O perfil do idoso no mercado de trabalho brasileiro: análise das pnads 2002 e 2012

O presente trabalho tem como finalidade traçar um perfil do idoso inserido no mercado de trabalho Brasileiro. Para a realização deste objetivo, de acordo com a literatura sobre mercado de trabalho e estudos realizados sobre o assunto, utilizaram-se modelos econométricos de resposta qualitativa, o logit e o probit, para a obtenção da probabilidade dos idosos brasileiros estarem inseridos no mercado de trabalho, a partir de variáveis independentes selecionadas. A mostra foi construída a partir de dados fornecidos pela Pesquisa Nacional de Amostra por Domicílios, a PNAD, para os anos de 2002 e 2012. Os resultados dos modelos apresentaram um perfil de idoso inserido no mercado de trabalho brasileiro como sendo residente de áreas rurais, branco, não aposentado e moradores principalmente dos estados do Sul e do Nordeste do país.

Insatisfação no emprego e suas implicações: uma análise para o mercado de trabalho do Rio Grande do Sul

O presente trabalho tem como finalidade traçar um perfil para o trabalhador insatisfeito do Rio Grande do Sul a partir de variáveis socioeconômicas ligadas às características pessoais, do núcleo familiar e do posto de trabalho do indivíduo. Para a realização deste objetivo, de acordo com os estudos já realizados dentro da literatura de “job satisfaction”, utilizou-se modelos econométricos de resposta qualitativa, o LOGIT e o PROBIT, para a obtenção da probabilidade de o trabalhador gaúcho estar ou não insatisfeito levanto as variáveis independentes selecionadas. A amostra foi construída a partir de dados fornecidos pela Pesquisa Anual de Amostra por Domicílios, a PNAD, dos anos de 2009, 2011 e 2012, excluindo-se o ano de 2010 no qual a PNAD não foi realizada. Os modelos estimados apresentaram bom ajustamento e resultados similares, apontando o perfil do trabalhador insatisfeito gaúcho como sendo aquele indivíduo que é negro, chefe de família, com baixa escolaridade e renda, residente da área urbana, que possui renda provenientes de outras fontes que não o trabalho, trabalhadores do setor informal e de áreas como a construção civil, comércio e serviços.

Análisis de la percepción de seguridad en puentes peatonales: una aproximación mediante modelación híbrida

Este artículo de investigación científica y tecnológica estudia la percepción de seguridad en el uso de puentes peatonales, empleando un enfoque sustentado en dos campos principales: el microeconómico y el psicológico. El trabajo hace la estimación simultánea de un modelo híbrido de elección y variables latentes con datos de una encuesta de preferencias declaradas, encontrando mejor ajuste que un modelo mixto de referencia, lo que indica que la percepción de seguridad determina el comportamiento de los peatones cuando se enfrentan a la decisión de usar o no un puente peatonal. Se encontró que el sexo, la edad y el nivel de estudios son atributos que inciden en la percepción de seguridad. El modelo calibrado sugiere varias estrategias para aumentar el uso de puentes peatonales que son discutidas, encontrando que el uso de barreras ocasiona una pérdida de utilidad, en los peatones, que debería ser estudiada como extensión del presente trabajo.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Mapeamento projetivo, associação de palavras e escala hedônica como instrumentos de avaliação da percepção de produtos de pescado por crianças da rede pública de ensino

Brazil is a country that is characterized by its low consumption of fish. With consumption records of 10.6 kg/ inhabitant/ year, it is lower than the recommended by the UN, that is 12 kg/ inhabitant/ year. The regular consumption of fish provides health gain for people and their introduction into the school feeding is an important strategy for the insertion of this food consumption habits in a population. In this context, the objective of this study was to understand the perception of fish with children from the public school system through the technical Projective Mapping (MP) and Association of Words (AP); and evaluate the acceptability of fish derivative in school meals. In the first instance with the intention to better understand the perception of children from different ages about the fish-based products, Projective Mapping techniques were applied through the use of food figures and word association. A total of 149 children from three public schools from Pato Branco, Paraná State, Brazil, took part in this study. Three groups of children aged 5-6, 7-8 and 9-10 years old were interviewed individually by six monitors experienced in applied sensory methods. Ten figures with healthy foods drawings (sushi, salad, fruit, fish, chicken), and less healthy foods (pizza, pudding, cake, hamburger, fries) were distributed to the children, who were asked to paste the figures in A3 sheet, so that the products they considered similar stayed near each other, and the ones considered very different stayed apart. After this, the children described the images and the image groups (Ultra Flash Profile). The results revealed that the MP technique was easily operated and understood by all the children and the use of images made its implementation easier. The results analysis also revealed different perceptions came from children from different ages and hedonic perceptions regarding the fish-based products had a greater weight in the percentage from older children. AP technique proved to be an important tool to understand the perception of fish by children, and strengthened the results previously obtained by the MP. In a second step it was evaluated the acceptance of fish burger (tilapia) in school meals. For this task, the school cooks were trained to prepare the hamburgers. For the evaluation of acceptance, the hedonic scale was used with 5 facial ratings (1 = disliked very much to 5 = liked a lot). Students from both genders, between 5 to 10 years old (n = 142) proved the burgers at lunchtime, representing the protein portion of the meal. The tilapia derivative products shown to be foods with important nutritional value and low calorie value. For the application of the multinomial logistic regression analysis there was no significant effect from the age and gender variation in the acceptance by children. However, statistical significance was determined in the interaction between these two variables. With 87 % acceptance rate there was potential for consumption of fish burgers in school meals.

Predicción de insolvencia en la empresa familiar

El objetivo de este trabajo es la construcción de un modelo para predecir la insolvencia de las empresas familiares. El hecho de centrarnos en esta tipología de empresas deriva de dos motivos esenciales: Primero, por la importante participación de la empresa familiar en el ámbito de la economía española, así como en la economía mundial (Allouche et al., 2008). España tiene en la actualidad 1,1 millones de empresas familiares, un 85% del total de empresas, las cuales generan siete millones de empleos directos, esto es, un 70% del empleo privado. Este hecho ha provocado que la investigación en el campo de la empresa familiar haya crecido significativamente durante las dos últimas décadas (Gómez-Mejia et al., 2011). Y segundo, porque pensamos que las diferencias y características propias de la empresa familiar deberían tomarse en consideración para la predicción de la insolvencia empresarial. Estas circunstancias han motivado el interés en analizar las causas que propician la insolvencia en las empresas familiares e intentar facilitar herramientas o estrategias a los gestores de las mismas, con vistas a evitarla y asegurar la viabilidad de sus empresas. Además, hasta la fecha no se ha estudiado la predicción de insolvencia en las empresas familiares, donde encontramos un gap que pretendemos cubrir con la presente investigación. En consecuencia, la inexistencia de trabajos empíricos con muestras específicas de empresas familiares, tanto españolas como internacionales, hace especialmente interesante que analicemos las causas que propician su posible insolvencia. Por ello, y con objeto de contar con un mayor margen para realizar estrategias que eviten la insolvencia de este tipo de empresas, pretendemos obtener modelos que tengan como objeto predecirla 1, 2 y 3 años antes de que ésta se produzca, comparándose las similitudes y diferencias de dichos modelos a medida que nos alejamos del momento de la insolvencia. Con objeto de resolver esta cuestión de investigación hemos dispuesto de seis muestras elaboradas a partir de una base de datos creada expresamente para el presente estudio, y que incluirá información económico-financiera de empresas familiares y no familiares, tanto solventes como insolventes. Estas muestras contienen un número suficiente de empresas para construir fiables modelos de predicción y conocer las variables predictivas propias de cada una de ellas. Así mismo, y con objeto de dotar de robustez a los modelos, se ha considerado un período total de análisis de ocho años, comprendidos entre el ejercicio 2005 y el 2012, periodo que abarcaría varios ciclos económicos y, en consecuencia, evita el riesgo de obtener modelos sólo válidos para épocas de crecimiento o, en su caso, de decrecimiento económico. En el análisis empírico desarrollado utilizaremos dos métodos diferentes para predecir la insolvencia: técnicas de regresión logística (LOGIT) y técnicas computacionales de redes neuronales (NN). Si bien los modelos LOGIT han tenido y siguen manteniendo una especial relevancia en los estudios realizados en esta materia en los últimos treinta y cinco años, los modelos NN se corresponden con metodologías más avanzadas, que han mostrado tener un importante potencial en el ámbito de la predicción. La principal ventaja de los modelos LOGIT reside, no sólo en la capacidad de predecir previamente si una empresa se espera resulte solvente e insolvente, sino en facilitar información respecto a cuáles son las variables que resultan significativamente explicativas de la insolvencia, y en consecuencia, permiten deducir estrategias adecuadas en la gestión de la empresa con objeto de asegurar la solvencia de la misma. Por su parte, los modelos NN presentan un gran potencial de clasificación, superando en la mayoría de los casos al LOGIT, si bien su utilidad explicativa está menos contrastada. Nuestro estudio contribuye a la literatura existente sobre predicción de insolvencia de varias formas. En primer lugar, construyendo modelos específicos para empresas familiares y no familiares, lo que puede mejorar la eficiencia en la predicción del fracaso empresarial y evitar el concurso de acreedores, así como las consecuencias negativas de la insolvencia empresarial para la economía en general, dada la importancia de la empresa familiar en el mundo. En segundo lugar, nuestras conclusiones sugieren que la relación entre la evolución de ciertas variables financieras y la insolvencia empresarial toma connotaciones específicas en el caso de las empresas familiares. Aunque los modelos de predicción de insolvencia confirman la importancia de algunas variables financieras comunes para ambos tipos de empresas (eficiencia y dimensión empresarial), también identifican factores específicos y únicos. Así, la rentabilidad es el factor diferenciador para las empresas familiares como lo es el apalancamiento para las empresas no familiares.

Incidence of Chronic and Other Knee Pain in Relation to Occupational Risk Factors in a Large Working Population.

International audience

L’automédication en République Démocratique du Congo : choix ou contrainte ?

Fondé sur l’analyse des données produites par l’enquête « 1-2-3 » de 2012 en République Démocratique du Congo, cet article propose une approche quantitative de l’automédication. Il fait apparaître, le caractère relativement circonscrit de cette pratique dans les déclarations des individus confrontés à un épisode de maladie et tente de rendre compte des choix qui les guident : consulter un professionnel de santé, affirmer recourir à l’automédication, s’abstenir de se soigner ou recourir à l’automédication par défaut. La construction d’un modèle logistique multinomial non-ordonné permet à cet égard de comparer les déterminants de ces décisions, considérées sous la forme d’une double alternative : consulter ou recourir à l’automédication, et, pour ceux qui ne sollicitent pas un professionnel de santé, s’automédiquer ou s’abstenir de toute démarche thérapeutique. L’article pointe ainsi les contraintes (économiques, géographiques, sociales et culturelles) qui pèsent sur ces choix tout en soulignant comment les individus cherchent à s’en affranchir.

Factors Influencing Children’s Full Immunization in Ethiopia

Child morbidity and mortality in Ethiopia is mainly due to vaccine preventable diseases. Although numerous interventions have been made since the 1980’s to increase vaccination coverage, the level of full immunization is low in the country. This study examines factors influencing children’s full immunization based on data on 1927 children aged 12-23 months extracted from the 2011 Ethiopian Demographic and Health Survey. Multinomial logistic regression model was fitted to identify predictors of full immunization. The result shows that only 24.3% of the children were fully immunized. There was significant difference between regions in immunization coverage in which Tigray, Dire Dawa, and Addis Ababa performed well. In Oromia, Afar, Somali, Benishangul-Gumuz, and Gambela regions, the likelihood of children’s full immunization was significantly lower. Children born to mothers living in households with better socio-economic status, with frequent access to media, and who visit health facilities for antenatal care were more likely to be fully immunized. The results imply the importance of narrowing regional differences, improving women’s socio-economic status and utilization of antenatal care services, and strengthening culture-sensitive media campaign as a means of achieving full immunization of all children

Work stress, smoking status and smoking intensity: an observational study of 46 190 employees

Study objective: To examine the relationship between work stress, as indicated by the job strain model and the effort-reward imbalance model, and smoking. Setting: Ten municipalities and 21 hospitals in Finland. Design and Participants: Binary logistic regression models for the prevalence of smoking were related to survey responses of 37 309 female and 8881 male Finnish public sector employees aged 17-65. Separate multinomial logistic regression models were calculated for smoking intensity for 8130 smokers. In addition, binary logistic regression models for ex-smoking were fitted among 16 277 former and current smokers. In all analyses, adjustments were made for age, basic education, occupational status, type of employment and marital status. Main results: Respondents with high effort-reward imbalance or lower rewards were more likely to be smokers. Among smokers, an increased likelihood of higher intensity of smoking was associated with higher job strain and higher effort-reward imbalance and their components such as low job control and low rewards. Smoking intensity was also higher in active jobs in women, in passive jobs and among employees with low effort expenditure. Among former and current smokers, high job strain, high effort-reward imbalance and high job demands were associated with a higher likelihood of being a current smoker. Lower effort was associated with a higher likelihood of ex-smoking. Conclusions: This evidence suggests an association between work stress and smoking and implies that smoking cessation programs may benefit from the taking into account the modification of stressful features of work environment. Key words: effort-reward imbalance; job strain; smoking. Abbreviations: OR, odds ratio; CI, confidence interval; SES, socioeconomic status

Effort-reward imbalance at work and the co-occurrence of lifestyle risk factors: cross-sectional survey in a sample of 36,127 public sector employees

Background In occupational life, a mismatch between high expenditure of effort and receiving few rewards may promote the co-occurrence of lifestyle risk factors, however, there is insufficient evidence to support or refute this hypothesis. The aim of this study is to examine the extent to which the dimensions of the Effort-Reward Imbalance (ERI) model – effort, rewards and ERI – are associated with the co-occurrence of lifestyle risk factors. Methods Based on data from the Finnish Public Sector Study, cross-sectional analyses were performed for 28,894 women and 7233 men. ERI was conceptualized as a ratio of effort and rewards. To control for individual differences in response styles, such as a personal disposition to answer negatively to questionnaires, occupational and organizational -level ecological ERI scores were constructed in addition to individual-level ERI scores. Risk factors included current smoking, heavy drinking, body mass index ≥25 kg/m2, and physical inactivity. Multinomial logistic regression models were used to estimate the likelihood of having one risk factor, two risk factors, and three or four risk factors. The associations between ERI and single risk factors were explored using binary logistic regression models. Results After adjustment for age, socioeconomic position, marital status, and type of job contract, women and men with high ecological ERI were 40% more likely to have simultaneously ≥3 lifestyle risk factors (vs. 0 risk factors) compared with their counterparts with low ERI. When examined separately, both low ecological effort and low ecological rewards were also associated with an elevated prevalence of risk factor co-occurrence. The results obtained with the individual-level scores were in the same direction. The associations of ecological ERI with single risk factors were generally less marked than the associations with the co-occurrence of risk factors. Conclusion This study suggests that a high ratio of occupational efforts relative to rewards may be associated with an elevated risk of having multiple lifestyle risk factors. However, an unexpected association between low effort and a higher likelihood of risk factor co-occurrence as well as the absence of data on overcommitment (and thereby a lack of full test of the ERI model) warrant caution in regard to the extent to which the entire ERI model is supported by our evidence.

Unemployed, Now What? The Effect of Immigration on Unemployment Transitions of Native-born Workers in the United States

Although one would expect the unemployed to be the population most likely affected by immigration, most of the studies have concentrated on investigating the effects immigration has on the employed population. Little is known of the effects of immigration on labor market transitions out of unemployment. Using the basic monthly Current Population Survey from 2001 and 2013 we match data for individuals who were interviewed in two consecutive months and identify workers who transition out of unemployment. We employ a multinomial model to examine the effects of immigration on the transition out of unemployment, using state-level immigration statistics. The results suggest that immigration does not affect the probabilities of native-born workers finding a job. Instead, we find that immigration is associated with smaller probabilities of remaining unemployed, but it is also associated with higher probabilities of workers leaving the labor force. This effect impacts mostly young and less educated people.

Certificação de qualidade versus qualidade da informação financeira: estudo para empresas portuguesas

O presente estudo tem como objetivo analisar se as empresas que têm implementado um Sistema de Gestão da Qualidade, certificadas segundo a norma da International Organization for Standardization (ISO) 9001, apresentam também uma boa qualidade da informação financeira. Neste sentido, pretende-se testar a expectável relação positiva entre a certificação de qualidade de uma empresa e a qualidade da sua informação financeira. Para isso, identificaram-se as empresas que possuem certificação do Sistema de Gestão de Qualidade, segundo a norma ISO 9001, enquanto a qualidade da informação financeira foi aferida utilizando como proxy os accruals discricionários estimados através do modelo Jones (1991). Utiliza-se um modelo logit para testar a relação pretendida, tendo como variável dependente a variável binária relativa à certificação de qualidade e como principal variável explicativa a qualidade da informação financeira. Com base nos resultados obtidos foi possível verificar a existência de uma relação positiva e estatisticamente significativa entre certificação de qualidade das empresas e a sua qualidade da informação financeira.

Happiness and victimization in Latin America

In recent decades, studies on economics have identified happiness as a life quality indicator that not only accounts for individuals’ socioeconomic improvement but also accounts for their interactions with institutions and public goods, such as personal safety and protection of life. This study examines the determinants of individual happiness of Latin American citizens by focusing on whether the individual had been a victim of a crime in the last twelve months. To do this, a generalized ordered logit with partial constraints is used to analyze data obtained from the Americas Barometer Survey of 2014. The individual self- reported level of life satisfaction is used to study its relationship with having been a victim of a crime during the previous year. The results suggest the existence of a negative relationship between having been a victim of a crime in the past twelve months and being very satisfied with life.