunisia using targeting performance indicators which consider the divergence observed between the beneficiary selection process at the regional level and the official identification of poor.

In the first part of this research, the author intend to quantify the degree of association between social programs coverage and poverty incidence, with special emphasis on the contribution of the center's targeting of regions and on the efforts of the regions themselves to reach their poor households. Next, the author measure four targeting errors: inclusion and exclusion errors, by eligibility and by poverty. These targeting errors help policymakers to understand how well a program is performing in terms of its specific eligibility criteria and knowing whether a social transfer is reaching poor people at the regional level.

The author’s empirical findings show there is a positive targeting differential which rejects the nullity of independence between poverty incidence and coverage of the program in Tunisia. The author then found that the overall targeting differential is accounted for by the intra-region component. After estimating the eligibility thresholds at the regional level, the author found that the targeting performance differs from one region to another, while some recorded gains that others did not.

To the best of our knowledge, no research has addressed the distinction between eligibility and poverty in the measurement of targeting errors. Thus, this paper contains research to fill this gap. It differs from previous studies in two important ways. First, the targeting errors will be estimated on the basis of the official eligibility criteria used at the regional level. Second, we estimate the eligibility thresholds by considering the programs coverage rate variations between regions based on quotas allocated to each governorate by the Ministry of Social Affairs (MSA).

The bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction.

This aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result.

The analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN).

This paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction.

Prioritising in social services is not new. What has changed in the last ten years is that there has been a more open debate about the way in which scarce resources are rationed and a move towards explicit policies of eligibility and targeting of services. Most authorities have developed some statement of priority with which to guide the allocation of resources, though authorities vary widely in their approach. This article looks at the current state of play in this controversial policy area.

This paper identifies the challenges of interpreting and implementing appropriate eligibility criteria and assessment processes in adult mental health services, with reference to an inner‐city Trust's own protocols. Central guidance, local interpretation and professional judgment are all legitimate contributions, but also confound both the concept and processes of entry to service.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

In the spirit of Polachek (1975) and the later work of Becker (1985) on the role of specialization within the family, we examine the relationship between fringe benefits and the division of labor within a married household. The provision of fringe benefits is complicated by their non-additive nature within the household, as well as IRS regulations that stipulate that they be offered in a non-discriminatory manner in order to maintain their tax-exempt status. We model family decisions within a framework in which one spouse specializes in childcare and as a result experiences a reduction in market productive capacity. Our model predicts that the forces toward specialization become stronger as the number of children increase, so that the spouse specializing in childcare will have some combination of lower wages, hours worked, and fringe benefits. We demonstrate that to the extent that labor markets are incomplete, the family is less likely to obtain health insurance from the employer of the spouse that specializes in childcare. Using data from the April 1993 CPS we find evidence consistent with our model.

After the first transgender athletes participated in the 2020 Tokyo Olympics, some called for the immediate exclusion of transgender women in sports while others argue that this exclusion is discriminatory behaviour in violation of human rights law. Under current standards, the International Olympic Committee (IOC) has sought to address the balance between fairness and inclusion by allowing eligibility to be decided on a sport-by-sport basis for all transgender athletes (IOC, 2021). What if an international federation or national governing body concludes that transgender athletes should be prohibited or subjected to rigorous conditions for participation? Would those qualifications stand up to legal challenge by an affected athlete? Will some athletes have better legal protection based on the location of their challenge?

The United States (US) and United Kingdom (UK) have domestic legal provisions that protect transgender individuals from discrimination based on their gender identity whereas the European Union (EU) includes protections based on sex alone (Patel, 2021). This chapter will discuss transgender inclusion policies in Olympic sport and address how potential challenges to the policies will be handled in court using existing case law. Legal precedent in similar challenges is rare. However, when eligibility policies do not explicitly ban transgender athletes or appear to be a targeted response to a specific athlete or individuals, it becomes more likely that those policies will not be considered discriminatory. Courts are hesitant to interfere in a governing body's policies if that organization appears to have a legitimate reason for its sport governance decisions.